mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 12:54:11 +08:00
Update (base update)
[ghstack-poisoned]
This commit is contained in:
Xuehai Pan
@ -8,6 +8,8 @@ if [[ "$GPU_ARCH_VERSION" == *"12.6"* ]]; then
|
||||
export TORCH_CUDA_ARCH_LIST="8.0;9.0"
|
||||
elif [[ "$GPU_ARCH_VERSION" == *"12.8"* ]]; then
|
||||
export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
|
||||
elif [[ "$GPU_ARCH_VERSION" == *"12.9"* ]]; then
|
||||
export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
|
||||
elif [[ "$GPU_ARCH_VERSION" == *"13.0"* ]]; then
|
||||
export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;11.0;12.0+PTX"
|
||||
fi
|
||||
|
||||
@ -113,6 +113,7 @@ case "$tag" in
|
||||
UCX_COMMIT=${_UCX_COMMIT}
|
||||
UCC_COMMIT=${_UCC_COMMIT}
|
||||
TRITON=yes
|
||||
INSTALL_MINGW=yes
|
||||
;;
|
||||
pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11)
|
||||
CUDA_VERSION=13.0.0
|
||||
@ -361,6 +362,7 @@ docker build \
|
||||
--build-arg "OPENBLAS=${OPENBLAS:-}" \
|
||||
--build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
|
||||
--build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
|
||||
--build-arg "INSTALL_MINGW=${INSTALL_MINGW:-}" \
|
||||
-f $(dirname ${DOCKERFILE})/Dockerfile \
|
||||
-t "$tmp_tag" \
|
||||
"$@" \
|
||||
|
||||
10
.ci/docker/common/install_mingw.sh
Normal file
10
.ci/docker/common/install_mingw.sh
Normal file
@ -0,0 +1,10 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -ex
|
||||
|
||||
# Install MinGW-w64 for Windows cross-compilation
|
||||
apt-get update
|
||||
apt-get install -y g++-mingw-w64-x86-64-posix
|
||||
|
||||
echo "MinGW-w64 installed successfully"
|
||||
x86_64-w64-mingw32-g++ --version
|
||||
@ -20,7 +20,7 @@ pip_install \
|
||||
|
||||
pip_install coloredlogs packaging
|
||||
pip_install onnxruntime==1.23.0
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||||
pip_install onnxscript==0.5.3
|
||||
pip_install onnxscript==0.5.4
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||||
|
||||
# Cache the transformers model to be used later by ONNX tests. We need to run the transformers
|
||||
# package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/
|
||||
|
||||
@ -39,9 +39,13 @@ case ${DOCKER_TAG_PREFIX} in
|
||||
DOCKER_GPU_BUILD_ARG=""
|
||||
;;
|
||||
rocm*)
|
||||
# we want the patch version of 7.0 instead
|
||||
if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
|
||||
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
|
||||
fi
|
||||
# we want the patch version of 6.4 instead
|
||||
if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
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||||
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
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||||
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
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||||
fi
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||||
BASE_TARGET=rocm
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||||
GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete
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||||
|
||||
@ -75,9 +75,13 @@ case ${image} in
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||||
DOCKERFILE_SUFFIX="_cuda_aarch64"
|
||||
;;
|
||||
manylinux2_28-builder:rocm*)
|
||||
# we want the patch version of 7.0 instead
|
||||
if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
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||||
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
|
||||
fi
|
||||
# we want the patch version of 6.4 instead
|
||||
if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
|
||||
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
|
||||
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
|
||||
fi
|
||||
TARGET=rocm_final
|
||||
MANY_LINUX_VERSION="2_28"
|
||||
|
||||
@ -103,6 +103,11 @@ COPY ci_commit_pins/torchbench.txt torchbench.txt
|
||||
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
|
||||
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
|
||||
|
||||
ARG INSTALL_MINGW
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||||
COPY ./common/install_mingw.sh install_mingw.sh
|
||||
RUN if [ -n "${INSTALL_MINGW}" ]; then bash ./install_mingw.sh; fi
|
||||
RUN rm install_mingw.sh
|
||||
|
||||
ARG TRITON
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||||
ARG TRITON_CPU
|
||||
|
||||
|
||||
@ -57,8 +57,8 @@ def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules
|
||||
logger.info("Successfully cloned %s", target)
|
||||
return r, commit
|
||||
|
||||
except GitCommandError as e:
|
||||
logger.error("Git operation failed: %s", e)
|
||||
except GitCommandError:
|
||||
logger.exception("Git operation failed")
|
||||
raise
|
||||
|
||||
|
||||
|
||||
@ -143,7 +143,7 @@ def sample_vllm_test_library():
|
||||
"pytest -v -s compile/test_decorator.py",
|
||||
],
|
||||
},
|
||||
"vllm_languagde_model_test_extended_generation_28_failure_test": {
|
||||
"vllm_language_model_test_extended_generation_28_failure_test": {
|
||||
"title": "Language Models Test (Extended Generation) 2.8 release failure",
|
||||
"id": "vllm_languagde_model_test_extended_generation_28_failure_test",
|
||||
"package_install": [
|
||||
|
||||
@ -63,7 +63,7 @@ class VllmBuildParameters:
|
||||
# DOCKERFILE_PATH: path to Dockerfile used when use_local_dockerfile is True"
|
||||
use_local_dockerfile: bool = env_bool_field("USE_LOCAL_DOCKERFILE", True)
|
||||
dockerfile_path: Path = env_path_field(
|
||||
"DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile.tmp_vllm"
|
||||
"DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile"
|
||||
)
|
||||
|
||||
# the cleaning script to remove torch dependencies from pip
|
||||
|
||||
@ -187,19 +187,22 @@ if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
|
||||
export USE_CUFILE=0
|
||||
else
|
||||
DEPS_LIST+=(
|
||||
"/usr/local/cuda/lib64/libnvToolsExt.so.1"
|
||||
"/usr/local/cuda/lib64/libcublas.so.12"
|
||||
"/usr/local/cuda/lib64/libcublasLt.so.12"
|
||||
"/usr/local/cuda/lib64/libcudart.so.12"
|
||||
"/usr/local/cuda/lib64/libnvrtc.so.12"
|
||||
"/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12")
|
||||
DEPS_SONAME+=(
|
||||
"libnvToolsExt.so.1"
|
||||
"libcublas.so.12"
|
||||
"libcublasLt.so.12"
|
||||
"libcudart.so.12"
|
||||
"libnvrtc.so.12"
|
||||
"libcupti.so.12")
|
||||
|
||||
if [[ $CUDA_VERSION != 12.9* ]]; then
|
||||
DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
|
||||
DEPS_SONAME+=("libnvToolsExt.so.1")
|
||||
fi
|
||||
fi
|
||||
else
|
||||
echo "Using nvidia libs from pypi."
|
||||
|
||||
@ -485,6 +485,22 @@ test_inductor_aoti() {
|
||||
/usr/bin/env "${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
|
||||
}
|
||||
|
||||
test_inductor_aoti_cross_compile_for_windows() {
|
||||
|
||||
TEST_REPORTS_DIR=$(pwd)/test/test-reports
|
||||
mkdir -p "$TEST_REPORTS_DIR"
|
||||
|
||||
# Set WINDOWS_CUDA_HOME environment variable
|
||||
WINDOWS_CUDA_HOME="$(pwd)/win-torch-wheel-extracted"
|
||||
export WINDOWS_CUDA_HOME
|
||||
|
||||
echo "WINDOWS_CUDA_HOME is set to: $WINDOWS_CUDA_HOME"
|
||||
echo "Contents:"
|
||||
ls -lah "$(pwd)/win-torch-wheel-extracted/lib/x64/" || true
|
||||
|
||||
python test/inductor/test_aoti_cross_compile_windows.py -k compile --package-dir "$TEST_REPORTS_DIR" --win-torch-lib-dir "$(pwd)/win-torch-wheel-extracted/torch/lib"
|
||||
}
|
||||
|
||||
test_inductor_cpp_wrapper_shard() {
|
||||
if [[ -z "$NUM_TEST_SHARDS" ]]; then
|
||||
echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
|
||||
@ -900,7 +916,7 @@ test_inductor_set_cpu_affinity(){
|
||||
export LD_PRELOAD="$JEMALLOC_LIB":"$LD_PRELOAD"
|
||||
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"
|
||||
|
||||
if [[ "${TEST_CONFIG}" != *aarch64* ]]; then
|
||||
if [[ "$(uname -m)" != "aarch64" ]]; then
|
||||
# Use Intel OpenMP for x86
|
||||
IOMP_LIB="$(dirname "$(which python)")/../lib/libiomp5.so"
|
||||
export LD_PRELOAD="$IOMP_LIB":"$LD_PRELOAD"
|
||||
@ -914,7 +930,7 @@ test_inductor_set_cpu_affinity(){
|
||||
cores=$((cpus / thread_per_core))
|
||||
|
||||
# Set number of cores to 16 on aarch64 for performance runs
|
||||
if [[ "${TEST_CONFIG}" == *aarch64* && $cores -gt 16 ]]; then
|
||||
if [[ "$(uname -m)" == "aarch64" && $cores -gt 16 ]]; then
|
||||
cores=16
|
||||
fi
|
||||
export OMP_NUM_THREADS=$cores
|
||||
@ -1615,6 +1631,7 @@ test_operator_benchmark() {
|
||||
TEST_REPORTS_DIR=$(pwd)/test/test-reports
|
||||
mkdir -p "$TEST_REPORTS_DIR"
|
||||
TEST_DIR=$(pwd)
|
||||
ARCH=$(uname -m)
|
||||
|
||||
test_inductor_set_cpu_affinity
|
||||
|
||||
@ -1629,7 +1646,7 @@ test_operator_benchmark() {
|
||||
pip_install pandas
|
||||
python check_perf_csv.py \
|
||||
--actual "${TEST_REPORTS_DIR}/operator_benchmark_eager_float32_cpu.csv" \
|
||||
--expected "expected_ci_operator_benchmark_eager_float32_cpu.csv"
|
||||
--expected "${ARCH}_expected_ci_operator_benchmark_eager_float32_cpu.csv"
|
||||
}
|
||||
|
||||
test_operator_microbenchmark() {
|
||||
@ -1666,7 +1683,7 @@ if [[ "${TEST_CONFIG}" == *numpy_2* ]]; then
|
||||
python -m pip install --pre numpy==2.0.2 scipy==1.13.1 numba==0.60.0
|
||||
fi
|
||||
python test/run_test.py --include dynamo/test_functions.py dynamo/test_unspec.py test_binary_ufuncs.py test_fake_tensor.py test_linalg.py test_numpy_interop.py test_tensor_creation_ops.py test_torch.py torch_np/test_basic.py
|
||||
elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" != *perf_cpu_aarch64* ]]; then
|
||||
elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" == 'default' ]]; then
|
||||
test_linux_aarch64
|
||||
elif [[ "${TEST_CONFIG}" == *backward* ]]; then
|
||||
test_forward_backward_compatibility
|
||||
@ -1717,6 +1734,8 @@ elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
|
||||
test_inductor_triton_cpu
|
||||
elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
|
||||
test_inductor_micro_benchmark
|
||||
elif [[ "${TEST_CONFIG}" == *aoti_cross_compile_for_windows* ]]; then
|
||||
test_inductor_aoti_cross_compile_for_windows
|
||||
elif [[ "${TEST_CONFIG}" == *huggingface* ]]; then
|
||||
install_torchvision
|
||||
id=$((SHARD_NUMBER-1))
|
||||
|
||||
4
.flake8
4
.flake8
@ -13,10 +13,6 @@ ignore =
|
||||
EXE001,
|
||||
# these ignores are from flake8-bugbear; please fix!
|
||||
B007,B008,B017,B019,B023,B028,B903,B905,B906,B907,B908,B910
|
||||
# these ignores are from flake8-comprehensions; please fix!
|
||||
C407,
|
||||
# these ignores are from flake8-logging-format; please fix!
|
||||
G100,G101,G200
|
||||
# these ignores are from flake8-simplify. please fix or ignore with commented reason
|
||||
SIM105,SIM108,SIM110,SIM111,SIM113,SIM114,SIM115,SIM116,SIM117,SIM118,SIM119,SIM12,
|
||||
# SIM104 is already covered by pyupgrade ruff
|
||||
|
||||
1
.github/ISSUE_TEMPLATE/ci-sev.md
vendored
1
.github/ISSUE_TEMPLATE/ci-sev.md
vendored
@ -8,6 +8,7 @@ assignees: ''
|
||||
---
|
||||
|
||||
> NOTE: Remember to label this issue with "`ci: sev`"
|
||||
> If you want autorevert to be disabled, keep the ci: disable-autorevert label
|
||||
|
||||
<!-- Add the `merge blocking` label to this PR to prevent PRs from being merged while this issue is open -->
|
||||
|
||||
|
||||
4
.github/ISSUE_TEMPLATE/disable-autorevert.md
vendored
4
.github/ISSUE_TEMPLATE/disable-autorevert.md
vendored
@ -1,7 +1,7 @@
|
||||
---
|
||||
name: DISABLE AUTOREVERT
|
||||
name: "D❌\U0001F519 ISABLE AUTOREVERT"
|
||||
about: Disables autorevert when open
|
||||
title: "❌\U0001F519 [DISABLE AUTOREVERT]"
|
||||
title: "[DISABLE AUTOREVERT]"
|
||||
labels: 'ci: disable-autorevert'
|
||||
assignees: ''
|
||||
|
||||
|
||||
@ -65,7 +65,7 @@ runs:
|
||||
cd .ci/lumen_cli
|
||||
python3 -m pip install -e .
|
||||
)
|
||||
MAX_JOBS="$(nproc --ignore=6)"
|
||||
MAX_JOBS="$(nproc --ignore=10)"
|
||||
export MAX_JOBS
|
||||
|
||||
# Split the comma-separated list and build each target
|
||||
|
||||
13
.github/actions/setup-rocm/action.yml
vendored
13
.github/actions/setup-rocm/action.yml
vendored
@ -111,3 +111,16 @@ runs:
|
||||
# This video group ID maps to subgid 1 inside the docker image due to the /etc/subgid entries.
|
||||
# The group name corresponding to group ID 1 can change depending on the OS, so both are necessary.
|
||||
echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd $DEVICE_FLAG --group-add video --group-add $render_gid --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host" >> "${GITHUB_ENV}"
|
||||
|
||||
- name: configure aws credentials
|
||||
id: aws_creds
|
||||
uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
|
||||
with:
|
||||
role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
|
||||
aws-region: us-east-1
|
||||
role-duration-seconds: 18000
|
||||
|
||||
- name: Login to Amazon ECR
|
||||
id: login-ecr
|
||||
continue-on-error: true
|
||||
uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
|
||||
|
||||
2
.github/ci_commit_pins/audio.txt
vendored
2
.github/ci_commit_pins/audio.txt
vendored
@ -1 +1 @@
|
||||
87ff22e49ed0e92576c4935ccb8c143daac4a3cd
|
||||
69bbe7363897764f9e758d851cd0340147d27f94
|
||||
|
||||
2
.github/ci_commit_pins/vision.txt
vendored
2
.github/ci_commit_pins/vision.txt
vendored
@ -1 +1 @@
|
||||
966da7e46f65d6d49df3e31214470a4fe5cc8e66
|
||||
faffd5cf673615583da6517275e361cb3dbc77e6
|
||||
|
||||
2
.github/ci_commit_pins/vllm.txt
vendored
2
.github/ci_commit_pins/vllm.txt
vendored
@ -1 +1 @@
|
||||
0ad9951c416d33c5da4f7a504fb162cbe62386f5
|
||||
e5192819208c4d68194844b7dfafbc00020d0dea
|
||||
|
||||
2
.github/ci_commit_pins/xla.txt
vendored
2
.github/ci_commit_pins/xla.txt
vendored
@ -1 +1 @@
|
||||
2a9138a26ee257fef05310ad3fecf7c55fe80d73
|
||||
0fa6e3129e61143224663e1ec67980d12b7ec4eb
|
||||
|
||||
@ -1,59 +1,71 @@
|
||||
# TODO(elainwy): remove this file after the torch nightly dockerfile is in sync in vllm repo
|
||||
# The vLLM Dockerfile is used to construct vLLM image against torch nightly and torch main that can be directly used for testing
|
||||
|
||||
ARG CUDA_VERSION=12.8.1
|
||||
ARG PYTHON_VERSION=3.12
|
||||
|
||||
# BUILD_BASE_IMAGE: used to setup python build xformers, and vllm wheels, It can be replaced with a different base image from local machine,
|
||||
# by default, it uses the torch-nightly-base stage from this docker image
|
||||
ARG BUILD_BASE_IMAGE=torch-nightly-base
|
||||
|
||||
# FINAL_BASE_IMAGE: used to set up vllm-instaled environment and build flashinfer,
|
||||
# by default, it uses devel-ubuntu22.04 official image.
|
||||
ARG FINAL_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04
|
||||
|
||||
# The logic is copied from https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile
|
||||
ARG GET_PIP_URL="https://bootstrap.pypa.io/get-pip.py"
|
||||
|
||||
|
||||
#################### TORCH NIGHTLY BASE IMAGE ####################
|
||||
# A base image for building vLLM with devel ubuntu 22.04, this is mainly used to build vllm in vllm builtkite ci
|
||||
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 as torch-nightly-base
|
||||
|
||||
ARG CUDA_VERSION
|
||||
ARG PYTHON_VERSION
|
||||
ARG GET_PIP_URL
|
||||
|
||||
# Install Python and other dependencies
|
||||
# Install system dependencies and uv, then create Python virtual environment
|
||||
RUN apt-get update -y \
|
||||
&& apt-get install -y ccache software-properties-common git curl wget sudo vim \
|
||||
&& add-apt-repository -y ppa:deadsnakes/ppa \
|
||||
&& apt-get update -y \
|
||||
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
|
||||
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
|
||||
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
|
||||
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
|
||||
&& curl -sS ${GET_PIP_URL} | python${PYTHON_VERSION} \
|
||||
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
|
||||
&& curl -LsSf https://astral.sh/uv/install.sh | sh \
|
||||
&& $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION} \
|
||||
&& rm -f /usr/bin/python3 /usr/bin/python3-config /usr/bin/pip \
|
||||
&& ln -s /opt/venv/bin/python3 /usr/bin/python3 \
|
||||
&& ln -s /opt/venv/bin/python3-config /usr/bin/python3-config \
|
||||
&& ln -s /opt/venv/bin/pip /usr/bin/pip \
|
||||
&& python3 --version && python3 -m pip --version
|
||||
|
||||
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
|
||||
# as it was causing spam when compiling the CUTLASS kernels
|
||||
# Ensure gcc >= 10 to avoid CUTLASS issues (bug 92519)
|
||||
RUN current_gcc_version=$(gcc -dumpversion | cut -f1 -d.) && \
|
||||
if command -v apt-get >/dev/null; then \
|
||||
if [ "$current_gcc_version" -lt 10 ]; then \
|
||||
echo "GCC version is $current_gcc_version, installing gcc-10..."; \
|
||||
apt-get update \
|
||||
&& apt-get install -y gcc-10 g++-10 \
|
||||
&& update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 100 \
|
||||
&& update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-10 100; \
|
||||
else \
|
||||
echo "GCC version is $current_gcc_version, no need to install gcc-10."; \
|
||||
fi \
|
||||
fi \
|
||||
&& gcc --version && g++ --version
|
||||
RUN apt-get install -y gcc-10 g++-10
|
||||
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
|
||||
RUN <<EOF
|
||||
gcc --version
|
||||
EOF
|
||||
|
||||
# install uv for faster pip installs
|
||||
# Install uv for faster pip installs
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
python3 -m pip install uv==0.8.4
|
||||
|
||||
ENV UV_HTTP_TIMEOUT=500
|
||||
ENV UV_INDEX_STRATEGY="unsafe-best-match"
|
||||
# Use copy mode to avoid hardlink failures with Docker cache mounts
|
||||
ENV UV_LINK_MODE=copy
|
||||
#################### TORCH NIGHTLY BASE IMAGE ####################
|
||||
|
||||
|
||||
#################### BASE BUILD IMAGE ####################
|
||||
FROM ${BUILD_BASE_IMAGE} AS base
|
||||
USER root
|
||||
|
||||
ARG CUDA_VERSION
|
||||
ARG PYTHON_VERSION
|
||||
|
||||
# Only work with PyTorch manylinux builder
|
||||
ENV PATH="/opt/python/cp312-cp312/bin:${PATH}"
|
||||
|
||||
# Install some system dependencies and double check python version
|
||||
RUN if command -v apt-get >/dev/null; then \
|
||||
apt-get update -y \
|
||||
&& apt-get install -y ccache software-properties-common git wget sudo vim; \
|
||||
else \
|
||||
dnf install -y git wget sudo; \
|
||||
fi \
|
||||
&& python3 --version && python3 -m pip --version
|
||||
|
||||
# Install uv for faster pip installs if not existed
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
python3 -m pip install uv==0.8.4
|
||||
|
||||
@ -62,51 +74,17 @@ ENV UV_INDEX_STRATEGY="unsafe-best-match"
|
||||
# Use copy mode to avoid hardlink failures with Docker cache mounts
|
||||
ENV UV_LINK_MODE=copy
|
||||
|
||||
#################### TORCH NIGHTLY BASE IMAGE ####################
|
||||
|
||||
|
||||
#################### BASE BUILD IMAGE ####################
|
||||
# A base image for building vLLM with torch nightly or torch wheels
|
||||
# prepare basic build environment
|
||||
FROM ${BUILD_BASE_IMAGE} AS base
|
||||
USER root
|
||||
|
||||
ARG CUDA_VERSION
|
||||
ARG PYTHON_VERSION
|
||||
|
||||
# TODO (huydhn): Only work with PyTorch manylinux builder
|
||||
ENV PATH="/opt/python/cp312-cp312/bin:${PATH}"
|
||||
|
||||
# Install some system dependencies and double check python version
|
||||
RUN if command -v apt-get >/dev/null; then \
|
||||
apt-get update -y \
|
||||
&& apt-get install -y ccache software-properties-common git curl wget sudo vim; \
|
||||
else \
|
||||
dnf install -y git curl wget sudo; \
|
||||
fi \
|
||||
&& python3 --version && python3 -m pip --version
|
||||
|
||||
# Install uv for faster pip installs if not existed
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
if ! python3 -m uv --version >/dev/null 2>&1; then \
|
||||
python3 -m pip install uv==0.8.4; \
|
||||
fi
|
||||
ENV UV_HTTP_TIMEOUT=500
|
||||
ENV UV_INDEX_STRATEGY="unsafe-best-match"
|
||||
# Use copy mode to avoid hardlink failures with Docker cache mounts
|
||||
ENV UV_LINK_MODE=copy
|
||||
|
||||
WORKDIR /workspace
|
||||
|
||||
# install build and runtime dependencies
|
||||
# Install build and runtime dependencies
|
||||
COPY requirements/common.txt requirements/common.txt
|
||||
COPY use_existing_torch.py use_existing_torch.py
|
||||
COPY pyproject.toml pyproject.toml
|
||||
|
||||
# install build and runtime dependencies without stable torch version
|
||||
# Install build and runtime dependencies without stable torch version
|
||||
RUN python3 use_existing_torch.py
|
||||
|
||||
# default mount file as placeholder, this just avoid the mount error
|
||||
# Default mount file as placeholder, this just avoid the mount error
|
||||
# change to a different vllm folder if this does not exist anymore
|
||||
ARG TORCH_WHEELS_PATH="./requirements"
|
||||
ARG PINNED_TORCH_VERSION
|
||||
@ -138,56 +116,36 @@ RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system -r requirements/common.txt
|
||||
|
||||
# Must put before installing xformers, so it can install the correct version of xfomrers.
|
||||
ARG xformers_cuda_arch_list='7.5;8.0+PTX;9.0a'
|
||||
ENV TORCH_CUDA_ARCH_LIST=${xformers_cuda_arch_list}
|
||||
|
||||
ARG max_jobs=16
|
||||
ENV MAX_JOBS=${max_jobs}
|
||||
|
||||
RUN echo ${TORCH_CUDA_ARCH_LIST}
|
||||
RUN echo ${MAX_JOBS}
|
||||
RUN pip freeze | grep -E 'ninja'
|
||||
RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
|
||||
export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
|
||||
git clone https://github.com/facebookresearch/xformers.git
|
||||
|
||||
# Build xformers with cuda and torch nightly/wheel
|
||||
# following official xformers guidance: https://github.com/facebookresearch/xformers#build
|
||||
# sha for https://github.com/facebookresearch/xformers/tree/v0.0.32.post2
|
||||
ARG XFORMERS_COMMIT=5d4b92a5e5a9c6c6d4878283f47d82e17995b468
|
||||
ENV CCACHE_DIR=/root/.cache/ccache
|
||||
pushd xformers
|
||||
git checkout v0.0.32.post2
|
||||
git submodule update --init --recursive
|
||||
python3 setup.py bdist_wheel --dist-dir=../xformers-dist --verbose
|
||||
popd
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/ccache \
|
||||
--mount=type=cache,target=/root/.cache/uv \
|
||||
echo 'git clone xformers...' \
|
||||
&& git clone https://github.com/facebookresearch/xformers.git --recursive \
|
||||
&& cd xformers \
|
||||
&& git checkout ${XFORMERS_COMMIT} \
|
||||
&& git submodule update --init --recursive \
|
||||
&& echo 'finish git clone xformers...' \
|
||||
&& rm -rf build \
|
||||
&& python3 setup.py bdist_wheel --dist-dir=../xformers-dist --verbose \
|
||||
&& cd .. \
|
||||
&& rm -rf xformers
|
||||
rm -rf xformers
|
||||
BASH
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system xformers-dist/*.whl --verbose
|
||||
uv pip install --system xformers-dist/*.whl
|
||||
|
||||
# Build can take a long time, and the torch nightly version fetched from url can be different in next docker stage.
|
||||
# track the nightly torch version used in the build, when we set up runtime environment we can make sure the version is the same
|
||||
RUN uv pip freeze | grep -i '^torch\|^torchvision\|^torchaudio' > torch_build_versions.txt
|
||||
|
||||
RUN cat torch_build_versions.txt
|
||||
RUN pip freeze | grep -E 'torch|xformers|torchvision|torchaudio'
|
||||
|
||||
#################### BASE BUILD IMAGE ####################
|
||||
|
||||
|
||||
#################### WHEEL BUILD IMAGE ####################
|
||||
# Image used to build vllm wheel
|
||||
FROM base AS build
|
||||
ARG TARGETPLATFORM
|
||||
|
||||
COPY . .
|
||||
|
||||
RUN python3 use_existing_torch.py
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
@ -197,20 +155,17 @@ ARG GIT_REPO_CHECK=0
|
||||
RUN --mount=type=bind,source=.git,target=.git \
|
||||
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
|
||||
|
||||
# Max jobs used by Ninja to build extensions
|
||||
ARG max_jobs=16
|
||||
ENV MAX_JOBS=${max_jobs}
|
||||
ARG nvcc_threads=4
|
||||
ARG nvcc_threads=8
|
||||
ENV NVCC_THREADS=$nvcc_threads
|
||||
ARG torch_cuda_arch_list='8.0 8.6 8.9 9.0'
|
||||
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
|
||||
|
||||
ARG USE_SCCACHE
|
||||
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
|
||||
ARG SCCACHE_REGION_NAME=us-west-2
|
||||
ARG SCCACHE_S3_NO_CREDENTIALS=0
|
||||
|
||||
# if USE_SCCACHE is set, use sccache to speed up compilation
|
||||
# Use sccache to speed up compilation
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
--mount=type=bind,source=.git,target=.git \
|
||||
if [ "$USE_SCCACHE" = "1" ]; then \
|
||||
@ -235,6 +190,9 @@ RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
&& sccache --show-stats; \
|
||||
fi
|
||||
|
||||
ARG torch_cuda_arch_list='8.0 8.6 8.9 9.0'
|
||||
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
|
||||
|
||||
ARG vllm_target_device="cuda"
|
||||
ENV VLLM_TARGET_DEVICE=${vllm_target_device}
|
||||
ENV CCACHE_DIR=/root/.cache/ccache
|
||||
@ -248,17 +206,10 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
|
||||
export VLLM_DOCKER_BUILD_CONTEXT=1 && \
|
||||
python3 setup.py bdist_wheel --dist-dir=vllm-dist --py-limited-api=cp38; \
|
||||
fi
|
||||
|
||||
RUN echo "[INFO] Listing current directory:" && \
|
||||
ls -al && \
|
||||
echo "[INFO] Showing torch_build_versions.txt content:" && \
|
||||
cat torch_build_versions.txt
|
||||
|
||||
#################### WHEEL BUILD IMAGE ####################
|
||||
|
||||
|
||||
################### VLLM INSTALLED IMAGE ####################
|
||||
# Setup clean environment for vLLM for test and api server using ubuntu22.04 with AOT flashinfer
|
||||
FROM ${FINAL_BASE_IMAGE} AS vllm-base
|
||||
USER root
|
||||
|
||||
@ -266,7 +217,7 @@ ARG CUDA_VERSION
|
||||
ARG PYTHON_VERSION
|
||||
ARG GET_PIP_URL
|
||||
|
||||
# TODO (huydhn): Only work with PyTorch manylinux builder
|
||||
# Only work with PyTorch manylinux builder
|
||||
ENV PATH="/opt/python/cp312-cp312/bin:${PATH}"
|
||||
|
||||
# prepare for environment starts
|
||||
@ -275,20 +226,19 @@ WORKDIR /workspace
|
||||
# Install Python and other dependencies
|
||||
RUN if command -v apt-get >/dev/null; then \
|
||||
apt-get update -y \
|
||||
&& apt-get install -y ccache software-properties-common git curl wget sudo vim \
|
||||
&& add-apt-repository -y ppa:deadsnakes/ppa \
|
||||
&& apt-get update -y \
|
||||
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
|
||||
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
|
||||
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
|
||||
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
|
||||
&& curl -sS ${GET_PIP_URL} | python${PYTHON_VERSION}; \
|
||||
&& apt-get install -y ccache software-properties-common git sudo vim python3-pip; \
|
||||
else \
|
||||
dnf install -y git curl wget sudo; \
|
||||
dnf install -y git wget sudo; \
|
||||
fi \
|
||||
&& curl -LsSf https://astral.sh/uv/install.sh | sh \
|
||||
&& $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION} \
|
||||
&& rm -f /usr/bin/python3 /usr/bin/python3-config /usr/bin/pip \
|
||||
&& ln -s /opt/venv/bin/python3 /usr/bin/python3 \
|
||||
&& ln -s /opt/venv/bin/python3-config /usr/bin/python3-config \
|
||||
&& ln -s /opt/venv/bin/pip /usr/bin/pip \
|
||||
&& python3 --version && python3 -m pip --version
|
||||
|
||||
# Get the torch versions, and whls used in previous stagtes for consistency
|
||||
# Get the torch versions, and whls used in previous stage
|
||||
COPY --from=base /workspace/torch_build_versions.txt ./torch_build_versions.txt
|
||||
COPY --from=base /workspace/xformers-dist /wheels/xformers
|
||||
COPY --from=build /workspace/vllm-dist /wheels/vllm
|
||||
@ -297,33 +247,29 @@ RUN echo "[INFO] Listing current directory before torch install step:" && \
|
||||
echo "[INFO] Showing torch_build_versions.txt content:" && \
|
||||
cat torch_build_versions.txt
|
||||
|
||||
# Install build and runtime dependencies, this is needed for flashinfer install
|
||||
COPY requirements/build.txt requirements/build.txt
|
||||
COPY use_existing_torch.py use_existing_torch.py
|
||||
RUN python3 use_existing_torch.py
|
||||
RUN cat requirements/build.txt
|
||||
|
||||
# Install uv for faster pip installs if not existed
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
if ! python3 -m uv --version > /dev/null 2>&1; then \
|
||||
python3 -m pip install uv==0.8.4; \
|
||||
fi
|
||||
python3 -m pip install uv==0.8.4
|
||||
|
||||
ENV UV_HTTP_TIMEOUT=500
|
||||
ENV UV_INDEX_STRATEGY="unsafe-best-match"
|
||||
# Use copy mode to avoid hardlink failures with Docker cache mounts
|
||||
ENV UV_LINK_MODE=copy
|
||||
|
||||
# Install build and runtime dependencies, this is needed for flashinfer install
|
||||
COPY requirements/build.txt requirements/build.txt
|
||||
COPY use_existing_torch.py use_existing_torch.py
|
||||
RUN python3 use_existing_torch.py
|
||||
RUN cat requirements/build.txt
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system -r requirements/build.txt
|
||||
|
||||
|
||||
# Default mount file as placeholder, this just avoid the mount error
|
||||
ARG TORCH_WHEELS_PATH="./requirements"
|
||||
# Install torch, torchaudio and torchvision
|
||||
# if TORCH_WHEELS_PATH is default "./requirements", it will pull the nightly versions using pip using torch_build_versions.txt
|
||||
# otherwise, it will use the whls from TORCH_WHEELS_PATH from the host machine
|
||||
# Install torch, torchaudio and torchvision. If TORCH_WHEELS_PATH is default
|
||||
# to ./requirements, it will pull the nightly versions using pip. Otherwise,
|
||||
# it will use the local wheels from TORCH_WHEELS_PATH
|
||||
RUN --mount=type=bind,source=${TORCH_WHEELS_PATH},target=/dist \
|
||||
--mount=type=cache,target=/root/.cache/uv \
|
||||
if [ -n "$TORCH_WHEELS_PATH" ] && [ "$TORCH_WHEELS_PATH" != "./requirements" ] && [ -d "/dist" ] && ls /dist/torch*.whl >/dev/null 2>&1; then \
|
||||
@ -344,18 +290,14 @@ RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
# Install xformers wheel from previous stage
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system /wheels/xformers/*.whl --verbose
|
||||
# Build flashinfer from source.
|
||||
|
||||
# Build FlashInfer from source
|
||||
ARG torch_cuda_arch_list='8.0;8.9;9.0a;10.0a;12.0'
|
||||
# install package for build flashinfer
|
||||
# see issue: https://github.com/flashinfer-ai/flashinfer/issues/738
|
||||
|
||||
RUN pip freeze | grep -E 'setuptools|packaging|build'
|
||||
|
||||
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
|
||||
# Build flashinfer for torch nightly from source around 10 mins
|
||||
|
||||
ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
|
||||
# Keep this in sync with https://github.com/vllm-project/vllm/blob/main/requirements/cuda.txt
|
||||
ARG FLASHINFER_GIT_REF="v0.2.14.post1"
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
git clone --depth 1 --recursive --shallow-submodules \
|
||||
--branch ${FLASHINFER_GIT_REF} \
|
||||
@ -367,7 +309,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
&& cd .. \
|
||||
&& rm -rf flashinfer
|
||||
|
||||
# install flashinfer python
|
||||
# Install FlashInfer
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system wheels/flashinfer/*.whl --verbose
|
||||
|
||||
@ -377,49 +319,6 @@ RUN uv pip freeze | grep -i '^torch\|^torchvision\|^torchaudio\|^xformers\|^vllm
|
||||
################### VLLM INSTALLED IMAGE ####################
|
||||
|
||||
|
||||
#################### UNITTEST IMAGE #############################
|
||||
FROM vllm-base as test
|
||||
|
||||
ENV UV_HTTP_TIMEOUT=500
|
||||
ENV UV_INDEX_STRATEGY="unsafe-best-match"
|
||||
# Use copy mode to avoid hardlink failures with Docker cache mounts
|
||||
ENV UV_LINK_MODE=copy
|
||||
|
||||
COPY tests/ tests/
|
||||
COPY examples examples
|
||||
COPY benchmarks benchmarks
|
||||
COPY ./vllm/collect_env.py .
|
||||
COPY requirements/common.txt requirements/common.txt
|
||||
COPY use_existing_torch.py use_existing_torch.py
|
||||
COPY pyproject.toml pyproject.toml
|
||||
# Install build and runtime dependencies without stable torch version
|
||||
COPY requirements/nightly_torch_test.txt requirements/nightly_torch_test.txt
|
||||
|
||||
RUN python3 use_existing_torch.py
|
||||
|
||||
# install packages
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system -r requirements/common.txt
|
||||
# enable fast downloads from hf (for testing)
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system hf_transfer
|
||||
ENV HF_HUB_ENABLE_HF_TRANSFER 1
|
||||
|
||||
# install development dependencies (for testing)
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system -e tests/vllm_test_utils
|
||||
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv pip install --system -r requirements/nightly_torch_test.txt
|
||||
|
||||
# Logging to confirm the torch versions
|
||||
RUN pip freeze | grep -E 'torch|xformers|vllm|flashinfer'
|
||||
|
||||
# Logging to confirm all the packages are installed
|
||||
RUN pip freeze
|
||||
|
||||
#################### UNITTEST IMAGE #############################
|
||||
|
||||
#################### EXPORT STAGE ####################
|
||||
FROM scratch as export-wheels
|
||||
|
||||
29
.github/labeler.yml
vendored
29
.github/labeler.yml
vendored
@ -133,3 +133,32 @@
|
||||
|
||||
"ciflow/vllm":
|
||||
- .github/ci_commit_pins/vllm.txt
|
||||
|
||||
"ciflow/b200":
|
||||
- test/test_matmul_cuda.py
|
||||
- test/test_scaled_matmul_cuda.py
|
||||
- test/inductor/test_fp8.py
|
||||
- aten/src/ATen/native/cuda/Blas.cpp
|
||||
- torch/**/*cublas*
|
||||
- torch/_inductor/kernel/mm.py
|
||||
- test/inductor/test_max_autotune.py
|
||||
- third_party/fbgemm
|
||||
|
||||
"ciflow/h100":
|
||||
- test/test_matmul_cuda.py
|
||||
- test/test_scaled_matmul_cuda.py
|
||||
- test/inductor/test_fp8.py
|
||||
- aten/src/ATen/native/cuda/Blas.cpp
|
||||
- torch/**/*cublas*
|
||||
- torch/_inductor/kernel/mm.py
|
||||
- test/inductor/test_max_autotune.py
|
||||
- third_party/fbgemm
|
||||
|
||||
"ciflow/rocm":
|
||||
- test/test_matmul_cuda.py
|
||||
- test/test_scaled_matmul_cuda.py
|
||||
- test/inductor/test_fp8.py
|
||||
- aten/src/ATen/native/cuda/Blas.cpp
|
||||
- torch/_inductor/kernel/mm.py
|
||||
- test/inductor/test_max_autotune.py
|
||||
- third_party/fbgemm
|
||||
|
||||
4
.github/pytorch-probot.yml
vendored
4
.github/pytorch-probot.yml
vendored
@ -3,6 +3,7 @@ ciflow_tracking_issue: 64124
|
||||
ciflow_push_tags:
|
||||
- ciflow/b200
|
||||
- ciflow/b200-symm-mem
|
||||
- ciflow/b200-distributed
|
||||
- ciflow/binaries
|
||||
- ciflow/binaries_libtorch
|
||||
- ciflow/binaries_wheel
|
||||
@ -15,7 +16,8 @@ ciflow_push_tags:
|
||||
- ciflow/inductor-micro-benchmark
|
||||
- ciflow/inductor-micro-benchmark-cpu-x86
|
||||
- ciflow/inductor-perf-compare
|
||||
- ciflow/inductor-perf-test-nightly-rocm
|
||||
- ciflow/inductor-perf-test-nightly-rocm-mi300
|
||||
- ciflow/inductor-perf-test-nightly-rocm-mi355
|
||||
- ciflow/inductor-perf-test-nightly-x86-zen
|
||||
- ciflow/inductor-periodic
|
||||
- ciflow/inductor-rocm
|
||||
|
||||
2
.github/scripts/filter_test_configs.py
vendored
2
.github/scripts/filter_test_configs.py
vendored
@ -512,6 +512,8 @@ def perform_misc_tasks(
|
||||
"keep-going",
|
||||
branch == MAIN_BRANCH
|
||||
or bool(tag and re.match(r"^trunk/[a-f0-9]{40}$", tag))
|
||||
# Pattern for tags created via manual run on HUD
|
||||
or bool(tag and re.match(r"^ciflow/[^/]+/[a-f0-9]{40}$", tag))
|
||||
or check_for_setting(labels, pr_body, "keep-going"),
|
||||
)
|
||||
set_output(
|
||||
|
||||
38
.github/scripts/generate_binary_build_matrix.py
vendored
38
.github/scripts/generate_binary_build_matrix.py
vendored
@ -16,16 +16,18 @@ from typing import Optional
|
||||
|
||||
|
||||
# NOTE: Please also update the CUDA sources in `PIP_SOURCES` in tools/nightly.py when changing this
|
||||
CUDA_ARCHES = ["12.6", "12.8", "13.0"]
|
||||
CUDA_ARCHES = ["12.6", "12.8", "12.9", "13.0"]
|
||||
CUDA_STABLE = "12.8"
|
||||
CUDA_ARCHES_FULL_VERSION = {
|
||||
"12.6": "12.6.3",
|
||||
"12.8": "12.8.1",
|
||||
"12.9": "12.9.1",
|
||||
"13.0": "13.0.0",
|
||||
}
|
||||
CUDA_ARCHES_CUDNN_VERSION = {
|
||||
"12.6": "9",
|
||||
"12.8": "9",
|
||||
"12.9": "9",
|
||||
"13.0": "9",
|
||||
}
|
||||
|
||||
@ -38,7 +40,7 @@ CPU_AARCH64_ARCH = ["cpu-aarch64"]
|
||||
|
||||
CPU_S390X_ARCH = ["cpu-s390x"]
|
||||
|
||||
CUDA_AARCH64_ARCHES = ["12.6-aarch64", "12.8-aarch64", "13.0-aarch64"]
|
||||
CUDA_AARCH64_ARCHES = ["12.6-aarch64", "12.8-aarch64", "12.9-aarch64", "13.0-aarch64"]
|
||||
|
||||
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
|
||||
@ -76,6 +78,23 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
|
||||
"nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | "
|
||||
"nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'"
|
||||
),
|
||||
"12.9": (
|
||||
"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'"
|
||||
),
|
||||
"13.0": (
|
||||
"nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | "
|
||||
"nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | "
|
||||
@ -222,7 +241,11 @@ def generate_libtorch_matrix(
|
||||
arches += CUDA_ARCHES
|
||||
arches += ROCM_ARCHES
|
||||
elif os == "windows":
|
||||
arches += CUDA_ARCHES
|
||||
# TODO (huydhn): Only build CUDA 12.9 for Linux. This logic is to be cleaned up
|
||||
# in 2.10
|
||||
windows_cuda_arches = CUDA_ARCHES.copy()
|
||||
windows_cuda_arches.remove("12.9")
|
||||
arches += windows_cuda_arches
|
||||
if libtorch_variants is None:
|
||||
libtorch_variants = [
|
||||
"shared-with-deps",
|
||||
@ -286,7 +309,11 @@ def generate_wheels_matrix(
|
||||
if os == "linux":
|
||||
arches += CUDA_ARCHES + ROCM_ARCHES + XPU_ARCHES
|
||||
elif os == "windows":
|
||||
arches += CUDA_ARCHES + XPU_ARCHES
|
||||
# TODO (huydhn): Only build CUDA 12.9 for Linux. This logic is to be cleaned up
|
||||
# in 2.10
|
||||
windows_cuda_arches = CUDA_ARCHES.copy()
|
||||
windows_cuda_arches.remove("12.9")
|
||||
arches += windows_cuda_arches + XPU_ARCHES
|
||||
elif os == "linux-aarch64":
|
||||
# Separate new if as the CPU type is different and
|
||||
# uses different build/test scripts
|
||||
@ -322,7 +349,7 @@ def generate_wheels_matrix(
|
||||
# cuda linux wheels require PYTORCH_EXTRA_INSTALL_REQUIREMENTS to install
|
||||
|
||||
if (
|
||||
arch_version in ["13.0", "12.8", "12.6"]
|
||||
arch_version in ["13.0", "12.9", "12.8", "12.6"]
|
||||
and os == "linux"
|
||||
or arch_version in CUDA_AARCH64_ARCHES
|
||||
):
|
||||
@ -386,5 +413,6 @@ def generate_wheels_matrix(
|
||||
|
||||
|
||||
validate_nccl_dep_consistency("13.0")
|
||||
validate_nccl_dep_consistency("12.9")
|
||||
validate_nccl_dep_consistency("12.8")
|
||||
validate_nccl_dep_consistency("12.6")
|
||||
|
||||
6
.github/scripts/trymerge.py
vendored
6
.github/scripts/trymerge.py
vendored
@ -1092,7 +1092,7 @@ class GitHubPR:
|
||||
editor = node["editor"]
|
||||
return GitHubComment(
|
||||
body_text=node["bodyText"],
|
||||
created_at=node["createdAt"] if "createdAt" in node else "",
|
||||
created_at=node.get("createdAt", ""),
|
||||
author_login=node["author"]["login"],
|
||||
author_url=node["author"].get("url", None),
|
||||
author_association=node["authorAssociation"],
|
||||
@ -2042,10 +2042,6 @@ def validate_revert(
|
||||
f"[{', '.join(allowed_reverters)}], but instead is {author_association}."
|
||||
)
|
||||
|
||||
# Raises exception if matching rule is not found, but ignores all status checks
|
||||
find_matching_merge_rule(
|
||||
pr, repo, skip_mandatory_checks=True, skip_internal_checks=True
|
||||
)
|
||||
commit_sha = get_pr_commit_sha(repo, pr)
|
||||
return (author_login, commit_sha)
|
||||
|
||||
|
||||
@ -177,6 +177,9 @@ jobs:
|
||||
runs-on: linux.rocm.gpu.mi250
|
||||
timeout-minutes: !{{ common.timeout_minutes }}
|
||||
!{{ upload.binary_env(config) }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
|
||||
2
.github/workflows/_docs.yml
vendored
2
.github/workflows/_docs.yml
vendored
@ -72,7 +72,7 @@ jobs:
|
||||
# Let's try to figure out how this can be improved
|
||||
timeout-minutes: 360
|
||||
- docs_type: python
|
||||
runner: ${{ inputs.runner_prefix }}linux.2xlarge
|
||||
runner: ${{ inputs.runner_prefix }}linux.c7i.2xlarge
|
||||
# It takes less than 30m to finish python docs unless there are issues
|
||||
timeout-minutes: 30
|
||||
# Set a fixed name for this job instead of using the current matrix-generated name, i.e. build-docs (cpp, linux.12xlarge, 180)
|
||||
|
||||
2
.github/workflows/_linux-build.yml
vendored
2
.github/workflows/_linux-build.yml
vendored
@ -37,7 +37,7 @@ on:
|
||||
runner:
|
||||
required: false
|
||||
type: string
|
||||
default: "linux.2xlarge"
|
||||
default: "linux.c7i.2xlarge"
|
||||
description: |
|
||||
Label of the runner this job should run on.
|
||||
test-matrix:
|
||||
|
||||
40
.github/workflows/_linux-test.yml
vendored
40
.github/workflows/_linux-test.yml
vendored
@ -224,6 +224,46 @@ jobs:
|
||||
continue-on-error: true
|
||||
uses: ./.github/actions/download-td-artifacts
|
||||
|
||||
- name: Download Windows torch wheel for cross-compilation
|
||||
if: matrix.win_torch_wheel_artifact != ''
|
||||
uses: seemethere/download-artifact-s3@1da556a7aa0a088e3153970611f6c432d58e80e6 # v4.2.0
|
||||
with:
|
||||
name: ${{ matrix.win_torch_wheel_artifact }}
|
||||
path: win-torch-wheel
|
||||
|
||||
- name: Extract Windows wheel and setup CUDA libraries
|
||||
if: matrix.win_torch_wheel_artifact != ''
|
||||
shell: bash
|
||||
run: |
|
||||
set -x
|
||||
|
||||
# Find the wheel file
|
||||
WHEEL_FILE=$(find win-torch-wheel -name "*.whl" -type f | head -n 1)
|
||||
if [ -z "$WHEEL_FILE" ]; then
|
||||
echo "Error: No wheel file found in win-torch-wheel directory"
|
||||
exit 1
|
||||
fi
|
||||
echo "Found wheel file: $WHEEL_FILE"
|
||||
|
||||
# Unzip the wheel file
|
||||
unzip -q "$WHEEL_FILE" -d win-torch-wheel-extracted
|
||||
echo "Extracted wheel contents"
|
||||
|
||||
# Setup CUDA libraries (cuda.lib and cudart.lib) directory
|
||||
mkdir -p win-torch-wheel-extracted/lib/x64
|
||||
if [ -f "win-torch-wheel/cuda.lib" ]; then
|
||||
mv win-torch-wheel/cuda.lib win-torch-wheel-extracted/lib/x64/
|
||||
echo "Moved cuda.lib to win-torch-wheel-extracted/lib/x64/"
|
||||
fi
|
||||
if [ -f "win-torch-wheel/cudart.lib" ]; then
|
||||
mv win-torch-wheel/cudart.lib win-torch-wheel-extracted/lib/x64/
|
||||
echo "Moved cudart.lib to win-torch-wheel-extracted/lib/x64/"
|
||||
fi
|
||||
|
||||
# Verify CUDA libraries are present
|
||||
echo "CUDA libraries:"
|
||||
ls -la win-torch-wheel-extracted/lib/x64/ || echo "No CUDA libraries found"
|
||||
|
||||
- name: Parse ref
|
||||
id: parse-ref
|
||||
run: .github/scripts/parse_ref.py
|
||||
|
||||
13
.github/workflows/_rocm-test.yml
vendored
13
.github/workflows/_rocm-test.yml
vendored
@ -102,19 +102,6 @@ jobs:
|
||||
exit 1
|
||||
fi
|
||||
|
||||
- name: configure aws credentials
|
||||
id: aws_creds
|
||||
uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
|
||||
with:
|
||||
role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
|
||||
aws-region: us-east-1
|
||||
role-duration-seconds: 18000
|
||||
|
||||
- name: Login to Amazon ECR
|
||||
id: login-ecr
|
||||
continue-on-error: true
|
||||
uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
|
||||
|
||||
- name: Calculate docker image
|
||||
id: calculate-docker-image
|
||||
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
|
||||
|
||||
25
.github/workflows/_win-build.yml
vendored
25
.github/workflows/_win-build.yml
vendored
@ -168,6 +168,31 @@ jobs:
|
||||
run: |
|
||||
.ci/pytorch/win-build.sh
|
||||
|
||||
# Collect Windows torch libs and CUDA libs for cross-compilation
|
||||
- name: Collect Windows CUDA libs for cross-compilation
|
||||
if: steps.build.outcome != 'skipped' && inputs.cuda-version != 'cpu'
|
||||
shell: bash
|
||||
run: |
|
||||
set -ex
|
||||
|
||||
# Create directory structure if does not exist
|
||||
mkdir -p /c/${{ github.run_id }}/build-results
|
||||
|
||||
# Copy CUDA libs
|
||||
CUDA_PATH="/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${{ inputs.cuda-version }}"
|
||||
|
||||
if [ -f "${CUDA_PATH}/lib/x64/cuda.lib" ]; then
|
||||
cp "${CUDA_PATH}/lib/x64/cuda.lib" /c/${{ github.run_id }}/build-results/
|
||||
fi
|
||||
|
||||
if [ -f "${CUDA_PATH}/lib/x64/cudart.lib" ]; then
|
||||
cp "${CUDA_PATH}/lib/x64/cudart.lib" /c/${{ github.run_id }}/build-results/
|
||||
fi
|
||||
|
||||
# List collected files
|
||||
echo "Collected CUDA libs:"
|
||||
ls -lah /c/${{ github.run_id }}/build-results/*.lib
|
||||
|
||||
# Upload to github so that people can click and download artifacts
|
||||
- name: Upload artifacts to s3
|
||||
if: steps.build.outcome != 'skipped'
|
||||
|
||||
62
.github/workflows/b200-distributed.yml
vendored
Normal file
62
.github/workflows/b200-distributed.yml
vendored
Normal file
@ -0,0 +1,62 @@
|
||||
name: CI for distributed tests on B200
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- .github/workflows/b200-distributed.yml
|
||||
workflow_dispatch:
|
||||
push:
|
||||
tags:
|
||||
- ciflow/b200-distributed/*
|
||||
schedule:
|
||||
- cron: 46 8 * * * # about 1:46am PDT
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
|
||||
get-label-type:
|
||||
if: github.repository_owner == 'pytorch'
|
||||
name: get-label-type
|
||||
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
|
||||
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: ${{ github.ref_type }}
|
||||
|
||||
linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200:
|
||||
name: linux-jammy-cuda12.8-py3.10-gcc11-build-distributed-b200
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runner: linux.12xlarge.memory
|
||||
build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
|
||||
cuda-arch-list: '10.0'
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "distributed", shard: 1, num_shards: 2, runner: "linux.dgx.b200.8" },
|
||||
{ config: "distributed", shard: 2, num_shards: 2, runner: "linux.dgx.b200.8" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
linux-jammy-cuda12_8-py3_10-gcc11-test-distributed-b200:
|
||||
name: linux-jammy-cuda12.8-py3.10-gcc11-test-b200
|
||||
uses: ./.github/workflows/_linux-test.yml
|
||||
needs:
|
||||
- linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200
|
||||
with:
|
||||
timeout-minutes: 1200
|
||||
build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
|
||||
docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200.outputs.test-matrix }}
|
||||
aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
|
||||
secrets: inherit
|
||||
2
.github/workflows/build-manywheel-images.yml
vendored
2
.github/workflows/build-manywheel-images.yml
vendored
@ -48,8 +48,10 @@ jobs:
|
||||
include: [
|
||||
{ name: "manylinux2_28-builder", tag: "cuda13.0", runner: "linux.9xlarge.ephemeral" },
|
||||
{ name: "manylinux2_28-builder", tag: "cuda12.8", runner: "linux.9xlarge.ephemeral" },
|
||||
{ name: "manylinux2_28-builder", tag: "cuda12.9", runner: "linux.9xlarge.ephemeral" },
|
||||
{ name: "manylinux2_28-builder", tag: "cuda12.6", runner: "linux.9xlarge.ephemeral" },
|
||||
{ name: "manylinuxaarch64-builder", tag: "cuda13.0", runner: "linux.arm64.2xlarge.ephemeral" },
|
||||
{ name: "manylinuxaarch64-builder", tag: "cuda12.9", runner: "linux.arm64.2xlarge.ephemeral" },
|
||||
{ name: "manylinuxaarch64-builder", tag: "cuda12.8", runner: "linux.arm64.2xlarge.ephemeral" },
|
||||
{ name: "manylinuxaarch64-builder", tag: "cuda12.6", runner: "linux.arm64.2xlarge.ephemeral" },
|
||||
{ name: "manylinux2_28-builder", tag: "rocm6.4", runner: "linux.9xlarge.ephemeral" },
|
||||
|
||||
19
.github/workflows/build-vllm-wheel.yml
vendored
19
.github/workflows/build-vllm-wheel.yml
vendored
@ -27,9 +27,8 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python-version: [ '3.12' ]
|
||||
# TODO (huydhn): Add cu130 after https://github.com/vllm-project/vllm/issues/24464 is resolved
|
||||
platform: [ 'manylinux_2_28_x86_64', 'manylinux_2_28_aarch64' ]
|
||||
device: [ 'cu128', 'cu129' ]
|
||||
device: [ 'cu128', 'cu129', 'cu130' ]
|
||||
include:
|
||||
- platform: manylinux_2_28_x86_64
|
||||
device: cu128
|
||||
@ -39,6 +38,10 @@ jobs:
|
||||
device: cu129
|
||||
manylinux-image: 'pytorch/manylinux2_28-builder:cuda12.9'
|
||||
runner: linux.12xlarge.memory
|
||||
- platform: manylinux_2_28_x86_64
|
||||
device: cu130
|
||||
manylinux-image: 'pytorch/manylinux2_28-builder:cuda13.0'
|
||||
runner: linux.12xlarge.memory
|
||||
- platform: manylinux_2_28_aarch64
|
||||
device: cu128
|
||||
manylinux-image: 'pytorch/manylinuxaarch64-builder:cuda12.8'
|
||||
@ -47,6 +50,11 @@ jobs:
|
||||
device: cu129
|
||||
manylinux-image: 'pytorch/manylinuxaarch64-builder:cuda12.9'
|
||||
runner: linux.arm64.r7g.12xlarge.memory
|
||||
exclude:
|
||||
# TODO (huydhn): Add cu130 aarch64 once PyTorch is on 2.9+ and
|
||||
# xformers is update to support 13.0
|
||||
- platform: manylinux_2_28_aarch64
|
||||
device: cu130
|
||||
name: "Build ${{ matrix.device }} vLLM wheel on ${{ matrix.platform }}"
|
||||
runs-on: ${{ matrix.runner }}
|
||||
timeout-minutes: 480
|
||||
@ -169,7 +177,12 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
platform: [ 'manylinux_2_28_x86_64', 'manylinux_2_28_aarch64' ]
|
||||
device: [ 'cu128', 'cu129' ]
|
||||
device: [ 'cu128', 'cu129', 'cu130' ]
|
||||
exclude:
|
||||
# TODO (huydhn): Add cu130 aarch64 once PyTorch is on 2.9+ and
|
||||
# xformers is update to support 13.0
|
||||
- platform: manylinux_2_28_aarch64
|
||||
device: cu130
|
||||
env:
|
||||
PLATFORM: ${{ matrix.platform }}
|
||||
BUILD_DEVICE: ${{ matrix.device }}
|
||||
|
||||
322
.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml
generated
vendored
322
.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml
generated
vendored
@ -204,6 +204,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_10-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.10"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_10-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_10-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.10"
|
||||
build_name: manywheel-py3_10-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_10-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -407,6 +453,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_11-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.11"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_11-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_11-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.11"
|
||||
build_name: manywheel-py3_11-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_11-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -610,6 +702,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_12-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.12"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_12-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_12-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.12"
|
||||
build_name: manywheel-py3_12-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_12-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -813,6 +951,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_13-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_13-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13"
|
||||
build_name: manywheel-py3_13-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -1016,6 +1200,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13t-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_13t-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_13t-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
build_name: manywheel-py3_13t-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13t-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -1219,6 +1449,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_14-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_14-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14"
|
||||
build_name: manywheel-py3_14-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -1422,6 +1698,52 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14t-cuda-aarch64-12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.arm64.r7g.12xlarge.memory
|
||||
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==3.3.20; 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 }}
|
||||
manywheel-py3_14t-cuda-aarch64-12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_14t-cuda-aarch64-12_9-build
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9-aarch64"
|
||||
GPU_ARCH_TYPE: cuda-aarch64
|
||||
DOCKER_IMAGE: manylinuxaarch64-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
build_name: manywheel-py3_14t-cuda-aarch64-12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14t-cuda-aarch64-13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
|
||||
74
.github/workflows/generated-linux-binary-libtorch-nightly.yml
generated
vendored
74
.github/workflows/generated-linux-binary-libtorch-nightly.yml
generated
vendored
@ -248,6 +248,74 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
libtorch-cuda12_9-shared-with-deps-release-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: libtorch
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: libtorch-cxx11-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
LIBTORCH_CONFIG: release
|
||||
LIBTORCH_VARIANT: shared-with-deps
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: libtorch-cuda12_9-shared-with-deps-release
|
||||
build_environment: linux-binary-libtorch
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
libtorch-cuda12_9-shared-with-deps-release-test: # Testing
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- libtorch-cuda12_9-shared-with-deps-release-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: libtorch
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: libtorch-cxx11-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
LIBTORCH_CONFIG: release
|
||||
LIBTORCH_VARIANT: shared-with-deps
|
||||
build_name: libtorch-cuda12_9-shared-with-deps-release
|
||||
build_environment: linux-binary-libtorch
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
libtorch-cuda12_9-shared-with-deps-release-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: libtorch-cuda12_9-shared-with-deps-release-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: libtorch
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: libtorch-cxx11-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
LIBTORCH_CONFIG: release
|
||||
LIBTORCH_VARIANT: shared-with-deps
|
||||
build_name: libtorch-cuda12_9-shared-with-deps-release
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
libtorch-cuda13_0-shared-with-deps-release-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -358,6 +426,9 @@ jobs:
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
LIBTORCH_CONFIG: release
|
||||
LIBTORCH_VARIANT: shared-with-deps
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -473,6 +544,9 @@ jobs:
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
LIBTORCH_CONFIG: release
|
||||
LIBTORCH_VARIANT: shared-with-deps
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
|
||||
504
.github/workflows/generated-linux-binary-manywheel-nightly.yml
generated
vendored
504
.github/workflows/generated-linux-binary-manywheel-nightly.yml
generated
vendored
@ -241,6 +241,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_10-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.10"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_10-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.10"
|
||||
build_name: manywheel-py3_10-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_10-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_10-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.10"
|
||||
build_name: manywheel-py3_10-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_10-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -347,6 +413,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.10"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -459,6 +528,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.10"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -835,6 +907,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_11-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.11"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_11-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.11"
|
||||
build_name: manywheel-py3_11-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_11-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_11-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.11"
|
||||
build_name: manywheel-py3_11-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_11-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -941,6 +1079,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.11"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -1053,6 +1194,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.11"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -1429,6 +1573,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_12-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.12"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_12-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.12"
|
||||
build_name: manywheel-py3_12-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_12-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_12-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.12"
|
||||
build_name: manywheel-py3_12-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_12-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -1535,6 +1745,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.12"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -1647,6 +1860,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.12"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -2023,6 +2239,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_13-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13"
|
||||
build_name: manywheel-py3_13-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_13-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13"
|
||||
build_name: manywheel-py3_13-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -2129,6 +2411,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.13"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -2241,6 +2526,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.13"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -2617,6 +2905,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13t-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_13t-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
build_name: manywheel-py3_13t-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13t-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_13t-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
build_name: manywheel-py3_13t-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_13t-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -2723,6 +3077,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -2835,6 +3192,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.13t"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -3211,6 +3571,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_14-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14"
|
||||
build_name: manywheel-py3_14-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_14-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14"
|
||||
build_name: manywheel-py3_14-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -3317,6 +3743,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.14"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -3429,6 +3858,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.14"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -3805,6 +4237,72 @@ jobs:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14t-cuda12_9-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
runner_prefix: "${{ 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==3.3.20; 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
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
needs:
|
||||
- manywheel-py3_14t-cuda12_9-build
|
||||
- get-label-type
|
||||
uses: ./.github/workflows/_binary-test-linux.yml
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
build_name: manywheel-py3_14t-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14t-cuda12_9-upload: # Uploading
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
needs: manywheel-py3_14t-cuda12_9-test
|
||||
with:
|
||||
PYTORCH_ROOT: /pytorch
|
||||
PACKAGE_TYPE: manywheel
|
||||
# TODO: This is a legacy variable that we eventually want to get rid of in
|
||||
# favor of GPU_ARCH_VERSION
|
||||
DESIRED_CUDA: cu129
|
||||
GPU_ARCH_VERSION: "12.9"
|
||||
GPU_ARCH_TYPE: cuda
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: cuda12.9
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
build_name: manywheel-py3_14t-cuda12_9
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
uses: ./.github/workflows/_binary-upload.yml
|
||||
|
||||
manywheel-py3_14t-cuda13_0-build:
|
||||
if: ${{ github.repository_owner == 'pytorch' }}
|
||||
uses: ./.github/workflows/_binary-build-linux.yml
|
||||
@ -3911,6 +4409,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
@ -4023,6 +4524,9 @@ jobs:
|
||||
DOCKER_IMAGE: manylinux2_28-builder
|
||||
DOCKER_IMAGE_TAG_PREFIX: rocm7.0
|
||||
DESIRED_PYTHON: "3.14t"
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
steps:
|
||||
- name: Setup ROCm
|
||||
uses: ./.github/actions/setup-rocm
|
||||
|
||||
@ -130,7 +130,7 @@ jobs:
|
||||
name: test-periodically
|
||||
uses: ./.github/workflows/_linux-test.yml
|
||||
needs: build
|
||||
if: github.event.schedule == '15 0,12 * * 1-6'
|
||||
if: github.event.schedule == '15 0 * * 1-6'
|
||||
with:
|
||||
build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm90
|
||||
dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-true-cppwrapper-true-aotinductor-true-freezing_cudagraphs-true-cudagraphs_low_precision-true
|
||||
|
||||
132
.github/workflows/inductor-perf-test-nightly-rocm-mi300.yml
vendored
Normal file
132
.github/workflows/inductor-perf-test-nightly-rocm-mi300.yml
vendored
Normal file
@ -0,0 +1,132 @@
|
||||
name: inductor-perf-nightly-rocm-mi300
|
||||
|
||||
on:
|
||||
push:
|
||||
tags:
|
||||
- ciflow/inductor-perf-test-nightly-rocm-mi300/*
|
||||
schedule:
|
||||
- cron: 15 0 * * *
|
||||
# NB: GitHub has an upper limit of 10 inputs here, so before we can sort it
|
||||
# out, let try to run torchao cudagraphs_low_precision as part of cudagraphs
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
training:
|
||||
description: Run training (on by default)?
|
||||
required: false
|
||||
type: boolean
|
||||
default: true
|
||||
inference:
|
||||
description: Run inference (on by default)?
|
||||
required: false
|
||||
type: boolean
|
||||
default: true
|
||||
default:
|
||||
description: Run inductor_default?
|
||||
required: false
|
||||
type: boolean
|
||||
default: false
|
||||
dynamic:
|
||||
description: Run inductor_dynamic_shapes?
|
||||
required: false
|
||||
type: boolean
|
||||
default: false
|
||||
cppwrapper:
|
||||
description: Run inductor_cpp_wrapper?
|
||||
required: false
|
||||
type: boolean
|
||||
default: false
|
||||
cudagraphs:
|
||||
description: Run inductor_cudagraphs?
|
||||
required: false
|
||||
type: boolean
|
||||
default: true
|
||||
freezing_cudagraphs:
|
||||
description: Run inductor_cudagraphs with freezing for inference?
|
||||
required: false
|
||||
type: boolean
|
||||
default: false
|
||||
aotinductor:
|
||||
description: Run aot_inductor for inference?
|
||||
required: false
|
||||
type: boolean
|
||||
default: false
|
||||
maxautotune:
|
||||
description: Run inductor_max_autotune?
|
||||
required: false
|
||||
type: boolean
|
||||
default: false
|
||||
benchmark_configs:
|
||||
description: The list of configs used the benchmark
|
||||
required: false
|
||||
type: string
|
||||
default: inductor_huggingface_perf_rocm_mi300,inductor_timm_perf_rocm_mi300,inductor_torchbench_perf_rocm_mi300
|
||||
|
||||
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' }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions: read-all
|
||||
|
||||
jobs:
|
||||
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 }}
|
||||
opt_out_experiments: lf
|
||||
|
||||
linux-jammy-rocm-py3_10-inductor-benchmark-build:
|
||||
if: github.repository_owner == 'pytorch'
|
||||
name: rocm-py3_10-inductor-benchmark-build
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
with:
|
||||
build-environment: linux-jammy-rocm-py3_10
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "inductor_huggingface_perf_rocm_mi300", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi300", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi300", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi300", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi300", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 1, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 2, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 3, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 4, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 5, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 6, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi300", shard: 7, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 1, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 2, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 3, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 4, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 5, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 6, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 7, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 8, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi300", shard: 9, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
linux-jammy-rocm-py3_10-inductor-benchmark-test:
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
name: rocm-py3_10-inductor-benchmark-test
|
||||
uses: ./.github/workflows/_rocm-test.yml
|
||||
needs: linux-jammy-rocm-py3_10-inductor-benchmark-build
|
||||
with:
|
||||
build-environment: linux-jammy-rocm-py3_10
|
||||
dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-true-cppwrapper-true-aotinductor-true-freezing_cudagraphs-true-cudagraphs_low_precision-true
|
||||
docker-image: ${{ needs.linux-jammy-rocm-py3_10-inductor-benchmark-build.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.linux-jammy-rocm-py3_10-inductor-benchmark-build.outputs.test-matrix }}
|
||||
timeout-minutes: 720
|
||||
# Disable monitor in perf tests for more investigation
|
||||
disable-monitor: true
|
||||
monitor-log-interval: 10
|
||||
monitor-data-collect-interval: 2
|
||||
secrets: inherit
|
||||
@ -1,11 +1,11 @@
|
||||
name: inductor-perf-nightly-rocm
|
||||
name: inductor-perf-nightly-rocm-mi355
|
||||
|
||||
on:
|
||||
push:
|
||||
tags:
|
||||
- ciflow/inductor-perf-test-nightly-rocm/*
|
||||
- ciflow/inductor-perf-test-nightly-rocm-mi355/*
|
||||
schedule:
|
||||
- cron: 0 7 * * 0,3
|
||||
- cron: 15 0 * * *
|
||||
# NB: GitHub has an upper limit of 10 inputs here, so before we can sort it
|
||||
# out, let try to run torchao cudagraphs_low_precision as part of cudagraphs
|
||||
workflow_dispatch:
|
||||
@ -59,7 +59,7 @@ on:
|
||||
description: The list of configs used the benchmark
|
||||
required: false
|
||||
type: string
|
||||
default: inductor_huggingface_perf_rocm,inductor_timm_perf_rocm,inductor_torchbench_perf_rocm
|
||||
default: inductor_huggingface_perf_rocm_mi355,inductor_timm_perf_rocm_mi355,inductor_torchbench_perf_rocm_mi355
|
||||
|
||||
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' }}
|
||||
@ -88,23 +88,27 @@ jobs:
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "inductor_huggingface_perf_rocm", shard: 1, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm", shard: 2, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm", shard: 3, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm", shard: 4, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_timm_perf_rocm", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 1, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 2, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 3, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 4, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 5, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 6, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 7, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm", shard: 8, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi355", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi355", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi355", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi355", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_huggingface_perf_rocm_mi355", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 1, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 2, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 3, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 4, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 5, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 6, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_timm_perf_rocm_mi355", shard: 7, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 1, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 2, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 3, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 4, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 5, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 6, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 7, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 8, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "inductor_torchbench_perf_rocm_mi355", shard: 9, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
11
.github/workflows/lint.yml
vendored
11
.github/workflows/lint.yml
vendored
@ -12,6 +12,7 @@ on:
|
||||
- landchecks/*
|
||||
tags:
|
||||
- ciflow/pull/*
|
||||
- ciflow/trunk/*
|
||||
workflow_dispatch:
|
||||
|
||||
permissions: read-all
|
||||
@ -32,10 +33,12 @@ jobs:
|
||||
name: Get changed files
|
||||
uses: ./.github/workflows/_get-changed-files.yml
|
||||
with:
|
||||
all_files: ${{ contains(github.event.pull_request.labels.*.name, 'lint-all-files') || contains(github.event.pull_request.labels.*.name, 'Reverted') }}
|
||||
all_files: ${{ contains(github.event.pull_request.labels.*.name, 'lint-all-files') || contains(github.event.pull_request.labels.*.name, 'Reverted') || github.event_name == 'push' }}
|
||||
|
||||
lintrunner-clang:
|
||||
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
|
||||
# Needed to prevent deduping on HUD
|
||||
name: lintrunner-clang-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
|
||||
needs: [get-label-type, get-changed-files]
|
||||
# Only run if there are changed files relevant to clangtidy / clangformat
|
||||
if: |
|
||||
@ -75,6 +78,7 @@ jobs:
|
||||
# fails to find types when it should
|
||||
lintrunner-mypy:
|
||||
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
|
||||
name: lintrunner-mypy-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
|
||||
needs: [get-label-type, get-changed-files]
|
||||
# Only run if there are changed files relevant to mypy
|
||||
if: |
|
||||
@ -99,6 +103,7 @@ jobs:
|
||||
|
||||
lintrunner-noclang:
|
||||
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
|
||||
name: lintrunner-noclang-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
|
||||
needs: [get-label-type, get-changed-files]
|
||||
with:
|
||||
timeout: 120
|
||||
@ -113,9 +118,9 @@ jobs:
|
||||
CHANGED_FILES="${{ needs.get-changed-files.outputs.changed-files }}"
|
||||
echo "Running all other linters"
|
||||
if [ "$CHANGED_FILES" = '*' ]; then
|
||||
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT --all-files" .github/scripts/lintrunner.sh
|
||||
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY --all-files" .github/scripts/lintrunner.sh
|
||||
else
|
||||
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT ${CHANGED_FILES}" .github/scripts/lintrunner.sh
|
||||
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
|
||||
fi
|
||||
|
||||
quick-checks:
|
||||
|
||||
61
.github/workflows/operator_benchmark.yml
vendored
61
.github/workflows/operator_benchmark.yml
vendored
@ -7,9 +7,11 @@ on:
|
||||
workflow_dispatch:
|
||||
inputs:
|
||||
test_mode:
|
||||
required: false
|
||||
type: string
|
||||
default: 'short'
|
||||
type: choice
|
||||
options:
|
||||
- 'short'
|
||||
- 'long'
|
||||
- 'all'
|
||||
description: tag filter for operator benchmarks, options from long, short, all
|
||||
schedule:
|
||||
# Run at 07:00 UTC every Sunday
|
||||
@ -28,38 +30,49 @@ permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
opbenchmark-build:
|
||||
x86-opbenchmark-build:
|
||||
if: github.repository_owner == 'pytorch'
|
||||
name: opbenchmark-build
|
||||
name: x86-opbenchmark-build
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
with:
|
||||
build-environment: linux-jammy-py3.10-gcc11-build
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-py3-gcc11-inductor-benchmarks
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
|
||||
{ config: "cpu_operator_benchmark_${{ inputs.test_mode || 'short' }}", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
opbenchmark-on-demand-build:
|
||||
if: ${{ github.event_name == 'workflow_dispatch' && github.repository_owner == 'pytorch' }}
|
||||
name: opbenchmark-on-demand-build
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
with:
|
||||
build-environment: linux-jammy-py3.10-gcc11-build
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-py3-gcc11-inductor-benchmarks
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "cpu_operator_benchmark_${{ inputs.test_mode }}", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
opbenchmark-test:
|
||||
name: opbenchmark-test
|
||||
x86-opbenchmark-test:
|
||||
name: x86-opbenchmark-test
|
||||
uses: ./.github/workflows/_linux-test.yml
|
||||
needs: opbenchmark-build
|
||||
needs: x86-opbenchmark-build
|
||||
with:
|
||||
build-environment: linux-jammy-py3.10-gcc11-build
|
||||
docker-image: ${{ needs.opbenchmark-build.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.opbenchmark-build.outputs.test-matrix }}
|
||||
docker-image: ${{ needs.x86-opbenchmark-build.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.x86-opbenchmark-build.outputs.test-matrix }}
|
||||
secrets: inherit
|
||||
|
||||
aarch64-opbenchmark-build:
|
||||
if: github.repository_owner == 'pytorch'
|
||||
name: aarch64-opbenchmark-build
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
with:
|
||||
build-environment: linux-jammy-aarch64-py3.10
|
||||
runner: linux.arm64.m7g.4xlarge
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.arm64.m8g.4xlarge" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
aarch64-opbenchmark-test:
|
||||
name: aarch64-opbenchmark-test
|
||||
uses: ./.github/workflows/_linux-test.yml
|
||||
needs: aarch64-opbenchmark-build
|
||||
with:
|
||||
build-environment: linux-jammy-aarch64-py3.10
|
||||
docker-image: ${{ needs.aarch64-opbenchmark-build.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.aarch64-opbenchmark-build.outputs.test-matrix }}
|
||||
secrets: inherit
|
||||
|
||||
10
.github/workflows/periodic.yml
vendored
10
.github/workflows/periodic.yml
vendored
@ -182,11 +182,11 @@ jobs:
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "nogpu_AVX512", shard: 1, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
|
||||
{ config: "nogpu_AVX512", shard: 2, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
|
||||
{ config: "nogpu_AVX512", shard: 3, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
|
||||
{ config: "nogpu_NO_AVX2", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
|
||||
{ config: "nogpu_NO_AVX2", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
|
||||
{ config: "nogpu_AVX512", shard: 1, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge" },
|
||||
{ config: "nogpu_AVX512", shard: 2, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge" },
|
||||
{ config: "nogpu_AVX512", shard: 3, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge" },
|
||||
{ config: "nogpu_NO_AVX2", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge" },
|
||||
{ config: "nogpu_NO_AVX2", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge" },
|
||||
{ config: "jit_legacy", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
1
.github/workflows/pull.yml
vendored
1
.github/workflows/pull.yml
vendored
@ -127,6 +127,7 @@ jobs:
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
runner: linux.2xlarge.memory
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build-environment: linux-jammy-py3.10-clang18-asan
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-py3-clang18-asan
|
||||
|
||||
12
.github/workflows/rocm-mi355.yml
vendored
12
.github/workflows/rocm-mi355.yml
vendored
@ -45,12 +45,12 @@ jobs:
|
||||
sync-tag: rocm-build
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
|
||||
{ config: "default", shard: 2, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
|
||||
{ config: "default", shard: 3, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
|
||||
{ config: "default", shard: 4, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
|
||||
{ config: "default", shard: 5, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
|
||||
{ config: "default", shard: 6, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
|
||||
{ config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "default", shard: 2, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "default", shard: 3, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "default", shard: 4, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "default", shard: 5, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
|
||||
{ config: "default", shard: 6, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
|
||||
1
.github/workflows/slow.yml
vendored
1
.github/workflows/slow.yml
vendored
@ -140,6 +140,7 @@ jobs:
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
runner: linux.2xlarge.memory
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build-environment: linux-jammy-py3.10-clang18-asan
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-py3-clang18-asan
|
||||
|
||||
25
.github/workflows/trunk.yml
vendored
25
.github/workflows/trunk.yml
vendored
@ -180,13 +180,13 @@ jobs:
|
||||
disable-monitor: false
|
||||
secrets: inherit
|
||||
|
||||
win-vs2022-cuda12_6-py3-build:
|
||||
name: win-vs2022-cuda12.6-py3
|
||||
win-vs2022-cuda12_8-py3-build:
|
||||
name: win-vs2022-cuda12.8-py3
|
||||
uses: ./.github/workflows/_win-build.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
build-environment: win-vs2022-cuda12.6-py3
|
||||
cuda-version: "12.6"
|
||||
build-environment: win-vs2022-cuda12.8-py3
|
||||
cuda-version: "12.8"
|
||||
runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"
|
||||
secrets: inherit
|
||||
|
||||
@ -200,6 +200,23 @@ jobs:
|
||||
cuda-arch-list: '8.0'
|
||||
secrets: inherit
|
||||
|
||||
# Test cross-compiled models with Windows libs extracted from wheel
|
||||
cross-compile-linux-test:
|
||||
name: cross-compile-linux-test
|
||||
uses: ./.github/workflows/_linux-test.yml
|
||||
needs:
|
||||
- linux-jammy-cuda12_8-py3_10-gcc11-build
|
||||
- get-label-type
|
||||
- win-vs2022-cuda12_8-py3-build
|
||||
with:
|
||||
build-environment: linux-jammy-cuda12.8-py3.10-gcc11
|
||||
docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build.outputs.docker-image }}
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "aoti_cross_compile_for_windows", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", win_torch_wheel_artifact: "win-vs2022-cuda12.8-py3" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
verify-cachebench-cpu-build:
|
||||
name: verify-cachebench-cpu-build
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
|
||||
2
.github/workflows/vllm.yml
vendored
2
.github/workflows/vllm.yml
vendored
@ -54,7 +54,7 @@ jobs:
|
||||
{ config: "vllm_pytorch_compilation_unit_tests", shard: 1, num_shards: 1, runner: "linux.g6.4xlarge.experimental.nvidia.gpu" },
|
||||
{ config: "vllm_lora_28_failure_test", shard: 1, num_shards: 1, runner: "linux.g6.4xlarge.experimental.nvidia.gpu" },
|
||||
{ config: "vllm_multi_model_test_28_failure_test", shard: 1, num_shards: 1, runner: "linux.g6.4xlarge.experimental.nvidia.gpu"},
|
||||
{ config: "vllm_languagde_model_test_extended_generation_28_failure_test", shard: 1, num_shards: 1, runner: "linux.g6.4xlarge.experimental.nvidia.gpu"},
|
||||
{ config: "vllm_language_model_test_extended_generation_28_failure_test", shard: 1, num_shards: 1, runner: "linux.g6.4xlarge.experimental.nvidia.gpu"},
|
||||
{ config: "vllm_distributed_test_2_gpu_28_failure_test", shard: 1, num_shards: 1, runner: "linux.g6.4xlarge.experimental.nvidia.gpu" },
|
||||
{ config: "vllm_lora_test", shard: 0, num_shards: 4, runner: "linux.g6.4xlarge.experimental.nvidia.gpu" },
|
||||
{ config: "vllm_lora_test", shard: 1, num_shards: 4, runner: "linux.g6.4xlarge.experimental.nvidia.gpu" },
|
||||
|
||||
2
.gitignore
vendored
2
.gitignore
vendored
@ -374,6 +374,7 @@ third_party/ruy/
|
||||
third_party/glog/
|
||||
|
||||
# Virtualenv
|
||||
.venv/
|
||||
venv/
|
||||
|
||||
# Log files
|
||||
@ -395,3 +396,4 @@ android/pytorch_android_torchvision/.cxx
|
||||
CLAUDE.local.md
|
||||
/test_*.py
|
||||
/debug_*.py
|
||||
CLAUDE_CONTEXT/
|
||||
|
||||
@ -209,6 +209,46 @@ command = [
|
||||
'@{{PATHSFILE}}'
|
||||
]
|
||||
|
||||
|
||||
[[linter]]
|
||||
code = 'PYREFLY'
|
||||
include_patterns = [
|
||||
'torch/**/*.py',
|
||||
'torch/**/*.pyi',
|
||||
'torchgen/**/*.py',
|
||||
'torchgen/**/*.pyi',
|
||||
'functorch/**/*.py',
|
||||
'functorch/**/*.pyi',
|
||||
]
|
||||
exclude_patterns = []
|
||||
command = [
|
||||
'python3',
|
||||
'tools/linter/adapters/pyrefly_linter.py',
|
||||
'--config=pyrefly.toml',
|
||||
]
|
||||
init_command = [
|
||||
'python3',
|
||||
'tools/linter/adapters/pip_init.py',
|
||||
'--dry-run={{DRYRUN}}',
|
||||
'numpy==2.1.0 ; python_version >= "3.12"',
|
||||
'expecttest==0.3.0',
|
||||
'pyrefly==0.36.2',
|
||||
'sympy==1.13.3',
|
||||
'types-requests==2.27.25',
|
||||
'types-pyyaml==6.0.2',
|
||||
'types-tabulate==0.8.8',
|
||||
'types-protobuf==5.29.1.20250403',
|
||||
'types-setuptools==79.0.0.20250422',
|
||||
'types-jinja2==2.11.9',
|
||||
'types-colorama==0.4.6',
|
||||
'filelock==3.18.0',
|
||||
'junitparser==2.1.1',
|
||||
'rich==14.1.0',
|
||||
'optree==0.17.0',
|
||||
'types-openpyxl==3.1.5.20250919',
|
||||
'types-python-dateutil==2.9.0.20251008'
|
||||
]
|
||||
|
||||
[[linter]]
|
||||
code = 'CLANGTIDY'
|
||||
include_patterns = [
|
||||
|
||||
14
CODEOWNERS
14
CODEOWNERS
@ -201,3 +201,17 @@ torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
|
||||
/torch/csrc/stable/ @janeyx99 @mikaylagawarecki
|
||||
/torch/headeronly/ @janeyx99
|
||||
/torch/header_only_apis.txt @janeyx99
|
||||
|
||||
# FlexAttention
|
||||
/torch/nn/attention/flex_attention.py @drisspg
|
||||
/torch/_higher_order_ops/flex_attention.py @drisspg
|
||||
/torch/_inductor/kernel/flex/ @drisspg
|
||||
/torch/_inductor/codegen/cpp_flex_attention_template.py @drisspg
|
||||
/test/inductor/test_flex_attention.py @drisspg
|
||||
/test/inductor/test_flex_decoding.py @drisspg
|
||||
|
||||
# Low Precision GEMMs
|
||||
/aten/src/ATen/native/cuda/Blas.cpp @drisspg @slayton58
|
||||
/aten/src/ATen/cuda/CUDABlas.cpp @drisspg @slayton58
|
||||
/aten/src/ATen/cuda/CUDABlas.h @drisspg @slayton58
|
||||
/test/test_scaled_matmul_cuda.py @drisspg @slayton58
|
||||
|
||||
@ -256,6 +256,7 @@ endif()
|
||||
IF(USE_FBGEMM_GENAI)
|
||||
set(FBGEMM_THIRD_PARTY ${PROJECT_SOURCE_DIR}/third_party/fbgemm/external/)
|
||||
set(FBGEMM_GENAI_SRCS ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize)
|
||||
|
||||
if(USE_CUDA)
|
||||
# To avoid increasing the build time/binary size unnecessarily, use an allow-list of kernels to build.
|
||||
# If you want to integrate a kernel from FBGEMM into torch, you have to add it here.
|
||||
@ -288,19 +289,23 @@ IF(USE_FBGEMM_GENAI)
|
||||
|
||||
set_target_properties(fbgemm_genai PROPERTIES POSITION_INDEPENDENT_CODE ON)
|
||||
|
||||
set(fbgemm_genai_mx8mx8bf16_grouped
|
||||
set(fbgemm_genai_cuh
|
||||
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/mx8mx8bf16_grouped/"
|
||||
"${FBGEMM_GENAI_SRCS}/"
|
||||
)
|
||||
|
||||
target_include_directories(fbgemm_genai PUBLIC
|
||||
target_include_directories(fbgemm_genai PRIVATE
|
||||
${FBGEMM_THIRD_PARTY}/cutlass/include
|
||||
${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
|
||||
${fbgemm_genai_mx8mx8bf16_grouped}
|
||||
${fbgemm_genai_cuh}
|
||||
${FBGEMM_GENAI_SRCS}/common/include/ # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
|
||||
${FBGEMM_GENAI_SRCS}/include/ # includes fbgemm_gpu/torch_ops.h
|
||||
)
|
||||
else()
|
||||
if(USE_ROCM)
|
||||
|
||||
# Add FBGEMM_GENAI include directories for torch_ops.h
|
||||
list(APPEND ATen_CUDA_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
|
||||
list(APPEND ATen_CUDA_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
|
||||
elseif(USE_ROCM)
|
||||
# Only include the kernels we want to build to avoid increasing binary size.
|
||||
file(GLOB_RECURSE fbgemm_genai_native_rocm_hip
|
||||
"${FBGEMM_GENAI_SRCS}/ck_extensions/fp8_rowwise_grouped/kernels/fp8_rowwise_grouped*.hip"
|
||||
@ -334,7 +339,7 @@ IF(USE_FBGEMM_GENAI)
|
||||
set_target_properties(fbgemm_genai PROPERTIES POSITION_INDEPENDENT_CODE ON)
|
||||
target_compile_definitions(fbgemm_genai PRIVATE FBGEMM_GENAI_NO_EXTENDED_SHAPES)
|
||||
|
||||
target_include_directories(fbgemm_genai PUBLIC
|
||||
target_include_directories(fbgemm_genai PRIVATE
|
||||
# FBGEMM version of Composable Kernel is used due to some customizations
|
||||
${FBGEMM_THIRD_PARTY}/composable_kernel/include
|
||||
${FBGEMM_THIRD_PARTY}/composable_kernel/library/include
|
||||
@ -343,7 +348,10 @@ IF(USE_FBGEMM_GENAI)
|
||||
${FBGEMM_GENAI_SRCS}/common/include/ # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
|
||||
${FBGEMM_GENAI_SRCS}/include/ # includes fbgemm_gpu/torch_ops.h
|
||||
)
|
||||
endif()
|
||||
|
||||
# Add FBGEMM_GENAI include directories for torch_ops.h
|
||||
list(APPEND ATen_HIP_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
|
||||
list(APPEND ATen_HIP_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
@ -692,12 +700,6 @@ if(USE_CUDA AND NOT USE_ROCM)
|
||||
list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/cutlass/include)
|
||||
list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/cutlass/tools/util/include)
|
||||
|
||||
# Add FBGEMM_GENAI include directories for torch_ops.h
|
||||
if(USE_FBGEMM_GENAI)
|
||||
list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
|
||||
list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
|
||||
endif()
|
||||
|
||||
if($ENV{ATEN_STATIC_CUDA})
|
||||
if(CUDA_VERSION VERSION_LESS_EQUAL 12.9)
|
||||
list(APPEND ATen_CUDA_DEPENDENCY_LIBS
|
||||
|
||||
@ -389,37 +389,16 @@ void fillVersion<DLManagedTensorVersioned>(
|
||||
// constructed out of ATen tensor
|
||||
template <class T>
|
||||
T* toDLPackImpl(const Tensor& src) {
|
||||
auto view = src;
|
||||
|
||||
// Detect whether there is need to normalize the strides
|
||||
// Background: gh-83069
|
||||
//
|
||||
// However, normalizing strides can come at a high-cost
|
||||
// to slow down toDLPack conversion 3x, so we
|
||||
// only normalize if needed.
|
||||
//
|
||||
// The following code detects whether the src follows
|
||||
// a continuous pattern. If the src follows such pattern (common-case)
|
||||
// then we do not need to normalize the strides.
|
||||
bool need_normalize_strides = src.dim() == 1 && src.size(0) == 1 && src.stride(0) != 1;
|
||||
// less common case, try normalizing the strides
|
||||
if (need_normalize_strides) {
|
||||
// create a new tensor with possibly normalized strides
|
||||
// gh-83069
|
||||
auto shape = src.sizes();
|
||||
view = src.as_strided(shape, {1}, src.storage_offset());
|
||||
}
|
||||
|
||||
ATenDLMTensor<T>* atDLMTensor(new ATenDLMTensor<T>);
|
||||
atDLMTensor->handle = view;
|
||||
atDLMTensor->handle = src;
|
||||
atDLMTensor->tensor.manager_ctx = atDLMTensor;
|
||||
atDLMTensor->tensor.deleter = &deleter<T>;
|
||||
atDLMTensor->tensor.dl_tensor.data = view.data_ptr();
|
||||
atDLMTensor->tensor.dl_tensor.data = src.data_ptr();
|
||||
atDLMTensor->tensor.dl_tensor.device = torchDeviceToDLDevice(src.device());
|
||||
atDLMTensor->tensor.dl_tensor.ndim = static_cast<int32_t>(src.dim());
|
||||
atDLMTensor->tensor.dl_tensor.dtype = getDLDataType(src);
|
||||
atDLMTensor->tensor.dl_tensor.shape = const_cast<int64_t*>(view.sizes().data());
|
||||
atDLMTensor->tensor.dl_tensor.strides = const_cast<int64_t*>(view.strides().data());
|
||||
atDLMTensor->tensor.dl_tensor.shape = const_cast<int64_t*>(src.sizes().data());
|
||||
atDLMTensor->tensor.dl_tensor.strides = const_cast<int64_t*>(src.strides().data());
|
||||
atDLMTensor->tensor.dl_tensor.byte_offset = 0;
|
||||
fillVersion(&atDLMTensor->tensor);
|
||||
|
||||
|
||||
@ -52,16 +52,16 @@ struct DLPackTraits {};
|
||||
|
||||
template <>
|
||||
struct DLPackTraits<DLManagedTensor> {
|
||||
inline static const char* capsule = "dltensor";
|
||||
inline static const char* used = "used_dltensor";
|
||||
inline static constexpr const char* capsule = "dltensor";
|
||||
inline static constexpr const char* used = "used_dltensor";
|
||||
inline static auto toDLPack = at::toDLPack;
|
||||
inline static auto fromDLPack = at::fromDLPack;
|
||||
};
|
||||
|
||||
template <>
|
||||
struct DLPackTraits<DLManagedTensorVersioned> {
|
||||
inline static const char* capsule = "dltensor_versioned";
|
||||
inline static const char* used = "used_dltensor_versioned";
|
||||
inline static constexpr const char* capsule = "dltensor_versioned";
|
||||
inline static constexpr const char* used = "used_dltensor_versioned";
|
||||
inline static auto toDLPack = at::toDLPackVersioned;
|
||||
inline static auto fromDLPack = at::fromDLPackVersioned;
|
||||
};
|
||||
|
||||
@ -4,6 +4,7 @@
|
||||
#include <c10/core/ScalarType.h>
|
||||
#include <c10/core/SymIntArrayRef.h>
|
||||
#include <c10/util/DimVector.h>
|
||||
#include <c10/util/Exception.h>
|
||||
#include <optional>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
@ -26,9 +27,7 @@ inline void infer_size_impl(
|
||||
std::optional<int64_t> infer_dim;
|
||||
for (int64_t dim = 0, ndim = shape.size(); dim != ndim; dim++) {
|
||||
if (TORCH_GUARD_OR_FALSE(sym_eq(shape[dim], -1))) {
|
||||
if (infer_dim) {
|
||||
throw std::runtime_error("only one dimension can be inferred");
|
||||
}
|
||||
TORCH_CHECK(!infer_dim, "only one dimension can be inferred");
|
||||
infer_dim = dim;
|
||||
} else {
|
||||
// in case of unbacked shape[dim] we assume it's not -1 and add a runtime
|
||||
|
||||
@ -58,7 +58,7 @@ namespace at {
|
||||
namespace{
|
||||
|
||||
// PyTorch allows operations to specify dim 0 and dim -1 on a scalar tensor.
|
||||
static bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
|
||||
bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
|
||||
return dim == 0 || dim == -1;
|
||||
}
|
||||
|
||||
@ -365,7 +365,7 @@ Tensor select_batching_rule(const Tensor& self, int64_t dim, int64_t index) {
|
||||
return self_physical.getPhysicalToLogicalMap().apply(result);
|
||||
}
|
||||
|
||||
static int64_t getGradInputPhysicalDim(int64_t dim, IntArrayRef input_sizes, int64_t num_batch_dims) {
|
||||
int64_t getGradInputPhysicalDim(int64_t dim, IntArrayRef input_sizes, int64_t num_batch_dims) {
|
||||
return maybe_wrap_dim(dim, static_cast<int64_t>(input_sizes.size())) + num_batch_dims;
|
||||
}
|
||||
|
||||
@ -488,7 +488,7 @@ Tensor view_as_complex_batching_rule(const Tensor& self) {
|
||||
// Checks that the smallest batch stride is greater than the largest example
|
||||
// stride. This is something we can support but we choose not to because it's
|
||||
// potentially error prone.
|
||||
static void checkBatchDimsAtFrontInLayout(IntArrayRef physical_strides, int64_t num_batch_dims) {
|
||||
void checkBatchDimsAtFrontInLayout(IntArrayRef physical_strides, int64_t num_batch_dims) {
|
||||
auto smallest_batch_stride = std::min_element(
|
||||
physical_strides.begin(), physical_strides.begin() + num_batch_dims);
|
||||
auto largest_example_stride = std::max_element(
|
||||
@ -508,7 +508,7 @@ static void checkBatchDimsAtFrontInLayout(IntArrayRef physical_strides, int64_t
|
||||
// given (sizes, strides, storage_offset) returns the maximum location that
|
||||
// can be indexed (or nullopt if such a location doesn't exist, e.g., tensors
|
||||
// with zero-size dims).
|
||||
static std::optional<int64_t> maximum_indexable_location(
|
||||
std::optional<int64_t> maximum_indexable_location(
|
||||
IntArrayRef sizes, IntArrayRef strides, int64_t storage_offset) {
|
||||
auto result = native::storage_size_for(sizes, strides);
|
||||
if (result == 0) {
|
||||
@ -521,7 +521,7 @@ static std::optional<int64_t> maximum_indexable_location(
|
||||
// This checks that the range of possible memory locations accessible by
|
||||
// x.as_strided(sizes, strides, maybe_storage_offset)
|
||||
// are within the bounds of possible memory locations accessible by x.
|
||||
static void checkBasicAsStridedValidForSlice(
|
||||
void checkBasicAsStridedValidForSlice(
|
||||
const Tensor& physical_tensor,
|
||||
int64_t num_batch_dims,
|
||||
IntArrayRef sizes,
|
||||
|
||||
@ -42,8 +42,14 @@ const PythonTorchFunctionTLS& PythonTorchFunctionTLS::get_state() {
|
||||
}
|
||||
|
||||
bool torch_function_mode_enabled() {
|
||||
return PythonTorchFunctionTLS::get_disabled_state() != TorchFunctionDisabledState::ALL_DISABLED &&
|
||||
PythonTorchFunctionTLS::stack_len() > 0;
|
||||
// Manually flatten because gcc is refusing to inline here. Note
|
||||
// that we are still calling __tls_get_addr twice here with GCC,
|
||||
// presumably because of
|
||||
// https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81501 (which says
|
||||
// the fix ships in GCC 16), but forcing inlining still improves
|
||||
// performance.
|
||||
const auto& ptfs = pythonTorchFunctionState;
|
||||
return ptfs.disabled_state_ != TorchFunctionDisabledState::ALL_DISABLED && !ptfs.stack_.empty();
|
||||
}
|
||||
|
||||
// This is needed to disambiguate the ternary torch function disabled states
|
||||
|
||||
@ -27,6 +27,7 @@ struct TORCH_API PythonTorchFunctionTLS {
|
||||
TorchFunctionDisabledState disabled_state_ =
|
||||
TorchFunctionDisabledState::ENABLED;
|
||||
std::vector<std::shared_ptr<c10::SafePyObject>> stack_;
|
||||
friend TORCH_API bool torch_function_mode_enabled();
|
||||
};
|
||||
|
||||
TORCH_API bool torch_function_mode_enabled();
|
||||
|
||||
@ -13,7 +13,7 @@ namespace {
|
||||
// and left at true for the rest of the execution.
|
||||
// It's an optimization so that users who never use default hooks don't need to
|
||||
// read the thread_local variables pack_hook_ and unpack_hook_.
|
||||
static bool is_initialized(false);
|
||||
bool is_initialized(false);
|
||||
}
|
||||
|
||||
static void assertSavedTensorHooksNotDisabled() {
|
||||
|
||||
@ -56,7 +56,7 @@ inline void get_strides(int64_t* strides, ArrayRef<OperandInfo> operands, int64_
|
||||
}
|
||||
}
|
||||
|
||||
static OptionalTensorRef make_otr(const TensorBase &tensor) {
|
||||
OptionalTensorRef make_otr(const TensorBase &tensor) {
|
||||
if (tensor.defined()) {
|
||||
return OptionalTensorRef(tensor);
|
||||
} else {
|
||||
|
||||
@ -36,7 +36,7 @@ namespace {
|
||||
using weakref_type = c10::weak_intrusive_ptr<TensorImpl, UndefinedTensorImpl>;
|
||||
using val_type = std::tuple<weakref_type, Tensor>;
|
||||
|
||||
static ska::flat_hash_map<TensorImpl*, val_type>& get_cached_casts() {
|
||||
ska::flat_hash_map<TensorImpl*, val_type>& get_cached_casts() {
|
||||
static ska::flat_hash_map<TensorImpl*, val_type> cached_casts;
|
||||
return cached_casts;
|
||||
}
|
||||
|
||||
@ -6,9 +6,9 @@ namespace at {
|
||||
|
||||
namespace {
|
||||
|
||||
static std::array<HostAllocator*, at::COMPILE_TIME_MAX_DEVICE_TYPES>
|
||||
std::array<HostAllocator*, at::COMPILE_TIME_MAX_DEVICE_TYPES>
|
||||
allocator_array{};
|
||||
static std::array<uint8_t, at::COMPILE_TIME_MAX_DEVICE_TYPES>
|
||||
std::array<uint8_t, at::COMPILE_TIME_MAX_DEVICE_TYPES>
|
||||
allocator_priority{};
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
@ -229,10 +229,10 @@ private:
|
||||
}
|
||||
|
||||
|
||||
static const uint32_t kPhilox10A = 0x9E3779B9;
|
||||
static const uint32_t kPhilox10B = 0xBB67AE85;
|
||||
static const uint32_t kPhiloxSA = 0xD2511F53;
|
||||
static const uint32_t kPhiloxSB = 0xCD9E8D57;
|
||||
static constexpr uint32_t kPhilox10A = 0x9E3779B9;
|
||||
static constexpr uint32_t kPhilox10B = 0xBB67AE85;
|
||||
static constexpr uint32_t kPhiloxSA = 0xD2511F53;
|
||||
static constexpr uint32_t kPhiloxSB = 0xCD9E8D57;
|
||||
};
|
||||
|
||||
typedef philox_engine Philox4_32;
|
||||
|
||||
@ -624,7 +624,14 @@ struct TORCH_API IValue final {
|
||||
IValue(const c10::SymBool& i) {
|
||||
if (auto mi = i.maybe_as_bool()) {
|
||||
tag = Tag::Bool;
|
||||
#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
|
||||
payload.u.as_int = *mi;
|
||||
#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
|
||||
/* due to byteorder if value assigned as_int, as_bool actually is not set correctly */
|
||||
payload.u.as_bool = *mi;
|
||||
#else
|
||||
#error Unexpected or undefined __BYTE_ORDER__
|
||||
#endif
|
||||
} else {
|
||||
tag = Tag::SymBool;
|
||||
payload.u.as_intrusive_ptr = i.toSymNodeImpl().release();
|
||||
|
||||
@ -8,6 +8,7 @@
|
||||
#include <ATen/cpu/vec/vec128/vec128_bfloat16_neon.h>
|
||||
#include <ATen/cpu/vec/vec128/vec128_float_neon.h>
|
||||
#include <ATen/cpu/vec/vec128/vec128_half_neon.h>
|
||||
#include <ATen/cpu/vec/vec128/vec128_int_aarch64.h>
|
||||
#endif
|
||||
|
||||
#include <ATen/cpu/vec/vec128/vec128_convert.h>
|
||||
|
||||
794
aten/src/ATen/cpu/vec/vec128/vec128_int_aarch64.h
Normal file
794
aten/src/ATen/cpu/vec/vec128/vec128_int_aarch64.h
Normal file
@ -0,0 +1,794 @@
|
||||
#pragma once
|
||||
|
||||
#include <ATen/cpu/vec/intrinsics.h>
|
||||
#include <ATen/cpu/vec/vec_base.h>
|
||||
#include <c10/macros/Macros.h>
|
||||
#include <c10/util/irange.h>
|
||||
|
||||
namespace at::vec {
|
||||
// Note [CPU_CAPABILITY namespace]
|
||||
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
// This header, and all of its subheaders, will be compiled with
|
||||
// different architecture flags for each supported set of vector
|
||||
// intrinsics. So we need to make sure they aren't inadvertently
|
||||
// linked together. We do this by declaring objects in an `inline
|
||||
// namespace` which changes the name mangling, but can still be
|
||||
// accessed as `at::vec`.
|
||||
inline namespace CPU_CAPABILITY {
|
||||
|
||||
#define VEC_INT_NEON_TEMPLATE(vl, bit) \
|
||||
template <> \
|
||||
struct is_vec_specialized_for<int##bit##_t> : std::bool_constant<true> {}; \
|
||||
\
|
||||
template <> \
|
||||
class Vectorized<int##bit##_t> { \
|
||||
using neon_type = int##bit##x##vl##_t; \
|
||||
\
|
||||
private: \
|
||||
neon_type values; \
|
||||
\
|
||||
public: \
|
||||
using value_type = int##bit##_t; \
|
||||
using size_type = int; \
|
||||
static constexpr size_type size() { \
|
||||
return vl; \
|
||||
} \
|
||||
Vectorized() { \
|
||||
values = vdupq_n_s##bit(0); \
|
||||
} \
|
||||
Vectorized(neon_type v) : values(v) {} \
|
||||
Vectorized(int##bit##_t val); \
|
||||
template < \
|
||||
typename... Args, \
|
||||
typename = std::enable_if_t<(sizeof...(Args) == size())>> \
|
||||
Vectorized(Args... vals) { \
|
||||
__at_align__ int##bit##_t buffer[size()] = {vals...}; \
|
||||
values = vld1q_s##bit(buffer); \
|
||||
} \
|
||||
operator neon_type() const { \
|
||||
return values; \
|
||||
} \
|
||||
static Vectorized<int##bit##_t> loadu( \
|
||||
const void* ptr, \
|
||||
int64_t count = size()); \
|
||||
void store(void* ptr, int64_t count = size()) const; \
|
||||
template <int64_t mask> \
|
||||
static Vectorized<int##bit##_t> blend( \
|
||||
const Vectorized<int##bit##_t>& a, \
|
||||
const Vectorized<int##bit##_t>& b); \
|
||||
static Vectorized<int##bit##_t> blendv( \
|
||||
const Vectorized<int##bit##_t>& a, \
|
||||
const Vectorized<int##bit##_t>& b, \
|
||||
const Vectorized<int##bit##_t>& mask_) { \
|
||||
return vbslq_s##bit(vreinterpretq_u##bit##_s##bit(mask_.values), b, a); \
|
||||
} \
|
||||
template <typename step_t> \
|
||||
static Vectorized<int##bit##_t> arange( \
|
||||
value_type base = 0, \
|
||||
step_t step = static_cast<step_t>(1)); \
|
||||
static Vectorized<int##bit##_t> set( \
|
||||
const Vectorized<int##bit##_t>& a, \
|
||||
const Vectorized<int##bit##_t>& b, \
|
||||
int64_t count = size()); \
|
||||
const int##bit##_t& operator[](int idx) const = delete; \
|
||||
int##bit##_t& operator[](int idx) = delete; \
|
||||
Vectorized<int##bit##_t> abs() const { \
|
||||
return vabsq_s##bit(values); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> real() const { \
|
||||
return values; \
|
||||
} \
|
||||
Vectorized<int##bit##_t> imag() const { \
|
||||
return vdupq_n_s##bit(0); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> conj() const { \
|
||||
return values; \
|
||||
} \
|
||||
Vectorized<int##bit##_t> neg() const { \
|
||||
return vnegq_s##bit(values); \
|
||||
} \
|
||||
int##bit##_t reduce_add() const { \
|
||||
return vaddvq_s##bit(values); \
|
||||
} \
|
||||
int##bit##_t reduce_max() const; \
|
||||
Vectorized<int##bit##_t> operator==( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return Vectorized<value_type>( \
|
||||
vreinterpretq_s##bit##_u##bit(vceqq_s##bit(values, other.values))); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> operator!=( \
|
||||
const Vectorized<int##bit##_t>& other) const; \
|
||||
Vectorized<int##bit##_t> operator<( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return Vectorized<value_type>( \
|
||||
vreinterpretq_s##bit##_u##bit(vcltq_s##bit(values, other.values))); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> operator<=( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return Vectorized<value_type>( \
|
||||
vreinterpretq_s##bit##_u##bit(vcleq_s##bit(values, other.values))); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> operator>( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return Vectorized<value_type>( \
|
||||
vreinterpretq_s##bit##_u##bit(vcgtq_s##bit(values, other.values))); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> operator>=( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return Vectorized<value_type>( \
|
||||
vreinterpretq_s##bit##_u##bit(vcgeq_s##bit(values, other.values))); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> eq(const Vectorized<int##bit##_t>& other) const; \
|
||||
Vectorized<int##bit##_t> ne(const Vectorized<int##bit##_t>& other) const; \
|
||||
Vectorized<int##bit##_t> gt(const Vectorized<int##bit##_t>& other) const; \
|
||||
Vectorized<int##bit##_t> ge(const Vectorized<int##bit##_t>& other) const; \
|
||||
Vectorized<int##bit##_t> lt(const Vectorized<int##bit##_t>& other) const; \
|
||||
Vectorized<int##bit##_t> le(const Vectorized<int##bit##_t>& other) const; \
|
||||
}; \
|
||||
template <> \
|
||||
Vectorized<int##bit##_t> inline operator+( \
|
||||
const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
|
||||
return vaddq_s##bit(a, b); \
|
||||
} \
|
||||
template <> \
|
||||
Vectorized<int##bit##_t> inline operator-( \
|
||||
const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
|
||||
return vsubq_s##bit(a, b); \
|
||||
} \
|
||||
template <> \
|
||||
Vectorized<int##bit##_t> inline operator&( \
|
||||
const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
|
||||
return vandq_s##bit(a, b); \
|
||||
} \
|
||||
template <> \
|
||||
Vectorized<int##bit##_t> inline operator|( \
|
||||
const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
|
||||
return vorrq_s##bit(a, b); \
|
||||
} \
|
||||
template <> \
|
||||
Vectorized<int##bit##_t> inline operator^( \
|
||||
const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
|
||||
return veorq_s##bit(a, b); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::eq( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return (*this == other) & Vectorized<int##bit##_t>(1); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::ne( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return (*this != other) & Vectorized<int##bit##_t>(1); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::gt( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return (*this > other) & Vectorized<int##bit##_t>(1); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::ge( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return (*this >= other) & Vectorized<int##bit##_t>(1); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::lt( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return (*this < other) & Vectorized<int##bit##_t>(1); \
|
||||
} \
|
||||
Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::le( \
|
||||
const Vectorized<int##bit##_t>& other) const { \
|
||||
return (*this <= other) & Vectorized<int##bit##_t>(1); \
|
||||
}
|
||||
|
||||
VEC_INT_NEON_TEMPLATE(2, 64)
|
||||
VEC_INT_NEON_TEMPLATE(4, 32)
|
||||
VEC_INT_NEON_TEMPLATE(8, 16)
|
||||
VEC_INT_NEON_TEMPLATE(16, 8)
|
||||
|
||||
inline int32_t Vectorized<int32_t>::reduce_max() const {
|
||||
return vmaxvq_s32(values);
|
||||
}
|
||||
|
||||
inline int16_t Vectorized<int16_t>::reduce_max() const {
|
||||
return vmaxvq_s16(values);
|
||||
}
|
||||
|
||||
inline int8_t Vectorized<int8_t>::reduce_max() const {
|
||||
return vmaxvq_s8(values);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline operator*(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
return vmulq_s32(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline operator*(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
return vmulq_s16(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline operator*(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
return vmulq_s8(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
inline Vectorized<int64_t> operator~(const Vectorized<int64_t>& a) {
|
||||
int64x2_t val = a;
|
||||
return ~val;
|
||||
}
|
||||
|
||||
template <>
|
||||
inline Vectorized<int32_t> operator~(const Vectorized<int32_t>& a) {
|
||||
return vmvnq_s32(a);
|
||||
}
|
||||
|
||||
template <>
|
||||
inline Vectorized<int16_t> operator~(const Vectorized<int16_t>& a) {
|
||||
return vmvnq_s16(a);
|
||||
}
|
||||
|
||||
template <>
|
||||
inline Vectorized<int8_t> operator~(const Vectorized<int8_t>& a) {
|
||||
return vmvnq_s8(a);
|
||||
}
|
||||
|
||||
inline Vectorized<int64_t> Vectorized<int64_t>::operator!=(
|
||||
const Vectorized<int64_t>& other) const {
|
||||
return ~(*this == other);
|
||||
}
|
||||
|
||||
inline Vectorized<int32_t> Vectorized<int32_t>::operator!=(
|
||||
const Vectorized<int32_t>& other) const {
|
||||
return ~(*this == other);
|
||||
}
|
||||
|
||||
inline Vectorized<int16_t> Vectorized<int16_t>::operator!=(
|
||||
const Vectorized<int16_t>& other) const {
|
||||
return ~(*this == other);
|
||||
}
|
||||
|
||||
inline Vectorized<int8_t> Vectorized<int8_t>::operator!=(
|
||||
const Vectorized<int8_t>& other) const {
|
||||
return ~(*this == other);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline minimum(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
return vminq_s32(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline minimum(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
return vminq_s16(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline minimum(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
return vminq_s8(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline maximum(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
return vmaxq_s32(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline maximum(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
return vmaxq_s16(a, b);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline maximum(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
return vmaxq_s8(a, b);
|
||||
}
|
||||
|
||||
template <int64_t mask>
|
||||
Vectorized<int64_t> Vectorized<int64_t>::blend(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding bit
|
||||
// in 'mask' is set, 0 otherwise.
|
||||
uint64x2_t maskArray = {
|
||||
(mask & 1LL) ? 0xFFFFFFFFFFFFFFFF : 0,
|
||||
(mask & 2LL) ? 0xFFFFFFFFFFFFFFFF : 0};
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s64(maskArray, b.values, a.values);
|
||||
}
|
||||
|
||||
template <int64_t mask>
|
||||
Vectorized<int32_t> Vectorized<int32_t>::blend(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding bit
|
||||
// in 'mask' is set, 0 otherwise.
|
||||
uint32x4_t maskArray = {
|
||||
(mask & 1LL) ? 0xFFFFFFFF : 0,
|
||||
(mask & 2LL) ? 0xFFFFFFFF : 0,
|
||||
(mask & 4LL) ? 0xFFFFFFFF : 0,
|
||||
(mask & 8LL) ? 0xFFFFFFFF : 0};
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s32(maskArray, b.values, a.values);
|
||||
}
|
||||
|
||||
template <int64_t mask>
|
||||
Vectorized<int16_t> Vectorized<int16_t>::blend(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding bit
|
||||
// in 'mask' is set, 0 otherwise.
|
||||
uint16x8_t maskArray = {
|
||||
(mask & 1LL) ? 0xFFFF : 0,
|
||||
(mask & 2LL) ? 0xFFFF : 0,
|
||||
(mask & 4LL) ? 0xFFFF : 0,
|
||||
(mask & 8LL) ? 0xFFFF : 0,
|
||||
(mask & 16LL) ? 0xFFFF : 0,
|
||||
(mask & 32LL) ? 0xFFFF : 0,
|
||||
(mask & 64LL) ? 0xFFFF : 0,
|
||||
(mask & 128LL) ? 0xFFFF : 0};
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s16(maskArray, b.values, a.values);
|
||||
}
|
||||
|
||||
template <int64_t mask>
|
||||
Vectorized<int8_t> Vectorized<int8_t>::blend(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding bit
|
||||
// in 'mask' is set, 0 otherwise.
|
||||
uint8x16_t maskArray = {
|
||||
(mask & 1LL) ? 0xFF : 0,
|
||||
(mask & 2LL) ? 0xFF : 0,
|
||||
(mask & 4LL) ? 0xFF : 0,
|
||||
(mask & 8LL) ? 0xFF : 0,
|
||||
(mask & 16LL) ? 0xFF : 0,
|
||||
(mask & 32LL) ? 0xFF : 0,
|
||||
(mask & 64LL) ? 0xFF : 0,
|
||||
(mask & 128LL) ? 0xFF : 0,
|
||||
(mask & 256LL) ? 0xFF : 0,
|
||||
(mask & 512LL) ? 0xFF : 0,
|
||||
(mask & 1024LL) ? 0xFF : 0,
|
||||
(mask & 2048LL) ? 0xFF : 0,
|
||||
(mask & 4096LL) ? 0xFF : 0,
|
||||
(mask & 8192LL) ? 0xFF : 0,
|
||||
(mask & 16384LL) ? 0xFF : 0,
|
||||
(mask & 32768LL) ? 0xFF : 0};
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s8(maskArray, b.values, a.values);
|
||||
}
|
||||
|
||||
#define VEC_INT_NEON_OPS(vl, bit) \
|
||||
inline Vectorized<int##bit##_t>::Vectorized(int##bit##_t val) { \
|
||||
values = vdupq_n_s##bit(val); \
|
||||
} \
|
||||
inline Vectorized<int##bit##_t> Vectorized<int##bit##_t>::loadu( \
|
||||
const void* ptr, int64_t count) { \
|
||||
if (count == size()) { \
|
||||
return vld1q_s##bit(reinterpret_cast<const int##bit##_t*>(ptr)); \
|
||||
} else { \
|
||||
__at_align__ int##bit##_t tmp_values[size()]; \
|
||||
for (const auto i : c10::irange(size())) { \
|
||||
tmp_values[i] = 0; \
|
||||
} \
|
||||
std::memcpy( \
|
||||
tmp_values, \
|
||||
reinterpret_cast<const int##bit##_t*>(ptr), \
|
||||
count * sizeof(int##bit##_t)); \
|
||||
return vld1q_s##bit(reinterpret_cast<const int##bit##_t*>(tmp_values)); \
|
||||
} \
|
||||
} \
|
||||
inline void Vectorized<int##bit##_t>::store(void* ptr, int64_t count) \
|
||||
const { \
|
||||
if (count == size()) { \
|
||||
vst1q_s##bit(reinterpret_cast<int##bit##_t*>(ptr), values); \
|
||||
} else { \
|
||||
int##bit##_t tmp_values[size()]; \
|
||||
vst1q_s##bit(reinterpret_cast<int##bit##_t*>(tmp_values), values); \
|
||||
std::memcpy(ptr, tmp_values, count * sizeof(int##bit##_t)); \
|
||||
} \
|
||||
}
|
||||
|
||||
VEC_INT_NEON_OPS(2, 64)
|
||||
VEC_INT_NEON_OPS(4, 32)
|
||||
VEC_INT_NEON_OPS(8, 16)
|
||||
VEC_INT_NEON_OPS(16, 8)
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline operator*(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
int64x2_t x = a;
|
||||
int64x2_t y = b;
|
||||
return x * y;
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline operator/(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
int64x2_t x = a;
|
||||
int64x2_t y = b;
|
||||
return x / y;
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline operator/(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
int32x4_t x = a;
|
||||
int32x4_t y = b;
|
||||
return x / y;
|
||||
}
|
||||
|
||||
inline int64_t Vectorized<int64_t>::reduce_max() const {
|
||||
return std::max(values[0], values[1]);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline minimum(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
int64x2_t x = a;
|
||||
int64x2_t y = b;
|
||||
return {std::min(x[0], y[0]), std::min(x[1], y[1])};
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline maximum(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
int64x2_t x = a;
|
||||
int64x2_t y = b;
|
||||
return {std::max(x[0], y[0]), std::max(x[1], y[1])};
|
||||
}
|
||||
|
||||
template <typename step_t>
|
||||
inline Vectorized<int64_t> Vectorized<int64_t>::arange(
|
||||
int64_t base,
|
||||
step_t step) {
|
||||
const Vectorized<int64_t> base_vec(base);
|
||||
const Vectorized<int64_t> step_vec(step);
|
||||
const int64x2_t step_sizes = {0, 1};
|
||||
return base_vec.values + step_sizes * step_vec.values;
|
||||
}
|
||||
|
||||
template <typename step_t>
|
||||
inline Vectorized<int32_t> Vectorized<int32_t>::arange(
|
||||
int32_t base,
|
||||
step_t step) {
|
||||
const Vectorized<int32_t> base_vec(base);
|
||||
const Vectorized<int32_t> step_vec(step);
|
||||
const int32x4_t step_sizes = {0, 1, 2, 3};
|
||||
return vmlaq_s32(base_vec, step_sizes, step_vec);
|
||||
}
|
||||
|
||||
template <typename step_t>
|
||||
inline Vectorized<int16_t> Vectorized<int16_t>::arange(
|
||||
int16_t base,
|
||||
step_t step) {
|
||||
const Vectorized<int16_t> base_vec(base);
|
||||
const Vectorized<int16_t> step_vec(step);
|
||||
const int16x8_t step_sizes = {0, 1, 2, 3, 4, 5, 6, 7};
|
||||
return vmlaq_s16(base_vec, step_sizes, step_vec);
|
||||
}
|
||||
|
||||
template <typename step_t>
|
||||
inline Vectorized<int8_t> Vectorized<int8_t>::arange(int8_t base, step_t step) {
|
||||
const Vectorized<int8_t> base_vec(base);
|
||||
const Vectorized<int8_t> step_vec(step);
|
||||
const int8x16_t step_sizes = {
|
||||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
|
||||
return vmlaq_s8(base_vec, step_sizes, step_vec);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline operator>>(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
int64x2_t x = a;
|
||||
int64x2_t y = b;
|
||||
uint64x2_t u = vreinterpretq_u64_s64(y);
|
||||
uint64x2_t z = {std::min(u[0], (uint64_t)63), std::min(u[1], (uint64_t)63)};
|
||||
return x >> vreinterpretq_s64_u64(z);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline operator>>(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
int32x4_t x = a;
|
||||
int32x4_t y = b;
|
||||
uint32x4_t bound = vdupq_n_u32(31);
|
||||
uint32x4_t z = vminq_u32(vreinterpretq_u32_s32(y), bound);
|
||||
return x >> vreinterpretq_s32_u32(z);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline operator>>(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
int16x8_t x = a;
|
||||
int16x8_t y = b;
|
||||
uint16x8_t bound = vdupq_n_u16(15);
|
||||
uint16x8_t z = vminq_u16(vreinterpretq_u16_s16(y), bound);
|
||||
return x >> vreinterpretq_s16_u16(z);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline operator>>(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
int8x16_t x = a;
|
||||
int8x16_t y = b;
|
||||
uint8x16_t bound = vdupq_n_u8(7);
|
||||
int8x16_t z = vreinterpretq_s8_u8(vminq_u8(vreinterpretq_u8_s8(y), bound));
|
||||
return x >> z;
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline operator<<(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b) {
|
||||
int64x2_t y = b;
|
||||
uint64x2_t u = vreinterpretq_u64_s64(y);
|
||||
uint64x2_t z = {std::min(u[0], (uint64_t)64), std::min(u[1], (uint64_t)64)};
|
||||
return vshlq_s64(a, vreinterpretq_s64_u64(z));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline operator<<(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b) {
|
||||
int32x4_t y = b;
|
||||
uint32x4_t bound = vdupq_n_u32(32);
|
||||
uint32x4_t z = vminq_u32(vreinterpretq_u32_s32(y), bound);
|
||||
return vshlq_s32(a, vreinterpretq_s32_u32(z));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline operator<<(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
int16x8_t y = b;
|
||||
uint16x8_t bound = vdupq_n_u16(16);
|
||||
uint16x8_t z = vminq_u16(vreinterpretq_u16_s16(y), bound);
|
||||
return vshlq_s16(a, vreinterpretq_s16_u16(z));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline operator<<(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
int8x16_t y = b;
|
||||
uint8x16_t bound = vdupq_n_u8(8);
|
||||
int8x16_t z = vreinterpretq_s8_u8(vminq_u8(vreinterpretq_u8_s8(y), bound));
|
||||
return vshlq_s8(a, z);
|
||||
}
|
||||
|
||||
inline Vectorized<int64_t> Vectorized<int64_t>::set(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& b,
|
||||
int64_t count) {
|
||||
if (count == 0) {
|
||||
return a;
|
||||
} else if (count >= 2) {
|
||||
return b;
|
||||
} else {
|
||||
int64x2_t c = {b.values[0], a.values[1]};
|
||||
return c;
|
||||
}
|
||||
}
|
||||
|
||||
inline Vectorized<int32_t> Vectorized<int32_t>::set(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& b,
|
||||
int64_t count) {
|
||||
if (count == 0) {
|
||||
return a;
|
||||
} else if (count >= 4) {
|
||||
return b;
|
||||
} else {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding
|
||||
// bit in 'mask' is set, 0 otherwise.
|
||||
uint32x4_t maskArray = {
|
||||
(count >= 1LL) ? 0xFFFFFFFF : 0,
|
||||
(count >= 2LL) ? 0xFFFFFFFF : 0,
|
||||
(count >= 3LL) ? 0xFFFFFFFF : 0,
|
||||
0};
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s32(maskArray, b.values, a.values);
|
||||
}
|
||||
}
|
||||
|
||||
inline Vectorized<int16_t> Vectorized<int16_t>::set(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b,
|
||||
int64_t count) {
|
||||
if (count == 0) {
|
||||
return a;
|
||||
} else if (count >= 8) {
|
||||
return b;
|
||||
} else {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding
|
||||
// bit in 'mask' is set, 0 otherwise.
|
||||
uint16x8_t maskArray = {
|
||||
static_cast<uint16_t>((count >= 1LL) ? 0xFFFF : 0),
|
||||
static_cast<uint16_t>((count >= 2LL) ? 0xFFFF : 0),
|
||||
static_cast<uint16_t>((count >= 3LL) ? 0xFFFF : 0),
|
||||
static_cast<uint16_t>((count >= 4LL) ? 0xFFFF : 0),
|
||||
static_cast<uint16_t>((count >= 5LL) ? 0xFFFF : 0),
|
||||
static_cast<uint16_t>((count >= 6LL) ? 0xFFFF : 0),
|
||||
static_cast<uint16_t>((count >= 7LL) ? 0xFFFF : 0),
|
||||
0};
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s16(maskArray, b.values, a.values);
|
||||
}
|
||||
}
|
||||
|
||||
inline Vectorized<int8_t> Vectorized<int8_t>::set(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b,
|
||||
int64_t count) {
|
||||
if (count == 0) {
|
||||
return a;
|
||||
} else if (count >= 16) {
|
||||
return b;
|
||||
} else {
|
||||
// Build an array of flags: each bit of element is 1 if the corresponding
|
||||
// bit in 'mask' is set, 0 otherwise.
|
||||
uint8x16_t maskArray = {
|
||||
static_cast<uint8_t>((count >= 1LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 2LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 3LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 4LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 5LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 6LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 7LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 8LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 9LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 10LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 11LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 12LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 13LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 14LL) ? 0xFF : 0),
|
||||
static_cast<uint8_t>((count >= 15LL) ? 0xFF : 0),
|
||||
0};
|
||||
|
||||
// Use BSL to select elements from b where the mask is 1, else from a
|
||||
return vbslq_s8(maskArray, b.values, a.values);
|
||||
}
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline operator/(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& b) {
|
||||
Vectorized<int32_t> highBitsA = vmovl_high_s16(a);
|
||||
Vectorized<int32_t> highBitsB = vmovl_high_s16(b);
|
||||
Vectorized<int32_t> lowBitsA = vmovl_s16(vget_low_s16(a));
|
||||
Vectorized<int32_t> lowBitsB = vmovl_s16(vget_low_s16(b));
|
||||
int32x4_t highBitsResult = highBitsA / highBitsB;
|
||||
int32x4_t lowBitsResult = lowBitsA / lowBitsB;
|
||||
return vuzp1q_s16(
|
||||
vreinterpretq_s16_s32(lowBitsResult),
|
||||
vreinterpretq_s16_s32(highBitsResult));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline operator/(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& b) {
|
||||
Vectorized<int16_t> highBitsA = vmovl_high_s8(a);
|
||||
Vectorized<int16_t> highBitsB = vmovl_high_s8(b);
|
||||
Vectorized<int16_t> lowBitsA = vmovl_s8(vget_low_s8(a));
|
||||
Vectorized<int16_t> lowBitsB = vmovl_s8(vget_low_s8(b));
|
||||
int16x8_t highBitsResult = highBitsA / highBitsB;
|
||||
int16x8_t lowBitsResult = lowBitsA / lowBitsB;
|
||||
return vuzp1q_s8(
|
||||
vreinterpretq_s8_s16(lowBitsResult),
|
||||
vreinterpretq_s8_s16(highBitsResult));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline clamp(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& min,
|
||||
const Vectorized<int64_t>& max) {
|
||||
return minimum(max, maximum(min, a));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline clamp(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& min,
|
||||
const Vectorized<int32_t>& max) {
|
||||
return minimum(max, maximum(min, a));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline clamp(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& min,
|
||||
const Vectorized<int16_t>& max) {
|
||||
return minimum(max, maximum(min, a));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline clamp(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& min,
|
||||
const Vectorized<int8_t>& max) {
|
||||
return minimum(max, maximum(min, a));
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline clamp_max(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& max) {
|
||||
return minimum(max, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline clamp_max(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& max) {
|
||||
return minimum(max, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline clamp_max(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& max) {
|
||||
return minimum(max, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline clamp_max(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& max) {
|
||||
return minimum(max, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int64_t> inline clamp_min(
|
||||
const Vectorized<int64_t>& a,
|
||||
const Vectorized<int64_t>& min) {
|
||||
return maximum(min, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int32_t> inline clamp_min(
|
||||
const Vectorized<int32_t>& a,
|
||||
const Vectorized<int32_t>& min) {
|
||||
return maximum(min, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int16_t> inline clamp_min(
|
||||
const Vectorized<int16_t>& a,
|
||||
const Vectorized<int16_t>& min) {
|
||||
return maximum(min, a);
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<int8_t> inline clamp_min(
|
||||
const Vectorized<int8_t>& a,
|
||||
const Vectorized<int8_t>& min) {
|
||||
return maximum(min, a);
|
||||
}
|
||||
|
||||
} // namespace CPU_CAPABILITY
|
||||
} // namespace at::vec
|
||||
@ -1377,7 +1377,7 @@ Vectorized<c10::quint8> inline maximum(
|
||||
#if (defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256))
|
||||
std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
|
||||
at::vec::Vectorized<int8_t> src) {
|
||||
auto s8x8 = vld1_s8(src.operator const int8_t*());
|
||||
auto s8x8 = vget_low_s8(src);
|
||||
auto s16x8 = vmovl_s8(s8x8);
|
||||
|
||||
auto s32x4_hi = vmovl_s16(vget_high_s16(s16x8));
|
||||
@ -1402,7 +1402,7 @@ std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
|
||||
|
||||
Vectorized<float> inline convert_int8_half_register_to_float(
|
||||
at::vec::Vectorized<int8_t> src) {
|
||||
auto s8x8 = vld1_s8(src.operator const int8_t*());
|
||||
auto s8x8 = vget_low_s8(src);
|
||||
auto s16x8 = vmovl_s8(s8x8);
|
||||
|
||||
auto s32x4_lo = vmovl_s16(vget_low_s16(s16x8));
|
||||
|
||||
@ -16,6 +16,8 @@
|
||||
#include <c10/util/irange.h>
|
||||
#include <c10/core/ScalarType.h>
|
||||
|
||||
#include <ATen/cuda/detail/BLASConstants.h>
|
||||
|
||||
#ifdef USE_ROCM
|
||||
#include <c10/cuda/CUDAStream.h>
|
||||
#include <hipblaslt/hipblaslt-ext.hpp>
|
||||
@ -108,7 +110,7 @@ static hipblasStatus_t rocBLASStatusToHIPStatus(rocblas_status error)
|
||||
|
||||
namespace {
|
||||
|
||||
static cublasOperation_t _cublasOpFromChar(char op) {
|
||||
cublasOperation_t _cublasOpFromChar(char op) {
|
||||
// NOLINTNEXTLINE(bugprone-switch-missing-default-case)
|
||||
switch (op) {
|
||||
case 'n':
|
||||
@ -128,7 +130,7 @@ static cublasOperation_t _cublasOpFromChar(char op) {
|
||||
"_cublasOpFromChar input should be 't', 'n' or 'c' but got `", op, "`");
|
||||
}
|
||||
|
||||
static void _cublasAdjustLdLevel2(int64_t m, int64_t n, int64_t* lda) {
|
||||
void _cublasAdjustLdLevel2(int64_t m, int64_t n, int64_t* lda) {
|
||||
// Note: leading dimensions generally are checked that they are > 0
|
||||
// and at least as big the result requires (even if the value won't
|
||||
// be used).
|
||||
@ -142,7 +144,7 @@ static void _cublasAdjustLdLevel2(int64_t m, int64_t n, int64_t* lda) {
|
||||
*lda = std::max<int64_t>(m, 1);
|
||||
}
|
||||
|
||||
static void _cublasAdjustLdLevel3(
|
||||
void _cublasAdjustLdLevel3(
|
||||
char transa,
|
||||
char transb,
|
||||
int64_t m,
|
||||
@ -1954,13 +1956,15 @@ void scaled_gemm(
|
||||
const void *result_scale_ptr,
|
||||
int64_t result_ld,
|
||||
ScalarType result_dtype,
|
||||
bool use_fast_accum) {
|
||||
bool use_fast_accum,
|
||||
const std::optional<Tensor>& alpha) {
|
||||
// Note: see `cublasCommonArgs` for various non-intuitive manipulations
|
||||
// of input arguments to this function.
|
||||
const auto computeType = CUBLAS_COMPUTE_32F;
|
||||
const auto scaleType = CUDA_R_32F;
|
||||
const float alpha_val = 1.0;
|
||||
const float beta_val = 0.0;
|
||||
// Note: alpha_val may change later depending on user-passed argument
|
||||
float alpha_val = 1.0;
|
||||
float beta_val = 0.0;
|
||||
CuBlasLtMatmulDescriptor computeDesc(computeType, scaleType);
|
||||
computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_TRANSA, _cublasOpFromChar(transa));
|
||||
computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_TRANSB, _cublasOpFromChar(transb));
|
||||
@ -2031,6 +2035,33 @@ void scaled_gemm(
|
||||
computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_EPILOGUE, CUBLASLT_EPILOGUE_BIAS);
|
||||
computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE, ScalarTypeToCudaDataType(bias_dtype));
|
||||
}
|
||||
|
||||
// Handle user-passed alpha
|
||||
float *alpha_ptr = &alpha_val;
|
||||
float *beta_ptr = &beta_val;
|
||||
|
||||
if (alpha.has_value()) {
|
||||
auto& a = alpha.value();
|
||||
|
||||
// if device-tensor
|
||||
if (a.is_cuda()) {
|
||||
// NOTE: there are lifetime requirements on device-side pointers for alpha/beta -- the value must be
|
||||
// valid & correct until the cublas call finishes (not is scheduled like host-side values). Thus
|
||||
// we need to use allocations for alpha/beta that have some guarantees on lifetime - a statically
|
||||
// managed 4B buffer for alpha that we'll copy the passed alpha value into, and constant memory
|
||||
// for beta respectively.
|
||||
float *user_alpha_ptr = at::cuda::detail::get_user_alpha_ptr();
|
||||
at::Tensor user_alpha = at::from_blob(user_alpha_ptr, {1}, TensorOptions().device(kCUDA).dtype(kFloat));
|
||||
user_alpha.copy_(a);
|
||||
// Tell cublasLt we're using device-side pointers for alpha/beta
|
||||
auto pointer_mode = CUBLASLT_POINTER_MODE_DEVICE;
|
||||
computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_POINTER_MODE, pointer_mode);
|
||||
alpha_ptr = user_alpha.data_ptr<float>();
|
||||
beta_ptr = at::cuda::detail::get_cublas_device_zero();
|
||||
} else {
|
||||
alpha_val = a.item<float>();
|
||||
}
|
||||
}
|
||||
// For other data types, use the get_scale_mode function based on scaling type
|
||||
// The SCALE_MODE attrs only exist in cuBLAS 12.8+/ROCm 7.0 or in recent hipblaslt,
|
||||
// but we must invoke get_scale_mode anyways to trigger the version checks.
|
||||
@ -2048,6 +2079,7 @@ void scaled_gemm(
|
||||
cublasLtMatmulHeuristicResult_t heuristicResult = {};
|
||||
int returnedResult = 0;
|
||||
cublasLtHandle_t ltHandle = at::cuda::getCurrentCUDABlasLtHandle();
|
||||
|
||||
TORCH_CUDABLAS_CHECK(cublasLtMatmulAlgoGetHeuristic(
|
||||
ltHandle,
|
||||
computeDesc.descriptor(),
|
||||
@ -2088,10 +2120,10 @@ void scaled_gemm(
|
||||
auto is_valid_status = hipblaslt_ext::matmulIsAlgoSupported(
|
||||
ltHandle,
|
||||
computeDesc.descriptor(),
|
||||
&alpha_val,
|
||||
alpha_ptr,
|
||||
Adesc.descriptor(),
|
||||
Bdesc.descriptor(),
|
||||
&beta_val,
|
||||
beta_ptr,
|
||||
Cdesc.descriptor(),
|
||||
Ddesc.descriptor(),
|
||||
all_algos[i].algo,
|
||||
@ -2110,17 +2142,14 @@ void scaled_gemm(
|
||||
cublasStatus_t cublasStatus = cublasLtMatmul(
|
||||
ltHandle,
|
||||
computeDesc.descriptor(),
|
||||
&alpha_val,
|
||||
alpha_ptr,
|
||||
mat1_ptr,
|
||||
Adesc.descriptor(),
|
||||
mat2_ptr,
|
||||
Bdesc.descriptor(),
|
||||
&beta_val,
|
||||
#ifdef USE_ROCM
|
||||
beta_ptr,
|
||||
// NOTE: always use result_ptr here, because cuBLASLt w/device beta=0 can't handle nullptr either
|
||||
result_ptr, // unused, since beta_val is 0, but hipblaslt can't handle nullptr
|
||||
#else
|
||||
nullptr,
|
||||
#endif // ifdef USE_ROCM
|
||||
Cdesc.descriptor(),
|
||||
result_ptr,
|
||||
Ddesc.descriptor(),
|
||||
|
||||
@ -161,7 +161,8 @@ void scaled_gemm(
|
||||
const void* result_scale_ptr,
|
||||
int64_t result_ld,
|
||||
ScalarType result_dtype,
|
||||
bool use_fast_accum);
|
||||
bool use_fast_accum,
|
||||
const std::optional<Tensor>& alpha);
|
||||
|
||||
#define CUDABLAS_BGEMM_ARGTYPES(Dtype) CUDABLAS_BGEMM_ARGTYPES_AND_C_DTYPE(Dtype, Dtype)
|
||||
|
||||
|
||||
@ -15,19 +15,19 @@ namespace cuda::detail {
|
||||
namespace {
|
||||
|
||||
// Total number of gpus in the system.
|
||||
static int64_t num_gpus;
|
||||
int64_t num_gpus;
|
||||
|
||||
// Ensures default_gens_cuda is initialized once.
|
||||
static std::deque<c10::once_flag> cuda_gens_init_flag;
|
||||
std::deque<c10::once_flag> cuda_gens_init_flag;
|
||||
|
||||
// Default, global CUDA generators, one per GPU.
|
||||
static std::vector<Generator> default_gens_cuda;
|
||||
std::vector<Generator> default_gens_cuda;
|
||||
|
||||
/*
|
||||
* Populates the global variables related to CUDA generators
|
||||
* Warning: this function must only be called once!
|
||||
*/
|
||||
static void initCUDAGenVector() {
|
||||
void initCUDAGenVector() {
|
||||
// Ensures we only call cudaGetDeviceCount only once.
|
||||
static bool num_gpu_init_flag [[maybe_unused]] = []() {
|
||||
num_gpus = static_cast<int32_t>(c10::cuda::device_count());
|
||||
@ -325,9 +325,9 @@ uint64_t CUDAGeneratorImpl::seed() {
|
||||
*/
|
||||
c10::intrusive_ptr<c10::TensorImpl> CUDAGeneratorImpl::get_state() const {
|
||||
// The RNG state comprises the seed, and an offset used for Philox.
|
||||
static const size_t seed_size = sizeof(uint64_t);
|
||||
static const size_t offset_size = sizeof(int64_t);
|
||||
static const size_t total_size = seed_size + offset_size;
|
||||
constexpr size_t seed_size = sizeof(uint64_t);
|
||||
constexpr size_t offset_size = sizeof(int64_t);
|
||||
constexpr size_t total_size = seed_size + offset_size;
|
||||
|
||||
auto state_tensor = at::detail::empty_cpu({(int64_t)total_size}, ScalarType::Byte, std::nullopt, std::nullopt, std::nullopt, std::nullopt);
|
||||
auto rng_state = state_tensor.data_ptr<uint8_t>();
|
||||
@ -346,9 +346,9 @@ c10::intrusive_ptr<c10::TensorImpl> CUDAGeneratorImpl::get_state() const {
|
||||
* and size of the internal state.
|
||||
*/
|
||||
void CUDAGeneratorImpl::set_state(const c10::TensorImpl& new_state) {
|
||||
static const size_t seed_size = sizeof(uint64_t);
|
||||
static const size_t offset_size = sizeof(int64_t);
|
||||
static const size_t total_size = seed_size + offset_size;
|
||||
constexpr size_t seed_size = sizeof(uint64_t);
|
||||
constexpr size_t offset_size = sizeof(int64_t);
|
||||
constexpr size_t total_size = seed_size + offset_size;
|
||||
|
||||
detail::check_rng_state(new_state);
|
||||
|
||||
|
||||
@ -177,7 +177,6 @@ inline void segmented_sort_pairs(
|
||||
}
|
||||
}
|
||||
|
||||
#if CUB_SUPPORTS_UNIQUE_BY_KEY()
|
||||
template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename ValuesOutputIteratorT, typename NumSelectedIteratorT>
|
||||
inline void unique_by_key(
|
||||
KeysInputIteratorT keys_in, ValuesInputIteratorT values_in,
|
||||
@ -193,7 +192,6 @@ inline void unique_by_key(
|
||||
CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceSelect::UniqueByKey,
|
||||
keys_in, values_in, keys_out_, values_out, num_selected, num_input_items, c10::cuda::getCurrentCUDAStream());
|
||||
}
|
||||
#endif
|
||||
|
||||
namespace impl {
|
||||
|
||||
@ -579,7 +577,6 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
|
||||
#endif
|
||||
}
|
||||
|
||||
#if CUB_SUPPORTS_SCAN_BY_KEY()
|
||||
|
||||
template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename ValuesOutputIteratorT>
|
||||
inline void inclusive_sum_by_key(KeysInputIteratorT keys, ValuesInputIteratorT input, ValuesOutputIteratorT output, int64_t num_items) {
|
||||
@ -607,7 +604,6 @@ inline void inclusive_scan_by_key(KeysInputIteratorT keys, ValuesInputIteratorT
|
||||
#endif
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT>
|
||||
void unique(InputIteratorT input, OutputIteratorT output,
|
||||
|
||||
@ -28,22 +28,6 @@
|
||||
#define USE_GLOBAL_CUB_WRAPPED_NAMESPACE() false
|
||||
#endif
|
||||
|
||||
// cub support for UniqueByKey is added to cub 1.16 in:
|
||||
// https://github.com/NVIDIA/cub/pull/405
|
||||
#if CUB_VERSION >= 101600
|
||||
#define CUB_SUPPORTS_UNIQUE_BY_KEY() true
|
||||
#else
|
||||
#define CUB_SUPPORTS_UNIQUE_BY_KEY() false
|
||||
#endif
|
||||
|
||||
// cub support for scan by key is added to cub 1.15
|
||||
// in https://github.com/NVIDIA/cub/pull/376
|
||||
#if CUB_VERSION >= 101500
|
||||
#define CUB_SUPPORTS_SCAN_BY_KEY() 1
|
||||
#else
|
||||
#define CUB_SUPPORTS_SCAN_BY_KEY() 0
|
||||
#endif
|
||||
|
||||
// cub support for cub::FutureValue is added to cub 1.15 in:
|
||||
// https://github.com/NVIDIA/cub/pull/305
|
||||
#if CUB_VERSION >= 101500
|
||||
|
||||
54
aten/src/ATen/cuda/detail/BLASConstants.cu
Normal file
54
aten/src/ATen/cuda/detail/BLASConstants.cu
Normal file
@ -0,0 +1,54 @@
|
||||
#include <ATen/Functions.h>
|
||||
#include <ATen/Tensor.h>
|
||||
#include <ATen/cuda/Exceptions.h>
|
||||
|
||||
#include <mutex>
|
||||
|
||||
namespace at {
|
||||
namespace cuda {
|
||||
namespace detail {
|
||||
|
||||
__device__ __constant__ float cublas_one_device;
|
||||
__device__ __constant__ float cublas_zero_device;
|
||||
|
||||
float *get_cublas_device_one() {
|
||||
static c10::once_flag init_flag;
|
||||
|
||||
c10::call_once(init_flag, []() {
|
||||
const float one = 1.f;
|
||||
AT_CUDA_CHECK(cudaMemcpyToSymbol(cublas_one_device, &one, sizeof(float)));
|
||||
});
|
||||
|
||||
float *ptr;
|
||||
AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_one_device));
|
||||
return ptr;
|
||||
}
|
||||
|
||||
float *get_cublas_device_zero() {
|
||||
static c10::once_flag init_flag;
|
||||
|
||||
c10::call_once(init_flag, []() {
|
||||
const float zero = 0.f;
|
||||
AT_CUDA_CHECK(cudaMemcpyToSymbol(cublas_zero_device, &zero, sizeof(float)));
|
||||
});
|
||||
|
||||
float *ptr;
|
||||
AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_zero_device));
|
||||
return ptr;
|
||||
}
|
||||
|
||||
float *get_user_alpha_ptr() {
|
||||
static float *alpha_ptr;
|
||||
|
||||
static c10::once_flag init_flag;
|
||||
|
||||
c10::call_once(init_flag, []() {
|
||||
AT_CUDA_CHECK(cudaMalloc(&alpha_ptr, sizeof(float)));
|
||||
});
|
||||
|
||||
return alpha_ptr;
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cuda
|
||||
} // namespace at
|
||||
11
aten/src/ATen/cuda/detail/BLASConstants.h
Normal file
11
aten/src/ATen/cuda/detail/BLASConstants.h
Normal file
@ -0,0 +1,11 @@
|
||||
#pragma once
|
||||
|
||||
#include <ATen/core/TensorBase.h>
|
||||
|
||||
namespace at::cuda::detail {
|
||||
|
||||
float *get_cublas_device_one();
|
||||
float *get_cublas_device_zero();
|
||||
float *get_user_alpha_ptr();
|
||||
|
||||
} // namespace at::cuda::detail
|
||||
@ -13,6 +13,7 @@
|
||||
#include <c10/core/ScalarType.h>
|
||||
|
||||
#include <ATen/cuda/tunable/TunableOp.h>
|
||||
#include <ATen/cuda/tunable/Tunable.h>
|
||||
#include <ATen/cuda/CUDABlas.h>
|
||||
#include <ATen/cuda/Exceptions.h>
|
||||
#include <c10/util/StringUtil.h>
|
||||
@ -150,6 +151,7 @@ inline std::string ScalarTypeToBLASType(c10::ScalarType scalar_type) {
|
||||
BLASType = "unknown";
|
||||
}
|
||||
return BLASType;
|
||||
|
||||
}
|
||||
|
||||
// Similar to Compute Type in GemmRocblas.h
|
||||
@ -244,33 +246,25 @@ inline std::string to_string_epilogue(const at::cuda::blas::GEMMAndBiasActivatio
|
||||
|
||||
namespace detail {
|
||||
|
||||
static bool NumericalCheck(ScalarType dtype, void* c, void* other_c, int64_t size) {
|
||||
static bool NumericalCheck(ScalarType dtype, void* c, void* other_c, int64_t size, const NumericalCheckConfig& config) {
|
||||
|
||||
if (!config.enabled) {
|
||||
return true; // skip when disabled
|
||||
}
|
||||
|
||||
auto options = at::TensorOptions().dtype(dtype).device(at::kCUDA);
|
||||
// comparison done as 1D tensor
|
||||
at::Tensor ref = at::from_blob(c, {size}, options);
|
||||
at::Tensor oth = at::from_blob(other_c, {size}, options);
|
||||
at::Tensor ref_float = ref.to(at::kFloat);
|
||||
at::Tensor oth_float = oth.to(at::kFloat);
|
||||
std::vector<double> atols{1e-1, 1e-2, 1e-3, 1e-4, 1e-5};
|
||||
std::vector<double> rtols{1e-1, 1e-2, 1e-3, 1e-4, 1e-5};
|
||||
double last_succeed_atol = 1;
|
||||
double last_succeed_rtol = 1;
|
||||
for (auto& atol : atols) {
|
||||
for (auto& rtol : rtols) {
|
||||
if (at::allclose(ref_float, oth_float, rtol, atol)) {
|
||||
last_succeed_atol = atol;
|
||||
last_succeed_rtol = rtol;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (last_succeed_atol == 1) {
|
||||
return false;
|
||||
}
|
||||
else {
|
||||
TUNABLE_LOG3("├──verify numerics: atol=", last_succeed_atol, ", rtol=", last_succeed_rtol);
|
||||
}
|
||||
|
||||
return true;
|
||||
const bool ok = at::allclose(ref_float, oth_float, config.rtol, config.atol);
|
||||
if (ok) {
|
||||
TUNABLE_LOG3("├──verify numerics: PASSED with atol=", config.atol, ", rtol=", config.rtol);
|
||||
} else {
|
||||
TUNABLE_LOG3("├──verify numerics: FAILED with atol=", config.atol, ", rtol=", config.rtol);
|
||||
}
|
||||
return ok;
|
||||
}
|
||||
|
||||
}
|
||||
@ -355,8 +349,10 @@ struct GemmParams : OpParams {
|
||||
}
|
||||
|
||||
TuningStatus NumericalCheck(GemmParams<T> *other) {
|
||||
auto* ctx = getTuningContext();
|
||||
auto cfg = ctx->GetNumericalCheckConfig();
|
||||
auto c_dtype = c10::CppTypeToScalarType<T>::value;
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
|
||||
}
|
||||
|
||||
char transa{};
|
||||
@ -449,8 +445,10 @@ struct GemmAndBiasParams : OpParams {
|
||||
}
|
||||
|
||||
TuningStatus NumericalCheck(GemmAndBiasParams<T> *other) {
|
||||
auto* ctx = getTuningContext();
|
||||
auto cfg = ctx->GetNumericalCheckConfig();
|
||||
auto c_dtype = c10::CppTypeToScalarType<T>::value;
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
|
||||
}
|
||||
|
||||
char transa{};
|
||||
@ -546,8 +544,10 @@ struct GemmStridedBatchedParams : OpParams {
|
||||
}
|
||||
|
||||
TuningStatus NumericalCheck(GemmStridedBatchedParams<T> *other) {
|
||||
auto* ctx = getTuningContext();
|
||||
auto cfg = ctx->GetNumericalCheckConfig();
|
||||
auto c_dtype = c10::CppTypeToScalarType<C_Dtype>::value;
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
|
||||
}
|
||||
|
||||
char transa{};
|
||||
@ -663,7 +663,9 @@ struct ScaledGemmParams : OpParams {
|
||||
}
|
||||
|
||||
TuningStatus NumericalCheck(ScaledGemmParams<T> *other) {
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
|
||||
auto* ctx = getTuningContext();
|
||||
auto cfg = ctx->GetNumericalCheckConfig();
|
||||
return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
|
||||
}
|
||||
|
||||
char transa{};
|
||||
|
||||
@ -145,7 +145,7 @@ programmatically since the settings become fixed. Use the C++ or Python APIs ins
|
||||
| PYTORCH_TUNABLEOP_VERBOSE | Default is 0. Set to 1 to enable basic logging. 2 for basic tuning status. 3 for full trace. |
|
||||
| PYTORCH_TUNABLEOP_VERBOSE_FILENAME | Default is "err" for stderr. Set to "out" for stdout or a filename for capturing verbose logging. |
|
||||
| PYTORCH_TUNABLEOP_FILENAME | Default is 'tunableop_results.csv'. |
|
||||
| PYTORCH_TUNABLEOP_NUMERICAL_CHECK | Default is 0. Set to 1 to enable. |
|
||||
| PYTORCH_TUNABLEOP_NUMERICAL_CHECK | Default is off. Set 'atol_rtol' to enable, for example "1e-5_1e-5". |
|
||||
| PYTORCH_TUNABLEOP_ROCBLAS_ENABLED | Default is 1. Set to 0 to disable rocblas being considered during tuning. |
|
||||
| PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED | Default is 1. Set to 0 to disable hipblaslt being considered during tuning. |
|
||||
| PYTORCH_TUNABLEOP_MAX_TUNING_DURATION_MS | Default is 30. Unit is milliseconds. |
|
||||
@ -173,10 +173,9 @@ All python APIs exist in the `torch.cuda.tunable` module.
|
||||
| get_max_tuning_iterations() -> int | |
|
||||
| set_filename(filename: str, insert_device_ordinal: bool = False) -> None | |
|
||||
| get_filename() -> str | |
|
||||
| set_numerical_check_tolerances(enable: bool, atol: float, rtol: float) -> None | Enable or disable numerical checking; atol and rtol default to 1e-5.
|
||||
| get_results() -> Tuple[str, str, str, float] | |
|
||||
| get_validators() -> Tuple[str, str] | |
|
||||
| write_file_on_exit(val: bool) -> None | Default is True. |
|
||||
| write_file(filename: Optional[str] = None) -> None | If filename not given, it will call get_filename(). |
|
||||
| read_file(filename: Optional[str] = None) -> None | If filename not given, it will call get_filename(). |
|
||||
| tune_gemm_in_file(filename: str) -> None | read an untuned file and tune GEMMs in it. |
|
||||
| mgpu_tune_gemm_in_file(filename_pattern: str, num_gpus: int) -> None: -> None | read one or more untuned files and tune all unique GEMMs on one or more GPUs. |
|
||||
|
||||
@ -107,14 +107,30 @@ void TuningResultsManager::AddImpl(const std::string& op_signature,
|
||||
}
|
||||
|
||||
void TuningResultsManager::Add(const std::string& op_signature, const std::string& params_signature, ResultEntry best) {
|
||||
std::scoped_lock l{lock_};
|
||||
bool is_new = false;
|
||||
ResultEntry inserted = ResultEntry::Null();
|
||||
|
||||
auto it = results_.find(op_signature);
|
||||
if (it == results_.end()) {
|
||||
it = results_.insert({op_signature, {}}).first;
|
||||
// ---- mutate maps under results lock ----
|
||||
{
|
||||
std::scoped_lock l{lock_};
|
||||
auto& km = results_[op_signature]; // creates if missing
|
||||
is_new = (km.find(params_signature) == km.end());
|
||||
AddImpl(op_signature, params_signature, std::move(best), km);
|
||||
if (is_new) {
|
||||
inserted = km.at(params_signature); // snapshot for I/O after unlocking
|
||||
}
|
||||
}
|
||||
if (!is_new) return; // only write once per unique (op, params)
|
||||
|
||||
TuningContext* ctx = getTuningContext();
|
||||
if (ctx->IsTuningEnabled() && !ctx->IsRecordUntunedEnabled()) {
|
||||
InitRealtimeAppend(ctx->GetFilename(), ctx->GetTuningResultsValidator().GetAllValidators());
|
||||
|
||||
if (is_new && realtime_out_ && realtime_out_->good()) {
|
||||
AppendResultLine(op_signature, params_signature, inserted);
|
||||
}
|
||||
}
|
||||
|
||||
AddImpl(op_signature, params_signature, std::move(best), it->second);
|
||||
}
|
||||
|
||||
void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std::string& op_signature,
|
||||
@ -150,6 +166,77 @@ void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std
|
||||
}
|
||||
}
|
||||
|
||||
void TuningResultsManager::InitRealtimeAppend(const std::string& filename, const std::unordered_map<std::string, std::string>& validators) {
|
||||
std::scoped_lock fl{realtime_file_mutex_};
|
||||
|
||||
if (realtime_out_ && realtime_out_->good() && realtime_filename_ == filename) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (realtime_out_ && realtime_filename_ != filename) {
|
||||
realtime_out_->flush();
|
||||
realtime_out_->close();
|
||||
realtime_out_.reset();
|
||||
validators_written_ = false;
|
||||
}
|
||||
|
||||
bool file_exists = false;
|
||||
bool file_empty = true;
|
||||
|
||||
{
|
||||
std::ifstream check_file(filename);
|
||||
if (check_file.good()) {
|
||||
file_exists = true;
|
||||
file_empty = (check_file.peek() == std::ifstream::traits_type::eof());
|
||||
}
|
||||
}
|
||||
|
||||
realtime_out_ = std::make_unique<std::ofstream>(filename, std::ios::out | std::ios::app);
|
||||
|
||||
if (!realtime_out_->good()) {
|
||||
TORCH_WARN("TunableOp realtime append: failed to open '", filename,"'");
|
||||
realtime_out_.reset();
|
||||
return;
|
||||
}
|
||||
|
||||
if(!file_exists || file_empty) {
|
||||
for(const auto& [key, val] : validators) {
|
||||
(*realtime_out_) << "Validator," << key << "," << val << std::endl;
|
||||
realtime_out_->flush();
|
||||
}
|
||||
validators_written_ = true;
|
||||
|
||||
TUNABLE_LOG2("Wrote validators to realtime output file");
|
||||
}
|
||||
|
||||
realtime_filename_ = filename;
|
||||
}
|
||||
|
||||
void TuningResultsManager::AppendResultLine(const std::string& op_sig, const std::string& param_sig, const ResultEntry& result) {
|
||||
std::scoped_lock fl{realtime_file_mutex_};
|
||||
|
||||
if(!realtime_out_ || !realtime_out_->good()) {
|
||||
return;
|
||||
}
|
||||
|
||||
(*realtime_out_) << op_sig << "," << param_sig << "," << result << std::endl;
|
||||
realtime_out_->flush(); //ensure immediate write to disk
|
||||
|
||||
TUNABLE_LOG3("Realtime append: ", op_sig, "(", param_sig, ") -> ", result);
|
||||
}
|
||||
|
||||
void TuningResultsManager::CloseRealtimeAppend() {
|
||||
std::scoped_lock fl{realtime_file_mutex_};
|
||||
|
||||
|
||||
if(realtime_out_) {
|
||||
realtime_out_->flush();
|
||||
realtime_out_->close();
|
||||
realtime_out_.reset();
|
||||
TUNABLE_LOG2("Closed realtime output file");
|
||||
}
|
||||
}
|
||||
|
||||
void TuningResultsManager::Delete(const std::string& op_signature, const std::string& params_signature) {
|
||||
std::scoped_lock l{lock_};
|
||||
|
||||
@ -396,7 +483,6 @@ TuningContext::TuningContext() :
|
||||
tuning_enable_{true},
|
||||
record_untuned_enable_{false},
|
||||
manager_initialized_{false},
|
||||
write_file_on_exit_{true},
|
||||
numerics_check_enable_{false},
|
||||
max_tuning_duration_ms_{30},
|
||||
max_tuning_iterations_{100},
|
||||
@ -417,20 +503,8 @@ TuningContext::~TuningContext() {
|
||||
// but doesn't do any computation itself.
|
||||
return;
|
||||
}
|
||||
auto filename = GetFilename();
|
||||
if (IsTunableOpEnabled() && IsTuningEnabled() && !filename.empty() && write_file_on_exit_) {
|
||||
if (results_count_from_input_file_ < GetTuningResultsManager().GetSize()) {
|
||||
if (results_count_from_input_file_ > 0) {
|
||||
TUNABLE_LOG1("additional tuning results available, rewriting file ", filename);
|
||||
}
|
||||
else {
|
||||
TUNABLE_LOG1("writing file ", filename);
|
||||
}
|
||||
if (!WriteFile(filename)) {
|
||||
TUNABLE_LOG1("failed to write file ", filename);
|
||||
}
|
||||
}
|
||||
}
|
||||
TUNABLE_LOG1("Closing File");
|
||||
GetTuningResultsManager().CloseRealtimeAppend(); // Since, we do instant logging by default now.
|
||||
|
||||
if (untuned_file_.good()) {
|
||||
untuned_file_.close();
|
||||
@ -511,20 +585,54 @@ std::ofstream& TuningContext::GetUntunedFile(){
|
||||
return untuned_file_;
|
||||
}
|
||||
|
||||
void TuningContext::WriteFileOnExit(bool value) {
|
||||
write_file_on_exit_ = value;
|
||||
}
|
||||
|
||||
void TuningContext::EnableNumericsCheck(bool value) {
|
||||
numerics_check_enable_ = value;
|
||||
}
|
||||
|
||||
bool TuningContext::IsNumericsCheckEnabled() const {
|
||||
const auto env = c10::utils::get_env("PYTORCH_TUNABLEOP_NUMERICAL_CHECK");
|
||||
if (env == "1") {
|
||||
return true;
|
||||
NumericalCheckConfig TuningContext::GetNumericalCheckConfig() const {
|
||||
const auto env_opt = c10::utils::get_env("PYTORCH_TUNABLEOP_NUMERICAL_CHECK");
|
||||
|
||||
if (!env_opt.has_value()) {
|
||||
return numerics_cfg_;
|
||||
}
|
||||
return numerics_check_enable_;
|
||||
|
||||
const std::string& env = env_opt.value();
|
||||
|
||||
if (env == "0") {
|
||||
return NumericalCheckConfig(false, 1e-5, 1e-5);
|
||||
}
|
||||
|
||||
const size_t underscore = env.find('_');
|
||||
|
||||
TORCH_CHECK(
|
||||
underscore != std::string::npos,
|
||||
"Invalid PYTORCH_TUNABLEOP_NUMERICAL_CHECK format. "
|
||||
"Expected 'atol_rtol', got: ",
|
||||
env);
|
||||
|
||||
double atol = 0.0;
|
||||
double rtol = 0.0;
|
||||
|
||||
try {
|
||||
atol = std::stod(env.substr(0, underscore));
|
||||
rtol = std::stod(env.substr(underscore + 1));
|
||||
} catch (const std::exception& e) {
|
||||
TORCH_CHECK(false, "Failed to parse PYTORCH_TUNABLEOP_NUMERICAL_CHECK: ", e.what());
|
||||
}
|
||||
|
||||
TORCH_CHECK( atol > 0.0 && rtol > 0.0, "Tolerance values must be positive. atol=", atol, ", rtol=", rtol);
|
||||
return NumericalCheckConfig(true, atol, rtol);
|
||||
}
|
||||
|
||||
void TuningContext::SetNumericalCheckConfig(bool enabled, double atol, double rtol) {
|
||||
TORCH_CHECK(atol > 0.0 && rtol > 0.0, "Numerical check tolerances must be positive");
|
||||
numerics_cfg_ = {enabled, atol, rtol};
|
||||
}
|
||||
|
||||
bool TuningContext::IsNumericsCheckEnabled() const {
|
||||
const auto cfg = GetNumericalCheckConfig();
|
||||
return cfg.enabled || numerics_check_enable_;
|
||||
}
|
||||
|
||||
void TuningContext::SetMaxTuningDurationMs(int max_duration_ms) {
|
||||
@ -634,11 +742,6 @@ TuningResultsManager& TuningContext::GetTuningResultsManager() {
|
||||
auto filename = GetFilename();
|
||||
if (!filename.empty() && !IsRecordUntunedEnabled()) {
|
||||
ReadFile(filename);
|
||||
// attempt immediately to open file for writing to catch errors early
|
||||
std::ofstream file(filename, std::ios::out | std::ios::app);
|
||||
if (!file.good()) {
|
||||
TORCH_WARN("failed to open file '", filename, "' for writing; your tuning results will not be saved");
|
||||
}
|
||||
}
|
||||
});
|
||||
return manager_;
|
||||
@ -744,27 +847,6 @@ bool TuningContext::ReadFile(const std::string& filename_) {
|
||||
return true;
|
||||
}
|
||||
|
||||
bool TuningContext::WriteFile(const std::string& filename_) {
|
||||
std::string filename = filename_.empty() ? GetFilename() : filename_;
|
||||
std::ofstream file(filename, std::ios::out | std::ios::trunc);
|
||||
if (!file.good()) {
|
||||
TUNABLE_LOG1("error opening tuning results file for writing ", filename);
|
||||
return false;
|
||||
}
|
||||
auto validators = GetTuningResultsValidator().GetAllValidators();
|
||||
for (const auto& [key, val] : validators) {
|
||||
file << "Validator," << key << "," << val << std::endl;
|
||||
}
|
||||
auto results = GetTuningResultsManager().Dump();
|
||||
for (const auto& [op_sig, kernelmap] : results) {
|
||||
for (const auto& [param_sig, result] : kernelmap) {
|
||||
file << op_sig << "," << param_sig << "," << result << std::endl;
|
||||
}
|
||||
}
|
||||
file.close();
|
||||
return true;
|
||||
}
|
||||
|
||||
namespace {
|
||||
|
||||
struct MaybeDelete {
|
||||
|
||||
@ -103,10 +103,24 @@ class TORCH_CUDA_CPP_API TuningResultsManager {
|
||||
|
||||
void RecordUntuned( std::ofstream& untuned_file, const std::string& op_signature,
|
||||
const std::string& params_signature, const std::string& blas_signature);
|
||||
|
||||
void InitRealtimeAppend(
|
||||
const std::string& filename,
|
||||
const std::unordered_map<std::string, std::string>& validators);
|
||||
|
||||
void AppendResultLine(const std::string& op_sig,
|
||||
const std::string& param_sig,
|
||||
const ResultEntry& result);
|
||||
|
||||
void CloseRealtimeAppend(); // For clean shutdown
|
||||
private:
|
||||
std::mutex lock_;
|
||||
std::mutex realtime_file_mutex_;
|
||||
std::unique_ptr<std::ofstream> realtime_out_;
|
||||
std::string realtime_filename_;
|
||||
ResultsMap results_;
|
||||
UntunedMap untuned_results_;
|
||||
bool validators_written_ = false;
|
||||
|
||||
};
|
||||
|
||||
@ -134,6 +148,16 @@ class TORCH_CUDA_CPP_API TuningResultsValidator {
|
||||
GetValidateFuncs validators_;
|
||||
};
|
||||
|
||||
struct NumericalCheckConfig {
|
||||
bool enabled{false};
|
||||
double atol{1e-5};
|
||||
double rtol{1e-5};
|
||||
|
||||
NumericalCheckConfig() = default;
|
||||
NumericalCheckConfig(bool e, double a, double r) : enabled(e), atol(a), rtol(r) {}
|
||||
};
|
||||
|
||||
|
||||
class TORCH_CUDA_CPP_API TuningContext {
|
||||
public:
|
||||
TuningContext();
|
||||
@ -155,6 +179,8 @@ class TORCH_CUDA_CPP_API TuningContext {
|
||||
|
||||
void EnableNumericsCheck(bool value);
|
||||
bool IsNumericsCheckEnabled() const;
|
||||
void SetNumericalCheckConfig(bool enabled, double atol, double rtol);
|
||||
NumericalCheckConfig GetNumericalCheckConfig() const;
|
||||
|
||||
void SetMaxTuningDurationMs(int max_duration_ms);
|
||||
int GetMaxTuningDurationMs() const;
|
||||
@ -185,10 +211,7 @@ class TORCH_CUDA_CPP_API TuningContext {
|
||||
void SetFilename(const std::string& filename, bool insert_device_ordinal=false);
|
||||
std::string GetFilename() const;
|
||||
|
||||
void WriteFileOnExit(bool value);
|
||||
|
||||
bool ReadFile(const std::string& filename={});
|
||||
bool WriteFile(const std::string& filename={});
|
||||
|
||||
template<class... Types>
|
||||
void Log(int level, Types... args) {
|
||||
@ -207,7 +230,6 @@ class TORCH_CUDA_CPP_API TuningContext {
|
||||
bool tuning_enable_;
|
||||
bool record_untuned_enable_;
|
||||
bool manager_initialized_;
|
||||
bool write_file_on_exit_;
|
||||
bool numerics_check_enable_;
|
||||
int max_tuning_duration_ms_;
|
||||
int max_tuning_iterations_;
|
||||
@ -222,6 +244,8 @@ class TORCH_CUDA_CPP_API TuningContext {
|
||||
std::ofstream untuned_file_;
|
||||
size_t results_count_from_input_file_;
|
||||
bool is_shutting_down_;
|
||||
|
||||
NumericalCheckConfig numerics_cfg_{};
|
||||
};
|
||||
|
||||
TORCH_CUDA_CPP_API TuningContext* getTuningContext();
|
||||
|
||||
@ -109,7 +109,8 @@ class DefaultScaledGemmOp : public Callable<ScaledGemmParams<T>> {
|
||||
params->c_scale_ptr,
|
||||
params->ldc,
|
||||
params->c_dtype,
|
||||
params->use_fast_accum);
|
||||
params->use_fast_accum,
|
||||
std::nullopt /* alpha */);
|
||||
return OK;
|
||||
}
|
||||
};
|
||||
|
||||
@ -267,28 +267,11 @@ class TunableOp {
|
||||
for (size_t i = 0; i < op_names_.size(); i++) {
|
||||
auto* candidate = ops_[op_names_[i]].get(); // borrow pointer
|
||||
|
||||
if (do_numerics_check) {
|
||||
ParamsT* numerical_params = params->DeepCopy(false);
|
||||
auto status = candidate->Call(numerical_params);
|
||||
if (status != OK) {
|
||||
numerical_params->Delete();
|
||||
TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
|
||||
continue;
|
||||
}
|
||||
status = reference_params->NumericalCheck(numerical_params);
|
||||
numerical_params->Delete();
|
||||
if (status != OK) {
|
||||
TUNABLE_LOG3("├──numerics check failed for id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
else {
|
||||
auto status = candidate->Call(reusable_params[0]);
|
||||
if (status != OK) {
|
||||
TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
// collect a small profile
|
||||
int approx_num_iter = 3;
|
||||
@ -310,6 +293,22 @@ class TunableOp {
|
||||
continue;
|
||||
}
|
||||
|
||||
if (do_numerics_check) {
|
||||
ParamsT* numerical_params = params->DeepCopy(false);
|
||||
auto status = candidate->Call(numerical_params);
|
||||
if (status != OK) {
|
||||
numerical_params->Delete();
|
||||
TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
|
||||
continue;
|
||||
}
|
||||
status = reference_params->NumericalCheck(numerical_params);
|
||||
numerical_params->Delete();
|
||||
if (status != OK) {
|
||||
TUNABLE_LOG3("├──numerics check failed for id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
// for warmup does user set max duration, max iters, or both?
|
||||
// warmup is skipped by default, i.e. warmup_iter = 0
|
||||
// warmup will be set to the non-zero value of max_warmup_duration
|
||||
|
||||
@ -2,6 +2,8 @@
|
||||
|
||||
#include <ATen/ATen.h>
|
||||
|
||||
#include <c10/util/Exception.h>
|
||||
|
||||
namespace at::native {
|
||||
|
||||
cudnnDataType_t getCudnnDataTypeFromScalarType(const at::ScalarType dtype) {
|
||||
@ -20,9 +22,10 @@ cudnnDataType_t getCudnnDataTypeFromScalarType(const at::ScalarType dtype) {
|
||||
} else if (dtype == at::kByte) {
|
||||
return CUDNN_DATA_UINT8;
|
||||
}
|
||||
std::string msg("getCudnnDataTypeFromScalarType() not supported for ");
|
||||
msg += toString(dtype);
|
||||
throw std::runtime_error(msg);
|
||||
TORCH_CHECK(false,
|
||||
"getCudnnDataTypeFromScalarType() not supported for ",
|
||||
toString(dtype)
|
||||
);
|
||||
}
|
||||
|
||||
cudnnDataType_t getCudnnDataType(const at::Tensor& tensor) {
|
||||
|
||||
@ -39,7 +39,7 @@ Tensor vdot_decomp(const Tensor& A, const Tensor& B) {
|
||||
// NB: I wrote this like this because we *might* want its for a future matmul
|
||||
// batch rule that isn't decomposed...
|
||||
// "tv" = tensor @ vector
|
||||
static std::tuple<Tensor, std::optional<int64_t>> tv_batch_rule(
|
||||
std::tuple<Tensor, std::optional<int64_t>> tv_batch_rule(
|
||||
const Tensor& self, std::optional<int64_t> self_bdim,
|
||||
const Tensor& other, std::optional<int64_t> other_bdim) {
|
||||
if (self_bdim && other_bdim) {
|
||||
@ -66,7 +66,7 @@ static std::tuple<Tensor, std::optional<int64_t>> tv_batch_rule(
|
||||
TORCH_INTERNAL_ASSERT(false, "can't get here");
|
||||
}
|
||||
|
||||
static std::tuple<Tensor, std::optional<int64_t>> mv_batch_rule(
|
||||
std::tuple<Tensor, std::optional<int64_t>> mv_batch_rule(
|
||||
const Tensor& self, std::optional<int64_t> self_bdim,
|
||||
const Tensor& other, std::optional<int64_t> other_bdim) {
|
||||
auto self_logical_rank = rankWithoutBatchDim(self, self_bdim);
|
||||
@ -79,7 +79,7 @@ static std::tuple<Tensor, std::optional<int64_t>> mv_batch_rule(
|
||||
return tv_batch_rule(self, self_bdim, other, other_bdim);
|
||||
}
|
||||
|
||||
static std::tuple<Tensor, std::optional<int64_t>> mm_batch_rule(
|
||||
std::tuple<Tensor, std::optional<int64_t>> mm_batch_rule(
|
||||
const Tensor& self, std::optional<int64_t> self_bdim,
|
||||
const Tensor& other, std::optional<int64_t> other_bdim) {
|
||||
auto self_logical_rank = rankWithoutBatchDim(self, self_bdim);
|
||||
@ -94,7 +94,7 @@ static std::tuple<Tensor, std::optional<int64_t>> mm_batch_rule(
|
||||
return std::make_tuple( at::matmul(self_, other_), 0 );
|
||||
}
|
||||
|
||||
static std::tuple<Tensor, std::optional<int64_t>> bmm_batch_rule(
|
||||
std::tuple<Tensor, std::optional<int64_t>> bmm_batch_rule(
|
||||
const Tensor& self, std::optional<int64_t> self_bdim,
|
||||
const Tensor& other, std::optional<int64_t> other_bdim) {
|
||||
auto self_logical_rank = rankWithoutBatchDim(self, self_bdim);
|
||||
@ -250,7 +250,7 @@ struct LinalgCheckMatrixBinaryRuleHelper<op_name, F, Func, typelist<A, B, T...>>
|
||||
}
|
||||
};
|
||||
|
||||
static void expect_at_least_rank(
|
||||
void expect_at_least_rank(
|
||||
const Tensor& tensor,
|
||||
std::optional<int64_t> tensor_bdim,
|
||||
int64_t expected_rank,
|
||||
@ -472,7 +472,7 @@ atol_rtol_tensor_batch_rule(
|
||||
return std::make_tuple(Func(input_, atol_, rtol_, hermitian), 0);
|
||||
}
|
||||
|
||||
static std::tuple<Tensor, std::optional<int64_t>>
|
||||
std::tuple<Tensor, std::optional<int64_t>>
|
||||
pinv_batch_rule(
|
||||
const Tensor& input, std::optional<int64_t> input_bdim, const std::optional<Tensor>& atol,
|
||||
const std::optional<int64_t> atol_bdim, const std::optional<Tensor>& rtol,
|
||||
|
||||
@ -213,40 +213,22 @@ static cudnn_grid_sample_backward_batch_rule(
|
||||
return grid_sample_backward_helper_out(std::move(bw_out), 0, 0, bdim_size);
|
||||
}
|
||||
|
||||
// TODO: replace with targetable functionalization
|
||||
// uses functional formulation for one_hot under vmap to be compatible with
|
||||
// fakeTensor/dynamic shapes and compiled functorch transforms.
|
||||
// mirrors the meta path in aten/src/ATen/native/Onehot.cpp,
|
||||
// but requires explicit positive num_classes under vmap to avoid
|
||||
// data-dependent output shapes.
|
||||
static Tensor one_hot_decomposition_hack(const Tensor &self, int64_t num_classes) {
|
||||
TORCH_CHECK(self.dtype() == kLong, "one_hot is only applicable to index tensor.");
|
||||
auto shape = self.sym_sizes().vec();
|
||||
|
||||
// empty tensor could be converted to one hot representation,
|
||||
// but shape inference is not possible.
|
||||
if (self.sym_numel() == 0) {
|
||||
if (num_classes <= 0) {
|
||||
TORCH_CHECK(false, "Can not infer total number of classes from empty tensor.");
|
||||
} else {
|
||||
shape.emplace_back(num_classes);
|
||||
return at::empty_symint(shape, self.options());
|
||||
}
|
||||
}
|
||||
|
||||
// disallow implicit inference under vmap; this would be data-dependent
|
||||
// and is intentionally guarded by Dynamo in torch/_dynamo/variables/torch.py.
|
||||
TORCH_CHECK(num_classes > 0, "When vmap-ing torch.nn.functional.one_hot, please "
|
||||
"provide an explicit positive num_classes argument.");
|
||||
|
||||
// Disabling all of the following checks. This is OK because scatter has checks too.
|
||||
// Maybe one_hot should be a primitive wrt autograd so we don't have to deal with this.
|
||||
// // non-empty tensor
|
||||
// if (self.device().type() != at::kCUDA) {
|
||||
// //for cuda, rely on device assert thrown by scatter
|
||||
// TORCH_CHECK(self.min().item().toLong() >= 0, "Class values must be non-negative.");
|
||||
// }
|
||||
// if (self.device().type() != at::kCUDA) {
|
||||
// //rely on device asserts from scatter to avoid sync here
|
||||
// TORCH_CHECK(num_classes > self.max().item().toLong(), "Class values must be smaller than num_classes.");
|
||||
// }
|
||||
|
||||
shape.emplace_back(num_classes);
|
||||
Tensor ret = at::zeros_symint(shape, self.options());
|
||||
return ret.scatter(-1, self.unsqueeze(-1), 1);
|
||||
const auto options = self.options();
|
||||
at::Tensor index = at::arange(num_classes, options);
|
||||
return at::eq(self.unsqueeze(-1), index).to(at::kLong);
|
||||
}
|
||||
|
||||
template <typename A, A a, typename C>
|
||||
|
||||
@ -12,13 +12,14 @@
|
||||
#include <ATen/native/IndexKernel.h>
|
||||
#include <ATen/native/IndexingUtils.h>
|
||||
#include <torch/library.h>
|
||||
#include <c10/util/Exception.h>
|
||||
|
||||
|
||||
// NOLINTBEGIN(bugprone-unchecked-optional-access)
|
||||
namespace at::functorch {
|
||||
|
||||
namespace {
|
||||
static bool any_has_value(ArrayRef<std::optional<int64_t>> bdims) {
|
||||
bool any_has_value(ArrayRef<std::optional<int64_t>> bdims) {
|
||||
for (const auto& bdim : bdims) {
|
||||
if (bdim.has_value()) {
|
||||
return true;
|
||||
@ -27,7 +28,7 @@ static bool any_has_value(ArrayRef<std::optional<int64_t>> bdims) {
|
||||
return false;
|
||||
}
|
||||
|
||||
static int64_t get_num_leading_nones(ArrayRef<std::optional<Tensor>> indices) {
|
||||
int64_t get_num_leading_nones(ArrayRef<std::optional<Tensor>> indices) {
|
||||
int64_t result = 0;
|
||||
for (const auto& idx : indices) {
|
||||
if (!idx.has_value() || !idx->defined()) {
|
||||
@ -39,7 +40,7 @@ static int64_t get_num_leading_nones(ArrayRef<std::optional<Tensor>> indices) {
|
||||
return result;
|
||||
}
|
||||
|
||||
static int64_t get_max_index_logical_dim(
|
||||
int64_t get_max_index_logical_dim(
|
||||
ArrayRef<std::optional<Tensor>> indices,
|
||||
ArrayRef<std::optional<int64_t>> indices_bdims) {
|
||||
int64_t max_logical_dim = -1;
|
||||
@ -56,7 +57,7 @@ static int64_t get_max_index_logical_dim(
|
||||
return max_logical_dim;
|
||||
}
|
||||
|
||||
static std::vector<std::optional<Tensor>> batchIndices(
|
||||
std::vector<std::optional<Tensor>> batchIndices(
|
||||
at::TensorOptions options,
|
||||
ArrayRef<std::optional<Tensor>> indices,
|
||||
ArrayRef<std::optional<int64_t>> indices_bdims,
|
||||
@ -94,9 +95,10 @@ static std::vector<std::optional<Tensor>> batchIndices(
|
||||
if (index.has_value() && index->sym_numel() != 0) {
|
||||
const auto idx_bdim = indices_bdims[i];
|
||||
indices_.emplace_back(maybePadToLogicalRank(moveBatchDimToFront(index.value(), idx_bdim), idx_bdim, maxLogicalRank));
|
||||
if (index.value().dtype() == kBool && indices_bdims[i].has_value()) {
|
||||
throw std::runtime_error("vmap: We do not support batching operators that can support dynamic shape. Attempting to batch over indexing with a boolean mask.");
|
||||
}
|
||||
TORCH_CHECK(
|
||||
!(index.value().dtype() == kBool) || !indices_bdims[i].has_value(),
|
||||
"vmap: We do not support batching operators that can support dynamic shape. Attempting to batch over indexing with a boolean mask."
|
||||
);
|
||||
} else {
|
||||
indices_.push_back(index);
|
||||
}
|
||||
@ -124,7 +126,7 @@ static std::vector<std::optional<Tensor>> batchIndices(
|
||||
|
||||
// Define an "advanced index" to be a selection object that is
|
||||
// a non-trivial Tensor (i.e. it does not represent :).
|
||||
static bool is_advanced_index(const std::optional<Tensor>& idx) {
|
||||
bool is_advanced_index(const std::optional<Tensor>& idx) {
|
||||
if (!idx.has_value()) {
|
||||
return false;
|
||||
}
|
||||
@ -135,7 +137,7 @@ static bool is_advanced_index(const std::optional<Tensor>& idx) {
|
||||
}
|
||||
|
||||
// See NOTE: [advanced indices adjacent] for definition
|
||||
static bool are_advanced_indices_adjacent(ArrayRef<std::optional<Tensor>> indices) {
|
||||
bool are_advanced_indices_adjacent(ArrayRef<std::optional<Tensor>> indices) {
|
||||
int64_t num_advanced_indices_regions = 0;
|
||||
bool in_advanced_indices_region = false;
|
||||
for (const auto& idx : indices) {
|
||||
@ -163,7 +165,7 @@ static bool are_advanced_indices_adjacent(ArrayRef<std::optional<Tensor>> indice
|
||||
// - result: Tensor[B, 4, 5, 6, 2, 3, 7, 8]
|
||||
// ------- ----
|
||||
// region2 region1
|
||||
static Tensor swap_regions(const Tensor& tensor, int64_t first_region_size, int64_t second_region_size) {
|
||||
Tensor swap_regions(const Tensor& tensor, int64_t first_region_size, int64_t second_region_size) {
|
||||
VmapDimVector permutation(tensor.dim(), 0);
|
||||
std::iota(permutation.begin(), permutation.end(), 0);
|
||||
std::rotate(
|
||||
@ -551,7 +553,7 @@ Tensor &_index_put_impl__plumbing(Tensor &self, const List<std::optional<Tensor>
|
||||
return self;
|
||||
}
|
||||
|
||||
static Tensor maybe_permute_values(
|
||||
Tensor maybe_permute_values(
|
||||
const Tensor& values,
|
||||
ArrayRef<std::optional<Tensor>> orig_indices,
|
||||
ArrayRef<std::optional<int64_t>> orig_indices_bdims) {
|
||||
@ -1050,7 +1052,7 @@ std::tuple<Tensor, std::optional<int64_t>> index_add_batch_rule(
|
||||
other, other_bdim, alpha, false);
|
||||
}
|
||||
|
||||
static std::tuple<Tensor,Tensor> binary_pointwise_align(
|
||||
std::tuple<Tensor,Tensor> binary_pointwise_align(
|
||||
const Tensor & self,
|
||||
std::optional<int64_t> self_bdim,
|
||||
const Tensor & mask,
|
||||
|
||||
@ -346,7 +346,7 @@ std::tuple<Tensor, std::optional<int64_t>> slice_batch_rule(
|
||||
return std::make_tuple(std::move(result), 0);
|
||||
}
|
||||
|
||||
static bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
|
||||
bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
|
||||
return dim == 0 || dim == -1;
|
||||
}
|
||||
|
||||
|
||||
@ -160,6 +160,10 @@ constexpr DispatchKeySet kKeysToPropagateToWrapper({
|
||||
DispatchKey::CUDA,
|
||||
DispatchKey::CPU,
|
||||
DispatchKey::PrivateUse1,
|
||||
DispatchKey::SparseCPU,
|
||||
DispatchKey::SparseCUDA,
|
||||
DispatchKey::SparseCsrCPU,
|
||||
DispatchKey::SparseCsrCUDA,
|
||||
});
|
||||
|
||||
inline DispatchKeySet getKeysToPropagateToWrapper(const Tensor& tensor, DispatchKeySet to_propagate=kKeysToPropagateToWrapper) {
|
||||
|
||||
@ -3,6 +3,7 @@
|
||||
#include <ATen/functorch/Macros.h>
|
||||
#include <ATen/core/dispatch/Dispatcher.h>
|
||||
#include <c10/core/impl/LocalDispatchKeySet.h>
|
||||
#include <c10/util/Exception.h>
|
||||
#include <optional>
|
||||
#include <bitset>
|
||||
#include <utility>
|
||||
@ -106,9 +107,10 @@ struct VmapInterpreterMeta {
|
||||
|
||||
template <typename T>
|
||||
friend void to_json(T& json_j, const VmapInterpreterMeta& json_t) {
|
||||
if (json_t.batchSize_.is_heap_allocated()) {
|
||||
throw std::runtime_error("Serialization for heap-allocated SymInt is not implemented yet");
|
||||
}
|
||||
TORCH_CHECK(
|
||||
!json_t.batchSize_.is_heap_allocated(),
|
||||
"Serialization for heap-allocated SymInt is not implemented yet"
|
||||
);
|
||||
json_j["batchSize"] = json_t.batchSize_.as_int_unchecked();
|
||||
json_j["randomness"] = static_cast<int64_t>(json_t.randomness_);
|
||||
}
|
||||
@ -302,7 +304,7 @@ struct Interpreter {
|
||||
} else if (meta.contains("Functionalize")) {
|
||||
json_t.meta_.emplace<FunctionalizeInterpreterMeta>(meta["Functionalize"].template get<FunctionalizeInterpreterMeta>());
|
||||
} else {
|
||||
throw std::runtime_error("unknown interpreter metadata type");
|
||||
TORCH_CHECK(false, "unknown interpreter metadata type");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -68,18 +68,18 @@ namespace at::functorch {
|
||||
|
||||
namespace{
|
||||
// PyTorch allows operations to specify dim 0 and dim -1 on a scalar tensor.
|
||||
static bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
|
||||
bool is_allowed_dim_on_scalar_tensor(int64_t dim) {
|
||||
return dim == 0 || dim == -1;
|
||||
}
|
||||
|
||||
static int64_t get_current_level() {
|
||||
int64_t get_current_level() {
|
||||
auto maybe_level = maybeCurrentDynamicLayer();
|
||||
TORCH_INTERNAL_ASSERT(maybe_level.has_value());
|
||||
return maybe_level->layerId();
|
||||
}
|
||||
|
||||
// This check should probably go into the dispatcher...
|
||||
static bool participatesInCurrentLevel(const Tensor& self) {
|
||||
bool participatesInCurrentLevel(const Tensor& self) {
|
||||
auto current_level = get_current_level();
|
||||
auto* maybe_batched_impl = maybeGetBatchedImpl(self);
|
||||
if (!maybe_batched_impl) {
|
||||
@ -90,7 +90,7 @@ static bool participatesInCurrentLevel(const Tensor& self) {
|
||||
return self_level == current_level;
|
||||
}
|
||||
|
||||
static bool participatesInCurrentLevel(ITensorListRef self) {
|
||||
bool participatesInCurrentLevel(ITensorListRef self) {
|
||||
for (const Tensor& tensor : self) {
|
||||
if (participatesInCurrentLevel(tensor)) {
|
||||
return true;
|
||||
@ -285,7 +285,7 @@ std::vector<Tensor> unbind_batching_rule(const Tensor& self, int64_t dim) {
|
||||
// given (sizes, strides, storage_offset) returns the maximum location that
|
||||
// can be indexed (or nullopt if such a location doesn't exist, e.g., tensors
|
||||
// with zero-size dims).
|
||||
static std::optional<c10::SymInt> maximum_indexable_location(
|
||||
std::optional<c10::SymInt> maximum_indexable_location(
|
||||
c10::SymIntArrayRef sizes, c10::SymIntArrayRef strides, const c10::SymInt& storage_offset) {
|
||||
auto result = native::storage_size_for(sizes, strides);
|
||||
if (result == 0) {
|
||||
@ -298,7 +298,7 @@ static std::optional<c10::SymInt> maximum_indexable_location(
|
||||
// This checks that the range of possible memory locations accessible by
|
||||
// x.as_strided(sizes, strides, maybe_storage_offset)
|
||||
// are within the bounds of possible memory locations accessible by x.
|
||||
static void checkBasicAsStridedValidForSlice(
|
||||
void checkBasicAsStridedValidForSlice(
|
||||
const Tensor& physical_tensor,
|
||||
int64_t num_batch_dims,
|
||||
c10::SymIntArrayRef sizes,
|
||||
|
||||
@ -6,6 +6,7 @@
|
||||
#include <ATen/functorch/BatchedTensorImpl.h>
|
||||
#include <ATen/Dispatch.h>
|
||||
#include <c10/util/irange.h>
|
||||
#include <c10/util/Exception.h>
|
||||
#include <ATen/NamedTensorUtils.h>
|
||||
#include <ATen/native/LinearAlgebraUtils.h>
|
||||
#include <ATen/native/xnnpack/Engine.h>
|
||||
@ -70,7 +71,7 @@ Tensor linear_hack(const Tensor& input, const Tensor& weight, const std::optiona
|
||||
return output;
|
||||
}
|
||||
|
||||
static inline at::Tensor apply_loss_reduction(const at::Tensor& unreduced, int64_t reduction) {
|
||||
inline at::Tensor apply_loss_reduction(const at::Tensor& unreduced, int64_t reduction) {
|
||||
if (reduction == at::Reduction::Mean) {
|
||||
return unreduced.mean();
|
||||
} else if (reduction == at::Reduction::Sum) {
|
||||
@ -108,9 +109,7 @@ Tensor binary_cross_entropy_with_logits_hack(
|
||||
}
|
||||
|
||||
Tensor trace_backward_decomp(const Tensor& grad, IntArrayRef sizes) {
|
||||
if (sizes.size() != 2) {
|
||||
throw std::runtime_error("expected matrix input");
|
||||
}
|
||||
TORCH_CHECK(sizes.size() == 2, "expected matrix input");
|
||||
auto grad_input = at::zeros(sizes[0] * sizes[1], grad.options());
|
||||
auto indices = at::arange(0, grad_input.numel(), sizes[1] + 1, grad.options().dtype(at::kLong));
|
||||
// Workaround using index_put instead of yet unsupported index_fill_
|
||||
@ -128,7 +127,7 @@ namespace {
|
||||
template<bool inplace>
|
||||
using Ctype = std::conditional_t<inplace, Tensor&, Tensor>;
|
||||
|
||||
static Tensor make_feature_noise(const Tensor& input) {
|
||||
Tensor make_feature_noise(const Tensor& input) {
|
||||
auto input_sizes = input.sizes();
|
||||
TORCH_CHECK(input.dim() >= 2, "Feature dropout requires at least 2 dimensions in the input");
|
||||
std::vector<int64_t> sizes;
|
||||
@ -142,7 +141,7 @@ static Tensor make_feature_noise(const Tensor& input) {
|
||||
return at::empty(sizes, input.options());
|
||||
}
|
||||
|
||||
static bool is_fused_kernel_acceptable(const Tensor& input, double p) {
|
||||
bool is_fused_kernel_acceptable(const Tensor& input, double p) {
|
||||
return (input.is_cuda() || input.is_xpu() || input.is_lazy() || input.is_privateuseone()) && p > 0 && p < 1 && input.numel() > 0;
|
||||
}
|
||||
|
||||
@ -211,7 +210,7 @@ ALIAS_SPECIALIZATION(_feature_dropout, true, false)
|
||||
ALIAS_SPECIALIZATION(_alpha_dropout, false, true )
|
||||
ALIAS_SPECIALIZATION(_feature_alpha_dropout, true, true )
|
||||
|
||||
static Tensor dropout(const Tensor& input, double p, bool train) {
|
||||
Tensor dropout(const Tensor& input, double p, bool train) {
|
||||
auto result = [&]() {
|
||||
NoNamesGuard guard;
|
||||
if (train && is_fused_kernel_acceptable(input, p)) {
|
||||
|
||||
@ -240,8 +240,8 @@ TORCH_META_FUNC(gelu_backward) (
|
||||
|
||||
namespace at::native {
|
||||
|
||||
static const double SELU_ALPHA = 1.6732632423543772848170429916717;
|
||||
static const double SELU_SCALE = 1.0507009873554804934193349852946;
|
||||
static constexpr double SELU_ALPHA = 1.6732632423543772848170429916717;
|
||||
static constexpr double SELU_SCALE = 1.0507009873554804934193349852946;
|
||||
|
||||
DEFINE_DISPATCH(elu_stub);
|
||||
DEFINE_DISPATCH(elu_backward_stub);
|
||||
|
||||
@ -24,7 +24,7 @@ namespace at::native {
|
||||
namespace {
|
||||
|
||||
template <typename scalar_t>
|
||||
static void adaptive_avg_pool3d_out_frame(
|
||||
void adaptive_avg_pool3d_out_frame(
|
||||
const scalar_t* input_p,
|
||||
scalar_t* output_p,
|
||||
int64_t sizeD,
|
||||
@ -176,7 +176,7 @@ void adaptive_avg_pool3d_out_cpu_template(
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
static void adaptive_avg_pool3d_backward_out_frame(
|
||||
void adaptive_avg_pool3d_backward_out_frame(
|
||||
scalar_t* gradInput_p,
|
||||
const scalar_t* gradOutput_p,
|
||||
int64_t sizeD,
|
||||
|
||||
@ -93,7 +93,7 @@ namespace {
|
||||
// 5d tensor B x D x T x H x W
|
||||
|
||||
template <typename scalar_t>
|
||||
static void adaptive_max_pool3d_single_out_frame(
|
||||
void adaptive_max_pool3d_single_out_frame(
|
||||
const scalar_t *input_p,
|
||||
scalar_t *output_p,
|
||||
int64_t *ind_p,
|
||||
@ -170,7 +170,7 @@ static void adaptive_max_pool3d_single_out_frame(
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
static void adaptive_max_pool3d_out_frame(
|
||||
void adaptive_max_pool3d_out_frame(
|
||||
const scalar_t *input_data,
|
||||
scalar_t *output_data,
|
||||
int64_t *indices_data,
|
||||
@ -202,7 +202,7 @@ static void adaptive_max_pool3d_out_frame(
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
static void adaptive_max_pool3d_backward_single_out_frame(
|
||||
void adaptive_max_pool3d_backward_single_out_frame(
|
||||
scalar_t *gradInput_p,
|
||||
const scalar_t *gradOutput_p,
|
||||
const int64_t *ind_p,
|
||||
@ -241,7 +241,7 @@ static void adaptive_max_pool3d_backward_single_out_frame(
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
static void adaptive_max_pool3d_backward_out_frame(
|
||||
void adaptive_max_pool3d_backward_out_frame(
|
||||
scalar_t *gradInput_data,
|
||||
const scalar_t *gradOutput_data,
|
||||
const int64_t *indices_data,
|
||||
|
||||
@ -153,7 +153,7 @@ namespace at::native {
|
||||
namespace {
|
||||
|
||||
template <typename scalar_t>
|
||||
static void avg_pool3d_out_frame(
|
||||
void avg_pool3d_out_frame(
|
||||
const scalar_t *input_p,
|
||||
scalar_t *output_p,
|
||||
int64_t nslices,
|
||||
@ -333,7 +333,7 @@ TORCH_IMPL_FUNC(avg_pool3d_out_cpu) (
|
||||
namespace {
|
||||
|
||||
template <typename scalar_t>
|
||||
static void avg_pool3d_backward_out_frame(
|
||||
void avg_pool3d_backward_out_frame(
|
||||
scalar_t *gradInput_p,
|
||||
const scalar_t *gradOutput_p,
|
||||
int64_t nslices,
|
||||
|
||||
@ -143,13 +143,13 @@ Tensor& cholesky_inverse_kernel_impl(Tensor& result, Tensor& infos, bool upper)
|
||||
For more info see https://github.com/pytorch/pytorch/issues/145801#issuecomment-2631781776
|
||||
*/
|
||||
template <typename T>
|
||||
static inline
|
||||
inline
|
||||
std::enable_if_t<std::is_floating_point_v<T>, int> lapack_work_to_int(const T val) {
|
||||
const auto next_after = std::nextafter(val, std::numeric_limits<T>::infinity());
|
||||
return std::max<int>(1, std::ceil(next_after));
|
||||
}
|
||||
template <typename T>
|
||||
static inline
|
||||
inline
|
||||
std::enable_if_t<c10::is_complex<T>::value, int> lapack_work_to_int(const T val) {
|
||||
return lapack_work_to_int(val.real());
|
||||
}
|
||||
@ -343,7 +343,7 @@ void linalg_eigh_kernel(const Tensor& eigenvalues, const Tensor& eigenvectors, c
|
||||
For further details, please see the LAPACK documentation for GEQRF.
|
||||
*/
|
||||
template <typename scalar_t>
|
||||
static void apply_geqrf(const Tensor& input, const Tensor& tau) {
|
||||
void apply_geqrf(const Tensor& input, const Tensor& tau) {
|
||||
#if !AT_BUILD_WITH_LAPACK()
|
||||
TORCH_CHECK(
|
||||
false,
|
||||
@ -1039,7 +1039,7 @@ void lu_solve_kernel(const Tensor& LU, const Tensor& pivots, const Tensor& B, Tr
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
static void apply_svd(const Tensor& A,
|
||||
void apply_svd(const Tensor& A,
|
||||
const bool full_matrices,
|
||||
const bool compute_uv,
|
||||
const Tensor& U,
|
||||
|
||||
@ -286,7 +286,7 @@ template void scal_fast_path<scalar_t>(int *n, scalar_t *a, scalar_t *x, int *in
|
||||
#if AT_BUILD_WITH_BLAS()
|
||||
template <>
|
||||
bool scal_use_fast_path<double>(int64_t n, int64_t incx) {
|
||||
auto intmax = std::numeric_limits<int>::max();
|
||||
auto constexpr intmax = std::numeric_limits<int>::max();
|
||||
return n <= intmax && incx <= intmax;
|
||||
}
|
||||
|
||||
@ -315,7 +315,7 @@ bool gemv_use_fast_path<float>(
|
||||
int64_t incx,
|
||||
[[maybe_unused]] float beta,
|
||||
int64_t incy) {
|
||||
auto intmax = std::numeric_limits<int>::max();
|
||||
auto constexpr intmax = std::numeric_limits<int>::max();
|
||||
return (m <= intmax) && (n <= intmax) && (lda <= intmax) &&
|
||||
(incx > 0) && (incx <= intmax) && (incy > 0) && (incy <= intmax);
|
||||
}
|
||||
|
||||
@ -71,7 +71,7 @@
|
||||
namespace at::native {
|
||||
namespace {
|
||||
|
||||
static void col2im_out_cpu_template(
|
||||
void col2im_out_cpu_template(
|
||||
Tensor& output,
|
||||
const Tensor& input_,
|
||||
IntArrayRef output_size,
|
||||
|
||||
@ -658,6 +658,7 @@ static void check_shape_forward(const at::Tensor& input,
|
||||
TORCH_CHECK(!params.is_output_padding_neg(), "negative output_padding is not supported");
|
||||
TORCH_CHECK(!params.is_stride_nonpos(), "non-positive stride is not supported");
|
||||
TORCH_CHECK(!params.is_dilation_neg(), "dilation should be greater than zero");
|
||||
TORCH_CHECK(groups > 0, "expected groups to be greater than 0, but got groups=", groups);
|
||||
|
||||
TORCH_CHECK(weight_dim == k,
|
||||
"Expected ", weight_dim, "-dimensional input for ", weight_dim,
|
||||
|
||||
@ -25,7 +25,7 @@ namespace at::native {
|
||||
|
||||
namespace {
|
||||
|
||||
static Tensor compute_columns2d(
|
||||
Tensor compute_columns2d(
|
||||
const Tensor& input,
|
||||
IntArrayRef padding,
|
||||
IntArrayRef stride,
|
||||
@ -93,7 +93,7 @@ static Tensor compute_columns2d(
|
||||
return columns.contiguous();
|
||||
}
|
||||
|
||||
static inline void slow_conv2d_shape_check(
|
||||
inline void slow_conv2d_shape_check(
|
||||
const Tensor& input,
|
||||
const Tensor& grad_output,
|
||||
const Tensor& weight,
|
||||
@ -205,7 +205,7 @@ static inline void slow_conv2d_shape_check(
|
||||
}
|
||||
}
|
||||
|
||||
static inline Tensor view_weight_2d(const Tensor& weight_,
|
||||
inline Tensor view_weight_2d(const Tensor& weight_,
|
||||
at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) {
|
||||
Tensor weight = weight_.contiguous(memory_format);
|
||||
if (weight.dim() == 4) {
|
||||
@ -220,7 +220,7 @@ static inline Tensor view_weight_2d(const Tensor& weight_,
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
static void slow_conv2d_update_output_frame(
|
||||
void slow_conv2d_update_output_frame(
|
||||
TensorAccessor<const scalar_t, 3> input,
|
||||
TensorAccessor<scalar_t, 3> output,
|
||||
TensorAccessor<const scalar_t, 2> weight,
|
||||
@ -480,7 +480,7 @@ void slow_conv2d_backward_weight_frame(
|
||||
}
|
||||
}
|
||||
|
||||
static void slow_conv2d_backward_weight_out_cpu_template(
|
||||
void slow_conv2d_backward_weight_out_cpu_template(
|
||||
Tensor& grad_weight,
|
||||
const Tensor& input,
|
||||
const Tensor& grad_output_,
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user