Disable AVX512 CPU dispatch by default (#80253) (#80356)

As it can be slower, see https://github.com/pytorch/pytorch/issues/80252
Update trunk test matrix to test AVX512 config in `nogpu_AVX512` flavor.
Kill `nogpu_noAVX` as AVX support were replaced with AVX512 when https://github.com/pytorch/pytorch/pull/61903 were landed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80253
Approved by: https://github.com/ngimel

(cherry picked from commit 14813536a7120f1104be2270be341b7a383415c5)

.bazelignore

@@ -1,3 +0,0 @@
-# We do not use this library in our Bazel build. It contains an
-# infinitely recursing symlink that makes Bazel very unhappy.
-third_party/ittapi/

.bazelrc

@@ -1,4 +1,4 @@
-build --cxxopt=--std=c++17
+build --cxxopt=--std=c++14
 build --copt=-I.
 # Bazel does not support including its cc_library targets as system
 # headers. We work around this for generated code
@@ -13,102 +13,15 @@ build:no-tty --curses no
 build:no-tty --progress_report_interval 10
 build:no-tty --show_progress_rate_limit 10
 
-# Build with GPU support by default.
+# Configuration to build with GPU support
-build --define=cuda=true
+build:gpu --define=cuda=true
-# rules_cuda configuration
-build --@rules_cuda//cuda:enable_cuda
-build --@rules_cuda//cuda:cuda_targets=sm_52
-build --@rules_cuda//cuda:compiler=nvcc
-build --repo_env=CUDA_PATH=/usr/local/cuda
-
-# Configuration to build without GPU support
-build:cpu-only --define=cuda=false
 # define a separate build folder for faster switching between configs
-build:cpu-only --platform_suffix=-cpu-only
+build:gpu --platform_suffix=-gpu
 # See the note on the config-less build for details about why we are
-# doing this. We must also do it for the "-cpu-only" platform suffix.
+# doing this. We must also do it for the "-gpu" platform suffix.
-build --copt=-isystem --copt=bazel-out/k8-fastbuild-cpu-only/bin
+build --copt=-isystem --copt=bazel-out/k8-fastbuild-gpu/bin
 # rules_cuda configuration
-build:cpu-only --@rules_cuda//cuda:enable_cuda=False
+build:gpu --@rules_cuda//cuda:enable_cuda
-
+build:gpu --@rules_cuda//cuda:cuda_targets=sm_52
-# Definition of --config=shell
+build:gpu --@rules_cuda//cuda:compiler=nvcc
-# interactive shell immediately before execution
+build:gpu --repo_env=CUDA_PATH=/usr/local/cuda
-build:shell --run_under="//tools/bazel_tools:shellwrap"
-
-# Disable all warnings for external repositories. We don't care about
-# their warnings.
-build --per_file_copt=^external/@-w
-
-# Set additional warnings to error level.
-#
-# Implementation notes:
-#  * we use file extensions to determine if we are using the C++
-#    compiler or the cuda compiler
-#  * we use ^// at the start of the regex to only permit matching
-#    PyTorch files. This excludes external repos.
-#
-# Note that because this is logically a command-line flag, it is
-# considered the word on what warnings are enabled. This has the
-# unfortunate consequence of preventing us from disabling an error at
-# the target level because those flags will come before these flags in
-# the action invocation. Instead we provide per-file exceptions after
-# this.
-#
-# On the bright side, this means we don't have to more broadly apply
-# the exceptions to an entire target.
-#
-# Looking for CUDA flags? We have a cu_library macro that we can edit
-# directly. Look in //tools/rules:cu.bzl for details. Editing the
-# macro over this has the following advantages:
-#  * making changes does not require discarding the Bazel analysis
-#    cache
-#  * it allows for selective overrides on individual targets since the
-#    macro-level opts will come earlier than target level overrides
-
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=all
-# The following warnings come from -Wall. We downgrade them from error
-# to warnings here.
-#
-# We intentionally use #pragma unroll, which is compiler specific.
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-error=unknown-pragmas
-
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=extra
-# The following warnings come from -Wextra. We downgrade them from error
-# to warnings here.
-#
-# unused-parameter-compare has a tremendous amount of violations in the
-# codebase. It will be a lot of work to fix them, just disable it for
-# now.
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-parameter
-# missing-field-parameters has both a large number of violations in
-# the codebase, but it also is used pervasively in the Python C
-# API. There are a couple of catches though:
-# * we use multiple versions of the Python API and hence have
-#   potentially multiple different versions of each relevant
-#   struct. They may have different numbers of fields. It will be
-#   unwieldy to support multiple versions in the same source file.
-# * Python itself for many of these structs recommends only
-#   initializing a subset of the fields. We should respect the API
-#   usage conventions of our dependencies.
-#
-# Hence, we just disable this warning altogether. We may want to clean
-# up some of the clear-cut cases that could be risky, but we still
-# likely want to have this disabled for the most part.
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-missing-field-initializers
-
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-function
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-variable
-
-build --per_file_copt='//:aten/src/ATen/RegisterCompositeExplicitAutograd\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterCompositeImplicitAutograd\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterMkldnnCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterNestedTensorCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterQuantizedCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterSparseCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterSparseCsrCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterNestedTensorMeta\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterSparseMeta\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterQuantizedMeta\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterZeroTensor\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:torch/csrc/lazy/generated/RegisterAutogradLazy\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:torch/csrc/lazy/generated/RegisterLazy\.cpp$'@-Wno-error=unused-function

.bazelversion

@@ -1 +1 @@
-6.1.1
+4.2.1

.buckconfig.oss

@@ -1,26 +1,15 @@
-[pt]
-  is_oss=1
-
 [buildfile]
-  name = BUCK.oss
+name = BUILD.buck
-  includes = //tools/build_defs/select.bzl
 
 [repositories]
   bazel_skylib = third_party/bazel-skylib/
-  ovr_config = .
 
 [download]
   in_build = true
 
 [cxx]
   cxxflags = -std=c++17
-  ldflags = -Wl,--no-undefined
   should_remap_host_platform = true
-  cpp = /usr/bin/clang
-  cc = /usr/bin/clang
-  cxx = /usr/bin/clang++
-  cxxpp = /usr/bin/clang++
-  ld = /usr/bin/clang++
 
 [project]
   default_flavors_mode=all

.ci/caffe2/common.sh

@@ -1,36 +0,0 @@
-set -ex
-
-LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
-ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
-TEST_DIR="$ROOT_DIR/test"
-gtest_reports_dir="${TEST_DIR}/test-reports/cpp"
-pytest_reports_dir="${TEST_DIR}/test-reports/python"
-
-# Figure out which Python to use
-PYTHON="$(which python)"
-if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
-  PYTHON=$(which "python${BASH_REMATCH[1]}")
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-    # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
-    unset HIP_PLATFORM
-    if which sccache > /dev/null; then
-        # Save sccache logs to file
-        sccache --stop-server || true
-        rm -f ~/sccache_error.log || true
-        SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
-
-        # Report sccache stats for easier debugging
-        sccache --zero-stats
-    fi
-fi
-
-# /usr/local/caffe2 is where the cpp bits are installed to in cmake-only
-# builds. In +python builds the cpp tests are copied to /usr/local/caffe2 so
-# that the test code in .ci/test.sh is the same
-INSTALL_PREFIX="/usr/local/caffe2"
-
-mkdir -p "$gtest_reports_dir" || true
-mkdir -p "$pytest_reports_dir" || true
-mkdir -p "$INSTALL_PREFIX" || true

.ci/caffe2/test.sh

@@ -1,172 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
-  pip install click mock tabulate networkx==2.0
-  pip -q install --user "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
-fi
-
-# Skip tests in environments where they are not built/applicable
-if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
-  echo 'Skipping tests'
-  exit 0
-fi
-if [[ "${BUILD_ENVIRONMENT}" == *-rocm* ]]; then
-  # temporary to locate some kernel issues on the CI nodes
-  export HSAKMT_DEBUG_LEVEL=4
-fi
-# These additional packages are needed for circleci ROCm builds.
-if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
-    # Need networkx 2.0 because bellmand_ford was moved in 2.1 . Scikit-image by
-    # defaults installs the most recent networkx version, so we install this lower
-    # version explicitly before scikit-image pulls it in as a dependency
-    pip install networkx==2.0
-    # click - onnx
-    pip install --progress-bar off click protobuf tabulate virtualenv mock typing-extensions
-fi
-
-# Find where cpp tests and Caffe2 itself are installed
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  # For cmake only build we install everything into /usr/local
-  cpp_test_dir="$INSTALL_PREFIX/cpp_test"
-  ld_library_path="$INSTALL_PREFIX/lib"
-else
-  # For Python builds we install into python
-  # cd to /usr first so the python import doesn't get confused by any 'caffe2'
-  # directory in cwd
-  python_installation="$(dirname $(dirname $(cd /usr && $PYTHON -c 'import os; import caffe2; print(os.path.realpath(caffe2.__file__))')))"
-  caffe2_pypath="$python_installation/caffe2"
-  cpp_test_dir="$python_installation/torch/test"
-  ld_library_path="$python_installation/torch/lib"
-fi
-
-################################################################################
-# C++ tests #
-################################################################################
-# Only run cpp tests in the first shard, don't run cpp tests a second time in the second shard
-if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
-  echo "Running C++ tests.."
-  for test in $(find "$cpp_test_dir" -executable -type f); do
-    case "$test" in
-      # skip tests we know are hanging or bad
-      */mkl_utils_test|*/aten/integer_divider_test)
-        continue
-        ;;
-      */scalar_tensor_test|*/basic|*/native_test)
-        if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-          continue
-        else
-          LD_LIBRARY_PATH="$ld_library_path" "$test"
-        fi
-        ;;
-      */*_benchmark)
-        LD_LIBRARY_PATH="$ld_library_path" "$test" --benchmark_color=false
-        ;;
-      *)
-        # Currently, we use a mixture of gtest (caffe2) and Catch2 (ATen). While
-        # planning to migrate to gtest as the common PyTorch c++ test suite, we
-        # currently do NOT use the xml test reporter, because Catch doesn't
-        # support multiple reporters
-        # c.f. https://github.com/catchorg/Catch2/blob/master/docs/release-notes.md#223
-        # which means that enabling XML output means you lose useful stdout
-        # output for Jenkins.  It's more important to have useful console
-        # output than it is to have XML output for Jenkins.
-        # Note: in the future, if we want to use xml test reporter once we switch
-        # to all gtest, one can simply do:
-        LD_LIBRARY_PATH="$ld_library_path" \
-            "$test" --gtest_output=xml:"$gtest_reports_dir/$(basename $test).xml"
-        ;;
-    esac
-  done
-fi
-
-################################################################################
-# Python tests #
-################################################################################
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  exit 0
-fi
-
-# If pip is installed as root, we must use sudo.
-# CircleCI docker images could install conda as jenkins user, or use the OS's python package.
-PIP=$(which pip)
-PIP_USER=$(stat --format '%U' $PIP)
-CURRENT_USER=$(id -u -n)
-if [[ "$PIP_USER" = root && "$CURRENT_USER" != root ]]; then
-  MAYBE_SUDO=sudo
-fi
-
-# Uninstall pre-installed hypothesis and coverage to use an older version as newer
-# versions remove the timeout parameter from settings which ideep/conv_transpose_test.py uses
-$MAYBE_SUDO pip -q uninstall -y hypothesis
-$MAYBE_SUDO pip -q uninstall -y coverage
-
-# "pip install hypothesis==3.44.6" from official server is unreliable on
-# CircleCI, so we host a copy on S3 instead
-$MAYBE_SUDO pip -q install attrs==18.1.0 -f https://s3.amazonaws.com/ossci-linux/wheels/attrs-18.1.0-py2.py3-none-any.whl
-$MAYBE_SUDO pip -q install coverage==4.5.1 -f https://s3.amazonaws.com/ossci-linux/wheels/coverage-4.5.1-cp36-cp36m-macosx_10_12_x86_64.whl
-$MAYBE_SUDO pip -q install hypothesis==3.44.6 -f https://s3.amazonaws.com/ossci-linux/wheels/hypothesis-3.44.6-py3-none-any.whl
-
-# Collect additional tests to run (outside caffe2/python)
-EXTRA_TESTS=()
-
-# CUDA builds always include NCCL support
-if [[ "$BUILD_ENVIRONMENT" == *-cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *-rocm* ]]; then
-  EXTRA_TESTS+=("$caffe2_pypath/contrib/nccl")
-fi
-
-rocm_ignore_test=()
-if [[ $BUILD_ENVIRONMENT == *-rocm* ]]; then
-  # Currently these tests are failing on ROCM platform:
-
-  # On ROCm, RCCL (distributed) development isn't complete.
-  # https://github.com/ROCmSoftwarePlatform/rccl
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/data_parallel_model_test.py")
-
-  # This test has been flaky in ROCm CI (but note the tests are
-  # cpu-only so should be unrelated to ROCm)
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/operator_test/blobs_queue_db_test.py")
-  # This test is skipped on Jenkins(compiled without MKL) and otherwise known flaky
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/ideep/convfusion_op_test.py")
-  # This test is skipped on Jenkins(compiled without MKL) and causing segfault on Circle
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/ideep/pool_op_test.py")
-fi
-
-echo "Running Python tests.."
-# locale setting is required by click package
-for loc in "en_US.utf8" "C.UTF-8"; do
-  if locale -a | grep "$loc" >/dev/null 2>&1; then
-    export LC_ALL="$loc"
-    export LANG="$loc"
-    break;
-  fi
-done
-
-# Some Caffe2 tests fail when run using AVX512 ISA, see https://github.com/pytorch/pytorch/issues/66111
-export DNNL_MAX_CPU_ISA=AVX2
-
-# Should still run even in the absence of SHARD_NUMBER
-if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
-  # TODO(sdym@meta.com) remove this when the linked issue resolved.
-  # py is temporary until https://github.com/Teemu/pytest-sugar/issues/241 is fixed
-  pip install --user py==1.11.0
-  pip install --user pytest-sugar
-  # NB: Warnings are disabled because they make it harder to see what
-  # the actual erroring test is
-  "$PYTHON" \
-    -m pytest \
-    -x \
-    -v \
-    --disable-warnings \
-    --junit-xml="$pytest_reports_dir/result.xml" \
-    --ignore "$caffe2_pypath/python/test/executor_test.py" \
-    --ignore "$caffe2_pypath/python/operator_test/matmul_op_test.py" \
-    --ignore "$caffe2_pypath/python/operator_test/pack_ops_test.py" \
-    --ignore "$caffe2_pypath/python/mkl/mkl_sbn_speed_test.py" \
-    --ignore "$caffe2_pypath/python/trt/test_pt_onnx_trt.py" \
-    ${rocm_ignore_test[@]} \
-    "$caffe2_pypath/python" \
-    "${EXTRA_TESTS[@]}"
-fi

.ci/docker/README.md

@@ -1,32 +0,0 @@
-# Docker images for GitHub CI
-
-This directory contains everything needed to build the Docker images
-that are used in our CI.
-
-The Dockerfiles located in subdirectories are parameterized to
-conditionally run build stages depending on build arguments passed to
-`docker build`. This lets us use only a few Dockerfiles for many
-images. The different configurations are identified by a freeform
-string that we call a _build environment_. This string is persisted in
-each image as the `BUILD_ENVIRONMENT` environment variable.
-
-See `build.sh` for valid build environments (it's the giant switch).
-
-## Contents
-
-* `build.sh` -- dispatch script to launch all builds
-* `common` -- scripts used to execute individual Docker build stages
-* `ubuntu` -- Dockerfile for Ubuntu image for CPU build and test jobs
-* `ubuntu-cuda` -- Dockerfile for Ubuntu image with CUDA support for nvidia-docker
-* `ubuntu-rocm` -- Dockerfile for Ubuntu image with ROCm support
-* `ubuntu-xpu` -- Dockerfile for Ubuntu image with XPU support
-
-## Usage
-
-```bash
-# Build a specific image
-./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
-
-# Set flags (see build.sh) and build image
-sudo bash -c 'PROTOBUF=1 ./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
-```

.ci/docker/build.sh

@@ -1,462 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-image="$1"
-shift
-
-if [ -z "${image}" ]; then
-  echo "Usage: $0 IMAGE"
-  exit 1
-fi
-
-function extract_version_from_image_name() {
-  eval export $2=$(echo "${image}" | perl -n -e"/$1(\d+(\.\d+)?(\.\d+)?)/ && print \$1")
-  if [ "x${!2}" = x ]; then
-    echo "variable '$2' not correctly parsed from image='$image'"
-    exit 1
-  fi
-}
-
-function extract_all_from_image_name() {
-  # parts $image into array, splitting on '-'
-  keep_IFS="$IFS"
-  IFS="-"
-  declare -a parts=($image)
-  IFS="$keep_IFS"
-  unset keep_IFS
-
-  for part in "${parts[@]}"; do
-    name=$(echo "${part}" | perl -n -e"/([a-zA-Z]+)\d+(\.\d+)?(\.\d+)?/ && print \$1")
-    vername="${name^^}_VERSION"
-    # "py" is the odd one out, needs this special case
-    if [ "x${name}" = xpy ]; then
-      vername=ANACONDA_PYTHON_VERSION
-    fi
-    # skip non-conforming fields such as "pytorch", "linux" or "bionic" without version string
-    if [ -n "${name}" ]; then
-      extract_version_from_image_name "${name}" "${vername}"
-    fi
-  done
-}
-
-# Use the same pre-built XLA test image from PyTorch/XLA
-if [[ "$image" == *xla* ]]; then
-  echo "Using pre-built XLA test image..."
-  exit 0
-fi
-
-if [[ "$image" == *-focal* ]]; then
-  UBUNTU_VERSION=20.04
-elif [[ "$image" == *-jammy* ]]; then
-  UBUNTU_VERSION=22.04
-elif [[ "$image" == *ubuntu* ]]; then
-  extract_version_from_image_name ubuntu UBUNTU_VERSION
-elif [[ "$image" == *centos* ]]; then
-  extract_version_from_image_name centos CENTOS_VERSION
-fi
-
-if [ -n "${UBUNTU_VERSION}" ]; then
-  OS="ubuntu"
-elif [ -n "${CENTOS_VERSION}" ]; then
-  OS="centos"
-else
-  echo "Unable to derive operating system base..."
-  exit 1
-fi
-
-DOCKERFILE="${OS}/Dockerfile"
-# When using ubuntu - 22.04, start from Ubuntu docker image, instead of nvidia/cuda docker image.
-if [[ "$image" == *cuda* && "$UBUNTU_VERSION" != "22.04" ]]; then
-  DOCKERFILE="${OS}-cuda/Dockerfile"
-elif [[ "$image" == *rocm* ]]; then
-  DOCKERFILE="${OS}-rocm/Dockerfile"
-elif [[ "$image" == *xpu* ]]; then
-  DOCKERFILE="${OS}-xpu/Dockerfile"
-elif [[ "$image" == *cuda*linter* ]]; then
-  # Use a separate Dockerfile for linter to keep a small image size
-  DOCKERFILE="linter-cuda/Dockerfile"
-elif [[ "$image" == *linter* ]]; then
-  # Use a separate Dockerfile for linter to keep a small image size
-  DOCKERFILE="linter/Dockerfile"
-fi
-
-# CMake 3.18 is needed to support CUDA17 language variant
-CMAKE_VERSION=3.18.5
-
-_UCX_COMMIT=00bcc6bb18fc282eb160623b4c0d300147f579af
-_UCC_COMMIT=7cb07a76ccedad7e56ceb136b865eb9319c258ea
-
-# It's annoying to rename jobs every time you want to rewrite a
-# configuration, so we hardcode everything here rather than do it
-# from scratch
-case "$image" in
-  pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda11.8-cudnn8-py3-gcc9)
-    CUDA_VERSION=11.8.0
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-py3-clang10-onnx)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=10
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    ONNX=yes
-    ;;
-  pytorch-linux-focal-py3-clang9-android-ndk-r21e)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=9
-    LLVMDEV=yes
-    PROTOBUF=yes
-    ANDROID=yes
-    ANDROID_NDK_VERSION=r21e
-    GRADLE_VERSION=6.8.3
-    NINJA_VERSION=1.9.0
-    ;;
-  pytorch-linux-focal-py3.8-clang10)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=10
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    VULKAN_SDK_VERSION=1.2.162.1
-    SWIFTSHADER=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-py3.11-clang10)
-    ANACONDA_PYTHON_VERSION=3.11
-    CLANG_VERSION=10
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    VULKAN_SDK_VERSION=1.2.162.1
-    SWIFTSHADER=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-py3.8-gcc9)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-rocm-n-1-py3)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=5.7
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-rocm-n-py3)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=6.0
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-xpu-2024.0-py3)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    BASEKIT_VERSION=2024.0.0-49522
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    ;;
-    pytorch-linux-jammy-py3.8-gcc11-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    DOCS=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-jammy-cuda11.8-cudnn8-py3.8-clang12)
-    ANACONDA_PYTHON_VERSION=3.8
-    CUDA_VERSION=11.8
-    CUDNN_VERSION=8
-    CLANG_VERSION=12
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-clang12-asan)
-    ANACONDA_PYTHON_VERSION=3.9
-    CLANG_VERSION=12
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-clang15-asan)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=15
-    CONDA_CMAKE=yes
-    VISION=yes
-    ;;
-  pytorch-linux-jammy-py3.8-gcc11)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    DOCS=yes
-    UNINSTALL_DILL=yes
-    ;;
-  pytorch-linux-jammy-py3-clang12-executorch)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=12
-    CONDA_CMAKE=yes
-    EXECUTORCH=yes
-    ;;
-  pytorch-linux-focal-linter)
-    # TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
-    # We will need to update mypy version eventually, but that's for another day. The task
-    # would be to upgrade mypy to 1.0.0 with Python 3.11
-    ANACONDA_PYTHON_VERSION=3.9
-    CONDA_CMAKE=yes
-    ;;
-  pytorch-linux-jammy-cuda11.8-cudnn8-py3.9-linter)
-    ANACONDA_PYTHON_VERSION=3.9
-    CUDA_VERSION=11.8
-    CONDA_CMAKE=yes
-    ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc11)
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
-    ACL=yes
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    ;;
-  *)
-    # Catch-all for builds that are not hardcoded.
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    echo "image '$image' did not match an existing build configuration"
-    if [[ "$image" == *py* ]]; then
-      extract_version_from_image_name py ANACONDA_PYTHON_VERSION
-    fi
-    if [[ "$image" == *cuda* ]]; then
-      extract_version_from_image_name cuda CUDA_VERSION
-      extract_version_from_image_name cudnn CUDNN_VERSION
-    fi
-    if [[ "$image" == *rocm* ]]; then
-      extract_version_from_image_name rocm ROCM_VERSION
-      NINJA_VERSION=1.9.0
-      TRITON=yes
-      # To ensure that any ROCm config will build using conda cmake
-      # and thus have LAPACK/MKL enabled
-      CONDA_CMAKE=yes
-    fi
-    if [[ "$image" == *centos7* ]]; then
-      NINJA_VERSION=1.10.2
-    fi
-    if [[ "$image" == *gcc* ]]; then
-      extract_version_from_image_name gcc GCC_VERSION
-    fi
-    if [[ "$image" == *clang* ]]; then
-      extract_version_from_image_name clang CLANG_VERSION
-    fi
-    if [[ "$image" == *devtoolset* ]]; then
-      extract_version_from_image_name devtoolset DEVTOOLSET_VERSION
-    fi
-    if [[ "$image" == *glibc* ]]; then
-      extract_version_from_image_name glibc GLIBC_VERSION
-    fi
-    if [[ "$image" == *cmake* ]]; then
-      extract_version_from_image_name cmake CMAKE_VERSION
-    fi
-  ;;
-esac
-
-tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
-
-#when using cudnn version 8 install it separately from cuda
-if [[ "$image" == *cuda*  && ${OS} == "ubuntu" ]]; then
-  IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
-  if [[ ${CUDNN_VERSION} == 8 ]]; then
-    IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
-  fi
-fi
-
-# Build image
-DOCKER_BUILDKIT=1 docker build \
-       --no-cache \
-       --progress=plain \
-       --build-arg "BUILD_ENVIRONMENT=${image}" \
-       --build-arg "PROTOBUF=${PROTOBUF:-}" \
-       --build-arg "LLVMDEV=${LLVMDEV:-}" \
-       --build-arg "DB=${DB:-}" \
-       --build-arg "VISION=${VISION:-}" \
-       --build-arg "UBUNTU_VERSION=${UBUNTU_VERSION}" \
-       --build-arg "CENTOS_VERSION=${CENTOS_VERSION}" \
-       --build-arg "DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}" \
-       --build-arg "GLIBC_VERSION=${GLIBC_VERSION}" \
-       --build-arg "CLANG_VERSION=${CLANG_VERSION}" \
-       --build-arg "ANACONDA_PYTHON_VERSION=${ANACONDA_PYTHON_VERSION}" \
-       --build-arg "GCC_VERSION=${GCC_VERSION}" \
-       --build-arg "CUDA_VERSION=${CUDA_VERSION}" \
-       --build-arg "CUDNN_VERSION=${CUDNN_VERSION}" \
-       --build-arg "TENSORRT_VERSION=${TENSORRT_VERSION}" \
-       --build-arg "ANDROID=${ANDROID}" \
-       --build-arg "ANDROID_NDK=${ANDROID_NDK_VERSION}" \
-       --build-arg "GRADLE_VERSION=${GRADLE_VERSION}" \
-       --build-arg "VULKAN_SDK_VERSION=${VULKAN_SDK_VERSION}" \
-       --build-arg "SWIFTSHADER=${SWIFTSHADER}" \
-       --build-arg "CMAKE_VERSION=${CMAKE_VERSION:-}" \
-       --build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
-       --build-arg "KATEX=${KATEX:-}" \
-       --build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
-       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx906;gfx90a}" \
-       --build-arg "IMAGE_NAME=${IMAGE_NAME}" \
-       --build-arg "UCX_COMMIT=${UCX_COMMIT}" \
-       --build-arg "UCC_COMMIT=${UCC_COMMIT}" \
-       --build-arg "CONDA_CMAKE=${CONDA_CMAKE}" \
-       --build-arg "TRITON=${TRITON}" \
-       --build-arg "ONNX=${ONNX}" \
-       --build-arg "DOCS=${DOCS}" \
-       --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
-       --build-arg "EXECUTORCH=${EXECUTORCH}" \
-       --build-arg "BASEKIT_VERSION=${BASEKIT_VERSION}" \
-       --build-arg "ACL=${ACL:-}" \
-       -f $(dirname ${DOCKERFILE})/Dockerfile \
-       -t "$tmp_tag" \
-       "$@" \
-       .
-
-# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
-# for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
-# find the correct image. As a result, here we have to replace the
-#   "$UBUNTU_VERSION" == "18.04-rc"
-# with
-#   "$UBUNTU_VERSION" == "18.04"
-UBUNTU_VERSION=$(echo ${UBUNTU_VERSION} | sed 's/-rc$//')
-
-function drun() {
-  docker run --rm "$tmp_tag" $*
-}
-
-if [[ "$OS" == "ubuntu" ]]; then
-
-  if !(drun lsb_release -a 2>&1 | grep -qF Ubuntu); then
-    echo "OS=ubuntu, but:"
-    drun lsb_release -a
-    exit 1
-  fi
-  if !(drun lsb_release -a 2>&1 | grep -qF "$UBUNTU_VERSION"); then
-    echo "UBUNTU_VERSION=$UBUNTU_VERSION, but:"
-    drun lsb_release -a
-    exit 1
-  fi
-fi
-
-if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
-  if !(drun python --version 2>&1 | grep -qF "Python $ANACONDA_PYTHON_VERSION"); then
-    echo "ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION, but:"
-    drun python --version
-    exit 1
-  fi
-fi
-
-if [ -n "$GCC_VERSION" ]; then
-  if !(drun gcc --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
-    echo "GCC_VERSION=$GCC_VERSION, but:"
-    drun gcc --version
-    exit 1
-  fi
-fi
-
-if [ -n "$CLANG_VERSION" ]; then
-  if !(drun clang --version 2>&1 | grep -qF "clang version $CLANG_VERSION"); then
-    echo "CLANG_VERSION=$CLANG_VERSION, but:"
-    drun clang --version
-    exit 1
-  fi
-fi
-
-if [ -n "$KATEX" ]; then
-  if !(drun katex --version); then
-    echo "KATEX=$KATEX, but:"
-    drun katex --version
-    exit 1
-  fi
-fi

.ci/docker/centos-rocm/Dockerfile

@@ -1,123 +0,0 @@
-ARG CENTOS_VERSION
-
-FROM centos:${CENTOS_VERSION}
-
-ARG CENTOS_VERSION
-
-# Set AMD gpu targets to build for
-ARG PYTORCH_ROCM_ARCH
-ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
-
-# Install required packages to build Caffe2
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Update CentOS git version
-RUN yum -y remove git
-RUN yum -y remove git-*
-RUN yum -y install https://packages.endpoint.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm || \
-    (yum -y install https://packages.endpointdev.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm && \
-    sed -i "s/packages.endpoint/packages.endpointdev/" /etc/yum.repos.d/endpoint.repo)
-RUN yum install -y git
-
-# Install devtoolset
-ARG DEVTOOLSET_VERSION
-COPY ./common/install_devtoolset.sh install_devtoolset.sh
-RUN bash ./install_devtoolset.sh && rm install_devtoolset.sh
-ENV BASH_ENV "/etc/profile"
-
-# (optional) Install non-default glibc version
-ARG GLIBC_VERSION
-COPY ./common/install_glibc.sh install_glibc.sh
-RUN if [ -n "${GLIBC_VERSION}" ]; then bash ./install_glibc.sh; fi
-RUN rm install_glibc.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install rocm
-ARG ROCM_VERSION
-COPY ./common/install_rocm.sh install_rocm.sh
-RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh
-COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
-RUN bash ./install_rocm_magma.sh
-RUN rm install_rocm_magma.sh
-ENV PATH /opt/rocm/bin:$PATH
-ENV PATH /opt/rocm/hcc/bin:$PATH
-ENV PATH /opt/rocm/hip/bin:$PATH
-ENV PATH /opt/rocm/opencl/bin:$PATH
-ENV PATH /opt/rocm/llvm/bin:$PATH
-ENV MAGMA_HOME /opt/rocm/magma
-ENV LANG en_US.utf8
-ENV LC_ALL en_US.utf8
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-ENV CMAKE_C_COMPILER cc
-ENV CMAKE_CXX_COMPILER c++
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ci_commit_pins/executorch.txt

@@ -1 +0,0 @@
-7f96f5a852ba452670255d28d59f1e6398141fbb

.ci/docker/ci_commit_pins/huggingface.txt

@@ -1 +0,0 @@
-243e186efbf7fb93328dd6b34927a4e8c8f24395

.ci/docker/ci_commit_pins/timm.txt

@@ -1 +0,0 @@
-730b907b4d45a4713cbc425cbf224c46089fd514

.ci/docker/ci_commit_pins/triton-rocm.txt

@@ -1 +0,0 @@
-0a22a91d04c2b4a029a69a198eac390089c3e891

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +0,0 @@
-989adb9a29496c22a36ef82ca69cad5dad536b9c

.ci/docker/common/cache_vision_models.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-# Cache the test models at ~/.cache/torch/hub/
-IMPORT_SCRIPT_FILENAME="/tmp/torchvision_import_script.py"
-as_jenkins echo 'import torchvision; torchvision.models.mobilenet_v2(pretrained=True); torchvision.models.mobilenet_v3_large(pretrained=True);' > "${IMPORT_SCRIPT_FILENAME}"
-
-pip_install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
-# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
-# so echo the command to a file and run the file instead
-conda_run python "${IMPORT_SCRIPT_FILENAME}"
-
-# Cleaning up
-conda_run pip uninstall -y torch torchvision
-rm "${IMPORT_SCRIPT_FILENAME}" || true

.ci/docker/common/common_utils.sh

@@ -1,36 +0,0 @@
-#!/bin/bash
-
-# Work around bug where devtoolset replaces sudo and breaks it.
-if [ -n "$DEVTOOLSET_VERSION" ]; then
-  export SUDO=/bin/sudo
-else
-  export SUDO=sudo
-fi
-
-as_jenkins() {
-  # NB: unsetting the environment variables works around a conda bug
-  # https://github.com/conda/conda/issues/6576
-  # NB: Pass on PATH and LD_LIBRARY_PATH to sudo invocation
-  # NB: This must be run from a directory that jenkins has access to,
-  # works around https://github.com/conda/conda-package-handling/pull/34
-  $SUDO -E -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
-}
-
-conda_install() {
-  # Ensure that the install command don't upgrade/downgrade Python
-  # This should be called as
-  #   conda_install pkg1 pkg2 ... [-c channel]
-  as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION" $*
-}
-
-conda_run() {
-  as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION --no-capture-output $*
-}
-
-pip_install() {
-  as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION pip install --progress-bar off $*
-}
-
-get_pinned_commit() {
-  cat "${1}".txt
-}

.ci/docker/common/install_acl.sh

@@ -1,16 +0,0 @@
-set -euo pipefail
-
-readonly version=v23.08
-readonly src_host=https://review.mlplatform.org/ml
-readonly src_repo=ComputeLibrary
-
-# Clone ACL
-[[ ! -d ${src_repo} ]] && git clone ${src_host}/${src_repo}.git
-cd ${src_repo}
-
-git checkout $version
-
-# Build with scons
-scons -j8  Werror=0 debug=0 neon=1 opencl=0 embed_kernels=0 \
-  os=linux arch=armv8a build=native multi_isa=1 \
-  fixed_format_kernels=1 openmp=1 cppthreads=0

.ci/docker/common/install_base.sh

@@ -1,160 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  # NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
-  # for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
-  # find the correct image. As a result, here we have to check for
-  #   "$UBUNTU_VERSION" == "18.04"*
-  # instead of
-  #   "$UBUNTU_VERSION" == "18.04"
-  if [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
-    cmake3="cmake=3.16*"
-    maybe_libiomp_dev=""
-  elif [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
-    cmake3="cmake=3.22*"
-    maybe_libiomp_dev=""
-  else
-    cmake3="cmake=3.5*"
-    maybe_libiomp_dev="libiomp-dev"
-  fi
-
-  if [[ "$CLANG_VERSION" == 15 ]]; then
-    maybe_libomp_dev="libomp-15-dev"
-  elif [[ "$CLANG_VERSION" == 12 ]]; then
-    maybe_libomp_dev="libomp-12-dev"
-  elif [[ "$CLANG_VERSION" == 10 ]]; then
-    maybe_libomp_dev="libomp-10-dev"
-  else
-    maybe_libomp_dev=""
-  fi
-
-  # HACK: UCC testing relies on libnccl library from NVIDIA repo, and version 2.16 crashes
-  # See https://github.com/pytorch/pytorch/pull/105260#issuecomment-1673399729
-  if [[ "$UBUNTU_VERSION" == "20.04"* && "$CUDA_VERSION" == "11.8"* ]]; then
-    maybe_libnccl_dev="libnccl2=2.15.5-1+cuda11.8 libnccl-dev=2.15.5-1+cuda11.8 --allow-downgrades --allow-change-held-packages"
-  else
-    maybe_libnccl_dev=""
-  fi
-
-  # Install common dependencies
-  apt-get update
-  # TODO: Some of these may not be necessary
-  ccache_deps="asciidoc docbook-xml docbook-xsl xsltproc"
-  deploy_deps="libffi-dev libbz2-dev libreadline-dev libncurses5-dev libncursesw5-dev libgdbm-dev libsqlite3-dev uuid-dev tk-dev"
-  numpy_deps="gfortran"
-  apt-get install -y --no-install-recommends \
-    $ccache_deps \
-    $numpy_deps \
-    ${deploy_deps} \
-    ${cmake3} \
-    apt-transport-https \
-    autoconf \
-    automake \
-    build-essential \
-    ca-certificates \
-    curl \
-    git \
-    libatlas-base-dev \
-    libc6-dbg \
-    ${maybe_libiomp_dev} \
-    libyaml-dev \
-    libz-dev \
-    libjemalloc2 \
-    libjpeg-dev \
-    libasound2-dev \
-    libsndfile-dev \
-    ${maybe_libomp_dev} \
-    ${maybe_libnccl_dev} \
-    software-properties-common \
-    wget \
-    sudo \
-    vim \
-    jq \
-    libtool \
-    vim \
-    unzip \
-    gpg-agent \
-    gdb
-
-  # Should resolve issues related to various apt package repository cert issues
-  # see: https://github.com/pytorch/pytorch/issues/65931
-  apt-get install -y libgnutls30
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-install_centos() {
-  # Need EPEL for many packages we depend on.
-  # See http://fedoraproject.org/wiki/EPEL
-  yum --enablerepo=extras install -y epel-release
-
-  ccache_deps="asciidoc docbook-dtds docbook-style-xsl libxslt"
-  numpy_deps="gcc-gfortran"
-  # Note: protobuf-c-{compiler,devel} on CentOS are too old to be used
-  # for Caffe2. That said, we still install them to make sure the build
-  # system opts to build/use protoc and libprotobuf from third-party.
-  yum install -y \
-    $ccache_deps \
-    $numpy_deps \
-    autoconf \
-    automake \
-    bzip2 \
-    cmake \
-    cmake3 \
-    curl \
-    gcc \
-    gcc-c++ \
-    gflags-devel \
-    git \
-    glibc-devel \
-    glibc-headers \
-    glog-devel \
-    hiredis-devel \
-    libstdc++-devel \
-    libsndfile-devel \
-    make \
-    opencv-devel \
-    sudo \
-    wget \
-    vim \
-    unzip \
-    gdb
-
-  # Cleanup
-  yum clean all
-  rm -rf /var/cache/yum
-  rm -rf /var/lib/yum/yumdb
-  rm -rf /var/lib/yum/history
-}
-
-# Install base packages depending on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    install_ubuntu
-    ;;
-  centos)
-    install_centos
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac
-
-# Install Valgrind separately since the apt-get version is too old.
-mkdir valgrind_build && cd valgrind_build
-VALGRIND_VERSION=3.20.0
-wget https://ossci-linux.s3.amazonaws.com/valgrind-${VALGRIND_VERSION}.tar.bz2
-tar -xjf valgrind-${VALGRIND_VERSION}.tar.bz2
-cd valgrind-${VALGRIND_VERSION}
-./configure --prefix=/usr/local
-make -j$[$(nproc) - 2]
-sudo make install
-cd ../../
-rm -rf valgrind_build
-alias valgrind="/usr/local/bin/valgrind"

.ci/docker/common/install_clang.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$CLANG_VERSION" ]; then
-
-  if [[ $CLANG_VERSION == 9 && $UBUNTU_VERSION == 18.04 ]]; then
-    sudo apt-get update
-    # gpg-agent is not available by default on 18.04
-    sudo apt-get install  -y --no-install-recommends gpg-agent
-    wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add  -
-    apt-add-repository "deb http://apt.llvm.org/bionic/ llvm-toolchain-bionic-${CLANG_VERSION} main"
-  elif [[ $UBUNTU_VERSION == 22.04 ]]; then
-    # work around ubuntu apt-get conflicts
-    sudo apt-get -y -f install
-  fi
-
-  sudo apt-get update
-  apt-get install -y --no-install-recommends clang-"$CLANG_VERSION"
-  apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"
-
-  # Install dev version of LLVM.
-  if [ -n "$LLVMDEV" ]; then
-    sudo apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"-dev
-  fi
-
-  # Use update-alternatives to make this version the default
-  update-alternatives --install /usr/bin/clang clang /usr/bin/clang-"$CLANG_VERSION" 50
-  update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-"$CLANG_VERSION" 50
-  # Override cc/c++ to clang as well
-  update-alternatives --install /usr/bin/cc cc /usr/bin/clang 50
-  update-alternatives --install /usr/bin/c++ c++ /usr/bin/clang++ 50
-
-  # clang's packaging is a little messed up (the runtime libs aren't
-  # added into the linker path), so give it a little help
-  clang_lib=("/usr/lib/llvm-$CLANG_VERSION/lib/clang/"*"/lib/linux")
-  echo "$clang_lib" > /etc/ld.so.conf.d/clang.conf
-  ldconfig
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.ci/docker/common/install_cmake.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "$CMAKE_VERSION" ]
-
-# Remove system cmake install so it won't get used instead
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    apt-get remove cmake -y
-    ;;
-  centos)
-    yum remove cmake -y
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac
-
-# Turn 3.6.3 into v3.6
-path=$(echo "${CMAKE_VERSION}" | sed -e 's/\([0-9].[0-9]\+\).*/v\1/')
-file="cmake-${CMAKE_VERSION}-Linux-x86_64.tar.gz"
-
-# Download and install specific CMake version in /usr/local
-pushd /tmp
-curl -Os --retry 3 "https://cmake.org/files/${path}/${file}"
-tar -C /usr/local --strip-components 1 --no-same-owner -zxf cmake-*.tar.gz
-rm -f cmake-*.tar.gz
-popd

.ci/docker/common/install_conda.sh

@@ -1,127 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Optionally install conda
-if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
-  BASE_URL="https://repo.anaconda.com/miniconda"
-
-  MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
-  MINOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
-
-if [[ $(uname -m) == "aarch64" ]]; then
-  BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
-  case "$MAJOR_PYTHON_VERSION" in
-    3)
-      CONDA_FILE="Miniforge3-Linux-aarch64.sh"
-    ;;
-    *)
-      echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
-      exit 1
-      ;;
-  esac
-else
-  case "$MAJOR_PYTHON_VERSION" in
-    3)
-      CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
-    ;;
-    *)
-      echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
-      exit 1
-      ;;
-  esac
-fi
-
-  mkdir -p /opt/conda
-  chown jenkins:jenkins /opt/conda
-
-  source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-  pushd /tmp
-  wget -q "${BASE_URL}/${CONDA_FILE}"
-  # NB: Manually invoke bash per https://github.com/conda/conda/issues/10431
-  as_jenkins bash "${CONDA_FILE}" -b -f -p "/opt/conda"
-  popd
-
-  # NB: Don't do this, rely on the rpath to get it right
-  #echo "/opt/conda/lib" > /etc/ld.so.conf.d/conda-python.conf
-  #ldconfig
-  sed -e 's|PATH="\(.*\)"|PATH="/opt/conda/bin:\1"|g' -i /etc/environment
-  export PATH="/opt/conda/bin:$PATH"
-
-  # Ensure we run conda in a directory that jenkins has write access to
-  pushd /opt/conda
-
-  # Prevent conda from updating to 4.14.0, which causes docker build failures
-  # See https://hud.pytorch.org/pytorch/pytorch/commit/754d7f05b6841e555cea5a4b2c505dd9e0baec1d
-  # Uncomment the below when resolved to track the latest conda update
-  # as_jenkins conda update -y -n base conda
-
-  if [[ $(uname -m) == "aarch64" ]]; then
-    export SYSROOT_DEP="sysroot_linux-aarch64=2.17"
-  else
-    export SYSROOT_DEP="sysroot_linux-64=2.17"
-  fi
-
-  # Install correct Python version
-  # Also ensure sysroot is using a modern GLIBC to match system compilers
-  as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
-             python="$ANACONDA_PYTHON_VERSION" \
-             ${SYSROOT_DEP}
-
-  # libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
-  # which is provided in libstdcxx 12 and up.
-  conda_install libstdcxx-ng=12.3.0 -c conda-forge
-
-  # Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
-  if [[ $(uname -m) == "aarch64" ]]; then
-    CONDA_COMMON_DEPS="astunparse pyyaml setuptools openblas==0.3.25=*openmp* ninja==1.11.1 scons==4.5.2"
-
-    if [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
-      conda_install numpy=1.24.4 ${CONDA_COMMON_DEPS}
-    else
-      conda_install numpy=1.26.2 ${CONDA_COMMON_DEPS}
-    fi
-  else
-    CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
-
-    if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
-      conda_install numpy=1.26.0 ${CONDA_COMMON_DEPS}
-    else
-      conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS}
-    fi
-  fi
-
-  # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
-  # and libpython-static for torch deploy
-  conda_install llvmdev=8.0.0 "libpython-static=${ANACONDA_PYTHON_VERSION}"
-
-  # Use conda cmake in some cases. Conda cmake will be newer than our supported
-  # min version (3.5 for xenial and 3.10 for bionic), so we only do it in those
-  # following builds that we know should use conda. Specifically, Ubuntu bionic
-  # and focal cannot find conda mkl with stock cmake, so we need a cmake from conda
-  if [ -n "${CONDA_CMAKE}" ]; then
-    conda_install cmake
-  fi
-
-  # Magma package names are concatenation of CUDA major and minor ignoring revision
-  # I.e. magma-cuda102 package corresponds to CUDA_VERSION=10.2 and CUDA_VERSION=10.2.89
-  if [ -n "$CUDA_VERSION" ]; then
-    conda_install magma-cuda$(TMP=${CUDA_VERSION/./};echo ${TMP%.*[0-9]}) -c pytorch
-  fi
-
-  # Install some other packages, including those needed for Python test reporting
-  pip_install -r /opt/conda/requirements-ci.txt
-
-  pip_install -U scikit-learn
-
-  if [ -n "$DOCS" ]; then
-    apt-get update
-    apt-get -y install expect-dev
-
-    # We are currently building docs with python 3.8 (min support version)
-    pip_install -r /opt/conda/requirements-docs.txt
-  fi
-
-  popd
-fi

.ci/docker/common/install_cudnn.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-if [[ ${CUDNN_VERSION} == 8 ]]; then
-    # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
-    mkdir tmp_cudnn
-    pushd tmp_cudnn
-    if [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
-        CUDNN_NAME="cudnn-linux-x86_64-8.9.2.26_cuda12-archive"
-        curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/${CUDNN_NAME}.tar.xz
-    elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
-        CUDNN_NAME="cudnn-linux-x86_64-8.7.0.84_cuda11-archive"
-        curl --retry 3 -OLs https://developer.download.nvidia.com/compute/redist/cudnn/v8.7.0/local_installers/11.8/${CUDNN_NAME}.tar.xz
-    else
-        print "Unsupported CUDA version ${CUDA_VERSION}"
-        exit 1
-    fi
-
-    tar xf ${CUDNN_NAME}.tar.xz
-    cp -a ${CUDNN_NAME}/include/* /usr/local/cuda/include/
-    cp -a ${CUDNN_NAME}/lib/* /usr/local/cuda/lib64/
-    popd
-    rm -rf tmp_cudnn
-    ldconfig
-fi

.ci/docker/common/install_cusparselt.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
-mkdir tmp_cusparselt && cd tmp_cusparselt
-
-if [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
-    CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.5.2.1-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz
-elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
-    CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.4.0.7-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz
-fi
-
-tar xf ${CUSPARSELT_NAME}.tar.xz
-cp -a ${CUSPARSELT_NAME}/include/* /usr/local/cuda/include/
-cp -a ${CUSPARSELT_NAME}/lib/* /usr/local/cuda/lib64/
-cd ..
-rm -rf tmp_cusparselt
-ldconfig

.ci/docker/common/install_docs_reqs.sh

@@ -1,25 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$KATEX" ]; then
-  apt-get update
-  # Ignore error if gpg-agent doesn't exist (for Ubuntu 16.04)
-  apt-get install -y gpg-agent || :
-
-  curl --retry 3 -sL https://deb.nodesource.com/setup_16.x | sudo -E bash -
-  sudo apt-get install -y nodejs
-
-  curl --retry 3 -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
-  echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
-
-  apt-get update
-  apt-get install -y --no-install-recommends yarn
-  yarn global add katex --prefix /usr/local
-
-  sudo apt-get -y install doxygen
-
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.ci/docker/common/install_executorch.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-clone_executorch() {
-  EXECUTORCH_PINNED_COMMIT=$(get_pinned_commit executorch)
-
-  # Clone the Executorch
-  git clone https://github.com/pytorch/executorch.git
-
-  # and fetch the target commit
-  pushd executorch
-  git checkout "${EXECUTORCH_PINNED_COMMIT}"
-  git submodule update --init
-  popd
-
-  chown -R jenkins executorch
-}
-
-install_buck2() {
-  pushd executorch/.ci/docker
-
-  BUCK2_VERSION=$(cat ci_commit_pins/buck2.txt)
-  source common/install_buck.sh
-
-  popd
-}
-
-install_conda_dependencies() {
-  pushd executorch/.ci/docker
-  # Install conda dependencies like flatbuffer
-  conda_install --file conda-env-ci.txt
-  popd
-}
-
-install_pip_dependencies() {
-  pushd executorch/.ci/docker
-  # Install all Python dependencies
-  pip_install -r requirements-ci.txt
-  popd
-}
-
-setup_executorch() {
-  pushd executorch
-  source .ci/scripts/utils.sh
-
-  install_flatc_from_source
-  pip_install .
-
-  # Make sure that all the newly generate files are owned by Jenkins
-  chown -R jenkins .
-  popd
-}
-
-clone_executorch
-install_buck2
-install_conda_dependencies
-install_pip_dependencies
-setup_executorch

.ci/docker/common/install_gcc.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$GCC_VERSION" ]; then
-
-  # Need the official toolchain repo to get alternate packages
-  add-apt-repository ppa:ubuntu-toolchain-r/test
-  apt-get update
-  apt-get install -y g++-$GCC_VERSION
-  update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
-
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.ci/docker/common/install_inductor_benchmark_deps.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-function install_huggingface() {
-  local version
-  commit=$(get_pinned_commit huggingface)
-  pip_install pandas==2.0.3
-  pip_install "git+https://github.com/huggingface/transformers@${commit}"
-}
-
-function install_timm() {
-  local commit
-  commit=$(get_pinned_commit timm)
-  pip_install pandas==2.0.3
-  pip_install "git+https://github.com/huggingface/pytorch-image-models@${commit}"
-  # Clean up
-  conda_run pip uninstall -y cmake torch torchvision triton
-}
-
-# Pango is needed for weasyprint which is needed for doctr
-conda_install pango
-install_huggingface
-install_timm

.ci/docker/common/install_linter.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-if [ -n "${UBUNTU_VERSION}" ]; then
-  apt update
-  apt-get install -y clang doxygen git graphviz nodejs npm libtinfo5
-fi
-
-# Do shallow clone of PyTorch so that we can init lintrunner in Docker build context
-git clone https://github.com/pytorch/pytorch.git --depth 1
-chown -R jenkins pytorch
-
-pushd pytorch
-# Install all linter dependencies
-pip_install -r requirements.txt
-conda_run lintrunner init
-
-# Cache .lintbin directory as part of the Docker image
-cp -r .lintbin /tmp
-popd
-
-# Node dependencies required by toc linter job
-npm install -g markdown-toc
-
-# Cleaning up
-rm -rf pytorch

.ci/docker/common/install_onnx.sh

@@ -1,51 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-retry () {
-    "$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
-}
-
-# A bunch of custom pip dependencies for ONNX
-pip_install \
-  beartype==0.15.0 \
-  filelock==3.9.0 \
-  flatbuffers==2.0 \
-  mock==5.0.1 \
-  ninja==1.10.2 \
-  networkx==2.0 \
-  numpy==1.24.2
-
-# ONNXRuntime should be installed before installing
-# onnx-weekly. Otherwise, onnx-weekly could be
-# overwritten by onnx.
-pip_install \
-  parameterized==0.8.1 \
-  pytest-cov==4.0.0 \
-  pytest-subtests==0.10.0 \
-  tabulate==0.9.0 \
-  transformers==4.36.2
-
-pip_install coloredlogs packaging
-
-pip_install onnxruntime==1.17.0
-pip_install onnx==1.15.0
-# pip_install "onnxscript@git+https://github.com/microsoft/onnxscript@3e869ef8ccf19b5ebd21c10d3e9c267c9a9fa729" --no-deps
-pip_install onnxscript==0.1.0.dev20240315 --no-deps
-
-# 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/
-IMPORT_SCRIPT_FILENAME="/tmp/onnx_import_script.py"
-as_jenkins echo 'import transformers; transformers.AutoModel.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3");' > "${IMPORT_SCRIPT_FILENAME}"
-
-# Need a PyTorch version for transformers to work
-pip_install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
-# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
-# so echo the command to a file and run the file instead
-conda_run python "${IMPORT_SCRIPT_FILENAME}"
-
-# Cleaning up
-conda_run pip uninstall -y torch
-rm "${IMPORT_SCRIPT_FILENAME}" || true

.ci/docker/common/install_openssl.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-OPENSSL=openssl-1.1.1k
-
-wget -q -O "${OPENSSL}.tar.gz" "https://ossci-linux.s3.amazonaws.com/${OPENSSL}.tar.gz"
-tar xf "${OPENSSL}.tar.gz"
-cd "${OPENSSL}"
-./config --prefix=/opt/openssl -d '-Wl,--enable-new-dtags,-rpath,$(LIBRPATH)'
-# NOTE: openssl install errors out when built with the -j option
-NPROC=$[$(nproc) - 2]
-make -j${NPROC}; make install_sw
-# Link the ssl libraries to the /usr/lib folder.
-sudo ln -s /opt/openssl/lib/lib* /usr/lib
-cd ..
-rm -rf "${OPENSSL}"

.ci/docker/common/install_protobuf.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-pb_dir="/usr/temp_pb_install_dir"
-mkdir -p $pb_dir
-
-# On the nvidia/cuda:9-cudnn7-devel-centos7 image we need this symlink or
-# else it will fail with
-#   g++: error: ./../lib64/crti.o: No such file or directory
-ln -s /usr/lib64 "$pb_dir/lib64"
-
-curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz" --retry 3
-
-tar -xvz --no-same-owner -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
-NPROC=$[$(nproc) - 2]
-pushd "$pb_dir" && ./configure && make -j${NPROC} && make -j${NPROC} check && sudo make -j${NRPOC} install && sudo ldconfig
-popd
-rm -rf $pb_dir

.ci/docker/common/install_rocm.sh

@@ -1,190 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-ver() {
-    printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
-}
-
-# Map ROCm version to AMDGPU version
-declare -A AMDGPU_VERSIONS=( ["5.0"]="21.50" ["5.1.1"]="22.10.1" ["5.2"]="22.20" )
-
-install_ubuntu() {
-    apt-get update
-    if [[ $UBUNTU_VERSION == 18.04 ]]; then
-      # gpg-agent is not available by default on 18.04
-      apt-get install -y --no-install-recommends gpg-agent
-    fi
-    if [[ $UBUNTU_VERSION == 20.04 ]]; then
-      # gpg-agent is not available by default on 20.04
-      apt-get install -y --no-install-recommends gpg-agent
-    fi
-    apt-get install -y kmod
-    apt-get install -y wget
-
-    # Need the libc++1 and libc++abi1 libraries to allow torch._C to load at runtime
-    apt-get install -y libc++1
-    apt-get install -y libc++abi1
-
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
-        # Add amdgpu repository
-        UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
-        local amdgpu_baseurl
-        if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
-          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
-        else
-          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/ubuntu"
-        fi
-        echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
-    fi
-
-    ROCM_REPO="ubuntu"
-    if [[ $(ver $ROCM_VERSION) -lt $(ver 4.2) ]]; then
-        ROCM_REPO="xenial"
-    fi
-
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
-        ROCM_REPO="${UBUNTU_VERSION_NAME}"
-    fi
-
-    # Add rocm repository
-    wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
-    local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
-    echo "deb [arch=amd64] ${rocm_baseurl} ${ROCM_REPO} main" > /etc/apt/sources.list.d/rocm.list
-    apt-get update --allow-insecure-repositories
-
-    DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
-                   rocm-dev \
-                   rocm-utils \
-                   rocm-libs \
-                   rccl \
-                   rocprofiler-dev \
-                   roctracer-dev
-
-    # precompiled miopen kernels added in ROCm 3.5, renamed in ROCm 5.5
-    # search for all unversioned packages
-    # if search fails it will abort this script; use true to avoid case where search fails
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 5.5) ]]; then
-        MIOPENHIPGFX=$(apt-cache search --names-only miopen-hip-gfx | awk '{print $1}' | grep -F -v . || true)
-        if [[ "x${MIOPENHIPGFX}" = x ]]; then
-          echo "miopen-hip-gfx package not available" && exit 1
-        else
-          DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENHIPGFX}
-        fi
-    else
-        MIOPENKERNELS=$(apt-cache search --names-only miopenkernels | awk '{print $1}' | grep -F -v . || true)
-        if [[ "x${MIOPENKERNELS}" = x ]]; then
-          echo "miopenkernels package not available" && exit 1
-        else
-          DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENKERNELS}
-        fi
-    fi
-
-    # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 6.0) ]]; then
-        for kdb in /opt/rocm/share/miopen/db/*.kdb
-        do
-            sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
-        done
-    fi
-
-    # Cleanup
-    apt-get autoclean && apt-get clean
-    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-install_centos() {
-
-  yum update -y
-  yum install -y kmod
-  yum install -y wget
-  yum install -y openblas-devel
-
-  yum install -y epel-release
-  yum install -y dkms kernel-headers-`uname -r` kernel-devel-`uname -r`
-
-  if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
-      # Add amdgpu repository
-      local amdgpu_baseurl
-      if [[ $OS_VERSION == 9 ]]; then
-          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/9.0/main/x86_64"
-      else
-        if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
-          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/7.9/main/x86_64"
-        else
-          amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/7.9/main/x86_64"
-        fi
-      fi
-      echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
-      echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
-      echo "baseurl=${amdgpu_baseurl}" >> /etc/yum.repos.d/amdgpu.repo
-      echo "enabled=1" >> /etc/yum.repos.d/amdgpu.repo
-      echo "gpgcheck=1" >> /etc/yum.repos.d/amdgpu.repo
-      echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/amdgpu.repo
-  fi
-
-  local rocm_baseurl="http://repo.radeon.com/rocm/yum/${ROCM_VERSION}"
-  echo "[ROCm]" > /etc/yum.repos.d/rocm.repo
-  echo "name=ROCm" >> /etc/yum.repos.d/rocm.repo
-  echo "baseurl=${rocm_baseurl}" >> /etc/yum.repos.d/rocm.repo
-  echo "enabled=1" >> /etc/yum.repos.d/rocm.repo
-  echo "gpgcheck=1" >> /etc/yum.repos.d/rocm.repo
-  echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/rocm.repo
-
-  yum update -y
-
-  yum install -y \
-                   rocm-dev \
-                   rocm-utils \
-                   rocm-libs \
-                   rccl \
-                   rocprofiler-dev \
-                   roctracer-dev
-
-  # precompiled miopen kernels; search for all unversioned packages
-  # if search fails it will abort this script; use true to avoid case where search fails
-  if [[ $(ver $ROCM_VERSION) -ge $(ver 5.5) ]]; then
-      MIOPENHIPGFX=$(yum -q search miopen-hip-gfx | grep miopen-hip-gfx | awk '{print $1}'| grep -F kdb. || true)
-      if [[ "x${MIOPENHIPGFX}" = x ]]; then
-        echo "miopen-hip-gfx package not available" && exit 1
-      else
-        yum install -y ${MIOPENHIPGFX}
-      fi
-  else
-      MIOPENKERNELS=$(yum -q search miopenkernels | grep miopenkernels- | awk '{print $1}'| grep -F kdb. || true)
-      if [[ "x${MIOPENKERNELS}" = x ]]; then
-        echo "miopenkernels package not available" && exit 1
-      else
-        yum install -y ${MIOPENKERNELS}
-      fi
-  fi
-
-  # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
-  if [[ $(ver $ROCM_VERSION) -ge $(ver 6.0) ]]; then
-      for kdb in /opt/rocm/share/miopen/db/*.kdb
-      do
-          sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
-      done
-  fi
-
-  # Cleanup
-  yum clean all
-  rm -rf /var/cache/yum
-  rm -rf /var/lib/yum/yumdb
-  rm -rf /var/lib/yum/history
-}
-
-# Install Python packages depending on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    install_ubuntu
-    ;;
-  centos)
-    install_centos
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac

.ci/docker/common/install_rocm_magma.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# "install" hipMAGMA into /opt/rocm/magma by copying after build
-git clone https://bitbucket.org/icl/magma.git
-pushd magma
-
-# Version 2.7.2 + ROCm related updates
-git checkout a1625ff4d9bc362906bd01f805dbbe12612953f6
-
-cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
-echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
-echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib' >> make.inc
-echo 'DEVCCFLAGS += --gpu-max-threads-per-block=256' >> make.inc
-export PATH="${PATH}:/opt/rocm/bin"
-if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then
-  amdgpu_targets=`echo $PYTORCH_ROCM_ARCH | sed 's/;/ /g'`
-else
-  amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
-fi
-for arch in $amdgpu_targets; do
-  echo "DEVCCFLAGS += --offload-arch=$arch" >> make.inc
-done
-# hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
-sed -i 's/^FOPENMP/#FOPENMP/g' make.inc
-make -f make.gen.hipMAGMA -j $(nproc)
-LANG=C.UTF-8 make lib/libmagma.so -j $(nproc) MKLROOT=/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION
-make testing/testing_dgemm -j $(nproc) MKLROOT=/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION
-popd
-mv magma /opt/rocm

.ci/docker/common/install_triton.sh

@@ -1,69 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-get_conda_version() {
-  as_jenkins conda list -n py_$ANACONDA_PYTHON_VERSION | grep -w $* | head -n 1 | awk '{print $2}'
-}
-
-conda_reinstall() {
-  as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y --force-reinstall $*
-}
-
-if [ -n "${ROCM_VERSION}" ]; then
-  TRITON_REPO="https://github.com/ROCmSoftwarePlatform/triton"
-  TRITON_TEXT_FILE="triton-rocm"
-else
-  TRITON_REPO="https://github.com/openai/triton"
-  TRITON_TEXT_FILE="triton"
-fi
-
-# The logic here is copied from .ci/pytorch/common_utils.sh
-TRITON_PINNED_COMMIT=$(get_pinned_commit ${TRITON_TEXT_FILE})
-
-if [ -n "${UBUNTU_VERSION}" ];then
-    apt update
-    apt-get install -y gpg-agent
-fi
-
-if [ -n "${CONDA_CMAKE}" ]; then
-  # Keep the current cmake and numpy version here, so we can reinstall them later
-  CMAKE_VERSION=$(get_conda_version cmake)
-  NUMPY_VERSION=$(get_conda_version numpy)
-fi
-
-if [ -z "${MAX_JOBS}" ]; then
-    export MAX_JOBS=$(nproc)
-fi
-
-if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}" == "7" ]]; then
-  # Triton needs at least gcc-9 to build
-  apt-get install -y g++-9
-
-  CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
-  # Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
-  add-apt-repository -y ppa:ubuntu-toolchain-r/test
-  apt-get install -y g++-9
-
-  CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-else
-  pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-fi
-
-if [ -n "${CONDA_CMAKE}" ]; then
-  # TODO: This is to make sure that the same cmake and numpy version from install conda
-  # script is used. Without this step, the newer cmake version (3.25.2) downloaded by
-  # triton build step via pip will fail to detect conda MKL. Once that issue is fixed,
-  # this can be removed.
-  #
-  # The correct numpy version also needs to be set here because conda claims that it
-  # causes inconsistent environment.  Without this, conda will attempt to install the
-  # latest numpy version, which fails ASAN tests with the following import error: Numba
-  # needs NumPy 1.20 or less.
-  conda_reinstall cmake="${CMAKE_VERSION}"
-  # Note that we install numpy with pip as conda might not have the version we want
-  pip_install --force-reinstall numpy=="${NUMPY_VERSION}"
-fi

.ci/docker/common/install_ucc.sh

@@ -1,53 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [[ -d "/usr/local/cuda/" ]];  then
-  with_cuda=/usr/local/cuda/
-else
-  with_cuda=no
-fi
-
-function install_ucx() {
-  set -ex
-  git clone --recursive https://github.com/openucx/ucx.git
-  pushd ucx
-  git checkout ${UCX_COMMIT}
-  git submodule update --init --recursive
-
-  ./autogen.sh
-  ./configure --prefix=$UCX_HOME      \
-      --enable-mt                     \
-      --with-cuda=$with_cuda          \
-      --enable-profiling              \
-      --enable-stats
-  time make -j
-  sudo make install
-
-  popd
-  rm -rf ucx
-}
-
-function install_ucc() {
-  set -ex
-  git clone --recursive https://github.com/openucx/ucc.git
-  pushd ucc
-  git checkout ${UCC_COMMIT}
-  git submodule update --init --recursive
-
-  ./autogen.sh
-  # We only run distributed tests on Tesla M60 and A10G
-  NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
-  ./configure --prefix=$UCC_HOME          \
-    --with-ucx=$UCX_HOME                  \
-    --with-cuda=$with_cuda                \
-    --with-nvcc-gencode="${NVCC_GENCODE}"
-  time make -j
-  sudo make install
-
-  popd
-  rm -rf ucc
-}
-
-install_ucx
-install_ucc

.ci/docker/common/install_xpu.sh

@@ -1,115 +0,0 @@
-#!/bin/bash
-set -xe
-
-
-# Intel® software for general purpose GPU capabilities.
-# Refer to https://dgpu-docs.intel.com/releases/stable_647_21_20230714.html
-
-# Intel® oneAPI Base Toolkit (version 2024.0.0) has been updated to include functional and security updates.
-# Refer to https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html
-
-# Users should update to the latest version as it becomes available
-
-function install_ubuntu() {
-    apt-get update -y
-    apt-get install -y gpg-agent wget
-
-    # Set up the repository. To do this, download the key to the system keyring
-    wget -qO - https://repositories.intel.com/gpu/intel-graphics.key \
-        | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
-    wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
-        | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
-
-    # Add the signed entry to APT sources and configure the APT client to use the Intel repository
-    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy/production/2328 unified" \
-        | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" \
-        | tee /etc/apt/sources.list.d/oneAPI.list
-
-    # Update the packages list and repository index
-    apt-get update
-
-    # The xpu-smi packages
-    apt-get install -y flex bison xpu-smi
-    # Compute and Media Runtimes
-    apt-get install -y \
-        intel-opencl-icd intel-level-zero-gpu level-zero \
-        intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
-        libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
-        libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
-    # Development Packages
-    apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
-    # Install Intel® oneAPI Base Toolkit
-    if [ -n "$BASEKIT_VERSION" ]; then
-        apt-get install intel-basekit=$BASEKIT_VERSION -y
-    else
-        apt-get install intel-basekit -y
-    fi
-
-    # Cleanup
-    apt-get autoclean && apt-get clean
-    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-function install_centos() {
-    dnf install -y 'dnf-command(config-manager)'
-    dnf config-manager --add-repo \
-        https://repositories.intel.com/gpu/rhel/8.6/production/2328/unified/intel-gpu-8.6.repo
-    # To add the EPEL repository needed for DKMS
-    dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
-        # https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
-
-    # Create the YUM repository file in the /temp directory as a normal user
-    tee > /tmp/oneAPI.repo << EOF
-[oneAPI]
-name=Intel® oneAPI repository
-baseurl=https://yum.repos.intel.com/oneapi
-enabled=1
-gpgcheck=1
-repo_gpgcheck=1
-gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-EOF
-
-    # Move the newly created oneAPI.repo file to the YUM configuration directory /etc/yum.repos.d
-    mv /tmp/oneAPI.repo /etc/yum.repos.d
-
-    # The xpu-smi packages
-    dnf install -y flex bison xpu-smi
-    # Compute and Media Runtimes
-    dnf install -y \
-        intel-opencl intel-media intel-mediasdk libmfxgen1 libvpl2\
-        level-zero intel-level-zero-gpu mesa-dri-drivers mesa-vulkan-drivers \
-        mesa-vdpau-drivers libdrm mesa-libEGL mesa-libgbm mesa-libGL \
-        mesa-libxatracker libvpl-tools intel-metrics-discovery \
-        intel-metrics-library intel-igc-core intel-igc-cm \
-        libva libva-utils intel-gmmlib libmetee intel-gsc intel-ocloc hwinfo clinfo
-    # Development packages
-    dnf install -y --refresh \
-        intel-igc-opencl-devel level-zero-devel intel-gsc-devel libmetee-devel \
-        level-zero-devel
-    # Install Intel® oneAPI Base Toolkit
-    dnf install intel-basekit -y
-
-    # Cleanup
-    dnf clean all
-    rm -rf /var/cache/yum
-    rm -rf /var/lib/yum/yumdb
-    rm -rf /var/lib/yum/history
-}
-
-
-# The installation depends on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-    ubuntu)
-        install_ubuntu
-    ;;
-    centos)
-        install_centos
-    ;;
-    *)
-        echo "Unable to determine OS..."
-        exit 1
-    ;;
-esac

.ci/docker/linter-cuda/Dockerfile

@@ -1,44 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install missing libomp-dev
-RUN apt-get update && apt-get install -y --no-install-recommends libomp-dev && apt-get autoclean && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install cuda and cudnn
-ARG CUDA_VERSION
-RUN wget -q https://raw.githubusercontent.com/pytorch/builder/main/common/install_cuda.sh -O install_cuda.sh
-RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh
-ENV DESIRED_CUDA ${CUDA_VERSION}
-ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
-
-# Note that Docker build forbids copying file outside the build context
-COPY ./common/install_linter.sh install_linter.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_linter.sh
-RUN rm install_linter.sh common_utils.sh
-
-USER jenkins
-CMD ["bash"]

.ci/docker/linter/Dockerfile

@@ -1,34 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Note that Docker build forbids copying file outside the build context
-COPY ./common/install_linter.sh install_linter.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_linter.sh
-RUN rm install_linter.sh common_utils.sh
-
-USER jenkins
-CMD ["bash"]

.ci/docker/requirements-ci.txt

@@ -1,313 +0,0 @@
-# Python dependencies required for unit tests
-
-#awscli==1.6 #this breaks some platforms
-#Description: AWS command line interface
-#Pinned versions: 1.6
-#test that import:
-
-boto3==1.19.12
-#Description: AWS SDK for python
-#Pinned versions: 1.19.12, 1.16.34
-#test that import:
-
-click
-#Description: Command Line Interface Creation Kit
-#Pinned versions:
-#test that import:
-
-coremltools==5.0b5 ; python_version < "3.12"
-#Description: Apple framework for ML integration
-#Pinned versions: 5.0b5
-#test that import:
-
-#dataclasses #this breaks some platforms
-#Description: Provides decorators for auto adding special methods to user classes
-#Pinned versions:
-#test that import:
-
-dill==0.3.7
-#Description: dill extends pickle with serializing and de-serializing for most built-ins
-#Pinned versions: 0.3.7
-#test that import: dynamo/test_replay_record.py test_dataloader.py test_datapipe.py test_serialization.py
-
-expecttest==0.1.6
-#Description: method for writing tests where test framework auto populates
-# the expected output based on previous runs
-#Pinned versions: 0.1.6
-#test that import:
-
-flatbuffers==2.0
-#Description: cross platform serialization library
-#Pinned versions: 2.0
-#test that import:
-
-hypothesis==5.35.1
-# Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
-#Description: advanced library for generating parametrized tests
-#Pinned versions: 3.44.6, 4.53.2
-#test that import: test_xnnpack_integration.py, test_pruning_op.py, test_nn.py
-
-junitparser==2.1.1
-#Description: unitparser handles JUnit/xUnit Result XML files
-#Pinned versions: 2.1.1
-#test that import:
-
-lark==0.12.0
-#Description: parser
-#Pinned versions: 0.12.0
-#test that import:
-
-librosa>=0.6.2 ; python_version < "3.11"
-#Description: A python package for music and audio analysis
-#Pinned versions: >=0.6.2
-#test that import: test_spectral_ops.py
-
-#mkl #this breaks linux-bionic-rocm4.5-py3.7
-#Description: Intel oneAPI Math Kernel Library
-#Pinned versions:
-#test that import: test_profiler.py, test_public_bindings.py, test_testing.py,
-#test_nn.py, test_mkldnn.py, test_jit.py, test_fx_experimental.py,
-#test_autograd.py
-
-#mkl-devel
-# see mkl
-
-#mock
-#Description: A testing library that allows you to replace parts of your
-#system under test with mock objects
-#Pinned versions:
-#test that import: test_modules.py, test_nn.py,
-#test_testing.py
-
-#MonkeyType # breaks pytorch-xla-linux-bionic-py3.7-clang8
-#Description: collects runtime types of function arguments and return
-#values, and can automatically generate stub files
-#Pinned versions:
-#test that import:
-
-mypy==1.8.0
-# Pin MyPy version because new errors are likely to appear with each release
-#Description: linter
-#Pinned versions: 1.8.0
-#test that import: test_typing.py, test_type_hints.py
-
-networkx==2.8.8
-#Description: creation, manipulation, and study of
-#the structure, dynamics, and functions of complex networks
-#Pinned versions: 2.8.8
-#test that import: functorch
-
-#ninja
-#Description: build system.  Note that it install from
-#here breaks things so it is commented out
-#Pinned versions: 1.10.0.post1
-#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
-
-numba==0.49.0 ; python_version < "3.9"
-numba==0.54.1 ; python_version == "3.9"
-numba==0.55.2 ; python_version == "3.10"
-#Description: Just-In-Time Compiler for Numerical Functions
-#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
-#test that import: test_numba_integration.py
-#For numba issue see https://github.com/pytorch/pytorch/issues/51511
-
-#numpy
-#Description: Provides N-dimensional arrays and linear algebra
-#Pinned versions: 1.20
-#test that import: test_view_ops.py, test_unary_ufuncs.py, test_type_promotion.py,
-#test_type_info.py, test_torch.py, test_tensorexpr_pybind.py, test_tensorexpr.py,
-#test_tensorboard.py, test_tensor_creation_ops.py, test_static_runtime.py,
-#test_spectral_ops.py, test_sort_and_select.py, test_shape_ops.py,
-#test_segment_reductions.py, test_reductions.py, test_pruning_op.py,
-#test_overrides.py, test_numpy_interop.py, test_numba_integration.py
-#test_nn.py, test_namedtensor.py, test_linalg.py, test_jit_cuda_fuser.py,
-#test_jit.py, test_indexing.py, test_datapipe.py, test_dataloader.py,
-#test_binary_ufuncs.py
-
-#onnxruntime
-#Description: scoring engine for Open Neural Network Exchange (ONNX) models
-#Pinned versions: 1.9.0
-#test that import:
-
-opt-einsum==3.3
-#Description: Python library to optimize tensor contraction order, used in einsum
-#Pinned versions: 3.3
-#test that import: test_linalg.py
-
-optree==0.9.1
-#Description: A library for tree manipulation
-#Pinned versions: 0.9.1
-#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
-#test_pytree.py, test_ops.py, test_control_flow.py, test_modules.py,
-#common_utils.py, test_eager_transforms.py, test_python_dispatch.py,
-#test_expanded_weights.py, test_decomp.py, test_overrides.py, test_masked.py,
-#test_ops.py, test_prims.py, test_subclass.py, test_functionalization.py,
-#test_schema_check.py, test_profiler_tree.py, test_meta.py, test_torchxla_num_output.py,
-#test_utils.py, test_proxy_tensor.py, test_memory_profiler.py, test_view_ops.py,
-#test_pointwise_ops.py, test_dtensor_ops.py, test_torchinductor.py, test_fx.py,
-#test_fake_tensor.py, test_mps.py
-
-pillow==10.2.0
-#Description:  Python Imaging Library fork
-#Pinned versions: 10.2.0
-#test that import:
-
-protobuf==3.20.2
-#Description:  Google’s data interchange format
-#Pinned versions: 3.20.1
-#test that import: test_tensorboard.py
-
-psutil
-#Description: information on running processes and system utilization
-#Pinned versions:
-#test that import: test_profiler.py, test_openmp.py, test_dataloader.py
-
-pytest==7.3.2
-#Description: testing framework
-#Pinned versions:
-#test that import: test_typing.py, test_cpp_extensions_aot.py, run_test.py
-
-pytest-xdist==3.3.1
-#Description: plugin for running pytest in parallel
-#Pinned versions:
-#test that import:
-
-pytest-flakefinder==1.1.0
-#Description: plugin for rerunning tests a fixed number of times in pytest
-#Pinned versions: 1.1.0
-#test that import:
-
-pytest-rerunfailures>=10.3
-#Description: plugin for rerunning failure tests in pytest
-#Pinned versions:
-#test that import:
-
-#pytest-benchmark
-#Description: fixture for benchmarking code
-#Pinned versions: 3.2.3
-#test that import:
-
-#pytest-sugar
-#Description: shows failures and errors instantly
-#Pinned versions:
-#test that import:
-
-xdoctest==1.1.0
-#Description: runs doctests in pytest
-#Pinned versions: 1.1.0
-#test that import:
-
-pygments==2.15.0
-#Description: support doctest highlighting
-#Pinned versions: 2.12.0
-#test that import: the doctests
-
-#PyYAML
-#Description: data serialization format
-#Pinned versions:
-#test that import:
-
-#requests
-#Description: HTTP library
-#Pinned versions:
-#test that import: test_type_promotion.py
-
-#rich
-#Description: rich text and beautiful formatting in the terminal
-#Pinned versions: 10.9.0
-#test that import:
-
-scikit-image==0.19.3 ; python_version < "3.10"
-scikit-image==0.20.0 ; python_version >= "3.10"
-#Description: image processing routines
-#Pinned versions:
-#test that import: test_nn.py
-
-#scikit-learn
-#Description: machine learning package
-#Pinned versions: 0.20.3
-#test that import:
-
-scipy==1.6.3 ; python_version < "3.10"
-scipy==1.8.1 ; python_version == "3.10"
-scipy==1.10.1 ; python_version == "3.11"
-# Pin SciPy because of failing distribution tests (see #60347)
-#Description: scientific python
-#Pinned versions: 1.6.3
-#test that import: test_unary_ufuncs.py, test_torch.py,test_tensor_creation_ops.py
-#test_spectral_ops.py, test_sparse_csr.py, test_reductions.py,test_nn.py
-#test_linalg.py, test_binary_ufuncs.py
-
-#tabulate
-#Description: Pretty-print tabular data
-#Pinned versions:
-#test that import:
-
-tb-nightly==2.13.0a20230426
-#Description: TensorBoard
-#Pinned versions:
-#test that import:
-
-# needed by torchgen utils
-typing-extensions
-#Description: type hints for python
-#Pinned versions:
-#test that import:
-
-#virtualenv
-#Description: virtual environment for python
-#Pinned versions:
-#test that import:
-
-unittest-xml-reporting<=3.2.0,>=2.0.0
-#Description: saves unit test results to xml
-#Pinned versions:
-#test that import:
-
-#wheel not found on aarch64, and source build requires rust
-lintrunner==0.10.7 ; platform_machine == "x86_64"
-#Description: all about linters!
-#Pinned versions: 0.10.7
-#test that import:
-
-rockset==1.0.3
-#Description: queries Rockset
-#Pinned versions: 1.0.3
-#test that import:
-
-ghstack==0.8.0
-#Description: ghstack tool
-#Pinned versions: 0.8.0
-#test that import:
-
-jinja2==3.1.3
-#Description: jinja2 template engine
-#Pinned versions: 3.1.3
-#test that import:
-
-pytest-cpp==2.3.0
-#Description: This is used by pytest to invoke C++ tests
-#Pinned versions: 2.3.0
-#test that import:
-
-z3-solver==4.12.2.0
-#Description: The Z3 Theorem Prover Project
-#Pinned versions:
-#test that import:
-
-tensorboard==2.13.0
-#Description: Also included in .ci/docker/requirements-docs.txt
-#Pinned versions:
-#test that import: test_tensorboard
-
-pywavelets==1.4.1 ; python_version < "3.12"
-pywavelets==1.5.0 ; python_version >= "3.12"
-#Description: This is a requirement of scikit-image, we need to pin
-# it here because 1.5.0 conflicts with numpy 1.21.2 used in CI
-#Pinned versions: 1.4.1
-#test that import:
-
-lxml==5.0.0.
-#Description: This is a requirement of unittest-xml-reporting
-
-# Python-3.9 binaries

.ci/docker/requirements-docs.txt

@@ -1,49 +0,0 @@
-sphinx==5.3.0
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 5.3.0
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
-
-# TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
-# but it doesn't seem to work and hangs around idly. The initial thought is probably
-# something related to Docker setup. We can investigate this later
-sphinxcontrib.katex==0.8.6
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 0.8.6
-
-matplotlib==3.5.3
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 3.5.3
-
-tensorboard==2.13.0
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 2.13.0
-
-breathe==4.34.0
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.34.0
-
-exhale==0.2.3
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.2.3
-
-docutils==0.16
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.16
-
-bs4==0.0.1
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.0.1
-
-IPython==8.12.0
-#Description: This is used to generate PyTorch functorch docs
-#Pinned versions: 8.12.0
-
-myst-nb==0.17.2
-#Description: This is used to generate PyTorch functorch docs
-#Pinned versions: 0.13.2
-
-# The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
-python-etcd==0.4.5
-sphinx-copybutton==0.5.0
-sphinx-panels==0.4.1
-myst-parser==0.18.1

.ci/docker/triton_version.txt

@@ -1 +0,0 @@
-3.0.0

.ci/docker/ubuntu-cuda/Dockerfile

@@ -1,157 +0,0 @@
-ARG UBUNTU_VERSION
-ARG CUDA_VERSION
-ARG IMAGE_NAME
-
-FROM ${IMAGE_NAME}
-
-ARG UBUNTU_VERSION
-ARG CUDA_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ARG CONDA_CMAKE
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install clang
-ARG CLANG_VERSION
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install UCC
-ARG UCX_COMMIT
-ARG UCC_COMMIT
-ENV UCX_COMMIT $UCX_COMMIT
-ENV UCC_COMMIT $UCC_COMMIT
-ENV UCX_HOME /usr
-ENV UCC_HOME /usr
-ADD ./common/install_ucc.sh install_ucc.sh
-RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
-RUN rm install_ucc.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-RUN bash ./install_openssl.sh
-ENV OPENSSL_DIR /opt/openssl
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton.txt triton.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-# See https://github.com/pytorch/pytorch/issues/82174
-# TODO(sdym@fb.com):
-# check if this is needed after full off Xenial migration
-ENV CARGO_NET_GIT_FETCH_WITH_CLI true
-RUN bash ./install_cache.sh && rm install_cache.sh
-ENV CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
-
-# Add jni.h for java host build
-COPY ./common/install_jni.sh install_jni.sh
-COPY ./java/jni.h jni.h
-RUN bash ./install_jni.sh && rm install_jni.sh
-
-# Install Open MPI for CUDA
-COPY ./common/install_openmpi.sh install_openmpi.sh
-RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
-RUN rm install_openmpi.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# AWS specific CUDA build guidance
-ENV TORCH_CUDA_ARCH_LIST Maxwell
-ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
-ENV CUDA_PATH /usr/local/cuda
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-
-# Install CUDNN
-ARG CUDNN_VERSION
-ARG CUDA_VERSION
-COPY ./common/install_cudnn.sh install_cudnn.sh
-RUN if [ "${CUDNN_VERSION}" -eq 8 ]; then bash install_cudnn.sh; fi
-RUN rm install_cudnn.sh
-
-# Install CUSPARSELT
-ARG CUDA_VERSION
-COPY ./common/install_cusparselt.sh install_cusparselt.sh
-RUN bash install_cusparselt.sh
-RUN rm install_cusparselt.sh
-
-# Delete /usr/local/cuda-11.X/cuda-11.X symlinks
-RUN if [ -h /usr/local/cuda-11.6/cuda-11.6 ]; then rm /usr/local/cuda-11.6/cuda-11.6; fi
-RUN if [ -h /usr/local/cuda-11.7/cuda-11.7 ]; then rm /usr/local/cuda-11.7/cuda-11.7; fi
-RUN if [ -h /usr/local/cuda-12.1/cuda-12.1 ]; then rm /usr/local/cuda-12.1/cuda-12.1; fi
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu-rocm/Dockerfile

@@ -1,113 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Set AMD gpu targets to build for
-ARG PYTORCH_ROCM_ARCH
-ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-ARG CLANG_VERSION
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install rocm
-ARG ROCM_VERSION
-COPY ./common/install_rocm.sh install_rocm.sh
-RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh
-COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
-RUN bash ./install_rocm_magma.sh
-RUN rm install_rocm_magma.sh
-ENV ROCM_PATH /opt/rocm
-ENV PATH /opt/rocm/bin:$PATH
-ENV PATH /opt/rocm/hcc/bin:$PATH
-ENV PATH /opt/rocm/hip/bin:$PATH
-ENV PATH /opt/rocm/opencl/bin:$PATH
-ENV PATH /opt/rocm/llvm/bin:$PATH
-ENV MAGMA_HOME /opt/rocm/magma
-ENV LANG C.UTF-8
-ENV LC_ALL C.UTF-8
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu-xpu/Dockerfile

@@ -1,118 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-ARG CLANG_VERSION
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ARG DOCS
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ENV DOCS=$DOCS
-COPY requirements-ci.txt requirements-docs.txt /opt/conda/
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install lcov for C++ code coverage
-COPY ./common/install_lcov.sh install_lcov.sh
-RUN  bash ./install_lcov.sh && rm install_lcov.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-RUN bash ./install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-ENV OPENSSL_DIR /opt/openssl
-RUN rm install_openssl.sh
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-# TODO: will add triton xpu commit
-COPY ci_commit_pins/triton.txt triton.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install XPU Dependencies
-ARG BASEKIT_VERSION
-COPY ./common/install_xpu.sh install_xpu.sh
-RUN bash ./install_xpu.sh && rm install_xpu.sh
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu/Dockerfile

@@ -1,199 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-ARG CLANG_VERSION
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ARG DOCS
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ENV DOCS=$DOCS
-COPY requirements-ci.txt requirements-docs.txt /opt/conda/
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
-RUN if [ -n "${UNINSTALL_DILL}" ]; then pip uninstall -y dill; fi
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install lcov for C++ code coverage
-COPY ./common/install_lcov.sh install_lcov.sh
-RUN  bash ./install_lcov.sh && rm install_lcov.sh
-
-# Install cuda and cudnn
-ARG CUDA_VERSION
-RUN wget -q https://raw.githubusercontent.com/pytorch/builder/main/common/install_cuda.sh -O install_cuda.sh
-RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh
-ENV DESIRED_CUDA ${CUDA_VERSION}
-ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
-
-# (optional) Install UCC
-ARG UCX_COMMIT
-ARG UCC_COMMIT
-ENV UCX_COMMIT $UCX_COMMIT
-ENV UCC_COMMIT $UCC_COMMIT
-ENV UCX_HOME /usr
-ENV UCC_HOME /usr
-ADD ./common/install_ucc.sh install_ucc.sh
-RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
-RUN rm install_ucc.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install Android NDK
-ARG ANDROID
-ARG ANDROID_NDK
-ARG GRADLE_VERSION
-COPY ./common/install_android.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-COPY ./android/AndroidManifest.xml AndroidManifest.xml
-COPY ./android/build.gradle build.gradle
-RUN if [ -n "${ANDROID}" ]; then bash ./install_android.sh; fi
-RUN rm install_android.sh cache_vision_models.sh common_utils.sh
-RUN rm AndroidManifest.xml
-RUN rm build.gradle
-ENV INSTALLED_ANDROID ${ANDROID}
-
-# (optional) Install Vulkan SDK
-ARG VULKAN_SDK_VERSION
-COPY ./common/install_vulkan_sdk.sh install_vulkan_sdk.sh
-RUN if [ -n "${VULKAN_SDK_VERSION}" ]; then bash ./install_vulkan_sdk.sh; fi
-RUN rm install_vulkan_sdk.sh
-
-# (optional) Install swiftshader
-ARG SWIFTSHADER
-COPY ./common/install_swiftshader.sh install_swiftshader.sh
-RUN if [ -n "${SWIFTSHADER}" ]; then bash ./install_swiftshader.sh; fi
-RUN rm install_swiftshader.sh
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-RUN bash ./install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-ENV OPENSSL_DIR /opt/openssl
-RUN rm install_openssl.sh
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton.txt triton.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt
-
-ARG EXECUTORCH
-# Build and install executorch
-COPY ./common/install_executorch.sh install_executorch.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/executorch.txt executorch.txt
-RUN if [ -n "${EXECUTORCH}" ]; then bash ./install_executorch.sh; fi
-RUN rm install_executorch.sh common_utils.sh executorch.txt
-
-ARG ONNX
-# Install ONNX dependencies
-COPY ./common/install_onnx.sh ./common/common_utils.sh ./
-RUN if [ -n "${ONNX}" ]; then bash ./install_onnx.sh; fi
-RUN rm install_onnx.sh common_utils.sh
-
-# (optional) Build ACL
-ARG ACL
-COPY ./common/install_acl.sh install_acl.sh
-RUN if [ -n "${ACL}" ]; then bash ./install_acl.sh; fi
-RUN rm install_acl.sh
-ENV INSTALLED_ACL ${ACL}
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Add jni.h for java host build
-COPY ./common/install_jni.sh install_jni.sh
-COPY ./java/jni.h jni.h
-RUN bash ./install_jni.sh && rm install_jni.sh
-
-# Install Open MPI for CUDA
-COPY ./common/install_openmpi.sh install_openmpi.sh
-RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
-RUN rm install_openmpi.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-
-# AWS specific CUDA build guidance
-ENV TORCH_CUDA_ARCH_LIST Maxwell
-ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
-ENV CUDA_PATH /usr/local/cuda
-
-USER jenkins
-CMD ["bash"]

.ci/onnx/README.md

@@ -1,14 +0,0 @@
-# Jenkins
-
-The scripts in this directory are the entrypoint for testing ONNX exporter.
-
-The environment variable `BUILD_ENVIRONMENT` is expected to be set to
-the build environment you intend to test. It is a hint for the build
-and test scripts to configure Caffe2 a certain way and include/exclude
-tests. Docker images, they equal the name of the image itself. For
-example: `py2-cuda9.0-cudnn7-ubuntu16.04`. The Docker images that are
-built on Jenkins and are used in triggered builds already have this
-environment variable set in their manifest. Also see
-`./docker/jenkins/*/Dockerfile` and search for `BUILD_ENVIRONMENT`.
-
-Our Jenkins installation is located at https://ci.pytorch.org/jenkins/.

.ci/onnx/common.sh

@@ -1,19 +0,0 @@
-set -ex
-
-LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
-ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
-TEST_DIR="$ROOT_DIR/test"
-pytest_reports_dir="${TEST_DIR}/test-reports/python"
-
-# Figure out which Python to use
-PYTHON="$(which python)"
-if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
-  PYTHON=$(which "python${BASH_REMATCH[1]}")
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-    # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
-    unset HIP_PLATFORM
-fi
-
-mkdir -p "$pytest_reports_dir" || true

.ci/onnx/test.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
-  # TODO: This can be removed later once vision is also part of the Docker image
-  pip install -q --user --no-use-pep517 "git+https://github.com/pytorch/vision.git@$(cat .github/ci_commit_pins/vision.txt)"
-  # JIT C++ extensions require ninja, so put it into PATH.
-  export PATH="/var/lib/jenkins/.local/bin:$PATH"
-  # NB: ONNX test is fast (~15m) so it's ok to retry it few more times to avoid any flaky issue, we
-  # need to bring this to the standard PyTorch run_test eventually. The issue will be tracked in
-  # https://github.com/pytorch/pytorch/issues/98626
-  "$ROOT_DIR/scripts/onnx/test.sh"
-fi

.ci/pytorch/README.md

@@ -1,42 +0,0 @@
-This directory contains scripts for our continuous integration.
-
-One important thing to keep in mind when reading the scripts here is
-that they are all based off of Docker images, which we build for each of
-the various system configurations we want to run on Jenkins.  This means
-it is very easy to run these tests yourself:
-
-1. Figure out what Docker image you want.  The general template for our
-   images look like:
-   ``registry.pytorch.org/pytorch/pytorch-$BUILD_ENVIRONMENT:$DOCKER_VERSION``,
-   where ``$BUILD_ENVIRONMENT`` is one of the build environments
-   enumerated in
-   [pytorch-dockerfiles](https://github.com/pytorch/pytorch/blob/master/.ci/docker/build.sh). The dockerfile used by jenkins can be found under the `.ci` [directory](https://github.com/pytorch/pytorch/blob/master/.ci/docker)
-
-2. Run ``docker run -it -u jenkins $DOCKER_IMAGE``, clone PyTorch and
-   run one of the scripts in this directory.
-
-The Docker images are designed so that any "reasonable" build commands
-will work; if you look in [build.sh](build.sh) you will see that it is a
-very simple script.  This is intentional.  Idiomatic build instructions
-should work inside all of our Docker images.  You can tweak the commands
-however you need (e.g., in case you want to rebuild with DEBUG, or rerun
-the build with higher verbosity, etc.).
-
-We have to do some work to make this so.  Here is a summary of the
-mechanisms we use:
-
-- We install binaries to directories like `/usr/local/bin` which
-  are automatically part of your PATH.
-
-- We add entries to the PATH using Docker ENV variables (so
-  they apply when you enter Docker) and `/etc/environment` (so they
-  continue to apply even if you sudo), instead of modifying
-  `PATH` in our build scripts.
-
-- We use `/etc/ld.so.conf.d` to register directories containing
-  shared libraries, instead of modifying `LD_LIBRARY_PATH` in our
-  build scripts.
-
-- We reroute well known paths like `/usr/bin/gcc` to alternate
-  implementations with `update-alternatives`, instead of setting
-  `CC` and `CXX` in our implementations.

.ci/pytorch/build.sh

@@ -1,367 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Required environment variable: $BUILD_ENVIRONMENT
-# (This is set by default in the Docker images we build, so you don't
-# need to set it yourself.
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-# shellcheck source=./common-build.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
-
-if [[ "$BUILD_ENVIRONMENT" == *-mobile-*build* ]]; then
-  exec "$(dirname "${BASH_SOURCE[0]}")/build-mobile.sh" "$@"
-fi
-
-echo "Python version:"
-python --version
-
-echo "GCC version:"
-gcc --version
-
-echo "CMake version:"
-cmake --version
-
-echo "Environment variables:"
-env
-
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  # Use jemalloc during compilation to mitigate https://github.com/pytorch/pytorch/issues/116289
-  export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2
-  echo "NVCC version:"
-  nvcc --version
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
-  if [[ "$BUILD_ENVIRONMENT" != *cuda11.3* && "$BUILD_ENVIRONMENT" != *clang* ]]; then
-    # TODO: there is a linking issue when building with UCC using clang,
-    # disable it for now and to be fix later.
-    # TODO: disable UCC temporarily to enable CUDA 12.1 in CI
-    export USE_UCC=1
-    export USE_SYSTEM_UCC=1
-  fi
-fi
-
-if [[ ${BUILD_ENVIRONMENT} == *"caffe2"* ]]; then
-  echo "Caffe2 build is ON"
-  export BUILD_CAFFE2=ON
-fi
-
-if [[ ${BUILD_ENVIRONMENT} == *"paralleltbb"* ]]; then
-  export ATEN_THREADING=TBB
-  export USE_TBB=1
-elif [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
-  export ATEN_THREADING=NATIVE
-fi
-
-# Enable LLVM dependency for TensorExpr testing
-if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-  export USE_LLVM=/opt/rocm/llvm
-  export LLVM_DIR=/opt/rocm/llvm/lib/cmake/llvm
-else
-  export USE_LLVM=/opt/llvm
-  export LLVM_DIR=/opt/llvm/lib/cmake/llvm
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *executorch* ]]; then
-  # To build test_edge_op_registration
-  export BUILD_EXECUTORCH=ON
-  export USE_CUDA=0
-fi
-
-if ! which conda; then
-  # In ROCm CIs, we are doing cross compilation on build machines with
-  # intel cpu and later run tests on machines with amd cpu.
-  # Also leave out two builds to make sure non-mkldnn builds still work.
-  if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
-    export USE_MKLDNN=1
-  else
-    export USE_MKLDNN=0
-  fi
-else
-  export CMAKE_PREFIX_PATH=/opt/conda
-
-  # Workaround required for MKL library linkage
-  # https://github.com/pytorch/pytorch/issues/119557
-  if [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
-    export CMAKE_LIBRARY_PATH="/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/lib/"
-    export CMAKE_INCLUDE_PATH="/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/include/"
-  fi
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
-  export USE_MKLDNN=1
-  export USE_MKLDNN_ACL=1
-  export ACL_ROOT_DIR=/ComputeLibrary
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *libtorch* ]]; then
-  POSSIBLE_JAVA_HOMES=()
-  POSSIBLE_JAVA_HOMES+=(/usr/local)
-  POSSIBLE_JAVA_HOMES+=(/usr/lib/jvm/java-8-openjdk-amd64)
-  POSSIBLE_JAVA_HOMES+=(/Library/Java/JavaVirtualMachines/*.jdk/Contents/Home)
-  # Add the Windows-specific JNI
-  POSSIBLE_JAVA_HOMES+=("$PWD/.circleci/windows-jni/")
-  for JH in "${POSSIBLE_JAVA_HOMES[@]}" ; do
-    if [[ -e "$JH/include/jni.h" ]] ; then
-      # Skip if we're not on Windows but haven't found a JAVA_HOME
-      if [[ "$JH" == "$PWD/.circleci/windows-jni/" && "$OSTYPE" != "msys" ]] ; then
-        break
-      fi
-      echo "Found jni.h under $JH"
-      export JAVA_HOME="$JH"
-      export BUILD_JNI=ON
-      break
-    fi
-  done
-  if [ -z "$JAVA_HOME" ]; then
-    echo "Did not find jni.h"
-  fi
-fi
-
-# Use special scripts for Android builds
-if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
-  export ANDROID_NDK=/opt/ndk
-  build_args=()
-  if [[ "${BUILD_ENVIRONMENT}" == *-arm-v7a* ]]; then
-    build_args+=("-DANDROID_ABI=armeabi-v7a")
-  elif [[ "${BUILD_ENVIRONMENT}" == *-arm-v8a* ]]; then
-    build_args+=("-DANDROID_ABI=arm64-v8a")
-  elif [[ "${BUILD_ENVIRONMENT}" == *-x86_32* ]]; then
-    build_args+=("-DANDROID_ABI=x86")
-  elif [[ "${BUILD_ENVIRONMENT}" == *-x86_64* ]]; then
-    build_args+=("-DANDROID_ABI=x86_64")
-  fi
-  if [[ "${BUILD_ENVIRONMENT}" == *vulkan* ]]; then
-    build_args+=("-DUSE_VULKAN=ON")
-  fi
-  build_args+=("-DUSE_LITE_INTERPRETER_PROFILER=OFF")
-  exec ./scripts/build_android.sh "${build_args[@]}" "$@"
-fi
-
-if [[ "$BUILD_ENVIRONMENT" != *android* && "$BUILD_ENVIRONMENT" == *vulkan* ]]; then
-  export USE_VULKAN=1
-  # shellcheck disable=SC1091
-  source /var/lib/jenkins/vulkansdk/setup-env.sh
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-  # hcc used to run out of memory, silently exiting without stopping
-  # the build process, leaving undefined symbols in the shared lib,
-  # causing undefined symbol errors when later running tests.
-  # We used to set MAX_JOBS to 4 to avoid, but this is no longer an issue.
-  if [ -z "$MAX_JOBS" ]; then
-    export MAX_JOBS=$(($(nproc) - 1))
-  fi
-
-  if [[ -n "$CI" && -z "$PYTORCH_ROCM_ARCH" ]]; then
-      # Set ROCM_ARCH to gfx906 for CI builds, if user doesn't override.
-      echo "Limiting PYTORCH_ROCM_ARCH to gfx906 for CI builds"
-      export PYTORCH_ROCM_ARCH="gfx906"
-  fi
-
-  # hipify sources
-  python tools/amd_build/build_amd.py
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/compiler/latest/env/vars.sh
-  export USE_XPU=1
-fi
-
-# sccache will fail for CUDA builds if all cores are used for compiling
-# gcc 7 with sccache seems to have intermittent OOM issue if all cores are used
-if [ -z "$MAX_JOBS" ]; then
-  if { [[ "$BUILD_ENVIRONMENT" == *cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *gcc7* ]]; } && which sccache > /dev/null; then
-    export MAX_JOBS=$(($(nproc) - 1))
-  fi
-fi
-
-# TORCH_CUDA_ARCH_LIST must be passed from an environment variable
-if [[ "$BUILD_ENVIRONMENT" == *cuda* && -z "$TORCH_CUDA_ARCH_LIST" ]]; then
-  echo "TORCH_CUDA_ARCH_LIST must be defined"
-  exit 1
-fi
-
-# We only build FlashAttention files for CUDA 8.0+, and they require large amounts of
-# memory to build and will OOM
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]] && [[ "$TORCH_CUDA_ARCH_LIST" == *"8.6"* || "$TORCH_CUDA_ARCH_LIST" == *"8.0"* ]]; then
-  echo "WARNING: FlashAttention files require large amounts of memory to build and will OOM"
-  echo "Setting MAX_JOBS=(nproc-2)/3 to reduce memory usage"
-  export MAX_JOBS="$(( $(nproc --ignore=2) / 3 ))"
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
-  export CC=clang
-  export CXX=clang++
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *-clang*-asan* ]]; then
-  export LDSHARED="clang --shared"
-  export USE_CUDA=0
-  export USE_ASAN=1
-  export UBSAN_FLAGS="-fno-sanitize-recover=all;-fno-sanitize=float-divide-by-zero;-fno-sanitize=float-cast-overflow"
-  unset USE_LLVM
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *no-ops* ]]; then
-  export USE_PER_OPERATOR_HEADERS=0
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *-pch* ]]; then
-    export USE_PRECOMPILED_HEADERS=1
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *linux-focal-py3.7-gcc7-build*  ]]; then
-  export USE_GLOO_WITH_OPENSSL=ON
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* ]]; then
-  export BUILD_STATIC_RUNTIME_BENCHMARK=ON
-fi
-
-WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
-sudo chown -R jenkins /var/lib/jenkins/workspace
-git config --global --add safe.directory /var/lib/jenkins/workspace
-
-if [[ "$BUILD_ENVIRONMENT" == *-bazel-* ]]; then
-  set -e
-
-  get_bazel
-  install_sccache_nvcc_for_bazel
-
-  # Leave 1 CPU free and use only up to 80% of memory to reduce the change of crashing
-  # the runner
-  BAZEL_MEM_LIMIT="--local_ram_resources=HOST_RAM*.8"
-  BAZEL_CPU_LIMIT="--local_cpu_resources=HOST_CPUS-1"
-
-  if [[ "$CUDA_VERSION" == "cpu" ]]; then
-    # Build torch, the Python module, and tests for CPU-only
-    tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" --config=cpu-only :torch :torch/_C.so :all_tests
-  else
-    tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" //...
-  fi
-else
-  # check that setup.py would fail with bad arguments
-  echo "The next three invocations are expected to fail with invalid command error messages."
-  ( ! get_exit_code python setup.py bad_argument )
-  ( ! get_exit_code python setup.py clean] )
-  ( ! get_exit_code python setup.py clean bad_argument )
-
-  if [[ "$BUILD_ENVIRONMENT" != *libtorch* ]]; then
-    # rocm builds fail when WERROR=1
-    # XLA test build fails when WERROR=1
-    # set only when building other architectures
-    # or building non-XLA tests.
-    if [[ "$BUILD_ENVIRONMENT" != *rocm*  &&
-          "$BUILD_ENVIRONMENT" != *xla* ]]; then
-      if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
-        # Install numpy-2.0 release candidate for builds
-        # Which should be backward compatible with Numpy-1.X
-        python -mpip install --pre numpy==2.0.0b1
-      fi
-      WERROR=1 python setup.py bdist_wheel
-    else
-      python setup.py bdist_wheel
-    fi
-    pip_install_whl "$(echo dist/*.whl)"
-
-    # TODO: I'm not sure why, but somehow we lose verbose commands
-    set -x
-
-    assert_git_not_dirty
-    # Copy ninja build logs to dist folder
-    mkdir -p dist
-    if [ -f build/.ninja_log ]; then
-      cp build/.ninja_log dist
-    fi
-
-    if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-      # remove sccache wrappers post-build; runtime compilation of MIOpen kernels does not yet fully support them
-      sudo rm -f /opt/cache/bin/cc
-      sudo rm -f /opt/cache/bin/c++
-      sudo rm -f /opt/cache/bin/gcc
-      sudo rm -f /opt/cache/bin/g++
-      pushd /opt/rocm/llvm/bin
-      if [[ -d original ]]; then
-        sudo mv original/clang .
-        sudo mv original/clang++ .
-      fi
-      sudo rm -rf original
-      popd
-    fi
-
-    CUSTOM_TEST_ARTIFACT_BUILD_DIR=${CUSTOM_TEST_ARTIFACT_BUILD_DIR:-"build/custom_test_artifacts"}
-    CUSTOM_TEST_USE_ROCM=$([[ "$BUILD_ENVIRONMENT" == *rocm* ]] && echo "ON" || echo "OFF")
-    CUSTOM_TEST_MODULE_PATH="${PWD}/cmake/public"
-    mkdir -pv "${CUSTOM_TEST_ARTIFACT_BUILD_DIR}"
-
-    # Build custom operator tests.
-    CUSTOM_OP_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-op-build"
-    CUSTOM_OP_TEST="$PWD/test/custom_operator"
-    python --version
-    SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-    mkdir -p "$CUSTOM_OP_BUILD"
-    pushd "$CUSTOM_OP_BUILD"
-    cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
-          -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
-    make VERBOSE=1
-    popd
-    assert_git_not_dirty
-
-    # Build jit hook tests
-    JIT_HOOK_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/jit-hook-build"
-    JIT_HOOK_TEST="$PWD/test/jit_hooks"
-    python --version
-    SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-    mkdir -p "$JIT_HOOK_BUILD"
-    pushd "$JIT_HOOK_BUILD"
-    cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
-          -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
-    make VERBOSE=1
-    popd
-    assert_git_not_dirty
-
-    # Build custom backend tests.
-    CUSTOM_BACKEND_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-backend-build"
-    CUSTOM_BACKEND_TEST="$PWD/test/custom_backend"
-    python --version
-    mkdir -p "$CUSTOM_BACKEND_BUILD"
-    pushd "$CUSTOM_BACKEND_BUILD"
-    cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
-          -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
-    make VERBOSE=1
-    popd
-    assert_git_not_dirty
-  else
-    # Test no-Python build
-    echo "Building libtorch"
-
-    # This is an attempt to mitigate flaky libtorch build OOM error. By default, the build parallelization
-    # is set to be the number of CPU minus 2. So, let's try a more conservative value here. A 4xlarge has
-    # 16 CPUs
-    MAX_JOBS=$(nproc --ignore=4)
-    export MAX_JOBS
-
-    # NB: Install outside of source directory (at the same level as the root
-    # pytorch folder) so that it doesn't get cleaned away prior to docker push.
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    mkdir -p ../cpp-build/caffe2
-    pushd ../cpp-build/caffe2
-    WERROR=1 VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
-    popd
-  fi
-fi
-
-if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]]; then
-  # export test times so that potential sharded tests that'll branch off this build will use consistent data
-  # don't do this for libtorch as libtorch is C++ only and thus won't have python tests run on its build
-  python tools/stats/export_test_times.py
-fi
-
-print_sccache_stats
-
-sudo chown -R "$WORKSPACE_ORIGINAL_OWNER_ID" /var/lib/jenkins/workspace

.ci/pytorch/common-build.sh

@@ -1,59 +0,0 @@
-#!/bin/bash
-# Required environment variables:
-#   $BUILD_ENVIRONMENT (should be set by your Docker image)
-
-if [[ "$BUILD_ENVIRONMENT" != *win-* ]]; then
-    # Save the absolute path in case later we chdir (as occurs in the gpu perf test)
-    script_dir="$( cd "$(dirname "${BASH_SOURCE[0]}")" || exit ; pwd -P )"
-
-    if which sccache > /dev/null; then
-        # Save sccache logs to file
-        sccache --stop-server > /dev/null  2>&1 || true
-        rm -f ~/sccache_error.log || true
-
-        function sccache_epilogue() {
-            echo "::group::Sccache Compilation Log"
-            echo '=================== sccache compilation log ==================='
-            python "$script_dir/print_sccache_log.py" ~/sccache_error.log 2>/dev/null || true
-            echo '=========== If your build fails, please take a look at the log above for possible reasons ==========='
-            sccache --show-stats
-            sccache --stop-server || true
-            echo "::endgroup::"
-        }
-
-        # Register the function here so that the error log can be printed even when
-        # sccache fails to start, i.e. timeout error
-        trap_add sccache_epilogue EXIT
-
-        if [[ -n "${SKIP_SCCACHE_INITIALIZATION:-}" ]]; then
-            # sccache --start-server seems to hang forever on self hosted runners for GHA
-            # so let's just go ahead and skip the --start-server altogether since it seems
-            # as though sccache still gets used even when the sscache server isn't started
-            # explicitly
-            echo "Skipping sccache server initialization, setting environment variables"
-            export SCCACHE_IDLE_TIMEOUT=0
-            export SCCACHE_ERROR_LOG=~/sccache_error.log
-            export RUST_LOG=sccache::server=error
-        elif [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-            SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
-        else
-            # increasing SCCACHE_IDLE_TIMEOUT so that extension_backend_test.cpp can build after this PR:
-            # https://github.com/pytorch/pytorch/pull/16645
-            SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 RUST_LOG=sccache::server=error sccache --start-server
-        fi
-
-        # Report sccache stats for easier debugging. It's ok if this commands
-        # timeouts and fails on MacOS
-        sccache --zero-stats || true
-    fi
-
-    if which ccache > /dev/null; then
-        # Report ccache stats for easier debugging
-        ccache --zero-stats
-        ccache --show-stats
-        function ccache_epilogue() {
-            ccache --show-stats
-        }
-        trap_add ccache_epilogue EXIT
-    fi
-fi

.ci/pytorch/common.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-# Common setup for all Jenkins scripts
-# shellcheck source=./common_utils.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-set -ex
-
-# Required environment variables:
-#   $BUILD_ENVIRONMENT (should be set by your Docker image)
-
-# Figure out which Python to use for ROCm
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-  # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
-  unset HIP_PLATFORM
-  export PYTORCH_TEST_WITH_ROCM=1
-  # temporary to locate some kernel issues on the CI nodes
-  export HSAKMT_DEBUG_LEVEL=4
-  # improve rccl performance for distributed tests
-  export HSA_FORCE_FINE_GRAIN_PCIE=1
-fi
-
-# TODO: Renable libtorch testing for MacOS, see https://github.com/pytorch/pytorch/issues/62598
-# shellcheck disable=SC2034
-BUILD_TEST_LIBTORCH=0

.ci/pytorch/common_utils.sh

@@ -1,240 +0,0 @@
-#!/bin/bash
-
-# Common util **functions** that can be sourced in other scripts.
-
-# note: printf is used instead of echo to avoid backslash
-# processing and to properly handle values that begin with a '-'.
-
-log() { printf '%s\n' "$*"; }
-error() { log "ERROR: $*" >&2; }
-fatal() { error "$@"; exit 1; }
-
-retry () {
-    "$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
-}
-
-# compositional trap taken from https://stackoverflow.com/a/7287873/23845
-# appends a command to a trap
-#
-# - 1st arg:  code to add
-# - remaining args:  names of traps to modify
-#
-trap_add() {
-    trap_add_cmd=$1; shift || fatal "${FUNCNAME[0]} usage error"
-    for trap_add_name in "$@"; do
-        trap -- "$(
-            # helper fn to get existing trap command from output
-            # of trap -p
-            extract_trap_cmd() { printf '%s\n' "$3"; }
-            # print existing trap command with newline
-            eval "extract_trap_cmd $(trap -p "${trap_add_name}")"
-            # print the new trap command
-            printf '%s\n' "${trap_add_cmd}"
-        )" "${trap_add_name}" \
-            || fatal "unable to add to trap ${trap_add_name}"
-    done
-}
-# set the trace attribute for the above function.  this is
-# required to modify DEBUG or RETURN traps because functions don't
-# inherit them unless the trace attribute is set
-declare -f -t trap_add
-
-function assert_git_not_dirty() {
-    # TODO: we should add an option to `build_amd.py` that reverts the repo to
-    #       an unmodified state.
-    if [[ "$BUILD_ENVIRONMENT" != *rocm* ]] && [[ "$BUILD_ENVIRONMENT" != *xla* ]] ; then
-        git_status=$(git status --porcelain | grep -v '?? third_party' || true)
-        if [[ $git_status ]]; then
-            echo "Build left local git repository checkout dirty"
-            echo "git status --porcelain:"
-            echo "${git_status}"
-            exit 1
-        fi
-    fi
-}
-
-function pip_install_whl() {
-  # This is used to install PyTorch and other build artifacts wheel locally
-  # without using any network connection
-  python3 -mpip install --no-index --no-deps "$@"
-}
-
-function pip_install() {
-  # retry 3 times
-  # old versions of pip don't have the "--progress-bar" flag
-  pip install --progress-bar off "$@" || pip install --progress-bar off "$@" || pip install --progress-bar off "$@" ||\
-  pip install "$@" || pip install "$@" || pip install "$@"
-}
-
-function pip_uninstall() {
-  # uninstall 2 times
-  pip uninstall -y "$@" || pip uninstall -y "$@"
-}
-
-function get_exit_code() {
-  set +e
-  "$@"
-  retcode=$?
-  set -e
-  return $retcode
-}
-
-function get_bazel() {
-  # Download and use the cross-platform, dependency-free Python
-  # version of Bazelisk to fetch the platform specific version of
-  # Bazel to use from .bazelversion.
-  retry curl --location --output tools/bazel \
-    https://raw.githubusercontent.com/bazelbuild/bazelisk/v1.16.0/bazelisk.py
-  shasum --algorithm=1 --check \
-    <(echo 'd4369c3d293814d3188019c9f7527a948972d9f8  tools/bazel')
-  chmod u+x tools/bazel
-}
-
-# This function is bazel specific because of the bug
-# in the bazel that requires some special paths massaging
-# as a workaround. See
-# https://github.com/bazelbuild/bazel/issues/10167
-function install_sccache_nvcc_for_bazel() {
-  sudo mv /usr/local/cuda/bin/nvcc /usr/local/cuda/bin/nvcc-real
-
-  # Write the `/usr/local/cuda/bin/nvcc`
-  cat << EOF | sudo tee /usr/local/cuda/bin/nvcc
-#!/bin/sh
-if [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then
-  exec sccache /usr/local/cuda/bin/nvcc "\$@"
-else
-  exec external/local_cuda/cuda/bin/nvcc-real "\$@"
-fi
-EOF
-
-  sudo chmod +x /usr/local/cuda/bin/nvcc
-}
-
-function install_monkeytype {
-  # Install MonkeyType
-  pip_install MonkeyType
-}
-
-
-function get_pinned_commit() {
-  cat .github/ci_commit_pins/"${1}".txt
-}
-
-function install_torchaudio() {
-  local commit
-  commit=$(get_pinned_commit audio)
-  if [[ "$1" == "cuda" ]]; then
-    # TODO: This is better to be passed as a parameter from _linux-test workflow
-    # so that it can be consistent with what is set in build
-    TORCH_CUDA_ARCH_LIST="8.0;8.6" pip_install --no-use-pep517 --user "git+https://github.com/pytorch/audio.git@${commit}"
-  else
-    pip_install --no-use-pep517 --user "git+https://github.com/pytorch/audio.git@${commit}"
-  fi
-
-}
-
-function install_torchtext() {
-  local data_commit
-  local text_commit
-  data_commit=$(get_pinned_commit data)
-  text_commit=$(get_pinned_commit text)
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/data.git@${data_commit}"
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/text.git@${text_commit}"
-}
-
-function install_torchvision() {
-  local orig_preload
-  local commit
-  commit=$(get_pinned_commit vision)
-  orig_preload=${LD_PRELOAD}
-  if [ -n "${LD_PRELOAD}" ]; then
-    # Silence dlerror to work-around glibc ASAN bug, see https://sourceware.org/bugzilla/show_bug.cgi?id=27653#c9
-    echo 'char* dlerror(void) { return "";}'|gcc -fpic -shared -o "${HOME}/dlerror.so" -x c -
-    LD_PRELOAD=${orig_preload}:${HOME}/dlerror.so
-  fi
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/vision.git@${commit}"
-  if [ -n "${LD_PRELOAD}" ]; then
-    LD_PRELOAD=${orig_preload}
-  fi
-}
-
-function install_tlparse() {
-  pip_install --user "tlparse==0.3.7"
-  PATH="$(python -m site --user-base)/bin:$PATH"
-}
-
-function install_torchrec_and_fbgemm() {
-  local torchrec_commit
-  torchrec_commit=$(get_pinned_commit torchrec)
-  local fbgemm_commit
-  fbgemm_commit=$(get_pinned_commit fbgemm)
-  pip_uninstall torchrec-nightly
-  pip_uninstall fbgemm-gpu-nightly
-  pip_install setuptools-git-versioning scikit-build pyre-extensions
-  # See https://github.com/pytorch/pytorch/issues/106971
-  CUDA_PATH=/usr/local/cuda-12.1 pip_install --no-use-pep517 --user "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#egg=fbgemm-gpu&subdirectory=fbgemm_gpu"
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
-}
-
-function clone_pytorch_xla() {
-  if [[ ! -d ./xla ]]; then
-    git clone --recursive --quiet https://github.com/pytorch/xla.git
-    pushd xla
-    # pin the xla hash so that we don't get broken by changes to xla
-    git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"
-    git submodule sync
-    git submodule update --init --recursive
-    popd
-  fi
-}
-
-function checkout_install_torchdeploy() {
-  local commit
-  commit=$(get_pinned_commit multipy)
-  pushd ..
-  git clone --recurse-submodules https://github.com/pytorch/multipy.git
-  pushd multipy
-  git checkout "${commit}"
-  python multipy/runtime/example/generate_examples.py
-  BUILD_CUDA_TESTS=1 pip install -e .
-  popd
-  popd
-}
-
-function test_torch_deploy(){
- pushd ..
- pushd multipy
- ./multipy/runtime/build/test_deploy
- ./multipy/runtime/build/test_deploy_gpu
- popd
- popd
-}
-
-function checkout_install_torchbench() {
-  local commit
-  commit=$(get_pinned_commit torchbench)
-  git clone https://github.com/pytorch/benchmark torchbench
-  pushd torchbench
-  git checkout "$commit"
-
-  if [ "$1" ]; then
-    python install.py --continue_on_fail models "$@"
-  else
-    # Occasionally the installation may fail on one model but it is ok to continue
-    # to install and test other models
-    python install.py --continue_on_fail
-  fi
-  popd
-}
-
-function print_sccache_stats() {
-  echo 'PyTorch Build Statistics'
-  sccache --show-stats
-
-  if [[ -n "${OUR_GITHUB_JOB_ID}" ]]; then
-    sccache --show-stats --stats-format json | jq .stats \
-      > "sccache-stats-${BUILD_ENVIRONMENT}-${OUR_GITHUB_JOB_ID}.json"
-  else
-    echo "env var OUR_GITHUB_JOB_ID not set, will not write sccache stats to json"
-  fi
-}

.ci/pytorch/create_test_cert.py

@@ -1,124 +0,0 @@
-from datetime import datetime, timedelta
-from tempfile import mkdtemp
-
-from cryptography import x509
-from cryptography.hazmat.primitives import hashes, serialization
-from cryptography.hazmat.primitives.asymmetric import rsa
-from cryptography.x509.oid import NameOID
-
-temp_dir = mkdtemp()
-print(temp_dir)
-
-
-def genrsa(path):
-    key = rsa.generate_private_key(
-        public_exponent=65537,
-        key_size=2048,
-    )
-    with open(path, "wb") as f:
-        f.write(
-            key.private_bytes(
-                encoding=serialization.Encoding.PEM,
-                format=serialization.PrivateFormat.TraditionalOpenSSL,
-                encryption_algorithm=serialization.NoEncryption(),
-            )
-        )
-    return key
-
-
-def create_cert(path, C, ST, L, O, key):
-    subject = issuer = x509.Name(
-        [
-            x509.NameAttribute(NameOID.COUNTRY_NAME, C),
-            x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
-            x509.NameAttribute(NameOID.LOCALITY_NAME, L),
-            x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
-        ]
-    )
-    cert = (
-        x509.CertificateBuilder()
-        .subject_name(subject)
-        .issuer_name(issuer)
-        .public_key(key.public_key())
-        .serial_number(x509.random_serial_number())
-        .not_valid_before(datetime.utcnow())
-        .not_valid_after(
-            # Our certificate will be valid for 10 days
-            datetime.utcnow()
-            + timedelta(days=10)
-        )
-        .add_extension(
-            x509.BasicConstraints(ca=True, path_length=None),
-            critical=True,
-        )
-        .sign(key, hashes.SHA256())
-    )
-    # Write our certificate out to disk.
-    with open(path, "wb") as f:
-        f.write(cert.public_bytes(serialization.Encoding.PEM))
-    return cert
-
-
-def create_req(path, C, ST, L, O, key):
-    csr = (
-        x509.CertificateSigningRequestBuilder()
-        .subject_name(
-            x509.Name(
-                [
-                    # Provide various details about who we are.
-                    x509.NameAttribute(NameOID.COUNTRY_NAME, C),
-                    x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
-                    x509.NameAttribute(NameOID.LOCALITY_NAME, L),
-                    x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
-                ]
-            )
-        )
-        .sign(key, hashes.SHA256())
-    )
-    with open(path, "wb") as f:
-        f.write(csr.public_bytes(serialization.Encoding.PEM))
-    return csr
-
-
-def sign_certificate_request(path, csr_cert, ca_cert, private_ca_key):
-    cert = (
-        x509.CertificateBuilder()
-        .subject_name(csr_cert.subject)
-        .issuer_name(ca_cert.subject)
-        .public_key(csr_cert.public_key())
-        .serial_number(x509.random_serial_number())
-        .not_valid_before(datetime.utcnow())
-        .not_valid_after(
-            # Our certificate will be valid for 10 days
-            datetime.utcnow()
-            + timedelta(days=10)
-            # Sign our certificate with our private key
-        )
-        .sign(private_ca_key, hashes.SHA256())
-    )
-    with open(path, "wb") as f:
-        f.write(cert.public_bytes(serialization.Encoding.PEM))
-    return cert
-
-
-ca_key = genrsa(temp_dir + "/ca.key")
-ca_cert = create_cert(
-    temp_dir + "/ca.pem",
-    "US",
-    "New York",
-    "New York",
-    "Gloo Certificate Authority",
-    ca_key,
-)
-
-pkey = genrsa(temp_dir + "/pkey.key")
-csr = create_req(
-    temp_dir + "/csr.csr",
-    "US",
-    "California",
-    "San Francisco",
-    "Gloo Testing Company",
-    pkey,
-)
-
-cert = sign_certificate_request(temp_dir + "/cert.pem", csr, ca_cert, ca_key)

.ci/pytorch/docker-build-test.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-docker build -t pytorch .

.ci/pytorch/docs-test.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-echo "Testing pytorch docs"
-
-cd docs
-make doctest

.ci/pytorch/functorch_doc_push_script.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-
-# This is where the local pytorch install in the docker image is located
-pt_checkout="/var/lib/jenkins/workspace"
-source "$pt_checkout/.ci/pytorch/common_utils.sh"
-echo "functorch_doc_push_script.sh: Invoked with $*"
-
-set -ex
-
-version=${DOCS_VERSION:-nightly}
-echo "version: $version"
-
-# Build functorch docs
-pushd $pt_checkout/functorch/docs
-make html
-popd
-
-git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
-pushd functorch_ghpages
-
-if [ "$version" == "main" ]; then
-  version=nightly
-fi
-
-git rm -rf "$version" || true
-mv "$pt_checkout/functorch/docs/build/html" "$version"
-
-git add "$version" || true
-git status
-git config user.email "soumith+bot@pytorch.org"
-git config user.name "pytorchbot"
-# If there aren't changes, don't make a commit; push is no-op
-git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
-git status
-
-if [[ "${WITH_PUSH:-}" == true ]]; then
-  git push -u origin gh-pages
-fi
-
-popd

.ci/pytorch/macos-build.sh

@@ -1,92 +0,0 @@
-#!/bin/bash
-
-# shellcheck disable=SC2034
-# shellcheck source=./macos-common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/macos-common.sh"
-# shellcheck source=./common-build.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
-
-# Build PyTorch
-if [ -z "${CI}" ]; then
-  export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
-fi
-
-# This helper function wraps calls to binaries with sccache, but only if they're not already wrapped with sccache.
-# For example, `clang` will be `sccache clang`, but `sccache clang` will not become `sccache sccache clang`.
-# The way this is done is by detecting the command of the parent pid of the current process and checking whether
-# that is sccache, and wrapping sccache around the process if its parent were not already sccache.
-function write_sccache_stub() {
-  output=$1
-  binary=$(basename "${output}")
-
-  printf "#!/bin/sh\nif [ \$(ps auxc \$(ps auxc -o ppid \$\$ | grep \$\$ | rev | cut -d' ' -f1 | rev) | tr '\\\\n' ' ' | rev | cut -d' ' -f2 | rev) != sccache ]; then\n  exec sccache %s \"\$@\"\nelse\n  exec %s \"\$@\"\nfi" "$(which "${binary}")" "$(which "${binary}")" > "${output}"
-  chmod a+x "${output}"
-}
-
-if which sccache > /dev/null; then
-  # Create temp directory for sccache shims
-  tmp_dir=$(mktemp -d)
-  trap 'rm -rfv ${tmp_dir}' EXIT
-  write_sccache_stub "${tmp_dir}/clang++"
-  write_sccache_stub "${tmp_dir}/clang"
-
-  export PATH="${tmp_dir}:$PATH"
-fi
-
-cross_compile_arm64() {
-  # Cross compilation for arm64
-  # Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
-  # that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
-  USE_DISTRIBUTED=0 CMAKE_OSX_ARCHITECTURES=arm64 MACOSX_DEPLOYMENT_TARGET=11.0 USE_MKLDNN=OFF USE_QNNPACK=OFF WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
-}
-
-compile_arm64() {
-  # Compilation for arm64
-  # TODO: Compile with OpenMP support (but this causes CI regressions as cross-compilation were done with OpenMP disabled)
-  USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
-}
-
-compile_x86_64() {
-  USE_DISTRIBUTED=0 WERROR=1 python setup.py bdist_wheel --plat-name=macosx_10_9_x86_64
-}
-
-build_lite_interpreter() {
-    echo "Testing libtorch (lite interpreter)."
-
-    CPP_BUILD="$(pwd)/../cpp_build"
-    # Ensure the removal of the tmp directory
-    trap 'rm -rfv ${CPP_BUILD}' EXIT
-    rm -rf "${CPP_BUILD}"
-    mkdir -p "${CPP_BUILD}/caffe2"
-
-    # It looks libtorch need to be built in "${CPP_BUILD}/caffe2 folder.
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    pushd "${CPP_BUILD}/caffe2" || exit
-    VERBOSE=1 DEBUG=1 python "${BUILD_LIBTORCH_PY}"
-    popd || exit
-
-    "${CPP_BUILD}/caffe2/build/bin/test_lite_interpreter_runtime"
-}
-
-print_cmake_info
-
-if [[ ${BUILD_ENVIRONMENT} = *arm64* ]]; then
-  if [[ $(uname -m) == "arm64" ]]; then
-    compile_arm64
-  else
-    cross_compile_arm64
-  fi
-elif [[ ${BUILD_ENVIRONMENT} = *lite-interpreter* ]]; then
-  export BUILD_LITE_INTERPRETER=1
-  build_lite_interpreter
-else
-  compile_x86_64
-fi
-
-if which sccache > /dev/null; then
-  print_sccache_stats
-fi
-
-python tools/stats/export_test_times.py
-
-assert_git_not_dirty

.ci/pytorch/macos-common.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-
-# Common prelude for macos-build.sh and macos-test.sh
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-sysctl -a | grep machdep.cpu
-
-# These are required for both the build job and the test job.
-# In the latter to test cpp extensions.
-export MACOSX_DEPLOYMENT_TARGET=11.1
-export CXX=clang++
-export CC=clang
-
-print_cmake_info() {
-  CMAKE_EXEC=$(which cmake)
-  echo "$CMAKE_EXEC"
-
-  CONDA_INSTALLATION_DIR=$(dirname "$CMAKE_EXEC")
-  # Print all libraries under cmake rpath for debugging
-  ls -la "$CONDA_INSTALLATION_DIR/../lib"
-
-  export CMAKE_EXEC
-  # Explicitly add conda env lib folder to cmake rpath to address the flaky issue
-  # where cmake dependencies couldn't be found. This seems to point to how conda
-  # links $CMAKE_EXEC to its package cache when cloning a new environment
-  install_name_tool -add_rpath @executable_path/../lib "${CMAKE_EXEC}" || true
-  # Adding the rpath will invalidate cmake signature, so signing it again here
-  # to trust the executable. EXC_BAD_ACCESS (SIGKILL (Code Signature Invalid))
-  # with an exit code 137 otherwise
-  codesign -f -s - "${CMAKE_EXEC}" || true
-}

.ci/pytorch/macos-test.sh

@@ -1,169 +0,0 @@
-#!/bin/bash
-
-# shellcheck disable=SC2034
-# shellcheck source=./macos-common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/macos-common.sh"
-
-if [[ -n "$CONDA_ENV" ]]; then
-  # Use binaries under conda environment
-  export PATH="$CONDA_ENV/bin":$PATH
-fi
-
-# Test that OpenMP is enabled for non-arm64 build
-if [[ ${BUILD_ENVIRONMENT} != *arm64* ]]; then
-  pushd test
-  if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available()))") == "1" ]]; then
-    echo "Build should have OpenMP enabled, but torch.backends.openmp.is_available() is False"
-    exit 1
-  fi
-  popd
-fi
-
-setup_test_python() {
-  # The CircleCI worker hostname doesn't resolve to an address.
-  # This environment variable makes ProcessGroupGloo default to
-  # using the address associated with the loopback interface.
-  export GLOO_SOCKET_IFNAME=lo0
-  echo "Ninja version: $(ninja --version)"
-  echo "Python version: $(which python) ($(python --version))"
-
-  # Increase default limit on open file handles from 256 to 1024
-  ulimit -n 1024
-}
-
-test_python_all() {
-  setup_test_python
-
-  time python test/run_test.py --verbose --exclude-jit-executor
-
-  assert_git_not_dirty
-}
-
-test_python_shard() {
-  if [[ -z "$NUM_TEST_SHARDS" ]]; then
-    echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
-    exit 1
-  fi
-
-  setup_test_python
-
-  time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --shard "$1" "$NUM_TEST_SHARDS"
-
-  assert_git_not_dirty
-}
-
-test_libtorch() {
-  # C++ API
-
-  if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
-    # NB: Install outside of source directory (at the same level as the root
-    # pytorch folder) so that it doesn't get cleaned away prior to docker push.
-    # But still clean it before we perform our own build.
-
-    echo "Testing libtorch"
-
-    CPP_BUILD="$PWD/../cpp-build"
-    rm -rf "$CPP_BUILD"
-    mkdir -p "$CPP_BUILD"/caffe2
-
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    pushd "$CPP_BUILD"/caffe2
-    VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
-    popd
-
-    MNIST_DIR="${PWD}/test/cpp/api/mnist"
-    python tools/download_mnist.py --quiet -d "${MNIST_DIR}"
-
-    # Unfortunately it seems like the test can't load from miniconda3
-    # without these paths being set
-    export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    TORCH_CPP_TEST_MNIST_PATH="${MNIST_DIR}" CPP_TESTS_DIR="${CPP_BUILD}/caffe2/bin" python test/run_test.py --cpp --verbose -i cpp/test_api
-
-    assert_git_not_dirty
-  fi
-}
-
-test_custom_backend() {
-  print_cmake_info
-
-  echo "Testing custom backends"
-  pushd test/custom_backend
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
-  make VERBOSE=1
-  popd
-
-  # Run Python tests and export a lowered module.
-  python test_custom_backend.py -v
-  python backend.py --export-module-to=model.pt
-  # Run C++ tests using the exported module.
-  build/test_custom_backend ./model.pt
-  rm -f ./model.pt
-  popd
-  assert_git_not_dirty
-}
-
-test_custom_script_ops() {
-  print_cmake_info
-
-  echo "Testing custom script operators"
-  pushd test/custom_operator
-  # Build the custom operator library.
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
-  make VERBOSE=1
-  popd
-
-  # Run tests Python-side and export a script module.
-  python test_custom_ops.py -v
-  python model.py --export-script-module=model.pt
-  # Run tests C++-side and load the exported script module.
-  build/test_custom_ops ./model.pt
-  popd
-  assert_git_not_dirty
-}
-
-test_jit_hooks() {
-  print_cmake_info
-
-  echo "Testing jit hooks in cpp"
-  pushd test/jit_hooks
-  # Build the custom operator library.
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
-  make VERBOSE=1
-  popd
-
-  # Run tests Python-side and export a script module.
-  python model.py --export-script-module=model
-  # Run tests C++-side and load the exported script module.
-  build/test_jit_hooks ./model
-  popd
-  assert_git_not_dirty
-}
-
-install_tlparse
-
-if [[ $NUM_TEST_SHARDS -gt 1 ]]; then
-  test_python_shard "${SHARD_NUMBER}"
-  if [[ "${SHARD_NUMBER}" == 1 ]]; then
-    test_libtorch
-    test_custom_script_ops
-  elif [[ "${SHARD_NUMBER}" == 2 ]]; then
-    test_jit_hooks
-    test_custom_backend
-  fi
-else
-  test_python_all
-  test_libtorch
-  test_custom_script_ops
-  test_jit_hooks
-  test_custom_backend
-fi

.ci/pytorch/multigpu-test.sh

@@ -1,55 +0,0 @@
-#!/bin/bash
-
-# Required environment variable: $BUILD_ENVIRONMENT
-# (This is set by default in the Docker images we build, so you don't
-# need to set it yourself.
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-echo "Testing pytorch"
-time python test/run_test.py --include test_cuda_multigpu test_cuda_primary_ctx --verbose
-
-# Disabling tests to see if they solve timeout issues; see https://github.com/pytorch/pytorch/issues/70015
-# python tools/download_mnist.py --quiet -d test/cpp/api/mnist
-# OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" build/bin/test_api
-time python test/run_test.py --verbose -i distributed/test_c10d_common
-time python test/run_test.py --verbose -i distributed/test_c10d_gloo
-time python test/run_test.py --verbose -i distributed/test_c10d_nccl
-time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
-time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
-time python test/run_test.py --verbose -i distributed/test_store
-time python test/run_test.py --verbose -i distributed/test_pg_wrapper
-time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
-# FSDP tests
-for f in test/distributed/fsdp/*.py ; do time python test/run_test.py --verbose -i "${f#*/}" ; done
-# ShardedTensor tests
-time python test/run_test.py --verbose -i distributed/checkpoint/test_checkpoint
-time python test/run_test.py --verbose -i distributed/checkpoint/test_file_system_checkpoint
-time python test/run_test.py --verbose -i distributed/_shard/sharding_spec/test_sharding_spec
-time python test/run_test.py --verbose -i distributed/_shard/sharding_plan/test_sharding_plan
-time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor
-time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor_reshard
-
-# functional collective tests
-time python test/run_test.py --verbose -i distributed/test_functional_api
-
-# DTensor tests
-time python test/run_test.py --verbose -i distributed/_tensor/test_random_ops
-time python test/run_test.py --verbose -i distributed/_tensor/test_dtensor_compile
-
-# DeviceMesh test
-time python test/run_test.py --verbose -i distributed/test_device_mesh
-
-# DTensor/TP tests
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_ddp_2d_parallel
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_fsdp_2d_parallel
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_examples
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_random_state.py
-
-# Other tests
-time python test/run_test.py --verbose -i test_cuda_primary_ctx
-time python test/run_test.py --verbose -i test_optim -- -k test_forloop_goes_right_direction_multigpu
-time python test/run_test.py --verbose -i test_optim -- -k test_mixed_device_dtype
-time python test/run_test.py --verbose -i test_foreach -- -k test_tensors_grouping
-assert_git_not_dirty

.ci/pytorch/perf_test/compare_with_baseline.py

@@ -1,90 +0,0 @@
-import argparse
-import json
-import math
-import sys
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--test-name", dest="test_name", action="store", required=True, help="test name"
-)
-parser.add_argument(
-    "--sample-stats",
-    dest="sample_stats",
-    action="store",
-    required=True,
-    help="stats from sample",
-)
-parser.add_argument(
-    "--update",
-    action="store_true",
-    help="whether to update baseline using stats from sample",
-)
-args = parser.parse_args()
-
-test_name = args.test_name
-
-if "cpu" in test_name:
-    backend = "cpu"
-elif "gpu" in test_name:
-    backend = "gpu"
-
-data_file_path = f"../{backend}_runtime.json"
-
-with open(data_file_path) as data_file:
-    data = json.load(data_file)
-
-if test_name in data:
-    mean = float(data[test_name]["mean"])
-    sigma = float(data[test_name]["sigma"])
-else:
-    # Let the test pass if baseline number doesn't exist
-    mean = sys.maxsize
-    sigma = 0.001
-
-print("population mean: ", mean)
-print("population sigma: ", sigma)
-
-# Let the test pass if baseline number is NaN (which happened in
-# the past when we didn't have logic for catching NaN numbers)
-if math.isnan(mean) or math.isnan(sigma):
-    mean = sys.maxsize
-    sigma = 0.001
-
-sample_stats_data = json.loads(args.sample_stats)
-
-sample_mean = float(sample_stats_data["mean"])
-sample_sigma = float(sample_stats_data["sigma"])
-
-print("sample mean: ", sample_mean)
-print("sample sigma: ", sample_sigma)
-
-if math.isnan(sample_mean):
-    raise Exception("""Error: sample mean is NaN""")
-elif math.isnan(sample_sigma):
-    raise Exception("""Error: sample sigma is NaN""")
-
-z_value = (sample_mean - mean) / sigma
-
-print("z-value: ", z_value)
-
-if z_value >= 3:
-    raise Exception(
-        f"""\n
-z-value >= 3, there is high chance of perf regression.\n
-To reproduce this regression, run
-`cd .ci/pytorch/perf_test/ && bash {test_name}.sh` on your local machine
-and compare the runtime before/after your code change.
-"""
-    )
-else:
-    print("z-value < 3, no perf regression detected.")
-    if args.update:
-        print("We will use these numbers as new baseline.")
-        new_data_file_path = f"../new_{backend}_runtime.json"
-        with open(new_data_file_path) as new_data_file:
-            new_data = json.load(new_data_file)
-        new_data[test_name] = {}
-        new_data[test_name]["mean"] = sample_mean
-        new_data[test_name]["sigma"] = max(sample_sigma, sample_mean * 0.1)
-        with open(new_data_file_path, "w") as new_data_file:
-            json.dump(new_data, new_data_file, indent=4)

.ci/pytorch/perf_test/update_commit_hash.py

@@ -1,13 +0,0 @@
-import json
-import sys
-
-data_file_path = sys.argv[1]
-commit_hash = sys.argv[2]
-
-with open(data_file_path) as data_file:
-    data = json.load(data_file)
-
-data["commit"] = commit_hash
-
-with open(data_file_path, "w") as data_file:
-    json.dump(data, data_file)

.ci/pytorch/print_sccache_log.py

@@ -1,17 +0,0 @@
-import sys
-
-log_file_path = sys.argv[1]
-
-with open(log_file_path) as f:
-    lines = f.readlines()
-
-for line in lines:
-    # Ignore errors from CPU instruction set, symbol existing testing,
-    # or compilation error formatting
-    ignored_keywords = [
-        "src.c",
-        "CheckSymbolExists.c",
-        "test_compilation_error_formatting",
-    ]
-    if all(keyword not in line for keyword in ignored_keywords):
-        print(line)

.ci/pytorch/win-build.sh

@@ -1,47 +0,0 @@
-#!/bin/bash
-
-# If you want to rebuild, run this with REBUILD=1
-# If you want to build with CUDA, run this with USE_CUDA=1
-# If you want to build without CUDA, run this with USE_CUDA=0
-
-if [ ! -f setup.py ]; then
-  echo "ERROR: Please run this build script from PyTorch root directory."
-  exit 1
-fi
-
-SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
-# shellcheck source=./common.sh
-source "$SCRIPT_PARENT_DIR/common.sh"
-# shellcheck source=./common-build.sh
-source "$SCRIPT_PARENT_DIR/common-build.sh"
-
-export TMP_DIR="${PWD}/build/win_tmp"
-TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
-export TMP_DIR_WIN
-export PYTORCH_FINAL_PACKAGE_DIR=${PYTORCH_FINAL_PACKAGE_DIR:-/c/w/build-results}
-if [[ -n "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
-    mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
-fi
-
-export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
-
-set +ex
-grep -E -R 'PyLong_(From|As)(Unsigned|)Long\(' --exclude=python_numbers.h --exclude=eval_frame.c torch/
-PYLONG_API_CHECK=$?
-if [[ $PYLONG_API_CHECK == 0 ]]; then
-  echo "Usage of PyLong_{From,As}{Unsigned}Long API may lead to overflow errors on Windows"
-  echo "because \`sizeof(long) == 4\` and \`sizeof(unsigned long) == 4\`."
-  echo "Please include \"torch/csrc/utils/python_numbers.h\" and use the correspoding APIs instead."
-  echo "PyLong_FromLong -> THPUtils_packInt32 / THPUtils_packInt64"
-  echo "PyLong_AsLong -> THPUtils_unpackInt (32-bit) / THPUtils_unpackLong (64-bit)"
-  echo "PyLong_FromUnsignedLong -> THPUtils_packUInt32 / THPUtils_packUInt64"
-  echo "PyLong_AsUnsignedLong -> THPUtils_unpackUInt32 / THPUtils_unpackUInt64"
-  exit 1
-fi
-set -ex
-
-"$SCRIPT_HELPERS_DIR"/build_pytorch.bat
-
-assert_git_not_dirty
-
-echo "BUILD PASSED"

.ci/pytorch/win-test-helpers/build_pytorch.bat

@@ -1,137 +0,0 @@
-if "%DEBUG%" == "1" (
-  set BUILD_TYPE=debug
-) ELSE (
-  set BUILD_TYPE=release
-)
-
-set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;C:\Program Files\Amazon\AWSCLI\bin;%PATH%
-
-:: This inflates our log size slightly, but it is REALLY useful to be
-:: able to see what our cl.exe commands are (since you can actually
-:: just copy-paste them into a local Windows setup to just rebuild a
-:: single file.)
-:: log sizes are too long, but leaving this here incase someone wants to use it locally
-:: set CMAKE_VERBOSE_MAKEFILE=1
-
-
-set INSTALLER_DIR=%SCRIPT_HELPERS_DIR%\installation-helpers
-
-call %INSTALLER_DIR%\install_magma.bat
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-
-call %INSTALLER_DIR%\install_sccache.bat
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-
-:: Miniconda has been installed as part of the Windows AMI with all the dependencies.
-:: We just need to activate it here
-call %INSTALLER_DIR%\activate_miniconda3.bat
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-
-call pip install mkl-include==2021.4.0 mkl-devel==2021.4.0
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-
-:: Override VS env here
-pushd .
-if "%VC_VERSION%" == "" (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64
-) else (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%VC_VERSION%
-)
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-@echo on
-popd
-
-if not "%USE_CUDA%"=="1" goto cuda_build_end
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%CUDA_VERSION%
-
-if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
-    echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
-    exit /b 1
-)
-rem version transformer, for example 10.1 to 10_1.
-if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
-    echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
-    exit /b 1
-)
-set VERSION_SUFFIX=%CUDA_VERSION:.=_%
-set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-
-:cuda_build_end
-
-set DISTUTILS_USE_SDK=1
-set PATH=%TMP_DIR_WIN%\bin;%PATH%
-
-:: The latest Windows CUDA test is running on AWS G5 runner with A10G GPU
-if "%TORCH_CUDA_ARCH_LIST%" == "" set TORCH_CUDA_ARCH_LIST=8.6
-
-:: The default sccache idle timeout is 600, which is too short and leads to intermittent build errors.
-set SCCACHE_IDLE_TIMEOUT=0
-set SCCACHE_IGNORE_SERVER_IO_ERROR=1
-sccache --stop-server
-sccache --start-server
-sccache --zero-stats
-set CMAKE_C_COMPILER_LAUNCHER=sccache
-set CMAKE_CXX_COMPILER_LAUNCHER=sccache
-
-set CMAKE_GENERATOR=Ninja
-
-if "%USE_CUDA%"=="1" (
-  :: randomtemp is used to resolve the intermittent build error related to CUDA.
-  :: code: https://github.com/peterjc123/randomtemp-rust
-  :: issue: https://github.com/pytorch/pytorch/issues/25393
-  ::
-  :: CMake requires a single command as CUDA_NVCC_EXECUTABLE, so we push the wrappers
-  :: randomtemp.exe and sccache.exe into a batch file which CMake invokes.
-  curl -kL https://github.com/peterjc123/randomtemp-rust/releases/download/v0.4/randomtemp.exe --output %TMP_DIR_WIN%\bin\randomtemp.exe
-  if errorlevel 1 exit /b
-  if not errorlevel 0 exit /b
-  echo @"%TMP_DIR_WIN%\bin\randomtemp.exe" "%TMP_DIR_WIN%\bin\sccache.exe" "%CUDA_PATH%\bin\nvcc.exe" %%* > "%TMP_DIR%/bin/nvcc.bat"
-  cat %TMP_DIR%/bin/nvcc.bat
-  set CUDA_NVCC_EXECUTABLE=%TMP_DIR%/bin/nvcc.bat
-  for /F "usebackq delims=" %%n in (`cygpath -m "%CUDA_PATH%\bin\nvcc.exe"`) do set CMAKE_CUDA_COMPILER=%%n
-  set CMAKE_CUDA_COMPILER_LAUNCHER=%TMP_DIR%/bin/randomtemp.exe;%TMP_DIR%\bin\sccache.exe
-)
-
-:: Print all existing environment variable for debugging
-set
-
-python setup.py bdist_wheel
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-sccache --show-stats
-python -c "import os, glob; os.system('python -mpip install --no-index --no-deps ' + glob.glob('dist/*.whl')[0])"
-(
-  if "%BUILD_ENVIRONMENT%"=="" (
-    echo NOTE: To run `import torch`, please make sure to activate the conda environment by running `call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3` in Command Prompt before running Git Bash.
-  ) else (
-    copy /Y "dist\*.whl" "%PYTORCH_FINAL_PACKAGE_DIR%"
-
-    :: export test times so that potential sharded tests that'll branch off this build will use consistent data
-    python tools/stats/export_test_times.py
-    robocopy /E ".additional_ci_files" "%PYTORCH_FINAL_PACKAGE_DIR%\.additional_ci_files"
-
-    :: Also save build/.ninja_log as an artifact
-    copy /Y "build\.ninja_log" "%PYTORCH_FINAL_PACKAGE_DIR%\"
-  )
-)
-
-sccache --show-stats --stats-format json | jq .stats > sccache-stats-%BUILD_ENVIRONMENT%-%OUR_GITHUB_JOB_ID%.json
-sccache --stop-server

.ci/pytorch/win-test-helpers/installation-helpers/activate_miniconda3.bat

@@ -1,26 +0,0 @@
-if "%BUILD_ENVIRONMENT%"=="" (
-  set CONDA_PARENT_DIR=%CD%
-) else (
-  set CONDA_PARENT_DIR=C:\Jenkins
-)
-
-
-:: Be conservative here when rolling out the new AMI with conda. This will try
-:: to install conda as before if it couldn't find the conda installation. This
-:: can be removed eventually after we gain enough confidence in the AMI
-if not exist %CONDA_PARENT_DIR%\Miniconda3 (
-  set INSTALL_FRESH_CONDA=1
-)
-
-if "%INSTALL_FRESH_CONDA%"=="1" (
-  curl --retry 3 --retry-all-errors -k https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
-  if errorlevel 1 exit /b
-  if not errorlevel 0 exit /b
-
-  %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
-  if errorlevel 1 exit /b
-  if not errorlevel 0 exit /b
-)
-
-:: Activate conda so that we can use its commands, i.e. conda, python, pip
-call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3

.ci/pytorch/win-test-helpers/installation-helpers/install_sccache.bat

@@ -1,13 +0,0 @@
-mkdir %TMP_DIR_WIN%\bin
-
-if "%REBUILD%"=="" (
-  IF EXIST %TMP_DIR_WIN%\bin\sccache.exe (
-    taskkill /im sccache.exe /f /t || ver > nul
-    del %TMP_DIR_WIN%\bin\sccache.exe || ver > nul
-  )
-  if "%BUILD_ENVIRONMENT%"=="" (
-    curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache-v0.7.4.exe --output %TMP_DIR_WIN%\bin\sccache.exe
-  ) else (
-    aws s3 cp s3://ossci-windows/sccache-v0.7.4.exe %TMP_DIR_WIN%\bin\sccache.exe
-  )
-)

.ci/pytorch/win-test-helpers/setup_pytorch_env.bat

@@ -1,54 +0,0 @@
-set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;C:\Program Files\Amazon\AWSCLI\bin;%PATH%
-
-:: Install Miniconda3
-set INSTALLER_DIR=%SCRIPT_HELPERS_DIR%\installation-helpers
-
-:: Miniconda has been installed as part of the Windows AMI with all the dependencies.
-:: We just need to activate it here
-call %INSTALLER_DIR%\activate_miniconda3.bat
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-
-:: PyTorch is now installed using the standard wheel on Windows into the conda environment.
-:: However, the test scripts are still frequently referring to the workspace temp directory
-:: build\torch. Rather than changing all these references, making a copy of torch folder
-:: from conda to the current workspace is easier. The workspace will be cleaned up after
-:: the job anyway
-xcopy /s %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
-
-pushd .
-if "%VC_VERSION%" == "" (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64
-) else (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%VC_VERSION%
-)
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-@echo on
-popd
-
-set DISTUTILS_USE_SDK=1
-
-if not "%USE_CUDA%"=="1" goto cuda_build_end
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%CUDA_VERSION%
-
-rem version transformer, for example 10.1 to 10_1.
-set VERSION_SUFFIX=%CUDA_VERSION:.=_%
-set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-set NUMBAPRO_CUDALIB=%CUDA_PATH%\bin
-set NUMBAPRO_LIBDEVICE=%CUDA_PATH%\nvvm\libdevice
-set NUMBAPRO_NVVM=%CUDA_PATH%\nvvm\bin\nvvm64_32_0.dll
-
-:cuda_build_end
-
-set PYTHONPATH=%TMP_DIR_WIN%\build;%PYTHONPATH%
-
-:: Print all existing environment variable for debugging
-set

.ci/pytorch/win-test-helpers/test_libtorch.bat

@@ -1,54 +0,0 @@
-call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
-if errorlevel 1 exit /b 1
-
-:: Save the current working directory so that we can go back there
-set CWD=%cd%
-
-set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\bin
-set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
-
-set TORCH_CPP_TEST_MNIST_PATH=%CWD%\test\cpp\api\mnist
-python tools\download_mnist.py --quiet -d %TORCH_CPP_TEST_MNIST_PATH%
-
-python test\run_test.py --cpp --verbose -i cpp/test_api
-if errorlevel 1 exit /b 1
-if not errorlevel 0 exit /b 1
-
-cd %TMP_DIR_WIN%\build\torch\test
-for /r "." %%a in (*.exe) do (
-    call :libtorch_check "%%~na" "%%~fa"
-    if errorlevel 1 goto fail
-)
-
-goto :eof
-
-:libtorch_check
-
-cd %CWD%
-set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\test
-
-:: Skip verify_api_visibility as it a compile level test
-if "%~1" == "verify_api_visibility" goto :eof
-
-echo Running "%~2"
-if "%~1" == "c10_intrusive_ptr_benchmark" (
-  :: NB: This is not a gtest executable file, thus couldn't be handled by pytest-cpp
-  call "%~2"
-  goto :eof
-)
-
-python test\run_test.py --cpp --verbose -i "cpp/%~1"
-if errorlevel 1 (
-  echo %1 failed with exit code %errorlevel%
-  goto fail
-)
-if not errorlevel 0 (
-  echo %1 failed with exit code %errorlevel%
-  goto fail
-)
-
-:eof
-exit /b 0
-
-:fail
-exit /b 1

.ci/pytorch/win-test-helpers/test_python_jit_legacy.bat

@@ -1,12 +0,0 @@
-call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
-
-echo Copying over test times file
-robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
-
-pushd test
-
-echo Run jit_profiling tests
-python run_test.py --include test_jit_legacy test_jit_fuser_legacy --verbose
-if ERRORLEVEL 1 exit /b 1
-
-popd

.ci/pytorch/win-test.sh

@@ -1,73 +0,0 @@
-#!/bin/bash
-set -ex
-
-SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
-# shellcheck source=./common.sh
-source "$SCRIPT_PARENT_DIR/common.sh"
-
-export TMP_DIR="${PWD}/build/win_tmp"
-TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
-export TMP_DIR_WIN
-export PROJECT_DIR="${PWD}"
-PROJECT_DIR_WIN=$(cygpath -w "${PROJECT_DIR}")
-export PROJECT_DIR_WIN
-export TEST_DIR="${PWD}/test"
-TEST_DIR_WIN=$(cygpath -w "${TEST_DIR}")
-export TEST_DIR_WIN
-export PYTORCH_FINAL_PACKAGE_DIR="${PYTORCH_FINAL_PACKAGE_DIR:-/c/w/build-results}"
-PYTORCH_FINAL_PACKAGE_DIR_WIN=$(cygpath -w "${PYTORCH_FINAL_PACKAGE_DIR}")
-export PYTORCH_FINAL_PACKAGE_DIR_WIN
-
-mkdir -p "$TMP_DIR"/build/torch
-
-export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
-
-if [[ "$TEST_CONFIG" = "force_on_cpu" ]]; then
-  # run the full test suite for force_on_cpu test
-  export USE_CUDA=0
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  # Used so that only cuda/rocm specific versions of tests are generated
-  # mainly used so that we're not spending extra cycles testing cpu
-  # devices on expensive gpu machines
-  export PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda"
-fi
-
-# TODO: Move both of them to Windows AMI
-python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==2.13.0
-
-# Install Z3 optional dependency for Windows builds.
-python -m pip install z3-solver==4.12.2.0
-
-run_tests() {
-    # Run nvidia-smi if available
-    for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do
-        if [[ -x "$path" ]]; then
-            "$path" || echo "true";
-            break
-        fi
-    done
-
-    if [[ $NUM_TEST_SHARDS -eq 1 ]]; then
-        "$SCRIPT_HELPERS_DIR"/test_python_shard.bat
-        "$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
-        "$SCRIPT_HELPERS_DIR"/test_custom_backend.bat
-        "$SCRIPT_HELPERS_DIR"/test_libtorch.bat
-    else
-        "$SCRIPT_HELPERS_DIR"/test_python_shard.bat
-        if [[ "${SHARD_NUMBER}" == 1 && $NUM_TEST_SHARDS -gt 1 ]]; then
-            "$SCRIPT_HELPERS_DIR"/test_libtorch.bat
-            if [[ "${USE_CUDA}" == "1" ]]; then
-              "$SCRIPT_HELPERS_DIR"/test_python_jit_legacy.bat
-            fi
-        elif [[ "${SHARD_NUMBER}" == 2 && $NUM_TEST_SHARDS -gt 1 ]]; then
-            "$SCRIPT_HELPERS_DIR"/test_custom_backend.bat
-            "$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
-        fi
-    fi
-}
-
-run_tests
-assert_git_not_dirty
-echo "TEST PASSED"

.circleci/README.md

@@ -1,4 +0,0 @@
-Warning
-=======
-
-PyTorch migration from CircleCI to github actions has been completed. All continuous integration & deployment workflows are defined in  `.github/workflows` folder

.circleci/cimodel/data/binary_build_data.py

@@ -0,0 +1,171 @@
+"""
+This module models the tree of configuration variants
+for "smoketest" builds.
+
+Each subclass of ConfigNode represents a layer of the configuration hierarchy.
+These tree nodes encapsulate the logic for whether a branch of the hierarchy
+should be "pruned".
+"""
+
+from collections import OrderedDict
+
+from cimodel.lib.conf_tree import ConfigNode
+import cimodel.data.dimensions as dimensions
+
+
+LINKING_DIMENSIONS = [
+    "shared",
+    "static",
+]
+
+
+DEPS_INCLUSION_DIMENSIONS = [
+    "with-deps",
+    "without-deps",
+]
+
+
+def get_processor_arch_name(gpu_version):
+    return "cpu" if not gpu_version else (
+        "cu" + gpu_version.strip("cuda") if gpu_version.startswith("cuda") else gpu_version
+    )
+
+CONFIG_TREE_DATA = OrderedDict(
+)
+
+# GCC config variants:
+#
+# All the nightlies (except libtorch with new gcc ABI) are built with devtoolset7,
+# which can only build with old gcc ABI. It is better than devtoolset3
+# because it understands avx512, which is needed for good fbgemm performance.
+#
+# Libtorch with new gcc ABI is built with gcc 5.4 on Ubuntu 16.04.
+LINUX_GCC_CONFIG_VARIANTS = OrderedDict(
+    manywheel=['devtoolset7'],
+    conda=['devtoolset7'],
+    libtorch=[
+        "devtoolset7",
+        "gcc5.4_cxx11-abi",
+    ],
+)
+
+WINDOWS_LIBTORCH_CONFIG_VARIANTS = [
+    "debug",
+    "release",
+]
+
+
+class TopLevelNode(ConfigNode):
+    def __init__(self, node_name, config_tree_data, smoke):
+        super(TopLevelNode, self).__init__(None, node_name)
+
+        self.config_tree_data = config_tree_data
+        self.props["smoke"] = smoke
+
+    def get_children(self):
+        return [OSConfigNode(self, x, c, p) for (x, (c, p)) in self.config_tree_data.items()]
+
+
+class OSConfigNode(ConfigNode):
+    def __init__(self, parent, os_name, gpu_versions, py_tree):
+        super(OSConfigNode, self).__init__(parent, os_name)
+
+        self.py_tree = py_tree
+        self.props["os_name"] = os_name
+        self.props["gpu_versions"] = gpu_versions
+
+    def get_children(self):
+        return [PackageFormatConfigNode(self, k, v) for k, v in self.py_tree.items()]
+
+
+class PackageFormatConfigNode(ConfigNode):
+    def __init__(self, parent, package_format, python_versions):
+        super(PackageFormatConfigNode, self).__init__(parent, package_format)
+
+        self.props["python_versions"] = python_versions
+        self.props["package_format"] = package_format
+
+
+    def get_children(self):
+        if self.find_prop("os_name") == "linux":
+            return [LinuxGccConfigNode(self, v) for v in LINUX_GCC_CONFIG_VARIANTS[self.find_prop("package_format")]]
+        elif self.find_prop("os_name") == "windows" and self.find_prop("package_format") == "libtorch":
+            return [WindowsLibtorchConfigNode(self, v) for v in WINDOWS_LIBTORCH_CONFIG_VARIANTS]
+        else:
+            return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]
+
+
+class LinuxGccConfigNode(ConfigNode):
+    def __init__(self, parent, gcc_config_variant):
+        super(LinuxGccConfigNode, self).__init__(parent, "GCC_CONFIG_VARIANT=" + str(gcc_config_variant))
+
+        self.props["gcc_config_variant"] = gcc_config_variant
+
+    def get_children(self):
+        gpu_versions = self.find_prop("gpu_versions")
+
+        # XXX devtoolset7 on CUDA 9.0 is temporarily disabled
+        # see https://github.com/pytorch/pytorch/issues/20066
+        if self.find_prop("gcc_config_variant") == 'devtoolset7':
+            gpu_versions = filter(lambda x: x != "cuda_90", gpu_versions)
+
+        # XXX disabling conda rocm build since docker images are not there
+        if self.find_prop("package_format") == 'conda':
+            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)
+
+        # XXX libtorch rocm build  is temporarily disabled
+        if self.find_prop("package_format") == 'libtorch':
+            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)
+
+        return [ArchConfigNode(self, v) for v in gpu_versions]
+
+
+class WindowsLibtorchConfigNode(ConfigNode):
+    def __init__(self, parent, libtorch_config_variant):
+        super(WindowsLibtorchConfigNode, self).__init__(parent, "LIBTORCH_CONFIG_VARIANT=" + str(libtorch_config_variant))
+
+        self.props["libtorch_config_variant"] = libtorch_config_variant
+
+    def get_children(self):
+        return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]
+
+
+class ArchConfigNode(ConfigNode):
+    def __init__(self, parent, gpu):
+        super(ArchConfigNode, self).__init__(parent, get_processor_arch_name(gpu))
+
+        self.props["gpu"] = gpu
+
+    def get_children(self):
+        return [PyVersionConfigNode(self, v) for v in self.find_prop("python_versions")]
+
+
+class PyVersionConfigNode(ConfigNode):
+    def __init__(self, parent, pyver):
+        super(PyVersionConfigNode, self).__init__(parent, pyver)
+
+        self.props["pyver"] = pyver
+
+    def get_children(self):
+        package_format = self.find_prop("package_format")
+        os_name = self.find_prop("os_name")
+
+        has_libtorch_variants = package_format == "libtorch" and os_name == "linux"
+        linking_variants = LINKING_DIMENSIONS if has_libtorch_variants else []
+
+        return [LinkingVariantConfigNode(self, v) for v in linking_variants]
+
+
+class LinkingVariantConfigNode(ConfigNode):
+    def __init__(self, parent, linking_variant):
+        super(LinkingVariantConfigNode, self).__init__(parent, linking_variant)
+
+    def get_children(self):
+        return [DependencyInclusionConfigNode(self, v) for v in DEPS_INCLUSION_DIMENSIONS]
+
+
+class DependencyInclusionConfigNode(ConfigNode):
+    def __init__(self, parent, deps_variant):
+        super(DependencyInclusionConfigNode, self).__init__(parent, deps_variant)
+
+        self.props["libtorch_variant"] = "-".join([self.parent.get_label(), self.get_label()])

.circleci/cimodel/data/binary_build_definitions.py

@@ -0,0 +1,243 @@
+from collections import OrderedDict
+
+import cimodel.data.simple.util.branch_filters as branch_filters
+import cimodel.data.binary_build_data as binary_build_data
+import cimodel.lib.conf_tree as conf_tree
+import cimodel.lib.miniutils as miniutils
+
+class Conf(object):
+    def __init__(self, os, gpu_version, pydistro, parms, smoke, libtorch_variant, gcc_config_variant, libtorch_config_variant):
+
+        self.os = os
+        self.gpu_version = gpu_version
+        self.pydistro = pydistro
+        self.parms = parms
+        self.smoke = smoke
+        self.libtorch_variant = libtorch_variant
+        self.gcc_config_variant = gcc_config_variant
+        self.libtorch_config_variant = libtorch_config_variant
+
+    def gen_build_env_parms(self):
+        elems = [self.pydistro] + self.parms + [binary_build_data.get_processor_arch_name(self.gpu_version)]
+        if self.gcc_config_variant is not None:
+            elems.append(str(self.gcc_config_variant))
+        if self.libtorch_config_variant is not None:
+            elems.append(str(self.libtorch_config_variant))
+        return elems
+
+    def gen_docker_image(self):
+        if self.gcc_config_variant == 'gcc5.4_cxx11-abi':
+            if self.gpu_version is None:
+                return miniutils.quote("pytorch/libtorch-cxx11-builder:cpu")
+            else:
+                return miniutils.quote(
+                    f"pytorch/libtorch-cxx11-builder:{self.gpu_version}"
+                )
+        if self.pydistro == "conda":
+            if self.gpu_version is None:
+                return miniutils.quote("pytorch/conda-builder:cpu")
+            else:
+                return miniutils.quote(
+                    f"pytorch/conda-builder:{self.gpu_version}"
+                )
+
+        docker_word_substitution = {
+            "manywheel": "manylinux",
+            "libtorch": "manylinux",
+        }
+
+        docker_distro_prefix = miniutils.override(self.pydistro, docker_word_substitution)
+
+        # The cpu nightlies are built on the pytorch/manylinux-cuda102 docker image
+        # TODO cuda images should consolidate into tag-base images similar to rocm
+        alt_docker_suffix = "cuda102" if not self.gpu_version else (
+            "rocm:" + self.gpu_version.strip("rocm") if self.gpu_version.startswith("rocm") else self.gpu_version)
+        docker_distro_suffix = alt_docker_suffix if self.pydistro != "conda" else (
+            "cuda" if alt_docker_suffix.startswith("cuda") else "rocm")
+        return miniutils.quote("pytorch/" + docker_distro_prefix + "-" + docker_distro_suffix)
+
+    def get_name_prefix(self):
+        return "smoke" if self.smoke else "binary"
+
+    def gen_build_name(self, build_or_test, nightly):
+
+        parts = [self.get_name_prefix(), self.os] + self.gen_build_env_parms()
+
+        if nightly:
+            parts.append("nightly")
+
+        if self.libtorch_variant:
+            parts.append(self.libtorch_variant)
+
+        if not self.smoke:
+            parts.append(build_or_test)
+
+        joined = "_".join(parts)
+        return joined.replace(".", "_")
+
+    def gen_workflow_job(self, phase, upload_phase_dependency=None, nightly=False):
+        job_def = OrderedDict()
+        job_def["name"] = self.gen_build_name(phase, nightly)
+        job_def["build_environment"] = miniutils.quote(" ".join(self.gen_build_env_parms()))
+        if self.smoke:
+            job_def["requires"] = [
+                "update_s3_htmls",
+            ]
+            job_def["filters"] = branch_filters.gen_filter_dict(
+                branches_list=["postnightly"],
+            )
+        else:
+            filter_branch = r"/.*/"
+            job_def["filters"] = branch_filters.gen_filter_dict(
+                branches_list=[filter_branch],
+                tags_list=[branch_filters.RC_PATTERN],
+            )
+        if self.libtorch_variant:
+            job_def["libtorch_variant"] = miniutils.quote(self.libtorch_variant)
+        if phase == "test":
+            if not self.smoke:
+                job_def["requires"] = [self.gen_build_name("build", nightly)]
+            if not (self.smoke and self.os == "macos") and self.os != "windows":
+                job_def["docker_image"] = self.gen_docker_image()
+
+            # fix this. only works on cuda not rocm
+            if self.os != "windows" and self.gpu_version:
+                job_def["use_cuda_docker_runtime"] = miniutils.quote("1")
+        else:
+            if self.os == "linux" and phase != "upload":
+                job_def["docker_image"] = self.gen_docker_image()
+
+        if phase == "test":
+            if self.gpu_version:
+                if self.os == "windows":
+                    job_def["executor"] = "windows-with-nvidia-gpu"
+                else:
+                    job_def["resource_class"] = "gpu.medium"
+
+        os_name = miniutils.override(self.os, {"macos": "mac"})
+        job_name = "_".join([self.get_name_prefix(), os_name, phase])
+        return {job_name : job_def}
+
+    def gen_upload_job(self, phase, requires_dependency):
+        """Generate binary_upload job for configuration
+
+        Output looks similar to:
+
+      - binary_upload:
+          name: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_upload
+          context: org-member
+          requires: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_test
+          filters:
+            branches:
+              only:
+                - nightly
+            tags:
+              only: /v[0-9]+(\\.[0-9]+)*-rc[0-9]+/
+          package_type: manywheel
+          upload_subfolder: cu113
+        """
+        return {
+            "binary_upload": OrderedDict({
+                "name": self.gen_build_name(phase, nightly=True),
+                "context": "org-member",
+                "requires": [self.gen_build_name(
+                    requires_dependency,
+                    nightly=True
+                )],
+                "filters": branch_filters.gen_filter_dict(
+                    branches_list=["nightly"],
+                    tags_list=[branch_filters.RC_PATTERN],
+                ),
+                "package_type": self.pydistro,
+                "upload_subfolder": binary_build_data.get_processor_arch_name(
+                    self.gpu_version,
+                ),
+            })
+        }
+
+def get_root(smoke, name):
+
+    return binary_build_data.TopLevelNode(
+        name,
+        binary_build_data.CONFIG_TREE_DATA,
+        smoke,
+    )
+
+
+def gen_build_env_list(smoke):
+
+    root = get_root(smoke, "N/A")
+    config_list = conf_tree.dfs(root)
+
+    newlist = []
+    for c in config_list:
+        conf = Conf(
+            c.find_prop("os_name"),
+            c.find_prop("gpu"),
+            c.find_prop("package_format"),
+            [c.find_prop("pyver")],
+            c.find_prop("smoke") and not (c.find_prop("os_name") == "macos_arm64"),  # don't test arm64
+            c.find_prop("libtorch_variant"),
+            c.find_prop("gcc_config_variant"),
+            c.find_prop("libtorch_config_variant"),
+        )
+        newlist.append(conf)
+
+    return newlist
+
+def predicate_exclude_macos(config):
+    return config.os == "linux" or config.os == "windows"
+
+def get_nightly_uploads():
+    configs = gen_build_env_list(False)
+    mylist = []
+    for conf in configs:
+        phase_dependency = "test" if predicate_exclude_macos(conf) else "build"
+        mylist.append(conf.gen_upload_job("upload", phase_dependency))
+
+    return mylist
+
+def get_post_upload_jobs():
+    return [
+        {
+            "update_s3_htmls": {
+                "name": "update_s3_htmls",
+                "context": "org-member",
+                "filters": branch_filters.gen_filter_dict(
+                    branches_list=["postnightly"],
+                ),
+            },
+        },
+    ]
+
+def get_nightly_tests():
+
+    configs = gen_build_env_list(False)
+    filtered_configs = filter(predicate_exclude_macos, configs)
+
+    tests = []
+    for conf_options in filtered_configs:
+        yaml_item = conf_options.gen_workflow_job("test", nightly=True)
+        tests.append(yaml_item)
+
+    return tests
+
+
+def get_jobs(toplevel_key, smoke):
+    jobs_list = []
+    configs = gen_build_env_list(smoke)
+    phase = "build" if toplevel_key == "binarybuilds" else "test"
+    for build_config in configs:
+        # don't test for macos_arm64 as it's cross compiled
+        if phase != "test" or build_config.os != "macos_arm64":
+            jobs_list.append(build_config.gen_workflow_job(phase, nightly=True))
+
+    return jobs_list
+
+
+def get_binary_build_jobs():
+    return get_jobs("binarybuilds", False)
+
+
+def get_binary_smoke_test_jobs():
+    return get_jobs("binarysmoketests", True)

.circleci/cimodel/data/dimensions.py

@@ -0,0 +1,23 @@
+PHASES = ["build", "test"]
+
+CUDA_VERSIONS = [
+    "102",
+    "113",
+    "116",
+]
+
+ROCM_VERSIONS = [
+    "4.3.1",
+    "4.5.2",
+]
+
+ROCM_VERSION_LABELS = ["rocm" + v for v in ROCM_VERSIONS]
+
+GPU_VERSIONS = [None] + ["cuda" + v for v in CUDA_VERSIONS] + ROCM_VERSION_LABELS
+
+STANDARD_PYTHON_VERSIONS = [
+    "3.7",
+    "3.8",
+    "3.9",
+    "3.10"
+]

.circleci/cimodel/data/pytorch_build_data.py

@@ -0,0 +1,280 @@
+from cimodel.lib.conf_tree import ConfigNode
+
+
+CONFIG_TREE_DATA = [
+]
+
+
+def get_major_pyver(dotted_version):
+    parts = dotted_version.split(".")
+    return "py" + parts[0]
+
+
+class TreeConfigNode(ConfigNode):
+    def __init__(self, parent, node_name, subtree):
+        super(TreeConfigNode, self).__init__(parent, self.modify_label(node_name))
+        self.subtree = subtree
+        self.init2(node_name)
+
+    def modify_label(self, label):
+        return label
+
+    def init2(self, node_name):
+        pass
+
+    def get_children(self):
+        return [self.child_constructor()(self, k, v) for (k, v) in self.subtree]
+
+
+class TopLevelNode(TreeConfigNode):
+    def __init__(self, node_name, subtree):
+        super(TopLevelNode, self).__init__(None, node_name, subtree)
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return DistroConfigNode
+
+
+class DistroConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["distro_name"] = node_name
+
+    def child_constructor(self):
+        distro = self.find_prop("distro_name")
+
+        next_nodes = {
+            "xenial": XenialCompilerConfigNode,
+            "bionic": BionicCompilerConfigNode,
+        }
+        return next_nodes[distro]
+
+
+class PyVerConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["pyver"] = node_name
+        self.props["abbreviated_pyver"] = get_major_pyver(node_name)
+        if node_name == "3.9":
+            self.props["abbreviated_pyver"] = "py3.9"
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class ExperimentalFeatureConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["experimental_feature"] = node_name
+
+    def child_constructor(self):
+        experimental_feature = self.find_prop("experimental_feature")
+
+        next_nodes = {
+            "asan": AsanConfigNode,
+            "xla": XlaConfigNode,
+            "mps": MPSConfigNode,
+            "vulkan": VulkanConfigNode,
+            "parallel_tbb": ParallelTBBConfigNode,
+            "crossref": CrossRefConfigNode,
+            "parallel_native": ParallelNativeConfigNode,
+            "onnx": ONNXConfigNode,
+            "libtorch": LibTorchConfigNode,
+            "important": ImportantConfigNode,
+            "build_only": BuildOnlyConfigNode,
+            "shard_test": ShardTestConfigNode,
+            "cuda_gcc_override": CudaGccOverrideConfigNode,
+            "pure_torch": PureTorchConfigNode,
+            "slow_gradcheck": SlowGradcheckConfigNode,
+        }
+        return next_nodes[experimental_feature]
+
+
+class SlowGradcheckConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["is_slow_gradcheck"] = True
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+class PureTorchConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "PURE_TORCH=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_pure_torch"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class XlaConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "XLA=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_xla"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+class MPSConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "MPS=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_mps"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class AsanConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "Asan=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_asan"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class ONNXConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "Onnx=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_onnx"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class VulkanConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "Vulkan=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_vulkan"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class ParallelTBBConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "PARALLELTBB=" + str(label)
+
+    def init2(self, node_name):
+        self.props["parallel_backend"] = "paralleltbb"
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class CrossRefConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["is_crossref"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class ParallelNativeConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "PARALLELNATIVE=" + str(label)
+
+    def init2(self, node_name):
+        self.props["parallel_backend"] = "parallelnative"
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class LibTorchConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "BUILD_TEST_LIBTORCH=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_libtorch"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class CudaGccOverrideConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["cuda_gcc_override"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class BuildOnlyConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["build_only"] = node_name
+
+    def child_constructor(self):
+        return ExperimentalFeatureConfigNode
+
+
+class ShardTestConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["shard_test"] = node_name
+
+    def child_constructor(self):
+        return ImportantConfigNode
+
+
+class ImportantConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return "IMPORTANT=" + str(label)
+
+    def init2(self, node_name):
+        self.props["is_important"] = node_name
+
+    def get_children(self):
+        return []
+
+
+class XenialCompilerConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return label or "<unspecified>"
+
+    def init2(self, node_name):
+        self.props["compiler_name"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+
+        return XenialCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
+
+
+class BionicCompilerConfigNode(TreeConfigNode):
+    def modify_label(self, label):
+        return label or "<unspecified>"
+
+    def init2(self, node_name):
+        self.props["compiler_name"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+
+        return BionicCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
+
+
+class XenialCompilerVersionConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["compiler_version"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return PyVerConfigNode
+
+
+class BionicCompilerVersionConfigNode(TreeConfigNode):
+    def init2(self, node_name):
+        self.props["compiler_version"] = node_name
+
+    # noinspection PyMethodMayBeStatic
+    def child_constructor(self):
+        return PyVerConfigNode

.circleci/cimodel/data/pytorch_build_definitions.py

@@ -0,0 +1,380 @@
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import cimodel.data.dimensions as dimensions
+import cimodel.lib.conf_tree as conf_tree
+import cimodel.lib.miniutils as miniutils
+from cimodel.data.pytorch_build_data import CONFIG_TREE_DATA, TopLevelNode
+from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
+from cimodel.data.simple.util.docker_constants import gen_docker_image
+
+
+@dataclass
+class Conf:
+    distro: str
+    parms: List[str]
+    parms_list_ignored_for_docker_image: Optional[List[str]] = None
+    pyver: Optional[str] = None
+    cuda_version: Optional[str] = None
+    rocm_version: Optional[str] = None
+    # TODO expand this to cover all the USE_* that we want to test for
+    #  tesnrorrt, leveldb, lmdb, redis, opencv, mkldnn, ideep, etc.
+    # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453608)
+    is_xla: bool = False
+    is_vulkan: bool = False
+    is_pure_torch: bool = False
+    restrict_phases: Optional[List[str]] = None
+    gpu_resource: Optional[str] = None
+    dependent_tests: List = field(default_factory=list)
+    parent_build: Optional["Conf"] = None
+    is_libtorch: bool = False
+    is_important: bool = False
+    parallel_backend: Optional[str] = None
+    build_only: bool = False
+
+    @staticmethod
+    def is_test_phase(phase):
+        return "test" in phase
+
+    # TODO: Eliminate the special casing for docker paths
+    # In the short term, we *will* need to support special casing as docker images are merged for caffe2 and pytorch
+    def get_parms(self, for_docker):
+        leading = []
+        # We just don't run non-important jobs on pull requests;
+        # previously we also named them in a way to make it obvious
+        # if self.is_important and not for_docker:
+        #    leading.append("AAA")
+        leading.append("pytorch")
+        if self.is_xla and not for_docker:
+            leading.append("xla")
+        if self.is_vulkan and not for_docker:
+            leading.append("vulkan")
+        if self.is_libtorch and not for_docker:
+            leading.append("libtorch")
+        if self.is_pure_torch and not for_docker:
+            leading.append("pure_torch")
+        if self.parallel_backend is not None and not for_docker:
+            leading.append(self.parallel_backend)
+
+        cuda_parms = []
+        if self.cuda_version:
+            cudnn = "cudnn8" if self.cuda_version.startswith("11.") else "cudnn7"
+            cuda_parms.extend(["cuda" + self.cuda_version, cudnn])
+        if self.rocm_version:
+            cuda_parms.extend([f"rocm{self.rocm_version}"])
+        result = leading + ["linux", self.distro] + cuda_parms + self.parms
+        if not for_docker and self.parms_list_ignored_for_docker_image is not None:
+            result = result + self.parms_list_ignored_for_docker_image
+        return result
+
+    def gen_docker_image_path(self):
+        parms_source = self.parent_build or self
+        base_build_env_name = "-".join(parms_source.get_parms(True))
+        image_name, _ = gen_docker_image(base_build_env_name)
+        return miniutils.quote(image_name)
+
+    def gen_docker_image_requires(self):
+        parms_source = self.parent_build or self
+        base_build_env_name = "-".join(parms_source.get_parms(True))
+        _, requires = gen_docker_image(base_build_env_name)
+        return miniutils.quote(requires)
+
+    def get_build_job_name_pieces(self, build_or_test):
+        return self.get_parms(False) + [build_or_test]
+
+    def gen_build_name(self, build_or_test):
+        return (
+            ("_".join(map(str, self.get_build_job_name_pieces(build_or_test))))
+            .replace(".", "_")
+            .replace("-", "_")
+        )
+
+    def get_dependents(self):
+        return self.dependent_tests or []
+
+    def gen_workflow_params(self, phase):
+        parameters = OrderedDict()
+        build_job_name_pieces = self.get_build_job_name_pieces(phase)
+
+        build_env_name = "-".join(map(str, build_job_name_pieces))
+        parameters["build_environment"] = miniutils.quote(build_env_name)
+        parameters["docker_image"] = self.gen_docker_image_path()
+        if Conf.is_test_phase(phase) and self.gpu_resource:
+            parameters["use_cuda_docker_runtime"] = miniutils.quote("1")
+        if Conf.is_test_phase(phase):
+            resource_class = "large"
+            if self.gpu_resource:
+                resource_class = "gpu." + self.gpu_resource
+            if self.rocm_version is not None:
+                resource_class = "pytorch/amd-gpu"
+            parameters["resource_class"] = resource_class
+        if phase == "build" and self.rocm_version is not None:
+            parameters["resource_class"] = "xlarge"
+        if hasattr(self, 'filters'):
+            parameters['filters'] = self.filters
+        if self.build_only:
+            parameters['build_only'] = miniutils.quote(str(int(True)))
+        return parameters
+
+    def gen_workflow_job(self, phase):
+        job_def = OrderedDict()
+        job_def["name"] = self.gen_build_name(phase)
+
+        if Conf.is_test_phase(phase):
+
+            # TODO When merging the caffe2 and pytorch jobs, it might be convenient for a while to make a
+            #  caffe2 test job dependent on a pytorch build job. This way we could quickly dedup the repeated
+            #  build of pytorch in the caffe2 build job, and just run the caffe2 tests off of a completed
+            #  pytorch build job (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259452641)
+
+            dependency_build = self.parent_build or self
+            job_def["requires"] = [dependency_build.gen_build_name("build")]
+            job_name = "pytorch_linux_test"
+        else:
+            job_name = "pytorch_linux_build"
+            job_def["requires"] = [self.gen_docker_image_requires()]
+
+        if not self.is_important:
+            job_def["filters"] = gen_filter_dict()
+        job_def.update(self.gen_workflow_params(phase))
+
+        return {job_name: job_def}
+
+
+# TODO This is a hack to special case some configs just for the workflow list
+class HiddenConf(object):
+    def __init__(self, name, parent_build=None, filters=None):
+        self.name = name
+        self.parent_build = parent_build
+        self.filters = filters
+
+    def gen_workflow_job(self, phase):
+        return {
+            self.gen_build_name(phase): {
+                "requires": [self.parent_build.gen_build_name("build")],
+                "filters": self.filters,
+            }
+        }
+
+    def gen_build_name(self, _):
+        return self.name
+
+class DocPushConf(object):
+    def __init__(self, name, parent_build=None, branch="master"):
+        self.name = name
+        self.parent_build = parent_build
+        self.branch = branch
+
+    def gen_workflow_job(self, phase):
+        return {
+            "pytorch_doc_push": {
+                "name": self.name,
+                "branch": self.branch,
+                "requires": [self.parent_build],
+                "context": "org-member",
+                "filters": gen_filter_dict(branches_list=["nightly"],
+                                           tags_list=RC_PATTERN)
+            }
+        }
+
+def gen_docs_configs(xenial_parent_config):
+    configs = []
+
+    configs.append(
+        HiddenConf(
+            "pytorch_python_doc_build",
+            parent_build=xenial_parent_config,
+            filters=gen_filter_dict(branches_list=["master", "main", "nightly"],
+                                    tags_list=RC_PATTERN),
+        )
+    )
+    configs.append(
+        DocPushConf(
+            "pytorch_python_doc_push",
+            parent_build="pytorch_python_doc_build",
+            branch="site",
+        )
+    )
+
+    configs.append(
+        HiddenConf(
+            "pytorch_cpp_doc_build",
+            parent_build=xenial_parent_config,
+            filters=gen_filter_dict(branches_list=["master", "main", "nightly"],
+                                    tags_list=RC_PATTERN),
+        )
+    )
+    configs.append(
+        DocPushConf(
+            "pytorch_cpp_doc_push",
+            parent_build="pytorch_cpp_doc_build",
+            branch="master",
+        )
+    )
+    return configs
+
+
+def get_root():
+    return TopLevelNode("PyTorch Builds", CONFIG_TREE_DATA)
+
+
+def gen_tree():
+    root = get_root()
+    configs_list = conf_tree.dfs(root)
+    return configs_list
+
+
+def instantiate_configs(only_slow_gradcheck):
+
+    config_list = []
+
+    root = get_root()
+    found_configs = conf_tree.dfs(root)
+    for fc in found_configs:
+
+        restrict_phases = None
+        distro_name = fc.find_prop("distro_name")
+        compiler_name = fc.find_prop("compiler_name")
+        compiler_version = fc.find_prop("compiler_version")
+        is_xla = fc.find_prop("is_xla") or False
+        is_asan = fc.find_prop("is_asan") or False
+        is_crossref = fc.find_prop("is_crossref") or False
+        is_onnx = fc.find_prop("is_onnx") or False
+        is_pure_torch = fc.find_prop("is_pure_torch") or False
+        is_vulkan = fc.find_prop("is_vulkan") or False
+        is_slow_gradcheck = fc.find_prop("is_slow_gradcheck") or False
+        parms_list_ignored_for_docker_image = []
+
+        if only_slow_gradcheck ^ is_slow_gradcheck:
+            continue
+
+        python_version = None
+        if compiler_name == "cuda" or compiler_name == "android":
+            python_version = fc.find_prop("pyver")
+            parms_list = [fc.find_prop("abbreviated_pyver")]
+        else:
+            parms_list = ["py" + fc.find_prop("pyver")]
+
+        cuda_version = None
+        rocm_version = None
+        if compiler_name == "cuda":
+            cuda_version = fc.find_prop("compiler_version")
+
+        elif compiler_name == "rocm":
+            rocm_version = fc.find_prop("compiler_version")
+            restrict_phases = ["build", "test1", "test2", "caffe2_test"]
+
+        elif compiler_name == "android":
+            android_ndk_version = fc.find_prop("compiler_version")
+            # TODO: do we need clang to compile host binaries like protoc?
+            parms_list.append("clang5")
+            parms_list.append("android-ndk-" + android_ndk_version)
+            android_abi = fc.find_prop("android_abi")
+            parms_list_ignored_for_docker_image.append(android_abi)
+            restrict_phases = ["build"]
+
+        elif compiler_name:
+            gcc_version = compiler_name + (fc.find_prop("compiler_version") or "")
+            parms_list.append(gcc_version)
+
+        if is_asan:
+            parms_list.append("asan")
+            python_version = fc.find_prop("pyver")
+            parms_list[0] = fc.find_prop("abbreviated_pyver")
+
+        if is_crossref:
+            parms_list_ignored_for_docker_image.append("crossref")
+
+        if is_onnx:
+            parms_list.append("onnx")
+            python_version = fc.find_prop("pyver")
+            parms_list[0] = fc.find_prop("abbreviated_pyver")
+            restrict_phases = ["build", "ort_test1", "ort_test2"]
+
+        if cuda_version:
+            cuda_gcc_version = fc.find_prop("cuda_gcc_override") or "gcc7"
+            parms_list.append(cuda_gcc_version)
+
+        is_libtorch = fc.find_prop("is_libtorch") or False
+        is_important = fc.find_prop("is_important") or False
+        parallel_backend = fc.find_prop("parallel_backend") or None
+        build_only = fc.find_prop("build_only") or False
+        shard_test = fc.find_prop("shard_test") or False
+        # TODO: fix pure_torch python test packaging issue.
+        if shard_test:
+            restrict_phases = ["build"] if restrict_phases is None else restrict_phases
+            restrict_phases.extend(["test1", "test2"])
+        if build_only or is_pure_torch:
+            restrict_phases = ["build"]
+
+        if is_slow_gradcheck:
+            parms_list_ignored_for_docker_image.append("old")
+            parms_list_ignored_for_docker_image.append("gradcheck")
+
+        gpu_resource = None
+        if cuda_version and cuda_version != "10":
+            gpu_resource = "medium"
+
+        c = Conf(
+            distro_name,
+            parms_list,
+            parms_list_ignored_for_docker_image,
+            python_version,
+            cuda_version,
+            rocm_version,
+            is_xla,
+            is_vulkan,
+            is_pure_torch,
+            restrict_phases,
+            gpu_resource,
+            is_libtorch=is_libtorch,
+            is_important=is_important,
+            parallel_backend=parallel_backend,
+            build_only=build_only,
+        )
+
+        # run docs builds on "pytorch-linux-xenial-py3.7-gcc5.4". Docs builds
+        # should run on a CPU-only build that runs on all PRs.
+        # XXX should this be updated to a more modern build?
+        if (
+            distro_name == "xenial"
+            and fc.find_prop("pyver") == "3.7"
+            and cuda_version is None
+            and parallel_backend is None
+            and not is_vulkan
+            and not is_pure_torch
+            and compiler_name == "gcc"
+            and fc.find_prop("compiler_version") == "5.4"
+        ):
+            c.filters = gen_filter_dict(branches_list=r"/.*/",
+                                        tags_list=RC_PATTERN)
+            c.dependent_tests = gen_docs_configs(c)
+
+        config_list.append(c)
+
+    return config_list
+
+
+def get_workflow_jobs(only_slow_gradcheck=False):
+
+    config_list = instantiate_configs(only_slow_gradcheck)
+
+    x = []
+    for conf_options in config_list:
+
+        phases = conf_options.restrict_phases or dimensions.PHASES
+
+        for phase in phases:
+
+            # TODO why does this not have a test?
+            if Conf.is_test_phase(phase) and conf_options.cuda_version == "10":
+                continue
+
+            x.append(conf_options.gen_workflow_job(phase))
+
+        # TODO convert to recursion
+        for conf in conf_options.get_dependents():
+            x.append(conf.gen_workflow_job("test"))
+
+    return x

.circleci/cimodel/data/simple/anaconda_prune_defintions.py

@@ -0,0 +1,28 @@
+from collections import OrderedDict
+
+from cimodel.data.simple.util.branch_filters import gen_filter_dict
+from cimodel.lib.miniutils import quote
+
+
+CHANNELS_TO_PRUNE = ["pytorch-nightly", "pytorch-test"]
+PACKAGES_TO_PRUNE = "pytorch torchvision torchaudio torchtext ignite torchcsprng"
+
+
+def gen_workflow_job(channel: str):
+    return OrderedDict(
+        {
+            "anaconda_prune": OrderedDict(
+                {
+                    "name": f"anaconda-prune-{channel}",
+                    "context": quote("org-member"),
+                    "packages": quote(PACKAGES_TO_PRUNE),
+                    "channel": channel,
+                    "filters": gen_filter_dict(branches_list=["postnightly"]),
+                }
+            )
+        }
+    )
+
+
+def get_workflow_jobs():
+    return [gen_workflow_job(channel) for channel in CHANNELS_TO_PRUNE]

.circleci/cimodel/data/simple/docker_definitions.py

@@ -0,0 +1,39 @@
+from collections import OrderedDict
+
+from cimodel.lib.miniutils import quote
+from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
+
+
+# NOTE: All hardcoded docker image builds have been migrated to GHA
+IMAGE_NAMES = [
+]
+
+# This entry should be an element from the list above
+# This should contain the image matching the "slow_gradcheck" entry in
+# pytorch_build_data.py
+SLOW_GRADCHECK_IMAGE_NAME = "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
+
+def get_workflow_jobs(images=IMAGE_NAMES, only_slow_gradcheck=False):
+    """Generates a list of docker image build definitions"""
+    ret = []
+    for image_name in images:
+        if image_name.startswith('docker-'):
+            image_name = image_name.lstrip('docker-')
+        if only_slow_gradcheck and image_name is not SLOW_GRADCHECK_IMAGE_NAME:
+            continue
+
+        parameters = OrderedDict({
+            "name": quote(f"docker-{image_name}"),
+            "image_name": quote(image_name),
+        })
+        if image_name == "pytorch-linux-xenial-py3.7-gcc5.4":
+            # pushing documentation on tags requires CircleCI to also
+            # build all the dependencies on tags, including this docker image
+            parameters['filters'] = gen_filter_dict(branches_list=r"/.*/",
+                                                    tags_list=RC_PATTERN)
+        ret.append(OrderedDict(
+            {
+                "docker_build_job": parameters
+            }
+        ))
+    return ret

.circleci/cimodel/data/simple/ios_definitions.py

@@ -0,0 +1,88 @@
+from cimodel.data.simple.util.versions import MultiPartVersion
+import cimodel.lib.miniutils as miniutils
+
+XCODE_VERSION = MultiPartVersion([12, 5, 1])
+
+
+class ArchVariant:
+    def __init__(self, name, custom_build_name=""):
+        self.name = name
+        self.custom_build_name = custom_build_name
+
+    def render(self):
+        extra_parts = [self.custom_build_name] if len(self.custom_build_name) > 0 else []
+        return "_".join([self.name] + extra_parts)
+
+
+def get_platform(arch_variant_name):
+    return "SIMULATOR" if arch_variant_name == "x86_64" else "OS"
+
+
+class IOSJob:
+    def __init__(self, xcode_version, arch_variant, is_org_member_context=True, extra_props=None):
+        self.xcode_version = xcode_version
+        self.arch_variant = arch_variant
+        self.is_org_member_context = is_org_member_context
+        self.extra_props = extra_props
+
+    def gen_name_parts(self, with_version_dots):
+
+        version_parts = self.xcode_version.render_dots_or_parts(with_version_dots)
+        build_variant_suffix = "_".join([self.arch_variant.render(), "build"])
+
+        return [
+            "pytorch",
+            "ios",
+        ] + version_parts + [
+            build_variant_suffix,
+        ]
+
+    def gen_job_name(self):
+        return "_".join(self.gen_name_parts(False))
+
+    def gen_tree(self):
+
+        platform_name = get_platform(self.arch_variant.name)
+
+        props_dict = {
+            "build_environment": "-".join(self.gen_name_parts(True)),
+            "ios_arch": self.arch_variant.name,
+            "ios_platform": platform_name,
+            "name": self.gen_job_name(),
+        }
+
+        if self.is_org_member_context:
+            props_dict["context"] = "org-member"
+
+        if self.extra_props:
+            props_dict.update(self.extra_props)
+
+        return [{"pytorch_ios_build": props_dict}]
+
+
+WORKFLOW_DATA = [
+    IOSJob(XCODE_VERSION, ArchVariant("x86_64"), is_org_member_context=False, extra_props={
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("x86_64", "full_jit"), is_org_member_context=False, extra_props={
+        "lite_interpreter": miniutils.quote(str(int(False)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("arm64"), extra_props={
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("arm64", "metal"), extra_props={
+        "use_metal": miniutils.quote(str(int(True))),
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("arm64", "full_jit"), extra_props={
+        "lite_interpreter": miniutils.quote(str(int(False)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("arm64", "custom"), extra_props={
+        "op_list": "mobilenetv2.yaml",
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("x86_64", "coreml"), is_org_member_context=False, extra_props={
+        "use_coreml": miniutils.quote(str(int(True))),
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+    IOSJob(XCODE_VERSION, ArchVariant("arm64", "coreml"), extra_props={
+        "use_coreml": miniutils.quote(str(int(True))),
+        "lite_interpreter": miniutils.quote(str(int(True)))}),
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/macos_definitions.py

@@ -0,0 +1,52 @@
+class MacOsJob:
+    def __init__(self, os_version, is_build=False, is_test=False, extra_props=tuple()):
+        # extra_props is tuple type, because mutable data structures for argument defaults
+        # is not recommended.
+        self.os_version = os_version
+        self.is_build = is_build
+        self.is_test = is_test
+        self.extra_props = dict(extra_props)
+
+    def gen_tree(self):
+        non_phase_parts = ["pytorch", "macos", self.os_version, "py3"]
+
+        extra_name_list = [name for name, exist in self.extra_props.items() if exist]
+        full_job_name_list = non_phase_parts + extra_name_list + [
+            'build' if self.is_build else None,
+            'test' if self.is_test else None,
+        ]
+
+        full_job_name = "_".join(list(filter(None, full_job_name_list)))
+
+        test_build_dependency = "_".join(non_phase_parts + ["build"])
+        extra_dependencies = [test_build_dependency] if self.is_test else []
+        job_dependencies = extra_dependencies
+
+        # Yes we name the job after itself, it needs a non-empty value in here
+        # for the YAML output to work.
+        props_dict = {"requires": job_dependencies, "name": full_job_name}
+
+        return [{full_job_name: props_dict}]
+
+
+WORKFLOW_DATA = [
+    MacOsJob("10_15", is_build=True),
+    MacOsJob("10_13", is_build=True),
+    MacOsJob(
+        "10_13",
+        is_build=False,
+        is_test=True,
+    ),
+    MacOsJob(
+        "10_13",
+        is_build=True,
+        is_test=True,
+        extra_props=tuple({
+            "lite_interpreter": True
+        }.items()),
+    )
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/mobile_definitions.py

@@ -0,0 +1,53 @@
+"""
+PyTorch Mobile PR builds (use linux host toolchain + mobile build options)
+"""
+
+import cimodel.lib.miniutils as miniutils
+import cimodel.data.simple.util.branch_filters
+
+
+class MobileJob:
+    def __init__(
+            self,
+            docker_image,
+            docker_requires,
+            variant_parts,
+            is_master_only=False):
+        self.docker_image = docker_image
+        self.docker_requires = docker_requires
+        self.variant_parts = variant_parts
+        self.is_master_only = is_master_only
+
+    def gen_tree(self):
+        non_phase_parts = [
+            "pytorch",
+            "linux",
+            "xenial",
+            "py3",
+            "clang5",
+            "mobile",
+        ] + self.variant_parts
+
+        full_job_name = "_".join(non_phase_parts)
+        build_env_name = "-".join(non_phase_parts)
+
+        props_dict = {
+            "build_environment": build_env_name,
+            "build_only": miniutils.quote(str(int(True))),
+            "docker_image": self.docker_image,
+            "requires": self.docker_requires,
+            "name": full_job_name,
+        }
+
+        if self.is_master_only:
+            props_dict["filters"] = cimodel.data.simple.util.branch_filters.gen_filter_dict()
+
+        return [{"pytorch_linux_build": props_dict}]
+
+
+WORKFLOW_DATA = [
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/nightly_ios.py

@@ -0,0 +1,85 @@
+import cimodel.data.simple.ios_definitions as ios_definitions
+import cimodel.lib.miniutils as miniutils
+
+
+class IOSNightlyJob:
+    def __init__(self,
+                 variant,
+                 is_full_jit=False,
+                 is_upload=False):
+
+        self.variant = variant
+        self.is_full_jit = is_full_jit
+        self.is_upload = is_upload
+
+    def get_phase_name(self):
+        return "upload" if self.is_upload else "build"
+
+    def get_common_name_pieces(self, with_version_dots):
+
+        extra_name_suffix = [self.get_phase_name()] if self.is_upload else []
+
+        extra_name = ["full_jit"] if self.is_full_jit else []
+
+        common_name_pieces = [
+            "ios",
+        ] + extra_name + [
+        ] + ios_definitions.XCODE_VERSION.render_dots_or_parts(with_version_dots) + [
+            "nightly",
+            self.variant,
+            "build",
+        ] + extra_name_suffix
+
+        return common_name_pieces
+
+    def gen_job_name(self):
+        return "_".join(["pytorch"] + self.get_common_name_pieces(False))
+
+    def gen_tree(self):
+        build_configs = BUILD_CONFIGS_FULL_JIT if self.is_full_jit else BUILD_CONFIGS
+        extra_requires = [x.gen_job_name() for x in build_configs] if self.is_upload else []
+
+        props_dict = {
+            "build_environment": "-".join(["libtorch"] + self.get_common_name_pieces(True)),
+            "requires": extra_requires,
+            "context": "org-member",
+            "filters": {"branches": {"only": "nightly"}},
+        }
+
+        if not self.is_upload:
+            props_dict["ios_arch"] = self.variant
+            props_dict["ios_platform"] = ios_definitions.get_platform(self.variant)
+            props_dict["name"] = self.gen_job_name()
+            props_dict["use_metal"] = miniutils.quote(str(int(True)))
+            props_dict["use_coreml"] = miniutils.quote(str(int(True)))
+
+        if self.is_full_jit:
+            props_dict["lite_interpreter"] = miniutils.quote(str(int(False)))
+
+        template_name = "_".join([
+            "binary",
+            "ios",
+            self.get_phase_name(),
+        ])
+
+        return [{template_name: props_dict}]
+
+
+BUILD_CONFIGS = [
+    IOSNightlyJob("x86_64"),
+    IOSNightlyJob("arm64"),
+]
+
+BUILD_CONFIGS_FULL_JIT = [
+    IOSNightlyJob("x86_64", is_full_jit=True),
+    IOSNightlyJob("arm64", is_full_jit=True),
+]
+
+WORKFLOW_DATA = BUILD_CONFIGS + BUILD_CONFIGS_FULL_JIT + [
+    IOSNightlyJob("binary", is_full_jit=False, is_upload=True),
+    IOSNightlyJob("binary", is_full_jit=True, is_upload=True),
+]
+
+
+def get_workflow_jobs():
+    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/util/branch_filters.py

@@ -0,0 +1,28 @@
+NON_PR_BRANCH_LIST = [
+    "main",
+    "master",
+    r"/ci-all\/.*/",
+    r"/release\/.*/",
+]
+
+PR_BRANCH_LIST = [
+    r"/gh\/.*\/head/",
+    r"/pull\/.*/",
+]
+
+RC_PATTERN = r"/v[0-9]+(\.[0-9]+)*-rc[0-9]+/"
+
+def gen_filter_dict(
+        branches_list=NON_PR_BRANCH_LIST,
+        tags_list=None
+):
+    """Generates a filter dictionary for use with CircleCI's job filter"""
+    filter_dict = {
+        "branches": {
+            "only": branches_list,
+        },
+    }
+
+    if tags_list is not None:
+        filter_dict["tags"] = {"only": tags_list}
+    return filter_dict

.circleci/cimodel/data/simple/util/docker_constants.py

@@ -0,0 +1,33 @@
+AWS_DOCKER_HOST = "308535385114.dkr.ecr.us-east-1.amazonaws.com"
+
+def gen_docker_image(container_type):
+    return (
+        "/".join([AWS_DOCKER_HOST, "pytorch", container_type]),
+        f"docker-{container_type}",
+    )
+
+def gen_docker_image_requires(image_name):
+    return [f"docker-{image_name}"]
+
+
+DOCKER_IMAGE_BASIC, DOCKER_REQUIREMENT_BASE = gen_docker_image(
+    "pytorch-linux-xenial-py3.7-gcc5.4"
+)
+
+DOCKER_IMAGE_CUDA_10_2, DOCKER_REQUIREMENT_CUDA_10_2 = gen_docker_image(
+    "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
+)
+
+DOCKER_IMAGE_GCC7, DOCKER_REQUIREMENT_GCC7 = gen_docker_image(
+    "pytorch-linux-xenial-py3.7-gcc7"
+)
+
+
+def gen_mobile_docker(specifier):
+    container_type = "pytorch-linux-xenial-py3-clang5-" + specifier
+    return gen_docker_image(container_type)
+
+
+DOCKER_IMAGE_ASAN, DOCKER_REQUIREMENT_ASAN = gen_mobile_docker("asan")
+
+DOCKER_IMAGE_NDK, DOCKER_REQUIREMENT_NDK = gen_mobile_docker("android-ndk-r19c")

.circleci/cimodel/data/simple/util/versions.py

@@ -0,0 +1,34 @@
+class MultiPartVersion:
+    def __init__(self, parts, prefix=""):
+        self.parts = parts
+        self.prefix = prefix
+
+    def prefixed_parts(self):
+        """
+        Prepends the first element of the version list
+        with the prefix string.
+        """
+        if self.parts:
+            return [self.prefix + str(self.parts[0])] + [str(part) for part in self.parts[1:]]
+        else:
+            return [self.prefix]
+
+    def render_dots(self):
+        return ".".join(self.prefixed_parts())
+
+    def render_dots_or_parts(self, with_dots):
+        if with_dots:
+            return [self.render_dots()]
+        else:
+            return self.prefixed_parts()
+
+
+class CudaVersion(MultiPartVersion):
+    def __init__(self, major, minor):
+        self.major = major
+        self.minor = minor
+
+        super().__init__([self.major, self.minor], "cuda")
+
+    def __str__(self):
+        return f"{self.major}.{self.minor}"

.circleci/cimodel/lib/conf_tree.py

@@ -0,0 +1,107 @@
+from dataclasses import dataclass, field
+from typing import Optional, Dict
+
+
+def X(val):
+    """
+    Compact way to write a leaf node
+    """
+    return val, []
+
+
+def XImportant(name):
+    """Compact way to write an important (run on PRs) leaf node"""
+    return (name, [("important", [X(True)])])
+
+
+@dataclass
+class Ver:
+    """
+    Represents a product with a version number
+    """
+    name: str
+    version: str = ""
+
+    def __str__(self):
+        return self.name + self.version
+
+
+@dataclass
+class ConfigNode:
+    parent: Optional['ConfigNode']
+    node_name: str
+    props: Dict[str, str] = field(default_factory=dict)
+
+    def get_label(self):
+        return self.node_name
+
+    # noinspection PyMethodMayBeStatic
+    def get_children(self):
+        return []
+
+    def get_parents(self):
+        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []
+
+    def get_depth(self):
+        return len(self.get_parents())
+
+    def get_node_key(self):
+        return "%".join(self.get_parents() + [self.get_label()])
+
+    def find_prop(self, propname, searched=None):
+        """
+        Checks if its own dictionary has
+        the property, otherwise asks parent node.
+        """
+
+        if searched is None:
+            searched = []
+
+        searched.append(self.node_name)
+
+        if propname in self.props:
+            return self.props[propname]
+        elif self.parent:
+            return self.parent.find_prop(propname, searched)
+        else:
+            # raise Exception('Property "%s" does not exist anywhere in the tree! Searched: %s' % (propname, searched))
+            return None
+
+
+def dfs_recurse(
+        node,
+        leaf_callback=lambda x: None,
+        discovery_callback=lambda x, y, z: None,
+        child_callback=lambda x, y: None,
+        sibling_index=0,
+        sibling_count=1):
+
+    discovery_callback(node, sibling_index, sibling_count)
+
+    node_children = node.get_children()
+    if node_children:
+        for i, child in enumerate(node_children):
+            child_callback(node, child)
+
+            dfs_recurse(
+                child,
+                leaf_callback,
+                discovery_callback,
+                child_callback,
+                i,
+                len(node_children),
+            )
+    else:
+        leaf_callback(node)
+
+
+def dfs(toplevel_config_node):
+
+    config_list = []
+
+    def leaf_callback(node):
+        config_list.append(node)
+
+    dfs_recurse(toplevel_config_node, leaf_callback)
+
+    return config_list

.circleci/cimodel/lib/miniutils.py

@@ -0,0 +1,10 @@
+def quote(s):
+    return sandwich('"', s)
+
+
+def sandwich(bread, jam):
+    return bread + jam + bread
+
+
+def override(word, substitutions):
+    return substitutions.get(word, word)

.circleci/cimodel/lib/miniyaml.py

@@ -0,0 +1,52 @@
+from collections import OrderedDict
+
+import cimodel.lib.miniutils as miniutils
+
+
+LIST_MARKER = "- "
+INDENTATION_WIDTH = 2
+
+
+def is_dict(data):
+    return type(data) in [dict, OrderedDict]
+
+
+def is_collection(data):
+    return is_dict(data) or type(data) is list
+
+
+def render(fh, data, depth, is_list_member=False):
+    """
+    PyYaml does not allow precise control over the quoting
+    behavior, especially for merge references.
+    Therefore, we use this custom YAML renderer.
+    """
+
+    indentation = " " * INDENTATION_WIDTH * depth
+
+    if is_dict(data):
+
+        tuples = list(data.items())
+        if type(data) is not OrderedDict:
+            tuples.sort()
+
+        for i, (k, v) in enumerate(tuples):
+            if not v:
+                continue
+            # If this dict is itself a list member, the first key gets prefixed with a list marker
+            list_marker_prefix = LIST_MARKER if is_list_member and not i else ""
+
+            trailing_whitespace = "\n" if is_collection(v) else " "
+            fh.write(indentation + list_marker_prefix + k + ":" + trailing_whitespace)
+
+            render(fh, v, depth + 1 + int(is_list_member))
+
+    elif type(data) is list:
+        for v in data:
+            render(fh, v, depth, True)
+
+    else:
+        # use empty quotes to denote an empty string value instead of blank space
+        modified_data = miniutils.quote(data) if data == "" else data
+        list_member_prefix = indentation + LIST_MARKER if is_list_member else ""
+        fh.write(list_member_prefix + str(modified_data) + "\n")

.circleci/codegen_validation/normalize_yaml_fragment.py

@@ -2,11 +2,10 @@
 
 import os
 import sys
-
 import yaml
 
 # Need to import modules that lie on an upward-relative path
-sys.path.append(os.path.join(sys.path[0], ".."))
+sys.path.append(os.path.join(sys.path[0], '..'))
 
 import cimodel.lib.miniyaml as miniyaml
 

.circleci/docker/README.md

@@ -0,0 +1,31 @@
+# Docker images for Jenkins
+
+This directory contains everything needed to build the Docker images
+that are used in our CI
+
+The Dockerfiles located in subdirectories are parameterized to
+conditionally run build stages depending on build arguments passed to
+`docker build`. This lets us use only a few Dockerfiles for many
+images. The different configurations are identified by a freeform
+string that we call a _build environment_. This string is persisted in
+each image as the `BUILD_ENVIRONMENT` environment variable.
+
+See `build.sh` for valid build environments (it's the giant switch).
+
+Docker builds are now defined with `.circleci/cimodel/data/simple/docker_definitions.py`
+
+## Contents
+
+* `build.sh` -- dispatch script to launch all builds
+* `common` -- scripts used to execute individual Docker build stages
+* `ubuntu-cuda` -- Dockerfile for Ubuntu image with CUDA support for nvidia-docker
+
+## Usage
+
+```bash
+# Build a specific image
+./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
+
+# Set flags (see build.sh) and build image
+sudo bash -c 'PROTOBUF=1 ./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
+```