Use counter instead of vector of futures in _parallel_run (#36159) (#36334)

Summary:
This should be faster than allocating one mutex, flag and conditional variable per task.

Using `std::atomic<size_t>` to count remaing tasks is not sufficient,
because modification of remaining counter and signalling conditional variable must happen atomically,
otherwise `wait()` might get invoked after `notify_one()` was called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36159

Test Plan: CI

Differential Revision: D20905411

Pulled By: malfet

fbshipit-source-id: facaf599693649c3f43edafc49f369e90d2f60de
(cherry picked from commit 986a8fdd6a18d9110f8bde59361967139450966b)
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

Co-authored-by: Nikita Shulga <nshulga@fb.com>

.circleci/README.md

@@ -466,7 +466,7 @@ But if you want to try, then I’d recommend
 # Always install miniconda 3, even if building for Python <3
 new_conda="~/my_new_conda"
 conda_sh="$new_conda/install_miniconda.sh"
-curl -o "$conda_sh" https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+curl -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
 chmod +x "$conda_sh"
 "$conda_sh" -b -p "$MINICONDA_ROOT"
 rm -f "$conda_sh"

.circleci/cimodel/data/binary_build_data.py

@@ -34,8 +34,6 @@ def get_processor_arch_name(cuda_version):
 
 LINUX_PACKAGE_VARIANTS = OrderedDict(
     manywheel=[
-        "2.7m",
-        "2.7mu",
         "3.5m",
         "3.6m",
         "3.7m",
@@ -43,7 +41,7 @@ LINUX_PACKAGE_VARIANTS = OrderedDict(
     ],
     conda=dimensions.STANDARD_PYTHON_VERSIONS,
     libtorch=[
-        "2.7m",
+        "3.7m",
     ],
 )
 
@@ -53,7 +51,7 @@ CONFIG_TREE_DATA = OrderedDict(
         wheel=dimensions.STANDARD_PYTHON_VERSIONS,
         conda=dimensions.STANDARD_PYTHON_VERSIONS,
         libtorch=[
-            "2.7",
+            "3.7",
         ],
     )),
 )

.circleci/cimodel/data/binary_build_definitions.py

@@ -67,9 +67,14 @@ class Conf(object):
             job_def["requires"].append("update_s3_htmls_for_nightlies_devtoolset7")
             job_def["filters"] = {"branches": {"only": "postnightly"}}
         else:
+            filter_branches = ["nightly"]
+            # we only want to add the release branch filter if we aren't
+            # uploading
+            if phase not in ["upload"]:
+                filter_branches.append(r"/release\/.*/")
             job_def["filters"] = {
                 "branches": {
-                    "only": "nightly"
+                    "only": filter_branches
                 },
                 # Will run on tags like v1.5.0-rc1, etc.
                 "tags": {

.circleci/cimodel/data/caffe2_build_data.py

@@ -4,7 +4,6 @@ from cimodel.lib.conf_tree import Ver
 
 CONFIG_TREE_DATA = [
     (Ver("ubuntu", "16.04"), [
-        ([Ver("gcc", "5")], [XImportant("onnx_py2")]),
         ([Ver("clang", "7")], [XImportant("onnx_main_py3.6"),
                                XImportant("onnx_ort1_py3.6"),
                                XImportant("onnx_ort2_py3.6")]),

.circleci/cimodel/data/caffe2_build_definitions.py

@@ -33,8 +33,7 @@ class Conf:
     # TODO: Eventually we can probably just remove the cudnn7 everywhere.
     def get_cudnn_insertion(self):
 
-        omit = self.language == "onnx_py2" \
-            or self.language == "onnx_main_py3.6" \
+        omit = self.language == "onnx_main_py3.6" \
             or self.language == "onnx_ort1_py3.6" \
             or self.language == "onnx_ort2_py3.6" \
             or set(self.compiler_names).intersection({"android", "mkl", "clang"}) \
@@ -71,11 +70,10 @@ class Conf:
     def gen_docker_image(self):
 
         lang_substitutions = {
-            "onnx_py2": "py2",
             "onnx_main_py3.6": "py3.6",
             "onnx_ort1_py3.6": "py3.6",
             "onnx_ort2_py3.6": "py3.6",
-            "cmake": "py2",
+            "cmake": "py3",
         }
 
         lang = miniutils.override(self.language, lang_substitutions)
@@ -85,7 +83,7 @@ class Conf:
     def gen_workflow_params(self, phase):
         parameters = OrderedDict()
         lang_substitutions = {
-            "onnx_py2": "onnx-py2",
+            "onnx_py3": "onnx-py3",
             "onnx_main_py3.6": "onnx-main-py3.6",
             "onnx_ort1_py3.6": "onnx-ort1-py3.6",
             "onnx_ort2_py3.6": "onnx-ort2-py3.6",
@@ -129,7 +127,7 @@ class Conf:
             job_name = "caffe2_" + self.get_platform() + "_build"
 
         if not self.is_important:
-            job_def["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/"]}}
+            job_def["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/", r"/release\/.*/"]}}
         job_def.update(self.gen_workflow_params(phase))
         return {job_name : job_def}
 

.circleci/cimodel/data/dimensions.py

@@ -8,7 +8,6 @@ CUDA_VERSIONS = [
 ]
 
 STANDARD_PYTHON_VERSIONS = [
-    "2.7",
     "3.5",
     "3.6",
     "3.7",

.circleci/cimodel/data/pytorch_build_definitions.py

@@ -114,7 +114,7 @@ class Conf:
         if not self.is_important:
             # If you update this, update
             # caffe2_build_definitions.py too
-            job_def["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/"]}}
+            job_def["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/", r"/release\/.*/"]}}
         job_def.update(self.gen_workflow_params(phase))
 
         return {job_name : job_def}

.circleci/docker/common/install_conda.sh

@@ -4,7 +4,7 @@ set -ex
 
 # Optionally install conda
 if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
-  BASE_URL="https://repo.continuum.io/miniconda"
+  BASE_URL="https://repo.anaconda.com/miniconda"
 
   MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
 

.circleci/scripts/binary_install_miniconda.sh

@@ -31,9 +31,9 @@ fi
 
 conda_sh="$workdir/install_miniconda.sh"
 if [[ "$(uname)" == Darwin ]]; then
-  curl --retry 3 -o "$conda_sh" https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+  curl --retry 3 -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
 else
-  curl --retry 3 -o "$conda_sh" https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+  curl --retry 3 -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
 fi
 chmod +x "$conda_sh"
 "$conda_sh" -b -p "$MINICONDA_ROOT"

.circleci/scripts/binary_populate_env.sh

@@ -2,6 +2,19 @@
 set -eux -o pipefail
 export TZ=UTC
 
+tagged_version() {
+  # Grabs version from either the env variable CIRCLE_TAG
+  # or the pytorch git described version
+  GIT_DESCRIBE="git --git-dir ${workdir}/pytorch/.git describe"
+  if [[ -n "${CIRCLE_TAG:-}" ]]; then
+    echo "${CIRCLE_TAG}"
+  elif ${GIT_DESCRIBE} --exact --tags >/dev/null; then
+    ${GIT_DESCRIBE} --tags
+  else
+    return 1
+  fi
+}
+
 # We need to write an envfile to persist these variables to following
 # steps, but the location of the envfile depends on the circleci executor
 if [[ "$(uname)" == Darwin ]]; then
@@ -47,15 +60,17 @@ export DATE="$(date -u +%Y%m%d)"
 #TODO: We should be pulling semver version from the base version.txt
 BASE_BUILD_VERSION="1.5.0.dev$DATE"
 # Change BASE_BUILD_VERSION to git tag when on a git tag
-if git describe --tags --exact >/dev/null 2>/dev/null; then
+# Use 'git -C' to make doubly sure we're in the correct directory for checking
+# the git tag
+if tagged_version >/dev/null; then
   # Switch upload folder to 'test/' if we are on a tag
   PIP_UPLOAD_FOLDER='test/'
   # Grab git tag, remove prefixed v and remove everything after -
   # Used to clean up tags that are for release candidates like v1.5.0-rc1
   # Turns tag v1.5.0-rc1 -> v1.5.0
-  BASE_BUILD_VERSION="$(git describe --tags | sed -e 's/^v//' -e 's/-.*$//')"
+  BASE_BUILD_VERSION="$(tagged_version | sed -e 's/^v//' -e 's/-.*$//')"
 fi
-if [[ "$(uname)" == 'Darwin' ]] || [[ "$DESIRED_CUDA" == "cu101" ]] || [[ "$PACKAGE_TYPE" == conda ]]; then
+if [[ "$(uname)" == 'Darwin' ]] || [[ "$DESIRED_CUDA" == "cu102" ]] || [[ "$PACKAGE_TYPE" == conda ]]; then
   export PYTORCH_BUILD_VERSION="${BASE_BUILD_VERSION}"
 else
   export PYTORCH_BUILD_VERSION="${BASE_BUILD_VERSION}+$DESIRED_CUDA"

.circleci/scripts/cpp_doc_push_script.sh

@@ -72,10 +72,10 @@ time python tools/setup_helpers/generate_code.py \
 
 # Build the docs
 pushd docs/cpp
-pip install breathe>=4.13.0 bs4 lxml six
+pip install breathe==4.13.0 bs4 lxml six
 pip install --no-cache-dir -e "git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme"
 pip install exhale>=0.2.1
-pip install sphinx>=2.0
+pip install sphinx==2.4.4
 # Uncomment once it is fixed
 # pip install -r requirements.txt
 time make VERBOSE=1 html -j

.circleci/verbatim-sources/caffe2-job-specs.yml

@@ -151,7 +151,7 @@
             # Install Anaconda if we need to
             if [ -n "${CAFFE2_USE_ANACONDA}" ]; then
               rm -rf ${TMPDIR}/anaconda
-              curl --retry 3 -o ${TMPDIR}/conda.sh https://repo.continuum.io/miniconda/Miniconda${ANACONDA_VERSION}-latest-MacOSX-x86_64.sh
+              curl --retry 3 -o ${TMPDIR}/conda.sh https://repo.anaconda.com/miniconda/Miniconda${ANACONDA_VERSION}-latest-MacOSX-x86_64.sh
               chmod +x ${TMPDIR}/conda.sh
               /bin/bash ${TMPDIR}/conda.sh -b -p ${TMPDIR}/anaconda
               rm -f ${TMPDIR}/conda.sh

.circleci/verbatim-sources/pytorch-job-specs.yml

@@ -20,16 +20,16 @@ jobs:
           export id=$(docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -t -d -w /var/lib/jenkins ${DOCKER_IMAGE})
 
           # TODO We may want to move the rebase logic to a separate step after checkout
-          # Rebase to master only if in xenial_py3_6_gcc5_4 case
-          if [[ "${CIRCLE_BRANCH}" != "master" && "${BUILD_ENVIRONMENT}" == *"gcc5"* ]]; then
-            echo "Merge master branch into $CIRCLE_BRANCH before build in environment $BUILD_ENVIRONMENT"
+          # Rebase to release/1.5 only if in xenial_py3_6_gcc5_4 case
+          if [[ "${CIRCLE_BRANCH}" != "release/1.5" && "${BUILD_ENVIRONMENT}" == *"gcc5"* ]]; then
+            echo "Merge release/1.5 branch into $CIRCLE_BRANCH before build in environment $BUILD_ENVIRONMENT"
             set -x
             git config --global user.email "circleci.ossci@gmail.com"
             git config --global user.name "CircleCI"
             git config remote.origin.url https://github.com/pytorch/pytorch.git
-            git config --add remote.origin.fetch +refs/heads/master:refs/remotes/origin/master
-            git fetch --tags --progress https://github.com/pytorch/pytorch.git +refs/heads/master:refs/remotes/origin/master --depth=100 --quiet
-            export GIT_MERGE_TARGET=`git log -n 1 --pretty=format:"%H" origin/master`
+            git config --add remote.origin.fetch +refs/heads/release/1.5:refs/remotes/origin/release/1.5
+            git fetch --tags --progress https://github.com/pytorch/pytorch.git +refs/heads/release/1.5:refs/remotes/origin/release/1.5 --depth=100 --quiet
+            export GIT_MERGE_TARGET=`git log -n 1 --pretty=format:"%H" origin/release/1.5`
             echo "GIT_MERGE_TARGET: " ${GIT_MERGE_TARGET}
             export GIT_COMMIT=${CIRCLE_SHA1}
             echo "GIT_COMMIT: " ${GIT_COMMIT}
@@ -38,7 +38,7 @@ jobs:
             git merge --allow-unrelated-histories --no-edit --no-ff ${GIT_MERGE_TARGET}
             set +x
           else
-            echo "Do NOT merge master branch into $CIRCLE_BRANCH in environment $BUILD_ENVIRONMENT"
+            echo "Do NOT merge release/1.5 branch into $CIRCLE_BRANCH in environment $BUILD_ENVIRONMENT"
           fi
 
           git submodule sync && git submodule update -q --init --recursive

.circleci/verbatim-sources/windows-build-test.yml

@@ -15,6 +15,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
       - pytorch_windows_test:
           name: pytorch_windows_vs2017_14.11_py36_cuda10.1_test1
           test_name: pytorch-windows-test1
@@ -32,6 +33,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
       - pytorch_windows_test:
           name: pytorch_windows_vs2017_14.11_py36_cuda10.1_test2
           test_name: pytorch-windows-test2
@@ -49,6 +51,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
       - pytorch_windows_build:
           name: pytorch_windows_vs2017_14.16_py36_cuda10.1_build
           cuda_version: "10"
@@ -64,6 +67,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
       - pytorch_windows_test:
           name: pytorch_windows_vs2017_14.16_py36_cuda10.1_test1
           test_name: pytorch-windows-test1
@@ -81,6 +85,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
       - pytorch_windows_test:
           name: pytorch_windows_vs2017_14.16_py36_cuda10.1_test2
           test_name: pytorch-windows-test2
@@ -98,6 +103,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
       - pytorch_windows_build:
           name: pytorch_windows_vs2019_py36_cuda10.1_build
           cuda_version: "10"

.circleci/verbatim-sources/workflows-binary-builds-smoke-subset.yml

@@ -7,12 +7,6 @@
       # pytorch-ci-hud to adjust the list of whitelisted builds
       # at https://github.com/ezyang/pytorch-ci-hud/blob/master/src/BuildHistoryDisplay.js
 
-      - binary_linux_build:
-          name: binary_linux_manywheel_2_7mu_cpu_devtoolset7_build
-          build_environment: "manywheel 2.7mu cpu devtoolset7"
-          requires:
-            - setup
-          docker_image: "pytorch/manylinux-cuda102"
       - binary_linux_build:
           name: binary_linux_manywheel_3_7m_cu102_devtoolset7_build
           build_environment: "manywheel 3.7m cu102 devtoolset7"
@@ -23,24 +17,21 @@
             branches:
               only:
                 - master
-      - binary_linux_build:
-          name: binary_linux_conda_2_7_cpu_devtoolset7_build
-          build_environment: "conda 2.7 cpu devtoolset7"
-          requires:
-            - setup
-          docker_image: "pytorch/conda-cuda"
+                - /ci-all\/.*/
+                - /release\/.*/
       # This binary build is currently broken, see https://github_com/pytorch/pytorch/issues/16710
       # - binary_linux_conda_3_6_cu90_devtoolset7_build
+      # TODO rename to remove python version for libtorch
       - binary_linux_build:
-          name: binary_linux_libtorch_2_7m_cpu_devtoolset7_shared-with-deps_build
-          build_environment: "libtorch 2.7m cpu devtoolset7"
+          name: binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_build
+          build_environment: "libtorch 3.7m cpu devtoolset7"
           requires:
             - setup
           libtorch_variant: "shared-with-deps"
           docker_image: "pytorch/manylinux-cuda102"
       - binary_linux_build:
-          name: binary_linux_libtorch_2_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build
-          build_environment: "libtorch 2.7m cpu gcc5.4_cxx11-abi"
+          name: binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build
+          build_environment: "libtorch 3.7m cpu gcc5.4_cxx11-abi"
           requires:
             - setup
           libtorch_variant: "shared-with-deps"
@@ -48,45 +39,30 @@
       # TODO we should test a libtorch cuda build, but they take too long
       # - binary_linux_libtorch_2_7m_cu90_devtoolset7_static-without-deps_build
       - binary_mac_build:
-          name: binary_macos_wheel_3_6_cpu_build
-          build_environment: "wheel 3.6 cpu"
-          requires:
-            - setup
-          filters:
-            branches:
-              only:
-                - master
-      - binary_mac_build:
-          name: binary_macos_conda_2_7_cpu_build
-          build_environment: "conda 2.7 cpu"
+          name: binary_macos_wheel_3_7_cpu_build
+          build_environment: "wheel 3.7 cpu"
           requires:
             - setup
           filters:
             branches:
               only:
                 - master
+                - /ci-all\/.*/
+                - /release\/.*/
       # This job has an average run time of 3 hours o.O
       # Now only running this on master to reduce overhead
+      # TODO rename to remove python version for libtorch
       - binary_mac_build:
-          name: binary_macos_libtorch_2_7_cpu_build
-          build_environment: "libtorch 2.7 cpu"
+          name: binary_macos_libtorch_3_7_cpu_build
+          build_environment: "libtorch 3.7 cpu"
           requires:
             - setup
           filters:
             branches:
               only:
                 - master
-      - binary_linux_test:
-          name: binary_linux_manywheel_2_7mu_cpu_devtoolset7_test
-          build_environment: "manywheel 2.7mu cpu devtoolset7"
-          requires:
-            - setup
-            - binary_linux_manywheel_2_7mu_cpu_devtoolset7_build
-          docker_image: "pytorch/manylinux-cuda102"
-          filters:
-            branches:
-              only:
-                - master
+                - /ci-all\/.*/
+                - /release\/.*/
       - binary_linux_test:
           name: binary_linux_manywheel_3_7m_cu102_devtoolset7_test
           build_environment: "manywheel 3.7m cu102 devtoolset7"
@@ -100,29 +76,25 @@
             branches:
               only:
                 - master
-      - binary_linux_test:
-          name: binary_linux_conda_2_7_cpu_devtoolset7_test
-          build_environment: "conda 2.7 cpu devtoolset7"
-          requires:
-            - setup
-            - binary_linux_conda_2_7_cpu_devtoolset7_build
-          docker_image: "pytorch/conda-cuda"
+                - /ci-all\/.*/
+                - /release\/.*/
       # This binary build is currently broken, see https://github_com/pytorch/pytorch/issues/16710
       # - binary_linux_conda_3_6_cu90_devtoolset7_test:
+      # TODO rename to remove python version for libtorch
       - binary_linux_test:
-          name: binary_linux_libtorch_2_7m_cpu_devtoolset7_shared-with-deps_test
-          build_environment: "libtorch 2.7m cpu devtoolset7"
+          name: binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_test
+          build_environment: "libtorch 3.7m cpu devtoolset7"
           requires:
             - setup
-            - binary_linux_libtorch_2_7m_cpu_devtoolset7_shared-with-deps_build
+            - binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_build
           libtorch_variant: "shared-with-deps"
           docker_image: "pytorch/manylinux-cuda102"
       - binary_linux_test:
-          name: binary_linux_libtorch_2_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_test
-          build_environment: "libtorch 2.7m cpu gcc5.4_cxx11-abi"
+          name: binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_test
+          build_environment: "libtorch 3.7m cpu gcc5.4_cxx11-abi"
           requires:
             - setup
-            - binary_linux_libtorch_2_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build
+            - binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build
           libtorch_variant: "shared-with-deps"
           docker_image: "pytorch/pytorch-binary-docker-image-ubuntu16.04:latest"
 

.circleci/verbatim-sources/workflows-docker-builder.yml

@@ -20,21 +20,12 @@
       - docker_build_job:
           name: "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
           image_name: "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
-      - docker_build_job:
-          name: "pytorch-linux-xenial-cuda9-cudnn7-py2"
-          image_name: "pytorch-linux-xenial-cuda9-cudnn7-py2"
       - docker_build_job:
           name: "pytorch-linux-xenial-cuda9-cudnn7-py3"
           image_name: "pytorch-linux-xenial-cuda9-cudnn7-py3"
       - docker_build_job:
           name: "pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc7"
           image_name: "pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc7"
-      - docker_build_job:
-          name: "pytorch-linux-xenial-py2.7.9"
-          image_name: "pytorch-linux-xenial-py2.7.9"
-      - docker_build_job:
-          name: "pytorch-linux-xenial-py2.7"
-          image_name: "pytorch-linux-xenial-py2.7"
       - docker_build_job:
           name: "pytorch-linux-xenial-py3-clang5-android-ndk-r19c"
           image_name: "pytorch-linux-xenial-py3-clang5-android-ndk-r19c"

.circleci/verbatim-sources/workflows-pytorch-android-gradle-build.yml

@@ -4,6 +4,8 @@
             branches:
               only:
                 - master
+                - /ci-all\/.*/
+                - /release\/.*/
           requires:
             - pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build
 
@@ -13,6 +15,8 @@
             branches:
               only:
                 - master
+                - /ci-all\/.*/
+                - /release\/.*/
           requires:
             - pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build
             - pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_64_build

.circleci/verbatim-sources/workflows-pytorch-mobile-builds.yml

@@ -31,6 +31,7 @@
               only:
                 - master
                 - /ci-all\/.*/
+                - /release\/.*/
           build_environment: "pytorch-linux-xenial-py3-clang5-mobile-code-analysis"
           build_only: "1"
           # Use LLVM-DEV toolchain in android-ndk-r19c docker image

.github/workflows/lint.yml

@@ -81,44 +81,6 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-  flake8-py2:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Setup Python
-        uses: actions/setup-python@v1
-        with:
-          python-version: 2.x
-          architecture: x64
-      - name: Fetch PyTorch
-        uses: actions/checkout@v1
-      - name: Checkout PR tip
-        run: |
-          set -eux
-          if [[ "${{ github.event_name }}" == "pull_request" ]]; then
-            # We are on a PR, so actions/checkout leaves us on a merge commit.
-            # Check out the actual tip of the branch.
-            git checkout ${{ github.event.pull_request.head.sha }}
-          fi
-          echo ::set-output name=commit_sha::$(git rev-parse HEAD)
-        id: get_pr_tip
-      - name: Run flake8
-        run: |
-          set -eux
-          pip install flake8
-          rm -rf .circleci tools/clang_format_new.py
-          flake8 --exit-zero > ${GITHUB_WORKSPACE}/flake8-output.txt
-          cat ${GITHUB_WORKSPACE}/flake8-output.txt
-      - name: Add annotations
-        uses: pytorch/add-annotations-github-action@master
-        with:
-          check_name: 'flake8-py2'
-          linter_output_path: 'flake8-output.txt'
-          commit_sha: ${{ steps.get_pr_tip.outputs.commit_sha }}
-          regex: '^(?<filename>.*?):(?<lineNumber>\d+):(?<columnNumber>\d+): (?<errorCode>\w\d+) (?<errorDesc>.*)'
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-
   clang-tidy:
     if: github.event_name == 'pull_request'
     runs-on: ubuntu-latest
@@ -198,6 +160,8 @@ jobs:
             -g"-torch/csrc/jit/export.cpp"            \
             -g"-torch/csrc/jit/import.cpp"            \
             -g"-torch/csrc/jit/netdef_converter.cpp"  \
+            -g"-torch/csrc/cuda/nccl.*"               \
+            -g"-torch/csrc/cuda/python_nccl.cpp"      \
             "$@" > ${GITHUB_WORKSPACE}/clang-tidy-output.txt
 
           cat ${GITHUB_WORKSPACE}/clang-tidy-output.txt

.jenkins/pytorch/build.sh

@@ -167,7 +167,7 @@ fi
 
 # Patch required to build xla
 if [[ "${BUILD_ENVIRONMENT}" == *xla* ]]; then
-  git clone --recursive https://github.com/pytorch/xla.git
+  git clone --recursive -b r1.5 https://github.com/pytorch/xla.git
   ./xla/scripts/apply_patches.sh
 fi
 

.jenkins/pytorch/macos-common.sh

@@ -13,12 +13,12 @@ mkdir -p ${WORKSPACE_DIR}
 # If a local installation of conda doesn't exist, we download and install conda
 if [ ! -d "${WORKSPACE_DIR}/miniconda3" ]; then
   mkdir -p ${WORKSPACE_DIR}
-  curl --retry 3 https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ${WORKSPACE_DIR}/miniconda3.sh
+  curl --retry 3 https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ${WORKSPACE_DIR}/miniconda3.sh
   retry bash ${WORKSPACE_DIR}/miniconda3.sh -b -p ${WORKSPACE_DIR}/miniconda3
 fi
 export PATH="${WORKSPACE_DIR}/miniconda3/bin:$PATH"
 source ${WORKSPACE_DIR}/miniconda3/bin/activate
-retry conda install -y mkl mkl-include numpy pyyaml setuptools cmake cffi ninja
+retry conda install -y mkl mkl-include numpy pyyaml=5.3 setuptools=46.0.0 cmake cffi ninja
 
 # The torch.hub tests make requests to GitHub.
 #

.jenkins/pytorch/multigpu-test.sh

@@ -20,7 +20,7 @@ if [ -n "${IN_CIRCLECI}" ]; then
     sudo apt-get install -y --allow-downgrades --allow-change-held-packages libnccl-dev=2.5.6-1+cuda10.1 libnccl2=2.5.6-1+cuda10.1
   fi
 
-  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-cudnn7-py2* ]]; then
+  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda10.1-cudnn7-py3* ]]; then
     # TODO: move this to Docker
     sudo apt-get update
     sudo apt-get install -y --allow-downgrades --allow-change-held-packages openmpi-bin libopenmpi-dev

.jenkins/pytorch/test.sh

@@ -21,7 +21,7 @@ if [ -n "${IN_CIRCLECI}" ]; then
     sudo apt-get -qq install --allow-downgrades --allow-change-held-packages libnccl-dev=2.5.6-1+cuda10.1 libnccl2=2.5.6-1+cuda10.1
   fi
 
-  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-cudnn7-py2* ]]; then
+  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda10.1-cudnn7-py3* ]]; then
     # TODO: move this to Docker
     sudo apt-get -qq update
     sudo apt-get -qq install --allow-downgrades --allow-change-held-packages openmpi-bin libopenmpi-dev
@@ -244,7 +244,7 @@ test_backward_compatibility() {
   pushd test/backward_compatibility
   python dump_all_function_schemas.py --filename new_schemas.txt
   pip_uninstall torch
-  pip_install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+  pip_install torch==1.4.0+cpu torchvision==0.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
   python check_backward_compatibility.py --new-schemas new_schemas.txt
   popd
   set +x

.jenkins/pytorch/win-test-helpers/installation-helpers/install_miniconda3.bat

@@ -5,7 +5,7 @@ if "%BUILD_ENVIRONMENT%"=="" (
 )
 if "%REBUILD%"=="" (
   IF EXIST %CONDA_PARENT_DIR%\Miniconda3 ( rd /s /q %CONDA_PARENT_DIR%\Miniconda3 )
-  curl --retry 3 -k https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
+  curl --retry 3 -k https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
   %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
 )
 call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3

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

@@ -13,7 +13,7 @@ if "%BUILD_ENVIRONMENT%"=="" (
 )
 if NOT "%BUILD_ENVIRONMENT%"=="" (
     IF EXIST %CONDA_PARENT_DIR%\Miniconda3 ( rd /s /q %CONDA_PARENT_DIR%\Miniconda3 )
-    curl --retry 3 https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
+    curl --retry 3 https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
     %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
 )
 call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3

aten/src/ATen/CMakeLists.txt

@@ -160,20 +160,18 @@ ENDIF(BLAS_FOUND)
 
 IF(LAPACK_FOUND)
   list(APPEND ATen_CPU_DEPENDENCY_LIBS ${LAPACK_LIBRARIES})
-  if(USE_CUDA)
+  if(USE_CUDA AND MSVC)
     # Although Lapack provides CPU (and thus, one might expect that ATen_cuda
     # would not need this at all), some of our libraries (magma in particular)
     # backend to CPU BLAS/LAPACK implementations, and so it is very important
     # we get the *right* implementation, because even if the symbols are the
     # same, LAPACK implementions may have different calling conventions.
     # This caused https://github.com/pytorch/pytorch/issues/7353
+    #
+    # We do NOT do this on Linux, since we just rely on torch_cpu to
+    # provide all of the symbols we need
     list(APPEND ATen_CUDA_DEPENDENCY_LIBS ${LAPACK_LIBRARIES})
   endif()
-  if(USE_ROCM)
-    # It's not altogether clear that HIP behaves the same way, but it
-    # seems safer to assume that it needs it too
-    list(APPEND ATen_HIP_DEPENDENCY_LIBS ${LAPACK_LIBRARIES})
-  endif()
 ENDIF(LAPACK_FOUND)
 
 IF (UNIX AND NOT APPLE)
@@ -331,8 +329,12 @@ IF(USE_CUDA AND NOT USE_ROCM)
   IF(USE_MAGMA)
     list(APPEND ATen_CUDA_DEPENDENCY_LIBS ${MAGMA_LIBRARIES})
     IF ($ENV{TH_BINARY_BUILD})
+      IF (MSVC)
+        # Do not do this on Linux: see Note [Extra MKL symbols for MAGMA in torch_cpu]
+        # in caffe2/CMakeLists.txt
         list(APPEND ATen_CUDA_DEPENDENCY_LIBS
           "${BLAS_LIBRARIES};${BLAS_LIBRARIES};${BLAS_LIBRARIES}")
+      ENDIF(MSVC)
     ENDIF($ENV{TH_BINARY_BUILD})
   ENDIF(USE_MAGMA)
   IF ($ENV{ATEN_STATIC_CUDA})

aten/src/ATen/ParallelNative.cpp

@@ -125,13 +125,15 @@ void _parallel_run(
   std::tie(num_tasks, chunk_size) =
       internal::calc_num_tasks_and_chunk_size(begin, end, grain_size);
 
+  struct {
     std::atomic_flag err_flag = ATOMIC_FLAG_INIT;
     std::exception_ptr eptr;
-  std::vector<std::shared_ptr<c10::ivalue::Future>> futures(num_tasks);
-  for (size_t task_id = 0; task_id < num_tasks; ++task_id) {
-    futures[task_id] = std::make_shared<c10::ivalue::Future>(c10::NoneType::get());
-  }
-  auto task = [f, &eptr, &err_flag, &futures, begin, end, chunk_size]
+    std::mutex mutex;
+    volatile size_t remaining;
+    std::condition_variable cv;
+  } state;
+
+  auto task = [f, &state, begin, end, chunk_size]
       (int /* unused */, size_t task_id) {
     int64_t local_start = begin + task_id * chunk_size;
     if (local_start < end) {
@@ -140,21 +142,30 @@ void _parallel_run(
         ParallelRegionGuard guard(task_id);
         f(local_start, local_end, task_id);
       } catch (...) {
-        if (!err_flag.test_and_set()) {
-          eptr = std::current_exception();
+        if (!state.err_flag.test_and_set()) {
+          state.eptr = std::current_exception();
         }
       }
     }
-    futures[task_id]->markCompleted();
+    {
+      std::unique_lock<std::mutex> lk(state.mutex);
+      if (--state.remaining == 0) {
+        state.cv.notify_one();
+      }
+    }
   };
+  state.remaining = num_tasks;
   _run_with_pool(task, num_tasks);
 
   // Wait for all tasks to finish.
-  for (size_t task_id = 0; task_id < num_tasks; ++task_id) {
-    futures[task_id]->wait();
+  {
+    std::unique_lock<std::mutex> lk(state.mutex);
+    if (state.remaining != 0) {
+      state.cv.wait(lk);
     }
-  if (eptr) {
-    std::rethrow_exception(eptr);
+  }
+  if (state.eptr) {
+    std::rethrow_exception(state.eptr);
   }
 }
 

aten/src/ATen/Utils.h

@@ -16,14 +16,6 @@
 #include <numeric>
 #include <memory>
 
-#if defined(__clang__)
-#define __ubsan_ignore_float_divide_by_zero__ __attribute__((no_sanitize("float-divide-by-zero")))
-#define __ubsan_ignore_vptr__ __attribute__((no_sanitize("vptr")))
-#else
-#define __ubsan_ignore_float_divide_by_zero__
-#define __ubsan_ignore_vptr__
-#endif
-
 #define AT_DISALLOW_COPY_AND_ASSIGN(TypeName) \
   TypeName(const TypeName&) = delete; \
   void operator=(const TypeName&) = delete

aten/src/ATen/core/custom_class.cpp

@@ -20,6 +20,10 @@ void registerCustomClass(at::ClassTypePtr class_type) {
 }
 
 at::ClassTypePtr getCustomClass(const std::string& name) {
+  // BC hack so we can upgrade a binary internally
+  if (name == "__torch__.torch.classes.SentencePiece") {
+    return getCustomClass("__torch__.torch.classes.fb.SentencePiece");
+  }
   return customClasses().count(name) ? customClasses()[name] : nullptr;
 }
 

aten/src/ATen/cpu/vec256/vec256_base.h

@@ -15,6 +15,7 @@
 #include <c10/util/math_compat.h>
 #include <ATen/native/cpu/zmath.h>
 #include <c10/util/TypeCast.h>
+#include <c10/macros/Macros.h>
 
 #if defined(__GNUC__)
 #define __at_align32__ __attribute__((aligned(32)))

aten/src/ATen/cudnn/Descriptors.h

@@ -145,7 +145,7 @@ private:
 
 std::ostream& operator<<(std::ostream & out, const TensorDescriptor& d);
 
-class FilterDescriptor
+class TORCH_CUDA_API FilterDescriptor
   : public Descriptor<cudnnFilterStruct,
                       &cudnnCreateFilterDescriptor,
                       &cudnnDestroyFilterDescriptor>

aten/src/ATen/native/BinaryOps.cpp

@@ -138,6 +138,10 @@ Tensor true_divide(const Tensor& self, const Tensor& divisor) {
   return iter.output();
 }
 
+Tensor& true_divide_(Tensor& self, const Tensor& divisor) {
+  return native::true_divide_out(self, self, divisor);
+}
+
 Tensor& floor_divide_out(Tensor& result, const Tensor& self, const Tensor& other) {
   auto iter = TensorIterator::binary_op(result, self, other,
     /*check_mem_overlap=*/true);
@@ -731,7 +735,11 @@ Tensor& fmod_(Tensor& self, Scalar other) {
 }
 
 Tensor true_divide(const Tensor& self, Scalar divisor) {
-  return at::true_divide(self, wrapped_scalar_tensor(divisor)); // redispatch!
+  return self.true_divide(wrapped_scalar_tensor(divisor)); // redispatch!
+}
+
+Tensor& true_divide_(Tensor& self, Scalar divisor) {
+  return self.true_divide_(wrapped_scalar_tensor(divisor)); // redispatch!
 }
 
 }

aten/src/ATen/native/DispatchStub.h

@@ -70,8 +70,8 @@ struct CAFFE2_API DispatchStub<rT (*)(Args...), T> {
       // they will still compute the same value for cpu_dispatch_ptr.
       if (!cpu_dispatch_ptr.load(std::memory_order_relaxed)) {
         FnPtr tmp_cpu_dispatch_ptr = nullptr;
-        cpu_dispatch_ptr.compare_exchange_weak(
-            tmp_cpu_dispatch_ptr, choose_cpu_impl(), std::memory_order_relaxed);
+        while(!cpu_dispatch_ptr.compare_exchange_weak(
+            tmp_cpu_dispatch_ptr, choose_cpu_impl(), std::memory_order_relaxed));
       }
       return (*cpu_dispatch_ptr)(std::forward<ArgTypes>(args)...);
     } else if (device_type == DeviceType::CUDA) {

aten/src/ATen/native/LinearAlgebra.cpp

@@ -533,7 +533,7 @@ Tensor frobenius_norm(const Tensor& self, IntArrayRef dim, bool keepdim) {
     return at::norm(self, 2, dim, keepdim, self.scalar_type());
   }
   if (self.is_complex()){
-    return at::sqrt(at::sum((self.conj() * self).real(), dim, keepdim));
+    return at::sqrt(at::sum(at::real(self.conj() * self), dim, keepdim));
   } else {
     return at::sqrt(at::sum((self * self), dim, keepdim));
   }
@@ -553,7 +553,7 @@ Tensor &frobenius_norm_out(
     return at::norm_out(result, self, 2, dim, keepdim, self.scalar_type());
   }
   if (self.is_complex()){
-    return at::sqrt_out(result, at::sum((self.conj() * self).real(), dim, keepdim));
+    return at::sqrt_out(result, at::sum(at::real(self.conj() * self), dim, keepdim));
   } else {
     return at::sqrt_out(result, at::sum((self * self), dim, keepdim));
   }

aten/src/ATen/native/ReduceOps.cpp

@@ -799,7 +799,7 @@ static Tensor &std_var_out(Tensor &result, const Tensor &self, IntArrayRef dim,
 
   if (at::isComplexType(self.scalar_type())){
     ScalarType dtype = c10::toValueType(get_dtype(result, self, {}, true));
-    Tensor real_in = self.real().to(dtype);
+    Tensor real_in = at::real(self).to(dtype);
     Tensor real_out = at::empty({0}, self.options().dtype(dtype));
     auto iter = make_reduction("std or var", real_out, real_in, dim, keepdim, dtype);
     if (iter.numel() == 0) {
@@ -807,7 +807,7 @@ static Tensor &std_var_out(Tensor &result, const Tensor &self, IntArrayRef dim,
     } else {
       std_var_stub(iter.device_type(), iter, unbiased, false);
     }
-    Tensor imag_in = self.imag().to(dtype);
+    Tensor imag_in = at::imag(self).to(dtype);
     Tensor imag_out = at::empty({0}, self.options().dtype(dtype));
     iter = make_reduction("std or var", imag_out, imag_in, dim, keepdim, dtype);
     if (iter.numel() == 0) {
@@ -845,7 +845,7 @@ static std::tuple<Tensor&,Tensor&> std_var_mean_out(const char* fname, Tensor &r
            ".");
   if (at::isComplexType(self.scalar_type())){
     ScalarType dtype = c10::toValueType(get_dtype(result1, self, {}, true));
-    Tensor real_in = self.real().to(dtype);
+    Tensor real_in = at::real(self).to(dtype);
     Tensor real_out_var = at::empty({0}, self.options().dtype(dtype));
     Tensor real_out_mean = at::empty({0}, self.options().dtype(dtype));
     auto iter = make_reduction(fname, real_out_var, real_out_mean, real_in, dim, keepdim, dtype);
@@ -855,7 +855,7 @@ static std::tuple<Tensor&,Tensor&> std_var_mean_out(const char* fname, Tensor &r
     } else {
       std_var_stub(iter.device_type(), iter, unbiased, false);
     }
-    Tensor imag_in = self.imag().to(dtype);
+    Tensor imag_in = at::imag(self).to(dtype);
     Tensor imag_out_var = at::empty({0}, self.options().dtype(dtype));
     Tensor imag_out_mean = at::empty({0}, self.options().dtype(dtype));
     iter = make_reduction(fname, imag_out_var, imag_out_mean, imag_in, dim, keepdim, dtype);

aten/src/ATen/native/TensorConversions.cpp

@@ -33,7 +33,7 @@ static inline Tensor to_impl(const Tensor& self, const TensorOptions& options, b
     if (self.is_non_overlapping_and_dense()) {
       // Copy all strides
       auto r = at::empty_strided(self.sizes(), self.strides(), options.memory_format(c10::nullopt));
-      r.copy_(self);
+      r.copy_(self, non_blocking);
       return r;
     } else {
       memory_format = self.suggest_memory_format();

aten/src/ATen/native/TensorFactories.cpp

@@ -99,7 +99,7 @@ Tensor _dim_arange(const Tensor& like, int64_t dim) {
 
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ empty ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Tensor empty_cpu(IntArrayRef size, const TensorOptions& options_, c10::optional<c10::MemoryFormat> optional_memory_format) {
-
+  TORCH_CHECK(!isComplexType(at::typeMetaToScalarType(options_.dtype())), "Complex dtype not supported.");
   TORCH_CHECK(
     !(options_.has_memory_format() && optional_memory_format.has_value()),
     "Cannot set memory_format both in TensorOptions and explicit argument; please delete "

aten/src/ATen/native/TensorShape.cpp

@@ -98,6 +98,15 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
         "output memory locations. Found overlap in input tensor ", i);
   }
 
+  // Dtypes should be the same
+  const auto first_in_cat = tensors[0];
+  for (int64_t i = 1; i < tensors.size(); i++) {
+    TORCH_CHECK(first_in_cat.dtype() == tensors[i].dtype(),
+              "Expected object of scalar type ", first_in_cat.dtype(),
+              " but got scalar type ", tensors[i].dtype(),
+              " for sequence element ", i, ".");
+  }
+
   auto should_skip = [](const Tensor& t) { return t.numel() == 0 && t.dim() == 1; };
   for (auto const &tensor : tensors) {
     if (should_skip(tensor)) {

aten/src/ATen/native/UnaryOps.cpp

@@ -73,11 +73,17 @@ Tensor& abs_(Tensor& self) { return unary_op_impl_(self, at::abs_out); }
 Tensor& angle_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, angle_stub); }
 Tensor angle(const Tensor& self) { return unary_op_impl(self, at::angle_out); }
 
-Tensor& real_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, real_stub); }
-Tensor real(const Tensor& self) { return unary_op_impl(self, at::real_out); }
+Tensor real(const Tensor& self) {
+  TORCH_CHECK(!self.is_complex(), "real is not yet implemented for complex tensors.");
+  return self;
+}
 
-Tensor& imag_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, imag_stub); }
-Tensor imag(const Tensor& self) { return unary_op_impl(self, at::imag_out); }
+Tensor imag(const Tensor& self) {
+  TORCH_CHECK(false, "imag is not yet implemented.");
+
+  // Note: unreachable
+  return at::zeros_like(self);
+}
 
 Tensor& conj_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, conj_stub); }
 Tensor conj(const Tensor& self) { return unary_op_impl(self, at::conj_out); }

aten/src/ATen/native/cpu/BinaryOpsKernel.cpp

@@ -7,6 +7,7 @@
 #include <ATen/native/TensorIterator.h>
 #include <ATen/native/BinaryOps.h>
 #include <ATen/native/cpu/Loops.h>
+#include <c10/macros/Macros.h>
 
 namespace at { namespace native {
 namespace {

aten/src/ATen/native/cuda/BinaryArithmeticKernel.cu

@@ -4,7 +4,7 @@
 #include <ATen/native/cuda/zmath.cuh>
 #include <ATen/native/TensorIterator.h>
 #include <ATen/native/BinaryOps.h>
-
+#include <c10/macros/Macros.h>
 
 // NOTE: CUDA on Windows requires that the enclosing function
 // of a __device__ lambda not have internal linkage.

aten/src/ATen/native/cuda/DilatedMaxPool2d.cu

@@ -358,7 +358,7 @@ void max_pool2d_with_indices_out_cuda_template(
 
   Tensor input = input_.contiguous(memory_format);
 
-  const int64_t in_stride_n = input.stride(-4);
+  const int64_t in_stride_n = input_.ndimension() == 4 ? input.stride(-4) : 0;
   const int64_t in_stride_c = input.stride(-3);
   const int64_t in_stride_h = input.stride(-2);
   const int64_t in_stride_w = input.stride(-1);
@@ -506,7 +506,7 @@ void max_pool2d_with_indices_backward_out_cuda_template(
   const int64_t inputHeight = input.size(-2);
   const int64_t inputWidth = input.size(-1);
 
-  const int64_t in_stride_n = input.stride(-4);
+  const int64_t in_stride_n = input.ndimension() == 4 ? input.stride(-4) : 0;
   const int64_t in_stride_c = input.stride(-3);
   const int64_t in_stride_h = input.stride(-2);
   const int64_t in_stride_w = input.stride(-1);

aten/src/ATen/native/cuda/Indexing.cu

@@ -198,7 +198,7 @@ void index_put_accum_kernel(Tensor & self, TensorList indices, const Tensor & va
       using device_ptr = thrust::device_ptr<int64_t>;
       const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-      linearIndex.div_(sliceSize);
+      linearIndex.floor_divide_(sliceSize);
       {
       sorted_indices.copy_(linearIndex);
       auto allocator = THCThrustAllocator(globalContext().lazyInitCUDA());

aten/src/ATen/native/cuda/Shape.cu

@@ -307,6 +307,15 @@ Tensor& cat_out_cuda(Tensor& out, TensorList inputs, int64_t dimension) {
                 "tensor ", i);
   }
 
+  // Dtypes should be the same
+  const auto first_in_cat = inputs[0];
+  for (int64_t i = 1; i < inputs.size(); i++) {
+    TORCH_CHECK(first_in_cat.dtype() == inputs[i].dtype(),
+              "Expected object of scalar type ", first_in_cat.dtype(),
+              " but got scalar type ", inputs[i].dtype(),
+              " for sequence element ", i, ".");
+  }
+
   for (int i = 0; i < inputs.size(); i++)
   {
     if (should_skip(inputs[i])) {
@@ -325,6 +334,12 @@ Tensor& cat_out_cuda(Tensor& out, TensorList inputs, int64_t dimension) {
   TORCH_CHECK(inputs.size() > 0, "invalid number of inputs ", inputs.size());
   TORCH_CHECK(dimension >= 0, "invalid dimension ", dimension);
 
+  for (const Tensor& t: inputs) {
+    TORCH_CHECK(t.device() == notSkippedTensor->device(),
+                "All input tensors must be on the same device. Received ",
+                t.device(), " and ", notSkippedTensor->device());
+  }
+
   c10::MemoryFormat memory_format = compute_output_memory_format(inputs);
 
   std::vector<int64_t> size(notSkippedTensor->sizes().vec());
@@ -355,17 +370,11 @@ Tensor& cat_out_cuda(Tensor& out, TensorList inputs, int64_t dimension) {
   // 4. The number of dimensions is <= 4
   // 5. All input tensors are contiguous (output tensor may be non-contig)
   // 6. All input tensors can use 32-bit indexing
-  // 7. All input tensors are on the same device
 
   const bool all32BitIndexable = std::all_of(inputs.begin(), inputs.end(),
     [] (const Tensor& t) {
       return at::cuda::detail::canUse32BitIndexMath(t);
     });
-  Device firstDevice = notSkippedTensor->device();
-  const bool allSameDevice = std::all_of(inputs.begin(), inputs.end(),
-    [firstDevice](const Tensor& t) {
-      return t.device() == firstDevice;
-    });
   const bool allContiguous = std::all_of(inputs.begin(), inputs.end(),
     [=](const Tensor& t) {
       return !t.defined() || t.is_contiguous(memory_format);
@@ -375,8 +384,7 @@ Tensor& cat_out_cuda(Tensor& out, TensorList inputs, int64_t dimension) {
       out.dim() <= CAT_ARRAY_MAX_INPUT_DIMS &&
       at::cuda::detail::canUse32BitIndexMath(out) &&
       allContiguous &&
-      all32BitIndexable &&
-      allSameDevice) {
+      all32BitIndexable) {
 
     AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(
         at::ScalarType::Half, at::ScalarType::Bool, at::ScalarType::BFloat16,

aten/src/ATen/native/cuda/SortingRadixSelect.cuh

@@ -125,7 +125,7 @@ struct TopKTypeConfig<at::Half> {
   static inline __device__ RadixType convert(at::Half v) {
 #if defined(__CUDA_ARCH__) || defined(__HIP_PLATFORM_HCC__)
     RadixType x = __half_as_ushort(v);
-    RadixType mask = -((x >> 15)) | 0x8000;
+    RadixType mask = (x & 0x00008000) ? 0x0000ffff : 0x00008000;
     return (v == v) ? (x ^ mask) : 0xffff;
 #else
     assert(false);
@@ -135,7 +135,7 @@ struct TopKTypeConfig<at::Half> {
 
   static inline __device__ at::Half deconvert(RadixType v) {
 #if defined(__CUDA_ARCH__) || defined(__HIP_PLATFORM_HCC__)
-    RadixType mask = ((v >> 15) - 1) | 0x8000;
+    RadixType mask = (v & 0x00008000) ? 0x00008000 : 0x0000ffff;
     return __ushort_as_half(v ^ mask);
 #else
     assert(false);

aten/src/ATen/native/cuda/TensorFactories.cu

@@ -44,6 +44,7 @@ Tensor& eye_out_cuda(Tensor& result, int64_t n, int64_t m) {
 }
 
 Tensor empty_cuda(IntArrayRef size, const TensorOptions& options, c10::optional<MemoryFormat> optional_memory_format) {
+  TORCH_CHECK(!isComplexType(at::typeMetaToScalarType(options.dtype())), "Complex dtype not supported.");
   AT_ASSERT(options.device().type() == at::DeviceType::CUDA);
   TORCH_INTERNAL_ASSERT(impl::variable_excluded_from_dispatch());
   TORCH_CHECK(!options.pinned_memory(), "Only dense CPU tensors can be pinned");

aten/src/ATen/native/native_functions.yaml

@@ -238,18 +238,12 @@
 
 - func: real(Tensor self) -> Tensor
   use_c10_dispatcher: full
-  variants: function, method
-  supports_named_tensor: True
-
-- func: real.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+  variants: function
   supports_named_tensor: True
 
 - func: imag(Tensor self) -> Tensor
   use_c10_dispatcher: full
-  variants: function, method
-  supports_named_tensor: True
-
-- func: imag.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+  variants: function
   supports_named_tensor: True
 
 - func: conj(Tensor self) -> Tensor
@@ -2872,7 +2866,7 @@
 
 - func: true_divide.Tensor(Tensor self, Tensor other) -> Tensor
   use_c10_dispatcher: full
-  variants: function
+  variants: function, method
   dispatch:
     CPU: true_divide
     CUDA: true_divide
@@ -2880,6 +2874,15 @@
     SparseCUDA: true_divide_sparse
   supports_named_tensor: True
 
+- func: true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
+  variants: method
+  dispatch:
+    CPU: true_divide_
+    CUDA: true_divide_
+    SparseCPU: true_divide_sparse_
+    SparseCUDA: true_divide_sparse_
+  supports_named_tensor: True
+
 - func: true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
   dispatch:
     CPU: true_divide_out
@@ -2890,7 +2893,11 @@
 
 - func: true_divide.Scalar(Tensor self, Scalar other) -> Tensor
   use_c10_dispatcher: full
-  variants: function
+  variants: function, method
+  supports_named_tensor: True
+
+- func: true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
+  variants: method
   supports_named_tensor: True
 
 - func: trunc(Tensor self) -> Tensor

aten/src/ATen/native/sparse/SparseTensorMath.cpp

@@ -272,6 +272,10 @@ SparseTensor& true_divide_out_sparse_scalar(
   return true_divide_out_sparse_zerodim(result, dividend, wrapped_scalar_tensor(divisor));
 }
 
+Tensor& true_divide_sparse_(Tensor& self, const Tensor& divisor) {
+  return true_divide_out_sparse_zerodim(self, self, divisor);
+}
+
 // --------------------------------------------------------------------
 // floor_divide(SparseTensor, Scalar)
 // --------------------------------------------------------------------

aten/src/ATen/native/sparse/cuda/SparseCUDATensor.cu

@@ -138,7 +138,7 @@ SparseTensor coalesce_sparse_cuda(const SparseTensor& self) {
       // broadcasting logic; instead, it will blast the elements from one
       // to the other so long as the numel is the same
       indicesSlice.copy_(indices1D);
-      indices1D.div_(self.size(d));
+      indices1D.floor_divide_(self.size(d));
       indicesSlice.add_(indices1D, -self.size(d));
     }
   }

aten/src/ATen/native/xnnpack/RegisterOpContextClass.cpp

@@ -14,7 +14,7 @@ namespace xnnpack {
 namespace {
 torch::jit::class_<XNNPackLinearOpContext> register_xnnpack_linear_op_context_class() {
   static auto register_linear_op_context_class =
-      torch::jit::class_<XNNPackLinearOpContext>("XNNPackLinearOpContext")
+      torch::jit::class_<XNNPackLinearOpContext>("xnnpack", "XNNPackLinearOpContext")
           .def_pickle(
               [](const c10::intrusive_ptr<XNNPackLinearOpContext>& op_context)
                   -> SerializationTypeLinearPrePack { // __getstate__
@@ -38,7 +38,7 @@ torch::jit::class_<XNNPackLinearOpContext> register_xnnpack_linear_op_context_cl
 
 torch::jit::class_<XNNPackConv2dOpContext> register_xnnpack_conv2d_op_context_class() {
   static auto register_conv2d_op_context_class =
-      torch::jit::class_<XNNPackConv2dOpContext>("XNNPackConv2dOpContext")
+      torch::jit::class_<XNNPackConv2dOpContext>("xnnpack", "XNNPackConv2dOpContext")
           .def_pickle(
               [](const c10::intrusive_ptr<XNNPackConv2dOpContext>& op_context)
                   -> SerializationTypeConv2dPrePack { // __getstate__
@@ -74,25 +74,25 @@ static auto registry =
   // Registering under _xnnpack namespace for now. As we add more backend requiring similar functionality
   // We can refactor the code and use a better namespace.
     torch::RegisterOperators()
-        .op("_xnnpack::linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.XNNPackLinearOpContext",
+        .op("_xnnpack::linear_prepack(Tensor W, Tensor? B=None) -> __torch__.torch.classes.xnnpack.XNNPackLinearOpContext",
             torch::RegisterOperators::options()
             .aliasAnalysis(at::AliasAnalysisKind::PURE_FUNCTION)
             .kernel<internal::linear::LinearPrePack>(
                 DispatchKey::CPUTensorId))
-        .op("_xnnpack::linear_packed(Tensor X, __torch__.torch.classes.XNNPackLinearOpContext W_prepack) -> Tensor Y",
+        .op("_xnnpack::linear_packed(Tensor X, __torch__.torch.classes.xnnpack.XNNPackLinearOpContext W_prepack) -> Tensor Y",
             torch::RegisterOperators::options()
             .aliasAnalysis(at::AliasAnalysisKind::PURE_FUNCTION)
             .kernel<internal::linear::LinearPacked>(
                 DispatchKey::CPUTensorId))
         .op("_xnnpack::conv2d_prepack(Tensor W, Tensor? B, int[2] stride, "
             "int[2] padding, int[2] dilation, int groups) "
-            "-> __torch__.torch.classes.XNNPackConv2dOpContext",
+            "-> __torch__.torch.classes.xnnpack.XNNPackConv2dOpContext",
             torch::RegisterOperators::options()
             .aliasAnalysis(at::AliasAnalysisKind::PURE_FUNCTION)
             .kernel<internal::convolution2d::Conv2dPrePack>(
                 DispatchKey::CPUTensorId))
         .op("_xnnpack::conv2d_packed(Tensor X, "
-            "__torch__.torch.classes.XNNPackConv2dOpContext W_prepack) -> Tensor Y",
+            "__torch__.torch.classes.xnnpack.XNNPackConv2dOpContext W_prepack) -> Tensor Y",
             torch::RegisterOperators::options()
             .aliasAnalysis(at::AliasAnalysisKind::PURE_FUNCTION)
             .kernel<internal::convolution2d::Conv2dPacked>(

aten/src/ATen/templates/TensorBody.h

@@ -423,6 +423,85 @@ class CAFFE2_API Tensor {
 
   // ~~~~~ Autograd API ~~~~~
 
+  /// \fn bool is_leaf() const;
+  ///
+  /// All Tensors that have `requires_grad()` which is ``false`` will be leaf Tensors by convention.
+  ///
+  /// For Tensors that have `requires_grad()` which is ``true``, they will be leaf Tensors if they were
+  /// created by the user. This means that they are not the result of an operation and so
+  /// `grad_fn()` is `nullptr`.
+  ///
+  /// Only leaf Tensors will have their `grad()` populated during a call to `backward()`.
+  /// To get `grad()` populated for non-leaf Tensors, you can use `retain_grad()`.
+  ///
+  /// Example:
+  /// @code
+  /// auto a = torch::rand(10, torch::requires_grad());
+  /// std::cout << a.is_leaf() << std::endl; // prints `true`
+  ///
+  /// auto b = torch::rand(10, torch::requires_grad()).to(torch::kCUDA);
+  /// std::cout << b.is_leaf() << std::endl; // prints `false`
+  /// // b was created by the operation that cast a cpu Tensor into a cuda Tensor
+  ///
+  /// auto c = torch::rand(10, torch::requires_grad()) + 2;
+  /// std::cout << c.is_leaf() << std::endl; // prints `false`
+  /// // c was created by the addition operation
+  ///
+  /// auto d = torch::rand(10).cuda();
+  /// std::cout << d.is_leaf() << std::endl; // prints `true`
+  /// // d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
+  ///
+  /// auto e = torch::rand(10).cuda().requires_grad_();
+  /// std::cout << e.is_leaf() << std::endl; // prints `true`
+  /// // e requires gradients and has no operations creating it
+  ///
+  /// auto f = torch::rand(10, torch::device(torch::kCUDA).requires_grad(true));
+  /// std::cout << f.is_leaf() << std::endl; // prints `true`
+  /// // f requires grad, has no operation creating it
+  /// @endcode
+
+  /// \fn void backward(const Tensor & gradient={}, bool keep_graph=false, bool create_graph=false) const;
+  ///
+  /// Computes the gradient of current tensor with respect to graph leaves.
+  ///
+  /// The graph is differentiated using the chain rule. If the tensor is
+  /// non-scalar (i.e. its data has more than one element) and requires
+  /// gradient, the function additionally requires specifying ``gradient``.
+  /// It should be a tensor of matching type and location, that contains
+  /// the gradient of the differentiated function w.r.t. this Tensor.
+  ///
+  /// This function accumulates gradients in the leaves - you might need to
+  /// zero them before calling it.
+  ///
+  /// \param gradient Gradient w.r.t. the
+  ///     tensor. If it is a tensor, it will be automatically converted
+  ///     to a Tensor that does not require grad unless ``create_graph`` is True.
+  ///     None values can be specified for scalar Tensors or ones that
+  ///     don't require grad. If a None value would be acceptable then
+  ///     this argument is optional.
+  /// \param keep_graph If ``false``, the graph used to compute
+  ///     the grads will be freed. Note that in nearly all cases setting
+  ///     this option to True is not needed and often can be worked around
+  ///     in a much more efficient way. Defaults to the value of
+  ///     ``create_graph``.
+  /// \param create_graph If ``true``, graph of the derivative will
+  ///     be constructed, allowing to compute higher order derivative
+  ///     products. Defaults to ``false``.
+
+  /// \fn Tensor detach() const;
+  ///
+  /// Returns a new Tensor, detached from the current graph.
+  /// The result will never require gradient.
+
+  /// \fn Tensor & detach_() const;
+  ///
+  /// Detaches the Tensor from the graph that created it, making it a leaf.
+  /// Views cannot be detached in-place.
+
+  /// \fn void retain_grad() const;
+  ///
+  /// Enables .grad() for non-leaf Tensors.
+
   Tensor& set_requires_grad(bool requires_grad) {
     impl_->set_requires_grad(requires_grad);
     return *this;
@@ -431,9 +510,16 @@ class CAFFE2_API Tensor {
     return impl_->requires_grad();
   }
 
+  /// Return a mutable reference to the gradient. This is conventionally
+  /// used as `t.grad() = x` to set a gradient to a completely new tensor.
   Tensor& grad() {
     return impl_->grad();
   }
+
+  /// This function returns an undefined tensor by default and returns a defined tensor
+  /// the first time a call to `backward()` computes gradients for this Tensor.
+  /// The attribute will then contain the gradients computed and future calls
+  /// to `backward()` will accumulate (add) gradients into it.
   const Tensor& grad() const {
     return impl_->grad();
   }
@@ -505,11 +591,38 @@ class CAFFE2_API Tensor {
   template <typename T>
   using hook_return_var_t = std::enable_if_t<std::is_same<typename std::result_of<T&(Tensor)>::type, Tensor>::value, unsigned>;
 
-  // Returns the index of the hook in the list which can be used to remove hook
-  // Register a hook with no return value
+  /// Registers a backward hook.
+  ///
+  /// The hook will be called every time a gradient with respect to the Tensor is computed.
+  /// The hook should have one of the following signature:
+  /// ```
+  /// hook(Tensor grad) -> Tensor
+  /// ```
+  /// ```
+  /// hook(Tensor grad) -> void
+  /// ```
+  /// The hook should not modify its argument, but it can optionally return a new gradient
+  /// which will be used in place of `grad`.
+  ///
+  /// This function returns the index of the hook in the list which can be used to remove hook.
+  ///
+  /// Example:
+  /// @code
+  /// auto v = torch::tensor({0., 0., 0.}, torch::requires_grad());
+  /// auto h = v.register_hook([](torch::Tensor grad){ return grad * 2; }); // double the gradient
+  /// v.backward(torch::tensor({1., 2., 3.}));
+  /// // This prints:
+  /// // ```
+  /// //  2
+  /// //  4
+  /// //  6
+  /// // [ CPUFloatType{3} ]
+  /// // ```
+  /// std::cout << v.grad() << std::endl;
+  /// v.remove_hook(h);  // removes the hook
+  /// @endcode
   template <typename T>
   hook_return_void_t<T> register_hook(T&& hook) const;
-  // Register a hook with variable return value
   template <typename T>
   hook_return_var_t<T> register_hook(T&& hook) const;
 
@@ -518,7 +631,7 @@ private:
 
 public:
 
-  // Remove hook at given position
+  /// Remove hook at given position
   void remove_hook(unsigned pos) const;
 
   // View Variables

aten/src/TH/THGeneral.h.in

@@ -69,12 +69,6 @@
 # define TH_UNUSED
 #endif
 
-#if defined(__clang__)
-#define __ubsan_ignore_float_divide_by_zero__ __attribute__((no_sanitize("float-divide-by-zero")))
-#else
-#define __ubsan_ignore_float_divide_by_zero__
-#endif
-
 #ifndef M_PI
 # define M_PI 3.14159265358979323846
 #endif

aten/src/THC/CMakeLists.txt

@@ -9,7 +9,7 @@ set(extra_src)
 # loop over all types
 foreach(THC_TYPE Byte Char Short Int Long Half Float Double)
    # loop over files which need to be split between types (because of long compile times)
-   foreach(THC_FILE TensorSort TensorMathPointwise TensorMathReduce TensorMasked)
+   foreach(THC_FILE TensorSort TensorMathPointwise TensorMathReduce TensorMasked TensorTopK)
       if(NOT EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/generated/THC${THC_FILE}${THC_TYPE}.cu")
          FILE(WRITE "${CMAKE_CURRENT_SOURCE_DIR}/generated/THC${THC_FILE}${THC_TYPE}.cu"
            "#include <THC/THC${THC_FILE}.cuh>\n#include <THC/THCTensor.hpp>\n\n#include <THC/generic/THC${THC_FILE}.cu>\n#include <THC/THCGenerate${THC_TYPE}Type.h>\n")
@@ -56,7 +56,6 @@ set(ATen_CUDA_SRCS ${ATen_CUDA_SRCS}
   ${CMAKE_CURRENT_SOURCE_DIR}/THCTensorIndex.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/THCTensorRandom.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/THCTensorScatterGather.cu
-  ${CMAKE_CURRENT_SOURCE_DIR}/THCTensorTopK.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/THCTensorSort.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/THCSortUtils.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/THCTensorMode.cu

aten/src/THC/THCTensorTopK.cu

@@ -1,19 +0,0 @@
-#include <THC/THC.h>
-#include <THC/THCReduceApplyUtils.cuh>
-#include <THC/THCTensorCopy.h>
-#include <THC/THCTensorMath.h>
-#include <THC/THCAsmUtils.cuh>
-#include <THC/THCScanUtils.cuh>
-#include <THC/THCTensorTypeUtils.cuh>
-#include <THC/THCTensorMathReduce.cuh>
-#include <ATen/WrapDimUtils.h>
-#include <algorithm> // for std::min
-
-#if CUDA_VERSION >= 7000 || defined __HIP_PLATFORM_HCC__
-#include <thrust/system/cuda/execution_policy.h>
-#endif
-
-#include <THC/THCTensorTopK.cuh>
-
-#include <THC/generic/THCTensorTopK.cu>
-#include <THC/THCGenerateAllTypes.h>

aten/src/THC/THCTensorTopK.cuh

@@ -1,6 +1,21 @@
 #ifndef THC_TENSOR_TOPK_CUH
 #define THC_TENSOR_TOPK_CUH
 
+#include <THC/THC.h>
+#include <THC/THCReduceApplyUtils.cuh>
+#include <THC/THCTensorCopy.h>
+#include <THC/THCTensorMath.h>
+#include <THC/THCAsmUtils.cuh>
+#include <THC/THCScanUtils.cuh>
+#include <THC/THCTensorTypeUtils.cuh>
+#include <THC/THCTensorMathReduce.cuh>
+#include <ATen/WrapDimUtils.h>
+#include <algorithm> // for std::min
+
+#if CUDA_VERSION >= 7000 || defined __HIP_PLATFORM_HCC__
+#include <thrust/system/cuda/execution_policy.h>
+#endif
+
 #include <c10/macros/Macros.h>
 #include <ATen/native/cuda/SortingRadixSelect.cuh>
 
@@ -52,6 +67,7 @@ __global__ void gatherTopK(TensorInfo<T, IndexType> input,
     inputSliceStart, outputSliceSize,
     inputSliceSize, inputWithinSliceStride,
     smem, &topKValue);
+  const auto topKConverted = at::native::TopKTypeConfig<T>::convert(topKValue);
 
   // Every value that is strictly less/greater than `pattern`
   // (depending on sort dir) in sorted int format is in the top-K.
@@ -74,11 +90,12 @@ __global__ void gatherTopK(TensorInfo<T, IndexType> input,
     bool inRange = (i < inputSliceSize);
     T v =
       inRange ? doLdg(&inputSliceStart[i * inputWithinSliceStride]) : ScalarConvert<int, T>::to(0);
+    const auto convertedV = at::native::TopKTypeConfig<T>::convert(v);
     bool hasTopK;
     if (Order) {
-      hasTopK = inRange && (THCNumerics<T>::gt(v, topKValue));
+      hasTopK = inRange && (convertedV > topKConverted);
     } else {
-      hasTopK = inRange && (THCNumerics<T>::lt(v, topKValue));
+      hasTopK = inRange && (convertedV < topKConverted);
     }
 
     int index;
@@ -111,7 +128,8 @@ __global__ void gatherTopK(TensorInfo<T, IndexType> input,
     bool inRange = (i < inputSliceSize);
     T v =
       inRange ? doLdg(&inputSliceStart[i * inputWithinSliceStride]) : ScalarConvert<int, T>::to(0);
-    bool hasTopK = inRange && (THCNumerics<T>::eq(v, topKValue));
+    const auto convertedV = at::native::TopKTypeConfig<T>::convert(v);
+    bool hasTopK = inRange && (convertedV == topKConverted);
 
     int index;
     int carry;

aten/src/THC/generated/THCTensorTopKByte.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateByteType.h>

aten/src/THC/generated/THCTensorTopKChar.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateCharType.h>

aten/src/THC/generated/THCTensorTopKDouble.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateDoubleType.h>

aten/src/THC/generated/THCTensorTopKFloat.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateFloatType.h>

aten/src/THC/generated/THCTensorTopKHalf.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateHalfType.h>

aten/src/THC/generated/THCTensorTopKInt.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateIntType.h>

aten/src/THC/generated/THCTensorTopKLong.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateLongType.h>

aten/src/THC/generated/THCTensorTopKShort.cu

@@ -0,0 +1,5 @@
+#include <THC/THCTensorTopK.cuh>
+#include <THC/THCTensor.hpp>
+
+#include <THC/generic/THCTensorTopK.cu>
+#include <THC/THCGenerateShortType.h>

c10/macros/Macros.h

@@ -23,6 +23,14 @@
 
 #include "c10/macros/Export.h"
 
+#if defined(__clang__)
+  #define __ubsan_ignore_float_divide_by_zero__ __attribute__((no_sanitize("float-divide-by-zero")))
+  #define __ubsan_ignore_float_cast_overflow__ __attribute__((no_sanitize("float-cast-overflow")))
+#else
+  #define __ubsan_ignore_float_divide_by_zero__
+  #define __ubsan_ignore_float_cast_overflow__
+#endif
+
 // Disable the copy and assignment operator for a class. Note that this will
 // disable the usage of the class in std containers.
 #define C10_DISABLE_COPY_AND_ASSIGN(classname) \

c10/util/Exception.cpp

@@ -66,24 +66,44 @@ void Error::AppendMessage(const std::string& new_msg) {
 namespace Warning {
 
 namespace {
-  WarningHandler* getHandler() {
+  WarningHandler* getBaseHandler() {
     static WarningHandler base_warning_handler_ = WarningHandler();
     return &base_warning_handler_;
   };
-  static thread_local WarningHandler* warning_handler_ = getHandler();
+
+  class ThreadWarningHandler {
+    public:
+      ThreadWarningHandler() = delete;
+
+      static WarningHandler* get_handler() {
+        if (!warning_handler_) {
+          warning_handler_ = getBaseHandler();
+        }
+        return warning_handler_;
+      }
+
+      static void set_handler(WarningHandler* handler) {
+        warning_handler_ = handler;
+      }
+
+    private:
+      static thread_local WarningHandler* warning_handler_;
+  };
+
+  thread_local WarningHandler* ThreadWarningHandler::warning_handler_ = nullptr;
 
 }
 
 void warn(SourceLocation source_location, const std::string& msg) {
-  warning_handler_->process(source_location, msg);
+  ThreadWarningHandler::get_handler()->process(source_location, msg);
 }
 
 void set_warning_handler(WarningHandler* handler) noexcept(true) {
-  warning_handler_ = handler;
+  ThreadWarningHandler::set_handler(handler);
 }
 
 WarningHandler* get_warning_handler() noexcept(true) {
-  return warning_handler_;
+  return ThreadWarningHandler::get_handler();
 }
 
 } // namespace Warning

c10/util/TypeCast.h

@@ -67,7 +67,7 @@ struct maybe_real<true, src_t> {
 
 template <typename dest_t, typename src_t>
 struct static_cast_with_inter_type {
-  C10_HOST_DEVICE static inline dest_t apply(src_t src) {
+  C10_HOST_DEVICE __ubsan_ignore_float_cast_overflow__ static inline dest_t apply(src_t src) {
     constexpr bool real = needs_real<dest_t, src_t>::value;
     return static_cast<dest_t>(
       static_cast<inter_copy_type_t<dest_t>>(maybe_real<real, src_t>::apply(src)));

caffe2/CMakeLists.txt

@@ -748,7 +748,7 @@ if (NOT INTERN_BUILD_MOBILE OR NOT BUILD_CAFFE2_MOBILE)
       target_include_directories(torch_cuda PUBLIC "${NVTOOLEXT_HOME}/include")
       # -INCLUDE is used to ensure torch_cuda is linked against in a project that relies on it.
       # Related issue: https://github.com/pytorch/pytorch/issues/31611
-      target_link_libraries(torch_cuda INTERFACE "-INCLUDE:\"?warp_size@cuda@at@@YAHXZ\"")
+      target_link_libraries(torch_cuda INTERFACE "-INCLUDE:?warp_size@cuda@at@@YAHXZ")
 
     elseif(APPLE)
       set(TORCH_CUDA_LIBRARIES
@@ -949,6 +949,31 @@ if (USE_OPENMP AND OPENMP_FOUND)
   target_link_libraries(torch_cpu PRIVATE ${OpenMP_CXX_LIBRARIES})
 endif()
 
+if ($ENV{TH_BINARY_BUILD})
+  if (NOT MSVC AND USE_CUDA AND NOT APPLE)
+    # Note [Extra MKL symbols for MAGMA in torch_cpu]
+    #
+    # When we build CUDA libraries and link against MAGMA, MAGMA makes use of
+    # some BLAS symbols in its CPU fallbacks when it has no GPU versions
+    # of kernels.  Previously, we ensured the BLAS symbols were filled in by
+    # MKL by linking torch_cuda with BLAS, but when we are statically linking
+    # against MKL (when we do wheel builds), this actually ends up pulling in a
+    # decent chunk of MKL into torch_cuda, inflating our torch_cuda binary
+    # size by 8M.  torch_cpu exposes most of the MKL symbols we need, but
+    # empirically we determined that there are four which it doesn't provide.  If
+    # we link torch_cpu with these --undefined symbols, we can ensure they
+    # do get pulled in, and then we can avoid statically linking in MKL to
+    # torch_cuda at all!
+    #
+    # We aren't really optimizing for binary size on Windows (and this link
+    # line doesn't work on Windows), so don't do it there.
+    #
+    # These linker commands do not work on OS X, do not attempt this there.
+    # (It shouldn't matter anyway, though, because OS X has dropped CUDA support)
+    set_target_properties(torch_cpu PROPERTIES LINK_FLAGS "-Wl,--undefined=mkl_lapack_slaed0 -Wl,--undefined=mkl_lapack_dlaed0 -Wl,--undefined=mkl_lapack_dormql -Wl,--undefined=mkl_lapack_sormql")
+  endif()
+endif()
+
 target_link_libraries(torch_cpu PUBLIC c10)
 target_link_libraries(torch_cpu PUBLIC ${Caffe2_PUBLIC_DEPENDENCY_LIBS})
 target_link_libraries(torch_cpu PRIVATE ${Caffe2_DEPENDENCY_LIBS})

caffe2/operators/fused_rowwise_nbitfake_conversion_ops.cc

@@ -1,6 +1,8 @@
 #include "caffe2/operators/fused_rowwise_nbitfake_conversion_ops.h"
 #include <fp16.h>
+#ifdef __AVX__
 #include <immintrin.h>
+#endif
 #include "c10/util/Registry.h"
 
 namespace caffe2 {

caffe2/operators/relu_op.cu

@@ -50,8 +50,13 @@ __global__ void ReluCUDAKernel<half2>(const int N, const half2* X, half2* Y) {
     Y[i] = __hmul2(__hgt2(__ldg(X + i), kZero), __ldg(X + i));
 #else
     const float2 xx = __half22float2(X[i]);
-    Y[i] =
-        __floats2half2_rn(xx.x > 0.0f ? xx.x : 0.0f, xx.y > 0.0f ? xx.y : 0.0f);
+    // There are explicit cast to float here, because it may otherwise cause ambiguity on ROCm and can be triggered
+    // sometimes:
+    //
+    //   error: conditional expression is ambiguous; 'const hip_impl::Scalar_accessor<float, Native_vec_, 0>' can be
+    //   converted to 'float' and vice versa
+    Y[i] = __floats2half2_rn(xx.x > 0.0f ? static_cast<float>(xx.x) : 0.0f,
+                             xx.y > 0.0f ? static_cast<float>(xx.y) : 0.0f);
 #endif
   }
 }
@@ -100,8 +105,14 @@ __global__ void ReluGradientCUDAKernel<half2>(
 #else
     const float2 dy = __half22float2(dY[i]);
     const float2 yy = __half22float2(Y[i]);
-    dX[i] =
-        __floats2half2_rn(yy.x > 0.0f ? dy.x : 0.0f, yy.y > 0.0f ? dy.y : 0.0f);
+    // There are explicit cast to float here, because it may otherwise cause ambiguity on ROCm and can be triggered
+    // sometimes:
+    //
+    //   error: conditional expression is ambiguous; 'const hip_impl::Scalar_accessor<float, Native_vec_, 1>' can be
+    //   converted to 'float' and vice versa
+
+     dX[i] = __floats2half2_rn(yy.x > 0.0f ? static_cast<float>(dy.x) : 0.0f,
+                               yy.y > 0.0f ? static_cast<float>(dy.y) : 0.0f);
 #endif
   }
 }

cmake/External/nccl.cmake

@@ -15,6 +15,7 @@ if (NOT __NCCL_INCLUDED)
     # this second replacement is needed when there are multiple archs
     string(REPLACE ";-gencode" " -gencode" NVCC_GENCODE "${NVCC_GENCODE}")
 
+    set(__NCCL_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/nccl")
     ExternalProject_Add(nccl_external
       SOURCE_DIR ${PROJECT_SOURCE_DIR}/third_party/nccl/nccl
       BUILD_IN_SOURCE 1
@@ -30,20 +31,49 @@ if (NOT __NCCL_INCLUDED)
         "CUDA_HOME=${CUDA_TOOLKIT_ROOT_DIR}"
         "NVCC=${CUDA_NVCC_EXECUTABLE}"
         "NVCC_GENCODE=${NVCC_GENCODE}"
-        "BUILDDIR=${CMAKE_CURRENT_BINARY_DIR}/nccl"
+        "BUILDDIR=${__NCCL_BUILD_DIR}"
         "VERBOSE=0"
         "-j"
-      BUILD_BYPRODUCTS "${CMAKE_CURRENT_BINARY_DIR}/nccl/lib/libnccl_static.a"
+        BUILD_BYPRODUCTS "${__NCCL_BUILD_DIR}/lib/libnccl_static.a"
       INSTALL_COMMAND ""
       )
 
+    # Detect objcopy version
+    execute_process (COMMAND "${CMAKE_OBJCOPY}" "--version" OUTPUT_VARIABLE OBJCOPY_VERSION_STR)
+    string(REGEX REPLACE "GNU objcopy version ([0-9])\\.([0-9]+).*" "\\1" OBJCOPY_VERSION_MAJOR ${OBJCOPY_VERSION_STR})
+    string(REGEX REPLACE "GNU objcopy version ([0-9])\\.([0-9]+).*" "\\2" OBJCOPY_VERSION_MINOR ${OBJCOPY_VERSION_STR})
+
+    if ((${OBJCOPY_VERSION_MAJOR} GREATER 2) OR ((${OBJCOPY_VERSION_MAJOR} EQUAL 2) AND (${OBJCOPY_VERSION_MINOR} GREATER 27)))
+      message(WARNING "Enabling NCCL library slimming")
+      add_custom_command(
+        OUTPUT "${__NCCL_BUILD_DIR}/lib/libnccl_slim_static.a"
+        DEPENDS nccl_external
+        COMMAND "${CMAKE_COMMAND}" -E make_directory "${__NCCL_BUILD_DIR}/objects"
+        COMMAND cd objects
+        COMMAND "${CMAKE_AR}" x "${__NCCL_BUILD_DIR}/lib/libnccl_static.a"
+        COMMAND for obj in all_gather_* all_reduce_* broadcast_* reduce_*.o$<SEMICOLON> do "${CMAKE_OBJCOPY}" --remove-relocations .nvFatBinSegment --remove-section __nv_relfatbin $$obj$<SEMICOLON> done
+       COMMAND "${CMAKE_AR}" cr "${__NCCL_BUILD_DIR}/lib/libnccl_slim_static.a" "*.o"
+        COMMAND cd -
+        COMMAND "${CMAKE_COMMAND}" -E remove_directory "${__NCCL_BUILD_DIR}/objects"
+        WORKING_DIRECTORY "${__NCCL_BUILD_DIR}"
+        COMMENT "Slimming NCCL"
+      )
+    add_custom_target(nccl_slim_external DEPENDS "${__NCCL_BUILD_DIR}/lib/libnccl_slim_static.a")
+    set(__NCCL_LIBRARY_DEP nccl_slim_external)
+    set(NCCL_LIBRARIES ${__NCCL_BUILD_DIR}/lib/libnccl_slim_static.a)
+  else()
+    message(WARNING "Objcopy version is too old to support NCCL library slimming")
+    set(__NCCL_LIBRARY_DEP nccl_external)
+    set(NCCL_LIBRARIES ${__NCCL_BUILD_DIR}/lib/libnccl_static.a)
+  endif()
+
+
     set(NCCL_FOUND TRUE)
     add_library(__caffe2_nccl INTERFACE)
     # The following old-style variables are set so that other libs, such as Gloo,
     # can still use it.
-    set(NCCL_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/nccl/include)
-    set(NCCL_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/nccl/lib/libnccl_static.a)
-    add_dependencies(__caffe2_nccl nccl_external)
+    set(NCCL_INCLUDE_DIRS ${__NCCL_BUILD_DIR}/include)
+    add_dependencies(__caffe2_nccl ${__NCCL_LIBRARY_DEP})
     target_link_libraries(__caffe2_nccl INTERFACE ${NCCL_LIBRARIES})
     target_include_directories(__caffe2_nccl INTERFACE ${NCCL_INCLUDE_DIRS})
   endif()

docs/cpp/source/Doxyfile

@@ -56,6 +56,10 @@ INPUT                  = ../../../aten/src/ATen/ATen.h \
                          ../../../c10/cuda/CUDAStream.h \
                          ../../../torch/csrc/api/include \
                          ../../../torch/csrc/api/src \
+                         ../../../torch/csrc/autograd/autograd.h \
+                         ../../../torch/csrc/autograd/custom_function.h \
+                         ../../../torch/csrc/autograd/function.h \
+                         ../../../torch/csrc/autograd/variable.h \
                          ../../../torch/csrc/autograd/generated/variable_factories.h \
                          ../../../torch/csrc/jit/runtime/custom_operator.h \
                          ../../../torch/csrc/jit/serialization/import.h \

docs/cpp/source/notes/tensor_creation.rst

@@ -281,7 +281,9 @@ change one property, this is quite practical.
 In conclusion, we can now compare how ``TensorOptions`` defaults, together with
 the abbreviated API for creating ``TensorOptions`` using free functions, allow
 tensor creation in C++ with the same convenience as in Python. Compare this
-call in Python::
+call in Python:
+
+.. code-block:: python
 
   torch.randn(3, 4, dtype=torch.float32, device=torch.device('cuda', 1), requires_grad=True)
 

docs/cpp/source/notes/tensor_indexing.rst

@@ -0,0 +1,99 @@
+Tensor Indexing API
+===================
+
+Indexing a tensor in the PyTorch C++ API works very similar to the Python API.
+All index types such as ``None`` / ``...`` / integer / boolean / slice / tensor
+are available in the C++ API, making translation from Python indexing code to C++
+very simple. The main difference is that, instead of using the ``[]``-operator
+similar to the Python API syntax, in the C++ API the indexing methods are:
+
+- ``torch::Tensor::index`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor5indexE8ArrayRefIN2at8indexing11TensorIndexEE>`_)
+- ``torch::Tensor::index_put_`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4N2at6Tensor10index_put_E8ArrayRefIN2at8indexing11TensorIndexEERK6Tensor>`_)
+
+It's also important to note that index types such as ``None`` / ``Ellipsis`` / ``Slice``
+live in the ``torch::indexing`` namespace, and it's recommended to put ``using namespace torch::indexing``
+before any indexing code for convenient use of those index types.
+
+Here are some examples of translating Python indexing code to C++:
+
+Getter
+------
+
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| Python                                                   | C++  (assuming ``using namespace torch::indexing``)                                  |
++==========================================================+======================================================================================+
+| ``tensor[None]``                                         | ``tensor.index({None})``                                                             |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[Ellipsis, ...]``                                | ``tensor.index({Ellipsis, "..."})``                                                  |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[1, 2]``                                         | ``tensor.index({1, 2})``                                                             |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[True, False]``                                  | ``tensor.index({true, false})``                                                      |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[1::2]``                                         | ``tensor.index({Slice(1, None, 2)})``                                                |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[torch.tensor([1, 2])]``                         | ``tensor.index({torch::tensor({1, 2})})``                                            |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[..., 0, True, 1::2, torch.tensor([1, 2])]``     | ``tensor.index({"...", 0, true, Slice(1, None, 2), torch::tensor({1, 2})})``         |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+
+Setter
+------
+
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| Python                                                   | C++  (assuming ``using namespace torch::indexing``)                                  |
++==========================================================+======================================================================================+
+| ``tensor[None] = 1``                                     | ``tensor.index_put_({None}, 1)``                                                     |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[Ellipsis, ...] = 1``                            | ``tensor.index_put_({Ellipsis, "..."}, 1)``                                          |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[1, 2] = 1``                                     | ``tensor.index_put_({1, 2}, 1)``                                                     |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[True, False] = 1``                              | ``tensor.index_put_({true, false}, 1)``                                              |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[1::2] = 1``                                     | ``tensor.index_put_({Slice(1, None, 2)}, 1)``                                        |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[torch.tensor([1, 2])] = 1``                     | ``tensor.index_put_({torch::tensor({1, 2})}, 1)``                                    |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+| ``tensor[..., 0, True, 1::2, torch.tensor([1, 2])] = 1`` | ``tensor.index_put_({"...", 0, true, Slice(1, None, 2), torch::tensor({1, 2})}, 1)`` |
++----------------------------------------------------------+--------------------------------------------------------------------------------------+
+
+
+Translating between Python/C++ index types
+------------------------------------------
+
+The one-to-one translation between Python and C++ index types is as follows:
+
++-------------------------+------------------------------------------------------------------------+
+| Python                  | C++ (assuming ``using namespace torch::indexing``)                     |
++=========================+========================================================================+
+| ``None``                | ``None``                                                               |
++-------------------------+------------------------------------------------------------------------+
+| ``Ellipsis``            | ``Ellipsis``                                                           |
++-------------------------+------------------------------------------------------------------------+
+| ``...``                 | ``"..."``                                                              |
++-------------------------+------------------------------------------------------------------------+
+| ``123``                 | ``123``                                                                |
++-------------------------+------------------------------------------------------------------------+
+| ``True``                | ``true``                                                               |
++-------------------------+------------------------------------------------------------------------+
+| ``False``               | ``false``                                                              |
++-------------------------+------------------------------------------------------------------------+
+| ``:`` or ``::``         | ``Slice()`` or ``Slice(None, None)`` or ``Slice(None, None, None)``    |
++-------------------------+------------------------------------------------------------------------+
+| ``1:`` or ``1::``       | ``Slice(1, None)`` or ``Slice(1, None, None)``                         |
++-------------------------+------------------------------------------------------------------------+
+| ``:3`` or ``:3:``       | ``Slice(None, 3)`` or ``Slice(None, 3, None)``                         |
++-------------------------+------------------------------------------------------------------------+
+| ``::2``                 | ``Slice(None, None, 2)``                                               |
++-------------------------+------------------------------------------------------------------------+
+| ``1:3``                 | ``Slice(1, 3)``                                                        |
++-------------------------+------------------------------------------------------------------------+
+| ``1::2``                | ``Slice(1, None, 2)``                                                  |
++-------------------------+------------------------------------------------------------------------+
+| ``:3:2``                | ``Slice(None, 3, 2)``                                                  |
++-------------------------+------------------------------------------------------------------------+
+| ``1:3:2``               | ``Slice(1, 3, 2)``                                                     |
++-------------------------+------------------------------------------------------------------------+
+| ``torch.tensor([1, 2])``| ``torch::tensor({1, 2})``                                              |
++-------------------------+------------------------------------------------------------------------+

docs/requirements.txt

@@ -1,4 +1,4 @@
-sphinx
+sphinx==2.4.4
 -e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
 sphinxcontrib.katex
 matplotlib

docs/source/amp.rst

@@ -13,6 +13,13 @@ use ``torch.float16`` (``half``). Some operations, like linear layers and convol
 are much faster in ``float16``. Other operations, like reductions, often require the dynamic
 range of ``float32``. Networks running in mixed precision try to match each operation to its appropriate datatype.
 
+.. warning::
+    :class:`torch.cuda.amp.GradScaler` is not a complete implementation of automatic mixed precision.
+    :class:`GradScaler` is only useful if you manually run regions of your model in ``float16``.
+    If you aren't sure how to choose op precision manually, the master branch and nightly pip/conda
+    builds include a context manager that chooses op precision automatically wherever it's enabled.
+    See the `master documentation<https://pytorch.org/docs/master/amp.html>`_ for details.
+
 .. contents:: :local:
 
 .. _gradient-scaling:

docs/source/distributed.rst

@@ -395,6 +395,8 @@ of 16
 .. autofunction:: all_gather_multigpu
 
 
+.. _distributed-launch:
+
 Launch utility
 --------------
 

docs/source/index.rst

@@ -16,7 +16,6 @@ PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
    :caption: Notes
 
    notes/*
-   PyTorch on XLA Devices <http://pytorch.org/xla/>
 
 .. toctree::
    :maxdepth: 1
@@ -46,7 +45,7 @@ PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
    onnx
    optim
    quantization
-   rpc
+   rpc/index.rst
    torch.random <random>
    sparse
    storage
@@ -62,24 +61,15 @@ PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
    name_inference
    torch.__config__ <__config__>
 
-.. toctree::
-   :glob:
-   :maxdepth: 2
-   :caption: torchvision Reference
-
-   torchvision/index
-
 .. toctree::
    :maxdepth: 1
-   :caption: torchaudio Reference
+   :caption: Libraries
    
+   PyTorch on XLA Devices <http://pytorch.org/xla/>
+   PyTorch Elastic (torchelastic) <https://pytorch.org/elastic/>
    torchaudio <https://pytorch.org/audio>
-
-.. toctree::
-   :maxdepth: 1
-   :caption: torchtext Reference
-
    torchtext <https://pytorch.org/text>
+   torchvision/index
 
 .. toctree::
    :glob:

docs/source/notes/amp_examples.rst

@@ -5,6 +5,13 @@ Automatic Mixed Precision examples
 
 .. currentmodule:: torch.cuda.amp
 
+.. warning::
+    :class:`torch.cuda.amp.GradScaler` is not a complete implementation of automatic mixed precision.
+    :class:`GradScaler` is only useful if you manually run regions of your model in ``float16``.
+    If you aren't sure how to choose op precision manually, the master branch and nightly pip/conda
+    builds include a context manager that chooses op precision automatically wherever it's enabled.
+    See the `master documentation<https://pytorch.org/docs/master/amp.html>`_ for details.
+
 .. contents:: :local:
 
 .. _gradient-scaling-examples:

docs/source/notes/cuda.rst

@@ -306,20 +306,30 @@ to overlap data transfers with computation.
 You can make the :class:`~torch.utils.data.DataLoader` return batches placed in
 pinned memory by passing ``pin_memory=True`` to its constructor.
 
-.. _cuda-nn-dataparallel-instead:
+.. _cuda-nn-ddp-instead:
 
-Use nn.DataParallel instead of multiprocessing
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Most use cases involving batched inputs and multiple GPUs should default to
-using :class:`~torch.nn.DataParallel` to utilize more than one GPU. Even with
-the GIL, a single Python process can saturate multiple GPUs.
-
-As of version 0.1.9, large numbers of GPUs (8+) might not be fully utilized.
-However, this is a known issue that is under active development. As always,
-test your use case.
+using :class:`~torch.nn.parallel.DistributedDataParallel` to utilize more
+than one GPU.
 
 There are significant caveats to using CUDA models with
 :mod:`~torch.multiprocessing`; unless care is taken to meet the data handling
 requirements exactly, it is likely that your program will have incorrect or
 undefined behavior.
+
+It is recommended to use :class:`~torch.nn.parallel.DistributedDataParallel`,
+instead of :class:`~torch.nn.DataParallel` to do multi-GPU training, even if
+there is only a single node.
+
+The difference between :class:`~torch.nn.parallel.DistributedDataParallel` and
+:class:`~torch.nn.DataParallel` is: :class:`~torch.nn.parallel.DistributedDataParallel`
+uses multiprocessing where a process is created for each GPU, while
+:class:`~torch.nn.DataParallel` uses multithreading. By using multiprocessing,
+each GPU has its dedicated process, this avoids the performance overhead caused
+by GIL of Python interpreter. 
+
+If you use :class:`~torch.nn.parallel.DistributedDataParallel`, you could use 
+`torch.distributed.launch` utility to launch your program, see :ref:`distributed-launch`.

docs/source/notes/multiprocessing.rst

@@ -45,7 +45,7 @@ the consumer process has references to the tensor, and the refcounting can not
 save you if the consumer process exits abnormally via a fatal signal. See
 :ref:`this section <multiprocessing-cuda-sharing-details>`.
 
-See also: :ref:`cuda-nn-dataparallel-instead`
+See also: :ref:`cuda-nn-ddp-instead`
 
 
 Best practices and tips

docs/source/rpc/index.rst

@@ -0,0 +1,24 @@
+.. _rpc-index:
+
+Distributed RPC Framework
+==============================
+
+The distributed RPC framework provides mechanisms for multi-machine model training through a set of primitives to allow for remote communication, and a higher-level API to automatically differentiate models split across several machines.
+
+-  :ref:`distributed-rpc-framework`
+
+Design Notes
+-----------
+The distributed autograd design note covers the design of the RPC-based distributed autograd framework that is useful for applications such as model parallel training.
+
+-  :ref:`distributed-autograd-design`
+
+The RRef design note covers the design of the :ref:`rref` (Remote REFerence) protocol used to refer to values on remote workers by the framework.
+
+-  :ref:`remote-reference-protocol`
+
+Tutorials
+---------
+The RPC tutorial introduces users to the RPC framework and provides two example applications using :ref:`torch.distributed.rpc<distributed-rpc-framework>` APIs.
+
+-  `Getting started with Distributed RPC Framework <https://pytorch.org/tutorials/intermediate/rpc_tutorial.html>`__

docs/source/rpc/rpc.rst

@@ -8,6 +8,8 @@ training through a set of primitives to allow for remote communication, and a
 higher-level API to automatically differentiate models split across several
 machines.
 
+.. warning ::
+     APIs in the RPC package are stable. There are multiple ongoing work items to improve performance and error handling, which will ship in future releases.
 
 
 Basics

docs/source/tensor_attributes.rst

@@ -210,3 +210,25 @@ Example::
     (1, 5)
 
 For more information on ``torch.sparse_coo`` tensors, see :ref:`sparse-docs`.
+
+torch.memory_format
+------------
+
+.. class:: torch.memory_format
+
+A :class:`torch.memory_format` is an object representing the memory format on which a :class:`torch.Tensor` is
+or will be allocated.
+
+Possible values are:
+
+- ``torch.contiguous_format``:
+  Tensor is or will be  allocated in dense non-overlapping memory. Strides represented by values in decreasing order.
+
+- ``torch.channels_last``:
+  Tensor is or will be  allocated in dense non-overlapping memory. Strides represented by values in 
+  ``strides[0] > strides[2] > strides[3] > strides[1] == 1`` aka NHWC order. 
+
+- ``torch.preserve_format``:
+  Used in functions like `clone` to preserve the memory format of the input tensor. If input tensor is 
+  allocated in dense non-overlapping memory, the output tensor strides will be copied from the input. 
+  Otherwise output strides will follow ``torch.contiguous_format``

docs/source/tensor_view.rst

@@ -49,8 +49,10 @@ For reference, here’s a full list of view ops in PyTorch:
 
 - Basic slicing and indexing op, e.g. ``tensor[0, 2:, 1:7:2]`` returns a view of base ``tensor``, see note below.
 - :meth:`~torch.Tensor.as_strided`
+- :meth:`~torch.Tensor.detach`
 - :meth:`~torch.Tensor.diagonal`
 - :meth:`~torch.Tensor.expand`
+- :meth:`~torch.Tensor.expand_as`
 - :meth:`~torch.Tensor.narrow`
 - :meth:`~torch.Tensor.permute`
 - :meth:`~torch.Tensor.select`

docs/source/tensors.rst

@@ -296,7 +296,6 @@ view of a storage and defines numeric operations on it.
    .. automethod:: hardshrink
    .. automethod:: histc
    .. automethod:: ifft
-   .. automethod:: imag
    .. automethod:: index_add_
    .. automethod:: index_add
    .. automethod:: index_copy_
@@ -413,7 +412,6 @@ view of a storage and defines numeric operations on it.
       :noindex:
    .. automethod:: remainder
    .. automethod:: remainder_
-   .. automethod:: real
    .. automethod:: renorm
    .. automethod:: renorm_
    .. automethod:: repeat
@@ -495,6 +493,8 @@ view of a storage and defines numeric operations on it.
    .. automethod:: tril_
    .. automethod:: triu
    .. automethod:: triu_
+   .. automethod:: true_divide
+   .. automethod:: true_divide_
    .. automethod:: trunc
    .. automethod:: trunc_
    .. automethod:: type

setup.py

@@ -352,10 +352,10 @@ def build_deps():
 ################################################################################
 
 # the list of runtime dependencies required by this built package
-install_requires = []
+install_requires = ['future']
 
 if sys.version_info <= (2, 7):
-    install_requires += ['future', 'typing']
+    install_requires += ['typing']
 
 missing_pydep = '''
 Missing build dependency: Unable to `import {importname}`.

test/backward_compatibility/check_backward_compatibility.py

@@ -21,100 +21,109 @@ white_list = [
     # We export some functions and classes for test_jit.py directly from libtorch.so,
     # it's not important to have BC for them
     ('_TorchScriptTesting.*', datetime.date(9999, 1, 1)),
-    ('aten::pop*', datetime.date(2020, 4, 1)),
-    ('aten::insert*', datetime.date(2020, 4, 1)),
-    ('aten::Delete*', datetime.date(2020, 4, 1)),
-    ('aten::clear*', datetime.date(2020, 4, 1)),
-    ('aten::_set_item*', datetime.date(2020, 4, 1)),
-    ('aten::copy*', datetime.date(2020, 4, 1)),
-    ('aten::extend*', datetime.date(2020, 4, 1)),
-    ('aten::reverse*', datetime.date(2020, 4, 1)),
-    ('aten::append*', datetime.date(2020, 4, 1)),
-    ('aten::list*', datetime.date(2020, 4, 1)),
-    ('aten::__getitem__*', datetime.date(2020, 4, 1)),
-    ('aten::len*', datetime.date(2020, 4, 1)),
-    ('aten::mul_*', datetime.date(2020, 4, 1)),
-    ('aten::slice*', datetime.date(2020, 4, 1)),
-    ('aten::add*', datetime.date(2020, 4, 1)),
-    ('aten::mul*', datetime.date(2020, 4, 1)),
-    ('aten::select*', datetime.date(2020, 4, 1)),
-    ('aten::add_*', datetime.date(2020, 4, 1)),
-    # _like default change, see https://github.com/pytorch/pytorch/issues/33580
-    ('aten::randn_like', datetime.date(2020, 3, 15)),
-    ('aten::full_like', datetime.date(2020, 3, 15)),
-    ('aten::empty_like', datetime.date(2020, 3, 15)),
-    ('aten::rand_like', datetime.date(2020, 3, 15)),
-    ('aten::ones_like', datetime.date(2020, 3, 15)),
-    ('aten::randint_like', datetime.date(2020, 3, 15)),
-    ('aten::zeros_like', datetime.date(2020, 3, 15)),
-    ('aten::floor_divide', datetime.date(2020, 4, 1)),
-    ('aten::Bool', datetime.date(2020, 4, 1)),
-    ('aten::Float', datetime.date(2020, 4, 1)),
-    ('aten::to', datetime.date(2020, 4, 1)),
-    ('aten::backward', datetime.date(2020, 4, 1)),
-    ('aten::len', datetime.date(2020, 4, 1)),
-    ('aten::remove', datetime.date(2020, 4, 1)),
-    ('aten::index', datetime.date(2020, 4, 1)),
-    ('aten::count', datetime.date(2020, 4, 1)),
-    ('aten::__contains__', datetime.date(2020, 4, 1)),
-    ('aten::sort', datetime.date(2020, 4, 1)),
-    ('aten::sorted', datetime.date(2020, 4, 1)),
-    ('aten::eq', datetime.date(2020, 4, 1)),
-    ('aten::ne', datetime.date(2020, 4, 1)),
-    ('aten::lt', datetime.date(2020, 4, 1)),
-    ('aten::gt', datetime.date(2020, 4, 1)),
-    ('aten::le', datetime.date(2020, 4, 1)),
-    ('aten::ge', datetime.date(2020, 4, 1)),
-    ('aten::divmod', datetime.date(2020, 4, 1)),
-    ('aten::__upsample_bilinear', datetime.date(2020, 4, 1)),
-    ('aten::__upsample', datetime.date(2020, 4, 1)),
-    ('aten::__upsample_nearest', datetime.date(2020, 4, 1)),
-    ('aten::__interpolate', datetime.date(2020, 4, 1)),
-    ('aten::fabs', datetime.date(2020, 4, 1)),
-    ('aten::gamma', datetime.date(2020, 4, 1)),
-    ('prim::abs', datetime.date(2020, 4, 1)),
-    ('aten::factorial', datetime.date(2020, 4, 1)),
-    ('aten::radians', datetime.date(2020, 4, 1)),
-    ('aten::degrees', datetime.date(2020, 4, 1)),
-    ('prim::acosh', datetime.date(2020, 4, 1)),
-    ('prim::atanh', datetime.date(2020, 4, 1)),
-    ('aten::asinh', datetime.date(2020, 4, 1)),
-    ('aten::floordiv', datetime.date(2020, 4, 1)),
-    ('prim::NumToTensor', datetime.date(2020, 4, 1)),
-    ('aten::sin', datetime.date(2020, 4, 1)),
-    ('aten::round', datetime.date(2020, 4, 1)),
-    ('aten::remainder', datetime.date(2020, 4, 1)),
-    ('aten::isfinite', datetime.date(2020, 4, 1)),
-    ('aten::sub', datetime.date(2020, 4, 1)),
-    ('aten::sqrt', datetime.date(2020, 4, 1)),
-    ('aten::log1p', datetime.date(2020, 4, 1)),
-    ('aten::acos', datetime.date(2020, 4, 1)),
-    ('aten::floor', datetime.date(2020, 4, 1)),
-    ('aten::exp', datetime.date(2020, 4, 1)),
-    ('aten::tan', datetime.date(2020, 4, 1)),
-    ('aten::sinh', datetime.date(2020, 4, 1)),
-    ('aten::ceil', datetime.date(2020, 4, 1)),
-    ('aten::atan', datetime.date(2020, 4, 1)),
-    ('aten::erf', datetime.date(2020, 4, 1)),
-    ('aten::erfc', datetime.date(2020, 4, 1)),
-    ('aten::cosh', datetime.date(2020, 4, 1)),
-    ('aten::expm1', datetime.date(2020, 4, 1)),
-    ('aten::isinf', datetime.date(2020, 4, 1)),
-    ('aten::lgamma', datetime.date(2020, 4, 1)),
-    ('aten::asin', datetime.date(2020, 4, 1)),
-    ('aten::log', datetime.date(2020, 4, 1)),
-    ('aten::log10', datetime.date(2020, 4, 1)),
-    ('aten::cos', datetime.date(2020, 4, 1)),
-    ('aten::tanh', datetime.date(2020, 4, 1)),
-    ('prim::min', datetime.date(2020, 4, 1)),
-    ('prim::max', datetime.date(2020, 4, 1)),
-    ('aten::_linear_packed', datetime.date(2020, 4, 1)),
-    ('aten::_linear_prepack', datetime.date(2020, 4, 1)),
-    ('aten::_conv2d_packed', datetime.date(2020, 4, 1)),
-    ('aten::_conv2d_prepack', datetime.date(2020, 4, 1)),
-    ('aten::confirmed_by_owner', datetime.date(2020, 3, 17)),
-    ('aten::owner', datetime.date(2020, 3, 27)),
-    ('aten::owner_name', datetime.date(2020, 3, 27)),
+    ('_caffe2', datetime.date(9999, 1, 1)),
+    ('_aten', datetime.date(9999, 1, 1)),
+    ('prim::', datetime.date(9999, 1, 1)),
+    ('onnx::', datetime.date(9999, 1, 1)),
+    ('aten::_set_item', datetime.date(9999, 1, 1)),
+    ('aten::setdefault', datetime.date(9999, 1, 1)),
+    ('aten::_test_optional_float', datetime.date(9999, 1, 1)),
+    ('aten::__upsample', datetime.date(9999, 1, 1)),
+    ('aten::__interpolate', datetime.date(9999, 1, 1)),
+    ('aten::divmod', datetime.date(9999, 1, 1)),
+    ('aten::fabs', datetime.date(9999, 1, 1)),
+    ('aten::gamma', datetime.date(9999, 1, 1)),
+    ('aten::abs', datetime.date(9999, 1, 1)),
+    ('aten::isinf', datetime.date(9999, 1, 1)),
+    ('aten::factorial', datetime.date(9999, 1, 1)),
+    ('aten::radians', datetime.date(9999, 1, 1)),
+    ('aten::degrees', datetime.date(9999, 1, 1)),
+    ('aten::acosh', datetime.date(9999, 1, 1)),
+    ('aten::atanh', datetime.date(9999, 1, 1)),
+    ('aten::asinh', datetime.date(9999, 1, 1)),
+    ('aten::floordiv', datetime.date(9999, 1, 1)),
+    ('aten::sorted', datetime.date(9999, 1, 1)),
+    ('aten::__contains__', datetime.date(9999, 1, 1)),
+    ('aten::count', datetime.date(9999, 1, 1)),
+    ('aten::remove', datetime.date(9999, 1, 1)),
+    ('aten::pop', datetime.date(9999, 1, 1)),
+    ('aten::insert', datetime.date(9999, 1, 1)),
+    ('aten::clear', datetime.date(9999, 1, 1)),
+    ('aten::copy', datetime.date(9999, 1, 1)),
+    ('aten::extend', datetime.date(9999, 1, 1)),
+    ('aten::reverse', datetime.date(9999, 1, 1)),
+    ('aten::append', datetime.date(9999, 1, 1)),
+    ('aten::list', datetime.date(9999, 1, 1)),
+    ('aten::__getitem__', datetime.date(9999, 1, 1)),
+    ('aten::len', datetime.date(9999, 1, 1)),
+    ('aten::backward', datetime.date(9999, 1, 1)),
+    ('aten::Float', datetime.date(9999, 1, 1)),
+    ('aten::Int', datetime.date(9999, 1, 1)),
+    ('aten::Bool', datetime.date(9999, 1, 1)),
+    ('aten::_ncf_view', datetime.date(9999, 1, 1)),
+    ('aten::_ncf_unsqueeze', datetime.date(9999, 1, 1)),
+    ('quantized::mul_scalar_relu_out', datetime.date(9999, 1, 1)),
+    ('quantized::mul_scalar_out', datetime.date(9999, 1, 1)),
+    ('quantized::mul_relu_out', datetime.date(9999, 1, 1)),
+    ('quantized::mul_out', datetime.date(9999, 1, 1)),
+    ('aten::tan', datetime.date(9999, 1, 1)),
+    ('aten::sub', datetime.date(9999, 1, 1)),
+    ('aten::sqrt', datetime.date(9999, 1, 1)),
+    ('aten::sort', datetime.date(9999, 1, 1)),
+    ('aten::slice', datetime.date(9999, 1, 1)),
+    ('aten::sinh', datetime.date(9999, 1, 1)),
+    ('aten::sin', datetime.date(9999, 1, 1)),
+    ('aten::round', datetime.date(9999, 1, 1)),
+    ('aten::remainder', datetime.date(9999, 1, 1)),
+    ('aten::full_like', datetime.date(9999, 1, 1)),
+    ('aten::real', datetime.date(9999, 1, 1)),
+    ('aten::randn_like', datetime.date(9999, 1, 1)),
+    ('aten::pow', datetime.date(9999, 1, 1)),
+    ('aten::floor', datetime.date(9999, 1, 1)),
+    ('quantized::cat_relu_out', datetime.date(9999, 1, 1)),
+    ('quantized::cat_out', datetime.date(9999, 1, 1)),
+    ('aten::neg', datetime.date(9999, 1, 1)),
+    ('quantized::add_out', datetime.date(9999, 1, 1)),
+    ('aten::expm1', datetime.date(9999, 1, 1)),
+    ('aten::ceil', datetime.date(9999, 1, 1)),
+    ('aten::add', datetime.date(9999, 1, 1)),
+    ('aten::acos', datetime.date(9999, 1, 1)),
+    ('aten::cudnn_convolution', datetime.date(9999, 1, 1)),
+    ('aten::cudnn_convolution_backward', datetime.date(9999, 1, 1)),
+    ('aten::cudnn_convolution_transpose', datetime.date(9999, 1, 1)),
+    ('aten::cudnn_convolution_transpose_backward', datetime.date(9999, 1, 1)),
+    ('aten::cudnn_convolution_backward_bias', datetime.date(9999, 1, 1)),
+    ('aten::cudnn_convolution_transpose_backward_bias', datetime.date(9999, 1, 1)),
+    ('aten::atan', datetime.date(9999, 1, 1)),
+    ('aten::log10', datetime.date(9999, 1, 1)),
+    ('quantized::add_scalar_out', datetime.date(9999, 1, 1)),
+    ('quantized::add_scalar_relu_out', datetime.date(9999, 1, 1)),
+    ('quantized::add_relu_out', datetime.date(9999, 1, 1)),
+    ('aten::exp', datetime.date(9999, 1, 1)),
+    ('aten::cosh', datetime.date(9999, 1, 1)),
+    ('aten::erf', datetime.date(9999, 1, 1)),
+    ('aten::imag', datetime.date(9999, 1, 1)),
+    ('aten::empty_like', datetime.date(9999, 1, 1)),
+    ('aten::eq', datetime.date(9999, 1, 1)),
+    ('aten::index', datetime.date(9999, 1, 1)),
+    ('aten::isfinite', datetime.date(9999, 1, 1)),
+    ('aten::leaky_relu_backward', datetime.date(9999, 1, 1)),
+    ('aten::lgamma', datetime.date(9999, 1, 1)),
+    ('aten::log1p', datetime.date(9999, 1, 1)),
+    ('aten::asin', datetime.date(9999, 1, 1)),
+    ('aten::cos', datetime.date(9999, 1, 1)),
+    ('aten::log', datetime.date(9999, 1, 1)),
+    ('aten::mul', datetime.date(9999, 1, 1)),
+    ('aten::ne', datetime.date(9999, 1, 1)),
+    ('aten::rand_like', datetime.date(9999, 1, 1)),
+    ('aten::randint_like', datetime.date(9999, 1, 1)),
+    ('aten::rrelu_with_noise_backward', datetime.date(9999, 1, 1)),
+    ('aten::select', datetime.date(9999, 1, 1)),
+    ('aten::tanh', datetime.date(9999, 1, 1)),
+    ('aten::add_', datetime.date(9999, 1, 1)),
+    ('aten::ones_like', datetime.date(9999, 1, 1)),
+    ('aten::to', datetime.date(9999, 1, 1)),
+    ('aten::zeros_like', datetime.date(9999, 1, 1)),
 ]
 
 
@@ -162,6 +171,15 @@ def check_bc(new_schema_dict):
     return is_bc
 
 
+blacklist = [
+    "torch.classes",
+    "Any",
+    "RRef",
+    "aten::setdefault",
+    "aten::_set_item",
+]
+
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Process some integers.')
     parser.add_argument(
@@ -176,6 +194,9 @@ if __name__ == '__main__':
             line = f.readline()
             if not line:
                 break
+            if any(w for w in blacklist if w in line):
+                # TODO Fix type __torch__.torch.classes.xxx
+                continue
 
             s = parse_schema(line.strip())
             slist = new_schema_dict.get(s.name, [])

test/cpp/api/functional.cpp

@@ -293,7 +293,7 @@ TEST_F(FunctionalTest, MultiLabelSoftMarginLossWeightedNoReduction) {
   auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
   auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
   auto weight = torch::tensor({0.1, 0.6, 0.4, 0.8}, torch::kFloat);
-  auto options = F::MultiLabelSoftMarginLossFuncOptions().reduction(torch::kNone).weight(weight);
+  auto options = F::MultilabelSoftMarginLossFuncOptions().reduction(torch::kNone).weight(weight);
   auto output =
       F::multilabel_soft_margin_loss(input, target, options);
   auto expected = torch::tensor({0.4876902, 0.3321295}, torch::kFloat);
@@ -1875,7 +1875,7 @@ TEST_F(FunctionalTest, Interpolate) {
     // 1D interpolation
     auto input = torch::ones({1, 1, 2});
     auto options = F::InterpolateFuncOptions()
-                       .size({4})
+                       .size(std::vector<int64_t>({4}))
                        .mode(torch::kNearest);
     auto output = F::interpolate(input, options);
     auto expected = torch::ones({1, 1, 4});
@@ -1889,7 +1889,7 @@ TEST_F(FunctionalTest, Interpolate) {
       for (const auto scale_factor : {0.5, 1.5, 2.0}) {
         auto input = torch::ones({1, 1, 2, 2});
         auto options = F::InterpolateFuncOptions()
-                           .scale_factor({scale_factor, scale_factor})
+                           .scale_factor(std::vector<double>({scale_factor, scale_factor}))
                            .mode(torch::kBilinear)
                            .align_corners(align_corners);
         auto output = F::interpolate(input, options);
@@ -1908,7 +1908,7 @@ TEST_F(FunctionalTest, Interpolate) {
         auto input = torch::ones({1, 1, 2, 2, 2});
         auto options =
             F::InterpolateFuncOptions()
-                .scale_factor({scale_factor, scale_factor, scale_factor})
+                .scale_factor(std::vector<double>({scale_factor, scale_factor, scale_factor}))
                 .mode(torch::kTrilinear)
                 .align_corners(align_corners);
         auto output = F::interpolate(input, options);
@@ -1924,13 +1924,13 @@ TEST_F(FunctionalTest, Interpolate) {
   {
     auto input = torch::randn({3, 2, 2});
     ASSERT_THROWS_WITH(
-        F::interpolate(input[0], F::InterpolateFuncOptions().size({4, 4})),
+        F::interpolate(input[0], F::InterpolateFuncOptions().size(std::vector<int64_t>({4, 4}))),
         "Input Error: Only 3D, 4D and 5D input Tensors supported (got 2D) "
         "for the modes: nearest | linear | bilinear | bicubic | trilinear (got kNearest)");
     ASSERT_THROWS_WITH(
         F::interpolate(
             torch::reshape(input, {1, 1, 1, 3, 2, 2}),
-            F::InterpolateFuncOptions().size({1, 1, 1, 3, 4, 4})),
+            F::InterpolateFuncOptions().size(std::vector<int64_t>({1, 1, 1, 3, 4, 4}))),
         "Input Error: Only 3D, 4D and 5D input Tensors supported (got 6D) "
         "for the modes: nearest | linear | bilinear | bicubic | trilinear (got kNearest)");
     ASSERT_THROWS_WITH(
@@ -1939,12 +1939,12 @@ TEST_F(FunctionalTest, Interpolate) {
     ASSERT_THROWS_WITH(
         F::interpolate(
             input,
-            F::InterpolateFuncOptions().size({3, 4, 4}).scale_factor({0.5})),
+            F::InterpolateFuncOptions().size(std::vector<int64_t>({3, 4, 4})).scale_factor(std::vector<double>({0.5}))),
         "only one of size or scale_factor should be defined");
     ASSERT_THROWS_WITH(
-        F::interpolate(input, F::InterpolateFuncOptions().scale_factor({3, 2})),
+        F::interpolate(input, F::InterpolateFuncOptions().scale_factor(std::vector<double>({3, 2}))),
         "scale_factor shape must match input shape. "
-        "Input is 1D, scale_factor size is 2");
+        "Input is 1D, scale_factor size is [3, 2]");
     ASSERT_THROWS_WITH(
         F::interpolate(
             input,
@@ -2328,9 +2328,15 @@ TEST_F(FunctionalTest, AlphaDropout) {
   auto input_std = input.std();
 
   for (const auto rate : {0.2, 0.5, 0.8}) {
-    auto output = F::alpha_dropout(input, F::AlphaDropoutFuncOptions().p(rate).training(false));
+    for (const auto inplace : {false, true}) {
+      auto input_ = input.clone();
+      auto output = F::alpha_dropout(input_, F::AlphaDropoutFuncOptions().p(rate).training(false).inplace(inplace));
       ASSERT_TRUE(torch::allclose(input_mean, output.mean(), 0.1));
       ASSERT_TRUE(torch::allclose(input_std, output.std(), 0.1));
+      if (inplace) {
+        ASSERT_TRUE(torch::allclose(input_, output));
+      }
+    }
   }
   auto output = F::detail::alpha_dropout(input, 0.5, false, false);
   ASSERT_TRUE(torch::allclose(input_mean, output.mean(), 0.1));
@@ -2343,9 +2349,15 @@ TEST_F(FunctionalTest, FeatureAlphaDropout) {
   auto input_std = input.std();
 
   for (const auto rate : {0.2, 0.5, 0.8}) {
-    auto output = F::feature_alpha_dropout(input, F::FeatureAlphaDropoutFuncOptions().p(rate).training(false));
+    for (const auto inplace : {false, true}) {
+      auto input_ = input.clone();
+      auto output = F::feature_alpha_dropout(input_, F::FeatureAlphaDropoutFuncOptions().p(rate).training(false).inplace(inplace));
       ASSERT_TRUE(torch::allclose(input_mean, output.mean(), 0.1));
       ASSERT_TRUE(torch::allclose(input_std, output.std(), 0.1));
+      if (inplace) {
+        ASSERT_TRUE(torch::allclose(input_, output));
+      }
+    }
   }
   auto output = F::feature_alpha_dropout(input);
   ASSERT_TRUE(torch::allclose(input_mean, output.mean(), 0.1));

test/cpp/api/modules.cpp

@@ -1300,55 +1300,82 @@ TEST_F(ModulesTest, FeatureAlphaDropout) {
 }
 
 TEST_F(ModulesTest, Dropout) {
-  Dropout dropout(0.5);
-  torch::Tensor x = torch::ones(100, torch::requires_grad());
+  for (const auto inplace : {false, true}) {
+    Dropout dropout(DropoutOptions(0.5).inplace(inplace));
+    torch::Tensor x = torch::ones(100);
+    if (!inplace) {
+      x.requires_grad_(true);
+    }
     torch::Tensor y = dropout(x);
 
-  y.backward(torch::ones_like(y));
     ASSERT_EQ(y.ndimension(), 1);
     ASSERT_EQ(y.size(0), 100);
     ASSERT_LT(y.sum().item<float>(), 130); // Probably
     ASSERT_GT(y.sum().item<float>(), 70); // Probably
+    if (inplace) {
+      ASSERT_TRUE(y.allclose(x));
+    } else {
+      y.backward(torch::ones_like(y));
+    }
 
     dropout->eval();
-  y = dropout(x);
+    y = dropout(torch::ones(100));
     ASSERT_EQ(y.sum().item<float>(), 100);
   }
+}
 
 TEST_F(ModulesTest, Dropout2d) {
-  Dropout2d dropout(0.5);
-  torch::Tensor x = torch::ones({10, 10}, torch::requires_grad());
+  for (const auto inplace : {false, true}) {
+    Dropout2d dropout(Dropout2dOptions(0.5).inplace(inplace));
+    torch::Tensor x = torch::ones({10, 10});
+    if (!inplace) {
+      x.requires_grad_(true);
+    }
     torch::Tensor y = dropout(x);
 
-  y.backward(torch::ones_like(y));
     ASSERT_EQ(y.ndimension(), 2);
     ASSERT_EQ(y.size(0), 10);
     ASSERT_EQ(y.size(1), 10);
     ASSERT_LT(y.sum().item<float>(), 130); // Probably
     ASSERT_GT(y.sum().item<float>(), 70); // Probably
+    if (inplace) {
+      ASSERT_TRUE(y.allclose(x));
+    } else {
+      y.backward(torch::ones_like(y));
+    }
 
     dropout->eval();
-  y = dropout(x);
+    y = dropout(torch::ones({10, 10}));
     ASSERT_EQ(y.sum().item<float>(), 100);
   }
+}
 
 TEST_F(ModulesTest, Dropout3d) {
-  Dropout3d dropout(0.5);
-  torch::Tensor x = torch::ones({4, 5, 5}, torch::requires_grad());
+  for (const auto inplace : {false, true}) {
+    Dropout3d dropout(Dropout3dOptions(0.5).inplace(inplace));
+    torch::Tensor x = torch::ones({4, 5, 5});
+    if (!inplace) {
+      x.requires_grad_(true);
+    }
     torch::Tensor y = dropout(x);
 
-  y.backward(torch::ones_like(y));
     ASSERT_EQ(y.ndimension(), 3);
     ASSERT_EQ(y.size(0), 4);
     ASSERT_EQ(y.size(1), 5);
     ASSERT_EQ(y.size(1), 5);
     ASSERT_LT(y.sum().item<float>(), 130); // Probably
     ASSERT_GT(y.sum().item<float>(), 70); // Probably
+    if (inplace) {
+      ASSERT_TRUE(y.allclose(x));
+    } else {
+      y.backward(torch::ones_like(y));
+    }
 
     dropout->eval();
-  y = dropout(x);
+    y = dropout(torch::ones({4, 5, 5}));
     ASSERT_EQ(y.sum().item<float>(), 100);
   }
+}
 
 TEST_F(ModulesTest, Parameters) {
   auto model = std::make_shared<NestedModel>();
@@ -2147,38 +2174,58 @@ TEST_F(ModulesTest, PairwiseDistance) {
 TEST_F(ModulesTest, ELU) {
   const auto size = 3;
   for (const auto alpha : {0.0, 0.42, 1.0, 4.2, 42.42}) {
-    ELU model {ELUOptions().alpha(alpha)};
+    for (const auto inplace : {false, true}) {
+      ELU model {ELUOptions().alpha(alpha).inplace(inplace)};
       auto x = torch::linspace(-10.0, 10.0, size * size * size);
-    x.resize_({size, size, size}).set_requires_grad(true);
+      x.resize_({size, size, size});
+      if (!inplace) {
+        x.requires_grad_(true);
+      }
+      auto x_orig = x.clone();
       auto y = model(x);
       torch::Tensor s = y.sum();
 
-    s.backward();
       ASSERT_EQ(s.ndimension(), 0);
 
       ASSERT_EQ(y.ndimension(), 3);
       ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-    auto y_exp = torch::max(torch::zeros_like(x), x) +
-                 torch::min(torch::zeros_like(x), alpha * (torch::exp(x) - 1.0));
+      auto y_exp = torch::max(torch::zeros_like(x_orig), x_orig) +
+                   torch::min(torch::zeros_like(x_orig), alpha * (torch::exp(x_orig) - 1.0));
       ASSERT_TRUE(torch::allclose(y, y_exp));
+      if (inplace) {
+        ASSERT_TRUE(torch::allclose(x, y_exp));
+      } else {
+        s.backward();
+      }
+    }
   }
 }
 
 TEST_F(ModulesTest, SELU) {
-  SELU model;
-  auto input = torch::randn({5, 5}, torch::requires_grad());
+  for (const auto inplace : {false, true}) {
+    SELU model(inplace);
+    auto input = torch::randn({5, 5});
+    if (!inplace) {
+      input.requires_grad_(true);
+    }
+    auto input_orig = input.clone();
     auto output = model->forward(input);
     const double scale = 1.0507009873554804934193349852946;
     const double alpha = 1.6732632423543772848170429916717;
     auto zero = torch::zeros_like(input);
     auto expected = scale *
-      (torch::max(zero, input) +
-       torch::min(zero, alpha * (torch::exp(input) - 1)));
+        (torch::max(zero, input_orig) +
+         torch::min(zero, alpha * (torch::exp(input_orig) - 1)));
     auto s = output.sum();
-  s.backward();
 
     ASSERT_EQ(s.ndimension(), 0);
     ASSERT_TRUE(output.allclose(expected));
+    if (inplace) {
+      ASSERT_TRUE(input.allclose(expected));
+    } else {
+      s.backward();
+    }
+  }
 }
 
 TEST_F(ModulesTest, Hardshrink) {
@@ -2192,7 +2239,6 @@ TEST_F(ModulesTest, Hardshrink) {
 
     s.backward();
     ASSERT_EQ(s.ndimension(), 0);
-
     ASSERT_EQ(y.ndimension(), 3);
     ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
     auto y_exp = (x.abs() > lambda) * x;
@@ -2204,21 +2250,30 @@ TEST_F(ModulesTest, Hardtanh) {
   const auto size = 3;
   for (const auto min_val : {-4.2, -1.0, -0.42, 0.0}) {
     for (const auto max_val : {0.42, 1.0, 4.2}) {
-      Hardtanh model {HardtanhOptions().min_val(min_val).max_val(max_val)};
+      for (const auto inplace : {false, true}) {
+        Hardtanh model {HardtanhOptions().min_val(min_val).max_val(max_val).inplace(inplace)};
         auto x = torch::linspace(-10.0, 10.0, size * size * size);
-      x.resize_({size, size, size}).set_requires_grad(true);
+        x.resize_({size, size, size});
+        if (!inplace) {
+          x.requires_grad_(true);
+        }
+        auto x_orig = x.clone();
         auto y = model(x);
         torch::Tensor s = y.sum();
 
-      s.backward();
         ASSERT_EQ(s.ndimension(), 0);
-
         ASSERT_EQ(y.ndimension(), 3);
         ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-      auto y_exp = (x < min_val) * min_val +
-                   ((x >= min_val) * (x <= max_val)) * x +
-                   (x > max_val) * max_val;
+        auto y_exp = (x_orig < min_val) * min_val +
+                     ((x_orig >= min_val) * (x_orig <= max_val)) * x_orig +
+                     (x_orig > max_val) * max_val;
         ASSERT_TRUE(torch::allclose(y, y_exp));
+        if (inplace) {
+          ASSERT_TRUE(torch::allclose(x, y_exp));
+        } else {
+          s.backward();
+        }
+      }
     }
   }
 }
@@ -2238,20 +2293,29 @@ TEST_F(ModulesTest, HardtanhMinValGEMaxVal) {
 
 TEST_F(ModulesTest, LeakyReLU) {
   const auto size = 3;
+  for (const auto inplace : {false, true}) {
     for (const auto negative_slope : {0.0, 0.42, 1.0}) {
-    LeakyReLU model {LeakyReLUOptions().negative_slope(negative_slope)};
+      LeakyReLU model {LeakyReLUOptions().negative_slope(negative_slope).inplace(inplace)};
       auto x = torch::linspace(-10.0, 10.0, size * size * size);
-    x.resize_({size, size, size}).set_requires_grad(true);
+      x.resize_({size, size, size});
+      if (!inplace) {
+        x.requires_grad_(true);
+      }
+      auto x_orig = x.clone();
       auto y = model(x);
       torch::Tensor s = y.sum();
 
-    s.backward();
       ASSERT_EQ(s.ndimension(), 0);
-
       ASSERT_EQ(y.ndimension(), 3);
       ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-    auto y_exp = (x < 0) * x * negative_slope + (x >= 0) * x;
+      auto y_exp = (x_orig < 0) * x_orig * negative_slope + (x_orig >= 0) * x_orig;
       ASSERT_TRUE(torch::allclose(y, y_exp));
+      if (inplace) {
+        ASSERT_TRUE(torch::allclose(x, y_exp));
+      } else {
+        s.backward();
+      }
+    }
   }
 }
 
@@ -2394,78 +2458,114 @@ TEST_F(ModulesTest, PReLU) {
 }
 
 TEST_F(ModulesTest, ReLU) {
+  for (const auto inplace : {false, true}) {
     const auto size = 3;
-  ReLU model;
+    ReLU model(inplace);
     auto x = torch::linspace(-10.0, 10.0, size * size * size);
-  x.resize_({size, size, size}).set_requires_grad(true);
+    x.resize_({size, size, size});
+    if (!inplace) {
+      x.requires_grad_(true);
+    }
+    auto x_orig = x.clone();
     auto y = model(x);
     torch::Tensor s = y.sum();
 
-  s.backward();
     ASSERT_EQ(s.ndimension(), 0);
-
     ASSERT_EQ(y.ndimension(), 3);
     ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-  auto y_exp = (x < 0) * 0 + (x >= 0) * x;
+    auto y_exp = (x_orig < 0) * 0 + (x_orig >= 0) * x_orig;
     ASSERT_TRUE(torch::allclose(y, y_exp));
+    if (inplace) {
+      ASSERT_TRUE(torch::allclose(x, y_exp));
+    } else {
+      s.backward();
+    }
+  }
 }
 
 TEST_F(ModulesTest, ReLU6) {
+  for (const auto inplace : {false, true}) {
     const auto size = 3;
-  ReLU6 model;
+    ReLU6 model(inplace);
     auto x = torch::linspace(-10.0, 10.0, size * size * size);
-  x.resize_({size, size, size}).set_requires_grad(true);
+    x.resize_({size, size, size});
+    if (!inplace) {
+      x.requires_grad_(true);
+    }
+    auto x_orig = x.clone();
     auto y = model(x);
     torch::Tensor s = y.sum();
 
-  s.backward();
     ASSERT_EQ(s.ndimension(), 0);
-
     ASSERT_EQ(y.ndimension(), 3);
     ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-  auto y_exp = (x < 0) * 0 + ((x >= 0) * (x <= 6)) * x + (x > 6) * 6;
+    auto y_exp = (x_orig < 0) * 0 + ((x_orig >= 0) * (x_orig <= 6)) * x_orig + (x_orig > 6) * 6;
     ASSERT_TRUE(torch::allclose(y, y_exp));
+    if (inplace) {
+      ASSERT_TRUE(torch::allclose(x, y_exp));
+    } else {
+      s.backward();
+    }
+  }
 }
 
 TEST_F(ModulesTest, RReLU) {
   const auto size = 3;
   for (const auto lower : {0.01, 0.1, 0.2}) {
     for (const auto upper : {0.3, 0.4, 0.5}) {
-      RReLU model {RReLUOptions().lower(lower).upper(upper)};
+      for (const auto inplace : {false, true}) {
+        RReLU model {RReLUOptions().lower(lower).upper(upper).inplace(inplace)};
         auto x = torch::linspace(-10.0, 10.0, size * size * size);
-      x.resize_({size, size, size}).set_requires_grad(true);
+        x.resize_({size, size, size});
+        if (!inplace) {
+          x.requires_grad_(true);
+        }
+        auto x_orig = x.clone();
         auto y = model(x);
         torch::Tensor s = y.sum();
 
-      s.backward();
         ASSERT_EQ(s.ndimension(), 0);
-
         ASSERT_EQ(y.ndimension(), 3);
         ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-      auto z = ((x >= 0) * (x == y) +
-        (x < 0) * (y >= x * upper) * (y <= lower * x)) * 1.0;
+        auto z = ((x_orig >= 0) * (x_orig == y) +
+          (x_orig < 0) * (y >= x_orig * upper) * (y <= lower * x_orig)) * 1.0;
         ASSERT_TRUE(torch::allclose(z, torch::ones_like(z)));
+        if (inplace) {
+          ASSERT_TRUE(torch::allclose(x, y));
+        } else {
+          s.backward();
+        }
+      }
     }
   }
 }
 
 TEST_F(ModulesTest, CELU) {
   const auto size = 3;
+  for (const auto inplace : {false, true}) {
     for (const auto alpha : {0.42, 1.0, 4.2, 42.42}) {
-    CELU model {CELUOptions().alpha(alpha)};
+      CELU model {CELUOptions().alpha(alpha).inplace(inplace)};
       auto x = torch::linspace(-10.0, 10.0, size * size * size);
-    x.resize_({size, size, size}).set_requires_grad(true);
+      x.resize_({size, size, size});
+      if (!inplace) {
+        x.requires_grad_(true);
+      }
+      auto x_orig = x.clone();
       auto y = model(x);
       torch::Tensor s = y.sum();
 
-    s.backward();
       ASSERT_EQ(s.ndimension(), 0);
-
       ASSERT_EQ(y.ndimension(), 3);
       ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
-    auto y_exp = torch::max(torch::zeros_like(x), x) +
-        torch::min(torch::zeros_like(x), alpha * (torch::exp(x / alpha) - 1.0));
+      auto y_exp = torch::max(torch::zeros_like(x_orig), x_orig) +
+          torch::min(torch::zeros_like(x_orig), alpha * (torch::exp(x_orig / alpha) - 1.0));
       ASSERT_TRUE(torch::allclose(y, y_exp));
+      if (inplace) {
+        ASSERT_TRUE(torch::allclose(x, y_exp));
+      } else {
+        s.backward();
+      }
+    }
   }
 }
 
@@ -2597,12 +2697,16 @@ TEST_F(ModulesTest, Threshold) {
         Threshold model {ThresholdOptions(threshold, value).inplace(inplace)};
         auto x = torch::linspace(-3.0, 3.0, 61);
         x.resize_({size, size, size});
-        auto y_exp = (x <= threshold) * value + (x > threshold) * x;
+        auto x_orig = x.clone();
+        auto y_exp = (x_orig <= threshold) * value + (x_orig > threshold) * x_orig;
         auto y = model(x);
 
         ASSERT_EQ(y.ndimension(), 3);
         ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
         ASSERT_TRUE(torch::allclose(y, y_exp));
+        if (inplace) {
+          ASSERT_TRUE(torch::allclose(x, y_exp));
+        }
       }
     }
   }
@@ -2611,7 +2715,7 @@ TEST_F(ModulesTest, Threshold) {
 TEST_F(ModulesTest, Upsampling1D) {
   {
     Upsample model(UpsampleOptions()
-                       .size({4})
+                       .size(std::vector<int64_t>({4}))
                        .mode(torch::kNearest));
     auto input = torch::ones({1, 1, 2}, torch::requires_grad());
     auto output = model->forward(input);
@@ -2627,7 +2731,7 @@ TEST_F(ModulesTest, Upsampling1D) {
       // test float scale factor up & down sampling
       for (const auto scale_factor : {0.5, 1.5, 2.0}) {
         Upsample model(UpsampleOptions()
-                           .scale_factor({scale_factor})
+                           .scale_factor(std::vector<double>({scale_factor}))
                            .mode(torch::kLinear)
                            .align_corners(align_corners));
         auto input = torch::ones({1, 1, 2}, torch::requires_grad());
@@ -2646,7 +2750,7 @@ TEST_F(ModulesTest, Upsampling1D) {
   {
     // linear (1D) upsampling spatial invariance
     Upsample model(UpsampleOptions()
-                       .scale_factor({3})
+                       .scale_factor(std::vector<double>({3}))
                        .mode(torch::kLinear)
                        .align_corners(false));
     auto input = torch::zeros({1, 1, 9});
@@ -2661,7 +2765,7 @@ TEST_F(ModulesTest, Upsampling1D) {
 TEST_F(ModulesTest, Upsampling2D) {
   {
     Upsample model(UpsampleOptions()
-                       .size({4, 4})
+                       .size(std::vector<int64_t>({4, 4}))
                        .mode(torch::kNearest));
     auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
     auto output = model->forward(input);
@@ -2677,7 +2781,7 @@ TEST_F(ModulesTest, Upsampling2D) {
       // test float scale factor up & down sampling
       for (const auto scale_factor : {0.5, 1.5, 2.0}) {
         Upsample model(UpsampleOptions()
-                           .scale_factor({scale_factor, scale_factor})
+                           .scale_factor(std::vector<double>({scale_factor, scale_factor}))
                            .mode(torch::kBilinear)
                            .align_corners(align_corners));
         auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
@@ -2698,7 +2802,7 @@ TEST_F(ModulesTest, Upsampling2D) {
       // test float scale factor up & down sampling
       for (const auto scale_factor : {0.5, 1.5, 2.0}) {
         Upsample model(UpsampleOptions()
-                           .scale_factor({scale_factor, scale_factor})
+                           .scale_factor(std::vector<double>({scale_factor, scale_factor}))
                            .mode(torch::kBicubic)
                            .align_corners(align_corners));
         auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
@@ -2719,7 +2823,7 @@ TEST_F(ModulesTest, Upsampling2D) {
 TEST_F(ModulesTest, Upsampling3D) {
   {
     Upsample model(UpsampleOptions()
-                       .size({4, 4, 4})
+                       .size(std::vector<int64_t>({4, 4, 4}))
                        .mode(torch::kNearest));
     auto input = torch::ones({1, 1, 2, 2, 2}, torch::requires_grad());
     auto output = model->forward(input);
@@ -2736,7 +2840,7 @@ TEST_F(ModulesTest, Upsampling3D) {
       for (const auto scale_factor : {0.5, 1.5, 2.0}) {
         Upsample model(
             UpsampleOptions()
-                .scale_factor({scale_factor, scale_factor, scale_factor})
+                .scale_factor(std::vector<double>({scale_factor, scale_factor, scale_factor}))
                 .mode(torch::kTrilinear)
                 .align_corners(align_corners));
         auto input = torch::ones({1, 1, 2, 2, 2}, torch::requires_grad());
@@ -3876,10 +3980,10 @@ TEST_F(ModulesTest, PrettyPrintConvTranspose) {
 
 TEST_F(ModulesTest, PrettyPrintUpsample) {
   ASSERT_EQ(
-      c10::str(Upsample(UpsampleOptions().size({2, 4, 4}))),
+      c10::str(Upsample(UpsampleOptions().size(std::vector<int64_t>({2, 4, 4})))),
       "torch::nn::Upsample(size=[2, 4, 4], mode=kNearest)");
   ASSERT_EQ(
-      c10::str(Upsample(UpsampleOptions().scale_factor({0.5, 1.5}).mode(torch::kBilinear))),
+      c10::str(Upsample(UpsampleOptions().scale_factor(std::vector<double>({0.5, 1.5})).mode(torch::kBilinear))),
       "torch::nn::Upsample(scale_factor=[0.5, 1.5], mode=kBilinear)");
 }
 
@@ -3987,15 +4091,27 @@ TEST_F(ModulesTest, PrettyPrintAdaptiveMaxPool) {
       c10::str(AdaptiveMaxPool2d(5)),
       "torch::nn::AdaptiveMaxPool2d(output_size=[5, 5])");
   ASSERT_EQ(
-      c10::str(AdaptiveMaxPool2d(std::vector<int64_t>{5, 6})),
+      c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({5, 6}))),
       "torch::nn::AdaptiveMaxPool2d(output_size=[5, 6])");
+  ASSERT_EQ(
+      c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({5, c10::nullopt}))),
+      "torch::nn::AdaptiveMaxPool2d(output_size=[5, None])");
+  ASSERT_EQ(
+      c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({c10::nullopt, c10::nullopt}))),
+      "torch::nn::AdaptiveMaxPool2d(output_size=[None, None])");
 
   ASSERT_EQ(
       c10::str(AdaptiveMaxPool3d(5)),
       "torch::nn::AdaptiveMaxPool3d(output_size=[5, 5, 5])");
   ASSERT_EQ(
-      c10::str(AdaptiveMaxPool3d(std::vector<int64_t>{5, 6, 7})),
+      c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({5, 6, 7}))),
       "torch::nn::AdaptiveMaxPool3d(output_size=[5, 6, 7])");
+  ASSERT_EQ(
+      c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({5, c10::nullopt, 7}))),
+      "torch::nn::AdaptiveMaxPool3d(output_size=[5, None, 7])");
+  ASSERT_EQ(
+      c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({c10::nullopt, c10::nullopt, c10::nullopt}))),
+      "torch::nn::AdaptiveMaxPool3d(output_size=[None, None, None])");
 }
 
 TEST_F(ModulesTest, PrettyPrintAdaptiveAvgPool) {
@@ -4007,15 +4123,27 @@ TEST_F(ModulesTest, PrettyPrintAdaptiveAvgPool) {
       c10::str(AdaptiveAvgPool2d(5)),
       "torch::nn::AdaptiveAvgPool2d(output_size=[5, 5])");
   ASSERT_EQ(
-      c10::str(AdaptiveAvgPool2d(std::vector<int64_t>{5, 6})),
+      c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({5, 6}))),
       "torch::nn::AdaptiveAvgPool2d(output_size=[5, 6])");
+  ASSERT_EQ(
+      c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({5, c10::nullopt}))),
+      "torch::nn::AdaptiveAvgPool2d(output_size=[5, None])");
+  ASSERT_EQ(
+      c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({c10::nullopt, c10::nullopt}))),
+      "torch::nn::AdaptiveAvgPool2d(output_size=[None, None])");
 
   ASSERT_EQ(
       c10::str(AdaptiveAvgPool3d(5)),
       "torch::nn::AdaptiveAvgPool3d(output_size=[5, 5, 5])");
   ASSERT_EQ(
-      c10::str(AdaptiveAvgPool3d(std::vector<int64_t>{5, 6, 7})),
+      c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({5, 6, 7}))),
       "torch::nn::AdaptiveAvgPool3d(output_size=[5, 6, 7])");
+  ASSERT_EQ(
+      c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({5, c10::nullopt, 7}))),
+      "torch::nn::AdaptiveAvgPool3d(output_size=[5, None, 7])");
+  ASSERT_EQ(
+      c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({c10::nullopt, c10::nullopt, c10::nullopt}))),
+      "torch::nn::AdaptiveAvgPool3d(output_size=[None, None, None])");
 }
 
 TEST_F(ModulesTest, PrettyPrintMaxUnpool) {

test/cpp/api/optim.cpp

@@ -26,7 +26,7 @@ bool test_optimizer_xor(Options options) {
       Linear(8, 1),
       Functional(torch::sigmoid));
 
-  const int64_t kBatchSize = 4;
+  const int64_t kBatchSize = 200;
   const int64_t kMaximumNumberOfEpochs = 3000;
 
   OptimizerClass optimizer(model->parameters(), options);
@@ -40,13 +40,21 @@ bool test_optimizer_xor(Options options) {
       inputs[i] = torch::randint(2, {2}, torch::kInt64);
       labels[i] = inputs[i][0].item<int64_t>() ^ inputs[i][1].item<int64_t>();
     }
+
     inputs.set_requires_grad(true);
+
+    auto step = [&](OptimizerClass& optimizer, Sequential model, torch::Tensor inputs, torch::Tensor labels) {
+      auto closure = [&]() {
         optimizer.zero_grad();
         auto x = model->forward(inputs);
-    torch::Tensor loss = torch::binary_cross_entropy(x, labels);
+        auto loss = torch::binary_cross_entropy(x, labels);
         loss.backward();
+        return loss;
+      };
+      return optimizer.step(closure);
+    };
 
-    optimizer.step();
+    torch::Tensor loss = step(optimizer, model, inputs, labels);
 
     running_loss = running_loss * 0.99 + loss.item<float>() * 0.01;
     if (epoch > kMaximumNumberOfEpochs) {
@@ -166,30 +174,66 @@ TEST(OptimTest, OptimizerAccessors) {
   optimizer_.state();
 }
 
-TEST(OptimTest, BasicInterface) {
+#define OLD_INTERFACE_WARNING_CHECK(func) \
+{ \
+  std::stringstream buffer;\
+  torch::test::CerrRedirect cerr_redirect(buffer.rdbuf());\
+  func;\
+  ASSERT_EQ(\
+    torch::test::count_substr_occurrences(\
+      buffer.str(),\
+      "will be removed"\
+    ),\
+  1);\
+}
+
+struct MyOptimizerOptions : public OptimizerCloneableOptions<MyOptimizerOptions> {
+  MyOptimizerOptions(double lr = 1.0) : lr_(lr) {};
+  TORCH_ARG(double, lr) = 1.0;
+};
+
+TEST(OptimTest, OldInterface) {
   struct MyOptimizer : Optimizer {
     using Optimizer::Optimizer;
     torch::Tensor step(LossClosure closure = nullptr) override { return {};}
+    explicit MyOptimizer(
+        std::vector<at::Tensor> params, MyOptimizerOptions defaults = {}) :
+          Optimizer({std::move(OptimizerParamGroup(params))}, std::make_unique<MyOptimizerOptions>(defaults)) {}
   };
   std::vector<torch::Tensor> parameters = {
       torch::ones({2, 3}), torch::zeros({2, 3}), torch::rand({2, 3})};
   {
     MyOptimizer optimizer(parameters);
-    ASSERT_EQ(optimizer.size(), parameters.size());
+    size_t size;
+    OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
+    ASSERT_EQ(size, parameters.size());
   }
   {
-    MyOptimizer optimizer;
-    ASSERT_EQ(optimizer.size(), 0);
-    optimizer.add_parameters(parameters);
-    ASSERT_EQ(optimizer.size(), parameters.size());
-    for (size_t p = 0; p < parameters.size(); ++p) {
-      ASSERT_TRUE(optimizer.parameters()[p].allclose(parameters[p]));
+    std::vector<at::Tensor> params;
+    MyOptimizer optimizer(params);
+
+    size_t size;
+    OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
+    ASSERT_EQ(size, 0);
+
+    OLD_INTERFACE_WARNING_CHECK(optimizer.add_parameters(parameters));
+
+    OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
+    ASSERT_EQ(size, parameters.size());
+
+    std::vector<torch::Tensor> params_;
+    OLD_INTERFACE_WARNING_CHECK(params_ = optimizer.parameters());
+    for (size_t p = 0; p < size; ++p) {
+      ASSERT_TRUE(params_[p].allclose(parameters[p]));
     }
   }
   {
     Linear linear(3, 4);
     MyOptimizer optimizer(linear->parameters());
-    ASSERT_EQ(optimizer.size(), linear->parameters().size());
+
+    size_t size;
+    OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
+    ASSERT_EQ(size, linear->parameters().size());
   }
 }
 
@@ -198,6 +242,11 @@ TEST(OptimTest, XORConvergence_SGD) {
       SGDOptions(0.1).momentum(0.9).nesterov(true).weight_decay(1e-6)));
 }
 
+TEST(OptimTest, XORConvergence_LBFGS) {
+  ASSERT_TRUE(test_optimizer_xor<LBFGS>(LBFGSOptions(1.0)));
+  ASSERT_TRUE(test_optimizer_xor<LBFGS>(LBFGSOptions(1.0).line_search_fn("strong_wolfe")));
+}
+
 TEST(OptimTest, XORConvergence_Adagrad) {
   ASSERT_TRUE(test_optimizer_xor<Adagrad>(
       AdagradOptions(1.0).weight_decay(1e-6).lr_decay(1e-3)));
@@ -375,7 +424,7 @@ TEST(OptimTest, AddParameter_LBFGS) {
   }
 
   LBFGS optimizer(std::vector<torch::Tensor>{}, 1.0);
-  optimizer.add_parameters(parameters);
+  OLD_INTERFACE_WARNING_CHECK(optimizer.add_parameters(parameters));
 
   optimizer.step([]() { return torch::tensor(1); });
 

test/cpp/api/serialize.cpp

@@ -64,7 +64,7 @@ void is_optimizer_state_equal(
 }
 
 template <typename OptimizerClass, typename DerivedOptimizerOptions, typename DerivedOptimizerParamState>
-void test_serialize_optimizer(DerivedOptimizerOptions options) {
+void test_serialize_optimizer(DerivedOptimizerOptions options, bool only_has_global_state = false) {
   auto model1 = Linear(5, 2);
   auto model2 = Linear(5, 2);
   auto model3 = Linear(5, 2);
@@ -125,9 +125,11 @@ void test_serialize_optimizer(DerivedOptimizerOptions options) {
   auto& optim3_2_state = optim3_2.state();
   auto& optim3_state = optim3.state();
 
-  // optim3_2 and optim1 should have param_groups and state of size 1 and 2 respectively
+  // optim3_2 and optim1 should have param_groups and state of size 1 and state_size respectively
   ASSERT_TRUE(optim3_2_param_groups.size() == 1);
-  ASSERT_TRUE(optim3_2_state.size() == 2);
+  // state_size = 2 for all optimizers except LBFGS as LBFGS only maintains one global state
+  int state_size = only_has_global_state ? 1 : 2;
+  ASSERT_TRUE(optim3_2_state.size() == state_size);
 
   // optim3_2 and optim1 should have param_groups and state of same size
   ASSERT_TRUE(optim3_2_param_groups.size() == optim3_param_groups.size());
@@ -668,39 +670,16 @@ TEST(SerializeTest, Optim_RMSprop) {
 }
 
 TEST(SerializeTest, Optim_LBFGS) {
-  auto options = LBFGSOptions();
+  test_serialize_optimizer<LBFGS, LBFGSOptions, LBFGSParamState>(LBFGSOptions(), true);
+  // bc compatibility check
   auto model1 = Linear(5, 2);
-  auto model2 = Linear(5, 2);
-  auto model3 = Linear(5, 2);
-
-  // Models 1, 2, 3 will have the same parameters.
-  auto model_tempfile = c10::make_tempfile();
-  torch::save(model1, model_tempfile.name);
-  torch::load(model2, model_tempfile.name);
-  torch::load(model3, model_tempfile.name);
-
-  auto param1 = model1->named_parameters();
-  auto param2 = model2->named_parameters();
-  auto param3 = model3->named_parameters();
-  for (const auto& p : param1) {
-    ASSERT_TRUE(p->allclose(param2[p.key()]));
-    ASSERT_TRUE(param2[p.key()].allclose(param3[p.key()]));
-  }
-  // Make some optimizers
-  auto optim1 = LBFGS(
-      {torch::optim::OptimizerParamGroup(model1->parameters())}, options);
-  auto optim2 = LBFGS(
-      model2->parameters(), options);
-  auto optim2_2 = LBFGS(
-      model2->parameters(), options);
-  auto optim3 = LBFGS(
-      model3->parameters(), options);
-  auto optim3_2 = LBFGS(
-      model3->parameters(), options);
+  auto model1_params = model1->parameters();
+  // added a tensor for lazy init check - when all params do not have entry in buffers
+  model1_params.emplace_back(torch::randn({2,3}));
+  auto optim1 = torch::optim::LBFGS(model1_params, torch::optim::LBFGSOptions());
 
   auto x = torch::ones({10, 5});
-
-  auto step = [&x](torch::optim::LossClosureOptimizer& optimizer, Linear model) {
+  auto step = [&x](torch::optim::Optimizer& optimizer, Linear model) {
     optimizer.zero_grad();
     auto y = model->forward(x).sum();
     y.backward();
@@ -708,56 +687,47 @@ TEST(SerializeTest, Optim_LBFGS) {
     optimizer.step(closure);
   };
 
-  // Do 2 steps of model1
-  step(optim1, model1);
   step(optim1, model1);
 
-  // Do 2 steps of model 2 without saving the optimizer
-  step(optim2, model2);
-  step(optim2_2, model2);
+  at::Tensor d, t, H_diag, prev_flat_grad, prev_loss;
+  std::deque<at::Tensor> old_dirs, old_stps;
 
-  // Do 1 step of model 3
-  step(optim3, model3);
+  const auto& params_ = optim1.param_groups()[0].params();
+  auto key_ = c10::guts::to_string(params_[0].unsafeGetTensorImpl());
+  const auto& optim1_state = static_cast<const LBFGSParamState&>(*(optim1.state().at(key_).get()));
+  d = optim1_state.d();
+  t = at::tensor(optim1_state.t());
+  H_diag = optim1_state.H_diag();
+  prev_flat_grad = optim1_state.prev_flat_grad();
+  prev_loss = at::tensor(optim1_state.prev_loss());
+  old_dirs = optim1_state.old_dirs();
 
-  // save the optimizer
-  auto optim_tempfile = c10::make_tempfile();
-  torch::save(optim3, optim_tempfile.name);
-  torch::load(optim3_2, optim_tempfile.name);
+  // write buffers to the file
+  auto optim_tempfile_old_format = c10::make_tempfile();
+  torch::serialize::OutputArchive output_archive;
+  output_archive.write("d", d, /*is_buffer=*/true);
+  output_archive.write("t", t, /*is_buffer=*/true);
+  output_archive.write("H_diag", H_diag, /*is_buffer=*/true);
+  output_archive.write("prev_flat_grad", prev_flat_grad, /*is_buffer=*/true);
+  output_archive.write("prev_loss", prev_loss, /*is_buffer=*/true);
+  write_tensors_to_archive(output_archive, "old_dirs", old_dirs);
+  write_tensors_to_archive(output_archive, "old_stps", old_stps);
+  output_archive.save_to(optim_tempfile_old_format.name);
 
-  auto& optim3_2_param_groups = optim3_2.param_groups();
-  auto& optim3_param_groups = optim3.param_groups();
-  auto& optim3_2_state = optim3_2.state();
-  auto& optim3_state = optim3.state();
+  auto optim1_2 = LBFGS(model1_params, torch::optim::LBFGSOptions());
+  OLD_SERIALIZATION_LOGIC_WARNING_CHECK(torch::load, optim1_2, optim_tempfile_old_format.name);
 
-  // LBFGS only supports 1 param_group
-  // optim3_2 and optim1 should have param_groups of size 1
-  ASSERT_TRUE(optim3_param_groups.size() == 1);
-  ASSERT_TRUE(optim3_2_param_groups.size() == 1);
-  // LBFGS only maintains one global state
-  ASSERT_TRUE(optim3_2_state.size() == 1);
-  ASSERT_TRUE(optim3_state.size() == 1);
+  const auto& params1_2_ = optim1_2.param_groups()[0].params();
+  auto param_key = c10::guts::to_string(params1_2_[0].unsafeGetTensorImpl());
+  auto& optim1_2_state = static_cast<LBFGSParamState&>(*(optim1_2.state().at(param_key).get()));
 
-  // checking correctness of serialization logic for optimizer.param_groups_ and optimizer.state_
-  for (int i = 0; i < optim3_2_param_groups.size(); i++) {
-    is_optimizer_param_group_equal<LBFGSOptions>(
-      optim3_2_param_groups[i], optim3_param_groups[i]);
-    is_optimizer_state_equal<LBFGSParamState>(optim3_2_state, optim3_state);
-  }
+  // old LBFGS didn't track func_evals, n_iter, ro, al values
+  optim1_2_state.func_evals(optim1_state.func_evals());
+  optim1_2_state.n_iter(optim1_state.n_iter());
+  optim1_2_state.ro(optim1_state.ro());
+  optim1_2_state.al(optim1_state.al());
 
-  // Do step2 for model 3
-  step(optim3_2, model3);
-
-  param1 = model1->named_parameters();
-  param2 = model2->named_parameters();
-  param3 = model3->named_parameters();
-  for (const auto& p : param1) {
-    const auto& name = p.key();
-    // Model 1 and 3 should be the same
-    ASSERT_TRUE(
-        param1[name].norm().item<float>() == param3[name].norm().item<float>());
-    ASSERT_TRUE(
-        param1[name].norm().item<float>() != param2[name].norm().item<float>());
-  }
+  is_optimizer_state_equal<LBFGSParamState>(optim1.state(), optim1_2.state());
 }
 
 TEST(SerializeTest, XOR_CUDA) {

test/cpp/jit/test_class_import.cpp

@@ -138,7 +138,7 @@ void testClassDerive() {
 static const auto torchbindSrc = R"JIT(
 class FooBar1234(Module):
   __parameters__ = []
-  f : __torch__.torch.classes._TorchScriptTesting_StackString
+  f : __torch__.torch.classes._TorchScriptTesting._StackString
   training : bool
   def forward(self: __torch__.FooBar1234) -> str:
     return (self.f).top()

test/cpp/jit/test_custom_class.cpp

@@ -66,7 +66,7 @@ struct PickleTester : torch::CustomClassHolder {
   std::vector<int64_t> vals;
 };
 
-static auto test = torch::class_<Foo>("_TorchScriptTesting_Foo")
+static auto test = torch::class_<Foo>("_TorchScriptTesting", "_Foo")
                        .def(torch::init<int64_t, int64_t>())
                        // .def(torch::init<>())
                        .def("info", &Foo::info)
@@ -75,7 +75,9 @@ static auto test = torch::class_<Foo>("_TorchScriptTesting_Foo")
                        .def("combine", &Foo::combine);
 
 static auto testStack =
-    torch::class_<MyStackClass<std::string>>("_TorchScriptTesting_StackString")
+    torch::class_<MyStackClass<std::string>>(
+        "_TorchScriptTesting",
+        "_StackString")
         .def(torch::init<std::vector<std::string>>())
         .def("push", &MyStackClass<std::string>::push)
         .def("pop", &MyStackClass<std::string>::pop)
@@ -101,7 +103,7 @@ static auto testStack =
 // clang-format on
 
 static auto testPickle =
-    torch::class_<PickleTester>("_TorchScriptTesting_PickleTester")
+    torch::class_<PickleTester>("_TorchScriptTesting", "_PickleTester")
         .def(torch::init<std::vector<int64_t>>())
         .def_pickle(
             [](c10::intrusive_ptr<PickleTester> self) { // __getstate__
@@ -129,7 +131,7 @@ torch::RegisterOperators& register_take_instance() {
   static auto instance_registry = torch::RegisterOperators().op(
       torch::RegisterOperators::options()
           .schema(
-          "_TorchScriptTesting::take_an_instance(__torch__.torch.classes._TorchScriptTesting_PickleTester x) -> Tensor Y")
+              "_TorchScriptTesting::take_an_instance(__torch__.torch.classes._TorchScriptTesting._PickleTester x) -> Tensor Y")
           .catchAllKernel<decltype(take_an_instance), &take_an_instance>());
   return instance_registry;
 }
@@ -146,7 +148,7 @@ void testTorchbindIValueAPI() {
   auto custom_class_obj = make_custom_class<MyStackClass<std::string>>(
       std::vector<std::string>{"foo", "bar"});
   m.define(R"(
-    def forward(self, s : __torch__.torch.classes._TorchScriptTesting_StackString):
+    def forward(self, s : __torch__.torch.classes._TorchScriptTesting._StackString):
       return s.pop(), s
   )");
 

test/cpp/jit/test_lite_interpreter.cpp

@@ -343,7 +343,8 @@ void testLiteInterpreterBuiltinFunction() {
 namespace {
 static auto reg =
     torch::jit::class_<TorchBindLiteInterpreterTestStruct>(
-        "_TorchScriptTesting_LiteInterpreterTest")
+        "_TorchScriptTesting",
+        "_LiteInterpreterTest")
         .def("get", &TorchBindLiteInterpreterTestStruct::get)
         .def_pickle(
             // __getattr__

test/cpp_api_parity/__init__.py

@@ -1,85 +0,0 @@
-from collections import namedtuple
-
-TorchNNTestParams = namedtuple(
-    'TorchNNTestParams',
-    [
-        'module_name',
-        'module_variant_name',
-        'test_instance',
-        'cpp_constructor_args',
-        'has_parity',
-        'device',
-    ]
-)
-
-CppArg = namedtuple('CppArg', ['type', 'value'])
-
-ParityStatus = namedtuple('ParityStatus', ['has_impl_parity', 'has_doc_parity'])
-
-TorchNNModuleMetadata = namedtuple(
-    'TorchNNModuleMetadata',
-    [
-        'cpp_default_constructor_args',
-        'num_attrs_recursive',
-        'python_ignored_constructor_args',
-        'python_ignored_attrs',
-        'python_optional_attribute_to_jit_type',
-        'cpp_sources',
-    ]
-)
-TorchNNModuleMetadata.__new__.__defaults__ = (None, None, [], [], {}, '')
-
-'''
-This function expects the parity tracker Markdown file to have the following format:
-
-```
-## package1_name
-
-API | Implementation Parity | Doc Parity
-------------- | ------------- | -------------
-API_Name|No|No
-...
-
-## package2_name
-
-API | Implementation Parity | Doc Parity
-------------- | ------------- | -------------
-API_Name|No|No
-...
-```
-
-The returned dict has the following format:
-
-```
-Dict[package_name]
-    -> Dict[api_name]
-        -> ParityStatus
-```
-'''
-def parse_parity_tracker_table(file_path):
-    def parse_parity_choice(str):
-        if str in ['Yes', 'No']:
-            return str == 'Yes'
-        else:
-            raise RuntimeError(
-                '{} is not a supported parity choice. The valid choices are "Yes" and "No".'.format(str))
-
-    parity_tracker_dict = {}
-
-    with open(file_path, 'r') as f:
-        all_text = f.read()
-        packages = all_text.split('##')
-        for package in packages[1:]:
-            lines = [line.strip() for line in package.split('\n') if line.strip() != '']
-            package_name = lines[0]
-            if package_name in parity_tracker_dict:
-                raise RuntimeError("Duplicated package name `{}` found in {}".format(package_name, file_path))
-            else:
-                parity_tracker_dict[package_name] = {}
-            for api_status in lines[3:]:
-                api_name, has_impl_parity_str, has_doc_parity_str = [x.strip() for x in api_status.split('|')]
-                parity_tracker_dict[package_name][api_name] = ParityStatus(
-                    has_impl_parity=parse_parity_choice(has_impl_parity_str),
-                    has_doc_parity=parse_parity_choice(has_doc_parity_str))
-
-    return parity_tracker_dict

test/cpp_api_parity/functional_impl_check.py

@@ -0,0 +1,237 @@
+# The purpose of this test is to check that we have implementation parity between
+# a Python `torch.nn.functional` function and its corresponding C++ `torch::nn::functional`
+# function. Concretely, this test does the following:
+#
+# 1. Get a test params dict from common_nn.py, run forward pass on the Python functional
+# created using the test params.
+#
+# 2. Serialize the Python functional's forward input arguments, deserialize them
+# in C++ and use them as input for the C++ functional's forward pass.
+#
+# 3. Run the forward pass on the C++ functional, and serialize the C++ functional's
+# forward output.
+#
+# 4. Compare Python/C++ functional's forward output. If they are the same, then we
+# have implementation parity between Python/C++ module.
+
+import tempfile
+from string import Template
+import re
+import pprint
+import os
+
+import torch
+from cpp_api_parity.utils import TorchNNFunctionalTestParams, TORCH_NN_COMMON_TEST_HARNESS, \
+    compile_cpp_code_inline, set_python_tensors_requires_grad, move_python_tensors_to_device, \
+    add_test, compute_cpp_args_construction_stmts_and_forward_arg_symbols, serialize_arg_dict_as_script_module, \
+    compute_arg_dict, decorate_test_fn, compute_temp_file_path, generate_error_msg, is_torch_nn_functional_test, \
+    try_remove_folder
+from cpp_api_parity.sample_functional import SAMPLE_FUNCTIONAL_CPP_SOURCE
+
+# Expected substitutions:
+#
+# ${functional_variant_name}  (e.g. `BCELoss_no_reduce`)
+# ${cpp_args_construction_stmts}
+# ${cpp_function_call}
+TORCH_NN_FUNCTIONAL_TEST_FORWARD = Template("""
+void ${functional_variant_name}_test_forward(
+    const std::string& arg_dict_file_path,
+    const std::string& forward_output_file_path) {
+  pybind11::gil_scoped_release no_gil;
+
+  namespace F = torch::nn::functional;
+
+  // Declare arguments
+  auto arg_dict = load_dict_from_file(arg_dict_file_path);
+  ${cpp_args_construction_stmts};
+
+  // Some functionals (such as `F::rrelu`) create random tensors in their call path.
+  // To make sure the random tensors created are the same in Python/C++, we need
+  // to set the RNG seed manually.
+  torch::manual_seed(0);
+
+  // Run function with arguments
+  auto cpp_output = ${cpp_function_call};
+
+  // Save the output into a file to be compared in Python later
+  write_ivalue_to_file(torch::IValue(cpp_output), forward_output_file_path);
+}
+""")
+
+def run_forward(unit_test_class, test_params):
+    device = test_params.device
+
+    inputs = set_python_tensors_requires_grad(move_python_tensors_to_device(
+        [arg_value for _, arg_value in test_params.arg_dict['input']], device))
+    inputs += move_python_tensors_to_device(
+        [arg_value for _, arg_value in test_params.arg_dict['target']], device)
+    inputs += move_python_tensors_to_device(
+        [arg_value for _, arg_value in test_params.arg_dict['extra_args']], device)
+
+    # Some functionals (such as `F.rrelu`) create random tensors in their call path.
+    # To make sure the random tensors created are the same in Python/C++, we need
+    # to set the RNG seed manually.
+    torch.manual_seed(0)
+    python_output = test_params.test_instance.constructor()(*inputs)
+
+    return python_output
+
+def test_forward(unit_test_class, test_params):
+    functional_variant_name = test_params.functional_variant_name
+    cpp_tmp_folder = test_params.cpp_tmp_folder
+    # Remove the temporary folder if it exists already
+    try_remove_folder(cpp_tmp_folder)
+    os.mkdir(cpp_tmp_folder)
+
+    # Run forward on Python functional
+    python_output = run_forward(unit_test_class, test_params)
+
+    # Save Python arguments to be used from C++ function
+    arg_dict_file_path = compute_temp_file_path(cpp_tmp_folder, functional_variant_name, 'arg_dict')
+    serialize_arg_dict_as_script_module(test_params.arg_dict).save(arg_dict_file_path)
+
+    cpp_test_name = '{}_test_forward'.format(test_params.functional_variant_name)
+    cpp_test_fn = getattr(unit_test_class.functional_impl_check_cpp_module, cpp_test_name)
+
+    def run_cpp_test_fn_and_check_output():
+        forward_output_file_path = compute_temp_file_path(cpp_tmp_folder, functional_variant_name, 'forward_output')
+
+        cpp_test_fn(arg_dict_file_path, forward_output_file_path)
+        cpp_output = torch.load(forward_output_file_path)
+
+        # Check that forward outputs are equal
+        unit_test_class.assertEqual(
+            python_output, cpp_output,
+            message=generate_error_msg("forward output", cpp_output, python_output))
+
+    run_cpp_test_fn_and_check_output()
+
+    # Remove temporary folder that stores C++ outputs
+    try_remove_folder(cpp_tmp_folder)
+
+def compute_functional_name(test_params_dict):
+    def camel_case_to_snake_case(camel_case_str):
+        return re.sub(r'(?<!^)(?=[A-Z])', '_', camel_case_str).lower()
+
+    if 'cpp_options_args' in test_params_dict:
+        # Expected format for `cpp_options_args`: `F::FunctionalFuncOptions(...)`
+        # Example output: `binary_cross_entropy`
+        return camel_case_to_snake_case(
+            test_params_dict['cpp_options_args'].split('(')[0].replace('F::', '').replace('FuncOptions', ''))
+    elif 'cpp_function_call' in test_params_dict:
+        # Expected format for `cpp_function_call`: `F::functional_name(...)`
+        # Example output: `binary_cross_entropy`
+        return test_params_dict['cpp_function_call'].split('(')[0].replace('F::', '')
+    else:
+        raise RuntimeError(
+            "`cpp_options_args` or `cpp_function_call` entry must be present in test params dict:\n{}".format(
+                pprint.pformat(test_params_dict)))
+
+def compute_cpp_function_call(test_params_dict, arg_dict, functional_name):
+    if 'cpp_function_call' in test_params_dict:
+        return test_params_dict['cpp_function_call']
+    elif 'cpp_options_args' in test_params_dict:
+        cpp_forward_args_symbols = [arg_name for arg_name, _ in 
+                                    arg_dict['input'] + arg_dict['target'] + arg_dict['extra_args']]
+        return 'F::{}({}, {})'.format(
+            functional_name, ", ".join(cpp_forward_args_symbols), test_params_dict['cpp_options_args'])
+    else:
+        raise RuntimeError(
+            "`cpp_options_args` or `cpp_function_call` entry must be present in test params dict:\n{}".format(
+                pprint.pformat(test_params_dict)))
+
+def process_test_params_for_functional(test_params_dict, device, test_instance_class):
+    test_instance = test_instance_class(**test_params_dict)
+    functional_name = compute_functional_name(test_params_dict)
+    assert test_instance.get_name().startswith('test_')
+    # Example output: `BCELoss_no_reduce_cuda`
+    functional_variant_name = test_instance.get_name()[5:] + (('_' + device) if device != 'cpu' else '')
+    arg_dict = compute_arg_dict(test_params_dict, test_instance)
+
+    return TorchNNFunctionalTestParams(
+        functional_name=functional_name,
+        functional_variant_name=functional_variant_name,
+        test_instance=test_instance,
+        cpp_function_call=compute_cpp_function_call(test_params_dict, arg_dict, functional_name),
+        arg_dict=arg_dict,
+        has_parity=test_params_dict.get('has_parity', True),
+        device=device,
+        cpp_tmp_folder=tempfile.mkdtemp(),
+    )
+
+def write_test_to_test_class(
+        unit_test_class, test_params_dict, test_instance_class, parity_table, devices):
+    assert is_torch_nn_functional_test(test_params_dict)
+
+    assert 'cpp_options_args' in test_params_dict or 'cpp_function_call' in test_params_dict, (
+        "To enable C++ API parity test, "
+        "`cpp_options_args` or `cpp_function_call` entry must be present in test params dict:\n{}. \n"
+        "If you are interested in adding the C++ API parity test, please see:\n"
+        "NOTE [How to check NN module / functional API parity between Python and C++ frontends]. \n"
+        "If not, please add `test_cpp_api_parity=False` to the test params dict and file an issue about this."
+    ).format(pprint.pformat(test_params_dict))
+
+    assert not ('cpp_options_args' in test_params_dict and 'cpp_function_call' in test_params_dict), (
+        "Only one of `cpp_options_args` and `cpp_function_call` entries "
+        "should be present in test params dict:\n{}").format(pprint.pformat(test_params_dict))
+
+    functional_name = compute_functional_name(test_params_dict)
+
+    assert hasattr(torch.nn.functional, functional_name), \
+        "`torch.nn.functional` doesn't have function `{}`. (Discovered while processing\n{}.)".format(
+            functional_name, pprint.pformat(test_params_dict))
+
+    functional_full_name = 'F::' + functional_name
+
+    assert functional_full_name in parity_table['torch::nn::functional'], (
+        "Please add `{}` entry to `torch::nn::functional` section of `test/cpp_api_parity/parity-tracker.md`. "
+        "(Discovered while processing\n{}.)").format(functional_full_name, pprint.pformat(test_params_dict))
+
+    for device in devices:
+        test_params = process_test_params_for_functional(
+            test_params_dict=test_params_dict,
+            device=device,
+            test_instance_class=test_instance_class,
+        )
+        unit_test_name = 'test_torch_nn_functional_{}'.format(test_params.functional_variant_name)
+        unit_test_class.functional_test_params_map[unit_test_name] = test_params
+
+        def test_fn(self):
+            test_forward(
+                unit_test_class=self, test_params=unit_test_class.functional_test_params_map[self._testMethodName])
+
+        test_fn = decorate_test_fn(
+            test_fn=test_fn,
+            test_cuda=test_params_dict.get('test_cuda', True),
+            has_impl_parity=parity_table['torch::nn::functional'][functional_full_name][0] and
+            test_params_dict.get('has_parity', True),
+            device=device)
+
+        add_test(unit_test_class, unit_test_name, test_fn)
+
+def generate_test_cpp_sources(test_params, template):
+    cpp_args_construction_stmts, _ = compute_cpp_args_construction_stmts_and_forward_arg_symbols(test_params)
+
+    test_cpp_sources = template.substitute(
+        functional_variant_name=test_params.functional_variant_name,
+        cpp_args_construction_stmts=";\n  ".join(cpp_args_construction_stmts),
+        cpp_function_call=test_params.cpp_function_call,
+    )
+    return test_cpp_sources
+
+# Build all C++ tests together, instead of once per test.
+def build_cpp_tests(unit_test_class, print_cpp_source=False):
+    assert len(unit_test_class.functional_test_params_map) > 0
+    cpp_sources = TORCH_NN_COMMON_TEST_HARNESS + SAMPLE_FUNCTIONAL_CPP_SOURCE
+    functions = []
+    for test_name, test_params in unit_test_class.functional_test_params_map.items():
+        cpp_sources += generate_test_cpp_sources(test_params=test_params, template=TORCH_NN_FUNCTIONAL_TEST_FORWARD)
+        functions.append('{}_test_forward'.format(test_params.functional_variant_name))
+    if print_cpp_source:
+        print(cpp_sources)
+
+    cpp_module = compile_cpp_code_inline(
+        name='functional_impl_check',
+        cpp_sources=cpp_sources,
+        functions=functions)
+    unit_test_class.functional_impl_check_cpp_module = cpp_module

test/cpp_api_parity/module_impl_check.py

@@ -0,0 +1,298 @@
+# The purpose of this test is to check that we have implementation parity between
+# a Python `torch.nn` module and its corresponding C++ `torch::nn` module. Concretely,
+# this test does the following:
+#
+# 1. Get a test params dict from common_nn.py, run forward and backward on the
+# Python module created using the test params.
+#
+# 2. Serialize the Python module's parameters / buffers and its forward input
+# arguments, deserialize them in C++ and load them into the C++ module.
+#
+# 3. Run the same forward and backward passes on the C++ module, and serialize
+# the C++ module's forward output and backward gradients.
+#
+# 4. Compare Python/C++ module's forward output and backward gradients. If they
+# are the same, then we have implementation parity between Python/C++ module.
+
+import tempfile
+from string import Template
+import types
+import pprint
+import os
+
+import torch
+from cpp_api_parity.utils import TorchNNModuleTestParams, TORCH_NN_COMMON_TEST_HARNESS, \
+    compile_cpp_code_inline, set_python_tensors_requires_grad, move_python_tensors_to_device, \
+    add_test, compute_cpp_args_construction_stmts_and_forward_arg_symbols, serialize_arg_dict_as_script_module, \
+    compute_arg_dict, decorate_test_fn, compute_temp_file_path, generate_error_msg, is_torch_nn_functional_test, \
+    try_remove_folder
+from cpp_api_parity.sample_module import SAMPLE_MODULE_CPP_SOURCE
+
+# Expected substitutions:
+#
+# ${module_variant_name}  (e.g. `Linear_no_bias_cpu`)
+# ${module_qualified_name}  (e.g. `torch::nn::Linear`)
+# ${cpp_args_construction_stmts}
+# ${cpp_constructor_args}
+# ${device}
+# ${cpp_forward_args_symbols}
+TORCH_NN_MODULE_TEST_FORWARD_BACKWARD = Template("""
+void ${module_variant_name}_test_forward_backward(
+    const std::string& arg_dict_file_path,
+    const std::string& module_file_path,
+    const std::string& forward_output_file_path,
+    const std::string& backward_grad_dict_file_path) {
+  pybind11::gil_scoped_release no_gil;
+
+  // Declare arguments
+  auto arg_dict = load_dict_from_file(arg_dict_file_path);
+  ${cpp_args_construction_stmts};
+
+  // Construct module and load params/buffers from Python module
+  ${module_qualified_name} module${cpp_constructor_args};
+  module->to(std::string("${device}"));
+  torch::load(module, module_file_path);
+
+  // Some modules (such as `RReLU`) create random tensors in their forward pass.
+  // To make sure the random tensors created are the same in Python/C++, we need
+  // to set the RNG seed manually.
+  torch::manual_seed(0);
+
+  // Forward pass
+  auto cpp_output = module(${cpp_forward_args_symbols});
+
+  // Save the output into a file to be compared in Python later
+  write_ivalue_to_file(torch::IValue(cpp_output), forward_output_file_path);
+
+  // Backward pass
+  cpp_output.sum().backward();
+
+  // Put all gradients into a c10::Dict, save it into a file to be compared in Python later
+  c10::Dict<std::string, torch::Tensor> grad_dict;
+  for (const auto& param : module->named_parameters()) {
+    torch::Tensor grad = param.value().grad();
+    if (grad.is_sparse()) {
+      grad_dict.insert(param.key() + "_grad_indices", grad.coalesce().indices());
+      grad_dict.insert(param.key() + "_grad_values", grad.coalesce().values());
+    } else {
+      grad_dict.insert(param.key() + "_grad", grad);
+    }
+  }
+
+  write_ivalue_to_file(torch::IValue(grad_dict), backward_grad_dict_file_path);
+}
+""")
+
+def run_python_forward_backward(unit_test_class, test_params):
+    device = test_params.device
+    module = test_params.test_instance.constructor(*test_params.test_instance.constructor_args).to(device)
+
+    inputs = set_python_tensors_requires_grad(move_python_tensors_to_device(
+        [arg_value for _, arg_value in test_params.arg_dict['input']], device))
+    inputs += move_python_tensors_to_device(
+        [arg_value for _, arg_value in test_params.arg_dict['target']], device)
+    inputs += move_python_tensors_to_device(
+        [arg_value for _, arg_value in test_params.arg_dict['extra_args']], device)
+
+    # Some modules (such as `RReLU`) create random tensors in their forward pass.
+    # To make sure the random tensors created are the same in Python/C++, we need
+    # to set the RNG seed manually.
+    torch.manual_seed(0)
+
+    # Forward pass
+    python_output = module(*inputs)
+
+    # NOTE: This is a workaround to allow any module to be traced.
+    # We can do this because we are only interested in transferring
+    # the Python module's parameters and buffers to the C++ module.
+    module.forward = types.MethodType(lambda self, input: input, module)
+    script_module = torch.jit.trace(module, torch.tensor(0))
+
+    # Backward pass
+    python_output.sum().backward()
+
+    # Put all gradients into a dict, to be compared later
+    python_grad_dict = {}
+    for name, param in module.named_parameters():
+        grad = param.grad
+        if grad.is_sparse:
+            python_grad_dict[name + "_grad_indices"] = grad.coalesce().indices()
+            python_grad_dict[name + "_grad_values"] = grad.coalesce().values()
+        else:
+            python_grad_dict[name + "_grad"] = grad
+
+    return script_module, python_output, python_grad_dict
+
+def test_forward_backward(unit_test_class, test_params):
+    module_variant_name = test_params.module_variant_name
+    cpp_tmp_folder = test_params.cpp_tmp_folder
+    # Remove the temporary folder if it exists already
+    try_remove_folder(cpp_tmp_folder)
+    os.mkdir(cpp_tmp_folder)
+
+    # Run forward and backward on Python module
+    script_module, python_output, python_grad_dict = run_python_forward_backward(unit_test_class, test_params)
+
+    # Save Python module and arguments to be used from C++ function
+    module_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'module')
+    arg_dict_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'arg_dict')
+    script_module.save(module_file_path)
+    serialize_arg_dict_as_script_module(test_params.arg_dict).save(arg_dict_file_path)
+
+    cpp_test_name = '{}_test_forward_backward'.format(test_params.module_variant_name)
+    cpp_test_fn = getattr(unit_test_class.module_impl_check_cpp_module, cpp_test_name)
+
+    def run_cpp_test_fn_and_check_output():
+        forward_output_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'forward_output')
+        backward_grad_dict_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'backward_grad_dict')
+
+        cpp_test_fn(arg_dict_file_path, module_file_path, forward_output_file_path, backward_grad_dict_file_path)
+        cpp_output = torch.load(forward_output_file_path)
+        cpp_grad_dict = torch.load(backward_grad_dict_file_path)
+
+        # Check that forward outputs are equal
+        unit_test_class.assertEqual(python_output, cpp_output,
+                                    message=generate_error_msg("forward output", cpp_output, python_output))
+
+        # Check that module parameter gradients are equal after backward pass
+        unit_test_class.assertEqual(
+            len(python_grad_dict), len(cpp_grad_dict),
+            message=generate_error_msg("# of parameters", len(cpp_grad_dict), len(python_grad_dict)))
+        for key in python_grad_dict:
+            param_name = None
+            for suffix in ['_grad', '_grad_indices', '_grad_values']:
+                if key.endswith(suffix):
+                    param_name = key[:-len(suffix)]
+                    break
+            assert param_name is not None
+            sparsity_str = 'sparse' if key.endswith('_grad_indices') or key.endswith('_grad_values') else 'dense'
+
+            unit_test_class.assertTrue(
+                key in cpp_grad_dict,
+                msg=generate_error_msg(
+                    "\"Does module have a parameter named `{}` with {} gradient?\"".format(param_name, sparsity_str),
+                    False, True))
+            unit_test_class.assertEqual(
+                python_grad_dict[key], cpp_grad_dict[key],
+                message=generate_error_msg(
+                    "`{}`'s {} gradient (`{}`)".format(param_name, sparsity_str, key),
+                    cpp_grad_dict[key], python_grad_dict[key]))
+
+    run_cpp_test_fn_and_check_output()
+
+    # Remove temporary folder that stores C++ outputs
+    try_remove_folder(cpp_tmp_folder)
+
+def compute_module_name(test_params_dict):
+    fullname = test_params_dict.get('fullname', None)
+    if fullname:
+        module_name = fullname.split('_')[0]
+    else:
+        module_name = test_params_dict.get('module_name')
+    return module_name
+
+def process_test_params_for_module(test_params_dict, device, test_instance_class):
+    module_name = compute_module_name(test_params_dict)
+    test_params_dict['constructor'] = test_params_dict.get('constructor', getattr(torch.nn, module_name))
+    test_instance = test_instance_class(**test_params_dict)
+    assert test_instance.get_name().startswith('test_')
+    # Example output: `BCELoss_weights_cuda`
+    module_variant_name = test_instance.get_name()[5:] + (('_' + device) if device != 'cpu' else '')
+
+    if 'constructor_args' in test_params_dict:
+        assert 'cpp_constructor_args' in test_params_dict, (
+            "If `constructor_args` is present in test params dict, to enable C++ API parity test, "
+            "`cpp_constructor_args` must be present in:\n{}"
+            "If you are interested in adding the C++ API parity test, please see:\n"
+            "NOTE [How to check NN module / functional API parity between Python and C++ frontends]. \n"
+            "If not, please add `test_cpp_api_parity=False` to the test params dict and file an issue about this."
+        ).format(pprint.pformat(test_params_dict))
+
+    return TorchNNModuleTestParams(
+        module_name=module_name,
+        module_variant_name=module_variant_name,
+        test_instance=test_instance,
+        cpp_constructor_args=test_params_dict.get('cpp_constructor_args', ''),
+        arg_dict=compute_arg_dict(test_params_dict, test_instance),
+        has_parity=test_params_dict.get('has_parity', True),
+        device=device,
+        cpp_tmp_folder=tempfile.mkdtemp(),
+    )
+
+def write_test_to_test_class(
+        unit_test_class, test_params_dict, test_instance_class, parity_table, devices):
+    assert not is_torch_nn_functional_test(test_params_dict)
+
+    module_name = compute_module_name(test_params_dict)
+
+    assert hasattr(torch.nn, module_name), (
+        "`torch.nn` doesn't have module `{}`. "
+        "If you are adding a new test, please set `fullname` using format `ModuleName_desc` "
+        "or set `module_name` using format `ModuleName` in the module test dict:\n{}"
+    ).format(module_name, pprint.pformat(test_params_dict))
+
+    module_full_name = 'torch::nn::' + module_name
+
+    assert module_full_name in parity_table['torch::nn'], (
+        "Please add `{}` entry to `torch::nn` section of `test/cpp_api_parity/parity-tracker.md`. "
+        "(Discovered while processing\n{}.)").format(module_full_name, pprint.pformat(test_params_dict))
+
+    for device in devices:
+        test_params = process_test_params_for_module(
+            test_params_dict=test_params_dict,
+            device=device,
+            test_instance_class=test_instance_class,
+        )
+        unit_test_name = 'test_torch_nn_{}'.format(test_params.module_variant_name)
+        unit_test_class.module_test_params_map[unit_test_name] = test_params
+
+        def test_fn(self):
+            test_forward_backward(
+                unit_test_class=self, test_params=unit_test_class.module_test_params_map[self._testMethodName])
+
+        test_fn = decorate_test_fn(
+            test_fn=test_fn,
+            test_cuda=test_params_dict.get('test_cuda', True),
+            has_impl_parity=parity_table['torch::nn'][module_full_name][0] and
+            test_params_dict.get('has_parity', True),
+            device=device)
+
+        add_test(unit_test_class, unit_test_name, test_fn)
+
+def generate_test_cpp_sources(test_params, template):
+    device = test_params.device
+
+    cpp_constructor_args = test_params.cpp_constructor_args
+    if cpp_constructor_args != '':
+        cpp_constructor_args = '({})'.format(cpp_constructor_args)
+
+    cpp_args_construction_stmts, cpp_forward_args_symbols = \
+        compute_cpp_args_construction_stmts_and_forward_arg_symbols(test_params)
+
+    test_cpp_sources = template.substitute(
+        module_variant_name=test_params.module_variant_name,
+        module_qualified_name='torch::nn::{}'.format(test_params.module_name),
+        cpp_args_construction_stmts=";\n  ".join(cpp_args_construction_stmts),
+        cpp_constructor_args=cpp_constructor_args,
+        cpp_forward_args_symbols=", ".join(cpp_forward_args_symbols),
+        device=device,
+    )
+    return test_cpp_sources
+
+# Build all C++ tests together, instead of once per test.
+def build_cpp_tests(unit_test_class, print_cpp_source=False):
+    assert len(unit_test_class.module_test_params_map) > 0
+    cpp_sources = TORCH_NN_COMMON_TEST_HARNESS + SAMPLE_MODULE_CPP_SOURCE
+    functions = []
+    for test_name, test_params in unit_test_class.module_test_params_map.items():
+        cpp_sources += generate_test_cpp_sources(
+            test_params=test_params, template=TORCH_NN_MODULE_TEST_FORWARD_BACKWARD)
+        functions.append('{}_test_forward_backward'.format(test_params.module_variant_name))
+    if print_cpp_source:
+        print(cpp_sources)
+
+    cpp_module = compile_cpp_code_inline(
+        name='module_impl_check',
+        cpp_sources=cpp_sources,
+        functions=functions)
+    unit_test_class.module_impl_check_cpp_module = cpp_module