Remove few xfails

Compiler should do the work for you

.ci/docker/build.sh

@@ -251,14 +251,6 @@ case "$tag" in
     UCC_COMMIT=${_UCC_COMMIT}
     INDUCTOR_BENCHMARKS=yes
     ;;
-  pytorch-linux-jammy-xpu-2024.0-py3)
-    ANACONDA_PYTHON_VERSION=3.9
-    GCC_VERSION=11
-    VISION=yes
-    XPU_VERSION=0.5
-    NINJA_VERSION=1.9.0
-    TRITON=yes
-    ;;
   pytorch-linux-jammy-xpu-2025.0-py3)
     ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11
@@ -267,6 +259,14 @@ case "$tag" in
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
+  pytorch-linux-jammy-xpu-2025.1-py3)
+    ANACONDA_PYTHON_VERSION=3.9
+    GCC_VERSION=11
+    VISION=yes
+    XPU_VERSION=2025.1
+    NINJA_VERSION=1.9.0
+    TRITON=yes
+    ;;
     pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11

.ci/docker/common/install_xpu.sh

@@ -26,7 +26,7 @@ function install_ubuntu() {
     wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
         | gpg --dearmor > /usr/share/keyrings/oneapi-archive-keyring.gpg.gpg
     echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg.gpg] \
-        https://apt.repos.intel.com/${XPU_REPO_NAME} all main" \
+        https://apt.repos.intel.com/oneapi all main" \
         | tee /etc/apt/sources.list.d/oneAPI.list
 
     # Update the packages list and repository index
@@ -74,7 +74,7 @@ function install_rhel() {
     tee > /etc/yum.repos.d/oneAPI.repo << EOF
 [oneAPI]
 name=Intel for Pytorch GPU dev repository
-baseurl=https://yum.repos.intel.com/${XPU_REPO_NAME}
+baseurl=https://yum.repos.intel.com/oneapi
 enabled=1
 gpgcheck=1
 repo_gpgcheck=1
@@ -118,7 +118,7 @@ function install_sles() {
         https://repositories.intel.com/gpu/sles/${VERSION_SP}${XPU_DRIVER_VERSION}/unified/intel-gpu-${VERSION_SP}.repo
     rpm --import https://repositories.intel.com/gpu/intel-graphics.key
     # To add the online network network package repository for the Intel Support Packages
-    zypper addrepo https://yum.repos.intel.com/${XPU_REPO_NAME} oneAPI
+    zypper addrepo https://yum.repos.intel.com/oneapi oneAPI
     rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
 
     # The xpu-smi packages
@@ -141,10 +141,10 @@ if [[ "${XPU_DRIVER_TYPE,,}" == "rolling" ]]; then
     XPU_DRIVER_VERSION=""
 fi
 
-XPU_REPO_NAME="intel-for-pytorch-gpu-dev"
-XPU_PACKAGES="intel-for-pytorch-gpu-dev-0.5 intel-pti-dev-0.9"
-if [[ "$XPU_VERSION" == "2025.0" ]]; then
-    XPU_REPO_NAME="oneapi"
+# Default use Intel® oneAPI Deep Learning Essentials 2025.0
+if [[ "$XPU_VERSION" == "2025.1" ]]; then
+    XPU_PACKAGES="intel-deep-learning-essentials-2025.1"
+else
     XPU_PACKAGES="intel-deep-learning-essentials-2025.0"
 fi
 

.ci/docker/manywheel/Dockerfile_2_28

@@ -174,6 +174,6 @@ ENV XPU_DRIVER_TYPE ROLLING
 RUN python3 -m pip install --upgrade pip && \
     python3 -mpip install cmake==3.28.4
 ADD ./common/install_xpu.sh install_xpu.sh
-ENV XPU_VERSION 2025.0
+ENV XPU_VERSION 2025.1
 RUN bash ./install_xpu.sh && rm install_xpu.sh
 RUN pushd /opt/_internal && tar -xJf static-libs-for-embedding-only.tar.xz && popd

.ci/manywheel/build_xpu.sh

@@ -20,7 +20,11 @@ fi
 source /opt/intel/oneapi/compiler/latest/env/vars.sh
 source /opt/intel/oneapi/pti/latest/env/vars.sh
 source /opt/intel/oneapi/umf/latest/env/vars.sh
+source /opt/intel/oneapi/ccl/latest/env/vars.sh
+source /opt/intel/oneapi/mpi/latest/env/vars.sh
 export USE_STATIC_MKL=1
+export USE_ONEMKL=1
+export USE_XCCL=1
 
 WHEELHOUSE_DIR="wheelhousexpu"
 LIBTORCH_HOUSE_DIR="libtorch_housexpu"

.ci/pytorch/build.sh

@@ -99,30 +99,6 @@ if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
   export ACL_ROOT_DIR=/ComputeLibrary
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *libtorch* ]]; then
-  POSSIBLE_JAVA_HOMES=()
-  POSSIBLE_JAVA_HOMES+=(/usr/local)
-  POSSIBLE_JAVA_HOMES+=(/usr/lib/jvm/java-8-openjdk-amd64)
-  POSSIBLE_JAVA_HOMES+=(/Library/Java/JavaVirtualMachines/*.jdk/Contents/Home)
-  # Add the Windows-specific JNI
-  POSSIBLE_JAVA_HOMES+=("$PWD/.circleci/windows-jni/")
-  for JH in "${POSSIBLE_JAVA_HOMES[@]}" ; do
-    if [[ -e "$JH/include/jni.h" ]] ; then
-      # Skip if we're not on Windows but haven't found a JAVA_HOME
-      if [[ "$JH" == "$PWD/.circleci/windows-jni/" && "$OSTYPE" != "msys" ]] ; then
-        break
-      fi
-      echo "Found jni.h under $JH"
-      export JAVA_HOME="$JH"
-      export BUILD_JNI=ON
-      break
-    fi
-  done
-  if [ -z "$JAVA_HOME" ]; then
-    echo "Did not find jni.h"
-  fi
-fi
-
 # Use special scripts for Android builds
 if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
   export ANDROID_NDK=/opt/ndk

.ci/pytorch/macos-test.sh

@@ -232,7 +232,8 @@ test_torchbench_smoketest() {
   mkdir -p "$TEST_REPORTS_DIR"
 
   local device=mps
-  local models=(hf_T5 llama BERT_pytorch dcgan hf_GPT2 yolov3 resnet152 sam pytorch_unet stable_diffusion_text_encoder moco speech_transformer)
+  local models=(hf_T5 llama BERT_pytorch dcgan hf_GPT2 yolov3 resnet152 sam pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo)
+  local hf_models=(GoogleFnet YituTechConvBert)
 
   for backend in eager inductor; do
 
@@ -253,6 +254,16 @@ test_torchbench_smoketest() {
             --output "$TEST_REPORTS_DIR/inductor_${backend}_torchbench_${dtype}_inference_${device}_accuracy.csv" || true
         fi
       done
+      for model in "${hf_models[@]}"; do
+        if [ "$backend" == "inductor" ]; then
+          PYTHONPATH="$(pwd)"/torchbench python benchmarks/dynamo/huggingface.py \
+            --performance --only "$model" --backend "$backend" --inference --devices "$device" "$dtype_arg" \
+            --output "$TEST_REPORTS_DIR/inductor_${backend}_torchbench_${dtype}_inference_${device}_performance.csv" || true
+          PYTHONPATH="$(pwd)"/torchbench python benchmarks/dynamo/huggingface.py \
+            --accuracy --only "$model" --backend "$backend" --inference --devices "$device" "$dtype_arg" \
+            --output "$TEST_REPORTS_DIR/inductor_${backend}_torchbench_${dtype}_inference_${device}_accuracy.csv" || true
+        fi
+      done
     done
 
     for dtype in notset amp; do

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

@@ -37,6 +37,11 @@ call %INSTALLER_DIR%\activate_miniconda3.bat
 if errorlevel 1 goto fail
 if not errorlevel 0 goto fail
 
+:: Update CMake
+call choco upgrade -y cmake --no-progress --installargs 'ADD_CMAKE_TO_PATH=System' --apply-install-arguments-to-dependencies --version=3.27.9
+if errorlevel 1 goto fail
+if not errorlevel 0 goto fail
+
 call pip install mkl-include==2021.4.0 mkl-devel==2021.4.0
 if errorlevel 1 goto fail
 if not errorlevel 0 goto fail
@@ -88,7 +93,7 @@ set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
 :cuda_build_end
 
 set DISTUTILS_USE_SDK=1
-set PATH=%TMP_DIR_WIN%\bin;%PATH%
+set PATH=%TMP_DIR_WIN%\bin;C:\Program Files\CMake\bin;%PATH%
 
 :: The latest Windows CUDA test is running on AWS G5 runner with A10G GPU
 if "%TORCH_CUDA_ARCH_LIST%" == "" set TORCH_CUDA_ARCH_LIST=8.6

.ci/pytorch/windows/internal/xpu_install.bat

@@ -10,53 +10,23 @@ if not "%CUDA_VERSION%" == "xpu" (
 set SRC_DIR=%NIGHTLIES_PYTORCH_ROOT%
 if not exist "%SRC_DIR%\temp_build" mkdir "%SRC_DIR%\temp_build"
 
-set XPU_INSTALL_MODE=%~1
-if "%XPU_INSTALL_MODE%"=="" goto xpu_bundle_install_start
-if "%XPU_INSTALL_MODE%"=="bundle" goto xpu_bundle_install_start
-if "%XPU_INSTALL_MODE%"=="driver" goto xpu_driver_install_start
-if "%XPU_INSTALL_MODE%"=="all" goto xpu_driver_install_start
-
-:arg_error
-
-echo Illegal XPU installation mode. The value can be "bundle"/"driver"/"all"
-echo If keep the value as space, will use default "bundle" mode
-exit /b 1
-
-:xpu_driver_install_start
-:: TODO Need more testing for driver installation
-set XPU_DRIVER_LINK=https://downloadmirror.intel.com/830975/gfx_win_101.5972.exe
-curl -o xpu_driver.exe --retry 3 --retry-all-errors -k %XPU_DRIVER_LINK%
-echo "XPU Driver installing..."
-start /wait "Intel XPU Driver Installer" "xpu_driver.exe"
-if errorlevel 1 exit /b 1
-del xpu_driver.exe
-if "%XPU_INSTALL_MODE%"=="driver" goto xpu_install_end
-
 :xpu_bundle_install_start
 
 set XPU_BUNDLE_PARENT_DIR=C:\Program Files (x86)\Intel\oneAPI
-set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-for-pytorch-gpu-dev_p_0.5.3.37_offline.exe
-set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.intel-for-pytorch-gpu-dev.product
-set XPU_BUNDLE_VERSION=0.5.3+31
+set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d6d6c17-ca2d-4735-9331-99447e4a1280/intel-deep-learning-essentials-2025.0.1.28_offline.exe
+set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.deep-learning-essentials.product
+set XPU_BUNDLE_VERSION=2025.0.1+20
 set XPU_BUNDLE_INSTALLED=0
 set XPU_BUNDLE_UNINSTALL=0
-set XPU_EXTRA_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-pti-dev_p_0.9.0.37_offline.exe
-set XPU_EXTRA_PRODUCT_NAME=intel.oneapi.win.intel-pti-dev.product
-set XPU_EXTRA_VERSION=0.9.0+36
+set XPU_EXTRA_URL=NULL
+set XPU_EXTRA_PRODUCT_NAME=intel.oneapi.win.compiler.product
+set XPU_EXTRA_VERSION=2025.0.1+1226
 set XPU_EXTRA_INSTALLED=0
 set XPU_EXTRA_UNINSTALL=0
 
-if not [%XPU_VERSION%]==[] if [%XPU_VERSION%]==[2025.0] (
-    set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d6d6c17-ca2d-4735-9331-99447e4a1280/intel-deep-learning-essentials-2025.0.1.28_offline.exe
-    set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.deep-learning-essentials.product
-    set XPU_BUNDLE_VERSION=2025.0.1+20
-    set XPU_BUNDLE_INSTALLED=0
-    set XPU_BUNDLE_UNINSTALL=0
-    set XPU_EXTRA_URL=NULL
-    set XPU_EXTRA_PRODUCT_NAME=intel.oneapi.win.compiler.product
-    set XPU_EXTRA_VERSION=2025.0.1+1226
-    set XPU_EXTRA_INSTALLED=0
-    set XPU_EXTRA_UNINSTALL=0
+if not [%XPU_VERSION%]==[] if [%XPU_VERSION%]==[2025.1] (
+    set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/1a9fff3d-04c2-4d77-8861-3d86c774b66f/intel-deep-learning-essentials-2025.1.1.26_offline.exe
+    set XPU_BUNDLE_VERSION=2025.1.1+23
 )
 
 :: Check if XPU bundle is target version or already installed

.ci/pytorch/windows/xpu.bat

@@ -26,6 +26,7 @@ set VS2022INSTALLDIR=%VS15INSTALLDIR%
 set XPU_BUNDLE_ROOT=%ProgramFiles(x86)%\Intel\oneAPI
 call "%XPU_BUNDLE_ROOT%\compiler\latest\env\vars.bat"
 call "%XPU_BUNDLE_ROOT%\ocloc\latest\env\vars.bat"
+set USE_ONEMKL=1
 IF ERRORLEVEL 1 goto :eof
 
 if exist "%NIGHTLIES_PYTORCH_ROOT%" cd %NIGHTLIES_PYTORCH_ROOT%\..

.circleci/scripts/binary_windows_build.sh

@@ -15,7 +15,7 @@ fi
 if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
     export VC_YEAR=2022
     export USE_SCCACHE=0
-    export XPU_VERSION=2025.0
+    export XPU_VERSION=2025.1
     export XPU_ENABLE_KINETO=1
 fi
 

.circleci/scripts/binary_windows_test.sh

@@ -8,7 +8,7 @@ export VC_YEAR=2019
 
 if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
     export VC_YEAR=2022
-    export XPU_VERSION=2025.0
+    export XPU_VERSION=2025.1
 fi
 
 pushd "$PYTORCH_ROOT/.ci/pytorch/"

.github/actions/upload-utilization-stats/action.yml

@@ -44,7 +44,7 @@ runs:
         retry_wait_seconds: 30
         command: |
           set -eu
-          python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3
+          python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 dataclasses_json==0.6.7
     - name: Upload utilizatoin stats to s3
       shell: bash
       run: |

.github/pytorch-probot.yml

@@ -25,7 +25,6 @@ ciflow_push_tags:
 - ciflow/unstable
 - ciflow/xpu
 - ciflow/torchbench
-- ciflow/autoformat
 - ciflow/op-benchmark
 - ciflow/pull
 - ciflow/h100

.github/scripts/generate_binary_build_matrix.py

@@ -88,17 +88,26 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cufile-cu12==1.13.0.11; platform_system == 'Linux' and platform_machine == 'x86_64'"
     ),
     "xpu": (
-        "intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | "
-        "intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | "
-        "intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | "
-        "intel-sycl-rt==2025.0.4; platform_system == 'Linux' | "
-        "intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | "
-        "intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | "
-        "intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | "
-        "intel-sycl-rt==2025.0.5; platform_system == 'Windows' | "
-        "tcmlib==1.2.0 | "
-        "umf==0.9.1 | "
-        "intel-pti==0.10.1"
+        "intel-cmplr-lib-rt==2025.1.1 | "
+        "intel-cmplr-lib-ur==2025.1.1 | "
+        "intel-cmplr-lic-rt==2025.1.1 | "
+        "intel-sycl-rt==2025.1.1 | "
+        "oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "onemkl-sycl-blas==2025.1.0 | "
+        "onemkl-sycl-dft==2025.1.0 | "
+        "onemkl-sycl-lapack==2025.1.0 | "
+        "onemkl-sycl-rng==2025.1.0 | "
+        "onemkl-sycl-sparse==2025.1.0 | "
+        "dpcpp-cpp-rt==2025.1.1 | "
+        "intel-opencl-rt==2025.1.1 | "
+        "mkl==2025.1.0 | "
+        "intel-openmp==2025.1.1 | "
+        "tbb==2022.1.0 | "
+        "tcmlib==1.3.0 | "
+        "umf==0.10.0 | "
+        "intel-pti==0.12.0"
     ),
 }
 

.github/scripts/trymerge.py

@@ -1938,6 +1938,7 @@ def get_ghstack_dependent_prs(
 
 def do_revert_prs(
     repo: GitRepo,
+    original_pr: GitHubPR,
     shas_and_prs: list[tuple[str, GitHubPR]],
     *,
     author_login: str,
@@ -1959,9 +1960,16 @@ def do_revert_prs(
 
     # Comment/reopen PRs
     for commit_sha, pr in shas_and_prs:
-        revert_message = (
-            f"@{pr.get_pr_creator_login()} your PR has been successfully reverted."
-        )
+        revert_message = ""
+        if pr.pr_num == original_pr.pr_num:
+            revert_message += (
+                f"@{pr.get_pr_creator_login()} your PR has been successfully reverted."
+            )
+        else:
+            revert_message += (
+                f"@{pr.get_pr_creator_login()} your PR has been reverted as part of the stack under "
+                f"#{original_pr.pr_num}.\n"
+            )
         if (
             pr.has_internal_changes()
             and not pr.has_no_connected_diff()
@@ -2013,6 +2021,7 @@ def try_revert(
 
     do_revert_prs(
         repo,
+        pr,
         shas_and_prs,
         author_login=author_login,
         extra_msg=extra_msg,

.github/workflows/docker-builds.yml

@@ -67,8 +67,8 @@ jobs:
           pytorch-linux-jammy-py3.9-gcc11,
           pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks,
           pytorch-linux-jammy-py3.12-halide,
-          pytorch-linux-jammy-xpu-2024.0-py3,
           pytorch-linux-jammy-xpu-2025.0-py3,
+          pytorch-linux-jammy-xpu-2025.1-py3,
           pytorch-linux-jammy-py3-clang15-asan,
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-focal-py3-clang10-onnx,

.github/workflows/generated-linux-binary-manywheel-nightly.yml

@@ -565,7 +565,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_9-xpu
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_9-xpu-test:  # Testing
@@ -1178,7 +1178,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_10-xpu
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_10-xpu-test:  # Testing
@@ -1859,7 +1859,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_11-xpu
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_11-xpu-test:  # Testing
@@ -2472,7 +2472,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_12-xpu
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_12-xpu-test:  # Testing
@@ -3085,7 +3085,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13-xpu
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13-xpu-test:  # Testing
@@ -3698,7 +3698,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13t-xpu
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13t-xpu-test:  # Testing

.github/workflows/generated-windows-binary-wheel-nightly.yml

@@ -1004,7 +1004,7 @@ jobs:
       GPU_ARCH_TYPE: xpu
       SKIP_ALL_TESTS: 1
       DESIRED_PYTHON: "3.9"
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     steps:
       # NOTE: These environment variables are put here so that they can be applied on every job equally
       #       They are also here because setting them at a workflow level doesn't give us access to the
@@ -2189,7 +2189,7 @@ jobs:
       GPU_ARCH_TYPE: xpu
       SKIP_ALL_TESTS: 1
       DESIRED_PYTHON: "3.10"
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     steps:
       # NOTE: These environment variables are put here so that they can be applied on every job equally
       #       They are also here because setting them at a workflow level doesn't give us access to the
@@ -3374,7 +3374,7 @@ jobs:
       GPU_ARCH_TYPE: xpu
       SKIP_ALL_TESTS: 1
       DESIRED_PYTHON: "3.11"
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     steps:
       # NOTE: These environment variables are put here so that they can be applied on every job equally
       #       They are also here because setting them at a workflow level doesn't give us access to the
@@ -4559,7 +4559,7 @@ jobs:
       GPU_ARCH_TYPE: xpu
       SKIP_ALL_TESTS: 1
       DESIRED_PYTHON: "3.12"
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     steps:
       # NOTE: These environment variables are put here so that they can be applied on every job equally
       #       They are also here because setting them at a workflow level doesn't give us access to the
@@ -5744,7 +5744,7 @@ jobs:
       GPU_ARCH_TYPE: xpu
       SKIP_ALL_TESTS: 1
       DESIRED_PYTHON: "3.13"
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     steps:
       # NOTE: These environment variables are put here so that they can be applied on every job equally
       #       They are also here because setting them at a workflow level doesn't give us access to the
@@ -6929,7 +6929,7 @@ jobs:
       GPU_ARCH_TYPE: xpu
       SKIP_ALL_TESTS: 1
       DESIRED_PYTHON: "3.13t"
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-ur==2025.0.4; platform_system == 'Linux' | intel-cmplr-lic-rt==2025.0.4; platform_system == 'Linux' | intel-sycl-rt==2025.0.4; platform_system == 'Linux' | intel-cmplr-lib-rt==2025.0.5; platform_system == 'Windows' | intel-cmplr-lib-ur==2025.0.5; platform_system == 'Windows' | intel-cmplr-lic-rt==2025.0.5; platform_system == 'Windows' | intel-sycl-rt==2025.0.5; platform_system == 'Windows' | tcmlib==1.2.0 | umf==0.9.1 | intel-pti==0.10.1
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: intel-cmplr-lib-rt==2025.1.1 | intel-cmplr-lib-ur==2025.1.1 | intel-cmplr-lic-rt==2025.1.1 | intel-sycl-rt==2025.1.1 | oneccl-devel==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | oneccl==2021.15.1; platform_system == 'Linux' and platform_machine == 'x86_64' | impi-rt==2021.15.0; platform_system == 'Linux' and platform_machine == 'x86_64' | onemkl-sycl-blas==2025.1.0 | onemkl-sycl-dft==2025.1.0 | onemkl-sycl-lapack==2025.1.0 | onemkl-sycl-rng==2025.1.0 | onemkl-sycl-sparse==2025.1.0 | dpcpp-cpp-rt==2025.1.1 | intel-opencl-rt==2025.1.1 | mkl==2025.1.0 | intel-openmp==2025.1.1 | tbb==2022.1.0 | tcmlib==1.3.0 | umf==0.10.0 | intel-pti==0.12.0
     steps:
       # NOTE: These environment variables are put here so that they can be applied on every job equally
       #       They are also here because setting them at a workflow level doesn't give us access to the

.github/workflows/inductor-perf-compare.yml

@@ -52,7 +52,7 @@ jobs:
       docker-image: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.test-matrix }}
       # disable monitor in perf tests for more investigation
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit

.github/workflows/inductor-perf-test-nightly-aarch64.yml

@@ -129,7 +129,9 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-aarch64-py3_10-inductor-build.outputs.test-matrix }}
       timeout-minutes: 720
       # disable monitor in perf tests for more investigation
-      disable-monitor: true
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
     secrets: inherit
 
 

.github/workflows/inductor-perf-test-nightly-h100.yml

@@ -122,7 +122,7 @@ jobs:
       test-matrix: ${{ needs.build.outputs.test-matrix }}
       timeout-minutes: 720
       # disable monitor in perf tests, next step is to enable it
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
@@ -139,7 +139,7 @@ jobs:
       test-matrix: ${{ needs.build.outputs.test-matrix }}
       timeout-minutes: 1440
       # disable monitor in perf tests, next step is to enable it
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
@@ -156,7 +156,7 @@ jobs:
       test-matrix: ${{ needs.build.outputs.test-matrix }}
       timeout-minutes: 720
       # disable monitor in perf tests for more investigation
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit

.github/workflows/inductor-perf-test-nightly-x86.yml

@@ -103,7 +103,7 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cpu-py3_9-gcc11-inductor-build.outputs.test-matrix }}
       timeout-minutes: 720
       # disable monitor in perf tests
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
@@ -121,7 +121,7 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cpu-py3_9-gcc11-inductor-build.outputs.test-matrix }}
       timeout-minutes: 720
       # disable monitor in perf tests
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit

.github/workflows/inductor-perf-test-nightly.yml

@@ -123,8 +123,7 @@ jobs:
       docker-image: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.test-matrix }}
       timeout-minutes: 720
-      # disable monitor in perf tests, next step is to enable it
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
@@ -141,7 +140,7 @@ jobs:
       test-matrix: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.test-matrix }}
       timeout-minutes: 1440
       # disable monitor in perf tests, next step is to enable it
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
@@ -157,8 +156,7 @@ jobs:
       docker-image: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build.outputs.test-matrix }}
       timeout-minutes: 720
-      # disable monitor in perf tests, next step is to enable it
-      disable-monitor: true
+      disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit

.github/workflows/inductor-periodic.yml

@@ -133,10 +133,6 @@ jobs:
       build-environment: linux-focal-cuda12.6-py3.10-gcc9-sm80
       docker-image: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build-gcp.outputs.docker-image }}
       test-matrix: ${{ needs.linux-focal-cuda12_6-py3_10-gcc9-inductor-build-gcp.outputs.test-matrix }}
-      # disable monitor in perf tests, next step is to enable it
-      disable-monitor: true
-      monitor-log-interval: 15
-      monitor-data-collect-interval: 4
     secrets: inherit
 
   linux-jammy-cpu-py3_9-gcc11-periodic-dynamo-benchmarks-build:

.github/workflows/lint-autoformat.yml

@@ -1,10 +1,8 @@
 name: Apply lint suggestions
 
 on:
-
-  push:
-    tags:
-      - ciflow/autoformat/*
+  pull_request:
+    types: [opened, synchronize, reopened, labeled, unlabeled]
 
 jobs:
   lintrunner-autoformat:
@@ -12,7 +10,7 @@ jobs:
       contents: read
       pull-requests: write
     runs-on: lf.linux.2xlarge
-    if: ${{ github.repository_owner == 'pytorch' && github.event.pull_request.user.login != 'ezyang' && github.event.pull_request.user.login != 'malfet' && !startsWith(github.head_ref, 'export-') }}
+    if: ${{ github.repository_owner == 'pytorch' && contains(github.event.pull_request.labels.*.name, 'autoformat') }}
     steps:
       - name: Checkout pytorch
         uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
@@ -21,12 +19,11 @@ jobs:
           fetch-depth: 0
       - name: Run lintrunner (nonretryable)
         continue-on-error: true
-        # we can't run all files here because only changes around where the diff are shown in the PR UI
         run: |
           set -ex
           python3 -m venv /tmp/venv
           source /tmp/venv/bin/activate
-          export ADDITIONAL_LINTRUNNER_ARGS="format"
+          export ADDITIONAL_LINTRUNNER_ARGS="format --all-files"
           bash .github/scripts/lintrunner.sh
       - name: Check for changes
         id: git-check

.github/workflows/pull.yml

@@ -532,15 +532,15 @@ jobs:
       test-matrix: ${{ needs.linux-focal-cuda12_4-py3_10-gcc9-inductor-build.outputs.test-matrix }}
     secrets: inherit
 
-  linux-jammy-xpu-2025_0-py3_9-build:
-    name: linux-jammy-xpu-2025.0-py3.9
+  linux-jammy-xpu-2025_1-py3_9-build:
+    name: linux-jammy-xpu-2025.1-py3.9
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
-      sync-tag: linux-xpu-2025-0-build
+      sync-tag: linux-xpu-2025-1-build
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
-      build-environment: linux-jammy-xpu-2025.0-py3.9
-      docker-image-name: ci-image:pytorch-linux-jammy-xpu-2025.0-py3
+      build-environment: linux-jammy-xpu-2025.1-py3.9
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-2025.1-py3
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 4, runner: "linux.idc.xpu" },

.github/workflows/trunk.yml

@@ -138,6 +138,7 @@ jobs:
       build-environment: win-vs2022-cpu-py3
       cuda-version: cpu
       test-matrix: ${{ needs.win-vs2022-cpu-py3-build.outputs.test-matrix }}
+      disable-monitor: false
     secrets: inherit
 
   win-vs2022-cuda12_6-py3-build:

.github/workflows/upload-test-stats.yml

@@ -2,7 +2,25 @@ name: Upload test stats
 
 on:
   workflow_run:
-    workflows: [pull, trunk, periodic, periodic-rocm-mi300, inductor, unstable, slow, unstable-periodic, inductor-periodic, rocm, rocm-mi300, inductor-micro-benchmark, inductor-micro-benchmark-x86, inductor-cu124, inductor-rocm, inductor-rocm-mi300, mac-mps]
+    workflows:
+      - pull
+      - trunk
+      - periodic
+      - periodic-rocm-mi300
+      - inductor
+      - unstable
+      - slow
+      - unstable-periodic
+      - inductor-periodic
+      - rocm
+      - rocm-mi300
+      - inductor-micro-benchmark
+      - inductor-micro-benchmark-x86
+      - inductor-cu124
+      - inductor-rocm
+      - inductor-rocm-mi300
+      - mac-mps
+      - linux-aarch64
     types:
       - completed
 

.github/workflows/xpu.yml

@@ -43,17 +43,38 @@ jobs:
         ]}
     secrets: inherit
 
-  linux-jammy-xpu-2025_0-py3_9-test:
-    name: linux-jammy-xpu-2025.0-py3.9
+  linux-jammy-xpu-2025_1-py3_9-build:
+    name: linux-jammy-xpu-2025.1-py3.9
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      sync-tag: linux-xpu-2025-1-build
+      runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
+      build-environment: linux-jammy-xpu-2025.1-py3.9
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-2025.1-py3
+      runner: linux.12xlarge
+      test-matrix: |
+        { include: [
+          { config: "default", shard: 1, num_shards: 6, runner: "linux.idc.xpu" },
+          { config: "default", shard: 2, num_shards: 6, runner: "linux.idc.xpu" },
+          { config: "default", shard: 3, num_shards: 6, runner: "linux.idc.xpu" },
+          { config: "default", shard: 4, num_shards: 6, runner: "linux.idc.xpu" },
+          { config: "default", shard: 5, num_shards: 6, runner: "linux.idc.xpu" },
+          { config: "default", shard: 6, num_shards: 6, runner: "linux.idc.xpu" },
+        ]}
+    secrets: inherit
+
+  linux-jammy-xpu-2025_1-py3_9-test:
+    name: linux-jammy-xpu-2025.1-py3.9
     uses: ./.github/workflows/_xpu-test.yml
-    needs: linux-jammy-xpu-2025_0-py3_9-build
+    needs: linux-jammy-xpu-2025_1-py3_9-build
     permissions:
       id-token: write
       contents: read
     with:
-      build-environment: linux-jammy-xpu-2025.0-py3.9
-      docker-image: ${{ needs.linux-jammy-xpu-2025_0-py3_9-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-xpu-2025_0-py3_9-build.outputs.test-matrix }}
+      build-environment: linux-jammy-xpu-2025.1-py3.9
+      docker-image: ${{ needs.linux-jammy-xpu-2025_1-py3_9-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-xpu-2025_1-py3_9-build.outputs.test-matrix }}
     secrets: inherit
 
   windows-xpu-2025_0-build:
@@ -67,3 +88,15 @@ jobs:
       xpu-version: '2025.0'
       vc-year: '2022'
     secrets: inherit
+
+  windows-xpu-2025_1-build:
+    if: github.repository_owner == 'pytorch'
+    name: win-vs2022-xpu-2025_1-py3
+    uses: ./.github/workflows/_win-build.yml
+    with:
+      build-environment: win-vs2022-xpu-py3
+      cuda-version: cpu
+      use-xpu: true
+      xpu-version: '2025.1'
+      vc-year: '2022'
+    secrets: inherit

.gitignore

@@ -212,15 +212,6 @@ docs/source/scripts/lr_scheduler_images/
 # Compiled MATLAB
 *.mex*
 
-# IPython notebook checkpoints
-.ipynb_checkpoints
-
-# Editor temporaries
-*.swn
-*.swo
-*.swp
-*~
-
 # NFS handle files
 **/.nfs*
 

.lintrunner.toml

@@ -1719,3 +1719,15 @@ include_patterns = [
    'torch/_dynamo/**',
 ]
 is_formatter = false
+
+[[linter]]
+code = 'TEST_DEVICE_BIAS'
+command = [
+    'python3',
+    'tools/linter/adapters/test_device_bias_linter.py',
+    '--',
+    '@{{PATHSFILE}}',
+]
+include_patterns = [
+    'test/**/test_*.py',
+]

CMakeLists.txt

@@ -222,7 +222,6 @@ option(
   BUILD_MOBILE_TEST
   "Build C++ test binaries for mobile (ARM) targets(need gtest and gbenchmark)"
   OFF)
-option(BUILD_JNI "Build JNI bindings" OFF)
 option(BUILD_MOBILE_AUTOGRAD
        "Build autograd function in mobile build (in development)" OFF)
 cmake_dependent_option(INSTALL_TEST "Install test binaries if BUILD_TEST is on"
@@ -259,6 +258,8 @@ option(USE_NATIVE_ARCH "Use -march=native" OFF)
 cmake_dependent_option(USE_MPS "Use MPS for macOS build" ON "MPS_FOUND" OFF)
 cmake_dependent_option(USE_NCCL "Use NCCL" ON
                        "USE_CUDA OR USE_ROCM;UNIX;NOT APPLE" OFF)
+cmake_dependent_option(USE_XCCL "Use XCCL" OFF
+                       "USE_XPU;UNIX;NOT APPLE" OFF)
 cmake_dependent_option(USE_RCCL "Use RCCL" ON USE_NCCL OFF)
 cmake_dependent_option(USE_STATIC_NCCL "Use static NCCL" OFF "USE_NCCL" OFF)
 cmake_dependent_option(USE_SYSTEM_NCCL "Use system-wide NCCL" OFF "USE_NCCL"
@@ -338,6 +339,8 @@ cmake_dependent_option(
     USE_C10D_GLOO "USE C10D GLOO" ON "USE_DISTRIBUTED;USE_GLOO" OFF)
 cmake_dependent_option(
     USE_C10D_NCCL "USE C10D NCCL" ON "USE_DISTRIBUTED;USE_NCCL" OFF)
+cmake_dependent_option(
+    USE_C10D_XCCL "USE C10D XCCL" ON "USE_DISTRIBUTED;USE_XCCL" OFF)
 cmake_dependent_option(
     USE_C10D_MPI "USE C10D MPI" ON "USE_DISTRIBUTED;USE_MPI" OFF)
 cmake_dependent_option(
@@ -1346,16 +1349,6 @@ if(BUILD_BINARY)
   add_subdirectory(binaries)
 endif()
 
-# ---[ JNI
-if(BUILD_JNI)
-  if(NOT MSVC)
-    string(APPEND CMAKE_CXX_FLAGS " -Wno-unused-variable")
-  endif()
-  set(BUILD_LIBTORCH_WITH_JNI 1)
-  set(FBJNI_SKIP_TESTS 1)
-  add_subdirectory(android/pytorch_android)
-endif()
-
 include(cmake/Summary.cmake)
 caffe2_print_configuration_summary()
 

android/README.md

@@ -1,240 +1,5 @@
-# Android
+# Android Demo App
 
-## Demo applications and tutorials
+Please refer to [pytorch-labs/executorch-examples](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo) for the Android demo app based on [ExecuTorch](https://github.com/pytorch/executorch).
 
-Demo applications with code walk-through can be find in [this github repo](https://github.com/pytorch/android-demo-app).
-
-## Publishing
-
-##### Release
-Release artifacts are published to jcenter:
-
-```groovy
-repositories {
-    jcenter()
-}
-
-# lite interpreter build
-dependencies {
-    implementation 'org.pytorch:pytorch_android_lite:1.10.0'
-    implementation 'org.pytorch:pytorch_android_torchvision_lite:1.10.0'
-}
-
-# full jit build
-dependencies {
-    implementation 'org.pytorch:pytorch_android:1.10.0'
-    implementation 'org.pytorch:pytorch_android_torchvision:1.10.0'
-}
-```
-
-##### Nightly
-
-Nightly(snapshots) builds are published every night from `master` branch to [nexus sonatype snapshots repository](https://oss.sonatype.org/#nexus-search;quick~pytorch_android)
-
-To use them repository must be specified explicitly:
-```groovy
-repositories {
-    maven {
-        url "https://oss.sonatype.org/content/repositories/snapshots"
-    }
-}
-
-# lite interpreter build
-dependencies {
-    ...
-    implementation 'org.pytorch:pytorch_android_lite:1.12.0-SNAPSHOT'
-    implementation 'org.pytorch:pytorch_android_torchvision_lite:1.12.0-SNAPSHOT'
-    ...
-}
-
-# full jit build
-dependencies {
-    ...
-    implementation 'org.pytorch:pytorch_android:1.12.0-SNAPSHOT'
-    implementation 'org.pytorch:pytorch_android_torchvision:1.12.0-SNAPSHOT'
-    ...
-}
-```
-The current nightly(snapshots) version is the value of `VERSION_NAME` in `gradle.properties` in current folder, at this moment it is `1.8.0-SNAPSHOT`.
-
-## Building PyTorch Android from Source
-
-In some cases you might want to use a local build of pytorch android, for example you may build custom libtorch binary with another set of operators or to make local changes.
-
-For this you can use `./scripts/build_pytorch_android.sh` script.
-```bash
-git clone https://github.com/pytorch/pytorch.git
-cd pytorch
-git submodule update --init --recursive
-bash ./scripts/build_pytorch_android.sh
-```
-
-The workflow contains several steps:
-
-1\. Build libtorch for android for all 4 android abis (armeabi-v7a, arm64-v8a, x86, x86_64)
-
-2\. Create symbolic links to the results of those builds:
-`android/pytorch_android/src/main/jniLibs/${abi}` to the directory with output libraries
-`android/pytorch_android/src/main/cpp/libtorch_include/${abi}` to the directory with headers. These directories are used to build `libpytorch.so` library that will be loaded on android device.
-
-3\. And finally run `gradle` in `android/pytorch_android` directory with task `assembleRelease`
-
-Script requires that Android SDK, Android NDK and gradle are installed.
-They are specified as environment variables:
-
-`ANDROID_HOME` - path to [Android SDK](https://developer.android.com/studio/command-line/sdkmanager.html)
-
-`ANDROID_NDK` - path to [Android NDK](https://developer.android.com/studio/projects/install-ndk). It's recommended to use NDK 21.x.
-
-`GRADLE_HOME` - path to [gradle](https://gradle.org/releases/)
-
-
-After successful build you should see the result as aar file:
-
-```bash
-$ find pytorch_android/build/ -type f -name *aar
-pytorch_android/build/outputs/aar/pytorch_android.aar
-pytorch_android_torchvision/build/outputs/aar/pytorch_android.aar
-```
-
-It can be used directly in android projects, as a gradle dependency:
-```groovy
-allprojects {
-    repositories {
-        flatDir {
-            dirs 'libs'
-        }
-    }
-}
-
-dependencies {
-    implementation(name:'pytorch_android', ext:'aar')
-    implementation(name:'pytorch_android_torchvision', ext:'aar')
-    ...
-    implementation 'com.facebook.soloader:nativeloader:0.10.5'
-    implementation 'com.facebook.fbjni:fbjni-java-only:0.2.2'
-}
-```
-We also have to add all transitive dependencies of our aars.
-As `pytorch_android` [depends](https://github.com/pytorch/pytorch/blob/master/android/pytorch_android/build.gradle#L76-L77) on `'com.facebook.soloader:nativeloader:0.10.5'` and `'com.facebook.fbjni:fbjni-java-only:0.2.2'`, we need to add them.
-(In case of using maven dependencies they are added automatically from `pom.xml`).
-
-You can check out [test app example](https://github.com/pytorch/pytorch/blob/master/android/test_app/app/build.gradle) that uses aars directly.
-
-## Linking to prebuilt libtorch library from gradle dependency
-
-In some cases, you may want to use libtorch from your android native build.
-You can do it without building libtorch android, using native libraries from PyTorch android gradle dependency.
-For that, you will need to add the next lines to your gradle build.
-```groovy
-android {
-...
-    configurations {
-       extractForNativeBuild
-    }
-...
-    compileOptions {
-        externalNativeBuild {
-            cmake {
-                arguments "-DANDROID_STL=c++_shared"
-            }
-        }
-    }
-...
-    externalNativeBuild {
-        cmake {
-            path "CMakeLists.txt"
-        }
-    }
-}
-
-dependencies {
-    extractForNativeBuild('org.pytorch:pytorch_android:1.10.0')
-}
-
-task extractAARForNativeBuild {
-    doLast {
-        configurations.extractForNativeBuild.files.each {
-            def file = it.absoluteFile
-            copy {
-                from zipTree(file)
-                into "$buildDir/$file.name"
-                include "headers/**"
-                include "jni/**"
-            }
-        }
-    }
-}
-
-tasks.whenTaskAdded { task ->
-  if (task.name.contains('externalNativeBuild')) {
-    task.dependsOn(extractAARForNativeBuild)
-  }
-}
-```
-
-pytorch_android aar contains headers to link in `headers` folder and native libraries in `jni/$ANDROID_ABI/`.
-As PyTorch native libraries use `ANDROID_STL` - we should use `ANDROID_STL=c++_shared` to have only one loaded binary of STL.
-
-The added task will unpack them to gradle build directory.
-
-In your native build you can link to them adding these lines to your CMakeLists.txt:
-
-
-```cmake
-# Relative path of gradle build directory to CMakeLists.txt
-set(build_DIR ${CMAKE_SOURCE_DIR}/build)
-
-file(GLOB PYTORCH_INCLUDE_DIRS "${build_DIR}/pytorch_android*.aar/headers")
-file(GLOB PYTORCH_LINK_DIRS "${build_DIR}/pytorch_android*.aar/jni/${ANDROID_ABI}")
-
-set(BUILD_SUBDIR ${ANDROID_ABI})
-target_include_directories(${PROJECT_NAME} PRIVATE
-  ${PYTORCH_INCLUDE_DIRS}
-)
-
-find_library(PYTORCH_LIBRARY pytorch_jni
-  PATHS ${PYTORCH_LINK_DIRS}
-  NO_CMAKE_FIND_ROOT_PATH)
-
-find_library(FBJNI_LIBRARY fbjni
-  PATHS ${PYTORCH_LINK_DIRS}
-  NO_CMAKE_FIND_ROOT_PATH)
-
-target_link_libraries(${PROJECT_NAME}
-  ${PYTORCH_LIBRARY})
-  ${FBJNI_LIBRARY})
-
-```
-If your CMakeLists.txt file is located in the same directory as your build.gradle, `set(build_DIR ${CMAKE_SOURCE_DIR}/build)` should work for you. But if you have another location of it, you may need to change it.
-
-After that, you can use libtorch C++ API from your native code.
-```cpp
-#include <string>
-#include <ATen/NativeFunctions.h>
-#include <torch/script.h>
-namespace pytorch_testapp_jni {
-namespace {
-    struct JITCallGuard {
-      c10::InferenceMode guard;
-      torch::jit::GraphOptimizerEnabledGuard no_optimizer_guard{false};
-    };
-}
-
-void loadAndForwardModel(const std::string& modelPath) {
-  JITCallGuard guard;
-  torch::jit::Module module = torch::jit::load(modelPath);
-  module.eval();
-  torch::Tensor t = torch::randn({1, 3, 224, 224});
-  c10::IValue t_out = module.forward({t});
-}
-}
-```
-
-To load torchscript model for mobile we need some special setup which is placed in `struct JITCallGuard` in this example. It may change in future, you can track the latest changes keeping an eye in our [pytorch android jni code]([https://github.com/pytorch/pytorch/blob/master/android/pytorch_android/src/main/cpp/pytorch_jni_jit.cpp#L28)
-
-[Example of linking to libtorch from aar](https://github.com/pytorch/pytorch/tree/master/android/test_app)
-
-## PyTorch Android API Javadoc
-
-You can find more details about the PyTorch Android API in the [Javadoc](https://pytorch.org/javadoc/).
+Please join our [Discord](https://discord.com/channels/1334270993966825602/1349854760299270284) for any questions.

android/build.gradle

@@ -1,40 +0,0 @@
-allprojects {
-    buildscript {
-        ext {
-            minSdkVersion = 21
-            targetSdkVersion = 28
-            compileSdkVersion = 28
-            buildToolsVersion = '28.0.3'
-
-            coreVersion = "1.2.0"
-            extJUnitVersion = "1.1.1"
-            runnerVersion = "1.2.0"
-            rulesVersion = "1.2.0"
-            junitVersion = "4.12"
-
-            fbjniJavaOnlyVersion = "0.2.2"
-            soLoaderNativeLoaderVersion = "0.10.5"
-        }
-
-        repositories {
-            google()
-            mavenLocal()
-            mavenCentral()
-            jcenter()
-        }
-
-        dependencies {
-            classpath 'com.android.tools.build:gradle:4.1.2'
-            classpath 'com.vanniktech:gradle-maven-publish-plugin:0.14.2'
-        }
-    }
-
-    repositories {
-        google()
-        jcenter()
-    }
-}
-
-ext.deps = [
-        jsr305: 'com.google.code.findbugs:jsr305:3.0.1',
-]

android/build_test_app.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-set -eux
-
-PYTORCH_DIR="$(cd $(dirname $0)/..; pwd -P)"
-PYTORCH_ANDROID_DIR=$PYTORCH_DIR/android
-
-echo "PYTORCH_DIR:$PYTORCH_DIR"
-
-source "$PYTORCH_ANDROID_DIR/common.sh"
-
-check_android_sdk
-check_gradle
-parse_abis_list "$@"
-build_android
-
-# To set proxy for gradle add following lines to ./gradle/gradle.properties:
-# systemProp.http.proxyHost=...
-# systemProp.http.proxyPort=8080
-# systemProp.https.proxyHost=...
-# systemProp.https.proxyPort=8080
-
-if [ "$CUSTOM_ABIS_LIST" = true ]; then
-  NDK_DEBUG=1 $GRADLE_PATH -PnativeLibsDoNotStrip=true -PABI_FILTERS=$ABIS_LIST -p $PYTORCH_ANDROID_DIR clean test_app:assembleDebug
-else
-  NDK_DEBUG=1 $GRADLE_PATH -PnativeLibsDoNotStrip=true -p $PYTORCH_ANDROID_DIR clean test_app:assembleDebug
-fi
-
-find $PYTORCH_ANDROID_DIR -type f -name *apk
-
-find $PYTORCH_ANDROID_DIR -type f -name *apk | xargs echo "To install apk run: $ANDROID_HOME/platform-tools/adb install -r "

android/build_test_app_custom.sh

@@ -1,32 +0,0 @@
-#!/bin/bash
-###############################################################################
-# This script tests the custom selective build flow for PyTorch Android, which
-# optimizes library size by only including ops used by a specific model.
-###############################################################################
-
-set -eux
-
-PYTORCH_DIR="$(cd $(dirname $0)/..; pwd -P)"
-PYTORCH_ANDROID_DIR="${PYTORCH_DIR}/android"
-BUILD_ROOT="${PYTORCH_DIR}/build_pytorch_android_custom"
-
-source "${PYTORCH_ANDROID_DIR}/common.sh"
-
-prepare_model_and_dump_root_ops() {
-  cd "${BUILD_ROOT}"
-  MODEL="${BUILD_ROOT}/MobileNetV2.pt"
-  ROOT_OPS="${BUILD_ROOT}/MobileNetV2.yaml"
-  python "${PYTORCH_ANDROID_DIR}/test_app/make_assets_custom.py"
-  cp "${MODEL}" "${PYTORCH_ANDROID_DIR}/test_app/app/src/main/assets/mobilenet2.pt"
-}
-
-# Start building
-mkdir -p "${BUILD_ROOT}"
-check_android_sdk
-check_gradle
-parse_abis_list "$@"
-prepare_model_and_dump_root_ops
-SELECTED_OP_LIST="${ROOT_OPS}" build_android
-
-# TODO: change this to build test_app instead
-$GRADLE_PATH -PABI_FILTERS=$ABIS_LIST -p $PYTORCH_ANDROID_DIR clean assembleRelease

android/common.sh

@@ -1,74 +0,0 @@
-#!/bin/bash
-set -eux
-
-##############################################################################
-# Common util functions for Android build scripts.
-##############################################################################
-
-if [ -z "$PYTORCH_DIR" ]; then
-  echo "PYTORCH_DIR not set!"
-  exit 1
-fi
-
-retry () {
-    "$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
-}
-
-check_android_sdk() {
-  if [ -z "$ANDROID_HOME" ]; then
-    echo "ANDROID_HOME not set; please set it to Android sdk directory"
-    exit 1
-  fi
-
-  if [ ! -d "$ANDROID_HOME" ]; then
-    echo "ANDROID_HOME not a directory; did you install it under $ANDROID_HOME?"
-    exit 1
-  fi
-  echo "ANDROID_HOME:$ANDROID_HOME"
-}
-
-check_gradle() {
-  GRADLE_PATH=$PYTORCH_DIR/android/gradlew
-  echo "GRADLE_PATH:$GRADLE_PATH"
-}
-
-parse_abis_list() {
-  # sync with https://github.com/pytorch/pytorch/blob/0ca0e02685a9d033ac4f04e2fa5c8ba6dbc5ae50/android/gradle.properties#L1
-  ABIS_LIST="armeabi-v7a,arm64-v8a,x86,x86_64"
-  CUSTOM_ABIS_LIST=false
-  if [ $# -gt 0 ]; then
-    ABIS_LIST=$1
-    CUSTOM_ABIS_LIST=true
-  fi
-
-  echo "ABIS_LIST:$ABIS_LIST"
-  echo "CUSTOM_ABIS_LIST:$CUSTOM_ABIS_LIST"
-}
-
-build_android() {
-  PYTORCH_ANDROID_DIR="$PYTORCH_DIR/android"
-  BUILD_ROOT="${BUILD_ROOT:-$PYTORCH_DIR}"
-  echo "BUILD_ROOT:$BUILD_ROOT"
-
-  LIB_DIR="$PYTORCH_ANDROID_DIR/pytorch_android/src/main/jniLibs"
-  INCLUDE_DIR="$PYTORCH_ANDROID_DIR/pytorch_android/src/main/cpp/libtorch_include"
-
-  # These directories only contain symbolic links.
-  rm -rf "$LIB_DIR" && mkdir -p "$LIB_DIR"
-  rm -rf "$INCLUDE_DIR" && mkdir -p "$INCLUDE_DIR"
-
-  for abi in $(echo "$ABIS_LIST" | tr ',' '\n')
-  do
-    echo "abi:$abi"
-    ANDROID_BUILD_ROOT="$BUILD_ROOT/build_android_$abi"
-    ANDROID_ABI="$abi" \
-      BUILD_ROOT="$ANDROID_BUILD_ROOT" \
-      "$PYTORCH_DIR/scripts/build_android.sh" \
-      -DANDROID_CCACHE="$(which ccache)" \
-      -DUSE_LITE_INTERPRETER_PROFILER="OFF"
-
-    echo "$abi build output lib,include at $ANDROID_BUILD_ROOT/install"
-    ln -s "$ANDROID_BUILD_ROOT/install/lib" "$LIB_DIR/$abi"
-    ln -s "$ANDROID_BUILD_ROOT/install/include" "$INCLUDE_DIR/$abi"
-  done
-}

android/gradle.properties

@@ -1,26 +0,0 @@
-ABI_FILTERS=armeabi-v7a,arm64-v8a,x86,x86_64
-
-VERSION_NAME=2.2.0-SNAPSHOT
-GROUP=org.pytorch
-MAVEN_GROUP=org.pytorch
-SONATYPE_STAGING_PROFILE=orgpytorch
-POM_URL=https://github.com/pytorch/pytorch/tree/master/android
-POM_SCM_URL=https://github.com/pytorch/pytorch.git
-POM_SCM_CONNECTION=scm:git:https://github.com/pytorch/pytorch
-POM_SCM_DEV_CONNECTION=scm:git:git@github.com:pytorch/pytorch.git
-POM_LICENSE_NAME=BSD 3-Clause
-POM_LICENSE_URL=https://github.com/pytorch/pytorch/blob/master/LICENSE
-POM_ISSUES_URL=https://github.com/pytorch/pytorch/issues
-POM_LICENSE_DIST=repo
-POM_DEVELOPER_ID=pytorch
-POM_DEVELOPER_NAME=pytorch
-
-# Gradle internals
-org.gradle.internal.repository.max.retries=1
-org.gradle.jvmargs=-XX:MaxMetaspaceSize=1024m
-
-android.useAndroidX=true
-android.enableJetifier=true
-
-nativeLibsDoNotStrip=false
-testAppAllVariantsEnabled=false

android/gradle/android_tasks.gradle

@@ -1,11 +0,0 @@
-afterEvaluate { project ->
-    if (POM_PACKAGING == 'aar') {
-        task headersJar(type: Jar) {
-            archiveClassifier.set('headers')
-            from("$rootDir/cxx/") {
-                include '**/*.h'
-            }
-        }
-        artifacts.add('archives', headersJar)
-    }
-}

android/gradle/release.gradle

@@ -1,3 +0,0 @@
-apply from: rootProject.file('gradle/android_tasks.gradle')
-
-apply plugin: 'com.vanniktech.maven.publish'

android/gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +0,0 @@
-distributionBase=GRADLE_USER_HOME
-distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-6.8.3-bin.zip
-zipStoreBase=GRADLE_USER_HOME
-zipStorePath=wrapper/dists

android/gradlew

@@ -1,172 +0,0 @@
-#!/usr/bin/env sh
-
-##############################################################################
-##
-##  Gradle start up script for UN*X
-##
-##############################################################################
-
-# Attempt to set APP_HOME
-# Resolve links: $0 may be a link
-PRG="$0"
-# Need this for relative symlinks.
-while [ -h "$PRG" ] ; do
-    ls=`ls -ld "$PRG"`
-    link=`expr "$ls" : '.*-> \(.*\)$'`
-    if expr "$link" : '/.*' > /dev/null; then
-        PRG="$link"
-    else
-        PRG=`dirname "$PRG"`"/$link"
-    fi
-done
-SAVED="`pwd`"
-cd "`dirname \"$PRG\"`/" >/dev/null
-APP_HOME="`pwd -P`"
-cd "$SAVED" >/dev/null
-
-APP_NAME="Gradle"
-APP_BASE_NAME=`basename "$0"`
-
-# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-DEFAULT_JVM_OPTS=""
-
-# Use the maximum available, or set MAX_FD != -1 to use that value.
-MAX_FD="maximum"
-
-warn () {
-    echo "$*"
-}
-
-die () {
-    echo
-    echo "$*"
-    echo
-    exit 1
-}
-
-# OS specific support (must be 'true' or 'false').
-cygwin=false
-msys=false
-darwin=false
-nonstop=false
-case "`uname`" in
-  CYGWIN* )
-    cygwin=true
-    ;;
-  Darwin* )
-    darwin=true
-    ;;
-  MINGW* )
-    msys=true
-    ;;
-  NONSTOP* )
-    nonstop=true
-    ;;
-esac
-
-CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
-
-# Determine the Java command to use to start the JVM.
-if [ -n "$JAVA_HOME" ] ; then
-    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
-        # IBM's JDK on AIX uses strange locations for the executables
-        JAVACMD="$JAVA_HOME/jre/sh/java"
-    else
-        JAVACMD="$JAVA_HOME/bin/java"
-    fi
-    if [ ! -x "$JAVACMD" ] ; then
-        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
-
-Please set the JAVA_HOME variable in your environment to match the
-location of your Java installation."
-    fi
-else
-    JAVACMD="java"
-    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-
-Please set the JAVA_HOME variable in your environment to match the
-location of your Java installation."
-fi
-
-# Increase the maximum file descriptors if we can.
-if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
-    MAX_FD_LIMIT=`ulimit -H -n`
-    if [ $? -eq 0 ] ; then
-        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
-            MAX_FD="$MAX_FD_LIMIT"
-        fi
-        ulimit -n $MAX_FD
-        if [ $? -ne 0 ] ; then
-            warn "Could not set maximum file descriptor limit: $MAX_FD"
-        fi
-    else
-        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
-    fi
-fi
-
-# For Darwin, add options to specify how the application appears in the dock
-if $darwin; then
-    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
-fi
-
-# For Cygwin, switch paths to Windows format before running java
-if $cygwin ; then
-    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
-    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
-    JAVACMD=`cygpath --unix "$JAVACMD"`
-
-    # We build the pattern for arguments to be converted via cygpath
-    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
-    SEP=""
-    for dir in $ROOTDIRSRAW ; do
-        ROOTDIRS="$ROOTDIRS$SEP$dir"
-        SEP="|"
-    done
-    OURCYGPATTERN="(^($ROOTDIRS))"
-    # Add a user-defined pattern to the cygpath arguments
-    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
-        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
-    fi
-    # Now convert the arguments - kludge to limit ourselves to /bin/sh
-    i=0
-    for arg in "$@" ; do
-        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
-        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
-
-        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
-            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
-        else
-            eval `echo args$i`="\"$arg\""
-        fi
-        i=$((i+1))
-    done
-    case $i in
-        (0) set -- ;;
-        (1) set -- "$args0" ;;
-        (2) set -- "$args0" "$args1" ;;
-        (3) set -- "$args0" "$args1" "$args2" ;;
-        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
-        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
-        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
-        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
-        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
-        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
-    esac
-fi
-
-# Escape application args
-save () {
-    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
-    echo " "
-}
-APP_ARGS=$(save "$@")
-
-# Collect all arguments for the java command, following the shell quoting and substitution rules
-eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
-
-# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
-if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
-  cd "$(dirname "$0")"
-fi
-
-exec "$JAVACMD" "$@"

android/gradlew.bat

@@ -1,84 +0,0 @@
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem  Gradle startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto init
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto init
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:init
-@rem Get command-line arguments, handling Windows variants
-
-if not "%OS%" == "Windows_NT" goto win9xME_args
-
-:win9xME_args
-@rem Slurp the command line arguments.
-set CMD_LINE_ARGS=
-set _SKIP=2
-
-:win9xME_args_slurp
-if "x%~1" == "x" goto execute
-
-set CMD_LINE_ARGS=%*
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
-
-@rem Execute Gradle
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
-
-:end
-@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
-
-:fail
-rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
-exit /b 1
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega

android/pytorch_android/CMakeLists.txt

@@ -1,184 +0,0 @@
-cmake_minimum_required(VERSION 3.5)
-option(BUILD_LITE_INTERPRETER "Master flag to build pytorch_jni_lite" ON)
-message(
-  STATUS
-  "BUILD_LITE_INTERPRETER (pytorch_jni_lite): ${BUILD_LITE_INTERPRETER}")
-
-if(BUILD_LITE_INTERPRETER)
-  project(pytorch_jni_lite CXX)
-  set(PYTORCH_JNI_TARGET pytorch_jni_lite)
-else()
-  project(pytorch_jni CXX)
-  set(PYTORCH_JNI_TARGET pytorch_jni)
-endif()
-
-include(GNUInstallDirs)
-
-set(CMAKE_CXX_STANDARD 17 CACHE STRING "The C++ standard whose features are requested to build this target.")
-set(CMAKE_VERBOSE_MAKEFILE ON)
-message(STATUS "ANDROID_STL:${ANDROID_STL}")
-
-set(TRACE_ENABLED OFF)
-if(DEFINED ENV{TRACE_ENABLED})
-  if($ENV{TRACE_ENABLED} STREQUAL "1")
-    message(STATUS "TRACE_ENABLED ON")
-    set(TRACE_ENABLED ON)
-  endif()
-endif()
-if(NOT TRACE_ENABLED)
-  message(STATUS "TRACE_ENABLED OFF")
-endif()
-
-set(USE_VULKAN OFF)
-
-set(pytorch_android_DIR ${CMAKE_CURRENT_LIST_DIR}/src/main/cpp)
-
-if(ANDROID_ABI)
-  set(USE_VULKAN ON)
-  set(libtorch_include_DIR ${pytorch_android_DIR}/libtorch_include/${ANDROID_ABI})
-  set(BUILD_SUBDIR ${ANDROID_ABI})
-elseif(BUILD_LIBTORCH_WITH_JNI)
-  # Don't need LIBTORCH_HOME if we're building from within PyTorch.
-else()
-  # Building against a pre-built libtorch.
-  if(NOT LIBTORCH_HOME)
-    message(FATAL_ERROR
-      "pytorch_android requires LIBTORCH_HOME to be defined for non-Android builds.")
-  endif()
-  set(libtorch_include_DIR ${LIBTORCH_HOME}/include)
-  link_directories(${LIBTORCH_HOME}/lib)
-  set(BUILD_SUBDIR host)
-endif()
-
-message(STATUS "libtorch dir:${libtorch_DIR}")
-
-configure_file(
-    ${pytorch_android_DIR}/cmake_macros.h.in
-    ${pytorch_android_DIR}/cmake_macros.h)
-
-
-if(BUILD_LITE_INTERPRETER)
-  file(GLOB pytorch_android_SOURCES
-    ${pytorch_android_DIR}/pytorch_jni_lite.cpp
-    ${pytorch_android_DIR}/pytorch_jni_common.cpp
-    ${pytorch_android_DIR}/pytorch_jni_common.h
-  )
-else()
-  file(GLOB pytorch_android_SOURCES
-    ${pytorch_android_DIR}/pytorch_jni_jit.cpp
-    ${pytorch_android_DIR}/pytorch_jni_common.cpp
-    ${pytorch_android_DIR}/pytorch_jni_common.h
-  )
-endif()
-add_library(${PYTORCH_JNI_TARGET} SHARED ${pytorch_android_SOURCES})
-
-if(APPLE)
-  # Need to add rpath so dlopen can find dependencies.
-  add_custom_command(TARGET pytorch_jni
-      POST_BUILD COMMAND
-      ${CMAKE_INSTALL_NAME_TOOL} -add_rpath "@loader_path"
-        $<TARGET_FILE:pytorch_jni>)
-endif()
-
-target_compile_options(${PYTORCH_JNI_TARGET} PRIVATE
-  -fexceptions
-)
-target_include_directories(${PYTORCH_JNI_TARGET} BEFORE
-PUBLIC $<BUILD_INTERFACE:${libtorch_include_DIR}>)
-
-set(fbjni_DIR ${CMAKE_CURRENT_LIST_DIR}/../libs/fbjni/)
-set(fbjni_BUILD_DIR ${CMAKE_BINARY_DIR}/fbjni/${BUILD_SUBDIR})
-
-add_subdirectory(${fbjni_DIR} ${fbjni_BUILD_DIR})
-
-# ---[ Vulkan deps
-if(USE_VULKAN)
-  set(Vulkan_LIBS)
-  set(Vulkan_INCLUDES)
-  include(${CMAKE_CURRENT_LIST_DIR}/../../cmake/VulkanDependencies.cmake)
-endif()
-
-if(ANDROID_ABI)
-  function(import_static_lib name)
-    add_library(${name} STATIC IMPORTED)
-    set_property(
-        TARGET ${name}
-        PROPERTY IMPORTED_LOCATION
-        ${CMAKE_CURRENT_LIST_DIR}/src/main/jniLibs/${ANDROID_ABI}/${name}.a)
-  endfunction(import_static_lib)
-
-  import_static_lib(libtorch)
-  import_static_lib(libtorch_cpu)
-  import_static_lib(libc10)
-  import_static_lib(libnnpack)
-  import_static_lib(libXNNPACK)
-  import_static_lib(libmicrokernels-prod)
-  import_static_lib(libpytorch_qnnpack)
-  import_static_lib(libpthreadpool)
-  import_static_lib(libeigen_blas)
-  import_static_lib(libcpuinfo)
-  import_static_lib(libclog)
-
-  # Link most things statically on Android.
-  set(pytorch_jni_LIBS
-      fbjni
-      -Wl,--gc-sections
-      -Wl,--whole-archive
-      libtorch
-      libtorch_cpu
-      -Wl,--no-whole-archive
-      libc10
-      libnnpack
-      libXNNPACK
-      libmicrokernels-prod
-      libpytorch_qnnpack
-      libpthreadpool
-      libeigen_blas
-      libcpuinfo
-      libclog
-  )
-else()
-  # Prefer dynamic linking on the host
-  set(pytorch_jni_LIBS
-      fbjni
-      torch
-      torch_cpu
-      c10
-      cpuinfo
-  )
-
-  if(USE_NNPACK)
-    list(APPEND pytorch_jni_LIBS nnpack)
-  endif()
-
-  if(USE_XNNPACK)
-    list(APPEND pytorch_jni_LIBS XNNPACK)
-    list(APPEND pytorch_jni_LIBS microkernels-prod)
-  endif()
-
-  if(USE_SYSTEM_PTHREADPOOL)
-    list(APPEND pytorch_jni_LIBS pthreadpool)
-  endif()
-
-  if(USE_PYTORCH_QNNPACK)
-    list(APPEND pytorch_jni_LIBS pytorch_qnnpack)
-    list(APPEND pytorch_jni_LIBS clog)
-  endif()
-
-endif()
-
-if(USE_VULKAN)
-  list(APPEND pytorch_jni_LIBS ${Vulkan_LIBS})
-endif()
-
-target_link_libraries(${PYTORCH_JNI_TARGET} ${pytorch_jni_LIBS})
-
-install(TARGETS ${PYTORCH_JNI_TARGET}
-  ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}) #For windows
-
-if(MSVC)
-  install(FILES $<TARGET_PDB_FILE:pytorch_jni> DESTINATION ${CMAKE_INSTALL_LIBDIR} OPTIONAL)
-  install(TARGETS ${PYTORCH_JNI_TARGET} DESTINATION ${CMAKE_INSTALL_LIBDIR})
-endif()

android/pytorch_android/build.gradle

@@ -1,163 +0,0 @@
-apply plugin: 'com.android.library'
-apply plugin: 'maven'
-
-android {
-    compileSdkVersion rootProject.compileSdkVersion
-    buildToolsVersion rootProject.buildToolsVersion
-
-    defaultConfig {
-        minSdkVersion rootProject.minSdkVersion
-        targetSdkVersion rootProject.targetSdkVersion
-        versionCode 0
-        versionName "0.1"
-
-        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
-        ndk {
-            abiFilters ABI_FILTERS.split(",")
-        }
-        externalNativeBuild {
-            cmake {
-              if(System.env.BUILD_LITE_INTERPRETER == '0') {
-                arguments "-DANDROID_STL=c++_shared", "-DBUILD_LITE_INTERPRETER=OFF", "-DUSE_LITE_INTERPRETER_PROFILER=OFF"
-              } else {
-                arguments "-DANDROID_STL=c++_shared", "-DUSE_LITE_INTERPRETER_PROFILER=OFF"
-              }
-            }
-        }
-    }
-    buildTypes {
-        debug {
-            minifyEnabled false
-            debuggable true
-        }
-        release {
-            minifyEnabled false
-        }
-    }
-    sourceSets {
-        main {
-            java {
-              if(System.env.BUILD_LITE_INTERPRETER == '0') {
-                println 'Build pytorch_jni'
-                exclude 'org/pytorch/LiteModuleLoader.java'
-                exclude 'org/pytorch/LiteNativePeer.java'
-              } else {
-                println 'Build pytorch_jni_lite'
-              }
-            }
-            jniLibs.srcDirs = ['src/main/jniLibs']
-            manifest.srcFile 'src/main/AndroidManifest.xml'
-        }
-        androidTest {
-            java {
-                if(System.env.BUILD_LITE_INTERPRETER == '0') {
-                    println 'Build test for full jit (pytorch_jni)'
-                    exclude 'org/pytorch/PytorchHostTests.java'
-                    exclude 'org/pytorch/PytorchLiteInstrumentedTests.java'
-                    exclude 'org/pytorch/suite/PytorchLiteInstrumentedTestSuite.java'
-                } else {
-                    println 'Build test for lite interpreter (pytorch_jni_lite)'
-                    exclude 'org/pytorch/PytorchHostTests.java'
-                    exclude 'org/pytorch/PytorchInstrumentedTests.java'
-                    exclude 'org/pytorch/suite/PytorchInstrumentedTestSuite.java'
-                }
-            }
-        }
-    }
-    externalNativeBuild {
-        cmake {
-            path "CMakeLists.txt"
-        }
-    }
-
-    packagingOptions {
-        if (nativeLibsDoNotStrip.toBoolean()) {
-            doNotStrip "**/*.so"
-            logger.warn('WARNING: nativeLibsDoNotStrip==true; debug symbols included')
-        }
-    }
-
-    useLibrary 'android.test.runner'
-    useLibrary 'android.test.base'
-    useLibrary 'android.test.mock'
-}
-
-dependencies {
-    implementation 'com.facebook.fbjni:fbjni-java-only:' + rootProject.fbjniJavaOnlyVersion
-    implementation 'com.facebook.soloader:nativeloader:' + rootProject.soLoaderNativeLoaderVersion
-
-    testImplementation 'junit:junit:' + rootProject.junitVersion
-    testImplementation 'androidx.test:core:' + rootProject.coreVersion
-
-    androidTestImplementation 'junit:junit:' + rootProject.junitVersion
-    androidTestImplementation 'androidx.test:core:' + rootProject.coreVersion
-    androidTestImplementation 'androidx.test.ext:junit:' + rootProject.extJUnitVersion
-    androidTestImplementation 'androidx.test:rules:' + rootProject.rulesVersion
-    androidTestImplementation 'androidx.test:runner:' + rootProject.runnerVersion
-}
-
-apply from: rootProject.file('gradle/release.gradle')
-
-task sourcesJar(type: Jar) {
-    from android.sourceSets.main.java.srcDirs
-    classifier = 'sources'
-}
-
-def getLibtorchHeadersDir() {
-  def abi = ABI_FILTERS.split(",")[0]
-  return "$rootDir/pytorch_android/src/main/cpp/libtorch_include/$abi"
-}
-
-afterEvaluate {
-  if (POM_PACKAGING == 'aar') {
-    android.libraryVariants.all { variant ->
-      variant.outputs.each { output ->
-        File f = output.outputFile
-        if (f.name.endsWith(".aar")) {
-          output.assemble.finalizedBy addFolderToAarTask(
-              "addHeadersToAar" + variant.name,
-              f.path,
-              getLibtorchHeadersDir(),
-              "headers")
-        }
-      }
-    }
-  }
-}
-
-tasks.whenTaskAdded { task ->
-  if (task.name.startsWith("bundle") && task.name.endsWith("Aar")) {
-    doLast {
-      addFolderToAar("addHeadersTo" + task.name, task.archivePath, getLibtorchHeadersDir(), 'headers')
-    }
-  }
-}
-
-def addFolderToAarTask(taskName, aarPath, folderPath, folderPathInAar) {
-  return tasks.register(taskName) {
-    doLast {
-      addFolderToAar(taskName, aarPath, folderPath, folderPathInAar)
-    }
-  }
-}
-
-def addFolderToAar(taskName, aarPath, folderPath, folderPathInAar) {
-    def tmpDir = file("${buildDir}/${taskName}")
-    tmpDir.mkdir()
-    def tmpDirFolder = file("${tmpDir.path}/${folderPathInAar}")
-    tmpDirFolder.mkdir()
-    copy {
-      from zipTree(aarPath)
-      into tmpDir
-    }
-    copy {
-      from fileTree(folderPath)
-      into tmpDirFolder
-    }
-    ant.zip(destfile: aarPath) {
-      fileset(dir: tmpDir.path)
-    }
-    delete tmpDir
-}
-
-artifacts.add('archives', sourcesJar)

android/pytorch_android/generate_test_asset.cpp

@@ -1,20 +0,0 @@
-#include <torch/csrc/jit/api/module.h>
-#include <torch/jit.h>
-#include <torch/script.h>
-
-#include <fstream>
-#include <iostream>
-#include <string>
-
-int main(int argc, char* argv[]) {
-  std::string input_file_path{argv[1]};
-  std::string output_file_path{argv[2]};
-
-  std::ifstream ifs(input_file_path);
-  std::stringstream buffer;
-  buffer << ifs.rdbuf();
-  torch::jit::Module m("TestModule");
-
-  m.define(buffer.str());
-  m.save(output_file_path);
-}

android/pytorch_android/generate_test_torchscripts.py

@@ -1,151 +0,0 @@
-from typing import Optional
-
-import torch
-from torch import Tensor
-
-
-OUTPUT_DIR = "src/androidTest/assets/"
-
-
-def scriptAndSave(module, fileName):
-    print("-" * 80)
-    script_module = torch.jit.script(module)
-    print(script_module.graph)
-    outputFileName = OUTPUT_DIR + fileName
-    # note that the lite interpreter model can also be used in full JIT
-    script_module._save_for_lite_interpreter(outputFileName)
-    print("Saved to " + outputFileName)
-    print("=" * 80)
-
-
-class Test(torch.jit.ScriptModule):
-    @torch.jit.script_method
-    def forward(self, input):
-        return None
-
-    @torch.jit.script_method
-    def eqBool(self, input: bool) -> bool:
-        return input
-
-    @torch.jit.script_method
-    def eqInt(self, input: int) -> int:
-        return input
-
-    @torch.jit.script_method
-    def eqFloat(self, input: float) -> float:
-        return input
-
-    @torch.jit.script_method
-    def eqStr(self, input: str) -> str:
-        return input
-
-    @torch.jit.script_method
-    def eqTensor(self, input: Tensor) -> Tensor:
-        return input
-
-    @torch.jit.script_method
-    def eqDictStrKeyIntValue(self, input: dict[str, int]) -> dict[str, int]:
-        return input
-
-    @torch.jit.script_method
-    def eqDictIntKeyIntValue(self, input: dict[int, int]) -> dict[int, int]:
-        return input
-
-    @torch.jit.script_method
-    def eqDictFloatKeyIntValue(self, input: dict[float, int]) -> dict[float, int]:
-        return input
-
-    @torch.jit.script_method
-    def listIntSumReturnTuple(self, input: list[int]) -> tuple[list[int], int]:
-        sum = 0
-        for x in input:
-            sum += x
-        return (input, sum)
-
-    @torch.jit.script_method
-    def listBoolConjunction(self, input: list[bool]) -> bool:
-        res = True
-        for x in input:
-            res = res and x
-        return res
-
-    @torch.jit.script_method
-    def listBoolDisjunction(self, input: list[bool]) -> bool:
-        res = False
-        for x in input:
-            res = res or x
-        return res
-
-    @torch.jit.script_method
-    def tupleIntSumReturnTuple(
-        self, input: tuple[int, int, int]
-    ) -> tuple[tuple[int, int, int], int]:
-        sum = 0
-        for x in input:
-            sum += x
-        return (input, sum)
-
-    @torch.jit.script_method
-    def optionalIntIsNone(self, input: Optional[int]) -> bool:
-        return input is None
-
-    @torch.jit.script_method
-    def intEq0None(self, input: int) -> Optional[int]:
-        if input == 0:
-            return None
-        return input
-
-    @torch.jit.script_method
-    def str3Concat(self, input: str) -> str:
-        return input + input + input
-
-    @torch.jit.script_method
-    def newEmptyShapeWithItem(self, input):
-        return torch.tensor([int(input.item())])[0]
-
-    @torch.jit.script_method
-    def testAliasWithOffset(self) -> list[Tensor]:
-        x = torch.tensor([100, 200])
-        a = [x[0], x[1]]
-        return a
-
-    @torch.jit.script_method
-    def testNonContiguous(self):
-        x = torch.tensor([100, 200, 300])[::2]
-        assert not x.is_contiguous()
-        assert x[0] == 100
-        assert x[1] == 300
-        return x
-
-    @torch.jit.script_method
-    def conv2d(self, x: Tensor, w: Tensor, toChannelsLast: bool) -> Tensor:
-        r = torch.nn.functional.conv2d(x, w)
-        if toChannelsLast:
-            r = r.contiguous(memory_format=torch.channels_last)
-        else:
-            r = r.contiguous()
-        return r
-
-    @torch.jit.script_method
-    def conv3d(self, x: Tensor, w: Tensor, toChannelsLast: bool) -> Tensor:
-        r = torch.nn.functional.conv3d(x, w)
-        if toChannelsLast:
-            r = r.contiguous(memory_format=torch.channels_last_3d)
-        else:
-            r = r.contiguous()
-        return r
-
-    @torch.jit.script_method
-    def contiguous(self, x: Tensor) -> Tensor:
-        return x.contiguous()
-
-    @torch.jit.script_method
-    def contiguousChannelsLast(self, x: Tensor) -> Tensor:
-        return x.contiguous(memory_format=torch.channels_last)
-
-    @torch.jit.script_method
-    def contiguousChannelsLast3d(self, x: Tensor) -> Tensor:
-        return x.contiguous(memory_format=torch.channels_last_3d)
-
-
-scriptAndSave(Test(), "test.pt")

android/pytorch_android/gradle.properties

@@ -1,4 +0,0 @@
-POM_NAME=pytorch_android_lite pytorch android api
-POM_DESCRIPTION=pytorch_android_lite pytorch android api
-POM_ARTIFACT_ID=pytorch_android_lite
-POM_PACKAGING=aar

android/pytorch_android/host/build.gradle

@@ -1,42 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-//
-// This source code is licensed under the Apache-2 license found in the
-// LICENSE file in the root directory of this source tree.
-
-plugins {
-    id 'java-library'
-}
-
-repositories {
-    mavenLocal()
-    jcenter()
-}
-
-sourceSets {
-    main {
-        java {
-            srcDir '../src/main/java'
-            exclude 'org/pytorch/PyTorchAndroid.java'
-            exclude 'org/pytorch/LitePyTorchAndroid.java'
-            exclude 'org/pytorch/LiteModuleLoader.java'
-            exclude 'org/pytorch/LiteNativePeer.java'
-        }
-    }
-    test {
-        java {
-            srcDir '../src/androidTest/java'
-            exclude '**/PytorchInstrumented*'
-            exclude '**/PytorchLiteInstrumented*'
-        }
-        resources.srcDirs = ["../src/androidTest/assets"]
-    }
-}
-
-dependencies {
-    compileOnly 'com.google.code.findbugs:jsr305:3.0.1'
-    implementation 'com.facebook.soloader:nativeloader:0.10.1'
-    implementation 'com.facebook.fbjni:fbjni-java-only:0.2.2'
-    testImplementation 'junit:junit:4.12'
-}
-
-apply from: rootProject.file('gradle/release.gradle')

android/pytorch_android/host/gradle.properties

@@ -1,4 +0,0 @@
-POM_NAME=pytorch_java_only pytorch java api
-POM_DESCRIPTION=pytorch_java_only pytorch java api
-POM_ARTIFACT_ID=pytorch_java_only
-POM_PACKAGING=jar

android/pytorch_android/src/androidTest/cpp/pytorch_jni_common_test.cpp

@@ -1,21 +0,0 @@
-// Copyright (c) Meta Platforms, Inc. and affiliates.
-//
-// This source code is licensed under the BSD-style license found in the
-// LICENSE file in the root directory of this source tree.
-
-#include <gtest/gtest.h>
-
-#include <ATen/core/type_factory.h>
-#include "caffe2/android/pytorch_android/src/main/cpp/pytorch_jni_common.h"
-
-using namespace ::testing;
-
-TEST(pytorch_jni_common_test, newJIValueFromAtIValue) {
-  auto dict = c10::impl::GenericDict(
-      c10::dynT<c10::IntType>(), c10::dynT<c10::StringType>());
-  auto dictCallback = [](auto&&) {
-    return facebook::jni::local_ref<pytorch_jni::JIValue>{};
-  };
-  EXPECT_NO_THROW(pytorch_jni::JIValue::newJIValueFromAtIValue(
-      dict, dictCallback, dictCallback));
-}

android/pytorch_android/src/androidTest/java/org/pytorch/PytorchHostTests.java

@@ -1,25 +0,0 @@
-package org.pytorch;
-
-import java.io.IOException;
-import java.io.InputStream;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.StandardCopyOption;
-import java.util.Objects;
-
-public class PytorchHostTests extends PytorchTestBase {
-
-  @Override
-  protected Module loadModel(String path) throws IOException {
-    return Module.load(assetFilePath(path));
-  }
-
-  private String assetFilePath(String assetName) throws IOException {
-    Path tempFile = Files.createTempFile("test", ".pt");
-    try (InputStream resource =
-        Objects.requireNonNull(getClass().getClassLoader().getResourceAsStream("test.pt"))) {
-      Files.copy(resource, tempFile, StandardCopyOption.REPLACE_EXISTING);
-    }
-    return tempFile.toAbsolutePath().toString();
-  }
-}

android/pytorch_android/src/androidTest/java/org/pytorch/PytorchInstrumentedTests.java

@@ -1,42 +0,0 @@
-package org.pytorch;
-
-import android.content.Context;
-import androidx.test.InstrumentationRegistry;
-import androidx.test.runner.AndroidJUnit4;
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import org.junit.runner.RunWith;
-
-@RunWith(AndroidJUnit4.class)
-public class PytorchInstrumentedTests extends PytorchTestBase {
-
-  @Override
-  protected Module loadModel(String path) throws IOException {
-    return Module.load(assetFilePath(path));
-  }
-
-  private String assetFilePath(String assetName) throws IOException {
-    final Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
-    File file = new File(appContext.getFilesDir(), assetName);
-    if (file.exists() && file.length() > 0) {
-      return file.getAbsolutePath();
-    }
-
-    try (InputStream is = appContext.getAssets().open(assetName)) {
-      try (OutputStream os = new FileOutputStream(file)) {
-        byte[] buffer = new byte[4 * 1024];
-        int read;
-        while ((read = is.read(buffer)) != -1) {
-          os.write(buffer, 0, read);
-        }
-        os.flush();
-      }
-      return file.getAbsolutePath();
-    } catch (IOException e) {
-      throw e;
-    }
-  }
-}

android/pytorch_android/src/androidTest/java/org/pytorch/PytorchLiteInstrumentedTests.java

@@ -1,42 +0,0 @@
-package org.pytorch;
-
-import android.content.Context;
-import androidx.test.InstrumentationRegistry;
-import androidx.test.runner.AndroidJUnit4;
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import org.junit.runner.RunWith;
-
-@RunWith(AndroidJUnit4.class)
-public class PytorchLiteInstrumentedTests extends PytorchTestBase {
-
-  @Override
-  protected Module loadModel(String path) throws IOException {
-    return LiteModuleLoader.load(assetFilePath(path));
-  }
-
-  private String assetFilePath(String assetName) throws IOException {
-    final Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
-    File file = new File(appContext.getFilesDir(), assetName);
-    if (file.exists() && file.length() > 0) {
-      return file.getAbsolutePath();
-    }
-
-    try (InputStream is = appContext.getAssets().open(assetName)) {
-      try (OutputStream os = new FileOutputStream(file)) {
-        byte[] buffer = new byte[4 * 1024];
-        int read;
-        while ((read = is.read(buffer)) != -1) {
-          os.write(buffer, 0, read);
-        }
-        os.flush();
-      }
-      return file.getAbsolutePath();
-    } catch (IOException e) {
-      throw e;
-    }
-  }
-}

android/pytorch_android/src/androidTest/java/org/pytorch/PytorchTestBase.java

@@ -1,694 +0,0 @@
-package org.pytorch;
-
-import static org.junit.Assert.assertArrayEquals;
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertFalse;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.assertTrue;
-
-import java.io.IOException;
-import java.util.HashMap;
-import java.util.Map;
-import org.junit.Test;
-import org.junit.Ignore;
-
-public abstract class PytorchTestBase {
-  private static final String TEST_MODULE_ASSET_NAME = "android_api_module.ptl";
-
-  @Test
-  public void testForwardNull() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final IValue input = IValue.from(Tensor.fromBlob(Tensor.allocateByteBuffer(1), new long[] {1}));
-    assertTrue(input.isTensor());
-    final IValue output = module.forward(input);
-    assertTrue(output.isNull());
-  }
-
-  @Test
-  public void testEqBool() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    for (boolean value : new boolean[] {false, true}) {
-      final IValue input = IValue.from(value);
-      assertTrue(input.isBool());
-      assertTrue(value == input.toBool());
-      final IValue output = module.runMethod("eqBool", input);
-      assertTrue(output.isBool());
-      assertTrue(value == output.toBool());
-    }
-  }
-
-  @Test
-  public void testEqInt() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    for (long value : new long[] {Long.MIN_VALUE, -1024, -1, 0, 1, 1024, Long.MAX_VALUE}) {
-      final IValue input = IValue.from(value);
-      assertTrue(input.isLong());
-      assertTrue(value == input.toLong());
-      final IValue output = module.runMethod("eqInt", input);
-      assertTrue(output.isLong());
-      assertTrue(value == output.toLong());
-    }
-  }
-
-  @Test
-  public void testEqFloat() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    double[] values =
-        new double[] {
-          -Double.MAX_VALUE,
-          Double.MAX_VALUE,
-          -Double.MIN_VALUE,
-          Double.MIN_VALUE,
-          -Math.exp(1.d),
-          -Math.sqrt(2.d),
-          -3.1415f,
-          3.1415f,
-          -1,
-          0,
-          1,
-        };
-    for (double value : values) {
-      final IValue input = IValue.from(value);
-      assertTrue(input.isDouble());
-      assertTrue(value == input.toDouble());
-      final IValue output = module.runMethod("eqFloat", input);
-      assertTrue(output.isDouble());
-      assertTrue(value == output.toDouble());
-    }
-  }
-
-  @Test
-  public void testEqTensor() throws IOException {
-    final long[] inputTensorShape = new long[] {1, 3, 224, 224};
-    final long numElements = Tensor.numel(inputTensorShape);
-    final float[] inputTensorData = new float[(int) numElements];
-    for (int i = 0; i < numElements; ++i) {
-      inputTensorData[i] = i;
-    }
-    final Tensor inputTensor = Tensor.fromBlob(inputTensorData, inputTensorShape);
-
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final IValue input = IValue.from(inputTensor);
-    assertTrue(input.isTensor());
-    assertTrue(inputTensor == input.toTensor());
-    final IValue output = module.runMethod("eqTensor", input);
-    assertTrue(output.isTensor());
-    final Tensor outputTensor = output.toTensor();
-    assertNotNull(outputTensor);
-    assertArrayEquals(inputTensorShape, outputTensor.shape());
-    float[] outputData = outputTensor.getDataAsFloatArray();
-    for (int i = 0; i < numElements; i++) {
-      assertTrue(inputTensorData[i] == outputData[i]);
-    }
-  }
-
-  @Test
-  public void testEqDictIntKeyIntValue() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final Map<Long, IValue> inputMap = new HashMap<>();
-
-    inputMap.put(Long.MIN_VALUE, IValue.from(-Long.MIN_VALUE));
-    inputMap.put(Long.MAX_VALUE, IValue.from(-Long.MAX_VALUE));
-    inputMap.put(0l, IValue.from(0l));
-    inputMap.put(1l, IValue.from(-1l));
-    inputMap.put(-1l, IValue.from(1l));
-
-    final IValue input = IValue.dictLongKeyFrom(inputMap);
-    assertTrue(input.isDictLongKey());
-
-    final IValue output = module.runMethod("eqDictIntKeyIntValue", input);
-    assertTrue(output.isDictLongKey());
-
-    final Map<Long, IValue> outputMap = output.toDictLongKey();
-    assertTrue(inputMap.size() == outputMap.size());
-    for (Map.Entry<Long, IValue> entry : inputMap.entrySet()) {
-      assertTrue(outputMap.get(entry.getKey()).toLong() == entry.getValue().toLong());
-    }
-  }
-
-  @Test
-  public void testEqDictStrKeyIntValue() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final Map<String, IValue> inputMap = new HashMap<>();
-
-    inputMap.put("long_min_value", IValue.from(Long.MIN_VALUE));
-    inputMap.put("long_max_value", IValue.from(Long.MAX_VALUE));
-    inputMap.put("long_0", IValue.from(0l));
-    inputMap.put("long_1", IValue.from(1l));
-    inputMap.put("long_-1", IValue.from(-1l));
-
-    final IValue input = IValue.dictStringKeyFrom(inputMap);
-    assertTrue(input.isDictStringKey());
-
-    final IValue output = module.runMethod("eqDictStrKeyIntValue", input);
-    assertTrue(output.isDictStringKey());
-
-    final Map<String, IValue> outputMap = output.toDictStringKey();
-    assertTrue(inputMap.size() == outputMap.size());
-    for (Map.Entry<String, IValue> entry : inputMap.entrySet()) {
-      assertTrue(outputMap.get(entry.getKey()).toLong() == entry.getValue().toLong());
-    }
-  }
-
-  @Test
-  public void testListIntSumReturnTuple() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-
-    for (int n : new int[] {0, 1, 128}) {
-      long[] a = new long[n];
-      long sum = 0;
-      for (int i = 0; i < n; i++) {
-        a[i] = i;
-        sum += a[i];
-      }
-      final IValue input = IValue.listFrom(a);
-      assertTrue(input.isLongList());
-
-      final IValue output = module.runMethod("listIntSumReturnTuple", input);
-
-      assertTrue(output.isTuple());
-      assertTrue(2 == output.toTuple().length);
-
-      IValue output0 = output.toTuple()[0];
-      IValue output1 = output.toTuple()[1];
-
-      assertArrayEquals(a, output0.toLongList());
-      assertTrue(sum == output1.toLong());
-    }
-  }
-
-  @Test
-  public void testOptionalIntIsNone() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-
-    assertFalse(module.runMethod("optionalIntIsNone", IValue.from(1l)).toBool());
-    assertTrue(module.runMethod("optionalIntIsNone", IValue.optionalNull()).toBool());
-  }
-
-  @Test
-  public void testIntEq0None() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-
-    assertTrue(module.runMethod("intEq0None", IValue.from(0l)).isNull());
-    assertTrue(module.runMethod("intEq0None", IValue.from(1l)).toLong() == 1l);
-  }
-
-  @Test(expected = IllegalArgumentException.class)
-  public void testRunUndefinedMethod() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    module.runMethod("test_undefined_method_throws_exception");
-  }
-
-  @Test
-  public void testTensorMethods() {
-    long[] shape = new long[] {1, 3, 224, 224};
-    final int numel = (int) Tensor.numel(shape);
-    int[] ints = new int[numel];
-    float[] floats = new float[numel];
-
-    byte[] bytes = new byte[numel];
-    for (int i = 0; i < numel; i++) {
-      bytes[i] = (byte) ((i % 255) - 128);
-      ints[i] = i;
-      floats[i] = i / 1000.f;
-    }
-
-    Tensor tensorBytes = Tensor.fromBlob(bytes, shape);
-    assertTrue(tensorBytes.dtype() == DType.INT8);
-    assertArrayEquals(bytes, tensorBytes.getDataAsByteArray());
-
-    Tensor tensorInts = Tensor.fromBlob(ints, shape);
-    assertTrue(tensorInts.dtype() == DType.INT32);
-    assertArrayEquals(ints, tensorInts.getDataAsIntArray());
-
-    Tensor tensorFloats = Tensor.fromBlob(floats, shape);
-    assertTrue(tensorFloats.dtype() == DType.FLOAT32);
-    float[] floatsOut = tensorFloats.getDataAsFloatArray();
-    assertTrue(floatsOut.length == numel);
-    for (int i = 0; i < numel; i++) {
-      assertTrue(floats[i] == floatsOut[i]);
-    }
-  }
-
-  @Test(expected = IllegalStateException.class)
-  public void testTensorIllegalStateOnWrongType() {
-    long[] shape = new long[] {1, 3, 224, 224};
-    final int numel = (int) Tensor.numel(shape);
-    float[] floats = new float[numel];
-    Tensor tensorFloats = Tensor.fromBlob(floats, shape);
-    assertTrue(tensorFloats.dtype() == DType.FLOAT32);
-    tensorFloats.getDataAsByteArray();
-  }
-
-  @Test
-  public void testEqString() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    String[] values =
-        new String[] {
-          "smoketest",
-          "проверка не латинских символов", // not latin symbols check
-          "#@$!@#)($*!@#$)(!@*#$"
-        };
-    for (String value : values) {
-      final IValue input = IValue.from(value);
-      assertTrue(input.isString());
-      assertTrue(value.equals(input.toStr()));
-      final IValue output = module.runMethod("eqStr", input);
-      assertTrue(output.isString());
-      assertTrue(value.equals(output.toStr()));
-    }
-  }
-
-  @Test
-  public void testStr3Concat() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    String[] values =
-        new String[] {
-          "smoketest",
-          "проверка не латинских символов", // not latin symbols check
-          "#@$!@#)($*!@#$)(!@*#$"
-        };
-    for (String value : values) {
-      final IValue input = IValue.from(value);
-      assertTrue(input.isString());
-      assertTrue(value.equals(input.toStr()));
-      final IValue output = module.runMethod("str3Concat", input);
-      assertTrue(output.isString());
-      String expectedOutput =
-          new StringBuilder().append(value).append(value).append(value).toString();
-      assertTrue(expectedOutput.equals(output.toStr()));
-    }
-  }
-
-  @Test
-  public void testEmptyShape() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final long someNumber = 43;
-    final IValue input = IValue.from(Tensor.fromBlob(new long[] {someNumber}, new long[] {}));
-    final IValue output = module.runMethod("newEmptyShapeWithItem", input);
-    assertTrue(output.isTensor());
-    Tensor value = output.toTensor();
-    assertArrayEquals(new long[] {}, value.shape());
-    assertArrayEquals(new long[] {someNumber}, value.getDataAsLongArray());
-  }
-
-  @Test
-  public void testAliasWithOffset() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final IValue output = module.runMethod("testAliasWithOffset");
-    assertTrue(output.isTensorList());
-    Tensor[] tensors = output.toTensorList();
-    assertEquals(100, tensors[0].getDataAsLongArray()[0]);
-    assertEquals(200, tensors[1].getDataAsLongArray()[0]);
-  }
-
-  @Test
-  public void testNonContiguous() throws IOException {
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final IValue output = module.runMethod("testNonContiguous");
-    assertTrue(output.isTensor());
-    Tensor value = output.toTensor();
-    assertArrayEquals(new long[] {2}, value.shape());
-    assertArrayEquals(new long[] {100, 300}, value.getDataAsLongArray());
-  }
-
-  @Test
-  public void testChannelsLast() throws IOException {
-    long[] inputShape = new long[] {1, 3, 2, 2};
-    long[] data = new long[] {1, 11, 101, 2, 12, 102, 3, 13, 103, 4, 14, 104};
-    Tensor inputNHWC = Tensor.fromBlob(data, inputShape, MemoryFormat.CHANNELS_LAST);
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final IValue outputNCHW = module.runMethod("contiguous", IValue.from(inputNHWC));
-    assertIValueTensor(
-        outputNCHW,
-        MemoryFormat.CONTIGUOUS,
-        new long[] {1, 3, 2, 2},
-        new long[] {1, 2, 3, 4, 11, 12, 13, 14, 101, 102, 103, 104});
-    final IValue outputNHWC = module.runMethod("contiguousChannelsLast", IValue.from(inputNHWC));
-    assertIValueTensor(outputNHWC, MemoryFormat.CHANNELS_LAST, inputShape, data);
-  }
-
-  @Test
-  public void testChannelsLast3d() throws IOException {
-    long[] shape = new long[] {1, 2, 2, 2, 2};
-    long[] dataNCHWD = new long[] {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-    long[] dataNHWDC = new long[] {1, 9, 2, 10, 3, 11, 4, 12, 5, 13, 6, 14, 7, 15, 8, 16};
-
-    Tensor inputNHWDC = Tensor.fromBlob(dataNHWDC, shape, MemoryFormat.CHANNELS_LAST_3D);
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-    final IValue outputNCHWD = module.runMethod("contiguous", IValue.from(inputNHWDC));
-    assertIValueTensor(outputNCHWD, MemoryFormat.CONTIGUOUS, shape, dataNCHWD);
-
-    Tensor inputNCHWD = Tensor.fromBlob(dataNCHWD, shape, MemoryFormat.CONTIGUOUS);
-    final IValue outputNHWDC =
-        module.runMethod("contiguousChannelsLast3d", IValue.from(inputNCHWD));
-    assertIValueTensor(outputNHWDC, MemoryFormat.CHANNELS_LAST_3D, shape, dataNHWDC);
-  }
-
-  @Test
-  public void testChannelsLastConv2d() throws IOException {
-    long[] inputShape = new long[] {1, 3, 2, 2};
-    long[] dataNCHW = new long[] {
-      111, 112,
-      121, 122,
-
-      211, 212,
-      221, 222,
-
-      311, 312,
-      321, 322};
-    Tensor inputNCHW = Tensor.fromBlob(dataNCHW, inputShape, MemoryFormat.CONTIGUOUS);
-    long[] dataNHWC = new long[] {
-      111, 211, 311,       112, 212, 312,
-
-      121, 221, 321,       122, 222, 322};
-    Tensor inputNHWC = Tensor.fromBlob(dataNHWC, inputShape, MemoryFormat.CHANNELS_LAST);
-    long[] weightShape = new long[] {3, 3, 1, 1};
-    long[] dataWeightOIHW = new long[] {
-      2, 0, 0,
-      0, 1, 0,
-      0, 0, -1};
-    Tensor wNCHW = Tensor.fromBlob(dataWeightOIHW, weightShape, MemoryFormat.CONTIGUOUS);
-    long[] dataWeightOHWI = new long[] {
-      2, 0, 0,
-      0, 1, 0,
-      0, 0, -1};
-
-    Tensor wNHWC = Tensor.fromBlob(dataWeightOHWI, weightShape, MemoryFormat.CHANNELS_LAST);
-
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-
-    final IValue outputNCHW =
-        module.runMethod("conv2d", IValue.from(inputNCHW), IValue.from(wNCHW), IValue.from(false));
-    assertIValueTensor(
-        outputNCHW,
-        MemoryFormat.CONTIGUOUS,
-        new long[] {1, 3, 2, 2},
-        new long[] {
-          2*111, 2*112,
-          2*121, 2*122,
-
-          211, 212,
-          221, 222,
-
-          -311, -312,
-          -321, -322});
-
-    final IValue outputNHWC =
-        module.runMethod("conv2d", IValue.from(inputNHWC), IValue.from(wNHWC), IValue.from(true));
-    assertIValueTensor(
-        outputNHWC,
-        MemoryFormat.CHANNELS_LAST,
-        new long[] {1, 3, 2, 2},
-        new long[] {
-          2*111, 211, -311,      2*112, 212, -312,
-          2*121, 221, -321,      2*122, 222, -322});
-  }
-
-  @Test
-  public void testChannelsLastConv3d() throws IOException {
-    long[] inputShape = new long[] {1, 3, 2, 2, 2};
-    long[] dataNCDHW = new long[] {
-      1111, 1112,
-      1121, 1122,
-      1211, 1212,
-      1221, 1222,
-
-      2111, 2112,
-      2121, 2122,
-      2211, 2212,
-      2221, 2222,
-
-      3111, 3112,
-      3121, 3122,
-      3211, 3212,
-      3221, 3222};
-    Tensor inputNCDHW = Tensor.fromBlob(dataNCDHW, inputShape, MemoryFormat.CONTIGUOUS);
-    long[] dataNDHWC = new long[] {
-      1111, 2111, 3111,
-      1112, 2112, 3112,
-
-      1121, 2121, 3121,
-      1122, 2122, 3122,
-
-      1211, 2211, 3211,
-      1212, 2212, 3212,
-
-      1221, 2221, 3221,
-      1222, 2222, 3222};
-
-    Tensor inputNDHWC = Tensor.fromBlob(dataNDHWC, inputShape, MemoryFormat.CHANNELS_LAST_3D);
-
-    long[] weightShape = new long[] {3, 3, 1, 1, 1};
-    long[] dataWeightOIDHW = new long[] {
-      2, 0, 0,
-      0, 1, 0,
-      0, 0, -1,
-    };
-    Tensor wNCDHW = Tensor.fromBlob(dataWeightOIDHW, weightShape, MemoryFormat.CONTIGUOUS);
-    long[] dataWeightODHWI = new long[] {
-      2, 0, 0,
-      0, 1, 0,
-      0, 0, -1,
-    };
-    Tensor wNDHWC = Tensor.fromBlob(dataWeightODHWI, weightShape, MemoryFormat.CHANNELS_LAST_3D);
-
-    final Module module = loadModel(TEST_MODULE_ASSET_NAME);
-
-    final IValue outputNCDHW =
-        module.runMethod("conv3d", IValue.from(inputNCDHW), IValue.from(wNCDHW), IValue.from(false));
-    assertIValueTensor(
-        outputNCDHW,
-        MemoryFormat.CONTIGUOUS,
-        new long[] {1, 3, 2, 2, 2},
-        new long[] {
-          2*1111, 2*1112,     2*1121, 2*1122,
-          2*1211, 2*1212,     2*1221, 2*1222,
-
-          2111, 2112,     2121, 2122,
-          2211, 2212,     2221, 2222,
-
-          -3111, -3112,     -3121, -3122,
-          -3211, -3212,     -3221, -3222});
-
-    final IValue outputNDHWC =
-        module.runMethod("conv3d", IValue.from(inputNDHWC), IValue.from(wNDHWC), IValue.from(true));
-    assertIValueTensor(
-        outputNDHWC,
-        MemoryFormat.CHANNELS_LAST_3D,
-        new long[] {1, 3, 2, 2, 2},
-        new long[] {
-          2*1111, 2111, -3111,      2*1112, 2112, -3112,
-          2*1121, 2121, -3121,      2*1122, 2122, -3122,
-
-          2*1211, 2211, -3211,      2*1212, 2212, -3212,
-          2*1221, 2221, -3221,      2*1222, 2222, -3222});
-  }
-
-  @Test
-  public void testMobileNetV2() throws IOException {
-    try {
-      final Module module = loadModel("mobilenet_v2.ptl");
-      final IValue inputs = module.runMethod("get_all_bundled_inputs");
-      assertTrue(inputs.isList());
-      final IValue input = inputs.toList()[0];
-      assertTrue(input.isTuple());
-      module.forward(input.toTuple()[0]);
-      assertTrue(true);
-    } catch (Exception ex) {
-      assertTrue("failed to run MobileNetV2 " + ex.getMessage(), false);
-    }
-  }
-
-  @Test
-  public void testPointwiseOps() throws IOException {
-    runModel("pointwise_ops");
-  }
-
-  @Test
-  public void testReductionOps() throws IOException {
-    runModel("reduction_ops");
-  }
-
-  @Test
-  public void testComparisonOps() throws IOException {
-    runModel("comparison_ops");
-  }
-
-  @Test
-  public void testOtherMathOps() throws IOException {
-    runModel("other_math_ops");
-  }
-
-  @Test
-  @Ignore
-  public void testSpectralOps() throws IOException {
-    // NB: This model fails without lite interpreter.  The error is as follows:
-    // RuntimeError: stft requires the return_complex parameter be given for real inputs
-    runModel("spectral_ops");
-  }
-
-  @Test
-  public void testBlasLapackOps() throws IOException {
-    runModel("blas_lapack_ops");
-  }
-
-  @Test
-  public void testSamplingOps() throws IOException {
-    runModel("sampling_ops");
-  }
-
-  @Test
-  public void testTensorOps() throws IOException {
-    runModel("tensor_general_ops");
-  }
-
-  @Test
-  public void testTensorCreationOps() throws IOException {
-    runModel("tensor_creation_ops");
-  }
-
-  @Test
-  public void testTensorIndexingOps() throws IOException {
-    runModel("tensor_indexing_ops");
-  }
-
-  @Test
-  public void testTensorTypingOps() throws IOException {
-    runModel("tensor_typing_ops");
-  }
-
-  @Test
-  public void testTensorViewOps() throws IOException {
-    runModel("tensor_view_ops");
-  }
-
-  @Test
-  public void testConvolutionOps() throws IOException {
-    runModel("convolution_ops");
-  }
-
-  @Test
-  public void testPoolingOps() throws IOException {
-    runModel("pooling_ops");
-  }
-
-  @Test
-  public void testPaddingOps() throws IOException {
-    runModel("padding_ops");
-  }
-
-  @Test
-  public void testActivationOps() throws IOException {
-    runModel("activation_ops");
-  }
-
-  @Test
-  public void testNormalizationOps() throws IOException {
-    runModel("normalization_ops");
-  }
-
-  @Test
-  public void testRecurrentOps() throws IOException {
-    runModel("recurrent_ops");
-  }
-
-  @Test
-  public void testTransformerOps() throws IOException {
-    runModel("transformer_ops");
-  }
-
-  @Test
-  public void testLinearOps() throws IOException {
-    runModel("linear_ops");
-  }
-
-  @Test
-  public void testDropoutOps() throws IOException {
-    runModel("dropout_ops");
-  }
-
-  @Test
-  public void testSparseOps() throws IOException {
-    runModel("sparse_ops");
-  }
-
-  @Test
-  public void testDistanceFunctionOps() throws IOException {
-    runModel("distance_function_ops");
-  }
-
-  @Test
-  public void testLossFunctionOps() throws IOException {
-    runModel("loss_function_ops");
-  }
-
-  @Test
-  public void testVisionFunctionOps() throws IOException {
-    runModel("vision_function_ops");
-  }
-
-  @Test
-  public void testShuffleOps() throws IOException {
-    runModel("shuffle_ops");
-  }
-
-  @Test
-  public void testNNUtilsOps() throws IOException {
-    runModel("nn_utils_ops");
-  }
-
-  @Test
-  public void testQuantOps() throws IOException {
-    runModel("general_quant_ops");
-  }
-
-  @Test
-  public void testDynamicQuantOps() throws IOException {
-    runModel("dynamic_quant_ops");
-  }
-
-  @Test
-  public void testStaticQuantOps() throws IOException {
-    runModel("static_quant_ops");
-  }
-
-  @Test
-  public void testFusedQuantOps() throws IOException {
-    runModel("fused_quant_ops");
-  }
-
-  @Test
-  public void testTorchScriptBuiltinQuantOps() throws IOException {
-    runModel("torchscript_builtin_ops");
-  }
-
-  @Test
-  public void testTorchScriptCollectionQuantOps() throws IOException {
-    runModel("torchscript_collection_ops");
-  }
-
-  static void assertIValueTensor(
-      final IValue ivalue,
-      final MemoryFormat memoryFormat,
-      final long[] expectedShape,
-      final long[] expectedData) {
-    assertTrue(ivalue.isTensor());
-    Tensor t = ivalue.toTensor();
-    assertEquals(memoryFormat, t.memoryFormat());
-    assertArrayEquals(expectedShape, t.shape());
-    assertArrayEquals(expectedData, t.getDataAsLongArray());
-  }
-
-  void runModel(final String name) throws IOException {
-    final Module storage_module = loadModel(name + ".ptl");
-    storage_module.forward();
-
-    // TODO enable this once the on-the-fly script is ready
-    // final Module on_the_fly_module = loadModel(name + "_temp.ptl");
-    // on_the_fly_module.forward();
-    assertTrue(true);
-  }
-
-  protected abstract Module loadModel(String assetName) throws IOException;
-}

android/pytorch_android/src/androidTest/java/org/pytorch/suite/PytorchInstrumentedTestSuite.java

@@ -1,9 +0,0 @@
-package org.pytorch.suite;
-
-import org.junit.runner.RunWith;
-import org.junit.runners.Suite;
-import org.pytorch.PytorchInstrumentedTests;
-
-@RunWith(Suite.class)
-@Suite.SuiteClasses({PytorchInstrumentedTests.class})
-public class PytorchInstrumentedTestSuite {}

android/pytorch_android/src/androidTest/java/org/pytorch/suite/PytorchLiteInstrumentedTestSuite.java

@@ -1,9 +0,0 @@
-package org.pytorch.suite;
-
-import org.junit.runner.RunWith;
-import org.junit.runners.Suite;
-import org.pytorch.PytorchLiteInstrumentedTests;
-
-@RunWith(Suite.class)
-@Suite.SuiteClasses({PytorchLiteInstrumentedTests.class})
-public class PytorchLiteInstrumentedTestSuite {}

android/pytorch_android/src/main/AndroidManifest.xml

@@ -1 +0,0 @@
-<manifest package="org.pytorch" />

android/pytorch_android/src/main/cpp/cmake_macros.h

@@ -1,3 +0,0 @@
-#pragma once
-
-/* #undef TRACE_ENABLED */

android/pytorch_android/src/main/cpp/cmake_macros.h.in

@@ -1,3 +0,0 @@
-#pragma once
-
-#cmakedefine TRACE_ENABLED

android/pytorch_android/src/main/cpp/pytorch_jni_common.cpp

@@ -1,697 +0,0 @@
-#include <cassert>
-#include <iostream>
-#include <memory>
-#include <string>
-
-#include <c10/core/MemoryFormat.h>
-#include <c10/util/irange.h>
-
-#include <fbjni/ByteBuffer.h>
-#include <fbjni/fbjni.h>
-
-#include "pytorch_jni_common.h"
-#if defined(__ANDROID__)
-#ifndef USE_PTHREADPOOL
-#define USE_PTHREADPOOL
-#endif /* USE_PTHREADPOOL */
-#include <caffe2/utils/threadpool/pthreadpool-cpp.h>
-#endif
-
-namespace pytorch_jni {
-
-c10::DeviceType deviceJniCodeToDeviceType(jint deviceJniCode) {
-  if (deviceJniCode == kDeviceCPU) {
-    return at::kCPU;
-  } else if (deviceJniCode == kDeviceVulkan) {
-    return at::kVulkan;
-  }
-
-  facebook::jni::throwNewJavaException(
-      facebook::jni::gJavaLangIllegalArgumentException, "Unknown device");
-}
-
-bool Trace::is_initialized_ = false;
-
-#if defined(TRACE_ENABLED) && defined(__ANDROID__)
-Trace::fp_ATrace_beginSection Trace::ATrace_beginSection;
-Trace::fp_ATrace_endSection Trace::ATrace_endSection;
-#endif
-
-void Trace::init() {
-#if defined(TRACE_ENABLED) && defined(__ANDROID__)
-  void* lib = dlopen("libandroid.so", RTLD_NOW || RTLD_LOCAL);
-  if (lib != NULL) {
-    Trace::ATrace_beginSection = reinterpret_cast<fp_ATrace_beginSection>(
-        dlsym(lib, "ATrace_beginSection"));
-    Trace::ATrace_endSection =
-        reinterpret_cast<fp_ATrace_endSection>(dlsym(lib, "ATrace_endSection"));
-  }
-#endif
-}
-
-// NOTE: Codes must be kept in sync with DType.java.
-// NOTE: Never serialize these, because they can change between releases.
-constexpr static int kTensorDTypeUInt8 = 1;
-constexpr static int kTensorDTypeInt8 = 2;
-constexpr static int kTensorDTypeInt32 = 3;
-constexpr static int kTensorDTypeFloat32 = 4;
-constexpr static int kTensorDTypeInt64 = 5;
-constexpr static int kTensorDTypeFloat64 = 6;
-
-constexpr static int kTensorMemoryFormatContiguous = 1;
-constexpr static int kTensorMemoryFormatChannelsLast = 2;
-constexpr static int kTensorMemoryFormatChannelsLast3d = 3;
-
-template <typename K = jobject, typename V = jobject>
-struct JHashMap
-    : facebook::jni::JavaClass<JHashMap<K, V>, facebook::jni::JMap<K, V>> {
-  constexpr static auto kJavaDescriptor = "Ljava/util/HashMap;";
-
-  using Super =
-      facebook::jni::JavaClass<JHashMap<K, V>, facebook::jni::JMap<K, V>>;
-
-  static facebook::jni::local_ref<JHashMap<K, V>> create() {
-    return Super::newInstance();
-  }
-
-  void put(
-      facebook::jni::alias_ref<facebook::jni::JObject::javaobject> key,
-      facebook::jni::alias_ref<facebook::jni::JObject::javaobject> value) {
-    static auto putMethod =
-        Super::javaClassStatic()
-            ->template getMethod<facebook::jni::alias_ref<
-                facebook::jni::JObject::javaobject>(
-                facebook::jni::alias_ref<facebook::jni::JObject::javaobject>,
-                facebook::jni::alias_ref<facebook::jni::JObject::javaobject>)>(
-                "put");
-    putMethod(Super::self(), key, value);
-  }
-};
-
-static at::Tensor newAtTensor(
-    facebook::jni::alias_ref<facebook::jni::JBuffer> jbuffer,
-    facebook::jni::alias_ref<jlongArray> jshape,
-    jint jdtype,
-    jint jmemoryFormat) {
-  const auto rank = jshape->size();
-  const auto shapeArr = jshape->getRegion(0, rank);
-  std::vector<int64_t> shapeVec{};
-  shapeVec.reserve(rank);
-  auto numel = 1;
-  for (const auto i : c10::irange(rank)) {
-    shapeVec.push_back(shapeArr[i]);
-    numel *= shapeArr[i];
-  }
-  JNIEnv* jni = facebook::jni::Environment::current();
-  caffe2::TypeMeta typeMeta{};
-  int dataElementSizeBytes = 0;
-  if (kTensorDTypeFloat32 == jdtype) {
-    dataElementSizeBytes = 4;
-    typeMeta = caffe2::TypeMeta::Make<float>();
-  } else if (kTensorDTypeInt32 == jdtype) {
-    dataElementSizeBytes = 4;
-    typeMeta = caffe2::TypeMeta::Make<int32_t>();
-  } else if (kTensorDTypeInt8 == jdtype) {
-    dataElementSizeBytes = 1;
-    typeMeta = caffe2::TypeMeta::Make<int8_t>();
-  } else if (kTensorDTypeUInt8 == jdtype) {
-    dataElementSizeBytes = 1;
-    typeMeta = caffe2::TypeMeta::Make<uint8_t>();
-  } else if (kTensorDTypeFloat64 == jdtype) {
-    dataElementSizeBytes = 8;
-    typeMeta = caffe2::TypeMeta::Make<double>();
-  } else if (kTensorDTypeInt64 == jdtype) {
-    dataElementSizeBytes = 8;
-    typeMeta = caffe2::TypeMeta::Make<int64_t>();
-  } else {
-    facebook::jni::throwNewJavaException(
-        facebook::jni::gJavaLangIllegalArgumentException,
-        "Unknown Tensor jdtype %d",
-        jdtype);
-  }
-  const auto dataCapacity = jni->GetDirectBufferCapacity(jbuffer.get());
-  if (dataCapacity != numel) {
-    facebook::jni::throwNewJavaException(
-        facebook::jni::gJavaLangIllegalArgumentException,
-        "Tensor dimensions(elements number:%d, element byte size:%d, total "
-        "bytes:%d) inconsistent with buffer capacity(%d)",
-        numel,
-        dataElementSizeBytes,
-        numel * dataElementSizeBytes,
-        dataCapacity);
-  }
-
-  if (jmemoryFormat == kTensorMemoryFormatChannelsLast) {
-    auto sizes = torch::IntArrayRef(shapeVec);
-    return torch::from_blob(
-        jni->GetDirectBufferAddress(jbuffer.get()),
-        sizes,
-        torch::IntArrayRef(c10::get_channels_last_strides_2d(sizes)),
-        at::TensorOptions(typeMeta).memory_format(
-            at::MemoryFormat::ChannelsLast));
-  } else if (jmemoryFormat == kTensorMemoryFormatChannelsLast3d) {
-    auto sizes = torch::IntArrayRef(shapeVec);
-    return torch::from_blob(
-        jni->GetDirectBufferAddress(jbuffer.get()),
-        sizes,
-        torch::IntArrayRef(c10::get_channels_last_strides_3d(sizes)),
-        at::TensorOptions(typeMeta).memory_format(
-            at::MemoryFormat::ChannelsLast3d));
-  }
-  return torch::from_blob(
-      jni->GetDirectBufferAddress(jbuffer.get()),
-      torch::IntArrayRef(shapeVec),
-      at::TensorOptions(typeMeta));
-}
-
-class TensorHybrid : public facebook::jni::HybridClass<TensorHybrid> {
- public:
-  constexpr static const char* kJavaDescriptor = "Lorg/pytorch/Tensor;";
-
-  explicit TensorHybrid(at::Tensor tensor) : tensor_(tensor) {}
-
-  static facebook::jni::local_ref<TensorHybrid::jhybriddata> initHybrid(
-      facebook::jni::alias_ref<TensorHybrid::javaobject> jTensorThis) {
-    static auto cls = TensorHybrid::javaClassStatic();
-    static const auto jMethodDTypeCode = cls->getMethod<jint()>("dtypeJniCode");
-    static const auto jMethodMemoryFormatCode =
-        cls->getMethod<jint()>("memoryFormatJniCode");
-    static const auto jFieldShape = cls->getField<jlongArray>("shape");
-    static const auto jMethodGetDataBuffer = cls->getMethod<
-        facebook::jni::local_ref<facebook::jni::JBuffer::javaobject>()>(
-        "getRawDataBuffer");
-
-    at::Tensor tensor = newAtTensor(
-        jMethodGetDataBuffer(jTensorThis),
-        jTensorThis->getFieldValue(jFieldShape),
-        jMethodDTypeCode(jTensorThis),
-        jMethodMemoryFormatCode(jTensorThis));
-    return makeCxxInstance(std::move(tensor));
-  }
-
-  static facebook::jni::local_ref<TensorHybrid::javaobject>
-  newJTensorFromAtTensor(const at::Tensor& input_tensor) {
-    // Java wrapper currently only supports contiguous tensors.
-
-    int jmemoryFormat = 0;
-    at::Tensor tensor{};
-    if (input_tensor.is_contiguous(at::MemoryFormat::ChannelsLast)) {
-      tensor = input_tensor;
-      jmemoryFormat = kTensorMemoryFormatChannelsLast;
-    } else if (input_tensor.is_contiguous(at::MemoryFormat::ChannelsLast3d)) {
-      tensor = input_tensor;
-      jmemoryFormat = kTensorMemoryFormatChannelsLast3d;
-    } else {
-      tensor = input_tensor.contiguous();
-      jmemoryFormat = kTensorMemoryFormatContiguous;
-    }
-
-    const auto scalarType = tensor.scalar_type();
-    int jdtype = 0;
-    if (at::kFloat == scalarType) {
-      jdtype = kTensorDTypeFloat32;
-    } else if (at::kInt == scalarType) {
-      jdtype = kTensorDTypeInt32;
-    } else if (at::kByte == scalarType) {
-      jdtype = kTensorDTypeUInt8;
-    } else if (at::kChar == scalarType) {
-      jdtype = kTensorDTypeInt8;
-    } else if (at::kLong == scalarType) {
-      jdtype = kTensorDTypeInt64;
-    } else if (at::kDouble == scalarType) {
-      jdtype = kTensorDTypeFloat64;
-    } else {
-      facebook::jni::throwNewJavaException(
-          facebook::jni::gJavaLangIllegalArgumentException,
-          "at::Tensor scalar type %s is not supported on java side",
-          c10::toString(scalarType));
-    }
-
-    const auto& tensorShape = tensor.sizes();
-    std::vector<jlong> tensorShapeVec;
-    for (const auto& s : tensorShape) {
-      tensorShapeVec.push_back(s);
-    }
-    facebook::jni::local_ref<jlongArray> jTensorShape =
-        facebook::jni::make_long_array(tensorShapeVec.size());
-    jTensorShape->setRegion(0, tensorShapeVec.size(), tensorShapeVec.data());
-
-    static auto cls = TensorHybrid::javaClassStatic();
-    facebook::jni::local_ref<facebook::jni::JByteBuffer> jTensorBuffer =
-        facebook::jni::JByteBuffer::wrapBytes(
-            (uint8_t*)tensor.data_ptr(), tensor.nbytes());
-    jTensorBuffer->order(facebook::jni::JByteOrder::nativeOrder());
-
-    static const auto jMethodNewTensor =
-        cls->getStaticMethod<facebook::jni::local_ref<TensorHybrid::javaobject>(
-            facebook::jni::alias_ref<facebook::jni::JByteBuffer>,
-            facebook::jni::alias_ref<jlongArray>,
-            jint,
-            jint,
-            facebook::jni::alias_ref<jhybriddata>)>("nativeNewTensor");
-    return jMethodNewTensor(
-        cls,
-        jTensorBuffer,
-        jTensorShape,
-        jdtype,
-        jmemoryFormat,
-        makeCxxInstance(tensor));
-  }
-
-  static at::Tensor newAtTensorFromJTensor(
-      facebook::jni::alias_ref<TensorHybrid::javaobject> jtensor) {
-    static auto cls = TensorHybrid::javaClassStatic();
-    static const auto dtypeMethod = cls->getMethod<jint()>("dtypeJniCode");
-    jint jdtype = dtypeMethod(jtensor);
-
-    static const auto memoryFormatMethod =
-        cls->getMethod<jint()>("memoryFormatJniCode");
-    jint jmemoryFormat = memoryFormatMethod(jtensor);
-
-    static const auto shapeField = cls->getField<jlongArray>("shape");
-    auto jshape = jtensor->getFieldValue(shapeField);
-
-    static auto dataBufferMethod = cls->getMethod<
-        facebook::jni::local_ref<facebook::jni::JBuffer::javaobject>()>(
-        "getRawDataBuffer");
-    facebook::jni::local_ref<facebook::jni::JBuffer> jbuffer =
-        dataBufferMethod(jtensor);
-    return newAtTensor(jbuffer, jshape, jdtype, jmemoryFormat);
-  }
-
-  at::Tensor tensor() const {
-    return tensor_;
-  }
-
- private:
-  friend HybridBase;
-  at::Tensor tensor_;
-};
-
-facebook::jni::local_ref<JIValue> JIValue::newJIValueFromStringDict(
-    c10::Dict<c10::IValue, c10::IValue> dict) {
-  static auto jMethodDictStringKey =
-      JIValue::javaClassStatic()
-          ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-              facebook::jni::alias_ref<facebook::jni::JMap<
-                  facebook::jni::alias_ref<facebook::jni::JString::javaobject>,
-                  facebook::jni::alias_ref<JIValue::javaobject>>>)>(
-              "dictStringKeyFrom");
-
-  auto jmap = JHashMap<
-      facebook::jni::alias_ref<facebook::jni::JString::javaobject>,
-      facebook::jni::alias_ref<JIValue::javaobject>>::create();
-  for (auto& pair : dict) {
-    jmap->put(
-        facebook::jni::make_jstring(pair.key().toStringRef()),
-        JIValue::newJIValueFromAtIValue(pair.value()));
-  }
-  return jMethodDictStringKey(JIValue::javaClassStatic(), jmap);
-}
-
-facebook::jni::local_ref<JIValue> JIValue::newJIValueFromIntDict(
-    c10::Dict<c10::IValue, c10::IValue> dict) {
-  static auto jMethodDictLongKey =
-      JIValue::javaClassStatic()
-          ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-              facebook::jni::alias_ref<facebook::jni::JMap<
-                  facebook::jni::alias_ref<facebook::jni::JLong::javaobject>,
-                  facebook::jni::alias_ref<JIValue::javaobject>>>)>(
-              "dictLongKeyFrom");
-  auto jmap = JHashMap<
-      facebook::jni::alias_ref<facebook::jni::JLong::javaobject>,
-      facebook::jni::alias_ref<JIValue::javaobject>>::create();
-  for (auto& pair : dict) {
-    jmap->put(
-        facebook::jni::JLong::valueOf(pair.key().toInt()),
-        JIValue::newJIValueFromAtIValue(pair.value()));
-  }
-  return jMethodDictLongKey(JIValue::javaClassStatic(), jmap);
-}
-
-facebook::jni::local_ref<JIValue> JIValue::newJIValueFromAtIValue(
-    const at::IValue& ivalue,
-    DictCallback stringDictCallback,
-    DictCallback intDictCallback) {
-  Trace _s{"jni::JIValue::newJIValueFromAtIValue"};
-  if (ivalue.isNone()) {
-    static auto jMethodOptionalNull =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>()>(
-                "optionalNull");
-    return jMethodOptionalNull(JIValue::javaClassStatic());
-  } else if (ivalue.isTensor()) {
-    static auto jMethodTensor =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::local_ref<TensorHybrid::javaobject>)>("from");
-    const auto& tensor = ivalue.toTensor();
-    return jMethodTensor(
-        JIValue::javaClassStatic(),
-        TensorHybrid::newJTensorFromAtTensor(tensor));
-  } else if (ivalue.isBool()) {
-    static auto jMethodBool =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(jboolean)>(
-                "from");
-    return jMethodBool(JIValue::javaClassStatic(), ivalue.toBool());
-  } else if (ivalue.isInt()) {
-    static auto jMethodInt =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(jlong)>("from");
-    return jMethodInt(JIValue::javaClassStatic(), ivalue.toInt());
-  } else if (ivalue.isDouble()) {
-    static auto jMethodDouble =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(jdouble)>(
-                "from");
-    return jMethodDouble(JIValue::javaClassStatic(), ivalue.toDouble());
-  } else if (ivalue.isString()) {
-    static auto jMethodString =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<facebook::jni::JString::javaobject>)>(
-                "from");
-    return jMethodString(
-        JIValue::javaClassStatic(),
-        facebook::jni::make_jstring(ivalue.toStringRef()));
-  } else if (ivalue.isTuple()) {
-    auto elementsVec = ivalue.toTupleRef().elements();
-    static auto jMethodTupleArr =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<facebook::jni::JArrayClass<
-                    JIValue::javaobject>::javaobject>)>("tupleFrom");
-    auto jElementsArray =
-        facebook::jni::JArrayClass<JIValue::javaobject>::newArray(
-            elementsVec.size());
-    auto index = 0;
-    for (const auto& e : elementsVec) {
-      (*jElementsArray)[index++] = JIValue::newJIValueFromAtIValue(e);
-    }
-    return jMethodTupleArr(JIValue::javaClassStatic(), jElementsArray);
-  } else if (ivalue.isBoolList()) {
-    auto list = ivalue.toBoolList();
-    static auto jMethodBoolListArr =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<jbooleanArray>)>("listFrom");
-    size_t n = list.size();
-    auto jArray = facebook::jni::make_boolean_array(n);
-    auto jArrayPinned = jArray->pin();
-    auto index = 0;
-    for (const auto& e : list) {
-      jArrayPinned[index++] = e;
-    }
-    return jMethodBoolListArr(JIValue::javaClassStatic(), jArray);
-  } else if (ivalue.isIntList()) {
-    auto list = ivalue.toIntList();
-    static auto jMethodLongListArr =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<jlongArray>)>("listFrom");
-    size_t n = list.size();
-    auto jArray = facebook::jni::make_long_array(n);
-    auto jArrayPinned = jArray->pin();
-    auto index = 0;
-    for (const auto& e : list) {
-      jArrayPinned[index++] = e;
-    }
-    return jMethodLongListArr(JIValue::javaClassStatic(), jArray);
-  } else if (ivalue.isDoubleList()) {
-    auto list = ivalue.toDoubleList();
-    static auto jMethoDoubleListArr =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<jdoubleArray>)>("listFrom");
-    size_t n = list.size();
-    auto jArray = facebook::jni::make_double_array(n);
-    auto jArrayPinned = jArray->pin();
-    auto index = 0;
-    for (const auto& e : list) {
-      jArrayPinned[index++] = e;
-    }
-    return jMethoDoubleListArr(JIValue::javaClassStatic(), jArray);
-  } else if (ivalue.isTensorList()) {
-    auto list = ivalue.toTensorList();
-    static auto jMethodTensorListArr =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<facebook::jni::JArrayClass<
-                    TensorHybrid::javaobject>::javaobject>)>("listFrom");
-    auto jArray =
-        facebook::jni::JArrayClass<TensorHybrid::javaobject>::newArray(
-            list.size());
-    auto index = 0;
-    for (const auto& e : list) {
-      (*jArray)[index++] = TensorHybrid::newJTensorFromAtTensor(e);
-    }
-    return jMethodTensorListArr(JIValue::javaClassStatic(), jArray);
-  } else if (ivalue.isList()) {
-    auto list = ivalue.toList();
-    static auto jMethodListArr =
-        JIValue::javaClassStatic()
-            ->getStaticMethod<facebook::jni::local_ref<JIValue>(
-                facebook::jni::alias_ref<facebook::jni::JArrayClass<
-                    JIValue::javaobject>::javaobject>)>("listFrom");
-    auto jArray =
-        facebook::jni::JArrayClass<JIValue::javaobject>::newArray(list.size());
-    auto index = 0;
-    for (const auto& e : list) {
-      (*jArray)[index++] = JIValue::newJIValueFromAtIValue(e);
-    }
-    return jMethodListArr(JIValue::javaClassStatic(), jArray);
-  } else if (ivalue.isGenericDict()) {
-    auto dict = ivalue.toGenericDict();
-    const auto keyType = dict.keyType();
-
-    if (!keyType) {
-      facebook::jni::throwNewJavaException(
-          facebook::jni::gJavaLangIllegalArgumentException,
-          "Unknown IValue-Dict key type");
-    }
-
-    if (*keyType == *c10::StringType::get()) {
-      return stringDictCallback(std::move(dict));
-    } else if (*keyType == *c10::IntType::get()) {
-      return intDictCallback(std::move(dict));
-    }
-
-    facebook::jni::throwNewJavaException(
-        facebook::jni::gJavaLangIllegalArgumentException,
-        "Unsupported IValue-Dict key type: %s",
-        keyType->str().c_str());
-  }
-
-  facebook::jni::throwNewJavaException(
-      facebook::jni::gJavaLangIllegalArgumentException,
-      "Unsupported IValue type %s",
-      ivalue.tagKind().c_str());
-}
-
-at::IValue JIValue::JIValueToAtIValue(
-    facebook::jni::alias_ref<JIValue> jivalue) {
-  Trace _s{"jni::JIValue::JIValueToAtIValue"};
-  static const auto typeCodeField =
-      JIValue::javaClassStatic()->getField<jint>("mTypeCode");
-  const auto typeCode = jivalue->getFieldValue(typeCodeField);
-  if (JIValue::kTypeCodeNull == typeCode) {
-    return at::IValue{};
-  } else if (JIValue::kTypeCodeTensor == typeCode) {
-    static const auto jMethodGetTensor =
-        JIValue::javaClassStatic()
-            ->getMethod<facebook::jni::alias_ref<TensorHybrid::javaobject>()>(
-                "toTensor");
-    return TensorHybrid::newAtTensorFromJTensor(jMethodGetTensor(jivalue));
-  } else if (JIValue::kTypeCodeBool == typeCode) {
-    static const auto jMethodGetBool =
-        JIValue::javaClassStatic()->getMethod<jboolean()>("toBool");
-    // explicit cast to bool as jboolean is defined as uint8_t, IValue ctor
-    // for int will be called for jboolean
-    bool b = jMethodGetBool(jivalue);
-    return at::IValue{b};
-  } else if (JIValue::kTypeCodeLong == typeCode) {
-    static const auto jMethodGetLong =
-        JIValue::javaClassStatic()->getMethod<jlong()>("toLong");
-    return at::IValue{(int64_t)jMethodGetLong(jivalue)};
-  } else if (JIValue::kTypeCodeDouble == typeCode) {
-    static const auto jMethodGetDouble =
-        JIValue::javaClassStatic()->getMethod<jdouble()>("toDouble");
-    return at::IValue{jMethodGetDouble(jivalue)};
-  } else if (JIValue::kTypeCodeString == typeCode) {
-    static const auto jMethodGetString =
-        JIValue::javaClassStatic()->getMethod<jstring()>("toStr");
-    return at::IValue{jMethodGetString(jivalue)->toStdString()};
-  } else if (JIValue::kTypeCodeTuple == typeCode) {
-    static const auto jMethodGetTuple =
-        JIValue::javaClassStatic()
-            ->getMethod<
-                facebook::jni::JArrayClass<JIValue::javaobject>::javaobject()>(
-                "toTuple");
-    auto jarray = jMethodGetTuple(jivalue);
-    size_t n = jarray->size();
-
-    std::vector<at::IValue> elements;
-    elements.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      auto jivalue_element = jarray->getElement(i);
-      auto element = JIValue::JIValueToAtIValue(jivalue_element);
-      elements.push_back(std::move(element));
-    }
-    return c10::ivalue::Tuple::create(std::move(elements));
-  } else if (JIValue::kTypeCodeBoolList == typeCode) {
-    static const auto jMethodGetBoolList =
-        JIValue::javaClassStatic()->getMethod<jbooleanArray()>("toBoolList");
-    auto jArray = jMethodGetBoolList(jivalue);
-    auto jArrayPinned = jArray->pin();
-    size_t n = jArrayPinned.size();
-    c10::List<bool> list{};
-    list.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      list.push_back(jArrayPinned[i]);
-    }
-    return at::IValue{std::move(list)};
-  } else if (JIValue::kTypeCodeLongList == typeCode) {
-    static const auto jMethodGetLongList =
-        JIValue::javaClassStatic()->getMethod<jlongArray()>("toLongList");
-    auto jArray = jMethodGetLongList(jivalue);
-    auto jArrayPinned = jArray->pin();
-    size_t n = jArrayPinned.size();
-    c10::List<int64_t> list{};
-    list.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      list.push_back(jArrayPinned[i]);
-    }
-    return at::IValue{std::move(list)};
-  } else if (JIValue::kTypeCodeDoubleList == typeCode) {
-    static const auto jMethodGetDoubleList =
-        JIValue::javaClassStatic()->getMethod<jdoubleArray()>("toDoubleList");
-    auto jArray = jMethodGetDoubleList(jivalue);
-    auto jArrayPinned = jArray->pin();
-    size_t n = jArrayPinned.size();
-    c10::List<double> list{};
-    list.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      list.push_back(jArrayPinned[i]);
-    }
-    return at::IValue{std::move(list)};
-  } else if (JIValue::kTypeCodeTensorList == typeCode) {
-    static const auto jMethodGetTensorList =
-        JIValue::javaClassStatic()
-            ->getMethod<facebook::jni::JArrayClass<
-                TensorHybrid::javaobject>::javaobject()>("toTensorList");
-    auto jArray = jMethodGetTensorList(jivalue);
-    size_t n = jArray->size();
-    c10::List<at::Tensor> list{};
-    list.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      list.push_back(
-          TensorHybrid::newAtTensorFromJTensor(jArray->getElement(i)));
-    }
-    return at::IValue{std::move(list)};
-  } else if (JIValue::kTypeCodeList == typeCode) {
-    static const auto jMethodGetList =
-        JIValue::javaClassStatic()
-            ->getMethod<
-                facebook::jni::JArrayClass<JIValue::javaobject>::javaobject()>(
-                "toList");
-    auto jarray = jMethodGetList(jivalue);
-    size_t n = jarray->size();
-    if (n == 0) {
-      return at::IValue{c10::impl::GenericList(c10::TensorType::get())};
-    }
-
-    auto jivalue_first_element = jarray->getElement(0);
-    auto first_element = JIValue::JIValueToAtIValue(jivalue_first_element);
-    c10::impl::GenericList list{c10::unshapedType(first_element.type())};
-    list.reserve(n);
-    list.push_back(first_element);
-    for (const auto i : c10::irange(1, n)) {
-      auto jivalue_element = jarray->getElement(i);
-      auto element = JIValue::JIValueToAtIValue(jivalue_element);
-      list.push_back(element);
-    }
-    return at::IValue{list};
-  } else if (JIValue::kTypeCodeDictStringKey == typeCode) {
-    static const auto jMethodGetDictStringKey =
-        JIValue::javaClassStatic()
-            ->getMethod<facebook::jni::JMap<jstring, JIValue::javaobject>::
-                            javaobject()>("toDictStringKey");
-    auto jmap = jMethodGetDictStringKey(jivalue);
-    auto it = jmap->begin();
-    if (it == jmap->end()) {
-      return at::IValue{c10::impl::GenericDict(
-          c10::StringType::get(), c10::TensorType::get())};
-    }
-
-    auto firstEntryValue = JIValue::JIValueToAtIValue(it->second);
-    c10::impl::GenericDict dict{
-        c10::StringType::get(), c10::unshapedType(firstEntryValue.type())};
-    dict.insert(it->first->toStdString(), firstEntryValue);
-    it++;
-    for (; it != jmap->end(); it++) {
-      dict.insert(
-          it->first->toStdString(), JIValue::JIValueToAtIValue(it->second));
-    }
-    return at::IValue{dict};
-  } else if (JIValue::kTypeCodeDictLongKey == typeCode) {
-    static const auto jMethodGetDictLongKey =
-        JIValue::javaClassStatic()
-            ->getMethod<facebook::jni::JMap<
-                facebook::jni::JLong::javaobject,
-                JIValue::javaobject>::javaobject()>("toDictLongKey");
-    auto jmap = jMethodGetDictLongKey(jivalue);
-    auto it = jmap->begin();
-    if (it == jmap->end()) {
-      return at::IValue{
-          c10::impl::GenericDict(c10::IntType::get(), c10::TensorType::get())};
-    }
-
-    auto firstEntryValue = JIValue::JIValueToAtIValue(it->second);
-    c10::impl::GenericDict dict{
-        c10::IntType::get(), c10::unshapedType(firstEntryValue.type())};
-    dict.insert((int64_t)it->first->longValue(), firstEntryValue);
-    it++;
-    for (; it != jmap->end(); it++) {
-      dict.insert(
-          (int64_t)it->first->longValue(),
-          JIValue::JIValueToAtIValue(it->second));
-    }
-    return at::IValue{dict};
-  }
-
-  facebook::jni::throwNewJavaException(
-      facebook::jni::gJavaLangIllegalArgumentException,
-      "Unknown IValue typeCode %d",
-      typeCode);
-}
-
-#if defined(__ANDROID__)
-class PyTorchAndroidJni : public facebook::jni::JavaClass<PyTorchAndroidJni> {
- public:
-  constexpr static auto kJavaDescriptor = "Lorg/pytorch/PyTorchAndroid;";
-
-  static void registerNatives() {
-    javaClassStatic()->registerNatives({
-        makeNativeMethod(
-            "nativeSetNumThreads", PyTorchAndroidJni::setNumThreads),
-    });
-  }
-
-  static void setNumThreads(facebook::jni::alias_ref<jclass>, jint numThreads) {
-    caffe2::pthreadpool()->set_thread_count(numThreads);
-  }
-};
-#endif
-
-void common_registerNatives() {
-  static const int once = []() {
-#if defined(__ANDROID__)
-    pytorch_jni::PyTorchAndroidJni::registerNatives();
-#endif
-    return 0;
-  }();
-  ((void)once);
-}
-
-} // namespace pytorch_jni

android/pytorch_android/src/main/cpp/pytorch_jni_common.h

@@ -1,137 +0,0 @@
-#pragma once
-
-#include <c10/util/FunctionRef.h>
-#include <fbjni/fbjni.h>
-#include <torch/csrc/api/include/torch/types.h>
-#include "caffe2/serialize/read_adapter_interface.h"
-
-#include "cmake_macros.h"
-
-#ifdef __ANDROID__
-#include <android/log.h>
-#define ALOGI(...) \
-  __android_log_print(ANDROID_LOG_INFO, "pytorch-jni", __VA_ARGS__)
-#define ALOGE(...) \
-  __android_log_print(ANDROID_LOG_ERROR, "pytorch-jni", __VA_ARGS__)
-#endif
-
-#if defined(TRACE_ENABLED) && defined(__ANDROID__)
-#include <android/trace.h>
-#include <dlfcn.h>
-#endif
-
-namespace pytorch_jni {
-
-constexpr static int kDeviceCPU = 1;
-constexpr static int kDeviceVulkan = 2;
-
-c10::DeviceType deviceJniCodeToDeviceType(jint deviceJniCode);
-
-class Trace {
- public:
-#if defined(TRACE_ENABLED) && defined(__ANDROID__)
-  typedef void* (*fp_ATrace_beginSection)(const char* sectionName);
-  typedef void* (*fp_ATrace_endSection)(void);
-
-  static fp_ATrace_beginSection ATrace_beginSection;
-  static fp_ATrace_endSection ATrace_endSection;
-#endif
-
-  static void ensureInit() {
-    if (!Trace::is_initialized_) {
-      init();
-      Trace::is_initialized_ = true;
-    }
-  }
-
-  static void beginSection(const char* name) {
-    Trace::ensureInit();
-#if defined(TRACE_ENABLED) && defined(__ANDROID__)
-    ATrace_beginSection(name);
-#endif
-  }
-
-  static void endSection() {
-#if defined(TRACE_ENABLED) && defined(__ANDROID__)
-    ATrace_endSection();
-#endif
-  }
-
-  Trace(const char* name) {
-    ensureInit();
-    beginSection(name);
-  }
-
-  ~Trace() {
-    endSection();
-  }
-
- private:
-  static void init();
-  static bool is_initialized_;
-};
-
-class MemoryReadAdapter final : public caffe2::serialize::ReadAdapterInterface {
- public:
-  explicit MemoryReadAdapter(const void* data, off_t size)
-      : data_(data), size_(size){};
-
-  size_t size() const override {
-    return size_;
-  }
-
-  size_t read(uint64_t pos, void* buf, size_t n, const char* what = "")
-      const override {
-    memcpy(buf, (int8_t*)(data_) + pos, n);
-    return n;
-  }
-
-  ~MemoryReadAdapter() {}
-
- private:
-  const void* data_;
-  off_t size_;
-};
-
-class JIValue : public facebook::jni::JavaClass<JIValue> {
-  using DictCallback = c10::function_ref<facebook::jni::local_ref<JIValue>(
-      c10::Dict<c10::IValue, c10::IValue>)>;
-
- public:
-  constexpr static const char* kJavaDescriptor = "Lorg/pytorch/IValue;";
-
-  constexpr static int kTypeCodeNull = 1;
-
-  constexpr static int kTypeCodeTensor = 2;
-  constexpr static int kTypeCodeBool = 3;
-  constexpr static int kTypeCodeLong = 4;
-  constexpr static int kTypeCodeDouble = 5;
-  constexpr static int kTypeCodeString = 6;
-
-  constexpr static int kTypeCodeTuple = 7;
-  constexpr static int kTypeCodeBoolList = 8;
-  constexpr static int kTypeCodeLongList = 9;
-  constexpr static int kTypeCodeDoubleList = 10;
-  constexpr static int kTypeCodeTensorList = 11;
-  constexpr static int kTypeCodeList = 12;
-
-  constexpr static int kTypeCodeDictStringKey = 13;
-  constexpr static int kTypeCodeDictLongKey = 14;
-
-  static facebook::jni::local_ref<JIValue> newJIValueFromAtIValue(
-      const at::IValue& ivalue,
-      DictCallback stringDictCallback = newJIValueFromStringDict,
-      DictCallback intDictCallback = newJIValueFromIntDict);
-
-  static at::IValue JIValueToAtIValue(
-      facebook::jni::alias_ref<JIValue> jivalue);
-
- private:
-  static facebook::jni::local_ref<JIValue> newJIValueFromStringDict(
-      c10::Dict<c10::IValue, c10::IValue>);
-  static facebook::jni::local_ref<JIValue> newJIValueFromIntDict(
-      c10::Dict<c10::IValue, c10::IValue>);
-};
-
-void common_registerNatives();
-} // namespace pytorch_jni

android/pytorch_android/src/main/cpp/pytorch_jni_jit.cpp

@@ -1,245 +0,0 @@
-#include <cassert>
-#include <iostream>
-#include <memory>
-#include <string>
-
-#include <fbjni/ByteBuffer.h>
-#include <fbjni/fbjni.h>
-
-#include <ATen/record_function.h>
-#include <torch/csrc/jit/runtime/print_handler.h>
-#include <torch/script.h>
-#include "caffe2/serialize/read_adapter_interface.h"
-
-#include "pytorch_jni_common.h"
-
-#ifdef __ANDROID__
-#include <android/asset_manager.h>
-#include <android/asset_manager_jni.h>
-#include <android/log.h>
-#endif
-
-namespace pytorch_jni {
-
-namespace {
-
-struct JITCallGuard {
-  // Inference only workload.
-  c10::InferenceMode guard;
-  // Disable graph optimizer to ensure list of unused ops are not changed for
-  // custom mobile build.
-  torch::jit::GraphOptimizerEnabledGuard no_optimizer_guard{false};
-};
-
-} // namespace
-
-class PytorchJni : public facebook::jni::HybridClass<PytorchJni> {
- private:
-  friend HybridBase;
-  torch::jit::Module module_;
-  c10::DeviceType deviceType_;
-
- public:
-  constexpr static auto kJavaDescriptor = "Lorg/pytorch/NativePeer;";
-
-  static facebook::jni::local_ref<jhybriddata> initHybrid(
-      facebook::jni::alias_ref<jclass>,
-      facebook::jni::alias_ref<jstring> modelPath,
-      facebook::jni::alias_ref<
-          facebook::jni::JMap<facebook::jni::JString, facebook::jni::JString>>
-          extraFiles,
-      jint device) {
-    return makeCxxInstance(modelPath, extraFiles, device);
-  }
-
-#ifdef __ANDROID__
-  static facebook::jni::local_ref<jhybriddata> initHybridAndroidAsset(
-      facebook::jni::alias_ref<jclass>,
-      facebook::jni::alias_ref<jstring> assetName,
-      facebook::jni::alias_ref<jobject> assetManager,
-      jint device) {
-    return makeCxxInstance(assetName, assetManager, device);
-  }
-#endif
-
-#ifdef TRACE_ENABLED
-  static std::unique_ptr<at::ObserverContext> onFunctionEnter(
-      const at::RecordFunction& fn) {
-    Trace::beginSection(fn.name().str());
-    return nullptr;
-  }
-
-  static void onFunctionExit(const at::RecordFunction&, at::ObserverContext*) {
-    Trace::endSection();
-  }
-#endif
-
-  static void preModuleLoadSetupOnce() {
-    auto qengines = at::globalContext().supportedQEngines();
-    if (std::find(qengines.begin(), qengines.end(), at::QEngine::QNNPACK) !=
-        qengines.end()) {
-      at::globalContext().setQEngine(at::QEngine::QNNPACK);
-    }
-
-#ifdef __ANDROID__
-    torch::jit::setPrintHandler([](const std::string& s) {
-      __android_log_print(ANDROID_LOG_DEBUG, "pytorch-print", "%s", s.c_str());
-    });
-#endif
-
-#ifdef TRACE_ENABLED
-    at::addGlobalCallback(
-        at::RecordFunctionCallback(&onFunctionEnter, &onFunctionExit)
-            .scopes({RecordScope::FUNCTION, RecordScope::USER_SCOPE}));
-#endif
-  }
-
-  void preModuleLoadSetup() {
-    static const int once = []() {
-      preModuleLoadSetupOnce();
-      return 0;
-    }();
-    ((void)once);
-  }
-
-  PytorchJni(
-      facebook::jni::alias_ref<jstring> modelPath,
-      facebook::jni::alias_ref<
-          facebook::jni::JMap<facebook::jni::JString, facebook::jni::JString>>
-          extraFiles,
-      jint device) {
-    preModuleLoadSetup();
-    JITCallGuard guard;
-    std::unordered_map<std::string, std::string> extra_files;
-    const auto has_extra = extraFiles && extraFiles->size() > 0;
-    if (has_extra) {
-      for (const auto& e : *extraFiles) {
-        extra_files[e.first->toStdString()] = "";
-      }
-    }
-    deviceType_ = deviceJniCodeToDeviceType(device);
-    module_ = torch::jit::load(
-        std::move(modelPath->toStdString()), std::nullopt, extra_files);
-    if (has_extra) {
-      static auto putMethod =
-          facebook::jni::JMap<facebook::jni::JString, facebook::jni::JString>::
-              javaClassStatic()
-                  ->template getMethod<facebook::jni::alias_ref<jobject>(
-                      facebook::jni::alias_ref<jobject>,
-                      facebook::jni::alias_ref<jobject>)>("put");
-      for (const auto& ef : extra_files) {
-        putMethod(
-            extraFiles,
-            facebook::jni::make_jstring(ef.first),
-            facebook::jni::make_jstring(ef.second));
-      }
-    }
-
-    module_.eval();
-  }
-
-#ifdef __ANDROID__
-  PytorchJni(
-      facebook::jni::alias_ref<jstring> assetName,
-      facebook::jni::alias_ref<jobject> assetManager,
-      jint device) {
-    preModuleLoadSetup();
-    JNIEnv* env = facebook::jni::Environment::current();
-    AAssetManager* mgr = AAssetManager_fromJava(env, assetManager.get());
-    if (!mgr) {
-      facebook::jni::throwNewJavaException(
-          facebook::jni::gJavaLangIllegalArgumentException,
-          "Unable to get asset manager");
-    }
-    AAsset* asset = AAssetManager_open(
-        mgr, assetName->toStdString().c_str(), AASSET_MODE_BUFFER);
-    if (!asset) {
-      facebook::jni::throwNewJavaException(
-          facebook::jni::gJavaLangIllegalArgumentException,
-          "Failed to open asset '%s'",
-          assetName->toStdString().c_str());
-    }
-    auto assetBuffer = AAsset_getBuffer(asset);
-    if (!assetBuffer) {
-      facebook::jni::throwNewJavaException(
-          facebook::jni::gJavaLangIllegalArgumentException,
-          "Could not get buffer for asset '%s'",
-          assetName->toStdString().c_str());
-    }
-    JITCallGuard guard;
-    module_ = torch::jit::load(std::make_unique<MemoryReadAdapter>(
-        assetBuffer, AAsset_getLength(asset)));
-    AAsset_close(asset);
-    module_.eval();
-    deviceType_ = deviceJniCodeToDeviceType(device);
-  }
-#endif
-
-  static void registerNatives() {
-    registerHybrid({
-        makeNativeMethod("initHybrid", PytorchJni::initHybrid),
-#ifdef __ANDROID__
-        makeNativeMethod(
-            "initHybridAndroidAsset", PytorchJni::initHybridAndroidAsset),
-#endif
-        makeNativeMethod("forward", PytorchJni::forward),
-        makeNativeMethod("runMethod", PytorchJni::runMethod),
-    });
-  }
-
-  facebook::jni::local_ref<JIValue> forward(
-      facebook::jni::alias_ref<
-          facebook::jni::JArrayClass<JIValue::javaobject>::javaobject>
-          jinputs) {
-    Trace _s{"jni::Module::forward"};
-    std::vector<at::IValue> inputs{};
-    size_t n = jinputs->size();
-    inputs.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
-      inputs.push_back(std::move(atIValue));
-    }
-    auto output = [&]() {
-      JITCallGuard guard;
-      return module_.forward(std::move(inputs));
-    }();
-    return JIValue::newJIValueFromAtIValue(output);
-  }
-
-  facebook::jni::local_ref<JIValue> runMethod(
-      facebook::jni::alias_ref<facebook::jni::JString::javaobject> jmethodName,
-      facebook::jni::alias_ref<
-          facebook::jni::JArrayClass<JIValue::javaobject>::javaobject>
-          jinputs) {
-    std::string methodName = jmethodName->toStdString();
-
-    std::vector<at::IValue> inputs{};
-    size_t n = jinputs->size();
-    inputs.reserve(n);
-    for (const auto i : c10::irange(n)) {
-      at::IValue atIValue = JIValue::JIValueToAtIValue(jinputs->getElement(i));
-      inputs.push_back(std::move(atIValue));
-    }
-    if (auto method = module_.find_method(methodName)) {
-      auto output = [&]() {
-        JITCallGuard guard;
-        return (*method)(std::move(inputs));
-      }();
-      return JIValue::newJIValueFromAtIValue(output);
-    }
-
-    facebook::jni::throwNewJavaException(
-        facebook::jni::gJavaLangIllegalArgumentException,
-        "Undefined method %s",
-        methodName.c_str());
-  }
-};
-
-} // namespace pytorch_jni
-
-JNIEXPORT jint JNICALL JNI_OnLoad(JavaVM* vm, void*) {
-  return facebook::jni::initialize(vm, [] {
-    pytorch_jni::common_registerNatives();
-    pytorch_jni::PytorchJni::registerNatives();
-  });
-}