Update base for Update on "Use object identity for deepcopy memo"

Copy of #126089, with some additional fixes & tests

Partial fix for #125635: previously, the deepcopy implementation would group together any tensors with any aliasing relationship and assign them to the same tensor. This was sort of good if you have two tensors `b = a.detach()`, because then if you deepcopy `list = [a, b]` to `list2 = list.deepcopy()`, then writes to `list2[0]` will also modify `list2[1]`. But for the most part, it's bad; (1) if you have `b = a.as_strided((4, 4), (16, 1), 16)`, then it'll make `b == a` in the deepcopied implementation, which is completely wrong; and (2) even if you have `b = a.detach()`, these are still initially two different tensors which become the same tensor after the old deepcopy implementation.

The new implementation only groups together tensors that have the same identity. This is a partial fix, but it's more reasonable. What changes:
* (becomes more correct): different views of the same base tensor will no longer all become equal after deepcopying
* (still kind of wrong): views won't actually alias each other after deepcopying.
* (arguably a minor regression): equivalent views of the same tensor will no longer be copied to the same tensor - so they won't alias.

BC breaking: C++ deepcopy interface changes from accepting `IValue::HashAliasedIValueMap memo` to accepting `IValue::HashIdentityIValueMap memo`. If there are objections, we can keep the old API. However, it seems likely that users generally won't try to deepcopy from C++.

Differential Revision: [D57340612](https://our.internmc.facebook.com/intern/diff/D57340612)

cc ezyang gchanan

[ghstack-poisoned]

.ci/docker/build.sh

@@ -84,13 +84,27 @@ fi
 # CMake 3.18 is needed to support CUDA17 language variant
 CMAKE_VERSION=3.18.5
 
-_UCX_COMMIT=00bcc6bb18fc282eb160623b4c0d300147f579af
-_UCC_COMMIT=7cb07a76ccedad7e56ceb136b865eb9319c258ea
+_UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
+_UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
 
 # It's annoying to rename jobs every time you want to rewrite a
 # configuration, so we hardcode everything here rather than do it
 # from scratch
 case "$image" in
+  pytorch-linux-focal-cuda12.4-cudnn8-py3-gcc9)
+    CUDA_VERSION=12.4.0
+    CUDNN_VERSION=8
+    ANACONDA_PYTHON_VERSION=3.10
+    GCC_VERSION=9
+    PROTOBUF=yes
+    DB=yes
+    VISION=yes
+    KATEX=yes
+    UCX_COMMIT=${_UCX_COMMIT}
+    UCC_COMMIT=${_UCC_COMMIT}
+    CONDA_CMAKE=yes
+    TRITON=yes
+    ;;
   pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9)
     CUDA_VERSION=12.1.1
     CUDNN_VERSION=8
@@ -105,6 +119,21 @@ case "$image" in
     CONDA_CMAKE=yes
     TRITON=yes
     ;;
+  pytorch-linux-focal-cuda12.4-cudnn8-py3-gcc9-inductor-benchmarks)
+    CUDA_VERSION=12.4.0
+    CUDNN_VERSION=8
+    ANACONDA_PYTHON_VERSION=3.10
+    GCC_VERSION=9
+    PROTOBUF=yes
+    DB=yes
+    VISION=yes
+    KATEX=yes
+    UCX_COMMIT=${_UCX_COMMIT}
+    UCC_COMMIT=${_UCC_COMMIT}
+    CONDA_CMAKE=yes
+    TRITON=yes
+    INDUCTOR_BENCHMARKS=yes
+    ;;
   pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks)
     CUDA_VERSION=12.1.1
     CUDNN_VERSION=8
@@ -134,6 +163,20 @@ case "$image" in
     CONDA_CMAKE=yes
     TRITON=yes
     ;;
+  pytorch-linux-focal-cuda12.4-cudnn8-py3-gcc9)
+    CUDA_VERSION=12.4.0
+    CUDNN_VERSION=8
+    ANACONDA_PYTHON_VERSION=3.10
+    GCC_VERSION=9
+    PROTOBUF=yes
+    DB=yes
+    VISION=yes
+    KATEX=yes
+    UCX_COMMIT=${_UCX_COMMIT}
+    UCC_COMMIT=${_UCC_COMMIT}
+    CONDA_CMAKE=yes
+    TRITON=yes
+    ;;
   pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9)
     CUDA_VERSION=12.1.1
     CUDNN_VERSION=8
@@ -226,7 +269,7 @@ case "$image" in
     PROTOBUF=yes
     DB=yes
     VISION=yes
-    BASEKIT_VERSION=2024.0.0-49522
+    XPU_VERSION=0.5
     NINJA_VERSION=1.9.0
     CONDA_CMAKE=yes
     TRITON=yes
@@ -403,7 +446,7 @@ docker build \
        --build-arg "DOCS=${DOCS}" \
        --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
        --build-arg "EXECUTORCH=${EXECUTORCH}" \
-       --build-arg "BASEKIT_VERSION=${BASEKIT_VERSION}" \
+       --build-arg "XPU_VERSION=${XPU_VERSION}" \
        --build-arg "ACL=${ACL:-}" \
        --build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
        --build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \

.ci/docker/common/install_cudnn.sh

@@ -4,7 +4,10 @@ if [[ ${CUDNN_VERSION} == 8 ]]; then
     # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
     mkdir tmp_cudnn
     pushd tmp_cudnn
-    if [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
+    if [[ ${CUDA_VERSION:0:4} == "12.4" ]]; then
+        CUDNN_NAME="cudnn-linux-x86_64-8.9.7.29_cuda12-archive"
+        curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/${CUDNN_NAME}.tar.xz
+    elif [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
         CUDNN_NAME="cudnn-linux-x86_64-8.9.2.26_cuda12-archive"
         curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/${CUDNN_NAME}.tar.xz
     elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then

.ci/docker/common/install_cusparselt.sh

@@ -5,9 +5,14 @@ set -ex
 # cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
 mkdir tmp_cusparselt && cd tmp_cusparselt
 
-if [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
-    CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.5.2.1-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz
+if [[ ${CUDA_VERSION:0:4} =~ ^12\.[1-4]$ ]]; then
+    arch_path='sbsa'
+    export TARGETARCH=${TARGETARCH:-$(uname -m)}
+    if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
+        arch_path='x86_64'
+    fi
+    CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.5.2.1-archive"
+    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
 elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
     CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.4.0.7-archive"
     curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz

.ci/docker/common/install_triton.sh

@@ -15,7 +15,7 @@ conda_reinstall() {
 if [ -n "${ROCM_VERSION}" ]; then
   TRITON_REPO="https://github.com/openai/triton"
   TRITON_TEXT_FILE="triton-rocm"
-elif [ -n "${BASEKIT_VERSION}" ]; then
+elif [ -n "${XPU_VERSION}" ]; then
   TRITON_REPO="https://github.com/intel/intel-xpu-backend-for-triton"
   TRITON_TEXT_FILE="triton-xpu"
 else

.ci/docker/common/install_xpu.sh

@@ -3,10 +3,7 @@ set -xe
 
 
 # Intel® software for general purpose GPU capabilities.
-# Refer to https://dgpu-docs.intel.com/releases/LTS_803.29_20240131.html
-
-# Intel® oneAPI Base Toolkit (version 2024.0.0) has been updated to include functional and security updates.
-# Refer to https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html
+# Refer to https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
 
 # Users should update to the latest version as it becomes available
 
@@ -17,14 +14,16 @@ function install_ubuntu() {
     # Set up the repository. To do this, download the key to the system keyring
     wget -qO - https://repositories.intel.com/gpu/intel-graphics.key \
         | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
-    wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
-        | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
+    wget -qO - https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
+        | gpg --dearmor --output /usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg
 
     # Add the signed entry to APT sources and configure the APT client to use the Intel repository
-    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" \
+    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] \
+        https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" \
         | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" \
-        | tee /etc/apt/sources.list.d/oneAPI.list
+    echo "deb [signed-by=/usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg] \
+        https://apt.repos.intel.com/intel-for-pytorch-gpu-dev all main" \
+        | tee /etc/apt/sources.list.d/intel-for-pytorch-gpu-dev.list
 
     # Update the packages list and repository index
     apt-get update
@@ -40,11 +39,11 @@ function install_ubuntu() {
         mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
     # Development Packages
     apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
-    # Install Intel® oneAPI Base Toolkit
-    if [ -n "$BASEKIT_VERSION" ]; then
-        apt-get install intel-basekit=$BASEKIT_VERSION -y
+    # Install Intel Support Packages
+    if [ -n "$XPU_VERSION" ]; then
+        apt-get install -y intel-for-pytorch-gpu-dev-${XPU_VERSION}
     else
-        apt-get install intel-basekit -y
+        apt-get install -y intel-for-pytorch-gpu-dev
     fi
 
     # Cleanup

.ci/docker/ubuntu-cuda/Dockerfile

@@ -152,6 +152,7 @@ RUN rm install_cusparselt.sh
 RUN if [ -h /usr/local/cuda-11.6/cuda-11.6 ]; then rm /usr/local/cuda-11.6/cuda-11.6; fi
 RUN if [ -h /usr/local/cuda-11.7/cuda-11.7 ]; then rm /usr/local/cuda-11.7/cuda-11.7; fi
 RUN if [ -h /usr/local/cuda-12.1/cuda-12.1 ]; then rm /usr/local/cuda-12.1/cuda-12.1; fi
+RUN if [ -h /usr/local/cuda-12.1/cuda-12.4 ]; then rm /usr/local/cuda-12.1/cuda-12.4; fi
 
 USER jenkins
 CMD ["bash"]

.ci/docker/ubuntu-xpu/Dockerfile

@@ -62,7 +62,7 @@ RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_d
 RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
 
 # Install XPU Dependencies
-ARG BASEKIT_VERSION
+ARG XPU_VERSION
 COPY ./common/install_xpu.sh install_xpu.sh
 RUN bash ./install_xpu.sh && rm install_xpu.sh
 

.ci/pytorch/build.sh

@@ -44,11 +44,6 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
   fi
 fi
 
-if [[ ${BUILD_ENVIRONMENT} == *"caffe2"* ]]; then
-  echo "Caffe2 build is ON"
-  export BUILD_CAFFE2=ON
-fi
-
 if [[ ${BUILD_ENVIRONMENT} == *"paralleltbb"* ]]; then
   export ATEN_THREADING=TBB
   export USE_TBB=1

.ci/pytorch/docs-test.sh

@@ -6,4 +6,4 @@ source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 echo "Testing pytorch docs"
 
 cd docs
-make doctest
+TERM=vt100 make doctest

.ci/pytorch/test.sh

@@ -588,6 +588,15 @@ test_inductor_torchbench_smoketest_perf() {
       "$TEST_REPORTS_DIR/inductor_training_smoketest_$test.csv" \
       --expected benchmarks/dynamo/expected_ci_perf_inductor_torchbench.csv
   done
+
+  # Perform some "warm-start" runs for a few huggingface models.
+  for test in AlbertForQuestionAnswering AllenaiLongformerBase DistilBertForMaskedLM DistillGPT2 GoogleFnet YituTechConvBert; do
+    python benchmarks/dynamo/huggingface.py --accuracy --training --amp --inductor --device cuda --warm-start-latency \
+      --only $test --output "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv"
+    python benchmarks/dynamo/check_accuracy.py \
+      --actual "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv" \
+      --expected "benchmarks/dynamo/ci_expected_accuracy/inductor_huggingface_training.csv"
+  done
 }
 
 test_inductor_torchbench_cpu_smoketest_perf(){

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-ea437b31ce316ea3d66fe73768c0dcb94edb79ad
+1980f8af5bcd0bb2ce51965cf79d8d4c25dad8a0

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-e3fc03314dab5f44e3ed9ccbba6c15fbca3285cd
+6f0b61e5d782913a0fc7743812f2a8e522189111

.github/pytorch-probot.yml

@@ -1,6 +1,5 @@
 tracking_issue: 24422
 ciflow_tracking_issue: 64124
-TD_rollout_issue: 123120
 ciflow_push_tags:
 - ciflow/binaries
 - ciflow/binaries_conda

.github/workflows/docker-builds.yml

@@ -38,6 +38,8 @@ jobs:
       matrix:
         runner: [linux.12xlarge]
         docker-image-name: [
+          pytorch-linux-focal-cuda12.4-cudnn8-py3-gcc9,
+          pytorch-linux-focal-cuda12.4-cudnn8-py3-gcc9-inductor-benchmarks,
           pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9,
           pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks,
           pytorch-linux-focal-cuda11.8-cudnn8-py3-gcc9,

CMakeLists.txt

@@ -181,11 +181,7 @@ option(BUILD_BINARY "Build C++ binaries" OFF)
 option(BUILD_DOCS "Build Caffe2 documentation" OFF)
 option(BUILD_CUSTOM_PROTOBUF "Build and use Caffe2's own protobuf under third_party" ON)
 option(BUILD_PYTHON "Build Python binaries" ON)
-option(BUILD_CAFFE2 "Master flag to build Caffe2" OFF)
 option(BUILD_LITE_INTERPRETER "Master flag to build Lite Interpreter" OFF)
-cmake_dependent_option(
-    BUILD_CAFFE2_OPS "Build Caffe2 operators" ON
-    "BUILD_CAFFE2" OFF)
 option(BUILD_SHARED_LIBS "Build libcaffe2.so" ON)
 cmake_dependent_option(
     CAFFE2_LINK_LOCAL_PROTOBUF "If set, build protobuf inside libcaffe2.so." ON
@@ -281,8 +277,8 @@ if(NOT DEFINED USE_VULKAN)
 endif()
 
 option(USE_SLEEF_FOR_ARM_VEC256 "Use sleef for arm" OFF)
-option(USE_SOURCE_DEBUG_ON_MOBILE "Enable " ON)
-option(USE_LITE_INTERPRETER_PROFILER "Enable " ON)
+option(USE_SOURCE_DEBUG_ON_MOBILE "Enable" ON)
+option(USE_LITE_INTERPRETER_PROFILER "Enable" ON)
 option(USE_VULKAN_FP16_INFERENCE "Vulkan - Use fp16 inference" OFF)
 option(USE_VULKAN_RELAXED_PRECISION "Vulkan - Use relaxed precision math in the kernels (mediump)" OFF)
 # option USE_XNNPACK: try to enable xnnpack by default.
@@ -635,7 +631,6 @@ if(INTERN_BUILD_MOBILE)
   endif()
   set(BUILD_PYTHON OFF)
   set(BUILD_FUNCTORCH OFF)
-  set(BUILD_CAFFE2_OPS OFF)
   set(USE_DISTRIBUTED OFF)
   set(NO_API ON)
   set(USE_FBGEMM OFF)
@@ -1208,13 +1203,6 @@ else()
       "shared libs.")
 endif()
 
-# ---[ Modules
-# If master flag for buildling Caffe2 is disabled, we also disable the
-# build for Caffe2 related operator modules.
-if(BUILD_CAFFE2)
-  add_subdirectory(modules)
-endif()
-
 # ---[ Binaries
 # Binaries will be built after the Caffe2 main libraries and the modules
 # are built. For the binaries, they will be linked to the Caffe2 main

CONTRIBUTING.md

@@ -667,7 +667,6 @@ only interested in a specific component.
 - Working on a test binary? Run `(cd build && ninja bin/test_binary_name)` to
   rebuild only that test binary (without rerunning cmake). (Replace `ninja` with
   `make` if you don't have ninja installed).
-- Don't need Caffe2?  Pass `BUILD_CAFFE2=0` to disable Caffe2 build.
 
 On the initial build, you can also speed things up with the environment
 variables `DEBUG`, `USE_DISTRIBUTED`, `USE_MKLDNN`, `USE_CUDA`, `USE_FLASH_ATTENTION`, `USE_MEM_EFF_ATTENTION`, `BUILD_TEST`, `USE_FBGEMM`, `USE_NNPACK` and `USE_QNNPACK`.
@@ -790,7 +789,7 @@ USE_PRECOMPILED_HEADERS=1 python setup.py develop
 ```
 
 This adds a build step where the compiler takes `<ATen/ATen.h>` and essentially
-dumps it's internal AST to a file so the compiler can avoid repeating itself for
+dumps its internal AST to a file so the compiler can avoid repeating itself for
 every `.cpp` file.
 
 One caveat is that when enabled, this header gets included in every file by default.
@@ -1196,7 +1195,7 @@ build_with_asan()
   LDFLAGS="-stdlib=libstdc++" \
   CFLAGS="-fsanitize=address -fno-sanitize-recover=all -shared-libasan -pthread" \
   CXX_FLAGS="-pthread" \
-  USE_CUDA=0 USE_OPENMP=0 BUILD_CAFFE2_OPS=0 USE_DISTRIBUTED=0 DEBUG=1 \
+  USE_CUDA=0 USE_OPENMP=0 USE_DISTRIBUTED=0 DEBUG=1 \
   python setup.py develop
 }
 
@@ -1321,7 +1320,7 @@ There are two possible choices for which commit to use:
 1. Checkout commit `B`, the head of the PR (manually committed by the PR
    author).
 2. Checkout commit `C`, the hypothetical result of what would happen if the PR
-   were merged into it's destination (usually `main`).
+   were merged into its destination (usually `main`).
 
 For all practical purposes, most people can think of the commit being used as
 commit `B` (choice **1**).

README.md

@@ -24,6 +24,9 @@ Our trunk health (Continuous Integration signals) can be found at [hud.pytorch.o
     - [NVIDIA Jetson Platforms](#nvidia-jetson-platforms)
   - [From Source](#from-source)
     - [Prerequisites](#prerequisites)
+      - [NVIDIA CUDA Support](#nvidia-cuda-support)
+      - [AMD ROCm Support](#amd-rocm-support)
+      - [Intel GPU Support](#intel-gpu-support)
     - [Install Dependencies](#install-dependencies)
     - [Get the PyTorch Source](#get-the-pytorch-source)
     - [Install PyTorch](#install-pytorch)
@@ -162,6 +165,7 @@ If you are installing from source, you will need:
 
 We highly recommend installing an [Anaconda](https://www.anaconda.com/download) environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.
 
+##### NVIDIA CUDA Support
 If you want to compile with CUDA support, [select a supported version of CUDA from our support matrix](https://pytorch.org/get-started/locally/), then install the following:
 - [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
 - [NVIDIA cuDNN](https://developer.nvidia.com/cudnn) v8.5 or above
@@ -174,13 +178,22 @@ Other potentially useful environment variables may be found in `setup.py`.
 
 If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are [available here](https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/)
 
+##### AMD ROCm Support
 If you want to compile with ROCm support, install
 - [AMD ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html) 4.0 and above installation
 - ROCm is currently supported only for Linux systems.
 
 If you want to disable ROCm support, export the environment variable `USE_ROCM=0`.
 Other potentially useful environment variables may be found in `setup.py`.
 
+##### Intel GPU Support
+If you want to compile with Intel GPU support, follow these
+- [PyTorch Prerequisites for Intel GPUs](https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html) instructions.
+- Intel GPU is currently supported only for Linux systems.
+
+If you want to disable Intel GPU support, export the environment variable `USE_XPU=0`.
+Other potentially useful environment variables may be found in `setup.py`.
+
 #### Install Dependencies
 
 **Common**
@@ -379,7 +392,7 @@ You can also pass the `CMAKE_VARS="..."` environment variable to specify additio
 See [setup.py](./setup.py) for the list of available variables.
 
 ```bash
-CMAKE_VARS="BUILD_CAFFE2=ON BUILD_CAFFE2_OPS=ON" make -f docker.Makefile
+make -f docker.Makefile
 ```
 
 ### Building the Documentation

RELEASE.md

@@ -262,7 +262,7 @@ requires `pytorchbot`, so it's only available in PyTorch atm.
 
 ### Cherry Picking Reverts
 
-If PR that has been cherry-picked into release branch has been reverted, it's cherry-pick must be reverted as well.
+If PR that has been cherry-picked into release branch has been reverted, its cherry-pick must be reverted as well.
 
 Reverts for changes that was committed into the main branch prior to the branch cut, must be propagated into release branch as well.