Merge branch 'release/1.2' into cherry-pick-18229-by-pytorch_bot_bot_

Author: manuelcandales

.ci/docker/common/install_pytorch.sh

@@ -12,39 +12,11 @@ source "$(dirname "${BASH_SOURCE[0]}")/utils.sh"
 
 install_domains() {
   echo "Install torchvision and torchaudio"
-  pip_install --no-build-isolation --user "git+https://github.com/pytorch/audio.git@${TORCHAUDIO_VERSION}"
-  pip_install --no-build-isolation --user "git+https://github.com/pytorch/vision.git@${TORCHVISION_VERSION}"
+  pip_install --force-reinstall --no-cache-dir torchvision==0.26.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/test/cpu
 }
 
 install_pytorch_and_domains() {
-  git clone https://github.com/pytorch/pytorch.git
-
-  # Fetch the target commit
-  pushd pytorch || true
-  git checkout "${TORCH_VERSION}"
-  git submodule update --init --recursive
-
-  chown -R ci-user .
-
-  export _GLIBCXX_USE_CXX11_ABI=1
-  # Then build and install PyTorch
-  conda_run python setup.py bdist_wheel
-  pip_install "$(echo dist/*.whl)"
-
-  # Grab the pinned audio and vision commits from PyTorch
-  TORCHAUDIO_VERSION=release/2.11
-  export TORCHAUDIO_VERSION
-  TORCHVISION_VERSION=release/0.26
-  export TORCHVISION_VERSION
-
-  install_domains
-
-  popd || true
-  # Clean up the cloned PyTorch repo to reduce the Docker image size
-  rm -rf pytorch
-
-  # Print sccache stats for debugging
-  as_ci_user sccache --show-stats
+  pip_install --force-reinstall --no-cache-dir torch==2.11.0 torchvision==0.26.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/test/cpu
 }
 
 install_pytorch_and_domains

.ci/scripts/unittest-linux-cmake.sh

@@ -19,6 +19,8 @@ if ! python -c "import tosa_serializer" >/dev/null 2>&1; then
     TOSA_SERIALIZATION_DIR="${TOSA_TOOLS_DIR}/serialization"
   fi
 
+  # NOTE: Will be removed when tosa-tools is installed via pypi
+  python -m pip install pybind11==2.10.4
   CMAKE_POLICY_VERSION_MINIMUM=3.5 BUILD_PYBIND=1 \
     python -m pip install --no-dependencies \
     "${TOSA_SERIALIZATION_DIR}"

.ci/scripts/utils.sh

@@ -84,73 +84,11 @@ dedupe_macos_loader_path_rpaths() {
 
 install_domains() {
   echo "Install torchvision and torchaudio"
-  pip install --no-build-isolation --user "git+https://github.com/pytorch/audio.git@${TORCHAUDIO_VERSION}"
-  pip install --no-build-isolation --user "git+https://github.com/pytorch/vision.git@${TORCHVISION_VERSION}"
+  pip install --force-reinstall --no-cache-dir torchvision==0.26.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/test/cpu
 }
 
 install_pytorch_and_domains() {
-  pushd .ci/docker || return
-  TORCH_VERSION=$(cat ci_commit_pins/pytorch.txt)
-  popd || return
-
-  git clone https://github.com/pytorch/pytorch.git
-
-  # Fetch the target commit
-  pushd pytorch || return
-  git checkout "${TORCH_VERSION}"
-
-  local system_name=$(uname)
-  if [[ "${system_name}" == "Darwin" ]]; then
-    local platform=$(python -c 'import sysconfig; import platform; v=platform.mac_ver()[0].split(".")[0]; platform=sysconfig.get_platform().split("-"); platform[1]=f"{v}_0"; print("_".join(platform))')
-  fi
-  local python_version=$(python -c 'import platform; v=platform.python_version_tuple(); print(f"{v[0]}{v[1]}")')
-  local torch_release=$(cat version.txt)
-  local torch_short_hash=${TORCH_VERSION:0:7}
-  local torch_wheel_path="cached_artifacts/pytorch/executorch/pytorch_wheels/${system_name}/${python_version}"
-  local torch_wheel_name="torch-${torch_release}%2Bgit${torch_short_hash}-cp${python_version}-cp${python_version}-${platform:-}.whl"
-
-  local cached_torch_wheel="https://gha-artifacts.s3.us-east-1.amazonaws.com/${torch_wheel_path}/${torch_wheel_name}"
-  # Cache PyTorch wheel is only needed on MacOS, Linux CI already has this as part
-  # of the Docker image
-  local torch_wheel_not_found=0
-  if [[ "${system_name}" == "Darwin" ]]; then
-    pip install "${cached_torch_wheel}" || torch_wheel_not_found=1
-  else
-    torch_wheel_not_found=1
-  fi
-
-  # Found no such wheel, we will build it from source then
-  if [[ "${torch_wheel_not_found}" == "1" ]]; then
-    echo "No cached wheel found, continue with building PyTorch at ${TORCH_VERSION}"
-
-    git submodule update --init --recursive
-    USE_DISTRIBUTED=1 python setup.py bdist_wheel
-    pip install "$(echo dist/*.whl)"
-
-    # Only AWS runners have access to S3
-    if command -v aws && [[ -z "${GITHUB_RUNNER:-}" ]]; then
-      for wheel_path in dist/*.whl; do
-        local wheel_name=$(basename "${wheel_path}")
-        echo "Caching ${wheel_name}"
-        aws s3 cp "${wheel_path}" "s3://gha-artifacts/${torch_wheel_path}/${wheel_name}"
-      done
-    fi
-  else
-    echo "Use cached wheel at ${cached_torch_wheel}"
-  fi
-
-  dedupe_macos_loader_path_rpaths
-  # Grab the pinned audio and vision commits from PyTorch
-  TORCHAUDIO_VERSION=release/2.11
-  export TORCHAUDIO_VERSION
-  TORCHVISION_VERSION=release/0.26
-  export TORCHVISION_VERSION
-
-  install_domains
-
-  popd || return
-  # Print sccache stats for debugging
-  sccache --show-stats || true
+  pip install --force-reinstall --no-cache-dir torch==2.11.0 torchvision==0.26.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/test/cpu
 }
 
 build_executorch_runner_buck2() {

.ci/scripts/wheel/pre_build_script.sh

@@ -26,7 +26,12 @@ fi
 # we should update the core job in test-infra to enable long paths before
 # checkout to avoid needing to do this.
 pushd extension/llm/tokenizers
-git submodule update --init
+UNAME_S=$(uname -s)
+if [[ $UNAME_S == *"MINGW"* || $UNAME_S == *"MSYS"* ]]; then
+  git -c http.sslBackend=openssl submodule update --init
+else
+  git submodule update --init
+fi
 popd
 
 # On Windows, enable symlinks and re-checkout the current revision to create

CONTRIBUTING.md

@@ -327,7 +327,7 @@ for their help in the PR comments.
 
 See https://hud.pytorch.org/hud/pytorch/executorch/main for the current state of <!-- @lint-ignore -->
 the CI (continuous integration) jobs. If `main` is broken, consider rebasing
-your PR onto the `viable/strict` branch, which points to the most recent
+your PR onto the `release/1.2` branch, which points to the most recent
 all-green commit.
 
 &nbsp;

backends/arm/requirements-arm-tosa.txt

@@ -7,3 +7,5 @@ ml_dtypes == 0.5.1
 flatbuffers == 24.3.25
 tosa-adapter-model-explorer == 0.1.0
 ai-edge-model-explorer >= 0.1.16
+# NOTE: Will be removed when tosa-tools is installed via pypi
+pybind11 == 2.10.4

backends/arm/scripts/docgen/ethos-u/backends-arm-ethos-u-overview.md.in

@@ -59,6 +59,14 @@ An example runtime application is available in [examples/arm/executor_runner](ht
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 For an in-depth explanation of the architecture of the executor_runner and the steps required for doing such an integration, please refer to [Ethos-U porting guide](https://github.com/pytorch/executorch/blob/main/examples/arm/ethos-u-porting-guide.md).
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_ethos_u`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_ethos_u)
+contains a complete DeiT-based export and runtime walkthrough. The README shows
+how to run `model_export/export_deit.py`, build the sample firmware, and convert
+test images into C arrays so the workflow described in this guide can be tried
+end to end.
+
 ### Ethos-U memory modes
 
 The Ethos-U NPU provides two distinct memory interfaces:
@@ -147,6 +155,10 @@ ExecuTorch for the Ethos-U backend, you automatically install the compiler conta
 
 **→{doc}`/backends/arm-ethos-u/tutorials/arm-ethos-u-tutorials` — Tutorials.**
 
+**→{doc}`/backends/arm-ethos-u/U55_op_support` — Ethos-U55 supported operators.**
+
+**→{doc}`/backends/arm-ethos-u/U85_op_support` — Ethos-U85 supported operators.**
+
 
 ```{toctree}
 :maxdepth: 2
@@ -157,4 +169,6 @@ arm-ethos-u-partitioner
 arm-ethos-u-quantization
 arm-ethos-u-troubleshooting
 tutorials/arm-ethos-u-tutorials
+U55_op_support
+U85_op_support
 ```

backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in

@@ -56,6 +56,21 @@ For more information on quantization, see [Quantization](arm-vgf-quantization.md
 The VGF backend can use the default ExecuTorch runner. The steps required for building and running it are explained in the [VGF Backend Tutorial](tutorials/vgf-getting-started.md). <!-- @lint-ignore -->
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_vgf`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_vgf)
+contains a ready-to-run DeiT image classification pipeline for VGF targets.
+The example README documents how to:
+
+- Export the quantized INT8 weights via `model_export/export_deit.py`.
+- Use the provided requirements file to install the ML SDK converter scripts
+  and produce a `.pte` artifact.
+- Build and launch the Vulkan-based runtime under `runtime/`, which loads the
+  `.pte` alongside the generated VGF blob.
+
+Following this walkthrough ensures you exercise the same lowering + runtime flow
+described in the rest of this guide but with a concrete end-to-end sample.
+
 ## Reference
 
 **→{doc}`/backends/arm-vgf/arm-vgf-partitioner` — Partitioner options.**

docs/README.md

@@ -40,7 +40,7 @@ To build the documentation locally:
 1. Clone the ExecuTorch repo to your machine.
 
    ```bash
-   git clone -b viable/strict https://github.com/pytorch/executorch.git && cd executorch
+   git clone -b release/1.2 https://github.com/pytorch/executorch.git && cd executorch
    ```
 
 1. If you don't have it already, start either a Python virtual environment:

docs/source/backends/arm-ethos-u/arm-ethos-u-overview.md

@@ -137,6 +137,14 @@ An example runtime application is available in [examples/arm/executor_runner](ht
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 For an in-depth explanation of the architecture of the executor_runner and the steps required for doing such an integration, please refer to [Ethos-U porting guide](https://github.com/pytorch/executorch/blob/main/examples/arm/ethos-u-porting-guide.md).
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_ethos_u`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_ethos_u)
+contains a complete DeiT-based export and runtime walkthrough. The README shows
+how to run `model_export/export_deit.py`, build the sample firmware, and convert
+test images into C arrays so the workflow described in this guide can be tried
+end to end.
+
 ### Ethos-U memory modes
 
 The Ethos-U NPU provides two distinct memory interfaces:
@@ -229,6 +237,7 @@ ExecuTorch for the Ethos-U backend, you automatically install the compiler conta
 
 **→{doc}`/backends/arm-ethos-u/U85_op_support` — Ethos-U85 supported operators.**
 
+
 ```{toctree}
 :maxdepth: 2
 :hidden: