2024-11-02 nightly release (09cf982)

.ci/docker/ci_commit_pins/pytorch.txt

@@ -1 +1 @@
-bd5482c7c3e1197e10c46ff739027f917d9c1fcc
+e47e8794499a4a0130ff4efb8713ff93f4b40c36

.ci/docker/common/install_clang.sh

@@ -13,7 +13,7 @@ install_ubuntu() {
   apt-get install -y --no-install-recommends clang-"$CLANG_VERSION"
   apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"
   # Also require LLD linker from llvm and libomp to build PyTorch from source
-  apt-get install -y lld "libomp-${CLANG_VERSION}-dev"
+  apt-get install -y lld "libomp-${CLANG_VERSION}-dev" "libc++-${CLANG_VERSION}-dev"
 
   # Use update-alternatives to make this version the default
   update-alternatives --install /usr/bin/clang clang /usr/bin/clang-"$CLANG_VERSION" 50

.ci/docker/requirements-ci.txt

@@ -1,5 +1,5 @@
 mpmath==1.3.0
-numpy==1.22.0; python_version == '3.10'
+numpy==1.21.3; python_version == '3.10'
 numpy==1.23.2; python_version == '3.11'
 numpy; python_version >= '3.12'
 PyYAML==6.0.1

.ci/scripts/setup-linux.sh

@@ -19,6 +19,7 @@ else
 fi
 
 # As Linux job is running inside a Docker container, all of its dependencies
-# have already been installed
-install_executorch
+# have already been installed, so we use PyTorch build from source here instead
+# of nightly. This allows CI to test against latest commits from PyTorch
+install_executorch "use-pt-pinned-commit"
 build_executorch_runner "${BUILD_TOOL}"

.ci/scripts/setup-qnn-deps.sh

@@ -31,8 +31,9 @@ install_qnn() {
 }
 
 setup_libc++() {
+  clang_version=$1
   sudo apt-get update
-  pkgs_to_check=('libc++-dev')
+  pkgs_to_check=("libc++-${clang_version}-dev")
   j=0
   while [ $j -lt ${#pkgs_to_check[*]} ]; do
     install_status=$(verify_pkg_installed ${pkgs_to_check[$j]})
@@ -47,5 +48,6 @@ setup_libc++() {
   done
 }
 
-setup_libc++
+# This needs to match with the clang version from the Docker image
+setup_libc++ 12
 install_qnn

.ci/scripts/utils.sh

@@ -20,8 +20,11 @@ install_executorch() {
   which pip
   # Install executorch, this assumes that Executorch is checked out in the
   # current directory.
-  # TODO(T199538337): clean up install scripts to use install_requirements.sh
-  ./install_requirements.sh --pybind xnnpack
+  if [[ "${1:-}" == "use-pt-pinned-commit" ]]; then
+    ./install_requirements.sh --pybind xnnpack --use-pt-pinned-commit
+  else
+    ./install_requirements.sh --pybind xnnpack
+  fi
   # Just print out the list of packages for debugging
   pip list
 }

.github/pull_request_template.md

@@ -0,0 +1,9 @@
+### Summary
+[PLEASE REMOVE] See [CONTRIBUTING.md's Pull Requests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#pull-requests) for ExecuTorch PR guidelines.
+
+[PLEASE REMOVE] If this PR closes an issue, please add a `Fixes #<issue-id>` line.
+
+[PLEASE REMOVE] If this PR introduces a fix or feature that should be the upcoming release notes, please add a "Release notes: <area>" label. For a list of available release notes labels, check out [CONTRIBUTING.md's Pull Requests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#pull-requests).
+
+### Test plan
+[PLEASE REMOVE] How did you test this PR? Please write down any manual commands you used and note down tests that you have written if applicable.

.github/workflows/_unittest.yml

@@ -37,6 +37,9 @@ jobs:
         CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON" \
         .ci/scripts/setup-linux.sh cmake
 
+        # Install llama3_2_vision dependencies.
+        PYTHON_EXECUTABLE=python ./examples/models/llama3_2_vision/install_requirements.sh
+
         # Run pytest with coverage
         pytest -n auto --cov=./ --cov-report=xml
         # Run gtest
@@ -67,6 +70,10 @@ jobs:
         ${CONDA_RUN} --no-capture-output \
         .ci/scripts/setup-macos.sh cmake
 
+        # Install llama3_2_vision dependencies.
+        PYTHON_EXECUTABLE=python ${CONDA_RUN} --no-capture-output \
+        ./examples/models/llama3_2_vision/install_requirements.sh
+
         # Run pytest with coverage
         ${CONDA_RUN} pytest -n auto --cov=./ --cov-report=xml
         # Run gtest

.github/workflows/trunk.yml

@@ -137,6 +137,7 @@ jobs:
       docker-image: executorch-ubuntu-22.04-arm-sdk
       submodules: 'true'
       ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+      timeout: 90
       script: |
         # The generic Linux job chooses to use base env, not the one setup by the image
         CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")
@@ -162,6 +163,7 @@ jobs:
       docker-image: executorch-ubuntu-22.04-arm-sdk
       submodules: 'true'
       ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+      timeout: 90
       script: |
         # The generic Linux job chooses to use base env, not the one setup by the image
         CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")

CONTRIBUTING.md

@@ -242,14 +242,27 @@ for basics.
    - Give the PR a clear and thorough description. Don't just describe what the PR
      does: the diff will do that. Explain *why* you are making this change, in a
      way that will make sense to someone years from now.
-   - Add the line `Test Plan:` (with that spelling, capitalization, and trailing
-     colon character), followed by lines containing repeatable instructions for
+   - Explain how you have tested your changes by including repeatable instructions for
      testing the PR.
      - If you added tests, this can be as simple as the command you used to run the
        tests.
      - If you tested the PR manually, include the steps and the outputs. Help a
        future editor understand how to test the code that you're modifying
        today.
+   - If your PR contains or is representative of a feature/bug fix that should be
+     called out in the release notes, please add a label for "Release notes: \<area\>",
+	 where \<area\> describes which part of ExecuTorch the change pertains to, e.g.
+	 "Release notes: runtime". Here are all of the categories:
+     - `Release notes: runtime`: changes related to the core runtime which loads the program methods, initializes delegates, and runs the lowered graph.
+     - `Release notes: exir`: changes to any internal representations, such as any edge-related dialects. Also any changes to passes that may modify the exir, such as memory planning.
+     - `Release notes: quantization`: changes to quantization.
+     - `Release notes: ops & kernels`: changes to the opset and any new / changed kernel implementations.
+     - `Release notes: api`: changes to public facing apis (any interfaces, pybinded runtime methods, etc.).
+     - `Release notes: backends`: changes to any of the backend delegates.
+     - `Release notes: build`: changes related to the build system, including major dependency upgrades, notable build flags, optimizations, etc.
+     - `Release notes: devtools`: changes to any of ExecuTorch's developer tools, for example the debugger & profiler.
+     - `Release notes: examples`: changes to any code under `examples/`.
+     - `Release notes: misc`: anything notable that doesn't belong in the above categories.
    - See https://github.com/pytorch/executorch/pull/3612 for an example PR that
      follows this advice.
 1. Before asking for a review, ensure that all [CI (continuous integration)

backends/apple/coreml/README.md

@@ -65,7 +65,7 @@ To quantize a Program in a Core ML favored way, the client may utilize **CoreMLQ
 import torch
 import executorch.exir
 
-from torch._export import capture_pre_autograd_graph
+from torch.export import export_for_training
 from torch.ao.quantization.quantize_pt2e import (
     convert_pt2e,
     prepare_pt2e,
@@ -93,7 +93,7 @@ class Model(torch.nn.Module):
 source_model = Model()
 example_inputs = (torch.randn((1, 3, 256, 256)), )
 
-pre_autograd_aten_dialect = capture_pre_autograd_graph(model, example_inputs)
+pre_autograd_aten_dialect = export_for_training(model, example_inputs).module()
 
 quantization_config = LinearQuantizerConfig.from_dict(
     {

docs/source/llm/getting-started.md

@@ -144,8 +144,7 @@ import torch
 
 from executorch.exir import EdgeCompileConfig, to_edge
 from torch.nn.attention import sdpa_kernel, SDPBackend
-from torch._export import capture_pre_autograd_graph
-from torch.export import export
+from torch.export import export, export_for_training
 
 from model import GPT
 
@@ -170,7 +169,7 @@ dynamic_shape = (
 # Trace the model, converting it to a portable intermediate representation.
 # The torch.no_grad() call tells PyTorch to exclude training-specific logic.
 with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
-    m = capture_pre_autograd_graph(model, example_inputs, dynamic_shapes=dynamic_shape)
+    m = export_for_training(model, example_inputs, dynamic_shapes=dynamic_shape).module()
     traced_model = export(m, example_inputs, dynamic_shapes=dynamic_shape)
 
 # Convert the model into a runnable ExecuTorch program.
@@ -462,7 +461,7 @@ from executorch.exir import EdgeCompileConfig, to_edge
 import torch
 from torch.export import export
 from torch.nn.attention import sdpa_kernel, SDPBackend
-from torch._export import capture_pre_autograd_graph
+from torch.export import export_for_training
 
 from model import GPT
 
@@ -489,7 +488,7 @@ dynamic_shape = (
 # Trace the model, converting it to a portable intermediate representation.
 # The torch.no_grad() call tells PyTorch to exclude training-specific logic.
 with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
-    m = capture_pre_autograd_graph(model, example_inputs, dynamic_shapes=dynamic_shape)
+    m = export_for_training(model, example_inputs, dynamic_shapes=dynamic_shape).module()
     traced_model = export(m, example_inputs, dynamic_shapes=dynamic_shape)
 
 # Convert the model into a runnable ExecuTorch program.
@@ -635,7 +634,7 @@ xnnpack_quant_config = get_symmetric_quantization_config(
 xnnpack_quantizer = XNNPACKQuantizer()
 xnnpack_quantizer.set_global(xnnpack_quant_config)
 
-m = capture_pre_autograd_graph(model, example_inputs)
+m = export_for_training(model, example_inputs).module()
 
 # Annotate the model for quantization. This prepares the model for calibration.
 m = prepare_pt2e(m, xnnpack_quantizer)

examples/models/llama3_2_vision/preprocess/model.py

@@ -26,6 +26,9 @@ class PreprocessConfig:
     max_num_tiles: int = 4
     tile_size: int = 224
     antialias: bool = False
+    # Used for reference eager model from torchtune.
+    resize_to_max_canvas: bool = False
+    possible_resolutions: Optional[List[Tuple[int, int]]] = None
 
 
 class CLIPImageTransformModel(EagerModelBase):