Merge commit '087fea06' into bump_to_087fea06

.github/workflows/pre-commit-all.yml

@@ -0,0 +1,15 @@
+name: pre-commit (all files on push)
+
+on:
+  push:
+    branches: [main, post-commit-test]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: actions/setup-python@v3
+    - uses: pre-commit/[email protected]
+      with:
+        extra_args: --color=always --all-files

.github/workflows/pre-commit.yml

@@ -0,0 +1,17 @@
+name: pre-commit
+
+on:
+  pull_request:
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        # requites to grab the history of the PR
+        fetch-depth: 0
+    - uses: actions/setup-python@v3
+    - uses: pre-commit/[email protected]
+      with:
+        extra_args: --color=always --from-ref ${{ github.event.pull_request.base.sha }} --to-ref ${{ github.event.pull_request.head.sha }}

.github/workflows/releaseSnapshotPackage.yml

@@ -44,7 +44,7 @@ jobs:
         uses: ad-m/[email protected]
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          branch: ${{ env.BRANCH_NAME }} 
+          branch: ${{ env.BRANCH_NAME }}
           tags: true
 
       - name: Create Release

.pre-commit-config.yaml

@@ -0,0 +1,21 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+exclude: "GeneratedTorchOps\\.td|abstract_interp_lib_gen\\.py|\\.excalidraw|\\.ipynb"
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: end-of-file-fixer
+    -   id: check-ast
+    -   id: check-yaml
+    -   id: check-added-large-files
+-   repo: https://github.com/psf/black
+    rev: 24.4.2
+    hooks:
+    -   id: black
+
+-   repo: https://github.com/pre-commit/mirrors-clang-format
+    rev: 'v18.1.4'
+    hooks:
+    -   id: clang-format

CMakeLists.txt

@@ -247,4 +247,4 @@ add_subdirectory(projects)
 # Finish with top-level Python bindings so it can handle additional deps.
 if(MLIR_ENABLE_BINDINGS_PYTHON)
   add_subdirectory(python)
-endif()
+endif()

README.md

@@ -1,4 +1,4 @@
-# The Torch-MLIR Project 
+# The Torch-MLIR Project
 
 The Torch-MLIR project aims to provide first class compiler support from the [PyTorch](https://pytorch.org) ecosystem to the MLIR ecosystem.
 
@@ -8,15 +8,15 @@ necessarily a reflection of the completeness or stability of the code, it
 does indicate that the project is not yet endorsed as a component of LLVM.
 
 [PyTorch](https://pytorch.org)
-PyTorch is an open source machine learning framework that facilitates the seamless transition from research and prototyping to production-level deployment. 
+PyTorch is an open source machine learning framework that facilitates the seamless transition from research and prototyping to production-level deployment.
 
 [MLIR](https://mlir.llvm.org)
 The MLIR project offers a novel approach for building extensible and reusable compiler architectures, which address the issue of software fragmentation, reduce the cost of developing domain-specific compilers, improve compilation for heterogeneous hardware, and promote compatibility between existing compilers.
 
 [Torch-MLIR](https://github.com/llvm/torch-mlir)
 Several vendors have adopted MLIR as the middle layer in their systems, enabling them to map frameworks such as PyTorch, JAX, and TensorFlow into MLIR and subsequently lower them to their target hardware. We have observed half a dozen custom lowerings from PyTorch to MLIR, making it easier for hardware vendors to focus on their unique value, rather than needing to implement yet another PyTorch frontend for MLIR. The ultimate aim is to be similar to the current hardware vendors adding LLVM target support, rather than each one implementing Clang or a C++ frontend.
 
-[![Release Build](https://github.com/llvm/torch-mlir/actions/workflows/buildRelease.yml/badge.svg)](https://github.com/llvm/torch-mlir/actions/workflows/buildRelease.yml)
+[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
 
 ## All the roads from PyTorch to Torch MLIR Dialect
 
@@ -76,7 +76,7 @@ pip install torch-mlir -f https://github.com/llvm/torch-mlir-release/releases/ex
 
 ## Demos
 
-### TorchScript ResNet18 
+### TorchScript ResNet18
 
 Standalone script to Convert a PyTorch ResNet18 model to MLIR and run it on the CPU Backend:
 

build_tools/autogen_ltc_backend.py

@@ -30,6 +30,7 @@
 TORCHGEN_DIR = Path(torchgen.__path__[0]).resolve()
 TORCH_MLIR_DIR = Path(__file__).resolve().parent.parent
 
+
 def reindent(text, prefix=""):
     return indent(dedent(text), prefix)
 
@@ -75,7 +76,11 @@ def lowering_function(self, schema: LazyIrSchema):
         )
 
         # Only create this variable if it's used to avoid Wunused-variable
-        operand_idx_counter = "size_t i = 0;" if "i++" in (emplace_arguments_str + emplace_kwarguments) else ""
+        operand_idx_counter = (
+            "size_t i = 0;"
+            if "i++" in (emplace_arguments_str + emplace_kwarguments)
+            else ""
+        )
 
         return reindent(
             f"""
@@ -111,12 +116,16 @@ def __init__(self, binary_dir):
         )
         assert self.torch_ops_file.exists()
         self.binary_dir = Path(binary_dir)
-        assert self.binary_dir.is_dir(), f"Binary directory not found: {self.binary_dir}"
+        assert (
+            self.binary_dir.is_dir()
+        ), f"Binary directory not found: {self.binary_dir}"
         self.source_yaml = self.binary_dir.joinpath("generated_native_functions.yaml")
         self.backend_path = TORCH_MLIR_DIR.joinpath(
             "projects", "ltc", "csrc", "base_lazy_backend"
         )
-        assert self.backend_path.is_dir(), f"Backend path not found: {self.backend_path}"
+        assert (
+            self.backend_path.is_dir()
+        ), f"Backend path not found: {self.backend_path}"
         self.generated_path = self.binary_dir.joinpath(
             "projects", "ltc", "csrc", "base_lazy_backend", "generated"
         )
@@ -168,8 +177,9 @@ def generate_native_functions(self):
         if ts_native_yaml_path.exists():
             ts_native_yaml = yaml.load(ts_native_yaml_path.read_text(), yaml.CLoader)
         else:
-            logging.warning(f"Could not find `ts_native_functions.yaml` at {ts_native_yaml_path}")
-
+            logging.warning(
+                f"Could not find `ts_native_functions.yaml` at {ts_native_yaml_path}"
+            )
 
         parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path)
         self.native_functions = parsed_yaml.native_functions

build_tools/ci/test_posix.sh

@@ -26,17 +26,16 @@ echo "::endgroup::"
 
 case $torch_version in
   nightly)
-    # Failing with: NotImplementedError: 
+    # Failing with: NotImplementedError:
     #   Could not run 'aten::empty.memory_format' with arguments from the 'Lazy' backend.
     # As of 2024-01-07
     # echo "::group::Run Lazy Tensor Core e2e integration tests"
     # python -m e2e_testing.main --config=lazy_tensor_core -v
     # echo "::endgroup::"
 
-    # TODO: There is one failing test in this group on stable. It could
-    # be xfailed vs excluding entirely.
-    echo "::group::Run TorchDynamo e2e integration tests"
-    python -m e2e_testing.main --config=torchdynamo -v
+    # TODO: Need to verify in the stable version
+    echo "::group::Run FxImporter e2e integration tests"
+    python -m e2e_testing.main --config=fx_importer -v
     echo "::endgroup::"
     ;;
   stable)

build_tools/python_deploy/build_linux_packages.sh

@@ -282,7 +282,7 @@ function _check_file_not_changed_by() {
 
 function test_in_tree() {
   local torch_version="$1"
-  
+
   echo ":::: Test in-tree"
   cmake --build /main_checkout/torch-mlir/build --target check-torch-mlir-all
 
@@ -308,10 +308,8 @@ function test_in_tree() {
       echo ":::: Run Onnx e2e integration tests"
       python -m e2e_testing.main --config=onnx -v
 
-      # Dynamo is changing a lot in nightly versions, and thus the implementation
-      # tends to become incompatible to the stable version.
-      echo ":::: Run TorchDynamo e2e integration tests"
-      python -m e2e_testing.main --config=torchdynamo -v
+      echo ":::: Run FxImporter e2e integration tests"
+      python -m e2e_testing.main --config=fx_importer -v
       ;;
     stable)
       echo ":::: Test with stable torch"

build_tools/scrape_releases.py

@@ -2,26 +2,28 @@
 
 See https://github.com/llvm/torch-mlir/issues/1374
 """
+
 import argparse
 import json
 
 import requests
 
 # Parse arguments
 parser = argparse.ArgumentParser()
-parser.add_argument('owner', type=str)
-parser.add_argument('repo', type=str)
+parser.add_argument("owner", type=str)
+parser.add_argument("repo", type=str)
 args = parser.parse_args()
 
 # Get releases
 response = requests.get(
-    f"https://api.github.com/repos/{args.owner}/{args.repo}/releases")
+    f"https://api.github.com/repos/{args.owner}/{args.repo}/releases"
+)
 body = json.loads(response.content)
 
 # Parse releases
 releases = []
 for row in body:
-    for asset in row['assets']:
+    for asset in row["assets"]:
         releases.append((asset["name"], asset["browser_download_url"]))
 
 # Output HTML
@@ -33,4 +35,4 @@
     html += f"    <a href='{url}'>{name}</a><br />\n"
 html += """  </body>
 </html>"""
-print(html)
+print(html)

docs/abstract_interp_lib.md

@@ -104,7 +104,7 @@ that this is minimal.
 
 ## Adding to the abstract interpretation library
 
-See [Adding a Shape and Dtype Function](adding_a_shape_and_dtype_function.md)
+See [Adding Abstract Interpretation Functions](adding_abstract_interpretation_functions.md)
 for details on how to add a shape and dtype function for an operator.
 
 ## Rationale

docs/add_ops.md

@@ -2,7 +2,6 @@
 
 Collected links and contacts for how to add ops to torch-mlir.
 
-
 <details>
 <summary>Turbine Camp: Start Here</summary>
 This document was previously known as `turbine-camp.md` to Nod.ai. "Turbine Camp" is part of Nod.ai's onboarding process. Welcome to turbine camp. This document originated at Nod.ai as a part of onboardding process, where new nod-ai folks learn about the architecture of our work by adding support for 2 ops to torch-mlir. I decided to put this into torch mlir because a lot of this is about torch-mlir.
@@ -27,6 +26,7 @@ The details of how we do it and helpful commands to help you set up each repo is
 PS: IREE is pronounced Eerie, and hence the ghost icon.
 
 ## How to begin
+0. Set up torch-mlir according to the instructions here: https://github.com/llvm/torch-mlir/blob/main/docs/development.md
 1. You will start by adding support for 2 ops in torch-mlir, to get you familiar with the center of our pipeline. Begin by reading [torch-mlir's documentation on how to implement a new torch op](https://github.com/llvm/torch-mlir/blob/main/docs/Torch-ops-E2E-implementation.md), and set up `llvm/torch_mlir` using https://github.com/llvm/torch-mlir/blob/main/docs/development.md
 2. Pick 1 of the yet-unimplemented from the following. You should choose something that looks easy to you. **Make sure you create an issue by clicking the little "target" icon to the right of the op, thereby marking the op as yours**
     - [TorchToLinalg ops tracking issue](https://github.com/nod-ai/SHARK-Turbine/issues/347)

docs/development.md

@@ -26,6 +26,19 @@ python -m pip install -r requirements.txt
 python -m pip install -r torchvision-requirements.txt
 ```
 
+## (Optional) Set up pre-commit
+
+This project uses [pre-commit](https://pre-commit.com/) in its CI. You can
+install it locally too in order to lint and fix your code prior to the CI
+complaining about it.
+
+```shell
+pip install pre-commit
+# You can run interactively with `pre-commit run`
+# or install hooks so it runs automatically:
+pre-commit install
+```
+
 ## CMake Build
 
 Two setups are possible to build: in-tree and out-of-tree. The in-tree setup is the most straightforward, as it will build LLVM dependencies as well.

docs/importers/onnx_importer.md

@@ -140,4 +140,3 @@ torch-mlir's representation:
 
 * `ConstantOfShape`: Mapped to `torch.vtensor.literal` with
   a corresponding `value` attribute.
-

docs/roadmap.md

@@ -277,4 +277,3 @@ directly provided a way to plug into this.
 
 Additionally, we can leverage the [`pytorch-jit-paritybench`](https://github.com/jansel/pytorch-jit-paritybench)
 to verify our end-to-end correctness on real models.
-

include/CMakeLists.txt

@@ -1,2 +1,2 @@
 add_subdirectory(torch-mlir)
-add_subdirectory(torch-mlir-dialects)
+add_subdirectory(torch-mlir-dialects)