[CI] Linter and formatter (#24)

Adds 'ruff' as Python linter and code formatter, and 'pre-commit' for
easy rules application both locally and in CI.
New lint CI workflow is also added.

The formatting tools are added to the 'dev' dependency group.
They are installed by default with `uv sync` and can be run locally
with the following cmd:
  pre-commit run -a

Author: adam-smnk

.github/workflows/lint.yml

@@ -0,0 +1,22 @@
+name: Lint
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  Lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v5
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+
+      - name: Install the project and its dependencies
+        run: |
+          uv sync
+
+      - name: Run pre-commit
+        run: |-
+          uv run pre-commit run --all-files

.pre-commit-config.yaml

@@ -0,0 +1,10 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.14.5
+  hooks:
+    # Run the linter.
+    - id: ruff-check
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format

ingress/mlir-gen/mlir_gen/main.py

@@ -136,9 +136,9 @@ def weights(
             assert k_as_num_inputs % block.k == 0, "invalid tile size for K dim"
             assert n_as_num_outputs % block.n == 0, "invalid tile size for N dim"
             if block.vnni:
-                assert (
-                    block.n % block.vnni == 0
-                ), "incompatible tile sizes for N and VNNI dims"
+                assert block.n % block.vnni == 0, (
+                    "incompatible tile sizes for N and VNNI dims"
+                )
                 shape = (
                     n_as_num_outputs // block.n,
                     k_as_num_inputs // block.k,

pyproject.toml

@@ -8,7 +8,9 @@ dependencies = [
 
 [dependency-groups]
 dev = [
-  "lit==18.1.8"  # Tool to configure, discover and run tests
+  "lit==18.1.8",  # Tool to configure, discover and run tests
+  "ruff==0.14.5", # Python linter and formatter
+  "pre-commit", # Tool to manage and apply pre-commit hooks
 ]
 
 [project.optional-dependencies]
@@ -82,3 +84,33 @@ include = ["lighthouse*"]
 
 [tool.setuptools.dynamic]
 version = {attr = "lighthouse.__version__"}
+
+[tool.ruff]
+src = ["lighthouse"]
+target-version = "py310"
+line-length = 88
+
+[tool.ruff.format]
+docstring-code-format = true
+quote-style = "double"
+
+# List of rules:
+# https://docs.astral.sh/ruff/rules/
+[tool.ruff.lint]
+select = [
+  "D419", # empty-docstring
+  "E", # Error
+  "F", # Pyflakes
+  "PERF", # Perflint
+  "RUF022", # __all__ is not sorted
+  "RUF030", # print() call in assert
+  "RUF034", # useless if-else
+  "RUF047", # empty else
+  "RUF200", # invalid pyproject.toml
+  "W", # Warning
+]
+ignore = [
+    "E501", # line-too-long
+    "PERF203", # try-except-in-loop
+    "PERF401", # manual-list-comprehension
+]

python/examples/ingress/torch/MLPModel/model.py

@@ -3,16 +3,11 @@
 import torch
 import torch.nn as nn
 
-import os
 
 class MLPModel(nn.Module):
     def __init__(self):
         super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(10, 32),
-            nn.ReLU(),
-            nn.Linear(32, 2)
-        )
+        self.net = nn.Sequential(nn.Linear(10, 32), nn.ReLU(), nn.Linear(32, 2))
 
     def forward(self, x):
         return self.net(x)

python/examples/ingress/torch/mlp_from_file.py

@@ -34,14 +34,16 @@
 # - Loads the MLPModel class and instantiates it with arguments obtained from 'get_init_inputs()'
 # - Calls get_sample_inputs() to get sample input tensors for shape inference
 # - Converts PyTorch model to linalg-on-tensors dialect operations using torch_mlir
+# fmt: off
 mlir_module_ir: ir.Module = import_from_file(
     model_path,                              # Path to the Python file containing the model
     model_class_name="MLPModel",             # Name of the PyTorch nn.Module class to convert
     init_args_fn_name="get_init_inputs",     # Function that returns args for model.__init__()
     sample_args_fn_name="get_sample_inputs", # Function that returns sample inputs to pass to 'model(...)'
     dialect="linalg-on-tensors",             # Target MLIR dialect (linalg ops on tensor types)
-    ir_context=ir_context                    # MLIR context for the conversion
+    ir_context=ir_context,                   # MLIR context for the conversion
 )
+# fmt: on
 
 # The PyTorch model is now converted to MLIR at this point. You can now convert
 # the MLIR module to a text form (e.g. 'str(mlir_module_ir)') and save it to a file.

python/examples/ingress/torch/mlp_from_model.py

@@ -31,9 +31,7 @@
 ir_context = ir.Context()
 # Step 2: Convert the PyTorch model to MLIR
 mlir_module_ir: ir.Module = import_from_model(
-    model,
-    sample_args=(sample_input,),
-    ir_context=ir_context
+    model, sample_args=(sample_input,), ir_context=ir_context
 )
 
 # The PyTorch model is now converted to MLIR at this point. You can now convert

python/lighthouse/ingress/torch/__init__.py

@@ -1,3 +1,8 @@
 """Provides functions to convert PyTorch models to MLIR."""
 
 from .importer import import_from_file, import_from_model
+
+__all__ = [
+    "import_from_file",
+    "import_from_model",
+]

python/lighthouse/ingress/torch/importer.py

@@ -3,7 +3,10 @@
 from pathlib import Path
 from typing import Iterable, Mapping
 
-from lighthouse.ingress.torch.utils import load_and_run_callable, maybe_load_and_run_callable
+from lighthouse.ingress.torch.utils import (
+    load_and_run_callable,
+    maybe_load_and_run_callable,
+)
 
 try:
     import torch
@@ -25,6 +28,7 @@
 
 from mlir import ir
 
+
 def import_from_model(
     model: nn.Module,
     sample_args: Iterable,
@@ -49,10 +53,10 @@ def import_from_model(
         ir_context (ir.Context, optional): An optional MLIR context to use for parsing
             the module. If not provided, the module is returned as a string.
         **kwargs: Additional keyword arguments passed to the ``torch_mlir.fx.export_and_import`` function.
-    
+
     Returns:
         str | ir.Module: The imported MLIR module as a string or an ir.Module if `ir_context` is provided.
-    
+
     Examples:
         >>> import torch
         >>> import torch.nn as nn
@@ -61,17 +65,22 @@ def import_from_model(
         ...     def __init__(self):
         ...         super().__init__()
         ...         self.fc = nn.Linear(10, 5)
+        ...
         ...     def forward(self, x):
         ...         return self.fc(x)
         >>> model = SimpleModel()
         >>> sample_input = (torch.randn(1, 10),)
         >>> #
         >>> # option 1: get MLIR module as a string
-        >>> mlir_module : str = import_from_model(model, sample_input, dialect="linalg-on-tensors")
-        >>> print(mlir_module) # prints the MLIR module in linalg-on-tensors dialect
+        >>> mlir_module: str = import_from_model(
+        ...     model, sample_input, dialect="linalg-on-tensors"
+        ... )
+        >>> print(mlir_module)  # prints the MLIR module in linalg-on-tensors dialect
         >>> # option 2: get MLIR module as an ir.Module
         >>> ir_context = ir.Context()
-        >>> mlir_module_ir : ir.Module = import_from_model(model, sample_input, dialect="tosa", ir_context=ir_context)
+        >>> mlir_module_ir: ir.Module = import_from_model(
+        ...     model, sample_input, dialect="tosa", ir_context=ir_context
+        ... )
     """
     if dialect == "linalg":
         raise ValueError(
@@ -134,45 +143,48 @@ def import_from_file(
         ir_context (ir.Context, optional): An optional MLIR context to use for parsing
             the module. If not provided, the module is returned as a string.
         **kwargs: Additional keyword arguments passed to the ``torch_mlir.fx.export_and_import`` function.
-    
+
     Returns:
         str | ir.Module: The imported MLIR module as a string or an ir.Module if `ir_context` is provided.
-    
+
     Examples:
         Given a file `path/to/model_file.py` with the following content:
         ```python
         import torch
         import torch.nn as nn
 
+
         class MyModel(nn.Module):
             def __init__(self):
                 super().__init__()
                 self.fc = nn.Linear(10, 5)
+
             def forward(self, x):
                 return self.fc(x)
 
+
         def get_inputs():
             return (torch.randn(1, 10),)
         ```
 
         The import script would look like:
         >>> from lighthouse.ingress.torch_import import import_from_file
         >>> # option 1: get MLIR module as a string
-        >>> mlir_module : str = import_from_file(
+        >>> mlir_module: str = import_from_file(
         ...     "path/to/model_file.py",
         ...     model_class_name="MyModel",
         ...     init_args_fn_name=None,
-        ...     dialect="linalg-on-tensors"
+        ...     dialect="linalg-on-tensors",
         ... )
-        >>> print(mlir_module) # prints the MLIR module in linalg-on-tensors dialect
+        >>> print(mlir_module)  # prints the MLIR module in linalg-on-tensors dialect
         >>> # option 2: get MLIR module as an ir.Module
         >>> ir_context = ir.Context()
-        >>> mlir_module_ir : ir.Module = import_from_file(
+        >>> mlir_module_ir: ir.Module = import_from_file(
         ...     "path/to/model_file.py",
         ...     model_class_name="MyModel",
         ...     init_args_fn_name=None,
         ...     dialect="linalg-on-tensors",
-        ...     ir_context=ir_context
+        ...     ir_context=ir_context,
         ... )
     """
     if isinstance(filepath, str):
@@ -191,24 +203,24 @@ def get_inputs():
         module,
         init_args_fn_name,
         default=tuple(),
-        error_msg=f"Init args function '{init_args_fn_name}' not found in {filepath}"
+        error_msg=f"Init args function '{init_args_fn_name}' not found in {filepath}",
     )
     model_init_kwargs = maybe_load_and_run_callable(
         module,
         init_kwargs_fn_name,
         default={},
-        error_msg=f"Init kwargs function '{init_kwargs_fn_name}' not found in {filepath}"
+        error_msg=f"Init kwargs function '{init_kwargs_fn_name}' not found in {filepath}",
     )
     sample_args = load_and_run_callable(
         module,
         sample_args_fn_name,
-        f"Sample args function '{sample_args_fn_name}' not found in {filepath}"
+        f"Sample args function '{sample_args_fn_name}' not found in {filepath}",
     )
     sample_kwargs = maybe_load_and_run_callable(
         module,
         sample_kwargs_fn_name,
         default={},
-        error_msg=f"Sample kwargs function '{sample_kwargs_fn_name}' not found in {filepath}"
+        error_msg=f"Sample kwargs function '{sample_kwargs_fn_name}' not found in {filepath}",
     )
 
     nn_model: nn.Module = model(*model_init_args, **model_init_kwargs)

python/lighthouse/ingress/torch/utils.py

@@ -43,8 +43,4 @@ def maybe_load_and_run_callable(
     """
     if symbol_name is None:
         return default
-    return load_and_run_callable(
-        module,
-        symbol_name,
-        error_msg=error_msg
-    )
+    return load_and_run_callable(module, symbol_name, error_msg=error_msg)