[CI] Linter and formatter

.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)

python/lighthouse/utils/__init__.py

@@ -7,3 +7,11 @@
     torch_to_memref,
     torch_to_packed_args,
 )
+
+__all__ = [
+    "get_packed_arg",
+    "memref_to_ctype",
+    "memrefs_to_packed_args",
+    "torch_to_memref",
+    "torch_to_packed_args",
+]