Reformat Python codes with black and introduce Python linting via GitHub actions

.flake8

@@ -0,0 +1,35 @@
+[flake8]
+enable-extensions = G
+select = B,C,E,F,G,P,SIM1,T4,W,B9,TOR0,TOR1,TOR2
+max-line-length = 120
+# C408 ignored because we like the dict keyword argument syntax
+# E501 is not flexible enough, we're using B950 instead
+ignore =
+    E203,E305,E402,E501,E721,E741,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,
+    # fix these lints in the future
+    E275,
+    # shebang has extra meaning in fbcode lints, so I think it's not worth trying
+    # to line this up with executable bit
+    EXE001,
+    # these ignores are from flake8-bugbear; please fix!
+    B007,B008,B017,B019,B023,B028,B903,B904,B905,B906,B907
+    # these ignores are from flake8-comprehensions; please fix!
+    C407,
+    # these ignores are from flake8-logging-format; please fix!
+    G100,G101,G200,G201,G202
+    # these ignores are from flake8-simplify. please fix or ignore with commented reason
+    SIM105,SIM108,SIM110,SIM111,SIM113,SIM114,SIM115,SIM116,SIM117,SIM118,SIM119,SIM12,
+    # flake8-simplify code styles
+    SIM102,SIM103,SIM106,SIM112,
+    # TorchFix codes that don't make sense for PyTorch itself:
+    # removed and deprecated PyTorch functions.
+    TOR001,TOR101,
+    # TODO(kit1980): fix all TOR102 issues
+    # `torch.load` without `weights_only` parameter is unsafe
+    TOR102,
+    P201,
+per-file-ignores =
+    __init__.py: F401
+optional-ascii-coding = True
+exclude =
+    ./.git,

.github/workflows/python_lint.yml

@@ -0,0 +1,25 @@
+name: Python Linting
+
+on: [push, pull_request]
+
+jobs:
+  lint-and-format:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.8'
+
+    - name: Install dependencies
+      run: |
+        pip install black flake8
+
+    - name: Check code formatting with Black
+      run: black --check .
+
+    - name: Run Flake8
+      run: flake8 .

et_converter/et_converter.py

@@ -10,10 +10,11 @@
 from .flexflow2chakra_converter import FlexFlow2ChakraConverter
 from .pytorch2chakra_converter import PyTorch2ChakraConverter
 
+
 def get_logger(log_filename: str) -> logging.Logger:
     formatter = logging.Formatter(
-            "%(levelname)s [%(asctime)s] %(message)s",
-            datefmt="%m/%d/%Y %I:%M:%S %p")
+        "%(levelname)s [%(asctime)s] %(message)s", datefmt="%m/%d/%Y %I:%M:%S %p"
+    )
 
     file_handler = FileHandler(log_filename, mode="w")
     file_handler.setLevel(logging.DEBUG)
@@ -30,64 +31,60 @@ def get_logger(log_filename: str) -> logging.Logger:
 
     return logger
 
+
 def main() -> None:
-    parser = argparse.ArgumentParser(
-            description="Execution Trace Converter"
-    )
+    parser = argparse.ArgumentParser(description="Execution Trace Converter")
     parser.add_argument(
-            "--input_type",
-            type=str,
-            default=None,
-            required=True,
-            help="Input execution trace type"
+        "--input_type",
+        type=str,
+        default=None,
+        required=True,
+        help="Input execution trace type",
     )
     parser.add_argument(
-            "--input_filename",
-            type=str,
-            default=None,
-            required=True,
-            help="Input execution trace filename"
+        "--input_filename",
+        type=str,
+        default=None,
+        required=True,
+        help="Input execution trace filename",
     )
     parser.add_argument(
-            "--output_filename",
-            type=str,
-            default=None,
-            required=True,
-            help="Output Chakra execution trace filename"
+        "--output_filename",
+        type=str,
+        default=None,
+        required=True,
+        help="Output Chakra execution trace filename",
     )
     parser.add_argument(
-            "--num_dims",
-            type=int,
-            default=None,
-            required=True,
-            help="Number of dimensions in the network topology"
+        "--num_dims",
+        type=int,
+        default=None,
+        required=True,
+        help="Number of dimensions in the network topology",
     )
     parser.add_argument(
-            "--num_npus",
-            type=int,
-            default=None,
-            required="Text" in sys.argv,
-            help="Number of NPUs in a system"
+        "--num_npus",
+        type=int,
+        default=None,
+        required="Text" in sys.argv,
+        help="Number of NPUs in a system",
     )
     parser.add_argument(
-            "--num_passes",
-            type=int,
-            default=None,
-            required="Text" in sys.argv,
-            help="Number of training passes"
+        "--num_passes",
+        type=int,
+        default=None,
+        required="Text" in sys.argv,
+        help="Number of training passes",
     )
     parser.add_argument(
-            "--npu_frequency",
-            type=int,
-            default=None,
-            required="FlexFlow" in sys.argv,
-            help="NPU frequency in MHz"
+        "--npu_frequency",
+        type=int,
+        default=None,
+        required="FlexFlow" in sys.argv,
+        help="NPU frequency in MHz",
     )
     parser.add_argument(
-            "--log_filename",
-            type=str,
-            default="debug.log",
-            help="Log filename"
+        "--log_filename", type=str, default="debug.log", help="Log filename"
     )
     args = parser.parse_args()
 
@@ -97,27 +94,27 @@ def main() -> None:
     try:
         if args.input_type == "Text":
             converter = Text2ChakraConverter(
-                    args.input_filename,
-                    args.output_filename,
-                    args.num_dims,
-                    args.num_npus,
-                    args.num_passes,
-                    logger)
+                args.input_filename,
+                args.output_filename,
+                args.num_dims,
+                args.num_npus,
+                args.num_passes,
+                logger,
+            )
             converter.convert()
         elif args.input_type == "FlexFlow":
             converter = FlexFlow2ChakraConverter(
-                    args.input_filename,
-                    args.output_filename,
-                    args.num_dims,
-                    args.npu_frequency,
-                    logger)
+                args.input_filename,
+                args.output_filename,
+                args.num_dims,
+                args.npu_frequency,
+                logger,
+            )
             converter.convert()
         elif args.input_type == "PyTorch":
             converter = PyTorch2ChakraConverter(
-                    args.input_filename,
-                    args.output_filename,
-                    args.num_dims,
-                    logger)
+                args.input_filename, args.output_filename, args.num_dims, logger
+            )
             converter.convert()
         else:
             logger.error(f"{args.input_type} unsupported")
@@ -127,5 +124,6 @@ def main() -> None:
         logger.debug(traceback.format_exc())
         sys.exit(1)
 
+
 if __name__ == "__main__":
     main()