fix typos

Signed-off-by: dchigarev <dmitry.chigarev@intel.com>

Author: dchigarev

ingress/Torch-MLIR/generate-mlir.py

@@ -22,15 +22,15 @@ def parse_args():
         "--model-state-path",
         type=str,
         required=False,
-        help="Path to the state file of the Torch model (usually has .pt or .pth extension).",
+        help="Path to a state file of the Torch model (usually has .pt or .pth extension).",
     )
     parser.add_argument(
         "--model-args",
         type=str,
         required=False,
         default="[]",
         help=""
-        "Positional arguments to pass to the model-entry "
+        "Positional arguments to pass to the model's entrypoint "
         "(note that this argument will be passed to an 'eval',"
         " so the string should contain a valid python code).",
     )
@@ -40,7 +40,7 @@ def parse_args():
         required=False,
         default="{}",
         help=""
-        "Keyword arguments to pass to the model-entry "
+        "Keyword arguments to pass to the model's entrypoint "
         "(note that this argument will be passed to an 'eval',"
         " so the string should contain a valid python code).",
     )
@@ -95,7 +95,7 @@ def generate_sample_args(shape_str, sample_fn_path) -> tuple[tuple, dict]:
     return load_callable_symbol(sample_fn_path)()
 
 
-def generate_mlir(model, dialect, sample_args, sample_kwargs):
+def generate_mlir(model, sample_args, sample_kwargs=None, dialect="linalg"):
     # Convert the Torch model to MLIR
     output_type = None
     if dialect == "torch":
@@ -109,6 +109,9 @@ def generate_mlir(model, dialect, sample_args, sample_kwargs):
     else:
         raise ValueError(f"Unsupported dialect: {dialect}")
 
+    if sample_kwargs is None:
+        sample_kwargs = {}
+
     model.eval()
     module = fx.export_and_import(
         model, *sample_args, output_type=output_type, **sample_kwargs
@@ -132,7 +135,7 @@ def main():
         args.sample_shapes, args.sample_fn
     )
     # Generate MLIR from the model
-    mlir_module = generate_mlir(model, args.dialect, sample_args, sample_kwargs)
+    mlir_module = generate_mlir(model, sample_args, sample_kwargs, args.dialect)
 
     # Print or save the MLIR module
     if args.out_mlir: