Merge branch 'main' into justinchu/debug-pass

Author: justinchuby

onnxscript/optimizer/_constant_folding.py

@@ -843,12 +843,10 @@ class FoldConstantsPass(ir.passes.InPlacePass):
     def __init__(
         self,
         *,
-        external_data_folder: str,
         shape_inference: bool,
         input_size_limit: int,
         output_size_limit: int,
     ) -> None:
-        self._external_data_folder = external_data_folder
         self._shape_inference = shape_inference
         self._input_size_limit = input_size_limit
         self._output_size_limit = output_size_limit
@@ -1117,7 +1115,6 @@ def __bool__(self) -> bool:
 
 def fold_constants(
     model: ir.Model,
-    external_data_folder: str = "",
     *,
     onnx_shape_inference: bool = False,
     input_size_limit: int = DEFAULT_CONSTANT_FOLD_INPUT_SIZE_LIMIT,
@@ -1128,8 +1125,6 @@ def fold_constants(
 
     Args:
         model: The ONNX model to optimize.
-        external_data_folder: Path to the folder containing external data
-            for the model. Defaults to an empty string.
         onnx_shape_inference: Whether to enable ONNX shape inference during
             constant folding. Defaults to False.
         input_size_limit: The maximum size (in bytes) of input tensors
@@ -1144,7 +1139,6 @@ def fold_constants(
 
     """
     folder_pass = FoldConstantsPass(
-        external_data_folder=external_data_folder,
         shape_inference=onnx_shape_inference,
         input_size_limit=input_size_limit,
         output_size_limit=output_size_limit,

onnxscript/optimizer/_optimizer.py

@@ -40,7 +40,6 @@ def optimize_ir(
         ir.passes.PassManager(
             [
                 _constant_folding.FoldConstantsPass(
-                    external_data_folder="",
                     shape_inference=onnx_shape_inference,
                     input_size_limit=input_size_limit,
                     output_size_limit=output_size_limit,