Set onnxoptimizer.optimize as default for peepholeoptimizer (#1550)

## Describe your changes

Set onnxoptimizer.optimize as default for peepholeoptimizer

## Checklist before requesting a review
- [ ] Add unit tests for this change.
- [ ] Make sure all tests can pass.
- [ ] Update documents if necessary.
- [ ] Lint and apply fixes to your code by running `lintrunner -a`
- [ ] Is this a user-facing change? If yes, give a description of this
change to be included in the release notes.
- [ ] Is this PR including examples changes? If yes, please remember to
update [example
documentation](https://github.com/microsoft/Olive/blob/main/docs/source/examples.md)
in a follow-up PR.

## (Optional) Issue link

docs/source/how-to/configure-workflows/model-opt-and-transform/onnx.md

@@ -55,6 +55,10 @@ The `OnnxPeepholeOptimizer` leverages `onnxscript` (https://onnxscript.ai/tutori
 | **Fuse Consecutive Slices**       | Fuses consecutive Slice operations.                                                 |
 | **Eliminate Unused Initializer**  | Eliminates unused initializers.                                                     |
 | **Eliminate Duplicate Initializer** | Eliminates duplicate initializers.                                  |
+| **Broadcast to MatMul**           | Converts broadcast patterns into MatMul operations for better efficiency.  |
+| **Cast Constant of Shape**        | Simplifies constant casting for shape operations.                          |
+| **GEMM to MatMul+Add**            | Converts GEMM operations into MatMul and Add for improved compatibility.   |
+| **No-Op Removal**                 | Removes redundant or no-op operations in the computation graph.            |
 
 Please refer to [OnnxPeepholeOptimizer](../../../reference/pass.rst#onnx_peephole_optimizer) for more details about the pass and its config parameters.
 

olive/passes/onnx/peephole_optimizer.py

@@ -4,7 +4,7 @@
 # --------------------------------------------------------------------------
 import logging
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any, Dict
 
 import numpy as np
 import onnx
@@ -236,35 +236,27 @@ def onnxscript_optimize(self):
         try:
             import onnxscript
         except ImportError:
-            logger.warning("Please install onnxscript to use the ONNX optimizer feature. Skip onnxscript optimization.")
+            logger.warning("Please install `onnxscript` to apply more optimization.")
             return
 
         onnxscript.optimizer.optimize(self.model)
 
+    def onnxoptimizer_optimize(self):
+        try:
+            from onnxoptimizer import optimize
+        except ImportError:
+            logger.warning("Please install `onnxoptimizer` to apply more optimization.")
+            return
+
+        self.model = optimize(self.model)
+
 
 class OnnxPeepholeOptimizer(Pass):
     """Optimize ONNX model by fusing nodes."""
 
     @classmethod
     def _default_config(cls, accelerator_spec: AcceleratorSpec) -> Dict[str, PassConfigParam]:
-        return {
-            "onnxoptimizer": PassConfigParam(
-                type_=bool,
-                default_value=False,
-                description="Whether to run the ONNX optimizer (https://github.com/onnx/optimizer/). Default is False.",
-            ),
-            "passes": PassConfigParam(
-                type_=List[str],
-                default_value=None,
-                description="List of passes of ONNX optimizer to run. Default is None.",
-            ),
-            "fixed_point": PassConfigParam(
-                type_=bool,
-                default_value=False,
-                description="Whether to run the fixed-point optimization of ONNX optimizer. Default is False.",
-            ),
-            **get_external_data_config(),
-        }
+        return get_external_data_config()
 
     def _run_for_config(
         self, model: ONNXModelHandler, config: Dict[str, Any], output_model_path: str
@@ -273,20 +265,11 @@ def _run_for_config(
 
         # optimize model
         peephole_optimizer = ModelOptimizer(model.model_path)
+        peephole_optimizer.onnxscript_optimize()
+        peephole_optimizer.onnxoptimizer_optimize()
         peephole_optimizer.fuse_transpose_qat()
         peephole_optimizer.patch_unsupported_argmax_operator()
         peephole_optimizer.fuse_reshape_operations()
-        peephole_optimizer.onnxscript_optimize()
-
-        if config["onnxoptimizer"]:
-            try:
-                from onnxoptimizer import optimize
-
-                peephole_optimizer.model = optimize(peephole_optimizer.model, config["passes"], config["fixed_point"])
-            except ImportError:
-                logger.warning(
-                    "Please install onnxoptimizer to use the ONNX optimizer feature. Skip onnxoptimizer optimization."
-                )
 
         # save the model to the output path and return the model
         return model_proto_to_olive_model(peephole_optimizer.model, output_model_path, config)

test/unit_test/passes/onnx/test_peephole_optimizer.py

@@ -65,16 +65,19 @@ def _make_model_for_patch_unsupported_argmax_operator(
     return model_proto_to_olive_model(model, filepath, config)
 
 
+@patch("onnxoptimizer.optimize")
 @patch("onnxscript.optimizer.optimize")
 def test_onnx_peephole_optimizer_pass_patch_unsupported_argmax_operator_modified(
-    mock_optimize, tmp_path, external_data_config
+    mock_onnxscript, mock_onnxoptimizer, tmp_path, external_data_config
 ):
     m = _make_model_for_patch_unsupported_argmax_operator(
         TensorProto.INT64, str(tmp_path / "input.onnx"), external_data_config
     )
     p = create_pass_from_dict(
         OnnxPeepholeOptimizer, external_data_config, disable_search=True, accelerator_spec=DEFAULT_GPU_CUDA_ACCELERATOR
     )
+    mock_onnxscript.return_value = m.load_model()
+    mock_onnxoptimizer.return_value = m.load_model()
 
     actual_model = p.run(m, str(tmp_path / "onnx"))
     assert Path(actual_model.model_path).exists()
@@ -193,40 +196,36 @@ def test_onnx_peephole_optimizer_pass_fuse_reshape_operations(tmp_path, external
 
 
 @patch("olive.passes.onnx.peephole_optimizer.model_proto_to_olive_model")
+@patch("onnxoptimizer.optimize")
 @patch("onnxscript.optimizer.optimize")
-def test_onnxscript(mock_optimize, mock_model_proto_to_olive_model, tmp_path):
+def test_onnxscript(mock_onnxscript, mock_onnxoptimizer, mock_model_proto_to_olive_model, tmp_path):
     # setup
     input_model = get_onnx_model()
     p = create_pass_from_dict(OnnxPeepholeOptimizer, {}, disable_search=True)
+    mock_onnxscript.return_value = input_model.load_model()
+    mock_onnxoptimizer.return_value = input_model.load_model()
     output_folder = str(tmp_path / "onnx")
 
     # execute
     p.run(input_model, output_folder)
 
     # assert
-    mock_optimize.assert_called_once_with(input_model.load_model())
+    mock_onnxscript.assert_called_once_with(input_model.load_model())
 
 
 @patch("olive.passes.onnx.peephole_optimizer.model_proto_to_olive_model")
 @patch("onnxoptimizer.optimize")
-def test_onnxoptimizer(mock_optimize, mock_model_proto_to_olive_model, tmp_path):
+@patch("onnxscript.optimizer.optimize")
+def test_onnxoptimizer(mock_onnxscript, mock_onnxoptimizer, mock_model_proto_to_olive_model, tmp_path):
     # setup
     input_model = get_onnx_model()
-    passes = ["pass"]
-    fixed_point = True
-    p = create_pass_from_dict(
-        OnnxPeepholeOptimizer,
-        {
-            "onnxoptimizer": True,
-            "passes": passes,
-            "fixed_point": fixed_point,
-        },
-        disable_search=True,
-    )
+    p = create_pass_from_dict(OnnxPeepholeOptimizer, {}, disable_search=True)
+    mock_onnxscript.return_value = input_model.load_model()
+    mock_onnxoptimizer.return_value = input_model.load_model()
     output_folder = str(tmp_path / "onnx")
 
     # execute
     p.run(input_model, output_folder)
 
     # assert
-    mock_optimize.assert_called_once_with(input_model.load_model(), passes, fixed_point)
+    mock_onnxoptimizer.assert_called_once()