Add fusions for SigLIP and Conformer-Encoder (microsoft#23528)

### Description
This PR adds fusions for [Google's SigLIP
model](https://huggingface.co/google/siglip-base-patch16-224/) and
Microsoft's internal conformer-encoder model.

Here is an example of how to run the ORT transformer optimizer for the
SigLIP model.
```
$ git clone https://github.com/microsoft/onnxruntime
$ cd onnxruntime/onnxruntime/python/tools/transformers
$ python3 optimizer.py --input /path/to/model.onnx --output /path/to/model_opt.onnx --model_type clip --num_heads 16 --hidden_size 1152 --use_external_data_format --opt_level 0 --disable_shape_inference
```

Here is an example of how to run the ORT transformer optimizer for the
conformer-encoder model.
```
$ git clone https://github.com/microsoft/onnxruntime
$ cd onnxruntime/onnxruntime/python/tools/transformers
$ python3 optimizer.py --input /path/to/model.onnx --output /path/to/model_opt.onnx --model_type conformer --num_heads 16 --hidden_size 1024 --use_external_data_format --opt_level 0 --disable_shape_inference --convert_attribute
```

### Motivation and Context
This PR helps optimize multi-modal models that use SigLIP for the vision
encoder and conformer-encoder for the speech encoder.

This PR uses changes from the following PRs:
- pytorch/pytorch#144801
- microsoft/onnxscript#2018
- microsoft/onnxscript#2019
- microsoft/onnxscript#2020
- microsoft/onnxscript#2021
- microsoft/onnxscript#2022
- microsoft/onnxscript#2024
- microsoft/onnxscript#2025
- microsoft/onnxscript#2029
- microsoft/onnxscript#2033

### Introduction of ONNX Script

This PR introduces [ONNX
Script](https://github.com/microsoft/onnxscript) into the ORT
transformer optimizer as an optional step via the
`fold_transpose_initializers()` method of the `DynamoOnnxHelper` class.

onnxruntime/python/tools/transformers/dynamo_onnx_helper.py

@@ -2,54 +2,61 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 # --------------------------------------------------------------------------
-import logging
+from collections.abc import Sequence
+from logging import getLogger
+from typing import Any
 
+import numpy as np
 import onnx
+from onnx import helper
+from onnx_model import OnnxModel
+
+logger = getLogger(__name__)
 
 
 class DynamoOnnxHelper:
     """
-    Helper class for processing ONNX models exported by torch Dynamo.
+    Helper class for processing ONNX models exported by Torch Dynamo.
     """
 
     def __init__(self, model: onnx.ModelProto):
-        self.model = model
+        self.model = OnnxModel(model)
 
     def update_edges(self, edge_mapping: dict) -> None:
         """
         Updates the edges in the model according to the given mapping.
         """
-        for node in self.model.graph.node:
+        for node in self.model.model.graph.node:
             for i in range(len(node.input)):
                 if node.input[i] in edge_mapping:
                     node.input[i] = edge_mapping[node.input[i]]
             for i in range(len(node.output)):
                 if node.output[i] in edge_mapping:
                     node.output[i] = edge_mapping[node.output[i]]
 
-        for graph_input in self.model.graph.input:
+        for graph_input in self.model.model.graph.input:
             if graph_input.name in edge_mapping:
                 graph_input.name = edge_mapping[graph_input.name]
-        for graph_output in self.model.graph.output:
+        for graph_output in self.model.model.graph.output:
             if graph_output.name in edge_mapping:
                 graph_output.name = edge_mapping[graph_output.name]
 
     def unroll_function(self, func_name: str) -> None:
         """
         Unrolls the function with the given name in the model.
         """
-        logging.info(f"Unrolling function {func_name}...")
+        logger.debug(f"Unrolling function {func_name}...")
         nodes_to_remove = []
         nodes_to_add = []
         edges_to_remove = []
         edges_to_add = []
-        for node in self.model.graph.node:
+        for node in self.model.model.graph.node:
             if node.op_type == func_name:
                 nodes_to_remove.append(node)
                 edges_to_remove.extend(list(node.input) + list(node.output))
 
         func_to_remove = None
-        for f in self.model.functions:
+        for f in self.model.model.functions:
             if f.name == func_name:
                 nodes_to_add.extend(list(f.node))
                 edges_to_add.extend(list(f.input) + list(f.output))
@@ -58,11 +65,11 @@ def unroll_function(self, func_name: str) -> None:
         assert len(edges_to_remove) == len(edges_to_add)
 
         for node in nodes_to_remove:
-            self.model.graph.node.remove(node)
+            self.model.model.graph.node.remove(node)
         for node in nodes_to_add:
-            self.model.graph.node.append(node)
+            self.model.model.graph.node.append(node)
         if func_to_remove is not None:
-            self.model.functions.remove(func_to_remove)
+            self.model.model.functions.remove(func_to_remove)
 
         edge_mapping = {}
         for i in range(len(edges_to_remove)):
@@ -79,26 +86,120 @@ def remove_function(self, func_name: str, input_id: int, output_id: int) -> None
         """
         edge_mapping = {}
         nodes_to_remove = []
-        for node in self.model.graph.node:
+        for node in self.model.model.graph.node:
             if node.op_type.find(func_name) != -1:
                 edge_mapping[node.input[input_id]] = node.output[output_id]
                 nodes_to_remove.append(node)
         for node in nodes_to_remove:
-            self.model.graph.node.remove(node)
+            self.model.model.graph.node.remove(node)
 
         self.update_edges(edge_mapping)
 
     def remove_dropout_layer(self) -> None:
         """
         Removes the dropout layer in the model.
         """
-        logging.info("Removing dropout layer...")
+        logger.debug("Removing dropout layer...")
         self.remove_function("Dropout", 0, 0)
 
     def remove_lm_head_layer(self) -> None:
         """
         Removes the LM head layer in the model.
         """
-        logging.info("Removing LM head layer...")
+        logger.debug("Removing LM head layer...")
         # bugbug: need to copy the right vi over
         self.remove_function("Linear_lm_head", 2, 0)
+
+    def add_initializer(self, name: str, data_type: int, dims: Sequence[int], vals: Any, raw: bool = True):
+        if raw:
+            np_type = helper.tensor_dtype_to_np_dtype(data_type)
+            if not isinstance(vals, np.ndarray):
+                bytes = np.array(vals, dtype=np_type).tobytes()
+            else:
+                bytes = vals.astype(np_type).tobytes()
+            tensor = helper.make_tensor(
+                name=name,
+                data_type=data_type,
+                dims=dims,
+                vals=bytes,
+                raw=True,
+            )
+        else:
+            tensor = helper.make_tensor(
+                name=name,
+                data_type=data_type,
+                dims=dims,
+                vals=vals,
+                raw=False,
+            )
+
+        self.model.add_initializer(tensor)
+        return tensor
+
+    def convert_constants_to_initializers(self, min_size: int = 1) -> None:
+        """
+        Converts Constant ops of size [min_size] or higher to initializers
+        """
+        logger.debug(f"Converting constants greater than size {min_size} to initializers")
+
+        constant_nodes = self.model.get_nodes_by_op_type("Constant")
+        nodes_to_remove = []
+
+        for node in constant_nodes:
+            # Get info from Constant op
+            np_data = self.model.get_constant_value(node.output[0])
+
+            # Skip if there are less than [min_size] elements
+            if np_data is None or np_data.size < min_size:
+                continue
+
+            # Add new initializer with same name as Constant op's output
+            for att in node.attribute:
+                if att.name == "value":
+                    self.add_initializer(
+                        name=node.output[0],
+                        data_type=att.t.data_type,
+                        dims=list(np_data.shape),
+                        vals=np_data,
+                    )
+                    break
+
+            nodes_to_remove.append(node)
+
+        # Remove Constant ops from graph
+        self.model.remove_nodes(nodes_to_remove)
+
+    def clear_metadata(self) -> None:
+        """
+        Clear metadata fields in all nodes
+        """
+        for graph in self.model.graphs():
+            graph.ClearField("metadata_props")
+        for node in self.model.nodes():
+            node.ClearField("metadata_props")
+
+    @staticmethod
+    def fold_transpose_initializers(model) -> None:
+        """
+        Constant fold Transpose initializers without changing the initializer names
+        """
+        from onnxscript import ir
+
+        for name, initializer in model.graph.initializers.items():
+            user_nodes = initializer.consumers()
+            if len(user_nodes) == 1 and user_nodes[0].op_type == "Transpose":
+                transpose_node = user_nodes[0]
+                perm = transpose_node.attributes.get("perm")
+                if perm is None:
+                    transposed_tensor = ir.tensor(initializer.const_value.numpy().transpose())
+                else:
+                    transposed_tensor = ir.tensor(initializer.const_value.numpy().transpose(perm.as_ints()))
+                new_initializer = ir.Value(
+                    name=initializer.name,
+                    shape=transposed_tensor.shape,
+                    type=ir.TensorType(transposed_tensor.dtype),
+                    const_value=transposed_tensor,
+                )
+                ir.convenience.replace_all_uses_with(transpose_node.outputs[0], new_initializer)
+                model.graph.initializers[name] = new_initializer
+                transpose_node.graph.remove(transpose_node, safe=True)

onnxruntime/python/tools/transformers/fusion_attention.py

@@ -838,12 +838,10 @@ def create_attention_node(
                 attention_inputs.append(past_kv)
 
             if add_qk_str:
-                mask_output_name = self.reshape_add_qk(add_qk_str)
-
-                # Add attention mask to attention node
+                # Add additional add to attention node (input name = attention_bias)
                 if not past_exists:
                     attention_inputs.append("")
-                attention_inputs.append(mask_output_name)
+                attention_inputs.append(add_qk_str)
 
             attention_outputs = [output]
             if present_k and present_v: