Update base for Migrate pattern rewrite commit on "Skip full model sh…

…ape inference if model > 2GB | feat(optimizer)"

[ghstack-poisoned]

.lintrunner.toml

@@ -61,6 +61,7 @@ exclude_patterns = [
     'onnxscript/_legacy_ir/protobuilder.py',  # FIXME
     'onnxscript/rewriter/onnxruntime/transformers/layernorm.py',  # FIXME
     'onnxscript/ir/serde.py',  # FIXME
+    'onnxrewriter/rewriter/pattern/generic_pattern_test.py',  # FIXME
 ]
 command = [
     'python',

onnxscript/_legacy_ir/__init__.py

@@ -224,6 +224,14 @@ def __str__(self) -> str:
             ]
         )
 
+    @property
+    def input_names(self) -> list[str]:
+        return [_.name for _ in self.original_graph_proto.input]
+
+    @property
+    def output_names(self) -> list[str]:
+        return [_.name for _ in self.original_graph_proto.output]
+
 
 class Function:
     def __init__(self, function_proto: onnx.FunctionProto):
@@ -272,15 +280,45 @@ def to_proto(self) -> onnx.AttributeProto:
 
 
 class Node:
-    def __init__(self, node_proto: onnx.NodeProto) -> None:
+    def __init__(
+        self,
+        node_proto: onnx.NodeProto,
+        populate_io: bool = False,
+    ) -> None:
         self.original_node_proto = node_proto
         self.domain: str = node_proto.domain
         self.version: int | None = None
         self.op_type: str = node_proto.op_type
-        self.inputs: list[Value | None] = []
-        self.outputs: list[Value | None] = []
+        if populate_io:
+            self.inputs: list[Value | None] = [Value(i) for i in node_proto.input]
+            self.outputs: list[Value | None] = [Value(i) for i in node_proto.output]
+        else:
+            self.inputs: list[Value | None] = []
+            self.outputs: list[Value | None] = []
         self.attributes: dict[str, int | float | RefAttr | Graph | list[Graph]] = {}
 
+    def __repr__(self) -> str:
+        return (
+            f"{self.op_type}({','.join(self.original_node_proto.input)})"
+            f"->{','.join(self.original_node_proto.output)}"
+        )
+
+    @property
+    def name(self) -> str:
+        return self.original_node_proto.name
+
+    @property
+    def input_names(self):
+        return self.original_node_proto.input
+
+    @property
+    def output_names(self):
+        return self.original_node_proto.output
+
+    @property
+    def attribute(self):
+        return self.original_node_proto.attribute
+
     def get_attribute(self, name: str) -> int | float | None:
         return self.attributes.get(name, None)
 

onnxscript/_legacy_ir/irbuilder.py

@@ -123,14 +123,14 @@ def process_initializer(self, init: onnx.TensorProto):
     def process_node(self, node):
         node_ir = ir.Node(node)
         self.current_graph_or_function.nodes.append(node_ir)
-        for input in node.input:
-            value = self.lookup(input)
+        for name in node.input:
+            value = self.lookup(name)
             node_ir.inputs.append(value)
             if value is not None:
                 value.uses.append(node_ir)
             else:
                 # TODO(titaiwang): Do something more than warnings?
-                warnings.warn(f"Use of undefined variable '{input}'.", stacklevel=1)
+                warnings.warn(f"Use of undefined variable {name!r}.", stacklevel=1)
         for index, output in enumerate(node.output):
             newvalue = ir.Value(name=output, node=node_ir, output_index=index)
             if self._current_function is not None: