diff --git a/onnxscript/_legacy_ir/__init__.py b/onnxscript/_legacy_ir/__init__.py
new file mode 100644
index 0000000000..bb4bd65a71
--- /dev/null
+++ b/onnxscript/_legacy_ir/__init__.py
@@ -0,0 +1,297 @@
+from __future__ import annotations
+
+
+import dataclasses
+from collections import deque
+from typing import List, Tuple, Union
+
+import numpy as np
+import onnx
+
+
+class Unknown:
+    """A special value used to indicate that a value is not a statically known constant.
+
+    We use this instead of None because None is a valid constant value (since ONNX
+    supports the Optional type).
+    """
+
+    instance = None
+
+    def __init__(self) -> None:
+        if Unknown.instance is not None:
+            raise ValueError("Unknown.instance is already set")
+        Unknown.instance = self
+
+
+# Singleton instance of Unknown
+unknown = Unknown()
+NotConstant = unknown
+
+# ConcreteValue: This type represents constant values that an ONNX variable can take.
+# TODO: Extend this to a recursive type to handle lists of tensors, etc., support optionals,
+# maps, etc.
+# TODO (rama): The value is sometimes stored as a numpy array, and sometimes as an ONNX TensorProto.
+# A uniform representation would be helpful, but we should avoid unnecessary conversions for
+# large tensors. Should be cleaned up in the new IR.
+ConcreteValue = Union[onnx.TensorProto, np.ndarray, Unknown, None]
+
+# SymbolicValue: This information is used to enable partial-evaluation and specialization
+# of sequence operations, as well as elimination of redundant Identity ops.
+# The symbolic value of a variable X can be:
+# - a string with the value "Y", indicating that "X" is a copy of "Y"
+# - a list of strings, indicating that "X" is a list of tensors, with their symbolic values
+# Eg., the symbolic value ["A", "B", "C"] indicates that the value of X is equal to
+# "SequenceConstruct(A, B, C)".
+# TODO: Technically, SymbolicValue should be a recursive type to handle lists of lists of
+# tensors, etc. However, we currently only handle lists of tensors.
+
+SymbolicValue = Union[str, List[str]]
+
+FunctionId = Tuple[str, str, str]
+
+
+def get_function_id(function: onnx.FunctionProto) -> FunctionId:
+    return (function.domain, function.name, getattr(function, "overload", ""))
+
+
+def get_function_id_from_node(node: onnx.NodeProto) -> FunctionId:
+    return (node.domain, node.op_type, getattr(node, "overload", ""))
+
+
+@dataclasses.dataclass
+class StaticValueInfo:
+    name: str
+    value: ConcreteValue = NotConstant
+    type: onnx.TypeProto | None = None
+    symbolic_value: SymbolicValue | None = None
+
+    def is_copy(self) -> bool:
+        return isinstance(self.symbolic_value, str)
+
+    def tensor_shape_proto(self) -> onnx.TensorShapeProto | None:
+        """Returns the shape of a tensor or None.
+
+        A return value of None could mean that the type is unknown or that the type is not a tensor
+        or that the tensor shape (that is, even the rank) is unknown.
+        """
+        type = self.type
+        if type and type.HasField("tensor_type") and type.tensor_type.HasField("shape"):
+            return type.tensor_type.shape
+        return None
+
+    @property
+    def shape(self) -> list[str | int | None] | None:
+        """Returns the shape in a list.
+
+        Str means that the shape is dynamic.
+        """
+        type = self.type
+        if type and type.HasField("tensor_type") and type.tensor_type.HasField("shape"):
+            dims = []
+            for dim in type.tensor_type.shape.dim:
+                if dim.HasField("dim_param"):
+                    dims.append(dim.dim_param)
+                elif dim.HasField("dim_value"):
+                    dims.append(dim.dim_value)
+                else:
+                    dims.append(None)
+            return dims
+        if self.value_as_np_array is not None:
+            return list(self.value_as_np_array.shape)
+        return None
+
+    @property
+    def element_type(self) -> int | None:
+        """Returns the element type of a tensor, or None if type is not known or is not a tensor."""
+        type = self.type
+        if type and type.HasField("tensor_type"):
+            return type.tensor_type.elem_type
+        return None
+
+    def identity_merge_from(self, other: StaticValueInfo) -> None:
+        """Merge the value of other into self.
+
+        This models the effect of an identity (copy) operation.
+        This will update static-analysis information based on incoming value.
+        """
+        if not isinstance(other, StaticValueInfo):
+            raise TypeError(f"Cannot merge {other} into {self}.")
+        if other.value is not NotConstant:
+            self.value = other.value
+        # TODO: merge and combine best shape information from both types.
+        if other.tensor_shape_proto() is not None and other.element_type is not None:
+            self.type = other.type
+        # We cannot copy symbolic value across different scopes.
+
+    # WIP: Extensions towards new IR: Note that the default construction of StaticValueInfo
+    # does not fill in the following fields. These fields are filled in by the IRBuilder
+    # which constructs the IR from the ONNX model.
+    node: Node | None = None
+    uses: list[Node] = dataclasses.field(default_factory=list)
+    output_index: int | None = None
+    is_output: bool = False
+
+    @property
+    def const_value(self) -> ConcreteValue:
+        return self.value
+
+    @property
+    def value_as_np_array(self) -> np.ndarray | None:
+        if isinstance(self.value, np.ndarray):
+            return self.value
+        if isinstance(self.value, onnx.TensorProto):
+            return onnx.numpy_helper.to_array(self.value)
+        return None
+
+    def def_node(self) -> Node | None:
+        return self.node
+
+    def def_index(self) -> int:
+        return self.output_index
+
+    def is_same_as(self, other: StaticValueInfo) -> bool:
+        """Returns true if this value represents the same IR object as the other value.
+
+        This is *not* value-equality, but rather object-equality.
+        """
+        return self is other
+
+    def __str__(self) -> str:
+        shape = self.shape
+        if shape is not None:
+            shape = [str(dim) for dim in shape]
+            shape_str = f"[{', '.join(shape)}]"
+        else:
+            shape_str = "None"
+        return (
+            f"StaticValueInfo({self.name}, shape:{shape_str}, dtype:{self.element_type}, "
+            f"{'has const value' if self.value is not unknown else 'no const value'}.)"
+        )
+
+
+Value = StaticValueInfo
+
+
+class Model:
+    def __init__(self) -> None:
+        self.gen_var_counter: int = 0
+
+    def set(
+        self,
+        model_proto: onnx.ModelProto,
+        graph: Graph,
+        functions: list[Function],
+        version_map: dict[str, int],
+    ) -> None:
+        """TODO. This is a temporary patch."""
+        self.original_model_proto = model_proto
+        self.graph = graph
+        self.functions = functions
+        self.version_map = version_map
+
+    def make_new_name(self):
+        # Temporary hack.
+        self.gen_var_counter += 1
+        return f"_gen_{self.gen_var_counter}"
+
+    def __str__(self) -> str:
+        # TODO: Naive string representation for debugging. Need to improve this.
+        return "\n".join(
+            [
+                f"ModelGraph: {self.graph}",
+                f"Functions: {self.functions}",
+                f"VersionMap: {self.version_map}",
+            ]
+        )
+
+
+class Graph:
+    def __init__(self, graph_proto: onnx.GraphProto):
+        self.original_graph_proto = graph_proto
+        self.nodes: deque[Node] = deque()
+        self.values: dict[str, Value] = {}
+
+    @property
+    def name(self) -> str:
+        return self.original_graph_proto.name
+
+    def __str__(self) -> str:
+        return "\n".join(
+            [
+                "Graph",
+                f"Nodes: {[str(n) for n in self.nodes]}",
+                f"Values: {[str(v) for v in self.values]}",
+            ]
+        )
+
+
+class Function:
+    def __init__(self, function_proto: onnx.FunctionProto):
+        self.original_function_proto = function_proto
+        self.nodes = deque()
+        self.values = {}
+
+    @property
+    def id(self) -> FunctionId:
+        return (self.domain, self.name, self.overload)
+
+    @property
+    def domain(self) -> str:
+        return self.original_function_proto.domain
+
+    @property
+    def name(self) -> str:
+        return self.original_function_proto.name
+
+    @property
+    def overload(self) -> str:
+        return getattr(self.original_function_proto, "overload", "")
+
+    def __str__(self) -> str:
+        return "\n".join(
+            [
+                "Function",
+                f"Nodes: {[str(n) for n in self.nodes]}",
+                f"Values: {[str(v) for v in self.values]}",
+            ]
+        )
+
+
+class RefAttr:
+    def __init__(self, name: str, ref_attr_name: str, type) -> None:
+        self.name = name
+        self.ref_attr_name = ref_attr_name
+        self.type = type
+
+    def to_proto(self) -> onnx.AttributeProto:
+        attr_proto = onnx.AttributeProto()
+        attr_proto.name = self.name
+        attr_proto.ref_attr_name = self.ref_attr_name
+        attr_proto.type = self.type
+        return attr_proto
+
+
+class Node:
+    def __init__(self, node_proto: onnx.NodeProto) -> 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] = []
+        self.attributes: dict[str, int | float | RefAttr | Graph | list[Graph]] = {}
+
+    def get_attribute(self, name: str) -> int | float | None:
+        return self.attributes.get(name, None)
+
+    def __str__(self) -> str:
+        return "\n".join(
+            [
+                "Node",
+                f"OpType: {self.op_type}",
+                f"Inputs: {self.inputs}",
+                f"Outputs: {self.outputs}",
+                f"Attributes: {self.attributes}",
+            ]
+        )
diff --git a/onnxscript/ir/irbuilder.py b/onnxscript/_legacy_ir/irbuilder.py
similarity index 99%
rename from onnxscript/ir/irbuilder.py
rename to onnxscript/_legacy_ir/irbuilder.py
index 6a7f681977..aa440faadd 100644
--- a/onnxscript/ir/irbuilder.py
+++ b/onnxscript/_legacy_ir/irbuilder.py
@@ -5,8 +5,8 @@
 
 import onnx
 
-from onnxscript import ir
-from onnxscript.ir import visitor
+import onnxscript._legacy_ir as ir
+from onnxscript._legacy_ir import visitor
 from onnxscript.utils import utils
 
 """ NOTE: IRBuilder and function visiting
diff --git a/onnxscript/ir/irbuilder_test.py b/onnxscript/_legacy_ir/irbuilder_test.py
similarity index 99%
rename from onnxscript/ir/irbuilder_test.py
rename to onnxscript/_legacy_ir/irbuilder_test.py
index 671dc35f6f..5312152582 100644
--- a/onnxscript/ir/irbuilder_test.py
+++ b/onnxscript/_legacy_ir/irbuilder_test.py
@@ -2,7 +2,7 @@
 
 import onnx.parser
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 
 
 class IRBuilderTest(unittest.TestCase):
diff --git a/onnxscript/ir/protobuilder.py b/onnxscript/_legacy_ir/protobuilder.py
similarity index 99%
rename from onnxscript/ir/protobuilder.py
rename to onnxscript/_legacy_ir/protobuilder.py
index 8d76c27fb9..87e4de667b 100644
--- a/onnxscript/ir/protobuilder.py
+++ b/onnxscript/_legacy_ir/protobuilder.py
@@ -4,7 +4,7 @@
 import onnx.helper
 from onnx.helper import make_attribute
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 
 
 class ModelProtoBuilder:
diff --git a/onnxscript/ir/protobuilder_test.py b/onnxscript/_legacy_ir/protobuilder_test.py
similarity index 99%
rename from onnxscript/ir/protobuilder_test.py
rename to onnxscript/_legacy_ir/protobuilder_test.py
index 3d9f798447..f20fbaac4e 100644
--- a/onnxscript/ir/protobuilder_test.py
+++ b/onnxscript/_legacy_ir/protobuilder_test.py
@@ -4,7 +4,7 @@
 import onnx.checker
 import onnx.parser
 
-from onnxscript.ir import irbuilder, protobuilder
+from onnxscript._legacy_ir import irbuilder, protobuilder
 from onnxscript.rewriter import pattern
 from onnxscript.rewriter.onnxruntime import instance_to_group_normalization
 
diff --git a/onnxscript/ir/visitor.py b/onnxscript/_legacy_ir/visitor.py
similarity index 99%
rename from onnxscript/ir/visitor.py
rename to onnxscript/_legacy_ir/visitor.py
index 973001f0e7..03a0835890 100644
--- a/onnxscript/ir/visitor.py
+++ b/onnxscript/_legacy_ir/visitor.py
@@ -7,7 +7,7 @@
 import numpy as np
 import onnx
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 from onnxscript.utils.utils import (
     get_initializer_type,
     is_control_flow_op,
diff --git a/onnxscript/ir/visitor_test.py b/onnxscript/_legacy_ir/visitor_test.py
similarity index 96%
rename from onnxscript/ir/visitor_test.py
rename to onnxscript/_legacy_ir/visitor_test.py
index 814b103458..e4559472e3 100644
--- a/onnxscript/ir/visitor_test.py
+++ b/onnxscript/_legacy_ir/visitor_test.py
@@ -2,7 +2,7 @@
 
 import onnx
 
-from onnxscript.ir import visitor
+from onnxscript._legacy_ir import visitor
 
 
 class FunctionCallsiteProtoTransformerTest(unittest.TestCase):
diff --git a/onnxscript/ir/__init__.py b/onnxscript/ir/__init__.py
index 69794b1c09..3494e5c335 100644
--- a/onnxscript/ir/__init__.py
+++ b/onnxscript/ir/__init__.py
@@ -1,6 +1,4 @@
 """In-memory intermediate representation for ONNX graphs."""
-from __future__ import annotations
-
 __all__ = [
     # Modules
     "serde",
@@ -108,298 +106,3 @@
     TypeProtocol,
     ValueProtocol,
 )
-
-import dataclasses
-from collections import deque
-from typing import List, Tuple, Union
-
-import numpy as np
-import onnx
-
-
-class Unknown:
-    """A special value used to indicate that a value is not a statically known constant.
-
-    We use this instead of None because None is a valid constant value (since ONNX
-    supports the Optional type).
-    """
-
-    instance = None
-
-    def __init__(self) -> None:
-        if Unknown.instance is not None:
-            raise ValueError("Unknown.instance is already set")
-        Unknown.instance = self
-
-
-# Singleton instance of Unknown
-unknown = Unknown()
-NotConstant = unknown
-
-# ConcreteValue: This type represents constant values that an ONNX variable can take.
-# TODO: Extend this to a recursive type to handle lists of tensors, etc., support optionals,
-# maps, etc.
-# TODO (rama): The value is sometimes stored as a numpy array, and sometimes as an ONNX TensorProto.
-# A uniform representation would be helpful, but we should avoid unnecessary conversions for
-# large tensors. Should be cleaned up in the new IR.
-ConcreteValue = Union[onnx.TensorProto, np.ndarray, Unknown, None]
-
-# SymbolicValue: This information is used to enable partial-evaluation and specialization
-# of sequence operations, as well as elimination of redundant Identity ops.
-# The symbolic value of a variable X can be:
-# - a string with the value "Y", indicating that "X" is a copy of "Y"
-# - a list of strings, indicating that "X" is a list of tensors, with their symbolic values
-# Eg., the symbolic value ["A", "B", "C"] indicates that the value of X is equal to
-# "SequenceConstruct(A, B, C)".
-# TODO: Technically, SymbolicValue should be a recursive type to handle lists of lists of
-# tensors, etc. However, we currently only handle lists of tensors.
-
-SymbolicValue = Union[str, List[str]]
-
-FunctionId = Tuple[str, str, str]
-
-
-def get_function_id(function: onnx.FunctionProto) -> FunctionId:
-    return (function.domain, function.name, getattr(function, "overload", ""))
-
-
-def get_function_id_from_node(node: onnx.NodeProto) -> FunctionId:
-    return (node.domain, node.op_type, getattr(node, "overload", ""))
-
-
-@dataclasses.dataclass
-class StaticValueInfo:
-    name: str
-    value: ConcreteValue = NotConstant
-    type: onnx.TypeProto | None = None
-    symbolic_value: SymbolicValue | None = None
-
-    def is_copy(self) -> bool:
-        return isinstance(self.symbolic_value, str)
-
-    def tensor_shape_proto(self) -> onnx.TensorShapeProto | None:
-        """Returns the shape of a tensor or None.
-
-        A return value of None could mean that the type is unknown or that the type is not a tensor
-        or that the tensor shape (that is, even the rank) is unknown.
-        """
-        type = self.type
-        if type and type.HasField("tensor_type") and type.tensor_type.HasField("shape"):
-            return type.tensor_type.shape
-        return None
-
-    @property
-    def shape(self) -> list[str | int | None] | None:
-        """Returns the shape in a list.
-
-        Str means that the shape is dynamic.
-        """
-        type = self.type
-        if type and type.HasField("tensor_type") and type.tensor_type.HasField("shape"):
-            dims = []
-            for dim in type.tensor_type.shape.dim:
-                if dim.HasField("dim_param"):
-                    dims.append(dim.dim_param)
-                elif dim.HasField("dim_value"):
-                    dims.append(dim.dim_value)
-                else:
-                    dims.append(None)
-            return dims
-        if self.value_as_np_array is not None:
-            return list(self.value_as_np_array.shape)
-        return None
-
-    @property
-    def element_type(self) -> int | None:
-        """Returns the element type of a tensor, or None if type is not known or is not a tensor."""
-        type = self.type
-        if type and type.HasField("tensor_type"):
-            return type.tensor_type.elem_type
-        return None
-
-    def identity_merge_from(self, other: StaticValueInfo) -> None:
-        """Merge the value of other into self.
-
-        This models the effect of an identity (copy) operation.
-        This will update static-analysis information based on incoming value.
-        """
-        if not isinstance(other, StaticValueInfo):
-            raise TypeError(f"Cannot merge {other} into {self}.")
-        if other.value is not NotConstant:
-            self.value = other.value
-        # TODO: merge and combine best shape information from both types.
-        if other.tensor_shape_proto() is not None and other.element_type is not None:
-            self.type = other.type
-        # We cannot copy symbolic value across different scopes.
-
-    # WIP: Extensions towards new IR: Note that the default construction of StaticValueInfo
-    # does not fill in the following fields. These fields are filled in by the IRBuilder
-    # which constructs the IR from the ONNX model.
-    node: Node | None = None
-    uses: list[Node] = dataclasses.field(default_factory=list)
-    output_index: int | None = None
-    is_output: bool = False
-
-    @property
-    def const_value(self) -> ConcreteValue:
-        return self.value
-
-    @property
-    def value_as_np_array(self) -> np.ndarray | None:
-        if isinstance(self.value, np.ndarray):
-            return self.value
-        if isinstance(self.value, onnx.TensorProto):
-            return onnx.numpy_helper.to_array(self.value)
-        return None
-
-    def def_node(self) -> Node | None:
-        return self.node
-
-    def def_index(self) -> int:
-        return self.output_index
-
-    def is_same_as(self, other: StaticValueInfo) -> bool:
-        """Returns true if this value represents the same IR object as the other value.
-
-        This is *not* value-equality, but rather object-equality.
-        """
-        return self is other
-
-    def __str__(self) -> str:
-        shape = self.shape
-        if shape is not None:
-            shape = [str(dim) for dim in shape]
-            shape_str = f"[{', '.join(shape)}]"
-        else:
-            shape_str = "None"
-        return (
-            f"StaticValueInfo({self.name}, shape:{shape_str}, dtype:{self.element_type}, "
-            f"{'has const value' if self.value is not unknown else 'no const value'}.)"
-        )
-
-
-Value = StaticValueInfo
-
-
-class Model:
-    def __init__(self) -> None:
-        self.gen_var_counter: int = 0
-
-    def set(
-        self,
-        model_proto: onnx.ModelProto,
-        graph: Graph,
-        functions: list[Function],
-        version_map: dict[str, int],
-    ) -> None:
-        """TODO. This is a temporary patch."""
-        self.original_model_proto = model_proto
-        self.graph = graph
-        self.functions = functions
-        self.version_map = version_map
-
-    def make_new_name(self):
-        # Temporary hack.
-        self.gen_var_counter += 1
-        return f"_gen_{self.gen_var_counter}"
-
-    def __str__(self) -> str:
-        # TODO: Naive string representation for debugging. Need to improve this.
-        return "\n".join(
-            [
-                f"ModelGraph: {self.graph}",
-                f"Functions: {self.functions}",
-                f"VersionMap: {self.version_map}",
-            ]
-        )
-
-
-class Graph:
-    def __init__(self, graph_proto: onnx.GraphProto):
-        self.original_graph_proto = graph_proto
-        self.nodes: deque[Node] = deque()
-        self.values: dict[str, Value] = {}
-
-    @property
-    def name(self) -> str:
-        return self.original_graph_proto.name
-
-    def __str__(self) -> str:
-        return "\n".join(
-            [
-                "Graph",
-                f"Nodes: {[str(n) for n in self.nodes]}",
-                f"Values: {[str(v) for v in self.values]}",
-            ]
-        )
-
-
-class Function:
-    def __init__(self, function_proto: onnx.FunctionProto):
-        self.original_function_proto = function_proto
-        self.nodes = deque()
-        self.values = {}
-
-    @property
-    def id(self) -> FunctionId:
-        return (self.domain, self.name, self.overload)
-
-    @property
-    def domain(self) -> str:
-        return self.original_function_proto.domain
-
-    @property
-    def name(self) -> str:
-        return self.original_function_proto.name
-
-    @property
-    def overload(self) -> str:
-        return getattr(self.original_function_proto, "overload", "")
-
-    def __str__(self) -> str:
-        return "\n".join(
-            [
-                "Function",
-                f"Nodes: {[str(n) for n in self.nodes]}",
-                f"Values: {[str(v) for v in self.values]}",
-            ]
-        )
-
-
-class RefAttr:
-    def __init__(self, name: str, ref_attr_name: str, type) -> None:
-        self.name = name
-        self.ref_attr_name = ref_attr_name
-        self.type = type
-
-    def to_proto(self) -> onnx.AttributeProto:
-        attr_proto = onnx.AttributeProto()
-        attr_proto.name = self.name
-        attr_proto.ref_attr_name = self.ref_attr_name
-        attr_proto.type = self.type
-        return attr_proto
-
-
-class Node:
-    def __init__(self, node_proto: onnx.NodeProto) -> 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] = []
-        self.attributes: dict[str, int | float | RefAttr | Graph | list[Graph]] = {}
-
-    def get_attribute(self, name: str) -> int | float | None:
-        return self.attributes.get(name, None)
-
-    def __str__(self) -> str:
-        return "\n".join(
-            [
-                "Node",
-                f"OpType: {self.op_type}",
-                f"Inputs: {self.inputs}",
-                f"Outputs: {self.outputs}",
-                f"Attributes: {self.attributes}",
-            ]
-        )
diff --git a/onnxscript/optimizer/constant_folding.py b/onnxscript/optimizer/constant_folding.py
index 914f6efaa2..61214a4ba1 100644
--- a/onnxscript/optimizer/constant_folding.py
+++ b/onnxscript/optimizer/constant_folding.py
@@ -7,8 +7,8 @@
 import onnx
 import onnx.reference.ops
 
-from onnxscript import ir
-from onnxscript.ir import visitor
+import onnxscript._legacy_ir as ir
+from onnxscript._legacy_ir import visitor
 from onnxscript.optimizer import evaluator
 from onnxscript.utils.utils import (
     is_control_flow_op,
diff --git a/onnxscript/optimizer/copy_propagation.py b/onnxscript/optimizer/copy_propagation.py
index dc6613e702..bb19fbdcec 100644
--- a/onnxscript/optimizer/copy_propagation.py
+++ b/onnxscript/optimizer/copy_propagation.py
@@ -5,7 +5,7 @@
 import onnx
 
 import onnxscript.optimizer.remove_unused
-from onnxscript.ir import visitor
+from onnxscript._legacy_ir import visitor
 from onnxscript.utils.utils import is_onnx_op
 
 
diff --git a/onnxscript/optimizer/evaluator.py b/onnxscript/optimizer/evaluator.py
index e8415ae245..9bd2b62841 100644
--- a/onnxscript/optimizer/evaluator.py
+++ b/onnxscript/optimizer/evaluator.py
@@ -14,7 +14,7 @@
 import onnx
 import onnx.reference.ops
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 from onnxscript.utils.utils import (
     get_node_attr_value,
 )
diff --git a/onnxscript/optimizer/simple_function_folding.py b/onnxscript/optimizer/simple_function_folding.py
index 03b86f9a5a..f965edef6e 100644
--- a/onnxscript/optimizer/simple_function_folding.py
+++ b/onnxscript/optimizer/simple_function_folding.py
@@ -7,8 +7,8 @@
 
 import onnx
 
-from onnxscript import ir
-from onnxscript.ir import visitor
+import onnxscript._legacy_ir as ir
+from onnxscript._legacy_ir import visitor
 from onnxscript.optimizer import remove_unused
 
 logger = logging.getLogger(__name__)
diff --git a/onnxscript/rewriter/__init__.py b/onnxscript/rewriter/__init__.py
index fa80841110..3fe036f228 100644
--- a/onnxscript/rewriter/__init__.py
+++ b/onnxscript/rewriter/__init__.py
@@ -14,7 +14,7 @@
 
 import onnx
 
-from onnxscript.ir import irbuilder, protobuilder
+from onnxscript._legacy_ir import irbuilder, protobuilder
 from onnxscript.rewriter import function_rule, pattern
 
 PatternRewriteRule = pattern.RewriteRule
diff --git a/onnxscript/rewriter/broadcast_to_matmul.py b/onnxscript/rewriter/broadcast_to_matmul.py
index 36cb58603c..3b8d59cb50 100644
--- a/onnxscript/rewriter/broadcast_to_matmul.py
+++ b/onnxscript/rewriter/broadcast_to_matmul.py
@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 from onnxscript.rewriter import pattern
 
 op = pattern.onnxop
diff --git a/onnxscript/rewriter/broadcast_to_matmul_test.py b/onnxscript/rewriter/broadcast_to_matmul_test.py
index 83fc3cd4e1..73cb59e635 100644
--- a/onnxscript/rewriter/broadcast_to_matmul_test.py
+++ b/onnxscript/rewriter/broadcast_to_matmul_test.py
@@ -2,7 +2,7 @@
 
 import onnx.parser
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 from onnxscript.rewriter import broadcast_to_matmul
 
 
diff --git a/onnxscript/rewriter/cast_constant_of_shape.py b/onnxscript/rewriter/cast_constant_of_shape.py
index a4b90df42c..b8a64f6d3f 100644
--- a/onnxscript/rewriter/cast_constant_of_shape.py
+++ b/onnxscript/rewriter/cast_constant_of_shape.py
@@ -6,7 +6,7 @@
 import numpy as np
 import onnx
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 from onnxscript.rewriter import pattern
 
 op = pattern.onnxop
diff --git a/onnxscript/rewriter/cast_constant_of_shape_test.py b/onnxscript/rewriter/cast_constant_of_shape_test.py
index e7aa5f515f..c459a40c4c 100644
--- a/onnxscript/rewriter/cast_constant_of_shape_test.py
+++ b/onnxscript/rewriter/cast_constant_of_shape_test.py
@@ -2,7 +2,7 @@
 
 import onnx.parser
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 from onnxscript.rewriter import cast_constant_of_shape
 
 
diff --git a/onnxscript/rewriter/function_rule.py b/onnxscript/rewriter/function_rule.py
index 1432ee2c2d..f36f790227 100644
--- a/onnxscript/rewriter/function_rule.py
+++ b/onnxscript/rewriter/function_rule.py
@@ -7,8 +7,8 @@
 import onnxscript
 from packaging import version
 
-from onnxscript import ir
-from onnxscript.ir import visitor
+import onnxscript._legacy_ir as ir
+from onnxscript._legacy_ir import visitor
 from onnxscript.rewriter import pattern
 
 logger = logging.getLogger(__name__)
diff --git a/onnxscript/rewriter/gemm_to_matmul_add_test.py b/onnxscript/rewriter/gemm_to_matmul_add_test.py
index a2cd339927..615d6311a0 100644
--- a/onnxscript/rewriter/gemm_to_matmul_add_test.py
+++ b/onnxscript/rewriter/gemm_to_matmul_add_test.py
@@ -2,7 +2,7 @@
 
 import onnx.parser
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 from onnxscript.rewriter import gemm_to_matmul_add
 
 
diff --git a/onnxscript/rewriter/no_op_test.py b/onnxscript/rewriter/no_op_test.py
index ece94b9ec0..e38d8b7c6a 100644
--- a/onnxscript/rewriter/no_op_test.py
+++ b/onnxscript/rewriter/no_op_test.py
@@ -3,7 +3,7 @@
 import onnx.parser
 import parameterized
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 from onnxscript.rewriter import no_op
 
 
diff --git a/onnxscript/rewriter/onnxruntime/__init__.py b/onnxscript/rewriter/onnxruntime/__init__.py
index b7357a1d0e..5a99d03749 100644
--- a/onnxscript/rewriter/onnxruntime/__init__.py
+++ b/onnxscript/rewriter/onnxruntime/__init__.py
@@ -2,7 +2,7 @@
 
 import onnx
 
-from onnxscript.ir import irbuilder, protobuilder
+from onnxscript._legacy_ir import irbuilder, protobuilder
 from onnxscript.optimizer import remove_unused
 from onnxscript.rewriter import function_rule, pattern
 from onnxscript.rewriter.onnxruntime import (
diff --git a/onnxscript/rewriter/onnxruntime/instance_to_group_normalization.py b/onnxscript/rewriter/onnxruntime/instance_to_group_normalization.py
index 3d974bea92..2c7b742719 100644
--- a/onnxscript/rewriter/onnxruntime/instance_to_group_normalization.py
+++ b/onnxscript/rewriter/onnxruntime/instance_to_group_normalization.py
@@ -6,7 +6,7 @@
 import numpy as np
 import onnx
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 from onnxscript.rewriter import pattern
 
 op = pattern.onnxop
diff --git a/onnxscript/rewriter/onnxruntime/instance_to_group_normalization_test.py b/onnxscript/rewriter/onnxruntime/instance_to_group_normalization_test.py
index dadca0546b..67ae0554f7 100644
--- a/onnxscript/rewriter/onnxruntime/instance_to_group_normalization_test.py
+++ b/onnxscript/rewriter/onnxruntime/instance_to_group_normalization_test.py
@@ -3,7 +3,7 @@
 import numpy as np
 import onnx.parser
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 from onnxscript.rewriter.onnxruntime import instance_to_group_normalization
 
 
diff --git a/onnxscript/rewriter/onnxruntime/softmax.py b/onnxscript/rewriter/onnxruntime/softmax.py
index 9ea48cf3e0..5b5a93fe64 100644
--- a/onnxscript/rewriter/onnxruntime/softmax.py
+++ b/onnxscript/rewriter/onnxruntime/softmax.py
@@ -5,7 +5,7 @@
 
 import onnx
 
-from onnxscript import ir
+import onnxscript._legacy_ir as ir
 from onnxscript.rewriter import pattern
 
 op = pattern.onnxop
diff --git a/onnxscript/rewriter/onnxruntime/softmax_test.py b/onnxscript/rewriter/onnxruntime/softmax_test.py
index 2b15a8b755..507c38c149 100644
--- a/onnxscript/rewriter/onnxruntime/softmax_test.py
+++ b/onnxscript/rewriter/onnxruntime/softmax_test.py
@@ -3,7 +3,7 @@
 import onnx.parser
 import parameterized
 
-from onnxscript.ir import irbuilder
+from onnxscript._legacy_ir import irbuilder
 from onnxscript.rewriter.onnxruntime import softmax
 
 
diff --git a/onnxscript/rewriter/pattern.py b/onnxscript/rewriter/pattern.py
index 43582db400..8dce188322 100644
--- a/onnxscript/rewriter/pattern.py
+++ b/onnxscript/rewriter/pattern.py
@@ -10,8 +10,8 @@
 import onnx.numpy_helper
 import onnx.printer
 
-from onnxscript import ir
-from onnxscript.ir import irbuilder
+import onnxscript._legacy_ir as ir
+from onnxscript._legacy_ir import irbuilder
 
 # Overview of the pattern module: The classes below are used to define both
 # patterns (that we search for) and replacements for rewrite rules.
diff --git a/onnxscript/rewriter/pattern_test.py b/onnxscript/rewriter/pattern_test.py
index e888391a88..7720d51ffa 100644
--- a/onnxscript/rewriter/pattern_test.py
+++ b/onnxscript/rewriter/pattern_test.py
@@ -4,7 +4,7 @@
 import numpy as np
 import onnx.parser
 
-from onnxscript.ir import irbuilder, protobuilder
+from onnxscript._legacy_ir import irbuilder, protobuilder
 from onnxscript.rewriter import cast_constant_of_shape, pattern
 
 logger = logging.getLogger(__name__)
diff --git a/tests/common/testutils.py b/tests/common/testutils.py
index 119147992b..a54d9fcfd4 100644
--- a/tests/common/testutils.py
+++ b/tests/common/testutils.py
@@ -14,7 +14,7 @@
 import onnxruntime
 
 from onnxscript import optimizer
-from onnxscript.ir import visitor
+from onnxscript._legacy_ir import visitor
 from onnxscript.rewriter import onnxruntime as ort_rewriter
 from onnxscript.utils import evaluation_utils