optimizer_test.py

# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0

from collections import OrderedDict
from typing import List, Optional, Dict
import io
import unittest
import os

import numpy as np  # type: ignore

try:
    import torch
    import torchvision as tv

    has_tv = True
except ImportError:
    has_tv = False

import onnx
import pytest
from onnx import (
    checker,
    helper,
    ModelProto,
    TensorProto,
    shape_inference,
    parser,
)
from onnx import numpy_helper
from onnx.numpy_helper import to_array

try:
    import onnxruntime as rt

    has_ort = True
except ImportError:
    has_ort = False

import onnxoptimizer


TensorShape = List[int]
TensorShapes = Dict[Optional[str], TensorShape]

LATEST_STABLE_OPSET_VERSION = 13


class TestOptimizer(unittest.TestCase):
    def _compare(
        self,
        model_opt: onnx.ModelProto,
        model_ori: onnx.ModelProto,
        n_times: int = 5,
        input_shapes: Optional[TensorShapes] = None,
        verbose=True,
    ) -> bool:
        """
        :param input_shapes: Shapes of generated random inputs
        :param model_opt: The simplified ONNX model
        :param model_ori: The original ONNX model
        :param n_times: Generate n random inputs
        """

        def get_shape_from_value_info_proto(v: onnx.ValueInfoProto) -> List[int]:
            return [dim.dim_value for dim in v.type.tensor_type.shape.dim]

        def get_value_info_all(
            m: onnx.ModelProto, name: str
        ) -> Optional[onnx.ValueInfoProto]:
            for v in m.graph.value_info:
                if v.name == name:
                    return v

            for v in m.graph.input:
                if v.name == name:
                    return v

            for v in m.graph.output:
                if v.name == name:
                    return v

            return None

        def get_shape(m: onnx.ModelProto, name: str) -> TensorShape:
            """
            Note: This method relies on onnx shape inference, which is not reliable. So only use it on input or output tensors
            """
            v = get_value_info_all(m, name)
            if v is not None:
                return get_shape_from_value_info_proto(v)
            raise RuntimeError('Cannot get shape of "{}"'.format(name))

        def get_elem_type(m: onnx.ModelProto, name: str) -> Optional[int]:
            v = get_value_info_all(m, name)
            if v is not None:
                return v.type.tensor_type.elem_type
            return None

        def get_np_type_from_elem_type(elem_type: int) -> int:
            sizes = (
                None,
                np.float32,
                np.uint8,
                np.int8,
                np.uint16,
                np.int16,
                np.int32,
                np.int64,
                str,
                bool,
                np.float16,
                np.double,
                np.uint32,
                np.uint64,
                np.complex64,
                np.complex128,
                np.float16,
            )
            assert len(sizes) == 17
            size = sizes[elem_type]
            assert size is not None
            return size

        def get_input_names(model: onnx.ModelProto) -> List[str]:
            input_names = list(
                set([ipt.name for ipt in model.graph.input])
                - set([x.name for x in model.graph.initializer])
            )
            return input_names

        def generate_rand_input(model, input_shapes: Optional[TensorShapes] = None):
            if input_shapes is None:
                input_shapes = {}
            input_names = get_input_names(model)
            full_input_shapes = {ipt: get_shape(model, ipt) for ipt in input_names}
            assert None not in input_shapes
            full_input_shapes.update(input_shapes)  # type: ignore
            for key in full_input_shapes:
                if np.prod(full_input_shapes[key]) <= 0:
                    raise RuntimeError(
                        'The shape of input "{}" has dynamic size, '
                        "please set an input shape manually".format(key)
                    )

            inputs = {
                ipt: np.array(
                    np.random.rand(*full_input_shapes[ipt]),
                    dtype=get_np_type_from_elem_type(get_elem_type(model, ipt)),
                )
                for ipt in input_names
            }
            return inputs

        def forward(
            model, inputs=None, input_shapes: Optional[TensorShapes] = None
        ) -> Dict[str, np.ndarray]:
            if input_shapes is None:
                input_shapes = {}
            sess_options = rt.SessionOptions()
            sess_options.graph_optimization_level = rt.GraphOptimizationLevel(0)
            sess_options.log_severity_level = 3
            sess = rt.InferenceSession(
                model.SerializeToString(),
                sess_options=sess_options,
                providers=["CPUExecutionProvider"],
            )
            if inputs is None:
                inputs = generate_rand_input(model, input_shapes=input_shapes)
            outputs = [x.name for x in sess.get_outputs()]
            run_options = rt.RunOptions()
            run_options.log_severity_level = 3
            res = OrderedDict(
                zip(outputs, sess.run(outputs, inputs, run_options=run_options))
            )
            return res

        if input_shapes is None:
            input_shapes = {}
        onnx.checker.check_model(model_opt)
        for i in range(n_times):
            rand_input = generate_rand_input(model_opt, input_shapes=input_shapes)
            res_ori = forward(model_ori, inputs=rand_input)
            res_opt = forward(model_opt, inputs=rand_input)

            for name in res_opt.keys():
                if not np.allclose(res_opt[name], res_ori[name], rtol=1e-4, atol=1e-5):
                    if verbose:
                        print(
                            "Tensor {} changes after optimization. The max diff is {}.".format(
                                name, np.max(np.abs(res_opt[name] - res_ori[name]))
                            )
                        )
                        print("After optimization:")
                        print(res_opt[name])
                        print("Before optimization:")
                        print(res_ori[name])
                        print("----------------")
                    return False
        return True

    # type: (Union[GraphProto, ModelProto], Sequence[Text], bool, **Any) -> ModelProto
    def _optimized(
        self,
        graph_or_model,
        opts,
        fixed_point=False,
        compare_result=True,
        check=True,
        input_shapes_for_comparing=None,
        **kwargs
    ):
        if compare_result and not check:
            self.fail("compare_result cannot be True if check is False")

        if isinstance(graph_or_model, ModelProto):
            orig_model = graph_or_model
        else:
            opset_imports = kwargs.pop("opset_imports", None)
            if opset_imports is None:
                opset_imports = [helper.make_opsetid("", LATEST_STABLE_OPSET_VERSION)]

            orig_model = helper.make_model(
                graph_or_model,
                producer_name="onnx-test",
                opset_imports=opset_imports,
                ir_version=10,
                **kwargs
            )
        if check:
            checker.check_model(orig_model)
        optimized_model = onnxoptimizer.optimize(orig_model, opts, fixed_point)
        # NOTE(daquexian): Some passes (like lift_lexical_references) generate illegal model intentionally
        if check:
            checker.check_model(optimized_model)
        if compare_result and len(optimized_model.graph.node) > 0:
            if has_ort:
                assert self._compare(
                    optimized_model, orig_model, input_shapes=input_shapes_for_comparing
                )
            else:
                print("Skip onnxruntime test because it is not installed.")
        return optimized_model

    # input_types and output_types are lists of triples of (name, type, shape)
    # NOTE(daquexian): only values that change across loop iterations should be in `input_types` and `output_types`. The pseudocode showing how loop op works is:
    # loop_value_inputs = graph_value_inputs
    # while cond:
    #   loop_value_outputs = body(loop_value_inputs)
    #   loop_value_inputs = loop_value_outputs
    # graph_value_outputs = loop_value_outputs
    def _make_fake_loop_op(
        self,
        body_nodes,  # type: Sequence[NodeProto]
        # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]]
        input_types,
        # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]]
        output_types,
        check_legality=True,
    ):  # type: (...) -> List[NodeProto]
        if check_legality:
            assert len(input_types) == len(output_types)
        zero = helper.make_tensor("trip_count_value", TensorProto.INT64, (), [1])
        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])
        # lcd is a dummy loop-carried dependency that only exists because
        # right now the schema checker is broken and assumes a variadic
        # input needs at least one value.
        graph_inputs = [
            helper.make_tensor_value_info("i", TensorProto.INT64, ()),
            helper.make_tensor_value_info("cond", TensorProto.BOOL, ()),
        ]
        for type, shape, name in input_types:
            graph_inputs.append(helper.make_tensor_value_info("_" + name, type, shape))
        graph_outputs = [helper.make_tensor_value_info("cond", TensorProto.BOOL, ())]
        for type, shape, name in output_types:
            graph_outputs.append(helper.make_tensor_value_info("_" + name, type, shape))
        body_graph = helper.make_graph(
            body_nodes, "body_graph", graph_inputs, graph_outputs
        )
        loop_inputs = ["trip_count", "condition"]
        loop_inputs.extend([name for _, _, name in input_types])
        # TODO: fix checker to accept 0-input variadic inputs
        if len(loop_inputs) == 2:
            loop_inputs.append("")
        loop_outputs = [name for _, _, name in output_types]
        retval_nodes = [
            helper.make_node("Constant", [], ["trip_count"], value=zero),
            helper.make_node("Constant", [], ["condition"], value=true),
            helper.make_node("Loop", loop_inputs, loop_outputs, body=body_graph),
        ]
        return retval_nodes

    def _make_fake_if_op(
        self,
        true_nodes,  # type: Sequence[NodeProto]
        false_nodes,  # type: Sequence[NodeProto]
        # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]]
        output_types,
    ):  # type: (...) -> List[NodeProto]
        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])
        true_graph = helper.make_graph(true_nodes, "true_graph", [], [])
        false_graph = helper.make_graph(false_nodes, "false_graph", [], [])
        if_inputs = ["condition"]
        if_outputs = [name for _, _, name in output_types]
        retval_nodes = [
            helper.make_node("Constant", [], ["condition"], value=true),
            helper.make_node(
                "If",
                if_inputs,
                if_outputs,
                then_branch=true_graph,
                else_branch=false_graph,
            ),
        ]
        return retval_nodes

    # fn is a function that takes a single node as argument
    # type: (GraphProto, Callable[[NodeProto], None]) -> None
    def _visit_all_nodes_recursive(self, graph, fn):
        for node in graph.node:
            fn(node)
            for attr in node.attribute:
                if attr.g is not None:
                    self._visit_all_nodes_recursive(attr.g, fn)
                if len(attr.graphs):
                    for gr in attr.graphs:
                        self._visit_all_nodes_recursive(gr, fn)

    def test_get_available_passes(self):  # type: () -> None
        # FIXME does not guarantees to be listing all
        graph = helper.make_graph([], "dummy_graph", [], [])
        list_of_passes = onnxoptimizer.get_available_passes()
        assert isinstance(list_of_passes, (list)) and len(list_of_passes) > 0
        for pass_name in list_of_passes:
            # If pass_name is invalid it throws a RuntimeError
            self._optimized(graph, [pass_name])

    def test_eliminate_identity_single_use(self):  # type: () -> None
        nodes = [
            helper.make_node("Add", ["X", "Y"], ["A"]),
            helper.make_node("Identity", ["A"], ["B"]),
        ]
        nodes.extend(
            self._make_fake_loop_op(
                [
                    helper.make_node("Relu", ["_B"], ["_B1"]),
                    helper.make_node("Identity", ["_B1"], ["_B2"]),
                ],
                [(TensorProto.FLOAT, (5,), "B")],
                [(TensorProto.FLOAT, (5,), "B2")],
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
            ],
            [
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("B2", TensorProto.FLOAT, (5,)),
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_identity"])

        # All identity nodes should have been eliminated
        def check_identity(node):  # type: (NodeProto) -> None
            assert node.op_type != "Identity"

        self._visit_all_nodes_recursive(optimized_model.graph, check_identity)
        # Use of the output from the Identity node in the main graph should
        # have been replaced with the input to the identity node
        assert len(optimized_model.graph.output) == 2
        assert optimized_model.graph.output[0].name == "B"
        # Use of the output from the Identity node in the loop graph should
        # have been replaced with the input to that identity node
        assert len(optimized_model.graph.node[3].attribute[0].g.output) == 2
        assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_B2"

    # type: () -> None
    def test_eliminate_identity_both_graph_input_and_output(self):
        # We should not eliminate an op when its input is also graph input,
        # and its output is also graph output, because we want to always keep
        # the name of graph input and output unchanged.
        identity = helper.make_node("Identity", ["A"], ["B"])
        graph = helper.make_graph(
            [identity],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))],
        )
        optimized_model = self._optimized(graph, ["eliminate_identity"])

        assert optimized_model.graph == graph

    def test_eliminate_if_with_const_true_cond(self):  # type: () -> None
        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])

        subgraph_output_info = helper.make_tensor_value_info(
            "C", TensorProto.FLOAT, (5,)
        )

        sin = helper.make_node("Sin", ["A"], ["B"])
        hard_sigmoid = helper.make_node(
            "HardSigmoid", ["B"], ["C"], alpha=0.4, beta=0.6
        )
        true_graph = helper.make_graph(
            [sin, hard_sigmoid], "true_graph", [], [subgraph_output_info]
        )

        identity = helper.make_node("Identity", ["A"], ["C"])
        false_graph = helper.make_graph(
            [identity], "false_graph", [], [subgraph_output_info]
        )

        graph = helper.make_graph(
            [
                helper.make_node("Constant", [], ["condition"], value=true),
                helper.make_node(
                    "If",
                    ["condition"],
                    ["result"],
                    then_branch=true_graph,
                    else_branch=false_graph,
                ),
            ],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
            [helper.make_tensor_value_info("result", TensorProto.FLOAT, (5,))],
        )
        optimized_model = self._optimized(graph, ["eliminate_if_with_const_cond"])
        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Sin"
        assert optimized_model.graph.node[2].op_type == "HardSigmoid"

    def test_eliminate_if_with_const_false_cond(self):  # type: () -> None
        true = helper.make_tensor("condition", TensorProto.BOOL, (), [False])

        subgraph_output_info = helper.make_tensor_value_info(
            "C", TensorProto.FLOAT, (5,)
        )

        sin = helper.make_node("Sin", ["A"], ["B"])
        hard_sigmoid = helper.make_node(
            "HardSigmoid", ["B"], ["C"], alpha=0.4, beta=0.6
        )
        true_graph = helper.make_graph(
            [sin, hard_sigmoid], "true_graph", [], [subgraph_output_info]
        )

        identity = helper.make_node("Identity", ["A"], ["C"])
        false_graph = helper.make_graph(
            [identity], "false_graph", [], [subgraph_output_info]
        )

        graph = helper.make_graph(
            [
                helper.make_node("Constant", [], ["condition"], value=true),
                helper.make_node(
                    "If",
                    ["condition"],
                    ["result"],
                    then_branch=true_graph,
                    else_branch=false_graph,
                ),
            ],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
            [helper.make_tensor_value_info("result", TensorProto.FLOAT, (5,))],
        )
        optimized_model = self._optimized(graph, ["eliminate_if_with_const_cond"])
        assert len(optimized_model.graph.node) == 2
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Identity"

    def test_eliminate_if_with_const_cond_with_subgraph_param(self):  # type: () -> None
        true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])

        subgraph_output_info = helper.make_tensor_value_info(
            "C", TensorProto.FLOAT, (5,)
        )

        sin = helper.make_node("Sin", ["A"], ["B"])
        add = helper.make_node("Add", ["B", "D"], ["C"])
        true_graph = helper.make_graph(
            [sin, add],
            "true_graph",
            [],
            [subgraph_output_info],
            [numpy_helper.from_array(np.ones((5,), dtype=np.float32), name="D")],
        )

        identity = helper.make_node("Identity", ["A"], ["C"])
        false_graph = helper.make_graph(
            [identity], "false_graph", [], [subgraph_output_info]
        )

        graph = helper.make_graph(
            [
                helper.make_node("Constant", [], ["condition"], value=true),
                helper.make_node(
                    "If",
                    ["condition"],
                    ["result"],
                    then_branch=true_graph,
                    else_branch=false_graph,
                ),
            ],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
            [helper.make_tensor_value_info("result", TensorProto.FLOAT, (5,))],
        )
        optimized_model = self._optimized(graph, ["eliminate_if_with_const_cond"])
        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Sin"
        assert optimized_model.graph.node[2].op_type == "Add"

    def test_eliminate_identity_graph_output(self):  # type: () -> None
        add = helper.make_node("Add", ["X", "Y"], ["A"])
        identity = helper.make_node("Identity", ["A"], ["B"])
        graph = helper.make_graph(
            [add, identity],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))],
        )
        optimized_model = self._optimized(graph, ["eliminate_identity"])

        for node in optimized_model.graph.node:
            assert node.op_type != "Identity"
        assert (
            len(optimized_model.graph.output) == 1
            and optimized_model.graph.output[0].name == "B"
        )
        assert len(optimized_model.graph.node) == 1

    def test_eliminate_identity_multiple_uses(self):  # type: () -> None
        identity = helper.make_node("Identity", ["X"], ["Y"])
        add = helper.make_node("Add", ["Z", "Y"], ["A"])
        mul = helper.make_node("Mul", ["A", "Y"], ["B"])
        graph = helper.make_graph(
            [identity, add, mul],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5,)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))],
        )
        optimized_model = self._optimized(graph, ["eliminate_identity"])

        for node in optimized_model.graph.node:
            assert node.op_type != "Identity"
        assert len(optimized_model.graph.node) == 2

    def test_not_fuse_non_nop_flatten(self):
        identity = helper.make_node("Identity", ["A"], ["X"])
        flatten = helper.make_node("Flatten", ["X"], ["B"], axis=2)
        graph = helper.make_graph(
            [identity, flatten],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 10, 3, 1, 1))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (10, 3))],
        )

        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])

        assert len(optimized_model.graph.node) == 2
        assert optimized_model.graph.node[0].op_type == "Identity"
        assert optimized_model.graph.node[1].op_type == "Flatten"

    def test_nop_flatten_axis0_graph_output(self):
        add = helper.make_node("Add", ["X", "Y"], ["A"])
        flatten = helper.make_node("Flatten", ["A"], ["B"], axis=0)
        graph = helper.make_graph(
            [add, flatten],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 10)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 10))],
            # the tensor_value_info of "A" is necessary to this optimizer
            value_info=[helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 10))],
        )
        # The existence of shape infos of graoh outputs is checked in _optimized
        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Add"

    def test_nop_flatten_axis0(self):
        identity = helper.make_node("Identity", ["A"], ["X"])
        flatten = helper.make_node("Flatten", ["X"], ["B"], axis=0)
        graph = helper.make_graph(
            [identity, flatten],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 10))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 10))],
            value_info=[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10))],
        )

        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_nop_flatten_axis1(self):
        identity = helper.make_node("Identity", ["A"], ["X"])
        flatten = helper.make_node("Flatten", ["X"], ["B"], axis=1)
        graph = helper.make_graph(
            [identity, flatten],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))],
            value_info=[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))],
        )

        optimized_model = self._optimized(graph, ["eliminate_nop_flatten"])

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_eliminate_duplicate_initializer(self):  # type: () -> None
        types = [(TensorProto.INT32, np.int32), (TensorProto.INT64, np.int64),
                (TensorProto.FLOAT, np.float32), (TensorProto.DOUBLE, np.float64)]
        for tp_type, np_type in types:
            add_1 = helper.make_node("Add", ["A", "I_0"], ["B"])
            add_2 = helper.make_node("Add", ["B", "I_1"], ["C"])
            i = np.random.rand(5).astype(np_type)
            graph = helper.make_graph(
                [add_1, add_2],
                "test",
                [helper.make_tensor_value_info("A", tp_type, (5,))],
                [helper.make_tensor_value_info("C", tp_type, (5,))],
                [
                    helper.make_tensor(
                        "I_0", tp_type, dims=(5,), vals=i.tobytes(), raw=True
                    ),
                    helper.make_tensor(
                        "I_1", tp_type, dims=(5,), vals=i.tobytes(), raw=True
                    ),
                ],
            )
            optimized_model = self._optimized(graph, ["eliminate_duplicate_initializer"])
            assert len(optimized_model.graph.node) == 2
            assert len(optimized_model.graph.initializer) == 1
            assert len(optimized_model.graph.input) == 1
            assert optimized_model.graph.node[0].input[1] == "I_0"

    def test_nop_cast(self):  # type: () -> None
        identity = helper.make_node("Identity", ["X"], ["A"])
        cast = helper.make_node("Cast", ["A"], ["B"], to=TensorProto.FLOAT)
        graph = helper.make_graph(
            [identity, cast],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))],
            value_info=[helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
        )

        optimized_model = self._optimized(graph, ["eliminate_nop_cast"])

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_nop_transpose_graph_output(self):  # type: () -> None
        add = helper.make_node("Add", ["X", "Y"], ["A"])
        trans = helper.make_node("Transpose", ["A"], ["B"], perm=[0, 1])
        graph = helper.make_graph(
            [add, trans],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))],
        )
        # The existence of shape infos of graoh outputs is checked in _optimized
        optimized_model = self._optimized(graph, ["eliminate_nop_transpose"])

        def check_transpose(node):  # type: (NodeProto) -> None
            assert node.op_type != "Transpose"

        self._visit_all_nodes_recursive(optimized_model.graph, check_transpose)
        assert len(optimized_model.graph.node) == 1

    def test_nop_transpose(self):  # type: () -> None
        nodes = [
            helper.make_node("Identity", ["A"], ["X"]),
            helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 1]),
        ]
        nodes.extend(
            self._make_fake_loop_op(
                [
                    helper.make_node("Identity", ["_Y"], ["Y1"]),
                    helper.make_node("Transpose", ["Y1"], ["_Y2"], perm=[0, 1]),
                ],
                [(TensorProto.FLOAT, (2, 3), "Y")],
                [(TensorProto.FLOAT, (2, 3), "Y2")],
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
            [
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3)),
                helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (2, 3)),
            ],
            value_info=[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))],
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_transpose"])

        def check_transpose(node):  # type: (NodeProto) -> None
            assert node.op_type != "Transpose"

        self._visit_all_nodes_recursive(optimized_model.graph, check_transpose)
        # Use of the output from the Transpose node in the main graph should
        # have been replaced with the input to the identity node
        assert len(optimized_model.graph.output) == 2
        assert optimized_model.graph.output[0].name == "Y"
        # Use of the output from the Transpose node in the loop graph should
        # have been replaced with the input to that identity node
        assert len(optimized_model.graph.node[3].attribute[0].g.output) == 2
        assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_Y2"

    def test_nop_transpose_default(self):  # type: () -> None
        identity = helper.make_node("Identity", ["A"], ["X"])
        trans = helper.make_node("Transpose", ["X"], ["Y"])
        graph = helper.make_graph(
            [identity, trans],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2))],
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_transpose"])

        assert optimized_model.graph == graph

    def test_nop_pad_opset10(self):  # type: () -> None
        identity = helper.make_node("Identity", ["A"], ["X"])
        pad = helper.make_node("Pad", ["X"], ["Y"], pads=[0, 0, 0, 0])
        graph = helper.make_graph(
            [identity, pad],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))],
        )
        optimized_model = self._optimized(
            graph,
            ["eliminate_nop_pad"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        def check_pad(node):  # type: (NodeProto) -> None
            assert node.op_type != "Pad"

        self._visit_all_nodes_recursive(optimized_model.graph, check_pad)
        assert len(optimized_model.graph.output) == 1
        assert optimized_model.graph.output[0].name == "Y"
        assert len(optimized_model.graph.node) == 1

    def test_nop_pad_graph_output(self):  # type: () -> None
        add = helper.make_node("Add", ["X", "Y"], ["A"])
        pad = helper.make_node("Pad", ["A", "Pads"], ["B"])
        graph = helper.make_graph(
            [add, pad],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(2,),
                    vals=np.array([0, 0]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        # The existence of shape infos of graoh outputs is checked in _optimized
        optimized_model = self._optimized(graph, ["eliminate_nop_pad"])

        def check_pad(node):  # type: (NodeProto) -> None
            assert node.op_type != "Pad"

        self._visit_all_nodes_recursive(optimized_model.graph, check_pad)
        assert len(optimized_model.graph.node) == 1

    def test_nop_pad(self):  # type: () -> None
        identity = helper.make_node("Identity", ["A"], ["X"])
        pad = helper.make_node("Pad", ["X", "Pads"], ["Y"])
        graph = helper.make_graph(
            [identity, pad],
            "test",
            [
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
            ],
            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(4,),
                    vals=np.array([0, 0, 0, 0]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_pad"])

        def check_pad(node):  # type: (NodeProto) -> None
            assert node.op_type != "Pad"

        self._visit_all_nodes_recursive(optimized_model.graph, check_pad)
        assert len(optimized_model.graph.output) == 1
        assert optimized_model.graph.output[0].name == "Y"
        assert len(optimized_model.graph.node) == 1

    def test_nop_pad_default_opset10(self):  # type: () -> None
        identity = helper.make_node("Identity", ["A"], ["X"])
        pad = helper.make_node("Pad", ["X"], ["Y"], pads=[0, 0, 1, 1])
        graph = helper.make_graph(
            [identity, pad],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))],
            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4))],
        )
        optimized_model = self._optimized(
            graph,
            ["eliminate_nop_pad"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph == graph

    def test_nop_pad_default(self):  # type: () -> None
        identity = helper.make_node("Identity", ["A"], ["X"])
        pad = helper.make_node("Pad", ["X", "Pads"], ["Y"])
        graph = helper.make_graph(
            [identity, pad],
            "test",
            [
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
            ],
            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(4,),
                    vals=np.array([0, 1, 0, 0]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_pad"])

        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph == graph

    def test_eliminate_unused_initializer(self):  # type: () -> None
        add = helper.make_node("Add", ["X", "Y"], ["Z"])
        graph = helper.make_graph(
            [add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
            [
                helper.make_tensor(
                    "A",
                    TensorProto.FLOAT,
                    dims=(2, 3),
                    vals=np.random.randn(2, 3).astype(np.float32).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_unused_initializer"])

        assert len(list(optimized_model.graph.initializer)) == 0

    def test_eliminate_unused_initializer_input(self):  # type: () -> None
        add = helper.make_node("Add", ["X", "Y"], ["Z"])
        graph = helper.make_graph(
            [add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
            [
                helper.make_tensor(
                    "A",
                    TensorProto.FLOAT,
                    dims=(2, 3),
                    vals=np.random.randn(2, 3).astype(np.float32).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_unused_initializer"])

        assert len(list(optimized_model.graph.initializer)) == 0
        assert len(optimized_model.graph.input) == 2

    # type: () -> None
    def test_eliminate_unused_initializer_no_eliminate_used_default(self):
        add = helper.make_node("Add", ["X", "A"], ["Z"])
        graph = helper.make_graph(
            [add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
            [
                helper.make_tensor(
                    "A",
                    TensorProto.FLOAT,
                    dims=(1, 2),
                    vals=np.random.randn(1, 2).astype(np.float32).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_unused_initializer"])

        assert len(list(optimized_model.graph.initializer)) == 1

    # type: () -> None
    def test_eliminate_unused_initializer_no_eliminate_used(self):
        nodes = [helper.make_node("Add", ["X", "A"], ["Z"])]
        nodes.extend(
            self._make_fake_loop_op(
                [helper.make_node("Add", ["_X", "A"], ["_Z2"])],
                [(TensorProto.FLOAT, (1, 2), "X")],
                [(TensorProto.FLOAT, (1, 2), "Z2")],
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))],
            [
                helper.make_tensor(
                    "A",
                    TensorProto.FLOAT,
                    dims=(1, 2),
                    vals=np.random.randn(1, 2).astype(np.float32).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_unused_initializer"])

        # Add, Constant (trip count), Constant (cond), Loop
        assert len(list(optimized_model.graph.node)) == 4
        assert optimized_model.graph.node[0].op_type == "Add"
        assert optimized_model.graph.output[0].name == "Z"
        # Add
        assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1
        assert optimized_model.graph.node[3].attribute[0].g.node[0].op_type == "Add"
        assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_Z2"

        assert len(list(optimized_model.graph.initializer)) == 1

    # type: () -> None
    def test_eliminate_unused_initializer_no_eliminate_output(self):
        add = helper.make_node("Add", ["X", "Y"], ["Z"])
        graph = helper.make_graph(
            [add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
            ],
            [
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3)),
            ],
            [
                helper.make_tensor(
                    "A",
                    TensorProto.FLOAT,
                    dims=(2, 3),
                    vals=np.random.randn(2, 3).astype(np.float32).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["eliminate_unused_initializer"])

        assert len(list(optimized_model.graph.initializer)) == 1
        assert "Z" in [o.name for o in optimized_model.graph.output]

    def test_extract_constant_to_initializer(self):  # type: () -> None
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        constant = helper.make_node(
            "Constant",
            [],
            ["A"],
            value=helper.make_tensor(
                name="bias",
                data_type=TensorProto.FLOAT,
                dims=(16, 1, 1),
                vals=np.random.randn(16).astype(np.float32).tolist(),
            ),
        )
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [conv, constant, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(graph, ["extract_constant_to_initializer"])

        self.assertEqual(len(optimized_model.graph.initializer), 1)
        init = optimized_model.graph.initializer[0]
        self.assertEqual(init.name, "A")
        self.assertEqual(init.dims, [16, 1, 1])
        self.assertEqual(init.data_type, TensorProto.FLOAT)

        self.assertEqual(
            [n.op_type for n in optimized_model.graph.node], ["Conv", "Add"]
        )

    def test_fuse_concats(self):  # type: () -> None
        nodes = [
            helper.make_node("Concat", ["A", "B", "C"], ["X"], axis=0),
            helper.make_node("Concat", ["D", "E", "F"], ["Y"], axis=0),
            helper.make_node("Concat", ["X", "G", "Y"], ["Z"], axis=0),
        ]
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 3, 4)),
                helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 3, 4)),
                helper.make_tensor_value_info("D", TensorProto.FLOAT, (4, 3, 4)),
                helper.make_tensor_value_info("E", TensorProto.FLOAT, (2, 3, 4)),
                helper.make_tensor_value_info("F", TensorProto.FLOAT, (4, 3, 4)),
                helper.make_tensor_value_info("G", TensorProto.FLOAT, (4, 3, 4)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (22, 3, 4))],
        )
        optimized_model = self._optimized(
            graph, ["fuse_consecutive_concats"], True
        )  # two passes are needed to simplify the graph to its simplest state.

        assert len(optimized_model.graph.node) == 1
        assert len(optimized_model.graph.node[0].input) == 7
        assert optimized_model.graph.node[0].input == [
            "A",
            "B",
            "C",
            "G",
            "D",
            "E",
            "F",
        ]
        assert optimized_model.graph.node[0].op_type == "Concat"

    def test_fuse_concats_different_axis(self):  # type: () -> None
        nodes = [
            helper.make_node("Concat", ["A", "B", "C"], ["X"], axis=0),
            helper.make_node("Concat", ["D", "E", "F"], ["Y"], axis=1),
            helper.make_node("Concat", ["X", "Y"], ["Z"], axis=2),
        ]
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 9, 4)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 9, 4)),
                helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 9, 4)),
                helper.make_tensor_value_info("D", TensorProto.FLOAT, (8, 3, 4)),
                helper.make_tensor_value_info("E", TensorProto.FLOAT, (8, 3, 4)),
                helper.make_tensor_value_info("F", TensorProto.FLOAT, (8, 3, 4)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (8, 9, 8))],
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_concats"])

        assert optimized_model.graph == graph

    def test_fuse_transpose(self):  # type: () -> None
        nodes = [
            helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2]),
            helper.make_node("Transpose", ["Y"], ["Z"], perm=[2, 0, 1]),
            helper.make_node("Transpose", ["Z"], ["A"], perm=[2, 0, 1]),
        ]
        nodes.extend(
            self._make_fake_loop_op(
                [
                    helper.make_node("Transpose", ["_X"], ["_Y2"], perm=[1, 0, 2]),
                    helper.make_node("Transpose", ["_Y2"], ["_Y3"], perm=[2, 0, 1]),
                    helper.make_node("Transpose", ["_Y3"], ["_Y4"], perm=[2, 0, 1]),
                ],
                [(TensorProto.FLOAT, (2, 3, 4), "X")],
                [(TensorProto.FLOAT, (2, 4, 3), "Y4")],
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))],
            [
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 4, 3)),
                helper.make_tensor_value_info("Y4", TensorProto.FLOAT, (4, 3, 2)),
            ],
        )
        original_model = helper.make_model(graph)
        shape_inference.infer_shapes(original_model)
        optimized_model = self._optimized(graph, ["fuse_consecutive_transposes"])
        shape_inference.infer_shapes(optimized_model)

        # Transpose, Constant (trip count), Constant (cond), Loop
        assert len(list(optimized_model.graph.node)) == 4
        # Transpose
        assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1

    def test_fuse_transpose_default_graph_output(self):  # type: () -> None
        add = helper.make_node("Add", ["X", "Y"], ["A"])
        trans1 = helper.make_node("Transpose", ["A"], ["B"])
        trans2 = helper.make_node("Transpose", ["B"], ["C"])
        graph = helper.make_graph(
            [add, trans1, trans2],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3)),
            ],
            [helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 3))],
        )
        # The existence of shape infos of graoh outputs is checked in _optimized
        optimized_model = self._optimized(graph, ["fuse_consecutive_transposes", "eliminate_deadend"])

        def check_transpose(node):  # type: (NodeProto) -> None
            assert node.op_type != "Transpose"

        self._visit_all_nodes_recursive(optimized_model.graph, check_transpose)
        assert len(optimized_model.graph.node) == 1

    def test_fuse_transpose_default(self):  # type: () -> None
        identity1 = helper.make_node("Identity", ["A"], ["X"])
        trans1 = helper.make_node("Transpose", ["X"], ["Y"])
        trans2 = helper.make_node("Transpose", ["Y"], ["Z"])
        identity2 = helper.make_node("Identity", ["Z"], ["B"])
        graph = helper.make_graph(
            [identity1, trans1, trans2, identity2],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3, 4))],
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_transposes", "eliminate_deadend"])

        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph.node[0].op_type == "Identity"
        assert optimized_model.graph.node[1].op_type == "Identity"

    def test_fuse_transpose_default_no_fuse(self):  # type: () -> None
        identity1 = helper.make_node("Identity", ["A"], ["X"])
        trans1 = helper.make_node("Transpose", ["X"], ["Y"])
        trans2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[0, 1, 2])
        identity2 = helper.make_node("Identity", ["Z"], ["B"])
        graph = helper.make_graph(
            [identity1, trans1, trans2, identity2],
            "test",
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4))],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 3, 2))],
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_transposes"])

        assert len(list(optimized_model.graph.node)) == 4
        assert optimized_model.graph == graph

    def test_fuse_transpose_into_gemm(self):  # type: () -> None
        nodes = [
            helper.make_node("Transpose", ["X"], ["A"], perm=[1, 0]),
            helper.make_node("Transpose", ["Y"], ["B"], perm=[1, 0]),
            helper.make_node("Gemm", ["A", "B", "C"], ["Z"]),
        ]
        nodes.extend(
            self._make_fake_loop_op(
                [
                    helper.make_node("Transpose", ["_X"], ["_A"], perm=[1, 0]),
                    helper.make_node("Transpose", ["Y"], ["_B"], perm=[1, 0]),
                    helper.make_node("Gemm", ["_A", "_B", "C"], ["_Z2"]),
                ],
                [(TensorProto.FLOAT, (2, 3), "X")],
                [(TensorProto.FLOAT, (3, 5), "Z2")],
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 2)),
                helper.make_tensor_value_info("C", TensorProto.FLOAT, (3, 5)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (3, 5))],
        )
        optimized_model = self._optimized(graph, ["fuse_transpose_into_gemm"])

        # Gemm, Constant (trip count), Constant (cond), Loop
        assert len(list(optimized_model.graph.node)) == 4
        assert optimized_model.graph.node[0].op_type == "Gemm"
        # Gemm
        assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1
        assert optimized_model.graph.node[3].attribute[0].g.node[0].op_type == "Gemm"

    def test_fuse_add_bias_into_conv_with_scalar_bias(self):  # type: () -> None
        nodes = [
            helper.make_node("Conv", ["X", "Y"], ["Z"]),
            helper.make_node("Add", ["Z", "A"], ["B"]),
        ]
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, ()),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        # Unsqueeze, Conv
        assert len(optimized_model.graph.node) == 4
        assert optimized_model.graph.node[0].op_type == "Unsqueeze"
        assert optimized_model.graph.node[1].op_type == "Constant"
        assert optimized_model.graph.node[2].op_type == "Tile"
        assert optimized_model.graph.node[3].op_type == "Conv"

    def test_fuse_add_bias_into_conv_use_weight_shape(self):  # type: () -> None
        nodes = [
            helper.make_node("Conv", ["X", "Y"], ["Z"]),
            helper.make_node("Add", ["Z", "A"], ["B"]),
        ]
        # FIXME(daquexian): It looks like subgraph cannot get value info from parent subgraph
        # nodes.extend(self._make_fake_loop_op(
        #     [helper.make_node("Conv", ["_X", "Y"], ["_Z"]),
        #      helper.make_node("Add", ["_Z", "A"], ["_B2"])],
        #     [(TensorProto.FLOAT, (1, 5, 3, 3), "X")],
        #     [(TensorProto.FLOAT, (1, 16, 1, 1), "B2")]))
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 1, 1)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        # # Squeeze, Conv, Constant (trip count), Constant (condition), Loop
        # assert len(list(optimized_model.graph.node)) == 5
        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph.node[0].op_type == "Squeeze"
        assert optimized_model.graph.node[1].op_type == "Conv"
        assert optimized_model.graph.output[0].name == "B"
        # # Squeeze, Conv
        # assert len(optimized_model.graph.node[4].attribute[0].g.node) == 2
        # assert optimized_model.graph.node[4].attribute[0].g.node[0].op_type == 'Squeeze'
        # assert optimized_model.graph.node[4].attribute[0].g.node[1].op_type == 'Conv'
        # # Output 1 since 0 is 'cond'
        # assert optimized_model.graph.node[4].attribute[0].g.output[1].name == 'B2'

    # type: () -> None
    def test_fuse_add_bias_into_conv_use_weight_shape_with_tile(self):
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [conv, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (1,)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        assert len(list(optimized_model.graph.node)) == 3
        assert len(optimized_model.graph.value_info) == 1
        assert (
            optimized_model.graph.value_info[0].type.tensor_type.elem_type
            == TensorProto.INT64
        )
        assert len(optimized_model.graph.value_info[0].type.tensor_type.shape.dim) == 1
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Tile"
        assert optimized_model.graph.node[2].op_type == "Conv"
        assert optimized_model.graph.output[0].name == "B"

    def test_fuse_add_bias_into_conv_use_conv_shape(self):  # type: () -> None
        sub = helper.make_node("Sub", ["M", "N"], ["Y"])
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [sub, conv, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("M", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("N", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 16, 1, 1)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 1))],
            value_info=[
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Sub"
        assert optimized_model.graph.node[1].op_type == "Squeeze"
        assert optimized_model.graph.node[2].op_type == "Conv"
        assert optimized_model.graph.output[0].name == "B"
        assert (
            optimized_model.graph.output[0].type.tensor_type.elem_type
            == TensorProto.FLOAT
        )
        assert len(optimized_model.graph.output[0].type.tensor_type.shape.dim) == 4

    # type: () -> None
    def test_fuse_add_bias_into_conv_use_move_constant(self):
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        constant = helper.make_node(
            "Constant",
            [],
            ["A"],
            value=helper.make_tensor(
                name="bias",
                data_type=TensorProto.FLOAT,
                dims=(16, 1, 1),
                vals=np.random.randn(16).astype(np.float32).tolist(),
            ),
        )
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [conv, constant, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 1))],
            value_info=[
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 1, 1)),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Squeeze"
        assert optimized_model.graph.node[2].op_type == "Conv"
        assert optimized_model.graph.output[0].name == "B"
        assert (
            optimized_model.graph.output[0].type.tensor_type.elem_type
            == TensorProto.FLOAT
        )
        assert len(optimized_model.graph.output[0].type.tensor_type.shape.dim) == 4

    # type: () -> None
    def test_fuse_add_bias_into_conv_squeeze_1d_bias_no_fuse(self):
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [conv, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (3,)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 1, 3))],
            value_info=[
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1)),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[1].op_type == "Add"

    # type: () -> None
    def test_fuse_add_bias_into_conv_squeeze_3d_bias_no_fuse(self):
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [conv, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 3, 3)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 3, 3))],
            value_info=[
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1)),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[1].op_type == "Add"

    # type: () -> None
    def test_fuse_add_bias_into_conv_squeeze_4d_bias_no_fuse(self):
        conv = helper.make_node("Conv", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "A"], ["B"])
        graph = helper.make_graph(
            [conv, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 16, 3, 3)),
            ],
            [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16, 3, 3))],
        )
        optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"])

        assert len(list(optimized_model.graph.node)) == 2
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[1].op_type == "Add"

    def test_fuse_matmul_add_bias_into_gemm(self):  # type: () -> None
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "B"], ["A"])
        graph = helper.make_graph(
            [matmul, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (16,)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))],
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm", "eliminate_deadend"])
        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Gemm"

    def test_fuse_matmul_add_bias_into_gemm_2d_bias(self):  # type: () -> None
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "B"], ["A"])
        graph = helper.make_graph(
            [matmul, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))],
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm", "eliminate_deadend"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Gemm"

    # type: () -> None
    def test_fuse_matmul_add_bias_into_gemm_2d_bias_same_shape(self):
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "B"], ["A"])
        graph = helper.make_graph(
            [matmul, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (32, 16)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))],
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm", "eliminate_deadend"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Gemm"

    def test_fuse_matmul_add_bias_into_gemm_must_keep_topological_order(self):
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"], name='matmul')
        bias = helper.make_node("MatMul", ["M", "N"], ["B"], name='bias')
        add = helper.make_node("Add", ["Z", "B"], ["A"], name='add')
        graph = helper.make_graph(
            [matmul, bias, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("M", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("N", TensorProto.FLOAT, (10, 16)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))],
            value_info=[
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (32, 16)),
            ]
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])

        assert len(list(optimized_model.graph.node)) == 3
        assert optimized_model.graph.node[0].op_type == "MatMul"
        assert optimized_model.graph.node[1].op_type == "MatMul"
        assert optimized_model.graph.node[2].op_type == "Gemm"

    # type: () -> None
    def test_fuse_matmul_add_bias_into_gemm_2d_bias_bcast_no_fuse(self):
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "B"], ["A"])
        graph = helper.make_graph(
            [matmul, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (16, 16)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 16))],
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_matmul_add_bias_into_gemm_3d_matmul_no_fuse(self):
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "B"], ["A"])
        graph = helper.make_graph(
            [matmul, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4, 3)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (3, 3)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 3))],
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_matmul_add_bias_into_gemm_3d_bias_no_fuse(self):
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        add = helper.make_node("Add", ["Z", "B"], ["A"])
        graph = helper.make_graph(
            [matmul, add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 1, 16)),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))],
        )
        # 3d bias for 2d matmul is not legal. So disable onnxruntime checking
        optimized_model = self._optimized(
            graph, ["fuse_matmul_add_bias_into_gemm"], compare_result=False
        )

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_matmul_add_bias_into_gemm_multiple_use_no_fuse(self):
        matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
        identity = helper.make_node("Identity", ["Z"], ["A1"])
        add = helper.make_node("Add", ["Z", "B"], ["A2"])
        graph = helper.make_graph(
            [matmul, add, identity],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
                helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16)),
            ],
            [
                helper.make_tensor_value_info("A1", TensorProto.FLOAT, (32, 16)),
                helper.make_tensor_value_info("A2", TensorProto.FLOAT, (32, 16)),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_pad_into_conv_no_optional_value_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_conv"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_conv_no_optional_value(self):  # type: () -> None
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_conv_with_optional_value(self):  # type: () -> None
        pad = helper.make_node(
            "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                ),
                helper.make_tensor(
                    "Constant_value",
                    TensorProto.FLOAT,
                    dims=(),
                    vals=np.array([0]).astype(np.float32).tobytes(),
                    raw=True,
                ),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [0, 0, 1, 1]

    # type: () -> None
    def test_fuse_pad_into_conv_with_nonzero_optional_value(self):
        pad = helper.make_node(
            "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                ),
                helper.make_tensor(
                    "Constant_value",
                    TensorProto.FLOAT,
                    dims=(),
                    # non-zero Constant_value -> so no pad
                    vals=np.array([25]).astype(np.float32).tobytes(),
                    raw=True,
                ),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_conv_1d_opset10(self):  # type: () -> None
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 1, 0, 0, 1]
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 30)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 32)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_conv"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1]

    def test_fuse_pad_into_conv_1d(self):  # type: () -> None
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 30)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 32)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(6,),
                    vals=np.array([0, 0, 1, 0, 0, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1]

    # type: () -> None
    def test_fuse_pad_into_conv_existing_conv_pad_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"], pads=[1, 1, 0, 0])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 4, 4)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_conv"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1, 1, 1]

    def test_fuse_pad_into_conv_existing_conv_pad(self):  # type: () -> None
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"], pads=[1, 1, 0, 0])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 4, 4)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "Conv"
        assert optimized_model.graph.node[0].attribute[0].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1, 1, 1]

    # type: () -> None
    def test_fuse_pad_into_conv_pad_feature_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 1, 0, 0, 0, 0, 0, 0]
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_conv"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_conv_pad_feature_no_fuse(self):  # type: () -> None
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 1, 0, 0, 0, 0, 0, 0]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_pad_into_conv_negative_pad_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, -1, -1]
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 4, 4)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_conv"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_conv_negative_pad_no_fuse(self):  # type: () -> None
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 4, 4)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, -1, -1])
                    .astype(np.int64)
                    .tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_pad_into_conv_reflection_pad_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="reflect", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_conv"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    # type: () -> None
    def test_fuse_pad_into_conv_reflection_pad_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="reflect")
        conv = helper.make_node("Conv", ["P", "Y"], ["Z"])
        graph = helper.make_graph(
            [pad, conv],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_conv"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_no_optional_value_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        avg_pool = helper.make_node(
            "AveragePool", ["P"], ["Z"], count_include_pad=1, kernel_shape=[3, 3]
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_maxpool_no_optional_value_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_avgpool_no_optional_value(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        avg_pool = helper.make_node(
            "AveragePool", ["P"], ["Z"], count_include_pad=1, kernel_shape=[3, 3]
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_maxpool_no_optional_value(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_avgpool_with_optional_value(self):
        pad = helper.make_node(
            "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
        )
        avg_pool = helper.make_node(
            "AveragePool", ["P"], ["Z"], count_include_pad=1, kernel_shape=[3, 3]
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                ),
                helper.make_tensor(
                    "Constant_value",
                    TensorProto.FLOAT,
                    dims=(),
                    vals=np.array([0]).astype(np.float32).tobytes(),
                    raw=True,
                ),
            ],
        )

        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_maxpool_with_optional_value(self):
        pad = helper.make_node(
            "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                ),
                helper.make_tensor(
                    "Constant_value",
                    TensorProto.FLOAT,
                    dims=(),
                    vals=np.array([0]).astype(np.float32).tobytes(),
                    raw=True,
                ),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [0, 0, 1, 1]

    def test_fuse_pad_into_avgpool_with_nonzero_optional_value(self):
        pad = helper.make_node(
            "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
        )
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                ),
                helper.make_tensor(
                    "Constant_value",
                    TensorProto.FLOAT,
                    dims=(),
                    # non-zero Constant_value -> so no pad
                    vals=np.array([25]).astype(np.float32).tobytes(),
                    raw=True,
                ),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_with_nonzero_optional_value(self):
        pad = helper.make_node(
            "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant"
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                ),
                helper.make_tensor(
                    "Constant_value",
                    TensorProto.FLOAT,
                    dims=(),
                    # non-zero Constant_value -> so no pad
                    vals=np.array([25]).astype(np.float32).tobytes(),
                    raw=True,
                ),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])
        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_1d_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 1, 0, 0, 1]
        )
        avg_pool = helper.make_node(
            "AveragePool", ["P"], ["Z"], count_include_pad=1, kernel_shape=[3]
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1]

    def test_fuse_pad_into_maxpool_1d_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 1, 0, 0, 1]
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [1, 1]

    def test_fuse_pad_into_avgpool_1d(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        avg_pool = helper.make_node(
            "AveragePool", ["P"], ["Z"], count_include_pad=1, kernel_shape=[3]
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(6,),
                    vals=np.array([0, 0, 1, 0, 0, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1]

    def test_fuse_pad_into_maxpool_1d(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(6,),
                    vals=np.array([0, 0, 1, 0, 0, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [1, 1]

    def test_fuse_pad_into_avgpool_existing_avgpool_pad_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        avg_pool = helper.make_node(
            "AveragePool",
            ["P"],
            ["Z"],
            count_include_pad=1,
            kernel_shape=[3, 3],
            pads=[1, 1, 0, 0],
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1, 1))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1, 1, 1]

    def test_fuse_pad_into_maxpool_existing_maxpool_pad_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        max_pool = helper.make_node(
            "MaxPool", ["P"], ["Z"], kernel_shape=[3, 3], pads=[1, 1, 0, 0]
        )
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1, 1))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [1, 1, 1, 1]

    def test_fuse_pad_into_avgpool_existing_avgpool_pad(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        avg_pool = helper.make_node(
            "AveragePool",
            ["P"],
            ["Z"],
            count_include_pad=1,
            kernel_shape=[3, 3],
            pads=[1, 1, 0, 0],
        )
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1, 1)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "AveragePool"
        assert optimized_model.graph.node[0].attribute[2].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[2].ints) == [1, 1, 1, 1]

    def test_fuse_pad_into_maxpool_existing_maxpool_pad(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        max_pool = helper.make_node(
            "MaxPool", ["P"], ["Z"], kernel_shape=[3, 3], pads=[1, 1, 0, 0]
        )
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 1, 1)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert len(list(optimized_model.graph.node)) == 1
        assert optimized_model.graph.node[0].op_type == "MaxPool"
        assert optimized_model.graph.node[0].attribute[1].name == "pads"
        assert list(optimized_model.graph.node[0].attribute[1].ints) == [1, 1, 1, 1]

    def test_fuse_pad_into_avgpool_pad_feature_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 1, 0, 0, 0, 0, 0, 0]
        )
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 4, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_pad_feature_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 1, 0, 0, 0, 0, 0, 0]
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 4, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_pad_feature_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 4, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 1, 0, 0, 0, 0, 0, 0]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_pad_feature_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 4, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 1, 0, 0, 0, 0, 0, 0]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_negative_pad_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, -1, -1]
        )
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_negative_pad_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, -1, -1]
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_negative_pad_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, -1, -1])
                    .astype(np.int64)
                    .tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_negative_pad_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="constant")
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, -1, -1])
                    .astype(np.int64)
                    .tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_reflection_pad_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="reflect", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_reflection_pad_no_fuse_opset10(self):
        pad = helper.make_node(
            "Pad", ["X"], ["P"], mode="reflect", pads=[0, 0, 0, 0, 0, 0, 1, 1]
        )
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_pad_into_pool"],
            False,
            opset_imports=[helper.make_opsetid("", 10)],
        )

        assert optimized_model.graph == graph

    def test_fuse_pad_into_avgpool_reflection_pad_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="reflect")
        avg_pool = helper.make_node("AveragePool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, avg_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_pad_into_maxpool_reflection_pad_no_fuse(self):
        pad = helper.make_node("Pad", ["X", "Pads"], ["P"], mode="reflect")
        max_pool = helper.make_node("MaxPool", ["P"], ["Z"], kernel_shape=[3, 3])
        graph = helper.make_graph(
            [pad, max_pool],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 5, 1, 1))],
            [
                helper.make_tensor(
                    "Pads",
                    TensorProto.INT64,
                    dims=(8,),
                    vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype(np.int64).tobytes(),
                    raw=True,
                )
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_pad_into_pool"])

        assert optimized_model.graph == graph

    def test_fuse_consecutive_squeezes(self):  # type: () -> None
        nodes = [
            helper.make_node("Squeeze", ["X", "X_axes"], ["Y"]),
            helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"]),
        ]
        nodes.extend(
            self._make_fake_loop_op(
                [
                    helper.make_node("Squeeze", ["_X", "X_axes"], ["_Y"]),
                    helper.make_node("Squeeze", ["_Y", "Y_axes"], ["_Z2"]),
                ],
                [(TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9), "X")],
                [(TensorProto.FLOAT, (2, 3, 1, 8, 9), "Z2")],
            )
        )
        initializers = [
            helper.make_tensor(
                name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True
            )
            for name, npa in [
                ("X_axes", np.array([0, 4, 5], dtype=np.int64)),
                ("Y_axes", np.array([0, 3], dtype=np.int64)),
            ]
        ]
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info(
                    "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)
                ),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (2, 3, 1, 8, 9))],
            initializer=initializers,
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])

        # Squeeze, Constant (trip count), Constant (cond), Loop
        assert optimized_model.graph.node[0].op_type == "Squeeze"
        for init in optimized_model.graph.initializer:
            if init.name == optimized_model.graph.node[0].input[1]:
                assert list(to_array(init)) == [0, 1, 4, 5, 6]
        assert len(list(optimized_model.graph.node)) == 4

    def test_fuse_consecutive_squeezes_default(self):  # type: () -> None
        squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"])
        squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])
        squeeze3 = helper.make_node("Squeeze", ["Z", "Z_axes"], ["A"])
        nodes = [squeeze1, squeeze2, squeeze3]
        initializers = [
            helper.make_tensor(
                name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True
            )
            for name, npa in [
                ("X_axes", np.array([0, 4, 5], dtype=np.int64)),
                ("Y_axes", np.array([0, 3], dtype=np.int64)),
                ("Z_axes", np.array([2], dtype=np.int64)),
            ]
        ]
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info(
                    "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)
                ),
            ],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 8, 9))],
            initializer=initializers,
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])

        assert optimized_model.graph.node[0].op_type == "Squeeze"
        for init in optimized_model.graph.initializer:
            if init.name == optimized_model.graph.node[0].input[1]:
                assert list(to_array(init)) == [0, 1, 4, 5, 6, 7]
        assert len(list(optimized_model.graph.node)) == 1

    def test_fuse_consecutive_squeezes_random(self):  # type: () -> None
        x_shape = [1, 1, 1, 3, 4, 1, 6, 1, 1, 9]
        s1_one_indices = [i for i, a in enumerate(x_shape) if a == 1]
        s1_axes = np.random.choice(
            s1_one_indices,
            size=np.random.randint(low=1, high=len(s1_one_indices) - 1),
            replace=False,
        ).astype(np.int64)
        s2_x_shape = [a for i, a in enumerate(x_shape) if i not in s1_axes]
        s2_one_indices = [i for i, a in enumerate(s2_x_shape) if a == 1]
        s2_axes = np.array(s2_one_indices).astype(np.int64)

        squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"])
        squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])
        initializers = [
            helper.make_tensor(
                name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True
            )
            for name, npa in [("X_axes", s1_axes), ("Y_axes", s2_axes)]
        ]
        nodes = [squeeze1, squeeze2]
        graph = helper.make_graph(
            nodes,
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, x_shape),
            ],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (3, 4, 6, 9))],
            initializer=initializers,
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])

        assert optimized_model.graph.node[0].op_type == "Squeeze"
        for init in optimized_model.graph.initializer:
            if init.name == optimized_model.graph.node[0].input[1]:
                assert list(to_array(init)) == [0, 1, 2, 5, 7, 8]
        assert len(list(optimized_model.graph.node)) == 1

    def test_fuse_consecutive_squeezes_multi_uses(self):  # type: () -> None
        squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"])
        add = helper.make_node("Add", ["Y", "A"], ["Z2"])
        squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])
        initializers = [
            helper.make_tensor(
                name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True
            )
            for name, npa in [
                ("X_axes", np.array([0, 4, 5], dtype=np.int64)),
                ("Y_axes", np.array([0, 3], dtype=np.int64)),
            ]
        ]
        graph = helper.make_graph(
            [squeeze1, add, squeeze2],
            "test",
            [
                helper.make_tensor_value_info(
                    "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)
                ),
                helper.make_tensor_value_info("A", TensorProto.FLOAT, (1,)),
            ],
            [
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (2, 3, 1, 8, 9)),
                helper.make_tensor_value_info(
                    "Z2", TensorProto.FLOAT, (1, 2, 3, 1, 1, 8, 9)
                ),
            ],
            initializer=initializers,
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"])

        assert optimized_model.graph.node[0].op_type == "Squeeze"
        assert optimized_model.graph.node[2].op_type == "Squeeze"
        assert optimized_model.graph.node[2].input[0] == "X"
        assert len(list(optimized_model.graph.node)) == 3
        for init in optimized_model.graph.initializer:
            if init.name == optimized_model.graph.node[0].input[1]:
                assert list(to_array(init)) == [0, 4, 5]
            if init.name == optimized_model.graph.node[2].input[1]:
                assert list(to_array(init)) == [0, 1, 4, 5, 6]

    @pytest.mark.xfail
    def test_fuse_consecutive_softmax_log_axis(self):  # type: () -> None
        for axis in range(3):
            softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=axis)
            log = helper.make_node("Log", ["Y"], ["Z"])
            graph = helper.make_graph(
                [softmax, log],
                "test",
                [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 7, 11))],
                [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11))],
            )
            optimized_model = self._optimized(graph, ["fuse_consecutive_log_softmax"])

            assert (
                optimized_model.graph.output[0].type.tensor_type.elem_type
                == TensorProto.FLOAT
            )
            assert len(optimized_model.graph.output) == 1
            assert len(optimized_model.graph.node) == 1
            assert optimized_model.graph.node[0].op_type == "LogSoftmax"
            assert optimized_model.graph.node[0].attribute[0].name == "axis"
            assert optimized_model.graph.node[0].attribute[0].i == axis

    def test_fuse_consecutive_softmax_log_side_effect(self):  # type: () -> None
        softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
        log = helper.make_node("Log", ["Y"], ["Z"])
        graph = helper.make_graph(
            [softmax, log],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 7, 11))],
            [
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11)),
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 7, 11)),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_log_softmax"])

        assert graph == optimized_model.graph

    # type: () -> None
    @pytest.mark.xfail
    def test_fuse_consecutive_softmax_log_multiple_out(self):
        softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
        log = helper.make_node("Log", ["Y"], ["Z"])
        exp = helper.make_node("Exp", ["Z"], ["Z1"])
        graph = helper.make_graph(
            [softmax, log, exp],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 7, 11))],
            [
                helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11)),
                helper.make_tensor_value_info("Z1", TensorProto.FLOAT, (5, 7, 11)),
            ],
        )
        optimized_model = self._optimized(graph, ["fuse_consecutive_log_softmax"])

        assert len(optimized_model.graph.output) == 2
        assert len(optimized_model.graph.node) == 2
        assert (
            optimized_model.graph.output[0].type.tensor_type.elem_type
            == TensorProto.FLOAT
        )
        assert (
            optimized_model.graph.output[1].type.tensor_type.elem_type
            == TensorProto.FLOAT
        )
        assert optimized_model.graph.node[0].op_type == "LogSoftmax"
        assert optimized_model.graph.node[0].attribute[0].name == "axis"
        assert optimized_model.graph.node[0].attribute[0].i == 2
        assert optimized_model.graph.node[1].op_type == "Exp"

    def test_preserve_value_info(self):  # type: () -> None
        trans1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])
        trans2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[2, 0, 1])
        trans3 = helper.make_node("Transpose", ["Z"], ["A"], perm=[2, 0, 1])
        graph = helper.make_graph(
            [trans1, trans2, trans3],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))],
            [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 4, 3))],
        )
        vi = helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))
        graph.value_info.extend([vi])
        optimized_model = self._optimized(graph, ["nop"])
        assert list(optimized_model.graph.value_info) == [vi]
        assert len(list(optimized_model.graph.node)) == 3

    def test_split(self):  # type: () -> None
        node = onnx.helper.make_node(
            "Constant",
            inputs=[],
            outputs=["X"],
            value=onnx.helper.make_tensor(
                name="X", data_type=TensorProto.FLOAT, dims=[1], vals=[5],
            ),
        )
        graph = helper.make_graph(
            [node],
            "test-optimize-split",
            [],
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1,))],
        )

        init_model = self._optimized(graph, ["split_init"])
        self.assertEqual(len(init_model.graph.node), 1)
        self.assertEqual(len(init_model.graph.output), 1)
        self.assertEqual(init_model.graph.node[0].op_type, "Constant")

        predict_model = self._optimized(graph, ["split_predict"])
        self.assertEqual(len(predict_model.graph.node), 0)
        self.assertEqual(len(predict_model.graph.input), 1)
        self.assertEqual(predict_model.graph.input[0].name, "X")

    def test_lift_lex_loop(self):  # type: () -> None
        nodes = [helper.make_node("Identity", ["X"], ["Y"])]
        # 'lift_lexical_references' is legacy code and I don't know how it works.
        # More error occurs if I make this loop op legal.
        # So don't check legality here
        nodes.extend(
            self._make_fake_loop_op(
                [
                    helper.make_node("Identity", ["X"], ["_Y2"]),
                    helper.make_node("Identity", ["Y"], ["_Y3"]),
                ],
                [],
                [(TensorProto.FLOAT, (5,), "Y2"), (TensorProto.FLOAT, (5,), "Y3")],
                check_legality=False,
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
            [
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (5,)),
            ],
        )
        # "lift_lexical_references" pass produces a graph that does not conform to
        # the ONNX spec. Disable checking.
        optimized_model = self._optimized(
            graph, ["lift_lexical_references"], compare_result=False
        )
        assert len(optimized_model.graph.node) == 4
        # body_graph, __control_inputs
        assert len(optimized_model.graph.node[3].attribute) == 2
        assert optimized_model.graph.node[3].attribute[1].name == "__control_inputs"
        assert optimized_model.graph.node[3].attribute[1].strings[0] == b"X"
        assert optimized_model.graph.node[3].attribute[1].strings[1] == b"Y"

    def test_lift_lex_if(self):  # type: () -> None
        nodes = [helper.make_node("Identity", ["X"], ["Y"])]
        nodes.extend(
            self._make_fake_if_op(
                [
                    helper.make_node("Identity", ["X"], ["_Y2"]),
                    helper.make_node("Identity", ["Y"], ["_Y3"]),
                ],
                [
                    helper.make_node("Identity", ["X"], ["_Y2"]),
                    helper.make_node("Identity", ["X"], ["_Y3"]),
                ],
                [(TensorProto.FLOAT, (5,), "Y2"), (TensorProto.FLOAT, (5,), "Y3")],
            )
        )
        graph = helper.make_graph(
            nodes,
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))],
            [
                helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)),
                helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (5,)),
            ],
        )
        # "If" node now diverges from ONNX schema. Disable checking.
        optimized_model = self._optimized(
            graph, ["lift_lexical_references"], compare_result=False
        )

        # Identity, Constant (condition), If
        assert len(optimized_model.graph.node) == 3
        # else_branch, then_branch, __control_inputs
        assert len(optimized_model.graph.node[2].attribute) == 3
        assert optimized_model.graph.node[2].attribute[2].name == "__control_inputs"
        assert optimized_model.graph.node[2].attribute[2].strings[0] == b"X"
        assert optimized_model.graph.node[2].attribute[2].strings[1] == b"Y"

    def test_fuse_bn_into_conv_simple(self):  # type: () -> None
        for (tensor_type, np_type) in [(TensorProto.FLOAT, np.float32)]:
            conv = helper.make_node("Conv", ["X", "W", "B"], ["Y"])
            bn = helper.make_node(
                "BatchNormalization", ["Y", "scale", "b", "mean", "var"], ["Z"]
            )

            W = np.random.randn(3, 2, 5, 5).astype(np_type) + 2
            B = np.random.randn(3,).astype(np_type) + 2
            scale = np.random.randn(3,).astype(np_type) + 2
            b = np.random.randn(3,).astype(np_type) + 2
            mean = np.random.randn(3,).astype(np_type) + 2
            var = np.abs(np.random.randn(3,).astype(np_type)) + 2

            initializers = [
                helper.make_tensor(
                    name, tensor_type, npa.shape, npa.tobytes(), raw=True
                )
                for name, npa in [
                    ("W", W),
                    ("B", B),
                    ("scale", scale),
                    ("b", b),
                    ("mean", mean),
                    ("var", var),
                ]
            ]
            graph = helper.make_graph(
                [conv, bn],
                "test",
                [helper.make_tensor_value_info("X", tensor_type, (5, 2, 28, 28))],
                [helper.make_tensor_value_info("Z", tensor_type, (5, 3, 24, 24))],
                initializer=initializers,
                value_info=[
                    helper.make_tensor_value_info("Y", tensor_type, (5, 3, 24, 24))
                ],
            )
            optimized_model = self._optimized(graph, ["fuse_bn_into_conv"]) # noqa

    def _internal_test_deadend_elimination(self, fixed):  # type: (bool) -> None
        softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
        log = helper.make_node("Log", ["Y"], ["Z"])
        exp = helper.make_node("Exp", ["Z"], ["Z1"])
        exp1 = helper.make_node("Log", ["Z"], ["Z2"])
        exp2 = helper.make_node("Sqrt", ["Z1"], ["Z3"])
        graph = helper.make_graph(
            [softmax, log, exp, exp1, exp2],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 7, 11))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11))],
        )
        optimized_model = self._optimized(graph, ["eliminate_deadend"], fixed)
        assert len(optimized_model.graph.output) == 1
        assert len(optimized_model.graph.node) == 2
        assert (
            optimized_model.graph.output[0].type.tensor_type.elem_type
            == TensorProto.FLOAT
        )
        assert optimized_model.graph.node[0].op_type == "Softmax"
        assert optimized_model.graph.node[0].attribute[0].name == "axis"
        assert optimized_model.graph.node[0].attribute[0].i == 2
        assert optimized_model.graph.node[1].op_type == "Log"

    def test_deadend_elimination_simple(self):  # type: () -> None
        self._internal_test_deadend_elimination(False)

    def test_deadend_elimination_simple_fixed(self):  # type: () -> None
        self._internal_test_deadend_elimination(True)

    def _get_argmax_output_shape(self, input_shape, axis, keepdims):
        assert keepdims
        output_shape = list(input_shape[:])
        output_shape[axis] = 1
        output_shape = tuple(output_shape)
        return output_shape

    # type: () -> None

    def test_eliminate_nop_monotone_argmax_basic_no_node_axis(self):
        input_shape = (5, 7, 11)
        for node_name in ["Exp"]:
            for axis in range(3):
                node = helper.make_node(node_name, ["X"], ["Y"])
                argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis)
                output_shape = self._get_argmax_output_shape(input_shape, axis, True)
                graph = helper.make_graph(
                    [node, argmax],
                    "test",
                    [
                        helper.make_tensor_value_info(
                            "X", TensorProto.FLOAT, input_shape
                        )
                    ],
                    [
                        helper.make_tensor_value_info(
                            "Z", TensorProto.INT64, output_shape
                        )
                    ],
                )
                optimized_model = self._optimized(
                    graph, ["eliminate_nop_monotone_argmax"]
                )
                assert len(optimized_model.graph.output) == 1
                assert len(optimized_model.graph.node) == 1
                assert (
                    optimized_model.graph.output[0].type.tensor_type.elem_type
                    == TensorProto.INT64
                )
                assert optimized_model.graph.node[0].op_type == "ArgMax"
                assert optimized_model.graph.node[0].attribute[0].name == "axis"
                assert optimized_model.graph.node[0].attribute[0].i == axis

    # type: () -> None
    def test_eliminate_nop_monotone_argmax_basic_with_node_axis(self):
        input_shape = (5, 7, 11)
        for node_name in ["Softmax", "LogSoftmax"]:
            for axis_n in range(3):
                for axis_max in range(3):
                    node = helper.make_node(node_name, ["X"], ["Y"], axis=axis_n)
                    argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis_max)
                    output_shape = self._get_argmax_output_shape(
                        input_shape, axis_max, True
                    )
                    graph = helper.make_graph(
                        [node, argmax],
                        "test",
                        [
                            helper.make_tensor_value_info(
                                "X", TensorProto.FLOAT, input_shape
                            )
                        ],
                        [
                            helper.make_tensor_value_info(
                                "Z", TensorProto.INT64, output_shape
                            )
                        ],
                    )
                    optimized_model = self._optimized(
                        graph, ["eliminate_nop_monotone_argmax"]
                    )
                    if axis_max == axis_n:
                        assert len(optimized_model.graph.output) == 1
                        assert len(optimized_model.graph.node) == 1
                        assert (
                            optimized_model.graph.output[0].type.tensor_type.elem_type
                            == TensorProto.INT64
                        )
                        assert optimized_model.graph.node[0].op_type == "ArgMax"
                        assert optimized_model.graph.node[0].attribute[0].name == "axis"
                        assert optimized_model.graph.node[0].attribute[0].i == axis_max
                    else:
                        assert optimized_model.graph == graph

    # type: () -> None
    def test_eliminate_nop_monotone_argmax_multiple_out(self):
        input_shape = (5, 7, 11)
        for node_name in ["Exp"]:
            for axis in range(3):
                node = helper.make_node(node_name, ["X"], ["Y"])
                node2 = helper.make_node(node_name, ["Y"], ["Z1"])
                argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis)
                argmax_output_shape = self._get_argmax_output_shape(
                    input_shape, axis, True
                )
                graph = helper.make_graph(
                    [node, node2, argmax],
                    "test",
                    [
                        helper.make_tensor_value_info(
                            "X", TensorProto.FLOAT, input_shape
                        )
                    ],
                    [
                        helper.make_tensor_value_info(
                            "Z", TensorProto.INT64, argmax_output_shape
                        ),
                        helper.make_tensor_value_info(
                            "Z1", TensorProto.FLOAT, input_shape
                        ),
                    ],
                )
                optimized_model = self._optimized(
                    graph, ["eliminate_nop_monotone_argmax"]
                )
                assert optimized_model.graph == graph

    # type: () -> None
    def test_eliminate_nop_monotone_argmax_consecutive(self):
        # type: (GraphProto, ModelProto, bool, int) -> None
        input_shape = (5, 7, 11)

        def _assertion(graph, optimized_model, axis_aligned, true_axis):
            if axis_aligned:
                assert len(optimized_model.graph.output) == 1
                assert len(optimized_model.graph.node) == 1
                assert (
                    optimized_model.graph.output[0].type.tensor_type.elem_type
                    == TensorProto.INT64
                )
                assert optimized_model.graph.node[0].op_type == "ArgMax"
                assert optimized_model.graph.node[0].attribute[0].name == "axis"
                assert optimized_model.graph.node[0].attribute[0].i == true_axis
            else:
                assert optimized_model.graph == graph

        # no axis X no axis test
        for node_name_0 in ["Exp"]:
            for node_name_1 in ["Exp"]:
                for axis in range(3):
                    node = helper.make_node(node_name_0, ["X"], ["Y"])
                    node2 = helper.make_node(node_name_1, ["Y"], ["Y1"])
                    argmax = helper.make_node("ArgMax", ["Y1"], ["Z"], axis=axis)
                    output_shape = self._get_argmax_output_shape(
                        input_shape, axis, True
                    )
                    graph = helper.make_graph(
                        [node, node2, argmax],
                        "test",
                        [
                            helper.make_tensor_value_info(
                                "X", TensorProto.FLOAT, input_shape
                            )
                        ],
                        [
                            helper.make_tensor_value_info(
                                "Z", TensorProto.INT64, output_shape
                            )
                        ],
                    )
                    optimized_model = self._optimized(
                        graph, ["eliminate_nop_monotone_argmax"], True
                    )
                    _assertion(graph, optimized_model, True, axis)
        # no axis X axis test
        for node_name_0 in ["Exp"]:
            for node_name_1 in ["Softmax", "LogSoftmax"]:
                for axis_0 in range(3):
                    for axis_1 in range(3):
                        node = helper.make_node(node_name_0, ["X"], ["Y"])
                        node2 = helper.make_node(
                            node_name_1, ["Y"], ["Y1"], axis=axis_0
                        )
                        argmax = helper.make_node("ArgMax", ["Y1"], ["Z"], axis=axis_1)
                        output_shape = self._get_argmax_output_shape(
                            input_shape, axis_1, True
                        )
                        graph = helper.make_graph(
                            [node, node2, argmax],
                            "test",
                            [
                                helper.make_tensor_value_info(
                                    "X", TensorProto.FLOAT, (5, 7, 11)
                                )
                            ],
                            [
                                helper.make_tensor_value_info(
                                    "Z", TensorProto.INT64, output_shape
                                )
                            ],
                        )
                        optimized_model = self._optimized(
                            graph, ["eliminate_nop_monotone_argmax"], True
                        )
                        _assertion(graph, optimized_model, axis_0 == axis_1, axis_1)
        # axis X axis test
        for node_name_0 in ["Softmax", "LogSoftmax"]:
            for node_name_1 in ["Softmax", "LogSoftmax"]:
                for axis_0 in range(3):
                    for axis_1 in range(3):
                        for axis_2 in range(3):
                            node = helper.make_node(
                                node_name_0, ["X"], ["Y"], axis=axis_0
                            )
                            node2 = helper.make_node(
                                node_name_1, ["Y"], ["Y1"], axis=axis_1
                            )
                            argmax = helper.make_node(
                                "ArgMax", ["Y1"], ["Z"], axis=axis_2
                            )
                            output_shape = self._get_argmax_output_shape(
                                input_shape, axis_2, True
                            )
                            graph = helper.make_graph(
                                [node, node2, argmax],
                                "test",
                                [
                                    helper.make_tensor_value_info(
                                        "X", TensorProto.FLOAT, input_shape
                                    )
                                ],
                                [
                                    helper.make_tensor_value_info(
                                        "Z", TensorProto.INT64, output_shape
                                    )
                                ],
                            )
                            optimized_model = self._optimized(
                                graph, ["eliminate_nop_monotone_argmax"], True
                            )
                            if (
                                axis_0 == axis_1
                            ):  # we can reduce both of the monotonic ops
                                _assertion(
                                    graph, optimized_model, axis_1 == axis_2, axis_2
                                )
                            elif (
                                axis_1 == axis_2
                            ):  # we can reduce one of the monotonic ops
                                assert len(optimized_model.graph.output) == 1
                                assert len(optimized_model.graph.node) == 2
                                assert (
                                    optimized_model.graph.output[
                                        0
                                    ].type.tensor_type.elem_type
                                    == TensorProto.INT64
                                )
                                assert (
                                    optimized_model.graph.node[-1].op_type == "ArgMax"
                                )
                                assert (
                                    optimized_model.graph.node[-1].attribute[0].name
                                    == "axis"
                                )
                                assert (
                                    optimized_model.graph.node[-1].attribute[0].i
                                    == axis_2
                                )
                            else:  # we can't reduce anything
                                assert optimized_model.graph == graph

    def test_eliminate_nop_dropout(self):  # type: () -> None
        node = helper.make_node("Dropout", ["X"], ["Y"])
        node1 = helper.make_node("Log", ["Y"], ["Z"])
        graph = helper.make_graph(
            [node, node1],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 7))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))],
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_dropout"], False)

        # we don't want to eliminate the dropoutin opset 12,
        # even when it';s an optional parameter (defaults to 0)
        assert optimized_model.graph == graph

    # type: () -> None
    def test_eliminate_nop_dropout_opset11_graph_output(self):
        node = helper.make_node("Log", ["X"], ["Y"])
        node1 = helper.make_node("Dropout", ["Y"], ["Z"], ratio=0.0)
        graph = helper.make_graph(
            [node, node1],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, 7))],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))],
        )
        optimized_model = self._optimized(
            graph,
            ["eliminate_nop_dropout"],
            False,
            opset_imports=[helper.make_opsetid("", 11)],
        )

        assert len(optimized_model.graph.output) == 1
        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Log"
        assert optimized_model.graph.output[0].name == "Z"

    def test_eliminate_nop_dropout_opset11(self):  # type: () -> None
        for ratio in [0.0, 0.5]:
            graph = parser.parse_graph(f"""
               agraph (float[5, 7] X) => (float[5, 7] Z)
               {{
                  Y = Dropout<ratio={ratio}> (X)
                  Z = Log(Y)
               }}
            """)
            optimized_model = self._optimized(
                graph,
                ["eliminate_nop_dropout"],
                False,
                opset_imports=[helper.make_opsetid("", 11)],
            )

            if ratio > 0.0:
                assert optimized_model.graph == graph
            else:
                assert len(optimized_model.graph.output) == 1
                assert len(optimized_model.graph.node) == 1
                assert optimized_model.graph.node[0].op_type == "Log"

    def test_eliminate_shape_op(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info("Y", TensorProto.INT64, [2])
        X2 = helper.make_tensor_value_info("X2", TensorProto.FLOAT, [3, 2])

        node_def = helper.make_node("Relu", ["X"], ["X2"],)

        node_def2 = helper.make_node("Shape", ["X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            value_info=[X2],
        )
        optimized_model = self._optimized(graph, ["eliminate_shape_op"], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Relu"

    # Some exporters use negative dim to represent dynamic shapes.
    def test_not_eliminate_shape_op_with_negative_dim(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, -1])
        Y = helper.make_tensor_value_info("Y", TensorProto.INT64, [2])
        X2 = helper.make_tensor_value_info("X2", TensorProto.FLOAT, [3, -1])

        node_def = helper.make_node("Relu", ["X"], ["X2"],)

        node_def2 = helper.make_node("Shape", ["X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            value_info=[X2],
        )
        optimized_model = self._optimized(graph, ["eliminate_shape_op"], False, compare_result=False)

        assert len(optimized_model.graph.node) == 2
        assert optimized_model.graph.node[0].op_type == "Relu"
        assert optimized_model.graph.node[1].op_type == "Shape"

    def test_eliminate_nop_expand(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])
        shape = helper.make_tensor(
            "shape", TensorProto.INT64, [2], np.array([1, 1], dtype=np.int64)
        )

        node_def = helper.make_node("Expand", ["X", "shape"], ["X2"],)

        node_def2 = helper.make_node("Identity", ["X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [shape],  # initialzer
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_expand"], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_rename_input_output(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])
        shape = helper.make_tensor(
            "shape", TensorProto.INT64, [2], np.array([1, 1], dtype=np.int64)
        )

        node_def = helper.make_node("Expand", ["X", "shape"], ["X2"],)

        node_def2 = helper.make_node("Identity", ["X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [shape],  # initialzer
        )
        optimized_model = self._optimized(
            graph, ["rename_input_output"], compare_result=False
        )

        assert optimized_model.graph.input[0].name == "input_0"
        assert optimized_model.graph.output[0].name == "output_0"

    def test_rename_input_output_with_env_variable(self):  # type: () -> None
        os.environ["OPTIMIZER_RENAME_INPUT_PATTERN"] = "INPUT__%d_"
        os.environ["OPTIMIZER_RENAME_OUTPUT_PATTERN"] = "OUTPUT__%d_"

        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2])
        X1 = helper.make_tensor_value_info("X1", TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])

        node_def = helper.make_node("Add", ["X", "X1"], ["X2"],)

        node_def2 = helper.make_node("Identity", ["X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2],  # nodes
            "test",  # name
            [X, X1],  # inputs
            [Y],  # outputs
        )
        optimized_model = self._optimized(
            graph, ["rename_input_output"], compare_result=False
        )

        assert optimized_model.graph.input[0].name == "INPUT__0_"
        assert optimized_model.graph.input[1].name == "INPUT__1_"
        assert optimized_model.graph.output[0].name == "OUTPUT__0_"

    def test_set_unique_name_for_node(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2])
        X1 = helper.make_tensor_value_info("X1", TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])

        node_def = helper.make_node("Add", ["X", "X1"], ["X2"],)

        node_def2 = helper.make_node("Identity", ["X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2],  # nodes
            "test",  # name
            [X, X1],  # inputs
            [Y],  # outputs
        )

        assert not graph.node[0].HasField("name")
        assert not graph.node[1].HasField("name")

        optimized_model = self._optimized(
            graph, ["set_unique_name_for_nodes"], compare_result=False
        )

        assert optimized_model.graph.node[0].HasField("name")
        assert optimized_model.graph.node[1].HasField("name")

    # def test_larger_model(self):  # type: () -> None
    #     os.environ["OPTIMIZER_RENAME_INPUT_PATTERN"] = "input_%d"
    #     os.environ["OPTIMIZER_RENAME_OUTPUT_PATTERN"] = "output_%d"
    #     X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1024, 1024, 1024])
    #     Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1024, 1024, 1024])
    #     one = helper.make_tensor('one', TensorProto.FLOAT, [1024, 1024, 1024],
    #         np.ones((1024, 1024, 1024), dtype=np.float32).tobytes(), raw=True)

    #     node_def = helper.make_node(
    #         'Mul',
    #         ['X', 'one'],
    #         ['X2'],
    #     )

    #     node_def2 = helper.make_node(
    #         'Identity',
    #         ['X2'],
    #         ['Y'],
    #     )

    #     graph = helper.make_graph(
    #         [node_def, node_def2],        # nodes
    #         'test',      # name
    #         [X],  # inputs
    #         [Y],  # outputs
    #         [one],
    #     )

    #     model = helper.make_model(graph)
    #     optimized_model = self._optimized(model, ["rename_input_output"], compare_result=False, check=False)

    #     assert isinstance(optimized_model, ModelProto)
    #     assert optimized_model.graph.input[0].name == "input_0"
    #     assert optimized_model.graph.output[0].name == "output_0"

    def test_fuse_concat_into_reshape1(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 5])
        shape = helper.make_tensor(
            "shape", TensorProto.INT64, [2], np.array([4, 5], dtype=np.int64)
        )
        indices = helper.make_tensor(
            "indices", TensorProto.INT64, [1], np.array([0], dtype=np.int64)
        )
        axes = helper.make_tensor(
            "axes", TensorProto.INT64, [1], np.array([0], dtype=np.int64)
        )
        X2 = helper.make_tensor_value_info("X2", TensorProto.INT64, [1])

        node_def = helper.make_node("Shape", ["X"], ["X1"],)
        node_def1 = helper.make_node("Gather", ["X1", "indices"], ["X2"], axis=0,)
        node_def2 = helper.make_node("Concat", ["X2", "shape"], ["X3"], axis=0,)
        node_def3 = helper.make_node("Reshape", ["X", "X3"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [shape, indices, axes],  # initialzer
            value_info=[X2],
        )
        optimized_model = self._optimized(
            graph, ["fuse_concat_into_reshape", "eliminate_deadend"], False
        )

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Reshape"

    def test_fuse_concat_and_reshape2(self):  # type: () -> None
        # This case is not supported by fuse_concat_and_reshape for now.
        # Just test that no invalid model is genereated.
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, ["N", 4, 5])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, ["N", 4, 5, 1])
        shape = helper.make_tensor(
            "shape", TensorProto.INT64, [1], np.array([1], dtype=np.int64)
        )

        node_def = helper.make_node("Shape", ["X"], ["X1"],)
        node_def2 = helper.make_node("Concat", ["X1", "shape"], ["X2"], axis=0,)
        node_def3 = helper.make_node("Reshape", ["X", "X2"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2, node_def3],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [shape],  # initialzer
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_concat_into_reshape", "eliminate_deadend"],
            False,
            input_shapes_for_comparing={"X": [2, 4, 5]},
        )

        assert len(optimized_model.graph.node) == 3

    def test_fuse_concat_into_reshape3(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, ["N", 4, 5])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, ["N", 4, 5, 1])
        shape = helper.make_tensor(
            "shape", TensorProto.INT32, [3], np.array([4, 5, 1], dtype=np.int64)
        )
        indices = helper.make_tensor(
            "indices", TensorProto.INT64, [1], np.array([0], dtype=np.int64)
        )
        axes = helper.make_tensor(
            "axes", TensorProto.INT64, [1], np.array([0], dtype=np.int64)
        )
        X3 = helper.make_tensor_value_info("X3", TensorProto.INT32, [1])

        node_def = helper.make_node("Shape", ["X"], ["X1"],)
        node_def1 = helper.make_node("Gather", ["X1", "indices"], ["X2"], axis=0,)
        node_def2 = helper.make_node("Cast", ["X2"], ["X3"], to=TensorProto.INT32)
        node_def3 = helper.make_node("Concat", ["X3", "shape"], ["X4"], axis=0,)
        node_def4 = helper.make_node("Cast", ["X4"], ["X5"], to=TensorProto.INT64)
        node_def5 = helper.make_node("Reshape", ["X", "X5"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3, node_def4, node_def5],
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [shape, indices, axes],  # initialzer
            value_info=[X3],
        )
        optimized_model = self._optimized(
            graph,
            ["fuse_concat_into_reshape", "eliminate_deadend"],
            False,
            input_shapes_for_comparing={"X": [2, 4, 5]},
        )

        assert len(optimized_model.graph.node) == 1

    def test_fuse_concat_into_reshape_multiple_consumers(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5])
        Y1 = helper.make_tensor_value_info("Y1", TensorProto.FLOAT, [3, 4, 5])
        Y2 = helper.make_tensor_value_info("Y2", TensorProto.INT64, [1])
        shape = helper.make_tensor(
            "shape", TensorProto.INT64, [2], np.array([4, 5], dtype=np.int64)
        )
        indices = helper.make_tensor(
            "indices", TensorProto.INT64, [1], np.array([0], dtype=np.int64)
        )
        axes = helper.make_tensor(
            "axes", TensorProto.INT64, [1], np.array([0], dtype=np.int64)
        )
        X2 = helper.make_tensor_value_info("X2", TensorProto.INT64, [1])

        node_def = helper.make_node("Shape", ["X"], ["X1"],)
        node_def1 = helper.make_node("Gather", ["X1", "indices"], ["X2"], axis=0,)
        node_def2 = helper.make_node("Concat", ["X2", "shape"], ["X3"], axis=0,)
        node_def3 = helper.make_node("Reshape", ["X", "X3"], ["Y1"],)
        node_def4 = helper.make_node("Shape", ["X3"], ["Y2"],)

        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3, node_def4],  # nodes
            "test",  # name
            [X],  # inputs
            [Y1, Y2],  # outputs
            [shape, indices, axes],  # initialzer
            value_info=[X2],
        )
        optimized_model = self._optimized(
            graph, ["fuse_concat_into_reshape", "eliminate_deadend"], False
        )

        assert len(optimized_model.graph.node) == 5
        assert optimized_model.graph.node[3].input[1] != "X3"

    def test_eliminate_shape_gather(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info("Y", TensorProto.INT64, [2])
        X2 = helper.make_tensor_value_info("X2", TensorProto.FLOAT, [3, 2])
        indices = helper.make_tensor(
            "indices", TensorProto.INT64, [1], np.array([1], dtype=np.int64)
        )

        node_def = helper.make_node("Relu", ["X"], ["X2"],)
        node_def2 = helper.make_node("Shape", ["X2"], ["X3"],)
        node_def3 = helper.make_node("Gather", ["X3", "indices"], ["X4"],)
        node_def4 = helper.make_node("Identity", ["X4"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def2, node_def3, node_def4],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [indices],  # initializer
            value_info=[X2],
        )
        optimized_model = self._optimized(graph, ["eliminate_shape_gather"], False)

        assert len(optimized_model.graph.node) == 3

    def test_eliminate_nop_reshape(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 5])
        shape = helper.make_tensor(
            "shape", TensorProto.INT64, [3], np.array([0, 4, 5], dtype=np.int64)
        )

        node_def = helper.make_node("Reshape", ["X", "shape"], ["X1"],)
        node_def1 = helper.make_node("Identity", ["X1"], ["Y"],)

        graph = helper.make_graph(
            [node_def, node_def1],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [shape],  # initialzer
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_reshape"], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_eliminate_nop_reshape_with_neg_1(self):  # type: () -> None
        graph = parser.parse_graph("""
           agraph (float[5, 7] X) => (float[5, 7] Z)
           {
              Shape = Constant<value=int64[2]{5, -1}> ()
              Y = Reshape (X, Shape)
              Z = Identity(Y)
           }
        """)
        optimized_model = self._optimized(graph, ["eliminate_nop_reshape", "eliminate_deadend"], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_eliminate_nop_reshape_with_neg_1_and_another_unknown_dim(self):  # type: () -> None
        graph = parser.parse_graph("""
           agraph (float[2, N, 4] X) => (float[2, 8, M, 2] Z)
           {
              Shape = Constant<value=int64[4]{2, 8, -1, 2}> ()
              Y = Reshape (X, Shape)
              Z = Identity(Y)
           }
        """)
        optimized_model = self._optimized(graph, ["eliminate_nop_reshape", "eliminate_deadend"], False, input_shapes_for_comparing={'X': [2, 4, 4]})

        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Reshape"
        assert optimized_model.graph.node[2].op_type == "Identity"

    def test_eliminate_nop_reshape_with_allowzero(self):  # type: () -> None
        graph = parser.parse_graph("""
           agraph (float[N, 0] X) => (float[0, M] Z)
           {
              Shape = Constant<value=int64[2]{0, 5}> ()
              Y = Reshape (X, Shape)
              Z = Identity(Y)
           }
        """)
        optimized_model = self._optimized(graph, ["eliminate_nop_reshape", "eliminate_deadend"], False, compare_result=False)

        assert len(optimized_model.graph.node) == 3

    def test_eliminate_shape_slice(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 5])
        start = helper.make_tensor(
            "start", TensorProto.INT64, [1], np.array([-1], dtype=np.int64)
        )
        end = helper.make_tensor(
            "end", TensorProto.INT64, [1], np.array([999], dtype=np.int64)
        )

        node_def = helper.make_node("Shape", ["X"], ["X1"],)
        node_def1 = helper.make_node("Slice", ["X1", "start", "end"], ["X2"],)
        node_def2 = helper.make_node("Cast", ["X2"], ["X3"], to=TensorProto.FLOAT)
        node_def3 = helper.make_node("Mul", ["X", "X3"], ["Y"])

        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [start, end],  # initialzer
        )
        optimized_model = self._optimized(
            graph, ["eliminate_slice_after_shape", "eliminate_deadend"], False
        )

        assert len(optimized_model.graph.node) == 2

    def test_fuse_einsum_into_matmul_1(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5, 6])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 5, 5])

        node_def = helper.make_node(
            "Einsum", ["X", "X"], ["Y"], equation="bhid,bhjd->bhij"
        )

        graph = helper.make_graph(
            [node_def], "test", [X], [Y],  # nodes  # name  # inputs  # outputs
        )
        optimized_model = self._optimized(graph, ["replace_einsum_with_matmul"], False)

        assert len(optimized_model.graph.node) == 2

    def test_fuse_einsum_into_matmul_2(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5, 6])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 6, 5])
        Z = helper.make_tensor_value_info("Z", TensorProto.FLOAT, [3, 4, 5, 5])

        node_def = helper.make_node(
            "Einsum", ["X", "Y"], ["Y1"], equation="bhij,bhjd->bhid"
        )
        node_def1 = helper.make_node("Identity", ["Y1"], ["Z"])
        graph = helper.make_graph(
            [node_def, node_def1],  # nodes
            "test",  # name
            [X, Y],  # inputs
            [Z],  # outputs
        )
        optimized_model = self._optimized(graph, ["replace_einsum_with_matmul"], False)

        assert len(optimized_model.graph.node) == 2
        assert optimized_model.graph.node[0].op_type == "MatMul"

    def test_eliminate_nop_concat(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5, 6])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 6, 5])

        node_def = helper.make_node(
            "Concat", ["X"], ["Y1"], axis=0
        )
        node_def1 = helper.make_node("Identity", ["Y1"], ["Y"])
        graph = helper.make_graph(
            [node_def, node_def1],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
        )
        optimized_model = self._optimized(graph, ["eliminate_nop_concat"], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_eliminate_nop_split1(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5, 6])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4, 6, 5])

        node_def = helper.make_node(
            "Split", ["X"], ["Y1"], axis=0, split=[3]
        )
        node_def1 = helper.make_node("Identity", ["Y1"], ["Y"])
        graph = helper.make_graph(
            [node_def, node_def1],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
        )
        opset_imports = [helper.make_opsetid("", 11)]
        optimized_model = self._optimized(graph, ["eliminate_nop_split"], False, opset_imports=opset_imports)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Identity"

    def test_eliminate_nop_split2(self):  # type: () -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4, 5, 6])
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4, 6, 5])
        Z = helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 4, 6, 5])

        node_def = helper.make_node(
            "Split", ["X"], ["Y1", "Y2"], axis=0, split=[2, 1]
        )
        node_def1 = helper.make_node("Identity", ["Y1"], ["Y"])
        node_def2 = helper.make_node("Identity", ["Y2"], ["Z"])
        graph = helper.make_graph(
            [node_def, node_def1, node_def2],  # nodes
            "test",  # name
            [X],  # inputs
            [Y, Z],  # outputs
        )
        opset_imports = [helper.make_opsetid("", 11)]
        optimized_model = self._optimized(graph, ["eliminate_nop_split"], False, opset_imports=opset_imports)

        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Split"

    def test_eliminate_nop_with_unit(self):  # type: () -> None
        X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [3, 2])
        Y = helper.make_tensor_value_info('Y', TensorProto.BOOL, [3, 2])
        one = helper.make_tensor('one', TensorProto.FLOAT, [2], np.array([1, 1], dtype=np.float32))
        zero = helper.make_tensor('zero', TensorProto.FLOAT, [2], np.array([0, 0], dtype=np.float32))
        bool_one = helper.make_tensor('bool_one', TensorProto.BOOL, [2], np.array([1, 1], dtype=np.int32))
        bool_zero = helper.make_tensor('bool_zero', TensorProto.BOOL, [2], np.array([0, 0], dtype=np.int32))
        empty = helper.make_tensor('empty', TensorProto.BOOL, [0], np.array([], dtype=bool))
        X9 = helper.make_tensor_value_info('X9', TensorProto.BOOL, [2])
        X10 = helper.make_tensor_value_info('X10', TensorProto.BOOL, [2])

        node_def = helper.make_node('Mul', ['X', 'one'], ['X1'],)
        node_def1 = helper.make_node('Mul', ['one', 'X1'], ['X2'],)
        node_def2 = helper.make_node('Add', ['X2', 'zero'], ['X3'],)
        node_def3 = helper.make_node('Add', ['zero', 'X3'], ['X4'],)
        node_def4 = helper.make_node('Sub', ['X4', 'zero'], ['X5'],)
        node_def5 = helper.make_node('Div', ['X5', 'one'], ['X6'],)
        node_def6 = helper.make_node('Pow', ['X6', 'one'], ['X7'],)
        node_def7 = helper.make_node('Sub', ['zero', 'X7'], ['X8'],)
        node_def8 = helper.make_node('Cast', ['X8'], ['X9'], to=TensorProto.BOOL)
        node_def9 = helper.make_node('Or', ['bool_zero', 'X9'], ['X10'],)
        node_def10 = helper.make_node('And', ['bool_one', 'X10'], ['X11'],)
        node_def11 = helper.make_node('Concat', ['empty', 'X11'], ['X12'], axis=0)
        node_def12 = helper.make_node('Concat', ['X12', 'X12', 'empty'], ['X13'], axis=0)
        node_def13 = helper.make_node('Identity', ['X13'], ['Y'],)

        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3, node_def4, node_def5,
            node_def6, node_def7, node_def8, node_def9, node_def10, node_def11,
            node_def12, node_def13],        # nodes
            'test',      # name
            [X],  # inputs
            [Y],  # outputs
            [one, zero, bool_one, bool_zero, empty],   # initialzer
            value_info=[X9, X10],
        )
        # onnxruntime crashes in CI, don't know why
        optimized_model = self._optimized(
            graph, ["eliminate_nop_with_unit", "eliminate_deadend", "eliminate_nop_concat"], True, compare_result=False)

        assert len(optimized_model.graph.node) == 4
        assert optimized_model.graph.node[2].op_type == 'Concat'
        assert len(optimized_model.graph.node[2].input) == 2

    def test_fuse_reduction_unsqueeze(self):  # type: () -> None
        # type: (Tuple[int, ...], List[int], List[int], bool) -> Tuple[int, ...]
        def _calculate_post_transform_shape(
            input_shape, reduction_axes, unsqueeze_axes, keepdim
        ):
            post_reduce_shape = None
            if keepdim:
                post_reduce_shape = tuple(
                    [
                        (x if i not in reduction_axes else 1)
                        for i, x in enumerate(input_shape)
                    ]
                )
            else:
                post_reduce_shape = tuple(
                    [x for i, x in enumerate(input_shape) if i not in reduction_axes]
                )
            post_unsqueeze_shape = list(post_reduce_shape)
            for ax in unsqueeze_axes:
                post_unsqueeze_shape.insert(ax, 1)
            return tuple(post_unsqueeze_shape)

        for reduction in [
            "ReduceL1",
            "ReduceL2",
            "ReduceLogSum",
            "ReduceLogSumExp",
            "ReduceMax",
            "ReduceMean",
            "ReduceMin",
            "ReduceProd",
            "ReduceSum",
            "ReduceSumSquare",
        ]:
            for axes1 in [[1], [1, 2], [2]]:
                for axes2 in [[0], [0, 1], [1]]:
                    for keepdim in [False, True]:
                        input_shape = (5, 7, 9)
                        output_shape = _calculate_post_transform_shape(
                            input_shape, axes1, axes2, keepdim
                        )  # type: Tuple[int, ...]
                        axes2_arr = np.array(axes2, dtype=np.int64)
                        graph_input = [
                            helper.make_tensor_value_info(
                                "X", TensorProto.FLOAT, input_shape
                            ),
                        ]
                        graph_initializer = [
                            helper.make_tensor(
                                "Y_axes",
                                TensorProto.INT64,
                                axes2_arr.shape,
                                axes2_arr.tobytes(),
                                raw=True,
                            )
                        ]
                        if reduction in ("ReduceSum"):
                            axes1_arr = np.array(axes1, dtype=np.int64)
                            node = helper.make_node(
                                reduction, ["X", "X_axes"], ["Y"], keepdims=keepdim
                            )
                            graph_initializer.append(
                                helper.make_tensor(
                                    "X_axes",
                                    TensorProto.INT64,
                                    axes1_arr.shape,
                                    axes1_arr.tobytes(),
                                    raw=True,
                                )
                            )
                        else:
                            node = helper.make_node(
                                reduction, ["X"], ["Y"], axes=axes1, keepdims=keepdim
                            )

                        node1 = helper.make_node("Unsqueeze", ["Y", "Y_axes"], ["Z"])
                        graph = helper.make_graph(
                            [node, node1],
                            "test",
                            graph_input,
                            [
                                helper.make_tensor_value_info(
                                    "Z", TensorProto.FLOAT, output_shape
                                )
                            ],
                            initializer=graph_initializer,
                        )
                        optimized_model = self._optimized(
                            graph, ["fuse_consecutive_reduce_unsqueeze"], False
                        )

                        if keepdim or axes1 != axes2:
                            assert optimized_model.graph == graph
                        else:
                            assert len(optimized_model.graph.output) == 1
                            assert len(optimized_model.graph.node) == 1
                            assert (
                                optimized_model.graph.output[
                                    0
                                ].type.tensor_type.elem_type
                                == TensorProto.FLOAT
                            )
                            assert optimized_model.graph.node[-1].op_type == reduction

                            if reduction in ("ReduceSum"):
                                for init in optimized_model.graph.initializer:
                                    if (
                                        init.name
                                        == optimized_model.graph.node[-1].input[1]
                                    ):
                                        assert list(to_array(init)) == axes1
                            else:
                                assert (
                                    optimized_model.graph.node[-1].attribute[0].name
                                    == "axes"
                                )
                                assert (
                                    optimized_model.graph.node[-1].attribute[0].ints
                                    == axes1
                                )
                            optimized_output_shape = tuple(
                                x.dim_value
                                for x in optimized_model.graph.output[
                                    0
                                ].type.tensor_type.shape.dim
                            )
                            assert optimized_output_shape == output_shape

    def test_split_predict_and_lift_lexical_references_for_caffe2_backend(self):
        model_str = b'\x08\x06\x12\x07pytorch\x1a\x031.9:\xe5\x02\n\'\x12\x011"\x08Constant*\x18\n\x05value*\x0c\x10\x07J\x08\x05\x00\x00\x00\x00\x00\x00\x00\xa0\x01\x04\n \x12\x012"\x08Constant*\x11\n\x05value*\x05\x10\tJ\x01\x01\xa0\x01\x04\n\xd1\x01\n\x011\n\x012\n\x03x.1\x12\x013"\x04Loop*\xba\x01\n\x04body2\xae\x01\n\x1a\n\x04x.11\n\x03i.1\x12\x017\x1a\x05Add_0"\x03Add\n\x1c\n\x012\x12\x018\x1a\nIdentity_1"\x08Identity\x12\x11torch-jit-export1Z\r\n\x03i.1\x12\x06\n\x04\x08\x07\x12\x00Z\x0e\n\x04cond\x12\x06\n\x04\x08\t\x12\x00Z\x1a\n\x04x.11\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03b\x0b\n\x018\x12\x06\n\x04\x08\t\x12\x00b\x17\n\x017\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03\xa0\x01\x05\x12\x10torch-jit-exportZ\x19\n\x03x.1\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03b\x17\n\x013\x12\x12\n\x10\x08\x07\x12\x0c\n\x02\x08\x01\n\x02\x08\x02\n\x02\x08\x03B\x02\x10\t'
        model = onnx.load_from_string(model_str)
        passes = [
            "fuse_consecutive_transposes",
            "eliminate_nop_transpose",
            "fuse_transpose_into_gemm",
            "lift_lexical_references",
            "split_predict",
        ]
        self._optimized(
            model, passes, fixed_point=True, compare_result=False, check=False
        )

    @pytest.mark.xfail
    @unittest.skipUnless(has_tv, "This test needs torchvision")
    def test_torchvision_fasterrcnn_fpn(self):  # type: () -> None
        model = tv.models.detection.fasterrcnn_resnet50_fpn(pretrained=False)
        x = (torch.rand(3, 300, 400), torch.rand(3, 500, 400))
        with io.BytesIO() as f:
            torch.onnx.export(model, x, f, opset_version=18)
            model = onnx.load_model_from_string(f.getvalue())
            self._optimized(
                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True
            )

    # # maskrcnn is only supported in opset 11 and higher
    # @unittest.skipUnless(has_tv, "This test needs torchvision")
    # def test_torchvision_maskrcnn_fpn_opset11(self):  # type: () -> None
    #     model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False)
    #     x = (torch.rand(3, 300, 400), torch.rand(3, 500, 400))
    #     with io.BytesIO() as f:
    #         torch.onnx.export(model, x, f, dynamo=False)
    #         model = onnx.load_model_from_string(f.getvalue())
    #         self._optimized(
    #             model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True
    #         )

    # keypointrcnn is only supported in opset 11 and higher
    @pytest.mark.xfail
    @unittest.skipUnless(has_tv, "This test needs torchvision")
    def test_torchvision_keypointrcnn_fpn(self):  # type: () -> None
        model = tv.models.detection.keypointrcnn_resnet50_fpn(pretrained=False)
        x = (torch.rand(3, 300, 400), torch.rand(3, 500, 400))
        with io.BytesIO() as f:
            torch.onnx.export(model, x, f, opset_version=18)
            model = onnx.load_model_from_string(f.getvalue())
            self._optimized(
                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True
            )

    @unittest.skipUnless(has_tv, "This test needs torchvision")
    def test_torchvision_shufflenet_v2(self):  # type: () -> None
        model = tv.models.shufflenet_v2_x1_0(pretrained=False).eval()
        x = torch.rand(1, 3, 224, 224)
        with io.BytesIO() as f:
            torch.onnx.export(model, x, f)
            model = onnx.load_model_from_string(f.getvalue())
            self._optimized(
                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True
            )

    @unittest.skipUnless(has_tv, "This test needs torchvision")
    def test_torchvision_mnasnet(self):  # type: () -> None
        model = tv.models.mnasnet1_0(pretrained=False).eval()
        x = torch.rand(1, 3, 224, 224)
        with io.BytesIO() as f:
            torch.onnx.export(model, x, f)
            model = onnx.load_model_from_string(f.getvalue())
            self._optimized(
                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True
            )

    @unittest.skipUnless(has_tv, "This test needs torchvision")
    def test_torchvision_deeplabv3(self):  # type: () -> None
        model = tv.models.segmentation.deeplabv3_resnet50(pretrained=False).eval()
        x = torch.rand(1, 3, 224, 224)
        with io.BytesIO() as f:
            torch.onnx.export(model, x, f)
            model = onnx.load_model_from_string(f.getvalue())
            self._optimized(
                model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True
            )

    def test_adjust_add(self):  # type: () -> None
        shapes = [3, 4, 5, 6]
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, shapes)
        Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, shapes)

        I0 = helper.make_tensor(
            "I0", TensorProto.FLOAT, shapes, np.zeros(shapes, dtype=np.float32)
        )

        I1 = helper.make_tensor(
            "I1", TensorProto.FLOAT, shapes, np.zeros(shapes, dtype=np.float32)
        )

        node_def = helper.make_node("Add", ["I0", "X"], ["Y1"])
        node_def1 = helper.make_node("Add", ["I1", "Y1"], ["Y2"])
        node_def2 = helper.make_node("Add", ["Y1", "Y2"], ["Y"])
        graph = helper.make_graph(
            [node_def, node_def1, node_def2],  # nodes
            "test",  # name
            [X],  # inputs
            [Y],  # outputs
            [I0, I1],  # initializer
        )
        optimized_model = self._optimized(graph, ["adjust_add", "eliminate_deadend"], False)
        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == "Add"
        assert optimized_model.graph.node[1].op_type == "Add"
        assert optimized_model.graph.node[0].input[0] == "X"
        assert optimized_model.graph.node[0].input[1] == "I0"
        assert optimized_model.graph.node[1].input[0] == "Y1"
        assert optimized_model.graph.node[1].input[1] == "I1"
        assert optimized_model.graph.node[2].input[0] == "Y1"
        assert optimized_model.graph.node[2].input[1] == "Y2"

    def test_adjust_slice_and_matmul(self):  # type: () -> None
        X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1])
        Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [3, 1, 6, 5])

        indices = helper.make_tensor('indices', TensorProto.INT64, [], [0])
        axes = helper.make_tensor('axes', TensorProto.INT64, [1], [0])
        t1 = helper.make_tensor('t1', TensorProto.FLOAT, [3, 4, 5, 6], np.random.rand(3, 4, 5, 6).astype(np.float32))
        t2 = helper.make_tensor('t2', TensorProto.FLOAT, [6, 6], np.random.rand(6, 6).astype(np.float32))

        start = helper.make_tensor('start', TensorProto.INT64, [1], [0])
        slice_axes = helper.make_tensor('slice_axes', TensorProto.INT64, [1], [1])
        node_def = helper.make_node('Shape', ['X'], ['X1'])
        node_def1 = helper.make_node('Gather', ['X1', 'indices'], ['X2'], axis=0)
        node_def2 = helper.make_node('Unsqueeze', ['X2', 'axes'], ['X3'])
        node_def3 = helper.make_node('Slice', ['t1', 'start', 'X3', 'slice_axes'], ['X4'])
        node_def4 = helper.make_node('MatMul', ['X4', 't2'], ['Y'])

        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3, node_def4, ],        # nodes
            'test',      # name
            [X],  # inputs
            [Y],  # outputs
            [indices, axes, t1, t2, start, slice_axes]
        )
        optimized_model = self._optimized(
            graph, ['adjust_slice_and_matmul', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 5
        assert optimized_model.graph.node[3].op_type == 'MatMul'
        assert optimized_model.graph.node[4].op_type == 'Slice'

    @pytest.mark.xfail
    def test_fuse_consecutive_slices_1(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[1, 3, 640, 640] X) => (float[1, 3, 320, 320] Z)
               {
                  t0 = Constant<value=int64[1]{0}>()
                  t1 = Constant<value=int64[1]{1}>()
                  t2 = Constant<value=int64[1]{2}>()
                  t3 = Constant<value=int64[1]{3}>()
                  t640 = Constant<value=int64[1]{640}>()
                  Y = Slice(X, t0, t640, t2, t2)
                  Z = Slice(Y, t0, t640, t3, t2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['extract_constant_to_initializer', 'fuse_consecutive_slices', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 5
        assert optimized_model.graph.node[0].op_type == 'Concat'
        assert optimized_model.graph.node[1].op_type == 'Concat'
        assert optimized_model.graph.node[2].op_type == 'Concat'
        assert optimized_model.graph.node[3].op_type == 'Concat'
        assert optimized_model.graph.node[4].op_type == 'Slice'

    @pytest.mark.xfail
    def test_fuse_consecutive_slices_2(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[1, 3, 640, 640] X) => (float[1, 3, 320, 320] Z)
               {
                  t0 = Constant<value=int64[1]{0}>()
                  t1 = Constant<value=int64[1]{1}>()
                  t2 = Constant<value=int64[1]{2}>()
                  t3 = Constant<value=int64[1]{3}>()
                  t640 = Constant<value=int64[1]{640}>()
                  Y = Slice(X, t1, t640, t2, t2)
                  Z = Slice(Y, t0, t640, t3, t2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['extract_constant_to_initializer', 'fuse_consecutive_slices', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 5
        assert optimized_model.graph.node[0].op_type == 'Concat'
        assert optimized_model.graph.node[1].op_type == 'Concat'
        assert optimized_model.graph.node[2].op_type == 'Concat'
        assert optimized_model.graph.node[3].op_type == 'Concat'
        assert optimized_model.graph.node[4].op_type == 'Slice'

    @pytest.mark.xfail
    def test_fuse_consecutive_slices_3(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[1, 3, 640, 640] X) => (float[1, 3, 320, 320] Z)
               {
                  t0 = Constant<value=int64[1]{0}>()
                  t1 = Constant<value=int64[1]{1}>()
                  t2 = Constant<value=int64[1]{2}>()
                  t3 = Constant<value=int64[1]{3}>()
                  t640 = Constant<value=int64[1]{640}>()
                  Y = Slice(X, t0, t640, t2, t2)
                  Z = Slice(Y, t1, t640, t3, t2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['extract_constant_to_initializer', 'fuse_consecutive_slices', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 5
        assert optimized_model.graph.node[0].op_type == 'Concat'
        assert optimized_model.graph.node[1].op_type == 'Concat'
        assert optimized_model.graph.node[2].op_type == 'Concat'
        assert optimized_model.graph.node[3].op_type == 'Concat'
        assert optimized_model.graph.node[4].op_type == 'Slice'

    @pytest.mark.xfail
    def test_fuse_consecutive_slices_4(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[1, 3, 640, 640] X) => (float[1, 3, 320, 320] Z)
               {
                  t0 = Constant<value=int64[1]{0}>()
                  t1 = Constant<value=int64[1]{1}>()
                  t2 = Constant<value=int64[1]{2}>()
                  t3 = Constant<value=int64[1]{3}>()
                  t640 = Constant<value=int64[1]{640}>()
                  Y = Slice(X, t1, t640, t2, t2)
                  Z = Slice(Y, t1, t640, t3, t2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['extract_constant_to_initializer', 'fuse_consecutive_slices', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 5
        assert optimized_model.graph.node[0].op_type == 'Concat'
        assert optimized_model.graph.node[1].op_type == 'Concat'
        assert optimized_model.graph.node[2].op_type == 'Concat'
        assert optimized_model.graph.node[3].op_type == 'Concat'
        assert optimized_model.graph.node[4].op_type == 'Slice'

    def test_fuse_consecutive_slices_with_same_axes(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[1, 3, 640, 640] X) => (float[1, 3, 320, 320] Z)
               {
                  t0 = Constant<value=int64[1]{0}>()
                  t1 = Constant<value=int64[1]{1}>()
                  t2 = Constant<value=int64[1]{2}>()
                  tn2 = Constant<value=int64[1]{-2}>()
                  t3 = Constant<value=int64[1]{3}>()
                  t640 = Constant<value=int64[1]{640}>()
                  Y = Slice(X, t1, t640, t2, t2)
                  Z = Slice(Y, t1, t640, tn2, t2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['extract_constant_to_initializer', 'fuse_consecutive_slices', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 2
        assert optimized_model.graph.node[0].op_type == 'Slice'
        assert optimized_model.graph.node[1].op_type == 'Slice'

    def test_eliminate_common_subexression(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[1, 1280] X) => (float[1, 1280] Z)
               {
                  S1 = Sigmoid(X)
                  S2 = Sigmoid(X)
                  M1 = Mul(X, S1)
                  M2 = Mul(X, S2)
                  Z = Add(M1, M2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['eliminate_common_subexpression', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 3
        assert optimized_model.graph.node[0].op_type == 'Sigmoid'
        assert optimized_model.graph.node[1].op_type == 'Mul'
        assert optimized_model.graph.node[2].op_type == 'Add'

    def test_eliminate_common_subexression_with_attribute(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[4, 64, 160, 160] X) => (float[4, 64, 160, 160] Z)
               {
                  R1 = ReduceMean<axes=[2, 3], keepdims=1>(X)
                  R2 = ReduceMean<axes=[2, 3], keepdims=1>(X)
                  S1 = Add(X, R1)
                  S2 = Add(X, R2)
                  M1 = Sqrt(S2)
                  Z = Div(S1, M1)
               }
            """)

        optimized_model = self._optimized(
            graph, ['eliminate_common_subexpression', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 4
        assert optimized_model.graph.node[0].op_type == 'ReduceMean'
        assert optimized_model.graph.node[1].op_type == 'Add'
        assert optimized_model.graph.node[2].op_type == 'Sqrt'
        assert optimized_model.graph.node[3].op_type == 'Div'

    def test_eliminate_common_subexression_with_attribute1(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[4, 64, 160, 160] X) => (float[4, 64, 160, 160] Z)
               {
                  R1 = ReduceMean<axes=[2, 3], keepdims=1>(X)
                  R2 = ReduceMean<axes=[3], keepdims=1>(X)
                  S1 = Add(X, R1)
                  S2 = Add(X, R2)
                  M1 = Sqrt(S2)
                  Z = Div(S1, M1)
               }
            """)

        optimized_model = self._optimized(
            graph, ['eliminate_common_subexpression', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 6

    def _test_fuse_qkv_with_opset(self, opset_version):  # type: (int) -> None
        X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 4096, 320])
        Y1 = helper.make_tensor_value_info("Y1", TensorProto.FLOAT, [1, 4096, 8, 40])
        Y2 = helper.make_tensor_value_info("Y2", TensorProto.FLOAT, [1, 4096, 8, 40])
        Y3 = helper.make_tensor_value_info("Y3", TensorProto.FLOAT, [1, 4096, 8, 40])

        I0 = helper.make_tensor(
            "I0", TensorProto.FLOAT, [320], np.random.random((320)).astype(np.float32)
        )

        I1 = helper.make_tensor(
            "I1", TensorProto.FLOAT, [320], np.random.random((320)).astype(np.float32)
        )

        I2 = helper.make_tensor(
            "I2", TensorProto.FLOAT, [320, 320], np.random.random((320, 320)).astype(np.float32)
        )

        I3 = helper.make_tensor(
            "I3", TensorProto.FLOAT, [320, 320], np.random.random((320, 320)).astype(np.float32)
        )

        I4 = helper.make_tensor(
            "I4", TensorProto.FLOAT, [320, 320], np.random.random((320, 320)).astype(np.float32)
        )

        shape = helper.make_tensor("shape", TensorProto.INT64, [4], [1, 4096, 8, 40])

        node_def = helper.make_node("Mul", ["X", "I0"], ["Z1"])
        node_def1 = helper.make_node("Add", ["Z1", "I1"], ["Z2"])
        node_def2 = helper.make_node("MatMul", ["Z2", "I2"], ["Z3"])
        node_def3 = helper.make_node("MatMul", ["Z2", "I3"], ["Z4"])
        node_def4 = helper.make_node("MatMul", ["Z2", "I4"], ["Z5"])
        node_def5 = helper.make_node("Reshape", ["Z3", "shape"], ["Y1"])
        node_def6 = helper.make_node("Reshape", ["Z4", "shape"], ["Y2"])
        node_def7 = helper.make_node("Reshape", ["Z5", "shape"], ["Y3"])
        graph = helper.make_graph(
            [node_def, node_def1, node_def2, node_def3, node_def4, node_def5, node_def6, node_def7],  # nodes
            "test",  # name
            [X],  # inputs
            [Y1, Y2, Y3],  # outputs
            [I0, I1, I2, I3, I4, shape],  # initializer
        )
        optimized_model = self._optimized(graph, ["fuse_qkv", "eliminate_deadend"], False, opset_imports=[helper.make_opsetid("", opset_version)])
        assert len(optimized_model.graph.node) == 8
        assert optimized_model.graph.node[0].op_type == "Mul"
        assert optimized_model.graph.node[1].op_type == "Add"
        assert optimized_model.graph.node[2].op_type == "Concat"
        assert optimized_model.graph.node[3].op_type == "MatMul"
        assert optimized_model.graph.node[4].op_type == "Split"

    def test_fuse_qkv(self):  # type: () -> None
        for opset_version in [11, 15]:
            self._test_fuse_qkv_with_opset(opset_version)
        self._test_fuse_qkv_with_opset(LATEST_STABLE_OPSET_VERSION)

    def test_fuse_consecutive_unsqueezes_opset13(self):  # type: () -> None
        graph = parser.parse_graph("""
               agraph (float[4, 64, 160, 160] X) => (float[1, 1, 1, 4, 64, 1, 160, 160, 1, 1, 1] Z)
               {
                  R1 = Constant<value=int64[4]{0, 1, 4, -1}>()
                  R2 = Constant<value=int64[3]{1, -1, -2}>()
                  S1 = Unsqueeze(X, R1)
                  Z = Unsqueeze(S1, R2)
               }
            """)

        optimized_model = self._optimized(
            graph, ['fuse_consecutive_unsqueezes', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Unsqueeze"

    def test_fuse_consecutive_unsqueezes_opset11(self):  # type: () -> None
        model = parser.parse_model("""
                <
                    ir_version: 7,
                    opset_import:["": 11]
                >
               agraph (float[4, 64, 160, 160] X) => (float[1, 1, 1, 4, 64, 1, 160, 160, 1, 1, 1] Z)
               {
                  S1 = Unsqueeze<axes=[0, 1, 4, -1]>(X)
                  Z = Unsqueeze<axes=[1, -1, -2]>(S1)
               }
            """)

        optimized_model = self._optimized(
            model, ['fuse_consecutive_unsqueezes', 'eliminate_deadend'], False)

        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Unsqueeze"

    def test_eliminate_consecutive_idempotent_op(self):
        model = parser.parse_model("""
                <
                    ir_version: 7,
                    opset_import:["": 11]
                >
               agraph (float[1, 4, 5] X) => (float[2, 5, 2] Z)
               {
                  Shape1 = Constant<value=int64[2]{5, 4}>()
                  Shape2 = Constant<value=int64[3]{1, 20, 1}>()
                  Shape3 = Constant<value=int64[3]{2, 5, 2}>()
                  T1 = Reshape(X, Shape1)
                  T2 = Reshape(T1, Shape2)
                  Z = Reshape(T2, Shape3)
               }
            """)

        optimized_model = self._optimized(
            model, ['eliminate_consecutive_idempotent_ops', 'eliminate_deadend'], True)

        assert len(optimized_model.graph.node) == 2
        assert optimized_model.graph.node[0].op_type == "Constant"
        assert optimized_model.graph.node[1].op_type == "Reshape"

    def test_eliminate_consecutive_idempotent_sign_op(self):
        model = parser.parse_model("""
                <
                    ir_version: 7,
                    opset_import:["": 11]
                >
               agraph (float[1, 2, 3] X) => (float[1, 2, 3] Z)
               {
                  T1 = Sign(X)
                  T2 = Sign(T1)
                  T3 = Sign(T2)
                  Z = Sign(T3)
               }
            """)

        optimized_model = self._optimized(
            model, ['eliminate_consecutive_idempotent_ops', 'eliminate_deadend'], True)
        assert len(optimized_model.graph.node) == 1
        assert optimized_model.graph.node[0].op_type == "Sign"

    def test_rewrite_where(self):
        model = parser.parse_model("""
                <
                    ir_version: 7,
                    opset_import:["": 11]
                >
               agraph (bool[4] A, float[4] X, float[4] Y) => (float[4] F, float[4] H)
               {
                  B = Not(A)
                  Z = Where(B, X, Y)
                  F = Sign(Z)
                  M = And(A,A)
                  G = Where(M, X, Y)
                  H = Sign(G)
               }
            """)

        optimized_model = self._optimized(
            model, ["rewrite_where"], True)

        assert len(optimized_model.graph.node) == 5
        assert set([i.op_type for i in optimized_model.graph.node]) == {'Where', 'And', 'Sign'}
        assert optimized_model.graph.node[0].input == ['A', 'Y', 'X']
        assert optimized_model.graph.node[3].input == ['M', 'X', 'Y']

    def test_split_predict_preserves_elem_type(self):
        """
        Test that split_predict pass preserves elem_type information
        when creating new inputs/outputs.
        
        This reproduces the bug where intermediate values without explicit
        value_info cause split_predict to generate invalid models with
        missing elem_type.
        """
        # Create a model where intermediate values don't have value_info
        # but should have their types inferred from inputs
        model = parser.parse_model("""
            <
                ir_version: 7,
                opset_import:["": 13]
            >
            agraph (float[2] X) => (float[2] Y)
            {
                # Pure operation that can go to init net
                one = Constant<value: tensor = float[2] {1.0, 1.0}>()
                added = Add(X, one)
                
                # Impure operation that must stay in predict net
                # This uses 'added', making it a boundary value
                random = RandomUniform<dtype: int = 1, shape: ints = [2]>()
                Y = Add(random, added)
            }
        """)
        
        # Optimize with split_predict
        optimized_model = self._optimized(
            model, ['split_predict'], False, compare_result=False, check=True)
        
        # Verify the optimized model is valid
        # The key check is that checker.check_model() passes, 
        # which is done by _optimized when check=True
        
        # Additionally verify all inputs have valid elem_type
        for input_val in optimized_model.graph.input:
            self.assertIsNotNone(input_val.type)
            self.assertTrue(input_val.type.HasField('tensor_type'))
            # elem_type must not be UNDEFINED (TensorProto.UNDEFINED = 0)
            self.assertNotEqual(input_val.type.tensor_type.elem_type, 
                              TensorProto.UNDEFINED,
                              f"Input {input_val.name} has UNDEFINED elem_type")


if __name__ == "__main__":
    unittest.main()