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optimizer_test.py
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4683 lines (4240 loc) · 196 KB
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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from collections import OrderedDict
from typing import Sequence, Text, Any, Tuple, List, Callable, Optional, Dict, Union
import io
import unittest
import os
import numpy as np # type: ignore
try:
import torch
import torchvision as tv
has_tv = True
except:
has_tv = False
import onnx
import pytest
from onnx import (
checker,
helper,
ModelProto,
TensorProto,
GraphProto,
NodeProto,
shape_inference,
parser,
)
from onnx import numpy_helper
from onnx.numpy_helper import to_array
try:
import onnxruntime as rt
has_ort = True
except:
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",
[