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FastDeploy/third_party/optimizer/onnxoptimizer/test/optimizer_test.py
Jason 6343b0db47 [Build] Support build with source code of Paddle2ONNX (#1559) 
* Add notes for tensors

* Optimize some apis

* move some warnings

* Support build with Paddle2ONNX

* Add protobuf support

* Fix compile on mac

* add clearn package script

* Add paddle2onnx code

* remove submodule

* Add onnx ocde

* remove softlink

* add onnx code

* fix error

* Add cmake file

* fix patchelf

* update paddle2onnx

* Delete .gitmodules

---------

Co-authored-by: PaddleCI <paddle_ci@example.com>
Co-authored-by: pangyoki <pangyoki@126.com>
Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
2023-03-17 10:03:22 +08:00

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# SPDX-License-Identifier: Apache-2.0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
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
from onnx import checker, helper, ModelProto, TensorProto, GraphProto, NodeProto, shape_inference
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, np.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, **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, **kwargs)
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)
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_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_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
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.float32)
graph = helper.make_graph(
[add_1, add_2],
"test",
[helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,))],
[helper.make_tensor_value_info("C", TensorProto.FLOAT, (5,))],
[helper.make_tensor("I_0", TensorProto.FLOAT,
dims=(5,),
vals=i.tobytes(),
raw=True),
helper.make_tensor("I_1", TensorProto.FLOAT,
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("Pads", TensorProto.INT64, (2,))],
[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("Pads", TensorProto.INT64, (4,))],
[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("Pads", TensorProto.INT64, (4,))],
[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"])
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"])
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"])
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"])
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"])
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_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("Pads", TensorProto.INT64, (8,)),
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("Pads", TensorProto.INT64, (8,)),
helper.make_tensor_value_info(
"Constant_value", TensorProto.FLOAT, ()),
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("Pads", TensorProto.INT64, (8,)),
helper.make_tensor_value_info(
"Constant_value", TensorProto.FLOAT, ()),
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("Pads", TensorProto.INT64, (6,)),
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("Pads", TensorProto.INT64, (8,)),
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("Pads", TensorProto.INT64, (8,)),
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("Pads", TensorProto.INT64, (8,)),
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("Pads", TensorProto.INT64, (8,)),
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_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("X_axes", TensorProto.INT64, [3]),
helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2])],
[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("X_axes", TensorProto.INT64, [3]),
helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2]),
helper.make_tensor_value_info("Z_axes", TensorProto.INT64, [1])],
[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(
"X_axes", TensorProto.INT64, s1_axes.shape),
helper.make_tensor_value_info("Y_axes", TensorProto.INT64, s2_axes.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("X_axes", TensorProto.INT64, [3]),
helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2]),
],
[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]
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
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"])
self.assertEqual(len(optimized_model.graph.node), 1)
self.assertEqual(optimized_model.graph.node[0].op_type, 'Conv')
self.assertEqual(len(optimized_model.graph.initializer), 2)
new_W = numpy_helper.to_array(optimized_model.graph.initializer[0])
new_b = numpy_helper.to_array(optimized_model.graph.initializer[1])
f = scale / np.sqrt(var + 1e-5)
np.testing.assert_almost_equal((B - mean) * f + b, new_b)
np.testing.assert_almost_equal(
W * f[:, np.newaxis, np.newaxis, np.newaxis], new_W)
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]:
node = helper.make_node("Dropout", ["X"], ["Y"], ratio=ratio)
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, 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_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),
helper.make_tensor_value_info("Y_axes", TensorProto.INT64, axes2_arr.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_input.append(
helper.make_tensor_value_info("X_axes", TensorProto.INT64, axes1_arr.shape))
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)
@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=11)
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, opset_version=11)
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
@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=11)
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)
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)
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)
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)
if __name__ == '__main__':
unittest.main()
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