1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
|
# Owner(s): ["module: onnx"]
import pytorch_test_common
import torch
import torch._dynamo
import torch.fx
from torch.onnx._internal.fx.passes import _utils as pass_utils
from torch.testing._internal import common_utils
class TestFxPasses(common_utils.TestCase):
def test_set_node_name_correctly_renames_when_new_name_collides_recursively(self):
def func(x, y, z):
return x + y + z
x = torch.randn(3)
y = torch.randn(3)
z = torch.randn(3)
gm, _ = torch._dynamo.export(func)(x, y, z)
torch._dynamo.reset()
# Purposely name the nodes in a way that will cause a recursive collision later.
# See :func:`set_node_name` for name collision renaming logic.
base_name = "tensor"
nodes = list(gm.graph.nodes)
for i, node in enumerate(nodes[1:]):
if i == 0:
node.name = base_name
else:
node.name = f"{base_name}.{i}"
# Run `set_node_name` and verify that the names are correct.
name_to_node = {node.name: node for node in gm.graph.nodes}
pass_utils.set_node_name(nodes[0], base_name, name_to_node)
assert nodes[0].name == base_name, f"Expected {base_name}, got {nodes[0].name}"
assert len({node.name for node in nodes}) == len(
nodes
), f"Expected all names to be unique, got {nodes}"
def test_set_node_name_succeeds_when_no_name_collisions(self):
def func(x, y, z):
return x + y + z
x = torch.randn(3)
y = torch.randn(3)
z = torch.randn(3)
gm, _ = torch._dynamo.export(func)(x, y, z)
torch._dynamo.reset()
# Run `set_node_name` and verify that the names are correct.
new_name = "some_tensor"
nodes = list(gm.graph.nodes)
name_to_node = {node.name: node for node in nodes}
pass_utils.set_node_name(nodes[1], new_name, name_to_node)
assert nodes[1].name == new_name, f"Expected {new_name}, got {nodes[0].name}"
assert len({node.name for node in nodes}) == len(
nodes
), f"Expected all names to be unique, got {nodes}"
def test_onnx_dynamo_export_raises_when_model_contains_unsupported_fx_nodes(self):
@torch.library.custom_op(
"mylibrary::foo_op", device_types="cpu", mutates_args=()
)
def foo_op(x: torch.Tensor) -> torch.Tensor:
return x + 1
@torch.library.custom_op(
"mylibrary::bar_op", device_types="cpu", mutates_args=()
)
def bar_op(x: torch.Tensor) -> torch.Tensor:
return x + 2
@foo_op.register_fake
def _(x):
return torch.empty_like(x)
@bar_op.register_fake
def _(x):
return torch.empty_like(x)
def func(x, y, z):
return foo_op(x) + bar_op(y) + z
x = torch.randn(3)
y = torch.randn(3)
z = torch.randn(3)
with self.assertRaises(torch.onnx.OnnxExporterError) as ctx:
torch.onnx.dynamo_export(func, x, y, z)
inner_exception = ctx.exception.__cause__
self.assertRegex(
str(inner_exception),
r"Unsupported FX nodes.*mylibrary\.foo_op.*mylibrary\.bar_op",
)
torch._dynamo.reset()
@common_utils.instantiate_parametrized_tests
class TestModularizePass(common_utils.TestCase):
@pytorch_test_common.xfail(
error_message="'torch_nn_modules_activation_GELU_used_gelu_1' not found",
reason="optimizer",
)
@common_utils.parametrize(
"is_exported_program",
[
common_utils.subtest(
True,
name="exported_program",
),
common_utils.subtest(
False,
name="nn_module",
),
],
)
def test_modularize_pass_succeeds_when_submodule_output_is_unused(
self, is_exported_program
):
# This is an ill-formed model, but exporter must not crash.
# It is illegal for submodule to have zero output. For modularization pass it can happen
# when the submodule output is unused, so no inner node is connected to any outer
# nodes.
# However, this also means the entire submodule should be erased by DCE. Hence
# it should never occur.
#
# Minified repro from Background_Matting. https://github.com/pytorch/benchmark/issues/1768
class TestModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.unused_relu = torch.nn.ReLU()
self.used_gelu = torch.nn.GELU()
def forward(self, x, y):
result = self.used_gelu(x + y)
unused_relu_result = self.unused_relu(x)
return result
if is_exported_program:
model = torch.export.export(
TestModule(), args=(torch.randn(3), torch.randn(3))
)
else:
model = TestModule()
onnx_program = torch.onnx.dynamo_export(model, torch.randn(3), torch.randn(3))
model_proto = onnx_program.model_proto
function_proto_names = [function.name for function in model_proto.functions]
self.assertIn(
"torch_nn_modules_activation_GELU_used_gelu_1", function_proto_names
)
self.assertFalse(any("ReLU" in name for name in function_proto_names))
@pytorch_test_common.xfail(
error_message="'torch_nn_modules_activation_ReLU_relu_1' not found",
reason="optimizer",
)
@common_utils.parametrize(
"is_exported_program",
[
common_utils.subtest(
True,
name="exported_program",
),
common_utils.subtest(
False,
name="nn_module",
),
],
)
def test_modularize_pass_succeeds_when_a_submodule_is_called_multiple_times(
self, is_exported_program
):
class TestModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.relu = torch.nn.ReLU()
def forward(self, x, y):
out = x + y
out = self.relu(out)
out = out + x
out = self.relu(out)
return out
if is_exported_program:
model = torch.export.export(
TestModule(), args=(torch.randn(3), torch.randn(3))
)
else:
model = TestModule()
onnx_program = torch.onnx.dynamo_export(model, torch.randn(3), torch.randn(3))
model_proto = onnx_program.model_proto
function_proto_names = [function.name for function in model_proto.functions]
self.assertIn("torch_nn_modules_activation_ReLU_relu_1", function_proto_names)
self.assertIn("torch_nn_modules_activation_ReLU_relu_2", function_proto_names)
@pytorch_test_common.xfail(
error_message="'torch_nn_modules_activation_ReLU_inner_module_relu_1' not found",
reason="optimizer",
)
@common_utils.parametrize(
"is_exported_program",
[
common_utils.subtest(
True,
name="exported_program",
),
common_utils.subtest(
False,
name="nn_module",
),
],
)
def test_modularize_pass_succeeds_when_a_submodule_is_called_from_multiple_layers(
self, is_exported_program
):
# Minified repro from basic_gnn_edgecnn.
class InnerModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.relu = torch.nn.ReLU()
def forward(self, x):
return self.relu(x)
class TestModule(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.inner_module = InnerModule()
def forward(self, x, y):
out = x + y
out = self.inner_module(out)
out = out + x
out = self.inner_module.relu(out)
return out
if is_exported_program:
model = torch.export.export(
TestModule(), args=(torch.randn(3), torch.randn(3))
)
else:
model = TestModule()
onnx_program = torch.onnx.dynamo_export(model, torch.randn(3), torch.randn(3))
model_proto = onnx_program.model_proto
function_proto_names = [function.name for function in model_proto.functions]
self.assertIn(
"torch_nn_modules_activation_ReLU_inner_module_relu_1", function_proto_names
)
self.assertIn(
"torch_nn_modules_activation_ReLU_inner_module_relu_2", function_proto_names
)
# local module qualified name is unstable in test environment depending on different test
# invocation methods.
self.assertTrue(
any("InnerModule_inner_module_1" in name for name in function_proto_names)
)
if __name__ == "__main__":
common_utils.run_tests()
|
