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# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import List, Optional
import onnx.shape_inference
from onnx import ModelProto, TensorProto, TensorShapeProto, ValueInfoProto, helper
from onnx.helper import make_model, make_tensor_value_info
class TestSymbolicShape(unittest.TestCase):
def _assert_valueinfo_shape(
self, onnx_model: ModelProto, value_infos: List[ValueInfoProto]
) -> None:
"""Assert onnx_model.value_info should be the same as expected value_infos
Instead of exact symbol, use -1 to represent symbolic shape in expected value_infos
"""
for expected_vi in value_infos:
shape = self._get_shape_from_name(onnx_model, expected_vi.name)
assert shape is not None, f"{onnx_model}"
if expected_vi.type.HasField("tensor_type"):
expected_shape = expected_vi.type.tensor_type.shape
elif expected_vi.type.HasField("sparse_tensor_type"):
expected_shape = expected_vi.type.sparse_tensor_type.shape
assert len(shape.dim) == len(expected_shape.dim), f"{onnx_model}"
for dim_i, dim in enumerate(shape.dim):
expected_dim = expected_shape.dim[dim_i]
# -1 means it's a symbolic shape
if expected_dim.dim_value == -1:
# symbolic dimension must exist
assert dim.dim_param, f"{onnx_model}"
else:
assert dim.dim_value == expected_dim.dim_value, f"{onnx_model}"
def _count_unique_dim_param_number(self, onnx_model: ModelProto) -> int:
"""Return the total number of unique symbolic shape"""
symbol_shape_set = set()
inputs = list(onnx_model.graph.input)
outputs = list(onnx_model.graph.output)
valueinfos = list(onnx_model.graph.value_info)
for v in inputs + outputs + valueinfos:
for dim in v.type.tensor_type.shape.dim:
if dim.dim_param:
symbol_shape_set.add(dim.dim_param)
return len(symbol_shape_set)
def _get_shape_from_name(
self, onnx_model: ModelProto, name: str
) -> Optional[TensorShapeProto]:
"""Get shape from tensor_type or sparse_tensor_type according to given name"""
inputs = list(onnx_model.graph.input)
outputs = list(onnx_model.graph.output)
valueinfos = list(onnx_model.graph.value_info)
for v in inputs + outputs + valueinfos:
if v.name == name:
if v.type.HasField("tensor_type"):
return v.type.tensor_type.shape # type: ignore
if v.type.HasField("sparse_tensor_type"):
return v.type.sparse_tensor_type.shape # type: ignore
return None
def test_concat_enable_symbolic(self) -> None:
concat = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["C"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat, cast],
inputs=[
helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, "A"]),
helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3]),
],
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, [2, None])
],
)
onnx_model = make_model(graph_def)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
self._assert_valueinfo_shape(
inferred_model, [make_tensor_value_info("C", TensorProto.FLOAT, (2, -1))]
)
# the symbolic shape of C and output should be the same
assert self._get_shape_from_name(
inferred_model, "C"
) == self._get_shape_from_name(inferred_model, "output")
def test_two_symbolic_concat(self) -> None:
concat1 = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
concat2 = helper.make_node(
"Concat", inputs=["C", "D"], outputs=["E"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["E"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat1, concat2, cast],
inputs=[
helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, "A"]),
helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3]),
helper.make_tensor_value_info("D", TensorProto.FLOAT, [2, "D"]),
],
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, [2, None])
],
)
onnx_model = make_model(graph_def)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
self._assert_valueinfo_shape(
inferred_model,
[
make_tensor_value_info("C", TensorProto.FLOAT, (2, -1)),
make_tensor_value_info("E", TensorProto.FLOAT, (2, -1)),
],
)
# the symbolic shape of E and output should be the same
assert self._get_shape_from_name(
inferred_model, "E"
) == self._get_shape_from_name(inferred_model, "output")
def test_duplicate_symbolic_shape(self) -> None:
concat1 = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
concat2 = helper.make_node(
"Concat", inputs=["C", "D"], outputs=["E"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["E"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat1, concat2, cast],
inputs=[
helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, "unk__0"]),
helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3]),
helper.make_tensor_value_info("D", TensorProto.FLOAT, [2, "unk__1"]),
],
outputs=[
helper.make_tensor_value_info(
"output", TensorProto.FLOAT, [2, "unk__0"]
)
],
)
onnx_model = make_model(graph_def)
original_count = self._count_unique_dim_param_number(onnx_model)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
inferred_count = self._count_unique_dim_param_number(inferred_model)
# to prevent duplicate so the inferred count will be count + 2
# new symbol 'unk__2' and 'unk__3' should be generated
# original: {'unk_0', 'unk__1'}
# inferred: {'unk_0', 'unk__1', 'unk__2', 'unk__3'}
assert inferred_count == original_count + 2, f"{inferred_model}{onnx_model}"
def test_unknown_shape(self) -> None:
concat = helper.make_node(
"Concat", inputs=["A", "B"], outputs=["C"], name="Concat", axis=1
)
cast = onnx.helper.make_node(
"Cast", inputs=["C"], outputs=["output"], to=TensorProto.FLOAT
)
graph_def = helper.make_graph(
name="test_graph",
nodes=[concat, cast],
inputs=[
helper.make_tensor_value_info(
"A", TensorProto.FLOAT, [3, None]
), # unknown shape
helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, None]),
],
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, [3, None])
],
)
onnx_model = make_model(graph_def)
inferred_model = onnx.shape_inference.infer_shapes(onnx_model, strict_mode=True)
self._assert_valueinfo_shape(
inferred_model, [make_tensor_value_info("C", TensorProto.FLOAT, (3, -1))]
)
# the symbolic shape of C and output should be the same
# ('unk__0', 'unk__1')
assert self._get_shape_from_name(
inferred_model, "C"
) == self._get_shape_from_name(inferred_model, "output")
if __name__ == "__main__":
unittest.main()
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