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Yifan Li (yf711) merged 7 commits into from
Nov 14, 2024
Merged

Re-enable test symbolic shape infer #22737

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test additioal python float type conversion
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assign sympy version when py>=3.9
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141 changes: 70 additions & 71 deletions onnxruntime/test/python/onnxruntime_test_python_symbolic_shape_infer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,27 +39,27 @@ def unique_element(lst):


class TestSymbolicShapeInference(unittest.TestCase):
# TODO: investigate why symbolic shape infer test failed for Python 3.10
# def test_symbolic_shape_infer(self):
# from pathlib import Path
# cwd = os.getcwd()
# test_model_dir = os.path.join(cwd, "..", "models")
# for filename in Path(test_model_dir).rglob("*.onnx"):
# if filename.name.startswith("."):
# continue # skip some bad model files
#
# # https://github.com/onnx/models/issues/562
# if any(model_name in str(filename) for model_name in skipped_models):
# print(f"Skip symbolic shape inference on : {filename!s}")
# continue
#
# print("Running symbolic shape inference on : " + str(filename))
# SymbolicShapeInference.infer_shapes(
# in_mp=onnx.load(str(filename)),
# auto_merge=True,
# int_max=100000,
# guess_output_rank=True,
# )
def test_symbolic_shape_infer(self):
from pathlib import Path

cwd = os.getcwd()
test_model_dir = os.path.join(cwd, "..", "models")
for filename in Path(test_model_dir).rglob("*.onnx"):
if filename.name.startswith("."):
continue # skip some bad model files

# https://github.com/onnx/models/issues/562
if any(model_name in str(filename) for model_name in skipped_models):
print(f"Skip symbolic shape inference on : {filename!s}")
continue

print("Running symbolic shape inference on : " + str(filename))
SymbolicShapeInference.infer_shapes(
in_mp=onnx.load(str(filename)),
auto_merge=True,
int_max=100000,
guess_output_rank=True,
)

def test_mismatched_types(self):
graph = helper.make_graph(
Expand Down Expand Up @@ -343,56 +343,55 @@ def test_einsum_sum(self):
def test_einsum_transpose(self):
self._test_einsum_one_input_impl(["a", "b"], ["b", "a"], "ij -> ji")

# TODO: investigate why symbolic shape infer test failed for Python 3.10
# def test_mul_precision(self):
# graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [1024])
# graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)
#
# # initializers
# value = numpy.array([0.5], dtype=numpy.float32)
# constant = numpy_helper.from_array(value, name="constant")
#
# nodes = [
# # Get the shape of the input tensor: `input_tensor_shape = [1024]`.
# onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
# # mul(1024, 0.5) => 512
# onnx.helper.make_node("Mul", ["input_shape", "constant"], ["output_shape"]),
# # Resize input
# onnx.helper.make_node(
# "Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
# ),
# ]
#
# graph_def = onnx.helper.make_graph(nodes, "TestMulPrecision", [graph_input], [graph_output], [constant])
# model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
# output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
# self.assertEqual(len(output_dims), 1)
# self.assertEqual(output_dims[0].dim_value, 512)

# def test_div_precision(self):
# graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [768])
# graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)
#
# # initializers
# value = numpy.array([1.5], dtype=numpy.float32)
# constant = numpy_helper.from_array(value, name="constant")
#
# nodes = [
# # Get the shape of the input tensor: `input_tensor_shape = [768]`.
# onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
# # div(768, 1.5) => 512
# onnx.helper.make_node("Div", ["input_shape", "constant"], ["output_shape"]),
# # Resize input
# onnx.helper.make_node(
# "Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
# ),
# ]
#
# graph_def = onnx.helper.make_graph(nodes, "TestDivPrecision", [graph_input], [graph_output], [constant])
# model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
# output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
# self.assertEqual(len(output_dims), 1)
# self.assertEqual(output_dims[0].dim_value, 512)
def test_mul_precision(self):
graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [1024])
graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)

# initializers
value = numpy.array([0.5], dtype=numpy.float32)
constant = numpy_helper.from_array(value, name="constant")

nodes = [
# Get the shape of the input tensor: `input_tensor_shape = [1024]`.
onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
# mul(1024, 0.5) => 512
onnx.helper.make_node("Mul", ["input_shape", "constant"], ["output_shape"]),
# Resize input
onnx.helper.make_node(
"Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
),
]

graph_def = onnx.helper.make_graph(nodes, "TestMulPrecision", [graph_input], [graph_output], [constant])
model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
self.assertEqual(len(output_dims), 1)
self.assertEqual(output_dims[0].dim_value, 512)

def test_div_precision(self):
graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [768])
graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)

# initializers
value = numpy.array([1.5], dtype=numpy.float32)
constant = numpy_helper.from_array(value, name="constant")

nodes = [
# Get the shape of the input tensor: `input_tensor_shape = [768]`.
onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
# div(768, 1.5) => 512
onnx.helper.make_node("Div", ["input_shape", "constant"], ["output_shape"]),
# Resize input
onnx.helper.make_node(
"Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
),
]

graph_def = onnx.helper.make_graph(nodes, "TestDivPrecision", [graph_input], [graph_output], [constant])
model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
self.assertEqual(len(output_dims), 1)
self.assertEqual(output_dims[0].dim_value, 512)

def test_quantize_linear(self):
"""
Expand Down
3 changes: 2 additions & 1 deletion tools/ci_build/github/linux/docker/scripts/manylinux/requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,8 @@ setuptools>=68.2.2
wheel
onnx==1.16.1
protobuf==4.21.12
sympy==1.12
sympy==1.12 ; python_version < '3.9'
sympy==1.13 ; python_version >= '3.9'
flatbuffers
neural-compressor>=2.2.1
triton