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Re-enable test symbolic shape infer (#22737)
### Description <!-- Describe your changes. --> It seems after CI updated to py310, numpy got updated to 2.0 and sympy 1.2 failed to cast float numpy array. Pointing sympy to 1.13 when py>=3.9 and re-enable unit test ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Error: Linux CPU CI
This commit is contained in:
Yifan Li
GitHub
parent
c02b398980
commit
562ddce270
2 changed files with 71 additions and 71 deletions
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@ -39,27 +39,27 @@ skipped_models = ["SSD-MobilenetV1", "SSD-int8", "Inception-1-int8"]
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class TestSymbolicShapeInference(unittest.TestCase):
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# TODO: investigate why symbolic shape infer test failed for Python 3.10
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# def test_symbolic_shape_infer(self):
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# from pathlib import Path
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# cwd = os.getcwd()
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# test_model_dir = os.path.join(cwd, "..", "models")
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# for filename in Path(test_model_dir).rglob("*.onnx"):
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# if filename.name.startswith("."):
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# continue # skip some bad model files
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#
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# # https://github.com/onnx/models/issues/562
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# if any(model_name in str(filename) for model_name in skipped_models):
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# print(f"Skip symbolic shape inference on : {filename!s}")
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# continue
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#
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# print("Running symbolic shape inference on : " + str(filename))
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# SymbolicShapeInference.infer_shapes(
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# in_mp=onnx.load(str(filename)),
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# auto_merge=True,
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# int_max=100000,
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# guess_output_rank=True,
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# )
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def test_symbolic_shape_infer(self):
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from pathlib import Path
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cwd = os.getcwd()
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test_model_dir = os.path.join(cwd, "..", "models")
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for filename in Path(test_model_dir).rglob("*.onnx"):
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if filename.name.startswith("."):
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continue # skip some bad model files
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# https://github.com/onnx/models/issues/562
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if any(model_name in str(filename) for model_name in skipped_models):
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print(f"Skip symbolic shape inference on : {filename!s}")
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continue
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print("Running symbolic shape inference on : " + str(filename))
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SymbolicShapeInference.infer_shapes(
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in_mp=onnx.load(str(filename)),
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auto_merge=True,
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int_max=100000,
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guess_output_rank=True,
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)
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def test_mismatched_types(self):
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graph = helper.make_graph(
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@ -343,56 +343,55 @@ class TestSymbolicShapeInferenceForOperators(unittest.TestCase):
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def test_einsum_transpose(self):
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self._test_einsum_one_input_impl(["a", "b"], ["b", "a"], "ij -> ji")
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# TODO: investigate why symbolic shape infer test failed for Python 3.10
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# def test_mul_precision(self):
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# graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [1024])
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# graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)
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#
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# # initializers
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# value = numpy.array([0.5], dtype=numpy.float32)
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# constant = numpy_helper.from_array(value, name="constant")
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#
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# nodes = [
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# # Get the shape of the input tensor: `input_tensor_shape = [1024]`.
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# onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
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# # mul(1024, 0.5) => 512
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# onnx.helper.make_node("Mul", ["input_shape", "constant"], ["output_shape"]),
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# # Resize input
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# onnx.helper.make_node(
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# "Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
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# ),
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# ]
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#
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# graph_def = onnx.helper.make_graph(nodes, "TestMulPrecision", [graph_input], [graph_output], [constant])
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# model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
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# output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
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# self.assertEqual(len(output_dims), 1)
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# self.assertEqual(output_dims[0].dim_value, 512)
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def test_mul_precision(self):
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graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [1024])
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graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)
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# def test_div_precision(self):
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# graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [768])
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# graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)
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#
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# # initializers
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# value = numpy.array([1.5], dtype=numpy.float32)
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# constant = numpy_helper.from_array(value, name="constant")
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#
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# nodes = [
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# # Get the shape of the input tensor: `input_tensor_shape = [768]`.
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# onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
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# # div(768, 1.5) => 512
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# onnx.helper.make_node("Div", ["input_shape", "constant"], ["output_shape"]),
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# # Resize input
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# onnx.helper.make_node(
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# "Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
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# ),
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# ]
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#
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# graph_def = onnx.helper.make_graph(nodes, "TestDivPrecision", [graph_input], [graph_output], [constant])
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# model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
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# output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
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# self.assertEqual(len(output_dims), 1)
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# self.assertEqual(output_dims[0].dim_value, 512)
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# initializers
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value = numpy.array([0.5], dtype=numpy.float32)
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constant = numpy_helper.from_array(value, name="constant")
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nodes = [
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# Get the shape of the input tensor: `input_tensor_shape = [1024]`.
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onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
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# mul(1024, 0.5) => 512
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onnx.helper.make_node("Mul", ["input_shape", "constant"], ["output_shape"]),
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# Resize input
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onnx.helper.make_node(
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"Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
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),
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]
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graph_def = onnx.helper.make_graph(nodes, "TestMulPrecision", [graph_input], [graph_output], [constant])
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model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
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output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
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self.assertEqual(len(output_dims), 1)
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self.assertEqual(output_dims[0].dim_value, 512)
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def test_div_precision(self):
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graph_input = onnx.helper.make_tensor_value_info("input", TensorProto.FLOAT, [768])
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graph_output = onnx.helper.make_tensor_value_info("output", TensorProto.FLOAT, None)
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# initializers
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value = numpy.array([1.5], dtype=numpy.float32)
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constant = numpy_helper.from_array(value, name="constant")
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nodes = [
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# Get the shape of the input tensor: `input_tensor_shape = [768]`.
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onnx.helper.make_node("Shape", ["input"], ["input_shape"]),
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# div(768, 1.5) => 512
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onnx.helper.make_node("Div", ["input_shape", "constant"], ["output_shape"]),
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# Resize input
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onnx.helper.make_node(
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"Resize", inputs=["input", "", "", "output_shape"], outputs=["output"], mode="nearest"
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),
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]
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graph_def = onnx.helper.make_graph(nodes, "TestDivPrecision", [graph_input], [graph_output], [constant])
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model = SymbolicShapeInference.infer_shapes(onnx.helper.make_model(graph_def))
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output_dims = unique_element(model.graph.output).type.tensor_type.shape.dim
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self.assertEqual(len(output_dims), 1)
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self.assertEqual(output_dims[0].dim_value, 512)
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def test_quantize_linear(self):
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"""
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@ -6,7 +6,8 @@ setuptools>=68.2.2
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wheel
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onnx==1.16.1
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protobuf==4.21.12
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sympy==1.12
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sympy==1.12 ; python_version < '3.9'
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sympy==1.13 ; python_version >= '3.9'
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flatbuffers
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neural-compressor>=2.2.1
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triton
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