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# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from parameterized import parameterized
import onnx
from onnx import GraphProto, OperatorSetIdProto, checker
class TestBasicFunctions(unittest.TestCase):
def check_graph(self, graph: GraphProto) -> None:
self.assertEqual(len(graph.node), 3)
self.assertEqual(graph.node[0].op_type, "MatMul")
self.assertEqual(graph.node[1].op_type, "Add")
self.assertEqual(graph.node[2].op_type, "Softmax")
def test_parse_graph(self) -> None:
input = """
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
graph = onnx.parser.parse_graph(input)
self.check_graph(graph)
def test_parse_model(self) -> None:
input = """
<
ir_version: 7,
opset_import: [ "" : 10, "com.microsoft": 1]
>
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
model = onnx.parser.parse_model(input)
self.assertEqual(model.ir_version, 7)
self.assertEqual(len(model.opset_import), 2)
self.check_graph(model.graph)
def test_parse_graph_error(self) -> None:
input = """
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul[X, W]
S = Add(T, B)
C = Softmax(S)
}
"""
self.assertRaises(
onnx.parser.ParseError, lambda: onnx.parser.parse_graph(input)
)
def test_parse_model_error(self) -> None:
input = """
<
ir_version: 7,
opset_import: [ "" : 10 "com.microsoft": 1]
>
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
self.assertRaises(
onnx.parser.ParseError, lambda: onnx.parser.parse_model(input)
)
def test_parse_function_with_attributes(self) -> None:
input = """
<
ir_version: 9,
opset_import: [ "" : 15, "custom_domain" : 1],
producer_name: "FunctionProtoTest",
producer_version: "1.0",
model_version: 1,
doc_string: "A test model for model local functions."
>
agraph (float[N] x) => (float[N] out)
{
out = custom_domain.Selu<alpha=2.0, gamma=3.0>(x)
}
<
domain: "custom_domain",
opset_import: [ "" : 15],
doc_string: "Test function proto"
>
Selu
<alpha: float=1.67326319217681884765625, gamma: float=1.05070102214813232421875>
(X) => (C)
{
constant_alpha = Constant<value_float: float=@alpha>()
constant_gamma = Constant<value_float: float=@gamma>()
alpha_x = CastLike(constant_alpha, X)
gamma_x = CastLike(constant_gamma, X)
exp_x = Exp(X)
alpha_x_exp_x = Mul(alpha_x, exp_x)
alpha_x_exp_x_ = Sub(alpha_x_exp_x, alpha_x)
neg = Mul(gamma_x, alpha_x_exp_x_)
pos = Mul(gamma_x, X)
_zero = Constant<value_float=0.0>()
zero = CastLike(_zero, X)
less_eq = LessOrEqual(X, zero)
C = Where(less_eq, neg, pos)
}
"""
model = onnx.parser.parse_model(input)
checker.check_model(model)
@parameterized.expand(
[
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu(x) }",
{},
),
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0>(x) }",
{"alpha": 2.0},
),
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<gamma=3.0>(x) }",
{"gamma": 3.0},
),
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0, gamma=3.0>(x) }",
{"alpha": 2.0, "gamma": 3.0},
),
]
)
def test_composite_parse_function_with_attributes(
self, graph_text: str, expected_attribute: dict
) -> None:
default_alpha = 1.67326319217681884765625
default_gamma = 1.05070102214813232421875
def expect_custom_node_attribute(node, attributes):
for key in attributes:
match_attr = [attr for attr in node.attribute if attr.name == key]
assert len(match_attr) == 1
assert match_attr[0].f == attributes[key]
def expect_model_function_attribute(model):
assert len(model.functions[0].attribute_proto) == 2
attr_proto_alpha = [
attr_proto
for attr_proto in model.functions[0].attribute_proto
if attr_proto.name == "alpha"
]
assert len(attr_proto_alpha) == 1 and attr_proto_alpha[0].f == default_alpha
attr_proto_gamma = [
attr_proto
for attr_proto in model.functions[0].attribute_proto
if attr_proto.name == "gamma"
]
assert len(attr_proto_gamma) == 1 and attr_proto_gamma[0].f == default_gamma
function_text = f"""
<
domain: "custom_domain",
opset_import: [ "" : 15],
doc_string: "Test function proto"
>
Selu
<alpha: float={default_alpha}, gamma: float={default_gamma}>
(X) => (C)
{{
constant_alpha = Constant<value_float: float=@alpha>()
constant_gamma = Constant<value_float: float=@gamma>()
alpha_x = CastLike(constant_alpha, X)
gamma_x = CastLike(constant_gamma, X)
exp_x = Exp(X)
alpha_x_exp_x = Mul(alpha_x, exp_x)
alpha_x_exp_x_ = Sub(alpha_x_exp_x, alpha_x)
neg = Mul(gamma_x, alpha_x_exp_x_)
pos = Mul(gamma_x, X)
_zero = Constant<value_float=0.0>()
zero = CastLike(_zero, X)
less_eq = LessOrEqual(X, zero)
C = Where(less_eq, neg, pos)
}}
"""
functions = [onnx.parser.parse_function(function_text)]
graph = onnx.parser.parse_graph(graph_text)
opset_imports = [
OperatorSetIdProto(domain="", version=15),
OperatorSetIdProto(domain="custom_domain", version=1),
]
model = onnx.helper.make_model(
graph, functions=functions, opset_imports=opset_imports
)
checker.check_model(model)
expect_model_function_attribute(model)
expect_custom_node_attribute(model.graph.node[0], expected_attribute)
def test_parse_node(self):
node = onnx.parser.parse_node(
"out1, out2 = SomeDomain.SomeOp <attr1 = 1> (in1, in2)"
)
self.assertEqual(list(node.input), ["in1", "in2"])
self.assertEqual(list(node.output), ["out1", "out2"])
self.assertEqual(len(node.attribute), 1)
attr_val = onnx.helper.get_node_attr_value(node, "attr1")
self.assertEqual(attr_val, 1)
self.assertEqual(node.domain, "SomeDomain")
self.assertEqual(node.op_type, "SomeOp")
@parameterized.expand(
[
("not_a_good_float", True),
("inf1", True),
("-inf1", True),
("nan0", True),
("-nan0", True),
("naninf", True),
("inf", False),
("-inf", False),
("infinity", False),
("-infinity", False),
("nan", False),
("-NaN", False),
]
)
def test_parse_various_float_values(self, test_literal, expect_exception):
model_text = f"""
<
ir_version: 8,
opset_import: ["" : 18, "this" : 1],
producer_name: "FunctionProtoTest",
producer_version: "1.0"
>
_func () => ()
{{
tmp = Constant <value_float = {test_literal}>()
}}
"""
if expect_exception:
self.assertRaises(
onnx.parser.ParseError, lambda: onnx.parser.parse_model(model_text)
)
else:
model = onnx.parser.parse_model(model_text)
self.assertEqual(model.ir_version, 8)
self.assertEqual(model.producer_name, "FunctionProtoTest")
self.assertEqual(model.producer_version, "1.0")
self.assertEqual(len(model.graph.node), 1)
self.assertEqual(len(model.graph.node[0].attribute), 1)
self.assertEqual(model.graph.node[0].attribute[0].name, "value_float")
self.assertEqual(
model.graph.node[0].attribute[0].type, onnx.AttributeProto.FLOAT
)
self.assertEqual(
str(model.graph.node[0].attribute[0].f), str(float(test_literal))
)
if __name__ == "__main__":
unittest.main()
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