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# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import contextlib
import unittest
from typing import List, Sequence
import parameterized
import onnx
from onnx import defs
class TestSchema(unittest.TestCase):
def test_get_schema(self) -> None:
defs.get_schema("Relu")
def test_typecheck(self) -> None:
defs.get_schema("Conv")
def test_attr_default_value(self) -> None:
v = defs.get_schema("BatchNormalization").attributes["epsilon"].default_value
self.assertEqual(type(v), onnx.AttributeProto)
self.assertEqual(v.type, onnx.AttributeProto.FLOAT)
def test_function_body(self) -> None:
self.assertEqual(
type(defs.get_schema("Selu").function_body), onnx.FunctionProto
)
class TestOpSchema(unittest.TestCase):
def test_init(self):
# Test that the constructor creates an OpSchema object
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertIsInstance(schema, defs.OpSchema)
def test_init_with_inputs(self) -> None:
op_schema = defs.OpSchema(
"test_op",
"test_domain",
1,
inputs=[defs.OpSchema.FormalParameter("input1", "T")],
type_constraints=[("T", ["tensor(int64)"], "")],
)
self.assertEqual(op_schema.name, "test_op")
self.assertEqual(op_schema.domain, "test_domain")
self.assertEqual(op_schema.since_version, 1)
self.assertEqual(len(op_schema.inputs), 1)
self.assertEqual(op_schema.inputs[0].name, "input1")
self.assertEqual(op_schema.inputs[0].type_str, "T")
self.assertEqual(len(op_schema.type_constraints), 1)
self.assertEqual(op_schema.type_constraints[0].type_param_str, "T")
self.assertEqual(
op_schema.type_constraints[0].allowed_type_strs, ["tensor(int64)"]
)
def test_init_creates_multi_input_output_schema(self) -> None:
op_schema = defs.OpSchema(
"test_op",
"test_domain",
1,
inputs=[
defs.OpSchema.FormalParameter("input1", "T"),
defs.OpSchema.FormalParameter("input2", "T"),
],
outputs=[
defs.OpSchema.FormalParameter("output1", "T"),
defs.OpSchema.FormalParameter("output2", "T"),
],
type_constraints=[("T", ["tensor(int64)"], "")],
attributes=[
defs.OpSchema.Attribute(
"attr1", defs.OpSchema.AttrType.INTS, "attr1 description"
)
],
)
self.assertEqual(len(op_schema.inputs), 2)
self.assertEqual(op_schema.inputs[0].name, "input1")
self.assertEqual(op_schema.inputs[0].type_str, "T")
self.assertEqual(op_schema.inputs[1].name, "input2")
self.assertEqual(op_schema.inputs[1].type_str, "T")
self.assertEqual(len(op_schema.outputs), 2)
self.assertEqual(op_schema.outputs[0].name, "output1")
self.assertEqual(op_schema.outputs[0].type_str, "T")
self.assertEqual(op_schema.outputs[1].name, "output2")
self.assertEqual(op_schema.outputs[1].type_str, "T")
self.assertEqual(len(op_schema.type_constraints), 1)
self.assertEqual(op_schema.type_constraints[0].type_param_str, "T")
self.assertEqual(
op_schema.type_constraints[0].allowed_type_strs, ["tensor(int64)"]
)
self.assertEqual(len(op_schema.attributes), 1)
self.assertEqual(op_schema.attributes["attr1"].name, "attr1")
self.assertEqual(
op_schema.attributes["attr1"].type, defs.OpSchema.AttrType.INTS
)
self.assertEqual(op_schema.attributes["attr1"].description, "attr1 description")
def test_init_without_optional_arguments(self) -> None:
op_schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(op_schema.name, "test_op")
self.assertEqual(op_schema.domain, "test_domain")
self.assertEqual(op_schema.since_version, 1)
self.assertEqual(len(op_schema.inputs), 0)
self.assertEqual(len(op_schema.outputs), 0)
self.assertEqual(len(op_schema.type_constraints), 0)
def test_name(self):
# Test that the name parameter is required and is a string
with self.assertRaises(TypeError):
defs.OpSchema(domain="test_domain", since_version=1) # type: ignore
with self.assertRaises(TypeError):
defs.OpSchema(123, "test_domain", 1) # type: ignore
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(schema.name, "test_op")
def test_domain(self):
# Test that the domain parameter is required and is a string
with self.assertRaises(TypeError):
defs.OpSchema(name="test_op", since_version=1) # type: ignore
with self.assertRaises(TypeError):
defs.OpSchema("test_op", 123, 1) # type: ignore
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(schema.domain, "test_domain")
def test_since_version(self):
# Test that the since_version parameter is required and is an integer
with self.assertRaises(TypeError):
defs.OpSchema("test_op", "test_domain") # type: ignore
schema = defs.OpSchema("test_op", "test_domain", 1)
self.assertEqual(schema.since_version, 1)
def test_doc(self):
schema = defs.OpSchema("test_op", "test_domain", 1, doc="test_doc")
self.assertEqual(schema.doc, "test_doc")
def test_inputs(self):
# Test that the inputs parameter is optional and is a sequence of FormalParameter tuples
inputs = [
defs.OpSchema.FormalParameter(
name="input1", type_str="T", description="The first input."
)
]
schema = defs.OpSchema(
"test_op",
"test_domain",
1,
inputs=inputs,
type_constraints=[("T", ["tensor(int64)"], "")],
)
self.assertEqual(len(schema.inputs), 1)
self.assertEqual(schema.inputs[0].name, "input1")
self.assertEqual(schema.inputs[0].type_str, "T")
self.assertEqual(schema.inputs[0].description, "The first input.")
def test_outputs(self):
# Test that the outputs parameter is optional and is a sequence of FormalParameter tuples
outputs = [
defs.OpSchema.FormalParameter(
name="output1", type_str="T", description="The first output."
)
]
schema = defs.OpSchema(
"test_op",
"test_domain",
1,
outputs=outputs,
type_constraints=[("T", ["tensor(int64)"], "")],
)
self.assertEqual(len(schema.outputs), 1)
self.assertEqual(schema.outputs[0].name, "output1")
self.assertEqual(schema.outputs[0].type_str, "T")
self.assertEqual(schema.outputs[0].description, "The first output.")
class TestFormalParameter(unittest.TestCase):
def test_init(self):
name = "input1"
type_str = "tensor(float)"
description = "The first input."
param_option = defs.OpSchema.FormalParameterOption.Single
is_homogeneous = True
min_arity = 1
differentiation_category = defs.OpSchema.DifferentiationCategory.Unknown
formal_parameter = defs.OpSchema.FormalParameter(
name,
type_str,
description,
param_option=param_option,
is_homogeneous=is_homogeneous,
min_arity=min_arity,
differentiation_category=differentiation_category,
)
self.assertEqual(formal_parameter.name, name)
self.assertEqual(formal_parameter.type_str, type_str)
self.assertEqual(formal_parameter.description, description)
self.assertEqual(formal_parameter.option, param_option)
self.assertEqual(formal_parameter.is_homogeneous, is_homogeneous)
self.assertEqual(formal_parameter.min_arity, min_arity)
self.assertEqual(
formal_parameter.differentiation_category, differentiation_category
)
class TestTypeConstraintParam(unittest.TestCase):
@parameterized.parameterized.expand(
[
("single_type", "T", ["tensor(float)"], "Test description"),
(
"double_types",
"T",
["tensor(float)", "tensor(int64)"],
"Test description",
),
("tuple", "T", ("tensor(float)", "tensor(int64)"), "Test description"),
]
)
def test_init(
self,
_: str,
type_param_str: str,
allowed_types: Sequence[str],
description: str,
) -> None:
type_constraint = defs.OpSchema.TypeConstraintParam(
type_param_str, allowed_types, description
)
self.assertEqual(type_constraint.description, description)
self.assertEqual(type_constraint.allowed_type_strs, list(allowed_types))
self.assertEqual(type_constraint.type_param_str, type_param_str)
class TestAttribute(unittest.TestCase):
def test_init(self):
name = "test_attr"
type_ = defs.OpSchema.AttrType.STRINGS
description = "Test attribute"
attribute = defs.OpSchema.Attribute(name, type_, description)
self.assertEqual(attribute.name, name)
self.assertEqual(attribute.type, type_)
self.assertEqual(attribute.description, description)
def test_init_with_default_value(self):
default_value = (
defs.get_schema("BatchNormalization").attributes["epsilon"].default_value
)
self.assertIsInstance(default_value, onnx.AttributeProto)
attribute = defs.OpSchema.Attribute("attr1", default_value, "attr1 description")
self.assertEqual(default_value, attribute.default_value)
self.assertEqual("attr1", attribute.name)
self.assertEqual("attr1 description", attribute.description)
@parameterized.parameterized_class(
[
# register to exist domain
{
"op_type": "CustomOp",
"op_version": 5,
"op_domain": "",
"trap_op_version": [1, 2, 6, 7],
},
# register to new domain
{
"op_type": "CustomOp",
"op_version": 5,
"op_domain": "test",
"trap_op_version": [1, 2, 6, 7],
},
]
)
class TestOpSchemaRegister(unittest.TestCase):
op_type: str
op_version: int
op_domain: str
# register some fake schema to check behavior
trap_op_version: List[int]
def setUp(self) -> None:
# Ensure the schema is unregistered
self.assertFalse(onnx.defs.has(self.op_type, self.op_domain))
def tearDown(self) -> None:
# Clean up the registered schema
for version in [*self.trap_op_version, self.op_version]:
with contextlib.suppress(onnx.defs.SchemaError):
onnx.defs.deregister_schema(self.op_type, version, self.op_domain)
def test_register_multi_schema(self):
for version in [*self.trap_op_version, self.op_version]:
op_schema = defs.OpSchema(
self.op_type,
self.op_domain,
version,
)
onnx.defs.register_schema(op_schema)
self.assertTrue(onnx.defs.has(self.op_type, version, self.op_domain))
for version in [*self.trap_op_version, self.op_version]:
# Also make sure the `op_schema` is accessible after register
registered_op = onnx.defs.get_schema(
op_schema.name, version, op_schema.domain
)
op_schema = defs.OpSchema(
self.op_type,
self.op_domain,
version,
)
self.assertEqual(str(registered_op), str(op_schema))
def test_using_the_specified_version_in_onnx_check(self):
input = f"""
<
ir_version: 7,
opset_import: [
"{self.op_domain}" : {self.op_version}
]
>
agraph (float[N, 128] X, int32 Y) => (float[N] Z)
{{
Z = {self.op_domain}.{self.op_type}<attr1=[1,2]>(X, Y)
}}
"""
model = onnx.parser.parse_model(input)
op_schema = defs.OpSchema(
self.op_type,
self.op_domain,
self.op_version,
inputs=[
defs.OpSchema.FormalParameter("input1", "T"),
defs.OpSchema.FormalParameter("input2", "int32"),
],
outputs=[
defs.OpSchema.FormalParameter("output1", "T"),
],
type_constraints=[("T", ["tensor(float)"], "")],
attributes=[
defs.OpSchema.Attribute(
"attr1", defs.OpSchema.AttrType.INTS, "attr1 description"
)
],
)
with self.assertRaises(onnx.checker.ValidationError):
onnx.checker.check_model(model, check_custom_domain=True)
onnx.defs.register_schema(op_schema)
# The fake schema will raise check exception if selected in checker
for version in self.trap_op_version:
onnx.defs.register_schema(
defs.OpSchema(
self.op_type,
self.op_domain,
version,
outputs=[
defs.OpSchema.FormalParameter("output1", "int32"),
],
)
)
onnx.checker.check_model(model, check_custom_domain=True)
def test_register_schema_raises_error_when_registering_a_schema_twice(self):
op_schema = defs.OpSchema(
self.op_type,
self.op_domain,
self.op_version,
)
onnx.defs.register_schema(op_schema)
with self.assertRaises(onnx.defs.SchemaError):
onnx.defs.register_schema(op_schema)
def test_deregister_the_specified_schema(self):
for version in [*self.trap_op_version, self.op_version]:
op_schema = defs.OpSchema(
self.op_type,
self.op_domain,
version,
)
onnx.defs.register_schema(op_schema)
self.assertTrue(onnx.defs.has(op_schema.name, version, op_schema.domain))
onnx.defs.deregister_schema(op_schema.name, self.op_version, op_schema.domain)
for version in self.trap_op_version:
self.assertTrue(onnx.defs.has(op_schema.name, version, op_schema.domain))
# Maybe has lesser op version in trap list
if onnx.defs.has(op_schema.name, self.op_version, op_schema.domain):
schema = onnx.defs.get_schema(
op_schema.name, self.op_version, op_schema.domain
)
self.assertLess(schema.since_version, self.op_version)
def test_deregister_schema_raises_error_when_opschema_does_not_exist(self):
with self.assertRaises(onnx.defs.SchemaError):
onnx.defs.deregister_schema(self.op_type, self.op_version, self.op_domain)
def test_legacy_schema_accessible_after_deregister(self):
op_schema = defs.OpSchema(
self.op_type,
self.op_domain,
self.op_version,
)
onnx.defs.register_schema(op_schema)
schema_a = onnx.defs.get_schema(
op_schema.name, op_schema.since_version, op_schema.domain
)
schema_b = onnx.defs.get_schema(op_schema.name, op_schema.domain)
def filter_schema(schemas):
return [op for op in schemas if op.name == op_schema.name]
schema_c = filter_schema(onnx.defs.get_all_schemas())
schema_d = filter_schema(onnx.defs.get_all_schemas_with_history())
self.assertEqual(len(schema_c), 1)
self.assertEqual(len(schema_d), 1)
# Avoid memory residue and access storage as much as possible
self.assertEqual(str(schema_a), str(op_schema))
self.assertEqual(str(schema_b), str(op_schema))
self.assertEqual(str(schema_c[0]), str(op_schema))
self.assertEqual(str(schema_d[0]), str(op_schema))
if __name__ == "__main__":
unittest.main()
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