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/*
* SPDX-License-Identifier: Apache-2.0
*/
#include "onnx/defs/schema.h"
using namespace ONNX_NAMESPACE;
namespace ONNX_NAMESPACE {
std::function<void(OpSchema&)> BinaryLogicDocGenerator_opset12(const char* name) {
return [=](OpSchema& schema) {
std::string doc;
POPULATE_OP_DOC_STR(doc = R"DOC(
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
{broadcast_doc}
)DOC";
ReplaceAll(doc, "{name}", name);
ReplaceAll(doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
schema.SetDoc(doc);
schema.Input(0, "A", "First input operand for the logical operator.", "T");
schema.Input(1, "B", "Second input operand for the logical operator.", "T");
schema.Output(0, "C", "Result tensor.", "T1");
schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
// Type inference
updateOutputElemType(ctx, 0, TensorProto::BOOL);
// Shape inference
if (hasNInputShapes(ctx, 2))
bidirectionalBroadcastShapeInference(
ctx.getInputType(0)->tensor_type().shape(),
ctx.getInputType(1)->tensor_type().shape(),
*ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
});
};
}
ONNX_OPERATOR_SET_SCHEMA(
Greater,
9,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset12("greater"))
.TypeConstraint("T", OpSchema::all_numeric_types(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Less,
9,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset12("less"))
.TypeConstraint("T", OpSchema::all_numeric_types(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Equal,
11,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset12("equal"))
.TypeConstraint(
"T",
{"tensor(bool)",
"tensor(uint8)",
"tensor(uint16)",
"tensor(uint32)",
"tensor(uint64)",
"tensor(int8)",
"tensor(int16)",
"tensor(int32)",
"tensor(int64)",
"tensor(float16)",
"tensor(float)",
"tensor(double)"},
"Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
inline void logicalOpInference_opset1(InferenceContext& ctx) {
updateOutputElemType(ctx, 0, TensorProto::BOOL);
if (hasInputShape(ctx, 0)) {
propagateShapeFromInputToOutput(ctx, 0, 0);
}
}
std::function<void(OpSchema&)> BinaryLogicDocGenerator_opset1(const char* name) {
return [=](OpSchema& schema) {
std::string doc;
POPULATE_OP_DOC_STR(doc = R"DOC(
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B`.
If broadcasting is enabled, the right-hand-side argument will be broadcasted
to match the shape of left-hand-side argument. See the doc of `Add` for a
detailed description of the broadcasting rules.
)DOC";
ReplaceAll(doc, "{name}", name););
schema.SetDoc(doc);
schema.Attr("broadcast", "Enable broadcasting", AttributeProto::INT, static_cast<int64_t>(0));
schema.Attr("axis", "If set, defines the broadcast dimensions.", AttributeProto::INT, OPTIONAL_VALUE);
schema.Input(0, "A", "Left input tensor for the logical operator.", "T");
schema.Input(1, "B", "Right input tensor for the logical operator.", "T");
schema.Output(0, "C", "Result tensor.", "T1");
schema.TypeAndShapeInferenceFunction(logicalOpInference_opset1);
};
}
std::function<void(OpSchema&)> BinaryLogicDocGenerator_opset7(const char* name) {
return [=](OpSchema& schema) {
std::string doc;
POPULATE_OP_DOC_STR(doc = R"DOC(
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
{broadcast_doc}
)DOC";
ReplaceAll(doc, "{name}", name);
ReplaceAll(doc, "{broadcast_doc}", GenerateBroadcastingDocMul().c_str()););
schema.SetDoc(doc);
schema.Input(0, "A", "First input operand for the logical operator.", "T");
schema.Input(1, "B", "Second input operand for the logical operator.", "T");
schema.Output(0, "C", "Result tensor.", "T1");
schema.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
updateOutputElemType(ctx, 0, TensorProto::BOOL);
if (hasNInputShapes(ctx, 2))
bidirectionalBroadcastShapeInference(
ctx.getInputType(0)->tensor_type().shape(),
ctx.getInputType(1)->tensor_type().shape(),
*ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape());
});
};
}
ONNX_OPERATOR_SET_SCHEMA(
And,
1,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset1("and"))
.TypeConstraint("T", {"tensor(bool)"}, "Constrain input to boolean tensor.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Or,
1,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset1("or"))
.TypeConstraint("T", {"tensor(bool)"}, "Constrain input to boolean tensor.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Xor,
1,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset1("xor"))
.TypeConstraint("T", {"tensor(bool)"}, "Constrain input to boolean tensor.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Greater,
1,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset1("greater"))
.TypeConstraint(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input to float tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Less,
1,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset1("less"))
.TypeConstraint(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input to float tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Equal,
1,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset1("equal"))
.TypeConstraint("T", {"tensor(bool)", "tensor(int32)", "tensor(int64)"}, "Constrain input to integral tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Equal,
7,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset7("equal"))
.TypeConstraint("T", {"tensor(bool)", "tensor(int32)", "tensor(int64)"}, "Constrain input to integral tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Greater,
7,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset7("greater"))
.TypeConstraint(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input to float tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
ONNX_OPERATOR_SET_SCHEMA(
Less,
7,
OpSchema()
.FillUsing(BinaryLogicDocGenerator_opset7("less"))
.TypeConstraint(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input to float tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
// Shares same doc generator as newer opset 16 version.
extern std::function<void(OpSchema&)> BinaryLogicDocGenerator(const char* name);
ONNX_OPERATOR_SET_SCHEMA(
LessOrEqual,
12,
OpSchema()
.FillUsing(BinaryLogicDocGenerator("less_equal"))
.TypeConstraint("T", OpSchema::all_numeric_types(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor.")
.TypeAndShapeInferenceFunction(InferenceFunction())
.FunctionBody(R"ONNX(
{
O1 = Less (A, B)
O2 = Equal (A, B)
C = Or (O1, O2)
}
)ONNX"));
ONNX_OPERATOR_SET_SCHEMA(
GreaterOrEqual,
12,
OpSchema()
.FillUsing(BinaryLogicDocGenerator("greater_equal"))
.TypeConstraint("T", OpSchema::all_numeric_types(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor.")
.TypeAndShapeInferenceFunction(InferenceFunction())
.FunctionBody(R"ONNX(
{
O1 = Greater (A, B)
O2 = Equal (A, B)
C = Or (O1, O2)
}
)ONNX"));
ONNX_OPERATOR_SET_SCHEMA(
Equal,
13,
OpSchema()
.FillUsing(BinaryLogicDocGenerator("equal"))
.TypeConstraint(
"T",
{"tensor(bool)",
"tensor(uint8)",
"tensor(uint16)",
"tensor(uint32)",
"tensor(uint64)",
"tensor(int8)",
"tensor(int16)",
"tensor(int32)",
"tensor(int64)",
"tensor(float16)",
"tensor(float)",
"tensor(double)",
"tensor(bfloat16)"},
"Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor."));
} // namespace ONNX_NAMESPACE
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