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//===- Traits.cpp - Common op traits shared by dialects -------------------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
#include "mlir/Dialect/Traits.h"
#include "mlir/IR/StandardTypes.h"
using namespace mlir;
/// Returns true if the given `type` supports NumPy broadcast semantics.
/// Specifically, the given `type` must be integer type, floating point type,
/// vector type, or ranked tensor type from integer or floating point types.
static bool isBroadcastableType(Type type) {
switch (type.getKind()) {
case StandardTypes::BF16:
case StandardTypes::F16:
case StandardTypes::F32:
case StandardTypes::F64:
case StandardTypes::Integer:
case StandardTypes::Vector:
return true;
case StandardTypes::RankedTensor:
return type.cast<RankedTensorType>().getElementType().isIntOrFloat();
default:
break;
}
return false;
}
/// Returns the result broadcast composition type from the two given types by
/// following NumPy broadcast semantics. Returns null type if the two given
/// types are not broadcast-compatible.
Type OpTrait::util::getBroadcastedType(Type type1, Type type2) {
// Make sure both types are able to participate in broadcasting.
if (!isBroadcastableType(type1) || !isBroadcastableType(type2))
return {};
// Returns the scalar type out of the given type.
auto getScalarType = [](Type type) -> Type {
if (auto vtType = type.dyn_cast<VectorOrTensorType>())
return vtType.getElementType();
return type;
};
// Make sure underlying scalar type is the same.
auto scalarType = getScalarType(type1);
if (scalarType != getScalarType(type2))
return {};
// Returns the type kind if the given type is a vector or ranked tensor type.
// Returns llvm::None otherwise.
auto getCompositeTypeKind =
[](Type type) -> llvm::Optional<StandardTypes::Kind> {
if (type.isa<VectorType>() || type.isa<RankedTensorType>())
return static_cast<StandardTypes::Kind>(type.getKind());
return llvm::None;
};
// Make sure the composite type, if has, is consistent.
auto compositeKind1 = getCompositeTypeKind(type1);
auto compositeKind2 = getCompositeTypeKind(type2);
llvm::Optional<StandardTypes::Kind> resultCompositeKind;
if (compositeKind1 && compositeKind2) {
// Disallow mixing vector and tensor.
if (compositeKind1 != compositeKind2)
return {};
resultCompositeKind = compositeKind1;
} else if (compositeKind1) {
resultCompositeKind = compositeKind1;
} else if (compositeKind2) {
resultCompositeKind = compositeKind2;
}
// Returns the shape of the given type.
auto getShape = [](Type type) -> ArrayRef<int64_t> {
if (auto vtType = type.dyn_cast<VectorOrTensorType>())
return vtType.getShape();
return {};
};
// Get the shape of each type.
auto shape1 = getShape(type1);
auto shape2 = getShape(type2);
// To compute the result broadcasted shape, we compare operand shapes
// element-wise: starting with the trailing dimensions, and working the
// way backward. Two dimensions are compatible when
// 1. they are equal, or
// 2. one of them is 1
// The result shape has the maximum among the two inputs at every
// dimension index.
SmallVector<int64_t, 4> resultShape;
if (shape1.size() > shape2.size()) {
std::copy(shape1.begin(), shape1.end(), std::back_inserter(resultShape));
} else {
std::copy(shape2.begin(), shape2.end(), std::back_inserter(resultShape));
}
auto i1 = shape1.rbegin(), e1 = shape1.rend();
auto i2 = shape2.rbegin(), e2 = shape2.rend();
auto iR = resultShape.rbegin();
// Check each dimension is consistent.
for (; i1 != e1 && i2 != e2; ++i1, ++i2, ++iR) {
if (*i1 == *i2 || *i2 == 1) {
*iR = *i1;
} else if (*i1 == 1) {
*iR = *i2;
} else {
// This dimension of the two operand types is incompatible.
return {};
}
}
// Compose the final broadcasted type
if (resultCompositeKind == StandardTypes::Vector)
return VectorType::get(resultShape, scalarType);
if (resultCompositeKind == StandardTypes::RankedTensor)
return RankedTensorType::get(resultShape, scalarType);
return scalarType;
}
bool OpTrait::impl::verifyCompatibleOperandBroadcast(const OperationInst *op) {
assert(op->getNumOperands() == 2 &&
"only support broadcast check on two operands");
assert(op->getNumResults() == 1 &&
"only support broadcast check on one result");
auto type1 = op->getOperand(0)->getType();
auto type2 = op->getOperand(1)->getType();
auto retType = op->getResult(0)->getType();
auto broadcastedType = util::getBroadcastedType(type1, type2);
if (!broadcastedType)
return op->emitOpError("operands don't have broadcast-compatible types");
if (broadcastedType != retType)
return op->emitOpError(
"result type is not broadcast-compatible with operand types");
return false;
}
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