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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"
#include "llvm/Support/FormatVariadic.h"
using namespace mlir;
bool OpTrait::util::getBroadcastedShape(ArrayRef<int64_t> shape1,
ArrayRef<int64_t> shape2,
SmallVectorImpl<int64_t> &resultShape) {
// 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.
resultShape.clear();
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 == -1 || *i2 == -1) {
// One or both dimensions is unknown. Follow TensorFlow behavior:
// - If either dimension is greater than 1, we assume that the program is
// correct, and the other dimension will be broadcast to match it.
// - If either dimension is 1, the other dimension is the output.
if (*i1 > 1) {
*iR = *i1;
} else if (*i2 > 1) {
*iR = *i2;
} else if (*i1 == 1) {
*iR = *i2;
} else if (*i2 == 1) {
*iR = *i1;
} else {
*iR = -1;
}
} else {
if (*i1 == *i2 || *i2 == 1) {
*iR = *i1;
} else if (*i1 == 1) {
*iR = *i2;
} else {
// This dimension of the two operand types is incompatible.
resultShape.clear();
return false;
}
}
}
return true;
}
/// Returns the shape of the given type. Scalars will be considered as having a
/// shape with zero dimensions.
static ArrayRef<int64_t> getShape(Type type) {
if (auto sType = type.dyn_cast<ShapedType>())
return sType.getShape();
return {};
}
/// Returns the result broadcast composition type from the two given types by
/// following NumPy broadcast semantics. Returned type may have dynamic shape if
/// either of the input types has dynamic shape. Returns null type if the two
/// given types are not broadcast-compatible.
Type OpTrait::util::getBroadcastedType(Type type1, Type type2) {
// Returns the scalar type out of the given type.
auto getScalarType = [](Type type) -> Type {
if (auto shapedType = type.dyn_cast<ShapedType>())
return shapedType.getElementType();
return type;
};
// Make sure underlying scalar type is the same.
auto scalarType = getScalarType(type1);
if (scalarType != getScalarType(type2))
return {};
// If one of the types is unranked tensor, then the other type shouldn't be
// vector and the result should have unranked tensor type.
if (type1.isa<UnrankedTensorType>() || type2.isa<UnrankedTensorType>()) {
if (type1.isa<VectorType>() || type2.isa<VectorType>())
return {};
return UnrankedTensorType::get(scalarType);
}
// Returns the type kind if the given type is a vector or ranked tensor type.
// Returns llvm::None otherwise.
auto getCompositeTypeKind = [](Type type) -> 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);
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;
}
// Get the shape of each type.
SmallVector<int64_t, 4> resultShape;
if (!getBroadcastedShape(getShape(type1), getShape(type2), resultShape))
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;
}
/// Returns true if the given types has both vector types and tensor types.
static bool hasBothVectorAndTensorType(ArrayRef<Type> types) {
return llvm::any_of(types, [](Type t) { return t.isa<VectorType>(); }) &&
llvm::any_of(types, [](Type t) { return t.isa<TensorType>(); });
}
static bool areCompatibleShapes(ArrayRef<int64_t> shape1,
ArrayRef<int64_t> shape2) {
auto isCompatible = [](int64_t dim1, int64_t dim2) {
return dim1 == dim2 || dim1 == -1 || dim2 == -1;
};
if (shape1.size() != shape2.size())
return false;
for (const auto &p : llvm::zip(shape1, shape2))
if (!isCompatible(std::get<0>(p), std::get<1>(p)))
return false;
return true;
}
LogicalResult OpTrait::impl::verifyCompatibleOperandBroadcast(Operation *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();
// We forbid broadcasting vector and tensor.
if (hasBothVectorAndTensorType({type1, type2, retType}))
return op->emitError("cannot broadcast vector with tensor");
if (retType.isa<UnrankedTensorType>())
return success();
bool isUnranked1 = type1.isa<UnrankedTensorType>();
bool isUnranked2 = type2.isa<UnrankedTensorType>();
// If both operands are unranked, then all result shapes are possible.
if (isUnranked1 && isUnranked2)
return success();
// If one of the operands is unranked, then the known dimensions in the result
// should be compatible with the other shaped operand.
if (isUnranked1 || isUnranked2) {
// Result should have higher rank than the shaped operand's rank and then
// the result's trailing dimensions should be compatible with the operand
// shape.
ArrayRef<int64_t> shape = getShape(!isUnranked1 ? type1 : type2);
ArrayRef<int64_t> actualSuffix = getShape(retType).take_back(shape.size());
if (!areCompatibleShapes(actualSuffix, shape))
return op->emitOpError()
<< "result type " << retType
<< " has shape incompatible with a ranked operand type";
return success();
}
// If both operands are shaped, then the computed broadcasted shape should be
// compatible with the result shape.
SmallVector<int64_t, 4> resultShape;
if (!util::getBroadcastedShape(getShape(type1), getShape(type2), resultShape))
return op->emitOpError("operands don't have broadcast-compatible shapes");
if (!areCompatibleShapes(resultShape, getShape(retType)))
return op->emitOpError() << "result type " << retType
<< " does not have shape compatible with the one "
"computed from the operand types";
return success();
}
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