//===- 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; /// 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: case StandardTypes::UnrankedTensor: return type.cast().getElementType().isIntOrFloat(); default: break; } return false; } /// 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) { // 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()) return vtType.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() || type2.isa()) { if (type1.isa() || type2.isa()) 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) -> llvm::Optional { if (type.isa() || type.isa()) return static_cast(type.getKind()); return llvm::None; }; // Make sure the composite type, if has, is consistent. auto compositeKind1 = getCompositeTypeKind(type1); auto compositeKind2 = getCompositeTypeKind(type2); llvm::Optional 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 { if (auto vtType = type.dyn_cast()) 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 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 Instruction *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( llvm::formatv("result type '{0}' is not the expected broadcasted type " "'{1}' compute from the operand types", retType, broadcastedType)); return false; }