//===- NumpyOps.cpp - Core numpy dialect ops --------------------*- C++ -*-===// // // This file is licensed under the Apache License v2.0 with LLVM Exceptions. // See https://llvm.org/LICENSE.txt for license information. // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception // //===----------------------------------------------------------------------===// #include "npcomp/Dialect/Numpy/IR/NumpyOps.h" #include "mlir/IR/Builders.h" #include "mlir/IR/FunctionImplementation.h" #include "mlir/IR/OpImplementation.h" #include "npcomp/Dialect/Basicpy/IR/BasicpyDialect.h" #include "npcomp/Dialect/Numpy/IR/NumpyDialect.h" using namespace mlir; using namespace mlir::NPCOMP; using namespace mlir::NPCOMP::Numpy; //----------------------------------------------------------------------------// // Type inference //----------------------------------------------------------------------------// /// Adds constraints to relating a unary op that accepts and returns either /// tensor or ndarray types where the dtype should be the same. /// Type constraints are added on the dtype, not the outer object type. static void constrainUnaryDtypeInvariantOp(Typing::CPA::Context &context, Value source, Value dest, Operation *op) { auto &env = context.getCurrentEnvironment(); auto *sourceTn = llvm::dyn_cast(env.mapValueToType(source)); auto *destTn = llvm::dyn_cast(env.mapValueToType(dest)); if (sourceTn && destTn && sourceTn->getFieldCount() == 1 && destTn->getFieldCount() == 1) { context.getConstraint(sourceTn->getFieldTypes().front(), destTn->getFieldTypes().front()); } } void CreateArrayFromTensorOp::addCPAConstraints(Typing::CPA::Context &context) { constrainUnaryDtypeInvariantOp(context, source(), dest(), *this); } void CopyToTensorOp::addCPAConstraints(Typing::CPA::Context &context) { constrainUnaryDtypeInvariantOp(context, source(), dest(), *this); } void BuiltinUfuncCallOp::addCPAConstraints(Typing::CPA::Context &context) { // TODO: This should really be a function call chosen so as to promote // arguments. For now, though, we just say that the result is constrained // to the inputs. Note that not all ufuncs transfer types like this. // We just pretend this is two unary functions that write into the output. for (auto input : inputs()) { constrainUnaryDtypeInvariantOp(context, input, output(), *this); } } namespace mlir { namespace NPCOMP { namespace Numpy { #define GET_OP_CLASSES #include "npcomp/Dialect/Numpy/IR/NumpyOps.cpp.inc" } // namespace Numpy } // namespace NPCOMP } // namespace mlir