mirror of https://github.com/llvm/torch-mlir
128 lines
4.3 KiB
C++
128 lines
4.3 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchTypes.h"
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#include "llvm/ADT/StringMap.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::TorchConversion;
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using namespace mlir::torch;
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static bool haveSameSizeAndElementType(TensorType lhs, TensorType rhs) {
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if (lhs.hasRank() != rhs.hasRank())
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return false;
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bool sameSize = lhs.hasRank() ? lhs.getShape().equals(rhs.getShape()) : true;
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bool sameElementType = lhs.getElementType() == rhs.getElementType();
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return sameElementType && sameSize;
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}
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//===----------------------------------------------------------------------===//
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// ToBuiltinTensorOp
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//===----------------------------------------------------------------------===//
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LogicalResult ToBuiltinTensorOp::verify() {
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auto resultType = getResult().getType().cast<TensorType>();
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auto operandType =
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getOperand().getType().cast<Torch::ValueTensorType>().toBuiltinTensor();
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if (!haveSameSizeAndElementType(resultType, operandType)) {
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return emitError()
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<< "operand and result must have the same size and dtype";
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}
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return success();
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}
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LogicalResult ToBuiltinTensorOp::inferReturnTypes(
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MLIRContext *context, Optional<Location> location, ValueRange operands,
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DictionaryAttr attributes, RegionRange regions,
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SmallVectorImpl<Type> &inferredReturnTypes) {
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auto resultType =
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operands[0].getType().cast<Torch::ValueTensorType>().toBuiltinTensor();
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if (!resultType)
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return failure();
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inferredReturnTypes.push_back(resultType);
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return success();
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}
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//===----------------------------------------------------------------------===//
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// FromBuiltinTensorOp
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//===----------------------------------------------------------------------===//
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LogicalResult FromBuiltinTensorOp::verify() {
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auto resultType =
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getResult().getType().cast<Torch::ValueTensorType>().toBuiltinTensor();
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auto operandType = getOperand().getType().cast<TensorType>();
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if (!haveSameSizeAndElementType(resultType, operandType)) {
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return emitError()
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<< "operand and result must have the same size and dtype";
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}
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return success();
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}
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//===----------------------------------------------------------------------===//
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// FromI64Op
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//===----------------------------------------------------------------------===//
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OpFoldResult FromI64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
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auto attr = operands[0].dyn_cast_or_null<mlir::IntegerAttr>();
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if (attr) {
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return attr;
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} else {
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return nullptr;
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}
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}
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//===----------------------------------------------------------------------===//
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// ToI64Op
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//===----------------------------------------------------------------------===//
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OpFoldResult ToI64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
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auto attr = operands[0].dyn_cast_or_null<mlir::IntegerAttr>();
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if (attr) {
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return attr;
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} else {
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return nullptr;
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}
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}
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//===----------------------------------------------------------------------===//
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// ToF64Op
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//===----------------------------------------------------------------------===//
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OpFoldResult ToF64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
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auto attr = operands[0].dyn_cast_or_null<mlir::FloatAttr>();
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if (attr) {
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return attr;
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} else {
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return nullptr;
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}
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}
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//===----------------------------------------------------------------------===//
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// FromF64Op
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//===----------------------------------------------------------------------===//
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OpFoldResult FromF64Op::fold(llvm::ArrayRef<mlir::Attribute> operands) {
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auto attr = operands[0].dyn_cast_or_null<mlir::FloatAttr>();
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if (attr) {
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return attr;
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} else {
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return nullptr;
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}
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}
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#define GET_OP_CLASSES
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#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.cpp.inc"
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