mirror of https://github.com/llvm/torch-mlir
289 lines
12 KiB
C++
289 lines
12 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/Conversion/TorchToStd/TorchToStd.h"
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#include "../PassDetail.h"
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#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
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#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Math/IR/Math.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Traits.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "torch-mlir/Conversion/Utils/Utils.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
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#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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// -----------------------------------------------------------------------------
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// Patterns (as this grows, it should be organized into multiple files)
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// -----------------------------------------------------------------------------
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// This is going to eventually be O(#torch operators), which is in the 100s.
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namespace {
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// Note: Confusingly, ATen's "dim" means "number of dimensions" which is what
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// MLIR calls "rank".
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class ConvertAtenDimOp : public OpConversionPattern<AtenDimOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenDimOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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auto rank = rewriter.create<tensor::RankOp>(op->getLoc(), adaptor.self());
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rewriter.replaceOpWithNewOp<arith::IndexCastOp>(
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op, getTypeConverter()->convertType(op.getType()), rank);
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return success();
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}
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};
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} // namespace
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namespace {
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class ConvertRuntimeAssertOp : public OpConversionPattern<RuntimeAssertOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(RuntimeAssertOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.replaceOpWithNewOp<cf::AssertOp>(op, adaptor.condition(),
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adaptor.message());
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return success();
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}
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};
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} // namespace
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namespace {
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template <typename AtenOp, typename BinOp>
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class ConvertAtenBinaryOp : public OpConversionPattern<AtenOp> {
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public:
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using OpConversionPattern<AtenOp>::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenOp op,
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typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.template replaceOpWithNewOp<BinOp>(op, adaptor.a(), adaptor.b());
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return success();
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}
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};
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} // namespace
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namespace {
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template <typename AtenOp, typename UnaryOp>
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class ConvertAtenUnaryOp : public OpConversionPattern<AtenOp> {
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public:
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using OpConversionPattern<AtenOp>::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenOp op,
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typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Type resultType =
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this->getTypeConverter()->convertType(op->getResult(0).getType());
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Value result = rewriter.create<UnaryOp>(op.getLoc(), adaptor.a());
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rewriter.replaceOp(
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op, convertScalarToDtype(rewriter, op.getLoc(), result, resultType));
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return success();
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}
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};
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} // namespace
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namespace {
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// Lowers aten integer comparison ops.
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template <typename AtenOp, arith::CmpIPredicate Pred>
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class ConvertAtenIntComparisonOp : public OpConversionPattern<AtenOp> {
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public:
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using OpConversionPattern<AtenOp>::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenOp op,
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typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.replaceOpWithNewOp<arith::CmpIOp>(op, Pred, adaptor.a(),
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adaptor.b());
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return success();
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}
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};
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} // namespace
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namespace {
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// Lowers aten float and float_int comparison ops.
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template <typename AtenOp, arith::CmpFPredicate Pred>
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class ConvertAtenFloatComparisonOp : public OpConversionPattern<AtenOp> {
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public:
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using OpConversionPattern<AtenOp>::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenOp op,
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typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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Value lhs = adaptor.a(), rhs = adaptor.b();
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rhs = convertScalarToDtype(rewriter, op.getLoc(), rhs, lhs.getType());
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rewriter.replaceOpWithNewOp<arith::CmpFOp>(op, Pred, lhs, rhs);
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return success();
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}
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};
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} // namespace
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// Tensors with integer types need to be converted to signless integer
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// element type. All tensors with element types other than integer can reuse
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// existing elements attribute.
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namespace {
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class ConvertTorchTensorLiteralOp
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: public OpConversionPattern<ValueTensorLiteralOp> {
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public:
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using OpConversionPattern<ValueTensorLiteralOp>::OpConversionPattern;
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using OpAdaptor = ValueTensorLiteralOp::Adaptor;
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LogicalResult
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matchAndRewrite(ValueTensorLiteralOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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MLIRContext *context = op->getContext();
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if (auto elements = op.valueAttr().dyn_cast<DenseIntElementsAttr>()) {
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Type elemTy = op.valueAttr().getElementType();
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unsigned bitWidth = elemTy.getIntOrFloatBitWidth();
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Type builtinTensorElemTy = IntegerType::get(context, bitWidth);
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(
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op, elements.mapValues(builtinTensorElemTy, [&](const APInt &v) {
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return APInt(bitWidth, v.getSExtValue());
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}));
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return success();
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}
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if (auto elements = op.valueAttr().dyn_cast<OpaqueElementsAttr>()) {
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if (auto type = elements.getType().dyn_cast<RankedTensorType>()) {
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if (auto intType = type.getElementType().dyn_cast<IntegerType>()) {
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Type builtinTensorElemTy =
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IntegerType::get(context, intType.getIntOrFloatBitWidth());
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auto shapedType =
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RankedTensorType::get(type.getShape(), builtinTensorElemTy);
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(
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op, OpaqueElementsAttr::get(elements.getDialect(), shapedType,
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elements.getValue()));
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return success();
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}
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}
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}
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.valueAttr());
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return success();
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}
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};
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} // namespace
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namespace {
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template <typename OpTy>
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class ConvertTorchConstantOp : public OpConversionPattern<OpTy> {
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public:
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using OpConversionPattern<OpTy>::OpConversionPattern;
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using OpAdaptor = typename OpTy::Adaptor;
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LogicalResult
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matchAndRewrite(OpTy op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.valueAttr());
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return success();
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}
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};
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} // namespace
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// -----------------------------------------------------------------------------
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// The pass
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// -----------------------------------------------------------------------------
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namespace {
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class ConvertTorchToStd : public ConvertTorchToStdBase<ConvertTorchToStd> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<func::FuncDialect>();
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registry.insert<arith::ArithmeticDialect>();
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registry.insert<tensor::TensorDialect>();
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registry.insert<cf::ControlFlowDialect>();
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registry.insert<math::MathDialect>();
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TorchConversion::getBackendTypeConversionDependentDialects(registry);
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}
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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ConversionTarget target(*context);
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target.addLegalDialect<Torch::TorchDialect, func::FuncDialect,
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arith::ArithmeticDialect, tensor::TensorDialect,
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cf::ControlFlowDialect, math::MathDialect>();
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TypeConverter typeConverter;
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typeConverter.addConversion([](Type type) { return type; });
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TorchConversion::setupBackendTypeConversion(target, typeConverter);
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RewritePatternSet patterns(context);
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target.addIllegalOp<AtenDimOp>();
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patterns.add<ConvertAtenDimOp>(typeConverter, context);
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target.addIllegalOp<RuntimeAssertOp>();
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patterns.add<ConvertRuntimeAssertOp>(typeConverter, context);
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target.addIllegalOp<AtenNeIntOp, AtenEqIntOp, AtenGtIntOp>();
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patterns
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.add<ConvertAtenIntComparisonOp<AtenNeIntOp, arith::CmpIPredicate::ne>>(
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typeConverter, context);
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patterns
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.add<ConvertAtenIntComparisonOp<AtenEqIntOp, arith::CmpIPredicate::eq>>(
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typeConverter, context);
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patterns.add<
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ConvertAtenIntComparisonOp<AtenGtIntOp, arith::CmpIPredicate::sgt>>(
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typeConverter, context);
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target.addIllegalOp<AtenGeFloatOp, AtenGeFloatIntOp, AtenNeFloatIntOp,
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AtenGtFloatIntOp>();
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patterns.add<
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ConvertAtenFloatComparisonOp<AtenGeFloatOp, arith::CmpFPredicate::UGE>>(
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typeConverter, context);
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patterns.add<ConvertAtenFloatComparisonOp<AtenGeFloatIntOp,
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arith::CmpFPredicate::UGE>>(
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typeConverter, context);
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patterns.add<ConvertAtenFloatComparisonOp<AtenNeFloatIntOp,
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arith::CmpFPredicate::UNE>>(
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typeConverter, context);
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patterns.add<ConvertAtenFloatComparisonOp<AtenGtFloatIntOp,
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arith::CmpFPredicate::UGT>>(
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typeConverter, context);
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target.addIllegalOp<ValueTensorLiteralOp>();
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patterns.add<ConvertTorchTensorLiteralOp>(typeConverter, context);
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target.addIllegalOp<ConstantBoolOp>();
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patterns.add<ConvertTorchConstantOp<ConstantBoolOp>>(typeConverter,
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context);
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target.addIllegalOp<Torch::ConstantFloatOp>();
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patterns.add<ConvertTorchConstantOp<Torch::ConstantFloatOp>>(typeConverter,
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context);
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target.addIllegalOp<Torch::ConstantIntOp>();
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patterns.add<ConvertTorchConstantOp<Torch::ConstantIntOp>>(typeConverter,
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context);
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target.addIllegalOp<AtenAddIntOp, AtenSubIntOp, AtenMulIntOp>();
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patterns.add<ConvertAtenBinaryOp<AtenAddIntOp, arith::AddIOp>>(
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typeConverter, context);
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patterns.add<ConvertAtenBinaryOp<AtenSubIntOp, arith::SubIOp>>(
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typeConverter, context);
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patterns.add<ConvertAtenBinaryOp<AtenMulIntOp, arith::MulIOp>>(
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typeConverter, context);
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target.addIllegalOp<AtenSubFloatOp>();
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patterns.add<ConvertAtenBinaryOp<AtenSubFloatOp, arith::SubFOp>>(
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typeConverter, context);
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target.addIllegalOp<AtenDivFloatOp>();
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patterns.add<ConvertAtenBinaryOp<AtenDivFloatOp, arith::DivFOp>>(
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typeConverter, context);
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target.addIllegalOp<AtenCeilFloatOp>();
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patterns.add<ConvertAtenUnaryOp<AtenCeilFloatOp, math::CeilOp>>(
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typeConverter, context);
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if (failed(applyPartialConversion(getOperation(), target,
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std::move(patterns))))
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return signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<func::FuncOp>>
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mlir::torch::createConvertTorchToStdPass() {
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return std::make_unique<ConvertTorchToStd>();
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}
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