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torch-mlir/lib/Conversion/TorchToArith/TorchToArith.cpp

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//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, 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
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "torch-mlir/Conversion/TorchToArith/TorchToArith.h"
#include "../PassDetail.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/IR/DialectResourceBlobManager.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Conversion/Utils/Utils.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
// -----------------------------------------------------------------------------
// Patterns (as this grows, it should be organized into multiple files)
// -----------------------------------------------------------------------------
// This is going to eventually be O(#torch operators), which is in the 100s.
namespace {
// Note: Confusingly, ATen's "dim" means "number of dimensions" which is what
// MLIR calls "rank".
class ConvertAtenDimOp : public OpConversionPattern<AtenDimOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenDimOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto rank = rewriter.create<tensor::RankOp>(op->getLoc(), adaptor.self());
rewriter.replaceOpWithNewOp<arith::IndexCastOp>(
op, getTypeConverter()->convertType(op.getType()), rank);
return success();
}
};
} // namespace
namespace {
class ConvertAtenIsFloatingPointOp
: public OpConversionPattern<AtenIsFloatingPointOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenIsFloatingPointOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto tensorType = op.self().getType().cast<BaseTensorType>();
bool result =
tensorType.hasDtype() && tensorType.getDtype().isa<mlir::FloatType>();
rewriter.replaceOpWithNewOp<arith::ConstantOp>(
op, BoolAttr::get(getContext(), result));
return success();
}
};
} // namespace
namespace {
class ConvertRuntimeAssertOp : public OpConversionPattern<RuntimeAssertOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(RuntimeAssertOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<cf::AssertOp>(op, adaptor.condition(),
adaptor.message());
return success();
}
};
} // namespace
namespace {
template <typename AtenOp, typename BinOp>
class ConvertAtenBinaryOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.template replaceOpWithNewOp<BinOp>(op, adaptor.a(), adaptor.b());
return success();
}
};
} // namespace
namespace {
template <typename AtenOp, typename UnaryOp>
class ConvertAtenUnaryOpToFloatMathOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value input = adaptor.a();
Type resultType =
this->getTypeConverter()->convertType(op->getResult(0).getType());
if (!input.getType().isa<mlir::FloatType>())
input = convertScalarToDtype(rewriter, loc, input, rewriter.getF64Type());
Value result = rewriter.create<UnaryOp>(loc, input);
rewriter.replaceOp(op,
convertScalarToDtype(rewriter, loc, result, resultType));
return success();
}
};
} // namespace
namespace {
// Lowers aten integer comparison ops.
template <typename AtenOp, arith::CmpIPredicate Pred>
class ConvertAtenIntComparisonOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<arith::CmpIOp>(op, Pred, adaptor.a(),
adaptor.b());
return success();
}
};
} // namespace
namespace {
// Lowers aten float and float_int comparison ops.
template <typename AtenOp, arith::CmpFPredicate Pred>
class ConvertAtenFloatComparisonOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.a(), rhs = adaptor.b();
rhs = convertScalarToDtype(rewriter, op.getLoc(), rhs, lhs.getType());
rewriter.replaceOpWithNewOp<arith::CmpFOp>(op, Pred, lhs, rhs);
return success();
}
};
} // namespace
// Tensors with integer types need to be converted to signless integer
// element type. All tensors with element types other than integer can reuse
// existing elements attribute.
namespace {
class ConvertTorchTensorLiteralOp
: public OpConversionPattern<ValueTensorLiteralOp> {
public:
using OpConversionPattern<ValueTensorLiteralOp>::OpConversionPattern;
using OpAdaptor = ValueTensorLiteralOp::Adaptor;
LogicalResult
matchAndRewrite(ValueTensorLiteralOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
MLIRContext *context = op->getContext();
if (auto elements = op.valueAttr().dyn_cast<DenseIntElementsAttr>()) {
Type elemTy = op.valueAttr().getElementType();
unsigned bitWidth = elemTy.getIntOrFloatBitWidth();
Type builtinTensorElemTy = IntegerType::get(context, bitWidth);
rewriter.replaceOpWithNewOp<arith::ConstantOp>(
op, elements.mapValues(builtinTensorElemTy, [&](const APInt &v) {
return APInt(bitWidth, v.getSExtValue());
}));
return success();
}
if (auto elements = op.valueAttr().dyn_cast<DenseResourceElementsAttr>()) {
if (auto type = elements.getType().dyn_cast<RankedTensorType>()) {
if (auto intType = type.getElementType().dyn_cast<IntegerType>()) {
Type builtinTensorElemTy =
IntegerType::get(context, intType.getIntOrFloatBitWidth());
auto shapedType =
RankedTensorType::get(type.getShape(), builtinTensorElemTy);
AsmResourceBlob *blob = elements.getRawHandle().getBlob();
assert(blob && "Expecting dense resource with a valid blob");
rewriter.replaceOpWithNewOp<arith::ConstantOp>(
op, DenseElementsAttr::get(shapedType, blob->getData()));
return success();
}
}
}
rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.valueAttr());
return success();
}
};
} // namespace
namespace {
template <typename OpTy>
class ConvertTorchConstantOp : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
using OpAdaptor = typename OpTy::Adaptor;
LogicalResult
matchAndRewrite(OpTy op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.valueAttr());
return success();
}
};
} // namespace
namespace {
template <typename OpTy>
class ConvertAtenAnyOrAllBoolOp : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
using OpAdaptor = typename OpTy::Adaptor;
virtual bool reductionFunction(ArrayRef<bool> inputArray) const = 0;
LogicalResult
matchAndRewrite(OpTy op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
SmallVector<Value> inputListTorchBool;
if (!getListConstructElements(op.self(), inputListTorchBool)) {
return rewriter.notifyMatchFailure(
op, "Unimplemented input list not constructed from ListConstruct");
}
SmallVector<bool> inputListBool;
for (Value v : inputListTorchBool) {
bool cst;
if (!matchPattern(v, m_TorchConstantBool(&cst)))
return rewriter.notifyMatchFailure(
op, "only support constant bool input list elements");
inputListBool.push_back(cst);
}
bool result = reductionFunction(inputListBool);
rewriter.replaceOpWithNewOp<arith::ConstantOp>(
op, rewriter.getBoolAttr(result));
return success();
}
};
class ConvertAtenAnyOp : public ConvertAtenAnyOrAllBoolOp<AtenAnyBoolOp> {
using ConvertAtenAnyOrAllBoolOp<AtenAnyBoolOp>::ConvertAtenAnyOrAllBoolOp;
bool reductionFunction(ArrayRef<bool> inputArray) const override {
return llvm::any_of(inputArray,
[](bool inputListElem) { return inputListElem; });
}
};
class ConvertAtenAllOp : public ConvertAtenAnyOrAllBoolOp<AtenAllBoolOp> {
using ConvertAtenAnyOrAllBoolOp<AtenAllBoolOp>::ConvertAtenAnyOrAllBoolOp;
bool reductionFunction(ArrayRef<bool> inputArray) const override {
return llvm::all_of(inputArray,
[](bool inputListElem) { return inputListElem; });
}
};
} // namespace
namespace {
template <typename OpTy, typename CmpOpTy, typename CmpOpPred, CmpOpPred Pred>
class ConvertAtenBoolLikeOp : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
using OpAdaptor = typename OpTy::Adaptor;
LogicalResult
matchAndRewrite(OpTy op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Type inputType = adaptor.a().getType();
Value cstZero = rewriter.create<arith::ConstantOp>(
loc, rewriter.getZeroAttr(inputType));
Value cstTrue =
rewriter.create<arith::ConstantOp>(loc, rewriter.getBoolAttr(true));
Value cstFalse =
rewriter.create<arith::ConstantOp>(loc, rewriter.getBoolAttr(false));
Value cmpPred;
cmpPred = rewriter.create<CmpOpTy>(loc, Pred, adaptor.a(), cstZero);
rewriter.replaceOpWithNewOp<arith::SelectOp>(op, cmpPred, cstTrue,
cstFalse);
return success();
}
};
} // namespace
// -----------------------------------------------------------------------------
// The pass
// -----------------------------------------------------------------------------
namespace {
class ConvertTorchToArith : public ConvertTorchToArithBase<ConvertTorchToArith> {
public:
void getDependentDialects(DialectRegistry &registry) const override {
registry.insert<func::FuncDialect>();
registry.insert<arith::ArithmeticDialect>();
registry.insert<tensor::TensorDialect>();
registry.insert<cf::ControlFlowDialect>();
registry.insert<math::MathDialect>();
TorchConversion::getBackendTypeConversionDependentDialects(registry);
}
void runOnOperation() override {
MLIRContext *context = &getContext();
ConversionTarget target(*context);
target.addLegalDialect<Torch::TorchDialect, func::FuncDialect,
arith::ArithmeticDialect, tensor::TensorDialect,
cf::ControlFlowDialect, math::MathDialect>();
TypeConverter typeConverter;
typeConverter.addConversion([](Type type) { return type; });
TorchConversion::setupBackendTypeConversion(target, typeConverter);
RewritePatternSet patterns(context);
target.addIllegalOp<AtenDimOp>();
patterns.add<ConvertAtenDimOp>(typeConverter, context);
target.addIllegalOp<AtenIsFloatingPointOp>();
patterns.add<ConvertAtenIsFloatingPointOp>(typeConverter, context);
target.addIllegalOp<RuntimeAssertOp>();
patterns.add<ConvertRuntimeAssertOp>(typeConverter, context);
target.addIllegalOp<AtenNeIntOp, AtenEqIntOp, AtenGtIntOp, AtenGeIntOp>();
patterns
.add<ConvertAtenIntComparisonOp<AtenNeIntOp, arith::CmpIPredicate::ne>>(
typeConverter, context);
patterns
.add<ConvertAtenIntComparisonOp<AtenEqIntOp, arith::CmpIPredicate::eq>>(
typeConverter, context);
patterns.add<
ConvertAtenIntComparisonOp<AtenGtIntOp, arith::CmpIPredicate::sgt>>(
typeConverter, context);
patterns.add<
ConvertAtenIntComparisonOp<AtenGeIntOp, arith::CmpIPredicate::sge>>(
typeConverter, context);
target.addIllegalOp<AtenGeFloatOp, AtenGeFloatIntOp, AtenNeFloatIntOp,
AtenGtFloatIntOp>();
patterns.add<
ConvertAtenFloatComparisonOp<AtenGeFloatOp, arith::CmpFPredicate::UGE>>(
typeConverter, context);
patterns.add<ConvertAtenFloatComparisonOp<AtenGeFloatIntOp,
arith::CmpFPredicate::UGE>>(
typeConverter, context);
patterns.add<ConvertAtenFloatComparisonOp<AtenNeFloatIntOp,
arith::CmpFPredicate::UNE>>(
typeConverter, context);
patterns.add<ConvertAtenFloatComparisonOp<AtenGtFloatIntOp,
arith::CmpFPredicate::UGT>>(
typeConverter, context);
target.addIllegalOp<ValueTensorLiteralOp>();
patterns.add<ConvertTorchTensorLiteralOp>(typeConverter, context);
target.addIllegalOp<ConstantBoolOp>();
patterns.add<ConvertTorchConstantOp<ConstantBoolOp>>(typeConverter,
context);
target.addIllegalOp<Torch::ConstantFloatOp>();
patterns.add<ConvertTorchConstantOp<Torch::ConstantFloatOp>>(typeConverter,
context);
target.addIllegalOp<Torch::ConstantIntOp>();
patterns.add<ConvertTorchConstantOp<Torch::ConstantIntOp>>(typeConverter,
context);
target.addIllegalOp<AtenAddIntOp, AtenSubIntOp, AtenMulIntOp>();
patterns.add<ConvertAtenBinaryOp<AtenAddIntOp, arith::AddIOp>>(
typeConverter, context);
patterns.add<ConvertAtenBinaryOp<AtenSubIntOp, arith::SubIOp>>(
typeConverter, context);
patterns.add<ConvertAtenBinaryOp<AtenMulIntOp, arith::MulIOp>>(
typeConverter, context);
target.addIllegalOp<AtenSubFloatOp>();
patterns.add<ConvertAtenBinaryOp<AtenSubFloatOp, arith::SubFOp>>(
typeConverter, context);
target.addIllegalOp<AtenDivFloatOp>();
patterns.add<ConvertAtenBinaryOp<AtenDivFloatOp, arith::DivFOp>>(
typeConverter, context);
target.addIllegalOp<AtenCeilFloatOp>();
patterns
.add<ConvertAtenUnaryOpToFloatMathOp<AtenCeilFloatOp, math::CeilOp>>(
typeConverter, context);
target.addIllegalOp<AtenSqrtIntOp>();
patterns.add<ConvertAtenUnaryOpToFloatMathOp<AtenSqrtIntOp, math::SqrtOp>>(
typeConverter, context);
target.addIllegalOp<AtenAnyBoolOp, AtenAllBoolOp>();
patterns.add<ConvertAtenAnyOp>(typeConverter, context);
patterns.add<ConvertAtenAllOp>(typeConverter, context);
target.addIllegalOp<AtenBoolFloatOp, AtenBoolIntOp>();
patterns.add<
ConvertAtenBoolLikeOp<AtenBoolFloatOp, arith::CmpFOp,
arith::CmpFPredicate, arith::CmpFPredicate::UNE>>(
typeConverter, context);
patterns.add<
ConvertAtenBoolLikeOp<AtenBoolIntOp, arith::CmpIOp,
arith::CmpIPredicate, arith::CmpIPredicate::ne>>(
typeConverter, context);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
return signalPassFailure();
}
};
} // namespace
std::unique_ptr<OperationPass<func::FuncOp>>
mlir::torch::createConvertTorchToArithPass() {
return std::make_unique<ConvertTorchToArith>();
}
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