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torch-mlir/lib/Conversion/TorchToLinalg/Reduction.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/TorchToLinalg/TorchToLinalg.h"
#include "../PassDetail.h"
#include "PopulatePatterns.h"
#include "Utils.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Matchers.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/TorchUpstream.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/APSInt.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
namespace {
// Aten maxdim lowering represents the MaxDim op as an linalg.indexed_generic
// op, producing two output buffers.
//
// The first output buffer contains the maximum value found. It is initialized
// to the minimum representable value of the input element type.
//
// The second output buffer contains the index of the found maximum value. It is
// initialized to 0 and is resulting integer type.
//
// The indexed_generic op updates both the maximum value and index if the
// current value exceeds the running max.
class ConvertAtenMaxDimOp : public OpConversionPattern<AtenMaxDimOp> {
public:
using OpConversionPattern<AtenMaxDimOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenMaxDimOp maxDimOp, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = maxDimOp.getLoc();
Value input = adaptor.self();
RankedTensorType valResultType =
getTypeConverter()
->convertType(maxDimOp.getResult(0).getType())
.cast<RankedTensorType>();
RankedTensorType idxResultType =
getTypeConverter()
->convertType(maxDimOp.getResult(1).getType())
.cast<RankedTensorType>();
RankedTensorType inputType = input.getType().cast<RankedTensorType>();
Type idxElementType = idxResultType.getElementType();
if (!idxElementType.isa<IntegerType>())
return rewriter.notifyMatchFailure(
maxDimOp,
"aten.max_dim to linalg.* requires integer-like result type");
bool keepDim = false;
if (!matchPattern(maxDimOp.keepdim(), m_TorchConstantBool(&keepDim)))
return rewriter.notifyMatchFailure(
maxDimOp, "aten.max_dim requires boolean value for keepdim");
int64_t dim;
if (!matchPattern(maxDimOp.dim(), m_TorchConstantInt(&dim)))
return rewriter.notifyMatchFailure(
maxDimOp, "aten.max_dim to linalg.* requires int value for Dim");
dim = toPositiveDim(dim, inputType.getRank());
if (!isValidDim(dim, inputType.getRank()))
return rewriter.notifyMatchFailure(maxDimOp, "dim is not a valid dim");
Type inElementType = inputType.getElementType();
if (!inElementType.isa<mlir::FloatType>()) {
return rewriter.notifyMatchFailure(
maxDimOp,
"aten.max_dim to linalg.* requires Float input element type");
}
// Constant op to account for the reduction along dim.
auto c1 = rewriter.create<arith::ConstantIndexOp>(loc, /*value=*/1);
SmallVector<Value> resultShape;
for (int64_t i = 0; i < inputType.getRank(); i++) {
if (dim != i) {
auto currentDimSize = rewriter.create<tensor::DimOp>(loc, input, i);
resultShape.push_back(currentDimSize);
} else if (keepDim)
resultShape.push_back(c1);
}
// First fill the output buffer for the index.
Value filledTensorIdx =
createZeroInitTensor(rewriter, loc, resultShape, idxElementType);
// Second fill the output buffer for the running max.
Value initTensorMax = rewriter.create<tensor::EmptyOp>(
loc, getAsOpFoldResult(resultShape), inElementType);
FloatAttr fillValueMaxAttr = rewriter.getFloatAttr(
inElementType,
APFloat::getLargest(
inElementType.cast<mlir::FloatType>().getFloatSemantics(), true));
Value fillValueMax =
rewriter.create<arith::ConstantOp>(loc, fillValueMaxAttr);
Value filledTensorMax =
rewriter.create<linalg::FillOp>(loc, fillValueMax, initTensorMax)
.result();
// Create the affine expressions that will be used to
// iterate over the input and output tensors.
// Here we also set the type of iterator: parallel or reduction.
SmallVector<AffineExpr> exprs;
SmallVector<utils::IteratorType> iteratorTypes;
SmallVector<AffineExpr> resultExprs;
for (auto size : llvm::enumerate(inputType.getShape())) {
exprs.push_back(rewriter.getAffineDimExpr(size.index()));
if (unsigned(dim) == size.index()) {
iteratorTypes.push_back(utils::IteratorType::reduction);
// If `keepDim`, create affine map to the first element
// in the current dimension.
if (keepDim)
resultExprs.push_back(rewriter.getAffineConstantExpr(0));
} else {
iteratorTypes.push_back(utils::IteratorType::parallel);
resultExprs.push_back(rewriter.getAffineDimExpr(size.index()));
}
}
auto maps = AffineMap::inferFromExprList({exprs, resultExprs, resultExprs});
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc,
ArrayRef<Type>({filledTensorMax.getType(), filledTensorIdx.getType()}),
input, ValueRange({filledTensorMax, filledTensorIdx}), maps,
iteratorTypes,
[&](OpBuilder &nestedBuilder, Location nestedLoc,
ValueRange blockArgs) {
Value newValue = blockArgs[0];
Value oldValue = blockArgs[1];
Value oldIndex = blockArgs[2];
Value newIndex = rewriter.create<arith::IndexCastOp>(
nestedLoc, oldIndex.getType(),
rewriter.create<linalg::IndexOp>(loc, dim));
auto resultMax = rewriter.create<arith::MaxFOp>(
nestedLoc, newValue, oldValue);
Value predicate = rewriter.create<arith::CmpFOp>(
nestedLoc, arith::CmpFPredicate::OGT, newValue, oldValue);
auto resultIndex = rewriter.create<arith::SelectOp>(
nestedLoc, predicate, newIndex, oldIndex);
nestedBuilder.create<linalg::YieldOp>(
nestedLoc, ValueRange({resultMax, resultIndex}));
});
// This cast is required to fix the shape in the case of keepDim=True
Value maxValuesCast = rewriter.create<tensor::CastOp>(
loc, valResultType, linalgOp.getResult(0));
Value maxIdxCast = rewriter.create<tensor::CastOp>(loc, idxResultType,
linalgOp.getResult(1));
rewriter.replaceOp(maxDimOp, {maxValuesCast, maxIdxCast});
return success();
}
};
} // namespace
static Value createInitElementForReduceOp(OpBuilder &b, Location loc,
Operation *op, Type elementType) {
if (isa<AtenSumOp, AtenSumDimIntListOp>(op))
return b.create<arith::ConstantOp>(loc, b.getZeroAttr(elementType));
if (isa<AtenMaxOp>(op)) {
if (elementType.isa<mlir::FloatType>())
return b.create<arith::ConstantOp>(
loc, b.getFloatAttr(
elementType,
APFloat::getLargest(
elementType.cast<mlir::FloatType>().getFloatSemantics(),
/*Negative=*/true)));
else if (elementType.isa<mlir::IntegerType>() &&
elementType.getIntOrFloatBitWidth() != 8)
return b.create<arith::ConstantOp>(
loc, b.getIntegerAttr(elementType,
APSInt::getSignedMinValue(
elementType.getIntOrFloatBitWidth())));
}
if (isa<AtenLinalgVectorNormOp>(op) || isa<AtenFrobeniusNormDimOp>(op))
return b.create<arith::ConstantOp>(loc, b.getZeroAttr(elementType));
op->emitError("unimplemented lowering in createInitElementForReduceOp");
return nullptr;
}
static Value createLinalgPayloadForReduceOp(OpBuilder &b, Location loc,
ValueRange payloadArgs,
Operation *op,
ArrayRef<Value> operands,
Type resultElementType) {
if (isa<AtenSumOp, AtenSumDimIntListOp>(op)) {
Value self =
convertScalarToDtype(b, loc, payloadArgs[0], resultElementType);
Value result = payloadArgs[1];
if (resultElementType.isa<mlir::FloatType>())
return b.create<arith::AddFOp>(loc, self, result);
else if (resultElementType.isa<mlir::IntegerType>())
return b.create<arith::AddIOp>(loc, self, result);
} else if (auto max = dyn_cast<AtenMaxOp>(op)) {
Value self =
convertScalarToDtype(b, loc, payloadArgs[0], resultElementType);
Value result = payloadArgs[1];
if (resultElementType.isa<mlir::FloatType>())
return b.create<arith::MaxFOp>(loc, self, result);
else if (resultElementType.isa<mlir::IntegerType>()) {
IntegerType intType = max.self()
.getType()
.cast<BaseTensorType>()
.getDtype()
.dyn_cast<mlir::IntegerType>();
if (intType.isUnsigned())
return b.create<arith::MaxUIOp>(loc, self, result);
if (intType.isSigned())
return b.create<arith::MaxSIOp>(loc, self, result);
}
} else if (isa<AtenLinalgVectorNormOp>(op)) {
// This creates payload for only the first of the two linalg.generic ops.
// TODO: Short-circuit operations if `ord` is zero or one.
Value elem = payloadArgs[0];
Value result = payloadArgs[1];
Value self = convertScalarToDtype(b, loc, elem, resultElementType);
auto abs = b.create<math::AbsFOp>(loc, self);
AtenLinalgVectorNormOp::Adaptor adaptor(operands);
Value ord = convertScalarToDtype(b, loc, adaptor.ord(), resultElementType);
auto pow = b.create<math::PowFOp>(loc, abs, ord);
return b.create<arith::AddFOp>(loc, pow, result);
} else if (isa<AtenFrobeniusNormDimOp>(op)) {
Value elem = payloadArgs[0];
Value result = payloadArgs[1];
Value self = convertScalarToDtype(b, loc, elem, resultElementType);
auto abs = b.create<math::AbsFOp>(loc, self);
Attribute twoAttr = b.getFloatAttr(resultElementType, 2.0);
auto ord = b.create<arith::ConstantOp>(loc, twoAttr);
auto pow = b.create<math::PowFOp>(loc, abs, ord);
return b.create<arith::AddFOp>(loc, pow, result);
}
op->emitError("unimplemented lowering in createLinalgPayloadForReduceOp");
return nullptr;
}
namespace {
class ConvertReductionOp : public ConversionPattern {
private:
/// Given a reduction operation that has the `keepdim` attribute and the
/// (optional) `dim` attribute, return the source tensor operand and the
/// literal values of the attributes or failure otherwise.
template <typename T>
FailureOr<torch_to_linalg::ReductionOpInfo>
computeReductionOpInfoForDimVariantOp(
T op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const {
auto opInfo = torch_to_linalg::ReductionOpInfo{false, Value{}, {}};
typename T::Adaptor adaptor(operands);
opInfo.tensorOperand = adaptor.self();
auto inputType = opInfo.tensorOperand.getType().cast<RankedTensorType>();
if (!matchPattern(op.keepdim(), m_TorchConstantBool(&opInfo.keepDim)))
return rewriter.notifyMatchFailure(op,
"`keepdim` must be a constant bool");
SmallVector<int64_t> dimList;
bool isNoneOrEmptyDimList =
op.dim().getType().template isa<Torch::NoneType>();
if (matchPattern(op.dim(), m_TorchListOfConstantInts(dimList))) {
// Fix negative dimensions, if any, before adding to the list.
for (int64_t dim : dimList) {
dim = toPositiveDim(dim, inputType.getRank());
// Drop invalid dimensions
if (isValidDim(dim, inputType.getRank()))
opInfo.dimSet.insert(dim);
}
if (dimList.empty())
isNoneOrEmptyDimList = true;
} else if (!isNoneOrEmptyDimList) {
return rewriter.notifyMatchFailure(
op, "`dim` argument must be a constant int list or None");
}
if (isNoneOrEmptyDimList) {
// If no dimensions were specified, reduce along all dimensions
for (int64_t i = 0; i < inputType.getRank(); i++)
opInfo.dimSet.insert(i);
}
return opInfo;
}
/// Given a reduction operation, return the source tensor operand and the
/// literal values of the `keepdim` and `dim` attributes, if any, or failure
/// otherwise.
FailureOr<torch_to_linalg::ReductionOpInfo>
computeReductionOpInfo(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const {
auto opInfo = torch_to_linalg::ReductionOpInfo{false, Value{}, {}};
if (isa<AtenMaxOp, AtenSumOp>(op)) {
opInfo.tensorOperand = operands[0];
auto inputType = opInfo.tensorOperand.getType().cast<RankedTensorType>();
// `AtenSumOp` and `AtenMaxOp` reduces along all the dimensions of the
// input tensor.
for (int64_t i = 0; i < inputType.getRank(); i++)
opInfo.dimSet.insert(i);
return opInfo;
}
if (auto sumOp = dyn_cast<AtenSumDimIntListOp>(op))
return computeReductionOpInfoForDimVariantOp(sumOp, operands, rewriter);
if (auto normOp = dyn_cast<AtenLinalgVectorNormOp>(op))
return computeReductionOpInfoForDimVariantOp(normOp, operands, rewriter);
if (auto normOp = dyn_cast<AtenFrobeniusNormDimOp>(op))
return computeReductionOpInfoForDimVariantOp(normOp, operands, rewriter);
return rewriter.notifyMatchFailure(op, "not a supported reduce op");
}
/// Generate a linalg.generic operation for pointwise exponentiation of each
/// element.
Value createElementwiseExp(Location loc, Type elemType, Value exponent,
Value inputTensor,
const torch_to_linalg::ReductionOpInfo &opInfo,
ConversionPatternRewriter &rewriter) const {
bool err = false;
auto powBodyBuilder = [&](OpBuilder &builder, Location loc,
ValueRange payloadArgs) {
Value elem = convertScalarToDtype(builder, loc, payloadArgs[0], elemType);
auto result = builder.create<math::PowFOp>(loc, elem, exponent);
if (result)
builder.create<linalg::YieldOp>(loc, Value{result});
err = !result;
};
Value powOp = torch_to_linalg::createElementwiseLinalgGeneric(
rewriter, loc, {inputTensor}, elemType, powBodyBuilder);
return err ? Value{} : powOp;
}
FailureOr<Value> createSecondReductionForVectorNormOp(
Location loc, Type elemType, AtenLinalgVectorNormOp op, Value ordOp,
Value firstReduction, const torch_to_linalg::ReductionOpInfo &opInfo,
ConversionPatternRewriter &rewriter) const {
// Cast `ord` to float so that we can readily pass it math.powf.
Value ordValue = convertScalarToDtype(rewriter, loc, ordOp, elemType);
// TODO: Add support for ord = {0, +inf, -inf}.
auto epsilon = 1e-5;
auto ordLiteral = 0.0;
if (matchPattern(ordValue, m_TorchConstantFloat(&ordLiteral)) &&
fabs(ordLiteral) < epsilon)
return rewriter.notifyMatchFailure(op, "unimplemented: L0 norm");
if (std::isinf(ordLiteral))
return rewriter.notifyMatchFailure(op, "unimplemented: ord = +/- inf");
// Raise each summed value to the inverse of the order of the norm.
Attribute oneAttr = rewriter.getFloatAttr(elemType, 1.0);
auto oneValue = rewriter.create<arith::ConstantOp>(loc, oneAttr);
auto inverseOrdValue =
rewriter.create<arith::DivFOp>(loc, oneValue, ordValue);
// Use the results of the first reduction operation from above to generate
// a second reduction operation.
Value reduceOp = createElementwiseExp(loc, elemType, inverseOrdValue,
firstReduction, opInfo, rewriter);
if (!reduceOp)
return rewriter.notifyMatchFailure(
op, "failed to create linalg.generic operation for element-wise "
"exponentiation");
return reduceOp;
}
/// Generate a linalg.generic operation for a reduction.
Value createReductionOp(Location loc, Type elemType, Operation *op,
ArrayRef<Value> operands,
const torch_to_linalg::ReductionOpInfo &opInfo,
ConversionPatternRewriter &rewriter) const {
bool err = false;
auto reductionBodyBuilder = [&](OpBuilder &builder, Location loc,
ValueRange payloadArgs) {
Value result = createLinalgPayloadForReduceOp(builder, loc, payloadArgs,
op, operands, elemType);
if (result)
builder.create<linalg::YieldOp>(loc, result);
err = !result;
};
Value initElem = createInitElementForReduceOp(rewriter, loc, op, elemType);
Value reduceOp = torch_to_linalg::createReductionLinalgGeneric(
rewriter, loc, opInfo, initElem, reductionBodyBuilder);
return err ? Value{} : reduceOp;
}
/// Depending on the operation, check validity of the result's element type.
LogicalResult
validateReductionElementType(Operation *op, Type elemType,
ConversionPatternRewriter &rewriter) const {
if ((isa<AtenLinalgVectorNormOp>(op) || isa<AtenFrobeniusNormDimOp>(op)) &&
!elemType.isa<mlir::FloatType>())
return rewriter.notifyMatchFailure(
op, "only float types are valid for vector norm ops");
// No checks for all other reduction operations
return success();
}
public:
ConvertReductionOp(TypeConverter &typeConverter, MLIRContext *context)
: ConversionPattern(typeConverter, MatchAnyOpTypeTag(), /*benefit=*/1,
context) {}
LogicalResult
matchAndRewrite(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return rewriter.notifyMatchFailure(
op, "invalid operand or result types to use with linalg on tensors");
FailureOr<torch_to_linalg::ReductionOpInfo> opInfo =
computeReductionOpInfo(op, operands, rewriter);
if (failed(opInfo))
return opInfo;
Location loc = op->getLoc();
auto resultType = getTypeConverter()
->convertType(op->getResult(0).getType())
.cast<RankedTensorType>();
Type elemType = resultType.getElementType();
LogicalResult elemTypeCheck =
validateReductionElementType(op, elemType, rewriter);
if (failed(elemTypeCheck))
return elemTypeCheck;
Value reduceOp =
createReductionOp(loc, elemType, op, operands, *opInfo, rewriter);
if (!reduceOp)
return rewriter.notifyMatchFailure(
op, "failed to create linalg.generic operation for reduction");
// If this is aten.linalg_vector_norm op, then we need to generate another
// linalg.generic op that references the first linalg.generic op.
if (auto normOp = dyn_cast<AtenLinalgVectorNormOp>(op)) {
AtenLinalgVectorNormOp::Adaptor adaptor(operands);
FailureOr<Value> secondReduceOp = createSecondReductionForVectorNormOp(
loc, elemType, normOp, adaptor.ord(), reduceOp, *opInfo, rewriter);
if (failed(secondReduceOp))
return secondReduceOp;
reduceOp = *secondReduceOp;
}
// If it is aten.frobenius_norm.dim op, take the square root of reduceOp as
// the final result
if (auto normOp = dyn_cast<AtenFrobeniusNormDimOp>(op)) {
auto halfAttr = rewriter.getFloatAttr(elemType, 0.5);
auto exp = rewriter.create<arith::ConstantOp>(loc, halfAttr);
reduceOp =
createElementwiseExp(loc, elemType, exp, reduceOp, *opInfo, rewriter);
}
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, reduceOp);
return success();
}
};
} // namespace
void mlir::torch::torch_to_linalg::populateReductionPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<AtenMaxDimOp>();
patterns.add<ConvertAtenMaxDimOp>(typeConverter, context);
target.addIllegalOp<AtenSumOp>();
target.addIllegalOp<AtenSumDimIntListOp>();
target.addIllegalOp<AtenMaxOp>();
target.addIllegalOp<AtenLinalgVectorNormOp>();
target.addIllegalOp<AtenFrobeniusNormDimOp>();
patterns.add<ConvertReductionOp>(typeConverter, context);
}
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