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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 "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Complex/IR/Complex.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/TorchToLinalg/Utils.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 max.dim (min.dim) lowering represents the MaxDimOp (MinDimOp) as an
// linalg.indexed_generic op, producing two output buffers.
//
// The first output buffer contains the maximum (minium) value found. It is
// initialized to the minimum (maximum) representable value of the input
// element type.
//
// The second output buffer contains the index of the found maximum (minimum)
// value. It is initialized to 0 and is resulting integer type.
//
// The indexed_generic op updates both the maximum (minimum) value and index
// if the current value exceeds the running max (min).
template <typename OpTy>
class ConvertAtenMinMaxDimOp : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
using OpConversionPattern<OpTy>::getTypeConverter;
using OpAdaptor = typename OpTy::Adaptor;
LogicalResult
matchAndRewrite(OpTy op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
static_assert(std::is_same<OpTy, AtenMaxDimOp>() ||
std::is_same<OpTy, AtenMinDimOp>());
constexpr bool isMax = std::is_same<OpTy, AtenMaxDimOp>();
const llvm::StringRef opName = op->getName().getStringRef();
Location loc = op.getLoc();
Value input = adaptor.getSelf();
auto typec = this->getTypeConverter();
auto valResultType =
cast<RankedTensorType>(typec->convertType(op.getResult(0).getType()));
auto idxResultType =
cast<RankedTensorType>(typec->convertType(op.getResult(1).getType()));
RankedTensorType inputType = cast<RankedTensorType>(input.getType());
Type idxElementType =
getElementTypeOrSelf(typec->convertType(idxResultType));
if (!isa<IntegerType>(idxElementType))
return rewriter.notifyMatchFailure(
op, opName + " to linalg.* requires integer-like result type");
bool keepDim = false;
if (!matchPattern(op.getKeepdim(), m_TorchConstantBool(&keepDim)))
return rewriter.notifyMatchFailure(
op, opName + " requires boolean value for keepdim");
int64_t dim;
if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
return rewriter.notifyMatchFailure(
op, opName + " to linalg.* requires int value for Dim");
dim = toPositiveDim(dim, inputType.getRank());
if (!isValidDim(dim, inputType.getRank()))
return rewriter.notifyMatchFailure(op, "dim is not a valid dim");
Type inElementType = inputType.getElementType();
bool isUnsigned = false;
if (!isa<mlir::FloatType>(inElementType)) {
if (isa<mlir::IntegerType>(inElementType)) {
auto integerTy = op.getSelf()
.getType()
.template cast<BaseTensorType>()
.getDtype()
.template dyn_cast<mlir::IntegerType>();
isUnsigned = integerTy.isUnsigned();
} else {
return rewriter.notifyMatchFailure(
op, opName + " to linalg.* requires Float or Integer "
"input element type");
}
}
// Constant op to account for the reduction along dim.
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);
}
}
// First fill the output buffer for the index.
Value filledTensorIdx =
createZeroInitTensor(rewriter, loc, resultShape, idxElementType);
// Second fill the output buffer for the running max or min.
Value initTensorVal = rewriter.create<tensor::EmptyOp>(
loc, getAsOpFoldResult(resultShape), inElementType);
Value fillValue;
if (isa<mlir::FloatType>(inElementType)) {
fillValue = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(
inElementType,
APFloat::getInf(
cast<mlir::FloatType>(inElementType).getFloatSemantics(),
/*Negative=*/isMax)));
} else if (!isUnsigned) {
auto width = cast<mlir::IntegerType>(inElementType).getWidth();
auto init = isMax ? APSInt::getSignedMinValue(width)
: APSInt::getSignedMaxValue(width);
fillValue = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(inElementType, init));
} else if (isUnsigned) {
auto width = cast<mlir::IntegerType>(inElementType).getWidth();
auto init = isMax ? APInt::getMinValue(width) : APInt::getMaxValue(width);
fillValue = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(inElementType, init));
}
Value filledTensorVal =
rewriter.create<linalg::FillOp>(loc, fillValue, initTensorVal).result();
SmallVector<utils::IteratorType> iteratorTypes(
inputType.getRank(), utils::IteratorType::parallel);
iteratorTypes[dim] = utils::IteratorType::reduction;
// 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<AffineExpr> resultExprs;
for (auto size :
llvm::enumerate(makeShapeTorchCompatible(inputType.getShape()))) {
exprs.push_back(rewriter.getAffineDimExpr(size.index()));
if (unsigned(dim) != size.index())
resultExprs.push_back(rewriter.getAffineDimExpr(size.index()));
}
auto maps = AffineMap::inferFromExprList({exprs, resultExprs, resultExprs},
rewriter.getContext());
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc,
ArrayRef<Type>({filledTensorVal.getType(), filledTensorIdx.getType()}),
input, ValueRange({filledTensorVal, 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));
Value resultVal, predicate;
if (isa<mlir::FloatType>(inElementType)) {
arith::CmpFPredicate predType;
if (isMax) {
predType = arith::CmpFPredicate::OGT;
resultVal = rewriter.create<arith::MaximumFOp>(
nestedLoc, newValue, oldValue);
} else {
predType = arith::CmpFPredicate::OLT;
resultVal = rewriter.create<arith::MinimumFOp>(
nestedLoc, newValue, oldValue);
}
predicate = rewriter.create<arith::CmpFOp>(nestedLoc, predType,
newValue, oldValue);
} else {
arith::CmpIPredicate predType;
if (isMax) {
predType = isUnsigned ? arith::CmpIPredicate::ugt
: arith::CmpIPredicate::sgt;
if (isUnsigned) {
resultVal = rewriter.create<arith::MaxUIOp>(nestedLoc, newValue,
oldValue);
} else {
resultVal = rewriter.create<arith::MaxSIOp>(nestedLoc, newValue,
oldValue);
}
} else {
predType = isUnsigned ? arith::CmpIPredicate::ult
: arith::CmpIPredicate::slt;
if (isUnsigned) {
resultVal = rewriter.create<arith::MinUIOp>(nestedLoc, newValue,
oldValue);
} else {
resultVal = rewriter.create<arith::MinSIOp>(nestedLoc, newValue,
oldValue);
}
}
predicate = rewriter.create<arith::CmpIOp>(nestedLoc, predType,
newValue, oldValue);
}
auto resultIndex = rewriter.create<arith::SelectOp>(
nestedLoc, predicate, newIndex, oldIndex);
nestedBuilder.create<linalg::YieldOp>(
nestedLoc, ValueRange({resultVal, resultIndex}));
});
if (!keepDim) {
Value rVal = rewriter.create<tensor::CastOp>(loc, valResultType,
linalgOp.getResult(0));
Value rIdx = rewriter.create<tensor::CastOp>(loc, idxResultType,
linalgOp.getResult(1));
llvm::SmallVector<Value> res{rVal, rIdx};
rewriter.replaceOp(op, res);
return success();
}
llvm::SmallVector<int64_t> valShape(valResultType.getShape());
llvm::SmallVector<int64_t> idxShape(idxResultType.getShape());
for (int i = dim, s = valShape.size() - 1; i < s; ++i) {
valShape[i] = valShape[i + 1];
idxShape[i] = idxShape[i + 1];
}
valShape.resize(valShape.size() - 1);
idxShape.resize(idxShape.size() - 1);
Value rVal = rewriter.create<tensor::CastOp>(
loc, valResultType.clone(valShape), linalgOp.getResult(0));
Value rIdx = rewriter.create<tensor::CastOp>(
loc, idxResultType.clone(idxShape), linalgOp.getResult(1));
SmallVector<ReassociationIndices> reassociation(valShape.size());
if (reassociation.size() > 0) {
for (int i = 0; i < dim; ++i)
reassociation[i].push_back(i);
reassociation[std::max<int64_t>(0, dim - 1)].push_back(dim);
for (int i = dim, s = reassociation.size(); i < s; ++i)
reassociation[i].push_back(i + 1);
}
valShape.push_back(0);
idxShape.push_back(0);
for (int i = dim, s = valShape.size() - 1; i < s; ++i) {
valShape[i + 1] = valShape[i];
idxShape[i + 1] = idxShape[i];
}
valShape[dim] = 1;
idxShape[dim] = 1;
Value unsqueezeVal = rewriter.create<tensor::ExpandShapeOp>(
loc, valResultType, rVal, reassociation);
Value unsqueezeIdx = rewriter.create<tensor::ExpandShapeOp>(
loc, idxResultType, rIdx, reassociation);
llvm::SmallVector<Value> unsqueezes = {unsqueezeVal, unsqueezeIdx};
rewriter.replaceOp(op, unsqueezes);
return success();
}
};
} // namespace
static Value createAbsOpForNormOps(OpBuilder &b, Location loc, Value elem,
Type resultElementType) {
if (elem.getType().isa<mlir::ComplexType>()) {
return b.create<complex::AbsOp>(loc, elem);
}
Value self = convertScalarToDtype(b, loc, elem, resultElementType);
return b.create<math::AbsFOp>(loc, self);
}
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<AtenProdOp, AtenProdDimIntOp>(op)) {
if (isa<mlir::FloatType>(elementType))
return b.create<arith::ConstantOp>(loc, b.getFloatAttr(elementType, 1.0));
else if (isa<mlir::IntegerType>(elementType))
return b.create<arith::ConstantOp>(loc, b.getIntegerAttr(elementType, 1));
}
if (isa<AtenMaxOp>(op)) {
if (isa<mlir::FloatType>(elementType))
return b.create<arith::ConstantOp>(
loc, b.getFloatAttr(
elementType,
APFloat::getInf(
cast<mlir::FloatType>(elementType).getFloatSemantics(),
/*Negative=*/true)));
else if (isa<mlir::IntegerType>(elementType) &&
elementType.getIntOrFloatBitWidth() != 8)
return b.create<arith::ConstantOp>(
loc, b.getIntegerAttr(elementType,
APSInt::getSignedMinValue(
elementType.getIntOrFloatBitWidth())));
}
if (isa<AtenMinOp>(op)) {
if (isa<mlir::FloatType>(elementType))
return b.create<arith::ConstantOp>(
loc, b.getFloatAttr(
elementType,
APFloat::getInf(
cast<mlir::FloatType>(elementType).getFloatSemantics(),
/*Negative=*/false)));
else if (isa<mlir::IntegerType>(elementType) &&
elementType.getIntOrFloatBitWidth() != 8)
return b.create<arith::ConstantOp>(
loc, b.getIntegerAttr(elementType,
APSInt::getSignedMaxValue(
elementType.getIntOrFloatBitWidth())));
}
if (isa<AtenLinalgVectorNormOp>(op) || isa<AtenFrobeniusNormDimOp>(op) ||
isa<AtenNormScalarOp>(op))
return b.create<arith::ConstantOp>(loc, b.getZeroAttr(elementType));
if (isa<AtenAllOp, AtenAllDimOp>(op)) {
return b.create<arith::ConstantOp>(loc, b.getBoolAttr(true));
}
if (isa<AtenAnyOp>(op)) {
return b.create<arith::ConstantOp>(loc, b.getBoolAttr(false));
}
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 (isa<mlir::FloatType>(resultElementType))
return b.create<arith::AddFOp>(loc, self, result);
else if (isa<mlir::IntegerType>(resultElementType))
return b.create<arith::AddIOp>(loc, self, result);
} else if (isa<AtenProdOp, AtenProdDimIntOp>(op)) {
Value self =
convertScalarToDtype(b, loc, payloadArgs[0], resultElementType);
Value result = payloadArgs[1];
if (isa<mlir::FloatType>(resultElementType))
return b.create<arith::MulFOp>(loc, self, result);
else if (isa<mlir::IntegerType>(resultElementType))
return b.create<arith::MulIOp>(loc, self, result);
} else if (auto max = dyn_cast<AtenMaxOp>(op)) {
Value self =
convertScalarToDtype(b, loc, payloadArgs[0], resultElementType);
Value result = payloadArgs[1];
if (isa<mlir::FloatType>(resultElementType))
return b.create<arith::MaximumFOp>(loc, self, result);
else if (isa<mlir::IntegerType>(resultElementType)) {
IntegerType intType = max.getSelf()
.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 (auto min = dyn_cast<AtenMinOp>(op)) {
Value self =
convertScalarToDtype(b, loc, payloadArgs[0], resultElementType);
Value result = payloadArgs[1];
if (isa<mlir::FloatType>(resultElementType))
return b.create<arith::MinimumFOp>(loc, self, result);
else if (isa<mlir::IntegerType>(resultElementType)) {
IntegerType intType = min.getSelf()
.getType()
.cast<BaseTensorType>()
.getDtype()
.dyn_cast<mlir::IntegerType>();
if (intType.isUnsigned())
return b.create<arith::MinUIOp>(loc, self, result);
if (intType.isSigned())
return b.create<arith::MinSIOp>(loc, self, result);
}
} else if (isa<AtenNormScalarOp>(op)) {
// This creates payload for only the first of the two linalg.generic ops.
// TODO: Short-circuit operations if `p` is zero or one.
Value elem = payloadArgs[0];
Value result = payloadArgs[1];
AtenNormScalarOp::Adaptor adaptor(operands);
Value p = convertScalarToDtype(b, loc, adaptor.getP(), resultElementType);
auto abs = createAbsOpForNormOps(b, loc, elem, resultElementType);
auto pow = b.create<math::PowFOp>(loc, abs, p);
return b.create<arith::AddFOp>(loc, pow, 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];
AtenLinalgVectorNormOp::Adaptor adaptor(operands);
Value ord =
convertScalarToDtype(b, loc, adaptor.getOrd(), resultElementType);
auto abs = createAbsOpForNormOps(b, loc, elem, 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];
TypedAttr twoAttr = b.getFloatAttr(resultElementType, 2.0);
auto ord = b.create<arith::ConstantOp>(loc, twoAttr);
auto abs = createAbsOpForNormOps(b, loc, elem, resultElementType);
auto pow = b.create<math::PowFOp>(loc, abs, ord);
return b.create<arith::AddFOp>(loc, pow, result);
} else if (isa<AtenAllOp, AtenAllDimOp>(op)) {
Value elem = payloadArgs[0];
Value result = payloadArgs[1];
Value self = convertScalarToDtype(b, loc, elem, resultElementType);
return b.create<arith::AndIOp>(loc, self, result);
} else if (isa<AtenAnyOp>(op)) {
Value elem = payloadArgs[0];
Value result = payloadArgs[1];
Value self = convertScalarToDtype(b, loc, elem, resultElementType);
return b.create<arith::OrIOp>(loc, self, 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.getSelf();
auto inputType = cast<RankedTensorType>(opInfo.tensorOperand.getType());
if (!matchPattern(op.getKeepdim(), m_TorchConstantBool(&opInfo.keepDim)))
return rewriter.notifyMatchFailure(op,
"`keepdim` must be a constant bool");
SmallVector<int64_t> dimList;
int64_t dim;
bool isNoneOrEmptyDimList = isa<Torch::NoneType>(op.getDim().getType());
if (matchPattern(op.getDim(), 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 (matchPattern(op.getDim(), m_TorchConstantInt(&dim))) {
dim = toPositiveDim(dim, inputType.getRank());
if (!isValidDim(dim, inputType.getRank()))
return rewriter.notifyMatchFailure(
op, "`dim` argument must be valid, invalid received.");
opInfo.dimSet.insert(dim);
} 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<AtenAnyOp, AtenAllOp, AtenMaxOp, AtenMinOp, AtenSumOp, AtenProdOp,
AtenNormScalarOp>(op)) {
opInfo.tensorOperand = operands[0];
auto inputType = cast<RankedTensorType>(opInfo.tensorOperand.getType());
// `AtenAny`, `AtenAll`, `AtenSumOp`, `AtenProdOp`, `AtenMaxOp`, and
// `AtenMinOp` each reduce 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 prodOp = dyn_cast<AtenProdDimIntOp>(op))
return computeReductionOpInfoForDimVariantOp(prodOp, 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);
if (auto allOp = dyn_cast<AtenAllDimOp>(op))
return computeReductionOpInfoForDimVariantOp(allOp, 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;
}
template <typename TOp>
FailureOr<Value>
createSecondReductionForNormOp(Location loc, Type elemType, TOp 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.
TypedAttr 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) ||
isa<AtenNormScalarOp>(op)) &&
!isa<mlir::FloatType>(elemType))
return rewriter.notifyMatchFailure(
op, "only float types are valid for vector norm ops");
if (isa<AtenAllDimOp>(op) && isa<mlir::IntegerType>(elemType) &&
elemType.getIntOrFloatBitWidth() == 8)
return rewriter.notifyMatchFailure(op, "uint8 is not supported");
// 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.norm.Scalar op, then we need to generate another
// linalg.generic op that references the first linalg.generic op.
if (isa<AtenNormScalarOp>(op)) {
AtenNormScalarOp::Adaptor adaptor(operands);
FailureOr<Value> secondReduceOp = createSecondReductionForNormOp(
loc, elemType, op, adaptor.getP(), reduceOp, *opInfo, rewriter);
if (failed(secondReduceOp))
return secondReduceOp;
reduceOp = *secondReduceOp;
}
// 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 = createSecondReductionForNormOp(
loc, elemType, normOp, adaptor.getOrd(), 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<ConvertAtenMinMaxDimOp<AtenMaxDimOp>>(typeConverter, context);
target.addIllegalOp<AtenMinDimOp>();
patterns.add<ConvertAtenMinMaxDimOp<AtenMinDimOp>>(typeConverter, context);
target.addIllegalOp<AtenSumOp>();
target.addIllegalOp<AtenAnyOp>();
target.addIllegalOp<AtenAllOp>();
target.addIllegalOp<AtenSumDimIntListOp>();
target.addIllegalOp<AtenProdOp>();
target.addIllegalOp<AtenProdDimIntOp>();
target.addIllegalOp<AtenMaxOp>();
target.addIllegalOp<AtenMinOp>();
target.addIllegalOp<AtenAllDimOp>();
target.addIllegalOp<AtenNormScalarOp>();
target.addIllegalOp<AtenLinalgVectorNormOp>();
target.addIllegalOp<AtenFrobeniusNormDimOp>();
patterns.add<ConvertReductionOp>(typeConverter, context);
}
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