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bolt/deps/llvm-18.1.8/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
Sam Vervaeck 174b6f4d4e
Embed LLVM 18.1.8
2025-02-14 19:21:04 +01:00

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//===- TosaToLinalg.cpp - Lowering Tosa to Linalg Dialect -----------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// These rewriters lower from the Tosa to the Linalg dialect.
//
//===----------------------------------------------------------------------===//
#include "mlir/Conversion/TosaToLinalg/TosaToLinalg.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Utils/Utils.h"
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpDefinition.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/DialectConversion.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/Sequence.h"
#include <numeric>
using namespace mlir;
using namespace mlir::tosa;
template <typename T>
static arith::ConstantOp
createConstFromIntAttribute(Operation *op, const std::string &attrName,
Type requiredAttrType, OpBuilder &rewriter) {
auto castedN = static_cast<T>(
cast<IntegerAttr>(op->getAttr(attrName)).getValue().getSExtValue());
return rewriter.create<arith::ConstantOp>(
op->getLoc(), IntegerAttr::get(requiredAttrType, castedN));
}
static Value
createLinalgBodyCalculationForElementwiseOp(Operation *op, ValueRange args,
ArrayRef<Type> resultTypes,
PatternRewriter &rewriter) {
Location loc = op->getLoc();
auto elementTy =
cast<ShapedType>(op->getOperand(0).getType()).getElementType();
// tosa::AbsOp
if (isa<tosa::AbsOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<math::AbsFOp>(loc, resultTypes, args);
if (isa<tosa::AbsOp>(op) && isa<IntegerType>(elementTy)) {
auto zero = rewriter.create<arith::ConstantOp>(
loc, rewriter.getZeroAttr(elementTy));
auto cmp = rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sgt,
args[0], zero);
auto neg = rewriter.create<arith::SubIOp>(loc, zero, args[0]);
return rewriter.create<arith::SelectOp>(loc, cmp, args[0], neg);
}
// tosa::AddOp
if (isa<tosa::AddOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<arith::AddFOp>(loc, resultTypes, args);
if (isa<tosa::AddOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::AddIOp>(loc, resultTypes, args);
// tosa::SubOp
if (isa<tosa::SubOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<arith::SubFOp>(loc, resultTypes, args);
if (isa<tosa::SubOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::SubIOp>(loc, resultTypes, args);
// tosa::MulOp
if (isa<tosa::MulOp>(op) && isa<FloatType>(elementTy)) {
if (dyn_cast<tosa::MulOp>(op).getShift() != 0) {
(void)rewriter.notifyMatchFailure(op,
"Cannot have shift value for float");
return nullptr;
}
return rewriter.create<arith::MulFOp>(loc, resultTypes, args);
}
// tosa::DivOp
if (isa<tosa::DivOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::DivSIOp>(loc, resultTypes, args);
// tosa::ReciprocalOp
if (isa<tosa::ReciprocalOp>(op) && isa<FloatType>(elementTy)) {
auto one =
rewriter.create<arith::ConstantOp>(loc, FloatAttr::get(elementTy, 1));
return rewriter.create<arith::DivFOp>(loc, resultTypes, one, args[0]);
}
if (isa<tosa::MulOp>(op) && isa<IntegerType>(elementTy)) {
Value a = args[0];
Value b = args[1];
auto shift =
cast<IntegerAttr>(op->getAttr("shift")).getValue().getSExtValue();
if (shift > 0) {
auto shiftConst =
rewriter.create<arith::ConstantIntOp>(loc, shift, /*bitwidth=*/8);
if (!a.getType().isInteger(32))
a = rewriter.create<arith::ExtSIOp>(loc, rewriter.getI32Type(), a);
if (!b.getType().isInteger(32))
b = rewriter.create<arith::ExtSIOp>(loc, rewriter.getI32Type(), b);
auto result = rewriter.create<tosa::ApplyScaleOp>(
loc, rewriter.getI32Type(), a, b, shiftConst,
rewriter.getBoolAttr(false));
if (elementTy.isInteger(32))
return result;
return rewriter.create<arith::TruncIOp>(loc, elementTy, result);
}
int aWidth = a.getType().getIntOrFloatBitWidth();
int bWidth = b.getType().getIntOrFloatBitWidth();
int cWidth = resultTypes[0].getIntOrFloatBitWidth();
if (aWidth < cWidth)
a = rewriter.create<arith::ExtSIOp>(loc, resultTypes[0], a);
if (bWidth < cWidth)
b = rewriter.create<arith::ExtSIOp>(loc, resultTypes[0], b);
return rewriter.create<arith::MulIOp>(loc, resultTypes, a, b);
}
// tosa::NegateOp
if (isa<tosa::NegateOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<arith::NegFOp>(loc, resultTypes, args);
if (isa<tosa::NegateOp>(op) && isa<IntegerType>(elementTy) &&
!cast<tosa::NegateOp>(op).getQuantizationInfo()) {
auto constant =
rewriter.create<arith::ConstantOp>(loc, IntegerAttr::get(elementTy, 0));
return rewriter.create<arith::SubIOp>(loc, resultTypes, constant, args[0]);
}
if (isa<tosa::NegateOp>(op) && isa<IntegerType>(elementTy) &&
cast<tosa::NegateOp>(op).getQuantizationInfo()) {
auto quantizationInfo = cast<tosa::NegateOp>(op).getQuantizationInfo();
int32_t inputBitWidth = elementTy.getIntOrFloatBitWidth();
int64_t inZp = quantizationInfo.value().getInputZp();
int64_t outZp = quantizationInfo.value().getOutputZp();
// Compute the maximum value that can occur in the intermediate buffer.
int64_t zpAdd = inZp + outZp;
int64_t maxValue = APInt::getSignedMaxValue(inputBitWidth).getSExtValue() +
std::abs(zpAdd) + 1;
// Convert that maximum value into the maximum bitwidth needed to represent
// it. We assume 48-bit numbers may be supported further in the pipeline.
int intermediateBitWidth = 64;
if (maxValue <= APInt::getSignedMaxValue(16).getSExtValue()) {
intermediateBitWidth = 16;
} else if (maxValue <= APInt::getSignedMaxValue(32).getSExtValue()) {
intermediateBitWidth = 32;
} else if (maxValue <= APInt::getSignedMaxValue(48).getSExtValue()) {
intermediateBitWidth = 48;
}
Type intermediateType = rewriter.getIntegerType(intermediateBitWidth);
Value zpAddValue = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(intermediateType, zpAdd));
// The negation can be applied by doing:
// outputValue = inZp + outZp - inputValue
auto ext = rewriter.create<arith::ExtSIOp>(loc, intermediateType, args[0]);
auto sub = rewriter.create<arith::SubIOp>(loc, zpAddValue, ext);
// Clamp to the negation range.
Value min = rewriter.create<arith::ConstantIntOp>(
loc, APInt::getSignedMinValue(inputBitWidth).getSExtValue(),
intermediateType);
Value max = rewriter.create<arith::ConstantIntOp>(
loc, APInt::getSignedMaxValue(inputBitWidth).getSExtValue(),
intermediateType);
auto clamp = clampIntHelper(loc, sub, min, max, rewriter);
// Truncate to the final value.
return rewriter.create<arith::TruncIOp>(loc, elementTy, clamp);
}
// tosa::BitwiseAndOp
if (isa<tosa::BitwiseAndOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::AndIOp>(loc, resultTypes, args);
// tosa::BitwiseOrOp
if (isa<tosa::BitwiseOrOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::OrIOp>(loc, resultTypes, args);
// tosa::BitwiseNotOp
if (isa<tosa::BitwiseNotOp>(op) && isa<IntegerType>(elementTy)) {
auto allOnesAttr = rewriter.getIntegerAttr(
elementTy, APInt::getAllOnes(elementTy.getIntOrFloatBitWidth()));
auto allOnes = rewriter.create<arith::ConstantOp>(loc, allOnesAttr);
return rewriter.create<arith::XOrIOp>(loc, resultTypes, args[0], allOnes);
}
// tosa::BitwiseXOrOp
if (isa<tosa::BitwiseXorOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::XOrIOp>(loc, resultTypes, args);
// tosa::LogicalLeftShiftOp
if (isa<tosa::LogicalLeftShiftOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::ShLIOp>(loc, resultTypes, args);
// tosa::LogicalRightShiftOp
if (isa<tosa::LogicalRightShiftOp>(op) && isa<IntegerType>(elementTy))
return rewriter.create<arith::ShRUIOp>(loc, resultTypes, args);
// tosa::ArithmeticRightShiftOp
if (isa<tosa::ArithmeticRightShiftOp>(op) && isa<IntegerType>(elementTy)) {
auto result = rewriter.create<arith::ShRSIOp>(loc, resultTypes, args);
auto round = cast<BoolAttr>(op->getAttr("round")).getValue();
if (!round) {
return result;
}
Type i1Ty = IntegerType::get(rewriter.getContext(), /*width=*/1);
auto one =
rewriter.create<arith::ConstantOp>(loc, IntegerAttr::get(elementTy, 1));
auto zero =
rewriter.create<arith::ConstantOp>(loc, IntegerAttr::get(elementTy, 0));
auto i1one =
rewriter.create<arith::ConstantOp>(loc, IntegerAttr::get(i1Ty, 1));
// Checking that input2 != 0
auto shiftValueGreaterThanZero = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::sgt, args[1], zero);
// Checking for the last bit of input1 to be 1
auto subtract =
rewriter.create<arith::SubIOp>(loc, resultTypes, args[1], one);
auto shifted =
rewriter.create<arith::ShRSIOp>(loc, resultTypes, args[0], subtract)
->getResults();
auto truncated =
rewriter.create<arith::TruncIOp>(loc, i1Ty, shifted, std::nullopt);
auto isInputOdd =
rewriter.create<arith::AndIOp>(loc, i1Ty, truncated, i1one);
auto shouldRound = rewriter.create<arith::AndIOp>(
loc, i1Ty, shiftValueGreaterThanZero, isInputOdd);
auto extended =
rewriter.create<arith::ExtUIOp>(loc, resultTypes, shouldRound);
return rewriter.create<arith::AddIOp>(loc, resultTypes, result, extended);
}
// tosa::ClzOp
if (isa<tosa::ClzOp>(op) && isa<IntegerType>(elementTy)) {
return rewriter.create<math::CountLeadingZerosOp>(loc, elementTy, args[0]);
}
// tosa::LogicalAnd
if (isa<tosa::LogicalAndOp>(op) && elementTy.isInteger(1))
return rewriter.create<arith::AndIOp>(loc, resultTypes, args);
// tosa::LogicalNot
if (isa<tosa::LogicalNotOp>(op) && elementTy.isInteger(1)) {
auto one = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(elementTy, 1));
return rewriter.create<arith::XOrIOp>(loc, resultTypes, args[0], one);
}
// tosa::LogicalOr
if (isa<tosa::LogicalOrOp>(op) && elementTy.isInteger(1))
return rewriter.create<arith::OrIOp>(loc, resultTypes, args);
// tosa::LogicalXor
if (isa<tosa::LogicalXorOp>(op) && elementTy.isInteger(1))
return rewriter.create<arith::XOrIOp>(loc, resultTypes, args);
// tosa::PowOp
if (isa<tosa::PowOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<mlir::math::PowFOp>(loc, resultTypes, args);
// tosa::RsqrtOp
if (isa<tosa::RsqrtOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<mlir::math::RsqrtOp>(loc, resultTypes, args);
// tosa::LogOp
if (isa<tosa::LogOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<mlir::math::LogOp>(loc, resultTypes, args);
// tosa::ExpOp
if (isa<tosa::ExpOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<mlir::math::ExpOp>(loc, resultTypes, args);
// tosa::TanhOp
if (isa<tosa::TanhOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<mlir::math::TanhOp>(loc, resultTypes, args);
// tosa::ErfOp
if (isa<tosa::ErfOp>(op) && llvm::isa<FloatType>(elementTy))
return rewriter.create<mlir::math::ErfOp>(loc, resultTypes, args);
// tosa::GreaterOp
if (isa<tosa::GreaterOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::OGT,
args[0], args[1]);
if (isa<tosa::GreaterOp>(op) && elementTy.isSignlessInteger())
return rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sgt,
args[0], args[1]);
// tosa::GreaterEqualOp
if (isa<tosa::GreaterEqualOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::OGE,
args[0], args[1]);
if (isa<tosa::GreaterEqualOp>(op) && elementTy.isSignlessInteger())
return rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge,
args[0], args[1]);
// tosa::EqualOp
if (isa<tosa::EqualOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::OEQ,
args[0], args[1]);
if (isa<tosa::EqualOp>(op) && elementTy.isSignlessInteger())
return rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq,
args[0], args[1]);
// tosa::SelectOp
if (isa<tosa::SelectOp>(op)) {
elementTy = cast<ShapedType>(op->getOperand(1).getType()).getElementType();
if (isa<FloatType>(elementTy) || isa<IntegerType>(elementTy))
return rewriter.create<arith::SelectOp>(loc, args[0], args[1], args[2]);
}
// tosa::MaximumOp
if (isa<tosa::MaximumOp>(op) && isa<FloatType>(elementTy)) {
return rewriter.create<arith::MaximumFOp>(loc, args[0], args[1]);
}
if (isa<tosa::MaximumOp>(op) && elementTy.isSignlessInteger()) {
auto predicate = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::sgt, args[0], args[1]);
return rewriter.create<arith::SelectOp>(loc, predicate, args[0], args[1]);
}
// tosa::MinimumOp
if (isa<tosa::MinimumOp>(op) && isa<FloatType>(elementTy)) {
return rewriter.create<arith::MinimumFOp>(loc, args[0], args[1]);
}
if (isa<tosa::MinimumOp>(op) && elementTy.isSignlessInteger()) {
auto predicate = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::slt, args[0], args[1]);
return rewriter.create<arith::SelectOp>(loc, predicate, args[0], args[1]);
}
// tosa::CeilOp
if (isa<tosa::CeilOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<math::CeilOp>(loc, resultTypes, args);
// tosa::FloorOp
if (isa<tosa::FloorOp>(op) && isa<FloatType>(elementTy))
return rewriter.create<math::FloorOp>(loc, resultTypes, args);
// tosa::ClampOp
if (isa<tosa::ClampOp>(op) && isa<FloatType>(elementTy)) {
bool losesInfo = false;
APFloat minApf = cast<FloatAttr>(op->getAttr("min_fp")).getValue();
APFloat maxApf = cast<FloatAttr>(op->getAttr("max_fp")).getValue();
minApf.convert(cast<FloatType>(elementTy).getFloatSemantics(),
APFloat::rmNearestTiesToEven, &losesInfo);
maxApf.convert(cast<FloatType>(elementTy).getFloatSemantics(),
APFloat::rmNearestTiesToEven, &losesInfo);
auto min = rewriter.create<arith::ConstantOp>(
loc, elementTy, rewriter.getFloatAttr(elementTy, minApf));
auto max = rewriter.create<arith::ConstantOp>(
loc, elementTy, rewriter.getFloatAttr(elementTy, maxApf));
return clampFloatHelper(loc, args[0], min, max, rewriter);
}
if (isa<tosa::ClampOp>(op) && isa<IntegerType>(elementTy)) {
auto intTy = cast<IntegerType>(elementTy);
int32_t min = static_cast<int32_t>(
cast<IntegerAttr>(op->getAttr("min_int")).getValue().getSExtValue());
int32_t max = static_cast<int32_t>(
cast<IntegerAttr>(op->getAttr("max_int")).getValue().getSExtValue());
if (intTy.isUnsignedInteger()) {
min = std::max<int32_t>(min, 0);
max = std::min<int32_t>(
max,
APInt::getMaxValue(intTy.getIntOrFloatBitWidth()).getSExtValue());
} else {
min = std::max<int32_t>(
min, APInt::getSignedMinValue(intTy.getIntOrFloatBitWidth())
.getSExtValue());
max = std::min<int32_t>(
max, APInt::getSignedMaxValue(intTy.getIntOrFloatBitWidth())
.getSExtValue());
}
auto minVal = rewriter.create<arith::ConstantIntOp>(
loc, min, intTy.getIntOrFloatBitWidth());
auto maxVal = rewriter.create<arith::ConstantIntOp>(
loc, max, intTy.getIntOrFloatBitWidth());
return clampIntHelper(loc, args[0], minVal, maxVal, rewriter);
}
// tosa::SigmoidOp
if (isa<tosa::SigmoidOp>(op) && isa<FloatType>(elementTy)) {
auto one =
rewriter.create<arith::ConstantOp>(loc, FloatAttr::get(elementTy, 1));
auto negate = rewriter.create<arith::NegFOp>(loc, resultTypes, args[0]);
auto exp = rewriter.create<mlir::math::ExpOp>(loc, resultTypes, negate);
auto added = rewriter.create<arith::AddFOp>(loc, resultTypes, exp, one);
return rewriter.create<arith::DivFOp>(loc, resultTypes, one, added);
}
// tosa::CastOp
if (isa<tosa::CastOp>(op)) {
Type srcTy = elementTy;
Type dstTy = resultTypes.front();
bool bitExtend =
srcTy.getIntOrFloatBitWidth() < dstTy.getIntOrFloatBitWidth();
if (srcTy == dstTy)
return args.front();
if (isa<FloatType>(srcTy) && isa<FloatType>(dstTy) && bitExtend)
return rewriter.create<arith::ExtFOp>(loc, resultTypes, args,
std::nullopt);
if (isa<FloatType>(srcTy) && isa<FloatType>(dstTy) && !bitExtend)
return rewriter.create<arith::TruncFOp>(loc, resultTypes, args,
std::nullopt);
// 1-bit integers need to be treated as signless.
if (srcTy.isInteger(1) && arith::UIToFPOp::areCastCompatible(srcTy, dstTy))
return rewriter.create<arith::UIToFPOp>(loc, resultTypes, args,
std::nullopt);
if (srcTy.isInteger(1) && isa<IntegerType>(dstTy) && bitExtend)
return rewriter.create<arith::ExtUIOp>(loc, resultTypes, args,
std::nullopt);
// Unsigned integers need an unrealized cast so that they can be passed
// to UIToFP.
if (srcTy.isUnsignedInteger() && isa<FloatType>(dstTy)) {
auto unrealizedCast =
rewriter
.create<UnrealizedConversionCastOp>(
loc, rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()),
args[0])
.getResult(0);
return rewriter.create<arith::UIToFPOp>(loc, resultTypes[0],
unrealizedCast);
}
// All other si-to-fp conversions should be handled by SIToFP.
if (arith::SIToFPOp::areCastCompatible(srcTy, dstTy))
return rewriter.create<arith::SIToFPOp>(loc, resultTypes, args,
std::nullopt);
// Casting to boolean, floats need to only be checked as not-equal to zero.
if (isa<FloatType>(srcTy) && dstTy.isInteger(1)) {
Value zero = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(srcTy, 0.0));
return rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UNE,
args.front(), zero);
}
if (arith::FPToSIOp::areCastCompatible(srcTy, dstTy)) {
auto intMin = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(
getElementTypeOrSelf(srcTy),
APInt::getSignedMinValue(dstTy.getIntOrFloatBitWidth())
.getSExtValue()));
auto intMax = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(
getElementTypeOrSelf(srcTy),
APInt::getSignedMaxValue(dstTy.getIntOrFloatBitWidth())
.getSExtValue()));
auto rounded = rewriter.create<math::RoundEvenOp>(loc, args[0]);
auto clamped = clampFloatHelper(loc, rounded, intMin, intMax, rewriter);
return rewriter.create<arith::FPToSIOp>(loc, dstTy, clamped);
}
// Casting to boolean, integers need to only be checked as not-equal to
// zero.
if (isa<IntegerType>(srcTy) && dstTy.isInteger(1)) {
Value zero = rewriter.create<arith::ConstantIntOp>(
loc, 0, srcTy.getIntOrFloatBitWidth());
return rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ne,
args.front(), zero);
}
if (isa<IntegerType>(srcTy) && isa<IntegerType>(dstTy) && bitExtend)
return rewriter.create<arith::ExtSIOp>(loc, resultTypes, args,
std::nullopt);
if (isa<IntegerType>(srcTy) && isa<IntegerType>(dstTy) && !bitExtend) {
return rewriter.create<arith::TruncIOp>(loc, dstTy, args[0]);
}
}
(void)rewriter.notifyMatchFailure(
op, "unhandled op for linalg body calculation for elementwise op");
return nullptr;
}
static Value expandRank(PatternRewriter &rewriter, Location loc, Value tensor,
int64_t rank) {
// No need to expand if we are already at the desired rank
auto shapedType = dyn_cast<ShapedType>(tensor.getType());
assert(shapedType && shapedType.hasRank() && "expected a ranked shaped type");
int64_t numExtraDims = rank - shapedType.getRank();
assert(numExtraDims >= 0 && "cannot expand tensor to a lower rank");
if (!numExtraDims)
return tensor;
// Compute reassociation indices
SmallVector<SmallVector<int64_t, 2>> reassociationIndices(
shapedType.getRank());
int64_t index = 0;
for (index = 0; index <= numExtraDims; index++)
reassociationIndices[0].push_back(index);
for (size_t position = 1; position < reassociationIndices.size(); position++)
reassociationIndices[position].push_back(index++);
// Compute result type
SmallVector<int64_t> resultShape;
for (index = 0; index < numExtraDims; index++)
resultShape.push_back(1);
for (auto size : shapedType.getShape())
resultShape.push_back(size);
auto resultType =
RankedTensorType::get(resultShape, shapedType.getElementType());
// Emit 'tensor.expand_shape' op
return rewriter.create<tensor::ExpandShapeOp>(loc, resultType, tensor,
reassociationIndices);
}
static SmallVector<Value> expandInputRanks(PatternRewriter &rewriter,
Location loc, Operation *operation) {
auto rank =
operation->getResultTypes().front().cast<RankedTensorType>().getRank();
return llvm::map_to_vector(operation->getOperands(), [&](Value operand) {
return expandRank(rewriter, loc, operand, rank);
});
}
using IndexPool = DenseMap<int64_t, Value>;
// Emit an 'arith.constant' op for the given index if it has not been created
// yet, or return an existing constant. This will prevent an excessive creation
// of redundant constants, easing readability of emitted code for unit tests.
static Value createIndex(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, int64_t index) {
auto [it, inserted] = indexPool.try_emplace(index);
if (inserted)
it->second =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(index));
return it->second;
}
static Value getTensorDim(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, Value tensor, int64_t index) {
auto indexValue = createIndex(rewriter, loc, indexPool, index);
return rewriter.create<tensor::DimOp>(loc, tensor, indexValue).getResult();
}
static OpFoldResult getOrFoldTensorDim(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, Value tensor,
int64_t index) {
auto shapedType = dyn_cast<ShapedType>(tensor.getType());
assert(shapedType && shapedType.hasRank() && "expected a ranked shaped type");
assert(index >= 0 && index < shapedType.getRank() && "index out of bounds");
if (shapedType.isDynamicDim(index))
return getTensorDim(rewriter, loc, indexPool, tensor, index);
return rewriter.getIndexAttr(shapedType.getDimSize(index));
}
static bool operandsAndResultsRanked(Operation *operation) {
auto isRanked = [](Value value) {
return isa<RankedTensorType>(value.getType());
};
return llvm::all_of(operation->getOperands(), isRanked) &&
llvm::all_of(operation->getResults(), isRanked);
}
// Compute the runtime dimension size for dimension 'dim' of the output by
// inspecting input 'operands', all of which are expected to have the same rank.
// This function returns a pair {targetSize, masterOperand}.
//
// The runtime size of the output dimension is returned either as a statically
// computed attribute or as a runtime SSA value.
//
// If the target size was inferred directly from one dominating operand, that
// operand is returned in 'masterOperand'. If the target size is inferred from
// multiple operands, 'masterOperand' is set to nullptr.
static std::pair<OpFoldResult, Value>
computeTargetSize(PatternRewriter &rewriter, Location loc, IndexPool &indexPool,
ValueRange operands, int64_t dim) {
// If any input operand contains a static size greater than 1 for this
// dimension, that is the target size. An occurrence of an additional static
// dimension greater than 1 with a different value is undefined behavior.
for (auto operand : operands) {
auto size = operand.getType().cast<RankedTensorType>().getDimSize(dim);
if (!ShapedType::isDynamic(size) && size > 1)
return {rewriter.getIndexAttr(size), operand};
}
// Filter operands with dynamic dimension
auto operandsWithDynamicDim =
llvm::to_vector(llvm::make_filter_range(operands, [&](Value operand) {
return operand.getType().cast<RankedTensorType>().isDynamicDim(dim);
}));
// If no operand has a dynamic dimension, it means all sizes were 1
if (operandsWithDynamicDim.empty())
return {rewriter.getIndexAttr(1), operands.front()};
// Emit code that computes the runtime size for this dimension. If there is
// only one operand with a dynamic dimension, it is considered the master
// operand that determines the runtime size of the output dimension.
auto targetSize =
getTensorDim(rewriter, loc, indexPool, operandsWithDynamicDim[0], dim);
if (operandsWithDynamicDim.size() == 1)
return {targetSize, operandsWithDynamicDim[0]};
// Calculate maximum size among all dynamic dimensions
for (size_t i = 1; i < operandsWithDynamicDim.size(); i++) {
auto nextSize =
getTensorDim(rewriter, loc, indexPool, operandsWithDynamicDim[i], dim);
targetSize = rewriter.create<arith::MaxUIOp>(loc, targetSize, nextSize);
}
return {targetSize, nullptr};
}
// Compute the runtime output size for all dimensions. This function returns
// a pair {targetShape, masterOperands}.
static std::pair<SmallVector<OpFoldResult>, SmallVector<Value>>
computeTargetShape(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, ValueRange operands) {
assert(!operands.empty());
auto rank = operands.front().getType().cast<RankedTensorType>().getRank();
SmallVector<OpFoldResult> targetShape;
SmallVector<Value> masterOperands;
for (auto dim : llvm::seq<int64_t>(0, rank)) {
auto [targetSize, masterOperand] =
computeTargetSize(rewriter, loc, indexPool, operands, dim);
targetShape.push_back(targetSize);
masterOperands.push_back(masterOperand);
}
return {targetShape, masterOperands};
}
static Value broadcastDynamicDimension(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, Value operand,
int64_t dim, OpFoldResult targetSize,
Value masterOperand) {
// Nothing to do if this is a static dimension
auto rankedTensorType = operand.getType().cast<RankedTensorType>();
if (!rankedTensorType.isDynamicDim(dim))
return operand;
// If the target size for this dimension was directly inferred by only taking
// this operand into account, there is no need to broadcast. This is an
// optimization that will prevent redundant control flow, and constitutes the
// main motivation for tracking "master operands".
if (operand == masterOperand)
return operand;
// Affine maps for 'linalg.generic' op
auto rank = rankedTensorType.getRank();
SmallVector<AffineExpr> affineExprs;
for (auto index : llvm::seq<int64_t>(0, rank)) {
auto affineExpr = index == dim ? rewriter.getAffineConstantExpr(0)
: rewriter.getAffineDimExpr(index);
affineExprs.push_back(affineExpr);
}
auto broadcastAffineMap =
AffineMap::get(rank, 0, affineExprs, rewriter.getContext());
auto identityAffineMap = rewriter.getMultiDimIdentityMap(rank);
SmallVector<AffineMap> affineMaps = {broadcastAffineMap, identityAffineMap};
// Check if broadcast is necessary
auto one = createIndex(rewriter, loc, indexPool, 1);
auto runtimeSize = getTensorDim(rewriter, loc, indexPool, operand, dim);
auto broadcastNecessary = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, runtimeSize, one);
// Emit 'then' region of 'scf.if'
auto emitThenRegion = [&](OpBuilder &opBuilder, Location loc) {
// Emit 'tensor.empty' op
SmallVector<OpFoldResult> outputTensorShape;
for (auto index : llvm::seq<int64_t>(0, rank)) {
auto size = index == dim ? targetSize
: getOrFoldTensorDim(rewriter, loc, indexPool,
operand, index);
outputTensorShape.push_back(size);
}
Value outputTensor = opBuilder.create<tensor::EmptyOp>(
loc, outputTensorShape, rankedTensorType.getElementType());
// Emit 'linalg.generic' op
auto resultTensor =
opBuilder
.create<linalg::GenericOp>(
loc, outputTensor.getType(), operand, outputTensor, affineMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) {
// Emit 'linalg.yield' op
opBuilder.create<linalg::YieldOp>(loc, blockArgs.front());
})
.getResult(0);
// Cast to original operand type if necessary
auto castResultTensor = rewriter.createOrFold<tensor::CastOp>(
loc, operand.getType(), resultTensor);
// Emit 'scf.yield' op
opBuilder.create<scf::YieldOp>(loc, castResultTensor);
};
// Emit 'else' region of 'scf.if'
auto emitElseRegion = [&](OpBuilder &opBuilder, Location loc) {
opBuilder.create<scf::YieldOp>(loc, operand);
};
// Emit 'scf.if' op
auto ifOp = rewriter.create<scf::IfOp>(loc, broadcastNecessary,
emitThenRegion, emitElseRegion);
return ifOp.getResult(0);
}
static Value broadcastDynamicDimensions(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, Value operand,
ArrayRef<OpFoldResult> targetShape,
ArrayRef<Value> masterOperands) {
size_t rank = operand.getType().cast<RankedTensorType>().getRank();
assert(targetShape.size() == rank);
assert(masterOperands.size() == rank);
for (auto index : llvm::seq<int64_t>(0, rank))
operand =
broadcastDynamicDimension(rewriter, loc, indexPool, operand, index,
targetShape[index], masterOperands[index]);
return operand;
}
static SmallVector<Value>
broadcastDynamicDimensions(PatternRewriter &rewriter, Location loc,
IndexPool &indexPool, ValueRange operands,
ArrayRef<OpFoldResult> targetShape,
ArrayRef<Value> masterOperands) {
// No need to broadcast for unary operations
if (operands.size() == 1)
return operands;
// Broadcast dynamic dimensions operand by operand
return llvm::map_to_vector(operands, [&](Value operand) {
return broadcastDynamicDimensions(rewriter, loc, indexPool, operand,
targetShape, masterOperands);
});
}
static LogicalResult
emitElementwiseComputation(PatternRewriter &rewriter, Location loc,
Operation *operation, ValueRange operands,
ArrayRef<OpFoldResult> targetShape) {
// Generate output tensor
auto resultType =
operation->getResultTypes().front().cast<RankedTensorType>();
Value outputTensor = rewriter.create<tensor::EmptyOp>(
loc, targetShape, resultType.getElementType());
// Create affine maps. Input affine maps broadcast static dimensions of size
// 1. The output affine map is an identity map.
//
auto rank = resultType.getRank();
auto affineMaps = llvm::map_to_vector(operands, [&](Value operand) {
auto shape = cast<ShapedType>(operand.getType()).getShape();
SmallVector<AffineExpr> affineExprs;
for (auto it : llvm::enumerate(shape)) {
auto affineExpr = it.value() == 1 ? rewriter.getAffineConstantExpr(0)
: rewriter.getAffineDimExpr(it.index());
affineExprs.push_back(affineExpr);
}
return AffineMap::get(rank, 0, affineExprs, rewriter.getContext());
});
affineMaps.push_back(rewriter.getMultiDimIdentityMap(rank));
// Emit 'linalg.generic' op
bool encounteredError = false;
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc, outputTensor.getType(), operands, outputTensor, affineMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) {
Value opResult = createLinalgBodyCalculationForElementwiseOp(
operation, blockArgs.take_front(operation->getNumOperands()),
{resultType.getElementType()}, rewriter);
if (!opResult) {
encounteredError = true;
return;
}
opBuilder.create<linalg::YieldOp>(loc, opResult);
});
if (encounteredError)
return rewriter.notifyMatchFailure(
operation, "unable to create linalg.generic body for elementwise op");
// Cast 'linalg.generic' result into original result type if needed
auto castResult = rewriter.createOrFold<tensor::CastOp>(
loc, resultType, linalgOp->getResult(0));
rewriter.replaceOp(operation, castResult);
return success();
}
static LogicalResult
elementwiseMatchAndRewriteHelper(Operation *operation,
PatternRewriter &rewriter) {
// Collect op properties
assert(operation->getNumResults() == 1 && "elementwise op expects 1 result");
assert(operation->getNumOperands() >= 1 &&
"elementwise op expects at least 1 operand");
if (!operandsAndResultsRanked(operation))
return rewriter.notifyMatchFailure(operation,
"Unranked tensors not supported");
// Lower operation
IndexPool indexPool;
auto loc = operation->getLoc();
auto expandedOperands = expandInputRanks(rewriter, loc, operation);
auto [targetShape, masterOperands] =
computeTargetShape(rewriter, loc, indexPool, expandedOperands);
auto broadcastOperands = broadcastDynamicDimensions(
rewriter, loc, indexPool, expandedOperands, targetShape, masterOperands);
return emitElementwiseComputation(rewriter, loc, operation, broadcastOperands,
targetShape);
}
// Returns the constant initial value for a given reduction operation. The
// attribute type varies depending on the element type required.
static TypedAttr createInitialValueForReduceOp(Operation *op, Type elementTy,
PatternRewriter &rewriter) {
if (isa<tosa::ReduceSumOp>(op) && isa<FloatType>(elementTy))
return rewriter.getFloatAttr(elementTy, 0.0);
if (isa<tosa::ReduceSumOp>(op) && isa<IntegerType>(elementTy))
return rewriter.getIntegerAttr(elementTy, 0);
if (isa<tosa::ReduceProdOp>(op) && isa<FloatType>(elementTy))
return rewriter.getFloatAttr(elementTy, 1.0);
if (isa<tosa::ReduceProdOp>(op) && isa<IntegerType>(elementTy))
return rewriter.getIntegerAttr(elementTy, 1);
if (isa<tosa::ReduceMinOp>(op) && isa<FloatType>(elementTy))
return rewriter.getFloatAttr(
elementTy, APFloat::getLargest(
cast<FloatType>(elementTy).getFloatSemantics(), false));
if (isa<tosa::ReduceMinOp>(op) && isa<IntegerType>(elementTy))
return rewriter.getIntegerAttr(
elementTy, APInt::getSignedMaxValue(elementTy.getIntOrFloatBitWidth()));
if (isa<tosa::ReduceMaxOp>(op) && isa<FloatType>(elementTy))
return rewriter.getFloatAttr(
elementTy, APFloat::getLargest(
cast<FloatType>(elementTy).getFloatSemantics(), true));
if (isa<tosa::ReduceMaxOp>(op) && isa<IntegerType>(elementTy))
return rewriter.getIntegerAttr(
elementTy, APInt::getSignedMinValue(elementTy.getIntOrFloatBitWidth()));
if (isa<tosa::ReduceAllOp>(op) && elementTy.isInteger(1))
return rewriter.getIntegerAttr(elementTy, APInt::getAllOnes(1));
if (isa<tosa::ReduceAnyOp>(op) && elementTy.isInteger(1))
return rewriter.getIntegerAttr(elementTy, APInt::getZero(1));
if (isa<tosa::ArgMaxOp>(op) && isa<FloatType>(elementTy))
return rewriter.getFloatAttr(
elementTy, APFloat::getLargest(
cast<FloatType>(elementTy).getFloatSemantics(), true));
if (isa<tosa::ArgMaxOp>(op) && isa<IntegerType>(elementTy))
return rewriter.getIntegerAttr(
elementTy, APInt::getSignedMinValue(elementTy.getIntOrFloatBitWidth()));
return {};
}
// Creates the body calculation for a reduction. The operations vary depending
// on the input type.
static Value createLinalgBodyCalculationForReduceOp(Operation *op,
ValueRange args,
Type elementTy,
PatternRewriter &rewriter) {
Location loc = op->getLoc();
if (isa<tosa::ReduceSumOp>(op) && isa<FloatType>(elementTy)) {
return rewriter.create<arith::AddFOp>(loc, args);
}
if (isa<tosa::ReduceSumOp>(op) && isa<IntegerType>(elementTy)) {
return rewriter.create<arith::AddIOp>(loc, args);
}
if (isa<tosa::ReduceProdOp>(op) && isa<FloatType>(elementTy)) {
return rewriter.create<arith::MulFOp>(loc, args);
}
if (isa<tosa::ReduceProdOp>(op) && isa<IntegerType>(elementTy)) {
return rewriter.create<arith::MulIOp>(loc, args);
}
if (isa<tosa::ReduceMinOp>(op) && isa<FloatType>(elementTy)) {
return rewriter.create<arith::MinimumFOp>(loc, args[0], args[1]);
}
if (isa<tosa::ReduceMinOp>(op) && isa<IntegerType>(elementTy)) {
auto predicate = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::slt, args[0], args[1]);
return rewriter.create<arith::SelectOp>(loc, predicate, args[0], args[1]);
}
if (isa<tosa::ReduceMaxOp>(op) && isa<FloatType>(elementTy)) {
return rewriter.create<arith::MaximumFOp>(loc, args[0], args[1]);
}
if (isa<tosa::ReduceMaxOp>(op) && isa<IntegerType>(elementTy)) {
auto predicate = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::sgt, args[0], args[1]);
return rewriter.create<arith::SelectOp>(loc, predicate, args[0], args[1]);
}
if (isa<tosa::ReduceAllOp>(op) && elementTy.isInteger(1))
return rewriter.create<arith::AndIOp>(loc, args);
if (isa<tosa::ReduceAnyOp>(op) && elementTy.isInteger(1))
return rewriter.create<arith::OrIOp>(loc, args);
return {};
}
// Performs the match and rewrite for reduction operations. This includes
// declaring a correctly sized initial value, and the linalg.generic operation
// that reduces across the specified axis.
static LogicalResult reduceMatchAndRewriteHelper(Operation *op, uint64_t axis,
PatternRewriter &rewriter) {
auto loc = op->getLoc();
auto inputTy = cast<ShapedType>(op->getOperand(0).getType());
auto resultTy = cast<ShapedType>(op->getResult(0).getType());
auto elementTy = resultTy.getElementType();
Value input = op->getOperand(0);
SmallVector<int64_t> reduceShape;
SmallVector<Value> dynDims;
for (unsigned i = 0; i < inputTy.getRank(); i++) {
if (axis != i) {
reduceShape.push_back(inputTy.getDimSize(i));
if (inputTy.isDynamicDim(i))
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
}
}
// First fill the output buffer with the init value.
auto emptyTensor =
rewriter
.create<tensor::EmptyOp>(loc, reduceShape, resultTy.getElementType(),
dynDims)
.getResult();
auto fillValueAttr = createInitialValueForReduceOp(op, elementTy, rewriter);
if (!fillValueAttr)
return rewriter.notifyMatchFailure(
op, "No initial value found for reduction operation");
auto fillValue = rewriter.create<arith::ConstantOp>(loc, fillValueAttr);
auto filledTensor = rewriter
.create<linalg::FillOp>(loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
bool didEncounterError = false;
auto linalgOp = rewriter.create<linalg::ReduceOp>(
loc, input, filledTensor, axis,
[&](OpBuilder &nestedBuilder, Location nestedLoc, ValueRange blockArgs) {
auto result = createLinalgBodyCalculationForReduceOp(
op, blockArgs, elementTy, rewriter);
if (result)
didEncounterError = true;
nestedBuilder.create<linalg::YieldOp>(loc, result);
});
if (!didEncounterError)
return rewriter.notifyMatchFailure(
op, "unable to create linalg.generic body for reduce op");
SmallVector<ReassociationExprs, 4> reassociationMap;
uint64_t expandInputRank =
cast<ShapedType>(linalgOp.getResults()[0].getType()).getRank();
reassociationMap.resize(expandInputRank);
for (uint64_t i = 0; i < expandInputRank; i++) {
int32_t dimToPush = i > axis ? i + 1 : i;
reassociationMap[i].push_back(rewriter.getAffineDimExpr(dimToPush));
}
if (expandInputRank != 0) {
int32_t expandedDim = axis < expandInputRank ? axis : expandInputRank - 1;
reassociationMap[expandedDim].push_back(
rewriter.getAffineDimExpr(expandedDim + 1));
}
// Lower directly to `tensor::ExpandShapeOp` instead of `tosa::ReshapeOp`,
// since here we know which dimension to expand, and `tosa::ReshapeOp` would
// not have access to such information. This matters when handling dynamically
// sized tensors.
rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
op, resultTy, linalgOp.getResults()[0], reassociationMap);
return success();
}
namespace {
template <typename SrcOp>
class PointwiseConverter : public OpRewritePattern<SrcOp> {
public:
using OpRewritePattern<SrcOp>::OpRewritePattern;
LogicalResult matchAndRewrite(SrcOp op,
PatternRewriter &rewriter) const final {
return elementwiseMatchAndRewriteHelper(op, rewriter);
}
};
class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
public:
using OpRewritePattern<tosa::RescaleOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::RescaleOp op,
PatternRewriter &rewriter) const final {
auto loc = op.getLoc();
auto input = op.getInput();
auto inputTy = cast<ShapedType>(op.getInput().getType());
auto outputTy = cast<ShapedType>(op.getOutput().getType());
unsigned rank = inputTy.getRank();
// This is an illegal configuration. terminate and log an error
if (op.getDoubleRound() && !op.getScale32())
return rewriter.notifyMatchFailure(
op, "tosa.rescale requires scale32 for double_round to be true");
SmallVector<Value> dynDims;
for (int i = 0; i < outputTy.getRank(); i++) {
if (outputTy.isDynamicDim(i)) {
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
}
}
// The shift and multiplier values.
SmallVector<int32_t> multiplierValues(op.getMultiplier());
SmallVector<int8_t> shiftValues(op.getShift());
// If we shift by more than the bitwidth, this just sets to 0.
for (int i = 0, s = multiplierValues.size(); i < s; i++) {
if (shiftValues[i] > 63) {
shiftValues[i] = 0;
multiplierValues[i] = 0;
}
}
// Double round only occurs if shift is greater than 31, check that this
// is ever true.
bool doubleRound =
op.getDoubleRound() &&
llvm::any_of(shiftValues, [](int32_t v) { return v > 31; });
SmallVector<AffineMap> indexingMaps = {
rewriter.getMultiDimIdentityMap(rank)};
SmallVector<Value, 4> genericInputs = {input};
// If we are rescaling per-channel then we need to store the multiplier
// values in a buffer.
Value multiplierConstant;
int64_t multiplierArg = 0;
if (multiplierValues.size() == 1) {
multiplierConstant = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(multiplierValues.front()));
} else {
SmallVector<AffineExpr, 2> multiplierExprs{
rewriter.getAffineDimExpr(rank - 1)};
auto multiplierType =
RankedTensorType::get({static_cast<int64_t>(multiplierValues.size())},
rewriter.getI32Type());
genericInputs.push_back(rewriter.create<arith::ConstantOp>(
loc, DenseIntElementsAttr::get(multiplierType, multiplierValues)));
indexingMaps.push_back(AffineMap::get(/*dimCount=*/rank,
/*symbolCount=*/0, multiplierExprs,
rewriter.getContext()));
multiplierArg = indexingMaps.size() - 1;
}
// If we are rescaling per-channel then we need to store the shift
// values in a buffer.
Value shiftConstant;
int64_t shiftArg = 0;
if (shiftValues.size() == 1) {
shiftConstant = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI8IntegerAttr(shiftValues.front()));
} else {
SmallVector<AffineExpr, 2> shiftExprs = {
rewriter.getAffineDimExpr(rank - 1)};
auto shiftType =
RankedTensorType::get({static_cast<int64_t>(shiftValues.size())},
rewriter.getIntegerType(8));
genericInputs.push_back(rewriter.create<arith::ConstantOp>(
loc, DenseIntElementsAttr::get(shiftType, shiftValues)));
indexingMaps.push_back(AffineMap::get(/*dimCount=*/rank,
/*symbolCount=*/0, shiftExprs,
rewriter.getContext()));
shiftArg = indexingMaps.size() - 1;
}
// Indexing maps for output values.
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(rank));
// Construct the indexing maps needed for linalg.generic ops.
Value emptyTensor = rewriter.create<tensor::EmptyOp>(
loc, outputTy.getShape(), outputTy.getElementType(),
ArrayRef<Value>({dynDims}));
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc, outputTy, genericInputs, ValueRange{emptyTensor}, indexingMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &nestedBuilder, Location nestedLoc,
ValueRange blockArgs) {
Value value = blockArgs[0];
Type valueTy = value.getType();
// For now we do all of our math in 64-bit. This is not optimal but
// should be correct for now, consider computing correct bit depth
// later.
int32_t inBitwidth = valueTy.getIntOrFloatBitWidth() > 32 ? 48 : 32;
auto inputZp = createConstFromIntAttribute<int32_t>(
op, "input_zp", nestedBuilder.getIntegerType(inBitwidth),
nestedBuilder);
auto outputZp = createConstFromIntAttribute<int32_t>(
op, "output_zp", nestedBuilder.getI32Type(), nestedBuilder);
Value multiplier = multiplierConstant ? multiplierConstant
: blockArgs[multiplierArg];
Value shift = shiftConstant ? shiftConstant : blockArgs[shiftArg];
if (valueTy.getIntOrFloatBitWidth() < 32) {
if (valueTy.isUnsignedInteger()) {
value = nestedBuilder
.create<UnrealizedConversionCastOp>(
nestedLoc,
nestedBuilder.getIntegerType(
valueTy.getIntOrFloatBitWidth()),
value)
.getResult(0);
value = nestedBuilder.create<arith::ExtUIOp>(
nestedLoc, nestedBuilder.getI32Type(), value);
} else {
value = nestedBuilder.create<arith::ExtSIOp>(
nestedLoc, nestedBuilder.getI32Type(), value);
}
}
value =
nestedBuilder.create<arith::SubIOp>(nestedLoc, value, inputZp);
value = nestedBuilder.create<tosa::ApplyScaleOp>(
loc, nestedBuilder.getI32Type(), value, multiplier, shift,
nestedBuilder.getBoolAttr(doubleRound));
// Move to the new zero-point.
value =
nestedBuilder.create<arith::AddIOp>(nestedLoc, value, outputZp);
// Saturate to the output size.
IntegerType outIntType =
cast<IntegerType>(blockArgs.back().getType());
unsigned outBitWidth = outIntType.getWidth();
int32_t intMin = APInt::getSignedMinValue(outBitWidth).getSExtValue();
int32_t intMax = APInt::getSignedMaxValue(outBitWidth).getSExtValue();
// Unsigned integers have a difference output value.
if (outIntType.isUnsignedInteger()) {
intMin = 0;
intMax = APInt::getMaxValue(outBitWidth).getZExtValue();
}
auto intMinVal = nestedBuilder.create<arith::ConstantOp>(
loc, nestedBuilder.getI32IntegerAttr(intMin));
auto intMaxVal = nestedBuilder.create<arith::ConstantOp>(
loc, nestedBuilder.getI32IntegerAttr(intMax));
value = clampIntHelper(nestedLoc, value, intMinVal, intMaxVal,
nestedBuilder);
if (outIntType.getWidth() < 32) {
value = nestedBuilder.create<arith::TruncIOp>(
nestedLoc, rewriter.getIntegerType(outIntType.getWidth()),
value);
if (outIntType.isUnsignedInteger()) {
value = nestedBuilder
.create<UnrealizedConversionCastOp>(nestedLoc,
outIntType, value)
.getResult(0);
}
}
nestedBuilder.create<linalg::YieldOp>(loc, value);
});
rewriter.replaceOp(op, linalgOp->getResults());
return success();
}
};
// Handle the resize case where the input is a 1x1 image. This case
// can entirely avoiding having extract operations which target much
// more difficult to optimize away.
class ResizeUnaryConverter : public OpRewritePattern<tosa::ResizeOp> {
public:
using OpRewritePattern<tosa::ResizeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::ResizeOp op,
PatternRewriter &rewriter) const final {
Location loc = op.getLoc();
ImplicitLocOpBuilder builder(loc, rewriter);
auto input = op.getInput();
auto inputTy = cast<RankedTensorType>(input.getType());
auto resultTy = cast<RankedTensorType>(op.getType());
const bool isBilinear = op.getMode() == "BILINEAR";
auto inputH = inputTy.getDimSize(1);
auto inputW = inputTy.getDimSize(2);
auto outputH = resultTy.getDimSize(1);
auto outputW = resultTy.getDimSize(2);
if (inputH != 1 || inputW != 1 || outputH != 1 || outputW != 1)
return rewriter.notifyMatchFailure(
op, "tosa.resize is not a pure 1x1->1x1 image operation");
// TODO(suderman): These string values should be declared the TOSA dialect.
if (op.getMode() != "NEAREST_NEIGHBOR" && op.getMode() != "BILINEAR")
return rewriter.notifyMatchFailure(
op, "tosa.resize mode should be NEAREST_NEIGHBOR or BILINEAR");
if (inputTy == resultTy) {
rewriter.replaceOp(op, input);
return success();
}
ArrayRef<int64_t> scale = op.getScale();
// Collapse the unit width and height away.
SmallVector<ReassociationExprs, 4> reassociationMap(2);
reassociationMap[0].push_back(builder.getAffineDimExpr(0));
reassociationMap[1].push_back(builder.getAffineDimExpr(1));
reassociationMap[1].push_back(builder.getAffineDimExpr(2));
reassociationMap[1].push_back(builder.getAffineDimExpr(3));
auto collapseTy =
RankedTensorType::get({inputTy.getDimSize(0), inputTy.getDimSize(3)},
inputTy.getElementType());
Value collapse = builder.create<tensor::CollapseShapeOp>(collapseTy, input,
reassociationMap);
// Get any dynamic shapes that appear in the input format.
llvm::SmallVector<Value> outputDynSize;
if (inputTy.isDynamicDim(0))
outputDynSize.push_back(builder.create<tensor::DimOp>(input, 0));
if (inputTy.isDynamicDim(3))
outputDynSize.push_back(builder.create<tensor::DimOp>(input, 3));
// Generate the elementwise operation for casting scaling the input value.
auto genericTy = collapseTy.clone(resultTy.getElementType());
Value empty = builder.create<tensor::EmptyOp>(
genericTy.getShape(), resultTy.getElementType(), outputDynSize);
auto genericMap = rewriter.getMultiDimIdentityMap(genericTy.getRank());
SmallVector<utils::IteratorType> iterators(genericTy.getRank(),
utils::IteratorType::parallel);
auto generic = builder.create<linalg::GenericOp>(
genericTy, ValueRange{collapse}, ValueRange{empty},
ArrayRef<AffineMap>{genericMap, genericMap}, iterators,
[=](OpBuilder &b, Location loc, ValueRange args) {
Value value = args[0];
// This is the quantized case.
if (inputTy.getElementType() != resultTy.getElementType()) {
value =
b.create<arith::ExtSIOp>(loc, resultTy.getElementType(), value);
if (isBilinear && scale[0] != 0) {
Value scaleY = b.create<arith::ConstantOp>(
loc, b.getI32IntegerAttr(scale[0]));
value = b.create<arith::MulIOp>(loc, value, scaleY);
}
if (isBilinear && scale[2] != 0) {
Value scaleX = b.create<arith::ConstantOp>(
loc, b.getI32IntegerAttr(scale[2]));
value = b.create<arith::MulIOp>(loc, value, scaleX);
}
}
b.create<linalg::YieldOp>(loc, value);
});
rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
op, resultTy, generic.getResults()[0], reassociationMap);
return success();
}
};
// TOSA resize with width or height of 1 may be broadcasted to a wider
// dimension. This is done by materializing a new tosa.resize without
// the broadcasting behavior, and an explicit broadcast afterwards.
class MaterializeResizeBroadcast : public OpRewritePattern<tosa::ResizeOp> {
public:
using OpRewritePattern<tosa::ResizeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::ResizeOp op,
PatternRewriter &rewriter) const final {
Location loc = op.getLoc();
ImplicitLocOpBuilder builder(loc, rewriter);
auto input = op.getInput();
auto inputTy = dyn_cast<RankedTensorType>(input.getType());
auto resultTy = dyn_cast<RankedTensorType>(op.getType());
if (!inputTy || !resultTy)
return rewriter.notifyMatchFailure(op,
"requires ranked input/output types");
auto batch = inputTy.getDimSize(0);
auto channels = inputTy.getDimSize(3);
auto inputH = inputTy.getDimSize(1);
auto inputW = inputTy.getDimSize(2);
auto outputH = resultTy.getDimSize(1);
auto outputW = resultTy.getDimSize(2);
if ((inputH != 1 || outputH == 1) && (inputW != 1 || outputW == 1))
return rewriter.notifyMatchFailure(
op, "tosa.resize has no broadcasting behavior");
// For any dimension that is broadcastable we generate a width of 1
// on the output.
llvm::SmallVector<int64_t> resizeShape;
resizeShape.push_back(batch);
resizeShape.push_back(inputH == 1 ? 1 : outputH);
resizeShape.push_back(inputW == 1 ? 1 : outputW);
resizeShape.push_back(channels);
auto resizeTy = resultTy.clone(resizeShape);
auto resize =
builder.create<tosa::ResizeOp>(resizeTy, input, op->getAttrs());
// Collapse an unit result dims.
SmallVector<ReassociationExprs, 4> reassociationMap(2);
reassociationMap[0].push_back(builder.getAffineDimExpr(0));
reassociationMap.back().push_back(builder.getAffineDimExpr(1));
if (inputH != 1)
reassociationMap.push_back({});
reassociationMap.back().push_back(builder.getAffineDimExpr(2));
if (inputW != 1)
reassociationMap.push_back({});
reassociationMap.back().push_back(builder.getAffineDimExpr(3));
llvm::SmallVector<int64_t> collapseShape{batch};
if (inputH != 1)
collapseShape.push_back(outputH);
if (inputW != 1)
collapseShape.push_back(outputW);
collapseShape.push_back(channels);
auto collapseTy = resultTy.clone(collapseShape);
Value collapse = builder.create<tensor::CollapseShapeOp>(collapseTy, resize,
reassociationMap);
// Broadcast the collapsed shape to the output result.
llvm::SmallVector<Value> outputDynSize;
if (inputTy.isDynamicDim(0))
outputDynSize.push_back(builder.create<tensor::DimOp>(input, 0));
if (inputTy.isDynamicDim(3))
outputDynSize.push_back(builder.create<tensor::DimOp>(input, 3));
SmallVector<utils::IteratorType> iterators(resultTy.getRank(),
utils::IteratorType::parallel);
Value empty = builder.create<tensor::EmptyOp>(
resultTy.getShape(), resultTy.getElementType(), outputDynSize);
SmallVector<AffineExpr, 4> inputExprs{rewriter.getAffineDimExpr(0)};
if (inputH != 1)
inputExprs.push_back(rewriter.getAffineDimExpr(1));
if (inputW != 1)
inputExprs.push_back(rewriter.getAffineDimExpr(2));
inputExprs.push_back(rewriter.getAffineDimExpr(3));
auto inputMap = AffineMap::get(resultTy.getRank(), /*symbolCount=*/0,
inputExprs, rewriter.getContext());
auto outputMap = rewriter.getMultiDimIdentityMap(resultTy.getRank());
rewriter.replaceOpWithNewOp<linalg::GenericOp>(
op, resultTy, ValueRange{collapse}, ValueRange{empty},
ArrayRef<AffineMap>{inputMap, outputMap}, iterators,
[=](OpBuilder &b, Location loc, ValueRange args) {
Value value = args[0];
b.create<linalg::YieldOp>(loc, value);
});
return success();
}
};
class GenericResizeConverter : public OpRewritePattern<tosa::ResizeOp> {
public:
using OpRewritePattern<tosa::ResizeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::ResizeOp op,
PatternRewriter &rewriter) const final {
Location loc = op.getLoc();
ImplicitLocOpBuilder b(loc, rewriter);
auto input = op.getInput();
auto inputTy = cast<ShapedType>(input.getType());
auto resultTy = cast<ShapedType>(op.getType());
auto resultETy = resultTy.getElementType();
bool floatingPointMode = resultETy.isF16() || resultETy.isF32();
auto floatTy = resultETy.isF16() ? b.getF16Type() : b.getF32Type();
auto imageH = inputTy.getShape()[1];
auto imageW = inputTy.getShape()[2];
auto dynamicDimsOr =
checkHasDynamicBatchDims(rewriter, op, {input, op.getOutput()});
if (!dynamicDimsOr.has_value())
return rewriter.notifyMatchFailure(
op, "unable to get dynamic dimensions of tosa.resize");
if (op.getMode() != "NEAREST_NEIGHBOR" && op.getMode() != "BILINEAR")
return rewriter.notifyMatchFailure(
op, "tosa.resize mode should be NEAREST_NEIGHBOR or BILINEAR");
SmallVector<AffineMap, 2> affineMaps = {
rewriter.getMultiDimIdentityMap(resultTy.getRank())};
auto emptyTensor = b.create<tensor::EmptyOp>(resultTy.getShape(), resultETy,
*dynamicDimsOr);
auto genericOp = b.create<linalg::GenericOp>(
resultTy, ValueRange({}), ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(resultTy.getRank()));
Value resize = genericOp.getResult(0);
{
OpBuilder::InsertionGuard regionGuard(b);
b.createBlock(&genericOp.getRegion(), genericOp.getRegion().end(),
TypeRange({resultETy}), loc);
Value batch = b.create<linalg::IndexOp>(0);
Value y = b.create<linalg::IndexOp>(1);
Value x = b.create<linalg::IndexOp>(2);
Value channel = b.create<linalg::IndexOp>(3);
Value zeroI32 =
b.create<arith::ConstantOp>(b.getZeroAttr(b.getI32Type()));
Value zeroFp = b.create<arith::ConstantOp>(b.getZeroAttr(floatTy));
Value hMax = b.create<arith::ConstantOp>(b.getI32IntegerAttr(imageH - 1));
Value wMax = b.create<arith::ConstantOp>(b.getI32IntegerAttr(imageW - 1));
Value inY = b.create<arith::IndexCastOp>(b.getI32Type(), y);
Value inX = b.create<arith::IndexCastOp>(b.getI32Type(), x);
ArrayRef<int64_t> offset = op.getOffset();
ArrayRef<int64_t> border = op.getBorder();
ArrayRef<int64_t> scale = op.getScale();
Value yScaleN, yScaleD, xScaleN, xScaleD;
yScaleN = b.create<arith::ConstantOp>(b.getI32IntegerAttr(scale[0]));
yScaleD = b.create<arith::ConstantOp>(b.getI32IntegerAttr(scale[1]));
xScaleN = b.create<arith::ConstantOp>(b.getI32IntegerAttr(scale[2]));
xScaleD = b.create<arith::ConstantOp>(b.getI32IntegerAttr(scale[3]));
Value yOffset, xOffset, yBorder, xBorder;
yOffset = b.create<arith::ConstantOp>(b.getI32IntegerAttr(offset[0]));
xOffset = b.create<arith::ConstantOp>(b.getI32IntegerAttr(offset[1]));
yBorder = b.create<arith::ConstantOp>(b.getI32IntegerAttr(border[0]));
xBorder = b.create<arith::ConstantOp>(b.getI32IntegerAttr(border[1]));
// Compute the ix and dx values for both the X and Y dimensions.
auto getIndexAndDeltaFp = [&](Value &index, Value &delta, Value in,
Value scaleN, Value scaleD, Value offset,
int size, ImplicitLocOpBuilder &b) {
if (size == 1) {
index = zeroI32;
delta = zeroFp;
return;
}
// x = x * scale_d + offset;
// ix = floor(x / scale_n)
// dx = x / scale_n - ix
Value val = b.create<arith::UIToFPOp>(floatTy, in);
scaleN = b.create<arith::UIToFPOp>(floatTy, scaleN);
scaleD = b.create<arith::UIToFPOp>(floatTy, scaleD);
offset = b.create<arith::SIToFPOp>(floatTy, offset);
val = b.create<arith::MulFOp>(val, scaleD);
val = b.create<arith::AddFOp>(val, offset);
val = b.create<arith::DivFOp>(val, scaleN);
index = b.create<math::FloorOp>(val);
delta = b.create<arith::SubFOp>(val, index);
index = b.create<arith::FPToSIOp>(b.getI32Type(), index);
};
// Compute the ix and dx values for the X and Y dimensions - int case.
auto getIndexAndDeltaInt = [&](Value &index, Value &delta, Value in,
Value scaleN, Value scaleD, Value offset,
int size, ImplicitLocOpBuilder &b) {
if (size == 1) {
index = zeroI32;
delta = zeroI32;
return;
}
// x = x * scale_d + offset;
// ix = floor(x / scale_n)
// dx = x - ix * scale_n;
Value val = b.create<arith::MulIOp>(in, scaleD);
val = b.create<arith::AddIOp>(val, offset);
index = b.create<arith::DivSIOp>(val, scaleN);
delta = b.create<arith::MulIOp>(index, scaleN);
delta = b.create<arith::SubIOp>(val, delta);
};
Value ix, iy, dx, dy;
if (floatingPointMode) {
getIndexAndDeltaFp(iy, dy, inY, yScaleN, yScaleD, yOffset, imageH, b);
getIndexAndDeltaFp(ix, dx, inX, xScaleN, xScaleD, xOffset, imageW, b);
} else {
getIndexAndDeltaInt(iy, dy, inY, yScaleN, yScaleD, yOffset, imageH, b);
getIndexAndDeltaInt(ix, dx, inX, xScaleN, xScaleD, xOffset, imageW, b);
}
if (op.getMode() == "NEAREST_NEIGHBOR") {
auto one = b.create<arith::ConstantOp>(b.getI32IntegerAttr(1));
auto getNearestIndexAndClamp = [&](Value val, Value dval, Value scale,
Value max, int size,
ImplicitLocOpBuilder &b) -> Value {
if (size == 1) {
return b.create<arith::ConstantIndexOp>(0);
}
Value pred;
if (floatingPointMode) {
auto h = b.create<arith::ConstantOp>(b.getFloatAttr(floatTy, 0.5f));
pred = b.create<arith::CmpFOp>(arith::CmpFPredicate::OGE, dval, h);
} else {
Value dvalDouble = b.create<arith::ShLIOp>(dval, one);
pred = b.create<arith::CmpIOp>(arith::CmpIPredicate::sge,
dvalDouble, scale);
}
auto offset = b.create<arith::SelectOp>(pred, one, zeroI32);
val = b.create<arith::AddIOp>(val, offset);
val = clampIntHelper(loc, val, zeroI32, max, b);
return b.create<arith::IndexCastOp>(b.getIndexType(), val);
};
iy = getNearestIndexAndClamp(iy, dy, yScaleN, hMax, imageH, b);
ix = getNearestIndexAndClamp(ix, dx, xScaleN, wMax, imageW, b);
Value result = b.create<tensor::ExtractOp>(
input, ValueRange{batch, iy, ix, channel});
b.create<linalg::YieldOp>(result);
} else {
// The mode here must be BILINEAR.
assert(op.getMode() == "BILINEAR");
auto oneVal = b.create<arith::ConstantOp>(b.getI32IntegerAttr(1));
auto getClampedIdxs = [&](Value &val0, Value &val1, int size, Value in,
Value max, ImplicitLocOpBuilder &b) {
val0 = in;
val1 = b.create<arith::AddIOp>(val0, oneVal);
val0 = clampIntHelper(loc, val0, zeroI32, max, b);
val1 = clampIntHelper(loc, val1, zeroI32, max, b);
val0 = b.create<arith::IndexCastOp>(b.getIndexType(), val0);
val1 = b.create<arith::IndexCastOp>(b.getIndexType(), val1);
};
// Linalg equivalent to the section below:
// int16_t iy0 = apply_max(iy, 0);
// int16_t iy1 = apply_min(iy + 1, IH - 1);
// int16_t ix0 = apply_max(ix, 0);
// int16_t ix1 = apply_min(ix + 1, IW - 1);
Value x0, x1, y0, y1;
getClampedIdxs(y0, y1, imageH, iy, hMax, b);
getClampedIdxs(x0, x1, imageW, ix, wMax, b);
Value y0x0 = b.create<tensor::ExtractOp>(
input, ValueRange{batch, y0, x0, channel});
Value y0x1 = b.create<tensor::ExtractOp>(
input, ValueRange{batch, y0, x1, channel});
Value y1x0 = b.create<tensor::ExtractOp>(
input, ValueRange{batch, y1, x0, channel});
Value y1x1 = b.create<tensor::ExtractOp>(
input, ValueRange{batch, y1, x1, channel});
if (floatingPointMode) {
auto oneVal =
b.create<arith::ConstantOp>(b.getFloatAttr(floatTy, 1.0f));
auto interpolate = [&](Value val0, Value val1, Value delta,
int inputSize,
ImplicitLocOpBuilder &b) -> Value {
if (inputSize == 1)
return val0;
Value oneMinusDelta = b.create<arith::SubFOp>(oneVal, delta);
Value mul0 = b.create<arith::MulFOp>(val0, oneMinusDelta);
Value mul1 = b.create<arith::MulFOp>(val1, delta);
return b.create<arith::AddFOp>(mul0, mul1);
};
// Linalg equivalent to the section below:
// topAcc = v00 * (unit_x - dx);
// topAcc += v01 * dx;
Value topAcc = interpolate(y0x0, y0x1, dx, imageW, b);
// Linalg equivalent to the section below:
// bottomAcc = v10 * (unit_x - dx);
// bottomAcc += v11 * dx;
Value bottomAcc = interpolate(y1x0, y1x1, dx, imageW, b);
// Linalg equivalent to the section below:
// result = topAcc * (unit_y - dy) + bottomAcc * dy
Value result = interpolate(topAcc, bottomAcc, dy, imageH, b);
b.create<linalg::YieldOp>(result);
} else {
// Perform in quantized space.
y0x0 = b.create<arith::ExtSIOp>(resultETy, y0x0);
y0x1 = b.create<arith::ExtSIOp>(resultETy, y0x1);
y1x0 = b.create<arith::ExtSIOp>(resultETy, y1x0);
y1x1 = b.create<arith::ExtSIOp>(resultETy, y1x1);
const int64_t deltaBitwidth = dx.getType().getIntOrFloatBitWidth();
if (resultETy.getIntOrFloatBitWidth() > deltaBitwidth) {
dx = b.create<arith::ExtSIOp>(resultETy, dx);
dy = b.create<arith::ExtSIOp>(resultETy, dy);
}
Value yScaleNExt = yScaleN;
Value xScaleNExt = xScaleN;
const int64_t scaleBitwidth =
xScaleN.getType().getIntOrFloatBitWidth();
if (resultETy.getIntOrFloatBitWidth() > scaleBitwidth) {
yScaleNExt = b.create<arith::ExtSIOp>(resultETy, yScaleN);
xScaleNExt = b.create<arith::ExtSIOp>(resultETy, xScaleN);
}
auto interpolate = [](Value val0, Value val1, Value weight1,
Value scale, int inputSize,
ImplicitLocOpBuilder &b) -> Value {
if (inputSize == 1)
return b.create<arith::MulIOp>(val0, scale);
Value weight0 = b.create<arith::SubIOp>(scale, weight1);
Value mul0 = b.create<arith::MulIOp>(val0, weight0);
Value mul1 = b.create<arith::MulIOp>(val1, weight1);
return b.create<arith::AddIOp>(mul0, mul1);
};
Value topAcc = interpolate(y0x0, y0x1, dx, xScaleNExt, imageW, b);
Value bottomAcc = interpolate(y1x0, y1x1, dx, xScaleNExt, imageW, b);
Value result =
interpolate(topAcc, bottomAcc, dy, yScaleNExt, imageH, b);
b.create<linalg::YieldOp>(result);
}
}
}
rewriter.replaceOp(op, resize);
return success();
}
};
// At the codegen level any identity operations should be removed. Any cases
// where identity is load-bearing (e.g. cross device computation) should be
// handled before lowering to codegen.
template <typename SrcOp>
class IdentityNConverter : public OpRewritePattern<SrcOp> {
public:
using OpRewritePattern<SrcOp>::OpRewritePattern;
LogicalResult matchAndRewrite(SrcOp op,
PatternRewriter &rewriter) const final {
rewriter.replaceOp(op, op.getOperation()->getOperands());
return success();
}
};
template <typename SrcOp>
class ReduceConverter : public OpRewritePattern<SrcOp> {
public:
using OpRewritePattern<SrcOp>::OpRewritePattern;
LogicalResult matchAndRewrite(SrcOp reduceOp,
PatternRewriter &rewriter) const final {
return reduceMatchAndRewriteHelper(reduceOp, reduceOp.getAxis(), rewriter);
}
};
class ReverseConverter : public OpRewritePattern<tosa::ReverseOp> {
public:
using OpRewritePattern<tosa::ReverseOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::ReverseOp op,
PatternRewriter &rewriter) const final {
auto loc = op.getLoc();
Value input = op.getInput();
auto inputTy = cast<ShapedType>(input.getType());
auto resultTy = cast<ShapedType>(op.getType());
auto axis = op.getAxis();
SmallVector<Value> dynDims;
for (int i = 0; i < inputTy.getRank(); i++) {
if (inputTy.isDynamicDim(i)) {
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
}
}
Value axisDimSize = rewriter.create<tensor::DimOp>(loc, input, axis);
// First fill the output buffer with the init value.
auto emptyTensor = rewriter
.create<tensor::EmptyOp>(loc, inputTy.getShape(),
inputTy.getElementType(),
ArrayRef<Value>({dynDims}))
.getResult();
SmallVector<AffineMap, 2> affineMaps = {
rewriter.getMultiDimIdentityMap(resultTy.getRank())};
rewriter.replaceOpWithNewOp<linalg::GenericOp>(
op, resultTy, ArrayRef<Value>({}), ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[&](OpBuilder &nestedBuilder, Location nestedLoc, ValueRange args) {
llvm::SmallVector<Value> indices;
for (unsigned int i = 0; i < inputTy.getRank(); i++) {
Value index =
rewriter.create<linalg::IndexOp>(nestedLoc, i).getResult();
if (i == axis) {
auto one = rewriter.create<arith::ConstantIndexOp>(nestedLoc, 1);
auto sizeMinusOne =
rewriter.create<arith::SubIOp>(nestedLoc, axisDimSize, one);
index = rewriter.create<arith::SubIOp>(nestedLoc, sizeMinusOne,
index);
}
indices.push_back(index);
}
auto extract = nestedBuilder.create<tensor::ExtractOp>(
nestedLoc, input, indices);
nestedBuilder.create<linalg::YieldOp>(op.getLoc(),
extract.getResult());
});
return success();
}
};
// This converter translate a tile operation to a reshape, broadcast, reshape.
// The first reshape minimally expands each tiled dimension to include a
// proceding size-1 dim. This dim is then broadcasted to the appropriate
// multiple.
struct TileConverter : public OpConversionPattern<tosa::TileOp> {
using OpConversionPattern<tosa::TileOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(tosa::TileOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto loc = op.getLoc();
auto input = op.getInput1();
auto inputTy = cast<ShapedType>(input.getType());
auto inputShape = inputTy.getShape();
auto resultTy = cast<ShapedType>(op.getType());
auto elementTy = inputTy.getElementType();
int64_t rank = inputTy.getRank();
ArrayRef<int64_t> multiples = op.getMultiples();
// Broadcast the newly added dimensions to their appropriate multiple.
SmallVector<int64_t, 2> genericShape;
for (int i = 0; i < rank; i++) {
int64_t dim = multiples[i];
genericShape.push_back(dim == -1 ? ShapedType::kDynamic : dim);
genericShape.push_back(inputShape[i]);
}
SmallVector<Value> dynDims;
for (int i = 0; i < inputTy.getRank(); i++) {
if (inputTy.isDynamicDim(i) || multiples[i] == -1) {
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
}
}
auto emptyTensor = rewriter.create<tensor::EmptyOp>(
op.getLoc(), genericShape, elementTy, dynDims);
// We needs to map the input shape to the non-broadcasted dimensions.
SmallVector<AffineExpr, 4> dimExprs;
dimExprs.reserve(rank);
for (unsigned i = 0; i < rank; ++i)
dimExprs.push_back(rewriter.getAffineDimExpr(i * 2 + 1));
auto readAffineMap =
AffineMap::get(/*dimCount=*/rank * 2, /*symbolCount=*/0, dimExprs,
rewriter.getContext());
SmallVector<AffineMap, 2> affineMaps = {
readAffineMap, rewriter.getMultiDimIdentityMap(genericShape.size())};
auto genericOp = rewriter.create<linalg::GenericOp>(
loc, RankedTensorType::get(genericShape, elementTy), input,
ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(genericShape.size()),
[&](OpBuilder &nestedBuilder, Location nestedLoc, ValueRange args) {
nestedBuilder.create<linalg::YieldOp>(op.getLoc(), *args.begin());
});
rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
op, resultTy, genericOp.getResult(0),
rewriter.getDenseI64ArrayAttr(resultTy.getShape()));
return success();
}
};
// Tosa argmax lowering represents the ArgMax op as an linalg.indexed_generic
// op, producing two output buffers.
//
// The first output buffer contains the index of the found maximum value. It is
// initialized to 0 and is resulting integer type.
//
// The second output buffer contains the maximum value found. It is initialized
// to the minimum representable value of the input element type. After being
// populated by indexed_generic, this buffer is disgarded as only the index is
// requested.
//
// The indexed_generic op updates both the maximum value and index if the
// current value exceeds the running max.
class ArgMaxConverter : public OpRewritePattern<tosa::ArgMaxOp> {
public:
using OpRewritePattern<tosa::ArgMaxOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::ArgMaxOp argmaxOp,
PatternRewriter &rewriter) const final {
auto loc = argmaxOp.getLoc();
Value input = argmaxOp.getInput();
auto inputTy = cast<ShapedType>(input.getType());
auto resultTy = cast<ShapedType>(argmaxOp.getOutput().getType());
auto inElementTy = inputTy.getElementType();
auto outElementTy = resultTy.getElementType();
int axis = argmaxOp.getAxis();
auto resultMaxTy = RankedTensorType::get(resultTy.getShape(), inElementTy);
if (!isa<IntegerType>(outElementTy))
return rewriter.notifyMatchFailure(
argmaxOp,
"tosa.arg_max to linalg.* requires integer-like result type");
SmallVector<Value> dynDims;
for (int i = 0; i < inputTy.getRank(); i++) {
if (inputTy.isDynamicDim(i) && i != axis) {
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
}
}
// First fill the output buffer for the index.
auto emptyTensorIdx = rewriter
.create<tensor::EmptyOp>(loc, resultTy.getShape(),
outElementTy, dynDims)
.getResult();
auto fillValueIdx = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(outElementTy, 0));
auto filledTensorIdx =
rewriter
.create<linalg::FillOp>(loc, ValueRange{fillValueIdx},
ValueRange{emptyTensorIdx})
.result();
// Second fill the output buffer for the running max.
auto emptyTensorMax = rewriter
.create<tensor::EmptyOp>(loc, resultTy.getShape(),
inElementTy, dynDims)
.getResult();
auto fillValueMaxAttr =
createInitialValueForReduceOp(argmaxOp, inElementTy, rewriter);
if (!fillValueMaxAttr)
return rewriter.notifyMatchFailure(
argmaxOp, "unsupported tosa.argmax element type");
auto fillValueMax =
rewriter.create<arith::ConstantOp>(loc, fillValueMaxAttr);
auto filledTensorMax =
rewriter
.create<linalg::FillOp>(loc, ValueRange{fillValueMax},
ValueRange{emptyTensorMax})
.result();
// We need to reduce along the arg-max axis, with parallel operations along
// the rest.
SmallVector<utils::IteratorType, 4> iteratorTypes;
iteratorTypes.resize(inputTy.getRank(), utils::IteratorType::parallel);
iteratorTypes[axis] = utils::IteratorType::reduction;
SmallVector<AffineExpr, 2> srcExprs;
SmallVector<AffineExpr, 2> dstExprs;
for (int i = 0, rank = inputTy.getRank(); i != rank; ++i) {
srcExprs.push_back(mlir::getAffineDimExpr(i, rewriter.getContext()));
if (axis != i)
dstExprs.push_back(mlir::getAffineDimExpr(i, rewriter.getContext()));
}
bool didEncounterError = false;
auto maps = AffineMap::inferFromExprList({srcExprs, dstExprs, dstExprs});
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc, ArrayRef<Type>({resultTy, resultMaxTy}), input,
ValueRange({filledTensorIdx, filledTensorMax}), maps, iteratorTypes,
[&](OpBuilder &nestedBuilder, Location nestedLoc,
ValueRange blockArgs) {
auto newValue = blockArgs[0];
auto oldIndex = blockArgs[1];
auto oldValue = blockArgs[2];
Value newIndex = rewriter.create<arith::IndexCastOp>(
nestedLoc, oldIndex.getType(),
rewriter.create<linalg::IndexOp>(loc, axis));
Value predicate;
if (isa<FloatType>(inElementTy)) {
predicate = rewriter.create<arith::CmpFOp>(
nestedLoc, arith::CmpFPredicate::OGT, newValue, oldValue);
} else if (isa<IntegerType>(inElementTy)) {
predicate = rewriter.create<arith::CmpIOp>(
nestedLoc, arith::CmpIPredicate::sgt, newValue, oldValue);
} else {
didEncounterError = true;
return;
}
auto resultMax = rewriter.create<arith::SelectOp>(
nestedLoc, predicate, newValue, oldValue);
auto resultIndex = rewriter.create<arith::SelectOp>(
nestedLoc, predicate, newIndex, oldIndex);
nestedBuilder.create<linalg::YieldOp>(
nestedLoc, ValueRange({resultIndex, resultMax}));
});
if (didEncounterError)
return rewriter.notifyMatchFailure(
argmaxOp, "unsupported tosa.argmax element type");
rewriter.replaceOp(argmaxOp, linalgOp.getResult(0));
return success();
}
};
class GatherConverter : public OpConversionPattern<tosa::GatherOp> {
public:
using OpConversionPattern<tosa::GatherOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(tosa::GatherOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const final {
auto input = adaptor.getOperands()[0];
auto indices = adaptor.getOperands()[1];
auto valuesTy =
dyn_cast_or_null<RankedTensorType>(op.getValues().getType());
auto resultTy = cast<ShapedType>(op.getType());
if (!valuesTy)
return rewriter.notifyMatchFailure(op, "unranked tensors not supported");
auto dynamicDims = inferDynamicDimsForGather(
rewriter, op.getLoc(), adaptor.getValues(), adaptor.getIndices());
auto resultElementTy = resultTy.getElementType();
auto loc = op.getLoc();
auto emptyTensor =
rewriter
.create<tensor::EmptyOp>(loc, resultTy.getShape(), resultElementTy,
dynamicDims)
.getResult();
SmallVector<AffineMap, 2> affineMaps = {
AffineMap::get(
/*dimCount=*/resultTy.getRank(), /*symbolCount=*/0,
{rewriter.getAffineDimExpr(0), rewriter.getAffineDimExpr(1)},
rewriter.getContext()),
rewriter.getMultiDimIdentityMap(resultTy.getRank())};
auto genericOp = rewriter.create<linalg::GenericOp>(
loc, ArrayRef<Type>({resultTy}), ValueRange{indices},
ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[&](OpBuilder &b, Location loc, ValueRange args) {
auto indexValue = args[0];
auto index0 = rewriter.create<linalg::IndexOp>(loc, 0);
Value index1 = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIndexType(), indexValue);
auto index2 = rewriter.create<linalg::IndexOp>(loc, 2);
Value extract = rewriter.create<tensor::ExtractOp>(
loc, input, ValueRange{index0, index1, index2});
rewriter.create<linalg::YieldOp>(loc, extract);
});
rewriter.replaceOp(op, genericOp.getResult(0));
return success();
}
static llvm::SmallVector<Value> inferDynamicDimsForGather(OpBuilder &builder,
Location loc,
Value values,
Value indices) {
llvm::SmallVector<Value> results;
auto addDynamicDimension = [&](Value source, int64_t dim) {
auto sz = tensor::getMixedSize(builder, loc, source, dim);
if (auto dimValue = llvm::dyn_cast_if_present<Value>(sz))
results.push_back(dimValue);
};
addDynamicDimension(values, 0);
addDynamicDimension(indices, 1);
addDynamicDimension(values, 2);
return results;
}
};
// Lowerings the TableOp to a series of gathers and numerica operations. This
// includes interpolation between the high/low values. For the I8 varient, this
// simplifies to a single gather operation.
class TableConverter : public OpRewritePattern<tosa::TableOp> {
public:
using OpRewritePattern<tosa::TableOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::TableOp op,
PatternRewriter &rewriter) const final {
auto loc = op.getLoc();
Value input = op.getInput();
Value table = op.getTable();
auto inputTy = cast<ShapedType>(input.getType());
auto tableTy = cast<ShapedType>(table.getType());
auto resultTy = cast<ShapedType>(op.getType());
auto inputElementTy = inputTy.getElementType();
auto tableElementTy = tableTy.getElementType();
auto resultElementTy = resultTy.getElementType();
SmallVector<Value> dynDims;
for (int i = 0; i < resultTy.getRank(); ++i) {
if (inputTy.isDynamicDim(i)) {
dynDims.push_back(
rewriter.create<tensor::DimOp>(loc, op.getOperand(0), i));
}
}
auto emptyTensor = rewriter
.create<tensor::EmptyOp>(loc, resultTy.getShape(),
resultElementTy, dynDims)
.getResult();
SmallVector<AffineMap, 2> affineMaps = {
rewriter.getMultiDimIdentityMap(resultTy.getRank()),
rewriter.getMultiDimIdentityMap(resultTy.getRank())};
auto genericOp = rewriter.create<linalg::GenericOp>(
loc, resultTy, ValueRange({input}), ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(resultTy.getRank()));
rewriter.replaceOp(op, genericOp.getResult(0));
{
OpBuilder::InsertionGuard regionGuard(rewriter);
Block *block = rewriter.createBlock(
&genericOp.getRegion(), genericOp.getRegion().end(),
TypeRange({inputElementTy, resultElementTy}), {loc, loc});
auto inputValue = block->getArgument(0);
rewriter.setInsertionPointToStart(block);
if (inputElementTy.isInteger(8) && tableElementTy.isInteger(8) &&
resultElementTy.isInteger(8)) {
Value index = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIndexType(), inputValue);
Value offset = rewriter.create<arith::ConstantIndexOp>(loc, 128);
index = rewriter.create<arith::AddIOp>(loc, rewriter.getIndexType(),
index, offset);
Value extract =
rewriter.create<tensor::ExtractOp>(loc, table, ValueRange{index});
rewriter.create<linalg::YieldOp>(loc, extract);
return success();
}
if (inputElementTy.isInteger(16) && tableElementTy.isInteger(16) &&
resultElementTy.isInteger(32)) {
Value extend = rewriter.create<arith::ExtSIOp>(
loc, rewriter.getI32Type(), inputValue);
auto offset = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(32768));
auto seven = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(7));
auto one = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(1));
auto b1111111 = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(127));
// Compute the index and fractional part from the input value:
// value = value + 32768
// index = value >> 7;
// fraction = 0x01111111 & value
auto extendAdd = rewriter.create<arith::AddIOp>(loc, extend, offset);
Value index = rewriter.create<arith::ShRUIOp>(loc, extendAdd, seven);
Value fraction =
rewriter.create<arith::AndIOp>(loc, extendAdd, b1111111);
// Extract the base and next values from the table.
// base = (int32_t) table[index];
// next = (int32_t) table[index + 1];
Value indexPlusOne = rewriter.create<arith::AddIOp>(loc, index, one);
index = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIndexType(), index);
indexPlusOne = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIndexType(), indexPlusOne);
Value base =
rewriter.create<tensor::ExtractOp>(loc, table, ValueRange{index});
Value next = rewriter.create<tensor::ExtractOp>(
loc, table, ValueRange{indexPlusOne});
base =
rewriter.create<arith::ExtSIOp>(loc, rewriter.getI32Type(), base);
next =
rewriter.create<arith::ExtSIOp>(loc, rewriter.getI32Type(), next);
// Use the fractional part to interpolate between the input values:
// result = (base << 7) + (next - base) * fraction
Value baseScaled = rewriter.create<arith::ShLIOp>(loc, base, seven);
Value diff = rewriter.create<arith::SubIOp>(loc, next, base);
Value diffScaled = rewriter.create<arith::MulIOp>(loc, diff, fraction);
Value result =
rewriter.create<arith::AddIOp>(loc, baseScaled, diffScaled);
rewriter.create<linalg::YieldOp>(loc, result);
return success();
}
}
return rewriter.notifyMatchFailure(
op, "unable to create body for tosa.table op");
}
};
struct RFFT2dConverter final : public OpRewritePattern<RFFT2dOp> {
using OpRewritePattern<RFFT2dOp>::OpRewritePattern;
static bool isRankedTensor(Type type) { return isa<RankedTensorType>(type); }
static OpFoldResult halfPlusOne(OpBuilder &builder, Location loc,
OpFoldResult ofr) {
auto one = builder.create<arith::ConstantIndexOp>(loc, 1);
auto two = builder.create<arith::ConstantIndexOp>(loc, 2);
auto value = getValueOrCreateConstantIndexOp(builder, loc, ofr);
auto divBy2 = builder.createOrFold<arith::DivUIOp>(loc, value, two);
auto plusOne = builder.createOrFold<arith::AddIOp>(loc, divBy2, one);
return getAsOpFoldResult(plusOne);
}
static RankedTensorType
computeOutputShape(OpBuilder &builder, Location loc, Value input,
llvm::SmallVectorImpl<Value> &dynamicSizes) {
// Get [N, H, W]
auto dims = tensor::getMixedSizes(builder, loc, input);
// Set W = (W / 2) + 1 to account for the half-sized W dimension of the
// output tensors.
dims[2] = halfPlusOne(builder, loc, dims[2]);
llvm::SmallVector<int64_t, 3> staticSizes;
dispatchIndexOpFoldResults(dims, dynamicSizes, staticSizes);
auto elementType =
input.getType().cast<RankedTensorType>().getElementType();
return RankedTensorType::get(staticSizes, elementType);
}
static Value createZeroTensor(PatternRewriter &rewriter, Location loc,
RankedTensorType type,
llvm::ArrayRef<Value> dynamicSizes) {
auto emptyTensor =
rewriter.create<tensor::EmptyOp>(loc, type, dynamicSizes);
auto fillValueAttr = rewriter.getZeroAttr(type.getElementType());
auto fillValue = rewriter.create<arith::ConstantOp>(loc, fillValueAttr);
auto filledTensor = rewriter
.create<linalg::FillOp>(loc, ValueRange{fillValue},
ValueRange{emptyTensor})
.result();
return filledTensor;
}
static Value castIndexToFloat(OpBuilder &builder, Location loc,
FloatType type, Value value) {
auto integerVal = builder.create<arith::IndexCastUIOp>(
loc,
type.getIntOrFloatBitWidth() > 32 ? builder.getI64Type()
: builder.getI32Type(),
value);
return builder.create<arith::UIToFPOp>(loc, type, integerVal);
}
static Value createLinalgIndex(OpBuilder &builder, Location loc,
FloatType type, int64_t index) {
auto indexVal = builder.create<linalg::IndexOp>(loc, index);
return castIndexToFloat(builder, loc, type, indexVal);
}
template <typename... Args>
static llvm::SmallVector<AffineExpr, 4> affineDimsExpr(OpBuilder &builder,
Args... args) {
return {builder.getAffineDimExpr(args)...};
}
LogicalResult matchAndRewrite(RFFT2dOp rfft2d,
PatternRewriter &rewriter) const override {
if (!llvm::all_of(rfft2d->getOperandTypes(), isRankedTensor) ||
!llvm::all_of(rfft2d->getResultTypes(), isRankedTensor)) {
return rewriter.notifyMatchFailure(rfft2d,
"only supports ranked tensors");
}
auto loc = rfft2d.getLoc();
auto input = rfft2d.getInput();
auto elementType =
input.getType().cast<ShapedType>().getElementType().cast<FloatType>();
// Compute the output type and set of dynamic sizes
llvm::SmallVector<Value> dynamicSizes;
auto outputType = computeOutputShape(rewriter, loc, input, dynamicSizes);
// Iterator types for the linalg.generic implementation
llvm::SmallVector<utils::IteratorType, 5> iteratorTypes = {
utils::IteratorType::parallel, utils::IteratorType::parallel,
utils::IteratorType::parallel, utils::IteratorType::reduction,
utils::IteratorType::reduction};
// Inputs/outputs to the linalg.generic implementation
llvm::SmallVector<Value> genericOpInputs = {input};
llvm::SmallVector<Value> genericOpOutputs = {
createZeroTensor(rewriter, loc, outputType, dynamicSizes),
createZeroTensor(rewriter, loc, outputType, dynamicSizes)};
// Indexing maps for input and output tensors
auto indexingMaps = AffineMap::inferFromExprList(llvm::ArrayRef{
affineDimsExpr(rewriter, 0, 3, 4), affineDimsExpr(rewriter, 0, 1, 2),
affineDimsExpr(rewriter, 0, 1, 2)});
// Width and height dimensions of the original input.
auto dimH = rewriter.createOrFold<tensor::DimOp>(loc, input, 1);
auto dimW = rewriter.createOrFold<tensor::DimOp>(loc, input, 2);
// Constants and dimension sizes
auto twoPiAttr = rewriter.getFloatAttr(elementType, 6.283185307179586);
auto twoPi = rewriter.create<arith::ConstantOp>(loc, twoPiAttr);
auto constH = castIndexToFloat(rewriter, loc, elementType, dimH);
auto constW = castIndexToFloat(rewriter, loc, elementType, dimW);
auto buildBody = [&](OpBuilder &builder, Location loc, ValueRange args) {
Value valReal = args[0];
Value sumReal = args[1];
Value sumImag = args[2];
// Indices for angle computation
auto oy = createLinalgIndex(builder, loc, elementType, 1);
auto ox = createLinalgIndex(builder, loc, elementType, 2);
auto iy = createLinalgIndex(builder, loc, elementType, 3);
auto ix = createLinalgIndex(builder, loc, elementType, 4);
// angle = 2 * pi() * ((iy * oy) / H + (ix * ox) / W)
auto iyXoy = builder.create<arith::MulFOp>(loc, iy, oy);
auto ixXox = builder.create<arith::MulFOp>(loc, ix, ox);
auto yComponent = builder.create<arith::DivFOp>(loc, iyXoy, constH);
auto xComponent = builder.create<arith::DivFOp>(loc, ixXox, constW);
auto sumXY = builder.create<arith::AddFOp>(loc, yComponent, xComponent);
auto angle = builder.create<arith::MulFOp>(loc, twoPi, sumXY);
// realComponent = valReal * cos(angle)
// imagComponent = valReal * sin(angle)
auto cosAngle = builder.create<math::CosOp>(loc, angle);
auto sinAngle = builder.create<math::SinOp>(loc, angle);
auto realComponent =
builder.create<arith::MulFOp>(loc, valReal, cosAngle);
auto imagComponent =
builder.create<arith::MulFOp>(loc, valReal, sinAngle);
// outReal = sumReal + realComponent
// outImag = sumImag - imagComponent
auto outReal = builder.create<arith::AddFOp>(loc, sumReal, realComponent);
auto outImag = builder.create<arith::SubFOp>(loc, sumImag, imagComponent);
builder.create<linalg::YieldOp>(loc, ValueRange{outReal, outImag});
};
rewriter.replaceOpWithNewOp<linalg::GenericOp>(
rfft2d, rfft2d.getResultTypes(), genericOpInputs, genericOpOutputs,
indexingMaps, iteratorTypes, buildBody);
return success();
}
};
struct FFT2dConverter final : OpRewritePattern<FFT2dOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(FFT2dOp fft2d,
PatternRewriter &rewriter) const override {
if (!llvm::all_of(fft2d->getOperandTypes(),
RFFT2dConverter::isRankedTensor) ||
!llvm::all_of(fft2d->getResultTypes(),
RFFT2dConverter::isRankedTensor)) {
return rewriter.notifyMatchFailure(fft2d, "only supports ranked tensors");
}
Location loc = fft2d.getLoc();
Value input_real = fft2d.getInputReal();
Value input_imag = fft2d.getInputImag();
BoolAttr inverse = fft2d.getInverseAttr();
auto real_el_ty = cast<FloatType>(
cast<ShapedType>(input_real.getType()).getElementType());
[[maybe_unused]] auto imag_el_ty = cast<FloatType>(
cast<ShapedType>(input_imag.getType()).getElementType());
assert(real_el_ty == imag_el_ty);
// Compute the output type and set of dynamic sizes
SmallVector<Value> dynamicSizes;
// Get [N, H, W]
auto dims = tensor::getMixedSizes(rewriter, loc, input_real);
SmallVector<int64_t, 3> staticSizes;
dispatchIndexOpFoldResults(dims, dynamicSizes, staticSizes);
auto outputType = RankedTensorType::get(staticSizes, real_el_ty);
// Iterator types for the linalg.generic implementation
SmallVector<utils::IteratorType, 5> iteratorTypes = {
utils::IteratorType::parallel, utils::IteratorType::parallel,
utils::IteratorType::parallel, utils::IteratorType::reduction,
utils::IteratorType::reduction};
// Inputs/outputs to the linalg.generic implementation
SmallVector<Value> genericOpInputs = {input_real, input_imag};
SmallVector<Value> genericOpOutputs = {
RFFT2dConverter::createZeroTensor(rewriter, loc, outputType,
dynamicSizes),
RFFT2dConverter::createZeroTensor(rewriter, loc, outputType,
dynamicSizes)};
// Indexing maps for input and output tensors
auto indexingMaps = AffineMap::inferFromExprList(
ArrayRef{RFFT2dConverter::affineDimsExpr(rewriter, 0, 3, 4),
RFFT2dConverter::affineDimsExpr(rewriter, 0, 3, 4),
RFFT2dConverter::affineDimsExpr(rewriter, 0, 1, 2),
RFFT2dConverter::affineDimsExpr(rewriter, 0, 1, 2)});
// Width and height dimensions of the original input.
auto dimH = rewriter.createOrFold<tensor::DimOp>(loc, input_real, 1);
auto dimW = rewriter.createOrFold<tensor::DimOp>(loc, input_real, 2);
// Constants and dimension sizes
auto twoPiAttr = rewriter.getFloatAttr(real_el_ty, 6.283185307179586);
auto twoPi = rewriter.create<arith::ConstantOp>(loc, twoPiAttr);
Value constH =
RFFT2dConverter::castIndexToFloat(rewriter, loc, real_el_ty, dimH);
Value constW =
RFFT2dConverter::castIndexToFloat(rewriter, loc, real_el_ty, dimW);
auto buildBody = [&](OpBuilder &builder, Location loc, ValueRange args) {
Value valReal = args[0];
Value valImag = args[1];
Value sumReal = args[2];
Value sumImag = args[3];
// Indices for angle computation
Value oy =
RFFT2dConverter::createLinalgIndex(builder, loc, real_el_ty, 1);
Value ox =
RFFT2dConverter::createLinalgIndex(builder, loc, real_el_ty, 2);
Value iy =
RFFT2dConverter::createLinalgIndex(builder, loc, real_el_ty, 3);
Value ix =
RFFT2dConverter::createLinalgIndex(builder, loc, real_el_ty, 4);
// float_t angle = sign_val * 2 * pi() * ((iy * oy) / H + (ix * ox) / W);
auto iyXoy = builder.create<arith::MulFOp>(loc, iy, oy);
auto ixXox = builder.create<arith::MulFOp>(loc, ix, ox);
auto yComponent = builder.create<arith::DivFOp>(loc, iyXoy, constH);
auto xComponent = builder.create<arith::DivFOp>(loc, ixXox, constW);
auto sumXY = builder.create<arith::AddFOp>(loc, yComponent, xComponent);
auto angle = builder.create<arith::MulFOp>(loc, twoPi, sumXY);
if (inverse.getValue()) {
angle = builder.create<arith::MulFOp>(
loc, angle,
rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(real_el_ty, -1.0)));
}
// realComponent = val_real * cos(a) + val_imag * sin(a);
// imagComponent = -val_real * sin(a) + val_imag * cos(a);
auto cosAngle = builder.create<math::CosOp>(loc, angle);
auto sinAngle = builder.create<math::SinOp>(loc, angle);
auto rcos = builder.create<arith::MulFOp>(loc, valReal, cosAngle);
auto rsin = builder.create<arith::MulFOp>(loc, valImag, sinAngle);
auto realComponent = builder.create<arith::AddFOp>(loc, rcos, rsin);
auto icos = builder.create<arith::MulFOp>(loc, valImag, cosAngle);
auto isin = builder.create<arith::MulFOp>(loc, valReal, sinAngle);
auto imagComponent = builder.create<arith::SubFOp>(loc, icos, isin);
// outReal = sumReal + realComponent
// outImag = sumImag - imagComponent
auto outReal = builder.create<arith::AddFOp>(loc, sumReal, realComponent);
auto outImag = builder.create<arith::AddFOp>(loc, sumImag, imagComponent);
builder.create<linalg::YieldOp>(loc, ValueRange{outReal, outImag});
};
rewriter.replaceOpWithNewOp<linalg::GenericOp>(
fft2d, fft2d.getResultTypes(), genericOpInputs, genericOpOutputs,
indexingMaps, iteratorTypes, buildBody);
return success();
}
};
} // namespace
void mlir::tosa::populateTosaToLinalgConversionPatterns(
RewritePatternSet *patterns) {
// We have multiple resize coverters to handle degenerate cases.
patterns->add<GenericResizeConverter>(patterns->getContext(),
/*benefit=*/100);
patterns->add<ResizeUnaryConverter>(patterns->getContext(),
/*benefit=*/200);
patterns->add<MaterializeResizeBroadcast>(patterns->getContext(),
/*benefit=*/300);
patterns->add<
// clang-format off
PointwiseConverter<tosa::AddOp>,
PointwiseConverter<tosa::SubOp>,
PointwiseConverter<tosa::MulOp>,
PointwiseConverter<tosa::DivOp>,
PointwiseConverter<tosa::NegateOp>,
PointwiseConverter<tosa::PowOp>,
PointwiseConverter<tosa::ReciprocalOp>,
PointwiseConverter<tosa::RsqrtOp>,
PointwiseConverter<tosa::LogOp>,
PointwiseConverter<tosa::ExpOp>,
PointwiseConverter<tosa::AbsOp>,
PointwiseConverter<tosa::TanhOp>,
PointwiseConverter<tosa::ErfOp>,
PointwiseConverter<tosa::BitwiseAndOp>,
PointwiseConverter<tosa::BitwiseOrOp>,
PointwiseConverter<tosa::BitwiseNotOp>,
PointwiseConverter<tosa::BitwiseXorOp>,
PointwiseConverter<tosa::LogicalAndOp>,
PointwiseConverter<tosa::LogicalNotOp>,
PointwiseConverter<tosa::LogicalOrOp>,
PointwiseConverter<tosa::LogicalXorOp>,
PointwiseConverter<tosa::CastOp>,
PointwiseConverter<tosa::LogicalLeftShiftOp>,
PointwiseConverter<tosa::LogicalRightShiftOp>,
PointwiseConverter<tosa::ArithmeticRightShiftOp>,
PointwiseConverter<tosa::ClzOp>,
PointwiseConverter<tosa::SelectOp>,
PointwiseConverter<tosa::GreaterOp>,
PointwiseConverter<tosa::GreaterEqualOp>,
PointwiseConverter<tosa::EqualOp>,
PointwiseConverter<tosa::MaximumOp>,
PointwiseConverter<tosa::MinimumOp>,
PointwiseConverter<tosa::CeilOp>,
PointwiseConverter<tosa::FloorOp>,
PointwiseConverter<tosa::ClampOp>,
PointwiseConverter<tosa::SigmoidOp>,
IdentityNConverter<tosa::IdentityOp>,
ReduceConverter<tosa::ReduceAllOp>,
ReduceConverter<tosa::ReduceAnyOp>,
ReduceConverter<tosa::ReduceMinOp>,
ReduceConverter<tosa::ReduceMaxOp>,
ReduceConverter<tosa::ReduceSumOp>,
ReduceConverter<tosa::ReduceProdOp>,
ArgMaxConverter,
GatherConverter,
RescaleConverter,
ReverseConverter,
RFFT2dConverter,
FFT2dConverter,
TableConverter,
TileConverter>(patterns->getContext());
// clang-format on
}
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