THU-DSP-LAB
/
llvm-project
mirror of https://github.com/THU-DSP-LAB/llvm-project.git
2
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity
llvm-project/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

2385 lines
93 KiB
C++
Raw Permalink Blame History

//===- 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/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/CoversionUtils.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/DialectConversion.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.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>(
op->getAttr(attrName).cast<IntegerAttr>().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 =
op->getOperand(0).getType().cast<ShapedType>().getElementType();
// tosa::AbsOp
if (isa<tosa::AbsOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<math::AbsFOp>(loc, resultTypes, args);
if (isa<tosa::AbsOp>(op) && elementTy.isa<IntegerType>()) {
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) && elementTy.isa<FloatType>())
return rewriter.create<arith::AddFOp>(loc, resultTypes, args);
if (isa<tosa::AddOp>(op) && elementTy.isa<IntegerType>())
return rewriter.create<arith::AddIOp>(loc, resultTypes, args);
// tosa::SubOp
if (isa<tosa::SubOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<arith::SubFOp>(loc, resultTypes, args);
if (isa<tosa::SubOp>(op) && elementTy.isa<IntegerType>())
return rewriter.create<arith::SubIOp>(loc, resultTypes, args);
// tosa::MulOp
if (isa<tosa::MulOp>(op) && elementTy.isa<FloatType>()) {
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) && elementTy.isa<IntegerType>())
return rewriter.create<arith::DivSIOp>(loc, resultTypes, args);
// tosa::ReciprocalOp
if (isa<tosa::ReciprocalOp>(op) && elementTy.isa<FloatType>()) {
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) && elementTy.isa<IntegerType>()) {
Value a = args[0];
Value b = args[1];
auto shift =
op->getAttr("shift").cast<IntegerAttr>().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) && elementTy.isa<FloatType>())
return rewriter.create<arith::NegFOp>(loc, resultTypes, args);
if (isa<tosa::NegateOp>(op) && elementTy.isa<IntegerType>() &&
!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) && elementTy.isa<IntegerType>() &&
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.
auto min = rewriter.create<arith::ConstantIntOp>(
loc, APInt::getSignedMinValue(inputBitWidth).getSExtValue(),
intermediateType);
auto 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) && elementTy.isa<IntegerType>())
return rewriter.create<arith::AndIOp>(loc, resultTypes, args);
// tosa::BitwiseOrOp
if (isa<tosa::BitwiseOrOp>(op) && elementTy.isa<IntegerType>())
return rewriter.create<arith::OrIOp>(loc, resultTypes, args);
// tosa::BitwiseNotOp
if (isa<tosa::BitwiseNotOp>(op) && elementTy.isa<IntegerType>()) {
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) && elementTy.isa<IntegerType>())
return rewriter.create<arith::XOrIOp>(loc, resultTypes, args);
// tosa::LogicalLeftShiftOp
if (isa<tosa::LogicalLeftShiftOp>(op) && elementTy.isa<IntegerType>())
return rewriter.create<arith::ShLIOp>(loc, resultTypes, args);
// tosa::LogicalRightShiftOp
if (isa<tosa::LogicalRightShiftOp>(op) && elementTy.isa<IntegerType>())
return rewriter.create<arith::ShRUIOp>(loc, resultTypes, args);
// tosa::ArithmeticRightShiftOp
if (isa<tosa::ArithmeticRightShiftOp>(op) && elementTy.isa<IntegerType>()) {
auto result = rewriter.create<arith::ShRSIOp>(loc, resultTypes, args);
auto round = op->getAttr("round").cast<BoolAttr>().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) && elementTy.isa<IntegerType>()) {
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) && elementTy.isa<FloatType>())
return rewriter.create<mlir::math::PowFOp>(loc, resultTypes, args);
// tosa::RsqrtOp
if (isa<tosa::RsqrtOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<mlir::math::RsqrtOp>(loc, resultTypes, args);
// tosa::LogOp
if (isa<tosa::LogOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<mlir::math::LogOp>(loc, resultTypes, args);
// tosa::ExpOp
if (isa<tosa::ExpOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<mlir::math::ExpOp>(loc, resultTypes, args);
// tosa::TanhOp
if (isa<tosa::TanhOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<mlir::math::TanhOp>(loc, resultTypes, args);
// tosa::GreaterOp
if (isa<tosa::GreaterOp>(op) && elementTy.isa<FloatType>())
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) && elementTy.isa<FloatType>())
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) && elementTy.isa<FloatType>())
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 = op->getOperand(1).getType().cast<ShapedType>().getElementType();
if (elementTy.isa<FloatType>() || elementTy.isa<IntegerType>())
return rewriter.create<arith::SelectOp>(loc, args[0], args[1], args[2]);
}
// tosa::MaximumOp
if (isa<tosa::MaximumOp>(op) && elementTy.isa<FloatType>()) {
return rewriter.create<arith::MaxFOp>(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) && elementTy.isa<FloatType>()) {
return rewriter.create<arith::MinFOp>(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) && elementTy.isa<FloatType>())
return rewriter.create<math::CeilOp>(loc, resultTypes, args);
// tosa::FloorOp
if (isa<tosa::FloorOp>(op) && elementTy.isa<FloatType>())
return rewriter.create<math::FloorOp>(loc, resultTypes, args);
// tosa::ClampOp
if (isa<tosa::ClampOp>(op) && elementTy.isa<FloatType>()) {
bool losesInfo = false;
APFloat minApf = op->getAttr("min_fp").cast<FloatAttr>().getValue();
APFloat maxApf = op->getAttr("max_fp").cast<FloatAttr>().getValue();
minApf.convert(elementTy.cast<FloatType>().getFloatSemantics(),
APFloat::rmNearestTiesToEven, &losesInfo);
maxApf.convert(elementTy.cast<FloatType>().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) && elementTy.isa<IntegerType>()) {
auto intTy = elementTy.cast<IntegerType>();
int32_t min = static_cast<int32_t>(
op->getAttr("min_int").cast<IntegerAttr>().getValue().getSExtValue());
int32_t max = static_cast<int32_t>(
op->getAttr("max_int").cast<IntegerAttr>().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) && elementTy.isa<FloatType>()) {
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 (srcTy.isa<FloatType>() && dstTy.isa<FloatType>() && bitExtend)
return rewriter.create<arith::ExtFOp>(loc, resultTypes, args,
std::nullopt);
if (srcTy.isa<FloatType>() && dstTy.isa<FloatType>() && !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) && dstTy.isa<IntegerType>() && 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() && dstTy.isa<FloatType>()) {
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 (srcTy.isa<FloatType>() && 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 zero = rewriter.create<arith::ConstantOp>(
loc, rewriter.getF32FloatAttr(0.0f));
auto half = rewriter.create<arith::ConstantOp>(
loc, rewriter.getF32FloatAttr(0.5f));
auto intMin = rewriter.create<arith::ConstantOp>(
loc, rewriter.getF32FloatAttr(
APInt::getSignedMinValue(dstTy.getIntOrFloatBitWidth())
.getSExtValue()));
auto intMax = rewriter.create<arith::ConstantOp>(
loc, rewriter.getF32FloatAttr(
APInt::getSignedMaxValue(dstTy.getIntOrFloatBitWidth())
.getSExtValue()));
auto added = rewriter.create<arith::AddFOp>(loc, args[0], half);
auto subbed = rewriter.create<arith::SubFOp>(loc, args[0], half);
auto negative = rewriter.create<arith::CmpFOp>(
loc, arith::CmpFPredicate::OLT, args[0], zero);
auto rounded =
rewriter.create<arith::SelectOp>(loc, negative, subbed, added);
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 (srcTy.isa<IntegerType>() && 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 (srcTy.isa<IntegerType>() && dstTy.isa<IntegerType>() && bitExtend)
return rewriter.create<arith::ExtSIOp>(loc, resultTypes, args,
std::nullopt);
if (srcTy.isa<IntegerType>() && dstTy.isa<IntegerType>() && !bitExtend) {
auto intMin = rewriter.create<arith::ConstantIntOp>(
loc,
APInt::getSignedMinValue(dstTy.getIntOrFloatBitWidth())
.getSExtValue(),
srcTy.getIntOrFloatBitWidth());
auto intMax = rewriter.create<arith::ConstantIntOp>(
loc,
APInt::getSignedMaxValue(dstTy.getIntOrFloatBitWidth())
.getSExtValue(),
srcTy.getIntOrFloatBitWidth());
auto clamped = clampIntHelper(loc, args[0], intMin, intMax, rewriter);
return rewriter.create<arith::TruncIOp>(loc, dstTy, clamped);
}
}
(void)rewriter.notifyMatchFailure(
op, "unhandled op for linalg body calculation for elementwise op");
return nullptr;
}
static LogicalResult
elementwiseMatchAndRewriteHelper(Operation *operation,
PatternRewriter &rewriter) {
auto loc = operation->getLoc();
assert(operation->getNumResults() == 1 &&
"All TOSA elementwise ops should only return a single result.");
auto results = operation->getResults();
auto resultTy = operation->getResult(0).getType().dyn_cast<ShapedType>();
if (!resultTy)
return rewriter.notifyMatchFailure(operation,
"All results must be a shaped type");
unsigned rank = resultTy.getRank();
// Construct the indexing maps needed for linalg.generic ops.
SmallVector<Type> bodyArgTypes;
for (Value in : operation->getOperands())
bodyArgTypes.emplace_back(getElementTypeOrSelf(in.getType()));
SmallVector<Type> opResultTypes;
SmallVector<Value> emptyTensors;
SmallVector<Value> dynDims;
dynDims.resize(results.front().getType().cast<ShapedType>().getRank());
for (auto arg : operation->getOperands()) {
auto operandTy = arg.getType().cast<ShapedType>();
for (int i = 0; i < operandTy.getRank(); i++) {
if (operandTy.isDynamicDim(i) && !dynDims[i])
dynDims[i] = rewriter.create<tensor::DimOp>(loc, arg, i);
}
}
SmallVector<Value> filteredDims = condenseValues(dynDims);
for (auto result : results) {
auto resultTy = result.getType().template cast<ShapedType>();
emptyTensors.push_back(rewriter.create<tensor::EmptyOp>(
loc, resultTy.getShape(), resultTy.getElementType(), filteredDims));
opResultTypes.push_back(result.getType());
}
auto bodyResultTypes = llvm::to_vector<4>(llvm::map_range(
emptyTensors, [](Value v) { return getElementTypeOrSelf(v); }));
SmallVector<Value, 2> operands;
SmallVector<AffineMap, 2> indexingMaps;
indexingMaps.reserve(operation->getNumOperands() + bodyResultTypes.size());
// Input indexing maps may be broadcasted.
for (Value operand : operation->getOperands()) {
ShapedType type = operand.getType().cast<ShapedType>();
if (type.getShape() == resultTy.getShape()) {
operands.push_back(operand);
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(rank));
continue;
}
SmallVector<int64_t, 5> newShape;
SmallVector<AffineExpr, 4> affineExprs;
newShape.reserve(type.getRank());
for (const auto &it : llvm::enumerate(type.getShape())) {
if (it.value() == resultTy.getDimSize(it.index())) {
newShape.push_back(it.value());
affineExprs.push_back(
mlir::getAffineDimExpr(it.index(), rewriter.getContext()));
}
}
if (newShape.size() != rank) {
operand = rewriter.create<tosa::ReshapeOp>(
loc, RankedTensorType::get(newShape, type.getElementType()), operand,
rewriter.getI64ArrayAttr(newShape));
}
operands.push_back(operand);
indexingMaps.push_back(AffineMap::get(
/*dimCount=*/rank, /*symbolCount=*/0, affineExprs,
rewriter.getContext()));
}
indexingMaps.append(operation->getNumResults(),
rewriter.getMultiDimIdentityMap(rank));
bool didEncounterError = false;
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc, opResultTypes, operands, emptyTensors, indexingMaps,
getNParallelLoopsAttrs(rank),
[&](OpBuilder &nestedBuilder, Location nestedLoc, ValueRange blockArgs) {
Value opResult = createLinalgBodyCalculationForElementwiseOp(
operation, blockArgs.take_front(operation->getNumOperands()),
bodyResultTypes, rewriter);
if (!opResult) {
didEncounterError = true;
return;
}
nestedBuilder.create<linalg::YieldOp>(loc, opResult);
});
if (didEncounterError)
return failure();
rewriter.replaceOp(operation, linalgOp->getResults());
return success();
}
// Returns the constant initial value for a given reduction operation. The
// attribute type varies depending on the element type required.
static Attribute createInitialValueForReduceOp(Operation *op, Type elementTy,
PatternRewriter &rewriter) {
if (isa<tosa::ReduceSumOp>(op) && elementTy.isa<FloatType>())
return rewriter.getFloatAttr(elementTy, 0.0);
if (isa<tosa::ReduceSumOp>(op) && elementTy.isa<IntegerType>())
return rewriter.getIntegerAttr(elementTy, 0);
if (isa<tosa::ReduceProdOp>(op) && elementTy.isa<FloatType>())
return rewriter.getFloatAttr(elementTy, 1.0);
if (isa<tosa::ReduceProdOp>(op) && elementTy.isa<IntegerType>())
return rewriter.getIntegerAttr(elementTy, 1);
if (isa<tosa::ReduceMinOp>(op) && elementTy.isa<FloatType>())
return rewriter.getFloatAttr(
elementTy, APFloat::getLargest(
elementTy.cast<FloatType>().getFloatSemantics(), false));
if (isa<tosa::ReduceMinOp>(op) && elementTy.isa<IntegerType>())
return rewriter.getIntegerAttr(
elementTy, APInt::getSignedMaxValue(elementTy.getIntOrFloatBitWidth()));
if (isa<tosa::ReduceMaxOp>(op) && elementTy.isa<FloatType>())
return rewriter.getFloatAttr(
elementTy, APFloat::getLargest(
elementTy.cast<FloatType>().getFloatSemantics(), true));
if (isa<tosa::ReduceMaxOp>(op) && elementTy.isa<IntegerType>())
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) && elementTy.isa<FloatType>())
return rewriter.getFloatAttr(
elementTy, APFloat::getLargest(
elementTy.cast<FloatType>().getFloatSemantics(), true));
if (isa<tosa::ArgMaxOp>(op) && elementTy.isa<IntegerType>())
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) && elementTy.isa<FloatType>()) {
return rewriter.create<arith::AddFOp>(loc, args);
}
if (isa<tosa::ReduceSumOp>(op) && elementTy.isa<IntegerType>()) {
return rewriter.create<arith::AddIOp>(loc, args);
}
if (isa<tosa::ReduceProdOp>(op) && elementTy.isa<FloatType>()) {
return rewriter.create<arith::MulFOp>(loc, args);
}
if (isa<tosa::ReduceProdOp>(op) && elementTy.isa<IntegerType>()) {
return rewriter.create<arith::MulIOp>(loc, args);
}
if (isa<tosa::ReduceMinOp>(op) && elementTy.isa<FloatType>()) {
return rewriter.create<arith::MinFOp>(loc, args[0], args[1]);
}
if (isa<tosa::ReduceMinOp>(op) && elementTy.isa<IntegerType>()) {
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) && elementTy.isa<FloatType>()) {
return rewriter.create<arith::MaxFOp>(loc, args[0], args[1]);
}
if (isa<tosa::ReduceMaxOp>(op) && elementTy.isa<IntegerType>()) {
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 = op->getOperand(0).getType().template cast<ShapedType>();
auto resultTy = op->getResult(0).getType().template cast<ShapedType>();
auto elementTy = resultTy.getElementType();
Value input = op->getOperand(0);
llvm::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));
}
}
Type reduceTy = RankedTensorType::get(reduceShape, resultTy.getElementType());
// 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();
SmallVector<AffineExpr, 2> srcExprs;
SmallVector<AffineExpr, 2> dstExprs;
SmallVector<utils::IteratorType, 4> iteratorTypes;
for (unsigned int i = 0, rank = inputTy.getRank(); i != rank; ++i) {
srcExprs.push_back(mlir::getAffineDimExpr(i, rewriter.getContext()));
iteratorTypes.push_back(axis == i ? utils::IteratorType::reduction
: utils::IteratorType::parallel);
if (axis != i)
dstExprs.push_back(mlir::getAffineDimExpr(i, rewriter.getContext()));
}
bool didEncounterError = false;
auto maps = AffineMap::inferFromExprList({srcExprs, dstExprs});
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc, reduceTy, input, filledTensor, maps, iteratorTypes,
[&](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 failure();
SmallVector<ReassociationExprs, 4> reassociationMap;
uint64_t expandInputRank =
linalgOp.getResults()[0].getType().cast<ShapedType>().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));
}
rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
op, resultTy, linalgOp.getResults()[0], reassociationMap);
return success();
}
static bool findIntermediateShape(ArrayRef<int64_t> lhsShape,
ArrayRef<int64_t> rhsShape,
SmallVector<int64_t> &intermediateShape,
bool isDynamic) {
if (isDynamic) {
// TODO (natashaknk): Make dynamic intermediate shape not always be rank-1
intermediateShape = {ShapedType::kDynamic};
return true;
}
if (lhsShape.empty() || rhsShape.empty()) {
intermediateShape = {};
return true;
}
unsigned currLhsDim = 0, currRhsDim = 0;
while (currLhsDim < lhsShape.size() && currRhsDim < rhsShape.size()) {
int64_t rhsSize = rhsShape[currRhsDim];
int64_t lhsSize = lhsShape[currLhsDim];
while (lhsSize != rhsSize && currLhsDim < lhsShape.size() &&
currRhsDim < rhsShape.size()) {
if (lhsSize < rhsSize) {
currLhsDim++;
lhsSize *= lhsShape[currLhsDim];
} else {
currRhsDim++;
rhsSize *= rhsShape[currRhsDim];
}
}
if (lhsSize == rhsSize) {
intermediateShape.push_back(lhsSize);
}
currRhsDim++;
currLhsDim++;
}
// If the iterators didn't reach the end and their leftover dimensions are not
// equal to 1 an intermediate shape was not found.
while (currLhsDim < lhsShape.size()) {
if (lhsShape[currLhsDim++] != 1) {
return false;
}
}
while (currRhsDim < rhsShape.size()) {
if (rhsShape[currRhsDim++] != 1) {
return false;
}
}
return true;
}
static bool createReassociationMapsForCollapse(
PatternRewriter &rewriter, ArrayRef<int64_t> srcShape,
ArrayRef<int64_t> dstShape,
SmallVector<ReassociationExprs, 4> &reassociationMap, bool isDynamic) {
// If the shape is dynamic, create a map for collapsing into one dimension.
if (isDynamic) {
SmallVector<AffineExpr, 2> exprs;
for (int i = 0, s = srcShape.size(); i < s; ++i)
exprs.push_back(rewriter.getAffineDimExpr(i));
reassociationMap = {exprs};
return true;
}
if (dstShape.empty()) {
reassociationMap = {};
return true;
}
reassociationMap.resize(dstShape.size());
unsigned currSrcDim = 0, currDstDim = 0;
while (currSrcDim < srcShape.size() && currDstDim < dstShape.size()) {
int64_t dstSize = dstShape[currDstDim];
int64_t srcSize = srcShape[currSrcDim];
while (srcSize < dstSize && currSrcDim < srcShape.size()) {
reassociationMap[currDstDim].push_back(
rewriter.getAffineDimExpr(currSrcDim++));
srcSize *= srcShape[currSrcDim];
}
if (srcSize == dstSize) {
reassociationMap[currDstDim].push_back(
rewriter.getAffineDimExpr(currSrcDim++));
// If the next dim in collapsedShape is not 1, treat subsequent dims in
// expandedShape which are 1 to be collapsed.
if (currDstDim == dstShape.size() - 1 || dstShape[currDstDim + 1] != 1) {
while (currSrcDim < srcShape.size() && srcShape[currSrcDim] == 1) {
reassociationMap[currDstDim].push_back(
rewriter.getAffineDimExpr(currSrcDim++));
}
}
}
currDstDim++;
}
// If both iterators didn't reach the end, we have leftover dimentions which
// implies that we have a mismatch in shape.
return currSrcDim == srcShape.size() && currDstDim == dstShape.size();
}
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 ReshapeConverterCollapse : public OpConversionPattern<tosa::ReshapeOp> {
public:
using OpConversionPattern<tosa::ReshapeOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(tosa::ReshapeOp reshape, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const final {
ShapedType operandTy = adaptor.getInput1().getType().cast<ShapedType>();
ShapedType resultTy = reshape.getType().template cast<ShapedType>();
bool isDynamic = !operandTy.hasStaticShape();
if (isDynamic && resultTy.getRank() != 1) {
return rewriter.notifyMatchFailure(
reshape, "Cannot collapse dynamic dims to more than one dimension");
}
if (operandTy == resultTy) {
rewriter.replaceOp(reshape, adaptor.getOperands()[0]);
return success();
}
SmallVector<ReassociationExprs, 4> reassociationMap;
if (!createReassociationMapsForCollapse(rewriter, operandTy.getShape(),
resultTy.getShape(),
reassociationMap, isDynamic)) {
return rewriter.notifyMatchFailure(
reshape,
"tosa.reshape Attempting to collapse into an incompatible shape");
}
SmallVector<int64_t> intermediateShape;
if (!findIntermediateShape(operandTy.getShape(), resultTy.getShape(),
intermediateShape, isDynamic)) {
return rewriter.notifyMatchFailure(
reshape, "tosa.reshape Cannot collapse into given shape");
}
rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(
reshape, resultTy, adaptor.getOperands()[0], reassociationMap);
return success();
}
};
class ReshapeConverterExpand : public OpConversionPattern<tosa::ReshapeOp> {
public:
using OpConversionPattern<tosa::ReshapeOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(tosa::ReshapeOp reshape, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const final {
ShapedType operandTy = adaptor.getInput1().getType().cast<ShapedType>();
ShapedType resultTy = reshape.getType().template cast<ShapedType>();
bool isDynamic = !operandTy.hasStaticShape();
if (operandTy == resultTy) {
rewriter.replaceOp(reshape, adaptor.getOperands()[0]);
return success();
}
if (isDynamic && operandTy.getRank() != 1) {
return rewriter.notifyMatchFailure(
reshape, "Cannot expand dynamic dims from more than one dimension");
}
SmallVector<ReassociationExprs, 4> reassociationMap;
if (!createReassociationMapsForCollapse(rewriter, resultTy.getShape(),
operandTy.getShape(),
reassociationMap, isDynamic)) {
return rewriter.notifyMatchFailure(
reshape,
"tosa.reshape Attempting to expand into an incompatible shape");
}
SmallVector<int64_t> intermediateShape;
if (!findIntermediateShape(operandTy.getShape(), resultTy.getShape(),
intermediateShape, isDynamic) ||
intermediateShape != operandTy.getShape()) {
return rewriter.notifyMatchFailure(
reshape, "tosa.reshape Cannot expand into given shape");
}
rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
reshape, resultTy, adaptor.getOperands()[0], reassociationMap);
return success();
}
};
class ReshapeConverterCollapseExpand
: public OpConversionPattern<tosa::ReshapeOp> {
public:
using OpConversionPattern<tosa::ReshapeOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(tosa::ReshapeOp reshape, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const final {
ShapedType operandTy = adaptor.getInput1().getType().cast<ShapedType>();
ShapedType resultTy = reshape.getType().template cast<ShapedType>();
bool isDynamic = !operandTy.hasStaticShape();
if (operandTy == resultTy) {
rewriter.replaceOp(reshape, adaptor.getOperands()[0]);
return success();
}
SmallVector<int64_t> intermediateShape;
if (!findIntermediateShape(resultTy.getShape(), operandTy.getShape(),
intermediateShape, isDynamic)) {
return rewriter.notifyMatchFailure(
reshape, "tosa.reshape Cannot identify an intermediate shape between "
"the given two shapes");
}
Value collapse = rewriter.create<tosa::ReshapeOp>(
reshape.getLoc(),
RankedTensorType::get(intermediateShape,
reshape.getType().getElementType()),
adaptor.getInput1());
Value expand =
rewriter.create<tosa::ReshapeOp>(reshape.getLoc(), resultTy, collapse);
rewriter.replaceOp(reshape, expand);
return success();
}
};
class TransposeConverter : public OpRewritePattern<tosa::TransposeOp> {
public:
using OpRewritePattern<tosa::TransposeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::TransposeOp op,
PatternRewriter &rewriter) const final {
DenseIntElementsAttr perms;
if (!matchPattern(op.getPerms(), m_Constant(&perms))) {
return failure();
}
auto loc = op.getLoc();
auto input = op->getOperand(0);
auto resultTy = op.getType().cast<ShapedType>();
SmallVector<Value> dynDims;
dynDims.resize(op->getResult(0).getType().cast<ShapedType>().getRank());
SmallVector<AffineExpr, 2> inputExprs;
inputExprs.resize(resultTy.getRank());
auto operandTy = input.getType().cast<ShapedType>();
for (const auto &permutation : llvm::enumerate(perms.getValues<APInt>())) {
auto index = permutation.index();
auto value = permutation.value().getZExtValue();
if (!operandTy.hasRank() || operandTy.isDynamicDim(index)) {
dynDims[value] = rewriter.create<tensor::DimOp>(loc, input, index);
}
inputExprs[value] = rewriter.getAffineDimExpr(index);
}
SmallVector<Value> filteredDims = condenseValues(dynDims);
auto emptyTensor = rewriter.create<tensor::EmptyOp>(
loc, resultTy.getShape(), resultTy.getElementType(), filteredDims);
SmallVector<AffineMap, 2> affineMaps = {
AffineMap::get(resultTy.getRank(), /*symbolCount=*/0, inputExprs,
rewriter.getContext()),
rewriter.getMultiDimIdentityMap(resultTy.getRank())};
rewriter.replaceOpWithNewOp<linalg::GenericOp>(
op, resultTy, op.getInput1(), ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(resultTy.getRank()),
[&](OpBuilder &nestedBuilder, Location nestedLoc, ValueRange args) {
nestedBuilder.create<linalg::YieldOp>(loc, *args.begin());
});
return success();
}
};
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 = op.getInput().getType().cast<ShapedType>();
auto outputTy = op.getOutput().getType().cast<ShapedType>();
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;
getValuesFromIntArrayAttribute(op.getMultiplier(), multiplierValues);
SmallVector<int8_t> shiftValues;
getValuesFromIntArrayAttribute(op.getShift(), shiftValues);
// 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 =
blockArgs.back().getType().cast<IntegerType>();
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 case where the resize operation is a regular broadcast. We
// perform this part separately to avoid generating Extract operations which
// are difficult to vectorize / optimize.
class BroadcastResizeConverter : 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 = input.getType().cast<RankedTensorType>();
auto resultTy = op.getType().cast<RankedTensorType>();
auto imageH = inputTy.getDimSize(1);
auto imageW = inputTy.getDimSize(2);
if (imageH != 1 || imageW != 1) {
return rewriter.notifyMatchFailure(
op, "tosa.resize is not a pure broadcast operation");
}
// TODO(suderman): These string values should be declared the TOSA dialect.
if (op.getMode() != "NEAREST_NEIGHBOR" && op.getMode() != "BILINEAR")
return failure();
const bool isBilinear = op.getMode() == "BILINEAR";
SmallVector<int32_t> scale;
getValuesFromIntArrayAttribute(op.getScale(), scale);
// Collapse the 1 dimensions away.
SmallVector<ReassociationExprs, 4> collapseMap(2);
collapseMap[0].push_back(builder.getAffineDimExpr(0));
collapseMap[1].push_back(builder.getAffineDimExpr(1));
collapseMap[1].push_back(builder.getAffineDimExpr(2));
collapseMap[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, collapseMap);
// Broadcast input to the output shape.
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));
llvm::SmallVector<AffineExpr> inputExprs{
rewriter.getAffineDimExpr(0),
rewriter.getAffineDimExpr(3),
};
auto inputMap = AffineMap::get(/*dimCount=*/4, /*symbolCount=*/0,
inputExprs, builder.getContext());
auto resultMap = rewriter.getMultiDimIdentityMap(resultTy.getRank());
SmallVector<utils::IteratorType> iterators(4,
utils::IteratorType::parallel);
Value empty = builder.create<tensor::EmptyOp>(
resultTy.getShape(), resultTy.getElementType(), outputDynSize);
auto generic = builder.create<linalg::GenericOp>(
resultTy, ValueRange{collapse}, ValueRange{empty},
ArrayRef<AffineMap>{inputMap, resultMap}, 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.replaceOp(op, generic.getResult(0));
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();
auto input = op.getInput();
auto inputTy = input.getType().cast<ShapedType>();
auto resultTy = op.getType().cast<ShapedType>();
auto resultElementTy = resultTy.getElementType();
auto imageH = inputTy.getShape()[1];
auto imageW = inputTy.getShape()[2];
auto dynamicDimsOr =
checkHasDynamicBatchDims(rewriter, op, {input, op.getOutput()});
if (!dynamicDimsOr.has_value())
return failure();
SmallVector<Value> dynamicDims = dynamicDimsOr.value();
if (op.getMode() != "NEAREST_NEIGHBOR" && op.getMode() != "BILINEAR")
return failure();
auto emptyTensor = rewriter.create<tensor::EmptyOp>(
loc, resultTy.getShape(), resultElementTy, dynamicDims);
SmallVector<AffineMap, 2> affineMaps = {
rewriter.getMultiDimIdentityMap(resultTy.getRank())};
Value resize = input;
auto genericOp = rewriter.create<linalg::GenericOp>(
loc, resultTy, ValueRange({}), ValueRange{emptyTensor}, affineMaps,
getNParallelLoopsAttrs(resultTy.getRank()));
resize = genericOp.getResult(0);
OpBuilder::InsertionGuard regionGuard(rewriter);
rewriter.createBlock(&genericOp.getRegion(), genericOp.getRegion().end(),
TypeRange({resultElementTy}), loc);
Value batch = rewriter.create<linalg::IndexOp>(loc, 0);
Value y = rewriter.create<linalg::IndexOp>(loc, 1);
Value x = rewriter.create<linalg::IndexOp>(loc, 2);
Value channel = rewriter.create<linalg::IndexOp>(loc, 3);
auto hwMin =
rewriter.create<arith::ConstantOp>(loc, rewriter.getI32IntegerAttr(0));
auto hMax = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(imageH - 1));
auto wMax = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(imageW - 1));
Value inY =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getI32Type(), y);
Value inX =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getI32Type(), x);
bool floatingPointMode = resultElementTy.isF32();
Value yScaleN, yScaleD, xScaleN, xScaleD, yOffset, xOffset, yBorder,
xBorder;
SmallVector<int32_t> scale, offset, border;
getValuesFromIntArrayAttribute(op.getScale(), scale);
getValuesFromIntArrayAttribute(op.getOffset(), offset);
getValuesFromIntArrayAttribute(op.getBorder(), border);
yScaleN = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(scale[0]));
yScaleD = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(scale[1]));
xScaleN = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(scale[2]));
xScaleD = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(scale[3]));
yOffset = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(offset[0]));
xOffset = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(offset[1]));
yBorder = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(border[0]));
xBorder = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(border[1]));
// Compute the the integer index and partial offset.
Value ix, iy, dx, dy;
// x = x * scale_d + offset;
// ix = floor(x / scale_n)
if (floatingPointMode) {
// dx = x / scale_n - ix
Value y =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), inY);
Value x =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), inX);
yScaleN =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), yScaleN);
yScaleD =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), yScaleD);
xScaleN =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), xScaleN);
xScaleD =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), xScaleD);
yOffset =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), yOffset);
xOffset =
rewriter.create<arith::UIToFPOp>(loc, rewriter.getF32Type(), xOffset);
y = rewriter.create<arith::MulFOp>(loc, y, yScaleD);
x = rewriter.create<arith::MulFOp>(loc, x, xScaleD);
y = rewriter.create<arith::AddFOp>(loc, y, yOffset);
x = rewriter.create<arith::AddFOp>(loc, x, xOffset);
y = rewriter.create<arith::DivFOp>(loc, y, yScaleN);
x = rewriter.create<arith::DivFOp>(loc, x, xScaleN);
iy = rewriter.create<math::FloorOp>(loc, y);
ix = rewriter.create<math::FloorOp>(loc, x);
dy = rewriter.create<arith::SubFOp>(loc, y, iy);
dx = rewriter.create<arith::SubFOp>(loc, x, ix);
iy = rewriter.create<arith::FPToSIOp>(loc, rewriter.getI32Type(), iy);
ix = rewriter.create<arith::FPToSIOp>(loc, rewriter.getI32Type(), ix);
} else {
// dx = x - ix * scale_n;
Value y = rewriter.create<arith::MulIOp>(loc, inY, yScaleD);
Value x = rewriter.create<arith::MulIOp>(loc, inX, xScaleD);
y = rewriter.create<arith::AddIOp>(loc, y, yOffset);
x = rewriter.create<arith::AddIOp>(loc, x, xOffset);
iy = rewriter.create<arith::DivUIOp>(loc, y, yScaleN);
ix = rewriter.create<arith::DivUIOp>(loc, x, xScaleN);
Value tempY = rewriter.create<arith::MulIOp>(loc, iy, yScaleN);
Value tempX = rewriter.create<arith::MulIOp>(loc, ix, xScaleN);
dy = rewriter.create<arith::SubIOp>(loc, y, tempY);
dx = rewriter.create<arith::SubIOp>(loc, x, tempX);
}
if (op.getMode() == "NEAREST_NEIGHBOR") {
Value yPred, xPred;
auto zeroVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(0));
auto oneVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(1));
// Round the index position towards the closest pixel location.
if (floatingPointMode) {
auto halfVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getF32FloatAttr(0.5f));
yPred = rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::OGE,
dy, halfVal);
xPred = rewriter.create<arith::CmpFOp>(loc, arith::CmpFPredicate::OGE,
dx, halfVal);
} else {
Value yScaleNHalfVal =
rewriter.create<arith::ShRSIOp>(loc, yScaleN, oneVal);
Value xScaleNHalfVal =
rewriter.create<arith::ShRSIOp>(loc, xScaleN, oneVal);
yPred = rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge,
dy, yScaleNHalfVal);
xPred = rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge,
dx, xScaleNHalfVal);
}
auto yOffset =
rewriter.create<arith::SelectOp>(loc, yPred, oneVal, zeroVal);
auto xOffset =
rewriter.create<arith::SelectOp>(loc, xPred, oneVal, zeroVal);
iy = rewriter.create<arith::AddIOp>(loc, iy, yOffset);
ix = rewriter.create<arith::AddIOp>(loc, ix, xOffset);
// Clamp the to be within the bounds of the input image.
iy = clampIntHelper(loc, iy, hwMin, hMax, rewriter);
ix = clampIntHelper(loc, ix, hwMin, wMax, rewriter);
// Read the value from the input array.
iy =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(), iy);
ix =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(), ix);
Value result = rewriter.create<tensor::ExtractOp>(
loc, input, ValueRange{batch, iy, ix, channel});
rewriter.create<linalg::YieldOp>(loc, result);
} else {
// The mode here must be BILINEAR.
assert(op.getMode() == "BILINEAR");
Value y0 = iy;
Value x0 = ix;
auto oneVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getI32IntegerAttr(1));
Value y1 = rewriter.create<arith::AddIOp>(loc, y0, oneVal);
Value x1 = rewriter.create<arith::AddIOp>(loc, x0, oneVal);
y0 = clampIntHelper(loc, y0, hwMin, hMax, rewriter);
y1 = clampIntHelper(loc, y1, hwMin, hMax, rewriter);
x0 = clampIntHelper(loc, x0, hwMin, wMax, rewriter);
x1 = clampIntHelper(loc, x1, hwMin, wMax, rewriter);
y0 =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(), y0);
y1 =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(), y1);
x0 =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(), x0);
x1 =
rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(), x1);
Value y0x0 = rewriter.create<tensor::ExtractOp>(
loc, input, ValueRange{batch, y0, x0, channel});
Value y0x1 = rewriter.create<tensor::ExtractOp>(
loc, input, ValueRange{batch, y0, x1, channel});
Value y1x0 = rewriter.create<tensor::ExtractOp>(
loc, input, ValueRange{batch, y1, x0, channel});
Value y1x1 = rewriter.create<tensor::ExtractOp>(
loc, input, ValueRange{batch, y1, x1, channel});
if (floatingPointMode) {
Value rightPart = dx;
auto oneVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getF32FloatAttr(1.0f));
Value leftPart = rewriter.create<arith::SubFOp>(loc, oneVal, dx);
y0x0 = rewriter.create<arith::MulFOp>(loc, y0x0, leftPart);
y0x1 = rewriter.create<arith::MulFOp>(loc, y0x1, rightPart);
Value topAcc = rewriter.create<arith::AddFOp>(loc, y0x0, y0x1);
y1x0 = rewriter.create<arith::MulFOp>(loc, y1x0, leftPart);
y1x1 = rewriter.create<arith::MulFOp>(loc, y1x1, rightPart);
Value bottomAcc = rewriter.create<arith::AddFOp>(loc, y1x0, y1x1);
Value bottomPart = dy;
Value topPart = rewriter.create<arith::SubFOp>(loc, oneVal, dy);
topAcc = rewriter.create<arith::MulFOp>(loc, topAcc, topPart);
bottomAcc = rewriter.create<arith::MulFOp>(loc, bottomAcc, bottomPart);
Value result = rewriter.create<arith::AddFOp>(loc, topAcc, bottomAcc);
rewriter.create<linalg::YieldOp>(loc, result);
} else {
// Perform in quantized space.
y0x0 = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, y0x0);
y0x1 = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, y0x1);
y1x0 = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, y1x0);
y1x1 = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, y1x1);
if (resultElementTy.getIntOrFloatBitWidth() > 32) {
dx = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, dx);
dy = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, dy);
}
Value topAcc, bottomAcc;
if (imageW == 1) {
topAcc = rewriter.create<arith::MulIOp>(loc, y0x0, xScaleN);
bottomAcc = rewriter.create<arith::MulIOp>(loc, y1x0, xScaleN);
} else {
Value rightPart = dx;
Value leftPart = rewriter.create<arith::SubIOp>(loc, xScaleN, dx);
y0x0 = rewriter.create<arith::MulIOp>(loc, y0x0, leftPart);
y0x1 = rewriter.create<arith::MulIOp>(loc, y0x1, rightPart);
topAcc = rewriter.create<arith::AddIOp>(loc, y0x0, y0x1);
y1x0 = rewriter.create<arith::MulIOp>(loc, y1x0, leftPart);
y1x1 = rewriter.create<arith::MulIOp>(loc, y1x1, rightPart);
bottomAcc = rewriter.create<arith::AddIOp>(loc, y1x0, y1x1);
}
Value result;
if (imageH == 1) {
result = rewriter.create<arith::MulIOp>(loc, topAcc, yScaleN);
} else {
Value bottomPart = dy;
Value topPart = rewriter.create<arith::SubIOp>(loc, yScaleN, dy);
topAcc = rewriter.create<arith::MulIOp>(loc, topAcc, topPart);
bottomAcc =
rewriter.create<arith::MulIOp>(loc, bottomAcc, bottomPart);
result = rewriter.create<arith::AddIOp>(loc, topAcc, bottomAcc);
}
rewriter.create<linalg::YieldOp>(loc, 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);
}
};
struct ConcatConverter : public OpConversionPattern<tosa::ConcatOp> {
using OpConversionPattern<tosa::ConcatOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(tosa::ConcatOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto inputType = op.getOperand(0).getType().template cast<ShapedType>();
auto resultType = op.getType().dyn_cast<RankedTensorType>();
Location loc = op.getLoc();
int axis = op.getAxis();
Value axisValue = rewriter.createOrFold<arith::ConstantOp>(
loc, rewriter.getIndexAttr(axis));
int rank = resultType.getRank();
SmallVector<Value, 3> offsets, sizes, strides;
sizes.reserve(rank);
strides.resize(rank, rewriter.create<arith::ConstantIndexOp>(loc, 1));
offsets.resize(rank, rewriter.create<arith::ConstantIndexOp>(loc, 0));
SmallVector<Value> dynDims;
for (int i = 0; i < rank; ++i) {
sizes.push_back(rewriter.createOrFold<tensor::DimOp>(
loc, adaptor.getOperands()[0], i));
if (inputType.isDynamicDim(i)) {
dynDims.push_back(
rewriter.create<tensor::DimOp>(loc, op.getOperand(0), i));
}
}
Value resultDimSize = sizes[axis];
for (auto arg : adaptor.getOperands().drop_front()) {
auto size = rewriter.createOrFold<tensor::DimOp>(loc, arg, axisValue);
resultDimSize =
rewriter.createOrFold<arith::AddIOp>(loc, resultDimSize, size);
}
sizes[axis] = resultDimSize;
Value emptyTensor = rewriter.create<tensor::EmptyOp>(
loc, resultType.getShape(), resultType.getElementType(), dynDims);
auto toOpFoldResult = [](Value v) -> OpFoldResult {
auto op = v.getDefiningOp<arith::ConstantIndexOp>();
if (!op)
return v;
return op.getValue();
};
Value result = emptyTensor;
for (auto arg : adaptor.getOperands()) {
sizes[axis] = rewriter.createOrFold<tensor::DimOp>(loc, arg, axisValue);
result = rewriter.createOrFold<tensor::InsertSliceOp>(
loc, arg, result,
llvm::to_vector(llvm::map_range(offsets, toOpFoldResult)),
llvm::to_vector(llvm::map_range(sizes, toOpFoldResult)),
llvm::to_vector(llvm::map_range(strides, toOpFoldResult)));
offsets[axis] =
rewriter.createOrFold<arith::AddIOp>(loc, offsets[axis], sizes[axis]);
}
rewriter.replaceOp(op, result);
return success();
}
};
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 = input.getType().template cast<ShapedType>();
auto resultTy = op.getType().template cast<ShapedType>();
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++) {
auto 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 = input.getType().cast<ShapedType>();
auto inputShape = inputTy.getShape();
auto resultTy = op.getType().cast<ShapedType>();
auto elementTy = inputTy.getElementType();
int64_t rank = inputTy.getRank();
SmallVector<int64_t> multiples;
getValuesFromIntArrayAttribute(op.getMultiples(), multiples);
// 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.getI64ArrayAttr(resultTy.getShape()));
return success();
}
};
class PadConverter : public OpRewritePattern<tosa::PadOp> {
public:
using OpRewritePattern<tosa::PadOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tosa::PadOp padOp,
PatternRewriter &rewriter) const final {
auto loc = padOp.getLoc();
auto input = padOp.getInput1();
auto padding = padOp.getPadding();
ShapedType inputTy = input.getType().cast<ShapedType>();
Type elementTy = inputTy.getElementType();
int64_t rank = inputTy.getRank();
// Setup the default constantAttr.
Value padConstant;
if (padOp.getPadConst()) {
padConstant = rewriter.createOrFold<tensor::ExtractOp>(
loc, padOp.getPadConst(), ValueRange({}));
} else {
Attribute constantAttr;
if (elementTy.isa<FloatType>()) {
constantAttr = rewriter.getFloatAttr(elementTy, 0.0);
} else if (elementTy.isa<IntegerType>() && !padOp.getQuantizationInfo()) {
constantAttr = rewriter.getIntegerAttr(elementTy, 0);
} else if (elementTy.isa<IntegerType>() && padOp.getQuantizationInfo()) {
int64_t value = padOp.getQuantizationInfo()->getInputZp();
constantAttr = rewriter.getIntegerAttr(elementTy, value);
}
if (constantAttr)
padConstant = rewriter.create<arith::ConstantOp>(loc, constantAttr);
}
if (!padConstant) {
return rewriter.notifyMatchFailure(
padOp, "tosa.pad was unable to determine the pad constant value.");
}
Value lowIndex =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(0));
Value highIndex =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(1));
SmallVector<OpFoldResult, 3> lowValues;
SmallVector<OpFoldResult, 3> highValues;
lowValues.reserve(rank);
highValues.reserve(rank);
for (int i = 0; i < rank; i++) {
Value inputIndex = rewriter.createOrFold<arith::ConstantIndexOp>(loc, i);
Value lowVal = rewriter.createOrFold<tensor::ExtractOp>(
loc, padding, ValueRange({inputIndex, lowIndex}));
Value highVal = rewriter.createOrFold<tensor::ExtractOp>(
loc, padding, ValueRange({inputIndex, highIndex}));
lowVal = rewriter.createOrFold<arith::IndexCastOp>(
loc, rewriter.getIndexType(), lowVal);
highVal = rewriter.createOrFold<arith::IndexCastOp>(
loc, rewriter.getIndexType(), highVal);
lowValues.push_back(lowVal);
highValues.push_back(highVal);
}
auto newPadOp = rewriter.create<tensor::PadOp>(
loc, padOp.getType(), input, lowValues, highValues, padConstant);
rewriter.replaceOp(padOp, newPadOp.getResult());
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 = input.getType().cast<ShapedType>();
auto resultTy = argmaxOp.getOutput().getType().cast<ShapedType>();
auto inElementTy = inputTy.getElementType();
auto outElementTy = resultTy.getElementType();
int axis = argmaxOp.getAxis();
auto resultMaxTy = RankedTensorType::get(resultTy.getShape(), inElementTy);
if (!outElementTy.isa<IntegerType>())
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 (inElementTy.isa<FloatType>()) {
predicate = rewriter.create<arith::CmpFOp>(
nestedLoc, arith::CmpFPredicate::OGT, newValue, oldValue);
} else if (inElementTy.isa<IntegerType>()) {
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 resultTy = op.getType().cast<ShapedType>();
auto dynamicDimsOr = checkHasDynamicBatchDims(
rewriter, op, {input, indices, op.getOutput()});
if (!dynamicDimsOr.has_value())
return failure();
SmallVector<Value> dynamicDims = dynamicDimsOr.value();
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();
}
};
// 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 = input.getType().cast<ShapedType>();
auto tableTy = table.getType().cast<ShapedType>();
auto resultTy = op.getType().cast<ShapedType>();
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");
}
};
} // 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<BroadcastResizeConverter>(patterns->getContext(),
/*benefit=*/200);
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::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,
ConcatConverter,
GatherConverter,
PadConverter,
ReshapeConverterCollapse,
ReshapeConverterExpand,
ReshapeConverterCollapseExpand,
RescaleConverter,
ReverseConverter,
TableConverter,
TileConverter,
TransposeConverter>(patterns->getContext());
// clang-format on
}
Reference in New Issue View Git Blame Copy Permalink