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torch-mlir/lib/Conversion/TorchToLinalg/Uncategorized.cpp

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//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"
#include "PopulatePatterns.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/IR/Matchers.h"
#include "torch-mlir/Conversion/TorchToLinalg/Utils.h"
#include "torch-mlir/Conversion/Utils/Utils.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include "llvm/ADT/APSInt.h"
#include <numeric>
#include <type_traits>
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
// Check if a ranked-tensor has the specified element type.
template <typename elementType> static bool hasElementType(Value tensor) {
auto tensorType = cast<RankedTensorType>(tensor.getType());
Type tensorElementType = tensorType.getElementType();
return isa<elementType>(tensorElementType);
}
template <arith::CmpFPredicate fpred, arith::CmpIPredicate iupred,
arith::CmpIPredicate ispred>
static Value createComparisonTemplate(OpBuilder &b, Location loc, Type type,
Value lhs, Value rhs) {
if (isa<mlir::FloatType>(type))
return b.create<arith::CmpFOp>(loc, fpred, lhs, rhs);
if (IntegerType intType = dyn_cast<mlir::IntegerType>(type)) {
if (intType.isUnsigned())
return b.create<arith::CmpIOp>(loc, iupred, lhs, rhs);
if (intType.isSigned())
return b.create<arith::CmpIOp>(loc, ispred, lhs, rhs);
assert(intType.getWidth() == 1);
return b.create<arith::CmpIOp>(loc, iupred, lhs, rhs);
}
llvm_unreachable("Unhandled element type for comparison");
}
static Value getZeroPoint(Value value) {
if (auto make = value.getDefiningOp<Aten_MakePerTensorQuantizedTensorOp>()) {
return make.getZeroPoint();
}
return nullptr;
}
static Value createGreaterThan(OpBuilder &b, Location loc, Type elementalType,
Value lhs, Value rhs) {
return createComparisonTemplate<arith::CmpFPredicate::OGT,
arith::CmpIPredicate::ugt,
arith::CmpIPredicate::sgt>(
b, loc, elementalType, lhs, rhs);
}
static Value createGreaterThanOrEqual(OpBuilder &b, Location loc,
Type elementalType, Value lhs,
Value rhs) {
return createComparisonTemplate<arith::CmpFPredicate::OGE,
arith::CmpIPredicate::uge,
arith::CmpIPredicate::sge>(
b, loc, elementalType, lhs, rhs);
}
static Value createLessThan(OpBuilder &b, Location loc, Type elementalType,
Value lhs, Value rhs) {
return createComparisonTemplate<arith::CmpFPredicate::OLT,
arith::CmpIPredicate::ult,
arith::CmpIPredicate::slt>(
b, loc, elementalType, lhs, rhs);
}
static Value createLessThanOrEqual(OpBuilder &b, Location loc,
Type elementalType, Value lhs, Value rhs) {
return createComparisonTemplate<arith::CmpFPredicate::OLE,
arith::CmpIPredicate::ule,
arith::CmpIPredicate::sle>(
b, loc, elementalType, lhs, rhs);
}
static Value createEqual(OpBuilder &b, Location loc, Type elementalType,
Value lhs, Value rhs) {
return createComparisonTemplate<arith::CmpFPredicate::OEQ,
arith::CmpIPredicate::eq,
arith::CmpIPredicate::eq>(
b, loc, elementalType, lhs, rhs);
}
static Value createNotEqual(OpBuilder &b, Location loc, Type elementalType,
Value lhs, Value rhs) {
return createComparisonTemplate<arith::CmpFPredicate::UNE,
arith::CmpIPredicate::ne,
arith::CmpIPredicate::ne>(
b, loc, elementalType, lhs, rhs);
}
static Value buildNormalCdf(OpBuilder &b, Location &loc, Value x, Value mean,
Value sigma) {
Type elementType = x.getType();
Value xMinusMean = b.create<arith::SubFOp>(loc, x, mean);
Value two = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 2));
Value sqrt2 = b.create<math::SqrtOp>(loc, two);
Value erfArg = b.create<arith::DivFOp>(loc, xMinusMean, sqrt2);
Value erf = b.create<math::ErfOp>(loc, erfArg);
Value one = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1));
Value erfPlus1 = b.create<arith::AddFOp>(loc, one, erf);
Value oneHalf =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0.5));
Value normalCdf = b.create<arith::MulFOp>(loc, oneHalf, erfPlus1);
return normalCdf;
}
static Value buildUnitNormalCdf(OpBuilder &b, Location &loc, Value x) {
Type elementType = x.getType();
Value zero = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0));
Value one = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1));
return buildNormalCdf(b, loc, x, zero, one);
}
template <typename MathOpTy>
static Value createFpOpWithDtype(OpBuilder &b, const TypeConverter *converter,
Value payloadArg, Operation *op) {
Type inTTy = cast<ValueTensorType>(op->getOperand(0).getType()).getDtype();
Type outTTy = cast<ValueTensorType>(op->getResult(0).getType()).getDtype();
Type outTy =
cast<RankedTensorType>(converter->convertType(op->getResult(0).getType()))
.getElementType();
Type computeTy = outTy;
if (isa<IntegerType>(computeTy))
computeTy = b.getF32Type();
Location loc = op->getLoc();
Value arg = convertScalarToDtype(b, loc, payloadArg, computeTy, inTTy);
auto newOp = b.create<MathOpTy>(loc, arg);
return convertScalarToDtype(b, loc, newOp, outTy, std::nullopt, outTTy);
}
template <class T, class... Ts>
struct is_any_same : std::disjunction<std::is_same<T, Ts>...> {};
template <typename OpTy>
static Value createCompareOp(OpBuilder &b, Location loc, OpTy op, Value lhs,
Value rhs) {
static_assert(
is_any_same<OpTy, AtenLtScalarOp, AtenLeScalarOp, AtenEqScalarOp,
AtenNeScalarOp, AtenGtScalarOp, AtenGeScalarOp,
AtenLtTensorOp, AtenLeTensorOp, AtenGtTensorOp,
AtenGeTensorOp, AtenEqTensorOp, AtenNeTensorOp>(),
"unimplemented: op type not supported");
Type lhsDtype = lhs.getType();
Type rhsDtype = rhs.getType();
Type elementalType = cast<BaseTensorType>(op.getSelf().getType()).getDtype();
if (lhsDtype.isIntOrFloat() && rhsDtype.isIntOrFloat()) {
if (isa<mlir::FloatType>(lhsDtype) && isa<mlir::IntegerType>(rhsDtype)) {
rhs = convertScalarToDtype(b, loc, rhs, lhsDtype);
elementalType = lhsDtype;
} else if (isa<mlir::IntegerType>(lhsDtype) &&
isa<mlir::FloatType>(rhsDtype)) {
lhs = convertScalarToDtype(b, loc, lhs, rhsDtype);
elementalType = rhsDtype;
} else {
// Both are either Integer or Float types, but the bit width might be
// different.
if (lhsDtype.getIntOrFloatBitWidth() > rhsDtype.getIntOrFloatBitWidth()) {
rhs = convertScalarToDtype(b, loc, rhs, lhsDtype);
} else {
lhs = convertScalarToDtype(b, loc, lhs, rhsDtype);
}
}
} else {
op.emitError("unimplemented: type promotion from tensor to scalar.");
return nullptr;
}
if constexpr (is_any_same<OpTy, AtenLtScalarOp, AtenLtTensorOp>()) {
return createLessThan(b, loc, elementalType, lhs, rhs);
}
if constexpr (is_any_same<OpTy, AtenLeScalarOp, AtenLeTensorOp>()) {
return createLessThanOrEqual(b, loc, elementalType, lhs, rhs);
}
if constexpr (is_any_same<OpTy, AtenGtScalarOp, AtenGtTensorOp>()) {
return createGreaterThan(b, loc, elementalType, lhs, rhs);
}
if constexpr (is_any_same<OpTy, AtenGeScalarOp, AtenGeTensorOp>()) {
return createGreaterThanOrEqual(b, loc, elementalType, lhs, rhs);
}
if constexpr (is_any_same<OpTy, AtenEqScalarOp, AtenEqTensorOp>()) {
return createEqual(b, loc, elementalType, lhs, rhs);
}
if constexpr (is_any_same<OpTy, AtenNeScalarOp, AtenNeTensorOp>()) {
return createNotEqual(b, loc, elementalType, lhs, rhs);
}
llvm_unreachable("unimplemented: op type not supported");
}
template <arith::CmpIPredicate predicate>
static LogicalResult
createTriangularMatrix(OpBuilder &b, Location loc, ValueRange payloadArgs,
Operation *op, ArrayRef<Value> operands, Value &result) {
auto inputType = cast<RankedTensorType>(operands[0].getType());
uint64_t inputRank = inputType.getRank();
// Use the indices of the two innermost dimensions.
auto rowIndex = b.create<linalg::IndexOp>(loc, inputRank - 2);
Value rowIndexI64 = castIndexToInt64(b, loc, rowIndex);
auto colIndex = b.create<linalg::IndexOp>(loc, inputRank - 1);
Value colIndexI64 = castIndexToInt64(b, loc, colIndex);
// columnIndex >= rowIndex + diagonal?
auto sum =
b.create<arith::AddIOp>(loc, rowIndexI64, /*diagonal=*/operands[1]);
auto pred = b.create<arith::CmpIOp>(loc, predicate, colIndexI64, sum);
Value scalar = payloadArgs[0];
Type elementType = inputType.getElementType();
Value zero = getConstant(b, loc, 0, elementType);
result = b.create<arith::SelectOp>(loc, pred, scalar, zero);
return success();
}
template <typename OpT>
Value createDivModePayload(OpBuilder &b, Location loc,
const TypeConverter *converter,
ValueRange payloadArgs, OpT op,
ArrayRef<Value> operands) {
static_assert(std::is_same_v<OpT, AtenDivTensorModeOp> ||
std::is_same_v<OpT, AtenDivScalarModeOp>,
"template type must be a tensor/scalar div mode");
typename OpT::Adaptor adaptor(operands);
Type dtype = cast<RankedTensorType>(converter->convertType(op.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(
b, loc,
std::is_same_v<OpT, AtenDivScalarModeOp> ? operands[1] : payloadArgs[1],
dtype);
Value quotient;
if (isa<mlir::FloatType>(dtype)) {
quotient = b.create<arith::DivFOp>(loc, lhs, rhs);
} else if (dtype.isUnsignedInteger()) {
quotient = b.create<arith::DivUIOp>(loc, lhs, rhs);
} else {
assert(dtype.isInteger() &&
"dtype should be an integer (signless or signed)");
quotient = b.create<arith::DivSIOp>(loc, lhs, rhs);
}
if (isa<Torch::NoneType>(op.getRoundingMode().getType()))
return quotient;
std::string roundingMode;
if (!matchPattern(op.getRoundingMode(), m_TorchConstantStr(roundingMode))) {
op.emitError("only support constant str rounding mode");
return nullptr;
}
assert((roundingMode == "trunc" || roundingMode == "floor") &&
"unsupported rounding mode");
if (roundingMode == "trunc") {
// "trunc" - rounds the results of the division towards zero. Equivalent
// to C-style integer division.
if (!isa<mlir::FloatType>(dtype)) {
// nothing to do for integers
return quotient;
}
// float
Value ceil = b.create<math::CeilOp>(loc, quotient);
Value floor = b.create<math::FloorOp>(loc, quotient);
Value cstZero = b.create<arith::ConstantOp>(loc, b.getZeroAttr(dtype));
Value pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT,
quotient, cstZero);
return b.create<arith::SelectOp>(loc, pred, ceil, floor);
}
if (roundingMode == "floor") {
// "floor" - rounds the results of the division down. Equivalent to
// floor division in Python (the // operator)
if (isa<mlir::FloatType>(dtype))
return b.create<math::FloorOp>(loc, quotient);
if (!dtype.isUnsignedInteger()) {
Type defaultIntToFloatType = b.getF64Type();
lhs = convertScalarToDtype(b, loc, lhs, defaultIntToFloatType);
rhs = convertScalarToDtype(b, loc, rhs, defaultIntToFloatType);
quotient = b.create<arith::DivFOp>(loc, lhs, rhs);
Value floor = b.create<math::FloorOp>(loc, quotient);
Value convert = convertScalarToDtype(b, loc, floor, dtype);
return convert;
}
}
return quotient;
}
static Value createLinalgPayloadCalculationForElementwiseOp(
OpBuilder &b, Location loc, const TypeConverter *converter,
ValueRange payloadArgs, Operation *op, ArrayRef<Value> operands) {
if (isa<AtenFloorOp>(op))
return b.create<math::FloorOp>(loc, payloadArgs[0]);
if (isa<AtenCeilOp>(op))
return b.create<math::CeilOp>(loc, payloadArgs[0]);
if (isa<AtenExpOp>(op)) {
return createFpOpWithDtype<math::ExpOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenExpm1Op>(op)) {
return createFpOpWithDtype<math::ExpM1Op>(b, converter, payloadArgs[0], op);
}
if (isa<AtenLogOp>(op)) {
return createFpOpWithDtype<math::LogOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenLog2Op>(op)) {
return createFpOpWithDtype<math::Log2Op>(b, converter, payloadArgs[0], op);
}
if (isa<AtenLog10Op>(op)) {
return createFpOpWithDtype<math::Log10Op>(b, converter, payloadArgs[0], op);
}
if (isa<AtenLog1pOp>(op)) {
return createFpOpWithDtype<math::Log1pOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenErfOp>(op)) {
return createFpOpWithDtype<math::ErfOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenSqrtOp>(op)) {
return createFpOpWithDtype<math::SqrtOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenRsqrtOp>(op)) {
return createFpOpWithDtype<math::RsqrtOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenNegOp>(op)) {
return createFpOpWithDtype<arith::NegFOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenSinOp>(op)) {
return createFpOpWithDtype<math::SinOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenSinhOp>(op)) {
return createFpOpWithDtype<math::SinhOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenAsinOp>(op)) {
return createFpOpWithDtype<math::AsinOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenAsinhOp>(op)) {
return createFpOpWithDtype<math::AsinhOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenCosOp>(op)) {
return createFpOpWithDtype<math::CosOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenCoshOp>(op)) {
return createFpOpWithDtype<math::CoshOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenAcosOp>(op)) {
return createFpOpWithDtype<math::AcosOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenAcoshOp>(op)) {
return createFpOpWithDtype<math::AcoshOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenTanOp>(op)) {
return createFpOpWithDtype<math::TanOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenTanhOp>(op)) {
return createFpOpWithDtype<math::TanhOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenAtanOp>(op)) {
return createFpOpWithDtype<math::AtanOp>(b, converter, payloadArgs[0], op);
}
if (isa<AtenAtanhOp>(op)) {
return createFpOpWithDtype<math::AtanhOp>(b, converter, payloadArgs[0], op);
}
if (auto clone = dyn_cast<AtenCloneOp>(op)) {
int64_t memoryFormat;
if (!isa<Torch::NoneType>(clone.getMemoryFormat().getType()) &&
(!matchPattern(clone.getMemoryFormat(),
m_TorchConstantInt(&memoryFormat)) ||
(memoryFormat != torch_upstream::MemoryFormat::Contiguous &&
memoryFormat != torch_upstream::MemoryFormat::ChannelsLast))) {
clone.emitError("unimplemented: only contiguous and channels last memory "
"format is supported");
return nullptr;
}
return payloadArgs[0];
}
if (auto bitwiseAndTensor = dyn_cast<AtenBitwiseAndTensorOp>(op)) {
if (isa<mlir::FloatType>(
cast<ValueTensorType>(bitwiseAndTensor.getType()).getDtype())) {
bitwiseAndTensor.emitError(
"Bitwise_And does not support floating point dtype");
return nullptr;
}
Type dtype = cast<RankedTensorType>(
converter->convertType(bitwiseAndTensor.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<arith::AndIOp>(loc, lhs, rhs);
}
if (auto bitwiseAndScalar = dyn_cast<AtenBitwiseAndScalarOp>(op)) {
Type dtype = cast<RankedTensorType>(
converter->convertType(bitwiseAndScalar.getType()))
.getElementType();
if (!isa<mlir::IntegerType>(dtype)) {
bitwiseAndScalar.emitError(
"bitwise_and.Scalar does not support non-integer input dtype.");
return nullptr;
}
Type resultElementType =
cast<BaseTensorType>(bitwiseAndScalar.getType()).getDtype();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value other = convertScalarToDtype(b, loc, operands[1], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
return b.create<arith::AndIOp>(loc, self, other);
}
if (auto bitwiseOrTensor = dyn_cast<AtenBitwiseOrTensorOp>(op)) {
if (isa<mlir::FloatType>(
cast<ValueTensorType>(bitwiseOrTensor.getType()).getDtype())) {
bitwiseOrTensor.emitError(
"Bitwise_Or does not support floating point dtype");
return nullptr;
}
Type dtype = cast<RankedTensorType>(
converter->convertType(bitwiseOrTensor.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<arith::OrIOp>(loc, lhs, rhs);
}
if (auto bitwiseXorTensor = dyn_cast<AtenBitwiseXorTensorOp>(op)) {
if (isa<mlir::FloatType>(
cast<ValueTensorType>(bitwiseXorTensor.getType()).getDtype())) {
bitwiseXorTensor.emitError(
"Bitwise_Xor does not support floating point dtype");
return nullptr;
}
Type dtype = cast<RankedTensorType>(
converter->convertType(bitwiseXorTensor.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<arith::XOrIOp>(loc, lhs, rhs);
}
if (auto bitwiseRightShiftTensor =
dyn_cast<AtenBitwiseRightShiftTensorOp>(op)) {
Type dtype = cast<RankedTensorType>(
converter->convertType(bitwiseRightShiftTensor.getType()))
.getElementType();
if (!isa<mlir::IntegerType>(dtype)) {
bitwiseRightShiftTensor.emitError(
"Bitwise_Right_Shift op does not support non-integer input dtype.");
return nullptr;
}
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<arith::ShRSIOp>(loc, lhs, rhs);
}
if (auto bitwiseLeftShiftTensor =
dyn_cast<AtenBitwiseLeftShiftTensorOp>(op)) {
Type dtype = cast<RankedTensorType>(
converter->convertType(bitwiseLeftShiftTensor.getType()))
.getElementType();
if (!isa<mlir::IntegerType>(dtype)) {
bitwiseLeftShiftTensor.emitError(
"Bitwise_Left_Shift op does not support non-integer input dtype.");
return nullptr;
}
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<arith::ShLIOp>(loc, lhs, rhs);
}
if (isa<AtenLogicalOrOp, AtenLogicalAndOp, AtenLogicalXorOp>(op)) {
MLIRContext *context = op->getContext();
Type floatDtype = mlir::FloatType::getF64(context);
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], floatDtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], floatDtype);
Value zero =
b.create<arith::ConstantOp>(loc, b.getFloatAttr(floatDtype, 0));
Value lhsTest = createNotEqual(b, loc, floatDtype, lhs, zero);
Value rhsTest = createNotEqual(b, loc, floatDtype, rhs, zero);
if (isa<AtenLogicalOrOp>(op)) {
return b.create<arith::OrIOp>(loc, lhsTest, rhsTest);
}
if (isa<AtenLogicalAndOp>(op)) {
return b.create<arith::AndIOp>(loc, lhsTest, rhsTest);
}
if (isa<AtenLogicalXorOp>(op)) {
return b.create<arith::XOrIOp>(loc, lhsTest, rhsTest);
}
llvm_unreachable("Unknown op type");
}
if (isa<AtenLogicalNotOp>(op)) {
MLIRContext *context = op->getContext();
Type floatDtype = mlir::FloatType::getF64(context);
Value self = convertScalarToDtype(b, loc, payloadArgs[0], floatDtype);
Value zero =
b.create<arith::ConstantOp>(loc, b.getFloatAttr(floatDtype, 0));
return createEqual(b, loc, floatDtype, self, zero);
}
if (isa<AtenAbsOp>(op)) {
if (isa<IntegerType>(payloadArgs[0].getType()))
return b.create<math::AbsIOp>(loc, payloadArgs[0]);
return b.create<math::AbsFOp>(loc, payloadArgs[0]);
}
if (isa<AtenIsinfOp>(op)) {
Value abs = b.create<math::AbsFOp>(loc, payloadArgs[0]);
Value infinity = b.create<arith::ConstantOp>(
loc,
b.getFloatAttr(abs.getType(), std::numeric_limits<double>::infinity()));
return createEqual(b, loc, abs.getType(), abs, infinity);
}
if (isa<AtenSigmoidOp>(op)) {
Type inTTy = cast<ValueTensorType>(op->getOperand(0).getType()).getDtype();
Type outTTy = cast<ValueTensorType>(op->getResult(0).getType()).getDtype();
Type outTy = cast<RankedTensorType>(
converter->convertType(op->getResult(0).getType()))
.getElementType();
Type computeTy = outTy;
if (isa<IntegerType>(computeTy))
computeTy = b.getF32Type();
Value arg = payloadArgs[0];
arg = convertScalarToDtype(b, loc, payloadArgs[0], computeTy, inTTy);
auto negate = b.create<arith::NegFOp>(loc, arg);
auto one =
b.create<arith::ConstantOp>(loc, FloatAttr::get(negate.getType(), 1));
auto exp = b.create<math::ExpOp>(loc, negate);
auto added = b.create<arith::AddFOp>(loc, exp, one);
auto div = b.create<arith::DivFOp>(loc, one, added);
return convertScalarToDtype(b, loc, div, outTy, std::nullopt, outTTy);
}
if (auto relu = dyn_cast<AtenReluOp>(op)) {
Value zeroPoint = getZeroPoint(relu.getSelf());
Value arg = payloadArgs[0];
auto intType = dyn_cast<mlir::IntegerType>(arg.getType());
if (zeroPoint && !intType) {
relu.emitError("unimplemented: non-integer quantized Relu.");
return nullptr;
}
auto reluTorchType = cast<ValueTensorType>(relu.getType());
bool isUnsigned =
torch_to_linalg::isUnsignedTorchType(reluTorchType.getDtype());
if (zeroPoint) {
int64_t zeroPointInt;
int64_t width = intType.getWidth();
assert(width < 64);
int64_t minForIntType = isUnsigned ? 0 : -(1 << (width - 1));
int64_t maxForIntType =
isUnsigned ? (1 << (width + 1)) - 1 : (1 << (width - 1)) - 1;
// check for constant zero point edge-cases:
if (matchPattern(zeroPoint, m_TorchConstantInt(&zeroPointInt))) {
if (zeroPointInt > maxForIntType) {
// TODO: figure out how to handle this case:
// current impl. quantizes output like input.
// If zero point > maxForIntType, ordinary relu should return 0.
// However, 0 isn't represented in such a quantization scheme.
relu.emitError(
"unimplemented: quantized relu for zero-point > max qint");
return nullptr;
}
if (zeroPointInt < minForIntType)
return arg;
}
zeroPoint = converter->materializeTargetConversion(
b, loc, converter->convertType(zeroPoint.getType()), zeroPoint);
auto minForIntTypeValue = b.create<arith::ConstantOp>(
loc, b.getIntegerAttr(zeroPoint.getType(), minForIntType));
auto maxForIntTypeValue = b.create<arith::ConstantOp>(
loc, b.getIntegerAttr(zeroPoint.getType(), maxForIntType));
auto zpLtMax = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt,
zeroPoint, maxForIntTypeValue);
b.create<cf::AssertOp>(
loc, zpLtMax,
b.getStringAttr("Invalid Quantization: quantized relu with "
"zero-point > max qint"));
auto zpLtMin = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt,
zeroPoint, minForIntTypeValue);
zeroPoint = b.create<arith::SelectOp>(loc, zpLtMin, minForIntTypeValue,
zeroPoint);
zeroPoint = b.create<arith::TruncIOp>(loc, arg.getType(), zeroPoint);
} else {
zeroPoint =
b.create<arith::ConstantOp>(loc, b.getZeroAttr(arg.getType()));
}
Value cmp;
if (intType) {
auto pred =
isUnsigned ? arith::CmpIPredicate::ugt : arith::CmpIPredicate::sgt;
cmp = b.create<arith::CmpIOp>(loc, pred, arg, zeroPoint);
} else {
cmp = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT, arg,
zeroPoint);
}
return b.create<arith::SelectOp>(loc, cmp, arg, zeroPoint);
}
if (auto round = dyn_cast<AtenRoundOp>(op)) {
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(round.getType()).getDtype())) {
round.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
return b.create<math::RoundEvenOp>(loc, payloadArgs[0]);
}
if (auto prelu = dyn_cast<AtenPreluOp>(op)) {
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(prelu.getType()).getDtype())) {
prelu.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Type elementType = payloadArgs[0].getType();
Value constZero =
b.create<arith::ConstantOp>(loc, b.getZeroAttr(elementType));
Value pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT,
payloadArgs[0], constZero);
Value positivePart =
b.create<arith::SelectOp>(loc, pred, payloadArgs[0], constZero);
Value negativePart =
b.create<arith::SelectOp>(loc, pred, constZero, payloadArgs[0]);
Value scale = convertScalarToDtype(b, loc, payloadArgs[1], elementType);
Value scaledNegativePart =
b.create<arith::MulFOp>(loc, negativePart, scale);
return b.create<arith::AddFOp>(loc, positivePart, scaledNegativePart);
}
if (auto gelu = dyn_cast<AtenGeluOp>(op)) {
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(gelu.getType()).getDtype())) {
gelu.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
// TODO: Take approximation into account.
std::string approximate;
if (!matchPattern(gelu.getApproximate(), m_TorchConstantStr(approximate))) {
gelu.emitError(
"unimplemented: expected approximate to be a constant str");
return nullptr;
}
if (approximate == "none") {
Value multiplier = buildUnitNormalCdf(b, loc, payloadArgs[0]);
return b.create<arith::MulFOp>(loc, payloadArgs[0], multiplier);
}
if (approximate == "tanh") {
// GELU(x)=0.5∗x∗(1+Tanh((2/π)^1/2 * (x+0.044715∗x^3)))
// Ref: https://pytorch.org/docs/stable/generated/torch.nn.GELU.html
Value cstThree = b.create<arith::ConstantOp>(
loc, IntegerAttr::get(IntegerType::get(op->getContext(), 64), 3));
Value xCube = b.create<math::FPowIOp>(loc, payloadArgs[0], cstThree);
Type elementType = payloadArgs[0].getType();
Value cstAlpha = b.create<arith::ConstantOp>(
loc, FloatAttr::get(elementType, 0.044715));
Value xCubeMulAlpha = b.create<arith::MulFOp>(loc, xCube, cstAlpha);
Value xPlusXCubeMulAlpha =
b.create<arith::AddFOp>(loc, payloadArgs[0], xCubeMulAlpha);
Value cstBeta = b.create<arith::ConstantOp>(
loc, FloatAttr::get(elementType, 0.7977240352174656));
Value betaMulX =
b.create<arith::MulFOp>(loc, cstBeta, xPlusXCubeMulAlpha);
Value tanh = b.create<math::TanhOp>(loc, betaMulX);
Value cstOne =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1.0));
Value onePlusTanh = b.create<arith::AddFOp>(loc, cstOne, tanh);
Value cstHalf =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0.5));
Value multiplier = b.create<arith::MulFOp>(loc, cstHalf, onePlusTanh);
return b.create<arith::MulFOp>(loc, payloadArgs[0], multiplier);
}
gelu.emitError("unimplemented: approximate value should be none or tanh");
return nullptr;
}
if (auto geluBackward = dyn_cast<AtenGeluBackwardOp>(op)) {
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(geluBackward.getType()).getDtype())) {
geluBackward.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
// TODO: Take approximation into account.
std::string approximate;
if (!matchPattern(geluBackward.getApproximate(),
m_TorchConstantStr(approximate)) ||
approximate != "none")
return nullptr;
Type elementType = payloadArgs[1].getType();
Value cstAlpha0 = b.create<arith::ConstantOp>(
loc, FloatAttr::get(elementType, 1.12837916709551257390));
Value cstAlpha1 = b.create<arith::ConstantOp>(
loc, FloatAttr::get(elementType, 0.70710678118654752440));
Value oneHalf =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0.5));
Value kAlpha = b.create<arith::MulFOp>(loc, cstAlpha0, cstAlpha1);
Value kAlphaHalf = b.create<arith::MulFOp>(loc, kAlpha, oneHalf);
Value negOneHalf =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, -0.5));
Value inputSquared =
b.create<arith::MulFOp>(loc, payloadArgs[1], payloadArgs[1]);
Value negHalfInputSquared =
b.create<arith::MulFOp>(loc, inputSquared, negOneHalf);
Value dinput = b.create<math::ExpOp>(loc, negHalfInputSquared);
Value cdf = buildUnitNormalCdf(b, loc, payloadArgs[1]);
Value dinputInput = b.create<arith::MulFOp>(loc, dinput, payloadArgs[1]);
Value dinputInputAlpha =
b.create<arith::MulFOp>(loc, dinputInput, kAlphaHalf);
Value cdfExt = b.create<arith::AddFOp>(loc, dinputInputAlpha, cdf);
return b.create<arith::MulFOp>(loc, payloadArgs[0], cdfExt);
}
if (auto hardtanhBackward = dyn_cast<AtenHardtanhBackwardOp>(op)) {
AtenHardtanhBackwardOp::Adaptor adaptor(operands);
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(hardtanhBackward.getType()).getDtype())) {
hardtanhBackward.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Value gradOutput = payloadArgs[0];
Type elementType = gradOutput.getType();
Value self = convertScalarToDtype(b, loc, payloadArgs[1], elementType);
Value constantZero =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0.0));
Value min = convertScalarToDtype(b, loc, adaptor.getMinVal(), elementType);
Value max = convertScalarToDtype(b, loc, adaptor.getMaxVal(), elementType);
Value lesser =
b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT, self, min);
Value greater =
b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT, self, max);
Value cmp = b.create<arith::OrIOp>(loc, lesser, greater);
return b.create<arith::SelectOp>(loc, cmp, constantZero, gradOutput);
}
if (auto add = dyn_cast<AtenAddTensorOp>(op)) {
AtenAddTensorOp::Adaptor adaptor(operands);
Type resultElementType = cast<BaseTensorType>(add.getType()).getDtype();
Type dtype = cast<RankedTensorType>(converter->convertType(add.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value alpha = convertScalarToDtype(b, loc, adaptor.getAlpha(), dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
if (isa<mlir::FloatType>(dtype)) {
Value scaled = b.create<arith::MulFOp>(loc, rhs, alpha);
return b.create<arith::AddFOp>(loc, lhs, scaled);
} else {
Value scaled = b.create<arith::MulIOp>(loc, rhs, alpha);
return b.create<arith::AddIOp>(loc, lhs, scaled);
}
}
if (auto sub = dyn_cast<AtenSubTensorOp>(op)) {
AtenSubTensorOp::Adaptor adaptor(operands);
Type dtype = cast<RankedTensorType>(converter->convertType(sub.getType()))
.getElementType();
Type resultElementType = cast<BaseTensorType>(sub.getType()).getDtype();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value alpha = convertScalarToDtype(b, loc, adaptor.getAlpha(), dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType,
/*originalScalar=*/sub.getAlpha());
if (isa<mlir::FloatType>(dtype)) {
Value scaled = b.create<arith::MulFOp>(loc, rhs, alpha);
return b.create<arith::SubFOp>(loc, lhs, scaled);
} else {
Value scaled = b.create<arith::MulIOp>(loc, rhs, alpha);
return b.create<arith::SubIOp>(loc, lhs, scaled);
}
}
if (auto subScalar = dyn_cast<AtenSubScalarOp>(op)) {
Type dtype =
cast<RankedTensorType>(converter->convertType(subScalar.getType()))
.getElementType();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value other = convertScalarToDtype(b, loc, operands[1], dtype);
Value alpha = convertScalarToDtype(
b, loc, operands[2], dtype, /*srcOriginalDtype=*/operands[2].getType(),
/*dstOriginalDtype=*/dtype);
if (isa<mlir::FloatType>(dtype)) {
Value mult = b.create<arith::MulFOp>(loc, other, alpha);
return b.create<arith::SubFOp>(loc, self, mult);
} else if (isa<mlir::IntegerType>(dtype)) {
Value mult = b.create<arith::MulIOp>(loc, other, alpha);
return b.create<arith::SubIOp>(loc, self, mult);
}
subScalar.emitError("unimplemented: dtype other than float and integer "
"types are not supported.");
return nullptr;
}
if (auto addScalar = dyn_cast<AtenAddScalarOp>(op)) {
Type dtype =
cast<RankedTensorType>(converter->convertType(addScalar.getType()))
.getElementType();
Type resultElementType =
cast<BaseTensorType>(addScalar.getType()).getDtype();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value other = convertScalarToDtype(b, loc, operands[1], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
Value alpha = convertScalarToDtype(b, loc, operands[2], dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/resultElementType);
if (isa<mlir::FloatType>(dtype)) {
Value mult = b.create<arith::MulFOp>(loc, other, alpha);
return b.create<arith::AddFOp>(loc, self, mult);
} else if (isa<mlir::IntegerType>(dtype)) {
Value mult = b.create<arith::MulIOp>(loc, other, alpha);
return b.create<arith::AddIOp>(loc, self, mult);
}
addScalar.emitError("unimplemented: dtype other than float and integer "
"types are not supported.");
return nullptr;
}
if (auto mul = dyn_cast<AtenMulTensorOp>(op)) {
AtenMulTensorOp::Adaptor adaptor(operands);
Type dtype = cast<RankedTensorType>(converter->convertType(mul.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
if (isa<mlir::FloatType>(dtype)) {
return b.create<arith::MulFOp>(loc, lhs, rhs);
} else if (isa<mlir::ComplexType>(dtype)) {
return b.create<complex::MulOp>(loc, lhs, rhs);
} else {
return b.create<arith::MulIOp>(loc, lhs, rhs);
}
}
if (auto atan2 = dyn_cast<AtenAtan2Op>(op)) {
Type dtype = cast<RankedTensorType>(converter->convertType(atan2.getType()))
.getElementType();
if (!isa<mlir::FloatType>(dtype)) {
atan2.emitError("Atan2 requires floating point result type");
return nullptr;
}
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<math::Atan2Op>(loc, lhs, rhs);
}
if (auto ltTensor = dyn_cast<AtenLtTensorOp>(op)) {
return createCompareOp(b, loc, ltTensor, payloadArgs[0], payloadArgs[1]);
}
if (auto leTensor = dyn_cast<AtenLeTensorOp>(op)) {
return createCompareOp(b, loc, leTensor, payloadArgs[0], payloadArgs[1]);
}
if (auto gtTensor = dyn_cast<AtenGtTensorOp>(op)) {
return createCompareOp(b, loc, gtTensor, payloadArgs[0], payloadArgs[1]);
}
if (auto geTensor = dyn_cast<AtenGeTensorOp>(op)) {
return createCompareOp(b, loc, geTensor, payloadArgs[0], payloadArgs[1]);
}
if (auto eqTensor = dyn_cast<AtenEqTensorOp>(op)) {
return createCompareOp(b, loc, eqTensor, payloadArgs[0], payloadArgs[1]);
}
if (auto neTensor = dyn_cast<AtenNeTensorOp>(op)) {
return createCompareOp(b, loc, neTensor, payloadArgs[0], payloadArgs[1]);
}
if (auto div = dyn_cast<AtenDivTensorOp>(op)) {
AtenDivTensorOp::Adaptor adaptor(operands);
Type dtype = cast<RankedTensorType>(converter->convertType(div.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
if (isa<mlir::FloatType>(dtype))
return b.create<arith::DivFOp>(loc, lhs, rhs);
else if (isa<mlir::IntegerType>(dtype)) {
if (dtype.isUnsignedInteger())
return b.create<arith::DivUIOp>(loc, lhs, rhs);
return b.create<arith::DivSIOp>(loc, lhs, rhs);
}
div.emitError("unimplemented: non-floating point and non-integer dtype");
return nullptr;
}
if (auto divScalarMode = dyn_cast<AtenDivScalarModeOp>(op)) {
return createDivModePayload(b, loc, converter, payloadArgs, divScalarMode,
operands);
}
if (auto divTensorMode = dyn_cast<AtenDivTensorModeOp>(op)) {
return createDivModePayload(b, loc, converter, payloadArgs, divTensorMode,
operands);
}
if (auto pow = dyn_cast<AtenPowScalarOp>(op)) {
Type dtype = cast<ValueTensorType>(pow.getType()).getDtype();
if (!isa<mlir::FloatType>(dtype)) {
pow.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Value selfPromoted = convertScalarToDtype(b, loc, operands[0], dtype);
Value expPromoted = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
return b.create<math::PowFOp>(loc, selfPromoted, expPromoted);
}
if (auto pow = dyn_cast<AtenPowTensorScalarOp>(op)) {
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(pow.getType()).getDtype())) {
pow.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Type dtype = cast<ValueTensorType>(pow.getSelf().getType()).getDtype();
Value expPromoted = convertScalarToDtype(b, loc, operands[1], dtype);
return b.create<math::PowFOp>(loc, payloadArgs[0], expPromoted);
}
if (auto pow = dyn_cast<AtenPowTensorTensorOp>(op)) {
Type dtype = cast<RankedTensorType>(converter->convertType(pow.getType()))
.getElementType();
if (!isa<mlir::FloatType>(dtype)) {
pow.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
return b.create<math::PowFOp>(loc, lhs, rhs);
}
if (auto imag = dyn_cast<AtenImagOp>(op)) {
Type dtype = cast<RankedTensorType>(converter->convertType(imag.getType()))
.getElementType();
if (!isa<mlir::FloatType>(dtype)) {
imag.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Value imagVal = b.create<complex::ImOp>(loc, payloadArgs[0]);
return imagVal;
}
if (auto real = dyn_cast<AtenRealOp>(op)) {
Type dtype = cast<RankedTensorType>(converter->convertType(real.getType()))
.getElementType();
if (!isa<mlir::FloatType>(dtype)) {
real.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Value realVal = b.create<complex::ReOp>(loc, payloadArgs[0]);
return realVal;
}
if (auto gtScalar = dyn_cast<AtenGtScalarOp>(op)) {
return createCompareOp(b, loc, gtScalar, payloadArgs[0], operands[1]);
}
if (auto geScalar = dyn_cast<AtenGeScalarOp>(op)) {
return createCompareOp(b, loc, geScalar, payloadArgs[0], operands[1]);
}
if (auto eqScalar = dyn_cast<AtenEqScalarOp>(op)) {
return createCompareOp(b, loc, eqScalar, payloadArgs[0], operands[1]);
}
if (auto neScalar = dyn_cast<AtenNeScalarOp>(op)) {
return createCompareOp(b, loc, neScalar, payloadArgs[0], operands[1]);
}
if (auto ltScalar = dyn_cast<AtenLtScalarOp>(op)) {
return createCompareOp(b, loc, ltScalar, payloadArgs[0], operands[1]);
}
if (auto leScalar = dyn_cast<AtenLeScalarOp>(op)) {
return createCompareOp(b, loc, leScalar, payloadArgs[0], operands[1]);
}
if (auto whereSelf = dyn_cast<AtenWhereSelfOp>(op)) {
Type dtype =
cast<RankedTensorType>(converter->convertType(whereSelf.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[2], dtype);
return b.create<arith::SelectOp>(loc, payloadArgs[0], lhs, rhs);
}
if (auto lerp = dyn_cast<AtenLerpTensorOp>(op)) {
if (!isa<mlir::FloatType>(
cast<ValueTensorType>(lerp.getType()).getDtype())) {
lerp.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
AtenLerpTensorOp::Adaptor adaptor(payloadArgs);
auto start = adaptor.getSelf();
auto end = adaptor.getEnd();
auto weight = adaptor.getWeight();
auto delta = b.create<arith::SubFOp>(loc, end, start);
auto weightedDelta = b.create<arith::MulFOp>(loc, delta, weight);
return b.create<arith::AddFOp>(loc, start, weightedDelta);
}
if (auto minimum = dyn_cast<AtenMinimumOp>(op)) {
Type dtype = cast<BaseTensorType>(minimum.getType()).getDtype();
Type elemTy =
cast<RankedTensorType>(converter->convertType(minimum.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], elemTy);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], elemTy);
Value pred = createLessThan(b, loc, dtype, lhs, rhs);
return b.create<arith::SelectOp>(loc, pred, lhs, rhs);
}
if (auto maximum = dyn_cast<AtenMaximumOp>(op)) {
Type dtype = cast<BaseTensorType>(maximum.getType()).getDtype();
Type elemTy =
cast<RankedTensorType>(converter->convertType(maximum.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], elemTy);
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], elemTy);
Value pred = createGreaterThan(b, loc, dtype, lhs, rhs);
return b.create<arith::SelectOp>(loc, pred, lhs, rhs);
}
if (auto clamp = dyn_cast<AtenClampOp>(op)) {
AtenClampOp::Adaptor adaptor(operands);
auto min = adaptor.getMin();
auto max = adaptor.getMax();
if (isa<Torch::OptionalType>(min.getType()) ||
isa<Torch::OptionalType>(max.getType())) {
clamp.emitError("unimplemented: runtime optional type");
return nullptr;
}
Type dtype = cast<RankedTensorType>(converter->convertType(clamp.getType()))
.getElementType();
if (!isa<mlir::FloatType, mlir::IntegerType>(dtype)) {
clamp.emitError("unimplement type for clamp");
return nullptr;
}
Type dstOriginalDtype = cast<BaseTensorType>(clamp.getType()).getDtype();
bool isUnsigned = isa<QUInt8Type>(dstOriginalDtype);
if (auto intTy = dyn_cast<IntegerType>(dstOriginalDtype)) {
isUnsigned = intTy.isUnsigned();
}
auto cmpSelect = [&](Value input, Value clamp, bool getMax) -> Value {
clamp = convertScalarToDtype(b, loc, clamp, dtype,
/*srcOriginalDtype=*/std::nullopt,
/*dstOriginalDtype=*/dstOriginalDtype);
Value pred;
if (isa<mlir::FloatType>(dtype)) {
auto cmp =
getMax ? arith::CmpFPredicate::UGT : arith::CmpFPredicate::ULT;
pred = b.create<arith::CmpFOp>(loc, cmp, input, clamp);
} else if (isa<mlir::IntegerType>(dtype)) {
auto cmp =
isUnsigned ? arith::CmpIPredicate::ult : arith::CmpIPredicate::slt;
if (getMax)
cmp = arith::invertPredicate(cmp);
pred = b.create<arith::CmpIOp>(loc, cmp, input, clamp);
}
return b.create<arith::SelectOp>(loc, pred, clamp, input);
};
auto result = payloadArgs[0];
if (!isa<Torch::NoneType>(min.getType()))
result = cmpSelect(result, min, /*getMax=*/false);
if (!isa<Torch::NoneType>(max.getType()))
result = cmpSelect(result, max, /*getMax=*/true);
return result;
}
if (auto clampTensor = dyn_cast<AtenClampTensorOp>(op)) {
AtenClampTensorOp::Adaptor adaptor(operands);
auto min = adaptor.getMin();
auto max = adaptor.getMax();
if (isa<Torch::OptionalType>(min.getType()) ||
isa<Torch::OptionalType>(max.getType())) {
clampTensor.emitError("unimplemented: runtime optional type");
return nullptr;
}
Type dtype =
cast<RankedTensorType>(converter->convertType(clampTensor.getType()))
.getElementType();
bool isMinNone = true;
auto result = payloadArgs[0];
if (!isa<Torch::NoneType>(min.getType())) {
isMinNone = false;
auto minPromoted = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
Value pred;
if (isa<mlir::FloatType>(dtype)) {
pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT, result,
minPromoted);
} else if (isa<mlir::IntegerType>(dtype)) {
pred = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt, result,
minPromoted);
} else {
clampTensor.emitError(
"unimplemented: dtype other than float and integer "
"types are not supported.");
return nullptr;
}
result = b.create<arith::SelectOp>(loc, pred, minPromoted, result);
}
if (!isa<Torch::NoneType>(max.getType())) {
max = isMinNone ? payloadArgs[1] : payloadArgs[2];
auto maxPromoted = convertScalarToDtype(b, loc, max, dtype);
Value pred;
if (isa<mlir::FloatType>(dtype)) {
pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT, result,
maxPromoted);
} else if (isa<mlir::IntegerType>(dtype)) {
pred = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sgt, result,
maxPromoted);
} else {
clampTensor.emitError(
"unimplemented: dtype other than float and integer "
"types are not supported.");
return nullptr;
}
result = b.create<arith::SelectOp>(loc, pred, maxPromoted, result);
}
return result;
}
if (auto rsub = dyn_cast<AtenRsubScalarOp>(op)) {
Type dtype = cast<RankedTensorType>(converter->convertType(rsub.getType()))
.getElementType();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value other = convertScalarToDtype(b, loc, operands[1], dtype);
Value alpha = convertScalarToDtype(
b, loc, operands[2], dtype, /*srcOriginalDtype=*/operands[2].getType(),
/*dstOriginalDtype=*/dtype);
if (isa<mlir::FloatType>(dtype)) {
Value mult = b.create<arith::MulFOp>(loc, self, alpha);
return b.create<arith::SubFOp>(loc, other, mult);
} else if (isa<mlir::IntegerType>(dtype)) {
Value mult = b.create<arith::MulIOp>(loc, self, alpha);
return b.create<arith::SubIOp>(loc, other, mult);
}
rsub.emitError("unimplemented: dtype other than float and integer "
"types are not supported.");
return nullptr;
}
if (auto mulScalar = dyn_cast<AtenMulScalarOp>(op)) {
Type dtype =
cast<RankedTensorType>(converter->convertType(mulScalar.getType()))
.getElementType();
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value rhs = convertScalarToDtype(b, loc, operands[1], dtype);
if (isa<mlir::FloatType>(dtype))
return b.create<arith::MulFOp>(loc, lhs, rhs);
if (isa<mlir::IntegerType>(dtype))
return b.create<arith::MulIOp>(loc, lhs, rhs);
mulScalar.emitError("unimplemented: Only integer/float dtype supported");
return nullptr;
}
if (auto atenToDtype = dyn_cast<AtenToDtypeOp>(op)) {
Value input = payloadArgs[0];
Type inputElementType =
cast<BaseTensorType>(atenToDtype.getSelf().getType()).getDtype();
Type dtype =
cast<RankedTensorType>(converter->convertType(atenToDtype.getType()))
.getElementType();
Type resultElementType;
int64_t dtypeInt;
if (!matchPattern(atenToDtype.getDtype(), m_TorchConstantInt(&dtypeInt))) {
atenToDtype.emitError("unimplemented: dtype must be a constant integer");
return nullptr;
}
FailureOr<Type> maybeResultElementType =
torch_to_linalg::getBackendTypeForScalarType(
atenToDtype->getContext(), (torch_upstream::ScalarType)dtypeInt);
if (failed(maybeResultElementType)) {
atenToDtype.emitError("unable to convert `dtypeInt` to builtin type");
return nullptr;
}
resultElementType = *maybeResultElementType;
Value result = convertScalarToDtype(b, loc, input, dtype,
/*srcOriginalDtype=*/inputElementType,
/*dstOriginalDtype=*/resultElementType);
return result;
}
if (auto divScalar = dyn_cast<AtenDivScalarOp>(op)) {
Type dtype =
cast<RankedTensorType>(converter->convertType(divScalar.getType()))
.getElementType();
if (!isa<mlir::FloatType>(dtype)) {
divScalar.emitError("unimplemented: non-floating point dtype");
return nullptr;
}
Value self = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value other = convertScalarToDtype(b, loc, operands[1], dtype);
return b.create<arith::DivFOp>(loc, self, other);
}
if (auto remScalar = dyn_cast<AtenRemainderScalarOp>(op)) {
Type newResultType =
cast<RankedTensorType>(converter->convertType(remScalar.getType()))
.getElementType();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], newResultType);
Value other = convertScalarToDtype(b, loc, operands[1], newResultType);
Value result;
if (isa<mlir::FloatType>(newResultType)) {
result = b.create<arith::RemFOp>(loc, self, other);
} else if (isa<mlir::IntegerType>(newResultType)) {
result = b.create<arith::RemSIOp>(loc, self, other);
} else {
remScalar.emitError(
"Unsupported type encountered for AtenRemainderScalarOp.");
}
return result;
}
if (auto remTensor = dyn_cast<AtenRemainderTensorOp>(op)) {
Type newResultType =
cast<RankedTensorType>(converter->convertType(remTensor.getType()))
.getElementType();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], newResultType);
Value other = convertScalarToDtype(b, loc, payloadArgs[1], newResultType);
Value result;
if (isa<mlir::FloatType>(newResultType)) {
result = b.create<arith::RemFOp>(loc, self, other);
} else if (isa<mlir::IntegerType>(newResultType)) {
result = b.create<arith::RemSIOp>(loc, self, other);
} else {
remTensor.emitError(
"Unsupported type encountered for AtenRemainderTensorOp.");
}
return result;
}
if (auto fmod = dyn_cast<AtenFmodTensorOp>(op)) {
Type newResultType =
cast<RankedTensorType>(converter->convertType(fmod.getType()))
.getElementType();
Value self = convertScalarToDtype(b, loc, payloadArgs[0], newResultType);
Value other = convertScalarToDtype(b, loc, payloadArgs[1], newResultType);
Value result;
if (isa<mlir::FloatType>(newResultType)) {
Value n = b.create<arith::DivFOp>(loc, self, other);
n = b.create<math::TruncOp>(loc, n);
Value n_y = b.create<arith::MulFOp>(loc, n, other);
result = b.create<arith::SubFOp>(loc, self, n_y);
} else if (isa<mlir::IntegerType>(newResultType)) {
Value n = b.create<arith::DivSIOp>(loc, self, other);
Value n_y = b.create<arith::MulIOp>(loc, n, other);
result = b.create<arith::SubIOp>(loc, self, n_y);
} else {
fmod.emitError("Unsupported type encountered for AtenFmodTensorOp.");
}
return result;
}
if (auto reciprocal = dyn_cast<AtenReciprocalOp>(op)) {
Type dtype =
cast<RankedTensorType>(converter->convertType(reciprocal.getType()))
.getElementType();
Value arg = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Type elementType = arg.getType();
// assert(element != 0)
auto zero =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0.0));
auto pred =
b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ONE, arg, zero);
b.create<cf::AssertOp>(
loc, pred, b.getStringAttr("unimplemented: tensor with zero element"));
auto one =
b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1.0));
return b.create<arith::DivFOp>(loc, one, arg);
}
if (auto thresholdOp = dyn_cast<AtenThresholdOp>(op)) {
// The approach used here is as follows:
// result = self <= threshold ? value : self
AtenThresholdOp::Adaptor adaptor(operands);
Type dtype =
cast<RankedTensorType>(converter->convertType(thresholdOp.getType()))
.getElementType();
Value self = payloadArgs[0];
Value threshold =
convertScalarToDtype(b, loc, adaptor.getThreshold(), dtype);
Value value = convertScalarToDtype(b, loc, adaptor.getValue(), dtype);
Value predicate;
if (isa<mlir::FloatType>(dtype))
predicate = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULE, self,
threshold);
else
predicate = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sle, self,
threshold);
return b.create<arith::SelectOp>(loc, predicate, value, self);
}
if (auto thresholdBackward = dyn_cast<AtenThresholdBackwardOp>(op)) {
// The approach used here is as follows:
// result = self <= threshold ? 0 : grad
AtenThresholdBackwardOp::Adaptor adaptor(operands);
Type dtype = cast<RankedTensorType>(
converter->convertType(thresholdBackward.getType()))
.getElementType();
Value grad = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
Value self = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
Value threshold =
convertScalarToDtype(b, loc, adaptor.getThreshold(), dtype);
Value constantZero = b.create<arith::ConstantOp>(loc, b.getZeroAttr(dtype));
Value predicate;
if (isa<mlir::FloatType>(dtype))
predicate = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULE, self,
threshold);
else
predicate = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sle, self,
threshold);
return b.create<arith::SelectOp>(loc, predicate, constantZero, grad);
}
if (auto fillScalar = dyn_cast<AtenFillScalarOp>(op)) {
AtenFillScalarOp::Adaptor adaptor(operands);
Type dtype =
cast<RankedTensorType>(converter->convertType(fillScalar.getType()))
.getElementType();
return convertScalarToDtype(b, loc, adaptor.getValue(), dtype);
}
if (auto maskedFillTensor = dyn_cast<AtenMaskedFillTensorOp>(op)) {
AtenMaskedFillScalarOp::Adaptor adaptor(operands);
Type dtype = cast<RankedTensorType>(
converter->convertType(maskedFillTensor.getType()))
.getElementType();
Value input = payloadArgs[0];
Value mask = payloadArgs[1];
Value fillValue = convertScalarToDtype(b, loc, payloadArgs[2], dtype);
return b.create<arith::SelectOp>(loc, mask, fillValue, input);
}
if (auto fillTensor = dyn_cast<AtenFillTensorOp>(op)) {
AtenFillTensorOp::Adaptor adaptor(operands);
Type dtype =
cast<RankedTensorType>(converter->convertType(fillTensor.getType()))
.getElementType();
return convertScalarToDtype(b, loc, payloadArgs[1], dtype);
}
if (auto triu = dyn_cast<AtenTriuOp>(op)) {
Value result;
if (failed(createTriangularMatrix<arith::CmpIPredicate::sge>(
b, loc, payloadArgs, op, operands, result)))
return nullptr;
return result;
}
if (auto tril = dyn_cast<AtenTrilOp>(op)) {
Value result;
if (failed(createTriangularMatrix<arith::CmpIPredicate::sle>(
b, loc, payloadArgs, op, operands, result)))
return nullptr;
return result;
}
if (auto bitwiseNot = dyn_cast<AtenBitwiseNotOp>(op)) {
Type elementType =
cast<RankedTensorType>(converter->convertType(bitwiseNot.getType()))
.getElementType();
if (isa<mlir::FloatType>(elementType)) {
bitwiseNot.emitError("Bitwise_Not does not support floating point dtype");
return nullptr;
}
Value allOnesVal = b.create<arith::ConstantOp>(
loc, b.getIntegerAttr(
elementType,
APSInt::getAllOnes(elementType.getIntOrFloatBitWidth())));
return b.create<arith::XOrIOp>(loc, payloadArgs[0], allOnesVal);
}
if (isa<AtenDequantizeTensorOp, AtenDequantizeSelfOp>(op)) {
auto value = payloadArgs[0];
auto valueTy = value.getType();
auto qtensor = op->getOperand(0);
auto qtensorTy = cast<ValueTensorType>(qtensor.getType()).getDtype();
Value zp, scale;
if (auto makeQTensor =
qtensor.getDefiningOp<Aten_MakePerTensorQuantizedTensorOp>()) {
zp = makeQTensor.getZeroPoint();
scale = makeQTensor.getScale();
}
if (auto quant = qtensor.getDefiningOp<AtenQuantizePerTensorOp>()) {
zp = quant.getZeroPoint();
scale = quant.getScale();
}
if (!zp || !scale) {
return nullptr;
}
auto outFpTy = payloadArgs[1].getType();
auto outBw = outFpTy.getIntOrFloatBitWidth();
auto outIntTy = b.getIntegerType(outBw);
if (valueTy != outIntTy) {
if (torch_to_linalg::isUnsignedTorchType(qtensorTy)) {
value = b.create<arith::ExtUIOp>(loc, outIntTy, value);
} else {
value = b.create<arith::ExtSIOp>(loc, outIntTy, value);
}
}
zp = converter->materializeTargetConversion(
b, loc, converter->convertType(zp.getType()), zp);
auto zpTy = zp.getType();
if (zpTy != outIntTy) {
zp = b.create<arith::TruncIOp>(loc, outIntTy, zp);
}
value = b.create<arith::SubIOp>(loc, value, zp);
// treat the i32 as a signed int regardless of original signed-ness
// this will prevent overflow from subtraction for unsigned quantizations.
value = b.create<arith::SIToFPOp>(loc, outFpTy, value);
scale = converter->materializeTargetConversion(
b, loc, converter->convertType(scale.getType()), scale);
if (scale.getType() != value.getType()) {
scale = b.create<arith::TruncFOp>(loc, value.getType(), scale);
}
value = b.create<arith::MulFOp>(loc, value, scale);
return value;
}
if (auto quant = dyn_cast<AtenQuantizePerTensorOp>(op)) {
Value value = payloadArgs[0];
Value scale = quant.getScale();
Value zp = quant.getZeroPoint();
auto valueTy = value.getType();
zp = converter->materializeTargetConversion(
b, loc, converter->convertType(zp.getType()), zp);
zp = b.create<arith::SIToFPOp>(loc, valueTy, zp);
scale = converter->materializeTargetConversion(
b, loc, converter->convertType(scale.getType()), scale);
scale = b.create<arith::TruncFOp>(loc, valueTy, scale);
value = b.create<arith::DivFOp>(loc, value, scale);
value = b.create<math::RoundEvenOp>(loc, value);
value = b.create<arith::AddFOp>(loc, value, zp);
auto destTy = payloadArgs[1].getType();
auto bitwidth = destTy.getIntOrFloatBitWidth();
bool isUnsigned = torch_to_linalg::isUnsignedTorchType(quant.getType());
APInt min = isUnsigned ? APInt::getMinValue(bitwidth)
: APInt::getSignedMinValue(bitwidth);
APInt max = isUnsigned ? APInt::getMaxValue(bitwidth)
: APInt::getSignedMaxValue(bitwidth);
double minI = isUnsigned ? static_cast<double>(min.getZExtValue())
: static_cast<double>(min.getSExtValue());
double maxI = isUnsigned ? static_cast<double>(max.getZExtValue())
: static_cast<double>(max.getSExtValue());
Value minVal =
b.create<arith::ConstantOp>(loc, b.getFloatAttr(valueTy, minI));
Value maxVal =
b.create<arith::ConstantOp>(loc, b.getFloatAttr(valueTy, maxI));
value = b.create<arith::MaximumFOp>(loc, value, minVal);
value = b.create<arith::MinimumFOp>(loc, value, maxVal);
if (isUnsigned) {
value = b.create<arith::FPToUIOp>(loc, destTy, value);
} else {
value = b.create<arith::FPToSIOp>(loc, destTy, value);
}
return value;
}
op->emitError("unimplemented lowering in "
"createLinalgPayloadCalculationForElementwiseOp");
return nullptr;
}
namespace {
// Converts an elementwise op.
// This specifically includes:
// - converting elementwise ops of any tensor arity
// - converting elementwise ops with any number of scalar captures (such as a
// scalar alpha to torch.aten.Add)
// - broadcasting of static size-1 dimensions
//
// Currently, we adopt the behavior that "size 1" broadcasting is a runtime
// error if it happens dynamically.
//
// Looking forward a bit, eventually, it probably makes sense to have
// a "linalg.generic-like" op for modeling a fused subgraph of numpy-broadcasted
// operands. Modeling elementwise ops that way is potentially useful to allow a
// more centralized reasoning about multiversioning. However a cost model will
// be needed for "pre-fusing" elementwise ops that way, as it can potentially be
// a pessimization. A mild extension of this pattern should work for such a
// general op.
class ConvertElementwiseOp : public ConversionPattern {
public:
ConvertElementwiseOp(TypeConverter &typeConverter, MLIRContext *context)
: ConversionPattern(typeConverter, MatchAnyOpTypeTag(), /*benefit=*/1,
context) {}
LogicalResult
matchAndRewrite(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const override {
if (!isa<AtenTanOp, AtenTanhOp, AtenSinhOp, AtenCoshOp, AtenReluOp,
AtenPreluOp, AtenGeluOp, AtenGeluBackwardOp, AtenAddTensorOp,
AtenMulTensorOp, AtenDivTensorOp, AtenDivTensorModeOp,
AtenDivScalarModeOp, AtenSubTensorOp, AtenAtan2Op,
AtenLerpTensorOp, AtenSigmoidOp, AtenExpOp, AtenExpm1Op,
AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
AtenClampTensorOp, AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp,
AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenPowScalarOp,
AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op,
AtenLog10Op, AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp,
AtenRemainderScalarOp, AtenRemainderTensorOp, AtenFmodTensorOp,
AtenAbsOp, AtenReciprocalOp, AtenBitwiseAndTensorOp,
AtenBitwiseAndScalarOp, AtenBitwiseOrTensorOp,
AtenBitwiseXorTensorOp, AtenBitwiseLeftShiftTensorOp,
AtenBitwiseRightShiftTensorOp, AtenGtScalarOp, AtenGeScalarOp,
AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp,
AtenCeilOp, AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp,
AtenNeTensorOp, AtenLtTensorOp, AtenLeTensorOp, AtenSubScalarOp,
AtenAddScalarOp, AtenThresholdOp, AtenThresholdBackwardOp,
AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp,
AtenNeScalarOp, AtenNegOp, AtenMaskedFillTensorOp, AtenLogicalOrOp,
AtenLogicalAndOp, AtenLogicalXorOp, AtenLogicalNotOp, AtenIsinfOp,
AtenTriuOp, AtenTrilOp, AtenBitwiseNotOp, AtenRoundOp,
AtenFillScalarOp, AtenFillTensorOp, AtenAtanOp, AtenAcosOp,
AtenAtanhOp, AtenAcoshOp, AtenAsinOp, AtenAsinhOp, AtenRealOp,
AtenImagOp, AtenDequantizeSelfOp, AtenDequantizeTensorOp,
AtenQuantizePerTensorOp>(op))
return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op->getLoc();
auto tensorOperands = llvm::to_vector<6>(llvm::make_filter_range(
operands, [](Value v) { return isa<RankedTensorType>(v.getType()); }));
auto resultType = cast<RankedTensorType>(
getTypeConverter()->convertType(op->getResult(0).getType()));
bool hadErrorCreatingPayload = false;
Value generic = torch_to_linalg::createElementwiseLinalgGeneric(
rewriter, loc, tensorOperands, resultType.getElementType(),
[&](OpBuilder &b, Location loc, ValueRange payloadArgs) {
Value result = createLinalgPayloadCalculationForElementwiseOp(
b, loc, getTypeConverter(), payloadArgs, op, operands);
if (!result) {
hadErrorCreatingPayload = true;
return;
}
b.create<linalg::YieldOp>(loc, result);
});
if (hadErrorCreatingPayload)
return failure();
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, generic);
return success();
}
};
} // namespace
// Given `input`, `target`, `nll_loss_forward` is given by:
// for i in range(0, len(target)):
// indi = target[i];
// nll_loss_forward[i] = -(input[i][indi]);
// TODO: `weight`operand is still to be taken care of.
namespace {
class ConvertAtenNllLossForwardOp
: public OpConversionPattern<AtenNllLossForwardOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenNllLossForwardOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op->getLoc();
Value input = adaptor.getSelf();
Value target = adaptor.getTarget();
Value weight = adaptor.getWeight();
int64_t reduction;
if (!matchPattern(op.getReduction(), m_TorchConstantInt(&reduction)))
return rewriter.notifyMatchFailure(op, "dim must be constant");
// TODO: Incorporate the weight argument.
if (!isa<mlir::torch::Torch::NoneType>(weight.getType()))
return rewriter.notifyMatchFailure(
op, "Unimplemented, the weight operand is not incorporated.");
Value ignoreIndex = adaptor.getIgnoreIndex();
Value ignoreIndexVal = castIntToIndex(rewriter, loc, ignoreIndex);
unsigned inputRank = cast<RankedTensorType>(input.getType()).getRank();
unsigned targetRank = cast<RankedTensorType>(target.getType()).getRank();
// TODO: Add support for k-dim loss.
if (inputRank > 2) {
return rewriter.notifyMatchFailure(
op, "expected input and target to be rank <= 2");
}
RankedTensorType resultType = cast<RankedTensorType>(
getTypeConverter()->convertType(op->getResult(0).getType()));
Type elementType = resultType.getElementType();
Value zeroVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getZeroAttr(elementType));
Value finalRes = torch_to_linalg::createElementwiseLinalgGeneric(
rewriter, loc, {target}, elementType,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value targetVal = args[0];
Value indTarget = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIndexType(), targetVal);
// The final result is given by:
// final_res = (indTarget == ignoreIndexVal) ? 0 :
// input[indI][IndTarget]
Value cmpEq = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, indTarget, ignoreIndexVal);
SmallVector<Value> extractionIndices{indTarget};
if (inputRank == 2) {
Value indI = rewriter.create<linalg::IndexOp>(loc, 0);
extractionIndices.insert(extractionIndices.begin(), indI);
}
Value result =
rewriter.create<tensor::ExtractOp>(loc, input, extractionIndices);
Value negate =
rewriter.create<arith::NegFOp>(loc, elementType, result);
Value selectFinal =
rewriter.create<arith::SelectOp>(loc, cmpEq, zeroVal, negate);
b.create<linalg::YieldOp>(loc, selectFinal);
});
llvm::iota_range<int64_t> dimsToReduce(0, targetRank,
/*inclusive=*/false);
DenseSet<int64_t> dimSet(dimsToReduce.begin(), dimsToReduce.end());
if (reduction == torch_upstream::Reduction::Sum ||
reduction == torch_upstream::Reduction::Mean) {
Value zeroIVal = rewriter.create<arith::ConstantOp>(
loc, rewriter.getZeroAttr(rewriter.getI32Type()));
auto countInfo = torch_to_linalg::ReductionOpInfo{false, target, dimSet};
Value numOfElems = torch_to_linalg::createReductionLinalgGeneric(
rewriter, loc, countInfo,
/*initElem=*/zeroIVal,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value targetVal = args[0];
Value indTarget = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIndexType(), targetVal);
Value cmpEq = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::ne, indTarget, ignoreIndexVal);
cmpEq = rewriter.create<arith::ExtUIOp>(loc, rewriter.getI32Type(),
cmpEq);
Value add = rewriter.create<arith::AddIOp>(loc, args[1], cmpEq);
rewriter.create<linalg::YieldOp>(loc, add);
});
numOfElems = rewriter.create<tensor::ExtractOp>(
loc, rewriter.getI32Type(), numOfElems, ArrayRef<Value>{});
numOfElems = convertScalarToDtype(rewriter, loc, numOfElems, elementType);
auto opInfo = torch_to_linalg::ReductionOpInfo{false, finalRes, dimSet};
finalRes = torch_to_linalg::createReductionLinalgGeneric(
rewriter, loc, opInfo,
/*initElem=*/zeroVal,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value newVal = args[0];
Value accumulator = args[1];
if (reduction == torch_upstream::Reduction::Mean)
newVal = b.create<arith::DivFOp>(loc, newVal, numOfElems);
Value result = b.create<arith::AddFOp>(loc, newVal, accumulator);
b.create<linalg::YieldOp>(loc, result);
});
}
// The implementation for the `total_weight` has been adopted from here:
// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/LossNLL.cpp#L154-L294
// As per the ref link, the `total_weight` value when the `weight` is
// `None`, is equal to `total_weight = batch_size - num_ignored_index`,
// where `batch_size` is equal to `target.shape[0]` when rank(target) > 0,
// otherwise 1. The value `num_ignored_index` is the number of elements of
// the `target` tensors that have been ignored.
if (reduction == torch_upstream::Reduction::None && inputRank == 2) {
Value totalWeight = createZeroInitTensor(rewriter, loc, {}, elementType);
rewriter.replaceOp(op, {finalRes, totalWeight});
return success();
}
Value numIgnoredIndex;
if (targetRank == 0) {
Value targetVal = rewriter.create<tensor::ExtractOp>(loc, target);
numIgnoredIndex = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, targetVal, ignoreIndex);
numIgnoredIndex = convertScalarToDtype(rewriter, loc, numIgnoredIndex,
ignoreIndex.getType());
} else {
Value zeroCstInt = rewriter.create<arith::ConstantOp>(
loc, rewriter.getZeroAttr(ignoreIndex.getType()));
auto opInfo =
torch_to_linalg::ReductionOpInfo{/*keepDim=*/false, target, dimSet};
numIgnoredIndex = torch_to_linalg::createReductionLinalgGeneric(
rewriter, loc, opInfo,
/*initElem=*/zeroCstInt,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value targetVal = args[0];
Value accumulator = args[1];
Value result = b.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, targetVal, ignoreIndex);
result = b.create<arith::AddIOp>(
loc,
convertScalarToDtype(rewriter, loc, result,
ignoreIndex.getType()),
accumulator);
b.create<linalg::YieldOp>(loc, result);
});
numIgnoredIndex =
rewriter.create<tensor::ExtractOp>(loc, numIgnoredIndex);
}
Value numtargetElems = getTensorSize(rewriter, loc, target);
Value totalWeightVal =
rewriter.create<arith::SubIOp>(loc, numtargetElems, numIgnoredIndex);
Value totalWeight = createInitTensor(
rewriter, loc, {}, elementType,
convertScalarToDtype(rewriter, loc, totalWeightVal, elementType));
rewriter.replaceOp(op, {finalRes, totalWeight});
return success();
}
};
} // namespace
/// Inverted STD: rSTD = 1 / sqrt(var + eps).
static Value calculateRSTD(OpBuilder &b, Location loc, Type elemTy, Value eps,
Value var) {
// The eps is always f64.
Value truncatedEps = b.create<arith::TruncFOp>(loc, elemTy, eps);
Value varPlusEps = b.create<arith::AddFOp>(loc, var, truncatedEps);
Value rSTD = b.create<math::RsqrtOp>(loc, varPlusEps);
return rSTD;
}
// Normalization formula:
// ((input - mean) * rSTD * weight + bias
static Value createLinalgPayloadCalculationForNormOpsWithRSTD(
OpBuilder &b, Location loc, Type elemTy, Value input, Value mean,
Value rSTD, Value eps, Value weight, Value bias) {
Value inputSubMean = b.create<arith::SubFOp>(loc, input, mean);
Value temp = b.create<arith::MulFOp>(loc, inputSubMean, rSTD);
Value timesWeight = b.create<arith::MulFOp>(loc, temp, weight);
Value plusBias = b.create<arith::AddFOp>(loc, timesWeight, bias);
return plusBias;
}
static Value createLinalgPayloadCalculationForNormOpsWithVar(
OpBuilder &b, Location loc, Type elemTy, Value input, Value mean, Value var,
Value eps, Value weight, Value bias) {
Value rSTD = calculateRSTD(b, loc, elemTy, eps, var);
Value result = createLinalgPayloadCalculationForNormOpsWithRSTD(
b, loc, elemTy, input, mean, rSTD, eps, weight, bias);
return result;
}
namespace {
class ConvertAtenBatchNormOp : public OpConversionPattern<AtenBatchNormOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenBatchNormOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
MLIRContext *context = op->getContext();
Location loc = op->getLoc();
Value input = adaptor.getInput();
Value weight = adaptor.getWeight();
Value bias = adaptor.getBias();
Value runningMean = adaptor.getRunningMean();
Value runningVar = adaptor.getRunningVar();
Value training = adaptor.getTraining();
Value eps = adaptor.getEps();
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
// TODO: Handle the None cases for the optional parameters:
// weight, bias.
if (failed(checkNotNone(rewriter, op, weight)) ||
failed(checkNotNone(rewriter, op, bias)) ||
failed(checkNotNone(rewriter, op, runningMean)) ||
failed(checkNotNone(rewriter, op, runningVar)))
return failure();
auto inputType = cast<RankedTensorType>(input.getType());
auto weightType = cast<RankedTensorType>(weight.getType());
auto biasType = cast<RankedTensorType>(bias.getType());
auto runningMeanType = cast<RankedTensorType>(runningMean.getType());
auto runningVarType = cast<RankedTensorType>(runningVar.getType());
auto inputRank = inputType.getRank();
if (inputRank < 2)
return rewriter.notifyMatchFailure(
op, "input should have rank larger than 1");
if (weightType.getRank() != 1 || biasType.getRank() != 1 ||
runningMeanType.getRank() != 1 || runningVarType.getRank() != 1) {
return rewriter.notifyMatchFailure(
op, "expect weight, bias, running_mean and running_var to be rank 1");
}
// TODO: Add support for training.
auto constFalse = rewriter.create<arith::ConstantOp>(
loc, IntegerAttr::get(IntegerType::get(context, 1), 0));
auto trainingFalse = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, training, constFalse);
rewriter.create<cf::AssertOp>(
loc, trainingFalse,
rewriter.getStringAttr("training is not supported for now"));
// num_features – C from an expected input of size (N,C,D,H,W ...)
Value numFeatures = rewriter.create<tensor::DimOp>(loc, input, 1);
auto contractingDim0EqualsNumFeatures = [&](Value v) {
auto dim0 = rewriter.create<tensor::DimOp>(loc, v, 0);
auto dim0Equal = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, numFeatures, dim0);
rewriter.create<cf::AssertOp>(
loc, dim0Equal,
rewriter.getStringAttr(
"expect the size of dim 0 equal to the number of features"));
};
if (!isAssumingStrictSymbolicShapes(rewriter)) {
contractingDim0EqualsNumFeatures(weight);
contractingDim0EqualsNumFeatures(bias);
contractingDim0EqualsNumFeatures(runningMean);
contractingDim0EqualsNumFeatures(runningVar);
}
auto indexingMap = AffineMap::get(
/*dimCount=*/inputRank,
/*symbolCount=*/0, rewriter.getAffineDimExpr(1), context);
SmallVector<AffineMap> indexingMaps = {
rewriter.getMultiDimIdentityMap(inputRank), // input
indexingMap, // weight
indexingMap, // bias
indexingMap, // runningMean
indexingMap, // runningVar
rewriter.getMultiDimIdentityMap(inputRank), // output
};
SmallVector<utils::IteratorType> iteratorTypes(
inputRank, utils::IteratorType::parallel);
Value batchNorm =
rewriter
.create<linalg::GenericOp>(
loc, input.getType(),
ValueRange{input, weight, bias, runningMean, runningVar}, input,
/*indexingMaps=*/indexingMaps,
/*iteratorTypes=*/iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value input = args[0], weight = args[1], bias = args[2],
mean = args[3], var = args[4];
Value result =
createLinalgPayloadCalculationForNormOpsWithVar(
b, loc, var.getType(), input, mean, var, eps, weight,
bias);
b.create<linalg::YieldOp>(loc, result);
})
.getResult(0);
Type newResultType = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, batchNorm);
return success();
}
};
} // namespace
namespace {
class ConvertAtenNllLossBackwardOp
: public OpConversionPattern<AtenNllLossBackwardOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenNllLossBackwardOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Location loc = op->getLoc();
Value gradOutput = adaptor.getGradOutput();
Value input = adaptor.getSelf();
Value target = adaptor.getTarget();
Value weight = adaptor.getWeight();
bool weightIsNone = isa<Torch::NoneType>(op.getWeight().getType());
Value ignoreIndex = castIntToIndex(rewriter, loc, adaptor.getIgnoreIndex());
Value totalWeight = adaptor.getTotalWeight();
auto inputType = cast<RankedTensorType>(input.getType());
int inputRank = inputType.getRank();
auto gradOutputType = cast<RankedTensorType>(gradOutput.getType());
Type resultElementType = gradOutputType.getElementType();
int64_t reduction;
if (!matchPattern(op.getReduction(), m_TorchConstantInt(&reduction)))
return rewriter.notifyMatchFailure(op, "dim must be constant");
if (!hasElementType<mlir::FloatType>(gradOutput) ||
!hasElementType<mlir::FloatType>(gradOutput) ||
(!weightIsNone && !hasElementType<mlir::FloatType>(weight))) {
return rewriter.notifyMatchFailure(
op, "`gradOutput`, 'weight', and `totalWeight` must be tensors of "
"type float");
}
if (!hasElementType<mlir::IntegerType>(target)) {
return rewriter.notifyMatchFailure(
op, "`target` must be a tensor of integer type");
}
auto outputSize = getTensorSizes(rewriter, loc, input);
Value gradInputTensor =
createZeroInitTensor(rewriter, loc, outputSize, resultElementType);
auto getAffineMapForSingleElementTensor = [&](Value tensor) {
auto tensorType = cast<RankedTensorType>(tensor.getType());
SmallVector<AffineExpr> affineExprs(tensorType.getRank(),
rewriter.getAffineConstantExpr(0));
return AffineMap::get(inputRank, /*symbolCount=*/0, affineExprs,
op->getContext());
};
AffineMap gradOutMap = AffineMap::get(inputRank, /*symbolCount=*/0,
rewriter.getAffineDimExpr(0));
if (reduction != torch_upstream::Reduction::None || inputRank == 1)
gradOutMap = getAffineMapForSingleElementTensor(gradOutput);
AffineMap targetMap = AffineMap::get(inputRank, /*symbolCount=*/0,
rewriter.getAffineDimExpr(0));
if (inputRank == 1)
targetMap = getAffineMapForSingleElementTensor(target);
AffineMap totalWeightMap = getAffineMapForSingleElementTensor(totalWeight);
AffineMap resultMap = rewriter.getMultiDimIdentityMap(inputRank);
SmallVector<AffineMap> indexingMaps{gradOutMap, targetMap, totalWeightMap,
resultMap};
SmallVector<utils::IteratorType> iteratorTypes(
inputRank, utils::IteratorType::parallel);
// The code generation is equivalent to the following pseudo-code:
//
// for batch_index in len(input.size(0)):
// for class_index in len(input.size(1)):
// target_elem = target[batch_index]
//
// if reduction == None:
// grad_out_elem = grad_output[batchIndex]
// else:
// grad_out_elem = grad_output[0]
//
// if reduction == Mean:
// total_weight_elem = total_weight[0]
// grad_out_elem /= total_weight_elem
//
// weight_elem = weight[target_elem] if weight != None else 1
//
// if target_elem != class_index or target_elem == ignore_index:
// grad_input_elem = -weight_elem * grad_out_elem
// else:
// grad_input_elem = 0
// grad_input[batch_index, target_elem] = grad_input_elem
//
// NOTE: In the case of not batch dimension, `batch_index` essentially
// becomes zero.
Value gradInput =
rewriter
.create<linalg::GenericOp>(
loc, gradInputTensor.getType(),
ValueRange{gradOutput, target, totalWeight}, gradInputTensor,
indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value gradOutElem = args[0];
Value targetElem = castIntToIndex(b, loc, args[1]);
Value totalWeightElem = args[2];
Value classIndex =
b.create<linalg::IndexOp>(loc, inputRank - 1);
if (reduction == torch_upstream::Reduction::Mean) {
gradOutElem = b.create<arith::DivFOp>(loc, gradOutElem,
totalWeightElem);
}
Value negGradOutElem =
b.create<arith::NegFOp>(loc, gradOutElem);
Value weightElem = getConstant(b, loc, 1, resultElementType);
if (!weightIsNone) {
weightElem =
b.create<tensor::ExtractOp>(loc, weight, targetElem);
}
Value weightedNegGradOutElem =
b.create<arith::MulFOp>(loc, weightElem, negGradOutElem);
Value targetNeqClassIndex = b.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::ne, targetElem, classIndex);
Value targetEqIgnoreIndex = b.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, targetElem, ignoreIndex);
Value gradInputIsZero = b.create<arith::OrIOp>(
loc, targetNeqClassIndex, targetEqIgnoreIndex);
Value zero = getConstant(b, loc, 0, resultElementType);
Value gradInElem = b.create<arith::SelectOp>(
loc, gradInputIsZero, zero, weightedNegGradOutElem);
b.create<linalg::YieldOp>(loc, gradInElem);
})
->getResult(0);
RankedTensorType resultType = cast<RankedTensorType>(
getTypeConverter()->convertType(op->getResult(0).getType()));
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, gradInput);
return success();
}
};
} // namespace
namespace {
class ConvertAtenDetachOp : public OpConversionPattern<AtenDetachOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenDetachOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
Type resultType = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
adaptor.getSelf());
return success();
}
};
} // namespace
namespace {
class ConvertPrimsSplitDimOp : public OpConversionPattern<PrimsSplitDimOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(PrimsSplitDimOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
auto aRankedTensorType = cast<RankedTensorType>(adaptor.getA().getType());
const TypeConverter *typeConverter = getTypeConverter();
auto resultRankedTensorType =
cast<RankedTensorType>(typeConverter->convertType(op.getType()));
// The dimension being split must be statically known.
int64_t dimInt;
if (!matchPattern(op.getDim(), m_TorchConstantInt(&dimInt)))
return failure();
SmallVector<ReassociationIndices> associations;
associations.reserve(aRankedTensorType.getRank());
for (unsigned i = 0; i < dimInt; ++i) {
associations.push_back(ReassociationIndices{i});
}
associations.push_back(ReassociationIndices{dimInt, dimInt + 1});
for (int i = dimInt + 2; i < resultRankedTensorType.getRank(); ++i) {
associations.push_back(ReassociationIndices{i});
}
auto expanded = rewriter.createOrFold<tensor::ExpandShapeOp>(
op.getLoc(), resultRankedTensorType, adaptor.getA(), associations);
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultRankedTensorType,
expanded);
return success();
}
};
} // namespace
namespace {
class ConvertPrimsCollapseOp : public OpConversionPattern<PrimsCollapseOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(PrimsCollapseOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
auto aRankedTensorType = cast<RankedTensorType>(adaptor.getA().getType());
const TypeConverter *typeConverter = getTypeConverter();
auto resultRankedTensorType =
cast<RankedTensorType>(typeConverter->convertType(op.getType()));
// Collapse range must be statically known.
int64_t startInt;
if (!matchPattern(op.getStart(), m_TorchConstantInt(&startInt)))
return failure();
int64_t endInt;
if (!matchPattern(op.getEnd(), m_TorchConstantInt(&endInt)))
return failure();
// Upstream MLIR is overly strict -- it fails verification if the
// collapse_shape is the identity op (i.e. when no dimensions are
// collapsed). We manually fold this case here.
if (startInt == endInt) {
rewriter.replaceOp(op, adaptor.getA());
return success();
}
SmallVector<ReassociationIndices> associations;
associations.reserve(resultRankedTensorType.getRank());
// An example of is where input shape is [3,4,5,6] and
// start = 1, and end = 2. The collapsed shape is then [3,4*5,6],
// with reassociation indices of [0], [1,2], and [3].
// Append the singleton dimensions before the collapsed dimensions.
for (unsigned i = 0; i < startInt; ++i) {
associations.push_back(ReassociationIndices{i});
}
// Append the collapsed dimensions.
ReassociationIndices collapseDims(endInt + 1 - startInt);
std::iota(collapseDims.begin(), collapseDims.end(), startInt);
associations.push_back(collapseDims);
// Append the singleton dimensions after the collapsed dimensions.
for (int i = endInt + 1; i < aRankedTensorType.getRank(); ++i) {
associations.push_back(ReassociationIndices{i});
}
rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(
op, resultRankedTensorType, adaptor.getA(), associations);
return success();
}
};
} // namespace
namespace {
class ConvertTensorStaticInfoCastOp
: public OpConversionPattern<TensorStaticInfoCastOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(TensorStaticInfoCastOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
RankedTensorType resultType = cast<RankedTensorType>(
getTypeConverter()->convertType(op->getResult(0).getType()));
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
adaptor.getOperand());
return success();
}
};
} // namespace
namespace {
class ConvertLogitOp : public OpConversionPattern<AtenLogitOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenLogitOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op->getLoc();
Value input = adaptor.getSelf();
Value eps = adaptor.getEps();
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
return failure();
bool handleEps = false;
if (succeeded(checkNotNone(rewriter, op, eps)))
handleEps = true;
if (handleEps && !isa<mlir::FloatType>(eps.getType())) {
op.emitError("Logit does not support non-floating point type");
return failure();
}
auto inputType = cast<RankedTensorType>(input.getType());
auto inputElementType = inputType.getElementType();
if (!isa<mlir::FloatType>(inputElementType)) {
op.emitError("Logit does not support non-floating point type");
return failure();
}
auto inputRank = inputType.getRank();
SmallVector<AffineMap> indexingMaps = {
rewriter.getMultiDimIdentityMap(inputRank), // input
rewriter.getMultiDimIdentityMap(inputRank), // output
};
SmallVector<utils::IteratorType> iteratorTypes(
inputRank, utils::IteratorType::parallel);
Value logit =
rewriter
.create<linalg::GenericOp>(
loc, input.getType(),
/*ins=*/input,
/*outs=*/input,
/*indexingMaps=*/indexingMaps,
/*iteratorTypes=*/iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value input = args[0];
TypedAttr oneAttr = b.getFloatAttr(inputElementType, 1.0);
Value oneValue = b.create<arith::ConstantOp>(loc, oneAttr);
Value zI;
if (!handleEps) {
zI = input;
} else {
Value truncEps =
b.create<arith::TruncFOp>(loc, inputElementType, eps);
Value oneMinusEps =
b.create<arith::SubFOp>(loc, oneValue, truncEps);
Value min =
b.create<arith::MinimumFOp>(loc, input, oneMinusEps);
Value clampedInput =
b.create<arith::MaximumFOp>(loc, min, truncEps);
zI = clampedInput;
}
Value probability =
b.create<arith::SubFOp>(loc, oneValue, zI);
Value odds = b.create<arith::DivFOp>(loc, zI, probability);
Value result = b.create<math::LogOp>(loc, odds);
b.create<linalg::YieldOp>(loc, result);
})
.getResult(0);
Type newResultType = getTypeConverter()->convertType(op.getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, logit);
return success();
}
};
} // namespace
namespace {
class ConvertAtenIntReprOp : public OpConversionPattern<AtenIntReprOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenIntReprOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
RankedTensorType resultType = cast<RankedTensorType>(
getTypeConverter()->convertType(op->getResult(0).getType()));
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
adaptor.getSelf());
return success();
}
};
} // namespace
namespace {
class ConvertDequantizePerChannel
: public OpConversionPattern<AtenDequantizeSelfOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenDequantizeSelfOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto loc = op.getLoc();
auto qoperand = op.getOperand();
auto make = qoperand.getDefiningOp<Aten_MakePerChannelQuantizedTensorOp>();
if (!make) {
return rewriter.notifyMatchFailure(op, "did not find per channel qint");
}
auto converter = getTypeConverter();
auto operand = make.getOperand(0);
auto scale = make.getScale();
auto zeropoint = make.getZeroPoint();
auto axis = make.getAxis();
IntegerAttr axisAttr;
if (!matchPattern(axis, m_Constant(&axisAttr))) {
return failure();
}
auto operandDTy = cast<ValueTensorType>(operand.getType()).getDtype();
auto zeropointDTy = cast<ValueTensorType>(zeropoint.getType()).getDtype();
operand = converter->materializeTargetConversion(
rewriter, loc, converter->convertType(operand.getType()), operand);
scale = converter->materializeTargetConversion(
rewriter, loc, converter->convertType(scale.getType()), scale);
zeropoint = converter->materializeTargetConversion(
rewriter, loc, converter->convertType(zeropoint.getType()), zeropoint);
auto resultType = cast<RankedTensorType>(
converter->convertType(op->getResult(0).getType()));
llvm::SmallVector<Value> dynSizes;
for (auto [index, dim] : llvm::enumerate(resultType.getShape())) {
if (ShapedType::isDynamic(dim)) {
dynSizes.push_back(rewriter.create<tensor::DimOp>(loc, operand, index));
}
}
llvm::SmallVector<utils::IteratorType> iterators(
resultType.getRank(), utils::IteratorType::parallel);
llvm::SmallVector<AffineMap> maps(
4, {rewriter.getMultiDimIdentityMap(resultType.getRank())});
auto broadcastMap = AffineMap::get(
resultType.getRank(), /*symbolCount=*/0,
{rewriter.getAffineDimExpr(axisAttr.getInt())}, rewriter.getContext());
maps[1] = broadcastMap;
maps[2] = broadcastMap;
auto empty =
rewriter.create<tensor::EmptyOp>(op.getLoc(), resultType, dynSizes);
auto linalgOp = rewriter.create<linalg::GenericOp>(
loc, resultType, ValueRange{operand, scale, zeropoint},
ValueRange{empty}, maps, iterators,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value operand = args[0];
Value scale = args[1];
Value zeropoint = args[2];
if (operandDTy.isUnsignedInteger(8)) {
operand = b.create<arith::ExtUIOp>(loc, b.getI32Type(), operand);
} else if (operandDTy.isSignedInteger(8)) {
operand = b.create<arith::ExtSIOp>(loc, b.getI32Type(), operand);
}
if (zeropointDTy.isUnsignedInteger(8)) {
zeropoint =
b.create<arith::ExtUIOp>(loc, b.getI32Type(), zeropoint);
} else if (zeropointDTy.isSignedInteger(8)) {
zeropoint =
b.create<arith::ExtSIOp>(loc, b.getI32Type(), zeropoint);
}
Value sub = rewriter.create<arith::SubIOp>(loc, operand, zeropoint);
Value fp =
rewriter.create<arith::SIToFPOp>(loc, args[3].getType(), sub);
Value mul = rewriter.create<arith::MulFOp>(loc, fp, scale);
b.create<linalg::YieldOp>(loc, mul);
});
rewriter.replaceOp(op, linalgOp.getResults());
return success();
}
};
} // namespace
namespace {
template <typename OpTy>
class ConvertCastEquivalentOp : public OpConversionPattern<OpTy> {
using OpConversionPattern<OpTy>::OpConversionPattern;
using OpAdaptor = typename OpTy::Adaptor;
LogicalResult
matchAndRewrite(OpTy op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto converter = this->getTypeConverter();
RankedTensorType resultType = cast<RankedTensorType>(
converter->convertType(op->getResult(0).getType()));
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
adaptor.getSelf());
return success();
}
};
} // namespace
namespace {
class ConvertAtenGridSamplerOp : public OpConversionPattern<AtenGridSamplerOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenGridSamplerOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op->getLoc();
Type int64type = rewriter.getI64Type();
Type floatType = rewriter.getF32Type();
Value zeroIndex = rewriter.create<arith::ConstantIndexOp>(loc, 0);
Value oneIndex = rewriter.create<arith::ConstantIndexOp>(loc, 1);
Value twoIndex = rewriter.create<arith::ConstantIndexOp>(loc, 2);
Value zeroFloat = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(floatType, 0.0));
Value oneFloat = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(floatType, 1.0));
Value twoFloat = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(floatType, 2.0));
Value input = adaptor.getInput();
auto inputType = cast<RankedTensorType>(input.getType());
auto inputShape = inputType.getShape();
Value innerDim0a = rewriter.create<tensor::DimOp>(loc, input, 2);
Value innerDim1a = rewriter.create<tensor::DimOp>(loc, input, 3);
Value innerDim0b =
rewriter.create<arith::SubIOp>(loc, innerDim0a, oneIndex);
Value innerDim1b =
rewriter.create<arith::SubIOp>(loc, innerDim1a, oneIndex);
Value innerDim0c =
rewriter.create<arith::IndexCastOp>(loc, int64type, innerDim0b);
Value innerDim1c =
rewriter.create<arith::IndexCastOp>(loc, int64type, innerDim1b);
Value innerDim0d =
rewriter.create<arith::SIToFPOp>(loc, floatType, innerDim0c);
Value innerDim1d =
rewriter.create<arith::SIToFPOp>(loc, floatType, innerDim1c);
Value innerDim0e =
rewriter.create<arith::DivFOp>(loc, innerDim0d, twoFloat);
Value innerDim1e =
rewriter.create<arith::DivFOp>(loc, innerDim1d, twoFloat);
Value grid = adaptor.getGrid();
auto gridType = cast<RankedTensorType>(grid.getType());
auto gridShape = gridType.getShape();
auto gridRank = gridType.getRank();
SmallVector<Value> extractGridOffsets0(gridRank, zeroIndex);
SmallVector<Value> extractGridShape = getTensorSizes(rewriter, loc, grid);
SmallVector<Value> extractGridStride(gridRank, oneIndex);
int64_t lastGridDim = gridRank - 1;
extractGridShape[lastGridDim] = oneIndex;
extractGridStride[lastGridDim] = twoIndex;
SmallVector<Value> extractGridOffsets1(gridRank, zeroIndex);
extractGridOffsets1[lastGridDim] = oneIndex;
SmallVector<int64_t> gridShapeExtracted(gridShape);
gridShapeExtracted.back() = 1;
SmallVector<int64_t> gridShapeCollapsed{gridShape[0], gridShape[1],
gridShape[2]};
auto grid0 = rewriter.create<tensor::ExtractSliceOp>(
loc, grid, extractGridOffsets0, extractGridShape, extractGridStride);
auto grid1 = rewriter.create<tensor::ExtractSliceOp>(
loc, grid, extractGridOffsets1, extractGridShape, extractGridStride);
SmallVector<ReassociationIndices> associations{ReassociationIndices{0},
ReassociationIndices{1},
ReassociationIndices{2, 3}};
auto gridCollapsed0 =
rewriter.create<tensor::CollapseShapeOp>(loc, grid0, associations);
auto gridCollapsed1 =
rewriter.create<tensor::CollapseShapeOp>(loc, grid1, associations);
AffineMap gridMap = AffineMap::get(4, 0,
{rewriter.getAffineDimExpr(0),
rewriter.getAffineDimExpr(2),
rewriter.getAffineDimExpr(3)},
op->getContext());
SmallVector<AffineMap> gridMaps{gridMap, gridMap,
rewriter.getMultiDimIdentityMap(gridRank)};
SmallVector<utils::IteratorType> gridIterators(
gridRank, utils::IteratorType::parallel);
SmallVector<int64_t> resultShape{inputShape[0], inputShape[1], gridShape[1],
gridShape[2]};
auto lambdaExtract = [](OpBuilder &b, Location loc, Value input, Value idxA,
Value idxB, Value idxC, Value idxD) -> Value {
SmallVector<Value> index{idxA, idxB, idxC, idxD};
Value result = b.create<tensor::ExtractOp>(loc, input, index);
return result;
};
auto lambdaLinear = [&](OpBuilder &b, Location loc, Value x, Value y,
Value d) -> Value {
Value dm = b.create<arith::SubFOp>(loc, oneFloat, d);
Value ra = b.create<arith::MulFOp>(loc, x, dm);
Value rb = b.create<arith::MulFOp>(loc, y, d);
Value res = b.create<arith::AddFOp>(loc, ra, rb);
return res;
};
auto lambdaNearest = [&](OpBuilder &b, Location loc, Value x, Value y,
Value d) -> Value {
Value halfConst = rewriter.create<arith::ConstantOp>(
loc, rewriter.getFloatAttr(floatType, 0.5));
Value checkClosest =
b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::OLT, d, halfConst);
Value res = b.create<arith::SelectOp>(loc, checkClosest, x, y);
return res;
};
auto lambdaInterpolate = [&](OpBuilder &b, Location loc, Value iMode,
Value x, Value y, Value d) -> Value {
Value linear = lambdaLinear(b, loc, x, y, d);
Value nearest = lambdaNearest(b, loc, x, y, d);
Value zeroInt =
b.create<arith::ConstantOp>(loc, b.getIntegerAttr(int64type, 0));
Value checkMode = b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq,
iMode, zeroInt);
Value res = b.create<arith::SelectOp>(loc, checkMode, linear, nearest);
return res;
};
auto resultType = cast<RankedTensorType>(
getTypeConverter()->convertType(op.getResult().getType()));
SmallVector<Value> resultSize{};
if (resultType.isDynamicDim(0))
resultSize.push_back(rewriter.create<tensor::DimOp>(loc, input, 0));
if (resultType.isDynamicDim(1))
resultSize.push_back(rewriter.create<tensor::DimOp>(loc, input, 1));
if (resultType.isDynamicDim(2))
resultSize.push_back(rewriter.create<tensor::DimOp>(loc, grid, 1));
if (resultType.isDynamicDim(3))
resultSize.push_back(rewriter.create<tensor::DimOp>(loc, grid, 2));
Value alignCorners = adaptor.getAlignCorners();
Value interMode = adaptor.getInterpolationMode();
Value resultFinal =
rewriter.create<tensor::EmptyOp>(loc, resultType, resultSize);
auto sGrid = rewriter.create<linalg::GenericOp>(
loc, TypeRange{resultType}, ValueRange{gridCollapsed0, gridCollapsed1},
ValueRange(resultFinal), gridMaps, gridIterators,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value gr0 = args[1];
Value gr1 = args[0];
Value gr0Half = b.create<arith::DivFOp>(loc, gr0, twoFloat);
Value gr1Half = b.create<arith::DivFOp>(loc, gr1, twoFloat);
Value gr0HalfSelect =
b.create<arith::SelectOp>(loc, alignCorners, zeroFloat, gr0Half);
Value gr1HalfSelect =
b.create<arith::SelectOp>(loc, alignCorners, zeroFloat, gr1Half);
Value gplus0 = b.create<arith::AddFOp>(loc, gr0, oneFloat);
Value gplus1 = b.create<arith::AddFOp>(loc, gr1, oneFloat);
Value gPlusMul0 = b.create<arith::MulFOp>(loc, gplus0, innerDim0e);
Value gPlusMul1 = b.create<arith::MulFOp>(loc, gplus1, innerDim1e);
Value result0 =
b.create<arith::AddFOp>(loc, gPlusMul0, gr0HalfSelect);
Value result1 =
b.create<arith::AddFOp>(loc, gPlusMul1, gr1HalfSelect);
Value checkLowerBound0 = b.create<arith::CmpFOp>(
loc, arith::CmpFPredicate::OLT, result0, zeroFloat);
Value checkLowerBound1 = b.create<arith::CmpFOp>(
loc, arith::CmpFPredicate::OLT, result1, zeroFloat);
Value lowerOrig0 = b.create<arith::FPToSIOp>(loc, int64type, result0);
Value lowerOrig1 = b.create<arith::FPToSIOp>(loc, int64type, result1);
Value zeroInt =
b.create<arith::ConstantOp>(loc, b.getIntegerAttr(int64type, 0));
Value oneInt =
b.create<arith::ConstantOp>(loc, b.getIntegerAttr(int64type, 1));
Value lowerSub0 = b.create<arith::SubIOp>(loc, lowerOrig0, oneInt);
Value lowerSub1 = b.create<arith::SubIOp>(loc, lowerOrig1, oneInt);
Value lower0 = b.create<arith::SelectOp>(loc, checkLowerBound0,
lowerSub0, lowerOrig0);
Value lower1 = b.create<arith::SelectOp>(loc, checkLowerBound1,
lowerSub1, lowerOrig1);
Value lowerValid0 =
b.create<arith::SelectOp>(loc, checkLowerBound0, zeroInt, lower0);
Value lowerValid1 =
b.create<arith::SelectOp>(loc, checkLowerBound1, zeroInt, lower1);
Value upper0 =
b.create<arith::AddIOp>(loc, int64type, lower0, oneInt);
Value upper1 =
b.create<arith::AddIOp>(loc, int64type, lower1, oneInt);
Value notValidUpper0 = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::sgt, upper0, innerDim0c);
Value notValidUpper1 = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::sgt, upper1, innerDim1c);
Value upperValid0 =
b.create<arith::SelectOp>(loc, notValidUpper0, lower0, upper0);
Value upperValid1 =
b.create<arith::SelectOp>(loc, notValidUpper1, lower1, upper1);
Value lw0 =
b.create<arith::IndexCastOp>(loc, b.getIndexType(), lowerValid0);
Value lw1 =
b.create<arith::IndexCastOp>(loc, b.getIndexType(), lowerValid1);
Value up0 =
b.create<arith::IndexCastOp>(loc, b.getIndexType(), upperValid0);
Value up1 =
b.create<arith::IndexCastOp>(loc, b.getIndexType(), upperValid1);
Value N = b.create<linalg::IndexOp>(loc, 0);
Value C = b.create<linalg::IndexOp>(loc, 1);
Value result00 = lambdaExtract(b, loc, input, N, C, lw0, lw1);
Value result00a = b.create<arith::SelectOp>(loc, checkLowerBound0,
zeroFloat, result00);
Value result00b = b.create<arith::SelectOp>(loc, checkLowerBound1,
zeroFloat, result00a);
Value result01 = lambdaExtract(b, loc, input, N, C, lw0, up1);
Value result01a = b.create<arith::SelectOp>(loc, notValidUpper1,
zeroFloat, result01);
Value result01b = b.create<arith::SelectOp>(loc, checkLowerBound0,
zeroFloat, result01a);
Value result10 = lambdaExtract(b, loc, input, N, C, up0, lw1);
Value result10a = b.create<arith::SelectOp>(loc, notValidUpper0,
zeroFloat, result10);
Value result10b = b.create<arith::SelectOp>(loc, checkLowerBound1,
zeroFloat, result10a);
Value result11 = lambdaExtract(b, loc, input, N, C, up0, up1);
Value result11a = b.create<arith::SelectOp>(loc, notValidUpper0,
zeroFloat, result11);
Value result11b = b.create<arith::SelectOp>(loc, notValidUpper1,
zeroFloat, result11a);
Value lw0a = b.create<arith::SIToFPOp>(loc, floatType, lower0);
Value lw1a = b.create<arith::SIToFPOp>(loc, floatType, lower1);
Value d1 = b.create<arith::SubFOp>(loc, result0, lw0a);
Value d0 = b.create<arith::SubFOp>(loc, result1, lw1a);
Value resultScaled0 =
lambdaInterpolate(b, loc, interMode, result00b, result01b, d0);
Value resultScaled1 =
lambdaInterpolate(b, loc, interMode, result10b, result11b, d0);
Value resultScaled = lambdaInterpolate(
b, loc, interMode, resultScaled0, resultScaled1, d1);
b.create<linalg::YieldOp>(loc, resultScaled);
});
rewriter.replaceOp(op, sGrid.getResults());
return success();
}
};
} // namespace
static Value NearestInterpolate(OpBuilder &b, Location loc,
SmallVector<Value> outputSizes, Value input,
SmallVector<Value> inputSizes,
SmallVector<Value> scaleValues,
std::string coordStr, std::string nearestMode) {
auto inputType = cast<RankedTensorType>(input.getType());
auto inputRank = inputType.getRank();
SmallVector<Value> indices;
for (unsigned i = 0; i < inputRank; i++) {
indices.push_back(b.create<linalg::IndexOp>(loc, i));
}
for (unsigned i = 2; i < inputRank; i++) {
Value outIndex = indices[i];
Value inputSizeFP =
b.create<arith::SIToFPOp>(loc, b.getF32Type(), inputSizes[i - 2]);
Value outputSizeFP =
b.create<arith::SIToFPOp>(loc, b.getF32Type(), outputSizes[i - 2]);
// scale = length_resized / length_original
// x_original = x_resized / scale
Value scale;
if (scaleValues.empty())
scale = b.create<arith::DivFOp>(loc, outputSizeFP, inputSizeFP);
else
scale = scaleValues[i - 2];
Value outInt = b.create<arith::IndexCastOp>(loc, b.getI64Type(), outIndex);
Value outFP = b.create<arith::SIToFPOp>(loc, b.getF32Type(), outInt);
Value proj;
if (coordStr.empty() || coordStr == "_asymmetric") {
proj = b.create<arith::DivFOp>(loc, outFP, scale);
} else if (coordStr == "_half_pixel") {
Value cstHalf = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.5));
Value add = b.create<arith::AddFOp>(loc, outFP, cstHalf);
Value div = b.create<arith::DivFOp>(loc, add, scale);
proj = b.create<arith::SubFOp>(loc, div, cstHalf);
} else {
llvm_unreachable("Unsupported coordination transformation mode");
}
Value nearestFP;
// get nearest pixel using floor
if (nearestMode == "floor" || nearestMode == "") {
nearestFP = b.create<math::FloorOp>(loc, proj);
} else if (nearestMode == "round_prefer_floor") {
Value cstHalf = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.5));
Value floor = b.create<math::FloorOp>(loc, proj);
Value ceil = b.create<math::CeilOp>(loc, proj);
Value decimal = b.create<arith::SubFOp>(loc, proj, floor);
Value cmp = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULE,
decimal, cstHalf);
nearestFP = b.create<arith::SelectOp>(loc, cmp, floor, ceil);
} else if (nearestMode == "round_prefer_ceil") {
Value cstHalf = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.5));
Value cstOne = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(1));
Value floor = b.create<math::FloorOp>(loc, proj);
Value ceil = b.create<math::CeilOp>(loc, proj);
Value decimal = b.create<arith::SubFOp>(loc, proj, floor);
Value cmp = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGE,
decimal, cstHalf);
nearestFP = b.create<arith::SelectOp>(loc, cmp, ceil, floor);
Value inputSizeMOne = b.create<arith::SubFOp>(loc, inputSizeFP, cstOne);
// don't extract out of bounds
nearestFP = b.create<arith::MinimumFOp>(loc, nearestFP, inputSizeMOne);
} else if (nearestMode == "ceil") {
Value cstOne = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(1));
Value inputSizeMOne = b.create<arith::SubFOp>(loc, inputSizeFP, cstOne);
nearestFP = b.create<math::CeilOp>(loc, proj);
nearestFP = b.create<arith::MinimumFOp>(loc, nearestFP, inputSizeMOne);
} else {
llvm_unreachable("Unsupported nearest mode");
}
Value nearestInt =
b.create<arith::FPToSIOp>(loc, b.getI64Type(), nearestFP);
Value nearest =
b.create<arith::IndexCastOp>(loc, b.getIndexType(), nearestInt);
indices[i] = nearest;
}
Value retVal = b.create<tensor::ExtractOp>(loc, input, indices);
return retVal;
}
static Value BilinearInterpolate(OpBuilder &b,
Aten__InterpolateSizeListScaleListOp op,
Location loc, SmallVector<Value> outputSizes,
Value input, SmallVector<Value> inputSizes,
SmallVector<Value> scaleValues,
std::string coordStr) {
unsigned dimOffset = 2;
auto inputType = cast<RankedTensorType>(input.getType());
auto inputRank = inputType.getRank();
Value cstOneEps =
b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(1.000001));
Value cstOneFloat = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(1.0));
Value cstHalf = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.5));
Value zero = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.0));
bool alignCornersBool;
matchPattern(op.getAlignCorners(), m_TorchConstantBool(&alignCornersBool));
SmallVector<Value> indices;
for (unsigned i = 0; i < inputRank; i++) {
indices.push_back(b.create<linalg::IndexOp>(loc, i));
}
SmallVector<Value> proj, projEps, high, low, highFP, lowFP;
for (unsigned i = 0; i < inputRank - dimOffset; i++) {
// length_original
Value inputFP =
b.create<arith::SIToFPOp>(loc, b.getF32Type(), inputSizes[i]);
// length_resized
Value outputSizeFP =
b.create<arith::SIToFPOp>(loc, b.getF32Type(), outputSizes[i]);
// scale = length_resized/length_original
Value scale;
if (alignCornersBool) {
// x_original = x_resized * (length_original - 1) / (length_resized - 1)
Value inputSubOne = b.create<arith::SubFOp>(loc, inputFP, cstOneFloat);
Value outputSizeSubOne =
b.create<arith::SubFOp>(loc, outputSizeFP, cstOneFloat);
Value cmp = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UEQ,
outputSizeSubOne, zero);
scale = b.create<arith::DivFOp>(loc, inputSubOne, outputSizeSubOne);
scale = b.create<arith::SelectOp>(loc, cmp, zero, scale);
coordStr = "_align_corners";
} else if (scaleValues.empty())
scale = b.create<arith::DivFOp>(loc, outputSizeFP, inputFP);
else
scale = scaleValues[i];
// y_resized
Value outInt = b.create<arith::IndexCastOp>(loc, b.getI64Type(),
indices[i + dimOffset]);
Value outFP = b.create<arith::SIToFPOp>(loc, b.getF32Type(), outInt);
Value preClip;
if (coordStr == "_align_corners") {
preClip = b.create<arith::MulFOp>(loc, outFP, scale);
}
if (coordStr == "_asymmetric") {
preClip = b.create<arith::DivFOp>(loc, outFP, scale);
}
if (coordStr == "_pytorch_half_pixel" || coordStr == "" ||
coordStr == "_half_pixel_symmetric") {
// half-pixel modes
// y_resized + 0.5
Value outPlusHalf = b.create<arith::AddFOp>(loc, outFP, cstHalf);
// (y_resized + 0.5) / scale
Value outDivScale = b.create<arith::DivFOp>(loc, outPlusHalf, scale);
// _ - 0.5
preClip = b.create<arith::SubFOp>(loc, outDivScale, cstHalf);
}
// for half_pixel_symmetric, need to compute offset from raw scales
if (coordStr == "_half_pixel_symmetric" && !scaleValues.empty()) {
Value outputSizeFromScale = b.create<arith::MulFOp>(loc, inputFP, scale);
Value adjustment =
b.create<arith::DivFOp>(loc, outputSizeFP, outputSizeFromScale);
Value cstTwo = b.create<arith::ConstantOp>(loc, b.getF32FloatAttr(2.0));
Value center = b.create<arith::DivFOp>(loc, inputFP, cstTwo);
Value oneMAdjustment =
b.create<arith::SubFOp>(loc, cstOneFloat, adjustment);
Value offset = b.create<arith::MulFOp>(loc, center, oneMAdjustment);
preClip = b.create<arith::AddFOp>(loc, offset, preClip);
}
// for pytorch half pixel , special case for length_resized == 1:
if (coordStr == "_pytorch_half_pixel") {
Value cmp = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UEQ,
outputSizeFP, cstOneFloat);
preClip = b.create<arith::SelectOp>(loc, cmp, zero, preClip);
}
// clip to 0,inf
Value max = b.create<arith::MaximumFOp>(loc, preClip, zero);
// length_original - 1.001
Value inputSubOneEps = b.create<arith::SubFOp>(loc, inputFP, cstOneEps);
Value inputSubOne = b.create<arith::SubFOp>(loc, inputFP, cstOneFloat);
// clip to [0,length_original - 1.001]
projEps.push_back(b.create<arith::MinimumFOp>(loc, max, inputSubOneEps));
proj.push_back(b.create<arith::MinimumFOp>(loc, max, inputSubOne));
lowFP.push_back(b.create<math::FloorOp>(loc, projEps[i]));
Value projPlusOne = b.create<arith::AddFOp>(loc, cstOneFloat, projEps[i]);
highFP.push_back(b.create<math::FloorOp>(loc, projPlusOne));
Value lowInt = b.create<arith::FPToSIOp>(loc, b.getI64Type(), lowFP[i]);
low.push_back(b.create<arith::IndexCastOp>(loc, b.getIndexType(), lowInt));
Value highInt = b.create<arith::FPToSIOp>(loc, b.getI64Type(), highFP[i]);
high.push_back(
b.create<arith::IndexCastOp>(loc, b.getIndexType(), highInt));
}
SmallVector<Value> cornerValues;
indices[dimOffset] = low[0];
indices[dimOffset + 1] = low[1];
Value p00 = b.create<tensor::ExtractOp>(loc, input, indices);
indices[dimOffset] = low[0];
indices[dimOffset + 1] = high[1];
Value p01 = b.create<tensor::ExtractOp>(loc, input, indices);
indices[dimOffset] = high[0];
indices[dimOffset + 1] = low[1];
Value p10 = b.create<tensor::ExtractOp>(loc, input, indices);
indices[dimOffset] = high[0];
indices[dimOffset + 1] = high[1];
Value p11 = b.create<tensor::ExtractOp>(loc, input, indices);
// Let Aij := area rect((yProj,xProj) <-> (y_i*,x_j*)),
// where i* = i+1 mod 2 and x_0 = xLow, x_1 = xHigh etc.
// We interpolate via the weighted average of pij by weights Aij
// the formula is retval = Sum(pij*Aij for i and j in range(2))
// Note: we do not need to divide by total rect area == 1
// lengths : Aij == dyi*dxj
Value dy0 = b.create<arith::SubFOp>(loc, highFP[0], proj[0]);
Value dy1 = b.create<arith::SubFOp>(loc, proj[0], lowFP[0]);
Value dx0 = b.create<arith::SubFOp>(loc, highFP[1], proj[1]);
Value dx1 = b.create<arith::SubFOp>(loc, proj[1], lowFP[1]);
// left = A00*p00 + A01*p01 = dy0(dx0p00 + dx1p01)
Value dx0p00 = b.create<arith::MulFOp>(loc, dx0, p00);
Value dx1p01 = b.create<arith::MulFOp>(loc, dx1, p01);
Value sum = b.create<arith::AddFOp>(loc, dx0p00, dx1p01);
Value left = b.create<arith::MulFOp>(loc, dy0, sum);
// right = A10*p10 + A11*p11 = dy1(dx0p10 + dx1p11)
Value dx0p10 = b.create<arith::MulFOp>(loc, dx0, p10);
Value dx1p11 = b.create<arith::MulFOp>(loc, dx1, p11);
sum = b.create<arith::AddFOp>(loc, dx0p10, dx1p11);
Value right = b.create<arith::MulFOp>(loc, dy1, sum);
return b.create<arith::AddFOp>(loc, left, right);
}
namespace {
class ConvertInterpolateOp
: public OpConversionPattern<Aten__InterpolateSizeListScaleListOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(Aten__InterpolateSizeListScaleListOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
std::string mode;
// note: to support onnx.Resize, we are passing some extra options through
// the mode attribute. For example, onnx.Resize with mode="linear" and
// coordinate_transformation_mode="asymmetric" will lower to an interpolate
// op with the non-standard mode="bilinear_asymmetric".
matchPattern(op.getMode(), m_TorchConstantStr(mode));
if (mode.substr(0, 8) != "bilinear" && mode.substr(0, 7) != "nearest") {
return failure();
}
Location loc = op->getLoc();
Value input = adaptor.getInput();
auto inputType = cast<RankedTensorType>(input.getType());
auto inputRank = inputType.getRank();
if (mode.substr(0, 8) == "bilinear" && inputRank != 4)
return rewriter.notifyMatchFailure(
op,
"cannot perform bilinear interpolation when input spatial dims != 2");
SmallVector<Value> outputSizeIntValues;
SmallVector<Value> inputSizes;
SmallVector<Value> ScaleFactorFloatValues;
for (unsigned i = 2; i < inputRank; i++) {
Value inputSize = getDimOp(rewriter, loc, input, i);
inputSizes.push_back(rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIntegerType(64), inputSize));
}
if (!isa<Torch::NoneType>(op.getScaleFactor().getType())) {
bool recompScale;
if (!matchPattern(op.getRecomputeScaleFactor(),
m_TorchConstantBool(&recompScale)))
recompScale = false;
SmallVector<Value> ScaleFactorTorchFloat;
if (!getListConstructElements(op.getScaleFactor(), ScaleFactorTorchFloat))
return rewriter.notifyMatchFailure(
op, "unimplemented: the output_size is not constructed from "
"ListConstruct");
ScaleFactorFloatValues = getTypeConvertedValues(
rewriter, loc, getTypeConverter(), ScaleFactorTorchFloat);
for (unsigned i = 0; i < inputRank - 2; i++) {
Value inputSizeFP = rewriter.create<arith::SIToFPOp>(
loc, rewriter.getF32Type(), inputSizes[i]);
ScaleFactorFloatValues[i] = rewriter.create<arith::TruncFOp>(
loc, inputSizeFP.getType(), ScaleFactorFloatValues[i]);
Value outputSize = rewriter.create<arith::MulFOp>(
loc, inputSizeFP, ScaleFactorFloatValues[i]);
outputSize = rewriter.create<math::FloorOp>(loc, outputSize);
outputSize = rewriter.create<arith::FPToSIOp>(
loc, rewriter.getI64Type(), outputSize);
outputSizeIntValues.push_back(outputSize);
}
if (recompScale)
ScaleFactorFloatValues.clear();
} else {
SmallVector<Value> outputSizeTorchInt;
if (!getListConstructElements(op.getSize(), outputSizeTorchInt))
return rewriter.notifyMatchFailure(
op, "unimplemented: the output_size is not constructed from "
"ListConstruct");
outputSizeIntValues = getTypeConvertedValues(
rewriter, loc, getTypeConverter(), outputSizeTorchInt);
}
SmallVector<Value> dims = getTensorSizesUntilDim(rewriter, loc, input, 1);
for (unsigned i = 2; i < inputRank; i++) {
dims.push_back(castIntToIndex(rewriter, loc, outputSizeIntValues[i - 2]));
}
Value outTensor = rewriter.create<tensor::EmptyOp>(
loc, getAsOpFoldResult(dims), inputType.getElementType());
AffineMap idMap = rewriter.getMultiDimIdentityMap(inputRank);
SmallVector<utils::IteratorType> iteratorTypes(
inputRank, utils::IteratorType::parallel);
Value finalRes =
rewriter
.create<linalg::GenericOp>(
loc, outTensor.getType(), ValueRange{}, outTensor,
/*indexingMaps=*/idMap,
/*iteratorTypes=*/iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value retVal;
if (mode.substr(0, 7) == "nearest") {
std::string coordTfMode =
mode.substr(7, mode.find(",") - 7);
std::string nearestMode =
(mode.find(",") == std::string::npos)
? ""
: mode.substr(mode.find(",") + 1);
retVal = NearestInterpolate(
b, loc, outputSizeIntValues, input, inputSizes,
ScaleFactorFloatValues, coordTfMode, nearestMode);
} else if (mode.substr(0, 8) == "bilinear") {
retVal = BilinearInterpolate(
b, op, loc, outputSizeIntValues, input, inputSizes,
ScaleFactorFloatValues, mode.substr(8));
}
b.create<linalg::YieldOp>(loc, retVal);
})
.getResult(0);
Type newResultType =
getTypeConverter()->convertType(op.getResult().getType());
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, finalRes);
return success();
}
};
} // namespace
namespace {
// This pattern row reduces a matrix, then returns the product of it's diagonal
// elements
class ConvertAtenLinalgDetOp : public OpConversionPattern<AtenLinalgDetOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenLinalgDetOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Location loc = op->getLoc();
MLIRContext *context = op->getContext();
Value input = adaptor.getA();
auto inputType = cast<RankedTensorType>(input.getType());
unsigned inputRank = inputType.getRank();
auto elemTy = inputType.getElementType();
bool isBatched = (inputRank == 3);
Value cstZero = rewriter.create<arith::ConstantIndexOp>(loc, 0);
Value cstOne = rewriter.create<arith::ConstantIndexOp>(loc, 1);
Value cstZeroF = getConstant(rewriter, loc, 0, elemTy);
// get some shapes
SmallVector<int64_t> inputShape(inputType.getShape());
SmallVector<int64_t> sliceShape(inputShape);
sliceShape.pop_back();
SmallVector<int64_t> diagShape({isBatched ? inputType.getShape()[0] : 1});
auto sliceTy = RankedTensorType::get(sliceShape, elemTy);
auto diagTy = RankedTensorType::get(diagShape, elemTy);
// get some sizes
SmallVector<Value> inputSizes = getTensorSizes(rewriter, loc, input);
Value chDim = isBatched ? inputSizes[0] : cstOne;
Value matDim = inputSizes[inputRank - 1];
Value matDimMinusOne = rewriter.create<arith::SubIOp>(loc, matDim, cstOne);
ArrayRef<Value> sliceSizes(inputSizes.begin(), inputSizes.end() - 1);
// initialize a tensor to store the diagonal elements found during row
// reduction
Value initDiags = rewriter.create<tensor::EmptyOp>(
loc, getAsOpFoldResult(sliceSizes), elemTy);
// loop over each pivot row in A. Get the diagonal, then reduce the
// subdiagonal Don't perform the loop on the last row since no further
// reduction is needed.
auto rowReductionLoop = rewriter.create<scf::ForOp>(
loc, /*start=*/cstZero, /*end=*/matDimMinusOne, /*step=*/cstOne,
/*yeild_to=*/ValueRange{input, initDiags}, /*body_lambda=*/
[&](OpBuilder &b, Location loc, Value row, ValueRange vals) {
// extract row i from input Tensor of shape CxNxN or shape
// NxN.
OpFoldResult cstOneFold = getAsOpFoldResult(cstOne);
OpFoldResult cstZeroFold = getAsOpFoldResult(cstZero);
SmallVector<OpFoldResult> offsets(inputRank, cstZeroFold);
offsets[inputRank - 2] = row;
SmallVector<OpFoldResult> strides(inputRank, cstOneFold);
auto sizes = getAsOpFoldResult(inputSizes);
sizes[inputRank - 2] = cstOneFold;
// offsets = [0, row, 0], sizes = [C, 1, N] -> pivot row
Value pivot = b.create<tensor::ExtractSliceOp>(
loc, sliceTy, vals[0], offsets, sizes, strides);
// extract diagonal elements and insert them into vals[1]
offsets.back() = row;
sizes.back() = cstOneFold;
// offsets = [0, row, row], sizes = [C, 1, 1] -> diag(row,row)
Value diag = b.create<tensor::ExtractSliceOp>(
loc, diagTy, vals[0], offsets, sizes, strides);
SmallVector<OpFoldResult> diagOffsets(inputRank - 1, cstZeroFold);
diagOffsets.back() = row;
SmallVector<OpFoldResult> diagStrides(inputRank - 1, cstOneFold);
SmallVector<OpFoldResult> diagSizes = getAsOpFoldResult(sliceSizes);
diagSizes.back() = cstOneFold;
// offsets = [0, row], sizes = [C, 1] insert to [C,N]
Value updatedDiags = b.create<tensor::InsertSliceOp>(
loc, diag, vals[1], diagOffsets, diagSizes, diagStrides);
// the subpivot matrix column size, as a Value, is matDim - row -
// cstOne. This can't be statically converted to an int64_t, since row
// is the loop index, so this is left as a dynamic dim.
SmallVector<int64_t> subPivotShape(inputType.getShape());
subPivotShape[inputRank - 2] = ShapedType::kDynamic;
ArrayRef<int64_t> subDiagShape(subPivotShape.begin(),
subPivotShape.end() - 1);
auto subPivotTy = RankedTensorType::get(subPivotShape, elemTy);
auto subDiagTy = RankedTensorType::get(subDiagShape, elemTy);
Value rowPlusOne = b.create<arith::AddIOp>(loc, row, cstOne);
offsets[inputRank - 2] = getAsOpFoldResult(rowPlusOne);
sizes[inputRank - 2] = getAsOpFoldResult(
b.create<arith::SubIOp>(loc, matDim, rowPlusOne));
// offsets = [0, row + 1, row], sizes = [C, N - row - 1, 1] -> A_j,row
// with j > row
Value subDiag = b.create<tensor::ExtractSliceOp>(
loc, subDiagTy, vals[0], offsets, sizes, strides);
offsets.back() = cstZeroFold;
sizes.back() = getAsOpFoldResult(matDim);
// offsets = [0, row + 1, 0], sizes = [C, N - row - 1, N] -> elements
// below pivot row
Value subPivot = b.create<tensor::ExtractSliceOp>(
loc, subPivotTy, vals[0], offsets, sizes, strides);
Value initResult = b.create<tensor::EmptyOp>(loc, sizes, elemTy);
// write a generic op to perform subpivot = subpivot -
// (subdiag/diag)*pivot
// d0 = batches, d1 = row, d2 = column -> pivot(d0,d2), diag(d0),
// subPivot(d0,d1,d2), subDiag(d0, d1); output(d0,d1,d2)
SmallVector<AffineExpr> allDims;
for (unsigned i = 0; i < inputRank; i++)
allDims.push_back(b.getAffineDimExpr(i));
SmallVector<AffineExpr> rowIterator(1, allDims[0]);
SmallVector<AffineExpr> colIterator;
SmallVector<AffineExpr> batchIterator;
if (isBatched) {
rowIterator.push_back(allDims[1]);
colIterator.push_back(allDims[0]);
colIterator.push_back(allDims[2]);
batchIterator.push_back(allDims[0]);
} else {
colIterator.push_back(allDims[1]);
batchIterator.push_back(getAffineConstantExpr(0, context));
}
SmallVector<AffineMap> indexingMaps;
indexingMaps.push_back(
AffineMap::get(inputRank, 0, colIterator, context));
indexingMaps.push_back(
AffineMap::get(inputRank, 0, batchIterator, context));
indexingMaps.push_back(b.getMultiDimIdentityMap(inputRank));
indexingMaps.push_back(
AffineMap::get(inputRank, 0, rowIterator, context));
indexingMaps.push_back(b.getMultiDimIdentityMap(inputRank));
SmallVector<utils::IteratorType> iteratorTypes(
inputRank, utils::IteratorType::parallel);
Value reducedSubPivot =
b.create<linalg::GenericOp>(
loc, subPivotTy, ValueRange{pivot, diag, subPivot, subDiag},
initResult, indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
// for d0 in batches, d1 in subpivotrows, d2 in columns
// let i represent the pivot row index (scf loop index)
Value pivotd0d2 = args[0];
Value diagd0 = args[1];
Value subPivotd0d1d2 = args[2];
Value subDiagd0d1 = args[3];
// coeff = A_d1,i / A_i,i
Value coeff =
b.create<arith::DivFOp>(loc, subDiagd0d1, diagd0);
auto cmp = b.create<arith::CmpFOp>(
loc, arith::CmpFPredicate::ONE, diagd0, cstZeroF);
b.create<cf::AssertOp>(
loc, cmp,
b.getStringAttr(
"unimplemented: determinants requiring "
"permutations and singular matrices"));
// coeff*A_i,d2
Value scaledPivotValue =
b.create<arith::MulFOp>(loc, coeff, pivotd0d2);
// result = A_d1,d2 - (A_d1,i/A_i,i)*A_i,d2
// so that when d2 = i, A_d1,i - (A_d1,i/A_i,i) * A_i,i = 0
Value result = b.create<arith::SubFOp>(loc, subPivotd0d1d2,
scaledPivotValue);
b.create<linalg::YieldOp>(loc, result);
})
.getResult(0);
Value rowReductionResult = b.create<tensor::InsertSliceOp>(
loc, reducedSubPivot, vals[0], offsets, sizes, strides);
b.create<scf::YieldOp>(loc,
ValueRange{rowReductionResult, updatedDiags});
});
Value allDiagsExceptLast = rowReductionLoop.getResult(1);
SmallVector<OpFoldResult> offsets(inputRank,
getAsOpFoldResult(matDimMinusOne));
SmallVector<OpFoldResult> strides(inputRank, getAsOpFoldResult(cstOne));
SmallVector<OpFoldResult> sizes(inputRank, getAsOpFoldResult(cstOne));
sizes[0] = getAsOpFoldResult(chDim);
if (isBatched)
offsets[0] = getAsOpFoldResult(cstZero);
Value lastDiag = rewriter.create<tensor::ExtractSliceOp>(
loc, diagTy, rowReductionLoop.getResult(0), offsets, sizes, strides);
offsets.pop_back();
strides.pop_back();
sizes.pop_back();
Value allDiags = rewriter.create<tensor::InsertSliceOp>(
loc, lastDiag, allDiagsExceptLast, offsets, sizes, strides);
// linalg generic to do reduce prod for allDiags along back dim.
// the result of that generic will be the determinant
SmallVector<AffineMap> indexingMaps;
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(inputRank - 1));
AffineExpr resultExpr = isBatched ? rewriter.getAffineDimExpr(0)
: getAffineConstantExpr(0, context);
indexingMaps.push_back(AffineMap::get(inputRank - 1, 0, resultExpr));
SmallVector<utils::IteratorType> iteratorTypes(
inputRank - 1, utils::IteratorType::parallel);
Value initDet = createInitTensor(rewriter, loc, ValueRange{chDim}, elemTy,
getConstant(rewriter, loc, 1.0, elemTy));
Value determinant =
rewriter
.create<linalg::GenericOp>(
loc, initDet.getType(), ValueRange{allDiags}, initDet,
indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value prod = b.create<arith::MulFOp>(loc, args[0], args[1]);
b.create<linalg::YieldOp>(loc, prod);
})
.getResult(0);
Type newResultType =
getTypeConverter()->convertType(op.getResult().getType());
if (isBatched) {
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType,
determinant);
return success();
}
Value detVal = rewriter.create<tensor::ExtractOp>(
loc, determinant, SmallVector<Value>(1, cstZero));
rewriter.replaceOpWithNewOp<tensor::FromElementsOp>(op, newResultType,
ValueRange{detVal});
return success();
}
};
} // namespace
void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<
AtenTanOp, AtenTanhOp, AtenSinhOp, AtenCoshOp, AtenAtanhOp, AtenAcoshOp,
AtenAsinOp, AtenAsinhOp, AtenReluOp, AtenGeluOp, AtenGeluBackwardOp,
AtenAddTensorOp, AtenMulTensorOp, AtenDivTensorOp, AtenDivTensorModeOp,
AtenDivScalarModeOp, AtenSubTensorOp, AtenLerpTensorOp, AtenSigmoidOp,
AtenMinimumOp, AtenAtan2Op, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
AtenClampTensorOp, AtenRsubScalarOp, AtenLogOp, AtenErfOp, AtenSqrtOp,
AtenFloorOp, AtenCeilOp, AtenPreluOp, AtenPowScalarOp,
AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op, AtenLog10Op,
AtenLog1pOp, AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp,
AtenBitwiseAndTensorOp, AtenBitwiseAndScalarOp, AtenBitwiseOrTensorOp,
AtenBitwiseXorTensorOp, AtenBitwiseLeftShiftTensorOp,
AtenBitwiseRightShiftTensorOp, AtenGtScalarOp, AtenGeScalarOp,
AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp, AtenWhereSelfOp,
AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp, AtenNeTensorOp,
AtenLtTensorOp, AtenLeTensorOp, AtenThresholdOp, AtenThresholdBackwardOp,
AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp,
AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp, AtenAtanOp,
AtenAcosOp, AtenLogicalXorOp, AtenLogicalNotOp, AtenIsinfOp, AtenTriuOp,
AtenTrilOp, AtenRemainderScalarOp, AtenFmodTensorOp,
AtenRemainderTensorOp, AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp,
AtenFillTensorOp, AtenRealOp, AtenImagOp, AtenDequantizeSelfOp,
AtenDequantizeTensorOp, AtenQuantizePerTensorOp>();
patterns.add<ConvertElementwiseOp>(typeConverter, context);
target.addIllegalOp<AtenNllLossForwardOp>();
patterns.add<ConvertAtenDetachOp>(typeConverter, context);
target.addIllegalOp<AtenDetachOp>();
patterns.add<ConvertAtenNllLossForwardOp>(typeConverter, context);
target.addIllegalOp<AtenBatchNormOp>();
patterns.add<ConvertAtenBatchNormOp>(typeConverter, context);
target.addIllegalOp<AtenLogitOp>();
patterns.add<ConvertLogitOp>(typeConverter, context);
target.addIllegalOp<PrimsCollapseOp>();
patterns.add<ConvertPrimsCollapseOp>(typeConverter, context);
target.addIllegalOp<PrimsSplitDimOp>();
patterns.add<ConvertPrimsSplitDimOp>(typeConverter, context);
target.addIllegalOp<AtenNllLossBackwardOp>();
patterns.add<ConvertAtenNllLossBackwardOp>(typeConverter, context);
patterns.add<ConvertTensorStaticInfoCastOp>(typeConverter, context);
target.addIllegalOp<TensorStaticInfoCastOp>();
patterns.add<ConvertAtenIntReprOp>(typeConverter, context);
target.addIllegalOp<AtenIntReprOp>();
patterns.add<ConvertCastEquivalentOp<Aten_MakePerChannelQuantizedTensorOp>>(
typeConverter, context);
target.addIllegalOp<Aten_MakePerChannelQuantizedTensorOp>();
patterns.add<ConvertCastEquivalentOp<Aten_MakePerTensorQuantizedTensorOp>>(
typeConverter, context);
target.addIllegalOp<Aten_MakePerTensorQuantizedTensorOp>();
patterns.add<ConvertDequantizePerChannel>(typeConverter, context);
target.addIllegalOp<AtenGridSamplerOp>();
patterns.add<ConvertAtenGridSamplerOp>(typeConverter, context);
target.addIllegalOp<Aten__InterpolateSizeListScaleListOp>();
patterns.add<ConvertInterpolateOp>(typeConverter, context);
target.addIllegalOp<AtenLinalgDetOp>();
patterns.add<ConvertAtenLinalgDetOp>(typeConverter, context);
}
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