mirror of https://github.com/llvm/torch-mlir
1884 lines
81 KiB
C++
1884 lines
81 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"
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#include "../PassDetail.h"
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#include "PopulatePatterns.h"
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#include "Utils.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Complex/IR/Complex.h"
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#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/Math/IR/Math.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/Matchers.h"
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#include "torch-mlir/Conversion/Utils/Utils.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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#include "llvm/ADT/APSInt.h"
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#include <numeric>
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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// Check if a ranked-tensor has the specified element type.
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template <typename elementType> static bool hasElementType(Value tensor) {
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auto tensorType = tensor.getType().cast<RankedTensorType>();
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Type tensorElementType = tensorType.getElementType();
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return tensorElementType.isa<elementType>();
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}
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template <arith::CmpFPredicate fpred, arith::CmpIPredicate iupred,
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arith::CmpIPredicate ispred>
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static Value createComparisonTemplate(OpBuilder &b, Location loc, Type type,
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Value lhs, Value rhs) {
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if (type.isa<mlir::FloatType>())
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return b.create<arith::CmpFOp>(loc, fpred, lhs, rhs);
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if (IntegerType intType = type.dyn_cast<mlir::IntegerType>()) {
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if (intType.isUnsigned())
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return b.create<arith::CmpIOp>(loc, iupred, lhs, rhs);
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if (intType.isSigned())
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return b.create<arith::CmpIOp>(loc, ispred, lhs, rhs);
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assert(intType.getWidth() == 1);
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return b.create<arith::CmpIOp>(loc, iupred, lhs, rhs);
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}
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llvm_unreachable("Unhandled element type for comparison");
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}
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static Value createGreaterThan(OpBuilder &b, Location loc, Type elementalType,
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Value lhs, Value rhs) {
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return createComparisonTemplate<arith::CmpFPredicate::OGT,
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arith::CmpIPredicate::ugt,
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arith::CmpIPredicate::sgt>(
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b, loc, elementalType, lhs, rhs);
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}
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static Value createGreaterThanOrEqual(OpBuilder &b, Location loc,
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Type elementalType, Value lhs,
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Value rhs) {
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return createComparisonTemplate<arith::CmpFPredicate::OGE,
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arith::CmpIPredicate::uge,
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arith::CmpIPredicate::sge>(
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b, loc, elementalType, lhs, rhs);
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}
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static Value createLessThan(OpBuilder &b, Location loc, Type elementalType,
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Value lhs, Value rhs) {
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return createComparisonTemplate<arith::CmpFPredicate::OLT,
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arith::CmpIPredicate::ult,
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arith::CmpIPredicate::slt>(
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b, loc, elementalType, lhs, rhs);
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}
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static Value createLessThanOrEqual(OpBuilder &b, Location loc,
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Type elementalType, Value lhs, Value rhs) {
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return createComparisonTemplate<arith::CmpFPredicate::OLE,
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arith::CmpIPredicate::ule,
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arith::CmpIPredicate::sle>(
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b, loc, elementalType, lhs, rhs);
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}
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static Value createEqual(OpBuilder &b, Location loc, Type elementalType,
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Value lhs, Value rhs) {
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return createComparisonTemplate<arith::CmpFPredicate::OEQ,
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arith::CmpIPredicate::eq,
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arith::CmpIPredicate::eq>(
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b, loc, elementalType, lhs, rhs);
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}
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static Value createNotEqual(OpBuilder &b, Location loc, Type elementalType,
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Value lhs, Value rhs) {
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return createComparisonTemplate<arith::CmpFPredicate::UNE,
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arith::CmpIPredicate::ne,
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arith::CmpIPredicate::ne>(
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b, loc, elementalType, lhs, rhs);
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}
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static Value buildNormalCdf(OpBuilder &b, Location &loc, Value x, Value mean,
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Value sigma) {
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Type elementType = x.getType();
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Value xMinusMean = b.create<arith::SubFOp>(loc, x, mean);
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Value two = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 2));
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Value sqrt2 = b.create<math::SqrtOp>(loc, two);
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Value erfArg = b.create<arith::DivFOp>(loc, xMinusMean, sqrt2);
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Value erf = b.create<math::ErfOp>(loc, erfArg);
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Value one = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1));
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Value erfPlus1 = b.create<arith::AddFOp>(loc, one, erf);
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Value oneHalf =
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b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0.5));
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Value normalCdf = b.create<arith::MulFOp>(loc, oneHalf, erfPlus1);
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return normalCdf;
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}
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static Value buildUnitNormalCdf(OpBuilder &b, Location &loc, Value x) {
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Type elementType = x.getType();
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Value zero = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 0));
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Value one = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1));
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return buildNormalCdf(b, loc, x, zero, one);
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}
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template <typename MathOpTy>
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static Value
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createCalculationForMathOpWithDtypeConversion(OpBuilder &b,
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const TypeConverter *converter,
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Value payloadArg, Operation *op) {
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Type dtype = converter->convertType(op->getResult(0).getType())
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.template cast<RankedTensorType>()
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.getElementType();
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Location loc = op->getLoc();
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Value arg = convertScalarToDtype(b, loc, payloadArg, dtype);
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return b.create<MathOpTy>(loc, arg);
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}
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template <typename OpTy>
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static Value createCompareTensorOp(OpBuilder &b, Location loc, OpTy op,
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Value lhs, Value rhs) {
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static_assert(std::is_same<OpTy, AtenLtTensorOp>() ||
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std::is_same<OpTy, AtenLeTensorOp>() ||
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std::is_same<OpTy, AtenGtTensorOp>() ||
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std::is_same<OpTy, AtenGeTensorOp>() ||
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std::is_same<OpTy, AtenEqTensorOp>() ||
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std::is_same<OpTy, AtenNeTensorOp>(),
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"unimplemented: op type not supported");
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Type lhsDtype = lhs.getType();
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Type rhsDtype = rhs.getType();
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// TODO: Type promotion in case of different `lhsDtype` and `rhsDtype` needs
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// to be handled.
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if (lhsDtype != rhsDtype) {
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op.emitError("unimplemented: lhs and rhs dtype must be same");
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return nullptr;
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}
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Type elementalType =
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op.getSelf().getType().template cast<BaseTensorType>().getDtype();
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if constexpr (std::is_same<OpTy, AtenLtTensorOp>()) {
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return createLessThan(b, loc, elementalType, lhs, rhs);
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}
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if constexpr (std::is_same<OpTy, AtenLeTensorOp>()) {
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return createLessThanOrEqual(b, loc, elementalType, lhs, rhs);
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}
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if constexpr (std::is_same<OpTy, AtenGtTensorOp>()) {
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return createGreaterThan(b, loc, elementalType, lhs, rhs);
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}
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if constexpr (std::is_same<OpTy, AtenGeTensorOp>()) {
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return createGreaterThanOrEqual(b, loc, elementalType, lhs, rhs);
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}
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if constexpr (std::is_same<OpTy, AtenEqTensorOp>()) {
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return createEqual(b, loc, elementalType, lhs, rhs);
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}
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if constexpr (std::is_same<OpTy, AtenNeTensorOp>()) {
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return createNotEqual(b, loc, elementalType, lhs, rhs);
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}
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llvm_unreachable("unimplemented: op type not supported");
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}
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template <arith::CmpIPredicate predicate>
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static LogicalResult
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createTriangularMatrix(OpBuilder &b, Location loc, ValueRange payloadArgs,
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Operation *op, ArrayRef<Value> operands, Value &result) {
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auto inputType = operands[0].getType().cast<RankedTensorType>();
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uint64_t inputRank = inputType.getRank();
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// Use the indices of the two innermost dimensions.
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auto rowIndex = b.create<linalg::IndexOp>(loc, inputRank - 2);
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Value rowIndexI64 = castIndexToInt64(b, loc, rowIndex);
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auto colIndex = b.create<linalg::IndexOp>(loc, inputRank - 1);
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Value colIndexI64 = castIndexToInt64(b, loc, colIndex);
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// columnIndex >= rowIndex + diagonal?
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auto sum =
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b.create<arith::AddIOp>(loc, rowIndexI64, /*diagonal=*/operands[1]);
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auto pred = b.create<arith::CmpIOp>(loc, predicate, colIndexI64, sum);
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Value scalar = payloadArgs[0];
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Type elementType = inputType.getElementType();
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Value zero = getConstant(b, loc, 0, elementType);
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result = b.create<arith::SelectOp>(loc, pred, scalar, zero);
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return success();
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}
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static Value createLinalgPayloadCalculationForElementwiseOp(
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OpBuilder &b, Location loc, const TypeConverter *converter,
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ValueRange payloadArgs, Operation *op, ArrayRef<Value> operands) {
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if (isa<AtenFloorOp>(op))
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return b.create<math::FloorOp>(loc, payloadArgs[0]);
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if (isa<AtenCeilOp>(op))
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return b.create<math::CeilOp>(loc, payloadArgs[0]);
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if (isa<AtenTanhOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::TanhOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenExpOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::ExpOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenExpm1Op>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::ExpM1Op>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenLogOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::LogOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenLog2Op>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::Log2Op>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenLog10Op>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::Log10Op>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenLog1pOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::Log1pOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenErfOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::ErfOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenSqrtOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::SqrtOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenRsqrtOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::RsqrtOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenNegOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<arith::NegFOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenSinOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::SinOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenCosOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::CosOp>(
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b, converter, payloadArgs[0], op);
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}
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if (isa<AtenAtanOp>(op)) {
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return createCalculationForMathOpWithDtypeConversion<math::AtanOp>(
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b, converter, payloadArgs[0], op);
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}
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if (auto clone = dyn_cast<AtenCloneOp>(op)) {
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int64_t memoryFormat;
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if (!clone.getMemoryFormat().getType().isa<Torch::NoneType>() &&
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(!matchPattern(clone.getMemoryFormat(),
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m_TorchConstantInt(&memoryFormat)) ||
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(memoryFormat != torch_upstream::MemoryFormat::Contiguous &&
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memoryFormat != torch_upstream::MemoryFormat::ChannelsLast))) {
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clone.emitError("unimplemented: only contiguous and channels last memory "
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"format is supported");
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return nullptr;
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}
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return payloadArgs[0];
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}
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if (auto bitwiseAndTensor = dyn_cast<AtenBitwiseAndTensorOp>(op)) {
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if (bitwiseAndTensor.getType()
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.cast<ValueTensorType>()
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.getDtype()
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.isa<mlir::FloatType>()) {
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bitwiseAndTensor.emitError(
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"Bitwise_And does not support floating point dtype");
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return nullptr;
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}
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Type dtype = converter->convertType(bitwiseAndTensor.getType())
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.cast<RankedTensorType>()
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.getElementType();
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Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
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Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
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return b.create<arith::AndIOp>(loc, lhs, rhs);
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}
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if (auto bitwiseAndScalar = dyn_cast<AtenBitwiseAndScalarOp>(op)) {
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Type dtype = converter->convertType(bitwiseAndScalar.getType())
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.cast<RankedTensorType>()
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.getElementType();
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if (!dtype.isa<mlir::IntegerType>()) {
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bitwiseAndScalar.emitError(
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"bitwise_and.Scalar does not support non-integer input dtype.");
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return nullptr;
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}
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Type resultElementType =
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bitwiseAndScalar.getType().cast<BaseTensorType>().getDtype();
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Value self = convertScalarToDtype(b, loc, payloadArgs[0], dtype,
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/*srcOriginalDtype=*/std::nullopt,
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/*dstOriginalDtype=*/resultElementType);
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Value other = convertScalarToDtype(b, loc, operands[1], dtype,
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/*srcOriginalDtype=*/std::nullopt,
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/*dstOriginalDtype=*/resultElementType);
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return b.create<arith::AndIOp>(loc, self, other);
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}
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if (auto bitwiseOrTensor = dyn_cast<AtenBitwiseOrTensorOp>(op)) {
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if (bitwiseOrTensor.getType()
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.cast<ValueTensorType>()
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.getDtype()
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.isa<mlir::FloatType>()) {
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bitwiseOrTensor.emitError(
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"Bitwise_Or does not support floating point dtype");
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return nullptr;
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}
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Type dtype = converter->convertType(bitwiseOrTensor.getType())
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.cast<RankedTensorType>()
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.getElementType();
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Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
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Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
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return b.create<arith::OrIOp>(loc, lhs, rhs);
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}
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if (auto bitwiseXorTensor = dyn_cast<AtenBitwiseXorTensorOp>(op)) {
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if (bitwiseXorTensor.getType()
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.cast<ValueTensorType>()
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.getDtype()
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.isa<mlir::FloatType>()) {
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bitwiseXorTensor.emitError(
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"Bitwise_Xor does not support floating point dtype");
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return nullptr;
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}
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Type dtype = converter->convertType(bitwiseXorTensor.getType())
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.cast<RankedTensorType>()
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.getElementType();
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Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
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Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
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return b.create<arith::XOrIOp>(loc, lhs, rhs);
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}
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if (auto bitwiseRightShiftTensor =
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dyn_cast<AtenBitwiseRightShiftTensorOp>(op)) {
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Type dtype = converter->convertType(bitwiseRightShiftTensor.getType())
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.cast<RankedTensorType>()
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.getElementType();
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if (!dtype.isa<mlir::IntegerType>()) {
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bitwiseRightShiftTensor.emitError(
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"Bitwise_Right_Shift op does not support non-integer input dtype.");
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return nullptr;
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}
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Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
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Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
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return b.create<arith::ShRSIOp>(loc, lhs, rhs);
|
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}
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if (isa<AtenLogicalOrOp, AtenLogicalAndOp, AtenLogicalXorOp>(op)) {
|
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MLIRContext *context = op->getContext();
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Type floatDtype = mlir::FloatType::getF64(context);
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Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], floatDtype);
|
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Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], floatDtype);
|
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Value zero =
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b.create<arith::ConstantOp>(loc, b.getFloatAttr(floatDtype, 0));
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Value lhsTest = createNotEqual(b, loc, floatDtype, lhs, zero);
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Value rhsTest = createNotEqual(b, loc, floatDtype, rhs, zero);
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if (isa<AtenLogicalOrOp>(op)) {
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return b.create<arith::OrIOp>(loc, lhsTest, rhsTest);
|
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}
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if (isa<AtenLogicalAndOp>(op)) {
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return b.create<arith::AndIOp>(loc, lhsTest, rhsTest);
|
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}
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if (isa<AtenLogicalXorOp>(op)) {
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return b.create<arith::XOrIOp>(loc, lhsTest, rhsTest);
|
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}
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llvm_unreachable("Unknown op type");
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}
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if (isa<AtenLogicalNotOp>(op)) {
|
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MLIRContext *context = op->getContext();
|
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Type floatDtype = mlir::FloatType::getF64(context);
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Value self = convertScalarToDtype(b, loc, payloadArgs[0], floatDtype);
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Value zero =
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b.create<arith::ConstantOp>(loc, b.getFloatAttr(floatDtype, 0));
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return createEqual(b, loc, floatDtype, self, zero);
|
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}
|
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if (isa<AtenAbsOp>(op))
|
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return b.create<math::AbsFOp>(loc, payloadArgs[0]);
|
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if (isa<AtenSigmoidOp>(op)) {
|
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auto negate = createCalculationForMathOpWithDtypeConversion<arith::NegFOp>(
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b, converter, payloadArgs[0], op);
|
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auto one =
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b.create<arith::ConstantOp>(loc, FloatAttr::get(negate.getType(), 1));
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auto exp = b.create<math::ExpOp>(loc, negate);
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auto added = b.create<arith::AddFOp>(loc, exp, one);
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return b.create<arith::DivFOp>(loc, one, added);
|
||
}
|
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if (auto relu = dyn_cast<AtenReluOp>(op)) {
|
||
if (!relu.getType()
|
||
.cast<ValueTensorType>()
|
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.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
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relu.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);
|
||
return b.create<arith::SelectOp>(loc, pred, payloadArgs[0], constZero);
|
||
}
|
||
if (auto round = dyn_cast<AtenRoundOp>(op)) {
|
||
if (!round.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
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 (!prelu.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
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 (!gelu.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
gelu.emitError("unimplemented: non-floating point dtype");
|
||
return nullptr;
|
||
}
|
||
// TODO: Take approximation into account.
|
||
std::string approximate;
|
||
if (!matchPattern(gelu.getApproximate(), m_TorchConstantStr(approximate)) ||
|
||
approximate != "none")
|
||
return nullptr;
|
||
Value cdf = buildUnitNormalCdf(b, loc, payloadArgs[0]);
|
||
return b.create<arith::MulFOp>(loc, payloadArgs[0], cdf);
|
||
}
|
||
if (auto geluBackward = dyn_cast<AtenGeluBackwardOp>(op)) {
|
||
if (!geluBackward.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
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 (!hardtanhBackward.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
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 dtype = converter->convertType(add.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
|
||
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
|
||
Value alpha = convertScalarToDtype(b, loc, adaptor.getAlpha(), dtype);
|
||
if (dtype.isa<mlir::FloatType>()) {
|
||
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 = converter->convertType(sub.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
Type resultElementType = sub.getType().cast<BaseTensorType>().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);
|
||
if (dtype.isa<mlir::FloatType>()) {
|
||
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 = converter->convertType(subScalar.getType())
|
||
.cast<RankedTensorType>()
|
||
.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);
|
||
if (dtype.isa<mlir::FloatType>()) {
|
||
Value mult = b.create<arith::MulFOp>(loc, other, alpha);
|
||
return b.create<arith::SubFOp>(loc, self, mult);
|
||
} else if (dtype.isa<mlir::IntegerType>()) {
|
||
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 = converter->convertType(addScalar.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
Type resultElementType =
|
||
addScalar.getType().cast<BaseTensorType>().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 (dtype.isa<mlir::FloatType>()) {
|
||
Value mult = b.create<arith::MulFOp>(loc, other, alpha);
|
||
return b.create<arith::AddFOp>(loc, self, mult);
|
||
} else if (dtype.isa<mlir::IntegerType>()) {
|
||
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 = converter->convertType(mul.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
|
||
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
|
||
if (dtype.isa<mlir::FloatType>()) {
|
||
return b.create<arith::MulFOp>(loc, lhs, rhs);
|
||
} else if (dtype.isa<mlir::ComplexType>()) {
|
||
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 = converter->convertType(atan2.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
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 createCompareTensorOp(b, loc, ltTensor, payloadArgs[0],
|
||
payloadArgs[1]);
|
||
}
|
||
if (auto leTensor = dyn_cast<AtenLeTensorOp>(op)) {
|
||
return createCompareTensorOp(b, loc, leTensor, payloadArgs[0],
|
||
payloadArgs[1]);
|
||
}
|
||
if (auto gtTensor = dyn_cast<AtenGtTensorOp>(op)) {
|
||
return createCompareTensorOp(b, loc, gtTensor, payloadArgs[0],
|
||
payloadArgs[1]);
|
||
}
|
||
if (auto geTensor = dyn_cast<AtenGeTensorOp>(op)) {
|
||
return createCompareTensorOp(b, loc, geTensor, payloadArgs[0],
|
||
payloadArgs[1]);
|
||
}
|
||
if (auto eqTensor = dyn_cast<AtenEqTensorOp>(op)) {
|
||
return createCompareTensorOp(b, loc, eqTensor, payloadArgs[0],
|
||
payloadArgs[1]);
|
||
}
|
||
if (auto neTensor = dyn_cast<AtenNeTensorOp>(op)) {
|
||
return createCompareTensorOp(b, loc, neTensor, payloadArgs[0],
|
||
payloadArgs[1]);
|
||
}
|
||
if (auto div = dyn_cast<AtenDivTensorOp>(op)) {
|
||
AtenDivTensorOp::Adaptor adaptor(operands);
|
||
Type dtype = converter->convertType(div.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
div.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<arith::DivFOp>(loc, lhs, rhs);
|
||
}
|
||
if (auto divTensorMode = dyn_cast<AtenDivTensorModeOp>(op)) {
|
||
AtenDivTensorModeOp::Adaptor adaptor(operands);
|
||
Type dtype = converter->convertType(divTensorMode.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
divTensorMode.emitError("unimplemented: non-floating point dtype");
|
||
return nullptr;
|
||
}
|
||
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
|
||
Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
|
||
Value div = b.create<arith::DivFOp>(loc, lhs, rhs);
|
||
|
||
if (divTensorMode.getRoundingMode().getType().isa<Torch::NoneType>())
|
||
return div;
|
||
|
||
std::string roundingMode;
|
||
if (!matchPattern(divTensorMode.getRoundingMode(),
|
||
m_TorchConstantStr(roundingMode))) {
|
||
divTensorMode.emitError("only support constant str rounding mode");
|
||
return nullptr;
|
||
}
|
||
if (roundingMode == "trunc") {
|
||
// "trunc" - rounds the results of the division towards zero. Equivalent
|
||
// to C-style integer division.
|
||
Value ceil = b.create<math::CeilOp>(loc, div);
|
||
Value floor = b.create<math::FloorOp>(loc, div);
|
||
Value cstZero = b.create<arith::ConstantOp>(loc, b.getZeroAttr(dtype));
|
||
Value pred =
|
||
b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT, div, 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)
|
||
return b.create<math::FloorOp>(loc, div);
|
||
}
|
||
divTensorMode.emitError("invalid rounding mode");
|
||
return nullptr;
|
||
}
|
||
|
||
if (auto pow = dyn_cast<AtenPowScalarOp>(op)) {
|
||
Type dtype = pow.getType().cast<ValueTensorType>().getDtype();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
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 (!pow.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
pow.emitError("unimplemented: non-floating point dtype");
|
||
return nullptr;
|
||
}
|
||
Type dtype = pow.getSelf().getType().cast<ValueTensorType>().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 = converter->convertType(pow.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
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 = converter->convertType(imag.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
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 = converter->convertType(real.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
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)) {
|
||
Type dtype = gtScalar.getSelf().getType().cast<BaseTensorType>().getDtype();
|
||
|
||
// TODO: `gtTensor` and `gtScalar` share similar code and can be called from
|
||
// one static function.
|
||
Value otherPromoted =
|
||
convertScalarToDtype(b, loc, operands[1], payloadArgs[0].getType());
|
||
|
||
if (dtype.isa<mlir::FloatType>())
|
||
return b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT,
|
||
payloadArgs[0], otherPromoted);
|
||
if (IntegerType intType = dtype.dyn_cast<mlir::IntegerType>()) {
|
||
if (!operands[1].getType().isa<mlir::IntegerType>()) {
|
||
// TODO: Promote tensor args from integer to float.
|
||
gtScalar.emitError(
|
||
"unimplemented: type promotion from tensor to scalar.");
|
||
return nullptr;
|
||
}
|
||
|
||
if (intType.isUnsigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ugt,
|
||
payloadArgs[0], otherPromoted);
|
||
if (intType.isSigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sgt,
|
||
payloadArgs[0], otherPromoted);
|
||
}
|
||
gtScalar.emitError("unimplemented: dtype isn't supported.");
|
||
return nullptr;
|
||
}
|
||
|
||
if (auto geScalar = dyn_cast<AtenGeScalarOp>(op)) {
|
||
Type dtype = geScalar.getSelf().getType().cast<BaseTensorType>().getDtype();
|
||
|
||
// TODO: The `AtenGeScalarOp` and `AtenGtScalarOp` share a lot of code that
|
||
// can be refactored.
|
||
Value otherPromoted =
|
||
convertScalarToDtype(b, loc, operands[1], payloadArgs[0].getType());
|
||
|
||
if (dtype.isa<mlir::FloatType>())
|
||
return b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGE,
|
||
payloadArgs[0], otherPromoted);
|
||
if (IntegerType intType = dtype.dyn_cast<mlir::IntegerType>()) {
|
||
if (!operands[1].getType().isa<mlir::IntegerType>()) {
|
||
// TODO: Promote tensor args from integer to float.
|
||
geScalar.emitError(
|
||
"unimplemented: type promotion from tensor to scalar.");
|
||
return nullptr;
|
||
}
|
||
|
||
if (intType.isUnsigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::uge,
|
||
payloadArgs[0], otherPromoted);
|
||
if (intType.isSigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge,
|
||
payloadArgs[0], otherPromoted);
|
||
}
|
||
geScalar.emitError("unimplemented: dtype isn't supported.");
|
||
return nullptr;
|
||
}
|
||
|
||
if (auto eqScalar = dyn_cast<AtenEqScalarOp>(op)) {
|
||
Type dtype = eqScalar.getSelf().getType().cast<BaseTensorType>().getDtype();
|
||
Value otherPromoted =
|
||
convertScalarToDtype(b, loc, operands[1], payloadArgs[0].getType());
|
||
|
||
if (dtype.isa<mlir::IntegerType>()) {
|
||
if (!operands[1].getType().isa<mlir::IntegerType>()) {
|
||
// TODO: Promote tensor operand from integer to float.
|
||
eqScalar.emitError(
|
||
"unimplemented: type promotion from tensor to scalar");
|
||
return nullptr;
|
||
}
|
||
}
|
||
return createEqual(b, loc, dtype, payloadArgs[0], otherPromoted);
|
||
}
|
||
|
||
if (auto neScalar = dyn_cast<AtenNeScalarOp>(op)) {
|
||
Type dtype = neScalar.getSelf().getType().cast<BaseTensorType>().getDtype();
|
||
Value otherPromoted =
|
||
convertScalarToDtype(b, loc, operands[1], payloadArgs[0].getType());
|
||
|
||
if (dtype.isa<mlir::IntegerType>()) {
|
||
if (!operands[1].getType().isa<mlir::IntegerType>()) {
|
||
// TODO: Promote tensor operand from integer to float.
|
||
neScalar.emitError(
|
||
"unimplemented: type promotion from tensor to scalar");
|
||
return nullptr;
|
||
}
|
||
}
|
||
return createNotEqual(b, loc, dtype, payloadArgs[0], otherPromoted);
|
||
}
|
||
|
||
if (auto ltScalar = dyn_cast<AtenLtScalarOp>(op)) {
|
||
Type dtype = ltScalar.getSelf().getType().cast<BaseTensorType>().getDtype();
|
||
Value otherPromoted =
|
||
convertScalarToDtype(b, loc, operands[1], payloadArgs[0].getType());
|
||
|
||
// TODO: Both tensor and scalar variants of `aten.gt` and `aten.lt` share
|
||
// a lot of code that can be refactored.
|
||
if (dtype.isa<mlir::FloatType>())
|
||
return b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT,
|
||
payloadArgs[0], otherPromoted);
|
||
if (IntegerType intType = dtype.dyn_cast<mlir::IntegerType>()) {
|
||
if (!operands[1].getType().isa<mlir::IntegerType>()) {
|
||
// TODO: Promote tensor operand from integer to float.
|
||
ltScalar.emitError(
|
||
"unimplemented: type promotion from tensor to scalar");
|
||
return nullptr;
|
||
}
|
||
if (intType.isUnsigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult,
|
||
payloadArgs[0], otherPromoted);
|
||
if (intType.isSigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt,
|
||
payloadArgs[0], otherPromoted);
|
||
}
|
||
ltScalar.emitError("unimplemented: dtype isn't supported.");
|
||
return nullptr;
|
||
}
|
||
|
||
if (auto leScalar = dyn_cast<AtenLeScalarOp>(op)) {
|
||
Type dtype = leScalar.getSelf().getType().cast<BaseTensorType>().getDtype();
|
||
Value otherPromoted =
|
||
convertScalarToDtype(b, loc, operands[1], payloadArgs[0].getType());
|
||
|
||
// TODO: The `AtenLeScalarOp` and `AtenLtScalarOp` share a lot of code
|
||
// that can be refactored.
|
||
if (dtype.isa<mlir::FloatType>())
|
||
return b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULE,
|
||
payloadArgs[0], otherPromoted);
|
||
if (IntegerType intType = dtype.dyn_cast<mlir::IntegerType>()) {
|
||
if (!operands[1].getType().isa<mlir::IntegerType>()) {
|
||
// TODO: Promote tensor operand from integer to float.
|
||
leScalar.emitError(
|
||
"unimplemented: type promotion from tensor to scalar");
|
||
return nullptr;
|
||
}
|
||
if (intType.isUnsigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ule,
|
||
payloadArgs[0], otherPromoted);
|
||
if (intType.isSigned())
|
||
return b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sle,
|
||
payloadArgs[0], otherPromoted);
|
||
}
|
||
leScalar.emitError("unimplemented: dtype isn't supported.");
|
||
return nullptr;
|
||
}
|
||
|
||
if (auto whereSelf = dyn_cast<AtenWhereSelfOp>(op)) {
|
||
Type dtype = converter->convertType(whereSelf.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 (!lerp.getType()
|
||
.cast<ValueTensorType>()
|
||
.getDtype()
|
||
.isa<mlir::FloatType>()) {
|
||
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 = minimum.getType().cast<BaseTensorType>().getDtype();
|
||
Type elemTy = converter->convertType(minimum.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 = maximum.getType().cast<BaseTensorType>().getDtype();
|
||
Type elemTy = converter->convertType(maximum.getType())
|
||
.cast<RankedTensorType>()
|
||
.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)) {
|
||
Type dtype = converter->convertType(clamp.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
clamp.emitError("unimplemented: non-floating point dtype");
|
||
return nullptr;
|
||
}
|
||
AtenClampOp::Adaptor adaptor(operands);
|
||
auto min = adaptor.getMin();
|
||
auto max = adaptor.getMax();
|
||
if (min.getType().isa<Torch::OptionalType>() ||
|
||
max.getType().isa<Torch::OptionalType>()) {
|
||
clamp.emitError("unimplemented: runtime optional type");
|
||
return nullptr;
|
||
}
|
||
auto result = payloadArgs[0];
|
||
if (!min.getType().isa<Torch::NoneType>()) {
|
||
auto minPromoted = convertScalarToDtype(b, loc, min, dtype);
|
||
auto pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT,
|
||
result, minPromoted);
|
||
result = b.create<arith::SelectOp>(loc, pred, minPromoted, result);
|
||
}
|
||
if (!max.getType().isa<Torch::NoneType>()) {
|
||
auto maxPromoted = convertScalarToDtype(b, loc, max, dtype);
|
||
auto pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT,
|
||
result, maxPromoted);
|
||
result = b.create<arith::SelectOp>(loc, pred, maxPromoted, result);
|
||
}
|
||
return result;
|
||
}
|
||
if (auto rsub = dyn_cast<AtenRsubScalarOp>(op)) {
|
||
Type dtype = converter->convertType(rsub.getType())
|
||
.cast<RankedTensorType>()
|
||
.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);
|
||
if (dtype.isa<mlir::FloatType>()) {
|
||
Value mult = b.create<arith::MulFOp>(loc, self, alpha);
|
||
return b.create<arith::SubFOp>(loc, other, mult);
|
||
} else if (dtype.isa<mlir::IntegerType>()) {
|
||
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 = converter->convertType(mulScalar.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
|
||
Value rhs = convertScalarToDtype(b, loc, operands[1], dtype);
|
||
if (dtype.isa<mlir::FloatType>())
|
||
return b.create<arith::MulFOp>(loc, lhs, rhs);
|
||
if (dtype.isa<mlir::IntegerType>())
|
||
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 dtype = converter->convertType(atenToDtype.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 = getTypeForScalarType(
|
||
atenToDtype->getContext(), (torch_upstream::ScalarType)dtypeInt,
|
||
IntegerType::Signless);
|
||
if (failed(maybeResultElementType)) {
|
||
atenToDtype.emitError("unable to convert `dtypeInt` to builtin type");
|
||
return nullptr;
|
||
}
|
||
resultElementType = *maybeResultElementType;
|
||
Value result = convertScalarToDtype(b, loc, input, dtype,
|
||
/*srcOriginalDtype=*/std::nullopt,
|
||
/*dstOriginalDtype=*/resultElementType);
|
||
return result;
|
||
}
|
||
if (auto divScalar = dyn_cast<AtenDivScalarOp>(op)) {
|
||
Type dtype = converter->convertType(divScalar.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (!dtype.isa<mlir::FloatType>()) {
|
||
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 = converter->convertType(remScalar.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
|
||
Value self = convertScalarToDtype(b, loc, payloadArgs[0], newResultType);
|
||
Value other = convertScalarToDtype(b, loc, operands[1], newResultType);
|
||
Value result;
|
||
|
||
if (newResultType.isa<mlir::FloatType>()) {
|
||
result = b.create<arith::RemFOp>(loc, self, other);
|
||
} else if (newResultType.isa<mlir::IntegerType>()) {
|
||
result = b.create<arith::RemSIOp>(loc, self, other);
|
||
} else {
|
||
remScalar.emitError(
|
||
"Unsupported type encountered for AtenRemainderScalarOp.");
|
||
}
|
||
|
||
return result;
|
||
}
|
||
if (auto reciprocal = dyn_cast<AtenReciprocalOp>(op)) {
|
||
Type dtype = converter->convertType(reciprocal.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 = converter->convertType(thresholdOp.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
|
||
Value self = payloadArgs[0];
|
||
Value threshold =
|
||
convertScalarToDtype(b, loc, adaptor.getThreshold(), dtype);
|
||
Value value = convertScalarToDtype(b, loc, adaptor.getValue(), dtype);
|
||
|
||
Value predicate;
|
||
if (dtype.isa<mlir::FloatType>())
|
||
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 = converter->convertType(thresholdBackward.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 (dtype.isa<mlir::FloatType>())
|
||
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 = converter->convertType(fillScalar.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
return convertScalarToDtype(b, loc, adaptor.getValue(), dtype);
|
||
}
|
||
if (auto maskedFillTensor = dyn_cast<AtenMaskedFillTensorOp>(op)) {
|
||
AtenMaskedFillScalarOp::Adaptor adaptor(operands);
|
||
Type dtype = converter->convertType(maskedFillTensor.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 = converter->convertType(fillTensor.getType())
|
||
.cast<RankedTensorType>()
|
||
.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 = converter->convertType(bitwiseNot.getType())
|
||
.cast<RankedTensorType>()
|
||
.getElementType();
|
||
if (elementType.isa<mlir::FloatType>()) {
|
||
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);
|
||
}
|
||
|
||
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<AtenTanhOp, AtenReluOp, AtenPreluOp, AtenGeluOp,
|
||
AtenGeluBackwardOp, AtenAddTensorOp, AtenMulTensorOp,
|
||
AtenDivTensorOp, AtenDivTensorModeOp, AtenSubTensorOp, AtenAtan2Op,
|
||
AtenLerpTensorOp, AtenSigmoidOp, AtenExpOp, AtenExpm1Op,
|
||
AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
|
||
AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp, AtenErfOp,
|
||
AtenSqrtOp, AtenFloorOp, AtenPowScalarOp, AtenPowTensorScalarOp,
|
||
AtenPowTensorTensorOp, AtenLog2Op, AtenLog10Op, AtenLog1pOp,
|
||
AtenRsqrtOp, AtenDivScalarOp, AtenRemainderScalarOp, AtenAbsOp,
|
||
AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenBitwiseAndScalarOp,
|
||
AtenBitwiseOrTensorOp, AtenBitwiseXorTensorOp,
|
||
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, AtenTriuOp,
|
||
AtenTrilOp, AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp,
|
||
AtenFillTensorOp, AtenAtanOp, AtenRealOp, AtenImagOp>(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 v.getType().isa<RankedTensorType>(); }));
|
||
auto resultType = getTypeConverter()
|
||
->convertType(op->getResult(0).getType())
|
||
.cast<RankedTensorType>();
|
||
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 (!weight.getType().isa<mlir::torch::Torch::NoneType>())
|
||
return rewriter.notifyMatchFailure(
|
||
op, "Unimplemented, the weight operand is not incorporated.");
|
||
|
||
Value ignoreIndex = adaptor.getIgnoreIndex();
|
||
Value ignoreIndexVal = castIntToIndex(rewriter, loc, ignoreIndex);
|
||
|
||
unsigned inputRank = input.getType().cast<RankedTensorType>().getRank();
|
||
unsigned targetRank = target.getType().cast<RankedTensorType>().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 = getTypeConverter()
|
||
->convertType(op->getResult(0).getType())
|
||
.cast<RankedTensorType>();
|
||
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 numOfElems = getTensorSize(rewriter, loc, finalRes);
|
||
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 = input.getType().cast<RankedTensorType>();
|
||
auto weightType = weight.getType().cast<RankedTensorType>();
|
||
auto biasType = bias.getType().cast<RankedTensorType>();
|
||
auto runningMeanType = runningMean.getType().cast<RankedTensorType>();
|
||
auto runningVarType = runningVar.getType().cast<RankedTensorType>();
|
||
|
||
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 = op.getWeight().getType().isa<Torch::NoneType>();
|
||
Value ignoreIndex = castIntToIndex(rewriter, loc, adaptor.getIgnoreIndex());
|
||
Value totalWeight = adaptor.getTotalWeight();
|
||
|
||
auto inputType = input.getType().cast<RankedTensorType>();
|
||
int inputRank = inputType.getRank();
|
||
auto gradOutputType = gradOutput.getType().cast<RankedTensorType>();
|
||
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 = tensor.getType().cast<RankedTensorType>();
|
||
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 = getTypeConverter()
|
||
->convertType(op->getResult(0).getType())
|
||
.cast<RankedTensorType>();
|
||
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 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 = adaptor.getA().getType().cast<RankedTensorType>();
|
||
const TypeConverter *typeConverter = getTypeConverter();
|
||
|
||
auto resultRankedTensorType =
|
||
typeConverter->convertType(op.getType()).cast<RankedTensorType>();
|
||
|
||
// 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 = getTypeConverter()
|
||
->convertType(op->getResult(0).getType())
|
||
.cast<RankedTensorType>();
|
||
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType,
|
||
adaptor.getOperand());
|
||
return success();
|
||
}
|
||
};
|
||
} // namespace
|
||
|
||
void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
|
||
TypeConverter &typeConverter, RewritePatternSet &patterns,
|
||
ConversionTarget &target) {
|
||
MLIRContext *context = patterns.getContext();
|
||
target.addIllegalOp<
|
||
AtenTanhOp, AtenReluOp, AtenGeluOp, AtenGeluBackwardOp, AtenAddTensorOp,
|
||
AtenMulTensorOp, AtenDivTensorOp, AtenDivTensorModeOp, AtenSubTensorOp,
|
||
AtenLerpTensorOp, AtenSigmoidOp, AtenMinimumOp, AtenAtan2Op,
|
||
AtenMaximumOp, AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp, AtenLogOp,
|
||
AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenCeilOp, AtenPreluOp,
|
||
AtenPowScalarOp, AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op,
|
||
AtenLog10Op, AtenLog1pOp, AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp,
|
||
AtenBitwiseAndTensorOp, AtenBitwiseAndScalarOp, AtenBitwiseOrTensorOp,
|
||
AtenBitwiseXorTensorOp, AtenBitwiseRightShiftTensorOp, AtenGtScalarOp,
|
||
AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp, AtenLeScalarOp,
|
||
AtenWhereSelfOp, AtenGtTensorOp, AtenGeTensorOp, AtenEqTensorOp,
|
||
AtenNeTensorOp, AtenLtTensorOp, AtenLeTensorOp, AtenThresholdOp,
|
||
AtenThresholdBackwardOp, AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp,
|
||
AtenCosOp, AtenNeScalarOp, AtenMaskedFillTensorOp, AtenLogicalOrOp,
|
||
AtenLogicalAndOp, AtenAtanOp, AtenLogicalXorOp, AtenLogicalNotOp,
|
||
AtenTriuOp, AtenTrilOp, AtenRemainderScalarOp, AtenBitwiseNotOp,
|
||
AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp, AtenRealOp,
|
||
AtenImagOp>();
|
||
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<PrimsCollapseOp>();
|
||
patterns.add<ConvertPrimsCollapseOp>(typeConverter, context);
|
||
|
||
target.addIllegalOp<AtenNllLossBackwardOp>();
|
||
patterns.add<ConvertAtenNllLossBackwardOp>(typeConverter, context);
|
||
patterns.add<ConvertTensorStaticInfoCastOp>(typeConverter, context);
|
||
target.addIllegalOp<TensorStaticInfoCastOp>();
|
||
}
|