mirror of https://github.com/llvm/torch-mlir
1103 lines
46 KiB
C++
1103 lines
46 KiB
C++
//===----------------------------------------------------------------------===//
|
|
//
|
|
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
// Also available under a BSD-style license. See LICENSE.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"
|
|
|
|
#include "../PassDetail.h"
|
|
#include "PopulatePatterns.h"
|
|
#include "Utils.h"
|
|
#include "mlir/Dialect/Arith/IR/Arith.h"
|
|
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
|
|
#include "mlir/Dialect/Linalg/IR/Linalg.h"
|
|
#include "mlir/Dialect/Math/IR/Math.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/IR/Matchers.h"
|
|
#include "torch-mlir/Conversion/Utils/Utils.h"
|
|
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
|
|
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
|
|
#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
|
|
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::torch;
|
|
using namespace mlir::torch::Torch;
|
|
|
|
static void createLinalgPayloadCalculationForGatherOps(
|
|
OpBuilder &b, Location loc, Value input, int64_t inputRank, Value index,
|
|
int64_t dim, int64_t outputRank) {
|
|
SmallVector<Value> indices;
|
|
for (int i = 0; i < inputRank; i++) {
|
|
if (i == dim) {
|
|
indices.push_back(castIntToIndex(b, loc, index));
|
|
} else {
|
|
// `outputRank` might be larger than `inputRank`. The `linalg::IndexOp`
|
|
// takes in the dimension of the output. Add `inputDimOffset` to
|
|
// related to the correct dimension of the output for dimension larger
|
|
// than the given `dim`.
|
|
int64_t inputDimOffset = i < dim ? 0 : outputRank - inputRank;
|
|
indices.push_back(b.create<linalg::IndexOp>(loc, i + inputDimOffset));
|
|
}
|
|
}
|
|
|
|
// Assert index < input.sizes[dim]
|
|
Value indexLTInputDim = b.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::slt, castIntToIndex(b, loc, index),
|
|
getDimOp(b, loc, input, dim));
|
|
b.create<cf::AssertOp>(
|
|
loc, indexLTInputDim,
|
|
b.getStringAttr("index must be smaller than dim size"));
|
|
|
|
// Assert index >= 0
|
|
Value cst0 = b.create<arith::ConstantOp>(loc, b.getZeroAttr(index.getType()));
|
|
Value indexGEThanZero =
|
|
b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge, index, cst0);
|
|
b.create<cf::AssertOp>(loc, indexGEThanZero,
|
|
b.getStringAttr("index must be larger or equal to 0"));
|
|
|
|
Value extract = b.create<tensor::ExtractOp>(loc, input, indices);
|
|
b.create<linalg::YieldOp>(loc, extract);
|
|
}
|
|
|
|
namespace {
|
|
class ConvertAtenGatherOp : public OpConversionPattern<AtenGatherOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenGatherOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
|
|
return failure();
|
|
Location loc = op->getLoc();
|
|
|
|
Value dimValue = op.dim();
|
|
int64_t dim;
|
|
if (!matchPattern(dimValue, m_TorchConstantInt(&dim)))
|
|
return op.emitError("unimplemented: dim is not constant");
|
|
|
|
Value indices = adaptor.index();
|
|
Value self = adaptor.self();
|
|
RankedTensorType newResultTy =
|
|
getTypeConverter()->convertType(op.getType()).cast<RankedTensorType>();
|
|
int64_t rank = newResultTy.getRank();
|
|
|
|
SmallVector<Value> sizes = getTensorSizes(rewriter, loc, indices);
|
|
Value result = createZeroInitTensor(rewriter, loc, sizes,
|
|
newResultTy.getElementType());
|
|
|
|
SmallVector<AffineMap, 2> affineMaps(2,
|
|
rewriter.getMultiDimIdentityMap(rank));
|
|
SmallVector<StringRef> iteratorTypes(rank, getParallelIteratorTypeName());
|
|
auto genericOp = rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, result.getType(), indices, result, affineMaps,
|
|
iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
auto index = args[0];
|
|
createLinalgPayloadCalculationForGatherOps(
|
|
b, loc, self, rank, index, dim, rank);
|
|
})
|
|
.getResult(0);
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultTy, genericOp);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
namespace {
|
|
class ConvertAtenEmbeddingOp : public OpConversionPattern<AtenEmbeddingOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenEmbeddingOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
|
|
return failure();
|
|
Location loc = op->getLoc();
|
|
Value weight = adaptor.weight();
|
|
Value indices = adaptor.indices();
|
|
RankedTensorType newResultType =
|
|
typeConverter->convertType(op.getType()).cast<RankedTensorType>();
|
|
|
|
auto weightTy = weight.getType().cast<RankedTensorType>();
|
|
if (weightTy.getRank() != 2)
|
|
return rewriter.notifyMatchFailure(op, "weight must be rank 2");
|
|
Value embeddingDim = getDimOp(rewriter, loc, weight, 1);
|
|
Type elemTy = weightTy.getElementType();
|
|
|
|
SmallVector<Value> sizes = getTensorSizes(rewriter, loc, indices);
|
|
sizes.push_back(embeddingDim);
|
|
int64_t resultRank = sizes.size();
|
|
|
|
auto indicesTy = indices.getType().cast<RankedTensorType>();
|
|
int64_t indicesRank = indicesTy.getRank();
|
|
SmallVector<AffineExpr> indicesExprs;
|
|
for (int i = 0; i < indicesRank; i++)
|
|
indicesExprs.push_back(rewriter.getAffineDimExpr(i));
|
|
auto indicesAffineMap = AffineMap::get(
|
|
/*dimCount=*/resultRank,
|
|
/*symbolCount=*/0, indicesExprs, op->getContext());
|
|
SmallVector<AffineMap, 2> indexingMaps = {
|
|
indicesAffineMap,
|
|
rewriter.getMultiDimIdentityMap(resultRank),
|
|
};
|
|
SmallVector<StringRef> iteratorTypes(sizes.size(),
|
|
getParallelIteratorTypeName());
|
|
Value initTensor =
|
|
rewriter.create<tensor::EmptyOp>(loc, getAsOpFoldResult(sizes), elemTy);
|
|
Value embeddingResult =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, initTensor.getType(), indices, initTensor,
|
|
/*indexingMaps=*/indexingMaps, /*iteratorTypes=*/iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
Value index = args[0];
|
|
createLinalgPayloadCalculationForGatherOps(
|
|
b, loc, weight, weightTy.getRank(), index, /*dim=*/0,
|
|
resultRank);
|
|
})
|
|
.getResult(0);
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType,
|
|
embeddingResult);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
namespace {
|
|
// AtenEmbeddingPaddingIdxOp
|
|
// SUM mode == integer 0
|
|
// Sums bags of embeddings together from a weight tensor based on an index and
|
|
// offset Vector. Example arguments weight = [[1, 3, 5, 3],
|
|
// [3, 4, 2, 1],
|
|
// [2, 2, 3, 2],
|
|
// [0, 4, 2, 1]]
|
|
//
|
|
// indices = [0, 2, 3, 1, 2, 3, 2, 1, 0, 1]
|
|
// offsets = [0, 3, 5]
|
|
//
|
|
// output_tensor = initZeroTensor(offsets_length, embedding_size)
|
|
//
|
|
// for i in range(offsets_length): <- dim0
|
|
// for j in range(indices_length): <- dim1
|
|
// for k in range(embedding_size): <- dim2
|
|
// if(offsets[i] <= j and j < offsets[i+1]):
|
|
// output_tensor[i][k] = output_tensor[i][k] +
|
|
// weight[indices[j]][k]
|
|
// else:
|
|
// break
|
|
//
|
|
// Indexing maps for linalg::Generic ops
|
|
//
|
|
//
|
|
// indices_indexing_map = (d0, d1, d2) -> (d1)
|
|
// offset_indexing_map = (d0, d1, d2) -> (d0)
|
|
// output_indexing_map = (d0, d1, d2) -> (d0, d2)
|
|
//
|
|
// TODO: Find an optimal lowering.
|
|
// current lowering is not optimal for bags of large embeddings.
|
|
// Since it traverses the output tensor multiple times.
|
|
//
|
|
//
|
|
|
|
class ConvertAtenEmbeddingBagPaddingIdxOp
|
|
: public OpConversionPattern<AtenEmbeddingBagPaddingIdxOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenEmbeddingBagPaddingIdxOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
|
|
return failure();
|
|
Location loc = op->getLoc();
|
|
auto context = op->getContext();
|
|
Value weight = adaptor.weight();
|
|
Value indices = adaptor.indices();
|
|
Value offsets = adaptor.offsets();
|
|
Value scaleGradByFreq = op.scale_grad_by_freq();
|
|
Value mode = op.mode();
|
|
Value sparse = op.sparse();
|
|
Value includeLastOffset = op.include_last_offset();
|
|
|
|
bool scaleGradByFreqBool;
|
|
if (!matchPattern(scaleGradByFreq,
|
|
m_TorchConstantBool(&scaleGradByFreqBool))) {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "scale_grad_by_freq is expected to be a constant boolean value.");
|
|
}
|
|
|
|
if (scaleGradByFreqBool) {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "Unimplemented: scale_grad_by_freq=True.");
|
|
}
|
|
|
|
int64_t modeInt;
|
|
if (!matchPattern(mode, m_TorchConstantInt(&modeInt))) {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "mode is expected to be a constant integer value.");
|
|
}
|
|
|
|
if (modeInt != torch_upstream::EmbeddingBagMode::MODE_SUM) {
|
|
return rewriter.notifyMatchFailure(
|
|
op,
|
|
"Unimplemented: Mean and Max mode are not supported yet for EmbeddingBag.");
|
|
}
|
|
|
|
bool isSparse;
|
|
if (!matchPattern(sparse, m_TorchConstantBool(&isSparse))) {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "sparse is expected to be a constant boolean value.");
|
|
}
|
|
|
|
if (isSparse) {
|
|
return rewriter.notifyMatchFailure(
|
|
op,
|
|
"Unimplemented: Sparse mode is not supported yet for EmbeddingBag.");
|
|
}
|
|
|
|
bool discardLastOffset;
|
|
if (!matchPattern(includeLastOffset,
|
|
m_TorchConstantBool(&discardLastOffset))) {
|
|
return rewriter.notifyMatchFailure(
|
|
op,
|
|
"include_last_offset is expected to be a constant boolean value.");
|
|
}
|
|
|
|
auto weightTy = weight.getType().cast<RankedTensorType>();
|
|
if (weightTy.getRank() != 2)
|
|
return rewriter.notifyMatchFailure(op, "weight must be rank 2");
|
|
|
|
auto indicesTy = indices.getType().cast<RankedTensorType>();
|
|
if (indicesTy.getRank() != 1)
|
|
return rewriter.notifyMatchFailure(op, "indices must be a vector");
|
|
|
|
auto offsetsTy = offsets.getType().cast<RankedTensorType>();
|
|
if (offsetsTy.getRank() != 1)
|
|
return rewriter.notifyMatchFailure(op, "offsets much be a vector");
|
|
|
|
Type weightElemTy = weightTy.getElementType();
|
|
|
|
int64_t iterationMapDimension = weightTy.getRank() + indicesTy.getRank();
|
|
SmallVector<AffineExpr> indicesExpr;
|
|
indicesExpr.push_back(mlir::getAffineDimExpr(1, context));
|
|
auto indicesIndexingMap =
|
|
AffineMap::get(/*dimCount=*/iterationMapDimension, /*symbolCount=*/0,
|
|
indicesExpr, context);
|
|
|
|
SmallVector<AffineExpr> offsetsExpr;
|
|
offsetsExpr.push_back(mlir::getAffineDimExpr(0, context));
|
|
|
|
auto offsetIndexingMap =
|
|
AffineMap::get(/*dimCount=*/iterationMapDimension, /*symbolCount=*/0,
|
|
offsetsExpr, context);
|
|
|
|
SmallVector<AffineExpr> outputExpr;
|
|
outputExpr.push_back(mlir::getAffineDimExpr(0, context));
|
|
outputExpr.push_back(mlir::getAffineDimExpr(2, context));
|
|
|
|
auto outputIndexingMap =
|
|
AffineMap::get(/*dimCount=*/iterationMapDimension, /*symbolCount=*/0,
|
|
outputExpr, context);
|
|
|
|
SmallVector<AffineMap, 3> indexingMaps = {
|
|
indicesIndexingMap,
|
|
offsetIndexingMap,
|
|
outputIndexingMap,
|
|
};
|
|
|
|
// Reduce along the indices dim
|
|
SmallVector<StringRef> iteratorTypes({getParallelIteratorTypeName(),
|
|
getReductionIteratorTypeName(),
|
|
getParallelIteratorTypeName()});
|
|
|
|
Value embeddingDim = getDimOp(rewriter, loc, weight, 1);
|
|
Value initTensor;
|
|
Value offsetsLength;
|
|
Value indicesLength;
|
|
if (!discardLastOffset) {
|
|
SmallVector<Value> sizes{getDimOp(rewriter, loc, offsets, 0),
|
|
embeddingDim};
|
|
|
|
initTensor = createZeroInitTensor(rewriter, loc, sizes, weightElemTy);
|
|
offsetsLength = getDimOp(rewriter, loc, offsets, 0);
|
|
indicesLength = getDimOp(rewriter, loc, indices, 0);
|
|
} else {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "Unimplemented: include last offset is not yet "
|
|
"supported for EmbeddingBag.");
|
|
}
|
|
|
|
Value embeddingBagResult =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, initTensor.getType(), ValueRange{indices, offsets},
|
|
initTensor,
|
|
/*indexingMaps=*/indexingMaps,
|
|
/*iteratorTypes=*/iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
Value indexInIndices = args[0];
|
|
Value offsetsI = args[1];
|
|
Value initTensorElem = args[2];
|
|
|
|
Value indexI = b.create<linalg::IndexOp>(loc, /*value=*/0);
|
|
Value indexIToInt = castIndexToInt64(b, loc, indexI);
|
|
Value one = getConstant(
|
|
b, loc, 1,
|
|
mlir::IntegerType::get(getContext(), 64,
|
|
IntegerType::Signless));
|
|
Value offsetIndexPlusOneInt =
|
|
b.create<arith::AddIOp>(loc, indexIToInt, one);
|
|
|
|
Value offsetIndexPlusOne =
|
|
castIntToIndex(b, loc, offsetIndexPlusOneInt);
|
|
Value checkLast = b.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::eq,
|
|
castIndexToInt64(b, loc, offsetsLength),
|
|
offsetIndexPlusOneInt);
|
|
Value nextOffset = b.create<arith::SelectOp>(
|
|
loc, checkLast, castIndexToInt64(b, loc, indicesLength),
|
|
b.create<tensor::ExtractOp>(loc, offsets,
|
|
offsetIndexPlusOne));
|
|
|
|
Value indicesIndex = castIndexToInt64(
|
|
b, loc, b.create<linalg::IndexOp>(loc, /*value=*/1));
|
|
|
|
Value offsetLessThanIndicesIndex = b.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::slt, offsetsI, indicesIndex);
|
|
Value offsetEqualToIndicesIndex = b.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::eq, offsetsI, indicesIndex);
|
|
Value offsetLessThanOrEqualToIndicesIndex =
|
|
b.create<arith::OrIOp>(loc, offsetLessThanIndicesIndex,
|
|
offsetEqualToIndicesIndex);
|
|
|
|
Value indicesIndexLessThanNextOffset =
|
|
b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt,
|
|
indicesIndex, nextOffset);
|
|
|
|
Value indicesIndexWithinBounds = b.create<arith::AndIOp>(
|
|
loc, offsetLessThanOrEqualToIndicesIndex,
|
|
indicesIndexLessThanNextOffset);
|
|
|
|
SmallVector<Value> indexIntoWeight;
|
|
indexIntoWeight.push_back(
|
|
castIntToIndex(b, loc, indexInIndices));
|
|
indexIntoWeight.push_back(
|
|
b.create<linalg::IndexOp>(loc, /*value=*/2));
|
|
Value weightElem = b.create<tensor::ExtractOp>(
|
|
loc, weight, indexIntoWeight);
|
|
|
|
Value addResult = b.create<arith::AddFOp>(loc, weightElem,
|
|
initTensorElem);
|
|
Value select =
|
|
b.create<arith::SelectOp>(loc, indicesIndexWithinBounds,
|
|
addResult, initTensorElem);
|
|
b.create<linalg::YieldOp>(loc, select);
|
|
})
|
|
.getResult(0);
|
|
|
|
// cast outputType.
|
|
auto restulType0 = typeConverter->convertType(op->getResult(0).getType());
|
|
Value castedEmbeddingBagResult =
|
|
rewriter.create<tensor::CastOp>(loc, restulType0, embeddingBagResult);
|
|
|
|
// offset2 tensor, this should be an empty tensor for the sum mode
|
|
SmallVector<Value> offsetResultSize;
|
|
Type offsetElemTy = offsetsTy.getElementType();
|
|
Value zeroDim = rewriter.create<arith::ConstantIndexOp>(loc, /*value=*/0);
|
|
offsetResultSize.push_back(zeroDim);
|
|
Value offsetResult = rewriter.create<tensor::EmptyOp>(
|
|
loc, getAsOpFoldResult(offsetResultSize), offsetElemTy);
|
|
auto resultType1 = typeConverter->convertType(op->getResult(1).getType());
|
|
Value castedOffsetResult =
|
|
rewriter.create<tensor::CastOp>(loc, resultType1, offsetResult);
|
|
|
|
SmallVector<Value> offsetSize = getTensorSizes(rewriter, loc, offsets);
|
|
// bagsize, vector of size offset with zeros, I think this is always just
|
|
// a vector of zeros in the sum mode
|
|
Value bagSize =
|
|
createZeroInitTensor(rewriter, loc, offsetSize, offsetElemTy);
|
|
auto resultType2 = typeConverter->convertType(op->getResult(2).getType());
|
|
Value castedBagSizeResult =
|
|
rewriter.create<tensor::CastOp>(loc, resultType2, bagSize);
|
|
|
|
// max indices, vector of size offset with zeros, this is also always a
|
|
// vector of zeros in the sum mode. Its mainly used in the max mode.
|
|
Value indicesOut =
|
|
createZeroInitTensor(rewriter, loc, offsetSize, offsetElemTy);
|
|
auto resultType3 = typeConverter->convertType(op->getResult(3).getType());
|
|
Value castedMaxIndices =
|
|
rewriter.create<tensor::CastOp>(loc, resultType3, indicesOut);
|
|
|
|
rewriter.replaceOp(op, {castedEmbeddingBagResult, castedOffsetResult,
|
|
castedBagSizeResult, castedMaxIndices});
|
|
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
namespace {
|
|
// Let's say we have an input tensor: initialized with some random values of
|
|
// size [4, 5, 6]. An index tensor (always 1-d): [0, 2] of size [2], and an
|
|
// integer argument dim = 1. The size of the output tensor will be [4, 2, 6].
|
|
// The approach is as follows:
|
|
//
|
|
// for i in range(input.size[0])
|
|
// for j in range(index.size[0])
|
|
// for k in range(input.size[2])
|
|
// indexValue = index[j]
|
|
// output[i,j,k] = input[i,indexValue,k]
|
|
|
|
class ConvertAtenIndexSelectOp : public OpConversionPattern<AtenIndexSelectOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenIndexSelectOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
|
|
return failure();
|
|
|
|
Location loc = op.getLoc();
|
|
Value input = adaptor.self();
|
|
Value indices = adaptor.index();
|
|
RankedTensorType inputType = input.getType().cast<RankedTensorType>();
|
|
RankedTensorType resultType = getTypeConverter()
|
|
->convertType(op->getResult(0).getType())
|
|
.cast<RankedTensorType>();
|
|
Type elementType = resultType.getElementType();
|
|
unsigned inputRank = inputType.getRank();
|
|
|
|
int64_t dimInt;
|
|
if (!matchPattern(op.dim(), m_TorchConstantInt(&dimInt)))
|
|
return op->emitError("unimplemented: dim is not constant");
|
|
|
|
SmallVector<Value> resultShape = getTensorSizes(rewriter, loc, input);
|
|
resultShape[dimInt] = getTensorSizes(rewriter, loc, indices)[0];
|
|
Value initTensor = rewriter.create<tensor::EmptyOp>(
|
|
loc, getAsOpFoldResult(resultShape), elementType);
|
|
|
|
SmallVector<AffineExpr> resultExpr;
|
|
AffineExpr indicesExpr = rewriter.getAffineDimExpr(dimInt);
|
|
SmallVector<StringRef> iteratorTypes;
|
|
|
|
for (unsigned i = 0; i < inputRank; i++) {
|
|
resultExpr.push_back(rewriter.getAffineDimExpr(i));
|
|
iteratorTypes.push_back(getParallelIteratorTypeName());
|
|
}
|
|
|
|
auto indexingMaps = AffineMap::inferFromExprList({indicesExpr, resultExpr});
|
|
|
|
Value finalRes =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, initTensor.getType(), ValueRange{indices}, initTensor,
|
|
/*indexingMaps=*/indexingMaps,
|
|
/*iteratorTypes=*/iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
Value index = rewriter.create<arith::IndexCastOp>(
|
|
loc, rewriter.getIndexType(), args[0]);
|
|
SmallVector<Value> indexTarget;
|
|
for (unsigned i = 0; i < inputRank; i++)
|
|
indexTarget.push_back(b.create<linalg::IndexOp>(loc, i));
|
|
indexTarget[dimInt] = index;
|
|
Value extractedElement =
|
|
b.create<tensor::ExtractOp>(loc, input, indexTarget);
|
|
b.create<linalg::YieldOp>(loc, extractedElement);
|
|
})
|
|
.getResult(0);
|
|
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, finalRes);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
// IndexTensor for multiple input tensors broadcasts their shapes to a common
|
|
// shape and then replaces the indexed dims with the indices given by the
|
|
// indexing tensors:
|
|
// x[i_1, i_2, ..., i_M] = result
|
|
// result[...] = x[i_1[...], i_2[...], ..., i_M[...]]
|
|
//
|
|
// where the result shape is computed as follows:
|
|
// 1. broadcast i_1, i_2, ..., i_M to a common shape
|
|
// 2. if i_1, i_2, ..., i_M is not contiguous, transpose the broadcasted
|
|
// shape to the beginning of the result shape, while removing the
|
|
// unchanged dims (marked by None)
|
|
// 3. Otherwise replace the indexed dims with the broadcasted shape
|
|
//
|
|
// e.g. x: [2, 3]
|
|
// x[[4], [6, 1]] -> x[6, 4]
|
|
namespace {
|
|
class ConvertAtenIndexTensorOp : public OpConversionPattern<AtenIndexTensorOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenIndexTensorOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
|
|
if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
|
|
return failure();
|
|
|
|
Location loc = op.getLoc();
|
|
Value input = adaptor.self();
|
|
Value indices = op.indices();
|
|
SmallVector<Value> indicesTuple;
|
|
if (!getListConstructElements(indices, indicesTuple)) {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unimplemented: the indices list is not from a list construct");
|
|
}
|
|
|
|
SmallVector<Value> indicesVal =
|
|
getTypeConvertedValues(rewriter, loc, getTypeConverter(), indicesTuple);
|
|
|
|
// Identify the indices with non-None index tensors and determine if they
|
|
// are contiguous within the input list.
|
|
SmallVector<int> indexTensorDims;
|
|
SmallVector<Value> indexTensors;
|
|
bool contiguous = true;
|
|
for (auto i : llvm::seq(0, (int)indicesVal.size())) {
|
|
Value index = indicesVal[i];
|
|
if (!index || failed(checkNotNone(rewriter, op, index)))
|
|
continue;
|
|
if (!indexTensorDims.empty() && indexTensorDims.back() != i - 1)
|
|
contiguous = false;
|
|
indexTensorDims.push_back(i);
|
|
indexTensors.push_back(index);
|
|
}
|
|
|
|
if (indexTensors.empty()) {
|
|
return rewriter.notifyMatchFailure(
|
|
op, "aten.index.Tensor: index tensor must not be None");
|
|
}
|
|
|
|
RankedTensorType inputType = input.getType().cast<RankedTensorType>();
|
|
RankedTensorType resultType = getTypeConverter()
|
|
->convertType(op->getResult(0).getType())
|
|
.cast<RankedTensorType>();
|
|
Type elementType = resultType.getElementType();
|
|
int inputRank = inputType.getRank();
|
|
int resultRank = resultType.getRank();
|
|
int firstIndexDim = indexTensorDims[0];
|
|
int replacedIndexCount = indexTensorDims.size();
|
|
int64_t startIndex = contiguous ? firstIndexDim : 0;
|
|
|
|
// Currently we only support statically sized index tensors or dynamic size
|
|
// index tensors without overlapping dynamic dims when there is more than
|
|
// one index tensor.
|
|
// TODO: Add support for dynamic size index tensors with overlapping
|
|
// dynamic dims.
|
|
SmallVector<Value> broadcastedIndexShape;
|
|
if (indexTensors.size() > 1) {
|
|
int maxRank = -1;
|
|
for (auto indexTensor : indexTensors) {
|
|
RankedTensorType indexTensorType =
|
|
indexTensor.getType().cast<RankedTensorType>();
|
|
maxRank = std::max(maxRank, (int)indexTensorType.getRank());
|
|
}
|
|
|
|
// Because we are assuming static shapes, we can get the shape of the
|
|
// broadcasted index tensors from the shape refinement pass
|
|
auto refinedResultShape = resultType.getShape();
|
|
for (auto i : llvm::seq(startIndex, startIndex + maxRank)) {
|
|
auto resultDimSize = refinedResultShape[i];
|
|
if (ShapedType::isDynamic(resultDimSize)) {
|
|
SmallVector<Value> dynamicDims;
|
|
int64_t staticDimSize = -1;
|
|
for (auto indexTensor : indexTensors) {
|
|
RankedTensorType indexTensorType =
|
|
indexTensor.getType().cast<RankedTensorType>();
|
|
int64_t indexTensorRank = indexTensorType.getRank();
|
|
if ((maxRank - indexTensorRank) > (i - startIndex))
|
|
continue;
|
|
int64_t dim = i - startIndex - maxRank + indexTensorRank;
|
|
if (ShapedType::isDynamic(indexTensorType.getShape()[dim]))
|
|
dynamicDims.push_back(getDimOp(rewriter, loc, indexTensor, dim));
|
|
else
|
|
staticDimSize =
|
|
std::max(staticDimSize, indexTensorType.getShape()[dim]);
|
|
}
|
|
if (dynamicDims.size() >= 2)
|
|
return rewriter.notifyMatchFailure(
|
|
op,
|
|
"unimplemented: index tensors with overlapping dynamic dims");
|
|
if (staticDimSize > 1) {
|
|
Value cstStaticDimSize = getConstant(rewriter, loc, staticDimSize,
|
|
rewriter.getIndexType());
|
|
auto equalToRunning = rewriter.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::eq, cstStaticDimSize,
|
|
dynamicDims[0]);
|
|
rewriter.create<cf::AssertOp>(loc, equalToRunning,
|
|
"mismatched size for broadcast");
|
|
}
|
|
broadcastedIndexShape.push_back(dynamicDims[0]);
|
|
} else {
|
|
broadcastedIndexShape.push_back(getConstant(
|
|
rewriter, loc, resultDimSize, rewriter.getIndexType()));
|
|
}
|
|
}
|
|
} else {
|
|
// For a single indexing tensor we can simply use its (dynamic) sizes
|
|
broadcastedIndexShape =
|
|
getTensorSizes(rewriter, loc, indexTensors.front());
|
|
}
|
|
|
|
// This result shape calculation assumes that there is only one
|
|
// index tensor, or all of the index tensors are statically shaped.
|
|
int broadcastRank = broadcastedIndexShape.size();
|
|
|
|
SmallVector<Value> resultShape;
|
|
if (contiguous) {
|
|
for (auto i : llvm::seq(0, firstIndexDim)) {
|
|
resultShape.push_back(getDimOp(rewriter, loc, input, i));
|
|
}
|
|
resultShape.append(broadcastedIndexShape);
|
|
for (auto i : llvm::seq((int)resultShape.size(), resultRank)) {
|
|
resultShape.push_back(getDimOp(rewriter, loc, input,
|
|
i - broadcastRank + replacedIndexCount));
|
|
}
|
|
} else {
|
|
resultShape.append(broadcastedIndexShape);
|
|
int j = 0;
|
|
for (auto i : llvm::seq(0, inputRank)) {
|
|
if (j < replacedIndexCount && i == indexTensorDims[j]) {
|
|
j++;
|
|
continue;
|
|
}
|
|
resultShape.push_back(getDimOp(rewriter, loc, input, i));
|
|
}
|
|
}
|
|
|
|
// Initialize the indexing maps for the generic op. Because we are assuming
|
|
// static shapes for the indexing tensors when there are more than 1, we can
|
|
// safely map all size 1 dims to 0 in the corresponding affine maps.
|
|
// TODO: For dynamic shapes, we have to either broadcast the index tensors
|
|
// to a common shape or introduce some form of control flow.
|
|
Value initTensor = rewriter.create<tensor::EmptyOp>(
|
|
loc, getAsOpFoldResult(resultShape), elementType);
|
|
SmallVector<AffineMap> indexingMaps;
|
|
SmallVector<StringRef> iteratorTypes;
|
|
|
|
for (auto indexTensor : indexTensors) {
|
|
RankedTensorType indexTensorType =
|
|
indexTensor.getType().cast<RankedTensorType>();
|
|
auto indexTensorShape = indexTensorType.getShape();
|
|
int rank = indexTensorShape.size();
|
|
SmallVector<AffineExpr> indicesExpr;
|
|
for (auto dim : llvm::seq(0, rank)) {
|
|
if (indexTensorShape[dim] == 1) {
|
|
indicesExpr.push_back(rewriter.getAffineConstantExpr(0));
|
|
continue;
|
|
}
|
|
indicesExpr.push_back(
|
|
rewriter.getAffineDimExpr(startIndex + broadcastRank - rank + dim));
|
|
}
|
|
indexingMaps.push_back(
|
|
AffineMap::get(resultRank, 0, indicesExpr, op->getContext()));
|
|
}
|
|
|
|
SmallVector<AffineExpr> resultExpr;
|
|
for (auto i : llvm::seq(0, resultRank)) {
|
|
resultExpr.push_back(rewriter.getAffineDimExpr(i));
|
|
iteratorTypes.push_back(getParallelIteratorTypeName());
|
|
}
|
|
|
|
indexingMaps.push_back(
|
|
AffineMap::get(resultRank, 0, resultExpr, op->getContext()));
|
|
|
|
Value finalRes =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, initTensor.getType(), indexTensors, initTensor,
|
|
indexingMaps, iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
SmallVector<Value> extractionIndices;
|
|
if (contiguous) {
|
|
for (auto i : llvm::seq(0, firstIndexDim)) {
|
|
extractionIndices.push_back(
|
|
b.create<linalg::IndexOp>(loc, i));
|
|
}
|
|
for (auto i : llvm::seq(0, (int)indexTensorDims.size())) {
|
|
extractionIndices.push_back(
|
|
castIntToIndex(b, loc, args[i]));
|
|
}
|
|
for (auto i :
|
|
llvm::seq((int)extractionIndices.size(), inputRank)) {
|
|
extractionIndices.push_back(b.create<linalg::IndexOp>(
|
|
loc, i + broadcastRank - replacedIndexCount));
|
|
}
|
|
} else {
|
|
int indexCount = 0, unchanged = 0;
|
|
for (auto i : llvm::seq(0, inputRank)) {
|
|
if (indexCount < replacedIndexCount &&
|
|
i == indexTensorDims[indexCount]) {
|
|
extractionIndices.push_back(
|
|
castIntToIndex(b, loc, args[indexCount++]));
|
|
continue;
|
|
}
|
|
extractionIndices.push_back(b.create<linalg::IndexOp>(
|
|
loc, broadcastRank + unchanged));
|
|
unchanged++;
|
|
}
|
|
}
|
|
Value extractedElement = b.create<tensor::ExtractOp>(
|
|
loc, input, extractionIndices);
|
|
b.create<linalg::YieldOp>(loc, extractedElement);
|
|
})
|
|
.getResult(0);
|
|
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, finalRes);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
// `getScaledDims` scales the `dim` value with a scale factor `ScaleFactor`.
|
|
// The `dim` and `scaleFactor` are assumed to be of index and float type
|
|
// respectively. `scaledDim = int(floor(float(dim) * scaleFactor))`.
|
|
static Value getScaledDims(OpBuilder &builder, Location loc, Value dim,
|
|
Value scaleFactor) {
|
|
|
|
Value dimInt = castIndexToInt64(builder, loc, dim);
|
|
Value dimFp =
|
|
builder.create<arith::SIToFPOp>(loc, scaleFactor.getType(), dimInt);
|
|
Value scaleDim = builder.create<arith::MulFOp>(loc, dimFp, scaleFactor);
|
|
Value floorDim = builder.create<math::FloorOp>(loc, scaleDim);
|
|
Value scaledDimToIndex = castIntToIndex(
|
|
builder, loc,
|
|
builder.create<arith::FPToSIOp>(loc, dimInt.getType(), floorDim));
|
|
|
|
return scaledDimToIndex;
|
|
}
|
|
|
|
// `getScaleFactor` returns the scale factor from input to output dimension.
|
|
// The `dim` and `scaledDim` are assumed to be of index and int64 type
|
|
// respectively. scale_factor = (scaled_dim // dim).
|
|
static Value getScaleFactor(OpBuilder &builder, Location loc, Value dim,
|
|
Value scaledDim) {
|
|
Value dimInt = castIndexToInt64(builder, loc, dim);
|
|
Value scaleFactorInt =
|
|
builder.create<arith::CeilDivSIOp>(loc, scaledDim, dimInt);
|
|
return scaleFactorInt;
|
|
}
|
|
|
|
// N, C, H, W = input_tensor.shape
|
|
// N, C, H_scaled, W_scaled = out_tensor.shape
|
|
// H_factor, W_factor = H_scaled/H, W_scaled/W
|
|
|
|
// for i in range(N):
|
|
// for j in range(C):
|
|
// for k in range(H_scaled):
|
|
// for l in range(W_scaled):
|
|
// out_tensor[i, j, k, l] = input[i, j, k//H_factor, l//W_factor]
|
|
|
|
namespace {
|
|
class ConvertAtenUpsampleNearest2dVecOp
|
|
: public OpConversionPattern<AtenUpsampleNearest2dVecOp> {
|
|
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenUpsampleNearest2dVecOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
|
|
Location loc = op->getLoc();
|
|
Value input = adaptor.input();
|
|
|
|
Type resultType = getTypeConverter()->convertType(op.getResult().getType());
|
|
auto inputType = input.getType().cast<RankedTensorType>();
|
|
auto inputRank = inputType.getRank();
|
|
Type elementType = inputType.getElementType();
|
|
|
|
SmallVector<Value> dims = getTensorSizes(rewriter, loc, input);
|
|
SmallVector<Value, 2> scaleFactorsInt;
|
|
|
|
// The dimension at which the scaling starts.
|
|
unsigned hDimOffset = 2;
|
|
|
|
if (!adaptor.scale_factors().getType().isa<Torch::NoneType>()) {
|
|
SmallVector<Value, 2> scaleFactorsTorchFloat;
|
|
if (!getListConstructElements(op.scale_factors(), scaleFactorsTorchFloat))
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unimplemented: the scale_factors is not constructed from "
|
|
"ListConstruct");
|
|
SmallVector<Value, 2> scaleFactorsFloatValues;
|
|
scaleFactorsFloatValues = getTypeConvertedValues(
|
|
rewriter, loc, getTypeConverter(), scaleFactorsTorchFloat);
|
|
// Convert float values to int values.
|
|
// int_value = (int64_t)ceil(float_value)
|
|
for (auto floatValue : scaleFactorsFloatValues) {
|
|
Value ceilVal = rewriter.create<math::CeilOp>(loc, floatValue);
|
|
Value intVal = rewriter.create<arith::FPToSIOp>(
|
|
loc, rewriter.getI64Type(), ceilVal);
|
|
scaleFactorsInt.push_back(intVal);
|
|
}
|
|
|
|
for (unsigned i = 0; i < scaleFactorsFloatValues.size(); i++)
|
|
dims[hDimOffset + i] = getScaledDims(
|
|
rewriter, loc, dims[hDimOffset + i], scaleFactorsFloatValues[i]);
|
|
|
|
} else {
|
|
|
|
SmallVector<Value, 2> outputSizeTorchInt;
|
|
if (!getListConstructElements(op.output_size(), outputSizeTorchInt))
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unimplemented: the output_size is not constructed from "
|
|
"ListConstruct");
|
|
SmallVector<Value, 2> outputSizeIntValues;
|
|
outputSizeIntValues = getTypeConvertedValues(
|
|
rewriter, loc, getTypeConverter(), outputSizeTorchInt);
|
|
|
|
for (unsigned i = 0; i < outputSizeTorchInt.size(); i++) {
|
|
auto scaleFactorVal = getScaleFactor(
|
|
rewriter, loc, dims[hDimOffset + i], outputSizeIntValues[i]);
|
|
scaleFactorsInt.push_back(scaleFactorVal);
|
|
dims[hDimOffset + i] =
|
|
castIntToIndex(rewriter, loc, outputSizeIntValues[i]);
|
|
}
|
|
}
|
|
|
|
Value outTensor = rewriter.create<tensor::EmptyOp>(
|
|
loc, getAsOpFoldResult(dims), elementType);
|
|
|
|
AffineMap idMap = rewriter.getMultiDimIdentityMap(inputRank);
|
|
SmallVector<StringRef> iteratorTypes(inputRank,
|
|
getParallelIteratorTypeName());
|
|
|
|
Value finalRes =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, outTensor.getType(), ValueRange{}, outTensor,
|
|
/*indexingMaps=*/idMap,
|
|
/*iteratorTypes=*/iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
SmallVector<Value> indices;
|
|
for (unsigned i = 0; i < inputRank; i++)
|
|
indices.push_back(b.create<linalg::IndexOp>(loc, i));
|
|
|
|
for (unsigned i = 0; i < (inputRank - hDimOffset); i++)
|
|
indices[i + hDimOffset] = b.create<arith::FloorDivSIOp>(
|
|
loc, indices[i + hDimOffset],
|
|
castIntToIndex(rewriter, loc, scaleFactorsInt[i]));
|
|
|
|
Value retVal =
|
|
b.create<tensor::ExtractOp>(loc, input, indices);
|
|
b.create<linalg::YieldOp>(loc, retVal);
|
|
})
|
|
.getResult(0);
|
|
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, finalRes);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
static Value getGradOutputValue(OpBuilder &builder, Location loc,
|
|
Value gradOutput, Type gradOutputElemType,
|
|
Value numBatch, Value numChannel,
|
|
Value inputIndexH, Value inputIndexW,
|
|
Value kernelIndexH, Value kernelIndexW,
|
|
SmallVector<Value> &gradOutputSizeIndexValues,
|
|
SmallVector<Value, 2> &scaleFactorsIntValues) {
|
|
Value constantOne = builder.create<arith::ConstantIndexOp>(loc, 1);
|
|
|
|
Value outputIndexH = builder.create<arith::MulIOp>(
|
|
loc, inputIndexH, castIntToIndex(builder, loc, scaleFactorsIntValues[0]));
|
|
outputIndexH = builder.create<arith::AddIOp>(loc, outputIndexH, kernelIndexH);
|
|
|
|
Value outputIndexW = builder.create<arith::MulIOp>(
|
|
loc, inputIndexW, castIntToIndex(builder, loc, scaleFactorsIntValues[1]));
|
|
outputIndexW = builder.create<arith::AddIOp>(loc, outputIndexW, kernelIndexW);
|
|
|
|
// Handling corner cases.
|
|
Value gradOutputHMinusOne = builder.create<arith::SubIOp>(
|
|
loc, gradOutputSizeIndexValues[2], constantOne);
|
|
Value predH = builder.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::sle, outputIndexH, gradOutputHMinusOne);
|
|
outputIndexH = builder.create<arith::SelectOp>(loc, predH, outputIndexH,
|
|
gradOutputHMinusOne);
|
|
|
|
Value gradOutputWMinusOne = builder.create<arith::SubIOp>(
|
|
loc, gradOutputSizeIndexValues[3], constantOne);
|
|
Value predW = builder.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::sle, outputIndexW, gradOutputWMinusOne);
|
|
outputIndexW = builder.create<arith::SelectOp>(loc, predW, outputIndexW,
|
|
gradOutputWMinusOne);
|
|
|
|
Value gradOutputValue = builder.create<tensor::ExtractOp>(
|
|
loc, gradOutput,
|
|
ValueRange{numBatch, numChannel, outputIndexH, outputIndexW});
|
|
Value constantZero =
|
|
builder.create<arith::ConstantOp>(loc, builder.getF32FloatAttr(0.0));
|
|
Value pred = builder.create<arith::AndIOp>(loc, predH, predW);
|
|
Value result = builder.create<arith::SelectOp>(
|
|
loc, pred, gradOutputValue,
|
|
convertScalarToDtype(builder, loc, constantZero, gradOutputElemType));
|
|
|
|
return result;
|
|
}
|
|
|
|
// The implementation of the `aten.upsample_nearest2d_backward.vec` op's
|
|
// lowering is as follows:
|
|
// gradOutput: Tensor of size [n, c, oh, ow]
|
|
// outTensor: Tensor of size [n, c, ih, iw], initialized with zero
|
|
// kh = ceil(oh/ih), kw = ceil(ow/iw)
|
|
//
|
|
// for i in range(n):
|
|
// for j in range(c):
|
|
// for p in range(ih):
|
|
// for q in range(iw):
|
|
// for x in range(kh):
|
|
// for y in range(kw):
|
|
// outTensor[i, j, p, q] += gradOutput[i, j, (p*kh)+x, (q*kw)+y]
|
|
namespace {
|
|
class ConvertAtenUpsampleNearest2dBackwardVecOp
|
|
: public OpConversionPattern<AtenUpsampleNearest2dBackwardVecOp> {
|
|
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenUpsampleNearest2dBackwardVecOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
|
|
Location loc = op->getLoc();
|
|
Value gradOutput = adaptor.grad_output();
|
|
|
|
Type resultType = getTypeConverter()->convertType(op.getResult().getType());
|
|
auto gradOutputType = gradOutput.getType().cast<RankedTensorType>();
|
|
auto gradOutputRank = gradOutputType.getRank();
|
|
Type elementType = gradOutputType.getElementType();
|
|
|
|
SmallVector<Value> gradOutputSizeIndexValues =
|
|
getTensorSizes(rewriter, loc, gradOutput);
|
|
SmallVector<Value> gradOutputSizeIntValues =
|
|
castIndexVectorToInt64Vector(rewriter, loc, gradOutputSizeIndexValues);
|
|
SmallVector<Value, 2> scaleFactorsFloatValues;
|
|
|
|
SmallVector<Value, 4> inputSizeTorchInt;
|
|
if (!getListConstructElements(op.input_size(), inputSizeTorchInt))
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unimplemented: the input_size is not constructed from "
|
|
"ListConstruct");
|
|
SmallVector<Value, 4> inputSizeIntValues;
|
|
inputSizeIntValues = getTypeConvertedValues(
|
|
rewriter, loc, getTypeConverter(), inputSizeTorchInt);
|
|
|
|
// The dimension at which the scaling starts.
|
|
unsigned hDimOffset = 2;
|
|
|
|
if (!op.scale_factors().getType().isa<Torch::NoneType>()) {
|
|
SmallVector<Value, 2> scaleFactorsTorchFloat;
|
|
if (!getListConstructElements(op.scale_factors(), scaleFactorsTorchFloat))
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unimplemented: the scale_factors is not constructed from "
|
|
"ListConstruct");
|
|
scaleFactorsFloatValues = getTypeConvertedValues(
|
|
rewriter, loc, getTypeConverter(), scaleFactorsTorchFloat);
|
|
} else {
|
|
for (unsigned i = hDimOffset; i < gradOutputRank; i++) {
|
|
auto scaleFactorVal = rewriter.create<arith::DivFOp>(
|
|
loc,
|
|
convertScalarToDtype(rewriter, loc, gradOutputSizeIntValues[i],
|
|
mlir::Float32Type::get(op->getContext())),
|
|
convertScalarToDtype(rewriter, loc, inputSizeIntValues[i],
|
|
mlir::Float32Type::get(op->getContext())));
|
|
scaleFactorsFloatValues.push_back(scaleFactorVal);
|
|
}
|
|
}
|
|
|
|
SmallVector<Value, 2> scaleFactorsIntValues;
|
|
for (auto v : scaleFactorsFloatValues)
|
|
scaleFactorsIntValues.push_back(convertScalarToDtype(
|
|
rewriter, loc, rewriter.create<math::CeilOp>(loc, v),
|
|
mlir::IntegerType::get(op->getContext(), 64)));
|
|
|
|
Value outTensor = createZeroInitTensor(
|
|
rewriter, loc,
|
|
castIntVectorToIndexVector(rewriter, loc, inputSizeIntValues),
|
|
elementType);
|
|
|
|
Value kernelTensor = rewriter.create<tensor::EmptyOp>(
|
|
loc,
|
|
getAsOpFoldResult(
|
|
castIntVectorToIndexVector(rewriter, loc, scaleFactorsIntValues)),
|
|
elementType);
|
|
unsigned kernelRank = scaleFactorsIntValues.size();
|
|
|
|
SmallVector<AffineExpr> affineExprs;
|
|
for (unsigned i = 0; i < gradOutputRank; i++)
|
|
affineExprs.push_back(rewriter.getAffineDimExpr(i));
|
|
|
|
AffineMap outputMap =
|
|
AffineMap::get(gradOutputRank + kernelRank,
|
|
/*symbolCount=*/0, affineExprs, op->getContext());
|
|
|
|
affineExprs.clear();
|
|
for (unsigned i = gradOutputRank; i < gradOutputRank + kernelRank; i++)
|
|
affineExprs.push_back(rewriter.getAffineDimExpr(i));
|
|
|
|
AffineMap kernelMap =
|
|
AffineMap::get(gradOutputRank + kernelRank,
|
|
/*symbolCount=*/0, affineExprs, op->getContext());
|
|
|
|
SmallVector<AffineMap> indexingMaps{kernelMap, outputMap};
|
|
SmallVector<StringRef> iteratorTypes(gradOutputRank,
|
|
getParallelIteratorTypeName());
|
|
iteratorTypes.push_back(getReductionIteratorTypeName());
|
|
iteratorTypes.push_back(getReductionIteratorTypeName());
|
|
|
|
Value finalRes =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, outTensor.getType(), ValueRange{kernelTensor},
|
|
ValueRange{outTensor},
|
|
/*indexingMaps=*/indexingMaps,
|
|
/*iteratorTypes=*/iteratorTypes,
|
|
[&](OpBuilder &b, Location loc, ValueRange args) {
|
|
Value n = rewriter.create<linalg::IndexOp>(loc, 0);
|
|
Value c = rewriter.create<linalg::IndexOp>(loc, 1);
|
|
Value ih = rewriter.create<linalg::IndexOp>(loc, 2);
|
|
Value iw = rewriter.create<linalg::IndexOp>(loc, 3);
|
|
Value kh = rewriter.create<linalg::IndexOp>(loc, 4);
|
|
Value kw = rewriter.create<linalg::IndexOp>(loc, 5);
|
|
Value accValue = getGradOutputValue(
|
|
rewriter, loc, gradOutput, elementType, n, c, ih, iw, kh,
|
|
kw, gradOutputSizeIndexValues, scaleFactorsIntValues);
|
|
Value outputVal = args[1];
|
|
outputVal =
|
|
rewriter.create<arith::AddFOp>(loc, outputVal, accValue);
|
|
b.create<linalg::YieldOp>(loc, outputVal);
|
|
})
|
|
->getResult(0);
|
|
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, finalRes);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
void mlir::torch::torch_to_linalg::
|
|
populateIndirectDataMovementPatternsAndLegality(
|
|
TypeConverter &typeConverter, RewritePatternSet &patterns,
|
|
ConversionTarget &target) {
|
|
MLIRContext *context = patterns.getContext();
|
|
target.addIllegalOp<AtenGatherOp>();
|
|
patterns.add<ConvertAtenGatherOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenEmbeddingOp>();
|
|
patterns.add<ConvertAtenEmbeddingOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenIndexSelectOp>();
|
|
patterns.add<ConvertAtenIndexSelectOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenIndexTensorOp>();
|
|
patterns.add<ConvertAtenIndexTensorOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenEmbeddingBagPaddingIdxOp>();
|
|
patterns.add<ConvertAtenEmbeddingBagPaddingIdxOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenUpsampleNearest2dVecOp>();
|
|
patterns.add<ConvertAtenUpsampleNearest2dVecOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenUpsampleNearest2dBackwardVecOp>();
|
|
patterns.add<ConvertAtenUpsampleNearest2dBackwardVecOp>(typeConverter, context);
|
|
}
|