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

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//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//
#include "torch-mlir/Conversion/TorchToLinalg/TorchToLinalg.h"
#include "../PassDetail.h"
#include "PopulatePatterns.h"
#include "Utils.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.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<linalg::InitTensorOp>(loc, 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 {
// 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<linalg::InitTensorOp>(loc, 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
// when there is more than one index tensor.
// TODO: Add support for dynamic size index tensors. This will probably
// require broadcasting the index tensors to a common shape.
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)) {
return rewriter.notifyMatchFailure(
op, "unimplemented: index tensors must have static shape if "
"there is more than one index tensor");
}
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<linalg::InitTensorOp>(loc, 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
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);
}
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