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torch-mlir/lib/Conversion/TorchToMhlo/ViewLikeOps.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/TorchToMhlo/TorchToMhlo.h"
#include "../PassDetail.h"
#include "./PopulatePatterns.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.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"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
#include <numeric>
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
using namespace mlir::torch::TorchConversion;
#ifdef TORCH_MLIR_ENABLE_MHLO_TRUNC_DIMSIZE_TO_I32
static constexpr size_t kMhloDimSizeBits = 32;
#else
static constexpr size_t kMhloDimSizeBits = 64;
#endif
namespace {
SmallVector<size_t> toPositiveDims(ArrayRef<int64_t> dims, int64_t rank) {
SmallVector<size_t> posDims;
posDims.reserve(rank);
std::transform(
dims.begin(), dims.end(), std::back_inserter(posDims),
[rank](int64_t d) -> size_t { return toPositiveDim(d, rank); });
return posDims;
}
FailureOr<SmallVector<Value, 4>>
getDimSizesOfTensor(PatternRewriter &rewriter, Operation *op, Value value,
ArrayRef<int64_t> inpDims) {
auto valueTy = value.getType().dyn_cast<RankedTensorType>();
if (!valueTy) {
return rewriter.notifyMatchFailure(
op, "getDimSizesOfTensor(): the input is not a ranked tensor");
}
auto rank = valueTy.getRank();
auto dims = toPositiveDims(inpDims, rank);
SmallVector<Value, 4> dimSizes;
dimSizes.reserve(dims.size());
auto loc = op->getLoc();
for (auto d : dims) {
dimSizes.emplace_back(rewriter.create<arith::IndexCastOp>(
loc, rewriter.getIntegerType(kMhloDimSizeBits),
rewriter.create<tensor::DimOp>(loc, value, d)));
}
return dimSizes;
}
FailureOr<SmallVector<Value, 4>>
getDimSizesOfTensor(PatternRewriter &rewriter, Operation *op, Value value) {
auto valueTy = value.getType().dyn_cast<RankedTensorType>();
if (!valueTy) {
return rewriter.notifyMatchFailure(
op, "getDimSizesOfTensor(): the input is not a ranked tensor");
}
auto rank = valueTy.getRank();
// Get int vector [0, 1, ..., rank-1]
std::vector<int64_t> dims(rank);
std::iota(dims.begin(), dims.end(), 0);
return getDimSizesOfTensor(rewriter, op, value, dims);
}
// A dimension index from torch.dialect might outside the range [0, dimSize].
// The function is used to normalize the input index into the range.
Value getNormalizedDimSizeInternal(PatternRewriter &rewriter, Operation *op,
Value index, Value dimSize) {
auto loc = op->getLoc();
Value zero = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(rewriter.getI64Type(), 0));
// To normalize index into range [-dimSize, dimSize]
// index = min(max(-dimSize, index), dimSize)
auto negDimSize = rewriter.create<arith::SubIOp>(loc, zero, dimSize);
index = rewriter.create<arith::MaxSIOp>(loc, negDimSize, index);
index = rewriter.create<arith::MinSIOp>(loc, dimSize, index);
auto dimSizePlusIndex = rewriter.create<arith::AddIOp>(loc, dimSize, index);
auto indexPositive = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::sge, index, zero);
// get positive index: (index >=0) ? index: index + dimSize
return rewriter.create<arith::SelectOp>(loc, indexPositive, index,
dimSizePlusIndex);
}
Value getDynamicSliceInternal(PatternRewriter &rewriter, Operation *op,
Value input, Value startIndex, Value endIndex,
Value step, size_t dimIndex,
ArrayRef<Value> dimSizes) {
auto loc = op->getLoc();
// startIndex & endIndex has been normailized into range [0, dSize]
Type intType = rewriter.getIntegerType(kMhloDimSizeBits);
Value zero = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(intType, 0));
Value one = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(intType, 1));
SmallVector<Value, 4> startIndices;
SmallVector<Value, 4> endIndices;
SmallVector<Value, 4> strides;
auto inputTy = input.getType().dyn_cast<RankedTensorType>();
size_t rank = inputTy.getRank();
startIndices.reserve(rank);
endIndices.reserve(rank);
strides.reserve(rank);
auto endIndexIsZero = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::eq, endIndex, zero);
endIndex = rewriter.create<arith::SelectOp>(loc, endIndexIsZero,
dimSizes[dimIndex], endIndex);
for (size_t r = 0; r < rank; ++r) {
if (r == dimIndex) {
startIndices.push_back(startIndex);
endIndices.push_back(endIndex);
strides.push_back(step);
} else {
startIndices.push_back(zero);
endIndices.push_back(dimSizes[r]);
strides.push_back(one);
}
}
auto startTensor =
rewriter.create<tensor::FromElementsOp>(loc, startIndices).getResult();
auto endTensor =
rewriter.create<tensor::FromElementsOp>(loc, endIndices).getResult();
auto stridesTensor =
rewriter.create<tensor::FromElementsOp>(loc, strides).getResult();
auto inputShape = inputTy.getShape();
SmallVector<int64_t, 4> sliceShape(inputShape.begin(), inputShape.end());
sliceShape[dimIndex] = ShapedType::kDynamicSize;
auto sliceoutputTy =
RankedTensorType::get(sliceShape, inputTy.getElementType());
return rewriter.create<mhlo::RealDynamicSliceOp>(
loc, sliceoutputTy, input, startTensor, endTensor, stridesTensor);
}
// Get a dynamic slice of the tensor from startIndex to endIndex with stride
// step on the specifed dimension. The input startIndex(default to 0),
// endIndex(default to dimSize), and step(default to 1) can be optional.
FailureOr<Value> getDynamicSlice(PatternRewriter &rewriter, Operation *op,
Value input,
llvm::Optional<Value> startIndexOpt,
llvm::Optional<Value> endIndexOpt,
llvm::Optional<Value> stepOpt, int64_t dim) {
auto loc = op->getLoc();
auto inputTy = input.getType().dyn_cast<RankedTensorType>();
auto rank = inputTy.getRank();
dim = (dim + rank) % rank;
Value dimSize = rewriter.create<arith::IndexCastOp>(
loc, rewriter.getI64Type(),
rewriter.create<tensor::DimOp>(loc, input, dim));
Value normStartIndex =
startIndexOpt
? getNormalizedDimSizeInternal(rewriter, op, *startIndexOpt, dimSize)
: rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(rewriter.getI64Type(), 0));
Value normEndIndex =
endIndexOpt
? getNormalizedDimSizeInternal(rewriter, op, *endIndexOpt, dimSize)
: dimSize;
Value step =
stepOpt ? *stepOpt
: rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(rewriter.getI64Type(), 1));
#ifdef TORCH_MLIR_ENABLE_MHLO_TRUNC_DIMSIZE_TO_I32
auto i32Type = rewriter.getIntegerType(kMhloDimSizeBits);
normStartIndex =
rewriter.create<arith::TruncIOp>(loc, i32Type, normStartIndex);
normEndIndex = rewriter.create<arith::TruncIOp>(loc, i32Type, normEndIndex);
step = rewriter.create<arith::TruncIOp>(loc, i32Type, step);
#endif
FailureOr<SmallVector<Value, 4>> dimSizesInfo =
getDimSizesOfTensor(rewriter, op, input);
if (failed(dimSizesInfo))
return rewriter.notifyMatchFailure(
op, "failed to get dimension sizes of the input");
auto dimSizes = *dimSizesInfo;
return getDynamicSliceInternal(rewriter, op, input, normStartIndex,
normEndIndex, step, dim, dimSizes);
}
template <typename AtenOpT>
class ConvertAtenOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override;
};
template <>
LogicalResult ConvertAtenOp<AtenSliceTensorOp>::matchAndRewrite(
AtenSliceTensorOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("only ranked tensor types are supported");
int64_t dim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
return rewriter.notifyMatchFailure(
op, "only constant dim is currently supported");
auto getOptionalVal = [&](Value val) -> llvm::Optional<Value> {
if (val.getType().isa<Torch::NoneType>()) {
return llvm::None;
} else {
return val;
}
};
llvm::Optional<Value> start = getOptionalVal(adaptor.start());
llvm::Optional<Value> end = getOptionalVal(adaptor.end());
llvm::Optional<Value> step = getOptionalVal(adaptor.step());
FailureOr<Value> sliceInfo =
getDynamicSlice(rewriter, op, self, start, end, step, dim);
if (failed(sliceInfo))
return op.emitError("can not create a dynmaic slice");
auto slice = *sliceInfo;
rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(
op, getTypeConverter()->convertType(op.getType()), slice);
return success();
}
// This defines a template to construct ops whose legalizations are
// specialized.
template <typename AtenOpT>
class ConvertAtenViewOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult matchAndRewrite(
AtenOpT op,
OpAdaptor adaptor,
ConversionPatternRewriter& rewriter) const override {
auto rankType =
adaptor.self().getType().template dyn_cast<RankedTensorType>();
if (!rankType)
return op.emitError("Only ranked tensor types are currently supported");
SmallVector<Value, 4> dimSizes;
if (!getAtenViewOpSizes(op, adaptor, rewriter, dimSizes)) {
return op.emitError("Dims size must be a list of Scalar");
}
auto loc = op.getLoc();
auto newRank = dimSizes.size();
if (newRank == 0 || rankType.getRank() == 0) {
rewriter.replaceOpWithNewOp<mhlo::ReshapeOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
adaptor.self());
return success();
}
std::for_each(dimSizes.begin(), dimSizes.end(), [&](Value& dSize) {
dSize = rewriter.create<ToI64Op>(loc, dSize).getResult();
return dSize;
});
#ifdef TORCH_MLIR_ENABLE_MHLO_TRUNC_DIMSIZE_TO_I32
// The i64 calculation is much slower than i32 on some devices, such as Nvidia GPU.
// One can truncate from i64 to i32 since dimension sizes are unlikely to exceed
// the range of i32(4GiB)
std::for_each(dimSizes.begin(), dimSizes.end(), [&](Value& dSize) {
// dimSize: cast i64 -> i32
dSize = rewriter.create<arith::TruncIOp>(loc, rewriter.getI32Type(), dSize);
return dSize;
});
#endif
Type intType = rewriter.getIntegerType(kMhloDimSizeBits);
Value numel = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(intType, 1));
for (auto d : dimSizes) {
numel = rewriter.create<arith::MulIOp>(loc, numel, d);
}
numel = rewriter.create<arith::IndexCastOp>(loc, rewriter.getIndexType(),
numel);
Value mhloShape = rewriter.create<tensor::FromElementsOp>(loc, dimSizes);
Value computedShape = rewriter.create<mhlo::ComputeReshapeShapeOp>(
loc, mhloShape.getType(), numel, mhloShape);
rewriter.replaceOpWithNewOp<mhlo::DynamicReshapeOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()),
adaptor.self(), computedShape);
return success();
}
bool getAtenViewOpSizes(
AtenOpT op,
OpAdaptor adaptor,
ConversionPatternRewriter& rewriter,
SmallVector<Value, 4>& dimSizes) const;
};
template <>
bool ConvertAtenViewOp<AtenViewOp>::getAtenViewOpSizes(
AtenViewOp op,
OpAdaptor adaptor,
ConversionPatternRewriter& rewriter,
SmallVector<Value, 4>& dimSizes) const {
return getListConstructElements(adaptor.size(), dimSizes);
}
template <>
bool ConvertAtenViewOp<AtenReshapeOp>::getAtenViewOpSizes(
AtenReshapeOp op,
OpAdaptor adaptor,
ConversionPatternRewriter& rewriter,
SmallVector<Value, 4>& dimSizes) const {
return getListConstructElements(adaptor.shape(), dimSizes);
}
FailureOr<Value> unsqueezeTensor(PatternRewriter &rewriter, Operation *op,
Value tensor,
ArrayRef<int64_t> inputUnsqzDims) {
// Returns a new tensor with dims of size 1 inserted at the specified
// position.
//
// The position indices (must be high to low dimension number of the returned
// tensor) are specified with unsqzDims. Indices must be in-order, and in
// range of tensor rank. Thus, unsqueeze a rank 1 tensor with {0, 2}, {0, 1,
// 3}, {0, 1, 2} are all valid dimension sets, but {0, 3}, {2} are not.
auto dimSizesInfo = getDimSizesOfTensor(rewriter, op, tensor);
if (failed(dimSizesInfo))
return rewriter.notifyMatchFailure(
op, "failed to get dimension sizes of the input");
auto dimSizes = *dimSizesInfo;
auto rank = dimSizes.size();
size_t newRank = rank + inputUnsqzDims.size();
auto unsqzDims = toPositiveDims(inputUnsqzDims, newRank);
for (size_t k = 0, sz = unsqzDims.size(); k < sz; ++k)
if (k > 1 && unsqzDims[k] <= unsqzDims[k - 1])
return rewriter.notifyMatchFailure(
op, "unsqueeze dimensions must be specified in order");
auto loc = op->getLoc();
auto rankTy = tensor.getType().dyn_cast<RankedTensorType>();
auto oldShape = rankTy.getShape();
Type intType = rewriter.getIntegerType(kMhloDimSizeBits);
auto one = rewriter.create<arith::ConstantOp>(
loc, rewriter.getIntegerAttr(intType, 1));
std::vector<Value> newDimSizes;
std::vector<int64_t> newShape;
newDimSizes.reserve(newRank);
newShape.reserve(newRank);
for (size_t k = 0, i = 0, j = 0; k < newRank; ++k) {
if (j < unsqzDims.size() && unsqzDims[j] == k) {
newDimSizes.push_back(one);
newShape.push_back(1);
j++;
} else {
newDimSizes.push_back(dimSizes[i]);
newShape.push_back(oldShape[i]);
i++;
}
}
auto outTy = RankedTensorType::get(newShape, rankTy.getElementType());
auto mhloShape = rewriter.create<tensor::FromElementsOp>(loc, newDimSizes);
return rewriter.create<mhlo::DynamicReshapeOp>(loc, outTy, tensor, mhloShape)
.getResult();
}
template <>
LogicalResult ConvertAtenOp<AtenSqueezeOp>::matchAndRewrite(
AtenSqueezeOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("only ranked tensor types are supported");
auto rank = selfTy.getRank();
if (rank == 0)
return rewriter.notifyMatchFailure(
op, "The rank of tensor must be greater than 0");
SmallVector<int64_t, 4> dims;
dims.reserve(rank);
for (int r = 0; r < rank; ++r) {
auto dSize = selfTy.getShape()[r];
if (dSize == ShapedType::kDynamicSize)
return rewriter.notifyMatchFailure(
op, "the size of the dimension being squeezed can't be unknown");
if (dSize != 1)
dims.push_back(r);
}
auto newDimSizesInfo = getDimSizesOfTensor(rewriter, op, self, dims);
if (failed(newDimSizesInfo))
return rewriter.notifyMatchFailure(
op, "failed to get dimension sizes of the input");
auto newDimSizes = *newDimSizesInfo;
auto mhloShape =
rewriter.create<tensor::FromElementsOp>(op.getLoc(), newDimSizes);
rewriter.replaceOpWithNewOp<mhlo::DynamicReshapeOp>(
op, getTypeConverter()->convertType(op.getType()), self, mhloShape);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenSqueezeDimOp>::matchAndRewrite(
AtenSqueezeDimOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto self = adaptor.self();
auto selfTy = self.getType().template cast<RankedTensorType>();
if (!selfTy)
return op.emitError("only ranked tensor types are supported");
int64_t dim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
return rewriter.notifyMatchFailure(
op, "only constant dim is currently supported");
auto rank = selfTy.getRank();
if (rank == 0)
return rewriter.notifyMatchFailure(
op, "the rank of tensor must be greater than 0");
dim = toPositiveDim(dim, rank);
if (selfTy.getShape()[dim] != 1) {
if (selfTy.getShape()[dim] == ShapedType::kDynamicSize)
return rewriter.notifyMatchFailure(
op, "the size of the dimension being squeezed is can't be unknown");
rewriter.replaceOp(op, adaptor.self());
return success();
}
SmallVector<int64_t, 4> dims(rank);
std::iota(dims.begin(), dims.end(), 0);
dims.erase(dims.begin() + dim);
auto newDimSizesInfo = getDimSizesOfTensor(rewriter, op, self, dims);
if (failed(newDimSizesInfo))
return rewriter.notifyMatchFailure(
op, "failed to get dimension sizes of the input");
auto newDimSizes = *newDimSizesInfo;
auto mhloShape =
rewriter.create<tensor::FromElementsOp>(op.getLoc(), newDimSizes);
rewriter.replaceOpWithNewOp<mhlo::DynamicReshapeOp>(
op, getTypeConverter()->convertType(op.getType()), self, mhloShape);
return success();
}
template <>
LogicalResult ConvertAtenOp<AtenUnsqueezeOp>::matchAndRewrite(
AtenUnsqueezeOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
if (!selfType) {
return op.emitError("only tensor types are currently supported");
}
int64_t dim;
if (!matchPattern(op.dim(), m_TorchConstantInt(&dim)))
return op->emitError("dim must be a Scalar constant");
auto unsqzTensorInfo = unsqueezeTensor(rewriter, op, adaptor.self(), {dim});
if (failed(unsqzTensorInfo))
return rewriter.notifyMatchFailure(op,
"failed to create unsqueezed tensor");
rewriter.replaceOp(op, *unsqzTensorInfo);
return success();
}
} // namespace
void mlir::torch::torch_to_mhlo::populateViewLikeOpPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
#define INSERT_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenOp<AtenOp>>(typeConverter, context);
INSERT_ATENOP_PATTERN(AtenSliceTensorOp);
INSERT_ATENOP_PATTERN(AtenSqueezeOp);
INSERT_ATENOP_PATTERN(AtenSqueezeDimOp);
INSERT_ATENOP_PATTERN(AtenUnsqueezeOp);
#undef INSERT_ATENOP_PATTERN
#define INSERT_VIEW_OP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenViewOp<AtenOp>>(typeConverter, context);
INSERT_VIEW_OP_PATTERN(AtenViewOp);
INSERT_VIEW_OP_PATTERN(AtenReshapeOp);
#undef INSERT_VIEW_OP_PATTERN
}
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