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torch-mlir/lib/Conversion/TorchToMhlo/Linear.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 "./MhloLegalizeUtils.h"
#include "./PopulatePatterns.h"
#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Tensor/IR/Tensor.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/Utils.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
namespace {
Value getBroadcastTensor(PatternRewriter &rewriter, Operation *op, Value tensor,
ArrayRef<int64_t> shape, ArrayRef<Value> dimSizes,
ArrayRef<int64_t> broadcastDims) {
auto tensorTy = tensor.getType().dyn_cast<RankedTensorType>();
auto loc = op->getLoc();
Value mhloShape = rewriter.create<tensor::FromElementsOp>(loc, dimSizes);
RankedTensorType outTy =
RankedTensorType::get(shape, tensorTy.getElementType());
RankedTensorType attrTy =
RankedTensorType::get({static_cast<int64_t>(broadcastDims.size())},
rewriter.getIntegerType(64));
auto broadcastAttr = DenseIntElementsAttr::get(attrTy, broadcastDims);
auto broadcast = rewriter.create<mhlo::DynamicBroadcastInDimOp>(
loc, outTy, tensor, mhloShape, broadcastAttr);
return broadcast;
}
Value getPermutedTensor(PatternRewriter &rewriter, Operation *op, Value input,
ArrayRef<int64_t> inpTransDims) {
auto inputTy = input.getType().dyn_cast<RankedTensorType>();
auto rank = inputTy.getRank();
auto transDims = mhlo::toPositiveDims(inpTransDims, rank);
auto inpShape = inputTy.getShape();
std::vector<int64_t> newShape;
newShape.reserve(rank);
for (auto d : transDims) {
newShape.push_back(inpShape[d]);
}
auto attrTy = RankedTensorType::get({static_cast<int64_t>(transDims.size())},
rewriter.getIntegerType(64));
auto permuteAttr = DenseIntElementsAttr::get(attrTy, transDims);
auto outTy = RankedTensorType::get(newShape, inputTy.getElementType());
auto result = rewriter.create<mhlo::TransposeOp>(op->getLoc(), outTy, input,
permuteAttr);
return result.getResult();
}
void getBmmBroadcast(PatternRewriter &rewriter, Operation *op, Value &inpLhs,
Value &inpRhs, int64_t leadingRank) {
Value lhs = inpLhs;
Value rhs = inpRhs;
auto lhsRankTy = inpLhs.getType().dyn_cast<RankedTensorType>();
auto rhsRankTy = inpRhs.getType().dyn_cast<RankedTensorType>();
auto lhsRank = lhsRankTy.getRank();
auto rhsRank = rhsRankTy.getRank();
// The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be
// broadcastable).
auto minRank = std::min(lhsRank, rhsRank);
auto leadingDims = llvm::to_vector<4>(llvm::seq<int64_t>(0, leadingRank));
auto broadcastDims = llvm::to_vector<4>(
llvm::seq<int64_t>(leadingRank, minRank + leadingRank));
auto lhsShape = lhsRankTy.getShape();
auto rhsShape = rhsRankTy.getShape();
if (lhsRank < rhsRank) {
std::vector<int64_t> newShape(rhsShape.begin(),
rhsShape.begin() + leadingRank);
newShape.insert(newShape.end(), lhsShape.begin(), lhsShape.end());
auto newDimSizes =
*mhlo::getDimSizesOfTensor(rewriter, op, rhs, leadingDims);
auto lhsDimSizes = *mhlo::getDimSizesOfTensor(rewriter, op, lhs);
newDimSizes.insert(newDimSizes.end(), lhsDimSizes.begin(),
lhsDimSizes.end());
lhs = getBroadcastTensor(rewriter, op, lhs, newShape, newDimSizes,
broadcastDims);
} else {
std::vector<int64_t> newShape(lhsShape.begin(),
lhsShape.begin() + leadingRank);
newShape.insert(newShape.end(), rhsShape.begin(), rhsShape.end());
auto newDimSizes =
*mhlo::getDimSizesOfTensor(rewriter, op, lhs, leadingDims);
auto rhsDimSizes = *mhlo::getDimSizesOfTensor(rewriter, op, rhs);
newDimSizes.insert(newDimSizes.end(), rhsDimSizes.begin(),
rhsDimSizes.end());
rhs = getBroadcastTensor(rewriter, op, rhs, newShape, newDimSizes,
broadcastDims);
}
inpLhs = lhs;
inpRhs = rhs;
}
// Perform the basic n-dim matmul operation encompassing the handling of
// broadcasting and dynamic shape propagation.
// All PyTorch ops that leverage matrix multiplication will derive this and
// implement their specialized input processing (e.g transpose), and output
// processing, e.g. GEMM or fully connected bias handling.
template <typename AtenOpT>
class ConvertAtenMatmulBaseOp : public OpConversionPattern<AtenOpT> {
public:
using OpConversionPattern<AtenOpT>::OpConversionPattern;
using OpAdaptor = typename AtenOpT::Adaptor;
// Each variant must implement corresponding parameter parsing options.
// Maintain separate input read functions for each variant because it is not
// necessarily true with all variants that the first two operands are the lhs
// and rhs.
virtual LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const {
return rewriter.notifyMatchFailure(
op,
"unimplemented matrix multiplication variant input parsing function");
}
LogicalResult performMatmul(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, Value &lhs,
Value &rhs, Value &output) const {
auto lhsTy = lhs.getType().cast<RankedTensorType>();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
auto lhsRank = lhsTy.getRank();
auto rhsRank = rhsTy.getRank();
auto lhsElemTy = lhsTy.getElementType();
auto rhsElemTy = rhsTy.getElementType();
if (lhsElemTy != rhsElemTy)
return op.emitError("matmul: input datatypes mismatched");
if (lhsRank < 1 || rhsRank < 1) {
return op.emitError("matmul: inputs can't be 0-rank");
}
if (lhsRank <= 2 && rhsRank <= 2) {
output = rewriter.create<mhlo::DotOp>(op->getLoc(), lhs, rhs, nullptr);
return success();
}
int64_t nBatchDims;
if (rhsRank <= 2) {
auto leadingRank = lhsRank - 2;
getBmmBroadcast(rewriter, op, lhs, rhs, leadingRank);
nBatchDims = leadingRank;
} else if (lhsRank <= 2) {
auto leadingRank = rhsRank - 2;
getBmmBroadcast(rewriter, op, lhs, rhs, leadingRank);
nBatchDims = leadingRank;
} else {
assert(rhsRank > 2 && lhsRank > 2);
auto leadingRank = std::max(lhsRank - rhsRank, rhsRank - lhsRank);
nBatchDims = std::max(lhsRank - 2, rhsRank - 2);
getBmmBroadcast(rewriter, op, lhs, rhs, leadingRank);
}
auto batchDims = llvm::to_vector<4>(llvm::seq<int64_t>(0, nBatchDims));
auto lhsContractingDim = nBatchDims + 1;
auto rhsContractingDim = nBatchDims;
if (lhsRank == 1)
lhsContractingDim = nBatchDims;
mhlo::DotDimensionNumbersAttr dotDimensionNumbers =
mhlo::DotDimensionNumbersAttr::get(
rewriter.getContext(),
/*lhsBatchingDimensions=*/batchDims,
/*rhsBatchingDimensions=*/batchDims,
/*lhsContractingDimensions=*/{lhsContractingDim},
/*rhsContractingDimensions=*/{rhsContractingDim});
auto resultTy = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>();
output = rewriter
.create<mhlo::DotGeneralOp>(op->getLoc(), resultTy, lhs, rhs,
dotDimensionNumbers, nullptr)
.getResult();
return success();
}
// The default version just reads two inputs, computes output and returns it.
// Other versions may add a bias, apply GEMM-style alpha/beta scaling etc.
virtual LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs, rhs;
if (failed(readMatMulInputs(op, adaptor, rewriter, lhs, rhs)))
return op.emitError("failed to read matmul inputs");
Value output;
if (failed(performMatmul(op, adaptor, rewriter, lhs, rhs, output)))
return op.emitError("failed to perform matmul operation");
rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(
op,
OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>(),
output);
return success();
}
};
// Legalizes the torch.matmul op for general n-dim matmul.
template <typename AtenOpT>
class ConvertAtenMatMulOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
public:
using ConvertAtenMatmulBaseOp<AtenOpT>::ConvertAtenMatmulBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const override {
lhs = adaptor.self();
auto lhsTy = lhs.getType().cast<RankedTensorType>();
rhs = adaptor.other();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
if (!lhsTy || !rhsTy)
return op.emitError(
"only ranked tensor types are supported in MHLO matmul");
return success();
}
};
// Implements handling of aten.mm and aten.bmm ops.
template <typename AtenOpT>
class ConvertAtenMmOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
public:
using ConvertAtenMatmulBaseOp<AtenOpT>::ConvertAtenMatmulBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const override {
lhs = adaptor.self();
auto lhsTy = lhs.getType().cast<RankedTensorType>();
rhs = adaptor.mat2();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
if (!lhsTy || !rhsTy)
return op.emitError(
"only ranked tensor types are supported in MHLO matmul");
auto lhsRank = lhsTy.getRank();
auto rhsRank = rhsTy.getRank();
if (isa<AtenMmOp>(op)) {
// Mm takes two 2D tensors.
if (lhsRank != 2 || rhsRank != 2)
return op.emitError("aten.mm called but matrix rank != 2");
} else if (isa<AtenBmmOp>(op)) {
// Bmm takes two 3D tensors.
if (lhsRank != 3 || rhsRank != 3)
return op.emitError("aten.bmm called but matrix rank != 3");
}
return success();
}
};
// Implements handling of aten.linear op.
template <typename AtenOpT>
class ConvertAtenLinearOp : public ConvertAtenMatmulBaseOp<AtenOpT> {
public:
using ConvertAtenMatmulBaseOp<AtenOpT>::ConvertAtenMatmulBaseOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult readMatMulInputs(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter,
Value &lhs, Value &rhs) const override {
lhs = adaptor.input();
auto lhsTy = lhs.getType().cast<RankedTensorType>();
rhs = adaptor.weight();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
if (!lhsTy || !rhsTy)
return op.emitError(
"only ranked tensor types are supported in MHLO matmul");
auto lhsRank = lhsTy.getRank();
auto rhsRank = rhsTy.getRank();
if (lhsRank != 2 && lhsRank != 3)
return op.emitError("aten.Linear called but input rank not 2 or 3");
if (rhsRank != 2 && rhsRank != 3)
return op.emitError("aten.Linear called but weight rank not 2 or 3");
return success();
}
// Override the default rewriter to perform RHS transpose and bias addition
// as well.
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs, rhs;
if (failed(readMatMulInputs(op, adaptor, rewriter, lhs, rhs)))
return op.emitError("failed to read matmul inputs");
// The aten.Linear op has a bias tensor that is added to the matmul
// output.
auto bias = adaptor.bias();
auto biasTy = bias.getType();
// MHLO does not mandate that elementwise op tensors need to be ranked.
if (!biasTy.template isa<Torch::NoneType>() &&
!biasTy.template isa<RankedTensorType>())
return op.emitError("only ranked tensor types are supported in MHLO "
"matmul for bias tensor");
// weight.T
rhs = getPermutedTensor(rewriter, op, rhs, {1, 0});
auto lhsTy = lhs.getType().cast<RankedTensorType>();
auto rhsTy = rhs.getType().cast<RankedTensorType>();
auto leadingRank = std::max(lhsTy.getRank() - rhsTy.getRank(),
rhsTy.getRank() - lhsTy.getRank());
getBmmBroadcast(rewriter, op, lhs, rhs, leadingRank);
auto resultRank = std::max(lhsTy.getRank(), rhsTy.getRank());
auto nBatchDims = resultRank - 2;
auto batchDims = llvm::to_vector<4>(llvm::seq<int64_t>(0, nBatchDims));
auto lhsContractingDim = nBatchDims + 1;
auto rhsContractingDim = nBatchDims;
mhlo::DotDimensionNumbersAttr dotDimensionNumbers =
mhlo::DotDimensionNumbersAttr::get(
rewriter.getContext(),
/*lhsBatchingDimensions=*/batchDims,
/*rhsBatchingDimensions=*/batchDims,
/*lhsContractingDimensions=*/{lhsContractingDim},
/*rhsContractingDimensions=*/{rhsContractingDim});
auto resultTy =
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType());
Value matmulOutput = rewriter.create<mhlo::DotGeneralOp>(
op->getLoc(), resultTy, lhs, rhs, dotDimensionNumbers, nullptr);
Value matmulPlusBias = matmulOutput;
if (!biasTy.template isa<Torch::NoneType>()) {
// Bias addition broadcasts to the matmul output shape.
matmulPlusBias =
rewriter
.create<chlo::BroadcastAddOp>(op->getLoc(), resultTy,
matmulOutput, bias, nullptr)
.getResult();
}
rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(op, resultTy, matmulPlusBias);
return success();
}
};
} // namespace
namespace {
class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
public:
using OpConversionPattern<AtenConvolutionOp>::OpConversionPattern;
using OpAdaptor = typename AtenConvolutionOp::Adaptor;
Value reshapeConvWeight(PatternRewriter &rewriter, Operation *op,
Value weight, int64_t groups) const {
auto weightTy = weight.getType().cast<RankedTensorType>();
auto weightElemTy = weightTy.getElementType();
auto rank = weightTy.getRank();
SmallVector<Value> weightShapeVec =
*mhlo::getDimSizesOfTensor(rewriter, op, weight);
auto weightShape = weightTy.getShape();
SmallVector<int64_t> weightShapeInt(rank);
std::copy(weightShape.begin(), weightShape.end(), weightShapeInt.begin());
// 1. [IC, OC, H, W, ...] => [G, IC//G, OC, H, W, ...]
Value GValue = rewriter.create<mlir::arith::ConstantOp>(
op->getLoc(), rewriter.getI64IntegerAttr(groups));
Value ICDivGValue = rewriter.create<mlir::arith::DivSIOp>(
op->getLoc(), weightShapeVec[0], GValue);
Value OCMulGValue = rewriter.create<mlir::arith::MulIOp>(
op->getLoc(), weightShapeVec[1], GValue);
weightShapeVec[0] = ICDivGValue;
weightShapeVec.insert(weightShapeVec.begin(), GValue);
if (weightShapeInt[0] == ShapedType::kDynamicSize) {
weightShapeInt.insert(weightShapeInt.begin(), groups);
} else {
weightShapeInt[0] /= groups;
weightShapeInt.insert(weightShapeInt.begin(), groups);
}
Value weightShapeTensor = rewriter.create<mlir::tensor::FromElementsOp>(
op->getLoc(), weightShapeVec);
weight = rewriter.create<mhlo::DynamicReshapeOp>(
op->getLoc(), RankedTensorType::get(weightShapeInt, weightElemTy),
weight, weightShapeTensor);
// 2. [G, IC//G, OC, H, W, ...] => [IC//G, G, OC, H, W, ...]
std::vector<int64_t> transposeDims(rank + 1);
for (int64_t i = 0; i <= rank; i++)
transposeDims[i] = i;
std::swap(transposeDims[1], transposeDims[0]);
weight = rewriter.create<mhlo::TransposeOp>(
op->getLoc(), weight, rewriter.getI64TensorAttr(transposeDims));
// 3. [IC//G, G, OC, H, W, ...] => [IC//G, G*OC, H, W, ...]
weightShapeInt.erase(weightShapeInt.begin());
if (weightShapeInt[1] != ShapedType::kDynamicSize) {
weightShapeInt[1] *= groups;
}
weightShapeVec.erase(weightShapeVec.begin());
weightShapeVec[1] = OCMulGValue;
weightShapeTensor = rewriter.create<mlir::tensor::FromElementsOp>(
op->getLoc(), weightShapeVec);
weight = rewriter.create<mhlo::DynamicReshapeOp>(
op->getLoc(), RankedTensorType::get(weightShapeInt, weightElemTy),
weight, weightShapeTensor);
return weight;
}
Value convertTransposedConv(AtenConvolutionOp op,
ConversionPatternRewriter &rewriter,
RankedTensorType outType, Value input,
Value weight, ArrayRef<int64_t> stride,
ArrayRef<int64_t> padding,
ArrayRef<int64_t> dilation,
ArrayRef<int64_t> outputPadding, int64_t groups,
bool needHandleOutputPadding) const {
auto inputTy = input.getType().cast<RankedTensorType>();
auto weightTy = weight.getType().cast<RankedTensorType>();
auto weightShape = weightTy.getShape();
auto nDims = inputTy.getRank();
auto nSpatialDims = nDims - 2;
auto convOutTy = outType;
if (needHandleOutputPadding) {
SmallVector<int64_t> outShape(nDims);
auto finalOutShape = outType.getShape();
std::copy(finalOutShape.begin(), finalOutShape.end(), outShape.begin());
for (int i = 2; i < nDims; ++i) {
if (finalOutShape[i] == ShapedType::kDynamicSize)
continue;
outShape[i] = finalOutShape[i] - outputPadding[i - 2];
}
convOutTy = RankedTensorType::get(outShape, outType.getElementType());
}
// Prepare for transposed convolution
SmallVector<int64_t> mhloStrideVec(nSpatialDims, 1);
DenseIntElementsAttr mhloStride = rewriter.getI64TensorAttr(mhloStrideVec);
SmallVector<int64_t> mhloPaddingVec(nSpatialDims * 2, 0);
for (int i = 0; i < nSpatialDims; ++i) {
int64_t padInt = dilation[i] * (weightShape[i + 2] - 1) - padding[i];
mhloPaddingVec[i * 2] = padInt;
mhloPaddingVec[i * 2 + 1] = padInt;
}
DenseIntElementsAttr mhloPadding = DenseIntElementsAttr::get(
RankedTensorType::get({nSpatialDims, 2}, rewriter.getI64Type()),
mhloPaddingVec);
SmallVector<int64_t> mhloLhsDilationVec(nSpatialDims);
std::copy(stride.begin(), stride.end(), mhloLhsDilationVec.begin());
DenseIntElementsAttr mhloLhsDilation =
rewriter.getI64TensorAttr(mhloLhsDilationVec);
SmallVector<int64_t> mhloRhsDilationVec(nSpatialDims);
std::copy(dilation.begin(), dilation.end(), mhloRhsDilationVec.begin());
DenseIntElementsAttr mhloRhsDilation =
rewriter.getI64TensorAttr(mhloRhsDilationVec);
DenseElementsAttr windowReversal;
ArrayAttr precisionConfig;
SmallVector<int64_t> spatialDims;
for (int i = 0; i < nSpatialDims; ++i) {
spatialDims.push_back(i + 2);
}
mhlo::ConvDimensionNumbersAttr dimensionNumbers =
mhlo::ConvDimensionNumbersAttr::get(
/*context=*/rewriter.getContext(), /*inputBatchDimension=*/0,
/*inputFeatureDimension=*/1,
/*inputSpatialDimensions=*/spatialDims,
/*kernelInputFeatureDimension=*/0,
/*kernelOutputFeatureDimension=*/1,
/*kernelSpatialDimensions=*/spatialDims,
/*outputBatchDimension=*/0, /*outputFeatureDimension=*/1,
/*outputSpatialDimensions=*/spatialDims);
// Reverse and transpose weight
weight = rewriter.create<mhlo::ReverseOp>(
op->getLoc(), weight, rewriter.getI64TensorAttr(spatialDims));
if (groups != 1) {
weight = reshapeConvWeight(rewriter, op, weight, groups);
}
// Create transposed convolution
auto transposedConvOp = rewriter.create<mhlo::ConvolutionOp>(
op->getLoc(), convOutTy, input, weight, mhloStride, mhloPadding,
mhloLhsDilation, mhloRhsDilation, windowReversal, dimensionNumbers,
static_cast<uint64_t>(groups), 1, precisionConfig);
// Handle output padding
if (!needHandleOutputPadding) {
return transposedConvOp.getResult();
}
SmallVector<int64_t> edgePaddingLowVec(nDims, 0);
SmallVector<int64_t> edgePaddingHighVec(nDims, 0);
SmallVector<int64_t> interiorPaddingVec(nDims, 0);
std::copy(outputPadding.begin(), outputPadding.end(),
edgePaddingHighVec.begin() + 2);
Value paddingValue =
mhlo::getConstTensor<float>(rewriter, op, {0.0}, {}).getValue();
paddingValue = mhlo::promoteType(rewriter, paddingValue, inputTy);
mlir::DenseIntElementsAttr edgePaddingLow =
rewriter.getI64VectorAttr(edgePaddingLowVec);
mlir::DenseIntElementsAttr edgePaddingHigh =
rewriter.getI64VectorAttr(edgePaddingHighVec);
mlir::DenseIntElementsAttr interiorPadding =
rewriter.getI64VectorAttr(interiorPaddingVec);
auto paddedOutput = rewriter.create<mhlo::PadOp>(
op->getLoc(), outType, transposedConvOp, paddingValue, edgePaddingLow,
edgePaddingHigh, interiorPadding);
return paddedOutput.getResult();
}
Value convertNormalConv(AtenConvolutionOp op,
ConversionPatternRewriter &rewriter,
RankedTensorType outType, Value input, Value weight,
ArrayRef<int64_t> stride, ArrayRef<int64_t> padding,
ArrayRef<int64_t> dilation, int64_t groups) const {
int64_t nDims = outType.getRank();
// Get mhlo::ConvolutionOp attributes
DenseIntElementsAttr mhloWindowStride = DenseIntElementsAttr::get(
RankedTensorType::get({static_cast<long int>(stride.size())},
rewriter.getI64Type()),
stride);
std::vector<int64_t> mhloPaddingVec;
for (size_t i = 0; i < padding.size(); i++) {
mhloPaddingVec.emplace_back(padding[i]);
mhloPaddingVec.emplace_back(padding[i]);
}
DenseIntElementsAttr mhloPadding = DenseIntElementsAttr::get(
RankedTensorType::get(
{static_cast<long int>(padding.size()), static_cast<long int>(2)},
rewriter.getI64Type()),
mhloPaddingVec);
DenseIntElementsAttr mhloRhsDilation = DenseIntElementsAttr::get(
RankedTensorType::get({static_cast<long int>(dilation.size())},
rewriter.getI64Type()),
dilation);
SmallVector<int64_t> spatialDimensions;
for (int64_t i = 2; i < nDims; i++) {
spatialDimensions.emplace_back(i);
}
mhlo::ConvDimensionNumbersAttr dimensionNumbers =
mhlo::ConvDimensionNumbersAttr::get(
/*context=*/rewriter.getContext(), /*inputBatchDimension=*/0,
/*inputFeatureDimension=*/1,
/*inputSpatialDimensions=*/spatialDimensions,
/*kernelInputFeatureDimension=*/1,
/*kernelOutputFeatureDimension=*/0,
/*kernelSpatialDimensions=*/spatialDimensions,
/*outputBatchDimension=*/0, /*outputFeatureDimension=*/1,
/*outputSpatialDimensions=*/spatialDimensions);
// mhlo::ConvolutionOp's optional attributes, leave them as default
DenseIntElementsAttr mhloLhsDilation;
DenseElementsAttr windowReversal;
ArrayAttr precisionConfig;
auto mhloConvOp = rewriter.create<mhlo::ConvolutionOp>(
op->getLoc(), outType, input, weight, mhloWindowStride, mhloPadding,
mhloLhsDilation, mhloRhsDilation, windowReversal, dimensionNumbers,
static_cast<uint64_t>(groups), 1, precisionConfig);
return mhloConvOp.getResult();
}
LogicalResult
matchAndRewrite(AtenConvolutionOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value input = adaptor.input();
Value weight = adaptor.weight();
// The input shape is [N, C, H, W]
auto inputTy = input.getType().template cast<RankedTensorType>();
// The weight shape is [OC, (IC//G), KH, KW]
// If transposed is set to true,
// the weight shape changes to [IC, (OC//G), KH, KW]
auto weightTy = weight.getType().template cast<RankedTensorType>();
auto outTy = getTypeConverter()
->convertType(op.getType())
.template cast<RankedTensorType>();
if (!inputTy || !weightTy || !outTy) {
return op.emitError("input, weight and output must be ranked tensors");
}
if (inputTy.getRank() < 3)
return op.emitError("only input with at least 3 dims valid");
SmallVector<int64_t> stride;
if (!matchPattern(op.stride(), m_TorchConstantIntList(stride))) {
return rewriter.notifyMatchFailure(op,
"non-const stride list unsupported");
}
SmallVector<int64_t> padding;
if (!matchPattern(op.padding(), m_TorchConstantIntList(padding))) {
return rewriter.notifyMatchFailure(op,
"non-const padding list unsupported");
}
SmallVector<int64_t> dilation;
if (!matchPattern(op.dilation(), m_TorchConstantIntList(dilation))) {
return rewriter.notifyMatchFailure(op,
"non-const dilation list unsupported");
}
SmallVector<int64_t> outputPadding;
if (!matchPattern(op.output_padding(),
m_TorchConstantIntList(outputPadding))) {
return rewriter.notifyMatchFailure(
op, "non-const output_padding list unsupported");
}
int64_t groups;
if (!matchPattern(op.groups(), m_TorchConstantInt(&groups))) {
return rewriter.notifyMatchFailure(op, "non-int groups unsupported");
}
bool transposed;
if (!matchPattern(op.transposed(), m_TorchConstantBool(&transposed))) {
return rewriter.notifyMatchFailure(op, "non-bool transposed unsupported");
}
// Whether need to handle outputpadding
bool needHandleOutputPadding = false;
for (int64_t i : outputPadding) {
if (i != 0) {
needHandleOutputPadding = true;
break;
}
}
// Op validation check
if (needHandleOutputPadding && !transposed) {
return op->emitError(
"output padding attr is valid only in transposed convolution");
}
assert(padding.size() == dilation.size() &&
padding.size() == stride.size() &&
padding.size() == static_cast<size_t>(inputTy.getRank()) - 2 &&
inputTy.getRank() == weightTy.getRank());
auto nSpatialDims = padding.size();
auto nDims = inputTy.getRank();
// Kernel size must be constant.
auto weightShape = weightTy.getShape();
for (int i = 2; i < nDims; ++i) {
if (weightShape[i] == ShapedType::kDynamicSize) {
return rewriter.notifyMatchFailure(
op, "only constant kernel size is supported");
}
}
Value mhloConvResult;
if (transposed) {
mhloConvResult = convertTransposedConv(
op, rewriter, outTy, input, weight, stride, padding, dilation,
outputPadding, groups, needHandleOutputPadding);
} else {
mhloConvResult = convertNormalConv(op, rewriter, outTy, input, weight,
stride, padding, dilation, groups);
}
auto bias = adaptor.bias();
// No bias provided
if (failed(checkNotNone(rewriter, op, op.bias()))) {
rewriter.replaceOp(op, mhloConvResult);
return success();
}
// Handle bias
if (!bias.getType().cast<RankedTensorType>()) {
return op.emitError("bias provided but not a ranked tensor");
}
auto biasTy = bias.getType().template cast<RankedTensorType>();
if (!biasTy.getElementType().isIntOrFloat()) {
return op.emitError("only floating-point or integer datatype "
"legalization for bias supported");
}
assert(biasTy.getRank() <= 1);
// Reshape and promote bias
auto inputUnsqzDims =
llvm::to_vector<4>(llvm::seq<int64_t>(-nSpatialDims, 0));
bias = *mhlo::unsqueezeTensor(rewriter, op, bias, inputUnsqzDims);
bias = mhlo::promoteType(rewriter, bias, outTy);
DenseIntElementsAttr bcastDimensions;
rewriter.replaceOpWithNewOp<chlo::BroadcastAddOp>(op, outTy, mhloConvResult,
bias, bcastDimensions);
return success();
}
};
} // namespace
void mlir::torch::torch_to_mhlo::populateLinearOpPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
#define INSERT_MATMUL_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMatMulOp<AtenOp>>(typeConverter, context);
INSERT_MATMUL_ATENOP_PATTERN(AtenMatmulOp);
#undef INSERT_MATMUL_ATEMOP_PATTERN
#define INSERT_MM_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMmOp<AtenOp>>(typeConverter, context);
INSERT_MM_ATENOP_PATTERN(AtenMmOp);
INSERT_MM_ATENOP_PATTERN(AtenBmmOp);
#undef INSERT_MM_ATEMOP_PATTERN
#define INSERT_LINEAR_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenLinearOp<AtenOp>>(typeConverter, context);
INSERT_LINEAR_ATENOP_PATTERN(AtenLinearOp);
#undef INSERT_LINEAR_ATEMOP_PATTERN
#define INSERT_CONVOLUTION_ATENOP_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenConvolutionOp>(typeConverter, context);
INSERT_CONVOLUTION_ATENOP_PATTERN(AtenConvolutionOp);
#undef INSERT_CONVOLUTION_ATENOP_PATTERN
}
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