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‎lib/Conversion/TorchToLinalg/Pooling.cpp‎

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@@ -1616,6 +1616,223 @@ class ConvertAtenAdaptivePoolOp : public OpConversionPattern<OpTy> {
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};
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} // namespace
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namespace {
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template <typename OpTy, typename PoolingOpTy, int Dim>
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class ConvertRoiAlignOp : public OpConversionPattern<OpTy> {
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public:
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using OpConversionPattern<OpTy>::OpConversionPattern;
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LogicalResult
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matchAndRewrite(OpTy op, typename OpTy::Adaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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Location loc = op->getLoc();
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const TypeConverter *typeConverter = this->getTypeConverter();
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Value result = op.getResult();
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uint64_t pooledHeight =
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cast<ConstantIntOp>(op.getPooledHeight().getDefiningOp()).getValue();
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uint64_t pooledWidth =
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cast<ConstantIntOp>(op.getPooledWidth().getDefiningOp()).getValue();
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uint64_t samplingRatio =
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cast<ConstantIntOp>(op.getSamplingRatio().getDefiningOp()).getValue();
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Value pooledH = op.getPooledHeight();
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Value pooledW = op.getPooledWidth();
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Value spatialScaleVal = op.getSpatialScale();
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llvm::APFloat spatialScale =
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cast<ConstantFloatOp>(op.getSpatialScale().getDefiningOp()).getValue();
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Value rois = op.getRois();
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Value input = op.getInput();
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// RankedTensorType inputType = input.getType();
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Value offset =
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rewriter.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.0));
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Type resultType = cast<RankedTensorType>(result.getType());
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Type resultElementType = resultType.getElementType();
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if (!op.getAligned()) {
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offset = rewriter.create<arith::ConstantOp>(loc, b.getF32FloatAttr(0.5));
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}
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Value lb = rewriter.create<arith::ConstantIndexOp>(loc, 0);
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Value ub0 = rewriter.create<tensor::DimOp>(loc, rois, 0);
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Value ub1 = rewriter.create<tensor::DimOp>(loc, input, 1);
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Value step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
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SmallVector<Value> finalOutputShape = {ub0, ub1, pooledH, pooledW};
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Value finalOutputTensor = rewriter.create<tensor::EmptyOp>(
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loc, getAsOpFoldResult(finalOutputShape), resultElementType);
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auto forLoop = rewriter.create<scf::ForOp>(
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loc, lb, ub0, step, ValueRange{},
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[&](OpBuilder &b1, Location loc, Value iv0, ValueRange args) {
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auto forLoop = b1.create<scf::ForOp>(
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loc, lb, ub1, step, ValueRange{},
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[&](OpBuilder &b, Location loc, Value iv1, ValueRange args) {
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// Step 1: Extract bounds for region of interest (roi)
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OpFoldResult zeroAttr = b.getI64IntegerAttr(0);
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OpFoldResult oneAttr = b.getI64IntegerAttr(1);
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OpFoldResult twoAttr = b.getI64IntegerAttr(2);
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OpFoldResult threeAttr = b.getI64IntegerAttr(3);
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OpFoldResult fourAttr = b.getI64IntegerAttr(4);
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OpFoldResult fiveAttr = b.getI64IntegerAttr(5);
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// SmallVector<Value> offsetVals{iv0, zeroAttr};
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// SmallVector<OpFoldResult> sizeVals{oneAttr, fiveAttr};
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SmallVector<OpFoldResult> strideVals{oneAttr, oneAttr, oneAttr,
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oneAttr};
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// Value extractRoiBounds = b.create<tensor::ExtractSliceOp>(
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// loc, rois, offsetVals, sizeVals, strideVals);
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Value lowY = b.create<tensor::ExtractOp>(
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loc, rois, ValueRange{iv0, oneAttr});
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Value lowX = b.create<tensor::ExtractOp>(
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loc, rois, ValueRange{iv0, twoAttr});
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Value highY = b.create<tensor::ExtractOp>(
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loc, rois, ValueRange{iv0, threeAttr});
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Value highX = b.create<tensor::ExtractOp>(
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loc, rois, ValueRange{iv0, fourAttr});
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lowY = b.create<arith::MulFOp>(loc, lowY, spatialScaleVal);
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lowX = b.create<arith::MulFOp>(loc, lowX, spatialScaleVal);
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highY = b.create<arith::MulFOp>(loc, highY, spatialScaleVal);
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highX = b.create<arith::MulFOp>(loc, highX, spatialScaleVal);
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lowY = b.create<arith::SubFOp>(loc, lowY, offset);
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lowX = b.create<arith::SubFOp>(loc, lowX, offset);
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highY = b.create<arith::SubFOp>(loc, highY, offset);
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highX = b.create<arith::SubFOp>(loc, highX, offset);
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// Step 2: Extract region of interest using bounds
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Value lowY_int = b.create<math::FloorOp>(loc, lowY);
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Value lowX_int = b.create<math::FloorOp>(loc, lowX);
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Value highY_int = b.create<math::CeilOp>(loc, highY);
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Value highX_int = b.create<math::CeilOp>(loc, highX);
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lowY_int =
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b.create<arith::FPToSIOp>(loc, b.getI64Type(), lowY_int);
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lowX_int =
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b.create<arith::FPToSIOp>(loc, b.getI64Type(), lowX_int);
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highY_int =
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b.create<arith::FPToSIOp>(loc, b.getI64Type(), highY_int);
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highX_int =
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b.create<arith::FPToSIOp>(loc, b.getI64Type(), highX_int);
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Value roiHeight =
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b.create<arith::SubIOp>(loc, highY_int, lowY_int);
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Value roiWidth =
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b.create<arith::SubIOp>(loc, highX_int, lowX_int);
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SmallVector<Value> roiOffsetVals{zeroAttr, iv1, lowY_int,
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lowX_int};
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SmallVector<Value> roiSizeVals{oneAttr, oneAttr, roiHeight,
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roiWidth};
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Value extractRoi = b.create<tensor::ExtractSliceOp>(
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loc, input, roiOffsetVals, roiSizeVals, strideVals);
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// Step 3: Perform bilinear interpolation over roi
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Value roiBinH = b.create<arith::SubOp>(loc, highY, lowY);
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Value roiBinW = b.create<arith::SubOp>(loc, highX, lowX);
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Value scaleH = b.create<arith::DivOp>(loc, roiBinH, pooledH);
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Value scaleW = b.create<arith::DivOp>(loc, roiBinW, pooledW);
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scaleH = b.create<arith::CeilOp>(loc, scaleH);
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scaleW = b.create<arith::CeilOp>(loc, scaleW);
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scaleH = b.create<arith::FPToSIOp>(loc, b.getI64Type(), scaleH);
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scaleW = b.create<arith::FPToSIOp>(loc, b.getI64Type(), scaleW);
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Value roiSampleHeight =
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b.create<arith::MulIOp>(loc, pooledH, scaleH);
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Value roiSampleWidth =
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b.create<arith::MulIOp>(loc, pooledW, scaleW);
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SmallVector<Value> outputSizeIntValues = {roiSampleHeight,
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roiSampleWidth};
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SmallVector<Value> dims =
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getTensorSizesUntilDim(b, loc, extractRoi, 1);
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for (unsigned i = 2; i < inputRank; i++) {
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dims.push_back(
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castIntToIndex(b, loc, outputSizeIntValues[i - 2]));
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}
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SmallVector<Value> inputSizes;
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auto inputType = cast<RankedTensorType>(extractRoi.getType());
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auto inputRank = inputType.getRank();
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for (unsigned i = 2; i < inputRank; i++) {
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Value inputSize = getDimOp(b, loc, extractRoi, i);
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inputSizes.push_back(b.create<arith::IndexCastOp>(
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loc, b.getIntegerType(64), roiSizeVals[i]));
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}
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Value outTensor = b.create<tensor::EmptyOp>(
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loc, getAsOpFoldResult(dims), inputType.getElementType());
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AffineMap idMap = b.getMultiDimIdentityMap(inputRank);
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SmallVector<utils::IteratorType> iteratorTypes(
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inputRank, utils::IteratorType::parallel);
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Value bilinearInterpolatedRoi =
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b.create<linalg::GenericOp>(
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loc, outTensor.getType(), ValueRange{}, outTensor,
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/*indexingMaps=*/idMap,
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/*iteratorTypes=*/iteratorTypes,
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value retVal = bilinearInterpolate(
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b, op, loc, outputSizeIntValues, extractRoi,
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inputSizes, ValueRange{}, "bilinear");
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b.create<linalg::YieldOp>(loc, retVal);
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})
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.getResult(0);
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// Step 4: Sum pool over interpolated values
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Value sumPool, paddedInput;
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SmallVector<Value> kernelSizeIntValues = {oneAttr, oneAttr,
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scaleH, scaleW};
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SmallVector<Value, 2> strideInts = {scaleH, scaleW};
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SmallVector<Value, 2> paddingInts = {zeroAttr, zeroAttr};
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SmallVector<Value, 2> dilationInts(oneAttr, 2);
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SmallVector<Value, 4> outTensorShape;
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if (failed(createPoolingOp<linalg::PoolingNchwSumOp>(
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op, b, self, /*supportNonFPInput=*/true, false,
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/*dimensionality=*/2, kernelSizeIntValues, strideInts,
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paddingInts, dilationInts,
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b.getZeroAttr(resultElementType), outTensorShape,
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paddedInput, sumPool)))
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return b.notifyMatchFailure(op, "unable to compute sumpool");
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// Step 5: elementwise division by number of sampling points
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// to compute avg pool
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Value outputTensor = b.create<tensor::EmptyOp>(
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loc, getAsOpFoldResult(outTensorShape), resultElementType);
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Value divisor = b.create<arith::MulIOp>(loc, scaleH, scaleW);
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Value avgPool =
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b.create<linalg::GenericOp>(
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loc, outputTensor.getType(), sumPool, outputTensor,
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/*indexingMaps=*/indexingMapsAvg,
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/*iteratorTypes=*/iteratorTypesAvg,
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value avg;
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if (isa<mlir::IntegerType>(resultElementType))
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avg = b.create<arith::DivSIOp>(loc, args[0],
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divisor);
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else if (isa<mlir::FloatType>(resultElementType))
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avg =
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b.create<arith::DivFOp>(loc, args[0], divisor);
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b.create<linalg::YieldOp>(loc, avg);
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})
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.getResult(0);
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SmallVector<OpFoldResult> finalStrides(inputRank, oneAttr);
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SmallVector<OpFoldResult> finalOffsets = {
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getAsOpFoldResult(iv0), getAsOpFoldResult(iv1), zeroAttr,
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zeroAttr};
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SmallVector<OpFoldResult> finalSizes = {
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oneAttr, oneAttr, getAsOpFoldResult(pooledH),
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getAsOpFoldResult(pooledW)};
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SmallVector<OpFoldResult> diagStrides(inputRank, oneAttr);
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finalOutputTensor = b.create<tensor::InsertSliceOp>(
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loc, finalOutputTensor, avgPool, finalOffsets, finalSizes,
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finalStrides);
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});
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});
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Type resultType = typeConverter->convertType(op.getType());
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b.replaceOp(op, finalOutputTensor);
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return success();
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}
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};
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} // namespace
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void mlir::torch::torch_to_linalg::populatePoolingPatternsAndLegality(
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TypeConverter &typeConverter, RewritePatternSet &patterns,
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ConversionTarget &target) {

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