124 lines
5.1 KiB
C++
124 lines
5.1 KiB
C++
//===- TosaDecomposeConv2D.cpp --------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// Decompose TOSA Conv2D operation to a series of TOSA Ops specifically
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// (1) Convert a 1x1 Convolution to a Reshape->FC->Reshape
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Tosa/IR/TosaOps.h"
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#include "mlir/Dialect/Tosa/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::tosa;
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namespace {
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SmallVector<int64_t> convertFromMlirShape(ArrayRef<int64_t> shape) {
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return to_vector(llvm::map_range(shape, [](int64_t dim) {
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return ShapedType::isDynamic(dim) ? -1 : dim;
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}));
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}
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struct Conv2DIsFullyConnected : public OpRewritePattern<tosa::Conv2DOp> {
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explicit Conv2DIsFullyConnected(MLIRContext *context)
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: OpRewritePattern(context) {}
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LogicalResult matchAndRewrite(tosa::Conv2DOp op,
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PatternRewriter &rewriter) const override {
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Value input = op.getInput();
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Value weight = op.getWeight();
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ShapedType inputType = input.getType().cast<ShapedType>();
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ShapedType weightType = weight.getType().cast<ShapedType>();
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ShapedType resultType = op.getType().cast<ShapedType>();
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auto numDynamic =
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llvm::count_if(inputType.getShape(), ShapedType::isDynamic);
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if (numDynamic > 1)
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return rewriter.notifyMatchFailure(
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op, "at most one dim in input may be dynamic");
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if (!weightType.hasRank())
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return rewriter.notifyMatchFailure(op, "unranked weight input");
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// Stride must be 1 for this optimization.
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for (APInt stride : op.getStride().getAsValueRange<IntegerAttr>()) {
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if (!stride.isOne())
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return failure();
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}
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// Only works for a 1x1 kernel.
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ArrayRef<int64_t> weightShape = weightType.getShape();
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if (weightShape[1] != 1 || weightShape[2] != 1)
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return failure();
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// Reshape input to [N,IH,IW,IC] -> [N * IH * IW, IC].
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ArrayRef<int64_t> inputShape = inputType.getShape();
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int64_t combined = ShapedType::kDynamic;
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if (numDynamic == 0)
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combined = inputShape[0] * inputShape[1] * inputShape[2];
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llvm::SmallVector<int64_t, 2> revisedInputShape{combined, inputShape[3]};
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auto revisedInputShapeType =
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RankedTensorType::get(revisedInputShape, inputType.getElementType());
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auto reshapedInput = rewriter
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.create<tosa::ReshapeOp>(
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op.getLoc(), revisedInputShapeType, input,
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rewriter.getI64ArrayAttr(
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convertFromMlirShape(revisedInputShape)))
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.getResult();
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// Reshape kernel to [OC,KH,KW,IC] -> [OC, IC].
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llvm::SmallVector<int64_t, 2> revisedWeightShape{weightShape[0],
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weightShape[3]};
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auto revisedWeightShapeType = RankedTensorType::get(
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revisedWeightShape,
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weight.getType().dyn_cast<RankedTensorType>().getElementType());
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auto reshapedWeight = rewriter
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.create<tosa::ReshapeOp>(
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op.getLoc(), revisedWeightShapeType, weight,
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rewriter.getI64ArrayAttr(
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convertFromMlirShape(revisedWeightShape)))
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.getResult();
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// Perform a fully connected network over the reshaped input and weight.
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llvm::SmallVector<int64_t, 2> fullyConnectedShape{combined, weightShape[0]};
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auto fullyConnectedShapeType =
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RankedTensorType::get(fullyConnectedShape, resultType.getElementType());
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Value fullyConnectedValue;
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if (op.getQuantizationInfo()) {
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fullyConnectedValue =
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rewriter
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.create<tosa::FullyConnectedOp>(
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op.getLoc(), fullyConnectedShapeType, reshapedInput,
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reshapedWeight, op.getBias(), *op.getQuantizationInfo())
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.getResult();
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} else {
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fullyConnectedValue = rewriter
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.create<tosa::FullyConnectedOp>(
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op.getLoc(), fullyConnectedShapeType,
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reshapedInput, reshapedWeight, op.getBias())
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.getResult();
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}
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// Reshape output to [N, IH, IW, OC].
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llvm::SmallVector<int64_t, 4> outputShape{inputShape[0], inputShape[1],
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inputShape[2], weightShape[0]};
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rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
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op, resultType, fullyConnectedValue,
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rewriter.getI64ArrayAttr(convertFromMlirShape(outputShape)));
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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::tosa::populateTosaDecomposeConv2D(MLIRContext *ctx,
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RewritePatternSet &patterns) {
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patterns.add<Conv2DIsFullyConnected>(ctx);
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}
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