b4e0507ce0
insert is soft deprecated, so remove all references so it's less likely to be used and can be easily removed in the future. Differential Revision: https://reviews.llvm.org/D120021
116 lines
4.6 KiB
C++
116 lines
4.6 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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#include "mlir/Pass/Pass.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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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.input();
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Value weight = op.weight();
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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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if (!inputType.hasStaticShape() || !weightType.hasRank()) {
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return failure();
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}
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// Stride must be 1 for this optimization.
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for (Attribute stride : op.stride().getValue()) {
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if (!stride.cast<IntegerAttr>().getValue().isOne()) {
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return failure();
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}
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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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}
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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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llvm::SmallVector<int64_t, 2> revisedInputShape{
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inputShape[0] * inputShape[1] * inputShape[2], inputShape[3]};
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auto revisedInputShapeType = RankedTensorType::get(
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revisedInputShape,
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input.getType().dyn_cast<RankedTensorType>().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(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(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{
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inputShape[0] * inputShape[1] * inputShape[2], weightShape[0]};
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auto fullyConnectedShapeType = RankedTensorType::get(
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fullyConnectedShape,
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resultType.dyn_cast<ShapedType>().getElementType());
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Value fullyConnectedValue;
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if (op.quantization_info()) {
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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.bias(), op.quantization_info().getValue())
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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.bias())
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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(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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