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
122 lines
4.8 KiB
C++
122 lines
4.8 KiB
C++
//===- TosaDecomposeDepthwise.cpp
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//------------------------------------------===//
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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 Depthwise operation to a series of TOSA Ops specifically
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// (1) Convert a 1x1 Depthwise to Reshape -> Mul -> Reshape -> Add
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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 DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
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explicit DepthwiseConv2DIsMul(MLIRContext *context)
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: OpRewritePattern(context) {}
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LogicalResult matchAndRewrite(tosa::DepthwiseConv2DOp 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.output().getType().cast<ShapedType>();
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Type inputEType = inputType.getElementType();
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if (!(inputType.hasStaticShape() && weightType.hasStaticShape() &&
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resultType.hasStaticShape())) {
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return failure();
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}
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// Quantization information needs to still be performed.
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if (op.quantization_info() || !inputEType.isa<FloatType>()) {
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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[0] != 1 || weightShape[1] != 1) {
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return failure();
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}
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// Reshape input to [N, H, W, C] -> [N, H, W, C, 1].
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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], 1};
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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 [KH, KW, C, M] -> [1, 1, 1, C, M].
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llvm::SmallVector<int64_t, 2> revisedWeightShape{1, 1, 1, weightShape[2],
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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 an elementwise mul over the reshaped input and weight.
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llvm::SmallVector<int64_t, 2> mulShape{inputShape[0], inputShape[1],
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inputShape[2], inputShape[3],
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weightShape[3]};
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auto mulShapeType = RankedTensorType::get(
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mulShape,
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weight.getType().dyn_cast<RankedTensorType>().getElementType());
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Value mulValue =
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rewriter
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.create<tosa::MulOp>(op.getLoc(), mulShapeType, reshapedInput,
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reshapedWeight, /*shift=*/0)
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.getResult();
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// Reshape output to [N, H, W, C * M].
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auto outputShape = op.output().getType().cast<ShapedType>().getShape();
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auto outputShapeType = RankedTensorType::get(
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outputShape,
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input.getType().dyn_cast<RankedTensorType>().getElementType());
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auto outputValue =
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rewriter.create<tosa::ReshapeOp>(op.getLoc(), outputShapeType, mulValue,
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rewriter.getI64ArrayAttr(outputShape));
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// Add in the bias.
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rewriter
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.replaceOpWithNewOp<tosa::AddOp>(op, outputShapeType, outputValue,
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op.bias())
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.getResult();
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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::populateTosaDecomposeDepthwise(MLIRContext *ctx,
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RewritePatternSet &patterns) {
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patterns.add<DepthwiseConv2DIsMul>(ctx);
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}
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