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//===- TosaDecomposeDepthwise.cpp -----------------------------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// Decompose TOSA Depthwise operation to a series of TOSA Ops specifically
// (1) Convert a 1x1 Depthwise to Reshape -> Mul -> Reshape -> Add
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Tosa/Transforms/Passes.h"
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
#include "mlir/Pass/Pass.h"
using namespace mlir;
using namespace mlir::tosa;
namespace {
struct DepthwiseConv2DIsMul : public OpRewritePattern<tosa::DepthwiseConv2DOp> {
explicit DepthwiseConv2DIsMul(MLIRContext *context)
: OpRewritePattern(context) {}
LogicalResult matchAndRewrite(tosa::DepthwiseConv2DOp op,
PatternRewriter &rewriter) const override {
Value input = op.getInput();
Value weight = op.getWeight();
ShapedType inputType = cast<ShapedType>(input.getType());
ShapedType weightType = cast<ShapedType>(weight.getType());
ShapedType resultType = cast<ShapedType>(op.getOutput().getType());
if (!(inputType.hasStaticShape() && weightType.hasStaticShape() &&
resultType.hasStaticShape())) {
return failure();
}
if (!llvm::all_of(op.getStride(), [](int64_t v) { return v == 1; }))
return failure();
// Only works for a 1x1 kernel.
ArrayRef<int64_t> weightShape = weightType.getShape();
if (weightShape[0] != 1 || weightShape[1] != 1) {
return failure();
}
// Reshape input to [N, H, W, C] -> [N, H, W, C, 1].
ArrayRef<int64_t> inputShape = inputType.getShape();
llvm::SmallVector<int64_t, 2> revisedInputShape{
inputShape[0], inputShape[1], inputShape[2], inputShape[3], 1};
inputType = RankedTensorType::get(
revisedInputShape,
dyn_cast<RankedTensorType>(input.getType()).getElementType());
input = rewriter
.create<tosa::ReshapeOp>(
op.getLoc(), inputType, input,
rewriter.getDenseI64ArrayAttr(revisedInputShape))
.getResult();
if (inputType.getElementType() != resultType.getElementType()) {
inputType = inputType.clone(resultType.getElementType());
input = rewriter.create<tosa::CastOp>(op.getLoc(), inputType, input);
}
if (weightType.getElementType() != resultType.getElementType()) {
weightType = weightType.clone(resultType.getElementType());
weight = rewriter.create<tosa::CastOp>(op.getLoc(), weightType, weight);
}
if (auto quantizationInfo = op.getQuantizationInfo()) {
auto iZp = quantizationInfo->getInputZp();
auto wZp = quantizationInfo->getWeightZp();
auto applyZp = [&](Value val, int64_t zp) -> Value {
if (zp == 0)
return val;
auto ety = cast<ShapedType>(val.getType()).getElementType();
std::vector<int64_t> shape(cast<ShapedType>(val.getType()).getRank(),
1);
auto zpTy = RankedTensorType::get(shape, ety);
auto zpAttr =
DenseElementsAttr::get(zpTy, rewriter.getIntegerAttr(ety, zp));
auto zpVal = rewriter.create<tosa::ConstOp>(op.getLoc(), zpTy, zpAttr);
return rewriter.create<tosa::SubOp>(op.getLoc(), val.getType(), val,
zpVal);
};
input = applyZp(input, iZp);
weight = applyZp(weight, wZp);
}
ArrayRef<int64_t> padAttr = op.getPad();
llvm::SmallVector<int64_t> pad(10, 0);
for (const auto &it : llvm::enumerate(padAttr))
pad[it.index() + 2] = it.value();
if (llvm::any_of(pad, [](int64_t p) { return p != 0; })) {
Type inputETy = inputType.getElementType();
Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
llvm::SmallVector<int64_t> newShape(inputType.getShape());
for (int i = 0, s = pad.size(); i < s; ++i) {
if (newShape[i / 2] != ShapedType::kDynamic) {
newShape[i / 2] += pad[i];
}
}
auto padSizeTy = RankedTensorType::get({5, 2}, rewriter.getI64Type());
auto padSize =
DenseIntElementsAttr::get(padSizeTy, ArrayRef<int64_t>(pad));
Value padSizeVal =
rewriter.create<tosa::ConstOp>(op->getLoc(), padSizeTy, padSize);
auto padTy = RankedTensorType::get({}, inputETy);
auto padAttr = DenseElementsAttr::get(padTy, zeroAttr);
Value padVal =
rewriter.create<tosa::ConstOp>(op->getLoc(), padTy, padAttr);
inputType = RankedTensorType::get(newShape, inputETy);
input = rewriter.create<tosa::PadOp>(op->getLoc(), inputType, input,
padSizeVal, padVal);
}
// Perform an elementwise mul over the reshaped input and weight.
llvm::SmallVector<int64_t, 2> mulShape{
inputType.getDimSize(0), inputType.getDimSize(1),
inputType.getDimSize(2), inputType.getDimSize(3), weightShape[3]};
auto mulShapeType = RankedTensorType::get(
mulShape,
dyn_cast<RankedTensorType>(weight.getType()).getElementType());
if (EqualizeRanks(rewriter, op.getLoc(), input, weight).failed()) {
return failure();
}
Value mulValue = rewriter
.create<tosa::MulOp>(op.getLoc(), mulShapeType, input,
weight, /*shift=*/0)
.getResult();
// Reshape output to [N, H, W, C * M].
auto outputShape = cast<ShapedType>(op.getOutput().getType()).getShape();
auto outputShapeType = RankedTensorType::get(
outputShape,
dyn_cast<RankedTensorType>(input.getType()).getElementType());
Value outputValue = rewriter.create<tosa::ReshapeOp>(
op.getLoc(), outputShapeType, mulValue,
rewriter.getDenseI64ArrayAttr(outputShape));
Value bias = op.getBias();
if (EqualizeRanks(rewriter, op.getLoc(), outputValue, bias).failed()) {
return failure();
}
// Add in the bias.
rewriter
.replaceOpWithNewOp<tosa::AddOp>(op, outputShapeType, outputValue, bias)
.getResult();
return success();
}
};
} // namespace
void mlir::tosa::populateTosaDecomposeDepthwise(MLIRContext *ctx,
RewritePatternSet &patterns) {
patterns.add<DepthwiseConv2DIsMul>(ctx);
}
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