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//===- TosaDecomposeConv2D.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 Conv2D operation to a series of TOSA Ops specifically
// (1) Convert a 1x1 Convolution to a Reshape->FC->Reshape
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
#include "mlir/Dialect/Tosa/Transforms/Passes.h"
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
using namespace mlir;
using namespace mlir::tosa;
namespace {
SmallVector<int64_t> convertFromMlirShape(ArrayRef<int64_t> shape) {
return to_vector(llvm::map_range(shape, [](int64_t dim) {
return ShapedType::isDynamic(dim) ? -1 : dim;
}));
}
struct Conv2DIsFullyConnected : public OpRewritePattern<tosa::Conv2DOp> {
explicit Conv2DIsFullyConnected(MLIRContext *context)
: OpRewritePattern(context) {}
LogicalResult matchAndRewrite(tosa::Conv2DOp 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.getType());
auto numDynamic =
llvm::count_if(inputType.getShape(), ShapedType::isDynamic);
if (numDynamic > 1)
return rewriter.notifyMatchFailure(
op, "at most one dim in input may be dynamic");
if (!weightType.hasRank())
return rewriter.notifyMatchFailure(op, "unranked weight input");
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[1] != 1 || weightShape[2] != 1)
return failure();
llvm::ArrayRef<int64_t> padAttr = op.getPad();
llvm::SmallVector<int64_t> pad(8, 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);
if (op.getQuantizationInfo()) {
auto quantizationInfo = op.getQuantizationInfo();
int64_t iZp = quantizationInfo->getInputZp();
if (!validIntegerRange(cast<IntegerType>(inputETy), iZp))
return rewriter.notifyMatchFailure(
op, "tosa.conv op quantization has zp outside of input range");
zeroAttr = rewriter.getIntegerAttr(inputETy, iZp);
}
llvm::SmallVector<int64_t> newShape(inputType.getShape());
for (int i = 0, s = newShape.size(); i < s; ++i) {
if (newShape[i] != ShapedType::kDynamic) {
newShape[i] += pad[i * 2] + pad[i * 2 + 1];
}
}
auto padSizeTy = RankedTensorType::get({4, 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);
}
// Reshape input to [N,IH,IW,IC] -> [N * IH * IW, IC].
ArrayRef<int64_t> inputShape = inputType.getShape();
int64_t combined = ShapedType::kDynamic;
if (numDynamic == 0)
combined = inputShape[0] * inputShape[1] * inputShape[2];
llvm::SmallVector<int64_t, 2> revisedInputShape{combined, inputShape[3]};
auto revisedInputShapeType =
RankedTensorType::get(revisedInputShape, inputType.getElementType());
auto reshapedInput = rewriter
.create<tosa::ReshapeOp>(
op.getLoc(), revisedInputShapeType, input,
rewriter.getDenseI64ArrayAttr(
convertFromMlirShape(revisedInputShape)))
.getResult();
// Reshape kernel to [OC,KH,KW,IC] -> [OC, IC].
llvm::SmallVector<int64_t, 2> revisedWeightShape{weightShape[0],
weightShape[3]};
auto revisedWeightShapeType = RankedTensorType::get(
revisedWeightShape,
dyn_cast<RankedTensorType>(weight.getType()).getElementType());
auto reshapedWeight = rewriter
.create<tosa::ReshapeOp>(
op.getLoc(), revisedWeightShapeType, weight,
rewriter.getDenseI64ArrayAttr(
convertFromMlirShape(revisedWeightShape)))
.getResult();
// Perform a fully connected network over the reshaped input and weight.
llvm::SmallVector<int64_t, 2> fullyConnectedShape{combined, weightShape[0]};
auto fullyConnectedShapeType =
RankedTensorType::get(fullyConnectedShape, resultType.getElementType());
Value fullyConnectedValue;
if (op.getQuantizationInfo()) {
fullyConnectedValue =
rewriter
.create<tosa::FullyConnectedOp>(
op.getLoc(), fullyConnectedShapeType, reshapedInput,
reshapedWeight, op.getBias(), *op.getQuantizationInfo())
.getResult();
} else {
fullyConnectedValue = rewriter
.create<tosa::FullyConnectedOp>(
op.getLoc(), fullyConnectedShapeType,
reshapedInput, reshapedWeight, op.getBias())
.getResult();
}
// Reshape output to [N, IH, IW, OC].
llvm::SmallVector<int64_t, 4> outputShape{inputShape[0], inputShape[1],
inputShape[2], weightShape[0]};
rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
op, resultType, fullyConnectedValue,
rewriter.getDenseI64ArrayAttr(convertFromMlirShape(outputShape)));
return success();
}
};
} // namespace
void mlir::tosa::populateTosaDecomposeConv2D(MLIRContext *ctx,
RewritePatternSet &patterns) {
patterns.add<Conv2DIsFullyConnected>(ctx);
}
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