TosaDecomposeDepthwise.cpp

Go to the documentation of this file.

//===- 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/IR/BuiltinTypes.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());
 
    // Any dimensions other than batchSize cannot be dynamic for input/output
    for (unsigned int i = 1; i < 4; ++i) {
      if (inputType.isDynamicDim(i) || resultType.isDynamicDim(i))
        return failure();
    }
 
    if (!weightType.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();
    }
 
    Type inputETy = inputType.getElementType();
    Type weightETy = weightType.getElementType();
    if (!inputETy.isIntOrFloat() || !weightETy.isIntOrFloat())
      return rewriter.notifyMatchFailure(op, "unsupported type");
 
    // Get and verify zero points.
    FailureOr<int64_t> maybeIZp = op.getInputZeroPoint();
    if (failed(maybeIZp))
      return rewriter.notifyMatchFailure(
          op, "input zero point cannot be statically determined");
 
    FailureOr<int64_t> maybeWZp = op.getWeightZeroPoint();
    if (failed(maybeWZp))
      return rewriter.notifyMatchFailure(
          op, "weight zero point cannot be statically determined");
 
    int64_t iZp = *maybeIZp;
    int64_t wZp = *maybeWZp;
    if (op.verifyInputZeroPoint(iZp).failed())
      return rewriter.notifyMatchFailure(
          op, "input zero point must be zero for non-int8 integer types");
    if (op.verifyWeightZeroPoint(wZp).failed())
      return rewriter.notifyMatchFailure(
          op, "weight zero point must be zero for non-int8 integer types");
 
    // 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());
    auto revisedInputShapeValue =
        getTosaConstShape(rewriter, op.getLoc(), revisedInputShape);
    input = tosa::ReshapeOp::create(rewriter, op.getLoc(), inputType, input,
                                    revisedInputShapeValue)
                .getResult();
 
    Type resultETy = resultType.getElementType();
    if (inputETy != resultETy) {
      inputType = inputType.clone(resultETy);
      input = tosa::CastOp::create(rewriter, op.getLoc(), inputType, input,
                                   /*input_unsigned*/ false);
    }
 
    if (weightETy != resultETy) {
      weightType = weightType.clone(resultETy);
      weight = tosa::CastOp::create(rewriter, op.getLoc(), weightType, weight,
                                    /*input_unsigned*/ false);
    }
 
    if (iZp != 0 || wZp != 0) {
 
      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 = tosa::ConstOp::create(rewriter, op.getLoc(), zpTy, zpAttr);
        return tosa::SubOp::create(rewriter, 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; })) {
      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];
        }
      }
 
      Value padSizeVal = getTosaConstShape(rewriter, op->getLoc(), pad);
 
      auto padTy = RankedTensorType::get({1}, inputETy);
      auto padAttr = DenseElementsAttr::get(padTy, zeroAttr);
      Value padVal =
          tosa::ConstOp::create(rewriter, op->getLoc(), padTy, padAttr);
      inputType = RankedTensorType::get(newShape, inputETy);
      input = tosa::PadOp::create(rewriter, 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();
    }
 
    auto shiftElementType = IntegerType::get(rewriter.getContext(), 8);
    auto shiftType = RankedTensorType::get({1}, shiftElementType);
    auto shiftZeroAttr = DenseElementsAttr::get(
        shiftType, rewriter.getIntegerAttr(shiftElementType, 0));
    Value constZero =
        tosa::ConstOp::create(rewriter, op.getLoc(), shiftType, shiftZeroAttr);
    Value mulValue = tosa::MulOp::create(rewriter, op.getLoc(), mulShapeType,
                                         input, weight, constZero)
                         .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());
    auto outputShapeValue =
        getTosaConstShape(rewriter, op->getLoc(), outputShape);
    Value outputValue = tosa::ReshapeOp::create(
        rewriter, op.getLoc(), outputShapeType, mulValue, outputShapeValue);
 
    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);
}