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//===- TransposeConv2D.cpp - Convolution transposition -------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/ValueRange.h"
#include "mlir/Transforms/DialectConversion.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/RWMutex.h"
#include <memory>
#include <numeric>
namespace mlir {
namespace linalg {
namespace {
// clang-format off
/// Convolution converter that applies the following rewrite:
///
/// Before:
///
/// %0 = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>,
/// strides = dense<2> : tensor<2xi64>}
/// ins (%input, %filter: tensor<1x4x4x6xf32>, tensor<8x2x2x6xf32>)
/// outs (%init: tensor<1x2x2x8xf32>) -> tensor<1x2x2x8xf32>
///
/// After:
///
/// %cst = arith.constant 0.000000e+00 : f32
/// %0 = tensor.empty() : tensor<2x2x6x8xf32>
/// %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<2x2x6x8xf32>) -> tensor<2x2x6x8xf32>
/// %transposed = linalg.transpose ins(%arg1 : tensor<8x2x2x6xf32>) outs(%1 : tensor<2x2x6x8xf32>)
/// permutation = [1, 2, 3, 0]
/// %2 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
/// ins(%arg0, %transposed : tensor<1x4x4x6xf32>, tensor<2x2x6x8xf32>) outs(%arg2 : tensor<1x2x2x8xf32>)
/// -> tensor<1x2x2x8xf32>
///
/// with an analogous example for the quantized case.
// clang-format on
template <typename FHWCConvOp, typename HWCFConvOp>
FailureOr<Operation *> transposeConv2DHelper(RewriterBase &rewriter,
FHWCConvOp op) {
// Construct a permutation of the filter tensor dimensions. For a 2D
// convolution this will be known statically as [1, 2, 3, 0].
SmallVector<int64_t> filterPerm({1, 2, 3, 0});
// Create the type for the transposed filter tensor.
auto filter = op->getOperand(1);
auto filterTy = cast<ShapedType>(filter.getType());
SmallVector<int64_t> newFilterShape(filterPerm.size());
std::generate(std::begin(newFilterShape), std::end(newFilterShape),
[dim = 0, &filterTy, &filterPerm]() mutable {
return filterTy.getShape()[filterPerm[dim++]];
});
// Because linalg.transpose expects an "out" parameter we need to pass it a
// tensor of zeros of the result type so here we construct that tensor.
auto inputType = op->getOperand(0).getType();
auto elementTy = cast<ShapedType>(inputType).getElementType();
auto loc = op->getLoc();
const auto isTensorOp = isa<TensorType>(inputType);
Value input;
if (isTensorOp) {
input = rewriter.create<tensor::EmptyOp>(loc, newFilterShape, elementTy)
.getResult();
} else {
input = rewriter
.create<memref::AllocOp>(
loc, MemRefType::get(newFilterShape, elementTy))
.getResult();
}
// We can then construct the transposition on our filter.
auto transpose =
rewriter.create<linalg::TransposeOp>(loc, filter, input, filterPerm);
Value newFilter;
if (isTensorOp) {
newFilter = transpose.getResult()[0];
} else {
newFilter = input;
}
SmallVector<Value> newInputs{op.getInputs()};
// The filter is always the second input argument, the other inputs can be
// left as they are.
newInputs[1] = newFilter;
// It is possible the convolution doesn't define any results and its
// out argument is just used instead.
SmallVector<Type> resultTy;
if (op.getNumResults()) {
resultTy.push_back(op->getResult(0).getType());
}
auto newConv =
rewriter.create<HWCFConvOp>(loc, resultTy, newInputs, op.getOutputs(),
op.getStrides(), op.getDilations());
rewriter.replaceOp(op, newConv);
return newConv.getOperation();
}
template <typename FHWCConvOp, typename HWCFConvOp>
class ConvConverter : public OpRewritePattern<FHWCConvOp> {
public:
using OpRewritePattern<FHWCConvOp>::OpRewritePattern;
LogicalResult matchAndRewrite(FHWCConvOp op,
PatternRewriter &rewriter) const final {
if (failed(transposeConv2DHelper<FHWCConvOp, HWCFConvOp>(rewriter, op))) {
return failure();
}
return success();
}
};
} // namespace
FailureOr<Operation *> transposeConv2D(RewriterBase &rewriter,
linalg::Conv2DNhwcFhwcOp op) {
return transposeConv2DHelper<linalg::Conv2DNhwcFhwcOp,
linalg::Conv2DNhwcHwcfOp>(rewriter, op);
}
FailureOr<Operation *> transposeConv2D(RewriterBase &rewriter,
linalg::Conv2DNhwcFhwcQOp op) {
return transposeConv2DHelper<linalg::Conv2DNhwcFhwcQOp,
linalg::Conv2DNhwcHwcfQOp>(rewriter, op);
}
void populateTranposeConv2DPatterns(RewritePatternSet &patterns) {
MLIRContext *context = patterns.getContext();
patterns.insert<
ConvConverter<linalg::Conv2DNhwcFhwcOp, linalg::Conv2DNhwcHwcfOp>,
ConvConverter<linalg::Conv2DNhwcFhwcQOp, linalg::Conv2DNhwcHwcfQOp>>(
context);
}
} // namespace linalg
} // namespace mlir
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