b1f2e2664e
Also avoid redoing index calculation. Differential Revision: https://reviews.llvm.org/D132274
91 lines
3.3 KiB
C++
91 lines
3.3 KiB
C++
//===- TosaFoldConstantTranspose.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
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// Fold TOSA Transpose operation on constant data
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
|
|
#include "mlir/Dialect/Tosa/Transforms/Passes.h"
|
|
#include "mlir/IR/Matchers.h"
|
|
#include "mlir/Pass/Pass.h"
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::tosa;
|
|
|
|
namespace {
|
|
|
|
struct TosaFoldConstantTranspose : public OpRewritePattern<tosa::TransposeOp> {
|
|
using OpRewritePattern::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(tosa::TransposeOp op,
|
|
PatternRewriter &rewriter) const override {
|
|
auto outputType = op.getType().cast<ShapedType>();
|
|
// TOSA supports quantized types.
|
|
if (!outputType.getElementType().isIntOrIndexOrFloat())
|
|
return failure();
|
|
|
|
ElementsAttr inputValues;
|
|
if (!matchPattern(op.getInput1(), m_Constant(&inputValues)))
|
|
return failure();
|
|
// Make sure the input is a constant that has a single user.
|
|
if (!llvm::hasSingleElement(op.getInput1().getDefiningOp()->getUsers()))
|
|
return failure();
|
|
|
|
DenseIntElementsAttr permAttr;
|
|
if (!matchPattern(op.getPerms(), m_Constant(&permAttr)))
|
|
return failure();
|
|
auto permValues = llvm::to_vector<6>(llvm::map_range(
|
|
// TOSA allows both 32- and 64-bit integer tensors here.
|
|
permAttr.getValues<APInt>(),
|
|
[](const APInt &val) { return val.getZExtValue(); }));
|
|
|
|
auto inputType = op.getInput1().getType().cast<ShapedType>();
|
|
ArrayRef<int64_t> inputShape = inputType.getShape();
|
|
int64_t numElements = inputType.getNumElements();
|
|
|
|
SmallVector<Attribute, 4> outputValues;
|
|
outputValues.resize(numElements);
|
|
|
|
// Transpose the input constant. Because we don't know its rank in advance,
|
|
// we need to loop over the range [0, element count) and delinearize the
|
|
// index.
|
|
auto attrValues = inputValues.getValues<Attribute>();
|
|
ArrayRef<int64_t> outputShape = outputType.getShape();
|
|
for (const auto &it : llvm::enumerate(attrValues)) {
|
|
SmallVector<uint64_t, 6> srcIndices(inputType.getRank(), 0);
|
|
int totalCount = it.index();
|
|
for (int dim = inputType.getRank() - 1; dim >= 0; --dim) {
|
|
srcIndices[dim] = totalCount % inputShape[dim];
|
|
totalCount /= inputShape[dim];
|
|
}
|
|
|
|
SmallVector<uint64_t, 6> dstIndices(outputType.getRank(), 0);
|
|
for (int dim = outputType.getRank() - 1; dim >= 0; --dim)
|
|
dstIndices[dim] = srcIndices[permValues[dim]];
|
|
|
|
uint64_t dstLinearIndex = dstIndices.front();
|
|
for (int dim = 1; dim < outputType.getRank(); ++dim)
|
|
dstLinearIndex = dstLinearIndex * outputShape[dim] + dstIndices[dim];
|
|
|
|
outputValues[dstLinearIndex] = it.value();
|
|
}
|
|
|
|
rewriter.replaceOpWithNewOp<tosa::ConstOp>(
|
|
op, outputType, DenseElementsAttr::get(outputType, outputValues));
|
|
return success();
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void mlir::tosa::populateTosaFoldConstantTransposePatterns(
|
|
MLIRContext *ctx, RewritePatternSet &patterns) {
|
|
patterns.add<TosaFoldConstantTranspose>(ctx);
|
|
}
|