cbb0981388
std::optional::value() has undesired exception checking semantics and is unavailable in older Xcode (see _LIBCPP_AVAILABILITY_BAD_OPTIONAL_ACCESS). The call sites block std::optional migration.
312 lines
13 KiB
C++
312 lines
13 KiB
C++
//===- DataLayoutPropagation.cpp -----------------------------------------===///
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Linalg/Passes.h"
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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
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#include "mlir/Dialect/Linalg/Utils/Utils.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tensor/Utils/Utils.h"
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#include "mlir/Dialect/Utils/IndexingUtils.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "llvm/Support/Debug.h"
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namespace mlir {
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#define GEN_PASS_DEF_LINALGDATALAYOUTPROPAGATION
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#include "mlir/Dialect/Linalg/Passes.h.inc"
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} // namespace mlir
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using namespace mlir;
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using namespace mlir::linalg;
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#define DEBUG_TYPE "linalg-data-layout-propagation"
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namespace {
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// The struct contains the infomation about mapping packing information to
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// the iteration domain of Linalg ops.
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struct PackInfo {
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int64_t getNumTiledLoops() const { return tileToPointMapping.size(); };
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// InnerDimsPos on iteration domain, which follows the order in pack ops.
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SmallVector<int64_t> tiledDimsPos;
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// The sizes of tiling data dimensions on iteration domain.
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llvm::DenseMap<int64_t, OpFoldResult> domainDimAndTileMapping;
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// The mapping from a dimension of iteration domain to the corresponding inner
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// tiling dimension on iteration domain.
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llvm::DenseMap<int64_t, int64_t> tileToPointMapping;
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// The permutation of outer dims (on domain).
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SmallVector<int64_t> outerDimsOnDomainPerm;
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Optional<Value> paddingValue;
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};
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static PackInfo getPackingInfoFromConsumer(
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AffineMap indexingMap, ArrayRef<OpFoldResult> innerTileSizes,
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ArrayRef<int64_t> innerDimsPos, ArrayRef<int64_t> outerDimsPerm,
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Optional<Value> paddingValue = std::nullopt) {
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LLVM_DEBUG(
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{ llvm::dbgs() << "--- Construct PackInfo From A Consumer ---\n"; });
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PackInfo packInfo;
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packInfo.paddingValue = paddingValue;
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int64_t origNumDims = indexingMap.getNumDims();
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SmallVector<AffineExpr> exprs(indexingMap.getResults());
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for (auto [index, innerDimPos, tileSize] :
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llvm::zip_equal(llvm::seq<unsigned>(0, innerDimsPos.size()),
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innerDimsPos, innerTileSizes)) {
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int64_t domainDimPos =
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exprs[innerDimPos].cast<AffineDimExpr>().getPosition();
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packInfo.tiledDimsPos.push_back(domainDimPos);
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packInfo.domainDimAndTileMapping[domainDimPos] = tileSize;
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packInfo.tileToPointMapping[domainDimPos] = origNumDims + index;
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LLVM_DEBUG({
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llvm::dbgs() << "map innerDimPos=" << innerDimPos
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<< " to iteration dimension (d" << domainDimPos << ", d"
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<< packInfo.tileToPointMapping[domainDimPos]
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<< "), which has size=("
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<< packInfo.domainDimAndTileMapping[domainDimPos] << ")\n";
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});
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}
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for (auto dim : outerDimsPerm)
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packInfo.outerDimsOnDomainPerm.push_back(indexingMap.getDimPosition(dim));
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if (!packInfo.outerDimsOnDomainPerm.empty()) {
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LLVM_DEBUG({
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llvm::dbgs() << "map outer dimsDimsPerm to ";
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for (auto dim : packInfo.outerDimsOnDomainPerm)
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llvm::dbgs() << dim << " ";
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llvm::dbgs() << "\n";
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});
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}
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return packInfo;
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}
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/// Returns a tuple for packed operand and indexing_map with the assumptions:
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/// 1) The generic op is the producer of the pack op.
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/// 2) The generic op has only one result.
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/// If the operand is a scalar or packing dimensions are all irrelevant to the
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/// operand, the opreand and the updated indexing map will be returned.
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/// Otherwise, it returns the packed operand and the updated indexing map. E.g.,
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///
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/// #map0 = affine_map<(d0, d1) -> (d0, d1)>
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/// #map1 = affine_map<(d0, d1) -> (d0)>
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/// #map2 = affine_map<(d0, d1) -> (d1)>
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/// %0 = linalg.generic {indexing_maps = [#map1, #map2, #map0],
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/// iterator_types = ["parallel", "parallel"]}
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/// ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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/// outs(%init : tensor<?x?xf32>) {
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/// ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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/// %4 = arith.addf %arg3, %arg4 : f32
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/// linalg.yield %4 : f32
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/// } -> tensor<?x?xf32>
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/// %1 = tensor.pack %0
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/// inner_dims_pos = [0, 1]
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/// inner_tiles = [8, 2]
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/// into %dest : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
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///
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/// Taking the first input operand as an example, the inner tile size of d1 is
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/// 8. Thus, the below operation and `affine_map<(d0, d1, d2, d3)> ->
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/// affine_map<(d1, d3)>` will be returned.
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///
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/// %pack = tensor.pack %arg0
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/// inner_dims_pos = [0]
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/// inner_tiles = [8]
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/// into %init : tensor<?xf32> -> tensor<?x8xf32>
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static std::tuple<Value, AffineMap>
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getOrCreatePackedViewOfOperand(OpBuilder &b, Location loc, PackInfo packInfo,
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GenericOp genericOp, OpOperand *opOperand) {
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int64_t numOrigLoops = genericOp.getNumLoops();
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int64_t numInnerLoops = packInfo.getNumTiledLoops();
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int64_t numLoops = numOrigLoops + numInnerLoops;
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AffineMap origIndexingMap = genericOp.getMatchingIndexingMap(opOperand);
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llvm::DenseMap<int64_t, int64_t> domainDimToOperandDim;
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SmallVector<AffineExpr> exprs(origIndexingMap.getResults());
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if (genericOp.isScalar(opOperand))
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return std::make_tuple(opOperand->get(),
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AffineMap::get(numLoops, 0, exprs, b.getContext()));
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// Step 1. Construct the information of packing data dimensions; append inner
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// dimensions to the indexing maps for the operand.
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for (auto [index, expr] : llvm::enumerate(exprs)) {
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int64_t dimPos = expr.cast<AffineDimExpr>().getPosition();
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domainDimToOperandDim[dimPos] = index;
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}
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SmallVector<int64_t> innerDimsPos;
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SmallVector<OpFoldResult> innerTileSizes;
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for (auto dimPos : packInfo.tiledDimsPos) {
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if (!domainDimToOperandDim.count(dimPos))
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continue;
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int64_t index = domainDimToOperandDim[dimPos];
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innerTileSizes.push_back(packInfo.domainDimAndTileMapping[dimPos]);
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innerDimsPos.push_back(index);
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exprs.push_back(b.getAffineDimExpr(packInfo.tileToPointMapping[dimPos]));
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}
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// Step 2. Fold transpose variants (i.e., outerDimsPerm) into generic op.
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// TODO: should we propagate the permutation of outer dims to the pack op?
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SmallVector<int64_t> outerDimsPerm;
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if (!packInfo.outerDimsOnDomainPerm.empty()) {
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SmallVector<int64_t> inversedOuterPerm =
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invertPermutationVector(packInfo.outerDimsOnDomainPerm);
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for (auto i : llvm::seq<unsigned>(0, origIndexingMap.getNumResults())) {
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int64_t dimPos = exprs[i].cast<AffineDimExpr>().getPosition();
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exprs[i] = b.getAffineDimExpr(inversedOuterPerm[dimPos]);
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}
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}
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auto indexingMap = AffineMap::get(numLoops, 0, exprs, b.getContext());
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// The operand does not have dimensions that relates to pack op.
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if (innerDimsPos.empty())
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return std::make_tuple(opOperand->get(), indexingMap);
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auto empty = tensor::PackOp::createDestinationTensor(
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b, loc, opOperand->get(), innerTileSizes, innerDimsPos, outerDimsPerm);
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auto packedOperand = b.create<tensor::PackOp>(
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loc, opOperand->get(), empty, innerDimsPos, innerTileSizes,
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packInfo.paddingValue, outerDimsPerm);
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return std::make_tuple(packedOperand, indexingMap);
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}
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/// Bubbles up tensor.pack op through elementwise generic op. This
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/// swap pack(generic) to generic(pack). The new generic op works on packed
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/// domain; pack ops are created for input and output operands. E.g.,
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///
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/// #map0 = affine_map<(d0, d1) -> (d0, d1)>
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/// %0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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/// %1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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/// %2 = tensor.empty(%0, %1) : tensor<?x?xf32>
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/// %3 = linalg.generic {indexing_maps = [#map0, #map0],
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/// iterator_types = ["parallel", "parallel"]}
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/// ins(%arg0 : tensor<?x?xf32>)
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/// outs(%2 : tensor<?x?xf32>) {
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/// ^bb0(%arg3: f32, %arg4: f32):
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/// %4 = arith.addf %arg3, %arg3 : f32
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/// linalg.yield %4 : f32
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/// } -> tensor<?x?xf32>
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/// %4 = tensor.pack %3
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/// inner_dims_pos = [0, 1]
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/// inner_tiles = [8, 2]
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/// into %dest : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
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///
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/// will be converted to
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///
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/// #map = affine_map<()[s0] -> (s0 ceildiv 8)>
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/// #map1 = affine_map<()[s0] -> (s0 ceildiv 2)>
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/// #map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
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/// %dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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/// %dim_0 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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/// %0 = affine.apply #map()[%dim]
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/// %1 = affine.apply #map1()[%dim_0]
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/// %2 = tensor.empty(%0, %1) : tensor<?x?x8x2xf32>
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/// %pack = tensor.pack %arg0
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/// inner_dims_pos = [0, 1]
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/// inner_tiles = [8, 2]
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/// into %2 : tensor<?x?xf32> -> tensor<?x?x8x2xf32>
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/// %3 = linalg.generic {indexing_maps = [#map2, #map2],
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/// iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
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/// ins(%pack : tensor<?x?x8x2xf32>)
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/// outs(%arg1 : tensor<?x?x8x2xf32>) {
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/// ^bb0(%in: f32, %out: f32):
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/// %4 = arith.addf %in, %in : f32
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/// linalg.yield %4 : f32
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/// } -> tensor<?x?x8x2xf32>
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static FailureOr<GenericOp>
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bubbleUpPackOpThroughElemGenericOp(RewriterBase &rewriter,
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tensor::PackOp packOp) {
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auto genericOp = packOp.getSource().getDefiningOp<GenericOp>();
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if (!genericOp)
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return failure();
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if (!isElementwise(genericOp))
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return failure();
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// TODO: Relax the restriction. We are able to bubble up the pack op through
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// multi-result generic op. It just needs more work.
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if (genericOp.getNumResults() != 1)
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return failure();
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// TODO: Add an option for allowing padding values. It could introduce
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// undefined behavior if we unconditionally propagate pack op through all
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// the ops. E.g., if the padding value is zero and there are division ops in
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// a generic op. Some values of padding area could be NaN (0/0).
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if (packOp.getPaddingValue())
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return failure();
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OpOperand *opOperand = genericOp.getDpsInitOperand(0);
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auto packInfo = getPackingInfoFromConsumer(
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genericOp.getMatchingIndexingMap(opOperand), packOp.getMixedTiles(),
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packOp.getInnerDimsPos(), packOp.getOuterDimsPerm(),
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packOp.getPaddingValue());
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Location loc = packOp.getLoc();
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SmallVector<Value> inputOperands;
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SmallVector<AffineMap> indexingMaps;
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for (OpOperand *inputOperand : genericOp.getDpsInputOperands()) {
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auto [packedOperand, packedIndexingMap] = getOrCreatePackedViewOfOperand(
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rewriter, loc, packInfo, genericOp, inputOperand);
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inputOperands.push_back(packedOperand);
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indexingMaps.push_back(packedIndexingMap);
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}
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int64_t numLoops = genericOp.getNumLoops();
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int64_t numInnerLoops = packInfo.getNumTiledLoops();
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int64_t newNumLoops = numLoops + numInnerLoops;
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SmallVector<utils::IteratorType> iterTypes =
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genericOp.getIteratorTypesArray();
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iterTypes.append(numInnerLoops, utils::IteratorType::parallel);
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// Rebuild the indexing map for the corresponding init operand.
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auto [packedOutOperand, packedOutIndexingMap] =
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getOrCreatePackedViewOfOperand(rewriter, loc, packInfo, genericOp,
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opOperand);
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SmallVector<AffineExpr> outExprs(
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packedOutIndexingMap.getResults().drop_back(numInnerLoops));
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// Apply transpose to the indexing map, because we'll replace the init operand
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// with the destination of pack op.
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auto outerDimsPerm = packOp.getOuterDimsPerm();
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if (!outerDimsPerm.empty()) {
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applyPermutationToVector<AffineExpr>(outExprs, outerDimsPerm);
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}
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for (int i = 0; i < numInnerLoops; ++i)
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outExprs.push_back(rewriter.getAffineDimExpr(numLoops + i));
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AffineMap outMap =
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AffineMap::get(newNumLoops, 0, outExprs, rewriter.getContext());
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indexingMaps.push_back(outMap);
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auto newGenericOp = rewriter.create<linalg::GenericOp>(
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loc, packOp.getDestType(), inputOperands, packOp.getDest(), indexingMaps,
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iterTypes, /*bodyBuild=*/nullptr,
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linalg::getPrunedAttributeList(genericOp));
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rewriter.cloneRegionBefore(genericOp.getRegion(), newGenericOp.getRegion(),
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newGenericOp.getRegion().begin());
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return newGenericOp;
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}
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// Wrapper pattern that applies bubbleUpPackOpThroughElemGenericOp method.
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struct BubbleUpPackOpThroughElemGenericOpPattern
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: public OpRewritePattern<tensor::PackOp> {
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using OpRewritePattern<tensor::PackOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(tensor::PackOp packOp,
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PatternRewriter &rewriter) const override {
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auto genericOp = bubbleUpPackOpThroughElemGenericOp(rewriter, packOp);
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if (failed(genericOp))
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return failure();
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rewriter.replaceOp(packOp, genericOp->getResults());
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return success();
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}
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};
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} // namespace
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void mlir::linalg::populateDataLayoutPropagationPatterns(
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RewritePatternSet &patterns) {
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patterns.insert<BubbleUpPackOpThroughElemGenericOpPattern>(
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patterns.getContext());
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}
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