[Mlir-commits] [mlir] [mlir][linalg] unfold projected permutation. (PR #114704)
Andrzej Warzyński
llvmlistbot at llvm.org
Thu Nov 7 06:56:51 PST 2024
================
@@ -0,0 +1,243 @@
+//===- UnfoldProjectedPermutation.cpp - extract projected projections ---===//
+//
+// 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/Linalg/Transforms/Transforms.h"
+#include <utility>
+
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include <map>
+#include <optional>
+#include <vector>
+
+using namespace mlir;
+using namespace mlir::linalg;
+
+namespace {
+
+/// This file implements pattern to decompose the input operand(s) of a
+/// linalg.generic that has a `transpose`, `broadcast` or a mixture of two,
+/// into explicit transpose and broadcast. Having them folded into the
+/// linalg.generic is a good optimization but sometimes we may want to unwrap
+/// i.e. `unfold` them as explicit transpose and broadcast. This rewrite
+/// pattern helps do it for each input operand. This is useful for instance
+/// when trying to recognize named ops.
+///
+/// The transpose, broadcast, or mixture of both, are expressed in the affine
+/// map of the operand. Technically it is essentially `projected permutation`.
+///
+/// Example
+///
+/// ```mlir
+///
+/// #projection = affine_map<(d0, d1, d2, d3, d4) -> (d2, d3, d1)>
+/// #identity = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
+/// ...
+/// %res = linalg.generic
+/// { indexing_maps = [#projection, #identity, #identity],
+/// iterator_types = ["parallel", "parallel", "parallel",
+/// "parallel", "parallel"]}
+/// ins(%x, %y : tensor<7x8x9xf32>, tensor<5x9x7x8x10xf32>)
+/// outs(%z : tensor<5x9x7x8x10xf32>) {
+/// ^bb0(%in: f32, %in_1: f32, %out: f32):
+/// %div = arith.divf %in, %in_1 : f32
+/// linalg.yield %div : f32
+/// } -> tensor<5x9x7x8x10xf32>
+/// ```
+///
+/// In the above IR operand `%x` map is a projected-permutation. This can be
+/// unfolded as:
+///
+/// ```mlir
+/// ...
+/// %x_trans = linalg.transpose
+/// ins(%x : tensor<7x8x9xf32>)
+/// outs(%e1 : tensor<9x7x8xf32>) permutation = [2, 0, 1]
+/// ...
+/// %x_trans_bc = linalg.broadcast
+/// ins(%x_trans : tensor<9x7x8xf32>)
+/// outs(%e2 : tensor<5x9x7x8x10xf32>) dimensions = [0, 4]
+/// %2 = linalg.div
+/// ins(%x_trans_bc, %y :
+/// tensor<5x9x7x8x10xf32>, tensor<5x9x7x8x10xf32>)
+/// outs(%arg2 : tensor<5x9x7x8x10xf32>) -> tensor<5x9x7x8x10xf32>
+///
+/// Note that linalg.generic has been 'specialized' to linalg.div.
+///
+/// To unfold it is more effective to transpose first and then do the broadcast.
+/// However, if transpose is done first, the permutation map needs to be
+/// expressed in terms of reduced dimension (as broadcast hasn't happened yet).
+/// Also, the broadcast dimensions in a linalg.generic come from other operands
+/// (those not broadcasted along that particular dimension). We work this out
+/// by computing the convex-polyhedron shape of the linalg.gneric iteration
+/// space from shapes of all the operands (inputs and outputs).
+///
+struct UnfoldProjectedPermutation : public OpRewritePattern<GenericOp> {
+ using OpRewritePattern<GenericOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(GenericOp genericOp,
+ PatternRewriter &rewriter) const override;
+};
+
+/// For the given `map` determine what dimensions are transposed
+/// and what dimensions are broadcasted.
+/// Returns :
+/// `isTransposed, isBroadcast,
+/// transpose-permutation, broadcast-dimensions`
+///
+std::tuple<bool, bool, SmallVector<int64_t>, SmallVector<int64_t>>
+computeTransposeBroadcast(AffineMap &map) {
+ assert(map.isProjectedPermutation(false) && "not a projection");
+ int64_t minorSize = map.getNumResults();
+
+ SmallVector<int64_t> minorResult;
+ for (int64_t i = 0; i < minorSize; ++i) {
+ auto expr = cast<AffineDimExpr>(map.getResults()[i]);
+ minorResult.push_back(expr.getPosition());
+ }
+
+ // If dims are not monotonically increasing then transpose is present.
+ SmallVector<int64_t> sortedResMap(minorResult);
+ std::sort(sortedResMap.begin(), sortedResMap.end());
+ bool hasTranspose = !std::equal(minorResult.begin(), minorResult.end(),
+ sortedResMap.begin(), sortedResMap.end());
+
+ // Walk the sorted map result to determine which dimensions are broadcasted.
+ SmallVector<int64_t> broadcast;
+ for (int64_t i = 0, j = 0; i < map.getNumInputs(); ++i) {
+ if (j < minorSize && sortedResMap[j] == i) {
+ j++;
+ continue;
+ }
+ broadcast.push_back(i);
+ }
+ bool hasBroadcast = !broadcast.empty();
+
+ /// Consider an operand `x : tensor<7x8x9>` of a genericOp that has
+ /// affine map `affine_map<(d0, d1, d2, d3, d4) -> (d2, d3, d1)>`
+ /// `x`s access is both transposed and brodcast. But when specifying
+ /// the `linalg.transpose(x : tensor<7x8x9>)` the dimensions need to be
+ /// specified as `affine_map<(d0,d1,d2) -> (d1, d2, d0)` instead of
+ /// refering to d3, d4. Therefore, re-base the transpose dimensions so
+ /// that they start from d0.
+ std::map<int64_t, int64_t> minorMap;
+ for (int64_t i = 0; i < minorSize; ++i)
+ minorMap.insert({sortedResMap[i], i});
+
+ // Re-map the dimensions.
+ SmallVector<int64_t> remappedResult(minorSize);
+ for (int64_t i = 0; i < minorSize; ++i)
+ remappedResult[i] = minorMap[minorResult[i]];
+
+ /// Calculate the permutation for the transpose.
+ SmallVector<int64_t> permutation(minorSize);
+ for (unsigned i = 0; i < minorSize; ++i) {
+ permutation[remappedResult[i]] = i;
+ }
+
+ return {hasTranspose, hasBroadcast, permutation, broadcast};
+}
+
+LogicalResult
+UnfoldProjectedPermutation::matchAndRewrite(GenericOp op,
+ PatternRewriter &rewriter) const {
+ if (!op.hasPureTensorSemantics() || op.isSingleInputOutput() ||
+ op.isSingleYieldOp() || !op.isAllParallelLoops())
+ return failure();
+
+ // All maps need to be projected permutations.
+ for (auto &opOperand : op->getOpOperands()) {
+ auto map = op.getMatchingIndexingMap(&opOperand);
+ if (!map.isProjectedPermutation(false))
+ return failure();
+ }
+
+ // Currently we handle only static shapes.
+ for (auto &operand : op->getOpOperands()) {
+ auto opType = cast<RankedTensorType>(operand.get().getType());
+ for (auto size : opType.getShape())
+ if (size == ShapedType::kDynamic)
+ return failure();
+ }
+
+ auto outputShape = op.getStaticLoopRanges();
+
+ auto loc = op.getLoc();
+ bool isChanged = false;
+ SmallVector<Value> newInitValues = op.getDpsInputs();
+ SmallVector<AffineMap> newMap = op.getIndexingMapsArray();
+
+ // Walk over each input operand and unfold if it is transposed, broadcast
+ // or mix of two via operand's affine-map.
+ for (int64_t i = 0; i < op.getNumDpsInputs(); ++i) {
+ auto &map = newMap[i];
+ auto inputRTType = cast<RankedTensorType>(newInitValues[i].getType());
+ auto elType = inputRTType.getElementType();
+
+ /// Nothing to do if map is already an identity.
+ if (map.isIdentity())
+ continue;
+
+ auto [hasTranspose, hasBroadcast, permutation, broadcastedDims] =
+ computeTransposeBroadcast(map);
+
+ if (hasTranspose) {
+ /// linalg.transpose permutes the dimensions of input using
+ /// rule: dim(result, i) = dim(input, permutation[i])
+ SmallVector<int64_t> transposedShape(map.getNumResults());
+ for (int64_t i = 0; i < map.getNumResults(); ++i)
+ transposedShape[i] = inputRTType.getShape()[permutation[i]];
+
+ Value emptyTensor =
+ rewriter.create<tensor::EmptyOp>(loc, transposedShape, elType);
+
+ auto transposeOp = rewriter.create<TransposeOp>(loc, newInitValues[i],
+ emptyTensor, permutation);
+ newInitValues[i] = transposeOp->getResult(0);
+ isChanged = true;
+ }
+
+ // Does it require broadcast
----------------
banach-space wrote:
[nit] Similar comment for `hasTranspose`
https://github.com/llvm/llvm-project/pull/114704
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