b6eb26fd0e
There are several pieces of pattern rewriting infra in IR/ that really shouldn't be there. This revision moves those pieces to a better location such that they are easier to evolve in the future(e.g. with PDL). More concretely this revision does the following: * Create a Transforms/GreedyPatternRewriteDriver.h and move the apply*andFold methods there. The definitions for these methods are already in Transforms/ so it doesn't make sense for the declarations to be in IR. * Create a new lib/Rewrite library and move PatternApplicator there. This new library will be focused on applying rewrites, and will also include compiling rewrites with PDL. Differential Revision: https://reviews.llvm.org/D89103
966 lines
43 KiB
C++
966 lines
43 KiB
C++
//===- Fusion.cpp - Implementation of linalg Fusion -----------------------===//
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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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//
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// This file implements the linalg dialect Fusion on tensors operations pass.
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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
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#include "mlir/Dialect/Linalg/IR/LinalgTypes.h"
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#include "mlir/Dialect/Linalg/Passes.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/IR/AffineExpr.h"
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#include "mlir/IR/AffineMap.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Support/LLVM.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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using namespace mlir;
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using namespace mlir::linalg;
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/// Implementation of fusion of generic ops and indexed_generic ops.
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// struct FuseGenericOpsOnTensors {
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static bool areTensorOpsFusable(LinalgOp producer, LinalgOp consumer,
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unsigned consumerIdx) {
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// Producer and consumer must have tensor semantics.
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if (!producer.hasTensorSemantics() || !consumer.hasTensorSemantics())
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return false;
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// Verify that
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// - the producer has all "parallel" iterator type.
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if (producer.getNumParallelLoops() != producer.getNumLoops())
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return false;
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// Get the consumer index map. The number of results of the consumer index
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// map must match the number of loops of the producer.
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AffineMap consumerIndexMap = consumer.getIndexingMap(consumerIdx);
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if (consumerIndexMap.getNumResults() != producer.getNumLoops())
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return false;
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// Finally the index_map for the result must be invertible. For now just
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// verify it is a permutation.
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AffineMap producerResultIndexMap = producer.getOutputIndexingMap(0);
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return producerResultIndexMap.isPermutation();
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}
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/// Append to `fusedOpIndexingMapAttrs` the indexing maps for the operands of
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/// the `producer` to use in the fused operation given the indexing map of the
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/// result of the producer in the consumer.
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static void getIndexingMapOfProducerOperandsInFusedOp(
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LinalgOp producer, AffineMap fusedConsumerArgIndexMap,
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SmallVectorImpl<Attribute> &fusedOpIndexingMapAttrs) {
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// The indexing map in the consumer op (fusedConsumerArgIndexMap) is a map
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// from consumer loop -> consumer arg tensor index/producer result tensor
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// index. The fused loop is same as the consumer loop. For each producer arg
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// the indexing map to be computed is a map from consumer loop -> producer
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// arg tensor index.
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AffineMap producerResultIndexMap = producer.getOutputIndexingMap(0);
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// producerResultIndexMap is a map from producer loop -> tensor index.
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// Compute the inverse to get map from tensor index -> producer loop.
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// The inverse is a map from producer result tensor index -> producer loop.
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AffineMap invProducerResultIndexMap =
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inversePermutation(producerResultIndexMap);
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assert(invProducerResultIndexMap &&
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"expected producer result indexig map to be invertible");
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for (unsigned argNum : llvm::seq<unsigned>(0, producer.getNumInputs())) {
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// argMap is a map from producer loop -> producer arg tensor index.
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AffineMap argMap = producer.getInputIndexingMap(argNum);
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// Compose argMap with invProducerResultIndexMap to get a map from
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// producer result tensor index -> producer arg tensor index.
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AffineMap t1 = argMap.compose(invProducerResultIndexMap);
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// Compose t1 with fusedConsumerArgIndexMap gives an indexing map from
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// consumer loop/ fused loop -> producer arg tensor index.
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AffineMap indexingMap = t1.compose(fusedConsumerArgIndexMap);
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fusedOpIndexingMapAttrs.push_back(AffineMapAttr::get(indexingMap));
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}
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}
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/// Generate the region of the fused tensor operation. The region of the fused
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/// op must be empty.
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static void generateFusedTensorOpRegion(PatternRewriter &rewriter,
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Operation *fusedOp, LinalgOp producer,
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LinalgOp consumer,
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AffineMap consumerToProducerLoopsMap,
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unsigned consumerIdx, unsigned nloops) {
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// Build the region of the fused op.
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Block &producerBlock = producer.getOperation()->getRegion(0).front();
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Block &consumerBlock = consumer.getOperation()->getRegion(0).front();
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Block *fusedBlock = new Block();
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fusedOp->getRegion(0).push_back(fusedBlock);
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BlockAndValueMapping mapper;
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OpBuilder::InsertionGuard guard(rewriter);
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rewriter.setInsertionPointToStart(fusedBlock);
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// The block arguments are
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// [index_0, index_1, ... ,
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// consumer_operand_0, ... , consumer_operand_(`consumerIdx`-1),
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// producer_operand_0, ... , producer_operand_(n-1)],
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// consumer_operand_(`consumerIdx`), .. consumer_operand_(m-1)]
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// , where n is the number of producer's operand and m is the number
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// consumer's operand.
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// If both `numProducerIndices` and `numConsumerIndices` are zero, this is a
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// generic op. In this case, there are no indices in block arguments.
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unsigned numProducerIndices = isa<IndexedGenericOp>(producer.getOperation())
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? producer.getNumLoops()
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: 0;
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unsigned numConsumerIndices = isa<IndexedGenericOp>(consumer.getOperation())
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? consumer.getNumLoops()
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: 0;
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unsigned numFusedOpIndices =
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(isa<IndexedGenericOp>(producer.getOperation()) ||
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isa<IndexedGenericOp>(consumer.getOperation()))
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? std::max(producer.getNumLoops(), consumer.getNumLoops())
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: 0;
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// Firstly, add all the indices to the block arguments.
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for (unsigned i = 0, e = numFusedOpIndices; i < e; ++i)
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fusedBlock->addArgument(rewriter.getIndexType());
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// Map the arguments for the unmodified args from the consumer.
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for (auto consumerArg : llvm::enumerate(consumerBlock.getArguments())) {
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if (consumerArg.index() == consumerIdx + numConsumerIndices) {
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// Map the arguments for the args from the producer.
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for (auto producerArg : llvm::enumerate(producerBlock.getArguments())) {
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// If producer is an indexed_generic op, map the indices from consumer
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// loop to producer loop (because the fusedOp is built based on
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// consumer's perspective).
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if (producerArg.index() < numProducerIndices) {
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auto newIndex = rewriter.create<mlir::AffineApplyOp>(
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producer.getLoc(),
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consumerToProducerLoopsMap.getSubMap(producerArg.index()),
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fusedBlock->getArguments().take_front(numFusedOpIndices));
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mapper.map(producerArg.value(), newIndex);
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} else {
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mapper.map(producerArg.value(),
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fusedBlock->addArgument(producerArg.value().getType()));
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}
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}
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continue;
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}
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// If consumer is an indexed_generic op, map the indices to the block
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// arguments directly. Otherwise, add the same type of arugment and map to
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// it.
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if (consumerArg.index() < numConsumerIndices) {
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mapper.map(consumerArg.value(),
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fusedBlock->getArgument(consumerArg.index()));
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} else {
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mapper.map(consumerArg.value(),
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fusedBlock->addArgument(consumerArg.value().getType()));
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}
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}
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// Add operations from producer (except the yield operation) to the fused
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// op.
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for (auto &op : producerBlock.getOperations()) {
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if (auto yieldOp = dyn_cast<linalg::YieldOp>(op)) {
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// Lookup the value the yield operation is mapped to.
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Value yieldVal = yieldOp.getOperand(0);
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if (Value clonedVal = mapper.lookupOrNull(yieldVal))
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mapper.map(consumerBlock.getArgument(consumerIdx + numConsumerIndices),
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clonedVal);
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continue;
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}
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rewriter.clone(op, mapper);
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}
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for (auto &op : consumerBlock.getOperations())
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rewriter.clone(op, mapper);
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}
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static Optional<SmallVector<Value, 1>>
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fuseTensorOpsImpl(LinalgOp producer, LinalgOp consumer, unsigned consumerIdx,
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PatternRewriter &rewriter,
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OperationFolder *folder = nullptr) {
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if (!areTensorOpsFusable(producer, consumer, consumerIdx))
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return llvm::None;
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unsigned numFusedOperands =
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producer.getNumInputs() + consumer.getNumInputs() - 1;
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// Compute the fused operands list,
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SmallVector<Value, 2> fusedOperands;
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fusedOperands.reserve(numFusedOperands);
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auto consumerOperands = consumer.getInputs();
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auto producerOperands = producer.getInputs();
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fusedOperands.assign(consumerOperands.begin(),
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std::next(consumerOperands.begin(), consumerIdx));
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fusedOperands.append(producerOperands.begin(), producerOperands.end());
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fusedOperands.append(std::next(consumerOperands.begin(), consumerIdx + 1),
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consumerOperands.end());
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// Compute indexing_maps for the fused operation. The indexing_maps for the
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// operands of the consumers that arent fused are the same. The
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// indexing_maps for the producers need to be computed based on the
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// indexing_map of the operand at consumerIdx in the consumer.
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SmallVector<Attribute, 4> fusedIndexMaps;
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auto consumerIndexMaps = consumer.indexing_maps();
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fusedIndexMaps.reserve(fusedOperands.size() + consumer.getNumOutputs());
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fusedIndexMaps.assign(consumerIndexMaps.begin(),
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std::next(consumerIndexMaps.begin(), consumerIdx));
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// Compute indexing maps for the producer args in the fused operation.
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getIndexingMapOfProducerOperandsInFusedOp(
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producer, consumer.getInputIndexingMap(consumerIdx), fusedIndexMaps);
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// Append the indexing maps for the remaining consumer operands.
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fusedIndexMaps.append(std::next(consumerIndexMaps.begin(), consumerIdx + 1),
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consumerIndexMaps.end());
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// Generate the fused op.
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// Tensor-level fusion is only on ops without initTensors and outputBuffers.
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LinalgOp fusedOp;
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if (isa<GenericOp>(producer.getOperation()) &&
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isa<GenericOp>(consumer.getOperation())) {
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fusedOp = rewriter
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.create<GenericOp>(consumer.getLoc(),
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consumer.getOperation()->getResultTypes(),
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/*inputs=*/fusedOperands,
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/*outputBuffers=*/ValueRange{},
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/*initTensors=*/ValueRange{},
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rewriter.getArrayAttr(fusedIndexMaps),
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consumer.iterator_types(),
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/*doc=*/nullptr,
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/*library_call=*/nullptr,
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/*symbol_source=*/nullptr)
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.getOperation();
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} else {
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fusedOp =
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rewriter
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.create<IndexedGenericOp>(consumer.getLoc(),
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consumer.getOperation()->getResultTypes(),
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/*inputs=*/fusedOperands,
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/*outputBuffers=*/ValueRange{},
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/*initTensors=*/ValueRange{},
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rewriter.getArrayAttr(fusedIndexMaps),
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consumer.iterator_types(),
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/*doc=*/nullptr,
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/*library_call=*/nullptr,
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/*symbol_source=*/nullptr)
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.getOperation();
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}
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// Construct an AffineMap from consumer loops to producer loops.
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// consumer loop -> tensor index
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AffineMap consumerResultIndexMap = consumer.getInputIndexingMap(consumerIdx);
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// producer loop -> tensor index
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AffineMap producerResultIndexMap = producer.getOutputIndexingMap(0);
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// tensor index -> producer loop
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AffineMap invProducerResultIndexMap =
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inversePermutation(producerResultIndexMap);
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assert(invProducerResultIndexMap &&
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"expected producer result indexig map to be invertible");
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// consumer loop -> producer loop
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AffineMap consumerToProducerLoopsMap =
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invProducerResultIndexMap.compose(consumerResultIndexMap);
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generateFusedTensorOpRegion(rewriter, fusedOp.getOperation(), producer,
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consumer, consumerToProducerLoopsMap, consumerIdx,
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consumer.getNumLoops());
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return SmallVector<Value, 1>(fusedOp.getOperation()->getResults());
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}
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/// Linearize the expressions in `sourceMap` based on the `reassociationMaps`
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/// provided, given the shape of the source tensor that corresponds to the
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/// `sourceMap`. Note that this implicitly assumes that the tensors dimensions
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/// are "row-major" ordered logically.
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///
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/// For example:
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///
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/// %0 = op ... : tensor<?x?x4x5xf32>
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/// with output index_map `affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>`
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///
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/// and reshape:
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/// %1 = linalg.tensor_reshape %0 [affine_map<(i, j, k, l) -> (i)>,
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/// affine_map<(i, j, k, l) -> (j, k, l)>] :
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/// tensor<?x?x4x5xf32> into tensor<?x?xf32>
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///
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/// would be rewritten into:
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/// %0 = op ... : tensor<?x?x4x5xf32>
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/// with output index_map
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/// `affine_map<(d0, d1, d2, d3) -> (d0, d1 * 20 + d2 * 5 + d3)>`
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static AffineMap linearizeCollapsedDims(AffineMap sourceMap,
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ArrayRef<int64_t> sourceShape,
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ArrayRef<AffineMap> reassociationMaps) {
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SmallVector<AffineExpr, 4> resultExprs;
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resultExprs.reserve(reassociationMaps.size());
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ArrayRef<AffineExpr> sourceExprs = sourceMap.getResults();
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MLIRContext *context = sourceMap.getContext();
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// Compute the result exprs based on the reassociation maps.
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for (AffineMap map : reassociationMaps) {
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ArrayRef<AffineExpr> collapsedDims = map.getResults();
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// Assume that they are in-order and contiguous (already checked in
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// verifier).
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assert(!collapsedDims.empty());
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unsigned startDim =
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collapsedDims.front().cast<AffineDimExpr>().getPosition();
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AffineExpr linearizedExpr = makeCanonicalStridedLayoutExpr(
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sourceShape.slice(startDim, collapsedDims.size()),
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sourceExprs.slice(startDim, collapsedDims.size()), context);
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resultExprs.push_back(linearizedExpr);
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}
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return AffineMap::get(sourceMap.getNumDims(), sourceMap.getNumSymbols(),
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resultExprs, context);
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}
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/// Checks if the `reshapeOp` can be fused with it consumer (if `asProducer` is
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/// true) or its producer (if `asProducer` is false) given the indexing map at
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/// its use.
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static bool isTensorReshapeOpFoldableByLinearization(TensorReshapeOp reshapeOp,
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AffineMap useIndexMap,
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bool asProducer) {
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RankedTensorType returnType = reshapeOp.getResultType();
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RankedTensorType operandType = reshapeOp.getSrcType();
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// Reshape is fusable with its consumer (i.e. reshape as a producer) when its
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// operand is of lesser rank than the result. Fusing when operand has higher
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// rank will require use of mods and divs in the indexing maps of the fused op
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// which would make it non-invertible. Similarly reshape is fused with its
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// producer (i.e. reshape as consumer) only if the return type has lesser
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// rank.
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if ((asProducer && reshapeOp.getSrcType().hasStaticShape() &&
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returnType.getRank() < operandType.getRank()) ||
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(!asProducer && reshapeOp.getResultType().hasStaticShape() &&
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operandType.getRank() < returnType.getRank()))
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return false;
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return useIndexMap.isPermutation();
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}
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/// Based on the type of `op` create a linalg op of the same type, i.e. if `op`
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/// is a linalg.generic operation, the create a `linalg.generic` operation with
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/// the given `args`. Expects `op` to be `linalg.generic` or
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/// `linalg.indexed_generic`.
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template <typename... Args>
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static LinalgOp createLinalgOpOfSameType(LinalgOp op, PatternRewriter &rewriter,
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Args... args) {
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if (isa<GenericOp>(op.getOperation()))
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return cast<LinalgOp>(rewriter.create<GenericOp>(args...).getOperation());
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if (isa<IndexedGenericOp>(op.getOperation()))
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return cast<LinalgOp>(
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rewriter.create<IndexedGenericOp>(args...).getOperation());
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llvm_unreachable(
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"expected only linalg.generic or linalg.indexed_generic ops");
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return nullptr;
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}
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/// Conditions for folding a generic/indexed-generic operation with a reshape op
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/// by expanding the iteration space dimensionality for tensor operations. These
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/// are preconditions assumed by `foldReshapeByDimExpansion` which implements
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/// the following fusion pattern.
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///
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/// Consider
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///
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/// %c = linalg.generic ins(%a, %b : memref<?x?x?xf32>, memref<?x?xf32>)
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/// indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d0, d2)>,
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/// affine_map<(d0, d1, d2) -> (d1, d2)>,
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/// affine_map<(d0, d1, d2) -> (d0, d2, d1)>]
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/// %d = linalg.tensor_reshape %c
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/// [affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1)>,
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/// affine_map<(d0, d1, d2, d3, d4, d5) -> (d2)>,
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/// affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>]
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/// : tensor<?x?x?xf32> into tensor<?x?x?x?x?x?xf32>
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///
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/// The reshape can be folded into the `linalgOp` if the
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/// generic/indexed-generic op loop dimensionality is increased to match the
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/// result (operand) of the tensor_reshape when the reshape is expanding
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/// (folding). The indexing_map of the fused tensor in the `linalgOp` and the
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/// reassociation map helps compute the indexing maps of the modified op. For
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/// the above example, based on the reassociation map it can be concluded that
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///
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/// - The loop used to access the first dimension of the fused tensor is split
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/// into two.
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/// - The loop used to access the second dimension of the fused tensor is kept
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/// as is.
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/// - The loop used to access the third dimension of the fused tensor is split
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/// into three.
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///
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/// i.e. (e0, e1, e2, e3, e4) is the domain of the indexing map of the modified
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/// op, then
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///
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/// d0 -> e0, e1
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/// d1 -> e2, e3, e4
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/// d2 -> e5
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///
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/// substituting this, the generic op can be rewritten as
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///
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/// %d = linalg.generic ins(%0, %1 : )
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/// indexing_maps =
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/// [affine_map<(e0, e1, e2, e3, e4, e5) -> (e2, e3, e4, e0, e1, e5)>,
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/// affine_map<(e0, e1, e2, e3, e4, e5) -> (e2, e3, e4, e5)>,
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/// affine_map<(e0, e1, e2, e3, e4, e5) -> (e0, e1, e5, e2, e3, e4)>]
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///
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/// Since operands to the linalg generic are now 5D, reshapes can be introduced
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/// to make it consistent
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///
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/// %0 = linalg.tensor_reshape %a
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/// [affine_map<(e0, e1, e2, e3, e4, e5) -> (e0, e1, e2),
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/// affine_map<(e0, e1, e2, e3, e4, e5) -> (e3, e4),
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/// affine_map<(e0, e1, e2, e3, e4, e5) -> (e5)]
|
|
/// : tensor<?x?x?xf32> into tensor<?x?x?x?x?x?xf32>
|
|
/// %1 = linalg.tensor_reshape %b
|
|
/// [affine_map<(e0, e1, e2, e3) -> (e0, e1, e2),
|
|
/// affine_map<(e0, e1, e2, e3) -> (e3)]
|
|
/// : tensor<?x?x?xf32> into tensor<?x?x?x?xf32>
|
|
///
|
|
/// The added reshapes are again expanding patterns, so they will get fused
|
|
/// with its producers if possible.
|
|
static bool isFusableWithReshapeByDimExpansion(LinalgOp linalgOp,
|
|
unsigned fusedTensorIndex) {
|
|
// Is fusable only if:
|
|
// - The linalgOp is a generic op.
|
|
// - All the indexing maps for operands in linalgOp are projected
|
|
// permutations.
|
|
// - The indexing map at the position representing the fused tensor is a
|
|
// permutation.
|
|
// - All the loops in linalgOp are parallel loops.
|
|
return isa<GenericOp>(linalgOp.getOperation()) &&
|
|
linalgOp.hasTensorSemantics() &&
|
|
llvm::all_of(linalgOp.indexing_maps().getValue().take_front(
|
|
linalgOp.getNumInputs()),
|
|
[](Attribute attr) {
|
|
return attr.cast<AffineMapAttr>()
|
|
.getValue()
|
|
.isProjectedPermutation();
|
|
}) &&
|
|
linalgOp.getIndexingMap(fusedTensorIndex).isPermutation() &&
|
|
llvm::all_of(linalgOp.iterator_types(), [](Attribute attr) {
|
|
return attr.cast<StringAttr>().getValue() ==
|
|
getParallelIteratorTypeName();
|
|
});
|
|
}
|
|
|
|
/// Implements the fusion of a tensor_reshape op and a generic/indexed_generic
|
|
/// op as explained in `isFusableWithReshapeByExpansion`. Assumes that those
|
|
/// conditions have been satisfied.
|
|
static Optional<SmallVector<Value, 1>>
|
|
fuseWithReshapeByExpansion(LinalgOp linalgOp, TensorReshapeOp reshapeOp,
|
|
unsigned fusedTensorIndex, PatternRewriter &rewriter,
|
|
OperationFolder *folder = nullptr) {
|
|
assert(isFusableWithReshapeByDimExpansion(linalgOp, fusedTensorIndex) &&
|
|
"preconditions for fuse operation failed");
|
|
// Check if reshape is expanding or collapsing.
|
|
bool isExpanding =
|
|
reshapeOp.getSrcType().getRank() < reshapeOp.getResultType().getRank();
|
|
RankedTensorType expandedType =
|
|
isExpanding ? reshapeOp.getResultType() : reshapeOp.getSrcType();
|
|
RankedTensorType foldedType =
|
|
isExpanding ? reshapeOp.getSrcType() : reshapeOp.getResultType();
|
|
AffineMap fusedIndexMap = linalgOp.getIndexingMap(fusedTensorIndex);
|
|
|
|
// The reshape is folding/expanding consecutive dimensions. Given the indexing
|
|
// map of the fused tensor find the number of dimensions each of the loops of
|
|
// the original op is expanded into. Also record the shape of the expanded
|
|
// dimensions.
|
|
ArrayRef<int64_t> expandedShape = expandedType.getShape();
|
|
SmallVector<unsigned, 4> numFoldedDims(foldedType.getRank(), 0);
|
|
SmallVector<SmallVector<int64_t, 4>, 4> expandedDimsShape(
|
|
expandedType.getRank());
|
|
auto reassociationMaps = reshapeOp.getReassociationMaps();
|
|
for (auto resultExpr : llvm::enumerate(fusedIndexMap.getResults())) {
|
|
unsigned pos = resultExpr.value().cast<AffineDimExpr>().getPosition();
|
|
AffineMap foldedDims = reassociationMaps[resultExpr.index()];
|
|
numFoldedDims[pos] = foldedDims.getNumResults();
|
|
ArrayRef<int64_t> shape = expandedShape.slice(
|
|
foldedDims.getResult(0).cast<AffineDimExpr>().getPosition(),
|
|
numFoldedDims[pos]);
|
|
expandedDimsShape[pos].assign(shape.begin(), shape.end());
|
|
}
|
|
|
|
// The remapping of the indices is then the prefix sum (inclusive) of the
|
|
// numFoldedDims.
|
|
SmallVector<unsigned, 4> remapping(numFoldedDims.size() + 1, 0);
|
|
unsigned sum = 0;
|
|
for (auto numFoldedDim : llvm::enumerate(numFoldedDims)) {
|
|
sum += numFoldedDim.value();
|
|
remapping[numFoldedDim.index() + 1] = sum;
|
|
}
|
|
|
|
SmallVector<AffineMap, 4> expandedOpIndexingMaps;
|
|
// Compute the modified indexing maps by replacing every loop (AffineDimExpr)
|
|
// in the original indexing map with the sequence of loops that it is expanded
|
|
// to.
|
|
for (AffineMap indexingMap : linalgOp.getIndexingMaps()) {
|
|
SmallVector<AffineExpr, 4> newExprs;
|
|
for (AffineExpr expr : indexingMap.getResults()) {
|
|
unsigned pos = expr.cast<AffineDimExpr>().getPosition();
|
|
for (unsigned newPos :
|
|
llvm::seq<unsigned>(remapping[pos], remapping[pos + 1])) {
|
|
newExprs.push_back(rewriter.getAffineDimExpr(newPos));
|
|
}
|
|
}
|
|
expandedOpIndexingMaps.push_back(
|
|
AffineMap::get(remapping.back(), indexingMap.getNumSymbols(), newExprs,
|
|
rewriter.getContext()));
|
|
}
|
|
|
|
// The operands of the expanded op are computed by reshaping the original
|
|
// operands. The reshape depends on the ordering of the loop used to access
|
|
// the tensor in the original operation, and are expanded into as many
|
|
// dimensions as the loop is expanded into (as computed by `remapping`).
|
|
auto getReshapeInfo =
|
|
[&](AffineMap operandIndexingMap,
|
|
SmallVectorImpl<ReassociationIndices> &reassociation,
|
|
SmallVectorImpl<int64_t> &expandedOpOperandShape) {
|
|
unsigned reshapeDims = 0;
|
|
for (AffineExpr expr : operandIndexingMap.getResults()) {
|
|
unsigned origDim = expr.cast<AffineDimExpr>().getPosition();
|
|
auto foldedDims = llvm::seq<int64_t>(
|
|
reshapeDims, reshapeDims + numFoldedDims[origDim]);
|
|
reassociation.emplace_back(foldedDims.begin(), foldedDims.end());
|
|
expandedOpOperandShape.append(expandedDimsShape[origDim].begin(),
|
|
expandedDimsShape[origDim].end());
|
|
reshapeDims += numFoldedDims[origDim];
|
|
}
|
|
};
|
|
SmallVector<Value, 4> expandedOpOperands;
|
|
for (auto operand : llvm::enumerate(linalgOp.getInputs())) {
|
|
if (operand.index() == fusedTensorIndex) {
|
|
expandedOpOperands.push_back(reshapeOp.src());
|
|
continue;
|
|
}
|
|
AffineMap indexingMap = linalgOp.getIndexingMap(operand.index());
|
|
SmallVector<ReassociationIndices, 4> reassociation;
|
|
SmallVector<int64_t, 4> expandedOperandShape;
|
|
getReshapeInfo(indexingMap, reassociation, expandedOperandShape);
|
|
Type expandedOperandType = RankedTensorType::get(
|
|
expandedOperandShape,
|
|
operand.value().getType().cast<ShapedType>().getElementType());
|
|
if (expandedOperandType != operand.value().getType()) {
|
|
expandedOpOperands.push_back(rewriter.create<TensorReshapeOp>(
|
|
linalgOp.getLoc(), expandedOperandType, operand.value(),
|
|
reassociation));
|
|
} else {
|
|
expandedOpOperands.push_back(operand.value());
|
|
}
|
|
}
|
|
SmallVector<Type, 1> resultTypes;
|
|
SmallVector<SmallVector<ReassociationIndices, 4>, 1> resultReassociation;
|
|
for (auto result : llvm::enumerate(linalgOp.getOperation()->getResults())) {
|
|
AffineMap indexingMap =
|
|
linalgOp.getIndexingMap(linalgOp.getNumInputs() + result.index());
|
|
SmallVector<ReassociationIndices, 4> reassociation;
|
|
SmallVector<int64_t, 4> expandedResultShape;
|
|
getReshapeInfo(indexingMap, reassociation, expandedResultShape);
|
|
resultTypes.push_back(RankedTensorType::get(
|
|
expandedResultShape,
|
|
result.value().getType().cast<ShapedType>().getElementType()));
|
|
resultReassociation.emplace_back(std::move(reassociation));
|
|
}
|
|
|
|
// The iterator types of the expanded op are all parallel.
|
|
SmallVector<StringRef, 4> iteratorTypes(remapping.back(),
|
|
getParallelIteratorTypeName());
|
|
|
|
LinalgOp fusedOp = createLinalgOpOfSameType(
|
|
linalgOp, rewriter, linalgOp.getLoc(), resultTypes,
|
|
/*inputs=*/expandedOpOperands,
|
|
/*outputBuffers=*/ValueRange{},
|
|
/*initTensors=*/ValueRange{}, expandedOpIndexingMaps, iteratorTypes);
|
|
Region &fusedRegion = fusedOp.getOperation()->getRegion(0);
|
|
// TODO: Add support for indexed generic op, which would need mapping the
|
|
// expanded dimensions to the original dimension arguments.
|
|
rewriter.cloneRegionBefore(linalgOp.getOperation()->getRegion(0), fusedRegion,
|
|
fusedRegion.begin());
|
|
|
|
// Reshape the result values to their original shape if this is a collapsing
|
|
// reshape folded into its consumer.
|
|
SmallVector<Value, 1> resultVals;
|
|
for (auto result : llvm::enumerate(linalgOp.getOperation()->getResults())) {
|
|
if (!isExpanding &&
|
|
resultTypes[result.index()] != result.value().getType()) {
|
|
resultVals.push_back(rewriter.create<TensorReshapeOp>(
|
|
linalgOp.getLoc(), result.value().getType(),
|
|
fusedOp.getOperation()->getResult(result.index()),
|
|
resultReassociation[result.index()]));
|
|
} else {
|
|
resultVals.push_back(fusedOp.getOperation()->getResult(result.index()));
|
|
}
|
|
}
|
|
// Assuming a single result.
|
|
return resultVals;
|
|
}
|
|
|
|
namespace {
|
|
|
|
/// Pattern to fold tensor_reshape op with its consumer by using the source of
|
|
/// the reshape op as the operand in the consumer (instead of the result of the
|
|
/// tensor_reshapeop) when the tensor_reshape op is collapsing. The
|
|
/// corresponding index map in the consumer needs to be modified to linearize
|
|
/// the folded dimension.
|
|
///
|
|
/// For example,
|
|
///
|
|
/// #map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
|
|
/// %0 = linalg.tensor_reshape %arg0
|
|
/// [affine_map<(i, j, k, l) -> (i)>, affine_map<(i, j, k, l) -> (j, k)>,
|
|
/// affine_map<(i, j, k, l) -> (l)>]
|
|
/// tensor<?x?x?xf32> into tensor<?x?x4x?xf32>
|
|
/// %1 = linalg.generic { indexing_maps = [#map0, #map0, #map0], ... }
|
|
/// ins(%0, %arg1 : tensor<?x?x4x?xf32>, tensor<?x?x4x?xf32>) ...
|
|
/// -> tensor<?x?x4x?xf32>
|
|
///
|
|
/// can be folded into
|
|
///
|
|
/// #map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1 * 4 + d2, d3)>
|
|
/// #map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
|
|
/// %0 = linalg.generic { indexing_maps = [#map0, #map1, #map1] ... }
|
|
/// ins(%arg0, %arg1 : tensor<?x?x?xf32>, tensor<?x?x4x?xf32>) ...
|
|
/// -> tensor<?x?x4x?xf32>
|
|
template <typename LinalgOpTy>
|
|
struct FoldProducerReshapeOpByLinearization
|
|
: public OpRewritePattern<LinalgOpTy> {
|
|
using OpRewritePattern<LinalgOpTy>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(LinalgOpTy op,
|
|
PatternRewriter &rewriter) const override {
|
|
if (!op.hasTensorSemantics())
|
|
return failure();
|
|
LinalgOp linalgOp = cast<LinalgOp>(op.getOperation());
|
|
for (auto operand : llvm::enumerate(linalgOp.getInputs())) {
|
|
TensorReshapeOp reshapeOp =
|
|
operand.value().getDefiningOp<TensorReshapeOp>();
|
|
if (!reshapeOp ||
|
|
!isTensorReshapeOpFoldableByLinearization(
|
|
reshapeOp, linalgOp.getInputIndexingMap(operand.index()),
|
|
/*asProducer =*/true))
|
|
continue;
|
|
|
|
// Compute the fused operands list,
|
|
SmallVector<Value, 2> fusedOperands(linalgOp.getInputs());
|
|
fusedOperands[operand.index()] = reshapeOp.src();
|
|
|
|
// Compute indexing_maps for the fused operation. The indexing_maps for
|
|
// the operands of the consumers that arent fused are the same.
|
|
SmallVector<AffineMap, 4> fusedIndexMaps = llvm::to_vector<4>(
|
|
op.indexing_maps().template getAsValueRange<AffineMapAttr>());
|
|
|
|
// Accepted consumer maps are either identity or permutation.
|
|
auto invMap = inversePermutation(fusedIndexMaps[operand.index()]);
|
|
|
|
// Compute the indexing map to use for the result of the producer.
|
|
AffineMap modifiedMap =
|
|
linearizeCollapsedDims(invMap, reshapeOp.getResultType().getShape(),
|
|
reshapeOp.getReassociationMaps());
|
|
for (AffineExpr expr : modifiedMap.getResults()) {
|
|
if (!expr.isPureAffine())
|
|
return failure();
|
|
}
|
|
fusedIndexMaps[operand.index()] = modifiedMap;
|
|
|
|
// Further check that the resulting index maps can be fused and
|
|
// inverted. Without this the resultant op is not legal.
|
|
if (!inversePermutation(concatAffineMaps(fusedIndexMaps)))
|
|
return op.emitRemark("fused op loop bound computation failed");
|
|
|
|
rewriter.startRootUpdate(op);
|
|
op.getOperation()->setOperands(fusedOperands);
|
|
op.indexing_mapsAttr(rewriter.getAffineMapArrayAttr(fusedIndexMaps));
|
|
rewriter.finalizeRootUpdate(op);
|
|
if (reshapeOp.use_empty())
|
|
rewriter.eraseOp(reshapeOp);
|
|
return success();
|
|
}
|
|
return op.emitRemark("no fusion candidates found");
|
|
}
|
|
};
|
|
|
|
/// Pattern to fuse a tensor_reshape op with its consumer generic op, when the
|
|
/// reshape op is collapsing dimensions. The dimensionality of the loop in the
|
|
/// consumer generic op is expanded.
|
|
struct FoldWithProducerReshapeOpByExpansion
|
|
: public OpRewritePattern<GenericOp> {
|
|
using OpRewritePattern<GenericOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(GenericOp genericOp,
|
|
PatternRewriter &rewriter) const override {
|
|
LinalgOp linalgOp = cast<LinalgOp>(genericOp.getOperation());
|
|
for (auto operand : llvm::enumerate(linalgOp.getInputs())) {
|
|
TensorReshapeOp reshapeOp =
|
|
operand.value().getDefiningOp<TensorReshapeOp>();
|
|
if (!reshapeOp)
|
|
continue;
|
|
|
|
// Fold only if
|
|
// - The tensor reshape op is folding.
|
|
// - All constraints of fusing with reshape by expansion are met.
|
|
if (reshapeOp.getSrcType().getRank() <
|
|
reshapeOp.getResultType().getRank() ||
|
|
!isFusableWithReshapeByDimExpansion(linalgOp, operand.index()))
|
|
continue;
|
|
|
|
Optional<SmallVector<Value, 1>> replacementValues =
|
|
fuseWithReshapeByExpansion(linalgOp, reshapeOp, operand.index(),
|
|
rewriter);
|
|
if (!replacementValues)
|
|
return failure();
|
|
rewriter.replaceOp(genericOp, replacementValues.getValue());
|
|
if (reshapeOp.use_empty())
|
|
rewriter.eraseOp(reshapeOp);
|
|
return success();
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
|
|
/// Pattern to fold tensor_reshape op with its producer. The corresponding index
|
|
/// map in the consumer needs to be modified to linearize the folded dimension.
|
|
struct FoldConsumerReshapeOpByLinearization
|
|
: public OpRewritePattern<TensorReshapeOp> {
|
|
using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
|
|
PatternRewriter &rewriter) const override {
|
|
LinalgOp producer = reshapeOp.src().getDefiningOp<LinalgOp>();
|
|
if (!producer ||
|
|
!isa<GenericOp, IndexedGenericOp>(producer.getOperation()) ||
|
|
!producer.hasTensorSemantics() || producer.getNumOutputs() != 1 ||
|
|
!isTensorReshapeOpFoldableByLinearization(
|
|
reshapeOp, producer.getOutputIndexingMap(0), /*asProducer =*/false))
|
|
return failure();
|
|
// The indexing_maps for the operands of the fused operation are same as
|
|
// those for the operands of the producer.
|
|
SmallVector<AffineMap, 4> fusedIndexMaps = llvm::to_vector<4>(
|
|
producer.indexing_maps().getAsValueRange<AffineMapAttr>());
|
|
|
|
auto invMap = inversePermutation(producer.getOutputIndexingMap(0));
|
|
|
|
// Compute the indexing map to use for the operand of the producer.
|
|
AffineMap modifiedMap =
|
|
linearizeCollapsedDims(invMap, reshapeOp.getSrcType().getShape(),
|
|
reshapeOp.getReassociationMaps());
|
|
for (AffineExpr expr : modifiedMap.getResults()) {
|
|
if (!expr.isPureAffine())
|
|
return reshapeOp.emitRemark("fused op indexing map is not affine");
|
|
}
|
|
fusedIndexMaps.back() = modifiedMap;
|
|
|
|
// Further check that the resulting index maps can be fused and
|
|
// inverted. Without this the resultant op is not legal.
|
|
if (!inversePermutation(concatAffineMaps(fusedIndexMaps)))
|
|
return reshapeOp.emitRemark("fused op loop bound computation failed");
|
|
|
|
LinalgOp fusedOp = createLinalgOpOfSameType(
|
|
producer, rewriter, rewriter.getUnknownLoc(), reshapeOp.getResultType(),
|
|
/*inputs=*/producer.getInputs(),
|
|
/*outputBuffers=*/ValueRange{},
|
|
/*initTensors=*/ValueRange{}, // no init tensors for now.
|
|
rewriter.getAffineMapArrayAttr(fusedIndexMaps),
|
|
producer.iterator_types(),
|
|
/*doc=*/nullptr,
|
|
/*library_call=*/nullptr,
|
|
/*symbol_source=*/nullptr);
|
|
auto &fusedRegion = fusedOp.getOperation()->getRegion(0);
|
|
rewriter.cloneRegionBefore(producer.getOperation()->getRegion(0),
|
|
fusedRegion, fusedRegion.begin());
|
|
rewriter.replaceOp(reshapeOp, fusedOp.getOperation()->getResults());
|
|
if (producer.use_empty())
|
|
rewriter.eraseOp(producer);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to fold a tensor_reshape op with its producer generic op if the
|
|
/// tensor_reshape op is expanding, by expanding the dimensionality of the loop
|
|
/// in the producer op.
|
|
struct FoldReshapeWithGenericOpByExpansion
|
|
: public OpRewritePattern<TensorReshapeOp> {
|
|
using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;
|
|
LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
|
|
PatternRewriter &rewriter) const override {
|
|
// Fold only if
|
|
// - The tensor reshape op is a expanding case.
|
|
// - All constraints of fusing with reshape by expansion are met.
|
|
if (reshapeOp.getSrcType().getRank() > reshapeOp.getResultType().getRank())
|
|
return failure();
|
|
LinalgOp producer = reshapeOp.src().getDefiningOp<LinalgOp>();
|
|
if (!producer || producer.getNumOutputs() != 1 ||
|
|
!isFusableWithReshapeByDimExpansion(producer, producer.getNumInputs()))
|
|
return failure();
|
|
Optional<SmallVector<Value, 1>> replacementValues =
|
|
fuseWithReshapeByExpansion(producer, reshapeOp, producer.getNumInputs(),
|
|
rewriter);
|
|
if (!replacementValues)
|
|
return failure();
|
|
rewriter.replaceOp(reshapeOp, replacementValues.getValue());
|
|
if (producer.use_empty())
|
|
rewriter.eraseOp(producer);
|
|
return success();
|
|
}
|
|
};
|
|
|
|
/// Pattern to fold a GenericOp/IndexedGenericOp with a splat constant.
|
|
template <typename LinalgOpTy>
|
|
struct FoldSplatConstants : public OpRewritePattern<LinalgOpTy> {
|
|
using OpRewritePattern<LinalgOpTy>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(LinalgOpTy op,
|
|
PatternRewriter &rewriter) const override {
|
|
if (!op.hasTensorSemantics())
|
|
return failure();
|
|
LinalgOp linalgOp = cast<LinalgOp>(op.getOperation());
|
|
for (auto operand : llvm::enumerate(linalgOp.getInputs())) {
|
|
ConstantOp constantOp = operand.value().getDefiningOp<ConstantOp>();
|
|
if (!constantOp ||
|
|
!constantOp.value().cast<DenseElementsAttr>().isSplat())
|
|
continue;
|
|
|
|
// The indexing_maps for the operands of the fused operation are same as
|
|
// those for the operands of the linalgOp without the indexing map at
|
|
// operand.index()
|
|
SmallVector<AffineMap, 4> fusedIndexMaps = llvm::to_vector<4>(
|
|
linalgOp.indexing_maps().getAsValueRange<AffineMapAttr>());
|
|
fusedIndexMaps.erase(std::next(fusedIndexMaps.begin(), operand.index()));
|
|
|
|
// The operands list is same as the linalgOp with the argument for
|
|
// constant index dropped.
|
|
SmallVector<Value, 4> fusedOperands(linalgOp.getInputs());
|
|
fusedOperands.erase(std::next(fusedOperands.begin(), operand.index()));
|
|
|
|
// Create a constant scalar value from the splat constant.
|
|
Value scalarConstant = rewriter.create<ConstantOp>(
|
|
constantOp.getLoc(),
|
|
constantOp.value().cast<DenseElementsAttr>().getSplatValue());
|
|
|
|
LinalgOp fusedOp = createLinalgOpOfSameType(
|
|
linalgOp, rewriter, rewriter.getUnknownLoc(),
|
|
linalgOp.getOperation()->getResultTypes(),
|
|
/*inputs=*/fusedOperands,
|
|
/*outputBuffers=*/ValueRange{},
|
|
/*initTensors=*/ValueRange{}, // no init tensors for now.
|
|
rewriter.getAffineMapArrayAttr(fusedIndexMaps),
|
|
linalgOp.iterator_types(),
|
|
/*doc=*/nullptr,
|
|
/*library_call=*/nullptr,
|
|
/*symbol_source=*/nullptr);
|
|
|
|
// Map the block argument corresponding to the replaced argument with the
|
|
// scalar constant.
|
|
Region &linalgOpRegion = linalgOp.getOperation()->getRegion(0);
|
|
Block &entryBlock = *linalgOpRegion.begin();
|
|
unsigned argIndex = entryBlock.getNumArguments() -
|
|
linalgOp.getNumInputs() + operand.index();
|
|
BlockAndValueMapping mapping;
|
|
mapping.map(entryBlock.getArgument(argIndex), scalarConstant);
|
|
Region &fusedRegion = fusedOp.getOperation()->getRegion(0);
|
|
rewriter.cloneRegionBefore(linalgOpRegion, fusedRegion,
|
|
fusedRegion.begin(), mapping);
|
|
rewriter.replaceOp(linalgOp, fusedOp.getOperation()->getResults());
|
|
if (constantOp.use_empty())
|
|
rewriter.eraseOp(constantOp);
|
|
return success();
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
Optional<SmallVector<Value, 1>>
|
|
mlir::linalg::fuseTensorOps(PatternRewriter &rewriter, Operation *consumer,
|
|
unsigned consumerIdx, OperationFolder *folder) {
|
|
if (consumerIdx >= consumer->getNumOperands())
|
|
return llvm::None;
|
|
Operation *producer = consumer->getOperand(consumerIdx).getDefiningOp();
|
|
if (!producer || producer->getNumResults() != 1)
|
|
return llvm::None;
|
|
|
|
// Fuse when consumer is GenericOp or IndexedGenericOp.
|
|
if (!isa<GenericOp, IndexedGenericOp>(consumer) ||
|
|
!isa<GenericOp, IndexedGenericOp>(producer))
|
|
return llvm::None;
|
|
|
|
return fuseTensorOpsImpl(cast<LinalgOp>(producer), cast<LinalgOp>(consumer),
|
|
consumerIdx, rewriter, folder);
|
|
}
|
|
|
|
namespace {
|
|
/// Patterns to fuse a generic op, with the producer of its operands.
|
|
template <typename LinalgOpTy>
|
|
struct FuseTensorOps : public OpRewritePattern<LinalgOpTy> {
|
|
using OpRewritePattern<LinalgOpTy>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(LinalgOpTy op,
|
|
PatternRewriter &rewriter) const override {
|
|
// Find the first operand that is defined by another generic op on tensors.
|
|
for (auto operandNum :
|
|
llvm::seq<unsigned>(0, op.getOperation()->getNumOperands())) {
|
|
Operation *producer =
|
|
op.getOperation()->getOperand(operandNum).getDefiningOp();
|
|
if (!producer)
|
|
continue;
|
|
Optional<SmallVector<Value, 1>> fusedOpResults =
|
|
fuseTensorOps(rewriter, op, operandNum);
|
|
if (fusedOpResults) {
|
|
rewriter.replaceOp(op, *fusedOpResults);
|
|
if (producer->use_empty())
|
|
rewriter.eraseOp(producer);
|
|
return success();
|
|
}
|
|
}
|
|
return failure();
|
|
}
|
|
};
|
|
|
|
/// Pass that fuses generic ops on tensors. Used only for testing.
|
|
struct FusionOfTensorOpsPass
|
|
: public LinalgFusionOfTensorOpsBase<FusionOfTensorOpsPass> {
|
|
void runOnOperation() override {
|
|
OwningRewritePatternList patterns;
|
|
Operation *op = getOperation();
|
|
populateLinalgTensorOpsFusionPatterns(op->getContext(), patterns);
|
|
applyPatternsAndFoldGreedily(op->getRegions(), patterns);
|
|
}
|
|
};
|
|
|
|
/// Pass to test folding of reshape op with generic/indexed_generic ops by
|
|
/// linearization.
|
|
struct FoldReshapeOpsByLinearizationPass
|
|
: public LinalgFoldReshapeOpsByLinearizationBase<
|
|
FoldReshapeOpsByLinearizationPass> {
|
|
void runOnOperation() override {
|
|
OwningRewritePatternList patterns;
|
|
Operation *op = getOperation();
|
|
populateFoldReshapeOpsByLinearizationPatterns(op->getContext(), patterns);
|
|
applyPatternsAndFoldGreedily(op->getRegions(), patterns);
|
|
}
|
|
};
|
|
|
|
} // namespace
|
|
|
|
void mlir::populateFoldReshapeOpsByLinearizationPatterns(
|
|
MLIRContext *context, OwningRewritePatternList &patterns) {
|
|
patterns.insert<FoldProducerReshapeOpByLinearization<GenericOp>,
|
|
FoldProducerReshapeOpByLinearization<IndexedGenericOp>,
|
|
FoldConsumerReshapeOpByLinearization>(context);
|
|
}
|
|
|
|
void mlir::populateFoldReshapeOpsByExpansionPatterns(
|
|
MLIRContext *context, OwningRewritePatternList &patterns) {
|
|
patterns.insert<FoldReshapeWithGenericOpByExpansion,
|
|
FoldWithProducerReshapeOpByExpansion>(context);
|
|
}
|
|
|
|
void mlir::populateLinalgTensorOpsFusionPatterns(
|
|
MLIRContext *context, OwningRewritePatternList &patterns) {
|
|
patterns.insert<FuseTensorOps<GenericOp>, FuseTensorOps<IndexedGenericOp>,
|
|
FoldSplatConstants<GenericOp>,
|
|
FoldSplatConstants<IndexedGenericOp>>(context);
|
|
populateFoldReshapeOpsByExpansionPatterns(context, patterns);
|
|
GenericOp::getCanonicalizationPatterns(patterns, context);
|
|
IndexedGenericOp::getCanonicalizationPatterns(patterns, context);
|
|
TensorReshapeOp::getCanonicalizationPatterns(patterns, context);
|
|
}
|
|
|
|
std::unique_ptr<Pass> mlir::createLinalgFusionOfTensorOpsPass() {
|
|
return std::make_unique<FusionOfTensorOpsPass>();
|
|
}
|
|
|
|
std::unique_ptr<Pass> mlir::createFoldReshapeOpsByLinearizationPass() {
|
|
return std::make_unique<FoldReshapeOpsByLinearizationPass>();
|
|
}
|