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//===- EmptyTensorElimination.cpp - tensor.empty op elimination -----------===//
//
// 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/Bufferization/Transforms/Passes.h"
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
#include "mlir/Dialect/Bufferization/Transforms/Transforms.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Dominance.h"
#include "mlir/Pass/Pass.h"
namespace mlir {
namespace bufferization {
#define GEN_PASS_DEF_EMPTYTENSORELIMINATION
#include "mlir/Dialect/Bufferization/Transforms/Passes.h.inc"
} // namespace bufferization
} // namespace mlir
using namespace mlir;
using namespace mlir::bufferization;
/// Return true if all `neededValues` are in scope at the given
/// `insertionPoint`.
static bool
neededValuesDominateInsertionPoint(const DominanceInfo &domInfo,
Operation *insertionPoint,
const SmallVector<Value> &neededValues) {
for (Value val : neededValues) {
if (auto bbArg = dyn_cast<BlockArgument>(val)) {
Block *owner = bbArg.getOwner();
if (!owner->findAncestorOpInBlock(*insertionPoint))
return false;
} else {
auto opResult = cast<OpResult>(val);
if (!domInfo.properlyDominates(opResult.getOwner(), insertionPoint))
return false;
}
}
return true;
}
/// Return true if the given `insertionPoint` dominates all uses of
/// `emptyTensorOp`.
static bool insertionPointDominatesUses(const DominanceInfo &domInfo,
Operation *insertionPoint,
Operation *emptyTensorOp) {
for (Operation *user : emptyTensorOp->getUsers())
if (!domInfo.dominates(insertionPoint, user))
return false;
return true;
}
/// Find a valid insertion point for a replacement of `emptyTensorOp`, assuming
/// that the replacement may use any value from `neededValues`.
static Operation *
findValidInsertionPoint(Operation *emptyTensorOp,
const SmallVector<Value> &neededValues) {
DominanceInfo domInfo;
// Gather all possible insertion points: the location of `emptyTensorOp` and
// right after the definition of each value in `neededValues`.
SmallVector<Operation *> insertionPointCandidates;
insertionPointCandidates.push_back(emptyTensorOp);
for (Value val : neededValues) {
// Note: The anchor op is using all of `neededValues`, so:
// * in case of a block argument: There must be at least one op in the block
// (the anchor op or one of its parents).
// * in case of an OpResult: There must be at least one op right after the
// defining op (the anchor op or one of its
// parents).
if (auto bbArg = dyn_cast<BlockArgument>(val)) {
insertionPointCandidates.push_back(
&bbArg.getOwner()->getOperations().front());
} else {
insertionPointCandidates.push_back(val.getDefiningOp()->getNextNode());
}
}
// Select first matching insertion point.
for (Operation *insertionPoint : insertionPointCandidates) {
// Check if all needed values are in scope.
if (!neededValuesDominateInsertionPoint(domInfo, insertionPoint,
neededValues))
continue;
// Check if the insertion point is before all uses.
if (!insertionPointDominatesUses(domInfo, insertionPoint, emptyTensorOp))
continue;
return insertionPoint;
}
// No suitable insertion point was found.
return nullptr;
}
/// Try to eliminate tensor::EmptyOps inside `op`. A tensor::EmptyOp is replaced
/// with the result of `rewriteFunc` if it is anchored on a matching
/// OpOperand. "Anchored" means that there is a path on the reverse SSA use-def
/// chain, starting from the OpOperand and always following the aliasing
/// OpOperand, that eventually ends at the tensor::EmptyOp.
///
/// E.g.:
/// %0 = tensor.empty() : tensor<10xf32>
/// %1 = linalg.fill ... outs(%0 : tensor<10xf32>)
/// %2 = tensor.insert_slice %0 into %t ...
///
/// In the above example, the anchor is the source operand of the insert_slice
/// op. When tracing back the reverse use-def chain, we end up at a
/// tensor.empty op.
LogicalResult mlir::bufferization::eliminateEmptyTensors(
RewriterBase &rewriter, Operation *op, OneShotAnalysisState &state,
AnchorMatchFn anchorMatchFunc, RewriteFn rewriteFunc) {
OpBuilder::InsertionGuard g(rewriter);
op->walk([&](Operation *op) {
for (OpOperand &operand : op->getOpOperands()) {
// Skip operands that do not bufferize inplace.
if (!state.isInPlace(operand))
continue;
// All values that are needed to create the replacement op.
SmallVector<Value> neededValues;
// Is this an anchor?
if (!anchorMatchFunc(operand, neededValues))
continue;
// Find tensor.empty ops on the reverse SSA use-def chain. Only follow
// equivalent tensors. I.e., stop when there are ops such as extract_slice
// on the path.
TraversalConfig config;
config.followEquivalentOnly = true;
config.alwaysIncludeLeaves = false;
SetVector<Value> emptyTensors = state.findValueInReverseUseDefChain(
operand.get(), /*condition=*/
[&](Value val) { return val.getDefiningOp<tensor::EmptyOp>(); },
config);
for (Value v : emptyTensors) {
Operation *emptyTensorOp = v.getDefiningOp();
// Replace only if the types match. We do not support slices or casts.
// TODO: This could be extended to support IR such as:
// %0 = tensor.empty() : tensor<128xf32>
// %1 = "some_op"(%0) : (tensor<128xf32>) -> (tensor<128xf32>)
// %2 = tensor.expand_shape %1 ...
// %3 = tensor.insert_slice %2 into ...
if (v.getType() != operand.get().getType())
continue;
// Find a suitable insertion point. If no suitable insertion point for
// the replacement can be found, skip this replacement.
Operation *insertionPoint =
findValidInsertionPoint(emptyTensorOp, neededValues);
if (!insertionPoint)
continue;
rewriter.setInsertionPoint(insertionPoint);
Value replacement =
rewriteFunc(rewriter, emptyTensorOp->getLoc(), operand);
if (!replacement)
continue;
// Replace the tensor::EmptyOp.
rewriter.replaceOp(emptyTensorOp, replacement);
state.resetCache();
}
}
});
return success();
}
/// Try to eliminate tensor::EmptyOps inside `op`. An tensor::EmptyOp can be
/// eliminated if it is eventually inserted into another tensor (and some other
/// conditions are met).
///
/// E.g.:
/// %0 = tensor.empty()
/// %1 = linalg.fill(%cst, %0) {inplace = [true]}
/// %2 = tensor.insert_slice %1 into %t[10][20][1]
///
/// tensor::EmptyOp elimination will try to fill %t inplace instead of filling a
/// new allocation %0 and inserting it into %t. This is done by replacing the
/// tensor::EmptyOp with:
///
/// %0 = tensor.extract_slice %t[10][20][1]
///
/// The analysis looks for matching ExtractSliceOp/InsertSliceOp pairs and lets
/// those bufferize inplace in the absence of other conflicts.
///
/// Starting from an InsertSliceOp, an tensor::EmptyOp at the end of the insert
/// source's reverse use-def chain is eliminated if:
/// * On the reverse use-def chain path from the InsertSliceOp to the
/// tensor::EmptyOp, all ops were decided to bufferize inplace and the buffer
/// relation is "equivalent" (TODO: can be relaxed if needed).
/// * The reverse use-def chain has exactly one end, which is the
/// tensor::EmptyOp.
template <typename OpTy>
static LogicalResult insertSliceLikeAnchoredEmptyTensorEliminationStep(
RewriterBase &rewriter, Operation *op, OneShotAnalysisState &state) {
return eliminateEmptyTensors(
rewriter, op, state,
/*anchorMatchFunc=*/
[&](OpOperand &operand, SmallVector<Value> &neededValues) {
auto insertSliceOp = dyn_cast<OpTy>(operand.getOwner());
if (!insertSliceOp)
return false;
if (&operand != &insertSliceOp->getOpOperand(0) /*source*/)
return false;
// Collect all values that are needed to construct the replacement op.
neededValues.append(insertSliceOp.getOffsets().begin(),
insertSliceOp.getOffsets().end());
neededValues.append(insertSliceOp.getSizes().begin(),
insertSliceOp.getSizes().end());
neededValues.append(insertSliceOp.getStrides().begin(),
insertSliceOp.getStrides().end());
neededValues.push_back(insertSliceOp.getDest());
return true;
},
/*rewriteFunc=*/
[](OpBuilder &b, Location loc, OpOperand &operand) {
auto insertOp = cast<OpTy>(operand.getOwner());
auto extractOp = b.create<tensor::ExtractSliceOp>(
loc, insertOp.getSourceType(), insertOp.getDest(),
insertOp.getMixedOffsets(), insertOp.getMixedSizes(),
insertOp.getMixedStrides());
return extractOp.getResult();
});
}
LogicalResult
mlir::bufferization::insertSliceAnchoredEmptyTensorEliminationStep(
RewriterBase &rewriter, Operation *op, OneShotAnalysisState &state) {
if (failed(insertSliceLikeAnchoredEmptyTensorEliminationStep<
tensor::InsertSliceOp>(rewriter, op, state)))
return failure();
if (failed(insertSliceLikeAnchoredEmptyTensorEliminationStep<
tensor::ParallelInsertSliceOp>(rewriter, op, state)))
return failure();
return success();
}
namespace {
struct EmptyTensorElimination
: public bufferization::impl::EmptyTensorEliminationBase<
EmptyTensorElimination> {
EmptyTensorElimination() = default;
void runOnOperation() override;
void getDependentDialects(DialectRegistry ®istry) const override {
registry
.insert<bufferization::BufferizationDialect, tensor::TensorDialect>();
}
};
} // namespace
void EmptyTensorElimination::runOnOperation() {
Operation *op = getOperation();
OneShotBufferizationOptions options;
options.allowReturnAllocs = true;
OneShotAnalysisState state(op, options);
if (failed(analyzeOp(op, state))) {
signalPassFailure();
return;
}
IRRewriter rewriter(op->getContext());
if (failed(bufferization::insertSliceAnchoredEmptyTensorEliminationStep(
rewriter, op, state)))
signalPassFailure();
}
std::unique_ptr<Pass> mlir::bufferization::createEmptyTensorEliminationPass() {
return std::make_unique<EmptyTensorElimination>();
}
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