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[mlir][bufferize] Improve resolveConflicts for ExtractSliceOp 

It is sometimes better to make a copy of the OpResult instead of making a copy of the OpOperand. E.g., when bufferizing tensor.extract_slice.

This implementation will eventually make parts of extract_slice's `bufferize` implementation obsolete (and simplify it). It will only need to handle in-place OpOperands.

Differential Revision: https://reviews.llvm.org/D126819
This commit is contained in:
Matthias Springer 2022-06-09 22:14:53 +02:00
parent 72a049d778
commit 87b46776c4
2 changed files with 63 additions and 5 deletions
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50
mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
View File

@ -44,7 +44,12 @@ constexpr const ::llvm::StringLiteral
LogicalResult BufferizableOpInterface::resolveTensorOpOperandConflicts(
RewriterBase &rewriter, const AnalysisState &state) {
OpBuilder::InsertionGuard g(rewriter);
Operation *op = getOperation();
SmallVector<OpOperand *> outOfPlaceOpOperands;
SmallVector<OpResult> outOfPlaceOpResults;
// Find all out-of-place OpOperands.
for (OpOperand &opOperand : op->getOpOperands()) {
Type operandType = opOperand.get().getType();
if (!operandType.isa<TensorType>())
@ -53,17 +58,52 @@ LogicalResult BufferizableOpInterface::resolveTensorOpOperandConflicts(
continue;
if (operandType.isa<UnrankedTensorType>())
return op->emitError("copies of unranked tensors are not supported");
auto tensorType = operandType.dyn_cast<RankedTensorType>();
if (!tensorType)
continue;
SmallVector<OpResult> aliasingOpResults =
state.getAliasingOpResult(opOperand);
if (aliasingOpResults.size() == 1 &&
!state.bufferizesToMemoryWrite(opOperand) &&
state.getAliasingOpOperand(aliasingOpResults.front()).size() == 1) {
// The op itself does not write but may create exactly one alias. Instead
// of copying the OpOperand, copy the OpResult. The OpResult can sometimes
// be smaller than the OpOperand (e.g., in the case of an extract_slice,
// where the result is usually a smaller part of the source).
outOfPlaceOpResults.push_back(aliasingOpResults.front());
} else {
// In all other cases, make a copy of the OpOperand.
outOfPlaceOpOperands.push_back(&opOperand);
}
}
// Insert copies of OpOperands.
rewriter.setInsertionPoint(op);
for (OpOperand *opOperand : outOfPlaceOpOperands) {
auto tensorType = opOperand->get().getType().cast<RankedTensorType>();
SmallVector<OpResult> aliasingOpResults =
state.getAliasingOpResult(*opOperand);
bool escape = llvm::any_of(
aliasingOpResults, [&](Value v) { return state.isTensorYielded(v); });
Value copy = rewriter.create<AllocTensorOp>(
op->getLoc(), tensorType, ValueRange(), opOperand.get(), escape);
rewriter.updateRootInPlace(op, [&]() { opOperand.set(copy); });
op->getLoc(), tensorType, ValueRange(), opOperand->get(), escape);
rewriter.updateRootInPlace(op, [&]() { opOperand->set(copy); });
}
// Insert copies of OpResults.
rewriter.setInsertionPointAfter(op);
for (OpResult opResult : outOfPlaceOpResults) {
auto tensorType = opResult.getType().cast<RankedTensorType>();
bool escape = state.isTensorYielded(opResult);
Value copy = rewriter.create<AllocTensorOp>(op->getLoc(), tensorType,
ValueRange(), opResult, escape);
SmallVector<OpOperand *> uses = llvm::to_vector(llvm::map_range(
opResult.getUses(), [](OpOperand &use) { return &use; }));
for (OpOperand *use : uses) {
// Do not update the alloc_tensor op that we just created.
if (use->getOwner() != copy.getDefiningOp())
rewriter.updateRootInPlace(use->getOwner(), [&]() { use->set(copy); });
}
}
return success();
}

18
mlir/test/Dialect/Tensor/one-shot-bufferize-tensor-copy-insertion.mlir Normal file
View File

@ -0,0 +1,18 @@
// RUN: mlir-opt %s -tensor-copy-insertion -split-input-file | FileCheck %s
// RUN: mlir-opt %s -tensor-copy-insertion="bufferize-function-boundaries allow-return-allocs" -split-input-file | FileCheck %s --check-prefix=CHECK-FUNC
// CHECK-LABEL: func @extract_slice(
// CHECK-SAME: %[[t:.*]]: tensor<?xf32>
// CHECK-FUNC-LABEL: func @extract_slice(
func.func @extract_slice(%t: tensor<?xf32>, %idx: index, %f: f32)
-> (tensor<5xf32>, tensor<?xf32>)
{
// CHECK: %[[extract_slice:.*]] = tensor.extract_slice %[[t]][10] [5] [1]
%0 = tensor.extract_slice %t[10][5][1] : tensor<?xf32> to tensor<5xf32>
// CHECK: %[[alloc:.*]] = bufferization.alloc_tensor() copy(%[[extract_slice]]) {escape = false} : tensor<5xf32>
// CHECK-FUNC: bufferization.alloc_tensor() copy(%{{.*}}) {escape = true} : tensor<5xf32>
// CHECK: %[[insert:.*]] = tensor.insert %{{.*}} into %[[alloc]]
%1 = tensor.insert %f into %0[%idx] : tensor<5xf32>
// CHECK: return %[[insert]], %[[t]]
return %1, %t : tensor<5xf32>, tensor<?xf32>
}