llvm-project/mlir/lib/Dialect/Linalg/Transforms/BufferizableOpInterfaceImpl.cpp
Matthias Springer b3ebe3beed [mlir][bufferize] Bufferize after TensorCopyInsertion
This change changes the bufferization so that it utilizes the new TensorCopyInsertion pass. One-Shot Bufferize no longer calls the One-Shot Analysis. Instead, it relies on the TensorCopyInsertion pass to make the entire IR fully inplacable. The `bufferize` implementations of all ops are simplified; they no longer have to account for out-of-place bufferization decisions. These were already materialized in the IR in the form of `bufferization.alloc_tensor` ops during the TensorCopyInsertion pass.

Differential Revision: https://reviews.llvm.org/D127652
2022-06-17 13:29:52 +02:00

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6.0 KiB
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//===- BufferizableOpInterfaceImpl.cpp - Impl. of BufferizableOpInterface -===//
//
// 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/BufferizableOpInterfaceImpl.h"
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Dialect.h"
#include "mlir/IR/Operation.h"
using namespace mlir;
using namespace linalg;
using namespace mlir::bufferization;
namespace {
// TODO: Ops in the linalg dialect can directly implement this interface.
/// Generic conversion for any LinalgOp on tensors.
static LogicalResult bufferizeLinalgOp(RewriterBase &rewriter, LinalgOp op,
BufferizationState &state) {
// Take a guard before anything else.
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPoint(op);
// Nothing to do. This op is already bufferized.
if (op.hasBufferSemantics())
return success();
// Ensure op has only tensors. Allow mixed tensor-buffer mode on a per-need
// basis.
if (!op.hasTensorSemantics())
return op->emitError() << "op does not have tensor semantics";
// New input operands for the cloned op.
SmallVector<Value> newInputBuffers;
newInputBuffers.reserve(op.getNumInputs());
for (OpOperand *opOperand : op.getInputOperands()) {
if (op.isScalar(opOperand)) {
newInputBuffers.push_back(opOperand->get());
continue;
}
newInputBuffers.push_back(state.getBuffer(rewriter, opOperand->get()));
}
// New output operands for the cloned op.
SmallVector<Value> newOutputBuffers;
for (OpResult opResult : op->getOpResults()) {
OpOperand *opOperand = op.getOutputOperand(opResult.getResultNumber());
Value resultBuffer = state.getBuffer(rewriter, opOperand->get());
newOutputBuffers.push_back(resultBuffer);
}
// Merge input/output operands.
SmallVector<Value> newOperands = newInputBuffers;
newOperands.append(newOutputBuffers.begin(), newOutputBuffers.end());
// Set insertion point now that potential alloc/dealloc are introduced.
rewriter.setInsertionPoint(op);
// Clone the op, but use the new operands. Move the existing block into the
// new op. Since the new op does not have any tensor results, it does not
// return anything.
assert(op->getNumRegions() == 1 && "expected that op has 1 region");
auto newOp = cast<LinalgOp>(op.cloneWithoutRegions(
rewriter, op.getLoc(), /*resultTypes=*/TypeRange{}, newOperands));
rewriter.inlineRegionBefore(op->getRegion(0), newOp->getRegion(0),
newOp->getRegion(0).begin());
// Replace the results of the old op with the new output buffers.
replaceOpWithBufferizedValues(rewriter, op, newOutputBuffers);
return success();
}
/// Bufferization of linalg.generic. Replace with a new linalg.generic that
/// operates entirely on memrefs.
template <typename OpTy>
struct LinalgOpInterface
: public BufferizableOpInterface::ExternalModel<LinalgOpInterface<OpTy>,
OpTy> {
bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
// Operand is read if it is used in the computation.
auto genericOp = cast<linalg::LinalgOp>(op);
return genericOp.payloadUsesValueFromOperand(&opOperand);
}
bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
// Operand is written to if it has an aliasing OpResult.
auto bufferizableOp = cast<BufferizableOpInterface>(op);
return !bufferizableOp.getAliasingOpResult(opOperand, state).empty();
}
SmallVector<OpOperand *>
getAliasingOpOperand(Operation *op, OpResult opResult,
const AnalysisState &state) const {
auto genericOp = cast<linalg::LinalgOp>(op);
// The i-th OpResult may alias with the i-th "out" tensor.
return {genericOp.getOutputOperand(opResult.getResultNumber())};
}
SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
auto genericOp = cast<linalg::LinalgOp>(op);
// The i-th "out" tensor may alias with the i-th OpResult.
if (genericOp.isOutputTensor(&opOperand))
return {genericOp.getTiedOpResult(&opOperand)};
return {};
}
BufferRelation bufferRelation(Operation *op, OpResult opResult,
const AnalysisState &state) const {
return BufferRelation::Equivalent;
}
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
BufferizationState &state) const {
return bufferizeLinalgOp(rewriter, cast<LinalgOp>(op), state);
}
};
/// Helper structure that iterates over all LinalgOps in `OpTys` and registers
/// the `BufferizableOpInterface` with each of them.
template <typename... Ops>
struct LinalgOpInterfaceHelper {
static void registerOpInterface(MLIRContext *ctx) {
(void)std::initializer_list<int>{
0, (Ops::template attachInterface<LinalgOpInterface<Ops>>(*ctx), 0)...};
}
};
} // namespace
void mlir::linalg::registerBufferizableOpInterfaceExternalModels(
DialectRegistry &registry) {
registry.addExtension(+[](MLIRContext *ctx, linalg::LinalgDialect *dialect) {
// Register all Linalg structured ops. `LinalgOp` is an interface and it is
// not possible to attach an external interface to an existing interface.
// Therefore, attach the `BufferizableOpInterface` to all ops one-by-one.
LinalgOpInterfaceHelper<
#define GET_OP_LIST
#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
>::registerOpInterface(ctx);
});
}