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