e07a7fd5c0
* Move Module Bufferization to the bufferization dialect. The implementation is split into `OneShotModuleBufferize.cpp` and `FuncBufferizableOpInterfaceImpl.cpp`, so that the external model implementation can be easily moved to the func dialect in the future. * Split and clean up test cases. A few test cases are still remaining in Linalg and will be updated separately. * `linalg.inplaceable` is renamed to `bufferization.writable` to accurately reflect its current usage. * Attributes and their verifiers are moved from the Linalg dialect to the Bufferization dialect. * Expand documentation. * Add a new flag to One-Shot Bufferize to allow for function boundary bufferization. Differential Revision: https://reviews.llvm.org/D122229
543 lines
22 KiB
C++
543 lines
22 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/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.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/Bufferization/Transforms/OneShotAnalysis.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/IR/Dialect.h"
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#include "mlir/IR/Operation.h"
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namespace mlir {
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namespace bufferization {
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namespace func_ext {
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void FuncAnalysisState::startFunctionAnalysis(FuncOp funcOp) {
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analyzedFuncOps[funcOp] = FuncOpAnalysisState::InProgress;
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auto createdEquiv = equivalentFuncArgs.try_emplace(funcOp, IndexMapping());
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auto createdAliasingOperands =
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aliasingFuncArgs.try_emplace(funcOp, IndexToIndexListMapping());
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auto createdAliasingResults =
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aliasingReturnVals.try_emplace(funcOp, IndexToIndexListMapping());
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auto createdRead = readBbArgs.try_emplace(funcOp, BbArgIndexSet());
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auto createdWritten = writtenBbArgs.try_emplace(funcOp, BbArgIndexSet());
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(void)createdEquiv;
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(void)createdAliasingOperands;
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(void)createdAliasingResults;
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(void)createdRead;
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(void)createdWritten;
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#ifndef NDEBUG
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assert(createdEquiv.second && "equivalence info exists already");
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assert(createdAliasingOperands.second && "aliasing info exists already");
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assert(createdAliasingResults.second && "aliasing info exists already");
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assert(createdRead.second && "bbarg access info exists already");
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assert(createdWritten.second && "bbarg access info exists already");
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#endif // NDEBUG
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}
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/// Return the unique ReturnOp that terminates `funcOp`.
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/// Return nullptr if there is no such unique ReturnOp.
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static func::ReturnOp getAssumedUniqueReturnOp(FuncOp funcOp) {
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func::ReturnOp returnOp;
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for (Block &b : funcOp.getBody()) {
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if (auto candidateOp = dyn_cast<func::ReturnOp>(b.getTerminator())) {
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if (returnOp)
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return nullptr;
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returnOp = candidateOp;
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}
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}
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return returnOp;
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}
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/// Return the index-th bufferized function argument type. This assumes that the
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/// specified argument is a tensor. If the tensor is ranked, a layout map may be
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/// specified by the user. If no layout map is specified, a fully dynamic map is
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/// used.
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static BaseMemRefType
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getBufferizedFunctionArgType(FuncOp funcOp, int64_t index,
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const BufferizationOptions &options) {
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auto tensorType =
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funcOp.getFunctionType().getInput(index).dyn_cast<TensorType>();
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assert(tensorType && "expected TensorType");
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BaseMemRefType memrefType = getMemRefType(tensorType, options);
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auto layoutAttr = funcOp.getArgAttrOfType<AffineMapAttr>(
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index, BufferizationDialect::kBufferLayoutAttrName);
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if (!layoutAttr)
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return memrefType;
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auto rankedMemrefType = memrefType.dyn_cast<MemRefType>();
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assert(rankedMemrefType && "buffer layout not supported on unranked tensors");
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return MemRefType::get(
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rankedMemrefType.getShape(), rankedMemrefType.getElementType(),
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layoutAttr.getValue(), rankedMemrefType.getMemorySpaceAsInt());
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}
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/// Return the FuncOp called by `callOp`.
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static FuncOp getCalledFunction(CallOpInterface callOp) {
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SymbolRefAttr sym = callOp.getCallableForCallee().dyn_cast<SymbolRefAttr>();
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if (!sym)
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return nullptr;
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return dyn_cast_or_null<FuncOp>(
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SymbolTable::lookupNearestSymbolFrom(callOp, sym));
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}
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/// Get FuncAnalysisState.
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static const FuncAnalysisState &
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getFuncAnalysisState(const AnalysisState &state) {
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Optional<const FuncAnalysisState *> maybeState =
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state.getDialectState<FuncAnalysisState>(
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func::FuncDialect::getDialectNamespace());
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assert(maybeState.hasValue() && "FuncAnalysisState does not exist");
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return **maybeState;
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}
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/// Return the state (phase) of analysis of the FuncOp.
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static FuncOpAnalysisState getFuncOpAnalysisState(const AnalysisState &state,
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FuncOp funcOp) {
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const FuncAnalysisState &funcState = getFuncAnalysisState(state);
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auto it = funcState.analyzedFuncOps.find(funcOp);
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if (it == funcState.analyzedFuncOps.end())
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return FuncOpAnalysisState::NotAnalyzed;
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return it->second;
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}
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/// Return the index of the bbArg in the given FuncOp that is equivalent to the
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/// specified return value (if any).
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static Optional<int64_t> getEquivalentFuncArgIdx(FuncOp funcOp,
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const FuncAnalysisState &state,
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int64_t returnValIdx) {
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auto funcOpIt = state.equivalentFuncArgs.find(funcOp);
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if (funcOpIt == state.equivalentFuncArgs.end())
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// No equivalence info stores for funcOp.
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return None;
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auto retValIt = funcOpIt->getSecond().find(returnValIdx);
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if (retValIt == funcOpIt->getSecond().end())
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// Return value has no equivalent bbArg.
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return None;
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return retValIt->getSecond();
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}
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struct CallOpInterface
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: public BufferizableOpInterface::ExternalModel<CallOpInterface,
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func::CallOp> {
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bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
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const AnalysisState &state) const {
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func::CallOp callOp = cast<func::CallOp>(op);
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FuncOp funcOp = getCalledFunction(callOp);
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assert(funcOp && "expected CallOp to a FuncOp");
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const FuncAnalysisState &funcState = getFuncAnalysisState(state);
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if (getFuncOpAnalysisState(state, funcOp) != FuncOpAnalysisState::Analyzed)
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// FuncOp not analyzed yet. Assume that OpOperand is read.
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return true;
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return funcState.readBbArgs.lookup(funcOp).contains(
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opOperand.getOperandNumber());
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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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func::CallOp callOp = cast<func::CallOp>(op);
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FuncOp funcOp = getCalledFunction(callOp);
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assert(funcOp && "expected CallOp to a FuncOp");
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const FuncAnalysisState &funcState = getFuncAnalysisState(state);
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if (getFuncOpAnalysisState(state, funcOp) != FuncOpAnalysisState::Analyzed)
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// FuncOp not analyzed yet. Assume that OpOperand is written.
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return true;
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return funcState.writtenBbArgs.lookup(funcOp).contains(
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opOperand.getOperandNumber());
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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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func::CallOp callOp = cast<func::CallOp>(op);
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FuncOp funcOp = getCalledFunction(callOp);
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assert(funcOp && "expected CallOp to a FuncOp");
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const FuncAnalysisState &funcState = getFuncAnalysisState(state);
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if (getFuncOpAnalysisState(state, funcOp) !=
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FuncOpAnalysisState::Analyzed) {
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// FuncOp not analyzed yet. Any OpResult may be aliasing.
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SmallVector<OpResult> result;
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for (OpResult opResult : op->getOpResults())
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if (opResult.getType().isa<TensorType>())
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result.push_back(opResult);
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return result;
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}
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// Get aliasing results from state.
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auto aliasingReturnVals =
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funcState.aliasingReturnVals.lookup(funcOp).lookup(
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opOperand.getOperandNumber());
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SmallVector<OpResult> result;
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for (int64_t resultIdx : aliasingReturnVals)
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result.push_back(callOp->getOpResult(resultIdx));
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return result;
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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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func::CallOp callOp = cast<func::CallOp>(op);
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FuncOp funcOp = getCalledFunction(callOp);
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assert(funcOp && "expected CallOp to a FuncOp");
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const FuncAnalysisState &funcState = getFuncAnalysisState(state);
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if (getFuncOpAnalysisState(state, funcOp) !=
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FuncOpAnalysisState::Analyzed) {
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// FuncOp not analyzed yet. Any OpOperand may be aliasing.
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SmallVector<OpOperand *> result;
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for (OpOperand &opOperand : op->getOpOperands())
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if (opOperand.get().getType().isa<TensorType>())
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result.push_back(&opOperand);
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return result;
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}
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// Get aliasing bbArgs from state.
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auto aliasingFuncArgs = funcState.aliasingFuncArgs.lookup(funcOp).lookup(
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opResult.getResultNumber());
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SmallVector<OpOperand *> result;
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for (int64_t bbArgIdx : aliasingFuncArgs)
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result.push_back(&callOp->getOpOperand(bbArgIdx));
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return result;
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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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/// All function arguments are writable. It is the responsibility of the
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/// CallOp to insert buffer copies where necessary.
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LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
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BufferizationState &state) const {
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func::CallOp callOp = cast<func::CallOp>(op);
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unsigned numResults = callOp.getNumResults();
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unsigned numOperands = callOp->getNumOperands();
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FuncOp funcOp = getCalledFunction(callOp);
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assert(funcOp && "expected CallOp to a FuncOp");
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FunctionType funcType = funcOp.getFunctionType();
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const FuncAnalysisState &funcState =
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getFuncAnalysisState(state.getAnalysisState());
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const OneShotBufferizationOptions &options =
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static_cast<const OneShotBufferizationOptions &>(state.getOptions());
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// Result types of the bufferized CallOp.
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SmallVector<Type> resultTypes;
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// Replacement values for the existing CallOp. These are usually the results
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// of the bufferized CallOp, unless a tensor result folds onto an operand.
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SmallVector<Value> replacementValues(numResults, Value());
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// For non-tensor results: A mapping from return val indices of the old
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// CallOp to return val indices of the bufferized CallOp.
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SmallVector<Optional<unsigned>> retValMapping(numResults, None);
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// Operands of the bufferized CallOp.
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SmallVector<Value> newOperands(numOperands, Value());
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// Based on previously gathered equivalence information, we know if a
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// tensor result folds onto an operand. These are the only tensor value
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// results that are supported at the moment.
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//
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// For tensors return values that do not fold onto an operand, additional
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// work is needed (TODO) to either:
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// * hoist a result into an inplaceable operand or
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// * devise a better representation to truly return a buffer.
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//
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// Note: If a function has no body, no equivalence information is
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// available. Consequently, a tensor return value cannot be proven to fold
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// onto a FuncOp bbArg, so calls to such functions are not bufferizable at
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// the moment.
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// 1. Compute the result types of the new CallOp. Tensor results that are
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// equivalent to a FuncOp bbArg are no longer returned.
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for (const auto &it : llvm::enumerate(callOp.getResultTypes())) {
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unsigned returnValIdx = it.index();
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Type returnType = it.value();
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if (!returnType.isa<TensorType>()) {
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// Non-tensor values are returned.
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retValMapping[returnValIdx] = resultTypes.size();
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resultTypes.push_back(returnType);
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continue;
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}
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if (Optional<int64_t> bbArgIdx =
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getEquivalentFuncArgIdx(funcOp, funcState, returnValIdx)) {
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// Return operands that are equivalent to some bbArg, are not
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// returned.
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FailureOr<Value> bufferOrFailure =
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state.getBuffer(rewriter, callOp->getOpOperand(*bbArgIdx));
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if (failed(bufferOrFailure))
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return failure();
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replacementValues[returnValIdx] = *bufferOrFailure;
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newOperands[*bbArgIdx] = *bufferOrFailure;
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continue;
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}
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if (!options.allowReturnAllocs)
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return callOp->emitError(
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"call to FuncOp that returns non-equivalent tensors not supported");
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// Returning a memref. This memref is not equivalent to any bbArg. It is
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// likely a newly allocated buffer. We may want to hoist such allocations
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// to the call site in the future.
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retValMapping[returnValIdx] = resultTypes.size();
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resultTypes.push_back(funcType.getResult(resultTypes.size()));
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}
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// 2. Rewrite tensor operands as memrefs based on `bufferizedFuncType`.
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for (OpOperand &opOperand : callOp->getOpOperands()) {
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unsigned idx = opOperand.getOperandNumber();
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Value tensorOperand = opOperand.get();
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// Non-tensor operands are just copied.
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if (!tensorOperand.getType().isa<TensorType>()) {
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newOperands[idx] = tensorOperand;
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continue;
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}
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// Retrieve buffers for tensor operands. Tensor operand buffers, who's
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// corresponding FuncOp bbArgs are equivalent to a returned tensor, were
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// already stored in `newOperands` during Step 1.
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Value buffer = newOperands[idx];
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if (!buffer) {
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FailureOr<Value> bufferOrFailure = state.getBuffer(rewriter, opOperand);
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if (failed(bufferOrFailure))
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return failure();
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buffer = *bufferOrFailure;
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}
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// Caller / callee type mismatch is handled with a CastOp.
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auto memRefType = funcType.getInput(idx);
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// Since we don't yet have a clear layout story, to_memref may
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// conservatively turn tensors into more dynamic memref than necessary.
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// If the memref type of the callee fails, introduce an extra memref.cast
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// that will either canonicalize away or fail compilation until we can do
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// something better.
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if (buffer.getType() != memRefType) {
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assert(
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memref::CastOp::areCastCompatible(buffer.getType(), memRefType) &&
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"CallOp::bufferize: cast incompatible");
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Value castBuffer = rewriter.create<memref::CastOp>(callOp.getLoc(),
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memRefType, buffer);
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buffer = castBuffer;
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}
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newOperands[idx] = buffer;
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}
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// 3. Create the new CallOp.
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Operation *newCallOp = rewriter.create<func::CallOp>(
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callOp.getLoc(), funcOp.getSymName(), resultTypes, newOperands);
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newCallOp->setAttrs(callOp->getAttrs());
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// Get replacement values for non-tensor / non-equivalent results.
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for (unsigned i = 0; i < replacementValues.size(); ++i) {
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if (replacementValues[i])
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continue;
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replacementValues[i] = newCallOp->getResult(*retValMapping[i]);
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}
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// 4. Replace the old op with the new op.
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replaceOpWithBufferizedValues(rewriter, callOp, replacementValues);
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return success();
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}
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};
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struct ReturnOpInterface
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: public BufferizableOpInterface::ExternalModel<ReturnOpInterface,
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func::ReturnOp> {
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bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
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const AnalysisState &state) const {
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return true;
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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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return false;
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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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return {};
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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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#ifndef NDEBUG
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auto returnOp = cast<func::ReturnOp>(op);
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assert(isa<FuncOp>(returnOp->getParentOp()) &&
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"only support FuncOp parent for ReturnOp");
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#endif // NDEBUG
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// ReturnOps are bufferized as part of FuncOps.
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return failure();
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}
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};
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struct FuncOpInterface
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: public BufferizableOpInterface::ExternalModel<FuncOpInterface, FuncOp> {
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/// Rewrite function bbArgs and return values into buffer form (using the
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/// canonical memref layout for now). This function bufferizes the function
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/// signature and the ReturnOp. When the entire function body has been
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/// bufferized, function return types can be switched to more concise memref
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/// types as part of `foldMemRefCasts`.
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///
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/// When a tensor function argument is known to be equivalent to a tensor
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/// result, it is dropped from the return values.
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///
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/// All function bbArgs are writable unless they are explicitly marked as
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/// read-only. Callers must insert copies when needed.
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///
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/// Note: Returning a memref is possible, but corresponding CallOp
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/// bufferizations fail unless `allowReturnAllocs`.
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LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
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BufferizationState &state) const {
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auto funcOp = cast<FuncOp>(op);
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FunctionType funcType = funcOp.getFunctionType();
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const FuncAnalysisState &funcState =
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getFuncAnalysisState(state.getAnalysisState());
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const BufferizationOptions &options = state.getOptions();
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// Construct the bufferized function type.
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SmallVector<Type> argTypes;
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for (const auto &it : llvm::enumerate(funcType.getInputs())) {
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Type argType = it.value();
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if (auto tensorType = argType.dyn_cast<TensorType>()) {
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argTypes.push_back(
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getBufferizedFunctionArgType(funcOp, it.index(), options));
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continue;
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}
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argTypes.push_back(argType);
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}
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// Bodiless functions are assumed opaque and we cannot know the
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// bufferization contract they want to enforce. As a consequence, only
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// support functions that don't return any tensors atm.
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if (funcOp.getBody().empty()) {
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SmallVector<Type> retTypes;
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for (Type resultType : funcType.getResults()) {
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if (resultType.isa<TensorType>())
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return funcOp->emitError() << "cannot bufferize bodiless function "
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<< "that returns a tensor";
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retTypes.push_back(resultType);
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}
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funcOp.setType(FunctionType::get(op->getContext(), argTypes, retTypes));
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return success();
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}
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// TODO: Support functions with multiple returns.
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func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
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assert(returnOp && "expected func with single return op");
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// 1. Rewrite the bbArgs. Turn every tensor bbArg into a memref bbArg.
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Block &frontBlock = funcOp.getBody().front();
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for (BlockArgument &bbArg : frontBlock.getArguments()) {
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auto tensorType = bbArg.getType().dyn_cast<TensorType>();
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// Non-tensor types stay the same.
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if (!tensorType)
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continue;
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// Collect all uses of the bbArg.
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SmallVector<OpOperand *> bbArgUses;
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for (OpOperand &use : bbArg.getUses())
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bbArgUses.push_back(&use);
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// Change the bbArg type to memref.
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Type memrefType =
|
|
getBufferizedFunctionArgType(funcOp, bbArg.getArgNumber(), options);
|
|
bbArg.setType(memrefType);
|
|
|
|
// Replace all uses of the original tensor bbArg.
|
|
rewriter.setInsertionPointToStart(&frontBlock);
|
|
if (!bbArgUses.empty()) {
|
|
// Insert to_tensor because the remaining function body has not been
|
|
// bufferized yet.
|
|
Value toTensorOp =
|
|
rewriter.create<bufferization::ToTensorOp>(funcOp.getLoc(), bbArg);
|
|
for (OpOperand *use : bbArgUses)
|
|
use->set(toTensorOp);
|
|
}
|
|
}
|
|
|
|
// 2. For each result, keep track of which inplace argument it reuses.
|
|
SmallVector<Value> returnValues;
|
|
for (OpOperand &returnOperand : returnOp->getOpOperands()) {
|
|
Value returnVal = returnOperand.get();
|
|
|
|
// If not a tensor type just forward it.
|
|
if (!returnVal.getType().isa<RankedTensorType>()) {
|
|
returnValues.push_back(returnVal);
|
|
continue;
|
|
}
|
|
|
|
// If return operand is equivalent to some bbArg, no need to return it.
|
|
if (Optional<int64_t> equivBbArgIdx = getEquivalentFuncArgIdx(
|
|
funcOp, funcState, returnOperand.getOperandNumber())) {
|
|
rewriter.setInsertionPoint(returnOp);
|
|
Location loc = returnOp.getLoc();
|
|
Value toMemrefOp = rewriter.create<bufferization::ToMemrefOp>(
|
|
loc, getMemRefType(returnVal.getType().cast<TensorType>(), options),
|
|
returnVal);
|
|
BlockArgument equivBbArg = funcOp.getArgument(*equivBbArgIdx);
|
|
// Note: This copy will fold away. It must be inserted here to ensure
|
|
// that `returnVal` still has at least one use and does not fold away.
|
|
if (failed(
|
|
createMemCpy(rewriter, loc, toMemrefOp, equivBbArg, options)))
|
|
return funcOp->emitError("could not generate copy for bbArg");
|
|
continue;
|
|
}
|
|
|
|
returnValues.push_back(*state.getBuffer(rewriter, returnOperand));
|
|
}
|
|
|
|
// 3. Rewrite the terminator without the in-place bufferizable values.
|
|
returnOp.operandsMutable().assign(returnValues);
|
|
|
|
// 4. Rewrite the FuncOp type to buffer form.
|
|
funcOp.setType(FunctionType::get(op->getContext(), argTypes,
|
|
ValueRange(returnValues).getTypes()));
|
|
|
|
return success();
|
|
}
|
|
|
|
/// Return `true` if the given function argument is writable.
|
|
bool isWritable(Operation *op, Value value,
|
|
const AnalysisState &state) const {
|
|
auto funcOp = cast<FuncOp>(op);
|
|
BlockArgument bbArg = value.dyn_cast<BlockArgument>();
|
|
assert(bbArg && "expected BlockArgument");
|
|
|
|
// "bufferization.writable" overrides other writability decisions. This is
|
|
// currently used for testing only.
|
|
if (BoolAttr writable = funcOp.getArgAttrOfType<BoolAttr>(
|
|
bbArg.getArgNumber(), BufferizationDialect::kWritableAttrName))
|
|
return writable.getValue();
|
|
|
|
// All function arguments are writable by default.
|
|
return true;
|
|
}
|
|
|
|
bool isAllocationHoistingBarrier(Operation *op) const { return true; }
|
|
};
|
|
|
|
} // namespace func_ext
|
|
} // namespace bufferization
|
|
} // namespace mlir
|
|
|
|
void mlir::bufferization::func_ext::
|
|
registerBufferizableOpInterfaceExternalModels(DialectRegistry ®istry) {
|
|
registry.addExtension(+[](MLIRContext *ctx, func::FuncDialect *dialect) {
|
|
func::CallOp::attachInterface<func_ext::CallOpInterface>(*ctx);
|
|
func::FuncOp::attachInterface<func_ext::FuncOpInterface>(*ctx);
|
|
func::ReturnOp::attachInterface<func_ext::ReturnOpInterface>(*ctx);
|
|
});
|
|
}
|