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llvm-capstone/mlir/lib/Dialect/Linalg/ComprehensiveBufferize/ModuleBufferization.cpp
River Riddle 4a3460a791 [mlir:FunctionOpInterface] Rename the "type" attribute to "function_type" 
This removes any potential confusion with the `getType` accessors
which correspond to SSA results of an operation, and makes it
clear what the intent is (i.e. to represent the type of the function).

Differential Revision: https://reviews.llvm.org/D121762
2022-03-16 17:07:04 -07:00

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//===- ModuleBufferization.cpp - Bufferization across Func. Boundaries ----===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// Module Bufferization is an extension of Comprehensive Bufferize that
// bufferizes function boundaries. It provides `BufferizableOpInterface`
// implementations for FuncOp, CallOp and ReturnOp.
//
// Module Bufferization is run via `runModuleBufferize(ModuleOp, ...)`. This
// function analyzes the given module and determines the order of analysis and
// bufferization: Functions that are called are processed before their
// respective callers.
//
// After analyzing a FuncOp, additional information about its bbArgs is
// gathered through PostAnalysisStepFns and stored in
// `ModuleAnalysisState`.
//
// * `equivalentFuncOpBBArgsAnalysis` determines the equivalent bbArg for each
// tensor return value (if any).
// * `funcOpBbArgReadWriteAnalysis` determines whether or not a tensor bbArg is
// read/written.
//
// Only tensors that are equivalent to some FuncOp bbArg may be returned.
// Bufferization currently fails if other tensors (in particular tensors that
// bufferize out-of-place and result in a new buffer allocation) are returned.
// In the future, such allocations could be hoisted to the caller.
//
// Example: `foo` fails bufferization because %0 is not equivalent to any bbArg.
// ```
// func @foo() -> tensor<?xf32> {
// %0 = linalg.init_tensor [...] : tensor<?xf32>
// return %0 : tensor<?xf32>
// }
// ```
//
// Module Bufferization implements the following calling convention.
//
// * In the absence of conflicts within a FuncOp, the FuncOp's bbArgs may always
// be written to in-place.
// * If a tensor operand of a CallOp is read after the CallOp, the operand of
// the CallOp must bufferize out-of-place.
//
// Example: The tensor.insert op bufferizes in-place because it is allowed to
// modify the buffer of `%t1` directly. The CallOp in `caller` must bufferize
// out-of-place because `%t0` is modified by the callee but read by the
// tensor.extract op. The analysis of CallOps decides whether an OpOperand must
// bufferize out-of-place based on results of `funcOpBbArgReadWriteAnalysis`.
// ```
// func @callee(%t1 : tensor<?xf32>) -> tensor<?xf32> {
// %f = ... : f32
// %0 = tensor.insert %f into %t1[...] : tensor<?xf32>
// return %0 : tensor<?xf32>
// }
//
// func @caller() -> () {
// %t0 = ... : tensor<?xf32>
// %1 = call @callee(%t0) : (tensor<?xf32>) -> (tensor<?xf32>)
// %2 = tensor.extract %1[...] : tensor<?xf32>
// }
// ```
//
// Note: If a function is external, `funcOpBbArgReadWriteAnalysis` cannot
// analyze the function body. In such a case, the CallOp analysis conservatively
// assumes that each tensor OpOperand is both read and written.
//
// TODO: Add FuncOp attributes so that bbArgs of external FuncOps can be marked
// as "not reading" and/or "not writing".
#include "mlir/Dialect/Linalg/ComprehensiveBufferize/ModuleBufferization.h"
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/IR/Operation.h"
using namespace mlir;
using namespace linalg;
using namespace tensor;
using namespace comprehensive_bufferize;
using namespace mlir::bufferization;
namespace {
/// The state of analysis of a FuncOp.
enum class FuncOpAnalysisState { NotAnalyzed, InProgress, Analyzed };
/// Extra analysis state that is required for bufferization of function
/// boundaries.
struct ModuleAnalysisState : public DialectAnalysisState {
/// A mapping of ReturnOp OpOperand indices to equivalent FuncOp BBArg
/// indices.
DenseMap<FuncOp, DenseMap<int64_t, int64_t>> equivalentFuncArgs;
/// A set of all read BlockArguments of FuncOps.
// Note: BlockArgument knows about its owner, so we do not need to store
// FuncOps here.
DenseSet<BlockArgument> readBbArgs;
/// A set of all written-to BlockArguments of FuncOps.
DenseSet<BlockArgument> writtenBbArgs;
/// Keep track of which FuncOps are fully analyzed or currently being
/// analyzed.
DenseMap<FuncOp, FuncOpAnalysisState> analyzedFuncOps;
// A list of functions in the order in which they are analyzed + bufferized.
SmallVector<FuncOp> orderedFuncOps;
// A mapping of FuncOps to their callers.
DenseMap<FuncOp, DenseSet<Operation *>> callerMap;
};
} // namespace
/// Get ModuleAnalysisState.
static const ModuleAnalysisState &
getModuleAnalysisState(const AnalysisState &state) {
Optional<const ModuleAnalysisState *> maybeState =
state.getDialectState<ModuleAnalysisState>(
func::FuncDialect::getDialectNamespace());
assert(maybeState.hasValue() && "ModuleAnalysisState does not exist");
return **maybeState;
}
/// Get or create ModuleAnalysisState.
static ModuleAnalysisState &getModuleAnalysisState(AnalysisState &state) {
return state.getOrCreateDialectState<ModuleAnalysisState>(
func::FuncDialect::getDialectNamespace());
}
/// Return the state (phase) of analysis of the FuncOp.
static FuncOpAnalysisState getFuncOpAnalysisState(const AnalysisState &state,
FuncOp funcOp) {
const ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
auto it = moduleState.analyzedFuncOps.find(funcOp);
if (it == moduleState.analyzedFuncOps.end())
return FuncOpAnalysisState::NotAnalyzed;
return it->second;
}
/// Return the unique ReturnOp that terminates `funcOp`.
/// Return nullptr if there is no such unique ReturnOp.
static func::ReturnOp getAssumedUniqueReturnOp(FuncOp funcOp) {
func::ReturnOp returnOp;
for (Block &b : funcOp.getBody()) {
if (auto candidateOp = dyn_cast<func::ReturnOp>(b.getTerminator())) {
if (returnOp)
return nullptr;
returnOp = candidateOp;
}
}
return returnOp;
}
namespace {
/// Annotate IR with the results of the analysis. For testing purposes only.
static void annotateEquivalentReturnBbArg(OpOperand &returnVal,
BlockArgument bbArg) {
const char *kEquivalentArgsAttr = "__equivalent_func_args__";
Operation *op = returnVal.getOwner();
SmallVector<int64_t> equivBbArgs;
if (op->hasAttr(kEquivalentArgsAttr)) {
auto attr = op->getAttr(kEquivalentArgsAttr).cast<ArrayAttr>();
equivBbArgs = llvm::to_vector<4>(llvm::map_range(attr, [](Attribute a) {
return a.cast<IntegerAttr>().getValue().getSExtValue();
}));
} else {
equivBbArgs.append(op->getNumOperands(), -1);
}
equivBbArgs[returnVal.getOperandNumber()] = bbArg.getArgNumber();
OpBuilder b(op->getContext());
op->setAttr(kEquivalentArgsAttr, b.getI64ArrayAttr(equivBbArgs));
}
/// Store function BlockArguments that are equivalent to a returned value in
/// ModuleAnalysisState.
static LogicalResult
equivalentFuncOpBBArgsAnalysis(Operation *op, AnalysisState &state,
BufferizationAliasInfo &aliasInfo,
SmallVector<Operation *> &newOps) {
ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
// Support only single return-terminated block in the function.
auto funcOp = cast<FuncOp>(op);
func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
assert(returnOp && "expected func with single return op");
for (OpOperand &returnVal : returnOp->getOpOperands())
if (returnVal.get().getType().isa<RankedTensorType>())
for (BlockArgument bbArg : funcOp.getArguments())
if (bbArg.getType().isa<RankedTensorType>())
if (aliasInfo.areEquivalentBufferizedValues(returnVal.get(), bbArg)) {
moduleState
.equivalentFuncArgs[funcOp][returnVal.getOperandNumber()] =
bbArg.getArgNumber();
if (state.getOptions().testAnalysisOnly)
annotateEquivalentReturnBbArg(returnVal, bbArg);
}
return success();
}
/// Return true if the buffer of the given tensor value is written to. Must not
/// be called for values inside not yet analyzed functions. (Post-analysis
/// steps do not have to be run yet, i.e., "in progress" is also OK.)
static bool isValueWritten(Value value, const AnalysisState &state,
const BufferizationAliasInfo &aliasInfo) {
#ifndef NDEBUG
assert(value.getType().isa<TensorType>() && "expected TensorType");
FuncOp funcOp;
if (auto bbArg = value.dyn_cast<BlockArgument>()) {
Operation *owner = bbArg.getOwner()->getParentOp();
funcOp = isa<FuncOp>(owner) ? cast<FuncOp>(owner)
: owner->getParentOfType<FuncOp>();
} else {
funcOp = value.getDefiningOp()->getParentOfType<FuncOp>();
}
assert(getFuncOpAnalysisState(state, funcOp) !=
FuncOpAnalysisState::NotAnalyzed &&
"FuncOp must be fully analyzed or analysis in progress");
#endif // NDEBUG
bool isWritten = false;
aliasInfo.applyOnAliases(value, [&](Value val) {
for (OpOperand &use : val.getUses())
if (state.isInPlace(use) && state.bufferizesToMemoryWrite(use))
isWritten = true;
});
return isWritten;
}
static void annotateFuncArgAccess(FuncOp funcOp, BlockArgument bbArg,
bool isRead, bool isWritten) {
OpBuilder b(funcOp.getContext());
Attribute accessType;
if (isRead && isWritten) {
accessType = b.getStringAttr("read-write");
} else if (isRead) {
accessType = b.getStringAttr("read");
} else if (isWritten) {
accessType = b.getStringAttr("write");
} else {
accessType = b.getStringAttr("none");
}
funcOp.setArgAttr(bbArg.getArgNumber(), "bufferization.access", accessType);
}
/// Determine which FuncOp bbArgs are read and which are written. If this
/// PostAnalysisStepFn is run on a function with unknown ops, it will
/// conservatively assume that such ops bufferize to a read + write.
static LogicalResult
funcOpBbArgReadWriteAnalysis(Operation *op, AnalysisState &state,
BufferizationAliasInfo &aliasInfo,
SmallVector<Operation *> &newOps) {
ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
auto funcOp = cast<FuncOp>(op);
// If the function has no body, conservatively assume that all args are
// read + written.
if (funcOp.getBody().empty()) {
for (BlockArgument bbArg : funcOp.getArguments()) {
moduleState.readBbArgs.insert(bbArg);
moduleState.writtenBbArgs.insert(bbArg);
}
return success();
}
for (BlockArgument bbArg : funcOp.getArguments()) {
if (!bbArg.getType().isa<TensorType>())
continue;
bool isRead = state.isValueRead(bbArg);
bool isWritten = isValueWritten(bbArg, state, aliasInfo);
if (state.getOptions().testAnalysisOnly)
annotateFuncArgAccess(funcOp, bbArg, isRead, isWritten);
if (isRead)
moduleState.readBbArgs.insert(bbArg);
if (isWritten)
moduleState.writtenBbArgs.insert(bbArg);
}
return success();
}
} // namespace
static bool isaTensor(Type t) { return t.isa<TensorType>(); }
/// If `value` is a memref::CastOp, return its source. Otherwise, return
/// `value` directly.
static Value getNonCastedValue(Value value) {
while (auto castOp = value.getDefiningOp<memref::CastOp>())
value = castOp.source();
return value;
}
/// Remove the attribute that triggers inplace bufferization on a FuncOp
/// argument `bbArg`.
static void removeBufferizationFuncArguments(BlockArgument bbArg) {
auto funcOp = cast<FuncOp>(bbArg.getOwner()->getParentOp());
funcOp.removeArgAttr(bbArg.getArgNumber(),
BufferizableOpInterface::kBufferLayoutAttrName);
funcOp.removeArgAttr(bbArg.getArgNumber(),
BufferizableOpInterface::kInplaceableAttrName);
}
/// Return the FuncOp called by `callOp`.
static FuncOp getCalledFunction(CallOpInterface callOp) {
SymbolRefAttr sym = callOp.getCallableForCallee().dyn_cast<SymbolRefAttr>();
if (!sym)
return nullptr;
return dyn_cast_or_null<FuncOp>(
SymbolTable::lookupNearestSymbolFrom(callOp, sym));
}
/// Return the FunctionType with `argumentTypes` and `resultTypes` where each
/// tensor is replaced by the corresponding buffer type.
/// In order for all the callers to agree, this *must* bufferize to the most
/// dynamic buffer type supported.
/// A later pass across all CallOps in the module can decide whether to simplify
/// the types of to version according to some cost model.
static FunctionType
getBufferizedFunctionType(MLIRContext *ctx, TypeRange argumentTypes,
TypeRange resultTypes,
const BufferizationOptions &options) {
auto rewrite = [&](Type t) -> Type {
// TODO: non-zero address space.
// TODO: layout information if relevant.
if (auto tensorType = t.dyn_cast<TensorType>())
return getMemRefType(tensorType, options);
return t;
};
auto argTypes = llvm::to_vector<4>(llvm::map_range(argumentTypes, rewrite));
auto retTypes = llvm::to_vector<4>(llvm::map_range(resultTypes, rewrite));
return FunctionType::get(ctx, argTypes, retTypes);
}
/// Gather equivalence info of CallOps.
/// Note: This only adds new equivalence info if `funcOp` was already analyzed.
// TODO: This does not handle cyclic function call graphs etc.
static void equivalenceAnalysis(FuncOp funcOp,
BufferizationAliasInfo &aliasInfo,
ModuleAnalysisState &moduleState) {
funcOp->walk([&](func::CallOp callOp) {
FuncOp calledFunction = getCalledFunction(callOp);
assert(calledFunction && "could not retrieved called FuncOp");
// No equivalence info available for the called function.
if (!moduleState.equivalentFuncArgs.count(calledFunction))
return WalkResult::skip();
for (auto it : moduleState.equivalentFuncArgs[calledFunction]) {
int64_t returnIdx = it.first;
int64_t bbargIdx = it.second;
Value returnVal = callOp.getResult(returnIdx);
Value argVal = callOp->getOperand(bbargIdx);
aliasInfo.unionEquivalenceClasses(returnVal, argVal);
}
return WalkResult::advance();
});
}
/// Rewrite the `funcOp` arguments analysis return values and terminator into
/// buffer form (using the canonical memref layout for now), according to the
/// inPlace-bufferizable information of the function arguments.
///
/// This relies on a buffer equivalence analysis of each return operand. When a
/// result buffer is equivalent to a BlockArgument of `funcOp`, it can be
/// dropped from the return values and becomes inplaceable at all callers. This
/// assumes all CallOp perform the necessary work to clone operands so as to
/// make them inplaceable. Reliance on this logic will need to be relaxed in the
/// future.
///
/// Note: Returning a memref currently fails bufferization. If such memrefs
/// originate from an op with an Alloc effect, they could be hoisted in the
/// future.
static LogicalResult bufferizeFuncOpBoundary(FuncOp funcOp,
RewriterBase &rewriter,
BufferizationState &state) {
const ModuleAnalysisState &moduleState =
getModuleAnalysisState(state.getAnalysisState());
// If nothing to do then we are done.
if (!llvm::any_of(funcOp.getFunctionType().getInputs(), isaTensor) &&
!llvm::any_of(funcOp.getFunctionType().getResults(), isaTensor))
return success();
// Get the bufferized FunctionType for funcOp or construct it if not yet
// available.
// TODO: Atm we have 3 cases:
// 1. if a function is called from within the Module, it must have bufferized
// to inplaceable tensor results.
// 2. if it is bodiless, it must have bufferized and is not allowed to have
// result tensors.
// 3. if it is not called internally, it still must bufferize to inplaceable
// tensor results and we construct it now (e.g. top-level function called
// externally).
// -> Figure out a better layering.
TypeRange resultTypes;
// Corner case: Bodiless FuncOp
// ============================
// The body of such functions is assumed opaque and we can't know the
// bufferization contract they want to enforce atm.
// As a consequence, only support functions that don't return any tensor atm.
if (funcOp.getBody().empty()) {
if (llvm::any_of(funcOp.getFunctionType().getResults(), isaTensor))
return funcOp->emitError() << "cannot bufferize bodiless function that "
<< "returns a tensor";
FunctionType bufferizedFuncType = getBufferizedFunctionType(
funcOp.getContext(), funcOp.getFunctionType().getInputs(), TypeRange{},
state.getOptions());
funcOp.setType(bufferizedFuncType);
return success();
}
// Support only single return-terminated block in the function.
func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
assert(returnOp && "expected func with single return op");
// 1. For each FuncOp result, keep track of which inplace argument it reuses.
SmallVector<Value> returnValues;
for (OpOperand &returnOperand : returnOp->getOpOperands()) {
Value returnVal = returnOperand.get();
// If not a renturn 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.
auto funcOpIt = moduleState.equivalentFuncArgs.find(funcOp);
if (funcOpIt != moduleState.equivalentFuncArgs.end() &&
funcOpIt->second.count(returnOperand.getOperandNumber()))
continue;
// Cast values at the call site if necessary.
returnValues.push_back(
getNonCastedValue(*state.getBuffer(rewriter, returnOperand)));
}
// 2. Rewrite the terminator without the inPlace bufferizable values.
ValueRange retValues{returnValues};
FunctionType bufferizedFuncType = getBufferizedFunctionType(
funcOp.getContext(), funcOp.getFunctionType().getInputs(),
retValues.getTypes(), state.getOptions());
OpBuilder b(returnOp);
b.create<func::ReturnOp>(returnOp.getLoc(), returnValues);
returnOp->erase();
// 3. Rewrite the bbArgs.
// Iterate on the original `numArgs` and replace them in order.
// This guarantees the argument order still matches after the rewrite.
Block &frontBlock = funcOp.getBody().front();
unsigned numArgs = frontBlock.getNumArguments();
for (unsigned idx = 0; idx < numArgs; ++idx) {
auto bbArg = frontBlock.getArgument(0);
auto tensorType = bbArg.getType().dyn_cast<TensorType>();
// Non-tensor types are just forwarded.
if (!tensorType) {
frontBlock.addArgument(bbArg.getType(), bbArg.getLoc());
bbArg.replaceAllUsesWith(frontBlock.getArguments().back());
frontBlock.eraseArgument(0);
continue;
}
// Get the buffer type from the bufferized function type.
Type memrefType = bufferizedFuncType.getInput(idx);
Value memref = frontBlock.addArgument(memrefType, bbArg.getLoc());
OpBuilder b(funcOp->getContext());
b.setInsertionPointToStart(&frontBlock);
// Replace all uses of bbArg through a ToMemRefOp.
for (auto &use : llvm::make_early_inc_range(bbArg.getUses())) {
if (auto toMemrefOp =
dyn_cast<bufferization::ToMemrefOp>(use.getOwner())) {
if (memref.getType() != toMemrefOp.memref().getType()) {
// Type has changed, insert a cast.
assert(memref::CastOp::areCastCompatible(
memref.getType(), toMemrefOp.memref().getType()) &&
"bufferizeFuncOpBoundary: cast incompatible");
auto castOp = b.create<memref::CastOp>(
funcOp.getLoc(), toMemrefOp.memref().getType(), memref);
toMemrefOp.memref().replaceAllUsesWith(castOp);
} else {
// Type did not change, replace directly.
toMemrefOp.memref().replaceAllUsesWith(memref);
}
}
}
// Replace all remaining uses by a to_tensor.
if (!bbArg.use_empty()) {
auto toTensorOp =
b.create<bufferization::ToTensorOp>(funcOp.getLoc(), memref);
bbArg.replaceAllUsesWith(toTensorOp);
}
frontBlock.eraseArgument(0);
// TODO: add support to erase aliasInfo entries if deemed necessary.
}
// 4. Rewrite the FuncOp type to buffer form.
funcOp.setType(bufferizedFuncType);
return success();
}
/// Store all functions of the `moduleOp` in `orderedFuncOps`, sorted by
/// callee-caller order (i.e. callees without callers first).
/// Store the map of FuncOp to all its callers in `callerMap`.
/// Return `failure()` if a cycle of calls is detected or if we are unable to
/// retrieve the called FuncOp from any CallOpInterface.
static LogicalResult
getFuncOpsOrderedByCalls(ModuleOp moduleOp,
SmallVectorImpl<FuncOp> &orderedFuncOps,
DenseMap<FuncOp, DenseSet<Operation *>> &callerMap) {
// For each FuncOp, the set of functions called by it (i.e. the union of
// symbols of all nested CallOpInterfaceOp).
DenseMap<FuncOp, DenseSet<FuncOp>> calledBy;
// For each FuncOp, the number of CallOpInterface it contains.
DenseMap<FuncOp, unsigned> numberCallOpsContainedInFuncOp;
WalkResult res = moduleOp.walk([&](FuncOp funcOp) -> WalkResult {
if (!funcOp.getBody().empty()) {
func::ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
if (!returnOp)
return funcOp->emitError()
<< "cannot bufferize a FuncOp with tensors and "
"without a unique ReturnOp";
}
numberCallOpsContainedInFuncOp[funcOp] = 0;
return funcOp.walk([&](CallOpInterface callOp) -> WalkResult {
// Only support CallOp for now.
if (!isa<func::CallOp>(callOp.getOperation()))
return callOp->emitError() << "expected a CallOp";
FuncOp calledFunction = getCalledFunction(callOp);
assert(calledFunction && "could not retrieved called FuncOp");
auto it = callerMap.try_emplace(calledFunction, DenseSet<Operation *>{});
it.first->getSecond().insert(callOp);
if (calledBy[calledFunction].count(funcOp) == 0) {
calledBy[calledFunction].insert(funcOp);
numberCallOpsContainedInFuncOp[funcOp]++;
}
return WalkResult::advance();
});
});
if (res.wasInterrupted())
return failure();
// Iteratively remove function operation that do not call any of the
// functions remaining in the callCounter map and add them to the worklist.
while (!numberCallOpsContainedInFuncOp.empty()) {
auto it = llvm::find_if(numberCallOpsContainedInFuncOp,
[](auto entry) { return entry.getSecond() == 0; });
if (it == numberCallOpsContainedInFuncOp.end())
return moduleOp.emitOpError(
"expected callgraph to be free of circular dependencies.");
orderedFuncOps.push_back(it->getFirst());
for (auto callee : calledBy[it->getFirst()])
numberCallOpsContainedInFuncOp[callee]--;
numberCallOpsContainedInFuncOp.erase(it);
}
return success();
}
static void
foreachCaller(const DenseMap<FuncOp, DenseSet<Operation *>> &callerMap,
FuncOp callee, llvm::function_ref<void(Operation *)> doit) {
auto itCallers = callerMap.find(callee);
if (itCallers == callerMap.end())
return;
for (Operation *caller : itCallers->second)
doit(caller);
}
/// Postprocess the linalg.buffer_layout annotation across function boundaries.
/// This is a purely mechanical process that may later become part of a
/// separate pass with its own layout assignment heuristic.
static void layoutPostProcessing(ModuleOp moduleOp) {
SmallVector<FuncOp> orderedFuncOps;
DenseMap<FuncOp, DenseSet<Operation *>> callerMap;
auto res = getFuncOpsOrderedByCalls(moduleOp, orderedFuncOps, callerMap);
(void)res;
assert(succeeded(res) && "unexpected getFuncOpsOrderedByCalls failure");
for (FuncOp funcOp : orderedFuncOps) {
DenseMap<Operation *, SmallVector<Value>> operandsPerCaller;
foreachCaller(callerMap, funcOp, [&](Operation *caller) {
operandsPerCaller.try_emplace(caller, SmallVector<Value>());
});
SmallVector<Type> argumentTypes;
// Iterate on each function argument and check it it was marked with a
// desired layout.
for (const auto &it :
llvm::enumerate(funcOp.getFunctionType().getInputs())) {
int argNumber = it.index();
Type inputType = it.value();
auto memrefType = inputType.dyn_cast<MemRefType>();
auto layoutAttr = funcOp.getArgAttrOfType<AffineMapAttr>(
argNumber, BufferizableOpInterface::kBufferLayoutAttrName);
AffineMap desiredLayoutMap =
layoutAttr ? layoutAttr.getValue() : AffineMap();
AffineMap currentLayoutMap =
memrefType ? getStridedLinearLayoutMap(memrefType) : AffineMap();
if (!memrefType || !layoutAttr || desiredLayoutMap == currentLayoutMap) {
argumentTypes.push_back(inputType);
foreachCaller(callerMap, funcOp, [&](Operation *caller) {
operandsPerCaller.find(caller)->getSecond().push_back(
caller->getOperand(argNumber));
});
continue;
}
// Compute the buffer type with desired layout and add to input argument
// types.
MemRefType desiredMemrefType = MemRefType::get(
memrefType.getShape(), memrefType.getElementType(), desiredLayoutMap);
argumentTypes.push_back(desiredMemrefType);
// If funcOp's body is not empty, change the bbArg type and propagate.
if (!funcOp.getBody().empty()) {
BlockArgument bbArg = funcOp.getArgument(argNumber);
bbArg.setType(desiredMemrefType);
OpBuilder b(bbArg.getContext());
b.setInsertionPointToStart(bbArg.getOwner());
assert(memref::CastOp::areCastCompatible(bbArg.getType(), memrefType) &&
"layoutPostProcessing: cast incompatible");
// Cast back to the original memrefType and let it canonicalize.
Value cast =
b.create<memref::CastOp>(funcOp.getLoc(), memrefType, bbArg);
bbArg.replaceAllUsesExcept(cast, cast.getDefiningOp());
}
// Cast to desired buffer type on all callers to `funcOp`.
// TODO: on the callee side, this may even have to trigger a copy to
// change the layout. For now let the memref::CastOp fail to verify in
// such cases.
auto castArg = [&](Operation *caller) {
OpBuilder b(caller);
assert(
memref::CastOp::areCastCompatible(
caller->getOperand(argNumber).getType(), desiredMemrefType) &&
"layoutPostProcessing.2: cast incompatible");
Value newOperand = b.create<memref::CastOp>(
funcOp.getLoc(), desiredMemrefType, caller->getOperand(argNumber));
operandsPerCaller.find(caller)->getSecond().push_back(newOperand);
};
foreachCaller(callerMap, funcOp, castArg);
}
// Set operands with cast buffer on all callers to `funcOp`.
foreachCaller(callerMap, funcOp, [&](Operation *caller) {
caller->setOperands(operandsPerCaller.lookup(caller));
});
// Finally set the funcOp type to update the arguments.
auto newFuncType = FunctionType::get(moduleOp.getContext(), argumentTypes,
funcOp.getFunctionType().getResults());
funcOp.setType(newFuncType);
}
}
namespace mlir {
namespace linalg {
namespace comprehensive_bufferize {
namespace std_ext {
/// Return the index of the bbArg in the given FuncOp that is equivalent to the
/// specified return value (if any).
static Optional<int64_t>
getEquivalentFuncArgIdx(FuncOp funcOp, const ModuleAnalysisState &state,
int64_t returnValIdx) {
auto funcOpIt = state.equivalentFuncArgs.find(funcOp);
if (funcOpIt == state.equivalentFuncArgs.end())
// No equivalence info stores for funcOp.
return None;
auto retValIt = funcOpIt->getSecond().find(returnValIdx);
if (retValIt == funcOpIt->getSecond().end())
// Return value has no equivalent bbArg.
return None;
return retValIt->getSecond();
}
struct CallOpInterface
: public BufferizableOpInterface::ExternalModel<CallOpInterface,
func::CallOp> {
bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
func::CallOp callOp = cast<func::CallOp>(op);
FuncOp funcOp = getCalledFunction(callOp);
assert(funcOp && "expected CallOp to a FuncOp");
const ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
if (getFuncOpAnalysisState(state, funcOp) != FuncOpAnalysisState::Analyzed)
// FuncOp not analyzed yet. Assume that OpOperand is read.
return true;
return moduleState.readBbArgs.contains(
funcOp.getArgument(opOperand.getOperandNumber()));
}
bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
func::CallOp callOp = cast<func::CallOp>(op);
FuncOp funcOp = getCalledFunction(callOp);
assert(funcOp && "expected CallOp to a FuncOp");
const ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
if (getFuncOpAnalysisState(state, funcOp) != FuncOpAnalysisState::Analyzed)
// FuncOp not analyzed yet. Assume that OpOperand is written.
return true;
return moduleState.writtenBbArgs.contains(
funcOp.getArgument(opOperand.getOperandNumber()));
}
SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
func::CallOp callOp = cast<func::CallOp>(op);
FuncOp funcOp = getCalledFunction(callOp);
assert(funcOp && "expected CallOp to a FuncOp");
const ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
SmallVector<OpResult> result;
for (int64_t resultIdx = 0; resultIdx < callOp->getNumResults();
++resultIdx)
if (Optional<int64_t> maybeArgNumber =
getEquivalentFuncArgIdx(funcOp, moduleState, resultIdx))
if (*maybeArgNumber == opOperand.getOperandNumber())
result.push_back(callOp->getOpResult(resultIdx));
return result;
}
SmallVector<OpOperand *>
getAliasingOpOperand(Operation *op, OpResult opResult,
const AnalysisState &state) const {
func::CallOp callOp = cast<func::CallOp>(op);
FuncOp funcOp = getCalledFunction(callOp);
assert(funcOp && "expected CallOp to a FuncOp");
const ModuleAnalysisState &moduleState = getModuleAnalysisState(state);
// TODO: We should be looking for aliasing block arguments here. The current
// condition is actually stronger than neccesary. Once we check for aliasing
// block arguments, we may be multiple.
if (Optional<int64_t> maybeArgNumber = getEquivalentFuncArgIdx(
funcOp, moduleState, opResult.getResultNumber()))
return {&op->getOpOperand(*maybeArgNumber)};
// Note: Returning a non-equivalent tensor from a FuncOp is currently not
// supported an will fail bufferization.
return {};
}
BufferRelation bufferRelation(Operation *op, OpResult opResult,
const AnalysisState &state) const {
return BufferRelation::Equivalent;
}
/// In a first approximation, all the function arguments of a FuncOp are
/// marked inplaceable. For now, it is the responsibility of the `callOp`
/// bufferization to allow FuncOp that are inplaceable to write inPlace.
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
BufferizationState &state) const {
func::CallOp callOp = cast<func::CallOp>(op);
unsigned numResults = callOp.getNumResults();
unsigned numOperands = callOp->getNumOperands();
FuncOp funcOp = getCalledFunction(callOp);
assert(funcOp && "expected CallOp to a FuncOp");
const ModuleAnalysisState &moduleState =
getModuleAnalysisState(state.getAnalysisState());
// Result types of the bufferized CallOp.
SmallVector<Type> resultTypes;
// Replacement values for the existing CallOp. These are usually the results
// of the bufferized CallOp, unless a tensor result folds onto an operand.
SmallVector<Value> replacementValues(numResults, Value());
// For non-tensor results: A mapping from return val indices of the old
// CallOp to return val indices of the bufferized CallOp.
SmallVector<Optional<unsigned>> retValMapping(numResults, None);
// Operands of the bufferized CallOp.
SmallVector<Value> newOperands(numOperands, Value());
// Based on previously gathered equivalence information, we know if a
// tensor result folds onto an operand. These are the only tensor value
// results that are supported at the moment.
//
// For tensors return values that do not fold onto an operand, additional
// work is needed (TODO) to either:
// * hoist a result into an inplaceable operand or
// * devise a better representation to truly return a buffer.
//
// Note: If a function has no body, no equivalence information is
// available. Consequently, a tensor return value cannot be proven to fold
// onto a FuncOp bbArg, so calls to such functions are not bufferizable at
// the moment.
// 1. Compute the result types of the new CallOp. Tensor results that are
// equivalent to a FuncOp bbArg are no longer returned.
for (const auto &it : llvm::enumerate(callOp.getResultTypes())) {
unsigned returnValIdx = it.index();
Type returnType = it.value();
if (!isaTensor(returnType)) {
// Non-tensor values are returned.
retValMapping[returnValIdx] = resultTypes.size();
resultTypes.push_back(returnType);
continue;
}
if (Optional<int64_t> bbArgIdx =
getEquivalentFuncArgIdx(funcOp, moduleState, returnValIdx)) {
// Return operands that are equivalent to some bbArg, are not
// returned.
FailureOr<Value> bufferOrFailure =
state.getBuffer(rewriter, callOp->getOpOperand(*bbArgIdx));
if (failed(bufferOrFailure))
return failure();
replacementValues[returnValIdx] = *bufferOrFailure;
newOperands[*bbArgIdx] = *bufferOrFailure;
continue;
}
return callOp->emitError(
"call to FuncOp that returns non-equivalent tensors not supported");
}
// 2. Compute bufferized FunctionType.
SmallVector<Type> argumentTypes{callOp->getOperandTypes()};
// Get the bufferized FunctionType for funcOp or construct it if not yet
// available.
FunctionType bufferizedFuncType = getBufferizedFunctionType(
funcOp.getContext(), argumentTypes, resultTypes, state.getOptions());
// 3. Rewrite tensor operands as memrefs based on `bufferizedFuncType`.
for (OpOperand &opOperand : callOp->getOpOperands()) {
unsigned idx = opOperand.getOperandNumber();
Value tensorOperand = opOperand.get();
// Non-tensor operands are just copied.
if (!tensorOperand.getType().isa<TensorType>()) {
newOperands[idx] = tensorOperand;
continue;
}
// Retrieve buffers for tensor operands. Tensor operand buffers, who's
// corresponding FuncOp bbArgs are equivalent to a returned tensor, were
// already stored in `newOperands` during Step 1.
Value buffer = newOperands[idx];
if (!buffer) {
FailureOr<Value> bufferOrFailure = state.getBuffer(rewriter, opOperand);
if (failed(bufferOrFailure))
return failure();
buffer = *bufferOrFailure;
}
// Caller / callee type mismatch is handled with a CastOp.
auto memRefType = bufferizedFuncType.getInput(idx);
// Since we don't yet have a clear layout story, to_memref may
// conservatively turn tensors into more dynamic memref than necessary.
// If the memref type of the callee fails, introduce an extra memref.cast
// that will either canonicalize away or fail compilation until we can do
// something better.
if (buffer.getType() != memRefType) {
assert(
memref::CastOp::areCastCompatible(buffer.getType(), memRefType) &&
"CallOp::bufferize: cast incompatible");
Value castBuffer = rewriter.create<memref::CastOp>(callOp.getLoc(),
memRefType, buffer);
buffer = castBuffer;
}
newOperands[idx] = buffer;
}
// 4. Create the new CallOp.
Operation *newCallOp = rewriter.create<func::CallOp>(
callOp.getLoc(), funcOp.getSymName(), resultTypes, newOperands);
newCallOp->setAttrs(callOp->getAttrs());
// Get replacement values for non-tensor / non-equivalent results.
for (unsigned i = 0; i < replacementValues.size(); ++i) {
if (replacementValues[i])
continue;
replacementValues[i] = newCallOp->getResult(*retValMapping[i]);
}
// 5. Replace the old op with the new op.
replaceOpWithBufferizedValues(rewriter, callOp, replacementValues);
return success();
}
};
struct ReturnOpInterface
: public BufferizableOpInterface::ExternalModel<ReturnOpInterface,
func::ReturnOp> {
bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
return true;
}
bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
return false;
}
SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
return {};
}
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
BufferizationState &state) const {
#ifndef NDEBUG
auto returnOp = cast<func::ReturnOp>(op);
assert(isa<FuncOp>(returnOp->getParentOp()) &&
"only support FuncOp parent for ReturnOp");
#endif // NDEBUG
return failure();
}
};
struct FuncOpInterface
: public BufferizableOpInterface::ExternalModel<FuncOpInterface, FuncOp> {
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
BufferizationState &state) const {
return failure();
}
/// 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");
// "linalg.inplaceable" overrides other writability decisions. This is
// currently used for testing only.
if (BoolAttr inplaceAttr = funcOp.getArgAttrOfType<BoolAttr>(
bbArg.getArgNumber(),
BufferizableOpInterface::kInplaceableAttrName))
return inplaceAttr.getValue();
// All function arguments are writable by default.
return true;
}
bool isAllocationHoistingBarrier(Operation *op) const { return true; }
};
} // namespace std_ext
} // namespace comprehensive_bufferize
} // namespace linalg
} // namespace mlir
void mlir::linalg::comprehensive_bufferize::std_ext::
registerModuleBufferizationExternalModels(DialectRegistry &registry) {
registry.addOpInterface<func::CallOp, std_ext::CallOpInterface>();
registry.addOpInterface<func::ReturnOp, std_ext::ReturnOpInterface>();
registry.addOpInterface<FuncOp, std_ext::FuncOpInterface>();
}
/// Set the attribute that triggers inplace bufferization on a FuncOp argument
/// `bbArg`.
static void setInPlaceFuncArgument(BlockArgument bbArg, bool inPlace) {
auto funcOp = cast<FuncOp>(bbArg.getOwner()->getParentOp());
funcOp.setArgAttr(bbArg.getArgNumber(),
BufferizableOpInterface::kInplaceableAttrName,
BoolAttr::get(bbArg.getContext(), inPlace));
}
/// Annotate the IR with the result of the analysis. For testing/debugging only.
static void annotateOpsWithBufferizationMarkers(FuncOp funcOp,
const AnalysisState &state) {
auto bufferizableOp = cast<BufferizableOpInterface>(funcOp.getOperation());
for (BlockArgument bbArg : funcOp.getArguments())
if (bbArg.getType().isa<TensorType>())
setInPlaceFuncArgument(bbArg, bufferizableOp.isWritable(bbArg, state));
}
LogicalResult mlir::linalg::comprehensive_bufferize::runModuleBufferize(
ModuleOp moduleOp, OneShotBufferizationOptions options) {
IRRewriter rewriter(moduleOp.getContext());
OneShotAnalysisState analysisState(moduleOp, options);
BufferizationState bufferizationState(analysisState);
ModuleAnalysisState &moduleState = getModuleAnalysisState(analysisState);
BufferizationAliasInfo &aliasInfo = analysisState.getAliasInfo();
if (failed(getFuncOpsOrderedByCalls(moduleOp, moduleState.orderedFuncOps,
moduleState.callerMap)))
return failure();
// Collect bbArg/return value information after the analysis.
options.addPostAnalysisStep(equivalentFuncOpBBArgsAnalysis);
options.addPostAnalysisStep(funcOpBbArgReadWriteAnalysis);
// Analyze ops.
for (FuncOp funcOp : moduleState.orderedFuncOps) {
// No body => no analysis.
if (funcOp.getBody().empty())
continue;
// Now analyzing function.
moduleState.analyzedFuncOps[funcOp] = FuncOpAnalysisState::InProgress;
// Analyze funcOp.
if (failed(analyzeOp(funcOp, analysisState)))
return failure();
// Gather equivalence info for CallOps.
// TODO: Make this a post-analysis step.
equivalenceAnalysis(funcOp, aliasInfo, moduleState);
// Mark op as fully analyzed.
moduleState.analyzedFuncOps[funcOp] = FuncOpAnalysisState::Analyzed;
// Add annotations to function arguments.
if (options.testAnalysisOnly)
annotateOpsWithBufferizationMarkers(funcOp, analysisState);
}
if (options.testAnalysisOnly)
return success();
// Bufferize function bodies.
for (FuncOp funcOp : moduleState.orderedFuncOps) {
// No body => no analysis.
if (funcOp.getBody().empty())
continue;
if (failed(bufferizeOp(funcOp, bufferizationState)))
return failure();
}
// Bufferize function boundaries.
for (FuncOp funcOp : moduleState.orderedFuncOps) {
// Note: It would be good to apply cleanups here but we cannot as aliasInfo
// would be invalidated.
if (failed(bufferizeFuncOpBoundary(funcOp, rewriter, bufferizationState)))
return failure();
if (!options.allowReturnAllocs &&
llvm::any_of(funcOp.getFunctionType().getResults(), [](Type t) {
return t.isa<MemRefType, UnrankedMemRefType>();
})) {
funcOp->emitError("memref return type is unsupported");
return failure();
}
}
// Finalize all buffers.
if (failed(finalizeBuffers(moduleOp, options)))
return failure();
// Perform a post-processing pass of layout modification at function boundary
// according to the kBufferLayoutAttrName.
layoutPostProcessing(moduleOp);
// Post-pass cleanup of inplaceable and buffer_layout attributes.
moduleOp.walk([&](FuncOp op) {
for (BlockArgument bbArg : op.getArguments())
removeBufferizationFuncArguments(bbArg);
});
return success();
}
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