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llvm / llvm-project / e40e17fcaf7ae28f16d4ece716f336b276ab8dce / . / mlir / lib / Dialect / Linalg / ComprehensiveBufferize / ComprehensiveBufferize.cpp
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//===- ComprehensiveBufferize.cpp - Single pass bufferization -------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// Perform inplace bufferization within function boundaries.
// This is a specialized pass that supports inplace analysis for a fixed subset
// of ops that have well-defined inplace semantics.
// This pass caters to high-performance codegen where buffer reuse is deemed
// critical: the pass should fail if the bufferized form of the function needs
// to return any buffer.
// Generic control-flow and branching are unsupported.
// Composability with extensible set of ops is not a first-class concern.
//
// Bufferization occurs by:
// a. performing an inPlace analysis `inPlaceAnalysisFuncOpBody`
// which marks each operation within the function with the
// `kInPlaceResultsAttrName` attribute.
// b. traversing each operation in the function and rewriting it in
// buffer form and keeping a BlockAndValueMapping mapping of the
// rewrites. New allocations are introduced during this step.
// TODO: Allocation + depending op hoisting to outermost enclosing
// sequential scope.
// c. at the end of this bufferization, 3 cases may occur:
// i. inplaceable function arguments may be reused in place after the
// function itself has been bufferized. This is encoded by IR resembling:
//
// ```
// #map = affine_map<(d0)[s0, s1] -> (d0 * s1 + s0)>
// func @foo(%A: tensor<?xf32> {linalg.inplaceable = true})
// -> tensor<?xf32> {
// %0 = bufferization.to_memref %A : memref<?xf32, #map>
// // ... uses of %0
// %res = bufferization.to_tensor %0 : memref<?xf32, #map>
// return %res : tensor<?xf32>
// }
// ```
//
// this is the cue for the bufferization of the function foo (and calls
// to it) may bufferize to `func @foo(%A: memref<?xf32, some_layout>)`.
// To fully achieve bufferization, an additional analysis is needed to
// determine whether function argument/operand pairs bufferize to a
// single inplace buffer argument (i.e. functions may return tensors in
// arbitrary order that may not match argument numbers).
//
// ii. results that don't map to an inplaceable function argument are
// generally allocated. Since memref semantics wrt ownership of the
// underlying memory region are not well-defined, comprehensive
// bufferization chooses to perform allocations in a scoped fashion:
// returning memrefs is always considered illegal.
// Such scenarios are encoded by IR resembling:
//
// ```
// #map = affine_map<(d0)[s0, s1] -> (d0 * s1 + s0)>
// func @foo(%A: tensor<?xf32> {linalg.inplaceable = true})
// -> tensor<?xf32> {
// %0 = bufferization.to_memref %A : memref<?xf32, #map>
// %1 = memref.dim %0, %c0 : memref<?xf32, #map>
// %2 = memref.alloc(%1) : memref<?xf32>
// %3 = memref.cast %2 : memref<?xf32> to memref<?xf32, #map>
// // ... uses of %3
// memref.dealloc %2 : memref<?xf32, #map>
// %res = bufferization.to_tensor %3 : memref<?xf32, #map>
// return %res : tensor<?xf32>
// }
// ```
//
// this is the cue for the bufferization of the function foo (and calls
// to it) that it must bufferize to `func @foo(%A: memref<?xf32,
// some_layout>,
// %B: memref<?xf32, some_layout>)` (i.e. make a cloned
// allocation of the result tensor)
// To fully achieve bufferization, the alloc/dealloc pair must be lifted
// out of the function at each call site.
//
// iii. as an optimization over ii., it may be possible to reuse an argument
// and only want to return a slice.
// This may forego allocation by letting *all* callers decide whether to
// pass a new *aliasing* memref function argument (i.e. a subview).
// Without loss of generality, callers may agree to allocate a new buffer
// to avoid this aliasing. Such scenarios are encoded by IR resembling:
//
// ```
// #map = affine_map<(d0)[s0, s1] -> (d0 * s1 + s0)>
// func @foo(%arg0: tensor<?xf32> {linalg.inplaceable = true})
// -> tensor<4xf32> {
// %0 = bufferization.to_memref %arg0 : memref<?xf32, #map>
// %1 = memref.subview %0[0] [4] [1] : memref<?xf32, #map> to
// memref<4xf32, #map>
// // ... inplace computes into %1
// %3 = bufferization.to_tensor %1 : memref<4xf32, #map>
// return %3 : tensor<4xf32>
// }
// ```
//
// Note: In the future, it may be worthwhile to design special bufferization
// ops to encode the desired semantics at function boundaries for i., ii. and
// iii.
//
// Lastly, note that layout map chosen to bufferize is the most dynamic
// canonical strided layout of the proper rank. This ensures compatibility with
// expected layouts after transformations. Combinations of memref.cast +
// canonicalization are responsible for clean ups.
#include "mlir/Dialect/Linalg/ComprehensiveBufferize/ComprehensiveBufferize.h"
#include <random>
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Linalg/ComprehensiveBufferize/BufferizableOpInterface.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/AsmState.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Dominance.h"
#include "mlir/IR/Operation.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/InferTypeOpInterface.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Pass/PassManager.h"
#include "llvm/ADT/DenseSet.h"
#include "llvm/ADT/ScopeExit.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/FormatVariadic.h"
#define DEBUG_TYPE "comprehensive-module-bufferize"
using namespace mlir;
using namespace linalg;
using namespace tensor;
using namespace comprehensive_bufferize;
#define DBGS() (llvm::dbgs() << '[' << DEBUG_TYPE << "] ")
#define LDBG(X) LLVM_DEBUG(DBGS() << X)
// Forward declarations.
#ifndef NDEBUG
static std::string printOperationInfo(Operation *, bool prefix = true);
static std::string printValueInfo(Value, bool prefix = true);
#endif
//===----------------------------------------------------------------------===//
// Generic helpers.
//===----------------------------------------------------------------------===//
static bool isaTensor(Type t) { return t.isa<TensorType>(); }
/// 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 unique ReturnOp that terminates `funcOp`.
/// Return nullptr if there is no such unique ReturnOp.
static ReturnOp getAssumedUniqueReturnOp(FuncOp funcOp) {
ReturnOp returnOp;
for (Block &b : funcOp.body()) {
if (auto candidateOp = dyn_cast<ReturnOp>(b.getTerminator())) {
if (returnOp)
return nullptr;
returnOp = candidateOp;
}
}
return returnOp;
}
//===----------------------------------------------------------------------===//
// Bufferization-specific attribute manipulation.
// These are for testing and debugging only. Bufferization information is
// stored in BufferizationAliasInfo. When run with `testAnalysisOnly`, the IR
// is annotated with the results of the analysis (copied from
// BufferizationAliasInfo), so that they can be checked in tests.
//===----------------------------------------------------------------------===//
/// Attribute marker to specify op results that can be bufferized inPlace.
constexpr StringLiteral kInPlaceResultsAttrName = "__inplace_results_attr__";
/// Mark whether OpResult can actually be bufferized inplace.
/// If `inPlace` is `true`, the use-def chain analysis has guaranteed that no
/// subsequent write would occur to the bufferized tensor value (i.e. the result
/// can be bufferized inplace).
static void setInPlaceOpResult(OpResult opResult, bool inPlace) {
if (!opResult)
return;
Operation *op = opResult.getOwner();
auto attr =
op->getAttr(kInPlaceResultsAttrName).dyn_cast_or_null<ArrayAttr>();
SmallVector<StringRef> inPlaceVector =
attr ? SmallVector<StringRef>(
llvm::to_vector<4>(attr.getAsValueRange<StringAttr>()))
: SmallVector<StringRef>(op->getNumResults(), "false");
LDBG("->set inPlace=" << inPlace << " <- #" << opResult.getResultNumber()
<< ": " << printOperationInfo(op) << "\n");
inPlaceVector[opResult.getResultNumber()] = inPlace ? "true" : "false";
op->setAttr(kInPlaceResultsAttrName,
OpBuilder(op).getStrArrayAttr(inPlaceVector));
}
/// 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));
}
/// 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);
}
//===----------------------------------------------------------------------===//
// Printing helpers.
//===----------------------------------------------------------------------===//
#ifndef NDEBUG
/// Helper method printing the bufferization information of a buffer / tensor.
static void printTensorOrBufferInfo(std::string prefix, Value value,
AsmState &state, llvm::raw_ostream &os) {
if (!value.getType().isa<ShapedType>())
return;
os << prefix;
value.printAsOperand(os, state);
os << " : " << value.getType();
}
/// Print the operation name and bufferization information.
static std::string printOperationInfo(Operation *op, bool prefix) {
std::string result;
llvm::raw_string_ostream os(result);
AsmState state(op->getParentOfType<mlir::FuncOp>());
StringRef tab = prefix ? "\n[" DEBUG_TYPE "]\t" : "";
os << tab << op->getName();
SmallVector<Value> shapedOperands;
for (OpOperand &opOperand : op->getOpOperands()) {
std::string prefix =
llvm::formatv("{0} -> #{1} ", tab, opOperand.getOperandNumber());
printTensorOrBufferInfo(prefix, opOperand.get(), state, os);
}
for (OpResult opResult : op->getOpResults()) {
std::string prefix =
llvm::formatv("{0} <- #{1} ", tab, opResult.getResultNumber());
printTensorOrBufferInfo(prefix, opResult, state, os);
}
return result;
}
/// Print the bufferization information for the defining op or block argument.
static std::string printValueInfo(Value value, bool prefix) {
auto *op = value.getDefiningOp();
if (op)
return printOperationInfo(op, prefix);
// Print the block argument bufferization information.
std::string result;
llvm::raw_string_ostream os(result);
AsmState state(value.getParentRegion()->getParentOfType<mlir::FuncOp>());
os << value;
printTensorOrBufferInfo("\n\t - ", value, state, os);
return result;
}
#endif
//===----------------------------------------------------------------------===//
// Bufferization-specific alias analysis.
//===----------------------------------------------------------------------===//
/// Return true if opOperand has been decided to bufferize in-place.
static bool isInplaceMemoryWrite(OpOperand &opOperand,
const BufferizationAliasInfo &aliasInfo) {
// Ops that do not bufferize to a memory write, cannot be write in-place.
if (!bufferizesToMemoryWrite(opOperand))
return false;
OpResult opResult = getAliasingOpResult(opOperand);
return opResult && aliasInfo.isInPlace(opResult);
}
/// Return true if, under current bufferization decisions, the buffer of `value`
/// is not writable.
static bool aliasesNonWritableBuffer(Value value,
const BufferizationAliasInfo &aliasInfo) {
LDBG("WRITABILITY ANALYSIS FOR " << printValueInfo(value) << "\n");
bool foundNonWritableBuffer = false;
aliasInfo.applyOnAliases(value, [&](Value v) {
// Some values are known to be writable.
if (aliasInfo.bufferizesToWritableMemory(v))
return;
// Query BufferizableOpInterface to see if the OpResult is writable.
// TODO: Out-of-place bufferized OpResult could be considered writable.
if (auto bufferizableOp = v.getDefiningOp<BufferizableOpInterface>())
if (bufferizableOp && bufferizableOp.isWritable(v))
return;
// Query BufferizableOpInterface to see if the BlockArgument is writable.
if (auto bbArg = v.dyn_cast<BlockArgument>())
if (auto bufferizableOp = dyn_cast<BufferizableOpInterface>(
bbArg.getOwner()->getParentOp()))
if (bufferizableOp.isWritable(bbArg))
return;
foundNonWritableBuffer = true;
});
if (foundNonWritableBuffer)
LDBG("--> NON WRITABLE\n");
else
LDBG("--> WRITABLE\n");
return foundNonWritableBuffer;
}
/// Return true if the buffer to which `operand` would bufferize is equivalent
/// to some buffer write.
static bool aliasesInPlaceWrite(Value value,
const BufferizationAliasInfo &aliasInfo) {
LDBG("----Start aliasesInPlaceWrite\n");
LDBG("-------for : " << printValueInfo(value) << '\n');
bool foundInplaceWrite = false;
aliasInfo.applyOnAliases(value, [&](Value v) {
for (auto &use : v.getUses()) {
if (isInplaceMemoryWrite(use, aliasInfo)) {
LDBG("-----------wants to bufferize to inPlace write: "
<< printOperationInfo(use.getOwner()) << '\n');
foundInplaceWrite = true;
return;
}
}
});
if (!foundInplaceWrite)
LDBG("----------->does not alias an inplace write\n");
return foundInplaceWrite;
}
/// Return true if `a` happens before `b`, i.e., `a` or one of its ancestors
/// properly dominates `b` and `b` is not inside `a`.
static bool happensBefore(Operation *a, Operation *b,
const DominanceInfo &domInfo) {
do {
// TODO: Instead of isProperAncestor + properlyDominates, we should use
// properlyDominatesImpl(a, b, /*enclosingOpOk=*/false)
if (a->isProperAncestor(b))
return false;
if (domInfo.properlyDominates(a, b))
return true;
} while ((a = a->getParentOp()));
return false;
}
/// Given sets of uses and writes, return true if there is a RaW conflict under
/// the assumption that all given reads/writes alias the same buffer and that
/// all given writes bufferize inplace.
///
/// A conflict is: According to SSA use-def chains, a read R is supposed to read
/// the result of a write W1. But because of bufferization decisions, R actually
/// reads another write W2.
static bool
hasReadAfterWriteInterference(const DenseSet<OpOperand *> &usesRead,
const DenseSet<OpOperand *> &usesWrite,
const DominanceInfo &domInfo,
const BufferizationAliasInfo &aliasInfo) {
for (OpOperand *uRead : usesRead) {
Operation *readingOp = uRead->getOwner();
// Find most recent write of uRead by following the SSA use-def chain. E.g.:
//
// %0 = "writing_op"(%t) : tensor<?x32> -> tensor<?xf32>
// %1 = "aliasing_op"(%0) : tensor<?x32> -> tensor<?xf32>
// %2 = "reading_op"(%1) : : tensor<?x32> -> not_a_tensor_type
//
// In the above example, if uRead is the OpOperand of reading_op, lastWrite
// is %0. Note that operations that create an alias but do not write (such
// as ExtractSliceOp) are skipped.
Value lastWrite = findLastPrecedingWrite(uRead->get());
// Look for conflicting memory writes. Potential conflicts are writes to an
// alias that have been decided to bufferize inplace.
for (OpOperand *uConflictingWrite : usesWrite) {
// Throughout this loop, check for multiple requirements that have to be
// met for uConflictingWrite to be an actual conflict.
Operation *conflictingWritingOp = uConflictingWrite->getOwner();
// Print some debug info.
LDBG("Found potential conflict:\n");
LDBG("READ = #" << uRead->getOperandNumber() << " of "
<< printOperationInfo(readingOp) << "\n");
LDBG("CONFLICTING WRITE = #"
<< uConflictingWrite->getOperandNumber() << " of "
<< printOperationInfo(conflictingWritingOp) << "\n");
// No conflict if the readingOp dominates conflictingWritingOp, i.e., the
// write is not visible when reading.
if (happensBefore(readingOp, conflictingWritingOp, domInfo))
continue;
// No conflict if the reading use equals the use of the conflicting write.
// A use cannot conflict with itself. Note: Just being the same op is not
// enough. It has to be the same use.
if (uConflictingWrite == uRead)
continue;
// No conflict if the op interface says so.
if (auto bufferizableOp = dyn_cast<BufferizableOpInterface>(readingOp))
if (bufferizableOp.isNotConflicting(uRead, uConflictingWrite,
aliasInfo))
continue;
if (conflictingWritingOp != readingOp)
if (auto bufferizableOp =
dyn_cast<BufferizableOpInterface>(conflictingWritingOp))
if (bufferizableOp.isNotConflicting(uRead, uConflictingWrite,
aliasInfo))
continue;
// Ops are not conflicting if they are in mutually exclusive regions.
if (insideMutuallyExclusiveRegions(readingOp, conflictingWritingOp))
continue;
LDBG("WRITE = #" << printValueInfo(lastWrite) << "\n");
// No conflict if the conflicting write happens before the last
// write.
if (Operation *writingOp = lastWrite.getDefiningOp()) {
if (happensBefore(conflictingWritingOp, writingOp, domInfo))
// conflictingWritingOp happens before writingOp. No conflict.
continue;
// No conflict if conflictingWritingOp is contained in writingOp.
if (writingOp->isProperAncestor(conflictingWritingOp))
continue;
} else {
auto bbArg = lastWrite.cast<BlockArgument>();
Block *block = bbArg.getOwner();
if (!block->findAncestorOpInBlock(*conflictingWritingOp))
// conflictingWritingOp happens outside of the block. No
// conflict.
continue;
}
// No conflict if the conflicting write and the last write are the same
// use.
if (getAliasingOpResult(*uConflictingWrite) == lastWrite)
continue;
// All requirements are met. Conflict found!
LDBG("CONFLICT CONFIRMED!\n\n");
return true;
}
}
LDBG("NOT A CONFLICT!\n\n");
return false;
}
/// Return true if bufferizing result inplace would create a conflict. A read R
/// and a write W of the same alias set is a conflict if inplace bufferization
/// of W changes the value read by R to a value different from the one that
/// would be expected by tracing back R's origin through SSA use-def chains.
/// A conflict can only be introduced by a new alias and/or an inplace
/// bufferization decision.
///
/// Example:
/// %0 = tensor.extract_slice %t[...][...][1, 1] {inplace?}
/// %1 = vector.transfer_write %v1, %t {inplace} : vector<5xf32>, tensor<?xf32>
/// %e = tensor.extract_slice %1
/// %2 = vector.transfer_write %v2, %0 {inplace} : vector<6xf32>, tensor<?xf32>
/// %3 = vector.transfer_read %e, %cst : tensor<?xf32>, vector<7xf32>
///
/// In the above example, the two TransferWriteOps have already been decided to
/// bufferize inplace. Bufferizing the ExtractSliceOp inplace would create a
/// conflict because:
/// * According to SSA use-def chains, we expect to read the result of %1.
/// * However, adding an alias {%0, %t} would mean that the second
/// TransferWriteOp overwrites the first one. Therefore, the TransferReadOp
/// would no longer be reading the result of %1.
///
/// If `checkConsistencyOnly` is true, this function checks if there is a
/// read-after-write conflict without bufferizing `operand` inplace. This would
/// indicate a problem with the current inplace bufferization decisions.
bool wouldCreateReadAfterWriteInterference(
OpOperand &operand, OpResult result, const DominanceInfo &domInfo,
const BufferizationAliasInfo &aliasInfo,
bool checkConsistencyOnly = false) {
#ifndef NDEBUG
SmallVector<OpOperand *> opOperands = getAliasingOpOperand(result);
assert(llvm::find(opOperands, &operand) != opOperands.end() &&
"operand and result do not match");
#endif // NDEBUG
// Helper function to iterate on aliases of `root` and capture the reads.
auto getAliasingReads = [&](DenseSet<OpOperand *> &res, Value root) {
aliasInfo.applyOnAliases(root, [&](Value alias) {
for (auto &use : alias.getUses())
// Read to a value that aliases root.
if (bufferizesToMemoryRead(use))
res.insert(&use);
});
};
// Helper function to iterate on aliases of `root` and capture the writes.
auto getAliasingInplaceWrites = [&](DenseSet<OpOperand *> &res, Value root) {
aliasInfo.applyOnAliases(root, [&](Value alias) {
for (auto &use : alias.getUses())
// Inplace write to a value that aliases root.
if (isInplaceMemoryWrite(use, aliasInfo))
res.insert(&use);
});
};
// Collect reads and writes of all aliases of OpOperand and OpResult.
DenseSet<OpOperand *> usesRead, usesWrite;
getAliasingReads(usesRead, operand.get());
getAliasingReads(usesRead, result);
getAliasingInplaceWrites(usesWrite, operand.get());
getAliasingInplaceWrites(usesWrite, result);
if (!checkConsistencyOnly && bufferizesToMemoryWrite(operand))
usesWrite.insert(&operand);
return hasReadAfterWriteInterference(usesRead, usesWrite, domInfo, aliasInfo);
}
/// Return true if bufferizing `opOperand` inplace with `opResult` would create
/// a write to a non-writable buffer.
static bool
wouldCreateWriteToNonWritableBuffer(OpOperand &opOperand, OpResult opResult,
const BufferizationAliasInfo &aliasInfo) {
#ifndef NDEBUG
SmallVector<OpOperand *> opOperands = getAliasingOpOperand(opResult);
assert(llvm::find(opOperands, &opOperand) != opOperands.end() &&
"operand and result do not match");
#endif // NDEBUG
// Certain buffers are not writeable:
// 1. A function bbArg that is not inplaceable or
// 2. A constant op.
assert(!aliasesNonWritableBuffer(opResult, aliasInfo) &&
"expected that opResult does not alias non-writable buffer");
bool nonWritable = aliasesNonWritableBuffer(opOperand.get(), aliasInfo);
if (!nonWritable)
return false;
// This is a problem only if the buffer is written to via some alias.
bool hasWrite = aliasesInPlaceWrite(opResult, aliasInfo) ||
aliasesInPlaceWrite(opOperand.get(), aliasInfo) ||
bufferizesToMemoryWrite(opOperand);
if (!hasWrite)
return false;
LDBG("->the corresponding buffer is not writeable\n");
return true;
}
//===----------------------------------------------------------------------===//
// Forward declarations.
//===----------------------------------------------------------------------===//
/// Return the op with Allocate MemoryEffect if `v` is equivalent to an such
/// an op. Return null otherwise.
static Operation *getEquivalentAlloc(Value value,
const BufferizationAliasInfo &aliasInfo);
/// Return the first argument of the enclosing FuncOp that is equivalent to `v`.
/// Return null if no such bbArg can be found.
static BlockArgument
getEquivalentEnclosingFuncBBArg(Value v,
const BufferizationAliasInfo &aliasInfo);
//===----------------------------------------------------------------------===//
// Bufferization-specific MemRefType support.
//===----------------------------------------------------------------------===//
/// 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) {
auto rewrite = [](Type t) -> Type {
// TODO: non-zero address space.
// TODO: layout information if relevant.
if (auto rankedTensorType = t.dyn_cast<RankedTensorType>())
return getDynamicMemRefType(rankedTensorType);
if (auto tensorType = t.dyn_cast<TensorType>())
return getContiguousOrUnrankedMemRefType(tensorType);
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);
}
/// If an entry for `funcOp` is available in `bufferizedFunctionTypes`, return
/// it. Otherwise, construct a new entry based on `argumentTypes` and
/// `resultTypes`.
// TODO: improve the layering.
static FunctionType getOrCreateBufferizedFunctionType(
FuncOp funcOp, TypeRange argumentTypes, TypeRange resultTypes,
DenseMap<FuncOp, FunctionType> &bufferizedFunctionTypes) {
auto it = bufferizedFunctionTypes.find(funcOp);
if (it != bufferizedFunctionTypes.end())
return it->second;
auto it2 = bufferizedFunctionTypes.try_emplace(
funcOp, getBufferizedFunctionType(funcOp.getContext(), argumentTypes,
resultTypes));
LDBG("FT: " << funcOp.getType() << " -> " << it2.first->second << "\n");
return it2.first->second;
}
//===----------------------------------------------------------------------===//
// Bufferization as simple BlockAndValueMapping rewrites.
//===----------------------------------------------------------------------===//
/// FuncOp always creates TensorToMemRef ops.
static LogicalResult bufferizeFuncOp(FuncOp funcOp, BufferizationState &state) {
// Take a guard before anything else.
OpBuilder b(funcOp->getContext());
b.setInsertionPointToStart(&funcOp.body().front());
// Create BufferCastOps for function args.
for (auto bbArg : funcOp.getArguments()) {
auto tensorType = bbArg.getType().dyn_cast<TensorType>();
if (!tensorType)
continue;
auto rankedTensorType = tensorType.dyn_cast<RankedTensorType>();
// Cast the tensor to the most dynamic buffer possible. Further
// canonicalizations will clean up.
Type memRefType = rankedTensorType
? getDynamicMemRefType(rankedTensorType)
: getContiguousOrUnrankedMemRefType(tensorType);
Value bufferCast =
b.create<bufferization::ToMemrefOp>(funcOp.getLoc(), memRefType, bbArg);
state.aliasInfo.insertNewBufferEquivalence(bufferCast, bbArg);
state.mapBuffer(bbArg, bufferCast);
}
// Bufferize function body.
return bufferize(&funcOp.body(), state);
}
//===----------------------------------------------------------------------===//
// Bufferization analyses.
//===----------------------------------------------------------------------===//
/// Determine if `operand` can be bufferized in-place with `result`.
static LogicalResult
bufferizableInPlaceAnalysisImpl(OpOperand &operand, OpResult result,
BufferizationAliasInfo &aliasInfo,
const DominanceInfo &domInfo) {
#ifndef NDEBUG
SmallVector<OpOperand *> opOperands = getAliasingOpOperand(result);
assert(llvm::find(opOperands, &operand) != opOperands.end() &&
"operand and result do not match");
#endif // NDEBUG
int64_t resultNumber = result.getResultNumber();
(void)resultNumber;
LDBG('\n');
LDBG("Inplace analysis for <- #" << resultNumber << " -> #"
<< operand.getOperandNumber() << " in "
<< printValueInfo(result) << '\n');
bool foundInterference =
wouldCreateWriteToNonWritableBuffer(operand, result, aliasInfo) ||
wouldCreateReadAfterWriteInterference(operand, result, domInfo,
aliasInfo);
if (foundInterference)
aliasInfo.bufferizeOutOfPlace(result);
else
aliasInfo.bufferizeInPlace(result, operand);
LDBG("Done inplace analysis for result #" << resultNumber << '\n');
return success();
}
/// Analyze the `ops` to determine which OpResults are inplaceable. Walk ops in
/// reverse and bufferize ops greedily. This is a good starter heuristic.
///
/// Even if an op does not read or write, it may still create an alias when
/// bufferized in-place. An example of such ops is tensor.extract_slice.
///
/// Rationale for bufferizing `%1 = tensor.extract_slice %0[...]` inplace:
///
/// When bufferized out of place, an ExtractSliceOp lowers to alloc + copy. This
/// cannot change the flow of information for either the source or the
/// result buffers.
///
/// When bufferized inplace, an ExtractSliceOp does not by itself create any
/// read or write from memory. Instead, it has the effect of merging the alias
/// sets of the source and the result buffers.
///
/// An analysis is required to ensure inplace bufferization would not result in
/// RaW dependence violations.
static LogicalResult inPlaceAnalysis(SmallVector<Operation *> &ops,
BufferizationAliasInfo &aliasInfo,
const DominanceInfo &domInfo,
unsigned analysisFuzzerSeed = 0) {
if (analysisFuzzerSeed) {
// This is a fuzzer. For testing purposes only. Randomize the order in which
// operations are analyzed. The bufferization quality is likely worse, but
// we want to make sure that no assertions are triggered anywhere.
std::mt19937 g(analysisFuzzerSeed);
llvm::shuffle(ops.begin(), ops.end(), g);
}
// Walk ops in reverse for better interference analysis.
for (Operation *op : reverse(ops))
for (OpOperand &opOperand : op->getOpOperands())
if (opOperand.get().getType().isa<TensorType>())
if (auto bufferizableOp = dyn_cast<BufferizableOpInterface>(op))
if (OpResult opResult = bufferizableOp.getAliasingOpResult(opOperand))
if (failed(bufferizableInPlaceAnalysisImpl(opOperand, opResult,
aliasInfo, domInfo)))
return failure();
return success();
}
/// Analyze the `funcOp` body to determine which OpResults are inplaceable.
static LogicalResult
inPlaceAnalysisFuncOpBody(FuncOp funcOp, BufferizationAliasInfo &aliasInfo,
const DominanceInfo &domInfo,
unsigned analysisFuzzerSeed = 0) {
LLVM_DEBUG(llvm::dbgs() << "\n\n");
LDBG("Begin InPlaceAnalysisFuncOpInternals:\n" << funcOp << '\n');
assert(funcOp && funcOp->getNumRegions() > 0 && !funcOp.body().empty() &&
"expected a funcOp definition with a body");
// Collect ops so we can build our own reverse traversal.
SmallVector<Operation *> ops;
funcOp.walk([&](Operation *op) {
// No tensors => no buffers.
if (none_of(op->getOperandTypes(), isaTensor) &&
none_of(op->getResultTypes(), isaTensor))
return;
ops.push_back(op);
});
// Set the function arguments marked with inplaceable to be known as
// bufferizing to a writeable memory.
for (BlockArgument bbArg : funcOp.getArguments()) {
BoolAttr inplaceAttr = funcOp.getArgAttrOfType<BoolAttr>(
bbArg.getArgNumber(), BufferizableOpInterface::kInplaceableAttrName);
if (inplaceAttr && inplaceAttr.getValue())
aliasInfo.setBufferizesToWritableMemory(bbArg);
}
LogicalResult res =
inPlaceAnalysis(ops, aliasInfo, domInfo, analysisFuzzerSeed);
LDBG("End InPlaceAnalysisFuncOpInternals:\n" << funcOp << '\n');
return res;
}
//===----------------------------------------------------------------------===//
// Bufferization entry-point for modules.
//===----------------------------------------------------------------------===//
/// Return the op with Allocate MemoryEffect if `v` is equivalent to such an
/// an op. Return null otherwise.
static Operation *getEquivalentAlloc(Value value,
const BufferizationAliasInfo &aliasInfo) {
Operation *res = nullptr;
aliasInfo.applyOnEquivalenceClass(value, [&](Value v) {
if (!res)
if (auto interface =
dyn_cast_or_null<MemoryEffectOpInterface>(v.getDefiningOp()))
if (auto effect =
interface.getEffectOnValue<MemoryEffects::Allocate>(v))
res = v.getDefiningOp();
});
return res;
}
/// Return the first argument of the enclosing FuncOp that is equivalent to `v`.
/// Return null if no such bbArg can be found.
static BlockArgument
getEquivalentEnclosingFuncBBArg(Value v,
const BufferizationAliasInfo &aliasInfo) {
if (!v.getType().isa<RankedTensorType>())
return nullptr;
Operation *op = v.getParentBlock()->getParentOp();
FuncOp funcOp = dyn_cast<FuncOp>(op);
if (!funcOp)
funcOp = op->getParentOfType<FuncOp>();
assert(funcOp && "expected non-null FuncOp");
for (BlockArgument bbArg : funcOp.getArguments()) {
if (!bbArg.getType().isa<RankedTensorType>())
continue;
if (aliasInfo.areEquivalentBufferizedValues(v, bbArg))
return bbArg;
}
return nullptr;
}
/// 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:
/// 1. 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 thefuture.
/// 2. an op with an Alloc effect, this currently fails bufferization but is a
/// candidate for hoisting and creating a new inplace operand at all caller
/// sites.
/// 3. if such a hoisting for 2. is not possible (e.g. data-dependent that
/// prevents hoisting), this is currently unsupported and will require a
/// refcounted buffer type.
static LogicalResult bufferizeFuncOpBoundary(
FuncOp funcOp, BufferizationAliasInfo &aliasInfo,
DenseMap<FuncOp, FunctionType> &bufferizedFunctionTypes) {
LLVM_DEBUG(DBGS() << "Begin bufferizeFuncOpBoundary:\n" << funcOp << "\n");
// If nothing to do then we are done.
if (!llvm::any_of(funcOp.getType().getInputs(), isaTensor) &&
!llvm::any_of(funcOp.getType().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.getType().getResults(), isaTensor))
return funcOp->emitError() << "cannot bufferize bodiless function that "
<< "returns a tensor";
FunctionType bufferizedFuncType =
getOrCreateBufferizedFunctionType(funcOp, funcOp.getType().getInputs(),
TypeRange{}, bufferizedFunctionTypes);
funcOp.setType(bufferizedFuncType);
LLVM_DEBUG(DBGS() << "End bufferizeFuncOpBoundary no fun body: " << funcOp);
return success();
}
// Support only single return-terminated block in the function.
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()) {
// If not a renturn tensor type just forward it.
if (!returnOperand.get().getType().isa<RankedTensorType>()) {
returnValues.push_back(returnOperand.get());
continue;
}
// If return operand is equivalent to some bbArg, no need to return it.
Value returnVal = returnOperand.get();
if (getEquivalentEnclosingFuncBBArg(returnVal, aliasInfo))
continue;
// TODO: Need to hoist above function boundary.
if (Operation *allocOp = getEquivalentAlloc(returnVal, aliasInfo)) {
returnValues.push_back(allocOp->getResult(0));
continue;
}
// Other cases legitimately need to return a tensor, this is currently not
// supported. For instance, if hoisting across function boundary has
// failed, it may be due to e.g. data-dependent sizes. In such a case, we
// would need a better type than memref.
int64_t returnIdx = returnOperand.getOperandNumber();
return returnOp->emitError()
<< "buffer result #" << returnIdx << " not produced by an alloc\n";
}
// 2. Rewrite the terminator without the inPlace bufferizable values.
ValueRange retValues{returnValues};
FunctionType bufferizedFuncType = getOrCreateBufferizedFunctionType(
funcOp, funcOp.getType().getInputs(), retValues.getTypes(),
bufferizedFunctionTypes);
OpBuilder b(returnOp);
b.create<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.body().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.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);
OpBuilder b(funcOp->getContext());
b.setInsertionPointToStart(&frontBlock);
// Replace all uses of bbArg through a ToMemrefOp by a memref::CastOp.
for (auto &use : llvm::make_early_inc_range(bbArg.getUses())) {
if (auto toMemrefOp =
dyn_cast<bufferization::ToMemrefOp>(use.getOwner())) {
auto castOp = b.create<memref::CastOp>(
funcOp.getLoc(), toMemrefOp.memref().getType(), memref);
toMemrefOp.memref().replaceAllUsesWith(castOp);
aliasInfo.insertNewBufferEquivalence(castOp.dest(),
toMemrefOp.memref());
}
}
// Replace all remaining uses by a tensor_load.
if (!bbArg.use_empty()) {
auto toTensorOp =
b.create<bufferization::ToTensorOp>(funcOp.getLoc(), memref);
aliasInfo.insertNewBufferEquivalence(toTensorOp, bbArg);
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);
LLVM_DEBUG(DBGS() << "End bufferizeFuncOpBoundary:\n" << funcOp);
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.body().empty()) {
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<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 (auto it : llvm::enumerate(funcOp.getType().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.body().empty()) {
BlockArgument bbArg = funcOp.getArgument(argNumber);
bbArg.setType(desiredMemrefType);
OpBuilder b(bbArg.getContext());
b.setInsertionPointToStart(bbArg.getOwner());
// 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);
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.getType().getResults());
funcOp.setType(newFuncType);
}
}
#ifndef NDEBUG
/// Assert that the current bufferization decisions are consistent.
static void checkAliasInfoConsistency(FuncOp funcOp,
const DominanceInfo &domInfo,
const BufferizationAliasInfo &aliasInfo) {
funcOp.walk([&](Operation *op) {
if (auto bufferizableOp = dyn_cast<BufferizableOpInterface>(op))
for (OpOperand &opOperand : op->getOpOperands())
if (opOperand.get().getType().isa<TensorType>())
if (OpResult opResult = bufferizableOp.getAliasingOpResult(opOperand))
// If this assertion fails, there is probably an inconsistent
// combination of "mustBufferizeInPlace" decisions.
assert(!wouldCreateReadAfterWriteInterference(
opOperand, opResult, domInfo, aliasInfo,
/*checkConsistencyOnly=*/true) &&
"found read after write conflict before running analysis");
});
}
#endif
/// Annotate the IR with the result of the analysis. For testing/debugging only.
static void
annotateOpsWithBufferizationMarkers(Operation *op,
const BufferizationAliasInfo &aliasInfo) {
op->walk([&](Operation *op) {
for (OpResult opResult : op->getResults()) {
if (opResult.getType().isa<TensorType>())
setInPlaceOpResult(opResult, aliasInfo.isInPlace(opResult));
if (auto funcOp = dyn_cast<FuncOp>(op))
for (BlockArgument bbArg : funcOp.getArguments())
if (bbArg.getType().isa<TensorType>())
setInPlaceFuncArgument(bbArg,
aliasInfo.bufferizesToWritableMemory(bbArg));
}
});
}
LogicalResult mlir::linalg::comprehensive_bufferize::runComprehensiveBufferize(
ModuleOp moduleOp, const BufferizationOptions &options) {
SmallVector<FuncOp> orderedFuncOps;
DenseMap<FuncOp, DenseSet<Operation *>> callerMap;
if (failed(getFuncOpsOrderedByCalls(moduleOp, orderedFuncOps, callerMap)))
return failure();
DominanceInfo domInfo(moduleOp);
BufferizationState state(moduleOp, *options.allocationFns);
BufferizationAliasInfo &aliasInfo = state.aliasInfo;
// Interestingly, all function args that are not visible outside of a module
// can be fully bufferized inplace by guaranteeing the CallOp is bufferized
// inplace. Therefore, we just bufferize funcOp as if none of its results were
// inplaceable, detect which operands are cloned internally and decide what to
// do at call sites.
for (FuncOp funcOp : orderedFuncOps) {
// No body => no analysis.
if (funcOp.body().empty())
continue;
// In a first approximation:
// =========================
// If the function is called, we can allocate on the caller side which lets
// us force inplace arguments at function boundaries.
// TODO: do not rely on this behavior.
if (callerMap.find(funcOp) != callerMap.end())
for (BlockArgument bbArg : funcOp.getArguments())
if (bbArg.getType().isa<TensorType>())
aliasInfo.setBufferizesToWritableMemory(bbArg);
#ifndef NDEBUG
checkAliasInfoConsistency(funcOp, domInfo, aliasInfo);
#endif // NDEBUG
// If the analysis fails, just return.
if (failed(inPlaceAnalysisFuncOpBody(funcOp, aliasInfo, domInfo,
options.analysisFuzzerSeed)))
return failure();
for (const std::unique_ptr<PostAnalysisStep> &step :
options.postAnalysisSteps) {
SmallVector<Operation *> newOps;
if (failed(step->run(funcOp, aliasInfo, domInfo, newOps)))
return failure();
// Analyze ops that were created by the PostAnalysisStep.
if (failed(inPlaceAnalysis(newOps, aliasInfo, domInfo)))
return failure();
}
// Bufferization phase.
if (!options.testAnalysisOnly) {
// Bufferize all ops in funcOp.
if (failed(bufferizeFuncOp(funcOp, state)))
return failure();
// Erase all obsolete ops.
state.eraseObsoleteOps();
}
}
// Annotate operations if we only want to report the analysis.
if (options.testAnalysisOnly) {
annotateOpsWithBufferizationMarkers(moduleOp, aliasInfo);
return success();
}
for (FuncOp funcOp : orderedFuncOps) {
// Note: It would be good to apply cleanups here but we cannot as aliasInfo
// would be invalidated.
if (failed(bufferizeFuncOpBoundary(funcOp, aliasInfo,
state.bufferizedFunctionTypes)))
return failure();
if (!options.allowReturnMemref &&
llvm::any_of(funcOp.getType().getResults(), [](Type t) {
return t.isa<MemRefType, UnrankedMemRefType>();
})) {
funcOp->emitError("memref return type is unsupported");
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();
}
/// Default allocation function that is used by the comprehensive bufferization
/// pass. The default currently creates a ranked memref using `memref.alloc`.
static Optional<Value> defaultAllocationFn(OpBuilder &b, Location loc,
MemRefType type,
ArrayRef<Value> dynShape) {
Value allocated = b.create<memref::AllocOp>(
loc, type, dynShape, b.getI64IntegerAttr(kBufferAlignments));
return allocated;
}
/// Default deallocation function that is used by the comprehensive
/// bufferization pass. It expects to recieve back the value called from the
/// `defaultAllocationFn`.
static void defaultDeallocationFn(OpBuilder &b, Location loc,
Value allocatedBuffer) {
b.create<memref::DeallocOp>(loc, allocatedBuffer);
}
/// Default memory copy function that is used by the comprehensive bufferization
/// pass. Creates a `memref.copy` op.
static void defaultMemCpyFn(OpBuilder &b, Location loc, Value from, Value to) {
b.create<memref::CopyOp>(loc, from, to);
}
std::unique_ptr<AllocationCallbacks>
mlir::linalg::comprehensive_bufferize::defaultAllocationCallbacks() {
return std::make_unique<AllocationCallbacks>(
defaultAllocationFn, defaultDeallocationFn, defaultMemCpyFn);
}
// Default constructor for BufferizationOptions that sets all allocation
// callbacks to their default functions.
BufferizationOptions::BufferizationOptions()
: allocationFns(defaultAllocationCallbacks()) {}
//===----------------------------------------------------------------------===//
// BufferizableOpInterface Implementations
//===----------------------------------------------------------------------===//
// TODO: Move these to a different file and BUILD target, so that they are
// decoupled from ComprehensiveBufferize.
namespace mlir {
namespace linalg {
namespace comprehensive_bufferize {
namespace std_ext {
struct CallOpInterface
: public BufferizableOpInterface::ExternalModel<CallOpInterface, CallOp> {
bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand) const {
// CallOpInterface alone doesn't bufferize to a memory read, one of the uses
// of the matching bbArg may. It is the responsibility of the caller to
// inspect bbArgs. In the absence of a BufferizationAliasInfo, we need to be
// conservative.
return true;
}
bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand) const {
// CallOpInterface alone doesn't bufferize to a memory write, one of the
// uses of the matching bbArg may. It is the responsibility of the caller to
// inspect bbArgs. In the absence of a BufferizationAliasInfo, we need to be
// conservative.
return true;
}
SmallVector<OpOperand *> getAliasingOpOperand(Operation *op,
OpResult opResult) const {
// TODO: Can we do better?
return {};
}
OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand) const {
// CallOpInterface is special, it needs to wait for the callee to be
// bufferized and needs to inspect the BufferAliasInfo object. It can't
// make a proper determination by itself and needs to be conservative.
return OpResult();
}
BufferRelation bufferRelation(Operation *op, OpOperand &opOperand) 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, OpBuilder &b,
BufferizationState &state) const {
CallOp callOp = cast<CallOp>(op);
FuncOp funcOp = getCalledFunction(callOp);
assert(isa<CallOp>(callOp.getOperation()) && funcOp &&
"expected Callop to a FuncOp");
// Take a guard before anything else.
OpBuilder::InsertionGuard g(b);
b.setInsertionPoint(callOp);
// 1. Filter return types:
// - if the callee is bodiless / external, we cannot inspect it and we
// cannot assume anything. We can just assert that it does not return a
// tensor as this would have to bufferize to "return a memref", whose
// semantics is ill-defined.
// - if the callee has a body, we perform inter-procedural equivalence
// analysis. When successful, a result folds onto an operand. When
// unsuccessful, additional work is needed to either:
// * hoist a result into an inplaceable operand or
// * devise a better representation to truly return a buffer.
SmallVector<Type> resultTypes;
SmallVector<Value> hoistedArguments;
if (funcOp.body().empty()) {
if (llvm::any_of(funcOp.getType().getResults(), isaTensor))
return callOp->emitError()
<< "cannot bufferize bodiless function that returns a tensor";
} else {
ReturnOp returnOp = getAssumedUniqueReturnOp(funcOp);
assert(returnOp && "expected func with single return op");
// For each FuncOp result, keep track of which inplace argument it reuses.
for (OpOperand &returnOperand : returnOp->getOpOperands()) {
Type returnType = returnOperand.get().getType();
if (!isaTensor(returnType)) {
resultTypes.push_back(returnType);
continue;
}
// If return operand is equivalent to some bbArg, no need to return it.
Value returnVal = returnOperand.get();
if (BlockArgument bbArg =
getEquivalentEnclosingFuncBBArg(returnVal, state.aliasInfo)) {
Value oldRes = callOp->getResult(returnOperand.getOperandNumber());
int64_t idx = bbArg.getArgNumber();
Value buffer = state.lookupBuffer(callOp->getOperand(idx));
// Add CallOp operand/result equivalence: this is interprocedural
// info.
state.aliasInfo.insertNewBufferEquivalence(oldRes, buffer);
state.mapBuffer(oldRes, buffer);
// Add a ToTensorOp to kill all uses of the CallOp return.
// Replace all uses of the CallOp results so we can erase the CallOp.
// This ToTensorOp must fold/DCE away or bufferization should be
// considered failed.
Value toTensor =
b.create<bufferization::ToTensorOp>(callOp.getLoc(), buffer);
oldRes.replaceAllUsesWith(toTensor);
// Add new op equivalence info.
state.aliasInfo.insertNewBufferEquivalence(toTensor, buffer);
state.mapBuffer(toTensor, buffer);
continue;
}
// TODO: Need to hoist above function boundary.
if (Operation *allocOp =
getEquivalentAlloc(returnVal, state.aliasInfo)) {
hoistedArguments.push_back(allocOp->getResult(0));
continue;
}
// Other cases legitimately need to return a tensor, this is currently
// not supported. For instance, if hoisting across function boundary has
// failed, it may be due to e.g. data-dependent sizes. In such a case,
// we would we need a better type than memref.
resultTypes.push_back(returnType);
int64_t returnIdx = returnOperand.getOperandNumber();
return returnOp->emitError() << "buffer result #" << returnIdx
<< " not produced by an alloc\n";
}
}
// 2. Compute bufferized FunctionType.
SmallVector<Type> argumentTypes{callOp->getOperandTypes()};
ValueRange hoistedArgs{hoistedArguments};
llvm::append_range(argumentTypes, hoistedArgs.getTypes());
// Get the bufferized FunctionType for funcOp or construct it if not yet
// available.
FunctionType bufferizedFuncType = getOrCreateBufferizedFunctionType(
funcOp, argumentTypes, resultTypes, state.bufferizedFunctionTypes);
// 3. Rewrite tensor operands as memrefs based on `bufferizedFuncType`.
SmallVector<Value> newOperands;
newOperands.reserve(callOp->getNumOperands());
for (OpOperand &opOperand : callOp->getOpOperands()) {
Value tensorOperand = opOperand.get();
// Non-tensor operands are just copied.
if (!tensorOperand.getType().isa<TensorType>()) {
newOperands.push_back(tensorOperand);
continue;
}
// Tensor operands are guaranteed to have been buferized.
int64_t idx = opOperand.getOperandNumber();
Value buffer = state.lookupBuffer(tensorOperand);
// Caller / callee type mistmatch is handled with a CastOp.
auto memRefType = bufferizedFuncType.getInput(idx);
// Since we don't yet have a clear layout story, buffer_cast 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) {
Value castBuffer =
b.create<memref::CastOp>(callOp.getLoc(), memRefType, buffer);
// Add new op equivalence info.
state.aliasInfo.insertNewBufferEquivalence(castBuffer, buffer);
state.mapBuffer(tensorOperand, castBuffer);
buffer = castBuffer;
}
newOperands.push_back(buffer);
}
// 4. Create the new CallOp.
Operation *newCallOp = b.create<CallOp>(callOp.getLoc(), funcOp.sym_name(),
resultTypes, newOperands);
newCallOp->setAttrs(callOp->getAttrs());
// 5. Delete the op at the end of bufferization.
state.markOpObsolete(callOp);
return success();
}
};
struct ReturnOpInterface
: public BufferizableOpInterface::ExternalModel<ReturnOpInterface,
ReturnOp> {
bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand) const {
return true;
}
bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand) const {
return false;
}
OpResult getAliasingOpResult(Operation *op, OpOperand &opOperand) const {
return OpResult();
}
LogicalResult bufferize(Operation *op, OpBuilder &b,
BufferizationState &state) const {
auto returnOp = cast<ReturnOp>(op);
// Take a guard before anything else.
OpBuilder::InsertionGuard g(b);
// Cannot insert after returnOp.
b.setInsertionPoint(returnOp);
assert(isa<FuncOp>(returnOp->getParentOp()) &&
"only support FuncOp parent for ReturnOp");
for (OpOperand &operand : returnOp->getOpOperands()) {
auto tensorType = operand.get().getType().dyn_cast<TensorType>();
if (!tensorType)
continue;
Value v = state.lookupBuffer(operand.get());
Value returnTensor =
b.create<bufferization::ToTensorOp>(returnOp.getLoc(), v);
operand.set(returnTensor);
state.aliasInfo.insertNewBufferEquivalence(returnTensor, v);
state.mapBuffer(returnTensor, v);
}
return success();
}
};
} // namespace std_ext
void registerBufferizableOpInterfaceExternalModels(DialectRegistry &registry) {
registry.addOpInterface<CallOp, std_ext::CallOpInterface>();
registry.addOpInterface<ReturnOp, std_ext::ReturnOpInterface>();
// Ops that are not bufferizable but are allocation hoisting barriers.
registry.addOpInterface<FuncOp, AllocationHoistingBarrierOnly<FuncOp>>();
}
} // namespace comprehensive_bufferize
} // namespace linalg
} // namespace mlir