Tres Popp 5550c82189 [mlir] Move casting calls from methods to function calls
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.

Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.

Caveats include:
- This clang-tidy script probably has more problems.
- This only touches C++ code, so nothing that is being generated.

Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
  for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443

Implementation:
This first patch was created with the following steps. The intention is
to only do automated changes at first, so I waste less time if it's
reverted, and so the first mass change is more clear as an example to
other teams that will need to follow similar steps.

Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
   additional check:
   https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
   and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
   them to a pure state.
4. Some changes have been deleted for the following reasons:
   - Some files had a variable also named cast
   - Some files had not included a header file that defines the cast
     functions
   - Some files are definitions of the classes that have the casting
     methods, so the code still refers to the method instead of the
     function without adding a prefix or removing the method declaration
     at the same time.

```
ninja -C $BUILD_DIR clang-tidy

run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
               -header-filter=mlir/ mlir/* -fix

rm -rf $BUILD_DIR/tools/mlir/**/*.inc

git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\
            mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\
            mlir/lib/**/IR/\
            mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\
            mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\
            mlir/test/lib/Dialect/Test/TestTypes.cpp\
            mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\
            mlir/test/lib/Dialect/Test/TestAttributes.cpp\
            mlir/unittests/TableGen/EnumsGenTest.cpp\
            mlir/test/python/lib/PythonTestCAPI.cpp\
            mlir/include/mlir/IR/
```

Differential Revision: https://reviews.llvm.org/D150123
2023-05-12 11:21:25 +02:00

557 lines
20 KiB
C++

//===- Bufferize.cpp - Bufferization utilities ----------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Bufferization/Transforms/Passes.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/Bufferization/Transforms/OneShotModuleBufferize.h"
#include "mlir/Dialect/Bufferization/Transforms/Transforms.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/IR/Operation.h"
#include "mlir/Interfaces/SideEffectInterfaces.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/Passes.h"
#include <optional>
namespace mlir {
namespace bufferization {
#define GEN_PASS_DEF_FINALIZINGBUFFERIZE
#define GEN_PASS_DEF_BUFFERIZATIONBUFFERIZE
#define GEN_PASS_DEF_ONESHOTBUFFERIZE
#include "mlir/Dialect/Bufferization/Transforms/Passes.h.inc"
} // namespace bufferization
} // namespace mlir
#define DEBUG_TYPE "bufferize"
using namespace mlir;
using namespace mlir::bufferization;
//===----------------------------------------------------------------------===//
// BufferizeTypeConverter
//===----------------------------------------------------------------------===//
static Value materializeToTensor(OpBuilder &builder, TensorType type,
ValueRange inputs, Location loc) {
assert(inputs.size() == 1);
assert(isa<BaseMemRefType>(inputs[0].getType()));
return builder.create<bufferization::ToTensorOp>(loc, type, inputs[0]);
}
/// Registers conversions into BufferizeTypeConverter
BufferizeTypeConverter::BufferizeTypeConverter() {
// Keep all types unchanged.
addConversion([](Type type) { return type; });
// Convert RankedTensorType to MemRefType.
addConversion([](RankedTensorType type) -> Type {
return MemRefType::get(type.getShape(), type.getElementType());
});
// Convert UnrankedTensorType to UnrankedMemRefType.
addConversion([](UnrankedTensorType type) -> Type {
return UnrankedMemRefType::get(type.getElementType(), 0);
});
addArgumentMaterialization(materializeToTensor);
addSourceMaterialization(materializeToTensor);
addTargetMaterialization([](OpBuilder &builder, BaseMemRefType type,
ValueRange inputs, Location loc) -> Value {
assert(inputs.size() == 1 && "expected exactly one input");
if (auto inputType = dyn_cast<MemRefType>(inputs[0].getType())) {
// MemRef to MemRef cast.
assert(inputType != type && "expected different types");
// Unranked to ranked and ranked to unranked casts must be explicit.
auto rankedDestType = dyn_cast<MemRefType>(type);
if (!rankedDestType)
return nullptr;
FailureOr<Value> replacement =
castOrReallocMemRefValue(builder, inputs[0], rankedDestType);
if (failed(replacement))
return nullptr;
return *replacement;
}
if (isa<TensorType>(inputs[0].getType())) {
// Tensor to MemRef cast.
return builder.create<bufferization::ToMemrefOp>(loc, type, inputs[0]);
}
llvm_unreachable("only tensor/memref input types supported");
});
}
void mlir::bufferization::populateBufferizeMaterializationLegality(
ConversionTarget &target) {
target.addLegalOp<bufferization::ToTensorOp, bufferization::ToMemrefOp>();
}
namespace {
// In a finalizing bufferize conversion, we know that all tensors have been
// converted to memrefs, thus, this op becomes an identity.
class BufferizeToTensorOp
: public OpConversionPattern<bufferization::ToTensorOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(bufferization::ToTensorOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOp(op, adaptor.getMemref());
return success();
}
};
} // namespace
namespace {
// In a finalizing bufferize conversion, we know that all tensors have been
// converted to memrefs, thus, this op becomes an identity.
class BufferizeToMemrefOp
: public OpConversionPattern<bufferization::ToMemrefOp> {
public:
using OpConversionPattern::OpConversionPattern;
LogicalResult
matchAndRewrite(bufferization::ToMemrefOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
rewriter.replaceOp(op, adaptor.getTensor());
return success();
}
};
} // namespace
void mlir::bufferization::populateEliminateBufferizeMaterializationsPatterns(
BufferizeTypeConverter &typeConverter, RewritePatternSet &patterns) {
patterns.add<BufferizeToTensorOp, BufferizeToMemrefOp>(typeConverter,
patterns.getContext());
}
namespace {
struct FinalizingBufferizePass
: public bufferization::impl::FinalizingBufferizeBase<
FinalizingBufferizePass> {
using FinalizingBufferizeBase<
FinalizingBufferizePass>::FinalizingBufferizeBase;
void runOnOperation() override {
auto func = getOperation();
auto *context = &getContext();
BufferizeTypeConverter typeConverter;
RewritePatternSet patterns(context);
ConversionTarget target(*context);
populateEliminateBufferizeMaterializationsPatterns(typeConverter, patterns);
// If all result types are legal, and all block arguments are legal (ensured
// by func conversion above), then all types in the program are legal.
//
// We also check that the operand types are legal to avoid creating invalid
// IR. For example, this prevents
// populateEliminateBufferizeMaterializationsPatterns from updating the
// types of the operands to a return op without updating the enclosing
// function.
target.markUnknownOpDynamicallyLegal(
[&](Operation *op) { return typeConverter.isLegal(op); });
if (failed(applyFullConversion(func, target, std::move(patterns))))
signalPassFailure();
}
};
static LayoutMapOption parseLayoutMapOption(const std::string &s) {
if (s == "fully-dynamic-layout-map")
return LayoutMapOption::FullyDynamicLayoutMap;
if (s == "identity-layout-map")
return LayoutMapOption::IdentityLayoutMap;
if (s == "infer-layout-map")
return LayoutMapOption::InferLayoutMap;
llvm_unreachable("invalid layout map option");
}
static OneShotBufferizationOptions::AnalysisHeuristic
parseHeuristicOption(const std::string &s) {
if (s == "bottom-up")
return OneShotBufferizationOptions::AnalysisHeuristic::BottomUp;
if (s == "top-down")
return OneShotBufferizationOptions::AnalysisHeuristic::TopDown;
llvm_unreachable("invalid analysisheuristic option");
}
struct OneShotBufferizePass
: public bufferization::impl::OneShotBufferizeBase<OneShotBufferizePass> {
OneShotBufferizePass() = default;
explicit OneShotBufferizePass(const OneShotBufferizationOptions &options)
: options(options) {}
void getDependentDialects(DialectRegistry &registry) const override {
registry
.insert<bufferization::BufferizationDialect, memref::MemRefDialect>();
registerAllocationOpInterfaceExternalModels(registry);
}
void runOnOperation() override {
OneShotBufferizationOptions opt;
if (!options) {
// Make new bufferization options if none were provided when creating the
// pass.
opt.allowReturnAllocs = allowReturnAllocs;
opt.allowUnknownOps = allowUnknownOps;
opt.analysisFuzzerSeed = analysisFuzzerSeed;
opt.analysisHeuristic = parseHeuristicOption(analysisHeuristic);
opt.copyBeforeWrite = copyBeforeWrite;
opt.createDeallocs = createDeallocs;
opt.setFunctionBoundaryTypeConversion(
parseLayoutMapOption(functionBoundaryTypeConversion));
if (mustInferMemorySpace)
opt.defaultMemorySpace = std::nullopt;
opt.printConflicts = printConflicts;
opt.testAnalysisOnly = testAnalysisOnly;
opt.bufferizeFunctionBoundaries = bufferizeFunctionBoundaries;
opt.noAnalysisFuncFilter = noAnalysisFuncFilter;
// Configure type converter.
LayoutMapOption unknownTypeConversionOption =
parseLayoutMapOption(unknownTypeConversion);
opt.unknownTypeConverterFn = [=](Value value, Attribute memorySpace,
const BufferizationOptions &options) {
auto tensorType = cast<TensorType>(value.getType());
if (unknownTypeConversionOption == LayoutMapOption::IdentityLayoutMap)
return bufferization::getMemRefTypeWithStaticIdentityLayout(
tensorType, memorySpace);
assert(unknownTypeConversionOption ==
LayoutMapOption::FullyDynamicLayoutMap &&
"invalid layout map option");
return bufferization::getMemRefTypeWithFullyDynamicLayout(tensorType,
memorySpace);
};
// Configure op filter.
OpFilter::Entry::FilterFn filterFn = [&](Operation *op) {
// Filter may be specified via options.
if (this->dialectFilter.hasValue())
return llvm::is_contained(this->dialectFilter,
op->getDialect()->getNamespace());
// No filter specified: All other ops are allowed.
return true;
};
opt.opFilter.allowOperation(filterFn);
} else {
opt = *options;
}
BufferizationStatistics statistics;
ModuleOp moduleOp = getOperation();
if (opt.bufferizeFunctionBoundaries) {
if (failed(runOneShotModuleBufferize(moduleOp, opt, &statistics))) {
signalPassFailure();
return;
}
} else {
assert(opt.noAnalysisFuncFilter.empty() &&
"invalid combination of bufferization flags");
if (failed(runOneShotBufferize(moduleOp, opt, &statistics))) {
signalPassFailure();
return;
}
}
// Set pass statistics.
this->numBufferAlloc = statistics.numBufferAlloc;
this->numBufferDealloc = statistics.numBufferDealloc;
this->numTensorInPlace = statistics.numTensorInPlace;
this->numTensorOutOfPlace = statistics.numTensorOutOfPlace;
if (opt.testAnalysisOnly)
return;
OpPassManager cleanupPipeline("builtin.module");
cleanupPipeline.addPass(createCanonicalizerPass());
cleanupPipeline.addPass(createCSEPass());
cleanupPipeline.addPass(createLoopInvariantCodeMotionPass());
(void)runPipeline(cleanupPipeline, moduleOp);
}
private:
std::optional<OneShotBufferizationOptions> options;
};
} // namespace
namespace {
struct BufferizationBufferizePass
: public bufferization::impl::BufferizationBufferizeBase<
BufferizationBufferizePass> {
void runOnOperation() override {
BufferizationOptions options = getPartialBufferizationOptions();
options.opFilter.allowDialect<BufferizationDialect>();
if (failed(bufferizeOp(getOperation(), options)))
signalPassFailure();
}
void getDependentDialects(DialectRegistry &registry) const override {
registry
.insert<bufferization::BufferizationDialect, memref::MemRefDialect>();
}
};
} // namespace
std::unique_ptr<Pass> mlir::bufferization::createBufferizationBufferizePass() {
return std::make_unique<BufferizationBufferizePass>();
}
std::unique_ptr<Pass> mlir::bufferization::createOneShotBufferizePass() {
return std::make_unique<OneShotBufferizePass>();
}
std::unique_ptr<Pass> mlir::bufferization::createOneShotBufferizePass(
const OneShotBufferizationOptions &options) {
return std::make_unique<OneShotBufferizePass>(options);
}
std::unique_ptr<OperationPass<func::FuncOp>>
mlir::bufferization::createFinalizingBufferizePass() {
return std::make_unique<FinalizingBufferizePass>();
}
//===----------------------------------------------------------------------===//
// BufferizableOpInterface-based Bufferization
//===----------------------------------------------------------------------===//
static bool isaTensor(Type t) { return isa<TensorType>(t); }
/// Return true if the given op has a tensor result or a tensor operand.
static bool hasTensorSemantics(Operation *op) {
if (auto funcOp = dyn_cast<FunctionOpInterface>(op)) {
bool hasTensorArg = any_of(funcOp.getArgumentTypes(), isaTensor);
bool hasTensorResult = any_of(funcOp.getResultTypes(), isaTensor);
return hasTensorArg || hasTensorResult;
}
bool hasTensorResult = any_of(op->getResultTypes(), isaTensor);
bool hasTensorOperand = any_of(op->getOperandTypes(), isaTensor);
return hasTensorResult || hasTensorOperand;
}
namespace {
/// A rewriter that keeps track of extra information during bufferization.
class BufferizationRewriter : public IRRewriter, public RewriterBase::Listener {
public:
BufferizationRewriter(MLIRContext *ctx, DenseSet<Operation *> &erasedOps,
DenseSet<Operation *> &toMemrefOps,
SmallVector<Operation *> &worklist,
const BufferizationOptions &options,
const OpFilter *opFilter,
BufferizationStatistics *statistics)
: IRRewriter(ctx), erasedOps(erasedOps), toMemrefOps(toMemrefOps),
worklist(worklist), analysisState(options), opFilter(opFilter),
statistics(statistics) {
setListener(this);
}
protected:
void notifyOperationRemoved(Operation *op) override {
// TODO: Walk can be removed when D144193 has landed.
op->walk([&](Operation *op) {
erasedOps.insert(op);
// Erase if present.
toMemrefOps.erase(op);
});
}
void notifyOperationInserted(Operation *op) override {
erasedOps.erase(op);
// Gather statistics about allocs and deallocs.
if (statistics) {
if (auto sideEffectingOp = dyn_cast<MemoryEffectOpInterface>(op)) {
statistics->numBufferAlloc += static_cast<int64_t>(
sideEffectingOp.hasEffect<MemoryEffects::Allocate>());
statistics->numBufferDealloc += static_cast<int64_t>(
sideEffectingOp.hasEffect<MemoryEffects::Free>());
}
}
// Keep track of to_memref ops.
if (isa<ToMemrefOp>(op)) {
toMemrefOps.insert(op);
return;
}
// Skip to_tensor ops.
if (isa<ToTensorOp>(op))
return;
// Skip non-tensor ops.
if (!hasTensorSemantics(op))
return;
// Skip ops that are not allowed to be bufferized.
auto const &options = analysisState.getOptions();
if (!options.isOpAllowed(op) || (opFilter && !opFilter->isOpAllowed(op)))
return;
// Add op to worklist.
worklist.push_back(op);
}
private:
/// A set of all erased ops.
DenseSet<Operation *> &erasedOps;
/// A set of all to_memref ops.
DenseSet<Operation *> &toMemrefOps;
/// The worklist of ops to be bufferized.
SmallVector<Operation *> &worklist;
/// The analysis state. Used for debug assertions and access to the
/// bufferization options.
const AnalysisState analysisState;
/// An extra op filter for bufferization.
const OpFilter *opFilter;
/// Bufferization statistics for debugging.
BufferizationStatistics *statistics;
};
} // namespace
LogicalResult bufferization::bufferizeOp(Operation *op,
const BufferizationOptions &options,
bool copyBeforeWrite,
const OpFilter *opFilter,
BufferizationStatistics *statistics) {
if (copyBeforeWrite) {
AnalysisState state(options);
if (failed(insertTensorCopies(op, state)))
return failure();
}
// Keep track of to_memref ops.
DenseSet<Operation *> toMemrefOps;
op->walk([&](ToMemrefOp toMemrefOp) { toMemrefOps.insert(toMemrefOp); });
// Gather all bufferizable ops in top-to-bottom order.
//
// We should ideally know the exact memref type of all operands when
// bufferizing an op. (This is the case when bufferizing top-to-bottom.)
// Otherwise, we have to use a memref type with a fully dynamic layout map to
// avoid copies. We are currently missing patterns for layout maps to
// canonicalize away (or canonicalize to more precise layouts).
//
// FuncOps must be bufferized before their bodies, so add them to the worklist
// first.
SmallVector<Operation *> worklist;
op->walk([&](func::FuncOp funcOp) {
if (hasTensorSemantics(funcOp))
worklist.push_back(funcOp);
});
op->walk<WalkOrder::PostOrder>([&](Operation *op) {
if (hasTensorSemantics(op) && !isa<func::FuncOp>(op))
worklist.push_back(op);
});
// Keep track of all erased ops.
DenseSet<Operation *> erasedOps;
// Bufferize all ops.
BufferizationRewriter rewriter(op->getContext(), erasedOps, toMemrefOps,
worklist, options, opFilter, statistics);
for (unsigned i = 0; i < worklist.size(); ++i) {
Operation *nextOp = worklist[i];
// Skip ops that were erased.
if (erasedOps.contains(nextOp))
continue;
// Skip ops that are not bufferizable or not allowed.
auto bufferizableOp = options.dynCastBufferizableOp(nextOp);
if (!bufferizableOp)
continue;
if (opFilter && !opFilter->isOpAllowed(nextOp))
continue;
// Skip ops that no longer have tensor semantics.
if (!hasTensorSemantics(nextOp))
continue;
// Bufferize the op.
LLVM_DEBUG(llvm::dbgs()
<< "//===-------------------------------------------===//\n"
<< "IR after bufferizing: " << nextOp->getName() << "\n");
rewriter.setInsertionPoint(nextOp);
if (failed(bufferizableOp.bufferize(rewriter, options))) {
LLVM_DEBUG(llvm::dbgs()
<< "failed to bufferize\n"
<< "//===-------------------------------------------===//\n");
return nextOp->emitError("failed to bufferize op");
}
LLVM_DEBUG(llvm::dbgs()
<< *op
<< "\n//===-------------------------------------------===//\n");
}
// Fold all to_memref(to_tensor(x)) pairs.
for (Operation *op : toMemrefOps) {
rewriter.setInsertionPoint(op);
(void)bufferization::foldToMemrefToTensorPair(rewriter,
cast<ToMemrefOp>(op));
}
// Remove all dead to_tensor ops.
op->walk<WalkOrder::PostOrder>([&](ToTensorOp toTensorOp) {
if (toTensorOp->getUses().empty()) {
rewriter.eraseOp(toTensorOp);
return WalkResult::skip();
}
return WalkResult::advance();
});
/// Check the result of bufferization. Return an error if an op was not
/// bufferized, unless partial bufferization is allowed.
if (options.allowUnknownOps)
return success();
for (Operation *op : worklist) {
// Skip ops that are entirely gone.
if (erasedOps.contains(op))
continue;
// Ops that no longer have tensor semantics (because they were updated
// in-place) are allowed.
if (!hasTensorSemantics(op))
continue;
// Continue ops that are not allowed.
if (!options.isOpAllowed(op))
continue;
if (opFilter && !opFilter->isOpAllowed(op))
continue;
// Ops without any uses and no side effects will fold away.
if (op->getUses().empty() && isMemoryEffectFree(op))
continue;
// ToTensorOps/ToMemrefOps are allowed in the output.
if (isa<ToTensorOp, ToMemrefOp>(op))
continue;
return op->emitError("op was not bufferized");
}
return success();
}
BufferizationOptions bufferization::getPartialBufferizationOptions() {
BufferizationOptions options;
options.allowUnknownOps = true;
options.createDeallocs = false;
options.enforceAliasingInvariants = false;
options.unknownTypeConverterFn = [](Value value, Attribute memorySpace,
const BufferizationOptions &options) {
return getMemRefTypeWithStaticIdentityLayout(
cast<TensorType>(value.getType()), memorySpace);
};
options.opFilter.allowDialect<BufferizationDialect>();
return options;
}