Files
clang-p2996/mlir/lib/Analysis/DataFlow/IntegerRangeAnalysis.cpp
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

234 lines
9.3 KiB
C++

//===- IntegerRangeAnalysis.cpp - Integer range analysis --------*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file defines the dataflow analysis class for integer range inference
// which is used in transformations over the `arith` dialect such as
// branch elimination or signed->unsigned rewriting
//
//===----------------------------------------------------------------------===//
#include "mlir/Analysis/DataFlow/IntegerRangeAnalysis.h"
#include "mlir/Analysis/DataFlow/ConstantPropagationAnalysis.h"
#include "mlir/Interfaces/InferIntRangeInterface.h"
#include "mlir/Interfaces/LoopLikeInterface.h"
#include "llvm/Support/Debug.h"
#include <optional>
#define DEBUG_TYPE "int-range-analysis"
using namespace mlir;
using namespace mlir::dataflow;
IntegerValueRange IntegerValueRange::getMaxRange(Value value) {
unsigned width = ConstantIntRanges::getStorageBitwidth(value.getType());
if (width == 0)
return {};
APInt umin = APInt::getMinValue(width);
APInt umax = APInt::getMaxValue(width);
APInt smin = width != 0 ? APInt::getSignedMinValue(width) : umin;
APInt smax = width != 0 ? APInt::getSignedMaxValue(width) : umax;
return IntegerValueRange{ConstantIntRanges{umin, umax, smin, smax}};
}
void IntegerValueRangeLattice::onUpdate(DataFlowSolver *solver) const {
Lattice::onUpdate(solver);
// If the integer range can be narrowed to a constant, update the constant
// value of the SSA value.
std::optional<APInt> constant = getValue().getValue().getConstantValue();
auto value = point.get<Value>();
auto *cv = solver->getOrCreateState<Lattice<ConstantValue>>(value);
if (!constant)
return solver->propagateIfChanged(
cv, cv->join(ConstantValue::getUnknownConstant()));
Dialect *dialect;
if (auto *parent = value.getDefiningOp())
dialect = parent->getDialect();
else
dialect = value.getParentBlock()->getParentOp()->getDialect();
solver->propagateIfChanged(
cv, cv->join(ConstantValue(IntegerAttr::get(value.getType(), *constant),
dialect)));
}
void IntegerRangeAnalysis::visitOperation(
Operation *op, ArrayRef<const IntegerValueRangeLattice *> operands,
ArrayRef<IntegerValueRangeLattice *> results) {
// If the lattice on any operand is unitialized, bail out.
if (llvm::any_of(operands, [](const IntegerValueRangeLattice *lattice) {
return lattice->getValue().isUninitialized();
})) {
return;
}
// Ignore non-integer outputs - return early if the op has no scalar
// integer results
bool hasIntegerResult = false;
for (auto it : llvm::zip(results, op->getResults())) {
Value value = std::get<1>(it);
if (value.getType().isIntOrIndex()) {
hasIntegerResult = true;
} else {
IntegerValueRangeLattice *lattice = std::get<0>(it);
propagateIfChanged(lattice,
lattice->join(IntegerValueRange::getMaxRange(value)));
}
}
if (!hasIntegerResult)
return;
auto inferrable = dyn_cast<InferIntRangeInterface>(op);
if (!inferrable)
return setAllToEntryStates(results);
LLVM_DEBUG(llvm::dbgs() << "Inferring ranges for " << *op << "\n");
SmallVector<ConstantIntRanges> argRanges(
llvm::map_range(operands, [](const IntegerValueRangeLattice *val) {
return val->getValue().getValue();
}));
auto joinCallback = [&](Value v, const ConstantIntRanges &attrs) {
auto result = dyn_cast<OpResult>(v);
if (!result)
return;
assert(llvm::is_contained(op->getResults(), result));
LLVM_DEBUG(llvm::dbgs() << "Inferred range " << attrs << "\n");
IntegerValueRangeLattice *lattice = results[result.getResultNumber()];
IntegerValueRange oldRange = lattice->getValue();
ChangeResult changed = lattice->join(IntegerValueRange{attrs});
// Catch loop results with loop variant bounds and conservatively make
// them [-inf, inf] so we don't circle around infinitely often (because
// the dataflow analysis in MLIR doesn't attempt to work out trip counts
// and often can't).
bool isYieldedResult = llvm::any_of(v.getUsers(), [](Operation *op) {
return op->hasTrait<OpTrait::IsTerminator>();
});
if (isYieldedResult && !oldRange.isUninitialized() &&
!(lattice->getValue() == oldRange)) {
LLVM_DEBUG(llvm::dbgs() << "Loop variant loop result detected\n");
changed |= lattice->join(IntegerValueRange::getMaxRange(v));
}
propagateIfChanged(lattice, changed);
};
inferrable.inferResultRanges(argRanges, joinCallback);
}
void IntegerRangeAnalysis::visitNonControlFlowArguments(
Operation *op, const RegionSuccessor &successor,
ArrayRef<IntegerValueRangeLattice *> argLattices, unsigned firstIndex) {
if (auto inferrable = dyn_cast<InferIntRangeInterface>(op)) {
LLVM_DEBUG(llvm::dbgs() << "Inferring ranges for " << *op << "\n");
// If the lattice on any operand is unitialized, bail out.
if (llvm::any_of(op->getOperands(), [&](Value value) {
return getLatticeElementFor(op, value)->getValue().isUninitialized();
}))
return;
SmallVector<ConstantIntRanges> argRanges(
llvm::map_range(op->getOperands(), [&](Value value) {
return getLatticeElementFor(op, value)->getValue().getValue();
}));
auto joinCallback = [&](Value v, const ConstantIntRanges &attrs) {
auto arg = dyn_cast<BlockArgument>(v);
if (!arg)
return;
if (!llvm::is_contained(successor.getSuccessor()->getArguments(), arg))
return;
LLVM_DEBUG(llvm::dbgs() << "Inferred range " << attrs << "\n");
IntegerValueRangeLattice *lattice = argLattices[arg.getArgNumber()];
IntegerValueRange oldRange = lattice->getValue();
ChangeResult changed = lattice->join(IntegerValueRange{attrs});
// Catch loop results with loop variant bounds and conservatively make
// them [-inf, inf] so we don't circle around infinitely often (because
// the dataflow analysis in MLIR doesn't attempt to work out trip counts
// and often can't).
bool isYieldedValue = llvm::any_of(v.getUsers(), [](Operation *op) {
return op->hasTrait<OpTrait::IsTerminator>();
});
if (isYieldedValue && !oldRange.isUninitialized() &&
!(lattice->getValue() == oldRange)) {
LLVM_DEBUG(llvm::dbgs() << "Loop variant loop result detected\n");
changed |= lattice->join(IntegerValueRange::getMaxRange(v));
}
propagateIfChanged(lattice, changed);
};
inferrable.inferResultRanges(argRanges, joinCallback);
return;
}
/// Given the results of getConstant{Lower,Upper}Bound() or getConstantStep()
/// on a LoopLikeInterface return the lower/upper bound for that result if
/// possible.
auto getLoopBoundFromFold = [&](std::optional<OpFoldResult> loopBound,
Type boundType, bool getUpper) {
unsigned int width = ConstantIntRanges::getStorageBitwidth(boundType);
if (loopBound.has_value()) {
if (loopBound->is<Attribute>()) {
if (auto bound =
dyn_cast_or_null<IntegerAttr>(loopBound->get<Attribute>()))
return bound.getValue();
} else if (auto value = loopBound->dyn_cast<Value>()) {
const IntegerValueRangeLattice *lattice =
getLatticeElementFor(op, value);
if (lattice != nullptr)
return getUpper ? lattice->getValue().getValue().smax()
: lattice->getValue().getValue().smin();
}
}
// Given the results of getConstant{Lower,Upper}Bound()
// or getConstantStep() on a LoopLikeInterface return the lower/upper
// bound
return getUpper ? APInt::getSignedMaxValue(width)
: APInt::getSignedMinValue(width);
};
// Infer bounds for loop arguments that have static bounds
if (auto loop = dyn_cast<LoopLikeOpInterface>(op)) {
std::optional<Value> iv = loop.getSingleInductionVar();
if (!iv) {
return SparseDataFlowAnalysis ::visitNonControlFlowArguments(
op, successor, argLattices, firstIndex);
}
std::optional<OpFoldResult> lowerBound = loop.getSingleLowerBound();
std::optional<OpFoldResult> upperBound = loop.getSingleUpperBound();
std::optional<OpFoldResult> step = loop.getSingleStep();
APInt min = getLoopBoundFromFold(lowerBound, iv->getType(),
/*getUpper=*/false);
APInt max = getLoopBoundFromFold(upperBound, iv->getType(),
/*getUpper=*/true);
// Assume positivity for uniscoverable steps by way of getUpper = true.
APInt stepVal =
getLoopBoundFromFold(step, iv->getType(), /*getUpper=*/true);
if (stepVal.isNegative()) {
std::swap(min, max);
} else {
// Correct the upper bound by subtracting 1 so that it becomes a <=
// bound, because loops do not generally include their upper bound.
max -= 1;
}
IntegerValueRangeLattice *ivEntry = getLatticeElement(*iv);
auto ivRange = ConstantIntRanges::fromSigned(min, max);
propagateIfChanged(ivEntry, ivEntry->join(IntegerValueRange{ivRange}));
return;
}
return SparseDataFlowAnalysis::visitNonControlFlowArguments(
op, successor, argLattices, firstIndex);
}