Files
clang-p2996/mlir/test/lib/Analysis/TestDataFlowFramework.cpp
donald chen 4b3f251bad [mlir] [dataflow] unify semantics of program point (#110344)
The concept of a 'program point' in the original data flow framework is
ambiguous. It can refer to either an operation or a block itself. This
representation has different interpretations in forward and backward
data-flow analysis. In forward data-flow analysis, the program point of
an operation represents the state after the operation, while in backward
data flow analysis, it represents the state before the operation. When
using forward or backward data-flow analysis, it is crucial to carefully
handle this distinction to ensure correctness.

This patch refactors the definition of program point, unifying the
interpretation of program points in both forward and backward data-flow
analysis.

How to integrate this patch?

For dense forward data-flow analysis and other analysis (except dense
backward data-flow analysis), the program point corresponding to the
original operation can be obtained by `getProgramPointAfter(op)`, and
the program point corresponding to the original block can be obtained by
`getProgramPointBefore(block)`.

For dense backward data-flow analysis, the program point corresponding
to the original operation can be obtained by
`getProgramPointBefore(op)`, and the program point corresponding to the
original block can be obtained by `getProgramPointAfter(block)`.

NOTE: If you need to get the lattice of other data-flow analyses in
dense backward data-flow analysis, you should still use the dense
forward data-flow approach. For example, to get the Executable state of
a block in dense backward data-flow analysis and add the dependency of
the current operation, you should write:

``getOrCreateFor<Executable>(getProgramPointBefore(op),
getProgramPointBefore(block))``

In case above, we use getProgramPointBefore(op) because the analysis we
rely on is dense backward data-flow, and we use
getProgramPointBefore(block) because the lattice we query is the result
of a non-dense backward data flow computation.

related dsscussion:
https://discourse.llvm.org/t/rfc-unify-the-semantics-of-program-points/80671/8
corresponding PSA:
https://discourse.llvm.org/t/psa-program-point-semantics-change/81479
2024-10-11 21:59:05 +08:00

187 lines
5.6 KiB
C++

//===- TestDataFlowFramework.cpp - Test data-flow analysis framework ------===//
//
// 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/Analysis/DataFlowFramework.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Pass/Pass.h"
#include <optional>
using namespace mlir;
namespace {
/// This analysis state represents an integer that is XOR'd with other states.
class FooState : public AnalysisState {
public:
MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(FooState)
using AnalysisState::AnalysisState;
/// Returns true if the state is uninitialized.
bool isUninitialized() const { return !state; }
/// Print the integer value or "none" if uninitialized.
void print(raw_ostream &os) const override {
if (state)
os << *state;
else
os << "none";
}
/// Join the state with another. If either is unintialized, take the
/// initialized value. Otherwise, XOR the integer values.
ChangeResult join(const FooState &rhs) {
if (rhs.isUninitialized())
return ChangeResult::NoChange;
return join(*rhs.state);
}
ChangeResult join(uint64_t value) {
if (isUninitialized()) {
state = value;
return ChangeResult::Change;
}
uint64_t before = *state;
state = before ^ value;
return before == *state ? ChangeResult::NoChange : ChangeResult::Change;
}
/// Set the value of the state directly.
ChangeResult set(const FooState &rhs) {
if (state == rhs.state)
return ChangeResult::NoChange;
state = rhs.state;
return ChangeResult::Change;
}
/// Returns the integer value of the state.
uint64_t getValue() const { return *state; }
private:
/// An optional integer value.
std::optional<uint64_t> state;
};
/// This analysis computes `FooState` across operations and control-flow edges.
/// If an op specifies a `foo` integer attribute, the contained value is XOR'd
/// with the value before the operation.
class FooAnalysis : public DataFlowAnalysis {
public:
MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(FooAnalysis)
using DataFlowAnalysis::DataFlowAnalysis;
LogicalResult initialize(Operation *top) override;
LogicalResult visit(ProgramPoint *point) override;
private:
void visitBlock(Block *block);
void visitOperation(Operation *op);
};
struct TestFooAnalysisPass
: public PassWrapper<TestFooAnalysisPass, OperationPass<func::FuncOp>> {
MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestFooAnalysisPass)
StringRef getArgument() const override { return "test-foo-analysis"; }
void runOnOperation() override;
};
} // namespace
LogicalResult FooAnalysis::initialize(Operation *top) {
if (top->getNumRegions() != 1)
return top->emitError("expected a single region top-level op");
if (top->getRegion(0).getBlocks().empty())
return top->emitError("expected at least one block in the region");
// Initialize the top-level state.
(void)getOrCreate<FooState>(getProgramPointBefore(&top->getRegion(0).front()))
->join(0);
// Visit all nested blocks and operations.
for (Block &block : top->getRegion(0)) {
visitBlock(&block);
for (Operation &op : block) {
if (op.getNumRegions())
return op.emitError("unexpected op with regions");
visitOperation(&op);
}
}
return success();
}
LogicalResult FooAnalysis::visit(ProgramPoint *point) {
if (!point->isBlockStart())
visitOperation(point->getPrevOp());
else
visitBlock(point->getBlock());
return success();
}
void FooAnalysis::visitBlock(Block *block) {
if (block->isEntryBlock()) {
// This is the initial state. Let the framework default-initialize it.
return;
}
ProgramPoint *point = getProgramPointBefore(block);
FooState *state = getOrCreate<FooState>(point);
ChangeResult result = ChangeResult::NoChange;
for (Block *pred : block->getPredecessors()) {
// Join the state at the terminators of all predecessors.
const FooState *predState = getOrCreateFor<FooState>(
point, getProgramPointAfter(pred->getTerminator()));
result |= state->join(*predState);
}
propagateIfChanged(state, result);
}
void FooAnalysis::visitOperation(Operation *op) {
ProgramPoint *point = getProgramPointAfter(op);
FooState *state = getOrCreate<FooState>(point);
ChangeResult result = ChangeResult::NoChange;
// Copy the state across the operation.
const FooState *prevState;
prevState = getOrCreateFor<FooState>(point, getProgramPointBefore(op));
result |= state->set(*prevState);
// Modify the state with the attribute, if specified.
if (auto attr = op->getAttrOfType<IntegerAttr>("foo")) {
uint64_t value = attr.getUInt();
result |= state->join(value);
}
propagateIfChanged(state, result);
}
void TestFooAnalysisPass::runOnOperation() {
func::FuncOp func = getOperation();
DataFlowSolver solver;
solver.load<FooAnalysis>();
if (failed(solver.initializeAndRun(func)))
return signalPassFailure();
raw_ostream &os = llvm::errs();
os << "function: @" << func.getSymName() << "\n";
func.walk([&](Operation *op) {
auto tag = op->getAttrOfType<StringAttr>("tag");
if (!tag)
return;
const FooState *state =
solver.lookupState<FooState>(solver.getProgramPointAfter(op));
assert(state && !state->isUninitialized());
os << tag.getValue() << " -> " << state->getValue() << "\n";
});
}
namespace mlir {
namespace test {
void registerTestFooAnalysisPass() { PassRegistration<TestFooAnalysisPass>(); }
} // namespace test
} // namespace mlir