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
Base `DataFlowAnalysis::visit` returns `LogicalResult`, but wrappers's
Sparse/Dense/Forward/Backward `visitOperation` doesn't.
Sometimes it's needed to abort solver early if some unrecoverable
condition detected inside analysis.
Update `visitOperation` to return `LogicalResult` and propagate it to
`solver.initializeAndRun()`. Only `visitOperation` is updated for now,
it's possible to update other hooks like `visitNonControlFlowArguments`,
bit it's not needed immediately and let's keep this PR small.
Hijacked `UnderlyingValueAnalysis` test analysis to test it.
The class `Lattice` should automatically delegate invocations of the
meet operator to the meet operation of the associated lattice value
class if that class provides a static function called `meet`. This
process fails for two reasons:
1. `Lattice::has_meet` checks for a member function `meet` without
arguments of the lattice value class, although it should check for a
static member function.
2. The function template `Lattice::meet<VT>()` implementing the default
meet operation directly in the lattice is always present and takes
precedence over the delegating function template `Lattice::meet<VT,
std::integral_constant<bool, true>>()`.
This change fixes the automatic delegation of the meet operation of a
lattice to the lattice value class in the presence of a static `meet`
function by conditionally enabling either the delegating function
template or the non-delegating function template and by changing
`Lattice::has_meet` so that it checks for a static `meet` member
function in the lattice value type.
The test from `TestSparseBackwardDataFlowAnalysis.cpp` is changed, such
that the `meet` function is not provided directly in the `WrittenTo`
lattice, but by the `Lattice` base class in order to trigger delegation
to a lattice value class.
The core implementation of the dataflow anlysis framework is
interpocedural by design. While this offers better analysis precision,
it also comes with additional cost as it takes longer for the analysis
to reach the fixpoint state. Add a configuration mechanism to the
dataflow solver to control whether it operates inteprocedurally or not
to offer clients a choice.
As a positive side effect, this change also adds hooks for explicitly
processing external/opaque function calls in the dataflow analyses,
e.g., based off of attributes present in the the function declaration or
call operation such as alias scopes and modref available in the LLVM
dialect.
This change should not affect existing analyses and the default solver
configuration remains interprocedural.
Co-authored-by: Jacob Peng <jacobmpeng@gmail.com>
Currently, data in `AbstractSparseBackwardDataFlowAnalysis` is
considered to flow one-to-one, in order, from the operands of an op
implementing `CallOpInterface` to the arguments of the function it is
calling.
This understanding of the data flow is inaccurate. The operands of such
an op that forward to the function arguments are obtained using a
method provided by `CallOpInterface` called `getArgOperands()`.
This commit fixes this bug by using `getArgOperands()` instead of
`getOperands()` to get the mapping from operands to function arguments
because not all operands necessarily forward to the function arguments
and even if they do, they don't necessarily have to be in the order in
which they appear in the op. The operands that don't get forwarded are
handled by the newly introduced `visitCallOperand()` function, which
works analogous to the `visitBranchOperand()` function.
This fix is also propagated to liveness analysis that earlier relied on
this incorrect implementation of the sparse backward dataflow analysis
framework and corrects some incorrect assumptions made in it.
Extra cleanup: Improved a comment and removed an unnecessary code line.
Signed-off-by: Srishti Srivastava <srishtisrivastava.ai@gmail.com>
Reviewed By: matthiaskramm, jcai19
Differential Revision: https://reviews.llvm.org/D157261
