This patch mechanically replaces None with std::nullopt where the
compiler would warn if None were deprecated. The intent is to reduce
the amount of manual work required in migrating from Optional to
std::optional.
This is part of an effort to migrate from llvm::Optional to
std::optional:
https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Currently, for sparse analyses, we always store a `Optional<ValueT>` in each lattice element. When it's `None`, we consider the lattice element as `uninitialized`.
However:
* Not all lattices have an `uninitialized` state. For example, `Executable` and `PredecessorState` have default values so they are always initialized.
* In dense analyses, we don't have the concept of an `uninitialized` state.
Given these inconsistencies, this patch removes `Lattice::isUninitialized()`. Individual analysis states are now default-constructed. If the default state of an analysis can be considered as "uninitialized" then this analysis should implement the following logic:
* Special join rule: `join(uninitialized, any) == any`.
* Special bail out logic: if any of the input states is uninitialized, exit the transfer function early.
Depends On D132086
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D132800
### Rationale
For a program point where we cannot reason about incoming dataflow (e.g. an argument of an entry block), the framework needs to initialize the state.
Currently, `AbstractSparseDataFlowAnalysis` initializes such state to the "pessimistic fixpoint", and `AbstractDenseDataFlowAnalysis` calls the state's `reset()` function.
However, entry states aren't necessarily the pessimistic fixpoint. Example: in reaching definition, the pessimistic fixpoint is `{all definitions}`, but the entry state is `{}`.
This awkwardness might be why the dense analysis API currently uses `reset()` instead of `markPessimisticFixpoint()`.
This patch consolidates entry point initialization into a single function `setToEntryState()`.
### API Location
Note that `setToEntryState()` is defined in the analysis rather than the lattice, so that we allow different analyses to use the same lattice but different entry states.
### Removal of the concept of optimistic/known value
The concept of optimistic/known value is too specific to SCCP.
Furthermore, the known value is not really used: In the current SCCP implementation, the known value (pessimistic fixpoint) is always `Attribute{}` (non-constant). This means there's no point storing a `knownValue` in each state.
If we do need to re-introduce optimistic/known value, we should put it in the SCCP analysis, not the sparse analysis API.
### Terminology
Please let me know if "entry state" is a good terminology.
I chose "entry" from Wikipedia (https://en.wikipedia.org/wiki/Data-flow_analysis#Basic_principles).
Another term I can think of is "boundary" (https://suif.stanford.edu/~courses/cs243/lectures/L3-DFA2-revised.pdf) which might be better since it also makes sense for backward analysis.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D132086
Depends On D131660
`defaultInitialize()` was introduced for the "nudging" behavior, which has been deleted.
Reviewed By: Mogball, rriddle
Differential Revision: https://reviews.llvm.org/D131746
Currently, in the MLIR `{Sparse,Dense}DataFlowAnalysis` API, there is a small optimization:
Before running a transfer function, if the "out state" is already at the pessimistic fixpoint (bottom lattice value), then we know that it cannot possibly be changed, therefore we can skip the transfer function.
I benchmarked and found that this optimization is ineffective, so we can remove it and simplify `{Sparse,Dense}DataFlowAnalysis`. In a subsequent patch, I plan to change/remove the concept of the pessimistic fixpoint so that the API is further simplified.
Benchmark: I ran the following tests 5 times (after 3 warmup runs), and timed the `initializeAndRun()` function.
| Test | Before (us) | After (us) |
| mlir-opt -test-dead-code-analysis mlir/test/Analysis/DataFlow/test-dead-code-analysis.mlir | 181.2536 | 187.7074 |
| mlir-opt -- -test-dead-code-analysis mlir/test/Analysis/DataFlow/test-last-modified-callgraph.mlir | 109.5504 | 105.0654 |
| mlir-opt -- -test-dead-code-analysis mlir/test/Analysis/DataFlow/test-last-modified.mlir | 333.3646 | 322.4224 |
| mlir-opt -- -allow-unregistered-dialect -sccp mlir/test/Analysis/DataFlow/test-combined-sccp.mlir | 1027.1492 | 1081.818 |
Note: `test-combined-sccp.mlir` is crafted by combining `mlir/test/Transforms/sccp.mlir`, `mlir/test/Transforms/sccp-structured.mlir` and `mlir/test/Transforms/sccp-callgraph.mlir`.
Reviewed By: aartbik, Mogball
Differential Revision: https://reviews.llvm.org/D131660
This patch introduces an implementation of dense data-flow analysis. Dense
data-flow analysis attaches a lattice before and after the execution of every
operation. The lattice state is propagated across operations by a user-defined
transfer function. The state is joined across control-flow and callgraph edges.
Thge patch provides an example pass that uses both a dense and a sparse analysis
together.
Depends on D127139
Reviewed By: rriddle, phisiart
Differential Revision: https://reviews.llvm.org/D127173
