Note that PointerUnion::{is,get} have been soft deprecated in
PointerUnion.h:
// FIXME: Replace the uses of is(), get() and dyn_cast() with
// isa<T>, cast<T> and the llvm::dyn_cast<T>
I'm not touching PointerUnion::dyn_cast for now because it's a bit
complicated; we could blindly migrate it to dyn_cast_if_present, but
we should probably use dyn_cast when the operand is known to be
non-null.
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
Allow customization of the `resolveCallable` method in the
`CallOpInterface`. This change allows for operations implementing this
interface to provide their own logic for resolving callables.
- Introduce the `resolveCallable` method, which does not include the
optional symbol table parameter. This method replaces the previously
existing extra class declaration `resolveCallable`.
- Introduce the `resolveCallableInTable` method, which incorporates the
symbol table parameter. This method replaces the previous extra class
declaration `resolveCallable` that used the optional symbol table
parameter.
This patch distinguishes between program points and lattice anchors in
data flow analysis, where lattice anchors represent locations where a
lattice can be attached, while program points denote points in program
execution.
Related discussions:
https://discourse.llvm.org/t/rfc-unify-the-semantics-of-program-points/80671/8
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 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>
The current implementation is not very ergonomic or descriptive: It uses `std::optional<unsigned>` where `std::nullopt` represents the parent op and `unsigned` is the region number.
This doesn't give us any useful methods specific to region control flow and makes the code fragile to changes due to now taking the region number into account.
This patch introduces a new type called `RegionBranchPoint`, replacing all uses of `std::optional<unsigned>` in the interface. It can be implicitly constructed from a region or a `RegionSuccessor`, can be compared with a region to check whether the branch point is branching from the parent, adds `isParent` to check whether we are coming from a parent op and adds `RegionSuccessor::parent` as a descriptive way to indicate branching from the parent.
Differential Revision: https://reviews.llvm.org/D159116
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
The `RegionBranchOpInterface` had a few fundamental issues caused by the API design of `getSuccessorRegions`.
It always required passing values for the `operands` parameter. This is problematic as the operands parameter actually changes meaning depending on which predecessor `index` is referring to. If coming from a region, you'd have to find a `RegionBranchTerminatorOpInterface` in that region, get its operand count, and then create a `SmallVector` of that size.
This is not only inconvenient, but also error-prone, which has lead to a bug in the implementation of a previously existing `getSuccessorRegions` overload.
Additionally, this made the method dual-use, trying to serve two different use-cases: 1) Trying to determine possible control flow edges between regions and 2) Trying to determine the region being branched to based on constant operands.
This patch fixes these issues by changing the interface methods and adding new ones:
* The `operands` argument of `getSuccessorRegions` has been removed. The method is now only responsible for returning possible control flow edges between regions.
* An optional `getEntrySuccessorRegions` method has been added. This is used to determine which regions are branched to from the parent op based on constant operands of the parent op. By default, it calls `getSuccessorRegions`. This is analogous to `getSuccessorForOperands` from `BranchOpInterface`.
* Add `getSuccessorRegions` to `RegionBranchTerminatorOpInterface`. This is used to get the possible successors of the terminator based on constant operands. By default, it calls the containing `RegionBranchOpInterface`s `getSuccessorRegions` method.
* `getSuccessorEntryOperands` was renamed to `getEntrySuccessorOperands` for consistency.
Differential Revision: https://reviews.llvm.org/D157506
This implication was already done de-facto and there were plenty of users and wrapper functions specifically used to handle the "return-like or RegionBranchTerminatorOpInterface" case. These simply existed due to up until recently missing features in ODS.
With the new capabilities of traits, we can make `ReturnLike` imply `RegionBranchTerminatorOpInterface` and auto generate proper definitions for its methods.
Various occurrences and wrapper methods used for `isa<RegionBranchTerminatorOpInterface>() || hasTrait<ReturnLike>()` have all been removed.
Differential Revision: https://reviews.llvm.org/D157402
Earlier, in the sparse backward dataflow analysis, data from the results
of an op implementing `RegionBranchOpInterface` was considered to flow
into the operands of every op that did not implement the
`RegionBranchTerminatorOpInterface` but was return-like and present
in a region of the former. It was thus also expected that the number of
results of the former be equal to the number of operands in the latter.
This understanding of dataflow is incorrect and thus this expectation is
also not justified. This commit fixes this incorrect understanding.
This commit ensures that these return-like ops are handled just like the
ops implementing the `RegionBranchTerminatorOpInterface`, which means
that, if this op has a region `A` whose successors are regions `B`, `C`,
and `D`, then data flows from the arguments (successor inputs) of `B`,
`C`, and `D` to the corresponding successor operands of this op.
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.
Also cleaned up some unnecessary comments from the test file.
Issue: https://github.com/llvm/llvm-project/issues/64139.
Signed-off-by: Srishti Srivastava <srishtisrivastava.ai@gmail.com>
Reviewed By: jcai19, matthiaskramm, Mogball
Differential Revision: https://reviews.llvm.org/D156376
In the MLIR dataflow analysis framework, when an `AnalysisState` is updated, it's dependents are enqueued to be visited.
Currently, there are two ways dependents are managed:
* `AnalysisState::dependents` stores a list of dependents. `DataFlowSolver::propagateIfChanged()` reads this list and enqueues them to the worklist.
* `AnalysisState::onUpdate()` allows custom logic to enqueue more to the worklist. This is called by `DataFlowSolver::propagateIfChanged()`.
This cleanup diff consolidates the two into `AnalysisState::onUpdate()`. This way, `DataFlowSolver` does not need to know the detail about `AnalysisState::dependents`, and the logic of dependency management is entirely handled by `AnalysisState`.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D154170
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.
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 patch updates all remaining uses of the deprecated functionality in
mlir/. This was done with clang-tidy as described below and further
modifications to GPUBase.td and OpenMPOpsInterfaces.td.
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:
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.
```
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
```
Differential Revision: https://reviews.llvm.org/D151542
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
Replace references to enumerate results with either result_pairs
(reference wrapper type) or structured bindings. I did not use
structured bindings everywhere as it wasn't clear to me it would
improve readability.
This is in preparation to the switch to zip semantics which won't
support non-const lvalue reference to elements:
https://reviews.llvm.org/D144503.
I chose to use values instead of const lvalue-refs because MLIR is
biased towards avoiding `const` local variables. This won't degrade
performance because currently `result_pair` is cheap to copy (size_t
+ iterator), and in the future, the enumerator iterator dereference
will return temporaries anyway.
Reviewed By: dblaikie
Differential Revision: https://reviews.llvm.org/D146006
This is part of an effort to migrate from llvm::Optional to
std::optional. This patch changes the way mlir-tblgen generates .inc
files, and modifies tests and documentation appropriately. It is a "no
compromises" patch, and doesn't leave the user with an unpleasant mix of
llvm::Optional and std::optional.
A non-trivial change has been made to ControlFlowInterfaces to split one
constructor into two, relating to a build failure on Windows.
See also: https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Signed-off-by: Ramkumar Ramachandra <r@artagnon.com>
Differential Revision: https://reviews.llvm.org/D138934
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
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
Integer range inference has been swapped to the new framework. The integer value range lattices automatically updates the corresponding constant value on update.
Depends on D127173
Reviewed By: krzysz00, rriddle
Differential Revision: https://reviews.llvm.org/D128866
This patch introduces a (forward) sparse data-flow analysis implemented with the data-flow analysis framework. The analysis interacts with liveness information that can be provided by dead-code analysis to be conditional. This patch re-implements SCCP using dead-code analysis and (conditional) constant propagation analyses.
Depends on D127064
Reviewed By: rriddle, phisiart
Differential Revision: https://reviews.llvm.org/D127139
This patch implements the analysis state classes needed for sparse data-flow analysis and implements a dead-code analysis using those states to determine liveness of blocks, control-flow edges, region predecessors, and function callsites.
Depends on D126751
Reviewed By: rriddle, phisiart
Differential Revision: https://reviews.llvm.org/D127064
