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
The methods in `SideEffectUtils.h` (and their implementations in
`SideEffectUtils.cpp`) seem to have similar intent to methods already
existing in `SideEffectInterfaces.h`. Move the decleration (and
implementation) from `SideEffectUtils.h` (and `SideEffectUtils.cpp`)
into `SideEffectInterfaces.h` (and `SideEffectInterface.cpp`).
Also drop the `SideEffectInterface::hasNoEffect` method in favor of
`mlir::isMemoryEffectFree` which actually recurses into the operation
instead of just relying on the `hasRecursiveMemoryEffectTrait`
exclusively.
Differential Revision: https://reviews.llvm.org/D137857
The defining Op may live in an unlinked block so its parent Op may be
null. Only assert it when the parent Op is not null.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D137306
This patch takes the first step towards a more principled modeling of undefined behavior in MLIR as discussed in the following discourse threads:
1. https://discourse.llvm.org/t/semantics-modeling-undefined-behavior-and-side-effects/4812
2. https://discourse.llvm.org/t/rfc-mark-tensor-dim-and-memref-dim-as-side-effecting/65729
This patch in particular does the following:
1. Introduces a ConditionallySpeculatable OpInterface that dynamically determines whether an Operation can be speculated.
2. Re-defines `NoSideEffect` to allow undefined behavior, making it necessary but not sufficient for speculation. Also renames it to `NoMemoryEffect`.
3. Makes LICM respect the above semantics.
4. Changes all ops tagged with `NoSideEffect` today to additionally implement ConditionallySpeculatable and mark themselves as always speculatable. This combined trait is named `Pure`. This makes this change NFC.
For out of tree dialects:
1. Replace `NoSideEffect` with `Pure` if the operation does not have any memory effects, undefined behavior or infinite loops.
2. Replace `NoSideEffect` with `NoSideEffect` otherwise.
The next steps in this process are (I'm proposing to do these in upcoming patches):
1. Update operations like `tensor.dim`, `memref.dim`, `scf.for`, `affine.for` to implement a correct hook for `ConditionallySpeculatable`. I'm also happy to update ops in other dialects if the respective dialect owners would like to and can give me some pointers.
2. Update other passes that speculate operations to consult `ConditionallySpeculatable` in addition to `NoMemoryEffect`. I could not find any other than LICM on a quick skim, but I could have missed some.
3. Add some documentation / FAQs detailing the differences between side effects, undefined behavior, speculatabilty.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D135505
This patch adds hermite normal form computation to Matrix. Part of this algorithm
lived in LinearTransform, being used for compuing column echelon form. This
patch moves the implementation to Matrix::hermiteNormalForm and generalises it
to compute the hermite normal form.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D133510
Only the main Presburger library under the Presburger directory has been switched to use arbitrary precision. Users have been changed to just cast returned values back to int64_t or to use newly added convenience functions that perform the same cast internally.
The performance impact of this has been tested by checking test runtimes after copy-pasting 100 copies of each function. Affine/simplify-structures.mlir goes from 0.76s to 0.80s after this patch. Its performance sees no regression compared to its original performance at commit 18a06d4f3a before a series of patches that I landed to offset the performance overhead of switching to arbitrary precision.
Affine/canonicalize.mlir and SCF/canonicalize.mlir show no noticable difference, staying at 2.02s and about 2.35s respectively.
Also, for Affine and SCF tests as a whole (no copy-pasting), the runtime remains about 0.09s on average before and after.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D129510
This patch refactors MAF to be defined over the universe in a given space
instead of being defined over a restricted domain.
The reasoning for this refactor is to store division representation for local
variables explicitly for the function outputs. This change is required for
unionLexMax/Min to support local variables which will be upstreamed after this
patch. Another reason for this refactor is to have a flattened form of
AffineMap as MultiAffineFunction.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D131864
The callgraph currently contains a special external node that is used both as the quasi caller for any externally callable as well as callees that could not be resolved.
This has one negative side effect however, which is the motivation for this patch: It leads to every externally callable which contains a call that could not be resolved (eg. an indirect call), to be put into one giant SCC when iterating over the SCCs of the call graph.
This patch fixes that issue by creating a second special callgraph node that acts as the callee for any unresolved callable. This breaks the cycles produced in the callgraph, yielding proper SCCs for all direct calls.
Differential Revision: https://reviews.llvm.org/D133585
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
Every dialect was dependent on `mlir-headers`, which was causing the
build of any single MLIR dialect to pull in a bunch of extra
dependencies that aren't needed. Now, MLIR dialects will need to
explicitly depend on `MLIR*IncGen` targets to pull in any needed
headers.
This does not impact the actual `mlir-header` target.
Consider the "simple" Arithmetic dialect. Before:
```
% ninja MLIRArithmeticDialect
[151/812] Building CXX object lib/TableGen/CMakeFiles/LLVMTableGen.dir/JSONBackend.cpp.o
```
After:
```
% ninja MLIRArithmeticDialect
[207/374] Building CXX object tools/mlir/lib/TableGen/CMakeFiles/MLIRTableGen.dir/GenInfo.cpp.o
```
(Both clean builds)
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D133132
It's only used from there, and this lets us remove the dependency from Analysis
to the Arith dialect.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D132928
### 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
This patch replaces calls to greatestCommonDivisor with std::gcd where
two arguments are of the same type. This means that
std::common_type_t of the argument type is the same as the argument
type.
We could drop calls to std::abs in some cases, but that's left for
another patch.
This patch replaces mlir::lcm with std::lcm, a C++17 feature.
Note that all the arguments to mlir::lcm are of int64_t with no
implicit type conversion as they are passed to mlir::lcm, which I've
verified by modifying mlir::lcm as:
template <typename TA, typename TB>
inline int64_t lcm(TA a, TB b) {
static_assert(std::is_same_v<TA, int64_t>);
static_assert(std::is_same_v<TB, int64_t>);
:
Currently, buffer deallocation considers arith.select to be
non-aliasing, which results in deallocs being inserted incorrectly. Since
arith.select doesn't implement any useful interfaces, this change just handles
it explicitly. Eventually this should probably be fixed properly, if this pass
is going to be used long term.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D132460
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 can easily overflow and it is possible for these unsigned overflows to result in incorrect results.
For example, the two LCMs could be 641 and 6700417, which multiply to 2^32 + 1, which overflows to 1.
Unsigned overflows already occur in the existing tests.
Also, when switching to arbitrary-precision arithmetic, this results in a many
large integer multiplications resulting in a significant slowdown.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D131184
When dead-code analysis is run at the scope of a function, call ops to
other functions at the same level were being marked as unreachable,
since the analysis optimistically assumes the call op to have no known
predecessors and that all predecessors are known, but the callee would
never get visited.
This patch fixes the bug by checking if a referenced function is above
the top-level op of the analysis, and is thus considered an external
callable.
Fixes#56830
Reviewed By: zero9178
Differential Revision: https://reviews.llvm.org/D130829
When this was updated in D127139 the update in-place case was no longer
marked as pessimistic. Add back in.
Differential Revision: https://reviews.llvm.org/D130453
llvm::sort is beneficial even when we use the iterator-based overload,
since it can optionally shuffle the elements (to detect
non-determinism). However llvm::sort is not usable everywhere, for
example, in compiler-rt.
Reviewed By: nhaehnle
Differential Revision: https://reviews.llvm.org/D130406
This change allows the user of LivenessBlockInfo to specify an op within the block and get a set of all values that are live as of that op. Semantically it relies on having a dominance-based region that has ordered operations. For DFG regions, computing liveness statically this way doesn't really make sense, it likely needs to be done at runtime.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D129447
This uses an int64_t-based fastpath for the common case and falls back to
SlowMPInt to handle the rare cases where larger numbers occur.
It uses `__builtin_*` for performance through the support in LLVM MathExtras.
Using this in the Presburger library results in a minor performance
*improvement* over any commit hash before sequence of patches
starting at d5e31cf38a.
This was previously reverted in 1e10d35ea9 due
to a build failure; relanding now with an attempted fix.
Reviewed By: Groverkss, ftynse
Differential Revision: https://reviews.llvm.org/D128811
This uses an int64_t-based fastpath for the common case and falls back to
SlowMPInt to handle the rare cases where larger numbers occur.
It uses `__builtin_*` for performance through the support in LLVM MathExtras.
Using this in the Presburger library results in a minor performance
*improvement* over any commit hash before sequence of patches
starting at d5e31cf38a.
Reviewed By: Groverkss, ftynse
Differential Revision: https://reviews.llvm.org/D128811
With SCCP and integer range analysis ported to the new framework, this old framework is redundant. Delete it.
Depends on D128866
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D128867
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 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
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 refactors existing implementations of division representation storage
into a new class, DivisionRepr. This refactoring is done so that the common
division utilities can be shared in an upcoming patch.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D129146
This patch implements a lexicographic max/min union of two PWMAFunctions.
The lexmax/lexmin union of two functions is defined as a function defined on
the union of the input domains of both functions, such that when only one of the
functions are defined, it outputs the same as that function, and if both are
defined, it outputs the lexmax/lexmin of the two outputs. On points where
neither function is defined, the union is not defined either.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D128829
"attachment" was a temporary name chosen for the information attached to a
variable in a PresburgerSpace. After the disambiguation of "variables" and
"identifiers" in PresburgerSpace, we use the word "identifiers" for this
information, since this information is used to "identify" these variables.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D128751
