This has been a TODO for a long time, and it brings about many advantages (namely nice accessors, and less fragile code). The existing overloads that accept ArrayRef are now treated as deprecated and will be removed in a followup (after a small grace period). Most of the upstream MLIR usages have been fixed by this commit, the rest will be handled in a followup.
Differential Revision: https://reviews.llvm.org/D110293
This pass transforms SCF.ForOp operations to SCF.WhileOp. The For loop condition is placed in the 'before' region of the while operation, and indctuion variable incrementation + the loop body in the 'after' region. The loop carried values of the while op are the induction variable (IV) of the for-loop + any iter_args specified for the for-loop.
Any 'yield' ops in the for-loop are rewritten to additionally yield the (incremented) induction variable.
This transformation is useful for passes where we want to consider structured control flow solely on the basis of a loop body and the computation of a loop condition. As an example, when doing high-level synthesis in CIRCT, the incrementation of an IV in a for-loop is "just another part" of a circuit datapath, and what we really care about is the distinction between our datapath and our control logic (the condition variable).
Differential Revision: https://reviews.llvm.org/D108454
This pass transforms SCF.ForOp operations to SCF.WhileOp. The For loop condition is placed in the 'before' region of the while operation, and indctuion variable incrementation + the loop body in the 'after' region. The loop carried values of the while op are the induction variable (IV) of the for-loop + any iter_args specified for the for-loop.
Any 'yield' ops in the for-loop are rewritten to additionally yield the (incremented) induction variable.
This transformation is useful for passes where we want to consider structured control flow solely on the basis of a loop body and the computation of a loop condition. As an example, when doing high-level synthesis in CIRCT, the incrementation of an IV in a for-loop is "just another part" of a circuit datapath, and what we really care about is the distinction between our datapath and our control logic (the condition variable).
Differential Revision: https://reviews.llvm.org/D108454
Generate an scf.for instead of an scf.if for the partial iteration. This is for consistency reasons: The peeling of linalg.tiled_loop also uses another loop for the partial iteration.
Note: Canonicalizations patterns may rewrite partial iterations to scf.if afterwards.
Differential Revision: https://reviews.llvm.org/D109568
Fold dim ops of scf.for results to dim ops of the respective iter args if the loop is shape preserving.
Differential Revision: https://reviews.llvm.org/D109430
The limitation on iter_args introduced with D108806 is too restricting. Changes of the runtime type should be allowed.
Extends the dim op canonicalization with a simple analysis to determine when it is safe to canonicalize.
Differential Revision: https://reviews.llvm.org/D109125
* Add `DimOfIterArgFolder`.
* Move existing cross-dialect canonicalization patterns to `LoopCanonicalization.cpp`.
* Rename `SCFAffineOpCanonicalization` pass to `SCFForLoopCanonicalization`.
* Expand documentaton of scf.for: The type of loop-carried variables may not change with iterations. (Not even the dynamic type.)
Differential Revision: https://reviews.llvm.org/D108806
* Add batched version of all `addId` variants, so that multiple IDs can be added at a time.
* Rename `addId` and variants to `insertId` and `appendId`. Most external users call `appendId`. Splitting `addId` into two functions also makes it possible to provide batched version for both. (Otherwise, the overloads are ambigious when calling `addId`.)
Differential Revision: https://reviews.llvm.org/D108532
* Add support for affine.max ops to SCF loop peeling pattern.
* Add support for affine.max ops to `AffineMinSCFCanonicalizationPattern`.
* Rename `AffineMinSCFCanonicalizationPattern` to `AffineOpSCFCanonicalizationPattern`.
* Rename `AffineMinSCFCanonicalization` pass to `SCFAffineOpCanonicalization`.
Differential Revision: https://reviews.llvm.org/D108009
This canonicalization simplifies affine.min operations inside "for loop"-like operations (e.g., scf.for and scf.parallel) based on two invariants:
* iv >= lb
* iv < lb + step * ((ub - lb - 1) floorDiv step) + 1
This commit adds a new pass `canonicalize-scf-affine-min` (instead of being a canonicalization pattern) to avoid dependencies between the Affine dialect and the SCF dialect.
Differential Revision: https://reviews.llvm.org/D107731
Do not apply loop peeling to loops that are contained in the partial iteration of an already peeled loop. This is to avoid code explosion when dealing with large loop nests. Can be controlled with a new pass option `skip-partial`.
Differential Revision: https://reviews.llvm.org/D108542
Simplify affine.min ops, enabling various other canonicalizations inside the peeled loop body.
affine.min ops such as:
```
map = affine_map<(d0)[s0, s1] -> (s0, -d0 + s1)>
%r = affine.min #affine.min #map(%iv)[%step, %ub]
```
are rewritten them into (in the case the peeled loop):
```
%r = %step
```
To determine how an affine.min op should be rewritten and to prove its correctness, FlatAffineConstraints is utilized.
Differential Revision: https://reviews.llvm.org/D107222
Expand ParallelLoopTilingPass with an inbound_check mode.
In default mode, the upper bound of the inner loop is from the min op; in
inbound_check mode, the upper bound of the inner loop is the step of the outer
loop and an additional inbound check will be emitted inside of the inner loop.
This was 'FIXME' in the original codes and a typical usage is for GPU backends,
thus the outer loop and inner loop can be mapped to blocks/threads in seperate.
Differential Revision: https://reviews.llvm.org/D105455
Replace some code snippets With scf::ForOp methods. Additionally,
share a listener at one more point (although this pattern is still
not safe to roll back currently)
Differential Revision: https://reviews.llvm.org/D107754
Add ForLoopBoundSpecialization pass, which specializes scf.for loops into a "main loop" where `step` divides the iteration space evenly and into an scf.if that handles the last iteration.
This transformation is useful for vectorization and loop tiling. E.g., when vectorizing loads/stores, programs will spend most of their time in the main loop, in which only unmasked loads/stores are used. Only the in the last iteration (scf.if), slower masked loads/stores are used.
Subsequent commits will apply this transformation in the SparseDialect and in Linalg's loop tiling.
Differential Revision: https://reviews.llvm.org/D105804
This is the first step to support software pipeline for scf.for loops.
This is only the transformation to create pipelined kernel and
prologue/epilogue.
The scheduling needs to be given by user as many different algorithm
and heuristic could be applied.
This currently doesn't handle loop arguments, this will be added in a
follow up patch.
Differential Revision: https://reviews.llvm.org/D105868
In cases where arithmetic (addi/muli) ops are performed on an scf.for loops induction variable with a single use, we can fold those ops directly into the scf.for loop.
For example, in the following code:
```
scf.for %i = %c0 to %arg1 step %c1 {
%0 = addi %arg2, %i : index
%1 = muli %0, %c4 : index
%2 = memref.load %arg0[%1] : memref<?xi32>
%3 = muli %2, %2 : i32
memref.store %3, %arg0[%1] : memref<?xi32>
}
```
we can lift `%0` up into the scf.for loop range, as it is the only user of %i:
```
%lb = addi %arg2, %c0 : index
%ub = addi %arg2, %i : index
scf.for %i = %lb to %ub step %c1 {
%1 = muli %0, %c4 : index
%2 = memref.load %arg0[%1] : memref<?xi32>
%3 = muli %2, %2 : i32
memref.store %3, %arg0[%1] : memref<?xi32>
}
```
Reviewed By: mehdi_amini, ftynse, Anthony
Differential Revision: https://reviews.llvm.org/D104289
* Remove dependency: Standard --> MemRef
* Add dependencies: GPUToNVVMTransforms --> MemRef, Linalg --> MemRef, MemRef --> Tensor
* Note: The `subtensor_insert_propagate_dest_cast` test case in MemRef/canonicalize.mlir will be moved to Tensor/canonicalize.mlir in a subsequent commit, which moves over the remaining Tensor ops from the Standard dialect to the Tensor dialect.
Differential Revision: https://reviews.llvm.org/D104506
Splitting the memref dialect lead to an introduction of several dependencies
to avoid compilation issues. The canonicalize pass also depends on the
memref dialect, but it shouldn't. This patch resolves the dependencies
and the unintuitive includes are removed. However, the dependency moves
to the constructor of the std dialect.
Differential Revision: https://reviews.llvm.org/D102060
This covers the extremely common case of replacing all uses of a Value
with a new op that is itself a user of the original Value.
This should also be a little bit more efficient than the
`SmallPtrSet<Operation *, 1>{op}` idiom that was being used before.
Differential Revision: https://reviews.llvm.org/D102373
Break up the dependency between SCF ops and substituteMin helper and make a
more generic version of AffineMinSCFCanonicalization. This reduce dependencies
between linalg and SCF and will allow the logic to be used with other kind of
ops. (Like ID ops).
Differential Revision: https://reviews.llvm.org/D100321
This doesn't change APIs, this just cleans up the many in-tree uses of these
names to use the new preferred names. We'll keep the old names around for a
couple weeks to help transitions.
Differential Revision: https://reviews.llvm.org/D99127
This updates the codebase to pass the context when creating an instance of
OwningRewritePatternList, and starts removing extraneous MLIRContext
parameters. There are many many more to be removed.
Differential Revision: https://reviews.llvm.org/D99028
This commit introduced a cyclic dependency:
Memref dialect depends on Standard because it used ConstantIndexOp.
Std depends on the MemRef dialect in its EDSC/Intrinsics.h
Working on a fix.
This reverts commit 8aa6c3765b.
Create the memref dialect and move several dialect-specific ops without
dependencies to other ops from std dialect to this dialect.
Moved ops:
AllocOp -> MemRef_AllocOp
AllocaOp -> MemRef_AllocaOp
DeallocOp -> MemRef_DeallocOp
MemRefCastOp -> MemRef_CastOp
GetGlobalMemRefOp -> MemRef_GetGlobalOp
GlobalMemRefOp -> MemRef_GlobalOp
PrefetchOp -> MemRef_PrefetchOp
ReshapeOp -> MemRef_ReshapeOp
StoreOp -> MemRef_StoreOp
TransposeOp -> MemRef_TransposeOp
ViewOp -> MemRef_ViewOp
The roadmap to split the memref dialect from std is discussed here:
https://llvm.discourse.group/t/rfc-split-the-memref-dialect-from-std/2667
Differential Revision: https://reviews.llvm.org/D96425
This better matches the rest of the infrastructure, is much simpler, and makes it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D93432
Given that OpState already implicit converts to Operator*, this seems reasonable.
The alternative would be to add more functions to OpState which forward to Operation.
Reviewed By: rriddle, ftynse
Differential Revision: https://reviews.llvm.org/D92266
- Address TODO in scf-bufferize: the argument materialization issue is
now fixed and the code is now in Transforms/Bufferize.cpp
- Tighten up finalizing-bufferize to avoid creating invalid IR when
operand types potentially change
- Tidy up the testing of func-bufferize, and move appropriate tests
to a new finalizing-bufferize.mlir
- The new stricter checking in finalizing-bufferize revealed that we
needed a DimOp conversion pattern (found when integrating into npcomp).
Previously, the converion infrastructure was blindly changing the
operand type during finalization, which happened to work due to
DimOp's tensor/memref polymorphism, but is generally not encouraged
(the new pattern is the way to tell the conversion infrastructure that
it is legal to change that type).
These includes have been deprecated in favor of BuiltinDialect.h, which contains the definitions of ModuleOp and FuncOp.
Differential Revision: https://reviews.llvm.org/D91572
This fixes a subtle issue, described in the comment starting with
"Clone the op without the regions and inline the regions from the old op",
which prevented this conversion from working on non-trivial examples.
Differential Revision: https://reviews.llvm.org/D90203
This class represents a rewrite pattern list that has been frozen, and thus immutable. This replaces the uses of OwningRewritePatternList in pattern driver related API, such as dialect conversion. When PDL becomes more prevalent, this API will allow for optimizing a set of patterns once without the need to do this per run of a pass.
Differential Revision: https://reviews.llvm.org/D89104
A "structural" type conversion is one where the underlying ops are
completely agnostic to the actual types involved and simply need to update
their types. An example of this is scf.if -- the scf.if op and the
corresponding scf.yield ops need to update their types accordingly to the
TypeConverter, but otherwise don't care what type conversions are happening.
To test the structural type conversions, it is convenient to define a
bufferize pass for a dialect, which exercises them nicely.
Differential Revision: https://reviews.llvm.org/D89757
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
