Commit Graph

9 Commits

Author SHA1 Message Date
rkayaith
13bd410962 [mlir][Pass] Include anchor op in -pass-pipeline
In D134622 the printed form of a pass manager is changed to include the
name of the op that the pass manager is anchored on. This updates the
`-pass-pipeline` argument format to include the anchor op as well, so
that the printed form of a pipeline can be directly passed to
`-pass-pipeline`. In most cases this requires updating
`-pass-pipeline='pipeline'` to
`-pass-pipeline='builtin.module(pipeline)'`.

This also fixes an outdated assert that prevented running a
`PassManager` anchored on `'any'`.

Reviewed By: rriddle

Differential Revision: https://reviews.llvm.org/D134900
2022-11-03 11:36:12 -04:00
River Riddle
cda6aa78f8 [mlir][NFC] Update textual references of func to func.func in Transform tests
The special case parsing of `func` operations is being removed.
2022-04-20 22:17:30 -07:00
River Riddle
3655069234 [mlir] Move the Builtin FuncOp to the Func dialect
This commit moves FuncOp out of the builtin dialect, and into the Func
dialect. This move has been planned in some capacity from the moment
we made FuncOp an operation (years ago). This commit handles the
functional aspects of the move, but various aspects are left untouched
to ease migration: func::FuncOp is re-exported into mlir to reduce
the actual API churn, the assembly format still accepts the unqualified
`func`. These temporary measures will remain for a little while to
simplify migration before being removed.

Differential Revision: https://reviews.llvm.org/D121266
2022-03-16 17:07:03 -07:00
River Riddle
87d6bf3728 [mlir][test] Generalize a bunch of FuncOp based passes to run on any operation/interfaces
A lot of test passes are currently anchored on FuncOp, but this
dependency
is generally just historical. A majority of these test passes can run on
any operation, or can operate on a specific interface
(FunctionOpInterface/SymbolOpInterface).
This allows for greatly reducing the API dependency on FuncOp, which
is slated to be moved out of the Builtin dialect.

Differential Revision: https://reviews.llvm.org/D121191
2022-03-08 12:25:32 -08:00
Lei Zhang
50000abe3c [mlir] Use affine.apply when distributing to processors
This makes it easy to compose the distribution computation with
other affine computations.

Reviewed By: mravishankar

Differential Revision: https://reviews.llvm.org/D98171
2021-03-09 08:37:20 -05:00
Alex Zinenko
60f443bb3b [mlir] Change dialect namespace loop->scf
All ops of the SCF dialect now use the `scf.` prefix instead of `loop.`. This
is a part of dialect renaming.

Differential Revision: https://reviews.llvm.org/D79844
2020-05-13 19:20:21 +02:00
Mehdi Amini
bab5bcf8fd Add a flag on the context to protect against creation of operations in unregistered dialects
Differential Revision: https://reviews.llvm.org/D76903
2020-03-30 19:37:31 +00:00
Mahesh Ravishankar
9cbbd8f4df Support lowering of imperfectly nested loops into GPU dialect.
The current lowering of loops to GPU only supports lowering of loop
nests where the loops mapped to workgroups and workitems are perfectly
nested. Here a new lowering is added to handle lowering of imperfectly
nested loop body with the following properties
1) The loops partitioned to workgroups are perfectly nested.
2) The loop body of the inner most loop partitioned to workgroups can
contain one or more loop nests that are to be partitioned across
workitems. Each individual loops nests partitioned to workitems should
also be perfectly nested.
3) The number of workgroups and workitems are not deduced from the
loop bounds but are passed in by the caller of the lowering as values.
4) For statements within the perfectly nested loop nest partitioned
across workgroups that are not loops, it is valid to have all threads
execute that statement. This is NOT verified.

PiperOrigin-RevId: 277958868
2019-11-01 10:52:06 -07:00
Nicolas Vasilache
db4cd1c8dc Utility function to map a loop on a parametric grid of virtual processors
This CL introduces a simple loop utility function which rewrites the bounds and step of a loop so as to become mappable on a regular grid of processors whose identifiers are given by SSA values.

A corresponding unit test is added.

For example, using CUDA terminology, and assuming a 2-d grid with processorIds = [blockIdx.x, threadIdx.x] and numProcessors = [gridDim.x, blockDim.x], the loop:
```
   loop.for %i = %lb to %ub step %step {
     ...
   }
```
is rewritten into a version resembling the following pseudo-IR:
```
   loop.for %i = %lb + threadIdx.x + blockIdx.x * blockDim.x to %ub
      step %gridDim.x * blockDim.x {
     ...
   }
```

PiperOrigin-RevId: 258945942
2019-07-19 11:40:31 -07:00