This revision untangles a few more conversion pieces and allows rewriting
the relatively intricate (and somewhat inconsistent) LowerGpuOpsToNVVMOpsPass
in a declarative fashion that provides a much better understanding and control.
Differential Revision: https://reviews.llvm.org/D157617
GPU code generation, and specifically the shared memory copy insertion
may introduce spurious barriers guarding read-after-read dependencies or
read-after-write on non-aliasing data, which degrades performance due to
unnecessary synchronization. Add a pattern and transform op that removes
such barriers by analyzing memory effects that the barrier actually
guards that are not also guarded by other barriers. The code is adapted
from the Polygeist incubator project.
Co-authored-by: William Moses <gh@wsmoses.com>
Co-authored-by: Ivan Radanov Ivanov <ivanov.i.aa@m.titech.ac.jp>
Reviewed By: nicolasvasilache, wsmoses
Differential Revision: https://reviews.llvm.org/D154720
Update operations in Transform dialect extensions defined in the Affine,
GPU, MemRef and Tensor dialects to use the more generic
`TransformHandleTypeInterface` type constraint instead of hardcoding
`PDL_Operation`. See
https://discourse.llvm.org/t/rfc-type-system-for-the-transform-dialect/65702
for motivation.
Remove the dependency on PDLDialect from these extensions.
Update tests to use `!transform.any_op` instead of `!pdl.operation`.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D150781
`scf.foreach_thread` defines mapping its loops to processors via an integer array, see an example below. A lowering can use this mapping. However, expressing mapping as an integer array is very confusing, especially when there are multiple levels of parallelism. In addition, the op does not verify the integer array. This change introduces device mapping attribute to make mapping descriptive and verifiable. Then it makes GPU transform dialect use it.
```
scf.foreach_thread (%i, %j) in (%c1, %c2) {
scf.foreach_thread (%i2, %j2) in (%c1, %c2)
{...} { thread_dim_mapping = [0, 1]}
} { thread_dim_mapping = [0, 1]}
```
It first introduces a `DeviceMappingInterface` which is an attribute interface. `scf.foreach_thread` defines its mapping via this interface. A lowering must define its attributes and implement this interface as well. This way gives us a clear validation.
The change also introduces two new attributes (`#gpu.thread<x/y/z>` and `#gpu.block<x,y,z>` ). After this change, the above code prints as below, as seen here, this way clarifies the loop mappings. The change also implements consuming of these two new attribute by the transform dialect. Transform dialect binds the outermost loops to the thread blocks and innermost loops to threads.
```
scf.foreach_thread (%i, %j) in (%c1, %c2) {
scf.foreach_thread (%i2, %j2) in (%c1, %c2)
{...} { thread_dim_mapping = [#gpu.thread<x>, #gpu.thread<y>]}
} { thread_dim_mapping = [#gpu.block<x>, #gpu.block<y>]}
```
Reviewed By: ftynse, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D137413
This revision adds GPU transform dialect. It also introduce a prefix such as "transform.gpu" for all ops related to this dialect.
MLIR already had two GPU transform op in linalg. This revision moves these ops into GPUTransformOps. The Ops are as follows:
`transform.structured.map_nested_foreach_thread_to_gpu_blocks` -> `transform.gpu.map_foreach_to_blocks`
This op selects the outermost (toplevel) foreach_thread and parallelize across GPU blocks. It can also generate `gpu_launch`.
`transform.structured.map_nested_foreach_thread_to_gpu_threads` -> `transform.gpu.map_nested_foreach_to_threads`
This op parallelizes nested foreach_thread that are inside `gpu_launch` across GPU threads.
It doesn't add new functionality, but there are some minor refactoring of the code.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D134800
