Remove the somewhat redundant rank attribute.
Before this change
```
mesh.cluster @mesh(rank = 3, dim_sizes = 2x3)
```
After
```
mesh.cluster @mesh(shape = 2x3x?)
```
The rank is instead determined by the provided shape. With this change
no longer `getDimSizes()` can be wrongly assumed to have size equal to
the cluster rank.
Now `getShape().size()` will always equal `getRank()`.
* Rename mesh.process_index -> mesh.process_multi_index.
* Add mesh.process_linear_index op.
* Add lowering of mesh.process_multi_index into an expression using
mesh.process_linear_index, mesh.cluster_shape and
affine.delinearize_index.
This is useful to lower mesh ops and prepare them for further lowering
where the runtime may have only the linear index of a device/process.
For example in MPI we have a rank (linear index) in a communicator.
Add verification and canonicalization for
broadcast, gather, recv, reduce, scatter, send and shift.
The canonicalizations only remove trivial collectives with empty
mesh_axes attrubutes.
Examle:
substitute
mesh.cluster @mesh0(rank = 2, dim_sizes = [0, 4])
with
mesh.cluster @mesh0(rank = 2, dim_sizes = ?x4)
Same as tensor/memref shapes. The only difference is for 0-rank shapes.
With tensors you would have something like `tensor<f32>`. Here to avoid
matching an empty string a 0-rank shape is denoted by `[]`.
Add a pass that propagates sharding information throughout the graph.
After this pass, each of the operations' operands and results is
annotated with a mesh.shard operation.
The pass is driven by a newly added ShardingInterface, and an implementation
for element-wise and matmul ops in the TOSA dialect is provided.
This reverts commit 9d9400d7de9b928e3018af97e8b381a4a6ba5162.
This reverts commit bda763aea0b854178c01eac9f309042d9aaa823b.
The buildbot is broken and tests are failing.
/llvm-project/mlir/lib/Dialect/Mesh/IR/MeshOps.cpp:73:1: error: non-void function does not return a value in all control paths [-Werror,-Wreturn-type]
}
^
1 error generated.
Add a pass that propagates sharding information throughout the graph.
After this pass, each of the operations' operands and results is
annotated with a `mesh.shard` operation, and the operations themselves
are added with sharding option attributes.
The pass is driven by a newly added `ShardingInterface`, and an implementation
for element-wise and matmul ops in the TOSA dialect is provided.
