Don't require that `mesh.shard` operations come in pairs. If there is
only a single `mesh.shard` operation we assume that the producer result
and consumer operand have the same sharding.
This op is the inverse of all-gather. It is useful to have an explicit
concise representation instead of having a blob of slicing logic.
Add lowering for the op that slices from the tensor based on the
in-group process index.
Make resharding generate an all-slice instead of inserting the slicing
logic directly.
* Split out `MeshDialect.h` form `MeshOps.h` that defines the dialect
class. Reduces include clutter if you care only about the dialect and
not the ops.
* Expose functions `getMesh` and `collectiveProcessGroupSize`. There
functions are useful for outside users of the dialect.
* Remove unused code.
* Remove examples and tests of mesh.shard attribute in tensor encoding.
Per the decision that Spmdization would be performed on sharding
annotations and there will be no tensors with sharding specified in the
type.
For more info see this RFC comment:
https://discourse.llvm.org/t/rfc-sharding-framework-design-for-device-mesh/73533/81
Rename
* Op mesh.cluster -> mesh.mesh
* Op mesh.cluster_shape -> mesh.mesh_shape
* variables and attributes.
The name `mesh` is more specific to what it really represents. It is a
mesh of devices.
The name `cluster` implies a broader posibility of device
configurations. When just the word `mesh` is used the meaning can often
be inferred from the context whether it refers to the mesh dialect or a
device mesh. The full name can be used when needed.
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.
