Allows linalg structured operations to be handled during spmdization and
sharding propagation.
There is only support for projected permutation indexing maps.
Implement OpAsmOpInterface for most ops to increase IR readability. For
example `mesh.process_linear_index` would produce a value with name
`proc_linear_idx`.
The two types duplicated mostly the same values.
Here they are decomposed to carry orthogonal and complimentary
information.
Use `utils::IteratorType` instead of `mesh::IteratorType`. It now has
only parallel and reduction values.
Rename `Partial` to `ReductionKind`.
Add `getReductionLoopIteratorKinds` method to `ShardingInterface`.
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.
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.
