By adding a few small helpers (e.g., a `splice` method to `_MeshLayout`, and making `_init_process_groups` static and thus stateless) we can substantially shorten the definition of the unflatten method, and help readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165556
Approved by: https://github.com/fduwjj
ghstack dependencies: #165554, #165555
The refactoring of DeviceMesh is heavily constrained by the signature of its constructor, which is a public API which contains some "legacy" concepts which we'd love to get rid of, such as an explicit/materialized `mesh` Tensor.
In other languages the solution to this would be to add a private overload of the constructor. Python doesn't natively allow this, but in this PR I managed to build something that approximates it.
This new private constructor basically only takes `_layout`, `_global_rank_permutation`, and `mesh_dim_names`.
With such a constructor we can effectively simplify a lot of callsites and get rid of the `_create_mesh_from_ranks` helper method. That's a good thing because it was instantiating many DeviceMeshes in a for loop, which always felt unnecessary.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165555
Approved by: https://github.com/fduwjj, https://github.com/fegin
ghstack dependencies: #165554
The goal of this PR is to avoid storing the explicit `mesh` Tensor inside each DeviceMesh, and instead compute it on-the-fly when the end user needs it, and try to replace all of its internal usages with `_layout` and the newly-introduced `_global_rank_permutation` Tensor. The name of this attribute is up for debate. The advantage of the `_global_rank_permutation` Tensor is that it is _the same_ Tensor for the root mesh and all its children, so it doesn't need to be copied/reallocated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165554
Approved by: https://github.com/fduwjj
By adding a few small helpers (e.g., a `splice` method to `_MeshLayout`, and making `_init_process_groups` static and thus stateless) we can substantially shorten the definition of the unflatten method, and help readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165556
Approved by: https://github.com/fduwjj
ghstack dependencies: #165554, #165555
PyTorch tensor iteration (.view, contiguous, broadcasting) and NumPy array indexing all follow lexicographic (row-major) order. In Lexicographic (lex) on (i0, i1, …, i{k-1}): the leftmost index(stride is larger) changes fastest and the rightmost index changes slowest and usually last dim is contiguous.
However original pycute is all based on co-lex, after porting their code into pytorch and some cosmetic change, we now make it lex so that we can use it for use cases like device mesh internal bookkeeping and other stuff as well.
Changes included in this PR:
1. We changes all API ported in, included prefix_product(stride inferring and rename it to suffix_product), idx2crd, crd2idx, coalesce, composition, complement, right_inverse and left_inverse to make sure they are working in the lex way.
2. Added more unit test cases for some API mentioned above since existing unit tests do not have full coverage.
3. One bug fix inside composition, which will lead to infinite recursive call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162690
Approved by: https://github.com/ezyang
ghstack dependencies: #162413, #162534, #162414
