Since we are in the middle of big refactoring and simplying the bookkeeping for device mesh. We found an interesting bug inside DeviceMesh flatten implementation. Here is the finding:
1. In unit test, we assume users can call `dp_cp_mesh._flatten()` many times but no backend will be created (aka cached).
2. From the implementation of slicing, we actually throw exception erroring out doing the `_flatten` more than once. But there is bug which was partially fixed in https://github.com/pytorch/pytorch/pull/160709 but it does not fixed the check for the case when we call the `_flatten` twice.
What's more important question to ask is, what behavior we want for `_flatten`? Do we allow calling `_flatten` multiple times (with same mesh_name)? I think we should, why?
1. We allow slicing for the same mesh_name or name_list multiple times, and we cache the PG behinds. Although we will return a new device mesh object everytime, when we compare them they are all the same (according to __eq__).
2. We actually cached the flattened mesh today inside `root_to_flatten_mapping` and actually do the early return but that line will never be reached if we error out before that.
Also we should allow a no-op for flatten a 1D mesh into itself's mesh_dim_name, I added a unit test for it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161311
Approved by: https://github.com/fegin
Summary: This adds a `_rank` field to DeviceMesh init that allows for instantiating a DeviceMesh without depending on `dist.get_rank()` which requires a global PG to be instantiated.
Test Plan:
```
buck2 test mode/opt -c fbcode.enable_gpu_sections=true //caffe2/test/distributed:device_mesh -- init_backend
```
Rollback Plan:
Differential Revision: D81981777
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162439
Approved by: https://github.com/kwen2501, https://github.com/fduwjj
This PR is greatly simplified now that it stacked on top of a PR that builds with distributed always. We only need to stub functions that may not be defined due to a backend not being enabled.
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159889
Approved by: https://github.com/wconstab
ghstack dependencies: #160449
Fix the `DeviceMesh._flatten` docstring example of use. Alternative fix would be to replace `mesh_3d["dp", "cp"]` with `mesh_3d["cp", "tp"]`.
(I verified the fix using the `gloo` backend)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162277
Approved by: https://github.com/ezyang
This PR is greatly simplified now that it stacked on top of a PR that builds with distributed always. We only need to stub functions that may not be defined due to a backend not being enabled.
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159889
Approved by: https://github.com/wconstab
ghstack dependencies: #160449
This PR is greatly simplified now that it stacked on top of a PR that builds with distributed always. We only need to stub functions that may not be defined due to a backend not being enabled.
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159889
Approved by: https://github.com/wconstab
ghstack dependencies: #160449
This PR is greatly simplified now that it stacked on top of a PR that builds with distributed always. We only need to stub functions that may not be defined due to a backend not being enabled.
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159889
Approved by: https://github.com/wconstab
ghstack dependencies: #160449
This PR is greatly simplified now that it stacked on top of a PR that builds with distributed always. We only need to stub functions that may not be defined due to a backend not being enabled.
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159889
Approved by: https://github.com/wconstab
ghstack dependencies: #160449
We don't create new PGs when doing slicing in DeviceMesh so it is relatively safe to relax the requirement of one can only do slicing from root mesh. But this does come with caveat when it is asymmetric, for example, only some have the sliced out submesh, for example. So aside from removing the requirement we also add a warning here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158899
Approved by: https://github.com/wz337
Fixed error message:
On main:
```
KeyError: ("Invalid mesh_dim_names ('dp_shard', 'dp_shard') specified. ", 'Found mesh dim indices to slice: [(1,), (1,)]. ', 'Mesh dim indices should be in ascending order.')
```
On PR:
```
KeyError: Invalid mesh_dim_names ('dp_shard', 'dp_shard') specified. Found mesh dim indices to slice: [(1,), (1,)]. Mesh dim indices should be in ascending order.'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157096
Approved by: https://github.com/Skylion007
as titled, this PR improves the device selection logic when user did not
set the device before calling the DeviceMesh constructor, as a device
manager, DeviceMesh should try to set the device for users in a good
way.
The behavior of set_device before:
* If user call init_process_group to init a world process group, we assume user already called set_device and we don't set the device for the user
* If user does not init a world process group by themselves, we init a world process group for the user and follow a heuristic to set the device.
This is ok but sometimes the set_device heuristic wouldn't work well (i.e. if user use TORCH_CUDA_VISBILE_DEVICES
So this PR improves the device selection logic to:
* If the default cuda context is initialized by the time we init DeviceMesh, then we assume user must called some cuda operation before therefore must have selected the device by themselves
* If not the above, then we check if envvars have "LOCAL_RANK" and "WORLD_SIZE" from the launcher (i.e. torchrun), if so, we use "LOCAL_RANK" to set the device for the current process, which is a very standard practice. (This solves the TORCH_CUDA_VISBILE_DEVICES issue)
* If not above, then we throw warning to users about situation, and fallback to the old heuristic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150897
Approved by: https://github.com/tianyu-l
ghstack dependencies: #150898
