For https://github.com/pytorch/pytorch/issues/114850, we will port distributed tests to Intel GPU. This PR will work on some test files under test/distributed. We could enable Intel GPU with following methods and try the best to keep the original code styles:
- instantiate_device_type_tests()
- use "torch.accelerator.current_accelerator()" to determine the accelerator backend
- use requires_accelerator_dist_backend to allow both nccl and xccl test
- enabled XPU for some test path
- Change the hardcoded world_size according to device_count.
- Unify some common code under torch/testing/_internal for multiple backend, for example:
Added xpu for Backend.backend_capability and dist.Backend.register_backend()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159473
Approved by: https://github.com/guangyey, https://github.com/d4l3k
Today we can initialize a mixed-backend process group (e.g. "cpu:gloo,cuda:nccl") but we can only pass one set of process group options.
However, when we call `split_group`, we retrieve that set of options from the parent PG and pass it to the ProcessGroup::groupSplit C++ API, which then attempts to propagate that set of options to all backends.
This leads to an assert on some user code, where ProcessGroupGloo::split is expecting gloo options but receives nccl options instead.
Arguably the APIs as currently designed are just broken; we should not ever expect a single set of backend options to apply across multiple backends. However, fixing this would require changing quite a few public APIs.
As a quick fix, since user-provided options really only exist for NCCL, just warn and fall-back to defaulted options for Gloo if non-gloo options are detected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162424
Approved by: https://github.com/d4l3k, https://github.com/fduwjj, https://github.com/H-Huang
For https://github.com/pytorch/pytorch/issues/114850, we will port distributed tests to Intel GPU. This PR will work on some test files under test/distributed. We could enable Intel GPU with following methods and try the best to keep the original code styles:
- instantiate_device_type_tests()
- use "torch.accelerator.current_accelerator()" to determine the accelerator backend
- use requires_accelerator_dist_backend to allow both nccl and xccl test
- enabled XPU for some test path
- Change the hardcoded world_size according to device_count.
- Unify some common code under torch/testing/_internal for multiple backend, for example:
Added xpu for Backend.backend_capability and dist.Backend.register_backend()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159473
Approved by: https://github.com/guangyey, https://github.com/d4l3k
Today we can initialize a mixed-backend process group (e.g. "cpu:gloo,cuda:nccl") but we can only pass one set of process group options.
However, when we call `split_group`, we retrieve that set of options from the parent PG and pass it to the ProcessGroup::groupSplit C++ API, which then attempts to propagate that set of options to all backends.
This leads to an assert on some user code, where ProcessGroupGloo::split is expecting gloo options but receives nccl options instead.
Arguably the APIs as currently designed are just broken; we should not ever expect a single set of backend options to apply across multiple backends. However, fixing this would require changing quite a few public APIs.
As a quick fix, since user-provided options really only exist for NCCL, just warn and fall-back to defaulted options for Gloo if non-gloo options are detected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162424
Approved by: https://github.com/d4l3k, https://github.com/fduwjj, https://github.com/H-Huang
For https://github.com/pytorch/pytorch/issues/114850, we will port distributed tests to Intel GPU. This PR will work on some test files under test/distributed. We could enable Intel GPU with following methods and try the best to keep the original code styles:
- instantiate_device_type_tests()
- use "torch.accelerator.current_accelerator()" to determine the accelerator backend
- use requires_accelerator_dist_backend to allow both nccl and xccl test
- enabled XPU for some test path
- Change the hardcoded world_size according to device_count.
- Unify some common code under torch/testing/_internal for multiple backend, for example:
Added xpu for Backend.backend_capability and dist.Backend.register_backend()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159473
Approved by: https://github.com/guangyey, https://github.com/d4l3k
`send_object_list` and `recv_object_list` use regular `send`/`recv` P2P ops which means that they will create 2-rank NCCL communicators between ranks if the communicators have not been initialized.
This adds an option `use_batch` which will call the send/recv with `batch_isend_irecv` which will re-use the communicators already initialized for collectives in the group.
---
BatchP2P ops, creates (or use existing) communicator keyed by device index
Regular P2P Ops, creates (or use existing) dedicated 2-rank communicators keyed by “rank1:rank2”
See:
c8205cb354/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp (L3980-L4008)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160342
Approved by: https://github.com/wconstab
Summary:
We found that we don't really set group_name inside group_split correctly, because we are setting group_name to `deviceTypeToBackend_` which is set after `setBackend`. Same thing as group_desc. I added more unit tests for it.
We need to setGroupName correctly, otherwise, this will break DeviceMesh use case when split_group is used in DeviceMesh
Also ncclx needs to be aware of that its Option is a subclass of BackendOption
Test Plan:
CI
Rollback Plan:
Differential Revision: D79201132
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159429
Approved by: https://github.com/xunnanxu
Add complex number unwrapping in functional collectives used by DTensor.
Complex tensors are not directly supported by underlying comm kernels
(e.g. nccl) but complex tensors can be viewed as real tensors of a
higher rank (added size-2 tensor dim represents real vs im component).
Collective output is then viewed as complex to restore the
original/expected shape and dtype.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157329
Approved by: https://github.com/XilunWu
Motivation:
PyTorch maintain a `default_device_backend_map` https://github.com/pytorch/pytorch/blob/main/torch/distributed/distributed_c10d.py#L269 , which indicates the default distributed backend if no backend name is specified in user frontend (like `init_process_group`).
Currently, `"xpu": XCCL` is also in this `default_device_backend_map`. However, if another process group name is registered as XPU distributed backend, it immediately replaces XCCL in this default map, which is not what we want.
Therefore, we would like to skip updating the default distributed backend if one is already registered in the map.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155320
Approved by: https://github.com/guangyey, https://github.com/d4l3k
Summary: This diff enhances the `get_process_group_ranks()` function to accept `group=None` as an optional argument. This allows the function to return all ranks associated with the default process group if no group is specified.
Test Plan:
contbuild & OSS CI
Rollback Plan:
Differential Revision: D75817800
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154902
Approved by: https://github.com/wz337
Summary: The error caused by the world size not being divisible by `group_size` is a common issue encountered by end-users when utilizing applications built on top of `new_subgroups()`. However, these applications may employ different variable names, such as `num_trainers_per_group`, which can make the current error messages less effective despite being correct. To address this, we have improved the error messages to display the actual numbers involved, thereby enhancing their clarity and usefulness.
Test Plan: contbuild & OSS CI
Differential Revision: D75226925
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154124
Approved by: https://github.com/wz337
Summary: The bug, introduced in https://github.com/pytorch/pytorch/pull/152765, was caused by passing the `group` parameter to the `get_rank()` function, which caused the function to return the rank of the entire group instead of the rank of the current process. The fix involves removing the `group` parameter from the `get_rank()` function call.
Test Plan: contbuild & OSS CI
Differential Revision: D74964213
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153798
Approved by: https://github.com/Skylion007
Summary:
Currently things are hardcoded to only work with nccl backend. Extend it
to allow NCCL + custom plugin backend.
The split-specific methods/attributes have not been added to the base
Backend and Options as some of them are specific to backend implementations.
Instead, explicit checks have been added to the split_group method for the
expected methods and attributes.
I am open to making them part of base Backend based if folks prefer.
Test Plan:
CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152175
Approved by: https://github.com/shuqiangzhang, https://github.com/kwen2501
