C++ side callbacks allow for advance users to get
access to the collective firehose.
It's worth mentioning and discussing the dire environment that those
callbacks are invoked. From either main thread of watchdog thread and
with a PTD lock held.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110307
Approved by: https://github.com/fduwjj
ghstack dependencies: #111061, #111072
This reverts commit 314a502eb0.
Changes since original PR:
Reland 1
* rename torch.distributed.hooks to torch.distributed._hooks
Reland 2
* make _hooks importable even if !distributed.is_available()
* handle cuda driver exit intermittent failure caused by new cuda api usage in callback caller (see prev PR in stack)
(original PR https://github.com/pytorch/pytorch/pull/108815 desc copied below)
Expose a set of observability hooks into C10D such that our users can
detect collectives failure both faster and more easily.
The design is similar to NCCL desync debug that it minimized the
overhead by doing most of the work out of the main thread.
This PR introduces a new module torch.distributed.hooks that exposes the following set of methods:
register_collective_start_hook
register_collective_end_hook
register_process_group_hook
The process group hook exposes PG creation on the member ranks and call them inline from the
the PG creation code. This is fine since this happens during initialization and a limited number of times.
The collective start/end hooks are fired from a single background thread. It reads
events from a C++ queue and dispatches over.
Queue notification is oddly done using a pipe, this is needed so python can abort the thread on shutdown
and have it as background thread. This is not possible with more reasonable choices like a condvar.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111072
Approved by: https://github.com/malfet
ghstack dependencies: #111061
This reverts commit ff0358b038.
(original PR https://github.com/pytorch/pytorch/pull/108815 desc copied below)
Expose a set of observability hooks into C10D such that our users can
detect collectives failure both faster and more easily.
The design is similar to NCCL desync debug that it minimized the
overhead by doing most of the work out of the main thread.
This PR introduces a new module torch.distributed.hooks that exposes the following set of methods:
register_collective_start_hook
register_collective_end_hook
register_process_group_hook
The process group hook exposes PG creation on the member ranks and call them inline from the
the PG creation code. This is fine since this happens during initialization and a limited number of times.
The collective start/end hooks are fired from a single background thread. It reads
events from a C++ queue and dispatches over.
Queue notification is oddly done using a pipe, this is needed so python can abort the thread on shutdown
and have it as background thread. This is not possible with more reasonable choices like a condvar.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110907
Approved by: https://github.com/fduwjj
Expose a set of observability hooks into C10D such that our users can
detect collectives failure both faster and more easily.
The design is similar to NCCL desync debug that it minimized the
overhead by doing most of the work out of the main thread.
This PR introduces a new module torch.distributed.hooks that exposes the following set of methods:
register_collective_start_hook
register_collective_end_hook
register_process_group_hook
The process group hook exposes PG creation on the member ranks and call them inline from the
the PG creation code. This is fine since this happens during initialization and a limited number of times.
The collective start/end hooks are fired from a single background thread. It reads
events from a C++ queue and dispatches over.
Queue notification is oddly done using a pipe, this is needed so python can abort the thread on shutdown
and have it as background thread. This is not possible with more reasonable choices like a condvar.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108815
Approved by: https://github.com/wconstab, https://github.com/fduwjj
…c10d
Fixes a broken header filters from #90699 and applies a few more clang-tidy fixes that are relevant from c10 and c10d. The header filter pattern was actually broken and the clang-tidy include pattern was redundant. Also fixed a few bugs in torch/distributed/c10d
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91178
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88330
### Implementation
Move backend-specific (NCCL, Gloo, etc) collective implementations to corresponding `Backend` class. Update ProcessGroup to support multiple backends and use dispatcher to calls backends based on tensor device type.
### Changes
#### c++ changes (ProcessGroup files, `Ops.cpp`, `init.cpp`)
- Update pybind definitions for new process group base class and new backend class
- Update pybinded backend class with collective definitions to keep BC with Python PG instances (e.g. `dist.ProcessGroupGloo`, `dist.ProcessGroupNCCL`) which are used in tests
- Switch `ProcessGroupGloo`, `ProcessGroupNCCL`, `ProcessGroupMPI`, `ProcessGroupUCC` to derive from the `Backend` class.
- Update CPU/CUDA `Ops.cpp` and `OpsImpl.cpp` to perform this dispatching by querying the backend using the device type
- Update internal dispatched implementation of `barrier` to use a tensor which allows operation to be dispatched.
- Update `allgather` collective to use `TensorList`. For some reason it was using the default implementation of `allgather` rather than dispatching it correctly. I still don't understand why and had originally filed an issue in 85122.
#### python changes (`distributed_c10d.py`, test files)
- Add BackendConfig class to specify the default configurations of backends and `get_backend_config()` API
- `get_backend()` deprecation warning
- `init_process_group` how returns a generic `ProcessGroup` object, it contains a list of backends (the ones stated above) which it will dispatch operations to.
- `new_group` updated to return the same as above
- Update `test_c10d_gloo.py`, Update `DistributedDataParallelTest` to use `init_process_group`, Update `ReducerTest`, update `test_broadcast_coalesced_gloo` to move from PG instance and gloo options
- Update `test_c10d_nccl.py`, Update `DistributedDataParallelTest` to use `init_process_group`
- Specific tests updated: `test_Backend_enum_class`
### Changes missing
- lazy initialization of backends
- support parsing of BackendConfig
### open questions
- Pure Python PG extensions (https://github.com/pytorch/pytorch/pull/66338)
# Example
This is a basic script (using 2 backends within a process group)
```python
# python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 basic_scenario.py
import torch.distributed as dist
import torch
import os
if __name__ == "__main__":
rank = os.environ.get("RANK")
# initialize with both gloo and nccl
dist.init_process_group()
# with gloo
dist.all_reduce(torch.tensor([1.0]))
print(f"Rank {rank} finished")
# with nccl
dist.all_reduce(torch.tensor([1.0], device=f"cuda:{rank}"))
```
Test Plan: Imported from OSS
Differential Revision: D42069829
Pulled By: H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90997
Approved by: https://github.com/awgu, https://github.com/fduwjj
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera, https://github.com/huydhn
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera
