Optional option to detect missing ranks (that can be mapped to host info via `rank_tracing_decoder` lambda argument) in store barrier operation.
This approach has been used in some form already, moving it to collectives API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132818
Approved by: https://github.com/d4l3k
Summary: We call `.get` in the elastic store barrier operation but we don't need the result. This switches it to use `.wait` instead which eliminates one network round trip as `get` internally does a wait first.
Test Plan:
CI + existing tests -- no behavior change
Differential Revision: D59396199
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130148
Approved by: https://github.com/kurman, https://github.com/wconstab
The current call passes in `['/actual/path']` to os.walk which is a string pointing to no path and thus silently leads to and empty traversal.
There is an unused function just above that handles that, so I guess this is what was supposed to be called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126103
Approved by: https://github.com/suo
Summary:
This makes barrier and rank operations linear instead of quadratic with the number of workers. This drastically improves performance for rendezvous when running with over 1000 hosts.
This uses 2 approaches for different areas:
* local rank assignment: each worker does 1 set and 1 get, local ranks are assigned on the rank 0 host in a O(n) operation which reduces total store operations to be linear with number of workers.
* exit_barrier: use a counter and a final flag so each worker has to do max 1 set, 1 get and 1 add.
At 4000 hosts we see torchelastic be able to run in as little as 10 seconds down from 373 seconds.
Test Plan:
This is testing using many small tests running on a remote cluster.
{D56549942}
```
torchx run --scheduler mast -- --image=torchelastic_benchmark --j=4000x1
```
Differential Revision: D56605193
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124982
Approved by: https://github.com/kiukchung, https://github.com/kurman
Summary:
When workers finish their work TE agent will start `synchronize_barrier` procedure. The barrier will wait for other agents at the end of the execution.
There is a race condition may happen: The barrier uses TCPStore which is located on Rank0. When Rank0 finishes the work, other ranks may still be in a process of executing `get_all` method. This means that some of them will fail because the TCPStore will be destroyed.
The fix adds additional check on Rank0 process: Rank0 process now waits for all other ranks to finish before terminating the process.
Test Plan: unit tests
Differential Revision: D35227180
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74931
Approved by: https://github.com/kiukchung
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61294
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60925
* Make `torch.distributed.launch` restarts to 0
* Remove unnecessary `-use_env` warning, move `-use_env` warnings
* Move `-use_env` warnings to `torch.distributed.launch`
* Make default log level WARNING
* Add new doc section around transitioning to `torch.distributed.run`
* Make `torch.distributed.launch` not use error-propagation
* Set default events handler to `null` that does not print events to console
* Add reference from `torch.distributed.launch` to `torch.distributed.run`
* Set correct preexec function that sends SIGTERM to child processes when parent dies
Issues resolved:
https://github.com/pytorch/pytorch/issues/60716https://github.com/pytorch/pytorch/issues/60754
Test Plan:
sandcastle
python -m torch.distributed.launch --nproc_per_node 2 main.py -> uses 0 restarts
python -m torch.distributed.run --nproc_per_node 2 main.py -> uses default for torchelastic, 0 restarts
python -m torch.distributed.launch --nproc_per_node=4 --use_env --no_python main.py -> produces error
python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py -> no warning
python -m torch.distributed.launch --nproc_per_node=4 --no_python main.py ->warning
Output of running torch.distributed.launch without --use_env:
$path/torch/distributed/launch.py:173: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torch.distributed.run.
Note that --use_env is set by default in torch.distributed.run.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ('LOCAL_RANK')` instead.
New section:
{F628923078}
{F628974089}
Reviewed By: cbalioglu
Differential Revision: D29559553
fbshipit-source-id: 03ed9ba638bf154354e1530ffc964688431edf6b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60925
* Make `torch.distributed.launch` restarts to 0
* Remove unnecessary `-use_env` warning, move `-use_env` warnings
* Move `-use_env` warnings to `torch.distributed.launch`
* Make default log level WARNING
* Add new doc section around transitioning to `torch.distributed.run`
* Make `torch.distributed.launch` not use error-propagation
* Set default events handler to `null` that does not print events to console
* Add reference from `torch.distributed.launch` to `torch.distributed.run`
* Set correct preexec function that sends SIGTERM to child processes when parent dies
Issues resolved:
https://github.com/pytorch/pytorch/issues/60716https://github.com/pytorch/pytorch/issues/60754
Test Plan:
sandcastle
python -m torch.distributed.launch --nproc_per_node 2 main.py -> uses 0 restarts
python -m torch.distributed.run --nproc_per_node 2 main.py -> uses default for torchelastic, 0 restarts
python -m torch.distributed.launch --nproc_per_node=4 --use_env --no_python main.py -> produces error
python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py -> no warning
python -m torch.distributed.launch --nproc_per_node=4 --no_python main.py ->warning
Output of running torch.distributed.launch without --use_env:
$path/torch/distributed/launch.py:173: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torch.distributed.run.
Note that --use_env is set by default in torch.distributed.run.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ('LOCAL_RANK')` instead.
New section:
{F628923078}
{F628974089}
Reviewed By: kiukchung, cbalioglu
Differential Revision: D29413019
fbshipit-source-id: 323bfbad9d0e4aba3b10ddd7a243ca6e48169630
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60807
Addresses: https://github.com/pytorch/pytorch/issues/60717
This warning should have been removed since this code is no longer in "experimental" mode.
Test Plan: N/A - just removing experimental warning that should've been removed.
Reviewed By: H-Huang, aivanou
Differential Revision: D29412972
fbshipit-source-id: 16a8a98abde70a4ae0c1ac1b14bda339cb44863a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53172
Pull Request resolved: https://github.com/pytorch/elastic/pull/141
Upstreams two modules to torch:
1. `torchelastic.rendezvous`
2. `torchelastic.utils`
These modules were chosen as `[1/n]` since they are the leaf modules in torchelastic.
==== NOTES: ====
1. I'm disabling etcd_rendezvous and etcd_server tests in CIRCLECI for the moment since I need to edit the test dockers to contain the etcd server binary (there's 4-5 test dockers - one for each platform so this is going to take some time for me to set up the environments and test) - T85992919.
2. I've fixed all lint errors on python files but there are ones on the cpp files on the ZeusRendezvous. I took a look at them, and I don't want to fix the linter errors right now for 2 major reasons:
1. Some of them are more than formatting changes (e.g. std::move vs pass by value) and I don't want to introduce bundled changes with the move
1. The old rendezvous code (the one we forked from in caffe2/fb) has the same problems and I think its better for us to deal with this when we deprecate caffe2/fb/rendezvous in favor of the one in torchelastic -T86012579.
Test Plan:
```
buck test mode/dev-nosan //caffe2/torch/distributed/elastic/utils/test/...
buck test mode/dev-nosan //caffe2/torch/distributed/elastic/utils/data/test/...
buck test mode/dev-nosan //caffe2/torch/distributed/elastic/rendezvous/test/...
buck test mode/dev-nosan //caffe2/torch/distributed/elastic/rendezvous/fb/...
buck test mode/dev-nosan //pytorch/elastic/torchelastic/...
```
\+ Sandcastle
Reviewed By: H-Huang
Differential Revision: D26718746
fbshipit-source-id: 67cc0350c3d847221cb3c3038f98f47915362f51
