This PR aims to support the following use case:
```python
def all_reduce_eager(x):
y = x * x
req = dist.all_reduce(y, op=dist.ReduceOp.SUM, async_op=True)
assert isinstance(req, torch.distributed.Work)
return y
@torch.compile(fullgraph=True)
def all_reduce_wait_compiled(y):
torch.ops.c10d_functional.wait_tensor(y)
return y * y
x = torch.ones(1280, 1280, device="cuda") + self.rank
with allow_inflight_collective_as_graph_input_ctx():
y = all_reduce_eager(x)
z = all_reduce_wait_compiled(y)
```
where the collective is issued in eager (with `async_op=True`) but waited in compiled region.
This is important for internal use cases such as TorchRec, where we issue collectives in eager for SparseArch all_to_all but want to wait for them in compiled region at beginning of OverArch, so that the all_to_all can be overlapped with the DenseArch compute that runs in parallel.
----
**Update**: Did two items to prevent regression to existing use cases:
1. Added memory-stressed test case to test_c10d_nccl.py `test_unwaited` to cover existing user's "not calling work.wait() for non-functional collective" use case
2. Gated all new `register_work()` / `unregister_work()` calls with `c10d::allow_inflight_collective_as_graph_input()` check, which is a new context manager that requires explicit user enablement (i.e. not on by default, so should not affect existing users).
The risk of this new version of PR causing regression should be very low.
------
Test commands:
- `pytest -rA test/distributed/test_inductor_collectives.py::TestCollectivesMultiProc::test_eager_async_allreduce_inductor_wait`
- `pytest -rA test/test_fx.py::TestDCE::test_keep_collectives`
- `pytest -rA test/test_fx.py::TestDCE::test_keep_collectives_no_overload`
- `pytest -rA test/distributed/test_c10d_functional_native.py::TestWithNCCL::test_wait_tensor`
- `pytest -rA test/distributed/test_c10d_functional_native.py::TestWithNCCL::test_unwaited`
- `pytest -rA test/distributed/test_c10d_nccl.py::CommTest::test_wait_tensor`
- `pytest -rA test/distributed/test_c10d_nccl.py::CommTest::test_unwaited`
- `pytest -rA test/distributed/_tensor/test_tensor_ops.py::DistTensorOpsTest::test_equal`
- `pytest -rA test/distributed/_tensor/test_random_ops.py::DistTensorRandomOpTest::test_manual_seed`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_ddp_baseline_aot_eager_multiprocess`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_aot_eager`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_setattr`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_unspecialized_forced_getattr_inline`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_unspecialized_forced_getattr_no_inline`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_asymmetric_compilation`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_automatic_dynamic_scalar`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_automatic_dynamic_speculation_divergence`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_automatic_dynamic_tensor`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_dim_mismatch`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_graph_break_empty_graph_still_collective`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_missing_source`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_scalar_missing_source`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_compiler_collectives_type_mismatch`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_ddp_activation_checkpointing`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_ddp_baseline_aot_eager_multiprocess`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_activation_checkpointing`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_aot_eager`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_inductor`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_setattr`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_unspecialized_forced_getattr_inline`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_fsdp_unspecialized_forced_getattr_no_inline`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_hf_bert_ddp_aot_eager`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_hf_bert_ddp_aot_eager_static_graph`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_hf_bert_ddp_inductor`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_hf_bert_ddp_inductor_static_graph`
- `pytest -rA test/distributed/test_dynamo_distributed.py::TestMultiProc::test_hf_bert_fsdp_activation_checkpointing`
- `pytest -rA test/distributed/_tensor/test_experimental_ops.py::DistOtherOpsTest::test_bernoulli`
- `pytest -rA test/distributed/_tensor/test_dtensor_compile.py::TestDTensorCompileE2E::test_tp_compile_fullgraph_is_seq_parallel_True`
- `pytest -rA test/distributed/test_inductor_collectives.py::TestCollectivesMultiProc::test_allreduce_inductor_cudagraph_trees`
- `python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --inductor --device cuda --inference --bfloat16 --total-partitions 2 --partition-id 1 --output inference_torchbench.csv --only moco`
------
Differential Revision: [D65023311](https://our.internmc.facebook.com/intern/diff/D65023311)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137763
Approved by: https://github.com/yifuwang
We introduced the dispatchable backend for a ProcessGroup and collective in https://github.com/pytorch/pytorch/issues/86225. This PR is a follow-up cleanup to clean up the option of a ProcessGroup and ask users to either set timeout or backend later on or directly create backend after creating a PG.
Also PGNCCL is using option class from ProcessGroup but we actually should use Option from backend class. So this PR is to make the type or name to be aligned with what we are doing in cpp side. I don't change the signature for the public API, so they still use args named "pg_options"
We need to make changes to the test to make it aligned with the change.
This is try to reland D62008954 by fixing internal errors.
Differential Revision: [D62483294](https://our.internmc.facebook.com/intern/diff/D62483294/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135653
Approved by: https://github.com/wz337, https://github.com/H-Huang
We introduced the dispatchable backend for a ProcessGroup and collective in https://github.com/pytorch/pytorch/issues/86225. This PR is a follow-up cleanup to clean up the option of a ProcessGroup and ask users to either set timeout or backend later on or directly create backend after creating a PG.
Also PGNCCL is using option class from ProcessGroup but we actually should use Option from backend class. So this PR is to make the type or name to be aligned with what we are doing in cpp side. I don't change the signature for the public API, so they still use args named "pg_options"
We need to make changes to the test to make it aligned with the change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132931
Approved by: https://github.com/H-Huang
Summary:
We need a way to allow user set a customized description for a process group, e.g. FSDP, PP.
Here are several use cases of user specified group_desc:
- Logging: we can easily match a log line and understand what's this collective/pg is used to.
- Pytorch traces (e.g. Kineto, Execution Trace) can benefit from the PG desc since trace analysis, benchmarks will be able to easily differentiate PG purpose like FSDP, PP.
- Lower layer collectives(e.g. NCCL) debug: we will be able to expose PG desc to NCCL communicator so NCCL layer operations can be easily correlated to a PG.
Solution: Add a group_desc field to c10d
Differential Revision: D55781850
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123472
Approved by: https://github.com/kwen2501
Fixes https://github.com/pytorch/pytorch/issues/114301
Previously, coalesced work (created by `end_coalescing`) is not watched by watchdog, which results in silent timeout.
The culprit is that we reset `coalescing_state_` to 0 before checking it to see if we should enqueue a work.
Example:
```
import torch
import torch.distributed as dist
from datetime import timedelta
dist.init_process_group(backend="nccl", timeout=timedelta(seconds=10))
rank = dist.get_rank()
world_size = dist.get_world_size()
device = torch.device(f"cuda:{rank}")
# Create tensors of different sizes to create hang
s = 100 * 1024 * 1024 * (world_size - rank)
with dist._coalescing_manager(device=device):
dist.all_reduce(torch.ones(s, device=device))
dist.broadcast(torch.ones(s, device=device), src=0)
torch.cuda.synchronize()
print(f"{dist.get_rank()} done")
```
Watchdog fires:
```
$ torchrun --nproc-per-node 2 example.py
...
[rank1]:[E ProcessGroupNCCL.cpp:545] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=2, OpType=COALESCED, NumelIn=18446744073709551615, NumelOut=18446744073709551615, Timeout(ms)=10000) ran for 10000 milliseconds before timing out.
[rank0]:[E ProcessGroupNCCL.cpp:545] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=2, OpType=COALESCED, NumelIn=18446744073709551615, NumelOut=18446744073709551615, Timeout(ms)=10000) ran for 10567 milliseconds before timing out.
...
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117682
Approved by: https://github.com/wconstab, https://github.com/fduwjj
This PR introduces a native version of c10d_functional ops. The main goal is to add collective support in AOTInductor and allow collective ops to work in multi-threaded native runtimes.
The native version also incorporated API improvements we wished to implement in Python c10d_functional:
- Removed `ranks` and `group_size` from collective op signatures which were proven to be redundant.
- Use tensor storage as opposed to `void*` to resolve in-flight work.
The native process group registration/resolution mechansim is only used for native c10d_functional in the PR. It will become the single source of truth in upcoming PRs.
The upcoming PRs will implement Inductor/AOTInductor support for c10d_functional, after which native c10d_functional will replace Python c10d_functional.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110570
Approved by: https://github.com/wanchaol
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
Collectives timing gates the tracking when a collective starts on device.
Currently it's enabled by set the NCCL_ENABLE_TIMING env var.
The goal of this PR is to make it possible to dynamically enable that flag so users of the PG hooks don't have to set that flag in order to have their hooks work.
The design is that once set, all new collectives will have such behavior so we track it on each Work object.
We make enableTiming_ atomic in PGNCCL to avoid races on non-TSO hardware.
To ensure consistency, we copy its value during Work construction and replace all previous usage of enableTiming_ from the PG with usages from the Work, which now has an immutable value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108814
Approved by: https://github.com/wconstab, https://github.com/fduwjj
ghstack dependencies: #108813
…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
### Changes
- Move ProcessGroup::Work into its own class and update all the references to it / header includes.
#### Motivation
In the future PRs we will repurpose ProcessGroup to instead contain a list of Backends (ProcessGroupNCCL/Gloo/UCC) and perform dispatching to them based on tensor type. This change is prevent a circular dependency with ProcessGroup depending on Backend and Backend depending on ProcessGroup::Work.
Differential Revision: [D38839212](https://our.internmc.facebook.com/intern/diff/D38839212)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83680
Approved by: https://github.com/kwen2501
This PR enables python process group usage with DDP by doing the following:
- Surface PG::Work::getFuture() as overridable()
- Use Work::getFuture() to retrieve values from a PG.
- Add _create_work_from_future python method that creates a Work object that wraps a Future.
To test this changes we use both strategies to run DDP with a python based PG.
The reason for offering two methods is that both have short-comings.
The wrapper method is harder to troubleshoot as there's no visibility of how the future is used.
The subclass method has memory management issues as can be noticed in the test suite by having to keep Work instances alive by storing them in PG fields.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79176
Approved by: https://github.com/rohan-varma
Summary:
`batch_isend_irecv` previously only worked for two-rank cases,
otherwise it would hang, e.g. pytorch/pytorch#73960. This Diff extends
`batch_isend_irecv` to support more than two ranks. The fix is by treating
the operation more like a collective rather than two-rank P2P when selecting
the communicator, since there can be more ranks participating in the batch call than "my" rank and "my" peer.
Rules:
- If `batch_isend_irecv` is the first collective call (including collectives and
all-to-all) in the `group` given as the argument, then all ranks of the
`group` are expected to participate in this call.
- Otherwise, if it is not the first collective call in the `group` (i.e. the
communicator has been initialized), then batched P2P communication involving
only subset of processes of the `group` is allowed.
Test Plan:
Added p2p_tests.py testing the following patterns:
+ sendrecv_neighbor(input, output) # Ring like neighbor exchange
+ sendrecv_ripple(input, output) # Exchange with growing distance (pytorch/pytorch#73960)
+ sendrecv_P2P(input, output) # Single P2P operation
+ isendrecv_P2P(input, output) # Single non-blocking P2P operation
+ isendrecv_P2P_batch(input, output, 0) # batched P2P between only two ranks
+ isendrecv_P2P_batch(input, output, 1) # batched P2P within a new group created for two ranks
Differential Revision: D35122664
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74701
Approved by: https://github.com/mingzhe09088, https://github.com/osalpekar
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73702
This PR adds logging to track usage of ProcessGroup backends. The
change involves adding an `init()` method to the ProcessGroup base class and
modifying all the subclasses to call it.
We can't add logging directly in the ProcessGroup constructor since
`getBackendName()` is pure virtual and can't be called from the base class
constructor. See
https://isocpp.org/wiki/faq/strange-inheritance#calling-virtuals-from-ctors for
more details.
ghstack-source-id: 150381852
Test Plan: check logging
Reviewed By: cbalioglu
Differential Revision: D34596295
fbshipit-source-id: 5baa7bfb53bdcd1b4032292c4e7715467f983288
(cherry picked from commit b2344144fe0c25db02c2e958f2acd772e9cd4dc8)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73166
This PR refactors, cleans up, and optimizes the implementation of `TORCH_DISTRIBUTED_DEBUG`. It also introduces three new user APIs: `get_debug_level()`, `set_debug_level()`, and `set_debug_level_from_env()` to retrieve and modify the debug level after a process has started.
ghstack-source-id: 149778566
Test Plan: Run the existing unit tests.
Reviewed By: rohan-varma
Differential Revision: D34371226
fbshipit-source-id: e18443b411adcbaf39b2ec999178c198052fcd5b
(cherry picked from commit 26d6bb1584b83a0490d8b766482656a5887fa21d)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71711
This will fix a ton of broken asserts that should always fire but never actually fire.
All would have been caught with `-Wstring-conversion` warnings enabled.
Test Plan: CI Pass
Differential Revision: D33743605
fbshipit-source-id: 062641f9d5d02c6e317c5a286fd01017cf77237f
(cherry picked from commit 639b42e04b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64943
Most ProcessGroup Collective APIs are pure virtual. As a result, c10d extensions need to override all of them and throw an error if they don't need certain APIs. This is too verbose for users. This commit changes those collective APIs to virtual and throws an error by default. Note that ProcessGroup is still an abstract class as `getBackendName` is a pure virtual function that all subclasses have to override.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang cbalioglu gcramer23
Test Plan: Imported from OSS
Reviewed By: cbalioglu
Differential Revision: D30906866
Pulled By: mrshenli
fbshipit-source-id: c4df8962d60350a44d2df72fd04f9dd6eadb9fa6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60543
Since now c10d is part of libtorch, it would also be nice if the sources lived all in one place.
ghstack-source-id: 132306292
Test Plan: It builds
Reviewed By: cbalioglu
Differential Revision: D29062002
fbshipit-source-id: d9e1301e9d73e1643fa0f0119cd2d618f1ad52e6
