This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This makes it more obvious what's going on when TCPStore shuts down while waiting on a remote key and also shows the remote address.
Test plan:
```
[W514 18:33:36.536327028 TCPStore.cpp:138] [c10d] recvValueWithTimeout failed on SocketImpl(fd=3, addr=[localhost]:34658, remote=[localhost]:1234): Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
```
```py
import os
rank = int(os.environ["RANK"])
import time
from torch import distributed as dist
store = dist.TCPStore(
host_name="localhost",
port=1234,
is_master=(rank == 0),
wait_for_workers=False,
)
time.sleep(1)
print("starting")
if rank != 0:
store.get("foo")
else:
time.sleep(1)
print("done")
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153586
Approved by: https://github.com/XilunWu
So that the tensor's lifetime management is the same as the management built for the NCCL, pre and post kernels.
Also so that on visualizers, they show up in the NCCL stream line. Otherwise if they show up in the compute line, user may get confused (my code does not have these kernels).
The check is thus moved after the point where we depend NCCL stream from the last compute kernel.
Also moved declaration of `checkForNan` from Utils.hpp to NCCLUtils.hpp, and renamed Utils.cu to NCCLUtils.cu.
Differential Revision: [D61957573](https://our.internmc.facebook.com/intern/diff/D61957573)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134300
Approved by: https://github.com/shuqiangzhang, https://github.com/wconstab
So that the tensor's lifetime management is the same as the management built for the NCCL, pre and post kernels.
Also so that on visualizers, they show up in the NCCL stream line. Otherwise if they show up in the compute line, user may get confused (my code does not have these kernels).
The check is thus moved after the point where we depend NCCL stream from the last compute kernel.
Also moved declaration of `checkForNan` from Utils.hpp to NCCLUtils.hpp, and renamed Utils.cu to NCCLUtils.cu.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134300
Approved by: https://github.com/shuqiangzhang, https://github.com/wconstab
This PR continues to clean clang-tidy warnings in torch/csrc/distributed/c10d, following #124701. In addition, libfmt dependency is added in CMake code to enable using it in the headers. The libfmt has to be added as private dependency to torch_cuda and torch_hip because they include torch/csrc/distributed/c10d/Utils.hpp which uses libfmt.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124987
Approved by: https://github.com/malfet
Summary:
`-Wunused-exception-parameter` has identified an unused exception parameter. This diff removes it.
This:
```
try {
...
} catch (exception& e) {
// no use of e
}
```
should instead be written as
```
} catch (exception&) {
```
If the code compiles, this is safe to land.
Test Plan: Sandcastle
Reviewed By: palmje
Differential Revision: D55548497
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123056
Approved by: https://github.com/Skylion007
Summary:
The getCvar* functions allow us to provide multiple environment variables for the same value. This allows us to deprecate some variables in favor of others, while still allowing users to temporarily use the old variables for some time.
Test Plan: OSS CI
Reviewed By: fduwjj, XilunWu
Differential Revision: D51225487
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113797
Approved by: https://github.com/fduwjj
We have a plethora of error types for various errors raised from c10d. These include `RuntimeError`, `TimeoutError`, `SocketError`, `DistBackendError` etc.
This results in messy code during error handling somewhat like this:
```
if "NCCL" in exception_str:
...
if "Timed out initializing process group in store based barrier on rank" in exception_str:
...
if "The client socket has timed out after" in exception_str:
...
if "Broken pipe" in exception_str:
...
if "Connection reset by peer" in exception_str:
...
```
To address this issue, in this PR I've ensured added these error types:
1. **DistError** - the base type of all distributed errors
2. **DistBackendError** - this already existed and referred to PG backend errors
3. **DistStoreError** - for errors originating from the store
4. **DistNetworkError** - for general network errors coming from the socket library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108191
Approved by: https://github.com/H-Huang
We have a plethora of error types for various errors raised from c10d. These include `RuntimeError`, `TimeoutError`, `SocketError`, `DistBackendError` etc.
This results in messy code during error handling somewhat like this:
```
if "NCCL" in exception_str:
...
if "Timed out initializing process group in store based barrier on rank" in exception_str:
...
if "The client socket has timed out after" in exception_str:
...
if "Broken pipe" in exception_str:
...
if "Connection reset by peer" in exception_str:
...
```
To address this issue, in this PR I've ensured added these error types:
1. **DistError** - the base type of all distributed errors
2. **DistBackendError** - this already existed and referred to PG backend errors
3. **DistStoreError** - for errors originating from the store
4. **DistNetworkError** - for general network errors coming from the socket library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107651
Approved by: https://github.com/H-Huang
Not only is this change usually shorter and more readable, it also can yield better performance. size() is not always a constant time operation (such as on LinkedLists), but empty() always is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93236
Approved by: https://github.com/malfet
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
