When the stub file `nn/parallel/distributed.pyi` was removed (#88701), some types that existed are no longer available. This pull request adds them back.
Just for reference, these types are used in pytorch-lightning's LightningCLI. Command line interfaces are created automatically, and having type hints make them nicer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136835
Approved by: https://github.com/kwen2501
Previously, when we slice out a submesh from a mesh, we assign the mesh as the parent mesh of the submesh. In this case, when we have a 3D mesh topology, the parent mesh of a 1D mesh sliced out from the 3D mesh is different from the parent mesh of the same 1D mesh sliced out from the 2D submesh of the 3D mesh. For example:
```
mesh_3d = init_device_mesh("cuda", (2,2,2), ("dim0", "dim1", "dim2"))
mesh_dim0 = mesh_3d["dim0"]
mesh_2d = mesh_2d["dim0", "dim1"]
mesh_dim0_2 = mesh_2d["dim0_2"]
# This would evaluate to be True
print(_mesh_resources.get_parent_mesh(mesh_dim0) != _mesh_resources.get_parent_mesh(mesh_dim0))
```
We can always reconstruct the mesh needed from the mesh dim names, as long as two dims come from the same root. For simplicity, we do not see the necessity of building a tree structure to represent child-parent relationship. Therefore, we are replacing the parent mesh concept with a root mesh concept in `_MeshEnv` so we would have:
```
mesh_3d = init_device_mesh("cuda", (2,2,2), ("dim0", "dim1", "dim2"))
mesh_dim0 = mesh_3d["dim0"]
mesh_2d = mesh_2d["dim0", "dim1"]
mesh_dim0_2 = mesh_2d["dim0_2"]
# This would evaluate to be True
print(_mesh_resources.get_root_mesh(mesh_dim0) == _mesh_resources.get_root_mesh(mesh_dim0))
```
With this change, we will have two types of meshes in an environment.
1. `device_mesh != _mesh_resources.get_root_mesh(device_mesh)` means that the device_mesh is created by slicing.
2. `device_mesh == _mesh_resources.get_root_mesh(device_mesh)` means that the device_mesh is a root mesh not created through slicing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132339
Approved by: https://github.com/wanchaol
ghstack dependencies: #132310, #132311
FSDP implements the following logic but its missing from DDP.
This PR adds an equivalent function for the same.
```python
def __getattr__(self, name: str) -> Any:
"""Forward missing attributes to the wrapped module."""
try:
return super().__getattr__(name) # defer to nn.Module's logic
except AttributeError:
return getattr(self._fsdp_wrapped_module, name)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128620
Approved by: https://github.com/awgu
Changes:
1. Make some arguments positional-only as we only support Python 3.8+
2. Clean up `torch.typename(obj)` implementation.
3. Update type annotations., especially `is_tensor()` and `is_masked_tensor()` using `TypeGuard`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129001
Approved by: https://github.com/malfet
Changes:
1. Make some arguments positional-only as we only support Python 3.8+
2. Clean up `torch.typename(obj)` implementation.
3. Update type annotations., especially `is_tensor()` and `is_masked_tensor()` using `TypeGuard`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129001
Approved by: https://github.com/malfet
Not requiring all functions to have types allows a lot of 'Any' types to slip in - which poison types and make mypy unable to properly typecheck the code. I want to flip the default so that new files are required to have fully typed defs and we can have a burndown list of files that fail to require full types.
The preceding stack of PRs (cut up simply to limit the number of file changes per PR "reasonable") adds `# mypy: allow-untyped-defs` to any file which didn't immediately pass mypy with the flag flipped. Due to changing files and merge conflicts it will probably be necessary to have several passes through before landing this final PR which turns the option on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127836
Approved by: https://github.com/oulgen, https://github.com/Skylion007
The fist DDP bucket is always being created of the size of `dist._DEFAULT_FIRST_BUCKET_BYTES` (1 MiB) by default regardless of `bucket_cap_mb`. The proposal is to set `bucket_cap_mb` as the one main bucket size if it was supplied by the user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121640
Approved by: https://github.com/wanchaol
The fist DDP bucket is always being created of the size of `dist._DEFAULT_FIRST_BUCKET_BYTES` (1 MiB) by default regardless of `bucket_cap_mb`. The proposal is to set `bucket_cap_mb` as the one main bucket size if it was supplied by the user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121640
Approved by: https://github.com/wanchaol
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves#126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
Update ruff to 0.4.1 .
This version fixes a lot false negatives/false positives, is 20-40% faster, and has various other bug fixes.
Below is a before and after table showing the execution time of ruff lint and ruff format in milliseconds courtesy of https://astral.sh/blog/ruff-v0.4.0
| Repository | Linter (v0.3) | Linter (v0.4) | Formatter (v0.3) | Formatter (v0.4) |
|----------------------------------------------------|---------------|---------------|------------------|------------------|
| [pytorch/pytorch](https://github.com/pytorch/pytorch) | 328.7 | 251.8 | 351.1 | 274.9 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124549
Approved by: https://github.com/ezyang
Automatic fixes that replaces certain list comprehensions with generator ones where appropriate so that they are immediately consumed. This is preview functionality in ruff for rule C419 and it was automatically applied.
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123960
Approved by: https://github.com/malfet
This PR enables DDP + TP using a TP internal API. This should not be the final implementation. A more sound implementation is to inline the TP internal API in DDP. In other words, DDP needs to be aware of DTensor so that we can support 2D state_dict.
This PR adds a compiled DDP + TP test to ensure the new compiled DDP fusion doesn't break TP all_reduce.
**TODOs**
- [x] Implement DDP allreduce fusion algorithm for Inductor post_grad pass.
- [x] Add unit tests to ensure the fusion doesn't DDP + TP.
- [ ] Group different PG and data type of all_reduces.
- [ ] Mixed precision supports and tests
- [ ] Implement the fusions with Inductor IR.
- [ ] Add auto bucketing based on Inductor profiling.
Differential Revision: [D54105050](https://our.internmc.facebook.com/intern/diff/D54105050/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120479
Approved by: https://github.com/wz337
ghstack dependencies: #113209
Differential Revision: [D49858057](https://our.internmc.facebook.com/intern/diff/D49858057/)
**TL;DR**
This PR implements 2 different DDP all_reduce fusions in Inductor post_grad fx passes. The two fusions are 1) fusion with concat op and 2) fusion with all_reduce_coalesced. When DDP detects that Python reducer is being used, DDP will automatically turn on the fusion.
This PR does not invent any algorithm and simply reflects the bucket size users set to DDP.
**Implementation Details**
*Fusion with concat op*
The idea of this fusion is to use a concat op to concatenate all the gradients into one tensor and perform one `all_reduce`. After the `wait` op of the `all_reduce`, splitting and reshaping will also be perform to get the individual gradient.
Because DDP needs to perform gradient scaling, the benefit of using this fusion is that we could perform the gradient scaling over the the concatenated buffer.
*Fusion with `all_reduce_coalesced`*
The idea of this fusion is to use `all_reduce_coalesced` op to directly perform the `all_reduce` over multiple buffers. This avoid the copy overhead but may not achieve the best NCCL performance. In addition, because there are multiple buffers, we could not do one simple gradient scaling but have to rely on `foreach_div` to help the gradient scaling.
**Limitations**
Current fusions do not distinguish `all_reduce` generated by different DDP modules. This is okay if all DDP instances use the same PG and data type. The support of multiple DDP instances with different PG and data type will come in the later PRs.
**TODOs**
- [x] Implement DDP allreduce fusion algorithm for Inductor post_grad pass.
- [ ] Add unit tests to ensure the fusion doesn't DDP + TP.
- [ ] Group different PG and data type of `all_reduce`s.
- [ ] Mixed precision supports and tests
- [ ] Implement the fusions with Inductor IR.
- [ ] Add auto bucketing based on Inductor profiling.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113209
Approved by: https://github.com/yf225
**Summary**
The reducer of `DistributedDataParallel` is implemented with C++ and it is not easy to trace the allreduce launched in the reducer. This PR modifies `DistributedDataParallel` to launch one allreduce per gradient when `compiled_autograd` is enabled. The changes allow us to use `compiled_autograd` to trace the allreduce and later be optimized (fused) in the Inductor.
**Key Logic**
1. If `ddp_python_hook` is True, we assume `compiled_autograd` is used. `DistributedDataParallel` registers `compiled_accum_grad_hook` for all parameters.
2. In the first forward() call, if `DistributedDataParallel` is not compiled, all `compiled_accum_grad_hook` are deregistered. If `DistributedDataParallel` is compiled, all `compiled_accum_grad_hook` will be compiled by `compiled_autograd`.
3. `compiled_accum_grad_hook` launches an allreduce to reduce the gradient of the parameter.
**Bucketing**
The compiled backward is slow because there is no bucketing for the allreduces. We rely on Inductor to bucket the allreduces.
The bucketing is done in a separate PR.
Differential Revision: [D49428482](https://our.internmc.facebook.com/intern/diff/D49428482/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110662
Approved by: https://github.com/wconstab
Work.result() returns a vector of tensors. This signature is problematic as some collectives may just return one tensor (e.g all-reduce), while some others may return multiple tensors (e.g. all-gather).
It would be clearer/easier for users to directly access the result via the tensor/tensorlist passed to the collective APIs.
Deprecating work.result() would also allow us to remove the `outputs_` field in the Work class, avoiding an "artificial" reference to the tensor, which could potentially hold up the tensor's memory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117565
Approved by: https://github.com/wconstab
Previously:
```
[W Utils.hpp:133] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function getCvarInt)
[W Utils.hpp:133] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function getCvarInt)
```
With this PR, those warnings disappear. They were introduced in #114077
This change was generated with this sed script, applied with `sed -i -f /tmp/x **/*.{py,hpp,cpp,cc}` and hand inspected.
```
s/\bNCCL_BLOCKING_WAIT\b/TORCH_NCCL_BLOCKING_WAIT/g
s/\bNCCL_ENABLE_TIMING\b/TORCH_NCCL_ENABLE_TIMING/g
s/\bNCCL_DESYNC_DEBUG\b/TORCH_NCCL_DESYNC_DEBUG/g
s/\bNCCL_ASYNC_ERROR_HANDLING\b/TORCH_NCCL_ASYNC_ERROR_HANDLING/g
s/\bENABLE_NCCL_HEALTH_CHECK\b/TORCH_ENABLE_NCCL_HEALTH_CHECK/g
s/\bNCCL_USE_TENSOR_REGISTER_ALLOCATOR_HOOK\b/TORCH_NCCL_USE_TENSOR_REGISTER_ALLOCATOR_HOOK/g
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114880
Approved by: https://github.com/kwen2501
https://github.com/pytorch/pytorch/pull/113580 introduced the `DDP._update_process_group` API. However, the implementation did not correctly reset all of the necessary state in the reducer. In particular if an error occurred during backward, DDP would end up in an incorrect state.
As a result, in this PR I've enhanced the unit test to test for this case and also appropriately fixed resetting Reducer state.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114194
Approved by: https://github.com/rohan-varma
# Motivation
If we would like to reinitialize DDP with a different PG with `torch.compile`, we need to do the following:
```
del old_ddp
del old_pg
pg = init_pg(...)
ddp = DDP(pg)
model = torch.compile(DDP)
```
This results in recompilation of the entire model and is very expensive. Since the only thing we need to update is the PG, we should be able to do this without having to compile the model again.
# Proposal
As a result, in this PR I've introduced an `_update_process_group` API which can dynamically update the underlying ProcessGroup used by DDP without needing to reinitialize DDP again.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113580
Approved by: https://github.com/fduwjj
Fixes#112604
Fixes docstring by following `pydocstyle` outputs.
- torch/nn/parallel/distributed.py
Before: 84
```
torch/nn/parallel/distributed.py:1 at module level:
D100: Missing docstring in public module
torch/nn/parallel/distributed.py:92 in private function `_cast_buffers`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:103 in private function `_setup_mixed_precision_params`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:103 in private function `_setup_mixed_precision_params`:
D401: First line should be in imperative mood (perhaps 'Create', not 'Creates')
torch/nn/parallel/distributed.py:143 in private function `_find_tensors`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:273 in private method `__init__`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:273 in private method `__init__`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
torch/nn/parallel/distributed.py:287 in private method `main_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:287 in private method `main_hook`:
D400: First line should end with a period (not 'd')
torch/nn/parallel/distributed.py:324 in private method `post_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:324 in private method `post_hook`:
D400: First line should end with a period (not 'l')
torch/nn/parallel/distributed.py:324 in private method `post_hook`:
D401: First line should be in imperative mood (perhaps 'Sync', not 'Syncs')
torch/nn/parallel/distributed.py:332 in public class `DistributedDataParallel`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:332 in public class `DistributedDataParallel`:
D400: First line should end with a period (not 'n')
torch/nn/parallel/distributed.py:633 in public method `__init__`:
D107: Missing docstring in __init__
torch/nn/parallel/distributed.py:960 in private method `_fire_reducer_autograd_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:960 in private method `_fire_reducer_autograd_hook`:
D401: First line should be in imperative mood (perhaps 'Fire', not 'Fires')
torch/nn/parallel/distributed.py:969 in private method `_root_copy_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:969 in private method `_root_copy_hook`:
D400: First line should end with a period (not 's')
torch/nn/parallel/distributed.py:1012 in private method `_module_wait_for_copy_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1012 in private method `_module_wait_for_copy_hook`:
D400: First line should end with a period (not 'e')
torch/nn/parallel/distributed.py:1050 in private method `_ddp_init_helper`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1050 in private method `_ddp_init_helper`:
D400: First line should end with a period (not ':')
torch/nn/parallel/distributed.py:1050 in private method `_ddp_init_helper`:
D401: First line should be in imperative mood (perhaps 'Initialize', not 'Initialization')
torch/nn/parallel/distributed.py:1146 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/nn/parallel/distributed.py:1154 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/nn/parallel/distributed.py:1222 in private method `_assign_modules_buffers`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1222 in private method `_assign_modules_buffers`:
D400: First line should end with a period (not 'o')
torch/nn/parallel/distributed.py:1222 in private method `_assign_modules_buffers`:
D401: First line should be in imperative mood (perhaps 'Assign', not 'Assigns')
torch/nn/parallel/distributed.py:1277 in private method `_get_parameters`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:1277 in private method `_get_parameters`:
D400: First line should end with a period (not 's')
torch/nn/parallel/distributed.py:1277 in private method `_get_parameters`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/parallel/distributed.py:1312 in public method `no_sync`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1312 in public method `no_sync`:
D400: First line should end with a period (not 'P')
torch/nn/parallel/distributed.py:1312 in public method `no_sync`:
D401: First line should be in imperative mood; try rephrasing (found 'A')
torch/nn/parallel/distributed.py:1340 in private method `_get_active_ddp_module`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:1340 in private method `_get_active_ddp_module`:
D403: First word of the first line should be properly capitalized ('Torchdynamo', not 'TorchDynamo')
torch/nn/parallel/distributed.py:1517 in public method `forward`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1527 in public method `scatter`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1530 in public method `to_kwargs`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1539 in public method `gather`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1542 in public method `train`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1617 in public method `join`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1617 in public method `join`:
D400: First line should end with a period (not 'f')
torch/nn/parallel/distributed.py:1617 in public method `join`:
D401: First line should be in imperative mood; try rephrasing (found 'A')
torch/nn/parallel/distributed.py:1723 in public method `join_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1723 in public method `join_hook`:
D400: First line should end with a period (not 'y')
torch/nn/parallel/distributed.py:1723 in public method `join_hook`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/parallel/distributed.py:1752 in public method `join_device`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1756 in public method `join_process_group`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1765 in private method `_register_buffer_comm_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1765 in private method `_register_buffer_comm_hook`:
D400: First line should end with a period (not 'e')
torch/nn/parallel/distributed.py:1765 in private method `_register_buffer_comm_hook`:
D401: First line should be in imperative mood (perhaps 'Allow', not 'Allows')
torch/nn/parallel/distributed.py:1805 in public method `register_comm_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1805 in public method `register_comm_hook`:
D400: First line should end with a period (not 'a')
torch/nn/parallel/distributed.py:1805 in public method `register_comm_hook`:
D401: First line should be in imperative mood (perhaps 'Register', not 'Registers')
torch/nn/parallel/distributed.py:1887 in private method `_register_builtin_comm_hook`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1887 in private method `_register_builtin_comm_hook`:
D400: First line should end with a period (not 'P')
torch/nn/parallel/distributed.py:1887 in private method `_register_builtin_comm_hook`:
D401: First line should be in imperative mood (perhaps 'Register', not 'Registers')
torch/nn/parallel/distributed.py:1914 in private method `_register_fused_optim`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:1914 in private method `_register_fused_optim`:
D400: First line should end with a period (not 'a')
torch/nn/parallel/distributed.py:1914 in private method `_register_fused_optim`:
D401: First line should be in imperative mood (perhaps 'Register', not 'Registers')
torch/nn/parallel/distributed.py:2005 in public method `will_sync_module_buffers`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:2060 in private method `_default_broadcast_coalesced`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:2060 in private method `_default_broadcast_coalesced`:
D400: First line should end with a period (not 'e')
torch/nn/parallel/distributed.py:2128 in private method `_get_data_parallel_params`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:2128 in private method `_get_data_parallel_params`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/parallel/distributed.py:2141 in private method `_set_params_and_buffers_to_ignore_for_model`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:2141 in private method `_set_params_and_buffers_to_ignore_for_model`:
D400: First line should end with a period (not 'r')
torch/nn/parallel/distributed.py:2141 in private method `_set_params_and_buffers_to_ignore_for_model`:
D401: First line should be in imperative mood (perhaps 'Set', not 'Sets')
torch/nn/parallel/distributed.py:2170 in private method `_get_ddp_logging_data`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:2170 in private method `_get_ddp_logging_data`:
D400: First line should end with a period (not 's')
torch/nn/parallel/distributed.py:2170 in private method `_get_ddp_logging_data`:
D401: First line should be in imperative mood; try rephrasing (found 'This')
torch/nn/parallel/distributed.py:2184 in private method `_set_ddp_runtime_logging_sample_rate`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:2184 in private method `_set_ddp_runtime_logging_sample_rate`:
D400: First line should end with a period (not 'g')
torch/nn/parallel/distributed.py:2184 in private method `_set_ddp_runtime_logging_sample_rate`:
D401: First line should be in imperative mood; try rephrasing (found 'This')
torch/nn/parallel/distributed.py:2202 in private method `_set_static_graph`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:2202 in private method `_set_static_graph`:
D400: First line should end with a period (not 'l')
torch/nn/parallel/distributed.py:2202 in private method `_set_static_graph`:
D401: First line should be in imperative mood; try rephrasing (found 'It')
torch/nn/parallel/distributed.py:2227 in private method `_remove_autograd_hooks`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/parallel/distributed.py:2227 in private method `_remove_autograd_hooks`:
D401: First line should be in imperative mood (perhaps 'Remove', not 'Removes')
torch/nn/parallel/distributed.py:2233 in private method `_check_reducer_finalized`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/parallel/distributed.py:2233 in private method `_check_reducer_finalized`:
D400: First line should end with a period (not 'd')
torch/nn/parallel/distributed.py:2233 in private method `_check_reducer_finalized`:
D401: First line should be in imperative mood (perhaps 'Check', not 'Checks')
84
```
After: 12
```
torch/nn/parallel/distributed.py:1 at module level:
D100: Missing docstring in public module
torch/nn/parallel/distributed.py:618 in public method `__init__`:
D107: Missing docstring in __init__
torch/nn/parallel/distributed.py:1133 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/nn/parallel/distributed.py:1141 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/nn/parallel/distributed.py:1503 in public method `forward`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1513 in public method `scatter`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1516 in public method `to_kwargs`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1525 in public method `gather`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1528 in public method `train`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1734 in public method `join_device`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1738 in public method `join_process_group`:
D102: Missing docstring in public method
torch/nn/parallel/distributed.py:1986 in public method `will_sync_module_buffers`:
D102: Missing docstring in public method
12
```
- torch/nn/utils/_named_member_accessor.py
Before: 23
```
torch/nn/utils/_named_member_accessor.py:12 in public function `set_tensor`:
D103: Missing docstring in public function
torch/nn/utils/_named_member_accessor.py:29 in public function `swap_tensor`:
D103: Missing docstring in public function
torch/nn/utils/_named_member_accessor.py:85 in public function `swap_submodule`:
D103: Missing docstring in public function
torch/nn/utils/_named_member_accessor.py:109 in public class `NamedMemberAccessor`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:109 in public class `NamedMemberAccessor`:
D400: First line should end with a period (not 's')
torch/nn/utils/_named_member_accessor.py:115 in public method `__init__`:
D107: Missing docstring in __init__
torch/nn/utils/_named_member_accessor.py:122 in public method `get_submodule`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:155 in public method `swap_submodule`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:164 in public method `get_tensor`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:185 in public method `set_tensor`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:194 in public method `del_tensor`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:211 in public method `swap_tensor`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:224 in public method `get_tensors`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:233 in public method `set_tensors`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:249 in public method `set_tensors_dict`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:261 in public method `del_tensors`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:276 in public method `swap_tensors`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:296 in public method `swap_tensors_dict`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_named_member_accessor.py:325 in public method `check_keys`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/utils/_named_member_accessor.py:340 in public method `named_parameters`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/utils/_named_member_accessor.py:349 in public method `named_buffers`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/utils/_named_member_accessor.py:358 in public method `named_tensors`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/nn/utils/_named_member_accessor.py:368 in public method `named_modules`:
D200: One-line docstring should fit on one line with quotes (found 3)
23
```
After: 4
```
torch/nn/utils/_named_member_accessor.py:12 in public function `set_tensor`:
D103: Missing docstring in public function
torch/nn/utils/_named_member_accessor.py:29 in public function `swap_tensor`:
D103: Missing docstring in public function
torch/nn/utils/_named_member_accessor.py:85 in public function `swap_submodule`:
D103: Missing docstring in public function
torch/nn/utils/_named_member_accessor.py:116 in public method `__init__`:
D107: Missing docstring in __init__
4
```
- torch/nn/utils/_per_sample_grad.py
Before: 3
```
torch/nn/utils/_per_sample_grad.py:12 in public function `call_for_per_sample_grads`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_per_sample_grad.py:12 in public function `call_for_per_sample_grads`:
D400: First line should end with a period (not ')')
torch/nn/utils/_per_sample_grad.py:12 in public function `call_for_per_sample_grads`:
D402: First line should not be the function's "signature"
3
```
After: 0
```
0
```
- torch/nn/utils/init.py
Before: 3
```
torch/nn/utils/init.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/init.py:6 in public function `skip_init`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/init.py:6 in public function `skip_init`:
D400: First line should end with a period (not 'g')
3
```
After: 1
```
torch/nn/utils/init.py:1 at module level:
D100: Missing docstring in public module
1
```
- torch/nn/utils/memory_format.py
Before: 4
```
torch/nn/utils/memory_format.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/memory_format.py:5 in public function `convert_conv2d_weight_memory_format`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/memory_format.py:5 in public function `convert_conv2d_weight_memory_format`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/memory_format.py:5 in public function `convert_conv2d_weight_memory_format`:
D400: First line should end with a period (not '`')
4
```
After: 1
```
torch/nn/utils/memory_format.py:1 at module level:
D100: Missing docstring in public module
1
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112657
Approved by: https://github.com/fduwjj
