Current implementation reads as: we will only actually use the "python_reducer" config if the DDP forward is compiled. Otherwise, we will silently fallback to C++ reducer + no DDPOptimizer.
I'm changing this behavior to always use the python reducer if the config is specified.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147123
Approved by: https://github.com/fegin
As `torch._C._scatter` is only defined for CUDA/ROCm (and may be XPU?)
This is a regression introduced by https://github.com/pytorch/pytorch/pull/141098 that went unnoticed due to https://github.com/pytorch/pytorch/issues/142206
Test plan:
```
python test_autograd.py -v -k test_dataparallel_saved_tensors_hooks
```
Before this change it failed with
```
ERROR: test_dataparallel_saved_tensors_hooks (__main__.TestMultithreadAutograd.test_dataparallel_saved_tensors_hooks)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/malfet/git/pytorch/pytorch/torch/testing/_internal/common_utils.py", line 3108, in wrapper
method(*args, **kwargs)
~~~~~~^^^^^^^^^^^^^^^^^
File "/Users/malfet/git/pytorch/pytorch/test/test_autograd.py", line 13074, in test_dataparallel_saved_tensors_hooks
model = torch.nn.DataParallel(Model())
File "/Users/malfet/git/pytorch/pytorch/torch/nn/parallel/data_parallel.py", line 153, in __init__
raise RuntimeError("no available devices were found")
RuntimeError: no available devices were found
```
After this change it passes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142448
Approved by: https://github.com/kit1980
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
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
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
Automated fixes to put imports that are only used in type hints into TYPE_CHECKING imports. This also enables the RUFF TCH rules which will automatically apply autofixes to move imports in and out of TYPE_CHECKING blocks as needed in the future, this will make the initial PyTorch import faster and will reduce cyclic dependencies.
Co-authored-by: Xuehai Pan <XuehaiPan@pku.edu.cn>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127688
Approved by: https://github.com/XuehaiPan, https://github.com/ezyang, https://github.com/malfet
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
