reland of https://github.com/pytorch/pytorch/pull/133113
I have to create a new PR because the previous reverted PR could not either be rebased, or imported successfully :(
----
Moving DTensor to be in the public namespace, to formally add the documentation page that includes all the public APIs. This includes:
* many path renames and path import fixes
* a dedicated doc page without too much content yet (adding in the next PRs)
* To preserve the BC for users still using the torch.distributed._tensor, I added a shim script to redirect old path calls to the new module
The BC preserving is evidented by the fact that all DTensor tests are still working without changing the public imports. So it's safe to land the changes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134203
Approved by: https://github.com/tianyu-l
Moving DTensor to be in the public namespace, to formally add the
documentation page that includes all the public APIs. This includes:
* many path renames and path import fixes
* a dedicated doc page without too much content yet (adding in the next
PRs)
* To preserve the BC for users still using the `torch.distributed._tensor`,
I added a shim script to redirect old path calls to the new module
The BC preserving is evidented by the fact that all DTensor tests are still
working without changing the public imports. So it's safe to land the
changes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133113
Approved by: https://github.com/XilunWu
ghstack dependencies: #133305, #133306
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.
I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
* Enable PERF402. Makes code more efficient and succinct by removing useless list copies that could be accomplished either via a list constructor or extend call. All test cases have noqa added since performance is not as sensitive in that folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115505
Approved by: https://github.com/malfet
Summary:
This diff fix the param unflattening when using FSDP together with TP. Currently we hardcode the `reshape_size` to be multiplied by 2, which instead should be the size of the process group.
Before the fix, example exception: `shape '[257, 514]' is invalid for input of size 264196`, where the process group size is 4 instead of 2.
Test Plan:
**CI**:
CI test
**Unit test**:
`buck2 test mode/dev-nosan //caffe2/test/distributed/tensor/parallel:fsdp_2d_parallel`
- Passed
**Test model with WHEN**:
- Verified that checkpoint can be saved and resumed successfully;
- Verified the accuracy with window_ne, which is on-par with baseline.
https://pxl.cl/3Wp8w
Differential Revision: D51826120
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115105
Approved by: https://github.com/fegin
It appears that `mypy` is now checking a few more previously-unchecked files; these files
are being found via import-following. Not sure exactly why they weren't being checked before.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114160
Approved by: https://github.com/eellison
ghstack dependencies: #114162
Summary: I had a job fail due to rank mismatch but didn't find enough information in the assertion message. This change makes the message more informative.
Test Plan:
CI tests and I ran a test job which failed as expected:
```
Rank 1 has different values for step: 8016.0. Other ranks: 7870.0
```
Differential Revision: D51322046
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113765
Approved by: https://github.com/wz337, https://github.com/fegin
For scenarios where FSDP is not the root module, the `_use_dtensor` flag would not be switched on. This PR fixes it by checking whether the submodule has the `device_mesh` and turn `_use_dtensor` flag on accordingly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113593
Approved by: https://github.com/fegin
Currently, when we have 2D composition, a global variable _extensions controls the 2D deviation we need to take in state_dict calls (See https://github.com/pytorch/pytorch/blob/release/2.1/torch/distributed/fsdp/_fsdp_extensions.py#L66-L68). This is problematic when we have both a 2D model and a plain FSDP model in the same dist environment, as the _extensions will be mistakenly turned on for the plain FSDP model, resulting in state_dict error (RuntimeError: No parent device_mesh is found for FSDP device_mesh.).
This PR binds _fsdp_extension to the FSDP instances to make sure that state_dict calls would not get interfered with each other when mixing both 2D and 1D parallelism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113237
Approved by: https://github.com/fduwjj, https://github.com/fegin
