This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
If `XLA_HLO_DEBUG` flag is enabled, generated a minified HLO graph when using the minifier. This function enables HLO minification support by porting the minified FX graph to StableHLO via the `save_torch_model_as_stablehlo` function.
This allows users to port the minified graph to compilers that are not compatible with TorchDynamo/Inductor workflow and use XLA instead. The purpose of this PR is to help XLA users debug accuracy and compilation errors. It will also be helpful for existing TorchDynamo/XLA workflow on `torchxla_trace_once` backend as well.
Fixes [#5461](https://github.com/pytorch/xla/issues/5461) in Torch XLA repo. CC @GleasonK @qihqi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109084
Approved by: https://github.com/anijain2305
When minifying extremely large repros, the minifier can run out of memory. This is because, for delta debugging, the minifier keeps a copy of every intermediate output in the network. This can easily put you over the memory limit for your GPU. To make matters worse, we cannot easily delta debug in such a situation, as delta debugging involves replacing intermediates with inputs, but doing so can cause an intermediate to become live longer than its actual extent in the original model (since inputs all have to be allocated up front).
The strategy in this PR is to use `load_tensor` from the previous PR to offer a low memory mode for delta debugging. Instead of putting intermediates as inputs, we instead load them in the middle of the graph in question. If, through DCE, the load_tensor ends up floating to the top of the graph, we can input-ify it. We now no longer save all intermediates in memory, but instead save them to disk. I used this to successfully minify the repro that helped us solve https://github.com/pytorch/pytorch/pull/100332
The testing is not very good. I can try to add more robust testing but it will involve a more involved refactor to FX minifier. Let me know if that's what you want.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100546
Approved by: https://github.com/anijain2305, https://github.com/voznesenskym
This will be the last disruptive functorch internals change.
Why are we moving these files?
- As a part of rationalizing functorch we are moving the code in
functorch/_src to torch/_functorch
- This is so that we can offer the functorch APIs as native PyTorch APIs
(coming soon) and resolve some internal build issues.
Why are we moving all of these files at once?
- It's better to break developers all at once rather than many times
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90091
Approved by: https://github.com/anijain2305, https://github.com/ezyang
This will be the last disruptive functorch internals change.
Why are we moving these files?
- As a part of rationalizing functorch we are moving the code in
functorch/_src to torch/_functorch
- This is so that we can offer the functorch APIs as native PyTorch APIs
(coming soon) and resolve some internal build issues.
Why are we moving all of these files at once?
- It's better to break developers all at once rather than many times
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88756
Approved by: https://github.com/ezyang
