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
Summary: We need an implementation of RedisRemoteCacheBackend with the same API that we're using for FbMemcacheRemoteFxGraphCacheBackend. So we'll stop using the Triton implementation and adapt a version for use by inductor. I also renamed parameters and cache entries to match our cache terminology.
Test Plan: Ran this command twice and inspected log output to ensure I got cache hits:
```
TORCH_LOGS=+torch._inductor.codecache TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE=1 python benchmarks/dynamo/torchbench.py --performance --inductor --device cuda --training --amp --print-compilation-time --only dcgan
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127480
Approved by: https://github.com/oulgen
Type checking Python is a pain. Here are my learnings:
* The types for heavily polymorphic code is going to be verbose, no way around it. I originally was hoping I could lean on polymorphism with a bounded TypeVar to compactly write signatures for many of the ValueRanges methods, but I ran into some unworkaroundable mypy bugs. Writing out all the types explicitly and using `@overload` liberally works pretty well, so I think I recommend people do that instead of trying to do fancy things.
* Sympy is missing annotations for assumptions, because they are all metaprogrammed. I don't really relish maintaining a typeshed for sympy, so I wrote a small mypy plugin to add them in.
* GADT style refinement is... just not a good idea in practice. Mypy easily gets confused whether or not a return value from a refined section is allowed for the outer return type. So many of these have been replaced with less informative implementation types and more informative external types via overloads. Hopefully this is good for use sites.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118870
Approved by: https://github.com/Skylion007, https://github.com/albanD
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
dmypy silently ignores follow_imports = skip, so to get parity between
dmypy and mypy we have to suck it up and type: ignore all of the sympy
typing problems.
The suppressions were added automatically with the following script generated by GPT-4:
```
import re
# Read the error file
with open("error_file.txt", "r") as f:
errors = f.readlines()
# Parse the lines with errors and error types
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
# Insert ignore comments in the source files
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/118469
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418, #118432, #118467, #118468
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
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
Since MYPYNOFOLLOW is about to turn on import following, there's no
reason to keep test_utils.py in the MYPYNOFOLLOW config. Moreover, I'm
not sure it still takes 10 minutes to typecheck this file; adding it to
the MYPY config takes `lintrunner --take MYPY --all-files` from 53s to
57s on my machine, which is substantial but not horrible. I guess we'll
see how it fares on CI.
(Note that we cannot simply merge MYPY and MYPYNOFOLLOW because the
latter config turns on `disallow_any_generics` and so is in that sense
stricter than the MYPY config.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113745
Approved by: https://github.com/clee2000
This added the numpy typing plugin to mypy config so that we could
use it for DeviceMesh typing annotations
Please see https://github.com/pytorch/pytorch/pull/92931 about why we need this. For example, we are currently saving the DeviceMesh's mesh field as torch.Tensor, where when we do sth like:
```python
with FakeTensorMode():
device_mesh = DeviceMesh("cuda", torch.arange(4))
```
It would throw error because FakeTensorMode or any TorchDispatchMode tracks every tensor creation and interactions. While DeviceMesh just want to save a nd-array to record the mesh topology, and would like to avoid the interaction with subsystems like FakeTensor, so we want to support saving `mesh` as numpy array instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92930
Approved by: https://github.com/ezyang, https://github.com/malfet
As per title.
Additionally we also introduce support for:
- Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation).
- Batch support with broadcasting for either of the arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078
Approved by: https://github.com/cpuhrsch
As per title.
Additionally we also introduce support for:
- Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation).
- Batch support with broadcasting for either of the arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078
Approved by: https://github.com/cpuhrsch
As per title.
Additionally we also introduce support for:
- Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation).
- Batch support with broadcasting for either of the arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078
Approved by: https://github.com/cpuhrsch
### Summary:
This PR updates the design of APoT Observer, Quantizer, and Tensor to be more consistent with their uniform counterparts in the PyTorch framework. APoT Observer now calculates alpha as the max between the absolute values of the max and min values in the input tensor. APoT Quantizer is modified so its instance methods quantize_APoT and dequantize_APoT are called by their global method counterparts. APoT Tensor is modified to account for the new method definition of the `quantize_APoT` from APoT Quantizer.
### Test Plan:
Run APoT Observer class unit tests with: `python pytorch/test/quantization/core/experimental/test_nonuniform_observer.py`
Run APoT Quantize class unit tests with: `python pytorch/test/quantization/core/experimental/test_quantizer.py`
Run APoT Tensor class unit tests with: `python pytorch/test/quantization/core/experimental/test_quantized_tensor.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80075
Approved by: https://github.com/jerryzh168
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74833
FSDP has 74 type ignores and more being added, it may be better to
exclude from typechecking until we can type it properly.
ghstack-source-id: 152354450
Test Plan: CI
Reviewed By: zhaojuanmao
Differential Revision: D35186441
fbshipit-source-id: d06d90a06dcdbd38033adc70d0e9374333d4dc9a
(cherry picked from commit 4b410e1480abeeb672ce14274215ef55bcf3285d)
Summary:
Fixes CI regression caused by https://github.com/pytorch/pytorch/issues/61119
Unlike Python, `.ini` string lists could not end with trailing comma.
Fixes CI on master
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61333
Reviewed By: bhosmer
Differential Revision: D29578696
Pulled By: malfet
fbshipit-source-id: b81e5f4c0a553299c4d4bee0a9bb73748910795f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61119
This change spilts the clang-tidy CI job into smaller steps and uses a
refactored version of the clang_tidy.py script.
The new folder structure is as follows:
```
tools/linter/clang_tidy
|_ __main__py
|_ requirements.txt
|_ run.py
|_ setup.sh
```
`__main__.py`
This script will run `tools/linter/clang_tidy/setup.sh` if a `build`
directory doesn't exist, mimicing what used to be done as a separate
step in the CI job.
After that, it will invoke `clang-tidy` with default arguments being
declared in the script itself (as opposed to declaring them in
lint.yml).
The reasoning behind this approach is two-fold:
- Make it easier to run `clang-tidy` locally using this script
- De-duplicate the option passing
`requirements.txt`
Contains a list of additional python dependencies needed by the
`clang-tidy` script.
`setup.sh`
If a build directory doesn't exist, this command will run the necessary
codegen and build commands for running `clang-tidy`
Example usage:
```
python3 tools/linter/clang_tidy --parallel
```
Notice that we don't have to put the `.py` at the end of `clang_tidy`.
Test Plan:
Run the following command:
```
python3 tools/linter/clang_tidy --paths torch/csrc/fx --parallel
```
Reviewed By: walterddr, janeyx99
Differential Revision: D29568582
Pulled By: 1ntEgr8
fbshipit-source-id: cd6d11c5cb8ba9f1344a87c35647a1cd8dd45b04
Summary:
Changes including:
- introduced `linter/`, `testing/`, `stats/` folders in `tools/`
- move appropriate scripts into these folders
- change grepped references in the pytorch/pytorch repo
Next step
- introduce `build/` folder for build scripts
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60473
Test Plan:
- CI (this is important b/c pytorch/test-infra also rely on some script reference.
- tools/tests/
Reviewed By: albanD
Differential Revision: D29352716
Pulled By: walterddr
fbshipit-source-id: bad40b5ce130b35dfd9e59b8af34f9025f3285fd
Summary:
During development it is common practice to put `type: ignore` comments on lines that are correct, but `mypy` doesn't recognize this. This often stems from the fact, that the used `mypy` version wasn't able to handle the used pattern.
With every new release `mypy` gets better at handling complex code. In addition to fix all the previously accepted but now failing patterns, we should also revisit all `type: ignore` comments to see if they are still needed or not. Fortunately, we don't need to do it manually: by adding `warn_unused_ignores = True` to the configuration, `mypy` will error out in case it encounters an `type: ignore` that is no longer needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60006
Reviewed By: jbschlosser, malfet
Differential Revision: D29133237
Pulled By: albanD
fbshipit-source-id: 41e82edc5cd5affa7ccedad044b59b94dad4425a
Summary:
JUnitXml.__iadd__() is very slow
But since testsuites are flattened anyway in
`convert_junit_to_testcases` concatenate flattened tests right away
As result, parsing test-reports folder with 393 files and 25+ test cases
takes .5 sec instead of 193 sec
Fix typing errors and add script to mypy-strict
Print warning, rather than abort if xml can not be parsed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57641
Reviewed By: samestep
Differential Revision: D28224401
Pulled By: malfet
fbshipit-source-id: 3efc079c1c0deef8fff5ddf083268885b28418f9
Summary:
Fixes https://github.com/pytorch/pytorch/issues/48141
~Mypy is complaining about a missing arg in a function call.~
```bash
torch/backends/_nnapi/serializer.py:806: error: Too few arguments for "_do_add_binary" [call-arg]
Found 1 error in 1 file (checked 1140 source files)
```
9392137dbe/torch/backends/_nnapi/serializer.py (L804-L806)
~dreiss, would you mind take a look when you have some cycles to spare and see what would be the appropriated value for `fuse_code` here? Thanks :)~
Edit: https://github.com/pytorch/pytorch/issues/48925 got merged a couple of days ago. The blocking part is now unblocked, and I just pushed the changes to make mypy happy again. This PR is ready for review.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48142
Reviewed By: ezyang
Differential Revision: D28006249
Pulled By: walterddr
fbshipit-source-id: 5e43eeba7143512a549efaad31541f86718add7c
