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
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
Fixes#103818
1. for some special nn.Modules, there are checks which only support cuda, so I add `privateuse1` check.
2. when get the device type for `privateuse1` by `torch._C._get_privateuse1_backend_name()`, it will get error in `torch.jit.script`, so I add a global variable to avoid this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103419
Approved by: https://github.com/albanD
I see https://github.com/pytorch/pytorch/issues/53103 says this might be problematic, but I'm a bit confused at this point, because it looks like ModuleList does in fact already adhere to the Sequence API
The big win here is that for homogenous ModuleLists, you now get typing for individual members, e.g.
`ModuleList([Linear(), Linear(), Linear()])[1]` properly has type `Linear`
If this looks good, I can do a followup PR to do similarly for `ModuleDict` and `Parameter[List,Dict]`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89135
Approved by: https://github.com/albanD
## Problem
When models have a lot of complex repeated layers, `print(module)` output becomes unfeasible to work with. For example, current output of `__repr__` for `t5-small` is `715 ` lines long.
## Solution
Using better `__repr__` it becomes `135`. For `t5-large`, current `__repr__` prints `1411` lines. Better `__repr__` — `135`. Same numer as for t5-small, because most of the layers are just repeated. For `EleutherAI/gpt-j-6B` number of lines reduces form `483` to just `24`.
Here's how it works: when ModuleList items have exactly the same `__repr__` instead of printing both of them, it prints f`N x {repr(item)}`. Current code supports cases when the same ModuleList has multiple repeating items, which is especially useful when first/last layer of a block is different from the reset of them.
Better `__repr__` should make model prints smaller, more beautiful and significantly more useful by highlighting the difference between repeated blocks instead of losing it in a wall of text.
## Motivating real-life example.
You can try it out in this [colab notebook](https://colab.research.google.com/drive/1PscpX_K1UemIDotl2raC4QMy_pTqDq7p?usp=sharing).
Current `__repr__` of gpt-j-6b output it too big to add it to this PR description:
```
GPTJModel(
(wte): Embedding(50400, 4096)
(drop): Dropout(p=0.0, inplace=False)
(h): ModuleList(
(0): GPTJBlock(
(ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(attn): GPTJAttention(
(attn_dropout): Dropout(p=0.0, inplace=False)
(resid_dropout): Dropout(p=0.0, inplace=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(out_proj): Linear(in_features=4096, out_features=4096, bias=False)
)
(mlp): GPTJMLP(
(fc_in): Linear(in_features=4096, out_features=16384, bias=True)
(fc_out): Linear(in_features=16384, out_features=4096, bias=True)
(act): NewGELUActivation()
(dropout): Dropout(p=0.0, inplace=False)
)
)
(1): GPTJBlock(
(ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(attn): GPTJAttention(
(attn_dropout): Dropout(p=0.0, inplace=False)
(resid_dropout): Dropout(p=0.0, inplace=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(out_proj): Linear(in_features=4096, out_features=4096, bias=False)
)
(mlp): GPTJMLP(
(fc_in): Linear(in_features=4096, out_features=16384, bias=True)
(fc_out): Linear(in_features=16384, out_features=4096, bias=True)
(act): NewGELUActivation()
(dropout): Dropout(p=0.0, inplace=False)
)
)
(2): GPTJBlock(
...
```
Better `__repr__` output looks like this:
```
GPTJModel(
(wte): Embedding(50400, 4096)
(drop): Dropout(p=0.0, inplace=False)
(h): ModuleList(
(0-27): 28 x GPTJBlock(
(ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(attn): GPTJAttention(
(attn_dropout): Dropout(p=0.0, inplace=False)
(resid_dropout): Dropout(p=0.0, inplace=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(out_proj): Linear(in_features=4096, out_features=4096, bias=False)
)
(mlp): GPTJMLP(
(fc_in): Linear(in_features=4096, out_features=16384, bias=True)
(fc_out): Linear(in_features=16384, out_features=4096, bias=True)
(act): NewGELUActivation()
(dropout): Dropout(p=0.0, inplace=False)
)
)
)
(ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90452
Approved by: https://github.com/albanD
Summary:
The only difference with plain list/dict now is that nn.Parameters are
handled specially and registered as parameters properly.
test_nn and parametrization works locally.
Will see in CI if DP is fixed as well.
Tentative fix for https://github.com/pytorch/pytorch/issues/36035
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70499
Reviewed By: jbschlosser, alexeib
Differential Revision: D34005332
Pulled By: albanD
fbshipit-source-id: 7e76b0873d0fec345cb537e2a6ecba0258e662b9
(cherry picked from commit dc1e6f8d86)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/68476
We implemented all of the following `dict` methods for `ParameterDict`
- `get `
- `setdefault`
- `popitem`
- `fromkeys`
- `copy`
- `__or__`
- `__ior__`
- `__reversed__`
- `__ror__`
The behavior of these new methods matches the expected behavior of python `dict` as defined by the language itself: https://docs.python.org/3/library/stdtypes.html#typesmapping
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69403
Reviewed By: albanD
Differential Revision: D33187111
Pulled By: jbschlosser
fbshipit-source-id: ecaa493837dbc9d8566ddbb113b898997e2debcb
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
