mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
d59a6864fb
68 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
PyTorch MergeBot
|
d59a6864fb |
Revert "[BE]: Update ruff to 0.285 (#107519)"
This reverts commit
|
||
|
Aaron Gokaslan
|
88ab3e4322 |
[BE]: Update ruff to 0.285 (#107519)
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 |
||
|
Justin Chu
|
4cc1745b13 |
[BE] f-stringify torch/ and scripts (#105538)
This PR is a follow up on the pyupgrade series to convert more strings to use f-strings using `flynt`. - https://docs.python.org/3/reference/lexical_analysis.html#f-strings - https://pypi.org/project/flynt/ Command used: ``` flynt torch/ -ll 120 flynt scripts/ -ll 120 flynt tools/ -ll 120 ``` and excluded `collect_env.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/105538 Approved by: https://github.com/ezyang, https://github.com/malfet |
||
|
Justin Chu
|
79c5e33349 |
[BE] Enable ruff's UP rules and autoformat nn/ mps/ and torch/ (#105436)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105436 Approved by: https://github.com/malfet, https://github.com/albanD |
||
|
ts
|
74dc2a53f6 |
Thread generator through trunc_normal_ (#100810)
This will solve @albertz's issue as described in #98200 , threading the generator argument through the trunc_normal_ function. I'm still working on #99796 (and won't let it stall out), but this fix doesn't trigger any JIT issues, so I think it might be helpful to get it merged now. Would be happy to iterate on this if there are any issues. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100810 Approved by: https://github.com/Skylion007, https://github.com/albanD |
||
|
loganthomas
|
c848a777e8 |
DOC: Various typo fixes (#97095)
Various typos found while browsing documentation/source code. Thank you for a wonderful deep-learning library! Pull Request resolved: https://github.com/pytorch/pytorch/pull/97095 Approved by: https://github.com/mikaylagawarecki, https://github.com/kit1980 |
||
|
joncrall
|
b136f3f310 |
More doctest refinements. (#83317)
Follow up to #82797 Now that the doctests themselves are in a better state, we should be able to enable xdoctest on the CI so they stay that way. @ezyang @vadimkantorov Pull Request resolved: https://github.com/pytorch/pytorch/pull/83317 Approved by: https://github.com/ezyang |
||
|
joncrall
|
4618371da5 |
Integrate xdoctest - Rebased (#82797)
This is a new version of #15648 based on the latest master branch. Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR. In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.) Fixes https://github.com/pytorch/pytorch/issues/71105 @ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797 Approved by: https://github.com/ezyang |
||
|
ProGamerGov
|
8def154e00 |
Fix multiple docstring type mistakes (#82474)
### Description * Docstrings using `(tuple of ints)` shows up as `(tuple of python:ints)`, so I fixed them by making the `int` no longer plural. Example: https://pytorch.org/docs/stable/generated/torch.permute.html#torch.permute * A docstring type in JIT had one of its types incorrectly highlighted as code. Example: https://pytorch.org/docs/stable/generated/torch.jit.script.html#torch.jit.script * I found some docstring type usages of `string` that had not yet been converted to `str` after #82410 * Some docstrings incorrectly listed their defaults inside the docstring types. * I also found a docstring that was missing its type ### Testing No testing should be required. --- In the developer guidelines, there should probably be standards listed for the docstring types. Pull Request resolved: https://github.com/pytorch/pytorch/pull/82474 Approved by: https://github.com/albanD |
||
|
Adam J. Stewart
|
dfde877c0b |
Add type hints for a few random functions/classes
Adds type hints for a few functions/classes that we use in [TorchGeo](https://github.com/microsoft/torchgeo). Pull Request resolved: https://github.com/pytorch/pytorch/pull/74171 Approved by: https://github.com/jbschlosser, https://github.com/anjali411 |
||
|
PyTorch MergeBot
|
80fe96c860 |
Revert "Add type hints for a few random functions/classes"
This reverts commit
|
||
|
Adam J. Stewart
|
cdb40eb528 |
Add type hints for a few random functions/classes
Adds type hints for a few functions/classes that we use in [TorchGeo](https://github.com/microsoft/torchgeo). Pull Request resolved: https://github.com/pytorch/pytorch/pull/74171 Approved by: https://github.com/jbschlosser |
||
|
Jake Tae
|
b5b296c4cf |
Fix: Make nn.init.orthogonal_ no-op for empty input
Fixes #73503. Pull Request resolved: https://github.com/pytorch/pytorch/pull/75553 Approved by: https://github.com/albanD |
||
|
Brian Hirsh
|
f87f753bb9 |
avoiding adding some functions to the public python API before 1.11 release (#72543)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72543
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D34085724
Pulled By: bdhirsh
fbshipit-source-id: 941d5a90a6fa5328268d623e0e2b01577e4132ca
(cherry picked from commit
|
||
|
Pritam Damania
|
8c505bbc86 |
Make ShardedTensor ctor more inline with torch.Tensor ctor (#72164)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72164
torch.Tensor ctor creates an empty tensor and this PR makes
ShardedTensor on par with that.
In particular we remove TensorInitParams and instead always a create an empty
tensor and then fill it in for things like ones, zeros, full etc. This is
inline with torch.ones etc. as well since even for those APIs we first create
an empty tensor and then fill it out.
ghstack-source-id: 148318045
Test Plan: waitforbuildbot
Reviewed By: wanchaol
Differential Revision: D33934603
fbshipit-source-id: 5655bbd726f29e74600ebe9f33f9dc5952b528f4
(cherry picked from commit
|
||
|
Pritam Damania
|
b199e3c842 |
Provide functionality to write custom ShardedTensor ops. (#69874)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69874 We have a handful of ops supported for ShardedTensor via ``__torch_function__`` dispatch. However, we currently can't cover all torch operators and having a way for users to extend this functionality will make this functionality much more general. In this PR, I've introduced a custom_sharded_op decorator which can be used to register a custom sharded op implementation. ghstack-source-id: 145841141 Test Plan: waitforbuildbot Reviewed By: wanchaol Differential Revision: D33078587 fbshipit-source-id: 5936b7ac25582e613653c19afa559219719ee54b |
||
|
Bo Wang
|
3596e13d45 |
Add torch.nn.init.normal_ and torch.nn.init.kaiming_uniform_ ops to ShardedTensor (#67057)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/67057 Extend ShardedTensor with torch.nn.init.[normal_, and kaiming_uniform_] ops Follow up from https://github.com/pytorch/pytorch/pull/63997 Test Plan: a) Unit Test (pytorch) ... $ python test/distributed/_sharded_tensor/ops/test_init.py TestShardedTensorNNInit --v or b) Manual run: Instruction here: https://docs.google.com/document/d/1_m1Hdo5w51-hhPlZ_F8Y6PIWrN7UgJZqiSpARYvhsaE/edit# s/uniform_/normal_ or kaiming_uniform_ Imported from OSS Reviewed By: pritamdamania87 Differential Revision: D31845654 fbshipit-source-id: e7aedc0972539da59f7b84bbbf617caf6b206d52 |
||
|
Bo Wang
|
b6df043f1f |
Add torch.nn.init.uniform_ operator to ShardedTensor. (#63997)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63997 Use torch_function to extend torch.nn.init.uniform_ The Init is done in SPMD fashion. Note that ideally we want to aggregate sharded tensors into a global tensor, init it and reshard. It's fine to run it SPMD since uniform is I.I.D indepenent and identifically distributed. Also enable unit test for test_linear.py for OSS test Test Plan: a) Unit Test (pytorch) ... $ python test/distributed/_sharded_tensor/ops/test_init.py TestShardedTensorNNInit --v (pytorch) ... $ python test/distributed/_sharded_tensor/ops/test_linear.py --v (before runs this command is no-op) or b) Manual run: Instruction here: https://docs.google.com/document/d/1_m1Hdo5w51-hhPlZ_F8Y6PIWrN7UgJZqiSpARYvhsaE/edit# Imported from OSS Reviewed By: pritamdamania87, anjali411 Differential Revision: D30563017 fbshipit-source-id: d1859f7682235bcb44515efc69ca92bc5e34fce1 |
||
|
lezcano
|
24087d07ca |
Deprecate QR (#57745)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/57745 Reviewed By: bdhirsh Differential Revision: D28318164 Pulled By: mruberry fbshipit-source-id: b8e3cb9d7ab33f30c8653ec39f932a8af8bd2a50 |
||
|
beningodfrey4
|
df1dfd879e |
Fix errors when initializing Linear with 0 in_features (#56505)
Summary: Fixes https://github.com/pytorch/pytorch/issues/48152 Pull Request resolved: https://github.com/pytorch/pytorch/pull/56505 Reviewed By: malfet Differential Revision: D27919590 Pulled By: jbschlosser fbshipit-source-id: 462ca280051f63c31ff588c38a9e436116c0f336 |
||
|
mrTsjolder
|
a7c7fc96ff |
Add doc warnings for default SELU gain (#54057)
Summary: Fixes https://github.com/pytorch/pytorch/issues/24991 and provides the alternative solution suggested in https://github.com/pytorch/pytorch/issues/53694. Also related to https://github.com/pytorch/pytorch/issues/54055 Attempt to make people aware of the difference between paper and implementation of SELU gain. Pull Request resolved: https://github.com/pytorch/pytorch/pull/54057 Reviewed By: ailzhang Differential Revision: D27292060 Pulled By: jbschlosser fbshipit-source-id: e0e303595e6a7d05d11dfb68735e1839f55987a2 |
||
|
Jean Kossaifi
|
70a43425e0 |
Simplify init._calculate_fan_in_and_fan_out (#53522)
Summary: This uses the shape of the tensor instead of directly indexing it. This is useful when extending PyTorch's tensor class, e.g. for lazy access. Since the `init` sub-module doesn't check for `torch_function`, it is not possibly to override its functions. Explicitly indexing the tensor will force a call to tensor() and reconstruct the full tensor/explicitly access the elements. Simply using the shape allows to avoid that. Fixes https://github.com/pytorch/pytorch/issues/53540 Pull Request resolved: https://github.com/pytorch/pytorch/pull/53522 Reviewed By: anjali411 Differential Revision: D26947794 Pulled By: jbschlosser fbshipit-source-id: 80cd65efed16383f21363cee2eb404c9bc05971c |
||
|
AJ San Joaquin
|
e9b369c25f |
Add SELU Activation to calculate_gain (#50664)
Summary:
Fixes #{[24991](https://github.com/pytorch/pytorch/issues/24991)}
I used a value of 0.75 as suggested in the forums by Thomas: https://discuss.pytorch.org/t/calculate-gain-tanh/20854/6
I verified that the value keeps the gradient stable for a 100-layer network.
Code to reproduce (from [jpeg729](https://discuss.pytorch.org/t/calculate-gain-tanh/20854/4)):
```python
import torch
import torch.nn.functional as F
import sys
a = torch.randn(1000,1000, requires_grad=True)
b = a
print (f"in: {a.std().item():.4f}")
for i in range(100):
l = torch.nn.Linear(1000,1000, bias=False)
torch.nn.init.xavier_normal_(l.weight, torch.nn.init.calculate_gain("selu"))
b = getattr(F, 'selu')(l(b))
if i % 10 == 0:
print (f"out: {b.std().item():.4f}", end=" ")
a.grad = None
b.sum().backward(retain_graph=True)
print (f"grad: {a.grad.abs().mean().item():.4f}")
```
Output:
```
in: 1.0008
out: 0.7968 grad: 0.6509
out: 0.3127 grad: 0.2760
out: 0.2404 grad: 0.2337
out: 0.2062 grad: 0.2039
out: 0.2056 grad: 0.1795
out: 0.2044 grad: 0.1977
out: 0.2005 grad: 0.2045
out: 0.2042 grad: 0.2273
out: 0.1944 grad: 0.2034
out: 0.2085 grad: 0.2464
```
I included the necessary documentation change, and it passes the _test_calculate_gain_nonlinear_ unittest.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50664
Reviewed By: mruberry
Differential Revision: D25942217
Pulled By: ngimel
fbshipit-source-id: 29ff1be25713484fa7c516df71b12fdaecfb9af8
|
||
|
Richard Barnes
|
b89827b73f |
Drop unused imports (#49972)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49972 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727352 fbshipit-source-id: 6b90717e161aeb1da8df30e67d586101d35d7d5f |
||
|
Richard Barnes
|
f83d57f99e |
[Don't review] Clean up type annotations in caffe2/torch/nn (#50079)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/50079 Test Plan: Sandcastle tests Reviewed By: xush6528 Differential Revision: D25718694 fbshipit-source-id: f535fb879bcd4cb4ea715adfd90bbffa3fcc1150 |
||
|
Xiang Gao
|
20ac736200 |
Remove py2 compatible future imports (#44735)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44735 Reviewed By: mruberry Differential Revision: D23731306 Pulled By: ezyang fbshipit-source-id: 0ba009a99e475ddbe22981be8ac636f8a1c8b02f |
||
|
F-G Fernandez
|
847d102e93 |
docs: Fixed docstring indentation for documentation (#37739)
Summary: Hello there, I was going through the default initialization of some layers, and ended up on the `torch.nn.init` documentation. As shown below, there was a slight issue with the docstrings of both `kaiming_normal_` and `kaiming_uniform_` that yielded a wrong list of function parameters:  This PR fixes the indentation in the corresponding docstrings. Any feedback is welcome! Pull Request resolved: https://github.com/pytorch/pytorch/pull/37739 Differential Revision: D21393728 Pulled By: ngimel fbshipit-source-id: 64523cb328e72d2e51c2c42b20a4545c1ec5f478 |
||
|
Ralf Gommers
|
78d5707041 |
Fix type annotations and make MyPy run on torch/ (#36584)
Summary: This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages. As a result, running `mypy` in the root dir of the repo now runs on: - `torch/` - `aten/src/ATen/function_wrapper.py` (the only file already covered in CI) In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`. Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through): <details> ``` torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx' torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch" torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2' torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2' torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx' torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util' torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework' torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer' torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...") torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items" torch/nn/_reduction.py:27: error: Name 'Optional' is not defined torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/_reduction.py:47: error: Name 'Optional' is not defined torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs torch/nn/modules/utils.py:27: error: Name 'List' is not defined torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment" torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction' caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed) caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs caffe2/python/__init__.py:7: error: Module has no attribute "CPU" caffe2/python/__init__.py:8: error: Module has no attribute "CUDA" caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN" caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL" caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL" caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP" caffe2/python/__init__.py:13: error: Module has no attribute "HIP" caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"? caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"? caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:35: error: Module has no attribute "cuda" caffe2/python/__init__.py:37: error: Module has no attribute "cuda" caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory" torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C' torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import) torch/_tensor_str.py:164: error: Not all arguments converted during string formatting torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/_linalg_utils.py:26: error: Name 'Optional' is not defined torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Optional' is not defined torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Optional' is not defined torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Optional' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched' torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...") torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...") torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm") torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Optional' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/nn/init.py:109: error: Name 'Tensor' is not defined torch/nn/init.py:126: error: Name 'Tensor' is not defined torch/nn/init.py:142: error: Name 'Tensor' is not defined torch/nn/init.py:165: error: Name 'Tensor' is not defined torch/nn/init.py:180: error: Name 'Tensor' is not defined torch/nn/init.py:194: error: Name 'Tensor' is not defined torch/nn/init.py:287: error: Name 'Tensor' is not defined torch/nn/init.py:315: error: Name 'Tensor' is not defined torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction' torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer' torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_" torch/jit/_builtins.py:80: error: Module has no attribute "stft" torch/jit/_builtins.py:81: error: Module has no attribute "cdist" torch/jit/_builtins.py:82: error: Module has no attribute "norm" torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm" torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm" torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C' torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...") torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import) torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass" torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache" torch/_lowrank.py:11: error: Name 'Tensor' is not defined torch/_lowrank.py:13: error: Name 'Optional' is not defined torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Optional' is not defined torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Optional' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:82: error: Name 'Tuple' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:130: error: Name 'Tensor' is not defined torch/_lowrank.py:130: error: Name 'Optional' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:130: error: Name 'Tuple' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:167: error: Name 'Tensor' is not defined torch/_lowrank.py:167: error: Name 'Optional' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:167: error: Name 'Tuple' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases torch/quantization/observer.py:45: error: Invalid base class "ABC" torch/quantization/observer.py:127: error: Name 'Tensor' is not defined torch/quantization/observer.py:127: error: Name 'Tuple' is not defined torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:192: error: Name 'Tensor' is not defined torch/quantization/observer.py:192: error: Name 'Tuple' is not defined torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:534: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tuple' is not defined torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:894: error: Cannot determine type of 'max_val' torch/quantization/observer.py:894: error: Cannot determine type of 'min_val' torch/quantization/observer.py:899: error: Cannot determine type of 'min_val' torch/quantization/observer.py:902: error: Name 'Tensor' is not defined torch/quantization/observer.py:925: error: Name 'Tensor' is not defined torch/quantization/observer.py:928: error: Cannot determine type of 'min_val' torch/quantization/observer.py:929: error: Cannot determine type of 'max_val' torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric" torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:289: error: Module has no attribute "ops" torch/nn/quantized/functional.py:290: error: Module has no attribute "ops" torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops" torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops" torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig' torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__" torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views' torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16 torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15 torch/functional.py:114: error: Name 'Tuple' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:114: error: Name 'Optional' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]" torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:204: note: Possible overload variants: torch/functional.py:204: note: def __setitem__(self, int, int) -> None torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:204: note: def __getitem__(self, int) -> int torch/functional.py:204: note: def __getitem__(self, slice) -> List[int] torch/functional.py:207: error: "Tensor" has no attribute "copy_" torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:212: note: Possible overload variants: torch/functional.py:212: note: def __setitem__(self, int, int) -> None torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:212: note: def __getitem__(self, int) -> int torch/functional.py:212: note: def __getitem__(self, slice) -> List[int] torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:334: error: Name 'Optional' is not defined torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]" torch/functional.py:431: error: Module has no attribute "stft" torch/functional.py:766: error: Module has no attribute "cdist" torch/functional.py:768: error: Module has no attribute "cdist" torch/functional.py:770: error: Module has no attribute "cdist" torch/functional.py:775: error: Name 'Optional' is not defined torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'Optional' is not defined torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'number' is not defined torch/functional.py:780: error: Name 'norm' already defined on line 775 torch/functional.py:785: error: Name 'Optional' is not defined torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:785: error: Name 'number' is not defined torch/functional.py:785: error: Name 'norm' already defined on line 775 torch/functional.py:790: error: Name 'Optional' is not defined torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:790: error: Name 'norm' already defined on line 775 torch/functional.py:795: error: Name 'norm' already defined on line 775 torch/functional.py:960: error: Name 'Any' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any") torch/functional.py:960: error: Name 'Tuple' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized" torch/functional.py:1041: error: Name 'Optional' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1041: error: Name 'Tuple' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1056: error: Name 'Optional' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1056: error: Name 'Tuple' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]") torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]") torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]") torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase" torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]") torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None") torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric" torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric" torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module' torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68 torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass" torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524 torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer' torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching' torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]") torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]") torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...") torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/__init__.py:158: error: Name 'transforms' is not defined torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse' torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import) torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import) torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore' torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore' torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584 Reviewed By: seemethere, ailzhang Differential Revision: D21155985 Pulled By: ezyang fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1 |
||
|
Enealor
|
8bcedf7da2 |
Adds truncated normal initializer (#32397)
Summary: This adds the `trunc_normal_` function to `torch.nn.init` which allows for modifying tensors in-place to values drawn from a truncated normal distribution. I chose to use the inverse CDF method to implement this. I have included the appropriate code in `test_nn.py` for verifying that the values are from the correct distribution. Reasons I chose this method: 1. Easily implemented to operate on memory in place, as the other initializers are. 1. No resampling delays 1. This method's main weakness is unlikely to be an issue. While the inverse CDF method can fail to generate the correct distribution when `b < mean` or `mean < a`, I expect users will choose `a` and `b` so that `a < mean < b`. This method is extremely effective in this case. Pull Request resolved: https://github.com/pytorch/pytorch/pull/32397 Differential Revision: D20550996 Pulled By: ezyang fbshipit-source-id: 298a325043a3fd7d1e24d266e3b9b6cc14f81829 |
||
|
Assaf Shocher
|
2c99ea8654 |
Dirac init compatibility with group convolutions (#32825)
Summary:
Initializing weights of group-conv with init.dirac_, and applying, previously resulted in an output that makes no sense:
```
x = torch.randn([1, 3, 3, 3])
print('input:\n', x)
conv_layer = torch.nn.Conv2d(3, 3, 3, padding=1, groups=3, bias=False)
torch.nn.init.dirac_(conv_layer.weight.data)
print('\noutput (before this PR):\n',conv_layer(x))
input:
tensor([[[[ 0.5369, -1.1428, 0.1031],
[ 0.4638, -0.0854, -0.6553],
[ 0.8321, -2.5926, -0.3214]],
[[-0.2289, -0.0895, 0.4407],
[ 1.2309, -1.2096, -1.5216],
[-0.1798, 1.1694, 0.3469]],
[[ 0.1905, 0.8095, 0.5490],
[-0.4525, -0.4284, -0.1141],
[ 1.1857, -0.9246, -0.5119]]]])
output (before this PR):
tensor([[[[ 0.5369, -1.1428, 0.1031],
[ 0.4638, -0.0854, -0.6553],
[ 0.8321, -2.5926, -0.3214]],
[[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000]],
[[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000]]]], grad_fn=<MkldnnConvolutionBackward>)
````
This PR allows introducing groups to the initialization:
```
torch.nn.init.dirac_(conv_layer.weight.data, groups=3)
print('output (after this PR):\n', conv_layer(x))
output (after this PR):
tensor([[[[ 0.5369, -1.1428, 0.1031],
[ 0.4638, -0.0854, -0.6553],
[ 0.8321, -2.5926, -0.3214]],
[[-0.2289, -0.0895, 0.4407],
[ 1.2309, -1.2096, -1.5216],
[-0.1798, 1.1694, 0.3469]],
[[ 0.1905, 0.8095, 0.5490],
[-0.4525, -0.4284, -0.1141],
[ 1.1857, -0.9246, -0.5119]]]], grad_fn=<MkldnnConvolutionBackward>)
```
When out_channels is different than input_channels, it does the natural thing which is applying identity in each group separately:
```
x = torch.randn([1, 2, 3, 3])
print('input:\n', x)
conv_layer = torch.nn.Conv2d(2, 4, 3, padding=1, groups=2, bias=False)
torch.nn.init.dirac_(conv_layer.weight.data, groups=2)
print('\noutput:\n', conv_layer(x))
input:
tensor([[[[ 1.2205, -0.6608, 0.8640],
[-0.5464, 1.1288, 1.4726],
[-0.6693, 0.4000, -1.7613]],
[[-0.8760, -0.8814, -0.4705],
[ 0.6283, -0.5943, 0.6873],
[-0.6852, 1.4723, 0.3325]]]])
output:
tensor([[[[ 1.2205, -0.6608, 0.8640],
[-0.5464, 1.1288, 1.4726],
[-0.6693, 0.4000, -1.7613]],
[[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000]],
[[-0.8760, -0.8814, -0.4705],
[ 0.6283, -0.5943, 0.6873],
[-0.6852, 1.4723, 0.3325]],
[[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000]]]], grad_fn=<MkldnnConvolutionBackward>)
```
Argument 'groups' defaults to 1 so it is backward compatible.
Tests are modified to include cases of with groups>1 but also contain groups=1 cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32825
Differential Revision: D19859926
Pulled By: vincentqb
fbshipit-source-id: 9dfdd24471ff14d79c442dfd28c1891aff812fdf
|
||
|
Neta Zmora
|
fa66a1498e |
Simplify _calculate_fan_in_and_fan_out (#29370)
Summary: The code checking `if dimensions == 2` is not needed because the case of a 2D tensor (Linear) is already handled by the statement: `receptive_field_size = 1` and this conditional: `if tensor.dim() > 2:` Pull Request resolved: https://github.com/pytorch/pytorch/pull/29370 Differential Revision: D18372987 Pulled By: albanD fbshipit-source-id: fcb4dddbc76b9f4414c6d88c0aa2fb4435bf3385 |
||
|
Robert
|
d081de67cf |
fix the document of kaiming initialization (#25638)
Summary: Based on https://github.com/pytorch/pytorch/issues/25549, I modified the comments for kaiming initialization in torch.nn.init.py Pull Request resolved: https://github.com/pytorch/pytorch/pull/25638 Differential Revision: D17915392 Pulled By: vincentqb fbshipit-source-id: 40f60c65d14790696ec03d7d91c764875efd6cf1 |
||
|
Kexuan Sun
|
ba6f65cf33 |
Add document of functions nn.init.ones_/zeros_ (#23145)
Summary: Functions `nn.init.ones_` and `nn.init.zeros_` were not documented. As mentioned in https://github.com/pytorch/pytorch/issues/9886 Pull Request resolved: https://github.com/pytorch/pytorch/pull/23145 Differential Revision: D16427108 Pulled By: soumith fbshipit-source-id: 4fac31e79717a436411ef5e107a829b403e576c9 |
||
|
David Riazati
|
10c4b98ade |
Remove weak script (#22212)
Summary: * Deletes all weak script decorators / associated data structures / methods * In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn` * Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods * `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand This should also fix https://github.com/pytorch/pytorch/issues/22212 Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212 Differential Revision: D15988346 Pulled By: driazati fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f |
||
|
selaselah
|
ae18f8e761 |
Fix latex formular error about *normal (#21000)
Summary: issue: https://github.com/pytorch/pytorch/issues/20903 the latex abort norm should be `\mathcal{N}(\text{mean}, \text{std}^2)` Pull Request resolved: https://github.com/pytorch/pytorch/pull/21000 Differential Revision: D15695335 Pulled By: ezyang fbshipit-source-id: 34dcca0acb20c297f876287e081cd44d11a3e516 |
||
|
Tongzhou Wang
|
343c1c21f2 |
update nn.init.calculate_gain doc example
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20131 Differential Revision: D15218126 Pulled By: ezyang fbshipit-source-id: 164c9d1573cd4d2c3689fb83b952e71862d4f1f2 |
||
|
davidriazati
|
c08f3d06c3 |
Add some of nn.init to weak script (#19640)
Summary: Stack from [ghstack](https://github.com/ezyang/ghstack): * **#19640 [jit] Add some of nn.init to weak script** Pull Request resolved: https://github.com/pytorch/pytorch/pull/19640 Pulled By: driazati Differential Revision: D15065332 fbshipit-source-id: 30df9f02e527cd5e5ebe34b7e003444eae96c66d |
||
|
Omegastick
|
31ff0ecd2b |
Fix torch::nn::init::orthogonal_ with CNNs (#18915)
Summary: Fixes #18518 I changed the C++ API torch::nn::init::orthogonal_ implementation to match the Python implementation. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18915 Differential Revision: D14851833 Pulled By: ezyang fbshipit-source-id: 45b5e9741582777c203e9ebed564ab3ac1f94baf |
||
|
Edward Yang
|
173f224570 |
Turn on F401: Unused import warning. (#18598)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3 |
||
|
ZhuBaohe
|
19a6de328f |
Correct docstring of vision/init functions
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17351 Differential Revision: D14276355 Pulled By: soumith fbshipit-source-id: 9b572b6a04eeb1e44cd93961edac76ed10f7b24e |
||
|
Tongzhou Wang
|
24e958a0a7 |
Move bernoulli into ATen (#10273)
Summary: + https://github.com/pytorch/pytorch/issues/10236 : torch.bernoulli's out kwarg is broken fixed in moving `bernoulli_out` to ATen + https://github.com/pytorch/pytorch/issues/9917 : BUG torch.bernoulli(p.expand(shape)) is broken fixed in moving all `bernoulli` ops in ATen to use the modern apply utils methods + https://github.com/pytorch/pytorch/issues/10357 : torch.bernoulli inconsistent gpu/cpu results fixed by adding CUDA asserts In order to use `curand_uniform4`, I made some changes to `CUDAApplyUtils.cuh`. Specifically, I introduced an optional template parameter `int step` to the `CUDA_tensor_applyN` methods, representing that we want to process `step` values at each time for each of the `N` tensors. The calling convention for `step = 1` (default) isn't changed. But if `step > 1`, the given lambda `op` must take in `int n` as its first argument, representing the number of valid values, because there may not be full `step` values at the boundary. E.g., here is what the `bernoulli(self, p_tensor)` call look like: ```cpp // The template argument `4` below indicates that we want to operate on four // element at each time. See NOTE [ CUDA_tensor_applyN helpers ] for details. at::cuda::CUDA_tensor_apply2<scalar_t, prob_t, 4>( ret, p, [seeds] __device__( int n, scalar_t& v1, scalar_t& v2, scalar_t& v3, scalar_t& v4, const prob_t& p1, const prob_t& p2, const prob_t& p3, const prob_t& p4) { curandStatePhilox4_32_10_t state; curand_init( seeds.first, blockIdx.x * blockDim.x + threadIdx.x, seeds.second, &state); float4 rand = curand_uniform4(&state); switch (n) { case 4: { assert(0 <= p4 && p4 <= 1); v4 = static_cast<scalar_t>(rand.w <= p4); } case 3: { assert(0 <= p3 && p3 <= 1); v3 = static_cast<scalar_t>(rand.z <= p3); } case 2: { assert(0 <= p2 && p2 <= 1); v2 = static_cast<scalar_t>(rand.y <= p2); } case 1: { assert(0 <= p1 && p1 <= 1); v1 = static_cast<scalar_t>(rand.x <= p1); } } } ); ``` Benchmarking on `torch.rand(200, 300, 400)` 20 times, each time with 20 loops: post patch ``` ➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py torch.bernoulli(x) 6.841588497161865 +- 0.05413117632269859 torch.bernoulli(xc) 0.05963418632745743 +- 0.0008014909108169377 x.bernoulli_() 0.4024486541748047 +- 0.0021550932433456182 xc.bernoulli_() 0.02167394384741783 +- 2.3818030967959203e-05 ``` pre-patch ``` ➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py torch.bernoulli(x) 12.394511222839355 +- 0.0966421514749527 torch.bernoulli(xc) 0.08970972150564194 +- 0.0038722590543329716 x.bernoulli_() 1.654480218887329 +- 0.02364428900182247 xc.bernoulli_() 0.058352887630462646 +- 0.003094920190051198 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/10273 Differential Revision: D9831294 Pulled By: SsnL fbshipit-source-id: 65e0655a36b90d5278b675d35cb5327751604088 |
||
|
rasbt
|
f9595e756e |
typo/grammar fixes (#11344)
Summary: Fixes some minor grammar issues in the code base. PS: I was actually looking for the following one but couldn't find it via grepping in this repo:  Any idea in which file this issue is raised? Pull Request resolved: https://github.com/pytorch/pytorch/pull/11344 Differential Revision: D9696454 Pulled By: soumith fbshipit-source-id: 8ffe494b1bf1efb0e35563381d9da2e1e8032a3c |
||
|
Rob Kunkle
|
6e85112f12 |
Adding katex rendering of equations, and required edits to equations. (#8848)
Summary: This fixes issue #8529. - Adds Katex extension to conf.py and requirements.txt - Fixes syntax differences in docs - Should allow documentation pages to render faster Pull Request resolved: https://github.com/pytorch/pytorch/pull/8848 Reviewed By: soumith Differential Revision: D8677702 Pulled By: goodlux fbshipit-source-id: c4a832c5879e0eebcb14763b35a41663331ba23f |
||
|
Vishwak Srinivasan
|
3e25b4af6d | Fix #8692 (#8699) | ||
|
Karan Dwivedi
|
dc0faab18d |
Add zeros_ and ones_ init + tests (#7488)
* Add zeros_ and ones_ init + tests * Dedup tests * Remove all occurences of as_variable |
||
|
Martin Tutek
|
c96f2624a2 |
Speedup sparse init (#6899)
* Sparse initialization speedup * +empty line * simplify indexing * Can't reproduce locally... * Can't reproduce locally...+ * Can't reproduce locally...+ * Fix test, cleanup |
||
|
li-roy
|
d564ecb4a5 |
Update docs with new tensor repr (#6454)
* Update docs with new tensor repr * remove cuda in dtype * remove changes to gloo submodule * [docs] document tensor.new_* ctor * [docs] Add docs for tensor.to(), tensor.float(), etc * [docs] Moar examples for docs. * [docs] Warning for tensor ctor copy behavior * Quick fix * [docs] Document requires_grad_() * [docs] Add example for requires_grad_() * update slogdet and *fft * update tensor rst * small fixes * update some docs * additional doc changes * update torch and tensor docs * finish changing tensor docs * fix flake8 * slogdet with negative det * Update functional.py tensor ctors * Fix nll_loss docs * reorder to move device up * torch.LongTensor -> torch.tensor or torch.empty in docs * update tensor constructors in docs * change tensor constructors * change constructors * change more Tensor() to tensor() * Show requires_grads_ docs * Fix set_default_dtype docs * Update docs with new tensor repr * remove cuda in dtype * remove changes to gloo submodule * [docs] document tensor.new_* ctor * [docs] Add docs for tensor.to(), tensor.float(), etc * [docs] Moar examples for docs. * [docs] Warning for tensor ctor copy behavior * Quick fix * [docs] Document requires_grad_() * [docs] Add example for requires_grad_() * update slogdet and *fft * update tensor rst * small fixes * update some docs * additional doc changes * update torch and tensor docs * finish changing tensor docs * fix flake8 * slogdet with negative det * Update functional.py tensor ctors * Fix nll_loss docs * reorder to move device up * torch.LongTensor -> torch.tensor or torch.empty in docs * update tensor constructors in docs * change tensor constructors * change constructors * change more Tensor() to tensor() * Show requires_grads_ docs * Fix set_default_dtype docs * Link to torch.no_grad, etc, from torch doc * Add dtype aliases to table * regen docs again * Tensor attributes stub page * link to inplace sampling * Link torch.dtype, device, and layout * fix dots after nonfinite floats * better layout docs |
||
|
Tony Beltramelli
|
7fcaf3b49e |
Update torch.nn.init and torch.nn.utils.clip_grad (#6173)
Introducing two updates. 1. Add param to He initialization scheme in torch.nn.init Problem solved: The function calculate_gain can take an argument to specify the type of non-linearity used. However, it wasn't possible to pass this argument directly to the He / Kaiming weight initialization function. 2. Add util to clip gradient value in torch.nn.utils.clip_grad Problem solved: DL libraries typically provide users with easy access to functions for clipping the gradients both using the norm and a fixed value. However, the utils clip_grad.py only had a function to clip the gradient norm. * add param to He initialization scheme in torch.nn.init * add util to clip gradient value in torch/nn/utils/clip_grad.py * update doc in torch.nn.utils.clip_grad * update and add test for torch.nn.utils.clip_grad * update function signature in torch.nn.utils.clip_grad to match suffix_ convention * ensure backward compatibility in torch.nn.utils.clip_grad * remove DeprecationWarning in torch.nn.utils.clip_grad * extend test and implementation of torch.nn.utils.clip_grad * update test and implementation torch.nn.utils.clip_grad |
||
|
Kento NOZAWA
|
3b58b859b2 | Fix typos in docs (#6389) | ||
|
Tongzhou Wang
|
4f05cb710e |
Add underscore to nn.init.* and deprecate the original ones (#6093)
Fixes #5946. * add underscore to nn.init.* and deprecate the original ones * add a test for deprecation |
