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cuda_stream_jit
189 Commits
| Author | SHA1 | Message | Date | |
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HyunJun
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a69910868a |
Fix possible padding length overflow in DistributedSampler (#45329)
Summary: Fixes https://github.com/pytorch/pytorch/issues/45324 This fix handles cases for `len(dataset) * 2 < num_replica` in DistributedSampler. (which previous code resulted in error.) Pull Request resolved: https://github.com/pytorch/pytorch/pull/45329 Reviewed By: mruberry Differential Revision: D24205035 Pulled By: rohan-varma fbshipit-source-id: f94329d9c1e7deaee41e5af319e7c7d0c741910c |
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Vitaly Fedyunin
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31ee5d8d8b |
Adding information how to control randomness with DataLoader (#45749)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45749 Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D24088407 Pulled By: VitalyFedyunin fbshipit-source-id: 398b73ec5e8c83000ebc692001da847fc0aaa48f |
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Andy Zhang
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ce82b522c8 |
Define objects using classes instead of namedtuples in torch.utils.data._utils.worker (#45870)
Summary:
This PR fixes a bug when torch is used with pyspark, by converting namedtuples in `torch.utils.data._utils.worker` into classes.
Before this PR, creating an IterableDataset and then running `list(torch.utils.data.DataLoader(MyIterableDataset(...), num_workers=2)))` will not terminate, if pyspark is also being used. This is because pyspark hijacks namedtuples to make them pickleable ([see here](https://github.com/apache/spark/blob/master/python/pyspark/serializers.py#L370)). So `_IterableDatasetStopIteration` would be modified, and then the check at [this line in dataloader.py](
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Erjia Guan
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96540e918c |
Add ShuffleDataset with buffer (#45290)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45290 Test Plan: Imported from OSS Reviewed By: gchanan Differential Revision: D24001084 Pulled By: erjia-guan fbshipit-source-id: d8a7455cf3f18e1f8c1edc53c42c1a99c8573c51 |
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Guilherme Leobas
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eb39542e67 |
Add typing annotations for torch.utils.data.* modules (#44136)
Summary: Fixes https://github.com/pytorch/pytorch/issues/44135 Pull Request resolved: https://github.com/pytorch/pytorch/pull/44136 Reviewed By: gchanan Differential Revision: D23963273 Pulled By: ezyang fbshipit-source-id: 939234dddbe89949bd8e5ff05d06f6c8add6935c |
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Natalia Gimelshein
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74c3dcd1d2 |
Revert D23725053: [pytorch][PR] change self.generator to generator
Test Plan: revert-hammer
Differential Revision:
D23725053 (
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Fang Zhang
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a011b86115 |
change self.generator to generator (#44461)
Summary: bug fix Pull Request resolved: https://github.com/pytorch/pytorch/pull/44461 Reviewed By: mruberry Differential Revision: D23725053 Pulled By: ngimel fbshipit-source-id: 89706313013d9eae96aaaf144924867457efd2c0 |
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Emilio Castillo
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5472426b9f |
Reset DataLoader workers instead of creating new ones (#35795)
Summary: This PR needs discussion as it changes the behavior of `DataLoader`. It can be closed if its not considered a good practice. Currently, the `DataLoader` spawns a new `_BaseDataLoaderIter` object every epoch, In the case of the multiprocess DataLoader, every epoch the worker processes are re-created and they make a copy of the original `Dataset` object. If users want to cache data or do some tracking on their datasets, all their data will be wiped out every epoch. Notice that this doesn't happen when the number of workers is 0. giving some inconsistencies with the multiprocess and serial data loaders. This PR keeps the `_BaseDataLoaderIter` object alive and just resets it within epochs, so the workers remain active and so their own `Dataset` objects. People seem to file issues about this often. Pull Request resolved: https://github.com/pytorch/pytorch/pull/35795 Reviewed By: ailzhang Differential Revision: D23426612 Pulled By: VitalyFedyunin fbshipit-source-id: e16950036bae35548cd0cfa78faa06b6c232a2ea |
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Akihiro Nitta
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f17d7a5556 |
Fix exception chaining in torch/ (#43836)
Summary: ## Motivation Fixes https://github.com/pytorch/pytorch/issues/43770. ## Description of the change This PR fixes exception chaining only in files under `torch/` where appropriate. To fix exception chaining, I used either: 1. `raise new_exception from old_exception` where `new_exception` itself seems not descriptive enough to debug or `old_exception` delivers valuable information. 2. `raise new_exception from None` where raising both of `new_exception` and `old_exception` seems a bit noisy and redundant. I subjectively chose which one to use from the above options. ## List of lines containing raise in except clause: I wrote [this simple script](https://gist.github.com/akihironitta/4223c1b32404b36c1b349d70c4c93b4d) using [ast](https://docs.python.org/3.8/library/ast.html#module-ast) to list lines where `raise`ing in `except` clause. - [x] |
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Ralf Gommers
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da32bf4cc6 |
Move type annotations for remaining torch.utils stub files inline (#43406)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43406 Reviewed By: mruberry Differential Revision: D23319736 Pulled By: malfet fbshipit-source-id: e25fbb49f27aa4893590b022441303d6d98263a9 |
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Ralf Gommers
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bcab2d6848 |
And type annotations for cpp_extension, utils.data, signal_handling (#42647)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42647 Reviewed By: ezyang Differential Revision: D22967041 Pulled By: malfet fbshipit-source-id: 35e124da0be56934faef56834a93b2b400decf66 |
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Rohan Varma
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5ed7cd0025 |
Allow drop_last option in DistributedSampler (#41171)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41171 DistributedSampler allows data to be split evenly across workers in DDP, but it has always added additional samples in order for the data to be evenly split in the case that the # of samples is not evenly divisible by the number of workers. This can cause issues such as when doing distributed validation accuracy, where multiple samples could be considered twice. This PR adds a drop_last option where the tail of the data is dropped such that the effective dataset size is still evenly divisible across the workers. This ensures that DDP can train fine (there is no uneven inputs) and each replica gets an equal number of data indices. ghstack-source-id: 108617516 Test Plan: Added unittest Reviewed By: mrshenli Differential Revision: D22449974 fbshipit-source-id: e3156b751f5262cc66437b9191818b78aee8ddea |
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yl-to
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1b55e2b043 |
add prefetch_factor for multiprocessing prefetching process (#41130)
Summary: fix https://github.com/pytorch/pytorch/issues/40604 Add parameter to Dataloader to configure the per-worker prefetch number. Before this edit, the prefetch process always prefetch 2 * num_workers data items, this commit help us make this configurable, e.x. you can specify to prefetch 10 * num_workers data items. Pull Request resolved: https://github.com/pytorch/pytorch/pull/41130 Reviewed By: izdeby Differential Revision: D22705288 Pulled By: albanD fbshipit-source-id: 2c483fce409735fef1351eb5aa0b033f8e596561 |
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Daiming Yang
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ad7133d3c1 |
Patch for #40026 RandomSampler generates samples one at a time when replacement=True (#41682)
Summary: Fix https://github.com/pytorch/pytorch/issues/32530 Fix/Patch https://github.com/pytorch/pytorch/pull/40026 Resubmit this patch and fix the type error. Force the input type to `manual_seed()` in `sampler.py` to be `int`. ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/41682 Reviewed By: izdeby Differential Revision: D22665477 Pulled By: ezyang fbshipit-source-id: 1725c8aa742c31e74321f20448f4b6a392afb38d |
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Shen Li
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86590f226e |
Revert D22519869: [pytorch][PR] RandomSampler generates samples one at a time when replacement=True
Test Plan: revert-hammer
Differential Revision:
D22519869 (
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Daiming Yang
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09647e1287 |
RandomSampler generates samples one at a time when replacement=True (#40026)
Summary: Fix https://github.com/pytorch/pytorch/issues/32530 I used the next() function to generate samples one at a time. To compensate replacement=False, I added a variable called "sample_list" to RandomSampler for random permutation. cc SsnL Pull Request resolved: https://github.com/pytorch/pytorch/pull/40026 Reviewed By: zhangguanheng66 Differential Revision: D22519869 Pulled By: ezyang fbshipit-source-id: be65850025864d659a713b3bc461b25d6d0048a2 |
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SsnL
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1922f2212a |
Make IterableDataset dataloader.__len__ warning clearer (#41175)
Summary: Based on discussion with jlucier (https://github.com/pytorch/pytorch/pull/38925#issuecomment-655859195) . `batch_size` change isn't made because data loader only has the notion of `batch_sampler`, not batch size. If `batch_size` dependent sharding is needed, users can still access it from their own code. Pull Request resolved: https://github.com/pytorch/pytorch/pull/41175 Differential Revision: D22456525 Pulled By: zou3519 fbshipit-source-id: 5281fcf14807f219de06e32107d5fe7d5b6a8623 |
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Wojciech Baranowski
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0e09511af9 |
type annotations for dataloader, dataset, sampler (#39392)
Summary: Fixes https://github.com/pytorch/pytorch/issues/38913 Pull Request resolved: https://github.com/pytorch/pytorch/pull/39392 Reviewed By: anjali411 Differential Revision: D22102489 Pulled By: zou3519 fbshipit-source-id: acb68d9521145f0b047214d62b5bdc5a0d1b9be4 |
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Mike Ruberry
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d753f1c2e1 |
Fixes formatting of vander, count_nonzero, DistributedSampler documentation (#41025)
Summary: Bundle of small edits to fix formatting. Pull Request resolved: https://github.com/pytorch/pytorch/pull/41025 Differential Revision: D22398364 Pulled By: mruberry fbshipit-source-id: 8d484cb52a1cf4a8eb1f64914574250c9fd5043d |
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mungsoo
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c38a5cba0d |
Remove duplicate assignment in collate.py (#40655)
Summary: Duplicated assignment Pull Request resolved: https://github.com/pytorch/pytorch/pull/40655 Reviewed By: ezyang Differential Revision: D22308827 Pulled By: colesbury fbshipit-source-id: 48361da8994b3ca00ef29e9afd3ec2672266f00a |
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Linyuan Gong
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0a75234934 |
Allow np.memmap objects (numpy arrays based on files) to be processed… (#39847)
Summary: Allow np.memmap objects to be processed by default_collate np.memmap objects has the same behavior as numpy arrays, and the only difference is that they are stored in a binary file on the disk. However, the default_collate function used by PyTorch DataLoader only accepts np.array, and rejects np.memmap by type checking. This commit allows np.memmap objects to be processed by default_collate. In this way, users can use in-disk large arrays with PyTorch DataLoader. Pull Request resolved: https://github.com/pytorch/pytorch/pull/39847 Reviewed By: ezyang Differential Revision: D22284650 Pulled By: zou3519 fbshipit-source-id: 003e3208a2afd1afc2e4640df14b3446201e00b4 |
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Tongzhou Wang
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23db54acdf |
[DataLoader] add repr for WorkerInfo (#39975)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39975 Differential Revision: D22039414 Pulled By: ezyang fbshipit-source-id: 230f68a91fca901bce652fdf88ba88167f39b978 |
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Tongzhou Wang
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019eeb3183 |
Kill DataLoader worker when we can't join (#39869)
Summary: There still are occasional reports of DataLoader workers not exiting (e.g., https://github.com/pytorch/pytorch/issues/39570). Before we figure out why, we should just kill them if the join timesout to prevent hanging. Pull Request resolved: https://github.com/pytorch/pytorch/pull/39869 Differential Revision: D22018501 Pulled By: ezyang fbshipit-source-id: 66a00d0f5b3e303b6106b336949176b3ff8ac8ae |
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ShawnZhong
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c8c53c802e |
Add generator= kwarg for DataLoader & random samplers (#39737)
Summary: Fix https://github.com/pytorch/pytorch/issues/39572 Add `generator=` kwarg for DataLoader & random samplers cc: SsnL, deeppatel4557, albanD, mitar Pull Request resolved: https://github.com/pytorch/pytorch/pull/39737 Differential Revision: D22019132 Pulled By: albanD fbshipit-source-id: 835e08b86c5396bc0b0e41057661306b15394d6e |
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Daiming Yang
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0b90b9cdd3 |
Allow shuffle when auto-batching disabled in DataLoader (#39865)
Summary: Fix https://github.com/pytorch/pytorch/issues/35761 cc SsnL Note: closed the other PR for this new branch. Pull Request resolved: https://github.com/pytorch/pytorch/pull/39865 Differential Revision: D22003612 Pulled By: ezyang fbshipit-source-id: 26aecd1b298fe99d3924f4c8157cd6cae2561c7c |
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Tongzhou Wang
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479b04e26a |
Improve DistributedSampler docs and add seed option (#39628)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39628 Differential Revision: D21920373 Pulled By: mrshenli fbshipit-source-id: d7d1005db6feef4a83a1a094b85fcff964bd0ac6 |
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Hong Xu
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283a3ff16d |
The exception raised when RandomSampler.replacement is non-boolean should be TypeError (#36547)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36547 Differential Revision: D21818752 Pulled By: ezyang fbshipit-source-id: 7502a24a0df134c44ac72959ba992777c873f8e9 |
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Donna Choi
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3d2fce6bc3 |
Change len(DataLoader) for IterableDataset (#38925)
Summary: Fix https://github.com/pytorch/pytorch/issues/36176 One-liner change to ensure that ```len(loader) == (len(dataset) // batch_size)``` for IterableDataset. Pull Request resolved: https://github.com/pytorch/pytorch/pull/38925 Differential Revision: D21731587 Pulled By: ezyang fbshipit-source-id: 59a086165a004c0c1c8a1ee0776b1444bd26de23 |
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Donna Choi
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8c07a98adc |
Error out of default_collate for lists of unequal size (#38492)
Summary: Fix issue https://github.com/pytorch/pytorch/issues/23141# In the below example ```default_collate``` collates each element of the list. Since the second element isn't present in all samples, it is discarded: ``` from torch.utils.data import Dataset from torch.utils.data import DataLoader import numpy as np class CustomDataset(Dataset): def __len__(self): return 2 def __getitem__(self, idx): tmp = { "foo": np.array([1, 2, 3]), "bar": ["X"] * (idx+1), } return tmp training = CustomDataset() for batch in DataLoader(training, batch_size=2): print(batch) ``` Yields ``` { 'foo': tensor( [ [1, 2, 3], [1, 2, 3] ] ), 'bar': [ ('X', 'X'), ] } ``` Based on discussion in the issue, it seems the best course of action is to error out in this case. This seems consistent with what is done for tensor elements, as seen in [TensorShape.cpp line 1066](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorShape.cpp#L1060) which is called when ```torch.stack``` is called. In this PR, I introduce a similar message to error out for lists. SsnL Pull Request resolved: https://github.com/pytorch/pytorch/pull/38492 Differential Revision: D21620396 Pulled By: ezyang fbshipit-source-id: 17f59fbb1ed1f0d9b2185c95b9ebe55ece701b0c |
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SsnL
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b5868b2833 |
Relax sampler check in BatchSampler (#38403)
Summary: Since the check was added in https://github.com/pytorch/pytorch/pull/6249, one can not pass an iterable as a sampler to the data loader anymore, which was a very handy feature (e.g., https://github.com/pytorch/pytorch/issues/1337). I think the check should be removed for two-fold reasons: 1. It is too strict. There is no reason that it should not be a general iterable. 2. It is inconsistent. In `DataLoader` (the main place where people use samplers), you can pass a general iterable as `batch_sampler` but not `sampler` due to this check. Pull Request resolved: https://github.com/pytorch/pytorch/pull/38403 Differential Revision: D21555958 Pulled By: soumith fbshipit-source-id: c7267bb99a31edd8f2750689205d6edc5dab5cff |
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Edward Yang
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6edf340338 |
Delete torch/__init__.pyi, deferring to direct extension stubs (#38157)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38157 This removes the error prone process of assembling `torch/__init__.pyi` (and frequently forgetting to expose things), since now we can simply rely on the true source file to get things done. Most of the old codegen in gen_pyi.py is now rerouted to various files: - `torch/_C/__init__.pyi` (the dumping pile of all misc bindings) - `torch/_C/_nn.pyi` (NN function bindings) - `torch/_C/_VariableFunctions.pyi` (torch function bindings) `torch.types` grew a bunch more definitions that previously where defined in `torch/__init__.pyi` Some miscellaneous changes - Fixed a bug where we treat single TensorList argument as implying varargs are accepted. This is actually only supported on IntList. This means we can correctly generate a stub for dequantize. - Add missing manual stub for nonzero - Switched torch/onnx/operators.py to directly refer to _C module, since apparently mypy doesn't think that methods prefixed with underscores get reexported. This may be a recurring theme; maybe we need to find a better way to solve it. Because I was really lazy, I dumped namedtuple definitions in both `torch._C` and `torch._C._VariableFunctions`. This is definitely wrong. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Test Plan: Imported from OSS Differential Revision: D21497400 Pulled By: ezyang fbshipit-source-id: 07b126141c82efaca37be27c07255cb2b9b3f064 |
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Rohan Varma
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deb4100928 |
[DistributedSampler] Only create torch.generator and seed when shuffling (#37604)
Summary: We don't need to create `torch.Generator()` and seed it if we are not shuffling. Pull Request resolved: https://github.com/pytorch/pytorch/pull/37604 Differential Revision: D21346167 Pulled By: rohan-varma fbshipit-source-id: 6ed560d236bc5c026a7d321755ddc02a29db1604 |
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Ralf Gommers
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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 |
||
|
Wojciech Baranowski
|
69e3ee2d5f |
DataLoader: properly diagnose exceeding file descriptor limit (#34768)
Summary: Fixes https://github.com/pytorch/pytorch/issues/973 Common failure scenario: * DataLoader creates workers and communicates with them through SHMs * Workers send back through an AF_UNIX socket file descriptors to SHMs containing data * The limit of open files gets fully used * A FD gets stripped from a socket message coming back from a worker, without the worker knowing this. * This causes a `RuntimeError: received 0 items of ancdata` in the standard `multiprocessing` package * The exception is not handled by PyTorch and so is presented to the users. After this change the user will see ``` Traceback (most recent call last): File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 761, in _try_get_data data = self._data_queue.get(timeout=timeout) File "/home/wbaranowski/miniconda3/envs/pytorch-cuda-dev/lib/python3.6/multiprocessing/queues.py", line 113, in get return _ForkingPickler.loads(res) File "/home/wbaranowski/git/Quansight/pytorch/torch/multiprocessing/reductions.py", line 294, in rebuild_storage_fd fd = df.detach() File "/home/wbaranowski/miniconda3/envs/pytorch-cuda-dev/lib/python3.6/multiprocessing/resource_sharer.py", line 58, in detach return reduction.recv_handle(conn) File "/home/wbaranowski/miniconda3/envs/pytorch-cuda-dev/lib/python3.6/multiprocessing/reduction.py", line 184, in recv_handle return recvfds(s, 1)[0] File "/home/wbaranowski/miniconda3/envs/pytorch-cuda-dev/lib/python3.6/multiprocessing/reduction.py", line 162, in recvfds len(ancdata)) RuntimeError: received 0 items of ancdata During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 787, in _try_get_data fs = [tempfile.NamedTemporaryFile() for i in range(10)] File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 787, in <listcomp> fs = [tempfile.NamedTemporaryFile() for i in range(10)] File "/home/wbaranowski/miniconda3/envs/pytorch-cuda-dev/lib/python3.6/tempfile.py", line 551, in NamedTemporaryFile (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type) File "/home/wbaranowski/miniconda3/envs/pytorch-cuda-dev/lib/python3.6/tempfile.py", line 262, in _mkstemp_inner fd = _os.open(file, flags, 0o600) OSError: [Errno 24] Too many open files: '/tmp/tmpnx_f6v_f' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "test_shm_leak.py", line 56, in <module> worker_init_fn=worker_init_fn File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 345, in __next__ data = self._next_data() File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 861, in _next_data idx, data = self._get_data() File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 828, in _get_data success, data = self._try_get_data() File "/home/wbaranowski/git/Quansight/pytorch/torch/utils/data/dataloader.py", line 791, in _try_get_data "Too many open files. Communication with the" RuntimeError: Too many open files. Communication with the workers is no longer possible. Please increase the limit using `ulimit -n` in the shell or change the sharing strategy by calling `torch.multiprocessing.set_sharing_strategy('file_system')` at the beginning of your code ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/34768 Differential Revision: D20538053 Pulled By: ezyang fbshipit-source-id: be4425cf2fa02aff61619b2b829c153cb1a867cb |
||
|
Hong Xu
|
817e4f9ef1 |
Correct a ValueError in dataloader to TypeError (#36244)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36244 Differential Revision: D20963949 Pulled By: ezyang fbshipit-source-id: 8c6aa4831021788052269e7aa8282d11eba4e085 |
||
|
Mathis Chenuet
|
17a01c7c7b |
feature: deterministic random_split (#34043)
Summary: ## 🚀 Feature Option to provide a seed (random_state) for random_split() like the sklearn API https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html. ## Motivation Useful for deterministic sampling & reproducible data generation (easily, without affecting the PRNG for other uses). See https://github.com/pytorch/pytorch/issues/32467 Pull Request resolved: https://github.com/pytorch/pytorch/pull/34043 Differential Revision: D20605678 Pulled By: ezyang fbshipit-source-id: 12b10bf72cd8a0d4264ae4d326064f806945d011 |
||
|
Tongzhou Wang
|
4ef854b4b4 |
Fix potential hang when exiting main process (#33721)
Summary:
The following script reproduces the hang
```py
import multiprocessing, logging
logger = multiprocessing.log_to_stderr()
logger.setLevel(multiprocessing.SUBDEBUG)
import torch
class Dataset:
def __len__(self):
return 23425
def __getitem__(self, idx):
return torch.randn(3, 128, 128), idx % 100
ds = Dataset()
trdl = torch.utils.data.DataLoader(ds, batch_size=64, num_workers=300, pin_memory=True, shuffle=True)
for e in range(1000):
for ii, (x, y) in enumerate(trdl):
print(f'tr {e: 5d} {ii: 5d} avg y={y.mean(dtype=torch.double).item()}')
if ii % 2 == 0:
print("="*200 + "BEFORE ERROR" + "="*200)
1/0
```
The process will hang at joining the putting thread of `data_queue` in **main process**. The root cause is that too many things are put in the queue from the **worker processes**, and the `put` at
|
||
|
Peter Bell
|
32c93099c4 |
Add typing info for data members of utils.data.sampler classes (#33679)
Summary: Fixes https://github.com/pytorch/pytorch/issues/33490 Pull Request resolved: https://github.com/pytorch/pytorch/pull/33679 Differential Revision: D20063099 Pulled By: ngimel fbshipit-source-id: 1bbf71a65408d117019ab38d7d095cfd337f5d1e |
||
|
DuckSoft
|
6a275b696e |
adding IterableDataset to utils.data.__init__ (#33543)
Summary: this shall fix issue https://github.com/pytorch/pytorch/issues/27820 again Pull Request resolved: https://github.com/pytorch/pytorch/pull/33543 Differential Revision: D20002446 Pulled By: vincentqb fbshipit-source-id: 7563a56fd6238efe8ea5626b02ba5e8fcda0780e |
||
|
vfdev
|
c6e0360812 |
Minor change of docstring example of WeightedRandomSampler (#30846)
Summary:
Previous example
```python
>>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True))
[0, 0, 0, 1, 0]
```
may seem misleading according to provided weights.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30846
Differential Revision: D19697367
Pulled By: ezyang
fbshipit-source-id: 3d6e3cd0cecb5272a368707ba35bc7acdbd82c30
|
||
|
Ralf Gommers
|
6305e4a88f |
Add warning and example for seeding to DistributedSampler (#32951)
Summary: Closes gh-31771 Also note that the `epoch` attribute is *only* used as a manual seed in each iteration (so it could easily be changed/renamed). Seeding consecutive iterations with `[0, 1, 2, ...]` is low-entropy, however in practice it probably doesn't matter when using the sampler in combination with a dataloader (because there won't be enough data nor epochs to run into statistical issues due to low-entropy seeding). So leaving that as is. Rendered docstring: <img width="534" alt="image" src="https://user-images.githubusercontent.com/98330/73701250-35134100-46e9-11ea-97b8-3baeb60fcb37.png"> Pull Request resolved: https://github.com/pytorch/pytorch/pull/32951 Differential Revision: D19729333 Pulled By: ezyang fbshipit-source-id: 3ddf90a3828b8bbae88aa2195a5d0b7d8ee1b066 |
||
|
Santiago Castro
|
6996f8d880 |
Add missing default_collate in dataloader.pyi
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28935 Differential Revision: D19698781 Pulled By: ezyang fbshipit-source-id: abdd735c98656ed16cd326529441d1fcec2ace3e |
||
|
Santiago Castro
|
167a892e99 |
Add missing shuffle attribute to DistributedSampler typing file
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28763 Differential Revision: D19698808 Pulled By: ezyang fbshipit-source-id: 7820acd7b0715ebf1d9ae954dca0058b6759075e |
||
|
Brian Wignall
|
f326045b37 |
Fix typos, via a Levenshtein-type corrector (#31523)
Summary: Should be non-semantic. Uses https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines to find likely typos, with https://github.com/bwignall/typochecker to help automate the checking. Uses an updated version of the tool used in https://github.com/pytorch/pytorch/pull/30606 . Pull Request resolved: https://github.com/pytorch/pytorch/pull/31523 Differential Revision: D19216749 Pulled By: mrshenli fbshipit-source-id: 7fd489cb9a77cd7e4950c1046f925d57524960ea |
||
|
Tongzhou Wang
|
c37de32b23 |
Enable len(dataloader) for iterable dataset (#23587)
Summary:
Copy-paste comment from code for reasoning:
```
# NOTE [ IterableDataset and __len__ ]
#
# For `IterableDataset`, `__len__` could be inaccurate when one naively
# does multi-processing data loading, since the samples will be duplicated.
# However, no real use case should be actually using that behavior, so
# it should count as a user error. We should generally trust user
# code to do the proper thing (e.g., configure each replica differently
# in `__iter__`), and give us the correct `__len__` if they choose to
# implement it (this will still throw if the dataset does not implement
# a `__len__`).
#
# To provide a further warning, we track if `__len__` was called on the
# `DataLoader`, save the returned value in `self._len_called`, and warn
# if the iterator ends up yielding more than this number of samples.
```
Fixes https://github.com/pytorch/pytorch/issues/30184
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23587
Differential Revision: D18852625
Pulled By: ailzhang
fbshipit-source-id: aea8d4d70c7f21aaa69b35908a6f43026493d826
|
||
|
Donald Pinckney
|
00bd9eae33 |
Fix typo in Dataset and IterableDataset docs (#28960)
Summary: Replaced "overrite" with "overwrite". Pull Request resolved: https://github.com/pytorch/pytorch/pull/28960 Differential Revision: D18246411 Pulled By: soumith fbshipit-source-id: dc0979a44b7c621a316823061760e0358c227727 |
||
|
henribru
|
440b192078 |
Type hints: Return Iterator instead of Iterable from __iter__ (#27445)
Summary:
`__iter__` methods are supposed to return iterators (https://docs.python.org/3/reference/datamodel.html#object.__iter__), but some of them are typed to return iterables, which is too general. This results in error messages such as `Iterable[Module[Any]]" has no attribute "__next__"` from Mypy. Technically this should also have caused a type error [here](
|
||
|
なるみ
|
d83389d327 |
Ignore F401 in all __init__.py without putting noqa (#25823)
Summary: By adding `per-file-ignores = __init__.py: F401` into `.flake8` with `flake8>=3.7`, we can ignore F410 in all `__init__.py` without putting `# noqa: F401` line by line. http://flake8.pycqa.org/en/latest/user/options.html?highlight=per-file-ignores#cmdoption-flake8-per-file-ignores Pull Request resolved: https://github.com/pytorch/pytorch/pull/25823 Differential Revision: D17252182 Pulled By: soumith fbshipit-source-id: 87b174075b79e4078953a7521bd1a8f82405646b |
||
|
DuckSoft
|
498ca083a6 |
adding IterableDataset to dataset.pyi (#27966)
Summary: this shall fix https://github.com/pytorch/pytorch/issues/27820 Pull Request resolved: https://github.com/pytorch/pytorch/pull/27966 Differential Revision: D17929633 Pulled By: ezyang fbshipit-source-id: ff3e0fb7f998b0771183288200c0859eb5f381dd |
||
|
Michael Steininger
|
4bcedb6670 |
Mark sampler and batch_sampler arguments as optional in the DataLoader interface (#27821)
Summary: Changelog: - DataLoader argument `sampler` is now of type `Optional[Sampler[int]]`instead of `Sampler[int]` - DataLoader argument `batch_sampler` is now of type `Optional[Sampler[Sequence[int]]]` instead of `Sampler[Sequence[int]]` Fixes https://github.com/pytorch/pytorch/issues/27737 Pull Request resolved: https://github.com/pytorch/pytorch/pull/27821 Differential Revision: D17906623 Pulled By: ezyang fbshipit-source-id: 088cacbb7e9f7988995f40b71adc3e719815f5ad |
