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88fe05e106
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148 Commits

Author SHA1 Message Date
KushajveerSingh
88fe05e106 [Docs] Update torch.(squeeze, split, set_printoptions, save) docs. (#39303) 
Summary:
I added the following to the docs:
1. `torch.save`.
    1. Added doc for `_use_new_zipfile_serialization` argument.
    2. Added a note telling that extension does not matter while saving.
    3. Added an example showing the use of above argument along with `pickle_protocol=5`.

2. `torch.split`
    1. Added an example showing the use of the function.

3. `torch.squeeze`
   1. Added a warning for batch_size=1 case.

4. `torch.set_printoptions`
    1. Changed the docs of `sci_mode` argument from
        ```
        sci_mode: Enable (True) or disable (False) scientific notation. If
                 None (default) is specified, the value is defined by `_Formatter`
        ```
        to
        ```
        sci_mode: Enable (True) or disable (False) scientific notation. If
                 None (default=False) is specified, the value is defined by
                `torch._tensor_str._Formatter`.
        ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39303

Differential Revision: D21904504

Pulled By: zou3519

fbshipit-source-id: 92a324257d09d6bcfa0b410d4578859782b94488
 2020-06-05 12:57:53 -07:00
Xiang Gao
ebd4125e7e [JIT] Make torch.unique_consecutive compatible (#39339) 
Summary:
A `unique_consecutive` version of https://github.com/pytorch/pytorch/pull/38156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39339

Differential Revision: D21823997

Pulled By: eellison

fbshipit-source-id: d14596a36ba36497e296da5a344e0376cef56f1b
 2020-06-02 14:54:29 -07:00
Ralf Gommers
d363cf4639 Fix incorrect __torch_function__ handling in einsum (#38741) 
Summary:
Closes gh-38479
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38741

Differential Revision: D21662512

Pulled By: ezyang

fbshipit-source-id: 247e3b50b8f2dd842c03be8d6ebe71910b619bc6
 2020-05-21 06:59:25 -07:00
Elias Ellison
eb3e9872c9 [JIT] make torch.unique compilable (#38156) 
Summary:
Fix for https://github.com/pytorch/pytorch/issues/37986

Follows the stack in https://github.com/pytorch/pytorch/pull/33783 stack to make functions in `torch/functional.py` resolve to their python implementations. Because the return type of `torch.unique` depends on `return_inverse` and `return_counts` I had to refactor the implementation to use our boolean_dispatch mechanism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38156

Differential Revision: D21504449

Pulled By: eellison

fbshipit-source-id: 7efb1dff3b5c00655da10168403ac4817286ff59
 2020-05-12 18:37:53 -07:00
moto
5a27ec09b8 Add Inverse Short Time Fourier Transform in ATen native (#35569) 
Summary:
Ported `torchaudio`'s implementation (test, and documentation as well) to ATen.

Note
 - Batch packing/unpacking is performed in Python. ATen implementation expects 4D input tensor.
 - The way `hop_length` is initialized in the same way as `stft` implementation. [The Torchaudio's version tried to mimic the same behavior but slightly different](7da61a4bee/torchaudio/functional.py (L152-L157)).

Closes https://github.com/pytorch/pytorch/issues/34827
Relates https://github.com/pytorch/pytorch/issues/3775
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35569

Differential Revision: D21178090

Pulled By: mthrok

fbshipit-source-id: 2701a8b241a36a6fb1b740c2fb2b07cb938185d4
 2020-04-24 12:14:55 -07:00
Alban Desmaison
3799d1d74a Fix many doc issues (#37099) 
Summary:
Fix https://github.com/pytorch/pytorch/issues/35643 https://github.com/pytorch/pytorch/issues/37063 https://github.com/pytorch/pytorch/issues/36307 https://github.com/pytorch/pytorch/issues/35861 https://github.com/pytorch/pytorch/issues/35299 https://github.com/pytorch/pytorch/issues/23108 https://github.com/pytorch/pytorch/issues/4661

Just a bunch of small updates on the doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37099

Differential Revision: D21185713

Pulled By: albanD

fbshipit-source-id: 4ac06d6709dc0da6109a6ad3daae75667ee5863e
 2020-04-23 10:01:03 -07:00
Ralf Gommers
78d5707041 Fix type annotations and make MyPy run on torch/ (#36584) 
Summary:
This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages.

As a result, running `mypy` in the root dir of the repo now runs on:
- `torch/`
- `aten/src/ATen/function_wrapper.py` (the only file already covered in CI)

In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`.

Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through):

<details>

```
torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx'
torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined
torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch"
torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2'
torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2'
torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx'
torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util'
torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework'
torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer'
torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...")
torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items"
torch/nn/_reduction.py:27: error: Name 'Optional' is not defined
torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/_reduction.py:47: error: Name 'Optional' is not defined
torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs
torch/nn/modules/utils.py:27: error: Name 'List' is not defined
torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment"
torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction'
caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs
torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed)
caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs
caffe2/python/__init__.py:7: error: Module has no attribute "CPU"
caffe2/python/__init__.py:8: error: Module has no attribute "CUDA"
caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN"
caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL"
caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL"
caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP"
caffe2/python/__init__.py:13: error: Module has no attribute "HIP"
caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"?
caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"?
caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:35: error: Module has no attribute "cuda"
caffe2/python/__init__.py:37: error: Module has no attribute "cuda"
caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory"
torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs
torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs
torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import)
torch/_tensor_str.py:164: error: Not all arguments converted during string formatting
torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_linalg_utils.py:26: error: Name 'Optional' is not defined
torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Optional' is not defined
torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Optional' is not defined
torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Optional' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched'
torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm")
torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Optional' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/nn/init.py:109: error: Name 'Tensor' is not defined
torch/nn/init.py:126: error: Name 'Tensor' is not defined
torch/nn/init.py:142: error: Name 'Tensor' is not defined
torch/nn/init.py:165: error: Name 'Tensor' is not defined
torch/nn/init.py:180: error: Name 'Tensor' is not defined
torch/nn/init.py:194: error: Name 'Tensor' is not defined
torch/nn/init.py:287: error: Name 'Tensor' is not defined
torch/nn/init.py:315: error: Name 'Tensor' is not defined
torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction'
torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer'
torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_"
torch/jit/_builtins.py:80: error: Module has no attribute "stft"
torch/jit/_builtins.py:81: error: Module has no attribute "cdist"
torch/jit/_builtins.py:82: error: Module has no attribute "norm"
torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm"
torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm"
torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C'
torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...")
torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import)
torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass"
torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache"
torch/_lowrank.py:11: error: Name 'Tensor' is not defined
torch/_lowrank.py:13: error: Name 'Optional' is not defined
torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Optional' is not defined
torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Optional' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:82: error: Name 'Tuple' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:130: error: Name 'Tensor' is not defined
torch/_lowrank.py:130: error: Name 'Optional' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:130: error: Name 'Tuple' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:167: error: Name 'Tensor' is not defined
torch/_lowrank.py:167: error: Name 'Optional' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:167: error: Name 'Tuple' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type
torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases
torch/quantization/observer.py:45: error: Invalid base class "ABC"
torch/quantization/observer.py:127: error: Name 'Tensor' is not defined
torch/quantization/observer.py:127: error: Name 'Tuple' is not defined
torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:192: error: Name 'Tensor' is not defined
torch/quantization/observer.py:192: error: Name 'Tuple' is not defined
torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:534: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tuple' is not defined
torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:894: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:894: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:899: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:902: error: Name 'Tensor' is not defined
torch/quantization/observer.py:925: error: Name 'Tensor' is not defined
torch/quantization/observer.py:928: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:929: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric"
torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:289: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:290: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops"
torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops"
torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined
torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig'
torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__"
torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views'
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16
torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15
torch/functional.py:114: error: Name 'Tuple' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:114: error: Name 'Optional' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]"
torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:204: note: Possible overload variants:
torch/functional.py:204: note:     def __setitem__(self, int, int) -> None
torch/functional.py:204: note:     def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:204: note:     def __getitem__(self, int) -> int
torch/functional.py:204: note:     def __getitem__(self, slice) -> List[int]
torch/functional.py:207: error: "Tensor" has no attribute "copy_"
torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:212: note: Possible overload variants:
torch/functional.py:212: note:     def __setitem__(self, int, int) -> None
torch/functional.py:212: note:     def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:212: note:     def __getitem__(self, int) -> int
torch/functional.py:212: note:     def __getitem__(self, slice) -> List[int]
torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:334: error: Name 'Optional' is not defined
torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]"
torch/functional.py:431: error: Module has no attribute "stft"
torch/functional.py:766: error: Module has no attribute "cdist"
torch/functional.py:768: error: Module has no attribute "cdist"
torch/functional.py:770: error: Module has no attribute "cdist"
torch/functional.py:775: error: Name 'Optional' is not defined
torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'Optional' is not defined
torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'number' is not defined
torch/functional.py:780: error: Name 'norm' already defined on line 775
torch/functional.py:785: error: Name 'Optional' is not defined
torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:785: error: Name 'number' is not defined
torch/functional.py:785: error: Name 'norm' already defined on line 775
torch/functional.py:790: error: Name 'Optional' is not defined
torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:790: error: Name 'norm' already defined on line 775
torch/functional.py:795: error: Name 'norm' already defined on line 775
torch/functional.py:960: error: Name 'Any' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any")
torch/functional.py:960: error: Name 'Tuple' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized"
torch/functional.py:1041: error: Name 'Optional' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1041: error: Name 'Tuple' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1056: error: Name 'Optional' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1056: error: Name 'Tuple' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]")
torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]")
torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]")
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase"
torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]")
torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None")
torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric"
torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module'
torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68
torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass"
torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524
torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer'
torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching'
torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined
torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]")
torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]")
torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...")
torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/__init__.py:158: error: Name 'transforms' is not defined
torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse'
torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore'
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore'
torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584

Reviewed By: seemethere, ailzhang

Differential Revision: D21155985

Pulled By: ezyang

fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1
 2020-04-22 14:17:08 -07:00
David Reiss
e75fb4356b Remove (most) Python 2 support from Python code (#35615) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615

Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).

Test Plan: CI

Differential Revision: D20842886

Pulled By: dreiss

fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
 2020-04-22 09:23:14 -07:00
Torsten Wörtwein
be52b7f0ea Documentation LU Decomposition: deriving L, U, and P (#36907) 
Summary:
Add note to LU decomposition to use `lu_unpack` to get `L`, `U`, and `P`.

Fixes https://github.com/pytorch/pytorch/issues/36752.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36907

Differential Revision: D21134545

Pulled By: albanD

fbshipit-source-id: 54d4872bb8c95dfb8048aedace9781f843ab8a30
 2020-04-21 07:40:21 -07:00
Kurt Mohler
2bc49a4b85 block_diag dense (#33449) 
Summary:
Add block_diag function for dense tensors, based on scipy.linalg.block_diag

Closes https://github.com/pytorch/pytorch/issues/31932
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33449

Differential Revision: D20943099

Pulled By: zou3519

fbshipit-source-id: 8b5c9476fb5af959aafa4169612c660396d9b717
 2020-04-13 10:04:55 -07:00
Edward Yang
83907ded1d Revert D20895316: [pytorch][PR] [JIT] List reland 
Test Plan: revert-hammer

Differential Revision:
D20895316

Original commit changeset: 9a2bc0e6bdcb

fbshipit-source-id: d135f0038cf240a0973ecfcd540121cbd4ecb5a7
 2020-04-08 14:40:10 -07:00
Elias Ellison
2afe171538 [JIT] List reland (#36146) 
Summary:
Relanding https://github.com/pytorch/pytorch/pull/33783
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36146

Differential Revision: D20895316

Pulled By: eellison

fbshipit-source-id: 9a2bc0e6bdcbd43f9abe51eadaa28f90bccafcc9
 2020-04-07 16:18:30 -07:00
Pearu Peterson
8bae1ed144 PCA and SVD for low-rank matrices, LOBPCG for positive-defined generalized eigenvalue problem - copy (#34721) 
Summary:
This is a copy of PR https://github.com/pytorch/pytorch/issues/29488 to help the merging process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34721

Differential Revision: D20444270

Pulled By: vincentqb

fbshipit-source-id: 042c56c8c0dae37834f52b4aee2deae7dd6fa659
 2020-03-16 14:13:30 -07:00
Edward Yang
4b929e5466 Revert D20193196: [pytorch][PR] PCA and SVD for low-rank matrices, LOBPCG for positive-defined generalized eigenvalue problem 
Test Plan: revert-hammer

Differential Revision:
D20193196

Original commit changeset: 78a487991242

fbshipit-source-id: 8da4f8cb17c45af41e8c0ce80bc72581eb10dbb8
 2020-03-11 09:24:34 -07:00
Pearu Peterson
2ec779d46c PCA and SVD for low-rank matrices, LOBPCG for positive-defined generalized eigenvalue problem (#29488) 
Summary:
This PR implements the following linear algebra algorithms for low-rank matrices:
- [x] Approximate `A` as `Q Q^H A` - using Algorithm 4.4 from [Halko et al, 2009](http://arxiv.org/abs/0909.4061).
  + exposed as `torch.lowrank.get_approximate_basis(A, q, niter=2, M=None) -> Q`
  + [x] dense matrices
  + [x] batches of dense matrices
  + [x] sparse matrices
  + [x] documentation
- [x] SVD - using Algorithm 5.1 from [Halko et al, 2009](http://arxiv.org/abs/0909.4061).
  + uses `torch.lowrank.get_approximate_basis`
  + exposed as `torch.svd_lowrank(A, q=6, niter=2, M=None) -> (U, S, V)`
  + [x] dense matrices
  + [x] batches of dense matrices
  + [x] sparse matrices
  + [x] documentation
- [x] PCA - using `torch.svd_lowrank`
  + uses `torch.svd_lowrank`
  + exposed as `torch.pca_lowrank(A, center=True, q=None, niter=2) -> (U, S, V)`
  + [x] dense matrices
  + [x] batches of dense matrices
  + [x] sparse matrices, uses non-centered sparse matrix algorithm
  + [x] documentation
- [x] generalized eigenvalue solver using the original LOBPCG algorithm [Knyazev, 2001](https://epubs.siam.org/doi/abs/10.1137/S1064827500366124)
  + exposed as `torch.lobpcg(A, B=None, k=1, method="basic", ...)`
  + [x] dense matrices
  + [x] batches of dense matrices
  + [x] sparse matrices
  + [x] documentation
- [x] generalized eigenvalue solver using robust LOBPCG with orthogonal basis selection [Stathopoulos, 2002](https://epubs.siam.org/doi/10.1137/S1064827500370883)
  + exposed as `torch.lobpcg(A, B=None, k=1, method="ortho", ...)`
  + [x] dense matrices
  + [x] batches of dense matrices
  + [x] sparse matrices
  + [x] documentation
- [x] generalized eigenvalue solver using the robust and efficient LOBPCG Algorithm 8 from [Duersch et al, 2018](https://epubs.siam.org/doi/abs/10.1137/17M1129830) that switches to orthogonal basis selection automatically
  + the "ortho" method improves iterations so rapidly that in the current test cases it does not make sense to use the basic iterations at all. If users will have matrices for which basic iterations could improve convergence then the `tracker` argument allows breaking the iteration process at user choice so that the user can switch to the orthogonal basis selection if needed. In conclusion, there is no need to implement Algorithm 8 at this point.
- [x] benchmarks
  + [x] `torch.svd` vs `torch.svd_lowrank`, see notebook [Low-rank SVD](https://github.com/Quansight/pearu-sandbox/blob/master/pytorch/Low-rank%20SVD.ipynb). In conclusion, the low-rank SVD is going to be useful only for large sparse matrices where the full-rank SVD will fail due to memory limitations.
  + [x] `torch.lobpcg` vs `scipy.sparse.linalg.lobpcg`, see notebook [LOBPCG - pytorch vs scipy](https://github.com/Quansight/pearu-sandbox/blob/master/pytorch/LOBPCG%20-%20pytorch%20vs%20scipy.ipynb). In conculsion, both implementations give the same results (up to numerical errors from different methods), scipy lobpcg implementation is generally faster.
  + [x] On very small tolerance cases, `torch.lobpcg` is more robust than `scipy.sparse.linalg.lobpcg` (see `test_lobpcg_scipy` results)

Resolves https://github.com/pytorch/pytorch/issues/8049.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29488

Differential Revision: D20193196

Pulled By: vincentqb

fbshipit-source-id: 78a4879912424595e6ea95a95e483a37487a907e
 2020-03-11 07:33:49 -07:00
vishwakftw
e025677e3c Remove **kwargs from torch.meshgrid (#34356) 
Summary:
Changelog:
- Remove **kwargs from torch.meshgrid as they serve no purpose

Closes https://github.com/pytorch/pytorch/issues/34206
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34356

Differential Revision: D20310971

Pulled By: zou3519

fbshipit-source-id: 97250051504aa3ec1e2a9af9296e7cc71872e5bf
 2020-03-09 12:07:43 -07:00
Shen Li
30680196e4 Revert D20121915: [JIT] Add support for list() 
Test Plan: revert-hammer

Differential Revision:
D20121915

Original commit changeset: c6c4ef444dbf

fbshipit-source-id: 829adb58780f4d0f41acebb3e7640a9c68bdbc1b
 2020-03-06 07:16:40 -08:00
Elias Ellison
f218842f2e [JIT] Add support for list() (#33818) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33818

Test Plan: Imported from OSS

Differential Revision: D20121915

Pulled By: eellison

fbshipit-source-id: c6c4ef444dbf1d4134dccb28c13315e225945b64
 2020-03-05 14:48:20 -08:00
Elias Ellison
479c3b0aa5 [JIT] add support for torch.norm (#33783) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33783

Fix for https://github.com/pytorch/pytorch/issues/20113

Test Plan: Imported from OSS

Differential Revision: D20121917

Pulled By: eellison

fbshipit-source-id: ffedcc40678cd80f5529ff9323088eed544e5158
 2020-03-05 14:46:24 -08:00
Elias Ellison
857eb4145e [JIT] add support for torch.cdist (#33737) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33737

Test Plan: Imported from OSS

Differential Revision: D20121916

Pulled By: eellison

fbshipit-source-id: b0427bbfd3ade1f3129c4a95a542fbc32c3abd76
 2020-02-26 18:37:37 -08:00
Elias Ellison
f31b1d3453 [JIT] add support for lu_unpack (#33736) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33736

Test Plan: Imported from OSS

Differential Revision: D20121914

Pulled By: eellison

fbshipit-source-id: 1136f4d7678a2233129aefe3e30234af385b8353
 2020-02-26 18:37:33 -08:00
Elias Ellison
4543cf4eb1 [JIT] add support for torch.lu to torchscript (#33724) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33724

Fix for https://github.com/pytorch/pytorch/issues/33381, partial fix of https://github.com/pytorch/pytorch/issues/30786

Test Plan: Imported from OSS

Differential Revision: D20077321

Pulled By: eellison

fbshipit-source-id: a1e6a0370712b36c9f66979098ac2f9d500ca5f6
 2020-02-26 18:37:28 -08:00
Elias Ellison
fddf73250d [JIT] fix resolving of functions in torch/functional. fix compilation of torch.stft (#33504) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33504

Fix resolution fo functions that are bound onto torch in torch/functional.py. This does not fix compilation of all of those functions, those will be done in follow ups. Does torch.stft as a start.

Fixes #21478

Test Plan: Imported from OSS

Differential Revision: D20014591

Pulled By: eellison

fbshipit-source-id: bb362f1b5479adbb890e72a54111ef716679d127
 2020-02-26 18:35:43 -08:00
Nathan Goldbaum
fa80299bdf __torch_function__ overrides for torch.functional and torch.nn.functional (#32799) 
Summary:
This adds `__torch_function__` support for all functions in `torch.functional` and `torch.nn.functional`.

The changes to C++ code and codegen scripts are to facilitate adding `__torch_function__` support for the native functions in `torch._C._nn`. Note that I moved the `handle_torch_function` C++ function to a header that both `python_torch_functions.cpp` and `python_nn_functions.cpp` include. The changes to `python_nn_functions.cpp` mirror the changes I made to `python_torch_functions.cpp` when `__torch_function__` support was first added in https://github.com/pytorch/pytorch/issues/27064. Due to the somewhat different way the `torch._C` and `torch._C._nn` namespaces are initialized I needed to create a new static reference to the `torch._C._nn` namespace (`THPNNVariableFunctions`). I'm not sure if that is the best way to do this. In principle I could import these namespaces in each kernel and avoid the global variable but that would have a runtime cost.

I added `__torch_function__` support to the Python functions in `torch.nn.functional` following the approach in https://github.com/pytorch/pytorch/issues/32194.

I re-enabled the test that checks if all functions in the `torch` namespace are explicitly tested for `__torch_function__` support. I also generalized the check to work for `torch.functional` and `torch.nn.functional` as well. This test was explicitly disabled in https://github.com/pytorch/pytorch/issues/30730 and I'm happy to disable it again if you think that's appropriate. I figured now was as good a time as any to try to re-enable it.

Finally I adjusted the existing torch API tests to suppress deprecation warnings and add keyword arguments used by some of the code in `torch.nn.functional` that were missed when I originally added the tests in https://github.com/pytorch/pytorch/issues/27064.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32799

Differential Revision: D19956809

Pulled By: ezyang

fbshipit-source-id: 40d34e0109cc4b9f3ef62f409d2d35a1d84e3d22
 2020-02-21 08:38:37 -08:00
Mike Ruberry
aa3c871739 Adds TestViewOps, updates documentation (#32512) 
Summary:
Understanding which ops return views and which return tensors with new storage is a common user issue, and an issue for developers connecting accelerators to PyTorch, too. This generic test suite verifies that ops which should return views do (and a few ops that shouldn't don't).  The documentation has also been updated for .t(), permute(), unfold(), and select() to clarify they return views.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32512

Differential Revision: D19659454

Pulled By: mruberry

fbshipit-source-id: b4334be9b698253a979e1bb8746fdb3ca24aa4e3
 2020-02-04 11:10:34 -08:00
Nathan Goldbaum
bab87e4b60 reimplement __torch_function__ overrides for torch.functional using inline logic (#32194) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/30831.

This improves the performance of operators in the `torch.functional` namespace that are overridable by `__torch_function__` implementations when supplied with `Tensor` operands.

Running the split benchmark in various configurations produces the following timings:

<details>
<summary>Expand for timings on <code>master</code> </summary>

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M8_N8_parts2_cpu
# Input: M: 8, N: 8, parts: 2, device: cpu
Forward Execution Time (us) : 3.340

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M8_N8_parts2_cuda
# Input: M: 8, N: 8, parts: 2, device: cuda
Forward Execution Time (us) : 3.333

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M256_N512_parts2_cpu
# Input: M: 256, N: 512, parts: 2, device: cpu
Forward Execution Time (us) : 3.366

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M256_N512_parts2_cuda
# Input: M: 256, N: 512, parts: 2, device: cuda
Forward Execution Time (us) : 3.385

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M512_N512_parts2_cpu
# Input: M: 512, N: 512, parts: 2, device: cpu
Forward Execution Time (us) : 3.468

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M512_N512_parts2_cuda
# Input: M: 512, N: 512, parts: 2, device: cuda
Forward Execution Time (us) : 3.416
```
</details>

<details>
<summary>Expand for timings with this pull request applied</summary>

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M8_N8_parts2_cpu
# Input: M: 8, N: 8, parts: 2, device: cpu
Forward Execution Time (us) : 2.261

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M8_N8_parts2_cuda
# Input: M: 8, N: 8, parts: 2, device: cuda
Forward Execution Time (us) : 2.223

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M256_N512_parts2_cpu
# Input: M: 256, N: 512, parts: 2, device: cpu
Forward Execution Time (us) : 2.237

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M256_N512_parts2_cuda
# Input: M: 256, N: 512, parts: 2, device: cuda
Forward Execution Time (us) : 2.218

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M512_N512_parts2_cpu
# Input: M: 512, N: 512, parts: 2, device: cpu
Forward Execution Time (us) : 2.259

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M512_N512_parts2_cuda
# Input: M: 512, N: 512, parts: 2, device: cuda
Forward Execution Time (us) : 2.234
```

</details>

<details>
<summary>Expand for timings on <code>master</code> with <code>__torch_function__</code> dispatch disabled </summary>

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M8_N8_parts2_cpu
# Input: M: 8, N: 8, parts: 2, device: cpu
Forward Execution Time (us) : 2.180

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M8_N8_parts2_cuda
# Input: M: 8, N: 8, parts: 2, device: cuda
Forward Execution Time (us) : 2.172

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M256_N512_parts2_cpu
# Input: M: 256, N: 512, parts: 2, device: cpu
Forward Execution Time (us) : 2.171

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M256_N512_parts2_cuda
# Input: M: 256, N: 512, parts: 2, device: cuda
Forward Execution Time (us) : 2.146

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M512_N512_parts2_cpu
# Input: M: 512, N: 512, parts: 2, device: cpu
Forward Execution Time (us) : 2.175

# Benchmarking PyTorch: split
# Mode: Eager
# Name: split_M512_N512_parts2_cuda
# Input: M: 512, N: 512, parts: 2, device: cuda
Forward Execution Time (us) : 2.152
```

</details>

So at least on the machine I'm testing on, this brings the overhead down to less than 100 ns. For comparison, the overhead for `__array_function__` in NumPy is about 850 ns on the same machine.

<details>
<summary>Expand for timings for NumPy <code>__array_function__</code> dispatch </summary>

```
In [1]: import numpy as np

In [2]: %timeit np.mean([1])
8.89 µs ± 17.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [3]: %timeit np.mean._implementation([1])
8.04 µs ± 28.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```

See [the implementation in NumPy](https://github.com/numpy/numpy/blob/master/numpy/core/overrides.py#L195) for why this measures `__array_function__` overhead.

</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32194

Differential Revision: D19410396

Pulled By: ezyang

fbshipit-source-id: ada788a4399c81cd7eb2d548aa04a2459e96634a
 2020-01-16 07:10:38 -08:00
Tongzhou Wang
b6f43afaca Fix tensordot allowing negative dims (#31954) 
Summary:
fixes https://github.com/pytorch/pytorch/issues/31926
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31954

Differential Revision: D19331847

Pulled By: zou3519

fbshipit-source-id: e30dd9517917c056a52be7d16f23247fe28f4e28
 2020-01-10 07:42:04 -08:00
TH3CHARLie
1296e2d55e C++ API parity: isinf (#31099) 
Summary:
fixes https://github.com/pytorch/pytorch/issues/31021, port the legacy binding method of `isinf` to C++ therefore support JIT
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31099

Differential Revision: D19314733

Pulled By: yf225

fbshipit-source-id: 5725c51d19c33b4fddd0fc9e7034078580bd534e
 2020-01-09 13:16:13 -08:00
Karl Ostmo
227d1a43a4 Revert D18838848: disable __torch_function__ overides for operators in torch.functional 
Test Plan: revert-hammer

Differential Revision:
D18838848

Original commit changeset: 22b8015d7b2f

fbshipit-source-id: fdaeffcd112990ed379782cf7216d3f1beeb2cb1
 2020-01-07 15:03:15 -08:00
Nathan Goldbaum
ca72df06ae disable __torch_function__ overides for operators in torch.functional (#30839) 
Summary:
For now I'm just removing the decorators from all of the currently overridable functions in `torch.functional`. This means they are no longer overridable, however this should fix the benchmark regressions reported in https://github.com/pytorch/pytorch/issues/30831. Moving forward we'll be looking at reducing the overhead of the python-level override mechanism and failing that, re-implementing all of these operators in C++.

cc hl475
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30839

Differential Revision: D18838848

Pulled By: ezyang

fbshipit-source-id: 22b8015d7b2f7a947f1ebc9632c998e081b48ad8
 2020-01-07 12:27:28 -08:00
Richard Zou
9047d4df45 Remove all remaining usages of BUILD_NAMEDTENSOR (#31116) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31116

Changelist:
- remove BUILD_NAMEDTENSOR macro
- remove torch._C._BUILD_NAMEDTENSOR
- remove all python behavior that relies on torch._C._BUILD_NAMEDTENSOR

Future:
- In the next diff, I will remove all usages of
ATen/core/EnableNamedTensor.h since that header doesn't do anything
anymore
- After that, we'll be done with the BUILD_NAMEDTENSOR removal.

Test Plan: - run CI

Differential Revision: D18934951

Pulled By: zou3519

fbshipit-source-id: 0a0df0f1f0470d0a01c495579333a2835aac9f5d
 2019-12-12 09:53:03 -08:00
Michael Suo
62b10721fb Actually make flake8 do something (#30892) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30892

Fixes all outstanding lints and actually installs a properly configured
flake8

Test Plan: Imported from OSS

Differential Revision: D18862825

Pulled By: suo

fbshipit-source-id: 08e9083338a7309272e17bb803feaa42e348aa85
 2019-12-06 17:50:50 -08:00
Nathan Goldbaum
9d3402e4cb Add the __torch_function__ API override mechanism (#30730) 
Summary:
This is a re-do of https://github.com/pytorch/pytorch/issues/27064, which was reverted (b8792c0438). This was landed at the same time as other work that added new operators to the `torch` namespace so the check for whether the `torch` namespace is exhaustively checked for overridability was triggering test failures.

I've temporarily disabled that check and added an explanatory comment that the check will be re-enabled in a future PR that will be merged during a time when the commit velocity on PyTorch is lower.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30730

Differential Revision: D18813270

Pulled By: ezyang

fbshipit-source-id: 70477c4656dca8fea6e7bc59259555041fcfbf68
 2019-12-04 13:19:07 -08:00
Edward Yang
b8792c0438 Revert D18645954: add __torch_function__ API override mechanism 
Test Plan: revert-hammer

Differential Revision:
D18645954

Original commit changeset: 54b5e4344d7a

fbshipit-source-id: 4a7aebb483e6b001130d6f384ccc53c5a808ab13
 2019-12-04 07:41:47 -08:00
Prasun Anand
d12786b24f add __torch_function__ API override mechanism (#27064) 
Summary:
Closes https://github.com/pytorch/pytorch/issues/24015 (see description of that issue for more details).

For a toy example, see the `DiagonalTensor` and `SubDiagonalTensor` class in test/test_overrides.py.

This PR currently contains:

* tests for `__torch_function__` behavior
* modification to `gen_python_functions` and `parse` function signatures and dispatched to correct overloaded argument.

This feature is inspired by and analogous to NumPy's `__array_function__` protocol ([see NumPy Enhancement Proposal 18](https://numpy.org/neps/nep-0018-array-function-protocol.html#trying-array-function-methods-until-the-right-one-works)).

### Benchmarks:
See Nathan's comment below: https://github.com/pytorch/pytorch/pull/27064#issuecomment-554601189
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27064

Differential Revision: D18645954

Pulled By: ezyang

fbshipit-source-id: 54b5e4344d7afdbcf996bb57191b0bdadc7b1767
 2019-12-04 05:56:46 -08:00
Pavel Belevich
cc81769e10 C++ API parity: isfinite 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30083

Test Plan: Imported from OSS

Differential Revision: D18594723

Pulled By: pbelevich

fbshipit-source-id: 5970e0aa6ef8994e9c4a741784fd053383aaceb7
 2019-11-19 20:00:05 -08:00
Will Feng
3bd0f476d4 Revert D18233037: C++ API parity: isfinite 
Test Plan: revert-hammer

Differential Revision:
D18233037

Original commit changeset: c76b9467bbc1

fbshipit-source-id: 97d2cfa9de767a8c3a0ca919f9d768e959fa484e
 2019-11-18 20:26:19 -08:00
Pavel Belevich
8df5e10ee9 C++ API parity: isfinite 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28918

Test Plan: Imported from OSS

Differential Revision: D18233037

Pulled By: pbelevich

fbshipit-source-id: c76b9467bbc1fbb2c9bf49855895c98438b36c12
 2019-11-18 19:06:57 -08:00
Vitaly Fedyunin
bf61405ed6 explicitly provide memory format when calling to *_like operators 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29387

Test Plan: Imported from OSS

Differential Revision: D18429729

Pulled By: VitalyFedyunin

fbshipit-source-id: c71264ed5d64ed7e5d8ea907413b6b8e7b67769a
 2019-11-11 17:57:34 -08:00
Xiang Gao
5f03ad9698 Add note to docs of torch.unique (#29165) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/19151
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29165

Differential Revision: D18319890

Pulled By: soumith

fbshipit-source-id: 162afaecd5371446bec2a1769e0a8848ecffb002
 2019-11-07 22:03:15 -08:00
Igor Fedan
75309b45f3 explicitly provide memory format when calling to clone() at Indexing.cpp 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28660

Test Plan: Imported from OSS

Differential Revision: D18333346

Pulled By: ifedan

fbshipit-source-id: 06590205d883a5096388a4ae318389244130972d
 2019-11-07 05:38:32 -08:00
Pearu Peterson
fd4f22e4ea Generalized LU factorization (#28608) 
Summary:
This PR implements support for generalized LU factorization that is required for various algorithms such as PCA (see issue https://github.com/pytorch/pytorch/issues/8049).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28608

Differential Revision: D18326449

Pulled By: ezyang

fbshipit-source-id: d4011d75710e06e87ddbf5ad9afae42ba3330548
 2019-11-05 12:27:40 -08:00
Igor Fedan
12dde7f58a cdist performance improvement for euclidean distance (#25799) 
Summary:
jacobrgardner https://github.com/pytorch/pytorch/issues/15253#issuecomment-491467128 preposed a way to speedup euclidean distance calculation. This PR is implementation of this solution for normal and batch version.

Also simonepri provided performance metrics https://github.com/pytorch/pytorch/issues/15253#issuecomment-502363581
![image](https://user-images.githubusercontent.com/12058312/64460756-44a24580-d0c9-11e9-9f7f-a5942f4c832d.png)

Current implementation has speedup comparing to jacobrgardner approach
![image](https://user-images.githubusercontent.com/12058312/64461495-5553bb00-d0cb-11e9-87e6-302b8cc7e12b.png)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25799

Differential Revision: D17964982

Pulled By: ifedan

fbshipit-source-id: bf7bd0dbfca51fd39e667da55139347480f30a2f
 2019-10-17 14:56:54 -07:00
Dominik1123
5797f5dd27 Update 'einsum' docstring to conform to PEP-484 (#27563) 
Summary:
[PEP-484](https://www.python.org/dev/peps/pep-0484/#arbitrary-argument-lists-and-default-argument-values) specifies that arbitrary argument lists, here `*operands`, should be annotated with the type of the single arguments, i.e. not indicating that the whole thing is wrapped into a `list` (which is a Python internal anyway). The previous docstring caused problems with type checkers for IDEs such as PyCharm ([see here](https://youtrack.jetbrains.com/issue/PY-38035)).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27563

Differential Revision: D17904748

Pulled By: soumith

fbshipit-source-id: 0a7fcbbb12e388e6fc40d48bf533652a96024757
 2019-10-15 14:35:24 -07:00
Iurii Zdebskyi
293e35a87c Fixed Error message for tensor.align_to (#27221) 
Summary:
Fixing this [issue1](https://github.com/pytorch/pytorch/issues/27074) and [issue2](https://github.com/pytorch/pytorch/issues/27073)
Tested via unit tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27221

Differential Revision: D17716235

Pulled By: izdeby

fbshipit-source-id: c7bafd16b469c91924ebc3dba77ca56424d4c93c
 2019-10-02 14:19:40 -07:00
vishwakftw
15b506068b Remove deprecated torch.gels (#26480) 
Summary:
Changelog:
- Remove `torch.gels` which was deprecated in v1.2.0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26480

Test Plan: - No tests were changed and all callsites for `torch.gels` where modified to `torch.lstsq` when `torch.lstsq` was introduced

Differential Revision: D17527207

Pulled By: zou3519

fbshipit-source-id: 28e2fa3a3bf30eb6b9029bb5aab198c4d570a950
 2019-09-23 07:15:39 -07:00
Richard Zou
7030f2c623 Implement tensor.align_to(names), torch.align_tensors(*tensors) (#23804) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23804

`output = tensor.align_to(names)` returns a view of `tensor` such that
`output.names = names`. Dimensions with the same names in `tensor` and
`output` have the same sizes; dimensions with new names have size 1.

The following must be true for this operation to succeed:
1) tensor.names must be a subsequence (not necessarily contiguous) of `names`
2) Aligning tensor.names to names must not change the absolute position from the
   right of any unnamed dimension.

In practice, these constraints mean that aligning cannot transpose
names.

Some examples:
- Tensor[C].align_to(C) -> Tensor[C]
- Tensor[N].align_to([N, C]) -> Tensor[N, C]
- Tensor[H, W].align_to([N, H, W, C]) -> Tensor[N, H, W, C]
- Tensor[None].align_to([N, None]) -> Tensor[N, None]
- Tensor[N].align_to([N, None None]) -> Tensor[N, None, None]

Examples of error cases:
- Tensor[W, H].align_to([N, H, W, C]) -> Error (not a subsequence)
- Tensor[None, H].align_to([None, H, W]) -> Error (would change the
absolute position from the right of a None dimension)

`torch.align_tensors(*tensors)` aligns the named dimensions of each
tensor according to the alignment rules so that they can be used in an
operation. More concretely, it aligns each tensor to the
longest names among the names of the tensors in `tensors`.

This allows users to emulate "broadcasting by names", which is one of
the things named tensors tries to enable. Here is an example:

```
imgs: Tensor[N, C, H, W]
scale: Tensor[N]

// Doesn't work because we do broadcasting by alignment by default
imgs * scale

// Does work
imgs, scale = torch.align_tensors(imgs, scale)
imas * scale
```

Future:
- Consider allowing broadcasting by names by default.

Test Plan:
- The diff looks pretty large but more than half of it is testing.
- new tests [namedtensor ci]

Differential Revision: D16657927

Pulled By: zou3519

fbshipit-source-id: e2f958bf5146c8ee3b694aba57d21b08e928a4e6
 2019-08-14 09:40:27 -07:00
Iurii Zdebskyi
865c7eea48 Changed tensor comparison return type from uint8 to bool (#21113) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21113
ghimport-source-id: 9c4ba63457a72bfc41894387e0b01be3fd9a9baf

Test Plan: Imported from OSS

Differential Revision: D15552204

Pulled By: izdeby

fbshipit-source-id: a608213668649d058e22b510d7755cb99e7d0037
 2019-08-01 07:54:53 -07:00
vishwakftw
b3a9a7a9b9 Rename gels to lstsq (#23460) 
Summary:
Changelog:
- Rename `gels` to `lstsq`
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lstsq` under the name `gels` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23460

Test Plan: - All tests should pass to confirm that the patch is correct

Differential Revision: D16547834

Pulled By: colesbury

fbshipit-source-id: b3bdb8f4c5d14c7716c3d9528e40324cc544e496
 2019-07-30 09:56:04 -07:00
vishwakftw
6dfecc7e01 Remove deprecated linear algebra functions (and methods) (#22841) 
Summary:
Changelog:
- Removed the following linear algebra functions in PyTorch in favor of the renamed operations
  - `btrifact` (use `lu` instead)
  - `btrifact_with_info` (use `lu` with `get_infos=True` instead)
  - `btrisolve` (use `lu_solve` instead)
  - `btriunpack` (use `lu_unpack` instead)
  - `gesv` (use `solve` instead)
  - `pstrf` (use `cholesky` instead)
  - `potrf` (use `cholesky` instead)
  - `potri` (use `cholesky_inverse` instead)
  - `potrs` (use `cholesky_solve` instead)
  - `trtrs` (use `triangular_solve` instead)

- Removed dead code after the removal of `pstrf`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22841

Test Plan:
- All existing tests should pass to verify that the removal is clean

Closes https://github.com/pytorch/pytorch/issues/22832

Differential Revision: D16346184

Pulled By: zou3519

fbshipit-source-id: f748d16ed7609c028de6adcbc28684d5a1af0678
 2019-07-19 11:43:06 -07:00