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27f9d3b0a17289500f1a2d24e4901a1e0fa9ea95
84 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
| 27f9d3b0a1 |
Flip default value for mypy disallow_untyped_defs [8/11] (#127845)
See #127836 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127845 Approved by: https://github.com/oulgen ghstack dependencies: #127842, #127843, #127844 |
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| 53e7de4b65 |
Issue 112599 - fix pydocstyle errors (#113177)
Fixes #112599 Fixed errors relating to pydocstyle in the following files. The remaining errors are related to docstrings at the module level and at methods within each module, `forward()`, `reset_parameters`, `__init__` ..etc pydocstyle torch/nn/modules/pooling.py --count before: 49 after: 29 **remaining errors:** ``` torch/nn/modules/pooling.py:1 at module level: D100: Missing docstring in public module torch/nn/modules/pooling.py:90 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:163 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:240 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:315 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:321 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:402 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:408 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:472 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:478 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:541 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:550 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:620 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:630 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:706 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:716 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:720 in public method `__setstate__`: D105: Missing docstring in magic method torch/nn/modules/pooling.py:774 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:792 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:845 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pooling.py:863 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:925 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:979 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:1026 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:1068 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:1111 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:1150 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:1189 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pooling.py:1228 in public method `forward`: D102: Missing docstring in public method ``` pydocstyle torch/nn/modules/upsampling.py --count before: 14 after: 7 **remaining:** ``` torch/nn/modules/upsampling.py:1 at module level: D100: Missing docstring in public module torch/nn/modules/upsampling.py:142 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/upsampling.py:156 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/upsampling.py:160 in public method `__setstate__`: D105: Missing docstring in magic method torch/nn/modules/upsampling.py:166 in public method `extra_repr`: D102: Missing docstring in public method torch/nn/modules/upsampling.py:216 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/upsampling.py:263 in public method `__init__`: D107: Missing docstring in __init__ ``` pydocstyle torch/nn/modules/rnn.py --count before: 47 after: 40 **remaining** ``` torch/nn/modules/rnn.py:1 at module level: D100: Missing docstring in public module torch/nn/modules/rnn.py:59 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:160 in public method `__setattr__`: D105: Missing docstring in magic method torch/nn/modules/rnn.py:225 in public method `reset_parameters`: D102: Missing docstring in public method torch/nn/modules/rnn.py:230 in public method `check_input`: D102: Missing docstring in public method torch/nn/modules/rnn.py:242 in public method `get_expected_hidden_size`: D102: Missing docstring in public method torch/nn/modules/rnn.py:256 in public method `check_hidden_size`: D102: Missing docstring in public method torch/nn/modules/rnn.py:272 in public method `check_forward_args`: D102: Missing docstring in public method torch/nn/modules/rnn.py:278 in public method `permute_hidden`: D102: Missing docstring in public method torch/nn/modules/rnn.py:284 in public method `extra_repr`: D102: Missing docstring in public method torch/nn/modules/rnn.py:305 in public method `__getstate__`: D105: Missing docstring in magic method torch/nn/modules/rnn.py:313 in public method `__setstate__`: D105: Missing docstring in magic method torch/nn/modules/rnn.py:355 in public method `all_weights`: D102: Missing docstring in public method torch/nn/modules/rnn.py:471 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:478 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:481 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:503 in public method `forward` (skipping F811): D102: Missing docstring in public method torch/nn/modules/rnn.py:762 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:768 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:771 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:774 in public method `get_expected_cell_size`: D102: Missing docstring in public method torch/nn/modules/rnn.py:786 in public method `check_forward_args`: D102: Missing docstring in public method torch/nn/modules/rnn.py:798 in public method `permute_hidden`: D102: Missing docstring in public method torch/nn/modules/rnn.py:809 in public method `forward` (skipping F811): D102: Missing docstring in public method torch/nn/modules/rnn.py:820 in public method `forward` (skipping F811): D102: Missing docstring in public method torch/nn/modules/rnn.py:1030 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1036 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1039 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1046 in public method `forward` (skipping F811): D102: Missing docstring in public method torch/nn/modules/rnn.py:1054 in public method `forward` (skipping F811): D102: Missing docstring in public method torch/nn/modules/rnn.py:1123 in public class `RNNCellBase`: D101: Missing docstring in public class torch/nn/modules/rnn.py:1134 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1152 in public method `extra_repr`: D102: Missing docstring in public method torch/nn/modules/rnn.py:1160 in public method `reset_parameters`: D102: Missing docstring in public method torch/nn/modules/rnn.py:1224 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1230 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/rnn.py:1327 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1332 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/rnn.py:1422 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/rnn.py:1427 in public method `forward`: D102: Missing docstring in public method ``` pydocstyle torch/nn/modules/pixelshuffle.py --count before: 13 after: 8 **remaining:** ``` torch/nn/modules/pixelshuffle.py:1 at module level: D100: Missing docstring in public module torch/nn/modules/pixelshuffle.py:52 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pixelshuffle.py:56 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pixelshuffle.py:59 in public method `extra_repr`: D102: Missing docstring in public method torch/nn/modules/pixelshuffle.py:105 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/pixelshuffle.py:109 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/pixelshuffle.py:112 in public method `extra_repr`: D102: Missing docstring in public method ``` pydocstyle torch/nn/modules/sparse.py --count before: 14 after: 8 **remaining errors:** ``` torch/nn/modules/sparse.py:1 at module level: D100: Missing docstring in public module torch/nn/modules/sparse.py:124 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/sparse.py:153 in public method `reset_parameters`: D102: Missing docstring in public method torch/nn/modules/sparse.py:162 in public method `forward`: D102: Missing docstring in public method torch/nn/modules/sparse.py:167 in public method `extra_repr`: D102: Missing docstring in public method torch/nn/modules/sparse.py:320 in public method `__init__`: D107: Missing docstring in __init__ torch/nn/modules/sparse.py:350 in public method `reset_parameters`: D102: Missing docstring in public method torch/nn/modules/sparse.py:396 in public method `extra_repr`: D102: Missing docstring in public method ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/113177 Approved by: https://github.com/ezyang |
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| 5b1cedacde |
[BE] [2/3] Rewrite super() calls in functorch and torch (#94588)
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.: |
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| caf1b27196 |
Fix Upsample/EmbeddingBag module printing (#93850)
The fix generalizes but I want someone else to holistically figure this out. Fixes https://github.com/pytorch/pytorch/issues/93233 Fixes https://github.com/pytorch/pytorch/issues/93512 Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/93850 Approved by: https://github.com/albanD |
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| ad782ff7df |
Enable xdoctest runner in CI for real this time (#83816)
Builds on #83317 and enables running the doctests. Just need to figure out what is causing the failures. Pull Request resolved: https://github.com/pytorch/pytorch/pull/83816 Approved by: https://github.com/ezyang, https://github.com/malfet |
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| 158a071034 |
add _freeze for embedding op (#86769)
Fixes #86663 Pull Request resolved: https://github.com/pytorch/pytorch/pull/86769 Approved by: https://github.com/albanD |
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| 4618371da5 |
Integrate xdoctest - Rebased (#82797)
This is a new version of #15648 based on the latest master branch. Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR. In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.) Fixes https://github.com/pytorch/pytorch/issues/71105 @ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797 Approved by: https://github.com/ezyang |
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| 357b7d589c |
Fix docstring inconsistencies: string -> str, boolean -> bool (#82410)
### Description Throughout the PyTorch docs and codebase, the `string` type in docstrings is referred to by two separate names. This leads to inconsistent docs, like you can see here: https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html#torch.nn.Conv3d This PR fixes this issue by ensuring that all mentions of the string type in docstrings, are using the same format that Sphinx generates hyperlinks for. ### Testing No testing should be required for this change Pull Request resolved: https://github.com/pytorch/pytorch/pull/82410 Approved by: https://github.com/jbschlosser |
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| 9db3c517de |
Add __all__ for torch.nn.modules, torch.distributed.elastic, torch.nn.utils submodules (#80240)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80240 Approved by: https://github.com/rohan-varma |
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| febff45900 |
Support factory kwargs in torch.nn modules (#54508)
Summary: Continuation of https://github.com/pytorch/pytorch/pull/53144 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54508 Reviewed By: albanD Differential Revision: D27939544 Pulled By: jbschlosser fbshipit-source-id: 4bf517e5f74f093e27ca38a85e732da65e44d805 |
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| 6d7d36d255 |
s/“pad”/"pad"/ in files introduced by #56065 (#56618)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/56618 Reviewed By: albanD Differential Revision: D27919343 Pulled By: malfet fbshipit-source-id: 2fac8ba5f399e050463141eba225da935c97a5ce |
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| 12b2bc94d7 |
Revert D27909732: [pytorch][PR] Support factory kwargs in torch.nn modules
Test Plan: revert-hammer
Differential Revision:
D27909732 (
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| 5a09def9b0 |
Support factory kwargs in torch.nn modules (#54508)
Summary: Continuation of https://github.com/pytorch/pytorch/pull/53144 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54508 Reviewed By: malfet Differential Revision: D27909732 Pulled By: jbschlosser fbshipit-source-id: d8684b2403ab7eb336371d118799146a2520bd76 |
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| 8a81c4dc27 |
Update padding_idx docs for EmbeddingBag to better match Embedding's (#56065)
Summary: Match updated `Embedding` docs from https://github.com/pytorch/pytorch/pull/54026 as closely as possible. Additionally, update the C++ side `Embedding` docs, since those were missed in the previous PR. There are 6 (!) places for docs: 1. Python module form in `sparse.py` - includes an additional line about newly constructed `Embedding`s / `EmbeddingBag`s 2. Python `from_pretrained()` in `sparse.py` (refers back to module docs) 3. Python functional form in `functional.py` 4. C++ module options - includes an additional line about newly constructed `Embedding`s / `EmbeddingBag`s 5. C++ `from_pretrained()` options 6. C++ functional options Pull Request resolved: https://github.com/pytorch/pytorch/pull/56065 Reviewed By: malfet Differential Revision: D27908383 Pulled By: jbschlosser fbshipit-source-id: c5891fed1c9d33b4b8cd63500a14c1a77d92cc78 |
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| 92d24e3060 |
Revert D27855386: [pytorch][PR] Support factory kwargs in torch.nn modules
Test Plan: revert-hammer
Differential Revision:
D27855386 (
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| 40483acc51 |
Support factory kwargs in torch.nn modules (#54508)
Summary: Continuation of https://github.com/pytorch/pytorch/pull/53144 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54508 Reviewed By: bdhirsh Differential Revision: D27855386 Pulled By: jbschlosser fbshipit-source-id: dabd505d2a04208e74b158570fb2859c736eea2c |
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| d05e7c163f |
Revert D27600457: [pytorch][PR] Support factory kwargs in torch.nn modules
Test Plan: revert-hammer
Differential Revision:
D27600457 (
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| 1077f87269 |
Support factory kwargs in torch.nn modules (#54508)
Summary: Continuation of https://github.com/pytorch/pytorch/pull/53144 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54508 Reviewed By: mrshenli Differential Revision: D27600457 Pulled By: jbschlosser fbshipit-source-id: b58bfee61c3917524b4622f63ef216c27a588eb1 |
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| 3fe4718d16 |
Add padding_idx argument to EmbeddingBag (#49237)
Summary: This PR adds a `padding_idx` parameter to `nn.EmbeddingBag` and `nn.functional.embedding_bag`. As with `nn.Embedding`'s `padding_idx` argument, if an embedding's index is equal to `padding_idx` it is ignored, so it is not included in the reduction. This PR does not add support for `padding_idx` for quantized or ONNX `EmbeddingBag` for opset10/11 (opset9 is supported). In these cases, an error is thrown if `padding_idx` is provided. Fixes https://github.com/pytorch/pytorch/issues/3194 Pull Request resolved: https://github.com/pytorch/pytorch/pull/49237 Reviewed By: walterddr, VitalyFedyunin Differential Revision: D26948258 Pulled By: jbschlosser fbshipit-source-id: 3ca672f7e768941f3261ab405fc7597c97ce3dfc |
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| 263d8ef4ef |
docs: fix formatting for embedding_bag (#54666)
Summary: fixes https://github.com/pytorch/pytorch/issues/43499 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54666 Reviewed By: H-Huang Differential Revision: D27411027 Pulled By: jbschlosser fbshipit-source-id: a84cc174155bd725e108d8f953a21bb8de8d9d23 |
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| 0bd96458ba |
Revert D26820202: Support mix of int32 and int64 offsets/indices for EmbeddingBag and its variants
Test Plan: revert-hammer
Differential Revision:
D26820202 (
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| f9097c43b9 |
Support mix of int32 and int64 offsets/indices for EmbeddingBag and its variants (#53655)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/53655 Currently EmbeddingBag and it variants support either int32 or int64 indices/offsets. We have use cases where there are mix of int32 and int64 indices which are not supported yet. To avoid introducing too many branches we could simply cast offsets type to indices type when they are not the same. Test Plan: unit tests Reviewed By: qizzzh Differential Revision: D26820202 fbshipit-source-id: 3e8f09523329ea12393ea92ee9a6315aa40a0b7f |
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| 27048c1dfa |
Remove legacy constructor calls from _torch_ folder. (#53889)
Summary: Fixes https://github.com/pytorch/pytorch/issues/53146 Related to https://github.com/pytorch/pytorch/issues/47112 As mentioned in https://github.com/pytorch/pytorch/issues/47112, the plan is to: 1. Verify that all `torch.Tensor()` scenarios are covered by other functions 2. Scrub internal `torch.Tensor()` uses 3. Update the docs and throw `TORCH_WARN_ONCE` if someone uses `torch.Tensor()` In this PR, I replaced all occurrences of `torch.Tensor` present in the _torch_ folder. Pull Request resolved: https://github.com/pytorch/pytorch/pull/53889 Reviewed By: walterddr, zou3519 Differential Revision: D27190743 Pulled By: jbschlosser fbshipit-source-id: 7ecc201d57935b8dbb98ae3718b60d95cb55a010 |
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| c1a39620b8 |
[nn] nn.Embedding : padding_idx doc update (#53809)
Summary: Follow-up of https://github.com/pytorch/pytorch/pull/53447 Reference: https://github.com/pytorch/pytorch/pull/53447#discussion_r590521051 Pull Request resolved: https://github.com/pytorch/pytorch/pull/53809 Reviewed By: bdhirsh Differential Revision: D27049643 Pulled By: jbschlosser fbshipit-source-id: 623a2a254783b86391dc2b0777b688506adb4c0e |
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| 45ddf113c9 |
[fix] nn.Embedding: allow changing the padding vector (#53447)
Summary: Fixes https://github.com/pytorch/pytorch/issues/53368 Pull Request resolved: https://github.com/pytorch/pytorch/pull/53447 Reviewed By: albanD Differential Revision: D26946284 Pulled By: jbschlosser fbshipit-source-id: 54e5eec7da86fa02b1b6e4a235d66976a80764fc |
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| 0ec717c830 |
Support int32 indices and offsets in nn.EmbeddingBag (#46758)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46758 It's in general helpful to support int32 indices and offsets, especially when such tensors are large and need to be transferred to accelerator backends. Since it may not be very useful to support the combination of int32 indices and int64 offsets, here we enforce that these two must have the same type. Test Plan: unit tests Reviewed By: ngimel Differential Revision: D24470808 fbshipit-source-id: 94b8a1d0b7fc9fe3d128247aa042c04d7c227f0b |
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| c68c3d0a02 |
[fix] nn.Embedding.from_pretrained : honour padding_idx argument (#47184)
Summary: Fixes https://github.com/pytorch/pytorch/issues/46585 (first snippet) Now the behaviour of `padding_idx` agrees with documentation. Pull Request resolved: https://github.com/pytorch/pytorch/pull/47184 Reviewed By: mruberry Differential Revision: D24682567 Pulled By: albanD fbshipit-source-id: 864bd34eb9099d367a3fcbb8f4f4ba2e2b270724 |
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| 9b52654620 |
annotate a few torch.nn.modules.* modules (#45772)
Summary: Fixes https://github.com/pytorch/pytorch/issues/45771 Pull Request resolved: https://github.com/pytorch/pytorch/pull/45772 Reviewed By: mruberry Differential Revision: D24682013 Pulled By: albanD fbshipit-source-id: e32bc4fe9c586c079f7070924a874c70f3d127fa |
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| ed1552a48f |
Add note about in-place weight modification for nn.Embedding (#45595)
Summary: Fixes https://github.com/pytorch/pytorch/issues/26596 Pull Request resolved: https://github.com/pytorch/pytorch/pull/45595 Reviewed By: albanD Differential Revision: D24143456 Pulled By: mruberry fbshipit-source-id: a884a32809105ce16959b40ec745ec873b3c8375 |
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| 4ab73c1f74 |
[docs] Fix EmbeddingBag docs (#45763)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45763 **Summary** This commit updates the documentation for `EmbeddingBag` to say that for bags of constant length with no per-sample weights, the class is equivalent to `Embedding` followed by `torch.sum(dim=1)`. The current docs say `dim=0` and this is readily falsifiable. **Test Plan** 1) Tried `Embedding` + `sum` with `dim`=0,1 in interpreter and compared to `EmbeddingBag` ``` >>> import torch >>> weights = torch.nn.Parameter(torch.randn(10, 3)) >>> e = torch.nn.Embedding(10, 3) >>> eb = torch.nn.EmbeddingBag(10, 3, mode="sum") >>> e.weight = weights >>> eb.weight = weights # Use 2D inputs because we are trying to test the case in which bags have constant length >>> inputs = torch.LongTensor([[4,1,2,7],[5,6,0,3]]) >>> eb(inputs) tensor([[-2.5497, -0.1556, -0.5166], [ 2.2528, -0.3627, 2.5822]], grad_fn=<EmbeddingBagBackward>) >>> torch.sum(e(inputs), dim=0) tensor([[ 1.6181, -0.8739, 0.8168], [ 0.0295, 2.3274, 1.2558], [-0.7958, -0.4228, 0.5961], [-1.1487, -1.5490, -0.6031]], grad_fn=<SumBackward1>) >>> torch.sum(e(inputs), dim=1) tensor([[-2.5497, -0.1556, -0.5166], [ 2.2528, -0.3627, 2.5822]], grad_fn=<SumBackward1>) ``` So clearly `torch.sum` with `dim=0` is not correct here. 2) Built docs and viewed in browser. *Before* <img width="882" alt="Captura de Pantalla 2020-10-02 a la(s) 12 26 20 p m" src="https://user-images.githubusercontent.com/4392003/94963035-557be100-04ac-11eb-986c-088965ac3050.png"> *After* <img width="901" alt="Captura de Pantalla 2020-10-05 a la(s) 11 26 51 a m" src="https://user-images.githubusercontent.com/4392003/95117732-ea294d80-06fd-11eb-9d6b-9b4e6c805cd0.png"> **Fixes** This commit closes #43197. Test Plan: Imported from OSS Reviewed By: ansley Differential Revision: D24118206 Pulled By: SplitInfinity fbshipit-source-id: cd0d6b5db33e415d8e04ba04f2c7074dcecf3eee |
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| 1c5c289b62 |
[pt] Add incude_last_offset option to EmbeddingBag mean and max (#42215)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42215 Specifically on https://github.com/pytorch/pytorch/pull/27477#discussion_r371402079 We would like to supported with include_last=True overall for other reduction types like mean and max. It now causes further code fragmentation in DPER (https://www.internalfb.com/intern/diff/D22794469/). More details: https://www.internalfb.com/intern/diff/D22794469/?dest_fbid=309597093427021&transaction_id=631457624153457 ghstack-source-id: 108733009 Test Plan: ``` buck test mode/dev-nosan //caffe2/test:nn -- "test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu" ``` ``` (base) [jianyuhuang@devbig281.ftw3.facebook.com: ~/fbsource/fbcode/caffe2/test] $ TORCH_SHOW_CPP_STACKTRACES=1 buck test mode/dev-nosan //caffe2/test: nn -- "test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu" --print-passing-details Parsing buck files: finished in 1.2 sec Building: finished in 5.5 sec (100%) 10130/10130 jobs, 2 updated Total time: 6.7 sec More details at https://www.internalfb.com/intern/buck/build/dbdc2063-69d8-45cb-9146-308a9e8505ef First unknown argument: --print-passing-details. Falling back to TestPilot classic. Trace available for this run at /tmp/testpilot.20200728-195414.1422748.log TestPilot test runner for Facebook. See https://fburl.com/testpilot for details. Testpilot build revision cd2638f1f47250eac058b8c36561760027d16add fbpkg f88726c8ebde4ba288e1172a348c7f46 at Mon Jul 27 18:11:43 2020 by twsvcscm from /usr/local/fbprojects/packages/testinfra.testpilot/887/t.par Discovering tests Running 1 test Started new test run: https://our.intern.facebook.com/intern/testinfra/testrun/844425097242375 ✓ caffe2/test:nn - test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu (test_nn.TestNNDeviceTypeCPU) 0.162 1/1 (passed) Test output: > /data/users/jianyuhuang/fbsource/fbcode/buck-out/dev/gen/caffe2/test/nn#binary,link-tree/torch/_utils_internal.py:103: DeprecationWarning: This is a NOOP in python >= 3.7, its just too dangerous with how we write code at facebook. Instead we patch os.fork and multiprocessing which can raise exceptions if a deadlock would happen. > threadSafeForkRegisterAtFork() > /usr/local/fbcode/platform007/lib/python3.7/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__ > return f(*args, **kwds) > test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu (test_nn.TestNNDeviceTypeCPU) ... Couldn't download test skip set, leaving all tests enabled... > ok > > ---------------------------------------------------------------------- > Ran 1 test in 0.162s > > OK Finished test run: https://our.intern.facebook.com/intern/testinfra/testrun/844425097242375 Summary (total time 5.54s): PASS: 1 FAIL: 0 SKIP: 0 FATAL: 0 TIMEOUT: 0 OMIT: 0 Did _not_ run with tpx. See https://fburl.com/tpx for details. ``` Reviewed By: dzhulgakov Differential Revision: D22801881 fbshipit-source-id: 80a624465727081bb9bf55c28419695a3d79c6e5 |
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| eace053398 |
Move all torch.nn.modules type annotations inline (#38211)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38211 Just because the annotations are inline doesn't mean the files type check; most of the newly annotated files have type errors and I added exclusions for them in mypy.ini. The payoff of moving all of these modules inline is I can delete the relevant code generation logic for the pyi files (which was added ignore annotations that weren't actually relevant anymore.) For the most part the translation was completely mechanical, but there were two hairy issues. First, I needed to work around a Python 3.6 and earlier bug where Generic has a nontrivial metaclass. This fix is in torch/jit/__init__.py. Second, module.py, we need to apply the same fix for avoiding contravariance checks that the pyi file used to have; this is done by declaring forward as a variable (rather than a function), which appears to be sufficient enough to get mypy to not contravariantly check input arguments. Because we aren't actually typechecking these modules in most cases, it is inevitable that some of these type annotations are wrong. I slavishly copied the old annotations from the pyi files unless there was an obvious correction I could make. These annotations will probably need fixing up later. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Test Plan: Imported from OSS Differential Revision: D21497397 Pulled By: ezyang fbshipit-source-id: 2b08bacc152c48f074e7edc4ee5dce1b77d83702 |
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| 78d5707041 |
Fix type annotations and make MyPy run on torch/ (#36584)
Summary: This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages. As a result, running `mypy` in the root dir of the repo now runs on: - `torch/` - `aten/src/ATen/function_wrapper.py` (the only file already covered in CI) In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`. Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through): <details> ``` torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx' torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch" torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2' torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2' torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx' torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util' torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework' torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer' torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...") torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items" torch/nn/_reduction.py:27: error: Name 'Optional' is not defined torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/_reduction.py:47: error: Name 'Optional' is not defined torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs torch/nn/modules/utils.py:27: error: Name 'List' is not defined torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment" torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction' caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed) caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs caffe2/python/__init__.py:7: error: Module has no attribute "CPU" caffe2/python/__init__.py:8: error: Module has no attribute "CUDA" caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN" caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL" caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL" caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP" caffe2/python/__init__.py:13: error: Module has no attribute "HIP" caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"? caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"? caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:35: error: Module has no attribute "cuda" caffe2/python/__init__.py:37: error: Module has no attribute "cuda" caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory" torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C' torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import) torch/_tensor_str.py:164: error: Not all arguments converted during string formatting torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/_linalg_utils.py:26: error: Name 'Optional' is not defined torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Optional' is not defined torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Optional' is not defined torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Optional' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched' torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...") torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...") torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm") torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Optional' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/nn/init.py:109: error: Name 'Tensor' is not defined torch/nn/init.py:126: error: Name 'Tensor' is not defined torch/nn/init.py:142: error: Name 'Tensor' is not defined torch/nn/init.py:165: error: Name 'Tensor' is not defined torch/nn/init.py:180: error: Name 'Tensor' is not defined torch/nn/init.py:194: error: Name 'Tensor' is not defined torch/nn/init.py:287: error: Name 'Tensor' is not defined torch/nn/init.py:315: error: Name 'Tensor' is not defined torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction' torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer' torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_" torch/jit/_builtins.py:80: error: Module has no attribute "stft" torch/jit/_builtins.py:81: error: Module has no attribute "cdist" torch/jit/_builtins.py:82: error: Module has no attribute "norm" torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm" torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm" torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C' torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...") torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import) torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass" torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache" torch/_lowrank.py:11: error: Name 'Tensor' is not defined torch/_lowrank.py:13: error: Name 'Optional' is not defined torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Optional' is not defined torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Optional' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:82: error: Name 'Tuple' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:130: error: Name 'Tensor' is not defined torch/_lowrank.py:130: error: Name 'Optional' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:130: error: Name 'Tuple' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:167: error: Name 'Tensor' is not defined torch/_lowrank.py:167: error: Name 'Optional' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:167: error: Name 'Tuple' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases torch/quantization/observer.py:45: error: Invalid base class "ABC" torch/quantization/observer.py:127: error: Name 'Tensor' is not defined torch/quantization/observer.py:127: error: Name 'Tuple' is not defined torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:192: error: Name 'Tensor' is not defined torch/quantization/observer.py:192: error: Name 'Tuple' is not defined torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:534: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tuple' is not defined torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:894: error: Cannot determine type of 'max_val' torch/quantization/observer.py:894: error: Cannot determine type of 'min_val' torch/quantization/observer.py:899: error: Cannot determine type of 'min_val' torch/quantization/observer.py:902: error: Name 'Tensor' is not defined torch/quantization/observer.py:925: error: Name 'Tensor' is not defined torch/quantization/observer.py:928: error: Cannot determine type of 'min_val' torch/quantization/observer.py:929: error: Cannot determine type of 'max_val' torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric" torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:289: error: Module has no attribute "ops" torch/nn/quantized/functional.py:290: error: Module has no attribute "ops" torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops" torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops" torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig' torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__" torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views' torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16 torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15 torch/functional.py:114: error: Name 'Tuple' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:114: error: Name 'Optional' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]" torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:204: note: Possible overload variants: torch/functional.py:204: note: def __setitem__(self, int, int) -> None torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:204: note: def __getitem__(self, int) -> int torch/functional.py:204: note: def __getitem__(self, slice) -> List[int] torch/functional.py:207: error: "Tensor" has no attribute "copy_" torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:212: note: Possible overload variants: torch/functional.py:212: note: def __setitem__(self, int, int) -> None torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:212: note: def __getitem__(self, int) -> int torch/functional.py:212: note: def __getitem__(self, slice) -> List[int] torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:334: error: Name 'Optional' is not defined torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]" torch/functional.py:431: error: Module has no attribute "stft" torch/functional.py:766: error: Module has no attribute "cdist" torch/functional.py:768: error: Module has no attribute "cdist" torch/functional.py:770: error: Module has no attribute "cdist" torch/functional.py:775: error: Name 'Optional' is not defined torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'Optional' is not defined torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'number' is not defined torch/functional.py:780: error: Name 'norm' already defined on line 775 torch/functional.py:785: error: Name 'Optional' is not defined torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:785: error: Name 'number' is not defined torch/functional.py:785: error: Name 'norm' already defined on line 775 torch/functional.py:790: error: Name 'Optional' is not defined torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:790: error: Name 'norm' already defined on line 775 torch/functional.py:795: error: Name 'norm' already defined on line 775 torch/functional.py:960: error: Name 'Any' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any") torch/functional.py:960: error: Name 'Tuple' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized" torch/functional.py:1041: error: Name 'Optional' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1041: error: Name 'Tuple' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1056: error: Name 'Optional' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1056: error: Name 'Tuple' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]") torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]") torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]") torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase" torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]") torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None") torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric" torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric" torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module' torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68 torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass" torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524 torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer' torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching' torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]") torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]") torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...") torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/__init__.py:158: error: Name 'transforms' is not defined torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse' torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import) torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import) torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore' torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore' torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584 Reviewed By: seemethere, ailzhang Differential Revision: D21155985 Pulled By: ezyang fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1 |
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| 3ada2e0d64 |
[pytorch][embeddingbag] Parallelize the EmbeddingBag operator (#4049)
Summary: Pull Request resolved: https://github.com/pytorch/glow/pull/4049 Pull Request resolved: https://github.com/pytorch/pytorch/pull/27477 We would like to add the intra-op parallelization support for the EmbeddingBag operator. This should bring speedup for the DLRM benchmark: https://github.com/pytorch/pytorch/pull/24385 Benchmark code: ``` from __future__ import absolute_import, division, print_function, unicode_literals import torch import time eb = torch.nn.EmbeddingBag(1000000, 64, mode='sum') input = torch.LongTensor(1500).random_(0, 1000000) offsets = torch.zeros(64, dtype=torch.int64) niter = 10000 s = time.time() for _ in range(niter): out = eb(input, offsets) time_per_iter = (time.time() - s) / niter print('time_per_iter', time_per_iter) print('GB/s', (input.numel() * 64 * 4 + out.numel() * 4) / time_per_iter / 1e9) ``` The following results are single core on Skylake T6: - Before our change (with the original caffe2::EmbeddingLookup) time_per_iter 6.313693523406982e-05 GB/s 6.341517821789133 - After our change using the EmbeddingLookupIdx API which takes the offsets instead of lengths. time_per_iter 5.7627105712890626e-05 GB/s 6.947841559053659 - With Intel's PR: https://github.com/pytorch/pytorch/pull/24385 time_per_iter 7.393271923065185e-05 GB/s 5.415518381664018 For multi-core performance, because Clang doesn't work with OMP, I can only see the single-core performance on SKL T6. ghstack-source-id: 97124557 Test Plan: With D16990830: ``` buck run mode/dev //caffe2/caffe2/perfkernels:embedding_bench ``` With D17750961: ``` buck run mode/opt //experimental/jianyuhuang/embeddingbag:eb buck run mode/opt-lto //experimental/jianyuhuang/embeddingbag:eb ``` OSS test ``` python run_test.py -i nn -- TestNNDeviceTypeCPU.test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu ``` Buck test ``` buck test mode/dev-nosan //caffe2/test:nn -- "test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu" OMP_NUM_THREADS=3 buck test mode/opt -c pytorch.parallel_backend=tbb //caffe2/test:nn -- "test_EmbeddingBag_per_sample_weights_and_new_offsets" --print-passing-details ``` Generate the AVX2 code for embedding_lookup_idx_avx2.cc: ``` python hp_emblookup_codegen.py --use-offsets ``` Differential Revision: D17768404 fbshipit-source-id: 8dcd15a62d75b737fa97e0eff17f347052675700 |
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| 10c4b98ade |
Remove weak script (#22212)
Summary: * Deletes all weak script decorators / associated data structures / methods * In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn` * Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods * `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand This should also fix https://github.com/pytorch/pytorch/issues/22212 Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212 Differential Revision: D15988346 Pulled By: driazati fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f |
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| 736bf7b46c |
Fix __constants__ for some nn modules (#21071)
Summary: A bunch of modules were missing entries for `__constants__` which was making their `__repr__`s not work. Others had `__constants__` that were not necessary since it was provided by some parent class instead. Fixes #20978 ](https://our.intern.facebook.com/intern/diff/15539518/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/21071 Pulled By: driazati Differential Revision: D15539518 fbshipit-source-id: 24bdd1ef41ef636eefd5d2bad4ab2d79646ed4f0 |
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| 3a39ce0f41 |
Fix reflection on weak modules, copy attributes (#20190)
Summary: * Constructs a new type at runtime so that `isinstance` checks work for weak modules assigned to `ScriptModule`s * Fix some extraneous names in `__constants__` * Add `in_features` and `out_features` to `nn.Linear` `__constants__` Fixes #19363 Pull Request resolved: https://github.com/pytorch/pytorch/pull/20190 Pulled By: driazati Differential Revision: D15302350 fbshipit-source-id: 1d4d21ed44ab9578a4bc2a72396a82e9bbcd387c |
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| 2a2007e5ac |
EmbeddingBag CPU forward with per_sample_weights. (#18735)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18735 ghimport-source-id: d81bef54dafd7167d2451250d7be478d3c013920 Reviewed By: cpuhrsch Differential Revision: D14851415 Pulled By: zou3519 fbshipit-source-id: cea6039e760ad571b90f0a536e420498f34be325 |
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| 173f224570 |
Turn on F401: Unused import warning. (#18598)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3 |
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| 5360984fbd |
Remove TH(CU)NN Sparse Linear (#17610)
Summary: Sparse Linear in TH(CU)NN implements sparse linear layers without using sparse matrices. It is currently not documented in PyTorch and there is no functional or module interface. This means it is unused from a PyTorch point of view. The reason for removing it is twofold: - The module uses sort, which I would like to move to ATen. - When we implement a SparseLinear layer, we would want to do it using sparse tensors, so it's not all that useful, anyway. I checked this on slack with soumith, I hope the above is an accurate representation. All bad ideas are my own. This is part of the ongoing work to move sort/topk/mode/median/kthvalue to ATen. Pull Request resolved: https://github.com/pytorch/pytorch/pull/17610 Differential Revision: D14280663 Pulled By: gchanan fbshipit-source-id: 289231d2c20626855ce2ceecd4f204b460c32378 |
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| 8852e21245 |
Correct recurrent/linear/dropout/sparse layers docstrings
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17238 Differential Revision: D14130811 Pulled By: soumith fbshipit-source-id: d3998ca7da46aec5a59220c6af489f71f3d60735 |
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| dbe6a7a9ff |
Unify the shape notation for all of the pytorch modules (#15741)
Summary: PR to update the shape notation for all of the torch.nn modules to take a unified form. The goal is to make these definitions machine-readable and those checkable by unifying the style across all of the different modules. Pull Request resolved: https://github.com/pytorch/pytorch/pull/15741 Differential Revision: D13709601 Pulled By: ezyang fbshipit-source-id: fb89a03903fdf0cd0dcf76f3e469b8582b2f3634 |
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| cdb8edce75 |
add from_pretrained method to EmbeddingBag (#15273)
Summary: The `EmbeddingBag` module does not include a `from_pretrained` method like the `Embedding` module. I added it for consistency between the two modules. Pull Request resolved: https://github.com/pytorch/pytorch/pull/15273 Differential Revision: D13547842 Pulled By: soumith fbshipit-source-id: 8ffde51ff0c1e8fc8310263b6f375da88089ff7d |
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| 6d63e9dbff |
Support Embedding + EmbeddingBag in Script + (Ignore flakey test) (#14509)
Summary: Resubmitting PR #14415 The tests added for Embedding + EmbeddingBag had random numbers as input, which affected the random number generator & caused the flakey test to break. Everything but the last two commits have already been accepted Pull Request resolved: https://github.com/pytorch/pytorch/pull/14509 Differential Revision: D13247917 Pulled By: eellison fbshipit-source-id: ea6963c47f666c07687787e2fa82020cddc6aa15 |
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| 5f07b33857 |
Revert D13219647: [pytorch][PR] Support Embedding + EmbeddingBag in Script
Differential Revision: D13219647 Original commit changeset: c90706aa6fbd fbshipit-source-id: d189e717ba0773de43d633876bc3a688830a9303 |
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| 7749804099 |
Support Embedding + EmbeddingBag in Script (#14415)
Summary: Add support for Embedding and EmbeddingBag in script. Both functions require with torch.no_grad(), which we don't have any plans to support in the near future. To work around this, I added a embedding_renorm function without derivatives. Pull Request resolved: https://github.com/pytorch/pytorch/pull/14415 Reviewed By: wanchaol Differential Revision: D13219647 Pulled By: eellison fbshipit-source-id: c90706aa6fbd48686eb10f3efdb65844be7b8717 |
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| 0db505bf27 |
Made docstrings for Embedding more accurate. (#13310)
Summary: Made the previous description for max_norm more precise, avoiding 'this' and describing what actually happens in the code. Pull Request resolved: https://github.com/pytorch/pytorch/pull/13310 Differential Revision: D12840813 Pulled By: SsnL fbshipit-source-id: 98090c884267a62ce93cd85da84252d46926dfa5 |
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| f9a99d5504 |
Specify default initialization schemes for modules in docs (#9038)
Summary: This closes #6906 . Reviewed By: ezyang Differential Revision: D8698632 Pulled By: weiyangfb fbshipit-source-id: 259c1dbdc264a8e9f83e196fa72d135babd97d48 |
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| f9926e4ce5 |
Fix EmbeddingBag max_norm option (#7959)
* fix EmbeddingBag max_norm option * flake8 * add warning to the embedding bag arg change |
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| 5f96a2d26a |
Add sparse gradient option to pretrained embedding (#7492)
* Add sparse gradient option to pretrained embedding * Add sparse gradient option to pretrained embedding * Trailing white space |
