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268 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Xuehai Pan
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8d45f555d7 |
[BE] [1/3] Rewrite super() calls in caffe2 and benchmarks (#94587)
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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Aaron Gokaslan
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8fce9a09cd |
[BE]: pyupgrade Python to 3.8 - imports and object inheritance only (#94308)
Apply parts of pyupgrade to torch (starting with the safest changes). This PR only does two things: removes the need to inherit from object and removes unused future imports. Pull Request resolved: https://github.com/pytorch/pytorch/pull/94308 Approved by: https://github.com/ezyang, https://github.com/albanD |
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Nikita Shulga
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1906eaf22f |
[BE] Get rid of future (#92596)
PyTorch has been Python-3.X+ for ages, so it's a shame to still rely on `future.utils` even in a deprecated Caffe2 codebase For the reference: https://peps.python.org/pep-0469/#migrating-directly-to-python-3 Pull Request resolved: https://github.com/pytorch/pytorch/pull/92596 Approved by: https://github.com/kit1980, https://github.com/orionr |
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Jeongmin Lee
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bca25d97ad |
[itemwise-dropout][1/x][low-level module] Implement Itemwise Sparse Feature Dropout in Dper3 (#59322)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/59322 Implement sparse feature dropout (with replacement) that can drop out individual items in each sparse feature. For example, the existing sparse feature dropout with replacement drops out whole feature (e.g., a list of page ids) when the feature is selected for drop out. This itemwise dropout assigns probability and drops out to individual items in sparse features. Test Plan: ``` buck test mode/dev caffe2/torch/fb/sparsenn:test ``` https://www.internalfb.com/intern/testinfra/testrun/281475166777899/ ``` buck test mode/dev //dper3/dper3/modules/tests:sparse_itemwise_dropout_with_replacement_test ``` https://www.internalfb.com/intern/testinfra/testrun/6473924504443423 ``` buck test mode/opt caffe2/caffe2/python:layers_test ``` https://www.internalfb.com/intern/testinfra/testrun/2533274848456607 ``` buck test mode/opt caffe2/caffe2/python/operator_test:sparse_itemwise_dropout_with_replacement_op_test ``` https://www.internalfb.com/intern/testinfra/testrun/8725724318782701 Reviewed By: Wakeupbuddy Differential Revision: D27867213 fbshipit-source-id: 8e173c7b3294abbc8bf8a3b04f723cb170446b96 |
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Adam Simpkins
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81b9aa743b |
[pytorch] Update caffe2/python to eliminate Pyre errors (#52083)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52083 This makes minor fixes in `caffe2/python` to address all errors currently reported by Pyre. I update the code to fix errors when doing so looked simple and safe, and added `pyre-fixme` comments in other places. ghstack-source-id: 121109695 Test Plan: Confirmed that Pyre no longer reports errors under `caffe2/python` Differential Revision: D26272279 fbshipit-source-id: b1eb19d323b613f23280ce9c71e800e874ca1162 |
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Samuel Marks
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e6779d4357 |
[*.py] Rename "Arguments:" to "Args:" (#49736)
Summary:
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue when I was parsing/generating from the TensorFlow codebase: inconsistent use of `Args:` and `Arguments:` in its docstrings.
```sh
(pytorch#c348fae)$ for name in 'Args:' 'Arguments:'; do
printf '%-10s %04d\n' "$name" "$(rg -IFtpy --count-matches "$name" | paste -s -d+ -- | bc)"; done
Args: 1095
Arguments: 0336
```
It is easy enough to extend my parsers to support both variants, however it looks like `Arguments:` is wrong anyway, as per:
- https://google.github.io/styleguide/pyguide.html#doc-function-args @ [`ddccc0f`](https://github.com/google/styleguide/blob/ddccc0f/pyguide.md)
- https://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @ [`9fc0fc0`](https://chromium.googlesource.com/chromiumos/docs/+/9fc0fc0/styleguide/python.md)
- https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @ [`c0ae8e3`](https://github.com/sphinx-contrib/napoleon/blob/c0ae8e3/docs/source/example_google.rst)
Therefore, only `Args:` is valid. This PR replaces them throughout the codebase.
PS: For related PRs, see tensorflow/tensorflow/pull/45420
PPS: The trackbacks automatically appearing below are sending the same changes to other repositories in the [PyTorch](https://github.com/pytorch) organisation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49736
Reviewed By: albanD
Differential Revision: D25710534
Pulled By: soumith
fbshipit-source-id: 61e8ff01abb433e9f78185c2d1d0cbd7c22c1619
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skyline75489
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46b83212d1 |
Remove unused six code for Python 2/3 compatibility (#48077)
Summary: This is basically a reborn version of https://github.com/pytorch/pytorch/issues/45254 . Ref: https://github.com/pytorch/pytorch/issues/42919 Pull Request resolved: https://github.com/pytorch/pytorch/pull/48077 Reviewed By: ngimel Differential Revision: D25687042 Pulled By: bugra fbshipit-source-id: 05f20a6f3c5212f73d0b1505b493b720e6cf74e5 |
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Tristan Rice
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dc7d8a889e |
caffe2: refactor context to allow being typed (#48340)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/48340 This changes the context managed classes from using a decorator to define them to using inheritance. Inheritance allows the python static type checking to work correctly. ``` context.define_context() class Bar(object): ... context.define_context(allow_default=True) class Foo(object): ... ``` becomes ``` class Foo(context.Managed): ... class Bar(context.DefaultManaged): ... ``` Behavior differences: * arg_name has been removed since it's not used anywhere * classes need to call `super()` in `__enter__/__exit__` methods if they override (none do) This also defines a context.pyi file to add types for python3. python2 support should not be affected Test Plan: ci buck test //caffe2/caffe2/python:context_test //caffe2/caffe2/python:checkpoint_test Reviewed By: dongyuzheng Differential Revision: D25133469 fbshipit-source-id: 16368bf723eeb6ce3308d6827f5ac5e955b4e29a |
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Bugra Akyildiz
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eec201c138 |
Add last_n_window_collector
Summary: Add `last_n_window_collector` as C2 supports and PyTorch currently does not have this operator: https://www.internalfb.com/intern/diffusion/FBS/browsefile/master/fbcode/caffe2/caffe2/operators/last_n_window_collector.cc?lines=139 ## Problem that we are solving This operator works on multiple pieces of data and collects last `n` element that has been seen. If you have the following pieces of data that has been passed around: ``` [1, 2, 3, 4] [5, 6, 7] [8, 9, 10, 11] ``` for 3 times and the number of collector is given to be 6. The expected result is: ``` [6, 7, 8, 9, 10, 11] ``` What this means is that, almost like we need a FIFO(First in First Out) mechanism where as we are passing this data through the collector, we will be pushing some other data at the end. In this particular example, in the first pass(the data is `[1, 2, 3, 4]`) , we hold `[1, 2, 3, 4]` in the queue as our queue size is 6. In the second pass(the data is `[5, 6, 7]`), we hold `[2, 3, 4, 5, 6, 7]` in the queue and since 1 is inserted the last, it will drop due to the size limitation of the queue. In the third pass(the data is `[8, 9, 10, 11]`), we hold `[6, 7, 8, 9, 10, 11]` in the queue and `2,3,4,5` are dropped due the the size of the queue. For multidimension case, when we have the following data: ``` [[1, 2], [2, 3], [3, 4], [4, 5]] [[5, 6], [6, 7], [7, 8]] [[8, 9], [9, 10], [10, 11], [11, 12]] ``` and our queue size is 6. In the first pass, we will have ` [[1, 2], [2, 3], [3, 4], [4, 5]]` In the second pass, we will have `[2, 3], [3, 4], [4, 5]] [[5, 6], [6, 7], [7, 8]]` In the third pass, we will have `[6, 7], [7, 8]] [[8, 9], [9, 10], [10, 11], [11, 12]]` ### The implementation I am using FIFO queue in Python which is in the collections library. This accepts `maxlen` argument which can be used to set the size of the queue. I am using last n indices of the tensor through list indices and in this operator, I am not doing copy. In the test plan, I have both single dimension tensors as well as multi-dimension tensors. ### Benchmark I used various different configurations and added a benchmark test. PyTorch implementation is much master than Caffe2 implementation: #### CPU Benchmark ``` torch_response.median 0.00019254473969340324 caffe_response.median 0.00030233583599794657 ``` #### GPU Benchmark ``` torch_response.mean 0.000081007429903838786 caffe_response.mean 0.00010279081099724863 ``` Test Plan: ### For CPU: ``` buck test //caffe2/torch/fb/sparsenn:test ``` ### For GPU: - Used an on-demand machine and did the following commands: ``` jf get D24435544 buck test mode/opt //caffe2/torch/fb/sparsenn:test ``` https://www.internalfb.com/intern/testinfra/testconsole/testrun/4222124688138052/ Reviewed By: dzhulgakov, radkris-git Differential Revision: D24435544 fbshipit-source-id: 8193b4746b20f2a4920fd4d41271341045cdcee1 |
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Bugra Akyildiz
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27c7158166 |
Remove __future__ imports for legacy Python2 supports (#45033)
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38 |
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Christopher Whelan
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5cd0f5e8ec |
[PyFI] Update hypothesis and switch from tp2 (#41645)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41645 Pull Request resolved: https://github.com/facebookresearch/pytext/pull/1405 Test Plan: buck test Reviewed By: thatch Differential Revision: D20323893 fbshipit-source-id: 54665d589568c4198e96a27f0ed8e5b41df7b86b |
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Xing Wang
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ebfff31e19 |
[distributedhogwild] Introducing new tags for distributed hogwild. (#42381)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42381 Introduce new tag to support distributed hogwild. Reviewed By: boryiingsu Differential Revision: D20484099 fbshipit-source-id: 5973495589e0a7ab185d3867b37437aa747f408a |
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Tristan Rice
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976e614915 |
caffe2: add PIPELINE tag (#41482)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41482 This adds a new tag for use with pipeline parallelism. Test Plan: CI Reviewed By: heslami Differential Revision: D22551487 fbshipit-source-id: 90910f458a9bce68f7ef684773322a49aa24494a |
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Eileen Pan
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f07816003a |
[2/n][Compute Meta] support analysis for null flag features
Summary: ## TLDR Support using NaN default value for missing dense features in RawInputProcessor for DPER2. In preparation for subsequent support for null flag features in compute meta. For train_eval this is already supported in DPER3 and we do not plan to support this in DPER2 train eval. Differential Revision: D22439142 fbshipit-source-id: 99ae9755bd41a5d5f43bf5a9a2819d64f3883005 |
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Will Feng (FAIAR)
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078669f6c3 |
Back out "[2/n][Compute Meta] support analysis for null flag features"
Summary: Original commit changeset: 46c59d849fa8 The original commit is breaking DPER3 release pipeline with the following failures: https://www.internalfb.com/intern/chronos/jobinstance?jobinstanceid=9007207344413239&smc=chronos_gp_admin_client&offset=0 ``` Child workflow f 202599639 failed with error: c10::Error: [enforce fail at operator.cc:76] blob != nullptr. op Save: Encountered a non-existing input blob: feature_preproc/feature_sparse_to_dense/default_float_value ``` https://www.internalfb.com/intern/chronos/jobinstance?jobinstanceid=9007207344855973&smc=chronos_gp_admin_client&offset=0 ``` Child workflow f 202629391 failed with error: c10::Error: [enforce fail at operator.cc:76] blob != nullptr. op Save: Encountered a non-existing input blob: tum_preproc/inductive/feature_sparse_to_dense/default_float_value ``` Related UBN tasks: T69529846, T68986110 Test Plan: Build a DPER3 package on top of this commit, and check that DPER3 release test `model_deliverability_test` is passing. Differential Revision: D22396317 fbshipit-source-id: 92d5b30cc146c005d6159a8d5bfe8973e2c546dd |
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Eileen Pan
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db39542509 |
[2/n][Compute Meta] support analysis for null flag features
Summary: ## TLDR Support using NaN default value for missing dense features in RawInputProcessor for DPER2. In preparation for subsequent support for null flag features in compute meta. For train_eval this is already supported in DPER3 and we do not plan to support this in DPER2 train eval. ## Overview Intern project plan to support adding dense flags for missing feature values instead of replacing with zero. ## Project plan : https://docs.google.com/document/d/1OsPUTjpJycwxWLCue3Tnb1mx0uDC_2KKWvC1Rwpo2NI/edit?usp=sharing ## Code paths: See https://fb.quip.com/eFXUA0tbDmNw for the call stack for all affected code paths. Test Plan: ## fblearner flow test 1. `flow-cli clone f197867430 --run-as-secure-group ads_personalization_systems --force-build` to build a ephemeral package and start a fblearner flow run (may fail) 2. Clone the new run and change the secure_group to `XXXX` and entitlement to `default` in the UI 3. Adds explicit_null_min_coverage flag 4. Optionally reduce `max_examples` since we only test pass/fail instead of quality. 5. Submit the run to test the change Example: f198538878 ## compare output coverages to daiquery runs 1. Randomly select null flag features from compute meta workflow output 2. Look up the feature id in feature metadata using feature name 3. Check against a daiquery sample of coverage to see if the coverage falls within guidelines. https://www.internalfb.com/intern/daiquery/workspace/275342740223489/192619942076136/ ## Sampled features: GFF_C66_ADS_USER_SUM_84_PAGE_TYPE_RATIO_EVENT_LIKE_IMPRESSION: 15694257 - original feature compute meta coverage: 0.999992 - daiquery feature coverage (10k rows): 0.69588 - null flag compute meta coverage: 0.293409 GFF_R1303_ADS_USER_SUM_7_PAGE_TYPE_COUNTER_CONVERSION: 16051183 - original feature compute meta coverage: 0.949868 - daiquery feature coverage: 0.82241 - null flag compute meta coverage: 0.151687 ## Unit tests: `buck test fblearner/flow/projects/dper/tests/workflows:ads_test` https://www.internalfb.com/intern/testinfra/testconsole/testrun/6192449504303863/ Differential Revision: D22026450 fbshipit-source-id: 46c59d849fa89253f14dc2b035c4c677cd6e3a4c |
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Eileen Pan
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4102fbdf08 |
[1/n] Allow dense NaN value in dper raw input processor output
Summary: ## TLDR Support using NaN default value for missing dense features in RawInputProcessor for *DPER2*. In preparation for subsequent support for null flag features in *compute meta*. For train_eval this is already supported in DPER3 and we do not plan to support this in DPER2 train eval. ## Overview Intern project plan to support adding dense flags for missing feature values instead of replacing with zero. Project plan : https://docs.google.com/document/d/1OsPUTjpJycwxWLCue3Tnb1mx0uDC_2KKWvC1Rwpo2NI/edit?usp=sharing ## Code paths: See https://fb.quip.com/eFXUA0tbDmNw for the call stack for all affected code paths. Test Plan: # A. DPER3 blob value inspection ## 1. Build local bento kernel in fbcode folder `buck build mode/dev-nosan //bento/kernels:bento_kernel_ads_ranking` ## 2. Use kernel `ads_ranking (local)` to print dense feature blob values n280239 ## 2.1 Try `default_dense_value = "0.0"` (default) ``` preproc_6/feature_preproc_6/dper_feature_processor_7/raw_input_proc_7/float_feature_sparse_to_dense_7/float_features [[0. ] [0. ] [0. ] [0. ] [0. ] [0. ] [0. ] [1. ] [1.7857143] [1.7777778] [1. ] [0. ] [0.5625 ] [0. ] [0. ] [0.8 ] [0. ] [1. ] [0.56 ] [0. ]] ``` ## 2.2 Try `default_dense_value = "123"` ``` preproc_2/feature_preproc_2/dper_feature_processor_3/raw_input_proc_3/float_feature_sparse_to_dense_3/float_features [[123. ] [123. ] [123. ] [123. ] [123. ] [123. ] [123. ] [ 1. ] [ 1.7857143] [ 1.7777778] [ 1. ] [123. ] [ 0.5625 ] [123. ] [123. ] [ 0.8 ] [123. ] [ 1. ] [ 0.56 ] [123. ]] ``` ## 2.3 Try `default_dense_value = float("nan")` ``` RuntimeError: [enforce fail at enforce_finite_op.h:40] std::isfinite(input_data[i]). Index 0 is not finite (e.g., NaN, Inf): -nan (Error from operator: input: "unary_4/logistic_regression_loss_4/average_loss_4/average_loss" name: "" type: "EnforceFinite" device_option { random_seed: 54 }) ``` which is expected due to nan input. # B. Unit test `buck test fblearner/flow/projects/dper/tests/preprocs:raw_feature_extractor_test` https://www.internalfb.com/intern/testinfra/testconsole/testrun/5348024586274923/ {F241336814} Differential Revision: D21961595 fbshipit-source-id: 3dcb153b3c7f42f391584f5e7f52f3d9c76de31f |
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Huayu Li
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cd4c3b48a6 |
Add LN after specialzied output embeddings and flexible LCE (#35178)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35178 * add layer norm (LN) after specialized output embeddings * add flexible lce inside specialized module Test Plan: * unit-tests * buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_4 -- * buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_6 -- * workflows * flexible lce: f177025325 {F232112501} * LN: f177025301 {F232112982} Differential Revision: D20586281 fbshipit-source-id: 664e77cb4cb5bec6646cafd2e4afb88aff27df03 |
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Ralf Gommers
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78d5707041 |
Fix type annotations and make MyPy run on torch/ (#36584)
Summary: This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages. As a result, running `mypy` in the root dir of the repo now runs on: - `torch/` - `aten/src/ATen/function_wrapper.py` (the only file already covered in CI) In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`. Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through): <details> ``` torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx' torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch" torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2' torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2' torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx' torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util' torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework' torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer' torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...") torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items" torch/nn/_reduction.py:27: error: Name 'Optional' is not defined torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/_reduction.py:47: error: Name 'Optional' is not defined torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs torch/nn/modules/utils.py:27: error: Name 'List' is not defined torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment" torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction' caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed) caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs caffe2/python/__init__.py:7: error: Module has no attribute "CPU" caffe2/python/__init__.py:8: error: Module has no attribute "CUDA" caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN" caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL" caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL" caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP" caffe2/python/__init__.py:13: error: Module has no attribute "HIP" caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"? caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"? caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:35: error: Module has no attribute "cuda" caffe2/python/__init__.py:37: error: Module has no attribute "cuda" caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory" torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C' torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import) torch/_tensor_str.py:164: error: Not all arguments converted during string formatting torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/_linalg_utils.py:26: error: Name 'Optional' is not defined torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Optional' is not defined torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Optional' is not defined torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Optional' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched' torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...") torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...") torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm") torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Optional' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/nn/init.py:109: error: Name 'Tensor' is not defined torch/nn/init.py:126: error: Name 'Tensor' is not defined torch/nn/init.py:142: error: Name 'Tensor' is not defined torch/nn/init.py:165: error: Name 'Tensor' is not defined torch/nn/init.py:180: error: Name 'Tensor' is not defined torch/nn/init.py:194: error: Name 'Tensor' is not defined torch/nn/init.py:287: error: Name 'Tensor' is not defined torch/nn/init.py:315: error: Name 'Tensor' is not defined torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction' torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer' torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_" torch/jit/_builtins.py:80: error: Module has no attribute "stft" torch/jit/_builtins.py:81: error: Module has no attribute "cdist" torch/jit/_builtins.py:82: error: Module has no attribute "norm" torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm" torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm" torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C' torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...") torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import) torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass" torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache" torch/_lowrank.py:11: error: Name 'Tensor' is not defined torch/_lowrank.py:13: error: Name 'Optional' is not defined torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Optional' is not defined torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Optional' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:82: error: Name 'Tuple' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:130: error: Name 'Tensor' is not defined torch/_lowrank.py:130: error: Name 'Optional' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:130: error: Name 'Tuple' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:167: error: Name 'Tensor' is not defined torch/_lowrank.py:167: error: Name 'Optional' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:167: error: Name 'Tuple' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases torch/quantization/observer.py:45: error: Invalid base class "ABC" torch/quantization/observer.py:127: error: Name 'Tensor' is not defined torch/quantization/observer.py:127: error: Name 'Tuple' is not defined torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:192: error: Name 'Tensor' is not defined torch/quantization/observer.py:192: error: Name 'Tuple' is not defined torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:534: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tuple' is not defined torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:894: error: Cannot determine type of 'max_val' torch/quantization/observer.py:894: error: Cannot determine type of 'min_val' torch/quantization/observer.py:899: error: Cannot determine type of 'min_val' torch/quantization/observer.py:902: error: Name 'Tensor' is not defined torch/quantization/observer.py:925: error: Name 'Tensor' is not defined torch/quantization/observer.py:928: error: Cannot determine type of 'min_val' torch/quantization/observer.py:929: error: Cannot determine type of 'max_val' torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric" torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:289: error: Module has no attribute "ops" torch/nn/quantized/functional.py:290: error: Module has no attribute "ops" torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops" torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops" torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig' torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__" torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views' torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16 torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15 torch/functional.py:114: error: Name 'Tuple' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:114: error: Name 'Optional' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]" torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:204: note: Possible overload variants: torch/functional.py:204: note: def __setitem__(self, int, int) -> None torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:204: note: def __getitem__(self, int) -> int torch/functional.py:204: note: def __getitem__(self, slice) -> List[int] torch/functional.py:207: error: "Tensor" has no attribute "copy_" torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:212: note: Possible overload variants: torch/functional.py:212: note: def __setitem__(self, int, int) -> None torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:212: note: def __getitem__(self, int) -> int torch/functional.py:212: note: def __getitem__(self, slice) -> List[int] torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:334: error: Name 'Optional' is not defined torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]" torch/functional.py:431: error: Module has no attribute "stft" torch/functional.py:766: error: Module has no attribute "cdist" torch/functional.py:768: error: Module has no attribute "cdist" torch/functional.py:770: error: Module has no attribute "cdist" torch/functional.py:775: error: Name 'Optional' is not defined torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'Optional' is not defined torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'number' is not defined torch/functional.py:780: error: Name 'norm' already defined on line 775 torch/functional.py:785: error: Name 'Optional' is not defined torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:785: error: Name 'number' is not defined torch/functional.py:785: error: Name 'norm' already defined on line 775 torch/functional.py:790: error: Name 'Optional' is not defined torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:790: error: Name 'norm' already defined on line 775 torch/functional.py:795: error: Name 'norm' already defined on line 775 torch/functional.py:960: error: Name 'Any' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any") torch/functional.py:960: error: Name 'Tuple' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized" torch/functional.py:1041: error: Name 'Optional' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1041: error: Name 'Tuple' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1056: error: Name 'Optional' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1056: error: Name 'Tuple' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]") torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]") torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]") torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase" torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]") torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None") torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric" torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric" torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module' torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68 torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass" torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524 torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer' torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching' torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]") torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]") torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...") torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/__init__.py:158: error: Name 'transforms' is not defined torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse' torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import) torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import) torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore' torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore' torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584 Reviewed By: seemethere, ailzhang Differential Revision: D21155985 Pulled By: ezyang fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1 |
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Huayu Li
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c75d06d854 |
Move gating part of SparseFeatureGating to local
Summary: in dper2, local net is hard-coded by whitelisting some layers. Add SparseFeatureGating related layers to local net explicitly.
Test Plan:
* workflow: f167812211
* QRT: fall back looks normal
{F228442018}
Differential Revision: D19852280
fbshipit-source-id: 6fecc3d745c3f742d029575a7b9fe320618f1863
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Brian Wignall
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f326045b37 |
Fix typos, via a Levenshtein-type corrector (#31523)
Summary: Should be non-semantic. Uses https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines to find likely typos, with https://github.com/bwignall/typochecker to help automate the checking. Uses an updated version of the tool used in https://github.com/pytorch/pytorch/pull/30606 . Pull Request resolved: https://github.com/pytorch/pytorch/pull/31523 Differential Revision: D19216749 Pulled By: mrshenli fbshipit-source-id: 7fd489cb9a77cd7e4950c1046f925d57524960ea |
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David Gisser
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91bdb872ce |
fix spelling mistake: excpected -> expected
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28817 Differential Revision: D18544562 Pulled By: dgisser fbshipit-source-id: 51f728e807f9c4bb30f58585d5b6f436cb880153 |
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Silun Wang
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28c1258f18 |
Scale init for batch-norm and layer-norm (#31983)
Summary: Per discussion with Fei Tian, we need to add a `scale_init_value` to scale down the output of normalization such as batch-norm and layer-norm. Currently we have `sparse_normalization_options` to normalize embedding pooling output. By default, scale = 1.0, we found it's better to set scale from 0.025 to 0.1 https://fb.quip.com/MiKUAibEaYhH Besides, I am removing the tags from normalizers because it makes more sense to calculate norm ops in distributed trainers, not ps. Pull Request resolved: https://github.com/pytorch/pytorch/pull/31983 Test Plan: Testing LN and BN after sum-pooling -- baseline f160348514 LN: f160348609 BN: f160348710 {F226106518} Layer norm after sum-pooling fwd_net https://fburl.com/sa4j207n Layer norm after dot-prod fwd_net https://fburl.com/twggwyvb ## Unit Tests Testing normalization after pooling ``` buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_4 -- test_sparse_pooling_batch_normalization buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_4 -- test_dense_sparse_pooling_batch_normalization buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_4 -- test_sparse_pooling_layer_normalization buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test_4 -- test_dense_sparse_pooling_layer_normalization ``` Testing normalization after dot-prod ``` buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_last_layer_use_batch_norm buck test caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_last_layer_use_layer_norm ``` Differential Revision: D19277618 Pulled By: SilunWang fbshipit-source-id: ea323e33e3647ba55d2e808ef09d94ad7b45b934 |
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Fei Tian
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809ee9d04c |
Enable personalized FC weight_init and sparse_emb weight_init (#31707)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/31707 Change the initialization value for FC weight init and sparse embedding lookup init. Previous default initialization is uniform(-\sqrt(1/input_dim), \sqrt(1/input_dim)); Now pass into a flexible hyperparameter, say \alpha into it, to change into uniform(-\sqrt(\alpha/input_dim), \sqrt(\alpha/input_dim)); Reviewed By: chonglinsun Differential Revision: D18825615 fbshipit-source-id: 4c5f2e07f2b3f5d642fd96d64dbf68892ebeb30b |
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Summer Deng
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a42d093db2 |
FCTransposed to FbFCPacked (#29766)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29766 Add FbgemmPackTranspose op to support the packing on FCTransposed weights Add FCTransposed to FbFCPacked transformation to Dper fp16 exporter Test Plan: ``` buck test mode/opt caffe2/caffe2/fb/fbgemm:fb_fc_packed_op_test ``` ``` buck test mode/opt caffe2/caffe2/python:layers_test ``` Differential Revision: D18482306 fbshipit-source-id: e8f1947b3d0d04892293509ebf88742f5f0f5997 |
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Brian Wignall
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e7fe64f6a6 |
Fix typos (#30606)
Summary: Should be non-semantic. Uses https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines to find likely typos. Pull Request resolved: https://github.com/pytorch/pytorch/pull/30606 Differential Revision: D18763028 Pulled By: mrshenli fbshipit-source-id: 896515a2156d062653408852e6c04b429fc5955c |
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Yangxin Zhong
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ed788ec780 |
Linearizable Label: Class Weights, Allow Missing Label, and Average by Batch Size (#29707)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29707 In D17885977, Linearizable label (a multi-class classification) was implemented in MTML. In this diff, we add several items for Linearizable label: - Assigning different weights to each class through ```model_def.tasks[i].class_weights```. - This option is a dictionary, the keys of which are indices of the classes and the values of which are weights for each class. - For example, if a linearizable-label task has 4 classes and its ```class_weights = {"0": 1, "1": 0.1, "2": 0.1, "3": 0.01}```, it means that in the loss function of this task, we assign weight 1 to its first class, weight 0.1 to its second and third class, and weight 0.01 to its forth class. The index/order of classes follows the logic of linearizable label. - Note that when you assign different weights to different classes, you need to correct the calibration by setting an appropriate ```model_def.tasks[i].calibration.linearizable_class_weight```. Basically, the class weights in calibration should be the reciprocals of the class weights in loss function. So the ```calibration.linearizable_class_weight = {"0": 1, "1": 10, "2": 10, "3": 100}``` for the example above. - Example FBLearner job: f150763093 - We also support ```model_def.allow_missing_label_with_zero_weight``` for linearizable label, which will ignore those examples with first label missing, by assigning zero weights to them in loss function. - We need to set ```allow_missing_label_with_zero_weight = true``` to enable it. - Example FBLearner job: f150763093 - Last but not least, we update caffe2 operator ```SoftmaxWithLoss``` to support loss averaged by batch size. - We need to set ```model_def.tasks[i].loss.softmaxLoss.average_by_batch_size = true``` to enable it. - Previously, the loss was averaged by weight sum of examples in batch, which is still the default behavior now (when ```average_by_batch_size = null``` or ```average_by_batch_size = false```). - Without this new feature, the calibration will be incorrect when applying non-equal-weight training among different classes to a linearizable task. - Example FBLearner job with ```average_by_batch_size = true``` results in a correct calibration: f150763093 - Example FBLearner job with ```average_by_batch_size = null``` results in an incorrect calibration: f150762990 Test Plan: buck test caffe2/caffe2/fb/dper/layer_models/tests:mtml_test_2 -- test_linearizable_label_task_with_class_weights buck test caffe2/caffe2/fb/dper/layer_models/tests:mtml_test_2 -- test_linearizable_label_task_with_zero_weight buck test caffe2/caffe2/fb/dper/layer_models/tests:mtml_test_2 -- test_linearizable_label_task_average_by_batch_size All tests passed. full canary: https://fburl.com/fblearner/troznfgh Reviewed By: chenshouyuan Differential Revision: D18461163 fbshipit-source-id: aaf3df031406ae94f74e2e365b57e47409ef0bfe |
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Kevin Chen
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1189f559cc |
Creating new layer FCWithBootstrap used in bootstrapping uncertainty approach (#29152)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29152 Bootstrapping uncertainty approach: bootstrap the last layer before the last fully-connected layer. FCWithBootstrap is a new layer to handle the logic for the bootstrapping process. Goal: - return a struct with the bootstrapped indices and bootstrapped predictions from this layer - separate the functionality in the train_net and eval_net - save the bootstrapped FC in this object so that the eval_net can use them during prediction time Reviewed By: wx1988 Differential Revision: D17822429 fbshipit-source-id: 15dec501503d581aeb69cb9ae9e8c3a3fbc7e7b5 |
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Peiyao Zhou
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46fefc98e2 |
Change dper3 loss module to match dper2 (#28265)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28265 Fix the difference in dper3 and dper2 when regressionLoss is used. Test Plan: test using dper2 model id f134632386 Comparison tool output before change: ``` FOUND OP DIFFERENT WITH DPER2!!! OP is of type ExpandDims OP inputs ['supervision:label'] OP outputs ['sparse_nn/regression_loss/mean_squared_error_loss/ExpandDims:0'] =============================== Finished all dper3 ops, number of good ops 11, bad ops 1, skipped 26 run_comparison for dper2 / dper3 nets running time: 0.0020143985748291016 result type: <class 'NoneType'> result: None ``` After change: ``` FOUND OP DIFFERENT WITH DPER2!!! OP is of type ExpandDims OP inputs ['sparse_nn_2/regression_loss_2/mean_squared_error_loss_8/Squeeze:0_grad'] OP outputs ['sparse_nn_2/over_arch_2/linear_2/FC_grad'] =============================== Finished all dper3 ops, number of good ops 19, bad ops 1, skipped 16 run_comparison for dper2 / dper3 nets running time: 0.0017991065979003906 result type: <class 'NoneType'> result: None ``` dper2 label part of net P111794577 dper3 label part of net after change P116817194 Reviewed By: kennyhorror Differential Revision: D17795740 fbshipit-source-id: 9faf96f5140f5a1efdf2985820bda3ca400f61fa |
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Long Jin
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76bf8f62f7 |
fix loss_weight for self_supervision
Summary: previously loss_weight is not used correctly for self-supervision branch Test Plan: buck test mode/dev-nosan //caffe2/caffe2/fb/dper/layer_models/models/experimental/tests:tum_test Reviewed By: xianjiec Differential Revision: D17862312 fbshipit-source-id: 554b793a5caa3886946c54333c81a0d8a10230d9 |
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Alyssa Wang
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4b1096c652 |
Fix predict net issue with LRU hash eviction
Summary: We are seeing error "[enforce fail at BlackBoxPredictor.cpp:134] ! !parameter_workspace->HasBlob(out). Net REMOTE of type predict_net writes to blob cat/NGRAM_QRT_VERSIONS_x_EVENT_TYPE_AUTO_FIRST_X/Pool_Option_0/Repeat_0/sparse_lookup/w which exists in the parameter workspace" in online testing for calibration models. I'm suspecting it's due to the op CopyRowsToTensorOp are being used in prediction Test Plan: f143080108 offline predict net does not contain CopyRowsToTensorNet, which looks right. Waiting for Olga to test online behavior dper2 canary: https://fburl.com/fblearner/sv3o3yj1 Differential Revision: D17741823 fbshipit-source-id: 19721b632b5ea9ebfa1ef9ae0e99d3a10c926287 |
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Benny Chen
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d23d62cb1e |
Fix unaries to export fp16 instead of fp32 when rest of the model export to int8
Summary: Currently accelerators does not have the concept for fp32, it only has understandings of fp16 and int8 in terms of data input. In order to fixe the issue here, we want to make sure unaries are turned into fp16 when we have the int8 exporter turned on. Reviewed By: kennyhorror Differential Revision: D17743791 fbshipit-source-id: 7322d23eb12ac3f813b525fc0ddd066f95c8ca85 |
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Lei Zhang
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0e8d4836e4 |
add feature name into module and update position weighted to match dper2
Test Plan: The notebook showed no diff for id score list https://our.intern.facebook.com/intern/anp/view/?id=154764 Reviewed By: alyssawangqq Differential Revision: D17649974 fbshipit-source-id: 84cb4ae372fc215295c2d0b139d65f4eacafae4a |
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Lin Jiang
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1f158adeee |
Add support for attention weight in SparseLookup (#26748)
Summary: Support attention weights input to SparseLookup. In attention sum pooling, if attention weights can be pre-calculated before embedding lookup, they can be passed to SparseLookup and processed by SparseLengthsWeightedSum op. One example is id_score attention sum pooling. Essentially the net is converted from: LengthsSum(Mul(Gather(keys, w), att_weight)) to: SpaseLenghtsWeightedSum(keys, w, att_weight) It unblocks potential efficiency gain with distributed training. Pull Request resolved: https://github.com/pytorch/pytorch/pull/26748 Test Plan: unit test Reviewed By: chocjy Differential Revision: D17553345 Pulled By: wheatkit fbshipit-source-id: 60cc3c4b0bc1eade5459ac598e85286f3849a412 |
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Swati Rallapalli
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e63addfff6 |
Exponential decay of the weight of task loss (#27508)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27508 Implemented a simple exponential decay of the weight of lr loss function, with a lower bound. Test Plan: buck test //caffe2/caffe2/fb/dper/layer_models/tests:mtml_test -- test_task_weight_decay https://our.intern.facebook.com/intern/testinfra/testrun/3377699729136308 canary: f140103452 Reviewed By: chenshouyuan Differential Revision: D17524101 fbshipit-source-id: 9a653e21a4ecb74dfc4ac949c9e3388f36ef3a20 |
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Xing Wang
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a1513dced3 |
Integrate FC fp16 exporter into Dper2 (#26582)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26582 Add the blob quantization. replace the op in the eval/predictor net. Test Plan: # Unit test: ----- buck build fblearner/flow/projects/dper/tests/validators:test_exporter_options_validators ./buck-out/gen/fblearner/flow/projects/dper/tests/validators/test_exporter_options_validators#binary.par ---- buck build caffe2/caffe2/fb/dper/layer_models/tests:exporter_test ./buck-out/gen/caffe2/caffe2/fb/dper/layer_models/tests/exporter_test-2.7#binary.par Reviewed By: chocjy Differential Revision: D17439720 fbshipit-source-id: 68de5d0322b0111aeca5ed552210bf80a4cddc78 |
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Xing Wang
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73ae23a4ea |
add support for real4bits quant (#25426)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/25426 Add embedding table 4bit quantization support. * add the conversion from fp32 to int4. * using brew to pass the context so that the 4bit operators are added when generating the predictor net. Reviewed By: kennyhorror, chocjy Differential Revision: D16859892 fbshipit-source-id: a06c3f0b56a7eabf9ca4a2b2cb6c63735030d70b |
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Swati Rallapalli
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c47ccfd01d |
Enable variable size embedding (#25782)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/25782 Enable variable size embedding for dot processor. We split the embedding matrix into multiple towers, based on the embedding size and perform dot product in a loop over each of the towers and finally concatenate all the dot product outputs. Test Plan: buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1: https://our.intern.facebook.com/intern/testinfra/testrun/3659174703037560 Specific unit tests -- buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_per_feature_emb_dim https://our.intern.facebook.com/intern/testinfra/testrun/3377699726358808 Reviewed By: chenshouyuan Differential Revision: D16690811 fbshipit-source-id: 8f5bce5aa5b272f5f795d4ac32bba814cc55210b |
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Xing Wang
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8a8844dc83 |
Add the sparse feature information during logging in sparse lookup layer (#24863)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24863 Add the sparse feature name in logging for ease of debugging Test Plan: ./buck-out/gen/caffe2/caffe2/fb/dper/layer_models/sparse_nn/pooling_test#binary.par -r test_simple_sum_pooling_named_exception Another test for id_score_list. the original sparse_key is equivalent to get_key(self.input_record)() P98343716 ./buck-out/gen/caffe2/caffe2/python/layers_test-2.7#binary.par -r test_get_key Reviewed By: chocjy Differential Revision: D16901964 fbshipit-source-id: 2523de2e290aca20afd0b909111541d3d152a588 |
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Yu Shi
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43a2fd0e24 |
Support focal loss in MTML
Summary: [Not in need of review at this time] Support focal loss in MTML (effectively dper2 in general) as described in https://arxiv.org/pdf/1708.02002.pdf. Adopt approach similar to Yuchen He's WIP diff D14008545 Test Plan: Passed the following unit tests buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_lr_loss_based_focal_loss buck test //caffe2/caffe2/fb/dper/layer_models/tests:mtml_test_2 -- test_mtml_with_lr_loss_based_focal_loss buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_lr_loss_based_focal_loss_with_stop_grad_in_focal_factor Passed ./fblearner/flow/projects/dper/canary.sh; URL to track workflow runs: https://fburl.com/fblearner/446ix5q6 Model based on V10 of this diff f133367092 Baseline model f133297603 Protobuf of train_net_1 https://our.intern.facebook.com/intern/everpaste/?color=0&handle=GEq30QIFW_7HJJoCAAAAAABMgz4Jbr0LAAAz Reviewed By: hychyc90, ellie-wen Differential Revision: D16795972 fbshipit-source-id: 7bacae3e2255293d337951c896e9104208235f33 |
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Bin Wen
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e78dad3593 |
Add BPR loss to TTSN (#24439)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24439 many literatures mentioned BPR is useful for improving recommendation quality. Add a BPR loss so that we can train TTSN with it. Would like to see if it can improve retrieval models. reference: https://arxiv.org/pdf/1205.2618.pdf Reviewed By: dragonxlwang Differential Revision: D16812513 fbshipit-source-id: 74488c714a37ccd10e0666d225751a845019eb94 |
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Fan Wang
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59094c409e |
Refactor and expose metadata of tum_history layer for online prediction
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24290 Reviewed By: xianjiec Differential Revision: D16570968 fbshipit-source-id: f68d42f3a8e1a6c8d30e00c2dd7f7efc1fb35d7c |
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Kevin Wilfong
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88b1f6619e |
Return list of AccessedFeatures from get_accessed_features (#23983)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23983 While testing I realized that model layers can extract different types of features from the same column. For example, MultifeedFeaturesTransform uses float and ID list features from the "features" column. get_accessed_features returns a map from column to AccessedFeatures, and AccessedFeatures only has the feature IDs for one feature type. This is incompatible with have multiple types of features per column, one type ends up overwriting another in the map. To fix this, I've modified get_accessed_features to return a map from column to a list of AccessedFeatures objects. Reviewed By: itomatik Differential Revision: D16693845 fbshipit-source-id: 2099aac8dc3920dd61de6b6ad5cf343c864803bc |
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Frank Jiang
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1439152e72 |
Make hashing default for bucket-weighted pooling (#24266)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24266 As title Reviewed By: huginhuangfb Differential Revision: D16775870 fbshipit-source-id: f919fdffa014ef3ce9a69fe173dd240e91813c3e |
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Levent Ertoz
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8d4956fd02 |
hook up dropout sparse with replacement operator
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23183 Reviewed By: ffjiang Differential Revision: D16428262 fbshipit-source-id: 0d6e17d15c898629bbd2826441f2c9701a78b0bd |
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Kevin Wilfong
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3ca7c0ffdb |
Add get_accessed_features function to ModelLayer class (#23036)
Summary: We need a way to figure get a complete list fo features that are used in training a model. One way to do this is to make it possible to get the list of features used in each Model Layer. Then once the model is complete we can go through the layers and aggregate the features. I've introduced a function to expose that information here, get_accessed_features, and implemented it in the FeatureSparseToDense layer to start with. I've tried to include the minimum amount of information to make this useful, while making it easy to integrate into the variety of model layers. This is, for example, why AccessedFeatures does not contain feature_names which is not always present in a model layer. I debated whether or not to include feature_type, but I think that's useful enough, and easy enough to figure out in a model layer, that it's worth including. Pull Request resolved: https://github.com/pytorch/pytorch/pull/23036 Test Plan: Added a unit test to verify the behavior of get_accessed_features in FeatureSparseToDense. aml_dper2-fblearner-flow-integration-tests failed due to a known issue D16355865 aml_dper3-fblearner-flow-integration-tests failed due to a known issue T47197113 I verified no tests in the integration tests failed to issues other than those known ones. DPER2 canaries: https://fburl.com/fblearner/1217voga Reviewed By: volkhin Differential Revision: D16365380 Pulled By: kevinwilfong fbshipit-source-id: 2dbb4d832628180336533f29f7d917cbad171950 |
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Alyssa Wang
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d9e15bccb0 |
Perform weight re-init for embedding table in sparse_lookup.py (#22348)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/22348 This is the last step of LRU hash eviction weight re-init. This diff checks if there's evicted values in sparse_lookup, if so call op created in D15709866 to re-init the values for indicies in evicted_values. Also created gradient op for the operator. The gradient op just passes the output gradient as input gradient. Reviewed By: itomatik Differential Revision: D16044736 fbshipit-source-id: 9afb85209b0de1038c5153bcb7dfc5f52e0b2abb |
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Alyssa Wang
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bb07f2d063 |
Pass LRU hash output evicted_values to SparseLookup (#21389)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/21389 As titled. To do weight re-init on evicted rows in embedding table, we need to pass the info of the evicted hashed values to SparseLookup, which is the layer model responsible for constructing the embedding table and do pooling. To pass evicted values, we need to adjust the output record of lru_sparse_hash to include the evicted values, and add optional input to all processors that needs to take in sparse segment. For SparseLookup to get the evicted values, its input record needs to be adjusted. Now the input record can have type IdList/IdScoreList/or a struct of feature + evicted values Reviewed By: itomatik Differential Revision: D15590307 fbshipit-source-id: e493881909830d5ca5806a743a2a713198c100c2 |
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Frank Jiang
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84a2d5d7aa |
Add hashing to bucket-weighted pooling (#20673)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20673 Add option to bucket-weighted pooling to hash the bucket so that any cardinality score can be used. Reviewed By: huginhuangfb Differential Revision: D15003509 fbshipit-source-id: 575a149de395f18fd7759f3edb485619f8aa5363 |
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Hongyu Xiong
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76a250d590 |
Add new regression loss function type to FBLearner (#21080)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/21080 Add Huber loss as a new option for regression training (refer to TensorFlow implementation: https://fburl.com/9va71wwo) # huber loss def huber(true, pred, delta): error = abs(true-pred) loss = 0.5 * min(error, delta)^2 + delta * max(error - delta, 0) return mean(loss) As a combination of MSE loss (`x < delta`) and MAE loss (`x >= delta`), the advantage of Huber loss is to reduce the training dependence on outlier. One thing worth to note is that Huber loss is not 2nd differential at `x = delta`. To further address this problem, one could consider adopt the loss of `LOG(cosh(x))`. Reviewed By: chintak Differential Revision: D15524377 fbshipit-source-id: 73acbe2728ce160c075f9acc65a1c21e3eb64e84 |