Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74662
Previously, we would not emit a check that `concrete_args` with value `None` matched that value during runtime. This fixes that and improves some of the warning messages
Test Plan: Imported from OSS
Reviewed By: Chillee
Differential Revision: D35137362
Pulled By: jamesr66a
fbshipit-source-id: 222a2c8a907748f90290f1c1b4ab8012b46099a0
(cherry picked from commit b960405ad87e57dcf62ca25dd4d4bdfc34c8744c)
Summary: Android only support a few scalar types as model return value. This diff improved the error message so user can know which type is not supported.
Test Plan: verified unsupported scalar type is printed
Differential Revision: D35104788
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74660
Approved by: https://github.com/kit1980
SSH setup was being hidden away in the setup step for both linux and
windows, this moves it out to it's own step so that users can know where
to click to get ssh details
Signed-off-by: Eli Uriegas <eliuriegasfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74773
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Approved by: https://github.com/suo, https://github.com/malfet
Summary:
Isn't foolproof since it doesn't include transitive deps of these python scripts, but it's better than nothing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74750
Reviewed By: bdhirsh
Differential Revision: D35145749
Pulled By: wconstab
fbshipit-source-id: ccd77cf18f68cc66c790f41f111833eca4101dac
(cherry picked from commit 5ef757ffd27837eb2b3c98935d66aecb1fc5acf9)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74241
Adds the following changes:
- During collection, replaces the vector of vector of int shapes, and vector of string dtypes. Instead pack the IValue details into InputOutputEncoder as flat AppendOnlyLists.
- This will save each IValue with a enum tag, metadata holding its dim and dtype, and the shapes.
- During Post-Processing, re-construct the vectors that are originally expected (struct Inputs).
Reviewed By: chaekit
Differential Revision: D34823546
Pulled By: aaronenyeshi
fbshipit-source-id: 718fccaa8aab16128da986d665564a8fef5436c8
(cherry picked from commit 96a47c068e55220e7b7224c8a1935033859b5cd2)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74758
See Title
Test Plan: NA
Reviewed By: jamesr66a
Differential Revision: D35147762
fbshipit-source-id: 34572bfb3aef5e14a06fe27dc3008308b40bdc34
(cherry picked from commit 190f429cdf6381b7d2c955ed9e9a2a62930d0582)
Summary:
This is the very first step for the UCC-NCCL integration. This PR lets `ProcessGroupNCCL` load the `torch_ucc.so` if the user specifies an environmental variable `TORCH_UCC_LIBRARY_PATH`. If this environment variable is not specified by the user, then there will be no visible change.
In the future, we may want to make PyTorch smart enough to automatically detect the `torch_ucc.so` in the user's system, but before doing that, I believe we should first make sure that `ProcessGroupUCC` is very well tested.
Note that in this PR, `ProcessGroupNCCL` just loads the library but will not use it. I am trying to make PRs small, so the usage of `torch_ucc.so` will be submitted in later PRs.
This PR requires the change in https://github.com/facebookresearch/torch_ucc/pull/56, otherwise `torch_ucc.so` can not be successfully loaded. But his PR can be landed separately without waiting for https://github.com/facebookresearch/torch_ucc/pull/56 because, in PyTorch's unit tests, UCC is never used or tested.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69552
Reviewed By: mruberry
Differential Revision: D34675212
Pulled By: jiayisuse
fbshipit-source-id: a3d1fb98340dbe3a931af555423863efd381f1ae
(cherry picked from commit 3778b6fabe70c26b5a65e6ddec641d2ef9113cd1)
Before:
```
Traceback (most recent call last):
File "/Users/pbelevich/PycharmProjects/PiPPy/test/t5_test.py", line 37, in <module>
t5_pipe_output = t5_pipe(input_ids=t5_input, decoder_attention_mask=None, decoder_input_ids=decoder_input_ids)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/Users/pbelevich/PycharmProjects/PiPPy/pippy/IR.py", line 251, in forward
return self.executor.run(*executor_args)
File "/Users/pbelevich/PycharmProjects/PiPPy/pippy/IR.py", line 155, in run
return super().run(*args, initial_env=initial_env)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/interpreter.py", line 121, in run
self.env[node] = self.run_node(node)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/interpreter.py", line 148, in run_node
return getattr(self, n.op)(n.target, args, kwargs)
File "/Users/pbelevich/PycharmProjects/PiPPy/pippy/IR.py", line 170, in call_module
return super().call_module(target, args, kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/interpreter.py", line 265, in call_module
return submod(*args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e.with_traceback(None)
AttributeError: 'NoneType' object has no attribute 'dtype'
```
After:
```
Traceback (most recent call last):
File "/Users/pbelevich/PycharmProjects/PiPPy/test/t5_test.py", line 37, in <module>
t5_pipe_output = t5_pipe(input_ids=t5_input, decoder_attention_mask=None, decoder_input_ids=decoder_input_ids)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/Users/pbelevich/PycharmProjects/PiPPy/pippy/IR.py", line 251, in forward
return self.executor.run(*executor_args)
File "/Users/pbelevich/PycharmProjects/PiPPy/pippy/IR.py", line 155, in run
return super().run(*args, initial_env=initial_env)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/interpreter.py", line 121, in run
self.env[node] = self.run_node(node)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/interpreter.py", line 148, in run_node
return getattr(self, n.op)(n.target, args, kwargs)
File "/Users/pbelevich/PycharmProjects/PiPPy/pippy/IR.py", line 170, in call_module
return super().call_module(target, args, kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/interpreter.py", line 265, in call_module
return submod(*args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 620, in wrapped_call
return cls_call(self, *args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 620, in wrapped_call
return cls_call(self, *args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 620, in wrapped_call
return cls_call(self, *args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 620, in wrapped_call
return cls_call(self, *args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 620, in wrapped_call
return cls_call(self, *args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 630, in wrapped_call
raise e
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/fx/graph_module.py", line 622, in wrapped_call
return super(cls, self).__call__(*args, **kwargs)
File "/Users/pbelevich/miniconda3/envs/PiPPy/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "<eval_with_key>.42", line 74, in forward
File "/Users/pbelevich/PycharmProjects/pbelevich-transformers/src/transformers/utils/fx.py", line 180, in wrapper
return func(*args, **kwargs)
File "/Users/pbelevich/PycharmProjects/pbelevich-transformers/src/transformers/modeling_utils.py", line 256, in create_extended_attention_mask_for_decoder
causal_mask = causal_mask.to(attention_mask.dtype)
AttributeError: 'NoneType' object has no attribute 'dtype'
```
The last lines of stack trace show where the problem is
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74655
Approved by: https://github.com/ansley, https://github.com/rohan-varma
Use nvidia cuda image without cudnn for cudnn 8 and up.
We want to decouple the CUDA and cudnn versions so that we can evolve these version separately.
We want to use cudnn 8.3.2 for following CUDA versions 11.3, 11.5 and 11.6.
We are using Official Nvidia Cuda ubuntu image. And installing cudnn 8.3.2 on top of it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74545
Approved by: https://github.com/malfet
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74601
Currently the behavior for general tensor shape op and general tensor value op are the same, so we can remove
this flag and merge with the is_general_tensor_value_op flag.
is_general_tensor_value_op flag is used in two places in prepare:
(1). dtype propgation: we only do dtype propgation when this flag is true (this will be refactor in the future to be more systematic)
(2). observer sharing, we'll use the input observer instance as output observer for an op if this flag is True
Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
Imported from OSS
Reviewed By: george-qi
Differential Revision: D35071438
fbshipit-source-id: 5e8f5fd84e37db0433a63fe0a0e212ce3c5908d6
(cherry picked from commit b4bbc9fa0e65f3768eb97ca8e84b7cbd7e840b67)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74353
Repatched `d00de0d43598522b8f6ab2de553b6aaf6768faa5` by Nora Belrose (norabelrose). With following changes:
* Register fake source of generated methods in linecache so that inspect.get_source will succeed.
* this patching is only triggered if the given dataclass passed to torch.jit.script previously. Effectively we make this feature opt-in.
## Original Summary:
Fixes#72901.
Since we can't get access to the source code for synthesized magic methods on dataclasses, we have to synthesize our own versions. torch/jit/_dataclass_impls.py has the code that does this.
What's supported
Synthesized __init__, __eq__, and the comparison magic methods when order=True is set on the dataclass decorator
Default values for fields
__post_init__, including using InitVar fields inside of __post_init__, on Python 3.8+
Overriding __eq__ or any of the comparison magic methods to provide your own implementation
What's not supported
Default factory initializers for fields
Frozen dataclasses
InitVar on Python 3.7
__repr__ and __hash__ (these are actually implemented, but the TorchScript interpreter won't call them)
Using the != operator on dataclasses inside TorchScript; this is because TorchScript requires that you implement __ne__ to use this operator, whereas in regular Python the != operator will resolve to the negation of whatever is returned by __eq__ if there's no __ne__. Dataclasses don't actually synthesize an __ne__ method for this reason. I've been toying with different ways to fix this but != is not working in this PR at the moment.
Test Plan:
unittest
Also run previously failed test:
```
buck test mode/dev-nosan //fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests -- --exact 'fblearner/flow/projects/fluent2/definition/transformers/contrib/faim/test:tests - test_mixmatch_multiclass (fblearner.flow.projects.fluent2.definition.transformers.contrib.faim.test.faim_mixmatch_test.TestFaimTransformerMixMatch)'
```
passes
Reviewed By: zhxchen17
Differential Revision: D34808842
fbshipit-source-id: 02f807cff1ea99e606333960225c71a239743a4b
(cherry picked from commit ec885a2bc04f9e5f65838fa5704d9a05815ebd37)
Summary:
Ref https://github.com/pytorch/pytorch/issues/72263 for cpp_custom_type_hack removal
`qlinear_prepack` and `qlinear_unpack` were updated to use torchbind
and the `cpp_custom_type_hack` overloads marked with a deprecation
warning in https://github.com/pytorch/pytorch/issues/38101 which was in the PyTorch 1.6 release. So, we are
safe to break BC here.
The deprecation warning only appears in unpack, but you can't use one
without the other I think that's still okay.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72680
Reviewed By: george-qi
Differential Revision: D35056994
Pulled By: jerryzh168
fbshipit-source-id: cc046b9fa00d0219a4510854204564f4ea23da4b
(cherry picked from commit 31abbf1142d86174a1980feced57e4c621b704d1)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74705
As the comment says, libdl might not be separate because it may be subsumed into libc.
Test Plan:
1) existing tests
2) this is being sent out on top of platform010 migration for caffe2
Reviewed By: d4l3k, r-barnes
Differential Revision: D35117159
fbshipit-source-id: c4a6de7c3412db695509bd25d529658cdf785e3d
(cherry picked from commit 563919d4c5fd7a9cbdc03d24b1afc5b6a2c09cc8)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74600
Following https://github.com/pytorch/pytorch/pull/74210, this PR adds the support for some ops
using the DefaultNodeQuantizeHandler in the backend_config_dict defintion for pytorch native backend
TODO: There is still a few ops we didn't handle with backend_config_dict path: gelu and softmax, need to discuss if we still need them, if so we can change the test
to use backend_config_dict and remove the DefaultNodeQuantizeHandler after that
Test Plan:
python test/test_quantization.py TestQuantizeFxOps
Imported from OSS
Reviewed By: andrewor14
Differential Revision: D35071437
fbshipit-source-id: 70351d2810ca1ac7dc09d4a9c239f6757ccb51ca
(cherry picked from commit 5e68f755a32ba7d90d6c73db9c2017f9c58d7fa5)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74663
In lightweight dispatch, we only need to register root ops. Unlike in the dispatcher world, the transitive closure of the operators doesn't need to go through dispatcher or op registry.
Test Plan: Rely on unit tests
Reviewed By: priyaramani
Differential Revision: D35104401
fbshipit-source-id: 1a2df571880ac3c8625985c01bd89a2bb9566af9
(cherry picked from commit 16207fa18e87908ec5e038a7f60f41893a236749)
Summary:
This change adds the following logic:
If lightweight dispatch is enabled, do not generate `TORCH_LIBARAY` API calls for operator schema and implementations, since these operators will be registered into JIT op registry.
`skip_dispatcher_op_registration` is an existing argument to `gen.py`. With that set, `RegisterDispatchKey.cpp` will not generate `m.def` and `m.impl` for each native function. This logic will be removed once we find a better way to skip op registration into dispatcher.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74664
Test Plan: Rely on unit tests for lightweight dispatch.
Reviewed By: priyaramani
Differential Revision: D34634300
Pulled By: larryliu0820
fbshipit-source-id: d87828f2c6c62f15024ce9e98823b09ee5a81336
(cherry picked from commit 3eb1c27547dea6accd9fa95496189f3699d91201)
Closes#44459
This migrates the python implementation of `_pad_circular` to ATen and
removes the old C++ implementation that had diverged from python.
Note that `pad` can't actually use this until the
forward-compatibility period is over.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73410
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74387
Make temporary python bindings for flatbuffer to test ScriptModule save / load.
(Note: this ignores all push blocking failures!)
Test Plan: unittest
Reviewed By: iseeyuan
Differential Revision: D34968080
fbshipit-source-id: d23b16abda6e4b7ecf6b1198ed6e00908a3db903
(cherry picked from commit 5cbbc390c5f54146a1c469106ab4a6286c754325)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74610
Adding python version to exported package and reading it on import as per this issue in github https://github.com/pytorch/pytorch/issues/74068
ghstack-source-id: 152003088
Test Plan: CI Tests
Reviewed By: PaliC
Differential Revision: D35062709
fbshipit-source-id: 04091a1255a09b96255112a60d31df127c424193
(cherry picked from commit ed39fd54b8b20918dac89a2873ecccf06aafd724)
Currently by default, reduce timer registration
is expected for all backend. if timer is not
registered throws assert in set_runtime_stats_and_log()
To allow registration of reducer timer for other
backends, moved the timer registration to another
file decoupling the internal interface.
Signed-off-by: Jeeja <jeejakp@habana.ai>
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71700
Approved by: https://github.com/rohan-varma
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74594
Extending `_save_for_mobile` and `_load_for_mobile` to support faltbuffer format with additional optional argument which is set to pick pickle by default.
Adding new binary target with suffix `_pickle_and_flatbuffer` to help migration.
Size test in D34909502 shows the size has regressed by ~40K but after removing pickle and comparing lite_predictors we have ~120K size measure that we will achieve when deprecating pickle and moving to flatbuffer
**BEFORE:**
```lang=mermaid
graph TD;
torch_core-->torch_mobile_deserialize;
torch_mobile_core-->torch_mobile_deserialize;
jit_module_saving-->torch_core;
jit_module_saving-->torch_mobile_core;
torch_mobile_deserialize-->caffe2_serialize;
torch_mobile_deserialize-->torch_mobile_module;
caffe2_serialize-->miniz;
flatbuffer_loader-->mobile_bytecode;
flatbuffer_serializer-->mobile_bytecode;
mobile_bytecode-->flatbuffer_2.0;
flatbuffer_loader-->torch_mobile_module;
flatbuffer_serializer-->torch_mobile_module;
```
**AFTER:**
```lang=mermaid
graph TD;
torch_core-->torch_mobile_deserialize;
torch_mobile_core-->torch_mobile_deserialize;
jit_module_saving-->torch_core;
jit_module_saving-->torch_mobile_core;
torch_mobile_deserialize-->caffe2_serialize;
torch_mobile_deserialize-->torch_mobile_module;
caffe2_serialize-->miniz;
flatbuffer_loader-->mobile_bytecode;
flatbuffer_serializer-->mobile_bytecode;
mobile_bytecode-->flatbuffer_2.0;
torch_mobile_deserialize_pickle_and_flatbuffer-->|new| flatbuffer_loader;
torch_mobile_deserialize_pickle_and_flatbuffer-->|new| torch_mobile_deserialize;
torch_mobile_core_pickle_and_flatbuffer-->|new| torch_mobile_deserialize_pickle_and_flatbuffer;
torch_core_pickle_and_flatbuffer-->|new| torch_mobile_deserialize_pickle_and_flatbuffer;
jit_module_saving_pickle_and_flatbuffer-->|new| torch_core_pickle_and_flatbuffer;
jit_module_saving_pickle_and_flatbuffer-->|new| torch_mobile_core_pickle_and_flatbuffer;
flatbuffer_serializer-->torch_mobile_module;
jit_module_saving_pickle_and_flatbuffer-->|new|jit_module_saving;
jit_module_saving_pickle_and_flatbuffer-->|new|flatbuffer_serializer;
flatbuffer_loader-->torch_mobile_module;
```
Original commit changeset: 780dfb6fd6ba
Original Phabricator Diff: D34805092 (284b2b7135)
ghstack-source-id: 152044801
(Note: this ignores all push blocking failures!)
Test Plan:
CI
```
~/fbsource/fbcode] cd ~/fbsource/fbcode/ && buck test -c fbcode.caffe2_enable_flatbuffer=1 //caffe2/test/cpp/jit:jit -- FlatbufferTest.ExtraFiles
Parsing buck files: finished in 0.9 sec
Building: finished in 5.3 sec (100%) 12992/54304 jobs, 0/54304 updated
Total time: 6.2 sec
More details at https://www.internalfb.com/intern/buck/build/2b387fff-f813-4cfa-b53f-eb2378630d4e
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: f93a84d6-e7ce-41a0-a97f-0ef3fa6d199d
Trace available for this run at /tmp/tpx-20220323-134108.766518-f93a84d6-e7ce-41a0-a97f-0ef3fa6d199d/trace.log
RemoteExecution session id: reSessionID-f93a84d6-e7ce-41a0-a97f-0ef3fa6d199d-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/4503599723101693
✓ ListingSuccess: caffe2/test/cpp/jit:jit : 486 tests discovered (19.122)
✓ Pass: caffe2/test/cpp/jit:jit - FlatbufferTest.ExtraFiles (0.187)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/4503599723101693
```
Similar Build Deps Dags
```
[pavithran@devvm5216.vll0 /data/users/pavithran/fbsource] buck query 'allpaths(//xplat/caffe2:torch_mobile_all_ops_pickle_and_flatbuffer, //xplat/caffe2:torch_mobile_deserialize_pickle_and_flatbuffer)' --output-format dot-compact | pastry
P486770901: https://www.internalfb.com/intern/paste/P486770901/
[pavithran@devvm5216.vll0 /data/users/pavithran/fbsource] buck query 'allpaths(//xplat/caffe2:torch_mobile_all_ops, //xplat/caffe2:torch_mobile_deserialize)' --output-format dot-compact | pastry
P486771278: https://www.internalfb.com/intern/paste/P486771278/
```
pickle_and_flatbuffer: https://www.internalfb.com/intern/dgw/graph/?build_id=P486770901
pickle: https://www.internalfb.com/intern/dgw/graph/?build_id=P486771278
Reviewed By: iseeyuan
Differential Revision: D35067157
fbshipit-source-id: 9044259c17a2e0da79bd6aedb28efbdfd57e23e0
(cherry picked from commit f738069ec3a72e79da56172741d027de514e9e5f)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74641
This makes dns hostname lookup failures retryable since in some environments such as Kubernetes they're not guaranteed to be resolvable until the job starts. Retrying this eliminates the race condition.
This also fixes `sandcastle_skip_if` when used on the class instead of the method. Previously they wouldn't inherit from TestCase so just wouldn't run under buck at all.
Fixes https://github.com/pytorch/pytorch/issues/73682
Test Plan:
Added a unit test
```
buck test //caffe2/test/distributed:test_store
```
Reviewed By: aivanou
Differential Revision: D35092284
fbshipit-source-id: d40bf187e52c41f551e4fe41c536b2b0015588ee
(cherry picked from commit f8908309d8ee64c25ee466a6b4922f34f2b7618e)
Summary:
This is an automated pull request to update the first-party submodule for [pytorch/FBGEMM](https://github.com/pytorch/FBGEMM).
New submodule commit: ef22aabc8b
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74633
Test Plan: Ensure that CI jobs succeed on GitHub before landing.
Reviewed By: jianyuh
Differential Revision: D35088395
Pulled By: geyyer
fbshipit-source-id: cb6808719a545c318302ed2770b3c7fa459fe169
(cherry picked from commit dfba6a9441136ff8563e80e6a09c555ad6a3af5a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74484
In my first attempt at this in December I stamped out specializations using variadic templates. However I'm able to get comparable performance using simple conditionals since the branch is very predictable and AppendOnlyList::emplace_back is low enough overhead that multiple calls don't cause an issue.
This is also a chance to do some BE: rather than force ops and backend events to use the same fields (which in practice means setting a bunch of default values when reporting backend events), I just split them and use a variant.
Test Plan: The single threaded benchmark (with no extra options set) improved considerably from ~0.88 us to ~0.62 us. The stress test benchmark improved modestly from ~6.1 us to ~5.8 us. So the bottleneck for multi-threading is somewhere else, but doing less wasted work is still able to move the needle a little bit.
Reviewed By: swolchok
Differential Revision: D34779994
fbshipit-source-id: 392bc7c6f12797fa5e18777063aa21210d9d2067
(cherry picked from commit f0a49ff7be8aa65bab2f6952cc2e6306c1edc24b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74637
Forgot to update the expect file in https://github.com/pytorch/pytorch/pull/74242. Reland to include changes in expect file.
Test Plan: unit test
Reviewed By: yinghai
Differential Revision: D35089989
fbshipit-source-id: 5e3ad9c696cf31cbc691d34fdb77eff26f92e38d
(cherry picked from commit 110ac12f5e2bcca7552d4b4691c7d98fafb21a57)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74581
As title, currently the quant_min/quant_max of the FakeQuantize are not populated to the observer. We plan to populate when they are both not None.
To do this we need to do
1. Remove the current default quant_min/quant_max value (0/255) as it's not universal for various dtype.
2. Move the upper bound/lower bound check before creating the observer.
Test Plan:
```
[jiaxuzhu@devvm3400.frc0 /data/users/jiaxuzhu/fbsource/fbcode] buck test mode/dev //caffe2/test:quantization -- --exact 'caffe2/test:quantization - test_quant_min_max_override (quantization.core.test_workflow_module.TestFakeQuantize)'
Parsing buck files: finished in 0.8 sec
Downloaded 0/2 artifacts, 0.00 bytes, 100.0% cache miss (for updated rules)
Building: finished in 9.5 sec (100%) 18535/84579 jobs, 2/84579 updated
Total time: 10.3 sec
More details at https://www.internalfb.com/intern/buck/build/1cab97ef-0788-4d06-92ed-a828995e3bde
BUILD SUCCEEDED
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id: 24be645e-eebc-45d6-8111-052ef1225fa0
Trace available for this run at /tmp/tpx-20220323-094106.724238-24be645e-eebc-45d6-8111-052ef1225fa0/trace.log
RemoteExecution session id: reSessionID-24be645e-eebc-45d6-8111-052ef1225fa0-tpx
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/5066549674998735
✓ ListingSuccess: caffe2/test:quantization : 483 tests discovered (20.179)
✓ Pass: caffe2/test:quantization - test_quant_min_max_override (quantization.core.test_workflow_module.TestFakeQuantize) (18.896)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/5066549674998735
```
Reviewed By: jerryzh168
Differential Revision: D34971236
fbshipit-source-id: 4407fd03116a296053256b333f7ce6d28dcc9c42
(cherry picked from commit f6980bccea802f220cc5b6dfe1bf3a3a3eef0a34)
Summary:
This PR moves the Dockerfile conda dependencies into a requirements-ci.txt (and begins the requirements file for other parts of CI as well). Packages are listed alphabetically in the requirements-ci.txt. Uncommented packages before the mkl package have been tested and confirmed to work on all platforms. Commented out packages before mkl have broken at least one platform and so have been comment out. There appears to be some randomness with certain platforms not passing tests so it might be good to run a number of tests for the same configuration to confirm if it is indeed these commented out packages that cause the errors.
Remaining is to test all commented out packages to ensure they work on all platforms. This will likely involve repeat runs of the same configurations to ensure it is indeed the packages that break the platforms and not random errors.
This PR makes progress on task https://github.com/pytorch/pytorch/issues/72556
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73404
Reviewed By: janeyx99
Differential Revision: D34730797
Pulled By: kstant0725
fbshipit-source-id: 3e4b171720fa33b604cebb9c6101d38ba11f2f8b
(cherry picked from commit 99cc445aadb95f92f6ef040f2d4b7c6c6d5b7f8b)
