Sebastian Messmer [Sat, 30 Mar 2019 07:03:46 +0000 (00:03 -0700)]
Fallback kernels (#18443)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18443
Allow registering a kernel without a dispatch key. In this case, the kernel becomes a fallback kernel that is called whenever no other kernel matches.
This is also useful for the legacy function based API (since that API doesn't know about dispatch keys) or any other custom ops that don't care about dispatch
and just want one kernel to be called no matter the dispatch key.
Reviewed By: dzhulgakov
Differential Revision:
D14603258
fbshipit-source-id:
242dc8871dad2989ca25079854d0cc97429e7199
Sebastian Messmer [Sat, 30 Mar 2019 07:03:46 +0000 (00:03 -0700)]
Introduce lambda-based kernel API (#18541)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18541
Allow registering lambdas as c10 kernels.
Reviewed By: dzhulgakov
Differential Revision:
D14653005
fbshipit-source-id:
f867cc776b1339e83b7a2e1935f5cf924cfba44a
Sebastian Messmer [Sat, 30 Mar 2019 07:03:46 +0000 (00:03 -0700)]
Report better errors when kernel or dispatch key are missing (#18302)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18302
These might be use cases we want to support in the future, but they don't work yet.
Let's at least report an error instead of doing segfaults or worse.
Reviewed By: dzhulgakov
Differential Revision:
D14572346
fbshipit-source-id:
49262ce131493bc887defe2978d8b22f202cd8cc
Sebastian Messmer [Sat, 30 Mar 2019 07:03:44 +0000 (00:03 -0700)]
Move stuff to cpp files (#18301)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18301
Move code out of headers and templates into source files and non-templates.
Reviewed By: dzhulgakov
Differential Revision:
D14572347
fbshipit-source-id:
9fd5d62d54000a95e93076cd73f591ba2c5c2653
Sebastian Messmer [Sat, 30 Mar 2019 07:03:44 +0000 (00:03 -0700)]
Check kernel against function schema in c10 op registration (#18256)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18256
This diff infers the function schema from the kernel function/functor and checks that it matches the specified function schema.
This diff does not allow (yet) to omit specifying the function schema in the registration API. That will come in a future diff.
Reviewed By: dzhulgakov
Differential Revision:
D14552738
fbshipit-source-id:
00202b489ede19f26ae686c97416b38c72c11532
Sebastian Messmer [Sat, 30 Mar 2019 07:03:44 +0000 (00:03 -0700)]
Add functor- and function-based kernel registration API (#18162)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18162
- Adds the API to register a functor- and function-based kernel.
- Change the experimental c10 ops to use this new API instead of the old one
- Deletes the old APIs in KernelRegistration.h and OpSchemaRegistration.h
Reviewed By: dzhulgakov
Differential Revision:
D14514239
fbshipit-source-id:
35b2f6e8f62964e54886450a6a5fac812ed20f26
Sebastian Messmer [Sat, 30 Mar 2019 07:03:43 +0000 (00:03 -0700)]
New operator registration MVP (#18161)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18161
This introduces version 0 for the new operator registration.
For now, it only works with kernels that are defined as stack-based functions.
This is actually not the intended public API for defining kernels, but it's the basis which is going to be used to define the public APIs (see diffs on top for them),
and it's also the API used for exposing caffe2 operators.
This diff also switches the mechanism for exposing caffe2 operators to the new mechanism.
Reviewed By: dzhulgakov
Differential Revision:
D14514231
fbshipit-source-id:
454ab7b5b46a10203aa27b175400d23f818dd1df
Junjie Bai [Sat, 30 Mar 2019 05:42:18 +0000 (22:42 -0700)]
Fix trt installation in CI (#18609)
Summary:
caffe2_py2_cuda9_0_cudnn7_ubuntu16_04_build is failing
```
...
Mar 29 04:44:46 Need to get 174 MB of archives.
Mar 29 04:44:46 After this operation, 576 MB of additional disk space will be used.
Mar 29 04:44:46 Do you want to continue? [Y/n] Abort.
Exited with code 1
...
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18609
Differential Revision:
D14694990
Pulled By: bddppq
fbshipit-source-id:
260446a8650f660a2baf123a3f17efdf0a8d6c64
David Riazati [Sat, 30 Mar 2019 02:06:06 +0000 (19:06 -0700)]
Attribute serialization improvements (#18188)
Summary:
* adds attributes to `ScriptModule.__getattr__` so they can be accessed in Python after re-importing
* full support for all the possible values for an `int64_t`
* this necessitated a bunch more `pushWhatever` functions, so re-introduced a templated version to cut down on duplicate code
* tests to validate references / value sharing works
* adds `torch.jit.Unpickler` which people can use to de-serialize the pickle files into Python / have a quick reference on how to do this without PyTorch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18188
Differential Revision:
D14527490
Pulled By: driazati
fbshipit-source-id:
efd15579cc04aa2e28c4b2c9490d82d849dee559
Cheng,Penghui [Sat, 30 Mar 2019 01:51:50 +0000 (18:51 -0700)]
support pre-convert filter format for mkldnn training mode and change 'OptimizeForIdeep' to 'OptimizeForMkldnn' (#15171)
Summary:
For MKL-DNN,the filter data will be reorderd to primitive format, it takes a lot of time.
So the patch provide a method to convert filter format before training.
And "OptimizeForIdeep" will be changed to "OptimizeForMkldnn" in this patch.
This patch depends on https://github.com/pytorch/pytorch/pull/12866
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15171
Differential Revision:
D14590741
Pulled By: yinghai
fbshipit-source-id:
07971c9977edac3c8eec08ca2c39cda639683492
Jerry Zhang [Sat, 30 Mar 2019 01:26:07 +0000 (18:26 -0700)]
Tensor construction codemod(raw_mutable_data) (#16373)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16373
motivation: https://github.com/pytorch/pytorch/pull/12407
This is a manual diff.
most of the fixes should be:
```
auto* Y = Output(0);
Y->Resize(dims);
Y->raw_mutable_data(dtype);
```
-->
```
auto* Y = Output(0, dims, at::dtype(dtype));
```
But there might be other cases.
Reviewed By: dzhulgakov
Differential Revision:
D13725460
fbshipit-source-id:
649a4b0e42f62cda1a60171dd9fa3e440dc9dca1
David Riazati [Sat, 30 Mar 2019 01:23:28 +0000 (18:23 -0700)]
Add hash() global (#18258)
Summary:
This adds `hash()` which supports `int`, `str`, and `float`. It relies on `std::hash` which is implementation defined, so the result of `hash()` in TorchScript is not the same as in Python, but should satisfy the same properties.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18258
Differential Revision:
D14692317
Pulled By: driazati
fbshipit-source-id:
909df5d024bb3feea157d5a203b7de53c72261c9
Elias Ellison [Sat, 30 Mar 2019 01:10:36 +0000 (18:10 -0700)]
Move fuser to test_jit_fuser (#18590)
Summary:
Start of breaking up test_jit.py
New files will have the format test_jit_* so they are easily grepable but remain in the same directory so we don't have to go through multiple sources for imports.
I am adding a test that's expected to fail to be sure it's running.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18590
Reviewed By: wanchaol
Differential Revision:
D14677094
Pulled By: eellison
fbshipit-source-id:
9782c6aa9525bb6f332fc75cfff004c83a417522
James Reed [Sat, 30 Mar 2019 00:06:08 +0000 (17:06 -0700)]
Experimental logging/counters API (#18235)
Summary:
This defines a generic counters API that users can utilize to provide monitoring functionality in e.g. a production service. We expose both counters for runtime internals as well as a TorchScript API to create user-defined counters. Synopsis of the API:
- `torch/csrc/jit/script/logging.h` specifies the externally-facing API in C++
- `torch/jit/_logging.py` specifies the Python API
We use an interface, `LoggerBase`, to define the interactions between users and a logging backend. Implementing a subclass of `LoggerBase` allows the user to handle these events in a custom way, such as logging into a DB or calling into an infra-specific counters API.
From the frontend perspective, we can create log events in two ways:
1. We provide an `add_stat_value(name, val)` function. This calls into the Logger backend with a key/value pair. For example, we might call `add_stat_value('foo', 1)` to bump an event counter.
2. We provide a `time_point()` function to record a timestamp in nanoseconds. This can be used in conjunction with `add_stat_value` to record runtime wall clock durations.
Examples of frontend usage can be found in `test_jit.py TestLogging`.
We provide a trivial `LockingLogger` implementation as an example and for testing purposes. It is likely not ready for production usage. It demonstrates that a backend implementing the API can do things like specify aggregation types and report these aggregate stats via the `get_counters()` API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18235
Differential Revision:
D14545060
Pulled By: jamesr66a
fbshipit-source-id:
04099543a1898cfdd411511e46e03d5dce9b4881
David Riazati [Sat, 30 Mar 2019 00:03:50 +0000 (17:03 -0700)]
Revert
D14668859: [pytorch][PR] Re-land Parsing file check
Differential Revision:
D14668859
Original commit changeset:
3825a35ddc61
fbshipit-source-id:
f3343ec6b63fe8f1f04959adfac4331865990047
Pieter Noordhuis [Fri, 29 Mar 2019 23:15:10 +0000 (16:15 -0700)]
Update argument names of torch::autograd::FunctionPostHook (#18140)
Summary:
They are called as (outputs, inputs) and were named (inputs, outputs).
Possible follow up fix is to make the outputs argument an lvalue to allow for calling multiple post hooks without ever copying outputs vector. It looks like the copy is now forced because the hook takes a const reference as input and returns an value. This would change the prototype of the function, so needs further discussion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18140
Differential Revision:
D14684498
Pulled By: pietern
fbshipit-source-id:
1bd3ddbdd1ff7fe0a18241de5a9ec745a4e7ef07
Soumith Chintala [Fri, 29 Mar 2019 23:02:02 +0000 (16:02 -0700)]
note on updating existing source (#18409)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/18388
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18409
Differential Revision:
D14597666
Pulled By: soumith
fbshipit-source-id:
156104c0cd19da06f6f96a225228d1e8cf831af1
eellison [Fri, 29 Mar 2019 22:35:37 +0000 (15:35 -0700)]
Re-land Parsing file check (#18570)
Summary:
The last time I tried to land it there was a merge race with the docs coverage test lol. Re-landing with the fix.
Re-land of https://github.com/pytorch/pytorch/pull/18304
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18570
Differential Revision:
D14668859
Pulled By: eellison
fbshipit-source-id:
3825a35ddc6179a0d433d70d22b5c1a96c20b21a
Spandan Tiwari [Fri, 29 Mar 2019 22:17:14 +0000 (15:17 -0700)]
Refactoring serialization of ONNX initializers to be name-based (Resubmission) (#17830)
Summary:
houseroad - this is the resubmission of https://github.com/pytorch/pytorch/pull/17420, as suggested.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17830
Reviewed By: zrphercule
Differential Revision:
D14398714
Pulled By: houseroad
fbshipit-source-id:
bda475f1ae8a5273ebdb0f6883fc66036c29d326
Mikhail Zolotukhin [Fri, 29 Mar 2019 22:01:36 +0000 (15:01 -0700)]
Initial implementation of InsertObserverNodes pass. (#18152)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18152
ghimport-source-id:
1dd5e62c4d93394dcd8d8af2871554575c8d3d1a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18152 Initial implementation of InsertObserverNodes pass.**
* #18151 Add quant-passes stubs.
gh-metadata: pytorch pytorch 18150 gh/zolotukhinm@gmail.com/2/head
Differential Revision:
D14584223
fbshipit-source-id:
30896acc1a8901d22c6a167eb87d2fbaafbbeb6f
Gu, Jinghui [Fri, 29 Mar 2019 21:06:09 +0000 (14:06 -0700)]
Fix bug in tensor feed which caused crash due to wrong tensor type (#18552)
Summary:
In blob feeder for ideep device, the wrong device option is given and led to a crash issue.
This patch aims to correct the device option to fix this bug.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18552
Differential Revision:
D14679838
Pulled By: yinghai
fbshipit-source-id:
bde11e6a6fe44822166881dcb7c9bd0b34b4ecf3
Gu, Jinghui [Fri, 29 Mar 2019 20:50:51 +0000 (13:50 -0700)]
Upgrade mkldnn-bridge to revert tensor capacity patch and prepare for DNNLOWP support (#18471)
Summary:
1. Upgrade mkldnn-bridge to revert tensor capacity patch to avoid ASAN issue.
2. Prepare for DNNLOWP support.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18471
Differential Revision:
D14621569
Pulled By: yinghai
fbshipit-source-id:
9df300b77d0f2acd1a4f63c2925b7a7cab7a474e
Yanghan Wang [Fri, 29 Mar 2019 20:31:45 +0000 (13:31 -0700)]
register BoxWithNMSLimit with C10
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17956
Reviewed By: houseroad
Differential Revision:
D14417300
fbshipit-source-id:
eb5e2ba84513b3b7bfa509dc442424b13fe9148f
Gregory Chanan [Fri, 29 Mar 2019 20:31:42 +0000 (13:31 -0700)]
Fix c10d build without nccl.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18582
Differential Revision:
D14672928
Pulled By: gchanan
fbshipit-source-id:
74e9805cbaf5ebe8e3f579fe08dad72eb410b80a
Will Feng [Fri, 29 Mar 2019 19:59:29 +0000 (12:59 -0700)]
Add named submodule support to nn::Sequential (#17552)
Summary:
Previously, we were not able to assign names to `nn::Sequential`'s submodules. This PR adds this feature to match the Python API. Example use:
```cpp
Sequential sequential(named_submodule({
{"linear", Linear(10, 3)},
{"conv2d", Conv2d(1, 2, 3)},
{"dropout", Dropout(0.5)},
{"batchnorm", BatchNorm(5)},
{"embedding", Embedding(4, 10)},
{"lstm", LSTM(4, 5)}
}));
```
It also enables loading parameters of Python `nn.Sequential` module with custom submodules names into C++ frontend, unblocking https://github.com/pytorch/vision/pull/728#issuecomment-
466661344.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17552
Differential Revision:
D14246834
Pulled By: yf225
fbshipit-source-id:
3030b5c5d68f6dd5d3e37ac4b4f98dc6d6d9ba72
Vishwak Srinivasan [Fri, 29 Mar 2019 19:58:23 +0000 (12:58 -0700)]
Rename `btriunpack` to `lu_unpack` (#18529)
Summary:
Changelog:
- Renames `btriunpack` to `lu_unpack` to remain consistent with the `lu` function interface.
- Rename all relevant tests, fix callsites
- Create a tentative alias for `lu_unpack` under the name `btriunpack` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18529
Differential Revision:
D14683161
Pulled By: soumith
fbshipit-source-id:
994287eaa15c50fd74c2f1c7646edfc61e8099b1
Elias Ellison [Fri, 29 Mar 2019 18:45:38 +0000 (11:45 -0700)]
fix lint (#18623)
Summary:
Fix lint
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18623
Differential Revision:
D14686265
Pulled By: eellison
fbshipit-source-id:
4bbe0f5bc58f508cbf4bc1baef2029ce1eaa42d8
Xiaodong Wang [Fri, 29 Mar 2019 18:04:23 +0000 (11:04 -0700)]
Manual hipify caffe2/distributed and rocm update (no hcc modules support) (#18088)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18088
Manually hipify the distributed folder
Reviewed By: bddppq
Differential Revision:
D14482702
fbshipit-source-id:
cc0abdf525b423ab1f18db8010d21e27c6668d36
Summer Deng [Fri, 29 Mar 2019 16:24:07 +0000 (09:24 -0700)]
Change dnnlowp log level from warning to v2 (#18576)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18576
As in title
Reviewed By: feiyu1990
Differential Revision:
D14670898
fbshipit-source-id:
1983099b2ba57daab393278553f10dcdb1812fdf
Stas Bekman [Fri, 29 Mar 2019 13:48:53 +0000 (06:48 -0700)]
multiline KeyError msg python bug workaround (#18557)
Summary:
make multiline KeyError msg readable by working around a python bug https://bugs.python.org/issue2651
discussion: https://github.com/pytorch/pytorch/issues/16647
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18557
Differential Revision:
D14681086
Pulled By: soumith
fbshipit-source-id:
acbd13a823302c854c3d364028ed414fd8ce6bc8
Søren Rasmussen [Fri, 29 Mar 2019 13:42:52 +0000 (06:42 -0700)]
ReduceLrOnPlateau: best=current -> best=copy(current) (#16364) (#16697)
Summary:
Fixes #16364
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16697
Differential Revision:
D14680879
Pulled By: soumith
fbshipit-source-id:
c50c22f3eacea4474fb3a04fe85fbf11d5a177c9
crcrpar [Fri, 29 Mar 2019 13:41:49 +0000 (06:41 -0700)]
make InstanceNorm1d raise an error if the input is 2D (#11992)
Summary:
Resolves #11991 .
Any comment is welcome.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11992
Differential Revision:
D14680974
Pulled By: soumith
fbshipit-source-id:
8e287a9c32bf43b35edc9d127f16ed6b72c61d91
Arunava [Fri, 29 Mar 2019 13:36:40 +0000 (06:36 -0700)]
Fixed torch.arange docs (#18604)
Summary:
Kindly let me know if its okay and if any places i need to make a fix. Closes #18534
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18604
Differential Revision:
D14680712
Pulled By: soumith
fbshipit-source-id:
030e4a3d8f7839cbe2b8a3ef386323f0d39eb81a
Junjie Bai [Fri, 29 Mar 2019 08:16:52 +0000 (01:16 -0700)]
Minor fixes in fastrnns benchmarks
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18613
Reviewed By: wanchaol
Differential Revision:
D14681838
fbshipit-source-id:
60bd5c9b09398c74335f003cd21ea32dd1c45876
Vishwak Srinivasan [Fri, 29 Mar 2019 07:27:48 +0000 (00:27 -0700)]
Rename `btrifact*` to `lu` (#18435)
Summary:
Changelog:
- Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`).
- Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple.
- Rename all tests, fix callsites
- Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage.
- Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435
Differential Revision:
D14680352
Pulled By: soumith
fbshipit-source-id:
af58dfc11fa53d9e8e0318c720beaf5502978cd8
Xiaomeng Yang [Fri, 29 Mar 2019 07:20:25 +0000 (00:20 -0700)]
Optimize relu op on GPU (#18506)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18506
Optimize relu op on GPU
Reviewed By: houseroad
Differential Revision:
D14633171
fbshipit-source-id:
bd3afa9a0bae1325d32ad4153736a0c7ecb0ec64
Lu Fang [Fri, 29 Mar 2019 06:44:20 +0000 (23:44 -0700)]
update of fbcode/onnx to
fb1a80692c1ab0bd27b1072f2e7bffacba336777 (#18585)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18585
Previous import was
b29e78a4efb8e5d8995f576bbf19a846807829b6
Included changes:
- **[
fb1a8069](https://github.com/onnx/onnx/commit/
fb1a8069)**: Fix wrongly handled attribute in MVN and test generating scripts (#1877) <Raymond Yang>
- **[
b22041c3](https://github.com/onnx/onnx/commit/
b22041c3)**: Add dilation attribute to MaxPool (#1864) <karljang>
Reviewed By: zrphercule, benoitsteiner
Differential Revision:
D14668623
fbshipit-source-id:
fa7f44b1ecc949d8dd654939d20b1e93db98b1d2
Lu Fang [Fri, 29 Mar 2019 06:17:18 +0000 (23:17 -0700)]
update of fbcode/foxi to
81e1683d6348eee4b5ed1145222dc2c41be4269c (#18596)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18596
Previous import was
2bcc4064c90e87b9638615c733485f07c47b7558
Included changes:
- **[81e1683](https://github.com/houseroad/foxi/commit/81e1683)**: Merge pull request #9 from zrphercule/add_foxi_quantization <Rui Zhu>
- **[580559c](https://github.com/houseroad/foxi/commit/580559c)**: char=>uint8 <zrphercule>
- **[1a572f7](https://github.com/houseroad/foxi/commit/1a572f7)**: add quantization <zrphercule>
Reviewed By: zrphercule
Differential Revision:
D14677404
fbshipit-source-id:
09429b3bf0e7783a25b8145020e505761bad887d
Elias Ellison [Fri, 29 Mar 2019 06:07:45 +0000 (23:07 -0700)]
Delete batch tensor (#18575)
Summary:
Deleting batch tensor since we are no longer maintaining the project and keeping it functional is blocking other improvements.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18575
Differential Revision:
D14671126
Pulled By: eellison
fbshipit-source-id:
b42d5b699c4d12171ed95e6d3a977532167f0d2c
Thomas Viehmann [Fri, 29 Mar 2019 05:20:08 +0000 (22:20 -0700)]
Update NNPACK to current master (#18580)
Summary:
This fixes builds on x86 (32 bits).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18580
Differential Revision:
D14672462
Pulled By: soumith
fbshipit-source-id:
7629b001c2bfa3e5b6ade7f1b03a8280232a4c16
Gemfield [Fri, 29 Mar 2019 04:35:17 +0000 (21:35 -0700)]
Enhance build_ios.sh to be consistent with build_android.sh (#18564)
Summary:
1, Enhance build_ios.sh to be consistent with build_android.sh;
2, Add docs for build_ios.sh.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18564
Differential Revision:
D14680752
Pulled By: soumith
fbshipit-source-id:
6d2667ed8a3c85a057a522838f5d0461dd4788cf
Hyungjoo Andrew Cho [Fri, 29 Mar 2019 03:49:43 +0000 (20:49 -0700)]
Serialization supports pathlib.Path object for the input argument (#18562)
Summary:
This will allow pathlib.Path object to the torch.load as an input argument.
Fixes #16607
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18562
Differential Revision:
D14668255
Pulled By: soumith
fbshipit-source-id:
0ae4f7c210918582912f2d1ef2a98f1ab288c540
Aurélien Roy [Fri, 29 Mar 2019 03:46:03 +0000 (20:46 -0700)]
Target and input sizes mismatch warning in L1 Loss / L1 Smooth Loss (#18565)
Summary:
Addind the same warning message already present in the mse_loss function to the L1 losses when input and target sizes are different.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18565
Differential Revision:
D14671415
Pulled By: soumith
fbshipit-source-id:
01f5e1fb1ea119dbb2aecf1d94d0cb462f284982
bddppq [Fri, 29 Mar 2019 01:07:10 +0000 (18:07 -0700)]
Resubmit PR-18512: Improved onnx export for 3 onnx ops (#18571)
Summary:
Fix ROCm CI failure
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18571
Differential Revision:
D14669323
Pulled By: bddppq
fbshipit-source-id:
022afe5c20e680295c9cfdfe1ec14650305955a8
Jeff Daily [Fri, 29 Mar 2019 00:43:22 +0000 (17:43 -0700)]
in caching allocator, ignore and clear the error if not ready
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18584
Differential Revision:
D14675041
Pulled By: bddppq
fbshipit-source-id:
c1fab797e0d224e0a481a0395a3f9975c4265ff6
Ilia Cherniavskii [Fri, 29 Mar 2019 00:42:47 +0000 (17:42 -0700)]
Add external callbacks into RecordFunction (#17844)
Summary:
Add a way to insert external callbacks into PT's RecordFunction
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17844
Differential Revision:
D14399664
Pulled By: ilia-cher
fbshipit-source-id:
76654799811fefd3ffed4abfb46ed95b492cebab
Jing Huang [Thu, 28 Mar 2019 23:58:54 +0000 (16:58 -0700)]
Implement rotated generate_proposals_op without opencv dependency (CPU version)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18533
Reviewed By: ezyang
Differential Revision:
D14648083
fbshipit-source-id:
e53e8f537100862f8015c4efa4efe4d387cef551
Ahmed Aly [Thu, 28 Mar 2019 22:58:24 +0000 (15:58 -0700)]
Use SetOutputTensor instead of copying outputs manually (#17770)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17770
As title
Reviewed By: dzhulgakov
Differential Revision:
D14370937
fbshipit-source-id:
f415490c38556cf03bb13dce3643775331483448
Shen Li [Thu, 28 Mar 2019 22:05:53 +0000 (15:05 -0700)]
Fix NCCL/Gloo process groups and DDP stream sync bug (#18465)
Summary:
DDP with NCCL backend uses a [worker stream](https://github.com/pytorch/pytorch/blob/
d3eb941ed96774efb8d89a0b20c9e49807ea85a7/torch/csrc/distributed/c10d/ddp.cpp#L142) to flatten grand batch
tensors, and passes the flattened tensor to [another stream](https://github.com/pytorch/pytorch/blob/
d3eb941ed96774efb8d89a0b20c9e49807ea85a7/torch/lib/c10d/ProcessGroupNCCL.cpp#L379) to
conduct ncclAllReduce. The flattened tensor has to record the
ncclAllReduce stream, otherwise multiple streams might access the
same memory space.
cc ppwwyyxx
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18465
Differential Revision:
D14613449
Pulled By: mrshenli
fbshipit-source-id:
b62773732552d12cc87b7adeb6897e9e11753ea9
Ahmed Aly [Thu, 28 Mar 2019 18:23:22 +0000 (11:23 -0700)]
Inference LSTM integration test (#18559)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18559
Adding integration test for inference LSTM
Reviewed By: houseroad
Differential Revision:
D14656698
fbshipit-source-id:
80fb2a72be30fcb695f4471b72bf9d6e3965bf81
Zachary DeVito [Thu, 28 Mar 2019 17:31:45 +0000 (10:31 -0700)]
Add Slot type to abstract the raw pointers being used for slots. (#18226)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18226
ghimport-source-id:
b9ec8651212875b30971cc6859d2ddec6559ae3a
If modules become first-class IValues, then the slots will no longer be raw pointers but (IValue, index) pairs. This commit inserts the Slot abstraction so that this change can be made in later patches.
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18226 Add Slot type to abstract the raw pointers being used for slots.**
Differential Revision:
D14542022
fbshipit-source-id:
b81d7f4334c983d663e7551bda82df43680d7c5f
Junjie Bai [Thu, 28 Mar 2019 17:18:46 +0000 (10:18 -0700)]
Revert
D14635130: Improved onnx export for 3 onnx ops.
Differential Revision:
D14635130
Original commit changeset:
d54a2b6e2950
fbshipit-source-id:
f624e2befdde245cb88435a95508b2a8e6b12e61
Benoit Steiner [Thu, 28 Mar 2019 15:52:01 +0000 (08:52 -0700)]
Improved onnx export for 3 onnx ops. (#18512)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18512
Ceil and Floor have been supported since version 6 of ONNX: export them using the native onnx ops instead of an Aten op.
Similarly, support for the Where op has been added in version 9, so we don't need to wrap these op in an Aten op.
Reviewed By: houseroad
Differential Revision:
D14635130
fbshipit-source-id:
d54a2b6e295074a6214b5939b21051a6735c9958
Elias Ellison [Thu, 28 Mar 2019 07:09:36 +0000 (00:09 -0700)]
Revert
D14652372: [pytorch][PR] Add parsing to file check
Differential Revision:
D14652372
Original commit changeset:
7430b9d1dc2b
fbshipit-source-id:
fa3d0f68515fe53447746469844d2db20c1292e0
Ilia Cherniavskii [Thu, 28 Mar 2019 04:07:36 +0000 (21:07 -0700)]
C++17.h: forward -> c10::guts::forward (#18492)
Summary:
Use c10::guts::forward instead of forward
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18492
Reviewed By: smessmer
Differential Revision:
D14625513
Pulled By: ilia-cher
fbshipit-source-id:
8bc4e20f102fe2a107a22f3e172882d60b95ab0e
Thomas Viehmann [Thu, 28 Mar 2019 03:17:01 +0000 (20:17 -0700)]
Use __ldg for CUDA kernels in fuser (#18540)
Summary:
While benchmarking a kernel with broadcasted inputs, I noticed
that is was much slower than a hand-coded kernel for the smae task.
The kernel in question computed a * b + c for a of shape
32 x 32 x 10240 and b and c of shape 1 x 32 x 1.
This patch accellerates said kernel from 450us to 250us on my GTX1080Ti.
I didn't change half because there doesn't seem to be __ldg for
half.
An alternative could be to sprinkle const and restrict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18540
Differential Revision:
D14657840
Pulled By: soumith
fbshipit-source-id:
408847346ec12d1d1d9b119ac50bbc70f0d9ed33
Sam Pepose [Thu, 28 Mar 2019 02:47:43 +0000 (19:47 -0700)]
Adds Cyclical Learning Rate and Momentum (#18001)
Summary:
This implements a cyclical learning rate (CLR) schedule with an optional inverse cyclical momentum. More info about CLR: https://github.com/bckenstler/CLR
This is finishing what #2016 started. Resolves #1909.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18001
Differential Revision:
D14451845
Pulled By: sampepose
fbshipit-source-id:
8f682e0c3dee3a73bd2b14cc93fcf5f0e836b8c9
Edward Yang [Thu, 28 Mar 2019 02:46:23 +0000 (19:46 -0700)]
Completely synchronize behavior of Facebook flake8 and public flake8. (#18538)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18538
ghimport-source-id:
665b09f158d1c5dd94686d4212792504b55b7f73
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18538 Completely synchronize behavior of Facebook flake8 and public flake8.**
Previously, developers at Facebook had the very funny experience
wherein /usr/local/bin/flake8 behaved differently than a freshly
installed flake8 from pip. In this commit, I add enough ignores to
.flake8 and install enough plugins to make the Facebook flake8
and public flake8 line up exactly. These means you don't have
to care which flake8 you use; they all will report accurate information
on your Python files.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision:
D14652336
fbshipit-source-id:
ba7776eaa139cf2e3df2e65349da6fd7c99acca4
Elias Ellison [Thu, 28 Mar 2019 02:21:32 +0000 (19:21 -0700)]
add slow tests annotation to some jit tests (#18545)
Summary:
Adds slow test annotation to the following very slow tests -
70.33s test/test_jit.py::TestScript::test_script_module_script_resnet
32.33s test/test_jit.py::TestBatched::test_beam_search
17.70s test/test_jit.py::TestBatched::test_greedy_search
15.58s test/test_jit.py::TestScript::test_script_module_trace_resnet18
The list of remaining slow tests is below. Let me know if you think any of the others should be added to slow tests as well. Slow tests will only run on master.
15.28s call test/test_jit.py::TestJit::test_export_batchnorm
12.96s call test/test_jit.py::TestEndToEndHybridFrontendModels::test_snli
11.65s call test/test_jit.py::TestEndToEndHybridFrontendModels::test_neural_style
6.38s call test/test_jit.py::TestJitGeneratedModule::test_nn_LocalResponseNorm_1d
5.96s call test/test_jit.py::TestJitGeneratedModule::test_nn_LocalResponseNorm_2d_uneven_pad
5.91s call test/test_jit.py::TestJitGeneratedModule::test_nn_LocalResponseNorm_3d_custom_params
4.76s call test/test_jit.py::TestJit::test_alexnet
3.82s call test/test_jit.py::TestScript::test_number_math
3.81s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_no_bias
3.76s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_groups_thnn
3.65s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride_pad1circular
3.49s call test/test_jit.py::TestBatched::test_lstm
3.33s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_pad2circular
3.19s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_stride1_pad2circular
3.11s call test/test_jit.py::TestEndToEndHybridFrontendModels::test_dcgan_models
3.11s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride_padding
3.11s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride
3.08s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_no_bias
3.08s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv1d_stride1_pad1circular
3.07s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_groups
3.05s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_dilated
3.05s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_with_multiplier
3.04s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_groups
3.03s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_dilated
3.02s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv2d_depthwise_dilated
3.02s call test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_dilated_strided
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18545
Differential Revision:
D14656064
Pulled By: eellison
fbshipit-source-id:
d17ee23c3b3679276cee983555d43e83ce099356
Elias Ellison [Thu, 28 Mar 2019 01:11:45 +0000 (18:11 -0700)]
Add parsing to file check (#18304)
Summary:
This allows you to embed checks in IR, making the test more readable.
E.g.
```
graph_str = 'graph(%0 : Double(5, 5)):
# CHECK: aten::relu
%1 : Double(5, 5) = aten::relu(%0)
return (%1)'
FileCheck().run(graph_str, parseIR(graph_str))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18304
Differential Revision:
D14652372
Pulled By: eellison
fbshipit-source-id:
7430b9d1dc2b7584704375aac02d7392ecec76a0
Elias Ellison [Wed, 27 Mar 2019 23:02:10 +0000 (16:02 -0700)]
bug fix for node with writers in create autodiff subgraph (#18491)
Summary:
Previously we were moving nodes with writers into differentiable subgraphs, without necessarily preserving whether or not they were written to. This can lead to bugs with CSE, which needs that context.
I'm not completely sure if there's anything else we can do to be more aggresive here - inline these nodes and not run CSE and just run constant pooling, or possibly something else, but I think we should land this correctness condition first and then possibly think further.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18491
Differential Revision:
D14648562
Pulled By: eellison
fbshipit-source-id:
bc1e444774ccdb708e22f0e06a477a221a231f9e
Xianjie Chen [Wed, 27 Mar 2019 21:52:13 +0000 (14:52 -0700)]
add extra info for the auto gen sum ops
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17934
Reviewed By: iroot900
Differential Revision:
D14418689
fbshipit-source-id:
9e11e461001467f0000ea7c355d5b0f0d738fa85
Vitaly Fedyunin [Wed, 27 Mar 2019 21:44:00 +0000 (14:44 -0700)]
Clarify error text of the pin_memory function
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18530
Reviewed By: ezyang
Differential Revision:
D14647578
Pulled By: VitalyFedyunin
fbshipit-source-id:
ddd70240d52d2e9a96e26f5a0dfea8d76fe25078
Wanchao Liang [Wed, 27 Mar 2019 21:39:33 +0000 (14:39 -0700)]
Move fast rnn benchmark to pytorch/pytorch
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18369
Differential Revision:
D14652039
Pulled By: wanchaol
fbshipit-source-id:
1177b1f60d96672c3e2c9d527b56ee06ca7c0af1
eellison [Wed, 27 Mar 2019 21:29:45 +0000 (14:29 -0700)]
Rename isTensor api -> isCompleteTensor (#18437)
Summary:
Is Tensor has been brought up as misleading a couple times, rename it isCompleteTensor for clarity.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18437
Differential Revision:
D14605223
Pulled By: eellison
fbshipit-source-id:
189f67f12cbecd76516a04e67d8145c260c79036
Elias Ellison [Wed, 27 Mar 2019 21:28:11 +0000 (14:28 -0700)]
Const trace error v2 (#18535)
Summary:
Trying to reland https://github.com/pytorch/pytorch/pull/18298
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18535
Differential Revision:
D14652391
Pulled By: eellison
fbshipit-source-id:
699e30045dd5f14f0a2b98378272045a292e1e2a
jithunnair-amd [Wed, 27 Mar 2019 21:16:01 +0000 (14:16 -0700)]
enable more unit tests (#18537)
Summary:
Enable unit tests working with ROCm 2.3. In particular, these are unit tests where we skipped for double data types previously and some tests for multi-GPU setups.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18537
Differential Revision:
D14651822
Pulled By: ezyang
fbshipit-source-id:
7dd575504ebe235a91489866c91000e9754b1235
Min Ni [Wed, 27 Mar 2019 18:14:32 +0000 (11:14 -0700)]
Skip tests if C2/ONNX models cannot be read (#18494)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18494
Today we have some C2 end2end test run requiring reading model data from external filesystem (for example, Gluster and AWS). This could be a source for flaky test when the external filesystems are not reachable during the tests.
In this diff, we add try/catch logic around where we download models and open model files from external system. In case such attempts fails, we will catch the excption and let the unittest skip the current test instead of failure.
I also refactor the code a little bit by removing some duplicated logic on downloading and build the c2 model data. It has been duplicated in two classes and a few functions...
Reviewed By: yinghai
Differential Revision:
D14442241
fbshipit-source-id:
da8bf56c8d096efa34ca2070de5cd10a18aad70c
zrphercule [Wed, 27 Mar 2019 18:11:01 +0000 (11:11 -0700)]
Add qtensors in caffe2 protobuf argument (#18486)
Summary:
We are about to merge onnxifi quantization support soon. Before that, I would like to merge this diff seperately to make sure it doesnt break anything.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18486
Reviewed By: bddppq, houseroad
Differential Revision:
D14626419
Pulled By: yinghai
fbshipit-source-id:
504c1eae60be1e629203267b59defb8b69d82c0a
Paul O’Shannessy [Wed, 27 Mar 2019 17:55:12 +0000 (10:55 -0700)]
Generate sphinx docs with secure content. (#18508)
Summary:
There are a number of pages in the docs that serve insecure content. AFAICT this is the sole source of that.
I wasn't sure if docs get regenerated for old versions as part of the automation, or if those would need to be manually done.
cf. https://github.com/pytorch/pytorch.github.io/pull/177
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18508
Differential Revision:
D14645665
Pulled By: zpao
fbshipit-source-id:
003563b06048485d4f539feb1675fc80bab47c1b
ZhuBaohe [Wed, 27 Mar 2019 17:15:20 +0000 (10:15 -0700)]
Fix loss functions doc (#18420)
Summary:
Correct docstring display error on web page caused by my previous PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18420
Differential Revision:
D14642467
Pulled By: soumith
fbshipit-source-id:
16fdd3301a4c5bad27fbcd8686f7fbfcc1e908ee
Edward Yang [Wed, 27 Mar 2019 15:01:15 +0000 (08:01 -0700)]
Upgrade flake8-bugbear to master, fix the new lints. (#18507)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18507
ghimport-source-id:
1c3642befad2da78a7e5f39d6d58732b85c76267
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18507 Upgrade flake8-bugbear to master, fix the new lints.**
It turns out Facebobok is internally using the unreleased master
flake8-bugbear, so upgrading it grabs a few more lints that Phabricator
was complaining about but we didn't get in open source.
A few of the getattr sites that I fixed look very suspicious (they're
written as if Python were a lazy language), but I didn't look more
closely into the matter.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision:
D14633682
fbshipit-source-id:
fc3f97c87dca40bbda943a1d1061953490dbacf8
peter [Wed, 27 Mar 2019 14:55:48 +0000 (07:55 -0700)]
Add export annotations for functions in c10 (#18464)
Summary:
Fixes #18461.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18464
Differential Revision:
D14620963
Pulled By: ezyang
fbshipit-source-id:
c11f3967de2ac69c7140767c8fe73a85555e9f40
Li Yu [Wed, 27 Mar 2019 06:41:35 +0000 (23:41 -0700)]
Back out "Revert
D14613517: [pytorch][PR] Updating onnxtrt submodule to master branch" (#18514)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18514
Original commit changeset:
d6267ddfc339
Reviewed By: bddppq
Differential Revision:
D14634476
fbshipit-source-id:
2633b0b4c512d71001e5c20cd79c0c0d7856f942
Lu Fang [Wed, 27 Mar 2019 04:51:10 +0000 (21:51 -0700)]
update of fbcode/onnx to
b29e78a4efb8e5d8995f576bbf19a846807829b6 (#18503)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18503
Previous import was
c05f2ae412daf8fd64136ca354b97ccf73e0ea6c
Included changes:
- **[
b29e78a4](https://github.com/onnx/onnx/commit/
b29e78a4)**: update copyright for open governance (#1885) <Prasanth Pulavarthi>
- **[
3b0ecd55](https://github.com/onnx/onnx/commit/
3b0ecd55)**: open governance (#1881) <Prasanth Pulavarthi>
- **[
bbe28349](https://github.com/onnx/onnx/commit/
bbe28349)**: Revert "Adding Reverse op (#1804)" (#1882) <Lu Fang>
- **[
5be3e223](https://github.com/onnx/onnx/commit/
5be3e223)**: Adding Reverse op (#1804) <Peyman Manikashani>
Reviewed By: zrphercule
Differential Revision:
D14632717
fbshipit-source-id:
2637a4090e7071a59caff3a910fa4f077906bf3c
Yinghai Lu [Wed, 27 Mar 2019 03:57:18 +0000 (20:57 -0700)]
Move weight offload inside backend construction functor (#18385)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18385
By moving the weight offload into the backend initialization function, we can instantiate the backend once by creating the OnnxifiOp once and then clean up the parameter workspace. And we need to keep hold of that instantiated net (OnnxifiOp) without cleaning it. Subsequent ctor of OnnxifiOp of the same model will hit the cached backend and they will not look into weight offloading, which is safe as the weight is already gone.
Reviewed By: ipiszy
Differential Revision:
D14590379
fbshipit-source-id:
f7f34016e09777ad3df0af487885cd14658e1044
Tongzhou Wang [Wed, 27 Mar 2019 03:55:25 +0000 (20:55 -0700)]
fix #16448 (#18479)
Summary:
Fixes #16448
bddppq
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18479
Differential Revision:
D14635360
Pulled By: ezyang
fbshipit-source-id:
4010319fbce050dd0bdf4da3cd1171b9737f3c4c
James Reed [Wed, 27 Mar 2019 03:47:23 +0000 (20:47 -0700)]
Add section about .code to docs
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18493
Differential Revision:
D14634677
Pulled By: jamesr66a
fbshipit-source-id:
9ee065f6ce4218f725b93deb4c64b4ef55926145
Stas Bekman [Wed, 27 Mar 2019 02:56:39 +0000 (19:56 -0700)]
how to use the `ccache` package on Ubuntu (#18495)
Summary:
Added full instructions for how to use the `ccache` package. Thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18495
Differential Revision:
D14635351
Pulled By: ezyang
fbshipit-source-id:
158e1052bae580e95f73644252fdbddcc0213128
peterjc123 [Wed, 27 Mar 2019 02:47:37 +0000 (19:47 -0700)]
Append c10 libs to TorchConfig.cmake (#18418)
Summary:
Fixes #18416.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18418
Differential Revision:
D14635322
Pulled By: ezyang
fbshipit-source-id:
81cb658f73583e4cd0358173617f747ebf4f7f8a
Xiang Gao [Wed, 27 Mar 2019 01:00:15 +0000 (18:00 -0700)]
Add some missing docs for tensor methods and attributes, new unittest to enforce tensors.rst no longer miss anything (#16057)
Summary:
This depend on https://github.com/pytorch/pytorch/pull/16039
This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`.
When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057
Differential Revision:
D14619550
Pulled By: ezyang
fbshipit-source-id:
e1c6dd6761142e2e48ec499e118df399e3949fcc
Li Yu [Wed, 27 Mar 2019 00:30:17 +0000 (17:30 -0700)]
Revert
D14613517: [pytorch][PR] Updating onnxtrt submodule to master branch
Differential Revision:
D14613517
Original commit changeset:
dd20d718db55
fbshipit-source-id:
d6267ddfc339d04f182e2de1750a601c8d6bf8c6
Junjie Bai [Wed, 27 Mar 2019 00:16:23 +0000 (17:16 -0700)]
Fix direct comparison of OperatorDef proto structs (#18466)
Summary:
arguments order is okay to be different
ajyu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18466
Differential Revision:
D14627258
Pulled By: bddppq
fbshipit-source-id:
430e1fb1bea2c5639a547ae7c1652368788c86b9
Soumith Chintala [Wed, 27 Mar 2019 00:14:26 +0000 (17:14 -0700)]
Revert
D14605905: [pytorch][PR] Add return_counts to torch.unique
Differential Revision:
D14605905
Original commit changeset:
555f5a12a8e2
fbshipit-source-id:
c7874f5987893e956c022180a37763d88bba38db
Sameer Indarapu [Tue, 26 Mar 2019 22:29:55 +0000 (15:29 -0700)]
Fix typo in Github links in elementwise_ops_schema.cc (#18018)
Summary:
s/elementwise_op_schema.cc/elementwise_ops_schema.cc
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18018
Differential Revision:
D14612291
Pulled By: soumith
fbshipit-source-id:
09276283b9ff92c039ce530165c62cc8421fb443
Tongzhou Wang [Tue, 26 Mar 2019 22:25:26 +0000 (15:25 -0700)]
Improve numerical precision of (s)logdet (#18449)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/18448 and https://github.com/pytorch/pytorch/issues/18450
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18449
Differential Revision:
D14611638
Pulled By: soumith
fbshipit-source-id:
4f1f27ab5316a92d2783e734169f599afed743cf
Soumith Chintala [Tue, 26 Mar 2019 22:23:43 +0000 (15:23 -0700)]
fix arange shape issue inconsistency across cpu and cuda (#18462)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/18363
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18462
Differential Revision:
D14620263
Pulled By: soumith
fbshipit-source-id:
223524cdda2f5d55c2ca8d4cdcf6f7a05a6c15eb
Kevin Chen [Tue, 26 Mar 2019 21:15:39 +0000 (14:15 -0700)]
Updating onnxtrt submodule to master branch
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18441
Differential Revision:
D14613517
Pulled By: bddppq
fbshipit-source-id:
dd20d718db55942df9cce7acd1151d6902bc57ff
BowenBao [Tue, 26 Mar 2019 20:00:29 +0000 (13:00 -0700)]
Minor fix for onnx ConstantOfShape export (#18199)
Summary:
Set value as tensor of 1 element instead of scalar, according to ONNX spec.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18199
Reviewed By: dzhulgakov
Differential Revision:
D14542588
Pulled By: houseroad
fbshipit-source-id:
70dc978d870ebe6ef37c519ba4a20061c3f07372
Xiang Gao [Tue, 26 Mar 2019 19:33:09 +0000 (12:33 -0700)]
Namedtuple return for solve, slogdet, sort, topk (#17093)
Summary:
More ops for https://github.com/pytorch/pytorch/issues/394. ~~Also need to rebase after landing #16186, because we need to update the whitelist of the new unit test added in #16186.~~
cc: ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17093
Differential Revision:
D14620068
Pulled By: ezyang
fbshipit-source-id:
deec5ffc9bf7624e0350c85392ee59789bad4237
Sebastian Messmer [Tue, 26 Mar 2019 19:29:02 +0000 (12:29 -0700)]
Expose c10 operators to caffe2 by operator name (#18160)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18160
When exposing a c10 operator to the caffe2 frontend, don't use the operator schema but use the operator name instead.
This allows us to get rid of the existing mechanism for operator schema registration in a diff stacked on top.
Reviewed By: dzhulgakov
Differential Revision:
D14513420
fbshipit-source-id:
6b08a9c6d9497eaf18b62361dd44bc07c7b4b76b
Edward Yang [Tue, 26 Mar 2019 19:19:14 +0000 (12:19 -0700)]
Test running a CUDA build on CPU machine. (#18242)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18242
ghimport-source-id:
b949d312a48226a34f90304162e910acee7c95cd
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18242 Test running a CUDA build on CPU machine.**
* #18362 Add ability to query if built with CUDA and MKL-DNN.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision:
D14584429
fbshipit-source-id:
b54de5b33f0c795a7d9605d30576cdf9b74050fd
Edward Yang [Tue, 26 Mar 2019 19:17:31 +0000 (12:17 -0700)]
Properly use cudaGetLastError return code. (#18485)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18485
I don't know how (1) we landed the wrong version of the patch and (2) how
this passed the push blocking test
Reviewed By: pjh5
Differential Revision:
D14621961
fbshipit-source-id:
0a3953d7adcdc79727a61c2acff65f436dcafe55
Xiaomeng Yang [Tue, 26 Mar 2019 19:13:51 +0000 (12:13 -0700)]
Move math::Axpy function to elementwise lib (#18316)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18316
Move math::Axpy function to elementwise lib
i-am-not-moving-c2-to-c10
Reviewed By: houseroad
Differential Revision:
D14574697
fbshipit-source-id:
7cfbb2da295c8966c5328bd6b577cce2638eea62
Gu, Jinghui [Tue, 26 Mar 2019 17:52:52 +0000 (10:52 -0700)]
Upgrade mkldnn to version 0.18.1 (#18463)
Summary:
Upgrade mkldnn to version 0.18.1
Fix the MKLDNN build issue if linking with MKL 2019.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18463
Differential Revision:
D14620228
Pulled By: ezyang
fbshipit-source-id:
136074ad0e4631e1dde4ca1b0af4ee6a41e50913
Pat Mellon [Tue, 26 Mar 2019 17:25:01 +0000 (10:25 -0700)]
Add Google tag (#17690)
Summary:
This PR adds a Global Site Tag to the site.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17690
Differential Revision:
D14620816
Pulled By: zou3519
fbshipit-source-id:
c02407881ce08340289123f5508f92381744e8e3
Gemfield [Tue, 26 Mar 2019 17:14:11 +0000 (10:14 -0700)]
remove redundant --install_dir parameter in GEN_COMMAND (#18473)
Summary:
remove redundant --install_dir parameter in GEN_COMMAND, since "--install_dir parameter " already contained in ${GEN_COMMAND}.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18473
Differential Revision:
D14620193
Pulled By: ezyang
fbshipit-source-id:
ee9953b5d055f4b8beb3557f95f6539051b0028a
Iurii Zdebskyi [Tue, 26 Mar 2019 16:55:50 +0000 (09:55 -0700)]
Resolving comments from Bool Tensor for CPU PR (#18165)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18165
ghimport-source-id:
55cb3fb63a25c2faab1725b4ec14c688bf45bd38
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18166 Bool Tensor for CUDA
* **#18165 Resolved comments from Bool Tensor for CPU PR**
-------
------------
This is a follow up PR that resolves some additional feedback on one the of previous Bool Tensor PRs.
gchanan, here is a list of almost all the comments from the original PR with respective fixes and replies:
**[utils/python_scalars.h]** why is this converting from uint8_t and not bool? (comment?)
When i was adding this, i was testing by creating a tensor and then calling its .tolist(). it worked for bool and uint8_t equally good so i left uint8_t as thought it makes more sense as we are calling PyBool_FromLong. �Changing it to bool.
**[ATen/Dispatch.h]**better name?.
fixed.
**[test/test_torch.py]** what about other factories, such as full? (and more).
There is a test that goes through the factory methods - test_tensor_factories_empty. i added some bool cases above it and added a comment that once CUDA will be done, i will unite them and it will iterate not just between CUDA and CPU but also all types. ��Adding all bool cases now. Will unite in CUDA PR.
**[generic/THTensorMath.h]** any changes in this file actually needed?
Bad merge. Fixed.
**[TH/THTensor.h]** this generates code for random, clampedRandom, and cappedRandom -- do we have tests for all of these with bool?
Added
**[c10/core/ScalarType.h]** I'm not very confident about the lack of Bool here -- can you look at the call sites and see what makes sense to do here?
Added bool to the macro and created a similar one without for a single case which fails the build with errors:
_./torch/csrc/jit/symbolic_variable.h:79:20: error: ambiguous overload for ‘operator*’ (operand types are ‘const torch::jit::SymbolicVariable’ and ‘torch::jit::Value*’)
return (*this) * insertConstant(rhs);_
Differential Revision:
D14605105
fbshipit-source-id:
abf82d50e8f8c50b386545ac068268651b28496d
Edward Yang [Tue, 26 Mar 2019 16:42:41 +0000 (09:42 -0700)]
Unify cudaGetDeviceCount implementations. (#18445)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18445
ghimport-source-id:
30d018737bf6989bc68b7e3676f44e0ca6141fde
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18242 Test running a CUDA build on CPU machine.
* **#18445 Unify cudaGetDeviceCount implementations.**
I went about doing this by searching for calls to cudaGetDeviceCount,
and then methodically replacing them with references to c10::cuda::device_count()
or at::cuda::device_count().
There is a point to doing this: the various implementations wildly differed
in their handling of what to do when cudaGetDeviceCount returns an error.
The final standardized behavior is that **all errors are swallowed** and
we return device count of zero. This indirectly fixes running CUDA builds
on CPU, which was broken in #17847.
I added 'noexcept' to the 'deviceCount' virtual method on DeviceGuardImpl.
This is a BC-breaking change for anyone inheriting from DeviceGuardImpl
but all you need to do is put 'noexcept' on your method and it is backwards
compatible with older libtorch.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision:
D14612189
fbshipit-source-id:
3c8d186e3dd623c0e27625212c7ce30f75d943cb
Christian Puhrsch [Tue, 26 Mar 2019 16:19:51 +0000 (09:19 -0700)]
Use TensorIterator for unary operations
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18309
Differential Revision:
D14591533
Pulled By: cpuhrsch
fbshipit-source-id:
a3b0788a481bddf1803c9f2d3289263d7364f8d7
projects / platform / upstream / pytorch.git / log