Wanchao Liang [Fri, 15 Feb 2019 05:37:08 +0000 (21:37 -0800)]
more test coverage on emitIf none dispatch (#16794)
Summary:
Follow up of #14533, add more test coverage for emitif metaprogramming conditions. Also delete some unwrap optional usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16794
Differential Revision:
D14096868
Pulled By: wanchaol
fbshipit-source-id:
ee1cec609c58d0dd65211249a90207be06649e71
ngimel [Fri, 15 Feb 2019 05:11:30 +0000 (21:11 -0800)]
Speed-up adaptive average pooling for the common case of size=1 output (#17011)
Summary:
When adaptive pooling has to produce a single pixel feature map, it is faster to do so by calling .mean(). Backward calls a pretty inefficient cuda kernel with atomics, which becomes ridiculously slow for halfs. For half this PR provides approx 30x speed-up for adaptive average pooling, which results in 30% end-to-end speed-up on senet. Improvements are smaller for float, but still significant (approx 5x).
Also this PR unifies handling of 3d (no batch dimension) and 4d tensors, using negative dimension indices.
cc ezyang for review.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17011
Reviewed By: ailzhang
Differential Revision:
D14078747
Pulled By: soumith
fbshipit-source-id:
0eb9255da2351190a6bcaf68c30e2ae2402a2dd9
Thomas Viehmann [Fri, 15 Feb 2019 02:41:35 +0000 (18:41 -0800)]
Improve example for torch.mode (#17069)
Summary:
This updates the example for `torch.mode` to show a case where there is a mode.
Also add a bit of a description to the explanation as well as being a bit more precise about "a" mode rather than "the" mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17069
Differential Revision:
D14078722
Pulled By: soumith
fbshipit-source-id:
837a238d53a9b8e868511acbdc258633975bea48
Yinghai Lu [Fri, 15 Feb 2019 01:45:36 +0000 (17:45 -0800)]
Create BackendTransformerBase to host common functions used for backend lowering (#17074)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17074
There are some common functionalities in backend lowering. This diff creates a base class which hosts these common stuff.
Reviewed By: ipiszy
Differential Revision:
D14073192
fbshipit-source-id:
9617603d0e73db6f7fcc5572756b9dbab506dae5
Zhizhen Qin [Fri, 15 Feb 2019 01:22:34 +0000 (17:22 -0800)]
Fix android crash when model detects nothing
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17119
Reviewed By: sf-wind
Differential Revision:
D14087835
Pulled By: ZhizhenQin
fbshipit-source-id:
32e61d46679bae645fd0bbec724513cfa5c553ab
kngwyu [Fri, 15 Feb 2019 01:07:12 +0000 (17:07 -0800)]
Fix some documentation links in torch.tensor (#17109)
Summary:
Currently it's broken https://pytorch.org/docs/stable/tensors.html#torch.Tensor.norm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17109
Differential Revision:
D14093567
Pulled By: ezyang
fbshipit-source-id:
b167cde2150ee97ccf5689fcf50ff8157acfce10
Michael Liu [Fri, 15 Feb 2019 00:21:50 +0000 (16:21 -0800)]
Apply modernize-use-override (2nd iteration)
Summary:
Use C++11’s override and remove virtual where applicable.
Change are automatically generated.
Reviewed By: Orvid
Differential Revision:
D14086124
fbshipit-source-id:
2005227d095d776ca3b4309a57f54e25782b9b58
James Reed [Thu, 14 Feb 2019 23:58:06 +0000 (15:58 -0800)]
Generalize catArray for contiguous inputs and dim != 0 (#17032)
Summary:
I noticed that we were sinking a lot of time into `cat` operations in machine translation on CPU, and drilled down to us doing the cat element-by-element, even though all the inputs were contiguous. The reason was we were doing the cat along a dimension that was not 0, and that caused us to not use the fast `memcpy` branch. This PR generalizes that branch.
Quick benchmark script:
```
import torch, time
tensors = [torch.rand(6, 2, 1024) for i in range(5)]
NITER = 1000
s = time.time()
for i in range(NITER):
torch.cat(tensors, dim=1)
print('time per iter ', (time.time() - s) / NITER)
```
Before:
```
time per iter 8.
089399337768554e-05
```
After:
```
time per iter 2.
183413505554199e-05
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17032
Differential Revision:
D14090038
Pulled By: jamesr66a
fbshipit-source-id:
2c733a84915896008ac95f2233f44894bd2573de
Wanchao Liang [Thu, 14 Feb 2019 23:37:42 +0000 (15:37 -0800)]
fix test_jit canonicalize_tensor_iterator
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17104
Differential Revision:
D14089928
Pulled By: wanchaol
fbshipit-source-id:
8b288514ab9ee8d24a11d39b75eef95783f28f20
Sebastian Messmer [Thu, 14 Feb 2019 23:06:53 +0000 (15:06 -0800)]
Use new constructor in USE_SIMPLE_CTOR_DTOR (#17080)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17080
This changes all operators using this macro to the new format
Reviewed By: dzhulgakov
Differential Revision:
D14078628
fbshipit-source-id:
67048e485e326765fd49567cc008633d3d500d5c
Xiaodong Wang [Thu, 14 Feb 2019 23:02:56 +0000 (15:02 -0800)]
Caffe2 TARGETS for HIP (#17076)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17076
OSS: slightely change the tools/amd_build/build_amd.py to add the output_directory for internal use. Also modify the renaming convention in hipify script to reflect the updated rules.
Reviewed By: bddppq
Differential Revision:
D13767218
fbshipit-source-id:
cbcadc51daab42197d545f204840dcc18176bb3d
Ailing Zhang [Thu, 14 Feb 2019 22:55:44 +0000 (14:55 -0800)]
maskrcnn & bert AD coverage part 1 (#16689)
Summary:
- Moved a few functions from `autograd` namespace to `aten` namespace to be visible from JIT nativeResolver.
- Added a hack to loop up keyword only argument. Will add proper support for kw only later
- Simulate function overload in aten using `_<number>` as function name suffix.
- Even `forward` returns multiple outputs like in `kthvalue`, there's at most one requires grad that we currently support.
- Removed the `TensorList` related ops here since partial `TensorList` support is prone to bugs. Our symbolic diff for `cat` was never tested with autodiff, and it seems broken. Need to find another proper way to support these ops(either by properly supporting `TensorList` or sth like `prim::ConstantChunk` and leave them for next PR.
Ops supported in this PR:
```
erf
expand_as
index
kthvalue
mean
permute
pow
rsub
select
sqrt
squeeze
t
to
topk
transpose
view
var
embedding
logsumexp
// grad is None
_dim_arange
contiguous
nonzero
ones_like
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16689
Differential Revision:
D14020806
Pulled By: ailzhang
fbshipit-source-id:
a5e2c144a7be5a0d39d7ac5f93cb402ec12503a5
jiej [Thu, 14 Feb 2019 22:40:13 +0000 (14:40 -0800)]
Second PR to restore reverted commit (#16224) (#17040)
Summary:
update:
1. global_reduce check for should_block_y_reduce first.
This avoids the enabling global_reduce without block_y_reduce. Leading to
accessing shared memory during global reduce without allocation.
2. updating block_y_reduce heuristics. Improves perf on tiny tensors
3. adding test case covering old cases where illegal memory access might occur
TensorIterator cuda launch configs update (#16224)
Update launch configs for TensorIterator gpu_reduce_kernel. Enable flexible
block dimension to improve efficiency for reduction cases with small fast
dimension.
Previously TensorIterator launches blocks with fixed 32x16 threads.
For cases like:
import torch
torch.randn(2**20, 4, device='cuda').sum(0)
The fixed launch config does handle coalesced memory access efficiently.
Updated launch configure enables flexible block dimension. Combining with
improved reduction scheme (using flexible vertical / horizontal reduction
instead of limited warp / block reduction in the old code), it ensures optimal
memory access pattern even with reduction on dimension with small stride.
Possible future improvements:
1. Precise dynamic shared memory allocation.
2. Using warp shuffle for vertical (block_y) reduction.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16224
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17040
Differential Revision:
D14078295
Pulled By: umanwizard
fbshipit-source-id:
ecc55054a5a4035e731f0196d633412225c3b06c
Yinghai Lu [Thu, 14 Feb 2019 22:22:51 +0000 (14:22 -0800)]
Remove fake inference for shape info in ONNXIFI transform (#17046)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17046
As we are moving to use bound shape inference, we can remove the awkward fake inference run path and make the code cleaner.
Reviewed By: ipiszy
Differential Revision:
D14061501
fbshipit-source-id:
b3ace98b3dabef3c3359086a0bb1410518cefa26
Gregory Chanan [Thu, 14 Feb 2019 21:45:04 +0000 (13:45 -0800)]
Update alexnet expect.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17122
Reviewed By: colesbury
Differential Revision:
D14090209
Pulled By: gchanan
fbshipit-source-id:
78c5961dd7d752b237782b6ed90c376bbd6d3145
Michael Kösel [Thu, 14 Feb 2019 21:42:27 +0000 (13:42 -0800)]
add clear functionality to list (#17050)
Summary:
Add clear functionality to list. See #16662
```python
import torch
torch.jit.script
def foo():
a = [1, 2, 3, 4]
a.clear()
return a
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17050
Differential Revision:
D14071799
Pulled By: driazati
fbshipit-source-id:
305551c16f7db127c43de0ad5885d9f10678e101
Yinghai Lu [Thu, 14 Feb 2019 21:33:52 +0000 (13:33 -0800)]
Moderate the dim type after LengthsRangeFill (#17096)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17096
LengthsRangeFill will take a batch size of lengths input and expand it into sequence. Later op should follow this type until it hits another batch type moderating op, e.g. SparseLengthsSum.
Reviewed By: ipiszy
Differential Revision:
D14079422
fbshipit-source-id:
1a26925d502c32875ea95c160268bf6a256cc955
jayleverett [Thu, 14 Feb 2019 19:46:55 +0000 (11:46 -0800)]
fix behavior of ConcatDataset w/ negative indices (#15756)
Summary:
Currently, when you pass a negative index to a `Dataset` created with `ConcatDataset`, it simply passes that index to the first dataset in the list. So if, for example, we took `concatenated_dataset[-1]`, this will give us the last entry of the *first* dataset, rather than the last entry of the *last* dataset, as we would expect.
This is a simple fix to support the expected behavior for negative indices.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15756
Reviewed By: ezyang
Differential Revision:
D14081811
Pulled By: fmassa
fbshipit-source-id:
a7783fd3fd9e1a8c00fd076c4978ca39ad5a8a2a
Dwarak Rajagopal [Thu, 14 Feb 2019 18:28:25 +0000 (10:28 -0800)]
Add support of count_include_pad and test end to end test for AveragePool (#17034)
Summary:
Add support of count_include_pad end to end test for AveragePool
We can export AveragePool from PyTorch with count_include_pad attribute. However, we don't directly support it in Caffe2's ONNX backend.
We also want to check whether we can pass the end to end test for average pool operator with count_include_pad attribute (pytorch => onnx => caffe2)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17034
Reviewed By: houseroad
Differential Revision:
D14060186
Pulled By: dwarakrajagopal
fbshipit-source-id:
10dae532611c71f8c8cfc3fa701cc7c1c1c02695
BowenBao [Thu, 14 Feb 2019 07:43:14 +0000 (23:43 -0800)]
Support nonzero onnx export
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17036
Differential Revision:
D14079676
Pulled By: houseroad
fbshipit-source-id:
562b538dd9ab330c26f15fdb34c98dc7a23571a1
Dmytro Dzhulgakov [Thu, 14 Feb 2019 06:53:56 +0000 (22:53 -0800)]
Add more headers to setup.py to make pytorch/benchmark work (#16890)
Summary:
Since we don't do tmp_install any more it's better to include all necessary headers.
cc kostmo for better suggestions of how to list all headers here
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16890
Differential Revision:
D14079848
Pulled By: dzhulgakov
fbshipit-source-id:
4522c80d05e5d91f99f6700cde46cac559330d28
Dmytro Dzhulgakov [Thu, 14 Feb 2019 06:38:24 +0000 (22:38 -0800)]
Clean up Storage/StorageImpl constructors (#16948)
Summary:
Small cleanup while doing https://github.com/pytorch/pytorch/pull/16857:
- rename C2 constructors as create_legacy
- remove duplicated constructors
- make resizable flag non-default
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16948
Differential Revision:
D14062755
Pulled By: dzhulgakov
fbshipit-source-id:
3b7b4ec9cdf67d2628cccc001156e040006b673e
Dmytro Dzhulgakov [Thu, 14 Feb 2019 06:18:27 +0000 (22:18 -0800)]
Safety check for negative alloc_cpu() attempt (#17071)
Summary:
Some legacy TH code was relying on alloc to throw when called with negative number!!! E.g. `torch.linspace(0, 1, -1)`. And it breaks ASAN build. I still believe alloc should receive size_t, but I added a safety enforce inside.
It should fix ASAN. I'll follow up with a proper fix for empty_cpu (which is probably the right place to do it) separately
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17071
Differential Revision:
D14074157
Pulled By: dzhulgakov
fbshipit-source-id:
3ed3bdb873e446edecb558e1df491310fd7179e3
svcscm [Thu, 14 Feb 2019 05:38:37 +0000 (21:38 -0800)]
Updating submodules
Reviewed By: cdelahousse
fbshipit-source-id:
b4e7a3850b01bbec56faa3eb0feb3bc6197c0393
Michael Liu [Thu, 14 Feb 2019 04:51:55 +0000 (20:51 -0800)]
Apply modernize-use-override - 2/2
Summary:
Use C++11’s override and remove virtual where applicable.
Change are automatically generated.
Reviewed By: Orvid
Differential Revision:
D14054721
fbshipit-source-id:
15d266fa1779b1e3ea6270f00841d7fb1e4d44ee
svcscm [Thu, 14 Feb 2019 04:49:07 +0000 (20:49 -0800)]
Updating submodules
Reviewed By: cdelahousse
fbshipit-source-id:
5d9763a6f26ba53c6402b978004aaa7508f4e354
ptrblck [Thu, 14 Feb 2019 04:42:45 +0000 (20:42 -0800)]
#16627 convert weights using torch.as_tensor to avoid warning (#17067)
Summary:
Minor change which fixes #16627
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17067
Differential Revision:
D14078726
Pulled By: soumith
fbshipit-source-id:
c04a5f1eff44e4a4b04b981f0ae8de6ff018515b
svcscm [Thu, 14 Feb 2019 04:26:10 +0000 (20:26 -0800)]
Updating submodules
Reviewed By: cdelahousse
fbshipit-source-id:
e074a865b859fd72b34b012505dfbd3a27a0cc41
Edward Yang [Thu, 14 Feb 2019 04:21:06 +0000 (20:21 -0800)]
Revert
D14062537: [pytorch][PR] Implement NetDef <--> JIT IR converters.
Differential Revision:
D14062537
Original commit changeset:
88b184ee7276
fbshipit-source-id:
01971bbe20daade40cc2cbf85fc08edb380b445c
Pritam Damania [Thu, 14 Feb 2019 03:41:25 +0000 (19:41 -0800)]
PyTorch model metadata. (#16275)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16275
Adding a generic string `metadata` field as part of the model to capture additional metadata with the model.
Reviewed By: dzhulgakov
Differential Revision:
D13579029
fbshipit-source-id:
7456ef2edbe73bb70bbb31889cecd94e0db329a2
Dmytro Dzhulgakov [Thu, 14 Feb 2019 03:28:05 +0000 (19:28 -0800)]
Trim libshm deps, move tempfile.h to c10 (#17019)
Summary:
libshm_manager doesn't need to depend on all of libtorch. It only uses tiny tempfile.h which can be moved to c10. I could just duplicate the file too, but it's not worth it as c10 is small enough.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17019
Differential Revision:
D14052688
Pulled By: dzhulgakov
fbshipit-source-id:
8797d15f8c7c49c49d40b7ab2f43aa3bf6becb0c
Mikhail Zolotukhin [Thu, 14 Feb 2019 02:15:57 +0000 (18:15 -0800)]
Implement NetDef <--> JIT IR converters. (#16967)
Summary:
Currently the converters are very straightforward, i.e. there is no code for trying to
preserve semantics, we're purely perform conversion from one format to another.
Two things that we might want to add/change:
1. Add semantic conversion as well (but probably it would be a good idea to keep
it separate as a temporary thing).
2. Make sure we don't mess with value names, as they are crucial for current
uses of NetDefs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16967
Differential Revision:
D14062537
Pulled By: ZolotukhinM
fbshipit-source-id:
88b184ee7276779e5e9152b149d69857515ad98a
David Riazati [Thu, 14 Feb 2019 01:56:52 +0000 (17:56 -0800)]
Remove IgnoredPythonOp sugared value
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17042
Differential Revision:
D14072497
Pulled By: driazati
fbshipit-source-id:
68fe3fa89c22e60142d758c8cbe0e6e258e7d5c2
Xiaomeng Yang [Thu, 14 Feb 2019 01:47:49 +0000 (17:47 -0800)]
Separate reduce functions from math (#16929)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16929
Separate CPU reduce functions from math
i-am-not-moving-c2-to-c10
Reviewed By: houseroad
Differential Revision:
D13999469
fbshipit-source-id:
bd628b15a6e3c1f04cc62aefffb0110690e1c0d1
Junjie Bai [Thu, 14 Feb 2019 01:12:01 +0000 (17:12 -0800)]
Skip test_cudnn_multiple_threads_same_device on ROCm (flaky) (#17061)
Summary:
cc iotamudelta
https://ci.pytorch.org/jenkins/job/pytorch-builds/job/py2-clang7-rocmdeb-ubuntu16.04-test/10722//console
https://ci.pytorch.org/jenkins/job/pytorch-builds/job/py2-clang7-rocmdeb-ubuntu16.04-test/10710//console
https://ci.pytorch.org/jenkins/job/pytorch-builds/job/py2-clang7-rocmdeb-ubuntu16.04-test/10753//console
https://ci.pytorch.org/jenkins/job/pytorch-builds/job/py2-devtoolset7-rocmrpm-centos7.5-test/1756//console
```
19:07:18 ======================================================================
19:07:18 FAIL: test_cudnn_multiple_threads_same_device (test_nn.TestNN)
19:07:18 ----------------------------------------------------------------------
19:07:18 Traceback (most recent call last):
19:07:18 File "/var/lib/jenkins/workspace/test/test_nn.py", line 3905, in test_cudnn_multiple_threads_same_device
19:07:18 (2048 - test_iters) * (2048 - test_iters))
19:07:18 File "/var/lib/jenkins/workspace/test/common_utils.py", line 453, in assertEqual
19:07:18 super(TestCase, self).assertLessEqual(abs(x - y), prec, message)
19:07:18 AssertionError: 3794704.0 not less than or equal to 1e-05 :
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17061
Differential Revision:
D14069324
Pulled By: bddppq
fbshipit-source-id:
e33b09abca217a62a8b577f9c332ea22985ef4ff
Tongliang Liao [Thu, 14 Feb 2019 01:08:40 +0000 (17:08 -0800)]
Support FC (Caffe2) -> Gemm (ONNX) with variable input shape. (#16184)
Summary:
For >2D input, previously the code uses static shape captured during tracing and reshape before/after `Gemm`.
Now we add `-1` to the first `Reshape`, and uses `Shape(X) => Slice(outer) => Concat(with -1 for inner) => Reshape` for the second.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16184
Differential Revision:
D14070754
Pulled By: ezyang
fbshipit-source-id:
86c69e9b254945b3406c07e122e57a00dfeba3df
Junjie Bai [Thu, 14 Feb 2019 00:57:30 +0000 (16:57 -0800)]
Make timeout in resnet50_trainer configurable (#17058)
Summary:
xw285cornell petrex dagamayank
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17058
Differential Revision:
D14068458
Pulled By: bddppq
fbshipit-source-id:
15df4007859067a22df4c6c407df4121e19aaf97
Boris Daskalov [Wed, 13 Feb 2019 23:30:39 +0000 (15:30 -0800)]
Make mkldnn Stream object thread_local and enable mkldnn thread-safe (#17022)
Summary:
This PR fixes following issue: https://github.com/pytorch/pytorch/issues/16828
It is a combination of two things:
1) MKLDNN streams are not thread-safe but are currently shared between different threads. This change makes them thread_local
2) By default MKLDNN primitives can share global memory and can't be invoked from multiple threads. This PR enables the MKLDNN_ENABLE_CONCURRENT_EXEC cmake configuration option that makes them thread-safe.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17022
Differential Revision:
D14069052
Pulled By: ezyang
fbshipit-source-id:
f8f7fcb86c40f5d751fb35dfccc2f802b6e137c6
Tongliang Liao [Wed, 13 Feb 2019 22:57:27 +0000 (14:57 -0800)]
Support conversion from Caffe2 MergeDim to ONNX Reshape + Squeeze. (#16189)
Summary:
`MergeDim` can be done by `Reshape([1, -1, 0, 0, ...]) + Squeeze`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16189
Differential Revision:
D14070676
Pulled By: ezyang
fbshipit-source-id:
28d7e9b35cc2c1dcbd4afb3fbdf7383e219b1777
vishwakftw [Wed, 13 Feb 2019 21:35:40 +0000 (13:35 -0800)]
Fix mvlgamma doc (#17045)
Summary:
Changelog:
- Fix the constant in the docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17045
Differential Revision:
D14068698
Pulled By: ezyang
fbshipit-source-id:
af040b9a9badea213785f5bf3b6daf4d90050eb2
Mikhail Zolotukhin [Wed, 13 Feb 2019 18:32:38 +0000 (10:32 -0800)]
Change IR graph print format to make it look more pythonic (#16986)
Summary:
This removes curly braces from the outputs (we have indentation to indicate scopes), also adds ':' after graph and blocks declaration and removes ';' from the return line. ".expect" tests are updated to keep up with it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16986
Differential Revision:
D14062540
Pulled By: ZolotukhinM
fbshipit-source-id:
7f8e2d11619152a21ef7f1f7f8579c49392c3eca
Gregory Chanan [Wed, 13 Feb 2019 18:27:23 +0000 (10:27 -0800)]
Turn off the ability for Declarations.cwrap entries to be methods.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17053
Differential Revision:
D14065887
Pulled By: gchanan
fbshipit-source-id:
5d06ac66d27d28d48c2aff2b0d911f34ea0cd6fd
Jaliya Ekanayake [Wed, 13 Feb 2019 18:26:15 +0000 (10:26 -0800)]
Remove chunk count check on the ChunkBuffer (#16868)
Summary:
Previously, the ChunkBuffer depends on the remaining chunk count to signal end of dataloading. This does not work with distributed samplers where each sampler only loads a subset of chunks. This refactor remove the dependency on the remaining chunk count at the ChunkBuffer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16868
Differential Revision:
D14066517
Pulled By: goldsborough
fbshipit-source-id:
293dfe282ceff326dff0876c2f75c2ee4f4463e2
Stefan Krah [Wed, 13 Feb 2019 17:24:04 +0000 (09:24 -0800)]
Use IndexError instead of RuntimeError in ATen CPU kernels
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17049
Reviewed By: ezyang
Differential Revision:
D14064700
Pulled By: fmassa
fbshipit-source-id:
3575db103bba5a7d82f574cbb082beca419151ec
Edward Yang [Wed, 13 Feb 2019 16:44:43 +0000 (08:44 -0800)]
Mark IntList as deprecated; add C10_DEPRECATED_USING (#16824)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16824
There was a big wooly yak getting the deprecated macros to work.
Gory details are in Deprecated.h
Reviewed By: smessmer
Differential Revision:
D13978429
fbshipit-source-id:
f148e5935ac36eacc481789d22c7a9443164fe95
Yinghai Lu [Wed, 13 Feb 2019 07:59:40 +0000 (23:59 -0800)]
Add more debugging facilities to ONNXIFI transform (#17043)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17043
Add more debugging facilities for ONXNIFI transform.
Reviewed By: ipiszy
Differential Revision:
D14019492
fbshipit-source-id:
8c258ccba2f8ce77db096031fc8a61e15bd8af93
svcscm [Wed, 13 Feb 2019 05:45:34 +0000 (21:45 -0800)]
Updating submodules
Reviewed By: cdelahousse
fbshipit-source-id:
399afdc341075c383227d0d410a30eeb6c1d3b08
svcscm [Wed, 13 Feb 2019 05:20:09 +0000 (21:20 -0800)]
Updating submodules
Reviewed By: cdelahousse
fbshipit-source-id:
edb216d2eca7120d0f7729b2e4640096a0341154
Dmytro Dzhulgakov [Wed, 13 Feb 2019 05:13:25 +0000 (21:13 -0800)]
unify c2 and TH allocator (#16892)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16892
Replaces https://github.com/pytorch/pytorch/pull/14517
Merged caffe2 and TH CPU Allocators. Mostly using the code from caffe2 allocators.
`memset` of caffe2 allocator is gone now. These two allocators should be almost the same.
Baseline:
```
Running ./tensor_allocation
Run on (48 X 2501 MHz CPU s)
CPU Caches:
L1 Data 32K (x24)
L1 Instruction 32K (x24)
L2 Unified 256K (x24)
L3 Unified 30720K (x2)
-------------------------------------------------------------------------
Benchmark Time CPU Iterations
-------------------------------------------------------------------------
BM_MakeStorageImpl 148 ns 148 ns 4676594
BM_StorageImplCtor 54 ns 54 ns
12957810
BM_MallocStorageImpl 62 ns 62 ns
11254745
BM_TensorImplCtor 22 ns 22 ns
31939472
BM_MallocTensorImpl 105 ns 105 ns 6505661
BM_Malloc_1 43 ns 43 ns
16464905
BM_MakeTensorFromStorage 126 ns 126 ns 5586116
BM_MakeVariableFromTensor 236 ns 236 ns 2995528
BM_ATenCPUTensorAllocationSmall1 319 ns 319 ns 2268884
BM_ATenCPUTensorAllocationSmall2 318 ns 318 ns 2163332
BM_ATenCPUTensorAllocationMedium1 403 ns 403 ns 1663228
BM_ATenCPUTensorAllocationMedium2 448 ns 448 ns 1595004
BM_ATenCPUTensorAllocationBig1 532 ns 532 ns 1352634
BM_ATenCPUTensorAllocationBig2 4486 ns 4486 ns 160978
```
Changed:
```
Running ./tensor_allocation
Run on (48 X 2501 MHz CPU s)
CPU Caches:
L1 Data 32K (x24)
L1 Instruction 32K (x24)
L2 Unified 256K (x24)
L3 Unified 30720K (x2)
-------------------------------------------------------------------------
Benchmark Time CPU Iterations
-------------------------------------------------------------------------
BM_MakeStorageImpl 141 ns 141 ns 4803576
BM_StorageImplCtor 55 ns 55 ns
13129391
BM_MallocStorageImpl 64 ns 64 ns
11088143
BM_TensorImplCtor 23 ns 23 ns
31616273
BM_MallocTensorImpl 101 ns 101 ns 7017585
BM_Malloc_1 39 ns 39 ns
18523954
BM_MakeTensorFromStorage 118 ns 118 ns 5877919
BM_MakeVariableFromTensor 452 ns 452 ns 1565722
BM_ATenCPUTensorAllocationSmall1 384 ns 384 ns 1819763
BM_ATenCPUTensorAllocationSmall2 389 ns 389 ns 1857483
BM_ATenCPUTensorAllocationMedium1 425 ns 425 ns 1646284
BM_ATenCPUTensorAllocationMedium2 430 ns 430 ns 1561319
BM_ATenCPUTensorAllocationBig1 508 ns 508 ns 1309969
BM_ATenCPUTensorAllocationBig2 3799 ns 3799 ns 173674
```
lstm benchmark:
Before:
```
INFO:lstm_bench:Iter: 1 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 21 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 41 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 61 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 81 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 101 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 121 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 141 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 161 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 181 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 201 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 221 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 241 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 261 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 281 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 301 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 321 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 341 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 361 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 381 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Done. Total EPS excluding 1st iteration: 0.8k
```
After:
```
INFO:lstm_bench:Iter: 1 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 21 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 41 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 61 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 81 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 101 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 121 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 141 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 161 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 181 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 201 / 390. Entries Per Second: 0.8k.
INFO:lstm_bench:Iter: 221 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 241 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 261 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 281 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 301 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 321 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 341 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 361 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Iter: 381 / 390. Entries Per Second: 0.7k.
INFO:lstm_bench:Done. Total EPS excluding 1st iteration: 0.8k
```
Reviewed By: ezyang
Differential Revision:
D13202632
fbshipit-source-id:
db6d2ec756ed15b0732b15396c82ad42302bb79d
svcscm [Wed, 13 Feb 2019 04:42:10 +0000 (20:42 -0800)]
Updating submodules
Reviewed By: cdelahousse
fbshipit-source-id:
7d730945dbdd7bb7d10192061229ee6e759a1a7f
Yinghai Lu [Wed, 13 Feb 2019 02:21:09 +0000 (18:21 -0800)]
Remove second output of Reshape during ONNXIFI transform (#17027)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17027
Glow doesn't support second output of Reshape right now and it's useless. For correctness, we do make sure that the second output of Reshape is of Constant type during bound shape inference.
Reviewed By: ipiszy
Differential Revision:
D14056555
fbshipit-source-id:
f39cca7ba941bf5a5cc3adc96e2b1f943cc0be93
Johannes M Dieterich [Wed, 13 Feb 2019 01:41:24 +0000 (17:41 -0800)]
enable more unit tests in test_nn (#16994)
Summary:
These tests work with ROCm 2.1.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16994
Differential Revision:
D14059802
Pulled By: bddppq
fbshipit-source-id:
8e2cbb13196c2e0283d3e02b7f761374bc580751
Johannes M Dieterich [Wed, 13 Feb 2019 01:18:40 +0000 (17:18 -0800)]
fix bicubic upsampling and enable tests (#17020)
Summary:
Fix macro name in ifdef guard, enable upsampling tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17020
Differential Revision:
D14059780
Pulled By: bddppq
fbshipit-source-id:
82c57d17d5bccdccb548c65d2b7a1ff8ab05af30
Jongsoo Park [Wed, 13 Feb 2019 01:00:33 +0000 (17:00 -0800)]
Fold col offsets into bias; optimize A symmetric quant (#16942)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16942
We can fold col offsets into bias if zero point of activation is constant.
fbgemm still needs to provide an option to pass col offsets in case zero point of activation keep changes (e.g., dynamic quantization).
A trick to optimize static quantization case is setting A zero point to 0 after folding into bias.
This diff also optimizes when weights use symmetric quantization. When B zero point is 0, we use PackAMatrix instead of PackAWithRowOffset .
TODO:
Ideally, PackAWithRowOffset should perform as fast as PackAMatrix when B_zero_point is 0 to make client code simpler
Same in PackAWithIm2Col and depth-wise convolution (group convolution is already doing this)
Reviewed By: csummersea
Differential Revision:
D14013931
fbshipit-source-id:
e4d313343e2a16a451eb910beed30e35de02a40c
Johannes M Dieterich [Wed, 13 Feb 2019 00:48:51 +0000 (16:48 -0800)]
enable unit tests in test_cuda that now pass with ROCm 2.1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17012
Differential Revision:
D14059761
Pulled By: bddppq
fbshipit-source-id:
8309c3ffe1efed42b5db69fdec26427413c3f224
Sebastian Messmer [Wed, 13 Feb 2019 00:47:53 +0000 (16:47 -0800)]
Register CUDA kernels for caffe2 operators (#16691)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16691
Previous diffs already introduced a macro that registers caffe2 CPU kernels with c10.
This now also registers the CUDA kernels with it.
Reviewed By: bwasti
Differential Revision:
D13901619
fbshipit-source-id:
c15e5b7081ff10e5219af460779b88d6e091a6a6
Johannes M Dieterich [Wed, 13 Feb 2019 00:45:09 +0000 (16:45 -0800)]
Enable test_jit tests that work on ROCm 2.1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17010
Differential Revision:
D14059748
Pulled By: bddppq
fbshipit-source-id:
7a1f7eee4f818dba91e741437415370973e4d429
Ying Zhang [Wed, 13 Feb 2019 00:37:50 +0000 (16:37 -0800)]
Extract ShapeInfo and some util functions into a separate file. (#17025)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17025
Extract ShapeInfo and some util functions into a separate file.
Reviewed By: yinghai
Differential Revision:
D14017432
fbshipit-source-id:
201db46bce6d52d9355a1a86925aa6206d0336bf
Yinghai Lu [Tue, 12 Feb 2019 22:43:44 +0000 (14:43 -0800)]
Allow customization of blob node in net_drawer (#16915)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16915
TSIA
Reviewed By: ipiszy
Differential Revision:
D14018010
fbshipit-source-id:
df5ccc06fa37f08e7a02a8acc466c4ad47afe04e
Yinghai Lu [Tue, 12 Feb 2019 22:43:43 +0000 (14:43 -0800)]
Ignore unknown_shaped tensor in bound shape inference (#16916)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16916
Two fixes for maximum effort bound shape inference
1. Ignore failed and unknown shape
2. Add specialization for `SparseLengthsWeightedSumFused8BitRowwise`.
Reviewed By: ipiszy
Differential Revision:
D14017810
fbshipit-source-id:
25cd68d35aa20b9ed077bdb562eb7f9deff0ab96
Pearu Peterson [Tue, 12 Feb 2019 22:14:30 +0000 (14:14 -0800)]
Workarounds to the lack of nvidia-smi and ldconfig programs in macosx (was PR 16968) (#16999)
Summary:
Fix issue #12174 for Mac OSX.
PS: This is a duplicate of PR #16968 that got messed up. Sorry for the confusion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16999
Differential Revision:
D14050669
Pulled By: zou3519
fbshipit-source-id:
a4594c03ae8e0ca91a4836408b6c588720162c9f
vishwakftw [Tue, 12 Feb 2019 21:34:44 +0000 (13:34 -0800)]
Dispatch the correct legacy function for geqrf_out and ormqr_out (#16964)
Summary:
This fixes the segfault.
Changelog:
- Modify the function calls in LegacyDefinitions for `geqrf_out` and `ormqr_out`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16964
Differential Revision:
D14025985
Pulled By: gchanan
fbshipit-source-id:
aa50e2c1694cbf3642273ee14b09ba12625c7d33
Davide Libenzi [Tue, 12 Feb 2019 21:34:11 +0000 (13:34 -0800)]
Register layout for XLA backend.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16946
Differential Revision:
D14054716
Pulled By: gchanan
fbshipit-source-id:
063495b99b9f7d29ca3ad2020a6bc90d36ba0d7d
Tongliang Liao [Tue, 12 Feb 2019 21:18:13 +0000 (13:18 -0800)]
Export ReduceMean/ReduceFrontMean/ReduceBackMean (Caffe2) to ReduceMean (ONNX). (#16727)
Summary:
The second input (`lengths`) is not supported.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16727
Differential Revision:
D14054105
Pulled By: houseroad
fbshipit-source-id:
36b8d00460f9623696439e1bd2a6bc60b7bb263c
James Reed [Tue, 12 Feb 2019 20:18:54 +0000 (12:18 -0800)]
Clean up allocations in FBGEMM linear (#16985)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16985
These statements were causing some redundant allocations + copying, so I cleaned
them up
Reviewed By: zdevito, wanchaol
Differential Revision:
D14031067
fbshipit-source-id:
f760fb29a2561894d52a2663f557b3e9ab1653de
Gregory Chanan [Tue, 12 Feb 2019 20:13:11 +0000 (12:13 -0800)]
Properly dispatch s_copy__cpu.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16974
Differential Revision:
D14030516
Pulled By: gchanan
fbshipit-source-id:
ba4cde5ebf2898d207efbc9117c1f1d6ccae861b
Gregory Chanan [Tue, 12 Feb 2019 20:11:45 +0000 (12:11 -0800)]
Get rid of unused THPStorage defines related to accreal.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16973
Differential Revision:
D14029538
Pulled By: gchanan
fbshipit-source-id:
b51f203ccff97695bf228772bb13e3e6b9bb6d1a
Yinghai Lu [Tue, 12 Feb 2019 19:18:52 +0000 (11:18 -0800)]
Fix AddAdjustBatchOp (#16997)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16997
1. Don't create multiple AdjustBatch ops for the same input name. We create it once and hook input to abc_post_adjust_batch.
2. Dangling tensor. The problem for such an error is still with AttachAdjustBatchOp. Considering such as net
```
op {
type : "Relu"
input: "X"
outpu: "Y"
}
op {
type : "Relu"
input: "Y"
output: "Y2"
}
external_output: "Y"
external_output: "Y2"
```
In this the output of first Relu will be used as an internal node as well as output. We cannot simply rename Y into Y_pre_batch_adjust. Basically, we need another pass in to check all the input of the ops in the net and rename Y into Y_pre_batch_adjust.
Reviewed By: bertmaher
Differential Revision:
D14041446
fbshipit-source-id:
f6553e287a8dfb14e4044cc20afaf3f290e5151b
Will Feng [Tue, 12 Feb 2019 18:49:38 +0000 (10:49 -0800)]
Roll back PyTorch DockerVersion to 282
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17013
Differential Revision:
D14052415
Pulled By: yf225
fbshipit-source-id:
df663fb46ee825174fe06b8d395979b3d4e84766
Karl Ostmo [Tue, 12 Feb 2019 18:41:45 +0000 (10:41 -0800)]
fix silent failure on Windows builds (#16984)
Summary:
Closes #16983
Remove backticks that are being interpreted by the shell. Add -e option to bash script to avoid future such failures
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16984
Reviewed By: yf225
Differential Revision:
D14039128
Pulled By: kostmo
fbshipit-source-id:
c31a1895377ca86c1b59e79351843cc8c4fd7de3
Theo [Tue, 12 Feb 2019 17:35:23 +0000 (09:35 -0800)]
Add module and name to func created with _jit_internal.boolean_dispatch (#16922)
Summary:
The use case for making this PR is the following bug :
(with F = torch.nn.functional)
`F.max_pool2d.__module__` is `torch._jit_internal`
`F.max_pool2d.__name__` is `fn`
With this PR you get:
`F.max_pool2d.__module__` is `torch.nn.functional`
`F.max_pool2d.__name__` is `max_pool2d`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16922
Differential Revision:
D14020053
Pulled By: driazati
fbshipit-source-id:
c109c1f04640f3b2b69bc4790b16fef7714025dd
Edward Yang [Tue, 12 Feb 2019 16:02:05 +0000 (08:02 -0800)]
More docs for methods in operator.h
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16826
Reviewed By: izdeby
Differential Revision:
D13979891
fbshipit-source-id:
df8391ffaff0d44845057bb839f05aea6fc5712c
Daniel [Tue, 12 Feb 2019 15:52:55 +0000 (07:52 -0800)]
Minor typo
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16980
Differential Revision:
D14033686
Pulled By: gchanan
fbshipit-source-id:
9f7967defc6795640e14157d0b701b185061741f
SsnL [Tue, 12 Feb 2019 15:49:48 +0000 (07:49 -0800)]
Fix allow_inf in assertEqual (#16959)
Summary:
gchanan pointed out in https://github.com/pytorch/pytorch/pull/16389 that `allow_inf` is treating `-inf` and `inf` as equal. This fixes it.
Also fixing #16448 since it's near and 2.1 has released.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16959
Differential Revision:
D14025297
Pulled By: gchanan
fbshipit-source-id:
95348309492e7ab65aa4d7aabb5a1800de66c5d6
Edward Yang [Tue, 12 Feb 2019 15:22:05 +0000 (07:22 -0800)]
Refine return type Stream to HIPStream in HIPStreamGuardMasqueradingAsCUDA (#16978)
Summary:
Previously, we used the templated class directly to provide
implementations. However, there is a subtle difference
between this, and CUDAStreamGuard: CUDAStreamGuard has refined types
for the Streams it returns. This lead to a compilation failure
of HIPified ddp.cpp. This commit lines them up more closely,
at the cost of copy-paste.
A possible alternate strategy would have been to extend the
InlineDeviceGuard templates to optionally accept refinements
for Stream. I leave this for future work.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16978
Differential Revision:
D14045346
Pulled By: ezyang
fbshipit-source-id:
2b101606e62e4db588027c57902ea739a2119410
Edward Yang [Tue, 12 Feb 2019 14:59:36 +0000 (06:59 -0800)]
Revert
D14030665: [pytorch][PR] [HOTFIX] Pin docker-ce version to the one expected by nvidia-docker2
Differential Revision:
D14030665
Original commit changeset:
dece6a5aa4d1
fbshipit-source-id:
885a464ec3d1c23d4e07630fa3b67e69a3eab1b8
Simeon Monov [Tue, 12 Feb 2019 08:12:03 +0000 (00:12 -0800)]
Parse the command line and check the arguments before build_deps() (#16914)
Summary:
This is needed to check for wrong arguments or --help options
before `build_deps()` is executed. Otherwise command line arguments
are not parsed and checked until `setup()` is run.
Fixes: #16707
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16914
Differential Revision:
D14041236
Pulled By: soumith
fbshipit-source-id:
41f635772ccf47f05114775d5a19ae04c495ab3b
Dmytro Dzhulgakov [Tue, 12 Feb 2019 07:15:54 +0000 (23:15 -0800)]
Fix and add testing for nullptr allocator in c2->pt conversion (#16857)
Summary:
Fixes the bug for when tensor is created on Caffe2 side, then passed to PT and resized. Now we just initialize allocator correctly.
Note that the code in raw_mutable_data() is still necessary because of non-resizable tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16857
Reviewed By: houseroad
Differential Revision:
D14019469
Pulled By: dzhulgakov
fbshipit-source-id:
14d3a3b946d718bbab747ea376903646b885706a
Dmytro Dzhulgakov [Tue, 12 Feb 2019 07:04:59 +0000 (23:04 -0800)]
Fix NERPredictor for zero initialization
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16931
Reviewed By: dragonxlwang
Differential Revision:
D14016749
fbshipit-source-id:
b5512c52cef77651bdba1e31f588ea649daacdd9
David Riazati [Tue, 12 Feb 2019 05:48:58 +0000 (21:48 -0800)]
Allow calling a Python function with a dict
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16989
Differential Revision:
D14037896
Pulled By: driazati
fbshipit-source-id:
5f26d2d8fabf0f267909a3383f19d984645f94d0
Kimish Patel [Mon, 11 Feb 2019 22:32:30 +0000 (14:32 -0800)]
Keep weights name unchanged during SsaRewrite (#16932)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16932
During onnxifi transformation net ssa is rewritten. At the last step the weight
names are changed back to what they were before. The diff keeps the weight
names unchanged thru the process.
Reviewed By: yinghai
Differential Revision:
D13972597
fbshipit-source-id:
7c29857f788a674edf625c073b345f2b44267b33
Will Feng [Mon, 11 Feb 2019 22:04:31 +0000 (14:04 -0800)]
Pin docker-ce version to the one expected by nvidia-docker2 (#16976)
Summary:
Fix errors such as https://circleci.com/gh/pytorch/pytorch/760715.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16976
Differential Revision:
D14030665
Pulled By: yf225
fbshipit-source-id:
dece6a5aa4d13ff771c18b4ce02a0b9f9572a379
Sebastian Messmer [Mon, 11 Feb 2019 22:03:45 +0000 (14:03 -0800)]
Expose GenerateProposals to PyTorch
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16880
Reviewed By: bwasti
Differential Revision:
D13998092
fbshipit-source-id:
23ab886ba137377312557fa718f262f4c8149cc7
Sebastian Messmer [Mon, 11 Feb 2019 22:03:45 +0000 (14:03 -0800)]
Expose BBoxTransform to pytorch
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16879
Reviewed By: bwasti
Differential Revision:
D13998093
fbshipit-source-id:
ddfe4bff83e9a1a4cedf1e520e6d2977b21cb3af
Sebastian Messmer [Mon, 11 Feb 2019 22:03:45 +0000 (14:03 -0800)]
Minimize templated code in caffe2 operator wrapper (#16965)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16965
Instead of having one large templated function to wrap the caffe2 op, minimize the amount of templated code.
Non-templated code can be reused between different operators and decreases binary size.
Reviewed By: orionr
Differential Revision:
D14018806
fbshipit-source-id:
bedd4152eec21dd8c5778446963826316d210543
Adam Paszke [Mon, 11 Feb 2019 21:31:06 +0000 (13:31 -0800)]
Don't keep unnecessary saved_inputs alive (#16583)
Summary:
Fixes #16577.
This greatly improves memory efficiency of certain ops like Dropout2d. Previously, they were implemented as `input * mask` where mask never requires_grad, but we didn't use that knowledge in forward, and (in case of a in-place dropout) kept input.clone() for the backward, when it would simply get ignored.
This patch tries to address this situation by emitting some guards for stores like this, but only if they are as simple, as checking if a single value requires_grad.
Interestingly, the same optimizations apply to methods like bmm, baddmm, etc., but _not to mm nor addmm_, because of how their derivatives are defined. Apparently they unnecessarily use `mat1` to compute the derivative of `mat1` just to improve the error message in case `mat1` was sparse. I'd like to apply this optimization to that case, but I don't want to loose the nicer error message, so if anyone has any ideas for solutions, please let me know...
Full list of operators affected by this patch:
* _nnpack_spatial_convolution
* addbmm
* addcdiv
* addcmul
* addmv
* addr
* baddbmm
* bmm
* cross
* div
* dot
* fmod
* ger
* index_add_
* mul
* mv
* scatter_add_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16583
Differential Revision:
D13900881
Pulled By: gchanan
fbshipit-source-id:
dd0aeb2ab58c4b6aa95b37b46d3255b3e014291c
Will Feng [Mon, 11 Feb 2019 20:48:17 +0000 (12:48 -0800)]
Enforce same input tensor storage in VariableType functions (#16305)
Summary:
In VariableType.cpp, when a function modifies its input tensors, it should only change the input tensors' storage data in-place, and should never change the input tensors' storage pointers. This PR adds checks for this, and also fixes functions that fail this test.
This is part of the Variable/Tensor merge work (https://github.com/pytorch/pytorch/issues/13638).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16305
Differential Revision:
D13897855
Pulled By: yf225
fbshipit-source-id:
0c4fc7eb530d30db88037b1f0981f6f8454d3b79
Sebastian Messmer [Mon, 11 Feb 2019 20:29:47 +0000 (12:29 -0800)]
Revert unneeded fixes in flat_hash_map (#16907)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16907
The begin()/end() fix actually doesn't make sense, see my comment on https://github.com/skarupke/flat_hash_map/pull/8
This diff removes it.
Reviewed By: ezyang
Differential Revision:
D13985779
fbshipit-source-id:
f08b02c941069e2a4e728e02a19b65dc72f96b41
Sebastian Messmer [Mon, 11 Feb 2019 20:29:47 +0000 (12:29 -0800)]
Fix constexpr in KernelRegistrationBuilder (#16906)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16906
In C++11, constexpr implies const, so these methods actually wouldn't be rvalue overloads as intended but const rvalue overloads.
Let's only apply the constexpr flag in C++14 to be safe.
Reviewed By: bddppq
Differential Revision:
D13998486
fbshipit-source-id:
a04d17ef0cc8f45e3d0a1ca9843d194f4f0f6f7f
Xiaodong Wang [Mon, 11 Feb 2019 20:27:12 +0000 (12:27 -0800)]
Catch cudaError_t return val (nodiscard in rocm) (#16399)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16399
Catching cudaError_t return values in a few places, because it's nodiscard in rocm. Unless we add -Wno-unused-result, it'll end up with a compilation error.
Also in c10/cuda/test, check whether a host has GPU or not. We were silently throwing out the error before (so not really testing the cuda api).
Reviewed By: bddppq
Differential Revision:
D13828281
fbshipit-source-id:
587d1cc31c20b836ce9594e3c18f067d322b2934
Thomas Viehmann [Mon, 11 Feb 2019 20:26:47 +0000 (12:26 -0800)]
optionally zero infinite losses in CTCLoss (#16199)
Summary:
Here is a stab at implementing an option to zero out infinite losses (and NaN gradients).
It might be nicer to move the zeroing to the respective kernels.
The default is currently `False` to mimic the old behaviour, but I'd be half inclined to set the default to `True`, because the behaviour wasn't consistent between CuDNN and Native anyways and the NaN gradients aren't terribly useful.
This topic seems to come up regularly, e.g. in #14335
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16199
Differential Revision:
D14020462
Pulled By: ezyang
fbshipit-source-id:
5ba8936c66ec6e61530aaf01175dc49f389ae428
Zhizhen Qin [Mon, 11 Feb 2019 20:24:10 +0000 (12:24 -0800)]
Merge binaries "convert_image_to_tensor" and "caffe2_benchmark" (#16875)
Summary:
Merge binaries "convert_image_to_tensor" and "caffe2_benchmark" to remove the overhead of writing to/reading from Tensor file.
*TODO next: TensorProtos is another overhead. No need for de-serialization.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16875
Reviewed By: sf-wind
Differential Revision:
D13997726
Pulled By: ZhizhenQin
fbshipit-source-id:
4dec17f0ebb59cf1438b9aba5421db2b41c47a9f
SsnL [Mon, 11 Feb 2019 19:59:17 +0000 (11:59 -0800)]
Fix missing CircleCI GPG key (#16961)
Summary:
I'm seeing a bunch of apt gpg key errors on CI with the following message:
```
An error occurred during the signature verification. The repository is not
updated and the previous index files will be used. GPG error:
https://packagecloud.io trusty InRelease: The following signatures couldn't
be verified because the public key is not available:
NO_PUBKEY
4E6910DFCB68C9CD
```
Most of the times apt will reuse the old cached version, but sometimes this results in a build failure: https://circleci.com/gh/pytorch/pytorch/758366?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link.
This should hopefully fix it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16961
Differential Revision:
D14028151
Pulled By: ezyang
fbshipit-source-id:
7648a0a58ece38d8d04916937a9fa17f34f8833e
Edward Yang [Mon, 11 Feb 2019 19:43:45 +0000 (11:43 -0800)]
Disable binary_linux_conda_3.6_cu90_build on PRs. (#16958)
Summary:
Issue tracked at https://github.com/pytorch/pytorch/issues/16710
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16958
Differential Revision:
D14028078
Pulled By: ezyang
fbshipit-source-id:
6c68f79775a156ef4a55ac450a5a0ecacc0e6af5
Xiaodong Wang [Mon, 11 Feb 2019 17:44:17 +0000 (09:44 -0800)]
Install Thrust package and stop patching (#16911)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16911
I think the Thrust package has want we want for /opt/rocm/include/thrust. We probably can stop patching it now.
Reviewed By: bddppq
Differential Revision:
D14015177
fbshipit-source-id:
8d9128783a790c39083a1b8b4771c2c18bd67d46
Eskil Jörgensen [Mon, 11 Feb 2019 16:22:15 +0000 (08:22 -0800)]
Make pin_memory and default_collate preserve namedtuples (#16440)
Summary:
Open issue: https://github.com/pytorch/pytorch/issues/3281
Corresponding PR (conflict): https://github.com/pytorch/pytorch/pull/4577
Another open issue: https://github.com/pytorch/pytorch/issues/14613
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16440
Differential Revision:
D14020901
Pulled By: ezyang
fbshipit-source-id:
4abe817fc43c281a510715d311bad544511995d3
Edward Yang [Mon, 11 Feb 2019 14:02:08 +0000 (06:02 -0800)]
Revert
D14020906: [pytorch][PR] Extend support for exporting reshape to onnx.
Differential Revision:
D14020906
Original commit changeset:
168616873044
fbshipit-source-id:
2730bb6990d41f3a9cef6625ea919c219733433d
Ivan Ogasawara [Mon, 11 Feb 2019 12:52:06 +0000 (04:52 -0800)]
Added scientific notation on set_printoptions (#16876)
Summary:
This PR fixes #15683
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16876
Differential Revision:
D14021703
Pulled By: soumith
fbshipit-source-id:
1f603a7d24e331831d8d389f4a704c6a5b070b0c
BowenBao [Mon, 11 Feb 2019 04:12:45 +0000 (20:12 -0800)]
Extend support for exporting reshape to onnx.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16632
Differential Revision:
D14020906
Pulled By: ezyang
fbshipit-source-id:
168616873044b980145a3554dab942bdec19efb2
eyyub.sari@epitech.eu [Mon, 11 Feb 2019 04:05:32 +0000 (20:05 -0800)]
Int8GivenTensorFill Operator Schema fix typo (#16204)
Summary:
Hi,
caffe2/operators/quantized/int8_given_tensor_fill_op.cc expects the value array to be named "values" but the operator schema describe "value" (no s). I guess it is a little typo but it made me losing a bit of time before understanding why I had this error by passing "value" instead of "values":
```
[F int8_given_tensor_fill_op.h:95] Check failed: output->t.numel() == values_.numel() output size: 3 given size: 0
Aborted (core dumped)
```
Thanks,
Eyyüb Sari
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16204
Differential Revision:
D14020476
Pulled By: ezyang
fbshipit-source-id:
a8a46bfc44ec125e7925ce4b7c79fdf99c890a50