Adam Paszke [Mon, 11 Feb 2019 03:32:15 +0000 (19:32 -0800)]
Add support for fusion of half batch norm with float stats (#16735)
Summary:
Fixes #16642.
cc ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16735
Differential Revision:
D14020310
Pulled By: ezyang
fbshipit-source-id:
ac78726f471d16d188eb998354d52bc79fe2c282
musikisomorphie [Mon, 11 Feb 2019 03:31:38 +0000 (19:31 -0800)]
Improve the Sparse matrix multiplication computational speed #16187 (#16905)
Summary:
Instead of converting coo to csr format of the sparse matrix in the original implementation, in my revision I directly use coo format for sparse dense matrix mutliplication.
On my linux machine it is 5 times faster than the original code:
```
(original code)
SIZE: 15000 DENSITY: 0.01 DEVICE: cpu
torch: 0.39403 seconds
np: 0.
00496674 seconds
torch/np: 79.3338
----------------------------------------
(my update)
SIZE: 15000 DENSITY: 0.01 DEVICE: cpu
torch: 0.0812583 seconds
np: 0.
00501871 seconds
torch/np: 16.1911
```
Further code feedback and running time tests are highly welcomed. I will keep revise my code if needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16905
Differential Revision:
D14020095
Pulled By: ezyang
fbshipit-source-id:
4ab94075344a55b375f22421e97a690e682baed5
Michael Carilli [Mon, 11 Feb 2019 03:31:23 +0000 (19:31 -0800)]
Allow dataloader to accept a custom memory pinning function (#16743)
Summary:
Renewed attempt at https://github.com/pytorch/pytorch/pull/14171
From the original PR:
> Currently, the pin_memory_batch function in the dataloader will return a batch comprised of any unrecognized type without pinning the data, because it doesn't know how.
>
>This behavior was preventing us from overlapping data prefetching in Mask-RCNN, whose custom collate_fn returns a custom batch type.
The old PR allowed the user to implement batch pinning for custom batch and data types by passing a custom pin function to the dataloader. slayton58 suggested a cleaner approach: allow the user to define a `pin_memory` method on their custom types, and have `pin_memory_batch` [check for the presence of that method](https://github.com/pytorch/pytorch/pull/16743/files#diff-9f154cbd884fe654066b1621fad654f3R56) in the incoming batch as a fallback. I've updated the test and docstrings accordingly.
The old PR was merged but then reverted due to weird cuda OOM errors on windows that may or may not have been related. I have no idea why my changes would cause such errors (then or now) but it's something to keep an eye out for.
fmassa and yf225 who were my POCs on the old PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16743
Differential Revision:
D13991745
Pulled By: ezyang
fbshipit-source-id:
74e71f62a03be453b4caa9f5524e9bc53467fa17
Hameer Abbasi [Mon, 11 Feb 2019 03:28:50 +0000 (19:28 -0800)]
Add abs for ByteTensor and CharTensor. (#16893)
Summary:
Fixes #15089
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16893
Differential Revision:
D14020115
Pulled By: ezyang
fbshipit-source-id:
6f3be6ed28d2d37667159be45959d400bc473451
Xiang Gao [Mon, 11 Feb 2019 02:10:59 +0000 (18:10 -0800)]
Support named tuple return from operators on JIT (#16253)
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/16233
The following changes are made:
- Modify `TupleType` to store optional field names
- Modify schema matching to return fill in those field names when creating `TupleType` as return type.
- Modify codegen of JIT to copy field names to schema string
- Modify `SchemaParser` to set field names of returned schema.
- Modify `SimpleValue::attr` to emit tuple indexing for named tuple.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16253
Reviewed By: ezyang
Differential Revision:
D13954298
Pulled By: zdevito
fbshipit-source-id:
247d483d78a0c9c12d1ba36e1f1ec6c3f1a3007b
Derek Kim [Sun, 10 Feb 2019 23:52:37 +0000 (15:52 -0800)]
Enhance the documentation for torch.nn.DataParallel (#15993)
Summary:
I found a few sentences in DataParallel docstring confusing, so I suggest this enhancement.
- Arbitrary arguments are allowed to be passed .... *INCLUDING* tensors (Not *EXCLUDING*)
- The original author said that "other types" are shallow-copied but I think actually only some builtin types are (effectively) shallow-copied. And "other types" are shared. Here is an example.
```python
import torch
from torch.nn import Module, DataParallel
from collections import deque
class MyModel(Module):
def forward(self, x):
x.append(None)
model = MyModel(); model.cuda()
model = DataParallel(model)
d = deque()
model.forward(d)
print(d)
```
This is a side note.
As far as I know, copying objects is not a specially frequent operation in python unlike some other languages. Notably, no copying is involved in assignment or function parameter passing. They are only name bindings and it is the whole point of "everything is object" python philosophy, I guess. If one keep this in mind, it may help you dealing with things like multithreading.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15993
Differential Revision:
D14020404
Pulled By: ezyang
fbshipit-source-id:
a38689c94d0b8f77be70447f34962d3a7cd25e2e
ZhuBaohe [Sun, 10 Feb 2019 22:34:31 +0000 (14:34 -0800)]
DOC: correct docstring for torch and torch.Tensor package (#16842)
Summary:
This PR is a simple fix for the mistake in the "tensor" and "torch.Tensor"doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16842
Differential Revision:
D14020300
Pulled By: ezyang
fbshipit-source-id:
3ab04f1223d6e60f8da578d04d759e385d23acbb
Thomas Viehmann [Sun, 10 Feb 2019 21:57:57 +0000 (13:57 -0800)]
find libnvToolsExt instead of using only hardcoded path (#16714)
Summary:
This changes the libnvToolsExt dependency to go through CMake find_library.
I have a machine where cuda libs, and libnvToolsExt in particular, are in the "usual library locations". It would be neat if we could find libnvToolsExt and use the path currently hardcoded as default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16714
Differential Revision:
D14020315
Pulled By: ezyang
fbshipit-source-id:
00be27be10b1863ca92fd585f273d50bded850f8
Xiang Gao [Sun, 10 Feb 2019 21:42:49 +0000 (13:42 -0800)]
Clean up autograd method tests
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16790
Differential Revision:
D14020305
Pulled By: ezyang
fbshipit-source-id:
3aa3362830cde35967a3895837a25b3cf3287569
Travis Johnston [Sun, 10 Feb 2019 19:44:54 +0000 (11:44 -0800)]
fixed LogSigmoid math string that wasn't rendering in documentation (#16900)
Summary:
The documentation for LogSigmoid says:
> Applies the element-wise function:
> \<blank\>
Now the documentation properly displays the math string.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16900
Differential Revision:
D14020097
Pulled By: ezyang
fbshipit-source-id:
41e229d0fcc6b9bb53367be548bf85286dc13546
drkw [Sun, 10 Feb 2019 18:41:46 +0000 (10:41 -0800)]
ctc_loss error message bug fix. (#16917)
Summary:
CTCLLoss argument error message is wrong.
Please fix this. (sorry if I made some mistakes.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16917
Differential Revision:
D14019983
Pulled By: ezyang
fbshipit-source-id:
3337a2e86da6f3f7594c73fddb73340494a19ce2
Will Feng [Sun, 10 Feb 2019 17:38:50 +0000 (09:38 -0800)]
Use non-Variable type for callsites that check type equality (#16325)
Summary:
When Variable and Tensor are merged, the dynamic type of the tensors passed to certain functions will become variables, and expecting `type()` on those variables to still return non-Variable types will cause type mismatch error.
One way to fix this problem is to use the thread-local guard `at::AutoNonVariableTypeMode` to force `type()` to return non-Variable type, but ideally we want to limit the use of `at::AutoNonVariableTypeMode` to be only in VariableType.cpp. Another way to fix the problem is to use `at::globalContext().getNonVariableType()` instead to get the non-Variable type of the tensor, which is what this PR is trying to achieve.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16325
Differential Revision:
D14012022
Pulled By: yf225
fbshipit-source-id:
77ef1d2a02f78bff0063bdd72596e34046f1e00d
Jiren Jin [Sun, 10 Feb 2019 04:12:32 +0000 (20:12 -0800)]
Fix the error in the note about `torch.device` documentation. (#16839)
Summary:
This PR is a simple fix for the mistake in the first note for `torch.device` in the "tensor attributes" doc.
```
>>> # You can substitute the torch.device with a string
>>> torch.randn((2,3), 'cuda:1')
```
Above code will cause error like below:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-53-
abdfafb67ab1> in <module>()
----> 1 torch.randn((2,3), 'cuda:1')
TypeError: randn() received an invalid combination of arguments - got (tuple, str), but expected one of:
* (tuple of ints size, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool requires_grad)
* (tuple of ints size, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool requires_grad)
```
Simply adding the argument name `device` solves the problem: `torch.randn((2,3), device='cuda:1')`.
However, another concern is that this note seems redundant as **there is already another note covering this usage**:
So maybe it's better to just remove this note?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16839
Reviewed By: ezyang
Differential Revision:
D13989209
Pulled By: gchanan
fbshipit-source-id:
ac255d52528da053ebfed18125ee6b857865ccaf
Johannes M Dieterich [Sat, 9 Feb 2019 19:20:18 +0000 (11:20 -0800)]
Register coalescer bug was fixed in ROCm 2.1 (#16923)
Summary:
Remove specialization/workaround for ROCm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16923
Differential Revision:
D14018521
Pulled By: bddppq
fbshipit-source-id:
d88162740bca6dc8ad37397dfbf8c84408074a00
Johannes M Dieterich [Sat, 9 Feb 2019 19:19:30 +0000 (11:19 -0800)]
Alignas is now correctly handled on ROCm (#16920)
Summary:
Post 2.1 release, packing is fixed and alignas works as expected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16920
Differential Revision:
D14018539
Pulled By: bddppq
fbshipit-source-id:
0ed4d9e9f36afb9b970812c3870082fd7f905455
Johannes M Dieterich [Sat, 9 Feb 2019 19:16:05 +0000 (11:16 -0800)]
Enable buildin bitonic sort (#16919)
Summary:
It now works post ROCm 2.1 release.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16919
Differential Revision:
D14018538
Pulled By: bddppq
fbshipit-source-id:
c4e1bafb53204a6d718b2d5054647d5715f23243
Junjie Bai [Sat, 9 Feb 2019 19:15:29 +0000 (11:15 -0800)]
Change the default image size from 227 to 224 in resnet50 trainer (#16924)
Summary:
cc xw285cornell
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16924
Differential Revision:
D14018509
Pulled By: bddppq
fbshipit-source-id:
fdbc9e94816ce6e4b1ca6f7261007bda7b80e1e5
Johannes M Dieterich [Sat, 9 Feb 2019 08:09:33 +0000 (00:09 -0800)]
enable unit tests working on ROCm 2.1 (#16871)
Summary:
This is the first round of enabling unit tests that work on ROCm 2.1 in my tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16871
Differential Revision:
D13997662
Pulled By: bddppq
fbshipit-source-id:
d909a3f7dd5fc8f85f126bf0613751c8e4ef949f
Elias Ellison [Sat, 9 Feb 2019 03:06:41 +0000 (19:06 -0800)]
Add suggest add to __constants__ message on save fail
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16850
Differential Revision:
D14014735
Pulled By: eellison
fbshipit-source-id:
7b6d5d5b64b9b107743cea1548cb4ee1b653977e
Chandler Zuo [Fri, 8 Feb 2019 23:20:15 +0000 (15:20 -0800)]
Make the exception raised from "numpy.dtype(numpy.void, (INT,))" less cryptic (#16809)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16809
https://fb.facebook.com/groups/
582508038765902/permalink/
736710343345670/?comment_id=
824042307945806&reply_comment_id=
824318864584817
numpy.dtype(numpy.void, (<INT>, )) raises a cryptic message "invalid itemsize in generic type tuple" that is hard to debug.
This diff adds the message to ask the user to investigate the error causing blob.
Reviewed By: kennyhorror
Differential Revision:
D13973359
fbshipit-source-id:
43a0c492ffafbabdfd7f7541c08a258e5ac0280f
Bram Wasti [Fri, 8 Feb 2019 22:57:57 +0000 (14:57 -0800)]
Revert
D13970381: [caffe2] Add visibility to registry class to fix ubsan error
Differential Revision:
D13970381
Original commit changeset:
763db24b8a98
fbshipit-source-id:
dda8672ed0bc6fecc4dde5ce73feb99e15205978
Nikita Shulga [Fri, 8 Feb 2019 22:20:31 +0000 (14:20 -0800)]
Extend Net.RunAllOnGPU() to support RecurrentNetwork op (#15713)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15713
[caffe2] Extend Net.RunAllOnGPU() to support RecurrentNetwork op
Reviewed By: dzhulgakov
Differential Revision:
D13576507
fbshipit-source-id:
f517127492c9d516ece663d42fef84338c70344e
James Reed [Fri, 8 Feb 2019 21:45:43 +0000 (13:45 -0800)]
delete critical section in TH*Tensor_addmm (#16889)
Summary:
This was serializing all calls to `addmm` (and any op that used it, in my case `bmm`) in the entire process, and led to downright atrocious performance in the TorchScript threaded runtime. Removing this gives a 2x throughput boost for high-load machine translation inference.
The original justification for this is dubious: there are other `gemm` callsites in the codebase that are not protected by critical sections. And in caffe2 land we never had any issues with nonreentrant BLAS libraries
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16889
Differential Revision:
D14008928
Pulled By: jamesr66a
fbshipit-source-id:
498e2133bd6564dba539a2d9751f4e61afbce608
Bram Wasti [Fri, 8 Feb 2019 19:42:22 +0000 (11:42 -0800)]
Revert
D13806753: [pytorch][PR] TensorIterator cuda launch configs update
Differential Revision:
D13806753
Original commit changeset:
37e45c7767b5
fbshipit-source-id:
74ac9f54f86853287b372ccf21fb37ed0e04a5d3
Elias Ellison [Fri, 8 Feb 2019 19:34:40 +0000 (11:34 -0800)]
Allow sequential modules in module list (#16882)
Summary:
Fix for https://github.com/pytorch/pytorch/issues/16845
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16882
Differential Revision:
D14007746
Pulled By: eellison
fbshipit-source-id:
d7918275cc1de6a67320619c3203463f66783343
Gu, Jinghui [Fri, 8 Feb 2019 19:17:59 +0000 (11:17 -0800)]
Impl ExpandDims op and fallback to CPU if needed (#15264)
Summary:
Impl ExpandDims op and fallback to CPU if needed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15264
Differential Revision:
D13808797
Pulled By: yinghai
fbshipit-source-id:
7795ec303a46e85f84e5490273db0ec76e8b9374
Bram Wasti [Fri, 8 Feb 2019 18:00:49 +0000 (10:00 -0800)]
Add visibility to registry class to fix ubsan error (#16792)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16792
fix
Reviewed By: ezyang
Differential Revision:
D13970381
fbshipit-source-id:
763db24b8a98a2757a63b77c70c8c68ba47f31e6
Edward Yang [Fri, 8 Feb 2019 17:29:59 +0000 (09:29 -0800)]
Remove Legacy entry point. (#16721)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16721
The very key line is we have to set the stream to the default
stream before calling the allocator. This is very interesting.
It shouldn't be necessary, but seemingly is!
Reviewed By: dzhulgakov
Differential Revision:
D13943193
fbshipit-source-id:
c21014917d9fe504fab0ad8abbc025787f559287
Edward Yang [Fri, 8 Feb 2019 17:29:59 +0000 (09:29 -0800)]
Deduplicate instances caching allocator, so that we only have one instance. (#16720)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16720
I'm taking the deduplication slowly because there is something here
that is causing problems, and I want to figure out what it is.
Reviewed By: dzhulgakov
Differential Revision:
D13943194
fbshipit-source-id:
cbc08fee5862fdcb393b9dd5b1d2ac7250f77c4b
Edward Yang [Fri, 8 Feb 2019 17:29:58 +0000 (09:29 -0800)]
Delete duplicate copy of THCCachingAllocator (round two). (#16615)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16615
This is another go at landing https://github.com/pytorch/pytorch/pull/16226
Now that the caching allocator is moved to c10_cuda, we can
delete the duplicate copy from Caffe2.
The difference between this and the previous PR is that this
version faithfully maintains the binding code; in particular,
we end up with a SECOND copy of the caching allocator in
this patch. I verified that this code does NOT cause a crash
in the workflow we canaried last time.
In further diffs, I plan to eliminate the second copy, and then
adjust the binding code.
Reviewed By: dzhulgakov
Differential Revision:
D13901067
fbshipit-source-id:
66331fd4eadffd0a5defb3cea532d5cd07287872
Junjie Bai [Fri, 8 Feb 2019 08:32:49 +0000 (00:32 -0800)]
Bump caffe2 docker images to 248 (#16863)
Summary:
Jenkins jobs update will be separate.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16863
Differential Revision:
D13994672
Pulled By: bddppq
fbshipit-source-id:
5b27879dc6ac11a42016fe7835e9124345005ebb
Sebastian Messmer [Fri, 8 Feb 2019 04:47:46 +0000 (20:47 -0800)]
Also register op schema when no kernels are registered
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16878
Reviewed By: bwasti
Differential Revision:
D13997959
fbshipit-source-id:
7527a560b03f672f76e95d4f22ae28ce24698cc1
Sebastian Messmer [Fri, 8 Feb 2019 04:47:45 +0000 (20:47 -0800)]
Don't automatically handle context parameter (#16867)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16867
Some caffe2 operators (example: BBoxTransform) have not just one template parameter which is the context, but might have multiple template parameters.
Because of this, we can't handle the context parameter inside the macro.
Reviewed By: bwasti
Differential Revision:
D13995696
fbshipit-source-id:
f55c3be913c8b125445a8d486846fc2fab587a63
Yinghai Lu [Fri, 8 Feb 2019 04:41:18 +0000 (20:41 -0800)]
Support onnxifi with partially shaped inferred net (#16877)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16877
That's it.
Reviewed By: ipiszy
Differential Revision:
D13997771
fbshipit-source-id:
f512c7f30b4a4747aca335a0769712c2a2cc2206
Pearu Peterson [Fri, 8 Feb 2019 04:27:40 +0000 (20:27 -0800)]
Robust determination of cudnn library and relevant conda packages. (#16859)
Summary:
This PR implements:
1. a fix to issue #12174 - determine the location of cudnn library using `ldconfig`
2. a fix to determine the installed conda packages (in recent versions of conda, the command `conda` is a Bash function that cannot be called within a python script, so using CONDA_EXE environment variable instead)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16859
Differential Revision:
D14000399
Pulled By: soumith
fbshipit-source-id:
905658ecacb0ca0587a162fade436de9582d32ab
Yinghai Lu [Fri, 8 Feb 2019 04:08:39 +0000 (20:08 -0800)]
Specialize LengthsRangeFill and SparseLengthsWeightedSum in bound shape inference (#16869)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16869
TSIA.
Reviewed By: ipiszy, rdzhabarov
Differential Revision:
D13994946
fbshipit-source-id:
7e507abc5a3c2834c92910e521387085c56e8b2e
Summer Deng [Fri, 8 Feb 2019 03:42:17 +0000 (19:42 -0800)]
Activation histogram net observer with multiple histogram files as output (#16855)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16855
Save the histogram of each net to a separate file
Reviewed By: jspark1105
Differential Revision:
D13991610
fbshipit-source-id:
a5be4e37a5e63567dcd7fdf99f451ee31bb350a5
David Riazati [Fri, 8 Feb 2019 02:21:30 +0000 (18:21 -0800)]
Allow dicts in C++ frontend (#16846)
Summary:
Fixes #16856
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16846
Differential Revision:
D13991103
Pulled By: driazati
fbshipit-source-id:
4830dd6f707fa90429b5d3070eeda0bee53d2f2b
Xiaomeng Yang [Fri, 8 Feb 2019 02:19:46 +0000 (18:19 -0800)]
Separate elementwise level2 math functions (#16753)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16753
Separate elementwise level2 math functions
i-am-not-moving-c2-to-c10
Reviewed By: houseroad
Differential Revision:
D13954928
fbshipit-source-id:
1ca7a5d3da96e32510f502e5e4e79168854bee67
Freddie Mendoza [Fri, 8 Feb 2019 02:15:44 +0000 (18:15 -0800)]
Fix (#2) ppc64le build break on git status --porcelain check (#16852)
Summary:
Add test/.hypothesis/ to .gitignore to pass git status --porcelain check in CI build
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16852
Differential Revision:
D14000206
Pulled By: soumith
fbshipit-source-id:
5da99a4bb242c12aa35776f7254f6399a7fa6d8c
Michael Suo [Fri, 8 Feb 2019 01:56:10 +0000 (17:56 -0800)]
doc updates for TorchScript (#16866)
Summary:
Some batched updates:
1. bool is a type now
2. Early returns are allowed now
3. The beginning of an FAQ section with some guidance on the best way to do GPU training + CPU inference
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16866
Differential Revision:
D13996729
Pulled By: suo
fbshipit-source-id:
3b884fd3a4c9632c9697d8f1a5a0e768fc918916
Alex Şuhan [Fri, 8 Feb 2019 01:31:52 +0000 (17:31 -0800)]
Fix autodiff of nll_loss
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16851
Differential Revision:
D13995046
Pulled By: wanchaol
fbshipit-source-id:
557c99f1d1825fa9b6031dd9fa8ba9b54205e8c4
James Reed [Fri, 8 Feb 2019 01:22:00 +0000 (17:22 -0800)]
aten::_convolution now participates in shape analysis (#16837)
Summary:
During tracing, we record `aten::_convolution` rather than `aten::convolution`. The schema for the former was not present in the shape analysis pass, and resulted in some missing shape information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16837
Differential Revision:
D13993831
Pulled By: jamesr66a
fbshipit-source-id:
ebb63bf628d81613258caf773a3af5930303ce5a
peter.yeh@amd.com [Fri, 8 Feb 2019 00:10:50 +0000 (16:10 -0800)]
Enable arg_ops_test/unique_ops_test on AMD/rocm (#16853)
Summary:
Verified both tests are passing on rocm 2.1 env.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16853
Differential Revision:
D13996279
Pulled By: bddppq
fbshipit-source-id:
c0df610d7d9ca8d80ed2d1339cdadef59105a71c
Johannes M Dieterich [Thu, 7 Feb 2019 22:17:14 +0000 (14:17 -0800)]
Update CI to recently released ROCm 2.1 release (#16808)
Summary:
* we do not need EAP packages any longer as the antistatic feature is now in the release
* consistently install the rccl package
* Skip one unit test that has regressed with 2.1
* Follow-up PRs will use 2.1 features once deployed on CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16808
Differential Revision:
D13992645
Pulled By: bddppq
fbshipit-source-id:
37ca9a1f104bb140bd2b56d403e32f04c4fbf4f0
Yinghai Lu [Thu, 7 Feb 2019 22:11:44 +0000 (14:11 -0800)]
Use bound shape inference in SparseNN tests (#16834)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16834
Inserting AdjustBatch ops will possibly change the names of the input/output, so we need to create a mapping and use the renamed names for external_inputs/outputs and input_shape_info for the onnxifi_net.
Reviewed By: ipiszy
Differential Revision:
D13982731
fbshipit-source-id:
c18b8a03d01490162929b2ca30c182d166001626
Davide Libenzi [Thu, 7 Feb 2019 22:11:33 +0000 (14:11 -0800)]
Add recognition for XLA device types.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16844
Differential Revision:
D13988805
Pulled By: gchanan
fbshipit-source-id:
4e89d6d2cde8bdac41739efa65cc91569a360953
Sebastian Messmer [Thu, 7 Feb 2019 21:52:49 +0000 (13:52 -0800)]
Fix and re-enable test case (#16643)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16643
The test was disabled in
D13908117 because it conflicted with another diff that was about to land.
Now fixed the merge conflict and re-landing it.
Reviewed By: ezyang
Differential Revision:
D13911775
fbshipit-source-id:
b790f1c3a3f207916eea41ac93bc104d011f629b
Sebastian Messmer [Thu, 7 Feb 2019 21:52:49 +0000 (13:52 -0800)]
C10_REGISTER_CAFFE2_OPERATOR: Macro for registering c2 kernels (#16548)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16548
With this macro, a caffe2 operator can now directly be registered with c10.
No need to write custom wrapper kernels anymore.
Differential Revision:
D13877076
fbshipit-source-id:
e56846238c5bb4b1989b79855fd44d5ecf089c9c
Jesse Hellemn [Thu, 7 Feb 2019 21:39:18 +0000 (13:39 -0800)]
Fix Anaconda logins on binary builds
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16848
Differential Revision:
D13993614
Pulled By: pjh5
fbshipit-source-id:
16854b06d01460b78d9dbe7bd0341b7332984795
Zhicheng Yan [Thu, 7 Feb 2019 21:08:06 +0000 (13:08 -0800)]
new embedding label type in image input op (#16835)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16835
We were using label type `multi_label_dense` to denote both 1) dense representation of integer label 2) embedding label of data type floating number.
This cause some issues as two cases have different assumption, such as for integer label, we will check whether label value is in [0, number_class - 1]. But such check should be skipped for `embedding label`.
Reviewed By: BIT-silence
Differential Revision:
D13985048
fbshipit-source-id:
1202cdfeea806eb47647e3f4a1ed9c104f72ad2c
Michael Antonov [Thu, 7 Feb 2019 20:42:38 +0000 (12:42 -0800)]
Update ATen internals to use int64_t for dimension indexing (#16739)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16739
Some code ATen locations seemed to use int, etc. inclorrectly where either
int64_t or size_t was required. Update them to use int64_t for dimension indexing where necessary.
Reviewed By: ezyang
Differential Revision:
D13950124
fbshipit-source-id:
aaf1cef783bf3c657aa03490f2616c35c816679f
Will Feng [Thu, 7 Feb 2019 19:58:50 +0000 (11:58 -0800)]
Make JIT attributes t_ and ts_ store Variable instead of Tensor (#16596)
Summary:
Discussed with zdevito and we want to use Variable (with `set_requires_grad(false)`) instead of Tensor in all parts of JIT, to eliminate the distinction and the conceptual overhead when trying to figure out which one to use.
This also helps with the Variable/Tensor merge work tracked at https://github.com/pytorch/pytorch/issues/13638, which will make common functions (such as `numel()` / `sizes()` / `dim()`) on Variable much faster when finished.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16596
Differential Revision:
D13979971
Pulled By: yf225
fbshipit-source-id:
c69119deec5bce0c22809081115f1012fdbb7d5a
David Riazati [Thu, 7 Feb 2019 19:50:27 +0000 (11:50 -0800)]
Better error when using a constant tensor (#16724)
Summary:
Fixes #16284
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16724
Differential Revision:
D13990531
Pulled By: driazati
fbshipit-source-id:
adbf47a07eddb3813fbe1322944abfe5fcff89fa
Richard Zou [Thu, 7 Feb 2019 19:09:10 +0000 (11:09 -0800)]
Backport the stable doc build on v1.0.1 to master (#16503)
Summary:
List of changes:
- Always push the final state of the doc build docker for debugging purposes.
- Adds code for the stable doc build. This code is never actually run on master, only the v1.0.1 branch. There is a big note for this behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16503
Differential Revision:
D13972469
Pulled By: zou3519
fbshipit-source-id:
68f459650ef0de200a34edd43fc1372143923972
Wanchao Liang [Thu, 7 Feb 2019 18:32:02 +0000 (10:32 -0800)]
Remove undefined tensor in jit script (#16379)
Summary:
This PR is a follow up of #15460, it did the following things:
* remove the undefined tensor semantic in jit script/tracing mode
* change ATen/JIT schema for at::index and other index related ops with `Tensor?[]` to align with what at::index is really doing and to adopt `optional[tensor]` in JIT
* change python_print to correctly print the exported script
* register both TensorList and ListOfOptionalTensor in JIT ATen ops to support both
* Backward compatibility for `torch.jit.annotate(Tensor, None)`
List of follow ups:
* remove the undefined tensor semantic in jit autograd, autodiff and grad_of
* remove prim::Undefined fully
For easy reviews, please turn on `hide white space changes` in diff settings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16379
Differential Revision:
D13855677
Pulled By: wanchaol
fbshipit-source-id:
0e21c14d7de250c62731227c81bfbfb7b7da20ab
Fritz Obermeyer [Thu, 7 Feb 2019 09:33:41 +0000 (01:33 -0800)]
Support multiple inheritance in torch.distributions (#16772)
Summary:
This adds calls to `super().__init__()` in three classes in torch.distributions.
This is needed when `Distribution` and `Transform` objects are used with multiple inheritance, as e.g. combined with `torch.nn.Module`s. For example
```py
class MyModule(torch.distributions.Transform, torch.nn.Module):
...
```
cc martinjankowiak esling who have wanted to use this pattern, e.g. in #16756
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16772
Differential Revision:
D13978633
Pulled By: soumith
fbshipit-source-id:
8bc6cca1747cd74d32135ee2fe588bba2ea796f1
vishwakftw [Thu, 7 Feb 2019 09:10:54 +0000 (01:10 -0800)]
Remove redundant wrappers in torch.distributions (#16807)
Summary:
Changelog:
- Remove torch.distributions.multivariate_normal._batch_diag : same functionality is provided by torch.diagonal
- Remove torch.distributions.lowrank_multivariate_normal._batch_vector_diag : same functionality is provided by torch.diag_embed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16807
Differential Revision:
D13985550
Pulled By: soumith
fbshipit-source-id:
25c7d00c52ff7f85e431134e9ce0d5dda453667b
Ying Zhang [Thu, 7 Feb 2019 08:33:29 +0000 (00:33 -0800)]
Insert AdjustBatchSizeOp into the predict_net. (#16811)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16811
As the title. The AdjustBatch ops will be inserted before and after the Onnxifi op to:
1) adjust batch/seq sizes to the ideal batch/seq size before these tensors are processed by the Onnxifi op;
2) adjust batch size to the original batch size for batches generated by the Onnxifi op.
Reviewed By: yinghai
Differential Revision:
D13967711
fbshipit-source-id:
471b25ae6a60bf5b7ebee1de6449e0389b6cafff
rohithkrn [Thu, 7 Feb 2019 08:21:21 +0000 (00:21 -0800)]
Unify gpu_support variable in python tests (#16748)
Summary:
Assign `has_gpu_support = has_cuda_support or has_hip_support` and make according changes in python tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16748
Differential Revision:
D13983132
Pulled By: bddppq
fbshipit-source-id:
ca496fd8c6ae3549b736bebd3ace7fa20a6dad7f
Mohana Rao [Thu, 7 Feb 2019 07:33:40 +0000 (23:33 -0800)]
Update Docker file section in README.md (#16812)
Summary:
Emphasize on the fact that docker build should be triggered from pytorch repo directory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16812
Differential Revision:
D13985531
Pulled By: soumith
fbshipit-source-id:
c6511d1e81476eb795b37fb0ad23e8951dbca617
Jie [Thu, 7 Feb 2019 07:05:49 +0000 (23:05 -0800)]
TensorIterator cuda launch configs update (#16224)
Summary:
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
Differential Revision:
D13806753
Pulled By: soumith
fbshipit-source-id:
37e45c7767b5748cf9ecf894fad306e040e2f79f
Sebastian Messmer [Thu, 7 Feb 2019 05:14:21 +0000 (21:14 -0800)]
Define layer_norm schema in caffe2 (#16535)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16535
There is now no need anymore to define the layer norm schema in a central location.
It can just be defined in caffe2 next to the kernel implementation.
Reviewed By: ezyang
Differential Revision:
D13869503
fbshipit-source-id:
c478153f8fd712ff6d507c794500286eb3583149
Sebastian Messmer [Thu, 7 Feb 2019 05:14:20 +0000 (21:14 -0800)]
Automatically register c10 ops with JIT (#16534)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16534
All c10 ops from the c10 dispatcher are now automatically registered with JIT
Reviewed By: dzhulgakov
Differential Revision:
D13869275
fbshipit-source-id:
5ab5dec5b983fe661f977f9d29d8036768cdcab6
Yinghai Lu [Thu, 7 Feb 2019 03:12:32 +0000 (19:12 -0800)]
Add AdjustBatch Op (#16676)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16676
This op is used for changing batch size (first dimension) of the tensor.
Reviewed By: bertmaher, ipiszy
Differential Revision:
D13929200
fbshipit-source-id:
4f2c3faec072d468be8301bf00c80d33adb3b5b3
bddppq [Thu, 7 Feb 2019 01:52:12 +0000 (17:52 -0800)]
Bring back running pytorch tests in rocm CI
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16829
Differential Revision:
D13982323
Pulled By: bddppq
fbshipit-source-id:
6ffadb96b9e2ebd64a29e38674a51401dfb211db
Zachary DeVito [Thu, 7 Feb 2019 01:22:47 +0000 (17:22 -0800)]
Rename DynamicType -> TensorType (#16787)
Summary:
```
import json
from subprocess import check_call
from pprint import pprint
renames = {
'c10::TensorType': 'DimentionedTensorType',
'c10::DynamicType': 'TensorType',
'c10::TensorTypePtr': 'DimentionedTensorTypePtr',
'c10::DynamicTypePtr': 'TensorTypePtr',
'c10::TypeKind::DynamicType': 'TensorType',
'c10::TypeKind::TensorType': 'DimentionedTensorType',
}
entries = json.loads(open('compile_commands.json', 'r').read())
build = None
sources = []
for e in entries:
name = e['file']
if not ('jit' in name or 'ATen/core' in name):
continue
build = e['directory']
sources.append(name)
args = ['clang-rename', '-i', '-force', '-pl']
for name in sorted(renames.keys()):
args += ['-qualified-name={}'.format(name), '-new-name={}'.format(renames[name])]
for source in sources:
cmd = args + [source]
pprint(args)
check_call(cmd, cwd=build)
check_call(['git', 'stash', 'push', '-m', 'rename'])
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16787
Differential Revision:
D13974132
Pulled By: zdevito
fbshipit-source-id:
8368fd53e17cff83707bbe77f2d7aad74f8ce60e
Yinghai Lu [Thu, 7 Feb 2019 00:13:24 +0000 (16:13 -0800)]
Use bound shape inference in onnxifi transform (#16598)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16598
ATT.
Reviewed By: bertmaher, rdzhabarov
Differential Revision:
D13893698
fbshipit-source-id:
8d2ad9814fe76924a46b450eb7ebd3601fbdbbc7
Soumith Chintala [Wed, 6 Feb 2019 23:46:39 +0000 (15:46 -0800)]
improve error message (#16719)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/16712
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16719
Differential Revision:
D13978688
Pulled By: ezyang
fbshipit-source-id:
61f8fa4c54c6969a58550e32e18be2eb9254ced7
Jongsoo Park [Wed, 6 Feb 2019 23:14:17 +0000 (15:14 -0800)]
int8 SpatialBN (#16796)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16796
SpatialBN int8 version
Reviewed By: dskhudia
Differential Revision:
D13971224
fbshipit-source-id:
e55fd608c161069daaa4e62c618bc14b01f32cb7
Jongsoo Park [Wed, 6 Feb 2019 23:10:07 +0000 (15:10 -0800)]
call istringstream clear after str (#16820)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16820
Sometimes parsing histogram was not working correctly due to changes in
D13633256
We need to call istringstream clear after str
Reviewed By: csummersea
Differential Revision:
D13977509
fbshipit-source-id:
ce3e8cb390641d8f0b5c9a7d6d6daadffeddbe11
Narine Kokhlikyan [Wed, 6 Feb 2019 22:29:35 +0000 (14:29 -0800)]
Replace resize_dim() with set_sizes_and_strides() (#16732)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16732
Use set_sizes_and_strides instead of resize_dim with.
Reviewed By: ezyang
Differential Revision:
D13947867
fbshipit-source-id:
067b096b1fde14b039690992a5fe3ace386b2789
Jongsoo Park [Wed, 6 Feb 2019 19:52:23 +0000 (11:52 -0800)]
no EIGEN engine for DeformConv (#16785)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16785
There's no EIGEN engine implemented for DeformConv but unit test was checking it.
Reviewed By: BIT-silence
Differential Revision:
D13967306
fbshipit-source-id:
e29c19f59f5700fc0501c59f45d60443b87ffedc
Jongsoo Park [Wed, 6 Feb 2019 19:52:23 +0000 (11:52 -0800)]
format deform_conv_test.py (#16786)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16786
Format to prepare
D13967306
Reviewed By: BIT-silence
Differential Revision:
D13967317
fbshipit-source-id:
2de895f8474b04c55ba067fbf788c553dc010c60
Yinghai Lu [Wed, 6 Feb 2019 18:23:01 +0000 (10:23 -0800)]
Fix/Improve bound shape inference with real net tests (#16597)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16597
This diff fixes some bugs in shape inference for `SparseLengthsSumFused8BitRowwise`. And added input shape inference for `Concat` when `add_axis=1`.
Reviewed By: bertmaher
Differential Revision:
D13892452
fbshipit-source-id:
6cd95697a6fabe6d78a5ce3cb749a3a1e51c68e7
Edward Yang [Wed, 6 Feb 2019 18:19:37 +0000 (10:19 -0800)]
Typofix (#16800)
Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16800
Differential Revision:
D13972592
Pulled By: ezyang
fbshipit-source-id:
45c352ac6090c8060bf75f44dec7205556986d88
Oleg Bogdanov [Wed, 6 Feb 2019 17:40:00 +0000 (09:40 -0800)]
caffe2 | MSVS compatibility fixes (#16765)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16765
Code changes required to build caffe2 for windows with toolchain used by FB.
Reviewed By: orionr
Differential Revision:
D13953258
fbshipit-source-id:
651823ec9d81ac70e32d4cce5bc2472434104733
Gu, Jinghui [Wed, 6 Feb 2019 17:25:42 +0000 (09:25 -0800)]
Fallback sum/add to CPU if needed (#15267)
Summary:
Fallback sum/add to CPU if needed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15267
Differential Revision:
D13935064
Pulled By: yinghai
fbshipit-source-id:
eb228683d00a0462a1970f849d35365bc98340d6
Lu Fang [Wed, 6 Feb 2019 17:17:37 +0000 (09:17 -0800)]
update of fbcode/onnx to
822d8df0a2a32233c6022f50a158817a0f19bdc7 (#16791)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16791
Previous import was
bfa8b335ab6d1ed7b688dc2ec96421a3fe9e644c
Included changes:
- **[822d8df](https://github.com/onnx/onnx/commit/822d8df)**: allow removed experimental ops in the checker for now (#1792) <Lu Fang>
Reviewed By: MisterTea
Differential Revision:
D13970103
fbshipit-source-id:
5feaaa6c65ba10901eeba0b63724d7e451e9b642
Freddie Mendoza [Wed, 6 Feb 2019 17:02:18 +0000 (09:02 -0800)]
Adding torch/lib64 in .gitignore for ppc64le CI build to pass (#16782)
Summary:
Adding torch/lib64 in .gitignore so that a git status --porcelain
check during CI build and test passes for ppc64le. During build
torch/lib64 is created and contains third-party libraries. This
should be ignored by the porcelain check
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16782
Differential Revision:
D13972794
Pulled By: ezyang
fbshipit-source-id:
5459c524eca42d396ac46e756a327980b4b1fa53
Edward Yang [Wed, 6 Feb 2019 16:17:55 +0000 (08:17 -0800)]
Revert
D13854304: [redo][c10] LayerNorm Registration Example
Differential Revision:
D13854304
Original commit changeset:
ec463ce22721
fbshipit-source-id:
4262b9a2ef486e1c7c0283ea021331ac97cc5f56
Edward Yang [Wed, 6 Feb 2019 16:17:54 +0000 (08:17 -0800)]
Revert
D13855525: [c10] Expose RoIAlign to torch
Differential Revision:
D13855525
Original commit changeset:
cfee7bb1544d
fbshipit-source-id:
0b4124b78c4082b52e592a1275069c879a9aed39
Edward Yang [Wed, 6 Feb 2019 16:17:53 +0000 (08:17 -0800)]
Revert
D13856086: [c10] Expose GenerateProposals to torch
Differential Revision:
D13856086
Original commit changeset:
a4873646a71a
fbshipit-source-id:
79b634426404236ddbc407d3796a350ad3dae5ca
Edward Yang [Wed, 6 Feb 2019 16:17:53 +0000 (08:17 -0800)]
Revert
D13864292: [c10] Expose BBoxTransform to pytorch
Differential Revision:
D13864292
Original commit changeset:
1f57664e7834
fbshipit-source-id:
37663b7e8213185ecaa5c219076fc7de64704549
Edward Yang [Wed, 6 Feb 2019 16:17:52 +0000 (08:17 -0800)]
Revert
D13865221: [c10] Expose BoxWithNMSLimit
Differential Revision:
D13865221
Original commit changeset:
8a3f1d420183
fbshipit-source-id:
0057be9619b660dcad8c01bae67b54400127577e
Edward Yang [Wed, 6 Feb 2019 16:17:52 +0000 (08:17 -0800)]
Revert
D13866214: [c10] Expose HeatmapMaxKeypoints to torch
Differential Revision:
D13866214
Original commit changeset:
2ca79037fc07
fbshipit-source-id:
d2c653f4f32cf0ea76875888f3523c0dc7db9960
Rodrigo Berriel [Wed, 6 Feb 2019 15:41:42 +0000 (07:41 -0800)]
Fix pip list format in collect_env (#16798)
Summary:
Since pip 18.0 (2018-07-22), `legacy` is no longer a valid choice for `pip list --format` as can be seen in the [Release Notes](https://pip.pypa.io/en/stable/news/#id62). Therefore, the options now are: `columns`, `freeze` and `json`. With `legacy`, this is how it looked like:
```
[...]
Versions of relevant libraries:
[pip3] numpy (1.16.1)
[pip3] torch (1.0.1)
[pip3] torchvision (0.2.1)
[...]
```
Changing to `freeze`, this is how it looks like:
```
[...]
Versions of relevant libraries:
[pip3] numpy==1.16.1
[pip3] torch==1.0.1
[pip3] torchvision==0.2.1
[...]
```
Currently, this is what happens:
```
[...]
Versions of relevant libraries:
[pip] Could not collect
[...]
```
The `freeze` option is also available in old pip, so this change is backwards compatible. Also, if we would like to keep the old style, which I think it is not necessary, I could easily change that.
---
In case anyone wants to know how `columns` looks like (I prefer `freeze`):
```
[...]
Versions of relevant libraries:
[pip3] numpy 1.16.1
[pip3] torch 1.0.1
[pip3] torchvision 0.2.1
[...]
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16798
Differential Revision:
D13971793
Pulled By: soumith
fbshipit-source-id:
3721d9079a2afa245e1185f725598901185ea4cd
Soumith Chintala [Wed, 6 Feb 2019 13:09:09 +0000 (05:09 -0800)]
disable default system-wide detection of gflags, glog, opencv, lmdb, leveldb (#16789)
Summary:
They can instead by enable by env flags USE_* (as always).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16789
Differential Revision:
D13971789
Pulled By: soumith
fbshipit-source-id:
d5eac9be677114be3fb15b43080faa0efdfff8ee
Zachary DeVito [Wed, 6 Feb 2019 06:36:48 +0000 (22:36 -0800)]
fix BUILD_CAFFE2_OPS
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16783
Differential Revision:
D13965061
Pulled By: zdevito
fbshipit-source-id:
6fe710ca51e2f338873b56f23256668ca3fe2032
Edward Yang [Wed, 6 Feb 2019 05:09:06 +0000 (21:09 -0800)]
Remove unnecessary typing import. (#16777)
Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16777
Differential Revision:
D13969679
Pulled By: ezyang
fbshipit-source-id:
d4728797a5927ae32628621c654eadb93c0e7682
Michael Suo [Wed, 6 Feb 2019 04:37:30 +0000 (20:37 -0800)]
Fix alias analysis for fork/wait (#16671)
Summary:
(review top commit only).
As expected, fork/wait introduces some corner cases into the alias analysis. The comments inline should describe the changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16671
Differential Revision:
D13963219
Pulled By: suo
fbshipit-source-id:
2bec6fc03a4989cf309fbb9473f3f2ffe2c31431
Ailing Zhang [Wed, 6 Feb 2019 02:59:45 +0000 (18:59 -0800)]
changes to apply xla patch (#16781)
Summary:
This PR will let xla tests passes after https://github.com/pytorch/xla/pull/183 is in.
Will add back the branch filters once it's ready.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16781
Differential Revision:
D13968976
Pulled By: ailzhang
fbshipit-source-id:
df3b173336b3247aa56ef723569a1f68cdfa56e0
Jerry Zhang [Wed, 6 Feb 2019 02:46:38 +0000 (18:46 -0800)]
Tensor construction codemod (#16568)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16568
In caffe2/caffe2/operators and caffe2/caffe2/fb/operators
(Resize + mutable_data) and (ResizeLike + mutable_data)
motivation: https://github.com/pytorch/pytorch/pull/12407
Reviewed By: dzhulgakov
Differential Revision:
D13863416
fbshipit-source-id:
90ad3971850b89bf4b2ac81e9fa59d3bc43dc1c9
David Riazati [Wed, 6 Feb 2019 02:22:17 +0000 (18:22 -0800)]
Warn when tracing legacy constructors
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16770
Differential Revision:
D13963581
Pulled By: driazati
fbshipit-source-id:
8f8cdfc455ba65be370fd952fc5e5c233525d002
David Riazati [Wed, 6 Feb 2019 01:52:07 +0000 (17:52 -0800)]
Use torch.zeros for nn.LSTM
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16779
Differential Revision:
D13963577
Pulled By: driazati
fbshipit-source-id:
dc9edc3d2096760737ecbe4b3dd441ed2d53f4ad
Roy Li [Tue, 5 Feb 2019 23:17:22 +0000 (15:17 -0800)]
Set SCCACHE_IDLE_TIMEOUT=1200 (#16728)
Summary:
Doubling the sccache timeout from default of 600.
the asan build of #16645 will fail without this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16728
Differential Revision:
D13963727
Pulled By: li-roy
fbshipit-source-id:
3614d75c1b46d663fa05b84f99d8a099283a8e64
Johannes M Dieterich [Tue, 5 Feb 2019 22:53:16 +0000 (14:53 -0800)]
Document hip-clang and its __HIP__ macro (#16771)
Summary:
In #16085 , we introduced initial hip-clang bring-up code. Document the use of the __HIP__ macro now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16771
Differential Revision:
D13961538
Pulled By: ezyang
fbshipit-source-id:
67f6226abcbe62e2f4efc291c84652199c464ca6
Edward Yang [Tue, 5 Feb 2019 22:39:43 +0000 (14:39 -0800)]
Rename IntList to IntArrayRef. (#16751)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16751
This was made more complicated by the fact that ivalue::IntList
is a thing. So I had to fix all of the sites where we referring
to IValue post facto.
The following codemods were run, in this order:
```
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntList IntArrayRef
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntArrayRef::create IntList::create
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in ivalue::IntArrayRef ivalue::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in Tag::IntArrayRef Tag::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in isIntArrayRef isIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in toIntArrayRef toIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'Shared<IntArrayRef>' 'Shared<IntList>'
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'intrusive_ptr<IntArrayRef>' 'intrusive_ptr<IntList>'
```
Some manual fixups were done afterwards; they can be reviewed separately
at https://github.com/pytorch/pytorch/pull/16752
Reviewed By: dzhulgakov
Differential Revision:
D13954363
fbshipit-source-id:
b5c40aacba042402155a2f5a229fa6db7992ac64
David Riazati [Tue, 5 Feb 2019 21:48:52 +0000 (13:48 -0800)]
dict values(), keys(), and len() (#16629)
Summary:
Adds some operations for dicts to match Python and tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16629
Differential Revision:
D13961144
Pulled By: driazati
fbshipit-source-id:
b31f27a4320ff62cd118b508fb0a13056535dc7c
Lu Fang [Tue, 5 Feb 2019 21:13:16 +0000 (13:13 -0800)]
update of fbcode/onnx to
bfa8b335ab6d1ed7b688dc2ec96421a3fe9e644c (#16767)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16767
Previous import was
875f7bbe537b9d6931d065977c192eaaf61e1179
Included changes:
- **[bfa8b33](https://github.com/onnx/onnx/commit/bfa8b33)**: [ONNXIFI]Add extension of onnxSetIOAndRunGraph (#1781) <Rui Zhu>
Reviewed By: zrphercule
Differential Revision:
D13959349
fbshipit-source-id:
4876d00a3f7033cf9d89554f8b4789acd6881f72
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