SsnL [Mon, 10 Dec 2018 15:33:26 +0000 (07:33 -0800)]
Allow converting CharTensor to np arrays (#14710)
Summary:
The other direction of #14700
cc soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14710
Reviewed By: weiyangfb
Differential Revision:
D13306052
Pulled By: soumith
fbshipit-source-id:
202d038f139cf05e01069ff8d05268c66354c983
Jongsoo Park [Mon, 10 Dec 2018 09:06:17 +0000 (01:06 -0800)]
pre-pack operation of dnnlowp conv with 16-bit accumulation (#14881)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14881
This diff allows us to pre-quantize and pre-pack weight matrix used in DNNLOWP_ACC16 .
The intended use pattern is run Int8ConvPackWeight in init_net that generates a packed weight and Int8Conv with DNNLOWP_ACC16 engine uses the the packed weight.
Reviewed By: csummersea
Differential Revision:
D13374662
fbshipit-source-id:
dd02b9a4eb7af1fe208aa857fcd0b445e6e395af
Zachary DeVito [Mon, 10 Dec 2018 06:45:18 +0000 (22:45 -0800)]
Respect -q of setup.py (#14972)
Summary:
1. Changes the prints along the 'rebuild' pathway to respect the '-q' flag of setup.py
A clean rebuild now only prints:
[zdevito@devgpu172.prn2 /data/users/zdevito/pytorch] python setup.py -q rebuild develop
[0/1] Install the project...
-- Install configuration: "RelWithDebInfo"
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
2. Deletes apparently dead calls to `generate_code`. Now that CMake builds these files,
it appears that it is getting called twice and the second version is never used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14972
Reviewed By: soumith
Differential Revision:
D13396330
Pulled By: zdevito
fbshipit-source-id:
83c45143bbc6a6d2c1cfee929291ec059f2b5dc3
SsnL [Mon, 10 Dec 2018 05:10:39 +0000 (21:10 -0800)]
_get_device_index supports parsing device strings
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14929
Reviewed By: weiyangfb
Differential Revision:
D13394498
Pulled By: soumith
fbshipit-source-id:
948c6118abdf6c1e1a8a17709333954cafb2345e
Soumith Chintala [Mon, 10 Dec 2018 04:41:44 +0000 (20:41 -0800)]
remove mingfeima mkldnn reference from README, as no longer necessary (#14975)
Summary: we now get mkldnn automatically from third_party/ideep
Differential Revision:
D13396480
Pulled By: soumith
fbshipit-source-id:
20f819ba4b78cbe9c7d0baeab1c575669cbf6c20
Zachary DeVito [Mon, 10 Dec 2018 00:29:38 +0000 (16:29 -0800)]
fixing some rebuild issues (#14969)
Summary:
This fixes rebuild issues with the ninja part of the build. With this patch all ninja files will now report `nothing to do` if nothing has changed assuming `BUILD_CAFFE2_OPS=0`.
1. This only does the python file processing for caffe2 when BUILD_CAFFE2_OPS=1, this part of the build file is written in such a way that it is always required to rerun and can take substantial time to move files around in the no-op build. In the future this part should be rewritten to use a faster method of copying the files or should treat copying the files as part of the build rules and only run when the files are out of date.
2. This points `sleef` to a patched version that fixes a dead build output that is causing everything to relink all the time. See https://github.com/shibatch/sleef/pull/231#partial-pull-merging for the upstream change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14969
Reviewed By: soumith
Differential Revision:
D13395998
Pulled By: zdevito
fbshipit-source-id:
ca85b7be9e99c5c578103c144ef0f2c3b927e724
vishwakftw [Sun, 9 Dec 2018 23:53:34 +0000 (15:53 -0800)]
Remove deprecated info argument in btrifact (#14935)
Summary:
As specified in title.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14935
Differential Revision:
D13394449
Pulled By: soumith
fbshipit-source-id:
569d59414f3a1a43ea641bded4b5433eb53e3490
Soumith Chintala [Sun, 9 Dec 2018 23:52:25 +0000 (15:52 -0800)]
add fix for CUDA 10 (#14971)
Summary:
Linux binaries-only fix for CUDA10
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14971
Differential Revision:
D13395932
Pulled By: soumith
fbshipit-source-id:
a72d6ab6b98c6c936e6391d55d2e4e45b9f1e6dd
Your Name [Sun, 9 Dec 2018 16:55:26 +0000 (08:55 -0800)]
Fix mismatched test_{full,ones,zeros}_like onnx expect files (#14956)
Summary:
master broken #14903
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14956
Differential Revision:
D13395363
Pulled By: bddppq
fbshipit-source-id:
31f0913843292e557807fd5a976f8907fa6cae4b
Yiming Wu [Sun, 9 Dec 2018 16:23:36 +0000 (08:23 -0800)]
fix auto grad summing for IfOp where intermediate output needs renaming (#14772)
Summary:
fix auto grad summing for IfOp where intermediate output needs renaming.
Bug before this diff:
- we only renames the output of IfOp without changing the subnet ops output
- this results in blob not found error
the unittest provides an example
this diff fix that for IfOp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14772
Differential Revision:
D13327090
Pulled By: harouwu
fbshipit-source-id:
ec40ee88526ace3619c54551e223dd71158a02f8
Spandan Tiwari [Sun, 9 Dec 2018 06:46:03 +0000 (22:46 -0800)]
Export ones_like, zeros_like and full_like using ONNX ConstantLike op. (#14903)
Summary:
This PR does the following:
1) Updates the ONNX export for `torch.zeros_like` and `torch.full_like` ops to use ONNX op `ConstantLike`. This reduces the export of experimental op `ConstantFill`, which may possibly be removed in future, see https://github.com/onnx/onnx/pull/1434).
2) It also adds export support for `torch.ones_like`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14903
Differential Revision:
D13383700
Pulled By: houseroad
fbshipit-source-id:
566d00a943e9497172fcd5a034b638a650ab13a2
Edward Yang [Sun, 9 Dec 2018 03:32:01 +0000 (19:32 -0800)]
Canonicalize all includes in PyTorch. (#14849)
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision:
D13363445
Pulled By: ezyang
fbshipit-source-id:
52361f878a672785f9306c9e9ab2513128092b68
Jongsoo Park [Sun, 9 Dec 2018 02:15:00 +0000 (18:15 -0800)]
race condition fix of calling mutable_data inside a openmp region (#14921)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14921
Fix race condition introduced in
D13188595 .
Let's reminder ourselves "never call mutable_data from an OpenMP region!!!"
Reviewed By: jianyuh
Differential Revision:
D13387692
fbshipit-source-id:
6a3aeedeeda55a9ede660de8f1f44d4eee76ae2b
Fei Sun [Sat, 8 Dec 2018 19:12:40 +0000 (11:12 -0800)]
Add crop argument, can crop rec as well, first resize and then crop
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14894
Reviewed By: llyfacebook
Differential Revision:
D13377604
Pulled By: sf-wind
fbshipit-source-id:
333d0d864e6c2dc85f405baa25ed58029d62750f
Marat Dukhan [Sat, 8 Dec 2018 10:45:41 +0000 (02:45 -0800)]
Switch Int8Sigmoid to QNNPACK (#14883)
Summary:
50x-100x speedup compared to current version.
Also, fixes a bug in the current version when batch size exceeds 1 (current version processes only the first image in this case).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14883
Differential Revision:
D13390655
Pulled By: Maratyszcza
fbshipit-source-id:
1b33a97bf2d0866d38faa2b42e64fd2859017898
Your Name [Sat, 8 Dec 2018 09:04:02 +0000 (01:04 -0800)]
ONNX changes to use int32_t (instead of enum) to store data type
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14926
Reviewed By: houseroad
Differential Revision:
D13390642
Pulled By: bddppq
fbshipit-source-id:
c2314b24d9384f188fda2b9a5cc16465ad39581e
Sebastian Messmer [Sat, 8 Dec 2018 08:26:14 +0000 (00:26 -0800)]
Remove at references from c10
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14432
Reviewed By: dzhulgakov
Differential Revision:
D13223904
fbshipit-source-id:
43b06e33e088e7789ccea6d92267936fe30d8571
Brennan Vincent [Sat, 8 Dec 2018 04:13:31 +0000 (20:13 -0800)]
Implement `std` for multiple dimensions on CPU devices. (#14535)
Summary:
Tested on a tensor with 1 billion elements and 3 dimensions on a powerful, highly
multi-core Linux machine.
parallelized: All operations (e.g., `t.std(1)`) that could be done in the old code are now several times faster. All
new operations (e.g., `t.std((0,2))` are significantly faster than the NumPy equivalents.
`t.std((0, 1, 2))`, a new operation, is logically equivalent to the
old `t.std()`, but faster.
serial: The above comment about old operationos now being faster still
holds, but `t.std((t1, ..., tn))` is now a few
times slower than `t.std()`. If this turns out to be important, we can
special-case that to use the old algorithm.
The approach is to create a new method, `TensorIterator::foreach_reduced_elt`,
valid for `TensorIterator`s that represent a dimension reduction. This
method calls a supplied function for each element in the output,
supplying it with the input elements that correspond to that output.
Given that primitive, we can implement reductions like the following pseudocode:
If there is more than one output element:
```
PARALLEL FOR EACH element IN output:
accumulator = identity
SERIAL FOR EACH data_point IN element.corresponding_input:
accumulator.update(data_point)
element = accumulator.to_output()
```
If there is only one output element, we still want to parallelize, so we
do so along the *input* instead:
```
accumulators[n_threads]
PARALLEL FOR EACH input_chunk IN input.chunks():
accumulators[thread_num()] = identity
SERIAL FOR EACH data_point IN input_chunk:
accumulators[thread_num()].update_with_data(data_point)
accumulator = identity
SERIAL FOR EACH acc in accumulators:
accumulator.update_with_other_accumulator(acc)
output_element = accumulator.to_output()
```
Note that accumulators and data points do not have to be the same type
in general, since it might be necessary to track arbitrary amounts of
data at intermediate stages.
For example, for `std`, we use a parallel version of Welford's
algorithm, which requies us to track the mean, second moment, and number
of elements, so the accumulator type for `std` contains three pieces of
data.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14535
Differential Revision:
D13283887
Pulled By: umanwizard
fbshipit-source-id:
8586b7bf00bf9f663c55d6f8323301e257f5ec3f
Orion Reblitz-Richardson [Sat, 8 Dec 2018 03:48:38 +0000 (19:48 -0800)]
Add CAFFE2_API to video processing functions (#14900)
Summary:
Extracted from https://github.com/pytorch/pytorch/pull/13733
Some tests were failing because these methods didn't have an export.
cc pjh5 yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14900
Reviewed By: pjh5
Differential Revision:
D13381130
Pulled By: orionr
fbshipit-source-id:
030536f8fb09765c09a7b0bd45400161053f2e18
Johannes M Dieterich [Sat, 8 Dec 2018 02:55:21 +0000 (18:55 -0800)]
Enable unit tests known to work on ROCm (#14011)
Summary:
* Enable unit tests known to work on ROCm.
* Disable a few that are known to be flaky for the time being.
* Use std::abs for Half
* No more special casing for ROCm in TensorMathReduce
* Document an important detail for a hardcoded block size w.r.t. ROCm in TensorMathReduce
ezyang bddppq for awareness
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14011
Differential Revision:
D13387679
Pulled By: bddppq
fbshipit-source-id:
4177f2a57b09d866ccbb82a24318f273e3292f71
Lu Fang [Sat, 8 Dec 2018 01:24:01 +0000 (17:24 -0800)]
update of fbcode/onnx to
aca8473a40cf43f01958c81b648efcee7f3a755a (#14865)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14865
Previous import was
42804705bdbf179d1a98394008417e1392013547
Included changes:
- **[aca8473](https://github.com/onnx/onnx/commit/aca8473)**: Add Erf operator for computing error function (#1675) <bddppq>
- **[3fc82ca](https://github.com/onnx/onnx/commit/3fc82ca)**: Add IsNaN operator. (#1656) <Pranav Sharma>
- **[0685f01](https://github.com/onnx/onnx/commit/0685f01)**: Add Sign Op (#1658) <Rui Zhu>
- **[2a8fae8](https://github.com/onnx/onnx/commit/2a8fae8)**: Fix unused var warning (#1669) <Yinghai Lu>
- **[e212833](https://github.com/onnx/onnx/commit/e212833)**: Update scan (#1653) <G. Ramalingam>
Reviewed By: zrphercule
Differential Revision:
D13370727
fbshipit-source-id:
13a93d5acc8d4758f682278ea162ec9124ced22d
rohithkrn [Sat, 8 Dec 2018 01:23:49 +0000 (17:23 -0800)]
Enable fp16 for MIOPEN operators in Caffe2 (#14905)
Summary:
This PR enables fp16 MIOPEN operators in Caffe2.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14905
Differential Revision:
D13383439
Pulled By: bddppq
fbshipit-source-id:
840afa8d08bef2952ca0039dee2423f1542bb330
Gu, Jinghui [Sat, 8 Dec 2018 00:42:39 +0000 (16:42 -0800)]
Upgrade MKL-DNN to version 0.17 (#14308)
Summary:
upgrade MKL-DNN to version 0.17
update mkldnn bridge to latest.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14308
Differential Revision:
D13383102
Pulled By: yinghai
fbshipit-source-id:
c434f0e0ddff2ee2c86db2d6c44a37298fd005a3
Daniel Bermond [Sat, 8 Dec 2018 00:37:30 +0000 (16:37 -0800)]
Fix build with OpenCV 4.0 (#14356)
Summary:
Fixes #14355
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14356
Differential Revision:
D13356237
Pulled By: bddppq
fbshipit-source-id:
2bf6ee21995c2c7b617c4e78ea7341f975f1b937
Sebastian Messmer [Sat, 8 Dec 2018 00:18:20 +0000 (16:18 -0800)]
Remove unused TensorImpl dependencies
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14792
Reviewed By: ezyang
Differential Revision:
D13336843
fbshipit-source-id:
12f84799a70c2e90a8b934dd8dc031c09a6782f0
Sebastian Messmer [Sat, 8 Dec 2018 00:18:20 +0000 (16:18 -0800)]
Remove TensorImpl -> context_base dependency (#14658)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14658
Remove this dependency by moving at::CopyBytes to c10.
The implementations for at::CopyBytes will have to live in aten/caffe2 for now because they're not unified for CUDA yet.
They'll be moved into c10/backend/xxx later.
Reviewed By: dzhulgakov
Differential Revision:
D13288655
fbshipit-source-id:
1c92379345308b3cd39a402779d7b7999613fc0d
Sebastian Messmer [Sat, 8 Dec 2018 00:18:19 +0000 (16:18 -0800)]
Fix include paths for TensorOptions
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14747
Reviewed By: ezyang
Differential Revision:
D13318645
fbshipit-source-id:
f5ba77a93f6019fbf5faffb47a2837c95fad474d
James Reed [Fri, 7 Dec 2018 23:06:48 +0000 (15:06 -0800)]
Update graph printouts in JIT docs (#14914)
Summary:
Tracing records variable names and we have new types and stuff in the IR, so this updates the graph printouts in the docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14914
Differential Revision:
D13385101
Pulled By: jamesr66a
fbshipit-source-id:
6477e4861f1ac916329853763c83ea157be77f23
Ailing Zhang [Fri, 7 Dec 2018 22:56:56 +0000 (14:56 -0800)]
Improve hub documentation (#14862)
Summary:
Added a few examples and explains to how publish/load models.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14862
Differential Revision:
D13384790
Pulled By: ailzhang
fbshipit-source-id:
008166e84e59dcb62c0be38a87982579524fb20e
James Reed [Fri, 7 Dec 2018 22:14:25 +0000 (14:14 -0800)]
USE_FBGEMM=True by default
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14868
Differential Revision:
D13383390
Pulled By: jamesr66a
fbshipit-source-id:
1880c07dfd239e19153bd4fde2ab2c8d0604f956
Sergei Nikolaev [Fri, 7 Dec 2018 21:52:56 +0000 (13:52 -0800)]
USE_TENSORRT support and TensorRT 5 compatibility
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13945
Differential Revision:
D13317525
Pulled By: yinghai
fbshipit-source-id:
8630dfec1bbc5aac19539e344e7c38a7fd8b051d
Orion Reblitz-Richardson [Fri, 7 Dec 2018 21:35:58 +0000 (13:35 -0800)]
Add __init__.py so files get picked up on install (#14898)
Summary:
This will let us install tests and other Caffe2 python code as a part of running Caffe2 tests in PyTorch.
Broken out of https://github.com/pytorch/pytorch/pull/13733/
cc pjh5 yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14898
Reviewed By: pjh5
Differential Revision:
D13381123
Pulled By: orionr
fbshipit-source-id:
0ec96629b0570f6cc2abb1d1d6fce084e7464dbe
Gregory Chanan [Fri, 7 Dec 2018 20:37:03 +0000 (12:37 -0800)]
Replace calls of Type::_th_tensor. (#14877)
Summary:
_th_tensor is moving off Type, so these calls need to be replaced.
Unfortunately, replacing these with a full-fledged solution [e.g. from_storage(..., TensorOptions)] is a bit complicated because the storage itself fully defines the Type (modulo variable). It's simpler to just wait for the Variable/Tensor merge rather than to solve this now, so instead I changed the call sites to: at::empty({0}, type.options()).set_(storage...).
This isn't great because we are also trying to get rid of Type::options, but this seems to be the lesser-of-two-evils.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14877
Differential Revision:
D13374310
Pulled By: gchanan
fbshipit-source-id:
eb953ed041507e6190d6f32e383912e5a08311cd
Peter Goldsborough [Fri, 7 Dec 2018 20:22:49 +0000 (12:22 -0800)]
Large scale fix of python-related files in torch/csrc/
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14515
Differential Revision:
D13247966
Pulled By: goldsborough
fbshipit-source-id:
7a127c508fc576a7a92626dd6b729f660162d628
PenghuiCheng [Fri, 7 Dec 2018 20:01:44 +0000 (12:01 -0800)]
Implementation of WeightedSum op for mkl-dnn and fix FC op output shape issue.
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14407
Reviewed By: yinghai
Differential Revision:
D13364364
Pulled By: wesolwsk
fbshipit-source-id:
e69bcd1bc52e35b2f0e45e5dc40184f1bd66605d
Yudong Guang [Fri, 7 Dec 2018 17:58:54 +0000 (09:58 -0800)]
Revert
D13205604: Move numa.{h, cc} to c10/util
Differential Revision:
D13205604
Original commit changeset:
54166492d318
fbshipit-source-id:
89b6833518c0b554668c88ae38d97fbc47e2de17
vishwakftw [Fri, 7 Dec 2018 15:25:55 +0000 (07:25 -0800)]
Expose torch.roll function and method (#14880)
Summary: Fixes #14859 .
Differential Revision:
D13376915
Pulled By: zou3519
fbshipit-source-id:
f1fc0e8492a159431a3fc0a19a41aa10429ecc80
Junjie Bai [Fri, 7 Dec 2018 08:07:05 +0000 (00:07 -0800)]
Make autograd engine compatible with hip
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14873
Differential Revision:
D13375053
Pulled By: bddppq
fbshipit-source-id:
f3051640386667bbf0566856ed433eb83276c39e
Jon Crall [Fri, 7 Dec 2018 07:55:34 +0000 (23:55 -0800)]
Fixed ConvT docstring (#14876)
Summary:
Fixes #14099
I attempted to be as consistent as possible with the formatting, hence why my equation reads d*(k - 1) instead of (k - 1)*d.
Also there is an unused variable on line 46: `n = self.in_channels`. I could fix that here too if that's not too out of scope.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14876
Differential Revision:
D13374317
Pulled By: soumith
fbshipit-source-id:
a9f110acafa58cdb4206956dbe3ab4738d48292d
svcscm [Fri, 7 Dec 2018 06:50:09 +0000 (22:50 -0800)]
Updating submodules
Reviewed By: yns88
fbshipit-source-id:
7da015701f18f8a0b5a8092aae02a42ede7bfd44
David Riazati [Fri, 7 Dec 2018 05:50:35 +0000 (21:50 -0800)]
Remove weak module test expect files (#14871)
Summary:
This PR removes some expect files that aren't really testing anything
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14871
Differential Revision:
D13373762
Pulled By: driazati
fbshipit-source-id:
e3537ee83df23b3b3b854f9b1253fd0cc8e9dd33
Wei Yang [Fri, 7 Dec 2018 01:58:16 +0000 (17:58 -0800)]
gradcheck (#14596)
Summary:
- allow gradcheck to take sparse tensor as input
- sparse output is not allowed yet at gradcheck
- add backward for `to_dense()` to get around sparse output
- calling gradcheck at test_sparse, so that we can use `_gen_sparse()` and also easily cover coalesced / uncoalesced test cases
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14596
Differential Revision:
D13271904
Pulled By: weiyangfb
fbshipit-source-id:
5317484104404fd38058884c86e987546011dd86
Teng Li [Fri, 7 Dec 2018 01:22:04 +0000 (17:22 -0800)]
Skipping two c10d tests only if there are multi-GPUs (#14860)
Summary:
Otherwise, these tests will fail, even though there are never meant to run on single GPU machines.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14860
Differential Revision:
D13369060
Pulled By: teng-li
fbshipit-source-id:
8a637a6d57335491ba8602cd09927700b2bbf8a0
Sebastian Messmer [Thu, 6 Dec 2018 23:52:15 +0000 (15:52 -0800)]
Move TensorOptions, DefaultTensorOptions to c10
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14746
Reviewed By: ezyang
Differential Revision:
D13318644
fbshipit-source-id:
b703d7dc67e75d9e9571c80d62a100c5fc4e84df
Marat Dukhan [Thu, 6 Dec 2018 23:12:35 +0000 (15:12 -0800)]
Switch Int8MaxPool operator to QNNPACK (#14832)
Summary:
1.6-2.4X speedup on ARM when compiled with gcc
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14832
Differential Revision:
D13358160
Pulled By: Maratyszcza
fbshipit-source-id:
39e9791886fac62650bb53a9df341889f0bb5d49
Richard Zou [Thu, 6 Dec 2018 22:55:55 +0000 (14:55 -0800)]
collect_env.py: get conda magma and mkl information (#14854)
Summary:
Fixes #12371
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14854
Differential Revision:
D13363635
Pulled By: zou3519
fbshipit-source-id:
f8b5d05038bf5ce451399dfeed558ae298178128
zrphercule [Thu, 6 Dec 2018 22:04:44 +0000 (14:04 -0800)]
Add LogSigmoid support in ONNX symbolic (#14830)
Summary:
Add LogSigmoid:
torch.LogSigmoid(x) = onnx.Log(onnx.Sigmoid(x))
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14830
Differential Revision:
D13353891
Pulled By: zrphercule
fbshipit-source-id:
bf456170b9e6c4edad07b3333cd5797f8e0fa97f
Ashwin Bharambe [Thu, 6 Dec 2018 21:44:33 +0000 (13:44 -0800)]
Kill GPU memory logs in normal runs (#14838)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14838
The GPU memory tracking logs are incredibly annoying and merely serve
to pollute output. I `VLOG(1)`ed them. Hopefully, this is non-controversial.
Reviewed By: kuttas
Differential Revision:
D13343290
fbshipit-source-id:
b3cae99346c97b66e97ea660061e15dc5c99b9fc
Junjie Bai [Thu, 6 Dec 2018 21:17:28 +0000 (13:17 -0800)]
Stop inserting static casts in Hipify (#14853)
Summary:
Latest hcc can now properly cast to correct type internally, so there is no need to insert static_cast in hipify scripts anymore.
However the hcc included in the latest ROCm release (1.9.2) doesn't have this fix, so leaving a flag to continue doing static_cast for those using the official ROCm releases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14853
Differential Revision:
D13363171
Pulled By: bddppq
fbshipit-source-id:
a36476a8511222ff3c933d31788e8a0ffb04f5ca
Jerry Zhang [Thu, 6 Dec 2018 19:16:07 +0000 (11:16 -0800)]
Tensor construction codemod - 3/3 (#14835)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14835
Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407
Reviewed By: bddppq
Differential Revision:
D13335184
fbshipit-source-id:
26d8247e16b30bdff045530034af9b72c76d066f
Jerry Zhang [Thu, 6 Dec 2018 19:14:48 +0000 (11:14 -0800)]
Tensor construction codemod - 1/3 (#14828)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14828
Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407
Reviewed By: bddppq
Differential Revision:
D13335160
fbshipit-source-id:
a3ae4c5a86bfbdaf2d5aa14e0eef57255e829fd4
Jerry Zhang [Thu, 6 Dec 2018 18:56:14 +0000 (10:56 -0800)]
Move numa.{h, cc} to c10/util (#14393)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14393
att
Reviewed By: ezyang
Differential Revision:
D13205604
fbshipit-source-id:
54166492d31827b0343ed070cc36a825dd86e2ed
Johannes M Dieterich [Thu, 6 Dec 2018 18:04:37 +0000 (10:04 -0800)]
Upgrade CI to ROCm 1.9.2 (#14216)
Summary:
Drop custom hcc/hip as the 1.9.2 release should contain the relevant patches therein.
Most notable feature in 1.9.2 is mixed precision support in rocBLAS and MIOpen. These features will be enabled by subsequent PRs.
bddppq ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14216
Differential Revision:
D13354294
Pulled By: bddppq
fbshipit-source-id:
2541d4a196af21c9432c1aff7f6e65b572628028
Jan Schlüter [Thu, 6 Dec 2018 17:29:01 +0000 (09:29 -0800)]
Allow linspace and logspace with steps=1 and start != end like numpy (#14748)
Summary:
`torch.linspace(0, 1, 1)` fails with `RuntimeError: invalid argument 3: invalid number of points at ../aten/src/TH/generic/THTensorMoreMath.cpp:2119`, while `np.linspace(0, 1, 1)` works fine.
Looking at the code, there is even a comment by gchanan asking: "NumPy allows you to pass different points even if n <= 1 -- should we?"
I would say "yes". Currently, I would need to handle the case of `steps == 1` or `steps == 0` separately, making sure to change the `end` when calling `torch.linspace`. This is impractical. If we support `start != end`, there are two possibilities for the result: Either we ensure the first value in the resulting sequence always equals `start`, or we ensure the last value in the resulting sequence always equals `end`. Numpy chose the former, which also allows it to support a boolean `endpoint` flag. I'd say we should follow numpy.
This PR adapts `linspace` and `logspace` to mimic the behavior of numpy, adapts the tests accordingly, and extends the docstrings to make clear what happens when passing `steps=1`.
If you decide against this PR, the error message should become explicit about what I did wrong, and the documentation should be extended to mention this restriction.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14748
Differential Revision:
D13356136
Pulled By: ezyang
fbshipit-source-id:
db85b8f0a98a5e24b3acd766132ab71c91794a82
Jie [Thu, 6 Dec 2018 16:57:39 +0000 (08:57 -0800)]
(#14580)
Summary:
Removes cast of half to float in torch.sum, with float16 input tensor and
float32 output tensor, instead we cast data when loading input in kernel.
This supposingly would save a kernel launch as well as a full global memory load
on promoted data type (float).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14580
Differential Revision:
D13356203
Pulled By: ezyang
fbshipit-source-id:
85e91225b880a65fe3ceb493371b9b36407fdf48
Ricardo Cuenca [Thu, 6 Dec 2018 16:57:31 +0000 (08:57 -0800)]
Consistent formatting in losses' docs
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14739
Differential Revision:
D13356143
Pulled By: ezyang
fbshipit-source-id:
9ae8316dd8ba6e910247b64cec22db63df10e11c
Alex Şuhan [Thu, 6 Dec 2018 16:56:25 +0000 (08:56 -0800)]
Add (partial) autodiff support for nll_loss (#14305)
Summary:
Not ready yet, need some comments / help with this. It's good enough for https://github.com/pytorch/xla immediate goals (forward + backward trace fusion), but there are at least two issues with it:
1. If we don't allow it, `test/test_jit.py` fails to cover the change.
2. If we allow the weight to be set, running `test/test_jit.py TestJitGenerated.test_nn_nll_loss` fails with:
```
======================================================================
ERROR: test_nn_nll_loss (__main__.TestJitGenerated)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test/test_jit.py", line 10001, in do_test
fn, f_args_variable, kwargs_variable, no_grad=no_grad)
File "test/test_jit.py", line 9360, in check_against_reference
outputs_test = self.runAndSaveRNG(func, recording_inputs, kwargs)
File "test/test_jit.py", line 425, in runAndSaveRNG
results = func(*inputs, **kwargs)
File "test/test_jit.py", line 9298, in script_fn
self.assertExportImport(CU.the_method.graph, tensors)
File "test/test_jit.py", line 415, in assertExportImport
self.assertExportImportModule(m, inputs)
File "test/test_jit.py", line 419, in assertExportImportModule
self.assertEqual(self.runAndSaveRNG(m.forward, inputs),
File "test/test_jit.py", line 425, in runAndSaveRNG
results = func(*inputs, **kwargs)
RuntimeError:
arguments for call are not valid:
for operator aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight, *, Tensor out) -> Tensor:
expected a value of type Tensor for argument 'total_weight' but found bool
<internally-created-node>
~ <--- HERE
for operator aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, Tensor total_weight) -> Tensor:
expected a value of type Tensor for argument 'total_weight' but found bool
<internally-created-node>
~ <--- HERE
for call at:
<internally-created-node>
~ <--- HERE
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14305
Differential Revision:
D13356265
Pulled By: ezyang
fbshipit-source-id:
504d783b2d87f923e698a6a4efc0fd9935a94a41
svcscm [Thu, 6 Dec 2018 11:18:17 +0000 (03:18 -0800)]
Updating submodules
Reviewed By: yns88
fbshipit-source-id:
2adbb6f97d4b8f067a2538fec855063510b0ca3f
svcscm [Thu, 6 Dec 2018 10:53:28 +0000 (02:53 -0800)]
Updating submodules
Reviewed By: yns88
fbshipit-source-id:
e0509413215f3b7578b825c52365fec4da625bd5
lcskrishna [Thu, 6 Dec 2018 07:52:42 +0000 (23:52 -0800)]
Fixed MIOpen RNN Segfault issue and enabled RNN test (#14810)
Summary:
This pull request contains changes for:
1. Added MIOpen RNN API miopenGetRNNLayerBiasSize and miopenGetRNNLayerParamSize.
2. Fixed usage of API miopenGetRNNLayerParam.
3. Modifying the RNN test to run using MIOpen engine.
Differential Revision:
D13355699
Pulled By: bddppq
fbshipit-source-id:
6f750657f8049c5446eca893880b397804120b69
Yinghai Lu [Thu, 6 Dec 2018 07:50:12 +0000 (23:50 -0800)]
Export complete subgraph io info when calling onnxGetBackendCompatibility (#14827)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14827
We need to send complete IO info when doing `onnxGetBackendCompatibility` to backend like Glow. Previously we are missing some info because sometimes we generate more than one nodes from one C2 op. This fixes the issue.
Reviewed By: jackm321
Differential Revision:
D13352049
fbshipit-source-id:
8d8ac70656a0ac42f3a0ccecad61456a4f3b2435
Huan Gui [Thu, 6 Dec 2018 06:51:23 +0000 (22:51 -0800)]
Fix clip gradient with empty input (#14709)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14709
As titled
Reviewed By: Wakeupbuddy
Differential Revision:
D13305554
fbshipit-source-id:
380062d4b0e4f9dc0207a27766cac7b8d05384d5
JerryShih [Thu, 6 Dec 2018 06:47:54 +0000 (22:47 -0800)]
Remove protobuf dependency in pytorch cmake file. (#14182)
Summary:
Currently, pytorch doesn't dependent on protobuf. So, we don't need to include the protobuf dir in pytorch cmake file.
And if we build caffe2 without custom-protobuf[1], we will have the protobuf mismatched problem.
[1]
https://github.com/pytorch/pytorch/blob/
92dbd0219f6fbdb1db105386386ccf92c0758e86/CMakeLists.txt#L65
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14182
Differential Revision:
D13356273
Pulled By: ezyang
fbshipit-source-id:
8120c3452d158dc51d70156433d7b9076c6aed47
Xiang Gao [Thu, 6 Dec 2018 06:44:27 +0000 (22:44 -0800)]
Optimize images (#14084)
Summary:
This is a PR that [ImgBot](https://imgbot.net/) opened on my fork https://github.com/zasdfgbnm/pytorch/pull/1, I forward it here. ImgBot does lossless compression on images to reduce file size.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14084
Differential Revision:
D13356293
Pulled By: ezyang
fbshipit-source-id:
731236d95ad870db8ccb99b03ed306704365242c
Aldian Fazrihady [Thu, 6 Dec 2018 06:31:39 +0000 (22:31 -0800)]
Prevent `profile_observer_test` from being run by CPU test (#14168)
Summary:
Fix CMakeLists.txt, so the test for CPU won't run profile_observer_test.cc, as currently it only supports GPU
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14168
Differential Revision:
D13356274
Pulled By: ezyang
fbshipit-source-id:
7d105f2e18675e5fab129864958148b0f18d582c
Achal Shah [Thu, 6 Dec 2018 06:30:07 +0000 (22:30 -0800)]
CAFFE2_INCLUDE_DIRS points to invalid path (#14306)
Summary:
I know that including CAFFE2_INCLUDE_DIRS in include headers are not necessary for newer cmakes. But I had this in one of my old projects and **cmake gave me error that "/usr/lib/include" is invalid path**.
It seems like "${_INSTALL_PREFIX}/lib/include" should be changed to "${_INSTALL_PREFIX}/include" as all caffe2 headers are in /include rather than /lib/include/
Please correct me if I am wrong?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14306
Differential Revision:
D13356246
Pulled By: ezyang
fbshipit-source-id:
e2d5d3c42352e59b245714ad90fd7a9ef48170d7
HB_alon [Thu, 6 Dec 2018 06:16:44 +0000 (22:16 -0800)]
use "Extension" instead of the unimported "setuptools.Extension" (#14475)
Summary:
use "Extension" instead of the unimported "setuptools.Extension"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14475
Differential Revision:
D13356219
Pulled By: ezyang
fbshipit-source-id:
5a3e7eb73a32d6bf09676efd9eddded5586435cd
Shuichi KITAGUCHI [Thu, 6 Dec 2018 06:07:45 +0000 (22:07 -0800)]
generate ATen core files with LF. (#14667)
Summary:
on Windows environment, some ATen core files (Type.h, Tensor.h, TensorMethods.h) are created and it's new line code is CRLF. (maybe enviconment dependant)
therefore, comparing files is failed in generate_outputs()agener917.py and compilation stopped.
this patch generates these files with LF forcibly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14667
Differential Revision:
D13356170
Pulled By: ezyang
fbshipit-source-id:
ef8cc3a6cc8bf3c45b78e9eb3df98cf47c0d33bb
Brendan Soffientini [Thu, 6 Dec 2018 05:53:36 +0000 (21:53 -0800)]
Remove outdated css file and refs in cpp conf.py (#14779)
Summary:
pytorch_theme.css is no longer necessary for the cpp or html docs site build. The new theme styles are located at https://github.com/pytorch/pytorch_sphinx_theme. The Lato font is also no longer used in the new theme.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14779
Differential Revision:
D13356125
Pulled By: ezyang
fbshipit-source-id:
c7635eb7512c7dcaddb9cad596ab3dbc96480144
vaeksare [Thu, 6 Dec 2018 05:24:58 +0000 (21:24 -0800)]
Fixes for some Windows compiler warnings (#14490)
Summary:
Implement some simple fixes to clean up windows build by fixing compiler warnings. Three main types of warnings were fixes:
1. GCC specific pragmas were changed to not be used on windows.
2. cmake flags that don't exist on windows were removed from windows build
3. Fix a macro that was defined multiple times on Windows.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14490
Differential Revision:
D13241988
Pulled By: ezyang
fbshipit-source-id:
38da8354f0e3a3b9c97e33309cdda9fd23c08247
Edward Yang [Thu, 6 Dec 2018 05:14:03 +0000 (21:14 -0800)]
Shut up "address will always evaluate to 'true'" warnings (#14774)
Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14774
Differential Revision:
D13327969
Pulled By: ezyang
fbshipit-source-id:
43380c89eedaaa89467952401b8fd3f5a9ad754a
Edward Yang [Thu, 6 Dec 2018 04:50:41 +0000 (20:50 -0800)]
HIPify less files in PyTorch (#14804)
Summary:
Stacked on #14803
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14804
Differential Revision:
D13347986
Pulled By: ezyang
fbshipit-source-id:
c93177b4ad51855660d0de36d042bfc542bd4be0
Junjie Bai [Thu, 6 Dec 2018 02:35:21 +0000 (18:35 -0800)]
Unify device argument parsing between torch and c10
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14786
Differential Revision:
D13334501
Pulled By: bddppq
fbshipit-source-id:
ae3536be1fe0dcd6a1552ec93629ecc9554c0d7c
Pieter Noordhuis [Thu, 6 Dec 2018 01:18:06 +0000 (17:18 -0800)]
Improve assertion failure message (#14813)
Summary:
See #14554.
I can't figure out how the reported issue can happen. The best next
thing is have more information when this happens again.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14813
Differential Revision:
D13351908
Pulled By: pietern
fbshipit-source-id:
61b30fcae2e34da54329d0893ca4921b6ad60f0d
Bram Wasti [Thu, 6 Dec 2018 01:16:24 +0000 (17:16 -0800)]
Add FunctionSchema based Operator Registry (#13789)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13789
This enables creation of operators with FunctionSchema and IValue
Reviewed By: smessmer
Differential Revision:
D13008791
fbshipit-source-id:
151efc88ac315f4a0ab0171a99774caaf767ef1e
Pieter Noordhuis [Thu, 6 Dec 2018 01:15:51 +0000 (17:15 -0800)]
Increase test timeout (#14814)
Summary:
It is possible that some sort of contention causes process scheduling
delays which in turn cause the timeout to *not* be hit.
Increased sleep here will decrease the probability of this happening.
Fixes #14555.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14814
Differential Revision:
D13351924
Pulled By: pietern
fbshipit-source-id:
1222cf0855408dfcb79f30f94694c790ee998cf9
Pieter Noordhuis [Thu, 6 Dec 2018 01:07:26 +0000 (17:07 -0800)]
Retry test on address already in use error (#14815)
Summary:
Thanks nairbv for the suggestion.
Also see #14589.
Fixes #14703.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14815
Differential Revision:
D13351913
Pulled By: pietern
fbshipit-source-id:
d11a4152505d0ce15592b13e417bb80551476a61
Lu Fang [Thu, 6 Dec 2018 01:04:39 +0000 (17:04 -0800)]
improve ONNX tests on torch.Linear
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14821
Reviewed By: zrphercule
Differential Revision:
D13348773
Pulled By: houseroad
fbshipit-source-id:
611ca6e28f715e5518649c8c16f702ac3433308c
Lin Huang [Wed, 5 Dec 2018 21:12:37 +0000 (13:12 -0800)]
Define THPStorage struct only once (rather than N times) (#14802)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14802
The definetion of THPStorage does not depend on any Real, its macro
defintion is unnecessary, refactor the code so that THPStorage is not macro
defined.
Reviewed By: ezyang
Differential Revision:
D13340445
fbshipit-source-id:
343393d0a36c868b9a06eea2ad9b80f5e395e947
Daya S Khudia [Wed, 5 Dec 2018 21:09:55 +0000 (13:09 -0800)]
File name change for FbgemmI8Depthwise.h and FbgemmI8Depthwise.cc (#14725)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14725
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/33
Renaming FbgemmI8Depthwise.h to FbgemmI8DepthwiseAvx2.h and FbgemmI8Depthwise.cc to FbgemmI8DepthwiseAvx2.cc since FbgemmI8DepthwiseAvx2.cc will be compiled with avx2 flags
Reviewed By: jianyuh
Differential Revision:
D13313898
fbshipit-source-id:
a8111eacf3d79a466ce0565bfe5f2f0b200a5c33
zrphercule [Wed, 5 Dec 2018 20:59:44 +0000 (12:59 -0800)]
Add torch.nn.RReLU support in symbolic (#14781)
Summary:
Now we support exporting torch.nn.RReLU in onnx.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14781
Reviewed By: houseroad
Differential Revision:
D13343872
Pulled By: zrphercule
fbshipit-source-id:
1e96b957de4fc2f5ba3959d42329807975419ae3
Daya S Khudia [Wed, 5 Dec 2018 19:50:57 +0000 (11:50 -0800)]
Move avx2 specific code in different source files (#28)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/28
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14516
This is the first diff in a series of diffs that will separate out avx2 specific code in separate files. The goal is to compile as little as possible code with avx2 and avx512 compiler flags.
Reviewed By: jianyuh
Differential Revision:
D13248376
fbshipit-source-id:
401c2e9d3cd96c420fd08c3efa011febce96ffbb
Marat Dukhan [Wed, 5 Dec 2018 19:39:46 +0000 (11:39 -0800)]
Validate matching input shapes in Int8Add operator (#14520)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14520
Default engine doesn't support broadcast semantics in Int8Add operator. This patch adds a check that shapes are equivalent.
Reviewed By: bertmaher
Differential Revision:
D13250922
fbshipit-source-id:
8526d07723bd9a34d54dee04d121c57f8b33c481
Tongzhou Wang [Wed, 5 Dec 2018 19:21:19 +0000 (11:21 -0800)]
fix stft arg types
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14800
Reviewed By: zou3519
Differential Revision:
D13340574
Pulled By: SsnL
fbshipit-source-id:
8b0dbbe299d1a362da0ecc0b1c0dadb2543ded5d
Edward Yang [Wed, 5 Dec 2018 18:57:00 +0000 (10:57 -0800)]
Improve HIPify performance (#14803)
Summary:
```
Improve performance of pyHIPIFY
Changes:
- Pre-compile regexes, don't use regexes when it's not necessary
(this saves us ~15%)
- Compile all substitutions for mappings into a single, non-backtracking
regex using a Trie. This gives big savings.
Before, running pyHIPIFY on all files took 15.8s. Now it takes 3.9s.
```
Stacked on #14769
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14803
Differential Revision:
D13342620
Pulled By: ezyang
fbshipit-source-id:
1cfa36b3236bbe24d07080a31cc788a52d740f40
Ailing Zhang [Wed, 5 Dec 2018 18:52:39 +0000 (10:52 -0800)]
Fix cuda multiprocessing cached memory (#14736)
Summary:
This PR fixes #11422
In the old world of CUDA IPC, when we want to share a tensor T from A to B, we have to share the whole CUDA mem allocation where T's storage sit in. And we casted it to the same type of storage of T's.
This causes problem when two different types of storage got allocated to the same CUDA mem block. When we try to reconstruct the second tensor, it will complain about wrong storage type.
In this PR we reconstruct the storage only (not the entire mem block). However, CUDA only allows one open memHandle once per process, we have to save the device pointer in a global cache so that we can reconstruct tensors as they come.
Thanks a ton to ezyang who helped design the solution and debugged the issue!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14736
Differential Revision:
D13335899
Pulled By: ailzhang
fbshipit-source-id:
cad69db392ed6f8fdc2b93a9dc2899f6d378c371
Peter Goldsborough [Wed, 5 Dec 2018 18:18:20 +0000 (10:18 -0800)]
Set and get default dtype (#13748)
Summary:
Replaces the `DefaultTensorOptions` with just a global default dtype that you can set and get like in Python.
Also, calls `set_default_dtype` in the implementation of `torch.set_default_dtype`. Right now these two default values are separate but will always be the same. Should we just bind `set_default_dtype` into Python? I think that might be good to do in a separate PR though.
ezyang gchanan
Also CC colesbury who wanted to do this for ATen for a while? What do you think about it?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13748
Differential Revision:
D13340207
Pulled By: goldsborough
fbshipit-source-id:
2689b09eb137fabb3a92d1ad1635782bee9398e8
Marat Dukhan [Wed, 5 Dec 2018 18:10:32 +0000 (10:10 -0800)]
Switch Int8AveragePool operator to QNNPACK (#14783)
Summary:
2.2-2.9X better performance on ARM when compiled with gcc (same bad perf when compiled with Clang)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14783
Differential Revision:
D13332680
Pulled By: Maratyszcza
fbshipit-source-id:
4c1138500c6b3026335e9bfe5f6be43b1ae2cefb
peterjc123 [Wed, 5 Dec 2018 17:50:41 +0000 (09:50 -0800)]
Update magma to 2.4.0 for Windows
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14738
Differential Revision:
D13341611
Pulled By: soumith
fbshipit-source-id:
39a49fc60e710cc32a463858c9cee57c182330e2
Edward Yang [Wed, 5 Dec 2018 17:21:13 +0000 (09:21 -0800)]
Unify build_caffe2_amd.py and build_pytorch_amd.py (#14769)
Summary:
I need to preserve ability to HIPify out-of-place files
only, so build_amd.py grows a --out-of-place-only flag.
Stacked on #14757
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14769
Differential Revision:
D13340154
Pulled By: ezyang
fbshipit-source-id:
1b855bc79e824ea94517a893236fd2c8ba4cb79d
Ilia Cherniavskii [Wed, 5 Dec 2018 16:40:54 +0000 (08:40 -0800)]
Default pool() option (#14636)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14636
Add a default CPU option for the pool()
Reviewed By: andrewwdye
Differential Revision:
D13281367
fbshipit-source-id:
92dbfce89c900a41731b6d1ff62bb97886c40f77
Francisco Massa [Wed, 5 Dec 2018 16:27:00 +0000 (08:27 -0800)]
Storage.clone maintains original device (#14751)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/14673
As pointed out by vishwakftw , the root case of the `deepcopy` issue was that `storage.clone()` would create a new storage in the default device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14751
Reviewed By: soumith
Differential Revision:
D13323061
Pulled By: fmassa
fbshipit-source-id:
bfe46ebd78f0b6cd9518c11d09de7849282ed2a2
svcscm [Wed, 5 Dec 2018 14:24:49 +0000 (06:24 -0800)]
Updating submodules
Reviewed By: yns88
fbshipit-source-id:
080e0034bd6353420383ac7b476af5a35eaba7c3
svcscm [Wed, 5 Dec 2018 10:53:49 +0000 (02:53 -0800)]
Updating submodules
Reviewed By: yns88
fbshipit-source-id:
e397238c7c477c4268e2dc89e530776fc89f18f8
Jongsoo Park [Wed, 5 Dec 2018 08:49:01 +0000 (00:49 -0800)]
include avx512vl to avx512 code path (#14733)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14733
We often also want to use AVX512VL instruction sets.
We already included AVX512F, AVX512DQ.
Skylake also has AVX512BW, AVX512CD we may want to later.
Reviewed By: duc0
Differential Revision:
D13317282
fbshipit-source-id:
82c8e401d82d5c3a5452fb4ccb6e5cb88d242bda
Adam Paszke [Wed, 5 Dec 2018 08:07:51 +0000 (00:07 -0800)]
Use AT_WARN for warnings in the JIT (#14770)
Summary:
Previously their implementation dispatched to prim::Print, which kept
printing the warnings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14770
Differential Revision:
D13327629
Pulled By: suo
fbshipit-source-id:
b9913f533d4530eb7c29146c39981ba7f72b6b68
Yinghai Lu [Wed, 5 Dec 2018 05:50:41 +0000 (21:50 -0800)]
Add output info when doing onnxGetBackendCompatibility (#14784)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14784
TSIA. To give more complete info to `onnxGetBackendCompatibility`.
Reviewed By: bertmaher, rdzhabarov
Differential Revision:
D13331989
fbshipit-source-id:
1064b93f7f474788f736e6f0c893dae915c6fb99
Adam Paszke [Wed, 5 Dec 2018 05:35:48 +0000 (21:35 -0800)]
Don't DCE PythonOp
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14773
Reviewed By: eellison
Differential Revision:
D13327673
Pulled By: suo
fbshipit-source-id:
236db3407c7eacac470530836e3d4d0dc323110c
Adam Paszke [Wed, 5 Dec 2018 04:35:51 +0000 (20:35 -0800)]
Improvements for symbolic AD (#14758)
Summary:
**Review only the last commit.**
This commit adds a few optimizations to AD, that let us dramatically
reduce the number of sizes we capture from forward.
We now:
- collapse chains of SumToSize
- avoid capturing sizes of tensors that are captured anyway
- more aggressively DCE the reverse code
- run CSE on the primal code to deduplicate `aten::size` calls
cc zou3519 zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14758
Differential Revision:
D13324440
Pulled By: zou3519
fbshipit-source-id:
45ccbc13605adcef2b461840c6089d3200000c72
Ailing Zhang [Wed, 5 Dec 2018 04:23:25 +0000 (20:23 -0800)]
Revert
D13289919: [pytorch][PR] [DataLoader] Refactor dataloader.py
Differential Revision:
D13289919
Original commit changeset:
d701bc7bb48f
fbshipit-source-id:
c350c491fefa98a0a7c0cf22cb832e78aeb15c3d
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