Summary:
It's been hard to understand how workers are launched and what code runs in the worker vs. main process, especially on Windows, which leads to many of our samples failing. This explains when workers run an how to make code work on Windows as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18091
Differential Revision: D15083766
Pulled By: soumith
fbshipit-source-id: 8a7e60defc8a72ec63874f657d7d5267d951dccf
Summary:
This PR adds TensorBoard logging support natively within PyTorch. It is based on the tensorboardX code developed by lanpa and relies on changes inside the tensorflow/tensorboard repo landing at https://github.com/tensorflow/tensorboard/pull/2065.
With these changes users can simply `pip install tensorboard; pip install torch` and then log PyTorch data directly to the TensorBoard protobuf format using
```
import torch
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
s1 = torch.rand(1)
writer.add_scalar('data/scalar1', s1[0], 0)
writer.close()
```
Design:
- `EventFileWriter` and `RecordWriter` from tensorboardX now live in tensorflow/tensorboard
- `SummaryWriter` and PyTorch-specific conversion from tensors, nn modules, etc. now live in pytorch/pytorch. We also support Caffe2 blobs and nets.
Action items:
- [x] `from torch.utils.tensorboard import SummaryWriter`
- [x] rename functions
- [x] unittests
- [x] move actual writing function to tensorflow/tensorboard in https://github.com/tensorflow/tensorboard/pull/2065
Review:
- Please review for PyTorch standard formatting, code usage, etc.
- Please verify unittest usage is correct and executing in CI
Any significant changes made here will likely be synced back to github.com/lanpa/tensorboardX/ in the future.
cc orionr, ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16196
Differential Revision: D15062901
Pulled By: orionr
fbshipit-source-id: 3812eb6aa07a2811979c5c7b70810261f9ea169e
Summary:
Also
1. Bump multiprocessing test timeout following python core tests
2. Fix one type of flakiness in `test_proper_exit`.
3. Add trace reporting when loader process hangs in `test_proper_exit` using `faulthandler`.
3. Give `test_proper_exit` another try.
I'll heavily retest this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19421
Differential Revision: D15063728
Pulled By: ezyang
fbshipit-source-id: 4e0d992622e11053c44a9ec237b88b9a28a4472c
Summary:
fix
- the order of `Arguments` in `RandomSampler` doc
- the meaningless check of `replacement`'s type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19113
Differential Revision: D15013081
Pulled By: ezyang
fbshipit-source-id: 39e367f42841de6814b1214eb9df7b75f14f747e
Summary:
* `torch.hub.list('pytorch/vision')` - show all available hub models in `pytorch/vision`
* `torch.hub.show('pytorch/vision', 'resnet18')` - show docstring & example for `resnet18` in `pytorch/vision`
* Moved `torch.utils.model_zoo.load_url` to `torch.hub.load_state_dict_from_url` and deprecate `torch.utils.model_zoo`
* We have too many env to control where the cache dir is, it's not very necessary. I actually want to unify `TORCH_HUB_DIR`, `TORCH_HOME` and `TORCH_MODEL_ZOO`, but haven't done it. (more suggestions are welcome!)
* Simplify `pytorch/vision` example in doc, it was used to show how how hub entrypoint can be written so had some confusing unnecessary args.
An example of hub usage is shown below
```
In [1]: import torch
In [2]: torch.hub.list('pytorch/vision', force_reload=True)
Downloading: "https://github.com/pytorch/vision/archive/master.zip" to /private/home/ailzhang/.torch/hub/master.zip
Out[2]: ['resnet18', 'resnet50']
In [3]: torch.hub.show('pytorch/vision', 'resnet18')
Using cache found in /private/home/ailzhang/.torch/hub/vision_master
Resnet18 model
pretrained (bool): a recommended kwargs for all entrypoints
args & kwargs are arguments for the function
In [4]: model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
Using cache found in /private/home/ailzhang/.torch/hub/vision_master
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18758
Differential Revision: D14883651
Pulled By: ailzhang
fbshipit-source-id: 6db6ab708a74121782a9154c44b0e190b23e8309
Summary:
Previously, when a user built PyTorch from source, but set the version string manually to be binary-formatted, it would've simply used CXX11_ABI=0 incorrectly.
We have this information available at runtime with `torch._C._GLIBCXX_USE_CXX11_ABI`, so this PR improves the situation by simply using that information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18994
Differential Revision: D14839393
Pulled By: soumith
fbshipit-source-id: ca92e0810b29ffe688be82326e02a64a5649a3ad
Summary:
I haven't had a chance to rigorously try these out yet so don't merge yet.
Closes#18725.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18963
Differential Revision: D14832897
Pulled By: ezyang
fbshipit-source-id: 4780e7a34126bc66ddbfd9d808dfc9e0edd77e68
Summary:
Hi. It seems that when building CPP-extensions with CUDA for Windows, an `extra_cuda_cflags` options are not properly forwarded to `nvcc`.
Use of extra CUDA options is necessary to build, for instance, a InplaceABN (https://github.com/mapillary/inplace_abn), which requires `--expt-extended-lambda` option.
This PR adds one line that correctly appends `extra_cuda_cflags`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18638
Differential Revision: D14704270
Pulled By: ezyang
fbshipit-source-id: e1e330d193d9afd5707a5437a74c0499460d2b90
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
...because gcc will have failures with very strange error messages
if you do.
This affects people with Debian/Ubuntu-provided NVCC, the PR should
not change anything for anyone else.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18127
Differential Revision: D14504386
Pulled By: soumith
fbshipit-source-id: 1aea168723cdc71cdcfffb3193ee116108ae755e
Summary:
Currently, we cannot run a checkpointed function with None argument.
```python
out = torch.utils.checkpoint.checkpoint(run_fn, input_var, None)
```
```
File "/home/tunz/anaconda3/envs/torchdev/lib/python3.7/site-packages/torch/utils/checkpoint.py", line 14, in detach_variable
x = inp.detach()
AttributeError: 'NoneType' object has no attribute 'detach'
```
This PR makes checkpoint function to safely handle None argument.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17969
Differential Revision: D14475148
Pulled By: ezyang
fbshipit-source-id: 9afe9e9aac511a6df1e1620e9ac341536890d451
Summary:
Indices in Subset were stored as tensors earlier
passing as list in random_split to ensure integer indexing
fixes: #17466
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17649
Differential Revision: D14400250
Pulled By: soumith
fbshipit-source-id: cd20a959f33773c4babf8e861ea37ec61c2713a0
Summary:
Currently, the fake tqdm implementation requires an input (whereas real tqdm does not).
This caused a problem in torchvision (https://github.com/pytorch/vision/pull/770), and seems likely to cause minor irritations elsewhere.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17636
Differential Revision: D14296530
Pulled By: ezyang
fbshipit-source-id: bc077d898773c93dab34c985a7b30525a43e558a
Summary:
Currently, when you pass a negative index to a `Dataset` created with `ConcatDataset`, it simply passes that index to the first dataset in the list. So if, for example, we took `concatenated_dataset[-1]`, this will give us the last entry of the *first* dataset, rather than the last entry of the *last* dataset, as we would expect.
This is a simple fix to support the expected behavior for negative indices.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15756
Reviewed By: ezyang
Differential Revision: D14081811
Pulled By: fmassa
fbshipit-source-id: a7783fd3fd9e1a8c00fd076c4978ca39ad5a8a2a
Summary:
Fix issue #12174 for Mac OSX.
PS: This is a duplicate of PR #16968 that got messed up. Sorry for the confusion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16999
Differential Revision: D14050669
Pulled By: zou3519
fbshipit-source-id: a4594c03ae8e0ca91a4836408b6c588720162c9f
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
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
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
Summary:
The current implementation of the `torch.utils.model_zoo.load_url`
function is prone to a race condition when creating the directory in
which it saves the loaded models, since it checks whether the
directory exists and then creates it in two separate steps. The
directory can be created after the check was made but before we
attempt to create the directory, resulting in an unhandled exception.
Instead, try to create the directory directly, and do nothing if it
already exists.
Note: for Python versions ≥ 3.2, we could simply use the
`exist_ok=True` flag on `os.makedirs`, but this is unavailable in
Python 2.7.
Signed-off-by: Antoine Busque <antoine.busque@elementai.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16578
Differential Revision: D13886470
Pulled By: soumith
fbshipit-source-id: 88815c8a65eec96caea32d6e9a7f83802502fdb9
Summary:
Rehash of previous attempts. This tries a different approach where we accept the install as specified in cmake (leaving bin/ include/ and lib/ alone), and then try to adjust the rest of the files to this more standard layout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16414
Differential Revision: D13863635
Pulled By: zdevito
fbshipit-source-id: 23725f5c64d7509bf3ca8f472dcdcad074de9828
Summary:
Some HTTP servers dont return Content-Length, account for that
Fixes: https://github.com/pytorch/pytorch/issues/16152
Differential Revision: D13858882
Pulled By: soumith
fbshipit-source-id: e4293e9368ed4c87548d22adec1ce0c25ea4bd8f
Summary:
1. Improve error message for better debugging info
2. Increase timeout
3. Also apply the windows worker failure detection mechanism on non-Windows platforms, for better robustness
Attempt to fix#14501
cc ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16249
Differential Revision: D13784702
Pulled By: ezyang
fbshipit-source-id: 09a7cff83ab9edce561ed69f9fb555ab35d1275f
Summary:
I fixed a very small extra parenthesis in a doctest.
I'm also going to use this issue as a place to propose the eventual inclusion of xdoctest (a pip installable library I wrote) in pytorch's test suite. I think there are a lot of problems with Python's built in doctest module, and I've built xdoctest to fix them. I would love for my project to get some exposure and its addition to PyTorch may benefit both projects. Please see the readme for more details on what xdoctest brings to the table over the builtin doctest module: https://github.com/Erotemic/xdoctest
I came across this small syntax error when working on ensuring xdoctest was compatible with pytorch. It isn't 100% there yet, but I'm working on it. My goal is to ensure that xdoctest is 100% compatible with all of torch's doctest out-of-the-box before writing up the PR. I'm also airing the idea out-loud before I commit too much time into this (or get my hopes up), so I'm attaching this little blurb to a no-brainer-merge PR to (1) demonstrate a little bit of value (because xdoctest flagged this syntax error) and (2) see how its received.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15646
Differential Revision: D13606111
Pulled By: soumith
fbshipit-source-id: d4492801a38ee0ae64ea0326a83239cee4d811a4
Summary:
Since #1323 tensors are shared with shared memory, but this feature is not active for numpy.
This PR fix this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14534
Differential Revision: D13561649
Pulled By: soumith
fbshipit-source-id: b6bc9e99fb91e8b675c2ef131fba9fa11c1647c0
Summary:
Same as #14668, and was approved there.
ailzhang , please apply this patch to Horizon's `data_streamer.py`: https://gist.github.com/SsnL/020fdb3d6b7016d81b6ba1d04cc41459 Thank you!
Below is the original description at #14668:
As I am working on tasks in https://github.com/pytorch/pytorch/issues/13023, I realized how unreadable the code is because all functions to be run in multiprocessing must be at top global level. Adding more functionalities to `dataloader.py` will only make things worse.
So in this PR, I refactor `dataloader.py` and move much of it into `data._utils`. E.g., the `_worker_loop` and related methods are now in `data._utils.worker`, signal handling code in `data._utils.signal_handling`, collating code in `data._utils.collate`, etc. This split, IMHO, makes code much clearer. I will base my future changes to DataLoader on top of this.
No functionality is changed, except that I added `torch._six.queue`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15331
Reviewed By: yf225
Differential Revision: D13503120
Pulled By: ailzhang
fbshipit-source-id: 94df16b4d80ad1102c437cde0d5a2e62cffe1f8e
Summary:
This PR enables C++ frontend modules to be bound into Python and added as submodules of Python modules. For this, I added lots of pybind11 bindings for the `torch::nn::Module` class, and modified the `torch.nn.Module` class in Python to have a new Metaclass that makes `isinstance(m, torch.nn.Module)` return true when `m` is a C++ frontend module. The methods and fields of C++ modules are bound in such a way that they work seamlessly as submodules of Python modules for most operations (one exception I know of: calling `.to()` ends up calling `.apply()` on each submodule with a Python lambda, which cannot be used in C++ -- this may require small changes on Python side).
I've added quite a bunch of tests to verify the bindings and equality with Python. I think I should also try out adding a C++ module as part of some large PyTorch module, like a WLM or something, and see if everything works smoothly.
The next step for inter-op across our system is ScriptModule <-> C++ Frontend Module inter-op. I think this will then also allow using C++ frontend modules from TorchScript.
apaszke zdevito
CC dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13481
Differential Revision: D12981996
Pulled By: goldsborough
fbshipit-source-id: 147370d3596ebb0e94c82cec92993a148fee50a7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14278
In this commit, we make checkpoint_sequential work for models with multiple tensor inputs. Previously, it only processed the first tensor and ignored the rest.
We introduce a new test in test/test_utils.py that replicates the issue referenced in this [GitHub issue](https://github.com/pytorch/pytorch/issues/11093), and we make sure that the test passes by changing the behavior of checkpoint_sequential to process all input tensors.
Reviewed By: ezyang
Differential Revision: D13144672
fbshipit-source-id: 24f58233a65a0f5b80b89c8d8cbced6f814004f7
