Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
Summary:
Copy-paste comment from code for reasoning:
```
# NOTE [ IterableDataset and __len__ ]
#
# For `IterableDataset`, `__len__` could be inaccurate when one naively
# does multi-processing data loading, since the samples will be duplicated.
# However, no real use case should be actually using that behavior, so
# it should count as a user error. We should generally trust user
# code to do the proper thing (e.g., configure each replica differently
# in `__iter__`), and give us the correct `__len__` if they choose to
# implement it (this will still throw if the dataset does not implement
# a `__len__`).
#
# To provide a further warning, we track if `__len__` was called on the
# `DataLoader`, save the returned value in `self._len_called`, and warn
# if the iterator ends up yielding more than this number of samples.
```
Fixes https://github.com/pytorch/pytorch/issues/30184
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23587
Differential Revision: D18852625
Pulled By: ailzhang
fbshipit-source-id: aea8d4d70c7f21aaa69b35908a6f43026493d826
Summary:
Fixes https://github.com/pytorch/pytorch/issues/28389
Intel's OpenMP implementation sets the thread affinity on the first call to an OpenMP function after a fork. By adding an atfork handler we can force this to happen before a user tries to set the affinity in their own DataLoader `worker_init_fn`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29006
Differential Revision: D18782456
Pulled By: ezyang
fbshipit-source-id: ce0b515256da0cf18ceb125e0cdec99a3311bbd3
Summary:
One fewer legacy decorator cluttering the test suite.
Functions relying on this decorator were updated or, in the case of test_sparse, the test suite was put back on double by default.
Note: this PR is blocked on https://github.com/pytorch/pytorch/issues/27599.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27628
Differential Revision: D17896254
Pulled By: mruberry
fbshipit-source-id: 13d460301f50ef4af7a660372432108164c0de1f
Summary:
This PR stop common_utils.py from setting the default tensor type when it's imported. See issue https://github.com/pytorch/pytorch/issues/27355. This is a frequent source of confusion for test writers.
Many tests relied on this setting (whether they knew it or not), and this PR also updates the test suite to pass without common_utils.py setting the default tensor type. Some larger test files now set the default floating dtype themselves, however. These test files are:
- test_autograd.py
- test_distributions.py
- test_jit.py
- test_nn.py
This is still a significant improvement from today, however. First, these files set the default floating dtype much more clearly than importing it from common_utils. Second, the rest of the test suite no longer sets this globally. Third, this PR is a springboard to updating those tests, too. In particular, as tests are made generic they can be moved aways from relying on this global setting.
Notable technical changes in this PR are:
- Significant updates to test_torch.py to make it pass without setting the default floating dtype globally.
- The default_floating_dtype decorator is now defined in common_utils, a couple versions of this operator were defined in test files previously.
- test_torch-specific parts of common_utils were refactored into test_torch.
- tensor creation methods in common_utils were updated to accept an optional dtype and device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27444
Differential Revision: D17795235
Pulled By: mruberry
fbshipit-source-id: 7f77271c0c836e69f183ad9057a2c4b29f09d2e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25005
Seeing a bunch of failures in TSAN mostly with the following error:
```
ThreadSanitizer: starting new threads after multi-threaded fork is not
supported. Dying (set die_after_fork=0 to override)
```
TSAN is unsafe to use in a multi-threaded program after fork() and setting
die_after_fork can lead to deadlocks. As a result, I'm disabling tsan.
ghstack-source-id: 88765698
Differential Revision: D16954347
fbshipit-source-id: 18895cd82b5052938284b46479d8470af2d74a06
Summary:
1. Prefixed underscores to any `DataLoaderIter` attribute that is not part of the data loader ctor argument list.
2. Prefixed `DataLoader.dataset_kind` with underscore because it only makes sense with the private enum `_DatasetKind`, and is an implementation detail.
3. Disallow setting `DataLoader.dataset` and `DataLoader.batch_sampler` after initializing a `DataLoader` because they affect other attributes in `__init__`.
These changes should not have major BC breaking effect since the big changes are on the iterator class and most users don't even store it. I GitHub searched `pin_memory_thread` and (while I didn't look through all result pages) results I see are forks of pytorch and blog posts on how data loader works.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23744
Differential Revision: D16732507
Pulled By: ezyang
fbshipit-source-id: 9f04d000b4200b8047f31eaa3473780b66cebd26
Summary:
When an exception occurs in one of the modules passed to `parallel_apply()`, it is caught and re-raised in the main thread. This preserves the original exception type and message, but has the traceback point at the position where it's re-raised, rather than the original point of failure.
This PR saves the exception information required to generate the traceback, and includes the original traceback in the message of the exception raised in the main thread.
Before:
```
...
File ".../torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File ".../torch/nn/parallel/parallel_apply.py", line 84, in parallel_apply
raise output
RuntimeError: expected type torch.FloatTensor but got torch.cuda.FloatTensor
```
After:
```
...
File ".../torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File ".../torch/nn/parallel/parallel_apply.py", line 88, in parallel_apply
''.join(traceback.format_exception(*exc_info)))
RuntimeError: Caught exception in replica 0. Original traceback and message:
Traceback (most recent call last):
...
File "../models/foo.py", line 319, in bar
baz = asdf / ghij[:, np.newaxis]
RuntimeError: expected type torch.FloatTensor but got torch.cuda.FloatTensor
```
I took care to raise an exception of the original type (in case the main code checks for that), but replaced the message. It helped me find a bug that did not occur outside `data_parallel()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18055
Differential Revision: D16444972
Pulled By: zhangguanheng66
fbshipit-source-id: ec436c9d4677fad18106a8046cfa835a20a101ce
Summary:
I learned from https://github.com/pytorch/pytorch/pull/22058 that `worker_kill` is just flaky, regardless of `hold_iter_reference`. So let's disable it altogether for now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22208
Differential Revision: D15990307
Pulled By: soumith
fbshipit-source-id: d7d3f4fe7eaac4987f240cb8fd032c73a84157d7
Summary:
This is a modified version of https://github.com/pytorch/pytorch/pull/14705 since commit structure for that PR is quite messy.
1. Add `IterableDataset`.
3. So we have 2 data loader mods: `Iterable` and `Map`.
1. `Iterable` if the `dataset` is an instance of `IterableDataset`
2. `Map` o.w.
3. Add better support for non-batch loading (i.e., `batch_size=None` and `batch_sampler=None`). This is useful in doing things like bulk loading.
3. Refactor `DataLoaderIter` into two classes, `_SingleProcessDataLoaderIter` and `_MultiProcessingDataLoaderIter`. Rename some methods to be more generic, e.g., `get_batch` -> `get_data`.
4. Add `torch.utils.data.get_worker_info` which returns worker information in a worker proc (e.g., worker id, dataset obj copy, etc.) and can be used in `IterableDataset.__iter__` and `worker_init_fn` to do per-worker configuration.
5. Add `ChainDataset`, which is the analog of `ConcatDataset` for `IterableDataset`.
7. Import torch.utils.data in `torch/__init__.py`
9. data loader examples and documentations
10. Use `get_worker_info` to detect whether we are in a worker process in `default_collate`
Closes https://github.com/pytorch/pytorch/issues/17909, https://github.com/pytorch/pytorch/issues/18096, https://github.com/pytorch/pytorch/issues/19946, and some of https://github.com/pytorch/pytorch/issues/13023
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19228
Reviewed By: bddppq
Differential Revision: D15058152
fbshipit-source-id: 9e081a901a071d7e4502b88054a34b450ab5ddde
Summary:
This doesn't have `strace` yet. But still have `faulthandler` to print stack traces at hanging. Also part of an attempt to isolate changes from #19228 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20166
Differential Revision: D15536504
Pulled By: ezyang
fbshipit-source-id: fe6e6e2e9899f30d8167436d7bc62b42883a3356
Summary:
This is an attempt to isolate unrelated changes from #19228 for easier review.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20150
Differential Revision: D15314891
Pulled By: ezyang
fbshipit-source-id: 8c429747ba83ad5aca4cdd8f8086bcf65a326921
Summary:
cc nairbv
All failures I have seen are of this combination. So let's just disable it for all cases. After #20063 I find it failing for py3 once.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20172
Differential Revision: D15266527
Pulled By: nairbv
fbshipit-source-id: afb9389dfc54a0878d52975ffa37a0fd2aa3a735
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:
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18291
ghimport-source-id: d6e95e899bd320407967df41435801e54864ba62
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18292 Add test for #17271 (torch.exp incorrect for 2**31 size tensor)
* **#18291 Correctly call superclass setUp in TestCase subclasses.**
This makes PYTORCH_TEST_SKIP_FAST work correctly for more
tests, reducing the wasted testing effort on our slow_test job.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14567643
fbshipit-source-id: 40cf1d6556e0dd0a0550ff3d9ffed8b6000f8191
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:
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 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
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:
Use case:
Some data loader tests rely on `psutil` (a third party lib). So they are guarded by `skipIf`. But we want to always test them on CI envs. With `IS_PYTORCH_CI`, we can raise if `psutil` is not found.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16006
Reviewed By: ezyang
Differential Revision: D13673957
Pulled By: yf225
fbshipit-source-id: c63a7138093f45333c0b371fed0bcc88b67f2a22
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:
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/14668
Reviewed By: soumith
Differential Revision: D13289919
Pulled By: ailzhang
fbshipit-source-id: d701bc7bb48f5dd7b163b5be941a9d27eb277a4c
Summary:
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 present PR adds the ability for the user to pass a `pin_fn` alongside any custom `collate_fn` to handle such custom types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14171
Differential Revision: D13166669
Pulled By: soumith
fbshipit-source-id: ca965f9841d4a259b3ca4413c8bd0d8743d433ab
Summary:
This should resolve the issue on ppc64le getting FAIL: test_proper_exit (__main__.TestDataLoader). This only happens when the CI build machine is very busy and fails with a timeout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14107
Differential Revision: D13103859
Pulled By: soumith
fbshipit-source-id: 268be80b59840853c5025f3211af272f68608fe5
