Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29762
Rename this API as discussed, since it's use cases extend beyond only
model parallelism.
ghstack-source-id: 94020627
Test Plan: Unit tests pass
Differential Revision: D18491743
fbshipit-source-id: d07676bb14f072c64da0ce99ee818bcc582efc57
Summary:
Small fixes to rpc docs:
- mark as experimental and subject to change
- Reference the distributed autograd design document in pytorch notes page.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29857
Differential Revision: D18526252
Pulled By: rohan-varma
fbshipit-source-id: e09757fa60a9f8fe9c76a868a418a1cd1c300eae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29927
With the docs page now up, we can update the links in the design doc
to point to the docs page.
ghstack-source-id: 94055423
Test Plan: waitforbuildbot
Differential Revision: D18541878
fbshipit-source-id: f44702d9a8296ccc0a5d58d56c3b6dc8a822b520
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29175
Updates our docs to include a design doc for distributed autograd.
Currently, this doc only covers the FAST mode algorithm. The Smart mode
algorithm section just refers to the original RFC.
There is a section for Distributed Optimizer that we can complete once we've
finalized the API for the same.
ghstack-source-id: 93701129
Test Plan: look at docs.
Differential Revision: D18318949
fbshipit-source-id: 670ea1b6bb84692f07facee26946bbc6ce8c650c
Summary:
cumsum/cumprod perform their own respective operations over a desired dimension, but there is no reduction in dimensions in the process, i.e. they are not reduction operations and hence just keep the input names of the tensor on which the operation is performed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29453
Differential Revision: D18455683
Pulled By: anjali411
fbshipit-source-id: 9e250d3077ff3d8f3405d20331f4b6ff05151a28
Summary:
Fixes https://github.com/pytorch/pytorch/issues/28658
I have added the link to the docs for `flatten_parameters`.
RNNBase is a superclass of RNN, LSTM and GRM classes. Should I add a link to `flatten_parameters()` in those sections as well ?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29196
Differential Revision: D18326815
Pulled By: ezyang
fbshipit-source-id: 4239019112e77753a0820aea95c981a2c868f5b0
Summary:
At the encouragement of Pyro developers and https://github.com/pytorch/pytorch/issues/13811, I have opened this PR to move the (2D) von Mises distribution upstream.
CC: fritzo neerajprad
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17168
Differential Revision: D18249048
Pulled By: ezyang
fbshipit-source-id: 3e6df9006c7b85da7c4f55307c5bfd54c2e254e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27254
`MultiplicativeLR` consumes a function providing the multiplicative factor at each epoch. It mimics `LambdaLR` in its syntax.
Test Plan: Imported from OSS
Differential Revision: D17728088
Pulled By: vincentqb
fbshipit-source-id: 1c4a8e19a4f24c87b5efccda01630c8a970dc5c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27927
This fixes
`WARNING: html_static_path entry '_images' does not exist`
by removing '_images' from conf.py. As far as I can tell, '_images' in
`html_static_path` is only necessary if images already exist in the
`_images` folder; otherwise, sphinx is able to auto-generate _images
into the build directory and populate it correctly.
Test Plan: - build and view the docs locally.
Differential Revision: D17915109
Pulled By: zou3519
fbshipit-source-id: ebcc1f331475f52c0ceadd3e97c3a4a0d606e14b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27850
Many of these are real problems in the documentation (i.e., link or
bullet point doesn't display correctly).
Test Plan: - built and viewed the documentation for each change locally.
Differential Revision: D17908123
Pulled By: zou3519
fbshipit-source-id: 65c92a352c89b90fb6b508c388b0874233a3817a
Summary:
People get confused with partial support otherwise: https://github.com/pytorch/pytorch/issues/27811#27729
Suggestions on where else put warnings are welcomed (probably in tutorials - cc SethHWeidman )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27829
Differential Revision: D17910931
Pulled By: dzhulgakov
fbshipit-source-id: 37a169a4bef01b94be59fe62a8f641c3ec5e9b7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27782
Warnings show up when running `make html` to build documentation. All of
the warnings are very reasonable and point to bugs in our docs. This PR
attempts to fix most of those warnings.
In the future we will add something to the CI that asserts that there
are no warnings in our docs.
Test Plan: - build and view changes locally
Differential Revision: D17887067
Pulled By: zou3519
fbshipit-source-id: 6bf4d08764759133b20983d6cd7f5d27e5ee3166
Summary:
resolves issues:
https://github.com/pytorch/pytorch/issues/27703
Updates to index for v1.3.0
* add javasphinx to the required sphinx plugins
* Update "Package Reference" to "Python API"
* Add in torchaudio and torchtext reference links so they show up across all docs not just the main page
* Add "Other Languages" section, add in C++ docs, add in Javadocs
* Add link to XLA docs under Notes: http://pytorch.org/xla/
this includes changes to:
docs/source/conf.py
docs/source/index.rst
docs/source/nn.rst
docs/requirements.txt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27721
Differential Revision: D17881973
Pulled By: jlin27
fbshipit-source-id: ccc1e9e4da17837ad99d25df997772613f76aea8
Summary:
- Update torch.rst to remove certain autofunction calls
- Add reference to Quantization Functions section in nn.rst
- Update javadocs for v1.3.0
- Update index.rst:
- Update "Package Reference" to "Python API"
- Add in torchaudio and torchtext reference links so they show up across all docs not just the main page
- Add "Other Languages" section, add in C++ docs, add in Javadocs
- Add link to XLA docs under Notes: http://pytorch.org/xla/
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27676
Differential Revision: D17850696
Pulled By: brianjo
fbshipit-source-id: 3de146f065222d1acd9a33aae3b543927a63532a
Summary:
This was written by Raghu, Jessica, Dmytro and myself.
This PR will accumulate additional changes (there are a few more things we need to add to this actual rst file). I'll probably add the related image files to this PR as well.
I'm breaking draft PR https://github.com/pytorch/pytorch/pull/27553 into more easily digestible pieces.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27559
Differential Revision: D17843414
Pulled By: gottbrath
fbshipit-source-id: 434689f255ac1449884acf81f10e0148d0d8d302
Summary:
Added Complex support with AVX to unary ops and binary ops.
I need to add nan propagation to minimum() and maximum() in the future.
In-tree changes to pytorch to support complex numbers are being submitted here.
Out-of-tree support for complex numbers is here: pytorch-cpu-strided-complex extension
Preliminary Benchmarks are here.
I tried rrii and riri and found that riri is better in most situations.
Divide is very slow because you can't reduce 1/(x+y)
Sqrt is also very slow.
Reciprocal could be sped up after I add conj()
Everything else is typically within 20% of the real number performance.
Questions:
Why does macOS not support mil? #if AT_MKL_ENABLED() && !defined(__APPLE__) in vml.h. MKL does support some complex operations like Abs, so I was curious about trying it.
Is MKL just calling AVX?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26500
Differential Revision: D17835431
Pulled By: ezyang
fbshipit-source-id: 6746209168fbeb567af340c22bf34af28286bd54
Summary:
According to https://github.com/pytorch/pytorch/issues/27285 , seems we do not intend to use shebang as an indication of Python version, thus
we enable EXE001 flake8 check.
For violations, we either remove shebang from non-executable Python scripts or grant them executable permission.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27560
Differential Revision: D17831782
Pulled By: ezyang
fbshipit-source-id: 6282fd3617b25676a6d959af0d318faf05c09b26
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27173
`docs/source/named_tensor.rst` is the entry point; most users will land
either here or the named tensor tutorial when looking to use named
tensors. We should strive to make this as readable, concise, and understandable
as possible.
`docs/source/name_inference.rst` lists all of the name inference rules.
It should be clear but it's hard to make it concise.
Please let me know if anything doesn't make sense and please propose
alternative wordings and/or restructuring to improve the documentation.
This should ultimately get cherry-picked into the 1.3 branch as one
monolithic commit so it would be good to get all necessary changes made
in this PR and not have any follow ups.
Test Plan: - built and reviewed locally with `cd docs/ && make html`.
Differential Revision: D17763046
Pulled By: zou3519
fbshipit-source-id: c7872184fc4b189d405b18dad77cad6899ae1522
Summary:
Adds comprehensive memory instrumentation to the CUDA caching memory allocator.
# Counters
Added comprehensive instrumentation for the following stats:
- Allocation requests (`allocation`)
- Allocated memory (`allocated_bytes`)
- Reserved segments from cudaMalloc (`segment`)
- Reserved memory (`reserved_bytes`)
- Active memory blocks (`active`)
- Active memory (`active_bytes`)
- Inactive, non-releasable blocks (`inactive_split`)
- Inactive, non-releasable memory (`inactive_split_bytes`)
- Number of failed cudaMalloc calls that result in a cache flush and retry (`cuda_malloc_retries`)
- Number of OOMs (`num_ooms`)
Except for the last two, these stats are segmented between all memory, large blocks, and small blocks. Along with the current value of each stat, historical counts of allocs/frees as well as peak usage are tracked by the allocator.
# Snapshots
Added the capability to get a "memory snapshot" – that is, to generate a complete dump of the allocator block/segment state.
# Implementation: major changes
- Added `torch.cuda.memory_stats()` (and associated C++ changes) which returns all instrumented stats as a dictionary.
- Added `torch.cuda.snapshot()` (and associated C++ changes) which returns a complete dump of the allocator block/segment state as a list of segments.
- Added memory summary generator in `torch.cuda.memory_summary()` for ease of client access to the instrumentation stats. Potentially useful to dump when catching OOMs. Sample output here: https://pastebin.com/uKZjtupq
# Implementation: minor changes
- Add error-checking helper functions for Python dicts and lists in `torch/csrc/utils/`.
- Existing memory management functions in `torch.cuda` moved from `__init__.py` to `memory.py` and star-imported to the main CUDA module.
- Add various helper functions to `torch.cuda` to return individual items from `torch.cuda.memory_stats()`.
- `torch.cuda.reset_max_memory_cached()` and `torch.cuda.reset_max_memory_allocated()` are deprecated in favor of `reset_peak_stats`. It's a bit difficult to think of a case where only one of those stats should be reset, and IMO this makes the peak stats collectively more consistent.
- `torch.cuda.memory_cached()` and `torch.cuda.max_memory_cached()` are deprecated in favor of `*memory_reserved()`.
- Style (add access modifiers in the allocator class, random nit fixes, etc.)
# Testing
- Added consistency check for stats in `test_cuda.py`. This verifies that the data from `memory_stats()` is faithful to the data from `snapshot()`.
- Ran on various basic workflows (toy example, CIFAR)
# Performance
Running the following speed benchmark: https://pastebin.com/UNndQg50
- Before this PR: 45.98 microseconds per tensor creation
- After this PR: 46.65 microseconds per tensor creation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27361
Differential Revision: D17758747
Pulled By: jma127
fbshipit-source-id: 5a84e82d696c40c505646b9a1b4e0c3bba38aeb6
Summary:
10 lines of error context (on both sides) is overkill, especially now
that we have line numbers. With a compilation stack of a couple
functions, it becomes a pain to scroll to the top of the stack to see
the real error every time.
This also fixes class names in the compilation stack to a format of
`ClassName.method_name` instead of the the full qualified name
Old output
```
clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor):
Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'.
:
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20
top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level)
batch_idx = torch.arange(num_images, device=device)[:, None]
objectness = objectness[batch_idx, top_n_idx]
levels = levels[batch_idx, top_n_idx]
proposals = proposals[batch_idx, top_n_idx]
final_boxes = []
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
# non-maximum suppression, independently done per level
keep = box_ops.batched_nms(boxes, scores, lvl, self.nms_thresh)
# keep only topk scoring predictions
keep = keep[:self.post_nms_top_n]
boxes, scores = boxes[keep], scores[keep]
final_boxes.append(boxes)
final_scores.append(scores)
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8
num_images = len(anchors)
num_anchors_per_level = [o[0].numel() for o in objectness]
objectness, pred_bbox_deltas = \
concat_box_prediction_layers(objectness, pred_bbox_deltas)
# apply pred_bbox_deltas to anchors to obtain the decoded proposals
# note that we detach the deltas because Faster R-CNN do not backprop through
# the proposals
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
losses = {}
if self.training:
assert targets is not None
labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets)
regression_targets = self.box_coder.encode(matched_gt_boxes, anchors)
loss_objectness, loss_rpn_box_reg = self.compute_loss(
objectness, pred_bbox_deltas, labels, regression_targets)
losses = {
'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8
"""
if self.training and targets is None:
raise ValueError("In training mode, targets should be passed")
original_image_sizes = [(img.shape[-2], img.shape[-3]) for img in images]
images, targets = self.transform(images, targets)
features = self.backbone(images.tensors)
if isinstance(features, torch.Tensor):
features = OrderedDict([(0, features)])
proposals, proposal_losses = self.rpn(images, features, targets)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
losses = {}
losses.update(detector_losses)
losses.update(proposal_losses)
# TODO: multiple return types??
# if self.training:
```
New output
```
RuntimeError:
clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor):
Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'.
:
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
losses = {}
'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8
if isinstance(features, torch.Tensor):
features = OrderedDict([(0, features)])
proposals, proposal_losses = self.rpn(images, features, targets)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess
```
](https://our.intern.facebook.com/intern/diff/17560963/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26765
Pulled By: driazati
Differential Revision: D17560963
fbshipit-source-id: e463548744b505ca17f0158079b80e08fda47d49
Summary:
Adds the method `add_hparams` to `torch.utils.tensorboard` API docs. Will want to have this in PyTorch 1.3 release.
cc sanekmelnikov lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27344
Differential Revision: D17753689
Pulled By: orionr
fbshipit-source-id: cc8636e0bdcf3f434444cd29471c62105491039d
Summary:
Resubmit of https://github.com/pytorch/pytorch/pull/25980.
Our old serialization was in tar (like `resnet18-5c106cde.pth` was in this format) so let's only support automatically unzip if checkpoints are zipfiles.
We can still manage to get it work with tarfile, but let's delay it when there's an ask.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26723
Differential Revision: D17551795
Pulled By: ailzhang
fbshipit-source-id: 00b4e7621f1e753ca9aa07b1fe356278c6693a1e
Summary:
This PR does a few small improvements to hub:
- add support `verbose` option in `torch.load`. Note that this mutes hitting cache message but keeps the message of first download as suggested. fixes https://github.com/pytorch/pytorch/issues/24791
- add support loading state dict from tar file or zip file in `torch.hub.load_state_dict_from_url`.
- add `torch.hub.download_url_to_file` as public API, and add BC bit for `_download_url_to_file`.
- makes hash check in filename optional through `check_hash`, many users don't have control over the naming, relaxing this constraint could potentially avoid duplicating download code on user end.
- move pytorch CI off `pytorch/vision` and use `ailzhang/torchhub_example` as a dedicated test repo. fixes https://github.com/pytorch/pytorch/issues/25865
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25980
Differential Revision: D17495679
Pulled By: ailzhang
fbshipit-source-id: 695df3e803ad5f9ca33cfbcf62f1a4f8cde0dbbe
Summary:
Changelog:
- Remove `torch.gels` which was deprecated in v1.2.0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26480
Test Plan: - No tests were changed and all callsites for `torch.gels` where modified to `torch.lstsq` when `torch.lstsq` was introduced
Differential Revision: D17527207
Pulled By: zou3519
fbshipit-source-id: 28e2fa3a3bf30eb6b9029bb5aab198c4d570a950
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26240
In particular adds support for empty/empty_like which is needed for memory layouts to work.
Test Plan: Imported from OSS
Differential Revision: D17443220
Pulled By: dzhulgakov
fbshipit-source-id: 9c9e25981999c0edaf40be104a5741e9c62a1333
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25263
This adds an api to return true in script and false in eager, which together with ignore allows guarding of not yet supported JIT features. Bikeshedding requested please.
cc zou3519
```
def foo():
if not torch.jit.is_scripting():
return torch.linear(...)
else:
return addmm(...)
```
Test Plan: Imported from OSS
Differential Revision: D17272443
Pulled By: eellison
fbshipit-source-id: de0f769c7eaae91de0007b98969183df93a91f42
Summary:
Improve handling of mixed-type tensor operations.
This PR affects the arithmetic (add, sub, mul, and div) operators implemented via TensorIterator (so dense but not sparse tensor ops).
For these operators, we will now promote to reasonable types where possible, following the rules defined in https://github.com/pytorch/pytorch/issues/9515, and error in cases where the cast would require floating point -> integral or non-boolean to boolean downcasts.
The details of the promotion rules are described here:
https://github.com/nairbv/pytorch/blob/promote_types_strict/docs/source/tensor_attributes.rst
Some specific backwards incompatible examples:
* now `int_tensor * float` will result in a float tensor, whereas previously the floating point operand was first cast to an int. Previously `torch.tensor(10) * 1.9` => `tensor(10)` because the 1.9 was downcast to `1`. Now the result will be the more intuitive `tensor(19)`
* Now `int_tensor *= float` will error, since the floating point result of this operation can't be cast into the in-place integral type result.
See more examples/detail in the original issue (https://github.com/pytorch/pytorch/issues/9515), in the above linked tensor_attributes.rst doc, or in the test_type_promotion.py tests added in this PR:
https://github.com/nairbv/pytorch/blob/promote_types_strict/test/test_type_promotion.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22273
Reviewed By: gchanan
Differential Revision: D16582230
Pulled By: nairbv
fbshipit-source-id: 4029cca891908cdbf4253e4513c617bba7306cb3
Summary:
All of the code examples should now run as unit tests, save for those
that require interaction (i.e. show `pdb` usage) and those that use
CUDA.
`save` had to be moved before `load` in `jit/__init__.py` so `load`
could use the file generated by `save`
](https://our.intern.facebook.com/intern/diff/17192417/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25668
Pulled By: driazati
Differential Revision: D17192417
fbshipit-source-id: 931b310ae0c3d2cc6affeabccae5296f53fe42bc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25262
Preserve the type of ignore'd functions on serialization. Currently we first compile an ignore'd function with it's annotated type when first compiling, but do not preserve it. This is important for being able to compile models with not-yet-supported features in JIT.
```
torch.jit.ignore
def unsupported(x):
return x
def foo():
if not torch.jit._is_scripting():
return torch.linear(...)
else:
return unsupported(...)
```
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D17199043
Pulled By: eellison
fbshipit-source-id: 1196fd94c207b9fbee1087e4b2ef7d4656a6647f
Summary:
Adds links to torchaudio and torchtext to docs index. We should eventually evolve this to bring the audio and text docs builds in like torchvision.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24245
Differential Revision: D17163539
Pulled By: soumith
fbshipit-source-id: 5754bdf7579208e291e53970b40f73ef119b758f
Summary:
I think...
I'm having issues building the site, but it appears to get rid of the error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25544
Differential Revision: D17157327
Pulled By: ezyang
fbshipit-source-id: 170235c52008ca78ff0d8740b2d7f5b67397b614
Summary:
I presume this is what was intended.
cc t-vi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25011
Differential Revision: D16980939
Pulled By: soumith
fbshipit-source-id: c55b22e119f3894bd124eb1dce4f92a719ac047a
Summary:
Another pass over the docs, this covers most of the remaining stuff
* content updates for new API
* adds links to functions instead of just names
* removes some useless indentations
* some more code examples + `testcode`s
](https://our.intern.facebook.com/intern/diff/16847964/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24445
Pulled By: driazati
Differential Revision: D16847964
fbshipit-source-id: cd0b403fe4a89802ce79289f7cf54ee0cea45073
Summary:
Stacked PRs
* #24445 - [jit] Misc doc updates #2
* **#24435 - [jit] Add docs to CI**
This integrates the [doctest](http://www.sphinx-doc.org/en/master/usage/extensions/doctest.html) module into `jit.rst` so that we can run our code examples as unit tests. They're added to `test_jit.py` under the `TestDocs` class (which takes about 30s to run). This should help prevent things like #24429 from happening in the future. They can be run manually by doing `cd docs && make doctest`.
* The test setup requires a hack since `doctest` defines everything in the `builtins` module which upsets `inspect`
* There are several places where the code wasn't testable (i.e. it threw an exception on purpose). This may be resolvable, but I'd prefer to leave that for a follow up. For now there are `TODO` comments littered around.
](https://our.intern.facebook.com/intern/diff/16840882/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24435
Pulled By: driazati
Differential Revision: D16840882
fbshipit-source-id: c4b26e7c374cd224a5a4a2d523163d7b997280ed
Summary:
This patch writes documentation for `Tensor.record_stream()`, which is not a documented API currently. I've discussed publishing it with colesbury in https://github.com/pytorch/pytorch/issues/23729.
The documentation is based on [the introduction at `CUDACachingAllocator.cpp`](25d1496d58/c10/cuda/CUDACachingAllocator.cpp (L47-L50)). ~~I didn't explain full details of the life cycle of memory blocks or stream awareness of the allocator for the consistent level of details with other documentations.~~ I explained about the stream awareness in a note block.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24078
Differential Revision: D16743526
Pulled By: zou3519
fbshipit-source-id: 05819c3cc96733e2ba93c0a7c0ca06933acb22f3
Summary:
This is a bunch of changes to the docs for stylistic changes,
correctness, and updates to the new script API / recent TorchScript
changes (i.e. namedtuple)
For reviewers, ping me to see a link of the rendered output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24371
Pulled By: driazati
Differential Revision: D16832417
fbshipit-source-id: a28e748cf1b590964ca0ae2dfb5d8259c766a203
Summary:
Stacked PRs
* #24258 - [jit] Add `trace_module` to docs
* **#24208 - [jit] Cleanup documentation around `script` and `trace`**
Examples / info was duplicated between `ScriptModule`, `script`, and
`trace`, so this PR consolidates it and moves some things around to make
the docs more clear.
For reviewers, if you want to see the rendered output, ping me for a
link
](https://our.intern.facebook.com/intern/diff/16746236/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24208
Pulled By: driazati
Differential Revision: D16746236
fbshipit-source-id: fac3c6e762a31c897b132b8421baa8d4d61f694c
Summary:
**Patch Description**:
Update the docs to reflect one no longer needs to install tensorboard nightly, as Tensorboard 1.14.0 was [released last week](https://github.com/tensorflow/tensorboard/releases/tag/1.14.0).
**Testing**:
Haven't actually tested pytorch with tensorboard 1.14 yet. I'll update this PR once I have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22026
Differential Revision: D16772136
Pulled By: orionr
fbshipit-source-id: 2e1e17300f304f50026837abbbc6ffb25704aac0
Summary:
This was previously buggy and not being displayed on master. This fixes
the issues with the script to generate the builtin function schemas and
moves it to its own page (it's 6000+ lines of schemas)
Sphinx looks like it will just keep going if it hits errors when importing modules, we should find out how to turn that off and put it in the CI
This also includes some other small fixes:
* removing internal only args from `script()` and `trace()` docs, this also requires manually keeping these argument lists up to date but I think the cleanliness is worth it
* removes outdated note about early returns
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24056
Pulled By: driazati
Differential Revision: D16742406
fbshipit-source-id: 9102ba14215995ffef5aaafcb66a6441113fad59
Summary:
Adds new people and reorders sections to make more sense
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23693
Differential Revision: D16618230
Pulled By: dzhulgakov
fbshipit-source-id: 74191b50c6603309a9e6d14960b7c666eec6abdd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23376
This uses master version of sphinxcontrib-katex as it only
recently got prerender support.
Fixes#20984
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D16582064
Pulled By: ezyang
fbshipit-source-id: 9ef24c5788c19572515ded2db2e8ebfb7a5ed44d
Summary:
Changelog:
- Rename `gels` to `lstsq`
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lstsq` under the name `gels` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23460
Test Plan: - All tests should pass to confirm that the patch is correct
Differential Revision: D16547834
Pulled By: colesbury
fbshipit-source-id: b3bdb8f4c5d14c7716c3d9528e40324cc544e496
Summary:
Use the recursive script API in the existing docs
TODO:
* Migration guide for 1.1 -> 1.2
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21612
Pulled By: driazati
Differential Revision: D16553734
fbshipit-source-id: fb6be81a950224390bd5d19b9b3de2d97b3dc515
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23417
Test Plan:
cd docs; make html
Imported from OSS
Differential Revision: D16523781
Pulled By: ilia-cher
fbshipit-source-id: d6c09e8a85d39e6185bbdc4b312fea44fcdfff06
Summary:
Thanks adefazio for the feedback, adding a note to the Contribution guide so that folks don't start working on code without checking with the maintainers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23513
Differential Revision: D16546685
Pulled By: soumith
fbshipit-source-id: 1ee8ade963703c88374aedecb8c9e5ed39d7722d
Summary:
This is still work in progress.
There are several more items to add to complete this doc, including
- [x] LHS indexing, index assignments.
- [x] Tensor List.
- [x] ~Shape/Type propagation.~
- [x] FAQs
Please review and share your thoughts, feel free to add anything that you think should be included as well. houseroad spandantiwari lara-hdr neginraoof
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23185
Differential Revision: D16459647
Pulled By: houseroad
fbshipit-source-id: b401c005f848d957541ba3b00e00c93ac2f4609b
Summary:
With this change you can now list multiple interfaces separated by
comma. ProcessGroupGloo creates a single Gloo context for every device
in the list (a context represents a connection to every other
rank). For every collective that is called, it will select the context
in a round robin fashion. The number of worker threads responsible for
executing the collectives is set to be twice the number of devices.
If you have a single physical interface, and wish to employ increased
parallelism, you can also specify
`GLOO_SOCKET_IFNAME=eth0,eth0,eth0,eth0`. This makes ProcessGroupGloo
use 4 connections per rank, 4 I/O threads, and 8 worker threads
responsible for executing the collectives.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22978
ghstack-source-id: 87006270
Differential Revision: D16339962
fbshipit-source-id: 9aa1dc93d8e131c1714db349b0cbe57e9e7266f1
Summary:
Covering fleet-wide profiling, api logging, etc.
It's my first time writing rst, so suggestions are definitely welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23010
Differential Revision: D16456721
Pulled By: dzhulgakov
fbshipit-source-id: 3d3018f41499d04db0dca865bb3a9652d8cdf90a
Summary:
I manually went through all functions in `torch.*` and corrected any mismatch between the arguments mentioned in doc and the ones actually taken by the function. This fixes https://github.com/pytorch/pytorch/issues/8698.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22973
Differential Revision: D16419602
Pulled By: yf225
fbshipit-source-id: 5562c9b0b95a0759abee41f967c45efacf2267c2
Summary:
This cleans up the `torch.utils.tensorboard` API to remove all kwargs usage (which isn't clear to the user) and removes the "experimental" warning in prep for our 1.2 release.
We also don't need the additional PyTorch version checks now that we are in the codebase itself.
cc ezyang lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21786
Reviewed By: natalialunova
Differential Revision: D15854892
Pulled By: orionr
fbshipit-source-id: 06b8498826946e578824d4b15c910edb3c2c20c6
Summary:
# What is this?
This is an implementation of the AdamW optimizer as implemented in [the fastai library](803894051b/fastai/callback.py) and as initially introduced in the paper [Decoupled Weight Decay Regularization](https://arxiv.org/abs/1711.05101). It decouples the weight decay regularization step from the optimization step during training.
There have already been several abortive attempts to push this into pytorch in some form or fashion: https://github.com/pytorch/pytorch/pull/17468, https://github.com/pytorch/pytorch/pull/10866, https://github.com/pytorch/pytorch/pull/3740, https://github.com/pytorch/pytorch/pull/4429. Hopefully this one goes through.
# Why is this important?
Via a simple reparameterization, it can be shown that L2 regularization has a weight decay effect in the case of SGD optimization. Because of this, L2 regularization became synonymous with the concept of weight decay. However, it can be shown that the equivalence of L2 regularization and weight decay breaks down for more complex adaptive optimization schemes. It was shown in the paper [Decoupled Weight Decay Regularization](https://arxiv.org/abs/1711.05101) that this is the reason why models trained with SGD achieve better generalization than those trained with Adam. Weight decay is a very effective regularizer. L2 regularization, in and of itself, is much less effective. By explicitly decaying the weights, we can achieve state-of-the-art results while also taking advantage of the quick convergence properties that adaptive optimization schemes have.
# How was this tested?
There were test cases added to `test_optim.py` and I also ran a [little experiment](https://gist.github.com/mjacar/0c9809b96513daff84fe3d9938f08638) to validate that this implementation is equivalent to the fastai implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21250
Differential Revision: D16060339
Pulled By: vincentqb
fbshipit-source-id: ded7cc9cfd3fde81f655b9ffb3e3d6b3543a4709
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:
Accidentally rebased the old PR and make it too messy. Find it here (https://github.com/pytorch/pytorch/pull/19274)
Create a PR for comments. The model is still WIP but I want to have some feedbacks before moving too far. The transformer model depends on several modules, like MultiheadAttention (landed).
Transformer is implemented based on the paper (https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf). Users have the flexibility to build a transformer with self-defined and/or built-in components (i.e encoder, decoder, encoder_layer, decoder_layer). Users could use Transformer class to build a standard transformer model and modify sub-layers as needed.
Add a few unit tests for the transformer module, as follow:
TestNN.test_Transformer_cell
TestNN.test_transformerencoderlayer
TestNN.test_transformerdecoderlayer
TestNN.test_transformer_args_check
TestScript.test_scriptmodule_transformer_cuda
There is another demonstration example for applying transformer module on the word language problem. https://github.com/pytorch/examples/pull/555
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20170
Differential Revision: D15417983
Pulled By: zhangguanheng66
fbshipit-source-id: 7ce771a7e27715acd9a23d60bf44917a90d1d572
Summary:
Something flaky is going on with `test_inplace_view_saved_output` on Windows.
With my PR #20598 applied, the test fails, even though there is no obvious reason it should be related, so the PR was reverted.
Based on commenting out various parts of my change and re-building, I think the problem is with the name -- renaming everything from `T` to `asdf` seems to make the test stop failing. I can't be sure that this is actually the case though, since I could just be seeing patterns in non-deterministic build output...
I spoke with colesbury offline and we agreed that it is okay to just disable this test on Windows for now and not block landing the main change. He will look into why it is failing.
**Test Plan:** I will wait to make sure the Windows CI suite passes before landing this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21175
Differential Revision: D15566970
Pulled By: umanwizard
fbshipit-source-id: edf223375d41faaab0a3a14dca50841f08030da3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20665
Add gelu activation forward on CPU in pytorch
Compare to current python implemented version of gelu in BERT model like
def gelu(self, x):
x * 0.5 * (1.0 + torch.erf(x / self.sqrt_two))
The torch.nn.functional.gelu function can reduce the forward time from 333ms to 109ms (with MKL) / 112ms (without MKL) for input size = [64, 128, 56, 56] on a devvm.
Reviewed By: zheng-xq
Differential Revision: D15400974
fbshipit-source-id: f606b43d1dd64e3c42a12c4991411d47551a8121
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21196
we'll add `quantize(quantizer)` as a tensor method later when we expose `quantizer` in Python frontend
Python
```
torch.quantize_linear(t, ...)
```
C++
```
at::quantize_linear(t, ...)
```
Differential Revision: D15577123
fbshipit-source-id: d0abeea488418fa9ab212f84b0b97ee237124240
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21156
we'll add `quantize(quantizer)` as a tensor method later when we expose `quantizer` in Python frontend
Python
```
torch.quantize_linear(t, ...)
```
C++
```
at::quantize_linear(t, ...)
```
Differential Revision: D15558784
fbshipit-source-id: 0b194750c423f51ad1ad5e9387a12b4d58d969a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20874
A criteria for what should go in Tensor method is whether numpy has it, for this one it does not
so we are removing it as a Tensor method, we can still call it as function.
Python
```
torch.quantize_linear(t, ...), torch.dequantize(t)
```
C++
```
at::quantize_linear(t, ...), at::dequantize(t)
```
Reviewed By: dzhulgakov
Differential Revision: D15477933
fbshipit-source-id: c8aa81f681e02f038d72e44f0c700632f1af8437
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20938
Dequantize_linear need not be exposed to the front end users.
It will only be used for the jit passes for q-dq insertion and op
substitution.
Differential Revision: D15446097
fbshipit-source-id: a5fbcf2bb72115122c9653e5089d014e2a2e891d
Summary:
I started adding support for the new **[mesh/point cloud](https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/tensorboard.md)** data type introduced to TensorBoard recently.
I created the functions to add the data, created the appropriate summaries.
This new data type however requires a **Merged** summary containing the data for the vertices, colors and faces.
I got stuck at this stage. Maybe someone can help. lanpa?
I converted the example code by Google to PyTorch:
```python
import numpy as np
import trimesh
import torch
from torch.utils.tensorboard import SummaryWriter
sample_mesh = 'https://storage.googleapis.com/tensorflow-graphics/tensorboard/test_data/ShortDance07_a175_00001.ply'
log_dir = 'runs/torch'
batch_size = 1
# Camera and scene configuration.
config_dict = {
'camera': {'cls': 'PerspectiveCamera', 'fov': 75},
'lights': [
{
'cls': 'AmbientLight',
'color': '#ffffff',
'intensity': 0.75,
}, {
'cls': 'DirectionalLight',
'color': '#ffffff',
'intensity': 0.75,
'position': [0, -1, 2],
}],
'material': {
'cls': 'MeshStandardMaterial',
'roughness': 1,
'metalness': 0
}
}
# Read all sample PLY files.
mesh = trimesh.load_remote(sample_mesh)
vertices = np.array(mesh.vertices)
# Currently only supports RGB colors.
colors = np.array(mesh.visual.vertex_colors[:, :3])
faces = np.array(mesh.faces)
# Add batch dimension, so our data will be of shape BxNxC.
vertices = np.expand_dims(vertices, 0)
colors = np.expand_dims(colors, 0)
faces = np.expand_dims(faces, 0)
# Create data placeholders of the same shape as data itself.
vertices_tensor = torch.as_tensor(vertices)
faces_tensor = torch.as_tensor(faces)
colors_tensor = torch.as_tensor(colors)
writer = SummaryWriter(log_dir)
writer.add_mesh('mesh_color_tensor', vertices=vertices_tensor, faces=faces_tensor,
colors=colors_tensor, config_dict=config_dict)
writer.close()
```
I tried adding only the vertex summary, hence the others are supposed to be optional.
I got the following error from TensorBoard and it also didn't display the points:
```
Traceback (most recent call last):
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/werkzeug/serving.py", line 302, in run_wsgi
execute(self.server.app)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/werkzeug/serving.py", line 290, in execute
application_iter = app(environ, start_response)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/backend/application.py", line 309, in __call__
return self.data_applications[clean_path](environ, start_response)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/werkzeug/wrappers/base_request.py", line 235, in application
resp = f(*args[:-2] + (request,))
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/plugins/mesh/mesh_plugin.py", line 252, in _serve_mesh_metadata
tensor_events = self._collect_tensor_events(request)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/plugins/mesh/mesh_plugin.py", line 188, in _collect_tensor_events
tensors = self._multiplexer.Tensors(run, instance_tag)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/backend/event_processing/plugin_event_multiplexer.py", line 400, in Tensors
return accumulator.Tensors(tag)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/backend/event_processing/plugin_event_accumulator.py", line 437, in Tensors
return self.tensors_by_tag[tag].Items(_TENSOR_RESERVOIR_KEY)
KeyError: 'mesh_color_tensor_COLOR'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20413
Differential Revision: D15500737
Pulled By: orionr
fbshipit-source-id: 426e8b966037d08c065bce5198fd485fd80a2b67
Summary:
To say that we don't do refinement on module attributes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20912
Differential Revision: D15496453
Pulled By: eellison
fbshipit-source-id: a1ab9fb0157a30fa1bb71d0793fcc9b1670c4926
Summary:
The current variance kernels compute mean at the same time. Many times we want both statistics together, so it seems reasonable to have a kwarg/function that allows us to get both values without launching an extra kernel.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18731
Differential Revision: D14726082
Pulled By: ifedan
fbshipit-source-id: 473cba0227b69eb2240dca5e61a8f4366df0e029
Summary:
As a part of supporting writing data into TensorBoard readable format, we show more example on how to use the function in addition to the API docs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20008
Reviewed By: natalialunova
Differential Revision: D15261502
Pulled By: orionr
fbshipit-source-id: 16611695a27e74bfcdf311e7cad40196e0947038
Summary:
This adds method details and corrects example on the page that didn't run properly. I've now confirmed that it runs in colab with nightly.
For those with internal access the rendered result can be seen at https://home.fburl.com/~orionr/pytorch-docs/tensorboard.html
cc lanpa, soumith, ezyang, brianjo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19915
Differential Revision: D15137430
Pulled By: orionr
fbshipit-source-id: 833368fb90f9d75231b8243b43de594b475b2cb1
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:
Changelog:
- Rename `potri` to `cholesky_inverse` to remain consistent with names of `cholesky` methods (`cholesky`, `cholesky_solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `cholesky_inverse` under the name `potri` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19498
Differential Revision: D15029901
Pulled By: ezyang
fbshipit-source-id: 2074286dc93d8744cdc9a45d54644fe57df3a57a
Summary:
This is a simple yet useful addition to the torch.nn modules: an identity module. This is a first draft - please let me know what you think and I will edit my PR.
There is no identity module - nn.Sequential() can be used, however it is argument sensitive so can't be used interchangably with any other module. This adds nn.Identity(...) which can be swapped with any module because it has dummy arguments. It's also more understandable than seeing an empty Sequential inside a model.
See discussion on #9160. The current solution is to use nn.Sequential(). However this won't work as follows:
```python
batch_norm = nn.BatchNorm2d
if dont_use_batch_norm:
batch_norm = Identity
```
Then in your network, you have:
```python
nn.Sequential(
...
batch_norm(N, momentum=0.05),
...
)
```
If you try to simply set `Identity = nn.Sequential`, this will fail since `nn.Sequential` expects modules as arguments. Of course there are many ways to get around this, including:
- Conditionally adding modules to an existing Sequential module
- Not using Sequential but writing the usual `forward` function with an if statement
- ...
**However, I think that an identity module is the most pythonic strategy,** assuming you want to use nn.Sequential.
Using the very simple class (this isn't the same as the one in my commit):
```python
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x):
return x
```
we can get around using nn.Sequential, and `batch_norm(N, momentum=0.05)` will work. There are of course other situations this would be useful.
Thank you.
Best,
Miles
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19249
Differential Revision: D15012969
Pulled By: ezyang
fbshipit-source-id: 9f47e252137a1679e306fd4c169dca832eb82c0c
Summary:
A few improvements while doing bert model
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19247
Differential Revision: D14989345
Pulled By: ailzhang
fbshipit-source-id: f4846813f62b6d497fbe74e8552c9714bd8dc3c7
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:
Changelog:
- Rename `btrisolve` to `lu_solve` to remain consistent with names of solve methods (`cholesky_solve`, `triangular_solve`, `solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lu_solve` under the name `btrisolve` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18726
Differential Revision: D14726237
Pulled By: zou3519
fbshipit-source-id: bf25f6c79062183a4153015e0ec7ebab2c8b986b
Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038
Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748
Reviewed By: dzhulgakov
Differential Revision: D14614640
Pulled By: bddppq
fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533
Summary:
Per our offline discussion, allow Tensors, ints, and floats to be casted to be bool when used in a conditional
Fix for https://github.com/pytorch/pytorch/issues/18381
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18755
Reviewed By: driazati
Differential Revision: D14752476
Pulled By: eellison
fbshipit-source-id: 149960c92afcf7e4cc4997bccc57f4e911118ff1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18230
Implementing minimum qtensor API to unblock other workstreams in quantization
Changes:
- Added Quantizer which represents different quantization schemes
- Added qint8 as a data type for QTensor
- Added a new ScalarType QInt8
- Added QTensorImpl for QTensor
- Added following user facing APIs
- quantize_linear(scale, zero_point)
- dequantize()
- q_scale()
- q_zero_point()
Reviewed By: dzhulgakov
Differential Revision: D14524641
fbshipit-source-id: c1c0ae0978fb500d47cdb23fb15b747773429e6c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18628
ghimport-source-id: d94b81a6f303883d97beaae25344fd591e13ce52
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18629 Provide flake8 install instructions.
* **#18628 Delete duplicated technical content from contribution_guide.rst**
There's useful guide in contributing_guide.rst, but the
technical bits were straight up copy-pasted from CONTRIBUTING.md,
and I don't think it makes sense to break the CONTRIBUTING.md
link. Instead, I deleted the duplicate bits and added a cross
reference to the rst document.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14701003
fbshipit-source-id: 3bbb102fae225cbda27628a59138bba769bfa288
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:
Changelog:
- Renames `btriunpack` to `lu_unpack` to remain consistent with the `lu` function interface.
- Rename all relevant tests, fix callsites
- Create a tentative alias for `lu_unpack` under the name `btriunpack` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18529
Differential Revision: D14683161
Pulled By: soumith
fbshipit-source-id: 994287eaa15c50fd74c2f1c7646edfc61e8099b1
Summary:
Changelog:
- Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`).
- Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple.
- Rename all tests, fix callsites
- Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage.
- Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435
Differential Revision: D14680352
Pulled By: soumith
fbshipit-source-id: af58dfc11fa53d9e8e0318c720beaf5502978cd8
Summary:
This implements a cyclical learning rate (CLR) schedule with an optional inverse cyclical momentum. More info about CLR: https://github.com/bckenstler/CLR
This is finishing what #2016 started. Resolves#1909.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18001
Differential Revision: D14451845
Pulled By: sampepose
fbshipit-source-id: 8f682e0c3dee3a73bd2b14cc93fcf5f0e836b8c9
Summary:
There are a number of pages in the docs that serve insecure content. AFAICT this is the sole source of that.
I wasn't sure if docs get regenerated for old versions as part of the automation, or if those would need to be manually done.
cf. https://github.com/pytorch/pytorch.github.io/pull/177
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18508
Differential Revision: D14645665
Pulled By: zpao
fbshipit-source-id: 003563b06048485d4f539feb1675fc80bab47c1b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18507
ghimport-source-id: 1c3642befad2da78a7e5f39d6d58732b85c76267
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18507 Upgrade flake8-bugbear to master, fix the new lints.**
It turns out Facebobok is internally using the unreleased master
flake8-bugbear, so upgrading it grabs a few more lints that Phabricator
was complaining about but we didn't get in open source.
A few of the getattr sites that I fixed look very suspicious (they're
written as if Python were a lazy language), but I didn't look more
closely into the matter.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14633682
fbshipit-source-id: fc3f97c87dca40bbda943a1d1061953490dbacf8
Summary:
This depend on https://github.com/pytorch/pytorch/pull/16039
This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`.
When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057
Differential Revision: D14619550
Pulled By: ezyang
fbshipit-source-id: e1c6dd6761142e2e48ec499e118df399e3949fcc
Summary:
This PR adds a Global Site Tag to the site.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17690
Differential Revision: D14620816
Pulled By: zou3519
fbshipit-source-id: c02407881ce08340289123f5508f92381744e8e3
Summary:
`SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111.
Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332.
This closes#9332 .
cc: soumith Balandat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505
Reviewed By: zou3519
Differential Revision: D9330179
Pulled By: ezyang
fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d
Summary:
This is to fix#16141 and similar issues.
The idea is to track a reference to every shared CUDA Storage and deallocate memory only after a consumer process deallocates received Storage.
ezyang Done with cleanup. Same (insignificantly better) performance as in file-per-share solution, but handles millions of shared tensors easily. Note [ ] documentation in progress.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16854
Differential Revision: D13994490
Pulled By: VitalyFedyunin
fbshipit-source-id: 565148ec3ac4fafb32d37fde0486b325bed6fbd1
Summary:
* Adds more headers for easier scanning
* Adds some line breaks so things are displayed correctly
* Minor copy/spelling stuff
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18234
Reviewed By: ezyang
Differential Revision: D14567737
Pulled By: driazati
fbshipit-source-id: 046d991f7aab8e00e9887edb745968cb79a29441
Summary:
Changelog:
- Renames `trtrs` to `triangular_solve` to remain consistent with `cholesky_solve` and `solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `triangular_solve` under the name `trtrs`, and add a deprecation warning to not promote usage.
- Move `isnan` to _torch_docs.py
- Remove unnecessary imports
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18213
Differential Revision: D14566902
Pulled By: ezyang
fbshipit-source-id: 544f57c29477df391bacd5de700bed1add456d3f
Summary:
Fixes Typo and a Link in the `docs/source/community/contribution_guide.rst`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18237
Differential Revision: D14566907
Pulled By: ezyang
fbshipit-source-id: 3a75797ab6b27d28dd5566d9b189d80395024eaf
Summary:
Changelog:
- Renames `gesv` to `solve` to remain consistent with `cholesky_solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `solve` under the name `gesv`, and add a deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18060
Differential Revision: D14503117
Pulled By: zou3519
fbshipit-source-id: 99c16d94e5970a19d7584b5915f051c030d49ff5
Summary:
Fix a very common typo in my name.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17949
Differential Revision: D14475162
Pulled By: ezyang
fbshipit-source-id: 91c2c364c56ecbbda0bd530e806a821107881480
Summary: Adding new documents to the PyTorch website to describe how PyTorch is governed, how to contribute to the project, and lists persons of interest.
Reviewed By: orionr
Differential Revision: D14394573
fbshipit-source-id: ad98b807850c51de0b741e3acbbc3c699e97b27f
Summary:
as title
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17476
Differential Revision: D14218312
Pulled By: suo
fbshipit-source-id: 64df096a3431a6f25cd2373f0959d415591fed15
Summary:
Based on https://github.com/pytorch/pytorch/pull/12413, with the following additional changes:
- Inside `native_functions.yml` move those outplace operators right next to everyone's corresponding inplace operators for convenience of checking if they match when reviewing
- `matches_jit_signature: True` for them
- Add missing `scatter` with Scalar source
- Add missing `masked_fill` and `index_fill` with Tensor source.
- Add missing test for `scatter` with Scalar source
- Add missing test for `masked_fill` and `index_fill` with Tensor source by checking the gradient w.r.t source
- Add missing docs to `tensor.rst`
Differential Revision: D14069925
Pulled By: ezyang
fbshipit-source-id: bb3f0cb51cf6b756788dc4955667fead6e8796e5
Summary:
one_hot docs is missing [here](https://pytorch.org/docs/master/nn.html#one-hot).
I dug around and could not find a way to get this working properly.
Differential Revision: D14104414
Pulled By: zou3519
fbshipit-source-id: 3f45c8a0878409d218da167f13b253772f5cc963
Summary:
This prevent people (reviewer, PR author) from forgetting adding things to `torch.rst`.
When something new is added to `_torch_doc.py` or `functional.py` but intentionally not in `torch.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16039
Differential Revision: D14070903
Pulled By: ezyang
fbshipit-source-id: 60f2a42eb5efe81be073ed64e54525d143eb643e
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
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
Summary:
Now that `cuda.get/set_rng_state` accept `device` objects, the default value should be an device object, and doc should mention so.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14324
Reviewed By: ezyang
Differential Revision: D13528707
Pulled By: soumith
fbshipit-source-id: 32fdac467dfea6d5b96b7e2a42dc8cfd42ba11ee
Summary:
Changelog:
- Renames `potrs` to `cholesky_solve` to remain consistent with Tensorflow and Scipy (not really, they call their function chol_solve)
- Default argument for upper in cholesky_solve is False. This will allow a seamless interface between `cholesky` and `cholesky_solve`, since the `upper` argument in both function are the same.
- Rename all tests
- Create a tentative alias for `cholesky_solve` under the name `potrs`, and add deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15334
Differential Revision: D13507724
Pulled By: soumith
fbshipit-source-id: b826996541e49d2e2bcd061b72a38c39450c76d0
Summary:
Some of the codeblocks were showing up as normal text and the "unsupported modules" table was formatted incorrectly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15227
Differential Revision: D13468847
Pulled By: driazati
fbshipit-source-id: eb7375710d4f6eca1d0f44dfc43c7c506300cb1e
Summary:
Documents what is supported in the script standard library.
* Adds `my_script_module._get_method('forward').schema()` method to get function schema from a `ScriptModule`
* Removes `torch.nn.functional` from the list of builtins. The only functions not supported are `nn.functional.fold` and `nn.functional.unfold`, but those currently just dispatch to their corresponding aten ops, so from a user's perspective it looks like they work.
* Allow printing of `IValue::Device` by getting its string representation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14912
Differential Revision: D13385928
Pulled By: driazati
fbshipit-source-id: e391691b2f87dba6e13be05d4aa3ed2f004e31da
