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
