This PR adds new Dynamic Shapes documentation and expands on the existing one.
- Adds a new structure with Intro, Core Concepts, Troubleshooting
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159428
Approved by: https://github.com/bobrenjc93
Co-authored-by: bobrenjc93 <bobren@meta.com>
Summary: In Pytorch 2.5 we added source code attribution to PT2 traces. Each Torch-Compiled Region will now have its frame id and frame compile id associated with it. Update the image in the doc and add a description of this in the doc itself
Test Plan:
{F1980179183}
Rollback Plan:
Differential Revision: D78118228
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158066
Approved by: https://github.com/aaronenyeshi
Updated nested tensor docs to be NJT-centric (instead of NST-centric). They now include:
* High-level description of NST vs. NJT + a recommendation to use NJT
* General NJT construction / usage
* torch.compile() integration w/ dynamic shapes
* Common errors and how to fix them
* Contribution guide
* Data layout / shape information (with diagram)
* Links to more extensive tutorials involving Transformers / SDPA / FlexAttention
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145402
Approved by: https://github.com/soulitzer
Add a new documentation to show one memory usage benefit brought by TorchDynamo-based ONNX exporter.
Also add a unit test to make sure TorchDynamo-based ONNX exporter works well under FakeTensorMode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139388
Approved by: https://github.com/xadupre
Summary:
Closes https://github.com/pytorch/pytorch/issues/51455
I think the current implementation is aggregating over the correct dimensions. The shape of `normalized_shape` is only used to determine the dimensions to aggregate over. The actual values of `normalized_shape` are used when `elementwise_affine=True` to initialize the weights and biases.
This PR updates the docstring to clarify how `normalized_shape` is used. Here is a short script comparing the implementations for tensorflow and pytorch:
```python
import torch
import torch.nn as nn
import tensorflow as tf
from tensorflow.keras.layers import LayerNormalization
rng = np.random.RandomState()
x = rng.randn(10, 20, 64, 64).astype(np.float32)
# slightly non-trival
x[:, :10, ...] = x[:, :10, ...] * 10 + 20
x[:, 10:, ...] = x[:, 10:, ...] * 30 - 100
# Tensorflow Layer norm
x_tf = tf.convert_to_tensor(x)
layer_norm_tf = LayerNormalization(axis=[-3, -2, -1], epsilon=1e-5)
output_tf = layer_norm_tf(x_tf)
output_tf_np = output_tf.numpy()
# PyTorch Layer norm
x_torch = torch.as_tensor(x)
layer_norm_torch = nn.LayerNorm([20, 64, 64], elementwise_affine=False)
output_torch = layer_norm_torch(x_torch)
output_torch_np = output_torch.detach().numpy()
# check tensorflow and pytorch
torch.testing.assert_allclose(output_tf_np, output_torch_np)
# manual comutation
manual_output = ((x_torch - x_torch.mean(dim=(-3, -2, -1), keepdims=True)) /
(x_torch.var(dim=(-3, -2, -1), keepdims=True, unbiased=False) + 1e-5).sqrt())
torch.testing.assert_allclose(output_torch, manual_output)
```
To get to the layer normalization as shown here:
<img width="157" alt="Screen Shot 2021-05-29 at 2 13 52 PM" src="https://user-images.githubusercontent.com/5402633/120080691-1e37f100-c088-11eb-9060-4f263e4cd093.png">
One needs to pass in `normalized_shape` with shape `x.dim() - 1` with the size of the channels and all spatial dimensions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59178
Reviewed By: ejguan
Differential Revision: D28931877
Pulled By: jbschlosser
fbshipit-source-id: 193e05205b9085bb190c221428c96d2ca29f2a70
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50791
Add a dedicated pipeline parallelism doc page explaining the APIs and
the overall value of the module.
ghstack-source-id: 120257168
Test Plan:
1) View locally
2) waitforbuildbot
Reviewed By: rohan-varma
Differential Revision: D25967981
fbshipit-source-id: b607b788703173a5fa4e3526471140506171632b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44224
The purpose of this file is to help developers on PT distributed get
upto speed on the code structure and layout for PT Distributed.
ghstack-source-id: 111644842
Test Plan: waitforbuildbot
Reviewed By: rohan-varma
Differential Revision: D23548377
fbshipit-source-id: 561d5b8e257642de172def8fdcc1311fae20690b
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:
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:
as title
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17476
Differential Revision: D14218312
Pulled By: suo
fbshipit-source-id: 64df096a3431a6f25cd2373f0959d415591fed15
Summary:
pytorch_theme.css is no longer necessary for the cpp or html docs site build. The new theme styles are located at https://github.com/pytorch/pytorch_sphinx_theme. The Lato font is also no longer used in the new theme.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14779
Differential Revision: D13356125
Pulled By: ezyang
fbshipit-source-id: c7635eb7512c7dcaddb9cad596ab3dbc96480144
