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Deprecate torchtext tutorials (#3088)
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* Deprecate torchtext tutorials and add redirects to main page.
* Remove torchtext from the requirements.txt
* Remove transformer tutorial and pipeline tutorial
* Deprecate Torchtext tutorials
* Restructure NLP to go under learning pytorch and remove torchtext from requirements.txt
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@svekars
svekars authored Oct 21, 2024
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1 change: 0 additions & 1 deletion .ci/docker/requirements.txt
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Expand Up @@ -16,7 +16,6 @@ matplotlib
librosa
torch==2.4
torchvision
torchtext
torchdata
networkx
PyHamcrest
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4 changes: 0 additions & 4 deletions .jenkins/validate_tutorials_built.py
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Expand Up @@ -11,7 +11,6 @@
NOT_RUN = [
"beginner_source/basics/intro", # no code
"beginner_source/onnx/intro_onnx",
"beginner_source/translation_transformer",
"beginner_source/profiler",
"beginner_source/saving_loading_models",
"beginner_source/introyt/captumyt",
Expand All @@ -22,9 +21,6 @@
"beginner_source/former_torchies/tensor_tutorial_old",
"beginner_source/examples_autograd/polynomial_autograd",
"beginner_source/examples_autograd/polynomial_custom_function",
"beginner_source/torchtext_custom_dataset_tutorial", # not building with 2.3 RC, might be able to turn on with GA
"beginner_source/text_sentiment_ngrams_tutorial", # not building with 2.3 RC, might be able to turn on with GA
"beginner_source/t5_tutorial", # re-enable after this is fixed: https://github.com/pytorch/text/issues/1756
"intermediate_source/mnist_train_nas", # used by ax_multiobjective_nas_tutorial.py
"intermediate_source/fx_conv_bn_fuser",
"intermediate_source/_torch_export_nightly_tutorial", # does not work on release
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4 changes: 0 additions & 4 deletions Makefile
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Expand Up @@ -82,10 +82,6 @@ download:
wget -nv -N http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz -P $(DATADIR)
tar $(TAROPTS) -xzf $(DATADIR)/vocab.tar.gz -C ./beginner_source/data/

# Download dataset for beginner_source/torchtext_custom_dataset_tutorial.py
wget -nv -N https://www.manythings.org/anki/deu-eng.zip -P $(DATADIR)
unzip -o $(DATADIR)/deu-eng.zip -d beginner_source/data/

# Download PennFudanPed dataset for intermediate_source/torchvision_tutorial.py
wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip -P $(DATADIR)
unzip -o $(DATADIR)/PennFudanPed.zip -d intermediate_source/data/
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4 changes: 0 additions & 4 deletions beginner_source/README.txt
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Expand Up @@ -20,7 +20,3 @@ Beginner Tutorials
5. nlp/* and deep_learning_nlp_tutorial.rst
Deep Learning for NLP with Pytorch
https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html

6. transformer_translation.py
Language Translation with Transformers
https://pytorch.org/tutorials/beginner/translation_transformer.html
251 changes: 5 additions & 246 deletions beginner_source/bettertransformer_tutorial.rst
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@@ -1,251 +1,10 @@
Fast Transformer Inference with Better Transformer
===============================================================
==================================================

**Author**: `Michael Gschwind <https://github.com/mikekgfb>`__
This tutorial has been deprecated.

This tutorial introduces Better Transformer (BT) as part of the PyTorch 1.12 release.
In this tutorial, we show how to use Better Transformer for production
inference with torchtext. Better Transformer is a production ready fastpath to
accelerate deployment of Transformer models with high performance on CPU and GPU.
The fastpath feature works transparently for models based either directly on
PyTorch core ``nn.module`` or with torchtext.

Models which can be accelerated by Better Transformer fastpath execution are those
using the following PyTorch core ``torch.nn.module`` classes ``TransformerEncoder``,
``TransformerEncoderLayer``, and ``MultiHeadAttention``. In addition, torchtext has
been updated to use the core library modules to benefit from fastpath acceleration.
(Additional modules may be enabled with fastpath execution in the future.)

Better Transformer offers two types of acceleration:

* Native multihead attention (MHA) implementation for CPU and GPU to improve overall execution efficiency.
* Exploiting sparsity in NLP inference. Because of variable input lengths, input
tokens may contain a large number of padding tokens for which processing may be
skipped, delivering significant speedups.

Fastpath execution is subject to some criteria. Most importantly, the model
must be executed in inference mode and operate on input tensors that do not collect
gradient tape information (e.g., running with torch.no_grad).

To follow this example in Google Colab, `click here
<https://colab.research.google.com/drive/1KZnMJYhYkOMYtNIX5S3AGIYnjyG0AojN?usp=sharing>`__.

Better Transformer Features in This Tutorial
--------------------------------------------

* Load pretrained models (created before PyTorch version 1.12 without Better Transformer)
* Run and benchmark inference on CPU with and without BT fastpath (native MHA only)
* Run and benchmark inference on (configurable) DEVICE with and without BT fastpath (native MHA only)
* Enable sparsity support
* Run and benchmark inference on (configurable) DEVICE with and without BT fastpath (native MHA + sparsity)

Additional Information
-----------------------
Additional information about Better Transformer may be found in the PyTorch.Org blog
`A Better Transformer for Fast Transformer Inference
<https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference//>`__.



1. Setup

1.1 Load pretrained models

We download the XLM-R model from the predefined torchtext models by following the instructions in
`torchtext.models <https://pytorch.org/text/main/models.html>`__. We also set the DEVICE to execute
on-accelerator tests. (Enable GPU execution for your environment as appropriate.)

.. code-block:: python
import torch
import torch.nn as nn
print(f"torch version: {torch.__version__}")
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
print(f"torch cuda available: {torch.cuda.is_available()}")
import torch, torchtext
from torchtext.models import RobertaClassificationHead
from torchtext.functional import to_tensor
xlmr_large = torchtext.models.XLMR_LARGE_ENCODER
classifier_head = torchtext.models.RobertaClassificationHead(num_classes=2, input_dim = 1024)
model = xlmr_large.get_model(head=classifier_head)
transform = xlmr_large.transform()
1.2 Dataset Setup

We set up two types of inputs: a small input batch and a big input batch with sparsity.

.. code-block:: python
small_input_batch = [
"Hello world",
"How are you!"
]
big_input_batch = [
"Hello world",
"How are you!",
"""`Well, Prince, so Genoa and Lucca are now just family estates of the
Buonapartes. But I warn you, if you don't tell me that this means war,
if you still try to defend the infamies and horrors perpetrated by
that Antichrist- I really believe he is Antichrist- I will have
nothing more to do with you and you are no longer my friend, no longer
my 'faithful slave,' as you call yourself! But how do you do? I see
I have frightened you- sit down and tell me all the news.`
It was in July, 1805, and the speaker was the well-known Anna
Pavlovna Scherer, maid of honor and favorite of the Empress Marya
Fedorovna. With these words she greeted Prince Vasili Kuragin, a man
of high rank and importance, who was the first to arrive at her
reception. Anna Pavlovna had had a cough for some days. She was, as
she said, suffering from la grippe; grippe being then a new word in
St. Petersburg, used only by the elite."""
]
Next, we select either the small or large input batch, preprocess the inputs and test the model.

.. code-block:: python
input_batch=big_input_batch
model_input = to_tensor(transform(input_batch), padding_value=1)
output = model(model_input)
output.shape
Finally, we set the benchmark iteration count:

.. code-block:: python
ITERATIONS=10
2. Execution

2.1 Run and benchmark inference on CPU with and without BT fastpath (native MHA only)

We run the model on CPU, and collect profile information:

* The first run uses traditional ("slow path") execution.
* The second run enables BT fastpath execution by putting the model in inference mode using `model.eval()` and disables gradient collection with `torch.no_grad()`.

You can see an improvement (whose magnitude will depend on the CPU model) when the model is executing on CPU. Notice that the fastpath profile shows most of the execution time
in the native `TransformerEncoderLayer` implementation `aten::_transformer_encoder_layer_fwd`.

.. code-block:: python
print("slow path:")
print("==========")
with torch.autograd.profiler.profile(use_cuda=False) as prof:
for i in range(ITERATIONS):
output = model(model_input)
print(prof)
model.eval()
print("fast path:")
print("==========")
with torch.autograd.profiler.profile(use_cuda=False) as prof:
with torch.no_grad():
for i in range(ITERATIONS):
output = model(model_input)
print(prof)
2.2 Run and benchmark inference on (configurable) DEVICE with and without BT fastpath (native MHA only)

We check the BT sparsity setting:

.. code-block:: python
model.encoder.transformer.layers.enable_nested_tensor
We disable the BT sparsity:

.. code-block:: python
model.encoder.transformer.layers.enable_nested_tensor=False
We run the model on DEVICE, and collect profile information for native MHA execution on DEVICE:

* The first run uses traditional ("slow path") execution.
* The second run enables BT fastpath execution by putting the model in inference mode using `model.eval()`
and disables gradient collection with `torch.no_grad()`.

When executing on a GPU, you should see a significant speedup, in particular for the small input batch setting:

.. code-block:: python
model.to(DEVICE)
model_input = model_input.to(DEVICE)
print("slow path:")
print("==========")
with torch.autograd.profiler.profile(use_cuda=True) as prof:
for i in range(ITERATIONS):
output = model(model_input)
print(prof)
model.eval()
print("fast path:")
print("==========")
with torch.autograd.profiler.profile(use_cuda=True) as prof:
with torch.no_grad():
for i in range(ITERATIONS):
output = model(model_input)
print(prof)
2.3 Run and benchmark inference on (configurable) DEVICE with and without BT fastpath (native MHA + sparsity)

We enable sparsity support:

.. code-block:: python
model.encoder.transformer.layers.enable_nested_tensor = True
We run the model on DEVICE, and collect profile information for native MHA and sparsity support execution on DEVICE:

* The first run uses traditional ("slow path") execution.
* The second run enables BT fastpath execution by putting the model in inference mode using `model.eval()` and disables gradient collection with `torch.no_grad()`.

When executing on a GPU, you should see a significant speedup, in particular for the large input batch setting which includes sparsity:

.. code-block:: python
model.to(DEVICE)
model_input = model_input.to(DEVICE)
print("slow path:")
print("==========")
with torch.autograd.profiler.profile(use_cuda=True) as prof:
for i in range(ITERATIONS):
output = model(model_input)
print(prof)
model.eval()
print("fast path:")
print("==========")
with torch.autograd.profiler.profile(use_cuda=True) as prof:
with torch.no_grad():
for i in range(ITERATIONS):
output = model(model_input)
print(prof)
Summary
-------

In this tutorial, we have introduced fast transformer inference with
Better Transformer fastpath execution in torchtext using PyTorch core
Better Transformer support for Transformer Encoder models. We have
demonstrated the use of Better Transformer with models trained prior to
the availability of BT fastpath execution. We have demonstrated and
benchmarked the use of both BT fastpath execution modes, native MHA execution
and BT sparsity acceleration.
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10 changes: 10 additions & 0 deletions beginner_source/t5_tutoria.rst
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T5-Base Model for Summarization, Sentiment Classification, and Translation
==========================================================================

This tutorial has been deprecated.

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<meta http-equiv="Refresh" content="3; url='https://pytorch.org/tutorials/'" />
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