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Preprocess
[[open-in-colab]]
Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, they need to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare your data for the model. In this tutorial, you'll learn that for:
Text, use a Tokenizer to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
Speech and audio, use a Feature extractor to extract sequential features from audio waveforms and convert them into tensors.
Image inputs use a ImageProcessor to convert images into tensors.
Multimodal inputs, use a Processor to combine a tokenizer and a feature extractor or image processor.
AutoProcessor always works and automatically chooses the correct class for the model you're using, whether you're using a tokenizer, image processor, feature extractor or processor.
Before you begin, install 🤗 Datasets so you can load some datasets to experiment with:
pip install datasets
Natural Language Processing
The main tool for preprocessing textual data is a tokenizer. A tokenizer splits text into tokens according to a set of rules. The tokens are converted into numbers and then tensors, which become the model inputs. Any additional inputs required by the model are added by the tokenizer.
If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer. This ensures the text is split the same way as the pretraining corpus, and uses the same corresponding tokens-to-index (usually referred to as the vocab) during pretraining.
Get started by loading a pretrained tokenizer with the [AutoTokenizer.from_pretrained] method. This downloads the vocab a model was pretrained with:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
Then pass your text to the tokenizer:
encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
The tokenizer returns a dictionary with three important items:
input_ids are the indices corresponding to each token in the sentence.
attention_mask indicates whether a token should be attended to or not.
token_type_ids identifies which sequence a token belongs to when there is more than one sequence.
Return your input by decoding the input_ids:
tokenizer.decode(encoded_input["input_ids"])
'[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
As you can see, the tokenizer added two special tokens - CLS and SEP (classifier and separator) - to the sentence. Not all models need
special tokens, but if they do, the tokenizer automatically adds them for you.
If there are several sentences you want to preprocess, pass them as a list to the tokenizer:
batch_sentences = [
"But what about second breakfast?",
"Don't think he knows about second breakfast, Pip.",
"What about elevensies?",
]
encoded_inputs = tokenizer(batch_sentences)
print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]]}
Pad
Sentences aren't always the same length which can be an issue because tensors, the model inputs, need to have a uniform shape. Padding is a strategy for ensuring tensors are rectangular by adding a special padding token to shorter sentences.
Set the padding parameter to True to pad the shorter sequences in the batch to match the longest sequence:
batch_sentences = [
"But what about second breakfast?",
"Don't think he knows about second breakfast, Pip.",
"What about elevensies?",
]
encoded_input = tokenizer(batch_sentences, padding=True)
print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
The first and third sentences are now padded with 0's because they are shorter.
Truncation
On the other end of the spectrum, sometimes a sequence may be too long for a model to handle. In this case, you'll need to truncate the sequence to a shorter length.
Set the truncation parameter to True to truncate a sequence to the maximum length accepted by the model:
batch_sentences = [
"But what about second breakfast?",
"Don't think he knows about second breakfast, Pip.",
"What about elevensies?",
]
encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
Check out the Padding and truncation concept guide to learn more different padding and truncation arguments.
Build tensors
Finally, you want the tokenizer to return the actual tensors that get fed to the model.
Set the return_tensors parameter to either pt for PyTorch, or tf for TensorFlow:
batch_sentences = [
"But what about second breakfast?",
"Don't think he knows about second breakfast, Pip.",
"What about elevensies?",
]
encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
print(encoded_input)
{'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}
</pt>
<tf>py
batch_sentences = [
"But what about second breakfast?",
"Don't think he knows about second breakfast, Pip.",
"What about elevensies?",
]
encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
print(encoded_input)
{'input_ids': ,
'token_type_ids': ,
'attention_mask': }
Different pipelines support tokenizer arguments in their __call__() differently. text-2-text-generation pipelines support (i.e. pass on)
only truncation. text-generation pipelines support max_length, truncation, padding and add_special_tokens.
In fill-mask pipelines, tokenizer arguments can be passed in the tokenizer_kwargs argument (dictionary).
Audio
For audio tasks, you'll need a feature extractor to prepare your dataset for the model. The feature extractor is designed to extract features from raw audio data, and convert them into tensors.
Load the MInDS-14 dataset (see the 🤗 Datasets tutorial for more details on how to load a dataset) to see how you can use a feature extractor with audio datasets:
from datasets import load_dataset, Audio
dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
Access the first element of the audio column to take a look at the input. Calling the audio column automatically loads and resamples the audio file:
dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, , -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
This returns three items:
array is the speech signal loaded - and potentially resampled - as a 1D array.
path points to the location of the audio file.
sampling_rate refers to how many data points in the speech signal are measured per second.
For this tutorial, you'll use the Wav2Vec2 model. Take a look at the model card, and you'll learn Wav2Vec2 is pretrained on 16kHz sampled speech audio. It is important your audio data's sampling rate matches the sampling rate of the dataset used to pretrain the model. If your data's sampling rate isn't the same, then you need to resample your data.
Use 🤗 Datasets' [~datasets.Dataset.cast_column] method to upsample the sampling rate to 16kHz:
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
Call the audio column again to resample the audio file:
dataset[0]["audio"]
{'array': array([ 2.3443763e-05, 2.1729663e-04, 2.2145823e-04, ,
3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 16000}
Next, load a feature extractor to normalize and pad the input. When padding textual data, a 0 is added for shorter sequences. The same idea applies to audio data. The feature extractor adds a 0 - interpreted as silence - to array.
Load the feature extractor with [AutoFeatureExtractor.from_pretrained]:
from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
Pass the audio array to the feature extractor. We also recommend adding the sampling_rate argument in the feature extractor in order to better debug any silent errors that may occur.
audio_input = [dataset[0]["audio"]["array"]]
feature_extractor(audio_input, sampling_rate=16000)
{'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ,
5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]}
Just like the tokenizer, you can apply padding or truncation to handle variable sequences in a batch. Take a look at the sequence length of these two audio samples:
dataset[0]["audio"]["array"].shape
(173398,)
dataset[1]["audio"]["array"].shape
(106496,)
Create a function to preprocess the dataset so the audio samples are the same lengths. Specify a maximum sample length, and the feature extractor will either pad or truncate the sequences to match it:
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays,
sampling_rate=16000,
padding=True,
max_length=100000,
truncation=True,
)
return inputs
Apply the preprocess_function to the first few examples in the dataset:
processed_dataset = preprocess_function(dataset[:5])
The sample lengths are now the same and match the specified maximum length. You can pass your processed dataset to the model now!
processed_dataset["input_values"][0].shape
(100000,)
processed_dataset["input_values"][1].shape
(100000,)
Computer vision
For computer vision tasks, you'll need an image processor to prepare your dataset for the model.
Image preprocessing consists of several steps that convert images into the input expected by the model. These steps
include but are not limited to resizing, normalizing, color channel correction, and converting images to tensors.
Image preprocessing often follows some form of image augmentation. Both image preprocessing and image augmentation
transform image data, but they serve different purposes:
Image augmentation alters images in a way that can help prevent overfitting and increase the robustness of the model. You can get creative in how you augment your data - adjust brightness and colors, crop, rotate, resize, zoom, etc. However, be mindful not to change the meaning of the images with your augmentations.
Image preprocessing guarantees that the images match the model’s expected input format. When fine-tuning a computer vision model, images must be preprocessed exactly as when the model was initially trained.
You can use any library you like for image augmentation. For image preprocessing, use the ImageProcessor associated with the model.
Load the food101 dataset (see the 🤗 Datasets tutorial for more details on how to load a dataset) to see how you can use an image processor with computer vision datasets:
Use 🤗 Datasets split parameter to only load a small sample from the training split since the dataset is quite large!
from datasets import load_dataset
dataset = load_dataset("food101", split="train[:100]")
Next, take a look at the image with 🤗 Datasets Image feature:
dataset[0]["image"]
Load the image processor with [AutoImageProcessor.from_pretrained]:
from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
First, let's add some image augmentation. You can use any library you prefer, but in this tutorial, we'll use torchvision's transforms module. If you're interested in using another data augmentation library, learn how in the Albumentations or Kornia notebooks.
Here we use Compose to chain together a couple of
transforms - RandomResizedCrop and ColorJitter.
Note that for resizing, we can get the image size requirements from the image_processor. For some models, an exact height and
width are expected, for others only the shortest_edge is defined.
from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose
size = (
image_processor.size["shortest_edge"]
if "shortest_edge" in image_processor.size
else (image_processor.size["height"], image_processor.size["width"])
)
_transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])
The model accepts pixel_values
as its input. ImageProcessor can take care of normalizing the images, and generating appropriate tensors.
Create a function that combines image augmentation and image preprocessing for a batch of images and generates pixel_values:
def transforms(examples):
images = [_transforms(img.convert("RGB")) for img in examples["image"]]
examples["pixel_values"] = image_processor(images, do_resize=False, return_tensors="pt")["pixel_values"]
return examples
In the example above we set do_resize=False because we have already resized the images in the image augmentation transformation,
and leveraged the size attribute from the appropriate image_processor. If you do not resize images during image augmentation,
leave this parameter out. By default, ImageProcessor will handle the resizing.
If you wish to normalize images as a part of the augmentation transformation, use the image_processor.image_mean,
and image_processor.image_std values.
Then use 🤗 Datasets[~datasets.Dataset.set_transform] to apply the transforms on the fly:
dataset.set_transform(transforms)
Now when you access the image, you'll notice the image processor has added pixel_values. You can pass your processed dataset to the model now!
dataset[0].keys()
Here is what the image looks like after the transforms are applied. The image has been randomly cropped and it's color properties are different.
import numpy as np
import matplotlib.pyplot as plt
img = dataset[0]["pixel_values"]
plt.imshow(img.permute(1, 2, 0))
For tasks like object detection, semantic segmentation, instance segmentation, and panoptic segmentation, ImageProcessor
offers post processing methods. These methods convert model's raw outputs into meaningful predictions such as bounding boxes,
or segmentation maps.
Pad
In some cases, for instance, when fine-tuning DETR, the model applies scale augmentation at training
time. This may cause images to be different sizes in a batch. You can use [DetrImageProcessor.pad]
from [DetrImageProcessor] and define a custom collate_fn to batch images together.
def collate_fn(batch):
pixel_values = [item["pixel_values"] for item in batch]
encoding = image_processor.pad(pixel_values, return_tensors="pt")
labels = [item["labels"] for item in batch]
batch = {}
batch["pixel_values"] = encoding["pixel_values"]
batch["pixel_mask"] = encoding["pixel_mask"]
batch["labels"] = labels
return batch
Multimodal
For tasks involving multimodal inputs, you'll need a processor to prepare your dataset for the model. A processor couples together two processing objects such as as tokenizer and feature extractor.
Load the LJ Speech dataset (see the 🤗 Datasets tutorial for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):
from datasets import load_dataset
lj_speech = load_dataset("lj_speech", split="train")
For ASR, you're mainly focused on audio and text so you can remove the other columns:
lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
Now take a look at the audio and text columns:
lj_speech[0]["audio"]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ,
7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
'sampling_rate': 22050}
lj_speech[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
Remember you should always resample your audio dataset's sampling rate to match the sampling rate of the dataset used to pretrain a model!
lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
Load a processor with [AutoProcessor.from_pretrained]:
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
Create a function to process the audio data contained in array to input_values, and tokenize text to labels. These are the inputs to the model:
def prepare_dataset(example):
audio = example["audio"]
example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
return example
Apply the prepare_dataset function to a sample:
prepare_dataset(lj_speech[0])
The processor has now added input_values and labels, and the sampling rate has also been correctly downsampled to 16kHz. You can pass your processed dataset to the model now! |
Run training on Amazon SageMaker
The documentation has been moved to hf.co/docs/sagemaker. This page will be removed in transformers 5.0.
Table of Content
Train Hugging Face models on Amazon SageMaker with the SageMaker Python SDK
Deploy Hugging Face models to Amazon SageMaker with the SageMaker Python SDK
|
How to convert a 🤗 Transformers model to TensorFlow?
Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when
designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that
adding TensorFlow compatibility to an existing model is simpler than adding a new model from scratch!
Whether you wish to have a deeper understanding of large TensorFlow models, make a major open-source contribution, or
enable TensorFlow for your model of choice, this guide is for you.
This guide empowers you, a member of our community, to contribute TensorFlow model weights and/or
architectures to be used in 🤗 Transformers, with minimal supervision from the Hugging Face team. Writing a new model
is no small feat, but hopefully this guide will make it less of a rollercoaster 🎢 and more of a walk in the park 🚶.
Harnessing our collective experiences is absolutely critical to make this process increasingly easier, and thus we
highly encourage that you suggest improvements to this guide!
Before you dive deeper, it is recommended that you check the following resources if you're new to 🤗 Transformers:
- General overview of 🤗 Transformers
- Hugging Face's TensorFlow Philosophy
In the remainder of this guide, you will learn what's needed to add a new TensorFlow model architecture, the
procedure to convert PyTorch into TensorFlow model weights, and how to efficiently debug mismatches across ML
frameworks. Let's get started!
Are you unsure whether the model you wish to use already has a corresponding TensorFlow architecture?
Check the model_type field of the config.json of your model of choice
(example). If the corresponding model folder in
🤗 Transformers has a file whose name starts with "modeling_tf", it means that it has a corresponding TensorFlow
architecture (example).
Step-by-step guide to add TensorFlow model architecture code
There are many ways to design a large model architecture, and multiple ways of implementing said design. However,
you might recall from our general overview of 🤗 Transformers
that we are an opinionated bunch - the ease of use of 🤗 Transformers relies on consistent design choices. From
experience, we can tell you a few important things about adding TensorFlow models:
Don't reinvent the wheel! More often than not, there are at least two reference implementations you should check: the
PyTorch equivalent of the model you are implementing and other TensorFlow models for the same class of problems.
Great model implementations survive the test of time. This doesn't happen because the code is pretty, but rather
because the code is clear, easy to debug and build upon. If you make the life of the maintainers easy with your
TensorFlow implementation, by replicating the same patterns as in other TensorFlow models and minimizing the mismatch
to the PyTorch implementation, you ensure your contribution will be long lived.
Ask for help when you're stuck! The 🤗 Transformers team is here to help, and we've probably found solutions to the same
problems you're facing.
Here's an overview of the steps needed to add a TensorFlow model architecture:
1. Select the model you wish to convert
2. Prepare transformers dev environment
3. (Optional) Understand theoretical aspects and the existing implementation
4. Implement the model architecture
5. Implement model tests
6. Submit the pull request
7. (Optional) Build demos and share with the world
1.-3. Prepare your model contribution
1. Select the model you wish to convert
Let's start off with the basics: the first thing you need to know is the architecture you want to convert. If you
don't have your eyes set on a specific architecture, asking the 🤗 Transformers team for suggestions is a great way to
maximize your impact - we will guide you towards the most prominent architectures that are missing on the TensorFlow
side. If the specific model you want to use with TensorFlow already has a TensorFlow architecture implementation in
🤗 Transformers but is lacking weights, feel free to jump straight into the
weight conversion section
of this page.
For simplicity, the remainder of this guide assumes you've decided to contribute with the TensorFlow version of
BrandNewBert (the same example as in the guide to add a new model from scratch).
Before starting the work on a TensorFlow model architecture, double-check that there is no ongoing effort to do so.
You can search for BrandNewBert on the
pull request GitHub page to confirm that there is no
TensorFlow-related pull request.
2. Prepare transformers dev environment
Having selected the model architecture, open a draft PR to signal your intention to work on it. Follow the
instructions below to set up your environment and open a draft PR.
Fork the repository by clicking on the 'Fork' button on the
repository's page. This creates a copy of the code under your GitHub user account.
Clone your transformers fork to your local disk, and add the base repository as a remote:
git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
Set up a development environment, for instance by running the following command:
python -m venv .env
source .env/bin/activate
pip install -e ".[dev]"
Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
failure with this command. If that's the case make sure to install TensorFlow then do:
pip install -e ".[quality]"
Note: You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
Create a branch with a descriptive name from your main branch
git checkout -b add_tf_brand_new_bert
Fetch and rebase to current main
git fetch upstream
git rebase upstream/main
Add an empty .py file in transformers/src/models/brandnewbert/ named modeling_tf_brandnewbert.py. This will
be your TensorFlow model file.
Push the changes to your account using:
git add .
git commit -m "initial commit"
git push -u origin add_tf_brand_new_bert
Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
future changes.
Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.
Now you have set up a development environment to port BrandNewBert to TensorFlow in 🤗 Transformers.
3. (Optional) Understand theoretical aspects and the existing implementation
You should take some time to read BrandNewBert's paper, if such descriptive work exists. There might be large
sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is
not to get a deep theoretical understanding of the paper, but to extract the necessary information required to
effectively re-implement the model in 🤗 Transformers using TensorFlow. That being said, you don't have to spend too
much time on the theoretical aspects, but rather focus on the practical ones, namely the existing model documentation
page (e.g. model docs for BERT).
After you've grasped the basics of the models you are about to implement, it's important to understand the existing
implementation. This is a great chance to confirm that a working implementation matches your expectations for the
model, as well as to foresee technical challenges on the TensorFlow side.
It's perfectly natural that you feel overwhelmed with the amount of information that you've just absorbed. It is
definitely not a requirement that you understand all facets of the model at this stage. Nevertheless, we highly
encourage you to clear any pressing questions in our forum.
4. Model implementation
Now it's time to finally start coding. Our suggested starting point is the PyTorch file itself: copy the contents of
modeling_brand_new_bert.py inside src/transformers/models/brand_new_bert/ into
modeling_tf_brand_new_bert.py. The goal of this section is to modify the file and update the import structure of
🤗 Transformers such that you can import TFBrandNewBert and
TFBrandNewBert.from_pretrained(model_repo, from_pt=True) successfully loads a working TensorFlow BrandNewBert model.
Sadly, there is no prescription to convert a PyTorch model into TensorFlow. You can, however, follow our selection of
tips to make the process as smooth as possible:
- Prepend TF to the name of all classes (e.g. BrandNewBert becomes TFBrandNewBert).
- Most PyTorch operations have a direct TensorFlow replacement. For example, torch.nn.Linear corresponds to
tf.keras.layers.Dense, torch.nn.Dropout corresponds to tf.keras.layers.Dropout, etc. If you're not sure
about a specific operation, you can use the TensorFlow documentation
or the PyTorch documentation.
- Look for patterns in the 🤗 Transformers codebase. If you come across a certain operation that doesn't have a direct
replacement, the odds are that someone else already had the same problem.
- By default, keep the same variable names and structure as in PyTorch. This will make it easier to debug, track
issues, and add fixes down the line.
- Some layers have different default values in each framework. A notable example is the batch normalization layer's
epsilon (1e-5 in PyTorch
and 1e-3 in TensorFlow).
Double-check the documentation!
- PyTorch's nn.Parameter variables typically need to be initialized within TF Layer's build(). See the following
example: PyTorch /
TensorFlow
- If the PyTorch model has a #copied from on top of a function, the odds are that your TensorFlow model can also
borrow that function from the architecture it was copied from, assuming it has a TensorFlow architecture.
- Assigning the name attribute correctly in TensorFlow functions is critical to do the from_pt=True weight
cross-loading. name is almost always the name of the corresponding variable in the PyTorch code. If name is not
properly set, you will see it in the error message when loading the model weights.
- The logic of the base model class, BrandNewBertModel, will actually reside in TFBrandNewBertMainLayer, a Keras
layer subclass (example).
TFBrandNewBertModel will simply be a wrapper around this layer.
- Keras models need to be built in order to load pretrained weights. For that reason, TFBrandNewBertPreTrainedModel
will need to hold an example of inputs to the model, the dummy_inputs
(example).
- If you get stuck, ask for help - we're here to help you! 🤗
In addition to the model file itself, you will also need to add the pointers to the model classes and related
documentation pages. You can complete this part entirely following the patterns in other PRs
(example). Here's a list of the needed manual
changes:
- Include all public classes of BrandNewBert in src/transformers/__init__.py
- Add BrandNewBert classes to the corresponding Auto classes in src/transformers/models/auto/modeling_tf_auto.py
- Add the lazy loading classes related to BrandNewBert in src/transformers/utils/dummy_tf_objects.py
- Update the import structures for the public classes in src/transformers/models/brand_new_bert/__init__.py
- Add the documentation pointers to the public methods of BrandNewBert in docs/source/en/model_doc/brand_new_bert.md
- Add yourself to the list of contributors to BrandNewBert in docs/source/en/model_doc/brand_new_bert.md
- Finally, add a green tick ✅ to the TensorFlow column of BrandNewBert in docs/source/en/index.md
When you're happy with your implementation, run the following checklist to confirm that your model architecture is
ready:
1. All layers that behave differently at train time (e.g. Dropout) are called with a training argument, which is
propagated all the way from the top-level classes
2. You have used #copied from whenever possible
3. TFBrandNewBertMainLayer and all classes that use it have their call function decorated with @unpack_inputs
4. TFBrandNewBertMainLayer is decorated with @keras_serializable
5. A TensorFlow model can be loaded from PyTorch weights using TFBrandNewBert.from_pretrained(model_repo, from_pt=True)
6. You can call the TensorFlow model using the expected input format
5. Add model tests
Hurray, you've implemented a TensorFlow model! Now it's time to add tests to make sure that your model behaves as
expected. As in the previous section, we suggest you start by copying the test_modeling_brand_new_bert.py file in
tests/models/brand_new_bert/ into test_modeling_tf_brand_new_bert.py, and continue by making the necessary
TensorFlow replacements. For now, in all .from_pretrained() calls, you should use the from_pt=True flag to load
the existing PyTorch weights.
After you're done, it's time for the moment of truth: run the tests! 😬
NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
The most likely outcome is that you'll see a bunch of errors. Don't worry, this is expected! Debugging ML models is
notoriously hard, and the key ingredient to success is patience (and breakpoint()). In our experience, the hardest
problems arise from subtle mismatches between ML frameworks, for which we have a few pointers at the end of this guide.
In other cases, a general test might not be directly applicable to your model, in which case we suggest an override
at the model test class level. Regardless of the issue, don't hesitate to ask for help in your draft pull request if
you're stuck.
When all tests pass, congratulations, your model is nearly ready to be added to the 🤗 Transformers library! 🎉
6.-7. Ensure everyone can use your model
6. Submit the pull request
Once you're done with the implementation and the tests, it's time to submit a pull request. Before pushing your code,
run our code formatting utility, make fixup 🪄. This will automatically fix any formatting issues, which would cause
our automatic checks to fail.
It's now time to convert your draft pull request into a real pull request. To do so, click on the "Ready for
review" button and add Joao (@gante) and Matt (@Rocketknight1) as reviewers. A model pull request will need
at least 3 reviewers, but they will take care of finding appropriate additional reviewers for your model.
After all reviewers are happy with the state of your PR, the final action point is to remove the from_pt=True flag in
.from_pretrained() calls. Since there are no TensorFlow weights, you will have to add them! Check the section
below for instructions on how to do it.
Finally, when the TensorFlow weights get merged, you have at least 3 reviewer approvals, and all CI checks are
green, double-check the tests locally one last time
NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
and we will merge your PR! Congratulations on the milestone 🎉
7. (Optional) Build demos and share with the world
One of the hardest parts about open-source is discovery. How can the other users learn about the existence of your
fabulous TensorFlow contribution? With proper communication, of course! 📣
There are two main ways to share your model with the community:
- Build demos. These include Gradio demos, notebooks, and other fun ways to show off your model. We highly
encourage you to add a notebook to our community-driven demos.
- Share stories on social media like Twitter and LinkedIn. You should be proud of your work and share
your achievement with the community - your model can now be used by thousands of engineers and researchers around
the world 🌍! We will be happy to retweet your posts and help you share your work with the community.
Adding TensorFlow weights to 🤗 Hub
Assuming that the TensorFlow model architecture is available in 🤗 Transformers, converting PyTorch weights into
TensorFlow weights is a breeze!
Here's how to do it:
1. Make sure you are logged into your Hugging Face account in your terminal. You can log in using the command
huggingface-cli login (you can find your access tokens here)
2. Run transformers-cli pt-to-tf --model-name foo/bar, where foo/bar is the name of the model repository
containing the PyTorch weights you want to convert
3. Tag @joaogante and @Rocketknight1 in the 🤗 Hub PR the command above has just created
That's it! 🎉
Debugging mismatches across ML frameworks 🐛
At some point, when adding a new architecture or when creating TensorFlow weights for an existing architecture, you
might come across errors complaining about mismatches between PyTorch and TensorFlow. You might even decide to open the
model architecture code for the two frameworks, and find that they look identical. What's going on? 🤔
First of all, let's talk about why understanding these mismatches matters. Many community members will use 🤗
Transformers models out of the box, and trust that our models behave as expected. When there is a large mismatch
between the two frameworks, it implies that the model is not following the reference implementation for at least one
of the frameworks. This might lead to silent failures, in which the model runs but has poor performance. This is
arguably worse than a model that fails to run at all! To that end, we aim at having a framework mismatch smaller than
1e-5 at all stages of the model.
As in other numerical problems, the devil is in the details. And as in any detail-oriented craft, the secret
ingredient here is patience. Here is our suggested workflow for when you come across this type of issues:
1. Locate the source of mismatches. The model you're converting probably has near identical inner variables up to a
certain point. Place breakpoint() statements in the two frameworks' architectures, and compare the values of the
numerical variables in a top-down fashion until you find the source of the problems.
2. Now that you've pinpointed the source of the issue, get in touch with the 🤗 Transformers team. It is possible
that we've seen a similar problem before and can promptly provide a solution. As a fallback, scan popular pages
like StackOverflow and GitHub issues.
3. If there is no solution in sight, it means you'll have to go deeper. The good news is that you've located the
issue, so you can focus on the problematic instruction, abstracting away the rest of the model! The bad news is
that you'll have to venture into the source implementation of said instruction. In some cases, you might find an
issue with a reference implementation - don't abstain from opening an issue in the upstream repository.
In some cases, in discussion with the 🤗 Transformers team, we might find that fixing the mismatch is infeasible.
When the mismatch is very small in the output layers of the model (but potentially large in the hidden states), we
might decide to ignore it in favor of distributing the model. The pt-to-tf CLI mentioned above has a --max-error
flag to override the error message at weight conversion time. |
Share a model
The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We encourage you to consider sharing your model with the community to help others save time and resources.
In this tutorial, you will learn two methods for sharing a trained or fine-tuned model on the Model Hub:
Programmatically push your files to the Hub.
Drag-and-drop your files to the Hub with the web interface.
To share a model with the community, you need an account on huggingface.co. You can also join an existing organization or create a new one.
Repository features
Each repository on the Model Hub behaves like a typical GitHub repository. Our repositories offer versioning, commit history, and the ability to visualize differences.
The Model Hub's built-in versioning is based on git and git-lfs. In other words, you can treat one model as one repository, enabling greater access control and scalability. Version control allows revisions, a method for pinning a specific version of a model with a commit hash, tag or branch.
As a result, you can load a specific model version with the revision parameter:
model = AutoModel.from_pretrained(
"julien-c/EsperBERTo-small", revision="v2.0.1" # tag name, or branch name, or commit hash
)
Files are also easily edited in a repository, and you can view the commit history as well as the difference:
Setup
Before sharing a model to the Hub, you will need your Hugging Face credentials. If you have access to a terminal, run the following command in the virtual environment where 🤗 Transformers is installed. This will store your access token in your Hugging Face cache folder (~/.cache/ by default):
huggingface-cli login
If you are using a notebook like Jupyter or Colaboratory, make sure you have the huggingface_hub library installed. This library allows you to programmatically interact with the Hub.
pip install huggingface_hub
Then use notebook_login to sign-in to the Hub, and follow the link here to generate a token to login with:
from huggingface_hub import notebook_login
notebook_login()
Convert a model for all frameworks
To ensure your model can be used by someone working with a different framework, we recommend you convert and upload your model with both PyTorch and TensorFlow checkpoints. While users are still able to load your model from a different framework if you skip this step, it will be slower because 🤗 Transformers will need to convert the checkpoint on-the-fly.
Converting a checkpoint for another framework is easy. Make sure you have PyTorch and TensorFlow installed (see here for installation instructions), and then find the specific model for your task in the other framework.
Specify from_tf=True to convert a checkpoint from TensorFlow to PyTorch:
pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
pt_model.save_pretrained("path/to/awesome-name-you-picked")
``
</pt>
<tf>
Specifyfrom_pt=True` to convert a checkpoint from PyTorch to TensorFlow:
tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
Then you can save your new TensorFlow model with its new checkpoint:
tf_model.save_pretrained("path/to/awesome-name-you-picked")
If a model is available in Flax, you can also convert a checkpoint from PyTorch to Flax:
flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
"path/to/awesome-name-you-picked", from_pt=True
)
Push a model during training
Sharing a model to the Hub is as simple as adding an extra parameter or callback. Remember from the fine-tuning tutorial, the [TrainingArguments] class is where you specify hyperparameters and additional training options. One of these training options includes the ability to push a model directly to the Hub. Set push_to_hub=True in your [TrainingArguments]:
training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
Pass your training arguments as usual to [Trainer]:
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
After you fine-tune your model, call [~transformers.Trainer.push_to_hub] on [Trainer] to push the trained model to the Hub. 🤗 Transformers will even automatically add training hyperparameters, training results and framework versions to your model card!
trainer.push_to_hub()
``
</pt>
<tf>
Share a model to the Hub with [PushToHubCallback]. In the [PushToHubCallback`] function, add:
An output directory for your model.
A tokenizer.
The hub_model_id, which is your Hub username and model name.
from transformers import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model"
)
Add the callback to fit, and 🤗 Transformers will push the trained model to the Hub:
model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback)
Use the push_to_hub function
You can also call push_to_hub directly on your model to upload it to the Hub.
Specify your model name in push_to_hub:
pt_model.push_to_hub("my-awesome-model")
This creates a repository under your username with the model name my-awesome-model. Users can now load your model with the from_pretrained function:
from transformers import AutoModel
model = AutoModel.from_pretrained("your_username/my-awesome-model")
If you belong to an organization and want to push your model under the organization name instead, just add it to the repo_id:
pt_model.push_to_hub("my-awesome-org/my-awesome-model")
The push_to_hub function can also be used to add other files to a model repository. For example, add a tokenizer to a model repository:
tokenizer.push_to_hub("my-awesome-model")
Or perhaps you'd like to add the TensorFlow version of your fine-tuned PyTorch model:
tf_model.push_to_hub("my-awesome-model")
Now when you navigate to your Hugging Face profile, you should see your newly created model repository. Clicking on the Files tab will display all the files you've uploaded to the repository.
For more details on how to create and upload files to a repository, refer to the Hub documentation here.
Upload with the web interface
Users who prefer a no-code approach are able to upload a model through the Hub's web interface. Visit huggingface.co/new to create a new repository:
From here, add some information about your model:
Select the owner of the repository. This can be yourself or any of the organizations you belong to.
Pick a name for your model, which will also be the repository name.
Choose whether your model is public or private.
Specify the license usage for your model.
Now click on the Files tab and click on the Add file button to upload a new file to your repository. Then drag-and-drop a file to upload and add a commit message.
Add a model card
To make sure users understand your model's capabilities, limitations, potential biases and ethical considerations, please add a model card to your repository. The model card is defined in the README.md file. You can add a model card by:
Manually creating and uploading a README.md file.
Clicking on the Edit model card button in your model repository.
Take a look at the DistilBert model card for a good example of the type of information a model card should include. For more details about other options you can control in the README.md file such as a model's carbon footprint or widget examples, refer to the documentation here. |
GPU inference
GPUs are the standard choice of hardware for machine learning, unlike CPUs, because they are optimized for memory bandwidth and parallelism. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed up GPU inference. In this guide, you'll learn how to use FlashAttention-2 (a more memory-efficient attention mechanism), BetterTransformer (a PyTorch native fastpath execution), and bitsandbytes to quantize your model to a lower precision. Finally, learn how to use 🤗 Optimum to accelerate inference with ONNX Runtime on Nvidia and AMD GPUs.
The majority of the optimizations described here also apply to multi-GPU setups!
FlashAttention-2
FlashAttention-2 is experimental and may change considerably in future versions.
FlashAttention-2 is a faster and more efficient implementation of the standard attention mechanism that can significantly speedup inference by:
additionally parallelizing the attention computation over sequence length
partitioning the work between GPU threads to reduce communication and shared memory reads/writes between them
FlashAttention-2 is currently supported for the following architectures:
* Bark
* Bart
* DistilBert
* Gemma
* GPTBigCode
* GPTNeo
* GPTNeoX
* Falcon
* Llama
* Llava
* VipLlava
* MBart
* Mistral
* Mixtral
* OPT
* Phi
* StableLm
* Starcoder2
* Qwen2
* Whisper
You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
Before you begin, make sure you have FlashAttention-2 installed.
pip install flash-attn --no-build-isolation
We strongly suggest referring to the detailed installation instructions to learn more about supported hardware and data types!
FlashAttention-2 is also supported on AMD GPUs and current support is limited to Instinct MI210 and Instinct MI250. We strongly suggest using this Dockerfile to use FlashAttention-2 on AMD GPUs.
To enable FlashAttention-2, pass the argument attn_implementation="flash_attention_2" to [~AutoModelForCausalLM.from_pretrained]:
thon
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
FlashAttention-2 can only be used when the model's dtype is fp16 or bf16. Make sure to cast your model to the appropriate dtype and load them on a supported device before using FlashAttention-2.
You can also set use_flash_attention_2=True to enable FlashAttention-2 but it is deprecated in favor of attn_implementation="flash_attention_2".
FlashAttention-2 can be combined with other optimization techniques like quantization to further speedup inference. For example, you can combine FlashAttention-2 with 8-bit or 4-bit quantization:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
load in 8bit
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
attn_implementation="flash_attention_2",
)
load in 4bit
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
attn_implementation="flash_attention_2",
)
Expected speedups
You can benefit from considerable speedups for inference, especially for inputs with long sequences. However, since FlashAttention-2 does not support computing attention scores with padding tokens, you must manually pad/unpad the attention scores for batched inference when the sequence contains padding tokens. This leads to a significant slowdown for batched generations with padding tokens.
To overcome this, you should use FlashAttention-2 without padding tokens in the sequence during training (by packing a dataset or concatenating sequences until reaching the maximum sequence length).
For a single forward pass on tiiuae/falcon-7b with a sequence length of 4096 and various batch sizes without padding tokens, the expected speedup is:
For a single forward pass on meta-llama/Llama-7b-hf with a sequence length of 4096 and various batch sizes without padding tokens, the expected speedup is:
For sequences with padding tokens (generating with padding tokens), you need to unpad/pad the input sequences to correctly compute the attention scores. With a relatively small sequence length, a single forward pass creates overhead leading to a small speedup (in the example below, 30% of the input is filled with padding tokens):
But for larger sequence lengths, you can expect even more speedup benefits:
FlashAttention is more memory efficient, meaning you can train on much larger sequence lengths without running into out-of-memory issues. You can potentially reduce memory usage up to 20x for larger sequence lengths. Take a look at the flash-attention repository for more details.
PyTorch scaled dot product attention
PyTorch's torch.nn.functional.scaled_dot_product_attention (SDPA) can also call FlashAttention and memory-efficient attention kernels under the hood. SDPA support is currently being added natively in Transformers and is used by default for torch>=2.1.1 when an implementation is available.
For now, Transformers supports SDPA inference and training for the following architectures:
* Bart
* GPTBigCode
* Falcon
* Gemma
* Llama
* Phi
* Idefics
* Whisper
* Mistral
* Mixtral
* StableLm
* Starcoder2
* Qwen2
FlashAttention can only be used for models with the fp16 or bf16 torch type, so make sure to cast your model to the appropriate type first. The memory-efficient attention backend is able to handle fp32 models.
By default, SDPA selects the most performant kernel available but you can check whether a backend is available in a given setting (hardware, problem size) with torch.backends.cuda.sdp_kernel as a context manager:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16).to("cuda")
convert the model to BetterTransformer
model.to_bettertransformer()
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
If you see a bug with the traceback below, try using the nightly version of PyTorch which may have broader coverage for FlashAttention:
```bash
RuntimeError: No available kernel. Aborting execution.
install PyTorch nightly
pip3 install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118
BetterTransformer
Some BetterTransformer features are being upstreamed to Transformers with default support for native torch.nn.scaled_dot_product_attention. BetterTransformer still has a wider coverage than the Transformers SDPA integration, but you can expect more and more architectures to natively support SDPA in Transformers.
Check out our benchmarks with BetterTransformer and scaled dot product attention in the Out of the box acceleration and memory savings of 🤗 decoder models with PyTorch 2.0 and learn more about the fastpath execution in the BetterTransformer blog post.
BetterTransformer accelerates inference with its fastpath (native PyTorch specialized implementation of Transformer functions) execution. The two optimizations in the fastpath execution are:
fusion, which combines multiple sequential operations into a single "kernel" to reduce the number of computation steps
skipping the inherent sparsity of padding tokens to avoid unnecessary computation with nested tensors
BetterTransformer also converts all attention operations to use the more memory-efficient scaled dot product attention (SDPA), and it calls optimized kernels like FlashAttention under the hood.
Before you start, make sure you have 🤗 Optimum installed.
Then you can enable BetterTransformer with the [PreTrainedModel.to_bettertransformer] method:
python
model = model.to_bettertransformer()
You can return the original Transformers model with the [~PreTrainedModel.reverse_bettertransformer] method. You should use this before saving your model to use the canonical Transformers modeling:
py
model = model.reverse_bettertransformer()
model.save_pretrained("saved_model")
bitsandbytes
bitsandbytes is a quantization library that includes support for 4-bit and 8-bit quantization. Quantization reduces your model size compared to its native full precision version, making it easier to fit large models onto GPUs with limited memory.
Make sure you have bitsandbytes and 🤗 Accelerate installed:
```bash
these versions support 8-bit and 4-bit
pip install bitsandbytes>=0.39.0 accelerate>=0.20.0
install Transformers
pip install transformers
4-bit
To load a model in 4-bit for inference, use the load_in_4bit parameter. The device_map parameter is optional, but we recommend setting it to "auto" to allow 🤗 Accelerate to automatically and efficiently allocate the model given the available resources in the environment.
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-2b5"
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 600MB of memory to the first GPU and 1GB of memory to the second GPU:
py
max_memory_mapping = {0: "600MB", 1: "1GB"}
model_name = "bigscience/bloom-3b"
model_4bit = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
)
8-bit
If you're curious and interested in learning more about the concepts underlying 8-bit quantization, read the Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using Hugging Face Transformers, Accelerate and bitsandbytes blog post.
To load a model in 8-bit for inference, use the load_in_8bit parameter. The device_map parameter is optional, but we recommend setting it to "auto" to allow 🤗 Accelerate to automatically and efficiently allocate the model given the available resources in the environment:
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-2b5"
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
If you're loading a model in 8-bit for text generation, you should use the [~transformers.GenerationMixin.generate] method instead of the [Pipeline] function which is not optimized for 8-bit models and will be slower. Some sampling strategies, like nucleus sampling, are also not supported by the [Pipeline] for 8-bit models. You should also place all inputs on the same device as the model:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "bigscience/bloom-2b5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
prompt = "Hello, my llama is cute"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 1GB of memory to the first GPU and 2GB of memory to the second GPU:
py
max_memory_mapping = {0: "1GB", 1: "2GB"}
model_name = "bigscience/bloom-3b"
model_8bit = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
)
Feel free to try running a 11 billion parameter T5 model or the 3 billion parameter BLOOM model for inference on Google Colab's free tier GPUs!
🤗 Optimum
Learn more details about using ORT with 🤗 Optimum in the Accelerated inference on NVIDIA GPUs and Accelerated inference on AMD GPUs guides. This section only provides a brief and simple example.
ONNX Runtime (ORT) is a model accelerator that supports accelerated inference on Nvidia GPUs, and AMD GPUs that use ROCm stack. ORT uses optimization techniques like fusing common operations into a single node and constant folding to reduce the number of computations performed and speedup inference. ORT also places the most computationally intensive operations on the GPU and the rest on the CPU to intelligently distribute the workload between the two devices.
ORT is supported by 🤗 Optimum which can be used in 🤗 Transformers. You'll need to use an [~optimum.onnxruntime.ORTModel] for the task you're solving, and specify the provider parameter which can be set to either CUDAExecutionProvider, ROCMExecutionProvider or TensorrtExecutionProvider. If you want to load a model that was not yet exported to ONNX, you can set export=True to convert your model on-the-fly to the ONNX format:
from optimum.onnxruntime import ORTModelForSequenceClassification
ort_model = ORTModelForSequenceClassification.from_pretrained(
"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
export=True,
provider="CUDAExecutionProvider",
)
Now you're free to use the model for inference:
from optimum.pipelines import pipeline
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english")
pipeline = pipeline(task="text-classification", model=ort_model, tokenizer=tokenizer, device="cuda:0")
result = pipeline("Both the music and visual were astounding, not to mention the actors performance.")
Combine optimizations
It is often possible to combine several of the optimization techniques described above to get the best inference performance possible for your model. For example, you can load a model in 4-bit, and then enable BetterTransformer with FlashAttention:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
load model in 4-bit
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", quantization_config=quantization_config)
enable BetterTransformer
model = model.to_bettertransformer()
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
enable FlashAttention
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
``` |
Efficient Training on Multiple CPUs
When training on a single CPU is too slow, we can use multiple CPUs. This guide focuses on PyTorch-based DDP enabling
distributed CPU training efficiently on bare metal and Kubernetes.
Intel® oneCCL Bindings for PyTorch
Intel® oneCCL (collective communications library) is a library for efficient distributed deep learning training implementing such collectives like allreduce, allgather, alltoall. For more information on oneCCL, please refer to the oneCCL documentation and oneCCL specification.
Module oneccl_bindings_for_pytorch (torch_ccl before version 1.12) implements PyTorch C10D ProcessGroup API and can be dynamically loaded as external ProcessGroup and only works on Linux platform now
Check more detailed information for oneccl_bind_pt.
Intel® oneCCL Bindings for PyTorch installation
Wheel files are available for the following Python versions:
| Extension Version | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10 |
| :---------------: | :--------: | :--------: | :--------: | :--------: | :---------: |
| 2.1.0 | | √ | √ | √ | √ |
| 2.0.0 | | √ | √ | √ | √ |
| 1.13.0 | | √ | √ | √ | √ |
| 1.12.100 | | √ | √ | √ | √ |
| 1.12.0 | | √ | √ | √ | √ |
Please run pip list | grep torch to get your pytorch_version.
pip install oneccl_bind_pt=={pytorch_version} -f https://developer.intel.com/ipex-whl-stable-cpu
where {pytorch_version} should be your PyTorch version, for instance 2.1.0.
Check more approaches for oneccl_bind_pt installation.
Versions of oneCCL and PyTorch must match.
oneccl_bindings_for_pytorch 1.12.0 prebuilt wheel does not work with PyTorch 1.12.1 (it is for PyTorch 1.12.0)
PyTorch 1.12.1 should work with oneccl_bindings_for_pytorch 1.12.100
Intel® MPI library
Use this standards-based MPI implementation to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. This component is part of the Intel® oneAPI HPC Toolkit.
oneccl_bindings_for_pytorch is installed along with the MPI tool set. Need to source the environment before using it.
for Intel® oneCCL >= 1.12.0
oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh
for Intel® oneCCL whose version < 1.12.0
torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))")
source $torch_ccl_path/env/setvars.sh
Intel® Extension for PyTorch installation
Intel Extension for PyTorch (IPEX) provides performance optimizations for CPU training with both Float32 and BFloat16 (refer to the single CPU section to learn more).
The following "Usage in Trainer" takes mpirun in Intel® MPI library as an example.
Usage in Trainer
To enable multi CPU distributed training in the Trainer with the ccl backend, users should add --ddp_backend ccl in the command arguments.
Let's see an example with the question-answering example
The following command enables training with 2 processes on one Xeon node, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance.
shell script
export CCL_WORKER_COUNT=1
export MASTER_ADDR=127.0.0.1
mpirun -n 2 -genv OMP_NUM_THREADS=23 \
python3 run_qa.py \
--model_name_or_path google-bert/bert-large-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
--no_cuda \
--ddp_backend ccl \
--use_ipex
The following command enables training with a total of four processes on two Xeons (node0 and node1, taking node0 as the main process), ppn (processes per node) is set to 2, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance.
In node0, you need to create a configuration file which contains the IP addresses of each node (for example hostfile) and pass that configuration file path as an argument.
shell script
cat hostfile
xxx.xxx.xxx.xxx #node0 ip
xxx.xxx.xxx.xxx #node1 ip
Now, run the following command in node0 and 4DDP will be enabled in node0 and node1 with BF16 auto mixed precision:
shell script
export CCL_WORKER_COUNT=1
export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
mpirun -f hostfile -n 4 -ppn 2 \
-genv OMP_NUM_THREADS=23 \
python3 run_qa.py \
--model_name_or_path google-bert/bert-large-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
--no_cuda \
--ddp_backend ccl \
--use_ipex \
--bf16
Usage with Kubernetes
The same distributed training job from the previous section can be deployed to a Kubernetes cluster using the
Kubeflow PyTorchJob training operator.
Setup
This example assumes that you have:
* Access to a Kubernetes cluster with Kubeflow installed
* kubectl installed and configured to access the Kubernetes cluster
* A Persistent Volume Claim (PVC) that can be used
to store datasets and model files. There are multiple options for setting up the PVC including using an NFS
storage class or a cloud storage bucket.
* A Docker container that includes your model training script and all the dependencies needed to run the script. For
distributed CPU training jobs, this typically includes PyTorch, Transformers, Intel Extension for PyTorch, Intel
oneCCL Bindings for PyTorch, and OpenSSH to communicate between the containers.
The snippet below is an example of a Dockerfile that uses a base image that supports distributed CPU training and then
extracts a Transformers release to the /workspace directory, so that the example scripts are included in the image:
```dockerfile
FROM intel/ai-workflows:torch-2.0.1-huggingface-multinode-py3.9
WORKDIR /workspace
Download and extract the transformers code
ARG HF_TRANSFORMERS_VER="4.35.2"
RUN mkdir transformers && \
curl -sSL --retry 5 https://github.com/huggingface/transformers/archive/refs/tags/v${HF_TRANSFORMERS_VER}.tar.gz | tar -C transformers --strip-components=1 -xzf -
The image needs to be built and copied to the cluster's nodes or pushed to a container registry prior to deploying the
PyTorchJob to the cluster.
PyTorchJob Specification File
The Kubeflow PyTorchJob is used to run the distributed
training job on the cluster. The yaml file for the PyTorchJob defines parameters such as:
* The name of the PyTorchJob
* The number of replicas (workers)
* The python script and it's parameters that will be used to run the training job
* The types of resources (node selector, memory, and CPU) needed for each worker
* The image/tag for the Docker container to use
* Environment variables
* A volume mount for the PVC
The volume mount defines a path where the PVC will be mounted in the container for each worker pod. This location can be
used for the dataset, checkpoint files, and the saved model after training completes.
The snippet below is an example of a yaml file for a PyTorchJob with 4 workers running the
question-answering example.
yaml
apiVersion: "kubeflow.org/v1"
kind: PyTorchJob
metadata:
name: transformers-pytorchjob
namespace: kubeflow
spec:
elasticPolicy:
rdzvBackend: c10d
minReplicas: 1
maxReplicas: 4
maxRestarts: 10
pytorchReplicaSpecs:
Worker:
replicas: 4 # The number of worker pods
restartPolicy: OnFailure
template:
spec:
containers:
- name: pytorch
image: <image name>:<tag> # Specify the docker image to use for the worker pods
imagePullPolicy: IfNotPresent
command:
- torchrun
- /workspace/transformers/examples/pytorch/question-answering/run_qa.py
- --model_name_or_path
- "google-bert/bert-large-uncased"
- --dataset_name
- "squad"
- --do_train
- --do_eval
- --per_device_train_batch_size
- "12"
- --learning_rate
- "3e-5"
- --num_train_epochs
- "2"
- --max_seq_length
- "384"
- --doc_stride
- "128"
- --output_dir
- "/tmp/pvc-mount/output"
- --no_cuda
- --ddp_backend
- "ccl"
- --use_ipex
- --bf16 # Specify --bf16 if your hardware supports bfloat16
env:
- name: LD_PRELOAD
value: "/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4.5.9:/usr/local/lib/libiomp5.so"
- name: TRANSFORMERS_CACHE
value: "/tmp/pvc-mount/transformers_cache"
- name: HF_DATASETS_CACHE
value: "/tmp/pvc-mount/hf_datasets_cache"
- name: LOGLEVEL
value: "INFO"
- name: CCL_WORKER_COUNT
value: "1"
- name: OMP_NUM_THREADS # Can be tuned for optimal performance
resources:
limits:
cpu: 200 # Update the CPU and memory limit values based on your nodes
memory: 128Gi
requests:
cpu: 200 # Update the CPU and memory request values based on your nodes
memory: 128Gi
volumeMounts:
- name: pvc-volume
mountPath: /tmp/pvc-mount
- mountPath: /dev/shm
name: dshm
restartPolicy: Never
nodeSelector: # Optionally use the node selector to specify what types of nodes to use for the workers
node-type: spr
volumes:
- name: pvc-volume
persistentVolumeClaim:
claimName: transformers-pvc
- name: dshm
emptyDir:
medium: Memory
To run this example, update the yaml based on your training script and the nodes in your cluster.
The CPU resource limits/requests in the yaml are defined in cpu units
where 1 CPU unit is equivalent to 1 physical CPU core or 1 virtual core (depending on whether the node is a physical
host or a VM). The amount of CPU and memory limits/requests defined in the yaml should be less than the amount of
available CPU/memory capacity on a single machine. It is usually a good idea to not use the entire machine's capacity in
order to leave some resources for the kubelet and OS. In order to get "guaranteed"
quality of service for the worker pods,
set the same CPU and memory amounts for both the resource limits and requests.
Deploy
After the PyTorchJob spec has been updated with values appropriate for your cluster and training job, it can be deployed
to the cluster using:
kubectl create -f pytorchjob.yaml
The kubectl get pods -n kubeflow command can then be used to list the pods in the kubeflow namespace. You should see
the worker pods for the PyTorchJob that was just deployed. At first, they will probably have a status of "Pending" as
the containers get pulled and created, then the status should change to "Running".
NAME READY STATUS RESTARTS AGE
transformers-pytorchjob-worker-0 1/1 Running 0 7m37s
transformers-pytorchjob-worker-1 1/1 Running 0 7m37s
transformers-pytorchjob-worker-2 1/1 Running 0 7m37s
transformers-pytorchjob-worker-3 1/1 Running 0 7m37s
The logs for worker can be viewed using kubectl logs -n kubeflow <pod name>. Add -f to stream the logs, for example:
kubectl logs -n kubeflow transformers-pytorchjob-worker-0 -f
After the training job completes, the trained model can be copied from the PVC or storage location. When you are done
with the job, the PyTorchJob resource can be deleted from the cluster using kubectl delete -f pytorchjob.yaml.
Summary
This guide covered running distributed PyTorch training jobs using multiple CPUs on bare metal and on a Kubernetes
cluster. Both cases utilize Intel Extension for PyTorch and Intel oneCCL Bindings for PyTorch for optimal training
performance, and can be used as a template to run your own workload on multiple nodes. |
Custom Tools and Prompts
If you are not aware of what tools and agents are in the context of transformers, we recommend you read the
Transformers Agents page first.
Transformers Agents is an experimental API that is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.
Creating and using custom tools and prompts is paramount to empowering the agent and having it perform new tasks.
In this guide we'll take a look at:
How to customize the prompt
How to use custom tools
How to create custom tools
Customizing the prompt
As explained in Transformers Agents agents can run in [~Agent.run] and [~Agent.chat] mode.
Both the run and chat modes underlie the same logic. The language model powering the agent is conditioned on a long
prompt and completes the prompt by generating the next tokens until the stop token is reached.
The only difference between the two modes is that during the chat mode the prompt is extended with
previous user inputs and model generations. This allows the agent to have access to past interactions,
seemingly giving the agent some kind of memory.
Structure of the prompt
Let's take a closer look at how the prompt is structured to understand how it can be best customized.
The prompt is structured broadly into four parts.
Introduction: how the agent should behave, explanation of the concept of tools.
Description of all the tools. This is defined by a <<all_tools>> token that is dynamically replaced at runtime with the tools defined/chosen by the user.
A set of examples of tasks and their solution
Current example, and request for solution.
To better understand each part, let's look at a shortened version of how the run prompt can look like:
````text
I will ask you to perform a task, your job is to come up with a series of simple commands in Python that will perform the task.
[]
You can print intermediate results if it makes sense to do so.
Tools:
- document_qa: This is a tool that answers a question about a document (pdf). It takes an input named document which should be the document containing the information, as well as a question that is the question about the document. It returns a text that contains the answer to the question.
- image_captioner: This is a tool that generates a description of an image. It takes an input named image which should be the image to the caption and returns a text that contains the description in English.
[]
Task: "Answer the question in the variable question about the image stored in the variable image. The question is in French."
I will use the following tools: translator to translate the question into English and then image_qa to answer the question on the input image.
Answer:
py
translated_question = translator(question=question, src_lang="French", tgt_lang="English")
print(f"The translated question is {translated_question}.")
answer = image_qa(image=image, question=translated_question)
print(f"The answer is {answer}")
Task: "Identify the oldest person in the document and create an image showcasing the result as a banner."
I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer.
Answer:
py
answer = document_qa(document, question="What is the oldest person?")
print(f"The answer is {answer}.")
image = image_generator("A banner showing " + answer)
[]
Task: "Draw me a picture of rivers and lakes"
I will use the following
`
The introduction (the text before "Tools:") explains precisely how the model shall behave and what it should do.
This part most likely does not need to be customized as the agent shall always behave the same way.
The second part (the bullet points below "Tools") is dynamically added upon calling run or chat. There are
exactly as many bullet points as there are tools in agent.toolbox and each bullet point consists of the name
and description of the tool:
text
- <tool.name>: <tool.description>
Let's verify this quickly by loading the document_qa tool and printing out the name and description.
from transformers import load_tool
document_qa = load_tool("document-question-answering")
print(f"- {document_qa.name}: {document_qa.description}")
which gives:
text
- document_qa: This is a tool that answers a question about a document (pdf). It takes an input named `document` which should be the document containing the information, as well as a `question` that is the question about the document. It returns a text that contains the answer to the question.
We can see that the tool name is short and precise. The description includes two parts, the first explaining
what the tool does and the second states what input arguments and return values are expected.
A good tool name and tool description are very important for the agent to correctly use it. Note that the only
information the agent has about the tool is its name and description, so one should make sure that both
are precisely written and match the style of the existing tools in the toolbox. In particular make sure the description
mentions all the arguments expected by name in code-style, along with the expected type and a description of what they
are.
Check the naming and description of the curated Transformers tools to better understand what name and
description a tool is expected to have. You can see all tools with the [Agent.toolbox] property.
The third part includes a set of curated examples that show the agent exactly what code it should produce
for what kind of user request. The large language models empowering the agent are extremely good at
recognizing patterns in a prompt and repeating the pattern with new data. Therefore, it is very important
that the examples are written in a way that maximizes the likelihood of the agent to generating correct,
executable code in practice.
Let's have a look at one example:
```text
Task: "Identify the oldest person in thedocument` and create an image showcasing the result as a banner."
I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer.
Answer:
py
answer = document_qa(document, question="What is the oldest person?")
print(f"The answer is {answer}.")
image = image_generator("A banner showing " + answer)
`
The pattern the model is prompted to repeat has three parts: The task statement, the agent's explanation of
what it intends to do, and finally the generated code. Every example that is part of the prompt has this exact
pattern, thus making sure that the agent will reproduce exactly the same pattern when generating new tokens.
The prompt examples are curated by the Transformers team and rigorously evaluated on a set of
problem statements
to ensure that the agent's prompt is as good as possible to solve real use cases of the agent.
The final part of the prompt corresponds to:
```text
Task: "Draw me a picture of rivers and lakes"
I will use the following
is a final and unfinished example that the agent is tasked to complete. The unfinished example
is dynamically created based on the actual user input. For the above example, the user ran:
py
agent.run("Draw me a picture of rivers and lakes")
The user input - a.k.a the task: "Draw me a picture of rivers and lakes" is cast into the
prompt template: "Task: \n\n I will use the following". This sentence makes up the final lines of the
prompt the agent is conditioned on, therefore strongly influencing the agent to finish the example
exactly in the same way it was previously done in the examples.
Without going into too much detail, the chat template has the same prompt structure with the
examples having a slightly different style, e.g.:
````text
[]
=====
Human: Answer the question in the variable question about the image stored in the variable image.
Assistant: I will use the tool image_qa to answer the question on the input image.
py
answer = image_qa(text=question, image=image)
print(f"The answer is {answer}")
Human: I tried this code, it worked but didn't give me a good result. The question is in French
Assistant: In this case, the question needs to be translated first. I will use the tool translator to do this.
py
translated_question = translator(question=question, src_lang="French", tgt_lang="English")
print(f"The translated question is {translated_question}.")
answer = image_qa(text=translated_question, image=image)
print(f"The answer is {answer}")
=====
[]
`
Contrary, to the examples of the run prompt, each chat prompt example has one or more exchanges between the
Human and the Assistant. Every exchange is structured similarly to the example of the run prompt.
The user's input is appended to behind Human: and the agent is prompted to first generate what needs to be done
before generating code. An exchange can be based on previous exchanges, therefore allowing the user to refer
to past exchanges as is done e.g. above by the user's input of "I tried this code" refers to the
previously generated code of the agent.
Upon running .chat, the user's input or task is cast into an unfinished example of the form:
text
Human: <user-input>\n\nAssistant:
which the agent completes. Contrary to the run command, the chat command then appends the completed example
to the prompt, thus giving the agent more context for the next chat turn.
Great now that we know how the prompt is structured, let's see how we can customize it!
Writing good user inputs
While large language models are getting better and better at understanding users' intentions, it helps
enormously to be as precise as possible to help the agent pick the correct task. What does it mean to be
as precise as possible?
The agent sees a list of tool names and their description in its prompt. The more tools are added the
more difficult it becomes for the agent to choose the correct tool and it's even more difficult to choose
the correct sequences of tools to run. Let's look at a common failure case, here we will only return
the code to analyze it.
from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
agent.run("Show me a tree", return_code=True)
gives:
``text
==Explanation from the agent==
I will use the following tool:image_segmenter` to create a segmentation mask for the image.
==Code generated by the agent==
mask = image_segmenter(image, prompt="tree")
which is probably not what we wanted. Instead, it is more likely that we want an image of a tree to be generated.
To steer the agent more towards using a specific tool it can therefore be very helpful to use important keywords that
are present in the tool's name and description. Let's have a look.
py
agent.toolbox["image_generator"].description
text
'This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which contains the image description and outputs an image.
The name and description make use of the keywords "image", "prompt", "create" and "generate". Using these words will most likely work better here. Let's refine our prompt a bit.
py
agent.run("Create an image of a tree", return_code=True)
gives:
``text
==Explanation from the agent==
I will use the following toolimage_generator` to generate an image of a tree.
==Code generated by the agent==
image = image_generator(prompt="tree")
Much better! That looks more like what we want. In short, when you notice that the agent struggles to
correctly map your task to the correct tools, try looking up the most pertinent keywords of the tool's name
and description and try refining your task request with it.
Customizing the tool descriptions
As we've seen before the agent has access to each of the tools' names and descriptions. The base tools
should have very precise names and descriptions, however, you might find that it could help to change the
the description or name of a tool for your specific use case. This might become especially important
when you've added multiple tools that are very similar or if you want to use your agent only for a certain
domain, e.g. image generation and transformations.
A common problem is that the agent confuses image generation with image transformation/modification when
used a lot for image generation tasks, e.g.
py
agent.run("Make an image of a house and a car", return_code=True)
returns
``text
==Explanation from the agent==
I will use the following toolsimage_generatorto generate an image of a house andimage_transformer` to transform the image of a car into the image of a house.
==Code generated by the agent==
house_image = image_generator(prompt="A house")
car_image = image_generator(prompt="A car")
house_car_image = image_transformer(image=car_image, prompt="A house")
which is probably not exactly what we want here. It seems like the agent has a difficult time
to understand the difference between image_generator and image_transformer and often uses the two together.
We can help the agent here by changing the tool name and description of image_transformer. Let's instead call it modifier
to disassociate it a bit from "image" and "prompt":
py
agent.toolbox["modifier"] = agent.toolbox.pop("image_transformer")
agent.toolbox["modifier"].description = agent.toolbox["modifier"].description.replace(
"transforms an image according to a prompt", "modifies an image"
)
Now "modify" is a strong cue to use the new image processor which should help with the above prompt. Let's run it again.
py
agent.run("Make an image of a house and a car", return_code=True)
Now we're getting:
``text
==Explanation from the agent==
I will use the following tools:image_generatorto generate an image of a house, thenimage_generator` to generate an image of a car.
==Code generated by the agent==
house_image = image_generator(prompt="A house")
car_image = image_generator(prompt="A car")
which is definitely closer to what we had in mind! However, we want to have both the house and car in the same image. Steering the task more toward single image generation should help:
py
agent.run("Create image: 'A house and car'", return_code=True)
``text
==Explanation from the agent==
I will use the following tool:image_generator` to generate an image.
==Code generated by the agent==
image = image_generator(prompt="A house and car")
Agents are still brittle for many use cases, especially when it comes to
slightly more complex use cases like generating an image of multiple objects.
Both the agent itself and the underlying prompt will be further improved in the coming
months making sure that agents become more robust to a variety of user inputs.
Customizing the whole prompt
To give the user maximum flexibility, the whole prompt template as explained in above
can be overwritten by the user. In this case make sure that your custom prompt includes an introduction section,
a tool section, an example section, and an unfinished example section. If you want to overwrite the run prompt template,
you can do as follows:
template = """ [] """
agent = HfAgent(your_endpoint, run_prompt_template=template)
Please make sure to have the <<all_tools>> string and the <<prompt>> defined somewhere in the template so that the agent can be aware
of the tools, it has available to it as well as correctly insert the user's prompt.
Similarly, one can overwrite the chat prompt template. Note that the chat mode always uses the following format for the exchanges:
```text
Human: <>
Assistant:
Therefore it is important that the examples of the custom chat prompt template also make use of this format.
You can overwrite the chat template at instantiation as follows.
thon
template = """ [] """
agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template)
Please make sure to have the <<all_tools>> string defined somewhere in the template so that the agent can be aware
of the tools, it has available to it.
In both cases, you can pass a repo ID instead of the prompt template if you would like to use a template hosted by someone in the community. The default prompts live in this repo as an example.
To upload your custom prompt on a repo on the Hub and share it with the community just make sure:
- to use a dataset repository
- to put the prompt template for the run command in a file named run_prompt_template.txt
- to put the prompt template for the chat command in a file named chat_prompt_template.txt
Using custom tools
In this section, we'll be leveraging two existing custom tools that are specific to image generation:
We replace huggingface-tools/image-transformation,
with diffusers/controlnet-canny-tool
to allow for more image modifications.
We add a new tool for image upscaling to the default toolbox:
diffusers/latent-upscaler-tool replace the existing image-transformation tool.
We'll start by loading the custom tools with the convenient [load_tool] function:
from transformers import load_tool
controlnet_transformer = load_tool("diffusers/controlnet-canny-tool")
upscaler = load_tool("diffusers/latent-upscaler-tool")
Upon adding custom tools to an agent, the tools' descriptions and names are automatically
included in the agents' prompts. Thus, it is imperative that custom tools have
a well-written description and name in order for the agent to understand how to use them.
Let's take a look at the description and name of controlnet_transformer:
py
print(f"Description: '{controlnet_transformer.description}'")
print(f"Name: '{controlnet_transformer.name}'")
gives
text
Description: 'This is a tool that transforms an image with ControlNet according to a prompt.
It takes two inputs: `image`, which should be the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the modified image.'
Name: 'image_transformer'
The name and description are accurate and fit the style of the curated set of tools.
Next, let's instantiate an agent with controlnet_transformer and upscaler:
py
tools = [controlnet_transformer, upscaler]
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=tools)
This command should give you the following info:
text
image_transformer has been replaced by <transformers_modules.diffusers.controlnet-canny-tool.bd76182c7777eba9612fc03c0
8718a60c0aa6312.image_transformation.ControlNetTransformationTool object at 0x7f1d3bfa3a00> as provided in `additional_tools`
The set of curated tools already has an image_transformer tool which is hereby replaced with our custom tool.
Overwriting existing tools can be beneficial if we want to use a custom tool exactly for the same task as an existing tool
because the agent is well-versed in using the specific task. Beware that the custom tool should follow the exact same API
as the overwritten tool in this case, or you should adapt the prompt template to make sure all examples using that
tool are updated.
The upscaler tool was given the name image_upscaler which is not yet present in the default toolbox and is therefore simply added to the list of tools.
You can always have a look at the toolbox that is currently available to the agent via the agent.toolbox attribute:
py
print("\n".join([f"- {a}" for a in agent.toolbox.keys()]))
text
- document_qa
- image_captioner
- image_qa
- image_segmenter
- transcriber
- summarizer
- text_classifier
- text_qa
- text_reader
- translator
- image_transformer
- text_downloader
- image_generator
- video_generator
- image_upscaler
Note how image_upscaler is now part of the agents' toolbox.
Let's now try out the new tools! We will re-use the image we generated in Transformers Agents Quickstart.
from diffusers.utils import load_image
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png"
)
Let's transform the image into a beautiful winter landscape:
py
image = agent.run("Transform the image: 'A frozen lake and snowy forest'", image=image)
``text
==Explanation from the agent==
I will use the following tool:image_transformer` to transform the image.
==Code generated by the agent==
image = image_transformer(image, prompt="A frozen lake and snowy forest")
The new image processing tool is based on ControlNet which can make very strong modifications to the image.
By default the image processing tool returns an image of size 512x512 pixels. Let's see if we can upscale it.
py
image = agent.run("Upscale the image", image)
``text
==Explanation from the agent==
I will use the following tool:image_upscaler` to upscale the image.
==Code generated by the agent==
upscaled_image = image_upscaler(image)
The agent automatically mapped our prompt "Upscale the image" to the just added upscaler tool purely based on the description and name of the upscaler tool
and was able to correctly run it.
Next, let's have a look at how you can create a new custom tool.
Adding new tools
In this section, we show how to create a new tool that can be added to the agent.
Creating a new tool
We'll first start by creating a tool. We'll add the not-so-useful yet fun task of fetching the model on the Hugging Face
Hub with the most downloads for a given task.
We can do that with the following code:
thon
from huggingface_hub import list_models
task = "text-classification"
model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
print(model.id)
For the task text-classification, this returns 'facebook/bart-large-mnli', for translation it returns 'google-t5/t5-base.
How do we convert this to a tool that the agent can leverage? All tools depend on the superclass Tool that holds the
main attributes necessary. We'll create a class that inherits from it:
thon
from transformers import Tool
class HFModelDownloadsTool(Tool):
pass
This class has a few needs:
- An attribute name, which corresponds to the name of the tool itself. To be in tune with other tools which have a
performative name, we'll name it model_download_counter.
- An attribute description, which will be used to populate the prompt of the agent.
- inputs and outputs attributes. Defining this will help the python interpreter make educated choices about types,
and will allow for a gradio-demo to be spawned when we push our tool to the Hub. They're both a list of expected
values, which can be text, image, or audio.
- A __call__ method which contains the inference code. This is the code we've played with above!
Here's what our class looks like now:
thon
from transformers import Tool
from huggingface_hub import list_models
class HFModelDownloadsTool(Tool):
name = "model_download_counter"
description = (
"This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. "
"It takes the name of the category (such as text-classification, depth-estimation, etc), and "
"returns the name of the checkpoint."
)
inputs = ["text"]
outputs = ["text"]
def __call__(self, task: str):
model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
return model.id
We now have our tool handy. Save it in a file and import it from your main script. Let's name this file
model_downloads.py, so the resulting import code looks like this:
thon
from model_downloads import HFModelDownloadsTool
tool = HFModelDownloadsTool()
In order to let others benefit from it and for simpler initialization, we recommend pushing it to the Hub under your
namespace. To do so, just call push_to_hub on the tool variable:
python
tool.push_to_hub("hf-model-downloads")
You now have your code on the Hub! Let's take a look at the final step, which is to have the agent use it.
Having the agent use the tool
We now have our tool that lives on the Hub which can be instantiated as such (change the user name for your tool):
thon
from transformers import load_tool
tool = load_tool("lysandre/hf-model-downloads")
In order to use it in the agent, simply pass it in the additional_tools parameter of the agent initialization method:
thon
from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool])
agent.run(
"Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?"
)
which outputs the following:text
==Code generated by the agent==
model = model_download_counter(task="text-to-video")
print(f"The model with the most downloads is {model}.")
audio_model = text_reader(model)
==Result==
The model with the most downloads is damo-vilab/text-to-video-ms-1.7b.
and generates the following audio.
| Audio |
|------------------------------------------------------------------------------------------------------------------------------------------------------|
| |
Depending on the LLM, some are quite brittle and require very exact prompts in order to work well. Having a well-defined
name and description of the tool is paramount to having it be leveraged by the agent.
Replacing existing tools
Replacing existing tools can be done simply by assigning a new item to the agent's toolbox. Here's how one would do so:
thon
from transformers import HfAgent, load_tool
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
agent.toolbox["image-transformation"] = load_tool("diffusers/controlnet-canny-tool")
Beware when replacing tools with others! This will also adjust the agent's prompt. This can be good if you have a better
prompt suited for the task, but it can also result in your tool being selected way more than others or for other
tools to be selected instead of the one you have defined.
Leveraging gradio-tools
gradio-tools is a powerful library that allows using Hugging
Face Spaces as tools. It supports many existing Spaces as well as custom Spaces to be designed with it.
We offer support for gradio_tools by using the Tool.from_gradio method. For example, we want to take
advantage of the StableDiffusionPromptGeneratorTool tool offered in the gradio-tools toolkit so as to
improve our prompts and generate better images.
We first import the tool from gradio_tools and instantiate it:
thon
from gradio_tools import StableDiffusionPromptGeneratorTool
gradio_tool = StableDiffusionPromptGeneratorTool()
We pass that instance to the Tool.from_gradio method:
thon
from transformers import Tool
tool = Tool.from_gradio(gradio_tool)
Now we can manage it exactly as we would a usual custom tool. We leverage it to improve our prompt
a rabbit wearing a space suit:
thon
from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool])
agent.run("Generate an image of the prompt after improving it.", prompt="A rabbit wearing a space suit")
The model adequately leverages the tool:
``text
==Explanation from the agent==
I will use the following tools:StableDiffusionPromptGeneratorto improve the prompt, thenimage_generator` to generate an image according to the improved prompt.
==Code generated by the agent==
improved_prompt = StableDiffusionPromptGenerator(prompt)
print(f"The improved prompt is {improved_prompt}.")
image = image_generator(improved_prompt)
Before finally generating the image:
gradio-tools requires textual inputs and outputs, even when working with different modalities. This implementation
works with image and audio objects. The two are currently incompatible, but will rapidly become compatible as we
work to improve the support.
Future compatibility with Langchain
We love Langchain and think it has a very compelling suite of tools. In order to handle these tools,
Langchain requires textual inputs and outputs, even when working with different modalities.
This is often the serialized version (i.e., saved to disk) of the objects.
This difference means that multi-modality isn't handled between transformers-agents and langchain.
We aim for this limitation to be resolved in future versions, and welcome any help from avid langchain
users to help us achieve this compatibility.
We would love to have better support. If you would like to help, please
open an issue and share what you have in mind. |
Padding and truncation
Batched inputs are often different lengths, so they can't be converted to fixed-size tensors. Padding and truncation are strategies for dealing with this problem, to create rectangular tensors from batches of varying lengths. Padding adds a special padding token to ensure shorter sequences will have the same length as either the longest sequence in a batch or the maximum length accepted by the model. Truncation works in the other direction by truncating long sequences.
In most cases, padding your batch to the length of the longest sequence and truncating to the maximum length a model can accept works pretty well. However, the API supports more strategies if you need them. The three arguments you need to are: padding, truncation and max_length.
The padding argument controls padding. It can be a boolean or a string:
True or 'longest': pad to the longest sequence in the batch (no padding is applied if you only provide
a single sequence).
'max_length': pad to a length specified by the max_length argument or the maximum length accepted
by the model if no max_length is provided (max_length=None). Padding will still be applied if you only provide a single sequence.
False or 'do_not_pad': no padding is applied. This is the default behavior.
The truncation argument controls truncation. It can be a boolean or a string:
True or 'longest_first': truncate to a maximum length specified by the max_length argument or
the maximum length accepted by the model if no max_length is provided (max_length=None). This will
truncate token by token, removing a token from the longest sequence in the pair until the proper length is
reached.
'only_second': truncate to a maximum length specified by the max_length argument or the maximum
length accepted by the model if no max_length is provided (max_length=None). This will only truncate
the second sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
'only_first': truncate to a maximum length specified by the max_length argument or the maximum
length accepted by the model if no max_length is provided (max_length=None). This will only truncate
the first sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
False or 'do_not_truncate': no truncation is applied. This is the default behavior.
The max_length argument controls the length of the padding and truncation. It can be an integer or None, in which case it will default to the maximum length the model can accept. If the model has no specific maximum input length, truncation or padding to max_length is deactivated.
The following table summarizes the recommended way to setup padding and truncation. If you use pairs of input sequences in any of the following examples, you can replace truncation=True by a STRATEGY selected in
['only_first', 'only_second', 'longest_first'], i.e. truncation='only_second' or truncation='longest_first' to control how both sequences in the pair are truncated as detailed before.
| Truncation | Padding | Instruction |
|--------------------------------------|-----------------------------------|---------------------------------------------------------------------------------------------|
| no truncation | no padding | tokenizer(batch_sentences) |
| | padding to max sequence in batch | tokenizer(batch_sentences, padding=True) or |
| | | tokenizer(batch_sentences, padding='longest') |
| | padding to max model input length | tokenizer(batch_sentences, padding='max_length') |
| | padding to specific length | tokenizer(batch_sentences, padding='max_length', max_length=42) |
| | padding to a multiple of a value | tokenizer(batch_sentences, padding=True, pad_to_multiple_of=8) |
| truncation to max model input length | no padding | tokenizer(batch_sentences, truncation=True) or |
| | | tokenizer(batch_sentences, truncation=STRATEGY) |
| | padding to max sequence in batch | tokenizer(batch_sentences, padding=True, truncation=True) or |
| | | tokenizer(batch_sentences, padding=True, truncation=STRATEGY) |
| | padding to max model input length | tokenizer(batch_sentences, padding='max_length', truncation=True) or |
| | | tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY) |
| | padding to specific length | Not possible |
| truncation to specific length | no padding | tokenizer(batch_sentences, truncation=True, max_length=42) or |
| | | tokenizer(batch_sentences, truncation=STRATEGY, max_length=42) |
| | padding to max sequence in batch | tokenizer(batch_sentences, padding=True, truncation=True, max_length=42) or |
| | | tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42) |
| | padding to max model input length | Not possible |
| | padding to specific length | tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42) or |
| | | tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42) | |
Distributed training with 🤗 Accelerate
As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU's on one machine or multiple GPU's across several machines. In this tutorial, learn how to customize your native PyTorch training loop to enable training in a distributed environment.
Setup
Get started by installing 🤗 Accelerate:
pip install accelerate
Then import and create an [~accelerate.Accelerator] object. The [~accelerate.Accelerator] will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device.
from accelerate import Accelerator
accelerator = Accelerator()
Prepare to accelerate
The next step is to pass all the relevant training objects to the [~accelerate.Accelerator.prepare] method. This includes your training and evaluation DataLoaders, a model and an optimizer:
train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
train_dataloader, eval_dataloader, model, optimizer
)
Backward
The last addition is to replace the typical loss.backward() in your training loop with 🤗 Accelerate's [~accelerate.Accelerator.backward]method:
for epoch in range(num_epochs):
for batch in train_dataloader:
outputs = model(**batch)
loss = outputs.loss
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
As you can see in the following code, you only need to add four additional lines of code to your training loop to enable distributed training!
+ from accelerate import Accelerator
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
accelerator = Accelerator()
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
train_dataloader, eval_dataloader, model, optimizer
)
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
outputs = model(**batch)
loss = outputs.loss
+ accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
Train
Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory.
Train with a script
If you are running your training from a script, run the following command to create and save a configuration file:
accelerate config
Then launch your training with:
accelerate launch train.py
Train with a notebook
🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to [~accelerate.notebook_launcher]:
from accelerate import notebook_launcher
notebook_launcher(training_function)
For more information about 🤗 Accelerate and its rich features, refer to the documentation. |
Community
This page regroups resources around 🤗 Transformers developed by the community.
Community resources:
| Resource | Description | Author |
|:----------|:-------------|------:|
| Hugging Face Transformers Glossary Flashcards | A set of flashcards based on the Transformers Docs Glossary that has been put into a form which can be easily learned/revised using Anki an open source, cross platform app specifically designed for long term knowledge retention. See this Introductory video on how to use the flashcards. | Darigov Research |
Community notebooks:
| Notebook | Description | Author | |
|:----------|:-------------|:-------------|------:|
| Fine-tune a pre-trained Transformer to generate lyrics | How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model | Aleksey Korshuk | |
| Train T5 in Tensorflow 2 | How to train T5 for any task using Tensorflow 2. This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using SQUAD | Muhammad Harris | |
| Train T5 on TPU | How to train T5 on SQUAD with Transformers and Nlp | Suraj Patil | |
| Fine-tune T5 for Classification and Multiple Choice | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning | Suraj Patil | |
| Fine-tune DialoGPT on New Datasets and Languages | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots | Nathan Cooper | |
| Long Sequence Modeling with Reformer | How to train on sequences as long as 500,000 tokens with Reformer | Patrick von Platen | |
| Fine-tune BART for Summarization | How to fine-tune BART for summarization with fastai using blurr | Wayde Gilliam | |
| Fine-tune a pre-trained Transformer on anyone's tweets | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model | Boris Dayma | |
| Optimize 🤗 Hugging Face models with Weights & Biases | A complete tutorial showcasing W&B integration with Hugging Face | Boris Dayma | |
| Pretrain Longformer | How to build a "long" version of existing pretrained models | Iz Beltagy | |
| Fine-tune Longformer for QA | How to fine-tune longformer model for QA task | Suraj Patil | |
| Evaluate Model with 🤗nlp | How to evaluate longformer on TriviaQA with nlp | Patrick von Platen | |
| Fine-tune T5 for Sentiment Span Extraction | How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning | Lorenzo Ampil | |
| Fine-tune DistilBert for Multiclass Classification | How to fine-tune DistilBert for multiclass classification with PyTorch | Abhishek Kumar Mishra | |
|Fine-tune BERT for Multi-label Classification|How to fine-tune BERT for multi-label classification using PyTorch|Abhishek Kumar Mishra ||
|Fine-tune T5 for Summarization|How to fine-tune T5 for summarization in PyTorch and track experiments with WandB|Abhishek Kumar Mishra ||
|Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing|How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing|Michael Benesty ||
|Pretrain Reformer for Masked Language Modeling| How to train a Reformer model with bi-directional self-attention layers | Patrick von Platen | |
|Expand and Fine Tune Sci-BERT| How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. | Tanmay Thakur | |
|Fine Tune BlenderBotSmall for Summarization using the Trainer API| How to fine-tune BlenderBotSmall for summarization on a custom dataset, using the Trainer API. | Tanmay Thakur | |
|Fine-tune Electra and interpret with Integrated Gradients | How to fine-tune Electra for sentiment analysis and interpret predictions with Captum Integrated Gradients | Eliza Szczechla | |
|fine-tune a non-English GPT-2 Model with Trainer class | How to fine-tune a non-English GPT-2 Model with Trainer class | Philipp Schmid | |
|Fine-tune a DistilBERT Model for Multi Label Classification task | How to fine-tune a DistilBERT Model for Multi Label Classification task | Dhaval Taunk | |
|Fine-tune ALBERT for sentence-pair classification | How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | Nadir El Manouzi | |
|Fine-tune Roberta for sentiment analysis | How to fine-tune a Roberta model for sentiment analysis | Dhaval Taunk | |
|Evaluating Question Generation Models | How accurate are the answers to questions generated by your seq2seq transformer model? | Pascal Zoleko | |
|Classify text with DistilBERT and Tensorflow | How to fine-tune DistilBERT for text classification in TensorFlow | Peter Bayerle | |
|Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail | How to warm-start a EncoderDecoderModel with a google-bert/bert-base-uncased checkpoint for summarization on CNN/Dailymail | Patrick von Platen | |
|Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum | How to warm-start a shared EncoderDecoderModel with a FacebookAI/roberta-base checkpoint for summarization on BBC/XSum | Patrick von Platen | |
|Fine-tune TAPAS on Sequential Question Answering (SQA) | How to fine-tune TapasForQuestionAnswering with a tapas-base checkpoint on the Sequential Question Answering (SQA) dataset | Niels Rogge | |
|Evaluate TAPAS on Table Fact Checking (TabFact) | How to evaluate a fine-tuned TapasForSequenceClassification with a tapas-base-finetuned-tabfact checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | Niels Rogge | |
|Fine-tuning mBART for translation | How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | Vasudev Gupta | |
|Fine-tune LayoutLM on FUNSD (a form understanding dataset) | How to fine-tune LayoutLMForTokenClassification on the FUNSD dataset for information extraction from scanned documents | Niels Rogge | |
|Fine-Tune DistilGPT2 and Generate Text | How to fine-tune DistilGPT2 and generate text | Aakash Tripathi | |
|Fine-Tune LED on up to 8K tokens | How to fine-tune LED on pubmed for long-range summarization | Patrick von Platen | |
|Evaluate LED on Arxiv | How to effectively evaluate LED on long-range summarization | Patrick von Platen | |
|Fine-tune LayoutLM on RVL-CDIP (a document image classification dataset) | How to fine-tune LayoutLMForSequenceClassification on the RVL-CDIP dataset for scanned document classification | Niels Rogge | |
|Wav2Vec2 CTC decoding with GPT2 adjustment | How to decode CTC sequence with language model adjustment | Eric Lam | |
|Fine-tune BART for summarization in two languages with Trainer class | How to fine-tune BART for summarization in two languages with Trainer class | Eliza Szczechla | |
|Evaluate Big Bird on Trivia QA | How to evaluate BigBird on long document question answering on Trivia QA | Patrick von Platen | |
| Create video captions using Wav2Vec2 | How to create YouTube captions from any video by transcribing the audio with Wav2Vec | Niklas Muennighoff | |
| Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and PyTorch Lightning | Niels Rogge | |
| Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and the 🤗 Trainer | Niels Rogge | |
| Evaluate LUKE on Open Entity, an entity typing dataset | How to evaluate LukeForEntityClassification on the Open Entity dataset | Ikuya Yamada | |
| Evaluate LUKE on TACRED, a relation extraction dataset | How to evaluate LukeForEntityPairClassification on the TACRED dataset | Ikuya Yamada | |
| Evaluate LUKE on CoNLL-2003, an important NER benchmark | How to evaluate LukeForEntitySpanClassification on the CoNLL-2003 dataset | Ikuya Yamada | |
| Evaluate BigBird-Pegasus on PubMed dataset | How to evaluate BigBirdPegasusForConditionalGeneration on PubMed dataset | Vasudev Gupta | |
| Speech Emotion Classification with Wav2Vec2 | How to leverage a pretrained Wav2Vec2 model for Emotion Classification on the MEGA dataset | Mehrdad Farahani | |
| Detect objects in an image with DETR | How to use a trained DetrForObjectDetection model to detect objects in an image and visualize attention | Niels Rogge | |
| Fine-tune DETR on a custom object detection dataset | How to fine-tune DetrForObjectDetection on a custom object detection dataset | Niels Rogge | |
| Finetune T5 for Named Entity Recognition | How to fine-tune T5 on a Named Entity Recognition Task | Ogundepo Odunayo | | |
Troubleshoot
Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we've seen and how you can resolve them. However, this guide isn't meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try:
Asking for help on the forums. There are specific categories you can post your question to, like Beginners or 🤗 Transformers. Make sure you write a good descriptive forum post with some reproducible code to maximize the likelihood that your problem is solved!
Create an Issue on the 🤗 Transformers repository if it is a bug related to the library. Try to include as much information describing the bug as possible to help us better figure out what's wrong and how we can fix it.
Check the Migration guide if you use an older version of 🤗 Transformers since some important changes have been introduced between versions.
For more details about troubleshooting and getting help, take a look at Chapter 8 of the Hugging Face course.
Firewalled environments
Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. When your script attempts to download model weights or datasets, the download will hang and then timeout with the following message:
ValueError: Connection error, and we cannot find the requested files in the cached path.
Please try again or make sure your Internet connection is on.
In this case, you should try to run 🤗 Transformers on offline mode to avoid the connection error.
CUDA out of memory
Training large models with millions of parameters can be challenging without the appropriate hardware. A common error you may encounter when the GPU runs out of memory is:
CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch)
Here are some potential solutions you can try to lessen memory use:
Reduce the per_device_train_batch_size value in [TrainingArguments].
Try using gradient_accumulation_steps in [TrainingArguments] to effectively increase overall batch size.
Refer to the Performance guide for more details about memory-saving techniques.
Unable to load a saved TensorFlow model
TensorFlow's model.save method will save the entire model - architecture, weights, training configuration - in a single file. However, when you load the model file again, you may run into an error because 🤗 Transformers may not load all the TensorFlow-related objects in the model file. To avoid issues with saving and loading TensorFlow models, we recommend you:
Save the model weights as a h5 file extension with model.save_weights and then reload the model with [~TFPreTrainedModel.from_pretrained]:
from transformers import TFPreTrainedModel
from tensorflow import keras
model.save_weights("some_folder/tf_model.h5")
model = TFPreTrainedModel.from_pretrained("some_folder")
Save the model with [~TFPretrainedModel.save_pretrained] and load it again with [~TFPreTrainedModel.from_pretrained]:
from transformers import TFPreTrainedModel
model.save_pretrained("path_to/model")
model = TFPreTrainedModel.from_pretrained("path_to/model")
ImportError
Another common error you may encounter, especially if it is a newly released model, is ImportError:
ImportError: cannot import name 'ImageGPTImageProcessor' from 'transformers' (unknown location)
For these error types, check to make sure you have the latest version of 🤗 Transformers installed to access the most recent models:
pip install transformers --upgrade
CUDA error: device-side assert triggered
Sometimes you may run into a generic CUDA error about an error in the device code.
RuntimeError: CUDA error: device-side assert triggered
You should try to run the code on a CPU first to get a more descriptive error message. Add the following environment variable to the beginning of your code to switch to a CPU:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
Another option is to get a better traceback from the GPU. Add the following environment variable to the beginning of your code to get the traceback to point to the source of the error:
import os
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
Incorrect output when padding tokens aren't masked
In some cases, the output hidden_state may be incorrect if the input_ids include padding tokens. To demonstrate, load a model and tokenizer. You can access a model's pad_token_id to see its value. The pad_token_id may be None for some models, but you can always manually set it.
from transformers import AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
model.config.pad_token_id
0
The following example shows the output without masking the padding tokens:
input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]])
output = model(input_ids)
print(output.logits)
tensor([[ 0.0082, -0.2307],
[ 0.1317, -0.1683]], grad_fn=)
Here is the actual output of the second sequence:
input_ids = torch.tensor([[7592]])
output = model(input_ids)
print(output.logits)
tensor([[-0.1008, -0.4061]], grad_fn=)
Most of the time, you should provide an attention_mask to your model to ignore the padding tokens to avoid this silent error. Now the output of the second sequence matches its actual output:
By default, the tokenizer creates an attention_mask for you based on your specific tokenizer's defaults.
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]])
output = model(input_ids, attention_mask=attention_mask)
print(output.logits)
tensor([[ 0.0082, -0.2307],
[-0.1008, -0.4061]], grad_fn=)
🤗 Transformers doesn't automatically create an attention_mask to mask a padding token if it is provided because:
Some models don't have a padding token.
For some use-cases, users want a model to attend to a padding token.
ValueError: Unrecognized configuration class XYZ for this kind of AutoModel
Generally, we recommend using the [AutoModel] class to load pretrained instances of models. This class
can automatically infer and load the correct architecture from a given checkpoint based on the configuration. If you see
this ValueError when loading a model from a checkpoint, this means the Auto class couldn't find a mapping from
the configuration in the given checkpoint to the kind of model you are trying to load. Most commonly, this happens when a
checkpoint doesn't support a given task.
For instance, you'll see this error in the following example because there is no GPT2 for question answering:
from transformers import AutoProcessor, AutoModelForQuestionAnswering
processor = AutoProcessor.from_pretrained("openai-community/gpt2-medium")
model = AutoModelForQuestionAnswering.from_pretrained("openai-community/gpt2-medium")
ValueError: Unrecognized configuration class for this kind of AutoModel: AutoModelForQuestionAnswering.
Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig,
|
Export to ONNX
Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into
a serialized format that can be loaded and executed on specialized runtimes and hardware.
🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or TensorFlow to serialized formats
such as ONNX and TFLite through its exporters module. 🤗 Optimum also provides a set of performance optimization tools to train
and run models on targeted hardware with maximum efficiency.
This guide demonstrates how you can export 🤗 Transformers models to ONNX with 🤗 Optimum, for the guide on exporting models to TFLite,
please refer to the Export to TFLite page.
Export to ONNX
ONNX (Open Neural Network eXchange) is an open standard that defines a common set of operators and a
common file format to represent deep learning models in a wide variety of frameworks, including PyTorch and
TensorFlow. When a model is exported to the ONNX format, these operators are used to
construct a computational graph (often called an intermediate representation) which
represents the flow of data through the neural network.
By exposing a graph with standardized operators and data types, ONNX makes it easy to
switch between frameworks. For example, a model trained in PyTorch can be exported to
ONNX format and then imported in TensorFlow (and vice versa).
Once exported to ONNX format, a model can be:
- optimized for inference via techniques such as graph optimization and quantization.
- run with ONNX Runtime via ORTModelForXXX classes,
which follow the same AutoModel API as the one you are used to in 🤗 Transformers.
- run with optimized inference pipelines,
which has the same API as the [pipeline] function in 🤗 Transformers.
🤗 Optimum provides support for the ONNX export by leveraging configuration objects. These configuration objects come
ready-made for a number of model architectures, and are designed to be easily extendable to other architectures.
For the list of ready-made configurations, please refer to 🤗 Optimum documentation.
There are two ways to export a 🤗 Transformers model to ONNX, here we show both:
export with 🤗 Optimum via CLI.
export with 🤗 Optimum with optimum.onnxruntime.
Exporting a 🤗 Transformers model to ONNX with CLI
To export a 🤗 Transformers model to ONNX, first install an extra dependency:
pip install optimum[exporters]
To check out all available arguments, refer to the 🤗 Optimum docs,
or view help in command line:
optimum-cli export onnx --help
To export a model's checkpoint from the 🤗 Hub, for example, distilbert/distilbert-base-uncased-distilled-squad, run the following command:
optimum-cli export onnx --model distilbert/distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
You should see the logs indicating progress and showing where the resulting model.onnx is saved, like this:
Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx
-[✓] ONNX model output names match reference model (start_logits, end_logits)
- Validating ONNX Model output "start_logits":
-[✓] (2, 16) matches (2, 16)
-[✓] all values close (atol: 0.0001)
- Validating ONNX Model output "end_logits":
-[✓] (2, 16) matches (2, 16)
-[✓] all values close (atol: 0.0001)
The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_onnx
The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you
saved both the model's weights and tokenizer files in the same directory (local_path). When using CLI, pass the
local_path to the model argument instead of the checkpoint name on 🤗 Hub and provide the --task argument.
You can review the list of supported tasks in the 🤗 Optimum documentation.
If task argument is not provided, it will default to the model architecture without any task specific head.
optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
The resulting model.onnx file can then be run on one of the many
accelerators that support the ONNX
standard. For example, we can load and run the model with ONNX
Runtime as follows:
thon
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("distilbert_base_uncased_squad_onnx")
model = ORTModelForQuestionAnswering.from_pretrained("distilbert_base_uncased_squad_onnx")
inputs = tokenizer("What am I using?", "Using DistilBERT with ONNX Runtime!", return_tensors="pt")
outputs = model(**inputs)
The process is identical for TensorFlow checkpoints on the Hub. For instance, here's how you would
export a pure TensorFlow checkpoint from the Keras organization:
optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
Exporting a 🤗 Transformers model to ONNX with optimum.onnxruntime
Alternative to CLI, you can export a 🤗 Transformers model to ONNX programmatically like so:
thon
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model_checkpoint = "distilbert_base_uncased_squad"
save_directory = "onnx/"
Load a model from transformers and export it to ONNX
ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
Save the onnx model and tokenizer
ort_model.save_pretrained(save_directory)
tokenizer.save_pretrained(save_directory)
Exporting a model for an unsupported architecture
If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
supported in optimum.exporters.onnx,
and if it is not, contribute to 🤗 Optimum
directly.
Exporting a model with transformers.onnx
tranformers.onnx is no longer maintained, please export models with 🤗 Optimum as described above. This section will be removed in the future versions.
To export a 🤗 Transformers model to ONNX with tranformers.onnx, install extra dependencies:
pip install transformers[onnx]
Use transformers.onnx package as a Python module to export a checkpoint using a ready-made configuration:
python -m transformers.onnx --model=distilbert/distilbert-base-uncased onnx/
This exports an ONNX graph of the checkpoint defined by the --model argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally.
The resulting model.onnx file can then be run on one of the many accelerators that support the ONNX standard. For example,
load and run the model with ONNX Runtime as follows:
thon
from transformers import AutoTokenizer
from onnxruntime import InferenceSession
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
session = InferenceSession("onnx/model.onnx")
ONNX Runtime expects NumPy arrays as input
inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")
outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
The required output names (like ["last_hidden_state"]) can be obtained by taking a look at the ONNX configuration of
each model. For example, for DistilBERT we have:
thon
from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig
config = DistilBertConfig()
onnx_config = DistilBertOnnxConfig(config)
print(list(onnx_config.outputs.keys()))
["last_hidden_state"]
The process is identical for TensorFlow checkpoints on the Hub. For example, export a pure TensorFlow checkpoint like so:
python -m transformers.onnx --model=keras-io/transformers-qa onnx/
To export a model that's stored locally, save the model's weights and tokenizer files in the same directory (e.g. local-pt-checkpoint),
then export it to ONNX by pointing the --model argument of the transformers.onnx package to the desired directory:
python -m transformers.onnx --model=local-pt-checkpoint onnx/ |
Fine-tune a pretrained model
[[open-in-colab]]
There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. 🤗 Transformers provides access to thousands of pretrained models for a wide range of tasks. When you use a pretrained model, you train it on a dataset specific to your task. This is known as fine-tuning, an incredibly powerful training technique. In this tutorial, you will fine-tune a pretrained model with a deep learning framework of your choice:
Fine-tune a pretrained model with 🤗 Transformers [Trainer].
Fine-tune a pretrained model in TensorFlow with Keras.
Fine-tune a pretrained model in native PyTorch.
Prepare a dataset
Before you can fine-tune a pretrained model, download a dataset and prepare it for training. The previous tutorial showed you how to process data for training, and now you get an opportunity to put those skills to the test!
Begin by loading the Yelp Reviews dataset:
from datasets import load_dataset
dataset = load_dataset("yelp_review_full")
dataset["train"][100]
{'label': 0,
'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularlythat takes something special!\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \"serving off their orders\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
As you now know, you need a tokenizer to process the text and include a padding and truncation strategy to handle any variable sequence lengths. To process your dataset in one step, use 🤗 Datasets map method to apply a preprocessing function over the entire dataset:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
If you like, you can create a smaller subset of the full dataset to fine-tune on to reduce the time it takes:
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
Train
At this point, you should follow the section corresponding to the framework you want to use. You can use the links
in the right sidebar to jump to the one you want - and if you want to hide all of the content for a given framework,
just use the button at the top-right of that framework's block!
Train with PyTorch Trainer
🤗 Transformers provides a [Trainer] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [Trainer] API supports a wide range of training options and features such as logging, gradient accumulation, and mixed precision.
Start by loading your model and specify the number of expected labels. From the Yelp Review dataset card, you know there are five labels:
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
You will see a warning about some of the pretrained weights not being used and some weights being randomly
initialized. Don't worry, this is completely normal! The pretrained head of the BERT model is discarded, and replaced with a randomly initialized classification head. You will fine-tune this new model head on your sequence classification task, transferring the knowledge of the pretrained model to it.
Training hyperparameters
Next, create a [TrainingArguments] class which contains all the hyperparameters you can tune as well as flags for activating different training options. For this tutorial you can start with the default training hyperparameters, but feel free to experiment with these to find your optimal settings.
Specify where to save the checkpoints from your training:
from transformers import TrainingArguments
training_args = TrainingArguments(output_dir="test_trainer")
Evaluate
[Trainer] does not automatically evaluate model performance during training. You'll need to pass [Trainer] a function to compute and report metrics. The 🤗 Evaluate library provides a simple accuracy function you can load with the [evaluate.load] (see this quicktour for more information) function:
import numpy as np
import evaluate
metric = evaluate.load("accuracy")
Call [~evaluate.compute] on metric to calculate the accuracy of your predictions. Before passing your predictions to compute, you need to convert the logits to predictions (remember all 🤗 Transformers models return logits):
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
If you'd like to monitor your evaluation metrics during fine-tuning, specify the evaluation_strategy parameter in your training arguments to report the evaluation metric at the end of each epoch:
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
Trainer
Create a [Trainer] object with your model, training arguments, training and test datasets, and evaluation function:
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
Then fine-tune your model by calling [~transformers.Trainer.train]:
trainer.train()
Train a TensorFlow model with Keras
You can also train 🤗 Transformers models in TensorFlow with the Keras API!
Loading data for Keras
When you want to train a 🤗 Transformers model with the Keras API, you need to convert your dataset to a format that
Keras understands. If your dataset is small, you can just convert the whole thing to NumPy arrays and pass it to Keras.
Let's try that first before we do anything more complicated.
First, load a dataset. We'll use the CoLA dataset from the GLUE benchmark,
since it's a simple binary text classification task, and just take the training split for now.
from datasets import load_dataset
dataset = load_dataset("glue", "cola")
dataset = dataset["train"] # Just take the training split for now
Next, load a tokenizer and tokenize the data as NumPy arrays. Note that the labels are already a list of 0 and 1s,
so we can just convert that directly to a NumPy array without tokenization!
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
tokenized_data = tokenizer(dataset["sentence"], return_tensors="np", padding=True)
Tokenizer returns a BatchEncoding, but we convert that to a dict for Keras
tokenized_data = dict(tokenized_data)
labels = np.array(dataset["label"]) # Label is already an array of 0 and 1
Finally, load, compile, and fit the model. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
from transformers import TFAutoModelForSequenceClassification
from tensorflow.keras.optimizers import Adam
Load and compile our model
model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
Lower learning rates are often better for fine-tuning transformers
model.compile(optimizer=Adam(3e-5)) # No loss argument!
model.fit(tokenized_data, labels)
You don't have to pass a loss argument to your models when you compile() them! Hugging Face models automatically
choose a loss that is appropriate for their task and model architecture if this argument is left blank. You can always
override this by specifying a loss yourself if you want to!
This approach works great for smaller datasets, but for larger datasets, you might find it starts to become a problem. Why?
Because the tokenized array and labels would have to be fully loaded into memory, and because NumPy doesn’t handle
“jagged” arrays, so every tokenized sample would have to be padded to the length of the longest sample in the whole
dataset. That’s going to make your array even bigger, and all those padding tokens will slow down training too!
Loading data as a tf.data.Dataset
If you want to avoid slowing down training, you can load your data as a tf.data.Dataset instead. Although you can write your own
tf.data pipeline if you want, we have two convenience methods for doing this:
[~TFPreTrainedModel.prepare_tf_dataset]: This is the method we recommend in most cases. Because it is a method
on your model, it can inspect the model to automatically figure out which columns are usable as model inputs, and
discard the others to make a simpler, more performant dataset.
[~datasets.Dataset.to_tf_dataset]: This method is more low-level, and is useful when you want to exactly control how
your dataset is created, by specifying exactly which columns and label_cols to include.
Before you can use [~TFPreTrainedModel.prepare_tf_dataset], you will need to add the tokenizer outputs to your dataset as columns, as shown in
the following code sample:
def tokenize_dataset(data):
# Keys of the returned dictionary will be added to the dataset as columns
return tokenizer(data["text"])
dataset = dataset.map(tokenize_dataset)
Remember that Hugging Face datasets are stored on disk by default, so this will not inflate your memory usage! Once the
columns have been added, you can stream batches from the dataset and add padding to each batch, which greatly
reduces the number of padding tokens compared to padding the entire dataset.
tf_dataset = model.prepare_tf_dataset(dataset["train"], batch_size=16, shuffle=True, tokenizer=tokenizer)
Note that in the code sample above, you need to pass the tokenizer to prepare_tf_dataset so it can correctly pad batches as they're loaded.
If all the samples in your dataset are the same length and no padding is necessary, you can skip this argument.
If you need to do something more complex than just padding samples (e.g. corrupting tokens for masked language
modelling), you can use the collate_fn argument instead to pass a function that will be called to transform the
list of samples into a batch and apply any preprocessing you want. See our
examples or
notebooks to see this approach in action.
Once you've created a tf.data.Dataset, you can compile and fit the model as before:
model.compile(optimizer=Adam(3e-5)) # No loss argument!
model.fit(tf_dataset)
Train in native PyTorch
[Trainer] takes care of the training loop and allows you to fine-tune a model in a single line of code. For users who prefer to write their own training loop, you can also fine-tune a 🤗 Transformers model in native PyTorch.
At this point, you may need to restart your notebook or execute the following code to free some memory:
py
del model
del trainer
torch.cuda.empty_cache()
Next, manually postprocess tokenized_dataset to prepare it for training.
Remove the text column because the model does not accept raw text as an input:
tokenized_datasets = tokenized_datasets.remove_columns(["text"])
Rename the label column to labels because the model expects the argument to be named labels:
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
Set the format of the dataset to return PyTorch tensors instead of lists:
tokenized_datasets.set_format("torch")
Then create a smaller subset of the dataset as previously shown to speed up the fine-tuning:
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
DataLoader
Create a DataLoader for your training and test datasets so you can iterate over batches of data:
from torch.utils.data import DataLoader
train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)
Load your model with the number of expected labels:
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
Optimizer and learning rate scheduler
Create an optimizer and learning rate scheduler to fine-tune the model. Let's use the AdamW optimizer from PyTorch:
from torch.optim import AdamW
optimizer = AdamW(model.parameters(), lr=5e-5)
Create the default learning rate scheduler from [Trainer]:
from transformers import get_scheduler
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
)
Lastly, specify device to use a GPU if you have access to one. Otherwise, training on a CPU may take several hours instead of a couple of minutes.
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
Get free access to a cloud GPU if you don't have one with a hosted notebook like Colaboratory or SageMaker StudioLab.
Great, now you are ready to train! 🥳
Training loop
To keep track of your training progress, use the tqdm library to add a progress bar over the number of training steps:
from tqdm.auto import tqdm
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
Evaluate
Just like how you added an evaluation function to [Trainer], you need to do the same when you write your own training loop. But instead of calculating and reporting the metric at the end of each epoch, this time you'll accumulate all the batches with [~evaluate.add_batch] and calculate the metric at the very end.
import evaluate
metric = evaluate.load("accuracy")
model.eval()
for batch in eval_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
metric.add_batch(predictions=predictions, references=batch["labels"])
metric.compute()
Additional resources
For more fine-tuning examples, refer to:
🤗 Transformers Examples includes scripts
to train common NLP tasks in PyTorch and TensorFlow.
🤗 Transformers Notebooks contains various notebooks on how to fine-tune a model for specific tasks in PyTorch and TensorFlow.
|
How 🤗 Transformers solve tasks
In What 🤗 Transformers can do, you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what's happening under the hood. There are many ways to solve a given task, some models may implement certain techniques or even approach the task from a new angle, but for Transformer models, the general idea is the same. Owing to its flexible architecture, most models are a variant of an encoder, decoder, or encoder-decoder structure. In addition to Transformer models, our library also has several convolutional neural networks (CNNs), which are still used today for computer vision tasks. We'll also explain how a modern CNN works.
To explain how tasks are solved, we'll walk through what goes on inside the model to output useful predictions.
Wav2Vec2 for audio classification and automatic speech recognition (ASR)
Vision Transformer (ViT) and ConvNeXT for image classification
DETR for object detection
Mask2Former for image segmentation
GLPN for depth estimation
BERT for NLP tasks like text classification, token classification and question answering that use an encoder
GPT2 for NLP tasks like text generation that use a decoder
BART for NLP tasks like summarization and translation that use an encoder-decoder
Before you go further, it is good to have some basic knowledge of the original Transformer architecture. Knowing how encoders, decoders, and attention work will aid you in understanding how different Transformer models work. If you're just getting started or need a refresher, check out our course for more information!
Speech and audio
Wav2Vec2 is a self-supervised model pretrained on unlabeled speech data and finetuned on labeled data for audio classification and automatic speech recognition.
This model has four main components:
A feature encoder takes the raw audio waveform, normalizes it to zero mean and unit variance, and converts it into a sequence of feature vectors that are each 20ms long.
Waveforms are continuous by nature, so they can't be divided into separate units like a sequence of text can be split into words. That's why the feature vectors are passed to a quantization module, which aims to learn discrete speech units. The speech unit is chosen from a collection of codewords, known as a codebook (you can think of this as the vocabulary). From the codebook, the vector or speech unit, that best represents the continuous audio input is chosen and forwarded through the model.
About half of the feature vectors are randomly masked, and the masked feature vector is fed to a context network, which is a Transformer encoder that also adds relative positional embeddings.
The pretraining objective of the context network is a contrastive task. The model has to predict the true quantized speech representation of the masked prediction from a set of false ones, encouraging the model to find the most similar context vector and quantized speech unit (the target label).
Now that wav2vec2 is pretrained, you can finetune it on your data for audio classification or automatic speech recognition!
Audio classification
To use the pretrained model for audio classification, add a sequence classification head on top of the base Wav2Vec2 model. The classification head is a linear layer that accepts the encoder's hidden states. The hidden states represent the learned features from each audio frame which can have varying lengths. To create one vector of fixed-length, the hidden states are pooled first and then transformed into logits over the class labels. The cross-entropy loss is calculated between the logits and target to find the most likely class.
Ready to try your hand at audio classification? Check out our complete audio classification guide to learn how to finetune Wav2Vec2 and use it for inference!
Automatic speech recognition
To use the pretrained model for automatic speech recognition, add a language modeling head on top of the base Wav2Vec2 model for connectionist temporal classification (CTC). The language modeling head is a linear layer that accepts the encoder's hidden states and transforms them into logits. Each logit represents a token class (the number of tokens comes from the task vocabulary). The CTC loss is calculated between the logits and targets to find the most likely sequence of tokens, which are then decoded into a transcription.
Ready to try your hand at automatic speech recognition? Check out our complete automatic speech recognition guide to learn how to finetune Wav2Vec2 and use it for inference!
Computer vision
There are two ways to approach computer vision tasks:
Split an image into a sequence of patches and process them in parallel with a Transformer.
Use a modern CNN, like ConvNeXT, which relies on convolutional layers but adopts modern network designs.
A third approach mixes Transformers with convolutions (for example, Convolutional Vision Transformer or LeViT). We won't discuss those because they just combine the two approaches we examine here.
ViT and ConvNeXT are commonly used for image classification, but for other vision tasks like object detection, segmentation, and depth estimation, we'll look at DETR, Mask2Former and GLPN, respectively; these models are better suited for those tasks.
Image classification
ViT and ConvNeXT can both be used for image classification; the main difference is that ViT uses an attention mechanism while ConvNeXT uses convolutions.
Transformer
ViT replaces convolutions entirely with a pure Transformer architecture. If you're familiar with the original Transformer, then you're already most of the way toward understanding ViT.
The main change ViT introduced was in how images are fed to a Transformer:
An image is split into square non-overlapping patches, each of which gets turned into a vector or patch embedding. The patch embeddings are generated from a convolutional 2D layer which creates the proper input dimensions (which for a base Transformer is 768 values for each patch embedding). If you had a 224x224 pixel image, you could split it into 196 16x16 image patches. Just like how text is tokenized into words, an image is "tokenized" into a sequence of patches.
A learnable embedding - a special [CLS] token - is added to the beginning of the patch embeddings just like BERT. The final hidden state of the [CLS] token is used as the input to the attached classification head; other outputs are ignored. This token helps the model learn how to encode a representation of the image.
The last thing to add to the patch and learnable embeddings are the position embeddings because the model doesn't know how the image patches are ordered. The position embeddings are also learnable and have the same size as the patch embeddings. Finally, all of the embeddings are passed to the Transformer encoder.
The output, specifically only the output with the [CLS] token, is passed to a multilayer perceptron head (MLP). ViT's pretraining objective is simply classification. Like other classification heads, the MLP head converts the output into logits over the class labels and calculates the cross-entropy loss to find the most likely class.
Ready to try your hand at image classification? Check out our complete image classification guide to learn how to finetune ViT and use it for inference!
CNN
This section briefly explains convolutions, but it'd be helpful to have a prior understanding of how they change an image's shape and size. If you're unfamiliar with convolutions, check out the Convolution Neural Networks chapter from the fastai book!
ConvNeXT is a CNN architecture that adopts new and modern network designs to improve performance. However, convolutions are still at the core of the model. From a high-level perspective, a convolution is an operation where a smaller matrix (kernel) is multiplied by a small window of the image pixels. It computes some features from it, such as a particular texture or curvature of a line. Then it slides over to the next window of pixels; the distance the convolution travels is known as the stride.
A basic convolution without padding or stride, taken from A guide to convolution arithmetic for deep learning.
You can feed this output to another convolutional layer, and with each successive layer, the network learns more complex and abstract things like hotdogs or rockets. Between convolutional layers, it is common to add a pooling layer to reduce dimensionality and make the model more robust to variations of a feature's position.
ConvNeXT modernizes a CNN in five ways:
Change the number of blocks in each stage and "patchify" an image with a larger stride and corresponding kernel size. The non-overlapping sliding window makes this patchifying strategy similar to how ViT splits an image into patches.
A bottleneck layer shrinks the number of channels and then restores it because it is faster to do a 1x1 convolution, and you can increase the depth. An inverted bottleneck does the opposite by expanding the number of channels and shrinking them, which is more memory efficient.
Replace the typical 3x3 convolutional layer in the bottleneck layer with depthwise convolution, which applies a convolution to each input channel separately and then stacks them back together at the end. This widens the network width for improved performance.
ViT has a global receptive field which means it can see more of an image at once thanks to its attention mechanism. ConvNeXT attempts to replicate this effect by increasing the kernel size to 7x7.
ConvNeXT also makes several layer design changes that imitate Transformer models. There are fewer activation and normalization layers, the activation function is switched to GELU instead of ReLU, and it uses LayerNorm instead of BatchNorm.
The output from the convolution blocks is passed to a classification head which converts the outputs into logits and calculates the cross-entropy loss to find the most likely label.
Object detection
DETR, DEtection TRansformer, is an end-to-end object detection model that combines a CNN with a Transformer encoder-decoder.
A pretrained CNN backbone takes an image, represented by its pixel values, and creates a low-resolution feature map of it. A 1x1 convolution is applied to the feature map to reduce dimensionality and it creates a new feature map with a high-level image representation. Since the Transformer is a sequential model, the feature map is flattened into a sequence of feature vectors that are combined with positional embeddings.
The feature vectors are passed to the encoder, which learns the image representations using its attention layers. Next, the encoder hidden states are combined with object queries in the decoder. Object queries are learned embeddings that focus on the different regions of an image, and they're updated as they progress through each attention layer. The decoder hidden states are passed to a feedforward network that predicts the bounding box coordinates and class label for each object query, or no object if there isn't one.
DETR decodes each object query in parallel to output N final predictions, where N is the number of queries. Unlike a typical autoregressive model that predicts one element at a time, object detection is a set prediction task (bounding box, class label) that makes N predictions in a single pass.
DETR uses a bipartite matching loss during training to compare a fixed number of predictions with a fixed set of ground truth labels. If there are fewer ground truth labels in the set of N labels, then they're padded with a no object class. This loss function encourages DETR to find a one-to-one assignment between the predictions and ground truth labels. If either the bounding boxes or class labels aren't correct, a loss is incurred. Likewise, if DETR predicts an object that doesn't exist, it is penalized. This encourages DETR to find other objects in an image instead of focusing on one really prominent object.
An object detection head is added on top of DETR to find the class label and the coordinates of the bounding box. There are two components to the object detection head: a linear layer to transform the decoder hidden states into logits over the class labels, and a MLP to predict the bounding box.
Ready to try your hand at object detection? Check out our complete object detection guide to learn how to finetune DETR and use it for inference!
Image segmentation
Mask2Former is a universal architecture for solving all types of image segmentation tasks. Traditional segmentation models are typically tailored towards a particular subtask of image segmentation, like instance, semantic or panoptic segmentation. Mask2Former frames each of those tasks as a mask classification problem. Mask classification groups pixels into N segments, and predicts N masks and their corresponding class label for a given image. We'll explain how Mask2Former works in this section, and then you can try finetuning SegFormer at the end.
There are three main components to Mask2Former:
A Swin backbone accepts an image and creates a low-resolution image feature map from 3 consecutive 3x3 convolutions.
The feature map is passed to a pixel decoder which gradually upsamples the low-resolution features into high-resolution per-pixel embeddings. The pixel decoder actually generates multi-scale features (contains both low- and high-resolution features) with resolutions 1/32, 1/16, and 1/8th of the original image.
Each of these feature maps of differing scales is fed successively to one Transformer decoder layer at a time in order to capture small objects from the high-resolution features. The key to Mask2Former is the masked attention mechanism in the decoder. Unlike cross-attention which can attend to the entire image, masked attention only focuses on a certain area of the image. This is faster and leads to better performance because the local features of an image are enough for the model to learn from.
Like DETR, Mask2Former also uses learned object queries and combines them with the image features from the pixel decoder to make a set prediction (class label, mask prediction). The decoder hidden states are passed into a linear layer and transformed into logits over the class labels. The cross-entropy loss is calculated between the logits and class label to find the most likely one.
The mask predictions are generated by combining the pixel-embeddings with the final decoder hidden states. The sigmoid cross-entropy and dice loss is calculated between the logits and the ground truth mask to find the most likely mask.
Ready to try your hand at object detection? Check out our complete image segmentation guide to learn how to finetune SegFormer and use it for inference!
Depth estimation
GLPN, Global-Local Path Network, is a Transformer for depth estimation that combines a SegFormer encoder with a lightweight decoder.
Like ViT, an image is split into a sequence of patches, except these image patches are smaller. This is better for dense prediction tasks like segmentation or depth estimation. The image patches are transformed into patch embeddings (see the image classification section for more details about how patch embeddings are created), which are fed to the encoder.
The encoder accepts the patch embeddings, and passes them through several encoder blocks. Each block consists of attention and Mix-FFN layers. The purpose of the latter is to provide positional information. At the end of each encoder block is a patch merging layer for creating hierarchical representations. The features of each group of neighboring patches are concatenated, and a linear layer is applied to the concatenated features to reduce the number of patches to a resolution of 1/4. This becomes the input to the next encoder block, where this whole process is repeated until you have image features with resolutions of 1/8, 1/16, and 1/32.
A lightweight decoder takes the last feature map (1/32 scale) from the encoder and upsamples it to 1/16 scale. From here, the feature is passed into a Selective Feature Fusion (SFF) module, which selects and combines local and global features from an attention map for each feature and then upsamples it to 1/8th. This process is repeated until the decoded features are the same size as the original image. The output is passed through two convolution layers and then a sigmoid activation is applied to predict the depth of each pixel.
Natural language processing
The Transformer was initially designed for machine translation, and since then, it has practically become the default architecture for solving all NLP tasks. Some tasks lend themselves to the Transformer's encoder structure, while others are better suited for the decoder. Still, other tasks make use of both the Transformer's encoder-decoder structure.
Text classification
BERT is an encoder-only model and is the first model to effectively implement deep bidirectionality to learn richer representations of the text by attending to words on both sides.
BERT uses WordPiece tokenization to generate a token embedding of the text. To tell the difference between a single sentence and a pair of sentences, a special [SEP] token is added to differentiate them. A special [CLS] token is added to the beginning of every sequence of text. The final output with the [CLS] token is used as the input to the classification head for classification tasks. BERT also adds a segment embedding to denote whether a token belongs to the first or second sentence in a pair of sentences.
BERT is pretrained with two objectives: masked language modeling and next-sentence prediction. In masked language modeling, some percentage of the input tokens are randomly masked, and the model needs to predict these. This solves the issue of bidirectionality, where the model could cheat and see all the words and "predict" the next word. The final hidden states of the predicted mask tokens are passed to a feedforward network with a softmax over the vocabulary to predict the masked word.
The second pretraining object is next-sentence prediction. The model must predict whether sentence B follows sentence A. Half of the time sentence B is the next sentence, and the other half of the time, sentence B is a random sentence. The prediction, whether it is the next sentence or not, is passed to a feedforward network with a softmax over the two classes (IsNext and NotNext).
The input embeddings are passed through multiple encoder layers to output some final hidden states.
To use the pretrained model for text classification, add a sequence classification head on top of the base BERT model. The sequence classification head is a linear layer that accepts the final hidden states and performs a linear transformation to convert them into logits. The cross-entropy loss is calculated between the logits and target to find the most likely label.
Ready to try your hand at text classification? Check out our complete text classification guide to learn how to finetune DistilBERT and use it for inference!
Token classification
To use BERT for token classification tasks like named entity recognition (NER), add a token classification head on top of the base BERT model. The token classification head is a linear layer that accepts the final hidden states and performs a linear transformation to convert them into logits. The cross-entropy loss is calculated between the logits and each token to find the most likely label.
Ready to try your hand at token classification? Check out our complete token classification guide to learn how to finetune DistilBERT and use it for inference!
Question answering
To use BERT for question answering, add a span classification head on top of the base BERT model. This linear layer accepts the final hidden states and performs a linear transformation to compute the span start and end logits corresponding to the answer. The cross-entropy loss is calculated between the logits and the label position to find the most likely span of text corresponding to the answer.
Ready to try your hand at question answering? Check out our complete question answering guide to learn how to finetune DistilBERT and use it for inference!
💡 Notice how easy it is to use BERT for different tasks once it's been pretrained. You only need to add a specific head to the pretrained model to manipulate the hidden states into your desired output!
Text generation
GPT-2 is a decoder-only model pretrained on a large amount of text. It can generate convincing (though not always true!) text given a prompt and complete other NLP tasks like question answering despite not being explicitly trained to.
GPT-2 uses byte pair encoding (BPE) to tokenize words and generate a token embedding. Positional encodings are added to the token embeddings to indicate the position of each token in the sequence. The input embeddings are passed through multiple decoder blocks to output some final hidden state. Within each decoder block, GPT-2 uses a masked self-attention layer which means GPT-2 can't attend to future tokens. It is only allowed to attend to tokens on the left. This is different from BERT's [mask] token because, in masked self-attention, an attention mask is used to set the score to 0 for future tokens.
The output from the decoder is passed to a language modeling head, which performs a linear transformation to convert the hidden states into logits. The label is the next token in the sequence, which are created by shifting the logits to the right by one. The cross-entropy loss is calculated between the shifted logits and the labels to output the next most likely token.
GPT-2's pretraining objective is based entirely on causal language modeling, predicting the next word in a sequence. This makes GPT-2 especially good at tasks that involve generating text.
Ready to try your hand at text generation? Check out our complete causal language modeling guide to learn how to finetune DistilGPT-2 and use it for inference!
For more information about text generation, check out the text generation strategies guide!
Summarization
Encoder-decoder models like BART and T5 are designed for the sequence-to-sequence pattern of a summarization task. We'll explain how BART works in this section, and then you can try finetuning T5 at the end.
BART's encoder architecture is very similar to BERT and accepts a token and positional embedding of the text. BART is pretrained by corrupting the input and then reconstructing it with the decoder. Unlike other encoders with specific corruption strategies, BART can apply any type of corruption. The text infilling corruption strategy works the best though. In text infilling, a number of text spans are replaced with a single [mask] token. This is important because the model has to predict the masked tokens, and it teaches the model to predict the number of missing tokens. The input embeddings and masked spans are passed through the encoder to output some final hidden states, but unlike BERT, BART doesn't add a final feedforward network at the end to predict a word.
The encoder's output is passed to the decoder, which must predict the masked tokens and any uncorrupted tokens from the encoder's output. This gives additional context to help the decoder restore the original text. The output from the decoder is passed to a language modeling head, which performs a linear transformation to convert the hidden states into logits. The cross-entropy loss is calculated between the logits and the label, which is just the token shifted to the right.
Ready to try your hand at summarization? Check out our complete summarization guide to learn how to finetune T5 and use it for inference!
For more information about text generation, check out the text generation strategies guide!
Translation
Translation is another example of a sequence-to-sequence task, which means you can use an encoder-decoder model like BART or T5 to do it. We'll explain how BART works in this section, and then you can try finetuning T5 at the end.
BART adapts to translation by adding a separate randomly initialized encoder to map a source language to an input that can be decoded into the target language. This new encoder's embeddings are passed to the pretrained encoder instead of the original word embeddings. The source encoder is trained by updating the source encoder, positional embeddings, and input embeddings with the cross-entropy loss from the model output. The model parameters are frozen in this first step, and all the model parameters are trained together in the second step.
BART has since been followed up by a multilingual version, mBART, intended for translation and pretrained on many different languages.
Ready to try your hand at translation? Check out our complete translation guide to learn how to finetune T5 and use it for inference!
For more information about text generation, check out the text generation strategies guide!
|
Benchmarks
Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed
and memory complexity of Transformer models.
[[open-in-colab]]
Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.
A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found here.
How to benchmark 🤗 Transformers models
The classes [PyTorchBenchmark] and [TensorFlowBenchmark] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the peak memory usage and required time for both inference and training.
Hereby, inference is defined by a single forward pass, and training is defined by a single forward pass and
backward pass.
The benchmark classes [PyTorchBenchmark] and [TensorFlowBenchmark] expect an object of type [PyTorchBenchmarkArguments] and
[TensorFlowBenchmarkArguments], respectively, for instantiation. [PyTorchBenchmarkArguments] and [TensorFlowBenchmarkArguments] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type bert-base-cased can be benchmarked.
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
args = PyTorchBenchmarkArguments(models=["google-bert/bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
benchmark = PyTorchBenchmark(args)
</pt>
<tf>py
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
args = TensorFlowBenchmarkArguments(
models=["google-bert/bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
)
benchmark = TensorFlowBenchmark(args)
Here, three arguments are given to the benchmark argument data classes, namely models, batch_sizes, and
sequence_lengths. The argument models is required and expects a list of model identifiers from the
model hub The list arguments batch_sizes and sequence_lengths define
the size of the input_ids on which the model is benchmarked. There are many more parameters that can be configured
via the benchmark argument data classes. For more detail on these one can either directly consult the files
src/transformers/benchmark/benchmark_args_utils.py, src/transformers/benchmark/benchmark_args.py (for PyTorch)
and src/transformers/benchmark/benchmark_args_tf.py (for Tensorflow). Alternatively, running the following shell
commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
respectively.
python examples/pytorch/benchmarking/run_benchmark.py --help
An instantiated benchmark object can then simply be run by calling benchmark.run().
results = benchmark.run()
print(results)
==================== INFERENCE - SPEED - RESULT ====================
Model Name Batch Size Seq Length Time in s
google-bert/bert-base-uncased 8 8 0.006
google-bert/bert-base-uncased 8 32 0.006
google-bert/bert-base-uncased 8 128 0.018
google-bert/bert-base-uncased 8 512 0.088
==================== INFERENCE - MEMORY - RESULT ====================
Model Name Batch Size Seq Length Memory in MB
google-bert/bert-base-uncased 8 8 1227
google-bert/bert-base-uncased 8 32 1281
google-bert/bert-base-uncased 8 128 1307
google-bert/bert-base-uncased 8 512 1539
==================== ENVIRONMENT INFORMATION ====================
transformers_version: 2.11.0
framework: PyTorch
use_torchscript: False
framework_version: 1.4.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 08:58:43.371351
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False
</pt>
<tf>bash
python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
An instantiated benchmark object can then simply be run by calling benchmark.run().
results = benchmark.run()
print(results)
results = benchmark.run()
print(results)
==================== INFERENCE - SPEED - RESULT ====================
Model Name Batch Size Seq Length Time in s
google-bert/bert-base-uncased 8 8 0.005
google-bert/bert-base-uncased 8 32 0.008
google-bert/bert-base-uncased 8 128 0.022
google-bert/bert-base-uncased 8 512 0.105
==================== INFERENCE - MEMORY - RESULT ====================
Model Name Batch Size Seq Length Memory in MB
google-bert/bert-base-uncased 8 8 1330
google-bert/bert-base-uncased 8 32 1330
google-bert/bert-base-uncased 8 128 1330
google-bert/bert-base-uncased 8 512 1770
==================== ENVIRONMENT INFORMATION ====================
transformers_version: 2.11.0
framework: Tensorflow
use_xla: False
framework_version: 2.2.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 09:26:35.617317
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False
By default, the time and the required memory for inference are benchmarked. In the example output above the first
two sections show the result corresponding to inference time and inference memory. In addition, all relevant
information about the computing environment, e.g. the GPU type, the system, the library versions, etc are printed
out in the third section under ENVIRONMENT INFORMATION. This information can optionally be saved in a .csv file
when adding the argument save_to_csv=True to [PyTorchBenchmarkArguments] and
[TensorFlowBenchmarkArguments] respectively. In this case, every section is saved in a separate
.csv file. The path to each .csv file can optionally be defined via the argument data classes.
Instead of benchmarking pre-trained models via their model identifier, e.g. google-bert/bert-base-uncased, the user can
alternatively benchmark an arbitrary configuration of any available model class. In this case, a list of
configurations must be inserted with the benchmark args as follows.
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
args = PyTorchBenchmarkArguments(
models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
)
config_base = BertConfig()
config_384_hid = BertConfig(hidden_size=384)
config_6_lay = BertConfig(num_hidden_layers=6)
benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
benchmark.run()
==================== INFERENCE - SPEED - RESULT ====================
Model Name Batch Size Seq Length Time in s
bert-base 8 128 0.006
bert-base 8 512 0.006
bert-base 8 128 0.018
bert-base 8 512 0.088
bert-384-hid 8 8 0.006
bert-384-hid 8 32 0.006
bert-384-hid 8 128 0.011
bert-384-hid 8 512 0.054
bert-6-lay 8 8 0.003
bert-6-lay 8 32 0.004
bert-6-lay 8 128 0.009
bert-6-lay 8 512 0.044
==================== INFERENCE - MEMORY - RESULT ====================
Model Name Batch Size Seq Length Memory in MB
bert-base 8 8 1277
bert-base 8 32 1281
bert-base 8 128 1307
bert-base 8 512 1539
bert-384-hid 8 8 1005
bert-384-hid 8 32 1027
bert-384-hid 8 128 1035
bert-384-hid 8 512 1255
bert-6-lay 8 8 1097
bert-6-lay 8 32 1101
bert-6-lay 8 128 1127
bert-6-lay 8 512 1359
==================== ENVIRONMENT INFORMATION ====================
transformers_version: 2.11.0
framework: PyTorch
use_torchscript: False
framework_version: 1.4.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 09:35:25.143267
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False
</pt>
<tf>py
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
args = TensorFlowBenchmarkArguments(
models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
)
config_base = BertConfig()
config_384_hid = BertConfig(hidden_size=384)
config_6_lay = BertConfig(num_hidden_layers=6)
benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
benchmark.run()
==================== INFERENCE - SPEED - RESULT ====================
Model Name Batch Size Seq Length Time in s
bert-base 8 8 0.005
bert-base 8 32 0.008
bert-base 8 128 0.022
bert-base 8 512 0.106
bert-384-hid 8 8 0.005
bert-384-hid 8 32 0.007
bert-384-hid 8 128 0.018
bert-384-hid 8 512 0.064
bert-6-lay 8 8 0.002
bert-6-lay 8 32 0.003
bert-6-lay 8 128 0.0011
bert-6-lay 8 512 0.074
==================== INFERENCE - MEMORY - RESULT ====================
Model Name Batch Size Seq Length Memory in MB
bert-base 8 8 1330
bert-base 8 32 1330
bert-base 8 128 1330
bert-base 8 512 1770
bert-384-hid 8 8 1330
bert-384-hid 8 32 1330
bert-384-hid 8 128 1330
bert-384-hid 8 512 1540
bert-6-lay 8 8 1330
bert-6-lay 8 32 1330
bert-6-lay 8 128 1330
bert-6-lay 8 512 1540
==================== ENVIRONMENT INFORMATION ====================
transformers_version: 2.11.0
framework: Tensorflow
use_xla: False
framework_version: 2.2.0
python_version: 3.6.10
system: Linux
cpu: x86_64
architecture: 64bit
date: 2020-06-29
time: 09:38:15.487125
fp16: False
use_multiprocessing: True
only_pretrain_model: False
cpu_ram_mb: 32088
use_gpu: True
num_gpus: 1
gpu: TITAN RTX
gpu_ram_mb: 24217
gpu_power_watts: 280.0
gpu_performance_state: 2
use_tpu: False
Again, inference time and required memory for inference are measured, but this time for customized configurations
of the BertModel class. This feature can especially be helpful when deciding for which configuration the model
should be trained.
Benchmark best practices
This section lists a couple of best practices one should be aware of when benchmarking a model.
Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
specifies on which device the code should be run by setting the CUDA_VISIBLE_DEVICES environment variable in the
shell, e.g. export CUDA_VISIBLE_DEVICES=0 before running the code.
The option no_multi_processing should only be set to True for testing and debugging. To ensure accurate
memory measurement it is recommended to run each memory benchmark in a separate process by making sure
no_multi_processing is set to True.
One should always state the environment information when sharing the results of a model benchmark. Results can vary
heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
useful for the community.
Sharing your benchmark
Previously all available core models (10 at the time) have been benchmarked for inference time, across many different
settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
done across CPUs (except for TensorFlow XLA) and GPUs.
The approach is detailed in the following blogpost and the results are
available here.
With the new benchmark tools, it is easier than ever to share your benchmark results with the community
PyTorch Benchmarking Results.
TensorFlow Benchmarking Results.
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