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Here you'll find techniques, tips and tricks that apply whether you are training a model, or running inference with it.
* [Instantiating a big model](big_models)
* [Troubleshooting performance issues](debugging) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/performance.md | https://huggingface.co/docs/transformers/en/performance/#training-and-inference | #training-and-inference | .md | 32_4 |
This document is far from being complete and a lot more needs to be added, so if you have additions or corrections to
make please don't hesitate to open a PR or if you aren't sure start an Issue and we can discuss the details there.
When making contributions that A is better than B, please try to include a reproducible benchmark and/or a link to the
source of that information (unless it comes directly from you). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/performance.md | https://huggingface.co/docs/transformers/en/performance/#contribute | #contribute | .md | 32_5 |
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|
🤗 Transformers is a library of pretrained state-of-the-art models for natural language processing (NLP), computer vision, and audio and speech processing tasks. Not only does the library contain Transformer models, but it also has non-Transformer models like modern convolutional networks for computer vision tasks. If you look at some of the most popular consumer products today, like smartphones, apps, and televisions, odds are that some kind of deep learning technology is behind it. Want to remove a background object from a picture taken by your smartphone? This is an example of a panoptic segmentation task (don't worry if you don't know what this means yet, we'll describe it in the following sections!).
This page provides an overview of the different speech and audio, computer vision, and NLP tasks that can be solved with the 🤗 Transformers library in just three lines of code! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#what--transformers-can-do | #what--transformers-can-do | .md | 33_1 |
Audio and speech processing tasks are a little different from the other modalities mainly because audio as an input is a continuous signal. Unlike text, a raw audio waveform can't be neatly split into discrete chunks the way a sentence can be divided into words. To get around this, the raw audio signal is typically sampled at regular intervals. If you take more samples within an interval, the sampling rate is higher, and the audio more closely resembles the original audio source.
Previous approaches preprocessed the audio to extract useful features from it. It is now more common to start audio and speech processing tasks by directly feeding the raw audio waveform to a feature encoder to extract an audio representation. This simplifies the preprocessing step and allows the model to learn the most essential features. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#audio | #audio | .md | 33_2 |
Audio classification is a task that labels audio data from a predefined set of classes. It is a broad category with many specific applications, some of which include:
* acoustic scene classification: label audio with a scene label ("office", "beach", "stadium")
* acoustic event detection: label audio with a sound event label ("car horn", "whale calling", "glass breaking")
* tagging: label audio containing multiple sounds (birdsongs, speaker identification in a meeting)
* music classification: label music with a genre label ("metal", "hip-hop", "country")
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="audio-classification", model="superb/hubert-base-superb-er")
>>> preds = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4532, 'label': 'hap'},
{'score': 0.3622, 'label': 'sad'},
{'score': 0.0943, 'label': 'neu'},
{'score': 0.0903, 'label': 'ang'}]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#audio-classification | #audio-classification | .md | 33_3 |
Automatic speech recognition (ASR) transcribes speech into text. It is one of the most common audio tasks due partly to speech being such a natural form of human communication. Today, ASR systems are embedded in "smart" technology products like speakers, phones, and cars. We can ask our virtual assistants to play music, set reminders, and tell us the weather.
But one of the key challenges Transformer architectures have helped with is in low-resource languages. By pretraining on large amounts of speech data, finetuning the model on only one hour of labeled speech data in a low-resource language can still produce high-quality results compared to previous ASR systems trained on 100x more labeled data.
```py
>>> from transformers import pipeline
>>> transcriber = pipeline(task="automatic-speech-recognition", model="openai/whisper-small")
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#automatic-speech-recognition | #automatic-speech-recognition | .md | 33_4 |
One of the first and earliest successful computer vision tasks was recognizing images of zip code numbers using a [convolutional neural network (CNN)](glossary#convolution). An image is composed of pixels, and each pixel has a numerical value. This makes it easy to represent an image as a matrix of pixel values. Each particular combination of pixel values describes the colors of an image.
Two general ways computer vision tasks can be solved are:
1. Use convolutions to learn the hierarchical features of an image from low-level features to high-level abstract things.
2. Split an image into patches and use a Transformer to gradually learn how each image patch is related to each other to form an image. Unlike the bottom-up approach favored by a CNN, this is kind of like starting out with a blurry image and then gradually bringing it into focus. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#computer-vision | #computer-vision | .md | 33_5 |
Image classification labels an entire image from a predefined set of classes. Like most classification tasks, there are many practical use cases for image classification, some of which include:
* healthcare: label medical images to detect disease or monitor patient health
* environment: label satellite images to monitor deforestation, inform wildland management or detect wildfires
* agriculture: label images of crops to monitor plant health or satellite images for land use monitoring
* ecology: label images of animal or plant species to monitor wildlife populations or track endangered species
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="image-classification")
>>> preds = classifier(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> print(*preds, sep="\n")
{'score': 0.4335, 'label': 'lynx, catamount'}
{'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
{'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
{'score': 0.0239, 'label': 'Egyptian cat'}
{'score': 0.0229, 'label': 'tiger cat'}
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#image-classification | #image-classification | .md | 33_6 |
Unlike image classification, object detection identifies multiple objects within an image and the objects' positions in an image (defined by the bounding box). Some example applications of object detection include:
* self-driving vehicles: detect everyday traffic objects such as other vehicles, pedestrians, and traffic lights
* remote sensing: disaster monitoring, urban planning, and weather forecasting
* defect detection: detect cracks or structural damage in buildings, and manufacturing defects
```py
>>> from transformers import pipeline
>>> detector = pipeline(task="object-detection")
>>> preds = detector(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"], "box": pred["box"]} for pred in preds]
>>> preds
[{'score': 0.9865,
'label': 'cat',
'box': {'xmin': 178, 'ymin': 154, 'xmax': 882, 'ymax': 598}}]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#object-detection | #object-detection | .md | 33_7 |
Image segmentation is a pixel-level task that assigns every pixel in an image to a class. It differs from object detection, which uses bounding boxes to label and predict objects in an image because segmentation is more granular. Segmentation can detect objects at a pixel-level. There are several types of image segmentation:
* instance segmentation: in addition to labeling the class of an object, it also labels each distinct instance of an object ("dog-1", "dog-2")
* panoptic segmentation: a combination of semantic and instance segmentation; it labels each pixel with a semantic class **and** each distinct instance of an object
Segmentation tasks are helpful in self-driving vehicles to create a pixel-level map of the world around them so they can navigate safely around pedestrians and other vehicles. It is also useful for medical imaging, where the task's finer granularity can help identify abnormal cells or organ features. Image segmentation can also be used in ecommerce to virtually try on clothes or create augmented reality experiences by overlaying objects in the real world through your camera.
```py
>>> from transformers import pipeline
>>> segmenter = pipeline(task="image-segmentation")
>>> preds = segmenter(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> print(*preds, sep="\n")
{'score': 0.9879, 'label': 'LABEL_184'}
{'score': 0.9973, 'label': 'snow'}
{'score': 0.9972, 'label': 'cat'}
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#image-segmentation | #image-segmentation | .md | 33_8 |
Depth estimation predicts the distance of each pixel in an image from the camera. This computer vision task is especially important for scene understanding and reconstruction. For example, in self-driving cars, vehicles need to understand how far objects like pedestrians, traffic signs, and other vehicles are to avoid obstacles and collisions. Depth information is also helpful for constructing 3D representations from 2D images and can be used to create high-quality 3D representations of biological structures or buildings.
There are two approaches to depth estimation:
* stereo: depths are estimated by comparing two images of the same image from slightly different angles
* monocular: depths are estimated from a single image
```py
>>> from transformers import pipeline
>>> depth_estimator = pipeline(task="depth-estimation")
>>> preds = depth_estimator(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#depth-estimation | #depth-estimation | .md | 33_9 |
NLP tasks are among the most common types of tasks because text is such a natural way for us to communicate. To get text into a format recognized by a model, it needs to be tokenized. This means dividing a sequence of text into separate words or subwords (tokens) and then converting these tokens into numbers. As a result, you can represent a sequence of text as a sequence of numbers, and once you have a sequence of numbers, it can be input into a model to solve all sorts of NLP tasks! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#natural-language-processing | #natural-language-processing | .md | 33_10 |
Like classification tasks in any modality, text classification labels a sequence of text (it can be sentence-level, a paragraph, or a document) from a predefined set of classes. There are many practical applications for text classification, some of which include:
* sentiment analysis: label text according to some polarity like `positive` or `negative` which can inform and support decision-making in fields like politics, finance, and marketing
* content classification: label text according to some topic to help organize and filter information in news and social media feeds (`weather`, `sports`, `finance`, etc.)
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="sentiment-analysis")
>>> preds = classifier("Hugging Face is the best thing since sliced bread!")
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.9991, 'label': 'POSITIVE'}]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#text-classification | #text-classification | .md | 33_11 |
In any NLP task, text is preprocessed by separating the sequence of text into individual words or subwords. These are known as [tokens](glossary#token). Token classification assigns each token a label from a predefined set of classes.
Two common types of token classification are:
* named entity recognition (NER): label a token according to an entity category like organization, person, location or date. NER is especially popular in biomedical settings, where it can label genes, proteins, and drug names.
* part-of-speech tagging (POS): label a token according to its part-of-speech like noun, verb, or adjective. POS is useful for helping translation systems understand how two identical words are grammatically different (bank as a noun versus bank as a verb).
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="ner")
>>> preds = classifier("Hugging Face is a French company based in New York City.")
>>> preds = [
... {
... "entity": pred["entity"],
... "score": round(pred["score"], 4),
... "index": pred["index"],
... "word": pred["word"],
... "start": pred["start"],
... "end": pred["end"],
... }
... for pred in preds
... ]
>>> print(*preds, sep="\n")
{'entity': 'I-ORG', 'score': 0.9968, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}
{'entity': 'I-ORG', 'score': 0.9293, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}
{'entity': 'I-ORG', 'score': 0.9763, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}
{'entity': 'I-MISC', 'score': 0.9983, 'index': 6, 'word': 'French', 'start': 18, 'end': 24}
{'entity': 'I-LOC', 'score': 0.999, 'index': 10, 'word': 'New', 'start': 42, 'end': 45}
{'entity': 'I-LOC', 'score': 0.9987, 'index': 11, 'word': 'York', 'start': 46, 'end': 50}
{'entity': 'I-LOC', 'score': 0.9992, 'index': 12, 'word': 'City', 'start': 51, 'end': 55}
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#token-classification | #token-classification | .md | 33_12 |
Question answering is another token-level task that returns an answer to a question, sometimes with context (open-domain) and other times without context (closed-domain). This task happens whenever we ask a virtual assistant something like whether a restaurant is open. It can also provide customer or technical support and help search engines retrieve the relevant information you're asking for.
There are two common types of question answering:
* extractive: given a question and some context, the answer is a span of text from the context the model must extract
* abstractive: given a question and some context, the answer is generated from the context; this approach is handled by the [`Text2TextGenerationPipeline`] instead of the [`QuestionAnsweringPipeline`] shown below
```py
>>> from transformers import pipeline
>>> question_answerer = pipeline(task="question-answering")
>>> preds = question_answerer(
... question="What is the name of the repository?",
... context="The name of the repository is huggingface/transformers",
... )
>>> print(
... f"score: {round(preds['score'], 4)}, start: {preds['start']}, end: {preds['end']}, answer: {preds['answer']}"
... )
score: 0.9327, start: 30, end: 54, answer: huggingface/transformers
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#question-answering | #question-answering | .md | 33_13 |
Summarization creates a shorter version of a text from a longer one while trying to preserve most of the meaning of the original document. Summarization is a sequence-to-sequence task; it outputs a shorter text sequence than the input. There are a lot of long-form documents that can be summarized to help readers quickly understand the main points. Legislative bills, legal and financial documents, patents, and scientific papers are a few examples of documents that could be summarized to save readers time and serve as a reading aid.
Like question answering, there are two types of summarization:
* extractive: identify and extract the most important sentences from the original text
* abstractive: generate the target summary (which may include new words not in the input document) from the original text; the [`SummarizationPipeline`] uses the abstractive approach
```py
>>> from transformers import pipeline
>>> summarizer = pipeline(task="summarization")
>>> summarizer(
... "In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles."
... )
[{'summary_text': ' The Transformer is the first sequence transduction model based entirely on attention . It replaces the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention . For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers .'}]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#summarization | #summarization | .md | 33_14 |
Translation converts a sequence of text in one language to another. It is important in helping people from different backgrounds communicate with each other, help translate content to reach wider audiences, and even be a learning tool to help people learn a new language. Along with summarization, translation is a sequence-to-sequence task, meaning the model receives an input sequence and returns a target output sequence.
In the early days, translation models were mostly monolingual, but recently, there has been increasing interest in multilingual models that can translate between many pairs of languages.
```py
>>> from transformers import pipeline
>>> text = "translate English to French: Hugging Face is a community-based open-source platform for machine learning."
>>> translator = pipeline(task="translation", model="google-t5/t5-small")
>>> translator(text)
[{'translation_text': "Hugging Face est une tribune communautaire de l'apprentissage des machines."}]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#translation | #translation | .md | 33_15 |
Language modeling is a task that predicts a word in a sequence of text. It has become a very popular NLP task because a pretrained language model can be finetuned for many other downstream tasks. Lately, there has been a lot of interest in large language models (LLMs) which demonstrate zero- or few-shot learning. This means the model can solve tasks it wasn't explicitly trained to do! Language models can be used to generate fluent and convincing text, though you need to be careful since the text may not always be accurate.
There are two types of language modeling:
* causal: the model's objective is to predict the next token in a sequence, and future tokens are masked
```py
>>> from transformers import pipeline
>>> prompt = "Hugging Face is a community-based open-source platform for machine learning."
>>> generator = pipeline(task="text-generation")
>>> generator(prompt) # doctest: +SKIP
```
* masked: the model's objective is to predict a masked token in a sequence with full access to the tokens in the sequence
```py
>>> text = "Hugging Face is a community-based open-source <mask> for machine learning."
>>> fill_mask = pipeline(task="fill-mask")
>>> preds = fill_mask(text, top_k=1)
>>> preds = [
... {
... "score": round(pred["score"], 4),
... "token": pred["token"],
... "token_str": pred["token_str"],
... "sequence": pred["sequence"],
... }
... for pred in preds
... ]
>>> preds
[{'score': 0.2236,
'token': 1761,
'token_str': ' platform',
'sequence': 'Hugging Face is a community-based open-source platform for machine learning.'}]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#language-modeling | #language-modeling | .md | 33_16 |
Multimodal tasks require a model to process multiple data modalities (text, image, audio, video) to solve a particular problem. Image captioning is an example of a multimodal task where the model takes an image as input and outputs a sequence of text describing the image or some properties of the image.
Although multimodal models work with different data types or modalities, internally, the preprocessing steps help the model convert all the data types into embeddings (vectors or list of numbers that holds meaningful information about the data). For a task like image captioning, the model learns relationships between image embeddings and text embeddings. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#multimodal | #multimodal | .md | 33_17 |
Document question answering is a task that answers natural language questions from a document. Unlike a token-level question answering task which takes text as input, document question answering takes an image of a document as input along with a question about the document and returns an answer. Document question answering can be used to parse structured documents and extract key information from it. In the example below, the total amount and change due can be extracted from a receipt.
```py
>>> from transformers import pipeline
>>> from PIL import Image
>>> import requests
>>> url = "https://huggingface.co/datasets/hf-internal-testing/example-documents/resolve/main/jpeg_images/2.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> doc_question_answerer = pipeline("document-question-answering", model="magorshunov/layoutlm-invoices")
>>> preds = doc_question_answerer(
... question="What is the total amount?",
... image=image,
... )
>>> preds
[{'score': 0.8531, 'answer': '17,000', 'start': 4, 'end': 4}]
```
Hopefully, this page has given you some more background information about all the types of tasks in each modality and the practical importance of each one. In the next [section](tasks_explained), you'll learn **how** 🤗 Transformers work to solve these tasks. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/task_summary.md | https://huggingface.co/docs/transformers/en/task_summary/#document-question-answering | #document-question-answering | .md | 33_18 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/hpo_train.md | https://huggingface.co/docs/transformers/en/hpo_train/ | .md | 34_0 |
|
🤗 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`] provides API for hyperparameter search. This doc shows how to enable it in example. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/hpo_train.md | https://huggingface.co/docs/transformers/en/hpo_train/#hyperparameter-search-using-trainer-api | #hyperparameter-search-using-trainer-api | .md | 34_1 |
[`Trainer`] supports four hyperparameter search backends currently:
[optuna](https://optuna.org/), [sigopt](https://sigopt.com/), [raytune](https://docs.ray.io/en/latest/tune/index.html) and [wandb](https://wandb.ai/site/sweeps).
you should install them before using them as the hyperparameter search backend
```bash
pip install optuna/sigopt/wandb/ray[tune]
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/hpo_train.md | https://huggingface.co/docs/transformers/en/hpo_train/#hyperparameter-search-backend | #hyperparameter-search-backend | .md | 34_2 |
Define the hyperparameter search space, different backends need different format.
For sigopt, see sigopt [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter), it's like following:
```py
>>> def sigopt_hp_space(trial):
... return [
... {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
... {
... "categorical_values": ["16", "32", "64", "128"],
... "name": "per_device_train_batch_size",
... "type": "categorical",
... },
... ]
```
For optuna, see optuna [object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py), it's like following:
```py
>>> def optuna_hp_space(trial):
... return {
... "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
... "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]),
... }
```
Optuna provides multi-objective HPO. You can pass `direction` in `hyperparameter_search` and define your own compute_objective to return multiple objective values. The Pareto Front (`List[BestRun]`) will be returned in hyperparameter_search, you should refer to the test case `TrainerHyperParameterMultiObjectOptunaIntegrationTest` in [test_trainer](https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py). It's like following
```py
>>> best_trials = trainer.hyperparameter_search(
... direction=["minimize", "maximize"],
... backend="optuna",
... hp_space=optuna_hp_space,
... n_trials=20,
... compute_objective=compute_objective,
... )
```
For raytune, see raytune [object_parameter](https://docs.ray.io/en/latest/tune/api/search_space.html), it's like following:
```py
>>> def ray_hp_space(trial):
... return {
... "learning_rate": tune.loguniform(1e-6, 1e-4),
... "per_device_train_batch_size": tune.choice([16, 32, 64, 128]),
... }
```
For wandb, see wandb [object_parameter](https://docs.wandb.ai/guides/sweeps/configuration), it's like following:
```py
>>> def wandb_hp_space(trial):
... return {
... "method": "random",
... "metric": {"name": "objective", "goal": "minimize"},
... "parameters": {
... "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
... "per_device_train_batch_size": {"values": [16, 32, 64, 128]},
... },
... }
```
Define a `model_init` function and pass it to the [`Trainer`], as an example:
```py
>>> def model_init(trial):
... return AutoModelForSequenceClassification.from_pretrained(
... model_args.model_name_or_path,
... from_tf=bool(".ckpt" in model_args.model_name_or_path),
... config=config,
... cache_dir=model_args.cache_dir,
... revision=model_args.model_revision,
... token=True if model_args.use_auth_token else None,
... )
```
Create a [`Trainer`] with your `model_init` function, training arguments, training and test datasets, and evaluation function:
```py
>>> trainer = Trainer(
... model=None,
... args=training_args,
... train_dataset=small_train_dataset,
... eval_dataset=small_eval_dataset,
... compute_metrics=compute_metrics,
... processing_class=tokenizer,
... model_init=model_init,
... data_collator=data_collator,
... )
```
Call hyperparameter search, get the best trial parameters, backend could be `"optuna"`/`"sigopt"`/`"wandb"`/`"ray"`. direction can be`"minimize"` or `"maximize"`, which indicates whether to optimize greater or lower objective.
You could define your own compute_objective function, if not defined, the default compute_objective will be called, and the sum of eval metric like f1 is returned as objective value.
```py
>>> best_trial = trainer.hyperparameter_search(
... direction="maximize",
... backend="optuna",
... hp_space=optuna_hp_space,
... n_trials=20,
... compute_objective=compute_objective,
... )
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/hpo_train.md | https://huggingface.co/docs/transformers/en/hpo_train/#how-to-enable-hyperparameter-search-in-example | #how-to-enable-hyperparameter-search-in-example | .md | 34_3 |
Currently, Hyperparameter search for DDP is enabled for optuna and sigopt. Only the rank-zero process will generate the search trial and pass the argument to other ranks. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/hpo_train.md | https://huggingface.co/docs/transformers/en/hpo_train/#hyperparameter-search-for-ddp-finetune | #hyperparameter-search-for-ddp-finetune | .md | 34_4 |
`transformers` is an opinionated framework; our philosophy is defined in the following [conceptual guide](./philosophy).
The core of that philosophy is exemplified by the [single model, single file](https://huggingface.co/blog/transformers-design-philosophy)
aspect of the library. This component's downside is that it limits the inheritance and importability of components from
files to others in the toolkit.
As a result, model components tend to be repeated across many files. There are as many attention layers defined
in `transformers` as there are models, and a significant number of those are identical to each other.
The unfortunate consequence is that independent implementations tend to diverge as fixes and changes get applied
to specific parts of the code.
In order to balance this issue, we introduced the concept of "copies" across the library. By adding a comment indicating
that code is a copy of another, we can enforce through CI and local commands that copies do not diverge. However,
while the complexity is low, this is often quite tedious to do.
And, finally, this contributes to adding a significant overhead to contributing models which we would like to remove.
This approach often requires model contributions to add modeling code (~1k lines), processor (~500 lines), tests, docs,
etc. Model contribution PRs rarely add less than 3-5k lines of code, with much of this code being boilerplate.
This raises the bar for contributions, and with Modular Transformers, we're aiming to lower the bar to a much more
acceptable point.
If you plan to add a model to `transformers` make sure you read [How to add a model to 🤗 Transformers?](https://huggingface.co/docs/transformers/add_new_model).
For any kind of contributions, see [CONTRIBUTING.md](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#modular-transformers | #modular-transformers | .md | 35_0 |
Modular Transformers introduces the concept of a "modular" file to a model folder. This modular file accepts code
that isn't typically accepted in modeling/processing files, as it allows importing from neighbouring models as well
as inheritance from classes to others.
This modular file defines models, processors, and the configuration class that would otherwise be defined in their
respective modules.
Finally, this feature introduces a new `linter` which will "unravel" the modular file into the "single model, single
file" directory structure. These files will get auto-generated every time the script is run; reducing the required
contributions to the modular file, and therefore only to the changes between the contributed model and others.
Model users will end up importing and using the single-file interface, so no change is expected here. Doing this, we
hope to combine the best of both worlds: enabling simple contributions while sticking to our philosophy.
This is therefore a replacement for the `# Copied from` markers, and previously contributed models can be expected to
be moved to the new Modular Transformers format in the coming months. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#what-is-it | #what-is-it | .md | 35_1 |
To generate a single file from the modular file, run the following command.
```bash
python utils/modular_model_converter.py --files-to-parse src/transformers/models/<your_model>/modular_<your_model>.py
```
The "linter", which unravels the inheritance and creates all single-files from the modular file, will flatten the
inheritance while trying to be invisible to Python users. At this time, the linter flattens a **single** level of
inheritance.
For example:
- If a configuration class inherits from another and adds/deletes an argument, the generated file will either directly
reference it (in case of addition) or completely remove it (in case of deletion).
- If a class inherits from another, for example: class GemmaModel(LlamaModel):, dependencies are automatically
inferred. All submodules will be automatically inferred from the superclass.
- If you define new functions in the `modular` and use them inside classes, the linter will automatically infer the
You should be able to write everything (the tokenizer, the image processor, the model, the config) in this `modular`
file, and the corresponding files will be created for you. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#details | #details | .md | 35_2 |
Run the command below to ensure the generated content matches `modular_<your_model>.py`
```bash
python utils/check_modular_conversion.py --files src/transformers/models/<your_model>/modular_<your_model>.py
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#enforcement | #enforcement | .md | 35_3 |
Here is a quick example with BERT and RoBERTa. The two models are intimately related: their modeling implementation
differs solely by a change in the embedding layer.
Instead of redefining the model entirely, here is what the `modular_roberta.py` file looks like for the modeling &
configuration classes (for the sake of the example, the tokenizer is ignored at this time as very different).
```python
from torch import nn
from ..bert.configuration_bert import BertConfig
from ..bert.modeling_bert import (
BertModel,
BertEmbeddings,
BertForMaskedLM
)
# The RoBERTa config is identical to BERT's config
class RobertaConfig(BertConfig):
model_type = 'roberta'
# We redefine the embeddings here to highlight the padding ID difference, and we redefine the position embeddings
class RobertaEmbeddings(BertEmbeddings):
def __init__(self, config):
super().__init__(config())
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
# The RoBERTa model is identical to the BERT model, except for the embedding layer.
# We redefine the embeddings above, so here there is no need to do additional work
class RobertaModel(BertModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = RobertaEmbeddings(config)
# The heads now only need to redefine the model inside to the correct `RobertaModel`
class RobertaForMaskedLM(BertForMaskedLM):
def __init__(self, config):
super().__init__(config)
self.model = RobertaModel(config)
```
Note that if you do not use the dependency that you defined, you will have the following error:
```bash
ValueError: You defined `RobertaEmbeddings` in the modular_roberta.py, it should be used
when you define `BertModel`, as it is one of it's direct dependencies. Make sure
you use it in the `__init__` function.
```
Additionally, you may find a list of examples here: | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#examples | #examples | .md | 35_4 |
It is not a replacement for the modeling code (yet?), and if your model is not based on anything else that ever existed, then you can add a `modeling` file as usual. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#what-it-is-not | #what-it-is-not | .md | 35_5 |
To remove attributes that are not used in your modular model, and that you don't want to see in the unravelled modeling:
```python
class GemmaModel(LlamaModel): | class GemmaModel(PreTrainedModel):
def __init__(self, config): | def __init__(self, config):
super().__init__(self, eos_token) | super().__init__(config)
del self.embed_tokens | self.padding_idx = config.pad_token_id
| self.vocab_size = config.vocab_size
|
| self.layers = nn.ModuleList(
| [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
| )
| self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
| self.rotary_emb = LlamaRotaryEmbedding(config=config)
| self.gradient_checkpointing = False
|
| # Initialize weights and apply final processing
| self.post_init()
```
If you check the original `LlamaModel`, it has a `embed_tokens` which was removed here (as you would expect!)
Removing a function is pretty similar, you just need to write it with a `raise ValueError("")` to mimick the behaviour you actually want when you remove a parent function in python.
```python
class GemmaTokenizer(LlamaTokenizer):
...
def get_spm_processor(self):
raise AttributeError("Not needed for Gemma")
def unk_token_length(self):
raise AttributeError("Not needed for Gemma")
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#removing-attributes-and-functions | #removing-attributes-and-functions | .md | 35_6 |
If you define a new function in the `modular` file to be used inside a class, say
```python
def my_new_function(*args, **kwargs):
# Do something here
pass
class GemmaModel(LlamaModel):
def forward(*args, **kwargs):
# Call the function
example = my_new_function(*args, **kwargs)
# continue here
```
the `my_new_function` function (and, recursively, any other new functions called in its body) will be automatically copy-pasted
in the file where it is used. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#define-new-functions | #define-new-functions | .md | 35_7 |
We recently shipped a few features that allow you to go from:
```python
class GemmaTokenizer(LlamaTokenizer, PretrainedTokenizerFast): | class GemmaModel(nn.Module):
def __init__(self, eos_token="</s>"): | def __init__(self):
eos_token = AddedToken(eos_token) | eos_token = AddedToken(eos_token)
PretrainedTokenizerFast.__init__(self, eos_token) | super().__init__(eos_token)
```
This is useful want you **don't** want to unravel the call to `super()`, and you want to differentiate which super init call you are doing! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#calling-super | #calling-super | .md | 35_8 |
We now also support special cases like
```python
class GemmaVisionModel(CLIPModel):
pass
```
where the name of your class `GemmaVision` is not the same as the modular `Gemma`. This is super useful for composite models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/modular_transformers.md | https://huggingface.co/docs/transformers/en/modular_transformers/#special-naming | #special-naming | .md | 35_9 |
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|
If training a model on a single GPU is too slow or if the model's weights do not fit in a single GPU's memory, transitioning
to a multi-GPU setup may be a viable option. Prior to making this transition, thoroughly explore all the strategies covered
in the [Methods and tools for efficient training on a single GPU](perf_train_gpu_one) as they are universally applicable
to model training on any number of GPUs. Once you have employed those strategies and found them insufficient for your
case on a single GPU, consider moving to multiple GPUs.
Transitioning from a single GPU to multiple GPUs requires the introduction of some form of parallelism, as the workload
must be distributed across the resources. Multiple techniques can be employed to achieve parallelism, such as data
parallelism, tensor parallelism, and pipeline parallelism. It's important to note that there isn't a one-size-fits-all
solution, and the optimal settings depend on the specific hardware configuration you are using.
This guide offers an in-depth overview of individual types of parallelism, as well as guidance on ways to combine
techniques and choosing an appropriate approach. For step-by-step tutorials on distributed training, please refer to
the [🤗 Accelerate documentation](https://huggingface.co/docs/accelerate/index).
<Tip>
While the main concepts discussed in this guide are likely applicable across frameworks, here we focus on
PyTorch-based implementations.
</Tip>
Before diving deeper into the specifics of each technique, let's go over the rough decision process when training
large models on a large infrastructure. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#efficient-training-on-multiple-gpus | #efficient-training-on-multiple-gpus | .md | 36_1 |
Begin by estimating how much vRAM is required to train your model. For models hosted on the 🤗 Hub, use our
[Model Memory Calculator](https://huggingface.co/spaces/hf-accelerate/model-memory-usage), which gives you
accurate calculations within a few percent margin.
**Parallelization strategy for a single Node / multi-GPU setup**
When training a model on a single node with multiple GPUs, your choice of parallelization strategy can significantly
impact performance. Here's a breakdown of your options:
**Case 1: Your model fits onto a single GPU**
If your model can comfortably fit onto a single GPU, you have two primary options:
1. DDP - Distributed DataParallel
2. [Zero Redundancy Optimizer (ZeRO)](https://arxiv.org/abs/1910.02054) - depending on the situation and configuration used, this method may or may not be faster, however, it's worth experimenting with it.
**Case 2: Your model doesn't fit onto a single GPU:**
If your model is too large for a single GPU, you have several alternatives to consider:
1. PipelineParallel (PP)
2. [ZeRO](https://arxiv.org/abs/1910.02054)
3. [TensorParallel](#tensor-parallelism) (TP)
With very fast inter-node connectivity (e.g., NVLINK or NVSwitch) all three strategies (PP, ZeRO, TP) should result in
similar performance. However, without these, PP will be faster than TP or ZeRO. The degree of TP may also
make a difference. It's best to experiment with your specific setup to determine the most suitable strategy.
TP is almost always used within a single node. That is TP size <= GPUs per node.
**Case 3: Largest layer of your model does not fit onto a single GPU**
1. If you are not using ZeRO, you have to use TensorParallel (TP), because PipelineParallel (PP) alone won't be sufficient to accommodate the large layer.
2. If you are using ZeRO, additionally adopt techniques from the [Methods and tools for efficient training on a single GPU](perf_train_gpu_one).
**Parallelization strategy for a multi-Node / multi-GPU setup**
* When you have fast inter-node connectivity (e.g., NVLINK or NVSwitch) consider using one of these options:
1. ZeRO - as it requires close to no modifications to the model
2. A combination of PipelineParallel(PP) with TensorParallel(TP) and DataParallel(DP) - this approach will result in fewer communications, but requires significant changes to the model
* When you have slow inter-node connectivity and still low on GPU memory:
1. Employ a combination of DataParallel(DP) with PipelineParallel(PP), TensorParallel(TP), and ZeRO.
In the following sections of this guide we dig deeper into how these different parallelism methods work. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#scalability-strategy | #scalability-strategy | .md | 36_2 |
Even with only 2 GPUs, you can readily leverage the accelerated training capabilities offered by PyTorch's built-in features,
such as `DataParallel` (DP) and `DistributedDataParallel` (DDP). Note that
[PyTorch documentation](https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html) recommends to prefer
`DistributedDataParallel` (DDP) over `DataParallel` (DP) for multi-GPU training as it works for all models.
Let's take a look at how these two methods work and what makes them different. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#data-parallelism | #data-parallelism | .md | 36_3 |
To understand the key differences in inter-GPU communication overhead between the two methods, let's review the processes per batch:
[DDP](https://pytorch.org/docs/master/notes/ddp.html):
- At the start time the main process replicates the model once from GPU 0 to the rest of GPUs
- Then for each batch:
1. Each GPU directly consumes its mini-batch of data.
2. During `backward`, once the local gradients are ready, they are averaged across all processes.
[DP](https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html):
For each batch:
1. GPU 0 reads the batch of data and then sends a mini-batch to each GPU.
2. The up-to-date model is replicated from GPU 0 to each GPU.
3. `forward` is executed, and output from each GPU is sent to GPU 0 to compute the loss.
4. The loss is distributed from GPU 0 to all GPUs, and `backward` is run.
5. Gradients from each GPU are sent to GPU 0 and averaged.
Key differences include:
1. DDP performs only a single communication per batch - sending gradients, while DP performs five different data exchanges per batch.
DDP copies data using [torch.distributed](https://pytorch.org/docs/master/distributed.html), while DP copies data within
the process via Python threads (which introduces limitations associated with GIL). As a result, **`DistributedDataParallel` (DDP) is generally faster than `DataParallel` (DP)** unless you have slow GPU card inter-connectivity.
2. Under DP, GPU 0 performs significantly more work than other GPUs, resulting in GPU under-utilization.
3. DDP supports distributed training across multiple machines, whereas DP does not.
This is not an exhaustive list of differences between DP and DDP, however, other nuances are out of scope of this guide.
You can get a deeper understanding of these methods by reading this [article](https://www.telesens.co/2019/04/04/distributed-data-parallel-training-using-pytorch-on-aws/).
Let's illustrate the differences between DP and DDP with an experiment. We'll benchmark the differences between DP and
DDP with an added context of NVLink presence:
* Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (`NV2` in `nvidia-smi topo -m`).
* Software: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`.
To disable the NVLink feature on one of the benchmarks, we use `NCCL_P2P_DISABLE=1`.
Here is the benchmarking code and outputs:
**DP**
```bash
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 110.5948, 'train_samples_per_second': 1.808, 'epoch': 0.69}
```
**DDP w/ NVlink**
```bash
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
```
**DDP w/o NVlink**
```bash
rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 131.4367, 'train_samples_per_second': 1.522, 'epoch': 0.69}
```
Here are the same benchmarking results gathered in a table for convenience:
| Type | NVlink | Time |
| :----- | ----- | ---: |
| 2:DP | Y | 110s |
| 2:DDP | Y | 101s |
| 2:DDP | N | 131s |
As you can see, in this case DP is ~10% slower than DDP with NVlink, but ~15% faster than DDP without NVlink.
The real difference will depend on how much data each GPU needs to sync with the others - the more there is to sync,
the more a slow link will impede the overall runtime. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#dataparallel-vs-distributeddataparallel | #dataparallel-vs-distributeddataparallel | .md | 36_4 |
ZeRO-powered data parallelism (ZeRO-DP) is illustrated in the following diagram from this [blog post](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/).
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-zero.png" alt="DeepSpeed-Image-1"/>
</div>
While it may appear complex, it is a very similar concept to `DataParallel` (DP). The difference is that instead of
replicating the full model parameters, gradients and optimizer states, each GPU stores only a slice of it. Then, at
run-time when the full layer parameters are needed just for the given layer, all GPUs synchronize to give each other
parts that they miss.
To illustrate this idea, consider a simple model with 3 layers (La, Lb, and Lc), where each layer has 3 parameters.
Layer La, for example, has weights a0, a1 and a2:
```
La | Lb | Lc
---|----|---
a0 | b0 | c0
a1 | b1 | c1
a2 | b2 | c2
```
If we have 3 GPUs, ZeRO-DP splits the model onto 3 GPUs like so:
```
GPU0:
La | Lb | Lc
---|----|---
a0 | b0 | c0
GPU1:
La | Lb | Lc
---|----|---
a1 | b1 | c1
GPU2:
La | Lb | Lc
---|----|---
a2 | b2 | c2
```
In a way, this is the same horizontal slicing as tensor parallelism, as opposed to Vertical
slicing, where one puts whole layer-groups on different GPUs. Now let's see how this works:
Each of these GPUs will get the usual mini-batch as it works in DP:
```
x0 => GPU0
x1 => GPU1
x2 => GPU2
```
The inputs are passed without modifications as if they would be processed by the original model.
First, the inputs get to the layer `La`. What happens at this point?
On GPU0: the x0 mini-batch requires the a0, a1, a2 parameters to do its forward path through the layer, but the GPU0 has only a0.
It will get a1 from GPU1 and a2 from GPU2, bringing all the pieces of the model together.
In parallel, GPU1 gets another mini-batch - x1. GPU1 has the a1 parameter, but needs a0 and a2, so it gets those from GPU0 and GPU2.
Same happens to GPU2 that gets the mini-batch x2. It gets a0 and a1 from GPU0 and GPU1.
This way each of the 3 GPUs gets the full tensors reconstructed and makes a forward pass with its own mini-batch.
As soon as the calculation is done, the data that is no longer needed gets dropped - it's only used during the calculation.
The reconstruction is done efficiently via a pre-fetch.
Then the whole process is repeated for layer Lb, then Lc forward-wise, and then backward Lc -> Lb -> La.
<Tip>
This mechanism is similar to an efficient group backpacking strategy: person A carries the tent, person B carries the stove,
and person C carries the axe. Each night they all share what they have with others and get from others what they don't have,
and in the morning they pack up their allocated type of gear and continue on their way. This is what ZeRO DP/Sharded DDP is.
Compare this strategy to the simple one where each person has to carry their own tent, stove and axe (similar to
DataParallel (DP and DDP) in PyTorch), which would be far more inefficient.
</Tip>
While reading the literature on this topic you may encounter the following synonyms: Sharded, Partitioned.
If you pay close attention the way ZeRO partitions the model's weights - it looks very similar to tensor parallelism
which will be discussed later. This is because it partitions/shards each layer's weights, unlike vertical model parallelism
which is discussed next.
Implementations:
- [DeepSpeed](https://www.deepspeed.ai/tutorials/zero/) ZeRO-DP stages 1+2+3
- [`Accelerate` integration](https://huggingface.co/docs/accelerate/en/usage_guides/deepspeed)
- [`transformers` integration](main_classes/trainer#trainer-integrations) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#zero-data-parallelism | #zero-data-parallelism | .md | 36_5 |
To explain Pipeline parallelism, we'll first look into Naive Model Parallelism (MP), also known as Vertical MP. This approach
involves distributing groups of model layers across multiple GPUs by assigning specific layers to specific GPUs with `.to()`.
As data flows through these layers, it is moved to the same GPU as the layer, while the other layers remain untouched.
We refer to this Model parallelism as "Vertical" because of how models are typically visualized. For example, the
following diagram shows an 8-layer model split vertically into two slices, placing layers 0-3 onto
GPU0 and 4-7 to GPU1:
```
================
| Layer | |
| 0 | |
| 1 | GPU0 |
| 2 | |
| 3 | |
================
| Layer | |
| 4 | |
| 5 | GPU1 |
| 6 | |
| 7 | |
================
```
In this example, when data moves from layer 0 to 3, it's no different from regular forward pass. However, passing data
from layer 3 to 4 requires moving it from GPU0 to GPU1, introducing a communication overhead. If the participating
GPUs are on the same compute node (e.g. same physical machine) this copying is fast, but if the GPUs are distributed
across different compute nodes (e.g. multiple machines), the communication overhead could be substantially greater.
Following that, layers 4 to 7 work as they would in the original model. Upon completion of the 7th layer, there is often
a need to send the data back to layer 0 where the labels are (or alternatively send the labels to the last layer). Now the loss can be
computed and the optimizer can do its work.
Naive Model Parallelism comes several shortcomings:
- **All but one GPU are idle at any given moment**: if 4 GPUs are used, it's nearly identical to quadrupling the amount of memory of a single GPU, and ignoring the rest of the hardware.
- **Overhead in data transfer between devices**: E.g. 4x 6GB cards will be able to accommodate the same size as 1x 24GB card using naive MP, but a single 24GB card will complete the training faster, because it doesn't have the data copying overhead. But, say, if you have 40GB cards and need to fit a 45GB model you can with 4x 40GB cards (but barely because of the gradient and optimizer states)
- **Copying shared embeddings**: Shared embeddings may need to get copied back and forth between GPUs.
Now that you are familiar with how the naive approach to model parallelism works and its shortcomings, let's look at Pipeline Parallelism (PP).
PP is almost identical to a naive MP, but it solves the GPU idling problem by chunking the incoming batch into micro-batches
and artificially creating a pipeline, which allows different GPUs to concurrently participate in the computation process.
The following illustration from the [GPipe paper](https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html)
shows the naive MP on the top, and PP on the bottom:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-gpipe-bubble.png" alt="MP vs PP"/>
</div>
At the bottom of the diagram, you can observe that the Pipeline Parallelism (PP) approach minimizes the number of idle
GPU zones, referred to as 'bubbles'. Both parts of the diagram show a parallelism level of degree 4, meaning that 4 GPUs
are involved in the pipeline. You can see that there's a forward path of 4 pipe stages (F0, F1, F2 and F3) followed by
a backward path in reverse order (B3, B2, B1, and B0).
PP introduces a new hyperparameter to tune - `chunks`, which determines how many data chunks are sent in a sequence
through the same pipe stage. For example, in the bottom diagram you can see `chunks=4`. GPU0 performs the same
forward path on chunk 0, 1, 2 and 3 (F0,0, F0,1, F0,2, F0,3) and then it waits for other GPUs to do complete their work.
Only when the other GPUs begin to complete their work, GPU0 starts to work again doing the backward path for chunks
3, 2, 1 and 0 (B0,3, B0,2, B0,1, B0,0).
Note that this is the same concept as gradient accumulation steps. PyTorch uses `chunks`, while DeepSpeed refers
to the same hyperparameter as gradient accumulation steps.
Because of the chunks, PP introduces the notion of micro-batches (MBS). DP splits the global data batch size into
mini-batches, so if you have a DP degree of 4, a global batch size of 1024 gets split up into 4 mini-batches of
256 each (1024/4). And if the number of `chunks` (or GAS) is 32 we end up with a micro-batch size of 8 (256/32). Each
Pipeline stage works with a single micro-batch at a time. To calculate the global batch size of the DP + PP setup,
use the formula: `mbs * chunks * dp_degree` (`8 * 32 * 4 = 1024`).
With `chunks=1` you end up with the naive MP, which is inefficient. With a large `chunks` value you end up with
tiny micro-batch sizes which is also inefficient. For this reason, we encourage to experiment with the `chunks` value to
find the one that leads to the most efficient GPUs utilization.
You may notice a bubble of "dead" time on the diagram that can't be parallelized because the last `forward` stage
has to wait for `backward` to complete the pipeline. The purpose of finding the best value for `chunks` is to enable a high
concurrent GPU utilization across all participating GPUs which translates to minimizing the size of the bubble.
Pipeline API solutions have been implemented in:
- PyTorch
- DeepSpeed
- Megatron-LM
These come with some shortcomings:
- They have to modify the model quite heavily, because Pipeline requires one to rewrite the normal flow of modules into a `nn.Sequential` sequence of the same, which may require changes to the design of the model.
- Currently the Pipeline API is very restricted. If you had a bunch of Python variables being passed in the very first stage of the Pipeline, you will have to find a way around it. Currently, the pipeline interface requires either a single Tensor or a tuple of Tensors as the only input and output. These tensors must have a batch size as the very first dimension, since pipeline is going to chunk the mini batch into micro-batches. Possible improvements are being discussed here https://github.com/pytorch/pytorch/pull/50693
- Conditional control flow at the level of pipe stages is not possible - e.g., Encoder-Decoder models like T5 require special workarounds to handle a conditional encoder stage.
- They have to arrange each layer so that the output of one layer becomes an input to the other layer.
More recent solutions include:
- Varuna
- Sagemaker
We have not experimented with Varuna and SageMaker but their papers report that they have overcome the list of problems
mentioned above and that they require smaller changes to the user's model.
Implementations:
- [PyTorch](https://pytorch.org/docs/stable/pipeline.html) (initial support in pytorch-1.8, and progressively getting improved in 1.9 and more so in 1.10). Some [examples](https://github.com/pytorch/pytorch/blob/master/benchmarks/distributed/pipeline/pipe.py)
- [DeepSpeed](https://www.deepspeed.ai/tutorials/pipeline/)
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation - no API.
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
- [OSLO](https://github.com/tunib-ai/oslo) - this is implemented based on the Hugging Face Transformers.
🤗 Transformers status: as of this writing none of the models supports full-PP. GPT2 and T5 models have naive MP support.
The main obstacle is being unable to convert the models to `nn.Sequential` and have all the inputs to be Tensors. This
is because currently the models include many features that make the conversion very complicated, and will need to be removed to accomplish that.
DeepSpeed and Megatron-LM integrations are available in [🤗 Accelerate](https://huggingface.co/docs/accelerate/main/en/usage_guides/deepspeed)
Other approaches:
DeepSpeed, Varuna and SageMaker use the concept of an [Interleaved Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html)
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-sagemaker-interleaved-pipeline.png" alt="Interleaved pipeline execution"/>
</div>
Here the bubble (idle time) is further minimized by prioritizing backward passes. Varuna further attempts to improve the
schedule by using simulations to discover the most efficient scheduling.
OSLO has pipeline parallelism implementation based on the Transformers without `nn.Sequential` conversion. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#from-naive-model-parallelism-to-pipeline-parallelism | #from-naive-model-parallelism-to-pipeline-parallelism | .md | 36_6 |
In Tensor Parallelism, each GPU processes a slice of a tensor and only aggregates the full tensor for operations requiring it.
To describe this method, this section of the guide relies on the concepts and diagrams from the [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
paper: [Efficient Large-Scale Language Model Training on GPU Clusters](https://arxiv.org/abs/2104.04473).
The main building block of any transformer is a fully connected `nn.Linear` followed by a nonlinear activation `GeLU`.
The dot dot-product part of it, following the Megatron's paper notation, can be written as `Y = GeLU(XA)`, where `X` is
an input vector, `Y` is the output vector, and `A` is the weight matrix.
If we look at the computation in matrix form, you can see how the matrix multiplication can be split between multiple GPUs:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-tp-parallel_gemm.png" alt="Parallel GEMM"/>
</div>
If we split the weight matrix `A` column-wise across `N` GPUs and perform matrix multiplications `XA_1` through `XA_n` in parallel,
then we will end up with `N` output vectors `Y_1, Y_2, ..., Y_n` which can be fed into `GeLU` independently:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-tp-independent-gelu.png" alt="Independent GeLU"/>
</div>
Using this principle, we can update a multi-layer perceptron of arbitrary depth, without the need for any synchronization
between GPUs until the very end, where we need to reconstruct the output vector from shards. The Megatron-LM paper authors
provide a helpful illustration for that:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-tp-parallel_shard_processing.png" alt="Parallel shard processing"/>
</div>
Parallelizing the multi-headed attention layers is even simpler, since they are already inherently parallel, due to having
multiple independent heads!
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-tp-parallel_self_attention.png" alt="Parallel self-attention"/>
</div>
Special considerations: TP requires very fast network, and therefore it's not advisable to do TP across more than one node.
Practically, if a node has 4 GPUs, the highest TP degree is therefore 4. If you need a TP degree of 8, you need to use
nodes that have at least 8 GPUs.
This section is based on the original much more [detailed TP overview](https://github.com/huggingface/transformers/issues/10321#issuecomment-783543530).
by [@anton-l](https://github.com/anton-l).
Alternative names:
- DeepSpeed calls it [tensor slicing](https://www.deepspeed.ai/training/#model-parallelism)
Implementations:
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation, as it's very model-specific
- [parallelformers](https://github.com/tunib-ai/parallelformers) (only inference at the moment)
- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
- [OSLO](https://github.com/tunib-ai/oslo) has the tensor parallelism implementation based on the Transformers.
SageMaker combines TP with DP for a more efficient processing.
🤗 Transformers status:
- core: not yet implemented in the core
- but if you want inference [parallelformers](https://github.com/tunib-ai/parallelformers) provides this support for most of our models. So until this is implemented in the core you can use theirs. And hopefully training mode will be supported too.
- Deepspeed-Inference also supports our BERT, GPT-2, and GPT-Neo models in their super-fast CUDA-kernel-based inference mode, see more [here](https://www.deepspeed.ai/tutorials/inference-tutorial/)
🤗 Accelerate integrates with [TP from Megatron-LM](https://huggingface.co/docs/accelerate/v0.23.0/en/usage_guides/megatron_lm). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#tensor-parallelism | #tensor-parallelism | .md | 36_7 |
The following diagram from the DeepSpeed [pipeline tutorial](https://www.deepspeed.ai/tutorials/pipeline/) demonstrates
how one can combine DP with PP.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-zero-dp-pp.png" alt="DP + PP-2d"/>
</div>
Here it's important to see how DP rank 0 doesn't see GPU2 and DP rank 1 doesn't see GPU3. To DP there is just GPUs 0
and 1 where it feeds data as if there were just 2 GPUs. GPU0 "secretly" offloads some of its load to GPU2 using PP.
And GPU1 does the same by enlisting GPU3 to its aid.
Since each dimension requires at least 2 GPUs, here you'd need at least 4 GPUs.
Implementations:
- [DeepSpeed](https://github.com/microsoft/DeepSpeed)
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972)
- [OSLO](https://github.com/tunib-ai/oslo)
🤗 Transformers status: not yet implemented | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#data-parallelism--pipeline-parallelism | #data-parallelism--pipeline-parallelism | .md | 36_8 |
To get an even more efficient training a 3D parallelism is used where PP is combined with TP and DP. This can be seen in the following diagram.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-deepspeed-3d.png" alt="dp-pp-tp-3d"/>
</div>
This diagram is from a blog post [3D parallelism: Scaling to trillion-parameter models](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/), which is a good read as well.
Since each dimension requires at least 2 GPUs, here you'd need at least 8 GPUs.
Implementations:
- [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972)
- [OSLO](https://github.com/tunib-ai/oslo)
🤗 Transformers status: not yet implemented, since we have no PP and TP. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#data-parallelism--pipeline-parallelism--tensor-parallelism | #data-parallelism--pipeline-parallelism--tensor-parallelism | .md | 36_9 |
One of the main features of DeepSpeed is ZeRO, which is a super-scalable extension of DP. It has already been
discussed in [ZeRO Data Parallelism](#zero-data-parallelism). Normally it's a standalone feature that doesn't require PP or TP.
But it can be combined with PP and TP.
When ZeRO-DP is combined with PP (and optionally TP) it typically enables only ZeRO stage 1 (optimizer sharding).
While it's theoretically possible to use ZeRO stage 2 (gradient sharding) with Pipeline Parallelism, it will have negative
performance impacts. There would need to be an additional reduce-scatter collective for every micro-batch to aggregate
the gradients before sharding, which adds a potentially significant communication overhead. By nature of Pipeline Parallelism,
small micro-batches are used and instead the focus is on trying to balance arithmetic intensity (micro-batch size) with
minimizing the Pipeline bubble (number of micro-batches). Therefore those communication costs are going to impact the performance.
In addition, there are already fewer layers than normal due to PP and so the memory savings won't be huge. PP already
reduces gradient size by ``1/PP``, and so gradient sharding savings on top of that are less significant than pure DP.
ZeRO stage 3 is not a good choice either for the same reason - more inter-node communications required.
And since we have ZeRO, the other benefit is ZeRO-Offload. Since this is stage 1 optimizer states can be offloaded to CPU.
Implementations:
- [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed) and [Megatron-Deepspeed from BigScience](https://github.com/bigscience-workshop/Megatron-DeepSpeed), which is the fork of the former repo.
- [OSLO](https://github.com/tunib-ai/oslo)
Important papers:
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](
https://arxiv.org/abs/2201.11990)
🤗 Transformers status: not yet implemented, since we have no PP and TP. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#zero-data-parallelism--pipeline-parallelism--tensor-parallelism | #zero-data-parallelism--pipeline-parallelism--tensor-parallelism | .md | 36_10 |
[FlexFlow](https://github.com/flexflow/FlexFlow) also solves the parallelization problem in a slightly different approach.
Paper: ["Beyond Data and Model Parallelism for Deep Neural Networks" by Zhihao Jia, Matei Zaharia, Alex Aiken](https://arxiv.org/abs/1807.05358)
It performs a sort of 4D Parallelism over Sample-Operator-Attribute-Parameter.
1. Sample = Data Parallelism (sample-wise parallel)
2. Operator = Parallelize a single operation into several sub-operations
3. Attribute = Data Parallelism (length-wise parallel)
4. Parameter = Model Parallelism (regardless of dimension - horizontal or vertical)
Examples:
* Sample
Let's take 10 batches of sequence length 512. If we parallelize them by sample dimension into 2 devices, we get 10 x 512 which becomes 5 x 2 x 512.
* Operator
If we perform layer normalization, we compute std first and mean second, and then we can normalize data.
Operator parallelism allows computing std and mean in parallel. So if we parallelize them by operator dimension into 2
devices (cuda:0, cuda:1), first we copy input data into both devices, and cuda:0 computes std, cuda:1 computes mean at the same time.
* Attribute
We have 10 batches of 512 length. If we parallelize them by attribute dimension into 2 devices, 10 x 512 will be 10 x 2 x 256.
* Parameter
It is similar with tensor model parallelism or naive layer-wise model parallelism.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-flexflow.jpeg" alt="flex-flow-soap"/>
</div>
The significance of this framework is that it takes resources like (1) GPU/TPU/CPU vs. (2) RAM/DRAM vs. (3)
fast-intra-connect/slow-inter-connect and it automatically optimizes all these algorithmically deciding which
parallelisation to use where.
One very important aspect is that FlexFlow is designed for optimizing DNN parallelizations for models with static and
fixed workloads, since models with dynamic behavior may prefer different parallelization strategies across iterations.
So the promise is very attractive - it runs a 30min simulation on the cluster of choice and it comes up with the best
strategy to utilise this specific environment. If you add/remove/replace any parts it'll run and re-optimize the plan
for that. And then you can train. A different setup will have its own custom optimization.
🤗 Transformers status: Transformers models are FX-trace-able via [transformers.utils.fx](https://github.com/huggingface/transformers/blob/master/src/transformers/utils/fx.py),
which is a prerequisite for FlexFlow, however, changes are required on the FlexFlow side to make it work with Transformers models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#flexflow | #flexflow | .md | 36_11 |
When training on multiple GPUs, you can specify the number of GPUs to use and in what order. This can be useful for instance when you have GPUs with different computing power and want to use the faster GPU first. The selection process works for both [DistributedDataParallel](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) and [DataParallel](https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html) to use only a subset of the available GPUs, and you don't need Accelerate or the [DeepSpeed integration](./main_classes/deepspeed). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#gpu-selection | #gpu-selection | .md | 36_12 |
For example, if you have 4 GPUs and you only want to use the first 2:
<hfoptions id="select-gpu">
<hfoption id="torchrun">
Use the `--nproc_per_node` to select how many GPUs to use.
```bash
torchrun --nproc_per_node=2 trainer-program.py ...
```
</hfoption>
<hfoption id="Accelerate">
Use `--num_processes` to select how many GPUs to use.
```bash
accelerate launch --num_processes 2 trainer-program.py ...
```
</hfoption>
<hfoption id="DeepSpeed">
Use `--num_gpus` to select how many GPUs to use.
```bash
deepspeed --num_gpus 2 trainer-program.py ...
```
</hfoption>
</hfoptions> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#number-of-gpus | #number-of-gpus | .md | 36_13 |
Now, to select which GPUs to use and their order, you'll use the `CUDA_VISIBLE_DEVICES` environment variable. It is easiest to set the environment variable in a `~/bashrc` or another startup config file. `CUDA_VISIBLE_DEVICES` is used to map which GPUs are used. For example, if you have 4 GPUs (0, 1, 2, 3) and you only want to run GPUs 0 and 2:
```bash
CUDA_VISIBLE_DEVICES=0,2 torchrun trainer-program.py ...
```
Only the 2 physical GPUs (0 and 2) are "visible" to PyTorch and these are mapped to `cuda:0` and `cuda:1` respectively. You can also reverse the order of the GPUs to use 2 first. Now, the mapping is `cuda:1` for GPU 0 and `cuda:0` for GPU 2.
```bash
CUDA_VISIBLE_DEVICES=2,0 torchrun trainer-program.py ...
```
You can also set the `CUDA_VISIBLE_DEVICES` environment variable to an empty value to create an environment without GPUs.
```bash
CUDA_VISIBLE_DEVICES= python trainer-program.py ...
```
<Tip warning={true}>
As with any environment variable, they can be exported instead of being added to the command line. However, this is not recommended because it can be confusing if you forget how the environment variable was setup and you end up using the wrong GPUs. Instead, it is common practice to set the environment variable for a specific training run on the same command line.
</Tip>
`CUDA_DEVICE_ORDER` is an alternative environment variable you can use to control how the GPUs are ordered. You can either order them by:
1. PCIe bus ID's that matches the order of [`nvidia-smi`](https://developer.nvidia.com/nvidia-system-management-interface) and [`rocm-smi`](https://rocm.docs.amd.com/projects/rocm_smi_lib/en/latest/.doxygen/docBin/html/index.html) for NVIDIA and AMD GPUs respectively
```bash
export CUDA_DEVICE_ORDER=PCI_BUS_ID
```
2. GPU compute ability
```bash
export CUDA_DEVICE_ORDER=FASTEST_FIRST
```
The `CUDA_DEVICE_ORDER` is especially useful if your training setup consists of an older and newer GPU, where the older GPU appears first, but you cannot physically swap the cards to make the newer GPU appear first. In this case, set `CUDA_DEVICE_ORDER=FASTEST_FIRST` to always use the newer and faster GPU first (`nvidia-smi` or `rocm-smi` still reports the GPUs in their PCIe order). Or you could also set `export CUDA_VISIBLE_DEVICES=1,0`. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/perf_train_gpu_many.md | https://huggingface.co/docs/transformers/en/perf_train_gpu_many/#order-of-gpus | #order-of-gpus | .md | 36_14 |
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|
An increasingly common use case for LLMs is **chat**. In a chat context, rather than continuing a single string
of text (as is the case with a standard language model), the model instead continues a conversation that consists
of one or more **messages**, each of which includes a **role**, like "user" or "assistant", as well as message text.
Much like tokenization, different models expect very different input formats for chat. This is the reason we added
**chat templates** as a feature. Chat templates are part of the tokenizer for text-only LLMs or processor for multimodal LLMs. They specify how to convert conversations,
represented as lists of messages, into a single tokenizable string in the format that the model expects.
Let's make this concrete with a quick example using the `mistralai/Mistral-7B-Instruct-v0.1` model:
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
>>> chat = [
... {"role": "user", "content": "Hello, how are you?"},
... {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
... {"role": "user", "content": "I'd like to show off how chat templating works!"},
... ]
>>> tokenizer.apply_chat_template(chat, tokenize=False)
"<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]"
```
Notice how the tokenizer has added the control tokens [INST] and [/INST] to indicate the start and end of
user messages (but not assistant messages!), and the entire chat is condensed into a single string.
If we use `tokenize=True`, which is the default setting, that string will also be tokenized for us.
Now, try the same code, but swap in the `HuggingFaceH4/zephyr-7b-beta` model instead, and you should get:
```text
<|user|>
Hello, how are you?</s>
<|assistant|>
I'm doing great. How can I help you today?</s>
<|user|>
I'd like to show off how chat templating works!</s>
```
Both Zephyr and Mistral-Instruct were fine-tuned from the same base model, `Mistral-7B-v0.1`. However, they were trained
with totally different chat formats. Without chat templates, you would have to write manual formatting code for each
model, and it's very easy to make minor errors that hurt performance! Chat templates handle the details of formatting
for you, allowing you to write universal code that works for any model. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#introduction | #introduction | .md | 37_1 |
As you can see in the example above, chat templates are easy to use. Simply build a list of messages, with `role`
and `content` keys, and then pass it to the [`~PreTrainedTokenizer.apply_chat_template`] or [`~ProcessorMixin.apply_chat_template`] method
depending on what type of model you are using. Once you do that,
you'll get output that's ready to go! When using chat templates as input for model generation, it's also a good idea
to use `add_generation_prompt=True` to add a [generation prompt](#what-are-generation-prompts). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#how-do-i-use-chat-templates | #how-do-i-use-chat-templates | .md | 37_2 |
Here's an example of preparing input for `model.generate()`, using `Zephyr` again:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceH4/zephyr-7b-beta"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint) # You may want to use bfloat16 and/or move to GPU here
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
print(tokenizer.decode(tokenized_chat[0]))
```
This will yield a string in the input format that Zephyr expects.
```text
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
```
Now that our input is formatted correctly for Zephyr, we can use the model to generate a response to the user's question:
```python
outputs = model.generate(tokenized_chat, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
This will yield:
```text
<|system|>
You are a friendly chatbot who always responds in the style of a pirate</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>
Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all.
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#usage-with-text-only-llms | #usage-with-text-only-llms | .md | 37_3 |
For multimodal LLMs such as [LLaVA](https://huggingface.co/llava-hf) the prompts can be formatted in a similar way. The only difference is you need to pass input images/videos as well along with the text. Each `"content"`
has to be a list containing either a text or an image/video.
Here's an example of preparing input for using `LLaVA` model:
```python
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id) # You may want to use bfloat16 and/or move to GPU here
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a friendly chatbot who always responds in the style of a pirate"}],
},
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "What are these?"},
],
},
]
processed_chat = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")
print(processor.batch_decode(processed_chat["input_ids"][:, :30]))
```
This yields a string in LLaVAs expected input format with many `<image>` tokens at the end.
The `<image>` tokens are placeholders and each one will be replaced by image embeddings when the mode is run in the forward call. The `processed_chat` can be further passed into [`~GenerationMixin.generate`] to generate text.
```text
'<|im_start|>system
You are a friendly chatbot who always responds in the style of a pirate<|im_end|><|im_start|>user <image><image><image><image><image><image><image><image>'
```
Arr, 'twas easy after all! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#usage-with-multimodal-llms | #usage-with-multimodal-llms | .md | 37_4 |
Yes, there is! Our text generation pipelines support chat inputs, which makes it easy to use chat models. In the past,
we used to use a dedicated "ConversationalPipeline" class, but this has now been deprecated and its functionality
has been merged into the [`TextGenerationPipeline`]. Let's try the `Zephyr` example again, but this time using
a pipeline:
```python
from transformers import pipeline
pipe = pipeline("text-generation", "HuggingFaceH4/zephyr-7b-beta")
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
print(pipe(messages, max_new_tokens=128)[0]['generated_text'][-1]) # Print the assistant's response
```
```text
{'role': 'assistant', 'content': "Matey, I'm afraid I must inform ye that humans cannot eat helicopters. Helicopters are not food, they are flying machines. Food is meant to be eaten, like a hearty plate o' grog, a savory bowl o' stew, or a delicious loaf o' bread. But helicopters, they be for transportin' and movin' around, not for eatin'. So, I'd say none, me hearties. None at all."}
```
The pipeline will take care of all the details of tokenization and calling `apply_chat_template` for you -
once the model has a chat template, all you need to do is initialize the pipeline and pass it the list of messages! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#is-there-an-automated-pipeline-for-chat | #is-there-an-automated-pipeline-for-chat | .md | 37_5 |
You may have noticed that the `apply_chat_template` method has an `add_generation_prompt` argument. This argument tells
the template to add tokens that indicate the start of a bot response. For example, consider the following chat:
```python
messages = [
{"role": "user", "content": "Hi there!"},
{"role": "assistant", "content": "Nice to meet you!"},
{"role": "user", "content": "Can I ask a question?"}
]
```
Here's what this will look like without a generation prompt, for a model that uses standard "ChatML" formatting:
```python
tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
"""
```
And here's what it looks like **with** a generation prompt:
```python
tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
```
Note that this time, we've added the tokens that indicate the start of a bot response. This ensures that when the model
generates text it will write a bot response instead of doing something unexpected, like continuing the user's
message. Remember, chat models are still just language models - they're trained to continue text, and chat is just a
special kind of text to them! You need to guide them with appropriate control tokens, so they know what they're
supposed to be doing.
Not all models require generation prompts. Some models, like LLaMA, don't have any
special tokens before bot responses. In these cases, the `add_generation_prompt` argument will have no effect. The exact
effect that `add_generation_prompt` has will depend on the template being used. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#what-are-generation-prompts | #what-are-generation-prompts | .md | 37_6 |
When passing a list of messages to `apply_chat_template` or `TextGenerationPipeline`, you can choose
to format the chat so the model will continue the final message in the chat instead of starting a new one. This is done
by removing any end-of-sequence tokens that indicate the end of the final message, so that the model will simply
extend the final message when it begins to generate text. This is useful for "prefilling" the model's response.
Here's an example:
```python
chat = [
{"role": "user", "content": "Can you format the answer in JSON?"},
{"role": "assistant", "content": '{"name": "'},
]
formatted_chat = tokenizer.apply_chat_template(chat, tokenize=True, return_dict=True, continue_final_message=True)
model.generate(**formatted_chat)
```
The model will generate text that continues the JSON string, rather than starting a new message. This approach
can be very useful for improving the accuracy of the model's instruction-following when you know how you want
it to start its replies.
Because `add_generation_prompt` adds the tokens that start a new message, and `continue_final_message` removes any
end-of-message tokens from the final message, it does not make sense to use them together. As a result, you'll
get an error if you try!
<Tip>
The default behaviour of `TextGenerationPipeline` is to set `add_generation_prompt=True` so that it starts a new
message. However, if the final message in the input chat has the "assistant" role, it will assume that this message is
a prefill and switch to `continue_final_message=True` instead, because most models do not support multiple
consecutive assistant messages. You can override this behaviour by explicitly passing the `continue_final_message`
argument when calling the pipeline.
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#what-does-continuefinalmessage-do | #what-does-continuefinalmessage-do | .md | 37_7 |
Yes! This is a good way to ensure that the chat template matches the tokens the model sees during training.
We recommend that you apply the chat template as a preprocessing step for your dataset. After this, you
can simply continue like any other language model training task. When training, you should usually set
`add_generation_prompt=False`, because the added tokens to prompt an assistant response will not be helpful during
training. Let's see an example:
```python
from transformers import AutoTokenizer
from datasets import Dataset
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
chat1 = [
{"role": "user", "content": "Which is bigger, the moon or the sun?"},
{"role": "assistant", "content": "The sun."}
]
chat2 = [
{"role": "user", "content": "Which is bigger, a virus or a bacterium?"},
{"role": "assistant", "content": "A bacterium."}
]
dataset = Dataset.from_dict({"chat": [chat1, chat2]})
dataset = dataset.map(lambda x: {"formatted_chat": tokenizer.apply_chat_template(x["chat"], tokenize=False, add_generation_prompt=False)})
print(dataset['formatted_chat'][0])
```
And we get:
```text
<|user|>
Which is bigger, the moon or the sun?</s>
<|assistant|>
The sun.</s>
```
From here, just continue training like you would with a standard language modelling task, using the `formatted_chat` column.
<Tip>
By default, some tokenizers add special tokens like `<bos>` and `<eos>` to text they tokenize. Chat templates should
already include all the special tokens they need, and so additional special tokens will often be incorrect or
duplicated, which will hurt model performance.
Therefore, if you format text with `apply_chat_template(tokenize=False)`, you should set the argument
`add_special_tokens=False` when you tokenize that text later. If you use `apply_chat_template(tokenize=True)`, you don't need to worry about this!
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#can-i-use-chat-templates-in-training | #can-i-use-chat-templates-in-training | .md | 37_8 |
The only argument that `apply_chat_template` requires is `messages`. However, you can pass any keyword
argument to `apply_chat_template` and it will be accessible inside the template. This gives you a lot of freedom to use
chat templates for many things. There are no restrictions on the names or the format of these arguments - you can pass
strings, lists, dicts or whatever else you want.
That said, there are some common use-cases for these extra arguments,
such as passing tools for function calling, or documents for retrieval-augmented generation. In these common cases,
we have some opinionated recommendations about what the names and formats of these arguments should be, which are
described in the sections below. We encourage model authors to make their chat templates compatible with this format,
to make it easy to transfer tool-calling code between models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#advanced-extra-inputs-to-chat-templates | #advanced-extra-inputs-to-chat-templates | .md | 37_9 |
"Tool use" LLMs can choose to call functions as external tools before generating an answer. When passing tools
to a tool-use model, you can simply pass a list of functions to the `tools` argument:
```python
import datetime
def current_time():
"""Get the current local time as a string."""
return str(datetime.now())
def multiply(a: float, b: float):
"""
A function that multiplies two numbers
Args:
a: The first number to multiply
b: The second number to multiply
"""
return a * b
tools = [current_time, multiply]
model_input = tokenizer.apply_chat_template(
messages,
tools=tools
)
```
In order for this to work correctly, you should write your functions in the format above, so that they can be parsed
correctly as tools. Specifically, you should follow these rules:
- The function should have a descriptive name
- Every argument must have a type hint
- The function must have a docstring in the standard Google style (in other words, an initial function description
followed by an `Args:` block that describes the arguments, unless the function does not have any arguments.
- Do not include types in the `Args:` block. In other words, write `a: The first number to multiply`, not
`a (int): The first number to multiply`. Type hints should go in the function header instead.
- The function can have a return type and a `Returns:` block in the docstring. However, these are optional
because most tool-use models ignore them. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#advanced-tool-use--function-calling | #advanced-tool-use--function-calling | .md | 37_10 |
The sample code above is enough to list the available tools for your model, but what happens if it wants to actually use
one? If that happens, you should:
1. Parse the model's output to get the tool name(s) and arguments.
2. Add the model's tool call(s) to the conversation.
3. Call the corresponding function(s) with those arguments.
4. Add the result(s) to the conversation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#passing-tool-results-to-the-model | #passing-tool-results-to-the-model | .md | 37_11 |
Let's walk through a tool use example, step by step. For this example, we will use an 8B `Hermes-2-Pro` model,
as it is one of the highest-performing tool-use models in its size category at the time of writing. If you have the
memory, you can consider using a larger model instead like [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
or [Mixtral-8x22B](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1), both of which also support tool use
and offer even stronger performance.
First, let's load our model and tokenizer:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")
```
Next, let's define a list of tools:
```python
def get_current_temperature(location: str, unit: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
unit: The unit to return the temperature in. (choices: ["celsius", "fahrenheit"])
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
def get_current_wind_speed(location: str) -> float:
"""
Get the current wind speed in km/h at a given location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current wind speed at the given location in km/h, as a float.
"""
return 6. # A real function should probably actually get the wind speed!
tools = [get_current_temperature, get_current_wind_speed]
```
Now, let's set up a conversation for our bot:
```python
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries. You should reply with the unit used in the queried location."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
```
Now, let's apply the chat template and generate a response:
```python
inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
```
And we get:
```text
<tool_call>
{"arguments": {"location": "Paris, France", "unit": "celsius"}, "name": "get_current_temperature"}
</tool_call><|im_end|>
```
The model has called the function with valid arguments, in the format requested by the function docstring. It has
inferred that we're most likely referring to the Paris in France, and it remembered that, as the home of SI units,
the temperature in France should certainly be displayed in Celsius.
<Tip>
The output format above is specific to the `Hermes-2-Pro` model we're using in this example. Other models may emit different
tool call formats, and you may need to do some manual parsing at this step. For example, `Llama-3.1` models will emit
slightly different JSON, with `parameters` instead of `arguments`. Regardless of the format the model outputs, you
should add the tool call to the conversation in the format below, with `tool_calls`, `function` and `arguments` keys.
</Tip>
Next, let's append the model's tool call to the conversation.
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
<Tip warning={true}>
If you're familiar with the OpenAI API, you should pay attention to an important difference here - the `tool_call` is
a dict, but in the OpenAI API it's a JSON string. Passing a string may cause errors or strange model behaviour!
</Tip>
Now that we've added the tool call to the conversation, we can call the function and append the result to the
conversation. Since we're just using a dummy function for this example that always returns 22.0, we can just append
that result directly.
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
<Tip>
Some model architectures, notably Mistral/Mixtral, also require a `tool_call_id` here, which should be
9 randomly-generated alphanumeric characters, and assigned to the `id` key of the tool call
dictionary. The same key should also be assigned to the `tool_call_id` key of the tool response dictionary below, so
that tool calls can be matched to tool responses. So, for Mistral/Mixtral models, the code above would be:
```python
tool_call_id = "9Ae3bDc2F" # Random ID, 9 alphanumeric characters
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "id": tool_call_id, "function": tool_call}]})
```
and
```python
messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": "get_current_temperature", "content": "22.0"})
```
</Tip>
Finally, let's let the assistant read the function outputs and continue chatting with the user:
```python
inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
```
And we get:
```text
The current temperature in Paris, France is 22.0 ° Celsius.<|im_end|>
```
Although this was a simple demo with dummy tools and a single call, the same technique works with
multiple real tools and longer conversations. This can be a powerful way to extend the capabilities of conversational
agents with real-time information, computational tools like calculators, or access to large databases. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#a-complete-tool-use-example | #a-complete-tool-use-example | .md | 37_12 |
Each function you pass to the `tools` argument of `apply_chat_template` is converted into a
[JSON schema](https://json-schema.org/learn/getting-started-step-by-step). These schemas
are then passed to the model chat template. In other words, tool-use models do not see your functions directly, and they
never see the actual code inside them. What they care about is the function **definitions** and the **arguments** they
need to pass to them - they care about what the tools do and how to use them, not how they work! It is up to you
to read their outputs, detect if they have requested to use a tool, pass their arguments to the tool function, and
return the response in the chat.
Generating JSON schemas to pass to the template should be automatic and invisible as long as your functions
follow the specification above, but if you encounter problems, or you simply want more control over the conversion,
you can handle the conversion manually. Here is an example of a manual schema conversion.
```python
from transformers.utils import get_json_schema
def multiply(a: float, b: float):
"""
A function that multiplies two numbers
Args:
a: The first number to multiply
b: The second number to multiply
"""
return a * b
schema = get_json_schema(multiply)
print(schema)
```
This will yield:
```json
{
"type": "function",
"function": {
"name": "multiply",
"description": "A function that multiplies two numbers",
"parameters": {
"type": "object",
"properties": {
"a": {
"type": "number",
"description": "The first number to multiply"
},
"b": {
"type": "number",
"description": "The second number to multiply"
}
},
"required": ["a", "b"]
}
}
}
```
If you wish, you can edit these schemas, or even write them from scratch yourself without using `get_json_schema` at
all. JSON schemas can be passed directly to the `tools` argument of
`apply_chat_template` - this gives you a lot of power to define precise schemas for more complex functions. Be careful,
though - the more complex your schemas, the more likely the model is to get confused when dealing with them! We
recommend simple function signatures where possible, keeping arguments (and especially complex, nested arguments)
to a minimum.
Here is an example of defining schemas by hand, and passing them directly to `apply_chat_template`:
```python
# A simple function that takes no arguments
current_time = {
"type": "function",
"function": {
"name": "current_time",
"description": "Get the current local time as a string.",
"parameters": {
'type': 'object',
'properties': {}
}
}
}
# A more complete function that takes two numerical arguments
multiply = {
'type': 'function',
'function': {
'name': 'multiply',
'description': 'A function that multiplies two numbers',
'parameters': {
'type': 'object',
'properties': {
'a': {
'type': 'number',
'description': 'The first number to multiply'
},
'b': {
'type': 'number', 'description': 'The second number to multiply'
}
},
'required': ['a', 'b']
}
}
}
model_input = tokenizer.apply_chat_template(
messages,
tools = [current_time, multiply]
)
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#understanding-tool-schemas | #understanding-tool-schemas | .md | 37_13 |
"Retrieval-augmented generation" or "RAG" LLMs can search a corpus of documents for information before responding
to a query. This allows models to vastly expand their knowledge base beyond their limited context size. Our
recommendation for RAG models is that their template
should accept a `documents` argument. This should be a list of documents, where each "document"
is a single dict with `title` and `contents` keys, both of which are strings. Because this format is much simpler
than the JSON schemas used for tools, no helper functions are necessary.
Here's an example of a RAG template in action:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
model_id = "CohereForAI/c4ai-command-r-v01-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
device = model.device # Get the device the model is loaded on
# Define conversation input
conversation = [
{"role": "user", "content": "What has Man always dreamed of?"}
]
# Define documents for retrieval-based generation
documents = [
{
"title": "The Moon: Our Age-Old Foe",
"text": "Man has always dreamed of destroying the moon. In this essay, I shall..."
},
{
"title": "The Sun: Our Age-Old Friend",
"text": "Although often underappreciated, the sun provides several notable benefits..."
}
]
# Tokenize conversation and documents using a RAG template, returning PyTorch tensors.
input_ids = tokenizer.apply_chat_template(
conversation=conversation,
documents=documents,
chat_template="rag",
tokenize=True,
add_generation_prompt=True,
return_tensors="pt").to(device)
# Generate a response
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
# Decode and print the generated text along with generation prompt
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
<Tip>
The `documents` input for retrieval-augmented generation is not widely supported, and many models have chat templates which simply ignore this input.
To verify if a model supports the `documents` input, you can read its model card, or `print(tokenizer.chat_template)` to see if the `documents` key is used anywhere.
One model class that does support it, though, is Cohere's [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024) and [Command-R+](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024), through their `rag` chat template. You can see additional examples of grounded generation using this feature in their model cards.
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#advanced-retrieval-augmented-generation | #advanced-retrieval-augmented-generation | .md | 37_14 |
The chat template for a model is stored on the `tokenizer.chat_template` attribute. If no chat template is set, the
default template for that model class is used instead. Let's take a look at a `Zephyr` chat template, though note this
one is a little simplified from the actual one!
```
{%- for message in messages %}
{{- '<|' + message['role'] + '|>\n' }}
{{- message['content'] + eos_token }}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|assistant|>\n' }}
{%- endif %}
```
If you've never seen one of these before, this is a [Jinja template](https://jinja.palletsprojects.com/en/3.1.x/templates/).
Jinja is a templating language that allows you to write simple code that generates text. In many ways, the code and
syntax resembles Python. In pure Python, this template would look something like this:
```python
for message in messages:
print(f'<|{message["role"]}|>')
print(message['content'] + eos_token)
if add_generation_prompt:
print('<|assistant|>')
```
Effectively, the template does three things:
1. For each message, print the role enclosed in `<|` and `|>`, like `<|user|>` or `<|assistant|>`.
2. Next, print the content of the message, followed by the end-of-sequence token.
3. Finally, if `add_generation_prompt` is set, print the assistant token, so that the model knows to start generating
an assistant response.
This is a pretty simple template but Jinja gives you a lot of flexibility to do more complex things! Let's see a Jinja
template that can format inputs similarly to the way LLaMA formats them (note that the real LLaMA template includes
handling for default system messages and slightly different system message handling in general - don't use this one
in your actual code!)
```
{%- for message in messages %}
{%- if message['role'] == 'user' %}
{{- bos_token + '[INST] ' + message['content'] + ' [/INST]' }}
{%- elif message['role'] == 'system' %}
{{- '<<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}
{%- elif message['role'] == 'assistant' %}
{{- ' ' + message['content'] + ' ' + eos_token }}
{%- endif %}
{%- endfor %}
```
Hopefully if you stare at this for a little bit you can see what this template is doing - it adds specific tokens like
`[INST]` and `[/INST]` based on the role of each message. User, assistant and system messages are clearly
distinguishable to the model because of the tokens they're wrapped in. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#advanced-how-do-chat-templates-work | #advanced-how-do-chat-templates-work | .md | 37_15 |
Simple, just write a jinja template and set `tokenizer.chat_template`. You may find it easier to start with an
existing template from another model and simply edit it for your needs! For example, we could take the LLaMA template
above and add "[ASST]" and "[/ASST]" to assistant messages:
```
{%- for message in messages %}
{%- if message['role'] == 'user' %}
{{- bos_token + '[INST] ' + message['content'].strip() + ' [/INST]' }}
{%- elif message['role'] == 'system' %}
{{- '<<SYS>>\\n' + message['content'].strip() + '\\n<</SYS>>\\n\\n' }}
{%- elif message['role'] == 'assistant' %}
{{- '[ASST] ' + message['content'] + ' [/ASST]' + eos_token }}
{%- endif %}
{%- endfor %}
```
Now, simply set the `tokenizer.chat_template` attribute. Next time you use [`~PreTrainedTokenizer.apply_chat_template`], it will
use your new template! This attribute will be saved in the `tokenizer_config.json` file, so you can use
[`~utils.PushToHubMixin.push_to_hub`] to upload your new template to the Hub and make sure everyone's using the right
template for your model!
```python
template = tokenizer.chat_template
template = template.replace("SYS", "SYSTEM") # Change the system token
tokenizer.chat_template = template # Set the new template
tokenizer.push_to_hub("model_name") # Upload your new template to the Hub!
```
The method [`~PreTrainedTokenizer.apply_chat_template`] which uses your chat template is called by the [`TextGenerationPipeline`] class, so
once you set the correct chat template, your model will automatically become compatible with [`TextGenerationPipeline`].
<Tip>
If you're fine-tuning a model for chat, in addition to setting a chat template, you should probably add any new chat
control tokens as special tokens in the tokenizer. Special tokens are never split,
ensuring that your control tokens are always handled as single tokens rather than being tokenized in pieces. You
should also set the tokenizer's `eos_token` attribute to the token that marks the end of assistant generations in your
template. This will ensure that text generation tools can correctly figure out when to stop generating text.
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#how-do-i-create-a-chat-template | #how-do-i-create-a-chat-template | .md | 37_16 |
Some models use different templates for different use cases. For example, they might use one template for normal chat
and another for tool-use, or retrieval-augmented generation. In these cases, `tokenizer.chat_template` is a dictionary.
This can cause some confusion, and where possible, we recommend using a single template for all use-cases. You can use
Jinja statements like `if tools is defined` and `{% macro %}` definitions to easily wrap multiple code paths in a
single template.
When a tokenizer has multiple templates, `tokenizer.chat_template` will be a `dict`, where each key is the name
of a template. The `apply_chat_template` method has special handling for certain template names: Specifically, it will
look for a template named `default` in most cases, and will raise an error if it can't find one. However, if a template
named `tool_use` exists when the user has passed a `tools` argument, it will use that instead. To access templates
with other names, pass the name of the template you want to the `chat_template` argument of
`apply_chat_template()`.
We find that this can be a bit confusing for users, though - so if you're writing a template yourself, we recommend
trying to put it all in a single template where possible! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#why-do-some-models-have-multiple-templates | #why-do-some-models-have-multiple-templates | .md | 37_17 |
When setting the template for a model that's already been trained for chat, you should ensure that the template
exactly matches the message formatting that the model saw during training, or else you will probably experience
performance degradation. This is true even if you're training the model further - you will probably get the best
performance if you keep the chat tokens constant. This is very analogous to tokenization - you generally get the
best performance for inference or fine-tuning when you precisely match the tokenization used during training.
If you're training a model from scratch, or fine-tuning a base language model for chat, on the other hand,
you have a lot of freedom to choose an appropriate template! LLMs are smart enough to learn to handle lots of different
input formats. One popular choice is the `ChatML` format, and this is a good, flexible choice for many use-cases.
It looks like this:
```
{%- for message in messages %}
{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
{%- endfor %}
```
If you like this one, here it is in one-liner form, ready to copy into your code. The one-liner also includes
handy support for [generation prompts](#what-are-generation-prompts), but note that it doesn't add BOS or EOS tokens!
If your model expects those, they won't be added automatically by `apply_chat_template` - in other words, the
text will be tokenized with `add_special_tokens=False`. This is to avoid potential conflicts between the template and
the `add_special_tokens` logic. If your model expects special tokens, make sure to add them to the template!
```python
tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
```
This template wraps each message in `<|im_start|>` and `<|im_end|>` tokens, and simply writes the role as a string, which
allows for flexibility in the roles you train with. The output looks like this:
```text
<|im_start|>system
You are a helpful chatbot that will do its best not to say anything so stupid that people tweet about it.<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I'm doing great!<|im_end|>
```
The "user", "system" and "assistant" roles are the standard for chat, and we recommend using them when it makes sense,
particularly if you want your model to operate well with [`TextGenerationPipeline`]. However, you are not limited
to these roles - templating is extremely flexible, and any string can be a role. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#what-template-should-i-use | #what-template-should-i-use | .md | 37_18 |
If you have any chat models, you should set their `tokenizer.chat_template` attribute and test it using
[`~PreTrainedTokenizer.apply_chat_template`], then push the updated tokenizer to the Hub. This applies even if you're
not the model owner - if you're using a model with an empty chat template, or one that's still using the default class
template, please open a [pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) to the model repository so that this attribute can be set properly!
Once the attribute is set, that's it, you're done! `tokenizer.apply_chat_template` will now work correctly for that
model, which means it is also automatically supported in places like `TextGenerationPipeline`!
By ensuring that models have this attribute, we can make sure that the whole community gets to use the full power of
open-source models. Formatting mismatches have been haunting the field and silently harming performance for too long -
it's time to put an end to them! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#i-want-to-add-some-chat-templates-how-should-i-get-started | #i-want-to-add-some-chat-templates-how-should-i-get-started | .md | 37_19 |
<Tip>
The easiest way to get started with writing Jinja templates is to take a look at some existing ones. You can use
`print(tokenizer.chat_template)` for any chat model to see what template it's using. In general, models that support tool use have
much more complex templates than other models - so when you're just getting started, they're probably a bad example
to learn from! You can also take a look at the
[Jinja documentation](https://jinja.palletsprojects.com/en/3.1.x/templates/#synopsis) for details
of general Jinja formatting and syntax.
</Tip>
Jinja templates in `transformers` are identical to Jinja templates elsewhere. The main thing to know is that
the conversation history will be accessible inside your template as a variable called `messages`.
You will be able to access `messages` in your template just like you can in Python, which means you can loop over
it with `{% for message in messages %}` or access individual messages with `{{ messages[0] }}`, for example.
You can also use the following tips to write clean, efficient Jinja templates: | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#advanced-template-writing-tips | #advanced-template-writing-tips | .md | 37_20 |
By default, Jinja will print any whitespace that comes before or after a block. This can be a problem for chat
templates, which generally want to be very precise with whitespace! To avoid this, we strongly recommend writing
your templates like this:
```
{%- for message in messages %}
{{- message['role'] + message['content'] }}
{%- endfor %}
```
rather than like this:
```
{% for message in messages %}
{{ message['role'] + message['content'] }}
{% endfor %}
```
Adding `-` will strip any whitespace that comes before the block. The second example looks innocent, but the newline
and indentation may end up being included in the output, which is probably not what you want! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#trimming-whitespace | #trimming-whitespace | .md | 37_21 |
Inside your template, you will have access several special variables. The most important of these is `messages`,
which contains the chat history as a list of message dicts. However, there are several others. Not every
variable will be used in every template. The most common other variables are:
- `tools` contains a list of tools in JSON schema format. Will be `None` or undefined if no tools are passed.
- `documents` contains a list of documents in the format `{"title": "Title", "contents": "Contents"}`, used for retrieval-augmented generation. Will be `None` or undefined if no documents are passed.
- `add_generation_prompt` is a bool that is `True` if the user has requested a generation prompt, and `False` otherwise. If this is set, your template should add the header for an assistant message to the end of the conversation. If your model doesn't have a specific header for assistant messages, you can ignore this flag.
- **Special tokens** like `bos_token` and `eos_token`. These are extracted from `tokenizer.special_tokens_map`. The exact tokens available inside each template will differ depending on the parent tokenizer.
<Tip>
You can actually pass any `kwarg` to `apply_chat_template`, and it will be accessible inside the template as a variable. In general,
we recommend trying to stick to the core variables above, as it will make your model harder to use if users have
to write custom code to pass model-specific `kwargs`. However, we're aware that this field moves quickly, so if you
have a new use-case that doesn't fit in the core API, feel free to use a new `kwarg` for it! If a new `kwarg`
becomes common we may promote it into the core API and create a standard, documented format for it.
</Tip> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#special-variables | #special-variables | .md | 37_22 |
There is also a short list of callable functions available to you inside your templates. These are:
- `raise_exception(msg)`: Raises a `TemplateException`. This is useful for debugging, and for telling users when they're
doing something that your template doesn't support.
- `strftime_now(format_str)`: Equivalent to `datetime.now().strftime(format_str)` in Python. This is used for getting
the current date/time in a specific format, which is sometimes included in system messages. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#callable-functions | #callable-functions | .md | 37_23 |
There are multiple implementations of Jinja in various languages. They generally have the same syntax,
but a key difference is that when you're writing a template in Python you can use Python methods, such as
`.lower()` on strings or `.items()` on dicts. This will break if someone tries to use your template on a non-Python
implementation of Jinja. Non-Python implementations are particularly common in deployment environments, where JS
and Rust are very popular.
Don't panic, though! There are a few easy changes you can make to your templates to ensure they're compatible across
all implementations of Jinja:
- Replace Python methods with Jinja filters. These usually have the same name, for example `string.lower()` becomes
`string|lower`, and `dict.items()` becomes `dict|items`. One notable change is that `string.strip()` becomes `string|trim`.
See the [list of built-in filters](https://jinja.palletsprojects.com/en/3.1.x/templates/#builtin-filters)
in the Jinja documentation for more.
- Replace `True`, `False` and `None`, which are Python-specific, with `true`, `false` and `none`.
- Directly rendering a dict or list may give different results in other implementations (for example, string entries
might change from single-quoted to double-quoted). Adding the `tojson` filter can help to ensure consistency here. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#compatibility-with-non-python-jinja | #compatibility-with-non-python-jinja | .md | 37_24 |
We mentioned above that `add_generation_prompt` is a special variable that will be accessible inside your template,
and is controlled by the user setting the `add_generation_prompt` flag. If your model expects a header for
assistant messages, then your template must support adding the header when `add_generation_prompt` is set.
Here is an example of a template that formats messages ChatML-style, with generation prompt support:
```text
{{- bos_token }}
{%- for message in messages %}
{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}
```
The exact content of the assistant header will depend on your specific model, but it should always be **the string
that represents the start of an assistant message**, so that if the user applies your template with
`add_generation_prompt=True` and then generates text, the model will write an assistant response. Also note that some
models do not need a generation prompt, because assistant messages always begin immediately after user messages.
This is particularly common for LLaMA and Mistral models, where assistant messages begin immediately after the `[/INST]`
token that ends user messages. In these cases, the template can ignore the `add_generation_prompt` flag.
Generation prompts are important! If your model requires a generation prompt but it is not set in the template, then
model generations will likely be severely degraded, or the model may display unusual behaviour like continuing
the final user message! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#writing-generation-prompts | #writing-generation-prompts | .md | 37_25 |
When this feature was introduced, most templates were quite small, the Jinja equivalent of a "one-liner" script.
However, with new models and features like tool-use and RAG, some templates can be 100 lines long or more. When
writing templates like these, it's a good idea to write them in a separate file, using a text editor. You can easily
extract a chat template to a file:
```python
open("template.jinja", "w").write(tokenizer.chat_template)
```
Or load the edited template back into the tokenizer:
```python
tokenizer.chat_template = open("template.jinja").read()
```
As an added bonus, when you write a long, multi-line template in a separate file, line numbers in that file will
exactly correspond to line numbers in template parsing or execution errors. This will make it much easier to
identify the source of issues. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#writing-and-debugging-larger-templates | #writing-and-debugging-larger-templates | .md | 37_26 |
Although chat templates do not enforce a specific API for tools (or for anything, really), we recommend
template authors try to stick to a standard API where possible. The whole point of chat templates is to allow code
to be transferable across models, so deviating from the standard tools API means users will have to write
custom code to use tools with your model. Sometimes it's unavoidable, but often with clever templating you can
make the standard API work!
Below, we'll list the elements of the standard API, and give tips on writing templates that will work well with it. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#writing-templates-for-tools | #writing-templates-for-tools | .md | 37_27 |
Your template should expect that the variable `tools` will either be null (if no tools are passed), or is a list
of JSON schema dicts. Our chat template methods allow users to pass tools as either JSON schema or Python functions, but when
functions are passed, we automatically generate JSON schema and pass that to your template. As a result, the
`tools` variable that your template receives will always be a list of JSON schema. Here is
a sample tool JSON schema:
```json
{
"type": "function",
"function": {
"name": "multiply",
"description": "A function that multiplies two numbers",
"parameters": {
"type": "object",
"properties": {
"a": {
"type": "number",
"description": "The first number to multiply"
},
"b": {
"type": "number",
"description": "The second number to multiply"
}
},
"required": ["a", "b"]
}
}
}
```
And here is some example code for handling tools in your chat template. Remember, this is just an example for a
specific format - your model will probably need different formatting!
```text
{%- if tools %}
{%- for tool in tools %}
{{- '<tool>' + tool['function']['name'] + '\n' }}
{%- for argument in tool['function']['parameters']['properties'] %}
{{- argument + ': ' + tool['function']['parameters']['properties'][argument]['description'] + '\n' }}
{%- endfor %}
{{- '\n</tool>' }}
{%- endif %}
{%- endif %}
```
The specific tokens and tool descriptions your template renders should of course be chosen to match the ones your model
was trained with. There is no requirement that your **model** understands JSON schema input, only that your template can translate
JSON schema into your model's format. For example, [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024)
was trained with tools defined using Python function headers, but the Command-R tool template accepts JSON schema,
converts types internally and renders the input tools as Python headers. You can do a lot with templates! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#tool-definitions | #tool-definitions | .md | 37_28 |
Tool calls, if present, will be a list attached to a message with the "assistant" role. Note that `tool_calls` is
always a list, even though most tool-calling models only support single tool calls at a time, which means
the list will usually only have a single element. Here is a sample message dict containing a tool call:
```json
{
"role": "assistant",
"tool_calls": [
{
"type": "function",
"function": {
"name": "multiply",
"arguments": {
"a": 5,
"b": 6
}
}
}
]
}
```
And a common pattern for handling them would be something like this:
```text
{%- if message['role'] == 'assistant' and 'tool_calls' in message %}
{%- for tool_call in message['tool_calls'] %}
{{- '<tool_call>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments']|tojson + '\n</tool_call>' }}
{%- endif %}
{%- endfor %}
{%- endif %}
```
Again, you should render the tool call with the formatting and special tokens that your model expects. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#tool-calls | #tool-calls | .md | 37_29 |
Tool responses have a simple format: They are a message dict with the "tool" role, a "name" key giving the name
of the called function, and a "content" key containing the result of the tool call. Here is a sample tool response:
```json
{
"role": "tool",
"name": "multiply",
"content": "30"
}
```
You don't need to use all of the keys in the tool response. For example, if your model doesn't expect the function
name to be included in the tool response, then rendering it can be as simple as:
```text
{%- if message['role'] == 'tool' %}
{{- "<tool_result>" + message['content'] + "</tool_result>" }}
{%- endif %}
```
Again, remember that the actual formatting and special tokens are model-specific - you should take a lot of care
to ensure that tokens, whitespace and everything else exactly match the format your model was trained with! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/chat_templating.md | https://huggingface.co/docs/transformers/en/chat_templating/#tool-responses | #tool-responses | .md | 37_30 |
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/ | .md | 38_0 |
|
The 🤗 Transformers library is often able to offer new models thanks to community contributors. But this can be a challenging project and requires an in-depth knowledge of the 🤗 Transformers library and the model to implement. At Hugging Face, we're trying to empower more of the community to actively add models and we've put together this guide to walk you through the process of adding a PyTorch model (make sure you have [PyTorch installed](https://pytorch.org/get-started/locally/)).
Along the way, you'll:
- get insights into open-source best practices
- understand the design principles behind one of the most popular deep learning libraries
- learn how to efficiently test large models
- learn how to integrate Python utilities like `black`, `ruff`, and `make fix-copies` to ensure clean and readable code
A Hugging Face team member will be available to help you along the way so you'll never be alone. 🤗 ❤️
To get started, open a [New model addition](https://github.com/huggingface/transformers/issues/new?assignees=&labels=New+model&template=new-model-addition.yml) issue for the model you want to see in 🤗 Transformers. If you're not especially picky about contributing a specific model, you can filter by the [New model label](https://github.com/huggingface/transformers/labels/New%20model) to see if there are any unclaimed model requests and work on it.
Once you've opened a new model request, the first step is to get familiar with 🤗 Transformers if you aren't already! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#how-to-add-a-model-to--transformers | #how-to-add-a-model-to--transformers | .md | 38_1 |
First, you should get a general overview of 🤗 Transformers. 🤗 Transformers is a very opinionated library, so there is a
chance that you don't agree with some of the library's philosophies or design choices. From our experience, however, we
found that the fundamental design choices and philosophies of the library are crucial to efficiently scale 🤗
Transformers while keeping maintenance costs at a reasonable level.
A good first starting point to better understand the library is to read the [documentation of our philosophy](philosophy). As a result of our way of working, there are some choices that we try to apply to all models:
- Composition is generally favored over-abstraction
- Duplicating code is not always bad if it strongly improves the readability or accessibility of a model
- Model files are as self-contained as possible so that when you read the code of a specific model, you ideally only
have to look into the respective `modeling_....py` file.
In our opinion, the library's code is not just a means to provide a product, *e.g.* the ability to use BERT for
inference, but also as the very product that we want to improve. Hence, when adding a model, the user is not only the
person who will use your model, but also everybody who will read, try to understand, and possibly tweak your code.
With this in mind, let's go a bit deeper into the general library design. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#general-overview-of--transformers | #general-overview-of--transformers | .md | 38_2 |
To successfully add a model, it is important to understand the interaction between your model and its config,
[`PreTrainedModel`], and [`PretrainedConfig`]. For exemplary purposes, we will
call the model to be added to 🤗 Transformers `BrandNewBert`.
Let's take a look:
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_overview.png"/>
As you can see, we do make use of inheritance in 🤗 Transformers, but we keep the level of abstraction to an absolute
minimum. There are never more than two levels of abstraction for any model in the library. `BrandNewBertModel`
inherits from `BrandNewBertPreTrainedModel` which in turn inherits from [`PreTrainedModel`] and
that's it. As a general rule, we want to make sure that a new model only depends on
[`PreTrainedModel`]. The important functionalities that are automatically provided to every new
model are [`~PreTrainedModel.from_pretrained`] and
[`~PreTrainedModel.save_pretrained`], which are used for serialization and deserialization. All of the
other important functionalities, such as `BrandNewBertModel.forward` should be completely defined in the new
`modeling_brand_new_bert.py` script. Next, we want to make sure that a model with a specific head layer, such as
`BrandNewBertForMaskedLM` does not inherit from `BrandNewBertModel`, but rather uses `BrandNewBertModel`
as a component that can be called in its forward pass to keep the level of abstraction low. Every new model requires a
configuration class, called `BrandNewBertConfig`. This configuration is always stored as an attribute in
[`PreTrainedModel`], and thus can be accessed via the `config` attribute for all classes
inheriting from `BrandNewBertPreTrainedModel`:
```python
model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert")
model.config # model has access to its config
```
Similar to the model, the configuration inherits basic serialization and deserialization functionalities from
[`PretrainedConfig`]. Note that the configuration and the model are always serialized into two
different formats - the model to a *pytorch_model.bin* file and the configuration to a *config.json* file. Calling
the model's [`~PreTrainedModel.save_pretrained`] will automatically call
the config's [`~PretrainedConfig.save_pretrained`], so that both model and configuration are saved. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#overview-of-models | #overview-of-models | .md | 38_3 |
When coding your new model, keep in mind that Transformers is an opinionated library and we have a few quirks of our
own regarding how code should be written :-)
1. The forward pass of your model should be fully written in the modeling file while being fully independent of other
models in the library. If you want to reuse a block from another model, copy the code and paste it with a
`# Copied from` comment on top (see [here](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160)
for a good example and [there](pr_checks#check-copies) for more documentation on Copied from).
2. The code should be fully understandable, even by a non-native English speaker. This means you should pick
descriptive variable names and avoid abbreviations. As an example, `activation` is preferred to `act`.
One-letter variable names are strongly discouraged unless it's an index in a for loop.
3. More generally we prefer longer explicit code to short magical one.
4. Avoid subclassing `nn.Sequential` in PyTorch but subclass `nn.Module` and write the forward pass, so that anyone
using your code can quickly debug it by adding print statements or breaking points.
5. Your function signature should be type-annotated. For the rest, good variable names are way more readable and
understandable than type annotations. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#code-style | #code-style | .md | 38_4 |
Not quite ready yet :-( This section will be added soon! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#overview-of-tokenizers | #overview-of-tokenizers | .md | 38_5 |
Everyone has different preferences of how to port a model so it can be very helpful for you to take a look at summaries
of how other contributors ported models to Hugging Face. Here is a list of community blog posts on how to port a model:
1. [Porting GPT2 Model](https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28) by [Thomas](https://huggingface.co/thomwolf)
2. [Porting WMT19 MT Model](https://huggingface.co/blog/porting-fsmt) by [Stas](https://huggingface.co/stas)
From experience, we can tell you that the most important things to keep in mind when adding a model are:
- Don't reinvent the wheel! Most parts of the code you will add for the new 🤗 Transformers model already exist
somewhere in 🤗 Transformers. Take some time to find similar, already existing models and tokenizers you can copy
from. [grep](https://www.gnu.org/software/grep/) and [rg](https://github.com/BurntSushi/ripgrep) are your
friends. Note that it might very well happen that your model's tokenizer is based on one model implementation, and
your model's modeling code on another one. *E.g.* FSMT's modeling code is based on BART, while FSMT's tokenizer code
is based on XLM.
- It's more of an engineering challenge than a scientific challenge. You should spend more time creating an
efficient debugging environment rather than trying to understand all theoretical aspects of the model in the paper.
- Ask for help, when you're stuck! Models are the core component of 🤗 Transformers so we at Hugging Face are more
than happy to help you at every step to add your model. Don't hesitate to ask if you notice you are not making
progress.
In the following, we try to give you a general recipe that we found most useful when porting a model to 🤗 Transformers.
The following list is a summary of everything that has to be done to add a model and can be used by you as a To-Do
List:
☐ (Optional) Understood the model's theoretical aspects<br>
☐ Prepared 🤗 Transformers dev environment<br>
☐ Set up debugging environment of the original repository<br>
☐ Created script that successfully runs the `forward()` pass using the original repository and checkpoint<br>
☐ Successfully added the model skeleton to 🤗 Transformers<br>
☐ Successfully converted original checkpoint to 🤗 Transformers checkpoint<br>
☐ Successfully ran `forward()` pass in 🤗 Transformers that gives identical output to original checkpoint<br>
☐ Finished model tests in 🤗 Transformers<br>
☐ Successfully added tokenizer in 🤗 Transformers<br>
☐ Run end-to-end integration tests<br>
☐ Finished docs<br>
☐ Uploaded model weights to the Hub<br>
☐ Submitted the pull request<br>
☐ (Optional) Added a demo notebook
To begin with, we usually recommend starting by getting a good theoretical understanding of `BrandNewBert`. However,
if you prefer to understand the theoretical aspects of the model *on-the-job*, then it is totally fine to directly dive
into the `BrandNewBert`'s code-base. This option might suit you better if your engineering skills are better than
your theoretical skill, if you have trouble understanding `BrandNewBert`'s paper, or if you just enjoy programming
much more than reading scientific papers. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#step-by-step-recipe-to-add-a-model-to--transformers | #step-by-step-recipe-to-add-a-model-to--transformers | .md | 38_6 |
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. That being said, you don't have to spend too much time on the
theoretical aspects, but rather focus on the practical ones, namely:
- What type of model is *brand_new_bert*? BERT-like encoder-only model? GPT2-like decoder-only model? BART-like
encoder-decoder model? Look at the [model_summary](model_summary) if you're not familiar with the differences between those.
- What are the applications of *brand_new_bert*? Text classification? Text generation? Seq2Seq tasks, *e.g.,*
summarization?
- What is the novel feature of the model that makes it different from BERT/GPT-2/BART?
- Which of the already existing [🤗 Transformers models](https://huggingface.co/transformers/#contents) is most
similar to *brand_new_bert*?
- What type of tokenizer is used? A sentencepiece tokenizer? Word piece tokenizer? Is it the same tokenizer as used
for BERT or BART?
After you feel like you have gotten a good overview of the architecture of the model, you might want to write to the
Hugging Face team with any questions you might have. This might include questions regarding the model's architecture,
its attention layer, etc. We will be more than happy to help you. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#1-optional-theoretical-aspects-of-brandnewbert | #1-optional-theoretical-aspects-of-brandnewbert | .md | 38_7 |
1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the ‘Fork' button on the
repository's page. This creates a copy of the code under your GitHub user account.
2. Clone your `transformers` fork to your local disk, and add the base repository as a remote:
```bash
git clone https://github.com/[your Github handle]/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git
```
3. Set up a development environment, for instance by running the following command:
```bash
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 the Deep Learning framework you are working with
(PyTorch, TensorFlow and/or Flax) then do:
```bash
pip install -e ".[quality]"
```
which should be enough for most use cases. You can then return to the parent directory
```bash
cd ..
```
4. We recommend adding the PyTorch version of *brand_new_bert* to Transformers. To install PyTorch, please follow the
instructions on https://pytorch.org/get-started/locally/.
**Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
5. To port *brand_new_bert*, you will also need access to its original repository:
```bash
git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
cd brand_new_bert
pip install -e .
```
Now you have set up a development environment to port *brand_new_bert* to 🤗 Transformers. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#2-next-prepare-your-environment | #2-next-prepare-your-environment | .md | 38_8 |
At first, you will work on the original *brand_new_bert* repository. Often, the original implementation is very
“researchy”. Meaning that documentation might be lacking and the code can be difficult to understand. But this should
be exactly your motivation to reimplement *brand_new_bert*. At Hugging Face, one of our main goals is to *make people
stand on the shoulders of giants* which translates here very well into taking a working model and rewriting it to make
it as **accessible, user-friendly, and beautiful** as possible. This is the number-one motivation to re-implement
models into 🤗 Transformers - trying to make complex new NLP technology accessible to **everybody**.
You should start thereby by diving into the original repository.
Successfully running the official pretrained model in the original repository is often **the most difficult** step.
From our experience, it is very important to spend some time getting familiar with the original code-base. You need to
figure out the following:
- Where to find the pretrained weights?
- How to load the pretrained weights into the corresponding model?
- How to run the tokenizer independently from the model?
- Trace one forward pass so that you know which classes and functions are required for a simple forward pass. Usually,
you only have to reimplement those functions.
- Be able to locate the important components of the model: Where is the model's class? Are there model sub-classes,
*e.g.* EncoderModel, DecoderModel? Where is the self-attention layer? Are there multiple different attention layers,
*e.g.* *self-attention*, *cross-attention*...?
- How can you debug the model in the original environment of the repo? Do you have to add *print* statements, can you
work with an interactive debugger like *ipdb*, or should you use an efficient IDE to debug the model, like PyCharm?
It is very important that before you start the porting process, you can **efficiently** debug code in the original
repository! Also, remember that you are working with an open-source library, so do not hesitate to open an issue, or
even a pull request in the original repository. The maintainers of this repository are most likely very happy about
someone looking into their code!
At this point, it is really up to you which debugging environment and strategy you prefer to use to debug the original
model. We strongly advise against setting up a costly GPU environment, but simply work on a CPU both when starting to
dive into the original repository and also when starting to write the 🤗 Transformers implementation of the model. Only
at the very end, when the model has already been successfully ported to 🤗 Transformers, one should verify that the
model also works as expected on GPU.
In general, there are two possible debugging environments for running the original model
- [Jupyter notebooks](https://jupyter.org/) / [google colab](https://colab.research.google.com/notebooks/intro.ipynb)
- Local python scripts.
Jupyter notebooks have the advantage that they allow for cell-by-cell execution which can be helpful to better split
logical components from one another and to have faster debugging cycles as intermediate results can be stored. Also,
notebooks are often easier to share with other contributors, which might be very helpful if you want to ask the Hugging
Face team for help. If you are familiar with Jupyter notebooks, we strongly recommend you work with them.
The obvious disadvantage of Jupyter notebooks is that if you are not used to working with them you will have to spend
some time adjusting to the new programming environment and you might not be able to use your known debugging tools
anymore, like `ipdb`.
For each code-base, a good first step is always to load a **small** pretrained checkpoint and to be able to reproduce a
single forward pass using a dummy integer vector of input IDs as an input. Such a script could look like this (in
pseudocode):
```python
model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = [0, 4, 5, 2, 3, 7, 9] # vector of input ids
original_output = model.predict(input_ids)
```
Next, regarding the debugging strategy, there are generally a few from which to choose from:
- Decompose the original model into many small testable components and run a forward pass on each of those for
verification
- Decompose the original model only into the original *tokenizer* and the original *model*, run a forward pass on
those, and use intermediate print statements or breakpoints for verification
Again, it is up to you which strategy to choose. Often, one or the other is advantageous depending on the original code
base.
If the original code-base allows you to decompose the model into smaller sub-components, *e.g.* if the original
code-base can easily be run in eager mode, it is usually worth the effort to do so. There are some important advantages
to taking the more difficult road in the beginning:
- at a later stage when comparing the original model to the Hugging Face implementation, you can verify automatically
for each component individually that the corresponding component of the 🤗 Transformers implementation matches instead
of relying on visual comparison via print statements
- it can give you some rope to decompose the big problem of porting a model into smaller problems of just porting
individual components and thus structure your work better
- separating the model into logical meaningful components will help you to get a better overview of the model's design
and thus to better understand the model
- at a later stage those component-by-component tests help you to ensure that no regression occurs as you continue
changing your code
[Lysandre's](https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed) integration checks for ELECTRA
gives a nice example of how this can be done.
However, if the original code-base is very complex or only allows intermediate components to be run in a compiled mode,
it might be too time-consuming or even impossible to separate the model into smaller testable sub-components. A good
example is [T5's MeshTensorFlow](https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow) library which is
very complex and does not offer a simple way to decompose the model into its sub-components. For such libraries, one
often relies on verifying print statements.
No matter which strategy you choose, the recommended procedure is often the same that you should start to debug the
starting layers first and the ending layers last.
It is recommended that you retrieve the output, either by print statements or sub-component functions, of the following
layers in the following order:
1. Retrieve the input IDs passed to the model
2. Retrieve the word embeddings
3. Retrieve the input of the first Transformer layer
4. Retrieve the output of the first Transformer layer
5. Retrieve the output of the following n - 1 Transformer layers
6. Retrieve the output of the whole BrandNewBert Model
Input IDs should thereby consists of an array of integers, *e.g.* `input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]`
The outputs of the following layers often consist of multi-dimensional float arrays and can look like this:
```
[[
[-0.1465, -0.6501, 0.1993, ..., 0.1451, 0.3430, 0.6024],
[-0.4417, -0.5920, 0.3450, ..., -0.3062, 0.6182, 0.7132],
[-0.5009, -0.7122, 0.4548, ..., -0.3662, 0.6091, 0.7648],
...,
[-0.5613, -0.6332, 0.4324, ..., -0.3792, 0.7372, 0.9288],
[-0.5416, -0.6345, 0.4180, ..., -0.3564, 0.6992, 0.9191],
[-0.5334, -0.6403, 0.4271, ..., -0.3339, 0.6533, 0.8694]]],
```
We expect that every model added to 🤗 Transformers passes a couple of integration tests, meaning that the original
model and the reimplemented version in 🤗 Transformers have to give the exact same output up to a precision of 0.001!
Since it is normal that the exact same model written in different libraries can give a slightly different output
depending on the library framework, we accept an error tolerance of 1e-3 (0.001). It is not enough if the model gives
nearly the same output, they have to be almost identical. Therefore, you will certainly compare the intermediate
outputs of the 🤗 Transformers version multiple times against the intermediate outputs of the original implementation of
*brand_new_bert* in which case an **efficient** debugging environment of the original repository is absolutely
important. Here is some advice to make your debugging environment as efficient as possible.
- Find the best way of debugging intermediate results. Is the original repository written in PyTorch? Then you should
probably take the time to write a longer script that decomposes the original model into smaller sub-components to
retrieve intermediate values. Is the original repository written in Tensorflow 1? Then you might have to rely on
TensorFlow print operations like [tf.print](https://www.tensorflow.org/api_docs/python/tf/print) to output
intermediate values. Is the original repository written in Jax? Then make sure that the model is **not jitted** when
running the forward pass, *e.g.* check-out [this link](https://github.com/google/jax/issues/196).
- Use the smallest pretrained checkpoint you can find. The smaller the checkpoint, the faster your debug cycle
becomes. It is not efficient if your pretrained model is so big that your forward pass takes more than 10 seconds.
In case only very large checkpoints are available, it might make more sense to create a dummy model in the new
environment with randomly initialized weights and save those weights for comparison with the 🤗 Transformers version
of your model
- Make sure you are using the easiest way of calling a forward pass in the original repository. Ideally, you want to
find the function in the original repository that **only** calls a single forward pass, *i.e.* that is often called
`predict`, `evaluate`, `forward` or `__call__`. You don't want to debug a function that calls `forward`
multiple times, *e.g.* to generate text, like `autoregressive_sample`, `generate`.
- Try to separate the tokenization from the model's *forward* pass. If the original repository shows examples where
you have to input a string, then try to find out where in the forward call the string input is changed to input ids
and start from this point. This might mean that you have to possibly write a small script yourself or change the
original code so that you can directly input the ids instead of an input string.
- Make sure that the model in your debugging setup is **not** in training mode, which often causes the model to yield
random outputs due to multiple dropout layers in the model. Make sure that the forward pass in your debugging
environment is **deterministic** so that the dropout layers are not used. Or use *transformers.utils.set_seed*
if the old and new implementations are in the same framework.
The following section gives you more specific details/tips on how you can do this for *brand_new_bert*. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#3-4-run-a-pretrained-checkpoint-using-the-original-repository | #3-4-run-a-pretrained-checkpoint-using-the-original-repository | .md | 38_9 |
Next, you can finally start adding new code to 🤗 Transformers. Go into the clone of your 🤗 Transformers' fork:
```bash
cd transformers
```
In the special case that you are adding a model whose architecture exactly matches the model architecture of an
existing model you only have to add a conversion script as described in [this section](#write-a-conversion-script).
In this case, you can just re-use the whole model architecture of the already existing model.
Otherwise, let's start generating a new model. We recommend using the following script to add a model starting from
an existing model:
```bash
transformers-cli add-new-model-like
```
You will be prompted with a questionnaire to fill in the basic information of your model.
**Open a Pull Request on the main huggingface/transformers repo**
Before starting to adapt the automatically generated code, now is the time to open a “Work in progress (WIP)” pull
request, *e.g.* “[WIP] Add *brand_new_bert*”, in 🤗 Transformers so that you and the Hugging Face team can work
side-by-side on integrating the model into 🤗 Transformers.
You should do the following:
1. Create a branch with a descriptive name from your main branch
```bash
git checkout -b add_brand_new_bert
```
2. Commit the automatically generated code:
```bash
git add .
git commit
```
3. Fetch and rebase to current main
```bash
git fetch upstream
git rebase upstream/main
```
4. Push the changes to your account using:
```bash
git push -u origin a-descriptive-name-for-my-changes
```
5. 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.
6. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.
In the following, whenever you have made some progress, don't forget to commit your work and push it to your account so
that it shows in the pull request. Additionally, you should make sure to update your work with the current main from
time to time by doing:
```bash
git fetch upstream
git merge upstream/main
```
In general, all questions you might have regarding the model or your implementation should be asked in your PR and
discussed/solved in the PR. This way, the Hugging Face team will always be notified when you are committing new code or
if you have a question. It is often very helpful to point the Hugging Face team to your added code so that the Hugging
Face team can efficiently understand your problem or question.
To do so, you can go to the “Files changed” tab where you see all of your changes, go to a line regarding which you
want to ask a question, and click on the “+” symbol to add a comment. Whenever a question or problem has been solved,
you can click on the “Resolve” button of the created comment.
In the same way, the Hugging Face team will open comments when reviewing your code. We recommend asking most questions
on GitHub on your PR. For some very general questions that are not very useful for the public, feel free to ping the
Hugging Face team by Slack or email.
**5. Adapt the generated models code for brand_new_bert**
At first, we will focus only on the model itself and not care about the tokenizer. All the relevant code should be
found in the generated files `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` and
`src/transformers/models/brand_new_bert/configuration_brand_new_bert.py`.
Now you can finally start coding :). The generated code in
`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` will either have the same architecture as BERT if
it's an encoder-only model or BART if it's an encoder-decoder model. At this point, you should remind yourself what
you've learned in the beginning about the theoretical aspects of the model: *How is the model different from BERT or
BART?*". Implement those changes which often means changing the *self-attention* layer, the order of the normalization
layer, etc… Again, it is often useful to look at the similar architecture of already existing models in Transformers to
get a better feeling of how your model should be implemented.
**Note** that at this point, you don't have to be very sure that your code is fully correct or clean. Rather, it is
advised to add a first *unclean*, copy-pasted version of the original code to
`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` until you feel like all the necessary code is
added. From our experience, it is much more efficient to quickly add a first version of the required code and
improve/correct the code iteratively with the conversion script as described in the next section. The only thing that
has to work at this point is that you can instantiate the 🤗 Transformers implementation of *brand_new_bert*, *i.e.* the
following command should work:
```python
from transformers import BrandNewBertModel, BrandNewBertConfig
model = BrandNewBertModel(BrandNewBertConfig())
```
The above command will create a model according to the default parameters as defined in `BrandNewBertConfig()` with
random weights, thus making sure that the `init()` methods of all components works.
Note that all random initialization should happen in the `_init_weights` method of your `BrandnewBertPreTrainedModel`
class. It should initialize all leaf modules depending on the variables of the config. Here is an example with the
BERT `_init_weights` method:
```py
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
```
You can have some more custom schemes if you need a special initialization for some modules. For instance, in
`Wav2Vec2ForPreTraining`, the last two linear layers need to have the initialization of the regular PyTorch `nn.Linear`
but all the other ones should use an initialization as above. This is coded like this:
```py
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, Wav2Vec2ForPreTraining):
module.project_hid.reset_parameters()
module.project_q.reset_parameters()
module.project_hid._is_hf_initialized = True
module.project_q._is_hf_initialized = True
elif isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
```
The `_is_hf_initialized` flag is internally used to make sure we only initialize a submodule once. By setting it to
`True` for `module.project_q` and `module.project_hid`, we make sure the custom initialization we did is not overridden later on,
the `_init_weights` function won't be applied to them.
**6. Write a conversion script**
Next, you should write a conversion script that lets you convert the checkpoint you used to debug *brand_new_bert* in
the original repository to a checkpoint compatible with your just created 🤗 Transformers implementation of
*brand_new_bert*. It is not advised to write the conversion script from scratch, but rather to look through already
existing conversion scripts in 🤗 Transformers for one that has been used to convert a similar model that was written in
the same framework as *brand_new_bert*. Usually, it is enough to copy an already existing conversion script and
slightly adapt it for your use case. Don't hesitate to ask the Hugging Face team to point you to a similar already
existing conversion script for your model.
- If you are porting a model from TensorFlow to PyTorch, a good starting point might be BERT's conversion script [here](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91)
- If you are porting a model from PyTorch to PyTorch, a good starting point might be BART's conversion script [here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py)
In the following, we'll quickly explain how PyTorch models store layer weights and define layer names. In PyTorch, the
name of a layer is defined by the name of the class attribute you give the layer. Let's define a dummy model in
PyTorch, called `SimpleModel` as follows:
```python
from torch import nn
class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.dense = nn.Linear(10, 10)
self.intermediate = nn.Linear(10, 10)
self.layer_norm = nn.LayerNorm(10)
```
Now we can create an instance of this model definition which will fill all weights: `dense`, `intermediate`,
`layer_norm` with random weights. We can print the model to see its architecture
```python
model = SimpleModel()
print(model)
```
This will print out the following:
```
SimpleModel(
(dense): Linear(in_features=10, out_features=10, bias=True)
(intermediate): Linear(in_features=10, out_features=10, bias=True)
(layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
)
```
We can see that the layer names are defined by the name of the class attribute in PyTorch. You can print out the weight
values of a specific layer:
```python
print(model.dense.weight.data)
```
to see that the weights were randomly initialized
```
tensor([[-0.0818, 0.2207, -0.0749, -0.0030, 0.0045, -0.1569, -0.1598, 0.0212,
-0.2077, 0.2157],
[ 0.1044, 0.0201, 0.0990, 0.2482, 0.3116, 0.2509, 0.2866, -0.2190,
0.2166, -0.0212],
[-0.2000, 0.1107, -0.1999, -0.3119, 0.1559, 0.0993, 0.1776, -0.1950,
-0.1023, -0.0447],
[-0.0888, -0.1092, 0.2281, 0.0336, 0.1817, -0.0115, 0.2096, 0.1415,
-0.1876, -0.2467],
[ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
0.2577, 0.0402],
[ 0.1502, 0.2465, 0.2566, 0.0693, 0.2352, -0.0530, 0.1859, -0.0604,
0.2132, 0.1680],
[ 0.1733, -0.2407, -0.1721, 0.1484, 0.0358, -0.0633, -0.0721, -0.0090,
0.2707, -0.2509],
[-0.1173, 0.1561, 0.2945, 0.0595, -0.1996, 0.2988, -0.0802, 0.0407,
0.1829, -0.1568],
[-0.1164, -0.2228, -0.0403, 0.0428, 0.1339, 0.0047, 0.1967, 0.2923,
0.0333, -0.0536],
[-0.1492, -0.1616, 0.1057, 0.1950, -0.2807, -0.2710, -0.1586, 0.0739,
0.2220, 0.2358]]).
```
In the conversion script, you should fill those randomly initialized weights with the exact weights of the
corresponding layer in the checkpoint. *E.g.*
```python
# retrieve matching layer weights, e.g. by
# recursive algorithm
layer_name = "dense"
pretrained_weight = array_of_dense_layer
model_pointer = getattr(model, "dense")
model_pointer.weight.data = torch.from_numpy(pretrained_weight)
```
While doing so, you must verify that each randomly initialized weight of your PyTorch model and its corresponding
pretrained checkpoint weight exactly match in both **shape and name**. To do so, it is **necessary** to add assert
statements for the shape and print out the names of the checkpoints weights. E.g. you should add statements like:
```python
assert (
model_pointer.weight.shape == pretrained_weight.shape
), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched"
```
Besides, you should also print out the names of both weights to make sure they match, *e.g.*
```python
logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}")
```
If either the shape or the name doesn't match, you probably assigned the wrong checkpoint weight to a randomly
initialized layer of the 🤗 Transformers implementation.
An incorrect shape is most likely due to an incorrect setting of the config parameters in `BrandNewBertConfig()` that
do not exactly match those that were used for the checkpoint you want to convert. However, it could also be that
PyTorch's implementation of a layer requires the weight to be transposed beforehand.
Finally, you should also check that **all** required weights are initialized and print out all checkpoint weights that
were not used for initialization to make sure the model is correctly converted. It is completely normal, that the
conversion trials fail with either a wrong shape statement or a wrong name assignment. This is most likely because either
you used incorrect parameters in `BrandNewBertConfig()`, have a wrong architecture in the 🤗 Transformers
implementation, you have a bug in the `init()` functions of one of the components of the 🤗 Transformers
implementation or you need to transpose one of the checkpoint weights.
This step should be iterated with the previous step until all weights of the checkpoint are correctly loaded in the
Transformers model. Having correctly loaded the checkpoint into the 🤗 Transformers implementation, you can then save
the model under a folder of your choice `/path/to/converted/checkpoint/folder` that should then contain both a
`pytorch_model.bin` file and a `config.json` file:
```python
model.save_pretrained("/path/to/converted/checkpoint/folder")
```
**7. Implement the forward pass**
Having managed to correctly load the pretrained weights into the 🤗 Transformers implementation, you should now make
sure that the forward pass is correctly implemented. In [Get familiar with the original repository](#3-4-run-a-pretrained-checkpoint-using-the-original-repository), you have already created a script that runs a forward
pass of the model using the original repository. Now you should write an analogous script using the 🤗 Transformers
implementation instead of the original one. It should look as follows:
```python
model = BrandNewBertModel.from_pretrained("/path/to/converted/checkpoint/folder")
input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
output = model(input_ids).last_hidden_states
```
It is very likely that the 🤗 Transformers implementation and the original model implementation don't give the exact
same output the very first time or that the forward pass throws an error. Don't be disappointed - it's expected! First,
you should make sure that the forward pass doesn't throw any errors. It often happens that the wrong dimensions are
used leading to a *Dimensionality mismatch* error or that the wrong data type object is used, *e.g.* `torch.long`
instead of `torch.float32`. Don't hesitate to ask the Hugging Face team for help, if you don't manage to solve
certain errors.
The final part to make sure the 🤗 Transformers implementation works correctly is to ensure that the outputs are
equivalent to a precision of `1e-3`. First, you should ensure that the output shapes are identical, *i.e.*
`outputs.shape` should yield the same value for the script of the 🤗 Transformers implementation and the original
implementation. Next, you should make sure that the output values are identical as well. This one of the most difficult
parts of adding a new model. Common mistakes why the outputs are not identical are:
- Some layers were not added, *i.e.* an *activation* layer was not added, or the residual connection was forgotten
- The word embedding matrix was not tied
- The wrong positional embeddings are used because the original implementation uses on offset
- Dropout is applied during the forward pass. To fix this make sure *model.training is False* and that no dropout
layer is falsely activated during the forward pass, *i.e.* pass *self.training* to [PyTorch's functional dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout)
The best way to fix the problem is usually to look at the forward pass of the original implementation and the 🤗
Transformers implementation side-by-side and check if there are any differences. Ideally, you should debug/print out
intermediate outputs of both implementations of the forward pass to find the exact position in the network where the 🤗
Transformers implementation shows a different output than the original implementation. First, make sure that the
hard-coded `input_ids` in both scripts are identical. Next, verify that the outputs of the first transformation of
the `input_ids` (usually the word embeddings) are identical. And then work your way up to the very last layer of the
network. At some point, you will notice a difference between the two implementations, which should point you to the bug
in the 🤗 Transformers implementation. From our experience, a simple and efficient way is to add many print statements
in both the original implementation and 🤗 Transformers implementation, at the same positions in the network
respectively, and to successively remove print statements showing the same values for intermediate presentations.
When you're confident that both implementations yield the same output, verify the outputs with
`torch.allclose(original_output, output, atol=1e-3)`, you're done with the most difficult part! Congratulations - the
work left to be done should be a cakewalk 😊.
**8. Adding all necessary model tests**
At this point, you have successfully added a new model. However, it is very much possible that the model does not yet
fully comply with the required design. To make sure, the implementation is fully compatible with 🤗 Transformers, all
common tests should pass. The Cookiecutter should have automatically added a test file for your model, probably under
the same `tests/models/brand_new_bert/test_modeling_brand_new_bert.py`. Run this test file to verify that all common
tests pass:
```bash
pytest tests/models/brand_new_bert/test_modeling_brand_new_bert.py
```
Having fixed all common tests, it is now crucial to ensure that all the nice work you have done is well tested, so that
- a) The community can easily understand your work by looking at specific tests of *brand_new_bert*
- b) Future changes to your model will not break any important feature of the model.
At first, integration tests should be added. Those integration tests essentially do the same as the debugging scripts
you used earlier to implement the model to 🤗 Transformers. A template of those model tests has already added by the
Cookiecutter, called `BrandNewBertModelIntegrationTests` and only has to be filled out by you. To ensure that those
tests are passing, run
```bash
RUN_SLOW=1 pytest -sv tests/models/brand_new_bert/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
```
<Tip>
In case you are using Windows, you should replace `RUN_SLOW=1` with `SET RUN_SLOW=1`
</Tip>
Second, all features that are special to *brand_new_bert* should be tested additionally in a separate test under
`BrandNewBertModelTester`/`BrandNewBertModelTest`. This part is often forgotten but is extremely useful in two
ways:
- It helps to transfer the knowledge you have acquired during the model addition to the community by showing how the
special features of *brand_new_bert* should work.
- Future contributors can quickly test changes to the model by running those special tests.
**9. Implement the tokenizer**
Next, we should add the tokenizer of *brand_new_bert*. Usually, the tokenizer is equivalent to or very similar to an
already existing tokenizer of 🤗 Transformers.
It is very important to find/extract the original tokenizer file and to manage to load this file into the 🤗
Transformers' implementation of the tokenizer.
To ensure that the tokenizer works correctly, it is recommended to first create a script in the original repository
that inputs a string and returns the `input_ids`. It could look similar to this (in pseudo-code):
```python
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
input_ids = model.tokenize(input_str)
```
You might have to take a deeper look again into the original repository to find the correct tokenizer function or you
might even have to do changes to your clone of the original repository to only output the `input_ids`. Having written
a functional tokenization script that uses the original repository, an analogous script for 🤗 Transformers should be
created. It should look similar to this:
```python
from transformers import BrandNewBertTokenizer
input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
tokenizer = BrandNewBertTokenizer.from_pretrained("/path/to/tokenizer/folder/")
input_ids = tokenizer(input_str).input_ids
```
When both `input_ids` yield the same values, as a final step a tokenizer test file should also be added.
Analogous to the modeling test files of *brand_new_bert*, the tokenization test files of *brand_new_bert* should
contain a couple of hard-coded integration tests.
**10. Run End-to-end integration tests**
Having added the tokenizer, you should also add a couple of end-to-end integration tests using both the model and the
tokenizer to `tests/models/brand_new_bert/test_modeling_brand_new_bert.py` in 🤗 Transformers.
Such a test should show on a meaningful
text-to-text sample that the 🤗 Transformers implementation works as expected. A meaningful text-to-text sample can
include *e.g.* a source-to-target-translation pair, an article-to-summary pair, a question-to-answer pair, etc… If none
of the ported checkpoints has been fine-tuned on a downstream task it is enough to simply rely on the model tests. In a
final step to ensure that the model is fully functional, it is advised that you also run all tests on GPU. It can
happen that you forgot to add some `.to(self.device)` statements to internal tensors of the model, which in such a
test would show in an error. In case you have no access to a GPU, the Hugging Face team can take care of running those
tests for you.
**11. Add Docstring**
Now, all the necessary functionality for *brand_new_bert* is added - you're almost done! The only thing left to add is
a nice docstring and a doc page. The Cookiecutter should have added a template file called
`docs/source/model_doc/brand_new_bert.md` that you should fill out. Users of your model will usually first look at
this page before using your model. Hence, the documentation must be understandable and concise. It is very useful for
the community to add some *Tips* to show how the model should be used. Don't hesitate to ping the Hugging Face team
regarding the docstrings.
Next, make sure that the docstring added to `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` is
correct and included all necessary inputs and outputs. We have a detailed guide about writing documentation and our docstring format [here](writing-documentation). It is always good to remind oneself that documentation should
be treated at least as carefully as the code in 🤗 Transformers since the documentation is usually the first contact
point of the community with the model.
**Code refactor**
Great, now you have added all the necessary code for *brand_new_bert*. At this point, you should correct some potential
incorrect code style by running:
```bash
make style
```
and verify that your coding style passes the quality check:
```bash
make quality
```
There are a couple of other very strict design tests in 🤗 Transformers that might still be failing, which shows up in
the tests of your pull request. This is often because of some missing information in the docstring or some incorrect
naming. The Hugging Face team will surely help you if you're stuck here.
Lastly, it is always a good idea to refactor one's code after having ensured that the code works correctly. With all
tests passing, now it's a good time to go over the added code again and do some refactoring.
You have now finished the coding part, congratulation! 🎉 You are Awesome! 😎
**12. Upload the models to the model hub**
In this final part, you should convert and upload all checkpoints to the model hub and add a model card for each
uploaded model checkpoint. You can get familiar with the hub functionalities by reading our [Model sharing and uploading Page](model_sharing). You should work alongside the Hugging Face team here to decide on a fitting name for each
checkpoint and to get the required access rights to be able to upload the model under the author's organization of
*brand_new_bert*. The `push_to_hub` method, present in all models in `transformers`, is a quick and efficient way to push your checkpoint to the hub. A little snippet is pasted below:
```python
brand_new_bert.push_to_hub("brand_new_bert")
# Uncomment the following line to push to an organization.
# brand_new_bert.push_to_hub("<organization>/brand_new_bert")
```
It is worth spending some time to create fitting model cards for each checkpoint. The model cards should highlight the
specific characteristics of this particular checkpoint, *e.g.* On which dataset was the checkpoint
pretrained/fine-tuned on? On what down-stream task should the model be used? And also include some code on how to
correctly use the model.
**13. (Optional) Add notebook**
It is very helpful to add a notebook that showcases in-detail how *brand_new_bert* can be used for inference and/or
fine-tuned on a downstream task. This is not mandatory to merge your PR, but very useful for the community.
**14. Submit your finished PR**
You're done programming now and can move to the last step, which is getting your PR merged into main. Usually, the
Hugging Face team should have helped you already at this point, but it is worth taking some time to give your finished
PR a nice description and eventually add comments to your code, if you want to point out certain design choices to your
reviewer. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#5-14-port-brandnewbert-to--transformers | #5-14-port-brandnewbert-to--transformers | .md | 38_10 |
Now, it's time to get some credit from the community for your work! Having completed a model addition is a major
contribution to Transformers and the whole NLP community. Your code and the ported pre-trained models will certainly be
used by hundreds and possibly even thousands of developers and researchers. You should be proud of your work and share
your achievements with the community.
**You have made another model that is super easy to access for everyone in the community! 🤯** | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#share-your-work | #share-your-work | .md | 38_11 |
We aim for `transformers` to have support for new model architectures and checkpoints as early as possible:
availability can range from day-0 (and hour-0) releases for some models, to a few days/weeks for others.
The availability of this is usually up to the model contributors, as well as how excited the community is for the
architecture.
We can split the model architecture possibilities in four sections:
- Day-0 integration
- Same-week integration
- Post-release integration
- Hub-first release
Let's dive into each of these and see how we (the transformers team) can help you contribute your architecture and get
your architecture to be very easily used by all members of the community. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#model-additions-and-their-timeline-when-is-a-model-added-to-transformers | #model-additions-and-their-timeline-when-is-a-model-added-to-transformers | .md | 38_12 |
For a day-0 integration to work, we'll usually want to work hand-in-hand with you directly. In order to keep your
architecture private until your checkpoints and release are ready, we'll work together in a private fork of
transformers.
If you plan on having a transformers-first release, this is a great option: we run CI ahead of time, ensure the
documentation is clear, and we aim to optimize your model as much as possible (providing quantization, optimizing it
with Flash-Attention/SDPA, optimizing the KV cache, etc).
We can also lend you a hand in adding the model, reviewing it early, and help you make sure the `transformers`
API works as expected!
If this is the path you wish to go with, we ask for you to reach out in advance, especially if the architecture is
particularly novel (at least a few days, but a few weeks will enable the absolute best integration). In order to reach
out, please contact transformers@huggingface.co 🤗. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#day-0-integration | #day-0-integration | .md | 38_13 |
A same-week integration usually happens when model authors do not reach out; but we see significant community
requests.
In order to specify you'd like for us to integrate a specific model, we'll redirect you to our
[issue tracker](https://github.com/huggingface/transformers/issues/new?assignees=&labels=New+model&projects=&template=new-model-addition.yml)
where you can request a specific model.
The more activity on the issue, the faster/more likely we are to integrate the model! | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#same-week-integration | #same-week-integration | .md | 38_14 |
A post-release integration usually happens when there has not been sufficient activity/requests to warrant a same-week
integration, or that we lack the sufficient bandwidth to integrate it.
We very gladly welcome community contributions in those instances; more than half of the library was contributed
by contributors external to Hugging Face. If this is something that is interesting to you, we recommend that you look
at our [open issues tagged with "New model"](https://github.com/huggingface/transformers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+model%22).
We recommend you try your hand at a heavily requested model as this will multiply the impact of your contribution.
We'll be there to help you in case that's your first contribution 🤗. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#post-release-integration | #post-release-integration | .md | 38_15 |
Finally, transformers has a "remote-code" possibility, in which contributions are not made within the toolkit, but on
the Hub. This can be particularly interesting for groups that are using `transformers` as a backbone for their project,
but don't have the bandwidth to contribute the model to transformers directly.
In case the model is very successful, then we'll very likely end up integrating it in `transformers` at the end - as this
provides better documentation, CI, maintenance, and optimizations - but this remains a great way to make your model
accessible day-0 with minimal friction.
This guide is a great starting point for a Hub-first release: [Custom models](./custom_models) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/add_new_model.md | https://huggingface.co/docs/transformers/en/add_new_model/#code-on-hub-release | #code-on-hub-release | .md | 38_16 |
<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/pipeline_webserver.md | https://huggingface.co/docs/transformers/en/pipeline_webserver/ | .md | 39_0 |
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