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Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Load ELI5 dataset Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset. from datasets import load_dataset eli5 = load_dataset("eli5", split="train_asks[:5000]") Split the dataset's train_asks split into a train and test set with the [~datasets.Dataset.train_test_split] method: eli5 = eli5.train_test_split(test_size=0.2) Then take a look at an example: eli5["train"][0] {'answers': {'a_id': ['c3d1aib', 'c3d4lya'], 'score': [6, 3], 'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.", "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]}, 'answers_urls': {'url': []}, 'document': '', 'q_id': 'nyxfp', 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?', 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']}, 'subreddit': 'askscience', 'title': 'Few questions about this space walk photograph.', 'title_urls': {'url': []}} While this may look like a lot, you're only really interested in the text field. What's cool about language modeling tasks is you don't need labels (also known as an unsupervised task) because the next word is the label. Preprocess For masked language modeling, the next step is to load a DistilRoBERTa tokenizer to process the text subfield: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") You'll notice from the example above, the text field is actually nested inside answers. This means you'll need to e xtract the text subfield from its nested structure with the flatten method: eli5 = eli5.flatten() eli5["train"][0] {'answers.a_id': ['c3d1aib', 'c3d4lya'], 'answers.score': [6, 3], 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.", "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"], 'answers_urls.url': [], 'document': '', 'q_id': 'nyxfp', 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?', 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'], 'subreddit': 'askscience', 'title': 'Few questions about this space walk photograph.', 'title_urls.url': []} Each subfield is now a separate column as indicated by the answers prefix, and the text field is a list now. Instead of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them. Here is a first preprocessing function to join the list of strings for each example and tokenize the result: def preprocess_function(examples): return tokenizer([" ".join(x) for x in examples["answers.text"]]) To apply this preprocessing function over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once, and increasing the number of processes with num_proc. Remove any columns you don't need: tokenized_eli5 = eli5.map( preprocess_function, batched=True, num_proc=4, remove_columns=eli5["train"].column_names, ) This dataset contains the token sequences, but some of these are longer than the maximum input length for the model. You can now use a second preprocessing function to - concatenate all the sequences - split the concatenated sequences into shorter chunks defined by block_size, which should be both shorter than the maximum input length and short enough for your GPU RAM. block_size = 128 def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of block_size. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } return result Apply the group_texts function over the entire dataset: lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4) Now create a batch of examples using [DataCollatorForLanguageModeling]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. Use the end-of-sequence token as the padding token and specify mlm_probability to randomly mask tokens each time you iterate over the data: from transformers import DataCollatorForLanguageModeling tokenizer.pad_token = tokenizer.eos_token data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15) Use the end-of-sequence token as the padding token and specify mlm_probability to randomly mask tokens each time you iterate over the data: from transformers import DataCollatorForLanguageModeling data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15, return_tensors="tf") Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load DistilRoBERTa with [AutoModelForMaskedLM]: from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("distilroberta-base") At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). Pass the training arguments to [Trainer] along with the model, datasets, and data collator. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_eli5_mlm_model", evaluation_strategy="epoch", learning_rate=2e-5, num_train_epochs=3, weight_decay=0.01, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=lm_dataset["train"], eval_dataset=lm_dataset["test"], data_collator=data_collator, ) trainer.train() Once training is completed, use the [~transformers.Trainer.evaluate] method to evaluate your model and get its perplexity: import math eval_results = trainer.evaluate() print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}") Perplexity: 8.76 Then share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import create_optimizer, AdamWeightDecay optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01) Then you can load DistilRoBERTa with [TFAutoModelForMaskedLM]: from transformers import TFAutoModelForMaskedLM model = TFAutoModelForMaskedLM.from_pretrained("distilroberta-base") Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( lm_dataset["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_test_set = model.prepare_tf_dataset( lm_dataset["test"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: import tensorflow as tf model.compile(optimizer=optimizer) # No loss argument! This can be done by specifying where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback callback = PushToHubCallback( output_dir="my_awesome_eli5_mlm_model", tokenizer=tokenizer, ) Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model: model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback]) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for masked language modeling, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Come up with some text you'd like the model to fill in the blank with, and use the special <mask> token to indicate the blank: text = "The Milky Way is a galaxy." The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for fill-mask with your model, and pass your text to it. If you like, you can use the top_k parameter to specify how many predictions to return: from transformers import pipeline mask_filler = pipeline("fill-mask", "stevhliu/my_awesome_eli5_mlm_model") mask_filler(text, top_k=3) [{'score': 0.5150994658470154, 'token': 21300, 'token_str': ' spiral', 'sequence': 'The Milky Way is a spiral galaxy.'}, {'score': 0.07087188959121704, 'token': 2232, 'token_str': ' massive', 'sequence': 'The Milky Way is a massive galaxy.'}, {'score': 0.06434620916843414, 'token': 650, 'token_str': ' small', 'sequence': 'The Milky Way is a small galaxy.'}] Tokenize the text and return the input_ids as PyTorch tensors. You'll also need to specify the position of the <mask> token: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_eli5_mlm_model") inputs = tokenizer(text, return_tensors="pt") mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1] Pass your inputs to the model and return the logits of the masked token: from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model") logits = model(**inputs).logits mask_token_logits = logits[0, mask_token_index, :] Then return the three masked tokens with the highest probability and print them out: top_3_tokens = torch.topk(mask_token_logits, 3, dim=1).indices[0].tolist() for token in top_3_tokens: print(text.replace(tokenizer.mask_token, tokenizer.decode([token]))) The Milky Way is a spiral galaxy. The Milky Way is a massive galaxy. The Milky Way is a small galaxy. `` </pt> <tf> Tokenize the text and return theinput_idsas TensorFlow tensors. You'll also need to specify the position of the` token: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_eli5_mlm_model") inputs = tokenizer(text, return_tensors="tf") mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1] Pass your inputs to the model and return the logits of the masked token: from transformers import TFAutoModelForMaskedLM model = TFAutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model") logits = model(**inputs).logits mask_token_logits = logits[0, mask_token_index, :] Then return the three masked tokens with the highest probability and print them out: top_3_tokens = tf.math.top_k(mask_token_logits, 3).indices.numpy() for token in top_3_tokens: print(text.replace(tokenizer.mask_token, tokenizer.decode([token]))) The Milky Way is a spiral galaxy. The Milky Way is a massive galaxy. The Milky Way is a small galaxy.

Efficient Inference on CPU This guide focuses on inferencing large models efficiently on CPU. BetterTransformer for faster inference We have recently integrated BetterTransformer for faster inference on CPU for text, image and audio models. Check the documentation about this integration here for more details. PyTorch JIT-mode (TorchScript) TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency. Comparing to default eager mode, jit mode in PyTorch normally yields better performance for model inference from optimization methodologies like operator fusion. For a gentle introduction to TorchScript, see the Introduction to PyTorch TorchScript tutorial. IPEX Graph Optimization with JIT-mode Intel® Extension for PyTorch provides further optimizations in jit mode for Transformers series models. It is highly recommended for users to take advantage of Intel® Extension for PyTorch with jit mode. Some frequently used operator patterns from Transformers models are already supported in Intel® Extension for PyTorch with jit mode fusions. Those fusion patterns like Multi-head-attention fusion, Concat Linear, Linear+Add, Linear+Gelu, Add+LayerNorm fusion and etc. are enabled and perform well. The benefit of the fusion is delivered to users in a transparent fashion. According to the analysis, ~70% of most popular NLP tasks in question-answering, text-classification, and token-classification can get performance benefits with these fusion patterns for both Float32 precision and BFloat16 Mixed precision. Check more detailed information for IPEX Graph Optimization. IPEX installation: IPEX release is following PyTorch, check the approaches for IPEX installation. Usage of JIT-mode To enable JIT-mode in Trainer for evaluaion or prediction, users should add jit_mode_eval in Trainer command arguments. for PyTorch >= 1.14.0. JIT-mode could benefit any models for prediction and evaluaion since dict input is supported in jit.trace for PyTorch < 1.14.0. JIT-mode could benefit models whose forward parameter order matches the tuple input order in jit.trace, like question-answering model In the case where the forward parameter order does not match the tuple input order in jit.trace, like text-classification models, jit.trace will fail and we are capturing this with the exception here to make it fallback. Logging is used to notify users. Take an example of the use cases on Transformers question-answering Inference using jit mode on CPU: python run_qa.py \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \ --dataset_name squad \ --do_eval \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/ \ --no_cuda \ --jit_mode_eval Inference with IPEX using jit mode on CPU: python run_qa.py \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \ --dataset_name squad \ --do_eval \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/ \ --no_cuda \ --use_ipex \ --jit_mode_eval

Attention mechanisms Most transformer models use full attention in the sense that the attention matrix is square. It can be a big computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and use a sparse version of the attention matrix to speed up training. LSH attention Reformer uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can consider only the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is modified to mask the current token (except at the first position), because it will give a query and a key equal (so very similar to each other). Since the hash can be a bit random, several hash functions are used in practice (determined by a n_rounds parameter) and then are averaged together. Local attention Longformer uses local attention: often, the local context (e.g., what are the two tokens to the left and right?) is enough to take action for a given token. Also, by stacking attention layers that have a small window, the last layer will have a receptive field of more than just the tokens in the window, allowing them to build a representation of the whole sentence. Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask: Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence length. Other tricks Axial positional encodings Reformer uses axial positional encodings: in traditional transformer models, the positional encoding E is a matrix of size \(l\) by \(d\), \(l\) being the sequence length and \(d\) the dimension of the hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU. To alleviate that, axial positional encodings consist of factorizing that big matrix E in two smaller matrices E1 and E2, with dimensions \(l_{1} \times d_{1}\) and \(l_{2} \times d_{2}\), such that \(l_{1} \times l_{2} = l\) and \(d_{1} + d_{2} = d\) (with the product for the lengths, this ends up being way smaller). The embedding for time step \(j\) in E is obtained by concatenating the embeddings for timestep \(j \% l1\) in E1 and \(j // l1\) in E2.

How to convert a 🤗 Transformers model to TensorFlow? Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that adding TensorFlow compatibility to an existing model is simpler than adding a new model from scratch! Whether you wish to have a deeper understanding of large TensorFlow models, make a major open-source contribution, or enable TensorFlow for your model of choice, this guide is for you. This guide empowers you, a member of our community, to contribute TensorFlow model weights and/or architectures to be used in 🤗 Transformers, with minimal supervision from the Hugging Face team. Writing a new model is no small feat, but hopefully this guide will make it less of a rollercoaster 🎢 and more of a walk in the park 🚶. Harnessing our collective experiences is absolutely critical to make this process increasingly easier, and thus we highly encourage that you suggest improvements to this guide! Before you dive deeper, it is recommended that you check the following resources if you're new to 🤗 Transformers: - General overview of 🤗 Transformers - Hugging Face's TensorFlow Philosophy In the remainder of this guide, you will learn what's needed to add a new TensorFlow model architecture, the procedure to convert PyTorch into TensorFlow model weights, and how to efficiently debug mismatches across ML frameworks. Let's get started! Are you unsure whether the model you wish to use already has a corresponding TensorFlow architecture?   Check the model_type field of the config.json of your model of choice (example). If the corresponding model folder in 🤗 Transformers has a file whose name starts with "modeling_tf", it means that it has a corresponding TensorFlow architecture (example). Step-by-step guide to add TensorFlow model architecture code There are many ways to design a large model architecture, and multiple ways of implementing said design. However, you might recall from our general overview of 🤗 Transformers that we are an opinionated bunch - the ease of use of 🤗 Transformers relies on consistent design choices. From experience, we can tell you a few important things about adding TensorFlow models: Don't reinvent the wheel! More often that not, there are at least two reference implementations you should check: the PyTorch equivalent of the model you are implementing and other TensorFlow models for the same class of problems. Great model implementations survive the test of time. This doesn't happen because the code is pretty, but rather because the code is clear, easy to debug and build upon. If you make the life of the maintainers easy with your TensorFlow implementation, by replicating the same patterns as in other TensorFlow models and minimizing the mismatch to the PyTorch implementation, you ensure your contribution will be long lived. Ask for help when you're stuck! The 🤗 Transformers team is here to help, and we've probably found solutions to the same problems you're facing. Here's an overview of the steps needed to add a TensorFlow model architecture: 1. Select the model you wish to convert 2. Prepare transformers dev environment 3. (Optional) Understand theoretical aspects and the existing implementation 4. Implement the model architecture 5. Implement model tests 6. Submit the pull request 7. (Optional) Build demos and share with the world 1.-3. Prepare your model contribution 1. Select the model you wish to convert Let's start off with the basics: the first thing you need to know is the architecture you want to convert. If you don't have your eyes set on a specific architecture, asking the 🤗 Transformers team for suggestions is a great way to maximize your impact - we will guide you towards the most prominent architectures that are missing on the TensorFlow side. If the specific model you want to use with TensorFlow already has a TensorFlow architecture implementation in 🤗 Transformers but is lacking weights, feel free to jump straight into the weight conversion section of this page. For simplicity, the remainder of this guide assumes you've decided to contribute with the TensorFlow version of BrandNewBert (the same example as in the guide to add a new model from scratch). Before starting the work on a TensorFlow model architecture, double-check that there is no ongoing effort to do so. You can search for BrandNewBert on the pull request GitHub page to confirm that there is no TensorFlow-related pull request. 2. Prepare transformers dev environment Having selected the model architecture, open an draft PR to signal your intention to work on it. Follow the instructions below to set up your environment and open a draft PR. Fork the repository by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account. Clone your transformers fork to your local disk, and add the base repository as a remote: git clone https://github.com/[your Github handle]/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git Set up a development environment, for instance by running the following command: python -m venv .env source .env/bin/activate pip install -e ".[dev]" Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a failure with this command. If that's the case make sure to install TensorFlow then do: pip install -e ".[quality]" Note: You don't need to have CUDA installed. Making the new model work on CPU is sufficient. Create a branch with a descriptive name from your main branch git checkout -b add_tf_brand_new_bert Fetch and rebase to current main git fetch upstream git rebase upstream/main Add an empty .py file in transformers/src/models/brandnewbert/ named modeling_tf_brandnewbert.py. This will be your TensorFlow model file. Push the changes to your account using: git add . git commit -m "initial commit" git push -u origin add_tf_brand_new_bert Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for future changes. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page. Now you have set up a development environment to port BrandNewBert to TensorFlow in 🤗 Transformers. 3. (Optional) Understand theoretical aspects and the existing implementation You should take some time to read BrandNewBert's paper, if such descriptive work exists. There might be large sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is not to get a deep theoretical understanding of the paper, but to extract the necessary information required to effectively re-implement the model in 🤗 Transformers using TensorFlow. That being said, you don't have to spend too much time on the theoretical aspects, but rather focus on the practical ones, namely the existing model documentation page (e.g. model docs for BERT). After you've grasped the basics of the models you are about to implement, it's important to understand the existing implementation. This is a great chance to confirm that a working implementation matches your expectations for the model, as well as to foresee technical challenges on the TensorFlow side. It's perfectly natural that you feel overwhelmed with the amount of information that you've just absorbed. It is definitely not a requirement that you understand all facets of the model at this stage. Nevertheless, we highly encourage you to clear any pressing questions in our forum. 4. Model implementation Now it's time to finally start coding. Our suggested starting point is the PyTorch file itself: copy the contents of modeling_brand_new_bert.py inside src/transformers/models/brand_new_bert/ into modeling_tf_brand_new_bert.py. The goal of this section is to modify the file and update the import structure of 🤗 Transformers such that you can import TFBrandNewBert and TFBrandNewBert.from_pretrained(model_repo, from_pt=True) successfully loads a working TensorFlow BrandNewBert model. Sadly, there is no prescription to convert a PyTorch model into TensorFlow. You can, however, follow our selection of tips to make the process as smooth as possible: - Prepend TF to the name of all classes (e.g. BrandNewBert becomes TFBrandNewBert). - Most PyTorch operations have a direct TensorFlow replacement. For example, torch.nn.Linear corresponds to tf.keras.layers.Dense, torch.nn.Dropout corresponds to tf.keras.layers.Dropout, etc. If you're not sure about a specific operation, you can use the TensorFlow documentation or the PyTorch documentation. - Look for patterns in the 🤗 Transformers codebase. If you come across a certain operation that doesn't have a direct replacement, the odds are that someone else already had the same problem. - By default, keep the same variable names and structure as in PyTorch. This will make it easier to debug, track issues, and add fixes down the line. - Some layers have different default values in each framework. A notable example is the batch normalization layer's epsilon (1e-5 in PyTorch and 1e-3 in TensorFlow). Double-check the documentation! - PyTorch's nn.Parameter variables typically need to be initialized within TF Layer's build(). See the following example: PyTorch / TensorFlow - If the PyTorch model has a #copied from on top of a function, the odds are that your TensorFlow model can also borrow that function from the architecture it was copied from, assuming it has a TensorFlow architecture. - Assigning the name attribute correctly in TensorFlow functions is critical to do the from_pt=True weight cross-loading. name is almost always the name of the corresponding variable in the PyTorch code. If name is not properly set, you will see it in the error message when loading the model weights. - The logic of the base model class, BrandNewBertModel, will actually reside in TFBrandNewBertMainLayer, a Keras layer subclass (example). TFBrandNewBertModel will simply be a wrapper around this layer. - Keras models need to be built in order to load pretrained weights. For that reason, TFBrandNewBertPreTrainedModel will need to hold an example of inputs to the model, the dummy_inputs (example). - If you get stuck, ask for help - we're here to help you! 🤗 In addition to the model file itself, you will also need to add the pointers to the model classes and related documentation pages. You can complete this part entirely following the patterns in other PRs (example). Here's a list of the needed manual changes: - Include all public classes of BrandNewBert in src/transformers/__init__.py - Add BrandNewBert classes to the corresponding Auto classes in src/transformers/models/auto/modeling_tf_auto.py - Include the modeling file in the documentation test file list in utils/documentation_tests.txt - Add the lazy loading classes related to BrandNewBert in src/transformers/utils/dummy_tf_objects.py - Update the import structures for the public classes in src/transformers/models/brand_new_bert/__init__.py - Add the documentation pointers to the public methods of BrandNewBert in docs/source/en/model_doc/brand_new_bert.md - Add yourself to the list of contributors to BrandNewBert in docs/source/en/model_doc/brand_new_bert.md - Finally, add a green tick ✅ to the TensorFlow column of BrandNewBert in docs/source/en/index.md When you're happy with your implementation, run the following checklist to confirm that your model architecture is ready: 1. All layers that behave differently at train time (e.g. Dropout) are called with a training argument, which is propagated all the way from the top-level classes 2. You have used #copied from whenever possible 3. TFBrandNewBertMainLayer and all classes that use it have their call function decorated with @unpack_inputs 4. TFBrandNewBertMainLayer is decorated with @keras_serializable 5. A TensorFlow model can be loaded from PyTorch weights using TFBrandNewBert.from_pretrained(model_repo, from_pt=True) 6. You can call the TensorFlow model using the expected input format 5. Add model tests Hurray, you've implemented a TensorFlow model! Now it's time to add tests to make sure that your model behaves as expected. As in the previous section, we suggest you start by copying the test_modeling_brand_new_bert.py file in tests/models/brand_new_bert/ into test_modeling_tf_brand_new_bert.py, and continue by making the necessary TensorFlow replacements. For now, in all .from_pretrained() calls, you should use the from_pt=True flag to load the existing PyTorch weights. After you're done, it's time for the moment of truth: run the tests! 😬 NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \ py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py The most likely outcome is that you'll see a bunch of errors. Don't worry, this is expected! Debugging ML models is notoriously hard, and the key ingredient to success is patience (and breakpoint()). In our experience, the hardest problems arise from subtle mismatches between ML frameworks, for which we have a few pointers at the end of this guide. In other cases, a general test might not be directly applicable to your model, in which case we suggest an override at the model test class level. Regardless of the issue, don't hesitate to ask for help in your draft pull request if you're stuck. When all tests pass, congratulations, your model is nearly ready to be added to the 🤗 Transformers library! 🎉 6.-7. Ensure everyone can use your model 6. Submit the pull request Once you're done with the implementation and the tests, it's time to submit a pull request. Before pushing your code, run our code formatting utility, make fixup 🪄. This will automatically fix any formatting issues, which would cause our automatic checks to fail. It's now time to convert your draft pull request into a real pull request. To do so, click on the "Ready for review" button and add Joao (@gante) and Matt (@Rocketknight1) as reviewers. A model pull request will need at least 3 reviewers, but they will take care of finding appropriate additional reviewers for your model. After all reviewers are happy with the state of your PR, the final action point is to remove the from_pt=True flag in .from_pretrained() calls. Since there are no TensorFlow weights, you will have to add them! Check the section below for instructions on how to do it. Finally, when the TensorFlow weights get merged, you have at least 3 reviewer approvals, and all CI checks are green, double-check the tests locally one last time NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \ py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py and we will merge your PR! Congratulations on the milestone 🎉 7. (Optional) Build demos and share with the world One of the hardest parts about open-source is discovery. How can the other users learn about the existence of your fabulous TensorFlow contribution? With proper communication, of course! 📣 There are two main ways to share your model with the community: - Build demos. These include Gradio demos, notebooks, and other fun ways to show off your model. We highly encourage you to add a notebook to our community-driven demos. - Share stories on social media like Twitter and LinkedIn. You should be proud of your work and share your achievement with the community - your model can now be used by thousands of engineers and researchers around the world 🌍! We will be happy to retweet your posts and help you share your work with the community. Adding TensorFlow weights to 🤗 Hub Assuming that the TensorFlow model architecture is available in 🤗 Transformers, converting PyTorch weights into TensorFlow weights is a breeze! Here's how to do it: 1. Make sure you are logged into your Hugging Face account in your terminal. You can log in using the command huggingface-cli login (you can find your access tokens here) 2. Run transformers-cli pt-to-tf --model-name foo/bar, where foo/bar is the name of the model repository containing the PyTorch weights you want to convert 3. Tag @joaogante and @Rocketknight1 in the 🤗 Hub PR the command above has just created That's it! 🎉 Debugging mismatches across ML frameworks 🐛 At some point, when adding a new architecture or when creating TensorFlow weights for an existing architecture, you might come across errors compaining about mismatches between PyTorch and TensorFlow. You might even decide to open the model architecture code for the two frameworks, and find that they look identical. What's going on? 🤔 First of all, let's talk about why understanding these mismatches matters. Many community members will use 🤗 Transformers models out of the box, and trust that our models behave as expected. When there is a large mismatch between the two frameworks, it implies that the model is not following the reference implementation for at least one of the frameworks. This might lead to silent failures, in which the model runs but has poor performance. This is arguably worse than a model that fails to run at all! To that end, we aim at having a framework mismatch smaller than 1e-5 at all stages of the model. As in other numerical problems, the devil is in the details. And as in any detail-oriented craft, the secret ingredient here is patience. Here is our suggested workflow for when you come across this type of issues: 1. Locate the source of mismatches. The model you're converting probably has near identical inner variables up to a certain point. Place breakpoint() statements in the two frameworks' architectures, and compare the values of the numerical variables in a top-down fashion until you find the source of the problems. 2. Now that you've pinpointed the source of the issue, get in touch with the 🤗 Transformers team. It is possible that we've seen a similar problem before and can promptly provide a solution. As a fallback, scan popular pages like StackOverflow and GitHub issues. 3. If there is no solution in sight, it means you'll have to go deeper. The good news is that you've located the issue, so you can focus on the problematic instruction, abstracting away the rest of the model! The bad news is that you'll have to venture into the source implementation of said instruction. In some cases, you might find an issue with a reference implementation - don't abstain from opening an issue in the upstream repository. In some cases, in dicussion with the 🤗 Transformers team, we might find that the fixing the mismatch is infeasible. When the mismatch is very small in the output layers of the model (but potentially large in the hidden states), we might decide to ignore it in favor of distributing the model. The pt-to-tf CLI mentioned above has a --max-error flag to override the error message at weight conversion time.

Installation Install 🤗 Transformers for whichever deep learning library you're working with, setup your cache, and optionally configure 🤗 Transformers to run offline. 🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning library you are using: PyTorch installation instructions. TensorFlow 2.0 installation instructions. Flax installation instructions. Install with pip You should install 🤗 Transformers in a virtual environment. If you're unfamiliar with Python virtual environments, take a look at this guide. A virtual environment makes it easier to manage different projects, and avoid compatibility issues between dependencies. Start by creating a virtual environment in your project directory: python -m venv .env Activate the virtual environment. On Linux and MacOs: source .env/bin/activate Activate Virtual environment on Windows .env/Scripts/activate Now you're ready to install 🤗 Transformers with the following command: pip install transformers For CPU-support only, you can conveniently install 🤗 Transformers and a deep learning library in one line. For example, install 🤗 Transformers and PyTorch with: pip install 'transformers[torch]' 🤗 Transformers and TensorFlow 2.0: pip install 'transformers[tf-cpu]' M1 / ARM Users You will need to install the following before installing TensorFLow 2.0 brew install cmake brew install pkg-config 🤗 Transformers and Flax: pip install 'transformers[flax]' Finally, check if 🤗 Transformers has been properly installed by running the following command. It will download a pretrained model: python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))" Then print out the label and score: [{'label': 'POSITIVE', 'score': 0.9998704791069031}] Install from source Install 🤗 Transformers from source with the following command: pip install git+https://github.com/huggingface/transformers This command installs the bleeding edge main version rather than the latest stable version. The main version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the main version may not always be stable. We strive to keep the main version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an Issue so we can fix it even sooner! Check if 🤗 Transformers has been properly installed by running the following command: python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))" Editable install You will need an editable install if you'd like to: Use the main version of the source code. Contribute to 🤗 Transformers and need to test changes in the code. Clone the repository and install 🤗 Transformers with the following commands: git clone https://github.com/huggingface/transformers.git cd transformers pip install -e . These commands will link the folder you cloned the repository to and your Python library paths. Python will now look inside the folder you cloned to in addition to the normal library paths. For example, if your Python packages are typically installed in ~/anaconda3/envs/main/lib/python3.7/site-packages/, Python will also search the folder you cloned to: ~/transformers/. You must keep the transformers folder if you want to keep using the library. Now you can easily update your clone to the latest version of 🤗 Transformers with the following command: cd ~/transformers/ git pull Your Python environment will find the main version of 🤗 Transformers on the next run. Install with conda Install from the conda channel huggingface: conda install -c huggingface transformers Cache setup Pretrained models are downloaded and locally cached at: ~/.cache/huggingface/hub. This is the default directory given by the shell environment variable TRANSFORMERS_CACHE. On Windows, the default directory is given by C:\Users\username\.cache\huggingface\hub. You can change the shell environment variables shown below - in order of priority - to specify a different cache directory: Shell environment variable (default): HUGGINGFACE_HUB_CACHE or TRANSFORMERS_CACHE. Shell environment variable: HF_HOME. Shell environment variable: XDG_CACHE_HOME + /huggingface. 🤗 Transformers will use the shell environment variables PYTORCH_TRANSFORMERS_CACHE or PYTORCH_PRETRAINED_BERT_CACHE if you are coming from an earlier iteration of this library and have set those environment variables, unless you specify the shell environment variable TRANSFORMERS_CACHE. Offline mode 🤗 Transformers is able to run in a firewalled or offline environment by only using local files. Set the environment variable TRANSFORMERS_OFFLINE=1 to enable this behavior. Add 🤗 Datasets to your offline training workflow by setting the environment variable HF_DATASETS_OFFLINE=1. For example, you would typically run a program on a normal network firewalled to external instances with the following command: python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en Run this same program in an offline instance with: HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \ python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en The script should now run without hanging or waiting to timeout because it knows it should only look for local files. Fetch models and tokenizers to use offline Another option for using 🤗 Transformers offline is to download the files ahead of time, and then point to their local path when you need to use them offline. There are three ways to do this: Download a file through the user interface on the Model Hub by clicking on the ↓ icon. Use the [PreTrainedModel.from_pretrained] and [PreTrainedModel.save_pretrained] workflow: Download your files ahead of time with [PreTrainedModel.from_pretrained]: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B") model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B") Save your files to a specified directory with [PreTrainedModel.save_pretrained]: tokenizer.save_pretrained("./your/path/bigscience_t0") model.save_pretrained("./your/path/bigscience_t0") Now when you're offline, reload your files with [PreTrainedModel.from_pretrained] from the specified directory: tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0") model = AutoModel.from_pretrained("./your/path/bigscience_t0") Programmatically download files with the huggingface_hub library: Install the huggingface_hub library in your virtual environment: python -m pip install huggingface_hub Use the hf_hub_download function to download a file to a specific path. For example, the following command downloads the config.json file from the T0 model to your desired path: from huggingface_hub import hf_hub_download hf_hub_download(repo_id="bigscience/T0_3B", filename="config.json", cache_dir="./your/path/bigscience_t0") Once your file is downloaded and locally cached, specify it's local path to load and use it: from transformers import AutoConfig config = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json") See the How to download files from the Hub section for more details on downloading files stored on the Hub.

The Transformer model family Since its introduction in 2017, the original Transformer model has inspired many new and exciting models that extend beyond natural language processing (NLP) tasks. There are models for predicting the folded structure of proteins, training a cheetah to run, and time series forecasting. With so many Transformer variants available, it can be easy to miss the bigger picture. What all these models have in common is they're based on the original Transformer architecture. Some models only use the encoder or decoder, while others use both. This provides a useful taxonomy to categorize and examine the high-level differences within models in the Transformer family, and it'll help you understand Transformers you haven't encountered before. If you aren't familiar with the original Transformer model or need a refresher, check out the How do Transformers work chapter from the Hugging Face course. Computer vision Convolutional network For a long time, convolutional networks (CNNs) were the dominant paradigm for computer vision tasks until the Vision Transformer demonstrated its scalability and efficiency. Even then, some of a CNN's best qualities, like translation invariance, are so powerful (especially for certain tasks) that some Transformers incorporate convolutions in their architecture. ConvNeXt flipped this exchange around and incorporated design choices from Transformers to modernize a CNN. For example, ConvNeXt uses non-overlapping sliding windows to patchify an image and a larger kernel to increase its global receptive field. ConvNeXt also makes several layer design choices to be more memory-efficient and improve performance, so it competes favorably with Transformers! Encoder[[cv-encoder]] The Vision Transformer (ViT) opened the door to computer vision tasks without convolutions. ViT uses a standard Transformer encoder, but its main breakthrough was how it treated an image. It splits an image into fixed-size patches and uses them to create an embedding, just like how a sentence is split into tokens. ViT capitalized on the Transformers' efficient architecture to demonstrate competitive results with the CNNs at the time while requiring fewer resources to train. ViT was soon followed by other vision models that could also handle dense vision tasks like segmentation as well as detection. One of these models is the Swin Transformer. It builds hierarchical feature maps (like a CNN 👀 and unlike ViT) from smaller-sized patches and merges them with neighboring patches in deeper layers. Attention is only computed within a local window, and the window is shifted between attention layers to create connections to help the model learn better. Since the Swin Transformer can produce hierarchical feature maps, it is a good candidate for dense prediction tasks like segmentation and detection. The SegFormer also uses a Transformer encoder to build hierarchical feature maps, but it adds a simple multilayer perceptron (MLP) decoder on top to combine all the feature maps and make a prediction. Other vision models, like BeIT and ViTMAE, drew inspiration from BERT's pretraining objective. BeIT is pretrained by masked image modeling (MIM); the image patches are randomly masked, and the image is also tokenized into visual tokens. BeIT is trained to predict the visual tokens corresponding to the masked patches. ViTMAE has a similar pretraining objective, except it must predict the pixels instead of visual tokens. What's unusual is 75% of the image patches are masked! The decoder reconstructs the pixels from the masked tokens and encoded patches. After pretraining, the decoder is thrown away, and the encoder is ready to be used in downstream tasks. Decoder[[cv-decoder]] Decoder-only vision models are rare because most vision models rely on an encoder to learn an image representation. But for use cases like image generation, the decoder is a natural fit, as we've seen from text generation models like GPT-2. ImageGPT uses the same architecture as GPT-2, but instead of predicting the next token in a sequence, it predicts the next pixel in an image. In addition to image generation, ImageGPT could also be finetuned for image classification. Encoder-decoder[[cv-encoder-decoder]] Vision models commonly use an encoder (also known as a backbone) to extract important image features before passing them to a Transformer decoder. DETR has a pretrained backbone, but it also uses the complete Transformer encoder-decoder architecture for object detection. The encoder learns image representations and combines them with object queries (each object query is a learned embedding that focuses on a region or object in an image) in the decoder. DETR predicts the bounding box coordinates and class label for each object query. Natural language processing Encoder[[nlp-encoder]] BERT is an encoder-only Transformer that randomly masks certain tokens in the input to avoid seeing other tokens, which would allow it to "cheat". The pretraining objective is to predict the masked token based on the context. This allows BERT to fully use the left and right contexts to help it learn a deeper and richer representation of the inputs. However, there was still room for improvement in BERT's pretraining strategy. RoBERTa improved upon this by introducing a new pretraining recipe that includes training for longer and on larger batches, randomly masking tokens at each epoch instead of just once during preprocessing, and removing the next-sentence prediction objective. The dominant strategy to improve performance is to increase the model size. But training large models is computationally expensive. One way to reduce computational costs is using a smaller model like DistilBERT. DistilBERT uses knowledge distillation - a compression technique - to create a smaller version of BERT while keeping nearly all of its language understanding capabilities. However, most Transformer models continued to trend towards more parameters, leading to new models focused on improving training efficiency. ALBERT reduces memory consumption by lowering the number of parameters in two ways: separating the larger vocabulary embedding into two smaller matrices and allowing layers to share parameters. DeBERTa added a disentangled attention mechanism where the word and its position are separately encoded in two vectors. The attention is computed from these separate vectors instead of a single vector containing the word and position embeddings. Longformer also focused on making attention more efficient, especially for processing documents with longer sequence lengths. It uses a combination of local windowed attention (attention only calculated from fixed window size around each token) and global attention (only for specific task tokens like [CLS] for classification) to create a sparse attention matrix instead of a full attention matrix. Decoder[[nlp-decoder]] GPT-2 is a decoder-only Transformer that predicts the next word in the sequence. It masks tokens to the right so the model can't "cheat" by looking ahead. By pretraining on a massive body of text, GPT-2 became really good at generating text, even if the text is only sometimes accurate or true. But GPT-2 lacked the bidirectional context from BERT's pretraining, which made it unsuitable for certain tasks. XLNET combines the best of both BERT and GPT-2's pretraining objectives by using a permutation language modeling objective (PLM) that allows it to learn bidirectionally. After GPT-2, language models grew even bigger and are now known as large language models (LLMs). LLMs demonstrate few- or even zero-shot learning if pretrained on a large enough dataset. GPT-J is an LLM with 6B parameters and trained on 400B tokens. GPT-J was followed by OPT, a family of decoder-only models, the largest of which is 175B and trained on 180B tokens. BLOOM was released around the same time, and the largest model in the family has 176B parameters and is trained on 366B tokens in 46 languages and 13 programming languages. Encoder-decoder[[nlp-encoder-decoder]] BART keeps the original Transformer architecture, but it modifies the pretraining objective with text infilling corruption, where some text spans are replaced with a single mask token. The decoder predicts the uncorrupted tokens (future tokens are masked) and uses the encoder's hidden states to help it. Pegasus is similar to BART, but Pegasus masks entire sentences instead of text spans. In addition to masked language modeling, Pegasus is pretrained by gap sentence generation (GSG). The GSG objective masks whole sentences important to a document, replacing them with a mask token. The decoder must generate the output from the remaining sentences. T5 is a more unique model that casts all NLP tasks into a text-to-text problem using specific prefixes. For example, the prefix Summarize: indicates a summarization task. T5 is pretrained by supervised (GLUE and SuperGLUE) training and self-supervised training (randomly sample and drop out 15% of tokens). Audio Encoder[[audio-encoder]] Wav2Vec2 uses a Transformer encoder to learn speech representations directly from raw audio waveforms. It is pretrained with a contrastive task to determine the true speech representation from a set of false ones. HuBERT is similar to Wav2Vec2 but has a different training process. Target labels are created by a clustering step in which segments of similar audio are assigned to a cluster which becomes a hidden unit. The hidden unit is mapped to an embedding to make a prediction. Encoder-decoder[[audio-encoder-decoder]] Speech2Text is a speech model designed for automatic speech recognition (ASR) and speech translation. The model accepts log mel-filter bank features extracted from the audio waveform and pretrained autoregressively to generate a transcript or translation. Whisper is also an ASR model, but unlike many other speech models, it is pretrained on a massive amount of ✨ labeled ✨ audio transcription data for zero-shot performance. A large chunk of the dataset also contains non-English languages, meaning Whisper can also be used for low-resource languages. Structurally, Whisper is similar to Speech2Text. The audio signal is converted to a log-mel spectrogram encoded by the encoder. The decoder generates the transcript autoregressively from the encoder's hidden states and the previous tokens. Multimodal Encoder[[mm-encoder]] VisualBERT is a multimodal model for vision-language tasks released shortly after BERT. It combines BERT and a pretrained object detection system to extract image features into visual embeddings, passed alongside text embeddings to BERT. VisualBERT predicts the masked text based on the unmasked text and the visual embeddings, and it also has to predict whether the text is aligned with the image. When ViT was released, ViLT adopted ViT in its architecture because it was easier to get the image embeddings this way. The image embeddings are jointly processed with the text embeddings. From there, ViLT is pretrained by image text matching, masked language modeling, and whole word masking. CLIP takes a different approach and makes a pair prediction of (image, text) . An image encoder (ViT) and a text encoder (Transformer) are jointly trained on a 400 million (image, text) pair dataset to maximize the similarity between the image and text embeddings of the (image, text) pairs. After pretraining, you can use natural language to instruct CLIP to predict the text given an image or vice versa. OWL-ViT builds on top of CLIP by using it as its backbone for zero-shot object detection. After pretraining, an object detection head is added to make a set prediction over the (class, bounding box) pairs. Encoder-decoder[[mm-encoder-decoder]] Optical character recognition (OCR) is a long-standing text recognition task that typically involves several components to understand the image and generate the text. TrOCR simplifies the process using an end-to-end Transformer. The encoder is a ViT-style model for image understanding and processes the image as fixed-size patches. The decoder accepts the encoder's hidden states and autoregressively generates text. Donut is a more general visual document understanding model that doesn't rely on OCR-based approaches. It uses a Swin Transformer as the encoder and multilingual BART as the decoder. Donut is pretrained to read text by predicting the next word based on the image and text annotations. The decoder generates a token sequence given a prompt. The prompt is represented by a special token for each downstream task. For example, document parsing has a special parsing token that is combined with the encoder hidden states to parse the document into a structured output format (JSON). Reinforcement learning Decoder[[rl-decoder]] The Decision and Trajectory Transformer casts the state, action, and reward as a sequence modeling problem. The Decision Transformer generates a series of actions that lead to a future desired return based on returns-to-go, past states, and actions. For the last K timesteps, each of the three modalities are converted into token embeddings and processed by a GPT-like model to predict a future action token. Trajectory Transformer also tokenizes the states, actions, and rewards and processes them with a GPT architecture. Unlike the Decision Transformer, which is focused on reward conditioning, the Trajectory Transformer generates future actions with beam search.

Summary of the tokenizers [[open-in-colab]] On this page, we will have a closer look at tokenization. As we saw in the preprocessing tutorial, tokenizing a text is splitting it into words or subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids is straightforward, so in this summary, we will focus on splitting a text into words or subwords (i.e. tokenizing a text). More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: Byte-Pair Encoding (BPE), WordPiece, and SentencePiece, and show examples of which tokenizer type is used by which model. Note that on each model page, you can look at the documentation of the associated tokenizer to know which tokenizer type was used by the pretrained model. For instance, if we look at [BertTokenizer], we can see that the model uses WordPiece. Introduction Splitting a text into smaller chunks is a task that is harder than it looks, and there are multiple ways of doing so. For instance, let's look at the sentence "Don't you love 🤗 Transformers? We sure do." A simple way of tokenizing this text is to split it by spaces, which would give: ["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."] This is a sensible first step, but if we look at the tokens "Transformers?" and "do.", we notice that the punctuation is attached to the words "Transformer" and "do", which is suboptimal. We should take the punctuation into account so that a model does not have to learn a different representation of a word and every possible punctuation symbol that could follow it, which would explode the number of representations the model has to learn. Taking punctuation into account, tokenizing our exemplary text would give: ["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."] Better. However, it is disadvantageous, how the tokenization dealt with the word "Don't". "Don't" stands for "do not", so it would be better tokenized as ["Do", "n't"]. This is where things start getting complicated, and part of the reason each model has its own tokenizer type. Depending on the rules we apply for tokenizing a text, a different tokenized output is generated for the same text. A pretrained model only performs properly if you feed it an input that was tokenized with the same rules that were used to tokenize its training data. spaCy and Moses are two popular rule-based tokenizers. Applying them on our example, spaCy and Moses would output something like: ["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."] As can be seen space and punctuation tokenization, as well as rule-based tokenization, is used here. Space and punctuation tokenization and rule-based tokenization are both examples of word tokenization, which is loosely defined as splitting sentences into words. While it's the most intuitive way to split texts into smaller chunks, this tokenization method can lead to problems for massive text corpora. In this case, space and punctuation tokenization usually generates a very big vocabulary (the set of all unique words and tokens used). E.g., Transformer XL uses space and punctuation tokenization, resulting in a vocabulary size of 267,735! Such a big vocabulary size forces the model to have an enormous embedding matrix as the input and output layer, which causes both an increased memory and time complexity. In general, transformers models rarely have a vocabulary size greater than 50,000, especially if they are pretrained only on a single language. So if simple space and punctuation tokenization is unsatisfactory, why not simply tokenize on characters? While character tokenization is very simple and would greatly reduce memory and time complexity it makes it much harder for the model to learn meaningful input representations. E.g. learning a meaningful context-independent representation for the letter "t" is much harder than learning a context-independent representation for the word "today". Therefore, character tokenization is often accompanied by a loss of performance. So to get the best of both worlds, transformers models use a hybrid between word-level and character-level tokenization called subword tokenization. Subword tokenization Subword tokenization algorithms rely on the principle that frequently used words should not be split into smaller subwords, but rare words should be decomposed into meaningful subwords. For instance "annoyingly" might be considered a rare word and could be decomposed into "annoying" and "ly". Both "annoying" and "ly" as stand-alone subwords would appear more frequently while at the same time the meaning of "annoyingly" is kept by the composite meaning of "annoying" and "ly". This is especially useful in agglutinative languages such as Turkish, where you can form (almost) arbitrarily long complex words by stringing together subwords. Subword tokenization allows the model to have a reasonable vocabulary size while being able to learn meaningful context-independent representations. In addition, subword tokenization enables the model to process words it has never seen before, by decomposing them into known subwords. For instance, the [~transformers.BertTokenizer] tokenizes "I have a new GPU!" as follows: from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") tokenizer.tokenize("I have a new GPU!") ["i", "have", "a", "new", "gp", "##u", "!"] Because we are considering the uncased model, the sentence was lowercased first. We can see that the words ["i", "have", "a", "new"] are present in the tokenizer's vocabulary, but the word "gpu" is not. Consequently, the tokenizer splits "gpu" into known subwords: ["gp" and "##u"]. "##" means that the rest of the token should be attached to the previous one, without space (for decoding or reversal of the tokenization). As another example, [~transformers.XLNetTokenizer] tokenizes our previously exemplary text as follows: from transformers import XLNetTokenizer tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased") tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.") ["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."] We'll get back to the meaning of those "▁" when we look at SentencePiece. As one can see, the rare word "Transformers" has been split into the more frequent subwords "Transform" and "ers". Let's now look at how the different subword tokenization algorithms work. Note that all of those tokenization algorithms rely on some form of training which is usually done on the corpus the corresponding model will be trained on. Byte-Pair Encoding (BPE) Byte-Pair Encoding (BPE) was introduced in Neural Machine Translation of Rare Words with Subword Units (Sennrich et al., 2015). BPE relies on a pre-tokenizer that splits the training data into words. Pretokenization can be as simple as space tokenization, e.g. GPT-2, Roberta. More advanced pre-tokenization include rule-based tokenization, e.g. XLM, FlauBERT which uses Moses for most languages, or GPT which uses Spacy and ftfy, to count the frequency of each word in the training corpus. After pre-tokenization, a set of unique words has been created and the frequency with which each word occurred in the training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to define before training the tokenizer. As an example, let's assume that after pre-tokenization, the following set of words including their frequency has been determined: ("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5) Consequently, the base vocabulary is ["b", "g", "h", "n", "p", "s", "u"]. Splitting all words into symbols of the base vocabulary, we obtain: ("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5) BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In the example above "h" followed by "u" is present 10 + 5 = 15 times (10 times in the 10 occurrences of "hug", 5 times in the 5 occurrences of "hugs"). However, the most frequent symbol pair is "u" followed by "g", occurring 10 + 5 + 5 = 20 times in total. Thus, the first merge rule the tokenizer learns is to group all "u" symbols followed by a "g" symbol together. Next, "ug" is added to the vocabulary. The set of words then becomes ("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5) BPE then identifies the next most common symbol pair. It's "u" followed by "n", which occurs 16 times. "u", "n" is merged to "un" and added to the vocabulary. The next most frequent symbol pair is "h" followed by "ug", occurring 15 times. Again the pair is merged and "hug" can be added to the vocabulary. At this stage, the vocabulary is ["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"] and our set of unique words is represented as ("hug", 10), ("p" "ug", 5), ("p" "un", 12), ("b" "un", 4), ("hug" "s", 5) Assuming, that the Byte-Pair Encoding training would stop at this point, the learned merge rules would then be applied to new words (as long as those new words do not include symbols that were not in the base vocabulary). For instance, the word "bug" would be tokenized to ["b", "ug"] but "mug" would be tokenized as ["<unk>", "ug"] since the symbol "m" is not in the base vocabulary. In general, single letters such as "m" are not replaced by the "<unk>" symbol because the training data usually includes at least one occurrence of each letter, but it is likely to happen for very special characters like emojis. As mentioned earlier, the vocabulary size, i.e. the base vocabulary size + the number of merges, is a hyperparameter to choose. For instance GPT has a vocabulary size of 40,478 since they have 478 base characters and chose to stop training after 40,000 merges. Byte-level BPE A base vocabulary that includes all possible base characters can be quite large if e.g. all unicode characters are considered as base characters. To have a better base vocabulary, GPT-2 uses bytes as the base vocabulary, which is a clever trick to force the base vocabulary to be of size 256 while ensuring that every base character is included in the vocabulary. With some additional rules to deal with punctuation, the GPT2's tokenizer can tokenize every text without the need for the symbol. GPT-2 has a vocabulary size of 50,257, which corresponds to the 256 bytes base tokens, a special end-of-text token and the symbols learned with 50,000 merges. WordPiece WordPiece is the subword tokenization algorithm used for BERT, DistilBERT, and Electra. The algorithm was outlined in Japanese and Korean Voice Search (Schuster et al., 2012) and is very similar to BPE. WordPiece first initializes the vocabulary to include every character present in the training data and progressively learns a given number of merge rules. In contrast to BPE, WordPiece does not choose the most frequent symbol pair, but the one that maximizes the likelihood of the training data once added to the vocabulary. So what does this mean exactly? Referring to the previous example, maximizing the likelihood of the training data is equivalent to finding the symbol pair, whose probability divided by the probabilities of its first symbol followed by its second symbol is the greatest among all symbol pairs. E.g. "u", followed by "g" would have only been merged if the probability of "ug" divided by "u", "g" would have been greater than for any other symbol pair. Intuitively, WordPiece is slightly different to BPE in that it evaluates what it loses by merging two symbols to ensure it's worth it. Unigram Unigram is a subword tokenization algorithm introduced in Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (Kudo, 2018). In contrast to BPE or WordPiece, Unigram initializes its base vocabulary to a large number of symbols and progressively trims down each symbol to obtain a smaller vocabulary. The base vocabulary could for instance correspond to all pre-tokenized words and the most common substrings. Unigram is not used directly for any of the models in the transformers, but it's used in conjunction with SentencePiece. At each training step, the Unigram algorithm defines a loss (often defined as the log-likelihood) over the training data given the current vocabulary and a unigram language model. Then, for each symbol in the vocabulary, the algorithm computes how much the overall loss would increase if the symbol was to be removed from the vocabulary. Unigram then removes p (with p usually being 10% or 20%) percent of the symbols whose loss increase is the lowest, i.e. those symbols that least affect the overall loss over the training data. This process is repeated until the vocabulary has reached the desired size. The Unigram algorithm always keeps the base characters so that any word can be tokenized. Because Unigram is not based on merge rules (in contrast to BPE and WordPiece), the algorithm has several ways of tokenizing new text after training. As an example, if a trained Unigram tokenizer exhibits the vocabulary: ["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"], "hugs" could be tokenized both as ["hug", "s"], ["h", "ug", "s"] or ["h", "u", "g", "s"]. So which one to choose? Unigram saves the probability of each token in the training corpus on top of saving the vocabulary so that the probability of each possible tokenization can be computed after training. The algorithm simply picks the most likely tokenization in practice, but also offers the possibility to sample a possible tokenization according to their probabilities. Those probabilities are defined by the loss the tokenizer is trained on. Assuming that the training data consists of the words \(x_{1}, \dots, x_{N}\) and that the set of all possible tokenizations for a word \(x_{i}\) is defined as \(S(x_{i})\), then the overall loss is defined as $$\mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )$$ SentencePiece All tokenization algorithms described so far have the same problem: It is assumed that the input text uses spaces to separate words. However, not all languages use spaces to separate words. One possible solution is to use language specific pre-tokenizers, e.g. XLM uses a specific Chinese, Japanese, and Thai pre-tokenizer). To solve this problem more generally, SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (Kudo et al., 2018) treats the input as a raw input stream, thus including the space in the set of characters to use. It then uses the BPE or unigram algorithm to construct the appropriate vocabulary. The [XLNetTokenizer] uses SentencePiece for example, which is also why in the example earlier the "▁" character was included in the vocabulary. Decoding with SentencePiece is very easy since all tokens can just be concatenated and "▁" is replaced by a space. All transformers models in the library that use SentencePiece use it in combination with unigram. Examples of models using SentencePiece are ALBERT, XLNet, Marian, and T5.

Custom Tools and Prompts If you are not aware of what tools and agents are in the context of transformers, we recommend you read the Transformers Agents page first. Transformers Agent is an experimental API that is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. Creating and using custom tools and prompts is paramount to empowering the agent and having it perform new tasks. In this guide we'll take a look at: How to customize the prompt How to use custom tools How to create custom tools Customizing the prompt As explained in Transformers Agents agents can run in [~Agent.run] and [~Agent.chat] mode. Both the run and chat modes underlie the same logic. The language model powering the agent is conditioned on a long prompt and completes the prompt by generating the next tokens until the stop token is reached. The only difference between the two modes is that during the chat mode the prompt is extended with previous user inputs and model generations. This allows the agent to have access to past interactions, seemingly giving the agent some kind of memory. Structure of the prompt Let's take a closer look at how the prompt is structured to understand how it can be best customized. The prompt is structured broadly into four parts. Introduction: how the agent should behave, explanation of the concept of tools. Description of all the tools. This is defined by a <<all_tools>> token that is dynamically replaced at runtime with the tools defined/chosen by the user. A set of examples of tasks and their solution Current example, and request for solution. To better understand each part, let's look at a shortened version of how the run prompt can look like: ````text I will ask you to perform a task, your job is to come up with a series of simple commands in Python that will perform the task. [] You can print intermediate results if it makes sense to do so. Tools: - document_qa: This is a tool that answers a question about a document (pdf). It takes an input named document which should be the document containing the information, as well as a question that is the question about the document. It returns a text that contains the answer to the question. - image_captioner: This is a tool that generates a description of an image. It takes an input named image which should be the image to the caption and returns a text that contains the description in English. [] Task: "Answer the question in the variable question about the image stored in the variable image. The question is in French." I will use the following tools: translator to translate the question into English and then image_qa to answer the question on the input image. Answer: py translated_question = translator(question=question, src_lang="French", tgt_lang="English") print(f"The translated question is {translated_question}.") answer = image_qa(image=image, question=translated_question) print(f"The answer is {answer}") Task: "Identify the oldest person in the document and create an image showcasing the result as a banner." I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer. Answer: py answer = document_qa(document, question="What is the oldest person?") print(f"The answer is {answer}.") image = image_generator("A banner showing " + answer) [] Task: "Draw me a picture of rivers and lakes" I will use the following ` The introduction (the text before "Tools:") explains precisely how the model shall behave and what it should do. This part most likely does not need to be customized as the agent shall always behave the same way. The second part (the bullet points below "Tools") is dynamically added upon calling run or chat. There are exactly as many bullet points as there are tools in agent.toolbox and each bullet point consists of the name and description of the tool: text - <tool.name>: <tool.description> Let's verify this quickly by loading the document_qa tool and printing out the name and description. from transformers import load_tool document_qa = load_tool("document-question-answering") print(f"- {document_qa.name}: {document_qa.description}") which gives: text - document_qa: This is a tool that answers a question about a document (pdf). It takes an input named `document` which should be the document containing the information, as well as a `question` that is the question about the document. It returns a text that contains the answer to the question. We can see that the tool name is short and precise. The description includes two parts, the first explaining what the tool does and the second states what input arguments and return values are expected. A good tool name and tool description are very important for the agent to correctly use it. Note that the only information the agent has about the tool is its name and description, so one should make sure that both are precisely written and match the style of the existing tools in the toolbox. In particular make sure the description mentions all the arguments expected by name in code-style, along with the expected type and a description of what they are. Check the naming and description of the curated Transformers tools to better understand what name and description a tool is expected to have. You can see all tools with the [Agent.toolbox] property. The third part includes a set of curated examples that show the agent exactly what code it should produce for what kind of user request. The large language models empowering the agent are extremely good at recognizing patterns in a prompt and repeating the pattern with new data. Therefore, it is very important that the examples are written in a way that maximizes the likelihood of the agent to generating correct, executable code in practice. Let's have a look at one example: ```text Task: "Identify the oldest person in thedocument` and create an image showcasing the result as a banner." I will use the following tools: document_qa to find the oldest person in the document, then image_generator to generate an image according to the answer. Answer: py answer = document_qa(document, question="What is the oldest person?") print(f"The answer is {answer}.") image = image_generator("A banner showing " + answer) ` The pattern the model is prompted to repeat has three parts: The task statement, the agent's explanation of what it intends to do, and finally the generated code. Every example that is part of the prompt has this exact pattern, thus making sure that the agent will reproduce exactly the same pattern when generating new tokens. The prompt examples are curated by the Transformers team and rigorously evaluated on a set of problem statements to ensure that the agent's prompt is as good as possible to solve real use cases of the agent. The final part of the prompt corresponds to: ```text Task: "Draw me a picture of rivers and lakes" I will use the following is a final and unfinished example that the agent is tasked to complete. The unfinished example is dynamically created based on the actual user input. For the above example, the user ran: py agent.run("Draw me a picture of rivers and lakes") The user input - a.k.a the task: "Draw me a picture of rivers and lakes" is cast into the prompt template: "Task: \n\n I will use the following". This sentence makes up the final lines of the prompt the agent is conditioned on, therefore strongly influencing the agent to finish the example exactly in the same way it was previously done in the examples. Without going into too much detail, the chat template has the same prompt structure with the examples having a slightly different style, e.g.: ````text [] ===== Human: Answer the question in the variable question about the image stored in the variable image. Assistant: I will use the tool image_qa to answer the question on the input image. py answer = image_qa(text=question, image=image) print(f"The answer is {answer}") Human: I tried this code, it worked but didn't give me a good result. The question is in French Assistant: In this case, the question needs to be translated first. I will use the tool translator to do this. py translated_question = translator(question=question, src_lang="French", tgt_lang="English") print(f"The translated question is {translated_question}.") answer = image_qa(text=translated_question, image=image) print(f"The answer is {answer}") ===== [] ` Contrary, to the examples of the run prompt, each chat prompt example has one or more exchanges between the Human and the Assistant. Every exchange is structured similarly to the example of the run prompt. The user's input is appended to behind Human: and the agent is prompted to first generate what needs to be done before generating code. An exchange can be based on previous exchanges, therefore allowing the user to refer to past exchanges as is done e.g. above by the user's input of "I tried this code" refers to the previously generated code of the agent. Upon running .chat, the user's input or task is cast into an unfinished example of the form: text Human: <user-input>\n\nAssistant: which the agent completes. Contrary to the run command, the chat command then appends the completed example to the prompt, thus giving the agent more context for the next chat turn. Great now that we know how the prompt is structured, let's see how we can customize it! Writing good user inputs While large language models are getting better and better at understanding users' intentions, it helps enormously to be as precise as possible to help the agent pick the correct task. What does it mean to be as precise as possible? The agent sees a list of tool names and their description in its prompt. The more tools are added the more difficult it becomes for the agent to choose the correct tool and it's even more difficult to choose the correct sequences of tools to run. Let's look at a common failure case, here we will only return the code to analyze it. from transformers import HfAgent agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder") agent.run("Show me a tree", return_code=True) gives: ``text ==Explanation from the agent== I will use the following tool:image_segmenter` to create a segmentation mask for the image. ==Code generated by the agent== mask = image_segmenter(image, prompt="tree") which is probably not what we wanted. Instead, it is more likely that we want an image of a tree to be generated. To steer the agent more towards using a specific tool it can therefore be very helpful to use important keywords that are present in the tool's name and description. Let's have a look. py agent.toolbox["image_generator"].description text 'This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which contains the image description and outputs an image. The name and description make use of the keywords "image", "prompt", "create" and "generate". Using these words will most likely work better here. Let's refine our prompt a bit. py agent.run("Create an image of a tree", return_code=True) gives: ``text ==Explanation from the agent== I will use the following toolimage_generator` to generate an image of a tree. ==Code generated by the agent== image = image_generator(prompt="tree") Much better! That looks more like what we want. In short, when you notice that the agent struggles to correctly map your task to the correct tools, try looking up the most pertinent keywords of the tool's name and description and try refining your task request with it. Customizing the tool descriptions As we've seen before the agent has access to each of the tools' names and descriptions. The base tools should have very precise names and descriptions, however, you might find that it could help to change the the description or name of a tool for your specific use case. This might become especially important when you've added multiple tools that are very similar or if you want to use your agent only for a certain domain, e.g. image generation and transformations. A common problem is that the agent confuses image generation with image transformation/modification when used a lot for image generation tasks, e.g. py agent.run("Make an image of a house and a car", return_code=True) returns ``text ==Explanation from the agent== I will use the following toolsimage_generatorto generate an image of a house andimage_transformer` to transform the image of a car into the image of a house. ==Code generated by the agent== house_image = image_generator(prompt="A house") car_image = image_generator(prompt="A car") house_car_image = image_transformer(image=car_image, prompt="A house") which is probably not exactly what we want here. It seems like the agent has a difficult time to understand the difference between image_generator and image_transformer and often uses the two together. We can help the agent here by changing the tool name and description of image_transformer. Let's instead call it modifier to disassociate it a bit from "image" and "prompt": py agent.toolbox["modifier"] = agent.toolbox.pop("image_transformer") agent.toolbox["modifier"].description = agent.toolbox["modifier"].description.replace( "transforms an image according to a prompt", "modifies an image" ) Now "modify" is a strong cue to use the new image processor which should help with the above prompt. Let's run it again. py agent.run("Make an image of a house and a car", return_code=True) Now we're getting: ``text ==Explanation from the agent== I will use the following tools:image_generatorto generate an image of a house, thenimage_generator` to generate an image of a car. ==Code generated by the agent== house_image = image_generator(prompt="A house") car_image = image_generator(prompt="A car") which is definitely closer to what we had in mind! However, we want to have both the house and car in the same image. Steering the task more toward single image generation should help: py agent.run("Create image: 'A house and car'", return_code=True) ``text ==Explanation from the agent== I will use the following tool:image_generator` to generate an image. ==Code generated by the agent== image = image_generator(prompt="A house and car") Agents are still brittle for many use cases, especially when it comes to slightly more complex use cases like generating an image of multiple objects. Both the agent itself and the underlying prompt will be further improved in the coming months making sure that agents become more robust to a variety of user inputs. Customizing the whole prompt To give the user maximum flexibility, the whole prompt template as explained in above can be overwritten by the user. In this case make sure that your custom prompt includes an introduction section, a tool section, an example section, and an unfinished example section. If you want to overwrite the run prompt template, you can do as follows: template = """ [] """ agent = HfAgent(your_endpoint, run_prompt_template=template) Please make sure to have the <<all_tools>> string and the <<prompt>> defined somewhere in the template so that the agent can be aware of the tools, it has available to it as well as correctly insert the user's prompt. Similarly, one can overwrite the chat prompt template. Note that the chat mode always uses the following format for the exchanges: ```text Human: <> Assistant: Therefore it is important that the examples of the custom chat prompt template also make use of this format. You can overwrite the chat template at instantiation as follows. template = """ [] """ agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template) Please make sure to have the <<all_tools>> string defined somewhere in the template so that the agent can be aware of the tools, it has available to it. In both cases, you can pass a repo ID instead of the prompt template if you would like to use a template hosted by someone in the community. The default prompts live in this repo as an example. To upload your custom prompt on a repo on the Hub and share it with the community just make sure: - to use a dataset repository - to put the prompt template for the run command in a file named run_prompt_template.txt - to put the prompt template for the chat command in a file named chat_prompt_template.txt Using custom tools In this section, we'll be leveraging two existing custom tools that are specific to image generation: We replace huggingface-tools/image-transformation, with diffusers/controlnet-canny-tool to allow for more image modifications. We add a new tool for image upscaling to the default toolbox: diffusers/latent-upscaler-tool replace the existing image-transformation tool. We'll start by loading the custom tools with the convenient [load_tool] function: from transformers import load_tool controlnet_transformer = load_tool("diffusers/controlnet-canny-tool") upscaler = load_tool("diffusers/latent-upscaler-tool") Upon adding custom tools to an agent, the tools' descriptions and names are automatically included in the agents' prompts. Thus, it is imperative that custom tools have a well-written description and name in order for the agent to understand how to use them. Let's take a look at the description and name of controlnet_transformer: py print(f"Description: '{controlnet_transformer.description}'") print(f"Name: '{controlnet_transformer.name}'") gives text Description: 'This is a tool that transforms an image with ControlNet according to a prompt. It takes two inputs: `image`, which should be the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the modified image.' Name: 'image_transformer' The name and description are accurate and fit the style of the curated set of tools. Next, let's instantiate an agent with controlnet_transformer and upscaler: py tools = [controlnet_transformer, upscaler] agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=tools) This command should give you the following info: text image_transformer has been replaced by <transformers_modules.diffusers.controlnet-canny-tool.bd76182c7777eba9612fc03c0 8718a60c0aa6312.image_transformation.ControlNetTransformationTool object at 0x7f1d3bfa3a00> as provided in `additional_tools` The set of curated tools already has an image_transformer tool which is hereby replaced with our custom tool. Overwriting existing tools can be beneficial if we want to use a custom tool exactly for the same task as an existing tool because the agent is well-versed in using the specific task. Beware that the custom tool should follow the exact same API as the overwritten tool in this case, or you should adapt the prompt template to make sure all examples using that tool are updated. The upscaler tool was given the name image_upscaler which is not yet present in the default toolbox and is therefore simply added to the list of tools. You can always have a look at the toolbox that is currently available to the agent via the agent.toolbox attribute: py print("\n".join([f"- {a}" for a in agent.toolbox.keys()])) text - document_qa - image_captioner - image_qa - image_segmenter - transcriber - summarizer - text_classifier - text_qa - text_reader - translator - image_transformer - text_downloader - image_generator - video_generator - image_upscaler Note how image_upscaler is now part of the agents' toolbox. Let's now try out the new tools! We will re-use the image we generated in Transformers Agents Quickstart. from diffusers.utils import load_image image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" ) Let's transform the image into a beautiful winter landscape: py image = agent.run("Transform the image: 'A frozen lake and snowy forest'", image=image) ``text ==Explanation from the agent== I will use the following tool:image_transformer` to transform the image. ==Code generated by the agent== image = image_transformer(image, prompt="A frozen lake and snowy forest") The new image processing tool is based on ControlNet which can make very strong modifications to the image. By default the image processing tool returns an image of size 512x512 pixels. Let's see if we can upscale it. py image = agent.run("Upscale the image", image) ``text ==Explanation from the agent== I will use the following tool:image_upscaler` to upscale the image. ==Code generated by the agent== upscaled_image = image_upscaler(image) The agent automatically mapped our prompt "Upscale the image" to the just added upscaler tool purely based on the description and name of the upscaler tool and was able to correctly run it. Next, let's have a look at how you can create a new custom tool. Adding new tools In this section, we show how to create a new tool that can be added to the agent. Creating a new tool We'll first start by creating a tool. We'll add the not-so-useful yet fun task of fetching the model on the Hugging Face Hub with the most downloads for a given task. We can do that with the following code: thon from huggingface_hub import list_models task = "text-classification" model = next(iter(list_models(filter=task, sort="downloads", direction=-1))) print(model.id) For the task text-classification, this returns 'facebook/bart-large-mnli', for translation it returns 't5-base. How do we convert this to a tool that the agent can leverage? All tools depend on the superclass Tool that holds the main attributes necessary. We'll create a class that inherits from it: thon from transformers import Tool class HFModelDownloadsTool(Tool): pass This class has a few needs: - An attribute name, which corresponds to the name of the tool itself. To be in tune with other tools which have a performative name, we'll name it model_download_counter. - An attribute description, which will be used to populate the prompt of the agent. - inputs and outputs attributes. Defining this will help the python interpreter make educated choices about types, and will allow for a gradio-demo to be spawned when we push our tool to the Hub. They're both a list of expected values, which can be text, image, or audio. - A __call__ method which contains the inference code. This is the code we've played with above! Here's what our class looks like now: thon from transformers import Tool from huggingface_hub import list_models class HFModelDownloadsTool(Tool): name = "model_download_counter" description = ( "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. " "It takes the name of the category (such as text-classification, depth-estimation, etc), and " "returns the name of the checkpoint." ) inputs = ["text"] outputs = ["text"] def __call__(self, task: str): model = next(iter(list_models(filter=task, sort="downloads", direction=-1))) return model.id We now have our tool handy. Save it in a file and import it from your main script. Let's name this file model_downloads.py, so the resulting import code looks like this: thon from model_downloads import HFModelDownloadsTool tool = HFModelDownloadsTool() In order to let others benefit from it and for simpler initialization, we recommend pushing it to the Hub under your namespace. To do so, just call push_to_hub on the tool variable: python tool.push_to_hub("hf-model-downloads") You now have your code on the Hub! Let's take a look at the final step, which is to have the agent use it. Having the agent use the tool We now have our tool that lives on the Hub which can be instantiated as such (change the user name for your tool): thon from transformers import load_tool tool = load_tool("lysandre/hf-model-downloads") In order to use it in the agent, simply pass it in the additional_tools parameter of the agent initialization method: thon from transformers import HfAgent agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool]) agent.run( "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?" ) which outputs the following:text ==Code generated by the agent== model = model_download_counter(task="text-to-video") print(f"The model with the most downloads is {model}.") audio_model = text_reader(model) ==Result== The model with the most downloads is damo-vilab/text-to-video-ms-1.7b. and generates the following audio. | Audio | |------------------------------------------------------------------------------------------------------------------------------------------------------| | | Depending on the LLM, some are quite brittle and require very exact prompts in order to work well. Having a well-defined name and description of the tool is paramount to having it be leveraged by the agent. Replacing existing tools Replacing existing tools can be done simply by assigning a new item to the agent's toolbox. Here's how one would do so: thon from transformers import HfAgent, load_tool agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder") agent.toolbox["image-transformation"] = load_tool("diffusers/controlnet-canny-tool") Beware when replacing tools with others! This will also adjust the agent's prompt. This can be good if you have a better prompt suited for the task, but it can also result in your tool being selected way more than others or for other tools to be selected instead of the one you have defined. Leveraging gradio-tools gradio-tools is a powerful library that allows using Hugging Face Spaces as tools. It supports many existing Spaces as well as custom Spaces to be designed with it. We offer support for gradio_tools by using the Tool.from_gradio method. For example, we want to take advantage of the StableDiffusionPromptGeneratorTool tool offered in the gradio-tools toolkit so as to improve our prompts and generate better images. We first import the tool from gradio_tools and instantiate it: thon from gradio_tools import StableDiffusionPromptGeneratorTool gradio_tool = StableDiffusionPromptGeneratorTool() We pass that instance to the Tool.from_gradio method: thon from transformers import Tool tool = Tool.from_gradio(gradio_tool) Now we can manage it exactly as we would a usual custom tool. We leverage it to improve our prompt a rabbit wearing a space suit: thon from transformers import HfAgent agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool]) agent.run("Generate an image of the prompt after improving it.", prompt="A rabbit wearing a space suit") The model adequately leverages the tool: ``text ==Explanation from the agent== I will use the following tools:StableDiffusionPromptGeneratorto improve the prompt, thenimage_generator` to generate an image according to the improved prompt. ==Code generated by the agent== improved_prompt = StableDiffusionPromptGenerator(prompt) print(f"The improved prompt is {improved_prompt}.") image = image_generator(improved_prompt) Before finally generating the image: gradio-tools requires textual inputs and outputs, even when working with different modalities. This implementation works with image and audio objects. The two are currently incompatible, but will rapidly become compatible as we work to improve the support. Future compatibility with Langchain We love Langchain and think it has a very compelling suite of tools. In order to handle these tools, Langchain requires textual inputs and outputs, even when working with different modalities. This is often the serialized version (i.e., saved to disk) of the objects. This difference means that multi-modality isn't handled between transformers-agents and langchain. We aim for this limitation to be resolved in future versions, and welcome any help from avid langchain users to help us achieve this compatibility. We would love to have better support. If you would like to help, please open an issue and share what you have in mind.

Custom hardware for training The hardware you use to run model training and inference can have a big effect on performance. For a deep dive into GPUs make sure to check out Tim Dettmer's excellent blog post. Let's have a look at some practical advice for GPU setups. GPU When you train bigger models you have essentially three options: - bigger GPUs - more GPUs - more CPU and NVMe (offloaded to by DeepSpeed-Infinity) Let's start at the case where you have a single GPU. Power and Cooling If you bought an expensive high end GPU make sure you give it the correct power and sufficient cooling. Power: Some high end consumer GPU cards have 2 and sometimes 3 PCI-E 8-Pin power sockets. Make sure you have as many independent 12V PCI-E 8-Pin cables plugged into the card as there are sockets. Do not use the 2 splits at one end of the same cable (also known as pigtail cable). That is if you have 2 sockets on the GPU, you want 2 PCI-E 8-Pin cables going from your PSU to the card and not one that has 2 PCI-E 8-Pin connectors at the end! You won't get the full performance out of your card otherwise. Each PCI-E 8-Pin power cable needs to be plugged into a 12V rail on the PSU side and can supply up to 150W of power. Some other cards may use a PCI-E 12-Pin connectors, and these can deliver up to 500-600W of power. Low end cards may use 6-Pin connectors, which supply up to 75W of power. Additionally you want the high-end PSU that has stable voltage. Some lower quality ones may not give the card the stable voltage it needs to function at its peak. And of course the PSU needs to have enough unused Watts to power the card. Cooling: When a GPU gets overheated it will start throttling down and will not deliver full performance and it can even shutdown if it gets too hot. It's hard to tell the exact best temperature to strive for when a GPU is heavily loaded, but probably anything under +80C is good, but lower is better - perhaps 70-75C is an excellent range to be in. The throttling down is likely to start at around 84-90C. But other than throttling performance a prolonged very high temperature is likely to reduce the lifespan of a GPU. Next let's have a look at one of the most important aspects when having multiple GPUs: connectivity. Multi-GPU Connectivity If you use multiple GPUs the way cards are inter-connected can have a huge impact on the total training time. If the GPUs are on the same physical node, you can run: nvidia-smi topo -m and it will tell you how the GPUs are inter-connected. On a machine with dual-GPU and which are connected with NVLink, you will most likely see something like: GPU0 GPU1 CPU Affinity NUMA Affinity GPU0 X NV2 0-23 N/A GPU1 NV2 X 0-23 N/A on a different machine w/o NVLink we may see: GPU0 GPU1 CPU Affinity NUMA Affinity GPU0 X PHB 0-11 N/A GPU1 PHB X 0-11 N/A The report includes this legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks So the first report NV2 tells us the GPUs are interconnected with 2 NVLinks, and the second report PHB we have a typical consumer-level PCIe+Bridge setup. Check what type of connectivity you have on your setup. Some of these will make the communication between cards faster (e.g. NVLink), others slower (e.g. PHB). Depending on the type of scalability solution used, the connectivity speed could have a major or a minor impact. If the GPUs need to sync rarely, as in DDP, the impact of a slower connection will be less significant. If the GPUs need to send messages to each other often, as in ZeRO-DP, then faster connectivity becomes super important to achieve faster training. NVlink NVLink is a wire-based serial multi-lane near-range communications link developed by Nvidia. Each new generation provides a faster bandwidth, e.g. here is a quote from Nvidia Ampere GA102 GPU Architecture: Third-Generation NVLink® GA102 GPUs utilize NVIDIA’s third-generation NVLink interface, which includes four x4 links, with each link providing 14.0625 GB/sec bandwidth in each direction between two GPUs. Four links provide 56.25 GB/sec bandwidth in each direction, and 112.5 GB/sec total bandwidth between two GPUs. Two RTX 3090 GPUs can be connected together for SLI using NVLink. (Note that 3-Way and 4-Way SLI configurations are not supported.) So the higher X you get in the report of NVX in the output of nvidia-smi topo -m the better. The generation will depend on your GPU architecture. Let's compare the execution of a gpt2 language model training over a small sample of wikitext. The results are: | NVlink | Time | | ----- | ---: | | Y | 101s | | N | 131s | You can see that NVLink completes the training ~23% faster. In the second benchmark we use NCCL_P2P_DISABLE=1 to tell the GPUs not to use NVLink. Here is the full benchmark code and outputs: ```bash DDP w/ NVLink rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch \ --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path 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 rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch.distributed.launch \ --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path 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} 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

Multilingual models for inference [[open-in-colab]] There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not all multilingual model usage is different though. Some models, like bert-base-multilingual-uncased, can be used just like a monolingual model. This guide will show you how to use multilingual models whose usage differs for inference. XLM XLM has ten different checkpoints, only one of which is monolingual. The nine remaining model checkpoints can be split into two categories: the checkpoints that use language embeddings and those that don't. XLM with language embeddings The following XLM models use language embeddings to specify the language used at inference: xlm-mlm-ende-1024 (Masked language modeling, English-German) xlm-mlm-enfr-1024 (Masked language modeling, English-French) xlm-mlm-enro-1024 (Masked language modeling, English-Romanian) xlm-mlm-xnli15-1024 (Masked language modeling, XNLI languages) xlm-mlm-tlm-xnli15-1024 (Masked language modeling + translation, XNLI languages) xlm-clm-enfr-1024 (Causal language modeling, English-French) xlm-clm-ende-1024 (Causal language modeling, English-German) Language embeddings are represented as a tensor of the same shape as the input_ids passed to the model. The values in these tensors depend on the language used and are identified by the tokenizer's lang2id and id2lang attributes. In this example, load the xlm-clm-enfr-1024 checkpoint (Causal language modeling, English-French): import torch from transformers import XLMTokenizer, XLMWithLMHeadModel tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024") model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024") The lang2id attribute of the tokenizer displays this model's languages and their ids: print(tokenizer.lang2id) {'en': 0, 'fr': 1} Next, create an example input: input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1 Set the language id as "en" and use it to define the language embedding. The language embedding is a tensor filled with 0 since that is the language id for English. This tensor should be the same size as input_ids. language_id = tokenizer.lang2id["en"] # 0 langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, , 0]) We reshape it to be of size (batch_size, sequence_length) langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1) Now you can pass the input_ids and language embedding to the model: outputs = model(input_ids, langs=langs) The run_generation.py script can generate text with language embeddings using the xlm-clm checkpoints. XLM without language embeddings The following XLM models do not require language embeddings during inference: xlm-mlm-17-1280 (Masked language modeling, 17 languages) xlm-mlm-100-1280 (Masked language modeling, 100 languages) These models are used for generic sentence representations, unlike the previous XLM checkpoints. BERT The following BERT models can be used for multilingual tasks: bert-base-multilingual-uncased (Masked language modeling + Next sentence prediction, 102 languages) bert-base-multilingual-cased (Masked language modeling + Next sentence prediction, 104 languages) These models do not require language embeddings during inference. They should identify the language from the context and infer accordingly. XLM-RoBERTa The following XLM-RoBERTa models can be used for multilingual tasks: xlm-roberta-base (Masked language modeling, 100 languages) xlm-roberta-large (Masked language modeling, 100 languages) XLM-RoBERTa was trained on 2.5TB of newly created and cleaned CommonCrawl data in 100 languages. It provides strong gains over previously released multilingual models like mBERT or XLM on downstream tasks like classification, sequence labeling, and question answering. M2M100 The following M2M100 models can be used for multilingual translation: facebook/m2m100_418M (Translation) facebook/m2m100_1.2B (Translation) In this example, load the facebook/m2m100_418M checkpoint to translate from Chinese to English. You can set the source language in the tokenizer: from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒." tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh") model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") Tokenize the text: encoded_zh = tokenizer(chinese_text, return_tensors="pt") M2M100 forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English: generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) 'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.' MBart The following MBart models can be used for multilingual translation: facebook/mbart-large-50-one-to-many-mmt (One-to-many multilingual machine translation, 50 languages) facebook/mbart-large-50-many-to-many-mmt (Many-to-many multilingual machine translation, 50 languages) facebook/mbart-large-50-many-to-one-mmt (Many-to-one multilingual machine translation, 50 languages) facebook/mbart-large-50 (Multilingual translation, 50 languages) facebook/mbart-large-cc25 In this example, load the facebook/mbart-large-50-many-to-many-mmt checkpoint to translate Finnish to English. You can set the source language in the tokenizer: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia." tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") Tokenize the text: encoded_en = tokenizer(en_text, return_tensors="pt") MBart forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English: generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) "Don't interfere with the wizard's affairs, because they are subtle, will soon get angry." If you are using the facebook/mbart-large-50-many-to-one-mmt checkpoint, you don't need to force the target language id as the first generated token otherwise the usage is the same.

What 🤗 Transformers can do 🤗 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! Audio 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. Audio classification 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") 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'}] Automatic speech recognition 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. 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.'} Computer vision One of the first and earliest successful computer vision tasks was recognizing images of zip code numbers using a convolutional neural network (CNN). 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: Use convolutions to learn the hierarchical features of an image from low-level features to high-level abstract things. 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. Image classification 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 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'} Object detection 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 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}}] Image segmentation 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. 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'} Depth estimation 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 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" ) Natural language processing 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! Text classification 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.) 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'}] Token classification In any NLP task, text is preprocessed by separating the sequence of text into individual words or subwords. These are known as tokens. 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). 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} Question answering 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 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 Summarization 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 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 .'}] Translation 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. 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="t5-small") translator(text) [{'translation_text': "Hugging Face est une tribune communautaire de l'apprentissage des machines."}] Language modeling 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 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 text = "Hugging Face is a community-based open-source 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.'}] Multimodal 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. Document question answering 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. from transformers import pipeline from PIL import Image import requests url = "https://datasets-server.huggingface.co/assets/hf-internal-testing/example-documents/--/hf-internal-testing--example-documents/test/2/image/image.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': 0.8531239628791809, '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, you'll learn how 🤗 Transformers work to solve these tasks.

Model outputs All models have outputs that are instances of subclasses of [~utils.ModelOutput]. Those are data structures containing all the information returned by the model, but that can also be used as tuples or dictionaries. Let's see how this looks in an example: thon from transformers import BertTokenizer, BertForSequenceClassification import torch tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") model = BertForSequenceClassification.from_pretrained("bert-base-uncased") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(**inputs, labels=labels) The outputs object is a [~modeling_outputs.SequenceClassifierOutput], as we can see in the documentation of that class below, it means it has an optional loss, a logits, an optional hidden_states and an optional attentions attribute. Here we have the loss since we passed along labels, but we don't have hidden_states and attentions because we didn't pass output_hidden_states=True or output_attentions=True. You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get None. Here for instance outputs.loss is the loss computed by the model, and outputs.attentions is None. When considering our outputs object as tuple, it only considers the attributes that don't have None values. Here for instance, it has two elements, loss then logits, so python outputs[:2] will return the tuple (outputs.loss, outputs.logits) for instance. When considering our outputs object as dictionary, it only considers the attributes that don't have None values. Here for instance, it has two keys that are loss and logits. We document here the generic model outputs that are used by more than one model type. Specific output types are documented on their corresponding model page. ModelOutput [[autodoc]] utils.ModelOutput - to_tuple BaseModelOutput [[autodoc]] modeling_outputs.BaseModelOutput BaseModelOutputWithPooling [[autodoc]] modeling_outputs.BaseModelOutputWithPooling BaseModelOutputWithCrossAttentions [[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions BaseModelOutputWithPoolingAndCrossAttentions [[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions BaseModelOutputWithPast [[autodoc]] modeling_outputs.BaseModelOutputWithPast BaseModelOutputWithPastAndCrossAttentions [[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions Seq2SeqModelOutput [[autodoc]] modeling_outputs.Seq2SeqModelOutput CausalLMOutput [[autodoc]] modeling_outputs.CausalLMOutput CausalLMOutputWithCrossAttentions [[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions CausalLMOutputWithPast [[autodoc]] modeling_outputs.CausalLMOutputWithPast MaskedLMOutput [[autodoc]] modeling_outputs.MaskedLMOutput Seq2SeqLMOutput [[autodoc]] modeling_outputs.Seq2SeqLMOutput NextSentencePredictorOutput [[autodoc]] modeling_outputs.NextSentencePredictorOutput SequenceClassifierOutput [[autodoc]] modeling_outputs.SequenceClassifierOutput Seq2SeqSequenceClassifierOutput [[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput MultipleChoiceModelOutput [[autodoc]] modeling_outputs.MultipleChoiceModelOutput TokenClassifierOutput [[autodoc]] modeling_outputs.TokenClassifierOutput QuestionAnsweringModelOutput [[autodoc]] modeling_outputs.QuestionAnsweringModelOutput Seq2SeqQuestionAnsweringModelOutput [[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput Seq2SeqSpectrogramOutput [[autodoc]] modeling_outputs.Seq2SeqSpectrogramOutput SemanticSegmenterOutput [[autodoc]] modeling_outputs.SemanticSegmenterOutput ImageClassifierOutput [[autodoc]] modeling_outputs.ImageClassifierOutput ImageClassifierOutputWithNoAttention [[autodoc]] modeling_outputs.ImageClassifierOutputWithNoAttention DepthEstimatorOutput [[autodoc]] modeling_outputs.DepthEstimatorOutput Wav2Vec2BaseModelOutput [[autodoc]] modeling_outputs.Wav2Vec2BaseModelOutput XVectorOutput [[autodoc]] modeling_outputs.XVectorOutput Seq2SeqTSModelOutput [[autodoc]] modeling_outputs.Seq2SeqTSModelOutput Seq2SeqTSPredictionOutput [[autodoc]] modeling_outputs.Seq2SeqTSPredictionOutput SampleTSPredictionOutput [[autodoc]] modeling_outputs.SampleTSPredictionOutput TFBaseModelOutput [[autodoc]] modeling_tf_outputs.TFBaseModelOutput TFBaseModelOutputWithPooling [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling TFBaseModelOutputWithPoolingAndCrossAttentions [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions TFBaseModelOutputWithPast [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast TFBaseModelOutputWithPastAndCrossAttentions [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions TFSeq2SeqModelOutput [[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput TFCausalLMOutput [[autodoc]] modeling_tf_outputs.TFCausalLMOutput TFCausalLMOutputWithCrossAttentions [[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions TFCausalLMOutputWithPast [[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast TFMaskedLMOutput [[autodoc]] modeling_tf_outputs.TFMaskedLMOutput TFSeq2SeqLMOutput [[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput TFNextSentencePredictorOutput [[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput TFSequenceClassifierOutput [[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput TFSeq2SeqSequenceClassifierOutput [[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput TFMultipleChoiceModelOutput [[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput TFTokenClassifierOutput [[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput TFQuestionAnsweringModelOutput [[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput TFSeq2SeqQuestionAnsweringModelOutput [[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput FlaxBaseModelOutput [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput FlaxBaseModelOutputWithPast [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast FlaxBaseModelOutputWithPooling [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling FlaxBaseModelOutputWithPastAndCrossAttentions [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions FlaxSeq2SeqModelOutput [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput FlaxCausalLMOutputWithCrossAttentions [[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions FlaxMaskedLMOutput [[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput FlaxSeq2SeqLMOutput [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput FlaxNextSentencePredictorOutput [[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput FlaxSequenceClassifierOutput [[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput FlaxSeq2SeqSequenceClassifierOutput [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput FlaxMultipleChoiceModelOutput [[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput FlaxTokenClassifierOutput [[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput FlaxQuestionAnsweringModelOutput [[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput FlaxSeq2SeqQuestionAnsweringModelOutput [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput

General Utilities This page lists all of Transformers general utility functions that are found in the file utils.py. Most of those are only useful if you are studying the general code in the library. Enums and namedtuples [[autodoc]] utils.ExplicitEnum [[autodoc]] utils.PaddingStrategy [[autodoc]] utils.TensorType Special Decorators [[autodoc]] utils.add_start_docstrings [[autodoc]] utils.add_start_docstrings_to_model_forward [[autodoc]] utils.add_end_docstrings [[autodoc]] utils.add_code_sample_docstrings [[autodoc]] utils.replace_return_docstrings Special Properties [[autodoc]] utils.cached_property Other Utilities [[autodoc]] utils._LazyModule

docstyle-ignore INSTALL_CONTENT = """ Transformers installation ! pip install transformers datasets To install from source instead of the last release, comment the command above and uncomment the following one. ! pip install git+https://github.com/huggingface/transformers.git """ notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] black_avoid_patterns = { "{processor_class}": "FakeProcessorClass", "{model_class}": "FakeModelClass", "{object_class}": "FakeObjectClass", }

Share a model The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We encourage you to consider sharing your model with the community to help others save time and resources. In this tutorial, you will learn two methods for sharing a trained or fine-tuned model on the Model Hub: Programmatically push your files to the Hub. Drag-and-drop your files to the Hub with the web interface. To share a model with the community, you need an account on huggingface.co. You can also join an existing organization or create a new one. Repository features Each repository on the Model Hub behaves like a typical GitHub repository. Our repositories offer versioning, commit history, and the ability to visualize differences. The Model Hub's built-in versioning is based on git and git-lfs. In other words, you can treat one model as one repository, enabling greater access control and scalability. Version control allows revisions, a method for pinning a specific version of a model with a commit hash, tag or branch. As a result, you can load a specific model version with the revision parameter: model = AutoModel.from_pretrained( "julien-c/EsperBERTo-small", revision="v2.0.1" # tag name, or branch name, or commit hash ) Files are also easily edited in a repository, and you can view the commit history as well as the difference: Setup Before sharing a model to the Hub, you will need your Hugging Face credentials. If you have access to a terminal, run the following command in the virtual environment where 🤗 Transformers is installed. This will store your access token in your Hugging Face cache folder (~/.cache/ by default): huggingface-cli login If you are using a notebook like Jupyter or Colaboratory, make sure you have the huggingface_hub library installed. This library allows you to programmatically interact with the Hub. pip install huggingface_hub Then use notebook_login to sign-in to the Hub, and follow the link here to generate a token to login with: from huggingface_hub import notebook_login notebook_login() Convert a model for all frameworks To ensure your model can be used by someone working with a different framework, we recommend you convert and upload your model with both PyTorch and TensorFlow checkpoints. While users are still able to load your model from a different framework if you skip this step, it will be slower because 🤗 Transformers will need to convert the checkpoint on-the-fly. Converting a checkpoint for another framework is easy. Make sure you have PyTorch and TensorFlow installed (see here for installation instructions), and then find the specific model for your task in the other framework. Specify from_tf=True to convert a checkpoint from TensorFlow to PyTorch: pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True) pt_model.save_pretrained("path/to/awesome-name-you-picked") `` </pt> <tf> Specifyfrom_pt=True` to convert a checkpoint from PyTorch to TensorFlow: tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True) Then you can save your new TensorFlow model with it's new checkpoint: tf_model.save_pretrained("path/to/awesome-name-you-picked") If a model is available in Flax, you can also convert a checkpoint from PyTorch to Flax: flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( "path/to/awesome-name-you-picked", from_pt=True ) Push a model during training Sharing a model to the Hub is as simple as adding an extra parameter or callback. Remember from the fine-tuning tutorial, the [TrainingArguments] class is where you specify hyperparameters and additional training options. One of these training options includes the ability to push a model directly to the Hub. Set push_to_hub=True in your [TrainingArguments]: training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True) Pass your training arguments as usual to [Trainer]: trainer = Trainer( model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset, compute_metrics=compute_metrics, ) After you fine-tune your model, call [~transformers.Trainer.push_to_hub] on [Trainer] to push the trained model to the Hub. 🤗 Transformers will even automatically add training hyperparameters, training results and framework versions to your model card! trainer.push_to_hub() `` </pt> <tf> Share a model to the Hub with [PushToHubCallback]. In the [PushToHubCallback`] function, add: An output directory for your model. A tokenizer. The hub_model_id, which is your Hub username and model name. from transformers import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model" ) Add the callback to fit, and 🤗 Transformers will push the trained model to the Hub: model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) Use the push_to_hub function You can also call push_to_hub directly on your model to upload it to the Hub. Specify your model name in push_to_hub: pt_model.push_to_hub("my-awesome-model") This creates a repository under your username with the model name my-awesome-model. Users can now load your model with the from_pretrained function: from transformers import AutoModel model = AutoModel.from_pretrained("your_username/my-awesome-model") If you belong to an organization and want to push your model under the organization name instead, just add it to the repo_id: pt_model.push_to_hub("my-awesome-org/my-awesome-model") The push_to_hub function can also be used to add other files to a model repository. For example, add a tokenizer to a model repository: tokenizer.push_to_hub("my-awesome-model") Or perhaps you'd like to add the TensorFlow version of your fine-tuned PyTorch model: tf_model.push_to_hub("my-awesome-model") Now when you navigate to the your Hugging Face profile, you should see your newly created model repository. Clicking on the Files tab will display all the files you've uploaded to the repository. For more details on how to create and upload files to a repository, refer to the Hub documentation here. Upload with the web interface Users who prefer a no-code approach are able to upload a model through the Hub's web interface. Visit huggingface.co/new to create a new repository: From here, add some information about your model: Select the owner of the repository. This can be yourself or any of the organizations you belong to. Pick a name for your model, which will also be the repository name. Choose whether your model is public or private. Specify the license usage for your model. Now click on the Files tab and click on the Add file button to upload a new file to your repository. Then drag-and-drop a file to upload and add a commit message. Add a model card To make sure users understand your model's capabilities, limitations, potential biases and ethical considerations, please add a model card to your repository. The model card is defined in the README.md file. You can add a model card by: Manually creating and uploading a README.md file. Clicking on the Edit model card button in your model repository. Take a look at the DistilBert model card for a good example of the type of information a model card should include. For more details about other options you can control in the README.md file such as a model's carbon footprint or widget examples, refer to the documentation here.

Text generation strategies Text generation is essential to many NLP tasks, such as open-ended text generation, summarization, translation, and more. It also plays a role in a variety of mixed-modality applications that have text as an output like speech-to-text and vision-to-text. Some of the models that can generate text include GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper. Check out a few examples that use [~transformers.generation_utils.GenerationMixin.generate] method to produce text outputs for different tasks: * Text summarization * Image captioning * Audio transcription Note that the inputs to the generate method depend on the model's modality. They are returned by the model's preprocessor class, such as AutoTokenizer or AutoProcessor. If a model's preprocessor creates more than one kind of input, pass all the inputs to generate(). You can learn more about the individual model's preprocessor in the corresponding model's documentation. The process of selecting output tokens to generate text is known as decoding, and you can customize the decoding strategy that the generate() method will use. Modifying a decoding strategy does not change the values of any trainable parameters. However, it can have a noticeable impact on the quality of the generated output. It can help reduce repetition in the text and make it more coherent. This guide describes: * default generation configuration * common decoding strategies and their main parameters * saving and sharing custom generation configurations with your fine-tuned model on 🤗 Hub Default text generation configuration A decoding strategy for a model is defined in its generation configuration. When using pre-trained models for inference within a [pipeline], the models call the PreTrainedModel.generate() method that applies a default generation configuration under the hood. The default configuration is also used when no custom configuration has been saved with the model. When you load a model explicitly, you can inspect the generation configuration that comes with it through model.generation_config: thon from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("distilgpt2") model.generation_config GenerationConfig { "_from_model_config": true, "bos_token_id": 50256, "eos_token_id": 50256, "transformers_version": "4.26.0.dev0" } Printing out the model.generation_config reveals only the values that are different from the default generation configuration, and does not list any of the default values. The default generation configuration limits the size of the output combined with the input prompt to a maximum of 20 tokens to avoid running into resource limitations. The default decoding strategy is greedy search, which is the simplest decoding strategy that picks a token with the highest probability as the next token. For many tasks and small output sizes this works well. However, when used to generate longer outputs, greedy search can start producing highly repetitive results. Customize text generation You can override any generation_config by passing the parameters and their values directly to the [generate] method: thon my_model.generate(**inputs, num_beams=4, do_sample=True) Even if the default decoding strategy mostly works for your task, you can still tweak a few things. Some of the commonly adjusted parameters include: max_new_tokens: the maximum number of tokens to generate. In other words, the size of the output sequence, not including the tokens in the prompt. num_beams: by specifying a number of beams higher than 1, you are effectively switching from greedy search to beam search. This strategy evaluates several hypotheses at each time step and eventually chooses the hypothesis that has the overall highest probability for the entire sequence. This has the advantage of identifying high-probability sequences that start with a lower probability initial tokens and would've been ignored by the greedy search. do_sample: if set to True, this parameter enables decoding strategies such as multinomial sampling, beam-search multinomial sampling, Top-K sampling and Top-p sampling. All these strategies select the next token from the probability distribution over the entire vocabulary with various strategy-specific adjustments. num_return_sequences: the number of sequence candidates to return for each input. This options is only available for the decoding strategies that support multiple sequence candidates, e.g. variations of beam search and sampling. Decoding strategies like greedy search and contrastive search return a single output sequence. Save a custom decoding strategy with your model If you would like to share your fine-tuned model with a specific generation configuration, you can: * Create a [GenerationConfig] class instance * Specify the decoding strategy parameters * Save your generation configuration with [GenerationConfig.save_pretrained], making sure to leave its config_file_name argument empty * Set push_to_hub to True to upload your config to the model's repo thon from transformers import AutoModelForCausalLM, GenerationConfig model = AutoModelForCausalLM.from_pretrained("my_account/my_model") generation_config = GenerationConfig( max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id ) generation_config.save_pretrained("my_account/my_model", push_to_hub=True) You can also store several generation configurations in a single directory, making use of the config_file_name argument in [GenerationConfig.save_pretrained]. You can later instantiate them with [GenerationConfig.from_pretrained]. This is useful if you want to store several generation configurations for a single model (e.g. one for creative text generation with sampling, and one for summarization with beam search). You must have the right Hub permissions to add configuration files to a model. thon from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig tokenizer = AutoTokenizer.from_pretrained("t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("t5-small") translation_generation_config = GenerationConfig( num_beams=4, early_stopping=True, decoder_start_token_id=0, eos_token_id=model.config.eos_token_id, pad_token=model.config.pad_token_id, ) translation_generation_config.save_pretrained("t5-small", "translation_generation_config.json", push_to_hub=True) You could then use the named generation config file to parameterize generation generation_config = GenerationConfig.from_pretrained("t5-small", "translation_generation_config.json") inputs = tokenizer("translate English to French: Configuration files are easy to use!", return_tensors="pt") outputs = model.generate(**inputs, generation_config=generation_config) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ['Les fichiers de configuration sont faciles à utiliser !'] Streaming The generate() supports streaming, through its streamer input. The streamer input is compatible any instance from a class that has the following methods: put() and end(). Internally, put() is used to push new tokens and end() is used to flag the end of text generation. The API for the streamer classes is still under development and may change in the future. In practice, you can craft your own streaming class for all sorts of purposes! We also have basic streaming classes ready for you to use. For example, you can use the [TextStreamer] class to stream the output of generate() into your screen, one word at a time: thon from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer tok = AutoTokenizer.from_pretrained("gpt2") model = AutoModelForCausalLM.from_pretrained("gpt2") inputs = tok(["An increasing sequence: one,"], return_tensors="pt") streamer = TextStreamer(tok) Despite returning the usual output, the streamer will also print the generated text to stdout. _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20) An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven, Decoding strategies Certain combinations of the generate() parameters, and ultimately generation_config, can be used to enable specific decoding strategies. If you are new to this concept, we recommend reading this blog post that illustrates how common decoding strategies work. Here, we'll show some of the parameters that control the decoding strategies and illustrate how you can use them. Greedy Search [generate] uses greedy search decoding by default so you don't have to pass any parameters to enable it. This means the parameters num_beams is set to 1 and do_sample=False. thon from transformers import AutoModelForCausalLM, AutoTokenizer prompt = "I look forward to" checkpoint = "distilgpt2" tokenizer = AutoTokenizer.from_pretrained(checkpoint) inputs = tokenizer(prompt, return_tensors="pt") model = AutoModelForCausalLM.from_pretrained(checkpoint) outputs = model.generate(**inputs) tokenizer.batch_decode(outputs, skip_special_tokens=True) ['I look forward to seeing you all again!\n\n\n\n\n\n\n\n\n\n\n'] Contrastive search The contrastive search decoding strategy was proposed in the 2022 paper A Contrastive Framework for Neural Text Generation. It demonstrates superior results for generating non-repetitive yet coherent long outputs. To learn how contrastive search works, check out this blog post. The two main parameters that enable and control the behavior of contrastive search are penalty_alpha and top_k: thon from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "gpt2-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) prompt = "Hugging Face Company is" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, penalty_alpha=0.6, top_k=4, max_new_tokens=100) tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Hugging Face Company is a family owned and operated business. \ We pride ourselves on being the best in the business and our customer service is second to none.\ \n\nIf you have any questions about our products or services, feel free to contact us at any time.\ We look forward to hearing from you!'] Multinomial sampling As opposed to greedy search that always chooses a token with the highest probability as the next token, multinomial sampling (also called ancestral sampling) randomly selects the next token based on the probability distribution over the entire vocabulary given by the model. Every token with a non-zero probability has a chance of being selected, thus reducing the risk of repetition. To enable multinomial sampling set do_sample=True and num_beams=1. thon from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "gpt2-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) prompt = "Today was an amazing day because" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, do_sample=True, num_beams=1, max_new_tokens=100) tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Today was an amazing day because we are now in the final stages of our trip to New York City which was very tough. \ It is a difficult schedule and a challenging part of the year but still worth it. I have been taking things easier and \ I feel stronger and more motivated to be out there on their tour. Hopefully, that experience is going to help them with \ their upcoming events which are currently scheduled in Australia.\n\nWe love that they are here. They want to make a \ name for themselves and become famous for what they'] Beam-search decoding Unlike greedy search, beam-search decoding keeps several hypotheses at each time step and eventually chooses the hypothesis that has the overall highest probability for the entire sequence. This has the advantage of identifying high-probability sequences that start with lower probability initial tokens and would've been ignored by the greedy search. To enable this decoding strategy, specify the num_beams (aka number of hypotheses to keep track of) that is greater than 1. thon from transformers import AutoModelForCausalLM, AutoTokenizer prompt = "It is astonishing how one can" checkpoint = "gpt2-medium" tokenizer = AutoTokenizer.from_pretrained(checkpoint) inputs = tokenizer(prompt, return_tensors="pt") model = AutoModelForCausalLM.from_pretrained(checkpoint) outputs = model.generate(**inputs, num_beams=5, max_new_tokens=50) tokenizer.batch_decode(outputs, skip_special_tokens=True) ['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of \ time."\n\nHe added: "I am very proud of the work I have been able to do in the last few years.\n\n"I have'] Beam-search multinomial sampling As the name implies, this decoding strategy combines beam search with multinomial sampling. You need to specify the num_beams greater than 1, and set do_sample=True to use this decoding strategy. thon from transformers import AutoTokenizer, AutoModelForSeq2SeqLM prompt = "translate English to German: The house is wonderful." checkpoint = "t5-small" tokenizer = AutoTokenizer.from_pretrained(checkpoint) inputs = tokenizer(prompt, return_tensors="pt") model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) outputs = model.generate(**inputs, num_beams=5, do_sample=True) tokenizer.decode(outputs[0], skip_special_tokens=True) 'Das Haus ist wunderbar.' Diverse beam search decoding The diverse beam search decoding strategy is an extension of the beam search strategy that allows for generating a more diverse set of beam sequences to choose from. To learn how it works, refer to Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models. This approach has three main parameters: num_beams, num_beam_groups, and diversity_penalty. The diversily penalty ensures the outputs are distinct across groups, and beam search is used within each group. thon from transformers import AutoTokenizer, AutoModelForSeq2SeqLM checkpoint = "google/pegasus-xsum" prompt = "The Permaculture Design Principles are a set of universal design principles \ that can be applied to any location, climate and culture, and they allow us to design \ the most efficient and sustainable human habitation and food production systems. \ Permaculture is a design system that encompasses a wide variety of disciplines, such \ as ecology, landscape design, environmental science and energy conservation, and the \ Permaculture design principles are drawn from these various disciplines. Each individual \ design principle itself embodies a complete conceptual framework based on sound \ scientific principles. When we bring all these separate principles together, we can \ create a design system that both looks at whole systems, the parts that these systems \ consist of, and how those parts interact with each other to create a complex, dynamic, \ living system. Each design principle serves as a tool that allows us to integrate all \ the separate parts of a design, referred to as elements, into a functional, synergistic, \ whole system, where the elements harmoniously interact and work together in the most \ efficient way possible." tokenizer = AutoTokenizer.from_pretrained(checkpoint) inputs = tokenizer(prompt, return_tensors="pt") model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) outputs = model.generate(**inputs, num_beams=5, num_beam_groups=5, max_new_tokens=30, diversity_penalty=1.0) tokenizer.decode(outputs[0], skip_special_tokens=True) 'The aim of this project is to create a new type of living system, one that is more sustainable and efficient than the current one.' This guide illustrates the main parameters that enable various decoding strategies. More advanced parameters exist for the [generate] method, which gives you even further control over the [generate] method's behavior. For the complete list of the available parameters, refer to the API documentation. Assisted Decoding Assisted decoding is a modification of the decoding strategies above that uses an assistant model with the same tokenizer (ideally a much smaller model) to greedily generate a few candidate tokens. The main model then validates the candidate tokens in a single forward pass, which speeds up the decoding process. Currently, only greedy search and sampling are supported with assisted decoding, and doesn't support batched inputs. To learn more about assisted decoding, check this blog post. To enable assisted decoding, set the assistant_model argument with a model. thon from transformers import AutoModelForCausalLM, AutoTokenizer prompt = "Alice and Bob" checkpoint = "EleutherAI/pythia-1.4b-deduped" assistant_checkpoint = "EleutherAI/pythia-160m-deduped" tokenizer = AutoTokenizer.from_pretrained(checkpoint) inputs = tokenizer(prompt, return_tensors="pt") model = AutoModelForCausalLM.from_pretrained(checkpoint) assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint) outputs = model.generate(**inputs, assistant_model=assistant_model) tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a'] When using assisted decoding with sampling methods, you can use the temperarure argument to control the randomness just like in multinomial sampling. However, in assisted decoding, reducing the temperature will help improving latency. thon from transformers import AutoModelForCausalLM, AutoTokenizer prompt = "Alice and Bob" checkpoint = "EleutherAI/pythia-1.4b-deduped" assistant_checkpoint = "EleutherAI/pythia-160m-deduped" tokenizer = AutoTokenizer.from_pretrained(checkpoint) inputs = tokenizer(prompt, return_tensors="pt") model = AutoModelForCausalLM.from_pretrained(checkpoint) assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint) outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5) tokenizer.batch_decode(outputs, skip_special_tokens=True) ["Alice and Bob are sitting on the sofa. Alice says, 'I'm going to my room"]

Logging 🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily. Currently the default verbosity of the library is WARNING. To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level. thon import transformers transformers.logging.set_verbosity_info() You can also use the environment variable TRANSFORMERS_VERBOSITY to override the default verbosity. You can set it to one of the following: debug, info, warning, error, critical. For example: TRANSFORMERS_VERBOSITY=error ./myprogram.py Additionally, some warnings can be disabled by setting the environment variable TRANSFORMERS_NO_ADVISORY_WARNINGS to a true value, like 1. This will disable any warning that is logged using [logger.warning_advice]. For example: TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py Here is an example of how to use the same logger as the library in your own module or script: thon from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger("transformers") logger.info("INFO") logger.warning("WARN") All the methods of this logging module are documented below, the main ones are [logging.get_verbosity] to get the current level of verbosity in the logger and [logging.set_verbosity] to set the verbosity to the level of your choice. In order (from the least verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are: transformers.logging.CRITICAL or transformers.logging.FATAL (int value, 50): only report the most critical errors. transformers.logging.ERROR (int value, 40): only report errors. transformers.logging.WARNING or transformers.logging.WARN (int value, 30): only reports error and warnings. This the default level used by the library. transformers.logging.INFO (int value, 20): reports error, warnings and basic information. transformers.logging.DEBUG (int value, 10): report all information. By default, tqdm progress bars will be displayed during model download. [logging.disable_progress_bar] and [logging.enable_progress_bar] can be used to suppress or unsuppress this behavior. Base setters [[autodoc]] logging.set_verbosity_error [[autodoc]] logging.set_verbosity_warning [[autodoc]] logging.set_verbosity_info [[autodoc]] logging.set_verbosity_debug Other functions [[autodoc]] logging.get_verbosity [[autodoc]] logging.set_verbosity [[autodoc]] logging.get_logger [[autodoc]] logging.enable_default_handler [[autodoc]] logging.disable_default_handler [[autodoc]] logging.enable_explicit_format [[autodoc]] logging.reset_format [[autodoc]] logging.enable_progress_bar [[autodoc]] logging.disable_progress_bar

Load pretrained instances with an AutoClass With so many different Transformer architectures, it can be challenging to create one for your checkpoint. As a part of 🤗 Transformers core philosophy to make the library easy, simple and flexible to use, an AutoClass automatically infer and load the correct architecture from a given checkpoint. The from_pretrained() method lets you quickly load a pretrained model for any architecture so you don't have to devote time and resources to train a model from scratch. Producing this type of checkpoint-agnostic code means if your code works for one checkpoint, it will work with another checkpoint - as long as it was trained for a similar task - even if the architecture is different. Remember, architecture refers to the skeleton of the model and checkpoints are the weights for a given architecture. For example, BERT is an architecture, while bert-base-uncased is a checkpoint. Model is a general term that can mean either architecture or checkpoint. In this tutorial, learn to: Load a pretrained tokenizer. Load a pretrained image processor Load a pretrained feature extractor. Load a pretrained processor. Load a pretrained model. AutoTokenizer Nearly every NLP task begins with a tokenizer. A tokenizer converts your input into a format that can be processed by the model. Load a tokenizer with [AutoTokenizer.from_pretrained]: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") Then tokenize your input as shown below: sequence = "In a hole in the ground there lived a hobbit." print(tokenizer(sequence)) {'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} AutoImageProcessor For vision tasks, an image processor processes the image into the correct input format. from transformers import AutoImageProcessor image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") AutoFeatureExtractor For audio tasks, a feature extractor processes the audio signal the correct input format. Load a feature extractor with [AutoFeatureExtractor.from_pretrained]: from transformers import AutoFeatureExtractor feature_extractor = AutoFeatureExtractor.from_pretrained( "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition" ) AutoProcessor Multimodal tasks require a processor that combines two types of preprocessing tools. For example, the LayoutLMV2 model requires an image processor to handle images and a tokenizer to handle text; a processor combines both of them. Load a processor with [AutoProcessor.from_pretrained]: from transformers import AutoProcessor processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased") AutoModel Finally, the AutoModelFor classes let you load a pretrained model for a given task (see here for a complete list of available tasks). For example, load a model for sequence classification with [AutoModelForSequenceClassification.from_pretrained]: from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased") Easily reuse the same checkpoint to load an architecture for a different task: from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased") For PyTorch models, the from_pretrained() method uses torch.load() which internally uses pickle and is known to be insecure. In general, never load a model that could have come from an untrusted source, or that could have been tampered with. This security risk is partially mitigated for public models hosted on the Hugging Face Hub, which are scanned for malware at each commit. See the Hub documentation for best practices like signed commit verification with GPG. TensorFlow and Flax checkpoints are not affected, and can be loaded within PyTorch architectures using the from_tf and from_flax kwargs for the from_pretrained method to circumvent this issue. Generally, we recommend using the AutoTokenizer class and the AutoModelFor class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next tutorial, learn how to use your newly loaded tokenizer, image processor, feature extractor and processor to preprocess a dataset for fine-tuning. Finally, the TFAutoModelFor classes let you load a pretrained model for a given task (see here for a complete list of available tasks). For example, load a model for sequence classification with [TFAutoModelForSequenceClassification.from_pretrained]: from transformers import TFAutoModelForSequenceClassification model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased") Easily reuse the same checkpoint to load an architecture for a different task: from transformers import TFAutoModelForTokenClassification model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased") Generally, we recommend using the AutoTokenizer class and the TFAutoModelFor class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next tutorial, learn how to use your newly loaded tokenizer, image processor, feature extractor and processor to preprocess a dataset for fine-tuning.

Utilities for Image Processors This page lists all the utility functions used by the image processors, mainly the functional transformations used to process the images. Most of those are only useful if you are studying the code of the image processors in the library. Image Transformations [[autodoc]] image_transforms.center_crop [[autodoc]] image_transforms.center_to_corners_format [[autodoc]] image_transforms.corners_to_center_format [[autodoc]] image_transforms.id_to_rgb [[autodoc]] image_transforms.normalize [[autodoc]] image_transforms.pad [[autodoc]] image_transforms.rgb_to_id [[autodoc]] image_transforms.rescale [[autodoc]] image_transforms.resize [[autodoc]] image_transforms.to_pil_image ImageProcessingMixin [[autodoc]] image_processing_utils.ImageProcessingMixin

Philosophy 🤗 Transformers is an opinionated library built for: machine learning researchers and educators seeking to use, study or extend large-scale Transformers models. hands-on practitioners who want to fine-tune those models or serve them in production, or both. engineers who just want to download a pretrained model and use it to solve a given machine learning task. The library was designed with two strong goals in mind: Be as easy and fast to use as possible: We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions, just three standard classes required to use each model: configuration, models, and a preprocessing class (tokenizer for NLP, image processor for vision, feature extractor for audio, and processor for multimodal inputs). All of these classes can be initialized in a simple and unified way from pretrained instances by using a common from_pretrained() method which downloads (if needed), caches and loads the related class instance and associated data (configurations' hyperparameters, tokenizers' vocabulary, and models' weights) from a pretrained checkpoint provided on Hugging Face Hub or your own saved checkpoint. On top of those three base classes, the library provides two APIs: [pipeline] for quickly using a model for inference on a given task and [Trainer] to quickly train or fine-tune a PyTorch model (all TensorFlow models are compatible with Keras.fit). As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to extend or build upon the library, just use regular Python, PyTorch, TensorFlow, Keras modules and inherit from the base classes of the library to reuse functionalities like model loading and saving. If you'd like to learn more about our coding philosophy for models, check out our Repeat Yourself blog post. Provide state-of-the-art models with performances as close as possible to the original models: We provide at least one example for each architecture which reproduces a result provided by the official authors of said architecture. The code is usually as close to the original code base as possible which means some PyTorch code may be not as pytorchic as it could be as a result of being converted TensorFlow code and vice versa. A few other goals: Expose the models' internals as consistently as possible: We give access, using a single API, to the full hidden-states and attention weights. The preprocessing classes and base model APIs are standardized to easily switch between models. Incorporate a subjective selection of promising tools for fine-tuning and investigating these models: A simple and consistent way to add new tokens to the vocabulary and embeddings for fine-tuning. Simple ways to mask and prune Transformer heads. Easily switch between PyTorch, TensorFlow 2.0 and Flax, allowing training with one framework and inference with another. Main concepts The library is built around three types of classes for each model: Model classes can be PyTorch models (torch.nn.Module), Keras models (tf.keras.Model) or JAX/Flax models (flax.linen.Module) that work with the pretrained weights provided in the library. Configuration classes store the hyperparameters required to build a model (such as the number of layers and hidden size). You don't always need to instantiate these yourself. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model). Preprocessing classes convert the raw data into a format accepted by the model. A tokenizer stores the vocabulary for each model and provide methods for encoding and decoding strings in a list of token embedding indices to be fed to a model. Image processors preprocess vision inputs, feature extractors preprocess audio inputs, and a processor handles multimodal inputs. All these classes can be instantiated from pretrained instances, saved locally, and shared on the Hub with three methods: from_pretrained() lets you instantiate a model, configuration, and preprocessing class from a pretrained version either provided by the library itself (the supported models can be found on the Model Hub) or stored locally (or on a server) by the user. save_pretrained() lets you save a model, configuration, and preprocessing class locally so that it can be reloaded using from_pretrained(). push_to_hub() lets you share a model, configuration, and a preprocessing class to the Hub, so it is easily accessible to everyone.

Benchmarks Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed and memory complexity of Transformer models. [[open-in-colab]] Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks. A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found here. How to benchmark 🤗 Transformers models The classes [PyTorchBenchmark] and [TensorFlowBenchmark] allow to flexibly benchmark 🤗 Transformers models. The benchmark classes allow us to measure the peak memory usage and required time for both inference and training. Hereby, inference is defined by a single forward pass, and training is defined by a single forward pass and backward pass. The benchmark classes [PyTorchBenchmark] and [TensorFlowBenchmark] expect an object of type [PyTorchBenchmarkArguments] and [TensorFlowBenchmarkArguments], respectively, for instantiation. [PyTorchBenchmarkArguments] and [TensorFlowBenchmarkArguments] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type bert-base-cased can be benchmarked. from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]) benchmark = PyTorchBenchmark(args) </pt> <tf>py from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments args = TensorFlowBenchmarkArguments( models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512] ) benchmark = TensorFlowBenchmark(args) Here, three arguments are given to the benchmark argument data classes, namely models, batch_sizes, and sequence_lengths. The argument models is required and expects a list of model identifiers from the model hub The list arguments batch_sizes and sequence_lengths define the size of the input_ids on which the model is benchmarked. There are many more parameters that can be configured via the benchmark argument data classes. For more detail on these one can either directly consult the files src/transformers/benchmark/benchmark_args_utils.py, src/transformers/benchmark/benchmark_args.py (for PyTorch) and src/transformers/benchmark/benchmark_args_tf.py (for Tensorflow). Alternatively, running the following shell commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow respectively. python examples/pytorch/benchmarking/run_benchmark.py --help An instantiated benchmark object can then simply be run by calling benchmark.run(). results = benchmark.run() print(results) ==================== INFERENCE - SPEED - RESULT ==================== Model Name Batch Size Seq Length Time in s bert-base-uncased 8 8 0.006 bert-base-uncased 8 32 0.006 bert-base-uncased 8 128 0.018 bert-base-uncased 8 512 0.088 ==================== INFERENCE - MEMORY - RESULT ==================== Model Name Batch Size Seq Length Memory in MB bert-base-uncased 8 8 1227 bert-base-uncased 8 32 1281 bert-base-uncased 8 128 1307 bert-base-uncased 8 512 1539 ==================== ENVIRONMENT INFORMATION ==================== transformers_version: 2.11.0 framework: PyTorch use_torchscript: False framework_version: 1.4.0 python_version: 3.6.10 system: Linux cpu: x86_64 architecture: 64bit date: 2020-06-29 time: 08:58:43.371351 fp16: False use_multiprocessing: True only_pretrain_model: False cpu_ram_mb: 32088 use_gpu: True num_gpus: 1 gpu: TITAN RTX gpu_ram_mb: 24217 gpu_power_watts: 280.0 gpu_performance_state: 2 use_tpu: False </pt> <tf>bash python examples/tensorflow/benchmarking/run_benchmark_tf.py --help An instantiated benchmark object can then simply be run by calling benchmark.run(). results = benchmark.run() print(results) results = benchmark.run() print(results) ==================== INFERENCE - SPEED - RESULT ==================== Model Name Batch Size Seq Length Time in s bert-base-uncased 8 8 0.005 bert-base-uncased 8 32 0.008 bert-base-uncased 8 128 0.022 bert-base-uncased 8 512 0.105 ==================== INFERENCE - MEMORY - RESULT ==================== Model Name Batch Size Seq Length Memory in MB bert-base-uncased 8 8 1330 bert-base-uncased 8 32 1330 bert-base-uncased 8 128 1330 bert-base-uncased 8 512 1770 ==================== ENVIRONMENT INFORMATION ==================== transformers_version: 2.11.0 framework: Tensorflow use_xla: False framework_version: 2.2.0 python_version: 3.6.10 system: Linux cpu: x86_64 architecture: 64bit date: 2020-06-29 time: 09:26:35.617317 fp16: False use_multiprocessing: True only_pretrain_model: False cpu_ram_mb: 32088 use_gpu: True num_gpus: 1 gpu: TITAN RTX gpu_ram_mb: 24217 gpu_power_watts: 280.0 gpu_performance_state: 2 use_tpu: False By default, the time and the required memory for inference are benchmarked. In the example output above the first two sections show the result corresponding to inference time and inference memory. In addition, all relevant information about the computing environment, e.g. the GPU type, the system, the library versions, etc are printed out in the third section under ENVIRONMENT INFORMATION. This information can optionally be saved in a .csv file when adding the argument save_to_csv=True to [PyTorchBenchmarkArguments] and [TensorFlowBenchmarkArguments] respectively. In this case, every section is saved in a separate .csv file. The path to each .csv file can optionally be defined via the argument data classes. Instead of benchmarking pre-trained models via their model identifier, e.g. bert-base-uncased, the user can alternatively benchmark an arbitrary configuration of any available model class. In this case, a list of configurations must be inserted with the benchmark args as follows. from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig args = PyTorchBenchmarkArguments( models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512] ) config_base = BertConfig() config_384_hid = BertConfig(hidden_size=384) config_6_lay = BertConfig(num_hidden_layers=6) benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay]) benchmark.run() ==================== INFERENCE - SPEED - RESULT ==================== Model Name Batch Size Seq Length Time in s bert-base 8 128 0.006 bert-base 8 512 0.006 bert-base 8 128 0.018 bert-base 8 512 0.088 bert-384-hid 8 8 0.006 bert-384-hid 8 32 0.006 bert-384-hid 8 128 0.011 bert-384-hid 8 512 0.054 bert-6-lay 8 8 0.003 bert-6-lay 8 32 0.004 bert-6-lay 8 128 0.009 bert-6-lay 8 512 0.044 ==================== INFERENCE - MEMORY - RESULT ==================== Model Name Batch Size Seq Length Memory in MB bert-base 8 8 1277 bert-base 8 32 1281 bert-base 8 128 1307 bert-base 8 512 1539 bert-384-hid 8 8 1005 bert-384-hid 8 32 1027 bert-384-hid 8 128 1035 bert-384-hid 8 512 1255 bert-6-lay 8 8 1097 bert-6-lay 8 32 1101 bert-6-lay 8 128 1127 bert-6-lay 8 512 1359 ==================== ENVIRONMENT INFORMATION ==================== transformers_version: 2.11.0 framework: PyTorch use_torchscript: False framework_version: 1.4.0 python_version: 3.6.10 system: Linux cpu: x86_64 architecture: 64bit date: 2020-06-29 time: 09:35:25.143267 fp16: False use_multiprocessing: True only_pretrain_model: False cpu_ram_mb: 32088 use_gpu: True num_gpus: 1 gpu: TITAN RTX gpu_ram_mb: 24217 gpu_power_watts: 280.0 gpu_performance_state: 2 use_tpu: False </pt> <tf>py from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig args = TensorFlowBenchmarkArguments( models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512] ) config_base = BertConfig() config_384_hid = BertConfig(hidden_size=384) config_6_lay = BertConfig(num_hidden_layers=6) benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay]) benchmark.run() ==================== INFERENCE - SPEED - RESULT ==================== Model Name Batch Size Seq Length Time in s bert-base 8 8 0.005 bert-base 8 32 0.008 bert-base 8 128 0.022 bert-base 8 512 0.106 bert-384-hid 8 8 0.005 bert-384-hid 8 32 0.007 bert-384-hid 8 128 0.018 bert-384-hid 8 512 0.064 bert-6-lay 8 8 0.002 bert-6-lay 8 32 0.003 bert-6-lay 8 128 0.0011 bert-6-lay 8 512 0.074 ==================== INFERENCE - MEMORY - RESULT ==================== Model Name Batch Size Seq Length Memory in MB bert-base 8 8 1330 bert-base 8 32 1330 bert-base 8 128 1330 bert-base 8 512 1770 bert-384-hid 8 8 1330 bert-384-hid 8 32 1330 bert-384-hid 8 128 1330 bert-384-hid 8 512 1540 bert-6-lay 8 8 1330 bert-6-lay 8 32 1330 bert-6-lay 8 128 1330 bert-6-lay 8 512 1540 ==================== ENVIRONMENT INFORMATION ==================== transformers_version: 2.11.0 framework: Tensorflow use_xla: False framework_version: 2.2.0 python_version: 3.6.10 system: Linux cpu: x86_64 architecture: 64bit date: 2020-06-29 time: 09:38:15.487125 fp16: False use_multiprocessing: True only_pretrain_model: False cpu_ram_mb: 32088 use_gpu: True num_gpus: 1 gpu: TITAN RTX gpu_ram_mb: 24217 gpu_power_watts: 280.0 gpu_performance_state: 2 use_tpu: False Again, inference time and required memory for inference are measured, but this time for customized configurations of the BertModel class. This feature can especially be helpful when deciding for which configuration the model should be trained. Benchmark best practices This section lists a couple of best practices one should be aware of when benchmarking a model. Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user specifies on which device the code should be run by setting the CUDA_VISIBLE_DEVICES environment variable in the shell, e.g. export CUDA_VISIBLE_DEVICES=0 before running the code. The option no_multi_processing should only be set to True for testing and debugging. To ensure accurate memory measurement it is recommended to run each memory benchmark in a separate process by making sure no_multi_processing is set to True. One should always state the environment information when sharing the results of a model benchmark. Results can vary heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very useful for the community. Sharing your benchmark Previously all available core models (10 at the time) have been benchmarked for inference time, across many different settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were done across CPUs (except for TensorFlow XLA) and GPUs. The approach is detailed in the following blogpost and the results are available here. With the new benchmark tools, it is easier than ever to share your benchmark results with the community PyTorch Benchmarking Results. TensorFlow Benchmarking Results.

Distributed training with 🤗 Accelerate As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU's on one machine or multiple GPU's across several machines. In this tutorial, learn how to customize your native PyTorch training loop to enable training in a distributed environment. Setup Get started by installing 🤗 Accelerate: pip install accelerate Then import and create an [~accelerate.Accelerator] object. The [~accelerate.Accelerator] will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device. from accelerate import Accelerator accelerator = Accelerator() Prepare to accelerate The next step is to pass all the relevant training objects to the [~accelerate.Accelerator.prepare] method. This includes your training and evaluation DataLoaders, a model and an optimizer: train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( train_dataloader, eval_dataloader, model, optimizer ) Backward The last addition is to replace the typical loss.backward() in your training loop with 🤗 Accelerate's [~accelerate.Accelerator.backward]method: for epoch in range(num_epochs): for batch in train_dataloader: outputs = model(**batch) loss = outputs.loss accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) As you can see in the following code, you only need to add four additional lines of code to your training loop to enable distributed training! + from accelerate import Accelerator from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler accelerator = Accelerator() model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) optimizer = AdamW(model.parameters(), lr=3e-5) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model.to(device) train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( train_dataloader, eval_dataloader, model, optimizer ) num_epochs = 3 num_training_steps = num_epochs * len(train_dataloader) lr_scheduler = get_scheduler( "linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps ) progress_bar = tqdm(range(num_training_steps)) model.train() for epoch in range(num_epochs): for batch in train_dataloader: outputs = model(**batch) loss = outputs.loss + accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) Train Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory. Train with a script If you are running your training from a script, run the following command to create and save a configuration file: accelerate config Then launch your training with: accelerate launch train.py Train with a notebook 🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to [~accelerate.notebook_launcher]: from accelerate import notebook_launcher notebook_launcher(training_function) For more information about 🤗 Accelerate and it's rich features, refer to the documentation.

Image captioning [[open-in-colab]] Image captioning is the task of predicting a caption for a given image. Common real world applications of it include aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning helps to improve content accessibility for people by describing images to them. This guide will show you how to: Fine-tune an image captioning model. Use the fine-tuned model for inference. Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate -q pip install jiwer -q We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in: thon from huggingface_hub import notebook_login notebook_login() Load the Pokémon BLIP captions dataset Use the 🤗 Dataset library to load a dataset that consists of {image-caption} pairs. To create your own image captioning dataset in PyTorch, you can follow this notebook. thon from datasets import load_dataset ds = load_dataset("lambdalabs/pokemon-blip-captions") ds bash DatasetDict({ train: Dataset({ features: ['image', 'text'], num_rows: 833 }) }) The dataset has two features, image and text. Many image captioning datasets contain multiple captions per image. In those cases, a common strategy is to randomly sample a caption amongst the available ones during training. Split the dataset’s train split into a train and test set with the [~datasets.Dataset.train_test_split] method: python ds = ds["train"].train_test_split(test_size=0.1) train_ds = ds["train"] test_ds = ds["test"] Let's visualize a couple of samples from the training set. thon from textwrap import wrap import matplotlib.pyplot as plt import numpy as np def plot_images(images, captions): plt.figure(figsize=(20, 20)) for i in range(len(images)): ax = plt.subplot(1, len(images), i + 1) caption = captions[i] caption = "\n".join(wrap(caption, 12)) plt.title(caption) plt.imshow(images[i]) plt.axis("off") sample_images_to_visualize = [np.array(train_ds[i]["image"]) for i in range(5)] sample_captions = [train_ds[i]["text"] for i in range(5)] plot_images(sample_images_to_visualize, sample_captions) Preprocess the dataset Since the dataset has two modalities (image and text), the pre-processing pipeline will preprocess images and the captions. To do so, load the processor class associated with the model you are about to fine-tune. thon from transformers import AutoProcessor checkpoint = "microsoft/git-base" processor = AutoProcessor.from_pretrained(checkpoint) The processor will internally pre-process the image (which includes resizing, and pixel scaling) and tokenize the caption. thon def transforms(example_batch): images = [x for x in example_batch["image"]] captions = [x for x in example_batch["text"]] inputs = processor(images=images, text=captions, padding="max_length") inputs.update({"labels": inputs["input_ids"]}) return inputs train_ds.set_transform(transforms) test_ds.set_transform(transforms) With the dataset ready, you can now set up the model for fine-tuning. Load a base model Load the "microsoft/git-base" into a AutoModelForCausalLM object. thon from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(checkpoint) Evaluate Image captioning models are typically evaluated with the Rouge Score or Word Error Rate. For this guide, you will use the Word Error Rate (WER). We use the 🤗 Evaluate library to do so. For potential limitations and other gotchas of the WER, refer to this guide. thon from evaluate import load import torch wer = load("wer") def compute_metrics(eval_pred): logits, labels = eval_pred predicted = logits.argmax(-1) decoded_labels = processor.batch_decode(labels, skip_special_tokens=True) decoded_predictions = processor.batch_decode(predicted, skip_special_tokens=True) wer_score = wer.compute(predictions=decoded_predictions, references=decoded_labels) return {"wer_score": wer_score} Train! Now, you are ready to start fine-tuning the model. You will use the 🤗 [Trainer] for this. First, define the training arguments using [TrainingArguments]. thon from transformers import TrainingArguments, Trainer model_name = checkpoint.split("/")[1] training_args = TrainingArguments( output_dir=f"{model_name}-pokemon", learning_rate=5e-5, num_train_epochs=50, fp16=True, per_device_train_batch_size=32, per_device_eval_batch_size=32, gradient_accumulation_steps=2, save_total_limit=3, evaluation_strategy="steps", eval_steps=50, save_strategy="steps", save_steps=50, logging_steps=50, remove_unused_columns=False, push_to_hub=True, label_names=["labels"], load_best_model_at_end=True, ) Then pass them along with the datasets and the model to 🤗 Trainer. python trainer = Trainer( model=model, args=training_args, train_dataset=train_ds, eval_dataset=test_ds, compute_metrics=compute_metrics, ) To start training, simply call [~Trainer.train] on the [Trainer] object. python trainer.train() You should see the training loss drop smoothly as training progresses. Once training is completed, share your model to the Hub with the [~Trainer.push_to_hub] method so everyone can use your model: python trainer.push_to_hub() Inference Take a sample image from test_ds to test the model. thon from PIL import Image import requests url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png" image = Image.open(requests.get(url, stream=True).raw) image Prepare image for the model. thon device = "cuda" if torch.cuda.is_available() else "cpu" inputs = processor(images=image, return_tensors="pt").to(device) pixel_values = inputs.pixel_values Call [generate] and decode the predictions. python generated_ids = model.generate(pixel_values=pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_caption) a drawing of a pink and blue pokemon Looks like the fine-tuned model generated a pretty good caption!

Model training anatomy To understand performance optimization techniques that one can apply to improve efficiency of model training speed and memory utilization, it's helpful to get familiar with how GPU is utilized during training, and how compute intensity varies depending on an operation performed. Let's start by exploring a motivating example of GPU utilization and the training run of a model. For the demonstration, we'll need to install a few libraries: pip install transformers datasets accelerate nvidia-ml-py3 The nvidia-ml-py3 library allows us to monitor the memory usage of the models from within Python. You might be familiar with the nvidia-smi command in the terminal - this library allows to access the same information in Python directly. Then, we create some dummy data: random token IDs between 100 and 30000 and binary labels for a classifier. In total, we get 512 sequences each with length 512 and store them in a [~datasets.Dataset] with PyTorch format. import numpy as np from datasets import Dataset seq_len, dataset_size = 512, 512 dummy_data = { "input_ids": np.random.randint(100, 30000, (dataset_size, seq_len)), "labels": np.random.randint(0, 1, (dataset_size)), } ds = Dataset.from_dict(dummy_data) ds.set_format("pt") To print summary statistics for the GPU utilization and the training run with the [Trainer] we define two helper functions: from pynvml import * def print_gpu_utilization(): nvmlInit() handle = nvmlDeviceGetHandleByIndex(0) info = nvmlDeviceGetMemoryInfo(handle) print(f"GPU memory occupied: {info.used//1024**2} MB.") def print_summary(result): print(f"Time: {result.metrics['train_runtime']:.2f}") print(f"Samples/second: {result.metrics['train_samples_per_second']:.2f}") print_gpu_utilization() Let's verify that we start with a free GPU memory: print_gpu_utilization() GPU memory occupied: 0 MB. That looks good: the GPU memory is not occupied as we would expect before we load any models. If that's not the case on your machine make sure to stop all processes that are using GPU memory. However, not all free GPU memory can be used by the user. When a model is loaded to the GPU also the kernels are loaded which can take up 1-2GB of memory. To see how much it is we load a tiny tensor into the GPU which triggers the kernels to be loaded as well. import torch torch.ones((1, 1)).to("cuda") print_gpu_utilization() GPU memory occupied: 1343 MB. We see that the kernels alone take up 1.3GB of GPU memory. Now let's see how much space the model uses. Load Model First, we load the bert-large-uncased model. We load the model weights directly to the GPU so that we can check how much space just the weights use. from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("bert-large-uncased").to("cuda") print_gpu_utilization() GPU memory occupied: 2631 MB. We can see that the model weights alone take up 1.3 GB of the GPU memory. The exact number depends on the specific GPU you are using. Note that on newer GPUs a model can sometimes take up more space since the weights are loaded in an optimized fashion that speeds up the usage of the model. Now we can also quickly check if we get the same result as with nvidia-smi CLI: nvidia-smi ```bash Tue Jan 11 08:58:05 2022 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 460.91.03 Driver Version: 460.91.03 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla V100-SXM2 On | 00000000:00:04.0 Off | 0 | | N/A 37C P0 39W / 300W | 2631MiB / 16160MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | 0 N/A N/A 3721 C nvs/codeparrot/bin/python 2629MiB | +-----------------------------------------------------------------------------+ We get the same number as before and you can also see that we are using a V100 GPU with 16GB of memory. So now we can start training the model and see how the GPU memory consumption changes. First, we set up a few standard training arguments: py default_args = { "output_dir": "tmp", "evaluation_strategy": "steps", "num_train_epochs": 1, "log_level": "error", "report_to": "none", } If you plan to run multiple experiments, in order to properly clear the memory between experiments, restart the Python kernel between experiments. Memory utilization at vanilla training Let's use the [Trainer] and train the model without using any GPU performance optimization techniques and a batch size of 4: from transformers import TrainingArguments, Trainer, logging logging.set_verbosity_error() training_args = TrainingArguments(per_device_train_batch_size=4, **default_args) trainer = Trainer(model=model, args=training_args, train_dataset=ds) result = trainer.train() print_summary(result) Time: 57.82 Samples/second: 8.86 GPU memory occupied: 14949 MB. We see that already a relatively small batch size almost fills up our GPU's entire memory. However, a larger batch size can often result in faster model convergence or better end performance. So ideally we want to tune the batch size to our model's needs and not to the GPU limitations. What's interesting is that we use much more memory than the size of the model. To understand a bit better why this is the case let's have look at a model's operations and memory needs. Anatomy of Model's Operations Transformers architecture includes 3 main groups of operations grouped below by compute-intensity. Tensor Contractions Linear layers and components of Multi-Head Attention all do batched matrix-matrix multiplications. These operations are the most compute-intensive part of training a transformer. Statistical Normalizations Softmax and layer normalization are less compute-intensive than tensor contractions, and involve one or more reduction operations, the result of which is then applied via a map. Element-wise Operators These are the remaining operators: biases, dropout, activations, and residual connections. These are the least compute-intensive operations. This knowledge can be helpful to know when analyzing performance bottlenecks. This summary is derived from Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020 Anatomy of Model's Memory We've seen that training the model uses much more memory than just putting the model on the GPU. This is because there are many components during training that use GPU memory. The components on GPU memory are the following: model weights optimizer states gradients forward activations saved for gradient computation temporary buffers functionality-specific memory A typical model trained in mixed precision with AdamW requires 18 bytes per model parameter plus activation memory. For inference there are no optimizer states and gradients, so we can subtract those. And thus we end up with 6 bytes per model parameter for mixed precision inference, plus activation memory. Let's look at the details. Model Weights: 4 bytes * number of parameters for fp32 training 6 bytes * number of parameters for mixed precision training (maintains a model in fp32 and one in fp16 in memory) Optimizer States: 8 bytes * number of parameters for normal AdamW (maintains 2 states) 2 bytes * number of parameters for 8-bit AdamW optimizers like bitsandbytes 4 bytes * number of parameters for optimizers like SGD with momentum (maintains only 1 state) Gradients 4 bytes * number of parameters for either fp32 or mixed precision training (gradients are always kept in fp32) Forward Activations size depends on many factors, the key ones being sequence length, hidden size and batch size. There are the input and output that are being passed and returned by the forward and the backward functions and the forward activations saved for gradient computation. Temporary Memory Additionally, there are all kinds of temporary variables which get released once the calculation is done, but in the moment these could require additional memory and could push to OOM. Therefore, when coding it's crucial to think strategically about such temporary variables and sometimes to explicitly free those as soon as they are no longer needed. Functionality-specific memory Then, your software could have special memory needs. For example, when generating text using beam search, the software needs to maintain multiple copies of inputs and outputs. forward vs backward Execution Speed For convolutions and linear layers there are 2x flops in the backward compared to the forward, which generally translates into ~2x slower (sometimes more, because sizes in the backward tend to be more awkward). Activations are usually bandwidth-limited, and it’s typical for an activation to have to read more data in the backward than in the forward (e.g. activation forward reads once, writes once, activation backward reads twice, gradOutput and output of the forward, and writes once, gradInput). As you can see, there are potentially a few places where we could save GPU memory or speed up operations. Now that you understand what affects GPU utilization and computation speed, refer to the Methods and tools for efficient training on a single GPU documentation page to learn about performance optimization techniques.

Run training on Amazon SageMaker The documentation has been moved to hf.co/docs/sagemaker. This page will be removed in transformers 5.0. Table of Content Train Hugging Face models on Amazon SageMaker with the SageMaker Python SDK Deploy Hugging Face models to Amazon SageMaker with the SageMaker Python SDK Frequently Asked Questions

Performance and Scalability Training large transformer models and deploying them to production present various challenges. During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment phase, the model can struggle to handle the required throughput in a production environment. This documentation aims to assist you in overcoming these challenges and finding the optimal setting for your use-case. The guides are divided into training and inference sections, as each comes with different challenges and solutions. Within each section you'll find separate guides for different hardware configurations, such as single GPU vs. multi-GPU for training or CPU vs. GPU for inference. Use this document as your starting point to navigate further to the methods that match your scenario. Training Training large transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where you have a single GPU. The methods that you can apply to improve training efficiency on a single GPU extend to other setups such as multiple GPU. However, there are also techniques that are specific to multi-GPU or CPU training. We cover them in separate sections. Methods and tools for efficient training on a single GPU: start here to learn common approaches that can help optimize GPU memory utilization, speed up the training, or both. Multi-GPU training section: explore this section to learn about further optimization methods that apply to a multi-GPU settings, such as data, tensor, and pipeline parallelism. CPU training section: learn about mixed precision training on CPU. Efficient Training on Multiple CPUs: learn about distributed CPU training. Training on TPU with TensorFlow: if you are new to TPUs, refer to this section for an opinionated introduction to training on TPUs and using XLA. Custom hardware for training: find tips and tricks when building your own deep learning rig. Hyperparameter Search using Trainer API Inference Efficient inference with large models in a production environment can be as challenging as training them. In the following sections we go through the steps to run inference on CPU and single/multi-GPU setups. Inference on a single CPU Inference on a single GPU Multi-GPU inference XLA Integration for TensorFlow Models Training and inference 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 Troubleshooting performance issues Contribute 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).

Tokenizer A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the Rust library 🤗 Tokenizers. The "Fast" implementations allows: a significant speed-up in particular when doing batched tokenization and additional methods to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token). The base classes [PreTrainedTokenizer] and [PreTrainedTokenizerFast] implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and "Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library (downloaded from HuggingFace's AWS S3 repository). They both rely on [~tokenization_utils_base.PreTrainedTokenizerBase] that contains the common methods, and [~tokenization_utils_base.SpecialTokensMixin]. [PreTrainedTokenizer] and [PreTrainedTokenizerFast] thus implement the main methods for using all the tokenizers: Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and encoding/decoding (i.e., tokenizing and converting to integers). Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece). Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the tokenizer for easy access and making sure they are not split during tokenization. [BatchEncoding] holds the output of the [~tokenization_utils_base.PreTrainedTokenizerBase]'s encoding methods (__call__, encode_plus and batch_encode_plus) and is derived from a Python dictionary. When the tokenizer is a pure python tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by these methods (input_ids, attention_mask). When the tokenizer is a "Fast" tokenizer (i.e., backed by HuggingFace tokenizers library), this class provides in addition several advanced alignment methods which can be used to map between the original string (character and words) and the token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding to a given token). PreTrainedTokenizer [[autodoc]] PreTrainedTokenizer - call - batch_decode - decode - encode - push_to_hub - all PreTrainedTokenizerFast The [PreTrainedTokenizerFast] depend on the tokenizers library. The tokenizers obtained from the 🤗 tokenizers library can be loaded very simply into 🤗 transformers. Take a look at the Using tokenizers from 🤗 tokenizers page to understand how this is done. [[autodoc]] PreTrainedTokenizerFast - call - batch_decode - decode - encode - push_to_hub - all BatchEncoding [[autodoc]] BatchEncoding

Text to speech [[open-in-colab]] Text-to-speech (TTS) is the task of creating natural-sounding speech from text, where the speech can be generated in multiple languages and for multiple speakers. The only text-to-speech model currently available in 🤗 Transformers is SpeechT5, though more will be added in the future. SpeechT5 is pre-trained on a combination of speech-to-text and text-to-speech data, allowing it to learn a unified space of hidden representations shared by both text and speech. This means that the same pre-trained model can be fine-tuned for different tasks. Furthermore, SpeechT5 supports multiple speakers through x-vector speaker embeddings. This guide illustrates how to: Fine-tune SpeechT5 that was originally trained on English speech on the Dutch (nl) language subset of the VoxPopuli dataset. Use your fine-tuned model for inference. Before you begin, make sure you have all the necessary libraries installed: pip install datasets soundfile speechbrain accelerate Install 🤗Transformers from source as not all the SpeechT5 features have been merged into an official release yet: pip install git+https://github.com/huggingface/transformers.git To follow this guide you will need a GPU. If you're working in a notebook, run the following line to check if a GPU is available: !nvidia-smi We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Load the dataset VoxPopuli is a large-scale multilingual speech corpus consisting of data sourced from 2009-2020 European Parliament event recordings. It contains labelled audio-transcription data for 15 European languages. In this guide, we are using the Dutch language subset, feel free to pick another subset. Note that VoxPopuli or any other automated speech recognition (ASR) dataset may not be the most suitable option for training TTS models. The features that make it beneficial for ASR, such as excessive background noise, are typically undesirable in TTS. However, finding top-quality, multilingual, and multi-speaker TTS datasets can be quite challenging. Let's load the data: from datasets import load_dataset, Audio dataset = load_dataset("facebook/voxpopuli", "nl", split="train") len(dataset) 20968 20968 examples should be sufficient for fine-tuning. SpeechT5 expects audio data to have a sampling rate of 16 kHz, so make sure the examples in the dataset meet this requirement: py dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) Preprocess the data Let's begin by defining the model checkpoint to use and loading the appropriate processor: from transformers import SpeechT5Processor checkpoint = "microsoft/speecht5_tts" processor = SpeechT5Processor.from_pretrained(checkpoint) Text cleanup for SpeechT5 tokenization Start by cleaning up the text data. You'll need the tokenizer part of the processor to process the text: tokenizer = processor.tokenizer The dataset examples contain raw_text and normalized_text features. When deciding which feature to use as the text input, consider that the SpeechT5 tokenizer doesn't have any tokens for numbers. In normalized_text the numbers are written out as text. Thus, it is a better fit, and we recommend using normalized_text as input text. Because SpeechT5 was trained on the English language, it may not recognize certain characters in the Dutch dataset. If left as is, these characters will be converted to <unk> tokens. However, in Dutch, certain characters like à are used to stress syllables. In order to preserve the meaning of the text, we can replace this character with a regular a. To identify unsupported tokens, extract all unique characters in the dataset using the SpeechT5Tokenizer which works with characters as tokens. To do this, write the extract_all_chars mapping function that concatenates the transcriptions from all examples into one string and converts it to a set of characters. Make sure to set batched=True and batch_size=-1 in dataset.map() so that all transcriptions are available at once for the mapping function. def extract_all_chars(batch): all_text = " ".join(batch["normalized_text"]) vocab = list(set(all_text)) return {"vocab": [vocab], "all_text": [all_text]} vocabs = dataset.map( extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=dataset.column_names, ) dataset_vocab = set(vocabs["vocab"][0]) tokenizer_vocab = {k for k, _ in tokenizer.get_vocab().items()} Now you have two sets of characters: one with the vocabulary from the dataset and one with the vocabulary from the tokenizer. To identify any unsupported characters in the dataset, you can take the difference between these two sets. The resulting set will contain the characters that are in the dataset but not in the tokenizer. dataset_vocab - tokenizer_vocab {' ', 'à', 'ç', 'è', 'ë', 'í', 'ï', 'ö', 'ü'} To handle the unsupported characters identified in the previous step, define a function that maps these characters to valid tokens. Note that spaces are already replaced by ▁ in the tokenizer and don't need to be handled separately. replacements = [ ("à", "a"), ("ç", "c"), ("è", "e"), ("ë", "e"), ("í", "i"), ("ï", "i"), ("ö", "o"), ("ü", "u"), ] def cleanup_text(inputs): for src, dst in replacements: inputs["normalized_text"] = inputs["normalized_text"].replace(src, dst) return inputs dataset = dataset.map(cleanup_text) Now that you have dealt with special characters in the text, it's time to shift focus to the audio data. Speakers The VoxPopuli dataset includes speech from multiple speakers, but how many speakers are represented in the dataset? To determine this, we can count the number of unique speakers and the number of examples each speaker contributes to the dataset. With a total of 20,968 examples in the dataset, this information will give us a better understanding of the distribution of speakers and examples in the data. from collections import defaultdict speaker_counts = defaultdict(int) for speaker_id in dataset["speaker_id"]: speaker_counts[speaker_id] += 1 By plotting a histogram you can get a sense of how much data there is for each speaker. import matplotlib.pyplot as plt plt.figure() plt.hist(speaker_counts.values(), bins=20) plt.ylabel("Speakers") plt.xlabel("Examples") plt.show() The histogram reveals that approximately one-third of the speakers in the dataset have fewer than 100 examples, while around ten speakers have more than 500 examples. To improve training efficiency and balance the dataset, we can limit the data to speakers with between 100 and 400 examples. def select_speaker(speaker_id): return 100 <= speaker_counts[speaker_id] <= 400 dataset = dataset.filter(select_speaker, input_columns=["speaker_id"]) Let's check how many speakers remain: len(set(dataset["speaker_id"])) 42 Let's see how many examples are left: len(dataset) 9973 You are left with just under 10,000 examples from approximately 40 unique speakers, which should be sufficient. Note that some speakers with few examples may actually have more audio available if the examples are long. However, determining the total amount of audio for each speaker requires scanning through the entire dataset, which is a time-consuming process that involves loading and decoding each audio file. As such, we have chosen to skip this step here. Speaker embeddings To enable the TTS model to differentiate between multiple speakers, you'll need to create a speaker embedding for each example. The speaker embedding is an additional input into the model that captures a particular speaker's voice characteristics. To generate these speaker embeddings, use the pre-trained spkrec-xvect-voxceleb model from SpeechBrain. Create a function create_speaker_embedding() that takes an input audio waveform and outputs a 512-element vector containing the corresponding speaker embedding. import os import torch from speechbrain.pretrained import EncoderClassifier spk_model_name = "speechbrain/spkrec-xvect-voxceleb" device = "cuda" if torch.cuda.is_available() else "cpu" speaker_model = EncoderClassifier.from_hparams( source=spk_model_name, run_opts={"device": device}, savedir=os.path.join("/tmp", spk_model_name), ) def create_speaker_embedding(waveform): with torch.no_grad(): speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform)) speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2) speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy() return speaker_embeddings It's important to note that the speechbrain/spkrec-xvect-voxceleb model was trained on English speech from the VoxCeleb dataset, whereas the training examples in this guide are in Dutch. While we believe that this model will still generate reasonable speaker embeddings for our Dutch dataset, this assumption may not hold true in all cases. For optimal results, we recommend training an X-vector model on the target speech first. This will ensure that the model is better able to capture the unique voice characteristics present in the Dutch language. Processing the dataset Finally, let's process the data into the format the model expects. Create a prepare_dataset function that takes in a single example and uses the SpeechT5Processor object to tokenize the input text and load the target audio into a log-mel spectrogram. It should also add the speaker embeddings as an additional input. def prepare_dataset(example): audio = example["audio"] example = processor( text=example["normalized_text"], audio_target=audio["array"], sampling_rate=audio["sampling_rate"], return_attention_mask=False, ) # strip off the batch dimension example["labels"] = example["labels"][0] # use SpeechBrain to obtain x-vector example["speaker_embeddings"] = create_speaker_embedding(audio["array"]) return example Verify the processing is correct by looking at a single example: processed_example = prepare_dataset(dataset[0]) list(processed_example.keys()) ['input_ids', 'labels', 'stop_labels', 'speaker_embeddings'] Speaker embeddings should be a 512-element vector: processed_example["speaker_embeddings"].shape (512,) The labels should be a log-mel spectrogram with 80 mel bins. import matplotlib.pyplot as plt plt.figure() plt.imshow(processed_example["labels"].T) plt.show() Side note: If you find this spectrogram confusing, it may be due to your familiarity with the convention of placing low frequencies at the bottom and high frequencies at the top of a plot. However, when plotting spectrograms as an image using the matplotlib library, the y-axis is flipped and the spectrograms appear upside down. Now apply the processing function to the entire dataset. This will take between 5 and 10 minutes. dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names) You'll see a warning saying that some examples in the dataset are longer than the maximum input length the model can handle (600 tokens). Remove those examples from the dataset. Here we go even further and to allow for larger batch sizes we remove anything over 200 tokens. def is_not_too_long(input_ids): input_length = len(input_ids) return input_length < 200 dataset = dataset.filter(is_not_too_long, input_columns=["input_ids"]) len(dataset) 8259 Next, create a basic train/test split: dataset = dataset.train_test_split(test_size=0.1) Data collator In order to combine multiple examples into a batch, you need to define a custom data collator. This collator will pad shorter sequences with padding tokens, ensuring that all examples have the same length. For the spectrogram labels, the padded portions are replaced with the special value -100. This special value instructs the model to ignore that part of the spectrogram when calculating the spectrogram loss. from dataclasses import dataclass from typing import Any, Dict, List, Union @dataclass class TTSDataCollatorWithPadding: processor: Any def call(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: input_ids = [{"input_ids": feature["input_ids"]} for feature in features] label_features = [{"input_values": feature["labels"]} for feature in features] speaker_features = [feature["speaker_embeddings"] for feature in features] # collate the inputs and targets into a batch batch = processor.pad(input_ids=input_ids, labels=label_features, return_tensors="pt") # replace padding with -100 to ignore loss correctly batch["labels"] = batch["labels"].masked_fill(batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100) # not used during fine-tuning del batch["decoder_attention_mask"] # round down target lengths to multiple of reduction factor if model.config.reduction_factor > 1: target_lengths = torch.tensor([len(feature["input_values"]) for feature in label_features]) target_lengths = target_lengths.new( [length - length % model.config.reduction_factor for length in target_lengths] ) max_length = max(target_lengths) batch["labels"] = batch["labels"][:, :max_length] # also add in the speaker embeddings batch["speaker_embeddings"] = torch.tensor(speaker_features) return batch In SpeechT5, the input to the decoder part of the model is reduced by a factor 2. In other words, it throws away every other timestep from the target sequence. The decoder then predicts a sequence that is twice as long. Since the original target sequence length may be odd, the data collator makes sure to round the maximum length of the batch down to be a multiple of 2. data_collator = TTSDataCollatorWithPadding(processor=processor) Train the model Load the pre-trained model from the same checkpoint as you used for loading the processor: from transformers import SpeechT5ForTextToSpeech model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint) The use_cache=True option is incompatible with gradient checkpointing. Disable it for training. model.config.use_cache = False Define the training arguments. Here we are not computing any evaluation metrics during the training process. Instead, we'll only look at the loss: thon from transformers import Seq2SeqTrainingArguments training_args = Seq2SeqTrainingArguments( output_dir="speecht5_finetuned_voxpopuli_nl", # change to a repo name of your choice per_device_train_batch_size=4, gradient_accumulation_steps=8, learning_rate=1e-5, warmup_steps=500, max_steps=4000, gradient_checkpointing=True, fp16=True, evaluation_strategy="steps", per_device_eval_batch_size=2, save_steps=1000, eval_steps=1000, logging_steps=25, report_to=["tensorboard"], load_best_model_at_end=True, greater_is_better=False, label_names=["labels"], push_to_hub=True, ) Instantiate the Trainer object and pass the model, dataset, and data collator to it. from transformers import Seq2SeqTrainer trainer = Seq2SeqTrainer( args=training_args, model=model, train_dataset=dataset["train"], eval_dataset=dataset["test"], data_collator=data_collator, tokenizer=processor, ) And with that, you're ready to start training! Training will take several hours. Depending on your GPU, it is possible that you will encounter a CUDA "out-of-memory" error when you start training. In this case, you can reduce the per_device_train_batch_size incrementally by factors of 2 and increase gradient_accumulation_steps by 2x to compensate. trainer.train() Push the final model to the 🤗 Hub: trainer.push_to_hub() Inference Great, now that you've fine-tuned a model, you can use it for inference! Load the model from the 🤗 Hub (make sure to use your account name in the following code snippet): model = SpeechT5ForTextToSpeech.from_pretrained("YOUR_ACCOUNT/speecht5_finetuned_voxpopuli_nl") Pick an example, here we'll take one from the test dataset. Obtain a speaker embedding. example = dataset["test"][304] speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0) Define some input text and tokenize it. text = "hallo allemaal, ik praat nederlands. groetjes aan iedereen!" Preprocess the input text: inputs = processor(text=text, return_tensors="pt") Create a spectrogram with your model: spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings) Visualize the spectrogram, if you'd like to: plt.figure() plt.imshow(spectrogram.T) plt.show() Finally, use the vocoder to turn the spectrogram into sound. with torch.no_grad(): speech = vocoder(spectrogram) from IPython.display import Audio Audio(speech.numpy(), rate=16000) In our experience, obtaining satisfactory results from this model can be challenging. The quality of the speaker embeddings appears to be a significant factor. Since SpeechT5 was pre-trained with English x-vectors, it performs best when using English speaker embeddings. If the synthesized speech sounds poor, try using a different speaker embedding. Increasing the training duration is also likely to enhance the quality of the results. Even so, the speech clearly is Dutch instead of English, and it does capture the voice characteristics of the speaker (compare to the original audio in the example). Another thing to experiment with is the model's configuration. For example, try using config.reduction_factor = 1 to see if this improves the results. Finally, it is essential to consider ethical considerations. Although TTS technology has numerous useful applications, it may also be used for malicious purposes, such as impersonating someone's voice without their knowledge or consent. Please use TTS judiciously and responsibly.

Inference on Specialized Hardware This document will be completed soon with information on how to infer on specialized hardware. In the meantime you can check out the guide for inference on CPUs.

Exporting 🤗 Transformers models to ONNX 🤗 Transformers provides a transformers.onnx package that enables you to convert model checkpoints to an ONNX graph by leveraging configuration objects. See the guide on exporting 🤗 Transformers models for more details. ONNX Configurations We provide three abstract classes that you should inherit from, depending on the type of model architecture you wish to export: Encoder-based models inherit from [~onnx.config.OnnxConfig] Decoder-based models inherit from [~onnx.config.OnnxConfigWithPast] Encoder-decoder models inherit from [~onnx.config.OnnxSeq2SeqConfigWithPast] OnnxConfig [[autodoc]] onnx.config.OnnxConfig OnnxConfigWithPast [[autodoc]] onnx.config.OnnxConfigWithPast OnnxSeq2SeqConfigWithPast [[autodoc]] onnx.config.OnnxSeq2SeqConfigWithPast ONNX Features Each ONNX configuration is associated with a set of features that enable you to export models for different types of topologies or tasks. FeaturesManager [[autodoc]] onnx.features.FeaturesManager

Instantiating a big model When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow from PyTorch is: Create your model with random weights. Load your pretrained weights. Put those pretrained weights in your random model. Step 1 and 2 both require a full version of the model in memory, which is not a problem in most cases, but if your model starts weighing several GigaBytes, those two copies can make you got our of RAM. Even worse, if you are using torch.distributed to launch a distributed training, each process will load the pretrained model and store these two copies in RAM. Note that the randomly created model is initialized with "empty" tensors, which take the space in memory without filling it (thus the random values are whatever was in this chunk of memory at a given time). The random initialization following the appropriate distribution for the kind of model/parameters instatiated (like a normal distribution for instance) is only performed after step 3 on the non-initialized weights, to be as fast as possible! In this guide, we explore the solutions Transformers offer to deal with this issue. Note that this is an area of active development, so the APIs explained here may change slightly in the future. Sharded checkpoints Since version 4.18.0, model checkpoints that end up taking more than 10GB of space are automatically sharded in smaller pieces. In terms of having one single checkpoint when you do model.save_pretrained(save_dir), you will end up with several partial checkpoints (each of which being of size < 10GB) and an index that maps parameter names to the files they are stored in. You can control the maximum size before sharding with the max_shard_size parameter, so for the sake of an example, we'll use a normal-size models with a small shard size: let's take a traditional BERT model. from transformers import AutoModel model = AutoModel.from_pretrained("bert-base-cased") If you save it using [~PreTrainedModel.save_pretrained], you will get a new folder with two files: the config of the model and its weights: import os import tempfile with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) print(sorted(os.listdir(tmp_dir))) ['config.json', 'pytorch_model.bin'] Now let's use a maximum shard size of 200MB: with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, max_shard_size="200MB") print(sorted(os.listdir(tmp_dir))) ['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json'] On top of the configuration of the model, we see three different weights files, and an index.json file which is our index. A checkpoint like this can be fully reloaded using the [~PreTrainedModel.from_pretrained] method: with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, max_shard_size="200MB") new_model = AutoModel.from_pretrained(tmp_dir) The main advantage of doing this for big models is that during step 2 of the workflow shown above, each shard of the checkpoint is loaded after the previous one, capping the memory usage in RAM to the model size plus the size of the biggest shard. Behind the scenes, the index file is used to determine which keys are in the checkpoint, and where the corresponding weights are stored. We can load that index like any json and get a dictionary: import json with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, max_shard_size="200MB") with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f: index = json.load(f) print(index.keys()) dict_keys(['metadata', 'weight_map']) The metadata just consists of the total size of the model for now. We plan to add other information in the future: index["metadata"] {'total_size': 433245184} The weights map is the main part of this index, which maps each parameter name (as usually found in a PyTorch model state_dict) to the file it's stored in: index["weight_map"] {'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin', 'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin', If you want to directly load such a sharded checkpoint inside a model without using [~PreTrainedModel.from_pretrained] (like you would do model.load_state_dict() for a full checkpoint) you should use [~modeling_utils.load_sharded_checkpoint]: from transformers.modeling_utils import load_sharded_checkpoint with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, max_shard_size="200MB") load_sharded_checkpoint(model, tmp_dir) Low memory loading Sharded checkpoints reduce the memory usage during step 2 of the workflow mentioned above, but in order to use that model in a low memory setting, we recommend leveraging our tools based on the Accelerate library. Please read the following guide for more information: Large model loading using Accelerate

Time Series Utilities This page lists all the utility functions and classes that can be used for Time Series based models. Most of those are only useful if you are studying the code of the time series models or you wish to add to the collection of distributional output classes. Distributional Output [[autodoc]] time_series_utils.NormalOutput [[autodoc]] time_series_utils.StudentTOutput [[autodoc]] time_series_utils.NegativeBinomialOutput

Trainer The [Trainer] class provides an API for feature-complete training in PyTorch for most standard use cases. It's used in most of the example scripts. Before instantiating your [Trainer], create a [TrainingArguments] to access all the points of customization during training. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. The [Trainer] contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods: get_train_dataloader -- Creates the training DataLoader. get_eval_dataloader -- Creates the evaluation DataLoader. get_test_dataloader -- Creates the test DataLoader. log -- Logs information on the various objects watching training. create_optimizer_and_scheduler -- Sets up the optimizer and learning rate scheduler if they were not passed at init. Note, that you can also subclass or override the create_optimizer and create_scheduler methods separately. create_optimizer -- Sets up the optimizer if it wasn't passed at init. create_scheduler -- Sets up the learning rate scheduler if it wasn't passed at init. compute_loss - Computes the loss on a batch of training inputs. training_step -- Performs a training step. prediction_step -- Performs an evaluation/test step. evaluate -- Runs an evaluation loop and returns metrics. predict -- Returns predictions (with metrics if labels are available) on a test set. The [Trainer] class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. When using it on your own model, make sure: your model always return tuples or subclasses of [~utils.ModelOutput]. your model can compute the loss if a labels argument is provided and that loss is returned as the first element of the tuple (if your model returns tuples) your model can accept multiple label arguments (use the label_names in your [TrainingArguments] to indicate their name to the [Trainer]) but none of them should be named "label". Here is an example of how to customize [Trainer] to use a weighted loss (useful when you have an unbalanced training set): thon from torch import nn from transformers import Trainer class CustomTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): labels = inputs.get("labels") # forward pass outputs = model(**inputs) logits = outputs.get("logits") # compute custom loss (suppose one has 3 labels with different weights) loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device)) loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1)) return (loss, outputs) if return_outputs else loss Another way to customize the training loop behavior for the PyTorch [Trainer] is to use callbacks that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms) and take decisions (like early stopping). Trainer [[autodoc]] Trainer - all Seq2SeqTrainer [[autodoc]] Seq2SeqTrainer - evaluate - predict TrainingArguments [[autodoc]] TrainingArguments - all Seq2SeqTrainingArguments [[autodoc]] Seq2SeqTrainingArguments - all Checkpoints By default, [Trainer] will save all checkpoints in the output_dir you set in the [TrainingArguments] you are using. Those will go in subfolder named checkpoint-xxx with xxx being the step at which the training was at. Resuming training from a checkpoint can be done when calling [Trainer.train] with either: resume_from_checkpoint=True which will resume training from the latest checkpoint resume_from_checkpoint=checkpoint_dir which will resume training from the specific checkpoint in the directory passed. In addition, you can easily save your checkpoints on the Model Hub when using push_to_hub=True. By default, all the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt the hub-strategy value of your [TrainingArguments] to either: "checkpoint": the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with trainer.train(resume_from_checkpoint="output_dir/last-checkpoint"). "all_checkpoints": all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository) Logging By default [Trainer] will use logging.INFO for the main process and logging.WARNING for the replicas if any. These defaults can be overridden to use any of the 5 logging levels with [TrainingArguments]'s arguments: log_level - for the main process log_level_replica - for the replicas Further, if [TrainingArguments]'s log_on_each_node is set to False only the main node will use the log level settings for its main process, all other nodes will use the log level settings for replicas. Note that [Trainer] is going to set transformers's log level separately for each node in its [Trainer.__init__]. So you may want to set this sooner (see the next example) if you tap into other transformers functionality before creating the [Trainer] object. Here is an example of how this can be used in an application: thon [] logger = logging.getLogger(name) Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) set the main code and the modules it uses to the same log-level according to the node log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) trainer = Trainer() And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated warnings you could run it as: my_app.py --log_level warning --log_level_replica error In the multi-node environment if you also don't want the logs to repeat for each node's main process, you will want to change the above to: my_app.py --log_level warning --log_level_replica error --log_on_each_node 0 and then only the main process of the first node will log at the "warning" level, and all other processes on the main node and all processes on other nodes will log at the "error" level. If you need your application to be as quiet as possible you could do: my_app.py --log_level error --log_level_replica error --log_on_each_node 0 (add --log_on_each_node 0 if on multi-node environment) Randomness When resuming from a checkpoint generated by [Trainer] all efforts are made to restore the python, numpy and pytorch RNG states to the same states as they were at the moment of saving that checkpoint, which should make the "stop and resume" style of training as close as possible to non-stop training. However, due to various default non-deterministic pytorch settings this might not fully work. If you want full determinism please refer to Controlling sources of randomness. As explained in the document, that some of those settings that make things deterministic (.e.g., torch.backends.cudnn.deterministic) may slow things down, therefore this can't be done by default, but you can enable those yourself if needed. Specific GPUs Selection Let's discuss how you can tell your program which GPUs are to be used and in what order. When using DistributedDataParallel to use only a subset of your GPUs, you simply specify the number of GPUs to use. For example, if you have 4 GPUs, but you wish to use the first 2 you can do: python -m torch.distributed.launch --nproc_per_node=2 trainer-program.py if you have either accelerate or deepspeed installed you can also accomplish the same by using one of: accelerate launch --num_processes 2 trainer-program.py deepspeed --num_gpus 2 trainer-program.py You don't need to use the Accelerate or the Deepspeed integration features to use these launchers. Until now you were able to tell the program how many GPUs to use. Now let's discuss how to select specific GPUs and control their order. The following environment variables help you control which GPUs to use and their order. CUDA_VISIBLE_DEVICES If you have multiple GPUs and you'd like to use only 1 or a few of those GPUs, set the environment variable CUDA_VISIBLE_DEVICES to a list of the GPUs to be used. For example, let's say you have 4 GPUs: 0, 1, 2 and 3. To run only on the physical GPUs 0 and 2, you can do: CUDA_VISIBLE_DEVICES=0,2 python -m torch.distributed.launch trainer-program.py So now pytorch will see only 2 GPUs, where your physical GPUs 0 and 2 are mapped to cuda:0 and cuda:1 correspondingly. You can even change their order: CUDA_VISIBLE_DEVICES=2,0 python -m torch.distributed.launch trainer-program.py Here your physical GPUs 0 and 2 are mapped to cuda:1 and cuda:0 correspondingly. The above examples were all for DistributedDataParallel use pattern, but the same method works for DataParallel as well: CUDA_VISIBLE_DEVICES=2,0 python trainer-program.py To emulate an environment without GPUs simply set this environment variable to an empty value like so: CUDA_VISIBLE_DEVICES= python trainer-program.py As with any environment variable you can, of course, export those instead of adding these to the command line, as in: export CUDA_VISIBLE_DEVICES=0,2 python -m torch.distributed.launch trainer-program.py but this approach can be confusing since you may forget you set up the environment variable earlier and not understand why the wrong GPUs are used. Therefore, it's a common practice to set the environment variable just for a specific run on the same command line as it's shown in most examples of this section. CUDA_DEVICE_ORDER There is an additional environment variable CUDA_DEVICE_ORDER that controls how the physical devices are ordered. The two choices are: ordered by PCIe bus IDs (matches nvidia-smi's order) - this is the default. export CUDA_DEVICE_ORDER=PCI_BUS_ID ordered by GPU compute capabilities export CUDA_DEVICE_ORDER=FASTEST_FIRST Most of the time you don't need to care about this environment variable, but it's very helpful if you have a lopsided setup where you have an old and a new GPUs physically inserted in such a way so that the slow older card appears to be first. One way to fix that is to swap the cards. But if you can't swap the cards (e.g., if the cooling of the devices gets impacted) then setting CUDA_DEVICE_ORDER=FASTEST_FIRST will always put the newer faster card first. It'll be somewhat confusing though since nvidia-smi will still report them in the PCIe order. The other solution to swapping the order is to use: export CUDA_VISIBLE_DEVICES=1,0 In this example we are working with just 2 GPUs, but of course the same would apply to as many GPUs as your computer has. Also if you do set this environment variable it's the best to set it in your ~/.bashrc file or some other startup config file and forget about it. Trainer Integrations The [Trainer] has been extended to support libraries that may dramatically improve your training time and fit much bigger models. Currently it supports third party solutions, DeepSpeed and PyTorch FSDP, which implement parts of the paper ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He. This provided support is new and experimental as of this writing. While the support for DeepSpeed and PyTorch FSDP is active and we welcome issues around it, we don't support the FairScale integration anymore since it has been integrated in PyTorch main (see the PyTorch FSDP integration) CUDA Extension Installation Notes As of this writing, Deepspeed require compilation of CUDA C++ code, before it can be used. While all installation issues should be dealt with through the corresponding GitHub Issues of Deepspeed, there are a few common issues that one may encounter while building any PyTorch extension that needs to build CUDA extensions. Therefore, if you encounter a CUDA-related build issue while doing the following: pip install deepspeed please, read the following notes first. In these notes we give examples for what to do when pytorch has been built with CUDA 10.2. If your situation is different remember to adjust the version number to the one you are after. Possible problem #1 While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA installed system-wide. For example, if you installed pytorch with cudatoolkit==10.2 in the Python environment, you also need to have CUDA 10.2 installed system-wide. The exact location may vary from system to system, but /usr/local/cuda-10.2 is the most common location on many Unix systems. When CUDA is correctly set up and added to the PATH environment variable, one can find the installation location by doing: which nvcc If you don't have CUDA installed system-wide, install it first. You will find the instructions by using your favorite search engine. For example, if you're on Ubuntu you may want to search for: ubuntu cuda 10.2 install. Possible problem #2 Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you may have: /usr/local/cuda-10.2 /usr/local/cuda-11.0 Now, in this situation you need to make sure that your PATH and LD_LIBRARY_PATH environment variables contain the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the last version was installed. If you encounter the problem, where the package build fails because it can't find the right CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned environment variables. First, you may look at their contents: echo $PATH echo $LD_LIBRARY_PATH so you get an idea of what is inside. It's possible that LD_LIBRARY_PATH is empty. PATH lists the locations of where executables can be found and LD_LIBRARY_PATH is for where shared libraries are to looked for. In both cases, earlier entries have priority over the later ones. : is used to separate multiple entries. Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by doing: export PATH=/usr/local/cuda-10.2/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH Note that we aren't overwriting the existing values, but prepending instead. Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do exist. lib64 sub-directory is where the various CUDA .so objects, like libcudart.so reside, it's unlikely that your system will have it named differently, but if it is adjust it to reflect your reality. Possible problem #3 Some older CUDA versions may refuse to build with newer compilers. For example, you my have gcc-9 but it wants gcc-7. There are various ways to go about it. If you can install the latest CUDA toolkit it typically should support the newer compiler. Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may already have it but it's not the default one, so the build system can't see it. If you have gcc-7 installed but the build system complains it can't find it, the following might do the trick: sudo ln -s /usr/bin/gcc-7 /usr/local/cuda-10.2/bin/gcc sudo ln -s /usr/bin/g++-7 /usr/local/cuda-10.2/bin/g++ Here, we are making a symlink to gcc-7 from /usr/local/cuda-10.2/bin/gcc and since /usr/local/cuda-10.2/bin/ should be in the PATH environment variable (see the previous problem's solution), it should find gcc-7 (and g++7) and then the build will succeed. As always make sure to edit the paths in the example to match your situation. PyTorch Fully Sharded Data parallel To accelerate training huge models on larger batch sizes, we can use a fully sharded data parallel model. This type of data parallel paradigm enables fitting more data and larger models by sharding the optimizer states, gradients and parameters. To read more about it and the benefits, check out the Fully Sharded Data Parallel blog. We have integrated the latest PyTorch's Fully Sharded Data Parallel (FSDP) training feature. All you need to do is enable it through the config. Required PyTorch version for FSDP support: PyTorch Nightly (or 1.12.0 if you read this after it has been released) as the model saving with FSDP activated is only available with recent fixes. Usage: Make sure you have added the distributed launcher -m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE if you haven't been using it already. Sharding Strategy: FULL_SHARD : Shards optimizer states + gradients + model parameters across data parallel workers/GPUs. For this, add --fsdp full_shard to the command line arguments. SHARD_GRAD_OP : Shards optimizer states + gradients across data parallel workers/GPUs. For this, add --fsdp shard_grad_op to the command line arguments. NO_SHARD : No sharding. For this, add --fsdp no_shard to the command line arguments. To offload the parameters and gradients to the CPU, add --fsdp "full_shard offload" or --fsdp "shard_grad_op offload" to the command line arguments. To automatically recursively wrap layers with FSDP using default_auto_wrap_policy, add --fsdp "full_shard auto_wrap" or --fsdp "shard_grad_op auto_wrap" to the command line arguments. To enable both CPU offloading and auto wrapping, add --fsdp "full_shard offload auto_wrap" or --fsdp "shard_grad_op offload auto_wrap" to the command line arguments. Remaining FSDP config is passed via --fsdp_config <path_to_fsdp_config.json>. It is either a location of FSDP json config file (e.g., fsdp_config.json) or an already loaded json file as dict. If auto wrapping is enabled, you can either use transformer based auto wrap policy or size based auto wrap policy. For transformer based auto wrap policy, it is recommended to specify fsdp_transformer_layer_cls_to_wrap in the config file. If not specified, the default value is model._no_split_modules when available. This specifies the list of transformer layer class name (case-sensitive) to wrap ,e.g, [BertLayer], [GPTJBlock], [T5Block] . This is important because submodules that share weights (e.g., embedding layer) should not end up in different FSDP wrapped units. Using this policy, wrapping happens for each block containing Multi-Head Attention followed by couple of MLP layers. Remaining layers including the shared embeddings are conveniently wrapped in same outermost FSDP unit. Therefore, use this for transformer based models. For size based auto wrap policy, please add fsdp_min_num_params in the config file. It specifies FSDP's minimum number of parameters for auto wrapping. fsdp_backward_prefetch can be specified in the config file. It controls when to prefetch next set of parameters. backward_pre and backward_pos are available options. For more information refer torch.distributed.fsdp.fully_sharded_data_parallel.BackwardPrefetch fsdp_forward_prefetch can be specified in the config file. It controls when to prefetch next set of parameters. If "True", FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. limit_all_gathers can be specified in the config file. If "True", FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers. Few caveats to be aware of - it is incompatible with generate, thus is incompatible with --predict_with_generate in all seq2seq/clm scripts (translation/summarization/clm etc.). Please refer issue #21667 PyTorch/XLA Fully Sharded Data parallel For all the TPU users, great news! PyTorch/XLA now supports FSDP. All the latest Fully Sharded Data Parallel (FSDP) training are supported. For more information refer to the Scaling PyTorch models on Cloud TPUs with FSDP and PyTorch/XLA implementation of FSDP All you need to do is enable it through the config. Required PyTorch/XLA version for FSDP support: >=2.0 Usage: Pass --fsdp "full shard" along with following changes to be made in --fsdp_config <path_to_fsdp_config.json>: - xla should be set to True to enable PyTorch/XLA FSDP. - xla_fsdp_settings The value is a dictionary which stores the XLA FSDP wrapping parameters. For a complete list of options, please see here. - xla_fsdp_grad_ckpt. When True, uses gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap. - You can either use transformer based auto wrap policy or size based auto wrap policy. - For transformer based auto wrap policy, it is recommended to specify fsdp_transformer_layer_cls_to_wrap in the config file. If not specified, the default value is model._no_split_modules when available. This specifies the list of transformer layer class name (case-sensitive) to wrap ,e.g, [BertLayer], [GPTJBlock], [T5Block] . This is important because submodules that share weights (e.g., embedding layer) should not end up in different FSDP wrapped units. Using this policy, wrapping happens for each block containing Multi-Head Attention followed by couple of MLP layers. Remaining layers including the shared embeddings are conveniently wrapped in same outermost FSDP unit. Therefore, use this for transformer based models. - For size based auto wrap policy, please add fsdp_min_num_params in the config file. It specifies FSDP's minimum number of parameters for auto wrapping. Using Trainer for accelerated PyTorch Training on Mac With PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac. Apple's Metal Performance Shaders (MPS) as a backend for PyTorch enables this and can be used via the new "mps" device. This will map computational graphs and primitives on the MPS Graph framework and tuned kernels provided by MPS. For more information please refer official documents Introducing Accelerated PyTorch Training on Mac and MPS BACKEND. We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing) on your MacOS machine. It has major fixes related to model correctness and performance improvements for transformer based models. Please refer to https://github.com/pytorch/pytorch/issues/82707 for more details. Benefits of Training and Inference using Apple Silicon Chips Enables users to train larger networks or batch sizes locally Reduces data retrieval latency and provides the GPU with direct access to the full memory store due to unified memory architecture. Therefore, improving end-to-end performance. Reduces costs associated with cloud-based development or the need for additional local GPUs. Pre-requisites: To install torch with mps support, please follow this nice medium article GPU-Acceleration Comes to PyTorch on M1 Macs. Usage: mps device will be used by default if available similar to the way cuda device is used. Therefore, no action from user is required. For example, you can run the official Glue text classififcation task (from the root folder) using Apple Silicon GPU with below command: ```bash export TASK_NAME=mrpc python examples/pytorch/text-classification/run_glue.py \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ \ --overwrite_output_dir A few caveats to be aware of Some PyTorch operations have not been implemented in mps and will throw an error. One way to get around that is to set the environment variable PYTORCH_ENABLE_MPS_FALLBACK=1, which will fallback to CPU for these operations. It still throws a UserWarning however. Distributed setups gloo and nccl are not working with mps device. This means that currently only single GPU of mps device type can be used. Finally, please, remember that, 🤗 Trainer only integrates MPS backend, therefore if you have any problems or questions with regards to MPS backend usage, please, file an issue with PyTorch GitHub. Using Accelerate Launcher with Trainer Accelerate now powers Trainer. In terms of what users should expect: - They can keep using the Trainer ingterations such as FSDP, DeepSpeed vis trainer arguments without any changes on their part. - They can now use Accelerate Launcher with Trainer (recommended). Steps to use Accelerate Launcher with Trainer: 1. Make sure 🤗 Accelerate is installed, you can't use the Trainer without it anyway. If not pip install accelerate. You may also need to update your version of Accelerate: pip install accelerate --upgrade 2. Run accelerate config and fill the questionnaire. Below are example accelerate configs: a. DDP Multi-node Multi-GPU config: yaml compute_environment: LOCAL_MACHINE distributed_type: MULTI_GPU downcast_bf16: 'no' gpu_ids: all machine_rank: 0 #change rank as per the node main_process_ip: 192.168.20.1 main_process_port: 9898 main_training_function: main mixed_precision: fp16 num_machines: 2 num_processes: 8 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false b. FSDP config: yaml compute_environment: LOCAL_MACHINE distributed_type: FSDP downcast_bf16: 'no' fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_forward_prefetch: true fsdp_offload_params: false fsdp_sharding_strategy: 1 fsdp_state_dict_type: FULL_STATE_DICT fsdp_sync_module_states: true fsdp_transformer_layer_cls_to_wrap: BertLayer fsdp_use_orig_params: true machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 2 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false c. DeepSpeed config pointing to a file: yaml compute_environment: LOCAL_MACHINE deepspeed_config: deepspeed_config_file: /home/user/configs/ds_zero3_config.json zero3_init_flag: true distributed_type: DEEPSPEED downcast_bf16: 'no' machine_rank: 0 main_training_function: main num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false d. DeepSpeed config using accelerate plugin: yaml compute_environment: LOCAL_MACHINE deepspeed_config: gradient_accumulation_steps: 1 gradient_clipping: 0.7 offload_optimizer_device: cpu offload_param_device: cpu zero3_init_flag: true zero_stage: 2 distributed_type: DEEPSPEED downcast_bf16: 'no' machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false Run the Trainer script with args other than the ones handled above by accelerate config or launcher args. Below is an example to run run_glue.py using accelerate launcher with FSDP config from above. ```bash cd transformers accelerate launch \ ./examples/pytorch/text-classification/run_glue.py \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 16 \ --learning_rate 5e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ \ --overwrite_output_dir You can also directly use the cmd args for accelerate launch. Above example would map to: ```bash cd transformers accelerate launch --num_processes=2 \ --use_fsdp \ --mixed_precision=bf16 \ --fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP \ --fsdp_transformer_layer_cls_to_wrap="BertLayer" \ --fsdp_sharding_strategy=1 \ --fsdp_state_dict_type=FULL_STATE_DICT \ ./examples/pytorch/text-classification/run_glue.py --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 16 \ --learning_rate 5e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ \ --overwrite_output_dir For more information, please refer the 🤗 Accelerate CLI guide: Launching your 🤗 Accelerate scripts. Sections that were moved: [ DeepSpeed | Installation | Deployment with multiple GPUs | Deployment with one GPU | Deployment in Notebooks | Configuration | Passing Configuration | Shared Configuration | ZeRO | ZeRO-2 Config | ZeRO-3 Config | NVMe Support | ZeRO-2 vs ZeRO-3 Performance | ZeRO-2 Example | ZeRO-3 Example | Optimizer | Scheduler | fp32 Precision | Automatic Mixed Precision | Batch Size | Gradient Accumulation | Gradient Clipping | Getting The Model Weights Out ]

Export to ONNX Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into a serialized format that can be loaded and executed on specialized runtimes and hardware. 🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or TensorFlow to serialized formats such as ONNX and TFLite through its exporters module. 🤗 Optimum also provides a set of performance optimization tools to train and run models on targeted hardware with maximum efficiency. This guide demonstrates how you can export 🤗 Transformers models to ONNX with 🤗 Optimum, for the guide on exporting models to TFLite, please refer to the Export to TFLite page. Export to ONNX ONNX (Open Neural Network eXchange) is an open standard that defines a common set of operators and a common file format to represent deep learning models in a wide variety of frameworks, including PyTorch and TensorFlow. When a model is exported to the ONNX format, these operators are used to construct a computational graph (often called an intermediate representation) which represents the flow of data through the neural network. By exposing a graph with standardized operators and data types, ONNX makes it easy to switch between frameworks. For example, a model trained in PyTorch can be exported to ONNX format and then imported in TensorFlow (and vice versa). Once exported to ONNX format, a model can be: - optimized for inference via techniques such as graph optimization and quantization. - run with ONNX Runtime via ORTModelForXXX classes, which follow the same AutoModel API as the one you are used to in 🤗 Transformers. - run with optimized inference pipelines, which has the same API as the [pipeline] function in 🤗 Transformers. 🤗 Optimum provides support for the ONNX export by leveraging configuration objects. These configuration objects come ready-made for a number of model architectures, and are designed to be easily extendable to other architectures. For the list of ready-made configurations, please refer to 🤗 Optimum documentation. There are two ways to export a 🤗 Transformers model to ONNX, here we show both: export with 🤗 Optimum via CLI. export with 🤗 Optimum with optimum.onnxruntime. Exporting a 🤗 Transformers model to ONNX with CLI To export a 🤗 Transformers model to ONNX, first install an extra dependency: pip install optimum[exporters] To check out all available arguments, refer to the 🤗 Optimum docs, or view help in command line: optimum-cli export onnx --help To export a model's checkpoint from the 🤗 Hub, for example, distilbert-base-uncased-distilled-squad, run the following command: optimum-cli export onnx --model distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/ You should see the logs indicating progress and showing where the resulting model.onnx is saved, like this: Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx -[✓] ONNX model output names match reference model (start_logits, end_logits) - Validating ONNX Model output "start_logits": -[✓] (2, 16) matches (2, 16) -[✓] all values close (atol: 0.0001) - Validating ONNX Model output "end_logits": -[✓] (2, 16) matches (2, 16) -[✓] all values close (atol: 0.0001) The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_onnx The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you saved both the model's weights and tokenizer files in the same directory (local_path). When using CLI, pass the local_path to the model argument instead of the checkpoint name on 🤗 Hub and provide the --task argument. You can review the list of supported tasks in the 🤗 Optimum documentation. If task argument is not provided, it will default to the model architecture without any task specific head. optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/ The resulting model.onnx file can then be run on one of the many accelerators that support the ONNX standard. For example, we can load and run the model with ONNX Runtime as follows: thon from transformers import AutoTokenizer from optimum.onnxruntime import ORTModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("distilbert_base_uncased_squad_onnx") model = ORTModelForQuestionAnswering.from_pretrained("distilbert_base_uncased_squad_onnx") inputs = tokenizer("What am I using?", "Using DistilBERT with ONNX Runtime!", return_tensors="pt") outputs = model(**inputs) The process is identical for TensorFlow checkpoints on the Hub. For instance, here's how you would export a pure TensorFlow checkpoint from the Keras organization: optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/ Exporting a 🤗 Transformers model to ONNX with optimum.onnxruntime Alternative to CLI, you can export a 🤗 Transformers model to ONNX programmatically like so: thon from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import AutoTokenizer model_checkpoint = "distilbert_base_uncased_squad" save_directory = "onnx/" Load a model from transformers and export it to ONNX ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) Save the onnx model and tokenizer ort_model.save_pretrained(save_directory) tokenizer.save_pretrained(save_directory) Exporting a model for an unsupported architecture If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is supported in optimum.exporters.onnx, and if it is not, contribute to 🤗 Optimum directly. Exporting a model with transformers.onnx tranformers.onnx is no longer maintained, please export models with 🤗 Optimum as described above. This section will be removed in the future versions. To export a 🤗 Transformers model to ONNX with tranformers.onnx, install extra dependencies: pip install transformers[onnx] Use transformers.onnx package as a Python module to export a checkpoint using a ready-made configuration: python -m transformers.onnx --model=distilbert-base-uncased onnx/ This exports an ONNX graph of the checkpoint defined by the --model argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally. The resulting model.onnx file can then be run on one of the many accelerators that support the ONNX standard. For example, load and run the model with ONNX Runtime as follows: thon from transformers import AutoTokenizer from onnxruntime import InferenceSession tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") session = InferenceSession("onnx/model.onnx") ONNX Runtime expects NumPy arrays as input inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np") outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs)) The required output names (like ["last_hidden_state"]) can be obtained by taking a look at the ONNX configuration of each model. For example, for DistilBERT we have: thon from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig config = DistilBertConfig() onnx_config = DistilBertOnnxConfig(config) print(list(onnx_config.outputs.keys())) ["last_hidden_state"] The process is identical for TensorFlow checkpoints on the Hub. For example, export a pure TensorFlow checkpoint like so: python -m transformers.onnx --model=keras-io/transformers-qa onnx/ To export a model that's stored locally, save the model's weights and tokenizer files in the same directory (e.g. local-pt-checkpoint), then export it to ONNX by pointing the --model argument of the transformers.onnx package to the desired directory: python -m transformers.onnx --model=local-pt-checkpoint onnx/

🤗 Transformers Notebooks You can find here a list of the official notebooks provided by Hugging Face. Also, we would like to list here interesting content created by the community. If you wrote some notebook(s) leveraging 🤗 Transformers and would like be listed here, please open a Pull Request so it can be included under the Community notebooks. Hugging Face's notebooks 🤗 Documentation notebooks You can open any page of the documentation as a notebook in Colab (there is a button directly on said pages) but they are also listed here if you need them: | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | Quicktour of the library | A presentation of the various APIs in Transformers || | | Summary of the tasks | How to run the models of the Transformers library task by task || | | Preprocessing data | How to use a tokenizer to preprocess your data || | | Fine-tuning a pretrained model | How to use the Trainer to fine-tune a pretrained model || | | Summary of the tokenizers | The differences between the tokenizers algorithm || | | Multilingual models | How to use the multilingual models of the library || | PyTorch Examples Natural Language Processing[[pytorch-nlp]] | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | Train your tokenizer | How to train and use your very own tokenizer || | | Train your language model | How to easily start using transformers || | | How to fine-tune a model on text classification| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | | | | How to fine-tune a model on language modeling| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | | | | How to fine-tune a model on token classification| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | | | | How to fine-tune a model on question answering| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | | | | How to fine-tune a model on multiple choice| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | | | | How to fine-tune a model on translation| Show how to preprocess the data and fine-tune a pretrained model on WMT. | | | | How to fine-tune a model on summarization| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | | | | How to train a language model from scratch| Highlight all the steps to effectively train Transformer model on custom data | | | | How to generate text| How to use different decoding methods for language generation with transformers | | | | How to generate text (with constraints)| How to guide language generation with user-provided constraints | | | | Reformer| How Reformer pushes the limits of language modeling | | | Computer Vision[[pytorch-cv]] | Notebook | Description | | | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:| | How to fine-tune a model on image classification (Torchvision) | Show how to preprocess the data using Torchvision and fine-tune any pretrained Vision model on Image Classification | | | | How to fine-tune a model on image classification (Albumentations) | Show how to preprocess the data using Albumentations and fine-tune any pretrained Vision model on Image Classification | | | | How to fine-tune a model on image classification (Kornia) | Show how to preprocess the data using Kornia and fine-tune any pretrained Vision model on Image Classification | | | | How to perform zero-shot object detection with OWL-ViT | Show how to perform zero-shot object detection on images with text queries | | | | How to fine-tune an image captioning model | Show how to fine-tune BLIP for image captioning on a custom dataset | | | | How to build an image similarity system with Transformers | Show how to build an image similarity system | | | | How to fine-tune a SegFormer model on semantic segmentation | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | | | | How to fine-tune a VideoMAE model on video classification | Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification | | | Audio[[pytorch-audio]] | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | How to fine-tune a speech recognition model in English| Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT | | | | How to fine-tune a speech recognition model in any language| Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice | | | | How to fine-tune a model on audio classification| Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting | | | Biological Sequences[[pytorch-bio]] | Notebook | Description | | | |:----------|:----------------------------------------------------------------------------------------|:-------------|------:| | How to fine-tune a pre-trained protein model | See how to tokenize proteins and fine-tune a large pre-trained protein "language" model | | | | How to generate protein folds | See how to go from protein sequence to a full protein model and PDB file | | | | How to fine-tune a Nucleotide Transformer model | See how to tokenize DNA and fine-tune a large pre-trained DNA "language" model | | | | Fine-tune a Nucleotide Transformer model with LoRA | Train even larger DNA models in a memory-efficient way | | | Other modalities[[pytorch-other]] | Notebook | Description | | | |:----------|:----------------------------------------------------------------------------------------|:-------------|------:| | Probabilistic Time Series Forecasting | See how to train Time Series Transformer on a custom dataset | | | Utility notebooks[[pytorch-utility]] | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | How to export model to ONNX| Highlight how to export and run inference workloads through ONNX | | How to use Benchmarks| How to benchmark models with transformers | | | TensorFlow Examples Natural Language Processing[[tensorflow-nlp]] | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | Train your tokenizer | How to train and use your very own tokenizer || | | Train your language model | How to easily start using transformers || | | How to fine-tune a model on text classification| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | | | | How to fine-tune a model on language modeling| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | | | | How to fine-tune a model on token classification| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | | | | How to fine-tune a model on question answering| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | | | | How to fine-tune a model on multiple choice| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | | | | How to fine-tune a model on translation| Show how to preprocess the data and fine-tune a pretrained model on WMT. | | | | How to fine-tune a model on summarization| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | | | Computer Vision[[tensorflow-cv]] | Notebook | Description | | | |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-------------|------:| | How to fine-tune a model on image classification | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification | | | | How to fine-tune a SegFormer model on semantic segmentation | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | | | Biological Sequences[[tensorflow-bio]] | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | How to fine-tune a pre-trained protein model | See how to tokenize proteins and fine-tune a large pre-trained protein "language" model | | | Utility notebooks[[tensorflow-utility]] | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | How to train TF/Keras models on TPU | See how to train at high speed on Google's TPU hardware | | | Optimum notebooks 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares. | Notebook | Description | | | |:----------|:-------------|:-------------|------:| | How to quantize a model with ONNX Runtime for text classification| Show how to apply static and dynamic quantization on a model using ONNX Runtime for any GLUE task. | | | | How to quantize a model with Intel Neural Compressor for text classification| Show how to apply static, dynamic and aware training quantization on a model using Intel Neural Compressor (INC) for any GLUE task. | | | | How to fine-tune a model on text classification with ONNX Runtime| Show how to preprocess the data and fine-tune a model on any GLUE task using ONNX Runtime. | | | | How to fine-tune a model on summarization with ONNX Runtime| Show how to preprocess the data and fine-tune a model on XSUM using ONNX Runtime. | | | Community notebooks: More notebooks developed by the community are available here.

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load IMDb dataset Start by loading the IMDb dataset from the 🤗 Datasets library: from datasets import load_dataset imdb = load_dataset("imdb") Then take a look at an example: imdb["test"][0] { "label": 0, "text": "I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \"Gene Roddenberry's Earth\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.", } There are two fields in this dataset: text: the movie review text. label: a value that is either 0 for a negative review or 1 for a positive review. Preprocess The next step is to load a DistilBERT tokenizer to preprocess the text field: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") Create a preprocessing function to tokenize text and truncate sequences to be no longer than DistilBERT's maximum input length: def preprocess_function(examples): return tokenizer(examples["text"], truncation=True) To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up map by setting batched=True to process multiple elements of the dataset at once: py tokenized_imdb = imdb.map(preprocess_function, batched=True) Now create a batch of examples using [DataCollatorWithPadding]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. from transformers import DataCollatorWithPadding data_collator = DataCollatorWithPadding(tokenizer=tokenizer) </pt> <tf>py from transformers import DataCollatorWithPadding data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric): import evaluate accuracy = evaluate.load("accuracy") Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy: import numpy as np def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) return accuracy.compute(predictions=predictions, references=labels) Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. Train Before you start training your model, create a map of the expected ids to their labels with id2label and label2id: id2label = {0: "NEGATIVE", 1: "POSITIVE"} label2id = {"NEGATIVE": 0, "POSITIVE": 1} If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load DistilBERT with [AutoModelForSequenceClassification] along with the number of expected labels, and the label mappings: from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer model = AutoModelForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id ) At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint. Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_model", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=2, weight_decay=0.01, evaluation_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_imdb["train"], eval_dataset=tokenized_imdb["test"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train() [Trainer] applies dynamic padding by default when you pass tokenizer to it. In this case, you don't need to specify a data collator explicitly. Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import create_optimizer import tensorflow as tf batch_size = 16 num_epochs = 5 batches_per_epoch = len(tokenized_imdb["train"]) // batch_size total_train_steps = int(batches_per_epoch * num_epochs) optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps) Then you can load DistilBERT with [TFAutoModelForSequenceClassification] along with the number of expected labels, and the label mappings: from transformers import TFAutoModelForSequenceClassification model = TFAutoModelForSequenceClassification.from_pretrained( "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id ) Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( tokenized_imdb["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_validation_set = model.prepare_tf_dataset( tokenized_imdb["test"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: import tensorflow as tf model.compile(optimizer=optimizer) # No loss argument! The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks. Pass your compute_metrics function to [~transformers.KerasMetricCallback]: from transformers.keras_callbacks import KerasMetricCallback metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set) Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="my_awesome_model", tokenizer=tokenizer, ) Then bundle your callbacks together: callbacks = [metric_callback, push_to_hub_callback] Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model: model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for text classification, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Grab some text you'd like to run inference on: text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three." The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for sentiment analysis with your model, and pass your text to it: from transformers import pipeline classifier = pipeline("sentiment-analysis", model="stevhliu/my_awesome_model") classifier(text) [{'label': 'POSITIVE', 'score': 0.9994940757751465}] You can also manually replicate the results of the pipeline if you'd like: Tokenize the text and return PyTorch tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model") inputs = tokenizer(text, return_tensors="pt") Pass your inputs to the model and return the logits: from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model") with torch.no_grad(): logits = model(**inputs).logits Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label: predicted_class_id = logits.argmax().item() model.config.id2label[predicted_class_id] 'POSITIVE' Tokenize the text and return TensorFlow tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model") inputs = tokenizer(text, return_tensors="tf") Pass your inputs to the model and return the logits: from transformers import TFAutoModelForSequenceClassification model = TFAutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model") logits = model(**inputs).logits Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label: predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0]) model.config.id2label[predicted_class_id] 'POSITIVE'

How 🤗 Transformers solve tasks In What 🤗 Transformers can do, you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what's happening under the hood. There are many ways to solve a given task, some models may implement certain techniques or even approach the task from a new angle, but for Transformer models, the general idea is the same. Owing to its flexible architecture, most models are a variant of an encoder, decoder, or encoder-decoder structure. In addition to Transformer models, our library also has several convolutional neural networks (CNNs), which are still used today for computer vision tasks. We'll also explain how a modern CNN works. To explain how tasks are solved, we'll walk through what goes on inside the model to output useful predictions. Wav2Vec2 for audio classification and automatic speech recognition (ASR) Vision Transformer (ViT) and ConvNeXT for image classification DETR for object detection Mask2Former for image segmentation GLPN for depth estimation BERT for NLP tasks like text classification, token classification and question answering that use an encoder GPT2 for NLP tasks like text generation that use a decoder BART for NLP tasks like summarization and translation that use an encoder-decoder Before you go further, it is good to have some basic knowledge of the original Transformer architecture. Knowing how encoders, decoders, and attention work will aid you in understanding how different Transformer models work. If you're just getting started or need a refresher, check out our course for more information! Speech and audio Wav2Vec2 is a self-supervised model pretrained on unlabeled speech data and finetuned on labeled data for audio classification and automatic speech recognition. This model has four main components: A feature encoder takes the raw audio waveform, normalizes it to zero mean and unit variance, and converts it into a sequence of feature vectors that are each 20ms long. Waveforms are continuous by nature, so they can't be divided into separate units like a sequence of text can be split into words. That's why the feature vectors are passed to a quantization module, which aims to learn discrete speech units. The speech unit is chosen from a collection of codewords, known as a codebook (you can think of this as the vocabulary). From the codebook, the vector or speech unit, that best represents the continuous audio input is chosen and forwarded through the model. About half of the feature vectors are randomly masked, and the masked feature vector is fed to a context network, which is a Transformer encoder that also adds relative positional embeddings. The pretraining objective of the context network is a contrastive task. The model has to predict the true quantized speech representation of the masked prediction from a set of false ones, encouraging the model to find the most similar context vector and quantized speech unit (the target label). Now that wav2vec2 is pretrained, you can finetune it on your data for audio classification or automatic speech recognition! Audio classification To use the pretrained model for audio classification, add a sequence classification head on top of the base Wav2Vec2 model. The classification head is a linear layer that accepts the encoder's hidden states. The hidden states represent the learned features from each audio frame which can have varying lengths. To create one vector of fixed-length, the hidden states are pooled first and then transformed into logits over the class labels. The cross-entropy loss is calculated between the logits and target to find the most likely class. Ready to try your hand at audio classification? Check out our complete audio classification guide to learn how to finetune Wav2Vec2 and use it for inference! Automatic speech recognition To use the pretrained model for automatic speech recognition, add a language modeling head on top of the base Wav2Vec2 model for connectionist temporal classification (CTC). The language modeling head is a linear layer that accepts the encoder's hidden states and transforms them into logits. Each logit represents a token class (the number of tokens comes from the task vocabulary). The CTC loss is calculated between the logits and targets to find the most likely sequence of tokens, which are then decoded into a transcription. Ready to try your hand at automatic speech recognition? Check out our complete automatic speech recognition guide to learn how to finetune Wav2Vec2 and use it for inference! Computer vision There are two ways to approach computer vision tasks: Split an image into a sequence of patches and process them in parallel with a Transformer. Use a modern CNN, like ConvNeXT, which relies on convolutional layers but adopts modern network designs. A third approach mixes Transformers with convolutions (for example, Convolutional Vision Transformer or LeViT). We won't discuss those because they just combine the two approaches we examine here. ViT and ConvNeXT are commonly used for image classification, but for other vision tasks like object detection, segmentation, and depth estimation, we'll look at DETR, Mask2Former and GLPN, respectively; these models are better suited for those tasks. Image classification ViT and ConvNeXT can both be used for image classification; the main difference is that ViT uses an attention mechanism while ConvNeXT uses convolutions. Transformer ViT replaces convolutions entirely with a pure Transformer architecture. If you're familiar with the original Transformer, then you're already most of the way toward understanding ViT. The main change ViT introduced was in how images are fed to a Transformer: An image is split into square non-overlapping patches, each of which gets turned into a vector or patch embedding. The patch embeddings are generated from a convolutional 2D layer which creates the proper input dimensions (which for a base Transformer is 768 values for each patch embedding). If you had a 224x224 pixel image, you could split it into 196 16x16 image patches. Just like how text is tokenized into words, an image is "tokenized" into a sequence of patches. A learnable embedding - a special [CLS] token - is added to the beginning of the patch embeddings just like BERT. The final hidden state of the [CLS] token is used as the input to the attached classification head; other outputs are ignored. This token helps the model learn how to encode a representation of the image. The last thing to add to the patch and learnable embeddings are the position embeddings because the model doesn't know how the image patches are ordered. The position embeddings are also learnable and have the same size as the patch embeddings. Finally, all of the embeddings are passed to the Transformer encoder. The output, specifically only the output with the [CLS] token, is passed to a multilayer perceptron head (MLP). ViT's pretraining objective is simply classification. Like other classification heads, the MLP head converts the output into logits over the class labels and calculates the cross-entropy loss to find the most likely class. Ready to try your hand at image classification? Check out our complete image classification guide to learn how to finetune ViT and use it for inference! CNN This section briefly explains convolutions, but it'd be helpful to have a prior understanding of how they change an image's shape and size. If you're unfamiliar with convolutions, check out the Convolution Neural Networks chapter from the fastai book! ConvNeXT is a CNN architecture that adopts new and modern network designs to improve performance. However, convolutions are still at the core of the model. From a high-level perspective, a convolution is an operation where a smaller matrix (kernel) is multiplied by a small window of the image pixels. It computes some features from it, such as a particular texture or curvature of a line. Then it slides over to the next window of pixels; the distance the convolution travels is known as the stride. A basic convolution without padding or stride, taken from A guide to convolution arithmetic for deep learning. You can feed this output to another convolutional layer, and with each successive layer, the network learns more complex and abstract things like hotdogs or rockets. Between convolutional layers, it is common to add a pooling layer to reduce dimensionality and make the model more robust to variations of a feature's position. ConvNeXT modernizes a CNN in five ways: Change the number of blocks in each stage and "patchify" an image with a larger stride and corresponding kernel size. The non-overlapping sliding window makes this patchifying strategy similar to how ViT splits an image into patches. A bottleneck layer shrinks the number of channels and then restores it because it is faster to do a 1x1 convolution, and you can increase the depth. An inverted bottleneck does the opposite by expanding the number of channels and shrinking them, which is more memory efficient. Replace the typical 3x3 convolutional layer in the bottleneck layer with depthwise convolution, which applies a convolution to each input channel separately and then stacks them back together at the end. This widens the network width for improved performance. ViT has a global receptive field which means it can see more of an image at once thanks to its attention mechanism. ConvNeXT attempts to replicate this effect by increasing the kernel size to 7x7. ConvNeXT also makes several layer design changes that imitate Transformer models. There are fewer activation and normalization layers, the activation function is switched to GELU instead of ReLU, and it uses LayerNorm instead of BatchNorm. The output from the convolution blocks is passed to a classification head which converts the outputs into logits and calculates the cross-entropy loss to find the most likely label. Object detection DETR, DEtection TRansformer, is an end-to-end object detection model that combines a CNN with a Transformer encoder-decoder. A pretrained CNN backbone takes an image, represented by its pixel values, and creates a low-resolution feature map of it. A 1x1 convolution is applied to the feature map to reduce dimensionality and it creates a new feature map with a high-level image representation. Since the Transformer is a sequential model, the feature map is flattened into a sequence of feature vectors that are combined with positional embeddings. The feature vectors are passed to the encoder, which learns the image representations using its attention layers. Next, the encoder hidden states are combined with object queries in the decoder. Object queries are learned embeddings that focus on the different regions of an image, and they're updated as they progress through each attention layer. The decoder hidden states are passed to a feedforward network that predicts the bounding box coordinates and class label for each object query, or no object if there isn't one. DETR decodes each object query in parallel to output N final predictions, where N is the number of queries. Unlike a typical autoregressive model that predicts one element at a time, object detection is a set prediction task (bounding box, class label) that makes N predictions in a single pass. DETR uses a bipartite matching loss during training to compare a fixed number of predictions with a fixed set of ground truth labels. If there are fewer ground truth labels in the set of N labels, then they're padded with a no object class. This loss function encourages DETR to find a one-to-one assignment between the predictions and ground truth labels. If either the bounding boxes or class labels aren't correct, a loss is incurred. Likewise, if DETR predicts an object that doesn't exist, it is penalized. This encourages DETR to find other objects in an image instead of focusing on one really prominent object. An object detection head is added on top of DETR to find the class label and the coordinates of the bounding box. There are two components to the object detection head: a linear layer to transform the decoder hidden states into logits over the class labels, and a MLP to predict the bounding box. Ready to try your hand at object detection? Check out our complete object detection guide to learn how to finetune DETR and use it for inference! Image segmentation Mask2Former is a universal architecture for solving all types of image segmentation tasks. Traditional segmentation models are typically tailored towards a particular subtask of image segmentation, like instance, semantic or panoptic segmentation. Mask2Former frames each of those tasks as a mask classification problem. Mask classification groups pixels into N segments, and predicts N masks and their corresponding class label for a given image. We'll explain how Mask2Former works in this section, and then you can try finetuning SegFormer at the end. There are three main components to Mask2Former: A Swin backbone accepts an image and creates a low-resolution image feature map from 3 consecutive 3x3 convolutions. The feature map is passed to a pixel decoder which gradually upsamples the low-resolution features into high-resolution per-pixel embeddings. The pixel decoder actually generates multi-scale features (contains both low- and high-resolution features) with resolutions 1/32, 1/16, and 1/8th of the original image. Each of these feature maps of differing scales is fed successively to one Transformer decoder layer at a time in order to capture small objects from the high-resolution features. The key to Mask2Former is the masked attention mechanism in the decoder. Unlike cross-attention which can attend to the entire image, masked attention only focuses on a certain area of the image. This is faster and leads to better performance because the local features of an image are enough for the model to learn from. Like DETR, Mask2Former also uses learned object queries and combines them with the image features from the pixel decoder to make a set prediction (class label, mask prediction). The decoder hidden states are passed into a linear layer and transformed into logits over the class labels. The cross-entropy loss is calculated between the logits and class label to find the most likely one. The mask predictions are generated by combining the pixel-embeddings with the final decoder hidden states. The sigmoid cross-entropy and dice loss is calculated between the logits and the ground truth mask to find the most likely mask. Ready to try your hand at object detection? Check out our complete image segmentation guide to learn how to finetune SegFormer and use it for inference! Depth estimation GLPN, Global-Local Path Network, is a Transformer for depth estimation that combines a SegFormer encoder with a lightweight decoder. Like ViT, an image is split into a sequence of patches, except these image patches are smaller. This is better for dense prediction tasks like segmentation or depth estimation. The image patches are transformed into patch embeddings (see the image classification section for more details about how patch embeddings are created), which are fed to the encoder. The encoder accepts the patch embeddings, and passes them through several encoder blocks. Each block consists of attention and Mix-FFN layers. The purpose of the latter is to provide positional information. At the end of each encoder block is a patch merging layer for creating hierarchical representations. The features of each group of neighboring patches are concatenated, and a linear layer is applied to the concatenated features to reduce the number of patches to a resolution of 1/4. This becomes the input to the next encoder block, where this whole process is repeated until you have image features with resolutions of 1/8, 1/16, and 1/32. A lightweight decoder takes the last feature map (1/32 scale) from the encoder and upsamples it to 1/16 scale. From here, the feature is passed into a Selective Feature Fusion (SFF) module, which selects and combines local and global features from an attention map for each feature and then upsamples it to 1/8th. This process is repeated until the decoded features are the same size as the original image. The output is passed through two convolution layers and then a sigmoid activation is applied to predict the depth of each pixel. Natural language processing The Transformer was initially designed for machine translation, and since then, it has practically become the default architecture for solving all NLP tasks. Some tasks lend themselves to the Transformer's encoder structure, while others are better suited for the decoder. Still, other tasks make use of both the Transformer's encoder-decoder structure. Text classification BERT is an encoder-only model and is the first model to effectively implement deep bidirectionality to learn richer representations of the text by attending to words on both sides. BERT uses WordPiece tokenization to generate a token embedding of the text. To tell the difference between a single sentence and a pair of sentences, a special [SEP] token is added to differentiate them. A special [CLS] token is added to the beginning of every sequence of text. The final output with the [CLS] token is used as the input to the classification head for classification tasks. BERT also adds a segment embedding to denote whether a token belongs to the first or second sentence in a pair of sentences. BERT is pretrained with two objectives: masked language modeling and next-sentence prediction. In masked language modeling, some percentage of the input tokens are randomly masked, and the model needs to predict these. This solves the issue of bidirectionality, where the model could cheat and see all the words and "predict" the next word. The final hidden states of the predicted mask tokens are passed to a feedforward network with a softmax over the vocabulary to predict the masked word. The second pretraining object is next-sentence prediction. The model must predict whether sentence B follows sentence A. Half of the time sentence B is the next sentence, and the other half of the time, sentence B is a random sentence. The prediction, whether it is the next sentence or not, is passed to a feedforward network with a softmax over the two classes (IsNext and NotNext). The input embeddings are passed through multiple encoder layers to output some final hidden states. To use the pretrained model for text classification, add a sequence classification head on top of the base BERT model. The sequence classification head is a linear layer that accepts the final hidden states and performs a linear transformation to convert them into logits. The cross-entropy loss is calculated between the logits and target to find the most likely label. Ready to try your hand at text classification? Check out our complete text classification guide to learn how to finetune DistilBERT and use it for inference! Token classification To use BERT for token classification tasks like named entity recognition (NER), add a token classification head on top of the base BERT model. The token classification head is a linear layer that accepts the final hidden states and performs a linear transformation to convert them into logits. The cross-entropy loss is calculated between the logits and each token to find the most likely label. Ready to try your hand at token classification? Check out our complete token classification guide to learn how to finetune DistilBERT and use it for inference! Question answering To use BERT for question answering, add a span classification head on top of the base BERT model. This linear layer accepts the final hidden states and performs a linear transformation to compute the span start and end logits corresponding to the answer. The cross-entropy loss is calculated between the logits and the label position to find the most likely span of text corresponding to the answer. Ready to try your hand at question answering? Check out our complete question answering guide to learn how to finetune DistilBERT and use it for inference! 💡 Notice how easy it is to use BERT for different tasks once it's been pretrained. You only need to add a specific head to the pretrained model to manipulate the hidden states into your desired output! Text generation GPT-2 is a decoder-only model pretrained on a large amount of text. It can generate convincing (though not always true!) text given a prompt and complete other NLP tasks like question answering despite not being explicitly trained to. GPT-2 uses byte pair encoding (BPE) to tokenize words and generate a token embedding. Positional encodings are added to the token embeddings to indicate the position of each token in the sequence. The input embeddings are passed through multiple decoder blocks to output some final hidden state. Within each decoder block, GPT-2 uses a masked self-attention layer which means GPT-2 can't attend to future tokens. It is only allowed to attend to tokens on the left. This is different from BERT's [mask] token because, in masked self-attention, an attention mask is used to set the score to 0 for future tokens. The output from the decoder is passed to a language modeling head, which performs a linear transformation to convert the hidden states into logits. The label is the next token in the sequence, which are created by shifting the logits to the right by one. The cross-entropy loss is calculated between the shifted logits and the labels to output the next most likely token. GPT-2's pretraining objective is based entirely on causal language modeling, predicting the next word in a sequence. This makes GPT-2 especially good at tasks that involve generating text. Ready to try your hand at text generation? Check out our complete causal language modeling guide to learn how to finetune DistilGPT-2 and use it for inference! For more information about text generation, check out the text generation strategies guide! Summarization Encoder-decoder models like BART and T5 are designed for the sequence-to-sequence pattern of a summarization task. We'll explain how BART works in this section, and then you can try finetuning T5 at the end. BART's encoder architecture is very similar to BERT and accepts a token and positional embedding of the text. BART is pretrained by corrupting the input and then reconstructing it with the decoder. Unlike other encoders with specific corruption strategies, BART can apply any type of corruption. The text infilling corruption strategy works the best though. In text infilling, a number of text spans are replaced with a single [mask] token. This is important because the model has to predict the masked tokens, and it teaches the model to predict the number of missing tokens. The input embeddings and masked spans are passed through the encoder to output some final hidden states, but unlike BERT, BART doesn't add a final feedforward network at the end to predict a word. The encoder's output is passed to the decoder, which must predict the masked tokens and any uncorrupted tokens from the encoder's output. This gives additional context to help the decoder restore the original text. The output from the decoder is passed to a language modeling head, which performs a linear transformation to convert the hidden states into logits. The cross-entropy loss is calculated between the logits and the label, which is just the token shifted to the right. Ready to try your hand at summarization? Check out our complete summarization guide to learn how to finetune T5 and use it for inference! For more information about text generation, check out the text generation strategies guide! Translation Translation is another example of a sequence-to-sequence task, which means you can use an encoder-decoder model like BART or T5 to do it. We'll explain how BART works in this section, and then you can try finetuning T5 at the end. BART adapts to translation by adding a separate randomly initialized encoder to map a source language to an input that can be decoded into the target language. This new encoder's embeddings are passed to the pretrained encoder instead of the original word embeddings. The source encoder is trained by updating the source encoder, positional embeddings, and input embeddings with the cross-entropy loss from the model output. The model parameters are frozen in this first step, and all the model parameters are trained together in the second step. BART has since been followed up by a multilingual version, mBART, intended for translation and pretrained on many different languages. Ready to try your hand at translation? Check out our complete translation guide to learn how to finetune T5 and use it for inference! For more information about text generation, check out the text generation strategies guide!

How to create a custom pipeline? In this guide, we will see how to create a custom pipeline and share it on the Hub or add it to the 🤗 Transformers library. First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes, dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible as it makes compatibility easier (even through other languages via JSON). Those will be the inputs of the pipeline (preprocess). Then define the outputs. Same policy as the inputs. The simpler, the better. Those will be the outputs of postprocess method. Start by inheriting the base class Pipeline with the 4 methods needed to implement preprocess, _forward, postprocess, and _sanitize_parameters. thon from transformers import Pipeline class MyPipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "maybe_arg" in kwargs: preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"] return preprocess_kwargs, {}, {} def preprocess(self, inputs, maybe_arg=2): model_input = Tensor(inputs["input_ids"]) return {"model_input": model_input} def _forward(self, model_inputs): # model_inputs == {"model_input": model_input} outputs = self.model(**model_inputs) # Maybe {"logits": Tensor()} return outputs def postprocess(self, model_outputs): best_class = model_outputs["logits"].softmax(-1) return best_class The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing pre/postprocessing on the CPU on different threads preprocess will take the originally defined inputs, and turn them into something feedable to the model. It might contain more information and is usually a Dict. _forward is the implementation detail and is not meant to be called directly. forward is the preferred called method as it contains safeguards to make sure everything is working on the expected device. If anything is linked to a real model it belongs in the _forward method, anything else is in the preprocess/postprocess. postprocess methods will take the output of _forward and turn it into the final output that was decided earlier. _sanitize_parameters exists to allow users to pass any parameters whenever they wish, be it at initialization time pipeline(., maybe_arg=4) or at call time pipe = pipeline(); output = pipe(., maybe_arg=4). The returns of _sanitize_parameters are the 3 dicts of kwargs that will be passed directly to preprocess, _forward, and postprocess. Don't fill anything if the caller didn't call with any extra parameter. That allows to keep the default arguments in the function definition which is always more "natural". A classic example would be a top_k argument in the post processing in classification tasks. thon pipe = pipeline("my-new-task") pipe("This is a test") [{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05} {"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}] pipe("This is a test", top_k=2) [{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}] In order to achieve that, we'll update our postprocess method with a default parameter to 5. and edit _sanitize_parameters to allow this new parameter. thon def postprocess(self, model_outputs, top_k=5): best_class = model_outputs["logits"].softmax(-1) # Add logic to handle top_k return best_class def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "maybe_arg" in kwargs: preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"] postprocess_kwargs = {} if "top_k" in kwargs: postprocess_kwargs["top_k"] = kwargs["top_k"] return preprocess_kwargs, {}, postprocess_kwargs Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy without requiring users to understand new kind of objects. It's also relatively common to support many different types of arguments for ease of use (audio files, can be filenames, URLs or pure bytes) Adding it to the list of supported tasks To register your new-task to the list of supported tasks, you have to add it to the PIPELINE_REGISTRY: thon from transformers.pipelines import PIPELINE_REGISTRY PIPELINE_REGISTRY.register_pipeline( "new-task", pipeline_class=MyPipeline, pt_model=AutoModelForSequenceClassification, ) You can specify a default model if you want, in which case it should come with a specific revision (which can be the name of a branch or a commit hash, here we took "abcdef") as well as the type: python PIPELINE_REGISTRY.register_pipeline( "new-task", pipeline_class=MyPipeline, pt_model=AutoModelForSequenceClassification, default={"pt": ("user/awesome_model", "abcdef")}, type="text", # current support type: text, audio, image, multimodal ) Share your pipeline on the Hub To share your custom pipeline on the Hub, you just have to save the custom code of your Pipeline subclass in a python file. For instance, let's say we want to use a custom pipeline for sentence pair classification like this: import numpy as np from transformers import Pipeline def softmax(outputs): maxes = np.max(outputs, axis=-1, keepdims=True) shifted_exp = np.exp(outputs - maxes) return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True) class PairClassificationPipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "second_text" in kwargs: preprocess_kwargs["second_text"] = kwargs["second_text"] return preprocess_kwargs, {}, {} def preprocess(self, text, second_text=None): return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework) def _forward(self, model_inputs): return self.model(**model_inputs) def postprocess(self, model_outputs): logits = model_outputs.logits[0].numpy() probabilities = softmax(logits) best_class = np.argmax(probabilities) label = self.model.config.id2label[best_class] score = probabilities[best_class].item() logits = logits.tolist() return {"label": label, "score": score, "logits": logits} The implementation is framework agnostic, and will work for PyTorch and TensorFlow models. If we have saved this in a file named pair_classification.py, we can then import it and register it like this: from pair_classification import PairClassificationPipeline from transformers.pipelines import PIPELINE_REGISTRY from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification PIPELINE_REGISTRY.register_pipeline( "pair-classification", pipeline_class=PairClassificationPipeline, pt_model=AutoModelForSequenceClassification, tf_model=TFAutoModelForSequenceClassification, ) Once this is done, we can use it with a pretrained model. For instance sgugger/finetuned-bert-mrpc has been fine-tuned on the MRPC dataset, which classifies pairs of sentences as paraphrases or not. from transformers import pipeline classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc") Then we can share it on the Hub by using the save_pretrained method in a Repository: from huggingface_hub import Repository repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline") classifier.save_pretrained("test-dynamic-pipeline") repo.push_to_hub() This will copy the file where you defined PairClassificationPipeline inside the folder "test-dynamic-pipeline", along with saving the model and tokenizer of the pipeline, before pushing everything in the repository {your_username}/test-dynamic-pipeline. After that anyone can use it as long as they provide the option trust_remote_code=True: from transformers import pipeline classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True) Add the pipeline to 🤗 Transformers If you want to contribute your pipeline to 🤗 Transformers, you will need to add a new module in the pipelines submodule with the code of your pipeline, then add it in the list of tasks defined in pipelines/__init__.py. Then you will need to add tests. Create a new file tests/test_pipelines_MY_PIPELINE.py with example with the other tests. The run_pipeline_test function will be very generic and run on small random models on every possible architecture as defined by model_mapping and tf_model_mapping. This is very important to test future compatibility, meaning if someone adds a new model for XXXForQuestionAnswering then the pipeline test will attempt to run on it. Because the models are random it's impossible to check for actual values, that's why there is a helper ANY that will simply attempt to match the output of the pipeline TYPE. You also need to implement 2 (ideally 4) tests. test_small_model_pt : Define 1 small model for this pipeline (doesn't matter if the results don't make sense) and test the pipeline outputs. The results should be the same as test_small_model_tf. test_small_model_tf : Define 1 small model for this pipeline (doesn't matter if the results don't make sense) and test the pipeline outputs. The results should be the same as test_small_model_pt. test_large_model_pt (optional): Tests the pipeline on a real pipeline where the results are supposed to make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make sure there is no drift in future releases. test_large_model_tf (optional): Tests the pipeline on a real pipeline where the results are supposed to make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make sure there is no drift in future releases.

Agents & Tools Transformers Agent is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. To learn more about agents and tools make sure to read the introductory guide. This page contains the API docs for the underlying classes. Agents We provide three types of agents: [HfAgent] uses inference endpoints for opensource models, [LocalAgent] uses a model of your choice locally and [OpenAiAgent] uses OpenAI closed models. HfAgent [[autodoc]] HfAgent LocalAgent [[autodoc]] LocalAgent OpenAiAgent [[autodoc]] OpenAiAgent AzureOpenAiAgent [[autodoc]] AzureOpenAiAgent Agent [[autodoc]] Agent - chat - run - prepare_for_new_chat Tools load_tool [[autodoc]] load_tool Tool [[autodoc]] Tool PipelineTool [[autodoc]] PipelineTool RemoteTool [[autodoc]] RemoteTool launch_gradio_demo [[autodoc]] launch_gradio_demo Agent Types Agents can handle any type of object in-between tools; tools, being completely multimodal, can accept and return text, image, audio, video, among other types. In order to increase compatibility between tools, as well as to correctly render these returns in ipython (jupyter, colab, ipython notebooks, ), we implement wrapper classes around these types. The wrapped objects should continue behaving as initially; a text object should still behave as a string, an image object should still behave as a PIL.Image. These types have three specific purposes: Calling to_raw on the type should return the underlying object Calling to_string on the type should return the object as a string: that can be the string in case of an AgentText but will be the path of the serialized version of the object in other instances Displaying it in an ipython kernel should display the object correctly AgentText [[autodoc]] transformers.tools.agent_types.AgentText AgentImage [[autodoc]] transformers.tools.agent_types.AgentImage AgentAudio [[autodoc]] transformers.tools.agent_types.AgentAudio

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Load ELI5 dataset Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset. from datasets import load_dataset eli5 = load_dataset("eli5", split="train_asks[:5000]") Split the dataset's train_asks split into a train and test set with the [~datasets.Dataset.train_test_split] method: eli5 = eli5.train_test_split(test_size=0.2) Then take a look at an example: eli5["train"][0] {'answers': {'a_id': ['c3d1aib', 'c3d4lya'], 'score': [6, 3], 'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.", "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]}, 'answers_urls': {'url': []}, 'document': '', 'q_id': 'nyxfp', 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?', 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']}, 'subreddit': 'askscience', 'title': 'Few questions about this space walk photograph.', 'title_urls': {'url': []}} While this may look like a lot, you're only really interested in the text field. What's cool about language modeling tasks is you don't need labels (also known as an unsupervised task) because the next word is the label. Preprocess The next step is to load a DistilGPT2 tokenizer to process the text subfield: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilgpt2") You'll notice from the example above, the text field is actually nested inside answers. This means you'll need to extract the text subfield from its nested structure with the flatten method: eli5 = eli5.flatten() eli5["train"][0] {'answers.a_id': ['c3d1aib', 'c3d4lya'], 'answers.score': [6, 3], 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.", "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"], 'answers_urls.url': [], 'document': '', 'q_id': 'nyxfp', 'selftext': 'URL_0\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?', 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'], 'subreddit': 'askscience', 'title': 'Few questions about this space walk photograph.', 'title_urls.url': []} Each subfield is now a separate column as indicated by the answers prefix, and the text field is a list now. Instead of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them. Here is a first preprocessing function to join the list of strings for each example and tokenize the result: def preprocess_function(examples): return tokenizer([" ".join(x) for x in examples["answers.text"]]) To apply this preprocessing function over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once, and increasing the number of processes with num_proc. Remove any columns you don't need: tokenized_eli5 = eli5.map( preprocess_function, batched=True, num_proc=4, remove_columns=eli5["train"].column_names, ) This dataset contains the token sequences, but some of these are longer than the maximum input length for the model. You can now use a second preprocessing function to - concatenate all the sequences - split the concatenated sequences into shorter chunks defined by block_size, which should be both shorter than the maximum input length and short enough for your GPU RAM. block_size = 128 def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of block_size. result = { k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } result["labels"] = result["input_ids"].copy() return result Apply the group_texts function over the entire dataset: lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4) Now create a batch of examples using [DataCollatorForLanguageModeling]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. Use the end-of-sequence token as the padding token and set mlm=False. This will use the inputs as labels shifted to the right by one element: from transformers import DataCollatorForLanguageModeling tokenizer.pad_token = tokenizer.eos_token data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) Use the end-of-sequence token as the padding token and set mlm=False. This will use the inputs as labels shifted to the right by one element: from transformers import DataCollatorForLanguageModeling data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf") Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial! You're ready to start training your model now! Load DistilGPT2 with [AutoModelForCausalLM]: from transformers import AutoModelForCausalLM, TrainingArguments, Trainer model = AutoModelForCausalLM.from_pretrained("distilgpt2") At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). Pass the training arguments to [Trainer] along with the model, datasets, and data collator. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_eli5_clm-model", evaluation_strategy="epoch", learning_rate=2e-5, weight_decay=0.01, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=lm_dataset["train"], eval_dataset=lm_dataset["test"], data_collator=data_collator, ) trainer.train() Once training is completed, use the [~transformers.Trainer.evaluate] method to evaluate your model and get its perplexity: import math eval_results = trainer.evaluate() print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}") Perplexity: 49.61 Then share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import create_optimizer, AdamWeightDecay optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01) Then you can load DistilGPT2 with [TFAutoModelForCausalLM]: from transformers import TFAutoModelForCausalLM model = TFAutoModelForCausalLM.from_pretrained("distilgpt2") Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( lm_dataset["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_test_set = model.prepare_tf_dataset( lm_dataset["test"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: import tensorflow as tf model.compile(optimizer=optimizer) # No loss argument! This can be done by specifying where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback callback = PushToHubCallback( output_dir="my_awesome_eli5_clm-model", tokenizer=tokenizer, ) Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model: model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback]) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for causal language modeling, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Come up with a prompt you'd like to generate text from: prompt = "Somatic hypermutation allows the immune system to" The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for text generation with your model, and pass your text to it: from transformers import pipeline generator = pipeline("text-generation", model="my_awesome_eli5_clm-model") generator(prompt) [{'generated_text': "Somatic hypermutation allows the immune system to be able to effectively reverse the damage caused by an infection.\n\n\nThe damage caused by an infection is caused by the immune system's ability to perform its own self-correcting tasks."}] Tokenize the text and return the input_ids as PyTorch tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model") inputs = tokenizer(prompt, return_tensors="pt").input_ids Use the [~transformers.generation_utils.GenerationMixin.generate] method to generate text. For more details about the different text generation strategies and parameters for controlling generation, check out the Text generation strategies page. from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model") outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95) Decode the generated token ids back into text: tokenizer.batch_decode(outputs, skip_special_tokens=True) ["Somatic hypermutation allows the immune system to react to drugs with the ability to adapt to a different environmental situation. In other words, a system of 'hypermutation' can help the immune system to adapt to a different environmental situation or in some cases even a single life. In contrast, researchers at the University of Massachusetts-Boston have found that 'hypermutation' is much stronger in mice than in humans but can be found in humans, and that it's not completely unknown to the immune system. A study on how the immune system"] `` </pt> <tf> Tokenize the text and return theinput_ids` as TensorFlow tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model") inputs = tokenizer(prompt, return_tensors="tf").input_ids Use the [~transformers.generation_tf_utils.TFGenerationMixin.generate] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the Text generation strategies page. from transformers import TFAutoModelForCausalLM model = TFAutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model") outputs = model.generate(input_ids=inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95) Decode the generated token ids back into text: tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Somatic hypermutation allows the immune system to detect the presence of other viruses as they become more prevalent. Therefore, researchers have identified a high proportion of human viruses. The proportion of virus-associated viruses in our study increases with age. Therefore, we propose a simple algorithm to detect the presence of these new viruses in our samples as a sign of improved immunity. A first study based on this algorithm, which will be published in Science on Friday, aims to show that this finding could translate into the development of a better vaccine that is more effective for']

Export to TorchScript This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases, with more code examples, a more flexible implementation, and benchmarks comparing Python-based codes with compiled TorchScript. According to the TorchScript documentation: TorchScript is a way to create serializable and optimizable models from PyTorch code. There are two PyTorch modules, JIT and TRACE, that allow developers to export their models to be reused in other programs like efficiency-oriented C++ programs. We provide an interface that allows you to export 🤗 Transformers models to TorchScript so they can be reused in a different environment than PyTorch-based Python programs. Here, we explain how to export and use our models using TorchScript. Exporting a model requires two things: model instantiation with the torchscript flag a forward pass with dummy inputs These necessities imply several things developers should be careful about as detailed below. TorchScript flag and tied weights The torchscript flag is necessary because most of the 🤗 Transformers language models have tied weights between their Embedding layer and their Decoding layer. TorchScript does not allow you to export models that have tied weights, so it is necessary to untie and clone the weights beforehand. Models instantiated with the torchscript flag have their Embedding layer and Decoding layer separated, which means that they should not be trained down the line. Training would desynchronize the two layers, leading to unexpected results. This is not the case for models that do not have a language model head, as those do not have tied weights. These models can be safely exported without the torchscript flag. Dummy inputs and standard lengths The dummy inputs are used for a models forward pass. While the inputs' values are propagated through the layers, PyTorch keeps track of the different operations executed on each tensor. These recorded operations are then used to create the trace of the model. The trace is created relative to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy input, and will not work for any other sequence length or batch size. When trying with a different size, the following error is raised: `The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2` We recommended you trace the model with a dummy input size at least as large as the largest input that will be fed to the model during inference. Padding can help fill the missing values. However, since the model is traced with a larger input size, the dimensions of the matrix will also be large, resulting in more calculations. Be careful of the total number of operations done on each input and follow the performance closely when exporting varying sequence-length models. Using TorchScript in Python This section demonstrates how to save and load models as well as how to use the trace for inference. Saving a model To export a BertModel with TorchScript, instantiate BertModel from the BertConfig class and then save it to disk under the filename traced_bert.pt: thon from transformers import BertModel, BertTokenizer, BertConfig import torch enc = BertTokenizer.from_pretrained("bert-base-uncased") Tokenizing input text text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]" tokenized_text = enc.tokenize(text) Masking one of the input tokens masked_index = 8 tokenized_text[masked_index] = "[MASK]" indexed_tokens = enc.convert_tokens_to_ids(tokenized_text) segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1] Creating a dummy input tokens_tensor = torch.tensor([indexed_tokens]) segments_tensors = torch.tensor([segments_ids]) dummy_input = [tokens_tensor, segments_tensors] Initializing the model with the torchscript flag Flag set to True even though it is not necessary as this model does not have an LM Head. config = BertConfig( vocab_size_or_config_json_file=32000, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True, ) Instantiating the model model = BertModel(config) The model needs to be in evaluation mode model.eval() If you are instantiating the model with from_pretrained you can also easily set the TorchScript flag model = BertModel.from_pretrained("bert-base-uncased", torchscript=True) Creating the trace traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors]) torch.jit.save(traced_model, "traced_bert.pt") Loading a model Now you can load the previously saved BertModel, traced_bert.pt, from disk and use it on the previously initialised dummy_input: thon loaded_model = torch.jit.load("traced_bert.pt") loaded_model.eval() all_encoder_layers, pooled_output = loaded_model(*dummy_input) Using a traced model for inference Use the traced model for inference by using its __call__ dunder method: python traced_model(tokens_tensor, segments_tensors) Deploy Hugging Face TorchScript models to AWS with the Neuron SDK AWS introduced the Amazon EC2 Inf1 instance family for low cost, high performance machine learning inference in the cloud. The Inf1 instances are powered by the AWS Inferentia chip, a custom-built hardware accelerator, specializing in deep learning inferencing workloads. AWS Neuron is the SDK for Inferentia that supports tracing and optimizing transformers models for deployment on Inf1. The Neuron SDK provides: Easy-to-use API with one line of code change to trace and optimize a TorchScript model for inference in the cloud. Out of the box performance optimizations for improved cost-performance. Support for Hugging Face transformers models built with either PyTorch or TensorFlow. Implications Transformers models based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, or its variants such as distilBERT and roBERTa run best on Inf1 for non-generative tasks such as extractive question answering, sequence classification, and token classification. However, text generation tasks can still be adapted to run on Inf1 according to this AWS Neuron MarianMT tutorial. More information about models that can be converted out of the box on Inferentia can be found in the Model Architecture Fit section of the Neuron documentation. Dependencies Using AWS Neuron to convert models requires a Neuron SDK environment which comes preconfigured on AWS Deep Learning AMI. Converting a model for AWS Neuron Convert a model for AWS NEURON using the same code from Using TorchScript in Python to trace a BertModel. Import the torch.neuron framework extension to access the components of the Neuron SDK through a Python API: python from transformers import BertModel, BertTokenizer, BertConfig import torch import torch.neuron You only need to modify the following line: diff - torch.jit.trace(model, [tokens_tensor, segments_tensors]) + torch.neuron.trace(model, [token_tensor, segments_tensors]) This enables the Neuron SDK to trace the model and optimize it for Inf1 instances. To learn more about AWS Neuron SDK features, tools, example tutorials and latest updates, please see the AWS NeuronSDK documentation.

Methods and tools for efficient training on a single GPU This guide demonstrates practical techniques that you can use to increase the efficiency of your model's training by optimizing memory utilization, speeding up the training, or both. If you'd like to understand how GPU is utilized during training, please refer to the Model training anatomy conceptual guide first. This guide focuses on practical techniques. If you have access to a machine with multiple GPUs, these approaches are still valid, plus you can leverage additional methods outlined in the multi-GPU section. When training large models, there are two aspects that should be considered at the same time: Data throughput/training time Model performance Maximizing the throughput (samples/second) leads to lower training cost. This is generally achieved by utilizing the GPU as much as possible and thus filling GPU memory to its limit. If the desired batch size exceeds the limits of the GPU memory, the memory optimization techniques, such as gradient accumulation, can help. However, if the preferred batch size fits into memory, there's no reason to apply memory-optimizing techniques because they can slow down the training. Just because one can use a large batch size, does not necessarily mean they should. As part of hyperparameter tuning, you should determine which batch size yields the best results and then optimize resources accordingly. The methods and tools covered in this guide can be classified based on the effect they have on the training process: | Method/tool | Improves training speed | Optimizes memory utilization | |:-----------------------------------------------------------|:------------------------|:-----------------------------| | Batch size choice | Yes | Yes | | Gradient accumulation | No | Yes | | Gradient checkpointing | No | Yes | | Mixed precision training | Yes | (No) | | Optimizer choice | Yes | Yes | | Data preloading | Yes | No | | DeepSpeed Zero | No | Yes | | torch.compile | Yes | No | Note: when using mixed precision with a small model and a large batch size, there will be some memory savings but with a large model and a small batch size, the memory use will be larger. You can combine the above methods to get a cumulative effect. These techniques are available to you whether you are training your model with [Trainer] or writing a pure PyTorch loop, in which case you can configure these optimizations with 🤗 Accelerate. If these methods do not result in sufficient gains, you can explore the following options: * Look into building your own custom Docker container with efficient softare prebuilds * Consider a model that uses Mixture of Experts (MoE) * Convert your model to BetterTransformer to leverage PyTorch native attention Finally, if all of the above is still not enough, even after switching to a server-grade GPU like A100, consider moving to a multi-GPU setup. All these approaches are still valid in a multi-GPU setup, plus you can leverage additional parallelism techniques outlined in the multi-GPU section. Batch size choice To achieve optimal performance, start by identifying the appropriate batch size. It is recommended to use batch sizes and input/output neuron counts that are of size 2^N. Often it's a multiple of 8, but it can be higher depending on the hardware being used and the model's dtype. For reference, check out NVIDIA's recommendation for input/output neuron counts and batch size for fully connected layers (which are involved in GEMMs (General Matrix Multiplications)). Tensor Core Requirements define the multiplier based on the dtype and the hardware. For instance, for fp16 data type a multiple of 8 is recommended, unless it's an A100 GPU, in which case use multiples of 64. For parameters that are small, consider also Dimension Quantization Effects. This is where tiling happens and the right multiplier can have a significant speedup. Gradient Accumulation The gradient accumulation method aims to calculate gradients in smaller increments instead of computing them for the entire batch at once. This approach involves iteratively calculating gradients in smaller batches by performing forward and backward passes through the model and accumulating the gradients during the process. Once a sufficient number of gradients have been accumulated, the model's optimization step is executed. By employing gradient accumulation, it becomes possible to increase the effective batch size beyond the limitations imposed by the GPU's memory capacity. However, it is important to note that the additional forward and backward passes introduced by gradient accumulation can slow down the training process. You can enable gradient accumulation by adding the gradient_accumulation_steps argument to [TrainingArguments]: py training_args = TrainingArguments(per_device_train_batch_size=1, gradient_accumulation_steps=4, **default_args) In the above example, your effective batch size becomes 4. Alternatively, use 🤗 Accelerate to gain full control over the training loop. Find the 🤗 Accelerate example further down in this guide. While it is advised to max out GPU usage as much as possible, a high number of gradient accumulation steps can result in a more pronounced training slowdown. Consider the following example. Let's say, the per_device_train_batch_size=4 without gradient accumulation hits the GPU's limit. If you would like to train with batches of size 64, do not set the per_device_train_batch_size to 1 and gradient_accumulation_steps to 64. Instead, keep per_device_train_batch_size=4 and set gradient_accumulation_steps=16. This results in the same effective batch size while making better use of the available GPU resources. For additional information, please refer to batch size and gradient accumulation benchmarks for RTX-3090 and A100. Gradient Checkpointing Some large models may still face memory issues even when the batch size is set to 1 and gradient accumulation is used. This is because there are other components that also require memory storage. Saving all activations from the forward pass in order to compute the gradients during the backward pass can result in significant memory overhead. The alternative approach of discarding the activations and recalculating them when needed during the backward pass, would introduce a considerable computational overhead and slow down the training process. Gradient checkpointing offers a compromise between these two approaches and saves strategically selected activations throughout the computational graph so only a fraction of the activations need to be re-computed for the gradients. For an in-depth explanation of gradient checkpointing, refer to this great article. To enable gradient checkpointing in the [Trainer], pass the corresponding a flag to [TrainingArguments]: py training_args = TrainingArguments( per_device_train_batch_size=1, gradient_accumulation_steps=4, gradient_checkpointing=True, **default_args ) Alternatively, use 🤗 Accelerate - find the 🤗 Accelerate example further in this guide. While gradient checkpointing may improve memory efficiency, it slows training by approximately 20%. Mixed precision training Mixed precision training is a technique that aims to optimize the computational efficiency of training models by utilizing lower-precision numerical formats for certain variables. Traditionally, most models use 32-bit floating point precision (fp32 or float32) to represent and process variables. However, not all variables require this high precision level to achieve accurate results. By reducing the precision of certain variables to lower numerical formats like 16-bit floating point (fp16 or float16), we can speed up the computations. Because in this approach some computations are performed in half-precision, while some are still in full precision, the approach is called mixed precision training. Most commonly mixed precision training is achieved by using fp16 (float16) data types, however, some GPU architectures (such as the Ampere architecture) offer bf16 and tf32 (CUDA internal data type) data types. Check out the NVIDIA Blog to learn more about the differences between these data types. fp16 The main advantage of mixed precision training comes from saving the activations in half precision (fp16). Although the gradients are also computed in half precision they are converted back to full precision for the optimization step so no memory is saved here. While mixed precision training results in faster computations, it can also lead to more GPU memory being utilized, especially for small batch sizes. This is because the model is now present on the GPU in both 16-bit and 32-bit precision (1.5x the original model on the GPU). To enable mixed precision training, set the fp16 flag to True: py training_args = TrainingArguments(per_device_train_batch_size=4, fp16=True, **default_args) If you prefer to use 🤗 Accelerate, find the 🤗 Accelerate example further in this guide. BF16 If you have access to an Ampere or newer hardware you can use bf16 for mixed precision training and evaluation. While bf16 has a worse precision than fp16, it has a much bigger dynamic range. In fp16 the biggest number you can have is 65535 and any number above that will result in an overflow. A bf16 number can be as large as 3.39e+38 (!) which is about the same as fp32 - because both have 8-bits used for the numerical range. You can enable BF16 in the 🤗 Trainer with: python training_args = TrainingArguments(bf16=True, **default_args) TF32 The Ampere hardware uses a magical data type called tf32. It has the same numerical range as fp32 (8-bits), but instead of 23 bits precision it has only 10 bits (same as fp16) and uses only 19 bits in total. It's "magical" in the sense that you can use the normal fp32 training and/or inference code and by enabling tf32 support you can get up to 3x throughput improvement. All you need to do is to add the following to your code: import torch torch.backends.cuda.matmul.allow_tf32 = True CUDA will automatically switch to using tf32 instead of fp32 where possible, assuming that the used GPU is from the Ampere series. According to NVIDIA research, the majority of machine learning training workloads show the same perplexity and convergence with tf32 training as with fp32. If you're already using fp16 or bf16 mixed precision it may help with the throughput as well. You can enable this mode in the 🤗 Trainer: python TrainingArguments(tf32=True, **default_args) tf32 can't be accessed directly via tensor.to(dtype=torch.tf32) because it is an internal CUDA data type. You need torch>=1.7 to use tf32 data types. For additional information on tf32 vs other precisions, please refer to the following benchmarks: RTX-3090 and A100. Optimizer choice The most common optimizer used to train transformer models is Adam or AdamW (Adam with weight decay). Adam achieves good convergence by storing the rolling average of the previous gradients; however, it adds an additional memory footprint of the order of the number of model parameters. To remedy this, you can use an alternative optimizer. For example if you have NVIDIA/apex installed, adamw_apex_fused will give you the fastest training experience among all supported AdamW optimizers. [Trainer] integrates a variety of optimizers that can be used out of box: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or adafactor. More optimizers can be plugged in via a third-party implementation. Let's take a closer look at two alternatives to AdamW optimizer - Adafactor (available in Trainer), and 8bit BNB quantized optimizer (third-party implementation). For comparison, for a 3B-parameter model, like “t5-3b”: * A standard AdamW optimizer will need 24GB of GPU memory because it uses 8 bytes for each parameter (83 => 24GB) * Adafactor optimizer will need more than 12GB. It uses slightly more than 4 bytes for each parameter, so 43 and then some extra. * 8bit BNB quantized optimizer will use only (2*3) 6GB if all optimizer states are quantized. Adafactor Adafactor doesn't store rolling averages for each element in weight matrices. Instead, it keeps aggregated information (sums of rolling averages row- and column-wise), significantly reducing its footprint. However, compared to Adam, Adafactor may have slower convergence in certain cases. You can switch to Adafactor by setting optim="adafactor" in [TrainingArguments]: py training_args = TrainingArguments(per_device_train_batch_size=4, optim="adafactor", **default_args) Combined with other approaches (gradient accumulation, gradient checkpointing, and mixed precision training) you can notice up to 3x improvement while maintaining the throughput! However, as mentioned before, the convergence of Adafactor can be worse than Adam. 8-bit Adam Instead of aggregating optimizer states like Adafactor, 8-bit Adam keeps the full state and quantizes it. Quantization means that it stores the state with lower precision and dequantizes it only for the optimization. This is similar to the idea behind mixed precision training. To use the 8-bit optimizer, you need to install it separately and then pass it as a custom optimizer to the [Trainer]. First, follow the installation guide in the GitHub repo to install the bitsandbytes library that implements the 8-bit Adam optimizer. Next you need to initialize the optimizer. This involves two steps: * First, group the model's parameters into two groups - one where weight decay should be applied, and the other one where it should not. Usually, biases and layer norm parameters are not weight decayed. * Then do some argument housekeeping to use the same parameters as the previously used AdamW optimizer. import bitsandbytes as bnb from torch import nn from transformers.trainer_pt_utils import get_parameter_names training_args = TrainingArguments(per_device_train_batch_size=4, **default_args) decay_parameters = get_parameter_names(model, [nn.LayerNorm]) decay_parameters = [name for name in decay_parameters if "bias" not in name] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if n in decay_parameters], "weight_decay": training_args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if n not in decay_parameters], "weight_decay": 0.0, }, ] optimizer_kwargs = { "betas": (training_args.adam_beta1, training_args.adam_beta2), "eps": training_args.adam_epsilon, } optimizer_kwargs["lr"] = training_args.learning_rate adam_bnb_optim = bnb.optim.Adam8bit( optimizer_grouped_parameters, betas=(training_args.adam_beta1, training_args.adam_beta2), eps=training_args.adam_epsilon, lr=training_args.learning_rate, ) To use the 8-bit optimizer with an existing pretrained model, you need to make a change to the embedding layer. Read this issue for more information. Finally, pass the custom optimizer as an argument to the Trainer: py trainer = Trainer(model=model, args=training_args, train_dataset=ds, optimizers=(adam_bnb_optim, None)) Combined with other approaches (gradient accumulation, gradient checkpointing, and mixed precision training), you can expect to get about a 3x memory improvement and even slightly higher throughput as using Adafactor. multi_tensor pytorch-nightly introduced torch.optim._multi_tensor which should significantly speed up the optimizers for situations with lots of small feature tensors. It should eventually become the default, but if you want to experiment with it sooner, take a look at this GitHub issue. Data preloading One of the important requirements to reach great training speed is the ability to feed the GPU at the maximum speed it can handle. By default, everything happens in the main process, and it might not be able to read the data from disk fast enough, and thus create a bottleneck, leading to GPU under-utilization. Configure the following arguments to reduce the bottleneck: DataLoader(pin_memory=True, ) - ensures the data gets preloaded into the pinned memory on CPU and typically leads to much faster transfers from CPU to GPU memory. DataLoader(num_workers=4, ) - spawn several workers to preload data faster. During training, watch the GPU utilization stats; if it's far from 100%, experiment with increasing the number of workers. Of course, the problem could be elsewhere, so many workers won't necessarily lead to better performance. When using [Trainer], the corresponding [TrainingArguments] are: dataloader_pin_memory (True by default), and dataloader_num_workers (defaults to 0). DeepSpeed ZeRO DeepSpeed is an open-source deep learning optimization library that is integrated with 🤗 Transformers and 🤗 Accelerate. It provides a wide range of features and optimizations designed to improve the efficiency and scalability of large-scale deep learning training. If your model fits onto a single GPU and you have enough space to fit a small batch size, you don't need to use DeepSpeed as it'll only slow things down. However, if the model doesn't fit onto a single GPU or you can't fit a small batch, you can leverage DeepSpeed ZeRO + CPU Offload, or NVMe Offload for much larger models. In this case, you need to separately install the library, then follow one of the guides to create a configuration file and launch DeepSpeed: For an in-depth guide on DeepSpeed integration with [Trainer], review the corresponding documentation, specifically the section for a single GPU. Some adjustments are required to use DeepSpeed in a notebook; please take a look at the corresponding guide. If you prefer to use 🤗 Accelerate, refer to 🤗 Accelerate DeepSpeed guide. Using torch.compile PyTorch 2.0 introduced a new compile function that doesn't require any modification to existing PyTorch code but can optimize your code by adding a single line of code: model = torch.compile(model). If using [Trainer], you only need to pass the torch_compile option in the [TrainingArguments]: python training_args = TrainingArguments(torch_compile=True, **default_args) torch.compile uses Python's frame evaluation API to automatically create a graph from existing PyTorch programs. After capturing the graph, different backends can be deployed to lower the graph to an optimized engine. You can find more details and benchmarks in PyTorch documentation. torch.compile has a growing list of backends, which can be found in by calling torchdynamo.list_backends(), each of which with its optional dependencies. Choose which backend to use by specifying it via torch_compile_backend in the [TrainingArguments]. Some of the most commonly used backends are: Debugging backends: * dynamo.optimize("eager") - Uses PyTorch to run the extracted GraphModule. This is quite useful in debugging TorchDynamo issues. * dynamo.optimize("aot_eager") - Uses AotAutograd with no compiler, i.e, just using PyTorch eager for the AotAutograd's extracted forward and backward graphs. This is useful for debugging, and unlikely to give speedups. Training & inference backends: * dynamo.optimize("inductor") - Uses TorchInductor backend with AotAutograd and cudagraphs by leveraging codegened Triton kernels Read more * dynamo.optimize("nvfuser") - nvFuser with TorchScript. Read more * dynamo.optimize("aot_nvfuser") - nvFuser with AotAutograd. Read more * dynamo.optimize("aot_cudagraphs") - cudagraphs with AotAutograd. Read more Inference-only backends: * dynamo.optimize("ofi") - Uses Torchscript optimize_for_inference. Read more * dynamo.optimize("fx2trt") - Uses Nvidia TensorRT for inference optimizations. Read more * dynamo.optimize("onnxrt") - Uses ONNXRT for inference on CPU/GPU. Read more * dynamo.optimize("ipex") - Uses IPEX for inference on CPU. Read more For an example of using torch.compile with 🤗 Transformers, check out this blog post on fine-tuning a BERT model for Text Classification using the newest PyTorch 2.0 features Using 🤗 Accelerate With 🤗 Accelerate you can use the above methods while gaining full control over the training loop and can essentially write the loop in pure PyTorch with some minor modifications. Suppose you have combined the methods in the [TrainingArguments] like so: py training_args = TrainingArguments( per_device_train_batch_size=1, gradient_accumulation_steps=4, gradient_checkpointing=True, fp16=True, **default_args, ) The full example training loop with 🤗 Accelerate is only a handful of lines of code long: from accelerate import Accelerator from torch.utils.data.dataloader import DataLoader dataloader = DataLoader(ds, batch_size=training_args.per_device_train_batch_size) if training_args.gradient_checkpointing: model.gradient_checkpointing_enable() accelerator = Accelerator(fp16=training_args.fp16) model, optimizer, dataloader = accelerator.prepare(model, adam_bnb_optim, dataloader) model.train() for step, batch in enumerate(dataloader, start=1): loss = model(**batch).loss loss = loss / training_args.gradient_accumulation_steps accelerator.backward(loss) if step % training_args.gradient_accumulation_steps == 0: optimizer.step() optimizer.zero_grad() First we wrap the dataset in a DataLoader. Then we can enable gradient checkpointing by calling the model's [~PreTrainedModel.gradient_checkpointing_enable] method. When we initialize the Accelerator we can specify if we want to use mixed precision training and it will take care of it for us in the [prepare] call. During the prepare call the dataloader will also be distributed across workers should we use multiple GPUs. We use the same 8-bit optimizer from the earlier example. Finally, we can add the main training loop. Note that the backward call is handled by 🤗 Accelerate. We can also see how gradient accumulation works: we normalize the loss, so we get the average at the end of accumulation and once we have enough steps we run the optimization. Implementing these optimization techniques with 🤗 Accelerate only takes a handful of lines of code and comes with the benefit of more flexibility in the training loop. For a full documentation of all features have a look at the Accelerate documentation. Efficient Software Prebuilds PyTorch's pip and conda builds come prebuilt with the cuda toolkit which is enough to run PyTorch, but it is insufficient if you need to build cuda extensions. At times, additional efforts may be required to pre-build some components. For instance, if you're using libraries like apex that don't come pre-compiled. In other situations figuring out how to install the right cuda toolkit system-wide can be complicated. To address these scenarios PyTorch and NVIDIA released a new version of NGC docker container which already comes with everything prebuilt. You just need to install your programs on it, and it will run out of the box. This approach is also useful if you want to tweak the pytorch source and/or make a new customized build. To find the docker image version you want start with PyTorch release notes, choose one of the latest monthly releases. Go into the release's notes for the desired release, check that the environment's components are matching your needs (including NVIDIA Driver requirements!) and then at the very top of that document go to the corresponding NGC page. If for some reason you get lost, here is the index of all PyTorch NGC images. Next follow the instructions to download and deploy the docker image. Mixture of Experts Some recent papers reported a 4-5x training speedup and a faster inference by integrating Mixture of Experts (MoE) into the Transformer models. Since it has been discovered that more parameters lead to better performance, this technique allows to increase the number of parameters by an order of magnitude without increasing training costs. In this approach every other FFN layer is replaced with a MoE Layer which consists of many experts, with a gated function that trains each expert in a balanced way depending on the input token's position in a sequence. (source: GLAM) You can find exhaustive details and comparison tables in the papers listed at the end of this section. The main drawback of this approach is that it requires staggering amounts of GPU memory - almost an order of magnitude larger than its dense equivalent. Various distillation and approaches are proposed to how to overcome the much higher memory requirements. There is direct trade-off though, you can use just a few experts with a 2-3x smaller base model instead of dozens or hundreds experts leading to a 5x smaller model and thus increase the training speed moderately while increasing the memory requirements moderately as well. Most related papers and implementations are built around Tensorflow/TPUs: GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity GLaM: Generalist Language Model (GLaM) And for Pytorch DeepSpeed has built one as well: DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale, Mixture of Experts - blog posts: 1, 2 and specific deployment with large transformer-based natural language generation models: blog post, Megatron-Deepspeed branch. Using PyTorch native attention PyTorch 2.0 released the native torch.nn.functional.scaled_dot_product_attention (SDPA), that allows to use fused GPU kernels as memory-efficient attention and flash attention. After installing the optimum package, the relevant internal modules can be replaced to use PyTorch's native attention with: python model = model.to_bettertransformer() Once converted, train the model as usual.

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate jiwer We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load MInDS-14 dataset Start by loading a smaller subset of the MInDS-14 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset. from datasets import load_dataset, Audio minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]") Split the dataset's train split into a train and test set with the [~Dataset.train_test_split] method: minds = minds.train_test_split(test_size=0.2) Then take a look at the dataset: minds DatasetDict({ train: Dataset({ features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'], num_rows: 16 }) test: Dataset({ features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'], num_rows: 4 }) }) While the dataset contains a lot of useful information, like lang_id and english_transcription, you'll focus on the audio and transcription in this guide. Remove the other columns with the [~datasets.Dataset.remove_columns] method: minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"]) Take a look at the example again: minds["train"][0] {'audio': {'array': array([-0.00024414, 0. , 0. , , 0.00024414, 0.00024414, 0.00024414], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'sampling_rate': 8000}, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"} There are two fields: audio: a 1-dimensional array of the speech signal that must be called to load and resample the audio file. transcription: the target text. Preprocess The next step is to load a Wav2Vec2 processor to process the audio signal: from transformers import AutoProcessor processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base") The MInDS-14 dataset has a sampling rate of 8000kHz (you can find this information in its dataset card), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model: minds = minds.cast_column("audio", Audio(sampling_rate=16_000)) minds["train"][0] {'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, , 2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'sampling_rate': 16000}, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"} As you can see in the transcription above, the text contains a mix of upper and lowercase characters. The Wav2Vec2 tokenizer is only trained on uppercase characters so you'll need to make sure the text matches the tokenizer's vocabulary: def uppercase(example): return {"transcription": example["transcription"].upper()} minds = minds.map(uppercase) Now create a preprocessing function that: Calls the audio column to load and resample the audio file. Extracts the input_values from the audio file and tokenize the transcription column with the processor. def prepare_dataset(batch): audio = batch["audio"] batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"]) batch["input_length"] = len(batch["input_values"][0]) return batch To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up map by increasing the number of processes with the num_proc parameter. Remove the columns you don't need with the [~datasets.Dataset.remove_columns] method: encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4) 🤗 Transformers doesn't have a data collator for ASR, so you'll need to adapt the [DataCollatorWithPadding] to create a batch of examples. It'll also dynamically pad your text and labels to the length of the longest element in its batch (instead of the entire dataset) so they are a uniform length. While it is possible to pad your text in the tokenizer function by setting padding=True, dynamic padding is more efficient. Unlike other data collators, this specific data collator needs to apply a different padding method to input_values and labels: import torch from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union @dataclass class DataCollatorCTCWithPadding: processor: AutoProcessor padding: Union[bool, str] = "longest" def call(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lengths and need # different padding methods input_features = [{"input_values": feature["input_values"][0]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt") labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt") # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels return batch Now instantiate your DataCollatorForCTCWithPadding: data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the word error rate (WER) metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric): import evaluate wer = evaluate.load("wer") Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the WER: import numpy as np def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids) label_str = processor.batch_decode(pred.label_ids, group_tokens=False) wer = wer.compute(predictions=pred_str, references=label_str) return {"wer": wer} Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load Wav2Vec2 with [AutoModelForCTC]. Specify the reduction to apply with the ctc_loss_reduction parameter. It is often better to use the average instead of the default summation: from transformers import AutoModelForCTC, TrainingArguments, Trainer model = AutoModelForCTC.from_pretrained( "facebook/wav2vec2-base", ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, ) At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the WER and save the training checkpoint. Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_asr_mind_model", per_device_train_batch_size=8, gradient_accumulation_steps=2, learning_rate=1e-5, warmup_steps=500, max_steps=2000, gradient_checkpointing=True, fp16=True, group_by_length=True, evaluation_strategy="steps", per_device_eval_batch_size=8, save_steps=1000, eval_steps=1000, logging_steps=25, load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=encoded_minds["train"], eval_dataset=encoded_minds["test"], tokenizer=processor, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() For a more in-depth example of how to finetune a model for automatic speech recognition, take a look at this blog post for English ASR and this post for multilingual ASR. Inference Great, now that you've finetuned a model, you can use it for inference! Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to! from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", "en-US", split="train") dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) sampling_rate = dataset.features["audio"].sampling_rate audio_file = dataset[0]["audio"]["path"] The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for automatic speech recognition with your model, and pass your audio file to it: from transformers import pipeline transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model") transcriber(audio_file) {'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'} The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better results! You can also manually replicate the results of the pipeline if you'd like: Load a processor to preprocess the audio file and transcription and return the input as PyTorch tensors: from transformers import AutoProcessor processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model") inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") Pass your inputs to the model and return the logits: from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model") with torch.no_grad(): logits = model(**inputs).logits Get the predicted input_ids with the highest probability, and use the processor to decode the predicted input_ids back into text: import torch predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) transcription ['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']

Efficient Training on Multiple CPUs When training on a single CPU is too slow, we can use multiple CPUs. This guide focuses on PyTorch-based DDP enabling distributed CPU training efficiently. Intel® oneCCL Bindings for PyTorch Intel® oneCCL (collective communications library) is a library for efficient distributed deep learning training implementing such collectives like allreduce, allgather, alltoall. For more information on oneCCL, please refer to the oneCCL documentation and oneCCL specification. Module oneccl_bindings_for_pytorch (torch_ccl before version 1.12) implements PyTorch C10D ProcessGroup API and can be dynamically loaded as external ProcessGroup and only works on Linux platform now Check more detailed information for oneccl_bind_pt. Intel® oneCCL Bindings for PyTorch installation: Wheel files are available for the following Python versions: | Extension Version | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10 | | :---------------: | :--------: | :--------: | :--------: | :--------: | :---------: | | 1.13.0 | | √ | √ | √ | √ | | 1.12.100 | | √ | √ | √ | √ | | 1.12.0 | | √ | √ | √ | √ | | 1.11.0 | | √ | √ | √ | √ | | 1.10.0 | √ | √ | √ | √ | | pip install oneccl_bind_pt=={pytorch_version} -f https://developer.intel.com/ipex-whl-stable-cpu where {pytorch_version} should be your PyTorch version, for instance 1.13.0. Check more approaches for oneccl_bind_pt installation. Versions of oneCCL and PyTorch must match. oneccl_bindings_for_pytorch 1.12.0 prebuilt wheel does not work with PyTorch 1.12.1 (it is for PyTorch 1.12.0) PyTorch 1.12.1 should work with oneccl_bindings_for_pytorch 1.12.100 Intel® MPI library Use this standards-based MPI implementation to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. This component is part of the Intel® oneAPI HPC Toolkit. oneccl_bindings_for_pytorch is installed along with the MPI tool set. Need to source the environment before using it. for Intel® oneCCL >= 1.12.0 oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)") source $oneccl_bindings_for_pytorch_path/env/setvars.sh for Intel® oneCCL whose version < 1.12.0 torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))") source $torch_ccl_path/env/setvars.sh IPEX installation: IPEX provides performance optimizations for CPU training with both Float32 and BFloat16, you could refer single CPU section. The following "Usage in Trainer" takes mpirun in Intel® MPI library as an example. Usage in Trainer To enable multi CPU distributed training in the Trainer with the ccl backend, users should add --ddp_backend ccl in the command arguments. Let's see an example with the question-answering example The following command enables training with 2 processes on one Xeon node, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance. shell script export CCL_WORKER_COUNT=1 export MASTER_ADDR=127.0.0.1 mpirun -n 2 -genv OMP_NUM_THREADS=23 \ python3 run_qa.py \ --model_name_or_path bert-large-uncased \ --dataset_name squad \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/debug_squad/ \ --no_cuda \ --ddp_backend ccl \ --use_ipex The following command enables training with a total of four processes on two Xeons (node0 and node1, taking node0 as the main process), ppn (processes per node) is set to 2, with one process running per one socket. The variables OMP_NUM_THREADS/CCL_WORKER_COUNT can be tuned for optimal performance. In node0, you need to create a configuration file which contains the IP addresses of each node (for example hostfile) and pass that configuration file path as an argument. shell script cat hostfile xxx.xxx.xxx.xxx #node0 ip xxx.xxx.xxx.xxx #node1 ip Now, run the following command in node0 and 4DDP will be enabled in node0 and node1 with BF16 auto mixed precision: shell script export CCL_WORKER_COUNT=1 export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip mpirun -f hostfile -n 4 -ppn 2 \ -genv OMP_NUM_THREADS=23 \ python3 run_qa.py \ --model_name_or_path bert-large-uncased \ --dataset_name squad \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/debug_squad/ \ --no_cuda \ --ddp_backend ccl \ --use_ipex \ --bf16

Community This page regroups resources around 🤗 Transformers developed by the community. Community resources: | Resource | Description | Author | |:----------|:-------------|------:| | Hugging Face Transformers Glossary Flashcards | A set of flashcards based on the Transformers Docs Glossary that has been put into a form which can be easily learnt/revised using Anki an open source, cross platform app specifically designed for long term knowledge retention. See this Introductory video on how to use the flashcards. | Darigov Research | Community notebooks: | Notebook | Description | Author | | |:----------|:-------------|:-------------|------:| | Fine-tune a pre-trained Transformer to generate lyrics | How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model | Aleksey Korshuk | | | Train T5 in Tensorflow 2 | How to train T5 for any task using Tensorflow 2. This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using SQUAD | Muhammad Harris | | | Train T5 on TPU | How to train T5 on SQUAD with Transformers and Nlp | Suraj Patil | | | Fine-tune T5 for Classification and Multiple Choice | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning | Suraj Patil | | | Fine-tune DialoGPT on New Datasets and Languages | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots | Nathan Cooper | | | Long Sequence Modeling with Reformer | How to train on sequences as long as 500,000 tokens with Reformer | Patrick von Platen | | | Fine-tune BART for Summarization | How to fine-tune BART for summarization with fastai using blurr | Wayde Gilliam | | | Fine-tune a pre-trained Transformer on anyone's tweets | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model | Boris Dayma | | | Optimize 🤗 Hugging Face models with Weights & Biases | A complete tutorial showcasing W&B integration with Hugging Face | Boris Dayma | | | Pretrain Longformer | How to build a "long" version of existing pretrained models | Iz Beltagy | | | Fine-tune Longformer for QA | How to fine-tune longformer model for QA task | Suraj Patil | | | Evaluate Model with 🤗nlp | How to evaluate longformer on TriviaQA with nlp | Patrick von Platen | | | Fine-tune T5 for Sentiment Span Extraction | How to fine-tune T5 for sentiment span extraction using a text-to-text format with PyTorch Lightning | Lorenzo Ampil | | | Fine-tune DistilBert for Multiclass Classification | How to fine-tune DistilBert for multiclass classification with PyTorch | Abhishek Kumar Mishra | | |Fine-tune BERT for Multi-label Classification|How to fine-tune BERT for multi-label classification using PyTorch|Abhishek Kumar Mishra || |Fine-tune T5 for Summarization|How to fine-tune T5 for summarization in PyTorch and track experiments with WandB|Abhishek Kumar Mishra || |Speed up Fine-Tuning in Transformers with Dynamic Padding / Bucketing|How to speed up fine-tuning by a factor of 2 using dynamic padding / bucketing|Michael Benesty || |Pretrain Reformer for Masked Language Modeling| How to train a Reformer model with bi-directional self-attention layers | Patrick von Platen | | |Expand and Fine Tune Sci-BERT| How to increase vocabulary of a pretrained SciBERT model from AllenAI on the CORD dataset and pipeline it. | Tanmay Thakur | | |Fine Tune BlenderBotSmall for Summarization using the Trainer API| How to fine tune BlenderBotSmall for summarization on a custom dataset, using the Trainer API. | Tanmay Thakur | | |Fine-tune Electra and interpret with Integrated Gradients | How to fine-tune Electra for sentiment analysis and interpret predictions with Captum Integrated Gradients | Eliza Szczechla | | |fine-tune a non-English GPT-2 Model with Trainer class | How to fine-tune a non-English GPT-2 Model with Trainer class | Philipp Schmid | | |Fine-tune a DistilBERT Model for Multi Label Classification task | How to fine-tune a DistilBERT Model for Multi Label Classification task | Dhaval Taunk | | |Fine-tune ALBERT for sentence-pair classification | How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | Nadir El Manouzi | | |Fine-tune Roberta for sentiment analysis | How to fine-tune a Roberta model for sentiment analysis | Dhaval Taunk | | |Evaluating Question Generation Models | How accurate are the answers to questions generated by your seq2seq transformer model? | Pascal Zoleko | | |Classify text with DistilBERT and Tensorflow | How to fine-tune DistilBERT for text classification in TensorFlow | Peter Bayerle | | |Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail | How to warm-start a EncoderDecoderModel with a bert-base-uncased checkpoint for summarization on CNN/Dailymail | Patrick von Platen | | |Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum | How to warm-start a shared EncoderDecoderModel with a roberta-base checkpoint for summarization on BBC/XSum | Patrick von Platen | | |Fine-tune TAPAS on Sequential Question Answering (SQA) | How to fine-tune TapasForQuestionAnswering with a tapas-base checkpoint on the Sequential Question Answering (SQA) dataset | Niels Rogge | | |Evaluate TAPAS on Table Fact Checking (TabFact) | How to evaluate a fine-tuned TapasForSequenceClassification with a tapas-base-finetuned-tabfact checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | Niels Rogge | | |Fine-tuning mBART for translation | How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | Vasudev Gupta | | |Fine-tune LayoutLM on FUNSD (a form understanding dataset) | How to fine-tune LayoutLMForTokenClassification on the FUNSD dataset for information extraction from scanned documents | Niels Rogge | | |Fine-Tune DistilGPT2 and Generate Text | How to fine-tune DistilGPT2 and generate text | Aakash Tripathi | | |Fine-Tune LED on up to 8K tokens | How to fine-tune LED on pubmed for long-range summarization | Patrick von Platen | | |Evaluate LED on Arxiv | How to effectively evaluate LED on long-range summarization | Patrick von Platen | | |Fine-tune LayoutLM on RVL-CDIP (a document image classification dataset) | How to fine-tune LayoutLMForSequenceClassification on the RVL-CDIP dataset for scanned document classification | Niels Rogge | | |Wav2Vec2 CTC decoding with GPT2 adjustment | How to decode CTC sequence with language model adjustment | Eric Lam | | |Fine-tune BART for summarization in two languages with Trainer class | How to fine-tune BART for summarization in two languages with Trainer class | Eliza Szczechla | | |Evaluate Big Bird on Trivia QA | How to evaluate BigBird on long document question answering on Trivia QA | Patrick von Platen | | | Create video captions using Wav2Vec2 | How to create YouTube captions from any video by transcribing the audio with Wav2Vec | Niklas Muennighoff | | | Fine-tune the Vision Transformer on CIFAR-10 using PyTorch Lightning | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and PyTorch Lightning | Niels Rogge | | | Fine-tune the Vision Transformer on CIFAR-10 using the 🤗 Trainer | How to fine-tune the Vision Transformer (ViT) on CIFAR-10 using HuggingFace Transformers, Datasets and the 🤗 Trainer | Niels Rogge | | | Evaluate LUKE on Open Entity, an entity typing dataset | How to evaluate LukeForEntityClassification on the Open Entity dataset | Ikuya Yamada | | | Evaluate LUKE on TACRED, a relation extraction dataset | How to evaluate LukeForEntityPairClassification on the TACRED dataset | Ikuya Yamada | | | Evaluate LUKE on CoNLL-2003, an important NER benchmark | How to evaluate LukeForEntitySpanClassification on the CoNLL-2003 dataset | Ikuya Yamada | | | Evaluate BigBird-Pegasus on PubMed dataset | How to evaluate BigBirdPegasusForConditionalGeneration on PubMed dataset | Vasudev Gupta | | | Speech Emotion Classification with Wav2Vec2 | How to leverage a pretrained Wav2Vec2 model for Emotion Classification on the MEGA dataset | Mehrdad Farahani | | | Detect objects in an image with DETR | How to use a trained DetrForObjectDetection model to detect objects in an image and visualize attention | Niels Rogge | | | Fine-tune DETR on a custom object detection dataset | How to fine-tune DetrForObjectDetection on a custom object detection dataset | Niels Rogge | | | Finetune T5 for Named Entity Recognition | How to fine-tune T5 on a Named Entity Recognition Task | Ogundepo Odunayo | |

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate seqeval We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load WNUT 17 dataset Start by loading the WNUT 17 dataset from the 🤗 Datasets library: from datasets import load_dataset wnut = load_dataset("wnut_17") Then take a look at an example: wnut["train"][0] {'id': '0', 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], 'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'] } Each number in ner_tags represents an entity. Convert the numbers to their label names to find out what the entities are: label_list = wnut["train"].features[f"ner_tags"].feature.names label_list [ "O", "B-corporation", "I-corporation", "B-creative-work", "I-creative-work", "B-group", "I-group", "B-location", "I-location", "B-person", "I-person", "B-product", "I-product", ] The letter that prefixes each ner_tag indicates the token position of the entity: B- indicates the beginning of an entity. I- indicates a token is contained inside the same entity (for example, the State token is a part of an entity like Empire State Building). 0 indicates the token doesn't correspond to any entity. Preprocess The next step is to load a DistilBERT tokenizer to preprocess the tokens field: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") As you saw in the example tokens field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set is_split_into_words=True to tokenize the words into subwords. For example: example = wnut["train"][0] tokenized_input = tokenizer(example["tokens"], is_split_into_words=True) tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"]) tokens ['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]'] However, this adds some special tokens [CLS] and [SEP] and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by: Mapping all tokens to their corresponding word with the word_ids method. Assigning the label -100 to the special tokens [CLS] and [SEP] so they're ignored by the PyTorch loss function (see CrossEntropyLoss). Only labeling the first token of a given word. Assign -100 to other subtokens from the same word. Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT's maximum input length: def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True) labels = [] for i, label in enumerate(examples[f"ner_tags"]): word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word. previous_word_idx = None label_ids = [] for word_idx in word_ids: # Set the special tokens to -100. if word_idx is None: label_ids.append(-100) elif word_idx != previous_word_idx: # Only label the first token of a given word. label_ids.append(label[word_idx]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once: tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True) Now create a batch of examples using [DataCollatorWithPadding]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. from transformers import DataCollatorForTokenClassification data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer) </pt> <tf>py from transformers import DataCollatorForTokenClassification data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the seqeval framework (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric). Seqeval actually produces several scores: precision, recall, F1, and accuracy. import evaluate seqeval = evaluate.load("seqeval") Get the NER labels first, and then create a function that passes your true predictions and true labels to [~evaluate.EvaluationModule.compute] to calculate the scores: import numpy as np labels = [label_list[i] for i in example[f"ner_tags"]] def compute_metrics(p): predictions, labels = p predictions = np.argmax(predictions, axis=2) true_predictions = [ [label_list[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels) ] true_labels = [ [label_list[l] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels) ] results = seqeval.compute(predictions=true_predictions, references=true_labels) return { "precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"], } Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. Train Before you start training your model, create a map of the expected ids to their labels with id2label and label2id: id2label = { 0: "O", 1: "B-corporation", 2: "I-corporation", 3: "B-creative-work", 4: "I-creative-work", 5: "B-group", 6: "I-group", 7: "B-location", 8: "I-location", 9: "B-person", 10: "I-person", 11: "B-product", 12: "I-product", } label2id = { "O": 0, "B-corporation": 1, "I-corporation": 2, "B-creative-work": 3, "I-creative-work": 4, "B-group": 5, "I-group": 6, "B-location": 7, "I-location": 8, "B-person": 9, "I-person": 10, "B-product": 11, "I-product": 12, } If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load DistilBERT with [AutoModelForTokenClassification] along with the number of expected labels, and the label mappings: from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer model = AutoModelForTokenClassification.from_pretrained( "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id ) At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the seqeval scores and save the training checkpoint. Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_wnut_model", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=2, weight_decay=0.01, evaluation_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_wnut["train"], eval_dataset=tokenized_wnut["test"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import create_optimizer batch_size = 16 num_train_epochs = 3 num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs optimizer, lr_schedule = create_optimizer( init_lr=2e-5, num_train_steps=num_train_steps, weight_decay_rate=0.01, num_warmup_steps=0, ) Then you can load DistilBERT with [TFAutoModelForTokenClassification] along with the number of expected labels, and the label mappings: from transformers import TFAutoModelForTokenClassification model = TFAutoModelForTokenClassification.from_pretrained( "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id ) Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( tokenized_wnut["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_validation_set = model.prepare_tf_dataset( tokenized_wnut["validation"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: import tensorflow as tf model.compile(optimizer=optimizer) # No loss argument! The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks. Pass your compute_metrics function to [~transformers.KerasMetricCallback]: from transformers.keras_callbacks import KerasMetricCallback metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set) Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="my_awesome_wnut_model", tokenizer=tokenizer, ) Then bundle your callbacks together: callbacks = [metric_callback, push_to_hub_callback] Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model: model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for token classification, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Grab some text you'd like to run inference on: text = "The Golden State Warriors are an American professional basketball team based in San Francisco." The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for NER with your model, and pass your text to it: from transformers import pipeline classifier = pipeline("ner", model="stevhliu/my_awesome_wnut_model") classifier(text) [{'entity': 'B-location', 'score': 0.42658573, 'index': 2, 'word': 'golden', 'start': 4, 'end': 10}, {'entity': 'I-location', 'score': 0.35856336, 'index': 3, 'word': 'state', 'start': 11, 'end': 16}, {'entity': 'B-group', 'score': 0.3064001, 'index': 4, 'word': 'warriors', 'start': 17, 'end': 25}, {'entity': 'B-location', 'score': 0.65523505, 'index': 13, 'word': 'san', 'start': 80, 'end': 83}, {'entity': 'B-location', 'score': 0.4668663, 'index': 14, 'word': 'francisco', 'start': 84, 'end': 93}] You can also manually replicate the results of the pipeline if you'd like: Tokenize the text and return PyTorch tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model") inputs = tokenizer(text, return_tensors="pt") Pass your inputs to the model and return the logits: from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model") with torch.no_grad(): logits = model(**inputs).logits Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label: predictions = torch.argmax(logits, dim=2) predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]] predicted_token_class ['O', 'O', 'B-location', 'I-location', 'B-group', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-location', 'B-location', 'O', 'O'] Tokenize the text and return TensorFlow tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model") inputs = tokenizer(text, return_tensors="tf") Pass your inputs to the model and return the logits: from transformers import TFAutoModelForTokenClassification model = TFAutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model") logits = model(**inputs).logits Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label: predicted_token_class_ids = tf.math.argmax(logits, axis=-1) predicted_token_class = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()] predicted_token_class ['O', 'O', 'B-location', 'I-location', 'B-group', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-location', 'B-location', 'O', 'O']

Efficient Inference on a Multiple GPUs This document contains information on how to efficiently infer on a multiple GPUs. Note: A multi GPU setup can use the majority of the strategies described in the single GPU section. You must be aware of simple techniques, though, that can be used for a better usage. BetterTransformer for faster inference We have recently integrated BetterTransformer for faster inference on multi-GPU for text, image and audio models. Check the documentation about this integration here for more details.

BERTology There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are: BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950 Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650 What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341 CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633 In order to help this new field develop, we have included a few additional features in the BERT/GPT/GPT-2 models to help people access the inner representations, mainly adapted from the great work of Paul Michel (https://arxiv.org/abs/1905.10650): accessing all the hidden-states of BERT/GPT/GPT-2, accessing all the attention weights for each head of BERT/GPT/GPT-2, retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650. To help you understand and use these features, we have added a specific example script: bertology.py while extract information and prune a model pre-trained on GLUE.

Pipelines The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the task summary for examples of use. There are two categories of pipeline abstractions to be aware about: The [pipeline] which is the most powerful object encapsulating all other pipelines. Task-specific pipelines are available for audio, computer vision, natural language processing, and multimodal tasks. The pipeline abstraction The pipeline abstraction is a wrapper around all the other available pipelines. It is instantiated as any other pipeline but can provide additional quality of life. Simple call on one item: thon pipe = pipeline("text-classification") pipe("This restaurant is awesome") [{'label': 'POSITIVE', 'score': 0.9998743534088135}] If you want to use a specific model from the hub you can ignore the task if the model on the hub already defines it: thon pipe = pipeline(model="roberta-large-mnli") pipe("This restaurant is awesome") [{'label': 'NEUTRAL', 'score': 0.7313136458396912}] To call a pipeline on many items, you can call it with a list. thon pipe = pipeline("text-classification") pipe(["This restaurant is awesome", "This restaurant is awful"]) [{'label': 'POSITIVE', 'score': 0.9998743534088135}, {'label': 'NEGATIVE', 'score': 0.9996669292449951}] To iterate over full datasets it is recommended to use a dataset directly. This means you don't need to allocate the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on GPU. If it doesn't don't hesitate to create an issue. thon import datasets from transformers import pipeline from transformers.pipelines.pt_utils import KeyDataset from tqdm.auto import tqdm pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0) dataset = datasets.load_dataset("superb", name="asr", split="test") KeyDataset (only pt) will simply return the item in the dict returned by the dataset item as we're not interested in the target part of the dataset. For sentence pair use KeyPairDataset for out in tqdm(pipe(KeyDataset(dataset, "file"))): print(out) # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"} # {"text": .} # . For ease of use, a generator is also possible: thon from transformers import pipeline pipe = pipeline("text-classification") def data(): while True: # This could come from a dataset, a database, a queue or HTTP request # in a server # Caveat: because this is iterative, you cannot use num_workers > 1 variable # to use multiple threads to preprocess data. You can still have 1 thread that # does the preprocessing while the main runs the big inference yield "This is a test" for out in pipe(data()): print(out) # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"} # {"text": .} # . [[autodoc]] pipeline Pipeline batching All pipelines can use batching. This will work whenever the pipeline uses its streaming ability (so when passing lists or Dataset or generator). thon from transformers import pipeline from transformers.pipelines.pt_utils import KeyDataset import datasets dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised") pipe = pipeline("text-classification", device=0) for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"): print(out) # [{'label': 'POSITIVE', 'score': 0.9998743534088135}] # Exactly the same output as before, but the content are passed # as batches to the model However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending on hardware, data and the actual model being used. Example where it's mostly a speedup: thon from transformers import pipeline from torch.utils.data import Dataset from tqdm.auto import tqdm pipe = pipeline("text-classification", device=0) class MyDataset(Dataset): def len(self): return 5000 def __getitem__(self, i): return "This is a test" dataset = MyDataset() for batch_size in [1, 8, 64, 256]: print("-" * 30) print(f"Streaming batch_size={batch_size}") for out in tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)): pass On GTX 970 Streaming no batching 100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s] Streaming batch_size=8 100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s] Streaming batch_size=64 100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s] Streaming batch_size=256 100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s] (diminishing returns, saturated the GPU) Example where it's most a slowdown: thon class MyDataset(Dataset): def len(self): return 5000 def __getitem__(self, i): if i % 64 == 0: n = 100 else: n = 1 return "This is a test" * n This is a occasional very long sentence compared to the other. In that case, the whole batch will need to be 400 tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on bigger batches, the program simply crashes. Streaming no batching 100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s] Streaming batch_size=8 100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s] Streaming batch_size=64 100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s] Streaming batch_size=256 0%| | 0/1000 [00:00<?, ?it/s] Traceback (most recent call last): File "/home/nicolas/src/transformers/test.py", line 42, in for out in tqdm(pipe(dataset, batch_size=256), total=len(dataset)): . q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head) RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch) There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of thumb: For users, a rule of thumb is: Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the only way to go. If you are latency constrained (live product doing inference), don't batch If you are using CPU, don't batch. If you are using throughput (you want to run your model on a bunch of static data), on GPU, then: If you have no clue about the size of the sequence_length ("natural" data), by default don't batch, measure and try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don't control the sequence_length.) If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push it until you get OOMs. The larger the GPU the more likely batching is going to be more interesting As soon as you enable batching, make sure you can handle OOMs nicely. Pipeline chunk batching zero-shot-classification and question-answering are slightly specific in the sense, that a single input might yield multiple forward pass of a model. Under normal circumstances, this would yield issues with batch_size argument. In order to circumvent this issue, both of these pipelines are a bit specific, they are ChunkPipeline instead of regular Pipeline. In short: python preprocessed = pipe.preprocess(inputs) model_outputs = pipe.forward(preprocessed) outputs = pipe.postprocess(model_outputs) Now becomes: python all_model_outputs = [] for preprocessed in pipe.preprocess(inputs): model_outputs = pipe.forward(preprocessed) all_model_outputs.append(model_outputs) outputs = pipe.postprocess(all_model_outputs) This should be very transparent to your code because the pipelines are used in the same way. This is a simplified view, since the pipeline can handle automatically the batch to ! Meaning you don't have to care about how many forward passes you inputs are actually going to trigger, you can optimize the batch_size independently of the inputs. The caveats from the previous section still apply. Pipeline custom code If you want to override a specific pipeline. Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most cases, so transformers could maybe support your use case. If you want to try simply you can: Subclass your pipeline of choice thon class MyPipeline(TextClassificationPipeline): def postprocess(): # Your code goes here scores = scores * 100 # And here my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ) or if you use pipeline function, then: my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline) That should enable you to do all the custom code you want. Implementing a pipeline Implementing a new pipeline Audio Pipelines available for audio tasks include the following. AudioClassificationPipeline [[autodoc]] AudioClassificationPipeline - call - all AutomaticSpeechRecognitionPipeline [[autodoc]] AutomaticSpeechRecognitionPipeline - call - all ZeroShotAudioClassificationPipeline [[autodoc]] ZeroShotAudioClassificationPipeline - call - all Computer vision Pipelines available for computer vision tasks include the following. DepthEstimationPipeline [[autodoc]] DepthEstimationPipeline - call - all ImageClassificationPipeline [[autodoc]] ImageClassificationPipeline - call - all ImageSegmentationPipeline [[autodoc]] ImageSegmentationPipeline - call - all ObjectDetectionPipeline [[autodoc]] ObjectDetectionPipeline - call - all VideoClassificationPipeline [[autodoc]] VideoClassificationPipeline - call - all ZeroShotImageClassificationPipeline [[autodoc]] ZeroShotImageClassificationPipeline - call - all ZeroShotObjectDetectionPipeline [[autodoc]] ZeroShotObjectDetectionPipeline - call - all Natural Language Processing Pipelines available for natural language processing tasks include the following. ConversationalPipeline [[autodoc]] Conversation [[autodoc]] ConversationalPipeline - call - all FillMaskPipeline [[autodoc]] FillMaskPipeline - call - all NerPipeline [[autodoc]] NerPipeline See [TokenClassificationPipeline] for all details. QuestionAnsweringPipeline [[autodoc]] QuestionAnsweringPipeline - call - all SummarizationPipeline [[autodoc]] SummarizationPipeline - call - all TableQuestionAnsweringPipeline [[autodoc]] TableQuestionAnsweringPipeline - call TextClassificationPipeline [[autodoc]] TextClassificationPipeline - call - all TextGenerationPipeline [[autodoc]] TextGenerationPipeline - call - all Text2TextGenerationPipeline [[autodoc]] Text2TextGenerationPipeline - call - all TokenClassificationPipeline [[autodoc]] TokenClassificationPipeline - call - all TranslationPipeline [[autodoc]] TranslationPipeline - call - all ZeroShotClassificationPipeline [[autodoc]] ZeroShotClassificationPipeline - call - all Multimodal Pipelines available for multimodal tasks include the following. DocumentQuestionAnsweringPipeline [[autodoc]] DocumentQuestionAnsweringPipeline - call - all FeatureExtractionPipeline [[autodoc]] FeatureExtractionPipeline - call - all ImageToTextPipeline [[autodoc]] ImageToTextPipeline - call - all VisualQuestionAnsweringPipeline [[autodoc]] VisualQuestionAnsweringPipeline - call - all Parent class: Pipeline [[autodoc]] Pipeline

Testing Let's take a look at how 🤗 Transformers models are tested and how you can write new tests and improve the existing ones. There are 2 test suites in the repository: tests -- tests for the general API examples -- tests primarily for various applications that aren't part of the API How transformers are tested Once a PR is submitted it gets tested with 9 CircleCi jobs. Every new commit to that PR gets retested. These jobs are defined in this config file, so that if needed you can reproduce the same environment on your machine. These CI jobs don't run @slow tests. There are 3 jobs run by github actions: torch hub integration: checks whether torch hub integration works. self-hosted (push): runs fast tests on GPU only on commits on main. It only runs if a commit on main has updated the code in one of the following folders: src, tests, .github (to prevent running on added model cards, notebooks, etc.) self-hosted runner: runs normal and slow tests on GPU in tests and examples: RUN_SLOW=1 pytest tests/ RUN_SLOW=1 pytest examples/ The results can be observed here. Running tests Choosing which tests to run This document goes into many details of how tests can be run. If after reading everything, you need even more details you will find them here. Here are some most useful ways of running tests. Run all: console pytest or: make test Note that the latter is defined as: python -m pytest -n auto --dist=loadfile -s -v ./tests/ which tells pytest to: run as many test processes as they are CPU cores (which could be too many if you don't have a ton of RAM!) ensure that all tests from the same file will be run by the same test process do not capture output run in verbose mode Getting the list of all tests All tests of the test suite: pytest --collect-only -q All tests of a given test file: pytest tests/test_optimization.py --collect-only -q Run a specific test module To run an individual test module: pytest tests/test_logging.py Run specific tests Since unittest is used inside most of the tests, to run specific subtests you need to know the name of the unittest class containing those tests. For example, it could be: pytest tests/test_optimization.py::OptimizationTest::test_adam_w Here: tests/test_optimization.py - the file with tests OptimizationTest - the name of the class test_adam_w - the name of the specific test function If the file contains multiple classes, you can choose to run only tests of a given class. For example: pytest tests/test_optimization.py::OptimizationTest will run all the tests inside that class. As mentioned earlier you can see what tests are contained inside the OptimizationTest class by running: pytest tests/test_optimization.py::OptimizationTest --collect-only -q You can run tests by keyword expressions. To run only tests whose name contains adam: pytest -k adam tests/test_optimization.py Logical and and or can be used to indicate whether all keywords should match or either. not can be used to negate. To run all tests except those whose name contains adam: pytest -k "not adam" tests/test_optimization.py And you can combine the two patterns in one: pytest -k "ada and not adam" tests/test_optimization.py For example to run both test_adafactor and test_adam_w you can use: pytest -k "test_adam_w or test_adam_w" tests/test_optimization.py Note that we use or here, since we want either of the keywords to match to include both. If you want to include only tests that include both patterns, and is to be used: pytest -k "test and ada" tests/test_optimization.py Run accelerate tests Sometimes you need to run accelerate tests on your models. For that you can just add -m accelerate_tests to your command, if let's say you want to run these tests on OPT run: RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py Run documentation tests In order to test whether the documentation examples are correct, you should check that the doctests are passing. As an example, let's use WhisperModel.forward's docstring: thon r""" Returns: Example: thon >>> import torch >>> from transformers import WhisperModel, WhisperFeatureExtractor >>> from datasets import load_dataset >>> model = WhisperModel.from_pretrained("openai/whisper-base") >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_features = inputs.input_features >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state >>> list(last_hidden_state.shape) [1, 2, 512] ```""" Just run the following line to automatically test every docstring example in the desired file: pytest --doctest-modules <path_to_file_or_dir> If the file has a markdown extention, you should add the --doctest-glob="*.md" argument. Run only modified tests You can run the tests related to the unstaged files or the current branch (according to Git) by using pytest-picked. This is a great way of quickly testing your changes didn't break anything, since it won't run the tests related to files you didn't touch. pip install pytest-picked pytest --picked All tests will be run from files and folders which are modified, but not yet committed. Automatically rerun failed tests on source modification pytest-xdist provides a very useful feature of detecting all failed tests, and then waiting for you to modify files and continuously re-rerun those failing tests until they pass while you fix them. So that you don't need to re start pytest after you made the fix. This is repeated until all tests pass after which again a full run is performed. pip install pytest-xdist To enter the mode: pytest -f or pytest --looponfail File changes are detected by looking at looponfailroots root directories and all of their contents (recursively). If the default for this value does not work for you, you can change it in your project by setting a configuration option in setup.cfg: ini [tool:pytest] looponfailroots = transformers tests or pytest.ini/tox.ini files: ini [pytest] looponfailroots = transformers tests This would lead to only looking for file changes in the respective directories, specified relatively to the ini-file’s directory. pytest-watch is an alternative implementation of this functionality. Skip a test module If you want to run all test modules, except a few you can exclude them by giving an explicit list of tests to run. For example, to run all except test_modeling_*.py tests: pytest *ls -1 tests/*py | grep -v test_modeling* Clearing state CI builds and when isolation is important (against speed), cache should be cleared: pytest --cache-clear tests Running tests in parallel As mentioned earlier make test runs tests in parallel via pytest-xdist plugin (-n X argument, e.g. -n 2 to run 2 parallel jobs). pytest-xdist's --dist= option allows one to control how the tests are grouped. --dist=loadfile puts the tests located in one file onto the same process. Since the order of executed tests is different and unpredictable, if running the test suite with pytest-xdist produces failures (meaning we have some undetected coupled tests), use pytest-replay to replay the tests in the same order, which should help with then somehow reducing that failing sequence to a minimum. Test order and repetition It's good to repeat the tests several times, in sequence, randomly, or in sets, to detect any potential inter-dependency and state-related bugs (tear down). And the straightforward multiple repetition is just good to detect some problems that get uncovered by randomness of DL. Repeat tests pytest-flakefinder: pip install pytest-flakefinder And then run every test multiple times (50 by default): pytest --flake-finder --flake-runs=5 tests/test_failing_test.py This plugin doesn't work with -n flag from pytest-xdist. There is another plugin pytest-repeat, but it doesn't work with unittest. Run tests in a random order pip install pytest-random-order Important: the presence of pytest-random-order will automatically randomize tests, no configuration change or command line options is required. As explained earlier this allows detection of coupled tests - where one test's state affects the state of another. When pytest-random-order is installed it will print the random seed it used for that session, e.g: pytest tests [] Using --random-order-bucket=module Using --random-order-seed=573663 So that if the given particular sequence fails, you can reproduce it by adding that exact seed, e.g.: pytest --random-order-seed=573663 [] Using --random-order-bucket=module Using --random-order-seed=573663 It will only reproduce the exact order if you use the exact same list of tests (or no list at all). Once you start to manually narrowing down the list you can no longer rely on the seed, but have to list them manually in the exact order they failed and tell pytest to not randomize them instead using --random-order-bucket=none, e.g.: pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py To disable the shuffling for all tests: pytest --random-order-bucket=none By default --random-order-bucket=module is implied, which will shuffle the files on the module levels. It can also shuffle on class, package, global and none levels. For the complete details please see its documentation. Another randomization alternative is: pytest-randomly. This module has a very similar functionality/interface, but it doesn't have the bucket modes available in pytest-random-order. It has the same problem of imposing itself once installed. Look and feel variations pytest-sugar pytest-sugar is a plugin that improves the look-n-feel, adds a progressbar, and show tests that fail and the assert instantly. It gets activated automatically upon installation. pip install pytest-sugar To run tests without it, run: pytest -p no:sugar or uninstall it. Report each sub-test name and its progress For a single or a group of tests via pytest (after pip install pytest-pspec): pytest --pspec tests/test_optimization.py Instantly shows failed tests pytest-instafail shows failures and errors instantly instead of waiting until the end of test session. pip install pytest-instafail pytest --instafail To GPU or not to GPU On a GPU-enabled setup, to test in CPU-only mode add CUDA_VISIBLE_DEVICES="": CUDA_VISIBLE_DEVICES="" pytest tests/test_logging.py or if you have multiple gpus, you can specify which one is to be used by pytest. For example, to use only the second gpu if you have gpus 0 and 1, you can run: CUDA_VISIBLE_DEVICES="1" pytest tests/test_logging.py This is handy when you want to run different tasks on different GPUs. Some tests must be run on CPU-only, others on either CPU or GPU or TPU, yet others on multiple-GPUs. The following skip decorators are used to set the requirements of tests CPU/GPU/TPU-wise: require_torch - this test will run only under torch require_torch_gpu - as require_torch plus requires at least 1 GPU require_torch_multi_gpu - as require_torch plus requires at least 2 GPUs require_torch_non_multi_gpu - as require_torch plus requires 0 or 1 GPUs require_torch_up_to_2_gpus - as require_torch plus requires 0 or 1 or 2 GPUs require_torch_tpu - as require_torch plus requires at least 1 TPU Let's depict the GPU requirements in the following table: | n gpus | decorator | |--------+--------------------------------| | >= 0 | @require_torch | | >= 1 | @require_torch_gpu | | >= 2 | @require_torch_multi_gpu | | < 2 | @require_torch_non_multi_gpu | | < 3 | @require_torch_up_to_2_gpus | For example, here is a test that must be run only when there are 2 or more GPUs available and pytorch is installed: python no-style @require_torch_multi_gpu def test_example_with_multi_gpu(): If a test requires tensorflow use the require_tf decorator. For example: python no-style @require_tf def test_tf_thing_with_tensorflow(): These decorators can be stacked. For example, if a test is slow and requires at least one GPU under pytorch, here is how to set it up: python no-style @require_torch_gpu @slow def test_example_slow_on_gpu(): Some decorators like @parametrized rewrite test names, therefore @require_* skip decorators have to be listed last for them to work correctly. Here is an example of the correct usage: python no-style @parameterized.expand() @require_torch_multi_gpu def test_integration_foo(): This order problem doesn't exist with @pytest.mark.parametrize, you can put it first or last and it will still work. But it only works with non-unittests. Inside tests: How many GPUs are available: thon from transformers.testing_utils import get_gpu_count n_gpu = get_gpu_count() # works with torch and tf Distributed training pytest can't deal with distributed training directly. If this is attempted - the sub-processes don't do the right thing and end up thinking they are pytest and start running the test suite in loops. It works, however, if one spawns a normal process that then spawns off multiple workers and manages the IO pipes. Here are some tests that use it: test_trainer_distributed.py test_deepspeed.py To jump right into the execution point, search for the execute_subprocess_async call in those tests. You will need at least 2 GPUs to see these tests in action: CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/test_trainer_distributed.py Output capture During test execution any output sent to stdout and stderr is captured. If a test or a setup method fails, its according captured output will usually be shown along with the failure traceback. To disable output capturing and to get the stdout and stderr normally, use -s or --capture=no: pytest -s tests/test_logging.py To send test results to JUnit format output: py.test tests --junitxml=result.xml Color control To have no color (e.g., yellow on white background is not readable): pytest --color=no tests/test_logging.py Sending test report to online pastebin service Creating a URL for each test failure: pytest --pastebin=failed tests/test_logging.py This will submit test run information to a remote Paste service and provide a URL for each failure. You may select tests as usual or add for example -x if you only want to send one particular failure. Creating a URL for a whole test session log: pytest --pastebin=all tests/test_logging.py Writing tests 🤗 transformers tests are based on unittest, but run by pytest, so most of the time features from both systems can be used. You can read here which features are supported, but the important thing to remember is that most pytest fixtures don't work. Neither parametrization, but we use the module parameterized that works in a similar way. Parametrization Often, there is a need to run the same test multiple times, but with different arguments. It could be done from within the test, but then there is no way of running that test for just one set of arguments. thon test_this1.py import unittest from parameterized import parameterized class TestMathUnitTest(unittest.TestCase): @parameterized.expand( [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ] ) def test_floor(self, name, input, expected): assert_equal(math.floor(input), expected) Now, by default this test will be run 3 times, each time with the last 3 arguments of test_floor being assigned the corresponding arguments in the parameter list. and you could run just the negative and integer sets of params with: pytest -k "negative and integer" tests/test_mytest.py or all but negative sub-tests, with: pytest -k "not negative" tests/test_mytest.py Besides using the -k filter that was just mentioned, you can find out the exact name of each sub-test and run any or all of them using their exact names. pytest test_this1.py --collect-only -q and it will list: test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer test_this1.py::TestMathUnitTest::test_floor_2_large_fraction So now you can run just 2 specific sub-tests: pytest test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer The module parameterized which is already in the developer dependencies of transformers works for both: unittests and pytest tests. If, however, the test is not a unittest, you may use pytest.mark.parametrize (or you may see it being used in some existing tests, mostly under examples). Here is the same example, this time using pytest's parametrize marker: thon test_this2.py import pytest @pytest.mark.parametrize( "name, input, expected", [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ], ) def test_floor(name, input, expected): assert_equal(math.floor(input), expected) Same as with parameterized, with pytest.mark.parametrize you can have a fine control over which sub-tests are run, if the -k filter doesn't do the job. Except, this parametrization function creates a slightly different set of names for the sub-tests. Here is what they look like: pytest test_this2.py --collect-only -q and it will list: test_this2.py::test_floor[integer-1-1.0] test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[large fraction-1.6-1] So now you can run just the specific test: pytest test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[integer-1-1.0] as in the previous example. Files and directories In tests often we need to know where things are relative to the current test file, and it's not trivial since the test could be invoked from more than one directory or could reside in sub-directories with different depths. A helper class transformers.test_utils.TestCasePlus solves this problem by sorting out all the basic paths and provides easy accessors to them: pathlib objects (all fully resolved): test_file_path - the current test file path, i.e. __file__ test_file_dir - the directory containing the current test file tests_dir - the directory of the tests test suite examples_dir - the directory of the examples test suite repo_root_dir - the directory of the repository src_dir - the directory of src (i.e. where the transformers sub-dir resides) stringified paths---same as above but these return paths as strings, rather than pathlib objects: test_file_path_str test_file_dir_str tests_dir_str examples_dir_str repo_root_dir_str src_dir_str To start using those all you need is to make sure that the test resides in a subclass of transformers.test_utils.TestCasePlus. For example: thon from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_local_locations(self): data_dir = self.tests_dir / "fixtures/tests_samples/wmt_en_ro" If you don't need to manipulate paths via pathlib or you just need a path as a string, you can always invoked str() on the pathlib object or use the accessors ending with _str. For example: thon from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_stringified_locations(self): examples_dir = self.examples_dir_str Temporary files and directories Using unique temporary files and directories are essential for parallel test running, so that the tests won't overwrite each other's data. Also we want to get the temporary files and directories removed at the end of each test that created them. Therefore, using packages like tempfile, which address these needs is essential. However, when debugging tests, you need to be able to see what goes into the temporary file or directory and you want to know it's exact path and not having it randomized on every test re-run. A helper class transformers.test_utils.TestCasePlus is best used for such purposes. It's a sub-class of unittest.TestCase, so we can easily inherit from it in the test modules. Here is an example of its usage: thon from transformers.testing_utils import TestCasePlus class ExamplesTests(TestCasePlus): def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() This code creates a unique temporary directory, and sets tmp_dir to its location. Create a unique temporary dir: python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() tmp_dir will contain the path to the created temporary dir. It will be automatically removed at the end of the test. Create a temporary dir of my choice, ensure it's empty before the test starts and don't empty it after the test. python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir("./xxx") This is useful for debug when you want to monitor a specific directory and want to make sure the previous tests didn't leave any data in there. You can override the default behavior by directly overriding the before and after args, leading to one of the following behaviors: before=True: the temporary dir will always be cleared at the beginning of the test. before=False: if the temporary dir already existed, any existing files will remain there. after=True: the temporary dir will always be deleted at the end of the test. after=False: the temporary dir will always be left intact at the end of the test. In order to run the equivalent of rm -r safely, only subdirs of the project repository checkout are allowed if an explicit tmp_dir is used, so that by mistake no /tmp or similar important part of the filesystem will get nuked. i.e. please always pass paths that start with ./. Each test can register multiple temporary directories and they all will get auto-removed, unless requested otherwise. Temporary sys.path override If you need to temporary override sys.path to import from another test for example, you can use the ExtendSysPath context manager. Example: thon import os from transformers.testing_utils import ExtendSysPath bindir = os.path.abspath(os.path.dirname(file)) with ExtendSysPath(f"{bindir}/.."): from test_trainer import TrainerIntegrationCommon # noqa Skipping tests This is useful when a bug is found and a new test is written, yet the bug is not fixed yet. In order to be able to commit it to the main repository we need make sure it's skipped during make test. Methods: A skip means that you expect your test to pass only if some conditions are met, otherwise pytest should skip running the test altogether. Common examples are skipping windows-only tests on non-windows platforms, or skipping tests that depend on an external resource which is not available at the moment (for example a database). A xfail means that you expect a test to fail for some reason. A common example is a test for a feature not yet implemented, or a bug not yet fixed. When a test passes despite being expected to fail (marked with pytest.mark.xfail), it’s an xpass and will be reported in the test summary. One of the important differences between the two is that skip doesn't run the test, and xfail does. So if the code that's buggy causes some bad state that will affect other tests, do not use xfail. Implementation Here is how to skip whole test unconditionally: python no-style @unittest.skip("this bug needs to be fixed") def test_feature_x(): or via pytest: python no-style @pytest.mark.skip(reason="this bug needs to be fixed") or the xfail way: python no-style @pytest.mark.xfail def test_feature_x(): Here is how to skip a test based on some internal check inside the test: python def test_feature_x(): if not has_something(): pytest.skip("unsupported configuration") or the whole module: thon import pytest if not pytest.config.getoption("--custom-flag"): pytest.skip("--custom-flag is missing, skipping tests", allow_module_level=True) or the xfail way: python def test_feature_x(): pytest.xfail("expected to fail until bug XYZ is fixed") Here is how to skip all tests in a module if some import is missing: python docutils = pytest.importorskip("docutils", minversion="0.3") Skip a test based on a condition: python no-style @pytest.mark.skipif(sys.version_info < (3,6), reason="requires python3.6 or higher") def test_feature_x(): or: python no-style @unittest.skipIf(torch_device == "cpu", "Can't do half precision") def test_feature_x(): or skip the whole module: python no-style @pytest.mark.skipif(sys.platform == 'win32', reason="does not run on windows") class TestClass(): def test_feature_x(self): More details, example and ways are here. Slow tests The library of tests is ever-growing, and some of the tests take minutes to run, therefore we can't afford waiting for an hour for the test suite to complete on CI. Therefore, with some exceptions for essential tests, slow tests should be marked as in the example below: python no-style from transformers.testing_utils import slow @slow def test_integration_foo(): Once a test is marked as @slow, to run such tests set RUN_SLOW=1 env var, e.g.: RUN_SLOW=1 pytest tests Some decorators like @parameterized rewrite test names, therefore @slow and the rest of the skip decorators @require_* have to be listed last for them to work correctly. Here is an example of the correct usage: python no-style @parameteriz ed.expand() @slow def test_integration_foo(): As explained at the beginning of this document, slow tests get to run on a scheduled basis, rather than in PRs CI checks. So it's possible that some problems will be missed during a PR submission and get merged. Such problems will get caught during the next scheduled CI job. But it also means that it's important to run the slow tests on your machine before submitting the PR. Here is a rough decision making mechanism for choosing which tests should be marked as slow: If the test is focused on one of the library's internal components (e.g., modeling files, tokenization files, pipelines), then we should run that test in the non-slow test suite. If it's focused on an other aspect of the library, such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine this approach we should have exceptions: All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is discussed in the following paragraphs. All tests that need to do a training not specifically optimized to be fast should be set to slow. We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to @slow. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked as @slow. If a test completes under 1 second on CI (including downloads if any) then it should be a normal test regardless. Collectively, all the non-slow tests need to cover entirely the different internals, while remaining fast. For example, a significant coverage can be achieved by testing with specially created tiny models with random weights. Such models have the very minimal number of layers (e.g., 2), vocab size (e.g., 1000), etc. Then the @slow tests can use large slow models to do qualitative testing. To see the use of these simply look for tiny models with: grep tiny tests examples Here is a an example of a script that created the tiny model stas/tiny-wmt19-en-de. You can easily adjust it to your specific model's architecture. It's easy to measure the run-time incorrectly if for example there is an overheard of downloading a huge model, but if you test it locally the downloaded files would be cached and thus the download time not measured. Hence check the execution speed report in CI logs instead (the output of pytest --durations=0 tests). That report is also useful to find slow outliers that aren't marked as such, or which need to be re-written to be fast. If you notice that the test suite starts getting slow on CI, the top listing of this report will show the slowest tests. Testing the stdout/stderr output In order to test functions that write to stdout and/or stderr, the test can access those streams using the pytest's capsys system. Here is how this is accomplished: thon import sys def print_to_stdout(s): print(s) def print_to_stderr(s): sys.stderr.write(s) def test_result_and_stdout(capsys): msg = "Hello" print_to_stdout(msg) print_to_stderr(msg) out, err = capsys.readouterr() # consume the captured output streams # optional: if you want to replay the consumed streams: sys.stdout.write(out) sys.stderr.write(err) # test: assert msg in out assert msg in err And, of course, most of the time, stderr will come as a part of an exception, so try/except has to be used in such a case: thon def raise_exception(msg): raise ValueError(msg) def test_something_exception(): msg = "Not a good value" error = "" try: raise_exception(msg) except Exception as e: error = str(e) assert msg in error, f"{msg} is in the exception:\n{error}" Another approach to capturing stdout is via contextlib.redirect_stdout: thon from io import StringIO from contextlib import redirect_stdout def print_to_stdout(s): print(s) def test_result_and_stdout(): msg = "Hello" buffer = StringIO() with redirect_stdout(buffer): print_to_stdout(msg) out = buffer.getvalue() # optional: if you want to replay the consumed streams: sys.stdout.write(out) # test: assert msg in out An important potential issue with capturing stdout is that it may contain \r characters that in normal print reset everything that has been printed so far. There is no problem with pytest, but with pytest -s these characters get included in the buffer, so to be able to have the test run with and without -s, you have to make an extra cleanup to the captured output, using re.sub(r'~.*\r', '', buf, 0, re.M). But, then we have a helper context manager wrapper to automatically take care of it all, regardless of whether it has some \r's in it or not, so it's a simple: thon from transformers.testing_utils import CaptureStdout with CaptureStdout() as cs: function_that_writes_to_stdout() print(cs.out) Here is a full test example: thon from transformers.testing_utils import CaptureStdout msg = "Secret message\r" final = "Hello World" with CaptureStdout() as cs: print(msg + final) assert cs.out == final + "\n", f"captured: {cs.out}, expecting {final}" If you'd like to capture stderr use the CaptureStderr class instead: thon from transformers.testing_utils import CaptureStderr with CaptureStderr() as cs: function_that_writes_to_stderr() print(cs.err) If you need to capture both streams at once, use the parent CaptureStd class: thon from transformers.testing_utils import CaptureStd with CaptureStd() as cs: function_that_writes_to_stdout_and_stderr() print(cs.err, cs.out) Also, to aid debugging test issues, by default these context managers automatically replay the captured streams on exit from the context. Capturing logger stream If you need to validate the output of a logger, you can use CaptureLogger: thon from transformers import logging from transformers.testing_utils import CaptureLogger msg = "Testing 1, 2, 3" logging.set_verbosity_info() logger = logging.get_logger("transformers.models.bart.tokenization_bart") with CaptureLogger(logger) as cl: logger.info(msg) assert cl.out, msg + "\n" Testing with environment variables If you want to test the impact of environment variables for a specific test you can use a helper decorator transformers.testing_utils.mockenv thon from transformers.testing_utils import mockenv class HfArgumentParserTest(unittest.TestCase): @mockenv(TRANSFORMERS_VERBOSITY="error") def test_env_override(self): env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None) At times an external program needs to be called, which requires setting PYTHONPATH in os.environ to include multiple local paths. A helper class transformers.test_utils.TestCasePlus comes to help: thon from transformers.testing_utils import TestCasePlus class EnvExampleTest(TestCasePlus): def test_external_prog(self): env = self.get_env() # now call the external program, passing env to it Depending on whether the test file was under the tests test suite or examples it'll correctly set up env[PYTHONPATH] to include one of these two directories, and also the src directory to ensure the testing is done against the current repo, and finally with whatever env[PYTHONPATH] was already set to before the test was called if anything. This helper method creates a copy of the os.environ object, so the original remains intact. Getting reproducible results In some situations you may want to remove randomness for your tests. To get identical reproducible results set, you will need to fix the seed: thon seed = 42 python RNG import random random.seed(seed) pytorch RNGs import torch torch.manual_seed(seed) torch.backends.cudnn.deterministic = True if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) numpy RNG import numpy as np np.random.seed(seed) tf RNG tf.random.set_seed(seed) Debugging tests To start a debugger at the point of the warning, do this: pytest tests/test_logging.py -W error::UserWarning --pdb Working with github actions workflows To trigger a self-push workflow CI job, you must: Create a new branch on transformers origin (not a fork!). The branch name has to start with either ci_ or ci- (main triggers it too, but we can't do PRs on main). It also gets triggered only for specific paths - you can find the up-to-date definition in case it changed since this document has been written here under push: Create a PR from this branch. Then you can see the job appear here. It may not run right away if there is a backlog. Testing Experimental CI Features Testing CI features can be potentially problematic as it can interfere with the normal CI functioning. Therefore if a new CI feature is to be added, it should be done as following. Create a new dedicated job that tests what needs to be tested The new job must always succeed so that it gives us a green ✓ (details below). Let it run for some days to see that a variety of different PR types get to run on it (user fork branches, non-forked branches, branches originating from github.com UI direct file edit, various forced pushes, etc. - there are so many) while monitoring the experimental job's logs (not the overall job green as it's purposefully always green) When it's clear that everything is solid, then merge the new changes into existing jobs. That way experiments on CI functionality itself won't interfere with the normal workflow. Now how can we make the job always succeed while the new CI feature is being developed? Some CIs, like TravisCI support ignore-step-failure and will report the overall job as successful, but CircleCI and Github Actions as of this writing don't support that. So the following workaround can be used: set +euo pipefail at the beginning of the run command to suppress most potential failures in the bash script. the last command must be a success: echo "done" or just true will do Here is an example: yaml - run: name: run CI experiment command: | set +euo pipefail echo "setting run-all-despite-any-errors-mode" this_command_will_fail echo "but bash continues to run" # emulate another failure false # but the last command must be a success echo "during experiment do not remove: reporting success to CI, even if there were failures" For simple commands you could also do: cmd_that_may_fail || true Of course, once satisfied with the results, integrate the experimental step or job with the rest of the normal jobs, while removing set +euo pipefail or any other things you may have added to ensure that the experimental job doesn't interfere with the normal CI functioning. This whole process would have been much easier if we only could set something like allow-failure for the experimental step, and let it fail without impacting the overall status of PRs. But as mentioned earlier CircleCI and Github Actions don't support it at the moment. You can vote for this feature and see where it is at these CI-specific threads: Github Actions: CircleCI:

Configuration The base class [PretrainedConfig] implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS S3 repository). Each derived config class implements model specific attributes. Common attributes present in all config classes are: hidden_size, num_attention_heads, and num_hidden_layers. Text models further implement: vocab_size. PretrainedConfig [[autodoc]] PretrainedConfig - push_to_hub - all

Utilities for Tokenizers This page lists all the utility functions used by the tokenizers, mainly the class [~tokenization_utils_base.PreTrainedTokenizerBase] that implements the common methods between [PreTrainedTokenizer] and [PreTrainedTokenizerFast] and the mixin [~tokenization_utils_base.SpecialTokensMixin]. Most of those are only useful if you are studying the code of the tokenizers in the library. PreTrainedTokenizerBase [[autodoc]] tokenization_utils_base.PreTrainedTokenizerBase - call - all SpecialTokensMixin [[autodoc]] tokenization_utils_base.SpecialTokensMixin Enums and namedtuples [[autodoc]] tokenization_utils_base.TruncationStrategy [[autodoc]] tokenization_utils_base.CharSpan [[autodoc]] tokenization_utils_base.TokenSpan

Create a custom architecture An AutoClass automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an AutoClass to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from just a few base classes. This could be particularly useful for anyone who is interested in studying, training or experimenting with a 🤗 Transformers model. In this guide, dive deeper into creating a custom model without an AutoClass. Learn how to: Load and customize a model configuration. Create a model architecture. Create a slow and fast tokenizer for text. Create an image processor for vision tasks. Create a feature extractor for audio tasks. Create a processor for multimodal tasks. Configuration A configuration refers to a model's specific attributes. Each model configuration has different attributes; for instance, all NLP models have the hidden_size, num_attention_heads, num_hidden_layers and vocab_size attributes in common. These attributes specify the number of attention heads or hidden layers to construct a model with. Get a closer look at DistilBERT by accessing [DistilBertConfig] to inspect it's attributes: from transformers import DistilBertConfig config = DistilBertConfig() print(config) DistilBertConfig { "activation": "gelu", "attention_dropout": 0.1, "dim": 768, "dropout": 0.1, "hidden_dim": 3072, "initializer_range": 0.02, "max_position_embeddings": 512, "model_type": "distilbert", "n_heads": 12, "n_layers": 6, "pad_token_id": 0, "qa_dropout": 0.1, "seq_classif_dropout": 0.2, "sinusoidal_pos_embds": false, "transformers_version": "4.16.2", "vocab_size": 30522 } [DistilBertConfig] displays all the default attributes used to build a base [DistilBertModel]. All attributes are customizable, creating space for experimentation. For example, you can customize a default model to: Try a different activation function with the activation parameter. Use a higher dropout ratio for the attention probabilities with the attention_dropout parameter. my_config = DistilBertConfig(activation="relu", attention_dropout=0.4) print(my_config) DistilBertConfig { "activation": "relu", "attention_dropout": 0.4, "dim": 768, "dropout": 0.1, "hidden_dim": 3072, "initializer_range": 0.02, "max_position_embeddings": 512, "model_type": "distilbert", "n_heads": 12, "n_layers": 6, "pad_token_id": 0, "qa_dropout": 0.1, "seq_classif_dropout": 0.2, "sinusoidal_pos_embds": false, "transformers_version": "4.16.2", "vocab_size": 30522 } Pretrained model attributes can be modified in the [~PretrainedConfig.from_pretrained] function: my_config = DistilBertConfig.from_pretrained("distilbert-base-uncased", activation="relu", attention_dropout=0.4) Once you are satisfied with your model configuration, you can save it with [~PretrainedConfig.save_pretrained]. Your configuration file is stored as a JSON file in the specified save directory: my_config.save_pretrained(save_directory="./your_model_save_path") To reuse the configuration file, load it with [~PretrainedConfig.from_pretrained]: my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json") You can also save your configuration file as a dictionary or even just the difference between your custom configuration attributes and the default configuration attributes! See the configuration documentation for more details. Model The next step is to create a model. The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like num_hidden_layers from the configuration are used to define the architecture. Every model shares the base class [PreTrainedModel] and a few common methods like resizing input embeddings and pruning self-attention heads. In addition, all models are also either a torch.nn.Module, tf.keras.Model or flax.linen.Module subclass. This means models are compatible with each of their respective framework's usage. Load your custom configuration attributes into the model: from transformers import DistilBertModel my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json") model = DistilBertModel(my_config) This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training. Create a pretrained model with [~PreTrainedModel.from_pretrained]: model = DistilBertModel.from_pretrained("distilbert-base-uncased") When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like: model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config) Load your custom configuration attributes into the model: from transformers import TFDistilBertModel my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json") tf_model = TFDistilBertModel(my_config) This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training. Create a pretrained model with [~TFPreTrainedModel.from_pretrained]: tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like: tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config) Model heads At this point, you have a base DistilBERT model which outputs the hidden states. The hidden states are passed as inputs to a model head to produce the final output. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i.e., you can't use DistilBERT for a sequence-to-sequence task like translation). For example, [DistilBertForSequenceClassification] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs. from transformers import DistilBertForSequenceClassification model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased") Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [DistilBertForQuestionAnswering] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output. from transformers import DistilBertForQuestionAnswering model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased") `` </pt> <tf> For example, [TFDistilBertForSequenceClassification`] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs. from transformers import TFDistilBertForSequenceClassification tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased") Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [TFDistilBertForQuestionAnswering] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output. from transformers import TFDistilBertForQuestionAnswering tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased") Tokenizer The last base class you need before using a model for textual data is a tokenizer to convert raw text to tensors. There are two types of tokenizers you can use with 🤗 Transformers: [PreTrainedTokenizer]: a Python implementation of a tokenizer. [PreTrainedTokenizerFast]: a tokenizer from our Rust-based 🤗 Tokenizer library. This tokenizer type is significantly faster - especially during batch tokenization - due to it's Rust implementation. The fast tokenizer also offers additional methods like offset mapping which maps tokens to their original words or characters. Both tokenizers support common methods such as encoding and decoding, adding new tokens, and managing special tokens. Not every model supports a fast tokenizer. Take a look at this table to check if a model has fast tokenizer support. If you trained your own tokenizer, you can create one from your vocabulary file: from transformers import DistilBertTokenizer my_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left") It is important to remember the vocabulary from a custom tokenizer will be different from the vocabulary generated by a pretrained model's tokenizer. You need to use a pretrained model's vocabulary if you are using a pretrained model, otherwise the inputs won't make sense. Create a tokenizer with a pretrained model's vocabulary with the [DistilBertTokenizer] class: from transformers import DistilBertTokenizer slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") Create a fast tokenizer with the [DistilBertTokenizerFast] class: from transformers import DistilBertTokenizerFast fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") By default, [AutoTokenizer] will try to load a fast tokenizer. You can disable this behavior by setting use_fast=False in from_pretrained. Image Processor An image processor processes vision inputs. It inherits from the base [~image_processing_utils.ImageProcessingMixin] class. To use, create an image processor associated with the model you're using. For example, create a default [ViTImageProcessor] if you are using ViT for image classification: from transformers import ViTImageProcessor vit_extractor = ViTImageProcessor() print(vit_extractor) ViTImageProcessor { "do_normalize": true, "do_resize": true, "image_processor_type": "ViTImageProcessor", "image_mean": [ 0.5, 0.5, 0.5 ], "image_std": [ 0.5, 0.5, 0.5 ], "resample": 2, "size": 224 } If you aren't looking for any customization, just use the from_pretrained method to load a model's default image processor parameters. Modify any of the [ViTImageProcessor] parameters to create your custom image processor: from transformers import ViTImageProcessor my_vit_extractor = ViTImageProcessor(resample="PIL.Image.BOX", do_normalize=False, image_mean=[0.3, 0.3, 0.3]) print(my_vit_extractor) ViTImageProcessor { "do_normalize": false, "do_resize": true, "image_processor_type": "ViTImageProcessor", "image_mean": [ 0.3, 0.3, 0.3 ], "image_std": [ 0.5, 0.5, 0.5 ], "resample": "PIL.Image.BOX", "size": 224 } Feature Extractor A feature extractor processes audio inputs. It inherits from the base [~feature_extraction_utils.FeatureExtractionMixin] class, and may also inherit from the [SequenceFeatureExtractor] class for processing audio inputs. To use, create a feature extractor associated with the model you're using. For example, create a default [Wav2Vec2FeatureExtractor] if you are using Wav2Vec2 for audio classification: from transformers import Wav2Vec2FeatureExtractor w2v2_extractor = Wav2Vec2FeatureExtractor() print(w2v2_extractor) Wav2Vec2FeatureExtractor { "do_normalize": true, "feature_extractor_type": "Wav2Vec2FeatureExtractor", "feature_size": 1, "padding_side": "right", "padding_value": 0.0, "return_attention_mask": false, "sampling_rate": 16000 } If you aren't looking for any customization, just use the from_pretrained method to load a model's default feature extractor parameters. Modify any of the [Wav2Vec2FeatureExtractor] parameters to create your custom feature extractor: from transformers import Wav2Vec2FeatureExtractor w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=8000, do_normalize=False) print(w2v2_extractor) Wav2Vec2FeatureExtractor { "do_normalize": false, "feature_extractor_type": "Wav2Vec2FeatureExtractor", "feature_size": 1, "padding_side": "right", "padding_value": 0.0, "return_attention_mask": false, "sampling_rate": 8000 } Processor For models that support multimodal tasks, 🤗 Transformers offers a processor class that conveniently wraps processing classes such as a feature extractor and a tokenizer into a single object. For example, let's use the [Wav2Vec2Processor] for an automatic speech recognition task (ASR). ASR transcribes audio to text, so you will need a feature extractor and a tokenizer. Create a feature extractor to handle the audio inputs: from transformers import Wav2Vec2FeatureExtractor feature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True) Create a tokenizer to handle the text inputs: from transformers import Wav2Vec2CTCTokenizer tokenizer = Wav2Vec2CTCTokenizer(vocab_file="my_vocab_file.txt") Combine the feature extractor and tokenizer in [Wav2Vec2Processor]: from transformers import Wav2Vec2Processor processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) With two basic classes - configuration and model - and an additional preprocessing class (tokenizer, image processor, feature extractor, or processor), you can create any of the models supported by 🤗 Transformers. Each of these base classes are configurable, allowing you to use the specific attributes you want. You can easily setup a model for training or modify an existing pretrained model to fine-tune.

Debugging Multi-GPU Network Issues Debug When training or inferencing with DistributedDataParallel and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues. wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py For example to test how 2 GPUs interact do: python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py If both processes can talk to each and allocate GPU memory each will print an OK status. For more GPUs or nodes adjust the arguments in the script. You will find a lot more details inside the diagnostics script and even a recipe to how you could run it in a SLURM environment. An additional level of debug is to add NCCL_DEBUG=INFO environment variable as follows: NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py This will dump a lot of NCCL-related debug information, which you can then search online if you find that some problems are reported. Or if you're not sure how to interpret the output you can share the log file in an Issue. Underflow and Overflow Detection This feature is currently available for PyTorch-only. For multi-GPU training it requires DDP (torch.distributed.launch). This feature can be used with any nn.Module-based model. If you start getting loss=NaN or the model inhibits some other abnormal behavior due to inf or nan in activations or weights one needs to discover where the first underflow or overflow happens and what led to it. Luckily you can accomplish that easily by activating a special module that will do the detection automatically. If you're using [Trainer], you just need to add: --debug underflow_overflow to the normal command line arguments, or pass debug="underflow_overflow" when creating the [TrainingArguments] object. If you're using your own training loop or another Trainer you can accomplish the same with: thon from transformers.debug_utils import DebugUnderflowOverflow debug_overflow = DebugUnderflowOverflow(model) [~debug_utils.DebugUnderflowOverflow] inserts hooks into the model that immediately after each forward call will test input and output variables and also the corresponding module's weights. As soon as inf or nan is detected in at least one element of the activations or weights, the program will assert and print a report like this (this was caught with google/mt5-small under fp16 mixed precision): Detected inf/nan during batch_number=0 Last 21 forward frames: abs min abs max metadata encoder.block.1.layer.1.DenseReluDense.dropout Dropout 0.00e+00 2.57e+02 input[0] 0.00e+00 2.85e+02 output [] encoder.block.2.layer.0 T5LayerSelfAttention 6.78e-04 3.15e+03 input[0] 2.65e-04 3.42e+03 output[0] None output[1] 2.25e-01 1.00e+04 output[2] encoder.block.2.layer.1.layer_norm T5LayerNorm 8.69e-02 4.18e-01 weight 2.65e-04 3.42e+03 input[0] 1.79e-06 4.65e+00 output encoder.block.2.layer.1.DenseReluDense.wi_0 Linear 2.17e-07 4.50e+00 weight 1.79e-06 4.65e+00 input[0] 2.68e-06 3.70e+01 output encoder.block.2.layer.1.DenseReluDense.wi_1 Linear 8.08e-07 2.66e+01 weight 1.79e-06 4.65e+00 input[0] 1.27e-04 2.37e+02 output encoder.block.2.layer.1.DenseReluDense.dropout Dropout 0.00e+00 8.76e+03 input[0] 0.00e+00 9.74e+03 output encoder.block.2.layer.1.DenseReluDense.wo Linear 1.01e-06 6.44e+00 weight 0.00e+00 9.74e+03 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense 1.79e-06 4.65e+00 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.dropout Dropout 3.18e-04 6.27e+04 input[0] 0.00e+00 inf output The example output has been trimmed in the middle for brevity. The second column shows the value of the absolute largest element, so if you have a closer look at the last few frames, the inputs and outputs were in the range of 1e4. So when this training was done under fp16 mixed precision the very last step overflowed (since under fp16 the largest number before inf is 64e3). To avoid overflows under fp16 the activations must remain way below 1e4, because 1e4 * 1e4 = 1e8 so any matrix multiplication with large activations is going to lead to a numerical overflow condition. At the very start of the trace you can discover at which batch number the problem occurred (here Detected inf/nan during batch_number=0 means the problem occurred on the first batch). Each reported frame starts by declaring the fully qualified entry for the corresponding module this frame is reporting for. If we look just at this frame: encoder.block.2.layer.1.layer_norm T5LayerNorm 8.69e-02 4.18e-01 weight 2.65e-04 3.42e+03 input[0] 1.79e-06 4.65e+00 output Here, encoder.block.2.layer.1.layer_norm indicates that it was a layer norm for the first layer, of the second block of the encoder. And the specific calls of the forward is T5LayerNorm. Let's look at the last few frames of that report: Detected inf/nan during batch_number=0 Last 21 forward frames: abs min abs max metadata [] encoder.block.2.layer.1.DenseReluDense.wi_0 Linear 2.17e-07 4.50e+00 weight 1.79e-06 4.65e+00 input[0] 2.68e-06 3.70e+01 output encoder.block.2.layer.1.DenseReluDense.wi_1 Linear 8.08e-07 2.66e+01 weight 1.79e-06 4.65e+00 input[0] 1.27e-04 2.37e+02 output encoder.block.2.layer.1.DenseReluDense.wo Linear 1.01e-06 6.44e+00 weight 0.00e+00 9.74e+03 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense 1.79e-06 4.65e+00 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.dropout Dropout 3.18e-04 6.27e+04 input[0] 0.00e+00 inf output The last frame reports for Dropout.forward function with the first entry for the only input and the second for the only output. You can see that it was called from an attribute dropout inside DenseReluDense class. We can see that it happened during the first layer, of the 2nd block, during the very first batch. Finally, the absolute largest input elements was 6.27e+04 and same for the output was inf. You can see here, that T5DenseGatedGeluDense.forward resulted in output activations, whose absolute max value was around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have Dropout which renormalizes the weights, after it zeroed some of the elements, which pushes the absolute max value to more than 64K, and we get an overflow (inf). As you can see it's the previous frames that we need to look into when the numbers start going into very large for fp16 numbers. Let's match the report to the code from models/t5/modeling_t5.py: thon class T5DenseGatedGeluDense(nn.Module): def init(self, config): super().init() self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.gelu_act = ACT2FN["gelu_new"] def forward(self, hidden_states): hidden_gelu = self.gelu_act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states Now it's easy to see the dropout call, and all the previous calls as well. Since the detection is happening in a forward hook, these reports are printed immediately after each forward returns. Going back to the full report, to act on it and to fix the problem, we need to go a few frames up where the numbers started to go up and most likely switch to the fp32 mode here, so that the numbers don't overflow when multiplied or summed up. Of course, there might be other solutions. For example, we could turn off amp temporarily if it's enabled, after moving the original forward into a helper wrapper, like so: thon def _forward(self, hidden_states): hidden_gelu = self.gelu_act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states import torch def forward(self, hidden_states): if torch.is_autocast_enabled(): with torch.cuda.amp.autocast(enabled=False): return self._forward(hidden_states) else: return self._forward(hidden_states) Since the automatic detector only reports on inputs and outputs of full frames, once you know where to look, you may want to analyse the intermediary stages of any specific forward function as well. In such a case you can use the detect_overflow helper function to inject the detector where you want it, for example: thon from debug_utils import detect_overflow class T5LayerFF(nn.Module): [] def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) detect_overflow(forwarded_states, "after layer_norm") forwarded_states = self.DenseReluDense(forwarded_states) detect_overflow(forwarded_states, "after DenseReluDense") return hidden_states + self.dropout(forwarded_states) You can see that we added 2 of these and now we track if inf or nan for forwarded_states was detected somewhere in between. Actually, the detector already reports these because each of the calls in the example above is a nn.Module, but let's say if you had some local direct calculations this is how you'd do that. Additionally, if you're instantiating the debugger in your own code, you can adjust the number of frames printed from its default, e.g.: thon from transformers.debug_utils import DebugUnderflowOverflow debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100) Specific batch absolute min and max value tracing The same debugging class can be used for per-batch tracing with the underflow/overflow detection feature turned off. Let's say you want to watch the absolute min and max values for all the ingredients of each forward call of a given batch, and only do that for batches 1 and 3. Then you instantiate this class as: python debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3]) And now full batches 1 and 3 will be traced using the same format as the underflow/overflow detector does. Batches are 0-indexed. This is helpful if you know that the program starts misbehaving after a certain batch number, so you can fast-forward right to that area. Here is a sample truncated output for such configuration: *** Starting batch number=1 *** abs min abs max metadata shared Embedding 1.01e-06 7.92e+02 weight 0.00e+00 2.47e+04 input[0] 5.36e-05 7.92e+02 output [] decoder.dropout Dropout 1.60e-07 2.27e+01 input[0] 0.00e+00 2.52e+01 output decoder T5Stack not a tensor output lm_head Linear 1.01e-06 7.92e+02 weight 0.00e+00 1.11e+00 input[0] 6.06e-02 8.39e+01 output T5ForConditionalGeneration not a tensor output *** Starting batch number=3 *** abs min abs max metadata shared Embedding 1.01e-06 7.92e+02 weight 0.00e+00 2.78e+04 input[0] 5.36e-05 7.92e+02 output [] Here you will get a huge number of frames dumped - as many as there were forward calls in your model, so it may or may not what you want, but sometimes it can be easier to use for debugging purposes than a normal debugger. For example, if a problem starts happening at batch number 150. So you can dump traces for batches 149 and 150 and compare where numbers started to diverge. You can also specify the batch number after which to stop the training, with: python debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3)

Custom Layers and Utilities This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling. Most of those are only useful if you are studying the code of the models in the library. Pytorch custom modules [[autodoc]] pytorch_utils.Conv1D [[autodoc]] modeling_utils.PoolerStartLogits - forward [[autodoc]] modeling_utils.PoolerEndLogits - forward [[autodoc]] modeling_utils.PoolerAnswerClass - forward [[autodoc]] modeling_utils.SquadHeadOutput [[autodoc]] modeling_utils.SQuADHead - forward [[autodoc]] modeling_utils.SequenceSummary - forward PyTorch Helper Functions [[autodoc]] pytorch_utils.apply_chunking_to_forward [[autodoc]] pytorch_utils.find_pruneable_heads_and_indices [[autodoc]] pytorch_utils.prune_layer [[autodoc]] pytorch_utils.prune_conv1d_layer [[autodoc]] pytorch_utils.prune_linear_layer TensorFlow custom layers [[autodoc]] modeling_tf_utils.TFConv1D [[autodoc]] modeling_tf_utils.TFSequenceSummary TensorFlow loss functions [[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss [[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss [[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss [[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss [[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss [[autodoc]] modeling_tf_utils.TFTokenClassificationLoss TensorFlow Helper Functions [[autodoc]] modeling_tf_utils.get_initializer [[autodoc]] modeling_tf_utils.keras_serializable [[autodoc]] modeling_tf_utils.shape_list

Training on TPUs Note: Most of the strategies introduced in the single GPU section (such as mixed precision training or gradient accumulation) and multi-GPU section are generic and apply to training models in general so make sure to have a look at it before diving into this section. This document will be completed soon with information on how to train on TPUs.

Notes: if you need to run on a specific GPU, which is different from GPU 0, you can't use CUDA_VISIBLE_DEVICES to limit the visible scope of available GPUs. Instead, you have to use the following syntax: deepspeed --include localhost:1 examples/pytorch/translation/run_translation.py In this example, we tell DeepSpeed to use GPU 1 (second gpu). Deployment with multiple Nodes The information in this section isn't not specific to the DeepSpeed integration and is applicable to any multi-node program. But DeepSpeed provides a deepspeed launcher that is easier to use than other launchers unless you are in a SLURM environment. For the duration of this section let's assume that you have 2 nodes with 8 gpus each. And you can reach the first node with ssh hostname1 and second node with ssh hostname2, and both must be able to reach each other via ssh locally without a password. Of course, you will need to rename these host (node) names to the actual host names you are working with. The torch.distributed.run launcher For example, to use torch.distributed.run, you could do: python -m torch.distributed.run --nproc_per_node=8 --nnode=2 --node_rank=0 --master_addr=hostname1 \ --master_port=9901 your_program.py <normal cl args> --deepspeed ds_config.json You have to ssh to each node and run this same command on each one of them! There is no rush, the launcher will wait until both nodes will synchronize. For more information please see torchrun. Incidentally, this is also the launcher that replaced torch.distributed.launch a few pytorch versions back. The deepspeed launcher To use the deepspeed launcher instead, you have to first create a hostfile file: hostname1 slots=8 hostname2 slots=8 and then you can launch it as: deepspeed --num_gpus 8 --num_nodes 2 --hostfile hostfile --master_addr hostname1 --master_port=9901 \ your_program.py <normal cl args> --deepspeed ds_config.json Unlike the torch.distributed.run launcher, deepspeed will automatically launch this command on both nodes! For more information please see Resource Configuration (multi-node). Launching in a SLURM environment In the SLURM environment the following approach can be used. The following is a slurm script launch.slurm which you will need to adapt it to your specific SLURM environment. ```bash SBATCH --job-name=test-nodes # name SBATCH --nodes=2 # nodes SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node! SBATCH --cpus-per-task=10 # number of cores per tasks SBATCH --gres=gpu:8 # number of gpus SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS) SBATCH --output=%x-%j.out # output file name export GPUS_PER_NODE=8 export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1) export MASTER_PORT=9901 srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \ --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \ --master_addr $MASTER_ADDR --master_port $MASTER_PORT \ your_program.py --deepspeed ds_config.json' All is left is to schedule it to run: sbatch launch.slurm srun will take care of launching the program simultaneously on all nodes. Use of Non-shared filesystem By default DeepSpeed expects that a multi-node environment uses a shared storage. If this is not the case and each node can only see the local filesystem, you need to adjust the config file to include a checkpoint_section with the following setting: json { "checkpoint": { "use_node_local_storage": true } } Alternatively, you can also use the [Trainer]'s --save_on_each_node argument, and the above config will be added automatically for you. Deployment in Notebooks The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so under certain setups we have to emulate it. If you're using only 1 GPU, here is how you'd have to adjust your training code in the notebook to use DeepSpeed. thon DeepSpeed requires a distributed environment even when only one process is used. This emulates a launcher in the notebook import os os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "9994" # modify if RuntimeError: Address already in use os.environ["RANK"] = "0" os.environ["LOCAL_RANK"] = "0" os.environ["WORLD_SIZE"] = "1" Now proceed as normal, plus pass the deepspeed config file training_args = TrainingArguments(, deepspeed="ds_config_zero3.json") trainer = Trainer() trainer.train() Note: stands for the normal arguments that you'd pass to the functions. If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. That is, you have to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented at the beginning of this section. If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated cell with: thon no-style %%bash cat <<'EOT' > ds_config_zero3.json { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } }, "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } EOT If the training script is in a normal file and not in the notebook cells, you can launch deepspeed normally via shell from a cell. For example, to use run_translation.py you would launch it with: python no-style !git clone https://github.com/huggingface/transformers !cd transformers; deepspeed examples/pytorch/translation/run_translation.py or with %%bash magic, where you can write a multi-line code for the shell program to run: thon no-style %%bash git clone https://github.com/huggingface/transformers cd transformers deepspeed examples/pytorch/translation/run_translation.py In such case you don't need any of the code presented at the beginning of this section. Note: While %%bash magic is neat, but currently it buffers the output so you won't see the logs until the process completes. Configuration For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer to the following documentation. You can find dozens of DeepSpeed configuration examples that address various practical needs in the DeepSpeedExamples repo: git clone https://github.com/microsoft/DeepSpeedExamples cd DeepSpeedExamples find . -name '*json' Continuing the code from above, let's say you're looking to configure the Lamb optimizer. So you can search through the example .json files with: grep -i Lamb $(find . -name '*json') Some more examples are to be found in the main repo as well. When using DeepSpeed you always need to supply a DeepSpeed configuration file, yet some configuration parameters have to be configured via the command line. You will find the nuances in the rest of this guide. To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features, including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed precision training if --fp16 is passed: ```json { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", } When you execute the program, DeepSpeed will log the configuration it received from the [Trainer] to the console, so you can see exactly what was the final configuration passed to it. Passing Configuration As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if you're not using the command line interface to configure the training, and instead instantiate the [Trainer] via [TrainingArguments] then for the deepspeed argument you can pass a nested dict. This allows you to create the configuration on the fly and doesn't require you to write it to the file system before passing it to [TrainingArguments]. To summarize you can do: python TrainingArguments(, deepspeed="/path/to/ds_config.json") or: python ds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params) TrainingArguments(, deepspeed=ds_config_dict) Shared Configuration This section is a must-read Some configuration values are required by both the [Trainer] and DeepSpeed to function correctly, therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those via the [Trainer] command line arguments. Additionally, some configuration values are derived automatically based on the model's configuration, so instead of remembering to manually adjust multiple values, it's the best to let the [Trainer] do the majority of configuration for you. Therefore, in the rest of this guide you will find a special configuration value: auto, which when set will be automatically replaced with the correct or most efficient value. Please feel free to choose to ignore this recommendation and set the values explicitly, in which case be very careful that your the [Trainer] arguments and DeepSpeed configurations agree. For example, are you using the same learning rate, or batch size, or gradient accumulation settings? if these mismatch the training may fail in very difficult to detect ways. You have been warned. There are multiple other values that are specific to DeepSpeed-only and those you will have to set manually to suit your needs. In your own programs, you can also use the following approach if you'd like to modify the DeepSpeed config as a master and configure [TrainingArguments] based on that. The steps are: Create or load the DeepSpeed configuration to be used as a master configuration Create the [TrainingArguments] object based on these values Do note that some values, such as scheduler.params.total_num_steps are calculated by [Trainer] during train, but you can of course do the math yourself. ZeRO Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. It supports 3 different levels (stages) of optimization. The first one is not quite interesting for scalability purposes, therefore this document focuses on stages 2 and 3. Stage 3 is further improved by the latest addition of ZeRO-Infinity. You will find more indepth information in the DeepSpeed documentation. The zero_optimization section of the configuration file is the most important part (docs), since that is where you define which ZeRO stages you want to enable and how to configure them. You will find the explanation for each parameter in the DeepSpeed docs. This section has to be configured exclusively via DeepSpeed configuration - the [Trainer] provides no equivalent command line arguments. Note: currently DeepSpeed doesn't validate parameter names, so if you misspell any, it'll use the default setting for the parameter that got misspelled. You can watch the DeepSpeed engine start up log messages to see what values it is going to use. ZeRO-2 Config The following is an example of configuration for ZeRO stage 2: json { "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 5e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 5e8, "contiguous_gradients": true } } Performance tuning: enabling offload_optimizer should reduce GPU RAM usage (it requires "stage": 2) "overlap_comm": true trades off increased GPU RAM usage to lower all-reduce latency. overlap_comm uses 4.5x the allgather_bucket_size and reduce_bucket_size values. So if they are set to 5e8, this requires a 9GB footprint (5e8 x 2Bytes x 2 x 4.5). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting OOM-errors you will need to reduce those parameters to about 2e8, which would require 3.6GB. You will want to do the same on larger capacity GPU as well, if you're starting to hit OOM. when reducing these buffers you're trading communication speed to avail more GPU RAM. The smaller the buffer size is, the slower the communication gets, and the more GPU RAM will be available to other tasks. So if a bigger batch size is important, getting a slightly slower training time could be a good trade. Additionally, deepspeed==0.4.4 added a new option round_robin_gradients which you can enable with: json { "zero_optimization": { "round_robin_gradients": true } } This is a stage 2 optimization for CPU offloading that parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. Performance benefit grows with gradient accumulation steps (more copying between optimizer steps) or GPU count (increased parallelism). ZeRO-3 Config The following is an example of configuration for ZeRO stage 3: json { "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true } } If you are getting OOMs, because your model or activations don't fit into the GPU memory and you have unutilized CPU memory offloading the optimizer states and parameters to CPU memory with "device": "cpu" may solve this limitation. If you don't want to offload to CPU memory, use none instead of cpu for the device entry. Offloading to NVMe is discussed further down. Pinned memory is enabled with pin_memory set to true. This feature can improve the throughput at the cost of making less memory available to other processes. Pinned memory is set aside to the specific process that requested it and its typically accessed much faster than normal CPU memory. Performance tuning: stage3_max_live_parameters: 1e9 stage3_max_reuse_distance: 1e9 If hitting OOM reduce stage3_max_live_parameters and stage3_max_reuse_distance. They should have minimal impact on performance unless you are doing activation checkpointing. 1e9 would consume ~2GB. The memory is shared by stage3_max_live_parameters and stage3_max_reuse_distance, so it's not additive, it's just 2GB total. stage3_max_live_parameters is the upper limit on how many full parameters you want to keep on the GPU at any given time. "reuse distance" is a metric we are using to figure out when will a parameter be used again in the future, and we use the stage3_max_reuse_distance to decide whether to throw away the parameter or to keep it. If a parameter is going to be used again in near future (less than stage3_max_reuse_distance) then we keep it to reduce communication overhead. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and backward passes a single layer granularity and want to keep the parameter in the forward recompute till the backward The following configuration values depend on the model's hidden size: reduce_bucket_size: hidden_size*hidden_size stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size stage3_param_persistence_threshold: 10 * hidden_size therefore set these values to auto and the [Trainer] will automatically assign the recommended values. But, of course, feel free to set these explicitly as well. stage3_gather_16bit_weights_on_model_save enables model fp16 weights consolidation when model gets saved. With large models and multiple GPUs this is an expensive operation both in terms of memory and speed. It's currently required if you plan to resume the training. Watch out for future updates that will remove this limitation and make things more flexible. If you're migrating from ZeRO-2 configuration note that allgather_partitions, allgather_bucket_size and reduce_scatter configuration parameters are not used in ZeRO-3. If you keep these in the config file they will just be ignored. sub_group_size: 1e9 sub_group_size controls the granularity in which parameters are updated during optimizer steps. Parameters are grouped into buckets of sub_group_size and each buckets is updated one at a time. When used with NVMe offload in ZeRO-Infinity, sub_group_size therefore controls the granularity in which model states are moved in and out of CPU memory from NVMe during the optimizer step. This prevents running out of CPU memory for extremely large models. You can leave sub_group_size to its default value of 1e9 when not using NVMe offload. You may want to change its default value in the following cases: Running into OOM during optimizer step: Reduce sub_group_size to reduce memory utilization of temporary buffers Optimizer Step is taking a long time: Increase sub_group_size to improve bandwidth utilization as a result of the increased data buffers. ZeRO-0 Config Note that we're listing Stage 0 and 1 last since they are rarely used. Stage 0 is disabling all types of sharding and just using DeepSpeed as DDP. You can turn it on with: json { "zero_optimization": { "stage": 0 } } This will essentially disable ZeRO without you needing to change anything else. ZeRO-1 Config Stage 1 is Stage 2 minus gradient sharding. You can always try it to speed things a tiny bit to only shard the optimizer states with: json { "zero_optimization": { "stage": 1 } } NVMe Support ZeRO-Infinity allows for training incredibly large models by extending GPU and CPU memory with NVMe memory. Thanks to smart partitioning and tiling algorithms each GPU needs to send and receive very small amounts of data during offloading so modern NVMe proved to be fit to allow for an even larger total memory pool available to your training process. ZeRO-Infinity requires ZeRO-3 enabled. The following configuration example enables NVMe to offload both optimizer states and the params: json { "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "nvme", "nvme_path": "/local_nvme", "pin_memory": true, "buffer_count": 4, "fast_init": false }, "offload_param": { "device": "nvme", "nvme_path": "/local_nvme", "pin_memory": true, "buffer_count": 5, "buffer_size": 1e8, "max_in_cpu": 1e9 }, "aio": { "block_size": 262144, "queue_depth": 32, "thread_count": 1, "single_submit": false, "overlap_events": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true }, } You can choose to offload both optimizer states and params to NVMe, or just one of them or none. For example, if you have copious amounts of CPU memory available, by all means offload to CPU memory only as it'd be faster (hint: "device": "cpu"). Here is the full documentation for offloading optimizer states and parameters. Make sure that your nvme_path is actually an NVMe, since it will work with the normal hard drive or SSD, but it'll be much much slower. The fast scalable training was designed with modern NVMe transfer speeds in mind (as of this writing one can have ~3.5GB/s read, ~3GB/s write peak speeds). In order to figure out the optimal aio configuration block you must run a benchmark on your target setup, as explained here. ZeRO-2 vs ZeRO-3 Performance ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather model weights in addition to what ZeRO-2 does. If ZeRO-2 meets your needs and you don't need to scale beyond a few GPUs then you may choose to stick to it. It's important to understand that ZeRO-3 enables a much higher scalability capacity at a cost of speed. It's possible to adjust ZeRO-3 configuration to make it perform closer to ZeRO-2: set stage3_param_persistence_threshold to a very large number - larger than the largest parameter, e.g., 6 * hidden_size * hidden_size. This will keep the parameters on the GPUs. turn off offload_params since ZeRO-2 doesn't have that option. The performance will likely improve significantly with just offload_params turned off, even if you don't change stage3_param_persistence_threshold. Of course, these changes will impact the size of the model you can train. So these help you to trade scalability for speed depending on your needs. ZeRO-2 Example Here is a full ZeRO-2 auto-configuration file ds_config_zero2.json: ```json { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } Here is a full ZeRO-2 all-enabled manually set configuration file. It is here mainly for you to see what the typical values look like, but we highly recommend using the one with multiple auto settings in it. ```json { "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": 3e-5, "betas": [0.8, 0.999], "eps": 1e-8, "weight_decay": 3e-7 } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 3e-5, "warmup_num_steps": 500 } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true }, "steps_per_print": 2000, "wall_clock_breakdown": false } ZeRO-3 Example Here is a full ZeRO-3 auto-configuration file ds_config_zero3.json: ```json { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } }, "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } Here is a full ZeRO-3 all-enabled manually set configuration file. It is here mainly for you to see what the typical values look like, but we highly recommend using the one with multiple auto settings in it. ```json { "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": 3e-5, "betas": [0.8, 0.999], "eps": 1e-8, "weight_decay": 3e-7 } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 3e-5, "warmup_num_steps": 500 } }, "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": 1e6, "stage3_prefetch_bucket_size": 0.94e6, "stage3_param_persistence_threshold": 1e4, "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true }, "steps_per_print": 2000, "wall_clock_breakdown": false } How to Choose Which ZeRO Stage and Offloads To Use For Best Performance So now you know there are all these different stages. How to decide which of them to use? This section will attempt to address this question. In general the following applies: Speed-wise (left is faster than right) Stage 0 (DDP) > Stage 1 > Stage 2 > Stage 2 + offload > Stage 3 > Stage 3 + offloads GPU Memory usage-wise (right is more GPU memory efficient than left) Stage 0 (DDP) < Stage 1 < Stage 2 < Stage 2 + offload < Stage 3 < Stage 3 + offloads So when you want to get the fastest execution while fitting into minimal number of GPUs, here is the process you could follow. We start with the fastest approach and if running into GPU OOM we then go to the next slower approach, but which will use less GPU memory. And so on and so forth. First of all set batch size to 1 (you can always use gradient accumulation for any desired effective batch size). Enable --gradient_checkpointing 1 (HF Trainer) or directly model.gradient_checkpointing_enable() - if OOM then Try ZeRO stage 2 first. if OOM then Try ZeRO stage 2 + offload_optimizer - if OOM then Switch to ZeRO stage 3 - if OOM then Enable offload_param to cpu - if OOM then Enable offload_optimizer to cpu - if OOM then If you still can't fit a batch size of 1 first check various default values and lower them if you can. For example, if you use generate and you don't use a wide search beam make it narrower as it'd take a lot of memory. Definitely use mixed half-precision over fp32 - so bf16 on Ampere and higher GPUs and fp16 on older gpu architectures. If you still OOM you could add more hardware or enable ZeRO-Infinity - that is switch offloads offload_param and offload_optimizer to nvme. You need to make sure it's a very fast nvme. As an anecdote I was able to infer BLOOM-176B on a tiny GPU using ZeRO-Infinity except it was extremely slow. But it worked! You can, of course, work through these steps in reverse by starting with the most GPU memory efficient config and then going backwards. Or try bi-secting it. Once you have your batch size 1 not leading to OOM, measure your effective throughput. Next try to increase the batch size to as large as you can, since the higher the batch size the more efficient the GPUs are as they perform the best when matrices they multiply are huge. Now the performance optimization game starts. You can turn off some offload features or step down in ZeRO stages and increase/decrease batch size and again measure your effective throughput. Rinse and repeat until satisfied. Don't spend forever on it, but if you're about to start a 3 months training - do spend a few days on it to find the most effective throughput-wise setup. So that your training cost will be the lowest and you will finish training faster. In the current crazy-paced ML world, if it takes you an extra month to train something you are likely to miss a golden opportunity. Of course, this is only me sharing an observation and in no way I'm trying to rush you. Before beginning to train BLOOM-176B I spent 2 days on this process and was able to increase throughput from 90 to 150 TFLOPs! This effort saved us more than one month of training time. These notes were written primarily for the training mode, but they should mostly apply for inference as well. For example, during inference Gradient Checkpointing is a no-op since it is only useful during training. Additionally, we found out that if you are doing a multi-GPU inference and not using DeepSpeed-Inference, Accelerate should provide a superior performance. Other quick related performance notes: - if you are training something from scratch always try to have tensors with shapes that are divisible by 16 (e.g. hidden size). For batch size try divisible by 2 at least. There are wave and tile quanitization divisibility that is hardware-specific if you want to squeeze even higher performance from your GPUs. Activation Checkpointing or Gradient Checkpointing Activation checkpointing and gradient checkpointing are two distinct terms that refer to the same methodology. It's very confusing but this is how it is. Gradient checkpointing allows one to trade speed for GPU memory, which either allows one to overcome a GPU OOM, or increase their batch size, which often leads to a better performance. HF Transformers models don't know anything about DeepSpeed's activation checkpointing, so if you try to enable that feature in the DeepSpeed config file, nothing will happen. Therefore you have two ways to take advantage of this very beneficial feature: If you want to use a HF Transformers models you can do model.gradient_checkpointing_enable() or use --gradient_checkpointing in the HF Trainer, which will automatically enable this for you. torch.utils.checkpoint is used there. If you write your own model and you want to use DeepSpeed's activation checkpointing you can use the API prescribed there. You can also take the HF Transformers modeling code and replace torch.utils.checkpoint with the DeepSpeed's API. The latter is more flexible since it allows you to offload the forward activations to the CPU memory instead of recalculating them. Optimizer and Scheduler As long as you don't enable offload_optimizer you can mix and match DeepSpeed and HuggingFace schedulers and optimizers, with the exception of using the combination of HuggingFace scheduler and DeepSpeed optimizer: | Combos | HF Scheduler | DS Scheduler | | HF Optimizer | Yes | Yes | | DS Optimizer | No | Yes | It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and GPU implementation (except LAMB). Optimizer DeepSpeed's main optimizers are Adam, AdamW, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are thus recommended to be used. It, however, can import other optimizers from torch. The full documentation is here. If you don't configure the optimizer entry in the configuration file, the [Trainer] will automatically set it to AdamW and will use the supplied values or the defaults for the following command line arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay. Here is an example of the auto-configured optimizer entry for AdamW: json { "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } } } Note that the command line arguments will set the values in the configuration file. This is so that there is one definitive source of the values and to avoid hard to find errors when for example, the learning rate is set to different values in different places. Command line rules. The values that get overridden are: lr with the value of --learning_rate betas with the value of --adam_beta1 --adam_beta2 eps with the value of --adam_epsilon weight_decay with the value of --weight_decay Therefore please remember to tune the shared hyperparameters on the command line. You can also set the values explicitly: json { "optimizer": { "type": "AdamW", "params": { "lr": 0.001, "betas": [0.8, 0.999], "eps": 1e-8, "weight_decay": 3e-7 } } } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. If you want to use another optimizer which is not listed above, you will have to add to the top level configuration. json { "zero_allow_untested_optimizer": true } Similarly to AdamW, you can configure other officially supported optimizers. Just remember that those may have different config values. e.g. for Adam you will want weight_decay around 0.01. Additionally, offload works the best when it's used with Deepspeed's CPU Adam optimizer. If you want to use a different optimizer with offload, since deepspeed==0.8.3 you need to also add: json { "zero_force_ds_cpu_optimizer": false } to the top level configuration. Scheduler DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate schedulers. The full documentation is here. Here is where the schedulers overlap between 🤗 Transformers and DeepSpeed: WarmupLR via --lr_scheduler_type constant_with_warmup WarmupDecayLR via --lr_scheduler_type linear. This is also the default value for --lr_scheduler_type, therefore, if you don't configure the scheduler this is scheduler that will get configured by default. If you don't configure the scheduler entry in the configuration file, the [Trainer] will use the values of --lr_scheduler_type, --learning_rate and --warmup_steps or --warmup_ratio to configure a 🤗 Transformers version of it. Here is an example of the auto-configured scheduler entry for WarmupLR: json { "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } } } Since "auto" is used the [Trainer] arguments will set the correct values in the configuration file. This is so that there is one definitive source of the values and to avoid hard to find errors when, for example, the learning rate is set to different values in different places. Command line rules. The values that get set are: warmup_min_lr with the value of 0. warmup_max_lr with the value of --learning_rate. warmup_num_steps with the value of --warmup_steps if provided. Otherwise will use --warmup_ratio multiplied by the number of training steps and rounded up. total_num_steps with either the value of --max_steps or if it is not provided, derived automatically at run time based on the environment and the size of the dataset and other command line arguments (needed for WarmupDecayLR). You can, of course, take over any or all of the configuration values and set those yourself: json { "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 0.001, "warmup_num_steps": 1000 } } } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. For example, for WarmupDecayLR, you can use the following entry: json { "scheduler": { "type": "WarmupDecayLR", "params": { "last_batch_iteration": -1, "total_num_steps": "auto", "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } } } and total_num_steps, warmup_max_lr, warmup_num_steps and total_num_steps will be set at loading time. fp32 Precision Deepspeed supports the full fp32 and the fp16 mixed precision. Because of the much reduced memory needs and faster speed one gets with the fp16 mixed precision, the only time you will want to not use it is when the model you're using doesn't behave well under this training mode. Typically this happens when the model wasn't pretrained in the fp16 mixed precision (e.g. often this happens with bf16-pretrained models). Such models may overflow or underflow leading to NaN loss. If this is your case then you will want to use the full fp32 mode, by explicitly disabling the otherwise default fp16 mixed precision mode with: json { "fp16": { "enabled": "false", } } If you're using the Ampere-architecture based GPU, pytorch version 1.7 and higher will automatically switch to using the much more efficient tf32 format for some operations, but the results will still be in fp32. For details and benchmarks, please, see TensorFloat-32(TF32) on Ampere devices. The document includes instructions on how to disable this automatic conversion if for some reason you prefer not to use it. With the 🤗 Trainer you can use --tf32 to enable it, or disable it with --tf32 0 or --no_tf32. By default the PyTorch default is used. Automatic Mixed Precision You can use automatic mixed precision with either a pytorch-like AMP way or the apex-like way: fp16 To configure pytorch AMP-like mode with fp16 (float16) set: json { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 } } and the [Trainer] will automatically enable or disable it based on the value of args.fp16_backend. The rest of config values are up to you. This mode gets enabled when --fp16 --fp16_backend amp or --fp16_full_eval command line args are passed. You can also enable/disable this mode explicitly: json { "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 } } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. Here is the documentation. bf16 If bf16 (bfloat16) is desired instead of fp16 then the following configuration section is to be used: json { "bf16": { "enabled": "auto" } } bf16 has the same dynamic range as fp32 and thus doesn't require loss scaling. This mode gets enabled when --bf16 or --bf16_full_eval command line args are passed. You can also enable/disable this mode explicitly: json { "bf16": { "enabled": true } } As of deepspeed==0.6.0 the bf16 support is new and experimental. If you use gradient accumulation with bf16-enabled, you need to be aware that it'll accumulate gradients in bf16, which may not be what you want due to this format's low precision, as it may lead to a lossy accumulation. A work is being done to fix that and provide an option to use a higher precision dtype (fp16 or fp32). NCCL Collectives There is the dtype of the training regime and there is a separate dtype that is used for communication collectives like various reduction and gathering/scattering operations. All gather/scatter ops are performed in the same dtype the data is in, so if you're using bf16 training regime it gets gathered in bf16 - gathering is a non-lossy operation. Various reduce operations can be quite lossy, for example when gradients are averaged across multiple-gpus, if the communications are done in fp16 or bf16 the outcome is likely be lossy - since when one ads multiple numbers in low precision the result isn't exact. More so with bf16 as it has a lower precision than fp16. Often fp16 is good enough as the loss is minimal when averaging grads which are typically very small. Therefore, by default for half precision training fp16 is used as the default for reduction operations. But you have full control over this functionality and if you choose you can add a small overhead and ensure that reductions will be using fp32 as the accumulation dtype and only when the result is ready it'll get downcast to the half precision dtype you're training in. In order to override the default you simply add a new configuration entry: json { "communication_data_type": "fp32" } The valid values as of this writing are "fp16", "bfp16", "fp32". note: stage zero 3 had a bug with regards to bf16 comm dtype that was fixed in deepspeed==0.8.1 apex To configure apex AMP-like mode set: json "amp": { "enabled": "auto", "opt_level": "auto" } and the [Trainer] will automatically configure it based on the values of args.fp16_backend and args.fp16_opt_level. This mode gets enabled when --fp16 --fp16_backend apex --fp16_opt_level 01 command line args are passed. You can also configure this mode explicitly: json { "amp": { "enabled": true, "opt_level": "O1" } } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. Here is the documentation. Batch Size To configure batch size, use: json { "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto" } and the [Trainer] will automatically set train_micro_batch_size_per_gpu to the value of args.per_device_train_batch_size and train_batch_size to args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps. You can also set the values explicitly: json { "train_batch_size": 12, "train_micro_batch_size_per_gpu": 4 } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. Gradient Accumulation To configure gradient accumulation set: json { "gradient_accumulation_steps": "auto" } and the [Trainer] will automatically set it to the value of args.gradient_accumulation_steps. You can also set the value explicitly: json { "gradient_accumulation_steps": 3 } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. Gradient Clipping To configure gradient gradient clipping set: json { "gradient_clipping": "auto" } and the [Trainer] will automatically set it to the value of args.max_grad_norm. You can also set the value explicitly: json { "gradient_clipping": 1.0 } But then you're on your own synchronizing the [Trainer] command line arguments and the DeepSpeed configuration. Getting The Model Weights Out As long as you continue training and resuming using DeepSpeed you don't need to worry about anything. DeepSpeed stores fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob pattern), and are saved under the normal checkpoint. FP16 Weights: When a model is saved under ZeRO-2, you end up having the normal pytorch_model.bin file with the model weights, but they are only the fp16 version of the weights. Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs, therefore "stage3_gather_16bit_weights_on_model_save": true is required to get the Trainer to save the fp16 version of the weights. If this setting is False pytorch_model.bin won't be created. This is because by default DeepSpeed's state_dict contains a placeholder and not the real weights. If we were to save this state_dict it won't be possible to load it back. json { "zero_optimization": { "stage3_gather_16bit_weights_on_model_save": true } } FP32 Weights: While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to the models hub or pass it to someone else you most likely will want to get the fp32 weights. This ideally shouldn't be done during training since this is a process that requires a lot of memory, and therefore best to be performed offline after the training is complete. But if desired and you have plenty of free CPU memory it can be done in the same training script. The following sections will discuss both approaches. Live FP32 Weights Recovery: This approach may not work if you model is large and you have little free CPU memory left, at the end of the training. If you have saved at least one checkpoint, and you want to use the latest one, you can do the following: thon from transformers.trainer_utils import get_last_checkpoint from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint checkpoint_dir = get_last_checkpoint(trainer.args.output_dir) fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) If you're using the --load_best_model_at_end class:~transformers.TrainingArguments argument (to track the best checkpoint), then you can finish the training by first saving the final model explicitly and then do the same as above: thon from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint checkpoint_dir = os.path.join(trainer.args.output_dir, "checkpoint-final") trainer.deepspeed.save_checkpoint(checkpoint_dir) fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) Note, that once load_state_dict_from_zero_checkpoint was run, the model will no longer be usable in the DeepSpeed context of the same application. i.e. you will need to re-initialize the deepspeed engine, since model.load_state_dict(state_dict) will remove all the DeepSpeed magic from it. So do this only at the very end of the training. Of course, you don't have to use class:~transformers.Trainer and you can adjust the examples above to your own trainer. If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply these yourself as is shown in the following example: thon from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu model = model.cpu() model.load_state_dict(state_dict) Offline FP32 Weights Recovery: DeepSpeed creates a special conversion script zero_to_fp32.py which it places in the top-level of the checkpoint folder. Using this script you can extract the weights at any point. The script is standalone and you no longer need to have the configuration file or a Trainer to do the extraction. Let's say your checkpoint folder looks like this: $ ls -l output_dir/checkpoint-1/ -rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/ -rw-rw-r-- 1 stas stas 12 Mar 27 13:16 latest -rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt -rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin -rw-rw-r-- 1 stas stas 623 Mar 27 20:42 scheduler.pt -rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json -rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model -rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json -rw-rw-r-- 1 stas stas 339 Mar 27 20:42 trainer_state.json -rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin -rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py* In this example there is just one DeepSpeed checkpoint sub-folder global_step1. Therefore to reconstruct the fp32 weights just run: python zero_to_fp32.py . pytorch_model.bin This is it. pytorch_model.bin will now contain the full fp32 model weights consolidated from multiple GPUs. The script will automatically be able to handle either a ZeRO-2 or ZeRO-3 checkpoint. python zero_to_fp32.py -h will give you usage details. The script will auto-discover the deepspeed sub-folder using the contents of the file latest, which in the current example will contain global_step1. Note: currently the script requires 2x general RAM of the final fp32 model weights. ZeRO-3 and Infinity Nuances ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature. ZeRO-Infinity further extends ZeRO-3 to support NVMe memory and multiple other speed and scalability improvements. While all the efforts were made for things to just work without needing any special changes to your models, in certain circumstances you may find the following information to be needed. Constructing Massive Models DeepSpeed/ZeRO-3 can handle models with Trillions of parameters which may not fit onto the existing RAM. In such cases, but also if you want the initialization to happen much faster, initialize the model using deepspeed.zero.Init() context manager (which is also a function decorator), like so: thon from transformers import T5ForConditionalGeneration, T5Config import deepspeed with deepspeed.zero.Init(): config = T5Config.from_pretrained("t5-small") model = T5ForConditionalGeneration(config) As you can see this gives you a randomly initialized model. If you want to use a pretrained model, model_class.from_pretrained will activate this feature as long as is_deepspeed_zero3_enabled() returns True, which currently is setup by the [TrainingArguments] object if the passed DeepSpeed configuration file contains ZeRO-3 config section. Thus you must create the [TrainingArguments] object before calling from_pretrained. Here is an example of a possible sequence: thon from transformers import AutoModel, Trainer, TrainingArguments training_args = TrainingArguments(, deepspeed=ds_config) model = AutoModel.from_pretrained("t5-small") trainer = Trainer(model=model, args=training_args, ) If you're using the official example scripts and your command line arguments include --deepspeed ds_config.json with ZeRO-3 config enabled, then everything is already done for you, since this is how example scripts are written. Note: If the fp16 weights of the model can't fit onto the memory of a single GPU this feature must be used. For full details on this method and other related features please refer to Constructing Massive Models. Also when loading fp16-pretrained models, you will want to tell from_pretrained to use torch_dtype=torch.float16. For details, please, see from_pretrained-torch-dtype. Gathering Parameters Under ZeRO-3 on multiple GPUs no single GPU has all the parameters unless it's the parameters for the currently executing layer. So if you need to access all parameters from all layers at once there is a specific method to do it. Most likely you won't need it, but if you do please refer to Gathering Parameters We do however use it internally in several places, one such example is when loading pretrained model weights in from_pretrained. We load one layer at a time and immediately partition it to all participating GPUs, as for very large models it won't be possible to load it on one GPU and then spread it out to multiple GPUs, due to memory limitations. Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like: python tensor([1.0], device="cuda:0", dtype=torch.float16, requires_grad=True) stress on tensor([1.]), or if you get an error where it says the parameter is of size 1, instead of some much larger multi-dimensional shape, this means that the parameter is partitioned and what you see is a ZeRO-3 placeholder. ZeRO Inference ZeRO Inference uses the same config as ZeRO-3 Training. You just don't need the optimizer and scheduler sections. In fact you can leave these in the config file if you want to share the same one with the training. They will just be ignored. Otherwise you just need to pass the usual [TrainingArguments] arguments. For example: deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json The only important thing is that you need to use a ZeRO-3 configuration, since ZeRO-2 provides no benefit whatsoever for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states. Here is an example of running run_translation.py under DeepSpeed deploying all available GPUs: deepspeed examples/pytorch/translation/run_translation.py \ --deepspeed tests/deepspeed/ds_config_zero3.json \ --model_name_or_path t5-small --output_dir output_dir \ --do_eval --max_eval_samples 50 --warmup_steps 50 \ --max_source_length 128 --val_max_target_length 128 \ --overwrite_output_dir --per_device_eval_batch_size 4 \ --predict_with_generate --dataset_config "ro-en" --fp16 \ --source_lang en --target_lang ro --dataset_name wmt16 \ --source_prefix "translate English to Romanian: " Since for inference there is no need for additional large memory used by the optimizer states and the gradients you should be able to fit much larger batches and/or sequence length onto the same hardware. Additionally DeepSpeed is currently developing a related product called Deepspeed-Inference which has no relationship to the ZeRO technology, but instead uses tensor parallelism to scale models that can't fit onto a single GPU. This is a work in progress and we will provide the integration once that product is complete. Memory Requirements Since Deepspeed ZeRO can offload memory to CPU (and NVMe) the framework provides utils that allow one to tell how much CPU and GPU memory will be needed depending on the number of GPUs being used. Let's estimate how much memory is needed to finetune "bigscience/T0_3B" on a single GPU: $ python -c 'from transformers import AutoModel; \ from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \ model = AutoModel.from_pretrained("bigscience/T0_3B"); \ estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)' [] Estimated memory needed for params, optim states and gradients for a: HW: Setup with 1 node, 1 GPU per node. SW: Model with 2783M total params, 65M largest layer params. per CPU | per GPU | Options 70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1 70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0 62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=1 62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=0 0.37GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=1 15.56GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=0 So you can fit it on a single 80GB GPU and no CPU offload, or a tiny 8GB GPU but then need ~60GB of CPU memory. (Remember this is just the memory for params, optimizer states and gradients - you will need a bit more memory for cuda kernels, activations and temps.) Then it's a tradeoff of cost vs speed. It'll be cheaper to buy/rent a smaller GPU (or less GPUs since you can use multiple GPUs with Deepspeed ZeRO. But then it'll be slower, so even if you don't care about how fast something will be done, the slowdown has a direct impact on the duration of using the GPU and thus bigger cost. So experiment and compare which works the best. If you have enough GPU memory make sure to disable the CPU/NVMe offload as it'll make everything faster. For example, let's repeat the same for 2 GPUs: ```bash $ python -c 'from transformers import AutoModel; \ from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \ model = AutoModel.from_pretrained("bigscience/T0_3B"); \ estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=2, num_nodes=1)' [] Estimated memory needed for params, optim states and gradients for a: HW: Setup with 1 node, 2 GPUs per node. SW: Model with 2783M total params, 65M largest layer params. per CPU | per GPU | Options 70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1 70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0 62.23GB | 2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=1 62.23GB | 2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=0 0.74GB | 23.58GB | offload_param=none, offload_optimizer=none, zero_init=1 31.11GB | 23.58GB | offload_param=none, offload_optimizer=none, zero_init=0 So here you'd want 2x 32GB GPUs or higher without offloading to CPU. For full information please see memory estimators. Filing Issues Here is how to file an issue so that we could quickly get to the bottom of the issue and help you to unblock your work. In your report please always include: the full Deepspeed config file in the report either the command line arguments if you were using the [Trainer] or [TrainingArguments] arguments if you were scripting the Trainer setup yourself. Please do not dump the [TrainingArguments] as it has dozens of entries that are irrelevant. Output of: python -c 'import torch; print(f"torch: {torch.__version__}")' python -c 'import transformers; print(f"transformers: {transformers.__version__}")' python -c 'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")' If possible include a link to a Google Colab notebook that we can reproduce the problem with. You can use this notebook as a starting point. Unless it's impossible please always use a standard dataset that we can use and not something custom. If possible try to use one of the existing examples to reproduce the problem with. Things to consider: Deepspeed is often not the cause of the problem. Some of the filed issues proved to be Deepspeed-unrelated. That is once Deepspeed was removed from the setup, the problem was still there. Therefore, if it's not absolutely obvious it's a DeepSpeed-related problem, as in you can see that there is an exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it. And only if the problem persists then do mentioned Deepspeed and supply all the required details. If it's clear to you that the issue is in the DeepSpeed core and not the integration part, please file the Issue directly with Deepspeed. If you aren't sure, please do not worry, either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if need be. Troubleshooting the deepspeed process gets killed at startup without a traceback If the deepspeed process gets killed at launch time without a traceback, that usually means that the program tried to allocate more CPU memory than your system has or your process is allowed to allocate and the OS kernel killed that process. This is because your configuration file most likely has either offload_optimizer or offload_param or both configured to offload to cpu. If you have NVMe, experiment with offloading to NVMe if you're running under ZeRO-3. Here is how you can estimate how much memory is needed for a specific model. training and/or eval/predict loss is NaN This often happens when one takes a model pre-trained in bf16 mixed precision mode and tries to use it under fp16 (with or without mixed precision). Most models trained on TPU and often the ones released by Google are in this category (e.g. almost all t5-based models). Here the solution is to either use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer). The other problem may have to do with using fp16. When you configure this section: json { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 } } and you see in your log that Deepspeed reports OVERFLOW! as follows: 0%| | 0/189 [00:00<?, ?it/s] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144 1%|▌ | 1/189 [00:00<01:26, 2.17it/s] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0 1%|█▏ [] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1 14%|████████████████▌ | 27/189 [00:14<01:13, 2.21it/s] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1 15%|█████████████████▏ | 28/189 [00:14<01:13, 2.18it/s] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1 15%|█████████████████▊ | 29/189 [00:15<01:13, 2.18it/s] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1 [] that means that the Deepspeed loss scaler can't figure out a scaling co-efficient that overcomes loss overflow. (the log was massaged to be more readable here.) In this case you usually need to raise the value of initial_scale_power. Setting it to "initial_scale_power": 32 will typically resolve the problem. Notes DeepSpeed works with the PyTorch [Trainer] but not TF [TFTrainer]. While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from source to best match your hardware and also if you need to enable certain features, like 1-bit Adam, which aren't available in the pypi distribution. You don't have to use the [Trainer] to use DeepSpeed with 🤗 Transformers - you can use any model with your own trainer, and you will have to adapt the latter according to the DeepSpeed integration instructions. Non-Trainer Deepspeed Integration The [~deepspeed.HfDeepSpeedConfig] is used to integrate Deepspeed into the 🤗 Transformers core functionality, when [Trainer] is not used. The only thing that it does is handling Deepspeed ZeRO-3 param gathering and automatically splitting the model onto multiple gpus during from_pretrained call. Everything else you have to do by yourself. When using [Trainer] everything is automatically taken care of. When not using [Trainer], to efficiently deploy DeepSpeed ZeRO-3, you must instantiate the [~deepspeed.HfDeepSpeedConfig] object before instantiating the model and keep that object alive. If you're using Deepspeed ZeRO-1 or ZeRO-2 you don't need to use HfDeepSpeedConfig at all. For example for a pretrained model: thon from transformers.deepspeed import HfDeepSpeedConfig from transformers import AutoModel import deepspeed ds_config = {} # deepspeed config object or path to the file must run before instantiating the model to detect zero 3 dschf = HfDeepSpeedConfig(ds_config) # keep this object alive model = AutoModel.from_pretrained("gpt2") engine = deepspeed.initialize(model=model, config_params=ds_config, ) or for non-pretrained model: thon from transformers.deepspeed import HfDeepSpeedConfig from transformers import AutoModel, AutoConfig import deepspeed ds_config = {} # deepspeed config object or path to the file must run before instantiating the model to detect zero 3 dschf = HfDeepSpeedConfig(ds_config) # keep this object alive config = AutoConfig.from_pretrained("gpt2") model = AutoModel.from_config(config) engine = deepspeed.initialize(model=model, config_params=ds_config, ) Please note that if you're not using the [Trainer] integration, you're completely on your own. Basically follow the documentation on the Deepspeed website. Also you have to configure explicitly the config file - you can't use "auto" values and you will have to put real values instead. HfDeepSpeedConfig [[autodoc]] deepspeed.HfDeepSpeedConfig - all Custom DeepSpeed ZeRO Inference Here is an example of how one could do DeepSpeed ZeRO Inference without using [Trainer] when one can't fit a model onto a single GPU. The solution includes using additional GPUs or/and offloading GPU memory to CPU memory. The important nuance to understand here is that the way ZeRO is designed you can process different inputs on different GPUs in parallel. The example has copious notes and is self-documenting. Make sure to: disable CPU offload if you have enough GPU memory (since it slows things down) enable bf16 if you own an Ampere or a newer GPU to make things faster. If you don't have that hardware you may enable fp16 as long as you don't use any model that was pre-trained in bf16 mixed precision (such as most t5 models). These usually overflow in fp16 and you will see garbage as output. thon !/usr/bin/env python This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model into a single GPU 1. Use 1 GPU with CPU offload 2. Or use multiple GPUs instead First you need to install deepspeed: pip install deepspeed Here we use a 3B "bigscience/T0_3B" model which needs about 15GB GPU RAM - so 1 largish or 2 small GPUs can handle it. or 1 small GPU and a lot of CPU memory. To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU - you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to process multiple inputs at once. The provided deepspeed config also activates CPU memory offloading, so chances are that if you have a lot of available CPU memory and you don't mind a slowdown you should be able to load a model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will run faster if you don't want offload to CPU - so disable that section then. To deploy on 1 gpu: deepspeed --num_gpus 1 t0.py or: python -m torch.distributed.run --nproc_per_node=1 t0.py To deploy on 2 gpus: deepspeed --num_gpus 2 t0.py or: python -m torch.distributed.run --nproc_per_node=2 t0.py from transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM from transformers.deepspeed import HfDeepSpeedConfig import deepspeed import os import torch os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warnings about parallelism in tokenizers distributed setup local_rank = int(os.getenv("LOCAL_RANK", "0")) world_size = int(os.getenv("WORLD_SIZE", "1")) torch.cuda.set_device(local_rank) deepspeed.init_distributed() model_name = "bigscience/T0_3B" config = AutoConfig.from_pretrained(model_name) model_hidden_size = config.d_model batch size has to be divisible by world_size, but can be bigger than world_size train_batch_size = 1 * world_size ds_config notes - enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be faster. - for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g. all official t5 models are bf16-pretrained - set offload_param.device to "none" or completely remove the offload_param section if you don't - want CPU offload - if using offload_param you can manually finetune stage3_param_persistence_threshold to control - which params should remain on gpus - the larger the value the smaller the offload size For indepth info on Deepspeed config see https://huggingface.co/docs/transformers/main/main_classes/deepspeed keeping the same format as json for consistency, except it uses lower case for true/false fmt: off ds_config = { "fp16": { "enabled": False }, "bf16": { "enabled": False }, "zero_optimization": { "stage": 3, "offload_param": { "device": "cpu", "pin_memory": True }, "overlap_comm": True, "contiguous_gradients": True, "reduce_bucket_size": model_hidden_size * model_hidden_size, "stage3_prefetch_bucket_size": 0.9 * model_hidden_size * model_hidden_size, "stage3_param_persistence_threshold": 10 * model_hidden_size }, "steps_per_print": 2000, "train_batch_size": train_batch_size, "train_micro_batch_size_per_gpu": 1, "wall_clock_breakdown": False } fmt: on next line instructs transformers to partition the model directly over multiple gpus using deepspeed.zero.Init when model's from_pretrained method is called. it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name) otherwise the model will first be loaded normally and only partitioned at forward time which is less efficient and when there is little CPU RAM may fail dschf = HfDeepSpeedConfig(ds_config) # keep this object alive now a model can be loaded. model = AutoModelForSeq2SeqLM.from_pretrained(model_name) initialise Deepspeed ZeRO and store only the engine object ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0] ds_engine.module.eval() # inference Deepspeed ZeRO can process unrelated inputs on each GPU. So for 2 gpus you process 2 inputs at once. If you use more GPUs adjust for more. And of course if you have just one input to process you then need to pass the same string to both gpus If you use only one GPU, then you will have only rank 0. rank = torch.distributed.get_rank() if rank == 0: text_in = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" elif rank == 1: text_in = "Is this review positive or negative? Review: this is the worst restaurant ever" tokenizer = AutoTokenizer.from_pretrained(model_name) inputs = tokenizer.encode(text_in, return_tensors="pt").to(device=local_rank) with torch.no_grad(): outputs = ds_engine.module.generate(inputs, synced_gpus=True) text_out = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"rank{rank}:\n in={text_in}\n out={text_out}") Let's save it as t0.py and run it: $ deepspeed --num_gpus 2 t0.py rank0: in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy out=Positive rank1: in=Is this review positive or negative? Review: this is the worst restaurant ever out=negative This was a very basic example and you will want to adapt it to your needs. generate nuances When using multiple GPUs with ZeRO Stage-3, one has to synchronize the GPUs by calling generate(, synced_gpus=True). If this is not done if one GPU finished generating before other GPUs the whole system will hang as the rest of the GPUs will not be able to received the shard of weights from the GPU that stopped generating. Starting from transformers>=4.28, if synced_gpus isn't explicitly specified, it'll be set to True automatically if these conditions are detected. But you can still override the value of synced_gpus if need to. Testing Deepspeed Integration If you submit a PR that involves DeepSpeed integration please note our CircleCI PR CI setup has no GPUs, so we only run tests requiring gpus on a different CI nightly. Therefore if you get a green CI report in your PR it doesn't mean DeepSpeed tests pass. To run DeepSpeed tests, please run at least: RUN_SLOW=1 pytest tests/deepspeed/test_deepspeed.py If you changed any of the modeling or pytorch examples code, then run the model zoo tests as well. The following will run all DeepSpeed tests: RUN_SLOW=1 pytest tests/deepspeed Main DeepSpeed Resources Project's github Usage docs API docs Blog posts Papers: ZeRO: Memory Optimizations Toward Training Trillion Parameter Models ZeRO-Offload: Democratizing Billion-Scale Model Training ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning Finally, please, remember that, HuggingFace [Trainer] only integrates DeepSpeed, therefore if you have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub.

Image Processor An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to segmentation masks. ImageProcessingMixin [[autodoc]] image_processing_utils.ImageProcessingMixin - from_pretrained - save_pretrained BatchFeature [[autodoc]] BatchFeature BaseImageProcessor [[autodoc]] image_processing_utils.BaseImageProcessor

Sharing custom models The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs. If you are writing a brand new model, it might be easier to start from scratch. In this tutorial, we will show you how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it with the community (with the code it relies on) so that anyone can use it, even if it's not present in the 🤗 Transformers library. We will illustrate all of this on a ResNet model, by wrapping the ResNet class of the timm library into a [PreTrainedModel]. Writing a custom configuration Before we dive into the model, let's first write its configuration. The configuration of a model is an object that will contain all the necessary information to build the model. As we will see in the next section, the model can only take a config to be initialized, so we really need that object to be as complete as possible. In our example, we will take a couple of arguments of the ResNet class that we might want to tweak. Different configurations will then give us the different types of ResNets that are possible. We then just store those arguments, after checking the validity of a few of them. thon from transformers import PretrainedConfig from typing import List class ResnetConfig(PretrainedConfig): model_type = "resnet" def __init__( self, block_type="bottleneck", layers: List[int] = [3, 4, 6, 3], num_classes: int = 1000, input_channels: int = 3, cardinality: int = 1, base_width: int = 64, stem_width: int = 64, stem_type: str = "", avg_down: bool = False, **kwargs, ): if block_type not in ["basic", "bottleneck"]: raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.") if stem_type not in ["", "deep", "deep-tiered"]: raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.") self.block_type = block_type self.layers = layers self.num_classes = num_classes self.input_channels = input_channels self.cardinality = cardinality self.base_width = base_width self.stem_width = stem_width self.stem_type = stem_type self.avg_down = avg_down super().__init__(**kwargs) The three important things to remember when writing you own configuration are the following: - you have to inherit from PretrainedConfig, - the __init__ of your PretrainedConfig must accept any kwargs, - those kwargs need to be passed to the superclass __init__. The inheritance is to make sure you get all the functionality from the 🤗 Transformers library, while the two other constraints come from the fact a PretrainedConfig has more fields than the ones you are setting. When reloading a config with the from_pretrained method, those fields need to be accepted by your config and then sent to the superclass. Defining a model_type for your configuration (here model_type="resnet") is not mandatory, unless you want to register your model with the auto classes (see last section). With this done, you can easily create and save your configuration like you would do with any other model config of the library. Here is how we can create a resnet50d config and save it: py resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True) resnet50d_config.save_pretrained("custom-resnet") This will save a file named config.json inside the folder custom-resnet. You can then reload your config with the from_pretrained method: py resnet50d_config = ResnetConfig.from_pretrained("custom-resnet") You can also use any other method of the [PretrainedConfig] class, like [~PretrainedConfig.push_to_hub] to directly upload your config to the Hub. Writing a custom model Now that we have our ResNet configuration, we can go on writing the model. We will actually write two: one that extracts the hidden features from a batch of images (like [BertModel]) and one that is suitable for image classification (like [BertForSequenceClassification]). As we mentioned before, we'll only write a loose wrapper of the model to keep it simple for this example. The only thing we need to do before writing this class is a map between the block types and actual block classes. Then the model is defined from the configuration by passing everything to the ResNet class: from transformers import PreTrainedModel from timm.models.resnet import BasicBlock, Bottleneck, ResNet from .configuration_resnet import ResnetConfig BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck} class ResnetModel(PreTrainedModel): config_class = ResnetConfig def __init__(self, config): super().__init__(config) block_layer = BLOCK_MAPPING[config.block_type] self.model = ResNet( block_layer, config.layers, num_classes=config.num_classes, in_chans=config.input_channels, cardinality=config.cardinality, base_width=config.base_width, stem_width=config.stem_width, stem_type=config.stem_type, avg_down=config.avg_down, ) def forward(self, tensor): return self.model.forward_features(tensor) For the model that will classify images, we just change the forward method: import torch class ResnetModelForImageClassification(PreTrainedModel): config_class = ResnetConfig def __init__(self, config): super().__init__(config) block_layer = BLOCK_MAPPING[config.block_type] self.model = ResNet( block_layer, config.layers, num_classes=config.num_classes, in_chans=config.input_channels, cardinality=config.cardinality, base_width=config.base_width, stem_width=config.stem_width, stem_type=config.stem_type, avg_down=config.avg_down, ) def forward(self, tensor, labels=None): logits = self.model(tensor) if labels is not None: loss = torch.nn.cross_entropy(logits, labels) return {"loss": loss, "logits": logits} return {"logits": logits} In both cases, notice how we inherit from PreTrainedModel and call the superclass initialization with the config (a bit like when you write a regular torch.nn.Module). The line that sets the config_class is not mandatory, unless you want to register your model with the auto classes (see last section). If your model is very similar to a model inside the library, you can re-use the same configuration as this model. You can have your model return anything you want, but returning a dictionary like we did for ResnetModelForImageClassification, with the loss included when labels are passed, will make your model directly usable inside the [Trainer] class. Using another output format is fine as long as you are planning on using your own training loop or another library for training. Now that we have our model class, let's create one: py resnet50d = ResnetModelForImageClassification(resnet50d_config) Again, you can use any of the methods of [PreTrainedModel], like [~PreTrainedModel.save_pretrained] or [~PreTrainedModel.push_to_hub]. We will use the second in the next section, and see how to push the model weights with the code of our model. But first, let's load some pretrained weights inside our model. In your own use case, you will probably be training your custom model on your own data. To go fast for this tutorial, we will use the pretrained version of the resnet50d. Since our model is just a wrapper around it, it's going to be easy to transfer those weights: import timm pretrained_model = timm.create_model("resnet50d", pretrained=True) resnet50d.model.load_state_dict(pretrained_model.state_dict()) Now let's see how to make sure that when we do [~PreTrainedModel.save_pretrained] or [~PreTrainedModel.push_to_hub], the code of the model is saved. Sending the code to the Hub This API is experimental and may have some slight breaking changes in the next releases. First, make sure your model is fully defined in a .py file. It can rely on relative imports to some other files as long as all the files are in the same directory (we don't support submodules for this feature yet). For our example, we'll define a modeling_resnet.py file and a configuration_resnet.py file in a folder of the current working directory named resnet_model. The configuration file contains the code for ResnetConfig and the modeling file contains the code of ResnetModel and ResnetModelForImageClassification. . └── resnet_model ├── __init__.py ├── configuration_resnet.py └── modeling_resnet.py The __init__.py can be empty, it's just there so that Python detects resnet_model can be use as a module. If copying a modeling files from the library, you will need to replace all the relative imports at the top of the file to import from the transformers package. Note that you can re-use (or subclass) an existing configuration/model. To share your model with the community, follow those steps: first import the ResNet model and config from the newly created files: py from resnet_model.configuration_resnet import ResnetConfig from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification Then you have to tell the library you want to copy the code files of those objects when using the save_pretrained method and properly register them with a given Auto class (especially for models), just run: py ResnetConfig.register_for_auto_class() ResnetModel.register_for_auto_class("AutoModel") ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification") Note that there is no need to specify an auto class for the configuration (there is only one auto class for them, [AutoConfig]) but it's different for models. Your custom model could be suitable for many different tasks, so you have to specify which one of the auto classes is the correct one for your model. Next, let's create the config and models as we did before: resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True) resnet50d = ResnetModelForImageClassification(resnet50d_config) pretrained_model = timm.create_model("resnet50d", pretrained=True) resnet50d.model.load_state_dict(pretrained_model.state_dict()) Now to send the model to the Hub, make sure you are logged in. Either run in your terminal: huggingface-cli login or from a notebook: from huggingface_hub import notebook_login notebook_login() You can then push to your own namespace (or an organization you are a member of) like this: py resnet50d.push_to_hub("custom-resnet50d") On top of the modeling weights and the configuration in json format, this also copied the modeling and configuration .py files in the folder custom-resnet50d and uploaded the result to the Hub. You can check the result in this model repo. See the sharing tutorial for more information on the push to Hub method. Using a model with custom code You can use any configuration, model or tokenizer with custom code files in its repository with the auto-classes and the from_pretrained method. All files and code uploaded to the Hub are scanned for malware (refer to the Hub security documentation for more information), but you should still review the model code and author to avoid executing malicious code on your machine. Set trust_remote_code=True to use a model with custom code: from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True) It is also strongly encouraged to pass a commit hash as a revision to make sure the author of the models did not update the code with some malicious new lines (unless you fully trust the authors of the models). py commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292" model = AutoModelForImageClassification.from_pretrained( "sgugger/custom-resnet50d", trust_remote_code=True, revision=commit_hash ) Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit hash of any commit. Registering a model with custom code to the auto classes If you are writing a library that extends 🤗 Transformers, you may want to extend the auto classes to include your own model. This is different from pushing the code to the Hub in the sense that users will need to import your library to get the custom models (contrarily to automatically downloading the model code from the Hub). As long as your config has a model_type attribute that is different from existing model types, and that your model classes have the right config_class attributes, you can just add them to the auto classes likes this: from transformers import AutoConfig, AutoModel, AutoModelForImageClassification AutoConfig.register("resnet", ResnetConfig) AutoModel.register(ResnetConfig, ResnetModel) AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification) Note that the first argument used when registering your custom config to [AutoConfig] needs to match the model_type of your custom config, and the first argument used when registering your custom models to any auto model class needs to match the config_class of those models.

Training on TPU with TensorFlow If you don't need long explanations and just want TPU code samples to get started with, check out our TPU example notebook! What is a TPU? A TPU is a Tensor Processing Unit. They are hardware designed by Google, which are used to greatly speed up the tensor computations within neural networks, much like GPUs. They can be used for both network training and inference. They are generally accessed through Google’s cloud services, but small TPUs can also be accessed directly for free through Google Colab and Kaggle Kernels. Because all TensorFlow models in 🤗 Transformers are Keras models, most of the methods in this document are generally applicable to TPU training for any Keras model! However, there are a few points that are specific to the HuggingFace ecosystem (hug-o-system?) of Transformers and Datasets, and we’ll make sure to flag them up when we get to them. What kinds of TPU are available? New users are often very confused by the range of TPUs, and the different ways to access them. The first key distinction to understand is the difference between TPU Nodes and TPU VMs. When you use a TPU Node, you are effectively indirectly accessing a remote TPU. You will need a separate VM, which will initialize your network and data pipeline and then forward them to the remote node. When you use a TPU on Google Colab, you are accessing it in the TPU Node style. Using TPU Nodes can have some quite unexpected behaviour for people who aren’t used to them! In particular, because the TPU is located on a physically different system to the machine you’re running your Python code on, your data cannot be local to your machine - any data pipeline that loads from your machine’s internal storage will totally fail! Instead, data must be stored in Google Cloud Storage where your data pipeline can still access it, even when the pipeline is running on the remote TPU node. If you can fit all your data in memory as np.ndarray or tf.Tensor, then you can fit() on that data even when using Colab or a TPU Node, without needing to upload it to Google Cloud Storage. 🤗Specific Hugging Face Tip🤗: The methods Dataset.to_tf_dataset() and its higher-level wrapper model.prepare_tf_dataset() , which you will see throughout our TF code examples, will both fail on a TPU Node. The reason for this is that even though they create a tf.data.Dataset it is not a “pure” tf.data pipeline and uses tf.numpy_function or Dataset.from_generator() to stream data from the underlying HuggingFace Dataset. This HuggingFace Dataset is backed by data that is on a local disc and which the remote TPU Node will not be able to read. The second way to access a TPU is via a TPU VM. When using a TPU VM, you connect directly to the machine that the TPU is attached to, much like training on a GPU VM. TPU VMs are generally easier to work with, particularly when it comes to your data pipeline. All of the above warnings do not apply to TPU VMs! This is an opinionated document, so here’s our opinion: Avoid using TPU Node if possible. It is more confusing and more difficult to debug than TPU VMs. It is also likely to be unsupported in future - Google’s latest TPU, TPUv4, can only be accessed as a TPU VM, which suggests that TPU Nodes are increasingly going to become a “legacy” access method. However, we understand that the only free TPU access is on Colab and Kaggle Kernels, which uses TPU Node - so we’ll try to explain how to handle it if you have to! Check the TPU example notebook for code samples that explain this in more detail. What sizes of TPU are available? A single TPU (a v2-8/v3-8/v4-8) runs 8 replicas. TPUs exist in pods that can run hundreds or thousands of replicas simultaneously. When you use more than a single TPU but less than a whole pod (for example, a v3-32), your TPU fleet is referred to as a pod slice. When you access a free TPU via Colab, you generally get a single v2-8 TPU. I keep hearing about this XLA thing. What’s XLA, and how does it relate to TPUs? XLA is an optimizing compiler, used by both TensorFlow and JAX. In JAX it is the only compiler, whereas in TensorFlow it is optional (but mandatory on TPU!). The easiest way to enable it when training a Keras model is to pass the argument jit_compile=True to model.compile(). If you don’t get any errors and performance is good, that’s a great sign that you’re ready to move to TPU! Debugging on TPU is generally a bit harder than on CPU/GPU, so we recommend getting your code running on CPU/GPU with XLA first before trying it on TPU. You don’t have to train for long, of course - just for a few steps to make sure that your model and data pipeline are working like you expect them to. XLA compiled code is usually faster - so even if you’re not planning to run on TPU, adding jit_compile=True can improve your performance. Be sure to note the caveats below about XLA compatibility, though! Tip born of painful experience: Although using jit_compile=True is a good way to get a speed boost and test if your CPU/GPU code is XLA-compatible, it can actually cause a lot of problems if you leave it in when actually training on TPU. XLA compilation will happen implicitly on TPU, so remember to remove that line before actually running your code on a TPU! How do I make my model XLA compatible? In many cases, your code is probably XLA-compatible already! However, there are a few things that work in normal TensorFlow that don’t work in XLA. We’ve distilled them into three core rules below: 🤗Specific HuggingFace Tip🤗: We’ve put a lot of effort into rewriting our TensorFlow models and loss functions to be XLA-compatible. Our models and loss functions generally obey rule #1 and #2 by default, so you can skip over them if you’re using transformers models. Don’t forget about these rules when writing your own models and loss functions, though! XLA Rule #1: Your code cannot have “data-dependent conditionals” What that means is that any if statement cannot depend on values inside a tf.Tensor. For example, this code block cannot be compiled with XLA! python if tf.reduce_sum(tensor) > 10: tensor = tensor / 2.0 This might seem very restrictive at first, but most neural net code doesn’t need to do this. You can often get around this restriction by using tf.cond (see the documentation here) or by removing the conditional and finding a clever math trick with indicator variables instead, like so: python sum_over_10 = tf.cast(tf.reduce_sum(tensor) > 10, tf.float32) tensor = tensor / (1.0 + sum_over_10) This code has exactly the same effect as the code above, but by avoiding a conditional, we ensure it will compile with XLA without problems! XLA Rule #2: Your code cannot have “data-dependent shapes” What this means is that the shape of all of the tf.Tensor objects in your code cannot depend on their values. For example, the function tf.unique cannot be compiled with XLA, because it returns a tensor containing one instance of each unique value in the input. The shape of this output will obviously be different depending on how repetitive the input Tensor was, and so XLA refuses to handle it! In general, most neural network code obeys rule #2 by default. However, there are a few common cases where it becomes a problem. One very common one is when you use label masking, setting your labels to a negative value to indicate that those positions should be ignored when computing the loss. If you look at NumPy or PyTorch loss functions that support label masking, you will often see code like this that uses boolean indexing: python label_mask = labels >= 0 masked_outputs = outputs[label_mask] masked_labels = labels[label_mask] loss = compute_loss(masked_outputs, masked_labels) mean_loss = torch.mean(loss) This code is totally fine in NumPy or PyTorch, but it breaks in XLA! Why? Because the shape of masked_outputs and masked_labels depends on how many positions are masked - that makes it a data-dependent shape. However, just like for rule #1, we can often rewrite this code to yield exactly the same output without any data-dependent shapes. python label_mask = tf.cast(labels >= 0, tf.float32) loss = compute_loss(outputs, labels) loss = loss * label_mask # Set negative label positions to 0 mean_loss = tf.reduce_sum(loss) / tf.reduce_sum(label_mask) Here, we avoid data-dependent shapes by computing the loss for every position, but zeroing out the masked positions in both the numerator and denominator when we calculate the mean, which yields exactly the same result as the first block while maintaining XLA compatibility. Note that we use the same trick as in rule #1 - converting a tf.bool to tf.float32 and using it as an indicator variable. This is a really useful trick, so remember it if you need to convert your own code to XLA! XLA Rule #3: XLA will need to recompile your model for every different input shape it sees This is the big one. What this means is that if your input shapes are very variable, XLA will have to recompile your model over and over, which will create huge performance problems. This commonly arises in NLP models, where input texts have variable lengths after tokenization. In other modalities, static shapes are more common and this rule is much less of a problem. How can you get around rule #3? The key is padding - if you pad all your inputs to the same length, and then use an attention_mask, you can get the same results as you’d get from variable shapes, but without any XLA issues. However, excessive padding can cause severe slowdown too - if you pad all your samples to the maximum length in the whole dataset, you might end up with batches consisting endless padding tokens, which will waste a lot of compute and memory! There isn’t a perfect solution to this problem. However, you can try some tricks. One very useful trick is to pad batches of samples up to a multiple of a number like 32 or 64 tokens. This often only increases the number of tokens by a small amount, but it hugely reduces the number of unique input shapes, because every input shape now has to be a multiple of 32 or 64. Fewer unique input shapes means fewer XLA compilations! 🤗Specific HuggingFace Tip🤗: Our tokenizers and data collators have methods that can help you here. You can use padding="max_length" or padding="longest" when calling tokenizers to get them to output padded data. Our tokenizers and data collators also have a pad_to_multiple_of argument that you can use to reduce the number of unique input shapes you see! How do I actually train my model on TPU? Once your training is XLA-compatible and (if you’re using TPU Node / Colab) your dataset has been prepared appropriately, running on TPU is surprisingly easy! All you really need to change in your code is to add a few lines to initialize your TPU, and to ensure that your model and dataset are created inside a TPUStrategy scope. Take a look at our TPU example notebook to see this in action! Summary There was a lot in here, so let’s summarize with a quick checklist you can follow when you want to get your model ready for TPU training: Make sure your code follows the three rules of XLA Compile your model with jit_compile=True on CPU/GPU and confirm that you can train it with XLA Either load your dataset into memory or use a TPU-compatible dataset loading approach (see notebook) Migrate your code either to Colab (with accelerator set to “TPU”) or a TPU VM on Google Cloud Add TPU initializer code (see notebook) Create your TPUStrategy and make sure dataset loading and model creation are inside the strategy.scope() (see notebook) Don’t forget to take jit_compile=True out again when you move to TPU! 🙏🙏🙏🥺🥺🥺 Call model.fit() You did it!

Quick tour [[open-in-colab]] Get up and running with 🤗 Transformers! Whether you're a developer or an everyday user, this quick tour will help you get started and show you how to use the [pipeline] for inference, load a pretrained model and preprocessor with an AutoClass, and quickly train a model with PyTorch or TensorFlow. If you're a beginner, we recommend checking out our tutorials or course next for more in-depth explanations of the concepts introduced here. Before you begin, make sure you have all the necessary libraries installed: !pip install transformers datasets You'll also need to install your preferred machine learning framework: pip install torch pip install tensorflow Pipeline The [pipeline] is the easiest and fastest way to use a pretrained model for inference. You can use the [pipeline] out-of-the-box for many tasks across different modalities, some of which are shown in the table below: For a complete list of available tasks, check out the pipeline API reference. | Task | Description | Modality | Pipeline identifier | |------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|-----------------------------------------------| | Text classification | assign a label to a given sequence of text | NLP | pipeline(task=“sentiment-analysis”) | | Text generation | generate text given a prompt | NLP | pipeline(task=“text-generation”) | | Summarization | generate a summary of a sequence of text or document | NLP | pipeline(task=“summarization”) | | Image classification | assign a label to an image | Computer vision | pipeline(task=“image-classification”) | | Image segmentation | assign a label to each individual pixel of an image (supports semantic, panoptic, and instance segmentation) | Computer vision | pipeline(task=“image-segmentation”) | | Object detection | predict the bounding boxes and classes of objects in an image | Computer vision | pipeline(task=“object-detection”) | | Audio classification | assign a label to some audio data | Audio | pipeline(task=“audio-classification”) | | Automatic speech recognition | transcribe speech into text | Audio | pipeline(task=“automatic-speech-recognition”) | | Visual question answering | answer a question about the image, given an image and a question | Multimodal | pipeline(task=“vqa”) | | Document question answering | answer a question about the document, given a document and a question | Multimodal | pipeline(task="document-question-answering") | | Image captioning | generate a caption for a given image | Multimodal | pipeline(task="image-to-text") | Start by creating an instance of [pipeline] and specifying a task you want to use it for. In this guide, you'll use the [pipeline] for sentiment analysis as an example: from transformers import pipeline classifier = pipeline("sentiment-analysis") The [pipeline] downloads and caches a default pretrained model and tokenizer for sentiment analysis. Now you can use the classifier on your target text: classifier("We are very happy to show you the 🤗 Transformers library.") [{'label': 'POSITIVE', 'score': 0.9998}] If you have more than one input, pass your inputs as a list to the [pipeline] to return a list of dictionaries: results = classifier(["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."]) for result in results: print(f"label: {result['label']}, with score: {round(result['score'], 4)}") label: POSITIVE, with score: 0.9998 label: NEGATIVE, with score: 0.5309 The [pipeline] can also iterate over an entire dataset for any task you like. For this example, let's choose automatic speech recognition as our task: import torch from transformers import pipeline speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h") Load an audio dataset (see the 🤗 Datasets Quick Start for more details) you'd like to iterate over. For example, load the MInDS-14 dataset: from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") # doctest: +IGNORE_RESULT You need to make sure the sampling rate of the dataset matches the sampling rate facebook/wav2vec2-base-960h was trained on: dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate)) The audio files are automatically loaded and resampled when calling the "audio" column. Extract the raw waveform arrays from the first 4 samples and pass it as a list to the pipeline: result = speech_recognizer(dataset[:4]["audio"]) print([d["text"] for d in result]) ['I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT', "FONDERING HOW I'D SET UP A JOIN TO HELL T WITH MY WIFE AND WHERE THE AP MIGHT BE", "I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE APSO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AN I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS", 'HOW DO I FURN A JOINA COUT'] For larger datasets where the inputs are big (like in speech or vision), you'll want to pass a generator instead of a list to load all the inputs in memory. Take a look at the pipeline API reference for more information. Use another model and tokenizer in the pipeline The [pipeline] can accommodate any model from the Hub, making it easy to adapt the [pipeline] for other use-cases. For example, if you'd like a model capable of handling French text, use the tags on the Hub to filter for an appropriate model. The top filtered result returns a multilingual BERT model finetuned for sentiment analysis you can use for French text: model_name = "nlptown/bert-base-multilingual-uncased-sentiment" Use [AutoModelForSequenceClassification] and [AutoTokenizer] to load the pretrained model and it's associated tokenizer (more on an AutoClass in the next section): from transformers import AutoTokenizer, AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) `` </pt> <tf> Use [TFAutoModelForSequenceClassification] and [AutoTokenizer] to load the pretrained model and it's associated tokenizer (more on anTFAutoClass` in the next section): from transformers import AutoTokenizer, TFAutoModelForSequenceClassification model = TFAutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) Specify the model and tokenizer in the [pipeline], and now you can apply the classifier on French text: classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) classifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.") [{'label': '5 stars', 'score': 0.7273}] If you can't find a model for your use-case, you'll need to finetune a pretrained model on your data. Take a look at our finetuning tutorial to learn how. Finally, after you've finetuned your pretrained model, please consider sharing the model with the community on the Hub to democratize machine learning for everyone! 🤗 AutoClass Under the hood, the [AutoModelForSequenceClassification] and [AutoTokenizer] classes work together to power the [pipeline] you used above. An AutoClass is a shortcut that automatically retrieves the architecture of a pretrained model from its name or path. You only need to select the appropriate AutoClass for your task and it's associated preprocessing class. Let's return to the example from the previous section and see how you can use the AutoClass to replicate the results of the [pipeline]. AutoTokenizer A tokenizer is responsible for preprocessing text into an array of numbers as inputs to a model. There are multiple rules that govern the tokenization process, including how to split a word and at what level words should be split (learn more about tokenization in the tokenizer summary). The most important thing to remember is you need to instantiate a tokenizer with the same model name to ensure you're using the same tokenization rules a model was pretrained with. Load a tokenizer with [AutoTokenizer]: from transformers import AutoTokenizer model_name = "nlptown/bert-base-multilingual-uncased-sentiment" tokenizer = AutoTokenizer.from_pretrained(model_name) Pass your text to the tokenizer: encoding = tokenizer("We are very happy to show you the 🤗 Transformers library.") print(encoding) {'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} The tokenizer returns a dictionary containing: input_ids: numerical representations of your tokens. attention_mask: indicates which tokens should be attended to. A tokenizer can also accept a list of inputs, and pad and truncate the text to return a batch with uniform length: pt_batch = tokenizer( ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."], padding=True, truncation=True, max_length=512, return_tensors="pt", ) </pt> <tf>py tf_batch = tokenizer( ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."], padding=True, truncation=True, max_length=512, return_tensors="tf", ) Check out the preprocess tutorial for more details about tokenization, and how to use an [AutoImageProcessor], [AutoFeatureExtractor] and [AutoProcessor] to preprocess image, audio, and multimodal inputs. AutoModel 🤗 Transformers provides a simple and unified way to load pretrained instances. This means you can load an [AutoModel] like you would load an [AutoTokenizer]. The only difference is selecting the correct [AutoModel] for the task. For text (or sequence) classification, you should load [AutoModelForSequenceClassification]: from transformers import AutoModelForSequenceClassification model_name = "nlptown/bert-base-multilingual-uncased-sentiment" pt_model = AutoModelForSequenceClassification.from_pretrained(model_name) See the task summary for tasks supported by an [AutoModel] class. Now pass your preprocessed batch of inputs directly to the model. You just have to unpack the dictionary by adding **: pt_outputs = pt_model(**pt_batch) The model outputs the final activations in the logits attribute. Apply the softmax function to the logits to retrieve the probabilities: from torch import nn pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1) print(pt_predictions) tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725], [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=) `` </pt> <tf> 🤗 Transformers provides a simple and unified way to load pretrained instances. This means you can load an [TFAutoModel] like you would load an [AutoTokenizer]. The only difference is selecting the correct [TFAutoModel] for the task. For text (or sequence) classification, you should load [TFAutoModelForSequenceClassification`]: from transformers import TFAutoModelForSequenceClassification model_name = "nlptown/bert-base-multilingual-uncased-sentiment" tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name) See the task summary for tasks supported by an [AutoModel] class. Now pass your preprocessed batch of inputs directly to the model. You can pass the tensors as-is: tf_outputs = tf_model(tf_batch) The model outputs the final activations in the logits attribute. Apply the softmax function to the logits to retrieve the probabilities: import tensorflow as tf tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1) tf_predictions # doctest: +IGNORE_RESULT All 🤗 Transformers models (PyTorch or TensorFlow) output the tensors before the final activation function (like softmax) because the final activation function is often fused with the loss. Model outputs are special dataclasses so their attributes are autocompleted in an IDE. The model outputs behave like a tuple or a dictionary (you can index with an integer, a slice or a string) in which case, attributes that are None are ignored. Save a model Once your model is fine-tuned, you can save it with its tokenizer using [PreTrainedModel.save_pretrained]: pt_save_directory = "./pt_save_pretrained" tokenizer.save_pretrained(pt_save_directory) # doctest: +IGNORE_RESULT pt_model.save_pretrained(pt_save_directory) When you are ready to use the model again, reload it with [PreTrainedModel.from_pretrained]: pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained") `` </pt> <tf> Once your model is fine-tuned, you can save it with its tokenizer using [TFPreTrainedModel.save_pretrained`]: tf_save_directory = "./tf_save_pretrained" tokenizer.save_pretrained(tf_save_directory) # doctest: +IGNORE_RESULT tf_model.save_pretrained(tf_save_directory) When you are ready to use the model again, reload it with [TFPreTrainedModel.from_pretrained]: tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained") One particularly cool 🤗 Transformers feature is the ability to save a model and reload it as either a PyTorch or TensorFlow model. The from_pt or from_tf parameter can convert the model from one framework to the other: from transformers import AutoModel tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) </pt> <tf>py from transformers import TFAutoModel tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) Custom model builds You can modify the model's configuration class to change how a model is built. The configuration specifies a model's attributes, such as the number of hidden layers or attention heads. You start from scratch when you initialize a model from a custom configuration class. The model attributes are randomly initialized, and you'll need to train the model before you can use it to get meaningful results. Start by importing [AutoConfig], and then load the pretrained model you want to modify. Within [AutoConfig.from_pretrained], you can specify the attribute you want to change, such as the number of attention heads: from transformers import AutoConfig my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12) Create a model from your custom configuration with [AutoModel.from_config]: from transformers import AutoModel my_model = AutoModel.from_config(my_config) `` </pt> <tf> Create a model from your custom configuration with [TFAutoModel.from_config`]: from transformers import TFAutoModel my_model = TFAutoModel.from_config(my_config) Take a look at the Create a custom architecture guide for more information about building custom configurations. Trainer - a PyTorch optimized training loop All models are a standard torch.nn.Module so you can use them in any typical training loop. While you can write your own training loop, 🤗 Transformers provides a [Trainer] class for PyTorch, which contains the basic training loop and adds additional functionality for features like distributed training, mixed precision, and more. Depending on your task, you'll typically pass the following parameters to [Trainer]: You'll start with a [PreTrainedModel] or a torch.nn.Module: from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased") [TrainingArguments] contains the model hyperparameters you can change like learning rate, batch size, and the number of epochs to train for. The default values are used if you don't specify any training arguments: from transformers import TrainingArguments training_args = TrainingArguments( output_dir="path/to/save/folder/", learning_rate=2e-5, per_device_train_batch_size=8, per_device_eval_batch_size=8, num_train_epochs=2, ) Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") Load a dataset: from datasets import load_dataset dataset = load_dataset("rotten_tomatoes") # doctest: +IGNORE_RESULT Create a function to tokenize the dataset: def tokenize_dataset(dataset): return tokenizer(dataset["text"]) Then apply it over the entire dataset with [~datasets.Dataset.map]: dataset = dataset.map(tokenize_dataset, batched=True) A [DataCollatorWithPadding] to create a batch of examples from your dataset: from transformers import DataCollatorWithPadding data_collator = DataCollatorWithPadding(tokenizer=tokenizer) Now gather all these classes in [Trainer]: from transformers import Trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset["train"], eval_dataset=dataset["test"], tokenizer=tokenizer, data_collator=data_collator, ) # doctest: +SKIP When you're ready, call [~Trainer.train] to start training: trainer.train() # doctest: +SKIP For tasks - like translation or summarization - that use a sequence-to-sequence model, use the [Seq2SeqTrainer] and [Seq2SeqTrainingArguments] classes instead. You can customize the training loop behavior by subclassing the methods inside [Trainer]. This allows you to customize features such as the loss function, optimizer, and scheduler. Take a look at the [Trainer] reference for which methods can be subclassed. The other way to customize the training loop is by using Callbacks. You can use callbacks to integrate with other libraries and inspect the training loop to report on progress or stop the training early. Callbacks do not modify anything in the training loop itself. To customize something like the loss function, you need to subclass the [Trainer] instead. Train with TensorFlow All models are a standard tf.keras.Model so they can be trained in TensorFlow with the Keras API. 🤗 Transformers provides the [~TFPreTrainedModel.prepare_tf_dataset] method to easily load your dataset as a tf.data.Dataset so you can start training right away with Keras' compile and fit methods. You'll start with a [TFPreTrainedModel] or a tf.keras.Model: from transformers import TFAutoModelForSequenceClassification model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased") Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") Create a function to tokenize the dataset: def tokenize_dataset(dataset): return tokenizer(dataset["text"]) # doctest: +SKIP Apply the tokenizer over the entire dataset with [~datasets.Dataset.map] and then pass the dataset and tokenizer to [~TFPreTrainedModel.prepare_tf_dataset]. You can also change the batch size and shuffle the dataset here if you'd like: dataset = dataset.map(tokenize_dataset) # doctest: +SKIP tf_dataset = model.prepare_tf_dataset( dataset["train"], batch_size=16, shuffle=True, tokenizer=tokenizer ) # doctest: +SKIP When you're ready, you can call compile and fit to start training. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: from tensorflow.keras.optimizers import Adam model.compile(optimizer=Adam(3e-5)) # No loss argument! model.fit(tf_dataset) # doctest: +SKIP What's next? Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate rouge_score We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load BillSum dataset Start by loading the smaller California state bill subset of the BillSum dataset from the 🤗 Datasets library: from datasets import load_dataset billsum = load_dataset("billsum", split="ca_test") Split the dataset into a train and test set with the [~datasets.Dataset.train_test_split] method: billsum = billsum.train_test_split(test_size=0.2) Then take a look at an example: billsum["train"][0] {'summary': 'Existing law authorizes state agencies to enter into contracts for the acquisition of goods or services upon approval by the Department of General Services. Existing law sets forth various requirements and prohibitions for those contracts, including, but not limited to, a prohibition on entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between spouses and domestic partners or same-sex and different-sex couples in the provision of benefits. Existing law provides that a contract entered into in violation of those requirements and prohibitions is void and authorizes the state or any person acting on behalf of the state to bring a civil action seeking a determination that a contract is in violation and therefore void. Under existing law, a willful violation of those requirements and prohibitions is a misdemeanor.\nThis bill would also prohibit a state agency from entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between employees on the basis of gender identity in the provision of benefits, as specified. By expanding the scope of a crime, this bill would impose a state-mandated local program.\nThe California Constitution requires the state to reimburse local agencies and school districts for certain costs mandated by the state. Statutory provisions establish procedures for making that reimbursement.\nThis bill would provide that no reimbursement is required by this act for a specified reason.', 'text': 'The people of the State of California do enact as follows:\n\n\nSECTION 1.\nSection 10295.35 is added to the Public Contract Code, to read:\n10295.35.\n(a) (1) Notwithstanding any other law, a state agency shall not enter into any contract for the acquisition of goods or services in the amount of one hundred thousand dollars ($100,000) or more with a contractor that, in the provision of benefits, discriminates between employees on the basis of an employee’s or dependent’s actual or perceived gender identity, including, but not limited to, the employee’s or dependent’s identification as transgender.\n(2) For purposes of this section, “contract” includes contracts with a cumulative amount of one hundred thousand dollars ($100,000) or more per contractor in each fiscal year.\n(3) For purposes of this section, an employee health plan is discriminatory if the plan is not consistent with Section 1365.5 of the Health and Safety Code and Section 10140 of the Insurance Code.\n(4) The requirements of this section shall apply only to those portions of a contractor’s operations that occur under any of the following conditions:\n(A) Within the state.\n(B) On real property outside the state if the property is owned by the state or if the state has a right to occupy the property, and if the contractor’s presence at that location is connected to a contract with the state.\n(C) Elsewhere in the United States where work related to a state contract is being performed.\n(b) Contractors shall treat as confidential, to the maximum extent allowed by law or by the requirement of the contractor’s insurance provider, any request by an employee or applicant for employment benefits or any documentation of eligibility for benefits submitted by an employee or applicant for employment.\n(c) After taking all reasonable measures to find a contractor that complies with this section, as determined by the state agency, the requirements of this section may be waived under any of the following circumstances:\n(1) There is only one prospective contractor willing to enter into a specific contract with the state agency.\n(2) The contract is necessary to respond to an emergency, as determined by the state agency, that endangers the public health, welfare, or safety, or the contract is necessary for the provision of essential services, and no entity that complies with the requirements of this section capable of responding to the emergency is immediately available.\n(3) The requirements of this section violate, or are inconsistent with, the terms or conditions of a grant, subvention, or agreement, if the agency has made a good faith attempt to change the terms or conditions of any grant, subvention, or agreement to authorize application of this section.\n(4) The contractor is providing wholesale or bulk water, power, or natural gas, the conveyance or transmission of the same, or ancillary services, as required for ensuring reliable services in accordance with good utility practice, if the purchase of the same cannot practically be accomplished through the standard competitive bidding procedures and the contractor is not providing direct retail services to end users.\n(d) (1) A contractor shall not be deemed to discriminate in the provision of benefits if the contractor, in providing the benefits, pays the actual costs incurred in obtaining the benefit.\n(2) If a contractor is unable to provide a certain benefit, despite taking reasonable measures to do so, the contractor shall not be deemed to discriminate in the provision of benefits.\n(e) (1) Every contract subject to this chapter shall contain a statement by which the contractor certifies that the contractor is in compliance with this section.\n(2) The department or other contracting agency shall enforce this section pursuant to its existing enforcement powers.\n(3) (A) If a contractor falsely certifies that it is in compliance with this section, the contract with that contractor shall be subject to Article 9 (commencing with Section 10420), unless, within a time period specified by the department or other contracting agency, the contractor provides to the department or agency proof that it has complied, or is in the process of complying, with this section.\n(B) The application of the remedies or penalties contained in Article 9 (commencing with Section 10420) to a contract subject to this chapter shall not preclude the application of any existing remedies otherwise available to the department or other contracting agency under its existing enforcement powers.\n(f) Nothing in this section is intended to regulate the contracting practices of any local jurisdiction.\n(g) This section shall be construed so as not to conflict with applicable federal laws, rules, or regulations. In the event that a court or agency of competent jurisdiction holds that federal law, rule, or regulation invalidates any clause, sentence, paragraph, or section of this code or the application thereof to any person or circumstances, it is the intent of the state that the court or agency sever that clause, sentence, paragraph, or section so that the remainder of this section shall remain in effect.\nSEC. 2.\nSection 10295.35 of the Public Contract Code shall not be construed to create any new enforcement authority or responsibility in the Department of General Services or any other contracting agency.\nSEC. 3.\nNo reimbursement is required by this act pursuant to Section 6 of Article XIII\u2009B of the California Constitution because the only costs that may be incurred by a local agency or school district will be incurred because this act creates a new crime or infraction, eliminates a crime or infraction, or changes the penalty for a crime or infraction, within the meaning of Section 17556 of the Government Code, or changes the definition of a crime within the meaning of Section 6 of Article XIII\u2009B of the California Constitution.', 'title': 'An act to add Section 10295.35 to the Public Contract Code, relating to public contracts.'} There are two fields that you'll want to use: text: the text of the bill which'll be the input to the model. summary: a condensed version of text which'll be the model target. Preprocess The next step is to load a T5 tokenizer to process text and summary: from transformers import AutoTokenizer checkpoint = "t5-small" tokenizer = AutoTokenizer.from_pretrained(checkpoint) The preprocessing function you want to create needs to: Prefix the input with a prompt so T5 knows this is a summarization task. Some models capable of multiple NLP tasks require prompting for specific tasks. Use the keyword text_target argument when tokenizing labels. Truncate sequences to be no longer than the maximum length set by the max_length parameter. prefix = "summarize: " def preprocess_function(examples): inputs = [prefix + doc for doc in examples["text"]] model_inputs = tokenizer(inputs, max_length=1024, truncation=True) labels = tokenizer(text_target=examples["summary"], max_length=128, truncation=True) model_inputs["labels"] = labels["input_ids"] return model_inputs To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once: tokenized_billsum = billsum.map(preprocess_function, batched=True) Now create a batch of examples using [DataCollatorForSeq2Seq]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. from transformers import DataCollatorForSeq2Seq data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint) </pt> <tf>py from transformers import DataCollatorForSeq2Seq data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors="tf") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the ROUGE metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric): import evaluate rouge = evaluate.load("rouge") Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the ROUGE metric: import numpy as np def compute_metrics(eval_pred): predictions, labels = eval_pred decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True) prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions] result["gen_len"] = np.mean(prediction_lens) return {k: round(v, 4) for k, v in result.items()} Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load T5 with [AutoModelForSeq2SeqLM]: from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) At this point, only three steps remain: Define your training hyperparameters in [Seq2SeqTrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the ROUGE metric and save the training checkpoint. Pass the training arguments to [Seq2SeqTrainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = Seq2SeqTrainingArguments( output_dir="my_awesome_billsum_model", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, weight_decay=0.01, save_total_limit=3, num_train_epochs=4, predict_with_generate=True, fp16=True, push_to_hub=True, ) trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=tokenized_billsum["train"], eval_dataset=tokenized_billsum["test"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import create_optimizer, AdamWeightDecay optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01) Then you can load T5 with [TFAutoModelForSeq2SeqLM]: from transformers import TFAutoModelForSeq2SeqLM model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint) Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( tokenized_billsum["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_test_set = model.prepare_tf_dataset( tokenized_billsum["test"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: import tensorflow as tf model.compile(optimizer=optimizer) # No loss argument! The last two things to setup before you start training is to compute the ROUGE score from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks. Pass your compute_metrics function to [~transformers.KerasMetricCallback]: from transformers.keras_callbacks import KerasMetricCallback metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set) Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="my_awesome_billsum_model", tokenizer=tokenizer, ) Then bundle your callbacks together: callbacks = [metric_callback, push_to_hub_callback] Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model: model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for summarization, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Come up with some text you'd like to summarize. For T5, you need to prefix your input depending on the task you're working on. For summarization you should prefix your input as shown below: text = "summarize: The Inflation Reduction Act lowers prescription drug costs, health care costs, and energy costs. It's the most aggressive action on tackling the climate crisis in American history, which will lift up American workers and create good-paying, union jobs across the country. It'll lower the deficit and ask the ultra-wealthy and corporations to pay their fair share. And no one making under $400,000 per year will pay a penny more in taxes." The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for summarization with your model, and pass your text to it: from transformers import pipeline summarizer = pipeline("summarization", model="stevhliu/my_awesome_billsum_model") summarizer(text) [{"summary_text": "The Inflation Reduction Act lowers prescription drug costs, health care costs, and energy costs. It's the most aggressive action on tackling the climate crisis in American history, which will lift up American workers and create good-paying, union jobs across the country."}] You can also manually replicate the results of the pipeline if you'd like: Tokenize the text and return the input_ids as PyTorch tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_billsum_model") inputs = tokenizer(text, return_tensors="pt").input_ids Use the [~transformers.generation_utils.GenerationMixin.generate] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API. from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("stevhliu/my_awesome_billsum_model") outputs = model.generate(inputs, max_new_tokens=100, do_sample=False) Decode the generated token ids back into text: tokenizer.decode(outputs[0], skip_special_tokens=True) 'the inflation reduction act lowers prescription drug costs, health care costs, and energy costs. it's the most aggressive action on tackling the climate crisis in american history. it will ask the ultra-wealthy and corporations to pay their fair share.' `` </pt> <tf> Tokenize the text and return theinput_ids` as TensorFlow tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_billsum_model") inputs = tokenizer(text, return_tensors="tf").input_ids Use the [~transformers.generation_tf_utils.TFGenerationMixin.generate] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API. from transformers import TFAutoModelForSeq2SeqLM model = TFAutoModelForSeq2SeqLM.from_pretrained("stevhliu/my_awesome_billsum_model") outputs = model.generate(inputs, max_new_tokens=100, do_sample=False) Decode the generated token ids back into text: tokenizer.decode(outputs[0], skip_special_tokens=True) 'the inflation reduction act lowers prescription drug costs, health care costs, and energy costs. it's the most aggressive action on tackling the climate crisis in american history. it will ask the ultra-wealthy and corporations to pay their fair share.'

Efficient Training on CPU This guide focuses on training large models efficiently on CPU. Mixed precision with IPEX IPEX is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above while CPUs with only AVX2 (e.g., AMD CPUs or older Intel CPUs) might result in a better performance under IPEX, but not guaranteed. IPEX provides performance optimizations for CPU training with both Float32 and BFloat16. The usage of BFloat16 is the main focus of the following sections. Low precision data type BFloat16 has been natively supported on the 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) with AVX512 instruction set and will be supported on the next generation of Intel® Xeon® Scalable Processors with Intel® Advanced Matrix Extensions (Intel® AMX) instruction set with further boosted performance. The Auto Mixed Precision for CPU backend has been enabled since PyTorch-1.10. At the same time, the support of Auto Mixed Precision with BFloat16 for CPU and BFloat16 optimization of operators has been massively enabled in Intel® Extension for PyTorch, and partially upstreamed to PyTorch master branch. Users can get better performance and user experience with IPEX Auto Mixed Precision. Check more detailed information for Auto Mixed Precision. IPEX installation: IPEX release is following PyTorch, to install via pip: | PyTorch Version | IPEX version | | :---------------: | :----------: | | 1.13 | 1.13.0+cpu | | 1.12 | 1.12.300+cpu | | 1.11 | 1.11.200+cpu | | 1.10 | 1.10.100+cpu | pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu Check more approaches for IPEX installation. Usage in Trainer To enable auto mixed precision with IPEX in Trainer, users should add use_ipex, bf16 and no_cuda in training command arguments. Take an example of the use cases on Transformers question-answering Training with IPEX using BF16 auto mixed precision on CPU: python run_qa.py \ --model_name_or_path bert-base-uncased \ --dataset_name squad \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/debug_squad/ \ --use_ipex \ --bf16 --no_cuda Practice example Blog: Accelerating PyTorch Transformers with Intel Sapphire Rapids

Quantize 🤗 Transformers models bitsandbytes Integration 🤗 Transformers is closely integrated with most used modules on bitsandbytes. You can load your model in 8-bit precision with few lines of code. This is supported by most of the GPU hardwares since the 0.37.0 release of bitsandbytes. Learn more about the quantization method in the LLM.int8() paper, or the blogpost about the collaboration. Since its 0.39.0 release, you can load any model that supports device_map using 4-bit quantization, leveraging FP4 data type. If you want to quantize your own pytorch model, check out this documentation from 🤗 Accelerate library. Here are the things you can do using bitsandbytes integration FP4 quantization Requirements Make sure that you have installed the requirements below before running any of the code snippets below. Latest bitsandbytes library pip install bitsandbytes>=0.39.0 Install latest accelerate pip install --upgrade accelerate Install latest transformers from source pip install --upgrade transformers Tips and best practices Advanced usage: Refer to this Google Colab notebook for advanced usage of 4-bit quantization with all the possible options. Faster inference with batch_size=1 : Since the 0.40.0 release of bitsandbytes, for batch_size=1 you can benefit from fast inference. Check out these release notes and make sure to have a version that is greater than 0.40.0 to benefit from this feature out of the box. Training: According to QLoRA paper, for training 4-bit base models (e.g. using LoRA adapters) one should use bnb_4bit_quant_type='nf4'. Inference: For inference, bnb_4bit_quant_type does not have a huge impact on the performance. However for consistency with the model's weights, make sure you use the same bnb_4bit_compute_dtype and torch_dtype arguments. Load a large model in 4bit By using load_in_4bit=True when calling the .from_pretrained method, you can divide your memory use by 4 (roughly). thon pip install transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "bigscience/bloom-1b7" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True) Note that once a model has been loaded in 4-bit it is currently not possible to push the quantized weights on the Hub. Note also that you cannot train 4-bit weights as this is not supported yet. However you can use 4-bit models to train extra parameters, this will be covered in the next section. Load a large model in 8bit You can load a model by roughly halving the memory requirements by using load_in_8bit=True argument when calling .from_pretrained method thon pip install transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "bigscience/bloom-1b7" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_8bit=True) Then, use your model as you would usually use a [PreTrainedModel]. You can check the memory footprint of your model with get_memory_footprint method. python print(model.get_memory_footprint()) With this integration we were able to load large models on smaller devices and run them without any issue. Note that once a model has been loaded in 8-bit it is currently not possible to push the quantized weights on the Hub except if you use the latest transformers and bitsandbytes. Note also that you cannot train 8-bit weights as this is not supported yet. However you can use 8-bit models to train extra parameters, this will be covered in the next section. Note also that device_map is optional but setting device_map = 'auto' is prefered for inference as it will dispatch efficiently the model on the available ressources. Advanced usecases Here we will cover some advanced usecases you can perform with FP4 quantization Change the compute dtype The compute dtype is used to change the dtype that will be used during computation. For example, hidden states could be in float32 but computation can be set to bf16 for speedups. By default, the compute dtype is set to float32. thon import torch from transformers import BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16) Using NF4 (Normal Float 4) data type You can also use the NF4 data type, which is a new 4bit datatype adapted for weights that have been initialized using a normal distribution. For that run: thon from transformers import BitsAndBytesConfig nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", ) model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config) Use nested quantization for more memory efficient inference We also advise users to use the nested quantization technique. This saves more memory at no additional performance - from our empirical observations, this enables fine-tuning llama-13b model on an NVIDIA-T4 16GB with a sequence length of 1024, batch size of 1 and gradient accumulation steps of 4. thon from transformers import BitsAndBytesConfig double_quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, ) model_double_quant = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=double_quant_config) Push quantized models on the 🤗 Hub You can push a quantized model on the Hub by naively using push_to_hub method. This will first push the quantization configuration file, then push the quantized model weights. Make sure to use bitsandbytes>0.37.2 (at this time of writing, we tested it on bitsandbytes==0.38.0.post1) to be able to use this feature. thon from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", device_map="auto", load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m") model.push_to_hub("bloom-560m-8bit") Pushing 8bit models on the Hub is strongely encouraged for large models. This will allow the community to benefit from the memory footprint reduction and loading for example large models on a Google Colab. Load a quantized model from the 🤗 Hub You can load a quantized model from the Hub by using from_pretrained method. Make sure that the pushed weights are quantized, by checking that the attribute quantization_config is present in the model configuration object. thon from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("{your_username}/bloom-560m-8bit", device_map="auto") `` Note that in this case, you don't need to specify the argumentsload_in_8bit=True, but you need to make sure thatbitsandbytesandaccelerateare installed. Note also thatdevice_mapis optional but settingdevice_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources. Advanced usecases This section is intended to advanced users, that want to explore what it is possible to do beyond loading and running 8-bit models. Offload between cpu and gpu One of the advanced usecase of this is being able to load a model and dispatch the weights between CPU and GPU. Note that the weights that will be dispatched on CPU will not be converted in 8-bit, thus kept in float32. This feature is intended for users that want to fit a very large model and dispatch the model between GPU and CPU. First, load a BitsAndBytesConfig from transformers and set the attribute llm_int8_enable_fp32_cpu_offload to True: thon from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True) Let's say you want to load bigscience/bloom-1b7 model, and you have just enough GPU RAM to fit the entire model except the lm_head. Therefore write a custom device_map as follows: python device_map = { "transformer.word_embeddings": 0, "transformer.word_embeddings_layernorm": 0, "lm_head": "cpu", "transformer.h": 0, "transformer.ln_f": 0, } And load your model as follows: python model_8bit = AutoModelForCausalLM.from_pretrained( "bigscience/bloom-1b7", device_map=device_map, quantization_config=quantization_config, ) And that's it! Enjoy your model! Play with llm_int8_threshold You can play with the llm_int8_threshold argument to change the threshold of the outliers. An "outlier" is a hidden state value that is greater than a certain threshold. This corresponds to the outlier threshold for outlier detection as described in LLM.int8() paper. Any hidden states value that is above this threshold will be considered an outlier and the operation on those values will be done in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but there are some exceptional systematic outliers that are very differently distributed for large models. These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6, but a lower threshold might be needed for more unstable models (small models, fine-tuning). This argument can impact the inference speed of the model. We suggest to play with this parameter to find which one is the best for your usecase. thon from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "bigscience/bloom-1b7" quantization_config = BitsAndBytesConfig( llm_int8_threshold=10, ) model_8bit = AutoModelForCausalLM.from_pretrained( model_id, device_map=device_map, quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_id) Skip the conversion of some modules Some models has several modules that needs to be not converted in 8-bit to ensure stability. For example Jukebox model has several lm_head modules that should be skipped. Play with llm_int8_skip_modules thon from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "bigscience/bloom-1b7" quantization_config = BitsAndBytesConfig( llm_int8_skip_modules=["lm_head"], ) model_8bit = AutoModelForCausalLM.from_pretrained( model_id, device_map=device_map, quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_id) Fine-tune a model that has been loaded in 8-bit With the official support of adapters in the Hugging Face ecosystem, you can fine-tune models that have been loaded in 8-bit. This enables fine-tuning large models such as flan-t5-large or facebook/opt-6.7b in a single google Colab. Please have a look at peft library for more details. Note that you don't need to pass device_map when loading the model for training. It will automatically load your model on your GPU. You can also set the device map to a specific device if needed (e.g. cuda:0, 0, torch.device('cuda:0')). Please note that device_map=auto should be used for inference only. BitsAndBytesConfig [[autodoc]] BitsAndBytesConfig Quantization with 🤗 optimum Please have a look at Optimum documentation to learn more about quantization methods that are supported by optimum and see if these are applicable for your usecase.

Hyperparameter Search using Trainer API 🤗 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. Hyperparameter Search backend [Trainer] supports four hyperparameter search backends currently: optuna, sigopt, raytune and wandb. you should install them before using them as the hyperparameter search backend pip install optuna/sigopt/wandb/ray[tune] How to enable Hyperparameter search in example Define the hyperparameter search space, different backends need different format. For sigopt, see sigopt object_parameter, it's like following: 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, it's like following: 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]), } For raytune, see raytune object_parameter, it's like following: 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, it's like following: 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: 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, use_auth_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: trainer = Trainer( model=None, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset, compute_metrics=compute_metrics, tokenizer=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. best_trial = trainer.hyperparameter_search( direction="maximize", backend="optuna", hp_space=optuna_hp_space, n_trials=20, compute_objective=compute_objective, ) Hyperparameter search For DDP finetune 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.

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load MInDS-14 dataset Start by loading the MInDS-14 dataset from the 🤗 Datasets library: from datasets import load_dataset, Audio minds = load_dataset("PolyAI/minds14", name="en-US", split="train") Split the dataset's train split into a smaller train and test set with the [~datasets.Dataset.train_test_split] method. This'll give you a chance to experiment and make sure everything works before spending more time on the full dataset. minds = minds.train_test_split(test_size=0.2) Then take a look at the dataset: minds DatasetDict({ train: Dataset({ features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'], num_rows: 450 }) test: Dataset({ features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'], num_rows: 113 }) }) While the dataset contains a lot of useful information, like lang_id and english_transcription, you'll focus on the audio and intent_class in this guide. Remove the other columns with the [~datasets.Dataset.remove_columns] method: minds = minds.remove_columns(["path", "transcription", "english_transcription", "lang_id"]) Take a look at an example now: minds["train"][0] {'audio': {'array': array([ 0. , 0. , 0. , , -0.00048828, -0.00024414, -0.00024414], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b9a5fbb1e6d0fbce91f52.wav', 'sampling_rate': 8000}, 'intent_class': 2} There are two fields: audio: a 1-dimensional array of the speech signal that must be called to load and resample the audio file. intent_class: represents the class id of the speaker's intent. To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name to an integer and vice versa: labels = minds["train"].features["intent_class"].names label2id, id2label = dict(), dict() for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label Now you can convert the label id to a label name: id2label[str(2)] 'app_error' Preprocess The next step is to load a Wav2Vec2 feature extractor to process the audio signal: from transformers import AutoFeatureExtractor feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") The MInDS-14 dataset has a sampling rate of 8000khz (you can find this information in it's dataset card), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model: minds = minds.cast_column("audio", Audio(sampling_rate=16_000)) minds["train"][0] {'audio': {'array': array([ 2.2098757e-05, 4.6582241e-05, -2.2803260e-05, , -2.8419291e-04, -2.3305941e-04, -1.1425107e-04], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b9a5fbb1e6d0fbce91f52.wav', 'sampling_rate': 16000}, 'intent_class': 2} Now create a preprocessing function that: Calls the audio column to load, and if necessary, resample the audio file. Checks if the sampling rate of the audio file matches the sampling rate of the audio data a model was pretrained with. You can find this information in the Wav2Vec2 model card. Set a maximum input length to batch longer inputs without truncating them. def preprocess_function(examples): audio_arrays = [x["array"] for x in examples["audio"]] inputs = feature_extractor( audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True ) return inputs To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up map by setting batched=True to process multiple elements of the dataset at once. Remove the columns you don't need, and rename intent_class to label because that's the name the model expects: encoded_minds = minds.map(preprocess_function, remove_columns="audio", batched=True) encoded_minds = encoded_minds.rename_column("intent_class", "label") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric): import evaluate accuracy = evaluate.load("accuracy") Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy: import numpy as np def compute_metrics(eval_pred): predictions = np.argmax(eval_pred.predictions, axis=1) return accuracy.compute(predictions=predictions, references=eval_pred.label_ids) Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load Wav2Vec2 with [AutoModelForAudioClassification] along with the number of expected labels, and the label mappings: from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer num_labels = len(id2label) model = AutoModelForAudioClassification.from_pretrained( "facebook/wav2vec2-base", num_labels=num_labels, label2id=label2id, id2label=id2label ) At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint. Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_mind_model", evaluation_strategy="epoch", save_strategy="epoch", learning_rate=3e-5, per_device_train_batch_size=32, gradient_accumulation_steps=4, per_device_eval_batch_size=32, num_train_epochs=10, warmup_ratio=0.1, logging_steps=10, load_best_model_at_end=True, metric_for_best_model="accuracy", push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=encoded_minds["train"], eval_dataset=encoded_minds["test"], tokenizer=feature_extractor, compute_metrics=compute_metrics, ) trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() For a more in-depth example of how to finetune a model for audio classification, take a look at the corresponding PyTorch notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to! from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) sampling_rate = dataset.features["audio"].sampling_rate audio_file = dataset[0]["audio"]["path"] The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for audio classification with your model, and pass your audio file to it: from transformers import pipeline classifier = pipeline("audio-classification", model="stevhliu/my_awesome_minds_model") classifier(audio_file) [ {'score': 0.09766869246959686, 'label': 'cash_deposit'}, {'score': 0.07998877018690109, 'label': 'app_error'}, {'score': 0.0781070664525032, 'label': 'joint_account'}, {'score': 0.07667109370231628, 'label': 'pay_bill'}, {'score': 0.0755252093076706, 'label': 'balance'} ] You can also manually replicate the results of the pipeline if you'd like: Load a feature extractor to preprocess the audio file and return the input as PyTorch tensors: from transformers import AutoFeatureExtractor feature_extractor = AutoFeatureExtractor.from_pretrained("stevhliu/my_awesome_minds_model") inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") Pass your inputs to the model and return the logits: from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("stevhliu/my_awesome_minds_model") with torch.no_grad(): logits = model(**inputs).logits Get the class with the highest probability, and use the model's id2label mapping to convert it to a label: import torch predicted_class_ids = torch.argmax(logits).item() predicted_label = model.config.id2label[predicted_class_ids] predicted_label 'cash_deposit'

Callbacks Callbacks are objects that can customize the behavior of the training loop in the PyTorch [Trainer] (this feature is not yet implemented in TensorFlow) that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms) and take decisions (like early stopping). Callbacks are "read only" pieces of code, apart from the [TrainerControl] object they return, they cannot change anything in the training loop. For customizations that require changes in the training loop, you should subclass [Trainer] and override the methods you need (see trainer for examples). By default a [Trainer] will use the following callbacks: [DefaultFlowCallback] which handles the default behavior for logging, saving and evaluation. [PrinterCallback] or [ProgressCallback] to display progress and print the logs (the first one is used if you deactivate tqdm through the [TrainingArguments], otherwise it's the second one). [~integrations.TensorBoardCallback] if tensorboard is accessible (either through PyTorch >= 1.4 or tensorboardX). [~integrations.WandbCallback] if wandb is installed. [~integrations.CometCallback] if comet_ml is installed. [~integrations.MLflowCallback] if mlflow is installed. [~integrations.NeptuneCallback] if neptune is installed. [~integrations.AzureMLCallback] if azureml-sdk is installed. [~integrations.CodeCarbonCallback] if codecarbon is installed. [~integrations.ClearMLCallback] if clearml is installed. [~integrations.DagsHubCallback] if dagshub is installed. [~integrations.FlyteCallback] if flyte is installed. The main class that implements callbacks is [TrainerCallback]. It gets the [TrainingArguments] used to instantiate the [Trainer], can access that Trainer's internal state via [TrainerState], and can take some actions on the training loop via [TrainerControl]. Available Callbacks Here is the list of the available [TrainerCallback] in the library: [[autodoc]] integrations.CometCallback - setup [[autodoc]] DefaultFlowCallback [[autodoc]] PrinterCallback [[autodoc]] ProgressCallback [[autodoc]] EarlyStoppingCallback [[autodoc]] integrations.TensorBoardCallback [[autodoc]] integrations.WandbCallback - setup [[autodoc]] integrations.MLflowCallback - setup [[autodoc]] integrations.AzureMLCallback [[autodoc]] integrations.CodeCarbonCallback [[autodoc]] integrations.NeptuneCallback [[autodoc]] integrations.ClearMLCallback [[autodoc]] integrations.DagsHubCallback [[autodoc]] integrations.FlyteCallback TrainerCallback [[autodoc]] TrainerCallback Here is an example of how to register a custom callback with the PyTorch [Trainer]: thon class MyCallback(TrainerCallback): "A callback that prints a message at the beginning of training" def on_train_begin(self, args, state, control, **kwargs): print("Starting training") trainer = Trainer( model, args, train_dataset=train_dataset, eval_dataset=eval_dataset, callbacks=[MyCallback], # We can either pass the callback class this way or an instance of it (MyCallback()) ) Another way to register a callback is to call trainer.add_callback() as follows: thon trainer = Trainer() trainer.add_callback(MyCallback) Alternatively, we can pass an instance of the callback class trainer.add_callback(MyCallback()) TrainerState [[autodoc]] TrainerState TrainerControl [[autodoc]] TrainerControl

Zero-shot object detection [[open-in-colab]] Traditionally, models used for object detection require labeled image datasets for training, and are limited to detecting the set of classes from the training data. Zero-shot object detection is supported by the OWL-ViT model which uses a different approach. OWL-ViT is an open-vocabulary object detector. It means that it can detect objects in images based on free-text queries without the need to fine-tune the model on labeled datasets. OWL-ViT leverages multi-modal representations to perform open-vocabulary detection. It combines CLIP with lightweight object classification and localization heads. Open-vocabulary detection is achieved by embedding free-text queries with the text encoder of CLIP and using them as input to the object classification and localization heads. associate images and their corresponding textual descriptions, and ViT processes image patches as inputs. The authors of OWL-ViT first trained CLIP from scratch and then fine-tuned OWL-ViT end to end on standard object detection datasets using a bipartite matching loss. With this approach, the model can detect objects based on textual descriptions without prior training on labeled datasets. In this guide, you will learn how to use OWL-ViT: - to detect objects based on text prompts - for batch object detection - for image-guided object detection Before you begin, make sure you have all the necessary libraries installed: pip install -q transformers Zero-shot object detection pipeline The simplest way to try out inference with OWL-ViT is to use it in a [pipeline]. Instantiate a pipeline for zero-shot object detection from a checkpoint on the Hugging Face Hub: thon from transformers import pipeline checkpoint = "google/owlvit-base-patch32" detector = pipeline(model=checkpoint, task="zero-shot-object-detection") Next, choose an image you'd like to detect objects in. Here we'll use the image of astronaut Eileen Collins that is a part of the NASA Great Images dataset. import skimage import numpy as np from PIL import Image image = skimage.data.astronaut() image = Image.fromarray(np.uint8(image)).convert("RGB") image Pass the image and the candidate object labels to look for to the pipeline. Here we pass the image directly; other suitable options include a local path to an image or an image url. We also pass text descriptions for all items we want to query the image for. predictions = detector( image, candidate_labels=["human face", "rocket", "nasa badge", "star-spangled banner"], ) predictions [{'score': 0.3571370542049408, 'label': 'human face', 'box': {'xmin': 180, 'ymin': 71, 'xmax': 271, 'ymax': 178}}, {'score': 0.28099656105041504, 'label': 'nasa badge', 'box': {'xmin': 129, 'ymin': 348, 'xmax': 206, 'ymax': 427}}, {'score': 0.2110239565372467, 'label': 'rocket', 'box': {'xmin': 350, 'ymin': -1, 'xmax': 468, 'ymax': 288}}, {'score': 0.13790413737297058, 'label': 'star-spangled banner', 'box': {'xmin': 1, 'ymin': 1, 'xmax': 105, 'ymax': 509}}, {'score': 0.11950037628412247, 'label': 'nasa badge', 'box': {'xmin': 277, 'ymin': 338, 'xmax': 327, 'ymax': 380}}, {'score': 0.10649408400058746, 'label': 'rocket', 'box': {'xmin': 358, 'ymin': 64, 'xmax': 424, 'ymax': 280}}] Let's visualize the predictions: from PIL import ImageDraw draw = ImageDraw.Draw(image) for prediction in predictions: box = prediction["box"] label = prediction["label"] score = prediction["score"] xmin, ymin, xmax, ymax = box.values() draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1) draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="white") image Text-prompted zero-shot object detection by hand Now that you've seen how to use the zero-shot object detection pipeline, let's replicate the same result manually. Start by loading the model and associated processor from a checkpoint on the Hugging Face Hub. Here we'll use the same checkpoint as before: from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint) processor = AutoProcessor.from_pretrained(checkpoint) Let's take a different image to switch things up. import requests url = "https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640" im = Image.open(requests.get(url, stream=True).raw) im Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the image for the model by resizing and normalizing it, and a [CLIPTokenizer] that takes care of the text inputs. text_queries = ["hat", "book", "sunglasses", "camera"] inputs = processor(text=text_queries, images=im, return_tensors="pt") Pass the inputs through the model, post-process, and visualize the results. Since the image processor resized images before feeding them to the model, you need to use the [~OwlViTImageProcessor.post_process_object_detection] method to make sure the predicted bounding boxes have the correct coordinates relative to the original image: import torch with torch.no_grad(): outputs = model(**inputs) target_sizes = torch.tensor([im.size[::-1]]) results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)[0] draw = ImageDraw.Draw(im) scores = results["scores"].tolist() labels = results["labels"].tolist() boxes = results["boxes"].tolist() for box, score, label in zip(boxes, scores, labels): xmin, ymin, xmax, ymax = box draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1) draw.text((xmin, ymin), f"{text_queries[label]}: {round(score,2)}", fill="white") im Batch processing You can pass multiple sets of images and text queries to search for different (or same) objects in several images. Let's use both an astronaut image and the beach image together. For batch processing, you should pass text queries as a nested list to the processor and images as lists of PIL images, PyTorch tensors, or NumPy arrays. images = [image, im] text_queries = [ ["human face", "rocket", "nasa badge", "star-spangled banner"], ["hat", "book", "sunglasses", "camera"], ] inputs = processor(text=text_queries, images=images, return_tensors="pt") Previously for post-processing you passed the single image's size as a tensor, but you can also pass a tuple, or, in case of several images, a list of tuples. Let's create predictions for the two examples, and visualize the second one (image_idx = 1). with torch.no_grad(): outputs = model(**inputs) target_sizes = [x.size[::-1] for x in images] results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes) image_idx = 1 draw = ImageDraw.Draw(images[image_idx]) scores = results[image_idx]["scores"].tolist() labels = results[image_idx]["labels"].tolist() boxes = results[image_idx]["boxes"].tolist() for box, score, label in zip(boxes, scores, labels): xmin, ymin, xmax, ymax = box draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1) draw.text((xmin, ymin), f"{text_queries[image_idx][label]}: {round(score,2)}", fill="white") images[image_idx] Image-guided object detection In addition to zero-shot object detection with text queries, OWL-ViT offers image-guided object detection. This means you can use an image query to find similar objects in the target image. Unlike text queries, only a single example image is allowed. Let's take an image with two cats on a couch as a target image, and an image of a single cat as a query: url = "http://images.cocodataset.org/val2017/000000039769.jpg" image_target = Image.open(requests.get(url, stream=True).raw) query_url = "http://images.cocodataset.org/val2017/000000524280.jpg" query_image = Image.open(requests.get(query_url, stream=True).raw) Let's take a quick look at the images: import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 2) ax[0].imshow(image_target) ax[1].imshow(query_image) In the preprocessing step, instead of text queries, you now need to use query_images: inputs = processor(images=image_target, query_images=query_image, return_tensors="pt") For predictions, instead of passing the inputs to the model, pass them to [~OwlViTForObjectDetection.image_guided_detection]. Draw the predictions as before except now there are no labels. with torch.no_grad(): outputs = model.image_guided_detection(**inputs) target_sizes = torch.tensor([image_target.size[::-1]]) results = processor.post_process_image_guided_detection(outputs=outputs, target_sizes=target_sizes)[0] draw = ImageDraw.Draw(image_target) scores = results["scores"].tolist() boxes = results["boxes"].tolist() for box, score, label in zip(boxes, scores, labels): xmin, ymin, xmax, ymax = box draw.rectangle((xmin, ymin, xmax, ymax), outline="white", width=4) image_target If you'd like to interactively try out inference with OWL-ViT, check out this demo:

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate sacrebleu We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load OPUS Books dataset Start by loading the English-French subset of the OPUS Books dataset from the 🤗 Datasets library: from datasets import load_dataset books = load_dataset("opus_books", "en-fr") Split the dataset into a train and test set with the [~datasets.Dataset.train_test_split] method: books = books["train"].train_test_split(test_size=0.2) Then take a look at an example: books["train"][0] {'id': '90560', 'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.', 'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}} translation: an English and French translation of the text. Preprocess The next step is to load a T5 tokenizer to process the English-French language pairs: from transformers import AutoTokenizer checkpoint = "t5-small" tokenizer = AutoTokenizer.from_pretrained(checkpoint) The preprocessing function you want to create needs to: Prefix the input with a prompt so T5 knows this is a translation task. Some models capable of multiple NLP tasks require prompting for specific tasks. Tokenize the input (English) and target (French) separately because you can't tokenize French text with a tokenizer pretrained on an English vocabulary. Truncate sequences to be no longer than the maximum length set by the max_length parameter. source_lang = "en" target_lang = "fr" prefix = "translate English to French: " def preprocess_function(examples): inputs = [prefix + example[source_lang] for example in examples["translation"]] targets = [example[target_lang] for example in examples["translation"]] model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True) return model_inputs To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] method. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once: tokenized_books = books.map(preprocess_function, batched=True) Now create a batch of examples using [DataCollatorForSeq2Seq]. It's more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. from transformers import DataCollatorForSeq2Seq data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint) from transformers import DataCollatorForSeq2Seq data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors="tf") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the SacreBLEU metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric): import evaluate metric = evaluate.load("sacrebleu") Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the SacreBLEU score: import numpy as np def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [[label.strip()] for label in labels] return preds, labels def compute_metrics(eval_preds): preds, labels = eval_preds if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) result = metric.compute(predictions=decoded_preds, references=decoded_labels) result = {"bleu": result["score"]} prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds] result["gen_len"] = np.mean(prediction_lens) result = {k: round(v, 4) for k, v in result.items()} return result Your compute_metrics function is ready to go now, and you'll return to it when you setup your training. Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load T5 with [AutoModelForSeq2SeqLM]: from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) At this point, only three steps remain: Define your training hyperparameters in [Seq2SeqTrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the SacreBLEU metric and save the training checkpoint. Pass the training arguments to [Seq2SeqTrainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = Seq2SeqTrainingArguments( output_dir="my_awesome_opus_books_model", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, weight_decay=0.01, save_total_limit=3, num_train_epochs=2, predict_with_generate=True, fp16=True, push_to_hub=True, ) trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=tokenized_books["train"], eval_dataset=tokenized_books["test"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train() ` Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import AdamWeightDecay optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01) Then you can load T5 with [TFAutoModelForSeq2SeqLM]: from transformers import TFAutoModelForSeq2SeqLM model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint) Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( tokenized_books["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_test_set = model.prepare_tf_dataset( tokenized_books["test"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: import tensorflow as tf model.compile(optimizer=optimizer) # No loss argument! The last two things to setup before you start training is to compute the SacreBLEU metric from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks. Pass your compute_metrics function to [~transformers.KerasMetricCallback]: from transformers.keras_callbacks import KerasMetricCallback metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set) Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="my_awesome_opus_books_model", tokenizer=tokenizer, ) Then bundle your callbacks together: callbacks = [metric_callback, push_to_hub_callback] Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model: model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for translation, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Inference Great, now that you've finetuned a model, you can use it for inference! Come up with some text you'd like to translate to another language. For T5, you need to prefix your input depending on the task you're working on. For translation from English to French, you should prefix your input as shown below: text = "translate English to French: Legumes share resources with nitrogen-fixing bacteria." The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for translation with your model, and pass your text to it: from transformers import pipeline translator = pipeline("translation", model="my_awesome_opus_books_model") translator(text) [{'translation_text': 'Legumes partagent des ressources avec des bactéries azotantes.'}] You can also manually replicate the results of the pipeline if you'd like: Tokenize the text and return the input_ids as PyTorch tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("my_awesome_opus_books_model") inputs = tokenizer(text, return_tensors="pt").input_ids Use the [~transformers.generation_utils.GenerationMixin.generate] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API. from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("my_awesome_opus_books_model") outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95) Decode the generated token ids back into text: tokenizer.decode(outputs[0], skip_special_tokens=True) 'Les lignées partagent des ressources avec des bactéries enfixant l'azote.' `` </pt> <tf> Tokenize the text and return theinput_ids` as TensorFlow tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("my_awesome_opus_books_model") inputs = tokenizer(text, return_tensors="tf").input_ids Use the [~transformers.generation_tf_utils.TFGenerationMixin.generate] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the Text Generation API. from transformers import TFAutoModelForSeq2SeqLM model = TFAutoModelForSeq2SeqLM.from_pretrained("my_awesome_opus_books_model") outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95) Decode the generated token ids back into text: tokenizer.decode(outputs[0], skip_special_tokens=True) 'Les lugumes partagent les ressources avec des bactéries fixatrices d'azote.'

LayoutLMv2 solves the document question-answering task by adding a question-answering head on top of the final hidden states of the tokens, to predict the positions of the start and end tokens of the answer. In other words, the problem is treated as extractive question answering: given the context, extract which piece of information answers the question. The context comes from the output of an OCR engine, here it is Google's Tesseract. Before you begin, make sure you have all the necessary libraries installed. LayoutLMv2 depends on detectron2, torchvision and tesseract. pip install -q transformers datasets pip install 'git+https://github.com/facebookresearch/detectron2.git' pip install torchvision sudo apt install tesseract-ocr pip install -q pytesseract Once you have installed all of the dependencies, restart your runtime. We encourage you to share your model with the community. Log in to your Hugging Face account to upload it to the 🤗 Hub. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Let's define some global variables. model_checkpoint = "microsoft/layoutlmv2-base-uncased" batch_size = 4 Load the data In this guide we use a small sample of preprocessed DocVQA that you can find on 🤗 Hub. If you'd like to use the full DocVQA dataset, you can register and download it on DocVQA homepage. If you do so, to proceed with this guide check out how to load files into a 🤗 dataset. from datasets import load_dataset dataset = load_dataset("nielsr/docvqa_1200_examples") dataset DatasetDict({ train: Dataset({ features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'], num_rows: 1000 }) test: Dataset({ features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'], num_rows: 200 }) }) As you can see, the dataset is split into train and test sets already. Take a look at a random example to familiarize yourself with the features. dataset["train"].features Here's what the individual fields represent: * id: the example's id * image: a PIL.Image.Image object containing the document image * query: the question string - natural language asked question, in several languages * answers: a list of correct answers provided by human annotators * words and bounding_boxes: the results of OCR, which we will not use here * answer: an answer matched by a different model which we will not use here Let's leave only English questions, and drop the answer feature which appears to contain predictions by another model. We'll also take the first of the answers from the set provided by the annotators. Alternatively, you can randomly sample it. updated_dataset = dataset.map(lambda example: {"question": example["query"]["en"]}, remove_columns=["query"]) updated_dataset = updated_dataset.map( lambda example: {"answer": example["answers"][0]}, remove_columns=["answer", "answers"] ) Note that the LayoutLMv2 checkpoint that we use in this guide has been trained with max_position_embeddings = 512 (you can find this information in the checkpoint's config.json file). We can truncate the examples but to avoid the situation where the answer might be at the end of a large document and end up truncated, here we'll remove the few examples where the embedding is likely to end up longer than 512. If most of the documents in your dataset are long, you can implement a sliding window strategy - check out this notebook for details. updated_dataset = updated_dataset.filter(lambda x: len(x["words"]) + len(x["question"].split()) < 512) At this point let's also remove the OCR features from this dataset. These are a result of OCR for fine-tuning a different model. They would still require some processing if we wanted to use them, as they do not match the input requirements of the model we use in this guide. Instead, we can use the [LayoutLMv2Processor] on the original data for both OCR and tokenization. This way we'll get the inputs that match model's expected input. If you want to process images manually, check out the LayoutLMv2 model documentation to learn what input format the model expects. updated_dataset = updated_dataset.remove_columns("words") updated_dataset = updated_dataset.remove_columns("bounding_boxes") Finally, the data exploration won't be complete if we don't peek at an image example. updated_dataset["train"][11]["image"] Preprocess the data The Document Question Answering task is a multimodal task, and you need to make sure that the inputs from each modality are preprocessed according to the model's expectations. Let's start by loading the [LayoutLMv2Processor], which internally combines an image processor that can handle image data and a tokenizer that can encode text data. from transformers import AutoProcessor processor = AutoProcessor.from_pretrained(model_checkpoint) Preprocessing document images First, let's prepare the document images for the model with the help of the image_processor from the processor. By default, image processor resizes the images to 224x224, makes sure they have the correct order of color channels, applies OCR with tesseract to get words and normalized bounding boxes. In this tutorial, all of these defaults are exactly what we need. Write a function that applies the default image processing to a batch of images and returns the results of OCR. image_processor = processor.image_processor def get_ocr_words_and_boxes(examples): images = [image.convert("RGB") for image in examples["image"]] encoded_inputs = image_processor(images) examples["image"] = encoded_inputs.pixel_values examples["words"] = encoded_inputs.words examples["boxes"] = encoded_inputs.boxes return examples To apply this preprocessing to the entire dataset in a fast way, use [~datasets.Dataset.map]. dataset_with_ocr = updated_dataset.map(get_ocr_words_and_boxes, batched=True, batch_size=2) Preprocessing text data Once we have applied OCR to the images, we need to encode the text part of the dataset to prepare it for the model. This involves converting the words and boxes that we got in the previous step to token-level input_ids, attention_mask, token_type_ids and bbox. For preprocessing text, we'll need the tokenizer from the processor. tokenizer = processor.tokenizer On top of the preprocessing mentioned above, we also need to add the labels for the model. For xxxForQuestionAnswering models in 🤗 Transformers, the labels consist of the start_positions and end_positions, indicating which token is at the start and which token is at the end of the answer. Let's start with that. Define a helper function that can find a sublist (the answer split into words) in a larger list (the words list). This function will take two lists as input, words_list and answer_list. It will then iterate over the words_list and check if the current word in the words_list (words_list[i]) is equal to the first word of answer_list (answer_list[0]) and if the sublist of words_list starting from the current word and of the same length as answer_list is equal to answer_list. If this condition is true, it means that a match has been found, and the function will record the match, its starting index (idx), and its ending index (idx + len(answer_list) - 1). If more than one match was found, the function will return only the first one. If no match is found, the function returns (None, 0, and 0). def subfinder(words_list, answer_list): matches = [] start_indices = [] end_indices = [] for idx, i in enumerate(range(len(words_list))): if words_list[i] == answer_list[0] and words_list[i : i + len(answer_list)] == answer_list: matches.append(answer_list) start_indices.append(idx) end_indices.append(idx + len(answer_list) - 1) if matches: return matches[0], start_indices[0], end_indices[0] else: return None, 0, 0 To illustrate how this function finds the position of the answer, let's use it on an example: example = dataset_with_ocr["train"][1] words = [word.lower() for word in example["words"]] match, word_idx_start, word_idx_end = subfinder(words, example["answer"].lower().split()) print("Question: ", example["question"]) print("Words:", words) print("Answer: ", example["answer"]) print("start_index", word_idx_start) print("end_index", word_idx_end) Question: Who is in cc in this letter? Words: ['wie', 'baw', 'brown', '&', 'williamson', 'tobacco', 'corporation', 'research', '&', 'development', 'internal', 'correspondence', 'to:', 'r.', 'h.', 'honeycutt', 'ce:', 't.f.', 'riehl', 'from:', '.', 'c.j.', 'cook', 'date:', 'may', '8,', '1995', 'subject:', 'review', 'of', 'existing', 'brainstorming', 'ideas/483', 'the', 'major', 'function', 'of', 'the', 'product', 'innovation', 'graup', 'is', 'to', 'develop', 'marketable', 'nove!', 'products', 'that', 'would', 'be', 'profitable', 'to', 'manufacture', 'and', 'sell.', 'novel', 'is', 'defined', 'as:', 'of', 'a', 'new', 'kind,', 'or', 'different', 'from', 'anything', 'seen', 'or', 'known', 'before.', 'innovation', 'is', 'defined', 'as:', 'something', 'new', 'or', 'different', 'introduced;', 'act', 'of', 'innovating;', 'introduction', 'of', 'new', 'things', 'or', 'methods.', 'the', 'products', 'may', 'incorporate', 'the', 'latest', 'technologies,', 'materials', 'and', 'know-how', 'available', 'to', 'give', 'then', 'a', 'unique', 'taste', 'or', 'look.', 'the', 'first', 'task', 'of', 'the', 'product', 'innovation', 'group', 'was', 'to', 'assemble,', 'review', 'and', 'categorize', 'a', 'list', 'of', 'existing', 'brainstorming', 'ideas.', 'ideas', 'were', 'grouped', 'into', 'two', 'major', 'categories', 'labeled', 'appearance', 'and', 'taste/aroma.', 'these', 'categories', 'are', 'used', 'for', 'novel', 'products', 'that', 'may', 'differ', 'from', 'a', 'visual', 'and/or', 'taste/aroma', 'point', 'of', 'view', 'compared', 'to', 'canventional', 'cigarettes.', 'other', 'categories', 'include', 'a', 'combination', 'of', 'the', 'above,', 'filters,', 'packaging', 'and', 'brand', 'extensions.', 'appearance', 'this', 'category', 'is', 'used', 'for', 'novel', 'cigarette', 'constructions', 'that', 'yield', 'visually', 'different', 'products', 'with', 'minimal', 'changes', 'in', 'smoke', 'chemistry', 'two', 'cigarettes', 'in', 'cne.', 'emulti-plug', 'te', 'build', 'yaur', 'awn', 'cigarette.', 'eswitchable', 'menthol', 'or', 'non', 'menthol', 'cigarette.', 'cigarettes', 'with', 'interspaced', 'perforations', 'to', 'enable', 'smoker', 'to', 'separate', 'unburned', 'section', 'for', 'future', 'smoking.', '«short', 'cigarette,', 'tobacco', 'section', '30', 'mm.', '«extremely', 'fast', 'buming', 'cigarette.', '«novel', 'cigarette', 'constructions', 'that', 'permit', 'a', 'significant', 'reduction', 'iretobacco', 'weight', 'while', 'maintaining', 'smoking', 'mechanics', 'and', 'visual', 'characteristics.', 'higher', 'basis', 'weight', 'paper:', 'potential', 'reduction', 'in', 'tobacco', 'weight.', '«more', 'rigid', 'tobacco', 'column;', 'stiffing', 'agent', 'for', 'tobacco;', 'e.g.', 'starch', 'colored', 'tow', 'and', 'cigarette', 'papers;', 'seasonal', 'promotions,', 'e.g.', 'pastel', 'colored', 'cigarettes', 'for', 'easter', 'or', 'in', 'an', 'ebony', 'and', 'ivory', 'brand', 'containing', 'a', 'mixture', 'of', 'all', 'black', '(black', 'paper', 'and', 'tow)', 'and', 'ail', 'white', 'cigarettes.', '499150498'] Answer: T.F. Riehl start_index 17 end_index 18 Once examples are encoded, however, they will look like this: encoding = tokenizer(example["question"], example["words"], example["boxes"]) tokenizer.decode(encoding["input_ids"]) [CLS] who is in cc in this letter? [SEP] wie baw brown & williamson tobacco corporation research & development We'll need to find the position of the answer in the encoded input. * token_type_ids tells us which tokens are part of the question, and which ones are part of the document's words. * tokenizer.cls_token_id will help find the special token at the beginning of the input. * word_ids will help match the answer found in the original words to the same answer in the full encoded input and determine the start/end position of the answer in the encoded input. With that in mind, let's create a function to encode a batch of examples in the dataset: def encode_dataset(examples, max_length=512): questions = examples["question"] words = examples["words"] boxes = examples["boxes"] answers = examples["answer"] # encode the batch of examples and initialize the start_positions and end_positions encoding = tokenizer(questions, words, boxes, max_length=max_length, padding="max_length", truncation=True) start_positions = [] end_positions = [] # loop through the examples in the batch for i in range(len(questions)): cls_index = encoding["input_ids"][i].index(tokenizer.cls_token_id) # find the position of the answer in example's words words_example = [word.lower() for word in words[i]] answer = answers[i] match, word_idx_start, word_idx_end = subfinder(words_example, answer.lower().split()) if match: # if match is found, use token_type_ids to find where words start in the encoding token_type_ids = encoding["token_type_ids"][i] token_start_index = 0 while token_type_ids[token_start_index] != 1: token_start_index += 1 token_end_index = len(encoding["input_ids"][i]) - 1 while token_type_ids[token_end_index] != 1: token_end_index -= 1 word_ids = encoding.word_ids(i)[token_start_index : token_end_index + 1] start_position = cls_index end_position = cls_index # loop over word_ids and increase token_start_index until it matches the answer position in words # once it matches, save the token_start_index as the start_position of the answer in the encoding for id in word_ids: if id == word_idx_start: start_position = token_start_index else: token_start_index += 1 # similarly loop over word_ids starting from the end to find the end_position of the answer for id in word_ids[::-1]: if id == word_idx_end: end_position = token_end_index else: token_end_index -= 1 start_positions.append(start_position) end_positions.append(end_position) else: start_positions.append(cls_index) end_positions.append(cls_index) encoding["image"] = examples["image"] encoding["start_positions"] = start_positions encoding["end_positions"] = end_positions return encoding Now that we have this preprocessing function, we can encode the entire dataset: encoded_train_dataset = dataset_with_ocr["train"].map( encode_dataset, batched=True, batch_size=2, remove_columns=dataset_with_ocr["train"].column_names ) encoded_test_dataset = dataset_with_ocr["test"].map( encode_dataset, batched=True, batch_size=2, remove_columns=dataset_with_ocr["test"].column_names ) Let's check what the features of the encoded dataset look like: encoded_train_dataset.features {'image': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='uint8', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'bbox': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'start_positions': Value(dtype='int64', id=None), 'end_positions': Value(dtype='int64', id=None)} Evaluation Evaluation for document question answering requires a significant amount of postprocessing. To avoid taking up too much of your time, this guide skips the evaluation step. The [Trainer] still calculates the evaluation loss during training so you're not completely in the dark about your model's performance. Extractive question answering is typically evaluated using F1/exact match. If you'd like to implement it yourself, check out the Question Answering chapter of the Hugging Face course for inspiration. Train Congratulations! You've successfully navigated the toughest part of this guide and now you are ready to train your own model. Training involves the following steps: * Load the model with [AutoModelForDocumentQuestionAnswering] using the same checkpoint as in the preprocessing. * Define your training hyperparameters in [TrainingArguments]. * Define a function to batch examples together, here the [DefaultDataCollator] will do just fine * Pass the training arguments to [Trainer] along with the model, dataset, and data collator. * Call [~Trainer.train] to finetune your model. from transformers import AutoModelForDocumentQuestionAnswering model = AutoModelForDocumentQuestionAnswering.from_pretrained(model_checkpoint) In the [TrainingArguments] use output_dir to specify where to save your model, and configure hyperparameters as you see fit. If you wish to share your model with the community, set push_to_hub to True (you must be signed in to Hugging Face to upload your model). In this case the output_dir will also be the name of the repo where your model checkpoint will be pushed. from transformers import TrainingArguments REPLACE THIS WITH YOUR REPO ID repo_id = "MariaK/layoutlmv2-base-uncased_finetuned_docvqa" training_args = TrainingArguments( output_dir=repo_id, per_device_train_batch_size=4, num_train_epochs=20, save_steps=200, logging_steps=50, evaluation_strategy="steps", learning_rate=5e-5, save_total_limit=2, remove_unused_columns=False, push_to_hub=True, ) Define a simple data collator to batch examples together. from transformers import DefaultDataCollator data_collator = DefaultDataCollator() Finally, bring everything together, and call [~Trainer.train]: from transformers import Trainer trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=encoded_train_dataset, eval_dataset=encoded_test_dataset, tokenizer=processor, ) trainer.train() To add the final model to 🤗 Hub, create a model card and call push_to_hub: trainer.create_model_card() trainer.push_to_hub() Inference Now that you have finetuned a LayoutLMv2 model, and uploaded it to the 🤗 Hub, you can use it for inference. The simplest way to try out your finetuned model for inference is to use it in a [Pipeline]. Let's take an example: example = dataset["test"][2] question = example["query"]["en"] image = example["image"] print(question) print(example["answers"]) 'Who is ‘presiding’ TRRF GENERAL SESSION (PART 1)?' ['TRRF Vice President', 'lee a. waller'] Next, instantiate a pipeline for document question answering with your model, and pass the image + question combination to it. from transformers import pipeline qa_pipeline = pipeline("document-question-answering", model="MariaK/layoutlmv2-base-uncased_finetuned_docvqa") qa_pipeline(image, question) [{'score': 0.9949808120727539, 'answer': 'Lee A. Waller', 'start': 55, 'end': 57}] You can also manually replicate the results of the pipeline if you'd like: 1. Take an image and a question, prepare them for the model using the processor from your model. 2. Forward the result or preprocessing through the model. 3. The model returns start_logits and end_logits, which indicate which token is at the start of the answer and which token is at the end of the answer. Both have shape (batch_size, sequence_length). 4. Take an argmax on the last dimension of both the start_logits and end_logits to get the predicted start_idx and end_idx. 5. Decode the answer with the tokenizer. import torch from transformers import AutoProcessor from transformers import AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("MariaK/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("MariaK/layoutlmv2-base-uncased_finetuned_docvqa") with torch.no_grad(): encoding = processor(image.convert("RGB"), question, return_tensors="pt") outputs = model(**encoding) start_logits = outputs.start_logits end_logits = outputs.end_logits predicted_start_idx = start_logits.argmax(-1).item() predicted_end_idx = end_logits.argmax(-1).item() processor.tokenizer.decode(encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]) 'lee a. waller'

Utilities for Generation This page lists all the utility functions used by [~generation.GenerationMixin.generate], [~generation.GenerationMixin.greedy_search], [~generation.GenerationMixin.contrastive_search], [~generation.GenerationMixin.sample], [~generation.GenerationMixin.beam_search], [~generation.GenerationMixin.beam_sample], [~generation.GenerationMixin.group_beam_search], and [~generation.GenerationMixin.constrained_beam_search]. Most of those are only useful if you are studying the code of the generate methods in the library. Generate Outputs The output of [~generation.GenerationMixin.generate] is an instance of a subclass of [~utils.ModelOutput]. This output is a data structure containing all the information returned by [~generation.GenerationMixin.generate], but that can also be used as tuple or dictionary. Here's an example: thon from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained("gpt2") model = GPT2LMHeadModel.from_pretrained("gpt2") inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt") generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True) The generation_output object is a [~generation.GreedySearchDecoderOnlyOutput], as we can see in the documentation of that class below, it means it has the following attributes: sequences: the generated sequences of tokens scores (optional): the prediction scores of the language modelling head, for each generation step hidden_states (optional): the hidden states of the model, for each generation step attentions (optional): the attention weights of the model, for each generation step Here we have the scores since we passed along output_scores=True, but we don't have hidden_states and attentions because we didn't pass output_hidden_states=True or output_attentions=True. You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get None. Here for instance generation_output.scores are all the generated prediction scores of the language modeling head, and generation_output.attentions is None. When using our generation_output object as a tuple, it only keeps the attributes that don't have None values. Here, for instance, it has two elements, loss then logits, so python generation_output[:2] will return the tuple (generation_output.sequences, generation_output.scores) for instance. When using our generation_output object as a dictionary, it only keeps the attributes that don't have None values. Here, for instance, it has two keys that are sequences and scores. We document here all output types. GreedySearchOutput [[autodoc]] generation.GreedySearchDecoderOnlyOutput [[autodoc]] generation.GreedySearchEncoderDecoderOutput [[autodoc]] generation.FlaxGreedySearchOutput SampleOutput [[autodoc]] generation.SampleDecoderOnlyOutput [[autodoc]] generation.SampleEncoderDecoderOutput [[autodoc]] generation.FlaxSampleOutput BeamSearchOutput [[autodoc]] generation.BeamSearchDecoderOnlyOutput [[autodoc]] generation.BeamSearchEncoderDecoderOutput BeamSampleOutput [[autodoc]] generation.BeamSampleDecoderOnlyOutput [[autodoc]] generation.BeamSampleEncoderDecoderOutput LogitsProcessor A [LogitsProcessor] can be used to modify the prediction scores of a language model head for generation. [[autodoc]] LogitsProcessor - call [[autodoc]] LogitsProcessorList - call [[autodoc]] LogitsWarper - call [[autodoc]] MinLengthLogitsProcessor - call [[autodoc]] MinNewTokensLengthLogitsProcessor - call [[autodoc]] TemperatureLogitsWarper - call [[autodoc]] RepetitionPenaltyLogitsProcessor - call [[autodoc]] TopPLogitsWarper - call [[autodoc]] TopKLogitsWarper - call [[autodoc]] TypicalLogitsWarper - call [[autodoc]] NoRepeatNGramLogitsProcessor - call [[autodoc]] SequenceBiasLogitsProcessor - call [[autodoc]] NoBadWordsLogitsProcessor - call [[autodoc]] PrefixConstrainedLogitsProcessor - call [[autodoc]] HammingDiversityLogitsProcessor - call [[autodoc]] ForcedBOSTokenLogitsProcessor - call [[autodoc]] ForcedEOSTokenLogitsProcessor - call [[autodoc]] InfNanRemoveLogitsProcessor - call [[autodoc]] TFLogitsProcessor - call [[autodoc]] TFLogitsProcessorList - call [[autodoc]] TFLogitsWarper - call [[autodoc]] TFTemperatureLogitsWarper - call [[autodoc]] TFTopPLogitsWarper - call [[autodoc]] TFTopKLogitsWarper - call [[autodoc]] TFMinLengthLogitsProcessor - call [[autodoc]] TFNoBadWordsLogitsProcessor - call [[autodoc]] TFNoRepeatNGramLogitsProcessor - call [[autodoc]] TFRepetitionPenaltyLogitsProcessor - call [[autodoc]] TFForcedBOSTokenLogitsProcessor - call [[autodoc]] TFForcedEOSTokenLogitsProcessor - call [[autodoc]] FlaxLogitsProcessor - call [[autodoc]] FlaxLogitsProcessorList - call [[autodoc]] FlaxLogitsWarper - call [[autodoc]] FlaxTemperatureLogitsWarper - call [[autodoc]] FlaxTopPLogitsWarper - call [[autodoc]] FlaxTopKLogitsWarper - call [[autodoc]] FlaxForcedBOSTokenLogitsProcessor - call [[autodoc]] FlaxForcedEOSTokenLogitsProcessor - call [[autodoc]] FlaxMinLengthLogitsProcessor - call StoppingCriteria A [StoppingCriteria] can be used to change when to stop generation (other than EOS token). [[autodoc]] StoppingCriteria - call [[autodoc]] StoppingCriteriaList - call [[autodoc]] MaxLengthCriteria - call [[autodoc]] MaxTimeCriteria - call Constraints A [Constraint] can be used to force the generation to include specific tokens or sequences in the output. [[autodoc]] Constraint [[autodoc]] PhrasalConstraint [[autodoc]] DisjunctiveConstraint [[autodoc]] ConstraintListState BeamSearch [[autodoc]] BeamScorer - process - finalize [[autodoc]] BeamSearchScorer - process - finalize [[autodoc]] ConstrainedBeamSearchScorer - process - finalize Utilities [[autodoc]] top_k_top_p_filtering [[autodoc]] tf_top_k_top_p_filtering Streamers [[autodoc]] TextStreamer [[autodoc]] TextIteratorStreamer

Contribute to 🤗 Transformers Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable. It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you. However you choose to contribute, please be mindful and respect our code of conduct. This guide was heavily inspired by the awesome scikit-learn guide to contributing. Ways to contribute There are several ways you can contribute to 🤗 Transformers: Fix outstanding issues with the existing code. Submit issues related to bugs or desired new features. Implement new models. Contribute to the examples or to the documentation. If you don't know where to start, there is a special Good First Issue listing. It will give you a list of open issues that are beginner-friendly and help you start contributing to open-source. Just comment in the issue that you'd like to work on it. For something slightly more challenging, you can also take a look at the Good Second Issue list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 All contributions are equally valuable to the community. 🥰 Fixing outstanding issues If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request! Submitting a bug-related issue or feature request Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback. Did you find a bug? The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. Before you report an issue, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask on the forum first. This helps us respond quicker to fixing issues related to the library versus general questions. Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: Your OS type and version and Python, PyTorch and TensorFlow versions when applicable. A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s. The full traceback if an exception is raised. Attach any other additional information, like screenshots, you think may help. To get the OS and software versions automatically, run the following command: transformers-cli env You can also run the same command from the root of the repository: python src/transformers/commands/transformers_cli.py env Do you want a new feature? If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: What is the motivation behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? Whatever it is, we'd love to hear about it! Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. Provide a code snippet that demonstrates the features usage. If the feature is related to a paper, please include a link. If your issue is well written we're already 80% of the way there by the time you create it. We have added templates to help you get started with your issue. Do you want to implement a new model? New models are constantly released and if you want to implement a new model, please provide the following information A short description of the model and link to the paper. Link to the implementation if it is open-sourced. Link to the model weights if they are available. If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! We have added a detailed guide and templates to help you get started with adding a new model, and we also have a more technical guide for how to add a model to 🤗 Transformers. Do you want to add documentation? We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! For more details about how to generate, build, and write the documentation, take a look at the documentation README. Create a Pull Request Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback. You will need basic git proficiency to contribute to 🤗 Transformers. While git is not the easiest tool to use, it has the greatest manual. Type git --help in a shell and enjoy! If you prefer books, Pro Git is a very good reference. You'll need Python 3.8 or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: Fork the repository by clicking on the Fork button on the repository's page. This creates a copy of the code under your GitHub user account. Clone your fork to your local disk, and add the base repository as a remote: git clone [email protected]:<your Github handle>/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git Create a new branch to hold your development changes: git checkout -b a-descriptive-name-for-my-changes 🚨 Do not work on the main branch! Set up a development environment by running the following command in a virtual environment: pip install -e ".[dev]" If 🤗 Transformers was already installed in the virtual environment, remove it with pip uninstall transformers before reinstalling it in editable mode with the -e flag. 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: pip install -e ".[quality]" which should be enough for most use cases. Develop the features on your branch. As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this: pytest tests/<TEST_TO_RUN>.py For more information about tests, check out the Testing guide. 🤗 Transformers relies on black and ruff to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with: make fixup This target is also optimized to only work with files modified by the PR you're working on. If you prefer to run the checks one after the other, the following command applies the style corrections: make style 🤗 Transformers also uses ruff and a few custom scripts to check for coding mistakes. Quality controls are run by the CI, but you can run the same checks with: make quality Finally, we have a lot of scripts to make sure we didn't forget to update some files when adding a new model. You can run these scripts with: make repo-consistency To learn more about those checks and how to fix any issues with them, check out the Checks on a Pull Request guide. If you're modifying documents under docs/source directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check make sure you install the documentation builder: pip install ".[docs]" Run the following command from the root of the repository: doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build This will build the documentation in the ~/tmp/test-build folder where you can inspect the generated Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. Once you're happy with your changes, add changed files with git add and record your changes locally with git commit: git add modified_file.py git commit Please remember to write good commit messages to clearly communicate the changes you made! To keep your copy of the code up to date with the original repository, rebase your branch on upstream/branch before you open a pull request or if requested by a maintainer: git fetch upstream git rebase upstream/main Push your changes to your branch: git push -u origin a-descriptive-name-for-my-changes If you've already opened a pull request, you'll need to force push with the --force flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. Now you can go to your fork of the repository on GitHub and click on Pull request to open a pull request. Make sure you tick off all the boxes in our checklist below. When you're ready, you can send your changes to the project maintainers for review. It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request. Pull request checklist ☐ The pull request title should summarize your contribution. ☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it). ☐ To indicate a work in progress please prefix the title with [WIP]. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged. ☐ Make sure existing tests pass. ☐ If adding a new feature, also add tests for it. - If you are adding a new model, make sure you use ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,) to trigger the common tests. - If you are adding new @slow tests, make sure they pass using RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py. - If you are adding a new tokenizer, write tests and make sure RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py passes. - CircleCI does not run the slow tests, but GitHub Actions does every night! ☐ All public methods must have informative docstrings (see modeling_bert.py for an example). ☐ Due to the rapidly growing repository, don't add any images, videos and other non-text files that'll significantly weigh down the repository. Instead, use a Hub repository such as hf-internal-testing to host these files and reference them by URL. We recommend placing documentation related images in the following repository: huggingface/documentation-images. You can open a PR on this dataset repostitory and ask a Hugging Face member to merge it. For more information about the checks run on a pull request, take a look at our Checks on a Pull Request guide. Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the tests folder and examples tests in the examples folder. We like pytest and pytest-xdist because it's faster. From the root of the repository, specify a path to a subfolder or a test file to run the test. python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model Similarly, for the examples directory, specify a path to a subfolder or test file to run the test. For example, the following command tests the text classification subfolder in the PyTorch examples directory: pip install -r examples/xxx/requirements.txt # only needed the first time python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification In fact, this is actually how our make test and make test-examples commands are implemented (not including the pip install)! You can also specify a smaller set of tests in order to test only the feature you're working on. By default, slow tests are skipped but you can set the RUN_SLOW environment variable to yes to run them. This will download many gigabytes of models so make sure you have enough disk space, a good internet connection or a lot of patience! Remember to specify a path to a subfolder or a test file to run the test. Otherwise, you'll run all the tests in the tests or examples folder, which will take a very long time! RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification Like the slow tests, there are other environment variables available which not enabled by default during testing: - RUN_CUSTOM_TOKENIZERS: Enables tests for custom tokenizers. - RUN_PT_FLAX_CROSS_TESTS: Enables tests for PyTorch + Flax integration. - RUN_PT_TF_CROSS_TESTS: Enables tests for TensorFlow + PyTorch integration. More environment variables and additional information can be found in the testing_utils.py. 🤗 Transformers uses pytest as a test runner only. It doesn't use any pytest-specific features in the test suite itself. This means unittest is fully supported. Here's how to run tests with unittest: python -m unittest discover -s tests -t . -v python -m unittest discover -s examples -t examples -v Style guide For documentation strings, 🤗 Transformers follows the Google Python Style Guide. Check our documentation writing guide for more information. Develop on Windows On Windows (unless you're working in Windows Subsystem for Linux or WSL), you need to configure git to transform Windows CRLF line endings to Linux LF line endings: git config core.autocrlf input One way to run the make command on Windows is with MSYS2: Download MSYS2, and we assume it's installed in C:\msys64. Open the command line C:\msys64\msys2.exe (it should be available from the Start menu). Run in the shell: pacman -Syu and install make with pacman -S make. Add C:\msys64\usr\bin to your PATH environment variable. You can now use make from any terminal (Powershell, cmd.exe, etc.)! 🎉 Sync a forked repository with upstream main (the Hugging Face repository) When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. If a PR is absolutely necessary, use the following steps after checking out your branch: git checkout -b your-branch-for-syncing git pull --squash --no-commit upstream main git commit -m '<your message without GitHub references>' git push --set-upstream origin your-branch-for-syncing

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login: from huggingface_hub import notebook_login notebook_login() Load SQuAD dataset Start by loading a smaller subset of the SQuAD dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset. from datasets import load_dataset squad = load_dataset("squad", split="train[:5000]") Split the dataset's train split into a train and test set with the [~datasets.Dataset.train_test_split] method: squad = squad.train_test_split(test_size=0.2) Then take a look at an example: squad["train"][0] {'answers': {'answer_start': [515], 'text': ['Saint Bernadette Soubirous']}, 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.', 'id': '5733be284776f41900661182', 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?', 'title': 'University_of_Notre_Dame' } There are several important fields here: answers: the starting location of the answer token and the answer text. context: background information from which the model needs to extract the answer. question: the question a model should answer. Preprocess The next step is to load a DistilBERT tokenizer to process the question and context fields: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") There are a few preprocessing steps particular to question answering tasks you should be aware of: Some examples in a dataset may have a very long context that exceeds the maximum input length of the model. To deal with longer sequences, truncate only the context by setting truncation="only_second". Next, map the start and end positions of the answer to the original context by setting return_offset_mapping=True. With the mapping in hand, now you can find the start and end tokens of the answer. Use the [~tokenizers.Encoding.sequence_ids] method to find which part of the offset corresponds to the question and which corresponds to the context. Here is how you can create a function to truncate and map the start and end tokens of the answer to the context: def preprocess_function(examples): questions = [q.strip() for q in examples["question"]] inputs = tokenizer( questions, examples["context"], max_length=384, truncation="only_second", return_offsets_mapping=True, padding="max_length", ) offset_mapping = inputs.pop("offset_mapping") answers = examples["answers"] start_positions = [] end_positions = [] for i, offset in enumerate(offset_mapping): answer = answers[i] start_char = answer["answer_start"][0] end_char = answer["answer_start"][0] + len(answer["text"][0]) sequence_ids = inputs.sequence_ids(i) # Find the start and end of the context idx = 0 while sequence_ids[idx] != 1: idx += 1 context_start = idx while sequence_ids[idx] == 1: idx += 1 context_end = idx - 1 # If the answer is not fully inside the context, label it (0, 0) if offset[context_start][0] > end_char or offset[context_end][1] < start_char: start_positions.append(0) end_positions.append(0) else: # Otherwise it's the start and end token positions idx = context_start while idx <= context_end and offset[idx][0] <= start_char: idx += 1 start_positions.append(idx - 1) idx = context_end while idx >= context_start and offset[idx][1] >= end_char: idx -= 1 end_positions.append(idx + 1) inputs["start_positions"] = start_positions inputs["end_positions"] = end_positions return inputs To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once. Remove any columns you don't need: tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad["train"].column_names) Now create a batch of examples using [DefaultDataCollator]. Unlike other data collators in 🤗 Transformers, the [DefaultDataCollator] does not apply any additional preprocessing such as padding. from transformers import DefaultDataCollator data_collator = DefaultDataCollator() </pt> <tf>py from transformers import DefaultDataCollator data_collator = DefaultDataCollator(return_tensors="tf") Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load DistilBERT with [AutoModelForQuestionAnswering]: from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased") At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, and data collator. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_qa_model", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=3, weight_decay=0.01, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_squad["train"], eval_dataset=tokenized_squad["test"], tokenizer=tokenizer, data_collator=data_collator, ) trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters: from transformers import create_optimizer batch_size = 16 num_epochs = 2 total_train_steps = (len(tokenized_squad["train"]) // batch_size) * num_epochs optimizer, schedule = create_optimizer( init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps, ) Then you can load DistilBERT with [TFAutoModelForQuestionAnswering]: from transformers import TFAutoModelForQuestionAnswering model = TFAutoModelForQuestionAnswering("distilbert-base-uncased") Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]: tf_train_set = model.prepare_tf_dataset( tokenized_squad["train"], shuffle=True, batch_size=16, collate_fn=data_collator, ) tf_validation_set = model.prepare_tf_dataset( tokenized_squad["test"], shuffle=False, batch_size=16, collate_fn=data_collator, ) Configure the model for training with compile: import tensorflow as tf model.compile(optimizer=optimizer) The last thing to setup before you start training is to provide a way to push your model to the Hub. This can be done by specifying where to push your model and tokenizer in the [~transformers.PushToHubCallback]: from transformers.keras_callbacks import PushToHubCallback callback = PushToHubCallback( output_dir="my_awesome_qa_model", tokenizer=tokenizer, ) Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model: model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=[callback]) Once training is completed, your model is automatically uploaded to the Hub so everyone can use it! For a more in-depth example of how to finetune a model for question answering, take a look at the corresponding PyTorch notebook or TensorFlow notebook. Evaluate Evaluation for question answering requires a significant amount of postprocessing. To avoid taking up too much of your time, this guide skips the evaluation step. The [Trainer] still calculates the evaluation loss during training so you're not completely in the dark about your model's performance. If have more time and you're interested in how to evaluate your model for question answering, take a look at the Question answering chapter from the 🤗 Hugging Face Course! Inference Great, now that you've finetuned a model, you can use it for inference! Come up with a question and some context you'd like the model to predict: question = "How many programming languages does BLOOM support?" context = "BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages." The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for question answering with your model, and pass your text to it: from transformers import pipeline question_answerer = pipeline("question-answering", model="my_awesome_qa_model") question_answerer(question=question, context=context) {'score': 0.2058267742395401, 'start': 10, 'end': 95, 'answer': '176 billion parameters and can generate text in 46 languages natural languages and 13'} You can also manually replicate the results of the pipeline if you'd like: Tokenize the text and return PyTorch tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("my_awesome_qa_model") inputs = tokenizer(question, context, return_tensors="pt") Pass your inputs to the model and return the logits: import torch from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("my_awesome_qa_model") with torch.no_grad(): outputs = model(**inputs) Get the highest probability from the model output for the start and end positions: answer_start_index = outputs.start_logits.argmax() answer_end_index = outputs.end_logits.argmax() Decode the predicted tokens to get the answer: predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens) '176 billion parameters and can generate text in 46 languages natural languages and 13' Tokenize the text and return TensorFlow tensors: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("my_awesome_qa_model") inputs = tokenizer(question, text, return_tensors="tf") Pass your inputs to the model and return the logits: from transformers import TFAutoModelForQuestionAnswering model = TFAutoModelForQuestionAnswering.from_pretrained("my_awesome_qa_model") outputs = model(**inputs) Get the highest probability from the model output for the start and end positions: answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0]) answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0]) Decode the predicted tokens to get the answer: predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens) '176 billion parameters and can generate text in 46 languages natural languages and 13'

Before you begin, make sure you have all the necessary libraries installed: pip install -q datasets transformers evaluate timm albumentations You'll use 🤗 Datasets to load a dataset from the Hugging Face Hub, 🤗 Transformers to train your model, and albumentations to augment the data. timm is currently required to load a convolutional backbone for the DETR model. We encourage you to share your model with the community. Log in to your Hugging Face account to upload it to the Hub. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Load the CPPE-5 dataset The CPPE-5 dataset contains images with annotations identifying medical personal protective equipment (PPE) in the context of the COVID-19 pandemic. Start by loading the dataset: from datasets import load_dataset cppe5 = load_dataset("cppe-5") cppe5 DatasetDict({ train: Dataset({ features: ['image_id', 'image', 'width', 'height', 'objects'], num_rows: 1000 }) test: Dataset({ features: ['image_id', 'image', 'width', 'height', 'objects'], num_rows: 29 }) }) You'll see that this dataset already comes with a training set containing 1000 images and a test set with 29 images. To get familiar with the data, explore what the examples look like. cppe5["train"][0] {'image_id': 15, 'image': , 'width': 943, 'height': 663, 'objects': {'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [[302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0]], 'category': [4, 4, 0, 0]}} The examples in the dataset have the following fields: - image_id: the example image id - image: a PIL.Image.Image object containing the image - width: width of the image - height: height of the image - objects: a dictionary containing bounding box metadata for the objects in the image: - id: the annotation id - area: the area of the bounding box - bbox: the object's bounding box (in the COCO format ) - category: the object's category, with possible values including Coverall (0), Face_Shield (1), Gloves (2), Goggles (3) and Mask (4) You may notice that the bbox field follows the COCO format, which is the format that the DETR model expects. However, the grouping of the fields inside objects differs from the annotation format DETR requires. You will need to apply some preprocessing transformations before using this data for training. To get an even better understanding of the data, visualize an example in the dataset. import numpy as np import os from PIL import Image, ImageDraw image = cppe5["train"][0]["image"] annotations = cppe5["train"][0]["objects"] draw = ImageDraw.Draw(image) categories = cppe5["train"].features["objects"].feature["category"].names id2label = {index: x for index, x in enumerate(categories, start=0)} label2id = {v: k for k, v in id2label.items()} for i in range(len(annotations["id"])): box = annotations["bbox"][i - 1] class_idx = annotations["category"][i - 1] x, y, w, h = tuple(box) draw.rectangle((x, y, x + w, y + h), outline="red", width=1) draw.text((x, y), id2label[class_idx], fill="white") image To visualize the bounding boxes with associated labels, you can get the labels from the dataset's metadata, specifically the category field. You'll also want to create dictionaries that map a label id to a label class (id2label) and the other way around (label2id). You can use them later when setting up the model. Including these maps will make your model reusable by others if you share it on the Hugging Face Hub. As a final step of getting familiar with the data, explore it for potential issues. One common problem with datasets for object detection is bounding boxes that "stretch" beyond the edge of the image. Such "runaway" bounding boxes can raise errors during training and should be addressed at this stage. There are a few examples with this issue in this dataset. To keep things simple in this guide, we remove these images from the data. remove_idx = [590, 821, 822, 875, 876, 878, 879] keep = [i for i in range(len(cppe5["train"])) if i not in remove_idx] cppe5["train"] = cppe5["train"].select(keep) Preprocess the data To finetune a model, you must preprocess the data you plan to use to match precisely the approach used for the pre-trained model. [AutoImageProcessor] takes care of processing image data to create pixel_values, pixel_mask, and labels that a DETR model can train with. The image processor has some attributes that you won't have to worry about: image_mean = [0.485, 0.456, 0.406 ] image_std = [0.229, 0.224, 0.225] These are the mean and standard deviation used to normalize images during the model pre-training. These values are crucial to replicate when doing inference or finetuning a pre-trained image model. Instantiate the image processor from the same checkpoint as the model you want to finetune. from transformers import AutoImageProcessor checkpoint = "facebook/detr-resnet-50" image_processor = AutoImageProcessor.from_pretrained(checkpoint) Before passing the images to the image_processor, apply two preprocessing transformations to the dataset: - Augmenting images - Reformatting annotations to meet DETR expectations First, to make sure the model does not overfit on the training data, you can apply image augmentation with any data augmentation library. Here we use Albumentations This library ensures that transformations affect the image and update the bounding boxes accordingly. The 🤗 Datasets library documentation has a detailed guide on how to augment images for object detection, and it uses the exact same dataset as an example. Apply the same approach here, resize each image to (480, 480), flip it horizontally, and brighten it: import albumentations import numpy as np import torch transform = albumentations.Compose( [ albumentations.Resize(480, 480), albumentations.HorizontalFlip(p=1.0), albumentations.RandomBrightnessContrast(p=1.0), ], bbox_params=albumentations.BboxParams(format="coco", label_fields=["category"]), ) The image_processor expects the annotations to be in the following format: {'image_id': int, 'annotations': List[Dict]}, where each dictionary is a COCO object annotation. Let's add a function to reformat annotations for a single example: def formatted_anns(image_id, category, area, bbox): annotations = [] for i in range(0, len(category)): new_ann = { "image_id": image_id, "category_id": category[i], "isCrowd": 0, "area": area[i], "bbox": list(bbox[i]), } annotations.append(new_ann) return annotations Now you can combine the image and annotation transformations to use on a batch of examples: transforming a batch def transform_aug_ann(examples): image_ids = examples["image_id"] images, bboxes, area, categories = [], [], [], [] for image, objects in zip(examples["image"], examples["objects"]): image = np.array(image.convert("RGB"))[:, :, ::-1] out = transform(image=image, bboxes=objects["bbox"], category=objects["category"]) area.append(objects["area"]) images.append(out["image"]) bboxes.append(out["bboxes"]) categories.append(out["category"]) targets = [ {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)} for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes) ] return image_processor(images=images, annotations=targets, return_tensors="pt") Apply this preprocessing function to the entire dataset using 🤗 Datasets [~datasets.Dataset.with_transform] method. This method applies transformations on the fly when you load an element of the dataset. At this point, you can check what an example from the dataset looks like after the transformations. You should see a tensor with pixel_values, a tensor with pixel_mask, and labels. cppe5["train"] = cppe5["train"].with_transform(transform_aug_ann) cppe5["train"][15] {'pixel_values': tensor([[[ 0.9132, 0.9132, 0.9132, , -1.9809, -1.9809, -1.9809], [ 0.9132, 0.9132, 0.9132, , -1.9809, -1.9809, -1.9809], [ 0.9132, 0.9132, 0.9132, , -1.9638, -1.9638, -1.9638], , [-1.5699, -1.5699, -1.5699, , -1.9980, -1.9980, -1.9980], [-1.5528, -1.5528, -1.5528, , -1.9980, -1.9809, -1.9809], [-1.5528, -1.5528, -1.5528, , -1.9980, -1.9809, -1.9809]], [[ 1.3081, 1.3081, 1.3081, , -1.8431, -1.8431, -1.8431], [ 1.3081, 1.3081, 1.3081, , -1.8431, -1.8431, -1.8431], [ 1.3081, 1.3081, 1.3081, , -1.8256, -1.8256, -1.8256], , [-1.3179, -1.3179, -1.3179, , -1.8606, -1.8606, -1.8606], [-1.3004, -1.3004, -1.3004, , -1.8606, -1.8431, -1.8431], [-1.3004, -1.3004, -1.3004, , -1.8606, -1.8431, -1.8431]], [[ 1.4200, 1.4200, 1.4200, , -1.6476, -1.6476, -1.6476], [ 1.4200, 1.4200, 1.4200, , -1.6476, -1.6476, -1.6476], [ 1.4200, 1.4200, 1.4200, , -1.6302, -1.6302, -1.6302], , [-1.0201, -1.0201, -1.0201, , -1.5604, -1.5604, -1.5604], [-1.0027, -1.0027, -1.0027, , -1.5604, -1.5430, -1.5430], [-1.0027, -1.0027, -1.0027, , -1.5604, -1.5430, -1.5430]]]), 'pixel_mask': tensor([[1, 1, 1, , 1, 1, 1], [1, 1, 1, , 1, 1, 1], [1, 1, 1, , 1, 1, 1], , [1, 1, 1, , 1, 1, 1], [1, 1, 1, , 1, 1, 1], [1, 1, 1, , 1, 1, 1]]), 'labels': {'size': tensor([800, 800]), 'image_id': tensor([756]), 'class_labels': tensor([4]), 'boxes': tensor([[0.7340, 0.6986, 0.3414, 0.5944]]), 'area': tensor([519544.4375]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}} You have successfully augmented the individual images and prepared their annotations. However, preprocessing isn't complete yet. In the final step, create a custom collate_fn to batch images together. Pad images (which are now pixel_values) to the largest image in a batch, and create a corresponding pixel_mask to indicate which pixels are real (1) and which are padding (0). def collate_fn(batch): pixel_values = [item["pixel_values"] for item in batch] encoding = image_processor.pad(pixel_values, return_tensors="pt") labels = [item["labels"] for item in batch] batch = {} batch["pixel_values"] = encoding["pixel_values"] batch["pixel_mask"] = encoding["pixel_mask"] batch["labels"] = labels return batch Training the DETR model You have done most of the heavy lifting in the previous sections, so now you are ready to train your model! The images in this dataset are still quite large, even after resizing. This means that finetuning this model will require at least one GPU. Training involves the following steps: 1. Load the model with [AutoModelForObjectDetection] using the same checkpoint as in the preprocessing. 2. Define your training hyperparameters in [TrainingArguments]. 3. Pass the training arguments to [Trainer] along with the model, dataset, image processor, and data collator. 4. Call [~Trainer.train] to finetune your model. When loading the model from the same checkpoint that you used for the preprocessing, remember to pass the label2id and id2label maps that you created earlier from the dataset's metadata. Additionally, we specify ignore_mismatched_sizes=True to replace the existing classification head with a new one. from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained( checkpoint, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True, ) In the [TrainingArguments] use output_dir to specify where to save your model, then configure hyperparameters as you see fit. It is important you do not remove unused columns because this will drop the image column. Without the image column, you can't create pixel_values. For this reason, set remove_unused_columns to False. If you wish to share your model by pushing to the Hub, set push_to_hub to True (you must be signed in to Hugging Face to upload your model). from transformers import TrainingArguments training_args = TrainingArguments( output_dir="detr-resnet-50_finetuned_cppe5", per_device_train_batch_size=8, num_train_epochs=10, fp16=True, save_steps=200, logging_steps=50, learning_rate=1e-5, weight_decay=1e-4, save_total_limit=2, remove_unused_columns=False, push_to_hub=True, ) Finally, bring everything together, and call [~transformers.Trainer.train]: from transformers import Trainer trainer = Trainer( model=model, args=training_args, data_collator=collate_fn, train_dataset=cppe5["train"], tokenizer=image_processor, ) trainer.train() If you have set push_to_hub to True in the training_args, the training checkpoints are pushed to the Hugging Face Hub. Upon training completion, push the final model to the Hub as well by calling the [~transformers.Trainer.push_to_hub] method. trainer.push_to_hub() Evaluate Object detection models are commonly evaluated with a set of COCO-style metrics. You can use one of the existing metrics implementations, but here you'll use the one from torchvision to evaluate the final model that you pushed to the Hub. To use the torchvision evaluator, you'll need to prepare a ground truth COCO dataset. The API to build a COCO dataset requires the data to be stored in a certain format, so you'll need to save images and annotations to disk first. Just like when you prepared your data for training, the annotations from the cppe5["test"] need to be formatted. However, images should stay as they are. The evaluation step requires a bit of work, but it can be split in three major steps. First, prepare the cppe5["test"] set: format the annotations and save the data to disk. import json format annotations the same as for training, no need for data augmentation def val_formatted_anns(image_id, objects): annotations = [] for i in range(0, len(objects["id"])): new_ann = { "id": objects["id"][i], "category_id": objects["category"][i], "iscrowd": 0, "image_id": image_id, "area": objects["area"][i], "bbox": objects["bbox"][i], } annotations.append(new_ann) return annotations Save images and annotations into the files torchvision.datasets.CocoDetection expects def save_cppe5_annotation_file_images(cppe5): output_json = {} path_output_cppe5 = f"{os.getcwd()}/cppe5/" if not os.path.exists(path_output_cppe5): os.makedirs(path_output_cppe5) path_anno = os.path.join(path_output_cppe5, "cppe5_ann.json") categories_json = [{"supercategory": "none", "id": id, "name": id2label[id]} for id in id2label] output_json["images"] = [] output_json["annotations"] = [] for example in cppe5: ann = val_formatted_anns(example["image_id"], example["objects"]) output_json["images"].append( { "id": example["image_id"], "width": example["image"].width, "height": example["image"].height, "file_name": f"{example['image_id']}.png", } ) output_json["annotations"].extend(ann) output_json["categories"] = categories_json with open(path_anno, "w") as file: json.dump(output_json, file, ensure_ascii=False, indent=4) for im, img_id in zip(cppe5["image"], cppe5["image_id"]): path_img = os.path.join(path_output_cppe5, f"{img_id}.png") im.save(path_img) return path_output_cppe5, path_anno Next, prepare an instance of a CocoDetection class that can be used with cocoevaluator. import torchvision class CocoDetection(torchvision.datasets.CocoDetection): def init(self, img_folder, image_processor, ann_file): super().init(img_folder, ann_file) self.image_processor = image_processor def getitem(self, idx): # read in PIL image and target in COCO format img, target = super(CocoDetection, self).getitem(idx) # preprocess image and target: converting target to DETR format, # resizing + normalization of both image and target) image_id = self.ids[idx] target = {"image_id": image_id, "annotations": target} encoding = self.image_processor(images=img, annotations=target, return_tensors="pt") pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension target = encoding["labels"][0] # remove batch dimension return {"pixel_values": pixel_values, "labels": target} im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"]) test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno) Finally, load the metrics and run the evaluation. import evaluate from tqdm import tqdm model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco) val_dataloader = torch.utils.data.DataLoader( test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn ) with torch.no_grad(): for idx, batch in enumerate(tqdm(val_dataloader)): pixel_values = batch["pixel_values"] pixel_mask = batch["pixel_mask"] labels = [ {k: v for k, v in t.items()} for t in batch["labels"] ] # these are in DETR format, resized + normalized # forward pass outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask) orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0) results = im_processor.post_process(outputs, orig_target_sizes) # convert outputs of model to COCO api module.add(prediction=results, reference=labels) del batch results = module.compute() print(results) Accumulating evaluation results DONE (t=0.08s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590 `` These results can be further improved by adjusting the hyperparameters in [~transformers.TrainingArguments`]. Give it a go! Inference Now that you have finetuned a DETR model, evaluated it, and uploaded it to the Hugging Face Hub, you can use it for inference. The simplest way to try out your finetuned model for inference is to use it in a [Pipeline]. Instantiate a pipeline for object detection with your model, and pass an image to it: from transformers import pipeline import requests url = "https://i.imgur.com/2lnWoly.jpg" image = Image.open(requests.get(url, stream=True).raw) obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5") obj_detector(image) You can also manually replicate the results of the pipeline if you'd like: image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5") with torch.no_grad(): inputs = image_processor(images=image, return_tensors="pt") outputs = model(**inputs) target_sizes = torch.tensor([image.size[::-1]]) results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08] Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9] Let's plot the result: draw = ImageDraw.Draw(image) for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] x, y, x2, y2 = tuple(box) draw.rectangle((x, y, x2, y2), outline="red", width=1) draw.text((x, y), model.config.id2label[label.item()], fill="white") image

In this guide you'll learn how to: create a depth estimation pipeline run depth estimation inference by hand Before you begin, make sure you have all the necessary libraries installed: pip install -q transformers Depth estimation pipeline The simplest way to try out inference with a model supporting depth estimation is to use the corresponding [pipeline]. Instantiate a pipeline from a checkpoint on the Hugging Face Hub: from transformers import pipeline checkpoint = "vinvino02/glpn-nyu" depth_estimator = pipeline("depth-estimation", model=checkpoint) Next, choose an image to analyze: from PIL import Image import requests url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640" image = Image.open(requests.get(url, stream=True).raw) image Pass the image to the pipeline. predictions = depth_estimator(image) The pipeline returns a dictionary with two entries. The first one, called predicted_depth, is a tensor with the values being the depth expressed in meters for each pixel. The second one, depth, is a PIL image that visualizes the depth estimation result. Let's take a look at the visualized result: predictions["depth"] Depth estimation inference by hand Now that you've seen how to use the depth estimation pipeline, let's see how we can replicate the same result by hand. Start by loading the model and associated processor from a checkpoint on the Hugging Face Hub. Here we'll use the same checkpoint as before: from transformers import AutoImageProcessor, AutoModelForDepthEstimation checkpoint = "vinvino02/glpn-nyu" image_processor = AutoImageProcessor.from_pretrained(checkpoint) model = AutoModelForDepthEstimation.from_pretrained(checkpoint) Prepare the image input for the model using the image_processor that will take care of the necessary image transformations such as resizing and normalization: pixel_values = image_processor(image, return_tensors="pt").pixel_values Pass the prepared inputs through the model: import torch with torch.no_grad(): outputs = model(pixel_values) predicted_depth = outputs.predicted_depth Visualize the results: import numpy as np interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ).squeeze() output = prediction.numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) depth

Troubleshoot Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we've seen and how you can resolve them. However, this guide isn't meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try: Asking for help on the forums. There are specific categories you can post your question to, like Beginners or 🤗 Transformers. Make sure you write a good descriptive forum post with some reproducible code to maximize the likelihood that your problem is solved! Create an Issue on the 🤗 Transformers repository if it is a bug related to the library. Try to include as much information describing the bug as possible to help us better figure out what's wrong and how we can fix it. Check the Migration guide if you use an older version of 🤗 Transformers since some important changes have been introduced between versions. For more details about troubleshooting and getting help, take a look at Chapter 8 of the Hugging Face course. Firewalled environments Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. When your script attempts to download model weights or datasets, the download will hang and then timeout with the following message: ValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on. In this case, you should try to run 🤗 Transformers on offline mode to avoid the connection error. CUDA out of memory Training large models with millions of parameters can be challenging without the appropriate hardware. A common error you may encounter when the GPU runs out of memory is: CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch) Here are some potential solutions you can try to lessen memory use: Reduce the per_device_train_batch_size value in [TrainingArguments]. Try using gradient_accumulation_steps in [TrainingArguments] to effectively increase overall batch size. Refer to the Performance guide for more details about memory-saving techniques. Unable to load a saved TensorFlow model TensorFlow's model.save method will save the entire model - architecture, weights, training configuration - in a single file. However, when you load the model file again, you may run into an error because 🤗 Transformers may not load all the TensorFlow-related objects in the model file. To avoid issues with saving and loading TensorFlow models, we recommend you: Save the model weights as a h5 file extension with model.save_weights and then reload the model with [~TFPreTrainedModel.from_pretrained]: from transformers import TFPreTrainedModel from tensorflow import keras model.save_weights("some_folder/tf_model.h5") model = TFPreTrainedModel.from_pretrained("some_folder") Save the model with [~TFPretrainedModel.save_pretrained] and load it again with [~TFPreTrainedModel.from_pretrained]: from transformers import TFPreTrainedModel model.save_pretrained("path_to/model") model = TFPreTrainedModel.from_pretrained("path_to/model") ImportError Another common error you may encounter, especially if it is a newly released model, is ImportError: ImportError: cannot import name 'ImageGPTImageProcessor' from 'transformers' (unknown location) For these error types, check to make sure you have the latest version of 🤗 Transformers installed to access the most recent models: pip install transformers --upgrade CUDA error: device-side assert triggered Sometimes you may run into a generic CUDA error about an error in the device code. RuntimeError: CUDA error: device-side assert triggered You should try to run the code on a CPU first to get a more descriptive error message. Add the following environment variable to the beginning of your code to switch to a CPU: import os os.environ["CUDA_VISIBLE_DEVICES"] = "" Another option is to get a better traceback from the GPU. Add the following environment variable to the beginning of your code to get the traceback to point to the source of the error: import os os.environ["CUDA_LAUNCH_BLOCKING"] = "1" Incorrect output when padding tokens aren't masked In some cases, the output hidden_state may be incorrect if the input_ids include padding tokens. To demonstrate, load a model and tokenizer. You can access a model's pad_token_id to see its value. The pad_token_id may be None for some models, but you can always manually set it. from transformers import AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") model.config.pad_token_id 0 The following example shows the output without masking the padding tokens: input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]]) output = model(input_ids) print(output.logits) tensor([[ 0.0082, -0.2307], [ 0.1317, -0.1683]], grad_fn=) Here is the actual output of the second sequence: input_ids = torch.tensor([[7592]]) output = model(input_ids) print(output.logits) tensor([[-0.1008, -0.4061]], grad_fn=) Most of the time, you should provide an attention_mask to your model to ignore the padding tokens to avoid this silent error. Now the output of the second sequence matches its actual output: By default, the tokenizer creates an attention_mask for you based on your specific tokenizer's defaults. attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]]) output = model(input_ids, attention_mask=attention_mask) print(output.logits) tensor([[ 0.0082, -0.2307], [-0.1008, -0.4061]], grad_fn=) 🤗 Transformers doesn't automatically create an attention_mask to mask a padding token if it is provided because: Some models don't have a padding token. For some use-cases, users want a model to attend to a padding token. ValueError: Unrecognized configuration class XYZ for this kind of AutoModel Generally, we recommend using the [AutoModel] class to load pretrained instances of models. This class can automatically infer and load the correct architecture from a given checkpoint based on the configuration. If you see this ValueError when loading a model from a checkpoint, this means the Auto class couldn't find a mapping from the configuration in the given checkpoint to the kind of model you are trying to load. Most commonly, this happens when a checkpoint doesn't support a given task. For instance, you'll see this error in the following example because there is no GPT2 for question answering: from transformers import AutoProcessor, AutoModelForQuestionAnswering processor = AutoProcessor.from_pretrained("gpt2-medium") model = AutoModelForQuestionAnswering.from_pretrained("gpt2-medium") ValueError: Unrecognized configuration class for this kind of AutoModel: AutoModelForQuestionAnswering. Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig,

Generation Each framework has a generate method for text generation implemented in their respective GenerationMixin class: PyTorch [~generation.GenerationMixin.generate] is implemented in [~generation.GenerationMixin]. TensorFlow [~generation.TFGenerationMixin.generate] is implemented in [~generation.TFGenerationMixin]. Flax/JAX [~generation.FlaxGenerationMixin.generate] is implemented in [~generation.FlaxGenerationMixin]. Regardless of your framework of choice, you can parameterize the generate method with a [~generation.GenerationConfig] class instance. Please refer to this class for the complete list of generation parameters, which control the behavior of the generation method. To learn how to inspect a model's generation configuration, what are the defaults, how to change the parameters ad hoc, and how to create and save a customized generation configuration, refer to the text generation strategies guide. The guide also explains how to use related features, like token streaming. GenerationConfig [[autodoc]] generation.GenerationConfig - from_pretrained - from_model_config - save_pretrained GenerationMixin [[autodoc]] generation.GenerationMixin - generate - compute_transition_scores - greedy_search - sample - beam_search - beam_sample - contrastive_search - group_beam_search - constrained_beam_search TFGenerationMixin [[autodoc]] generation.TFGenerationMixin - generate - compute_transition_scores FlaxGenerationMixin [[autodoc]] generation.FlaxGenerationMixin - generate

Efficient Inference on a Single GPU In addition to this guide, relevant information can be found as well in the guide for training on a single GPU and the guide for inference on CPUs. Better Transformer: PyTorch-native transformer fastpath PyTorch-native nn.MultiHeadAttention attention fastpath, called BetterTransformer, can be used with Transformers through the integration in the 🤗 Optimum library. PyTorch's attention fastpath allows to speed up inference through kernel fusions and the use of nested tensors. Detailed benchmarks can be found in this blog post. After installing the optimum package, to use Better Transformer during inference, the relevant internal modules are replaced by calling [~PreTrainedModel.to_bettertransformer]: python model = model.to_bettertransformer() The method [~PreTrainedModel.reverse_bettertransformer] allows to go back to the original modeling, which should be used before saving the model in order to use the canonical transformers modeling: python model = model.reverse_bettertransformer() model.save_pretrained("saved_model") As of PyTorch 2.0, the attention fastpath is supported for both encoders and decoders. The list of supported architectures can be found here. bitsandbytes integration for FP4 mixed-precision inference You can install bitsandbytes and benefit from easy model compression on GPUs. Using FP4 quantization you can expect to reduce up to 8x the model size compared to its native full precision version. Check out below how to get started. Note that this feature can also be used in a multi GPU setup. Requirements Latest bitsandbytes library pip install bitsandbytes>=0.39.0 Install latest accelerate from source pip install git+https://github.com/huggingface/accelerate.git Install latest transformers from source pip install git+https://github.com/huggingface/transformers.git Running FP4 models - single GPU setup - Quickstart You can quickly run a FP4 model on a single GPU by running the following code: from transformers import AutoModelForCausalLM model_name = "bigscience/bloom-2b5" model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True) `` Note thatdevice_mapis optional but settingdevice_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources. Running FP4 models - multi GPU setup The way to load your mixed 4-bit model in multiple GPUs is as follows (same command as single GPU setup): py model_name = "bigscience/bloom-2b5" model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True) But you can control the GPU RAM you want to allocate on each GPU using accelerate. Use the max_memory argument as follows: py max_memory_mapping = {0: "600MB", 1: "1GB"} model_name = "bigscience/bloom-3b" model_4bit = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping ) In this example, the first GPU will use 600MB of memory and the second 1GB. Advanced usage For more advanced usage of this method, please have a look at the quantization documentation page. bitsandbytes integration for Int8 mixed-precision matrix decomposition Note that this feature can also be used in a multi GPU setup. From the paper LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale, we support Hugging Face integration for all models in the Hub with a few lines of code. The method reduces nn.Linear size by 2 for float16 and bfloat16 weights and by 4 for float32 weights, with close to no impact to the quality by operating on the outliers in half-precision. Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) a systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no predictive degradation is possible for very large models. For more details regarding the method, check out the paper or our blogpost about the integration. Note, that you would require a GPU to run mixed-8bit models as the kernels have been compiled for GPUs only. Make sure that you have enough GPU memory to store the quarter (or half if your model weights are in half precision) of the model before using this feature. Below are some notes to help you use this module, or follow the demos on Google colab. Requirements If you have bitsandbytes<0.37.0, make sure you run on NVIDIA GPUs that support 8-bit tensor cores (Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100). For bitsandbytes>=0.37.0, all GPUs should be supported. Install the correct version of bitsandbytes by running: pip install bitsandbytes>=0.31.5 Install accelerate pip install accelerate>=0.12.0 Running mixed-Int8 models - single GPU setup After installing the required libraries, the way to load your mixed 8-bit model is as follows: from transformers import AutoModelForCausalLM model_name = "bigscience/bloom-2b5" model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) For text generation, we recommend: using the model's generate() method instead of the pipeline() function. Although inference is possible with the pipeline() function, it is not optimized for mixed-8bit models, and will be slower than using the generate() method. Moreover, some sampling strategies are like nucleaus sampling are not supported by the pipeline() function for mixed-8bit models. placing all inputs on the same device as the model. Here is a simple example: from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "bigscience/bloom-2b5" tokenizer = AutoTokenizer.from_pretrained(model_name) model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) prompt = "Hello, my llama is cute" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") generated_ids = model.generate(**inputs) outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) Running mixed-int8 models - multi GPU setup The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup): py model_name = "bigscience/bloom-2b5" model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) But you can control the GPU RAM you want to allocate on each GPU using accelerate. Use the max_memory argument as follows: py max_memory_mapping = {0: "1GB", 1: "2GB"} model_name = "bigscience/bloom-3b" model_8bit = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping ) In this example, the first GPU will use 1GB of memory and the second 2GB. Colab demos With this method you can infer on models that were not possible to infer on a Google Colab before. Check out the demo for running T5-11b (42GB in fp32)! Using 8-bit quantization on Google Colab: Or this demo for BLOOM-3B:

Before you begin, make sure you have all the necessary libraries installed: pip install -q pytorchvideo transformers evaluate You will use PyTorchVideo (dubbed pytorchvideo) to process and prepare the videos. We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Load UCF101 dataset Start by loading a subset of the UCF-101 dataset. This will give you a chance to experiment and make sure everything works before spending more time training on the full dataset. from huggingface_hub import hf_hub_download hf_dataset_identifier = "sayakpaul/ucf101-subset" filename = "UCF101_subset.tar.gz" file_path = hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type="dataset") After the subset has been downloaded, you need to extract the compressed archive: import tarfile with tarfile.open(file_path) as t: t.extractall(".") At a high level, the dataset is organized like so: UCF101_subset/ train/ BandMarching/ video_1.mp4 video_2.mp4 Archery video_1.mp4 video_2.mp4 val/ BandMarching/ video_1.mp4 video_2.mp4 Archery video_1.mp4 video_2.mp4 test/ BandMarching/ video_1.mp4 video_2.mp4 Archery video_1.mp4 video_2.mp4 The (sorted) video paths appear like so: 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c04.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c06.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c01.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c02.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c06.avi' You will notice that there are video clips belonging to the same group / scene where group is denoted by g in the video file paths. v_ApplyEyeMakeup_g07_c04.avi and v_ApplyEyeMakeup_g07_c06.avi, for example. For the validation and evaluation splits, you wouldn't want to have video clips from the same group / scene to prevent data leakage. The subset that you are using in this tutorial takes this information into account. Next up, you will derive the set of labels present in the dataset. Also, create two dictionaries that'll be helpful when initializing the model: label2id: maps the class names to integers. id2label: maps the integers to class names. class_labels = sorted({str(path).split("/")[2] for path in all_video_file_paths}) label2id = {label: i for i, label in enumerate(class_labels)} id2label = {i: label for label, i in label2id.items()} print(f"Unique classes: {list(label2id.keys())}.") Unique classes: ['ApplyEyeMakeup', 'ApplyLipstick', 'Archery', 'BabyCrawling', 'BalanceBeam', 'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk', 'BenchPress']. There are 10 unique classes. For each class, there are 30 videos in the training set. Load a model to fine-tune Instantiate a video classification model from a pretrained checkpoint and its associated image processor. The model's encoder comes with pre-trained parameters, and the classification head is randomly initialized. The image processor will come in handy when writing the preprocessing pipeline for our dataset. from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification model_ckpt = "MCG-NJU/videomae-base" image_processor = VideoMAEImageProcessor.from_pretrained(model_ckpt) model = VideoMAEForVideoClassification.from_pretrained( model_ckpt, label2id=label2id, id2label=id2label, ignore_mismatched_sizes=True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint ) While the model is loading, you might notice the following warning: Some weights of the model checkpoint at MCG-NJU/videomae-base were not used when initializing VideoMAEForVideoClassification: [, 'decoder.decoder_layers.1.attention.output.dense.bias', 'decoder.decoder_layers.2.attention.attention.key.weight'] - This IS expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. The warning is telling us we are throwing away some weights (e.g. the weights and bias of the classifier layer) and randomly initializing some others (the weights and bias of a new classifier layer). This is expected in this case, because we are adding a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do. Note that this checkpoint leads to better performance on this task as the checkpoint was obtained fine-tuning on a similar downstream task having considerable domain overlap. You can check out this checkpoint which was obtained by fine-tuning MCG-NJU/videomae-base-finetuned-kinetics. Prepare the datasets for training For preprocessing the videos, you will leverage the PyTorchVideo library. Start by importing the dependencies we need. import pytorchvideo.data from pytorchvideo.transforms import ( ApplyTransformToKey, Normalize, RandomShortSideScale, RemoveKey, ShortSideScale, UniformTemporalSubsample, ) from torchvision.transforms import ( Compose, Lambda, RandomCrop, RandomHorizontalFlip, Resize, ) For the training dataset transformations, use a combination of uniform temporal subsampling, pixel normalization, random cropping, and random horizontal flipping. For the validation and evaluation dataset transformations, keep the same transformation chain except for random cropping and horizontal flipping. To learn more about the details of these transformations check out the official documentation of PyTorchVideo. Use the image_processor associated with the pre-trained model to obtain the following information: Image mean and standard deviation with which the video frame pixels will be normalized. Spatial resolution to which the video frames will be resized. Start by defining some constants. mean = image_processor.image_mean std = image_processor.image_std if "shortest_edge" in image_processor.size: height = width = image_processor.size["shortest_edge"] else: height = image_processor.size["height"] width = image_processor.size["width"] resize_to = (height, width) num_frames_to_sample = model.config.num_frames sample_rate = 4 fps = 30 clip_duration = num_frames_to_sample * sample_rate / fps Now, define the dataset-specific transformations and the datasets respectively. Starting with the training set: train_transform = Compose( [ ApplyTransformToKey( key="video", transform=Compose( [ UniformTemporalSubsample(num_frames_to_sample), Lambda(lambda x: x / 255.0), Normalize(mean, std), RandomShortSideScale(min_size=256, max_size=320), RandomCrop(resize_to), RandomHorizontalFlip(p=0.5), ] ), ), ] ) train_dataset = pytorchvideo.data.Ucf101( data_path=os.path.join(dataset_root_path, "train"), clip_sampler=pytorchvideo.data.make_clip_sampler("random", clip_duration), decode_audio=False, transform=train_transform, ) The same sequence of workflow can be applied to the validation and evaluation sets: val_transform = Compose( [ ApplyTransformToKey( key="video", transform=Compose( [ UniformTemporalSubsample(num_frames_to_sample), Lambda(lambda x: x / 255.0), Normalize(mean, std), Resize(resize_to), ] ), ), ] ) val_dataset = pytorchvideo.data.Ucf101( data_path=os.path.join(dataset_root_path, "val"), clip_sampler=pytorchvideo.data.make_clip_sampler("uniform", clip_duration), decode_audio=False, transform=val_transform, ) test_dataset = pytorchvideo.data.Ucf101( data_path=os.path.join(dataset_root_path, "test"), clip_sampler=pytorchvideo.data.make_clip_sampler("uniform", clip_duration), decode_audio=False, transform=val_transform, ) Note: The above dataset pipelines are taken from the official PyTorchVideo example. We're using the pytorchvideo.data.Ucf101() function because it's tailored for the UCF-101 dataset. Under the hood, it returns a pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset object. LabeledVideoDataset class is the base class for all things video in the PyTorchVideo dataset. So, if you want to use a custom dataset not supported off-the-shelf by PyTorchVideo, you can extend the LabeledVideoDataset class accordingly. Refer to the data API documentation to learn more. Also, if your dataset follows a similar structure (as shown above), then using the pytorchvideo.data.Ucf101() should work just fine. You can access the num_videos argument to know the number of videos in the dataset. print(train_dataset.num_videos, val_dataset.num_videos, test_dataset.num_videos) (300, 30, 75) Visualize the preprocessed video for better debugging import imageio import numpy as np from IPython.display import Image def unnormalize_img(img): """Un-normalizes the image pixels.""" img = (img * std) + mean img = (img * 255).astype("uint8") return img.clip(0, 255) def create_gif(video_tensor, filename="sample.gif"): """Prepares a GIF from a video tensor. The video tensor is expected to have the following shape: (num_frames, num_channels, height, width). """ frames = [] for video_frame in video_tensor: frame_unnormalized = unnormalize_img(video_frame.permute(1, 2, 0).numpy()) frames.append(frame_unnormalized) kargs = {"duration": 0.25} imageio.mimsave(filename, frames, "GIF", **kargs) return filename def display_gif(video_tensor, gif_name="sample.gif"): """Prepares and displays a GIF from a video tensor.""" video_tensor = video_tensor.permute(1, 0, 2, 3) gif_filename = create_gif(video_tensor, gif_name) return Image(filename=gif_filename) sample_video = next(iter(train_dataset)) video_tensor = sample_video["video"] display_gif(video_tensor) Train the model Leverage Trainer from 🤗 Transformers for training the model. To instantiate a Trainer, you need to define the training configuration and an evaluation metric. The most important is the TrainingArguments, which is a class that contains all the attributes to configure the training. It requires an output folder name, which will be used to save the checkpoints of the model. It also helps sync all the information in the model repository on 🤗 Hub. Most of the training arguments are self-explanatory, but one that is quite important here is remove_unused_columns=False. This one will drop any features not used by the model's call function. By default it's True because usually it's ideal to drop unused feature columns, making it easier to unpack inputs into the model's call function. But, in this case, you need the unused features ('video' in particular) in order to create pixel_values (which is a mandatory key our model expects in its inputs). from transformers import TrainingArguments, Trainer model_name = model_ckpt.split("/")[-1] new_model_name = f"{model_name}-finetuned-ucf101-subset" num_epochs = 4 args = TrainingArguments( new_model_name, remove_unused_columns=False, evaluation_strategy="epoch", save_strategy="epoch", learning_rate=5e-5, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, warmup_ratio=0.1, logging_steps=10, load_best_model_at_end=True, metric_for_best_model="accuracy", push_to_hub=True, max_steps=(train_dataset.num_videos // batch_size) * num_epochs, ) The dataset returned by pytorchvideo.data.Ucf101() doesn't implement the __len__ method. As such, we must define max_steps when instantiating TrainingArguments. Next, you need to define a function to compute the metrics from the predictions, which will use the metric you'll load now. The only preprocessing you have to do is to take the argmax of our predicted logits: import evaluate metric = evaluate.load("accuracy") def compute_metrics(eval_pred): predictions = np.argmax(eval_pred.predictions, axis=1) return metric.compute(predictions=predictions, references=eval_pred.label_ids) A note on evaluation: In the VideoMAE paper, the authors use the following evaluation strategy. They evaluate the model on several clips from test videos and apply different crops to those clips and report the aggregate score. However, in the interest of simplicity and brevity, we don't consider that in this tutorial. Also, define a collate_fn, which will be used to batch examples together. Each batch consists of 2 keys, namely pixel_values and labels. def collate_fn(examples): # permute to (num_frames, num_channels, height, width) pixel_values = torch.stack( [example["video"].permute(1, 0, 2, 3) for example in examples] ) labels = torch.tensor([example["label"] for example in examples]) return {"pixel_values": pixel_values, "labels": labels} Then you just pass all of this along with the datasets to Trainer: trainer = Trainer( model, args, train_dataset=train_dataset, eval_dataset=val_dataset, tokenizer=image_processor, compute_metrics=compute_metrics, data_collator=collate_fn, ) You might wonder why you passed along the image_processor as a tokenizer when you preprocessed the data already. This is only to make sure the image processor configuration file (stored as JSON) will also be uploaded to the repo on the Hub. Now fine-tune our model by calling the train method: train_results = trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() Inference Great, now that you have fine-tuned a model, you can use it for inference! Load a video for inference: sample_test_video = next(iter(test_dataset)) The simplest way to try out your fine-tuned model for inference is to use it in a pipeline. Instantiate a pipeline for video classification with your model, and pass your video to it: from transformers import pipeline video_cls = pipeline(model="my_awesome_video_cls_model") video_cls("https://huggingface.co/datasets/sayakpaul/ucf101-subset/resolve/main/v_BasketballDunk_g14_c06.avi") [{'score': 0.9272987842559814, 'label': 'BasketballDunk'}, {'score': 0.017777055501937866, 'label': 'BabyCrawling'}, {'score': 0.01663011871278286, 'label': 'BalanceBeam'}, {'score': 0.009560945443809032, 'label': 'BandMarching'}, {'score': 0.0068979403004050255, 'label': 'BaseballPitch'}] You can also manually replicate the results of the pipeline if you'd like. def run_inference(model, video): # (num_frames, num_channels, height, width) perumuted_sample_test_video = video.permute(1, 0, 2, 3) inputs = { "pixel_values": perumuted_sample_test_video.unsqueeze(0), "labels": torch.tensor( [sample_test_video["label"]] ), # this can be skipped if you don't have labels available. } device = torch.device("cuda" if torch.cuda.is_available() else "cpu") inputs = {k: v.to(device) for k, v in inputs.items()} model = model.to(device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits return logits Now, pass your input to the model and return the logits: logits = run_inference(trained_model, sample_test_video["video"]) Decoding the logits, we get: predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) Predicted class: BasketballDunk ```

Export to TFLite TensorFlow Lite is a lightweight framework for deploying machine learning models on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. TFLite is designed to optimize and run models efficiently on these devices with limited computational power, memory, and power consumption. A TensorFlow Lite model is represented in a special efficient portable format identified by the .tflite file extension. 🤗 Optimum offers functionality to export 🤗 Transformers models to TFLite through the exporters.tflite module. For the list of supported model architectures, please refer to 🤗 Optimum documentation. To export a model to TFLite, install the required dependencies: pip install optimum[exporters-tf] To check out all available arguments, refer to the 🤗 Optimum docs, or view help in command line: optimum-cli export tflite --help To export a model's checkpoint from the 🤗 Hub, for example, bert-base-uncased, run the following command: optimum-cli export tflite --model bert-base-uncased --sequence_length 128 bert_tflite/ You should see the logs indicating progress and showing where the resulting model.tflite is saved, like this: Validating TFLite model -[✓] TFLite model output names match reference model (logits) - Validating TFLite Model output "logits": -[✓] (1, 128, 30522) matches (1, 128, 30522) -[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05) The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05: - logits: max diff = 5.817413330078125e-05. The exported model was saved at: bert_tflite The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you saved both the model's weights and tokenizer files in the same directory (local_path). When using CLI, pass the local_path to the model argument instead of the checkpoint name on 🤗 Hub.

sections: local: index title: 🤗 Transformers local: quicktour title: Quick tour local: installation title: Installation title: Get started sections: local: pipeline_tutorial title: Run inference with pipelines local: autoclass_tutorial title: Write portable code with AutoClass local: preprocessing title: Preprocess data local: training title: Fine-tune a pretrained model local: run_scripts title: Train with a script local: accelerate title: Set up distributed training with 🤗 Accelerate local: model_sharing title: Share your model local: transformers_agents title: Agents title: Tutorials sections: sections: local: tasks/sequence_classification title: Text classification local: tasks/token_classification title: Token classification local: tasks/question_answering title: Question answering local: tasks/language_modeling title: Causal language modeling local: tasks/masked_language_modeling title: Masked language modeling local: tasks/translation title: Translation local: tasks/summarization title: Summarization local: tasks/multiple_choice title: Multiple choice title: Natural Language Processing isExpanded: false sections: local: tasks/audio_classification title: Audio classification local: tasks/asr title: Automatic speech recognition title: Audio isExpanded: false sections: local: tasks/image_classification title: Image classification local: tasks/semantic_segmentation title: Semantic segmentation local: tasks/video_classification title: Video classification local: tasks/object_detection title: Object detection local: tasks/zero_shot_object_detection title: Zero-shot object detection local: tasks/zero_shot_image_classification title: Zero-shot image classification local: tasks/monocular_depth_estimation title: Depth estimation title: Computer Vision isExpanded: false sections: local: tasks/image_captioning title: Image captioning local: tasks/document_question_answering title: Document Question Answering local: tasks/text-to-speech title: Text to speech title: Multimodal isExpanded: false title: Task Guides sections: local: fast_tokenizers title: Use fast tokenizers from 🤗 Tokenizers local: multilingual title: Run inference with multilingual models local: generation_strategies title: Customize text generation strategy local: create_a_model title: Use model-specific APIs local: custom_models title: Share a custom model local: sagemaker title: Run training on Amazon SageMaker local: serialization title: Export to ONNX local: tflite title: Export to TFLite local: torchscript title: Export to TorchScript local: benchmarks title: Benchmarks local: notebooks title: Notebooks with examples local: community title: Community resources local: custom_tools title: Custom Tools and Prompts local: troubleshooting title: Troubleshoot title: Developer guides sections: local: performance title: Overview sections: local: perf_train_gpu_one title: Methods and tools for efficient training on a single GPU local: perf_train_gpu_many title: Multiple GPUs and parallelism local: perf_train_cpu title: Efficient training on CPU local: perf_train_cpu_many title: Distributed CPU training local: perf_train_tpu title: Training on TPUs local: perf_train_tpu_tf title: Training on TPU with TensorFlow local: perf_train_special title: Training on Specialized Hardware local: perf_hardware title: Custom hardware for training local: hpo_train title: Hyperparameter Search using Trainer API title: Efficient training techniques sections: local: perf_infer_cpu title: Inference on CPU local: perf_infer_gpu_one title: Inference on one GPU local: perf_infer_gpu_many title: Inference on many GPUs local: perf_infer_special title: Inference on Specialized Hardware title: Optimizing inference local: big_models title: Instantiating a big model local: debugging title: Troubleshooting local: tf_xla title: XLA Integration for TensorFlow Models title: Performance and scalability sections: local: contributing title: How to contribute to transformers? local: add_new_model title: How to add a model to 🤗 Transformers? local: add_tensorflow_model title: How to convert a 🤗 Transformers model to TensorFlow? local: add_new_pipeline title: How to add a pipeline to 🤗 Transformers? local: testing title: Testing local: pr_checks title: Checks on a Pull Request title: Contribute sections: local: philosophy title: Philosophy local: glossary title: Glossary local: task_summary title: What 🤗 Transformers can do local: tasks_explained title: How 🤗 Transformers solve tasks local: model_summary title: The Transformer model family local: tokenizer_summary title: Summary of the tokenizers local: attention title: Attention mechanisms local: pad_truncation title: Padding and truncation local: bertology title: BERTology local: perplexity title: Perplexity of fixed-length models local: pipeline_webserver title: Pipelines for webserver inference local: model_memory_anatomy title: Model training anatomy title: Conceptual guides sections: sections: local: main_classes/agent title: Agents and Tools local: model_doc/auto title: Auto Classes local: main_classes/callback title: Callbacks local: main_classes/configuration title: Configuration local: main_classes/data_collator title: Data Collator local: main_classes/keras_callbacks title: Keras callbacks local: main_classes/logging title: Logging local: main_classes/model title: Models local: main_classes/text_generation title: Text Generation local: main_classes/onnx title: ONNX local: main_classes/optimizer_schedules title: Optimization local: main_classes/output title: Model outputs local: main_classes/pipelines title: Pipelines local: main_classes/processors title: Processors local: main_classes/quantization title: Quantization local: main_classes/tokenizer title: Tokenizer local: main_classes/trainer title: Trainer local: main_classes/deepspeed title: DeepSpeed Integration local: main_classes/feature_extractor title: Feature Extractor local: main_classes/image_processor title: Image Processor title: Main Classes sections: isExpanded: false sections: local: model_doc/albert title: ALBERT local: model_doc/bart title: BART local: model_doc/barthez title: BARThez local: model_doc/bartpho title: BARTpho local: model_doc/bert title: BERT local: model_doc/bert-generation title: BertGeneration local: model_doc/bert-japanese title: BertJapanese local: model_doc/bertweet title: Bertweet local: model_doc/big_bird title: BigBird local: model_doc/bigbird_pegasus title: BigBirdPegasus local: model_doc/biogpt title: BioGpt local: model_doc/blenderbot title: Blenderbot local: model_doc/blenderbot-small title: Blenderbot Small local: model_doc/bloom title: BLOOM local: model_doc/bort title: BORT local: model_doc/byt5 title: ByT5 local: model_doc/camembert title: CamemBERT local: model_doc/canine title: CANINE local: model_doc/codegen title: CodeGen local: model_doc/convbert title: ConvBERT local: model_doc/cpm title: CPM local: model_doc/cpmant title: CPMANT local: model_doc/ctrl title: CTRL local: model_doc/deberta title: DeBERTa local: model_doc/deberta-v2 title: DeBERTa-v2 local: model_doc/dialogpt title: DialoGPT local: model_doc/distilbert title: DistilBERT local: model_doc/dpr title: DPR local: model_doc/electra title: ELECTRA local: model_doc/encoder-decoder title: Encoder Decoder Models local: model_doc/ernie title: ERNIE local: model_doc/ernie_m title: ErnieM local: model_doc/esm title: ESM local: model_doc/flan-t5 title: FLAN-T5 local: model_doc/flan-ul2 title: FLAN-UL2 local: model_doc/flaubert title: FlauBERT local: model_doc/fnet title: FNet local: model_doc/fsmt title: FSMT local: model_doc/funnel title: Funnel Transformer local: model_doc/openai-gpt title: GPT local: model_doc/gpt_neo title: GPT Neo local: model_doc/gpt_neox title: GPT NeoX local: model_doc/gpt_neox_japanese title: GPT NeoX Japanese local: model_doc/gptj title: GPT-J local: model_doc/gpt2 title: GPT2 local: model_doc/gpt_bigcode title: GPTBigCode local: model_doc/gptsan-japanese title: GPTSAN Japanese local: model_doc/gpt-sw3 title: GPTSw3 local: model_doc/herbert title: HerBERT local: model_doc/ibert title: I-BERT local: model_doc/jukebox title: Jukebox local: model_doc/led title: LED local: model_doc/llama title: LLaMA local: model_doc/llama2 title: Llama2 local: model_doc/longformer title: Longformer local: model_doc/longt5 title: LongT5 local: model_doc/luke title: LUKE local: model_doc/m2m_100 title: M2M100 local: model_doc/marian title: MarianMT local: model_doc/markuplm title: MarkupLM local: model_doc/mbart title: MBart and MBart-50 local: model_doc/mega title: MEGA local: model_doc/megatron-bert title: MegatronBERT local: model_doc/megatron_gpt2 title: MegatronGPT2 local: model_doc/mluke title: mLUKE local: model_doc/mobilebert title: MobileBERT local: model_doc/mpnet title: MPNet local: model_doc/mra title: MRA local: model_doc/mt5 title: MT5 local: model_doc/mvp title: MVP local: model_doc/nezha title: NEZHA local: model_doc/nllb title: NLLB local: model_doc/nllb-moe title: NLLB-MoE local: model_doc/nystromformer title: Nyströmformer local: model_doc/open-llama title: Open-Llama local: model_doc/opt title: OPT local: model_doc/pegasus title: Pegasus local: model_doc/pegasus_x title: PEGASUS-X local: model_doc/phobert title: PhoBERT local: model_doc/plbart title: PLBart local: model_doc/prophetnet title: ProphetNet local: model_doc/qdqbert title: QDQBert local: model_doc/rag title: RAG local: model_doc/realm title: REALM local: model_doc/reformer title: Reformer local: model_doc/rembert title: RemBERT local: model_doc/retribert title: RetriBERT local: model_doc/roberta title: RoBERTa local: model_doc/roberta-prelayernorm title: RoBERTa-PreLayerNorm local: model_doc/roc_bert title: RoCBert local: model_doc/roformer title: RoFormer local: model_doc/rwkv title: RWKV local: model_doc/splinter title: Splinter local: model_doc/squeezebert title: SqueezeBERT local: model_doc/switch_transformers title: SwitchTransformers local: model_doc/t5 title: T5 local: model_doc/t5v1.1 title: T5v1.1 local: model_doc/tapex title: TAPEX local: model_doc/transfo-xl title: Transformer XL local: model_doc/ul2 title: UL2 local: model_doc/umt5 title: UMT5 local: model_doc/xmod title: X-MOD local: model_doc/xglm title: XGLM local: model_doc/xlm title: XLM local: model_doc/xlm-prophetnet title: XLM-ProphetNet local: model_doc/xlm-roberta title: XLM-RoBERTa local: model_doc/xlm-roberta-xl title: XLM-RoBERTa-XL local: model_doc/xlm-v title: XLM-V local: model_doc/xlnet title: XLNet local: model_doc/yoso title: YOSO title: Text models isExpanded: false sections: local: model_doc/beit title: BEiT local: model_doc/bit title: BiT local: model_doc/conditional_detr title: Conditional DETR local: model_doc/convnext title: ConvNeXT local: model_doc/convnextv2 title: ConvNeXTV2 local: model_doc/cvt title: CvT local: model_doc/deformable_detr title: Deformable DETR local: model_doc/deit title: DeiT local: model_doc/deta title: DETA local: model_doc/detr title: DETR local: model_doc/dinat title: DiNAT local: model_doc/dinov2 title: DINO V2 local: model_doc/dit title: DiT local: model_doc/dpt title: DPT local: model_doc/efficientformer title: EfficientFormer local: model_doc/efficientnet title: EfficientNet local: model_doc/focalnet title: FocalNet local: model_doc/glpn title: GLPN local: model_doc/imagegpt title: ImageGPT local: model_doc/levit title: LeViT local: model_doc/mask2former title: Mask2Former local: model_doc/maskformer title: MaskFormer local: model_doc/mobilenet_v1 title: MobileNetV1 local: model_doc/mobilenet_v2 title: MobileNetV2 local: model_doc/mobilevit title: MobileViT local: model_doc/mobilevitv2 title: MobileViTV2 local: model_doc/nat title: NAT local: model_doc/poolformer title: PoolFormer local: model_doc/pvt title: Pyramid Vision Transformer (PVT) local: model_doc/regnet title: RegNet local: model_doc/resnet title: ResNet local: model_doc/segformer title: SegFormer local: model_doc/swiftformer title: SwiftFormer local: model_doc/swin title: Swin Transformer local: model_doc/swinv2 title: Swin Transformer V2 local: model_doc/swin2sr title: Swin2SR local: model_doc/table-transformer title: Table Transformer local: model_doc/timesformer title: TimeSformer local: model_doc/upernet title: UperNet local: model_doc/van title: VAN local: model_doc/videomae title: VideoMAE local: model_doc/vit title: Vision Transformer (ViT) local: model_doc/vit_hybrid title: ViT Hybrid local: model_doc/vit_mae title: ViTMAE local: model_doc/vit_msn title: ViTMSN local: model_doc/vivit title: ViViT local: model_doc/yolos title: YOLOS title: Vision models isExpanded: false sections: local: model_doc/audio-spectrogram-transformer title: Audio Spectrogram Transformer local: model_doc/bark title: Bark local: model_doc/clap title: CLAP local: model_doc/encodec title: EnCodec local: model_doc/hubert title: Hubert local: model_doc/mctct title: MCTCT local: model_doc/mms title: MMS local: model_doc/musicgen title: MusicGen local: model_doc/sew title: SEW local: model_doc/sew-d title: SEW-D local: model_doc/speech_to_text title: Speech2Text local: model_doc/speech_to_text_2 title: Speech2Text2 local: model_doc/speecht5 title: SpeechT5 local: model_doc/unispeech title: UniSpeech local: model_doc/unispeech-sat title: UniSpeech-SAT local: model_doc/wav2vec2 title: Wav2Vec2 local: model_doc/wav2vec2-conformer title: Wav2Vec2-Conformer local: model_doc/wav2vec2_phoneme title: Wav2Vec2Phoneme local: model_doc/wavlm title: WavLM local: model_doc/whisper title: Whisper local: model_doc/xls_r title: XLS-R local: model_doc/xlsr_wav2vec2 title: XLSR-Wav2Vec2 title: Audio models isExpanded: false sections: local: model_doc/align title: ALIGN local: model_doc/altclip title: AltCLIP local: model_doc/blip title: BLIP local: model_doc/blip-2 title: BLIP-2 local: model_doc/bridgetower title: BridgeTower local: model_doc/chinese_clip title: Chinese-CLIP local: model_doc/clip title: CLIP local: model_doc/clipseg title: CLIPSeg local: model_doc/data2vec title: Data2Vec local: model_doc/deplot title: DePlot local: model_doc/donut title: Donut local: model_doc/flava title: FLAVA local: model_doc/git title: GIT local: model_doc/groupvit title: GroupViT local: model_doc/instructblip title: InstructBLIP local: model_doc/layoutlm title: LayoutLM local: model_doc/layoutlmv2 title: LayoutLMV2 local: model_doc/layoutlmv3 title: LayoutLMV3 local: model_doc/layoutxlm title: LayoutXLM local: model_doc/lilt title: LiLT local: model_doc/lxmert title: LXMERT local: model_doc/matcha title: MatCha local: model_doc/mgp-str title: MGP-STR local: model_doc/oneformer title: OneFormer local: model_doc/owlvit title: OWL-ViT local: model_doc/perceiver title: Perceiver local: model_doc/pix2struct title: Pix2Struct local: model_doc/sam title: Segment Anything local: model_doc/speech-encoder-decoder title: Speech Encoder Decoder Models local: model_doc/tapas title: TAPAS local: model_doc/trocr title: TrOCR local: model_doc/tvlt title: TVLT local: model_doc/vilt title: ViLT local: model_doc/vision-encoder-decoder title: Vision Encoder Decoder Models local: model_doc/vision-text-dual-encoder title: Vision Text Dual Encoder local: model_doc/visual_bert title: VisualBERT local: model_doc/xclip title: X-CLIP title: Multimodal models isExpanded: false sections: local: model_doc/decision_transformer title: Decision Transformer local: model_doc/trajectory_transformer title: Trajectory Transformer title: Reinforcement learning models isExpanded: false sections: local: model_doc/autoformer title: Autoformer local: model_doc/informer title: Informer local: model_doc/time_series_transformer title: Time Series Transformer title: Time series models isExpanded: false sections: local: model_doc/graphormer title: Graphormer title: Graph models title: Models sections: local: internal/modeling_utils title: Custom Layers and Utilities local: internal/pipelines_utils title: Utilities for pipelines 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Optimization The .optimization module provides: an optimizer with weight decay fixed that can be used to fine-tuned models, and several schedules in the form of schedule objects that inherit from _LRSchedule: a gradient accumulation class to accumulate the gradients of multiple batches AdamW (PyTorch) [[autodoc]] AdamW AdaFactor (PyTorch) [[autodoc]] Adafactor AdamWeightDecay (TensorFlow) [[autodoc]] AdamWeightDecay [[autodoc]] create_optimizer Schedules Learning Rate Schedules (Pytorch) [[autodoc]] SchedulerType [[autodoc]] get_scheduler [[autodoc]] get_constant_schedule [[autodoc]] get_constant_schedule_with_warmup [[autodoc]] get_cosine_schedule_with_warmup [[autodoc]] get_cosine_with_hard_restarts_schedule_with_warmup [[autodoc]] get_linear_schedule_with_warmup [[autodoc]] get_polynomial_decay_schedule_with_warmup [[autodoc]] get_inverse_sqrt_schedule Warmup (TensorFlow) [[autodoc]] WarmUp Gradient Strategies GradientAccumulator (TensorFlow) [[autodoc]] GradientAccumulator

Before you begin, make sure you have all the necessary libraries installed: pip install transformers datasets evaluate We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in: from huggingface_hub import notebook_login notebook_login() Load Food-101 dataset Start by loading a smaller subset of the Food-101 dataset from the 🤗 Datasets library. This will give you a chance to experiment and make sure everything works before spending more time training on the full dataset. from datasets import load_dataset food = load_dataset("food101", split="train[:5000]") Split the dataset's train split into a train and test set with the [~datasets.Dataset.train_test_split] method: food = food.train_test_split(test_size=0.2) Then take a look at an example: food["train"][0] {'image': , 'label': 79} Each example in the dataset has two fields: image: a PIL image of the food item label: the label class of the food item To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name to an integer and vice versa: labels = food["train"].features["label"].names label2id, id2label = dict(), dict() for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label Now you can convert the label id to a label name: id2label[str(79)] 'prime_rib' Preprocess The next step is to load a ViT image processor to process the image into a tensor: from transformers import AutoImageProcessor checkpoint = "google/vit-base-patch16-224-in21k" image_processor = AutoImageProcessor.from_pretrained(checkpoint) Apply some image transformations to the images to make the model more robust against overfitting. Here you'll use torchvision's transforms module, but you can also use any image library you like. Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation: from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std) size = ( image_processor.size["shortest_edge"] if "shortest_edge" in image_processor.size else (image_processor.size["height"], image_processor.size["width"]) ) _transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize]) Then create a preprocessing function to apply the transforms and return the pixel_values - the inputs to the model - of the image: def transforms(examples): examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]] del examples["image"] return examples To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.with_transform] method. The transforms are applied on the fly when you load an element of the dataset: food = food.with_transform(transforms) Now create a batch of examples using [DefaultDataCollator]. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing such as padding. from transformers import DefaultDataCollator data_collator = DefaultDataCollator() To avoid overfitting and to make the model more robust, add some data augmentation to the training part of the dataset. Here we use Keras preprocessing layers to define the transformations for the training data (includes data augmentation), and transformations for the validation data (only center cropping, resizing and normalizing). You can use tf.imageor any other library you prefer. from tensorflow import keras from tensorflow.keras import layers size = (image_processor.size["height"], image_processor.size["width"]) train_data_augmentation = keras.Sequential( [ layers.RandomCrop(size[0], size[1]), layers.Rescaling(scale=1.0 / 127.5, offset=-1), layers.RandomFlip("horizontal"), layers.RandomRotation(factor=0.02), layers.RandomZoom(height_factor=0.2, width_factor=0.2), ], name="train_data_augmentation", ) val_data_augmentation = keras.Sequential( [ layers.CenterCrop(size[0], size[1]), layers.Rescaling(scale=1.0 / 127.5, offset=-1), ], name="val_data_augmentation", ) Next, create functions to apply appropriate transformations to a batch of images, instead of one image at a time. import numpy as np import tensorflow as tf from PIL import Image def convert_to_tf_tensor(image: Image): np_image = np.array(image) tf_image = tf.convert_to_tensor(np_image) # expand_dims() is used to add a batch dimension since # the TF augmentation layers operates on batched inputs. return tf.expand_dims(tf_image, 0) def preprocess_train(example_batch): """Apply train_transforms across a batch.""" images = [ train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"] ] example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images] return example_batch def preprocess_val(example_batch): """Apply val_transforms across a batch.""" images = [ val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"] ] example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images] return example_batch Use 🤗 Datasets [~datasets.Dataset.set_transform] to apply the transformations on the fly: py food["train"].set_transform(preprocess_train) food["test"].set_transform(preprocess_val) As a final preprocessing step, create a batch of examples using DefaultDataCollator. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing, such as padding. from transformers import DefaultDataCollator data_collator = DefaultDataCollator(return_tensors="tf") Evaluate Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric): import evaluate accuracy = evaluate.load("accuracy") Then create a function that passes your predictions and labels to [~evaluate.EvaluationModule.compute] to calculate the accuracy: import numpy as np def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) return accuracy.compute(predictions=predictions, references=labels) Your compute_metrics function is ready to go now, and you'll return to it when you set up your training. Train If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here! You're ready to start training your model now! Load ViT with [AutoModelForImageClassification]. Specify the number of labels along with the number of expected labels, and the label mappings: from transformers import AutoModelForImageClassification, TrainingArguments, Trainer model = AutoModelForImageClassification.from_pretrained( checkpoint, num_labels=len(labels), id2label=id2label, label2id=label2id, ) At this point, only three steps remain: Define your training hyperparameters in [TrainingArguments]. It is important you don't remove unused columns because that'll drop the image column. Without the image column, you can't create pixel_values. Set remove_unused_columns=False to prevent this behavior! The only other required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer] will evaluate the accuracy and save the training checkpoint. Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function. Call [~Trainer.train] to finetune your model. training_args = TrainingArguments( output_dir="my_awesome_food_model", remove_unused_columns=False, evaluation_strategy="epoch", save_strategy="epoch", learning_rate=5e-5, per_device_train_batch_size=16, gradient_accumulation_steps=4, per_device_eval_batch_size=16, num_train_epochs=3, warmup_ratio=0.1, logging_steps=10, load_best_model_at_end=True, metric_for_best_model="accuracy", push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=food["train"], eval_dataset=food["test"], tokenizer=image_processor, compute_metrics=compute_metrics, ) trainer.train() Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model: trainer.push_to_hub() If you are unfamiliar with fine-tuning a model with Keras, check out the basic tutorial first! To fine-tune a model in TensorFlow, follow these steps: 1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule. 2. Instantiate a pre-trained model. 3. Convert a 🤗 Dataset to a tf.data.Dataset. 4. Compile your model. 5. Add callbacks and use the fit() method to run the training. 6. Upload your model to 🤗 Hub to share with the community. Start by defining the hyperparameters, optimizer and learning rate schedule: from transformers import create_optimizer batch_size = 16 num_epochs = 5 num_train_steps = len(food["train"]) * num_epochs learning_rate = 3e-5 weight_decay_rate = 0.01 optimizer, lr_schedule = create_optimizer( init_lr=learning_rate, num_train_steps=num_train_steps, weight_decay_rate=weight_decay_rate, num_warmup_steps=0, ) Then, load ViT with [TFAutoModelForImageClassification] along with the label mappings: from transformers import TFAutoModelForImageClassification model = TFAutoModelForImageClassification.from_pretrained( checkpoint, id2label=id2label, label2id=label2id, ) Convert your datasets to the tf.data.Dataset format using the [~datasets.Dataset.to_tf_dataset] and your data_collator: converting our train dataset to tf.data.Dataset tf_train_dataset = food["train"].to_tf_dataset( columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator ) converting our test dataset to tf.data.Dataset tf_eval_dataset = food["test"].to_tf_dataset( columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator ) Configure the model for training with compile(): from tensorflow.keras.losses import SparseCategoricalCrossentropy loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer=optimizer, loss=loss) To compute the accuracy from the predictions and push your model to the 🤗 Hub, use Keras callbacks. Pass your compute_metrics function to KerasMetricCallback, and use the PushToHubCallback to upload the model: from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset) push_to_hub_callback = PushToHubCallback( output_dir="food_classifier", tokenizer=image_processor, save_strategy="no", ) callbacks = [metric_callback, push_to_hub_callback] Finally, you are ready to train your model! Call fit() with your training and validation datasets, the number of epochs, and your callbacks to fine-tune the model: model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks) Epoch 1/5 250/250 [==============================] - 313s 1s/step - loss: 2.5623 - val_loss: 1.4161 - accuracy: 0.9290 Epoch 2/5 250/250 [==============================] - 265s 1s/step - loss: 0.9181 - val_loss: 0.6808 - accuracy: 0.9690 Epoch 3/5 250/250 [==============================] - 252s 1s/step - loss: 0.3910 - val_loss: 0.4303 - accuracy: 0.9820 Epoch 4/5 250/250 [==============================] - 251s 1s/step - loss: 0.2028 - val_loss: 0.3191 - accuracy: 0.9900 Epoch 5/5 250/250 [==============================] - 238s 949ms/step - loss: 0.1232 - val_loss: 0.3259 - accuracy: 0.9890 Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. You can now use it for inference! For a more in-depth example of how to finetune a model for image classification, take a look at the corresponding PyTorch notebook. Inference Great, now that you've fine-tuned a model, you can use it for inference! Load an image you'd like to run inference on: ds = load_dataset("food101", split="validation[:10]") image = ds["image"][0] The simplest way to try out your finetuned model for inference is to use it in a [pipeline]. Instantiate a pipeline for image classification with your model, and pass your image to it: from transformers import pipeline classifier = pipeline("image-classification", model="my_awesome_food_model") classifier(image) [{'score': 0.31856709718704224, 'label': 'beignets'}, {'score': 0.015232225880026817, 'label': 'bruschetta'}, {'score': 0.01519392803311348, 'label': 'chicken_wings'}, {'score': 0.013022331520915031, 'label': 'pork_chop'}, {'score': 0.012728818692266941, 'label': 'prime_rib'}] You can also manually replicate the results of the pipeline if you'd like: Load an image processor to preprocess the image and return the input as PyTorch tensors: from transformers import AutoImageProcessor import torch image_processor = AutoImageProcessor.from_pretrained("my_awesome_food_model") inputs = image_processor(image, return_tensors="pt") Pass your inputs to the model and return the logits: from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("my_awesome_food_model") with torch.no_grad(): logits = model(**inputs).logits Get the predicted label with the highest probability, and use the model's id2label mapping to convert it to a label: predicted_label = logits.argmax(-1).item() model.config.id2label[predicted_label] 'beignets' Load an image processor to preprocess the image and return the input as TensorFlow tensors: from transformers import AutoImageProcessor image_processor = AutoImageProcessor.from_pretrained("MariaK/food_classifier") inputs = image_processor(image, return_tensors="tf") Pass your inputs to the model and return the logits: from transformers import TFAutoModelForImageClassification model = TFAutoModelForImageClassification.from_pretrained("MariaK/food_classifier") logits = model(**inputs).logits Get the predicted label with the highest probability, and use the model's id2label mapping to convert it to a label: predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0]) model.config.id2label[predicted_class_id] 'beignets'

Keras callbacks When training a Transformers model with Keras, there are some library-specific callbacks available to automate common tasks: KerasMetricCallback [[autodoc]] KerasMetricCallback PushToHubCallback [[autodoc]] PushToHubCallback

XLA Integration for TensorFlow Models [[open-in-colab]] Accelerated Linear Algebra, dubbed XLA, is a compiler for accelerating the runtime of TensorFlow Models. From the official documentation: XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. Using XLA in TensorFlow is simple – it comes packaged inside the tensorflow library, and it can be triggered with the jit_compile argument in any graph-creating function such as tf.function. When using Keras methods like fit() and predict(), you can enable XLA simply by passing the jit_compile argument to model.compile(). However, XLA is not limited to these methods - it can also be used to accelerate any arbitrary tf.function. Several TensorFlow methods in 🤗 Transformers have been rewritten to be XLA-compatible, including text generation for models such as GPT2, T5 and OPT, as well as speech processing for models such as Whisper. While the exact amount of speed-up is very much model-dependent, for TensorFlow text generation models inside 🤗 Transformers, we noticed a speed-up of ~100x. This document will explain how you can use XLA for these models to get the maximum amount of performance. We’ll also provide links to additional resources if you’re interested to learn more about the benchmarks and our design philosophy behind the XLA integration. Running TF functions with XLA Let us consider the following model in TensorFlow: import tensorflow as tf model = tf.keras.Sequential( [tf.keras.layers.Dense(10, input_shape=(10,), activation="relu"), tf.keras.layers.Dense(5, activation="softmax")] ) The above model accepts inputs having a dimension of (10, ). We can use the model for running a forward pass like so: Generate random inputs for the model. batch_size = 16 input_vector_dim = 10 random_inputs = tf.random.normal((batch_size, input_vector_dim)) Run a forward pass. _ = model(random_inputs) In order to run the forward pass with an XLA-compiled function, we’d need to do: py xla_fn = tf.function(model, jit_compile=True) _ = xla_fn(random_inputs) The default call() function of the model is used for compiling the XLA graph. But if there’s any other model function you want to compile into XLA that’s also possible with: py my_xla_fn = tf.function(model.my_xla_fn, jit_compile=True) Running a TF text generation model with XLA from 🤗 Transformers To enable XLA-accelerated generation within 🤗 Transformers, you need to have a recent version of transformers installed. You can install it by running: pip install transformers --upgrade And then you can run the following code: import tensorflow as tf from transformers import AutoTokenizer, TFAutoModelForCausalLM Will error if the minimal version of Transformers is not installed. from transformers.utils import check_min_version check_min_version("4.21.0") tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="") model = TFAutoModelForCausalLM.from_pretrained("gpt2") input_string = ["TensorFlow is"] One line to create an XLA generation function xla_generate = tf.function(model.generate, jit_compile=True) tokenized_input = tokenizer(input_string, return_tensors="tf") generated_tokens = xla_generate(**tokenized_input, num_beams=2) decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) print(f"Generated -- {decoded_text}") Generated -- TensorFlow is an open-source, open-source, distributed-source application # framework for the As you can notice, enabling XLA on generate() is just a single line of code. The rest of the code remains unchanged. However, there are a couple of gotchas in the above code snippet that are specific to XLA. You need to be aware of those to realize the speed-ups that XLA can bring in. We discuss these in the following section. Gotchas to be aware of When you are executing an XLA-enabled function (like xla_generate() above) for the first time, it will internally try to infer the computation graph, which is time-consuming. This process is known as “tracing”. You might notice that the generation time is not fast. Successive calls of xla_generate() (or any other XLA-enabled function) won’t have to infer the computation graph, given the inputs to the function follow the same shape with which the computation graph was initially built. While this is not a problem for modalities with fixed input shapes (e.g., images), you must pay attention if you are working with variable input shape modalities (e.g., text). To ensure xla_generate() always operates with the same input shapes, you can specify the padding arguments when calling the tokenizer. import tensorflow as tf from transformers import AutoTokenizer, TFAutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="") model = TFAutoModelForCausalLM.from_pretrained("gpt2") input_string = ["TensorFlow is"] xla_generate = tf.function(model.generate, jit_compile=True) Here, we call the tokenizer with padding options. tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf") generated_tokens = xla_generate(**tokenized_input, num_beams=2) decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) print(f"Generated -- {decoded_text}") This way, you can ensure that the inputs to xla_generate() will always receive inputs with the shape it was traced with and thus leading to speed-ups in the generation time. You can verify this with the code below: import time import tensorflow as tf from transformers import AutoTokenizer, TFAutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="") model = TFAutoModelForCausalLM.from_pretrained("gpt2") xla_generate = tf.function(model.generate, jit_compile=True) for input_string in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]: tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf") start = time.time_ns() generated_tokens = xla_generate(**tokenized_input, num_beams=2) end = time.time_ns() print(f"Execution time -- {(end - start) / 1e6:.1f} ms\n") On a Tesla T4 GPU, you can expect the outputs like so: ```bash Execution time -- 30819.6 ms Execution time -- 79.0 ms Execution time -- 78.9 ms `` The first call toxla_generate()` is time-consuming because of tracing, but the successive calls are orders of magnitude faster. Keep in mind that any change in the generation options at any point with trigger re-tracing and thus leading to slow-downs in the generation time. We didn’t cover all the text generation options 🤗 Transformers provides in this document. We encourage you to read the documentation for advanced use cases. Additional Resources Here, we leave you with some additional resources if you want to delve deeper into XLA in 🤗 Transformers and in general. This Colab Notebook provides an interactive demonstration if you want to fiddle with the XLA-compatible encoder-decoder (like T5) and decoder-only (like GPT2) text generation models. This blog post provides an overview of the comparison benchmarks for XLA-compatible models along with a friendly introduction to XLA in TensorFlow. This blog post discusses our design philosophy behind adding XLA support to the TensorFlow models in 🤗 Transformers. Recommended posts for learning more about XLA and TensorFlow graphs in general: XLA: Optimizing Compiler for Machine Learning Introduction to graphs and tf.function Better performance with tf.function

Data Collator Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of the same type as the elements of train_dataset or eval_dataset. To be able to build batches, data collators may apply some processing (like padding). Some of them (like [DataCollatorForLanguageModeling]) also apply some random data augmentation (like random masking) on the formed batch. Examples of use can be found in the example scripts or example notebooks. Default data collator [[autodoc]] data.data_collator.default_data_collator DefaultDataCollator [[autodoc]] data.data_collator.DefaultDataCollator DataCollatorWithPadding [[autodoc]] data.data_collator.DataCollatorWithPadding DataCollatorForTokenClassification [[autodoc]] data.data_collator.DataCollatorForTokenClassification DataCollatorForSeq2Seq [[autodoc]] data.data_collator.DataCollatorForSeq2Seq DataCollatorForLanguageModeling [[autodoc]] data.data_collator.DataCollatorForLanguageModeling - numpy_mask_tokens - tf_mask_tokens - torch_mask_tokens DataCollatorForWholeWordMask [[autodoc]] data.data_collator.DataCollatorForWholeWordMask - numpy_mask_tokens - tf_mask_tokens - torch_mask_tokens DataCollatorForPermutationLanguageModeling [[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling - numpy_mask_tokens - tf_mask_tokens - torch_mask_tokens

Using pipelines for a webserver Creating an inference engine is a complex topic, and the "best" solution will most likely depend on your problem space. Are you on CPU or GPU? Do you want the lowest latency, the highest throughput, support for many models, or just highly optimize 1 specific model? There are many ways to tackle this topic, so what we are going to present is a good default to get started which may not necessarily be the most optimal solution for you. The key thing to understand is that we can use an iterator, just like you would on a dataset, since a webserver is basically a system that waits for requests and treats them as they come in. Usually webservers are multiplexed (multithreaded, async, etc..) to handle various requests concurrently. Pipelines on the other hand (and mostly the underlying models) are not really great for parallelism; they take up a lot of RAM, so it's best to give them all the available resources when they are running or it's a compute-intensive job. We are going to solve that by having the webserver handle the light load of receiving and sending requests, and having a single thread handling the actual work. This example is going to use starlette. The actual framework is not really important, but you might have to tune or change the code if you are using another one to achieve the same effect. Create server.py: from starlette.applications import Starlette from starlette.responses import JSONResponse from starlette.routing import Route from transformers import pipeline import asyncio async def homepage(request): payload = await request.body() string = payload.decode("utf-8") response_q = asyncio.Queue() await request.app.model_queue.put((string, response_q)) output = await response_q.get() return JSONResponse(output) async def server_loop(q): pipe = pipeline(model="bert-base-uncased") while True: (string, response_q) = await q.get() out = pipe(string) await response_q.put(out) app = Starlette( routes=[ Route("/", homepage, methods=["POST"]), ], ) @app.on_event("startup") async def startup_event(): q = asyncio.Queue() app.model_queue = q asyncio.create_task(server_loop(q)) Now you can start it with: uvicorn server:app And you can query it: ```bash curl -X POST -d "test [MASK]" http://localhost:8000/ [{"score":0.7742936015129089,"token":1012,"token_str":".","sequence":"test."},] And there you go, now you have a good idea of how to create a webserver! What is really important is that we load the model only once, so there are no copies of the model on the webserver. This way, no unnecessary RAM is being used. Then the queuing mechanism allows you to do fancy stuff like maybe accumulating a few items before inferring to use dynamic batching: py (string, rq) = await q.get() strings = [] queues = [] while True: try: (string, rq) = await asyncio.wait_for(q.get(), timeout=0.001) # 1ms except asyncio.exceptions.TimeoutError: break strings.append(string) queues.append(rq) strings outs = pipe(strings, batch_size=len(strings)) for rq, out in zip(queues, outs): await rq.put(out) Do not activate this without checking it makes sense for your load! The proposed code is optimized for readability, not for being the best code. First of all, there's no batch size limit which is usually not a great idea. Next, the timeout is reset on every queue fetch, meaning you could wait much more than 1ms before running the inference (delaying the first request by that much). It would be better to have a single 1ms deadline. This will always wait for 1ms even if the queue is empty, which might not be the best since you probably want to start doing inference if there's nothing in the queue. But maybe it does make sense if batching is really crucial for your use case. Again, there's really no one best solution. Few things you might want to consider Error checking There's a lot that can go wrong in production: out of memory, out of space, loading the model might fail, the query might be wrong, the query might be correct but still fail to run because of a model misconfiguration, and so on. Generally, it's good if the server outputs the errors to the user, so adding a lot of try..except statements to show those errors is a good idea. But keep in mind it may also be a security risk to reveal all those errors depending on your security context. Circuit breaking Webservers usually look better when they do circuit breaking. It means they return proper errors when they're overloaded instead of just waiting for the query indefinitely. Return a 503 error instead of waiting for a super long time or a 504 after a long time. This is relatively easy to implement in the proposed code since there is a single queue. Looking at the queue size is a basic way to start returning errors before your webserver fails under load. Blocking the main thread Currently PyTorch is not async aware, and computation will block the main thread while running. That means it would be better if PyTorch was forced to run on its own thread/process. This wasn't done here because the code is a lot more complex (mostly because threads and async and queues don't play nice together). But ultimately it does the same thing. This would be important if the inference of single items were long (> 1s) because in this case, it means every query during inference would have to wait for 1s before even receiving an error. Dynamic batching In general, batching is not necessarily an improvement over passing 1 item at a time (see batching details for more information). But it can be very effective when used in the correct setting. In the API, there is no dynamic batching by default (too much opportunity for a slowdown). But for BLOOM inference - which is a very large model - dynamic batching is essential to provide a decent experience for everyone.

Glossary This glossary defines general machine learning and 🤗 Transformers terms to help you better understand the documentation. A attention mask The attention mask is an optional argument used when batching sequences together. This argument indicates to the model which tokens should be attended to, and which should not. For example, consider these two sequences: thon from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-cased") sequence_a = "This is a short sequence." sequence_b = "This is a rather long sequence. It is at least longer than the sequence A." encoded_sequence_a = tokenizer(sequence_a)["input_ids"] encoded_sequence_b = tokenizer(sequence_b)["input_ids"] The encoded versions have different lengths: thon len(encoded_sequence_a), len(encoded_sequence_b) (8, 19) Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length of the second one, or the second one needs to be truncated down to the length of the first one. In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask it to pad like this: thon padded_sequences = tokenizer([sequence_a, sequence_b], padding=True) We can see that 0s have been added on the right of the first sentence to make it the same length as the second one: thon padded_sequences["input_ids"] [[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]] This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the position of the padded indices so that the model does not attend to them. For the [BertTokenizer], 1 indicates a value that should be attended to, while 0 indicates a padded value. This attention mask is in the dictionary returned by the tokenizer under the key "attention_mask": thon padded_sequences["attention_mask"] [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] autoencoding models See encoder models and masked language modeling autoregressive models See causal language modeling and decoder models B backbone The backbone is the network (embeddings and layers) that outputs the raw hidden states or features. It is usually connected to a head which accepts the features as its input to make a prediction. For example, [ViTModel] is a backbone without a specific head on top. Other models can also use [VitModel] as a backbone such as DPT. C causal language modeling A pretraining task where the model reads the texts in order and has to predict the next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future tokens at a certain timestep. channel Color images are made up of some combination of values in three channels - red, green, and blue (RGB) - and grayscale images only have one channel. In 🤗 Transformers, the channel can be the first or last dimension of an image's tensor: [n_channels, height, width] or [height, width, n_channels]. connectionist temporal classification (CTC) An algorithm which allows a model to learn without knowing exactly how the input and output are aligned; CTC calculates the distribution of all possible outputs for a given input and chooses the most likely output from it. CTC is commonly used in speech recognition tasks because speech doesn't always cleanly align with the transcript for a variety of reasons such as a speaker's different speech rates. convolution A type of layer in a neural network where the input matrix is multiplied element-wise by a smaller matrix (kernel or filter) and the values are summed up in a new matrix. This is known as a convolutional operation which is repeated over the entire input matrix. Each operation is applied to a different segment of the input matrix. Convolutional neural networks (CNNs) are commonly used in computer vision. D decoder input IDs This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a way specific to each model. Most encoder-decoder models (BART, T5) create their decoder_input_ids on their own from the labels. In such models, passing the labels is the preferred way to handle training. Please check each model's docs to see how they handle these input IDs for sequence to sequence training. decoder models Also referred to as autoregressive models, decoder models involve a pretraining task (called causal language modeling) where the model reads the texts in order and has to predict the next word. It's usually done by reading the whole sentence with a mask to hide future tokens at a certain timestep. deep learning (DL) Machine learning algorithms which uses neural networks with several layers. E encoder models Also known as autoencoding models, encoder models take an input (such as text or images) and transform them into a condensed numerical representation called an embedding. Oftentimes, encoder models are pretrained using techniques like masked language modeling, which masks parts of the input sequence and forces the model to create more meaningful representations. F feature extraction The process of selecting and transforming raw data into a set of features that are more informative and useful for machine learning algorithms. Some examples of feature extraction include transforming raw text into word embeddings and extracting important features such as edges or shapes from image/video data. feed forward chunking In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers. The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for bert-base-uncased). For an input of size [batch_size, sequence_length], the memory required to store the intermediate feed forward embeddings [batch_size, sequence_length, config.intermediate_size] can account for a large fraction of the memory use. The authors of Reformer: The Efficient Transformer noticed that since the computation is independent of the sequence_length dimension, it is mathematically equivalent to compute the output embeddings of both feed forward layers [batch_size, config.hidden_size]_0, , [batch_size, config.hidden_size]_n individually and concat them afterward to [batch_size, sequence_length, config.hidden_size] with n = sequence_length, which trades increased computation time against reduced memory use, but yields a mathematically equivalent result. For models employing the function [apply_chunking_to_forward], the chunk_size defines the number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time complexity. If chunk_size is set to 0, no feed forward chunking is done. finetuned models Finetuning is a form of transfer learning which involves taking a pretrained model, freezing its weights, and replacing the output layer with a newly added model head. The model head is trained on your target dataset. See the Fine-tune a pretrained model tutorial for more details, and learn how to fine-tune models with 🤗 Transformers. H head The model head refers to the last layer of a neural network that accepts the raw hidden states and projects them onto a different dimension. There is a different model head for each task. For example: [GPT2ForSequenceClassification] is a sequence classification head - a linear layer - on top of the base [GPT2Model]. [ViTForImageClassification] is an image classification head - a linear layer on top of the final hidden state of the CLS token - on top of the base [ViTModel]. [Wav2Vec2ForCTC] ia a language modeling head with CTC on top of the base [Wav2Vec2Model]. I image patch Vision-based Transformers models split an image into smaller patches which are linearly embedded, and then passed as a sequence to the model. You can find the patch_size - or resolution - of the model in it's configuration. inference Inference is the process of evaluating a model on new data after training is complete. See the Pipeline for inference tutorial to learn how to perform inference with 🤗 Transformers. input IDs The input ids are often the only required parameters to be passed to the model as input. They are token indices, numerical representations of tokens building the sequences that will be used as input by the model. Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT tokenizer, which is a WordPiece tokenizer: thon from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-cased") sequence = "A Titan RTX has 24GB of VRAM" The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary. thon tokenized_sequence = tokenizer.tokenize(sequence) The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix is added for "RA" and "M": thon print(tokenized_sequence) ['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M'] These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding the sentence to the tokenizer, which leverages the Rust implementation of 🤗 Tokenizers for peak performance. thon inputs = tokenizer(sequence) The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The token indices are under the key input_ids: thon encoded_sequence = inputs["input_ids"] print(encoded_sequence) [101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102] Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special IDs the model sometimes uses. If we decode the previous sequence of ids, thon decoded_sequence = tokenizer.decode(encoded_sequence) we will see thon print(decoded_sequence) [CLS] A Titan RTX has 24GB of VRAM [SEP] because this is the way a [BertModel] is going to expect its inputs. L labels The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its predictions and the expected value (the label). These labels are different according to the model head, for example: For sequence classification models, ([BertForSequenceClassification]), the model expects a tensor of dimension (batch_size) with each value of the batch corresponding to the expected label of the entire sequence. For token classification models, ([BertForTokenClassification]), the model expects a tensor of dimension (batch_size, seq_length) with each value corresponding to the expected label of each individual token. For masked language modeling, ([BertForMaskedLM]), the model expects a tensor of dimension (batch_size, seq_length) with each value corresponding to the expected label of each individual token: the labels being the token ID for the masked token, and values to be ignored for the rest (usually -100). For sequence to sequence tasks, ([BartForConditionalGeneration], [MBartForConditionalGeneration]), the model expects a tensor of dimension (batch_size, tgt_seq_length) with each value corresponding to the target sequences associated with each input sequence. During training, both BART and T5 will make the appropriate decoder_input_ids and decoder attention masks internally. They usually do not need to be supplied. This does not apply to models leveraging the Encoder-Decoder framework. For image classification models, ([ViTForImageClassification]), the model expects a tensor of dimension (batch_size) with each value of the batch corresponding to the expected label of each individual image. For semantic segmentation models, ([SegformerForSemanticSegmentation]), the model expects a tensor of dimension (batch_size, height, width) with each value of the batch corresponding to the expected label of each individual pixel. For object detection models, ([DetrForObjectDetection]), the model expects a list of dictionaries with a class_labels and boxes key where each value of the batch corresponds to the expected label and number of bounding boxes of each individual image. For automatic speech recognition models, ([Wav2Vec2ForCTC]), the model expects a tensor of dimension (batch_size, target_length) with each value corresponding to the expected label of each individual token. Each model's labels may be different, so be sure to always check the documentation of each model for more information about their specific labels! The base models ([BertModel]) do not accept labels, as these are the base transformer models, simply outputting features. large language models (LLM) A generic term that refers to transformer language models (GPT-3, BLOOM, OPT) that were trained on a large quantity of data. These models also tend to have a large number of learnable parameters (e.g. 175 billion for GPT-3). M masked language modeling (MLM) A pretraining task where the model sees a corrupted version of the texts, usually done by masking some tokens randomly, and has to predict the original text. multimodal A task that combines texts with another kind of inputs (for instance images). N Natural language generation (NLG) All tasks related to generating text (for instance, Write With Transformers, translation). Natural language processing (NLP) A generic way to say "deal with texts". Natural language understanding (NLU) All tasks related to understanding what is in a text (for instance classifying the whole text, individual words). P pipeline A pipeline in 🤗 Transformers is an abstraction referring to a series of steps that are executed in a specific order to preprocess and transform data and return a prediction from a model. Some example stages found in a pipeline might be data preprocessing, feature extraction, and normalization. For more details, see Pipelines for inference. pixel values A tensor of the numerical representations of an image that is passed to a model. The pixel values have a shape of [batch_size, num_channels, height, width], and are generated from an image processor. pooling An operation that reduces a matrix into a smaller matrix, either by taking the maximum or average of the pooled dimension(s). Pooling layers are commonly found between convolutional layers to downsample the feature representation. position IDs Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of each token. Therefore, the position IDs (position_ids) are used by the model to identify each token's position in the list of tokens. They are an optional parameter. If no position_ids are passed to the model, the IDs are automatically created as absolute positional embeddings. Absolute positional embeddings are selected in the range [0, config.max_position_embeddings - 1]. Some models use other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings. preprocessing The task of preparing raw data into a format that can be easily consumed by machine learning models. For example, text is typically preprocessed by tokenization. To gain a better idea of what preprocessing looks like for other input types, check out the Preprocess tutorial. pretrained model A model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods involve a self-supervised objective, which can be reading the text and trying to predict the next word (see causal language modeling) or masking some words and trying to predict them (see masked language modeling). Speech and vision models have their own pretraining objectives. For example, Wav2Vec2 is a speech model pretrained on a contrastive task which requires the model to identify the "true" speech representation from a set of "false" speech representations. On the other hand, BEiT is a vision model pretrained on a masked image modeling task which masks some of the image patches and requires the model to predict the masked patches (similar to the masked language modeling objective). R recurrent neural network (RNN) A type of model that uses a loop over a layer to process texts. representation learning A subfield of machine learning which focuses on learning meaningful representations of raw data. Some examples of representation learning techniques include word embeddings, autoencoders, and Generative Adversarial Networks (GANs). S sampling rate A measurement in hertz of the number of samples (the audio signal) taken per second. The sampling rate is a result of discretizing a continuous signal such as speech. self-attention Each element of the input finds out which other elements of the input they should attend to. self-supervised learning A category of machine learning techniques in which a model creates its own learning objective from unlabeled data. It differs from unsupervised learning and supervised learning in that the learning process is supervised, but not explicitly from the user. One example of self-supervised learning is masked language modeling, where a model is passed sentences with a proportion of its tokens removed and learns to predict the missing tokens. semi-supervised learning A broad category of machine learning training techniques that leverages a small amount of labeled data with a larger quantity of unlabeled data to improve the accuracy of a model, unlike supervised learning and unsupervised learning. An example of a semi-supervised learning approach is "self-training", in which a model is trained on labeled data, and then used to make predictions on the unlabeled data. The portion of the unlabeled data that the model predicts with the most confidence gets added to the labeled dataset and used to retrain the model. sequence-to-sequence (seq2seq) Models that generate a new sequence from an input, like translation models, or summarization models (such as Bart or T5). stride In convolution or pooling, the stride refers to the distance the kernel is moved over a matrix. A stride of 1 means the kernel is moved one pixel over at a time, and a stride of 2 means the kernel is moved two pixels over at a time. supervised learning A form of model training that directly uses labeled data to correct and instruct model performance. Data is fed into the model being trained, and its predictions are compared to the known labels. The model updates its weights based on how incorrect its predictions were, and the process is repeated to optimize model performance. T token A part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords) or a punctuation symbol. token Type IDs Some models' purpose is to do classification on pairs of sentences or question answering. These require two different sequences to be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the classifier ([CLS]) and separator ([SEP]) tokens. For example, the BERT model builds its two sequence input as such: thon [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP] We can use our tokenizer to automatically generate such a sentence by passing the two sequences to tokenizer as two arguments (and not a list, like before) like this: thon from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-cased") sequence_a = "HuggingFace is based in NYC" sequence_b = "Where is HuggingFace based?" encoded_dict = tokenizer(sequence_a, sequence_b) decoded = tokenizer.decode(encoded_dict["input_ids"]) which will return: thon print(decoded) [CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP] This is enough for some models to understand where one sequence ends and where another begins. However, other models, such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying the two types of sequence in the model. The tokenizer returns this mask as the "token_type_ids" entry: thon encoded_dict["token_type_ids"] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1] The first sequence, the "context" used for the question, has all its tokens represented by a 0, whereas the second sequence, corresponding to the "question", has all its tokens represented by a 1. Some models, like [XLNetModel] use an additional token represented by a 2. transfer learning A technique that involves taking a pretrained model and adapting it to a dataset specific to your task. Instead of training a model from scratch, you can leverage knowledge obtained from an existing model as a starting point. This speeds up the learning process and reduces the amount of training data needed. transformer Self-attention based deep learning model architecture. U unsupervised learning A form of model training in which data provided to the model is not labeled. Unsupervised learning techniques leverage statistical information of the data distribution to find patterns useful for the task at hand.

Utilities for Trainer This page lists all the utility functions used by [Trainer]. Most of those are only useful if you are studying the code of the Trainer in the library. Utilities [[autodoc]] EvalPrediction [[autodoc]] IntervalStrategy [[autodoc]] enable_full_determinism [[autodoc]] set_seed [[autodoc]] torch_distributed_zero_first Callbacks internals [[autodoc]] trainer_callback.CallbackHandler Distributed Evaluation [[autodoc]] trainer_pt_utils.DistributedTensorGatherer Distributed Evaluation [[autodoc]] HfArgumentParser Debug Utilities [[autodoc]] debug_utils.DebugUnderflowOverflow

Efficient Training on Multiple GPUs When training on a single GPU is too slow or the model weights don't fit in a single GPUs memory we use a multi-GPU setup. Switching from a single GPU to multiple requires some form of parallelism as the work needs to be distributed. There are several techniques to achieve parallism such as data, tensor, or pipeline parallism. However, there is no one solution to fit them all and which settings works best depends on the hardware you are running on. While the main concepts most likely will apply to any other framework, this article is focused on PyTorch-based implementations. Note: Most of the strategies introduced in the single GPU section (such as mixed precision training or gradient accumulation) are generic and apply to training models in general so make sure to have a look at it before diving into the following sections such as multi-GPU or CPU training. We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D parallelism to enable an even faster training and to support even bigger models. Various other powerful alternative approaches will be presented. Concepts The following is the brief description of the main concepts that will be described later in depth in this document. DataParallel (DP) - the same setup is replicated multiple times, and each being fed a slice of the data. The processing is done in parallel and all setups are synchronized at the end of each training step. TensorParallel (TP) - each tensor is split up into multiple chunks, so instead of having the whole tensor reside on a single gpu, each shard of the tensor resides on its designated gpu. During processing each shard gets processed separately and in parallel on different GPUs and the results are synced at the end of the step. This is what one may call horizontal parallelism, as the splitting happens on horizontal level. PipelineParallel (PP) - the model is split up vertically (layer-level) across multiple GPUs, so that only one or several layers of the model are places on a single gpu. Each gpu processes in parallel different stages of the pipeline and working on a small chunk of the batch. Zero Redundancy Optimizer (ZeRO) - Also performs sharding of the tensors somewhat similar to TP, except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model doesn't need to be modified. It also supports various offloading techniques to compensate for limited GPU memory. Sharded DDP - is another name for the foundational ZeRO concept as used by various other implementations of ZeRO. Before diving deeper into the specifics of each concept we first have a look at the rough decision process when training large models on a large infrastructure. Scalability Strategy ⇨ Single Node / Multi-GPU * Model fits onto a single GPU: 1. DDP - Distributed DP 2. ZeRO - may or may not be faster depending on the situation and configuration used Model doesn't fit onto a single GPU: PP ZeRO TP With very fast intra-node connectivity of NVLINK or NVSwitch all three should be mostly on par, without these PP will be faster than TP or ZeRO. The degree of TP may also make a difference. Best to experiment to find the winner on your particular setup. TP is almost always used within a single node. That is TP size <= gpus per node. Largest Layer not fitting into a single GPU: If not using ZeRO - must use TP, as PP alone won't be able to fit. With ZeRO see the same entry for "Single GPU" above ⇨ Multi-Node / Multi-GPU When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model PP+TP+DP - less communications, but requires massive changes to the model when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP+ZeRO-1 Data Parallelism Most users with just 2 GPUs already enjoy the increased training speed up thanks to DataParallel (DP) and DistributedDataParallel (DDP) that are almost trivial to use. This is a built-in feature of Pytorch. Note that in general it is advised to use DDP as it is better maintained and works for all models while DP might fail for some models. PyTorch documentation itself recommends the use of DDP. DP vs DDP DistributedDataParallel (DDP) is typically faster than DataParallel (DP), but it is not always the case: * while DP is python threads-based, DDP is multiprocess-based - and as such it has no python threads limitations, such as GIL * on the other hand a slow inter-connectivity between the GPU cards could lead to an actual slower outcome with DDP Here are the main differences in the inter-GPU communication overhead between the two modes: DDP: At the start time the main process replicates the model once from gpu 0 to the rest of gpus Then for each batch: each gpu consumes each own mini-batch of data directly during backward, once the local gradients are ready, they are then averaged across all processes DP: For each batch: 1. gpu 0 reads the batch of data and then sends a mini-batch to each gpu 2. replicates the up-to-date model from gpu 0 to each gpu 3. runs forward and sends output from each gpu to gpu 0, computes loss 4. scatters loss from gpu 0 to all gpus, runs backward 5. sends gradients from each gpu to gpu 0 and averages those The only communication DDP performs per batch is sending gradients, whereas DP does 5 different data exchanges per batch. DP copies data within the process via python threads, whereas DDP copies data via torch.distributed. Under DP gpu 0 performs a lot more work than the rest of the gpus, thus resulting in under-utilization of gpus. You can use DDP across multiple machines, but this is not the case with DP. There are other differences between DP and DDP but they aren't relevant to this discussion. If you want to go really deep into understanding these 2 modes, this article is highly recommended, as it has great diagrams, includes multiple benchmarks and profiler outputs on various hardware, explains all the nuances that you may need to know. Let's look at an actual benchmark: | Type | NVlink | Time | | :----- | ----- | ---: | | 2:DP | Y | 110s | | 2:DDP | Y | 101s | | 2:DDP | N | 131s | Analysis: Here DP is ~10% slower than DDP w/ NVlink, but ~15% faster than DDP w/o 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 slow down the total runtime. Here is the full benchmark code and outputs: NCCL_P2P_DISABLE=1 was used to disable the NVLink feature on the corresponding benchmark. DP rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \ python examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path 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 rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \ python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path 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 rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \ python -m torch.distributed.launch --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path 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} 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 ZeRO Data Parallelism ZeRO-powered data parallelism (ZeRO-DP) is described on the following diagram from this blog post It can be difficult to wrap one's head around it, but in reality the concept is quite simple. This is just the usual DataParallel (DP), except, instead of replicating the full model params, gradients and optimizer states, each GPU stores only a slice of it. And then at run-time when the full layer params are needed just for the given layer, all GPUs synchronize to give each other parts that they miss - this is it. Consider this simple model with 3 layers, where each layer has 3 params: La | Lb | Lc ---|----|--- a0 | b0 | c0 a1 | b1 | c1 a2 | b2 | c2 Layer La has weights a0, a1 and a2. If we have 3 GPUs, the Sharded DDP (= 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, if you imagine the typical DNN diagram. Vertical slicing is where one puts whole layer-groups on different GPUs. But it's just the starting point. Now each of these GPUs will get the usual mini-batch as it works in DP: x0 => GPU0 x1 => GPU1 x2 => GPU2 The inputs are unmodified - they think they are going to be processed by the normal model. First, the inputs hit the layer La. Let's focus just on GPU0: x0 needs a0, a1, a2 params to do its forward path, but GPU0 has only a0 - it gets sent a1 from GPU1 and a2 from GPU2, bringing all pieces of the model together. In parallel, GPU1 gets mini-batch x1 and it only has a1, but needs a0 and a2 params, so it gets those from GPU0 and GPU2. Same happens to GPU2 that gets input x2. It gets a0 and a1 from GPU0 and GPU1, and with its a2 it reconstructs the full tensor. All 3 GPUs get the full tensors reconstructed and a forward happens. 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. And the whole process is repeated for layer Lb, then Lc forward-wise, and then backward Lc -> Lb -> La. To me this sounds like an efficient group backpacking weight distribution strategy: person A carries the tent person B carries the stove person C carries the axe Now 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 Sharded DDP / Zero DP. Compare this strategy to the simple one where each person has to carry their own tent, stove and axe, which would be far more inefficient. This is DataParallel (DP and DDP) in Pytorch. 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 ZeRO-DP stages 1+2+3 transformers integration Naive Model Parallelism (Vertical) and Pipeline Parallelism Naive Model Parallelism (MP) is where one spreads groups of model layers across multiple GPUs. The mechanism is relatively simple - switch the desired layers .to() the desired devices and now whenever the data goes in and out those layers switch the data to the same device as the layer and leave the rest unmodified. We refer to it as Vertical MP, because if you remember how most models are drawn, we slice the layers vertically. For example, if the following diagram shows an 8-layer model: =================== =================== | 0 | 1 | 2 | 3 | | 4 | 5 | 6 | 7 | =================== =================== gpu0 gpu1 we just sliced it in 2 vertically, placing layers 0-3 onto GPU0 and 4-7 to GPU1. Now while data travels from layer 0 to 1, 1 to 2 and 2 to 3 this is just the normal model. But when data needs to pass from layer 3 to layer 4 it needs to travel from GPU0 to GPU1 which introduces a communication overhead. If the participating GPUs are on the same compute node (e.g. same physical machine) this copying is pretty fast, but if the GPUs are located on different compute nodes (e.g. multiple machines) the communication overhead could be significantly larger. Then layers 4 to 5 to 6 to 7 are as a normal model would have and when the 7th layer completes we often 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. Problems: - the main deficiency and why this one is called "naive" MP, is that all but one GPU is idle at any given moment. So if 4 GPUs are used, it's almost identical to quadrupling the amount of memory of a single GPU, and ignoring the rest of the hardware. Plus there is the overhead of copying the data between devices. So 4x 6GB cards will be able to accommodate the same size as 1x 24GB card using naive MP, except the latter will complete the training faster, since 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) - shared embeddings may need to get copied back and forth between GPUs. Pipeline Parallelism (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 shows the naive MP on the top, and PP on the bottom: It's easy to see from the bottom diagram how PP has less dead zones, where GPUs are idle. The idle parts are referred to as the "bubble". Both parts of the diagram show a parallelism that is of degree 4. That is 4 GPUs are participating in the pipeline. So there is the forward path of 4 pipe stages F0, F1, F2 and F3 and then the return reverse order backward path of B3, B2, B1 and B0. PP introduces a new hyper-parameter to tune and it's chunks which defines how many chunks of data are sent in a sequence through the same pipe stage. For example, in the bottom diagram you can see that 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 their work and only when their work is starting to be complete, 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 conceptually this is the same concept as gradient accumulation steps (GAS). Pytorch uses chunks, whereas DeepSpeed refers to the same hyper-parameter as GAS. Because of the chunks, PP introduces the concept 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 we then do: mbs*chunks*dp_degree (8*32*4=1024). Let's go back to the diagram. With chunks=1 you end up with the naive MP, which is very inefficient. With a very large chunks value you end up with tiny micro-batch sizes which could be not every efficient either. So one has to experiment to find the value that leads to the highest efficient utilization of the gpus. While the diagram shows that there is a bubble of "dead" time 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. There are 2 groups of solutions - the traditional Pipeline API and the more modern solutions that make things much easier for the end user. Traditional Pipeline API solutions: - PyTorch - DeepSpeed - Megatron-LM Modern solutions: - Varuna - Sagemaker Problems with traditional Pipeline API solutions: - 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. - have to arrange each layer so that the output of one model becomes an input to the other model. We are yet to experiment with Varuna and SageMaker but their papers report that they have overcome the list of problems mentioned above and that they require much smaller changes to the user's model. Implementations: - Pytorch (initial support in pytorch-1.8, and progressively getting improved in 1.9 and more so in 1.10). Some examples - DeepSpeed - Megatron-LM has an internal implementation - no API. - Varuna - SageMaker - this is a proprietary solution that can only be used on AWS. - 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. Other approaches: DeepSpeed, Varuna and SageMaker use the concept of an Interleaved Pipeline Here the bubble (idle time) is further minimized by prioritizing backward passes. Varuna further tries 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 converting. Tensor Parallelism In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large-Scale Language Model Training on GPU Clusters. The main building block of any transformer is a fully connected nn.Linear followed by a nonlinear activation GeLU. Following the Megatron's paper notation, we can write the dot-product part of it as Y = GeLU(XA), where X and Y are the input and output vectors, and A is the weight matrix. If we look at the computation in matrix form, it's easy to see how the matrix multiplication can be split between multiple GPUs: 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: Using this principle, we can update an MLP 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: Parallelizing the multi-headed attention layers is even simpler, since they are already inherently parallel, due to having multiple independent heads! 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. by @anton-l. SageMaker combines TP with DP for a more efficient processing. Alternative names: - DeepSpeed calls it tensor slicing Implementations: - Megatron-LM has an internal implementation, as it's very model-specific - parallelformers (only inference at the moment) - SageMaker - this is a proprietary solution that can only be used on AWS. - OSLO has the tensor parallelism implementation based on the Transformers. 🤗 Transformers status: - core: not yet implemented in the core - but if you want inference 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 DP+PP The following diagram from the DeepSpeed pipeline tutorial demonstrates how one combines DP with PP. 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 - Megatron-LM - Varuna - SageMaker - OSLO 🤗 Transformers status: not yet implemented DP+PP+TP 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. This diagram is from a blog post 3D parallelism: Scaling to trillion-parameter models, 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 - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP. - Megatron-LM - Varuna - SageMaker - OSLO 🤗 Transformers status: not yet implemented, since we have no PP and TP. ZeRO DP+PP+TP 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. 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 bad 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 hurt. 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 and Megatron-Deepspeed from BigScience, which is the fork of the former repo. - OSLO Important papers: Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model 🤗 Transformers status: not yet implemented, since we have no PP and TP. 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 It performs a sort of 4D Parallelism over Sample-Operator-Attribute-Parameter. Sample = Data Parallelism (sample-wise parallel) Operator = Parallelize a single operation into several sub-operations Attribute = Data Parallelism (length-wise parallel) 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 be 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. 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: not yet integrated. We already have our models FX-trace-able via transformers.utils.fx, which is a prerequisite for FlexFlow, so someone needs to figure out what needs to be done to make FlexFlow work with our models. Which Strategy To Use When Here is a very rough outline at which parallelism strategy to use when. The first on each list is typically faster. ⇨ Single GPU Model fits onto a single GPU: Normal use Model doesn't fit onto a single GPU: ZeRO + Offload CPU and optionally NVMe as above plus Memory Centric Tiling (see below for details) if the largest layer can't fit into a single GPU Largest Layer not fitting into a single GPU: ZeRO - Enable Memory Centric Tiling (MCT). It allows you to run arbitrarily large layers by automatically splitting them and executing them sequentially. MCT reduces the number of parameters that are live on a GPU, but it does not affect the activation memory. As this need is very rare as of this writing a manual override of torch.nn.Linear needs to be done by the user. ⇨ Single Node / Multi-GPU Model fits onto a single GPU: DDP - Distributed DP ZeRO - may or may not be faster depending on the situation and configuration used Model doesn't fit onto a single GPU: PP ZeRO TP With very fast intra-node connectivity of NVLINK or NVSwitch all three should be mostly on par, without these PP will be faster than TP or ZeRO. The degree of TP may also make a difference. Best to experiment to find the winner on your particular setup. TP is almost always used within a single node. That is TP size <= gpus per node. Largest Layer not fitting into a single GPU: If not using ZeRO - must use TP, as PP alone won't be able to fit. With ZeRO see the same entry for "Single GPU" above ⇨ Multi-Node / Multi-GPU When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model PP+TP+DP - less communications, but requires massive changes to the model when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP+ZeRO-1

Processors Processors can mean two different things in the Transformers library: - the objects that pre-process inputs for multi-modal models such as Wav2Vec2 (speech and text) or CLIP (text and vision) - deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD. Multi-modal processors Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text, vision and audio). This is handled by objects called processors, which group together two or more processing objects such as tokenizers (for the text modality), image processors (for vision) and feature extractors (for audio). Those processors inherit from the following base class that implements the saving and loading functionality: [[autodoc]] ProcessorMixin Deprecated processors All processors follow the same architecture which is that of the [~data.processors.utils.DataProcessor]. The processor returns a list of [~data.processors.utils.InputExample]. These [~data.processors.utils.InputExample] can be converted to [~data.processors.utils.InputFeatures] in order to be fed to the model. [[autodoc]] data.processors.utils.DataProcessor [[autodoc]] data.processors.utils.InputExample [[autodoc]] data.processors.utils.InputFeatures GLUE General Language Understanding Evaluation (GLUE) is a benchmark that evaluates the performance of models across a diverse set of existing NLU tasks. It was released together with the paper GLUE: A multi-task benchmark and analysis platform for natural language understanding This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI. Those processors are: [~data.processors.utils.MrpcProcessor] [~data.processors.utils.MnliProcessor] [~data.processors.utils.MnliMismatchedProcessor] [~data.processors.utils.Sst2Processor] [~data.processors.utils.StsbProcessor] [~data.processors.utils.QqpProcessor] [~data.processors.utils.QnliProcessor] [~data.processors.utils.RteProcessor] [~data.processors.utils.WnliProcessor] Additionally, the following method can be used to load values from a data file and convert them to a list of [~data.processors.utils.InputExample]. [[autodoc]] data.processors.glue.glue_convert_examples_to_features XNLI The Cross-Lingual NLI Corpus (XNLI) is a benchmark that evaluates the quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on MultiNLI: pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili). It was released together with the paper XNLI: Evaluating Cross-lingual Sentence Representations This library hosts the processor to load the XNLI data: [~data.processors.utils.XnliProcessor] Please note that since the gold labels are available on the test set, evaluation is performed on the test set. An example using these processors is given in the run_xnli.py script. SQuAD The Stanford Question Answering Dataset (SQuAD) is a benchmark that evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper SQuAD: 100,000+ Questions for Machine Comprehension of Text. The second version (v2.0) was released alongside the paper Know What You Don't Know: Unanswerable Questions for SQuAD. This library hosts a processor for each of the two versions: Processors Those processors are: [~data.processors.utils.SquadV1Processor] [~data.processors.utils.SquadV2Processor] They both inherit from the abstract class [~data.processors.utils.SquadProcessor] [[autodoc]] data.processors.squad.SquadProcessor - all Additionally, the following method can be used to convert SQuAD examples into [~data.processors.utils.SquadFeatures] that can be used as model inputs. [[autodoc]] data.processors.squad.squad_convert_examples_to_features These processors as well as the aforementioned method can be used with files containing the data as well as with the tensorflow_datasets package. Examples are given below. Example usage Here is an example using the processors as well as the conversion method using data files: thon Loading a V2 processor processor = SquadV2Processor() examples = processor.get_dev_examples(squad_v2_data_dir) Loading a V1 processor processor = SquadV1Processor() examples = processor.get_dev_examples(squad_v1_data_dir) features = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=args.doc_stride, max_query_length=max_query_length, is_training=not evaluate, ) Using tensorflow_datasets is as easy as using a data file: thon tensorflow_datasets only handle Squad V1. tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) features = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=args.doc_stride, max_query_length=max_query_length, is_training=not evaluate, ) Another example using these processors is given in the run_squad.py script.

How to add a model to 🤗 Transformers? 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). If you're interested in implementing a TensorFlow model, take a look at the How to convert a 🤗 Transformers model to TensorFlow guide! 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 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 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! General overview of 🤗 Transformers 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. 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 that will use your model, but also everybody that 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. Overview of models 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: 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 [~PreTrainedModel.save_pretrained] will automatically call [~PretrainedConfig.save_pretrained], so that both model and configuration are saved. Code style 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 :-) 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 for a good example). 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. More generally we prefer longer explicit code to short magical one. 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. Your function signature should be type-annotated. For the rest, good variable names are way more readable and understandable than type annotations. Overview of tokenizers Not quite ready yet :-( This section will be added soon! Step-by-step recipe to add a model to 🤗 Transformers 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: Porting GPT2 Model by Thomas Porting WMT19 MT Model by 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 and rg 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 on creating an efficient debugging environment 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 that 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 ☐ Prepared 🤗 Transformers dev environment ☐ Set up debugging environment of the original repository ☐ Created script that successfully runs the forward() pass using the original repository and checkpoint ☐ Successfully added the model skeleton to 🤗 Transformers ☐ Successfully converted original checkpoint to 🤗 Transformers checkpoint ☐ Successfully ran forward() pass in 🤗 Transformers that gives identical output to original checkpoint ☐ Finished model tests in 🤗 Transformers ☐ Successfully added tokenizer in 🤗 Transformers ☐ Run end-to-end integration tests ☐ Finished docs ☐ Uploaded model weights to the Hub ☐ Submitted the pull request ☐ (Optional) Added a demo notebook To begin with, we usually recommend to start 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. 1. (Optional) Theoretical aspects of BrandNewBert 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 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 making it different from BERT/GPT-2/BART? Which of the already existing 🤗 Transformers models 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. 2. Next prepare your environment Fork the repository by clicking on the ‘Fork' button on the repository's page. This creates a copy of the code under your GitHub user account. Clone your transformers fork to your local disk, and add the base repository as a remote: git clone https://github.com/[your Github handle]/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git Set up a development environment, for instance by running the following command: python -m venv .env source .env/bin/activate pip install -e ".[dev]" Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a failure with this command. If that's the case make sure to install the Deep Learning framework you are working with (PyTorch, TensorFlow and/or Flax) then do: pip install -e ".[quality]" which should be enough for most use cases. You can then return to the parent directory cd .. 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. To port brand_new_bert, you will also need access to its original repository: 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. 3.-4. Run a pretrained checkpoint using the original repository 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, that 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 / google colab 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 to 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 that 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 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 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 in 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: Retrieve the input IDs passed to the model Retrieve the word embeddings Retrieve the input of the first Transformer layer Retrieve the output of the first Transformer layer Retrieve the output of the following n - 1 Transformer layers 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 the 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 is 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 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. 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. 5.-14. Port BrandNewBert to 🤗 Transformers Next, you can finally start adding new code to 🤗 Transformers. Go into the clone of your 🤗 Transformers' fork: 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. 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. You have two choices here: transformers-cli add-new-model-like to add a new model like an existing one transformers-cli add-new-model to add a new model from our template (will look like BERT or Bart depending on the type of model you select) In both cases, you will be prompted with a questionnaire to fill the basic information of your model. The second command requires to install cookiecutter, you can find more information on it here. 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: Create a branch with a descriptive name from your main branch git checkout -b add_brand_new_bert Commit the automatically generated code: git add . git commit Fetch and rebase to current main git fetch upstream git rebase upstream/main Push the changes to your account using: git push -u origin a-descriptive-name-for-my-changes Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for future changes. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page. In the following, whenever you have done 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: 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 to change 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: thon 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 isinstnace(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 If you are porting a model from PyTorch to PyTorch, a good starting point might be BART's conversion script here 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: thon 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 thon 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. thon 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 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, 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 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, verifying 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: 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 is already added by the Cookiecutter, called BrandNewBertModelIntegrationTests and only has to be filled out by you. To ensure that those tests are passing, run RUN_SLOW=1 pytest -sv tests/models/brand_new_bert/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests In case you are using Windows, you should replace RUN_SLOW=1 with SET RUN_SLOW=1 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 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 .$?-, numbers 2872 234 12 and words." model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/") input_ids = model.tokenize(input_str) You might have to take a deeper look again into the original repository to find the correct tokenizer function or you might even have to do changes to your clone of the original repository to only output the input_ids. Having written a functional tokenization script that uses the original repository, an analogous script for 🤗 Transformers should be created. It should look similar to this: thon from transformers import BrandNewBertTokenizer input_str = "This is a long example input string containing special characters .$?-, 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. It is always to 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: make style and verify that your coding style passes the quality check: 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. 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: thon brand_new_bert.push_to_hub("brand_new_bert") Uncomment the following line to push to an organization. brand_new_bert.push_to_hub("/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. Share your work!! 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 achievement with the community. You have made another model that is super easy to access for everyone in the community! 🤯

Fine-tune a pretrained model [[open-in-colab]] There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. 🤗 Transformers provides access to thousands of pretrained models for a wide range of tasks. When you use a pretrained model, you train it on a dataset specific to your task. This is known as fine-tuning, an incredibly powerful training technique. In this tutorial, you will fine-tune a pretrained model with a deep learning framework of your choice: Fine-tune a pretrained model with 🤗 Transformers [Trainer]. Fine-tune a pretrained model in TensorFlow with Keras. Fine-tune a pretrained model in native PyTorch. Prepare a dataset Before you can fine-tune a pretrained model, download a dataset and prepare it for training. The previous tutorial showed you how to process data for training, and now you get an opportunity to put those skills to the test! Begin by loading the Yelp Reviews dataset: from datasets import load_dataset dataset = load_dataset("yelp_review_full") dataset["train"][100] {'label': 0, 'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularlythat takes something special!\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \"serving off their orders\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'} As you now know, you need a tokenizer to process the text and include a padding and truncation strategy to handle any variable sequence lengths. To process your dataset in one step, use 🤗 Datasets map method to apply a preprocessing function over the entire dataset: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", truncation=True) tokenized_datasets = dataset.map(tokenize_function, batched=True) If you like, you can create a smaller subset of the full dataset to fine-tune on to reduce the time it takes: small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) Train At this point, you should follow the section corresponding to the framework you want to use. You can use the links in the right sidebar to jump to the one you want - and if you want to hide all of the content for a given framework, just use the button at the top-right of that framework's block! Train with PyTorch Trainer 🤗 Transformers provides a [Trainer] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [Trainer] API supports a wide range of training options and features such as logging, gradient accumulation, and mixed precision. Start by loading your model and specify the number of expected labels. From the Yelp Review dataset card, you know there are five labels: from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5) You will see a warning about some of the pretrained weights not being used and some weights being randomly initialized. Don't worry, this is completely normal! The pretrained head of the BERT model is discarded, and replaced with a randomly initialized classification head. You will fine-tune this new model head on your sequence classification task, transferring the knowledge of the pretrained model to it. Training hyperparameters Next, create a [TrainingArguments] class which contains all the hyperparameters you can tune as well as flags for activating different training options. For this tutorial you can start with the default training hyperparameters, but feel free to experiment with these to find your optimal settings. Specify where to save the checkpoints from your training: from transformers import TrainingArguments training_args = TrainingArguments(output_dir="test_trainer") Evaluate [Trainer] does not automatically evaluate model performance during training. You'll need to pass [Trainer] a function to compute and report metrics. The 🤗 Evaluate library provides a simple accuracy function you can load with the [evaluate.load] (see this quicktour for more information) function: import numpy as np import evaluate metric = evaluate.load("accuracy") Call [~evaluate.compute] on metric to calculate the accuracy of your predictions. Before passing your predictions to compute, you need to convert the predictions to logits (remember all 🤗 Transformers models return logits): def compute_metrics(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) return metric.compute(predictions=predictions, references=labels) If you'd like to monitor your evaluation metrics during fine-tuning, specify the evaluation_strategy parameter in your training arguments to report the evaluation metric at the end of each epoch: from transformers import TrainingArguments, Trainer training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch") Trainer Create a [Trainer] object with your model, training arguments, training and test datasets, and evaluation function: trainer = Trainer( model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset, compute_metrics=compute_metrics, ) Then fine-tune your model by calling [~transformers.Trainer.train]: trainer.train() Train a TensorFlow model with Keras You can also train 🤗 Transformers models in TensorFlow with the Keras API! Loading data for Keras When you want to train a 🤗 Transformers model with the Keras API, you need to convert your dataset to a format that Keras understands. If your dataset is small, you can just convert the whole thing to NumPy arrays and pass it to Keras. Let's try that first before we do anything more complicated. First, load a dataset. We'll use the CoLA dataset from the GLUE benchmark, since it's a simple binary text classification task, and just take the training split for now. from datasets import load_dataset dataset = load_dataset("glue", "cola") dataset = dataset["train"] # Just take the training split for now Next, load a tokenizer and tokenize the data as NumPy arrays. Note that the labels are already a list of 0 and 1s, so we can just convert that directly to a NumPy array without tokenization! from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") tokenized_data = tokenizer(dataset["sentence"], return_tensors="np", padding=True) Tokenizer returns a BatchEncoding, but we convert that to a dict for Keras tokenized_data = dict(tokenized_data) labels = np.array(dataset["label"]) # Label is already an array of 0 and 1 Finally, load, compile, and fit the model. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to: from transformers import TFAutoModelForSequenceClassification from tensorflow.keras.optimizers import Adam Load and compile our model model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased") Lower learning rates are often better for fine-tuning transformers model.compile(optimizer=Adam(3e-5)) # No loss argument! model.fit(tokenized_data, labels) You don't have to pass a loss argument to your models when you compile() them! Hugging Face models automatically choose a loss that is appropriate for their task and model architecture if this argument is left blank. You can always override this by specifying a loss yourself if you want to! This approach works great for smaller datasets, but for larger datasets, you might find it starts to become a problem. Why? Because the tokenized array and labels would have to be fully loaded into memory, and because NumPy doesn’t handle “jagged” arrays, so every tokenized sample would have to be padded to the length of the longest sample in the whole dataset. That’s going to make your array even bigger, and all those padding tokens will slow down training too! Loading data as a tf.data.Dataset If you want to avoid slowing down training, you can load your data as a tf.data.Dataset instead. Although you can write your own tf.data pipeline if you want, we have two convenience methods for doing this: [~TFPreTrainedModel.prepare_tf_dataset]: This is the method we recommend in most cases. Because it is a method on your model, it can inspect the model to automatically figure out which columns are usable as model inputs, and discard the others to make a simpler, more performant dataset. [~datasets.Dataset.to_tf_dataset]: This method is more low-level, and is useful when you want to exactly control how your dataset is created, by specifying exactly which columns and label_cols to include. Before you can use [~TFPreTrainedModel.prepare_tf_dataset], you will need to add the tokenizer outputs to your dataset as columns, as shown in the following code sample: def tokenize_dataset(data): # Keys of the returned dictionary will be added to the dataset as columns return tokenizer(data["text"]) dataset = dataset.map(tokenize_dataset) Remember that Hugging Face datasets are stored on disk by default, so this will not inflate your memory usage! Once the columns have been added, you can stream batches from the dataset and add padding to each batch, which greatly reduces the number of padding tokens compared to padding the entire dataset. tf_dataset = model.prepare_tf_dataset(dataset["train"], batch_size=16, shuffle=True, tokenizer=tokenizer) Note that in the code sample above, you need to pass the tokenizer to prepare_tf_dataset so it can correctly pad batches as they're loaded. If all the samples in your dataset are the same length and no padding is necessary, you can skip this argument. If you need to do something more complex than just padding samples (e.g. corrupting tokens for masked language modelling), you can use the collate_fn argument instead to pass a function that will be called to transform the list of samples into a batch and apply any preprocessing you want. See our examples or notebooks to see this approach in action. Once you've created a tf.data.Dataset, you can compile and fit the model as before: model.compile(optimizer=Adam(3e-5)) # No loss argument! model.fit(tf_dataset) Train in native PyTorch [Trainer] takes care of the training loop and allows you to fine-tune a model in a single line of code. For users who prefer to write their own training loop, you can also fine-tune a 🤗 Transformers model in native PyTorch. At this point, you may need to restart your notebook or execute the following code to free some memory: py del model del trainer torch.cuda.empty_cache() Next, manually postprocess tokenized_dataset to prepare it for training. Remove the text column because the model does not accept raw text as an input: tokenized_datasets = tokenized_datasets.remove_columns(["text"]) Rename the label column to labels because the model expects the argument to be named labels: tokenized_datasets = tokenized_datasets.rename_column("label", "labels") Set the format of the dataset to return PyTorch tensors instead of lists: tokenized_datasets.set_format("torch") Then create a smaller subset of the dataset as previously shown to speed up the fine-tuning: small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) DataLoader Create a DataLoader for your training and test datasets so you can iterate over batches of data: from torch.utils.data import DataLoader train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8) eval_dataloader = DataLoader(small_eval_dataset, batch_size=8) Load your model with the number of expected labels: from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5) Optimizer and learning rate scheduler Create an optimizer and learning rate scheduler to fine-tune the model. Let's use the AdamW optimizer from PyTorch: from torch.optim import AdamW optimizer = AdamW(model.parameters(), lr=5e-5) Create the default learning rate scheduler from [Trainer]: from transformers import get_scheduler num_epochs = 3 num_training_steps = num_epochs * len(train_dataloader) lr_scheduler = get_scheduler( name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps ) Lastly, specify device to use a GPU if you have access to one. Otherwise, training on a CPU may take several hours instead of a couple of minutes. import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model.to(device) Get free access to a cloud GPU if you don't have one with a hosted notebook like Colaboratory or SageMaker StudioLab. Great, now you are ready to train! 🥳 Training loop To keep track of your training progress, use the tqdm library to add a progress bar over the number of training steps: from tqdm.auto import tqdm progress_bar = tqdm(range(num_training_steps)) model.train() for epoch in range(num_epochs): for batch in train_dataloader: batch = {k: v.to(device) for k, v in batch.items()} outputs = model(**batch) loss = outputs.loss loss.backward() optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) Evaluate Just like how you added an evaluation function to [Trainer], you need to do the same when you write your own training loop. But instead of calculating and reporting the metric at the end of each epoch, this time you'll accumulate all the batches with [~evaluate.add_batch] and calculate the metric at the very end. import evaluate metric = evaluate.load("accuracy") model.eval() for batch in eval_dataloader: batch = {k: v.to(device) for k, v in batch.items()} with torch.no_grad(): outputs = model(**batch) logits = outputs.logits predictions = torch.argmax(logits, dim=-1) metric.add_batch(predictions=predictions, references=batch["labels"]) metric.compute() Additional resources For more fine-tuning examples, refer to: 🤗 Transformers Examples includes scripts to train common NLP tasks in PyTorch and TensorFlow. 🤗 Transformers Notebooks contains various notebooks on how to fine-tune a model for specific tasks in PyTorch and TensorFlow.

Pipelines for inference The [pipeline] makes it simple to use any model from the Hub for inference on any language, computer vision, speech, and multimodal tasks. Even if you don't have experience with a specific modality or aren't familiar with the underlying code behind the models, you can still use them for inference with the [pipeline]! This tutorial will teach you to: Use a [pipeline] for inference. Use a specific tokenizer or model. Use a [pipeline] for audio, vision, and multimodal tasks. Take a look at the [pipeline] documentation for a complete list of supported tasks and available parameters. Pipeline usage While each task has an associated [pipeline], it is simpler to use the general [pipeline] abstraction which contains all the task-specific pipelines. The [pipeline] automatically loads a default model and a preprocessing class capable of inference for your task. Start by creating a [pipeline] and specify an inference task: from transformers import pipeline generator = pipeline(task="automatic-speech-recognition") Pass your input text to the [pipeline]: generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac") {'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP LIVE UP THE TRUE MEANING OF ITS TREES'} Not the result you had in mind? Check out some of the most downloaded automatic speech recognition models on the Hub to see if you can get a better transcription. Let's try openai/whisper-large: generator = pipeline(model="openai/whisper-large") generator("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.'} Now this result looks more accurate! We really encourage you to check out the Hub for models in different languages, models specialized in your field, and more. You can check out and compare model results directly from your browser on the Hub to see if it fits or handles corner cases better than other ones. And if you don't find a model for your use case, you can always start training your own! If you have several inputs, you can pass your input as a list: py generator( [ "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac", "https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac", ] ) If you want to iterate over a whole dataset, or want to use it for inference in a webserver, check out dedicated parts Using pipelines on a dataset Using pipelines for a webserver Parameters [pipeline] supports many parameters; some are task specific, and some are general to all pipelines. In general you can specify parameters anywhere you want: py generator = pipeline(model="openai/whisper-large", my_parameter=1) out = generator() # This will use `my_parameter=1`. out = generator(, my_parameter=2) # This will override and use `my_parameter=2`. out = generator() # This will go back to using `my_parameter=1`. Let's check out 3 important ones: Device If you use device=n, the pipeline automatically puts the model on the specified device. This will work regardless of whether you are using PyTorch or Tensorflow. py generator = pipeline(model="openai/whisper-large", device=0) If the model is too large for a single GPU, you can set device_map="auto" to allow 🤗 Accelerate to automatically determine how to load and store the model weights. !pip install accelerate generator = pipeline(model="openai/whisper-large", device_map="auto") Note that if device_map="auto" is passed, there is no need to add the argument device=device when instantiating your pipeline as you may encounter some unexpected behavior! Batch size By default, pipelines will not batch inference for reasons explained in detail here. The reason is that batching is not necessarily faster, and can actually be quite slower in some cases. But if it works in your use case, you can use: py generator = pipeline(model="openai/whisper-large", device=0, batch_size=2) audio_filenames = [f"audio_{i}.flac" for i in range(10)] texts = generator(audio_filenames) This runs the pipeline on the 10 provided audio files, but it will pass them in batches of 2 to the model (which is on a GPU, where batching is more likely to help) without requiring any further code from you. The output should always match what you would have received without batching. It is only meant as a way to help you get more speed out of a pipeline. Pipelines can also alleviate some of the complexities of batching because, for some pipelines, a single item (like a long audio file) needs to be chunked into multiple parts to be processed by a model. The pipeline performs this chunk batching for you. Task specific parameters All tasks provide task specific parameters which allow for additional flexibility and options to help you get your job done. For instance, the [transformers.AutomaticSpeechRecognitionPipeline.__call__] method has a return_timestamps parameter which sounds promising for subtitling videos: Not using whisper, as it cannot provide timestamps. generator = pipeline(model="facebook/wav2vec2-large-960h-lv60-self", return_timestamps="word") generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac") {'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED', 'chunks': [{'text': 'I', 'timestamp': (1.22, 1.24)}, {'text': 'HAVE', 'timestamp': (1.42, 1.58)}, {'text': 'A', 'timestamp': (1.66, 1.68)}, {'text': 'DREAM', 'timestamp': (1.76, 2.14)}, {'text': 'BUT', 'timestamp': (3.68, 3.8)}, {'text': 'ONE', 'timestamp': (3.94, 4.06)}, {'text': 'DAY', 'timestamp': (4.16, 4.3)}, {'text': 'THIS', 'timestamp': (6.36, 6.54)}, {'text': 'NATION', 'timestamp': (6.68, 7.1)}, {'text': 'WILL', 'timestamp': (7.32, 7.56)}, {'text': 'RISE', 'timestamp': (7.8, 8.26)}, {'text': 'UP', 'timestamp': (8.38, 8.48)}, {'text': 'AND', 'timestamp': (10.08, 10.18)}, {'text': 'LIVE', 'timestamp': (10.26, 10.48)}, {'text': 'OUT', 'timestamp': (10.58, 10.7)}, {'text': 'THE', 'timestamp': (10.82, 10.9)}, {'text': 'TRUE', 'timestamp': (10.98, 11.18)}, {'text': 'MEANING', 'timestamp': (11.26, 11.58)}, {'text': 'OF', 'timestamp': (11.66, 11.7)}, {'text': 'ITS', 'timestamp': (11.76, 11.88)}, {'text': 'CREED', 'timestamp': (12.0, 12.38)}]} As you can see, the model inferred the text and also outputted when the various words were pronounced in the sentence. There are many parameters available for each task, so check out each task's API reference to see what you can tinker with! For instance, the [~transformers.AutomaticSpeechRecognitionPipeline] has a chunk_length_s parameter which is helpful for working on really long audio files (for example, subtitling entire movies or hour-long videos) that a model typically cannot handle on its own. If you can't find a parameter that would really help you out, feel free to request it! Using pipelines on a dataset The pipeline can also run inference on a large dataset. The easiest way we recommend doing this is by using an iterator: def data(): for i in range(1000): yield f"My example {i}" pipe = pipeline(model="gpt2", device=0) generated_characters = 0 for out in pipe(data()): generated_characters += len(out[0]["generated_text"]) The iterator data() yields each result, and the pipeline automatically recognizes the input is iterable and will start fetching the data while it continues to process it on the GPU (this uses DataLoader under the hood). This is important because you don't have to allocate memory for the whole dataset and you can feed the GPU as fast as possible. Since batching could speed things up, it may be useful to try tuning the batch_size parameter here. The simplest way to iterate over a dataset is to just load one from 🤗 Datasets: KeyDataset is a util that will just output the item we're interested in. from transformers.pipelines.pt_utils import KeyDataset from datasets import load_dataset pipe = pipeline(model="hf-internal-testing/tiny-random-wav2vec2", device=0) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:10]") for out in pipe(KeyDataset(dataset, "audio")): print(out) Using pipelines for a webserver Creating an inference engine is a complex topic which deserves it's own page. Link Vision pipeline Using a [pipeline] for vision tasks is practically identical. Specify your task and pass your image to the classifier. The image can be a link or a local path to the image. For example, what species of cat is shown below? from transformers import pipeline vision_classifier = pipeline(model="google/vit-base-patch16-224") preds = vision_classifier( images="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] preds [{'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'}] Text pipeline Using a [pipeline] for NLP tasks is practically identical. from transformers import pipeline This model is a zero-shot-classification model. It will classify text, except you are free to choose any label you might imagine classifier = pipeline(model="facebook/bart-large-mnli") classifier( "I have a problem with my iphone that needs to be resolved asap!!", candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"], ) {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]} Multimodal pipeline The [pipeline] supports more than one modality. For example, a visual question answering (VQA) task combines text and image. Feel free to use any image link you like and a question you want to ask about the image. The image can be a URL or a local path to the image. For example, if you use this invoice image: from transformers import pipeline vqa = pipeline(model="impira/layoutlm-document-qa") vqa( image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", question="What is the invoice number?", ) [{'score': 0.42515, 'answer': 'us-001', 'start': 16, 'end': 16}] To run the example above you need to have pytesseract installed in addition to 🤗 Transformers: sudo apt install -y tesseract-ocr pip install pytesseract Using pipeline on large models with 🤗 accelerate: You can easily run pipeline on large models using 🤗 accelerate! First make sure you have installed accelerate with pip install accelerate. First load your model using device_map="auto"! We will use facebook/opt-1.3b for our example. pip install accelerate import torch from transformers import pipeline pipe = pipeline(model="facebook/opt-1.3b", torch_dtype=torch.bfloat16, device_map="auto") output = pipe("This is a cool example!", do_sample=True, top_p=0.95) You can also pass 8-bit loaded models if you install bitsandbytes and add the argument load_in_8bit=True pip install accelerate bitsandbytes import torch from transformers import pipeline pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True}) output = pipe("This is a cool example!", do_sample=True, top_p=0.95) Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM!

Zero-shot image classification [[open-in-colab]] Zero-shot image classification is a task that involves classifying images into different categories using a model that was not explicitly trained on data containing labeled examples from those specific categories. Traditionally, image classification requires training a model on a specific set of labeled images, and this model learns to "map" certain image features to labels. When there's a need to use such model for a classification task that introduces a new set of labels, fine-tuning is required to "recalibrate" the model. In contrast, zero-shot or open vocabulary image classification models are typically multi-modal models that have been trained on a large dataset of images and associated descriptions. These models learn aligned vision-language representations that can be used for many downstream tasks including zero-shot image classification. This is a more flexible approach to image classification that allows models to generalize to new and unseen categories without the need for additional training data and enables users to query images with free-form text descriptions of their target objects . In this guide you'll learn how to: create a zero-shot image classification pipeline run zero-shot image classification inference by hand Before you begin, make sure you have all the necessary libraries installed: pip install -q transformers Zero-shot image classification pipeline The simplest way to try out inference with a model supporting zero-shot image classification is to use the corresponding [pipeline]. Instantiate a pipeline from a checkpoint on the Hugging Face Hub: thon from transformers import pipeline checkpoint = "openai/clip-vit-large-patch14" detector = pipeline(model=checkpoint, task="zero-shot-image-classification") Next, choose an image you'd like to classify. from PIL import Image import requests url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640" image = Image.open(requests.get(url, stream=True).raw) image Pass the image and the candidate object labels to the pipeline. Here we pass the image directly; other suitable options include a local path to an image or an image url. The candidate labels can be simple words like in this example, or more descriptive. predictions = classifier(image, candidate_labels=["fox", "bear", "seagull", "owl"]) predictions [{'score': 0.9996670484542847, 'label': 'owl'}, {'score': 0.000199399160919711, 'label': 'seagull'}, {'score': 7.392891711788252e-05, 'label': 'fox'}, {'score': 5.96074532950297e-05, 'label': 'bear'}] Zero-shot image classification by hand Now that you've seen how to use the zero-shot image classification pipeline, let's take a look how you can run zero-shot image classification manually. Start by loading the model and associated processor from a checkpoint on the Hugging Face Hub. Here we'll use the same checkpoint as before: from transformers import AutoProcessor, AutoModelForZeroShotImageClassification model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint) processor = AutoProcessor.from_pretrained(checkpoint) Let's take a different image to switch things up. from PIL import Image import requests url = "https://unsplash.com/photos/xBRQfR2bqNI/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjc4Mzg4ODEx&force=true&w=640" image = Image.open(requests.get(url, stream=True).raw) image Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the image for the model by resizing and normalizing it, and a tokenizer that takes care of the text inputs. candidate_labels = ["tree", "car", "bike", "cat"] inputs = processor(images=image, text=candidate_labels, return_tensors="pt", padding=True) Pass the inputs through the model, and post-process the results: import torch with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits_per_image[0] probs = logits.softmax(dim=-1).numpy() scores = probs.tolist() result = [ {"score": score, "label": candidate_label} for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0]) ] result [{'score': 0.998572, 'label': 'car'}, {'score': 0.0010570387, 'label': 'bike'}, {'score': 0.0003393686, 'label': 'tree'}, {'score': 3.1572064e-05, 'label': 'cat'}]

Utilities for FeatureExtractors This page lists all the utility functions that can be used by the audio [FeatureExtractor] in order to compute special features from a raw audio using common algorithms such as Short Time Fourier Transform or log mel spectrogram. Most of those are only useful if you are studying the code of the audio processors in the library. Audio Transformations [[autodoc]] audio_utils.hertz_to_mel [[autodoc]] audio_utils.mel_to_hertz [[autodoc]] audio_utils.mel_filter_bank [[autodoc]] audio_utils.optimal_fft_length [[autodoc]] audio_utils.window_function [[autodoc]] audio_utils.spectrogram [[autodoc]] audio_utils.power_to_db [[autodoc]] audio_utils.amplitude_to_db

Transformers Agent Transformers Agent is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. Transformers version v4.29.0, building on the concept of tools and agents. You can play with in this colab. In short, it provides a natural language API on top of transformers: we define a set of curated tools and design an agent to interpret natural language and to use these tools. It is extensible by design; we curated some relevant tools, but we'll show you how the system can be extended easily to use any tool developed by the community. Let's start with a few examples of what can be achieved with this new API. It is particularly powerful when it comes to multimodal tasks, so let's take it for a spin to generate images and read text out loud. py agent.run("Caption the following image", image=image) | Input | Output | |-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------| | | A beaver is swimming in the water | py agent.run("Read the following text out loud", text=text) | Input | Output | |-------------------------------------------------------------------------------------------------------------------------|----------------------------------------------| | A beaver is swimming in the water | your browser does not support the audio element. py agent.run( "In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?", document=document, ) | Input | Output | |-----------------------------------------------------------------------------------------------------------------------------|----------------| | | ballroom foyer | Quickstart Before being able to use agent.run, you will need to instantiate an agent, which is a large language model (LLM). We provide support for openAI models as well as opensource alternatives from BigCode and OpenAssistant. The openAI models perform better (but require you to have an openAI API key, so cannot be used for free); Hugging Face is providing free access to endpoints for BigCode and OpenAssistant models. To start with, please install the agents extras in order to install all default dependencies. pip install transformers[agents] To use openAI models, you instantiate an [OpenAiAgent] after installing the openai dependency: pip install openai from transformers import OpenAiAgent agent = OpenAiAgent(model="text-davinci-003", api_key="") To use BigCode or OpenAssistant, start by logging in to have access to the Inference API: from huggingface_hub import login login("") Then, instantiate the agent from transformers import HfAgent Starcoder agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder") StarcoderBase agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoderbase") OpenAssistant agent = HfAgent(url_endpoint="https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5") This is using the inference API that Hugging Face provides for free at the moment. If you have your own inference endpoint for this model (or another one) you can replace the URL above with your URL endpoint. StarCoder and OpenAssistant are free to use and perform admirably well on simple tasks. However, the checkpoints don't hold up when handling more complex prompts. If you're facing such an issue, we recommend trying out the OpenAI model which, while sadly not open-source, performs better at this given time. You're now good to go! Let's dive into the two APIs that you now have at your disposal. Single execution (run) The single execution method is when using the [~Agent.run] method of the agent: py agent.run("Draw me a picture of rivers and lakes.") It automatically selects the tool (or tools) appropriate for the task you want to perform and runs them appropriately. It can perform one or several tasks in the same instruction (though the more complex your instruction, the more likely the agent is to fail). py agent.run("Draw me a picture of the sea then transform the picture to add an island") Every [~Agent.run] operation is independent, so you can run it several times in a row with different tasks. Note that your agent is just a large-language model, so small variations in your prompt might yield completely different results. It's important to explain as clearly as possible the task you want to perform. We go more in-depth on how to write good prompts here. If you'd like to keep a state across executions or to pass non-text objects to the agent, you can do so by specifying variables that you would like the agent to use. For example, you could generate the first image of rivers and lakes, and ask the model to update that picture to add an island by doing the following: python picture = agent.run("Generate a picture of rivers and lakes.") updated_picture = agent.run("Transform the image in `picture` to add an island to it.", picture=picture) This can be helpful when the model is unable to understand your request and mixes tools. An example would be: py agent.run("Draw me the picture of a capybara swimming in the sea") Here, the model could interpret in two ways: - Have the text-to-image generate a capybara swimming in the sea - Or, have the text-to-image generate capybara, then use the image-transformation tool to have it swim in the sea In case you would like to force the first scenario, you could do so by passing it the prompt as an argument: py agent.run("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea") Chat-based execution (chat) The agent also has a chat-based approach, using the [~Agent.chat] method: py agent.chat("Generate a picture of rivers and lakes") py agent.chat("Transform the picture so that there is a rock in there") This is an interesting approach when you want to keep the state across instructions. It's better for experimentation, but will tend to be much better at single instructions rather than complex instructions (which the [~Agent.run] method is better at handling). This method can also take arguments if you would like to pass non-text types or specific prompts. ⚠️ Remote execution For demonstration purposes and so that this can be used with all setups, we have created remote executors for several of the default tools the agent has access. These are created using inference endpoints. To see how to set up remote executors tools yourself, we recommend reading the custom tool guide. In order to run with remote tools, specifying remote=True to either [~Agent.run] or [~Agent.chat] is sufficient. For example, the following command could be run on any device efficiently, without needing significant RAM or GPU: py agent.run("Draw me a picture of rivers and lakes", remote=True) The same can be said for [~Agent.chat]: py agent.chat("Draw me a picture of rivers and lakes", remote=True) What's happening here? What are tools, and what are agents? Agents The "agent" here is a large language model, and we're prompting it so that it has access to a specific set of tools. LLMs are pretty good at generating small samples of code, so this API takes advantage of that by prompting the LLM gives a small sample of code performing a task with a set of tools. This prompt is then completed by the task you give your agent and the description of the tools you give it. This way it gets access to the doc of the tools you are using, especially their expected inputs and outputs, and can generate the relevant code. Tools Tools are very simple: they're a single function, with a name, and a description. We then use these tools' descriptions to prompt the agent. Through the prompt, we show the agent how it would leverage tools to perform what was requested in the query. This is using brand-new tools and not pipelines, because the agent writes better code with very atomic tools. Pipelines are more refactored and often combine several tasks in one. Tools are meant to be focused on one very simple task only. Code-execution?! This code is then executed with our small Python interpreter on the set of inputs passed along with your tools. We hear you screaming "Arbitrary code execution!" in the back, but let us explain why that is not the case. The only functions that can be called are the tools you provided and the print function, so you're already limited in what can be executed. You should be safe if it's limited to Hugging Face tools. Then, we don't allow any attribute lookup or imports (which shouldn't be needed anyway for passing along inputs/outputs to a small set of functions) so all the most obvious attacks (and you'd need to prompt the LLM to output them anyway) shouldn't be an issue. If you want to be on the super safe side, you can execute the run() method with the additional argument return_code=True, in which case the agent will just return the code to execute and you can decide whether to do it or not. The execution will stop at any line trying to perform an illegal operation or if there is a regular Python error with the code generated by the agent. A curated set of tools We identify a set of tools that can empower such agents. Here is an updated list of the tools we have integrated in transformers: Document question answering: given a document (such as a PDF) in image format, answer a question on this document (Donut) Text question answering: given a long text and a question, answer the question in the text (Flan-T5) Unconditional image captioning: Caption the image! (BLIP) Image question answering: given an image, answer a question on this image (VILT) Image segmentation: given an image and a prompt, output the segmentation mask of that prompt (CLIPSeg) Speech to text: given an audio recording of a person talking, transcribe the speech into text (Whisper) Text to speech: convert text to speech (SpeechT5) Zero-shot text classification: given a text and a list of labels, identify to which label the text corresponds the most (BART) Text summarization: summarize a long text in one or a few sentences (BART) Translation: translate the text into a given language (NLLB) These tools have an integration in transformers, and can be used manually as well, for example: from transformers import load_tool tool = load_tool("text-to-speech") audio = tool("This is a text to speech tool") Custom tools While we identify a curated set of tools, we strongly believe that the main value provided by this implementation is the ability to quickly create and share custom tools. By pushing the code of a tool to a Hugging Face Space or a model repository, you're then able to leverage the tool directly with the agent. We've added a few transformers-agnostic tools to the huggingface-tools organization: Text downloader: to download a text from a web URL Text to image: generate an image according to a prompt, leveraging stable diffusion Image transformation: modify an image given an initial image and a prompt, leveraging instruct pix2pix stable diffusion Text to video: generate a small video according to a prompt, leveraging damo-vilab The text-to-image tool we have been using since the beginning is a remote tool that lives in huggingface-tools/text-to-image! We will continue releasing such tools on this and other organizations, to further supercharge this implementation. The agents have by default access to tools that reside on huggingface-tools. We explain how to you can write and share your tools as well as leverage any custom tool that resides on the Hub in following guide. Code generation So far we have shown how to use the agents to perform actions for you. However, the agent is only generating code that we then execute using a very restricted Python interpreter. In case you would like to use the code generated in a different setting, the agent can be prompted to return the code, along with tool definition and accurate imports. For example, the following instruction python agent.run("Draw me a picture of rivers and lakes", return_code=True) returns the following code thon from transformers import load_tool image_generator = load_tool("huggingface-tools/text-to-image") image = image_generator(prompt="rivers and lakes") that you can then modify and execute yourself.

Use tokenizers from 🤗 Tokenizers The [PreTrainedTokenizerFast] depends on the 🤗 Tokenizers library. The tokenizers obtained from the 🤗 Tokenizers library can be loaded very simply into 🤗 Transformers. Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines: thon from tokenizers import Tokenizer from tokenizers.models import BPE from tokenizers.trainers import BpeTrainer from tokenizers.pre_tokenizers import Whitespace tokenizer = Tokenizer(BPE(unk_token="[UNK]")) trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]) tokenizer.pre_tokenizer = Whitespace() files = [] tokenizer.train(files, trainer) We now have a tokenizer trained on the files we defined. We can either continue using it in that runtime, or save it to a JSON file for future re-use. Loading directly from the tokenizer object Let's see how to leverage this tokenizer object in the 🤗 Transformers library. The [PreTrainedTokenizerFast] class allows for easy instantiation, by accepting the instantiated tokenizer object as an argument: thon from transformers import PreTrainedTokenizerFast fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer) This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to the tokenizer page for more information. Loading from a JSON file In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer: thon tokenizer.save("tokenizer.json") The path to which we saved this file can be passed to the [PreTrainedTokenizerFast] initialization method using the tokenizer_file parameter: thon from transformers import PreTrainedTokenizerFast fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json") This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to the tokenizer page for more information.