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The Partnership: Amazon SageMaker and Hugging Face | philschmid | March 23, 2021 | the-partnership-amazon-sagemaker-and-hugging-face | partnerships, aws | https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face | Accelerating Machine Learning from Science to Production In addition to Hugging Face DLCs, we created a first-class Hugging Face extension to the SageMaker Python-sdk to accelerate data science teams, reducing the time required to set up and run experiments from days to minutes. You can use the Hugging Face DLCs with the Automatic Model Tuning capability of Amazon SageMaker, in order to automatically optimize your training hyperparameters and quickly increase the accuracy of your models. Thanks to the SageMaker Studio web-based Integrated Development Environment (IDE), you can easily track and compare your experiments and your training artifacts. | [
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The Partnership: Amazon SageMaker and Hugging Face | philschmid | March 23, 2021 | the-partnership-amazon-sagemaker-and-hugging-face | partnerships, aws | https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face | Distributed Training: Model Parallel You can use SageMaker Model Parallelism Library out of the box for distributed training. We added the functionality of Model Parallelism directly into the Trainer. If your train.py uses the Trainer API you only need to define the distribution parameter in the HuggingFace Estimator. For detailed information about the adjustments take a look here. - Example Notebook python # configuration for running training on smdistributed Model Parallel mpi_options = { "enabled" : True, "processes_per_host" : 8 } smp_options = { "enabled":True, "parameters": { "microbatches": 4, "placement_strategy": "spread", "pipeline": "interleaved", "optimize": "speed", "partitions": 4, "ddp": True, } } distribution={ "smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options } # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3dn.24xlarge', instance_count=2, role=role, transformers_version='4.4.2', pytorch_version='1.6.0', py_version='py36', hyperparameters = hyperparameters, distribution = distribution ) | [
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The Partnership: Amazon SageMaker and Hugging Face | philschmid | March 23, 2021 | the-partnership-amazon-sagemaker-and-hugging-face | partnerships, aws | https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face | Create a HuggingFace Estimator and train our model In order to create a SageMaker Trainingjob we can use a HuggingFace Estimator. The Estimator handles the end-to-end Amazon SageMaker training. In an Estimator, we define which fine-tuning script should be used as entry_point, which instance_type should be used, which hyperparameters are passed in. In addition to this, a number of advanced controls are available, such as customizing the output and checkpointing locations, specifying the local storage size or network configuration. SageMaker takes care of starting and managing all the required Amazon EC2 instances for us with the Hugging Face DLC, it uploads the provided fine-tuning script, for example, our train.py, then downloads the data from the S3 bucket, sess.default_bucket(), into the container. Once the data is ready, the training job will start automatically by running. bash /opt/conda/bin/python train.py --epochs 1 --model_name distilbert-base-uncased --train_batch_size 32 The hyperparameters you define in the HuggingFace Estimator are passed in as named arguments. python from sagemaker.huggingface import HuggingFace # hyperparameters, which are passed into the training job hyperparameters={'epochs': 1, 'train_batch_size': 32, 'model_name':'distilbert-base-uncased' } # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3.2xlarge', instance_count=1, role=role, transformers_version='4.6', pytorch_version='1.7', py_version='py36', hyperparameters = hyperparameters ) To start our training we call the .fit() method and pass our S3 uri as input. python # starting the train job with our uploaded datasets as input huggingface_estimator.fit({'train': training_input_path, 'test': test_input_path}) --- | [
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The Partnership: Amazon SageMaker and Hugging Face | philschmid | March 23, 2021 | the-partnership-amazon-sagemaker-and-hugging-face | partnerships, aws | https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face | Distributed Training: Data-Parallel You can use SageMaker Data Parallelism Library out of the box for distributed training. We added the functionality of Data Parallelism directly into the Trainer. If your train.py uses the Trainer API you only need to define the distribution parameter in the HuggingFace Estimator. - Example Notebook PyTorch - Example Notebook TensorFlow python # configuration for running training on smdistributed Data Parallel distribution = {'smdistributed':{'dataparallel':{ 'enabled': True }}} # create the Estimator huggingface_estimator = HuggingFace( entry_point='train.py', source_dir='./scripts', instance_type='ml.p3dn.24xlarge', instance_count=2, role=role, transformers_version='4.4.2', pytorch_version='1.6.0', py_version='py36', hyperparameters = hyperparameters distribution = distribution ) The "Getting started: End-to-End Text Classification 🧭" example can be used for distributed training without any changes. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | BigBird block sparse attention BigBird block sparse attention is just an efficient implementation of what we discussed above. Each token is attending some **global tokens**, **sliding tokens**, & **random tokens** instead of attending to **all** other tokens. The authors hardcoded the attention matrix for multiple query components separately; and used a cool trick to speed up training/inference on GPU and TPU.  *Note: on the top, we have 2 extra sentences. As you can notice, every token is just switched by one place in both sentences. This is how sliding attention is implemented. Whenq[i]is multiplied withk[i,0:3], we will get a sliding attention score forq[i](whereiis index of element in sequence).* You can find the actual implementation ofblock_sparseattention [here](https://github.com/vasudevgupta7/transformers/blob/5f2d6a0c93ca2017961199aa04a344b9b779d454/src/transformers/models/big_bird/modeling_big_bird.py#L513). This may look very scary 😨😨 now. But this article will surely ease your life in understanding the code. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Sliding Attention The sequence of key tokens is copied 2 times with each element shifted to the right in one of the copies and to the left in the other copy. Now if we multiply query sequence vectors by these 3 sequence vectors, we will cover all the sliding tokens. Computational complexity is simplyO(3xn) = O(n). Referring to the above picture, the orange boxes represent the sliding attention. You can see 3 sequences at the top of the figure with 2 of them shifted by one token (1 to the left, 1 to the right). ```python # what we want to do Q[i] x [K[i-1], K[i], K[i+1]] for i = 1:-1 # efficient implementation in code (assume dot product multiplication 👇) [Q[0], Q[1], Q[2], ......, Q[n-2], Q[n-1]] x [K[1], K[2], K[3], ......, K[n-1], K[0]] [Q[0], Q[1], Q[2], ......, Q[n-1]] x [K[n-1], K[0], K[1], ......, K[n-2]] [Q[0], Q[1], Q[2], ......, Q[n-1]] x [K[0], K[1], K[2], ......, K[n-1]] # Each sequence is getting multiplied by only 3 sequences to keepwindow_size = 3. # Some computations might be missing; this is just a rough idea. ``` | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Implementation **Recap:** In regular BERT attention, a sequence of tokens i.e. \\( X = x_1, x_2, ...., x_n \\) is projected through a dense layer into \\( Q,K,V \\) and the attention score \\( Z \\) is calculated as \\( Z=Softmax(QK^T) \\). In the case of BigBird block sparse attention, the same algorithm is used but only with some selected query & key vectors. Let's have a look at how bigbird block sparse attention is implemented. To begin with, let's assume \\(b, r, s, g\\) representblock_size,num_random_blocks,num_sliding_blocks,num_global_blocks, respectively. Visually, we can illustrate the components of big bird's block sparse attention with \\(b=4, r=1, g=2, s=3, d=5\\) as follows: <img src="assets/18_big_bird/intro.png" width=500 height=250> Attention scores for \\({q}_{1}, {q}_{2}, {q}_{3:n-2}, {q}_{n-1}, {q}_{n}\\) are calculated separately as described below: --- Attention score for \\(\mathbf{q}_{1}\\) represented by \\(a_1\\) where \\(a_1=Softmax(q_1 * K^T)\\), is nothing but attention score between all the tokens in 1st block with all the other tokens in the sequence.  \\(q_1\\) represents 1st block, \\(g_i\\) represents \\(i\\) block. We are simply performing normal attention operation between \\(q_1\\) & \\(g\\) (i.e. all the keys). --- For calculating attention score for tokens in seconcd block, we are gathering the first three blocks, the last block, and the fifth block. Then we can compute \\(a_2 = Softmax(q_2 * concat(k_1, k_2, k_3, k_5, k_7)\\).  *I am representing tokens by \\(g, r, s\\) just to represent their nature explicitly (i.e. showing global, random, sliding tokens), else they are \\(k\\) only.* --- For calculating attention score for \\({q}_{3:n-2}\\), we will gather global, sliding, random keys & will compute the normal attention operation over \\({q}_{3:n-2}\\) and the gathered keys. Note that sliding keys are gathered using the special shifting trick as discussed earlier in the sliding attention section.  --- For calculating attention score for tokens in previous to last block (i.e. \\({q}_{n-1}\\)), we are gathering the first block, last three blocks, and the third block. Then we can apply the formula \\({a}_{n-1} = Softmax({q}_{n-1} * concat(k_1, k_3, k_5, k_6, k_7))\\). This is very similar to what we did for \\(q_2\\).  --- Attention score for \\(\mathbf{q}_{n}\\) is represented by \\(a_n\\) where \\(a_n=Softmax(q_n * K^T)\\), and is nothing but attention score between all the tokens in the last block with all the other tokens in sequence. This is very similar to what we did for \\( q_1 \\) .  --- Let's combine the above matrices to get the final attention matrix. This attention matrix can be used to get a representation of all the tokens.  *blue -> global blocks,red -> random blocks,orange -> sliding blocksThis attention matrix is just for illustration. During the forward pass, we aren't storingwhiteblocks, but are computing a weighted value matrix (i.e. representation of each token) directly for each separated components as discussed above.* Now, we have covered the hardest part of block sparse attention, i.e. its implementation. Hopefully, you now have a better background to understand the actual code. Feel free to dive into it and to connect each part of the code with one of the components above. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | What tokens should be attended to? We will give a practical example of how attention works by considering the sentence "BigBird is now available in HuggingFace for extractive question answering". In BERT-like attention, every word would simply attend to all other tokens. Put mathematically, this would mean that each queried token \( \text{query-token} \in {\text{BigBird},\text{is},\text{now},\text{available},\text{in},\text{HuggingFace},\text{for},\text{extractive},\text{question},\text{answering}} \), would attend to the full list of \( \text{key-tokens} = \left[\text{BigBird},\text{is},\text{now},\text{available},\text{in},\text{HuggingFace},\text{for},\text{extractive},\text{question},\text{answering} \right]\). Let's think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo-code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. python >>> # let's consider following sentence as an example >>> example = ['BigBird', 'is', 'now', 'available', 'in', 'HuggingFace', 'for', 'extractive', 'question', 'answering'] >>> # further let's assume, we're trying to understand the representation of 'available' i.e. >>> query_token = 'available' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently 'available' token doesn't have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention. python >>> # considering `window_size = 3`, we will consider 1 token to left & 1 to right of 'available' >>> # left token: 'now' ; right token: 'in' >>> sliding_tokens = ["now", "available", "in"] >>> # let's update our collection with the above tokens >>> key_tokens.append(sliding_tokens) Long-range dependencies: For some tasks, it is crucial to capture long-range relationships between tokens. E.g., in question-answering the model needs to compare each token of the context to the whole question to be able to figure out which part of the context is useful for a correct answer. If most of the context tokens would just attend to other context tokens, but not to the question, it becomes much harder for the model to filter important context tokens from less important context tokens. Now,BigBirdproposes two ways of allowing long-term attention dependencies while staying computationally efficient. * **Global tokens:** Introduce some tokens which will attend to every token and which are attended by every token. Eg: *"HuggingFace is building nice libraries for easy NLP"*. Now, let's say *'building'* is defined as a global token, and the model needs to know the relation among *'NLP'* & *'HuggingFace'* for some task (Note: these 2 tokens are at two extremes); Now having *'building'* attend globally to all other tokens will probably help the model to associate *'NLP'* with *'HuggingFace'*. ```python >>> # let's assume 1st & last token to beglobal, then >>> global_tokens = ["BigBird", "answering"] >>> # fill up global tokens in our key tokens collection >>> key_tokens.append(global_tokens) ``` * **Random tokens:** Select some tokens randomly which will transfer information by transferring to other tokens which in turn can transfer to other tokens. This may reduce the cost of information travel from one token to other. ```python >>> # now we can choosertoken randomly from our example sentence >>> # let's choose 'is' assumingr=1>>> random_tokens = ["is"] # Note: it is chosen compleletly randomly; so it can be anything else also. >>> # fill random tokens to our collection >>> key_tokens.append(random_tokens) >>> # it's time to see what tokens are in ourkey_tokenslist >>> key_tokens {'now', 'is', 'in', 'answering', 'available', 'BigBird'} # Now, 'available' (query we choose in our 1st step) will attend only these tokens instead of attending the complete sequence ``` This way, the query token attends only to a subset of all possible tokens while yielding a good approximation of full attention. The same approach will is used for all other queried tokens. But remember, the whole point here is to approximateBERT's full attention as efficiently as possible. Simply making each queried token attend all key tokens as it's done for BERT can be computed very effectively as a sequence of matrix multiplication on modern hardware, like GPUs. However, a combination of sliding, global & random attention appears to imply sparse matrix multiplication, which is harder to implement efficiently on modern hardware. One of the major contributions ofBigBirdis the proposition of ablock sparseattention mechanism that allows computing sliding, global & random attention effectively. Let's look into it! | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Using BigBird with 🤗Transformers You can use BigBirdModel just like any other 🤗 model. Let's see some code below: python from transformers import BigBirdModel # loading bigbird from its pretrained checkpoint model = BigBirdModel.from_pretrained("google/bigbird-roberta-base") # This will init the model with default configuration i.e. attention_type = "block_sparse" num_random_blocks = 3, block_size = 64. # But You can freely change these arguments with any checkpoint. These 3 arguments will just change the number of tokens each query token is going to attend. model = BigBirdModel.from_pretrained("google/bigbird-roberta-base", num_random_blocks=2, block_size=16) # By setting attention_type to `original_full`, BigBird will be relying on the full attention of n^2 complexity. This way BigBird is 99.9 % similar to BERT. model = BigBirdModel.from_pretrained("google/bigbird-roberta-base", attention_type="original_full") There are total 3 checkpoints available in 🤗Hub (at the point of writing this article): bigbird-roberta-base, bigbird-roberta-large, bigbird-base-trivia-itc. The first two checkpoints come from pretraining BigBirdForPretraining with masked_lm loss; while the last one corresponds to the checkpoint after finetuning BigBirdForQuestionAnswering on trivia-qa dataset. Let's have a look at minimal code you can write (in case you like to use your PyTorch trainer), to use 🤗's BigBird model for fine-tuning your tasks. python # let's consider our task to be question-answering as an example from transformers import BigBirdForQuestionAnswering, BigBirdTokenizer import torch device = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda") # lets initialize bigbird model from pretrained weights with randomly initialized head on its top model = BigBirdForQuestionAnswering.from_pretrained("google/bigbird-roberta-base", block_size=64, num_random_blocks=3) tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") model.to(device) dataset = "torch.utils.data.DataLoader object" optimizer = "torch.optim object" epochs = ... # very minimal training loop for e in range(epochs): for batch in dataset: model.train() batch = {k: batch[k].to(device) for k in batch} # forward pass output = model(**batch) # back-propogation output["loss"].backward() optimizer.step() optimizer.zero_grad() # let's save final weights in a local directory model.save_pretrained("<YOUR-WEIGHTS-DIR>") # let's push our weights to 🤗Hub from huggingface_hub import ModelHubMixin ModelHubMixin.push_to_hub("<YOUR-WEIGHTS-DIR>", model_id="<YOUR-FINETUNED-ID>") # using finetuned model for inference question = ["How are you doing?", "How is life going?"] context = ["<some big context having ans-1>", "<some big context having ans-2>"] batch = tokenizer(question, context, return_tensors="pt") batch = {k: batch[k].to(device) for k in batch} model = BigBirdForQuestionAnswering.from_pretrained("<YOUR-FINETUNED-ID>") model.to(device) with torch.no_grad(): start_logits, end_logits = model(**batch).to_tuple() # now decode start_logits, end_logits with what ever strategy you want. # Note: # This was very minimal code (in case you want to use raw PyTorch) just for showing how BigBird can be used very easily # I would suggest using 🤗Trainer to have access for a lot of features It's important to keep the following points in mind while working with big bird: * Sequence length must be a multiple of block size i.e. seqlen % block_size = 0. You need not worry since 🤗Transformers will automatically <pad> (to smallest multiple of block size which is greater than sequence length) if batch sequence length is not a multiple of block_size. * Currently, HuggingFace version doesn't support ETC and hence only 1st & last block will be global. * Current implementation doesn't support num_random_blocks = 0. * It's recommended by authors to set attention_type = "original_full" when sequence length < 1024. * This must hold: seq_length > global_token + random_tokens + sliding_tokens + buffer_tokens where global_tokens = 2 x block_size, sliding_tokens = 3 x block_size, random_tokens = num_random_blocks x block_size & buffer_tokens = num_random_blocks x block_size. In case you fail to do that, 🤗Transformers will automatically switch attention_type to original_full with a warning. * When using big bird as decoder (or using BigBirdForCasualLM), attention_type should be original_full. But you need not worry, 🤗Transformers will automatically switch attention_type to original_full in case you forget to do that. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Introduction Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its \(O(n^2)\) time & memory complexity (where \(n\) is sequence length). Hence, it's computationally very expensive to apply transformer-based models on long sequences \(n > 512\). Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗's recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this post is to give the reader an in-depth understanding of big bird implementation & ease one's life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird's attention is an approximation of BERT's full attention and therefore does not strive to be better than BERT's full attention, but rather to be more efficient. It simply allows to apply transformer-based models to much longer sequences since BERT's quadratic memory requirement quickly becomes unbearable. Simply put, if we would have \(\infty\) compute & \(\infty\) time, BERT's attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! --- Some of the main questions one might have when working with standard BERT-like attention include: * Do all tokens really have to attend to all other tokens? * Why not compute attention only over important tokens? * How to decide what tokens are important? * How to attend to just a few tokens in a very efficient way? --- In this blog post, we will try to answer those questions. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | ITC vs ETC The BigBird model can be trained using 2 different strategies: **ITC** & **ETC**. ITC (internal transformer construction) is simply what we discussed above. In ETC (extended transformer construction), some additional tokens are made global such that they will attend to / will be attended by all tokens. ITC requires less compute since very few tokens are global while at the same time the model can capture sufficient global information (also with the help of random attention). On the other hand, ETC can be very helpful for tasks in which we need a lot of global tokens such asquestion-answering for which the entire question should be attended to globally by the context to be able to relate the context correctly to the question. Note: It is shown in the Big Bird paper that in many ETC experiments, the number of random blocks is set to 0. This is reasonable given our discussions above in the graph section. The table below summarizes ITC & ETC: | | ITC | ETC | |----------------------------------------------|---------------------------------------|--------------------------------------| | Attention Matrix with global attention | \( A = \begin{bmatrix} 1 & 1 & 1 & 1 & 1 & 1 & 1 \ 1 & & & & & & 1 \ 1 & & & & & & 1 \ 1 & & & & & & 1 \ 1 & & & & & & 1 \ 1 & & & & & & 1 \ 1 & 1 & 1 & 1 & 1 & 1 & 1 \end{bmatrix} \) | \( B = \begin{bmatrix} 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \ 1 & 1 & 1 & & & & & & 1 \ 1 & 1 & 1 & & & & & & 1 \ 1 & 1 & 1 & & & & & & 1 \ 1 & 1 & 1 & & & & & & 1 \ 1 & 1 & 1 & & & & & & 1 \ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \end{bmatrix} \) | | global_tokens | 2 x block_size | extra_tokens + 2 x block_size | | random_tokens | num_random_blocks x block_size | num_random_blocks x block_size | | sliding_tokens | 3 x block_size | 3 x block_size | | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | End Notes The original implementation of block sparse attention matrix** can be found here. You can find 🤗's version here. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Global Attention For global attention, each query is simply attending to all the other tokens in the sequence & is attended by every other token. Let's assumeVasudev(1st token) &them(last token) to be global (in the above figure). You can see that these tokens are directly connected to all other tokens (blue boxes). ```python # pseudo code Q -> Query martix (seq_length, head_dim) K -> Key matrix (seq_length, head_dim) # 1st & last token attends all other tokens Q[0] x [K[0], K[1], K[2], ......, K[n-1]] Q[n-1] x [K[0], K[1], K[2], ......, K[n-1]] # 1st & last token getting attended by all other tokens K[0] x [Q[0], Q[1], Q[2], ......, Q[n-1]] K[n-1] x [Q[0], Q[1], Q[2], ......, Q[n-1]] ``` | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Understanding the need for global, sliding, random keys with Graphs First, let's get a better understanding ofglobal,sliding&randomattention using graphs and try to understand how the combination of these three attention mechanisms yields a very good approximation of standardBert-likeattention. <img src="assets/18_big_bird/global.png" width=250 height=250> <img src="assets/18_big_bird/sliding.png" width=250 height=250> <img src="assets/18_big_bird/random.png" width=250 height=250> <br> *The above figure showsglobal(left),sliding(middle) &random(right) connections respectively as a graph. Each node corresponds to a token and each line represents an attention score. If no connection is made between 2 tokens, then an attention score is assumed to 0.*  <img src="assets/18_big_bird/full.png" width=230 height=230> **BigBird block sparse attention** is a combination of sliding, global & random connections (total 10 connections) as shown ingifin left. While a graph of **normal attention** (right) will have all 15 connections (note: total 6 nodes are present). You can simply think of normal attention as all the tokens attending globally \\( {}^1 \\). **Normal attention:** Model can transfer information from one token to another token directly in a single layer since each token is queried over every other token and is attended by every other token. Let's consider an example similar to what is shown in the above figures. If the model needs to associate *'going'* with *'now'*, it can simply do that in a single layer since there is a direct connection joining both the tokens. **Block sparse attention:** If the model needs to share information between two nodes (or tokens), information will have to travel across various other nodes in the path for some of the tokens; since all the nodes are not directly connected in a single layer. *Eg.*, assuming model needs to associate *'going'* with *'now'*, then if only sliding attention is present the flow of information among those 2 tokens, is defined by the path:going -> am -> i -> now(i.e. it will have to travel over 2 other tokens). Hence, we may need multiple layers to capture the entire information of the sequence. Normal attention can capture this in a single layer. In an extreme case, this could mean that as many layers as input tokens are needed. If, however, we introduce some global tokens information can travel via the path:going -> i -> now(which is shorter). If we in addition introduce random connections it can travel via:going -> am -> now. With the help of random connections & global connections, information can travel very rapidly (with just a few layers) from one token to the next. In case, we have many global tokens, then we may not need random connections since there will be multiple short paths through which information can travel. This is the idea behind keepingnum_random_tokens = 0when working with a variant of BigBird, called ETC (more on this in later sections). \\( {}^1 \\) In these graphics, we are assuming that the attention matrix is symmetric **i.e.** \\(\mathbf{A}_{ij} = \mathbf{A}_{ji}\\) since in a graph if some token **A** attends **B**, then **B** will also attend **A**. You can see from the figure of the attention matrix shown in the next section that this assumption holds for most tokens in BigBird | Attention Type |global_tokens|sliding_tokens|random_tokens| |-----------------|-------------------|------------------|------------------------------------| |original_full|n| 0 | 0 | |block_sparse| 2 xblock_size| 3 xblock_size|num_random_blocksxblock_size| *original_fullrepresentsBERT's attention whileblock_sparserepresentsBigBird's attention. Wondering what theblock_sizeis? We will cover that in later sections. For now, consider it to be 1 for simplicity* | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Time & Memory complexity | Attention Type | Sequence length | Time & Memory Complexity | |-----------------|-----------------|--------------------------| |original_full| 512 |T| | | 1024 | 4 xT| | | 4096 | 64 xT| |block_sparse| 1024 | 2 xT| | | 4096 | 8 xT| *Comparison of time & space complexity of BERT attention and BigBird block sparse attention.* <details> <summary>Expand this snippet in case you wanna see the calculations</summary> ```md BigBird time complexity = O(w x n + r x n + g x n) BERT time complexity = O(n^2) Assumptions: w = 3 x 64 r = 3 x 64 g = 2 x 64 When seqlen = 512 => **time complexity in BERT = 512^2** When seqlen = 1024 => time complexity in BERT = (2 x 512)^2 => **time complexity in BERT = 4 x 512^2** => time complexity in BigBird = (8 x 64) x (2 x 512) => **time complexity in BigBird = 2 x 512^2** When seqlen = 4096 => time complexity in BERT = (8 x 512)^2 => **time complexity in BERT = 64 x 512^2** => compute in BigBird = (8 x 64) x (8 x 512) => compute in BigBird = 8 x (512 x 512) => **time complexity in BigBird = 8 x 512^2** ``` </details> | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | What's next? @patrickvonplaten has made a really cool notebook on how to evaluate BigBirdForQuestionAnswering on the trivia-qa dataset. Feel free to play with BigBird using that notebook. You will soon find BigBird Pegasus-like model in the library for long document summarization💥. | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | # Understanding BigBird's Block Sparse Attention | [
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Understanding BigBird's Block Sparse Attention | vasudevgupta | March 31, 2021 | big-bird | community, research, nlp | https://huggingface.co/blog/big-bird | Random Attention Random attention is ensuring that each query token will attend a few random tokens as well. For the actual implementation, this means that the model gathers some tokens randomly and computes their attention score. ```python # r1, r2, r are some random indices; Note: r1, r2, r3 are different for each row 👇 Q[1] x [Q[r1], Q[r2], ......, Q[r]] . . . Q[n-2] x [Q[r1], Q[r2], ......, Q[r]] # leaving 0th & (n-1)th token since they are already global ``` **Note:** The current implementation further divides sequence into blocks & each notation is defined w.r.to block instead of tokens. Let's discuss this in more detail in the next section. | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Set up a development environment and install sagemaker After our SageMaker Notebook Instance is running we can select either Jupyer Notebook or JupyterLab and create a new Notebook with the conda_pytorch_p36 kernel. Note: The use of Jupyter is optional: We could also launch SageMaker Training jobs from anywhere we have an SDK installed, connectivity to the cloud and appropriate permissions, such as a Laptop, another IDE or a task scheduler like Airflow or AWS Step Functions. After that we can install the required dependencies bash !pip install transformers "datasets[s3]" sagemaker --upgrade install git-lfs for model upload. bash !curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.rpm.sh | sudo bash !sudo yum install git-lfs -y !git lfs install To run training on SageMaker we need to create a sagemaker Session and provide an IAM role with the right permission. This IAM role will be later attached to the TrainingJob enabling it to download data, e.g. from Amazon S3. python import sagemaker sess = sagemaker.Session() role = sagemaker.get_execution_role() print(f"IAM role arn used for running training: {role}") print(f"S3 bucket used for storing artifacts: {sess.default_bucket()}") --- # Choose 🤗 Transformers examples/ script The 🤗 Transformers repository contains several examples/scripts for fine-tuning models on tasks from language-modeling to token-classification. In our case, we are using the run_summarization.py from the seq2seq/ examples. Note: you can use this tutorial as-is to train your model on a different examples script. Since the HuggingFace Estimator has git support built-in, we can specify a training script stored in a GitHub repository as entry_point and source_dir. We are going to use the transformers 4.4.2 DLC which means we need to configure the v4.4.2 as the branch to pull the compatible example scripts. python #git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.4.2'} # v4.4.2 is referring to the `transformers_version you use in the estimator. # used due an missing package in v4.4.2 git_config = {'repo': 'https://github.com/philschmid/transformers.git','branch': 'master'} # v4.4.2 is referring to the `transformers_version you use in the estimator. --- | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Create a HuggingFace estimator and start training The last step before training is creating a HuggingFace estimator. The Estimator handles the end-to-end Amazon SageMaker training. We define which fine-tuning script should be used as entry_point, which instance_type should be used, and which hyperparameters are passed in. python from sagemaker.huggingface import HuggingFace # create the Estimator huggingface_estimator = HuggingFace( entry_point='run_summarization.py', # script source_dir='./examples/seq2seq', # relative path to example git_config=git_config, instance_type='ml.p3dn.24xlarge', instance_count=2, transformers_version='4.4.2', pytorch_version='1.6.0', py_version='py36', role=role, hyperparameters = hyperparameters, distribution = distribution ) As instance_type we are using ml.p3dn.24xlarge, which contains 8x NVIDIA A100 with an instance_count of 2. This means we are going to run training on 16 GPUs and a total_batch_size of 16*4=64. We are going to train a 400 Million Parameter model with a total_batch_size of 64, which is just wow. To start our training we call the .fit() method. python # starting the training job huggingface_estimator.fit() bash 2021-04-01 13:00:35 Starting - Starting the training job... 2021-04-01 13:01:03 Starting - Launching requested ML instancesProfilerReport-1617282031: InProgress 2021-04-01 13:02:23 Starting - Preparing the instances for training...... 2021-04-01 13:03:25 Downloading - Downloading input data... 2021-04-01 13:04:04 Training - Downloading the training image............... 2021-04-01 13:06:33 Training - Training image download completed. Training in progress .... .... 2021-04-01 13:16:47 Uploading - Uploading generated training model 2021-04-01 13:27:49 Completed - Training job completed Training seconds: 2882 Billable seconds: 2882 The training seconds are 2882 because they are multiplied by the number of instances. If we calculate 2882/2=1441 is it the duration from "Downloading the training image" to "Training job completed". Converted to real money, our training on 16 NVIDIA Tesla V100-GPU for a State-of-the-Art summarization model comes down to ~28$. --- | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Usage from transformers import pipeline summarizer = pipeline("summarization", model="philschmid/{model_name}") conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? Philipp: Sure you can use the new Hugging Face Deep Learning Container. Jeff: ok. Jeff: and how can I get started? Jeff: where can I find documentation? Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face ''' nlp(conversation) | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | `{model_name}` This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container. For more information look at: - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html) - [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker) - [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html) - [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html) - [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Hyperparameters {hyperparameters} | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Configure distributed training and hyperparameters Next, we will define our hyperparameters and configure our distributed training strategy. As hyperparameter, we can define any Seq2SeqTrainingArguments and the ones defined in run_summarization.py. python # hyperparameters, which are passed into the training job hyperparameters={ 'per_device_train_batch_size': 4, 'per_device_eval_batch_size': 4, 'model_name_or_path':'facebook/bart-large-cnn', 'dataset_name':'samsum', 'do_train':True, 'do_predict': True, 'predict_with_generate': True, 'output_dir':'/opt/ml/model', 'num_train_epochs': 3, 'learning_rate': 5e-5, 'seed': 7, 'fp16': True, } # configuration for running training on smdistributed Data Parallel distribution = {'smdistributed':{'dataparallel':{ 'enabled': True }}} Since, we are using SageMaker Data Parallelism our total_batch_size will be per_device_train_batch_size * n_gpus. --- | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Upload the fine-tuned model to huggingface.co Since our model achieved a pretty good score we are going to upload it to huggingface.co, create a model_card and test it with the Hosted Inference widget. To upload a model you need to create an account here. We can download our model from Amazon S3 and unzip it using the following snippet. python import os import tarfile from sagemaker.s3 import S3Downloader local_path = 'my_bart_model' os.makedirs(local_path, exist_ok = True) # download model from S3 S3Downloader.download( s3_uri=huggingface_estimator.model_data, # s3 uri where the trained model is located local_path=local_path, # local path where *.tar.gz will be saved sagemaker_session=sess # sagemaker session used for training the model ) # unzip model tar = tarfile.open(f"{local_path}/model.tar.gz", "r:gz") tar.extractall(path=local_path) tar.close() os.remove(f"{local_path}/model.tar.gz") Before we are going to upload our model to huggingface.co we need to create a model_card. The model_card describes the model and includes hyperparameters, results, and specifies which dataset was used for training. To create a model_card we create a README.md in our local_path python # read eval and test results with open(f"{local_path}/eval_results.json") as f: eval_results_raw = json.load(f) eval_results={} eval_results["eval_rouge1"] = eval_results_raw["eval_rouge1"] eval_results["eval_rouge2"] = eval_results_raw["eval_rouge2"] eval_results["eval_rougeL"] = eval_results_raw["eval_rougeL"] eval_results["eval_rougeLsum"] = eval_results_raw["eval_rougeLsum"] with open(f"{local_path}/test_results.json") as f: test_results_raw = json.load(f) test_results={} test_results["test_rouge1"] = test_results_raw["test_rouge1"] test_results["test_rouge2"] = test_results_raw["test_rouge2"] test_results["test_rougeL"] = test_results_raw["test_rougeL"] test_results["test_rougeLsum"] = test_results_raw["test_rougeLsum"] After we extract all the metrics we want to include we are going to create our README.md. Additionally to the automated generation of the results table we add the metrics manually to the metadata of our model card under model-index python import json MODEL_CARD_TEMPLATE = """ --- language: en tags: - sagemaker - bart - summarization license: apache-2.0 datasets: - samsum model-index: - name: {model_name} results: - task: name: Abstractive Text Summarization type: abstractive-text-summarization dataset: name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization" type: samsum metrics: - name: Validation ROGUE-1 type: rogue-1 value: 42.621 - name: Validation ROGUE-2 type: rogue-2 value: 21.9825 - name: Validation ROGUE-L type: rogue-l value: 33.034 - name: Test ROGUE-1 type: rogue-1 value: 41.3174 - name: Test ROGUE-2 type: rogue-2 value: 20.8716 - name: Test ROGUE-L type: rogue-l value: 32.1337 widget: - text: | Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? Philipp: Sure you can use the new Hugging Face Deep Learning Container. Jeff: ok. Jeff: and how can I get started? Jeff: where can I find documentation? Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face --- | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Model and Dataset We are going to fine-tune facebook/bart-large-cnn on the samsum dataset. "BART is sequence-to-sequence model trained with denoising as pretraining objective." [REF] The samsum dataset contains about 16k messenger-like conversations with summaries. json {"id": "13818513", "summary": "Amanda baked cookies and will bring Jerry some tomorrow.", "dialogue": "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"} --- | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Results | key | value | | --- | ----- | {eval_table} {test_table} """ # Generate model card (todo: add more data from Trainer) model_card = MODEL_CARD_TEMPLATE.format( model_name=f"{hyperparameters['model_name_or_path'].split('/')[1]}-{hyperparameters['dataset_name']}", hyperparameters=json.dumps(hyperparameters, indent=4, sort_keys=True), eval_table="\n".join(f"| {k} | {v} |" for k, v in eval_results.items()), test_table="\n".join(f"| {k} | {v} |" for k, v in test_results.items()), ) with open(f"{local_path}/README.md", "w") as f: f.write(model_card) After we have our unzipped model and model card located in my_bart_model we can use the either huggingface_hub SDK to create a repository and upload it to huggingface.co – or just to https://huggingface.co/new an create a new repository and upload it. python from getpass import getpass from huggingface_hub import HfApi, Repository hf_username = "philschmid" # your username on huggingface.co hf_email = "[email protected]" # email used for commit repository_name = f"{hyperparameters['model_name_or_path'].split('/')[1]}-{hyperparameters['dataset_name']}" # repository name on huggingface.co password = getpass("Enter your password:") # creates a prompt for entering password # get hf token token = HfApi().login(username=hf_username, password=password) # create repository repo_url = HfApi().create_repo(token=token, name=repository_name, exist_ok=True) # create a Repository instance model_repo = Repository(use_auth_token=token, clone_from=repo_url, local_dir=local_path, git_user=hf_username, git_email=hf_email) # push model to the hub model_repo.push_to_hub() --- | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | # Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker In case you missed it: on March 25th we announced a collaboration with Amazon SageMaker to make it easier to create State-of-the-Art Machine Learning models, and ship cutting-edge NLP features faster. Together with the SageMaker team, we built 🤗 Transformers optimized Deep Learning Containers to accelerate training of Transformers-based models. Thanks AWS friends!🤗 🚀 With the new HuggingFace estimator in the SageMaker Python SDK, you can start training with a single line of code. The announcement blog post provides all the information you need to know about the integration, including a "Getting Started" example and links to documentation, examples, and features. listed again here: - 🤗 Transformers Documentation: Amazon SageMaker - Example Notebooks - Amazon SageMaker documentation for Hugging Face - Python SDK SageMaker documentation for Hugging Face - Deep Learning Container If you're not familiar with Amazon SageMaker: "Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models." [REF] --- # Tutorial We will use the new Hugging Face DLCs and Amazon SageMaker extension to train a distributed Seq2Seq-transformer model on the summarization task using the transformers and datasets libraries, and then upload the model to huggingface.co and test it. As distributed training strategy we are going to use SageMaker Data Parallelism, which has been built into the Trainer API. To use data-parallelism we only have to define the distribution parameter in our HuggingFace estimator. python # configuration for running training on smdistributed Data Parallel distribution = {'smdistributed':{'dataparallel':{ 'enabled': True }}} In this tutorial, we will use an Amazon SageMaker Notebook Instance for running our training job. You can learn here how to set up a Notebook Instance. What are we going to do: - Set up a development environment and install sagemaker - Choose 🤗 Transformers examples/ script - Configure distributed training and hyperparameters - Create a HuggingFace estimator and start training - Upload the fine-tuned model to huggingface.co - Test inference | [
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Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | philschmid | April 8, 2021 | sagemaker-distributed-training-seq2seq | guide, partnerships, aws, nlp | https://huggingface.co/blog/sagemaker-distributed-training-seq2seq | Test inference After we uploaded our model we can access it at https://huggingface.co/{hf_username}/{repository_name} python print(f"https://huggingface.co/{hf_username}/{repository_name}") And use the "Hosted Inference API" widget to test it. https://huggingface.co/philschmid/bart-large-cnn-samsum | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | Optimizer The optimizer is also wrapped in a special container that will perform the necessary operations in the step to make mixed precision work. It will also properly handle device placement of the state dict if its non-empty or loaded from a checkpoint. | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | One launcher to rule them all The scripts using Accelerate will be completely compatible with your traditional launchers, such as torch.distributed.launch. But remembering all the arguments to them is a bit annoying and when you've setup your instance with 4 GPUs, you'll run most of your trainings using them all. Accelerate comes with a handy CLI that works in two steps: bash accelerate config This will trigger a little questionnaire about your setup, which will create a config file you can edit with all the defaults for your training commands. Then bash accelerate launch path_to_script.py --args_to_the_script will launch your training script using those default. The only thing you have to do is provide all the arguments needed by your training script. To make this launcher even more awesome, you can use it to spawn an AWS instance using SageMaker. Look at this guide to discover how! | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | Easy integration! Let's first have a look at an example: diff import torch import torch.nn.functional as F from datasets import load_dataset + from accelerate import Accelerator + accelerator = Accelerator() - device = 'cpu' + device = accelerator.device model = torch.nn.Transformer().to(device) optim = torch.optim.Adam(model.parameters()) dataset = load_dataset('my_dataset') data = torch.utils.data.DataLoader(dataset, shuffle=True) + model, optim, data = accelerator.prepare(model, optim, data) model.train() for epoch in range(10): for source, targets in data: source = source.to(device) targets = targets.to(device) optimizer.zero_grad() output = model(source) loss = F.cross_entropy(output, targets) - loss.backward() + accelerator.backward(loss) optimizer.step() By just adding five lines of code to any standard PyTorch training script, you can now run said script on any kind of distributed setting, as well as with or without mixed precision. 🤗 Accelerate even handles the device placement for you, so you can simplify the training loop above even further: diff import torch import torch.nn.functional as F from datasets import load_dataset + from accelerate import Accelerator + accelerator = Accelerator() - device = 'cpu' - model = torch.nn.Transformer().to(device) + model = torch.nn.Transformer() optim = torch.optim.Adam(model.parameters()) dataset = load_dataset('my_dataset') data = torch.utils.data.DataLoader(dataset, shuffle=True) + model, optim, data = accelerator.prepare(model, optim, data) model.train() for epoch in range(10): for source, targets in data: - source = source.to(device) - targets = targets.to(device) optimizer.zero_grad() output = model(source) loss = F.cross_entropy(output, targets) - loss.backward() + accelerator.backward(loss) optimizer.step() In contrast, here are the changes needed to have this code run with distributed training are the followings: diff + import os import torch import torch.nn.functional as F from datasets import load_dataset + from torch.utils.data import DistributedSampler + from torch.nn.parallel import DistributedDataParallel + local_rank = int(os.environ.get("LOCAL_RANK", -1)) - device = 'cpu' + device = device = torch.device("cuda", local_rank) model = torch.nn.Transformer().to(device) + model = DistributedDataParallel(model) optim = torch.optim.Adam(model.parameters()) dataset = load_dataset('my_dataset') + sampler = DistributedSampler(dataset) - data = torch.utils.data.DataLoader(dataset, shuffle=True) + data = torch.utils.data.DataLoader(dataset, sampler=sampler) model.train() for epoch in range(10): + sampler.set_epoch(epoch) for source, targets in data: source = source.to(device) targets = targets.to(device) optimizer.zero_grad() output = model(source) loss = F.cross_entropy(output, targets) loss.backward() optimizer.step() These changes will make your training script work for multiple GPUs, but your script will then stop working on CPU or one GPU (unless you start adding if statements everywhere). Even more annoying, if you wanted to test your script on TPUs you would need to change different lines of codes. Same for mixed precision training. The promise of 🤗 Accelerate is: - to keep the changes to your training loop to the bare minimum so you have to learn as little as possible. - to have the same functions work for any distributed setup, so only have to learn one API. | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | DataLoader This is where most of the magic is hidden. As you have seen in the code example, the library does not rely on a DistributedSampler, it will actually work with any sampler you might pass to your dataloader (if you ever had to write a distributed version of your custom sampler, there is no more need for that!). The dataloader is wrapped in a container that will only grab the indices relevant to the current process in the sampler (or skip the batches for the other processes if you use an IterableDataset) and put the batches on the proper device. For this to work, Accelerate provides a utility function that will synchronize the random number generators on each of the processes run during distributed training. By default, it only synchronizes the generator of your sampler, so your data augmentation will be different on each process, but the random shuffling will be the same. You can of course use this utility to synchronize more RNGs if you need it. python accelerator.backward(loss) This last line adds the necessary steps for the backward pass (mostly for mixed precision but other integrations will require some custom behavior here). | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | What about evaluation? Evaluation can either be run normally on all processes, or if you just want it to run on the main process, you can use the handy test: python if accelerator.is_main_process(): # Evaluation loop But you can also very easily run a distributed evaluation using Accelerate, here is what you would need to add to your evaluation loop: diff + eval_dataloader = accelerator.prepare(eval_dataloader) predictions, labels = [], [] for source, targets in eval_dataloader: with torch.no_grad(): output = model(source) - predictions.append(output.cpu().numpy()) - labels.append(targets.cpu().numpy()) + predictions.append(accelerator.gather(output).cpu().numpy()) + labels.append(accelerator.gather(targets).cpu().numpy()) predictions = np.concatenate(predictions) labels = np.concatenate(labels) + predictions = predictions[:len(eval_dataloader.dataset)] + labels = label[:len(eval_dataloader.dataset)] metric_compute(predictions, labels) Like for the training, you need to add one line to prepare your evaluation dataloader. Then you can just use accelerator.gather to gather across processes the tensors of predictions and labels. The last line to add truncates the predictions and labels to the number of examples in your dataset because the prepared evaluation dataloader will return a few more elements to make sure batches all have the same size on each process. | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | How does it work? To see how the library works in practice, let's have a look at each line of code we need to add to a training loop. python accelerator = Accelerator() On top of giving the main object that you will use, this line will analyze from the environment the type of distributed training run and perform the necessary initialization. You can force a training on CPU or a mixed precision training by passing cpu=True or fp16=True to this init. Both of those options can also be set using the launcher for your script. python model, optim, data = accelerator.prepare(model, optim, data) This is the main bulk of the API and will prepare the three main type of objects: models (torch.nn.Module), optimizers (torch.optim.Optimizer) and dataloaders (torch.data.dataloader.DataLoader). | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | 🤗 Accelerate Run your raw PyTorch training scripts on any kind of device. Most high-level libraries above PyTorch provide support for distributed training and mixed precision, but the abstraction they introduce require a user to learn a new API if they want to customize the underlying training loop. 🤗 Accelerate was created for PyTorch users who like to have full control over their training loops but are reluctant to write (and maintain) the boilerplate code needed to use distributed training (for multi-GPU on one or several nodes, TPUs, ...) or mixed precision training. Plans forward include support for fairscale, deepseed, AWS SageMaker specific data-parallelism and model parallelism. It provides two things: a simple and consistent API that abstracts that boilerplate code and a launcher command to easily run those scripts on various setups. | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | # Introducing 🤗 Accelerate | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | How to get involved? To get started, just pip install accelerate or see the documentation for more install options. Accelerate is a fully open-sourced project, you can find it on GitHub, have a look at its documentation or skim through our basic examples. Please let us know if you have any issue or feature you would like the library to support. For all questions, the forums is the place to check! For more complex examples in situation, you can look at the official Transformers examples. Each folder contains a run_task_no_trainer.py that leverages the Accelerate library! | [
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Introducing 🤗 Accelerate | sgugger | April 16, 2021 | accelerate-library | guide | https://huggingface.co/blog/accelerate-library | Model Model preparation include wrapping it in the proper container (for instance DistributedDataParallel) and putting it on the proper device. Like with a regular distributed training, you will need to unwrap your model for saving, or to access its specific methods, which can be done with accelerator.unwrap_model(model). | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 9. Conclusion Through this blog post, we covered out-of-box BERT inference performance one can expect for PyTorch and TensorFlow, from a simple PyPi install and without further tuning. It is important to highlight results provided here reflects out-of-the-box framework setup hence, they might not provide the absolute best performances. We decided to not include optimizations as part of this blog post to focus on hardware and efficiency. Optimizations will be discussed in the second part! 🚀 Then, we covered and detailed the impact, and the importance of setting the thread affinity along with the trade-off between the target problem size, and the number of cores required for achieving the task. Also, it is important to define which criteria (i.e. latency vs throughput) to use when optimizing your deployment as the resulting setups might be totally different. On a more general note, small problem sizes (short sequences and/or small batches) might require much fewer cores to achieve the best possible latency than big problems (very long sequences and/or big batches). It is interesting to cover all these aspects when thinking about the final deployment platform as it might cut the cost of the infrastructure drastically. For instance, our 48 cores machine charges 4.848\$/h whereas a smaller instances with only 8 cores lowers the cost to 0.808\$/h, leading to a 6x cost reduction. Last but not least, many of the knobs discussed along this blog post can be automatically tuned through a launcher script highly inspired from the original script made by Intel and available here. The launcher script is able to automatically starts your python process(es) with the correct thread affinity, effectively splitting resources across instances along with many other performances tips! We will detail many of this tips in the second part 🧐. In the follow-up blog post, more advanced settings and tuning techniques to decrease model latency even further will be involved, such as: - Launcher script walk-through - Tuning the memory allocation library - Using Linux's Transparent Huge Pages mechanisms - Using vendor-specific Math/Parallel libraries Stay tuned! 🤗 | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 5.1. Introduction There are multiple ways to improve the latency and throughput for tasks such as BERT inference. Improvements and tuning can be performed at various levels from enabling Operating System features, swapping dependent libraries with more performant ones, carefully tuning framework properties and, last but not least, using parallelization logic leveraging all the cores on the CPU(s). For the remainder of this blog post we will focus on the latter, also known as Multiple Inference Stream. The idea is simple: Allocate multiple instances of the same model and assign the execution of each instance to a dedicated, non-overlapping subset of the CPU cores in order to have truly parallel instances. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 5.2. Cores and Threads on Modern CPUs On our way towards optimizing CPU inference for better usage of the CPU cores you might have already seen -at least for the past 20 years- modern CPUs specifications report "cores" and "hardware threads" or "physical" and "logical" numbers. These notions refer to a mechanism called Simultaneous Multi-Threading (SMT) or Hyper-Threading on Intel's platforms. To illustrate this, imagine two tasks A and B, executing in parallel, each on its own software thread. At some point, there is a high probability these two tasks will have to wait for some resources to be fetched from main memory, SSD, HDD or even the network. If the threads are scheduled on different physical cores, with no hyper-threading, during these periods the core executing the task is in an Idle state waiting for the resources to arrive, and effectively doing nothing... and hence not getting fully utilized Now, with SMT, the two software threads for task A and B can be scheduled on the same physical core, such that their execution is interleaved on that physical core: Task A and Task B will execute simultaneously on the physical core and when one task is halted, the other task can still continue execution on the core thereby increasing the utilization of that core.
Figure 3. Illustration of Intel Hyper Threading technology (SMT)
The figure 3. above simplifies the situation by assuming single core setup. If you want some more details on how SMT works on multi-cores CPUs, please refer to these two articles with very deep technical explanations of the behavior: - Intel® Hyper-Threading Technology - Technical User Guide (12) - Introduction to Hyper-Threading Technology (13) Back to our model inference workload... If you think about it, in a perfect world with a fully optimized setup, computations take the majority of time. In this context, using the logical cores shouldn't bring us any performance benefit because both logical cores (hardware threads) compete for the core’s execution resources. As a result, the tasks being a majority of general matrix multiplications (gemms (14)), they are inherently CPU bounds and does not benefits from SMT. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 5.3. Leveraging Multi-Socket servers and CPU affinity Nowadays servers bring many cores, some of them even support multi-socket setups (i.e. multiple CPUs on the motherboard). On Linux, the command lscpu reports all the specifications and topology of the CPUs present on the system: shell ubuntu@some-ec2-machine:~$ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 96 On-line CPU(s) list: 0-95 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz Stepping: 7 CPU MHz: 1200.577 CPU max MHz: 3900.0000 CPU min MHz: 1200.0000 BogoMIPS: 6000.00 Virtualization: VT-x L1d cache: 1.5 MiB L1i cache: 1.5 MiB L2 cache: 48 MiB L3 cache: 71.5 MiB NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 In our case we have a machine with 2 sockets, each socket providing 24 physical cores with 2 threads per cores (SMT). Another interesting characteristic is the notion of NUMA node (0, 1) which represents how cores and memory are being mapped on the system. Non-Uniform Memory Access (NUMA) is the opposite of Uniform Memory Access (UMA) where the whole memory pool is accessible by all the cores through a single unified bus between sockets and the main memory. NUMA on the other hand splits the memory pool and each CPU socket is responsible to address a subset of the memory, reducing the congestion on the bus.
Figure 5. Difference illustration of UMA and NUMA architectures (source (15))
In order to fully utilize the potential of such a beefy machine, we need to ensure our model instances are correctly dispatched across all the physical cores on all sockets along with enforcing memory allocation to be "NUMA-aware". On Linux, NUMA's process configuration can be tuned through numactl which provides an interface to bind a process to a set of CPU cores (referred as Thread Affinity). Also, it allows tuning the memory allocation policy, making sure the memory allocated for the process is as close as possible to the cores' memory pool (referred as Explicit Memory Allocation Directives). Note: Setting both cores and memory affinities is important here. Having computations done on socket 0 and memory allocated on socket 1 would ask the system to go over the sockets shared bus to exchange memory, thus leading to an undesired overhead. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 5.4. Tuning Thread Affinity & Memory Allocation Policy Now that we have all the knobs required to control the resources' allocation of our model instances we go further and see how to effectively deploy those and see the impact on latency and throughput. Let's go gradually to get a sense of what is the impact of each command and parameter. First, we start by launching our inference model without any tuning, and we observe how the computations are being dispatched on CPU cores (Left). shell python3 src/main.py model=bert-base-cased backend.name=pytorch batch_size=1 sequence_length=128 Then we specify the core and memory affinity through numactl using all the physical cores and only a single thread (thread 0) per core (Right): shell numactl -C 0-47 -m 0,1 python3 src/main.py model=bert-base-cased backend.name=pytorch batch_size=1 sequence_length=128
Figure 6. Linux htop command side-by-side results without & with Thread Affinity set
As you can see, without any specific tuning, PyTorch and TensorFlow dispatch the work on a single socket, using all the logical cores in that socket (both threads on 24 cores). Also, as we highlighted earlier, we do not want to leverage the SMT feature in our case, so we set the process' thread affinity to target only 1 hardware thread. Note, this is specific to this run and can vary depending on individual setups. Hence, it is recommended to check thread affinity settings for each specific use-case. Let's take sometime from here to highlight what we did with numactl: - -C 0-47 indicates to numactl what is the thread affinity (cores 0 to 47). - -m 0,1 indicates to numactl to allocate memory on both CPU sockets If you wonder why we are binding the process to cores [0...47], you need to go back to look at the output of lscpu. From there you will find the section NUMA node0 and NUMA node1 which has the form NUMA node<X> <logical ids> In our case, each socket is one NUMA node and there are 2 NUMA nodes. Each socket or each NUMA node has 24 physical cores and 2 hardware threads per core, so 48 logical cores. For NUMA node 0, 0-23 are hardware thread 0 and 24-47 are hardware thread 1 on the 24 physical cores in socket 0. Likewise, for NUMA node 1, 48-71 are hardware thread 0 and 72-95 are hardware thread 1 on the 24 physical cores in socket 1. As we are targeting just 1 thread per physical core, as explained earlier, we pick only thread 0 on each core and hence logical processors 0-47. Since we are using both sockets, we need to also bind the memory allocations accordingly (0,1). Please note that using both sockets may not always give the best results, particularly for small problem sizes. The benefit of using compute resources across both sockets might be reduced or even negated by cross-socket communication overhead. | [
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] |
Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 |
# Scaling up BERT-like model Inference on modern CPU - Part 1 | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 3. Baselines All the results below were run on Amazon Web Services (AWS) c5.metal instance leveraging an Intel Xeon Platinum 8275 CPU (48 cores/96 threads). The choice of this instance provides all the useful CPU features to speedup Deep Learning workloads such as: - AVX512 instructions set (which might not be leveraged out-of-the-box by the various frameworks) - Intel Deep Learning Boost (also known as Vector Neural Network Instruction - VNNI) which provides specialized CPU instructions for running quantized networks (using int8 data type) The choice of using metal instance is to avoid any virtualization issue which can arise when using cloud providers. This gives us full control of the hardware, especially while targeting the NUMA (Non-Unified Memory Architecture) controller, which we will cover later in this post. The operating system was Ubuntu 20.04 (LTS) and all the experiments were conducted using Hugging Face transformers version 4.5.0, PyTorch 1.8.1 & Google TensorFlow 2.4.0 | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 4. Out of the box results
Figure 1. PyTorch (1.8.1) vs Google TensorFlow (2.4.1) out of the box
Figure 2. PyTorch (1.8.1) vs Google TensorFlow (2.4.1) out of the box - (Bigger Batch Size)
Straigh to the point, out-of-the-box, PyTorch shows better inference results over TensorFlow for all the configurations tested here. It is important to note the results out-of-the-box might not reflect the "optimal" setup for both PyTorch and TensorFlow and thus it can look deceiving here. One possible way to explain such difference between the two frameworks might be the underlying technology to execute parallel sections within operators. PyTorch internally uses OpenMP (10) along with Intel MKL (now oneDNN) (11) for efficient linear algebra computations whereas TensorFlow relies on Eigen and its own threading implementation. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | References 1. Benchmarking Transformers: PyTorch and TensorFlow 2. HuggingFace's Transformers: State-of-the-art Natural Language Processing 3. HuggingFace's Model Hub 4. BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin & al. 2018) 5. Illustrated Transformer blogpost from Jay Alammar 6. PyTorch - TorchScript 7. Google Accelerated Linear Algebra (XLA) 8. ONNX Runtime - Optimize and Accelerate Machine Learning Inferencing and Training 9. Q8BERT - Quantized 8Bit BERT (Zafrir & al. 2019) 10. OpenMP 11. Intel oneDNN 12. Intel® Hyper-Threading Technology - Technical User Guide 13. Introduction to Hyper-Threading Technology 14. BLAS (Basic Linear Algebra Subprogram) - Wikipedia 15. Optimizing Applications for NUMA | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 6. Core count scaling - Does using more cores actually improve performance? When thinking about possible ways to improve our model inference performances, the first rational solution might be to throw some more resources to do the same amount of work. Through the rest of this blog series, we will refer to this setup as Core Count Scaling meaning, only the number of cores used on the system to achieve the task will vary. This is also often referred as Strong Scaling in the HPC world. At this stage, you may wonder what is the point of allocating only a subset of the cores rather than throwing all the horses at the task to achieve minimum latency. Indeed, depending on the problem-size, throwing more resources to the task might give better results. It is also possible that for small problems putting more CPU cores at work doesn't improve the final latency. In order to illustrate this, the figure 6. below takes different problem sizes (batch_size = 1, sequence length = {32, 128, 512}) and reports the latencies with respect to the number of CPU cores used for running computations for both PyTorch and TensorFlow. Limiting the number of resources involved in computation is done by limiting the CPU cores involved in intra operations (intra here means inside an operator doing computation, also known as "kernel"). This is achieved through the following APIs: - PyTorch: torch.set_num_threads(x) - TensorFlow: tf.config.threading.set_intra_op_parallelism_threads(x)
Figure 7. Latency measurements
As you can see, depending on the problem size, the number of threads involved in the computations has a positive impact on the latency measurements. For small-sized problems & medium-sized problems using only one socket would give the best performance. For large-sized problems, the overhead of the cross-socket communication is covered by the computations cost, thus benefiting from using all the cores available on the both sockets. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 5. Scaling BERT Inference to increase overall throughput on modern CPU | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | Acknowledgments - Omry Yadan (Facebook FAIR) - Author of OmegaConf & Hydra for all the tips setting up Hydra correctly. - All Intel & Intel Labs' NLP colleagues - For the ongoing optimizations and research efforts they are putting into transformers and more generally in the NLP field. - Hugging Face colleagues - For all the comments and improvements in the reviewing process. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 2. Benchmarking methodology When it comes to leveraging BERT-like models from Hugging Face's model hub, there are many knobs which can be tuned to make things faster. Also, in order to quantify what "faster" means, we will rely on widely adopted metrics: - Latency: Time it takes for a single execution of the model (i.e. forward call) - Throughput: Number of executions performed in a fixed amount of time These two metrics will help us understand the benefits and tradeoffs along this blog post. The benchmarking methodology was reimplemented from scratch in order to integrate the latest features provided by transformers and also to let the community run and share benchmarks in an hopefully easier way. The whole framework is now based on Facebook AI & Research's Hydra configuration library allowing us to easily report and track all the items involved while running the benchmark, hence increasing the overall reproducibility. You can find the whole structure of the project here On the 2021 version, we kept the ability to run inference workloads through PyTorch and Tensorflow as in the previous blog (1) along with their traced counterpart TorchScript (6), Google Accelerated Linear Algebra (XLA) (7). Also, we decided to include support for ONNX Runtime (8) as it provides many optimizations specifically targeting transformers based models which makes it a strong candidate to consider when discussing performance. Last but not least, this new unified benchmarking environment will allow us to easily run inference for different scenarios such as Quantized Models (Zafrir & al.) (9) using less precise number representations (float16, int8, int4). This method known as quantization has seen an increased adoption among all major hardware providers. In the near future, we would like to integrate additional methods we are actively working on at Hugging Face, namely Distillation, Pruning & Sparsificaton. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 7.1. How-to allocate multiple independent instances Let's start simple, if we want to spawn 2 instances, one on each socket with 24 cores assigned: shell numactl -C 0-23 -m 0 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=24 numactl -C 24-47 -m 1 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=24 Starting from here, each instance does not share any resource with the other, and everything is operating at maximum efficiency from a hardware perspective. The latency measurements are identical to what a single instance would achieve, but throughput is actually 2x higher as the two instances operate in a truly parallel way. We can further increase the number of instances, lowering the number of cores assigned for each instance. Let's run 4 independent instances, each of them effectively bound to 12 CPU cores. shell numactl -C 0-11 -m 0 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=12 numactl -C 12-23 -m 0 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=12 numactl -C 24-35 -m 1 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=12 numactl -C 36-47 -m 1 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=12 The outcomes remain the same, our 4 instances are effectively running in a truly parallel manner. The latency will be slightly higher than the example before (2x less cores being used), but the throughput will be again 2x higher. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 7. Multi-Stream Inference - Using multiple instances in parallel If you're still reading this, you should now be in good shape to set up parallel inference workloads on CPU. Now, we are going to highlight some possibilities offered by the powerful hardware we have, and tuning the knobs described before, to scale our inference as linearly as possible. In the following section we will explore another possible scaling solution Batch Size Scaling, but before diving into this, let's take a look at how we can leverage Linux tools in order to assign Thread Affinity allowing effective model instance parallelism. Instead of throwing more cores to the task as you would do in the core count scaling setup, now we will be using more model instances. Each instance will run independently on its own subset of the hardware resources in a truly parallel fashion on a subset of the CPU cores. | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 7.2. Smart dispatching - Allocating different model instances for different problem sizes One another possibility offered by this setup is to have multiple instances carefully tuned for various problem sizes. With a smart dispatching approach, one can redirect incoming requests to the right configuration giving the best latency depending on the request workload. shell # Small-sized problems (sequence length <= 32) use only 8 cores (on socket 0 - 8/24 cores used) numactl -C 0-7 -m 0 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=32 backend.name=pytorch backend.num_threads=8 # Medium-sized problems (32 > sequence <= 384) use remaining 16 cores (on socket 0 - (8+16)/24 cores used) numactl -C 8-23 -m 0 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=128 backend.name=pytorch backend.num_threads=16 # Large sized problems (sequence >= 384) use the entire CPU (on socket 1 - 24/24 cores used) numactl -C 24-37 -m 1 python3 src/main.py model=bert-base-cased batch_size=1 sequence_length=384 backend.name=pytorch backend.num_threads=24 | [
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 8. Batch size scaling - Improving throughput and latency with multiple parallel & independent model instances One another very interesting direction for scaling up inference is to actually put some more model instances into the pool along with reducing the actual workload each instance receives proportionally. This method actually changes both the size of the problem (batch size), and the resources involved in the computation (cores). To illustrate, imagine you have a server with C CPU cores, and you want to run a workload containing B samples with S tokens. You can represent this workload as a tensor of shape [B, S], B being the size of the batch and S being the maximum sequence length within the B samples. For all the instances (N), each of them executes on C / N cores and would receive a subset of the task [B / N, S]. Each instance doesn't receive the global batch but instead, they all receive a subset of it [B / N, S] thus the name Batch Size Scaling. In order to highlight the benefits of such scaling method, the charts below reports both the latencies when scaling up model instances along with the effects on the throughput. When looking at the results, let's focus on the latency and the throughput aspects: On one hand, we are taking the maximum latency over the pool of instances to reflect the time it takes to process all the samples in the batch. Putting it differently, as instances operate in a truly parallel fashion, the time it takes to gather all the batch chunks from all the instances is driven by the longest time it takes for individual instance in the pool to get their chunk done. As you can see below on Figure 7., the actual latency gain when increasing the number of instances is really dependent of the problem size. In all cases, we can find an optimal resource allocation (batch size & number of instances) to minimize our latency but, there is no specific pattern on the number of cores to involve in the computation. Also, it is important to notice the results might look totally different on another system (i.e. Operating System, Kernel Version, Framework version, etc.) Figure 8. sums up the best multi-instance configuration when targeting minimum latency by taking the minimum over the number of instances involved. For instance, for {batch = 8, sequence length = 128} using 4 instances (each with {batch = 2} and 12 cores) gives the best latency measurements. The Figure 9. reports all the setups minimizing latency for both PyTorch and TensorFlow for various problem-sizes. Spoiler: There are numerous other optimizations we will discuss in a follow-up blog post which will substantially impact this chart.
Figure 8. Max latency evolution with respect to number of instances for a total batch size of 8
Figure 9. Optimal number of instance minimizing overall latency for a total batch size of 8
On a second hand, we observe the throughput as the sum of all the model instance executing in parallel. It allows us to visualize the scalability of the system when adding more and more instances each of them with fewer resources but also proportional workload. Here, the results show almost linear scalability and thus an optimal hardware usage.
Figure 10. Sum throughput with respect to number of instances for a total batch size of 8
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Scaling-up BERT Inference on CPU (Part 1) | mfuntowicz | April 20, 2021 | bert-cpu-scaling-part-1 | guide, nlp, partnerships, intel | https://huggingface.co/blog/bert-cpu-scaling-part-1 | 1. Context and Motivations Back in October 2019, my colleague Lysandre Debut published a comprehensive (at the time) inference performance benchmarking blog (1). Since then, 🤗 transformers (2) welcomed a tremendous number of new architectures and thousands of new models were added to the 🤗 hub (3) which now counts more than 9,000 of them as of first quarter of 2021. As the NLP landscape keeps trending towards more and more BERT-like models being used in production, it remains challenging to efficiently deploy and run these architectures at scale. This is why we recently introduced our 🤗 Inference API: to let you focus on building value for your users and customers, rather than digging into all the highly technical aspects of running such models. This blog post is the first part of a series which will cover most of the hardware and software optimizations to better leverage CPUs for BERT model inference. For this initial blog post, we will cover the hardware part: - Setting up a baseline - Out of the box results - Practical & technical considerations when leveraging modern CPUs for CPU-bound tasks - Core count scaling - Does increasing the number of cores actually give better performance? - Batch size scaling - Increasing throughput with multiple parallel & independent model instances We decided to focus on the most famous Transformer model architecture, BERT (Delvin & al. 2018) (4). While we focus this blog post on BERT-like models to keep the article concise, all the described techniques can be applied to any architecture on the Hugging Face model hub. In this blog post we will not describe in detail the Transformer architecture - to learn about that I can't recommend enough the Illustrated Transformer blogpost from Jay Alammar (5). Today's goals are to give you an idea of where we are from an Open Source perspective using BERT-like models for inference on PyTorch and TensorFlow, and also what you can easily leverage to speedup inference. | [
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Using & Mixing Hugging Face Models with Gradio 2.0 | abidlabs | May 25, 2021 | gradio | open-source-collab, guide | https://huggingface.co/blog/gradio | # Using & Mixing Hugging Face Models with Gradio 2.0 > | [
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Using & Mixing Hugging Face Models with Gradio 2.0 | abidlabs | May 25, 2021 | gradio | open-source-collab, guide | https://huggingface.co/blog/gradio | Cross-posted from the Gradio blog. The Hugging Face Model Hub has more than 10,000 machine learning models submitted by users. You’ll find all kinds of natural language processing models that, for example, translate between Finnish and English or recognize Chinese speech. More recently, the Hub has expanded to even include models for image classification and audio processing. Hugging Face has always worked to make models accessible and easy to use. The transformers library makes it possible to load a model in a few lines of code. After a model is loaded, it can be used to make predictions on new data programmatically. But it’s not just programmers that are using machine learning models! An increasingly common scenario in machine learning is demoing models to interdisciplinary teams or letting non-programmers use models (to help discover biases, failure points, etc.). The Gradio library lets machine learning developers create demos and GUIs from machine learning models very easily, and share them for free with your collaborators as easily as sharing a Google docs link. Now, we’re excited to share that the Gradio 2.0 library lets you load and use almost any Hugging Face model with a GUI in just 1 line of code. Here’s an example: By default, this uses HuggingFace’s hosted Inference API (you can supply your own API key or use the public access without an API key), or you can also run pip install transformers and run the model computations locally if you’d like. Do you want to customize the demo? You can override any of the default parameters of the Interface class by passing in your own parameters: But wait, there’s more! With 10,000 models already on Model Hub, we see models not just as standalone pieces of code, but as lego pieces that can be composed and mixed to create more sophisticated applications and demos. For example, Gradio lets you load multiple models in parallel (imagine you want to compare 4 different text generation models from Hugging Face to see which one is the best for your use case): Or put your models in series. This makes it easy to build complex applications built from multiple machine learning models. For example, here we can build an application to translate and summarize Finnish news articles in 3 lines of code: You can even mix multiple models in series compared to each other in parallel (we’ll let you try that yourself!). To try any of this out, just install Gradio (pip install gradio) and pick a Hugging Face model you want to try. Start building with Gradio and Hugging Face 🧱⛏️ | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | # Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API In many Machine Learning applications, the amount of available labeled data is a barrier to producing a high-performing model. The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning. In this blog post, we'll explain what Few-Shot Learning is, and explore how a large language model called GPT-Neo, and the 🤗 Accelerated Inference API, can be used to generate your own predictions. | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | Responsible Use Few-Shot Learning is a powerful technique but also presents unique pitfalls that need to be taken into account when designing uses cases. To illustrate this, let's consider the default Sentiment Analysis setting provided in the widget. After seeing three examples of sentiment classification, the model makes the following predictions 4 times out of 5, with temperature set to 0.1: > | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | 🤗 Accelerated Inference API The Accelerated Inference API is our hosted service to run inference on any of the 10,000+ models publicly available on the 🤗 Model Hub, or your own private models, via simple API calls. The API includes acceleration on CPU and GPU with up to 100x speedup compared to out of the box deployment of Transformers. To integrate Few-Shot Learning predictions with GPT-Neo in your own apps, you can use the 🤗 Accelerated Inference API with the code snippet below. You can find your API Token here, if you don't have an account you can get started here. python import json import requests API_TOKEN = "" def query(payload='',parameters=None,options={'use_cache': False}): API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-neo-2.7B" headers = {"Authorization": f"Bearer {API_TOKEN}"} body = {"inputs":payload,'parameters':parameters,'options':options} response = requests.request("POST", API_URL, headers=headers, data= json.dumps(body)) try: response.raise_for_status() except requests.exceptions.HTTPError: return "Error:"+" ".join(response.json()['error']) else: return response.json()[0]['generated_text'] parameters = { 'max_new_tokens':25, # number of generated tokens 'temperature': 0.5, # controlling the randomness of generations 'end_sequence': "###" # stopping sequence for generation } prompt="...." # few-shot prompt data = query(prompt,parameters,options) --- | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | Best practices for responsible use - Make sure people know which parts of their user experience depend on the outputs of the ML system - If possible, give users the ability to opt-out - Provide a mechanism for users to give feedback on the model decision, and to override it - Monitor feedback, especially model failures, for groups of users that may be disproportionately affected What needs most to be avoided is to use the model to automatically make decisions for, or about, a user, without opportunity for a human to provide input or correct the output. Several regulations, such as GDPR in Europe, require that users be provided an explanation for automatic decisions made about them. --- To use GPT-Neo or any Hugging Face model in your own application, you can start a free trial of the 🤗 Accelerated Inference API. If you need help mitigating bias in models and AI systems, or leveraging Few-Shot Learning, the 🤗 Expert Acceleration Program can offer your team direct premium support from the Hugging Face team. | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | Practical Insights Here are some practical insights, which help you get started using GPT-Neo and the 🤗 Accelerated Inference API. Since GPT-Neo (2.7B) is about 60x smaller than GPT-3 (175B), it does not generalize as well to zero-shot problems and needs 3-4 examples to achieve good results. When you provide more examples GPT-Neo understands the task and takes the end_sequence into account, which allows us to control the generated text pretty well. The hyperparameter End Sequence, Token Length & Temperature can be used to control the text-generation of the model and you can use this to your advantage to solve the task you need. The Temperature controlls the randomness of your generations, lower temperature results in less random generations and higher temperature results in more random generations. In the example, you can see how important it is to define your hyperparameter. These can make the difference between solving your task or failing miserably. --- | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | > Tweet: "I'm a disabled happy person" > Sentiment: Negative What could go wrong? Imagine that you are using sentiment analysis to aggregate reviews of products on an online shopping website: a possible outcome could be that items useful to people with disabilities would be automatically down-ranked - a form of automated discrimination. For more on this specific issue, we recommend the ACL 2020 paper Social Biases in NLP Models as Barriers for Persons with Disabilities. Because Few-Shot Learning relies more directly on information and associations picked up from pre-training, it makes it more sensitive to this type of failures. How to minimize the risk of harm? Here are some practical recommendations. | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | What is GPT-Neo? GPT-Neo is a family of transformer-based language models from EleutherAI based on the GPT architecture. EleutherAI's primary goal is to train a model that is equivalent in size to GPT-3 and make it available to the public under an open license. All of the currently available GPT-Neo checkpoints are trained with the Pile dataset, a large text corpus that is extensively documented in (Gao et al., 2021). As such, it is expected to function better on the text that matches the distribution of its training text; we recommend keeping this in mind when designing your examples. --- | [
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Few-shot learning in practice: GPT-NEO and the 🤗 Accelerated Inference API | philschmid | June 3, 2021 | few-shot-learning-gpt-neo-and-inference-api | guide, nlp | https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api | What is Few-Shot Learning? Few-Shot Learning refers to the practice of feeding a machine learning model with a very small amount of training data to guide its predictions, like a few examples at inference time, as opposed to standard fine-tuning techniques which require a relatively large amount of training data for the pre-trained model to adapt to the desired task with accuracy. This technique has been mostly used in computer vision, but with some of the latest Language Models, like EleutherAI GPT-Neo and OpenAI GPT-3, we can now use it in Natural Language Processing (NLP). In NLP, Few-Shot Learning can be used with Large Language Models, which have learned to perform a wide number of tasks implicitly during their pre-training on large text datasets. This enables the model to generalize, that is to understand related but previously unseen tasks, with just a few examples. Few-Shot NLP examples consist of three main components: - Task Description: A short description of what the model should do, e.g. "Translate English to French" - Examples: A few examples showing the model what it is expected to predict, e.g. "sea otter => loutre de mer" - Prompt: The beginning of a new example, which the model should complete by generating the missing text, e.g. "cheese => " Image from Language Models are Few-Shot Learners Creating these few-shot examples can be tricky, since you need to articulate the “task” you want the model to perform through them. A common issue is that models, especially smaller ones, are very sensitive to the way the examples are written. An approach to optimize Few-Shot Learning in production is to learn a common representation for a task and then train task-specific classifiers on top of this representation. OpenAI showed in the GPT-3 Paper that the few-shot prompting ability improves with the number of language model parameters. Image from Language Models are Few-Shot Learners Let's now take a look at how at how GPT-Neo and the 🤗 Accelerated Inference API can be used to generate your own Few-Shot Learning predictions! --- | [
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Sentence Transformers in the 🤗 Hub | nreimers | June 28, 2021 | sentence-transformers-in-the-hub | open-source-collab, nlp | https://huggingface.co/blog/sentence-transformers-in-the-hub | What's next? Moving forward, we want to make this integration even more useful. In our roadmap, we expect training and evaluation data to be included in the automatically created model card, like is the case in transformers from version v4.8. And what's next for you? We're very excited to see your contributions! If you already have a Sentence Transformer repo in the Hub, you can now enable the widget and Inference API by changing the model card metadata. yaml --- tags: - sentence-transformers - sentence-similarity # Or feature-extraction! --- If you don't have any model in the Hub and want to learn more about Sentence Transformers, head to www.SBERT.net! | [
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Sentence Transformers in the 🤗 Hub | nreimers | June 28, 2021 | sentence-transformers-in-the-hub | open-source-collab, nlp | https://huggingface.co/blog/sentence-transformers-in-the-hub | Would you like to integrate your library to the Hub? This integration is possible thanks to the huggingface_hub library which has all our widgets and the API for all our supported libraries. If you would like to integrate your library to the Hub, we have a guide for you! | [
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Sentence Transformers in the 🤗 Hub | nreimers | June 28, 2021 | sentence-transformers-in-the-hub | open-source-collab, nlp | https://huggingface.co/blog/sentence-transformers-in-the-hub | References 1. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. https://arxiv.org/abs/1908.10084 | [
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Sentence Transformers in the 🤗 Hub | nreimers | June 28, 2021 | sentence-transformers-in-the-hub | open-source-collab, nlp | https://huggingface.co/blog/sentence-transformers-in-the-hub | Unleashing the Power of Sharing So why is this powerful? In a matter of minutes, you can share your trained models with the whole community. python from sentence_transformers import SentenceTransformer # Load or train a model model.save_to_hub("my_new_model") Now you will have a repository in the Hub which hosts your model. A model card was automatically created. It describes the architecture by listing the layers and shows how to use the model with both Sentence Transformers and 🤗 Transformers. You can also try out the widget and use the Inference API straight away! If this was not exciting enough, your models will also be easily discoverable by filtering for all Sentence Transformers models. | [
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Sentence Transformers in the 🤗 Hub | nreimers | June 28, 2021 | sentence-transformers-in-the-hub | open-source-collab, nlp | https://huggingface.co/blog/sentence-transformers-in-the-hub | # Sentence Transformers in the Hugging Face Hub Over the past few weeks, we've built collaborations with many Open Source frameworks in the machine learning ecosystem. One that gets us particularly excited is Sentence Transformers. Sentence Transformers is a framework for sentence, paragraph and image embeddings. This allows to derive semantically meaningful embeddings (1) which is useful for applications such as semantic search or multi-lingual zero shot classification. As part of Sentence Transformers v2 release, there are a lot of cool new features: - Sharing your models in the Hub easily. - Widgets and Inference API for sentence embeddings and sentence similarity. - Better sentence-embeddings models available (benchmark and models in the Hub). With over 90 pretrained Sentence Transformers models for more than 100 languages in the Hub, anyone can benefit from them and easily use them. Pre-trained models can be loaded and used directly with few lines of code: python from sentence_transformers import SentenceTransformer sentences = ["Hello World", "Hallo Welt"] model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) But not only this. People will probably want to either demo their models or play with other models easily, so we're happy to announce the release of two new widgets in the Hub! The first one is the feature-extraction widget which shows the sentence embedding. sentence-transformers/distilbert-base-nli-max-tokens Hosted inference API Feature Extraction Compute This model is currently loaded and running on the Inference API. JSON Output Maximize But seeing a bunch of numbers might not be very useful to you (unless you're able to understand the embeddings from a quick look, which would be impressive!). We're also introducing a new widget for a common use case of Sentence Transformers: computing sentence similarity. sentence-transformers/paraphrase-MiniLM-L6-v2 Hosted inference API Sentence Similarity Source Sentence Sentences to compare to Add Sentence Compute This model can be loaded on the Inference API on-demand. JSON Output Maximize Of course, on top of the widgets, we also provide API endpoints in our Inference API that you can use to programmatically call your models! python import json import requests API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/paraphrase-MiniLM-L6-v2" headers = {"Authorization": "Bearer YOUR_TOKEN"} def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() data = query( { "inputs": { "source_sentence": "That is a happy person", "sentences": [ "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] } } ) | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Deploy one of the 10,000+ Hugging Face Transformers to Amazon SageMaker for Inference To deploy a model directly from the Hugging Face Model Hub to Amazon SageMaker, we need to define two environment variables when creating the HuggingFaceModel. We need to define: * HF_MODEL_ID: defines the model id, which will be automatically loaded from huggingface.co/models when creating or SageMaker Endpoint. The 🤗 Hub provides 10,000+ models all available through this environment variable. * HF_TASK: defines the task for the used 🤗 Transformers pipeline. A full list of tasks can be found here. python from sagemaker.huggingface.model import HuggingFaceModel # Hub Model configuration. <https://huggingface.co/models> hub = { 'HF_MODEL_ID':'distilbert-base-uncased-distilled-squad', # model_id from hf.co/models 'HF_TASK':'question-answering' # NLP task you want to use for predictions } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( env=hub, # configuration for loading model from Hub role=role, # iam role with permissions to create an Endpoint transformers_version="4.6", # transformers version used pytorch_version="1.7", # pytorch version used ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, instance_type="ml.m5.xlarge" ) # example request, you always need to define "inputs" data = { "inputs": { "question": "What is used for inference?", "context": "My Name is Philipp and I live in Nuremberg. This model is used with sagemaker for inference." } } # request predictor.predict(data) After we run our request we can delete the endpoint again with. python # delete endpoint predictor.delete_endpoint() --- # FAQ 🎯 You can find the complete Frequently Asked Questions in the documentation. Q: Which models can I deploy for Inference? A: You can deploy: * any 🤗 Transformers model trained in Amazon SageMaker, or other compatible platforms and that can accommodate the SageMaker Hosting design * any of the 10,000+ publicly available Transformer models from the Hugging Face Model Hub, or * your private models hosted in your Hugging Face premium account! Q: Which pipelines, tasks are supported by the Inference Toolkit? A: The Inference Toolkit and DLC support any of the transformers pipelines. You can find the full list here Q: Do I have to use the transformers pipelines when hosting SageMaker endpoints? A: No, you can also write your custom inference code to serve your own models and logic, documented here. Q: Do I have to use the SageMaker Python SDK to use the Hugging Face Deep Learning Containers (DLCs)? A: You can use the Hugging Face DLC without the SageMaker Python SDK and deploy your models to SageMaker with other SDKs, such as the AWS CLI, boto3 or Cloudformation. The DLCs are also available through Amazon ECR and can be pulled and used in any environment of choice. Q: Why should I use the Hugging Face Deep Learning Containers? A: The DLCs are fully tested, maintained, optimized deep learning environments that require no installation, configuration, or maintenance. In particular, our inference DLC comes with a pre-written serving stack, which drastically lowers the technical bar of DL serving. Q: How is my data and code secured by Amazon SageMaker? A: Amazon SageMaker provides numerous security mechanisms including encryption at rest and in transit, Virtual Private Cloud (VPC) connectivity, and Identity and Access Management (IAM). To learn more about security in the AWS cloud and with Amazon SageMaker, you can visit Security in Amazon SageMaker and AWS Cloud Security. Q: Is this available in my region? A: For a list of the supported regions, please visit the AWS region table for all AWS global infrastructure. Q: Do you offer premium support or support SLAs for this solution? A: AWS Technical Support tiers are available from AWS and cover development and production issues for AWS products and services - please refer to AWS Support for specifics and scope. If you have questions which the Hugging Face community can help answer and/or benefit from, please post them in the Hugging Face forum. --- If you need premium support from the Hugging Face team to accelerate your NLP roadmap, our Expert Acceleration Program offers direct guidance from our open-source, science, and ML Engineering teams. | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | API - Inference Toolkit Description Using thetransformers pipelines, we designed an API, which makes it easy for you to benefit from all pipelines features. The API has a similar interface than the 🤗 Accelerated Inference API, meaning your inputs need to be defined in the inputs key and if you want additional supported pipelines parameters you can add them in the parameters key. Below you can find examples for requests. python # text-classification request body { "inputs": "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days." } # question-answering request body { "inputs": { "question": "What is used for inference?", "context": "My Name is Philipp and I live in Nuremberg. This model is used with sagemaker for inference." } } # zero-shot classification request body { "inputs": "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!", "parameters": { "candidate_labels": [ "refund", "legal", "faq" ] } } # Getting started 🧭 In this guide we will use the new Hugging Face Inference DLCs and Amazon SageMaker Python SDK to deploy two transformer models for inference. In the first example, we deploy for inference a Hugging Face Transformer model trained in Amazon SageMaker. In the second example, we directly deploy one of the 10,000+ publicly available Hugging Face Transformers models from the Model Hub to Amazon SageMaker for Inference. | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Samples/Documentation - Hugging Face documentation for Amazon SageMaker - Deploy models to Amazon SageMaker - Amazon SageMaker documentation for Hugging Face - Python SDK SageMaker documentation for Hugging Face - Deep Learning Container - Notebook: Deploy one of the 10 000+ Hugging Face Transformers to Amazon SageMaker for Inference - Notebook: Deploy a Hugging Face Transformer model from S3 to SageMaker for inference --- # SageMaker Hugging Face Inference Toolkit ⚙️ In addition to the Hugging Face Transformers-optimized Deep Learning Containers for inference, we have created a new Inference Toolkit for Amazon SageMaker. This new Inference Toolkit leverages the pipelines from the transformers library to allow zero-code deployments of models without writing any code for pre- or post-processing. In the "Getting Started" section below you find two examples of how to deploy your models to Amazon SageMaker. In addition to the zero-code deployment, the Inference Toolkit supports "bring your own code" methods, where you can override the default methods. You can learn more about "bring your own code" in the documentation here or you can check out the sample notebook "deploy custom inference code to Amazon SageMaker". | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Blog/Video - Video: Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker - Video: Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Deploy a trained Hugging Face Transformer model to SageMaker for inference There are two ways to deploy your SageMaker trained Hugging Face model. You can either deploy it after your training is finished, or you can deploy it later, using the model_data pointing to your saved model on Amazon S3. In addition to the two below-mentioned options, you can also instantiate Hugging Face endpoints with lower-level SDK such as boto3 and AWS CLI, Terraform and with CloudFormation templates. | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | pseudo code start | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | # Deploy Hugging Face models easily with Amazon SageMaker 🏎 Earlier this year we announced a strategic collaboration with Amazon to make it easier for companies to use Hugging Face in Amazon SageMaker, and ship cutting-edge Machine Learning features faster. We introduced new Hugging Face Deep Learning Containers (DLCs) to train Hugging Face Transformer models in Amazon SageMaker. Today, we are excited to share a new inference solution with you that makes it easier than ever to deploy Hugging Face Transformers with Amazon SageMaker! With the new Hugging Face Inference DLCs, you can deploy your trained models for inference with just one more line of code, or select any of the 10,000+ publicly available models from the Model Hub, and deploy them with Amazon SageMaker. Deploying models in SageMaker provides you with production-ready endpoints that scale easily within your AWS environment, with built-in monitoring and a ton of enterprise features. It's been an amazing collaboration and we hope you will take advantage of it! Here's how to use the new SageMaker Hugging Face Inference Toolkit to deploy Transformers-based models: python from sagemaker.huggingface import HuggingFaceModel # create Hugging Face Model Class and deploy it as SageMaker Endpoint huggingface_model = HuggingFaceModel(...).deploy() That's it! 🚀 To learn more about accessing and using the new Hugging Face DLCs with the Amazon SageMaker Python SDK, check out the guides and resources below. --- # Resources, Documentation & Samples 📄 Below you can find all the important resources for deploying your models to Amazon SageMaker. | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | # create HuggingFace estimator for running training huggingface_estimator = HuggingFace(....) # starting the train job with our uploaded datasets as input huggingface_estimator.fit(...) | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | pseudo code end | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | # deploy model to SageMaker Inference predictor = hf_estimator.deploy(initial_instance_count=1, instance_type="ml.m5.xlarge") # example request, you always need to define "inputs" data = { "inputs": "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days." } # request predictor.predict(data) After we run our request we can delete the endpoint again with. python # delete endpoint predictor.delete_endpoint() | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Deploy the model directly after training with the Estimator class If you deploy your model directly after training, you need to ensure that all required model artifacts are saved in your training script, including the tokenizer and the model. A benefit of deploying directly after training is that SageMaker model container metadata will contain the source training job, providing lineage from training job to deployed model. python from sagemaker.huggingface import HuggingFace | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Setting up the environment We will use an Amazon SageMaker Notebook Instance for the example. You can learn here how to set up a Notebook Instance. To get started, jump into your Jupyter Notebook or JupyterLab and create a new Notebook with the conda_pytorch_p36 kernel. Note: The use of Jupyter is optional: We could also launch SageMaker API calls from anywhere we have an SDK installed, connectivity to the cloud, and appropriate permissions, such as a Laptop, another IDE, or a task scheduler like Airflow or AWS Step Functions. After that we can install the required dependencies. bash pip install "sagemaker>=2.48.0" --upgrade To deploy a model on SageMaker, we need to create a sagemaker Session and provide an IAM role with the right permission. The get_execution_role method is provided by the SageMaker SDK as an optional convenience. You can also specify the role by writing the specific role ARN you want your endpoint to use. This IAM role will be later attached to the Endpoint, e.g. download the model from Amazon S3. python import sagemaker sess = sagemaker.Session() role = sagemaker.get_execution_role() --- | [
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Deploy Hugging Face models easily with Amazon SageMaker | philschmid | July 8, 2021 | deploy-hugging-face-models-easily-with-amazon-sagemaker | guide, partnerships, aws | https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker | Deploy the model from pre-trained checkpoints using the HuggingFaceModel class If you've already trained your model and want to deploy it at some later time, you can use the model_data argument to specify the location of your tokenizer and model weights. python from sagemaker.huggingface.model import HuggingFaceModel # create Hugging Face Model Class huggingface_model = HuggingFaceModel( model_data="s3://models/my-bert-model/model.tar.gz", # path to your trained sagemaker model role=role, # iam role with permissions to create an Endpoint transformers_version="4.6", # transformers version used pytorch_version="1.7", # pytorch version used ) # deploy model to SageMaker Inference predictor = huggingface_model.deploy( initial_instance_count=1, instance_type="ml.m5.xlarge" ) # example request, you always need to define "inputs" data = { "inputs": "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days." } # request predictor.predict(data) After we run our request, we can delete the endpoint again with: python # delete endpoint predictor.delete_endpoint() | [
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Welcome spaCy to the 🤗 Hub | osanseviero | July 13, 2021 | spacy | open-source-collab, nlp | https://huggingface.co/blog/spacy | Would you like to integrate your library to the Hub? This integration is possible thanks to the huggingface_hub library which has all our widgets and the API for all our supported libraries. If you would like to integrate your library to the Hub, we have a guide for you! | [
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Welcome spaCy to the 🤗 Hub | osanseviero | July 13, 2021 | spacy | open-source-collab, nlp | https://huggingface.co/blog/spacy | Widgets This integration includes support for NER widgets, so all models with a NER component will have this out of the box! Coming soon there will be support for text classification and POS. spacy/en_core_web_sm Hosted inference API Token Classification Compute This model is currently loaded and running on the Inference API. JSON Output Maximize | [
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Welcome spaCy to the 🤗 Hub | osanseviero | July 13, 2021 | spacy | open-source-collab, nlp | https://huggingface.co/blog/spacy | # Welcome spaCy to the Hugging Face Hub spaCy is a popular library for advanced Natural Language Processing used widely across industry. spaCy makes it easy to use and train pipelines for tasks like named entity recognition, text classification, part of speech tagging and more, and lets you build powerful applications to process and analyze large volumes of text. Hugging Face makes it really easy to share your spaCy pipelines with the community! With a single command, you can upload any pipeline package, with a pretty model card and all required metadata auto-generated for you. The inference API currently supports NER out-of-the-box, and you can try out your pipeline interactively in your browser. You'll also get a live URL for your package that you can pip install from anywhere for a smooth path from prototype all the way to production! | [
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Welcome spaCy to the 🤗 Hub | osanseviero | July 13, 2021 | spacy | open-source-collab, nlp | https://huggingface.co/blog/spacy | Using existing models All models from the Hub can be directly installed using pip install. bash pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl python # Using spacy.load(). import spacy nlp = spacy.load("en_core_web_sm") # Importing as module. import en_core_web_sm nlp = en_core_web_sm.load() When you open a repository, you can click Use in spaCy and you will be given a working snippet that you can use to install and load the model! You can even make HTTP requests to call the models from the Inference API, which is useful in production settings. Here is an example of a simple request: bash curl -X POST --data '{"inputs": "Hello, this is Omar"}' https://api-inference.huggingface.co/models/spacy/en_core_web_sm >>> [{"entity_group":"PERSON","word":"Omar","start":15,"end":19,"score":1.0}] And for larger-scale use cases, you can click "Deploy > Accelerated Inference" and see how to do this with Python. | [
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Welcome spaCy to the 🤗 Hub | osanseviero | July 13, 2021 | spacy | open-source-collab, nlp | https://huggingface.co/blog/spacy | Finding models Over 60 canonical models can be found in the spaCy org. These models are from the latest 3.1 release, so you can try the latest realesed models right now! On top of this, you can find all spaCy models from the community here https://huggingface.co/models?filter=spacy. | [
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Welcome spaCy to the 🤗 Hub | osanseviero | July 13, 2021 | spacy | open-source-collab, nlp | https://huggingface.co/blog/spacy | Sharing your models But probably the coolest feature is that now you can very easily share your models with the spacy-huggingface-hub library, which extends the spaCy CLI with a new command, huggingface-hub push. bash huggingface-cli login python -m spacy package ./en_ner_fashion ./output --build wheel cd ./output/en_ner_fashion-0.0.0/dist python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl In just a minute, you can get your packaged model in the Hub, try it out directly in the browser, and share it with the rest of the community. All the required metadata will be uploaded for you and you even get a cool model card. Try it out and share your models with the community! | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Tokenizer The first brick of our model is called a tokenizer and takes care of transforming raw text into vocabulary indices. Because we are training a model for Bengali, which is not very similar to English, we need to implement language-specific preprocessing as a part of our tokenizer. We can view it as a sequence of operations: 1. Normalization: includes all preprocessing operations on raw text data. This was the step at which we have made the most changes, because removing certain details can either change the meaning of the text or leave it the same, depending on the language. For example, the standard ALBERT normalizer removes the accents, while for the Bengali language, we need to keep them, because they contain information about the vowels. As a result, we use the following operations: NMT normalization, NFKC normalization, removal of multiple spaces, homogenization of recurring Unicode characters in the Bengali language, and lowercasing. 2. Pretokenization describes rules for splitting the input (for example, by whitespace) to enforce specific token boundaries. As in the original work, we have chosen to keep the whitespace out of the tokens. Therefore, to distinguish the words from each other and not to have multiple single-space tokens, each token corresponding to the beginning of a word starts with a special character “_” (U+2581). In addition, we isolated all punctuation and digits from other characters to condense our vocabulary. 3. Tokenizer modeling: It is at this level that the text is mapped into a sequence of elements of a vocabulary. There are several algorithms for this, such as Byte-Pair Encoding (BPE) or Unigram, and most of them need to build the vocabulary from a text corpus. Following the setup of ALBERT, we used the Unigram Language Model approach, training a vocabulary of 32k tokens on the deduplicated Bengali part of the OSCAR dataset. 4. Post-processing: After tokenization, we might want to add several special tokens required by the architecture, such as starting the sequence with a special token [CLS] or separating two segments with a special token [SEP]. Since our main architecture is the same as the original ALBERT, we keep the same post-processing: specifically, we add a [CLS] token at the beginning of each example and a [SEP] token both between two segments and at the end. 💡 Read more information about each component in Tokenizers doc You can reuse our tokenizer by running the following code: python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("neuropark/sahajBERT") | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Adaptive averaging Now that we have discussed the overall training procedure, there remains one more question: how do we actually aggregate the gradients of participants? Most home computers cannot easily accept incoming connections, and the download speed might also become a constraint. Since we rely on volunteer hardware for experiments, a central server is not really a viable option, as it will quickly face overload when scaling to tens of clients and hundreds of millions of parameters. Most data-parallel training runs today don't use this strategy anyway; instead, they rely on All-Reduce — an efficient all-to-all communication primitive. Thanks to clever algorithmic optimizations, each node can compute the global average without sending the entire local gradient to every peer. Because All-Reduce is decentralized, it seems like a good choice; however, we still need to take the diversity of hardware and network setups into account. For example, some volunteers might join from computers that have slow network but powerful GPUs, some might have better connectivity only to a subset of other peers, and some may be firewalled from incoming connections. It turns out we can actually come up with an optimal data transfer strategy on the fly by leveraging this information about performance! On a high level, we split the entire gradient vector into parts depending on the Internet speed of each peer: those with the fastest connection aggregate the largest parts. Also, if some nodes do not accept incoming connections, they simply send their data for aggregation but do not compute the average themselves. Depending on the conditions, this adaptive algorithm can recover well-known distributed DL algorithms and improve on them with a hybrid strategy, as demonstrated below. Examples of different averaging strategies with the adaptive algorithm. 💡 The core techniques for decentralized training are available in Hivemind. Check out the repo and learn how to use this library in your own projects! | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Why should we do it? These days, many highest-quality NLP systems are based on large pretrained Transformers. In general, their quality improves with size: you can achieve unparalleled results in natural language understanding and generation by scaling up the parameter count and leveraging the abundance of unlabeled text data. Unfortunately, we use these pretrained models not only because it's convenient. The hardware resources for training Transformers on large datasets often exceed anything affordable to a single person and even most commercial or research organizations. Take, for example, BERT: its training was estimated to cost about $7,000, and for the largest models like GPT-3, this number can be as high as $12 million! This resource limitation might seem obvious and inevitable, but is there really no alternative to using pretrained models for the broader ML community? However, there might be a way out of this situation: to come up with a solution, we only need to take a look around. It might be the case that the computational resources we're looking for are already there; for example, many of us have powerful computers with gaming or workstation GPUs at home. You might've already guessed that we're going to join their power similarly to Folding@home, Rosetta@home, Leela Chess Zero or different BOINC projects that leverage volunteer computing, but the approach is even more general. For instance, several laboratories can join their smaller clusters to utilize all the available resources, and some might want to join the experiment using inexpensive cloud instances. To a skeptical mind, it might seem that we're missing a key factor here: data transfer in distributed DL is often a bottleneck, since we need to aggregate the gradients from multiple workers. Indeed, any naïve approach to distributed training over the Internet is bound to fail, as most participants don't have gigabit connections and might disconnect from the network at any time. So how on Earth can you train anything with a household data plan? :) As a solution to this problem, we propose a new training algorithm, called Distributed Deep Learning in Open Collaborations (or DeDLOC), which is described in detail in our recently released preprint. Now, let’s find out what are the core ideas behind this algorithm! | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | sahajBERT As always, having a well-designed algorithmic framework doesn't mean that it will work as intended in practice, because some assumptions may not hold true in actual training runs. To verify the competitive performance of this technology and to showcase its potential, we organized a special collaborative event to pretrain a masked language model for the Bengali language. Even though it is the fifth most spoken native language in the world, it has very few masked language models openly available, which emphasizes the importance of tools that can empower the community, unlocking a plethora of opportunities in the field. We conducted this experiment with real volunteers from the Neuropark community and used openly available datasets (OSCAR and Wikipedia), because we wanted to have a fully reproducible example that might serve as an inspiration for other groups. Below, we describe the detailed setup of our training run and demonstrate its results. | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Architecture For our experiment, we chose ALBERT (A Lite BERT) — a model for language representations that is pretrained with Masked Language Modeling (MLM) and Sentence Order Prediction (SOP) as objectives. We use this architecture because weight sharing makes it very parameter-efficient: for example, ALBERT-large has ~18M trainable parameters and performs comparably to BERT-base with ~108M weights on the GLUE benchmark. It means that there is less data to exchange between the peers, which is crucial in our setup, as it significantly speeds up each training iteration. 💡 Want to know more about ALBERT? Paper Transformers doc | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Evaluation To evaluate the performance of sahajBERT, we finetuned it on two downstream tasks in Bengali: - Named entity recognition (NER) on the Bengali split of WikiANN. The goal of this task is to classify each token in the input text into one of the following categories: person, organization, location, or none of them. - News Category Classification (NCC) on the Soham articles dataset from IndicGLUE. The goal of this task is to predict the category to which belong the input text. We evaluated it during training on the NER task to check that everything was going well; as you can see on the following plot, this was indeed the case! Evaluation metrics of fine-tuned models on the NER task from different checkpoints of pre-trained models. At the end of training, we compared sahajBERT with three other pretrained language models: XLM-R Large, IndicBert, and bnRoBERTa. In the table below, you can see that our model has results comparable to the best Bengali language models available on HF Hub, even though our model has only ~18M trained parameters, while, for instance, XLM-R (a strong multilingual baseline), has ~559M parameters and was trained on several hundred V100 GPUs. | Model | NER F1 (mean ± std) | NCC Accuracy (mean ± std) | |:-------------:|:-------------:|:-------------:| |sahajBERT | 95.45 ± 0.53| 91.97 ± 0.47| |XLM-R-large | 96.48 ± 0.22| 90.05 ± 0.38| |IndicBert | 92.52 ± 0.45| 74.46 ± 1.91| |bnRoBERTa |82.32 ± 0.67|80.94 ± 0.45| These models are available on the Hub as well. You can test them directly by playing with the Hosted Inference API widget on their Model Cards or by loading them directly in your Python code. | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | Collaborative event The sahajBERT collaborative training event took place from May 12 to May 21. The event brought together 40 participants, 30 of whom were Bengali-speaking volunteers, and 10 were volunteers from one of the authors' organizations. These 40 volunteers joined the Neuropark Discord channel to receive all information regarding the event and participate in discussions. To join the experiment, volunteers were asked to: 1. Send their username to the moderators to be allowlisted; 2. Open the provided notebook locally, on Google Colaboratory, or on Kaggle; 3. Run one code cell and fill in their Hugging Face credentials when requested; 4. Watch the training loss decrease on the shared dashboards! For security purposes, we set up an authorization system so that only members of the Neuropark community could train the model. Sparing you the technical details, our authorization protocol allows us to guarantee that every participant is in the allowlist and to acknowledge the individual contribution of each peer. In the following figure, you can see the activity of each volunteer. Over the experiment, the volunteers logged in 600 different sessions. Participants regularly launched multiple runs in parallel, and many of them spread out the runs they launched over time. The runs of individual participants lasted 4 hours on average, and the maximum length was 21 hours. You can read more about the participation statistics in the paper. Chart showing participants of the sahajBERT experiment. Circle radius is relative to the total number of processed batches, the circle is greyed if the participant is not active. Every purple square represents an active device, darker color corresponds to higher performance Along with the resources provided by participants, we also used 16 preemptible (cheap but frequently interrupted) single-GPU T4 cloud instances to ensure the stability of the run. The cumulative runtime for the experiment was 234 days, and in the figure below you can see parts of the loss curve that each peer contributed to! The final model was uploaded to the Model Hub, so you can download and play with it if you want to: https://hf.co/neuropark/sahajBERT | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | sahajBERT-NER Model card: https://hf.co/neuropark/sahajBERT-NER python from transformers import ( AlbertForTokenClassification, TokenClassificationPipeline, PreTrainedTokenizerFast, ) # Initialize tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NER") # Initialize model model = AlbertForTokenClassification.from_pretrained("neuropark/sahajBERT-NER") # Initialize pipeline pipeline = TokenClassificationPipeline(tokenizer=tokenizer, model=model) raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me output = pipeline(raw_text) | [
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Deep Learning over the Internet: Training Language Models Collaboratively | mryab | July 15, 2021 | collaborative-training | research | https://huggingface.co/blog/collaborative-training | sahajBERT-NCC Model card: https://hf.co/neuropark/sahajBERT-NER python from transformers import ( AlbertForSequenceClassification, TextClassificationPipeline, PreTrainedTokenizerFast, ) # Initialize tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NCC") # Initialize model model = AlbertForSequenceClassification.from_pretrained("neuropark/sahajBERT-NCC") # Initialize pipeline pipeline = TextClassificationPipeline(tokenizer=tokenizer, model=model) raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me output = pipeline(raw_text) | [
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