Hugging Face + FastAI (Winner)

Published: August 02, 2021
Training Transformers with FastAI
  1. 1.
    Source of this blogpost
  2. 2.
    Setting up a work Environment
    1. 1.
    2. 2.
      Necessary Libraries
    3. 3.
      Necessary imports
  3. 3.
    Do we need FastAI / Is 🤗 libraries not sufficient
    1. 1.
      Highlights of 🤗
    2. 2.
      Highlights of FastAI
  4. 4.
    Blurr API Highlights
  5. 5.
Source of this blogpost:
  • This blogpost is my understanding of the Transformers library after participating and learning from the HuggingFace Course with FastAI bent - generously organized by Weights & Biases and weight lifted by some great folks Wayde Gilliam, Sanyam Bhutani, Zach Muller, Andrea Pessl & Morgan McGuire. Sorry, if I have missed anyone, but thank you all for the great hard work to bring this to the masses.
  • Also this blogpost is submitted as part of the blogpost competition held by the team which was announced in session-3.
Setting up a work Environment:
  • For using local installations use conda/mamba and then utilize the fastconda channel to grab all the necessary fastai related libraries.
  • While learning this course, I am using google colab pro and this needs few settings before we get started.
  • First thing we need is to set the Runtime-Type, so for this setting, navigate to the colab -> Runtime Tab and click on “Change Runtime Type”.
Screen Shot 2021-08-02 at 2 14 24 PM
  • On the “Change Runtime Type” pop-up select the below options.
Screen Shot 2021-08-02 at 2 16 14 PM
  • You are all set as per environment.
Necessary Libraries:
  • Before we import anything, we need to install the required libraries. The following lines are needed as per our requirement on whether we are using fastai, HFTransformers, blurt api, adaptnlp api or fast hugs etc.
    !pip install -U fastai
    !pip install transformers[sentencepiece]
    !pip install ohmeow-blurr
    !pip install git+[email protected]
    !pip install -qq git+git://
  • In the following tutorial I am going to explain blurr api which is a wrapper on top of fastai to use 🤗 transformers.
Necessary Imports:
  • We need to import the necessary libraries as needed. But always a best practice is to keep all the imports on top of the notebook to keep it clean and readable.
import torch
from fastai.text.all import *
from datasets import load_dataset, concatenate_datasets
from transformers import *
from blurr.utils import *
from import *
from blurr.modeling.core import *
Do we need FastAI / Is 🤗 libraries not sufficient:
Highlights of 🤗:
  • The transformers library is great & the Pipeline API is self sufficient to tokenize, train and infer on the dataset.
  • Some of the best things about Pipeline API is the ease of having prebuilt checkpoints, AutoXXX classes like AutoModel, AutoTokenizer etc.
Highlights of FastAI:
  • Though we have all the functionality in the 🤗, there are lot of things we can improve and experiment with.
  • One of the main advantage of having wrappers like blurr, adapnlp or fast hugs is the flexibility of looking at each step and customize as per requirement.
  • We have lots of great tools like learning rate finder, augmenting techniques, debugging data blocks etc.
  • But out of the box if we want to wrap/integrate the external objects like 🤗 transformers we need to build the functions from scratch. This problem is solved by API’s like Blurr.
Blurr API Highlights:
The following steps will detail the Blurr API. But to make it easy and understandable we will compare each step with fastai:
  • Building an application using Blurr API is mostly similar to building one in fastAI but with very easy and convenient functions.
  • Couple of steps like downloading datasets, unzipping, wrapping them in to paths etc can be done in native fastAI functions like below:
    path = untar_data(URLs.IMDB)
    files = get_text_files(path, folders = ['train', 'test', 'unsup'])
  • But the Blurr API has lots of convenient functions like BLURR*.*get_hf_objects to get the hugging face objects, which are vital in constructing out the further steps in building an application like constructing data blocks.
  • In FastAI, tokenization can be done using the following code snippet
    spacy = WordTokenizer()
    tkn = Tokenizer(spacy)
  • But in case of integrating 🤗 api we prefer using their config and that can be done using their AutoConfig class like below:
config = AutoConfig.from_pretrained(pretrained_model_name)
  • Once we have the config, we can have our Blurr magic happen with below code snippet:
hf_arch, hf_config, hf_tokenizer, hf_model = BLURR.get_hf_objects(pretrained_model_name, model_cls=model_cls, config=config)
  • In the above step, the get_hf_objects will return the model, config, arch and tokenizer in one step.
  • The next step would be to construct a data loader. If we are using FastAI then we can do as below:
get_imdb = partial(get_text_files, folders=['train', 'test', 'unsup'])
dls_lm = DataBlock(
blocks=TextBlock.from_folder(path, is_lm=True),
get_items=get_imdb, splitter=RandomSplitter(0.1)
).dataloaders(path, path=path, bs=128, seq_len=80)
  • But if we want to do that using Blurr API, its lot more easier and convenient like below:
blocks = (HF_TextBlock(hf_arch, hf_config, hf_tokenizer, hf_model), CategoryBlock)
dblock = DataBlock(blocks=blocks, get_x=ColReader('text'), get_y=ColReader('label'), splitter=ColSplitter())
dls = dblock.dataloaders(imdb_df, bs=4)
  • So the next step would be to create a Learner object which is our crucial step. In Fast AI & Blurr API we can achieve with below code snippet:
learn = language_model_learner(
dls_lm, AWD_LSTM, drop_mult=0.3,
metrics=[accuracy, Perplexity()]).to_fp16()
learn.fit_one_cycle(1, 2e-2)
#Blurr API
learn = Learner(dls,
opt_func=partial(Adam, decouple_wd=True),
learn.fit_one_cycle(3, lr_max=1e-3)
  • And the rest of the steps like finding learning rate, freezing, unfreezing, or getting predictions can be done in native fastAI using below:
#Predictions, losses
preds, targs, losses = learn.get_preds(with_loss=True)
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