First off, let me explain that a “decoder” in the context of language models is essentially just a way to generate output text based on some input prompt or question. The Bart (Bidirectional Encoder Representations from Transformers) model specifically uses transformer architecture for its decoding process, which involves passing through multiple layers and attention mechanisms to produce more accurate and relevant results.
Now Let’s get started with the FlaxBart implementation in TensorFlow 2.0. The first thing you might notice is that it looks a bit different from other models you may have seen before this is because Flax (a library for building neural networks) uses a functional programming style instead of the more traditional object-oriented approach. This can make things a little less intuitive at first, but once you get used to it, it’s actually pretty straightforward!
Here’s an example script that demonstrates how to use FlaxBart for decoding:
# Import necessary libraries
import jax.numpy as np # Importing numpy from jax library
from flax import linen_util # Importing linen_util from flax library
from flax.linen import Module # Importing Module from flax library
from flax.training import train_state # Importing train_state from flax library
from transformers import BartConfig, TFBartForConditionalGeneration # Importing BartConfig and TFBartForConditionalGeneration from transformers library
# Load the pre-trained model and set up training parameters
config = BartConfig() # Creating an instance of BartConfig
model = TFBartForConditionalGeneration(config) # Creating an instance of TFBartForConditionalGeneration with the BartConfig instance as parameter
params = model.init(np.random.get_prng(), np.zeros((1, config.max_position_embeddings), dtype=np.float32)) # Initializing the model parameters with a random generator and a zero array of specified shape
optimizer = linen_util.adam() # Creating an instance of adam optimizer from linen_util library
train_state = train_state.create(apply_fn=model.apply, params=params, tx=optimizer) # Creating a train_state object with the model's apply function, parameters, and optimizer as parameters
# Define the input and output data for training
input_ids = np.array([[101, 2048, 3072, ...]], dtype=np.int64) # Creating an array of input tokens with specified shape and data type
targets = np.array([[5000], [5001], [5002], ...]], dtype=np.int64) # Creating an array of output tokens with specified shape and data type
# Run training loop for specified number of epochs and steps per epoch
for i in range(num_epochs): # Looping through the specified number of epochs
for j in range(steps_per_epoch): # Looping through the specified number of steps per epoch
loss, grads = model.step_and_update(train_state, input_ids=input_ids[j], target=targets[j]) # Calculating the loss and gradients using the model's step_and_update function with the train_state object and input and output tokens as parameters
So what’s actually happening in this script? First we load a pre-trained Bart model (which is essentially just a fancy way of saying “we already have some weights that are good at generating text”) and initialize our training state. Then we define some input data (in the form of token IDs) and output data (also as token IDs), which will be used to train the model over multiple epochs and steps per epoch.
During each step, we pass in a batch of input tokens and their corresponding target outputs, calculate the loss using the FlaxBart decoder layer, and update our parameters based on that loss (using gradient descent). This process is repeated for as many epochs and steps as desired, until the model has learned to generate output text with high accuracy.
Of course, there’s a lot more going on under the hood here than what I’ve described so far but hopefully this gives you a basic idea of how FlaxBart works! If you have any questions or want to learn more about neural networks and language models in general, feel free to ask.