PyTorch is an open-source ML framework used for building deep learning models. It provides a (flexible and dynamic) computational graph. It makes it suitable for research, experimentation and production deployment.
In LLMs, PyTorch is used in GPT-like pre-trained transformers. There are two purposes. First is model development. PyTorch is used to design and implement the architecture of an LLM. It provides high-level interface. It facilitates inclusion of components such as layers, activation functions and optimization algorithms. Since it is dynamic, there could be easy experimentation and prototyping. The second purpose is to train the model and fine-tune it.
LLMs are trained and fine-tuned on vast amounts of data. The models are pre-trained (using unsupervised pre-training and self-supervised learning). PyTorch facilitates implementation of training algorithms, support for distributed computing and enables evaluation of model performance during training.
Let us see how PyTorch is used in building a simple language model. Let us create an RNN model for generating text character.
First of all, install PyTorch. Here we write a a Python script to define, train and use a simple character-level language model. It trains the model on dummy data, and then generates characters using the trained model.
Python Copy code
import torch
import torch.nn as nn
import torch.optim as optim
#Define the RNN model
class CharRNN (nn.module):
def_init_(self, input_size, hidden_size, output_size):
super (CharRNN, self). _ init_( )
self.hidden_size=hidden_size
self.rnn=nnRNN (input_size, hidden_size, batch_first=True)
# Define some hyperparameters
input_size=100 # Size of input vocabulary
hidden_size=128 # Number of hidden units
output_size=100 # size of output vocabulary
seq_length=20 # length of input sequences
num_epochs= 100 # Number of training epochs.
#Create an instance of the model
model= CharRNN (input_size, hidden_size,output_size)
#Define loss function and optimizer
data=torch.randn(100, seq_length, input_size
#Training loop
for epoch in range (num_epochs):
model.train( )
optimizer.zero-grd( )
hidden=criterion (outputs, labels. view (-1)
#Forward pass
outputs, _=model (data, hidden)
outputs=outputs. view (-1, output_ size}
loss =criterion (outputs, labels.view (-1)
#Backward pass and optimization
loss.backward ( )
optimizer. step( )
if (epoch +1) %==0:
print{f ‘ Epoch { (epoch +1) /[( num-epochs], Loss: (loss.item( ): , 4f )’}
#Generating text
def generate_text( model, start_char =A’, length =100) :
model.eval( )
with torcch.no_grad ( ):
input_char=torch.zeros (1,1, input_size)
input_char[ 0,0,ord(start_char)]=1
hidden=None
output_text=start_char
for in range( length):
output, hidden=model( input_char, hidden)
predicted_char=
torch.argmax (output_probs).item.( )
output-text+=chr(predicted _char=1
input_char.fill_(0)
input_char{ 0,0, predicted_char}=1
return output_text
#Generate text using trained model
generate_text=generate_text(model, start_char=A’, length 200)
print (Generated Text : “)
print (generated_text)
Another alternative to PyTorch for designing neural networks is TensorFlow that offers similar capabilities and is widely used in both research and industry.
Prior to the advent of PyTorch and Tensor Flow, neural networks were designed by using lower-level libraries and frameworks such as Theano and Caffe. These provided the building blocks for constructing neural networks. These often required manual coding and were less flexible than the modern networks. Researchers and developers had to implement neural network architectures and algorithms from scratch. It was not only time consuming but also error prone. Besides, it was necessary to use specialized hardware and software optimization to achieve acceptable performance (for training and inference). Of course, actual networks could be designed in the absence of PyTorch and TensorFlow, but the process was cumbersome and less accessible to a wider audience.
PyTorch emerged in 2016. It was developed by Facebook’s AI Research Lab. TensorFlow was released by Google in 2015. Both have come on the scene with a little gap of a year. Both have been used widely for deep learning tasks. These can be used by anyone as they are open-source frameworks with extensive documentation, tutorials and community support.