A Practical Guide to Building GPT-2 with PyTorch (Part 2)

Author(s): Amit Kharel

Originally published on Towards AI.

This is the second part of the GPT-2 from scratch project. If you haven’t read the first part yet, I highly recommend getting familiar with the language model basics before continuing.

Build and Train GPT-2 (Part 1)

Final Loss:

In this section, we will add the GPT-2 parts one by one and then train & evaluate how the model performs in each stage. Here’s how it goes:

a. Positional Encoding + Fully Connected Layer (NN)

b. (Masked) Self-Attention + Normalization

c. (Masked) Multi-Head Attention

d. Multiple GPT Decoder Blocks

e. Improving Tokenizer

f. Final GPT-2 Training

To recall from previous part, our model looks like below:

Simple Bi-Gram Model

Code:

import torch.nn as nnimport torch.nn.functional as F# used to define size of embeddingsd_model = vocab_size class GPT(nn.Module): def __init__(self, vocab_size, d_model): super().__init__() self.wte = nn.Embedding(vocab_size, d_model) # word token embeddings def forward(self, inputs, targets = None): logits = self.wte(inputs) # dim -> batch_size, sequence_length, d_model loss = None if targets != None: batch_size, sequence_length, d_model = logits.shape # to calculate loss for all token embeddings in a batch # kind of a requirement for cross_entropy logits = logits.view(batch_size * sequence_length, d_model) targets = targets.view(batch_size * sequence_length) loss = F.cross_entropy(logits, targets) return logits, loss def generate(self, inputs, max_new_tokens): # this will store… Read the full blog for free on Medium.

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Published via Towards AI