Deepdive: Custom Collate in DataLoader, Gemini cookbook, Build LLM from scratch

Optimize your large language model (LLM) training with PyTorch by using a custom collate function in DataLoader. This technique dynamically pads text sequences in each batch to match the longest sequence, reducing wasted computation from unnecessary padding. Learn how to implement a custom collate function using pad_sequence from torch.nn.utils.rnn and integrate it with DataLoader for efficient handling of variable-length sequences in your LLM training workflow.

Custom Collate Function in DataLoader

When training large language models (LLMs) on text data, you often have sequences of varying lengths in each batch. To efficiently handle this, you can use PyTorch’s DataLoader with a custom collate function. 

This function dynamically pads the sequences in each batch to the length of the longest sequence, rather than padding to a fixed maximum length. This reduces wasted computation from unnecessary padding. 

To do so, implement a custom collate_fn that pads the text sequences using pad_sequence from torch.nn.utils.rnn. Then, pass this custom_collate_fn to the DataLoader to enable dynamic batching of variable length sequences.

from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader

# Define a custom collate function
def custom_collate_fn(batch):
    texts, labels = zip(*batch)
    texts = [torch.tensor(t)
            for t in texts]
    texts_padded = pad_sequence(texts,
            batch_first=True,
            padding_value=0)
    labels = torch.tensor(labels)
    return texts_padded, labels

# DataLoader with custom collate_fn
data_loader = DataLoader(your_dataset,
            batch_size=2,
            collate_fn=custom_collate_fn)

Gemini cookbook

A collection of guides and examples for the Gemini API, including quickstart tutorials for writing prompts and using different features of the API, and examples of things you can build.

Build an LLM from Scratch Chapter 5: “Pretraining an LLM on Unlabeled Data

Chapter 5 of Sebastian Raschka’s “Build an LLM from Scratch” book, titled “Pretraining an LLM on Unlabeled Data,” is now available. This chapter advances the series by focusing on the implementation of a training function and the initiation of pretraining for the LLM.

Key topics covered include:

• Computing the training and validation set losses to assess the quality of text generated by the LLM during training.
• Implementing a training function and starting the pretraining process.
• Techniques for saving and loading model weights, allowing for the continuation of training at different stages.
• Loading pretrained weights from OpenAI to enhance model performance.