Debugging and Monitoring LLMs With Weights & Biases

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Introduction

Large Language Models, or LLMs for short, are becoming a big deal in the world of technology. They're powerful and can do a lot, but they're not always easy to handle. Just like when building a big tower, you want to make sure everything goes right from the start to the finish. That's where Weights & Biases, often called W&B, comes in. It's a tool that helps people keep an eye on how their models are doing. In this article, we'll talk about why it's so important to watch over LLMs, how W&B helps with that, and how to use it. Let's dive in!

Large Language Models (LLMs)

Large Language Models (LLMs) are machine learning models trained on vast amounts of text data to understand and generate human-like text. They excel in processing and producing language, enabling various applications like translation, summarization, and conversation.

LLMs, such as GPT-3 by OpenAI, utilize deep learning architectures to learn patterns and relationships in the data, making them capable of sophisticated language tasks. Through training on diverse datasets, they aim to comprehend context, semantics, and nuances akin to human communication.

When discussing the forefront of natural language processing, several Large Language Models (LLMs) consistently emerge:

The Need for Debugging & Monitoring LLMs

Understanding and overseeing Large Language Models (LLMs) is much like supervising an intricate machine: they're powerful, and versatile, but require keen oversight.

Firstly, think about the intricacy of LLMs. They far surpass the complexity of your typical day-to-day machine learning models. While they hold immense potential to revolutionize tasks involving language - think customer support, content creation, and translations - their intricate designs can sometimes misfire. If we're not careful, instead of a smooth conversation with a chatbot, users might encounter bewildering responses, leading to user frustration and diminished trust.

Then there's the matter of resources. Training LLMs isn't just about the time; it's also financially demanding. Each hiccup, if not caught early, can translate to unnecessary expenditures. It's much like constructing a skyscraper; mid-way errors are costlier to rectify than those identified in the blueprint phase.

Introduction to Weights & Biases

Source

Weights & Biases (W&B) is a cutting-edge platform tailored for machine learning practitioners. It offers a suite of tools designed to help streamline the model development process, from tracking experiments to visualizing results.

With W&B, researchers and developers can efficiently monitor their LLM training progress, compare different model versions, and collaborate with team members. It's an invaluable asset for anyone looking to optimize and scale their machine-learning workflows.

How to Use W&B for Debugging & Monitoring LLMs

In the hands-on section of this article, we will adhere to the following structured approach, illustrated in the diagram below. We will fine-tune our model and leverage Weights and biases to save critical metrics, tables, and visualizations. This will empower us with deeper insights, enabling efficient debugging and monitoring of our Large Language Models.

1. Setting up Weights and Biases

a. Importing Necessary Libraries

import torch
import wandb
from transformers import BertTokenizer, BertForSequenceClassification
from torch.utils.data import DataLoader, random_split
from datasets import load_dataset

Intizailaizing W&B
# Initialize W&B
wandb.init(project='llm_monitoring', name='bert_example')

b. Loading the BERT Model

# Load tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

2. Fine-tuning your Model

a. Loading your dataset

dataset = load_dataset('Load your dataset')

b. Fine-tuning the model

for epoch in range(config.epochs):
    model.train()
    for batch in train_dataloader:
 
       # ……….
       # Continue training process here
       # ………..

3. Tracking Metrics

# Log the validation metrics to W&B
    wandb.log({
        "Epoch": epoch,
        "Validation Loss": avg_val_loss,
        "Validation Accuracy": val_accuracy
    })

4. Graph Visualizations

a. Plotting and logging Training Loss Graph

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(train_losses, label="Training Loss", color='blue')
ax.set(title="Training Losses", xlabel="Epoch", ylabel="Loss")
wandb.log({"Training Loss Curve": wandb.Image(fig)})

b. Plotting and logging Validation Loss Graph

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(val_losses, label="Validation Loss", color='orange')
ax.set(title="Validation Losses", xlabel="Epoch", ylabel="Loss")
wandb.log({"Validation Loss Curve": wandb.Image(fig)})

c. Plotting and Log Validation Accuracy Graph

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(val_accuracies, label="Validation Accuracy", color='green')
ax.set(title="Validation Accuracies", xlabel="Epoch", ylabel="Accuracy")
wandb.log({"Validation Accuracy Curve": wandb.Image(fig)})

d. Plotting and Log Training Accuracy Graph

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(train_accuracies, label="Training Accuracy", color='blue')
ax.set(title="Training Accuracies", xlabel="Epoch", ylabel="Accuracy")
wandb.log({"Training Accuracy Curve": wandb.Image(fig)})

5. Manual Checkups

questions = ["What's the weather like?", "Who won the world cup?", "How do you make an omelette?", "Why is the sky blue?", "When is the next holiday?"]
old_model_responses = ["It's sunny.", "France won the last one.", "Mix eggs and fry them.", "Because of the atmosphere.", "It's on December 25th."]
new_model_responses = ["The weather is clear and sunny.", "Brazil was the champion in the previous world cup.", "Whisk the eggs, add fillings, and cook in a pan.", "Due to Rayleigh scattering.", "The upcoming holiday is on New Year's Eve."]
 
# Create a W&B Table
table = wandb.Table(columns=["question", "old_model_response", "new_model_response"])
for q, old, new in zip(questions, old_model_responses, new_model_responses):
    table.add_data(q, old, new)
 
# Log the table to W&B
wandb.log({"NLP Responses Comparison": table})

6. Closing the W&B run after all logs are uploaded

wandb.finish()

Conclusion

Large Language Models have truly transformed the landscape of technology. Their vast capabilities are nothing short of amazing, but like all powerful tools, they require understanding and attention. Fortunately, with platforms like Weights & Biases, we have a handy toolkit to guide us. It reminds us that while LLMs are game-changers, they still need a bit of oversight.

Author Bio

Mostafa Ibrahim is a dedicated software engineer based in London, where he works in the dynamic field of Fintech. His professional journey is driven by a passion for cutting-edge technologies, particularly in the realms of machine learning and bioinformatics. When he's not immersed in coding or data analysis, Mostafa loves to travel.

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