Evals Dashboard for Finetuning

Here’s a draft blog post:

Why

Monitoring the learning rate, gradient norm spikes, and losses during fine-tuning a large language model like LLaMA is crucial for achieving good performance and preventing overfitting. As we scale up our models to handle increasingly larger datasets, it’s essential to have a framework in place that allows us to track these key metrics and make data-driven decisions about hyperparameter tuning.

Introduction

When fine-tuning a pre-trained language model like LLaMA on large datasets, it’s common to encounter issues with slow learning rates or sudden spikes in gradient norms. These issues can be challenging to diagnose without proper monitoring tools. In this post, we’ll introduce a simple framework for integrating a dashboard that tracks the learning rate, gradient norm spikes, and losses during fine-tuning.

Content

To implement our dashboard, we’ll use a combination of popular Python libraries: torch, transformers, and matplotlib. Here’s a step-by-step guide:

1. Set up your environment

First, make sure you have the necessary dependencies installed:

import torch
from transformers import LLaMAForSequenceClassification, AutoTokenizer
import matplotlib.pyplot as plt

2. Load your dataset and model

Load your large dataset (e.g., a CSV file or a database) and pre-trained LLaMA model:

dataset = pd.read_csv('your_data.csv')
model = LLaMAForSequenceClassification.from_pretrained('llama-base')
tokenizer = AutoTokenizer.from_pretrained('llama-base')

3. Fine-tune your model

Fine-tune the model on your dataset using a suitable optimizer and scheduler:

optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)

for epoch in range(10):
    model.train()
    for batch in dataset:
        inputs = tokenizer.encode(batch['text'], return_tensors='pt', max_length=512, truncation=True)
        labels = batch['label']
        optimizer.zero_grad()
        outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
        loss = torch.nn.CrossEntropyLoss()(outputs, labels)
        loss.backward()
        optimizer.step()
    scheduler.step()

4. Track key metrics

Track the learning rate, gradient norm spikes, and losses during fine-tuning:

lr_values = []
grad_norms = []
losses = []

for epoch in range(10):
    model.train()
    for batch in dataset:
        # ... (same as above)
        loss_value = loss.item()
        grad_norm_value = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0).item()
        lr_value = optimizer.param_groups[0]['lr']
        lr_values.append(lr_value)
        grad_norms.append(grad_norm_value)
        losses.append(loss_value)

plt.plot(lr_values, label='Learning Rate')
plt.plot(grad_norms, label='Gradient Norm Spikes')
plt.plot(losses, label='Loss')
plt.legend()
plt.show()

5. Visualize your dashboard

Use matplotlib to create a simple dashboard that displays the tracked metrics:

import matplotlib.pyplot as plt

fig, ax1 = plt.subplots()

ax1.plot(lr_values, 'b-', label='Learning Rate')
ax1.plot(grad_norms, 'r--', label='Gradient Norm Spikes')
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Value')

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

ax2.plot(losses, 'g-.', label='Loss')
ax2.set_ylabel('Loss Value')

plt.legend(loc='upper right')
plt.show()

Conclusion

In this post, we’ve introduced a simple framework for integrating a dashboard that tracks key metrics during fine-tuning a pre-trained LLaMA model on large datasets. By monitoring the learning rate, gradient norm spikes, and losses, you’ll be able to identify potential issues early on and make data-driven decisions about hyperparameter tuning. Next steps include exploring more advanced visualization techniques and integrating this framework with your existing workflow.

Note: This is a v0.5 draft generated by llama3. Will be updated with actual content.