Training Monitoring and Dataset Engineering

Overview

In our previous lesson, we explored the fundamentals of training language models, focusing on the basic optimization techniques and computational strategies. Now we'll dive deeper into two critical aspects of the training process: how to effectively monitor your training runs and how to engineer high-quality datasets that lead to better models.

Model training is both a science and an art — without proper monitoring, you're flying blind, and without well-engineered datasets, even the best architecture will underperform. This lesson equips you with the knowledge to track your model's progress and prepare data that maximizes learning efficiency.

Learning Objectives

After completing this lesson, you will be able to:

• Identify and track key metrics during language model training
• Implement effective monitoring systems for distributed training
• Diagnose common training issues through metric analysis
• Apply advanced dataset engineering techniques
• Implement data quality filtering and enhancement methods
• Balance dataset composition for improved model capabilities

Training Monitoring: The Compass for Model Development

Why Monitoring Matters

Training large language models is like navigating a vast ocean — without proper instruments, it's easy to get lost or sail in circles.

Analogy: Training Monitoring as a Health Dashboard

Think of training monitoring as a comprehensive health dashboard for your model:

• Vital Signs: Loss curves and learning rates are like heart rate and blood pressure
• Long-term Indicators: Validation metrics are like cholesterol levels, showing long-term health
• Warning Systems: Gradient statistics are like pain signals, indicating potential problems
• Growth Charts: Performance across tasks shows overall development, like height/weight charts

Essential Monitoring Metrics

Loss Curves: The Primary Indicator

Interpreting Loss Curves

• Healthy Convergence: Gradually decreasing loss that eventually plateaus
• Overfitting: Training loss continues to decrease while validation loss increases
• Underfitting: Both losses remain high and don't decrease significantly
• Oscillation: Spiky or unstable loss curves indicate learning rate issues

Beyond Loss: Advanced Metrics

1. Gradient Statistics:

   • Gradient Norm: Measures overall gradient magnitude
   • Gradient-to-Weight Ratio: Relative change applied to weights
   • Layer-wise Gradient Distribution: Identifies problematic layers

2. Weight Statistics:

   • Weight Norm: Tracks overall magnitude of weights
   • Weight Update Ratio: Percentage change in weights per step
   • Spectral Norm: Measures maximum eigenvalue of weight matrices

3. Attention Patterns:

   • Attention Entropy: Measures how focused vs. distributed attention is
   • Head Specialization: Shows which heads focus on specific patterns
   • Cross-layer Attention Correlation: Reveals layer interactions

{"tool": "code-editor", "defaultValue": "# Example code for monitoring gradient statistics import torch import numpy as np import matplotlib.pyplot as plt

def track_gradient_stats(model, step): """Track gradient statistics during training.""" stats = {} total_norm = 0.0 layer_norms = []

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# Calculate gradient norms by layer
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for name, param in model.named_parameters():
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    if param.grad is not None:
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        param_norm = param.grad.detach().data.norm(2)
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        layer_norms.append((name, param_norm.item()))
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        total_norm += param_norm.item() ** 2
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total_norm = total_norm ** 0.5
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stats['total_norm'] = total_norm
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stats['layer_norms'] = layer_norms
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# Calculate gradient-to-weight ratio
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grad_to_weight = []
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for name, param in model.named_parameters():
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    if param.grad is not None:
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        weight_norm = param.detach().data.norm(2).item()
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        if weight_norm > 0:
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            grad_norm = param.grad.detach().data.norm(2).item()
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            ratio = grad_norm / weight_norm
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            grad_to_weight.append((name, ratio))
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stats['grad_to_weight'] = grad_to_weight
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# Log to your monitoring system
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log_metrics(stats, step)
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return stats"}

Common Monitoring Tools

1. TensorBoard: Visualization for TensorFlow and PyTorch
2. Weights & Biases (W&B): Comprehensive experiment tracking
3. MLflow: Open-source platform for ML lifecycle
4. Neptune.ai: Metadata store for MLOps
5. Custom Monitoring: Tailored solutions for specific needs

Choosing the Right Monitoring System

Diagnosing Training Issues

Gradient Explosion

Symptoms:

• Sudden spike in loss values
• NaN or extremely large loss
• Rapidly growing gradient norms

Solutions:

• Gradient clipping
• Lower learning rate
• Check for improper initialization
• Investigate data outliers

Gradient Vanishing

Symptoms:

• Training progresses very slowly
• Lower layers update minimally
• Very small gradient norms

Solutions:

• Better initialization methods
• Residual connections
• Alternative activation functions
• Normalization techniques

Learning Rate Issues

Dataset Engineering: The Art of Better Data

From Data Collection to Dataset Engineering

Dataset engineering goes beyond simply gathering data—it involves thoughtful curation and enhancement.

Analogy: Dataset Engineering as Cooking

Think of dataset engineering as preparing a gourmet meal:

• Ingredients Selection: Choosing quality data sources
• Preparation: Cleaning and preprocessing
• Recipe Proportions: Balancing different data types
• Seasoning: Adding synthetic or augmented examples
• Tasting: Evaluating and iterating on the dataset

Quality Filtering Techniques

Statistical Filters

1. n-gram Statistics:

   • Measure repetition of words and phrases
   • Identify machine-generated text
   • Flag content with unusual patterns

2. Perplexity Filtering:

   • Use existing language models to score text quality
   • Remove content with abnormally high perplexity
   • Prioritize naturally flowing text

3. Entropy-based Filtering:

   • Measure information density and diversity
   • Remove content with very low or very high entropy
   • Ensure content has appropriate complexity

Example: Perplexity-based Filtering

{"tool": "code-editor", "defaultValue": "import torch from transformers import GPT2LMHeadModel, GPT2Tokenizer

def calculate_perplexity(text, model_name='gpt2'): """Calculate the perplexity of text using a pre-trained model.""" tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) model.eval()

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inputs = tokenizer(text, return_tensors='pt')
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with torch.no_grad():
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    outputs = model(**inputs, labels=inputs['input_ids'])
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loss = outputs.loss
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perplexity = torch.exp(loss)
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return perplexity.item()

def filter_by_perplexity(texts, threshold=100.0): """Filter out texts with perplexity above a threshold.""" filtered_texts = [] scores = []

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for text in texts:
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    perplexity = calculate_perplexity(text)
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    scores.append(perplexity)
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    if perplexity <= threshold:
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        filtered_texts.append(text)
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print(f'Kept {len(filtered_texts)}/{len(texts)} texts ({len(filtered_texts)/len(texts):.1%})')
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return filtered_texts, scores"}

Dataset Composition and Balancing

Carefully balancing dataset composition impacts what the model learns and how well it generalizes.

Example: RedPajama Dataset Composition

Balancing Strategies

1. Proportional Sampling: Weight data sources based on quality and relevance
2. Temperature Sampling: Control diversity using temperature parameter
3. Dynamic Rebalancing: Adjust composition based on validation performance
4. Domain-specific Enrichment: Increase proportion of targeted domains

Data Augmentation for Language Models

Unlike computer vision, language augmentation requires careful handling to preserve meaning.

Effective Augmentation Techniques

1. Back-translation: Translate text to another language and back
2. Paraphrasing: Use models to generate alternative phrasings
3. Synonym Replacement: Substitute words with semantically similar ones
4. Word Dropout: Randomly remove words to increase robustness
5. Sentence Reordering: Change paragraph structure while preserving meaning

Implementing Back-translation

{"tool": "code-editor", "defaultValue": "from transformers import MarianMTModel, MarianTokenizer

def back_translate(text, source_lang='en', target_lang='fr'): """Augment text via back-translation.""" # Load translation models forward_model_name = f'Helsinki-NLP/opus-mt-{source_lang}-{target_lang}' backward_model_name = f'Helsinki-NLP/opus-mt-{target_lang}-{source_lang}'

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# Forward translation tokenizer and model
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forward_tokenizer = MarianTokenizer.from_pretrained(forward_model_name)
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forward_model = MarianMTModel.from_pretrained(forward_model_name)
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# Backward translation tokenizer and model
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backward_tokenizer = MarianTokenizer.from_pretrained(backward_model_name)
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backward_model = MarianMTModel.from_pretrained(backward_model_name)
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# Translate to target language
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forward_inputs = forward_tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
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forward_outputs = forward_model.generate(**forward_inputs)
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intermediate_text = forward_tokenizer.decode(forward_outputs[0], skip_special_tokens=True)
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# Translate back to source language
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backward_inputs = backward_tokenizer(intermediate_text, return_tensors='pt', padding=True, truncation=True, max_length=512)
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backward_outputs = backward_model.generate(**backward_inputs)
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back_translated_text = backward_tokenizer.decode(backward_outputs[0], skip_special_tokens=True)
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return back_translated_text

Example usage

original_text = "The transformer architecture revolutionized natural language processing." augmented_text = back_translate(original_text, source_lang='en', target_lang='fr') print(f'Original: {original_text}') print(f'Augmented: {augmented_text}')"}

Synthetic Data Generation

Using Existing Models to Generate Training Data

1. Self-improvement: Using model-generated data to improve the same model
2. Data Distillation: Distilling knowledge from larger models
3. Task-specific Generation: Creating targeted examples for specific capabilities
4. Adversarial Examples: Generating difficult cases to improve robustness

Example: Generating Synthetic Question-Answer Pairs

{"tool": "code-editor", "defaultValue": "from transformers import pipeline

def generate_qa_pairs(context, num_questions=3): """Generate synthetic question-answer pairs from a context.""" # Load question generation model question_generator = pipeline('text2text-generation', model='valhalla/t5-base-qg-hl')

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# Load answer extraction model
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qa_model = pipeline('question-answering', model='deepset/roberta-base-squad2')
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qa_pairs = []
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# First approach: Generate questions, then extract answers
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generated_questions = question_generator(
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    f"generate questions: {context}", 
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    max_length=128, 
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    num_return_sequences=num_questions
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)
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for item in generated_questions:
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    question = item['generated_text']
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    # Find answer to the generated question
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    answer = qa_model(question=question, context=context)
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    qa_pairs.append({
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        'question': question,
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        'answer': answer['answer'],
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        'score': answer['score']
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    })
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return qa_pairs

Example usage

context = """ The transformer architecture was introduced in the paper 'Attention Is All You Need' by Vaswani et al. in 2017. It revolutionized natural language processing by replacing recurrent neural networks with self-attention mechanisms. """

qa_pairs = generate_qa_pairs(context) for i, pair in enumerate(qa_pairs): print(f"Q{i+1}: {pair['question']}") print(f"A{i+1}: {pair['answer']} (confidence: {pair['score']:.2f})") print()"}

Putting It All Together: Integrated Monitoring and Dataset Engineering

The Iterative Improvement Cycle

Case Study: Identifying Data Quality Issues Through Monitoring

When monitoring your training process, certain patterns can reveal data quality issues:

1. Plateau at High Loss: May indicate noisy or contradictory examples
2. Task-specific Underperformance: Shows gaps in domain coverage
3. Inconsistent Learning: Some batches cause spikes in gradient norms
4. Memorization Patterns: Model learns to copy rather than generalize

Data-Model Co-evolution

As models evolve, so should datasets:

• Larger models require higher-quality data
• Advanced capabilities need targeted examples
• Domain expertise becomes more important
• Evaluation drives dataset improvements

Practical Exercises

Exercise 1: Implement Basic Training Monitoring

Implement a monitoring system for a transformer language model that tracks:

• Training and validation loss
• Learning rate
• Gradient norms
• Sample predictions on a test set

Exercise 2: Perplexity-based Data Filtering

Use a pre-trained language model to:

1. Calculate perplexity scores for a dataset
2. Analyze the distribution of scores
3. Determine an appropriate filtering threshold
4. Compare model performance before and after filtering

Exercise 3: Dataset Composition Analysis

For a language model training dataset:

1. Analyze the composition by source, domain, and content type
2. Identify potential imbalances or gaps
3. Propose a rebalancing strategy
4. Implement a sampling method to achieve the desired composition

Conclusion

Effective monitoring and dataset engineering are inseparable aspects of successful language model development. By implementing robust monitoring systems, you can detect issues early and make data-driven decisions. Through thoughtful dataset engineering, you can improve model performance without architectural changes.

In the next lesson, we'll explore fine-tuning techniques and parameter-efficient methods to adapt pre-trained models to specific tasks while maintaining their general capabilities.

Additional Resources

Papers

• "Quality Filtering for Training Data: A Case Study on Large Language Models" (Penedo et al., 2023)
• "Data-juicer: A One-Stop Data Processing System for Large Language Models" (Chen et al., 2023)
• "The Role of Data Quality in Training Language Models" (Dodge et al., 2021)

Tools

• Weights & Biases
• TensorBoard
• Data-Juicer
• TextFlint (Text augmentation library)

Blog Posts

• "A Guide to LLM Training Monitoring"
• "Dataset Engineering is the New Feature Engineering"
• "The Patterns of Data Quality in LLM Training"