Training Fundamentals and Optimization for Language Models

Overview

In our previous lessons, we explored various transformer architectures and their evolution. We've learned what these models can do and how they differ in design. Now, we face the critical question: how do we effectively train these models to achieve peak performance?

This lesson focuses on the practical aspects of training large language models. We'll explore the entire training pipeline, from dataset preparation to distributed computing strategies and advanced optimization techniques. Understanding these fundamentals is essential whether you're fine-tuning existing models or building new ones from scratch.

Learning Objectives

After completing this lesson, you will be able to:

• Design and prepare datasets for pre-training and fine-tuning language models
• Understand the computational challenges of training large models and how to address them
• Implement distributed training strategies across multiple devices and machines
• Apply advanced optimization techniques to improve training stability and efficiency
• Diagnose and resolve common training issues
• Evaluate training progress and determine when a model is converged

Dataset Preparation: The Foundation of Model Quality

The Critical Role of Data

The quality, diversity, and scale of training data directly impact model performance—often more than architectural improvements. As the saying goes: "garbage in, garbage out."

Analogy: Training Data as Nutrition

Think of training data as the nutrition for an AI model:

• Quality: Just as an athlete needs clean, high-quality food, models need high-quality data
• Diversity: Like a balanced diet provides all necessary nutrients, diverse data provides broad knowledge
• Quantity: Both growing bodies and growing models need sufficient quantities of inputs
• Preparation: Raw ingredients must be processed appropriately, just as raw text needs to be processed

Pre-training Datasets: Scale and Diversity

For pre-training large language models, datasets typically include:

1. Web text: Filtered content from Common Crawl, WebText, etc.
2. Books: BookCorpus, Project Gutenberg, etc.
3. Scientific papers: arXiv, PubMed, etc.
4. Code: GitHub, StackOverflow, etc.
5. Wikipedia: Encyclopedic knowledge in multiple languages

Dataset Size Comparison

Data Cleaning and Filtering

Raw data from the internet contains noise, duplicates, and potentially harmful content. Data cleaning involves:

1. Deduplication: Removing exact and near-duplicate content
2. Quality Filtering: Heuristics for content quality (e.g., punctuation ratio, word diversity)
3. Harmful Content Removal: Filtering toxic, illegal, or private information
4. PII Redaction: Removing personally identifiable information

The Cleaning-Coverage Trade-off

Tokenization Approaches

As we covered in the text preprocessing lesson, there are several ways to tokenize text:

Fine-tuning Datasets

Fine-tuning datasets are typically smaller, task-specific, and often require:

• Labels or aligned pairs: For supervised learning
• High-quality curation: Often manually reviewed
• Balanced class distribution: For classification tasks
• Diverse samples: To prevent overfitting

Popular fine-tuning datasets include:

• GLUE/SuperGLUE: Benchmark suites for language understanding
• SQuAD: Question answering
• MNLI: Natural language inference
• WMT: Machine translation

Computational Challenges and Solutions

The Compute Equation: Memory, Speed, and Scale

Training large language models faces three main computational challenges:

1. Memory constraints: Model parameters, activations, and gradients
2. Computational intensity: FLOPs required for forward and backward passes
3. Training time: Epochs needed to achieve convergence

Analogy: Building a Skyscraper

Training a large language model is like building a skyscraper:

• Memory constraints are like the amount of land available for the foundation
• Computational intensity is like the number of workers and equipment needed
• Training time is like the construction schedule
• Distributed training is like coordinating multiple construction crews
• Optimization techniques are like improved building methods and materials

GPU Memory Anatomy

A typical training setup must fit:

• Model parameters: Weights and biases
• Optimizer states: Momentum terms, adaptive learning rates
• Activations: Forward pass outputs
• Gradients: Backward pass computations
• Temporary buffers: For operations like attention

Memory Optimization Techniques

Several techniques can reduce memory requirements:

1. Mixed Precision Training: Using FP16/BF16 instead of FP32
2. Gradient Checkpointing: Trading computation for memory
3. Gradient Accumulation: Simulating larger batches with smaller ones
4. Optimizer Memory Reduction: Techniques like 8-bit Adam
5. Activation Offloading: Moving activations to CPU RAM when not needed

How Gradient Checkpointing Works

Mixed Precision Training

Mixed precision leverages lower-precision formats to reduce memory usage and speed up computation on modern GPUs.

Implementation with PyTorch

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from torch.cuda.amp import autocast, GradScaler
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# Create model and optimizer
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model = TransformerModel().cuda()
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optimizer = torch.optim.Adam(model.parameters())
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scaler = GradScaler()
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# Training loop
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for epoch in range(num_epochs):
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    for batch in dataloader:
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        optimizer.zero_grad()
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        # Forward pass with autocast (uses mixed precision)
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        with autocast():
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            outputs = model(batch)
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            loss = compute_loss(outputs, batch)
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        # Backward pass with gradient scaling
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        scaler.scale(loss).backward()
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        # Optimizer step with unscaling
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        scaler.unscale_(optimizer)
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        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
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        scaler.step(optimizer)
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        scaler.update()

Gradient Accumulation

Gradient accumulation simulates larger batch sizes by accumulating gradients over multiple forward-backward passes.

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accumulation_steps = 8  # Effectively multiplies batch size by 8
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model.zero_grad()
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for i, batch in enumerate(dataloader):
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    # Forward pass
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    outputs = model(batch)
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    loss = compute_loss(outputs, batch)
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    # Normalize loss to account for accumulation
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    loss = loss / accumulation_steps
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    # Backward pass
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    loss.backward()
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    # Optimizer step every accumulation_steps
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    if (i + 1) % accumulation_steps == 0:
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        optimizer.step()
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        model.zero_grad()

Distributed Training Strategies

The Need for Distribution

As models grow, single-device training becomes impractical:

• GPT-3 (175B parameters) would require ~700GB for FP32 parameters alone
• Training time on a single device would be prohibitively long

Parallel Training Paradigms

Data Parallelism

In data parallelism, the model is replicated across devices, but each processes different data.

Implementation with PyTorch Distributed Data Parallel (DDP):

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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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# Initialize process group
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dist.init_process_group(backend='nccl')
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local_rank = dist.get_rank()
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torch.cuda.set_device(local_rank)
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# Create model on current device
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model = TransformerModel().cuda()
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# Wrap with DDP
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ddp_model = DDP(model, device_ids=[local_rank])
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# Distributed sampler for dataloader
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train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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dataloader = DataLoader(dataset, sampler=train_sampler, batch_size=batch_size)
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# Training loop
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for epoch in range(num_epochs):
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    train_sampler.set_epoch(epoch)
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    for batch in dataloader:
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        outputs = ddp_model(batch.cuda())
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        loss = compute_loss(outputs, batch)
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        loss.backward()
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        optimizer.step()
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        optimizer.zero_grad()

Model Parallelism

Model parallelism splits the model itself across multiple devices.

Pipeline Parallelism

Pipeline parallelism combines aspects of both data and model parallelism.

Tensor Parallelism

Tensor parallelism splits individual operations (e.g., matrix multiplications) across devices.

Hybrid Parallelism: The 3D Approach

Modern training systems like Megatron-LM combine multiple parallelism strategies:

• Data Parallelism: Across nodes
• Pipeline Parallelism: Across GPU groups
• Tensor Parallelism: Within GPU groups

Zero Redundancy Optimizer (ZeRO)

ZeRO eliminates memory redundancy in data parallel training:

• ZeRO Stage 1: Shards optimizer states
• ZeRO Stage 2: Shards gradients + Stage 1
• ZeRO Stage 3: Shards parameters + Stage 2

Advanced Optimization Techniques

Learning Rate Scheduling

Learning rate scheduling is crucial for stable and effective training.

Common Schedules

Implementation in PyTorch

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from torch.optim.lr_scheduler import LambdaLR
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def get_warmup_linear_decay_scheduler(optimizer, warmup_steps, total_steps):
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    def lr_lambda(current_step):
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        if current_step < warmup_steps:
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            # Linear warmup
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            return current_step / max(1, warmup_steps)
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        else:
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            # Linear decay
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            return max(0.0, (total_steps - current_step) / max(1, total_steps - warmup_steps))
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    return LambdaLR(optimizer, lr_lambda)
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# Usage
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optimizer = torch.optim.AdamW(model.parameters(), lr=0.0001)
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scheduler = get_warmup_linear_decay_scheduler(optimizer, warmup_steps=1000, total_steps=10000)
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# In training loop
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scheduler.step()

Weight Initialization

Proper weight initialization prevents exploding/vanishing gradients and speeds up convergence:

1. Xavier/Glorot Initialization: Designed for tanh activations
2. He Initialization: Optimized for ReLU activations
3. Layer-specific strategies: Special treatment for embedding, attention, and output layers

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def initialize_transformer_weights(module):
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    if isinstance(module, nn.Linear):
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        # Special init for output projection
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        if module.out_features == config.vocab_size:
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            nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * config.num_layers))
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        else:
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            nn.init.normal_(module.weight, mean=0.0, std=0.02)
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        if module.bias is not None:
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            nn.init.zeros_(module.bias)
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    elif isinstance(module, nn.Embedding):
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        nn.init.normal_(module.weight, mean=0.0, std=0.02)
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        if module.padding_idx is not None:
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            module.weight.data[module.padding_idx].zero_()
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    elif isinstance(module, nn.LayerNorm):
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        nn.init.ones_(module.weight)
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        nn.init.zeros_(module.bias)
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# Apply to model
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model.apply(initialize_transformer_weights)

Gradient Clipping

Gradient clipping prevents exploding gradients:

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# Global norm clipping
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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# Value clipping
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torch.nn.utils.clip_grad_value_(model.parameters(), clip_value=0.5)

Adaptive Optimizers

Advanced optimizers improve convergence and stability:

Optimizer Memory Requirements

AdamW Implementation

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optimizer = torch.optim.AdamW(
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    model.parameters(),
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    lr=1e-4,
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    betas=(0.9, 0.999),
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    eps=1e-8,
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    weight_decay=0.01
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)

Weight Decay and Regularization

Weight decay helps prevent overfitting and improves generalization:

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# Apply different weight decay to different parameter groups
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optimizer = torch.optim.AdamW([
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    {'params': model.embedding.parameters(), 'weight_decay': 0.0},  # No decay for embeddings
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    {'params': model.encoder.parameters(), 'weight_decay': 0.01},
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    {'params': model.decoder.parameters(), 'weight_decay': 0.01},
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    {'params': model.output_layer.parameters(), 'weight_decay': 0.1}  # Higher decay for output
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], lr=1e-4)

Monitoring and Debugging Training

Key Metrics to Track

Common Training Issues and Solutions

Learning Rate Finder

Finding optimal learning rates automatically:

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from torch_lr_finder import LRFinder
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model = TransformerModel()
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optimizer = torch.optim.AdamW(model.parameters())
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criterion = torch.nn.CrossEntropyLoss()
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lr_finder = LRFinder(model, optimizer, criterion, device="cuda")
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lr_finder.range_test(train_dataloader, end_lr=10, num_iter=100)
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lr_finder.plot()  # Visually inspect to find optimal LR
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lr_finder.reset()  # Reset model and optimizer to continue training

A Complete Training Pipeline

Putting It All Together

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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.cuda.amp import autocast, GradScaler
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from transformers import get_scheduler
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def train(config):
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    # Initialize distributed environment
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    dist.init_process_group(backend='nccl')
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    local_rank = dist.get_rank()
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    torch.cuda.set_device(local_rank)
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    # Create model, optimizer, and scheduler
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    model = TransformerModel(config).cuda()
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    model = DDP(model, device_ids=[local_rank])
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    # Optimizer with parameter groups
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    optimizer = torch.optim.AdamW([
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        {'params': model.module.embedding.parameters(), 'weight_decay': 0.0},
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        {'params': model.module.encoder.parameters()},
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        {'params': model.module.decoder.parameters()},
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    ], lr=config.learning_rate, weight_decay=config.weight_decay)
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    # Learning rate scheduler
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    num_training_steps = len(train_dataloader) * config.num_epochs
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    lr_scheduler = get_scheduler(
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        name="linear", 
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        optimizer=optimizer,
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        num_warmup_steps=int(0.1 * num_training_steps),
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        num_training_steps=num_training_steps
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    )
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    # Grad scaler for mixed precision
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    scaler = GradScaler()
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    # Training loop
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    for epoch in range(config.num_epochs):
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        model.train()
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        train_dataloader.sampler.set_epoch(epoch)
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        for step, batch in enumerate(train_dataloader):
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            # Move batch to device
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            batch = {k: v.cuda() for k, v in batch.items()}
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            # Zero gradients
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            optimizer.zero_grad()
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            # Gradient accumulation loop
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            for micro_step in range(config.gradient_accumulation_steps):
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                # Get micro-batch
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                micro_batch = get_micro_batch(batch, micro_step, config.gradient_accumulation_steps)
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                # Forward pass with mixed precision
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                with autocast():
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                    outputs = model(**micro_batch)
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                    loss = outputs.loss / config.gradient_accumulation_steps
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                # Backward pass with gradient scaling
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                scaler.scale(loss).backward()
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            # Gradient clipping
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            scaler.unscale_(optimizer)
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            torch.nn.utils.clip_grad_norm_(model.parameters(), config.max_grad_norm)
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            # Optimizer step
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            scaler.step(optimizer)
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            scaler.update()
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            lr_scheduler.step()
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            # Log metrics
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            if step % config.logging_steps == 0 and local_rank == 0:
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                log_metrics(loss, lr_scheduler.get_last_lr()[0], step, epoch)
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            # Save checkpoint
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            if step % config.save_steps == 0 and local_rank == 0:
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                save_checkpoint(model, optimizer, lr_scheduler, epoch, step)
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        # Evaluation at end of epoch
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        if local_rank == 0:
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            evaluate(model, eval_dataloader)
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    # Final model saving
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    if local_rank == 0:
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        model.module.save_pretrained(config.output_dir)

Future Directions in Training Optimization

Emergent Techniques

1. Mixture of Experts (MoE): Training larger models with conditional computation
2. Efficient Attention Mechanisms: Linear and sub-quadratic attention variants
3. Neural Architecture Search (NAS): Automated discovery of efficient architectures
4. Lifelong Learning: Continuous training with new data without forgetting

Mixture of Experts (MoE) Approach

Summary

In this lesson, we've covered:

1. Dataset Preparation:

   • Data collection, cleaning, and tokenization
   • Trade-offs between quality, diversity, and scale
   • Preparing pre-training and fine-tuning datasets

2. Computational Challenges:

   • Memory constraints and optimization techniques
   • Mixed precision training and gradient accumulation
   • Efficient parameter management

3. Distributed Training Strategies:

   • Data, model, pipeline, and tensor parallelism
   • Hybrid approaches for massive models
   • ZeRO optimizer for memory optimization

4. Advanced Optimization Techniques:

   • Learning rate scheduling and warmup
   • Specialized optimizers and weight decay
   • Gradient clipping and normalization techniques

5. Training Monitoring and Debugging:

   • Key metrics to track
   • Common issues and solutions
   • Tools for optimization

Understanding these training fundamentals is essential for successfully implementing and training language models at any scale, from fine-tuning smaller models to training massive architectures from scratch.

Practice Exercises

1. Dataset Preparation:

   • Build a text cleaning pipeline for web data
   • Implement different quality filtering heuristics
   • Compare the effect of different tokenization strategies

2. Memory Optimization:

   • Implement mixed precision training for a transformer model
   • Compare different gradient accumulation strategies
   • Measure the impact of gradient checkpointing on memory usage

3. Distributed Training:

   • Set up multi-GPU training with PyTorch DDP
   • Experiment with different data loading strategies
   • Compare throughput with and without distributed training

4. Optimization Techniques:

   • Implement and compare different learning rate schedulers
   • Test the effect of weight decay on model performance
   • Experiment with different gradient clipping thresholds

Additional Resources

• Efficient Training on Multiple GPUs
• DeepSpeed: Extreme-scale Model Training
• Megatron-LM: Training Multi-Billion Parameter Models
• Colossal-AI: A Unified Deep Learning System
• Flash Attention: Fast and Memory-Efficient Exact Attention
• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
• Parameter-Efficient Transfer Learning