Progressive Distillation

from specific pipeline wrappers, providing a transparent view of the training dynamics.

import os
import torch
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    Trainer,
    TrainingArguments,
    DataCollatorWithPadding
)
from datasets import load_dataset

class ProgressiveDistillationOrchestrator:
    """
    Manages a chain of distillation steps.
    Each step trains a student model using the output of the previous step as teacher.
    """
    
    def __init__(self, dataset_name: str, task_name: str):
        self.dataset = load_dataset(dataset_name, task_name)
        self.chain_artifacts = {}

    def run_chain(self, chain_config: list[dict]) -> str:
        """
        Executes the distillation chain sequentially.
        
        Args:
            chain_config: List of dicts defining each step.
                          Must contain 'student_base', 'output_dir', and optional 'teacher'.
        
        Returns:
            Path to the final student model.
        """
        current_teacher_path = None
        
        for step_idx, step in enumerate(chain_config):
            print(f"--- Executing Step {step_idx + 1} ---")
            
            # Determine teacher: explicit override or result of previous step
            teacher_path = step.get("teacher") or current_teacher_path
            
            # Execute distillation for this step
            trained_model_path = self._execute_step(
                teacher_path=teacher_path,
                student_base=step["student_base"],
                output_dir=step["output_dir"],
                hyperparams=step.get("hyperparams", {})
            )
            
            # Update chain state
            current_teacher_path = trained_model_path
            self.chain_artifacts[f"step_{step_idx}"] = trained_model_path
            
        return current_teacher_path

    def _execute_step(
        self, 
        teacher_path: str | None, 
        student_base: str, 
        output_dir: str, 
        hyperparams: dict
    ) -> str:
        """
        Trains a student model, optionally using a teacher for distillation.
        """
        tokenizer = AutoTokenizer.from_pretrained(student_base)
        
        # Load student
        student_model = AutoModelForSequenceClassification.from_pretrained(student_base)
        
        # Load teacher if provided
        teacher_model = None
        if teacher_path:
            teacher_model = AutoModelForSequenceClassification.from_pretrained(teacher_path)
            teacher_model.eval()
        
        # Prepare data
        def tokenize_fn(batch):
            return tokenizer(batch["sentence"], truncation=True, max_length=128)
        
        tokenized_ds = self.dataset.map(tokenize_fn, batched=True)
        data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
        
        # Configure training arguments
        training_args = TrainingArguments(
            output_dir=output_dir,
            evaluation_strategy="epoch",
            save_strategy="epoch",
            learning_rate=hyperparams.get("learning_rate", 2e-5),
            num_train_epochs=hyperparams.get("num_train_epochs", 3),
            per_device_train_batch_size=hyperparams.get("batch_size", 32),
            load_best_model_at_end=True,
        )
        
        # Initialize Trainer with custom logic for distillation
        trainer = DistillationTrainer(
            model=student_model,
            teacher=teacher_model,
            args=training_args,
            train_dataset=tokenized_ds["train"],
            eval_dataset=tokenized_ds["validation"],
            tokenizer=tokenizer,
            data_collator=data_collator,
        )
        
        trainer.train()
        trainer.save_model(output_dir)
        
        return output_dir


class DistillationTrainer(Trainer):
    """
    Custom Trainer that computes a combined loss:
    L = alpha * L_task + (1 - alpha) * L_distillation
    """
    
    def __init__(self, teacher=None, alpha=0.5, temperature=2.0, **kwargs):
        super().__init__(**kwargs)
        self.teacher = teacher
        self.alpha = alpha
        self.temperature = temperature
        self.loss_fn = torch.nn.KLDivLoss(reduction="batchmean")

    def compute_loss(self, model, inputs, return_outputs=False):
        # Student forward pass
        student_outputs = model(**inputs)
        student_logits = student_outputs.logits
        
        # Task loss (Cross Entropy)
        labels = inputs.pop("labels")
        task_loss = torch.nn.functional.cross_entropy(student_logits, labels)
        
        if self.teacher is not None:
            # Teacher forward pass (no gradients)
            with torch.no_grad():
                teacher_outputs = self.teacher(**inputs)
                teacher_logits = teacher_outputs.logits
            
            # Distillation loss (KL Divergence on softened logits)
            soft_targets = torch.softmax(teacher_logits / self.temperature, dim=-1)
            soft_predictions = torch.log_softmax(student_logits / self.temperature, dim=-1)
            distill_loss = self.loss_fn(soft_predictions, soft_targets) * (self.temperature ** 2)
            
            # Combined loss
            total_loss = self.alpha * task_loss + (1 - self.alpha) * distill_loss
        else:
            total_loss = task_loss
            
        return (total_loss, student_outputs) if return_outputs else total_loss

Key Implementation Details:

• Orchestrator Pattern: The ProgressiveDistillationOrchestrator manages state and file paths, ensuring that each step consumes the artifact of the previous step. This avoids memory leaks by loading models from disk rather than keeping the entire chain in RAM.
• Custom Loss Function: The DistillationTrainer implements a weighted loss combining Cross-Entropy (for task accuracy) and KL-Divergence (for knowledge transfer). The alpha parameter controls the trade-off, and temperature smooths the probability distribution to reveal dark knowledge.
• Teacher Inference Mode: The teacher model is set to eval() and wrapped in torch.no_grad() to prevent gradient computation, reducing memory overhead during training.

Pitfall Guide

1. The "Big Bang" Distillation

   • Mistake: Attempting to distill a massive teacher directly into a tiny student.
   • Explanation: The capacity gap causes the student to fail to converge or learn only superficial features. The KL-divergence gradient becomes too noisy for the small model to follow.
   • Fix: Insert intermediate models. If the ratio exceeds 10x, add a medium-capacity step to bridge the gap.

2. Uninitialized Student Weights

   • Mistake: Training a small student from random initialization.
   • Explanation: Small models lack the data efficiency to learn robust representations from scratch, even with distillation signals. They require the semantic foundation provided by pretraining.
   • Fix: Always initialize the student from a pretrained checkpoint (e.g., bert-tiny, distilbert).

3. Static Distillation Hyperparameters

   • Mistake: Using the same alpha and temperature for every step in the chain.
   • Explanation: As the student shrinks, its capacity to absorb soft targets changes. A temperature that works for a medium model may overwhelm a tiny model.
   • Fix: Tune hyperparameters per step. Smaller students often benefit from lower temperatures and higher task-loss weights in early epochs.

4. Teacher Task Mismatch

   • Mistake: Using a base language model or a teacher trained on a different domain as the teacher.
   • Explanation: The teacher must provide task-specific decision boundaries. A generic teacher provides no useful signal for classification, leading to negative transfer.
   • Fix: Ensure every teacher in the chain is fine-tuned on the target dataset before serving as a teacher.

5. Evaluation Drift

   • Mistake: Monitoring training loss instead of validation accuracy.
   • Explanation: Distillation loss can decrease while task accuracy stagnates, especially if the teacher is overconfident.
   • Fix: Implement strict validation checks. Use early stopping based on validation accuracy, not loss.

6. Resource Contention

   • Mistake: Loading teacher and student models simultaneously without memory management.
   • Explanation: In a chain, keeping all models in memory can exhaust GPU RAM, especially with larger intermediate models.
   • Fix: Use the disk-based orchestrator pattern shown above. Load the teacher only during the training step and release it immediately after.

7. Ignoring Quantization Synergy

   • Mistake: Treating distillation as the final optimization step.
   • Explanation: Progressive distillation produces a model that is accurate but not necessarily optimized for inference speed.
   • Fix: Apply post-training quantization (PTQ) or dynamic quantization to the final student model to maximize edge performance.

Production Bundle

Action Checklist

• Audit Capacity Gaps: Calculate parameter ratios between teacher and student. Insert intermediate steps if ratio > 10x.
• Select Pretrained Bases: Verify all student models in the chain are initialized from pretrained checkpoints.
• Define Chain Topology: Map out the sequence of models (e.g., Large → Mini → Tiny → Femto) and save paths.
• Configure Hyperparameters: Set alpha, temperature, and learning rates per step based on model size.
• Implement Sequential Orchestrator: Use a pipeline that saves checkpoints to disk and loads them sequentially.
• Establish Baseline: Run direct fine-tuning on the final student to measure the delta provided by distillation.
• Validate Task Alignment: Confirm every teacher is fine-tuned on the target task before use.
• Apply Inference Optimization: Quantize the final model for deployment.

Decision Matrix

Configuration Template

Use this YAML structure to define your distillation chain. This format supports reproducibility and easy modification of hyperparameters per step.

distillation_chain:
  metadata:
    dataset: "nyu-mll/glue"
    task: "sst2"
    max_length: 128
  
  steps:
    - step_id: "large_to_mini"
      teacher: "assemblyai/bert-large-uncased-sst2"
      student_base: "google/bert_uncased_L-4_H-256_A-4"
      output_dir: "./artifacts/step1_mini"
      hyperparams:
        learning_rate: 1e-4
        num_train_epochs: 5
        batch_size: 32
        alpha: 0.5
        temperature: 2.0

    - step_id: "mini_to_tiny"
      teacher: "./artifacts/step1_mini"
      student_base: "google/bert_uncased_L-2_H-128_A-2"
      output_dir: "./artifacts/step2_tiny"
      hyperparams:
        learning_rate: 1e-4
        num_train_epochs: 5
        batch_size: 32
        alpha: 0.6
        temperature: 1.5

    - step_id: "tiny_to_femto"
      teacher: "./artifacts/step2_tiny"
      student_base: "neuml/bert-hash-femto"
      output_dir: "./artifacts/step3_femto"
      hyperparams:
        learning_rate: 3e-4
        num_train_epochs: 5
        batch_size: 32
        alpha: 0.7
        temperature: 1.0

Quick Start Guide

1. Install Dependencies:

   pip install transformers datasets torch
   

2. Define Your Chain: Create a configuration file specifying the sequence of models and hyperparameters. Ensure teachers are task-fine-tuned.
3. Run the Orchestrator: Execute the ProgressiveDistillationOrchestrator with your config. Monitor validation accuracy at each step to detect degradation early.
4. Export and Deploy: Once the chain completes, export the final student model. Apply quantization if targeting constrained hardware, then deploy to your inference runtime.