knowledge-distillation

# Knowledge Distillation: Compressing LLMs ## When to Use This Skill Use Knowledge Distillation when you need to: - **Compress models** from 70B → 7B while retaining 90%+ performance - **Transfer capabilities** from proprietary models (GPT-4) to open-source (LLaMA, Mistral) - **Reduce inference costs** by deploying smaller student models - **Create specialized models** by distilling domain-specific knowledge - **Improve small models** using synthetic data from large teachers **Key Techniques**: Temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, response distillation **Papers**: Hinton et al. 2015 (arXiv 1503.02531), MiniLLM (arXiv 2306.08543), KD Survey (arXiv 2402.13116) ## Installation ```bash # Standard transformers pip install transformers datasets accelerate # For training pip install torch deepspeed wandb # Optional: MiniLLM implementation git clone https://github.com/microsoft/LMOps cd LMOps/minillm pip install -e . ``` ## Quick Start ### Basic Knowledge Distillation ```python import torch import torch.nn.functional as F from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments # 1. Load teacher (large) and student (small) models teacher = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-70b-hf", # Large teacher torch_dtype=torch.float16, device_map="auto" ) student = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-hf", # Small student torch_dtype=torch.float16, device_map="cuda:0" ) tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-70b-hf") # 2. Define distillation loss def distillation_loss(student_logits, teacher_logits, labels, temperature=2.0, alpha=0.5): """ Combine hard loss (cross-entropy) with soft loss (KL divergence). Args: temperature: Softens probability distributions (higher = softer) alpha: Weight for distillation loss (1-alpha for hard loss) """ # Hard loss: Standard cross-entropy with true labels hard_loss = F.cross_entropy(student_logits.view(-1, student_logits.size(-1)), labels.view(-1)) # Soft loss: KL divergence between student and teacher soft_targets = F.softmax(teacher_logits / temperature, dim=-1) soft_student = F.log_softmax(student_logits / temperature, dim=-1) soft_loss = F.kl_div(soft_student, soft_targets, reduction='batchmean') * (temperature ** 2) # Combined loss return alpha * soft_loss + (1 - alpha) * hard_loss # 3. Training loop for batch in dataloader: # Teacher forward (no grad) with torch.no_grad(): teacher_outputs = teacher(**batch) teacher_logits = teacher_outputs.logits # Student forward student_outputs = student(**batch) student_logits = student_outputs.logits # Compute distillation loss loss = distillation_loss( student_logits, teacher_logits, batch['labels'], temperature=2.0, alpha=0.7 # 70% soft, 30% hard ) # Backward and optimize loss.backward() optimizer.step() optimizer.zero_grad() ``` ### MiniLLM (Reverse KLD) **Source**: arXiv 2306.08543 (2024) **Innovation**: Use reverse KLD instead of forward KLD for better generative model distillation. ```python def reverse_kl_loss(student_logits, teacher_logits, temperature=1.0): """ Reverse KL divergence: KL(Teacher || Student) Better for generative models than forward KL. """ # Teacher distribution (target) p_teacher = F.softmax(teacher_logits / temperature, dim=-1) # Student distribution (model) log_p_student = F.log_softmax(student_logits / temperature, dim=-1) # Reverse KL: Sum over teacher, student learns to cover teacher's modes reverse_kl = -(p_teacher * log_p_student).sum(dim=-1).mean() return reverse_kl * (temperature ** 2) # Training with MiniLLM for batch in dataloader: with torch.no_grad(): teacher_logits = teacher(**batch).logits student_logits = student(**batch).logits # Reverse KLD (better for generation) loss = reverse_kl_loss(student_logits, teacher_logits, temperature=1.0) loss.backward() optimizer.step() ``` **Why reverse KL?** - **Forward KL** (standard): Student learns to match teacher's *mean* - **Reverse KL** (MiniLLM): Student learns to *cover* all teacher's modes - Better for diverse text generation ### Response Distillation ```python # Generate synthetic data from teacher, train student to imitate # 1. Generate synthetic responses from teacher prompts = ["Explain AI:", "What is ML?", "Define NLP:"] teacher_responses = [] for prompt in prompts: inputs = tokenizer(prompt, return_tensors='pt').to(teacher.device) outputs = teacher.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7) response = tokenizer.decode(outputs[0], skip_special_tokens=True) teacher_responses.append(response) # 2. Train student on teacher's responses (standard fine-tuning) train_dataset = [ {"text": f"{prompt}\n{response}"} for prompt, response in zip(prompts, teacher_responses) ] # 3. Fine-tune student trainer = Trainer( model=student, args=TrainingArguments(output_dir="./student", num_train_epochs=3, learning_rate=2e-5), train_dataset=train_dataset, ) trainer.train() ``` ## Core Concepts ### 1. Temperature Scaling **Purpose**: Soften probability distributions to expose teacher's uncertainty. ```python # Low temperature (T=1): Sharp distribution logits = [3.0, 2.0, 1.0] probs_T1 = softmax(logits / 1.0) # [0.67, 0.24, 0.09] # High temperature (T=4): Soft distribution probs_T4 = softmax(logits / 4.0) # [0.42, 0.34, 0.24] # Higher T reveals more information about relative rankings ``` **Rule**: Use T=2-5 for distillation (2 is common default). ### 2. Loss Function Components ```python # Total loss = alpha * soft_loss + (1 - alpha) * hard_loss # Soft loss: Learn from teacher's knowledge soft_loss = KL(student || teacher) # Hard loss: Learn from ground truth labels hard_loss = CrossEntropy(student_output, true_labels) # Typical values: alpha = 0.5 # Balanced alpha = 0.7 # More emphasis on teacher alpha = 0.3 # More emphasis on labels ``` ### 3. Forward vs Reverse KLD ```python # Forward KL: KL(Student || Teacher) # - Student matches teacher's average behavior # - Mode-seeking: Student focuses on teacher's highest probability modes # - Good for classification # Reverse KL: KL(Teacher || Student) # - Student covers all of teacher's behaviors # - Mode-covering: Student learns diverse behaviors # - Good for generation (MiniLLM) ``` ## Training Strategies ### Strategy 1: Logit Distillation ```python # Train student to match teacher's logits directly def logit_distillation_trainer(student, teacher, dataloader, temperature=2.0): optimizer = torch.optim.AdamW(student.parameters(), lr=2e-5) for epoch in range(3): for batch in dataloader: # Get logits with torch.no_grad(): teacher_logits = teacher(**batch).logits student_logits = student(**batch).logits # MSE on logits (alternative to KLD) loss = F.mse_loss(student_logits, teacher_logits) # Or use KLD # loss = F.kl_div( # F.log_softmax(student_logits/temperature, dim=-1), # F.softmax(teacher_logits/temperature, dim=-1), # reduction='batchmean' # ) * (temperature ** 2) loss.backward() optimizer.step() optimizer.zero_grad() return student ``` ### Strategy 2: Two-Stage Distillation ```python # Stage 1: Distill from teacher student = distill(teacher, student, epochs=5) # Stage 2: Fine-tune on task-specific data student = fine_tune(student, task_data, epochs=3) # Results in better task performance than single-stage ``` ### Strategy 3: Multi-Teacher Distillation ```python # Learn from multiple expert teachers def multi_teacher_distillation(student, teachers, batch): """Distill from ensemble of teachers.""" teacher_logits_list = [] # Get logits from all teachers with torch.no_grad(): for teacher in teachers: logits = teacher(**batch).logits teacher_logits_list.append(logits) # Average teacher predictions avg_teacher_logits = torch.stack(teacher_logits_list).mean(dim=0) # Student learns from ensemble student_logits = student(**batch).logits loss = F.kl_div( F.log_softmax(student_logits, dim=-1), F.softmax(avg_teacher_logits, dim=-1), reduction='batchmean' ) return loss ``` ## Production Deployment ### Complete Training Script ```python from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling def train_distilled_model( teacher_name="meta-llama/Llama-2-70b-hf", student_name="meta-llama/Llama-2-7b-hf", output_dir="./distilled-llama-7b", temperature=2.0, alpha=0.7, ): # Load models teacher = AutoModelForCausalLM.from_pretrained(teacher_name, torch_dtype=torch.float16, device_map="auto") student = AutoModelForCausalLM.from_pretrained(student_name, torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(teacher_name) # Custom trainer with distillation class DistillationTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): # Student forward outputs_student = model(**inputs) student_logits = outputs_student.logits # Teacher forward (no grad) with torch.no_grad(): outputs_teacher = teacher(**inputs) teacher_logits = outputs_teacher.logits # Distillation loss soft_targets = F.softmax(teacher_logits / temperature, dim=-1) soft_student = F.log_softmax(student_logits / temperature, dim=-1) soft_loss = F.kl_div(soft_student, soft_targets, reduction='batchmean') * (temperature ** 2) # Hard loss hard_loss = outputs_student.loss # Combined loss = alpha * soft_loss + (1 - alpha) * hard_loss return (loss, outputs_student) if return_outputs else loss # Training arguments training_args = TrainingArguments( output_dir=output_dir, num_train_epochs=3, per_device_train_batch_size=4, gradient_accumulation_steps=8, learning_rate=2e-5, warmup_steps=500, logging_steps=100, save_steps=1000, bf16=True, gradient_checkpointing=True, ) # Train trainer = DistillationTrainer( model=student, args=training_args, train_dataset=train_dataset, data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False), ) trainer.train() student.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) # Usage train_distilled_model( teacher_name="meta-llama/Llama-2-70b-hf", student_name="meta-llama/Llama-2-7b-hf", temperature=2.0, alpha=0.7 ) ``` ## Best Practices ### 1. Hyperparameter Selection ```python # Temperature T = 1.0 # Sharp (less knowledge transfer) T = 2.0 # Standard (good balance) T = 5.0 # Soft (more knowledge transfer) # Alpha (weight) alpha = 0.5 # Balanced alpha = 0.7 # Emphasize teacher knowledge alpha = 0.9 # Strong distillation # Rule: Higher T + higher alpha = stronger distillation ``` ### 2. Model Size Ratio ```python # Good ratios (teacher/student) 70B / 7B = 10× # Excellent 13B / 1B = 13× # Good 7B / 1B = 7× # Acceptable # Avoid too large gap 70B / 1B = 70× # Too large, ineffective ``` ### 3. Data Quality ```python # Best: Use teacher-generated data + real data train_data = { "teacher_generated": 70%, # Diverse, high-quality "real_data": 30% # Ground truth } # Avoid: Only real data (doesn't utilize teacher fully) ``` ## Evaluation ```python from transformers import pipeline # Compare student vs teacher teacher_pipe = pipeline("text-generation", model=teacher) student_pipe = pipeline("text-generation", model=student) prompts = ["Explain quantum computing:", "What is AI?"] for prompt in prompts: teacher_out = teacher_pipe(prompt, max_new_tokens=100) student_out = student_pipe(prompt, max_new_tokens=100) print(f"Prompt: {prompt}") print(f"Teacher: {teacher_out[0]['generated_text']}") print(f"Student: {student_out[0]['generated_text']}")