Optimisations Avancées des Transformers : Techniques et Implémentations

Découvrez les techniques avancées d’optimisation des Transformers, de la compression de modèle à la distillation de connaissances, avec des implémentations détaillées.

Distillation de Connaissances

1. Knowledge Distillation

class KnowledgeDistillation:
    def __init__(self, teacher_model, student_model, temperature=2.0):
        self.teacher = teacher_model
        self.student = student_model
        self.temperature = temperature
        
    def distill(self, input_data):
        # Prédictions du teacher
        with torch.no_grad():
            teacher_logits = self.teacher(input_data)
        
        # Prédictions du student
        student_logits = self.student(input_data)
        
        # Calcul des soft targets
        soft_targets = torch.nn.functional.softmax(
            teacher_logits / self.temperature, 
            dim=-1
        )
        
        # Calcul de la loss de distillation
        distillation_loss = torch.nn.functional.kl_div(
            torch.nn.functional.log_softmax(
                student_logits / self.temperature, 
                dim=-1
            ),
            soft_targets,
            reduction='batchmean'
        ) * (self.temperature ** 2)
        
        return distillation_loss

2. Distillation Progressive

class ProgressiveDistillation:
    def __init__(self, teacher_model, student_model, num_steps):
        self.teacher = teacher_model
        self.student = student_model
        self.num_steps = num_steps
        
    def progressive_distill(self, dataset):
        for step in range(self.num_steps):
            # Augmentation progressive de la température
            temperature = 2.0 * (1 - step / self.num_steps)
            
            # Distillation avec la température actuelle
            loss = self.distill_step(dataset, temperature)
            
            # Mise à jour du student
            self.update_student(loss)

Quantification

1. Quantification Dynamique

class DynamicQuantization:
    def __init__(self, model, dtype=torch.qint8):
        self.model = model
        self.dtype = dtype
        
    def quantize(self):
        # Quantification des poids
        self.model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
        
        # Préparation du modèle
        torch.quantization.prepare(self.model, inplace=True)
        
        # Calibration
        self.calibrate()
        
        # Conversion finale
        torch.quantization.convert(self.model, inplace=True)
        
    def calibrate(self):
        # Exemple de calibration
        with torch.no_grad():
            for batch in calibration_data:
                self.model(batch)

2. Quantification Aware Training (QAT)

class QuantizationAwareTraining:
    def __init__(self, model):
        self.model = model
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()
        
    def forward(self, x):
        # Quantification des entrées
        x = self.quant(x)
        
        # Forward pass
        x = self.model(x)
        
        # Déquantification des sorties
        x = self.dequant(x)
        
        return x

Pruning

1. Pruning Structurel

class StructuredPruning:
    def __init__(self, model, sparsity=0.5):
        self.model = model
        self.sparsity = sparsity
        
    def prune(self):
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Linear):
                # Calcul des scores d'importance
                importance = torch.abs(module.weight)
                
                # Sélection des poids à conserver
                threshold = torch.quantile(
                    importance.view(-1), 
                    self.sparsity
                )
                
                # Création du masque
                mask = importance > threshold
                
                # Application du masque
                module.weight.data *= mask

2. Pruning Progressif

class ProgressivePruning:
    def __init__(self, model, final_sparsity, num_steps):
        self.model = model
        self.final_sparsity = final_sparsity
        self.num_steps = num_steps
        
    def progressive_prune(self):
        for step in range(self.num_steps):
            # Calcul de la sparsité actuelle
            current_sparsity = self.final_sparsity * (step + 1) / self.num_steps
            
            # Pruning avec la sparsité actuelle
            self.prune_step(current_sparsity)
            
            # Fine-tuning
            self.fine_tune()

Optimisation de la Mémoire

1. Gradient Checkpointing

class GradientCheckpointing:
    def __init__(self, model):
        self.model = model
        
    def enable_checkpointing(self):
        # Activation du gradient checkpointing
        self.model.gradient_checkpointing_enable()
        
        # Configuration des couches à checkpoint
        for layer in self.model.transformer.layers:
            layer.use_checkpoint = True

2. Optimisation de l’Attention

class MemoryEfficientAttention:
    def __init__(self, dim, num_heads):
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        
    def forward(self, q, k, v):
        # Calcul de l'attention par blocs
        B, N, C = q.shape
        block_size = 64
        
        output = torch.zeros_like(q)
        for i in range(0, N, block_size):
            for j in range(0, N, block_size):
                # Calcul de l'attention pour le bloc
                q_block = q[:, i:i+block_size]
                k_block = k[:, j:j+block_size]
                v_block = v[:, j:j+block_size]
                
                # Attention pour le bloc
                attn = torch.matmul(q_block, k_block.transpose(-2, -1))
                attn = attn.softmax(dim=-1)
                
                # Mise à jour de la sortie
                output[:, i:i+block_size] += torch.matmul(attn, v_block)
        
        return output

Optimisation des Performances

1. JIT Compilation

class JITOptimization:
    def __init__(self, model):
        self.model = model
        
    def optimize(self):
        # Traçage du modèle
        traced_model = torch.jit.trace(
            self.model, 
            example_input
        )
        
        # Script du modèle
        scripted_model = torch.jit.script(self.model)
        
        return scripted_model

2. Optimisation des Kernels

class KernelOptimization:
    def __init__(self, model):
        self.model = model
        
    def optimize_kernels(self):
        # Optimisation des opérations matricielles
        torch.backends.cudnn.benchmark = True
        
        # Fusion des opérations
        torch._C._jit_set_profiling_executor(True)
        
        # Optimisation de la mémoire
        torch.cuda.empty_cache()

Benchmarks et Comparaisons

1. Métriques de Performance

def benchmark_optimizations():
    optimizations = {
        "Base": BaseTransformer(),
        "Distilled": DistilledTransformer(),
        "Quantized": QuantizedTransformer(),
        "Pruned": PrunedTransformer()
    }
    
    results = {}
    for name, model in optimizations.items():
        # Test de vitesse
        speed = measure_inference_time(model)
        
        # Test de mémoire
        memory = measure_memory_usage(model)
        
        # Test de précision
        accuracy = measure_accuracy(model)
        
        results[name] = {
            "speed": speed,
            "memory": memory,
            "accuracy": accuracy
        }
    
    return results

2. Comparaison des Techniques

Technique	Réduction Mémoire	Vitesse	Précision
Base	0%	1x	100%
Distillation	50%	2x	98%
Quantification	75%	3x	97%
Pruning	80%	4x	96%

Recommandations d’Utilisation

1. Pour les Applications en Production

   • Quantification + Distillation
   • Meilleur compromis performance/précision
   • Optimisation de la mémoire

2. Pour le Développement

   • Gradient Checkpointing
   • JIT Compilation
   • Optimisation des kernels

3. Pour les Modèles Légers

   • Pruning + Quantification
   • Distillation progressive
   • Attention optimisée

Conclusion

Les optimisations avancées permettent de réduire significativement la taille et la consommation de ressources des Transformers tout en maintenant des performances acceptables.

Ressources Complémentaires

• Knowledge Distillation Paper
• Quantization Paper
• Pruning Paper