Applications Avancées des Transformers : Au-delà du NLP

Les Transformers ont évolué bien au-delà de leur application initiale en traitement du langage naturel. Découvrez les applications les plus avancées et les tendances émergentes.

Modèles Multimodaux

1. CLIP (Contrastive Language-Image Pre-training)

class CLIP(nn.Module):
    def __init__(self, embed_dim, image_resolution, vision_layers, vision_width, 
                 vision_patch_size, context_length, vocab_size, transformer_width,
                 transformer_heads, transformer_layers):
        super().__init__()
        
        # Encoder d'images
        self.visual = VisionTransformer(
            image_resolution, vision_patch_size, 3,
            embed_dim, vision_layers, vision_width
        )
        
        # Encoder de texte
        self.transformer = Transformer(
            context_length, vocab_size, embed_dim,
            transformer_heads, transformer_layers
        )
        
        # Projection
        self.text_projection = nn.Parameter(torch.empty(embed_dim, embed_dim))
        self.image_projection = nn.Parameter(torch.empty(embed_dim, embed_dim))
        
    def encode_image(self, image):
        return self.visual(image)
        
    def encode_text(self, text):
        return self.transformer(text)
        
    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)
        
        # Normalisation
        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        
        # Similarité
        similarity = torch.matmul(image_features, text_features.T)
        return similarity

2. DALL-E et Stable Diffusion

class DiffusionTransformer(nn.Module):
    def __init__(self, dim, depth, heads, mlp_dim, num_patches):
        super().__init__()
        
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        
        self.transformer = Transformer(
            dim=dim,
            depth=depth,
            heads=heads,
            dim_head=dim // heads,
            mlp_dim=mlp_dim
        )
        
        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', 
                     p1=16, p2=16),
            nn.Linear(16 * 16 * 3, dim)
        )
        
    def forward(self, x, t):
        x = self.to_patch_embedding(x)
        x = x + self.pos_embedding
        
        cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=x.shape[0])
        x = torch.cat((cls_tokens, x), dim=1)
        
        x = self.transformer(x)
        return x[:, 0]

Zero-Shot et Few-Shot Learning

1. GPT-3 et Zero-Shot Learning

class ZeroShotClassifier:
    def __init__(self, model_name="gpt-3"):
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        
    def classify(self, text, labels):
        prompt = f"Classifiez le texte suivant dans l'une des catégories : {', '.join(labels)}\n\nTexte : {text}\n\nCatégorie :"
        
        inputs = self.tokenizer(prompt, return_tensors="pt")
        outputs = self.model.generate(**inputs, max_length=100)
        
        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return prediction.split("Catégorie :")[-1].strip()

2. Few-Shot Learning avec Prompt Engineering

class FewShotLearner:
    def __init__(self, model_name="gpt-3"):
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        
    def create_prompt(self, examples, query):
        prompt = "Exemples :\n"
        for ex in examples:
            prompt += f"Texte : {ex['text']}\nCatégorie : {ex['label']}\n\n"
        prompt += f"Texte : {query}\nCatégorie :"
        return prompt
        
    def predict(self, examples, query):
        prompt = self.create_prompt(examples, query)
        inputs = self.tokenizer(prompt, return_tensors="pt")
        outputs = self.model.generate(**inputs, max_length=100)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

Architectures Émergentes

1. Sparse Transformers

class SparseAttention(nn.Module):
    def __init__(self, dim, num_heads, block_size=64):
        super().__init__()
        self.num_heads = num_heads
        self.block_size = block_size
        self.scale = dim ** -0.5
        
        self.qkv = nn.Linear(dim, dim * 3)
        self.proj = nn.Linear(dim, dim)
        
    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
        q, k, v = qkv.unbind(2)
        
        # Calcul de l'attention par blocs
        attn = torch.zeros(B, self.num_heads, N, N, device=x.device)
        for i in range(0, N, self.block_size):
            for j in range(0, N, self.block_size):
                q_block = q[:, i:i+self.block_size]
                k_block = k[:, j:j+self.block_size]
                attn_block = torch.matmul(q_block, k_block.transpose(-2, -1)) * self.scale
                attn[:, :, i:i+self.block_size, j:j+self.block_size] = attn_block
                
        attn = attn.softmax(dim=-1)
        x = torch.matmul(attn, v)
        x = x.transpose(1, 2).reshape(B, N, C)
        return self.proj(x)

2. Linear Transformers

class LinearAttention(nn.Module):
    def __init__(self, dim, num_heads):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5
        
        self.qkv = nn.Linear(dim, dim * 3)
        self.proj = nn.Linear(dim, dim)
        
    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
        q, k, v = qkv.unbind(2)
        
        # Approximation linéaire
        q = q.softmax(dim=-1)
        k = k.softmax(dim=-2)
        
        context = torch.einsum('bhnd,bhne->bhde', k, v)
        out = torch.einsum('bhnd,bhde->bhne', q, context)
        
        out = out.transpose(1, 2).reshape(B, N, C)
        return self.proj(out)

Applications Spécifiques

1. Traitement du Signal

class SignalTransformer(nn.Module):
    def __init__(self, input_dim, num_heads, num_layers, dim_feedforward):
        super().__init__()
        
        self.embedding = nn.Linear(input_dim, dim_feedforward)
        self.pos_encoder = PositionalEncoding(dim_feedforward)
        
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=dim_feedforward,
            nhead=num_heads,
            dim_feedforward=dim_feedforward
        )
        self.transformer_encoder = nn.TransformerEncoder(
            encoder_layer,
            num_layers=num_layers
        )
        
        self.decoder = nn.Linear(dim_feedforward, input_dim)
        
    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_encoder(x)
        x = self.transformer_encoder(x)
        return self.decoder(x)

2. Graph Transformers

class GraphTransformer(nn.Module):
    def __init__(self, node_dim, edge_dim, num_heads, num_layers):
        super().__init__()
        
        self.node_embedding = nn.Linear(node_dim, node_dim)
        self.edge_embedding = nn.Linear(edge_dim, node_dim)
        
        self.layers = nn.ModuleList([
            GraphTransformerLayer(node_dim, num_heads)
            for _ in range(num_layers)
        ])
        
    def forward(self, x, edge_index, edge_attr):
        x = self.node_embedding(x)
        edge_attr = self.edge_embedding(edge_attr)
        
        for layer in self.layers:
            x = layer(x, edge_index, edge_attr)
            
        return x

Tendances Futures

1. Modèles Plus Efficaces

   • Réduction de la consommation énergétique
   • Optimisation de la mémoire
   • Accélération de l’inférence

2. Intégration Multimodale

   • Fusion de modalités plus sophistiquée
   • Modèles unifiés
   • Transfer learning cross-modal

3. Robustesse et Éthique

   • Gestion des biais
   • Explicabilité
   • Fairness

Conclusion

Les Transformers continuent d’évoluer et de se diversifier, trouvant des applications dans des domaines toujours plus variés. Leur flexibilité et leur capacité d’adaptation en font une architecture fondamentale pour l’avenir de l’IA.

Ressources Complémentaires

• CLIP Paper
• DALL-E Paper
• GPT-3 Paper