Advanced Applications of Transformers: Beyond NLP

Transformers have evolved well beyond their original application in natural language processing. Discover the most advanced applications and emerging trends.

Multimodal Models

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__()
        
        # Image encoder
        self.visual = VisionTransformer(
            image_resolution, vision_patch_size, 3,
            embed_dim, vision_layers, vision_width
        )
        
        # Text encoder
        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)
        
        # Normalization
        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        
        # Similarity
        similarity = torch.matmul(image_features, text_features.T)
        return similarity

2. DALL-E and 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 and Few-Shot Learning

1. GPT-3 and 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 with 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)

Emerging Architectures

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)
        
        # Block-wise attention computation
        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)
        
        # Linear approximation
        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)

Specific Applications

1. Signal Processing

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

Future Trends

1. More Efficient Models

   • Reduced energy consumption
   • Memory optimization
   • Faster inference

2. Multimodal Integration

   • More sophisticated modality fusion
   • Unified models
   • Cross-modal transfer learning

3. Robustness and Ethics

   • Bias mitigation
   • Explainability
   • Fairness

Conclusion

Transformers continue to evolve and diversify, finding applications in an ever-wider range of domains. Their flexibility and adaptability make them a foundational architecture for the future of AI.

Additional Resources

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