claudson/model.py

import torch
import torch.nn as nn
import faiss
import math

from model_args import ModelArgs
from transformer import Transformer
from tranny import FAISSRetriever

# Define a new combined model class
class CombinedMultiModalTransformer(nn.Module):
    def __init__(self, args: ModelArgs, knowledge_base: faiss.Index):
        super(CombinedMultiModalTransformer, self).__init__()
        self.args = args
        self.transformer = Transformer(args)
        
        # Multi-modal components
        self.audio_encoder = nn.Sequential(
            nn.Conv1d(256, 256, kernel_size=11, stride=2, padding='same'),
            nn.ReLU(),
            nn.Conv1d(256, args.dim, kernel_size=11, stride=2, padding='same'),
            nn.ReLU()
        )
        
        self.image_encoder = nn.Sequential(
            # ResNet50 layers
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(2048, args.dim)
        )
        
        # Music generation components
        self.pitch_embedding = nn.Embedding(128, args.dim)
        self.duration_embedding = nn.Embedding(32, args.dim)
        self.velocity_embedding = nn.Embedding(128, args.dim)
        
        # Anomaly detection components
        self.anomaly_detector = nn.Sequential(
            nn.Linear(args.dim, args.dim),
            nn.ReLU(),
            nn.Linear(args.dim, 1),
            nn.Sigmoid()
        )
        
        # RAG components
        self.knowledge_base = FAISSRetriever(knowledge_base)
        self.query_encoder = nn.Sequential(
            nn.Linear(args.dim, args.dim),
            nn.ReLU(),
            nn.Linear(args.dim, args.dim)
        )
        
    def forward(self, inputs, task):
        if task == 'text_generation':
            # RAG component
            query_embedding = self.query_encoder(self.transformer.embed(inputs))
            retrieved_docs = self.knowledge_base.search(query_embedding, k=5)
            
            # Concatenate retrieved docs with input
            inputs = torch.cat([retrieved_docs, inputs], dim=1)
            
            # Pass through transformer
            logits = self.transformer(inputs)
            return logits
        
        elif task == 'speech_recognition':
            x = self.audio_encoder(inputs)
            logits = self.transformer(x)
            return logits
        
        elif task == 'image_captioning':
            image_features = self.image_encoder(inputs)
            logits = self.transformer(image_features)
            return logits
        
        elif task == 'music_generation':
            pitch, duration, velocity = inputs
            x = self.pitch_embedding(pitch) + self.duration_embedding(duration) + self.velocity_embedding(velocity)
            logits = self.transformer(x)
            return logits
        
        elif task == 'anomaly_detection':
            x = self.transformer.embed(inputs)
            anomaly_scores = self.anomaly_detector(x)
            return anomaly_scores
        
        else:
            raise ValueError(f"Unknown task: {task}")

# Example usage
if __name__ == "__main__":
    args = ModelArgs()
    # Create a dummy knowledge base for testing
    knowledge_base = faiss.IndexFlatL2(args.dim)
    model = CombinedMultiModalTransformer(args, knowledge_base)
    # Define inputs and tasks
    # inputs = ...
    # task = 'text_generation'
    # output = model(inputs, task)
    # print(output.size())