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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()) |