""" OmniCoreX Core Model Definition This module defines the core neural architecture of OmniCoreX - the ultimate AI brain integrating infinite knowledge streams with unparalleled adaptive reasoning and real-time decision making. Features: - Multi-stream knowledge integration layers for combining diverse inputs. - Adaptive reasoning modules enabling dynamic context-aware inference. - Hierarchical transformer blocks optimized for scalability and modularity. """ import torch import torch.nn as nn import torch.nn.functional as F class MultiStreamEncoder(nn.Module): """ Encodes multiple knowledge streams with dedicated encoders and fuses representations. """ def __init__(self, stream_configs, embed_dim): """ Args: stream_configs (dict): {stream_name: input_dim} mapping input sizes per stream. embed_dim (int): Dimension of embedding vectors after encoding. """ super().__init__() self.stream_encoders = nn.ModuleDict() for name, input_dim in stream_configs.items(): self.stream_encoders[name] = nn.Sequential( nn.Linear(input_dim, embed_dim), nn.LayerNorm(embed_dim), nn.ReLU(inplace=True) ) self.fusion_layer = nn.Linear(len(stream_configs)*embed_dim, embed_dim) self.norm = nn.LayerNorm(embed_dim) def forward(self, input_streams): """ Args: input_streams (dict): {stream_name: tensor of shape (batch_size, seq_len, input_dim)} Returns: fused_embed: Tensor of shape (batch_size, seq_len, embed_dim) """ encoded_streams = [] for name, encoder in self.stream_encoders.items(): x = input_streams[name] # shape: (batch, seq_len, input_dim) encoded = encoder(x) # (batch, seq_len, embed_dim) encoded_streams.append(encoded) concat_embeds = torch.cat(encoded_streams, dim=-1) # (batch, seq_len, embed_dim * n_streams) fused = self.fusion_layer(concat_embeds) # (batch, seq_len, embed_dim) fused = self.norm(fused) return fused class AdaptiveReasoningBlock(nn.Module): """ Transformer block with adaptive reasoning capability through dynamic gating and context-modulated feed-forward networks. """ def __init__(self, embed_dim, num_heads, dropout=0.1): super().__init__() self.self_attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout) self.norm1 = nn.LayerNorm(embed_dim) self.norm2 = nn.LayerNorm(embed_dim) # Context gating mechanism dynamically modulates FFN self.context_gate = nn.Sequential( nn.Linear(embed_dim, embed_dim), nn.Sigmoid() ) self.ffn = nn.Sequential( nn.Linear(embed_dim, embed_dim * 4), nn.GELU(), nn.Dropout(dropout), nn.Linear(embed_dim * 4, embed_dim), nn.Dropout(dropout), ) def forward(self, x, context=None, attn_mask=None, key_padding_mask=None): """ Args: x: Input tensor (seq_len, batch_size, embed_dim) context: Optional context tensor (seq_len, batch_size, embed_dim) for gating. attn_mask: Optional attention mask. key_padding_mask: Optional padding mask. Returns: Tensor of shape (seq_len, batch_size, embed_dim) """ # Self-attention attn_out, _ = self.self_attn(x, x, x, attn_mask=attn_mask, key_padding_mask=key_padding_mask) x = x + attn_out x = self.norm1(x) # Context gating gate = self.context_gate(context) if context is not None else torch.ones_like(x) ffn_out = self.ffn(x) x = x + gate * ffn_out x = self.norm2(x) return x class OmniCoreXModel(nn.Module): """ Core OmniCoreX Model combining multi-stream encoding and adaptive reasoning layers. Arguments: stream_configs (dict): Dictionary of input stream names and their feature dims. embed_dim (int): Embedding dimension for all transformer layers. num_layers (int): Number of adaptive reasoning transformer blocks. num_heads (int): Number of attention heads. dropout (float): Dropout rate. """ def __init__(self, stream_configs, embed_dim=768, num_layers=24, num_heads=12, dropout=0.1): super().__init__() # Multi-stream encoder to fuse heterogeneous knowledge sources self.encoder = MultiStreamEncoder(stream_configs, embed_dim) # Stack of adaptive reasoning transformer layers self.layers = nn.ModuleList([ AdaptiveReasoningBlock(embed_dim, num_heads, dropout) for _ in range(num_layers) ]) self.norm = nn.LayerNorm(embed_dim) # Output projection to vocabulary or downstream embedding space self.output_head = nn.Linear(embed_dim, embed_dim) # Placeholder, plug model head as needed def forward(self, input_streams, context_streams=None, attn_mask=None, key_padding_mask=None): """ Forward pass through OmniCoreX. Args: input_streams (dict): Input tensors keyed by stream name. context_streams (dict or None): Optional context passed to reasoning blocks. attn_mask (Tensor or None): Optional attention mask. key_padding_mask (Tensor or None): Optional key padding mask. Returns: output: Tensor shaped (batch_size, seq_len, embed_dim) """ # Encode multi-stream inputs x = self.encoder(input_streams) # (batch, seq_len, embed_dim) # Change to seq_len, batch, embed_dim for transformer layers x = x.transpose(0, 1) # (seq_len, batch, embed_dim) # Prepare context tensors if given for each layer or None if context_streams is not None: context_embeds = self.encoder(context_streams).transpose(0, 1) else: context_embeds = None for layer in self.layers: x = layer(x, context=context_embeds, attn_mask=attn_mask, key_padding_mask=key_padding_mask) x = self.norm(x) x = x.transpose(0, 1) # back to (batch, seq_len, embed_dim) output = self.output_head(x) return output if __name__ == "__main__": # Simple test run with dummy data batch_size = 2 seq_len = 16 stream_configs = { "text": 128, "image": 256, "sensor": 64 } model = OmniCoreXModel(stream_configs=stream_configs, embed_dim=128, num_layers=4, num_heads=4) # Generate dummy inputs per stream inputs = { name: torch.randn(batch_size, seq_len, input_dim) for name, input_dim in stream_configs.items() } outputs = model(inputs) print(f"Output shape: {outputs.shape}") # Expected (batch_size, seq_len, embed_dim)