Create model.py

model.py

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+"""
+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)
+