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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)
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