hyper_graph.py

# HYPER_GRAPH CHUNK 1 START
import torch
import torch.nn as nn
from transformers import PretrainedConfig, PreTrainedModel

class GodConfig(PretrainedConfig):
    model_type = "hyper_graph_meta_transformer"
    def __init__(self, vocab_size=64000, hidden_size=4096, num_hidden_layers=32, num_attention_heads=32, max_position_embeddings=16384, pad_token_id=1, **kwargs):
        super().__init__(pad_token_id=pad_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.max_position_embeddings = max_position_embeddings
# HYPER_GRAPH CHUNK 1 END
# HYPER_GRAPH CHUNK 2 START
class SelfReferentialHyperGraph(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.state_projection = nn.Linear(hidden_size, hidden_size)
        self.dynamic_kernel = nn.Sequential(
            nn.Linear(hidden_size, hidden_size // 4),
            nn.GELU(),
            nn.Linear(hidden_size // 4, hidden_size)
        )
        self.layer_norm = nn.LayerNorm(hidden_size)
        self.register_buffer("quantum_manifold_alpha", torch.randn(4096) * 0.0001)
        self.register_buffer("quantum_manifold_beta", torch.randn(4096) * 0.0002)

    def forward(self, hidden_states):
        meta_gradients = self.dynamic_kernel(hidden_states)
        manifold_shift = torch.sigmoid(self.state_projection(hidden_states))
        optimized_states = hidden_states + (meta_gradients * manifold_shift)
        return self.layer_norm(optimized_states)
# HYPER_GRAPH CHUNK 2 END