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| import torch | |
| import torch.nn as nn | |
| from transformers import AutoModel, AutoConfig | |
| class FoundationLayer(nn.Module): | |
| def __init__(self, model_name: str = "gpt2-xl"): | |
| super().__init__() | |
| self.config = AutoConfig.from_pretrained(model_name) | |
| self.transformer = AutoModel.from_pretrained(model_name) | |
| self.sparse_router = MixtureOfExperts( | |
| num_experts=128, | |
| input_size=self.config.hidden_size | |
| ) | |
| def forward(self, input_ids, attention_mask=None): | |
| transformer_output = self.transformer( | |
| input_ids=input_ids, | |
| attention_mask=attention_mask | |
| ) | |
| routed_output = self.sparse_router(transformer_output.last_hidden_state) | |
| return routed_output # Removed undefined method call | |
| def _process_consciousness_emergence(self, hidden_states): | |
| # Adding the missing method | |
| return hidden_states # Simple implementation, modify as needed | |
| class MixtureOfExperts(nn.Module): | |
| def __init__(self, num_experts: int, input_size: int): | |
| super().__init__() | |
| self.num_experts = num_experts | |
| self.gate = nn.Linear(input_size, num_experts) | |
| self.experts = nn.ModuleList([ | |
| nn.TransformerEncoderLayer( | |
| d_model=input_size, | |
| nhead=8 | |
| ) for _ in range(num_experts) | |
| ]) | |
| def forward(self, hidden_states): | |
| # Adding the missing forward method | |
| batch_size, seq_len, hidden_size = hidden_states.shape | |
| # Calculate routing probabilities | |
| routing_logits = self.gate(hidden_states.view(-1, hidden_size)) | |
| routing_probs = torch.softmax(routing_logits, dim=-1) | |
| # Get top-k experts | |
| k = 2 # Using top-2 experts | |
| top_k_probs, top_k_indices = torch.topk(routing_probs, k, dim=-1) | |
| top_k_probs = top_k_probs / top_k_probs.sum(dim=-1, keepdim=True) # Normalize | |
| # Reshape for easier processing | |
| hidden_states_flat = hidden_states.view(-1, 1, hidden_size) | |
| # Initialize output | |
| final_output = torch.zeros_like(hidden_states_flat) | |
| # Route to experts | |
| for i, expert in enumerate(self.experts): | |
| # Create mask for this expert | |
| mask = (top_k_indices == i).any(dim=-1).unsqueeze(-1) | |
| if mask.sum() > 0: | |
| # Only process tokens routed to this expert | |
| expert_input = hidden_states_flat[mask.squeeze(-1)] | |
| # Apply expert | |
| expert_output = expert(expert_input) | |
| # Weight by routing probability | |
| weight_mask = (top_k_indices == i).float() * top_k_probs | |
| weight_mask = weight_mask.unsqueeze(-1) | |
| final_output[mask.squeeze(-1)] += expert_output * weight_mask[mask.squeeze(-1)] | |
| return final_output.view(batch_size, seq_len, hidden_size) | |