# --- START OF FILE architectureV3.py --- import torch import torch.nn as nn import torch.nn.functional as F from transformers import Phi3Config, Phi3ForCausalLM from transformers.modeling_outputs import CausalLMOutputWithPast from typing import Optional, Dict, Tuple from dataclasses import dataclass @dataclass class CausalLMOutputWithLTM(CausalLMOutputWithPast): loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None ltm_state: Optional[torch.Tensor] = None # The returned LTM state # --- BUILDING BLOCK 1: Hierarchical VectorMemoryHead (Stateless) --- class VectorMemoryHead(nn.Module): def __init__(self, hidden_dim: int, num_memory_slots: int, num_heads: int, ff_dim: int, num_long_term_memory_slots: int = 0, device=None, dtype=None): super().__init__() self.hidden_dim = hidden_dim self.num_memory_slots = num_memory_slots self.num_long_term_memory_slots = num_long_term_memory_slots self.use_long_term_memory = self.num_long_term_memory_slots > 0 encoder_layer = nn.TransformerEncoderLayer( d_model=hidden_dim, nhead=num_heads, dim_feedforward=ff_dim, dropout=0.1, batch_first=True, device=device, dtype=dtype) self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=1) self.memory_queries = nn.Parameter(torch.randn(1, num_memory_slots, hidden_dim, device=device, dtype=dtype)) self.memory_attention = nn.MultiheadAttention( embed_dim=hidden_dim, num_heads=num_heads, dropout=0.1, batch_first=True, device=device, dtype=dtype) self.memory_layernorm = nn.LayerNorm(hidden_dim, device=device, dtype=dtype) self.decoder_attention = nn.MultiheadAttention( embed_dim=hidden_dim, num_heads=num_heads, dropout=0.1, batch_first=True, device=device, dtype=dtype) self.decoder_layernorm = nn.LayerNorm(hidden_dim, device=device, dtype=dtype) self.decoder_ffn = nn.Sequential( nn.Linear(hidden_dim, ff_dim, device=device, dtype=dtype), nn.ReLU(), nn.Linear(ff_dim, hidden_dim, device=device, dtype=dtype)) if self.use_long_term_memory: self.memory_update_gate = nn.Sequential( nn.Linear(hidden_dim, hidden_dim, device=device, dtype=dtype), nn.Sigmoid()) self.ltm_retrieval_attention = nn.MultiheadAttention( embed_dim=hidden_dim, num_heads=num_heads, dropout=0.1, batch_first=True, device=device, dtype=dtype) def forward(self, memory_input_sequence: torch.Tensor, long_term_memory: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: batch_size = memory_input_sequence.shape[0] new_ltm_state = long_term_memory queries = self.memory_queries.expand(batch_size, -1, -1) encoded_vectors = self.encoder(memory_input_sequence) compressed_memory, _ = self.memory_attention(query=queries, key=encoded_vectors, value=encoded_vectors) compressed_memory = self.memory_layernorm(compressed_memory + queries) final_memory_context = compressed_memory if self.use_long_term_memory and long_term_memory is not None: retrieved_ltm, _ = self.ltm_retrieval_attention( query=compressed_memory, key=long_term_memory, value=long_term_memory) l1_summary = compressed_memory.mean(dim=1, keepdim=True) update_gate = self.memory_update_gate(l1_summary) new_ltm_state = (update_gate * l1_summary) + ((1 - update_gate) * long_term_memory) final_memory_context = final_memory_context + retrieved_ltm reconstructed, _ = self.decoder_attention(query=encoded_vectors, key=final_memory_context, value=final_memory_context) reconstructed_vectors = self.decoder_layernorm(reconstructed + encoded_vectors) reconstructed_vectors = self.decoder_ffn(reconstructed_vectors) return compressed_memory, reconstructed_vectors, new_ltm_state # --- BUILDING BLOCK 2: ReflectiveMemoryLayer --- class ReflectiveMemoryLayer(nn.Module): def __init__(self, original_layer: nn.Linear, global_input_dim: int, memory_dim: int, num_memory_slots: int, memory_num_heads: int, global_state_storage: Dict): super().__init__() self.input_dim, self.output_dim = original_layer.in_features, original_layer.out_features self.memory_dim, self.global_state_storage = memory_dim, global_state_storage self.linear = original_layer # Keep the original linear layer frozen self.refinement_passes: int = 2 device, dtype = self.linear.weight.device, self.linear.weight.dtype self.local_state_proj = nn.Linear(self.input_dim, memory_dim, device=device, dtype=dtype) self.global_state_proj = nn.Linear(global_input_dim, memory_dim, device=device, dtype=dtype) self.memory_head = VectorMemoryHead( hidden_dim=memory_dim, num_memory_slots=num_memory_slots, num_heads=memory_num_heads, ff_dim=memory_dim * 2, num_long_term_memory_slots=32, device=device, dtype=dtype) self.thought_critique_attention = nn.MultiheadAttention( embed_dim=memory_dim, num_heads=memory_num_heads, dropout=0.1, batch_first=True, device=device, dtype=dtype) self.thought_layernorm = nn.LayerNorm(memory_dim, device=device, dtype=dtype) self.correction_head = nn.Linear(memory_dim, 2 * self.output_dim, device=device, dtype=dtype) self.last_corrected_activation, self.last_additive_correction = None, None self.last_memory_input, self.last_reconstructed_from_memory = None, None def forward(self, x: torch.Tensor): base_output = self.linear(x) if 'embeds' not in self.global_state_storage: return base_output global_embeds = self.global_state_storage['embeds'] if global_embeds.shape[1] != x.shape[1]: global_embeds = global_embeds[:, -x.shape[1]:, :] B, S, _ = x.shape # CRITICAL FIX: Always detach LTM state to prevent backward through previous graphs ltm_state = self.global_state_storage.get('ltm', None) if ltm_state is not None: ltm_state = ltm_state.detach() proj_local = self.local_state_proj(x) proj_global = self.global_state_proj(global_embeds) memory_input = torch.stack([proj_global, proj_local], dim=2) memory_input_flat = memory_input.view(B * S, 2, self.memory_dim) # *** FIX: Expand LTM state to match the flattened token dimension (B*S) *** ltm_state_expanded = None if ltm_state is not None: ltm_state_expanded = ltm_state.repeat_interleave(S, dim=0) compressed_mem_flat, recon_flat, new_ltm_state_expanded = self.memory_head(memory_input_flat, ltm_state_expanded) # *** FIX: Condense updated LTM state back to batch dimension B *** if new_ltm_state_expanded is not None: num_ltm_slots = new_ltm_state_expanded.shape[1] new_ltm_condensed = new_ltm_state_expanded.view(B, S, num_ltm_slots, self.memory_dim).mean(dim=1) # CRITICAL FIX: Always detach when storing in global state self.global_state_storage['ltm'] = new_ltm_condensed.detach() initial_thought = compressed_mem_flat.mean(dim=1).view(B, S, self.memory_dim) current_thought = initial_thought if not self.training and self.refinement_passes > 0: with torch.no_grad(): for _ in range(self.refinement_passes): current_thought_flat = current_thought.view(B * S, 1, self.memory_dim) internal_ref, _ = self.memory_head.decoder_attention( query=current_thought_flat, key=compressed_mem_flat, value=compressed_mem_flat) external_crit, _ = self.thought_critique_attention( query=current_thought_flat, key=memory_input_flat, value=memory_input_flat) refined_thought = current_thought + internal_ref.view(B,S,-1) + external_crit.view(B,S,-1) current_thought = self.thought_layernorm(refined_thought) thought_for_correction = current_thought if not self.training else initial_thought raw_correction = self.correction_head(thought_for_correction) gate, value = torch.chunk(raw_correction, 2, dim=-1) final_activation = base_output * torch.sigmoid(gate.to(x.dtype)) + value.to(x.dtype) if self.training: # CRITICAL FIX: Detach tensors stored for debugging/analysis self.last_corrected_activation = final_activation.detach() self.last_additive_correction = value.detach() self.last_memory_input = memory_input.detach() self.last_reconstructed_from_memory = recon_flat.view(B, S, 2, self.memory_dim).detach() return final_activation # --- BUILDING BLOCK 3: The Full Custom Model with State Management --- class Phi3WithReflectiveMemoryForCausalLM(Phi3ForCausalLM): def __init__(self, config): super().__init__(config) self.global_state_storage = {} self.target_layer_path = "model.layers.15.mlp.gate_up_proj" self.memory_dim, self.num_long_term_memory_slots = 128, 32 # CRITICAL FIX: Ensure embeddings are detached when stored def embedding_hook(module, input, output): self.global_state_storage['embeds'] = output.detach() self.model.embed_tokens.register_forward_hook(embedding_hook) try: original_layer = self.get_submodule(self.target_layer_path) custom_layer = ReflectiveMemoryLayer( original_layer=original_layer, global_input_dim=config.hidden_size, memory_dim=self.memory_dim, num_memory_slots=16, memory_num_heads=4, global_state_storage=self.global_state_storage) parent_path = ".".join(self.target_layer_path.split('.')[:-1]) setattr(self.get_submodule(parent_path), self.target_layer_path.split('.')[-1], custom_layer) print(f"Successfully replaced '{self.target_layer_path}' with ReflectiveMemoryLayer.") except AttributeError: print(f"Could not find target layer '{self.target_layer_path}'. Model remains unmodified.") def _init_ltm_state(self, batch_size, device, dtype): # *** FIX: Initialize LTM state per item in the batch (no hardcoded hack) *** return torch.zeros( batch_size, self.num_long_term_memory_slots, self.memory_dim, device=device, dtype=dtype) def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[list[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ltm_state: Optional[torch.Tensor] = None): return_dict = return_dict if return_dict is not None else self.config.use_return_dict # CRITICAL FIX: Don't clear global state storage completely, just reset embeds # This prevents losing LTM state continuity if 'embeds' in self.global_state_storage: del self.global_state_storage['embeds'] # *** FIX: Initialize LTM state if not provided, for both training and first step of inference *** if ltm_state is None: batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0] ltm_state = self._init_ltm_state(batch_size, self.device, self.dtype) # CRITICAL FIX: Ensure LTM state is detached when stored self.global_state_storage['ltm'] = ltm_state.detach() if ltm_state is not None else None outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) hidden_states = outputs[0] logits = self.lm_head(hidden_states).float() loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss() loss = loss_fct(logits[..., :-1, :].contiguous().view(-1, self.config.vocab_size), labels[..., 1:].contiguous().view(-1)) # Note: Auxiliary losses from main.py are calculated outside the model forward pass. # CRITICAL FIX: Ensure returned LTM state is detached new_ltm_state = self.global_state_storage.get('ltm', None) if new_ltm_state is not None: new_ltm_state = new_ltm_state.detach() if not return_dict: output = (logits,) + outputs[1:] + (new_ltm_state,) return (loss,) + output if loss is not None else output return CausalLMOutputWithLTM( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ltm_state=new_ltm_state)