DynamicKV-LLM Pretrain v1.1.2

model/model.py

@@ -608,7 +608,9 @@ class MiniMindLM(PreTrainedModel):
         self.layers = nn.ModuleList([MiniMindBlock(l, params) for l in range(self.n_layers)])
         self.norm = RMSNorm(params.dim, eps=params.norm_eps)
         self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
+        self.database_output = nn.Linear(params.dim, params.knowledge_length, bias=False)
         self.tok_embeddings.weight = self.output.weight
+        self.database_output.weight = self.output.weight
 
         # Calculate input dimension
         input_dim = (self.params.max_seq_len-1)*self.params.n_layers
@@ -629,7 +631,7 @@ class MiniMindLM(PreTrainedModel):
             nn.Conv1d(128*8, 128, kernel_size=1, padding='same'),
             nn.Conv1d(128, self.params.knowledge_length, kernel_size=1, padding='same')
         )
-
+        
         # Specific layers for q path
         self.downsample_q_specific = nn.Sequential(
             nn.Conv1d(128*8, 512, kernel_size=1, padding='same')
@@ -684,23 +686,22 @@ class MiniMindLM(PreTrainedModel):
                 
                 # Compute shared downsampling layer once
                 shared_features = self.shared_downsample(h_tensor_detached)
-                z_v = self.downsample_v_specific(shared_features)#这里需要从emb返回为token
-                batch_z, seq_len, dim_z = z_v.shape
-                embedding_weights = self.tok_embeddings.weight.detach()#[vocab_size, dim]
-                z_v_flat = z_v.reshape(-1, z_v.shape[-1]) #  [batch_size_z * knowledge_len, dim]
                 
-                # 余弦相似度版本
-                # z_v_flat_norm = F.normalize(z_v_flat, p=2, dim=1)
-                # embedding_weights_norm = F.normalize(embedding_weights, p=2, dim=1)
-                # similarity_scores = torch.matmul(z_v_flat_norm, embedding_weights_norm.transpose(0, 1))
-                # token_indices_flat = torch.argmax(similarity_scores, dim=1) #  [batch_size_z * seq_len]
-                # token_indices = token_indices_flat.reshape(batch_size, -1)
-
-                distances = torch.cdist(z_v_flat, embedding_weights, p=2)
-                token_indices_flat = torch.argmin(distances, dim=1)
-                token_indices = token_indices_flat.reshape(batch_z,-1)
+                # Get features from v path - now we output embedding-dimension vectors
+                z_v_features = self.downsample_v_specific(shared_features)
+                batch_z, seq_len, dim_z = z_v_features.shape
+                
+                # Reshape to batch_size * knowledge_length, dim
+                z_v_flat = z_v_features.reshape(-1, dim_z)
+                
+                # Direct token prediction - like the main language model head
+                token_logits = self.database_output(z_v_flat)  # [batch_z * seq_len, vocab_size]
+                # Get token indices directly from logits
+                token_indices_flat = torch.argmax(token_logits, dim=-1)
+                token_indices = token_indices_flat.reshape(batch_z, -1)
+                
+                # Process query path as before
                 z_q = self.downsample_q_specific(shared_features)
-                # import pdb;pdb.set_trace()
                 z_k = self.extract_db.q_to_k(z_q)
                 self.extract_db.updata_value(z_k, token_indices)