Support batch training

modeling_chatglm.py

@@ -818,33 +818,37 @@ class ChatGLMModel(ChatGLMPreTrainedModel):
         return past_key_values
 
     @staticmethod
-    def get_masks(self, seq, device):
-        context_length = seq.index(self.config.bos_token_id) + 1
-
-        attention_mask = torch.ones((1, len(seq), len(seq)), device=device)
+    def get_masks(self, input_ids, device):
+        batch_size, seq_length = input_ids.shape
+        context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
+        attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
         attention_mask.tril_()
-        attention_mask[..., :context_length - 1] = 1
+        for i, context_length in enumerate(context_lengths):
+            attention_mask[i, :, :context_length] = 1
         attention_mask.unsqueeze_(1)
         attention_mask = (attention_mask < 0.5).bool()
 
         return attention_mask
 
-    def get_position_ids(self, seq, mask_position, device, gmask=False):
-        context_length = len(seq)
+    def get_position_ids(self, input_ids, mask_positions, device, gmask=False):
+        batch_size, seq_length = input_ids.shape
+        context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
         if self.position_encoding_2d:
-            seq_length = seq.index(self.config.bos_token_id)
-            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length)
             if not gmask:
-                position_ids[seq_length:] = mask_position
-            block_position_ids = torch.cat((
-                torch.zeros(seq_length, dtype=torch.long, device=device),
-                torch.arange(context_length - seq_length, dtype=torch.long, device=device) + 1
-            ))
-            position_ids = torch.stack((position_ids, block_position_ids), dim=0)
+                for i, context_length in enumerate(context_lengths):
+                    position_ids[i, context_length:] = mask_positions[i]
+            block_position_ids = [torch.cat((
+                torch.zeros(context_length, dtype=torch.long, device=device),
+                torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
+            )) for context_length in context_lengths]
+            block_position_ids = torch.stack(block_position_ids, dim=0)
+            position_ids = torch.stack((position_ids, block_position_ids), dim=1)
         else:
-            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length)
             if not gmask:
-                position_ids[context_length - 1:] = mask_position
+                for i, context_length in enumerate(context_lengths):
+                    position_ids[context_length:] = mask_positions[i]
 
         position_ids = position_ids.unsqueeze(0)
 
@@ -890,16 +894,15 @@ class ChatGLMModel(ChatGLMPreTrainedModel):
                 past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device)
             else:
                 past_key_values = tuple([None] * len(self.layers))
-            seq = input_ids[0].tolist()
 
             if attention_mask is None:
                 attention_mask = self.get_masks(
-                    seq=seq,
+                    input_ids,
                     device=input_ids.device
                 )
 
             if self.pre_seq_len is not None:
-                prefix_attention_mask = torch.ones(1, 1, len(seq), self.pre_seq_len).to(attention_mask.device)
+                prefix_attention_mask = torch.ones(1, 1, input_ids.size(-1), self.pre_seq_len).to(attention_mask.device)
                 prefix_attention_mask = (prefix_attention_mask < 0.5).bool()
                 attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3)
 
@@ -908,10 +911,10 @@ class ChatGLMModel(ChatGLMPreTrainedModel):
                 mask_token = MASK if MASK in input_ids else gMASK
                 use_gmask = False if MASK in input_ids else gMASK
 
-                mask_position = seq.index(mask_token)
+                mask_positions = [seq.tolist().index(mask_token) for seq in input_ids]
                 position_ids = self.get_position_ids(
-                    seq=seq,
-                    mask_position=mask_position,
+                    input_ids,
+                    mask_positions=mask_positions,
                     device=input_ids.device,
                     gmask=use_gmask
                 )