Fix batch input

tokenization_chatglm.py

@@ -177,7 +177,7 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
 
     vocab_files_names = {"vocab_file": "ice_text.model"}
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids"]
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
 
     def __init__(
             self,
@@ -397,7 +397,7 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
         needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
 
         # Initialize attention mask if not present.
-        if needs_to_be_padded or return_attention_mask:
+        if return_attention_mask:
             context_length = required_input.index(bos_token_id)
             attention_mask = np.ones((1, seq_length, seq_length))
             attention_mask = np.tril(attention_mask)
@@ -405,7 +405,7 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
             attention_mask = np.bool_(attention_mask < 0.5)
             encoded_inputs["attention_mask"] = attention_mask
 
-        if needs_to_be_padded or return_attention_mask:
+        if return_attention_mask:
             mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
             mask_position = required_input.index(mask_token)
             context_length = required_input.index(bos_token_id)