[`Llama`] remove prompt and fix prefix finetuning (#25565)

* nit

* update

* make sure use_default_system_prompt is saved

* update checkpointing

* consistency

* use_default_system_prompt for test

src/transformers/models/llama/modeling_llama.py

@@ -683,7 +683,7 @@ class LlamaModel(LlamaPreTrainedModel):
                 def create_custom_forward(module):
                     def custom_forward(*inputs):
                         # None for past_key_value
-                        return module(*inputs, output_attentions, None)
+                        return module(*inputs, past_key_value, output_attentions)
 
                     return custom_forward
 
@@ -692,7 +692,6 @@ class LlamaModel(LlamaPreTrainedModel):
                     hidden_states,
                     attention_mask,
                     position_ids,
-                    None,
                 )
             else:
                 layer_outputs = decoder_layer(

src/transformers/models/llama/tokenization_llama.py

@@ -113,6 +113,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
         add_bos_token=True,
         add_eos_token=False,
         clean_up_tokenization_spaces=False,
+        use_default_system_prompt=True,
         spaces_between_special_tokens=False,
         legacy=None,
         **kwargs,
@@ -131,6 +132,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
             add_eos_token=add_eos_token,
             sp_model_kwargs=self.sp_model_kwargs,
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            use_default_system_prompt=use_default_system_prompt,
             spaces_between_special_tokens=spaces_between_special_tokens,
             legacy=legacy,
             **kwargs,
@@ -149,8 +151,9 @@ class LlamaTokenizer(PreTrainedTokenizer):
         self.vocab_file = vocab_file
         self.add_bos_token = add_bos_token
         self.add_eos_token = add_eos_token
-        self.sp_model = self.get_spm_processor()
+        self.use_default_system_prompt = use_default_system_prompt
 
+        self.sp_model = self.get_spm_processor()
         self.unk_token_length = len(self.sp_model.encode(str(self.unk_token)))
 
     # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
@@ -390,8 +393,12 @@ class LlamaTokenizer(PreTrainedTokenizer):
             `List[int]`:
                 Input ids for the conversation.
         """
+        if self.use_default_system_prompt:
             if len(conversation.past_user_inputs) > 0:
-            if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:
+                if (
+                    not conversation.past_user_inputs[0].startswith(B_SYS)
+                    or E_SYS not in conversation.past_user_inputs[0]
+                ):
                     conversation.past_user_inputs[0] = (
                         B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                     )

src/transformers/models/llama/tokenization_llama_fast.py

@@ -110,6 +110,7 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
         eos_token="</s>",
         add_bos_token=True,
         add_eos_token=False,
+        use_default_system_prompt=True,
         **kwargs,
     ):
         super().__init__(
@@ -119,12 +120,13 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
             unk_token=unk_token,
             bos_token=bos_token,
             eos_token=eos_token,
+            use_default_system_prompt=use_default_system_prompt,
             **kwargs,
         )
         self._add_bos_token = add_bos_token
         self._add_eos_token = add_eos_token
         self.update_post_processor()
-
+        self.use_default_system_prompt = use_default_system_prompt
         self.vocab_file = vocab_file
         self.can_save_slow_tokenizer = False if not self.vocab_file else True
 
@@ -212,8 +214,12 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
             `List[int]`:
                 Input ids for the conversation.
         """
+        if self.use_default_system_prompt:
             if len(conversation.past_user_inputs) > 0:
-            if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:
+                if (
+                    not conversation.past_user_inputs[0].startswith(B_SYS)
+                    or E_SYS not in conversation.past_user_inputs[0]
+                ):
                     conversation.past_user_inputs[0] = (
                         B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                     )

tests/pipelines/test_pipelines_conversational.py

@@ -220,7 +220,7 @@ class ConversationalPipelineTests(unittest.TestCase):
     @require_torch
     @slow
     def test_integration_torch_conversation_llama2_input_ids(self):
-        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", use_default_system_prompt=True)
 
         conversation = Conversation(
             "What is so great about #1?",