max_tokens to max_new_tokens

examples/internlm2_agent_cli_demo.py

@@ -24,6 +24,7 @@ def main():
     model = HFTransformer(
         path=args.path,
         meta_template=META,
+        max_new_tokens=1024,
         top_p=0.8,
         top_k=None,
         temperature=0.1,
@@ -69,7 +70,7 @@ def input_prompt():
         print('\nInternLm2：', end='')
         current_length = 0
         last_status = None
-        for agent_return in chatbot.stream_chat(history, max_new_tokens=512):
+        for agent_return in chatbot.stream_chat(history):
             status = agent_return.state
             if status not in [
                     AgentStatusCode.STREAM_ING, AgentStatusCode.CODING,

examples/internlm2_agent_web_demo.py

@@ -120,6 +120,7 @@ def init_model(self, option, ip=None):
             model_name='internlm2-chat-20b',
             url=model_url,
             meta_template=META,
+            max_new_tokens=1024,
             top_p=0.8,
             top_k=100,
             temperature=0,

examples/model_cli_demo.py

@@ -26,6 +26,7 @@ def main():
     model = HFTransformer(
         path=args.path,
         meta_template=META,
+        max_new_tokens=1024,
         top_p=0.8,
         top_k=None,
         temperature=0.1,
@@ -51,8 +52,7 @@ def input_prompt():
             history = [dict(role='user', content=prompt)]
         print('\nInternLm2：', end='')
         current_length = 0
-        for status, response, _ in model.stream_chat(
-                history, max_new_tokens=512):
+        for status, response, _ in model.stream_chat(history):
             print(response[current_length:], end='', flush=True)
             current_length = len(response)
         history.append(dict(role='assistant', content=response))

lagent/llms/base_api.py

@@ -152,7 +152,7 @@ def __init__(self,
                  template_parser: 'APITemplateParser' = APITemplateParser,
                  meta_template: Optional[Dict] = None,
                  *,
-                 max_tokens: int = 512,
+                 max_new_tokens: int = 512,
                  top_p: float = 0.8,
                  top_k: float = None,
                  temperature: float = 0.8,
@@ -169,7 +169,7 @@ def __init__(self,
         if isinstance(stop_words, str):
             stop_words = [stop_words]
         self.gen_params = dict(
-            max_tokens=max_tokens,
+            max_new_tokens=max_new_tokens,
             top_p=top_p,
             top_k=top_k,
             temperature=temperature,

lagent/llms/base_llm.py

@@ -99,8 +99,8 @@ class BaseModel:
 
     Args:
         path (str): The path to the model.
-        max_seq_len (int): The maximum sequence length of the model. Defaults
-            to 2048.
+        max_new_tokens (int): Maximum length of output expected to be generated by the model. Defaults
+            to 512.
         tokenizer_only (bool): If True, only the tokenizer will be initialized.
             Defaults to False.
         meta_template (list of dict, optional): The model's meta prompt
@@ -116,7 +116,7 @@ def __init__(self,
                  template_parser: 'LMTemplateParser' = LMTemplateParser,
                  meta_template: Optional[List[Dict]] = None,
                  *,
-                 max_tokens: int = 512,
+                 max_new_tokens: int = 512,
                  top_p: float = 0.8,
                  top_k: float = None,
                  temperature: float = 0.8,
@@ -133,7 +133,7 @@ def __init__(self,
         if isinstance(stop_words, str):
             stop_words = [stop_words]
         self.gen_params = dict(
-            max_tokens=max_tokens,
+            max_new_tokens=max_new_tokens,
             top_p=top_p,
             top_k=top_k,
             temperature=temperature,

lagent/llms/huggingface.py

@@ -1,6 +1,5 @@
 import copy
 import logging
-import warnings
 from typing import Dict, List, Optional, Union
 
 from lagent.schema import ModelStatusCode
@@ -19,8 +18,6 @@ class HFTransformer(BaseModel):
 
     Args:
         path (str): The name or path to HuggingFace's model.
-        max_seq_len (int): The maximum length of the input sequence. Defaults
-            to 2048.
         tokenizer_path (str): The path to the tokenizer. Defaults to None.
         tokenizer_kwargs (dict): Keyword arguments for the tokenizer.
             Defaults to {}.
@@ -157,49 +154,9 @@ def stream_generate(
                 eos_token_id.extend(self.additional_eos_token_id)
             eos_token_id_tensor = torch.tensor(eos_token_id).to(
                 input_ids.device) if eos_token_id is not None else None
-            has_default_max_length = (
-                kwargs.get('max_length') is None
-                and generation_config.max_length is not None)
-            if (has_default_max_length
-                    and generation_config.max_new_tokens is None):
-                warnings.warn(
-                    "Using `max_length`'s default"
-                    f'({generation_config.max_length})'
-                    'to control the generation length. '
-                    'This behaviour is deprecated and will be removed'
-                    ' from the config in v5 of Transformers -- we'
-                    ' recommend using `max_new_tokens` to control the'
-                    ' maximum length of the generation.',
-                    UserWarning,
-                )
-            elif generation_config.max_new_tokens is not None:
-                generation_config.max_length = (
-                    generation_config.max_new_tokens + input_ids_seq_length)
-                if not has_default_max_length:
-                    logger.warn(  # pylint: disable=W4902
-                        'Both `max_new_tokens`'
-                        f'(={generation_config.max_new_tokens})'
-                        'and `max_length`'
-                        f'(={generation_config.max_length})'
-                        ' seem to have been set.`max_new_tokens`'
-                        ' will take precedence. Please refer to'
-                        ' the documentation for more information. '
-                        '(https://huggingface.co/docs/transformers/main/en'
-                        '/main_classes/text_generation)',
-                        UserWarning,
-                    )
-
-            if input_ids_seq_length >= generation_config.max_length:
-                input_ids_string = 'input_ids'
-                logger.warning(
-                    f'Input length of {input_ids_string}'
-                    f' is {input_ids_seq_length},'
-                    ' but `max_length` is set to'
-                    f' {generation_config.max_length}.'
-                    'This can lead to unexpected behavior.'
-                    ' You should consider increasing `max_new_tokens`.')
-
-            # 2. Set generation parameters if not already defined
+            generation_config.max_length = (
+                generation_config.max_new_tokens + input_ids_seq_length)
+            # Set generation parameters if not already defined
             logits_processor = self.logits_processor
             stopping_criteria = self.stopping_criteria
 

lagent/llms/lmdepoly_wrapper.py

@@ -46,7 +46,6 @@ def generate(self,
                  inputs: Union[str, List[str]],
                  session_id: int = 2967,
                  request_id: str = '',
-                 max_tokens: int = 512,
                  sequence_start: bool = True,
                  sequence_end: bool = True,
                  **kwargs):
@@ -57,7 +56,6 @@ def generate(self,
             inputs (str, List[str]): user's prompt(s) in this round
             session_id (int): the identical id of a session
             request_id (str): the identical id of this round conversation
-            max_tokens (int): the expected generated token numbers
             sequence_start (bool): start flag of a session
             sequence_end (bool): end flag of a session
 
@@ -72,9 +70,12 @@ def generate(self,
         assert isinstance(session_id, int), \
             f'INT session id is required, but got {type(session_id)}'
 
+        self.chatbot.cfg = self._update_gen_params(**kwargs)
+        max_new_tokens = self.chatbot.cfg.max_new_tokens
+
         logger = get_logger(log_level=self.chatbot.log_level)
         logger.info(f'session {session_id}, request_id {request_id}, '
-                    f'max_out_len {max_tokens}')
+                    f'max_out_len {max_new_tokens}')
 
         if self.chatbot._session is None:
             sequence_start = True
@@ -88,12 +89,9 @@ def generate(self,
         self.chatbot._session.request_id = request_id
         self.chatbot._session.response = ''
 
-        self.chatbot.cfg = self._update_gen_params(
-            max_tokens=max_tokens, **kwargs)
-
         status, res, _ = None, '', 0
         for status, res, _ in self.chatbot._stream_infer(
-                self.chatbot._session, prompt, max_tokens, sequence_start,
+                self.chatbot._session, prompt, max_new_tokens, sequence_start,
                 sequence_end):
             status = self.state_map.get(status)
             if status < ModelStatusCode.END:
@@ -111,7 +109,6 @@ def stream_chat(self,
                     inputs: List[dict],
                     session_id: int = 2967,
                     request_id: str = '',
-                    max_tokens: int = 512,
                     sequence_start: bool = True,
                     sequence_end: bool = True,
                     **kwargs):
@@ -122,7 +119,6 @@ def stream_chat(self,
             session_id (int): the identical id of a session
             inputs (List[dict]): user's inputs in this round conversation
             request_id (str): the identical id of this round conversation
-            max_tokens (int): the expected generated token numbers
             sequence_start (bool): start flag of a session
             sequence_end (bool): end flag of a session
 
@@ -134,9 +130,12 @@ def stream_chat(self,
         assert isinstance(session_id, int), \
             f'INT session id is required, but got {type(session_id)}'
 
+        self.chatbot.cfg = self._update_gen_params(**kwargs)
+        max_new_tokens = self.chatbot.cfg.max_new_tokens
+
         logger = get_logger(log_level=self.chatbot.log_level)
         logger.info(f'session {session_id}, request_id {request_id}, '
-                    f'max_out_len {max_tokens}')
+                    f'max_out_len {max_new_tokens}')
 
         if self.chatbot._session is None:
             sequence_start = True
@@ -150,13 +149,10 @@ def stream_chat(self,
         self.chatbot._session.request_id = request_id
         self.chatbot._session.response = ''
 
-        self.chatbot.cfg = self._update_gen_params(
-            max_tokens=max_tokens, **kwargs)
         prompt = self.template_parser(inputs)
-
         status, res, _ = None, '', 0
         for status, res, _ in self.chatbot._stream_infer(
-                self.chatbot._session, prompt, max_tokens, sequence_start,
+                self.chatbot._session, prompt, max_new_tokens, sequence_start,
                 sequence_end):
             status = self.state_map.get(status)
             # The stop symbol also appears in the output of the last STREAM_ING state.
@@ -246,9 +242,7 @@ def generate(self,
             batched = False
         prompt = inputs
         gen_params = self.update_gen_params(**kwargs)
-        max_tokens = gen_params.pop('max_tokens')
         gen_config = GenerationConfig(**gen_params)
-        gen_config.max_new_tokens = max_tokens
         response = self.model.batch_infer(
             prompt, gen_config=gen_config, do_preprocess=do_preprocess)
         response = [resp.text for resp in response]
@@ -336,6 +330,8 @@ def generate(self,
             batched = False
 
         gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        gen_params.update(max_tokens=max_new_tokens)
 
         resp = [''] * len(inputs)
         for text in self.client.completions_v1(
@@ -383,6 +379,8 @@ def stream_chat(self,
             generated token number
         """
         gen_params = self.update_gen_params(**kwargs)
+        max_new_tokens = gen_params.pop('max_new_tokens')
+        gen_params.update(max_tokens=max_new_tokens)
         prompt = self.template_parser(inputs)
 
         resp = ''

lagent/llms/openai.py

@@ -108,6 +108,8 @@ def chat(
         assert isinstance(inputs, list)
         if isinstance(inputs[0], dict):
             inputs = [inputs]
+        if 'max_tokens' in gen_params:
+            raise NotImplementedError('unsupported parameter: max_tokens')
         gen_params = {**self.gen_params, **gen_params}
         with ThreadPoolExecutor(max_workers=20) as executor:
             tasks = [
@@ -133,7 +135,7 @@ def _chat(self, messages: List[dict], **gen_params) -> str:
 
         # Hold out 100 tokens due to potential errors in tiktoken calculation
         max_tokens = min(
-            gen_params.pop('max_tokens'),
+            gen_params.pop('max_new_tokens'),
             self.context_window - len(self.tokenize(str(input))) - 100)
         if max_tokens <= 0:
             return ''