mirror of https://github.com/hpcaitech/ColossalAI
[colossal-llama2] add stream chat examlple for chat version model (#5428)
* add stream chat for chat version * remove os.system clear * modify function namepull/5430/head
Camille Zhong
9 months ago
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GitHub
parent
68f55a709c
commit
743e7fad2f
@ -0,0 +1,247 @@
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from copy import deepcopy
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from typing import Optional, List, Dict, Tuple, Callable, Any
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import torch
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from torch import nn
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from transformers import PreTrainedTokenizer
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from transformers.utils import logging
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from transformers.generation.utils import GenerationConfig, LogitsProcessorList, StoppingCriteriaList
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logger = logging.get_logger(__name__)
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def get_prompt_template(
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input_query:str,
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history:List[Dict]= None,
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roles:list = ["", "Human", "Assistant"],
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) -> str:
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"""
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Generates a prompt template for chat models based on input and history.
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Args:
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input_query (str): User's current input query.
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history (List[Dict], optional): List of past conversations, each a dict with 'role' and 'message'.
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roles (list): Specifies the roles in the conversation, defaults to ["", "Human", "Assistant"].
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Returns:
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str: A formatted prompt including the input query and history.
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"""
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prompt = ""
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if history is None:
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new_history = []
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else:
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new_history = deepcopy(history)
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new_history.append({"role": roles[1], "message": input_query.strip()})
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new_history.append({"role": roles[2], "message": None})
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for _, item in enumerate(new_history):
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role = item.get("role")
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message = item.get("message")
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if role == roles[0]:
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prompt += f"<s>{message}\n\n"
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else:
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if message:
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prompt += f"{role}: <s>{message}</s>"
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else:
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prompt += f"{role}: <s>"
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return prompt
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@torch.inference_mode()
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def streaming_chat(
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model: Any,
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tokenizer: PreTrainedTokenizer,
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input_query: str,
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history: List[Dict] = None,
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roles: list = ["", "Human", "Assistant"],
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past_key_values: Tuple[Tuple[torch.FloatTensor, Any], Any] = None,
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temperature: float = 0.8,
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top_p: float = 0.95,
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top_k: int = 50,
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do_sample: bool = True,
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length_penalty: float = 1.2,
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max_new_tokens: int = 512,
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logits_processor: LogitsProcessorList = None,
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return_past_key_values: bool = False,
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**kwargs,
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):
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"""
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Streaming chat responses generation with a given model and tokenizer.
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Args:
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model (Any): The language model to generate responses.
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tokenizer (PreTrainedTokenizer): Tokenizer compatible with the model, used for encoding inputs and decoding responses.
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input_query (str): The current user input to respond to.
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history (List[Dict], optional): A list of past conversations, where each conversation is a dictionary with keys 'role' and 'message'.
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roles (list): Roles involved in the conversation, defaults to ["", "Human", "Assistant"].
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past_key_values (Tuple[Tuple[torch.FloatTensor, Any], Any], optional): Past key values for incremental decoding.
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temperature (float): The temperature value for token sampling, defaults to 0.8.
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top_p (float): Nucleus sampling probability threshold, defaults to 0.95.
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top_k (int): Top-K filtering threshold, defaults to 50.
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do_sample (bool): Whether to sample responses, defaults to True.
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length_penalty (float): Penalty for response length, defaults to 1.2.
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max_new_tokens (int): Maximum number of new tokens to generate, defaults to 512.
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logits_processor (LogitsProcessorList, optional): Custom logits processors, defaults to None.
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return_past_key_values (bool): Whether to return past key values for further incremental decoding, defaults to False.
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**kwargs: Additional keyword arguments for generation.
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Yields:
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Tuple[str, List[Dict], Optional[Tuple[Tuple[torch.FloatTensor, Any], Any]]]: A tuple containing the generated response, updated history, and
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optionally the updated past key values if `return_past_key_values` is True.
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Ensures padding is on the left side for the tokenizer.
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"""
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assert tokenizer.padding_side == "left", "Current generation only supports left padding."
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if history is None:
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history = []
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if logits_processor is None:
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logits_processor = LogitsProcessorList()
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generation_kwargs = {
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'temperature': temperature,
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'top_p': top_p,
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'top_k': top_k,
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'do_sample': do_sample,
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'max_new_tokens': max_new_tokens,
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'length_penalty': length_penalty,
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'use_cache': True,
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**kwargs
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}
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prompt_str = get_prompt_template(input_query, history=history, roles=roles)
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eos_token_id = [tokenizer.eos_token_id]
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inputs = tokenizer(prompt_str, return_tensors="pt").to(model.device)
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history.append({"role": roles[1], "message": input_query.strip()})
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history.append({"role": roles[2], "message": None})
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for outputs in stream_generate(model, **inputs, past_key_values=past_key_values,
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eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
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**generation_kwargs):
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if return_past_key_values:
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outputs, past_key_values = outputs
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outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
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response = tokenizer.decode(outputs)
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history[-1]["message"] = response.strip()
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if return_past_key_values:
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yield response, history, past_key_values
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else:
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yield response, history
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@torch.inference_mode()
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def stream_generate(
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model: Any,
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input_ids: torch.Tensor,
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generation_config: Optional[GenerationConfig] = None,
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logits_processor: Optional[LogitsProcessorList] = None,
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stopping_criteria: Optional[StoppingCriteriaList] = None,
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prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
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return_past_key_values: bool = False,
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**kwargs,
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):
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"""
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Generates sequences of token ids using the specified model and generation parameters.
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Adapted from https://huggingface.co/THUDM/chatglm3-6b/blob/main/modeling_chatglm.py
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Args:
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model (Any): The model used for generating sequences of token ids.
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input_ids (torch.Tensor): The sequence used as a prompt for the generation or as model inputs to the encoder.
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generation_config (Optional[GenerationConfig]): The generation configuration to be used as base parametrization for the generation call.
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logits_processor (Optional[LogitsProcessorList]): Custom logits processors that complement the default logits processors built from arguments
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and generation config.
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stopping_criteria (Optional[StoppingCriteriaList]): Custom stopping criteria that complement the default stopping criteria built from arguments
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and a generation config.
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prefix_allowed_tokens_fn (Optional[Callable[[int, torch.Tensor], List[int]]]): Function to constrain token generation.
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return_past_key_values (bool): Whether to return past key values for further incremental decoding, defaults to False.
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**kwargs: Additional parameters for model generation.
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Yields:
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torch.Tensor: The generated token IDs, updated after each generation step.
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Optional[Tuple[Tuple[torch.FloatTensor, Any], Any]]: The past key values, returned if `return_past_key_values` is True, defaults to False.
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"""
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input_ids_len = input_ids.size(1)
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if generation_config is None:
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generation_config = model.generation_config
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generation_config = deepcopy(generation_config)
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model_kwargs = generation_config.update(**kwargs)
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eos_token_id = generation_config.eos_token_id
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if isinstance(eos_token_id, int):
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eos_token_id = [eos_token_id]
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eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
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if generation_config.max_new_tokens is not None:
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generation_config.max_length = generation_config.max_new_tokens + input_ids_len
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if input_ids_len >= generation_config.max_length:
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input_ids_string = "decoder_input_ids" if model.config.is_encoder_decoder else "input_ids"
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logger.warning(
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f"Input length of {input_ids_string} is {input_ids_len}, but `max_length` is set to"
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f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
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" increasing `max_new_tokens`."
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)
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logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
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stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
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# prepare distribution pre_processing samplers
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logits_processor = model._get_logits_processor(
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generation_config=generation_config,
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input_ids_seq_length=input_ids_len,
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encoder_input_ids=input_ids,
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prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
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logits_processor=logits_processor,
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)
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# prepare stopping criteria
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stopping_criteria = model._get_stopping_criteria(
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generation_config=generation_config, stopping_criteria=stopping_criteria
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)
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logits_warper = model._get_logits_warper(generation_config)
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unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
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scores = None
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while True:
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model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
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# forward pass to get next token
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outputs = model(
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**model_inputs,
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return_dict=True,
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output_attentions=False,
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output_hidden_states=False,
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)
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# NOTE: this is correct only in left padding mode
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# pre-process distribution
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next_token_logits = outputs.logits[:, -1, :]
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next_token_scores = logits_processor(input_ids, next_token_logits)
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next_token_scores = logits_warper(input_ids, next_token_scores)
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# sample
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probs = nn.functional.softmax(next_token_scores, dim=-1)
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if generation_config.do_sample:
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next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
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else:
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next_tokens = torch.argmax(probs, dim=-1)
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# update generated ids, model inputs, and length for next step
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input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
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model_kwargs = model._update_model_kwargs_for_generation(
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outputs, model_kwargs, is_encoder_decoder=model.config.is_encoder_decoder
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)
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unfinished_sequences = unfinished_sequences.mul(
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next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
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)
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if return_past_key_values:
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yield input_ids, outputs.past_key_values
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else:
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yield input_ids
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# stop when each sentence is finished, or if exceed the maximum length
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if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
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break
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@ -0,0 +1,55 @@
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import os
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import argparse
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from colossal_llama2.utils.stream_chat_patch import streaming_chat
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SYSTEM = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
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def main(args):
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model = AutoModelForCausalLM.from_pretrained(args.model_path).cuda().eval()
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
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past_key_values, history = None, []
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roles = ["", "Human", "Assistant"]
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history = []
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history.append({"role": roles[0], "message": SYSTEM})
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while True:
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input_query = input(f"\n{roles[1]}: ")
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if input_query.strip() == "exit":
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break
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if input_query.strip() == "clear":
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past_key_values, history = None, []
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continue
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print(f"\n{roles[2]}: ", end="")
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gen_len = 0
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for response, history, past_key_values in streaming_chat(
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model, tokenizer, input_query, history=history, roles=roles,
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temperature = args.temperature,
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top_p = args.top_p,
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top_k = args.top_k,
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do_sample = args.do_sample,
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length_penalty = args.length_penalty,
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max_new_tokens = args.max_new_tokens,
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past_key_values=past_key_values,
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return_past_key_values=True):
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output = response[gen_len:]
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print(output, end="", flush=True)
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gen_len = len(response)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_path', type=str, default=None, help="path to chat version model")
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parser.add_argument('--tokenizer_path', type=str, default=None, help="path to chat version tokenizer")
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parser.add_argument('--temperature', type=float, default=0.8, help="set temperature")
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parser.add_argument('--top_p', type=float, default=0.95, help="set top p value")
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parser.add_argument('--top_k', type=int, default=50, help="set top k value")
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parser.add_argument('--do_sample', type=bool, default=True, help="whether turn on do_sample or not")
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parser.add_argument('--length_penalty', type=float, default=1.2, help="set length penalty")
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parser.add_argument('--max_new_tokens', type=int, default=512, help="set max new tokens")
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args = parser.parse_args()
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main(args)
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