[colossal-llama2] add stream chat examlple for chat version model (#5428)

* add stream chat for chat version

* remove os.system clear

* modify function name

applications/Colossal-LLaMA-2/colossal_llama2/utils/stream_chat_patch.py

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

applications/Colossal-LLaMA-2/stream_chat_example.py

@@ -0,0 +1,55 @@
+import os
+import argparse
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from colossal_llama2.utils.stream_chat_patch import streaming_chat
+
+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."
+
+def main(args):
+    model = AutoModelForCausalLM.from_pretrained(args.model_path).cuda().eval()
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
+
+    past_key_values, history = None, []
+    roles = ["", "Human", "Assistant"]
+
+    history = []
+    history.append({"role": roles[0], "message": SYSTEM})
+
+    while True:
+        input_query = input(f"\n{roles[1]}: ")
+        if input_query.strip() == "exit":
+            break
+        if input_query.strip() == "clear":
+            past_key_values, history = None, []
+            continue
+
+        print(f"\n{roles[2]}: ", end="")
+        gen_len = 0
+        for response, history, past_key_values in streaming_chat(
+            model, tokenizer, input_query, history=history, roles=roles,
+            temperature = args.temperature,
+            top_p = args.top_p,
+            top_k = args.top_k,
+            do_sample = args.do_sample,
+            length_penalty = args.length_penalty,
+            max_new_tokens = args.max_new_tokens,
+            past_key_values=past_key_values,
+            return_past_key_values=True):
+
+            output = response[gen_len:]
+            print(output, end="", flush=True)
+            gen_len = len(response)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_path', type=str, default=None, help="path to chat version model")
+    parser.add_argument('--tokenizer_path', type=str, default=None, help="path to chat version tokenizer")
+    parser.add_argument('--temperature', type=float, default=0.8, help="set temperature")
+    parser.add_argument('--top_p', type=float, default=0.95, help="set top p value")
+    parser.add_argument('--top_k', type=int, default=50, help="set top k value")
+    parser.add_argument('--do_sample', type=bool, default=True, help="whether turn on do_sample or not")
+    parser.add_argument('--length_penalty', type=float, default=1.2, help="set length penalty")
+    parser.add_argument('--max_new_tokens', type=int, default=512, help="set max new tokens")
+    args = parser.parse_args()
+    main(args)