LLM: improve response speed in multi-turn chat (#9299)
* update * fix stop word and add chatglm2 support * remove system prompt
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binbin Deng
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8ef8e25178
2 changed files with 283 additions and 55 deletions
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@ -13,6 +13,31 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/mit-han-lab/streaming-llm/blob/main/examples/run_streaming_llama.py
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# which is licensed under the MIT license:
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#
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# MIT License
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#
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# Copyright (c) 2023 MIT HAN Lab
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import torch
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import argparse
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@ -27,46 +52,98 @@ from transformers.generation.stopping_criteria import StoppingCriteriaList
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from colorama import Fore
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from bigdl.llm import optimize_model
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from kv_cache import StartRecentKVCache
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SYSTEM_PROMPT = "A chat between a curious human <human> and an artificial intelligence assistant <bot>.\
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The assistant gives helpful, detailed, and polite answers to the human's questions."
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HUMAN_ID = "<human>"
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BOT_ID = "<bot>"
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# chat_history formated in [(iput_str, output_str)]
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def format_prompt(input_str,
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chat_history):
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prompt = [f"{SYSTEM_PROMPT}\n"]
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for history_input_str, history_output_str in chat_history:
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prompt.append(f"{HUMAN_ID} {history_input_str}\n{BOT_ID} {history_output_str}\n")
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prompt.append(f"{HUMAN_ID} {input_str}\n{BOT_ID} ")
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return "".join(prompt)
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def stream_chat(model,
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tokenizer,
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stopping_criteria,
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input_str,
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chat_history):
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prompt = format_prompt(input_str, chat_history)
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# print(prompt)
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input_ids = tokenizer([prompt], return_tensors="pt")
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(input_ids, streamer=streamer, max_new_tokens=512, stopping_criteria=stopping_criteria)
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from threading import Thread
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# to ensure non-blocking access to the generated text, generation process should be ran in a separate thread
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thread = Thread(target=model.generate, kwargs=generate_kwargs)
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thread.start()
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output_str = []
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@torch.no_grad()
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def greedy_generate(model, tokenizer, input_ids, past_key_values, max_gen_len):
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print(Fore.BLUE+"BigDL-LLM: "+Fore.RESET, end="")
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for partial_output_str in streamer:
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output_str.append(partial_output_str)
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# remove the last HUMAN_ID if exists
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print(partial_output_str.replace(f"{HUMAN_ID}", ""), end="")
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outputs = model(
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input_ids=input_ids,
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past_key_values=past_key_values,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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pred_token_idx = outputs.logits[:, -1, :].argmax(dim=-1).unsqueeze(1)
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generated_ids = [pred_token_idx.item()]
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pos = 0
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for _ in range(max_gen_len - 1):
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outputs = model(
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input_ids=pred_token_idx,
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past_key_values=past_key_values,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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pred_token_idx = outputs.logits[:, -1, :].argmax(dim=-1).unsqueeze(1)
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generated_ids.append(pred_token_idx.item())
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generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True,
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clean_up_tokenization_spaces=True,
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spaces_between_special_tokens=False)
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chat_history.append((input_str, "".join(output_str).replace(f"{HUMAN_ID}", "").rstrip()))
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now = len(generated_text) - 1
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if now > pos:
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if '\n<' in generated_text:
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break
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else:
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print("".join(generated_text[pos:now]), end="", flush=True)
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pos = now
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if pred_token_idx == tokenizer.eos_token_id:
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break
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print(" ".join(generated_text[pos:]).strip('\n<'), flush=True)
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return past_key_values
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@torch.no_grad()
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def stream_chat(model, tokenizer, kv_cache=None, max_gen_len=512):
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past_key_values = None
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while True:
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user_input = input(Fore.GREEN+"\nHuman: "+Fore.RESET)
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if user_input == "stop": # let's stop the conversation when user input "stop"
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break
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prompt = f"{HUMAN_ID} {user_input}\n{BOT_ID} "
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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seq_len = input_ids.shape[1]
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if kv_cache is not None:
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space_needed = seq_len + max_gen_len
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past_key_values = kv_cache.evict_for_space(past_key_values, space_needed)
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past_key_values = greedy_generate(
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model, tokenizer, input_ids, past_key_values, max_gen_len=max_gen_len
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)
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@torch.no_grad()
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def chatglm2_stream_chat(model, tokenizer):
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chat_history = []
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past_key_values = None
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current_length = 0
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stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(HUMAN_ID, tokenizer)])
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max_past_length = 2048
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while True:
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user_input = input(Fore.GREEN+"\nHuman: "+Fore.RESET)
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if user_input == "stop": # let's stop the conversation when user input "stop"
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break
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print(Fore.BLUE+"BigDL-LLM: "+Fore.RESET, end="")
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prompt = f"问：{user_input}\n答："
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for response, chat_history, past_key_values in model.stream_chat(tokenizer, prompt,
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history=chat_history,
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stopping_criteria=stopping_criteria,
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past_key_values=past_key_values,
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return_past_key_values=True):
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print(response[current_length:], end="", flush=True)
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current_length = len(response)
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if past_key_values[0][0].shape[0] > max_past_length:
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# To avoid out of memory, only keep recent key_values
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new_values_list = []
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for i in range(len(past_key_values)):
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new_value = []
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for val in past_key_values[i]:
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new_v = val[-max_past_length:]
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new_value.append(new_v)
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new_values_list.append(tuple(new_value))
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past_key_values = tuple(new_values_list)
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def auto_select_model(model_name):
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try:
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@ -100,17 +177,10 @@ if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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stopping_criteria = StoppingCriteriaList([StopSequenceCriteria(HUMAN_ID, tokenizer)])
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chat_history = []
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while True:
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with torch.inference_mode():
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user_input = input(Fore.GREEN+"\nHuman: "+Fore.RESET)
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if user_input == "stop": # let's stop the conversation when user input "stop"
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break
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if model.config.architectures is not None and model.config.architectures[0] == "ChatGLMModel":
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chatglm2_stream_chat(model=model, tokenizer=tokenizer)
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else:
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kv_cache = StartRecentKVCache()
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stream_chat(model=model,
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tokenizer=tokenizer,
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stopping_criteria=stopping_criteria,
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input_str=user_input,
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chat_history=chat_history)
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kv_cache=kv_cache)
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158
python/llm/portable-zip/kv_cache.py
Normal file
158
python/llm/portable-zip/kv_cache.py
Normal file
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@ -0,0 +1,158 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/mit-han-lab/streaming-llm/blob/main/streaming_llm/kv_cache.py
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# which is licensed under the MIT license:
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#
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# MIT License
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#
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# Copyright (c) 2023 MIT HAN Lab
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import torch
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def slice1d(x, start, end):
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return x[:, start:end, ...]
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def slice2d(x, start, end):
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return x[:, :, start:end, ...]
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def slice3d(x, start, end):
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return x[:, :, :, start:end, ...]
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DIM_TO_SLICE = {
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1: slice1d,
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2: slice2d,
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3: slice3d,
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}
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class StartRecentKVCache:
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def __init__(
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self,
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start_size=4,
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recent_size=512,
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k_seq_dim=2,
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v_seq_dim=2,
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):
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print(f"StartRecentKVCache: {start_size}, {recent_size}")
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self.start_size = start_size
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self.recent_size = recent_size
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self.cache_size = start_size + recent_size
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self.k_seq_dim = k_seq_dim
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self.v_seq_dim = v_seq_dim
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self.k_slice = DIM_TO_SLICE[k_seq_dim]
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self.v_slice = DIM_TO_SLICE[v_seq_dim]
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def __call__(self, past_key_values):
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if past_key_values is None:
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return None
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seq_len = past_key_values[0][0].size(self.k_seq_dim)
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if seq_len <= self.cache_size:
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return past_key_values
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return [
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[
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torch.cat(
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[
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self.k_slice(k, 0, self.start_size),
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self.k_slice(k, seq_len - self.recent_size, seq_len),
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],
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dim=self.k_seq_dim,
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),
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torch.cat(
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[
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self.v_slice(v, 0, self.start_size),
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self.v_slice(v, seq_len - self.recent_size, seq_len),
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],
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dim=self.v_seq_dim,
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),
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]
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for k, v in past_key_values
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]
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def evict_for_space(self, past_key_values, num_coming):
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if past_key_values is None:
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return None
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seq_len = past_key_values[0][0].size(self.k_seq_dim)
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if seq_len + num_coming <= self.cache_size:
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return past_key_values
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return [
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[
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torch.cat(
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[
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self.k_slice(k, 0, self.start_size),
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self.k_slice(
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k, seq_len - self.recent_size + num_coming, seq_len
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),
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],
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dim=self.k_seq_dim,
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),
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torch.cat(
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[
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self.v_slice(v, 0, self.start_size),
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self.v_slice(
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v, seq_len - self.recent_size + num_coming, seq_len
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),
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],
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dim=self.v_seq_dim,
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),
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]
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for k, v in past_key_values
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]
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def evict_range(self, past_key_values, start, end):
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if past_key_values is None:
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return None
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seq_len = past_key_values[0][0].size(self.k_seq_dim)
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assert start <= end and end <= seq_len
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return [
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[
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torch.cat(
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[
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self.k_slice(k, 0, start),
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self.k_slice(k, end, seq_len),
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],
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dim=self.k_seq_dim,
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),
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torch.cat(
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[
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self.v_slice(v, 0, start),
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self.v_slice(v, end, seq_len),
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],
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dim=self.v_seq_dim,
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),
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]
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for k, v in past_key_values
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]
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