测试大语言模型在嵌入式设备部署的可能性——模型TinyLlama-1.1B-Chat-v1.0_llama 嵌入式

测试模型TinyLlama-1.1B-Chat-v1.0修改推理参数，观察参数变化与推理时间变化之间的关系。
本地环境：

处理器 Intel® Core™ i5-8400 CPU @ 2.80GHz 2.80 GHz
机带 RAM 16.0 GB (15.9 GB 可用)
集显 Intel® UHD Graphics 630
独显 NVIDIA GeForce GTX 1050

主要测试修改：

outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)

源代码来源（镜像）：https://hf-mirror.com/TinyLlama/TinyLlama-1.1B-Chat-v1.0

'''
https://hf-mirror.com/TinyLlama/TinyLlama-1.1B-Chat-v1.0
测试tinyLlama 1.1B效果不错，比Qwen1.8B经过量化的都好很多
'''

# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import os
from datetime import datetime
import torch

os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
from transformers import pipeline

'''
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://hf-mirror.com/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    # {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
    {"role": "user", "content": "你叫什么名字?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
'''

# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# ...
def load_pipeline():
    pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16,
                    device_map="auto")
    return pipe

def generate_text(content, length=20):
    """
    根据给定的prompt生成文本
    """
    messages = [
        {
            "role": "提示",
            "content": "这是个友好的聊天机器人...",
        },
        # {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
        {"role": "user", "content": content},
    ]
    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    datetime1 = datetime.now()
    outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
    print(outputs[0]["generated_text"])
    datetime2 = datetime.now()
    time12_interval = datetime2 - datetime1
    print("时间间隔", time12_interval)
    if False:
        outputs = pipe(prompt, max_new_tokens=32, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
        print(outputs[0]["generated_text"])
        datetime3 = datetime.now()
        time23_interval = datetime3 - datetime2
        print("时间间隔2", time23_interval)
        outputs = pipe(prompt, max_new_tokens=32, do_sample=False, top_k=50)
        print(outputs[0]["generated_text"])
        datetime4 = datetime.now()
        time34_interval = datetime4 - datetime3
        print("时间间隔3", time34_interval)
        outputs = pipe(prompt, max_new_tokens=32, do_sample=True, temperature=0.7, top_k=30, top_p=0.95)
        print(outputs[0]["generated_text"])
        datetime5 = datetime.now()
        time45_interval = datetime5 - datetime4
        print("时间间隔4", time45_interval)
        outputs = pipe(prompt, max_new_tokens=32, do_sample=False, top_k=30)
        print(outputs[0]["generated_text"])
        datetime6 = datetime.now()
        time56_interval = datetime6 - datetime5
        print("时间间隔5", time56_interval)
        outputs = pipe(prompt, max_new_tokens=12, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
        print(outputs[0]["generated_text"])
        datetime7 = datetime.now()
        time67_interval = datetime7 - datetime6
        print("时间间隔6", time67_interval)

    '''
    结论：修改top_p不会显著降低推理时间，并且中英文相同的问题，中文问题推理时间是英文的两倍
    do_sample修改成False基本不会降低推理时间
    只有max_new_tokens才能显著降低推理时间，但是max_new_tokens与推理时间不是呈线性关系
    比如max_new_tokens=256，推理时间2分钟
    当max_new_tokens=32的时候，推理时间才会变成约1分钟
    因此，不如将max_new_tokens设置大些用于获取比较完整的答案
    '''

    return outputs

if __name__ == "__main__":
    '''
    main function
    '''
    global pipe
    pipe = load_pipeline()

    # print('load pipe ok')

    while True:
        prompt = input("请输入一个提示（或输入'exit'退出）：")
        if prompt.lower() == 'exit':
            break
        try:
            generated_text = generate_text(prompt)
            print("生成的文本：")
            print(generated_text[0]["generated_text"])
        except Exception as e:
            print("发生错误：", e)
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请输入一个提示（或输入'exit'退出）：如何开门？
<|user|>
如何开门？</s>
<|assistant|>
Certainly! Opening a door is a simple process that involves several steps. Here are the general steps to follow to open a door:

1. Turn off the lock: Turn off the lock with the key by pressing the "lock" button.

2. Press the handle: Use the handle to push the door open. If the door is mechanical, you may need to turn a knob or pull the door handle to activate the door.

3. Release the latch: Once the door is open, release the latch by pulling it backward.

4. Slide the door: Slide the door forward by pushing it against the wall with your feet or using a push bar.

5. Close the door: Once the door is open, close it by pressing the lock button or pulling the handle backward.

6. Use a second key: If the lock has a second key, make sure it is properly inserted and then turn it to the correct position to unlock the door.

Remember to always double-check the locks before opening a door, as some locks can be tricky to open. If you're unsure about the correct procedure for opening a door,
时间间隔 0:04:23.561065
生成的文本：
<|user|>
如何开门？</s>
<|assistant|>
Certainly! Opening a door is a simple process that involves several steps. Here are the general steps to follow to open a door:

1. Turn off the lock: Turn off the lock with the key by pressing the "lock" button.

2. Press the handle: Use the handle to push the door open. If the door is mechanical, you may need to turn a knob or pull the door handle to activate the door.

3. Release the latch: Once the door is open, release the latch by pulling it backward.

4. Slide the door: Slide the door forward by pushing it against the wall with your feet or using a push bar.

5. Close the door: Once the door is open, close it by pressing the lock button or pulling the handle backward.

6. Use a second key: If the lock has a second key, make sure it is properly inserted and then turn it to the correct position to unlock the door.

Remember to always double-check the locks before opening a door, as some locks can be tricky to open. If you're unsure about the correct procedure for opening a door,
请输入一个提示（或输入'exit'退出）：
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