long_context.md

长文本外推

长文本外推指 LLM 推理时处理比训练文本更长数据的能力。TurboMind 引擎目前支持 LlamaDynamicNTKScalingRotaryEmbedding, 并与 HuggingFace 的实现对齐。

如何使用

如果要直接加载 HuggingFace 格式的模型，可以通过修改 TurbomindEngineConfig 参数的方式赋予模型外推能力。将 session_len 修改为外推的长度，并将 rope_scaling_factor 修改为不小于 1.0 的值。

以具有 1M 上下文长度的internlm2_5-7b-chat-1m为例，可以使用如下方式，激活长文本推理能力：

from lmdeploy import pipeline, GenerationConfig, TurbomindEngineConfig

backend_config = TurbomindEngineConfig(
        rope_scaling_factor=2.5,
        session_len=1000000,
        max_batch_size=1,
        cache_max_entry_count=0.7,
        tp=4)
pipe = pipeline('internlm/internlm2_5-7b-chat-1m', backend_config=backend_config)
prompt = 'Use a long prompt to replace this sentence'
gen_config = GenerationConfig(top_p=0.8,
                              top_k=40,
                              temperature=0.8,
                              max_new_tokens=1024)
response = pipe(prompt, gen_config=gen_config)
print(response)

评测

我们使用多种方式评测 LMDeploy 长文本推理能力，分别是 passkey retrieval 实验、大海捞针实验 和计算困惑度

Passkey Retrieval

执行如下代码，可以测试在长文本中找到特殊 key 成功和失败的次数

import numpy as np
from lmdeploy import pipeline
from lmdeploy import TurbomindEngineConfig
import time

session_len = 1000000
backend_config = TurbomindEngineConfig(
        rope_scaling_factor=2.5,
        session_len=session_len,
        max_batch_size=1,
        cache_max_entry_count=0.7,
        tp=4)
pipe = pipeline('internlm/internlm2_5-7b-chat-1m', backend_config=backend_config)


def passkey_retrieval(session_len, n_round=5):
    # create long context input
    tok = pipe.tokenizer
    task_description = 'There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.'
    garbage = 'The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.'

    for _ in range(n_round):
        start = time.perf_counter()
        n_times = (session_len - 1000) // len(tok.encode(garbage))
        n_garbage_prefix = np.random.randint(0, n_times)
        n_garbage_suffix = n_times - n_garbage_prefix
        garbage_prefix = ' '.join([garbage] * n_garbage_prefix)
        garbage_suffix = ' '.join([garbage] * n_garbage_suffix)
        pass_key = np.random.randint(1, 50000)
        information_line = f'The pass key is {pass_key}. Remember it. {pass_key} is the pass key.'  # noqa: E501
        final_question = 'What is the pass key? The pass key is'
        lines = [
            task_description,
            garbage_prefix,
            information_line,
            garbage_suffix,
            final_question,
        ]

        # inference
        prompt = ' '.join(lines)
        response = pipe([prompt])
        print(pass_key, response)
        end = time.perf_counter()
        print(f'duration: {end - start} s')

passkey_retrieval(session_len, 5)

在 A100-80G GPU上，执行上述实验，每轮测试大约需要 364 秒

大海捞针

可使用 OpenCompass 进行测评，具体使用方法，请参考文档

困惑度

下面展示使用 LMDeploy 计算困惑度的用法

from transformers import AutoTokenizer
from lmdeploy import TurbomindEngineConfig, pipeline
import numpy as np

# load model and tokenizer
model_repoid_or_path = 'internlm/internlm2_5-7b-chat-1m'
backend_config = TurbomindEngineConfig(
        rope_scaling_factor=2.5,
        session_len=1000000,
        max_batch_size=1,
        cache_max_entry_count=0.7,
        tp=4)
pipe = pipeline(model_repoid_or_path, backend_config=backend_config)
tokenizer = AutoTokenizer.from_pretrained(model_repoid_or_path, trust_remote_code=True)

# get perplexity
text = 'Use a long prompt to replace this sentence'
input_ids = tokenizer.encode(text)
loss = pipe.get_ppl(input_ids)[0]
print(ppl)