安庆市网站建设_网站建设公司_响应式开发_seo优化

vLLM 是一款专为大语言模型推理加速而设计的框架，实现了 KV 缓存内存几乎零浪费，解决了内存管理瓶颈问题。

更多 vLLM 中文文档及教程可访问 →vllm.hyper.ai/

*在线运行 vLLM 入门教程：零基础分步指南

源码 examples/offline_inference/prefix_caching.py

# SPDX-License-Identifier: Apache-2.0 from vllm import LLM, SamplingParams from vllm.distributed import cleanup_dist_env_and_memory # 注意:这只是一个正在运行的示例。用于基准测试， # 请参阅基准 benchmarks/benchmark_prefix_caching.py # 常见前缀。 prefix = ( "You are an expert school principal, skilled in effectively managing " "faculty and staff. Draft 10-15 questions for a potential first grade " "Head Teacher for my K-12, all-girls', independent school that emphasizes " "community, joyful discovery, and life-long learning. The candidate is " "coming in for a first-round panel interview for a 8th grade Math " "teaching role. They have 5 years of previous teaching experience " "as an assistant teacher at a co-ed, public school with experience " "in middle school math teaching. Based on these information, fulfill " "the following paragraph: ") # 样本提示。 prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] generating_prompts = [prefix + prompt for prompt in prompts] # 创建一个采样参数对象。 sampling_params = SamplingParams(temperature=0.0) # 创建一个没有前缀缓存的 LLM 作为基线。 regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.4) print("Results without `enable_prefix_caching`") # 从提示中生成文本。输出是 RequestOutput 对象的包含提示，生成的文本和其他信息的对象列表。 outputs = regular_llm.generate(generating_prompts, sampling_params) regular_generated_texts = [] # 打印输出。 for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text regular_generated_texts.append(generated_text) print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") print("-" * 80) # 破坏 LLM 对象并释放 GPU 内存。 del regular_llm cleanup_dist_env_and_memory() # 使用启用前缀缓存创建一个 LLM。 prefix_cached_llm = LLM(model="facebook/opt-125m", enable_prefix_caching=True, gpu_memory_utilization=0.4) # 预热，以便计算共享的提示 KV 缓存。 prefix_cached_llm.generate(generating_prompts[0], sampling_params) # 使用前缀缓存生成。 outputs = prefix_cached_llm.generate(generating_prompts, sampling_params) print("Results with `enable_prefix_caching`") cached_generated_texts = [] # 打印输出。您应该看到与以前相同的输出。 for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text cached_generated_texts.append(generated_text) print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") print("-" * 80) # 比较结果并显示加速 generated_same = all([ regular_generated_texts[i] == cached_generated_texts[i] for i in range(len(prompts)) ]) print(f"Generated answers are the same: {generated_same}")