59_实时性模型：选择低延迟LLM

用户请求 → 输入预处理 → 模型推理 → 输出生成 → 后处理 → 用户接收

# 2025年最新PagedAttention实现示例
from vllm import PagedAttentionEngine

# 初始化优化的PagedAttention引擎
engine = PagedAttentionEngine(
    model="gpt2-large",
    max_num_seqs=1000,
    max_seq_len=4096,
    page_size=16,
    enable_chunked_prefill=True,
    prefetch_ratio=0.3  # 自适应预取比例
)

# 高效处理批量请求
results = engine.generate_batch([
    {"prompt": "编写一个函数", "max_tokens": 100},
    {"prompt": "解释量子计算", "max_tokens": 150}
], temperature=0.7)

# vLLM 2025最新版本部署示例
from vllm import LLM, SamplingParams
from vllm.engine.arg_utils import EngineArgs

# 配置优化参数
engine_args = EngineArgs(
    model="mistralai/Mistral-7B-v0.3",
    tensor_parallel_size=2,  # 使用2个GPU并行
    max_num_seqs=2000,       # 最大并发序列数
    max_model_len=8192,      # 最大模型长度
    trust_remote_code=True,
    quantization="awq",      # 使用AWQ量化
    enforce_eager=False,     # 启用图优化
    enable_chunked_prefill=True,  # 启用分块预填充
    swap_space=4,            # 4GB CPU内存作为交换空间
)

# 初始化LLM引擎
llm = LLM(engine_args=engine_args)

# 配置采样参数
sampling_params = SamplingParams(
    temperature=0.7,
    max_tokens=100,
    top_p=0.95,
    presence_penalty=0.1,
)

# 高效处理请求
outputs = llm.generate([
    "解释量子计算的基本原理",
    "编写一个Python快速排序算法"
], sampling_params)

# LMDeploy 2025版本部署与优化示例
from lmdeploy import TurbomindEngineConfig, pipeline

# 配置低延迟优化参数
config = TurbomindEngineConfig(
    model_name_or_path="meta-llama/Llama-3-8B-Instruct",
    max_batch_size=512,
    max_prefill_token_num=4096,
    max_seq_len=8192,
    quantization="w4a16",  # 4位权重，16位激活量化
    block_size=16,
    enable_prefix_caching=True,  # 启用前缀缓存
    use_context_fmha=True,      # 使用Flash Multi-Head Attention
    kv_cache_dtype="int8",      # KV缓存INT8量化
)

# 创建推理管道
pipe = pipeline(
    model_path="meta-llama/Llama-3-8B-Instruct",
    backend_config=config
)

# 启用流式输出和低延迟模式
response = pipe.stream_infer(
    prompt="什么是机器学习?",
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
    low_latency_mode=True,  # 启用低延迟模式
    cache_prompt=True       # 缓存提示以加速重复请求
)

# TensorRT-LLM优化示例（适用于GPT-OSS模型）
import tensorrt_llm
from tensorrt_llm import LLMConfig, TensorRTLLM

# 配置TensorRT-LLM优化参数
config = LLMConfig(
    model_name="openai/gpt-oss-20b",
    tensor_parallel_size=1,
    dtype="float16",
    quant_mode="int8_smoothquant",  # 使用SmoothQuant INT8量化
    enable_context_fmha=True,
    paged_kv_cache=True,
    use_custom_all_reduce=False,
    max_batch_size=128,
    max_input_len=4096,
    max_output_len=1024,
)

# 构建优化引擎
llm = TensorRTLLM(config)
llm.build()

# 运行优化推理
def generate_with_trt_llm(prompts, max_tokens=100):
    outputs = llm.generate(
        prompts=prompts,
        max_new_tokens=max_tokens,
        temperature=0.7,
        top_p=0.9,
        streaming=False,
        profile=False
    )
    return outputs

# GPTQ 2025量化示例
from transformers import AutoModelForCausalLM, AutoTokenizer
from gptq_2025 import quantize_model

# 加载基础模型
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3-70B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-70B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)

# 应用GPTQ 2025量化
quantized_model = quantize_model(
    model=model,
    tokenizer=tokenizer,
    bits=4,                       # INT4量化
    group_size=128,               # 分组大小
    damp_percent=0.1,             # 阻尼百分比
    sym=True,                     # 对称量化
    desc_act=True,                # 激活描述符
    use_triton=True,              # 使用Triton优化
    auto_find_params=True         # 自动查找最佳参数
)

# 保存量化模型
quantized_model.save_pretrained("./llama3-70b-gptq-4bit")
tokenizer.save_pretrained("./llama3-70b-gptq-4bit")

# AWQ 2.0量化示例
from awq_2025 import AutoAWQForCausalLM, AwqConfig

# 配置AWQ量化参数
awq_config = AwqConfig(
    bits=4,
    group_size=128,
    zero_point=True,
    q_group_size=128,
    version="GEMM",
    use_act_order=True,
    module_override={"mlp": {"bits": 8}}  # MLP层使用INT8量化
)

# 量化并加载模型
model = AutoAWQForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.3",
    awq_config=awq_config,
    device_map="auto"
)

# 进行量化后微调（可选）
model.quantize_and_finetune(
    dataset="wikitext",
    num_epochs=1,
    learning_rate=1e-5,
    batch_size=8
)

# 保存优化后的模型
model.save_quantized("./mistral-7b-awq2.0-4bit")

# 量化模型部署优化示例
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# 加载量化模型
tokenizer = AutoTokenizer.from_pretrained("./llama3-70b-gptq-4bit")
model = AutoModelForCausalLM.from_pretrained(
    "./llama3-70b-gptq-4bit",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

# 启用推理优化
model.eval()
with torch.no_grad():
    # 启用CUDA图优化（适用于固定输入大小场景）
    torch.cuda.empty_cache()
    torch.backends.cudnn.benchmark = True
    
    # 预热模型
    dummy_input = tokenizer("测试", return_tensors="pt").to("cuda")
    for _ in range(3):
        _ = model.generate(
            **dummy_input,
            max_new_tokens=10,
            use_cache=True
        )
    
    # 实际推理
    inputs = tokenizer("什么是人工智能?", return_tensors="pt").to("cuda")
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
        use_cache=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Ollama 2025版本部署命令
# 拉取并运行量化模型
ollama run llama3:8b-instruct-q4_0

# 或自定义模型配置
cat > mistral-edge.mod << EOF
FROM mistral:7b-instruct-v0.3
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
PARAMETER num_thread 8
SYSTEM "你是一个运行在边缘设备上的高效AI助手"
EOF

# 创建并运行自定义模型
ollama create mistral-edge -f mistral-edge.mod
ollama run mistral-edge

# 克隆并编译最新版本
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make -j LLAMA_CUBLAS=1 LLAMA_NATIVE=1

# 转换并量化模型
python convert.py models/llama-3-8b-instruct
./quantize models/llama-3-8b-instruct/ggml-model-f16.gguf models/llama-3-8b-instruct-q4_k_m.gguf q4_k_m

# 运行优化的推理服务
./server -m models/llama-3-8b-instruct-q4_k_m.gguf -c 4096 -t 8 --port 8080 --host 0.0.0.0 --mmap 1

# ONNX Runtime Mobile部署示例
import onnxruntime
from transformers import AutoTokenizer

# 配置推理会话
session_options = onnxruntime.SessionOptions()
session_options.enable_mem_pattern = True
session_options.enable_cpu_mem_arena = True
session_options.intra_op_num_threads = 4
session_options.inter_op_num_threads = 1

# 使用移动优化配置
session_options.add_session_config_entry("session.load_model_format", "ONNX")
session_options.add_session_config_entry("ep.cpu.use_mlas", "1")

# 创建推理会话
ort_session = onnxruntime.InferenceSession(
    "llama-3-8b-instruct-optimized.onnx",
    sess_options=session_options,
    providers=["CPUExecutionProvider"]
)

# 加载分词器
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3-8B-Instruct")

# 推理函数
def generate_text(prompt, max_length=100):
    inputs = tokenizer(prompt, return_tensors="np")
    input_ids = inputs["input_ids"]
    attention_mask = inputs["attention_mask"]
    
    # 执行推理
    outputs = ort_session.run(
        None,
        {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "max_length": np.array([max_length], dtype=np.int32)
        }
    )
    
    return tokenizer.decode(outputs[0][0], skip_special_tokens=True)

# GPU内存优化示例
import torch

# 启用内存优化
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True  # 启用TF32加速

# 内存碎片整理
def optimize_gpu_memory():
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()

# 使用内存池
torch.cuda.set_per_process_memory_fraction(0.85)  # 限制进程内存使用

# 混合精度计算
def mixed_precision_inference(model, inputs):
    with torch.autocast(device_type="cuda", dtype=torch.float16):
        return model.generate(**inputs, max_new_tokens=100)

# 使用vLLM的张量并行示例
from vllm import LLM, SamplingParams

# 配置4路张量并行
llm = LLM(
    model="meta-llama/Llama-3-70B-Instruct",
    tensor_parallel_size=4,  # 使用4个GPU
    gpu_memory_utilization=0.9,  # 最大GPU内存利用率
    max_num_seqs=500,
    trust_remote_code=True
)

# 配置采样参数
sampling_params = SamplingParams(
    temperature=0.7,
    max_tokens=100,
    top_p=0.95
)

# 生成文本
outputs = llm.generate(
    ["解释深度学习的基本原理"],
    sampling_params
)

# 使用Triton优化的注意力计算示例
import torch
import triton
import triton.language as tl

@triton.jit
def attention_kernel(
    q_ptr, k_ptr, v_ptr, out_ptr,
    batch, heads, seq_len, dim,
    qk_scale, BLOCK_SIZE: tl.constexpr,
):
    # Triton kernel实现高效注意力计算
    # ...

def optimized_attention(q, k, v):
    # 准备输入
    batch, heads, seq_len, dim = q.shape
    
    # 调用优化的Triton kernel
    output = torch.empty_like(q)
    grid = (batch * heads, triton.cdiv(seq_len, BLOCK_SIZE))
    attention_kernel[grid](
        q, k, v, output,
        batch, heads, seq_len, dim,
        1.0 / (dim ** 0.5),
        BLOCK_SIZE=128,
    )
    
    return output

# 异构计算架构配置示例
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import checkpoint_wrapper

# 定义异构执行策略
class HeterogeneousExecutionStrategy:
    def __init__(self, model, device_map):
        self.model = model
        self.device_map = device_map
    
    def execute(self, input_ids, attention_mask):
        # 第一部分在CPU上执行简单预处理
        with torch.no_grad():
            # 低精度层在专用加速器上执行
            for layer_idx, layer in enumerate(self.model.layers):
                if layer_idx in self.device_map["accelerator"]:
                    layer.to("cuda:1")  # 假设cuda:1是专用加速器
                elif layer_idx in self.device_map["gpu"]:
                    layer.to("cuda:0")  # 通用GPU
                else:
                    layer.to("cpu")  # CPU
            
            # 执行推理
            outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
            return outputs

# 使用示例
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.3")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.3", torch_dtype=torch.float16)

# 定义异构设备映射
device_map = {
    "accelerator": list(range(0, 8)),  # 前8层在专用加速器
    "gpu": list(range(8, 24)),         # 中间层在GPU
    "cpu": list(range(24, 32))         # 最后层在CPU
}

strategy = HeterogeneousExecutionStrategy(model, device_map)

# 执行推理
inputs = tokenizer("什么是人工智能?", return_tensors="pt")
outputs = strategy.execute(**inputs)

# 多级缓存架构实现示例
class MultiLevelCache:
    def __init__(self, config):
        # L1缓存：内存中的热点提示词
        self.l1_cache = {}  # 字典实现的快速查找
        self.l1_capacity = config["l1_capacity"]
        
        # L2缓存：磁盘上的频繁请求
        self.l2_cache = {}  # 可以替换为更持久的存储
        self.l2_capacity = config["l2_capacity"]
        
        # 访问计数器，用于LRU策略
        self.access_count = {}
    
    def get(self, key):
        # 首先检查L1缓存
        if key in self.l1_cache:
            self.access_count[key] = self.access_count.get(key, 0) + 1
            return self.l1_cache[key]
        
        # 然后检查L2缓存
        if key in self.l2_cache:
            # 提升到L1缓存
            value = self.l2_cache[key]
            self._add_to_l1(key, value)
            self.access_count[key] = self.access_count.get(key, 0) + 1
            return value
        
        return None
    
    def set(self, key, value, level=1):
        if level == 1:
            self._add_to_l1(key, value)
        elif level == 2:
            self._add_to_l2(key, value)
    
    def _add_to_l1(self, key, value):
        # L1缓存满时，移除访问最少的项
        if len(self.l1_cache) >= self.l1_capacity:
            # 找到访问次数最少的键
            min_key = min(self.l1_cache.keys(), key=lambda k: self.access_count.get(k, 0))
            # 移到L2缓存
            self._add_to_l2(min_key, self.l1_cache[min_key])
            # 从L1移除
            del self.l1_cache[min_key]
        
        self.l1_cache[key] = value
    
    def _add_to_l2(self, key, value):
        # L2缓存满时，移除最老的项
        if len(self.l2_cache) >= self.l2_capacity:
            # 简单的FIFO策略
            oldest_key = next(iter(self.l2_cache.keys()))
            del self.l2_cache[oldest_key]
        
        self.l2_cache[key] = value

# 使用示例
cache_config = {
    "l1_capacity": 1000,  # 内存中缓存1000个提示
    "l2_capacity": 10000  # 磁盘上缓存10000个提示
}

cache = MultiLevelCache(cache_config)

# 缓存预热
common_prompts = ["解释机器学习", "什么是深度学习", "如何优化LLM性能"]
for prompt in common_prompts:
    cache.set(prompt, f"预计算的响应: {prompt}")

# 在推理服务中使用
async def llm_inference_service(prompt):
    # 首先检查缓存
    cached_response = cache.get(prompt)
    if cached_response:
        return cached_response, "cache_hit"
    
    # 缓存未命中，执行实际推理
    # response = model.generate(prompt)
    response = f"生成的响应: {prompt}"
    
    # 存入缓存
    cache.set(prompt, response)
    return response, "cache_miss"

# 模型并行与数据并行结合的部署示例
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

# 初始化分布式环境
def init_distributed():
    dist.init_process_group("nccl")
    local_rank = int(dist.get_rank())
    torch.cuda.set_device(local_rank)
    return local_rank

# 加载并分片模型
def load_sharded_model(local_rank, world_size):
    # 模型并行：将模型层分配到不同GPU
    model = AutoModelForCausalLM.from_pretrained(
        "meta-llama/Llama-3-70B-Instruct",
        torch_dtype=torch.float16,
        device_map={f"cuda:{i}": list(range(i*10, (i+1)*10)) for i in range(world_size)}
    )
    
    # 数据并行：处理不同批次的请求
    model = DDP(model, device_ids=[local_rank])
    return model

# 优化的分布式推理函数
def distributed_inference(model, tokenizer, prompts, local_rank, batch_size=8):
    results = []
    
    # 批处理请求以提高吞吐量
    for i in range(0, len(prompts), batch_size):
        batch = prompts[i:i+batch_size]
        
        # 预处理
        inputs = tokenizer(batch, return_tensors="pt", padding=True, truncation=True)
        inputs = {k: v.to(f"cuda:{local_rank}") for k, v in inputs.items()}
        
        # 执行推理
        with torch.no_grad():
            outputs = model.module.generate(
                **inputs,
                max_new_tokens=100,
                temperature=0.7,
                top_p=0.9,
                use_cache=True
            )
        
        # 后处理
        for output in outputs:
            results.append(tokenizer.decode(output, skip_special_tokens=True))
    
    return results

# 实时聊天系统架构示例
import asyncio
import websockets
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from vllm import LLM, SamplingParams

# 初始化优化的LLM引擎
class ChatEngine:
    def __init__(self):
        # 使用vLLM进行高性能推理
        self.llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.3", 
                      tensor_parallel_size=2, 
                      max_model_len=8192, 
                      quantization="awq")
        self.tokenizer = self.llm.get_tokenizer()
        self.sampling_params = SamplingParams(
            temperature=0.7,
            max_tokens=100,
            top_p=0.9,
            presence_penalty=0.1,
            frequency_penalty=0.1
        )
        
        # 会话缓存
        self.session_cache = {}
        
        # 提示模板
        self.prompt_template = "<s>[INST] {context}\n{user_message} [/INST]"
    
    async def process_request(self, session_id, user_message, max_history=10):
        # 获取或创建会话历史
        if session_id not in self.session_cache:
            self.session_cache[session_id] = []
        
        # 限制历史长度，避免上下文过长
        history = self.session_cache[session_id][-max_history:]
        context = "\n".join([f"User: {h[0]}\nAssistant: {h[1]}" for h in history])
        
        # 构建完整提示
        prompt = self.prompt_template.format(
            context=context,
            user_message=user_message
        )
        
        # 执行高性能推理
        outputs = self.llm.generate([prompt], self.sampling_params)
        response = outputs[0].outputs[0].text.strip()
        
        # 更新会话历史
        self.session_cache[session_id].append((user_message, response))
        
        return response
    
    # 流式输出实现
    async def stream_response(self, session_id, user_message):
        # 获取或创建会话历史
        if session_id not in self.session_cache:
            self.session_cache[session_id] = []
        
        history = self.session_cache[session_id][-10:]
        context = "\n".join([f"User: {h[0]}\nAssistant: {h[1]}" for h in history])
        
        prompt = self.prompt_template.format(
            context=context,
            user_message=user_message
        )
        
        # 启用流式输出
        stream_params = SamplingParams(**self.sampling_params.to_dict(), stream=True)
        
        # 逐token生成并yield
        full_response = ""
        async for output in self.llm.generate_async([prompt], stream_params):
            token = output.outputs[0].text
            full_response += token
            yield token
        
        # 更新会话历史
        self.session_cache[session_id].append((user_message, full_response))

# WebSocket服务器实现
chat_engine = ChatEngine()

async def handle_connection(websocket, path):
    session_id = path.strip("/")
    print(f"New connection: {session_id}")
    
    try:
        async for message in websocket:
            # 异步处理请求
            async for token in chat_engine.stream_response(session_id, message):
                # 实时发送token
                await websocket.send(token)
            # 发送结束标记
            await websocket.send("<END>")
    except websockets.exceptions.ConnectionClosed:
        print(f"Connection closed: {session_id}")

# 启动服务器
start_server = websockets.serve(handle_connection, "localhost", 8765)

asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()

# 代码补全系统优化示例
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

class CodeCompletionEngine:
    def __init__(self):
        # 使用代码特化的模型
        self.model = AutoModelForCausalLM.from_pretrained(
            "deepseek-ai/deepseek-coder-16b-base",
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-16b-base")
        
        # 代码语法缓存
        self.syntax_cache = {}
        
        # 预编译常用代码模式
        self._precompile_patterns()
    
    def _precompile_patterns(self):
        # 预编译常见代码模式以加速推理
        common_patterns = [
            "def function", "for i in", "if condition",
            "import module", "class MyClass", "with open"
        ]
        
        for pattern in common_patterns:
            tokens = self.tokenizer(pattern, return_tensors="pt").input_ids.to("cuda")
            with torch.no_grad():
                # 预计算隐藏状态
                hidden_states = self.model(tokens).last_hidden_state
                self.syntax_cache[pattern] = hidden_states
    
    def complete_code(self, prefix, max_tokens=50, temperature=0.2):
        # 快速前缀匹配
        for pattern, cached_states in self.syntax_cache.items():
            if prefix.endswith(pattern):
                # 复用缓存的隐藏状态
                # 这里是概念示例，实际实现需要更复杂的逻辑
                pass
        
        # 常规推理路径
        inputs = self.tokenizer(prefix, return_tensors="pt").to("cuda")
        
        with torch.no_grad():
            # 低延迟生成配置
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                temperature=temperature,
                top_p=0.9,
                use_cache=True,
                num_return_sequences=1,
                do_sample=False,  # 贪婪解码以加速
                pad_token_id=self.tokenizer.eos_token_id
            )
        
        # 提取补全部分
        completion = self.tokenizer.decode(
            outputs[0][inputs.input_ids.shape[1]:],
            skip_special_tokens=True
        )
        
        return completion

# 实时语音交互优化架构示例
import asyncio
import torch
import numpy as np
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import AutoModelForCausalLM, AutoTokenizer

class VoiceInteractionSystem:
    def __init__(self):
        # 语音识别模型
        self.whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
        self.whisper_model = WhisperForConditionalGeneration.from_pretrained(
            "openai/whisper-large-v3", 
            torch_dtype=torch.float16
        ).to("cuda")
        
        # 语音处理配置
        self.sample_rate = 16000
        self.chunk_size = 3000  # 约0.18秒的音频
        self.buffer = []
        
        # LLM配置
        self.llm = AutoModelForCausalLM.from_pretrained(
            "mistralai/Mistral-7B-Instruct-v0.3",
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
        
        # 启用流式处理
        self.streaming_enabled = True
        self.silence_threshold = 0.01
        self.silence_frames = 0
        self.max_silence_frames = 30  # 约2秒无声音判定为结束
    
    async def process_audio_stream(self, audio_chunk):
        # 异步处理音频块
        self.buffer.append(audio_chunk)
        
        # 语音活动检测
        is_speech = np.max(np.abs(audio_chunk)) > self.silence_threshold
        
        if not is_speech:
            self.silence_frames += 1
            # 检测到语音结束
            if self.silence_frames > self.max_silence_frames and self.buffer:
                text = await self.transcribe_audio()
                if text.strip():
                    # 并行启动LLM处理
                    asyncio.create_task(self.process_with_llm(text))
                self.buffer = []
                self.silence_frames = 0
        else:
            self.silence_frames = 0
            # 实时转录（可选）
            if len(self.buffer) > 10:  # 积累一定音频后开始转录
                text = await self.transcribe_audio()
                # 可以选择在这里开始LLM处理的预热
    
    async def transcribe_audio(self):
        # 语音转文字
        full_audio = np.concatenate(self.buffer)
        
        # 预处理
        input_features = self.whisper_processor(
            full_audio, 
            sampling_rate=self.sample_rate,
            return_tensors="pt"
        ).input_features.to("cuda")
        
        # 低延迟转录配置
        with torch.no_grad():
            predicted_ids = self.whisper_model.generate(
                input_features,
                max_new_tokens=100,
                language="zh",
                task="transcribe",
                return_timestamps=False,
                do_sample=False  # 快速模式
            )
        
        transcription = self.whisper_processor.batch_decode(
            predicted_ids, 
            skip_special_tokens=True
        )[0]
        
        return transcription
    
    async def process_with_llm(self, text):
        # LLM处理文本
        prompt = f"<s>[INST] {text} [/INST]"
        inputs = self.tokenizer(prompt, return_tensors="pt").to("cuda")
        
        # 流式输出配置
        if self.streaming_enabled:
            # 这里是流式生成的占位实现
            # 实际实现需要更复杂的异步处理逻辑
            pass
        else:
            # 完整生成
            with torch.no_grad():
                outputs = self.llm.generate(
                    **inputs,
                    max_new_tokens=150,
                    temperature=0.7,
                    top_p=0.9,
                    use_cache=True
                )
            
            response = self.tokenizer.decode(
                outputs[0][inputs.input_ids.shape[1]:],
                skip_special_tokens=True
            )
            
            # 转换为语音输出（省略TTS实现）
            await self.speak_response(response)
    
    async def speak_response(self, text):
        # TTS实现（占位）
        print(f"Speaking: {text}")
        # 实际应用中需要集成TTS系统

# 标准化测试数据集构建示例
import json
import numpy as np
from transformers import AutoTokenizer

def build_standard_test_dataset(output_path):
    # 不同长度和复杂度的测试提示
    test_cases = [
        # 短提示测试
        {"category": "short", "prompt": "解释AI", "description": "极短提示"},
        {"category": "short", "prompt": "写一个Hello World程序", "description": "简单编程任务"},
        {"category": "short", "prompt": "什么是机器学习?", "description": "基础问题"},
        
        # 中等长度提示
        {"category": "medium", "prompt": "比较Python和JavaScript的主要区别，特别是在Web开发方面", "description": "比较类问题"},
        {"category": "medium", "prompt": "解释量子计算的基本原理，并举例说明其潜在应用", "description": "解释类问题"},
        {"category": "medium", "prompt": "写一个函数，计算斐波那契数列的第n项，要求时间复杂度为O(n)", "description": "算法编程任务"},
        
        # 长提示测试
        {"category": "long", "prompt": "详细分析大语言模型的环保影响，包括训练过程中的能源消耗、碳排放，以及行业在提高能源效率方面的最新进展。请提供具体数据和案例研究。", "description": "复杂分析任务"},
        
        # 多轮对话测试
        {"category": "multi_turn", "conversation": [
            {"role": "user", "content": "什么是递归函数?"},
            {"role": "assistant", "content": "递归函数是指在函数定义中调用自身的函数。"},
            {"role": "user", "content": "请举例说明，并解释递归的优缺点。"}
        ], "description": "多轮对话"},
        
        # 代码生成测试
        {"category": "code", "prompt": "编写一个Python函数，使用快速排序算法对列表进行排序，并包含详细注释。", "description": "代码生成"},
        
        # 数学推理测试
        {"category": "math", "prompt": "一个直角三角形的两条直角边分别为3和4，求斜边长度和三角形的面积。", "description": "数学计算"},
        
        # 长上下文处理测试
        {"category": "context", "prompt": "基于以下背景信息，回答问题：\n背景信息：机器学习是人工智能的一个分支，它赋予计算机从数据中学习而无需明确编程的能力。机器学习的核心是通过算法使计算机系统能够识别模式、做出决策并不断改进。机器学习可以分为监督学习、无监督学习和强化学习三大类。监督学习使用标记数据进行训练，无监督学习处理未标记数据，而强化学习通过与环境的互动来学习最佳行为。\n\n问题：机器学习的三大主要类型是什么？它们的主要区别是什么？", "description": "长上下文理解"}
    ]
    
    # 保存测试数据集
    with open(output_path, 'w', encoding='utf-8') as f:
        json.dump(test_cases, f, ensure_ascii=False, indent=2)
    
    print(f"测试数据集已保存到: {output_path}")
    return test_cases

def run_performance_test(model, tokenizer, test_dataset, max_new_tokens=100, num_runs=5):
    results = {}
    
    for case in test_dataset:
        case_id = f"{case['category']}_{len(results.get(case['category'], []))}"
        results.setdefault(case['category'], [])
        
        # 获取提示文本
        if case['category'] == 'multi_turn':
            # 处理多轮对话
            messages = case['conversation']
            # 构建多轮对话提示（根据模型要求格式化）
            prompt = ""
            for msg in messages:
                if msg['role'] == 'user':
                    prompt += f"<s>[INST] {msg['content']} [/INST] "
                elif msg['role'] == 'assistant':
                    prompt += f"{msg['content']} "
            prompt = prompt.strip()
        else:
            prompt = case['prompt']
        
        # 运行多次取平均
        latencies = []
        first_token_times = []
        token_gen_times = []
        
        for i in range(num_runs):
            # 预热（第一次运行不计入统计）
            if i == 0:
                try:
                    _ = model.generate(
                        **tokenizer(prompt, return_tensors="pt").to("cuda"),
                        max_new_tokens=5
                    )
                except:
                    pass
                continue
            
            # 测量完整延迟
            start_time = torch.cuda.Event(enable_timing=True)
            end_time = torch.cuda.Event(enable_timing=True)
            torch.cuda.synchronize()
            
            inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
            
            start_time.record()
            outputs = model.generate(
                **inputs,
                max_new_tokens=max_new_tokens,
                temperature=0.7,
                top_p=0.9,
                use_cache=True,
                output_scores=True,
                return_dict_in_generate=True
            )
            end_time.record()
            
            torch.cuda.synchronize()
            full_latency = start_time.elapsed_time(end_time)  # 毫秒
            latencies.append(full_latency)
            
            # 计算token生成速度（简化计算，实际需要更精确的计时）
            input_length = inputs.input_ids.shape[1]
            output_length = outputs.sequences.shape[1] - input_length
            if output_length > 0:
                token_gen_time = full_latency / output_length  # 毫秒/词
                token_gen_times.append(token_gen_time)
        
        # 保存结果
        results[case['category']].append({
            'id': case_id,
            'description': case['description'],
            'prompt_length': len(prompt),
            'avg_latency': np.mean(latencies) if latencies else 0,
            'p95_latency': np.percentile(latencies, 95) if latencies else 0,
            'avg_token_gen_time': np.mean(token_gen_times) if token_gen_times else 0
        })
    
    return results

def analyze_performance_results(results):
    # 计算各类别的平均性能
    category_avg = {}
    
    for category, cases in results.items():
        avg_latencies = [case['avg_latency'] for case in cases]
        p95_latencies = [case['p95_latency'] for case in cases]
        token_gen_times = [case['avg_token_gen_time'] for case in cases if case['avg_token_gen_time'] > 0]
        
        category_avg[category] = {
            'avg_latency': np.mean(avg_latencies),
            'p95_latency': np.mean(p95_latencies),
            'avg_token_gen_time': np.mean(token_gen_times) if token_gen_times else 0,
            'num_cases': len(cases)
        }
    
    # 打印摘要
    print("==== 性能测试结果摘要 ====")
    for category, stats in category_avg.items():
        print(f"\n{category}:")
        print(f"  平均延迟: {stats['avg_latency']:.2f} ms")
        print(f"  P95延迟: {stats['p95_latency']:.2f} ms")
        print(f"  平均token生成时间: {stats['avg_token_gen_time']:.2f} ms/token")
    
    return category_avg

# 性能瓶颈诊断工具示例
import torch
import time
import psutil
import GPUtil
from memory_profiler import profile

class PerformanceDiagnostics:
    def __init__(self):
        self.start_time = 0
        self.end_time = 0
        self.memory_before = 0
        self.memory_after = 0
        self.gpu_memory_before = 0
        self.gpu_memory_after = 0
    
    def start(self):
        """开始性能监控"""
        self.start_time = time.time()
        self.memory_before = psutil.Process().memory_info().rss / 1024 / 1024  # MB
        
        # GPU内存监控
        gpus = GPUtil.getGPUs()
        if gpus:
            self.gpu_memory_before = gpus[0].memoryUsed
        
        # CUDA事件计时
        self.cuda_start = torch.cuda.Event(enable_timing=True)
        self.cuda_end = torch.cuda.Event(enable_timing=True)
        torch.cuda.synchronize()
        self.cuda_start.record()
    
    def stop(self):
        """停止性能监控并返回结果"""
        # CUDA计时结束
        self.cuda_end.record()
        torch.cuda.synchronize()
        cuda_time_ms = self.cuda_start.elapsed_time(self.cuda_end)
        
        # 内存监控
        self.end_time = time.time()
        self.memory_after = psutil.Process().memory_info().rss / 1024 / 1024  # MB
        
        # GPU内存监控
        gpus = GPUtil.getGPUs()
        gpu_memory_used = 0
        if gpus:
            self.gpu_memory_after = gpus[0].memoryUsed
            gpu_memory_used = self.gpu_memory_after - self.gpu_memory_before
        
        # 返回诊断结果
        return {
            'wall_time_ms': (self.end_time - self.start_time) * 1000,
            'cuda_time_ms': cuda_time_ms,
            'cpu_memory_increase_mb': self.memory_after - self.memory_before,
            'gpu_memory_increase_mb': gpu_memory_used,
            'cpu_memory_total_mb': self.memory_after,
            'gpu_memory_total_mb': self.gpu_memory_after if gpus else 0
        }
    
    def analyze_inference_bottlenecks(self, model, tokenizer, prompts):
        """分析推理过程中的性能瓶颈"""
        bottlenecks = []
        
        # 1. 分析输入预处理
        diag = PerformanceDiagnostics()
        diag.start()
        for prompt in prompts:
            _ = tokenizer(prompt, return_tensors="pt")
        preprocess_time = diag.stop()['wall_time_ms']
        
        if preprocess_time > 10:  # 超过10ms考虑优化
            bottlenecks.append({
                'stage': 'input_preprocessing',
                'time_ms': preprocess_time,
                'suggestion': '考虑批量处理或缓存tokenizer结果'
            })
        
        # 2. 分析模型前向传播
        diag = PerformanceDiagnostics()
        diag.start()
        
        # 准备输入
        inputs = tokenizer(prompts[:2], return_tensors="pt", padding=True).to("cuda")
        
        # 仅运行前向传播
        with torch.no_grad():
            _ = model(**inputs)
        
        forward_time = diag.stop()
        
        if forward_time['cuda_time_ms'] > 50:  # 超过50ms考虑优化
            bottlenecks.append({
                'stage': 'model_forward',
                'time_ms': forward_time['cuda_time_ms'],
                'memory_increase_mb': forward_time['gpu_memory_increase_mb'],
                'suggestion': '考虑量化、模型剪枝或使用更高效的推理框架'
            })
        
        # 3. 分析生成过程
        diag = PerformanceDiagnostics()
        diag.start()
        
        with torch.no_grad():
            _ = model.generate(
                **inputs,
                max_new_tokens=20,
                temperature=0.7,
                use_cache=True
            )
        
        generate_time = diag.stop()
        
        if generate_time['cuda_time_ms'] > 100:  # 超过100ms考虑优化
            bottlenecks.append({
                'stage': 'generation',
                'time_ms': generate_time['cuda_time_ms'],
                'suggestion': '优化解码策略，考虑使用beam search替代或减少生成token数'
            })
        
        # 打印瓶颈分析
        if bottlenecks:
            print("==== 性能瓶颈分析 ====")
            for b in bottlenecks:
                print(f"\n瓶颈阶段: {b['stage']}")
                print(f"  耗时: {b['time_ms']:.2f} ms")
                if 'memory_increase_mb' in b:
                    print(f"  内存增长: {b['memory_increase_mb']:.2f} MB")
                print(f"  优化建议: {b['suggestion']}")
        else:
            print("未检测到明显性能瓶颈")
        
        return bottlenecks

# 未来算法优化方向的概念实现
class FutureOptimizedModel:
    def __init__(self):
        # 稀疏激活准备
        self.expert_weights = []  # 多个专家网络的权重
        self.router = None        # 路由网络，决定使用哪些专家
        
        # 动态精度控制
        self.precision_levels = {  # 不同精度级别
            'high': torch.float16,
            'medium': torch.bfloat16,
            'low': torch.int8
        }
        
        # 条件计算配置
        self.computation_budget = {}
        
        # 自适应架构调整
        self.layer_config = None
    
    def dynamic_expert_selection(self, inputs):
        """动态选择参与计算的专家"""
        # 路由网络预测重要性
        importance_scores = self.router(inputs)
        
        # 只激活最重要的几个专家（例如前2个）
        top_k = 2
        top_indices = torch.topk(importance_scores, top_k).indices
        
        # 只计算选中的专家
        outputs = []
        for idx in top_indices:
            expert_output = self.expert_weights[idx](inputs)
            outputs.append(expert_output)
        
        # 组合专家输出
        combined_output = torch.stack(outputs).mean(dim=0)
        return combined_output
    
    def adaptive_precision_control(self, inputs, complexity_score):
        """根据输入复杂度动态调整精度"""
        # 根据复杂度选择精度
        if complexity_score > 0.8:
            precision = self.precision_levels['high']
        elif complexity_score > 0.4:
            precision = self.precision_levels['medium']
        else:
            precision = self.precision_levels['low']
        
        # 转换输入到目标精度
        inputs = inputs.to(precision)
        
        # 使用选定精度执行计算
        # ...
        
        return results
    
    def conditional_computation(self, inputs, task_type):
        """根据任务类型分配计算资源"""
        # 根据任务类型获取计算预算
        budget = self.computation_budget.get(task_type, 1.0)
        
        # 计算预算影响网络深度和宽度
        num_layers_to_use = int(len(self.layer_config) * budget)
        
        # 只使用部分网络层
        x = inputs
        for layer in self.layer_config[:num_layers_to_use]:
            x = layer(x)
        
        return x

# 企业低延迟AI战略规划框架
class EnterpriseLLMStrategy:
    def __init__(self, industry, current_capabilities):
        self.industry = industry
        self.current_capabilities = current_capabilities
        self.roadmap = []
    
    def assess_readiness(self):
        """评估企业当前的AI就绪度"""
        # 技术基础设施评估
        infrastructure_gap = self._evaluate_infrastructure()
        
        # 人才队伍评估
        talent_gap = self._evaluate_talent()
        
        # 数据就绪度评估
        data_readiness = self._evaluate_data()
        
        return {
            'infrastructure': infrastructure_gap,
            'talent': talent_gap,
            'data': data_readiness
        }
    
    def develop_roadmap(self, assessment_results, time_horizon=3):
        """制定分阶段实施路线图"""
        # 短期目标（0-1年）
        short_term = self._develop_short_term_goals(assessment_results)
        
        # 中期目标（1-2年）
        mid_term = self._develop_mid_term_goals(assessment_results)
        
        # 长期目标（2-3年）
        long_term = self._develop_long_term_goals(assessment_results)
        
        self.roadmap = {
            'short_term': short_term,
            'mid_term': mid_term,
            'long_term': long_term
        }
        
        return self.roadmap
    
    def _evaluate_infrastructure(self):
        # 基础设施评估逻辑
        # ...
        return "infrastructure_gap_analysis"
    
    def _evaluate_talent(self):
        # 人才评估逻辑
        # ...
        return "talent_gap_analysis"
    
    def _evaluate_data(self):
        # 数据评估逻辑
        # ...
        return "data_readiness_score"
    
    def _develop_short_term_goals(self, assessment):
        # 短期目标制定
        goals = [
            "建立基础GPU集群",
            "部署开源低延迟推理框架",
            "培训核心技术团队"
        ]
        return goals
    
    def _develop_mid_term_goals(self, assessment):
        # 中期目标制定
        goals = [
            "开发行业特化模型",
            "实现混合云部署架构",
            "构建实时监控系统"
        ]
        return goals
    
    def _develop_long_term_goals(self, assessment):
        # 长期目标制定
        goals = [
            "实现边缘-云协同架构",
            "构建自适应AI系统",
            "建立AI创新中心"
        ]
        return goals