存储突破：LLM in a Flash 技术复现

# 更新系统包
sudo apt-get update

# 安装 Python 和虚拟环境
sudo apt-get install python3.8 python3.8-venv python3.8-dev

# 安装 CUDA Toolkit
sudo apt-get install nvidia-cuda-toolkit

# 配置 Python 虚拟环境
python3.8 -m venv llm_flash_env
source llm_flash_env/bin/activate

# 安装 PyTorch（以 CUDA 11.2 为例）
pip3 install torch==1.10.0+cu112 torchvision==0.11.0+cu112 torchaudio===0.10.0+cu112 -f https://download.pytorch.org/whl/cu112/torch_stable.html

import torch
import torch.nn as nn
import torch.nn.functional as F

class FlashAttention(nn.Module):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads"

    def forward(self, query, key, value):
        batch_size = query.shape[0]
        seq_len = query.shape[1]

        # 将查询、键、值分头
        query = query.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)

        # 计算注意力分数
        scores = torch.matmul(query, key.transpose(-1, -2)) / (self.head_dim ** 0.5)

        # 分块计算注意力分数，避免内存占用过高
        block_size = 128  # 根据硬件调整块大小
        num_blocks = (seq_len + block_size - 1) // block_size

        for i in range(num_blocks):
            start = i * block_size
            end = min((i + 1) * block_size, seq_len)
            block_scores = scores[:, :, start:end, :]

            # 应用掩码（可选，根据具体任务）
            # mask = torch.tril(torch.ones(block_scores.shape[2], block_scores.shape[3])).to(block_scores.device)
            # block_scores = block_scores.masked_fill(mask == 0, float('-inf'))

            # 计算 softmax
            attn_weights = F.softmax(block_scores, dim=-1)

            # 更新输出
            if i == 0:
                output = torch.matmul(attn_weights, value[:, :, start:end, :])
            else:
                output = torch.cat([output, torch.matmul(attn_weights, value[:, :, start:end, :])], dim=2)

        # 合并头
        output = output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.embed_dim)

        return output

# 示例用法
if __name__ == "__main__":
    batch_size = 2
    seq_len = 1024
    embed_dim = 1024
    num_heads = 16

    query = torch.randn(batch_size, seq_len, embed_dim)
    key = torch.randn(batch_size, seq_len, embed_dim)
    value = torch.randn(batch_size, seq_len, embed_dim)

    flash_attn = FlashAttention(embed_dim, num_heads)
    output = flash_attn(query, key, value)
    print("Flash Attention 输出形状:", output.shape)

class KVCache:
    def __init__(self, max_size, embed_dim, num_heads):
        self.max_size = max_size
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.cache = {}  # 以序列 ID 为键，缓存键和值

    def store(self, seq_id, key, value):
        # 存储键和值到缓存
        if seq_id not in self.cache:
            self.cache[seq_id] = {'key': [], 'value': []}

        self.cache[seq_id]['key'].append(key)
        self.cache[seq_id]['value'].append(value)

        # 如果缓存超出最大大小，执行淘汰策略
        if len(self.cache) > self.max_size:
            self._evict()

    def retrieve(self, seq_id):
        # 从缓存中检索键和值
        if seq_id in self.cache:
            return self.cache[seq_id]['key'], self.cache[seq_id]['value']
        else:
            return None, None

    def _evict(self):
        # 淘汰策略：简单起见，这里采用随机淘汰
        # 实际应用中，可以基于序列生成概率等指标
        import random
        seq_id_to_evict = random.choice(list(self.cache.keys()))
        del self.cache[seq_id_to_evict]

    def optimize_storage(self):
        # 优化缓存存储结构，将列表转换为张量
        for seq_id in self.cache:
            keys = self.cache[seq_id]['key']
            values = self.cache[seq_id]['value']

            # 将列表转换为张量
            self.cache[seq_id]['key'] = torch.stack(keys)
            self.cache[seq_id]['value'] = torch.stack(values)

# 示例用法
if __name__ == "__main__":
    max_cache_size = 100
    embed_dim = 1024
    num_heads = 16
    seq_id = 1

    kv_cache = KVCache(max_cache_size, embed_dim, num_heads)

    # 生成示例键和值
    key = torch.randn(num_heads, 100, embed_dim // num_heads)
    value = torch.randn(num_heads, 100, embed_dim // num_heads)

    # 存储到缓存
    kv_cache.store(seq_id, key, value)

    # 检索缓存
    retrieved_key, retrieved_value = kv_cache.retrieve(seq_id)
    print("检索到的键形状:", retrieved_key.shape if retrieved_key is not None else "未找到")
    print("检索到的值形状:", retrieved_value.shape if retrieved_value is not None else "未找到")

    # 优化存储
    kv_cache.optimize_storage()
    optimized_key, optimized_value = kv_cache.retrieve(seq_id)
    print("优化后检索到的键形状:", optimized_key.shape if optimized_key is not None else "未找到")
    print("优化后检索到的值形状:", optimized_value.shape if optimized_value is not found else "未找到")

import torch
import torch.nn as nn
import threading
import queue

class AsyncDataLoader:
    def __init__(self, dataset, batch_size, num_threads=2):
        self.dataset = dataset
        self.batch_size = batch_size
        self.num_threads = num_threads
        self.data_queue = queue.Queue(maxsize=10)  # 数据队列，最大存储 10 个批次
        self.threads = []
        self.stop_flag = False

    def _worker(self):
        while not self.stop_flag:
            try:
                # 加载一个批次的数据
                batch = []
                for _ in range(self.batch_size):
                    data = next(self.dataset)
                    batch.append(data)

                # 将批次数据转换为张量并放入队列
                batch_tensor = torch.stack(batch)
                self.data_queue.put(batch_tensor)

            except Exception as e:
                print(f"数据加载线程出错: {e}")
                self.stop_flag = True

    def start(self):
        # 启动多个线程加载数据
        for _ in range(self.num_threads):
            thread = threading.Thread(target=self._worker)
            thread.daemon = True
            thread.start()
            self.threads.append(thread)

    def get_batch(self):
        # 从队列中获取一个批次的数据
        return self.data_queue.get()

    def stop(self):
        # 停止数据加载
        self.stop_flag = True
        for thread in self.threads:
            thread.join()

class PipelineModel(nn.Module):
    def __init__(self):
        super().__init__()
        # 定义流水线各个阶段的模块
        self.stage1 = nn.Sequential(
            nn.Embedding(10000, 1024),
            nn.Linear(1024, 1024),
            nn.ReLU()
        )
        self.stage2 = nn.Sequential(
            FlashAttention(1024, 16),
            nn.Linear(1024, 1024),
            nn.ReLU()
        )
        self.stage3 = nn.Sequential(
            nn.Linear(1024, 1024),
            nn.ReLU(),
            nn.Linear(1024, 10000)
        )

    def forward(self, x):
        # 流水线执行
        # 实际应用中，不同阶段可以在不同设备上执行
        x = self.stage1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        return x

# 示例用法
if __name__ == "__main__":
    # 创建示例数据集
    class ExampleDataset:
        def __iter__(self):
            return self

        def __next__(self):
            # 生成随机数据模拟输入
            return torch.randint(0, 10000, (1024,))

    dataset = ExampleDataset()
    batch_size = 8

    # 初始化异步数据加载器
    async_loader = AsyncDataLoader(dataset, batch_size)
    async_loader.start()

    # 初始化流水线模型
    model = PipelineModel()

    try:
        while True:
            # 从队列中获取数据
            batch_data = async_loader.get_batch()

            # 前向传播
            output = model(batch_data)

            # 模拟计算损失和反向传播（实际应用中需要根据具体任务定义）
            loss = output.mean()
            loss.backward()

            print("处理一个批次数据，输出形状:", output.shape)

    except KeyboardInterrupt:
        print("停止训练")

    finally:
        # 停止数据加载
        async_loader.stop()

class LLMInFlashSystem:
    def __init__(self, model, max_cache_size, embed_dim, num_heads):
        self.model = model
        self.kv_cache = KVCache(max_cache_size, embed_dim, num_heads)
        self.embed_dim = embed_dim
        self.num_heads = num_heads

    def generate(self, input_ids, max_length=50):
        batch_size = input_ids.shape[0]
        generated_ids = input_ids.clone()

        # 初始化 KV Cache
        self.kv_cache.cache = {}

        for _ in range(max_length):
            # 获取当前输入的最后一个 token
            current_input = generated_ids[:, -1:]

            # 前向传播，计算注意力并存储到 KV Cache
            with torch.no_grad():
                outputs = self.model(current_input)
                next_token_logits = outputs.logits[:, -1]

                # 在 KV Cache 中存储键和值（实际应用中需要从模型输出中提取键和值）
                # 这里简化处理，实际实现会更复杂
                # self.kv_cache.store(generated_ids[:, 0].item(), key, value)

            # 采样下一个 token
            probs = F.softmax(next_token_logits, dim=-1)
            next_token = torch.multinomial(probs, num_samples=1)

            # 添加到生成的序列中
            generated_ids = torch.cat([generated_ids, next_token], dim=-1)

        return generated_ids

    def optimize(self, hardware_info):
        # 根据硬件信息调优系统参数
        # 硬件信息可以包括 GPU 型号、显存大小、CPU 核心数等

        # 示例：根据显存大小调整 KV Cache 最大大小
        if hardware_info["gpu_memory"] < 16:  # 单位：GB
            self.kv_cache.max_size = 50
        elif hardware_info["gpu_memory"] < 32:
            self.kv_cache.max_size = 100
        else:
            self.kv_cache.max_size = 200

        # 其他调优参数...

# 示例用法
if __name__ == "__main__":
    # 定义一个简单的模型（实际应用中会更复杂）
    class SimpleModel(nn.Module):
        def __init__(self):
            super().__init__()
            self.embedding = nn.Embedding(10000, 1024)
            self.flash_attn = FlashAttention(1024, 16)
            self.fc = nn.Linear(1024, 10000)

        def forward(self, input_ids):
            x = self.embedding(input_ids)
            x = self.flash_attn(x, x, x)
            x = self.fc(x)
            return x

    model = SimpleModel()
    max_cache_size = 100
    embed_dim = 1024
    num_heads = 16

    # 初始化系统
    llm_system = LLMInFlashSystem(model, max_cache_size, embed_dim, num_heads)

    # 模拟硬件信息
    hardware_info = {
        "gpu_memory": 24  # 单位：GB
    }

    # 调优系统
    llm_system.optimize(hardware_info)

    # 生成文本
    input_ids = torch.randint(0, 10000, (2, 1))  # 2 个序列，每个序列 1 个 token
    generated_ids = llm_system.generate(input_ids, max_length=50)
    print("生成的序列形状:", generated_ids.shape)