【大模型】- Jamba 混合 SSM-Transformer

宋 算法
  2026-08-19 09:21 2026-08-19 09:21 创建   2026-08-21 16:20:11 2026-08-21 16:20:11 更新      1.6k 字  8 分钟

Jamba 混合 SSM-Transformer

结合 SSM 和 Transformer 的优势

类型： 学习　｜　语言： Python　｜　🏷 前置：《DeepSeek-V3》（本系列第 19 篇）

学习目标

  • 理解 SSM 和 Transformer 的结合
  • 实现 Jamba 架构
  • 分析混合架构的优势
  • 优化 SSM-Transformer 混合
  • 应用于长序列处理

Jamba 概述

Jamba 是 AI21 Labs 开发的混合架构，结合了 Mamba（SSM）和 Transformer 的优势。

为什么混合 SSM 和 Transformer

SSM（状态空间模型）的优势：

  • 线性时间复杂度：O(n)
  • 高效处理长序列
  • 低内存使用

Transformer 的优势：

  • 强大的上下文学习能力
  • 成熟的训练技术
  • 良好的可解释性

Jamba 的设计

  • 交替使用 Mamba 层和 Transformer 层
  • 使用 Mixture of Experts（MoE）
  • 支持长上下文（256K tokens）

实现

Mamba 层

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

class MambaBlock(nn.Module):
    def __init__(self, dim, d_state=16, d_conv=4, expand=2):
        super().__init__()
        self.dim = dim
        self.d_state = d_state
        self.d_conv = d_conv
        self.expand = expand
        
        # 输入投影
        self.in_proj = nn.Linear(dim, dim * expand * 2, bias=False)
        
        # 1D 卷积
        self.conv1d = nn.Conv1d(
            in_channels=dim * expand,
            out_channels=dim * expand,
            kernel_size=d_conv,
            groups=dim * expand,
            padding=d_conv - 1
        )
        
        # SSM 参数
        self.x_proj = nn.Linear(dim * expand, d_state * 2, bias=False)
        self.dt_proj = nn.Linear(d_state, dim * expand, bias=True)
        
        # A 参数（对数空间）
        self.A_log = nn.Parameter(torch.randn(dim * expand, d_state))
        
        # D 参数
        self.D = nn.Parameter(torch.ones(dim * expand))
        
        # 输出投影
        self.out_proj = nn.Linear(dim * expand, dim, bias=False)
    
    def forward(self, x):
        batch_size, seq_len, _ = x.shape
        
        # 输入投影
        xz = self.in_proj(x)  # (batch, seq_len, dim * expand * 2)
        x, z = xz.chunk(2, dim=-1)
        
        # 转换为 (batch, channels, seq_len) 用于 1D 卷积
        x = x.transpose(1, 2)
        x = self.conv1d(x)[:, :, :seq_len]
        x = x.transpose(1, 2)
        
        # 激活
        x = F.silu(x)
        
        # SSM 参数
        x_dbl = self.x_proj(x)  # (batch, seq_len, d_state * 2)
        x_db, x_dt = x_dbl.chunk(2, dim=-1)
        
        # A 参数
        A = -torch.exp(self.A_log)
        
        # dt 参数
        dt = self.dt_proj(x_dt)
        dt = F.softplus(dt)
        
        # SSM 计算
        y = self.ssm(x, x_db, A, dt, self.D)
        
        # 门控
        y = y * F.silu(z)
        
        # 输出投影
        return self.out_proj(y)
    
    def ssm(self, x, x_db, A, dt, D):
        """状态空间模型计算"""
        batch_size, seq_len, dim = x.shape
        
        # 离散化
        dA = torch.exp(dt.unsqueeze(-1) * A)
        dB = dt.unsqueeze(-1) * x_db.unsqueeze(2)
        
        # 选择性扫描
        y = torch.zeros_like(x)
        h = torch.zeros(batch_size, dim, self.d_state, device=x.device)
        
        for i in range(seq_len):
            h = dA[:, i] * h + dB[:, i] * x[:, i].unsqueeze(-1)
            y[:, i] = (h * D).sum(-1)
        
        return y

Transformer 层

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class TransformerBlock(nn.Module):
    def __init__(self, dim, n_heads):
        super().__init__()
        self.attention = MultiHeadAttention(dim, n_heads)
        self.feed_forward = FeedForward(dim)
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
    
    def forward(self, x):
        x = x + self.attention(self.norm1(x))
        x = x + self.feed_forward(self.norm2(x))
        return x

Jamba 架构

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class Jamba(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        
        # 嵌入层
        self.embed = nn.Embedding(config["vocab_size"], config["dim"])
        
        # 混合层：交替使用 Mamba 和 Transformer
        self.layers = nn.ModuleList()
        for i in range(config["n_layers"]):
            if i % config["mamba_interval"] == 0:
                # Mamba 层
                self.layers.append(MambaBlock(
                    config["dim"],
                    d_state=config["d_state"],
                    d_conv=config["d_conv"],
                    expand=config["expand"]
                ))
            else:
                # Transformer 层
                self.layers.append(TransformerBlock(
                    config["dim"],
                    config["n_heads"]
                ))
        
        # 输出层
        self.norm = nn.LayerNorm(config["dim"])
        self.head = nn.Linear(config["dim"], config["vocab_size"], bias=False)
    
    def forward(self, idx, targets=None):
        x = self.embed(idx)
        
        for layer in self.layers:
            x = layer(x)
        
        x = self.norm(x)
        logits = self.head(x)
        
        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)),
                targets.view(-1)
            )
        
        return logits, loss

Mixture of Experts

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class JambaMoE(nn.Module):
    def __init__(self, dim, n_experts, top_k):
        super().__init__()
        self.dim = dim
        self.n_experts = n_experts
        self.top_k = top_k
        
        # 专家
        self.experts = nn.ModuleList([
            FeedForward(dim) for _ in range(n_experts)
        ])
        
        # 路由器
        self.router = nn.Linear(dim, n_experts, bias=False)
    
    def forward(self, x):
        batch_size, seq_len, _ = x.shape
        
        # 计算路由权重
        router_logits = self.router(x)
        router_weights = F.softmax(router_logits, dim=-1)
        
        # 选择 top-k 专家
        topk_weights, topk_indices = torch.topk(router_weights, self.top_k, dim=-1)
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
        
        # 计算专家输出
        output = torch.zeros_like(x)
        
        for i in range(batch_size):
            for j in range(seq_len):
                for k in range(self.top_k):
                    expert_idx = topk_indices[i, j, k].item()
                    weight = topk_weights[i, j, k].item()
                    
                    expert_input = x[i, j].unsqueeze(0)
                    expert_output = self.experts[expert_idx](expert_input)
                    
                    output[i, j] += weight * expert_output.squeeze(0)
        
        return output

性能分析

计算复杂度

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def analyze_complexity(seq_len, dim, n_layers, mamba_ratio=0.5):
    """分析计算复杂度"""
    
    mamba_layers = int(n_layers * mamba_ratio)
    transformer_layers = n_layers - mamba_layers
    
    # Mamba 复杂度：O(n)
    mamba_flops = mamba_layers * seq_len * dim * dim
    
    # Transformer 复杂度：O(n²)
    transformer_flops = transformer_layers * seq_len * seq_len * dim
    
    total_flops = mamba_flops + transformer_flops
    
    print(f"Mamba 层数: {mamba_layers}")
    print(f"Transformer 层数: {transformer_layers}")
    print(f"Mamba FLOPs: {mamba_flops / 1e9:.2f}G")
    print(f"Transformer FLOPs: {transformer_flops / 1e9:.2f}G")
    print(f"总 FLOPs: {total_flops / 1e9:.2f}G")
    
    return total_flops

内存使用

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def analyze_memory(seq_len, dim, n_layers, mamba_ratio=0.5):
    """分析内存使用"""
    
    mamba_layers = int(n_layers * mamba_ratio)
    transformer_layers = n_layers - mamba_layers
    
    # Mamba 内存：O(n)
    mamba_memory = mamba_layers * seq_len * dim * 4  # 4 bytes per float
    
    # Transformer 内存：O(n²)
    transformer_memory = transformer_layers * seq_len * seq_len * 4
    
    total_memory = mamba_memory + transformer_memory
    
    print(f"Mamba 内存: {mamba_memory / 1e9:.2f}GB")
    print(f"Transformer 内存: {transformer_memory / 1e9:.2f}GB")
    print(f"总内存: {total_memory / 1e9:.2f}GB")
    
    return total_memory

长序列性能

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def long_sequence_benchmark():
    """长序列基准测试"""
    seq_lengths = [1024, 4096, 16384, 65536, 262144]
    
    for seq_len in seq_lengths:
        # 创建模型
        config = {
            "dim": 4096,
            "n_layers": 32,
            "n_heads": 32,
            "vocab_size": 32000,
            "mamba_interval": 4,
            "d_state": 16,
            "d_conv": 4,
            "expand": 2,
        }
        
        model = Jamba(config)
        
        # 前向传播
        x = torch.randint(0, config["vocab_size"], (1, seq_len))
        
        start_time = time.time()
        with torch.no_grad():
            output = model(x)
        time_taken = time.time() - start_time
        
        # 内存使用
        memory = torch.cuda.max_memory_allocated() / 1024 / 1024 if torch.cuda.is_available() else 0
        
        print(f"序列长度 {seq_len}:")
        print(f"  时间: {time_taken:.4f}s")
        print(f"  内存: {memory:.2f}MB")

与其他架构比较

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def compare_with_other_architectures():
    """与其他架构比较"""
    
    architectures = {
        "Transformer": {
            "complexity": "O(n²)",
            "memory": "O(n²)",
            "long_sequence": "差",
            "training": "成熟",
        },
        "Mamba": {
            "complexity": "O(n)",
            "memory": "O(n)",
            "long_sequence": "优秀",
            "training": "较新",
        },
        "Jamba": {
            "complexity": "O(n * n_mamba + n² * n_transformer)",
            "memory": "O(n * n_mamba + n² * n_transformer)",
            "long_sequence": "良好",
            "training": "混合",
        },
    }
    
    for name, arch in architectures.items():
        print(f"\n{name}:")
        for key, value in arch.items():
            print(f"  {key}: {value}")

应用场景

长文档处理

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def long_document_processing():
    """长文档处理"""
    model = Jamba(config)
    
    # 处理长文档
    document = "长文档内容..."
    
    # 分块处理
    chunk_size = 4096
    chunks = [document[i:i+chunk_size] for i in range(0, len(document), chunk_size)]
    
    # 处理每个块
    outputs = []
    for chunk in chunks:
        inputs = tokenizer(chunk, return_tensors="pt")
        output = model(**inputs)
        outputs.append(output)
    
    # 合并结果
    final_output = torch.cat(outputs, dim=1)
    
    return final_output

代码生成

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def code_generation():
    """代码生成"""
    model = Jamba(config)
    
    # 生成代码
    prompt = "def fibonacci(n):"
    
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7
    )
    
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return generated_code

优化建议

  1. Mamba 间隔：通常每 4 层使用一个 Mamba 层效果最好
  2. 专家数量：MoE 专家数量影响性能和内存
  3. 状态维度：d_state 影响 SSM 的表达能力
  4. 卷积核大小：d_conv 影响局部依赖的捕捉

总结

Jamba 通过混合 SSM 和 Transformer，结合了两者的优势。Mamba 层提供线性复杂度和高效长序列处理，Transformer 层提供强大的上下文学习能力。这种混合架构在长序列任务上表现出色。

下一步

下一课将介绍异步 Hogwild 推理，优化并发推理。

📚 本文改编自 AI Engineering from Scratch（MIT License · 作者 Rohit Ghumare），中文内容来自官方中文镜像。原课程共 503 课 · 20 阶段 · 免费开源，教程网站见 aiengineeringfromscratch.com。

  • 标题: 【大模型】- Jamba 混合 SSM-Transformer
  • 作者: 宋
  • 创建于 : 2026-08-19 09:21:00
  • 更新于 : 2026-08-21 16:20:11
  • 链接: https://sxl-space.tk/2026/08/19/010_LLM/010_LLM-21-Jamba/
  • 版权声明: 版权所有 © 宋，禁止转载。
目录
【大模型】- Jamba 混合 SSM-Transformer
  1. 1. Jamba 混合 SSM-Transformer
  2. 2. 学习目标
  3. 3. Jamba 概述
    1. 3.1. 为什么混合 SSM 和 Transformer
    2. 3.2. Jamba 的设计
  4. 4. 实现
    1. 4.1. Mamba 层
    2. 4.2. Transformer 层
    3. 4.3. Jamba 架构
  5. 5. Mixture of Experts
  6. 6. 性能分析
    1. 6.1. 计算复杂度
    2. 6.2. 内存使用
    3. 6.3. 长序列性能
  7. 7. 与其他架构比较
  8. 8. 应用场景
    1. 8.1. 长文档处理
    2. 8.2. 代码生成
  9. 9. 优化建议
  10. 10. 总结
  11. 11. 下一步
  12. 12. 下一课将介绍异步 Hogwild 推理，优化并发推理。