Llm大模型

LLama2模型结构方面的改进

LLama系列模型

llama系列模型是闭源gpt3.5火爆之后开源的强大经典decoder-only模型，后面诞生的诸多llm多多少少都带有LLama的影子。

LLama2模型结构的主要改进

1. 将Layer_norm更换为RMS_norm

在NLP模型中，归一化对模型训练过程中防止loss起飞有重要作用。

  • 经典layer norm计算公式如下： $$ y = \frac {x - E(x)} {\sqrt{Var(x) + \epsilon}} * \gamma + \beta $$

$$ E(x) = \frac {1} {N} \sum^{N}_{i=0} {x_i} $$

$$ Var(x) = \frac {1} {N} \sum^{N}_{i=0}{(x_i - E(x))^2} $$

其中,$γ$和$β$是可学习的参数。分母加上一个极小的数$ε$防止分母为0。

  • RMS norm其实是layer norm的变体，为了加快计算，省去了求均值的过程，也删除了偏置值$β$。

$$ y = \frac {x} { \sqrt {Mean(x^2) + \epsilon}} * \gamma $$

$$ Mean(x^2) = \frac {1} {N} \sum^{N}_{i=0}({x_i}^2) $$ $γ$是可学习的参数

class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps  # ε
        self.gama = nn.Parameter(torch.ones(dim)) #可学习参数γ
​
    def _norm(self, x):
        # RMSNorm
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
​
    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * self.gama

2. Q在与K相乘之前，先使用RoPE进行位置编码

rope

# 作者：CodeLearner
# 链接：https://zhuanlan.zhihu.com/p/649756898

def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
    # 计算词向量元素两两分组以后，每组元素对应的旋转角度 
    # arange生成[0,2,4...126]
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    # t = [0,....end]
    t = torch.arange(end, device=freqs.device)  # type: ignore
    # t为列向量 freqs为行向量做外积
    # freqs.shape = (t.len(),freqs.len()) #shape (end,dim//2)
    freqs = torch.outer(t, freqs).float()  # type: ignore
    # 生成复数
    # torch.polar(abs,angle) -> abs*cos(angle) + abs*sin(angle)*j
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
    # freqs_cis.shape  = (end,dim//2)
    return freqs_cis
​
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
    # ndim为x的维度数 ,此时应该为4
    ndim = x.ndim
    assert 0 <= 1 < ndim
    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
    # (1,x.shape[1],1,x.shape[-1])
    return freqs_cis.view(*shape)
​
def apply_rotary_emb(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    # xq.shape = [bsz, seqlen, self.n_local_heads, self.head_dim]
    # xq_.shape = [bsz, seqlen, self.n_local_heads, self.head_dim//2 , 2]
    # torch.view_as_complex用于将二维向量转换为复数域 torch.view_as_complex即([x,y]) -> (x+yj)
    # 所以经过view_as_complex变换后xq_.shape = [bsz, seqlen, self.n_local_heads, self.head_dim//2]
    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    
    
    freqs_cis = reshape_for_broadcast(freqs_cis, xq_) # freqs_cis.shape = (1,x.shape[1],1,x.shape[-1])
    
    # xq_ 与freqs_cis广播哈达玛积
    # [bsz, seqlen, self.n_local_heads, self.head_dim//2] * [1,seqlen,1,self.head_dim//2]
    # torch.view_as_real用于将复数再转换回实数向量, 再经过flatten展平第4个维度 
    # [bsz, seqlen, self.n_local_heads, self.head_dim//2] ->[bsz, seqlen, self.n_local_heads, self.head_dim//2,2 ] ->[bsz, seqlen, self.n_local_heads, self.head_dim]
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)
# 精简版Attention
class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.wq = Linear(...)
        self.wk = Linear(...)
        self.wv = Linear(...)
        
        self.freqs_cis = precompute_freqs_cis(dim, max_seq_len * 2)
​
    def forward(self, x: torch.Tensor):
        bsz, seqlen, _ = x.shape
        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
         # attention 操作之前，应用旋转位置编码
        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
        #...
        # 进行后续Attention计算
        scores = torch.matmul(xq, xk.transpose(1, 2)) / math.sqrt(dim)
        scores = F.softmax(scores.float(), dim=-1)
        output = torch.matmul(scores, xv)  # (batch_size, seq_len, dim)
  # ......

3. 引入KV Cache，并采用Group Query Attention

kv-cache 出处见图片水印。


# 作者：CodeLearner
# 链接：https://zhuanlan.zhihu.com/p/649756898

def mha(x, c_attn, c_proj, n_head, kvcache=None):  # [n_seq, n_embd] -> [n_seq, n_embd]
    # qkv projection
    # when we pass kvcache, n_seq = 1. so we will compute new_q, new_k and new_v
    x = linear(x, **c_attn)  # [n_seq, n_embd] -> [n_seq, 3*n_embd]
    # split into qkv
    qkv = np.split(x, 3, axis=-1)  # [n_seq, 3*n_embd] -> [3, n_seq, n_embd]
    if kvcache:
        # qkv
        new_q, new_k, new_v = qkv  # new_q, new_k, new_v = [1, n_embd]
        old_k, old_v = kvcache
        k = np.vstack([old_k, new_k]) # k = [n_seq, n_embd], where n_seq = prev_n_seq + 1
        v = np.vstack([old_v, new_v]) # v = [n_seq, n_embd], where n_seq = prev_n_seq + 1
        qkv = [new_q, k, v]

4. 用SiLU激活函数代替RELU/GELU

act fun 这里先介绍Swish激活函数，计算公式如下： $$ Swish(x) = x · \sigma (\beta x) $$

$$ \sigma (x) = \frac{1} {1 + e^{-x} } $$

其中，$σ$是Sigmoid函数， $\beta$ 是一个参数，上图为$β=0.5$时的函数图像。当$β=1$时，Swish函数就是SiLU函数。

5. 使用分组查询注意力 (Grouped Query Attention, GQA)

maq_gqa

# copied from llama2

def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
    bs, slen, n_kv_heads, head_dim = x.shape
    if n_rep == 1: # MHA
        return x
    return ( # MQA / GQA
        x[:, :, :, None, :]
        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
    )


class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
        model_parallel_size = fs_init.get_model_parallel_world_size()
        self.n_local_heads = args.n_heads // model_parallel_size
        self.n_local_kv_heads = self.n_kv_heads // model_parallel_size # 此处 
        self.n_rep = self.n_local_heads // self.n_local_kv_heads # 此处 几个组
        self.head_dim = args.dim // args.n_heads

        self.wq = ColumnParallelLinear(
            args.dim,
            args.n_heads * self.head_dim,
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.wk = ColumnParallelLinear(
            args.dim,
            self.n_kv_heads * self.head_dim, # 初始化为单个组内的一份
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.wv = ColumnParallelLinear(
            args.dim,
            self.n_kv_heads * self.head_dim, # # 初始化为单个组内的一份
            bias=False,
            gather_output=False,
            init_method=lambda x: x,
        )
        self.wo = RowParallelLinear(
            args.n_heads * self.head_dim,
            args.dim,
            bias=False,
            input_is_parallel=True,
            init_method=lambda x: x,
        )

        self.cache_k = torch.zeros(
            (
                args.max_batch_size,
                args.max_seq_len,
                self.n_local_kv_heads,
                self.head_dim,
            )
        ).cuda()
        self.cache_v = torch.zeros(
            (
                args.max_batch_size,
                args.max_seq_len,
                self.n_local_kv_heads,
                self.head_dim,
            )
        ).cuda()

    def forward(
        self,
        x: torch.Tensor,
        start_pos: int,
        freqs_cis: torch.Tensor,
        mask: Optional[torch.Tensor],
    ):
        bsz, seqlen, _ = x.shape
        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)

        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)

        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)

        self.cache_k = self.cache_k.to(xq)
        self.cache_v = self.cache_v.to(xq)

        self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
        self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv

        keys = self.cache_k[:bsz, : start_pos + seqlen]
        values = self.cache_v[:bsz, : start_pos + seqlen]

        # repeat k/v heads if n_kv_heads < n_heads # 单个组扩展为完整head
        keys = repeat_kv(keys, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
        values = repeat_kv(values, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)

        xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
        keys = keys.transpose(1, 2)
        values = values.transpose(1, 2)
        scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
        if mask is not None:
            scores = scores + mask  # (bs, n_local_heads, seqlen, cache_len + seqlen)
        scores = F.softmax(scores.float(), dim=-1).type_as(xq)
        output = torch.matmul(scores, values)  # (bs, n_local_heads, seqlen, head_dim)
        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
        return self.wo(output)
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