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引言

今天介绍LLAMA2模型引入的关于注意力的改进——分组查询注意力(Grouped-query attention,GQA)¹。

Transformer中的多头注意力在解码阶段来说是一个性能瓶颈。多查询注意力²通过共享单个key和value头，同时不减少query头来提升性能。多查询注意力可能导致质量下降和训练不稳定，因此常用的是分组查询注意力。

然后我们结合上篇文章³探讨的旋转位置编码，将选择位置编码应用到分组查询注意力上。

多头注意力

我们先回顾以下原始多头注意力的实现。

import torch
from torch import nn, Tensorimport math
from dataclasses import dataclass@dataclass
class ModelArgs:hidden_size: int = 512num_heads: int = 8attention_dropout: float = 0.1class MultiHeadAttention(nn.Module):def __init__(self, args: ModelArgs) -> None:super().__init__()self.hidden_size = args.hidden_sizeself.num_heads = args.num_headsself.head_dim = self.hidden_size // self.num_headsself.attention_dropout = args.attention_dropoutself.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)def forward(self, hidden_states: Tensor, attention_mask: Tensor = None):batch_size, seq_len, _ = hidden_states.shapequery_states, key_states, value_states = (self.q_proj(hidden_states),self.k_proj(hidden_states),self.v_proj(hidden_states),)query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)key_states = key_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)if attention_mask is not None:causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]attn_weights = attn_weights + causal_mask# upcast attention to fp32 see https://github.com/huggingface/transformers/pull/17437attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)attn_output = torch.matmul(attn_weights, value_states)attn_output = attn_output.transpose(1, 2).contiguous()attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)attn_output = self.o_proj(attn_output)return attn_output

别忘了测试一下：

    args = ModelArgs()attention = MultiHeadAttention(args)inputs = torch.randn(32, 8, args.hidden_size)print(attention(inputs).shape)

torch.Size([32, 8, 512])

原始多头注意力就不再赘述了，之前的文章有过详细介绍。

分组查询注意力

分组查询注意力使用折中数量的key-value头(超过一个，但少于多头注意力全部的头数量)来提升性能。

多头注意力、分组查询注意力以及多查询注意力之间的区别如下：

该图来自参考1中的论文。

如上图所示，分组查询注意力是针对多头注意力的一种改进，每组Query头(这里两个Query一组)共享同一个Key和Value头，使得推理更加高效。

实际上在实现的时候，会将共享的Key和Value头进行广播(复制)成与Query头相同的数量：

这样，我们就可以像普通多头注意力一样去计算了。

我们增加num_key_value_heads表示key、value头数；num_heads还是表示query头数。

@dataclass
class ModelArgs:hidden_size: int = 512num_heads: int = 8num_key_value_heads: int = 4attention_dropout: float = 0.1

分组查询注意力和多查询注意力可以合并在一起实现：

class GroupedQueryAttention(nn.Module):def __init__(self, args: ModelArgs) -> None:super().__init__()self.hidden_size = args.hidden_sizeself.num_heads = args.num_heads# 每个头的维度计算和之前一样self.head_dim = self.hidden_size // self.num_heads# 保存key/value头数self.num_key_value_heads = args.num_key_value_heads# 每组内要复制的次数,若为1，即退化为多头注意力；若为num_heads，则为多查询注意力self.num_key_value_groups = self.num_heads // args.num_key_value_headsself.attention_dropout = args.attention_dropoutself.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)# 注意Key和Value的映射这里节省了参数，加速了推理效率。self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)# 最后的输出映射和之前一样self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)def forward(self, hidden_states: Tensor, attention_mask: Tensor = None):batch_size, seq_len, _ = hidden_states.shapequery_states, key_states, value_states = (self.q_proj(hidden_states),self.k_proj(hidden_states),self.v_proj(hidden_states),)query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)# 转换为对应的形状key_states = key_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)value_states = value_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)# 重复num_key_value_groups次，使得和query头数一致key_states = repeat_kv(key_states, self.num_key_value_groups)value_states = repeat_kv(value_states, self.num_key_value_groups)# 后面和普通多头注意力一样计算attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)if attention_mask is not None:causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]attn_weights = attn_weights + causal_mask# upcast attention to fp32 see https://github.com/huggingface/transformers/pull/17437attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)attn_output = torch.matmul(attn_weights, value_states)attn_output = attn_output.transpose(1, 2).contiguous()attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)attn_output = self.o_proj(attn_output)return attn_output

其中num_key_value_groups为每组内要复制的次数,若为1，即退化为多头注意力；若为num_heads，则为多查询注意力。

复制时调用repeat_kv方法，如其名所示，只针对key和value：

def repeat_kv(hidden_states: Tensor, n_rep: int) -> Tensor:"""The hidden states go from (batch, num_key_value_heads, seq_len, head_dim) to (batch, num_attention_heads, seq_len, head_dim)n_rep is the number of repeat times."""batch, num_key_value_heads, seq_len, head_dim = hidden_states.shapeif n_rep == 1:# do nothingreturn hidden_states# add a new dimension and repeat n_rep timeshidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, seq_len, head_dim)# reshape to (batch, num_attention_heads, seq_len, head_dim)return hidden_states.reshape(batch, num_key_value_heads * n_rep, seq_len, head_dim)

有了分组查询注意力，下面我们来看如何应用上篇文章³介绍的旋转位置编码到query和key上。

应用旋转位置编码

注意，实现的时候要考虑维度，因此代码和上篇文章的旋转位置编码³有所不同。

首先，我们实现RotaryEmbedding，它缓存了频率张量inv_freq的计算。

class RotaryEmbedding(nn.Module):def __init__(self, dim: int, max_position_embeddings: int = 2048, theta: int = 10000):super().__init__()self.dim = dim  # head dimself.max_position_embeddings = max_position_embeddingsself.theta = thetainv_freq = 1.0 / (theta** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))self.register_buffer("inv_freq", inv_freq, persistent=False)# 不需要计算梯度@torch.no_grad()def forward(self, position_ids: torch.LongTensor):freqs = torch.outer(position_ids, self.inv_freq).float()return torch.polar(torch.ones_like(freqs), freqs)

该实现修改自旋转位置编码文章³中的precompute_freqs_cis函数。

然后我们改写apply_rotary_emb函数，主要是确定了输入和输出维度的正确性：

def apply_rotary_emb(q: Tensor, k: Tensor, freq_cis: Tensor):"""Args:q (Tensor): (batch_size, num_heads, seq_len, head_dim)k (Tensor): (batch_size, num_key_value_heads, seq_len, head_dim)freq_cis (Tensor): (seq_len, batch_size)"""# q_ (batch_size, num_heads, seq_len, head_dim // 2, 2)q_ = q.float().reshape(*q.shape[:-1], -1, 2)# k_ (batch_size, num_key_value_heads, seq_len, head_dim // 2, 2)k_ = k.float().reshape(*k.shape[:-1], -1, 2)# turn to complex# q_ (batch_size, num_heads, seq_len, head_dim // 2)q_ = torch.view_as_complex(q_)# k_ (batch_size, num_key_value_heads, seq_len, head_dim // 2)k_ = torch.view_as_complex(k_)# freq_cis (batch_size, 1, seq_len, 1)freq_cis = reshape_for_broadcast(freq_cis, q_)# 应用旋转操作，然后将结果转回实数# view_as_real (batch_size, num_heads, seq_len, head_dim // 2, 2)# xq_out (batch_size, num_heads, seq_len, head_dim)xq_out = torch.view_as_real(q_ * freq_cis).flatten(-2)# view_as_real (batch_size, num_key_value_heads, seq_len, head_dim // 2, 2)# xk_out (batch_size, num_key_value_heads, seq_len, head_dim)xk_out = torch.view_as_real(k_ * freq_cis).flatten(-2)return xq_out.type_as(q), xk_out.type_as(k)

其中需要调用reshape_for_broadcast将频率张量的维度从(seq_len, batch_size)调整到(batch_size, 1, seq_len, 1)：

def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):"""Args:freqs_cis (torch.Tensor): (seq_len, batch_size)x (torch.Tensor): (batch_size, num_heads, seq_len, head_dim // 2)"""# enumerate(x.shape) = [(0, batch_size), (1, num_heads), (2, seq_len), (3, head_dim // 2)]# (batch_size, 1, seq_len, 1)shape = [d if i == 0 or i == 2 else 1 for i, d in enumerate(x.shape)]return freqs_cis.view(*shape)

我们把每个维度都写出来就不会出错。

再确保下repeat_kv函数的维度：

def repeat_kv(hidden_states: Tensor, n_rep: int) -> Tensor:"""The hidden states go from (batch, num_key_value_heads seq_len, head_dim) to (batch, num_attention_heads, seq_len, head_dim)n_rep is the number of repeat times."""batch, num_key_value_heads, seq_len, head_dim = hidden_states.shapeif n_rep == 1:# do nothingreturn hidden_states# add a new dimension and repeat n_rep timeshidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, seq_len, head_dim)# reshape to (batch, num_attention_heads, seq_len, head_dim)return hidden_states.reshape(batch, num_key_value_heads * n_rep, seq_len, head_dim)

最后将旋转位置编码整合到GroupedQueryAttention中：

class GroupedQueryAttention(nn.Module):def __init__(self, args: ModelArgs) -> None:super().__init__()self.hidden_size = args.hidden_sizeself.num_heads = args.num_heads# 每个头的维度计算和之前一样self.head_dim = self.hidden_size // self.num_heads# 保存key/value头数self.num_key_value_heads = args.num_key_value_heads# 每组内要复制的次数,若为1，即退化为多头注意力；若为num_heads，则为多查询注意力self.num_key_value_groups = self.num_heads // args.num_key_value_headsself.attention_dropout = args.attention_dropoutself.max_position_embeddings = args.max_position_embeddingsself.rope_theta = args.thetaself.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)# 注意Key和Value的映射这里节省了参数，加速了推理效率。self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)# 最后的输出映射和之前一样self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)# 定义了RotaryEmbedding实例self.rotary_emb = RotaryEmbedding(self.head_dim,max_position_embeddings=self.max_position_embeddings,theta=self.rope_theta,)def forward(self,hidden_states: Tensor,attention_mask: Tensor = None,position_ids: torch.LongTensor = None,):batch_size, seq_len, _ = hidden_states.shapequery_states, key_states, value_states = (self.q_proj(hidden_states),self.k_proj(hidden_states),self.v_proj(hidden_states),)# query_states(batch_size, num_heads, seq_len, head_dim)query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)# 转换为对应的形状# key_states (batch_size, num_key_value_heads, seq_len, head_dim)key_states = key_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)# value_states (batch_size, num_key_value_heads, seq_len, head_dim)value_states = value_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)# 计算频率张量# freq_cis (seq_len, batch_size)freq_cis = self.rotary_emb(position_ids)# 针对query和key应用旋转位置编码# query_states (batch_size, num_heads, seq_len, head_dim)# key_states (batch_size, num_key_value_heads, seq_len, head_dim)query_states, key_states = apply_rotary_emb(query_states, key_states, freq_cis)# 重复num_key_value_groups次，使得和query头数一致# key_states (batch_size, num_heads, seq_len, head_dim)key_states = repeat_kv(key_states, self.num_key_value_groups)# value_states (batch_size, num_heads, seq_len, head_dim)value_states = repeat_kv(value_states, self.num_key_value_groups)# 后面和普通多头注意力一样计算attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)if attention_mask is not None:causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]attn_weights = attn_weights + causal_mask# upcast attention to fp32 see https://github.com/huggingface/transformers/pull/17437attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)attn_output = torch.matmul(attn_weights, value_states)attn_output = attn_output.transpose(1, 2).contiguous()attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)attn_output = self.o_proj(attn_output)return attn_output

主要修改是在调用repeat_kv之前应用旋转位置编码到(每个Attention的)query和key中：

# 计算频率张量
# freq_cis (seq_len, batch_size)
freq_cis = self.rotary_emb(position_ids)# 针对query和key应用旋转位置编码
# query_states (batch_size, num_heads, seq_len, head_dim)
# key_states (batch_size, num_key_value_heads, seq_len, head_dim)
query_states, key_states = apply_rotary_emb(query_states, key_states, freq_cis)

这里简单探讨下为什么旋转位置编码只是应用到query和key上，没有应用到value上，考虑Attention的计算公式：
$$\begin{array}{rl}{a}_{m,n}& =\frac{\exp (\frac{{q\text{q}q}_m^T{k\text{k}k}_n}{\sqrt{d}})}{{\sum }_{j=1}^N\exp \frac{{q\text{q}q}_m^T{k\text{k}k}_j}{\sqrt{d}}}\\ {o\text{o}o}_m& =\sum _{n=1}^N{a}_{m,n}{v\text{v}v}_n\end{array}$$

我们可以看到，实际上只有query和key之间会进行交互(点乘)，而value只是用于计算加权和，不参与交互，因此没有必要应用旋转位置编码，但也可以尝试应用到value上。

苏神在博客也说了：“通过在q,k中施行该位置编码，那么效果就等价于相对位置编码，而如果还需要显式的绝对位置信息，则可以同时在v上也施行这种位置编码。总的来说，我们通过绝对位置的操作，可以达到绝对位置的效果，也能达到相对位置的效果。”

最后，进行一个简单的测试：

@dataclass
class ModelArgs:hidden_size: int = 512num_heads: int = 8num_key_value_heads: int = 4attention_dropout: float = 0.1max_position_embeddings: int = 2048theta: int = 10000if __name__ == "__main__":args = ModelArgs()attention = GroupedQueryAttention(args)inputs = torch.randn(32, 16, args.hidden_size)seq_len = inputs.size(1)position_ids = torch.arange(seq_len, dtype=torch.long)print(attention(inputs, position_ids=position_ids).shape)

torch.Size([32, 16, 512])

参考

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1. [论文翻译]GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints ↩︎

2. Fast Transformer Decoding: One Write-Head is All You Need ↩︎

3. Llama改进之——RoPE旋转位置编码 ↩︎ ↩︎ ↩︎ ↩︎