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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#xff0c;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…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 dataclassdataclass
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, biasFalse)self.k_proj nn.Linear(self.hidden_size, self.num_heads * self.head_dim, biasFalse)self.v_proj nn.Linear(self.hidden_size, self.num_heads * self.head_dim, biasFalse)self.o_proj nn.Linear(self.hidden_size, self.hidden_size, biasFalse)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, dtypetorch.float32).to(query_states.dtype)attn_weights nn.functional.dropout(attn_weights, pself.attention_dropout, trainingself.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, biasFalse)# 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, biasFalse)self.v_proj nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, biasFalse)# 最后的输出映射和之前一样self.o_proj nn.Linear(self.hidden_size, self.hidden_size, biasFalse)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, dtypetorch.float32).to(query_states.dtype)attn_weights nn.functional.dropout(attn_weights, pself.attention_dropout, trainingself.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, dtypetorch.int64).float() / self.dim))self.register_buffer(inv_freq, inv_freq, persistentFalse)# 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, biasFalse)# 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, biasFalse)self.v_proj nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, biasFalse)# 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, biasFalse)# 定义了RotaryEmbedding实例self.rotary_emb RotaryEmbedding(self.head_dim,max_position_embeddingsself.max_position_embeddings,thetaself.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, dtypetorch.float32).to(query_states.dtype)attn_weights nn.functional.dropout(attn_weights, pself.attention_dropout, trainingself.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 a m , n exp ( q m T k n d ) ∑ j 1 N exp q m T k j d o m ∑ n 1 N a m , n v n \begin{aligned} a_{m,n} \frac{\exp(\frac{\pmb q^T_m \pmb k_n}{\sqrt d})}{\sum_{j1}^N \exp \frac{\pmb q^T_m \pmb k_j}{\sqrt d}} \\ \pmb o_m \sum_{n1}^N a_{m,n}\pmb v_n \\ \end{aligned} am,nooom∑j1Nexpd qqqmTkkkjexp(d qqqmTkkkn)n1∑Nam,nvvvn
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, dtypetorch.long)print(attention(inputs, position_idsposition_ids).shape)
torch.Size([32, 16, 512])参考 [论文翻译]GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints ↩︎ Fast Transformer Decoding: One Write-Head is All You Need ↩︎ Llama改进之——RoPE旋转位置编码 ↩︎ ↩︎ ↩︎ ↩︎
