flash attention的调用
时间: 2024-12-31 21:16:23 浏览: 6
### 调用Flash Attention实现
在现代深度学习框架中,调用Flash Attention已经变得更为简便。对于PyTorch 2及以上版本,可以直接利用内置函数`torch.nn.functional.scaled_dot_product_attention()`来应用缩放点积注意力机制[^2]。
下面是一个具体的Python代码片段展示如何在一个简单的多头自注意层中集成并使用Flash Attention:
```python
import torch
from torch import nn
class MultiHeadAttention(nn.Module):
def __init__(self, embed_dim=512, num_heads=8, dropout=0.1, bias=True):
super(MultiHeadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.qkv_proj = nn.Linear(embed_dim, 3 * embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.dropout_p = dropout
def _shape(self, tensor, seq_len, batch_size):
return tensor.view(batch_size, seq_len, self.num_heads, -1).transpose(1, 2).contiguous()
def forward(self, query, key, value, attn_mask=None, position_ids=None):
batch_size, tgt_len, src_len = query.size(0), query.size(1), key.size(1)
qkv_same = torch.equal(query, key) and torch.equal(key, value)
if qkv_same:
# Self-attention
qkv = self.qkv_proj(query).chunk(3, dim=-1)
q, k, v = map(lambda t: self._shape(t, src_len, batch_size), qkv)
else:
raise NotImplementedError("Cross-attention not implemented")
if position_ids is not None:
# Pass additional positional information to Flash Attention implementation.
pass
output = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_mask,
dropout_p=self.dropout_p if self.training else 0.,
is_causal=False
)
output = output.transpose(1, 2).reshape(batch_size, tgt_len, self.embed_dim)
output = self.out_proj(output)
return output
```
此段代码定义了一个名为`MultiHeadAttention`的类,它继承自`nn.Module`。该模块实现了带有Flash Attention特性的多头自我关注机制。特别值得注意的是,在前向传播过程中,通过调用`scaled_dot_product_attention`方法执行实际的关注计算,并允许传递额外的位置ID给Flash Attention组件以增强其性能表现[^3]。
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