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optimize attention calc logic (#2010)

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Co-authored-by: wangzeyuan <wangzeyuan@agora.io>
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wzy3650 2025-02-12 11:57:17 +08:00 committed by GitHub
parent ca3cc4997a
commit 282ae1d9b2
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1 changed files with 15 additions and 40 deletions
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GPT_SoVITS/AR/models/t2s_model.py
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@ -145,45 +145,21 @@ class T2SBlock:
else: else:
attn = scaled_dot_product_attention(q, k, v, attn_mask) attn = scaled_dot_product_attention(q, k, v, attn_mask)
attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim) attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b) attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)
if padding_mask is not None: x = x + attn
for i in range(batch_size): x = F.layer_norm(
# mask = padding_mask[i,:,0] x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
if self.false.device!= padding_mask.device: )
self.false = self.false.to(padding_mask.device) x = x + self.mlp.forward(x)
idx = torch.where(padding_mask[i,:,0]==self.false)[0] x = F.layer_norm(
x_item = x[i,idx,:].unsqueeze(0) x,
attn_item = attn[i,idx,:].unsqueeze(0) [self.hidden_dim],
x_item = x_item + attn_item self.norm_w2,
x_item = F.layer_norm( self.norm_b2,
x_item, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1 self.norm_eps2,
) )
x_item = x_item + self.mlp.forward(x_item)
x_item = F.layer_norm(
x_item,
[self.hidden_dim],
self.norm_w2,
self.norm_b2,
self.norm_eps2,
)
x[i,idx,:] = x_item.squeeze(0)
x = self.to_mask(x, padding_mask)
else:
x = x + attn
x = F.layer_norm(
x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
)
x = x + self.mlp.forward(x)
x = F.layer_norm(
x,
[self.hidden_dim],
self.norm_w2,
self.norm_b2,
self.norm_eps2,
)
return x, k_cache, v_cache return x, k_cache, v_cache
def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor, attn_mask:Optional[torch.Tensor]=None, torch_sdpa:bool=True): def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor, attn_mask:Optional[torch.Tensor]=None, torch_sdpa:bool=True):
@ -206,8 +182,7 @@ class T2SBlock:
else: else:
attn = scaled_dot_product_attention(q, k, v, attn_mask) attn = scaled_dot_product_attention(q, k, v, attn_mask)
attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim) attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
attn = F.linear(attn, self.out_w, self.out_b) attn = F.linear(attn, self.out_w, self.out_b)
x = x + attn x = x + attn
@ -662,7 +637,7 @@ class Text2SemanticDecoder(nn.Module):
xy_attn_mask = xy_mask.logical_or(_xy_padding_mask) xy_attn_mask = xy_mask.logical_or(_xy_padding_mask)
xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1) xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1)
xy_attn_mask = xy_attn_mask.bool() xy_attn_mask = xy_attn_mask.bool()
xy_padding_mask = xy_padding_mask.view(bsz, src_len, 1).expand(-1, -1, self.model_dim) xy_padding_mask = xy_padding_mask.view(bsz, src_len, 1)
###### decode ##### ###### decode #####
y_list = [None]*y.shape[0] y_list = [None]*y.shape[0]