mirror of
https://github.com/wassname/pytorch-transformer-ts.git
synced 2026-08-11 11:24:32 +08:00
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from .Modules import ScaledDotProductAttention
|
|
|
|
|
|
class MultiHeadAttention(nn.Module):
|
|
""" Multi-Head Attention module """
|
|
|
|
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1, normalize_before=True):
|
|
super().__init__()
|
|
|
|
self.normalize_before = normalize_before
|
|
self.n_head = n_head
|
|
self.d_k = d_k
|
|
self.d_v = d_v
|
|
|
|
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
|
|
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
|
|
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
|
|
nn.init.xavier_uniform_(self.w_qs.weight)
|
|
nn.init.xavier_uniform_(self.w_ks.weight)
|
|
nn.init.xavier_uniform_(self.w_vs.weight)
|
|
|
|
self.fc = nn.Linear(d_v * n_head, d_model)
|
|
nn.init.xavier_uniform_(self.fc.weight)
|
|
|
|
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5, attn_dropout=dropout)
|
|
|
|
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
def forward(self, q, k, v, mask=None):
|
|
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
|
|
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
|
|
|
|
residual = q
|
|
if self.normalize_before:
|
|
q = self.layer_norm(q)
|
|
|
|
# Pass through the pre-attention projection: b x lq x (n*dv)
|
|
# Separate different heads: b x lq x n x dv
|
|
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
|
|
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
|
|
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
|
|
|
|
# Transpose for attention dot product: b x n x lq x dv
|
|
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
|
|
|
|
if mask is not None:
|
|
if len(mask.size()) == 3:
|
|
mask = mask.unsqueeze(1) # For head axis broadcasting.
|
|
|
|
output, attn = self.attention(q, k, v, mask=mask)
|
|
|
|
# Transpose to move the head dimension back: b x lq x n x dv
|
|
# Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
|
|
output = output.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
|
|
output = self.dropout(self.fc(output))
|
|
output += residual
|
|
|
|
if not self.normalize_before:
|
|
output = self.layer_norm(output)
|
|
return output, attn
|
|
|
|
|
|
class PositionwiseFeedForward(nn.Module):
|
|
""" Two-layer position-wise feed-forward neural network. """
|
|
|
|
def __init__(self, d_in, d_hid, dropout=0.1, normalize_before=True):
|
|
super().__init__()
|
|
|
|
self.normalize_before = normalize_before
|
|
|
|
self.w_1 = nn.Linear(d_in, d_hid)
|
|
self.w_2 = nn.Linear(d_hid, d_in)
|
|
|
|
self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
|
|
#self.layer_norm = GraphNorm(d_in)
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
def forward(self, x):
|
|
residual = x
|
|
if self.normalize_before:
|
|
x = self.layer_norm(x)
|
|
|
|
x = F.gelu(self.w_1(x))
|
|
x = self.dropout(x)
|
|
x = self.w_2(x)
|
|
x = self.dropout(x)
|
|
x = x + residual
|
|
|
|
if not self.normalize_before:
|
|
x = self.layer_norm(x)
|
|
return x
|
|
|