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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| 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): | |
| super().__init__() | |
| 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) | |
| self.w_ks = nn.Linear(d_model, n_head * d_k) | |
| self.w_vs = nn.Linear(d_model, n_head * d_v) | |
| self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5)) | |
| self.layer_norm = nn.LayerNorm(d_model) | |
| self.fc = nn.Linear(n_head * d_v, d_model) | |
| 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, _ = q.size() | |
| sz_b, len_k, _ = k.size() | |
| sz_b, len_v, _ = v.size() | |
| residual = q | |
| 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) | |
| q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk | |
| k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk | |
| v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv | |
| mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x .. | |
| output, attn = self.attention(q, k, v, mask=mask) | |
| output = output.view(n_head, sz_b, len_q, d_v) | |
| output = ( | |
| output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) | |
| ) # b x lq x (n*dv) | |
| output = self.dropout(self.fc(output)) | |
| output = self.layer_norm(output + residual) | |
| return output, attn | |
| class PositionwiseFeedForward(nn.Module): | |
| """A two-feed-forward-layer module""" | |
| def __init__(self, d_in, d_hid, kernel_size, dropout=0.1): | |
| super().__init__() | |
| # Use Conv1D | |
| # position-wise | |
| self.w_1 = nn.Conv1d( | |
| d_in, | |
| d_hid, | |
| kernel_size=kernel_size[0], | |
| padding=(kernel_size[0] - 1) // 2, | |
| ) | |
| # position-wise | |
| self.w_2 = nn.Conv1d( | |
| d_hid, | |
| d_in, | |
| kernel_size=kernel_size[1], | |
| padding=(kernel_size[1] - 1) // 2, | |
| ) | |
| self.layer_norm = nn.LayerNorm(d_in) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| residual = x | |
| output = x.transpose(1, 2) | |
| output = self.w_2(F.relu(self.w_1(output))) | |
| output = output.transpose(1, 2) | |
| output = self.dropout(output) | |
| output = self.layer_norm(output + residual) | |
| return output | |