98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from .Modules import ScaledDotProductAttention
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class MultiHeadAttention(nn.Module):
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"""Multi-Head Attention module"""
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def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
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super().__init__()
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self.n_head = n_head
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self.d_k = d_k
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self.d_v = d_v
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self.w_qs = nn.Linear(d_model, n_head * d_k)
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self.w_ks = nn.Linear(d_model, n_head * d_k)
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self.w_vs = nn.Linear(d_model, n_head * d_v)
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self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
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self.layer_norm = nn.LayerNorm(d_model)
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self.fc = nn.Linear(n_head * d_v, d_model)
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self.dropout = nn.Dropout(dropout)
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def forward(self, q, k, v, mask=None):
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d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
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sz_b, len_q, _ = q.size()
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sz_b, len_k, _ = k.size()
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sz_b, len_v, _ = v.size()
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residual = q
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q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
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k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
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v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
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q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
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k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
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v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
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mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
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output, attn = self.attention(q, k, v, mask=mask)
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output = output.view(n_head, sz_b, len_q, d_v)
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output = (
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output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1)
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) # b x lq x (n*dv)
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output = self.dropout(self.fc(output))
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output = self.layer_norm(output + residual)
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return output, attn
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class PositionwiseFeedForward(nn.Module):
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"""A two-feed-forward-layer module"""
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def __init__(self, d_in, d_hid, kernel_size, dropout=0.1):
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super().__init__()
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# Use Conv1D
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# position-wise
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self.w_1 = nn.Conv1d(
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d_in,
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d_hid,
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kernel_size=kernel_size[0],
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padding=(kernel_size[0] - 1) // 2,
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)
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# position-wise
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self.w_2 = nn.Conv1d(
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d_hid,
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d_in,
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kernel_size=kernel_size[1],
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padding=(kernel_size[1] - 1) // 2,
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)
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self.layer_norm = nn.LayerNorm(d_in)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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residual = x
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output = x.transpose(1, 2)
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output = self.w_2(F.relu(self.w_1(output)))
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output = output.transpose(1, 2)
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output = self.dropout(output)
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output = self.layer_norm(output + residual)
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return output
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