Spaces:
Build error
Build error
File size: 7,290 Bytes
4304c2f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
# adapted from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/fastpitch/transformer.py
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from common import get_mask_from_lengths, LinearNorm
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super(PositionalEmbedding, self).__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
self.register_buffer("inv_freq", inv_freq)
def forward(self, pos_seq, bsz=None):
sinusoid_inp = torch.matmul(
torch.unsqueeze(pos_seq, -1), torch.unsqueeze(self.inv_freq, 0)
)
pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=1)
if bsz is not None:
return pos_emb[None, :, :].expand(bsz, -1, -1)
else:
return pos_emb[None, :, :]
class PositionwiseConvFF(nn.Module):
def __init__(self, d_model, d_inner, kernel_size, dropout, pre_lnorm=False):
super(PositionwiseConvFF, self).__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.CoreNet = nn.Sequential(
nn.Conv1d(d_model, d_inner, kernel_size, 1, (kernel_size // 2)),
nn.ReLU(),
# nn.Dropout(dropout), # worse convergence
nn.Conv1d(d_inner, d_model, kernel_size, 1, (kernel_size // 2)),
nn.Dropout(dropout),
)
self.layer_norm = nn.LayerNorm(d_model)
self.pre_lnorm = pre_lnorm
def forward(self, inp):
return self._forward(inp)
def _forward(self, inp):
if self.pre_lnorm:
# layer normalization + positionwise feed-forward
core_out = inp.transpose(1, 2)
core_out = self.CoreNet(self.layer_norm(core_out).to(inp.dtype))
core_out = core_out.transpose(1, 2)
# residual connection
output = core_out + inp
else:
# positionwise feed-forward
core_out = inp.transpose(1, 2)
core_out = self.CoreNet(core_out)
core_out = core_out.transpose(1, 2)
# residual connection + layer normalization
output = self.layer_norm(inp + core_out).to(inp.dtype)
return output
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1, pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head = d_head
self.scale = 1 / (d_head**0.5)
self.pre_lnorm = pre_lnorm
self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head)
self.drop = nn.Dropout(dropout)
self.dropatt = nn.Dropout(dropatt)
self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, inp, attn_mask=None):
return self._forward(inp, attn_mask)
def _forward(self, inp, attn_mask=None):
residual = inp
if self.pre_lnorm:
# layer normalization
inp = self.layer_norm(inp)
n_head, d_head = self.n_head, self.d_head
head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=2)
head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head)
head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head)
head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head)
q = head_q.permute(2, 0, 1, 3).reshape(-1, inp.size(1), d_head)
k = head_k.permute(2, 0, 1, 3).reshape(-1, inp.size(1), d_head)
v = head_v.permute(2, 0, 1, 3).reshape(-1, inp.size(1), d_head)
attn_score = torch.bmm(q, k.transpose(1, 2))
attn_score.mul_(self.scale)
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(1).to(attn_score.dtype)
attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1)
attn_score.masked_fill_(attn_mask.to(torch.bool), -float("inf"))
attn_prob = F.softmax(attn_score, dim=2)
attn_prob = self.dropatt(attn_prob)
attn_vec = torch.bmm(attn_prob, v)
attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head)
attn_vec = (
attn_vec.permute(1, 2, 0, 3)
.contiguous()
.view(inp.size(0), inp.size(1), n_head * d_head)
)
# linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
# residual connection + layer normalization
output = self.layer_norm(residual + attn_out)
output = output.to(attn_out.dtype)
return output
class TransformerLayer(nn.Module):
def __init__(
self, n_head, d_model, d_head, d_inner, kernel_size, dropout, **kwargs
):
super(TransformerLayer, self).__init__()
self.dec_attn = MultiHeadAttn(n_head, d_model, d_head, dropout, **kwargs)
self.pos_ff = PositionwiseConvFF(d_model, d_inner, kernel_size, dropout)
def forward(self, dec_inp, mask=None):
output = self.dec_attn(dec_inp, attn_mask=~mask.squeeze(2))
output *= mask
output = self.pos_ff(output)
output *= mask
return output
class FFTransformer(nn.Module):
def __init__(
self,
in_dim,
out_dim=1,
n_layers=6,
n_head=1,
d_head=64,
d_inner=1024,
kernel_size=3,
dropout=0.1,
dropatt=0.1,
dropemb=0.0,
):
super(FFTransformer, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.n_head = n_head
self.d_head = d_head
self.pos_emb = PositionalEmbedding(self.in_dim)
self.drop = nn.Dropout(dropemb)
self.layers = nn.ModuleList()
for _ in range(n_layers):
self.layers.append(
TransformerLayer(
n_head,
in_dim,
d_head,
d_inner,
kernel_size,
dropout,
dropatt=dropatt,
)
)
self.dense = LinearNorm(in_dim, out_dim)
def forward(self, dec_inp, in_lens):
# B, C, T --> B, T, C
inp = dec_inp.transpose(1, 2)
mask = get_mask_from_lengths(in_lens)[..., None]
pos_seq = torch.arange(inp.size(1), device=inp.device).to(inp.dtype)
pos_emb = self.pos_emb(pos_seq) * mask
out = self.drop(inp + pos_emb)
for layer in self.layers:
out = layer(out, mask=mask)
out = self.dense(out).transpose(1, 2)
return out
|