Spaces:
Running
Running
File size: 6,004 Bytes
0883aa1 |
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 |
# 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
import torch.nn as nn
import numpy as np
from .Layers import FFTBlock
from text.symbols import symbols
PAD = 0
UNK = 1
BOS = 2
EOS = 3
PAD_WORD = "<blank>"
UNK_WORD = "<unk>"
BOS_WORD = "<s>"
EOS_WORD = "</s>"
def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
"""Sinusoid position encoding table"""
def cal_angle(position, hid_idx):
return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)
def get_posi_angle_vec(position):
return [cal_angle(position, hid_j) for hid_j in range(d_hid)]
sinusoid_table = np.array(
[get_posi_angle_vec(pos_i) for pos_i in range(n_position)]
)
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
if padding_idx is not None:
# zero vector for padding dimension
sinusoid_table[padding_idx] = 0.0
return torch.FloatTensor(sinusoid_table)
class Encoder(nn.Module):
"""Encoder"""
def __init__(self, config):
super(Encoder, self).__init__()
n_position = config["max_seq_len"] + 1
n_src_vocab = len(symbols) + 1
d_word_vec = config["transformer"]["encoder_hidden"]
n_layers = config["transformer"]["encoder_layer"]
n_head = config["transformer"]["encoder_head"]
d_k = d_v = (
config["transformer"]["encoder_hidden"]
// config["transformer"]["encoder_head"]
)
d_model = config["transformer"]["encoder_hidden"]
d_inner = config["transformer"]["conv_filter_size"]
kernel_size = config["transformer"]["conv_kernel_size"]
dropout = config["transformer"]["encoder_dropout"]
self.max_seq_len = config["max_seq_len"]
self.d_model = d_model
self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=PAD)
self.position_enc = nn.Parameter(
get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0),
requires_grad=False,
)
self.layer_stack = nn.ModuleList(
[
FFTBlock(
d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout
)
for _ in range(n_layers)
]
)
def forward(self, src_seq, mask, return_attns=False):
enc_slf_attn_list = []
batch_size, max_len = src_seq.shape[0], src_seq.shape[1]
# -- Prepare masks
slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
# -- Forward
if not self.training and src_seq.shape[1] > self.max_seq_len:
enc_output = self.src_word_emb(src_seq) + get_sinusoid_encoding_table(
src_seq.shape[1], self.d_model
)[: src_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to(
src_seq.device
)
else:
enc_output = self.src_word_emb(src_seq) + self.position_enc[
:, :max_len, :
].expand(batch_size, -1, -1)
for enc_layer in self.layer_stack:
enc_output, enc_slf_attn = enc_layer(
enc_output, mask=mask, slf_attn_mask=slf_attn_mask
)
if return_attns:
enc_slf_attn_list += [enc_slf_attn]
return enc_output
class Decoder(nn.Module):
"""Decoder"""
def __init__(self, config):
super(Decoder, self).__init__()
n_position = config["max_seq_len"] + 1
d_word_vec = config["transformer"]["decoder_hidden"]
n_layers = config["transformer"]["decoder_layer"]
n_head = config["transformer"]["decoder_head"]
d_k = d_v = (
config["transformer"]["decoder_hidden"]
// config["transformer"]["decoder_head"]
)
d_model = config["transformer"]["decoder_hidden"]
d_inner = config["transformer"]["conv_filter_size"]
kernel_size = config["transformer"]["conv_kernel_size"]
dropout = config["transformer"]["decoder_dropout"]
self.max_seq_len = config["max_seq_len"]
self.d_model = d_model
self.position_enc = nn.Parameter(
get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0),
requires_grad=False,
)
self.layer_stack = nn.ModuleList(
[
FFTBlock(
d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout
)
for _ in range(n_layers)
]
)
def forward(self, enc_seq, mask, return_attns=False):
dec_slf_attn_list = []
batch_size, max_len = enc_seq.shape[0], enc_seq.shape[1]
# -- Forward
if not self.training and enc_seq.shape[1] > self.max_seq_len:
# -- Prepare masks
slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
dec_output = enc_seq + get_sinusoid_encoding_table(
enc_seq.shape[1], self.d_model
)[: enc_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to(
enc_seq.device
)
else:
max_len = min(max_len, self.max_seq_len)
# -- Prepare masks
slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
dec_output = enc_seq[:, :max_len, :] + self.position_enc[
:, :max_len, :
].expand(batch_size, -1, -1)
mask = mask[:, :max_len]
slf_attn_mask = slf_attn_mask[:, :, :max_len]
for dec_layer in self.layer_stack:
dec_output, dec_slf_attn = dec_layer(
dec_output, mask=mask, slf_attn_mask=slf_attn_mask
)
if return_attns:
dec_slf_attn_list += [dec_slf_attn]
return dec_output, mask
|