/
pytorch_transformer.py
143 lines (97 loc) · 4.32 KB
/
pytorch_transformer.py
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
from __future__ import print_function
import torch
import os
import tqdm
import pdb
import numpy as np
import platform
import re
import argparse
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
def angle_defn(pos, i, d_model_size):
angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model_size))
return pos * angle_rates
def positional_encoding(position, d_model_size):
# create the sinusoidal pattern for the positional encoding
angle_rads = angle_defn(np.arange(position)[:, np.newaxis], np.arange(d_model_size)[np.newaxis, :], d_model_size)
sines = np.sin(angle_rads[:, 0::2])
cosines = np.cos(angle_rads[:, 1::2])
pos_encoding = torch.tensor(np.concatenate([sines, cosines], axis=-1)[np.newaxis, ...], dtype=torch.float)
return pos_encoding
def scaled_dot_product_attention(q, k, v, mask):
# calculate attention
matmul_qk = torch.matmul(q, k.permute(0,1,3,2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
scaled_attention_logits += (mask * -1e9)
attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
output = torch.matmul(attention_weights, v)
return output
class MultiHeadAttention(torch.nn.Module):
def __init__(self, d_model_size, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model_size = d_model_size
self.depth = int(d_model_size / self.num_heads)
self.Wq = torch.nn.Linear(d_model_size, d_model_size)
self.Wk = torch.nn.Linear(d_model_size, d_model_size)
self.Wv = torch.nn.Linear(d_model_size, d_model_size)
self.dense = torch.nn.Linear(d_model_size, d_model_size)
def split_into_heads(self, x, batch_size):
x = x.reshape(batch_size, -1, self.num_heads, self.depth)
return x.permute([0, 2, 1, 3])
def forward(self, v, k, q, mask):
batch_size = q.shape[0]
q = self.Wq(q)
k = self.Wk(k)
v = self.Wv(v)
q = self.split_into_heads(q, batch_size)
k = self.split_into_heads(k, batch_size)
v = self.split_into_heads(v, batch_size)
scaled_attention = scaled_dot_product_attention(q, k, v, mask).permute([0, 2, 1, 3])
original_size_attention = scaled_attention.reshape(batch_size, -1, self.d_model_size)
output = self.dense(original_size_attention)
return output
def point_wise_feed_forward_network(d_model_size, dff):
return torch.nn.Sequential(torch.nn.Linear(d_model_size, dff), torch.nn.ReLU(), torch.nn.Linear(dff, d_model_size))
class EncoderLayer(torch.nn.Module):
def __init__(self, d_model_size, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
self.ffn = point_wise_feed_forward_network(d_model_size, dff)
self.layernorm1 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.layernorm2 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.dropout1 = torch.nn.Dropout(rate)
self.dropout2 = torch.nn.Dropout(rate)
def forward(self, x, mask):
normed = self.layernorm1(x)
attn_output = self.multi_head_attention(normed, normed, normed, mask)
attn_output = self.dropout1(attn_output)
out1 = x + attn_output
out2 = self.layernorm2(out1)
ffn_output = self.ffn(out2)
ffn_output = self.dropout2(ffn_output)
out2 = out1 + ffn_output
return out2
class Encoder(torch.nn.Module):
def __init__(self, num_layers=48, d_model_size=1280, num_heads=16, dff=8192, input_vocab_size=50000,
rate=0.1, **kwargs):
super(Encoder, self).__init__()
self.d_model_size = d_model_size
self.num_layers = num_layers
self.pos_encoding = positional_encoding(input_vocab_size, self.d_model_size).to('cuda')
for i in range(num_layers):
setattr(self, "layer%i" % i, EncoderLayer(d_model_size, num_heads, dff, rate))
self.layernorm = torch.nn.LayerNorm(d_model_size, eps=1e-6)
self.dropout = torch.nn.Dropout(rate)
def forward(self, x):
seq_len = x.shape[1]
mask = torch.triu(torch.ones(seq_len, seq_len), 1).to('cuda')
x *= np.sqrt(self.d_model_size)
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x)
for i in range(self.num_layers):
x = getattr(self, "layer%i" % i)(x, mask)
return self.layernorm(x)