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a7532fa | 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 | import torch
import torch.nn.functional as F
import torch.nn as nn
import math
embedding = 256
heads = 4
layers = 4
dropout = 0.1
msl = 160
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:x.size(1), :].transpose(0, 1)
class CausalSelfAttention(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super().__init__()
assert d_model % nhead == 0
self.nhead = nhead
self.head_dim = d_model // nhead
self.dropout = dropout
self.qkv = nn.Linear(d_model, d_model * 3)
self.out_proj = nn.Linear(d_model, d_model)
def forward(self, x):
B, T, C = x.shape
# Create Q, K, V
q, k, v = self.qkv(x).chunk(3, dim=-1)
# [B, T, C] -> [B, heads, T, head_dim]
q = q.view(B, T, self.nhead, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.nhead, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.nhead, self.head_dim).transpose(1, 2)
y = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
dropout_p=self.dropout if self.training else 0.0,
is_causal=True
)
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(y)
class TransformerBlock(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super().__init__()
self.norm1 = nn.LayerNorm(d_model)
self.attention = CausalSelfAttention(
d_model,
nhead,
dropout
)
self.norm2 = nn.LayerNorm(d_model)
self.ffn = nn.Sequential(
nn.Linear(d_model, d_model * 4),
nn.GELU(),
nn.Linear(d_model * 4, d_model),
nn.Dropout(dropout)
)
def forward(self, x):
x = x + self.attention(self.norm1(x))
x = x + self.ffn(self.norm2(x))
return x
class TransformerLanguageModel(nn.Module):
def __init__(
self,
vocab_size,
d_model=512,
nhead=8,
num_layers=8,
dropout=0.1,
max_seq_len=160
):
super().__init__()
self.d_model = d_model
self.max_seq_len = max_seq_len
self.token_embedding = nn.Embedding(
vocab_size,
d_model
)
self.positional_encoding = PositionalEncoding(
d_model,
max_seq_len
)
self.transformer = nn.ModuleList([
TransformerBlock(
d_model,
nhead,
dropout
)
for _ in range(num_layers)
])
self.final_norm = nn.LayerNorm(d_model)
self.output_layer = nn.Linear(
d_model,
vocab_size,
bias=False
)
def forward(self, src):
x = self.token_embedding(src)
x = self.positional_encoding(x)
for layer in self.transformer:
x = layer(x)
x = self.final_norm(x)
return self.output_layer(x) |