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79caa33 | 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 | from torch import nn
import torch
import math
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super().__init__()
assert d_model % num_heads == 0, \
"d_model must be divisible by num_heads"
self.d_model = d_model
self.num_heads = num_heads
self.d_head = d_model // num_heads
self.q_proj = nn.Linear(d_model, d_model)
self.k_proj = nn.Linear(d_model, d_model)
self.v_proj = nn.Linear(d_model, d_model)
self.o_proj = nn.Linear(d_model, d_model)
def forward(self, X):
Q = self.q_proj(X)
K = self.k_proj(X)
V = self.v_proj(X)
batch, sequence, _ = X.shape
Q = Q.reshape(
batch,
sequence,
self.num_heads,
self.d_head
)
K = K.reshape(
batch,
sequence,
self.num_heads,
self.d_head
)
V = V.reshape(
batch,
sequence,
self.num_heads,
self.d_head
)
Q = Q.transpose(1, 2)
K = K.transpose(1, 2)
V = V.transpose(1, 2)
scores = torch.matmul(
Q,
K.transpose(-2, -1)
)
scaled_scores = scores / math.sqrt(self.d_head)
mask = torch.tril(
torch.ones(
sequence,
sequence,
device=X.device,
dtype=torch.bool
)
)
masked_scores = scaled_scores.masked_fill(
~mask,
-math.inf
)
attention_weights = torch.softmax(
masked_scores,
dim=-1
)
attention_output = torch.matmul(
attention_weights,
V
)
attention_output = attention_output.transpose(1, 2)
attention_output = attention_output.reshape(
batch,
sequence,
self.d_model
)
attention_output = self.o_proj(attention_output)
return attention_output
class FeedForwardNetwork(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.linear1 = nn.Linear(d_model, d_ff)
self.linear2 = nn.Linear(d_ff, d_model)
def forward(self, x):
x = self.linear1(x)
x = torch.relu(x)
x = self.linear2(x)
return x
class TransformerBlock(nn.Module):
def __init__(self, d_model, num_heads, d_ff):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.attention = MultiHeadAttention(
d_model,
num_heads
)
self.ln2 = nn.LayerNorm(d_model)
self.ffn = FeedForwardNetwork(
d_model,
d_ff
)
def forward(self, x):
x_norm = self.ln1(x)
x = x + self.attention(x_norm)
x_norm = self.ln2(x)
x = x + self.ffn(x_norm)
return x
class Transformer(nn.Module):
def __init__(
self,
vocab_size,
max_sequence_length,
d_model,
num_heads,
d_ff,
num_layers
):
super().__init__()
self.vocab_size = vocab_size
self.max_sequence_length = max_sequence_length
self.d_model = d_model
self.token_embedding = nn.Embedding(
vocab_size,
d_model
)
self.position_embedding = nn.Embedding(
max_sequence_length,
d_model
)
self.blocks = nn.ModuleList([
TransformerBlock(
d_model,
num_heads,
d_ff
)
for _ in range(num_layers)
])
self.final_ln = nn.LayerNorm(d_model)
self.lm_head = nn.Linear(
d_model,
vocab_size
)
def forward(self, x):
_, sequence = x.shape
assert sequence <= self.max_sequence_length, \
"Sequence length exceeds max_sequence_length"
token_embeddings = self.token_embedding(x)
positions = torch.arange(
sequence,
device=x.device
)
position_embeddings = self.position_embedding(
positions
)
x = token_embeddings + position_embeddings
for block in self.blocks:
x = block(x)
x = self.final_ln(x)
logits = self.lm_head(x)
return logits |