Example of self-attention model
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import torch | |
import torch.nn as nn | |
class SelfAttention(nn.Module): | |
def __init__(self, embed_size, heads): | |
super(SelfAttention, self).__init__() | |
self.embed_size = embed_size | |
self.heads = heads | |
self.head_dim = embed_size // heads | |
assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by number of heads" | |
self.values = nn.Linear(self.head_dim, self.head_dim, bias=False) | |
self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False) | |
self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False) | |
self.fc_out = nn.Linear(heads * self.head_dim, embed_size) | |
def forward(self, values, keys, query, mask): | |
N = query.shape[0] | |
value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1] | |
# Split embedding into self.heads pieces | |
values = values.reshape(N, value_len, self.heads, self.head_dim) | |
keys = keys.reshape(N, key_len, self.heads, self.head_dim) | |
query = query.reshape(N, query_len, self.heads, self.head_dim) | |
values = self.values(values) | |
keys = self.keys(keys) | |
queries = self.queries(query) | |
energy = torch.einsum("nqhd, nkhd -> nhqk", [queries, keys]) | |
if mask is not None: | |
energy = energy.masked_fill(mask == 0, float("-1e20")) | |
attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3) | |
out = torch.einsum("nhql, nlhd -> nqhd", [attention, values]).reshape(N, query_len, self.heads * self.head_dim) | |
out = self.fc_out(out) | |
return out | |
class TransformerBlock(nn.Module): | |
def __init__(self, embed_size, heads, dropout, forward_expansion): | |
super(TransformerBlock, self).__init__() | |
self.attention = SelfAttention(embed_size, heads) | |
self.norm1 = nn.LayerNorm(embed_size) | |
self.norm2 = nn.LayerNorm(embed_size) | |
self.feed_forward = nn.Sequential( | |
nn.Linear(embed_size, forward_expansion * embed_size), | |
nn.ReLU(), | |
nn.Linear(forward_expansion * embed_size, embed_size) | |
) | |
self.dropout = nn.Dropout(dropout) | |
def forward(self, value, key, query, mask): | |
attention = self.attention(value, key, query, mask) | |
x = self.dropout(self.norm1(attention + query)) | |
forward = self.feed_forward(x) | |
out = self.dropout(self.norm2(forward + x)) | |
return out | |
class Encoder(nn.Module): | |
def __init__(self, src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length): | |
super(Encoder, self).__init__() | |
self.embed_size = embed_size | |
self.device = device | |
self.word_embedding = nn.Embedding(src_vocab_size, embed_size) | |
self.position_embedding = nn.Embedding(max_length, embed_size) | |
self.layers = nn.ModuleList([TransformerBlock(embed_size, heads, dropout, forward_expansion) for _ in range(num_layers)]) | |
self.dropout = nn.Dropout(dropout) | |
def forward(self, x, mask): | |
# x est une séquence de seq_length mots, correspondant à des vecteurs creux d'un vocabulaire de N mots. | |
N, seq_length = x.shape | |
positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device) | |
out = self.dropout(self.word_embedding(x) + self.position_embedding(positions)) | |
for layer in self.layers: | |
out = layer(out, out, out, mask) | |
return out |
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