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multiplied token embeddings by sqrt of embedding dimension #2661

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multiplied token embeddings by sqrt of embedding dimension #2661

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10 changes: 8 additions & 2 deletions chapter_natural-language-processing-pretraining/bert.md
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,7 @@ class BERTEncoder(nn.Block):
def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads,
num_blks, dropout, max_len=1000, **kwargs):
super(BERTEncoder, self).__init__(**kwargs)
self.num_hiddens = num_hiddens
self.token_embedding = nn.Embedding(vocab_size, num_hiddens)
self.segment_embedding = nn.Embedding(2, num_hiddens)
self.blks = nn.Sequential()
Expand All @@ -210,7 +211,9 @@ class BERTEncoder(nn.Block):
def forward(self, tokens, segments, valid_lens):
# Shape of `X` remains unchanged in the following code snippet:
# (batch size, max sequence length, `num_hiddens`)
X = self.token_embedding(tokens) + self.segment_embedding(segments)
# the embedding values are multiplied by the square root of the embedding dimension
# to rescale before they are summed up
X = self.token_embedding(tokens)*math.sqrt(self.num_hiddens) + self.segment_embedding(segments)
X = X + self.pos_embedding.data(ctx=X.ctx)[:, :X.shape[1], :]
for blk in self.blks:
X = blk(X, valid_lens)
Expand All @@ -225,6 +228,7 @@ class BERTEncoder(nn.Module):
def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads,
num_blks, dropout, max_len=1000, **kwargs):
super(BERTEncoder, self).__init__(**kwargs)
self.num_hiddens = num_hiddens
self.token_embedding = nn.Embedding(vocab_size, num_hiddens)
self.segment_embedding = nn.Embedding(2, num_hiddens)
self.blks = nn.Sequential()
Expand All @@ -239,7 +243,9 @@ class BERTEncoder(nn.Module):
def forward(self, tokens, segments, valid_lens):
# Shape of `X` remains unchanged in the following code snippet:
# (batch size, max sequence length, `num_hiddens`)
X = self.token_embedding(tokens) + self.segment_embedding(segments)
# the embedding values are multiplied by the square root of the embedding dimension
# to rescale before they are summed up
X = self.token_embedding(tokens)*math.sqrt(self.num_hiddens) + self.segment_embedding(segments)
X = X + self.pos_embedding[:, :X.shape[1], :]
for blk in self.blks:
X = blk(X, valid_lens)
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