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Support various BERT relative position embeddings (2nd) (#8276)

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* Support BERT relative position embeddings

* Fix typo in README.md

* Address review comment

* Fix failing tests

* [tiny] Fix style_doc.py check by adding an empty line to configuration_bert.py

* make fix copies

* fix configs of electra and albert and fix longformer

* remove copy statement from longformer

* fix albert

* fix electra

* Add bert variants forward tests for various position embeddings

* [tiny] Fix style for test_modeling_bert.py

* improve docstring

* [tiny] improve docstring and remove unnecessary dependency

* [tiny] Remove unused import

* re-add to ALBERT

* make embeddings work for ALBERT

* add test for albert

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
zhiheng-huang
2020-11-24 05:40:53 -08:00
committed by GitHub
parent 9e71aa2f8f
commit 2c83b3c38d
16 changed files with 327 additions and 33 deletions
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28
src/transformers/models/roberta/modeling_roberta.py
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@@ -83,6 +83,7 @@ class RobertaEmbeddings(nn.Module):
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
self.position_embedding_type = config.position_embedding_type
# End copy
self.padding_idx = config.pad_token_id
@@ -114,10 +115,12 @@ class RobertaEmbeddings(nn.Module):
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
@@ -159,6 +162,10 @@ class RobertaSelfAttention(nn.Module):
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = config.position_embedding_type
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
@@ -193,6 +200,23 @@ class RobertaSelfAttention(nn.Module):
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)