SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Add use_lang_emb to config

Browse Source
This commit is contained in:
Shijie Wu
2019-08-23 20:33:01 -04:00
parent e85123d398
commit f1b018740c
1 changed files with 6 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
pytorch_transformers/modeling_xlm.py
View File

@@ -114,6 +114,7 @@ class XLMConfig(PretrainedConfig):
causal=False,
asm=False,
n_langs=1,
use_lang_emb=True,
max_position_embeddings=512,
embed_init_std=2048 ** -0.5,
layer_norm_eps=1e-12,
@@ -157,6 +158,7 @@ class XLMConfig(PretrainedConfig):
self.causal = causal
self.asm = asm
self.n_langs = n_langs
self.use_lang_emb = use_lang_emb
self.layer_norm_eps = layer_norm_eps
self.bos_index = bos_index
self.eos_index = eos_index
@@ -488,7 +490,7 @@ class XLMModel(XLMPreTrainedModel):
"""
ATTRIBUTES = ['encoder', 'eos_index', 'pad_index', # 'with_output',
'n_langs', 'n_words', 'dim', 'n_layers', 'n_heads',
'n_langs', 'use_lang_emb', 'n_words', 'dim', 'n_layers', 'n_heads',
'hidden_dim', 'dropout', 'attention_dropout', 'asm',
'asm_cutoffs', 'asm_div_value']
@@ -507,6 +509,7 @@ class XLMModel(XLMPreTrainedModel):
# dictionary / languages
self.n_langs = config.n_langs
self.use_lang_emb = config.use_lang_emb
self.n_words = config.n_words
self.eos_index = config.eos_index
self.pad_index = config.pad_index
@@ -529,7 +532,7 @@ class XLMModel(XLMPreTrainedModel):
self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
if config.sinusoidal_embeddings:
create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
if config.n_langs > 1:
if config.n_langs > 1 and config.use_lang_emb:
self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)
self.layer_norm_emb = nn.LayerNorm(self.dim, eps=config.layer_norm_eps)
@@ -628,7 +631,7 @@ class XLMModel(XLMPreTrainedModel):
# embeddings
tensor = self.embeddings(input_ids)
tensor = tensor + self.position_embeddings(position_ids).expand_as(tensor)
if langs is not None:
if langs is not None and self.use_lang_emb:
tensor = tensor + self.lang_embeddings(langs)
if token_type_ids is not None:
tensor = tensor + self.embeddings(token_type_ids)