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from transformers import PretrainedConfig
class HLMEncoderConfig(PretrainedConfig):
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_dropout_prob=0.1,
layer_norm_eps=1e-7,
sandwich=False,
sandwich_size=0,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout_prob = hidden_dropout_prob
self.layer_norm_eps = layer_norm_eps
if sandwich:
self.sandwich_size = num_hidden_layers // 6
else:
self.sandwich_size = sandwich_size
class HLMConfig(PretrainedConfig):
model_type = "hlm"
def __init__(
self,
vocab_size=512,
type_vocab_size=2,
embedding_size=-1,
max_seq_length=256,
max_word_length=16,
initializer_range=0.02,
pad_token_id=0,
intra_word_encoder={},
inter_word_encoder={},
residual_word_embedding=False,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.type_vocab_size = type_vocab_size
self.embedding_size = embedding_size
self.initializer_range = initializer_range
self.max_seq_length = max_seq_length
self.max_word_length = max_word_length
self.pad_token_id = pad_token_id
self.intra_word_encoder = HLMEncoderConfig(**intra_word_encoder)
self.inter_word_encoder = HLMEncoderConfig(**inter_word_encoder)
self.hidden_size = self.inter_word_encoder.hidden_size
self.residual_word_embedding = residual_word_embedding
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