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# coding=utf-8
"""Mitre model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MitreConfig(PretrainedConfig):
model_type = "mitre"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=160025,
max_position_embeddings=256,
decoder_layers=24,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
use_cache=True,
is_encoder_decoder=False,
activation_function="relu",
d_model=1024,
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=2,
scale_embedding=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.use_cache = use_cache
self.num_hidden_layers = decoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.is_decoder = True
self.is_encoder_decoder = False
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
)
MitreConfig.register_for_auto_class("AutoConfig") |