from transformers.utils import logging from transformers.configuration_utils import PretrainedConfig logger = logging.get_logger(__name__) class OpenBAConfig(PretrainedConfig): model_type = "openba" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "hidden_size", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers" } def __init__( self, vocab_size=32128, hidden_size=512, kv_channels=64, ffn_hidden_size=2048, num_layers=12, num_decoder_layers=None, hidden_dropout=0.1, attention_dropout=0.1, num_heads=8, is_encoder_decoder=True, use_cache=True, initializer_factor=1.0, pad_token_id=0, eos_token_id=1, decoder_start_token_id=0, add_qkv_bias=False, add_ffn_bias=False, add_lm_head_bias=False, max_seq_length=1024, decoder_max_seq_length=256, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.kv_channels = kv_channels self.ffn_hidden_size = ffn_hidden_size self.num_layers = num_layers self.num_decoder_layers = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.initializer_factor = initializer_factor self.num_heads = num_heads self.add_qkv_bias = add_qkv_bias self.add_ffn_bias = add_ffn_bias self.add_lm_head_bias = add_lm_head_bias self.max_seq_length = max_seq_length self.decoder_max_seq_length = decoder_max_seq_length self.use_cache = use_cache super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, is_encoder_decoder=is_encoder_decoder, **kwargs, )