| 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 |
| ) |
| 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, |
| ) |
