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