| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class GPTRefactConfig(PretrainedConfig): | |
| model_type = "gpt_refact" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| attribute_map = { | |
| "hidden_size": "n_embd", | |
| "max_position_embeddings": "n_positions", | |
| "num_attention_heads": "n_head", | |
| "num_hidden_layers": "n_layer", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size: int = 49216, | |
| n_positions: int = 4096, | |
| n_embd: int = 1024, | |
| n_layer: int = 32, | |
| n_head: int = 64, | |
| max_position_embeddings: int = 4096, | |
| multi_query: bool = True, | |
| layer_norm_epsilon: float = 1e-5, | |
| initializer_range: float = 0.02, | |
| use_cache: bool = True, | |
| eos_token_id: int = 0, | |
| attention_softmax_in_fp32: bool = True, | |
| scale_attention_softmax_in_fp32: bool = True, | |
| attention_bias_in_fp32: bool = True, | |
| torch_dtype: str = 'bfloat16', | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.n_positions = n_positions | |
| self.n_embd = n_embd | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_inner = None | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.initializer_range = initializer_range | |
| self.use_cache = use_cache | |
| self.attention_softmax_in_fp32 = attention_softmax_in_fp32 | |
| self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32 | |
| self.attention_bias_in_fp32 = attention_bias_in_fp32 | |
| self.multi_query = multi_query | |
| self.max_position_embeddings = max_position_embeddings | |
| self.torch_dtype = torch_dtype | |
| super().__init__(eos_token_id=eos_token_id, **kwargs) | |