| 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=49216, |
| n_positions=1024, |
| n_embd=768, |
| n_layer=12, |
| n_head=12, |
| n_inner=None, |
| resid_pdrop=0.1, |
| embd_pdrop=0.1, |
| attn_pdrop=0.1, |
| layer_norm_epsilon=1e-5, |
| initializer_range=0.02, |
| scale_attn_weights=True, |
| use_cache=True, |
| bos_token_id=-1, |
| eos_token_id=0, |
| max_position_embeddings: int = 2048, |
| multi_query: bool = True, |
| attention_softmax_in_fp32=False, |
| scale_attention_softmax_in_fp32=False, |
| **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 = n_inner |
| self.resid_pdrop = resid_pdrop |
| self.embd_pdrop = embd_pdrop |
| self.attn_pdrop = attn_pdrop |
| self.layer_norm_epsilon = layer_norm_epsilon |
| self.initializer_range = initializer_range |
| self.scale_attn_weights = scale_attn_weights |
| 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.bos_token_id = bos_token_id |
| self.eos_token_id = eos_token_id |
|
|
| self.multi_query = multi_query |
| self.max_position_embeddings = max_position_embeddings |
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
|
|
