| import math | |
| from typing import Optional , Union | |
| from transformers import PretrainedConfig | |
| class MambaConfig(PretrainedConfig): | |
| model_type = "mamba" | |
| def __init__( | |
| self, | |
| vocab_size=50277, | |
| d_state=16, | |
| d_model=2560, | |
| d_conv=4, | |
| expand=2, | |
| conv_bias=True, | |
| bias=False, | |
| n_layer=64, | |
| dt_rank: Union[int, str] = "auto", | |
| pad_vocab_size_multiple=8, | |
| initializer_range=0.02, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.n_layer= n_layer | |
| self.conv_bias = conv_bias | |
| self.expand = expand | |
| self.pad_vocab_size_multiple = pad_vocab_size_multiple | |
| self.d_conv = d_conv | |
| self.d_model = d_model | |
| self.d_state = d_state | |
| self.d_inner = int(self.expand * self.d_model) | |
| self.dt_rank = dt_rank | |
| self.initializer_range = initializer_range | |
| self.bias = bias | |
| if self.dt_rank == 'auto': | |
| self.dt_rank = math.ceil(self.d_model / 16) | |
| if self.vocab_size % self.pad_vocab_size_multiple != 0: | |
| self.vocab_size += (self.pad_vocab_size_multiple | |
| - self.vocab_size % self.pad_vocab_size_multiple) | |
| super().__init__( | |
| **kwargs, | |
| ) |