| from transformers import PretrainedConfig | |
| class FlashSTUConfig(PretrainedConfig): | |
| model_type = "FlashSTU" | |
| def __init__( | |
| self, | |
| bsz: int = 8, | |
| n_embd: int = 768, | |
| n_heads: int = 12, | |
| n_layers: int = 12, | |
| seq_len: int = 4096, | |
| window_size: int = 64, | |
| vocab_size: int = 200064, | |
| mlp_scale: int = 4, | |
| bias: bool = False, | |
| dropout: float = 0.0, | |
| num_eigh: int = 16, | |
| use_hankel_L: bool = False, | |
| use_flash_fft: bool = True, | |
| use_approx: bool = True, | |
| softcap: float = 50.0, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.bsz = bsz | |
| self.n_embd = n_embd | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.seq_len = seq_len | |
| self.window_size = window_size | |
| self.vocab_size = vocab_size | |
| self.mlp_scale = mlp_scale | |
| self.bias = bias | |
| self.dropout = dropout | |
| self.num_eigh = num_eigh | |
| self.use_hankel_L = use_hankel_L | |
| self.use_flash_fft = use_flash_fft | |
| self.use_approx = use_approx | |
| self.softcap = softcap | |