| |
|
|
| import math |
| from typing import Dict, Optional |
|
|
| from transformers.configuration_utils import PretrainedConfig |
|
|
|
|
| class SambaConfig(PretrainedConfig): |
|
|
| model_type = "samba" |
|
|
| def __init__( |
| self, |
| hidden_size: int = 2304, |
| state_size: int = 16, |
| num_hidden_layers: int = 18, |
| norm_eps=1e-5, |
| pad_token_id: int = 0, |
| bos_token_id: int = 1, |
| eos_token_id: int = 2, |
| expand: int = 2, |
| conv_kernel: int = 4, |
| use_bias: bool = False, |
| use_conv_bias: bool = True, |
| hidden_act: str = "swish", |
| initializer_range: str = 0.02, |
| residual_in_fp32: bool = False, |
| time_step_rank: str = "auto", |
| time_step_scale: float = 1.0, |
| time_step_min: float = 0.001, |
| time_step_max: float = 0.1, |
| time_step_init_scheme: str = "random", |
| time_step_floor: float = 1e-4, |
| max_position_embeddings: int = 2048, |
| attn: Optional[Dict] = { |
| 'layers': (1, 3, 5, 7, 9, 11, 13, 15, 17), |
| 'num_heads': 18, |
| 'num_kv_heads': 18, |
| 'qkv_bias': False, |
| 'window_size': 2048, |
| 'rope_theta': 10000. |
| }, |
| hidden_ratio: Optional[int] = 4, |
| rescale_prenorm_residual: bool = False, |
| use_cache: bool = True, |
| fuse_norm: bool = True, |
| fuse_swiglu: bool = True, |
| fuse_cross_entropy: bool = True, |
| vocab_size: int = 32000, |
| tie_word_embeddings: bool = False, |
| **kwargs, |
| ): |
| self.hidden_size = hidden_size |
| self.state_size = state_size |
| self.num_hidden_layers = num_hidden_layers |
| self.norm_eps = norm_eps |
| self.conv_kernel = conv_kernel |
| self.expand = expand |
| self.intermediate_size = int(expand * self.hidden_size) |
| self.bos_token_id = bos_token_id |
| self.eos_token_id = eos_token_id |
| self.pad_token_id = pad_token_id |
| self.use_bias = use_bias |
| self.use_conv_bias = use_conv_bias |
| self.hidden_act = hidden_act |
| self.initializer_range = initializer_range |
| self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank |
| self.time_step_scale = time_step_scale |
| self.time_step_min = time_step_min |
| self.time_step_max = time_step_max |
| self.time_step_init_scheme = time_step_init_scheme |
| self.time_step_floor = time_step_floor |
| self.max_position_embeddings = max_position_embeddings |
| self.attn = attn |
| self.hidden_ratio = hidden_ratio |
| self.rescale_prenorm_residual = rescale_prenorm_residual |
| self.residual_in_fp32 = residual_in_fp32 |
| self.use_cache = use_cache |
|
|
| self.fuse_norm = fuse_norm |
| self.fuse_swiglu = fuse_swiglu |
| self.fuse_cross_entropy = fuse_cross_entropy |
| self.vocab_size = vocab_size |
|
|
| super().__init__( |
| bos_token_id=bos_token_id, |
| eos_token_id=eos_token_id, |
| pad_token_id=pad_token_id, |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs |
| ) |
|
|
