| from typing import List |
| from transformers import PretrainedConfig |
|
|
|
|
| class TimeMoeConfig(PretrainedConfig): |
| model_type = "time_moe" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| input_size: int = 1, |
| hidden_size: int = 4096, |
| intermediate_size: int = 22016, |
| horizon_lengths: List[int] = 1, |
| num_hidden_layers: int = 32, |
| num_attention_heads: int = 32, |
| num_key_value_heads: int = None, |
| hidden_act: str = "silu", |
| num_experts_per_tok: int = 2, |
| num_experts: int = 1, |
| max_position_embeddings: int = 32768, |
| initializer_range: float = 0.02, |
| rms_norm_eps: float = 1e-6, |
| use_cache: bool = True, |
| use_dense: bool = False, |
| rope_theta: int = 10000, |
| attention_dropout: float = 0.0, |
| apply_aux_loss: bool = True, |
| router_aux_loss_factor: float = 0.02, |
| tie_word_embeddings: bool = False, |
| **kwargs, |
| ): |
| self.input_size = input_size |
| self.hidden_size = hidden_size |
| self.intermediate_size = intermediate_size |
| self.max_position_embeddings = max_position_embeddings |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
|
|
| if num_key_value_heads is None: |
| num_key_value_heads = num_attention_heads |
|
|
| self.num_key_value_heads = num_key_value_heads |
| self.hidden_act = hidden_act |
| if isinstance(horizon_lengths, int): |
| horizon_lengths = [horizon_lengths] |
| self.horizon_lengths = horizon_lengths |
| self.num_experts_per_tok = num_experts_per_tok |
| self.num_experts = num_experts |
| self.initializer_range = initializer_range |
| self.rms_norm_eps = rms_norm_eps |
| self.use_cache = use_cache |
| self.use_dense = use_dense |
| self.rope_theta = rope_theta |
| self.attention_dropout = attention_dropout |
| self.apply_aux_loss = apply_aux_loss |
| self.router_aux_loss_factor = router_aux_loss_factor |
|
|
| assert self.use_dense ^ self.apply_aux_loss, 'Both use_dense and apply_aux_loss cannot be set to True or False at the same time.' |
|
|
| kwargs.pop('tie_word_embeddings', None) |
| super().__init__( |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
|
|
