| | 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, |
| | ) |
| |
|
