| from typing import List | |
| from transformers import PretrainedConfig | |
| class SundialConfig(PretrainedConfig): | |
| model_type = "sundial" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| input_token_len: int = 16, | |
| hidden_size: int = 768, | |
| intermediate_size: int = 3072, | |
| output_token_lens: List[int] = [720], | |
| num_hidden_layers: int = 12, | |
| num_attention_heads: int = 12, | |
| hidden_act: str = "silu", | |
| use_cache: bool = True, | |
| rope_theta: int = 10000, | |
| dropout_rate: float = 0.1, | |
| initializer_range: float = 0.02, | |
| max_position_embeddings: int = 10000, | |
| flow_loss_depth: int = 3, | |
| num_sampling_steps: int = 50, | |
| diffusion_batch_mul: int = 4, | |
| **kwargs, | |
| ): | |
| self.input_token_len = input_token_len | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.output_token_lens = output_token_lens | |
| self.use_cache = use_cache | |
| self.rope_theta = rope_theta | |
| self.dropout_rate = dropout_rate | |
| self.initializer_range = initializer_range | |
| self.max_position_embeddings = max_position_embeddings | |
| self.flow_loss_depth = flow_loss_depth | |
| self.num_sampling_steps = num_sampling_steps | |
| self.diffusion_batch_mul = diffusion_batch_mul | |
| super().__init__( | |
| **kwargs, | |
| ) | |