| from transformers import PretrainedConfig, PreTrainedModel | |
| class DiffusionConfig(PretrainedConfig): | |
| """Configuration class for Diffusion-LLM model.""" | |
| model_type = "diffusionLM" | |
| def __init__( | |
| self, | |
| vocab_size: int = 50257, | |
| hidden_size: int = 768, | |
| num_hidden_layers: int = 12, | |
| num_attention_heads: int = 12, | |
| intermediate_size: int = 3072, | |
| hidden_dropout_prob: float = 0.1, | |
| attention_probs_dropout_prob: float = 0.1, | |
| max_position_embeddings: int = 1024, | |
| initializer_range: float = 0.02, | |
| layer_norm_eps: float = 1e-12, | |
| pad_token_id: int = 0, | |
| mask_token_id: int = 50256, | |
| eos_token_id: int = 50256, | |
| num_timesteps: int = 100, | |
| time_embed_dim: int = 128, | |
| **kwargs | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.mask_token_id = mask_token_id | |
| self.eos_token_id = eos_token_id | |
| self.num_timesteps = num_timesteps | |
| self.time_embed_dim = time_embed_dim |