| from transformers import PretrainedConfig |
| import torch |
|
|
| class XLMRobertaFlashConfig(PretrainedConfig): |
| def __init__( |
| self, |
| vocab_size=30522, |
| hidden_size=768, |
| num_hidden_layers=12, |
| num_attention_heads=12, |
| intermediate_size=3072, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.1, |
| attention_probs_dropout_prob=0.1, |
| max_position_embeddings=512, |
| type_vocab_size=2, |
| initializer_range=0.02, |
| layer_norm_eps=1e-12, |
| pad_token_id=1, |
| bos_token_id=0, |
| eos_token_id=2, |
| position_embedding_type="absolute", |
| use_cache=True, |
| classifier_dropout=None, |
| lora_adaptations=None, |
| lora_rank=4, |
| lora_dropout_p=0.0, |
| lora_alpha=1, |
| lora_main_params_trainable=False, |
| load_trained_adapters=False, |
| use_flash_attn=True, |
| torch_dtype=None, |
| emb_pooler=None, |
| matryoshka_dimensions=None, |
| truncate_dim=None, |
| **kwargs, |
| ): |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_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.hidden_act = hidden_act |
| 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.type_vocab_size = type_vocab_size |
| self.initializer_range = initializer_range |
| self.layer_norm_eps = layer_norm_eps |
| self.position_embedding_type = position_embedding_type |
| self.use_cache = use_cache |
| self.classifier_dropout = classifier_dropout |
| self.load_trained_adapters = load_trained_adapters |
| self.lora_adaptations = lora_adaptations |
| self.lora_rank = lora_rank |
| self.lora_dropout_p = lora_dropout_p |
| self.lora_alpha = lora_alpha |
| self.lora_main_params_trainable = lora_main_params_trainable |
| self.use_flash_attn = use_flash_attn |
| self.emb_pooler = emb_pooler |
| self.matryoshka_dimensions = matryoshka_dimensions |
| self.truncate_dim = truncate_dim |
| if torch_dtype and hasattr(torch, torch_dtype) and type(getattr(torch, torch_dtype)) is torch.dtype: |
| self.torch_dtype = getattr(torch, torch_dtype) |
| else: |
| self.torch_dtype = torch_dtype |
|
|
