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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
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