Spaces:
Runtime error
Runtime error
File size: 7,471 Bytes
ad93086 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
# from transformers import BertPreTrainedModel,BertConfig
# import torch.nn as nn
# import torch
# from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
# from transformers import XLMRobertaModel,XLMRobertaTokenizer
# from typing import Optional
# from modules import torch_utils
#
#
# class BertSeriesConfig(BertConfig):
# 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=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
#
# super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
# self.project_dim = project_dim
# self.pooler_fn = pooler_fn
# self.learn_encoder = learn_encoder
#
# class RobertaSeriesConfig(XLMRobertaConfig):
# def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
# super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
# self.project_dim = project_dim
# self.pooler_fn = pooler_fn
# self.learn_encoder = learn_encoder
#
#
# class BertSeriesModelWithTransformation(BertPreTrainedModel):
#
# _keys_to_ignore_on_load_unexpected = [r"pooler"]
# _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
# config_class = BertSeriesConfig
#
# def __init__(self, config=None, **kargs):
# # modify initialization for autoloading
# if config is None:
# config = XLMRobertaConfig()
# config.attention_probs_dropout_prob= 0.1
# config.bos_token_id=0
# config.eos_token_id=2
# config.hidden_act='gelu'
# config.hidden_dropout_prob=0.1
# config.hidden_size=1024
# config.initializer_range=0.02
# config.intermediate_size=4096
# config.layer_norm_eps=1e-05
# config.max_position_embeddings=514
#
# config.num_attention_heads=16
# config.num_hidden_layers=24
# config.output_past=True
# config.pad_token_id=1
# config.position_embedding_type= "absolute"
#
# config.type_vocab_size= 1
# config.use_cache=True
# config.vocab_size= 250002
# config.project_dim = 1024
# config.learn_encoder = False
# super().__init__(config)
# self.roberta = XLMRobertaModel(config)
# self.transformation = nn.Linear(config.hidden_size,config.project_dim)
# # self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
# # self.pooler = lambda x: x[:,0]
# # self.post_init()
#
# self.has_pre_transformation = True
# if self.has_pre_transformation:
# self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
# self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# self.post_init()
#
# def encode(self,c):
# device = torch_utils.get_param(self).device
# text = self.tokenizer(c,
# truncation=True,
# max_length=77,
# return_length=False,
# return_overflowing_tokens=False,
# padding="max_length",
# return_tensors="pt")
# text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
# text["attention_mask"] = torch.tensor(
# text['attention_mask']).to(device)
# features = self(**text)
# return features['projection_state']
#
# def forward(
# self,
# input_ids: Optional[torch.Tensor] = None,
# attention_mask: Optional[torch.Tensor] = None,
# token_type_ids: Optional[torch.Tensor] = None,
# position_ids: Optional[torch.Tensor] = None,
# head_mask: Optional[torch.Tensor] = None,
# inputs_embeds: Optional[torch.Tensor] = None,
# encoder_hidden_states: Optional[torch.Tensor] = None,
# encoder_attention_mask: Optional[torch.Tensor] = None,
# output_attentions: Optional[bool] = None,
# return_dict: Optional[bool] = None,
# output_hidden_states: Optional[bool] = None,
# ) :
# r"""
# """
#
# return_dict = return_dict if return_dict is not None else self.config.use_return_dict
#
#
# outputs = self.roberta(
# input_ids=input_ids,
# attention_mask=attention_mask,
# token_type_ids=token_type_ids,
# position_ids=position_ids,
# head_mask=head_mask,
# inputs_embeds=inputs_embeds,
# encoder_hidden_states=encoder_hidden_states,
# encoder_attention_mask=encoder_attention_mask,
# output_attentions=output_attentions,
# output_hidden_states=True,
# return_dict=return_dict,
# )
#
# # # last module outputs
# # sequence_output = outputs[0]
#
#
# # # project every module
# # sequence_output_ln = self.pre_LN(sequence_output)
#
# # # pooler
# # pooler_output = self.pooler(sequence_output_ln)
# # pooler_output = self.transformation(pooler_output)
# # projection_state = self.transformation(outputs.last_hidden_state)
#
# if self.has_pre_transformation:
# sequence_output2 = outputs["hidden_states"][-2]
# sequence_output2 = self.pre_LN(sequence_output2)
# projection_state2 = self.transformation_pre(sequence_output2)
#
# return {
# "projection_state": projection_state2,
# "last_hidden_state": outputs.last_hidden_state,
# "hidden_states": outputs.hidden_states,
# "attentions": outputs.attentions,
# }
# else:
# projection_state = self.transformation(outputs.last_hidden_state)
# return {
# "projection_state": projection_state,
# "last_hidden_state": outputs.last_hidden_state,
# "hidden_states": outputs.hidden_states,
# "attentions": outputs.attentions,
# }
#
#
# # return {
# # 'pooler_output':pooler_output,
# # 'last_hidden_state':outputs.last_hidden_state,
# # 'hidden_states':outputs.hidden_states,
# # 'attentions':outputs.attentions,
# # 'projection_state':projection_state,
# # 'sequence_out': sequence_output
# # }
#
#
# class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
# base_model_prefix = 'roberta'
# config_class= RobertaSeriesConfig
|