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| # 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 = 768 | |
| # 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() | |
| # | |
| # 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) | |
| # | |
| # 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 | |