165 lines
7.0 KiB
Python
165 lines
7.0 KiB
Python
from transformers import BertPreTrainedModel,BertConfig
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import torch.nn as nn
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import torch
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from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
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from transformers import XLMRobertaModel,XLMRobertaTokenizer
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from typing import Optional
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class BertSeriesConfig(BertConfig):
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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):
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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)
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self.project_dim = project_dim
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self.pooler_fn = pooler_fn
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self.learn_encoder = learn_encoder
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class RobertaSeriesConfig(XLMRobertaConfig):
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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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.project_dim = project_dim
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self.pooler_fn = pooler_fn
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self.learn_encoder = learn_encoder
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class BertSeriesModelWithTransformation(BertPreTrainedModel):
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_keys_to_ignore_on_load_unexpected = [r"pooler"]
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_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
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config_class = BertSeriesConfig
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def __init__(self, config=None, **kargs):
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# modify initialization for autoloading
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if config is None:
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config = XLMRobertaConfig()
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config.attention_probs_dropout_prob= 0.1
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config.bos_token_id=0
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config.eos_token_id=2
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config.hidden_act='gelu'
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config.hidden_dropout_prob=0.1
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config.hidden_size=1024
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config.initializer_range=0.02
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config.intermediate_size=4096
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config.layer_norm_eps=1e-05
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config.max_position_embeddings=514
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config.num_attention_heads=16
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config.num_hidden_layers=24
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config.output_past=True
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config.pad_token_id=1
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config.position_embedding_type= "absolute"
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config.type_vocab_size= 1
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config.use_cache=True
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config.vocab_size= 250002
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config.project_dim = 1024
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config.learn_encoder = False
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super().__init__(config)
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self.roberta = XLMRobertaModel(config)
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self.transformation = nn.Linear(config.hidden_size,config.project_dim)
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# self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
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# self.pooler = lambda x: x[:,0]
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# self.post_init()
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self.has_pre_transformation = True
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if self.has_pre_transformation:
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self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
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self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.post_init()
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def encode(self,c):
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device = next(self.parameters()).device
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text = self.tokenizer(c,
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truncation=True,
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max_length=77,
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return_length=False,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt")
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text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
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text["attention_mask"] = torch.tensor(
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text['attention_mask']).to(device)
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features = self(**text)
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return features['projection_state']
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def forward(
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self,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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encoder_attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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) :
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r"""
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.roberta(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=True,
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return_dict=return_dict,
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)
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# # last module outputs
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# sequence_output = outputs[0]
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# # project every module
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# sequence_output_ln = self.pre_LN(sequence_output)
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# # pooler
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# pooler_output = self.pooler(sequence_output_ln)
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# pooler_output = self.transformation(pooler_output)
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# projection_state = self.transformation(outputs.last_hidden_state)
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if self.has_pre_transformation:
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sequence_output2 = outputs["hidden_states"][-2]
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sequence_output2 = self.pre_LN(sequence_output2)
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projection_state2 = self.transformation_pre(sequence_output2)
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return {
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"projection_state": projection_state2,
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"last_hidden_state": outputs.last_hidden_state,
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"hidden_states": outputs.hidden_states,
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"attentions": outputs.attentions,
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}
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else:
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projection_state = self.transformation(outputs.last_hidden_state)
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return {
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"projection_state": projection_state,
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"last_hidden_state": outputs.last_hidden_state,
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"hidden_states": outputs.hidden_states,
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"attentions": outputs.attentions,
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}
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# return {
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# 'pooler_output':pooler_output,
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# 'last_hidden_state':outputs.last_hidden_state,
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# 'hidden_states':outputs.hidden_states,
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# 'attentions':outputs.attentions,
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# 'projection_state':projection_state,
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# 'sequence_out': sequence_output
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# }
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class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
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base_model_prefix = 'roberta'
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config_class= RobertaSeriesConfig
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