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import torch |
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import torch.nn as nn |
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from transformers.modeling_outputs import ( |
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BaseModelOutput, |
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SequenceClassifierOutput, |
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) |
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from typing import Optional, Union, Tuple |
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from .configuration_glm2 import gLM2Config |
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from .modeling_glm2 import gLM2Model, gLM2PreTrainedModel |
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from transformers import PretrainedConfig |
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from typing import List |
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class gLM2ClassicationConfig(gLM2Config): |
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def __init__(self, num_classes: int = 2, **kwargs): |
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super().__init__(**kwargs) |
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self.num_classes = num_classes |
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self.auto_map['AutoModelForSequenceClassification'] = "extension_glm2.gLM2ForSequenceClassification" |
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class gLM2ForSequenceClassification(gLM2PreTrainedModel): |
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config_class = gLM2ClassicationConfig |
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def __init__(self, config: gLM2ClassicationConfig): |
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super().__init__(config) |
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self.glm2 = gLM2Model(config) |
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self.score = nn.Linear(config.dim, config.num_classes, bias=False) |
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self.post_init() |
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def get_input_embeddings(self): |
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return self.glm2.tok_embeddings |
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def set_input_embeddings(self, value): |
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self.glm2.tok_embeddings = value |
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def forward( |
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self, |
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input_ids: torch.Tensor, |
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attention_mask: Optional[torch.Tensor] = None, |
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labels: Optional[torch.LongTensor] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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**kwargs, |
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) -> Union[Tuple, SequenceClassifierOutput]: |
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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.glm2( |
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input_ids, |
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attention_mask=attention_mask, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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token_embeddings = outputs[0] |
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cls_token = token_embeddings[:, 0, :] |
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logits = self.score(cls_token) |
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loss = None |
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if labels is not None: |
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labels = labels.to(logits.device) |
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loss_fct = nn.CrossEntropyLoss() |
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loss = loss_fct(logits.view(-1, self.config.num_classes), labels.view(-1)) |
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if not return_dict: |
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output = (logits,) + outputs[2:] |
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return ((loss,) + output) if loss is not None else output |
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return SequenceClassifierOutput( |
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loss=loss, |
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logits=logits, |
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hidden_states=outputs.hidden_states, |
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) |
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