custom_trans.py

import torch
import torch.nn as nn
from sentence_transformers import models

class CustTrans(models.Transformer):

  def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self.curr_task_type = None
      self._rebuild_taskembedding(['sts', 'quora'])

  def forward(self, inputs, task_type=None):

    enc = self.auto_model(**inputs).last_hidden_state

    if task_type == None:
      task_type = self.curr_task_type

    if  task_type in self.task_types:
      idx = torch.tensor(self.task_types.index(task_type), device=self.TaskEmbedding.weight.device)
      hyp = self.TaskEmbedding(idx)
      inputs['token_embeddings'] = self._project(enc, hyp)

    else:
      inputs['token_embeddings'] = enc

    return inputs

  def _set_curr_task_type(self, task_type):
    self.curr_task_type = task_type

  def _set_taskembedding_grad(self, value):
        self.TaskEmbedding.weight.requires_grad = value

  def _set_transformer_grad(self, value):
        for param in self.auto_model.parameters():
          param.requires_grad = value

  def _rebuild_taskembedding(self, task_types):
    self.task_types = task_types
    self.task_emb = 1 - torch.eye(len(self.task_types),768)
    self.TaskEmbedding = nn.Embedding(len(self.task_types), 768).from_pretrained(self.task_emb)

  def _project(self, v, normal_hyper):
    # return v - torch.dot(v, normal_hyper)*normal_hyper / torch.norm(normal_hyper)**2
    return v*normal_hyper