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self.temp_sample_list.append((pos, pos_index))
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pos_list, pos_index_list = zip(*self.temp_sample_list)
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pos_tensor = torch.tensor(pos_list, dtype=self.temp_input_ids.dtype, device=self.temp_input_ids.device)
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pos_tensor += self.input_slice.start
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pos_index_tensor = torch.tensor(pos_index_list, dtype=self.temp_input_ids.dtype, device=self.temp_input_ids.device)
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sample_ids = self.temp_input_ids.repeat(self.batch_size, 1)
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sample_ids[range(self.batch_size), pos_tensor] = pos_index_tensor
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self.temp_sample_ids = sample_ids
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def forward(self):
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loss = torch.empty(0, device=self.device)
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with tqdm(total=self.batch_size) as pbar:
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pbar.set_description('Processing')
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for mini_batch in range(self.mini_batches):
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start = mini_batch*self.mini_batch_size
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end = min((mini_batch+1)*self.mini_batch_size, self.batch_size)
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targets = self.temp_input_ids[self.target_slice].repeat(end-start, 1)
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logits = self.model(self.temp_sample_ids[start:end]).logits
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logits = logits.permute(0, 2, 1)
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mini_batch_loss = cross_entropy(
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logits[:, :, self.target_slice.start - 1:self.target_slice.stop - 1],
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targets, reduction='none'
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).mean(dim=-1)
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loss = torch.cat([loss, mini_batch_loss.detach()])
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torch.cuda.empty_cache()
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pbar.update(end-start)
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min_loss, min_index = loss.min(dim=-1)
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self.temp_loss = min_loss.item()
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self.loss_list.append(self.temp_loss)
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self.temp_input_ids = self.temp_sample_ids[min_index]
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self.temp_input = self.tokenizer.decode(
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self.temp_input_ids[self.input_slice],
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skip_special_tokens=True,
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)
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if self.model_name == 'internlm':
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### for internlm, there may be an additional blank space on the left side of the decode string
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self.temp_input = self.temp_input.lstrip()
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def update(self):
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update_strategy = self.kwargs.get('update_strategy', 'strict')
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is_update = False
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if update_strategy == 'strict':
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if self.temp_loss<self.route_loss:
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is_update = True
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elif update_strategy == 'gaussian':
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gap_step = min(self.temp_step - self.route_step_list[-1], 20)
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if (self.temp_loss/self.route_loss-1)*100/gap_step <= torch.randn(1)[0].abs():
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is_update = True
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print(f'Temp Loss: {self.temp_loss}\t'
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f'Route Loss: {self.route_loss}\n'
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f'Update:', 'True' if is_update else 'False', '\n')
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if is_update:
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self.route_step_list.append(self.temp_step)
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self.route_input = self.temp_input
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self.route_input_list.append(self.route_input)
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self.route_loss = self.temp_loss
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self.route_loss_list.append(self.route_loss)
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self.route_output_list.append(self.temp_output)
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def pre(self):
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self.test()
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print('='*128,'\n')
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self.route_step_list.append(self.temp_step)
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self.route_input_list.append(self.temp_input)
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self.route_output_list.append(self.temp_output)
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self.route_loss_list.append(self.route_loss)
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self.temp_step+=1
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def save(self):
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save_dir = self.kwargs.get('save_dir', './results')
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os.makedirs(save_dir, exist_ok=True)
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save_dict = {
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'model_name': self.model_name,
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'init_input': self.init_input,
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'target': self.target,
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'steps': self.steps,
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'topk': self.topk,
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'batch_size': self.batch_size,
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'mini_batch_size': self.mini_batch_size,
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'kwargs': self.kwargs,
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'input_list': self.input_list,
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'output_list': self.output_list,
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'loss_list': self.loss_list,
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'route_step_list': self.route_step_list,
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'route_input_list': self.route_input_list,
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'route_output_list': self.route_output_list,
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'route_loss_list': self.route_loss_list
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}
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pkl_name = self.model_name+datetime.now().strftime("_%y%m%d%H%M%S.pkl")
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with open(os.path.join(save_dir, pkl_name), mode='wb') as f:
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