class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
docstr: Test if the loss trainer works fine
code: def test_loss_trainer(self):
      """
      Test if the loss trainer works fine
      """
      # initialize dataset
      dummy_dataset = self._init_dummy_dataset()
  
      self.gpt2_model.eval()
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
  
      dummy_queries = [
          torch.tensor([1, 2, 3, 4]),
          torch.tensor([1, 2, 3, 4, 5, 6, 7]),
      ]
      dummy_responses = [
          torch.tensor([5, 6, 7, 8, 9]),
          torch.tensor([8, 9, 10, 11, 12, 13]),
      ]
      dummy_scores = torch.Tensor([1, 2])
  
      ppo_trainer.config.mini_batch_size = 1
      ppo_trainer.config.batch_size = 1
      model_inputs = ppo_trainer.prepare_model_inputs(
          dummy_queries, dummy_responses
      )
      all_logprobs, _, values, mask = ppo_trainer.batched_forward_pass(
          self.gpt2_model, dummy_queries, dummy_responses, model_inputs
      )
  
      # dummy values
      ref_logprobs = all_logprobs + 1
      logits = torch.exp(all_logprobs)
      vpreds = values + 0.1
  
      score, non_score, kls = ppo_trainer.compute_rewards(
          dummy_scores, all_logprobs, ref_logprobs, mask
      )
      values, advantages, returns = ppo_trainer.compute_advantages(
          values, score, mask
      )
  
      # just make sure a dummy loss is computed
      idx = 0
      pg_loss, v_loss, _ = ppo_trainer.loss(
          all_logprobs[idx].unsqueeze(0),
          values[idx].unsqueeze(0),
          logits[idx].unsqueeze(0),
          vpreds[idx].unsqueeze(0),
          ref_logprobs[idx].unsqueeze(0),
          mask[idx].unsqueeze(0),
          advantages[idx].unsqueeze(0),
          returns[idx].unsqueeze(0),
      )
  
      self.assertAlmostEqual(pg_loss.item(), 2.0494, 4)
      self.assertAlmostEqual(v_loss.item(), 0.07110, 4)
  
      # check if we get same results with masked parts removed
      pg_loss_unmasked, v_loss_unmasked, _ = ppo_trainer.loss(
          apply_mask(all_logprobs[idx], mask[idx]).unsqueeze(0),
          apply_mask(values[idx], mask[idx]).unsqueeze(0),
          apply_mask(logits[idx], mask[idx]).unsqueeze(0),
          apply_mask(vpreds[idx], mask[idx]).unsqueeze(0),
          apply_mask(ref_logprobs[idx], mask[idx]).unsqueeze(0),
          apply_mask(mask[idx], mask[idx]).unsqueeze(0),
          apply_mask(advantages[idx], mask[idx]).unsqueeze(0),
          apply_mask(returns[idx], mask[idx]).unsqueeze(0),
      )
      self.assertAlmostEqual(pg_loss_unmasked.item(), 2.0494, 4)
      self.assertAlmostEqual(v_loss_unmasked.item(), 0.07110, 4)
  
imports: import copy
  import fnmatch
  import gc
  import re
  import tempfile
  import unittest
  import pytest
  import torch
  from huggingface_hub import HfApi, HfFolder, delete_repo
  from parameterized import parameterized
  from pytest import mark
  from requests.exceptions import HTTPError
  from transformers import AutoTokenizer
  from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed
  from trl.core import respond_to_batch
  from .testing_constants import CI_HUB_ENDPOINT, CI_HUB_USER, CI_HUB_USER_TOKEN
  from .testing_utils import require_peft, require_torch_multi_gpu
label_desc: Use image node_id 17 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_loss_trainer() without return types