class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
code: def test_ppo_trainer_kl_penalty(self):
      dummy_dataset = self._init_dummy_dataset()
  
      log_probs = torch.Tensor([[0.5, 0.2, 0.1], [0.6, 0.2, 0.1]])
      ref_log_probs = torch.Tensor([[0.4, 0.3, 0.0], [0.7, 0.1, 0.3]])
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
  
      expected_output = torch.Tensor(
          [[0.1000, -0.1000, 0.1000], [-0.1000, 0.1000, -0.2000]]
      )
      self.assertTrue(
          torch.allclose(
              ppo_trainer._kl_penalty(log_probs, ref_log_probs),
              expected_output,
          )
      )
  
      self.ppo_config.kl_penalty = "abs"
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
  
      expected_output = torch.Tensor(
          [[0.1000, 0.1000, 0.1000], [0.1000, 0.1000, 0.2000]]
      )
      self.assertTrue(
          torch.allclose(
              ppo_trainer._kl_penalty(log_probs, ref_log_probs),
              expected_output,
          )
      )
  
      self.ppo_config.kl_penalty = "mse"
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
  
      expected_output = torch.Tensor(
          [[0.0050, 0.0050, 0.0050], [0.0050, 0.0050, 0.0200]]
      )
      self.assertTrue(
          torch.allclose(
              ppo_trainer._kl_penalty(log_probs, ref_log_probs),
              expected_output,
          )
      )
  
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 20 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_ppo_trainer_kl_penalty() without return types