class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
code: def test_ppo_step_with_no_ref(self):
      # initialize dataset
      dummy_dataset = self._init_dummy_dataset()
      self.gpt2_model = (
          AutoModelForCausalLMWithValueHead.from_pretrained(
              self.model_id
          )
      )
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
      dummy_dataloader = ppo_trainer.dataloader
      # train model with ppo
      for query_tensor, response_tensor in dummy_dataloader:
          # define a reward for response
          # (this could be any reward such as human feedback or output from another model)
          reward = [torch.tensor(1.0), torch.tensor(0.0)]
          # train model
          train_stats = ppo_trainer.step(
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
          break
  
      for name, param in ppo_trainer.model.named_parameters():
          self.assertTrue(
              param.grad is not None,
              f"Parameter {name} has no gradient",
          )
  
      # ref model should not be trained
      for name, param in ppo_trainer.ref_model.named_parameters():
          self.assertTrue(
              param.grad is None, f"Parameter {name} has a gradient"
          )
  
      # initialize a new gpt2 model:
      model = AutoModelForCausalLMWithValueHead.from_pretrained(
          self.model_id
      )
      for name, param in ppo_trainer.ref_model.named_parameters():
          if "v_head" not in name:
              name = name.replace("pretrained_model.", "")
  
              self.assertTrue(
                  torch.allclose(
                      param.cpu(), model.state_dict()[name].cpu()
                  ),
                  f"Parameter {name} has changed from the original model",
              )
  
      # Finally check stats
      for stat in EXPECTED_STATS:
          assert stat in train_stats.keys()
  
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 11 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_ppo_step_with_no_ref() without return types