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_sgd_lr_scheduler(self):
      # initialize dataset
      dummy_dataset = self._init_dummy_dataset()
      optimizer = torch.optim.SGD(self.gpt2_model.parameters(), lr=0.01)
      lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
          optimizer, gamma=0.9
      )
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          optimizer=optimizer,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
          lr_scheduler=lr_scheduler,
      )
      dummy_dataloader = ppo_trainer.dataloader
  
      self.assertTrue(
          isinstance(ppo_trainer.optimizer.optimizer, torch.optim.SGD)
      )
      self.assertTrue(
          isinstance(
              ppo_trainer.lr_scheduler.scheduler,
              torch.optim.lr_scheduler.ExponentialLR,
          )
      )
  
      # 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
          _ = ppo_trainer.step(
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
          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"
          )
  
      # Finally check stats
      for stat in EXPECTED_STATS:
          assert stat in train_stats.keys()
  
      # assert that the LR has increased for exponential decay
      self.assertTrue(
          train_stats["ppo/learning_rate"]
          > self.ppo_config.learning_rate
      )
  
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 10 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_ppo_step_with_no_ref_sgd_lr_scheduler() without return types