class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
docstr: Test if the training loop works fine without passing a dataset
code: def test_ppo_step_no_dataset(self):
      """
      Test if the training loop works fine without passing a dataset
      """
      query_txt = "This morning I went to the "
      query_tensor = self.gpt2_tokenizer.encode(
          query_txt, return_tensors="pt"
      )
      self.ppo_config.batch_size = 1
  
      response_tensor = respond_to_batch(self.gpt2_model, query_tensor)
  
      # Check that this warns the user about batch size
      with self.assertWarns(UserWarning):
          ppo_trainer = PPOTrainer(
              config=self.ppo_config,
              model=self.gpt2_model,
              ref_model=self.gpt2_model_ref,
              tokenizer=self.gpt2_tokenizer,
          )
      # train model with ppo
      reward = [torch.tensor([1.0])]
      # train model - this should work fine
      train_stats = ppo_trainer.step(
          [query_tensor[0]], [response_tensor[0]], reward
      )
  
      # check gradients
      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"
          )
  
      # check train stats
      for stat in EXPECTED_STATS:
          self.assertTrue(
              stat in train_stats, f"Train stats should contain {stat}"
          )
  
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 16 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_ppo_step_no_dataset() without return types