class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
docstr: Test PPO step with no reference model and custom layers
  For shared layers configuration, all the layers after the `num_shared_layers` are considered as custom layers
  therefore the gradients should be computed for these layers only.
code: def test_ppo_step_with_no_ref_custom_layers(self):
      """
      Test PPO step with no reference model and custom layers
      For shared layers configuration, all the layers after the `num_shared_layers` are considered as custom layers
      therefore the gradients should be computed for these layers only.
      """
      # initialize dataset
      dummy_dataset = self._init_dummy_dataset()
      self.gpt2_model = (
          AutoModelForCausalLMWithValueHead.from_pretrained(
              self.model_id
          )
      )
      num_shared_layers = 1
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
          num_shared_layers=num_shared_layers,
      )
      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
  
      pattern = r".*transformer\.h\.(\d+)\..*"
      final_layers = ["ln_f", "v_head", "lm_head"]
  
      for name, param in ppo_trainer.model.named_parameters():
          if re.match(pattern, name):
              layer_number = int(re.match(pattern, name).groups(0)[0])
              if layer_number < num_shared_layers:
                  self.assertTrue(
                      param.grad is None,
                      f"Parameter {name} has a gradient",
                  )
              else:
                  self.assertTrue(
                      param.grad is not None,
                      f"Parameter {name} has no gradient",
                  )
          elif any([layer in name for layer in final_layers]):
              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"
          )
  
      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 12 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_ppo_step_with_no_ref_custom_layers() without return types