class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
code: def test_grad_accumulation(self):
      dummy_dataset = self._init_dummy_dataset()
  
      torch.manual_seed(0)
      gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(
          self.model_id, summary_dropout_prob=0.0
      )
      gpt2_model_clone = copy.deepcopy(gpt2_model)
  
      self.ppo_config.mini_batch_size = 2
      self.ppo_config.ppo_epochs = 1
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=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(1.0)]
          # train model by running a step twice
          _ = ppo_trainer.step(
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
          break
  
      model_grad = gpt2_model.v_head.summary.weight
  
      self.ppo_config.mini_batch_size = 1
      self.ppo_config.gradient_accumulation_steps = 2
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=gpt2_model_clone,
          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(1.0)]
          # train model by running a step twice
          _ = ppo_trainer.step(
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
          break
  
      model_grad_acc = gpt2_model_clone.v_head.summary.weight
      self.assertTrue(
          torch.allclose(
              model_grad_acc, model_grad, rtol=1e-3, atol=1e-3
          )
      )
  
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 27 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_grad_accumulation() without return types