class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
code: def test_peft_model_ppo_trainer_multi_gpu(self):
      from peft import LoraConfig, get_peft_model
      from transformers import AutoModelForCausalLM
  
      lora_config = LoraConfig(
          r=16,
          lora_alpha=32,
          lora_dropout=0.05,
          bias="none",
          task_type="CAUSAL_LM",
      )
      gpt2_model = AutoModelForCausalLM.from_pretrained(
          "gpt2",
          device_map="balanced",
          max_memory={0: "500MB", 1: "500MB"},
      )
  
      self.assertTrue(set(gpt2_model.hf_device_map.values()) == {0, 1})
  
      # this line is very important
      def make_inputs_require_grad(module, input, output):
          output.requires_grad_(True)
  
      gpt2_model.get_input_embeddings().register_forward_hook(
          make_inputs_require_grad
      )
  
      peft_model = get_peft_model(gpt2_model, lora_config)
      model = AutoModelForCausalLMWithValueHead.from_pretrained(
          peft_model
      )
  
      self.assertTrue(model.is_sequential_parallel)
  
      dummy_dataset = self._init_dummy_dataset()
      self.ppo_config.batch_size = 2
      self.ppo_config.mini_batch_size = 1
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
  
      self.assertTrue(ppo_trainer.ref_model is None)
  
      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 by running a step twice
          _ = ppo_trainer.step(
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
  
          ppo_trainer.model.train()
          ppo_trainer.model.gradient_checkpointing_enable()
          _ = ppo_trainer.step(
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
          break
  
      # check gradients
      for name, param in model.named_parameters():
          if "lora" in name or "v_head" in name:
              self.assertTrue(
                  param.grad is not None,
                  f"Parameter {name} has a no gradient",
              )
          else:
              self.assertTrue(
                  param.grad is None, f"Parameter {name} has a gradient"
              )
  
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 25 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_peft_model_ppo_trainer_multi_gpu() without return types