class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
code: def test_peft_model_ppo_adapter_rm_trainer(self):
      from peft import LoraConfig, get_peft_model
      from transformers import (
          AutoModelForCausalLM,
          AutoModelForSequenceClassification,
      )
  
      dummy_inputs = torch.LongTensor(
          [[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]]
      )
      rm_lora_config = LoraConfig(
          r=16,
          lora_alpha=32,
          lora_dropout=0.05,
          bias="none",
          task_type="SEQ_CLS",
      )
  
      reward_model = AutoModelForSequenceClassification.from_pretrained(
          self.model_id
      )
      reward_model = get_peft_model(reward_model, rm_lora_config)
      dummy_optim = torch.optim.Adam(
          filter(lambda p: p.requires_grad, reward_model.parameters()),
          lr=1e-3,
      )
  
      previous_rm_logits = reward_model(dummy_inputs).logits
      loss = previous_rm_logits.mean()
      loss.backward()
  
      dummy_optim.step()
      reward_model.eval()
  
      original_rm_logits = reward_model(dummy_inputs).logits
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          reward_model.save_pretrained(tmpdirname)
  
          lora_config = LoraConfig(
              r=16,
              lora_alpha=32,
              lora_dropout=0.05,
              bias="none",
              task_type="CAUSAL_LM",
          )
          gpt2_model = AutoModelForCausalLM.from_pretrained(
              self.model_id
          )
  
          # 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,
              reward_adapter=tmpdirname,
          )
  
          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
  
          new_logits = ppo_trainer.model.compute_reward_score(
              dummy_inputs
          )
          self.assertTrue(
              not torch.allclose(
                  previous_rm_logits, new_logits[:, -1, :]
              )
          )
          self.assertTrue(
              torch.allclose(original_rm_logits, new_logits[:, -1, :])
          )
  
          # check gradients
          for name, param in model.named_parameters():
              if ("lora" in name or "v_head" in name) and (
                  "reward" not 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 23 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_peft_model_ppo_adapter_rm_trainer() without return types