class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
docstr: Test if the loss trainer works fine
code: def test_batched_forward_pass(self, name):
      """
      Test if the loss trainer works fine
      """
      # initialize dataset
      dummy_dataset = self._init_dummy_dataset()
  
      dummy_queries = [
          torch.tensor([1, 2, 3, 4]),
          torch.tensor([1, 2, 3, 4, 5, 6, 7]),
      ]
      dummy_responses = [
          torch.tensor([5, 6, 7, 8, 9]),
          torch.tensor([8, 9, 10, 11, 12, 13]),
      ]
  
      if name == "gpt2":
          model = self.gpt2_model
          tokenizer = self.gpt2_tokenizer
      elif name == "bloom":
          model = self.bloom_model
          tokenizer = self.bloom_tokenizer
      elif name == "t5":
          model = self.t5_model
          tokenizer = self.t5_tokenizer
  
      model.eval()
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=model,
          ref_model=None,
          tokenizer=tokenizer,
          dataset=dummy_dataset,
      )
  
      # we test all combinations of fwd_bs and bs:
      # if fwd_bs=bs=1: no padding is applied and only one forward pass
      # if fwd_bs=1/bs=2: padding is applied and results computed in two fwd passes
      # if fwd_bs=bs=2: padding is applied and results computed in one fwd pass
  
      ppo_trainer.config.mini_batch_size = 1
      ppo_trainer.config.batch_size = 1
  
      model_inputs = ppo_trainer.prepare_model_inputs(
          [dummy_queries[0]], [dummy_responses[0]]
      )
      (
          logprobs_0,
          logits_0,
          values_0,
          mask_0,
      ) = ppo_trainer.batched_forward_pass(
          model, [dummy_queries[0]], [dummy_responses[0]], model_inputs
      )
  
      ppo_trainer.config.batch_size = 2
      model_inputs = ppo_trainer.prepare_model_inputs(
          dummy_queries, dummy_responses
      )
      (
          logprobs_1,
          logits_1,
          values_1,
          mask_1,
      ) = ppo_trainer.batched_forward_pass(
          model, dummy_queries, dummy_responses, model_inputs
      )
  
      ppo_trainer.config.mini_batch_size = 2
      model_inputs = ppo_trainer.prepare_model_inputs(
          dummy_queries, dummy_responses
      )
      (
          logprobs_2,
          logits_2,
          values_2,
          mask_2,
      ) = ppo_trainer.batched_forward_pass(
          model, dummy_queries, dummy_responses, model_inputs
      )
  
      self.assertLessEqual(
          abs_diff_masked_tensors(
              logprobs_1, logprobs_2, mask_1, mask_2
          ),
          1e-4,
      )
      self.assertLessEqual(
          abs_diff_masked_tensors(values_1, values_2, mask_1, mask_2),
          1e-4,
      )
  
      self.assertLessEqual(
          abs_diff_masked_tensors(
              logprobs_0, logprobs_2[:1], mask_0, mask_2[:1]
          ),
          1e-4,
      )
      self.assertLessEqual(
          abs_diff_masked_tensors(
              values_0, values_2[:1], mask_0, mask_2[:1]
          ),
          1e-4,
      )
  
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 18 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_batched_forward_pass(name) without return types