docstr: Simply tests if passing a transformers model to `SFTTrainer` loads and runs the trainer
  as expected in mixed precision + different scenarios of gradient_checkpointing.
docstr_tok: ["Simply","tests","if","passing","a","transformers","model","to","`","SFTTrainer","`","loads","and","runs","the","trainer","as","expected","in","mixed","precision","+","different","scenarios","of","gradient_checkpointing","."]
code: def test_sft_trainer_transformers_mp_gc(
      self, model_name, packing, gradient_checkpointing_kwargs
  ):
      """
      Simply tests if passing a transformers model to `SFTTrainer` loads and runs the trainer
      as expected in mixed precision + different scenarios of gradient_checkpointing.
      """
      with tempfile.TemporaryDirectory() as tmp_dir:
          args = TrainingArguments(
              output_dir=tmp_dir,
              logging_strategy="no",
              report_to="none",
              per_device_train_batch_size=2,
              max_steps=10,
              fp16=True,  # this is sufficient to enable amp
              gradient_checkpointing=True,
              gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
          )
  
          model = AutoModelForCausalLM.from_pretrained(model_name)
          tokenizer = AutoTokenizer.from_pretrained(model_name)
  
          trainer = SFTTrainer(
              model,
              args=args,
              tokenizer=tokenizer,
              train_dataset=self.train_dataset,
              eval_dataset=self.eval_dataset,
              packing=packing,
              dataset_text_field=self.dataset_text_field,
              max_seq_length=self.max_seq_length,
          )
  
          trainer.train()
  
      release_memory(model, trainer)
  
imports: import gc
  import itertools
  import tempfile
  import unittest
  import torch
  from accelerate.utils.memory import release_memory
  from datasets import load_dataset
  from parameterized import parameterized
  from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments
  from trl import SFTTrainer, is_peft_available
  from ..testing_utils import require_bitsandbytes, require_peft, require_torch_gpu, require_torch_multi_gpu
  from .testing_constants import DEVICE_MAP_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST, PACKING_OPTIONS
label_desc: Use image node_id 7 for calling the SFTTrainerSlowTester obj's underlying member method code with example usage: obj.test_sft_trainer_transformers_mp_gc(model_name, packing, gradient_checkpointing_kwargs) without return types