code: def test_simple_generation(self):
      # This test is a bit flaky. For some GPU architectures, pytorch sets by default allow_fp16_reduced_precision_reduction = True and some operations
      # do not give the same results under this configuration, especially torch.baddmm and torch.bmm. https://pytorch.org/docs/stable/notes/numerical_accuracy.html#fp16-on-mi200
      # As we leave the default value (True) for allow_fp16_reduced_precision_reduction , the tests failed when running in half-precision with smaller models (560m)
      # Please see: https://pytorch.org/docs/stable/notes/cuda.html#reduced-precision-reduction-in-fp16-gemms
      # This discrepancy is observed only when using small models and seems to be stable for larger models.
      # Our conclusion is that these operations are flaky for small inputs but seems to be stable for larger inputs (for the functions `baddmm` and `bmm`), and therefore for larger models.
  
      # Here is a summary of an ablation study of our observations
      # EXPECTED_OUTPUT = "I enjoy walking with my cute dog, and I love to watch the kids play. I am a very active person, and I am a very good listener. I am a very good person, and I am a very good person. I am a"
      # 560m + allow_fp16_reduced_precision_reduction = False  + torch.bmm  ==> PASS
      # 560m + allow_fp16_reduced_precision_reduction = False  + torch.baddm  ==> PASS
      # 560m + allow_fp16_reduced_precision_reduction = True  + torch.baddm  ==> PASS
      # 560m + allow_fp16_reduced_precision_reduction = True  + torch.bmm  ==> FAIL
  
      # EXPECTED_OUTPUT = "I enjoy walking with my cute dog, but I also enjoy hiking, biking, and swimming. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love"
      # >=1b1 + allow_fp16_reduced_precision_reduction = True  + torch.baddm  ==> PASS  (for use_cache=True and use_cache=False)
      # >=1b1 + allow_fp16_reduced_precision_reduction = True  + torch.bmm  ==> PASS
      # >=1b1 + allow_fp16_reduced_precision_reduction = False  + torch.bmm  ==> PASS
  
      path_560m = "bigscience/bloom-560m"
      model = BloomForCausalLM.from_pretrained(
          path_560m, use_cache=True, revision="gs555750"
      ).cuda()
      model = model.eval()
      tokenizer = BloomTokenizerFast.from_pretrained(path_560m)
  
      input_sentence = "I enjoy walking with my cute dog"
      # This output has been obtained using fp32 model on the huggingface DGX workstation - NVIDIA A100 GPU
      EXPECTED_OUTPUT = (
          "I enjoy walking with my cute dog, and I love to watch the kids play with the kids. I am a very "
          "active person, and I enjoy working out, and I am a very active person. I am a very active person, and I"
      )
  
      input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
      greedy_output = model.generate(input_ids.cuda(), max_length=50)
  
      self.assertEqual(
          tokenizer.decode(greedy_output[0], skip_special_tokens=True),
          EXPECTED_OUTPUT,
      )
  
imports: import math
  import unittest
  from transformers import BloomConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 13 for calling the BloomModelTest obj's underlying member method code with example usage: obj.test_simple_generation() without return types