docstr: Tests that Cache.reorder_cache is retrocompatible with the legacy code path
docstr_tok: ["Tests","that","Cache.reorder_cache","is","retrocompatible","with","the","legacy","code","path"]
code: def test_reorder_cache_retrocompatibility(self):
      """Tests that Cache.reorder_cache is retrocompatible with the legacy code path"""
      legacy_reorder_fn = (
          LlamaForCausalLM._reorder_cache
      )  # An example of a legacy `_reorder_cache` function
  
      legacy_cache = ()
      new_cache = DynamicCache()
  
      # Creates a new cache with 10 layers in both formats
      for layer_idx in range(10):
          new_key = torch.rand((4, 4, 8, 16))
          new_value = torch.rand((4, 4, 8, 16))
          new_cache.update(new_key, new_value, layer_idx)
          legacy_cache += ((new_key, new_value),)
  
      # Let's create some dummy beam indices. From the shape above, it is equivalent to the case where num_beams=4
      # and batch_size=1
      beam_idx = torch.randint(low=0, high=4, size=(4,))
  
      legacy_cache_reordered = legacy_reorder_fn(legacy_cache, beam_idx)
      new_cache.reorder_cache(beam_idx)
  
      # Let's check that the results are the same
      for layer_idx in range(10):
          for key_value_idx in range(2):
              self.assertTrue(
                  torch.allclose(
                      new_cache[layer_idx][key_value_idx],
                      legacy_cache_reordered[layer_idx][key_value_idx],
                  )
              )
  
file_path: transformers/tests/test_cache_utils.py
imports: import unittest
  from transformers import set_seed
  from transformers.testing_utils import is_torch_available, require_auto_gptq, require_torch, require_torch_gpu, slow
label_desc: Use image node_id 2 for calling the CacheTest obj's underlying member method code with example usage: obj.test_reorder_cache_retrocompatibility() without return types