Fix on "cache position" for assisted generation (#30068)

* clean commit history I hope

* get kv seq length correctly

* PR suggestions

* Update src/transformers/testing_utils.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* add comment

* give gpt bigcode it's own overriden method

* remove code

---------

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

tests/generation/test_utils.py

@@ -1091,8 +1091,9 @@ class GenerationTesterMixin:
             )
             self.assertListEqual(low_output.tolist(), high_output.tolist())
 
+    @parameterized.expand([("random",), ("same",)])
     @is_flaky()  # Read NOTE (1) below. If there are API issues, all attempts will fail.
-    def test_assisted_decoding_matches_greedy_search(self):
+    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         # This test ensures that the assisted generation does not introduce output changes over greedy search.
         # NOTE (1): The sentence above is true most of the time, there is a tiny difference in the logits due to matmul
         # shape differences -- and it may result in a different output. The input shape difference happens in the
@@ -1151,7 +1152,13 @@ class GenerationTesterMixin:
             }
             output_greedy = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
 
-            assistant_model = model
+            # test with the same assistant model or randomly init one
+            # in the first case all candidate tokens are accepted, in the second none is accepted
+            # case when some are accepted and some not is hard to reproduce, so let's hope this catches most errors :)
+            if assistant_type == "random":
+                assistant_model = model_class(config).to(torch_device).eval()
+            else:
+                assistant_model = model
             assistant_model.generation_config.num_assistant_tokens = 2  # see b)
             assistant_model.generation_config.num_assistant_tokens_schedule = "constant"  # see b)
             generation_kwargs.update({"assistant_model": assistant_model})