fix num_assistant_tokens with heuristic schedule (#28759)

* fix heuristic num_assistant_tokens_schedule

* Update src/transformers/generation/configuration_utils.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/generation/candidate_generator.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update utils.py

check that candidate_generator.assistant_model exists since some some speculations (like ngram and PLD) don't have assistant_model attribute

* Update src/transformers/generation/candidate_generator.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update tests/generation/test_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* make fixup

* merge conflict

* fix docstring

* make fixup

---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/generation/candidate_generator.py

@@ -225,7 +225,10 @@ class AssistedCandidateGenerator(CandidateGenerator):
         # Adjust the max number of assistant tokens to use in the next iteration. This is a simple heuristic,
         # probably can be improved -- we want to balance the benefits of getting assistant tokens correct with the
         # cost of forecasting incorrect assistant tokens.
-        if self.assistant_model.generation_config.num_assistant_tokens_schedule == "heuristic":
+        if self.assistant_model.generation_config.num_assistant_tokens_schedule in {
+            "heuristic",
+            "heuristic_transient",
+        }:
             if num_matches == int(self.num_assistant_tokens):
                 self.num_assistant_tokens += 2.0
             else:

src/transformers/generation/configuration_utils.py

@@ -249,8 +249,9 @@ class GenerationConfig(PushToHubMixin):
 
         num_assistant_tokens_schedule (`str`, *optional*, defaults to `"heuristic"`):
             Defines the schedule at which max assistant tokens shall be changed during inference.
-            - `"_heuristic_`: When all _speculative_ tokens are correct, increase `num_assistant_tokens` by 2 else
-              reduce by 1
+            - `"heuristic"`: When all speculative tokens are correct, increase `num_assistant_tokens` by 2 else
+              reduce by 1. `num_assistant_tokens` value is persistent over multiple generation calls with the same assistant model.
+            - `"heuristic_transient"`: Same as `"heuristic"` but `num_assistant_tokens` is reset to its initial value after each generation call.
             - `"constant"`: `num_assistant_tokens` stays unchanged during generation
 
         > Parameters specific to the caching mechanism:

src/transformers/generation/utils.py

@@ -4561,6 +4561,13 @@ class GenerationMixin:
         if streamer is not None:
             streamer.end()
 
+        if (
+            hasattr(candidate_generator, "assistant_model")
+            and candidate_generator.assistant_model.generation_config.num_assistant_tokens_schedule == "heuristic"
+        ):
+            candidate_generator.assistant_model.generation_config.num_assistant_tokens = (
+                candidate_generator.num_assistant_tokens
+            )
         if return_dict_in_generate:
             if self.config.is_encoder_decoder:
                 return GenerateEncoderDecoderOutput(

tests/generation/test_utils.py

@@ -3490,3 +3490,49 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
             encoder_outputs=encoder_outputs,
         )
         self.assertListEqual(outputs_assisted.tolist(), outputs_foo.tolist())
+
+    def test_assisted_decoding_num_assistant_tokens_heuristic_schedule(self):
+        # This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
+
+        prompt = "Alice and Bob"
+        checkpoint = "EleutherAI/pythia-160m-deduped"
+        tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+        inputs = tokenizer(prompt, return_tensors="pt")
+
+        model = AutoModelForCausalLM.from_pretrained(checkpoint)
+
+        assistant_model = model
+        assistant_model.generation_config.num_assistant_tokens = 5
+        assistant_model.generation_config.num_assistant_tokens_schedule = "heuristic"
+        generation_kwargs = {
+            "eos_token_id": -1,
+            "max_new_tokens": 5,
+            "do_sample": False,
+            "assistant_model": assistant_model,
+        }
+        model.generate(**inputs, **generation_kwargs)
+        # update_candidate_strategy is called only once and therefore, assistant_model.generation_config.num_assistant_tokens should be either 4 or 7
+        self.assertTrue(assistant_model.generation_config.num_assistant_tokens in (4, 7))
+
+    def test_assisted_decoding_num_assistant_tokens_heuristic_transient_schedule(self):
+        # This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
+
+        prompt = "Alice and Bob"
+        checkpoint = "EleutherAI/pythia-160m-deduped"
+        tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+        inputs = tokenizer(prompt, return_tensors="pt")
+
+        model = AutoModelForCausalLM.from_pretrained(checkpoint)
+
+        assistant_model = model
+        assistant_model.generation_config.num_assistant_tokens = 5
+        assistant_model.generation_config.num_assistant_tokens_schedule = "heuristic_transient"
+        generation_kwargs = {
+            "eos_token_id": -1,
+            "max_new_tokens": 5,
+            "do_sample": False,
+            "assistant_model": assistant_model,
+        }
+        model.generate(**inputs, **generation_kwargs)
+        # update_candidate_strategy is called once but assistant_model.generation_config.num_assistant_tokens should stay 5
+        self.assertEqual(assistant_model.generation_config.num_assistant_tokens, 5)