Add early stopping for Bark generation via logits processor (#26675)

* add early stopping logits processor

* black formmated

* indent

* follow method signature

* actual logic

* check for None

* address comments on docstrings and method signature

* add unit test under `LogitsProcessorTest` wip

* unit test passing

* black formatted

* condition per sample

* add to BarkModelIntegrationTests

* wip BarkSemanticModelTest

* rename and add to kwargs handling

* not add to BarkSemanticModelTest

* correct logic and assert last outputs tokens different in test

* doc-builder style

* read from kwargs as well

* assert len of with less than that of without

* ruff

* add back seed and test case

* add original impl default suggestion

* doc-builder

* rename and use softmax

* switch back to LogitsProcessor and update docs wording

* camelCase and spelling and saving compute

* assert strictly less than

* assert less than

* expand test_generate_semantic_early_stop instead

src/transformers/generation/logits_process.py

@@ -1749,3 +1749,35 @@ class UnbatchedClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
         unconditional_logits = torch.nn.functional.log_softmax(logits[:, -1], dim=-1)
         out = self.guidance_scale * (scores - unconditional_logits) + unconditional_logits
         return out
+
+
+class BarkEosPrioritizerLogitsProcessor(LogitsProcessor):
+    r"""This processor ensures that the EOS token is selected if its probability is greater than the `min_eos_p`.
+
+    Args:
+        eos_token_id (`Union[int, List[int]]`):
+            The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+        min_eos_p (`float`, *optional*):
+            Minimum end of speech threshold.
+    """
+
+    def __init__(self, eos_token_id: Union[int, List[int]], min_eos_p: float):
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        self.eos_token_id = eos_token_id
+        if min_eos_p is not None and min_eos_p <= 0:
+            raise ValueError(f"`min_eos_p` has to be a positive float, but is {min_eos_p}")
+        self.min_eos_p = min_eos_p
+
+    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        if self.min_eos_p:
+            probs = torch.nn.functional.softmax(scores.float(), dim=-1)
+            # create scores full of -inf except for the eos_token_id
+            early_stop_scores = torch.ones_like(scores) * -float("inf")
+            early_stop_scores[:, self.eos_token_id] = scores[:, self.eos_token_id]
+
+            do_early_stop = probs[:, self.eos_token_id] > self.min_eos_p
+            scores = torch.where(do_early_stop, early_stop_scores, scores)
+
+        return scores

src/transformers/models/bark/generation_configuration_bark.py

@@ -44,6 +44,7 @@ class BarkSemanticGenerationConfig(GenerationConfig):
         semantic_vocab_size=10_000,
         max_input_semantic_length=256,
         semantic_rate_hz=49.9,
+        min_eos_p=None,
         **kwargs,
     ):
         """Class that holds a generation configuration for [`BarkSemanticModel`].
@@ -86,6 +87,10 @@ class BarkSemanticGenerationConfig(GenerationConfig):
                 Max length of semantic input vector.
             semantic_rate_hz (`float`, *optional*, defaults to 49.9):
                 Semantic rate in Hertz.
+            min_eos_p (`float`, *optional*):
+                Minimum threshold of the probability of the EOS token for it to be sampled. This is an early stopping
+                strategy to mitigate potential unwanted generations at the end of a prompt. The original implementation
+                suggests a default value of 0.2.
         """
         super().__init__(
             temperature=temperature,
@@ -107,6 +112,7 @@ class BarkSemanticGenerationConfig(GenerationConfig):
         self.semantic_vocab_size = semantic_vocab_size
         self.max_input_semantic_length = max_input_semantic_length
         self.semantic_rate_hz = semantic_rate_hz
+        self.min_eos_p = min_eos_p
 
 
 class BarkCoarseGenerationConfig(GenerationConfig):

src/transformers/models/bark/modeling_bark.py

@@ -21,7 +21,11 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-from ...generation.logits_process import AlternatingCodebooksLogitsProcessor, SuppressTokensLogitsProcessor
+from ...generation.logits_process import (
+    AlternatingCodebooksLogitsProcessor,
+    BarkEosPrioritizerLogitsProcessor,
+    SuppressTokensLogitsProcessor,
+)
 from ...modeling_outputs import CausalLMOutputWithPast, MaskedLMOutput
 from ...modeling_utils import PreTrainedModel, get_parameter_device
 from ...utils import (
@@ -798,12 +802,17 @@ class BarkSemanticModel(BarkCausalModel):
 
         suppress_tokens_logits_processor = SuppressTokensLogitsProcessor(tokens_to_suppress)
 
+        min_eos_p = kwargs.get("min_eos_p", semantic_generation_config.min_eos_p)
+        early_stopping_logits_processor = BarkEosPrioritizerLogitsProcessor(
+            eos_token_id=semantic_generation_config.eos_token_id, min_eos_p=min_eos_p
+        )
+
         # pass input_ids in order to stay consistent with the transformers generate method even though it is not used
         # (except to get the input seq_len - that's why we keep the first 257 tokens)
         semantic_output = super().generate(
             torch.ones((batch_size, max_input_semantic_length + 1), dtype=torch.int).to(self.device),
             input_embeds=input_embeds,
-            logits_processor=[suppress_tokens_logits_processor],
+            logits_processor=[suppress_tokens_logits_processor, early_stopping_logits_processor],
             generation_config=semantic_generation_config,
             **kwargs,
         )  # size: 10048
@@ -1559,7 +1568,8 @@ class BarkModel(BarkPreTrainedModel):
 
         kwargs_semantic = {
             # if "attention_mask" is set, it should not be passed to CoarseModel and FineModel
-            "attention_mask": kwargs.pop("attention_mask", None)
+            "attention_mask": kwargs.pop("attention_mask", None),
+            "min_eos_p": kwargs.pop("min_eos_p", None),
         }
         kwargs_coarse = {}
         kwargs_fine = {}

tests/generation/test_logits_process.py

@@ -53,6 +53,7 @@ if is_torch_available():
         TypicalLogitsWarper,
         UnbatchedClassifierFreeGuidanceLogitsProcessor,
     )
+    from transformers.generation.logits_process import BarkEosPrioritizerLogitsProcessor
 
 
 @require_torch
@@ -800,3 +801,19 @@ class LogitsProcessorTest(unittest.TestCase):
         self.assertAlmostEqual(out[0].item(), res[0].item())
         self.assertAlmostEqual(out[1].item(), res[1].item())
         self.assertAlmostEqual(out[2].item(), res[2].item())
+
+    def test_early_stop_processor(self):
+        input_ids = None
+        eos_token_id = 2
+        min_eos_p = 0.1  ## some small float
+
+        scores = self._get_uniform_logits(2, 4)
+        scores[0][eos_token_id] = -6  ## less than log(min_eos_p)
+
+        esp = BarkEosPrioritizerLogitsProcessor(eos_token_id=eos_token_id, min_eos_p=min_eos_p)
+        actual_scores = esp(input_ids, scores)
+        expected_scores_list = [
+            scores[0].tolist(),
+            [float("-inf"), float("-inf"), scores[0][0], float("-inf")],
+        ]
+        self.assertListEqual(actual_scores.tolist(), expected_scores_list)

tests/models/bark/test_modeling_bark.py

@@ -917,7 +917,51 @@ class BarkModelIntegrationTests(unittest.TestCase):
                 temperature=1.0,
                 semantic_generation_config=self.semantic_generation_config,
             )
+        self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids)
+
+    @slow
+    def test_generate_semantic_early_stop(self):
+        input_ids = self.inputs
+        min_eos_p = 0.01
+
+        # fmt: off
+        # check first ids
+        expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,]
+        # fmt: on
 
+        # Should be able to read min_eos_p from kwargs
+        with torch.no_grad():
+            torch.manual_seed(0)
+            output_ids_without_min_eos_p = self.model.semantic.generate(
+                **input_ids,
+                do_sample=False,
+                temperature=0.9,
+                semantic_generation_config=self.semantic_generation_config,
+            )
+            torch.manual_seed(0)
+            output_ids_kwargs = self.model.semantic.generate(
+                **input_ids,
+                do_sample=False,
+                temperature=0.9,
+                semantic_generation_config=self.semantic_generation_config,
+                min_eos_p=min_eos_p,
+            )
+        self.assertListEqual(output_ids_without_min_eos_p[0, : len(expected_output_ids)].tolist(), expected_output_ids)
+        self.assertLess(len(output_ids_kwargs[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist()))
+
+        # Should be able to read min_eos_p from the semantic generation config
+        self.semantic_generation_config.min_eos_p = min_eos_p
+        with torch.no_grad():
+            torch.manual_seed(0)
+            output_ids = self.model.semantic.generate(
+                **input_ids,
+                do_sample=False,
+                temperature=0.9,
+                semantic_generation_config=self.semantic_generation_config,
+            )
+
+        self.assertEqual(output_ids.shape, output_ids_kwargs.shape)
+        self.assertLess(len(output_ids[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist()))
         self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids)
 
     @slow
@@ -1022,25 +1066,29 @@ class BarkModelIntegrationTests(unittest.TestCase):
         input_ids = self.inputs
 
         with torch.no_grad():
+            torch.manual_seed(0)
             self.model.generate(
                 **input_ids, do_sample=False, temperature=1.0, coarse_do_sample=True, coarse_temperature=0.7
             )
-            self.model.generate(
+            output_ids_without_min_eos_p = self.model.generate(
                 **input_ids,
-                do_sample=False,
-                temperature=1.0,
+                do_sample=True,
+                temperature=0.9,
                 coarse_do_sample=True,
                 coarse_temperature=0.7,
                 fine_temperature=0.3,
             )
-            self.model.generate(
+
+            output_ids_with_min_eos_p = self.model.generate(
                 **input_ids,
                 do_sample=True,
-                temperature=0.6,
-                penalty_alpha=0.6,
-                semantic_temperature=0.9,
-                coarse_temperature=0.2,
-                fine_temperature=0.1,
+                temperature=0.9,
+                coarse_temperature=0.7,
+                fine_temperature=0.3,
+                min_eos_p=0.1,
+            )
+        self.assertLess(
+            len(output_ids_with_min_eos_p[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())
         )
 
     @require_torch_gpu