Generate: add SequenceBiasLogitsProcessor (#24334)

docs/source/en/internal/generation_utils.md

@@ -141,6 +141,9 @@ generation.
 [[autodoc]] NoRepeatNGramLogitsProcessor
     - __call__
 
+[[autodoc]] SequenceBiasLogitsProcessor
+    - __call__
+
 [[autodoc]] NoBadWordsLogitsProcessor
     - __call__
 

src/transformers/__init__.py

@@ -970,6 +970,7 @@ else:
             "PhrasalConstraint",
             "PrefixConstrainedLogitsProcessor",
             "RepetitionPenaltyLogitsProcessor",
+            "SequenceBiasLogitsProcessor",
             "StoppingCriteria",
             "StoppingCriteriaList",
             "TemperatureLogitsWarper",
@@ -4733,6 +4734,7 @@ if TYPE_CHECKING:
             PhrasalConstraint,
             PrefixConstrainedLogitsProcessor,
             RepetitionPenaltyLogitsProcessor,
+            SequenceBiasLogitsProcessor,
             StoppingCriteria,
             StoppingCriteriaList,
             TemperatureLogitsWarper,

src/transformers/generation/__init__.py

@@ -56,6 +56,7 @@ else:
         "NoRepeatNGramLogitsProcessor",
         "PrefixConstrainedLogitsProcessor",
         "RepetitionPenaltyLogitsProcessor",
+        "SequenceBiasLogitsProcessor",
         "EncoderRepetitionPenaltyLogitsProcessor",
         "TemperatureLogitsWarper",
         "TopKLogitsWarper",
@@ -182,6 +183,7 @@ if TYPE_CHECKING:
             NoRepeatNGramLogitsProcessor,
             PrefixConstrainedLogitsProcessor,
             RepetitionPenaltyLogitsProcessor,
+            SequenceBiasLogitsProcessor,
             TemperatureLogitsWarper,
             TopKLogitsWarper,
             TopPLogitsWarper,

src/transformers/generation/configuration_utils.py

@@ -142,11 +142,8 @@ class GenerationConfig(PushToHubMixin):
         no_repeat_ngram_size (`int`, *optional*, defaults to 0):
             If set to int > 0, all ngrams of that size can only occur once.
         bad_words_ids(`List[List[int]]`, *optional*):
-            List of token ids that are not allowed to be generated. In order to get the token ids of the words that
-            should not appear in the generated text, make sure to set `add_prefix_space=True` when initializing the
-            tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. The `add_prefix_space`
-            argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours come from
-            `pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
+            List of list of token ids that are not allowed to be generated. Check
+            [`~generation.NoBadWordsLogitsProcessor`] for further documentation and examples.
         force_words_ids(`List[List[int]]` or `List[List[List[int]]]`, *optional*):
             List of token ids that must be generated. If given a `List[List[int]]`, this is treated as a simple list of
             words that must be included, the opposite to `bad_words_ids`. If given `List[List[List[int]]]`, this
@@ -183,6 +180,10 @@ class GenerationConfig(PushToHubMixin):
             A list of pairs of integers which indicates a mapping from generation indices to token indices that will be
             forced before sampling. For example, `[[1, 123]]` means the second generated token will always be a token
             of index 123.
+        sequence_bias (`Dict[Tuple[int], float]`, *optional*)):
+            Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
+            sequence being selected, while negative biases do the opposite. Check
+            [`~generation.SequenceBiasLogitsProcessor`] for further documentation and examples.
 
         > Parameters that define the output variables of `generate`
 
@@ -262,6 +263,7 @@ class GenerationConfig(PushToHubMixin):
         self.suppress_tokens = kwargs.pop("suppress_tokens", None)
         self.begin_suppress_tokens = kwargs.pop("begin_suppress_tokens", None)
         self.forced_decoder_ids = kwargs.pop("forced_decoder_ids", None)
+        self.sequence_bias = kwargs.pop("sequence_bias", None)
 
         # Parameters that define the output variables of `generate`
         self.num_return_sequences = kwargs.pop("num_return_sequences", 1)

src/transformers/generation/logits_process.py

@@ -15,7 +15,7 @@
 
 import inspect
 import math
-from typing import Callable, Iterable, List, Optional, Tuple, Union
+from typing import Callable, Dict, Iterable, List, Tuple, Union
 
 import numpy as np
 import torch
@@ -539,140 +539,218 @@ class EncoderNoRepeatNGramLogitsProcessor(LogitsProcessor):
         return scores
 
 
-class NoBadWordsLogitsProcessor(LogitsProcessor):
+class SequenceBiasLogitsProcessor(LogitsProcessor):
     """
-    [`LogitsProcessor`] that enforces that specified sequences will never be sampled.
+    [`LogitsProcessor`] that applies an additive bias on sequences. The bias is applied to the last token of a sequence
+    when the next generated token can complete it. Consequently, to take the most of biasing sequences with more than
+    one token, consider using beam methods (to gracefully work around partially completed sequences that have a
+    negative bias) and applying the bias to their prefixes (to ensure the bias is applied earlier).
+
+    <Tip>
+
+    In order to get the token ids of the sequences that you want to bias, make sure to set `add_prefix_space=True` when
+    initializing the tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. The
+    `add_prefix_space` argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours
+    come from `pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
+
+    </Tip>
 
     Args:
-        bad_words_ids (`List[List[int]]`):
-            List of list of token ids that are not allowed to be generated. In order to get the token ids of the words
-            that should not appear in the generated text, make sure to set `add_prefix_space=True` when initializing
-            the tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. The `add_prefix_space`
-            argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours come from
-            `pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
-        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.
-    """
+        sequence_bias (`Dict[Tuple[int], float]`):
+            Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
+            sequence being selected, while negative biases do the opposite. If a sequence has a length of 1, its bias
+            will always be applied. Otherwise, the bias will only be applied if the sequence in question is about to be
+            completed (in the token selection step after this processor is applied).
 
-    def __init__(self, bad_words_ids: List[List[int]], eos_token_id: Union[int, List[int]]):
-        if not isinstance(bad_words_ids, List) or len(bad_words_ids) == 0:
-            raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
-        if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad_words_ids):
-            raise ValueError(f"`bad_words_ids` has to be a list of lists, but is {bad_words_ids}.")
-        if any(
-            any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in bad_word_ids)
-            for bad_word_ids in bad_words_ids
-        ):
-            raise ValueError(
-                f"Each list in `bad_words_ids` has to be a list of positive integers, but is {bad_words_ids}."
-            )
+    Examples:
 
-        if eos_token_id is None:
-            eos_token_id = []
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
-        bad_words_ids = list(
-            filter(lambda bad_token_seq: all([bad_token_seq != [i] for i in eos_token_id]), bad_words_ids)
-        )
-        self.bad_words_id_length_1 = []
-        self.bad_words_id_length_greater_than_1 = []
-        for word in bad_words_ids:
-            if len(word) == 1:
-                self.bad_words_id_length_1.append(word[0])
-            else:
-                self.bad_words_id_length_greater_than_1.append(word)
+    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    >>> inputs = tokenizer(["The full name of Donald is Donald"], return_tensors="pt")
 
-        self.static_bad_words_mask: Optional[torch.LongTensor] = None
+    >>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=4)
+    >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
+    The full name of Donald is Donald J. Trump Jr
 
-        for banned_token_seq in self.bad_words_id_length_greater_than_1:
-            if len(banned_token_seq) == 0:
-                raise ValueError(f"Banned words token sequences {bad_words_ids} cannot have an empty list")
+    >>> # Now let's control generation through a bias. Please note that the tokenizer is initialized differently!
+    >>> tokenizer_with_prefix_space = AutoTokenizer.from_pretrained("gpt2", add_prefix_space=True)
+
+
+    >>> def get_tokens_as_tuple(word):
+    ...     return tuple(tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0])
 
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-        if self.static_bad_words_mask is None and len(self.bad_words_id_length_1) > 0:
-            self.static_bad_words_mask = self._calc_static_bad_word_mask(scores)
 
-        dynamic_banned_tokens = self._calc_banned_bad_words_ids(input_ids.tolist())
-        scores = self._set_scores_to_inf_for_banned_tokens(scores, dynamic_banned_tokens)
+    >>> # If we add a negative bias without beam search, it may become "stuck" in a prefix without good continuations
+    >>> sequence_bias = {get_tokens_as_tuple("Trump"): -10.0}
+    >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, sequence_bias=sequence_bias)
+    >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
+    The full name of Donald is Donald J. Donald,
 
+    >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
+    >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
+    The full name of Donald is Donald Rumsfeld,
+
+    >>> # We can also add a positive bias to nudge the model towards specific tokens or continuations
+    >>> sequence_bias = {get_tokens_as_tuple("Donald Duck"): 10.0}
+    >>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
+    >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
+    The full name of Donald is Donald Duck.
+    ```
+    """
+
+    def __init__(self, sequence_bias: Dict[Tuple[int], float]):
+        self.sequence_bias = sequence_bias
+        self._validate_arguments()
+
+        # Bias variables that will be populated on the first call (for retrocompatibility purposes, the vocabulary size
+        # is infered in the first usage, which inhibits initializing here)
+        self.sequences_length_greater_than_1 = []
+        self.length_1_bias = None
+        self.length_greather_than_1_bias = None
+        self.prepared_bias_variables = False
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        # 1 - Prepares the bias tensors. This is only needed the first time the logit processor is called.
+        if not self.prepared_bias_variables:
+            self._prepare_bias_variables(scores)
+
+        # 2 - prepares an empty bias to add
+        bias = torch.zeros_like(scores)
+
+        # 3 - include the bias from length = 1
+        bias += self.length_1_bias
+
+        # 4 - include the bias from length > 1, after determining which biased sequences may be completed.
+        # `matching_mask` is a (batch_size, vocab_size) boolean mask that is True for all tokens whose corresponding
+        # bias should be applied. The bias is applied on the last token of the sequence, if (and only if) the sequence
+        # may become complete this iteration.
+        matching_mask = torch.zeros_like(scores, dtype=torch.bool)
+        for sequence_ids in self.sequences_length_greater_than_1:
+            if len(sequence_ids) > input_ids.shape[1]:  # the sequence is longer than the context, ignore
+                continue
+            prefix_length = len(sequence_ids) - 1
+            last_token = sequence_ids[-1]
+            matching_rows = torch.eq(
+                input_ids[:, -prefix_length:],
+                torch.tensor(sequence_ids[:-1], dtype=input_ids.dtype, device=input_ids.device),
+            ).prod(dim=1)
+            matching_mask[:, last_token] |= matching_rows.bool()
+        bias += torch.where(matching_mask, self.length_greather_than_1_bias, 0.0)
+
+        # 5 - apply the bias to the scores
+        scores = scores + bias
         return scores
 
-    def _calc_static_bad_word_mask(self, scores: torch.FloatTensor) -> torch.BoolTensor:
-        static_bad_words_mask = torch.zeros(scores.shape[1])
-        static_bad_words_mask[self.bad_words_id_length_1] = 1
-        return static_bad_words_mask.unsqueeze(0).to(scores.device).bool()
-
-    def _tokens_match(self, prev_tokens: List[int], tokens: List[int]) -> bool:
-        if len(tokens) == 0:
-            # if bad word tokens is just one token always ban it
-            return True
-        elif len(tokens) > len(prev_tokens):
-            # if bad word tokens are longer then prev input_ids they can't be equal
-            return False
+    def _prepare_bias_variables(self, scores: torch.FloatTensor):
+        vocabulary_size = scores.shape[-1]
+        sequence_bias = self.sequence_bias
+        tokens_with_bias = []
+
+        # Check biased tokens out of bounds
+        invalid_biases = []
+        for sequence_ids in sequence_bias:
+            for token_id in sequence_ids:
+                if token_id >= vocabulary_size:
+                    invalid_biases.append(token_id)
+        if len(invalid_biases) > 0:
+            raise ValueError(
+                f"The model vocabulary size is {vocabulary_size}, but the following tokens were being biased: "
+                f"{invalid_biases}"
+            )
+
+        # Precompute the bias tensors to be applied. Sequences of length 1 are kept separately, as they can be applied
+        # with simpler logic.
+        self.length_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
+        self.length_greather_than_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
+        for sequence_ids, bias in sequence_bias.items():
+            if len(sequence_ids) == 1:
+                self.length_1_bias[sequence_ids[-1]] = bias
             else:
-            return prev_tokens[-len(tokens) :] == tokens
+                self.sequences_length_greater_than_1.append(sequence_ids)
+                if self.length_greather_than_1_bias[sequence_ids[-1]] != 0.0:
+                    raise ValueError(
+                        "Setting a bias on sequences that share a common token termination is not yet supported. "
+                        "Please open an issue if you see this error message (after checking that it doesn't already "
+                        "exist)."
+                    )
+                self.length_greather_than_1_bias[sequence_ids[-1]] = bias
+            tokens_with_bias.append(sequence_ids[-1])
 
-    def _calc_banned_bad_words_ids(self, prev_input_ids: List[List[int]]) -> Iterable[int]:
-        banned_tokens = []
-        for prev_input_ids_slice in prev_input_ids:
-            banned_tokens_slice = []
-            for banned_token_seq in self.bad_words_id_length_greater_than_1:
-                if self._tokens_match(prev_input_ids_slice, banned_token_seq[:-1]):
-                    banned_tokens_slice.append(banned_token_seq[-1])
+        self.prepared_bias_variables = True
 
-            banned_tokens.append(banned_tokens_slice)
+    def _validate_arguments(self):
+        sequence_bias = self.sequence_bias
+        if not isinstance(sequence_bias, dict) or len(sequence_bias) == 0:
+            raise ValueError(f"`sequence_bias` has to be a non-empty dictionary, but is {sequence_bias}.")
+        if any(not isinstance(sequence_ids, tuple) for sequence_ids in sequence_bias.keys()):
+            raise ValueError(f"`sequence_bias` has to be a dict with tuples as keys, but is {sequence_bias}.")
+        if any(
+            any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in sequence_ids)
+            or len(sequence_ids) == 0
+            for sequence_ids in sequence_bias.keys()
+        ):
+            raise ValueError(
+                f"Each key in `sequence_bias` has to be a non-empty tuple of positive integers, but is "
+                f"{sequence_bias}."
+            )
+        if any(not isinstance(bias, float) for bias in sequence_bias.values()):
+            raise ValueError(f"`sequence_bias` has to be a dict with floats as values, but is {sequence_bias}.")
 
-        return banned_tokens
 
-    def _set_scores_to_inf_for_banned_tokens(
-        self, scores: torch.Tensor, banned_tokens: List[List[int]]
-    ) -> torch.Tensor:
+class NoBadWordsLogitsProcessor(SequenceBiasLogitsProcessor):
     """
-        Modifies the scores in place by setting the banned token positions to `-inf`. Banned token is expected to be a
-        list of list of banned tokens to ban in the format [[batch index, vocabulary position],...
+    [`LogitsProcessor`] that enforces that specified sequences will never be selected.
+
+    <Tip>
+
+    In order to get the token ids of the words that should not appear in the generated text, make sure to set
+    `add_prefix_space=True` when initializing the tokenizer, and use `tokenizer(bad_words,
+    add_special_tokens=False).input_ids`. The `add_prefix_space` argument is only supported for some slow tokenizers,
+    as fast tokenizers' prefixing behaviours come from `pre tokenizers`. Read more
+    [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
+
+    </Tip>
 
     Args:
-            scores: logits distribution of shape (batch size, vocabulary size)
-            banned_tokens: list of list of tokens to ban of length (batch_size)
+        bad_words_ids (`List[List[int]]`):
+            List of list of token ids that are not allowed to be generated.
+        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.
     """
-        banned_mask_list = []
-        for idx, batch_banned_tokens in enumerate(banned_tokens):
-            for token in batch_banned_tokens:
-                # Eliminates invalid bad word IDs that are over the vocabulary size.
-                if token <= scores.shape[1]:
-                    banned_mask_list.append([idx, token])
-                else:
-                    logger.error(
-                        f"An invalid bad word ID is defined: {token}. This ID is not contained in the "
-                        "vocabulary, and is therefore ignored."
-                    )
-        if not banned_mask_list and self.static_bad_words_mask is None:
-            return scores
 
-        else:
-            if banned_mask_list:
-                indices = torch.ones(len(banned_mask_list))
-                banned_mask = torch.LongTensor(banned_mask_list, device=indices.device)
-                # A sparse tensor is generated from a list of coordinates: [[0, 1], [0, 2], [2, 0]]. A conversion to dense tensor generates:
-                # [ 0  1  1 ]
-                # [ 0  0  0 ]
-                # [ 1  0  0 ]
-
-                banned_mask = (
-                    torch.sparse.LongTensor(banned_mask.t(), indices, scores.size())
-                    .to(scores.device)
-                    .to_dense()
-                    .bool()
+    def __init__(self, bad_words_ids: List[List[int]], eos_token_id: Union[int, List[int]]):
+        self.bad_word_ids = bad_words_ids
+        self._validate_arguments()
+
+        # Filter EOS token from bad_words_ids
+        if eos_token_id is None:
+            eos_token_id = []
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        bad_words_ids = list(
+            filter(lambda bad_token_seq: all([bad_token_seq != [i] for i in eos_token_id]), bad_words_ids)
         )
 
-                if self.static_bad_words_mask is not None:
-                    banned_mask = torch.bitwise_or(banned_mask, self.static_bad_words_mask)
-            else:
-                banned_mask = self.static_bad_words_mask
+        # Forbidding a sequence is equivalent to setting its bias to -inf
+        sequence_bias = {tuple(sequence): float("-inf") for sequence in bad_words_ids}
+        super().__init__(sequence_bias=sequence_bias)
 
-            scores = scores.masked_fill(banned_mask, -float("inf"))
-            return scores
+    def _validate_arguments(self):
+        bad_words_ids = self.bad_word_ids
+        if not isinstance(bad_words_ids, list) or len(bad_words_ids) == 0:
+            raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
+        if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad_words_ids):
+            raise ValueError(f"`bad_words_ids` has to be a list of lists, but is {bad_words_ids}.")
+        if any(
+            any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in bad_word_ids)
+            for bad_word_ids in bad_words_ids
+        ):
+            raise ValueError(
+                f"Each list in `bad_words_ids` has to be a list of positive integers, but is {bad_words_ids}."
+            )
 
 
 class PrefixConstrainedLogitsProcessor(LogitsProcessor):

src/transformers/generation/utils.py

@@ -56,6 +56,7 @@ from .logits_process import (
     NoRepeatNGramLogitsProcessor,
     PrefixConstrainedLogitsProcessor,
     RepetitionPenaltyLogitsProcessor,
+    SequenceBiasLogitsProcessor,
     SuppressTokensAtBeginLogitsProcessor,
     SuppressTokensLogitsProcessor,
     TemperatureLogitsWarper,
@@ -842,8 +843,9 @@ class GenerationMixin:
         # instantiate processors list
         processors = LogitsProcessorList()
 
-        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
-        # all samplers can be found in `generation_utils_samplers.py`
+        if generation_config.sequence_bias is not None:
+            processors.append(SequenceBiasLogitsProcessor(sequence_bias=generation_config.sequence_bias))
+
         if generation_config.diversity_penalty is not None and generation_config.diversity_penalty > 0.0:
             processors.append(
                 HammingDiversityLogitsProcessor(

src/transformers/utils/dummy_pt_objects.py

@@ -240,6 +240,13 @@ class RepetitionPenaltyLogitsProcessor(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class SequenceBiasLogitsProcessor(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class StoppingCriteria(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/generation/test_logits_process.py

@@ -46,6 +46,7 @@ if is_torch_available():
         NoRepeatNGramLogitsProcessor,
         PrefixConstrainedLogitsProcessor,
         RepetitionPenaltyLogitsProcessor,
+        SequenceBiasLogitsProcessor,
         TemperatureLogitsWarper,
         TopKLogitsWarper,
         TopPLogitsWarper,
@@ -512,6 +513,27 @@ class LogitsProcessorTest(unittest.TestCase):
         filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
         self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3))
 
+    def test_bias_dist_processor(self):
+        vocab_size = 5
+        batch_size = 2
+
+        input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
+        positive_bias = {(1,): 100.0, (4,): 100.0}
+        negative_bias = {(1, 0): -100.0, (0, 1, 2): -100.0, (1, 3, 1, 3): -100.0}
+        sequence_bias = {**positive_bias, **negative_bias}
+
+        # scores = 0 to facilitate checks
+        scores = torch.zeros((batch_size, vocab_size), dtype=torch.float, device=torch_device)
+
+        bias_dist_proc = SequenceBiasLogitsProcessor(sequence_bias=sequence_bias)
+        filtered_scores = bias_dist_proc(input_ids, scores.clone())
+
+        # batch 1: positive bias: tokens (1, 4); negative bias: tokens (0, 3); neutral: tokens (2)
+        # batch 2: positive bias: tokens (1, 4); negative bias: tokens (0, 2); neutral: tokens (3)
+        self.assertListEqual(
+            filtered_scores.tolist(), [[-100.0, 100.0, 0.0, -100.0, 100.0], [-100.0, 100.0, -100.0, 0.0, 100.0]]
+        )
+
     def test_processor_list(self):
         batch_size = 4
         sequence_length = 10