Constrained Beam Search [*With* Disjunctive Decoding] (#15761)

* added classes to get started with constrained beam search

* in progress, think i can directly force tokens now but not yet with the round robin

* think now i have total control, now need to code the bank selection

* technically works as desired, need to optimize and fix design choices leading to undersirable outputs

* complete PR #1 without disjunctive decoding

* removed incorrect tests

* Delete k.txt

* Delete test.py

* Delete test.sh

* revert changes to test scripts

* genutils

* full implementation with testing, no disjunctive yet

* shifted docs

* passing all tests realistically ran locally

* removing accidentally included print statements

* fixed source of error in initial PR test

* fixing the get_device() vs device trap

* fixed documentation docstrings about constrained_beam_search

* fixed tests having failing for Speech2TextModel's floating point inputs

* fix cuda long tensor

* added examples and testing for them and founx & fixed a bug in beam_search and constrained_beam_search

* deleted accidentally added test halting code with assert False

* code reformat

* Update tests/test_generation_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py

* fixing based on comments on PR

* took out the testing code that should but work fails without the beam search moditification ; style changes

* fixing comments issues

* docstrings for ConstraintListState

* typo in PhrsalConstraint docstring

* docstrings improvements

* finished adding what is sort of an opinionated implementation of disjunctive generation, but it revealed errors in inner beam search logic during testing.

* fixed bug found in constrained beam search that used beam_idx that were not global across all the batches

* disjunctive constraint working 100% correctly

* passing all tests

* Accidentally included mlruns

* Update src/transformers/generation_beam_constraints.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/generation_beam_constraints.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* complete overhaul of type complexities and other nits

* strict type checks in generate()

* fixing second round of feedback by narsil

* fixed failing generation test because of type check overhaul

* generation test fail fix

* fixing test fails

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/internal/generation_utils.mdx

@@ -229,6 +229,8 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 [[autodoc]] PhrasalConstraint
 
+[[autodoc]] DisjunctiveConstraint
+
 [[autodoc]] ConstraintListState
 
 ## BeamSearch

src/transformers/__init__.py

@@ -623,6 +623,7 @@ if is_torch_available():
     _import_structure["generation_beam_constraints"] = [
         "Constraint",
         "ConstraintListState",
+        "DisjunctiveConstraint",
         "PhrasalConstraint",
     ]
     _import_structure["generation_beam_search"] = ["BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer"]
@@ -2857,7 +2858,12 @@ if TYPE_CHECKING:
             TextDataset,
             TextDatasetForNextSentencePrediction,
         )
-        from .generation_beam_constraints import Constraint, ConstraintListState, PhrasalConstraint
+        from .generation_beam_constraints import (
+            Constraint,
+            ConstraintListState,
+            DisjunctiveConstraint,
+            PhrasalConstraint,
+        )
         from .generation_beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
         from .generation_logits_process import (
             ForcedBOSTokenLogitsProcessor,

src/transformers/generation_beam_constraints.py

@@ -1,7 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Optional, Union
-
-import torch
+from typing import List, Optional
 
 
 class Constraint(ABC):
@@ -137,37 +135,38 @@ class PhrasalConstraint(Constraint):
             The id of the token that must be generated by the output.
     """
 
-    def __init__(self, token_ids: Union[List[int], torch.LongTensor]):
+    def __init__(self, token_ids: List[int]):
         super(Constraint, self).__init__()
 
-        is_int_list = isinstance(token_ids, List) and isinstance(token_ids[0], int)
-        is_tensor = isinstance(token_ids, torch.Tensor)
-        is_int_tensor = (
-            is_tensor and token_ids.dtype in [torch.int16, torch.int32, torch.int64] and len(token_ids.size()) == 1
-        )
-        not_positive = torch.any(token_ids < 0) if is_tensor else len([t for t in token_ids if t < 0]) > 0
-        if isinstance(token_ids, int) or not (is_int_list or is_int_tensor) or not_positive:
-            raise ValueError(f"`token_ids` has to be a single list or tensor of positive integers but is {token_ids}")
-
-        if not is_tensor:
-            token_ids = torch.tensor(token_ids)
+        if not isinstance(token_ids, list) or len(token_ids) == 0:
+            raise ValueError(f"`token_ids` has to be a non-emtpy list, but is {token_ids}.")
+        if any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids):
+            raise ValueError(f"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.")
 
         self.token_ids = token_ids
 
-        self.seqlen = self.token_ids.size(0)
+        self.seqlen = len(self.token_ids)
         self.fulfilled_idx = -1  # the index of the currently fulfilled step
         self.completed = False
 
     def advance(self):
+        if self.completed:
+            return None
         return self.token_ids[self.fulfilled_idx + 1]
 
     def does_advance(self, token_id: int):
+        if not isinstance(token_id, int):
+            raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(token_id)}")
+
         if self.completed:
             return False
-        # move to cpu to guarantee no device issues.
-        return token_id.cpu() == self.token_ids[self.fulfilled_idx + 1].cpu()
+
+        return token_id == self.token_ids[self.fulfilled_idx + 1]
 
     def update(self, token_id: int):
+        if not isinstance(token_id, int):
+            raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(token_id)}")
+
         stepped = False
         completed = False
         reset = False
@@ -202,6 +201,151 @@ class PhrasalConstraint(Constraint):
         return new_constraint
 
 
+class DisjunctiveTrie:
+    def __init__(self, nested_token_ids: List[List[int]], no_subsets=True):
+        r"""
+        A helper class that builds a trie with the words represented in `nested_token_ids`.
+        """
+        self.max_height = max([len(one) for one in nested_token_ids])
+
+        root = dict()
+        for token_ids in nested_token_ids:
+            level = root
+            for tidx, token_id in enumerate(token_ids):
+                if token_id not in level:
+                    level[token_id] = dict()
+
+                level = level[token_id]
+
+        if no_subsets and self.has_subsets(root, nested_token_ids):
+            raise ValueError(
+                f"Each list in `nested_token_ids` can't be a complete subset of another list, but is {nested_token_ids}."
+            )
+
+        self.trie = root
+
+    def next_tokens(self, current_seq):
+        """
+        The next possible tokens that will progress the trie, given the current sequence of tokens in `current_seq`.
+        """
+        start = self.trie
+
+        for current_token in current_seq:
+            start = start[current_token]
+
+        next_tokens = list(start.keys())
+
+        return next_tokens
+
+    def reached_leaf(self, current_seq):
+        next_tokens = self.next_tokens(current_seq)
+
+        return len(next_tokens) == 0
+
+    def count_leaves(self, root):
+        next_nodes = list(root.values())
+        if len(next_nodes) == 0:
+            return 1
+        else:
+            return sum([self.count_leaves(nn) for nn in next_nodes])
+
+    def has_subsets(self, trie, nested_token_ids):
+        """
+        Returns whether # of leaves == # of words. Otherwise some word is a subset of another.
+        """
+        leaf_count = self.count_leaves(trie)
+        return len(nested_token_ids) != leaf_count
+
+
+class DisjunctiveConstraint(Constraint):
+    r"""
+    A special [`Constraint`] that is fulfilled by fulfilling just one of several constraints.
+
+    Args:
+        nested_token_ids (`List[List[int]]`): a list of words, where each word is a list of ids. This constraint
+        is fulfilled by generating just one from the list of words.
+    """
+
+    def __init__(self, nested_token_ids: List[List[int]]):
+        super(Constraint, self).__init__()
+
+        if not isinstance(nested_token_ids, list) or len(nested_token_ids) == 0:
+            raise ValueError(f"`nested_token_ids` has to be a non-emtpy list, but is {nested_token_ids}.")
+        if any(not isinstance(token_ids, list) for token_ids in nested_token_ids):
+            raise ValueError(f"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.")
+        if any(
+            any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
+            for token_ids in nested_token_ids
+        ):
+            raise ValueError(
+                f"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."
+            )
+
+        self.trie = DisjunctiveTrie(nested_token_ids)
+        self.token_ids = nested_token_ids
+
+        self.seqlen = self.trie.max_height
+        self.current_seq = []
+        self.completed = False
+
+    def advance(self):
+        token_list = self.trie.next_tokens(self.current_seq)
+
+        if len(token_list) == 0:
+            return None
+        else:
+            return token_list
+
+    def does_advance(self, token_id: int):
+        if not isinstance(token_id, int):
+            raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(token_id)}")
+
+        next_tokens = self.trie.next_tokens(self.current_seq)
+
+        return token_id in next_tokens
+
+    def update(self, token_id: int):
+        if not isinstance(token_id, int):
+            raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(token_id)}")
+
+        stepped = False
+        completed = False
+        reset = False
+
+        if self.does_advance(token_id):
+            self.current_seq.append(token_id)
+            stepped = True
+        else:
+            reset = True
+            self.reset()
+
+        completed = self.trie.reached_leaf(self.current_seq)
+        self.completed = completed
+
+        return stepped, completed, reset
+
+    def reset(self):
+        self.completed = False
+        self.current_seq = []
+
+    def remaining(self):
+        if self.completed:
+            # since this can be completed without reaching max height
+            return 0
+        else:
+            return self.seqlen - len(self.current_seq)
+
+    def copy(self, stateful=False):
+        new_constraint = DisjunctiveConstraint(self.token_ids)
+
+        if stateful:
+            new_constraint.seq_len = self.seqlen
+            new_constraint.current_seq = self.current_seq
+            new_constraint.completed = self.completed
+
+        return new_constraint
+
+
 class ConstraintListState:
     r"""
     A class for beam scorers to track its progress through a list of constraints.
@@ -215,7 +359,7 @@ class ConstraintListState:
         self.constraints = constraints
 
         # max # of steps required to fulfill a given constraint
-        self.max_seqlen = max([c.seqlen for c in constraints if isinstance(c, PhrasalConstraint)])
+        self.max_seqlen = max([c.seqlen for c in constraints])
         self.n_constraints = len(constraints)
         self.completed = False
 
@@ -249,26 +393,33 @@ class ConstraintListState:
         Though we don't care which constraint is fulfilled first, if we are in the progress of fulfilling a constraint,
         that's the only one we'll return.
         """
-        if self.inprogress_constraint is None:
         token_list = []
+        if self.inprogress_constraint is None:
             for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                 advance = constraint.advance()
+                if isinstance(advance, int):
                     token_list.append(advance)
+                elif isinstance(advance, list):
+                    token_list.extend(advance)
         else:
-            token_list = [self.inprogress_constraint.advance()]
+            advance = self.inprogress_constraint.advance()
+            if isinstance(advance, int):
+                token_list.append(advance)
+            elif isinstance(advance, list):
+                token_list.extend(advance)
 
         if len(token_list) == 0:
             return None
         else:
-            return torch.stack(token_list)
+            return token_list
 
-    def reset(self, token_ids: Optional[torch.LongTensor]):
+    def reset(self, token_ids: Optional[List[int]]):
         """
         token_ids: the tokens generated thus far to reset the state of the progress through constraints.
         """
         self.init_state()
 
-        if token_ids is not None and token_ids.size(0) > 0:
+        if token_ids is not None:
             for token in token_ids:
                 # completes or steps **one** constraint
                 complete, stepped = self.add(token)
@@ -277,9 +428,10 @@ class ConstraintListState:
                 if self.completed:
                     break
 
-        return self
+    def add(self, token_id: int):
+        if not isinstance(token_id, int):
+            raise ValueError(f"`token_id` should be an `int`, but is `{token_id}`.")
 
-    def add(self, token_id: Union[int, torch.LongTensor]):
         complete, stepped = False, False
 
         if self.completed:
@@ -324,8 +476,8 @@ class ConstraintListState:
 
                     if not stepped:
                         raise Exception(
-                            "constraint.update(token_id) is not yielding incremental progress, "
-                            "even though constraint.does_advance(token_id) is true."
+                            "`constraint.update(token_id)` is not yielding incremental progress, "
+                            "even though `constraint.does_advance(token_id)` is true."
                         )
 
                     if complete:

src/transformers/generation_beam_search.py

@@ -443,7 +443,7 @@ class ConstrainedBeamSearchScorer(BeamScorer):
 
     def check_completes_constraints(self, sequence):
         new_state = self.make_constraint_states(1)[0]
-        new_state = new_state.reset(sequence)
+        new_state.reset(sequence)
         return new_state.completed
 
     def process(
@@ -484,6 +484,7 @@ class ConstrainedBeamSearchScorer(BeamScorer):
                 - **next_beam_scores** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Updated scores of
                   all
                 non-finished beams.
+
                 - **next_beam_tokens** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Next tokens to be
                   added
                 to the non-finished beam_hypotheses.
@@ -537,7 +538,7 @@ class ConstrainedBeamSearchScorer(BeamScorer):
                     if is_beam_token_worse_than_top_num_beams:
                         continue
 
-                    completes_constraint = self.check_completes_constraints(input_ids[batch_beam_idx])
+                    completes_constraint = self.check_completes_constraints(input_ids[batch_beam_idx].cpu().tolist())
                     if completes_constraint:
                         beam_hyp.add(
                             input_ids[batch_beam_idx].clone(),
@@ -628,23 +629,23 @@ class ConstrainedBeamSearchScorer(BeamScorer):
             # hypotheses.
 
             topk_state = topk_contraint_states[seq_idx]
-            topk_state.reset(full_hypotheses[seq_idx])
+            topk_state.reset(full_hypotheses[seq_idx].cpu().tolist())
 
             advance_state = advance_constraint_states[seq_idx]
-            advance_state.reset(pre_seq)
+            advance_state.reset(pre_seq.cpu().tolist())
 
             if not advance_state.completed:
-                advance_tokens = advance_state.advance()
-                for advance_token in advance_tokens.to(device):
+                advance_tokens = torch.LongTensor(advance_state.advance()).to(device)
+                for advance_token in advance_tokens:
                     # since adding each `advance_token` leads to a different hypothesis, create new state instance.
                     new_state = advance_state.copy(stateful=True)
-                    new_state.add(advance_token)
+                    new_state.add(advance_token.cpu().tolist())
 
                     advance_seq = torch.cat((pre_seq, advance_token.unsqueeze(0)), -1).cpu().tolist()
                     if advance_seq not in track_new["new_seqs"]:
                         # prevent duplicates, which are basically bound to happen in this process.
                         track_new["new_seqs"].append(advance_seq)
-                        track_new["new_indices"].append(seq_idx)
+                        track_new["new_indices"].append(sidx + seq_idx)  # idx -> global idx across all the batches
                         track_new["new_tokens"].append(advance_token)
                         track_new["new_scores"].append(this_batch_token_scores[seq_idx].take(advance_token))
                         track_new["new_states"].append(new_state)
@@ -673,8 +674,9 @@ class ConstrainedBeamSearchScorer(BeamScorer):
 
                 advance_state = advance_constraint_states[seq_idx]
 
-                advance_state.reset(advance_seq)
                 advance_seq = advance_seq.cpu().tolist()
+
+                advance_state.reset(advance_seq)
                 if advance_seq not in track_new["new_seqs"]:
                     # but still don't want to have duplicates
                     track_new["new_seqs"].append(advance_seq)
@@ -745,7 +747,7 @@ class ConstrainedBeamSearchScorer(BeamScorer):
                 final_score = final_beam_scores[batch_beam_idx].item()
                 final_tokens = input_ids[batch_beam_idx]
 
-                completes_constraint = self.check_completes_constraints(final_tokens)
+                completes_constraint = self.check_completes_constraints(final_tokens.cpu().tolist())
                 if completes_constraint:
                     beam_hyp.add(final_tokens, final_score)
                     ids_collect.append(beam_id)

src/transformers/generation_utils.py

@@ -24,7 +24,7 @@ import torch.distributed as dist
 from torch import nn
 
 from .file_utils import ModelOutput
-from .generation_beam_constraints import Constraint
+from .generation_beam_constraints import Constraint, DisjunctiveConstraint, PhrasalConstraint
 from .generation_beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
 from .generation_logits_process import (
     EncoderNoRepeatNGramLogitsProcessor,
@@ -818,6 +818,7 @@ class GenerationMixin:
         typical_p: Optional[float] = None,
         repetition_penalty: Optional[float] = None,
         bad_words_ids: Optional[Iterable[int]] = None,
+        force_words_ids: Optional[Union[Iterable[int], Iterable[Iterable[int]]]] = None,
         bos_token_id: Optional[int] = None,
         pad_token_id: Optional[int] = None,
         eos_token_id: Optional[int] = None,
@@ -904,6 +905,11 @@ class GenerationMixin:
                 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, use `tokenizer(bad_words, add_prefix_space=True,
                 add_special_tokens=False).input_ids`.
+            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 triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081),
+                where one can allow different forms of each word.
             num_return_sequences(`int`, *optional*, defaults to 1):
                 The number of independently computed returned sequences for each element in the batch.
             max_time(`float`, *optional*, defaults to None):
@@ -1038,10 +1044,18 @@ class GenerationMixin:
         >>> bad_words_ids = tokenizer(
         ...     ["idiot", "stupid", "shut up"], add_prefix_space=True, add_special_tokens=False
         >>> ).input_ids
+        >>> # get tokens of words that we want generated
+        >>> force_words_ids = tokenizer(["runs", "loves"], add_prefix_space=True, add_special_tokens=False).input_ids
         >>> # encode input context
         >>> input_ids = tokenizer(input_context, return_tensors="pt").input_ids
         >>> # generate sequences without allowing bad_words to be generated
-        >>> outputs = model.generate(input_ids=input_ids, max_length=20, do_sample=True, bad_words_ids=bad_words_ids)
+        >>> outputs = model.generate(
+        ...     input_ids=input_ids,
+        ...     max_length=20,
+        ...     do_sample=True,
+        ...     bad_words_ids=bad_words_ids,
+        ...     force_words_ids=force_words_ids,
+        ... )
         >>> print("Generated:", tokenizer.decode(outputs[0], skip_special_tokens=True))
         ```"""
         # 1. Set generation parameters if not already defined
@@ -1138,14 +1152,20 @@ class GenerationMixin:
             )
 
         # 6. determine generation mode
-        is_constraint_gen_mode = constraints is not None
-        is_greedy_gen_mode = (num_beams == 1) and (num_beam_groups == 1) and do_sample is False and constraints is None
-        is_sample_gen_mode = (num_beams == 1) and (num_beam_groups == 1) and do_sample is True and constraints is None
-        is_beam_gen_mode = (num_beams > 1) and (num_beam_groups == 1) and do_sample is False and constraints is None
-        is_beam_sample_gen_mode = (
-            (num_beams > 1) and (num_beam_groups == 1) and do_sample is True and constraints is None
+        is_constraint_gen_mode = constraints is not None or force_words_ids is not None
+        is_greedy_gen_mode = (
+            (num_beams == 1) and (num_beam_groups == 1) and do_sample is False and not is_constraint_gen_mode
         )
-        is_group_beam_gen_mode = (num_beams > 1) and (num_beam_groups > 1) and constraints is None
+        is_sample_gen_mode = (
+            (num_beams == 1) and (num_beam_groups == 1) and do_sample is True and not is_constraint_gen_mode
+        )
+        is_beam_gen_mode = (
+            (num_beams > 1) and (num_beam_groups == 1) and do_sample is False and not is_constraint_gen_mode
+        )
+        is_beam_sample_gen_mode = (
+            (num_beams > 1) and (num_beam_groups == 1) and do_sample is True and not is_constraint_gen_mode
+        )
+        is_group_beam_gen_mode = (num_beams > 1) and (num_beam_groups > 1) and not is_constraint_gen_mode
 
         if num_beam_groups > num_beams:
             raise ValueError("`num_beam_groups` has to be smaller or equal to `num_beams`")
@@ -1356,9 +1376,46 @@ class GenerationMixin:
             if num_beam_groups is not None and num_beam_groups > 1:
                 raise ValueError("`num_beam_groups` not supported yet for constrained generation.")
 
+            final_constraints = []
+            if constraints is not None:
+                final_constraints = constraints
+
+            if force_words_ids is not None:
+
+                def typeerror():
+                    raise ValueError(
+                        "`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
+                        f"of positive integers, but is {force_words_ids}."
+                    )
+
+                if not isinstance(force_words_ids, list) or len(force_words_ids) == 0:
+                    typeerror()
+
+                for word_ids in force_words_ids:
+                    if isinstance(word_ids[0], list):
+                        if not isinstance(word_ids, list) or len(word_ids) == 0:
+                            typeerror()
+                        if any(not isinstance(token_ids, list) for token_ids in word_ids):
+                            typeerror()
+                        if any(
+                            any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
+                            for token_ids in word_ids
+                        ):
+                            typeerror()
+
+                        constraint = DisjunctiveConstraint(word_ids)
+                    else:
+                        if not isinstance(word_ids, list) or len(word_ids) == 0:
+                            typeerror()
+                        if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
+                            typeerror()
+
+                        constraint = PhrasalConstraint(word_ids)
+                    final_constraints.append(constraint)
+
             # 10. prepare beam search scorer
             constrained_beam_scorer = ConstrainedBeamSearchScorer(
-                constraints=constraints,
+                constraints=final_constraints,
                 batch_size=batch_size,
                 num_beams=num_beams,
                 device=self.device,

src/transformers/utils/dummy_pt_objects.py

@@ -94,6 +94,13 @@ class ConstraintListState(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class DisjunctiveConstraint(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class PhrasalConstraint(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/generation/test_generation_beam_constraints.py

@@ -0,0 +1,115 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a clone of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import require_torch
+
+
+if is_torch_available():
+    import torch
+
+    from transformers.generation_beam_constraints import DisjunctiveConstraint
+
+
+@require_torch
+class ConstraintTest(unittest.TestCase):
+    def test_input_types(self):
+        # For consistency across different places the DisjunctiveConstraint is called,
+        # dc.token_ids is a list of integers. It is also initialized only by integers.
+
+        cset = [[1, 2, 4], [1, 2, 3, 4]]
+        dc = DisjunctiveConstraint(cset)
+        self.assertTrue(isinstance(dc.token_ids, list))
+
+        with self.assertRaises(ValueError):
+            DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))
+
+        with self.assertRaises(ValueError):
+            DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])
+
+    def test_check_illegal_input(self):
+        # We can't have constraints that are complete subsets of another. This leads to a preverse
+        # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
+        # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
+        # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
+        # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
+        cset = [[1, 2], [1, 2, 3, 4]]
+
+        with self.assertRaises(ValueError):
+            DisjunctiveConstraint(cset)  # fails here
+
+    def test_example_progression(self):
+        cset = [[1, 2, 3], [1, 2, 4]]
+
+        dc = DisjunctiveConstraint(cset)
+
+        stepped, completed, reset = dc.update(1)
+        desired = stepped is True and completed is False and reset is False
+        self.assertTrue(desired)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.current_seq == [1])
+
+        stepped, completed, reset = dc.update(2)
+        desired = stepped is True and completed is False and reset is False
+        self.assertTrue(desired)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.current_seq == [1, 2])
+
+        stepped, completed, reset = dc.update(3)
+        desired = stepped is True and completed is True and reset is False
+        self.assertTrue(desired)
+        self.assertTrue(dc.completed)  # Completed!
+        self.assertTrue(dc.current_seq == [1, 2, 3])
+
+    def test_example_progression_unequal_three_mid_and_reset(self):
+        cset = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
+
+        dc = DisjunctiveConstraint(cset)
+
+        stepped, completed, reset = dc.update(1)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.current_seq == [1])
+
+        stepped, completed, reset = dc.update(2)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.current_seq == [1, 2])
+
+        stepped, completed, reset = dc.update(4)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.current_seq == [1, 2, 4])
+
+        stepped, completed, reset = dc.update(5)
+        self.assertTrue(dc.completed)  # Completed!
+        self.assertTrue(dc.current_seq == [1, 2, 4, 5])
+
+        dc.reset()
+
+        stepped, completed, reset = dc.update(1)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.remaining() == 3)
+        self.assertTrue(dc.current_seq == [1])
+
+        stepped, completed, reset = dc.update(2)
+        self.assertTrue(not dc.completed)
+        self.assertTrue(dc.remaining() == 2)
+        self.assertTrue(dc.current_seq == [1, 2])
+
+        stepped, completed, reset = dc.update(5)
+        self.assertTrue(dc.completed)  # Completed!
+        self.assertTrue(dc.remaining() == 0)
+        self.assertTrue(dc.current_seq == [1, 2, 5])

tests/generation/test_generation_beam_search.py

@@ -25,7 +25,7 @@ from ..test_modeling_common import floats_tensor, ids_tensor
 if is_torch_available():
     import torch
 
-    from transformers.generation_beam_constraints import PhrasalConstraint
+    from transformers.generation_beam_constraints import DisjunctiveConstraint, PhrasalConstraint
     from transformers.generation_beam_search import BeamHypotheses, BeamSearchScorer, ConstrainedBeamSearchScorer
 
 
@@ -260,10 +260,10 @@ class ConstrainedBeamSearchTester:
         self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
 
         if constraints is None:
-            force_tokens = torch.randint(10, 50, (1, 2)).type(torch.LongTensor)[0]
-            constraints = [
-                PhrasalConstraint(force_tokens),
-            ]
+            force_tokens = torch.randint(10, 50, (1, 2))[0].tolist()
+            disjunctive_tokens = torch.randint(10, 50, (2, 2)).tolist()
+
+            constraints = [PhrasalConstraint(force_tokens), DisjunctiveConstraint(disjunctive_tokens)]
             self.constraints = constraints
         # cannot be randomely generated
         self.eos_token_id = vocab_size + 1
@@ -331,7 +331,13 @@ class ConstrainedBeamSearchTester:
     ):
         # check too many eos tokens
         constrained_beam_scorer = self.prepare_constrained_beam_scorer()
-        fulfilling_sequence = torch.stack([constraint.token_ids for constraint in self.constraints]).flatten()
+        stacked_token_ids = []
+        for constraint in self.constraints:
+            token_ids = constraint.token_ids
+            token_ids = token_ids[0] if isinstance(token_ids[0], list) else token_ids
+            stacked_token_ids = stacked_token_ids + token_ids
+
+        fulfilling_sequence = torch.LongTensor(stacked_token_ids)
         fulfill_len = fulfilling_sequence.size(0)
         input_ids[:, :fulfill_len] = fulfilling_sequence
 
@@ -398,7 +404,14 @@ class ConstrainedBeamSearchTester:
         max_length = self.sequence_length + 1
 
         # for testing finalize, we do want to have fulfilled constraints
-        fulfilling_sequence = torch.stack([constraint.token_ids for constraint in self.constraints]).flatten()
+        stacked_token_ids = []
+        for constraint in self.constraints:
+            token_ids = constraint.token_ids
+            token_ids = token_ids[0] if isinstance(token_ids[0], list) else token_ids
+            stacked_token_ids = stacked_token_ids + token_ids
+
+        fulfilling_sequence = torch.LongTensor(stacked_token_ids)
+
         fulfill_len = fulfilling_sequence.size(0)
         input_ids[:, :fulfill_len] = fulfilling_sequence
 
@@ -451,9 +464,17 @@ class ConstrainedBeamSearchTester:
         self.parent.assertNotEqual(sequences[2, -1].item(), self.eos_token_id)
 
         # test that the constraint is indeed fulfilled
-        for output in sequences:
-            for constraint in constraints:
+        for (output, constraint) in [(s, c) for s in sequences for c in constraints]:
             forced_token_ids = constraint.token_ids
+            if isinstance(forced_token_ids[0], list):
+                # disjunctive case
+                flag = False
+                for token_ids in forced_token_ids:
+                    if self._check_sequence_inside_sequence(output, token_ids):
+                        flag = True
+                        break
+                self.parent.assertEqual(flag, True)
+            else:
                 self.parent.assertEqual(self._check_sequence_inside_sequence(output, forced_token_ids), True)
 
         # now test that if `num_beam_hyps_to_keep` is 3 => all beams are returned
@@ -479,18 +500,23 @@ class ConstrainedBeamSearchTester:
         self.parent.assertListEqual(list(sequence_scores.shape), [self.num_beams * self.batch_size])
 
     def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
+        # check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
         # set to same device. we don't care what device.
-        tensor_1, tensor_2 = tensor_1.cpu(), tensor_2.cpu()
 
-        in_order = tensor_1.size(0) <= tensor_2.size(0)
+        if not isinstance(tensor_1, list):
+            tensor_1 = tensor_1.cpu().tolist()
+        if not isinstance(tensor_2, list):
+            tensor_2 = tensor_2.cpu().tolist()
+
+        in_order = len(tensor_1) <= len(tensor_2)
         longer = tensor_2 if in_order else tensor_1
         shorter = tensor_1 if in_order else tensor_2
 
         flag = False
-        chunk_size = shorter.size(0)
-        for chunk_idx in range(longer.size(0) - chunk_size + 1):
+        chunk_size = len(shorter)
+        for chunk_idx in range(len(longer) - chunk_size + 1):
             subseq = longer[chunk_idx : chunk_idx + chunk_size]
-            if torch.equal(subseq, shorter):
+            if subseq == shorter:
                 flag = True
                 break
 

tests/generation/test_generation_utils.py

@@ -39,7 +39,7 @@ if is_torch_available():
         VisionEncoderDecoderModel,
         top_k_top_p_filtering,
     )
-    from transformers.generation_beam_constraints import PhrasalConstraint
+    from transformers.generation_beam_constraints import DisjunctiveConstraint, PhrasalConstraint
     from transformers.generation_beam_search import BeamSearchScorer, ConstrainedBeamSearchScorer
     from transformers.generation_logits_process import (
         ForcedBOSTokenLogitsProcessor,
@@ -1202,7 +1202,7 @@ class GenerationTesterMixin:
                 min_id = 3
                 max_id = 100
 
-            force_tokens = torch.randint(min_id, max_id, (1, 2)).type(torch.LongTensor)[0]
+            force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
             constraints = [
                 PhrasalConstraint(force_tokens),
             ]
@@ -1227,7 +1227,7 @@ class GenerationTesterMixin:
 
             # check `generate()` and `constrained_beam_search()` are equal for `num_return_sequences`
             # Sample constraints
-            force_tokens = torch.randint(min_id, max_id, (1, 2)).type(torch.LongTensor)[0]
+            force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
             constraints = [
                 PhrasalConstraint(force_tokens),
             ]
@@ -1288,7 +1288,7 @@ class GenerationTesterMixin:
                 # otherwise this throws an error for Speech2TextModel since its inputs are floating points
                 min_id = 3
                 max_id = 100
-            force_tokens = torch.randint(min_id, max_id, (1, 2)).type(torch.LongTensor)[0]
+            force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
             constraints = [
                 PhrasalConstraint(force_tokens),
             ]
@@ -1499,18 +1499,23 @@ class GenerationTesterMixin:
         )
 
     def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
+        # check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
         # set to same device. we don't care what device.
-        tensor_1, tensor_2 = tensor_1.cpu(), tensor_2.cpu()
 
-        in_order = tensor_1.size(0) <= tensor_2.size(0)
+        if not isinstance(tensor_1, list):
+            tensor_1 = tensor_1.cpu().tolist()
+        if not isinstance(tensor_2, list):
+            tensor_2 = tensor_2.cpu().tolist()
+
+        in_order = len(tensor_1) <= len(tensor_2)
         longer = tensor_2 if in_order else tensor_1
         shorter = tensor_1 if in_order else tensor_2
 
         flag = False
-        chunk_size = shorter.size(0)
-        for chunk_idx in range(longer.size(0) - chunk_size + 1):
+        chunk_size = len(shorter)
+        for chunk_idx in range(len(longer) - chunk_size + 1):
             subseq = longer[chunk_idx : chunk_idx + chunk_size]
-            if torch.equal(subseq, shorter):
+            if subseq == shorter:
                 flag = True
                 break
 
@@ -2315,8 +2320,8 @@ class GenerationIntegrationTests(unittest.TestCase):
         model = GPT2LMHeadModel.from_pretrained("../gpt2").to(torch_device)
         tokenizer = GPT2Tokenizer.from_pretrained("../gpt2")
 
-        force_tokens = tokenizer.encode(" scared", return_tensors="pt").to(torch_device)[0]
-        force_tokens_2 = tokenizer.encode(" big weapons", return_tensors="pt").to(torch_device)[0]
+        force_tokens = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
+        force_tokens_2 = tokenizer("big weapons", add_prefix_space=True, add_special_tokens=False).input_ids
 
         constraints = [
             PhrasalConstraint(force_tokens),
@@ -2346,6 +2351,105 @@ class GenerationIntegrationTests(unittest.TestCase):
             ],
         )
 
+    @slow
+    def test_constrained_beam_search_mixed(self):
+        model = GPT2LMHeadModel.from_pretrained("../gpt2").to(torch_device)
+        tokenizer = GPT2Tokenizer.from_pretrained("../gpt2")
+
+        force_phrase = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
+        flexible_phrases = tokenizer(
+            ["scream", "screams", "screaming", "screamed"], add_prefix_space=True, add_special_tokens=False
+        ).input_ids
+
+        constraints = [
+            PhrasalConstraint(force_phrase),
+            DisjunctiveConstraint(flexible_phrases),
+        ]
+
+        starting_text = ["The soldiers", "The child"]
+
+        input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
+
+        outputs = model.generate(
+            input_ids,
+            constraints=constraints,
+            num_beams=10,
+            num_return_sequences=1,
+            no_repeat_ngram_size=1,
+            # max_length=20,
+            remove_invalid_values=True,
+        )
+
+        generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+        self.assertListEqual(
+            generated_text,
+            [
+                "The soldiers, who were all scared and screaming at each other as they tried to get out of the",
+                "The child was taken to a local hospital where she screamed and scared for her life, police said.",
+            ],
+        )
+
+    @slow
+    def test_constrained_beam_search_mixed_mixin(self):
+        model = GPT2LMHeadModel.from_pretrained("../gpt2").to(torch_device)
+        tokenizer = GPT2Tokenizer.from_pretrained("../gpt2")
+
+        force_word = "scared"
+        force_flexible = ["scream", "screams", "screaming", "screamed"]
+
+        force_words_ids = [
+            tokenizer([force_word], add_prefix_space=True, add_special_tokens=False).input_ids,
+            tokenizer(force_flexible, add_prefix_space=True, add_special_tokens=False).input_ids,
+        ]
+
+        starting_text = ["The soldiers", "The child"]
+
+        input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)
+
+        outputs = model.generate(
+            input_ids,
+            force_words_ids=force_words_ids,
+            num_beams=10,
+            num_return_sequences=1,
+            no_repeat_ngram_size=1,
+            remove_invalid_values=True,
+        )
+
+        generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+        self.assertListEqual(
+            generated_text,
+            [
+                "The soldiers, who were all scared and screaming at each other as they tried to get out of the",
+                "The child was taken to a local hospital where she screamed and scared for her life, police said.",
+            ],
+        )
+
+    @slow
+    def test_constrained_beam_search_example_translation_mixin(self):
+        tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        encoder_input_str = "translate English to German: How old are you?"
+        force_words = ["sind"]
+
+        input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+        force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids
+
+        outputs = model.generate(
+            input_ids,
+            force_words_ids=force_words_ids,
+            num_beams=10,
+            num_return_sequences=1,
+            no_repeat_ngram_size=1,
+            remove_invalid_values=True,
+        )
+
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+        self.assertListEqual(outputs, ["Wie alter sind Sie?"])
+
     @slow
     def test_constrained_beam_search_example_integration(self):
         tokenizer = AutoTokenizer.from_pretrained("t5-base")
@@ -2389,3 +2493,43 @@ class GenerationIntegrationTests(unittest.TestCase):
         outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
 
         self.assertListEqual(outputs, ["Wie alter sind Sie?"])
+
+    def test_constrained_beam_search_mixin_type_checks(self):
+        tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        encoder_input_str = "translate English to German: How old are you?"
+        input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+
+        with self.assertRaises(ValueError):
+            force_words = ["sind"]
+            force_words_ids = tokenizer(force_words, return_tensors="pt").input_ids
+            model.generate(
+                input_ids,
+                force_words_ids=force_words_ids,
+                num_beams=10,
+                num_return_sequences=1,
+                no_repeat_ngram_size=1,
+                remove_invalid_values=True,
+            )
+
+        with self.assertRaises(ValueError):
+            force_words = ["sind"]
+            force_words_ids = [tokenizer(force_words, return_tensors="pt").input_ids]
+            model.generate(
+                input_ids,
+                force_words_ids=force_words_ids,
+                num_beams=10,
+                num_return_sequences=1,
+                no_repeat_ngram_size=1,
+                remove_invalid_values=True,
+            )
+
+        with self.assertRaises(ValueError):
+            model.generate(input_ids, force_words_ids=[])
+
+        with self.assertRaises(ValueError):
+            model.generate(input_ids, force_words_ids=[[-1]])
+
+        with self.assertRaises(ValueError):
+            model.generate(input_ids, force_words_ids=[[[-1]]])