Removed `max_length` from being mandatory within `generate`. (#11314)

* Removed `max_length` from being mandatory within `generate`.

- Moving on to fully using `StoppingCriteria` for `greedy` and `sample`
modes.
- `max_length` still used for `beam_search` and `group_beam_search`
(Follow up PR)
- Fixes a bug with MaxLengthStoppingCriteria (we should stop as soon a
we hit the max_length, the comparison needs to be or equal, that affects
the tests).
- Added options to use `logits_processor` and `stopping_criteria`
directly within `generate` function (so some users can define their own
`logits_processor` and `stopping_criteria`).
- Modified the backward compat tests to make sure we issue a warning.

* Fix `max_length` argument in `generate`.

* Moving validate to being functional.

- Renamed `smax_length` to `stoppping_max_length`.

* Removing `logits_processor` and `stopping_criteria` from `generate`
arguments.

* Deepcopy.

* Fix global variable name.

src/transformers/generation_stopping_criteria.py

 import time
 import warnings
 from abc import ABC
+from copy import deepcopy
 from typing import Optional
 
 import torch
@@ -8,7 +9,7 @@ import torch
 from .file_utils import add_start_docstrings
 
 
-LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
+STOPPING_CRITERIA_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
             Indices of input sequence tokens in the vocabulary.
@@ -33,7 +34,7 @@ LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
 class StoppingCriteria(ABC):
     """Abstract base class for all stopping criteria that can be applied during generation."""
 
-    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, score: torch.FloatTensor, **kwargs) -> bool:
         raise NotImplementedError("StoppingCriteria needs to be subclassed")
 
@@ -51,9 +52,9 @@ class MaxLengthCriteria(StoppingCriteria):
     def __init__(self, max_length: int):
         self.max_length = max_length
 
-    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        return input_ids.shape[-1] > self.max_length
+        return input_ids.shape[-1] >= self.max_length
 
 
 class MaxTimeCriteria(StoppingCriteria):
@@ -73,25 +74,29 @@ class MaxTimeCriteria(StoppingCriteria):
         self.max_time = max_time
         self.initial_timestamp = time.time() if initial_timestamp is None else initial_timestamp
 
-    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
         return time.time() - self.initial_timestamp > self.max_time
 
 
 class StoppingCriteriaList(list):
-    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
         return any(criteria(input_ids, scores) for criteria in self)
 
-
-def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_length: int):
-    found = False
-    for stopping_criterium in stopping_criteria:
-        if isinstance(stopping_criterium, MaxLengthCriteria):
-            found = True
-            if stopping_criterium.max_length != max_length:
-                warnings.warn(
-                    "You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning
-                )
-    if not found:
-        stopping_criteria.append(MaxLengthCriteria(max_length=max_length))
+    @property
+    def max_length(self) -> Optional[int]:
+        for stopping_criterium in self:
+            if isinstance(stopping_criterium, MaxLengthCriteria):
+                return stopping_criterium.max_length
+        return None
+
+
+def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_length: int) -> StoppingCriteriaList:
+    stopping_max_length = stopping_criteria.max_length
+    new_stopping_criteria = deepcopy(stopping_criteria)
+    if stopping_max_length is not None and stopping_max_length != max_length:
+        warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning)
+    elif stopping_max_length is None:
+        new_stopping_criteria.append(MaxLengthCriteria(max_length=max_length))
+    return new_stopping_criteria

src/transformers/generation_utils.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
@@ -564,6 +565,7 @@ class GenerationMixin:
         This class returns a :obj:`~transformers.LogitsProcessorList` list object that contains all relevant
         :obj:`~transformers.LogitsProcessor` instances used to modify the scores of the language model head.
         """
+        processors = LogitsProcessorList()
 
         # init warp parameters
         repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
@@ -589,7 +591,6 @@ class GenerationMixin:
             remove_invalid_values if remove_invalid_values is not None else self.config.remove_invalid_values
         )
         # 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`
@@ -629,7 +630,6 @@ class GenerationMixin:
         max_length: Optional[int],
         max_time: Optional[float],
     ) -> StoppingCriteriaList:
-
         stopping_criteria = StoppingCriteriaList()
         if max_length is not None:
             stopping_criteria.append(MaxLengthCriteria(max_length=max_length))
@@ -859,9 +859,9 @@ class GenerationMixin:
         """
 
         # set init values
+        max_length = max_length if max_length is not None else self.config.max_length
         num_beams = num_beams if num_beams is not None else self.config.num_beams
         num_beam_groups = num_beam_groups if num_beam_groups is not None else self.config.num_beam_groups
-        max_length = max_length if max_length is not None else self.config.max_length
         do_sample = do_sample if do_sample is not None else self.config.do_sample
         num_return_sequences = (
             num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
@@ -958,10 +958,13 @@ class GenerationMixin:
             remove_invalid_values=remove_invalid_values,
         )
 
-        stopping_criteria = self._get_stopping_criteria(
-            max_length=max_length,
-            max_time=max_time,
-        )
+        stopping_criteria = self._get_stopping_criteria(max_length=max_length, max_time=max_time)
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
 
         if is_greedy_gen_mode:
             if num_return_sequences > 1:
@@ -974,7 +977,6 @@ class GenerationMixin:
                 input_ids,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
-                max_length=max_length,
                 pad_token_id=pad_token_id,
                 eos_token_id=eos_token_id,
                 output_scores=output_scores,
@@ -1003,7 +1005,6 @@ class GenerationMixin:
                 logits_processor=logits_processor,
                 logits_warper=logits_warper,
                 stopping_criteria=stopping_criteria,
-                max_length=max_length,
                 pad_token_id=pad_token_id,
                 eos_token_id=eos_token_id,
                 output_scores=output_scores,
@@ -1021,9 +1022,12 @@ class GenerationMixin:
             if num_return_sequences > num_beams:
                 raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
 
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
+
             beam_scorer = BeamSearchScorer(
                 batch_size=batch_size,
-                max_length=max_length,
+                max_length=stopping_criteria.max_length,
                 num_beams=num_beams,
                 device=self.device,
                 length_penalty=length_penalty,
@@ -1039,7 +1043,6 @@ class GenerationMixin:
                 beam_scorer,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
-                max_length=max_length,
                 pad_token_id=pad_token_id,
                 eos_token_id=eos_token_id,
                 output_scores=output_scores,
@@ -1056,9 +1059,11 @@ class GenerationMixin:
             batch_size = input_ids.shape[0] * num_return_sequences
 
             length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
             beam_scorer = BeamSearchScorer(
                 batch_size=batch_size,
-                max_length=max_length,
+                max_length=stopping_criteria.max_length,
                 num_beams=num_beams,
                 device=self.device,
                 length_penalty=length_penalty,
@@ -1079,7 +1084,6 @@ class GenerationMixin:
                 logits_processor=logits_processor,
                 logits_warper=logits_warper,
                 stopping_criteria=stopping_criteria,
-                max_length=max_length,
                 pad_token_id=pad_token_id,
                 eos_token_id=eos_token_id,
                 output_scores=output_scores,
@@ -1100,10 +1104,13 @@ class GenerationMixin:
             if num_beams % num_beam_groups != 0:
                 raise ValueError("`num_beams` should be divisible by `num_beam_groups` for group beam search.")
 
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
+
             diverse_beam_scorer = BeamSearchScorer(
                 batch_size=batch_size,
-                max_length=max_length,
                 num_beams=num_beams,
+                max_length=stopping_criteria.max_length,
                 device=self.device,
                 length_penalty=length_penalty,
                 do_early_stopping=early_stopping,
@@ -1119,7 +1126,6 @@ class GenerationMixin:
                 diverse_beam_scorer,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
-                max_length=max_length,
                 pad_token_id=pad_token_id,
                 eos_token_id=eos_token_id,
                 output_scores=output_scores,
@@ -1160,7 +1166,8 @@ class GenerationMixin:
                 :class:`~transformers.StoppingCriteria` used to tell if the generation loop should stop.
 
             max_length (:obj:`int`, `optional`, defaults to 20):
-                The maximum length of the sequence to be generated.
+                **DEPRECATED**. Use :obj:`logits_processor` or :obj:`stopping_criteria` directly to cap the number of
+                generated tokens. The maximum length of the sequence to be generated.
             pad_token_id (:obj:`int`, `optional`):
                 The id of the `padding` token.
             eos_token_id (:obj:`int`, `optional`):
@@ -1220,8 +1227,12 @@ class GenerationMixin:
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-        max_length = max_length if max_length is not None else self.config.max_length
-        validate_stopping_criteria(stopping_criteria, max_length)
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
         output_scores = output_scores if output_scores is not None else self.config.output_scores
@@ -1251,7 +1262,7 @@ class GenerationMixin:
         cur_len = input_ids.shape[-1]
 
         this_peer_finished = False  # used by synced_gpus only
-        while cur_len < max_length:
+        while True:
 
             if synced_gpus:
                 # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
@@ -1384,7 +1395,8 @@ class GenerationMixin:
                 :class:`~transformers.LogitsWarper` used to warp the prediction score distribution of the language
                 modeling head applied before multinomial sampling at each generation step.
             max_length (:obj:`int`, `optional`, defaults to 20):
-                The maximum length of the sequence to be generated.
+                **DEPRECATED**. Use :obj:`logits_processor` or :obj:`stopping_criteria` directly to cap the number of
+                generated tokens. The maximum length of the sequence to be generated.
             pad_token_id (:obj:`int`, `optional`):
                 The id of the `padding` token.
             eos_token_id (:obj:`int`, `optional`):
@@ -1452,8 +1464,12 @@ class GenerationMixin:
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-        max_length = max_length if max_length is not None else self.config.max_length
-        validate_stopping_criteria(stopping_criteria, max_length)
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
         logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
@@ -1485,7 +1501,7 @@ class GenerationMixin:
 
         this_peer_finished = False  # used by synced_gpus only
         # auto-regressive generation
-        while cur_len < max_length:
+        while True:
 
             if synced_gpus:
                 # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
@@ -1620,7 +1636,8 @@ class GenerationMixin:
                 An instance of :class:`~transformers.StoppingCriteriaList`. List of instances of class derived from
                 :class:`~transformers.StoppingCriteria` used to tell if the generation loop should stop.
             max_length (:obj:`int`, `optional`, defaults to 20):
-                The maximum length of the sequence to be generated.
+                **DEPRECATED**. Use :obj:`logits_processor` or :obj:`stopping_criteria` directly to cap the number of
+                generated tokens. The maximum length of the sequence to be generated.
             pad_token_id (:obj:`int`, `optional`):
                 The id of the `padding` token.
             eos_token_id (:obj:`int`, `optional`):
@@ -1700,8 +1717,14 @@ class GenerationMixin:
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-        max_length = max_length if max_length is not None else self.config.max_length
-        validate_stopping_criteria(stopping_criteria, max_length)
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        if len(stopping_criteria) == 0:
+            warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
         output_scores = output_scores if output_scores is not None else self.config.output_scores
@@ -1740,7 +1763,7 @@ class GenerationMixin:
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
         this_peer_finished = False  # used by synced_gpus only
-        while cur_len < max_length:
+        while True:
 
             if synced_gpus:
                 # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
@@ -1770,7 +1793,7 @@ class GenerationMixin:
             # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
             # cannot be generated both before and after the `F.log_softmax` operation.
             next_token_logits = self.adjust_logits_during_generation(
-                next_token_logits, cur_len=cur_len, max_length=max_length
+                next_token_logits, cur_len=cur_len, max_length=None
             )
 
             next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
@@ -1907,7 +1930,8 @@ class GenerationMixin:
                 :class:`~transformers.LogitsWarper` used to warp the prediction score distribution of the language
                 modeling head applied before multinomial sampling at each generation step.
             max_length (:obj:`int`, `optional`, defaults to 20):
-                The maximum length of the sequence to be generated.
+                **DEPRECATED**. Use :obj:`logits_processor` or :obj:`stopping_criteria` directly to cap the number of
+                generated tokens. The maximum length of the sequence to be generated.
             pad_token_id (:obj:`int`, `optional`):
                 The id of the `padding` token.
             eos_token_id (:obj:`int`, `optional`):
@@ -1994,7 +2018,12 @@ class GenerationMixin:
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-        max_length = max_length if max_length is not None else self.config.max_length
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
         output_scores = output_scores if output_scores is not None else self.config.output_scores
@@ -2028,7 +2057,7 @@ class GenerationMixin:
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
         this_peer_finished = False  # used by synced_gpus only
-        while cur_len < max_length:
+        while True:
 
             if synced_gpus:
                 # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
@@ -2058,7 +2087,7 @@ class GenerationMixin:
             # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
             # cannot be generated both before and after the `F.log_softmax` operation.
             next_token_logits = self.adjust_logits_during_generation(
-                next_token_logits, cur_len=cur_len, max_length=max_length
+                next_token_logits, cur_len=cur_len, max_length=None
             )
 
             next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
@@ -2195,7 +2224,8 @@ class GenerationMixin:
                 An instance of :class:`~transformers.StoppingCriteriaList`. List of instances of class derived from
                 :class:`~transformers.StoppingCriteria` used to tell if the generation loop should stop.
             max_length (:obj:`int`, `optional`, defaults to 20):
-                The maximum length of the sequence to be generated.
+                **DEPRECATED**. Use :obj:`logits_processor` or :obj:`stopping_criteria` directly to cap the number of
+                generated tokens. The maximum length of the sequence to be generated.
             pad_token_id (:obj:`int`, `optional`):
                 The id of the `padding` token.
             eos_token_id (:obj:`int`, `optional`):
@@ -2279,8 +2309,12 @@ class GenerationMixin:
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-        max_length = max_length if max_length is not None else self.config.max_length
-        validate_stopping_criteria(stopping_criteria, max_length)
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
         pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
         output_scores = output_scores if output_scores is not None else self.config.output_scores
@@ -2324,7 +2358,7 @@ class GenerationMixin:
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
         this_peer_finished = False  # used by synced_gpus only
-        while cur_len < max_length:
+        while True:
 
             if synced_gpus:
                 # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
@@ -2378,7 +2412,7 @@ class GenerationMixin:
                 # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
                 # cannot be generated both before and after the `F.log_softmax` operation.
                 next_token_logits = self.adjust_logits_during_generation(
-                    next_token_logits, cur_len=cur_len, max_length=max_length
+                    next_token_logits, cur_len=cur_len, max_length=None
                 )
 
                 next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * group_size, vocab_size)

tests/test_generation_stopping_criteria.py

@@ -40,10 +40,10 @@ class StoppingCriteriaTestCase(unittest.TestCase):
 
         self.assertFalse(criteria(input_ids, scores))
 
-        input_ids, scores = self._get_tensors(10)
+        input_ids, scores = self._get_tensors(9)
         self.assertFalse(criteria(input_ids, scores))
 
-        input_ids, scores = self._get_tensors(11)
+        input_ids, scores = self._get_tensors(10)
         self.assertTrue(criteria(input_ids, scores))
 
     def test_max_length_criteria(self):
@@ -52,10 +52,10 @@ class StoppingCriteriaTestCase(unittest.TestCase):
         input_ids, scores = self._get_tensors(5)
         self.assertFalse(criteria(input_ids, scores))
 
-        input_ids, scores = self._get_tensors(10)
+        input_ids, scores = self._get_tensors(9)
         self.assertFalse(criteria(input_ids, scores))
 
-        input_ids, scores = self._get_tensors(11)
+        input_ids, scores = self._get_tensors(10)
         self.assertTrue(criteria(input_ids, scores))
 
     def test_max_time_criteria(self):
@@ -73,7 +73,6 @@ class StoppingCriteriaTestCase(unittest.TestCase):
         with self.assertWarns(UserWarning):
             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 11)
 
-        stopping_criteria = StoppingCriteriaList()
-        validate_stopping_criteria(stopping_criteria, 11)
+        stopping_criteria = validate_stopping_criteria(StoppingCriteriaList(), 11)
 
         self.assertEqual(len(stopping_criteria), 1)

tests/test_generation_utils.py

@@ -1358,13 +1358,14 @@ class GenerationIntegrationTests(unittest.TestCase):
             bos_token_id=bart_model.config.bos_token_id,
         )
 
-        bart_model.greedy_search(
-            input_ids,
-            max_length=max_length,
-            pad_token_id=bart_model.config.pad_token_id,
-            eos_token_id=bart_model.config.eos_token_id,
-            **model_kwargs,
-        )
+        with self.assertWarns(UserWarning):
+            bart_model.greedy_search(
+                input_ids,
+                max_length=max_length,
+                pad_token_id=bart_model.config.pad_token_id,
+                eos_token_id=bart_model.config.eos_token_id,
+                **model_kwargs,
+            )
 
     def test_max_length_backward_compat_sample(self):
         article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
@@ -1381,13 +1382,14 @@ class GenerationIntegrationTests(unittest.TestCase):
             bos_token_id=bart_model.config.bos_token_id,
         )
         with torch.no_grad():
-            bart_model.sample(
-                input_ids,
-                max_length=max_length,
-                pad_token_id=bart_model.config.pad_token_id,
-                eos_token_id=bart_model.config.eos_token_id,
-                **model_kwargs,
-            )
+            with self.assertWarns(UserWarning):
+                bart_model.sample(
+                    input_ids,
+                    max_length=max_length,
+                    pad_token_id=bart_model.config.pad_token_id,
+                    eos_token_id=bart_model.config.eos_token_id,
+                    **model_kwargs,
+                )
 
     def test_max_length_backward_compat_beam_search(self):
         article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
@@ -1413,9 +1415,10 @@ class GenerationIntegrationTests(unittest.TestCase):
             num_beams=num_beams,
             device=torch_device,
         )
-        _ = bart_model.beam_search(
-            input_ids, num_beams=num_beams, max_length=max_length, beam_scorer=beam_scorer, **model_kwargs
-        )
+        with self.assertWarns(UserWarning):
+            _ = bart_model.beam_search(
+                input_ids, num_beams=num_beams, max_length=max_length, beam_scorer=beam_scorer, **model_kwargs
+            )
 
     def test_max_length_backward_compat_group_beam_search(self):
         article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
@@ -1445,9 +1448,10 @@ class GenerationIntegrationTests(unittest.TestCase):
             num_beam_hyps_to_keep=num_return_sequences,
             num_beam_groups=num_beam_groups,
         )
-        bart_model.group_beam_search(
-            input_ids, diverse_beam_scorer, num_beams=num_beams, max_length=max_length, **model_kwargs
-        )
+        with self.assertWarns(UserWarning):
+            bart_model.group_beam_search(
+                input_ids, diverse_beam_scorer, num_beams=num_beams, max_length=max_length, **model_kwargs
+            )
 
     def test_max_length_warning_if_different(self):
         article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""