🚨🚨 Generate: standardize beam search behavior across frameworks (#21368)

src/transformers/generation/beam_search.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 from abc import ABC, abstractmethod
 from collections import UserDict
 from typing import List, Optional, Tuple, Union
@@ -130,8 +129,6 @@ class BeamSearchScorer(BeamScorer):
     Args:
         batch_size (`int`):
             Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
-        max_length (`int`):
-            The maximum length of the sequence to be generated.
         num_beams (`int`):
             Number of beams for beam search.
         device (`torch.device`):
@@ -142,14 +139,20 @@ class BeamSearchScorer(BeamScorer):
             the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
             likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
             `length_penalty` < 0.0 encourages shorter sequences.
-        do_early_stopping (`bool`, *optional*, defaults to `False`):
-            Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
+        do_early_stopping (`bool` or `str`, *optional*, defaults to `False`):
+            Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
+            `True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
+            heuristic is applied and the generation stops when is it very unlikely to find better candidates;
+            `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
+            beam search algorithm).
         num_beam_hyps_to_keep (`int`, *optional*, defaults to 1):
             The number of beam hypotheses that shall be returned upon calling
             [`~transformer.BeamSearchScorer.finalize`].
         num_beam_groups (`int`):
             Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
             See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
+        max_length (`int`, *optional*):
+            The maximum length of the sequence to be generated.
     """
 
     def __init__(
@@ -158,10 +161,10 @@ class BeamSearchScorer(BeamScorer):
         num_beams: int,
         device: torch.device,
         length_penalty: Optional[float] = 1.0,
-        do_early_stopping: Optional[bool] = False,
+        do_early_stopping: Optional[Union[bool, str]] = False,
         num_beam_hyps_to_keep: Optional[int] = 1,
         num_beam_groups: Optional[int] = 1,
-        **kwargs,
+        max_length: Optional[int] = None,
     ):
         self.num_beams = num_beams
         self.device = device
@@ -177,6 +180,7 @@ class BeamSearchScorer(BeamScorer):
                 num_beams=self.num_beams,
                 length_penalty=self.length_penalty,
                 early_stopping=self.do_early_stopping,
+                max_length=max_length,
             )
             for _ in range(batch_size)
         ]
@@ -194,13 +198,6 @@ class BeamSearchScorer(BeamScorer):
                 f" divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}."
             )
 
-        if "max_length" in kwargs:
-            warnings.warn(
-                "Passing `max_length` to BeamSearchScorer is deprecated and has no effect. "
-                "`max_length` should be passed directly to `beam_search(...)`, `beam_sample(...)`"
-                ", or `group_beam_search(...)`."
-            )
-
     @property
     def is_done(self) -> bool:
         return self._done.all()
@@ -402,8 +399,6 @@ class ConstrainedBeamSearchScorer(BeamScorer):
     Args:
         batch_size (`int`):
             Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
-        max_length (`int`):
-            The maximum length of the sequence to be generated.
         num_beams (`int`):
             Number of beams for beam search.
         constraints (`List[Constraint]`):
@@ -417,14 +412,20 @@ class ConstrainedBeamSearchScorer(BeamScorer):
             the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
             likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
             `length_penalty` < 0.0 encourages shorter sequences.
-        do_early_stopping (`bool`, *optional*, defaults to `False`):
-            Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
+        do_early_stopping (`bool` or `str`, *optional*, defaults to `False`):
+            Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
+            `True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
+            heuristic is applied and the generation stops when is it very unlikely to find better candidates;
+            `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
+            beam search algorithm).
         num_beam_hyps_to_keep (`int`, *optional*, defaults to 1):
             The number of beam hypotheses that shall be returned upon calling
             [`~transformer.BeamSearchScorer.finalize`].
         num_beam_groups (`int`):
             Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
             See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
+        max_length (`int`, *optional*):
+            The maximum length of the sequence to be generated.
     """
 
     def __init__(
@@ -434,10 +435,10 @@ class ConstrainedBeamSearchScorer(BeamScorer):
         constraints: List[Constraint],
         device: torch.device,
         length_penalty: Optional[float] = 1.0,
-        do_early_stopping: Optional[bool] = False,
+        do_early_stopping: Optional[Union[bool, str]] = False,
         num_beam_hyps_to_keep: Optional[int] = 1,
         num_beam_groups: Optional[int] = 1,
-        **kwargs,
+        max_length: Optional[int] = None,
     ):
         self.num_beams = num_beams
         self.device = device
@@ -454,6 +455,7 @@ class ConstrainedBeamSearchScorer(BeamScorer):
                 num_beams=self.num_beams,
                 length_penalty=self.length_penalty,
                 early_stopping=self.do_early_stopping,
+                max_length=max_length,
             )
             for _ in range(batch_size)
         ]
@@ -471,13 +473,6 @@ class ConstrainedBeamSearchScorer(BeamScorer):
                 f" divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}."
             )
 
-        if "max_length" in kwargs:
-            warnings.warn(
-                "Passing `max_length` to ConstrainedBeamSearchScorer is deprecated and has no effect. "
-                "`max_length` should be passed directly to `beam_search(...)`, `beam_sample(...)`"
-                ", or `group_beam_search(...)`."
-            )
-
     @property
     def is_done(self) -> bool:
         return self._done.all()
@@ -865,16 +860,23 @@ class ConstrainedBeamSearchScorer(BeamScorer):
 
 
 class BeamHypotheses:
-    def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool):
+    def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
         """
         Initialize n-best list of hypotheses.
         """
         self.length_penalty = length_penalty
         self.early_stopping = early_stopping
+        self.max_length = max_length
         self.num_beams = num_beams
         self.beams = []
         self.worst_score = 1e9
 
+        if not isinstance(self.early_stopping, bool) and self.max_length is None:
+            raise ValueError(
+                "When `do_early_stopping` is set to a string, `max_length` must be defined. Ensure it is passed to the"
+                " BeamScorer class instance at initialization time."
+            )
+
     def __len__(self):
         """
         Number of hypotheses in the list.
@@ -903,9 +905,26 @@ class BeamHypotheses:
 
         if len(self) < self.num_beams:
             return False
-        elif self.early_stopping:
+
+        # `True`: stop as soon as at least `num_beams` hypotheses are finished
+        if self.early_stopping is True:
             return True
+        # `False`: heuristic -- compute best possible score from `cur_len`, even though it is not entirely accurate
+        #  when `length_penalty` is positive. See the discussion below for more details.
+        # https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
+        elif self.early_stopping is False:
+            highest_attainable_score = best_sum_logprobs / cur_len**self.length_penalty
+            ret = self.worst_score >= highest_attainable_score
+            return ret
+        # `"never"`: compute the best possible score, depending on the signal of `length_penalty`
+        else:
+            # `length_penalty` > 0.0 -> max denominator is obtaned from `max_length`, not from `cur_len` -> min
+            # abs(`highest_attainable_score`) is obtained -> `highest_attainable_score` is negative, hence we obtain
+            # its max this way
+            if self.length_penalty > 0.0:
+                highest_attainable_score = best_sum_logprobs / self.max_length**self.length_penalty
+            # the opposite logic applies here (max `highest_attainable_score` from `cur_len`)
             else:
-            cur_score = best_sum_logprobs / cur_len**self.length_penalty
-            ret = self.worst_score >= cur_score
+                highest_attainable_score = best_sum_logprobs / cur_len**self.length_penalty
+            ret = self.worst_score >= highest_attainable_score
             return ret

src/transformers/generation/configuration_utils.py

@@ -71,8 +71,12 @@ class GenerationConfig(PushToHubMixin):
             `min_new_tokens`. Its effect is overridden by `min_new_tokens`, if also set.
         min_new_tokens (`int`, *optional*):
             The minimum numbers of tokens to generate, ignoring the number of tokens in the prompt.
-        early_stopping (`bool`, *optional*, defaults to `False`):
-            Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
+        early_stopping (`bool` or `str`, *optional*, defaults to `False`):
+            Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
+            `True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
+            heuristic is applied and the generation stops when is it very unlikely to find better candidates;
+            `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
+            beam search algorithm).
         max_time(`float`, *optional*):
             The maximum amount of time you allow the computation to run for in seconds. generation will still finish
             the current pass after allocated time has been passed.
@@ -290,6 +294,9 @@ class GenerationConfig(PushToHubMixin):
                     logger.error(f"Can't set {key} with value {value} for {self}")
                     raise err
 
+        # Validate the values of the attributes
+        self.validate()
+
     def __eq__(self, other):
         self_dict = self.__dict__.copy()
         other_dict = other.__dict__.copy()
@@ -302,6 +309,14 @@ class GenerationConfig(PushToHubMixin):
     def __repr__(self):
         return f"{self.__class__.__name__} {self.to_json_string()}"
 
+    def validate(self):
+        """
+        Validates the values of the attributes of the GenerationConfig instance, and raises a `ValueError` if any of
+        the values are invalid.
+        """
+        if self.early_stopping not in {True, False, "never"}:
+            raise ValueError(f"`early_stopping` must be a boolean or 'never', but is {self.early_stopping}.")
+
     def save_pretrained(
         self,
         save_directory: Union[str, os.PathLike],

src/transformers/generation/flax_utils.py

@@ -19,7 +19,7 @@ import copy
 import inspect
 import warnings
 from functools import partial
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Union
 
 import numpy as np
 
@@ -275,6 +275,7 @@ class FlaxGenerationMixin:
 
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
+        generation_config.validate()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # set init values
@@ -633,7 +634,7 @@ class FlaxGenerationMixin:
         pad_token_id: Optional[int] = None,
         eos_token_id: Optional[int] = None,
         length_penalty: Optional[float] = None,
-        early_stopping: Optional[bool] = None,
+        early_stopping: Optional[Union[bool, str]] = None,
         logits_processor: Optional[FlaxLogitsProcessorList] = None,
         trace: bool = True,
         params: Optional[Dict[str, jnp.ndarray]] = None,
@@ -733,14 +734,22 @@ class FlaxGenerationMixin:
             not_max_length_yet = state.cur_len < max_length
 
             # 2. can the new beams still improve?
-            best_running_score = state.running_scores[:, -1:] / (max_length**length_penalty)
+            # early_stopping == False -> apply heuristic = always get the best score from `cur_len`. See the discussion
+            # below for more details.
+            # https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
+            # early_stopping == "never" -> compute the best score from max_length or cur_len, depending on the sign of
+            #   length_penalty. Positive length_penalty favors longer sequences, thus we use max_length there.
+            if early_stopping == "never" and length_penalty > 0.0:
+                best_running_score = state.running_scores[:, :1] / (max_length**length_penalty)
+            else:
+                best_running_score = state.running_scores[:, :1] / (state.cur_len**length_penalty)
             worst_finished_score = jnp.where(
                 state.is_sent_finished, jnp.min(state.scores, axis=1, keepdims=True), np.array(-1.0e7)
             )
-            improvement_still_possible = jnp.all(worst_finished_score < best_running_score)
+            improvement_still_possible = jnp.any(best_running_score > worst_finished_score)
 
             # 3. is there still a beam that has not finished?
-            still_open_beam = ~(jnp.all(state.is_sent_finished) & early_stopping)
+            still_open_beam = ~(jnp.all(state.is_sent_finished) & (early_stopping is True))
 
             return not_max_length_yet & still_open_beam & improvement_still_possible
 
@@ -813,7 +822,7 @@ class FlaxGenerationMixin:
             # 5. Get running sequences scores for next
             # Determine the top k beam indices (from top 2*k beams) from log probs
             # and gather top k beams (from top 2*k beams).
-            next_topk_indices = jnp.flip(lax.top_k(running_topk_log_probs, k=num_beams)[1], axis=1)
+            next_topk_indices = lax.top_k(running_topk_log_probs, k=num_beams)[1]
             next_running_sequences, next_running_scores = gather_beams(
                 [topk_sequences, running_topk_log_probs], next_topk_indices, batch_size, num_beams
             )
@@ -824,10 +833,9 @@ class FlaxGenerationMixin:
             # - make sure no scores can be added anymore if beam is full
             # - make sure still running sequences cannot be chosen as finalized beam
             topk_log_probs = topk_log_probs / (state.cur_len**length_penalty)
-            beams_in_batch_are_full = (
-                jnp.broadcast_to(state.is_sent_finished.all(axis=-1, keepdims=True), did_topk_just_finished.shape)
-                & early_stopping
-            )
+            beams_in_batch_are_full = jnp.broadcast_to(
+                state.is_sent_finished.all(axis=-1, keepdims=True), did_topk_just_finished.shape
+            ) & (early_stopping is True)
             add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
             topk_log_probs += add_penalty * np.array(-1.0e7)
 
@@ -838,7 +846,7 @@ class FlaxGenerationMixin:
             merged_sequences = jnp.concatenate([state.sequences, topk_sequences], axis=1)
             merged_scores = jnp.concatenate([state.scores, topk_log_probs], axis=1)
             merged_is_sent_finished = jnp.concatenate([state.is_sent_finished, did_topk_just_finished], axis=1)
-            topk_merged_indices = jnp.flip(lax.top_k(merged_scores, k=num_beams)[1], axis=1)
+            topk_merged_indices = lax.top_k(merged_scores, k=num_beams)[1]
             next_sequences, next_scores, next_is_sent_finished = gather_beams(
                 [merged_sequences, merged_scores, merged_is_sent_finished], topk_merged_indices, batch_size, num_beams
             )
@@ -877,7 +885,7 @@ class FlaxGenerationMixin:
         scores = jnp.where(none_finished[:, None], state.scores, state.running_scores)
 
         # take best beam for each batch
-        sequences = sequences[:, -1]
-        scores = scores[:, -1]
+        sequences = sequences[:, 0]
+        scores = scores[:, 0]
 
         return FlaxBeamSearchOutput(sequences=sequences, scores=scores)

src/transformers/generation/tf_utils.py

@@ -611,6 +611,7 @@ class TFGenerationMixin:
 
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
+        generation_config.validate()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 2. Cast input dtypes to tf.int32 unless they're floats (which happens for some image models)
@@ -1808,7 +1809,7 @@ class TFGenerationMixin:
         pad_token_id: Optional[int] = None,
         eos_token_id: Optional[int] = None,
         length_penalty: Optional[float] = None,
-        early_stopping: Optional[bool] = None,
+        early_stopping: Optional[Union[bool, str]] = None,
         logits_processor: Optional[TFLogitsProcessorList] = None,
         logits_warper: Optional[TFLogitsProcessorList] = None,
         num_return_sequences: Optional[int] = None,
@@ -1838,8 +1839,12 @@ class TFGenerationMixin:
                 to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
                 the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
                 while `length_penalty` < 0.0 encourages shorter sequences.
-            early_stopping (`bool`, *optional*, defaults to `False`):
-                Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not.
+            early_stopping (`bool` or `str`, *optional*, defaults to `False`):
+                Controls the stopping condition for beam-based methods, like beam-search. It accepts the following
+                values: `True`, where the generation stops as soon as there are `num_beams` complete candidates;
+                `False`, where an heuristic is applied and the generation stops when is it very unlikely to find better
+                candidates; `"never"`, where the beam search procedure only stops when there cannot be better
+                candidates (canonical beam search algorithm).
             logits_processor (`[TFLogitsProcessorList]`, *optional*):
                 An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
                 used to modify the prediction scores of the language modeling head applied at each generation step.
@@ -2009,16 +2014,24 @@ class TFGenerationMixin:
             not_max_length_yet = cur_len < max_length
 
             # 2. can the new beams still improve?
+            # early_stopping == False -> apply heuristic = always get the best score from `cur_len`. See the discussion
+            # below for more details.
+            # https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
+            # early_stopping == "never" -> compute the best score from max_length or cur_len, depending on the sign of
+            #   length_penalty. Positive length_penalty favors longer sequences, thus we use max_length there.
+            if early_stopping == "never" and length_penalty > 0.0:
                 best_running_score = running_scores[:, :1] / (max_length**length_penalty)
+            else:
+                best_running_score = running_scores[:, :1] / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
             worst_finished_score = tf.where(
                 is_sent_finished, tf.math.reduce_min(scores, axis=1, keepdims=True), -1.0e9
             )
-            improvement_still_possible = tf.math.reduce_all(worst_finished_score < best_running_score)
+            improvement_still_possible = tf.math.reduce_any(best_running_score > worst_finished_score)
 
             # 3. is there still a beam that has not finished?
-            still_open_beam = ~(tf.math.reduce_all(is_sent_finished) & early_stopping)
+            still_open_beam = ~(tf.math.reduce_all(is_sent_finished) & (early_stopping is True))
 
-            return not_max_length_yet & (still_open_beam | improvement_still_possible)
+            return not_max_length_yet & still_open_beam & improvement_still_possible
 
         def beam_search_body_fn(
             cur_len,
@@ -2140,12 +2153,9 @@ class TFGenerationMixin:
             # - make sure no scores can be added anymore if beam is full
             # - make sure still running sequences cannot be chosen as finalized beam
             topk_log_probs = topk_log_probs / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
-            beams_in_batch_are_full = (
-                tf.broadcast_to(
+            beams_in_batch_are_full = tf.broadcast_to(
                 tf.math.reduce_all(is_sent_finished, axis=-1, keepdims=True), shape_list(did_topk_just_finished)
-                )
-                & early_stopping
-            )
+            ) & (early_stopping is True)
             add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
             topk_log_probs += tf.cast(add_penalty, tf.float32) * -1.0e9
 
@@ -2239,7 +2249,7 @@ class TFGenerationMixin:
         sequences = tf.where(none_finished[:, None, None], sequences, running_sequences)
         scores = tf.where(none_finished[:, None], scores, running_scores)
 
-        # Take best beams for each batch (the score is sorted in ascending order)
+        # Take best beams for each batch (the score is sorted in descending order)
         sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
         scores = flatten_beam_dim(scores[:, :num_return_sequences])
 

src/transformers/generation/utils.py

@@ -1190,6 +1190,7 @@ class GenerationMixin:
 
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
+        generation_config.validate()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 2. Set generation parameters if not already defined
@@ -1458,6 +1459,7 @@ class GenerationMixin:
                 length_penalty=generation_config.length_penalty,
                 do_early_stopping=generation_config.early_stopping,
                 num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                max_length=generation_config.max_length,
             )
             # 12. interleave input_ids with `num_beams` additional sequences per batch
             input_ids, model_kwargs = self._expand_inputs_for_generation(
@@ -1493,6 +1495,7 @@ class GenerationMixin:
                 device=inputs_tensor.device,
                 length_penalty=generation_config.length_penalty,
                 do_early_stopping=generation_config.early_stopping,
+                max_length=generation_config.max_length,
             )
 
             # 13. interleave input_ids with `num_beams` additional sequences per batch
@@ -1536,12 +1539,12 @@ class GenerationMixin:
             beam_scorer = BeamSearchScorer(
                 batch_size=batch_size,
                 num_beams=generation_config.num_beams,
-                max_length=stopping_criteria.max_length,
                 device=inputs_tensor.device,
                 length_penalty=generation_config.length_penalty,
                 do_early_stopping=generation_config.early_stopping,
                 num_beam_hyps_to_keep=generation_config.num_return_sequences,
                 num_beam_groups=generation_config.num_beam_groups,
+                max_length=generation_config.max_length,
             )
             # 12. interleave input_ids with `num_beams` additional sequences per batch
             input_ids, model_kwargs = self._expand_inputs_for_generation(
@@ -1629,6 +1632,7 @@ class GenerationMixin:
                 length_penalty=generation_config.length_penalty,
                 do_early_stopping=generation_config.early_stopping,
                 num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                max_length=generation_config.max_length,
             )
             # 12. interleave input_ids with `num_beams` additional sequences per batch
             input_ids, model_kwargs = self._expand_inputs_for_generation(

src/transformers/models/rag/modeling_rag.py

@@ -1566,6 +1566,7 @@ class RagTokenForGeneration(RagPreTrainedModel):
                 length_penalty=generation_config.length_penalty,
                 do_early_stopping=generation_config.early_stopping,
                 num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                max_length=generation_config.max_length,
             )
             return self.beam_search(
                 input_ids,

tests/generation/test_utils.py

@@ -2034,59 +2034,6 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
                 **model_kwargs,
             )
 
-    def test_beam_search_warning_if_max_length_is_passed(self):
-        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
-        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
-        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
-            torch_device
-        )
-
-        batch_size = 1
-        num_beams = 3
-
-        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
-        input_ids = input_ids.expand(num_beams, -1)
-        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
-
-        # pretend decoder_input_ids correspond to first encoder input id
-        decoder_input_ids = input_ids[:, :1]
-
-        stopping_criteria_max_length = 18
-        stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)])
-
-        with self.assertWarns(UserWarning):
-            beam_scorer = BeamSearchScorer(
-                batch_size=batch_size,
-                num_beams=num_beams,
-                device=torch_device,
-                max_length=10,
-            )
-
-        generated_ids = bart_model.beam_search(
-            decoder_input_ids,
-            num_beams=num_beams,
-            stopping_criteria=stopping_criteria,
-            beam_scorer=beam_scorer,
-            **model_kwargs,
-        )
-
-        beam_scorer_no_max_len = BeamSearchScorer(
-            batch_size=batch_size,
-            num_beams=num_beams,
-            device=torch_device,
-        )
-
-        generated_ids_no_max_len = bart_model.beam_search(
-            decoder_input_ids,
-            num_beams=num_beams,
-            stopping_criteria=stopping_criteria,
-            beam_scorer=beam_scorer_no_max_len,
-            **model_kwargs,
-        )
-
-        # BeamSearchScorer max_length should not influence "real" max_length
-        self.assertEqual(generated_ids.tolist(), generated_ids_no_max_len.tolist())
-
     def test_custom_stopping_criteria_overload_error(self):
         article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
         bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")

tests/models/bart/test_modeling_flax_bart.py

@@ -426,7 +426,7 @@ class FlaxBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationT
         )
 
         input_ids = tokenizer(input_str, return_tensors="np").input_ids
-        sequences = model.generate(input_ids, num_beams=2, max_length=20).sequences
+        sequences = model.generate(input_ids, num_beams=2, min_length=None, max_length=20).sequences
 
         output_str = tokenizer.batch_decode(sequences)[0]
 

tests/models/gpt2/test_modeling_flax_gpt2.py

@@ -224,7 +224,7 @@ class FlaxGPT2ModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittes
         output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
 
         expected_string = [
-            "Hello this is a long string of words. I'm going to try to explain what I mean.",
+            "Hello this is a long string of words. I'm going to start with the first one.\n",
             "Hey, I'm not sure if I'm going to be able to do",
         ]
 

tests/models/t5/test_modeling_flax_t5.py

@@ -1076,7 +1076,7 @@ class FlaxT5ModelIntegrationTests(unittest.TestCase):
         expected_summaries = [
             'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a'
             " cell phone video of the final seconds . \"one can hear cries of 'My God' in several languages,\" one"
-            " magazine says . all 150 on board were killed when germanwings flight 9525 crashed .",
+            " magazine says . all 150 on board were killed in the crash .",
             "the formal accession was marked by a ceremony at The Hague, in the Netherlands . the ICC opened a"
             " preliminary examination into the situation in the occupied Palestinian territory . as members of the"
             " court, Palestinians may be subject to counter-charges as well .",