FlaxBart (#11537)

* Start working on FlaxBart

* Create modeling_flax_bart.py

* Write FlaxBartAttention

* Add FlaxBartEncoderLayer

* Add FlaxBartDecoderLayer and some typing

* Add helepr function for FlaxBart

* shift_tokens_right

* _make_causal_mask

* _expand_mask

* Add PositionalEmbedding and fix init_std naming

* Add FlaxBartPretrainedModel

* Add FlaxBartEncoder

* Add FlaxBartEncoder

* Add FlaxBartEncoder among modules to be imported

* YET WE CANNOT INITIALIZE THAT!! :(

* Make BartEncoder working

Change BartEncoder to instance of nn.Module so far

* Add FlaxBartDecoder

* Add FlaxBartModel

* TODO to make model run -> Prepapre model inputs

* Resolve padding

* Add FlaxBartModel

* Add FlaxBartModel into importable modules

* Remove FlaxBartEncoder and FlaxBartDecoder from importable modules

* make style; not properly working

* make style; make quality not pass due to some import I left

* Remove TODO for padding_idx in nn.Embed so far

* Add FlaxBartForConditionalGeneration

* Incorporate Flax model output classes, i.e. return_dict

* Add another models and incorporate use_cache arg

* Add FlaxBartForSequenceClassification and FlaxBartForQuestionAnswering

* Incorporate use_cache arg from PyTorch implementation

* Add all necessary Flax output utils

* Add FlaxBartForCausalLM; not working yet'

* Add minor improvements; still lacks some functionality

* Update docs, src and tests

* Add support of FlaxBart to docs/source

* Fix some bugs in FlaxBart souce code

* Add some neccessary tests for FlaxBart models - jit_compilation not passing

* Fix tests and add test_head_masking

* Fix tests for @jax.jit computation

* Add test_head_masking

* Migrate FlaxBart tests from jax.numpy to numpy

* Remove FlaxBartForCausalLM

* Clean repo

* fix bart model weight structure

* Fix FlaxBartForSequenceClassification

Slicing is not possible to use below jit, therefore, selecting sentence
representation from hidden_states must be changed.

* Allow FlaxBartForSequenceClassification for testing pt_flax equivalence

* Allow testing for FlaxBartForQA for pt_flax equivalence

* Add a comment to FlaxBartForSequenceClassification + change noise from 1e-3 to 1e-6

* remove past_key_values

* remove inputs_mebeds and make input_ids required

* add position ids

* re-write attention layer

* fix dataclass

* fix pos embeds and attention output

* fix pos embeds

* expose encode method

* expose decode method

* move docstring to top

* add cache for causal attn layer

* remove head masking for now

* s2s greedy search first pass

* boom boom

* fix typos

* fix greedy generate for bart

* use encoder, decoder layers instead of num_hidden_layers

* handle encoder_outputs

* cleanup

* simplify decoding

* more clean-up

* typos

* Change header + add {decoder_,}position_ids into 2 models

* add BartConfig

* fix existing tests

* add encode, decode methods

* Fix shift_tokens_right for JIT compilation + clarify one condition

* fix decode

* encoder => encode

* simplify generate

* add tests for encode and decode

* style

* add tests for cache

* fix equivalence tests

* sample generate now works with seq2seq

* generation tests

* initialize dense layers

* docstring and cleanup

* quality

* remove get/set input_embeddings

* address Patricks suggestions

* decode for every model, remove encoder_outputs from call

* update tests accordingly

* decode returns only decoder outputs and logits

* fix arguments

* doc encode, decode methods

* correct base_model_prefix

* fix test for seq classif model

* fix docs

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

docs/source/index.rst

@@ -299,7 +299,7 @@ Flax), PyTorch, and/or TensorFlow.
 +=============================+================+================+=================+====================+==============+
 |           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/bart.rst

@@ -131,6 +131,7 @@ BartForQuestionAnswering
 .. autoclass:: transformers.BartForQuestionAnswering
     :members: forward
 
+
 BartForCausalLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -138,7 +139,6 @@ BartForCausalLM
     :members: forward
 
 
-
 TFBartModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -151,3 +151,32 @@ TFBartForConditionalGeneration
 
 .. autoclass:: transformers.TFBartForConditionalGeneration
     :members: call
+
+
+FlaxBartModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBartModel
+    :members: __call__, encode, decode
+
+
+FlaxBartForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBartForConditionalGeneration
+    :members: __call__, encode, decode
+
+
+FlaxBartForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBartForSequenceClassification
+    :members: __call__, encode, decode
+
+
+FlaxBartForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxBartForQuestionAnswering
+    :members: __call__, encode, decode
+

src/transformers/__init__.py

@@ -1508,6 +1508,14 @@ if is_flax_available():
             "FlaxAutoModelForTokenClassification",
         ]
     )
+    _import_structure["models.bart"].extend(
+        [
+            "FlaxBartForConditionalGeneration",
+            "FlaxBartForQuestionAnswering",
+            "FlaxBartForSequenceClassification",
+            "FlaxBartModel",
+        ]
+    )
     _import_structure["models.bert"].extend(
         [
             "FlaxBertForMaskedLM",
@@ -2808,6 +2816,12 @@ if TYPE_CHECKING:
             FlaxAutoModelForSequenceClassification,
             FlaxAutoModelForTokenClassification,
         )
+        from .models.bart import (
+            FlaxBartForConditionalGeneration,
+            FlaxBartForQuestionAnswering,
+            FlaxBartForSequenceClassification,
+            FlaxBartModel,
+        )
         from .models.bert import (
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,

src/transformers/generation_flax_utils.py

@@ -101,12 +101,23 @@ class FlaxGenerationMixin:
             state = body_fn(state)
         return state
 
+    def _prepare_encoder_decoder_kwargs_for_generation(self, input_ids, model_kwargs):
+        encoder_kwargs = {
+            argument: value
+            for argument, value in model_kwargs.items()
+            if not (argument.startswith("decoder_") or argument.startswith("cross_attn"))
+        }
+        model_kwargs["encoder_outputs"] = self.encode(input_ids, return_dict=True, **encoder_kwargs)
+        return model_kwargs
+
     def generate(
         self,
         input_ids: jax_xla.DeviceArray,
         max_length: Optional[int] = None,
         pad_token_id: Optional[int] = None,
+        bos_token_id: Optional[int] = None,
         eos_token_id: Optional[int] = None,
+        decoder_start_token_id: Optional[int] = None,
         do_sample: Optional[bool] = None,
         prng_key: Optional[jax_xla.DeviceArray] = None,
         top_k: Optional[int] = None,
@@ -147,6 +158,8 @@ class FlaxGenerationMixin:
                 The id of the `beginning-of-sequence` token.
             eos_token_id (:obj:`int`, `optional`):
                 The id of the `end-of-sequence` token.
+            decoder_start_token_id (:obj:`int`, `optional`):
+                If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
             trace (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 Whether to trace generation. Setting ``trace=False`` should only be used for debugging and will lead to
                 a considerably slower runtime.
@@ -170,10 +183,23 @@ class FlaxGenerationMixin:
         """
         # set init values
         max_length = max_length if max_length is not None else self.config.max_length
+        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
         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
+        decoder_start_token_id = (
+            decoder_start_token_id if decoder_start_token_id else self.config.decoder_start_token_id
+        )
         prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
 
+        if decoder_start_token_id is None and self.config.is_encoder_decoder:
+            raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.")
+
+        if self.config.is_encoder_decoder:
+            # add encoder_outputs to model_kwargs
+            model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, model_kwargs)
+            # prepare decoder_input_ids for generation
+            input_ids = jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
         do_sample = do_sample if do_sample is not None else self.config.do_sample
 
         if do_sample:
@@ -246,10 +272,11 @@ class FlaxGenerationMixin:
         # per batch-item state bit indicating if sentence has finished.
         is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
 
-        model = self
+        # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
-
+        # and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
+        model = self.decode if self.config.is_encoder_decoder else self
         # initialize model specific kwargs
-        model_kwargs = model.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
+        model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
 
         # initialize state
         state = GreedyState(
@@ -277,8 +304,7 @@ class FlaxGenerationMixin:
             next_token = next_token[:, None]
 
             next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
-            next_model_kwargs = model.update_inputs_for_generation(model_outputs, model_kwargs)
+            next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
-
             return GreedyState(
                 cur_len=state.cur_len + 1,
                 sequences=next_sequences,
@@ -288,6 +314,7 @@ class FlaxGenerationMixin:
             )
 
         # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
+        if input_ids.shape[1] > 1:
             state = greedy_search_body_fn(state)
 
         if not trace:
@@ -327,10 +354,12 @@ class FlaxGenerationMixin:
         # per batch-item state bit indicating if sentence has finished.
         is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
 
-        model = self
+        # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
+        # and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
+        model = self.decode if self.config.is_encoder_decoder else self
 
         # initialize model specific kwargs
-        model_kwargs = model.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
+        model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
 
         # initialize state
         state = SampleState(
@@ -366,7 +395,7 @@ class FlaxGenerationMixin:
             next_token = next_token[:, None]
 
             next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
-            next_model_kwargs = model.update_inputs_for_generation(model_outputs, model_kwargs)
+            next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
 
             return SampleState(
                 cur_len=state.cur_len + 1,
@@ -378,6 +407,7 @@ class FlaxGenerationMixin:
             )
 
         # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
+        if input_ids.shape[1] > 1:
             state = sample_search_body_fn(state)
 
         if not trace:

src/transformers/modeling_flax_outputs.py

@@ -106,6 +106,154 @@ class FlaxBaseModelOutputWithPooling(ModelOutput):
     attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
 
 
+@flax.struct.dataclass
+class FlaxBaseModelOutputWithPastAndCrossAttentions(ModelOutput):
+    """
+    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
+
+    Args:
+        last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+
+            If :obj:`past_key_values` is used only the last hidden-state of the sequences of shape :obj:`(batch_size,
+            1, hidden_size)` is output.
+        past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            Tuple of :obj:`tuple(jax_xla.DeviceArray)` of length :obj:`config.n_layers`, with each tuple having 2
+            tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            ``config.is_encoder_decoder=True`` 2 additional tensors of shape :obj:`(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+            ``config.is_encoder_decoder=True`` in the cross-attention blocks) that can be used (see
+            :obj:`past_key_values` input) to speed up sequential decoding.
+        hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` and ``config.add_cross_attention=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+    """
+
+    last_hidden_state: jax_xla.DeviceArray = None
+    past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None
+    hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+
+
+@flax.struct.dataclass
+class FlaxSeq2SeqModelOutput(ModelOutput):
+    """
+    Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
+    decoding.
+
+    Args:
+        last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the decoder of the model.
+
+            If :obj:`past_key_values` is used only the last hidden-state of the sequences of shape :obj:`(batch_size,
+            1, hidden_size)` is output.
+        past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            Tuple of :obj:`tuple(jax_xla.DeviceArray)` of length :obj:`config.n_layers`, with each tuple having 2
+            tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional
+            tensors of shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
+        decoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+        decoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+        encoder_last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+        encoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    last_hidden_state: jax_xla.DeviceArray = None
+    past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None
+    decoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    decoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_last_hidden_state: Optional[jax_xla.DeviceArray] = None
+    encoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+
+
+@flax.struct.dataclass
+class FlaxCausalLMOutputWithCrossAttentions(ModelOutput):
+    """
+    Base class for causal language model (or autoregressive) outputs.
+
+    Args:
+        logits (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Cross attentions weights after the attention softmax, used to compute the weighted average in the
+            cross-attention heads.
+        past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` tuples of length :obj:`config.n_layers`, with each tuple containing the
+            cached key, value states of the self-attention and the cross-attention layers if model is used in
+            encoder-decoder setting. Only relevant if ``config.is_decoder = True``.
+
+            Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
+            :obj:`past_key_values` input) to speed up sequential decoding.
+    """
+
+    logits: jax_xla.DeviceArray = None
+    past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None
+    hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+
+
 @flax.struct.dataclass
 class FlaxMaskedLMOutput(ModelOutput):
     """
@@ -135,6 +283,63 @@ class FlaxMaskedLMOutput(ModelOutput):
 FlaxCausalLMOutput = FlaxMaskedLMOutput
 
 
+@flax.struct.dataclass
+class FlaxSeq2SeqLMOutput(ModelOutput):
+    """
+    Base class for sequence-to-sequence language models outputs.
+
+    Args:
+        logits (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            Tuple of :obj:`tuple(jax_xla.DeviceArray)` of length :obj:`config.n_layers`, with each tuple having 2
+            tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional
+            tensors of shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
+        decoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+        decoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+        encoder_last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+        encoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    logits: jax_xla.DeviceArray = None
+    past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None
+    decoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    decoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_last_hidden_state: Optional[jax_xla.DeviceArray] = None
+    encoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+
+
 @flax.struct.dataclass
 class FlaxNextSentencePredictorOutput(ModelOutput):
     """
@@ -188,6 +393,63 @@ class FlaxSequenceClassifierOutput(ModelOutput):
     attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
 
 
+@flax.struct.dataclass
+class FlaxSeq2SeqSequenceClassifierOutput(ModelOutput):
+    """
+    Base class for outputs of sequence-to-sequence sentence classification models.
+
+    Args:
+        logits (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            Tuple of :obj:`tuple(jax_xla.DeviceArray)` of length :obj:`config.n_layers`, with each tuple having 2
+            tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional
+            tensors of shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
+        decoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+        decoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+        encoder_last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+        encoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    logits: jax_xla.DeviceArray = None
+    past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None
+    decoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    decoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_last_hidden_state: Optional[jax_xla.DeviceArray] = None
+    encoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+
+
 @flax.struct.dataclass
 class FlaxMultipleChoiceModelOutput(ModelOutput):
     """
@@ -269,3 +531,63 @@ class FlaxQuestionAnsweringModelOutput(ModelOutput):
     end_logits: jax_xla.DeviceArray = None
     hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
     attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+
+
+@flax.struct.dataclass
+class FlaxSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
+    """
+    Base class for outputs of sequence-to-sequence question answering models.
+
+    Args:
+        start_logits (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length)`):
+            Span-start scores (before SoftMax).
+        end_logits (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length)`):
+            Span-end scores (before SoftMax).
+        past_key_values (:obj:`tuple(tuple(jax_xla.DeviceArray))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
+            Tuple of :obj:`tuple(jax_xla.DeviceArray)` of length :obj:`config.n_layers`, with each tuple having 2
+            tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional
+            tensors of shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
+        decoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+        decoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+        cross_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+            weighted average in the cross-attention heads.
+        encoder_last_hidden_state (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder of the model.
+        encoder_hidden_states (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for the output of the embeddings + one for the output of each
+            layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+        encoder_attentions (:obj:`tuple(jax_xla.DeviceArray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`jax_xla.DeviceArray` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+            self-attention heads.
+    """
+
+    start_logits: jax_xla.DeviceArray = None
+    end_logits: jax_xla.DeviceArray = None
+    past_key_values: Optional[Tuple[Tuple[jax_xla.DeviceArray]]] = None
+    decoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    decoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    cross_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_last_hidden_state: Optional[jax_xla.DeviceArray] = None
+    encoder_hidden_states: Optional[Tuple[jax_xla.DeviceArray]] = None
+    encoder_attentions: Optional[Tuple[jax_xla.DeviceArray]] = None

src/transformers/models/auto/modeling_flax_auto.py

@@ -18,6 +18,12 @@
 from collections import OrderedDict
 
 from ...utils import logging
+from ..bart.modeling_flax_bart import (
+    FlaxBartForConditionalGeneration,
+    FlaxBartForQuestionAnswering,
+    FlaxBartForSequenceClassification,
+    FlaxBartModel,
+)
 from ..bert.modeling_flax_bert import (
     FlaxBertForMaskedLM,
     FlaxBertForMultipleChoice,
@@ -49,7 +55,7 @@ from ..roberta.modeling_flax_roberta import (
 )
 from ..vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
 from .auto_factory import auto_class_factory
-from .configuration_auto import BertConfig, CLIPConfig, ElectraConfig, GPT2Config, RobertaConfig, ViTConfig
+from .configuration_auto import BartConfig, BertConfig, CLIPConfig, ElectraConfig, GPT2Config, RobertaConfig, ViTConfig
 
 
 logger = logging.get_logger(__name__)
@@ -60,6 +66,7 @@ FLAX_MODEL_MAPPING = OrderedDict(
         # Base model mapping
         (RobertaConfig, FlaxRobertaModel),
         (BertConfig, FlaxBertModel),
+        (BartConfig, FlaxBartModel),
         (GPT2Config, FlaxGPT2Model),
         (ElectraConfig, FlaxElectraModel),
         (CLIPConfig, FlaxCLIPModel),
@@ -72,6 +79,7 @@ FLAX_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         # Model for pre-training mapping
         (RobertaConfig, FlaxRobertaForMaskedLM),
         (BertConfig, FlaxBertForPreTraining),
+        (BartConfig, FlaxBartForConditionalGeneration),
         (ElectraConfig, FlaxElectraForPreTraining),
     ]
 )
@@ -81,6 +89,7 @@ FLAX_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
         # Model for Masked LM mapping
         (RobertaConfig, FlaxRobertaForMaskedLM),
         (BertConfig, FlaxBertForMaskedLM),
+        (BartConfig, FlaxBartForConditionalGeneration),
         (ElectraConfig, FlaxElectraForMaskedLM),
     ]
 )
@@ -104,6 +113,7 @@ FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         # Model for Sequence Classification mapping
         (RobertaConfig, FlaxRobertaForSequenceClassification),
         (BertConfig, FlaxBertForSequenceClassification),
+        (BartConfig, FlaxBartForSequenceClassification),
         (ElectraConfig, FlaxElectraForSequenceClassification),
     ]
 )
@@ -113,6 +123,7 @@ FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
         # Model for Question Answering mapping
         (RobertaConfig, FlaxRobertaForQuestionAnswering),
         (BertConfig, FlaxBertForQuestionAnswering),
+        (BartConfig, FlaxBartForQuestionAnswering),
         (ElectraConfig, FlaxElectraForQuestionAnswering),
     ]
 )

src/transformers/models/bart/__init__.py

@@ -17,7 +17,13 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...file_utils import (
+    _BaseLazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -43,6 +49,13 @@ if is_torch_available():
 if is_tf_available():
     _import_structure["modeling_tf_bart"] = ["TFBartForConditionalGeneration", "TFBartModel", "TFBartPretrainedModel"]
 
+if is_flax_available():
+    _import_structure["modeling_flax_bart"] = [
+        "FlaxBartForConditionalGeneration",
+        "FlaxBartForQuestionAnswering",
+        "FlaxBartForSequenceClassification",
+        "FlaxBartModel",
+    ]
 
 if TYPE_CHECKING:
     from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig
@@ -66,6 +79,14 @@ if TYPE_CHECKING:
     if is_tf_available():
         from .modeling_tf_bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel
 
+    if is_flax_available():
+        from .modeling_flax_bart import (
+            FlaxBartForConditionalGeneration,
+            FlaxBartForQuestionAnswering,
+            FlaxBartForSequenceClassification,
+            FlaxBartModel,
+        )
+
 else:
     import importlib
     import os

src/transformers/utils/dummy_flax_objects.py

@@ -149,6 +149,42 @@ class FlaxAutoModelForTokenClassification:
         requires_backends(cls, ["flax"])
 
 
+class FlaxBartForConditionalGeneration:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxBartForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxBartForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxBartModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
 class FlaxBertForMaskedLM:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])

tests/test_modeling_flax_bart.py

@@ -0,0 +1,417 @@
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# 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 copy 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
+
+import numpy as np
+import timeout_decorator  # noqa
+
+from transformers import BartConfig, is_flax_available
+from transformers.testing_utils import require_flax, slow
+
+from .test_generation_flax_utils import FlaxGenerationTesterMixin
+from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
+
+
+if is_flax_available():
+    import os
+
+    # The slow tests are often failing with OOM error on GPU
+    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
+    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
+    os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
+
+    import jax
+    import jax.numpy as jnp
+    from transformers.models.bart.modeling_flax_bart import (
+        FlaxBartForConditionalGeneration,
+        FlaxBartForQuestionAnswering,
+        FlaxBartForSequenceClassification,
+        FlaxBartModel,
+        shift_tokens_right,
+    )
+
+
+def prepare_bart_inputs_dict(
+    config,
+    input_ids,
+    decoder_input_ids=None,
+    attention_mask=None,
+    decoder_attention_mask=None,
+    head_mask=None,
+    decoder_head_mask=None,
+    cross_attn_head_mask=None,
+):
+    if attention_mask is None:
+        attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
+    if decoder_attention_mask is None:
+        decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
+    if head_mask is None:
+        head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
+    if decoder_head_mask is None:
+        decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
+    if cross_attn_head_mask is None:
+        cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
+    return {
+        "input_ids": input_ids,
+        "decoder_input_ids": decoder_input_ids,
+        "attention_mask": attention_mask,
+        "decoder_attention_mask": attention_mask,
+    }
+
+
+class FlaxBartModelTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_labels=False,
+        vocab_size=99,
+        hidden_size=16,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=4,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=32,
+        eos_token_id=2,
+        pad_token_id=1,
+        bos_token_id=0,
+        initializer_range=0.02,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.eos_token_id = eos_token_id
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.initializer_range = initializer_range
+
+    def prepare_config_and_inputs(self):
+        input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
+        input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
+
+        decoder_input_ids = shift_tokens_right(input_ids, 1, 2)
+
+        config = BartConfig(
+            vocab_size=self.vocab_size,
+            d_model=self.hidden_size,
+            encoder_layers=self.num_hidden_layers,
+            decoder_layers=self.num_hidden_layers,
+            encoder_attention_heads=self.num_attention_heads,
+            decoder_attention_heads=self.num_attention_heads,
+            encoder_ffn_dim=self.intermediate_size,
+            decoder_ffn_dim=self.intermediate_size,
+            dropout=self.hidden_dropout_prob,
+            attention_dropout=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            eos_token_id=self.eos_token_id,
+            bos_token_id=self.bos_token_id,
+            pad_token_id=self.pad_token_id,
+            initializer_range=self.initializer_range,
+            use_cache=False,
+        )
+        inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids)
+        return config, inputs_dict
+
+    def prepare_config_and_inputs_for_common(self):
+        config, inputs_dict = self.prepare_config_and_inputs()
+        return config, inputs_dict
+
+    def check_use_cache_forward(self, model_class_name, config, inputs_dict):
+        max_decoder_length = 20
+        model = model_class_name(config)
+
+        encoder_outputs = model.encode(inputs_dict["input_ids"])
+
+        decoder_input_ids, decoder_attention_mask = (
+            inputs_dict["decoder_input_ids"],
+            inputs_dict["decoder_attention_mask"],
+        )
+
+        past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
+        decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
+
+        decoder_position_ids = jnp.broadcast_to(
+            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
+            (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
+        )
+        outputs_cache = model.decode(
+            decoder_input_ids[:, :-1],
+            encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask,
+            past_key_values=past_key_values,
+            decoder_position_ids=decoder_position_ids,
+        )
+
+        decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
+        outputs_cache_next = model.decode(
+            decoder_input_ids[:, -1:],
+            encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask,
+            past_key_values=outputs_cache.past_key_values,
+            decoder_position_ids=decoder_position_ids,
+        )
+
+        outputs = model.decode(decoder_input_ids, encoder_outputs)
+
+        diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
+        self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
+
+    def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
+        max_decoder_length = 20
+        model = model_class_name(config)
+
+        encoder_outputs = model.encode(inputs_dict["input_ids"])
+
+        decoder_input_ids, decoder_attention_mask = (
+            inputs_dict["decoder_input_ids"],
+            inputs_dict["decoder_attention_mask"],
+        )
+
+        decoder_attention_mask_cache = jnp.concatenate(
+            [
+                decoder_attention_mask,
+                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
+            ],
+            axis=-1,
+        )
+
+        past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
+        decoder_position_ids = jnp.broadcast_to(
+            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
+            (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
+        )
+
+        outputs_cache = model.decode(
+            decoder_input_ids[:, :-1],
+            encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask_cache,
+            past_key_values=past_key_values,
+            decoder_position_ids=decoder_position_ids,
+        )
+        decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
+        outputs_cache_next = model.decode(
+            decoder_input_ids[:, -1:],
+            encoder_outputs,
+            past_key_values=outputs_cache.past_key_values,
+            decoder_attention_mask=decoder_attention_mask_cache,
+            decoder_position_ids=decoder_position_ids,
+        )
+
+        outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
+
+        diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
+        self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
+
+
+@require_flax
+class BartHeadTests(unittest.TestCase):
+    vocab_size = 99
+
+    def _get_config_and_data(self):
+        input_ids = np.array(
+            [
+                [71, 82, 18, 33, 46, 91, 2],
+                [68, 34, 26, 58, 30, 82, 2],
+                [5, 97, 17, 39, 94, 40, 2],
+                [76, 83, 94, 25, 70, 78, 2],
+                [87, 59, 41, 35, 48, 66, 2],
+                [55, 13, 16, 58, 5, 2, 1],  # note padding
+                [64, 27, 31, 51, 12, 75, 2],
+                [52, 64, 86, 17, 83, 39, 2],
+                [48, 61, 9, 24, 71, 82, 2],
+                [26, 1, 60, 48, 22, 13, 2],
+                [21, 5, 62, 28, 14, 76, 2],
+                [45, 98, 37, 86, 59, 48, 2],
+                [70, 70, 50, 9, 28, 0, 2],
+            ],
+            dtype=np.int64,
+        )
+
+        batch_size = input_ids.shape[0]
+        config = BartConfig(
+            vocab_size=self.vocab_size,
+            d_model=24,
+            encoder_layers=2,
+            decoder_layers=2,
+            encoder_attention_heads=2,
+            decoder_attention_heads=2,
+            encoder_ffn_dim=32,
+            decoder_ffn_dim=32,
+            max_position_embeddings=48,
+            eos_token_id=2,
+            pad_token_id=1,
+            bos_token_id=0,
+        )
+        return config, input_ids, batch_size
+
+    def test_sequence_classification_forward(self):
+        config, input_ids, batch_size = self._get_config_and_data()
+        model = FlaxBartForSequenceClassification(config)
+        outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
+        expected_shape = (batch_size, config.num_labels)
+        self.assertEqual(outputs["logits"].shape, expected_shape)
+
+    def test_question_answering_forward(self):
+        config, input_ids, batch_size = self._get_config_and_data()
+        model = FlaxBartForQuestionAnswering(config)
+        outputs = model(input_ids=input_ids)
+
+        self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
+        self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
+
+    # @timeout_decorator.timeout(1)  # not working with the decorator so far
+    def test_lm_forward(self):
+        config, input_ids, batch_size = self._get_config_and_data()
+        lm_model = FlaxBartForConditionalGeneration(config)
+        outputs = lm_model(input_ids=input_ids)
+        expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
+        self.assertEqual(outputs["logits"].shape, expected_shape)
+
+    def test_lm_uneven_forward(self):
+        config = BartConfig(
+            vocab_size=self.vocab_size,
+            d_model=14,
+            encoder_layers=2,
+            decoder_layers=2,
+            encoder_attention_heads=2,
+            decoder_attention_heads=2,
+            encoder_ffn_dim=8,
+            decoder_ffn_dim=8,
+            max_position_embeddings=48,
+        )
+        lm_model = FlaxBartForConditionalGeneration(config)
+        context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
+        summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
+        outputs = lm_model(input_ids=context, decoder_input_ids=summary)
+        expected_shape = (*summary.shape, config.vocab_size)
+        self.assertEqual(outputs["logits"].shape, expected_shape)
+
+    def test_shift_tokens_right(self):
+        input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
+        shifted = shift_tokens_right(input_ids, 1, 2)
+        n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
+        n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
+        self.assertEqual(shifted.shape, input_ids.shape)
+        self.assertEqual(n_pad_after, n_pad_before - 1)
+        self.assertTrue(np.equal(shifted[:, 0], 2).all())
+
+
+@require_flax
+class FlaxBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
+    is_encoder_decoder = True
+    all_model_classes = (
+        (
+            FlaxBartModel,
+            FlaxBartForConditionalGeneration,
+            FlaxBartForSequenceClassification,
+            FlaxBartForQuestionAnswering,
+        )
+        if is_flax_available()
+        else ()
+    )
+    all_generative_model_classes = (FlaxBartForConditionalGeneration,) if is_flax_available() else ()
+
+    def setUp(self):
+        self.model_tester = FlaxBartModelTester(self)
+
+    def test_use_cache_forward(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
+        for model_class in self.all_model_classes:
+            self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
+
+    def test_use_cache_forward_with_attn_mask(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
+        for model_class in self.all_model_classes:
+            self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
+
+    def test_encode(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            with self.subTest(model_class.__name__):
+                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+                model = model_class(config)
+
+                @jax.jit
+                def encode_jitted(input_ids, attention_mask=None, **kwargs):
+                    return model.encode(input_ids=input_ids, attention_mask=attention_mask)
+
+                with self.subTest("JIT Enabled"):
+                    jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
+
+                with self.subTest("JIT Disabled"):
+                    with jax.disable_jit():
+                        outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
+
+                self.assertEqual(len(outputs), len(jitted_outputs))
+                for jitted_output, output in zip(jitted_outputs, outputs):
+                    self.assertEqual(jitted_output.shape, output.shape)
+
+    def test_decode(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            with self.subTest(model_class.__name__):
+                model = model_class(config)
+                encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
+
+                prepared_inputs_dict = {
+                    "decoder_input_ids": inputs_dict["decoder_input_ids"],
+                    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
+                    "encoder_outputs": encoder_outputs,
+                }
+
+                @jax.jit
+                def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
+                    return model.decode(
+                        decoder_input_ids=decoder_input_ids,
+                        decoder_attention_mask=decoder_attention_mask,
+                        encoder_outputs=encoder_outputs,
+                    )
+
+                with self.subTest("JIT Enabled"):
+                    jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
+
+                with self.subTest("JIT Disabled"):
+                    with jax.disable_jit():
+                        outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
+
+                self.assertEqual(len(outputs), len(jitted_outputs))
+                for jitted_output, output in zip(jitted_outputs, outputs):
+                    self.assertEqual(jitted_output.shape, output.shape)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_class_name in self.all_model_classes:
+            model = model_class_name.from_pretrained("facebook/bart-base", from_pt=True)
+            # FlaxBartForSequenceClassification expects eos token in input_ids
+            input_ids = np.ones((1, 1)) * model.config.eos_token_id
+            outputs = model(input_ids)
+            self.assertIsNotNone(outputs)

tests/test_modeling_flax_common.py

@@ -22,6 +22,7 @@ import numpy as np
 
 import transformers
 from transformers import is_flax_available, is_torch_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import is_pt_flax_cross_test, require_flax
 
 
@@ -31,6 +32,7 @@ if is_flax_available():
     import jax
     import jax.numpy as jnp
     import jaxlib.xla_extension as jax_xla
+    from transformers import FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
@@ -42,6 +44,14 @@ if is_torch_available():
     import torch
 
 
+def _config_zero_init(config):
+    configs_no_init = copy.deepcopy(config)
+    for key in configs_no_init.__dict__.keys():
+        if "_range" in key or "_std" in key or "initializer_factor" in key:
+            setattr(configs_no_init, key, 1e-10)
+    return configs_no_init
+
+
 def ids_tensor(shape, vocab_size, rng=None):
     """Creates a random int32 tensor of the shape within the vocab size."""
     if rng is None:
@@ -87,6 +97,7 @@ def random_attention_mask(shape, rng=None):
 class FlaxModelTesterMixin:
     model_tester = None
     all_model_classes = ()
+    is_encoder_decoder = False
 
     def _prepare_for_class(self, inputs_dict, model_class):
         inputs_dict = copy.deepcopy(inputs_dict)
@@ -156,6 +167,9 @@ class FlaxModelTesterMixin:
                 pt_model_class = getattr(transformers, pt_model_class_name)
 
                 pt_model = pt_model_class(config).eval()
+                # Flax models don't use the `use_cache` option and cache is not returned as a default.
+                # So we disable `use_cache` here for PyTorch model.
+                pt_model.config.use_cache = False
                 fx_model = model_class(config, dtype=jnp.float32)
 
                 fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
@@ -167,7 +181,7 @@ class FlaxModelTesterMixin:
                 fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
                 self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
                 for fx_output, pt_output in zip(fx_outputs, pt_outputs):
-                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
+                    self.assert_almost_equals(fx_output, pt_output.numpy(), 1e-3)
 
                 with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(tmpdirname)
@@ -178,7 +192,10 @@ class FlaxModelTesterMixin:
                     len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
                 )
                 for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
-                    self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
+                    if not isinstance(
+                        fx_output_loaded, tuple
+                    ):  # TODO(Patrick, Daniel) - let's discard use_cache for now
+                        self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 1e-3)
 
     @is_pt_flax_cross_test
     def test_equivalence_flax_to_pt(self):
@@ -195,6 +212,9 @@ class FlaxModelTesterMixin:
                 pt_model_class = getattr(transformers, pt_model_class_name)
 
                 pt_model = pt_model_class(config).eval()
+                # Flax models don't use the `use_cache` option and cache is not returned as a default.
+                # So we disable `use_cache` here for PyTorch model.
+                pt_model.config.use_cache = False
                 fx_model = model_class(config, dtype=jnp.float32)
 
                 pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
@@ -207,8 +227,9 @@ class FlaxModelTesterMixin:
 
                 fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
                 self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
+
                 for fx_output, pt_output in zip(fx_outputs, pt_outputs):
-                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
+                    self.assert_almost_equals(fx_output, pt_output.numpy(), 1e-3)
 
                 with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(tmpdirname)
@@ -221,7 +242,8 @@ class FlaxModelTesterMixin:
                     len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
                 )
                 for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded):
-                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
+                    if not isinstance(fx_output, tuple):  # TODO(Patrick, Daniel) - let's discard use_cache for now
+                        self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-3)
 
     def test_from_pretrained_save_pretrained(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -276,6 +298,7 @@ class FlaxModelTesterMixin:
 
                 self.assertEqual(len(outputs), len(jitted_outputs))
                 for jitted_output, output in zip(jitted_outputs, outputs):
+
                     self.assertEqual(jitted_output.shape, output.shape)
 
     def test_forward_signature(self):
@@ -287,6 +310,15 @@ class FlaxModelTesterMixin:
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             arg_names = [*signature.parameters.keys()]
 
+            if model.config.is_encoder_decoder:
+                expected_arg_names = [
+                    "input_ids",
+                    "attention_mask",
+                    "decoder_input_ids",
+                    "decoder_attention_mask",
+                ]
+                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
+            else:
                 expected_arg_names = ["input_ids", "attention_mask"]
                 self.assertListEqual(arg_names[:2], expected_arg_names)
 
@@ -306,9 +338,16 @@ class FlaxModelTesterMixin:
             model = model_class(config)
 
             outputs = model(**self._prepare_for_class(inputs_dict, model_class))
-            hidden_states = outputs.hidden_states
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
 
-            self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            if hasattr(self.model_tester, "encoder_seq_length"):
+                seq_length = self.model_tester.encoder_seq_length
+            else:
                 seq_length = self.model_tester.seq_length
 
             self.assertListEqual(
@@ -316,6 +355,19 @@ class FlaxModelTesterMixin:
                 [seq_length, self.model_tester.hidden_size],
             )
 
+            if config.is_encoder_decoder:
+                hidden_states = outputs.decoder_hidden_states
+
+                self.assertIsInstance(hidden_states, (list, tuple))
+                self.assertEqual(len(hidden_states), expected_num_layers)
+                seq_len = getattr(self.model_tester, "seq_length", None)
+                decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
+
+                self.assertListEqual(
+                    list(hidden_states[0].shape[-2:]),
+                    [decoder_seq_length, self.model_tester.hidden_size],
+                )
+
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         for model_class in self.all_model_classes:
@@ -333,13 +385,17 @@ class FlaxModelTesterMixin:
         config.return_dict = True
 
         seq_length = getattr(self.model_tester, "seq_length", None)
+        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length)
+        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_length)
+        decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
+        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
 
         for model_class in self.all_model_classes:
             inputs_dict["output_attentions"] = True
             inputs_dict["output_hidden_states"] = False
             model = model_class(config)
             outputs = model(**self._prepare_for_class(inputs_dict, model_class))
-            attentions = outputs.attentions
+            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
 
             # check that output_attentions also work using config
@@ -347,21 +403,57 @@ class FlaxModelTesterMixin:
             config.output_attentions = True
             model = model_class(config)
             outputs = model(**self._prepare_for_class(inputs_dict, model_class))
-            attentions = outputs.attentions
+            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
 
             self.assertListEqual(
                 list(attentions[0].shape[-3:]),
-                [self.model_tester.num_attention_heads, seq_length, seq_length],
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
             )
             out_len = len(outputs)
 
+            if self.is_encoder_decoder:
+                correct_outlen = 5
+
+                # Question Answering model returns start_logits and end_logits
+                if model_class in get_values(FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
+                    correct_outlen += 1  # start_logits and end_logits instead of only 1 output
+
+                self.assertEqual(out_len, correct_outlen)
+
+                # decoder attentions
+                decoder_attentions = outputs.decoder_attentions
+                self.assertIsInstance(decoder_attentions, (list, tuple))
+                self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
+                self.assertListEqual(
+                    list(decoder_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
+                )
+
+                # cross attentions
+                cross_attentions = outputs.cross_attentions
+                self.assertIsInstance(cross_attentions, (list, tuple))
+                self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
+                self.assertListEqual(
+                    list(cross_attentions[0].shape[-3:]),
+                    [
+                        self.model_tester.num_attention_heads,
+                        decoder_seq_length,
+                        encoder_key_length,
+                    ],
+                )
+
             # Check attention is always last and order is fine
             inputs_dict["output_attentions"] = True
             inputs_dict["output_hidden_states"] = True
             model = model_class(config)
             outputs = model(**self._prepare_for_class(inputs_dict, model_class))
 
+            if hasattr(self.model_tester, "num_hidden_states_types"):
+                added_hidden_states = self.model_tester.num_hidden_states_types
+            elif self.is_encoder_decoder:
+                added_hidden_states = 2
+            else:
                 added_hidden_states = 1
             self.assertEqual(out_len + added_hidden_states, len(outputs))
 
@@ -370,5 +462,5 @@ class FlaxModelTesterMixin:
 
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]),
-                [self.model_tester.num_attention_heads, seq_length, seq_length],
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
             )