Add Flax RoFormer (#15005)

* Add FlaxRoFormer

* Clean code + make quality

* Fix output pooling for FlaxRoFormerForMultipleChoiceModule

* Apply suggestions from code review

* add flax model to repos
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/index.mdx

@@ -246,7 +246,7 @@ Flax), PyTorch, and/or TensorFlow.
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          SegFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             SEW             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            SEW-D            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/model_doc/roformer.mdx

@@ -123,3 +123,33 @@ This model was contributed by [junnyu](https://huggingface.co/junnyu). The origi
 
 [[autodoc]] TFRoFormerForQuestionAnswering
     - call
+
+## FlaxRoFormerModel
+
+[[autodoc]] FlaxRoFormerModel
+    - __call__
+
+## FlaxRoFormerForMaskedLM
+
+[[autodoc]] FlaxRoFormerForMaskedLM
+    - __call__
+
+## FlaxRoFormerForSequenceClassification
+
+[[autodoc]] FlaxRoFormerForSequenceClassification
+    - __call__
+
+## FlaxRoFormerForMultipleChoice
+
+[[autodoc]] FlaxRoFormerForMultipleChoice
+    - __call__
+
+## FlaxRoFormerForTokenClassification
+
+[[autodoc]] FlaxRoFormerForTokenClassification
+    - __call__
+
+## FlaxRoFormerForQuestionAnswering
+
+[[autodoc]] FlaxRoFormerForQuestionAnswering
+    - __call__

src/transformers/__init__.py

@@ -2084,6 +2084,17 @@ if is_flax_available():
             "FlaxRobertaPreTrainedModel",
         ]
     )
+    _import_structure["models.roformer"].extend(
+        [
+            "FlaxRoFormerForMaskedLM",
+            "FlaxRoFormerForMultipleChoice",
+            "FlaxRoFormerForQuestionAnswering",
+            "FlaxRoFormerForSequenceClassification",
+            "FlaxRoFormerForTokenClassification",
+            "FlaxRoFormerModel",
+            "FlaxRoFormerPreTrainedModel",
+        ]
+    )
     _import_structure["models.t5"].extend(["FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel"])
     _import_structure["models.vision_encoder_decoder"].append("FlaxVisionEncoderDecoderModel")
     _import_structure["models.vision_text_dual_encoder"].extend(["FlaxVisionTextDualEncoderModel"])
@@ -3819,6 +3830,15 @@ if TYPE_CHECKING:
             FlaxRobertaModel,
             FlaxRobertaPreTrainedModel,
         )
+        from .models.roformer import (
+            FlaxRoFormerForMaskedLM,
+            FlaxRoFormerForMultipleChoice,
+            FlaxRoFormerForQuestionAnswering,
+            FlaxRoFormerForSequenceClassification,
+            FlaxRoFormerForTokenClassification,
+            FlaxRoFormerModel,
+            FlaxRoFormerPreTrainedModel,
+        )
         from .models.t5 import FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel
         from .models.vision_encoder_decoder import FlaxVisionEncoderDecoderModel
         from .models.vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

src/transformers/models/auto/modeling_flax_auto.py

@@ -50,6 +50,7 @@ FLAX_MODEL_MAPPING_NAMES = OrderedDict(
         ("wav2vec2", "FlaxWav2Vec2Model"),
         ("marian", "FlaxMarianModel"),
         ("blenderbot", "FlaxBlenderbotModel"),
+        ("roformer", "FlaxRoFormerModel"),
     ]
 )
 
@@ -66,6 +67,7 @@ FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
         ("t5", "FlaxT5ForConditionalGeneration"),
         ("mt5", "FlaxMT5ForConditionalGeneration"),
         ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
+        ("roformer", "FlaxRoFormerForMaskedLM"),
     ]
 )
 
@@ -80,6 +82,7 @@ FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
         ("bart", "FlaxBartForConditionalGeneration"),
         ("electra", "FlaxElectraForMaskedLM"),
         ("mbart", "FlaxMBartForConditionalGeneration"),
+        ("roformer", "FlaxRoFormerForMaskedLM"),
     ]
 )
 
@@ -132,6 +135,7 @@ FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("bart", "FlaxBartForSequenceClassification"),
         ("electra", "FlaxElectraForSequenceClassification"),
         ("mbart", "FlaxMBartForSequenceClassification"),
+        ("roformer", "FlaxRoFormerForSequenceClassification"),
     ]
 )
 
@@ -146,6 +150,7 @@ FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("bart", "FlaxBartForQuestionAnswering"),
         ("electra", "FlaxElectraForQuestionAnswering"),
         ("mbart", "FlaxMBartForQuestionAnswering"),
+        ("roformer", "FlaxRoFormerForQuestionAnswering"),
     ]
 )
 
@@ -158,6 +163,7 @@ FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("bert", "FlaxBertForTokenClassification"),
         ("big_bird", "FlaxBigBirdForTokenClassification"),
         ("electra", "FlaxElectraForTokenClassification"),
+        ("roformer", "FlaxRoFormerForTokenClassification"),
     ]
 )
 
@@ -170,6 +176,7 @@ FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
         ("bert", "FlaxBertForMultipleChoice"),
         ("big_bird", "FlaxBigBirdForMultipleChoice"),
         ("electra", "FlaxElectraForMultipleChoice"),
+        ("roformer", "FlaxRoFormerForMultipleChoice"),
     ]
 )
 

src/transformers/models/roformer/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...file_utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
 
 
 _import_structure = {
@@ -59,6 +59,19 @@ if is_tf_available():
     ]
 
 
+if is_flax_available():
+    _import_structure["modeling_flax_roformer"] = [
+        "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "FlaxRoFormerForMaskedLM",
+        "FlaxRoFormerForMultipleChoice",
+        "FlaxRoFormerForQuestionAnswering",
+        "FlaxRoFormerForSequenceClassification",
+        "FlaxRoFormerForTokenClassification",
+        "FlaxRoFormerModel",
+        "FlaxRoFormerPreTrainedModel",
+    ]
+
+
 if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig
     from .tokenization_roformer import RoFormerTokenizer
@@ -95,6 +108,18 @@ if TYPE_CHECKING:
             TFRoFormerPreTrainedModel,
         )
 
+    if is_flax_available():
+        from .modeling_flax_roformer import (
+            FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            FlaxRoFormerForMaskedLM,
+            FlaxRoFormerForMultipleChoice,
+            FlaxRoFormerForQuestionAnswering,
+            FlaxRoFormerForSequenceClassification,
+            FlaxRoFormerForTokenClassification,
+            FlaxRoFormerModel,
+            FlaxRoFormerPreTrainedModel,
+        )
+
 
 else:
     import sys

src/transformers/utils/dummy_flax_objects.py

@@ -1316,6 +1316,90 @@ class FlaxRobertaPreTrainedModel:
         requires_backends(self, ["flax"])
 
 
+class FlaxRoFormerForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRoFormerForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRoFormerForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRoFormerForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRoFormerForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRoFormerModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRoFormerPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
 class FlaxT5ForConditionalGeneration:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])

tests/test_modeling_flax_roformer.py

+# 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
+
+from transformers import RoFormerConfig, is_flax_available
+from transformers.testing_utils import require_flax, slow
+
+from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
+
+
+if is_flax_available():
+    import jax.numpy as jnp
+    from transformers.models.roformer.modeling_flax_roformer import (
+        FlaxRoFormerForMaskedLM,
+        FlaxRoFormerForMultipleChoice,
+        FlaxRoFormerForQuestionAnswering,
+        FlaxRoFormerForSequenceClassification,
+        FlaxRoFormerForTokenClassification,
+        FlaxRoFormerModel,
+    )
+
+
+class FlaxRoFormerModelTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_attention_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_choices=4,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_attention_mask = use_attention_mask
+        self.use_token_type_ids = use_token_type_ids
+        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.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_choices = num_choices
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        attention_mask = None
+        if self.use_attention_mask:
+            attention_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        config = RoFormerConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+        )
+
+        return config, input_ids, token_type_ids, attention_mask
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, token_type_ids, attention_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
+        return config, inputs_dict
+
+
+@require_flax
+class FlaxRoFormerModelTest(FlaxModelTesterMixin, unittest.TestCase):
+
+    test_head_masking = True
+
+    all_model_classes = (
+        (
+            FlaxRoFormerModel,
+            FlaxRoFormerForMaskedLM,
+            FlaxRoFormerForSequenceClassification,
+            FlaxRoFormerForTokenClassification,
+            FlaxRoFormerForMultipleChoice,
+            FlaxRoFormerForQuestionAnswering,
+        )
+        if is_flax_available()
+        else ()
+    )
+
+    def setUp(self):
+        self.model_tester = FlaxRoFormerModelTester(self)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_class_name in self.all_model_classes:
+            model = model_class_name.from_pretrained("junnyu/roformer_chinese_small", from_pt=True)
+            outputs = model(np.ones((1, 1)))
+            self.assertIsNotNone(outputs)
+
+
+@require_flax
+class FlaxRoFormerModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
+        input_ids = jnp.array([[0, 1, 2, 3, 4, 5]])
+        output = model(input_ids)[0]
+
+        vocab_size = 50000
+
+        expected_shape = (1, 6, vocab_size)
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = jnp.array(
+            [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]
+        )
+
+        self.assertTrue(jnp.allclose(output[:, :3, :3], expected_slice, atol=1e-4))