albert flax (#13294)

* albert flax

* year -> 2021

* docstring updated for flax

* removed head_mask

* removed from_pt

* removed passing attention_mask to embedding layer

docs/source/index.rst

@@ -321,7 +321,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
 +=============================+================+================+=================+====================+==============+
-|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/albert.rst

@@ -43,7 +43,8 @@ Tips:
   similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
   number of (repeating) layers.
 
-This model was contributed by `lysandre <https://huggingface.co/lysandre>`__. The original code can be found `here
+This model was contributed by `lysandre <https://huggingface.co/lysandre>`__. This model jax version was contributed by
+`kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
 <https://github.com/google-research/ALBERT>`__.
 
 AlbertConfig
@@ -174,3 +175,52 @@ TFAlbertForQuestionAnswering
 
 .. autoclass:: transformers.TFAlbertForQuestionAnswering
     :members: call
+
+
+FlaxAlbertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertModel
+    :members: __call__
+
+
+FlaxAlbertForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertForPreTraining
+    :members: __call__
+
+
+FlaxAlbertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertForMaskedLM
+    :members: __call__
+
+
+FlaxAlbertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertForSequenceClassification
+    :members: __call__
+
+
+FlaxAlbertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertForMultipleChoice
+    :members: __call__
+
+
+FlaxAlbertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertForTokenClassification
+    :members: __call__
+
+
+FlaxAlbertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAlbertForQuestionAnswering
+    :members: __call__

src/transformers/__init__.py

@@ -1642,6 +1642,18 @@ if is_flax_available():
         "FlaxTopPLogitsWarper",
     ]
     _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"]
+    _import_structure["models.albert"].extend(
+        [
+            "FlaxAlbertForMaskedLM",
+            "FlaxAlbertForMultipleChoice",
+            "FlaxAlbertForPreTraining",
+            "FlaxAlbertForQuestionAnswering",
+            "FlaxAlbertForSequenceClassification",
+            "FlaxAlbertForTokenClassification",
+            "FlaxAlbertModel",
+            "FlaxAlbertPreTrainedModel",
+        ]
+    )
     _import_structure["models.auto"].extend(
         [
             "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
@@ -3152,6 +3164,16 @@ if TYPE_CHECKING:
             FlaxTopPLogitsWarper,
         )
         from .modeling_flax_utils import FlaxPreTrainedModel
+        from .models.albert import (
+            FlaxAlbertForMaskedLM,
+            FlaxAlbertForMultipleChoice,
+            FlaxAlbertForPreTraining,
+            FlaxAlbertForQuestionAnswering,
+            FlaxAlbertForSequenceClassification,
+            FlaxAlbertForTokenClassification,
+            FlaxAlbertModel,
+            FlaxAlbertPreTrainedModel,
+        )
         from .models.auto import (
             FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
             FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,

src/transformers/models/albert/__init__.py

@@ -20,6 +20,7 @@ from typing import TYPE_CHECKING
 
 from ...file_utils import (
     _LazyModule,
+    is_flax_available,
     is_sentencepiece_available,
     is_tf_available,
     is_tokenizers_available,
@@ -65,6 +66,17 @@ if is_tf_available():
         "TFAlbertPreTrainedModel",
     ]
 
+if is_flax_available():
+    _import_structure["modeling_flax_albert"] = [
+        "FlaxAlbertForMaskedLM",
+        "FlaxAlbertForMultipleChoice",
+        "FlaxAlbertForPreTraining",
+        "FlaxAlbertForQuestionAnswering",
+        "FlaxAlbertForSequenceClassification",
+        "FlaxAlbertForTokenClassification",
+        "FlaxAlbertModel",
+        "FlaxAlbertPreTrainedModel",
+    ]
 
 if TYPE_CHECKING:
     from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
@@ -103,6 +115,17 @@ if TYPE_CHECKING:
             TFAlbertPreTrainedModel,
         )
 
+    if is_flax_available():
+        from .modeling_flax_albert import (
+            FlaxAlbertForMaskedLM,
+            FlaxAlbertForMultipleChoice,
+            FlaxAlbertForPreTraining,
+            FlaxAlbertForQuestionAnswering,
+            FlaxAlbertForSequenceClassification,
+            FlaxAlbertForTokenClassification,
+            FlaxAlbertModel,
+            FlaxAlbertPreTrainedModel,
+        )
 else:
     import sys
 

src/transformers/models/auto/modeling_flax_auto.py

@@ -29,6 +29,7 @@ FLAX_MODEL_MAPPING_NAMES = OrderedDict(
     [
         # Base model mapping
         ("distilbert", "FlaxDistilBertModel"),
+        ("albert", "FlaxAlbertModel"),
         ("roberta", "FlaxRobertaModel"),
         ("bert", "FlaxBertModel"),
         ("big_bird", "FlaxBigBirdModel"),
@@ -49,6 +50,7 @@ FLAX_MODEL_MAPPING_NAMES = OrderedDict(
 FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
     [
         # Model for pre-training mapping
+        ("albert", "FlaxAlbertForPreTraining"),
         ("roberta", "FlaxRobertaForMaskedLM"),
         ("bert", "FlaxBertForPreTraining"),
         ("big_bird", "FlaxBigBirdForPreTraining"),
@@ -65,6 +67,7 @@ FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
     [
         # Model for Masked LM mapping
         ("distilbert", "FlaxDistilBertForMaskedLM"),
+        ("albert", "FlaxAlbertForMaskedLM"),
         ("roberta", "FlaxRobertaForMaskedLM"),
         ("bert", "FlaxBertForMaskedLM"),
         ("big_bird", "FlaxBigBirdForMaskedLM"),
@@ -104,6 +107,7 @@ FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Sequence Classification mapping
         ("distilbert", "FlaxDistilBertForSequenceClassification"),
+        ("albert", "FlaxAlbertForSequenceClassification"),
         ("roberta", "FlaxRobertaForSequenceClassification"),
         ("bert", "FlaxBertForSequenceClassification"),
         ("big_bird", "FlaxBigBirdForSequenceClassification"),
@@ -117,6 +121,7 @@ FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
     [
         # Model for Question Answering mapping
         ("distilbert", "FlaxDistilBertForQuestionAnswering"),
+        ("albert", "FlaxAlbertForQuestionAnswering"),
         ("roberta", "FlaxRobertaForQuestionAnswering"),
         ("bert", "FlaxBertForQuestionAnswering"),
         ("big_bird", "FlaxBigBirdForQuestionAnswering"),
@@ -130,6 +135,7 @@ FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Token Classification mapping
         ("distilbert", "FlaxDistilBertForTokenClassification"),
+        ("albert", "FlaxAlbertForTokenClassification"),
         ("roberta", "FlaxRobertaForTokenClassification"),
         ("bert", "FlaxBertForTokenClassification"),
         ("big_bird", "FlaxBigBirdForTokenClassification"),
@@ -141,6 +147,7 @@ FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
     [
         # Model for Multiple Choice mapping
         ("distilbert", "FlaxDistilBertForMultipleChoice"),
+        ("albert", "FlaxAlbertForMultipleChoice"),
         ("roberta", "FlaxRobertaForMultipleChoice"),
         ("bert", "FlaxBertForMultipleChoice"),
         ("big_bird", "FlaxBigBirdForMultipleChoice"),

src/transformers/utils/dummy_flax_objects.py

@@ -76,6 +76,74 @@ class FlaxPreTrainedModel:
         requires_backends(cls, ["flax"])
 
 
+class FlaxAlbertForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxAlbertForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxAlbertForPreTraining:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxAlbertForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxAlbertForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxAlbertForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxAlbertModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxAlbertPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
 FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = None
 
 

tests/test_modeling_flax_albert.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 AlbertConfig, 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.albert.modeling_flax_albert import (
+        FlaxAlbertForMaskedLM,
+        FlaxAlbertForMultipleChoice,
+        FlaxAlbertForPreTraining,
+        FlaxAlbertForQuestionAnswering,
+        FlaxAlbertForSequenceClassification,
+        FlaxAlbertForTokenClassification,
+        FlaxAlbertModel,
+    )
+
+
+class FlaxAlbertModelTester(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 = AlbertConfig(
+            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 FlaxAlbertModelTest(FlaxModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            FlaxAlbertModel,
+            FlaxAlbertForPreTraining,
+            FlaxAlbertForMaskedLM,
+            FlaxAlbertForMultipleChoice,
+            FlaxAlbertForQuestionAnswering,
+            FlaxAlbertForSequenceClassification,
+            FlaxAlbertForTokenClassification,
+            FlaxAlbertForQuestionAnswering,
+        )
+        if is_flax_available()
+        else ()
+    )
+
+    def setUp(self):
+        self.model_tester = FlaxAlbertModelTester(self)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_class_name in self.all_model_classes:
+            model = model_class_name.from_pretrained("albert-base-v2")
+            outputs = model(np.ones((1, 1)))
+            self.assertIsNotNone(outputs)
+
+
+@require_flax
+class FlaxAlbertModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_no_head_absolute_embedding(self):
+        model = FlaxAlbertModel.from_pretrained("albert-base-v2")
+        input_ids = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
+        attention_mask = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
+        output = model(input_ids, attention_mask=attention_mask)[0]
+        expected_shape = (1, 11, 768)
+        self.assertEqual(output.shape, expected_shape)
+        expected_slice = np.array(
+            [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]
+        )
+
+        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))