transformers/tests/models/distilbert/test_modeling_flax_distilbert.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 DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
    )


class FlaxDistilBertModelTester(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])

        config = DistilBertConfig(
            vocab_size=self.vocab_size,
            dim=self.hidden_size,
            n_layers=self.num_hidden_layers,
            n_heads=self.num_attention_heads,
            hidden_dim=self.intermediate_size,
            hidden_act=self.hidden_act,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
            tie_weights_=True,
        )

        return config, input_ids, attention_mask

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, attention_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict


@require_flax
class FlaxDistilBertModelTest(FlaxModelTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            FlaxDistilBertModel,
            FlaxDistilBertForMaskedLM,
            FlaxDistilBertForMultipleChoice,
            FlaxDistilBertForQuestionAnswering,
            FlaxDistilBertForSequenceClassification,
            FlaxDistilBertForTokenClassification,
            FlaxDistilBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )

    def setUp(self):
        self.model_tester = FlaxDistilBertModelTester(self)

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            model = model_class_name.from_pretrained("distilbert-base-uncased")
            outputs = model(np.ones((1, 1)))
            self.assertIsNotNone(outputs)


@require_flax
class FlaxDistilBertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_no_head_absolute_embedding(self):
        model = FlaxDistilBertModel.from_pretrained("distilbert-base-uncased")
        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([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))