test_modeling_tf_roberta.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
from __future__ import absolute_import, division, print_function

import unittest

from transformers import RobertaConfig, is_tf_available

from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .utils import CACHE_DIR, require_tf, slow


if is_tf_available():
    import tensorflow as tf
    import numpy
    from transformers.modeling_tf_roberta import (
        TFRobertaModel,
        TFRobertaForMaskedLM,
        TFRobertaForSequenceClassification,
        TFRobertaForTokenClassification,
        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
    )


@require_tf
class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):

    all_model_classes = (
        (TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification) if is_tf_available() else ()
    )

    class TFRobertaModelTester(object):
        def __init__(
            self,
            parent,
            batch_size=13,
            seq_length=7,
            is_training=True,
            use_input_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_labels=3,
            num_choices=4,
            scope=None,
        ):
            self.parent = parent
            self.batch_size = batch_size
            self.seq_length = seq_length
            self.is_training = is_training
            self.use_input_mask = use_input_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_labels = num_labels
            self.num_choices = num_choices
            self.scope = scope

        def prepare_config_and_inputs(self):
            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

            input_mask = None
            if self.use_input_mask:
                input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

            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)

            sequence_labels = None
            token_labels = None
            choice_labels = None
            if self.use_labels:
                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
                choice_labels = ids_tensor([self.batch_size], self.num_choices)

            config = RobertaConfig(
                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,
                initializer_range=self.initializer_range,
            )

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

        def create_and_check_roberta_model(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = TFRobertaModel(config=config)
            inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            sequence_output = model(inputs)[0]

            inputs = [input_ids, input_mask]
            sequence_output = model(inputs)[0]

            sequence_output = model(input_ids)[0]

            result = {
                "sequence_output": sequence_output.numpy(),
            }
            self.parent.assertListEqual(
                list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
            )

        def create_and_check_roberta_for_masked_lm(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = TFRobertaForMaskedLM(config=config)
            prediction_scores = model([input_ids, input_mask, token_type_ids])[0]
            result = {
                "prediction_scores": prediction_scores.numpy(),
            }
            self.parent.assertListEqual(
                list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size]
            )

        def create_and_check_roberta_for_token_classification(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_labels = self.num_labels
            model = TFRobertaForTokenClassification(config=config)
            inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            (logits,) = model(inputs)
            result = {
                "logits": logits.numpy(),
            }
            self.parent.assertListEqual(
                list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels]
            )

        def prepare_config_and_inputs_for_common(self):
            config_and_inputs = self.prepare_config_and_inputs()
            (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
            ) = config_and_inputs
            inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict

    def setUp(self):
        self.model_tester = TFRobertaModelTest.TFRobertaModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_roberta_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_roberta_model(*config_and_inputs)

    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFRobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)


class TFRobertaModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_masked_lm(self):
        model = TFRobertaForMaskedLM.from_pretrained("roberta-base")

        input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = [1, 11, 50265]
        self.assertEqual(list(output.numpy().shape), expected_shape)
        # compare the actual values for a slice.
        expected_slice = tf.constant(
            [[[33.8843, -4.3107, 22.7779], [4.6533, -2.8099, 13.6252], [1.8222, -3.6898, 8.8600]]]
        )
        self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3))

    @slow
    def test_inference_no_head(self):
        model = TFRobertaModel.from_pretrained("roberta-base")

        input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        # compare the actual values for a slice.
        expected_slice = tf.constant(
            [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0539, -0.0174], [0.0548, 0.0799, 0.1687]]]
        )
        self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3))

    @slow
    def test_inference_classification_head(self):
        model = TFRobertaForSequenceClassification.from_pretrained("roberta-large-mnli")

        input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = [1, 3]
        self.assertEqual(list(output.numpy().shape), expected_shape)
        expected_tensor = tf.constant([[-0.9469, 0.3913, 0.5118]])
        self.assertTrue(numpy.allclose(output.numpy(), expected_tensor.numpy(), atol=1e-3))