test_modeling_deberta.py

# coding=utf-8
# Copyright 2018 Microsoft Authors and the HuggingFace Inc. team.
#
# 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 random
import unittest

import numpy as np

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor


if is_torch_available():
    import torch

    from transformers import (  # XxxForMaskedLM,; XxxForQuestionAnswering,; XxxForTokenClassification,
        DebertaConfig,
        DebertaForSequenceClassification,
        DebertaModel,
    )
    from transformers.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


@require_torch
class DebertaModelTest(ModelTesterMixin, unittest.TestCase):

    all_model_classes = (
        (
            DebertaModel,
            DebertaForSequenceClassification,
        )  # , DebertaForMaskedLM, DebertaForQuestionAnswering, DebertaForTokenClassification)
        if is_torch_available()
        else ()
    )

    test_torchscript = False
    test_pruning = False
    test_head_masking = False
    is_encoder_decoder = False

    class DebertaModelTester(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,
            relative_attention=False,
            position_biased_input=True,
            pos_att_type="None",
            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.relative_attention = relative_attention
            self.position_biased_input = position_biased_input
            self.pos_att_type = pos_att_type
            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 = DebertaConfig(
                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,
                relative_attention=self.relative_attention,
                position_biased_input=self.position_biased_input,
                pos_att_type=self.pos_att_type,
            )

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

        def check_loss_output(self, result):
            self.parent.assertListEqual(list(result.loss.size()), [])

        def create_and_check_deberta_model(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = DebertaModel(config=config)
            model.to(torch_device)
            model.eval()
            sequence_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)[0]
            sequence_output = model(input_ids, token_type_ids=token_type_ids)[0]
            sequence_output = model(input_ids)[0]

            self.parent.assertListEqual(
                list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size]
            )

        def create_and_check_deberta_for_sequence_classification(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_labels = self.num_labels
            model = DebertaForSequenceClassification(config)
            model.to(torch_device)
            model.eval()
            result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
            self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
            self.check_loss_output(result)

        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 = DebertaModelTest.DebertaModelTester(self)
        self.config_tester = ConfigTester(self, config_class=DebertaConfig, hidden_size=37)

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

    def test_deberta_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*config_and_inputs)

    def test_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*config_and_inputs)

    @unittest.skip(reason="Model not available yet")
    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*config_and_inputs)

    @unittest.skip(reason="Model not available yet")
    def test_for_question_answering(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*config_and_inputs)

    @unittest.skip(reason="Model not available yet")
    def test_for_token_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = DebertaModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


@require_torch
@require_sentencepiece
@require_tokenizers
class DebertaModelIntegrationTest(unittest.TestCase):
    @unittest.skip(reason="Model not available yet")
    def test_inference_masked_lm(self):
        pass

    @slow
    def test_inference_no_head(self):
        random.seed(0)
        np.random.seed(0)
        torch.manual_seed(0)
        torch.cuda.manual_seed_all(0)
        model = DebertaModel.from_pretrained("microsoft/deberta-base")

        input_ids = torch.tensor([[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 = torch.tensor(
            [[[-0.0218, -0.6641, -0.3665], [-0.3907, -0.4716, -0.6640], [0.7461, 1.2570, -0.9063]]]
        )
        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4), f"{output[:, :3, :3]}")