261 lines
10 KiB
Python
261 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2018 Microsoft Authors and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import unittest
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import numpy as np
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from transformers import is_torch_available
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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if is_torch_available():
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import torch
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from transformers import ( # XxxForMaskedLM,; XxxForQuestionAnswering,; XxxForTokenClassification,
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DebertaConfig,
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DebertaForSequenceClassification,
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DebertaModel,
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)
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from transformers.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
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@require_torch
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class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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DebertaModel,
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DebertaForSequenceClassification,
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) # , DebertaForMaskedLM, DebertaForQuestionAnswering, DebertaForTokenClassification)
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if is_torch_available()
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else ()
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)
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test_torchscript = False
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test_pruning = False
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test_head_masking = False
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is_encoder_decoder = False
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class DebertaModelTester(object):
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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relative_attention=False,
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position_biased_input=True,
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pos_att_type="None",
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.relative_attention = relative_attention
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self.position_biased_input = position_biased_input
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self.pos_att_type = pos_att_type
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = DebertaConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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relative_attention=self.relative_attention,
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position_biased_input=self.position_biased_input,
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pos_att_type=self.pos_att_type,
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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(list(result["loss"].size()), [])
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def create_and_check_deberta_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DebertaModel(config=config)
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model.to(torch_device)
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model.eval()
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sequence_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)[0]
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sequence_output = model(input_ids, token_type_ids=token_type_ids)[0]
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sequence_output = model(input_ids)[0]
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result = {
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"sequence_output": sequence_output,
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
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)
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def create_and_check_deberta_for_sequence_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = DebertaForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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loss, logits = model(
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input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
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)
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result = {
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"loss": loss,
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"logits": logits,
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}
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self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
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self.check_loss_output(result)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = DebertaModelTest.DebertaModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DebertaConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_deberta_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_deberta_model(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_deberta_for_sequence_classification(*config_and_inputs)
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@unittest.skip(reason="Model not available yet")
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_deberta_for_masked_lm(*config_and_inputs)
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@unittest.skip(reason="Model not available yet")
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_deberta_for_question_answering(*config_and_inputs)
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@unittest.skip(reason="Model not available yet")
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_deberta_for_token_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = DebertaModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class DebertaModelIntegrationTest(unittest.TestCase):
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@unittest.skip(reason="Model not available yet")
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def test_inference_masked_lm(self):
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pass
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@slow
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def test_inference_no_head(self):
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random.seed(0)
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np.random.seed(0)
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torch.manual_seed(0)
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torch.cuda.manual_seed_all(0)
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model = DebertaModel.from_pretrained("microsoft/deberta-base")
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input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[[[-0.0218, -0.6641, -0.3665], [-0.3907, -0.4716, -0.6640], [0.7461, 1.2570, -0.9063]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4), f"{output[:, :3, :3]}")
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