Remove dependency on pytest for running tests (#2055)
* Switch to plain unittest for skipping slow tests.
Add a RUN_SLOW environment variable for running them.
* Switch to plain unittest for PyTorch dependency.
* Switch to plain unittest for TensorFlow dependency.
* Avoid leaking open files in the test suite.
This prevents spurious warnings when running tests.
* Fix unicode warning on Python 2 when running tests.
The warning was:
UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
* Support running PyTorch tests on a GPU.
Reverts 27e015bd.
* Tests no longer require pytest.
* Make tests pass on cuda
This commit is contained in:
Aymeric Augustin
committed by
Julien Chaumond
Julien Chaumond
parent
e4679cddce
commit
35401fe50f
@@ -18,12 +18,12 @@ from __future__ import print_function
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import unittest
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import shutil
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import pytest
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from transformers import is_torch_available
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from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor)
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from .configuration_common_test import ConfigTester
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from .utils import require_torch, slow, torch_device
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if is_torch_available():
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from transformers import (BertConfig, BertModel, BertForMaskedLM,
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@@ -31,11 +31,9 @@ if is_torch_available():
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BertForQuestionAnswering, BertForSequenceClassification,
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BertForTokenClassification, BertForMultipleChoice)
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from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
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else:
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pytestmark = pytest.mark.skip("Require Torch")
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@pytest.mark.usefixtures("use_cuda")
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@require_torch
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class BertModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
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@@ -67,7 +65,6 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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num_labels=3,
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num_choices=4,
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scope=None,
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device='cpu',
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):
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self.parent = parent
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self.batch_size = batch_size
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@@ -91,26 +88,25 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.device = device
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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).to(self.device)
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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).to(self.device)
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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).to(self.device)
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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).to(self.device)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(self.device)
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choice_labels = ids_tensor([self.batch_size], self.num_choices).to(self.device)
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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 = BertConfig(
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vocab_size_or_config_json_file=self.vocab_size,
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@@ -144,7 +140,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = BertModel(config=config)
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model.to(input_ids.device)
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model.to(torch_device)
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model.eval()
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sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
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@@ -161,6 +157,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_model_as_decoder(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask):
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model = BertModel(config)
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model.to(torch_device)
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model.eval()
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sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask)
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sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
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@@ -177,6 +174,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = BertForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
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result = {
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@@ -190,6 +188,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_model_for_masked_lm_as_decoder(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask):
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model = BertForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask)
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loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels, encoder_hidden_states=encoder_hidden_states)
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@@ -204,6 +203,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = BertForNextSentencePrediction(config=config)
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model.to(torch_device)
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model.eval()
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loss, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels)
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result = {
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@@ -217,6 +217,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = BertForPreTraining(config=config)
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model.to(torch_device)
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model.eval()
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loss, prediction_scores, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
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masked_lm_labels=token_labels, next_sentence_label=sequence_labels)
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@@ -235,6 +236,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = BertForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
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start_positions=sequence_labels, end_positions=sequence_labels)
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@@ -254,6 +256,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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config.num_labels = self.num_labels
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model = BertForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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result = {
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@@ -268,6 +271,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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config.num_labels = self.num_labels
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model = BertForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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result = {
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@@ -282,6 +286,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def create_and_check_bert_for_multiple_choice(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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config.num_choices = self.num_choices
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model = BertForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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@@ -313,10 +318,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_bert_model(self, use_cuda=False):
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# ^^ This could be a real fixture
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if use_cuda:
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self.model_tester.device = "cuda"
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def test_bert_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_bert_model(*config_and_inputs)
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@@ -356,7 +358,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs)
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@pytest.mark.slow
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@slow
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/transformers_test/"
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for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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