[EncoderDecoder Tests] Improve tests (#4046)
* Hoist bert model tester for patric * indent * make tests work * Update tests/test_modeling_bert.py Co-authored-by: Julien Chaumond <chaumond@gmail.com> Co-authored-by: sshleifer <sshleifer@gmail.com> Co-authored-by: Julien Chaumond <chaumond@gmail.com>
This commit is contained in:
Patrick von Platen
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GitHub
GitHub
parent
6af3306a1d
commit
8e67573a64
@@ -38,6 +38,368 @@ if is_torch_available():
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from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
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class BertModelTester:
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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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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.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 = BertConfig(
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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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 prepare_config_and_inputs_for_decoder(self):
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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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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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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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encoder_hidden_states,
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encoder_attention_mask,
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)
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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_bert_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 = BertModel(config=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)
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sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids)
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result = {
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"sequence_output": sequence_output,
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"pooled_output": pooled_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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self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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def create_and_check_bert_model_as_decoder(
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self,
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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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(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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sequence_output, pooled_output = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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encoder_hidden_states=encoder_hidden_states,
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)
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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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result = {
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"sequence_output": sequence_output,
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"pooled_output": pooled_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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self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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def create_and_check_bert_for_masked_lm(
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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 = 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(
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input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels
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)
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
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)
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self.check_loss_output(result)
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def create_and_check_bert_model_for_masked_lm_as_decoder(
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self,
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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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(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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masked_lm_labels=token_labels,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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loss, prediction_scores = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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masked_lm_labels=token_labels,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
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)
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self.check_loss_output(result)
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def create_and_check_bert_for_next_sequence_prediction(
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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 = 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(
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input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels,
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)
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result = {
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"loss": loss,
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"seq_relationship_score": seq_relationship_score,
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}
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self.parent.assertListEqual(list(result["seq_relationship_score"].size()), [self.batch_size, 2])
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self.check_loss_output(result)
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def create_and_check_bert_for_pretraining(
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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 = 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(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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masked_lm_labels=token_labels,
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next_sentence_label=sequence_labels,
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)
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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"seq_relationship_score": seq_relationship_score,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
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)
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self.parent.assertListEqual(list(result["seq_relationship_score"].size()), [self.batch_size, 2])
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self.check_loss_output(result)
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def create_and_check_bert_for_question_answering(
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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 = 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(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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result = {
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"loss": loss,
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"start_logits": start_logits,
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"end_logits": end_logits,
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}
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self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
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self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
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self.check_loss_output(result)
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def create_and_check_bert_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 = BertForSequenceClassification(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 create_and_check_bert_for_token_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 = 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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"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.seq_length, self.num_labels])
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self.check_loss_output(result)
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def create_and_check_bert_for_multiple_choice(
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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_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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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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loss, logits = model(
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multiple_choice_inputs_ids,
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attention_mask=multiple_choice_input_mask,
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token_type_ids=multiple_choice_token_type_ids,
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labels=choice_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_choices])
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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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@require_torch
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class BertModelTest(ModelTesterMixin, unittest.TestCase):
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@@ -55,376 +417,8 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
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else ()
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)
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class BertModelTester(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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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.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:
|
||||
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 = BertConfig(
|
||||
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,
|
||||
is_decoder=False,
|
||||
initializer_range=self.initializer_range,
|
||||
)
|
||||
|
||||
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
|
||||
def prepare_config_and_inputs_for_decoder(self):
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = self.prepare_config_and_inputs()
|
||||
|
||||
config.is_decoder = True
|
||||
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
|
||||
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
)
|
||||
|
||||
def check_loss_output(self, result):
|
||||
self.parent.assertListEqual(list(result["loss"].size()), [])
|
||||
|
||||
def create_and_check_bert_model(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = BertModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
||||
sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
|
||||
sequence_output, pooled_output = model(input_ids)
|
||||
|
||||
result = {
|
||||
"sequence_output": sequence_output,
|
||||
"pooled_output": pooled_output,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
|
||||
)
|
||||
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
|
||||
|
||||
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,
|
||||
):
|
||||
model = BertModel(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
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,
|
||||
)
|
||||
sequence_output, pooled_output = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
)
|
||||
sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
||||
|
||||
result = {
|
||||
"sequence_output": sequence_output,
|
||||
"pooled_output": pooled_output,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
|
||||
)
|
||||
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
|
||||
|
||||
def create_and_check_bert_for_masked_lm(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = BertForMaskedLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
loss, prediction_scores = model(
|
||||
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"prediction_scores": prediction_scores,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
|
||||
)
|
||||
self.check_loss_output(result)
|
||||
|
||||
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,
|
||||
):
|
||||
model = BertForMaskedLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
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,
|
||||
)
|
||||
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,
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"prediction_scores": prediction_scores,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
|
||||
)
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_bert_for_next_sequence_prediction(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = BertForNextSentencePrediction(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
loss, seq_relationship_score = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
next_sentence_label=sequence_labels,
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"seq_relationship_score": seq_relationship_score,
|
||||
}
|
||||
self.parent.assertListEqual(list(result["seq_relationship_score"].size()), [self.batch_size, 2])
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_bert_for_pretraining(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = BertForPreTraining(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
loss, prediction_scores, seq_relationship_score = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
masked_lm_labels=token_labels,
|
||||
next_sentence_label=sequence_labels,
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"prediction_scores": prediction_scores,
|
||||
"seq_relationship_score": seq_relationship_score,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
|
||||
)
|
||||
self.parent.assertListEqual(list(result["seq_relationship_score"].size()), [self.batch_size, 2])
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_bert_for_question_answering(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = BertForQuestionAnswering(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
loss, start_logits, end_logits = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
start_positions=sequence_labels,
|
||||
end_positions=sequence_labels,
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"start_logits": start_logits,
|
||||
"end_logits": end_logits,
|
||||
}
|
||||
self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
|
||||
self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_bert_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 = BertForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
loss, logits = model(
|
||||
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"logits": logits,
|
||||
}
|
||||
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_bert_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 = BertForTokenClassification(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
loss, logits = model(
|
||||
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"logits": logits,
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels]
|
||||
)
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_bert_for_multiple_choice(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
config.num_choices = self.num_choices
|
||||
model = BertForMultipleChoice(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
loss, logits = model(
|
||||
multiple_choice_inputs_ids,
|
||||
attention_mask=multiple_choice_input_mask,
|
||||
token_type_ids=multiple_choice_token_type_ids,
|
||||
labels=choice_labels,
|
||||
)
|
||||
result = {
|
||||
"loss": loss,
|
||||
"logits": logits,
|
||||
}
|
||||
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
|
||||
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 = BertModelTest.BertModelTester(self)
|
||||
self.model_tester = BertModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
|
||||
@@ -21,7 +21,7 @@ from transformers import is_torch_available
|
||||
|
||||
# TODO(PVP): this line reruns all the tests in BertModelTest; not sure whether this can be prevented
|
||||
# for now only run module with pytest tests/test_modeling_encoder_decoder.py::EncoderDecoderModelTest
|
||||
from .test_modeling_bert import BertModelTest
|
||||
from .test_modeling_bert import BertModelTester
|
||||
from .utils import require_torch, slow, torch_device
|
||||
|
||||
|
||||
@@ -34,7 +34,7 @@ if is_torch_available():
|
||||
@require_torch
|
||||
class EncoderDecoderModelTest(unittest.TestCase):
|
||||
def prepare_config_and_inputs_bert(self):
|
||||
bert_model_tester = BertModelTest.BertModelTester(self)
|
||||
bert_model_tester = BertModelTester(self)
|
||||
encoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
|
||||
(
|
||||
|
||||
Reference in New Issue
Block a user