[cleanup] Hoist ModelTester objects to top level (#4939)
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
This commit is contained in:
Amil Khare
@@ -32,6 +32,138 @@ if is_tf_available():
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)
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class TFElectraModelTester:
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def __init__(
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self, parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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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 = ElectraConfig(
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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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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_electra_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 = TFElectraModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(sequence_output,) = model(inputs)
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inputs = [input_ids, input_mask]
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(sequence_output,) = model(inputs)
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(sequence_output,) = model(input_ids)
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result = {
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"sequence_output": sequence_output.numpy(),
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
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)
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def create_and_check_electra_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 = TFElectraForMaskedLM(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(prediction_scores,) = model(inputs)
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size]
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)
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def create_and_check_electra_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 = TFElectraForPreTraining(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(prediction_scores,) = model(inputs)
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(list(result["prediction_scores"].shape), [self.batch_size, self.seq_length])
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def create_and_check_electra_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 = TFElectraForTokenClassification(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(logits,) = model(inputs)
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result = {
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"logits": logits.numpy(),
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}
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self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels])
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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_tf
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class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
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@@ -41,163 +173,8 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
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else ()
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)
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class TFElectraModelTester(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:
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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 = ElectraConfig(
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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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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_electra_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 = TFElectraModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(sequence_output,) = model(inputs)
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inputs = [input_ids, input_mask]
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(sequence_output,) = model(inputs)
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(sequence_output,) = model(input_ids)
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result = {
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"sequence_output": sequence_output.numpy(),
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
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)
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def create_and_check_electra_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 = TFElectraForMaskedLM(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(prediction_scores,) = model(inputs)
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size]
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)
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def create_and_check_electra_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 = TFElectraForPreTraining(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(prediction_scores,) = model(inputs)
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(list(result["prediction_scores"].shape), [self.batch_size, self.seq_length])
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def create_and_check_electra_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 = TFElectraForTokenClassification(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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(logits,) = model(inputs)
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result = {
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"logits": logits.numpy(),
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}
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self.parent.assertListEqual(
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list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels]
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)
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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 = TFElectraModelTest.TFElectraModelTester(self)
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self.model_tester = TFElectraModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37)
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def test_config(self):
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