From daf8bebcddb9cbef356e92c628cd9ca1a9e89923 Mon Sep 17 00:00:00 2001 From: Aymeric Augustin Date: Sun, 22 Dec 2019 15:27:41 +0100 Subject: [PATCH] Remove unused GPTModelTester. It isn't imported anywhere. --- tests/test_modeling_common.py | 192 +--------------------------------- 1 file changed, 1 insertion(+), 191 deletions(-) diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index b2ef0c74d..8fbd3374b 100644 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -27,7 +27,7 @@ import uuid from transformers import is_torch_available -from .utils import CACHE_DIR, require_torch, slow, torch_device +from .utils import require_torch, slow, torch_device if is_torch_available(): @@ -612,196 +612,6 @@ class ModelTesterMixin: outputs = model(**inputs_dict) -class GPTModelTester(ModelTesterMixin): - def __init__( - self, - parent, - batch_size=13, - seq_length=7, - is_training=True, - use_position_ids=True, - use_token_type_ids=True, - use_labels=True, - vocab_size=99, - n_positions=33, - hidden_size=32, - num_hidden_layers=5, - num_attention_heads=4, - n_choices=3, - type_sequence_label_size=2, - initializer_range=0.02, - num_labels=3, - scope=None, - config_class=None, - base_model_class=None, - lm_head_model_class=None, - double_head_model_class=None, - ): - self.parent = parent - self.batch_size = batch_size - self.seq_length = seq_length - self.is_training = is_training - self.use_position_ids = use_position_ids - self.use_token_type_ids = use_token_type_ids - self.use_labels = use_labels - self.vocab_size = vocab_size - self.n_positions = n_positions - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.n_choices = n_choices - self.type_sequence_label_size = type_sequence_label_size - self.initializer_range = initializer_range - self.num_labels = num_labels - self.scope = scope - self.config_class = config_class - self.base_model_class = base_model_class - self.lm_head_model_class = lm_head_model_class - self.double_head_model_class = double_head_model_class - self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class) - - def prepare_config_and_inputs(self): - total_num_tokens = self.vocab_size - input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens) - - position_ids = None - if self.use_position_ids: - position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions) - - token_type_ids = None - if self.use_token_type_ids: - total_voc = self.vocab_size - token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc) - - mc_labels = None - lm_labels = None - mc_token_ids = None - if self.use_labels: - mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) - lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels) - mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length) - - config = self.config_class( - vocab_size=self.vocab_size, - n_positions=self.n_positions, - n_embd=self.hidden_size, - n_layer=self.num_hidden_layers, - n_head=self.num_attention_heads, - initializer_range=self.initializer_range, - ) - - return (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) - - def create_and_check_base_model( - self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids - ): - model = self.base_model_class(config) - model.to(torch_device) - model.eval() - - with torch.no_grad(): - outputs = model(input_ids, position_ids, token_type_ids) - outputs = model(input_ids, position_ids) - outputs = model(input_ids) - - hidden_state = outputs[0] - self.parent.assertListEqual( - list(hidden_state.size()), [self.batch_size, self.n_choices, self.seq_length, self.hidden_size] - ) - - def create_and_check_lm_head( - self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids - ): - model = self.lm_head_model_class(config) - model.to(torch_device) - model.eval() - with torch.no_grad(): - outputs = model(input_ids, position_ids, token_type_ids, lm_labels) - loss, lm_logits = outputs[:2] - - total_voc = self.vocab_size - self.parent.assertListEqual( - list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc] - ) - self.parent.assertListEqual(list(loss.size()), []) - - def create_and_check_presents( - self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids - ): - for model_class in self.all_model_classes: - model = model_class(config) - model.to(torch_device) - model.eval() - with torch.no_grad(): - outputs = model(input_ids) - presents = outputs[-1] - self.parent.assertEqual(self.num_hidden_layers, len(presents)) - self.parent.assertListEqual( - list(presents[0].size()), - [ - 2, - self.batch_size * self.n_choices, - self.num_attention_heads, - self.seq_length, - self.hidden_size // self.num_attention_heads, - ], - ) - - def create_and_check_double_heads( - self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids - ): - model = self.double_head_model_class(config) - model.to(torch_device) - model.eval() - with torch.no_grad(): - outputs = model( - input_ids, - mc_token_ids, - lm_labels=lm_labels, - mc_labels=mc_labels, - token_type_ids=token_type_ids, - position_ids=position_ids, - ) - lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4] - loss = [lm_loss, mc_loss] - - total_voc = self.vocab_size - self.parent.assertListEqual( - list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc] - ) - self.parent.assertListEqual(list(mc_logits.size()), [self.batch_size, self.n_choices]) - self.parent.assertListEqual([list(l.size()) for l in loss], [[], []]) - - def create_and_check_model_from_pretrained(self): - for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]: - model = self.base_model_class.from_pretrained(model_name, cache_dir=CACHE_DIR) - self.parent.assertIsNotNone(model) - - def prepare_config_and_inputs_for_common(self): - config_and_inputs = self.prepare_config_and_inputs() - (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) = config_and_inputs - inputs_dict = {"input_ids": input_ids} - return config, inputs_dict - - def run_common_tests(self, test_presents=False): - config_and_inputs = self.prepare_config_and_inputs() - self.create_and_check_base_model(*config_and_inputs) - - config_and_inputs = self.prepare_config_and_inputs() - self.create_and_check_lm_head(*config_and_inputs) - - config_and_inputs = self.prepare_config_and_inputs() - self.create_and_check_double_heads(*config_and_inputs) - - if test_presents: - config_and_inputs = self.prepare_config_and_inputs() - self.create_and_check_presents(*config_and_inputs) - - @slow - def run_slow_tests(self): - self.create_and_check_model_from_pretrained() - - class ConfigTester(object): def __init__(self, parent, config_class=None, **kwargs): self.parent = parent