mirror of
https://github.com/saymrwulf/transformers.git
synced 2026-05-14 20:58:08 +00:00
Remove unused GPTModelTester.
It isn't imported anywhere.
This commit is contained in:
Aymeric Augustin
parent
345c23a60f
commit
daf8bebcdd
1 changed files with 1 additions and 191 deletions
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@ -27,7 +27,7 @@ import uuid
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from transformers import is_torch_available
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from .utils import CACHE_DIR, require_torch, slow, torch_device
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from .utils import require_torch, slow, torch_device
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if is_torch_available():
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@ -612,196 +612,6 @@ class ModelTesterMixin:
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outputs = model(**inputs_dict)
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class GPTModelTester(ModelTesterMixin):
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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_position_ids=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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n_positions=33,
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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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n_choices=3,
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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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scope=None,
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config_class=None,
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base_model_class=None,
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lm_head_model_class=None,
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double_head_model_class=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_position_ids = use_position_ids
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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.n_positions = n_positions
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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.n_choices = n_choices
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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.scope = scope
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self.config_class = config_class
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self.base_model_class = base_model_class
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self.lm_head_model_class = lm_head_model_class
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self.double_head_model_class = double_head_model_class
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self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
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def prepare_config_and_inputs(self):
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total_num_tokens = self.vocab_size
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input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
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position_ids = None
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if self.use_position_ids:
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position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
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token_type_ids = None
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if self.use_token_type_ids:
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total_voc = self.vocab_size
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token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
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mc_labels = None
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lm_labels = None
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mc_token_ids = None
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if self.use_labels:
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mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
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mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
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config = self.config_class(
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vocab_size=self.vocab_size,
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n_positions=self.n_positions,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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initializer_range=self.initializer_range,
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)
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return (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids)
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def create_and_check_base_model(
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self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids
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):
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model = self.base_model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(input_ids, position_ids, token_type_ids)
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outputs = model(input_ids, position_ids)
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outputs = model(input_ids)
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hidden_state = outputs[0]
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self.parent.assertListEqual(
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list(hidden_state.size()), [self.batch_size, self.n_choices, self.seq_length, self.hidden_size]
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)
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def create_and_check_lm_head(
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self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids
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):
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model = self.lm_head_model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
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loss, lm_logits = outputs[:2]
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total_voc = self.vocab_size
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self.parent.assertListEqual(
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list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]
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)
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self.parent.assertListEqual(list(loss.size()), [])
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def create_and_check_presents(
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self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids
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):
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(input_ids)
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presents = outputs[-1]
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self.parent.assertEqual(self.num_hidden_layers, len(presents))
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self.parent.assertListEqual(
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list(presents[0].size()),
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[
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2,
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self.batch_size * self.n_choices,
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self.num_attention_heads,
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self.seq_length,
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self.hidden_size // self.num_attention_heads,
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],
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)
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def create_and_check_double_heads(
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self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids
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):
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model = self.double_head_model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(
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input_ids,
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mc_token_ids,
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lm_labels=lm_labels,
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mc_labels=mc_labels,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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)
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lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
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loss = [lm_loss, mc_loss]
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total_voc = self.vocab_size
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self.parent.assertListEqual(
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list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]
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)
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self.parent.assertListEqual(list(mc_logits.size()), [self.batch_size, self.n_choices])
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self.parent.assertListEqual([list(l.size()) for l in loss], [[], []])
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def create_and_check_model_from_pretrained(self):
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for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
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model = self.base_model_class.from_pretrained(model_name, cache_dir=CACHE_DIR)
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self.parent.assertIsNotNone(model)
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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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(config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) = config_and_inputs
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inputs_dict = {"input_ids": input_ids}
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return config, inputs_dict
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def run_common_tests(self, test_presents=False):
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config_and_inputs = self.prepare_config_and_inputs()
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self.create_and_check_base_model(*config_and_inputs)
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config_and_inputs = self.prepare_config_and_inputs()
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self.create_and_check_lm_head(*config_and_inputs)
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config_and_inputs = self.prepare_config_and_inputs()
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self.create_and_check_double_heads(*config_and_inputs)
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if test_presents:
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config_and_inputs = self.prepare_config_and_inputs()
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self.create_and_check_presents(*config_and_inputs)
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@slow
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def run_slow_tests(self):
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self.create_and_check_model_from_pretrained()
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class ConfigTester(object):
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def __init__(self, parent, config_class=None, **kwargs):
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self.parent = parent
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