OpenAI GPT-2 now depends on CommonTests.
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Lysandre
@@ -210,6 +210,9 @@ class CommonTestCases:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if "head_mask" in inputs_dict:
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del inputs_dict["head_mask"]
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for model_class in self.all_model_classes:
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config.output_attentions = True
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config.output_hidden_states = False
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@@ -18,31 +18,194 @@ from __future__ import print_function
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import unittest
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import pytest
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import shutil
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from pytorch_transformers import (GPT2Config, GPT2Model,
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from pytorch_transformers import (GPT2Config, GPT2Model, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
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GPT2LMHeadModel, GPT2DoubleHeadsModel)
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from .modeling_common_test import CommonTestCases, ConfigTester
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from .modeling_common_test import CommonTestCases, ConfigTester, ids_tensor
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class GPT2ModelTest(unittest.TestCase):
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class GPT2ModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel)
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class GPT2ModelTester(object):
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def __init__(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_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_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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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 = GPT2Config(
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vocab_size_or_config_json_file=self.vocab_size,
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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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# 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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n_positions=self.max_position_embeddings,
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n_ctx=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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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return config, input_ids, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_and_check_gpt2_model(self, config, input_ids, head_mask, token_type_ids, *args):
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model = GPT2Model(config=config)
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model.eval()
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model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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model(input_ids, token_type_ids=token_type_ids)
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sequence_output, presents = model(input_ids)
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result = {
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"sequence_output": sequence_output,
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"presents": presents,
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(len(result["presents"]), config.n_layer)
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def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
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model = GPT2LMHeadModel(config)
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model.eval()
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loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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result = {
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"loss": loss,
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"lm_logits": lm_logits
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}
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
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model = GPT2DoubleHeadsModel(config)
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model.eval()
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loss, lm_logits, mc_logits, _ = model(input_ids, token_type_ids=token_type_ids, lm_labels=input_ids)
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result = {
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"loss": loss,
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"lm_logits": lm_logits
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}
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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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, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
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inputs_dict = {
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'input_ids': input_ids,
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'token_type_ids': token_type_ids,
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'head_mask': head_mask
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}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = GPT2ModelTest.GPT2ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
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def test_config(self):
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config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
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config_tester.run_common_tests()
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self.config_tester.run_common_tests()
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def test_model(self):
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model_tester = CommonTestCases.GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
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lm_head_model_class=GPT2LMHeadModel,
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double_head_model_class=GPT2DoubleHeadsModel)
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model_tester.run_common_tests(test_presents=True)
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def test_gpt2_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
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def test_gpt2_lm_head_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
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def test_gpt2_double_lm_head_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
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@pytest.mark.slow
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def test_pretrained(self):
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model_tester = CommonTestCases.GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
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lm_head_model_class=GPT2LMHeadModel,
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double_head_model_class=GPT2DoubleHeadsModel)
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model_tester.run_slow_tests()
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/pytorch_transformers_test/"
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for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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