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- # coding=utf-8
- # Copyright 2020 The HuggingFace Team. All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
-
- import unittest
-
- from transformers import OpenAIGPTConfig, is_tf_available
- from transformers.testing_utils import require_tf, slow
-
- from .test_configuration_common import ConfigTester
- from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
-
-
- if is_tf_available():
- import tensorflow as tf
-
- from transformers.models.openai.modeling_tf_openai import (
- TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
- TFOpenAIGPTDoubleHeadsModel,
- TFOpenAIGPTForSequenceClassification,
- TFOpenAIGPTLMHeadModel,
- TFOpenAIGPTModel,
- )
-
-
- class TFOpenAIGPTModelTester:
- def __init__(
- self,
- parent,
- ):
- self.parent = parent
- self.batch_size = 13
- self.seq_length = 7
- self.is_training = True
- self.use_token_type_ids = True
- self.use_input_mask = True
- self.use_labels = True
- self.use_mc_token_ids = True
- self.vocab_size = 99
- self.hidden_size = 32
- self.num_hidden_layers = 5
- self.num_attention_heads = 4
- self.intermediate_size = 37
- self.hidden_act = "gelu"
- self.hidden_dropout_prob = 0.1
- self.attention_probs_dropout_prob = 0.1
- self.max_position_embeddings = 512
- self.type_vocab_size = 16
- self.type_sequence_label_size = 2
- self.initializer_range = 0.02
- self.num_labels = 3
- self.num_choices = 4
- self.scope = None
- self.pad_token_id = self.vocab_size - 1
-
- def prepare_config_and_inputs(self):
- input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
- input_mask = None
- if self.use_input_mask:
- input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
-
- token_type_ids = None
- if self.use_token_type_ids:
- token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
-
- mc_token_ids = None
- if self.use_mc_token_ids:
- mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
-
- sequence_labels = None
- token_labels = None
- choice_labels = None
- 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 = OpenAIGPTConfig(
- vocab_size=self.vocab_size,
- n_embd=self.hidden_size,
- n_layer=self.num_hidden_layers,
- n_head=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,
- n_positions=self.max_position_embeddings,
- n_ctx=self.max_position_embeddings,
- # type_vocab_size=self.type_vocab_size,
- # initializer_range=self.initializer_range,
- pad_token_id=self.pad_token_id,
- )
-
- head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
-
- return (
- config,
- input_ids,
- input_mask,
- head_mask,
- token_type_ids,
- mc_token_ids,
- sequence_labels,
- token_labels,
- choice_labels,
- )
-
- def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
- model = TFOpenAIGPTModel(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
-
- inputs = [input_ids, input_mask]
- result = model(inputs)
-
- result = model(input_ids)
-
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
-
- def create_and_check_openai_gpt_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
- model = TFOpenAIGPTLMHeadModel(config=config)
- inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
-
- def create_and_check_openai_gpt_double_head(
- self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
- ):
- model = TFOpenAIGPTDoubleHeadsModel(config=config)
-
- multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
- multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
- multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
-
- inputs = {
- "input_ids": multiple_choice_inputs_ids,
- "mc_token_ids": mc_token_ids,
- "attention_mask": multiple_choice_input_mask,
- "token_type_ids": multiple_choice_token_type_ids,
- }
- result = model(inputs)
- self.parent.assertEqual(
- result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
- )
- self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
-
- def create_and_check_openai_gpt_for_sequence_classification(
- self, config, input_ids, input_mask, head_mask, token_type_ids, *args
- ):
- config.num_labels = self.num_labels
- sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
- inputs = {
- "input_ids": input_ids,
- "attention_mask": input_mask,
- "token_type_ids": token_type_ids,
- "labels": sequence_labels,
- }
- model = TFOpenAIGPTForSequenceClassification(config)
- result = model(inputs)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
-
- def prepare_config_and_inputs_for_common(self):
- config_and_inputs = self.prepare_config_and_inputs()
-
- (
- config,
- input_ids,
- input_mask,
- head_mask,
- token_type_ids,
- mc_token_ids,
- 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
-
-
- @require_tf
- class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
-
- all_model_classes = (
- (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
- if is_tf_available()
- else ()
- )
- all_generative_model_classes = (
- (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
- ) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
- test_head_masking = False
- test_onnx = False
-
- def setUp(self):
- self.model_tester = TFOpenAIGPTModelTester(self)
- self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
-
- def test_config(self):
- self.config_tester.run_common_tests()
-
- def test_openai_gpt_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
-
- def test_openai_gpt_lm_head(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_openai_gpt_lm_head(*config_and_inputs)
-
- def test_openai_gpt_double_head(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs)
-
- def test_model_common_attributes(self):
- config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-
- for model_class in self.all_model_classes:
- model = model_class(config)
- assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
-
- if model_class in self.all_generative_model_classes:
- x = model.get_output_embeddings()
- assert isinstance(x, tf.keras.layers.Layer)
- name = model.get_bias()
- assert name is None
- else:
- x = model.get_output_embeddings()
- assert x is None
- name = model.get_bias()
- assert name is None
-
- def test_openai_gpt_sequence_classification_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)
-
- @slow
- def test_model_from_pretrained(self):
- for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
- model = TFOpenAIGPTModel.from_pretrained(model_name)
- self.assertIsNotNone(model)
-
-
- @require_tf
- class TFOPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
- @slow
- def test_lm_generate_openai_gpt(self):
- model = TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
- input_ids = tf.convert_to_tensor([[481, 4735, 544]], dtype=tf.int32) # the president is
- expected_output_ids = [
- 481,
- 4735,
- 544,
- 246,
- 963,
- 870,
- 762,
- 239,
- 244,
- 40477,
- 244,
- 249,
- 719,
- 881,
- 487,
- 544,
- 240,
- 244,
- 603,
- 481,
- ] # the president is a very good man. " \n " i\'m sure he is, " said the
-
- output_ids = model.generate(input_ids, do_sample=False)
- self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
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