test_modeling_openai.py 9.9 KB
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
# 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.
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import unittest

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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_generation_utils import GenerationTesterMixin
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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if is_torch_available():
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    import torch
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    from transformers import (
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        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
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        OpenAIGPTConfig,
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        OpenAIGPTDoubleHeadsModel,
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        OpenAIGPTForSequenceClassification,
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        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
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    )

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class OpenAIGPTModelTester:
    def __init__(
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        self,
        parent,
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    ):
        self.parent = parent
        self.batch_size = 13
        self.seq_length = 7
        self.is_training = True
        self.use_token_type_ids = True
        self.use_labels = 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
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        self.pad_token_id = self.vocab_size - 1
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    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        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)

        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,
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            n_ctx=self.max_position_embeddings,
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            # type_vocab_size=self.type_vocab_size,
            # initializer_range=self.initializer_range
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            pad_token_id=self.pad_token_id,
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        )

        head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)

        return (
            config,
            input_ids,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
        model = OpenAIGPTModel(config=config)
        model.to(torch_device)
        model.eval()

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        result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
        result = model(input_ids, token_type_ids=token_type_ids)
        result = model(input_ids)
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        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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    def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
        model = OpenAIGPTLMHeadModel(config)
        model.to(torch_device)
        model.eval()

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        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (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):
        model = OpenAIGPTDoubleHeadsModel(config)
        model.to(torch_device)
        model.eval()

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        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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    def create_and_check_openai_gpt_for_sequence_classification(
        self, config, input_ids, head_mask, token_type_ids, *args
    ):
        config.num_labels = self.num_labels
        model = OpenAIGPTForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        # print(config.num_labels, sequence_labels.size())
        sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
        result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

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    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "head_mask": head_mask,
        }

        return config, inputs_dict


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@require_torch
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class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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    all_model_classes = (
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        (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
        if is_torch_available()
        else ()
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    )
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    all_generative_model_classes = (
        (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
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    # special case for DoubleHeads model
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
                    dtype=torch.long,
                    device=torch_device,
                )
                inputs_dict["input_ids"] = inputs_dict["labels"]
                inputs_dict["token_type_ids"] = inputs_dict["labels"]
                inputs_dict["mc_token_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.num_choices),
                    dtype=torch.long,
                    device=torch_device,
                )
                inputs_dict["mc_labels"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
        return inputs_dict

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    def setUp(self):
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        self.model_tester = OpenAIGPTModelTester(self)
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        self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
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    def test_config(self):
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        self.config_tester.run_common_tests()
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    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_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*config_and_inputs)

    def test_openai_gpt_double_lm_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
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    def test_openai_gpt_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)

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    @slow
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    def test_model_from_pretrained(self):
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        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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            model = OpenAIGPTModel.from_pretrained(model_name)
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            self.assertIsNotNone(model)
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@require_torch
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class OPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_openai_gpt(self):
        model = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
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        model.to(torch_device)
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        input_ids = torch.tensor([[481, 4735, 544]], dtype=torch.long, device=torch_device)  # the president is
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        expected_output_ids = [
            481,
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            4735,
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            544,
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            246,
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            40477,
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            719,
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            487,
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            244,
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            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)
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        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)