269 lines
9.9 KiB
Python
269 lines
9.9 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# 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,
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OpenAIGPTModel,
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)
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class OpenAIGPTModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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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):
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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 = OpenAIGPTConfig(
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vocab_size=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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pad_token_id=self.pad_token_id,
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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 (
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config,
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input_ids,
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head_mask,
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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
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model = OpenAIGPTModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(input_ids, token_type_ids=token_type_ids)
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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):
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model = OpenAIGPTLMHeadModel(config)
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model.to(torch_device)
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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, ())
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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):
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model = OpenAIGPTDoubleHeadsModel(config)
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model.to(torch_device)
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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, ())
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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(
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self, config, input_ids, head_mask, token_type_ids, *args
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):
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config.num_labels = self.num_labels
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model = OpenAIGPTForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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# print(config.num_labels, sequence_labels.size())
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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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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(
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config,
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input_ids,
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head_mask,
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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = 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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@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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (
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(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
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) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
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# special case for DoubleHeads model
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["input_ids"] = inputs_dict["labels"]
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inputs_dict["token_type_ids"] = inputs_dict["labels"]
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inputs_dict["mc_token_ids"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.num_choices),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["mc_labels"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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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):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
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def test_openai_gpt_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_openai_gpt_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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def test_openai_gpt_classification_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_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):
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@slow
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def test_lm_generate_openai_gpt(self):
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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 = [
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481,
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4735,
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544,
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246,
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963,
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870,
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762,
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239,
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244,
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40477,
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244,
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249,
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719,
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881,
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487,
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544,
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240,
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244,
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603,
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481,
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] # the president is a very good man. " \n " i\'m sure he is, " said the
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output_ids = model.generate(input_ids, do_sample=False)
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self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
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