# Copyright (c) 2022 PaddlePaddle Authors. 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 parameterized import parameterized_class from paddlenlp.transformers import ( CTRLConfig, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, ) from ...testing_utils import slow from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask class CTRLModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_token_type_ids=True, use_input_mask=True, use_labels=True, use_mc_token_ids=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_token_type_ids = use_token_type_ids self.use_input_mask = use_input_mask self.use_labels = use_labels self.use_mc_token_ids = use_mc_token_ids self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope 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 = random_attention_mask([self.batch_size, self.seq_length]) 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 = self.get_config() 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 get_config(self): return CTRLConfig( 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, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = CTRLModel(config=config) model.eval() model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask) model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = CTRLLMHeadModel(config) model.eval() result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) self.parent.assertIsInstance(result[0].item(), float) self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size]) 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 def create_and_check_ctrl_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): config.num_classes = self.num_labels model = CTRLForSequenceClassification(config) model.eval() sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) result = model(input_ids=input_ids, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(len(result), 2) self.parent.assertEqual(result[1].shape, [self.batch_size, self.num_labels]) @parameterized_class( ("use_labels"), [ [False], [True], ], ) class CTRLModelTest(ModelTesterMixin, unittest.TestCase): base_model_class = CTRLModel return_dict = False use_labels = False all_model_classes = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) def setUp(self): super().setUp() self.model_tester = CTRLModelTester(self) def test_ctrl_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_model(*config_and_inputs) def test_ctrl_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_ctrl_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_for_sequence_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): names = ["sshleifer-tiny-ctrl"] for model_name in names: model = CTRLModel.from_pretrained(model_name) self.assertIsNotNone(model)