# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # 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. from __future__ import annotations import unittest import paddle from parameterized import parameterized_class from paddlenlp.transformers import ( ArtistConfig, ArtistForConditionalGeneration, ArtistModel, ) from tests.transformers.test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) class ArtistModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_input_mask=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_input_mask = use_input_mask 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 = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64") input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length], dtype="int64") sequence_labels = None token_labels = None choice_labels = None if self.parent.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size, dtype="int64") token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels, dtype="int64") choice_labels = ids_tensor([self.batch_size], self.num_choices, dtype="int64") config = self.get_config() return ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) def get_config(self): return ArtistConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=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, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = paddle.cast( ids_tensor([self.batch_size, self.seq_length], vocab_size=2), dtype="float32" ) return ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, } return config, inputs_dict def create_and_check_artist_model(self, config, input_ids, input_mask, *args): model = ArtistModel(config) model.eval() result = model(input_ids, use_cache=True, return_dict=self.parent.return_dict) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertEqual(len(result[1]), config.num_hidden_layers) def create_and_check_conditional_generation(self, config, input_ids, input_mask, *args): model = ArtistForConditionalGeneration(config) model.eval() result = model(input_ids, use_cache=True, return_dict=self.parent.return_dict) self.parent.assertEqual(len(result[1]), config.num_hidden_layers) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size]) @parameterized_class( ("return_dict", "use_labels"), [ [False, False], [False, True], [True, False], [True, True], ], ) class ArtistModelTest(ModelTesterMixin, unittest.TestCase): base_model_class = ArtistModel use_labels = False return_dict = False all_model_classes = (ArtistModel, ArtistForConditionalGeneration) def setUp(self): self.model_tester = ArtistModelTester(self) def test_artist_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_artist_model(*config_and_inputs) def test_conditional_generation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_conditional_generation(*config_and_inputs)