# Copyright (c) 2022 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. import unittest from parameterized import parameterized_class from paddlenlp.transformers.rw.configuration import RWConfig from paddlenlp.transformers.rw.modeling import RWForCausalLM, RWModel from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask class RWModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, 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=2, initializer_range=0.02, pad_token_id=0, type_sequence_label_size=2, use_relative_position=True, num_labels=3, num_choices=4, num_classes=3, scope=None, multi_query=True, bias=False, parallel_attn=True, output_attentions=False, ): 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.use_token_type_ids = use_token_type_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.initializer_range = initializer_range self.pad_token_id = pad_token_id self.type_sequence_label_size = type_sequence_label_size self.use_relative_position = use_relative_position self.num_classes = num_classes self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.multi_query = multi_query self.bias = bias self.parallel_attn = parallel_attn self.output_attentions = output_attentions 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) sequence_labels = None token_labels = None choice_labels = None if self.parent and self.parent.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() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict def get_config(self): return RWConfig( 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, pad_token_id=self.pad_token_id, use_relative_position=self.use_relative_position, num_class=self.num_classes, num_labels=self.num_labels, num_choices=self.num_choices, multi_query=self.multi_query, bias=self.bias, parallel_attn=self.parallel_attn, output_attentions=self.output_attentions, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RWModel(config) model.eval() result = model( input_ids, attention_mask=input_mask, return_dict=self.parent.return_dict, ) 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]) result = model(input_ids, use_cache=True, output_attentions=True, return_dict=self.parent.return_dict) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) result = model( input_ids, use_cache=True, output_attentions=True, output_hidden_states=True, return_dict=self.parent.return_dict, ) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) def create_and_check_for_causal_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = RWForCausalLM(config) model.eval() result = model( input_ids, attention_mask=input_mask, labels=input_ids, return_dict=self.parent.return_dict, ) self.parent.assertEqual(result[0].shape, []) self.parent.assertEqual(result[1].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 RWModelTest(ModelTesterMixin, unittest.TestCase): base_model_class = RWModel return_dict: bool = False use_labels: bool = False use_test_inputs_embeds: bool = True all_model_classes = ( RWModel, RWForCausalLM, ) def setUp(self): self.model_tester = RWModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_causal_model(*config_and_inputs)