# coding=utf-8 # Copyright 2022 The HuggingFace Inc. 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. """ Testing suite for the PyTorch CpmBee model. """ import unittest from transformers.testing_utils import is_torch_available, require_torch, tooslow from ...generation.test_utils import torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( CpmBeeConfig, CpmBeeForCausalLM, CpmBeeModel, CpmBeeTokenizer, ) @require_torch class CpmBeeModelTester: def __init__( self, parent, batch_size=2, seq_length=8, is_training=True, use_token_type_ids=False, use_input_mask=False, use_labels=False, use_mc_token_ids=False, vocab_size=99, hidden_size=32, num_hidden_layers=3, num_attention_heads=4, intermediate_size=37, num_buckets=32, max_distance=128, position_bias_num_segment_buckets=32, init_std=1.0, return_dict=True, ): 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.num_buckets = num_buckets self.max_distance = max_distance self.position_bias_num_segment_buckets = position_bias_num_segment_buckets self.init_std = init_std self.return_dict = return_dict def prepare_config_and_inputs(self): input_ids = {} input_ids["input_ids"] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).type(torch.int32) input_ids["use_cache"] = False config = self.get_config() return (config, input_ids) def get_config(self): return CpmBeeConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, dim_ff=self.intermediate_size, position_bias_num_buckets=self.num_buckets, position_bias_max_distance=self.max_distance, position_bias_num_segment_buckets=self.position_bias_num_segment_buckets, use_cache=True, init_std=self.init_std, return_dict=self.return_dict, ) def create_and_check_cpmbee_model(self, config, input_ids, *args): model = CpmBeeModel(config=config) model.to(torch_device) model.eval() hidden_states = model(**input_ids).last_hidden_state self.parent.assertEqual(hidden_states.shape, (self.batch_size, self.seq_length, config.hidden_size)) def create_and_check_lm_head_model(self, config, input_ids, *args): model = CpmBeeForCausalLM(config) model.to(torch_device) input_ids["input_ids"] = input_ids["input_ids"].to(torch_device) model.eval() model_output = model(**input_ids) self.parent.assertEqual( model_output.logits.shape, (self.batch_size, self.seq_length, config.vocab_size), ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict @require_torch class CpmBeeModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (CpmBeeModel, CpmBeeForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": CpmBeeModel, "text-generation": CpmBeeForCausalLM} if is_torch_available() else {} ) test_pruning = False test_missing_keys = False test_mismatched_shapes = False test_head_masking = False test_resize_embeddings = False def setUp(self): self.model_tester = CpmBeeModelTester(self) self.config_tester = ConfigTester(self, config_class=CpmBeeConfig) def test_config(self): self.config_tester.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def test_inputs_embeds(self): unittest.skip("CPMBee doesn't support input_embeds.")(self.test_inputs_embeds) def test_retain_grad_hidden_states_attentions(self): unittest.skip( "CPMBee doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\ So is attentions. We strongly recommand you use loss to tune model." )(self.test_retain_grad_hidden_states_attentions) def test_cpmbee_model(self): config, inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_cpmbee_model(config, inputs) def test_cpmbee_lm_head_model(self): config, inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(config, inputs) @require_torch class CpmBeeForCausalLMlIntegrationTest(unittest.TestCase): @tooslow def test_simple_generation(self): texts = {"input": "今天天气不错，", "": ""} model = CpmBeeForCausalLM.from_pretrained("openbmb/cpm-bee-10b") tokenizer = CpmBeeTokenizer.from_pretrained("openbmb/cpm-bee-10b") output_texts = model.generate(texts, tokenizer) expected_output = {"input": "今天天气不错，", "": "适合睡觉。"} self.assertEqual(expected_output[""], output_texts[""])