# coding=utf-8 # Copyright 2022 The OpenBMB Team and 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 CPMAnt 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 if is_torch_available(): import torch from transformers import ( CpmAntConfig, CpmAntForCausalLM, CpmAntModel, CpmAntTokenizer, ) @require_torch class CpmAntModelTester: 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, prompt_length=8, prompt_types=8, segment_types=8, 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.prompt_length = prompt_length self.prompt_types = prompt_types self.segment_types = segment_types 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 CpmAntConfig( 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, prompt_types=self.prompt_types, prompt_length=self.prompt_length, segment_types=self.segment_types, use_cache=True, init_std=self.init_std, return_dict=self.return_dict, ) def create_and_check_cpmant_model(self, config, input_ids, *args): model = CpmAntModel(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 = CpmAntForCausalLM(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 + config.prompt_types * config.prompt_length), ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict @require_torch class CpmAntModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (CpmAntModel, CpmAntForCausalLM) 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 = CpmAntModelTester(self) self.config_tester = ConfigTester(self, config_class=CpmAntConfig) 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("CPMAnt doesn't support input_embeds.")(self.test_inputs_embeds) def test_retain_grad_hidden_states_attentions(self): unittest.skip( "CPMAnt 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_cpmant_model(self): config, inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_cpmant_model(config, inputs) def test_cpmant_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 CpmAntModelIntegrationTest(unittest.TestCase): @tooslow def test_inference_masked_lm(self): texts = "今天天气真好！" model_path = "openbmb/cpm-ant-10b" model = CpmAntModel.from_pretrained(model_path) tokenizer = CpmAntTokenizer.from_pretrained(model_path) inputs = tokenizer(texts, return_tensors="pt") hidden_states = model(**inputs).last_hidden_state expected_slice = torch.tensor( [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]], ) self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2)) @require_torch class CpmAntForCausalLMlIntegrationTest(unittest.TestCase): @tooslow def test_inference_casual(self): texts = "今天天气真好！" model_path = "openbmb/cpm-ant-10b" model = CpmAntForCausalLM.from_pretrained(model_path) tokenizer = CpmAntTokenizer.from_pretrained(model_path) inputs = tokenizer(texts, return_tensors="pt") hidden_states = model(**inputs).logits expected_slice = torch.tensor( [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]], ) self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2)) @tooslow def test_simple_generation(self): model_path = "openbmb/cpm-ant-10b" model = CpmAntForCausalLM.from_pretrained(model_path) tokenizer = CpmAntTokenizer.from_pretrained(model_path) texts = "今天天气不错，" expected_output = "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的" model_inputs = tokenizer(texts, return_tensors="pt") token_ids = model.generate(**model_inputs) output_texts = tokenizer.batch_decode(token_ids) self.assertEqual(expected_output, output_texts) @tooslow def test_batch_generation(self): model_path = "openbmb/cpm-ant-10b" model = CpmAntForCausalLM.from_pretrained(model_path) tokenizer = CpmAntTokenizer.from_pretrained(model_path) texts = ["今天天气不错，", "新年快乐，万事如意！"] expected_output = [ "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的", "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的", ] model_inputs = tokenizer(texts, return_tensors="pt", padding=True) token_ids = model.generate(**model_inputs) output_texts = tokenizer.batch_decode(token_ids) self.assertEqual(expected_output, output_texts)