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| """ 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，", "<ans>": ""} |
| 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，", "<ans>": "适合睡觉。"} |
| self.assertEqual(expected_output["<ans>"], output_texts["<ans>"]) |
|
|
