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