238 lines
9.5 KiB
Python
238 lines
9.5 KiB
Python
# coding=utf-8
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# Copyright 2022 The OpenBMB Team and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch CPMAnt 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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CpmAntConfig,
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CpmAntForCausalLM,
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CpmAntModel,
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CpmAntTokenizer,
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)
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@require_torch
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class CpmAntModelTester:
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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=2,
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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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prompt_length=8,
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prompt_types=8,
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segment_types=8,
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init_std=0.02,
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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.prompt_length = prompt_length
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self.prompt_types = prompt_types
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self.segment_types = segment_types
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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 CpmAntConfig(
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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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prompt_types=self.prompt_types,
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prompt_length=self.prompt_length,
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segment_types=self.segment_types,
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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_cpmant_model(self, config, input_ids, *args):
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model = CpmAntModel(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 = CpmAntForCausalLM(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 + config.prompt_types * config.prompt_length),
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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 CpmAntModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} 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 = CpmAntModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CpmAntConfig)
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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("CPMAnt 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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"CPMAnt 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_cpmant_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_cpmant_model(config, inputs)
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def test_cpmant_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 CpmAntModelIntegrationTest(unittest.TestCase):
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@tooslow
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def test_inference_masked_lm(self):
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texts = "今天天气真好！"
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model_path = "openbmb/cpm-ant-10b"
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model = CpmAntModel.from_pretrained(model_path)
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tokenizer = CpmAntTokenizer.from_pretrained(model_path)
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inputs = tokenizer(texts, return_tensors="pt")
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hidden_states = model(**inputs).last_hidden_state
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expected_slice = torch.tensor(
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[[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]],
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)
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self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2))
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@require_torch
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class CpmAntForCausalLMlIntegrationTest(unittest.TestCase):
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@tooslow
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def test_inference_casual(self):
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texts = "今天天气真好！"
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model_path = "openbmb/cpm-ant-10b"
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model = CpmAntForCausalLM.from_pretrained(model_path)
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tokenizer = CpmAntTokenizer.from_pretrained(model_path)
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inputs = tokenizer(texts, return_tensors="pt")
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hidden_states = model(**inputs).logits
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expected_slice = torch.tensor(
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[[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]],
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)
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self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2))
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@tooslow
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def test_simple_generation(self):
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model_path = "openbmb/cpm-ant-10b"
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model = CpmAntForCausalLM.from_pretrained(model_path)
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tokenizer = CpmAntTokenizer.from_pretrained(model_path)
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texts = "今天天气不错，"
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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"
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model_inputs = tokenizer(texts, return_tensors="pt")
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token_ids = model.generate(**model_inputs)
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output_texts = tokenizer.batch_decode(token_ids)
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self.assertEqual(expected_output, output_texts)
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@tooslow
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def test_batch_generation(self):
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model_path = "openbmb/cpm-ant-10b"
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model = CpmAntForCausalLM.from_pretrained(model_path)
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tokenizer = CpmAntTokenizer.from_pretrained(model_path)
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texts = ["今天天气不错，", "新年快乐，万事如意！"]
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expected_output = [
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"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",
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"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",
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]
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model_inputs = tokenizer(texts, return_tensors="pt", padding=True)
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token_ids = model.generate(**model_inputs)
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output_texts = tokenizer.batch_decode(token_ids)
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self.assertEqual(expected_output, output_texts)
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