# 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)