昇思MindSpore学习笔记2-01 LLM原理和实践 --基于 MindSpore 实现 BERT 对话情绪识别

摘要：

通过识别BERT对话情绪状态的实例，展现在昇思MindSpore AI框架中大语言模型的原理和实际使用方法、步骤。

一、环境配置

%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14，如需更换mindspore版本，可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
# 该案例在 mindnlp 0.3.1 版本完成适配，如果发现案例跑不通，可以指定mindnlp版本，执行`!pip install mindnlp==0.3.1`
!pip install mindnlp

输出：

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
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Collecting tokenizers (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ba/26/139bd2371228a0e203da7b3e3eddcb02f45b2b7edd91df00e342e4b55e13/tokenizers-0.19.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.6 MB)━━━━━━━━━━━━━━━━ 3.6/3.6 MB 14.7 MB/s eta 0:00:00a 0:00:01
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Collecting pytest==7.2.0 (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/67/68/a5eb36c3a8540594b6035e6cdae40c1ef1b6a2bfacbecc3d1a544583c078/pytest-7.2.0-py3-none-any.whl (316 kB)━━━━━━━━━━━━━━━━ 316.8/316.8 kB 16.7 MB/s eta 0:00:00
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Collecting pyarrow-hotfix (from datasets->mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e4/f4/9ec2222f5f5f8ea04f66f184caafd991a39c8782e31f5b0266f101cb68ca/pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)
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Collecting xxhash (from datasets->mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7c/b9/93f860969093d5d1c4fa60c75ca351b212560de68f33dc0da04c89b7dc1b/xxhash-3.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (220 kB)━━━━━━━━━━━━━━━━ 220.6/220.6 kB 15.6 MB/s eta 0:00:00
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Collecting pygtrie<3.0,>=2.1 (from pyctcdecode->mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ec/cd/bd196b2cf014afb1009de8b0f05ecd54011d881944e62763f3c1b1e8ef37/pygtrie-2.5.0-py3-none-any.whl (25 kB)
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Collecting aiosignal>=1.1.2 (from aiohttp->datasets->mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl (7.6 kB)
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Building wheels for collected packages: jiebaBuilding wheel for jieba (setup.py) ... doneCreated wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314459 sha256=352f23b7dc8b4bade2f918165e055bc707601544400a4918136ba69f220ce9f6Stored in directory: /home/nginx/.cache/pip/wheels/1a/76/68/b6d79c4db704bb18d54f6a73ab551185f4711f9730c0c15d97
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[notice] To update, run: python -m pip install --upgrade pip

显示mindspore模块的基本信息

!pip show mindspore

输出：

Name: mindspore
Version: 2.2.14
Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Home-page: https://www.mindspore.cn
Author: The MindSpore Authors
Author-email: [e-mail]
License: Apache 2.0
Location: /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages
Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy
Required-by: mindnlp

二、模型简介

BERT是一种新型语言模型

全称：Bidirectional Encoder Representations from Transformers

中文：双向表达的编码变换

Google发布于2018年

用于自然语言处理场景类似的预训练语言模型有：

        问答

        命名实体识别

        自然语言推理

        文本分类等

BERT模型涉及

        Transformer的Encoder

        双向结构

BERT模型的主要创新点

        pre-train方法

                用Masked Language Model捕捉词语

                用Next Sentence Prediction捕捉句子

用Masked Language Model方法训练BERT对话时

        随机把语料库中15%的单词做Mask操作。

        Mask操作的三种情况：

                80%的单词直接用[Mask]替换

                10%的单词直接替换成另一个新的单词

                10%的单词保持不变。

问答Question Answering (QA) 

自然语言推断Natural Language Inference (NLI)

Next Sentence Prediction预训练任务

        目的：

                让模型理解两个句子之间的联系。

        训练内容：

                输入是句子A和B

                B有一半的几率是A的下一句

                预测B是不是A的下一句

        训练结果：

                Embedding table

                12层Transformer权重（BERT-BASE）

                或24层Transformer权重（BERT-LARGE）。

        微调Fine-tuning下游任务：

                文本分类

                相似度判断

                阅读理解等。

对话情绪识别Emotion Detection（简称EmoTect）

        对话文本

        判断文本情绪类别

                积极

                消极

                中性

        计算置信度。

示例：

导入mindspore dataset nn context mindnlp等模块

import os
​
import mindspore
from mindspore.dataset import text, GeneratorDataset, transforms
from mindspore import nn, context
​
from mindnlp._legacy.engine import Trainer, Evaluator
from mindnlp._legacy.engine.callbacks import CheckpointCallback, BestModelCallback
from mindnlp._legacy.metrics import Accuracy

输出：

Building prefix dict from the default dictionary ...
Dumping model to file cache /tmp/jieba.cache
Loading model cost 1.037 seconds.
Prefix dict has been built successfully.

三、准备数据集

1. 数据集说明

实验数据集采用百度飞桨的机器人聊天数据

        已标注

        分词预处理

数据两列，制表符（'\t'）分隔：

        情绪分类

                0消极

                1中性

                2积极

        中文文本

                空格分词

                utf8编码

数据示例：

label--text_a
0--谁骂人了？我从来不骂人，我骂的都不是人，你是人吗 ？
1--我有事等会儿就回来和你聊
2--我见到你很高兴谢谢你帮我

2.下载数据集

# download dataset
!wget https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz -O emotion_detection.tar.gz
!tar xvf emotion_detection.tar.gz

输出：

--2024-07-01 13:38:50--  https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz
Resolving baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)... 119.249.103.5, 113.200.2.111, 2409:8c04:1001:1203:0:ff:b0bb:4f27
Connecting to baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)|119.249.103.5|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1710581 (1.6M) [application/x-gzip]
Saving to: ‘emotion_detection.tar.gz’emotion_detection.t 100%[===================>]   1.63M  8.04MB/s    in 0.2s    2024-07-01 13:38:50 (8.04 MB/s) - ‘emotion_detection.tar.gz’ saved [1710581/1710581]data/
data/test.tsv
data/infer.tsv
data/dev.tsv
data/train.tsv
data/vocab.txt

3.定义数据集类

# prepare dataset
class SentimentDataset:"""Sentiment Dataset"""
​def __init__(self, path):self.path = pathself._labels, self._text_a = [], []self._load()
​def _load(self):with open(self.path, "r", encoding="utf-8") as f:dataset = f.read()lines = dataset.split("\n")for line in lines[1:-1]:label, text_a = line.split("\t")self._labels.append(int(label))self._text_a.append(text_a)
​def __getitem__(self, index):return self._labels[index], self._text_a[index]
​def __len__(self):return len(self._labels)

四、数据加载和数据预处理

数据加载和预处理函数

process_dataset()

import numpy as np
​
def process_dataset(source, tokenizer, max_seq_len=64, batch_size=32, shuffle=True):is_ascend = mindspore.get_context('device_target') == 'Ascend'column_names = ["label", "text_a"]dataset = GeneratorDataset(source, column_names=column_names, shuffle=shuffle)# transformstype_cast_op = transforms.TypeCast(mindspore.int32)def tokenize_and_pad(text):if is_ascend:tokenized = tokenizer(text, padding='max_length', 
truncation=True, max_length=max_seq_len)else:tokenized = tokenizer(text)return tokenized['input_ids'], tokenized['attention_mask']# map dataset
dataset = dataset.map(operations=tokenize_and_pad, input_columns="text_a", 
output_columns=['input_ids', 'attention_mask'])
dataset = dataset.map(operations=[type_cast_op], input_columns="label", 
output_columns='labels')# batch datasetif is_ascend:dataset = dataset.batch(batch_size)else:dataset = dataset.padded_batch(batch_size, 
pad_info={'input_ids': (None, tokenizer.pad_token_id),
'attention_mask': (None, 0)})return dataset

数据预处理部分采用静态Shape处理：

        昇腾NPU环境下暂不支持动态Shape

from mindnlp.transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')

输出：

100%━━━━━━━━━━━━━━━━━━━━━ 49.0/49.0 [00:00<00:00, 3.05kB/s]━107k/0.00 [00:05<00:00, 36.3kB/s]━263k/0.00 [00:15<00:00, 10.2kB/s]━━━━━━━━━━━━━━━━━━━━━ 624/? [00:00<00:00, 56.0kB/s]

tokenizer.pad_token_id

输出：

0

取训练数据集的列名：

dataset_train = process_dataset(SentimentDataset("data/train.tsv"), tokenizer)
dataset_val   = process_dataset(SentimentDataset("data/dev.tsv"  ), tokenizer)
dataset_test  = process_dataset(SentimentDataset("data/test.tsv" ), tokenizer, shuffle=False)
dataset_train.get_col_names()

输出：

['input_ids', 'attention_mask', 'labels']

遍历显示训练数据集

print(next(dataset_train.create_tuple_iterator()))

输出：

[Tensor(shape=[32, 64], dtype=Int64, value=
[[ 101, 2769, 4638 ...    0,    0,    0],[ 101, 2769, 3221 ...    0,    0,    0],[ 101,  758, 1282 ...    0,    0,    0],...[ 101, 1217,  678 ...    0,    0,    0],[ 101,  872,  679 ...    0,    0,    0],[ 101,  872, 3766 ...    0,    0,    0]]),Tensor(shape=[32, 64], dtype=Int64, value=
[[1, 1, 1 ... 0, 0, 0],[1, 1, 1 ... 0, 0, 0],[1, 1, 1 ... 0, 0, 0],...[1, 1, 1 ... 0, 0, 0],[1, 1, 1 ... 0, 0, 0],[1, 1, 1 ... 0, 0, 0]]),Tensor(shape=[32], dtype=Int32, value=[1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,  1, 1, 1, 2, 2, 1, 1, 1])]

五、模型构建

BERT 模型

        BertForSequenceClassification模块构建

                加载预训练权重

                设置情感三分类

        自动混合精度

        实例化优化器

        实例化评价指标

        设置模型训练的权重保存策略

        构建训练器

        模型开始训练

from mindnlp.transformers import BertForSequenceClassification, BertModel
from mindnlp._legacy.amp import auto_mixed_precision
​
# set bert config and define parameters for training
model = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=3)
model = auto_mixed_precision(model, 'O1')
​
optimizer = nn.Adam(model.trainable_params(), learning_rate=2e-5)

(), learning_rate=2e-5)

输出：

100%━━━━━━━━━━━━━━━━━━ 392M/392M [00:53<00:00, 6.82MB/s]

The following parameters in checkpoint files are not loaded:

['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']

The following parameters in models are missing parameter:

['classifier.weight', 'classifier.bias']

metric = Accuracy()
# define callbacks to save checkpoints
ckpoint_cb = CheckpointCallback(save_path='checkpoint', ckpt_name='bert_emotect', epochs=1, keep_checkpoint_max=2)
best_model_cb = BestModelCallback(save_path='checkpoint', ckpt_name='bert_emotect_best', auto_load=True)
# 构建训练器
trainer = Trainer(network=model, train_dataset=dataset_train,eval_dataset=dataset_val, metrics=metric,epochs=5, optimizer=optimizer, callbacks=[ckpoint_cb, best_model_cb])%%time
# start training
trainer.run(tgt_columns="labels")

输出：

The train will start from the checkpoint saved in 'checkpoint'.
Epoch  0: 100%━━━━━━━━━━━━━━ 302/302 [04:07<00:00,  2.25s/it, loss=0.3460012]
Checkpoint: 'bert_emotect_epoch_0.ckpt' has been saved in epoch: 0.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:07<00:00,  1.07it/s]
Evaluate Score: {'Accuracy': 0.9351851851851852}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 0.---------------
Epoch  1: 100%━━━━━━━━━━━━━━ 302/302 [02:38<00:00,  1.95it/s, loss=0.19017023]
Checkpoint: 'bert_emotect_epoch_1.ckpt' has been saved in epoch: 1.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:05<00:00,  7.48it/s]
Evaluate Score: {'Accuracy': 0.9564814814814815}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 1.---------------
Epoch  2: 100%━━━━━━━━━━━━━━ 302/302 [02:40<00:00,  1.92it/s, loss=0.12662967]
The maximum number of stored checkpoints has been reached.
Checkpoint: 'bert_emotect_epoch_2.ckpt' has been saved in epoch: 2.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:04<00:00,  7.59it/s]
Evaluate Score: {'Accuracy': 0.9740740740740741}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 2.---------------
Epoch  3: 100%━━━━━━━━━━━━━━ 302/302 [02:40<00:00,  1.92it/s, loss=0.08593981]
The maximum number of stored checkpoints has been reached.
Checkpoint: 'bert_emotect_epoch_3.ckpt' has been saved in epoch: 3.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:04<00:00,  7.51it/s]
Evaluate Score: {'Accuracy': 0.9833333333333333}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 3.---------------
Epoch  4: 100%━━━━━━━━━━━━━━ 302/302 [02:41<00:00,  1.92it/s, loss=0.05900709]
The maximum number of stored checkpoints has been reached.
Checkpoint: 'bert_emotect_epoch_4.ckpt' has been saved in epoch: 4.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:04<00:00,  7.39it/s]
Evaluate Score: {'Accuracy': 0.9879629629629629}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 4.---------------
Loading best model from 'checkpoint' with '['Accuracy']': [0.9879629629629629]...
---------------The model is already load the best model from 'bert_emotect_best.ckpt'.---------------
CPU times: user 22min 58s, sys: 13min 25s, total: 36min 24s
Wall time: 15min 30s

六、模型验证

验证评估

        测试数据集

        准确率

evaluator = Evaluator(network=model, eval_dataset=dataset_test, metrics=metric)
evaluator.run(tgt_columns="labels")

输出：

Evaluate: 100%━━━━━━━━━━━━━━ 33/33 [00:08<00:00,  1.20s/it]

Evaluate Score: {'Accuracy': 0.8822393822393823}

七、模型推理

遍历推理数据集，展示结果与标签。

dataset_infer = SentimentDataset("data/infer.tsv")
def predict(text, label=None):label_map = {0: "消极", 1: "中性", 2: "积极"}
​text_tokenized = Tensor([tokenizer(text).input_ids])logits = model(text_tokenized)predict_label = logits[0].asnumpy().argmax()info = f"inputs: '{text}', predict: '{label_map[predict_label]}'"if label is not None:info += f" , label: '{label_map[label]}'"print(info)
from mindspore import Tensor
​
for label, text in dataset_infer:predict(text, label)

输出：

inputs: '我 要 客观', predict: '中性' , label: '中性'
inputs: '靠 你 真是 说 废话 吗', predict: '消极' , label: '消极'
inputs: '口嗅 会', predict: '中性' , label: '中性'
inputs: '每次 是 表妹 带 窝 飞 因为 窝路痴', predict: '中性' , label: '中性'
inputs: '别说 废话 我 问 你 个 问题', predict: '消极' , label: '消极'
inputs: '4967 是 新加坡 那 家 银行', predict: '中性' , label: '中性'
inputs: '是 我 喜欢 兔子', predict: '积极' , label: '积极'
inputs: '你 写 过 黄山 奇石 吗', predict: '中性' , label: '中性'
inputs: '一个一个 慢慢来', predict: '中性' , label: '中性'
inputs: '我 玩 过 这个 一点 都 不 好玩', predict: '消极' , label: '消极'
inputs: '网上 开发 女孩 的 QQ', predict: '中性' , label: '中性'
inputs: '背 你 猜 对 了', predict: '中性' , label: '中性'
inputs: '我 讨厌 你 ， 哼哼 哼 。 。', predict: '消极' , label: '消极'

inputs: '我 讨厌 你 ， 哼哼 哼 。 。', predict: '消极' , label: '消极'

八、自定义推理数据集

predict("家人们咱就是说一整个无语住了 绝绝子叠buff")

输出：

inputs: '家人们咱就是说一整个无语住了 绝绝子叠buff', predict: '中性'