运行paddlenlp入门示例：训练与演算

0. 环境

win10 + NVIDIA GeForce GTX 1660 Ti 6GB
python3.9
cuda 10.2
cudnn 7.6.5
paddlepaddle 2.2.0（已经搭建好GPU版本）

1. 安装PaddleNLP

python -m pip install --upgrade paddlenlp -i https://pypi.org/simple

2. 运行脚本

2.1 创建文件E:\Workspaces\python\nlp\pynlp_10min.py，添加以下内容

        

import paddlenlp as ppnlp
from paddlenlp.datasets import load_dataset

train_ds, dev_ds, test_ds = load_dataset(
    "chnsenticorp", splits=["train", "dev", "test"])

print(train_ds.label_list)

for data in train_ds.data[:5]:
    print(data)

# 设置想要使用模型的名称
MODEL_NAME = "ernie-1.0"

tokenizer = ppnlp.transformers.ErnieTokenizer.from_pretrained(MODEL_NAME)
ernie_model = ppnlp.transformers.ErnieModel.from_pretrained(MODEL_NAME)

import paddle

# 将原始输入文本切分token，
tokens = tokenizer._tokenize("请输入测试样例")
print("Tokens: {}".format(tokens))

# token映射为对应token id
tokens_ids = tokenizer.convert_tokens_to_ids(tokens)
print("Tokens id: {}".format(tokens_ids))

# 拼接上预训练模型对应的特殊token ，如[CLS]、[SEP]
tokens_ids = tokenizer.build_inputs_with_special_tokens(tokens_ids)

# 转化成paddle框架数据格式
tokens_pd = paddle.to_tensor([tokens_ids])
print("Tokens : {}".format(tokens_pd))

# 此时即可输入ERNIE模型中得到相应输出
sequence_output, pooled_output = ernie_model(tokens_pd)
print("Token wise output: {}, Pooled output: {}".format(sequence_output.shape, pooled_output.shape))

# 一行代码完成切分token，映射token ID以及拼接特殊token
encoded_text = tokenizer(text="请输入测试样例")
for key, value in encoded_text.items():
    print("{}:\n\t{}".format(key, value))

# 转化成paddle框架数据格式
input_ids = paddle.to_tensor([encoded_text['input_ids']])
print("input_ids : {}".format(input_ids))
segment_ids = paddle.to_tensor([encoded_text['token_type_ids']])
print("token_type_ids : {}".format(segment_ids))

# 此时即可输入ERNIE模型中得到相应输出
sequence_output, pooled_output = ernie_model(input_ids, segment_ids)
print("Token wise output: {}, Pooled output: {}".format(sequence_output.shape, pooled_output.shape))

# 单句输入
single_seg_input = tokenizer(text="请输入测试样例")
# 句对输入
multi_seg_input = tokenizer(text="请输入测试样例1", text_pair="请输入测试样例2")

print("单句输入token (str): {}".format(tokenizer.convert_ids_to_tokens(single_seg_input['input_ids'])))
print("单句输入token (int): {}".format(single_seg_input['input_ids']))
print("单句输入segment ids : {}".format(single_seg_input['token_type_ids']))

print()
print("句对输入token (str): {}".format(tokenizer.convert_ids_to_tokens(multi_seg_input['input_ids'])))
print("句对输入token (int): {}".format(multi_seg_input['input_ids']))
print("句对输入segment ids : {}".format(multi_seg_input['token_type_ids']))

# Highlight: padding到统一长度
encoded_text = tokenizer(text="请输入测试样例",  max_seq_len=15)

for key, value in encoded_text.items():
    print("{}:\n\t{}".format(key, value))
	
# ---------------------------------------------------------------------------------------------------
# 数据读入

from functools import partial
from paddlenlp.data import Stack, Tuple, Pad
import numpy as np

def convert_example(example,
                    tokenizer,
                    max_seq_length=512,
                    is_test=False):
   
    # 将原数据处理成model可读入的格式，enocded_inputs是一个dict，包含input_ids、token_type_ids等字段
    encoded_inputs = tokenizer(
        text=example["text"], max_seq_len=max_seq_length)

    # input_ids：对文本切分token后，在词汇表中对应的token id
    input_ids = encoded_inputs["input_ids"]
    # token_type_ids：当前token属于句子1还是句子2，即上述图中表达的segment ids
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        # label：情感极性类别
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    else:
        # qid：每条数据的编号
        qid = np.array([example["qid"]], dtype="int64")
        return input_ids, token_type_ids, qid
def create_dataloader(dataset,
                      trans_fn=None,
                      mode='train',
                      batch_size=1,
                      batchify_fn=None):
    
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == 'train' else False
    if mode == "train":
        sampler = paddle.io.DistributedBatchSampler(
            dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        sampler = paddle.io.BatchSampler(
            dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    dataloader = paddle.io.DataLoader(
        dataset, batch_sampler=sampler, collate_fn=batchify_fn)
    return dataloader


# 模型运行批处理大小
batch_size = 8     # 32
max_seq_length = 128

trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length)
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # segment
    Stack(dtype="int64")  # label
): [data for data in fn(samples)]
train_data_loader = create_dataloader(
    train_ds,
    mode='train',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)
dev_data_loader = create_dataloader(
    dev_ds,
    mode='dev',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)
	
	
	
# ---------------------------------------------------------------------------------------------------
# PaddleNLP一键加载预训练模型

ernie_model = ppnlp.transformers.ErnieModel.from_pretrained(MODEL_NAME)
model = ppnlp.transformers.ErnieForSequenceClassification.from_pretrained(MODEL_NAME, num_classes=len(train_ds.label_list))
	
	
# ---------------------------------------------------------------------------------------------------
# 设置Fine-Tune优化策略，接入评价指标

from paddlenlp.transformers import LinearDecayWithWarmup

# 训练过程中的最大学习率
learning_rate = 5e-5 
# 训练轮次
epochs = 1 #3
# 学习率预热比例
warmup_proportion = 0.1
# 权重衰减系数，类似模型正则项策略，避免模型过拟合
weight_decay = 0.01

num_training_steps = len(train_data_loader) * epochs
lr_scheduler = LinearDecayWithWarmup(learning_rate, num_training_steps, warmup_proportion)
optimizer = paddle.optimizer.AdamW(
    learning_rate=lr_scheduler,
    parameters=model.parameters(),
    weight_decay=weight_decay,
    apply_decay_param_fun=lambda x: x in [
        p.name for n, p in model.named_parameters()
        if not any(nd in n for nd in ["bias", "norm"])
    ])

criterion = paddle.nn.loss.CrossEntropyLoss()
metric = paddle.metric.Accuracy()
	
	
	
# ---------------------------------------------------------------------------------------------------
# 模型训练与评估
import paddle.nn.functional as F
def evaluate(model, criterion, metric, data_loader):
    model.eval()
    metric.reset()
    losses = []
    for batch in data_loader:
        input_ids, token_type_ids, labels = batch
        logits = model(input_ids, token_type_ids)
        loss = criterion(logits, labels)
        losses.append(loss.numpy())
        correct = metric.compute(logits, labels)
        metric.update(correct)
        accu = metric.accumulate()
    # print("eval loss: %.5f, accu: %.5f" % (np.mean(losses), accu))
    model.train()
    metric.reset()
    return  np.mean(losses), accu
    
    
global_step = 0
for epoch in range(1, epochs + 1):
    for step, batch in enumerate(train_data_loader, start=1):
        input_ids, segment_ids, labels = batch
        logits = model(input_ids, segment_ids)
        loss = criterion(logits, labels)
        probs = F.softmax(logits, axis=1)
        correct = metric.compute(probs, labels)
        metric.update(correct)
        acc = metric.accumulate()

        global_step += 1
        if global_step % 10 == 0 :
            print("global step %d, epoch: %d, batch: %d, loss: %.5f, acc: %.5f" % (global_step, epoch, step, loss, acc))
        loss.backward()
        optimizer.step()
        lr_scheduler.step()
        optimizer.clear_grad()
    evaluate(model, criterion, metric, dev_data_loader)

model.save_pretrained('E:\\Workspaces\\python\\nlp\\checkpoint')
tokenizer.save_pretrained('E:\\Workspaces\\python\\nlp\\checkpoint')
	
	
# ---------------------------------------------------------------------------------------------------
# 模型预测
from utils import predict

data = [
    {"text":'这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般'},
    {"text":'怀着十分激动的心情放映，可是看着看着发现，在放映完毕后，出现一集米老鼠的动画片'},
    {"text":'作为老的四星酒店，房间依然很整洁，相当不错。机场接机服务很好，可以在车上办理入住手续，节省时间。'},
]
label_map = {0: 'negative', 1: 'positive'}

results = predict(
    model, data, tokenizer, label_map, batch_size=batch_size)
for idx, text in enumerate(data):
    print('Data: {} \t Lable: {}'.format(text, results[idx]))
	

2.2 创建文件E:\Workspaces\python\nlp\utils.py，添加以下内容

import numpy as np
import paddle
import paddle.nn.functional as F
from paddlenlp.data import Stack, Tuple, Pad


def predict(model, data, tokenizer, label_map, batch_size=1):
    """
    Predicts the data labels.

    Args:
        model (obj:`paddle.nn.Layer`): A model to classify texts.
        data (obj:`List(Example)`): The processed data whose each element is a Example (numedtuple) object.
            A Example object contains `text`(word_ids) and `se_len`(sequence length).
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` 
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        label_map(obj:`dict`): The label id (key) to label str (value) map.
        batch_size(obj:`int`, defaults to 1): The number of batch.

    Returns:
        results(obj:`dict`): All the predictions labels.
    """
    examples = []
    for text in data:
        input_ids, segment_ids = convert_example(
            text,
            tokenizer,
            max_seq_length=128,
            is_test=True)
        examples.append((input_ids, segment_ids))

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input id
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # segment id
    ): fn(samples)

    # Seperates data into some batches.
    batches = []
    one_batch = []
    for example in examples:
        one_batch.append(example)
        if len(one_batch) == batch_size:
            batches.append(one_batch)
            one_batch = []
    if one_batch:
        # The last batch whose size is less than the config batch_size setting.
        batches.append(one_batch)

    results = []
    model.eval()
    for batch in batches:
        input_ids, segment_ids = batchify_fn(batch)
        input_ids = paddle.to_tensor(input_ids)
        segment_ids = paddle.to_tensor(segment_ids)
        logits = model(input_ids, segment_ids)
        probs = F.softmax(logits, axis=1)
        idx = paddle.argmax(probs, axis=1).numpy()
        idx = idx.tolist()
        labels = [label_map[i] for i in idx]
        results.extend(labels)
    return results


@paddle.no_grad()
def evaluate(model, criterion, metric, data_loader):
    """
    Given a dataset, it evals model and computes the metric.

    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
        criterion(obj:`paddle.nn.Layer`): It can compute the loss.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
    """
    model.eval()
    metric.reset()
    losses = []
    for batch in data_loader:
        input_ids, token_type_ids, labels = batch
        logits = model(input_ids, token_type_ids)
        loss = criterion(logits, labels)
        losses.append(loss.numpy())
        correct = metric.compute(logits, labels)
        metric.update(correct)
        accu = metric.accumulate()
    print("eval loss: %.5f, accu: %.5f" % (np.mean(losses), accu))
    model.train()
    metric.reset()


def convert_example(example, tokenizer, max_seq_length=512, is_test=False):
    """
    Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
    by concatenating and adding special tokens. And creates a mask from the two sequences passed 
    to be used in a sequence-pair classification task.
        
    A BERT sequence has the following format:

    - single sequence: ``[CLS] X [SEP]``
    - pair of sequences: ``[CLS] A [SEP] B [SEP]``

    A BERT sequence pair mask has the following format:
    ::
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |

    If only one sequence, only returns the first portion of the mask (0's).


    Args:
        example(obj:`list[str]`): List of input data, containing text and label if it have label.
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` 
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        max_seq_len(obj:`int`): The maximum total input sequence length after tokenization. 
            Sequences longer than this will be truncated, sequences shorter will be padded.
        is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.

    Returns:
        input_ids(obj:`list[int]`): The list of token ids.
        token_type_ids(obj: `list[int]`): List of sequence pair mask.
        label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
    """
    encoded_inputs = tokenizer(text=example["text"], max_seq_len=max_seq_length)
    input_ids = encoded_inputs["input_ids"]
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    else:
        return input_ids, token_type_ids


def create_dataloader(dataset,
                      mode='train',
                      batch_size=1,
                      batchify_fn=None,
                      trans_fn=None):
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == 'train' else False
    if mode == 'train':
        batch_sampler = paddle.io.DistributedBatchSampler(
            dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        batch_sampler = paddle.io.BatchSampler(
            dataset, batch_size=batch_size, shuffle=shuffle)

    return paddle.io.DataLoader(
        dataset=dataset,
        batch_sampler=batch_sampler,
        collate_fn=batchify_fn,
        return_list=True)

3. 运行结果

 

4. 小结

4.1 缺少了utils.py，在参考[3]中找到了。

4.2 当GPU内存不够用时候，需要将batch_size降低。原本batch_size是32的，我的GPU顶不住，改为8，可以顺利运行本示例。

参考

参考[1]，10分钟完成高精度中文情感分析
https://paddlenlp.readthedocs.io/zh/latest/get_started/quick_start.html
参考[2]，超简单【推特文本情感13分类练习赛】高分baseline
https://blog.csdn.net/weixin_41450123/article/details/120520141?spm=1001.2014.3001.5501
参考[3]，『NLP经典项目集』02：使用预训练模型ERNIE优化情感分析，https://blog.csdn.net/qq_15821487/article/details/117123555