Python PaddleNLP实现自动生成虎年藏头诗

一、 数据处理

本项目中利用古诗数据集作为训练集，编码器接收古诗的每个字的开头，解码器利用编码器的信息生成所有的诗句。为了诗句之间的连贯性，编码器同时也在诗头之前加上之前诗句的信息。举例：

“白日依山尽，黄河入海流，欲穷千里目，更上一层楼。” 可以生成两个样本：

样本一：编码器输入，“白”；解码器输入，“白日依山尽，黄河入海流”

样本二：编码器输入，“白日依山尽，黄河入海流。欲”；解码器输入，“欲穷千里目，更上一层楼。”

1.paddlenlp升级

!pip install -u paddlenlp

looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
collecting paddlenlp
[?25l  downloading https://pypi.tuna.tsinghua.edu.cn/packages/17/9b/4535ccf0e96c302a3066bd2e4d0f44b6b1a73487c6793024475b48466c32/paddlenlp-2.2.3-py3-none-any.whl (1.2mb)
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installing collected packages: paddlenlp
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    uninstalling paddlenlp-2.1.1:
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2.提取诗头

import re
poems_file = open("./data/data70759/poems_zh.txt", encoding="utf8")
# 对读取的每一行诗句，统计每一句的词头
poems_samples = []
poems_prefix = []
poems_heads = []
for line in poems_file.readlines():
    line_ = re.sub('。', ' ', line)
    line_ = line_.split()
    # 生成训练样本
    for i, p in enumerate(line_):
        poems_heads.append(p[0])
        poems_prefix.append('。'.join(line_[:i]))
        poems_samples.append(p + '。')


# 输出文件信息
for i in range(20):
    print("poems heads:{}, poems_prefix: {}, poems:{}".format(poems_heads[i], poems_prefix[i], poems_samples[i]))

poems heads:欲, poems_prefix: , poems:欲出未出光辣达，千山万山如火发。
poems heads:须, poems_prefix: 欲出未出光辣达，千山万山如火发, poems:须臾走向天上来，逐却残星赶却月。
poems heads:未, poems_prefix: , poems:未离海底千山黑，才到天中万国明。
poems heads:满, poems_prefix: , poems:满目*四望幽，白云高卷嶂烟收。
poems heads:日, poems_prefix: 满目*四望幽，白云高卷嶂烟收, poems:日回禽影穿疏木，风递猿声入小楼。
poems heads:远, poems_prefix: 满目*四望幽，白云高卷嶂烟收。日回禽影穿疏木，风递猿声入小楼, poems:远岫似屏横碧落，断帆如叶截中流。
poems heads:片, poems_prefix: , poems:片片飞来静又闲，楼头江上复山前。
poems heads:飘, poems_prefix: 片片飞来静又闲，楼头江上复山前, poems:飘零尽日不归去，帖破清光万里天。
poems heads:因, poems_prefix: , poems:因登巨石知来处，勃勃元生绿藓痕。
poems heads:静, poems_prefix: 因登巨石知来处，勃勃元生绿藓痕, poems:静即等闲藏草木，动时顷刻徧乾坤。
poems heads:横, poems_prefix: 因登巨石知来处，勃勃元生绿藓痕。静即等闲藏草木，动时顷刻徧乾坤, poems:横天未必朋元恶，捧日还曾瑞至尊。
poems heads:不, poems_prefix: 因登巨石知来处，勃勃元生绿藓痕。静即等闲藏草木，动时顷刻徧乾坤。横天未必朋元恶，捧日还曾瑞至尊, poems:不独朝朝在巫峡，楚王何事谩劳魂。
poems heads:若, poems_prefix: , poems:若教作镇居中国，争得泥金在泰山。
poems heads:才, poems_prefix: , poems:才闻暖律先偷眼，既待和风始展眉。
poems heads:嚼, poems_prefix: , poems:嚼处春冰敲齿冷，咽时雪液沃心寒。
poems heads:蒙, poems_prefix: , poems:蒙君知重惠琼实，薄起金刀钉玉深。
poems heads:深, poems_prefix: , poems:深妆玉瓦平无垅，乱拂芦花细有声。
poems heads:片, poems_prefix: , poems:片逐银蟾落醉觥。
poems heads:巧, poems_prefix: , poems:巧剪银花乱，轻飞玉叶狂。
poems heads:寒, poems_prefix: , poems:寒艳芳姿色尽明。

3.生成词表

# 用paddlenlp生成词表文件，由于诗文的句式较短，我们以单个字作为词单元生成词表
from paddlenlp.data import vocab

vocab = vocab.build_vocab(poems_samples, unk_token="<unk>", pad_token="<pad>", bos_token="<", eos_token=">")
vocab_size = len(vocab)

print("vocab size", vocab_size)
print("word to idx:", vocab.token_to_idx)

4.定义dataset

# 定义数据读取器
from paddle.io import dataset, batchsampler, dataloader
import numpy as np

class poemdataset(dataset):
    def __init__(self, poems_data, poems_heads, poems_prefix, vocab, encoder_max_len=128, decoder_max_len=32):
        super(poemdataset, self).__init__()
        self.poems_data = poems_data
        self.poems_heads = poems_heads
        self.poems_prefix = poems_prefix
        self.vocab = vocab
        self.tokenizer = lambda x: [vocab.token_to_idx[x_] for x_ in x]
        self.encoder_max_len = encoder_max_len
        self.decoder_max_len = decoder_max_len

    def __getitem__(self, idx):
        eos_id = vocab.token_to_idx[vocab.eos_token]
        bos_id = vocab.token_to_idx[vocab.bos_token]
        pad_id = vocab.token_to_idx[vocab.pad_token]
        # 确保encoder和decoder的输出都小于最大长度
        poet = self.poems_data[idx][:self.decoder_max_len - 2]  # -2 包含bos_id和eos_id
        prefix = self.poems_prefix[idx][- (self.encoder_max_len - 3):]  # -3 包含bos_id, eos_id, 和head的编码
        # 对输入输出编码

        sample = [bos_id] + self.tokenizer(poet) + [eos_id]
        prefix = self.tokenizer(prefix) if prefix else []
        heads = prefix + [bos_id] + self.tokenizer(self.poems_heads[idx]) + [eos_id] 
        sample_len = len(sample)
        heads_len = len(heads)
        sample = sample + [pad_id] * (self.decoder_max_len - sample_len)
        heads = heads + [pad_id] * (self.encoder_max_len - heads_len)
        mask = [1] * (sample_len - 1) + [0] * (self.decoder_max_len - sample_len) # -1 to make equal to out[2]
        out = [np.array(d, "int64") for d in [heads, heads_len, sample, sample, mask]]
        out[2] = out[2][:-1]
        out[3] = out[3][1:, np.newaxis]
        return out

    def shape(self):
        return [([none, self.encoder_max_len], 'int64', 'src'),
                ([none, 1], 'int64', 'src_length'),
                ([none, self.decoder_max_len - 1],'int64', 'trg')], \
               [([none, self.decoder_max_len - 1, 1], 'int64', 'label'),
                ([none, self.decoder_max_len - 1], 'int64', 'trg_mask')]


    def __len__(self):
        return len(self.poems_data)

dataset = poemdataset(poems_samples, poems_heads, poems_prefix, vocab)
batch_sampler = batchsampler(dataset, batch_size=2048)
data_loader = dataloader(dataset, batch_sampler=batch_sampler)

二、定义模型并训练

1.模型定义

from seq2seq.models import seq2seqmodel
from paddlenlp.metrics import perplexity
from seq2seq.loss import crossentropycriterion
import paddle
from paddle.static import inputspec

# 参数
lr = 1e-6
max_epoch = 20
models_save_path = "./checkpoints"

encoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "dropout": .2,
                    "direction": "bidirectional", "mode": "gru"}
decoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "direction": "forward",
                    "dropout": .2, "mode": "gru", "use_attention": true}

# inputs shape and label shape
inputs_shape, labels_shape = dataset.shape()
inputs_list = [inputspec(input_shape[0], input_shape[1], input_shape[2]) for input_shape in inputs_shape]
labels_list = [inputspec(label_shape[0], label_shape[1], label_shape[2]) for label_shape in labels_shape]

net = seq2seqmodel(encoder_attrs, decoder_attrs)
model = paddle.model(net, inputs_list, labels_list)

model.load("./final_models/model")

opt = paddle.optimizer.adam(learning_rate=lr, parameters=model.parameters())

model.prepare(opt, crossentropycriterion(), perplexity())

w0122 21:03:30.616776   166 device_context.cc:447] please note: device: 0, gpu compute capability: 7.0, driver api version: 10.1, runtime api version: 10.1
w0122 21:03:30.620450   166 device_context.cc:465] device: 0, cudnn version: 7.6.

2.模型训练

# 训练，训练时间较长，已提供了训练好的模型（./final_models/model）
model.fit(train_data=data_loader, epochs=max_epoch, eval_freq=1, save_freq=5, save_dir=models_save_path, shuffle=true)

3.模型保存

# 保存
model.save("./final_models/model")

三、生成藏头诗

import warnings

def post_process_seq(seq, bos_idx, eos_idx, output_bos=false, output_eos=false):
    """
    post-process the decoded sequence.
    """
    eos_pos = len(seq) - 1
    for i, idx in enumerate(seq):
        if idx == eos_idx:
            eos_pos = i
            break
    seq = [idx for idx in seq[:eos_pos + 1]
           if (output_bos or idx != bos_idx) and (output_eos or idx != eos_idx)]
    return seq

# 定义用于生成祝福语的类
from paddlenlp.data.tokenizer import jiebatokenizer

class genpoems():
    # content (str): the str to generate poems, like "恭喜发财"
    # vocab: the instance of paddlenlp.data.vocab.vocab
    # model: the inference model
    def __init__(self, vocab, model):
        self.bos_id = vocab.token_to_idx[vocab.bos_token]
        self.eos_id = vocab.token_to_idx[vocab.eos_token]
        self.pad_id = vocab.token_to_idx[vocab.pad_token]
        self.tokenizer = lambda x: [vocab.token_to_idx[x_] for x_ in x]
        self.model = model
        self.vocab = vocab

    def gen(self, content, max_len=128):
        # max_len is the encoder_max_len in seq2seq model.
        out = []
        vocab_list = list(vocab.token_to_idx.keys())
        for w in content:
            if w in vocab_list:
                content = re.sub("([。，])", '', content)
                heads = out[- (max_len - 3):] + [self.bos_id] + self.tokenizer(w) + [self.eos_id]
                len_heads = len(heads)
                heads = heads + [self.pad_id] * (max_len - len_heads)
                x = paddle.to_tensor([heads], dtype="int64")
                len_x = paddle.to_tensor([len_heads], dtype='int64')
                pred = self.model.predict_batch(inputs = [x, len_x])[0]
                out += self._get_results(pred)[0]
            else:
                warnings.warn("{} is not in vocab list, so it is skipped.".format(w))
                pass
        out = ''.join([self.vocab.idx_to_token[id] for id in out])
        return out
    
    def _get_results(self, pred):
        pred = pred[:, :, np.newaxis] if len(pred.shape) == 2 else pred
        pred = np.transpose(pred, [0, 2, 1])
        outs = []
        for beam in pred[0]:
            id_list = post_process_seq(beam, self.bos_id, self.eos_id)
            outs.append(id_list)
        return outs

# 载入预测模型
from seq2seq.models import seq2seqinfermodel
import paddle

encoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "dropout": .2,
                    "direction": "bidirectional", "mode": "gru"}
decoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "direction": "forward",
                    "dropout": .2, "mode": "gru", "use_attention": true}

infer_model = paddle.model(seq2seqinfermodel(encoder_attrs,
                                             decoder_attrs,
                                             bos_id=vocab.token_to_idx[vocab.bos_token],
                                             eos_id=vocab.token_to_idx[vocab.eos_token],
                                             beam_size=10,
                                             max_out_len=256))
infer_model.load("./final_models/model")

# 送新年祝福
# 当然，表白也可以
generator = genpoems(vocab, infer_model)

content = "生龙活虎"
poet = generator.gen(content)
for line in poet.strip().split('。'):
    try:
        print("{}\t{}。".format(line[0], line))
    except:
        pass

输出结果

生    生涯不可见，何处不相逢。
龙    龙虎不知何处，人间不见人间。
活    活人不是人间事，不觉人间不可识。
虎    虎豹相逢不可寻，不知何处不相识。

总结

这个项目介绍了如何训练一个生成藏头诗的模型，从结果可以看出，模型已经具有一定的生成诗句的能力。但是，限于训练集规模和训练时间，生成的诗句还有很大的改进空间，未来还将进一步优化这个模型，敬请期待。

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