wehub-resource-sync
247 lines
7.5 KiB
Markdown
247 lines
7.5 KiB
Markdown
# PaddleNLP Data API
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This module provides common APIs for constructing effective data processing pipelines in NLP tasks.
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## API List
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| API | Description |
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| ------------------------------- | :------------------------------------------- |
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| `paddlenlp.data.Stack` | Stack N input data with the same shape to build a batch |
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| `paddlenlp.data.Pad` | Stack N input data to build a batch, each input will be padded to the maximum length among the N inputs |
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| `paddlenlp.data.Tuple` | Wrap multiple batchify functions together to form a tuple |
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| `paddlenlp.data.Dict` | Wrap multiple batchify functions together to form a dict |
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| `paddlenlp.data.SamplerHelper` | Build iterable sampler for `Dataloader` |
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| `paddlenlp.data.Vocab` | Map between text tokens and IDs |
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| `paddlenlp.data.JiebaTokenizer` | Jieba tokenizer |
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## API Usage
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The above APIs are all used to assist in building `DataLoader`. The three important initialization parameters of `DataLoader` are `dataset`, `batch_sampler`, and `collate_fn`.
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`paddlenlp.data.Vocab` and `paddlenlp.data.JiebaTokenizer` are used when constructing `dataset` to handle the mapping between text tokens and IDs.
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`paddlenlp.data.SamplerHelper` is used to build an iterable `batch_sampler`.
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`paddlenlp.data.Stack`, `paddlenlp.data.Pad`, `paddlenlp.data.Tuple`, and `paddlenlp.data.Dict` are used to build the `collate_fn` function that generates mini-batches.
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### Data Preprocessing
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#### `paddlenlp.data.Vocab`
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The `paddlenlp.data.Vocab` class is a vocabulary that includes a series of methods for mapping between text tokens and IDs. It supports building vocabulary from files, dictionaries, json, and other sources.
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```python
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from paddlenlp.data import Vocab
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# Build from file
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vocab1 = Vocab.load_vocabulary(vocab_file_path)
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# Build from dictionary
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# dic = {'unk':0, 'pad':1, 'bos':2, 'eos':3, ...}
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vocab2 = Vocab.from_dict(dic)
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# Build from json (usually for restoring previously saved Vocab objects from json_str or json files)
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# json_str method
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json_str = vocab1.to_json()
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vocab3 = Vocab.from_json(json_str)
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# json file method
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vocab1.to_json(json_file_path)
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vocab4 = Vocab.from_json(json_file_path)
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```
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#### `paddlenlp.data.JiebaTokenizer`
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`paddlenlp.data.JiebaTokenizer` requires initializing with a `paddlenlp.data.Vocab` class, containing the `cut` tokenization method and the `encode` method for converting plain text sentences to ids.
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```python
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from paddlenlp.data import Vocab, JiebaTokenizer
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# Vocabulary file path (download the vocabulary file first when running the sample)
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# wget https://bj.bcebos.com/paddlenlp/data/senta_word_dict.txt
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vocab_file_path = './senta_word_dict.txt'
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# Build vocabulary
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vocab = Vocab.load_vocabulary(
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vocab_file_path,
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unk_token='[UNK]',
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pad_token='[PAD]')
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tokenizer = JiebaTokenizer(vocab)
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tokens = tokenizer.cut('I love you, China') # ['I love you', 'China']
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ids = tokenizer.encode('I love you, China') # [1170578, 575565]
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```
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### Building `Sampler`
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#### `paddlenlp.data.SamplerHelper`
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`paddlenlp.data.SamplerHelper` serves to build iterable samplers for `DataLoader`, containing methods like `shuffle`, `sort`, `batch`, `shard`, etc., providing flexible usage for users.
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```python
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from paddlenlp.data import SamplerHelper
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from paddle.io import Dataset
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class MyDataset(Dataset):
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def __init__(self):
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super(MyDataset, self).__init__()
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self.data = [
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[[1, 2, 3, 4], [1]],
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[[5, 6, 7], [0]],
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[[8, 9], [1]],
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]
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def __getitem__(self, index):
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data = self.data[index][0]
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label = self.data[index][1]
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return data, label
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def __len__(self):
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return len(self.data)
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dataset = MyDataset()
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# SamplerHelper returns an iterable of data indices, generated indices: [0, 1, 2]
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sampler = SamplerHelper(dataset)
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# `shuffle()` randomly shuffles the index order, generated indices: [0, 2, 1]
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sampler = sampler.shuffle()
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# sort() arranges indices based on specified key within buffer_size samples
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# Example: sort by length of the first field in ascending order, generated indices: [2, 0, 1]
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key = (lambda x, data_source: len(data_source[x][0]))
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sampler = sampler.sort(key=key, buffer_size=2)
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# batch() creates mini-batches according to batch_size, generated indices: [[2, 0], [1]]
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sampler = sampler.batch(batch_size=2)
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# shard() splits dataset for multi-GPU training, current GPU indices: [[2, 0]]
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sampler = sampler.shard(num_replicas=2)
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```
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### Constructing `collate_fn`
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#### `paddlenlp.data.Stack`
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`paddlenlp.data.Stack` is used to create batches. Its inputs must have identical shapes, and the output is a batch formed by stacking these inputs.
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```python
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from paddlenlp.data import Stack
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a = [1, 2, 3, 4]
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b = [3, 4, 5, 6]
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c = [5, 6, 7, 8]
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result = Stack()([a, b, c])
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"""
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[[1, 2, 3, 4],
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[3, 4, 5, 6],
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[5, 6, 7, 8]]
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"""
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```
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#### `paddlenlp.data.Pad`
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`paddlenlp.data.Pad` is used to create batches. It first pads all input data to the maximum length, then stacks them to form batch data.
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```python
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from paddlenlp.data import Pad
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a = [1, 2]
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b = [3, 4, 5]
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c = [6, 7, 8, 9]
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result = Pad(pad_val=0)([a, b, c])
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"""
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[[1, 2, 0, 0],
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[3, 4, 5, 0],
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[6, 7, 8, 9]]
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"""
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```
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```python
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from paddlenlp.data import Pad
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a = [1, 2, 3, 4]
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b = [5, 6, 7]
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c = [8, 9]
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result = Pad(pad_val=0)([a, b, c])
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"""
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[[1, 2, 3, 4],
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[5, 6, 7, 0],
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[8, 9, 0, 0]]
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"""
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```
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#### `paddlenlp.data.Tuple`
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`paddlenlp.data.Tuple` wraps multiple batch functions together into a tuple.
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```python
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from paddlenlp.data import Stack, Pad, Tuple
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data = [
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[[1, 2, 3, 4], [1]],
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[[5, 6, 7], [0]],
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[[8, 9], [1]],
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]
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batchify_fn = Tuple(Pad(pad_val=0), Stack())
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ids, label = batchify_fn(data)
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"""
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ids:
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[[1, 2, 3, 4],
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[5, 6, 7, 0],
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[8, 9, 0, 0]]
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label: [[1], [0], [1]]
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"""
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```
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#### `paddlenlp.data.Dict`
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`paddlenlp.data.Dict` wraps multiple batch functions together into a dictionary.
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```python
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from paddlenlp.data import Stack, Pad, Dict
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data = [
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{'labels':[1], 'token_ids':[1, 2, 3, 4]},
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{'labels':[0], 'token_ids':[5, 6, 7]},
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{'labels':[1], 'token_ids':[8, 9]},
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]
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batchify_fn = Dict({'token_ids':Pad(pad_val=0), 'labels':Stack()})
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ids, label = batchify_fn(data)
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"""
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ids:
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[[1, 2, 3, 4],
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[5, 6, 7, 0],
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[8, 9, 0, 0]]
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label: [[1], [0], [1]]
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"""
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```
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### Comprehensive Example
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```python
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from paddlenlp.data import Vocab, JiebaTokenizer, Stack, Pad, Tuple, SamplerHelper
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from paddlenlp.datasets import load_dataset
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from paddle.io import DataLoader
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# Vocabulary file path, example program needs to download vocabulary file first
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# wget https://bj.bcebos.com/paddlenlp/data/senta_word_dict.txt
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vocab_file_path = './senta_word_dict.txt'
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# Build vocabulary
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vocab = Vocab.load_vocabulary(
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vocab_file_path,
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unk_token='[UNK]',
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pad_token='[PAD]')
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# Initialize tokenizer
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tokenizer = JiebaTokenizer(vocab)
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def convert_example(example):
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text, label = example['text'], example['label']
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ids = tokenizer.encode(text)
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label = [label]
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return ids, label
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dataset = load_dataset('chnsenticorp', splits='train')
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dataset = dataset.map(convert_example, lazy=True)
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pad_id = vocab.token_to_idx[vocab.pad_token]
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batchify_fn = Tuple(
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Pad(axis=0, pad_val=pad_id), # ids
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Stack(dtype='int64') # label
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)
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batch_sampler = SamplerHelper(dataset).shuffle().batch(batch_size=16)
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data_loader = DataLoader(
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dataset,
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batch_sampler=batch_sampler,
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collate_fn=batchify_fn,
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return_list=True)
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# Test dataset
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for batch in data_loader:
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ids, label = batch
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print(ids.shape, label.shape)
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print(ids)
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print(label)
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break
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```
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