From e0b23b16db59b249bc4ffbcbe45f4d8f99b7bbd8 Mon Sep 17 00:00:00 2001 From: xuyige Date: Mon, 24 Jun 2019 21:44:43 +0800 Subject: [PATCH] update data loader of matching --- fastNLP/io/file_utils.py | 31 +++++++ fastNLP/modules/encoder/embedding.py | 41 ++------- .../matching/data/MatchingDataLoader.py | 92 ++++++++++++------- reproduction/matching/matching_esim.py | 65 +++++++++++++ reproduction/matching/model/esim.py | 21 ++++- 5 files changed, 178 insertions(+), 72 deletions(-) create mode 100644 reproduction/matching/matching_esim.py diff --git a/fastNLP/io/file_utils.py b/fastNLP/io/file_utils.py index d178626b..04970cb3 100644 --- a/fastNLP/io/file_utils.py +++ b/fastNLP/io/file_utils.py @@ -10,6 +10,37 @@ import shutil import hashlib +PRETRAINED_BERT_MODEL_DIR = { + 'en': 'bert-base-cased-f89bfe08.zip', + 'en-base-uncased': 'bert-base-uncased-3413b23c.zip', + 'en-base-cased': 'bert-base-cased-f89bfe08.zip', + 'en-large-uncased': 'bert-large-uncased-20939f45.zip', + 'en-large-cased': 'bert-large-cased-e0cf90fc.zip', + + 'cn': 'bert-base-chinese-29d0a84a.zip', + 'cn-base': 'bert-base-chinese-29d0a84a.zip', + + 'multilingual': 'bert-base-multilingual-cased-1bd364ee.zip', + 'multilingual-base-uncased': 'bert-base-multilingual-uncased-f8730fe4.zip', + 'multilingual-base-cased': 'bert-base-multilingual-cased-1bd364ee.zip', +} + +PRETRAINED_ELMO_MODEL_DIR = { + 'en': 'elmo_en-d39843fe.tar.gz', + 'cn': 'elmo_cn-5e9b34e2.tar.gz' +} + +PRETRAIN_STATIC_FILES = { + 'en': 'glove.840B.300d-cc1ad5e1.tar.gz', + 'en-glove-840b-300': 'glove.840B.300d-cc1ad5e1.tar.gz', + 'en-glove-6b-50': "glove.6B.50d-a6028c70.tar.gz", + 'en-word2vec-300': "GoogleNews-vectors-negative300-be166d9d.tar.gz", + 'en-fasttext': "cc.en.300.vec-d53187b2.gz", + 'cn': "tencent_cn-dab24577.tar.gz", + 'cn-fasttext': "cc.zh.300.vec-d68a9bcf.gz", +} + + def cached_path(url_or_filename: str, cache_dir: Path=None) -> Path: """ 给定一个url或者文件名(可以是具体的文件名，也可以是文件)，先在cache_dir下寻找该文件是否存在，如果不存在则去下载, 并 diff --git a/fastNLP/modules/encoder/embedding.py b/fastNLP/modules/encoder/embedding.py index c6c95bb7..a58668da 100644 --- a/fastNLP/modules/encoder/embedding.py +++ b/fastNLP/modules/encoder/embedding.py @@ -26,6 +26,7 @@ from ...core.dataset import DataSet from ...core.batch import DataSetIter from ...core.sampler import SequentialSampler from ...core.utils import _move_model_to_device, _get_model_device +from ...io.file_utils import PRETRAINED_BERT_MODEL_DIR, PRETRAINED_ELMO_MODEL_DIR, PRETRAIN_STATIC_FILES class Embedding(nn.Module): @@ -187,15 +188,6 @@ class StaticEmbedding(TokenEmbedding): super(StaticEmbedding, self).__init__(vocab) # 优先定义需要下载的static embedding有哪些。这里估计需要自己搞一个server， - PRETRAIN_STATIC_FILES = { - 'en': 'glove.840B.300d-cc1ad5e1.tar.gz', - 'en-glove-840b-300': 'glove.840B.300d-cc1ad5e1.tar.gz', - 'en-glove-6b-50': "glove.6B.50d-a6028c70.tar.gz", - 'en-word2vec-300': "GoogleNews-vectors-negative300-be166d9d.tar.gz", - 'en-fasttext': "cc.en.300.vec-d53187b2.gz", - 'cn': "tencent_cn-dab24577.tar.gz", - 'cn-fasttext': "cc.zh.300.vec-d68a9bcf.gz", - } # 得到cache_path if model_dir_or_name.lower() in PRETRAIN_STATIC_FILES: @@ -231,7 +223,7 @@ class StaticEmbedding(TokenEmbedding): :return: """ requires_grads = set([param.requires_grad for name, param in self.named_parameters() - if 'words_to_words' not in name]) + if 'words_to_words' not in name]) if len(requires_grads) == 1: return requires_grads.pop() else: @@ -244,8 +236,8 @@ class StaticEmbedding(TokenEmbedding): continue param.requires_grad = value - def _load_with_vocab(self, embed_filepath, vocab, dtype=np.float32, padding='', unknown='', normalize=True, - error='ignore', init_method=None): + def _load_with_vocab(self, embed_filepath, vocab, dtype=np.float32, padding='', unknown='', + normalize=True, error='ignore', init_method=None): """ 从embed_filepath这个预训练的词向量中抽取出vocab这个词表的词的embedding。EmbedLoader将自动判断embed_filepath是 word2vec(第一行只有两个元素)还是glove格式的数据。 @@ -329,11 +321,6 @@ class ContextualEmbedding(TokenEmbedding): """ 由于动态embedding生成比较耗时，所以可以把每句话embedding缓存下来，这样就不需要每次都运行生成过程。 - Example:: - - >>> - - :param datasets: DataSet对象 :param batch_size: int, 生成cache的sentence表示时使用的batch的大小 :param device: 参考 :class::fastNLP.Trainer 的device @@ -363,7 +350,7 @@ class ContextualEmbedding(TokenEmbedding): seq_len = words.ne(pad_index).sum(dim=-1) max_len = words.size(1) # 因为有些情况可能包含CLS, SEP, 从后面往前计算比较安全。 - seq_len_from_behind =(max_len - seq_len).tolist() + seq_len_from_behind = (max_len - seq_len).tolist() word_embeds = self(words).detach().cpu().numpy() for b in range(words.size(0)): length = seq_len_from_behind[b] @@ -446,9 +433,6 @@ class ElmoEmbedding(ContextualEmbedding): self.layers = layers # 根据model_dir_or_name检查是否存在并下载 - PRETRAINED_ELMO_MODEL_DIR = {'en': 'elmo_en-d39843fe.tar.gz', - 'cn': 'elmo_cn-5e9b34e2.tar.gz'} - if model_dir_or_name.lower() in PRETRAINED_ELMO_MODEL_DIR: PRETRAIN_URL = _get_base_url('elmo') model_name = PRETRAINED_ELMO_MODEL_DIR[model_dir_or_name] @@ -532,21 +516,8 @@ class BertEmbedding(ContextualEmbedding): def __init__(self, vocab: Vocabulary, model_dir_or_name: str='en-base-uncased', layers: str='-1', pool_method: str='first', include_cls_sep: bool=False, requires_grad: bool=False): super(BertEmbedding, self).__init__(vocab) - # 根据model_dir_or_name检查是否存在并下载 - PRETRAINED_BERT_MODEL_DIR = {'en': 'bert-base-cased-f89bfe08.zip', - 'en-base-uncased': 'bert-base-uncased-3413b23c.zip', - 'en-base-cased': 'bert-base-cased-f89bfe08.zip', - 'en-large-uncased': 'bert-large-uncased-20939f45.zip', - 'en-large-cased': 'bert-large-cased-e0cf90fc.zip', - - 'cn': 'bert-base-chinese-29d0a84a.zip', - 'cn-base': 'bert-base-chinese-29d0a84a.zip', - - 'multilingual': 'bert-base-multilingual-cased-1bd364ee.zip', - 'multilingual-base-uncased': 'bert-base-multilingual-uncased-f8730fe4.zip', - 'multilingual-base-cased': 'bert-base-multilingual-cased-1bd364ee.zip', - } + # 根据model_dir_or_name检查是否存在并下载 if model_dir_or_name.lower() in PRETRAINED_BERT_MODEL_DIR: PRETRAIN_URL = _get_base_url('bert') model_name = PRETRAINED_BERT_MODEL_DIR[model_dir_or_name] diff --git a/reproduction/matching/data/MatchingDataLoader.py b/reproduction/matching/data/MatchingDataLoader.py index 139b1d4f..4868598a 100644 --- a/reproduction/matching/data/MatchingDataLoader.py +++ b/reproduction/matching/data/MatchingDataLoader.py @@ -6,31 +6,58 @@ from typing import Union, Dict from fastNLP.core.const import Const from fastNLP.core.vocabulary import Vocabulary -from fastNLP.core.dataset import DataSet from fastNLP.io.base_loader import DataInfo -from fastNLP.io.dataset_loader import JsonLoader -from fastNLP.io.file_utils import _get_base_url, cached_path +from fastNLP.io.dataset_loader import JsonLoader, DataSetLoader +from fastNLP.io.file_utils import _get_base_url, cached_path, PRETRAINED_BERT_MODEL_DIR from fastNLP.modules.encoder._bert import BertTokenizer -class MatchingLoader(JsonLoader): +class MatchingLoader(DataSetLoader): """ 别名：:class:`fastNLP.io.MatchingLoader` :class:`fastNLP.io.dataset_loader.MatchingLoader` 读取Matching任务的数据集 """ - def __init__(self, fields=None, paths: dict=None): - super(MatchingLoader, self).__init__(fields=fields) + def __init__(self, paths: dict=None): + """ + :param dict paths: key是数据集名称（如train、dev、test），value是对应的文件名 + """ self.paths = paths def _load(self, path): - return super(MatchingLoader, self)._load(path) - - def process(self, paths: Union[str, Dict[str, str]], dataset_name=None, - to_lower=False, char_information=False, seq_len_type: str=None, - bert_tokenizer: str=None, get_index=True, set_input: Union[list, str, bool]=True, + """ + :param str path: 待读取数据集的路径名 + :return: fastNLP.DataSet ds: 返回一个DataSet对象，里面必须包含3个field：其中两个分别为两个句子 + 的原始字符串文本，第三个为标签 + """ + raise NotImplementedError + + def process(self, paths: Union[str, Dict[str, str]], dataset_name: str=None, + to_lower=False, seq_len_type: str=None, bert_tokenizer: str=None, + get_index=True, set_input: Union[list, str, bool]=True, set_target: Union[list, str, bool] = True, concat: Union[str, list, bool]=None, ) -> DataInfo: + """ + :param paths: str或者Dict[str, str]。如果是str，则为数据集所在的文件夹或者是全路径文件名：如果是文件夹， + 则会从self.paths里面找对应的数据集名称与文件名。如果是Dict，则为数据集名称（如train、dev、test）和 + 对应的全路径文件名。 + :param str dataset_name: 如果在paths里传入的是一个数据集的全路径文件名，那么可以用dataset_name来定义 + 这个数据集的名字，如果不定义则默认为train。 + :param bool to_lower: 是否将文本自动转为小写。默认值为False。 + :param str seq_len_type: 提供的seq_len类型，支持 ``seq_len`` ：提供一个数字作为句子长度； ``mask`` : + 提供一个0/1的mask矩阵作为句子长度； ``bert`` ：提供segment_type_id（第一个句子为0，第二个句子为1）和 + attention mask矩阵（0/1的mask矩阵）。默认值为None，即不提供seq_len + :param str bert_tokenizer: bert tokenizer所使用的词表所在的文件夹路径 + :param bool get_index: 是否需要根据词表将文本转为index + :param set_input: 如果为True，则会自动将相关的field（名字里含有Const.INPUT的）设置为input，如果为False + 则不会将任何field设置为input。如果传入str或者List[str]，则会根据传入的内容将相对应的field设置为input， + 于此同时其他field不会被设置为input。默认值为True。 + :param set_target: set_target将控制哪些field可以被设置为target，用法与set_input一致。默认值为True。 + :param concat: 是否需要将两个句子拼接起来。如果为False则不会拼接。如果为True则会在两个句子之间插入一个。 + 如果传入一个长度为4的list，则分别表示插在第一句开始前、第一句结束后、第二句开始前、第二句结束后的标识符。如果 + 传入字符串 ``bert`` ，则会采用bert的拼接方式，等价于['[CLS]', '[SEP]', '', '[SEP]']. + :return: + """ if isinstance(set_input, str): set_input = [set_input] if isinstance(set_target, str): @@ -69,19 +96,6 @@ class MatchingLoader(JsonLoader): is_input=auto_set_input) if bert_tokenizer is not None: - PRETRAINED_BERT_MODEL_DIR = {'en': 'bert-base-cased-f89bfe08.zip', - 'en-base-uncased': 'bert-base-uncased-3413b23c.zip', - 'en-base-cased': 'bert-base-cased-f89bfe08.zip', - 'en-large-uncased': 'bert-large-uncased-20939f45.zip', - 'en-large-cased': 'bert-large-cased-e0cf90fc.zip', - - 'cn': 'bert-base-chinese-29d0a84a.zip', - 'cn-base': 'bert-base-chinese-29d0a84a.zip', - - 'multilingual': 'bert-base-multilingual-cased-1bd364ee.zip', - 'multilingual-base-uncased': 'bert-base-multilingual-uncased-f8730fe4.zip', - 'multilingual-base-cased': 'bert-base-multilingual-cased-1bd364ee.zip', - } if bert_tokenizer.lower() in PRETRAINED_BERT_MODEL_DIR: PRETRAIN_URL = _get_base_url('bert') model_name = PRETRAINED_BERT_MODEL_DIR[bert_tokenizer] @@ -128,14 +142,14 @@ class MatchingLoader(JsonLoader): for fields in data_set.get_field_names(): if Const.INPUT in fields: data_set.apply(lambda x: len(x[fields]), - new_field_name=fields.replace(Const.INPUT, Const.TARGET), + new_field_name=fields.replace(Const.INPUT, Const.INPUT_LEN), is_input=auto_set_input) elif seq_len_type == 'mask': for data_name, data_set in data_info.datasets.items(): for fields in data_set.get_field_names(): if Const.INPUT in fields: data_set.apply(lambda x: [1] * len(x[fields]), - new_field_name=fields.replace(Const.INPUT, Const.TARGET), + new_field_name=fields.replace(Const.INPUT, Const.INPUT_LEN), is_input=auto_set_input) elif seq_len_type == 'bert': for data_name, data_set in data_info.datasets.items(): @@ -152,11 +166,18 @@ class MatchingLoader(JsonLoader): if bert_tokenizer is not None: words_vocab = Vocabulary(padding='[PAD]', unknown='[UNK]') + with open(os.path.join(model_dir, 'vocab.txt'), 'r') as f: + lines = f.readlines() + lines = [line.strip() for line in lines] + words_vocab.add_word_lst(lines) + words_vocab.build_vocab() else: words_vocab = Vocabulary() - words_vocab = words_vocab.from_dataset(*data_set_list, - field_name=[n for n in data_set_list[0].get_field_names() - if (Const.INPUT in n)]) + words_vocab = words_vocab.from_dataset(*[d for n, d in data_info.datasets.items() if 'train' in n], + field_name=[n for n in data_set_list[0].get_field_names() + if (Const.INPUT in n)], + no_create_entry_dataset=[d for n, d in data_info.datasets.items() + if 'train' not in n]) target_vocab = Vocabulary(padding=None, unknown=None) target_vocab = target_vocab.from_dataset(*data_set_list, field_name=Const.TARGET) data_info.vocabs = {Const.INPUT: words_vocab, Const.TARGET: target_vocab} @@ -173,14 +194,14 @@ class MatchingLoader(JsonLoader): for data_name, data_set in data_info.datasets.items(): if isinstance(set_input, list): - data_set.set_input(set_input) + data_set.set_input(*set_input) if isinstance(set_target, list): - data_set.set_target(set_target) + data_set.set_target(*set_target) return data_info -class SNLILoader(MatchingLoader): +class SNLILoader(MatchingLoader, JsonLoader): """ 别名：:class:`fastNLP.io.SNLILoader` :class:`fastNLP.io.dataset_loader.SNLILoader` @@ -203,10 +224,13 @@ class SNLILoader(MatchingLoader): 'train': 'snli_1.0_train.jsonl', 'dev': 'snli_1.0_dev.jsonl', 'test': 'snli_1.0_test.jsonl'} - super(SNLILoader, self).__init__(fields=fields, paths=paths) + # super(SNLILoader, self).__init__(fields=fields, paths=paths) + MatchingLoader.__init__(self, paths=paths) + JsonLoader.__init__(self, fields=fields) def _load(self, path): - ds = super(SNLILoader, self)._load(path) + # ds = super(SNLILoader, self)._load(path) + ds = JsonLoader._load(self, path) def parse_tree(x): t = Tree.fromstring(x) diff --git a/reproduction/matching/matching_esim.py b/reproduction/matching/matching_esim.py new file mode 100644 index 00000000..3da6141f --- /dev/null +++ b/reproduction/matching/matching_esim.py @@ -0,0 +1,65 @@ + +import argparse +import torch + +from fastNLP.core import Trainer, Tester, Adam, AccuracyMetric, Const +from fastNLP.modules.encoder.embedding import ElmoEmbedding, StaticEmbedding + +from reproduction.matching.data.MatchingDataLoader import SNLILoader +from reproduction.matching.model.esim import ESIMModel + +argument = argparse.ArgumentParser() +argument.add_argument('--embedding', choices=['glove', 'elmo'], default='glove') +argument.add_argument('--batch-size-per-gpu', type=int, default=128) +argument.add_argument('--n-epochs', type=int, default=100) +argument.add_argument('--lr', type=float, default=1e-4) +argument.add_argument('--seq-len-type', choices=['mask', 'seq_len'], default='seq_len') +argument.add_argument('--save-dir', type=str, default=None) +arg = argument.parse_args() + +bert_dirs = 'path/to/bert/dir' + +# load data set +data_info = SNLILoader().process( + paths='path/to/snli/data/dir', to_lower=True, seq_len_type=arg.seq_len_type, bert_tokenizer=None, + get_index=True, concat=False, +) + +# load embedding +if arg.embedding == 'elmo': + embedding = ElmoEmbedding(data_info.vocabs[Const.INPUT], requires_grad=True) +elif arg.embedding == 'glove': + embedding = StaticEmbedding(data_info.vocabs[Const.INPUT], requires_grad=True) +else: + raise ValueError(f'now we only support elmo or glove embedding for esim model!') + +# define model +model = ESIMModel(embedding) + +# define trainer +trainer = Trainer(train_data=data_info.datasets['train'], model=model, + optimizer=Adam(lr=arg.lr, model_params=model.parameters()), + batch_size=torch.cuda.device_count() * arg.batch_size_per_gpu, + n_epochs=arg.n_epochs, print_every=-1, + dev_data=data_info.datasets['dev'], + metrics=AccuracyMetric(), metric_key='acc', + device=[i for i in range(torch.cuda.device_count())], + check_code_level=-1, + save_path=arg.save_path) + +# train model +trainer.train(load_best_model=True) + +# define tester +tester = Tester( + data=data_info.datasets['test'], + model=model, + metrics=AccuracyMetric(), + batch_size=torch.cuda.device_count() * arg.batch_size_per_gpu, + device=[i for i in range(torch.cuda.device_count())], +) + +# test model +tester.test() + + diff --git a/reproduction/matching/model/esim.py b/reproduction/matching/model/esim.py index 0551bbdb..d55034e7 100644 --- a/reproduction/matching/model/esim.py +++ b/reproduction/matching/model/esim.py @@ -30,24 +30,37 @@ class ESIMModel(BaseModel): self.bi_attention = SoftmaxAttention() self.rnn_high = BiRNN(self.embedding.embed_size, hidden_size, dropout_rate=dropout_rate) - # self.rnn_high = LSTM(hidden_size, hidden_size, dropout=dropout_rate, bidirectional=True) + # self.rnn_high = LSTM(hidden_size, hidden_size, dropout=dropout_rate, bidirectional=True,) self.classifier = nn.Sequential(nn.Dropout(p=dropout_rate), nn.Linear(8 * hidden_size, hidden_size), nn.Tanh(), nn.Dropout(p=dropout_rate), nn.Linear(hidden_size, num_labels)) + + self.dropout_rnn = nn.Dropout(p=dropout_rate) + nn.init.xavier_uniform_(self.classifier[1].weight.data) nn.init.xavier_uniform_(self.classifier[4].weight.data) def forward(self, words1, words2, seq_len1, seq_len2, target=None): - mask1 = seq_len_to_mask(seq_len1) - mask2 = seq_len_to_mask(seq_len2) + """ + :param words1: [batch, seq_len] + :param words2: [batch, seq_len] + :param seq_len1: [batch] + :param seq_len2: [batch] + :param target: + :return: + """ + mask1 = seq_len_to_mask(seq_len1, words1.size(1)) + mask2 = seq_len_to_mask(seq_len2, words2.size(1)) a0 = self.embedding(words1) # B * len * emb_dim b0 = self.embedding(words2) a0, b0 = self.dropout_embed(a0), self.dropout_embed(b0) a = self.rnn(a0, mask1.byte()) # a: [B, PL, 2 * H] b = self.rnn(b0, mask2.byte()) + # a = self.dropout_rnn(self.rnn(a0, seq_len1)[0]) # a: [B, PL, 2 * H] + # b = self.dropout_rnn(self.rnn(b0, seq_len2)[0]) ai, bi = self.bi_attention(a, mask1, b, mask2) @@ -58,6 +71,8 @@ class ESIMModel(BaseModel): a_h = self.rnn_high(a_f, mask1.byte()) # ma: [B, PL, 2 * H] b_h = self.rnn_high(b_f, mask2.byte()) + # a_h = self.dropout_rnn(self.rnn_high(a_f, seq_len1)[0]) # ma: [B, PL, 2 * H] + # b_h = self.dropout_rnn(self.rnn_high(b_f, seq_len2)[0]) a_avg = self.mean_pooling(a_h, mask1, dim=1) a_max, _ = self.max_pooling(a_h, mask1, dim=1)