- update dpcnn

- add train_idcnn
- update sst loader

fastNLP/io/data_loader/sst.py

@@ -5,10 +5,8 @@ from ..base_loader import DataInfo, DataSetLoader
 from ...core.vocabulary import VocabularyOption, Vocabulary
 from ...core.dataset import DataSet
 from ...core.instance import Instance
-from ..embed_loader import EmbeddingOption, EmbedLoader
+from ..utils import check_dataloader_paths, get_tokenizer
 
-spacy.prefer_gpu()
-sptk = spacy.load('en')
 
 class SSTLoader(DataSetLoader):
     URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'
@@ -37,6 +35,7 @@ class SSTLoader(DataSetLoader):
             tag_v['0'] = tag_v['1']
             tag_v['4'] = tag_v['3']
         self.tag_v = tag_v
+        self.tokenizer = get_tokenizer()
 
     def _load(self, path):
         """
@@ -55,29 +54,37 @@ class SSTLoader(DataSetLoader):
             ds.append(Instance(words=words, target=tag))
         return ds
 
-    @staticmethod
-    def _get_one(data, subtree):
+    def _get_one(self, data, subtree):
         tree = Tree.fromstring(data)
         if subtree:
-            return [([x.text for x in sptk.tokenizer(' '.join(t.leaves()))], t.label()) for t in tree.subtrees() ]
-        return [([x.text for x in sptk.tokenizer(' '.join(tree.leaves()))], tree.label())]
+            return [([x.text for x in self.tokenizer(' '.join(t.leaves()))], t.label()) for t in tree.subtrees() ]
+        return [([x.text for x in self.tokenizer(' '.join(tree.leaves()))], tree.label())]
 
     def process(self,
-                paths,
-                train_ds: Iterable[str] = None,
+                paths, train_subtree=True,
                 src_vocab_op: VocabularyOption = None,
-                tgt_vocab_op: VocabularyOption = None,
-                src_embed_op: EmbeddingOption = None):
+                tgt_vocab_op: VocabularyOption = None,):
+        paths = check_dataloader_paths(paths)
         input_name, target_name = 'words', 'target'
         src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op)
         tgt_vocab = Vocabulary(unknown=None, padding=None) \
             if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op)
 
-        info = DataInfo(datasets=self.load(paths))
-        _train_ds = [info.datasets[name]
-                     for name in train_ds] if train_ds else info.datasets.values()
-        src_vocab.from_dataset(*_train_ds, field_name=input_name)
-        tgt_vocab.from_dataset(*_train_ds, field_name=target_name)
+        info = DataInfo()
+        origin_subtree = self.subtree
+        self.subtree = train_subtree
+        info.datasets['train'] = self._load(paths['train'])
+        self.subtree = origin_subtree
+        for n, p in paths.items():
+            if n != 'train':
+                info.datasets[n] = self._load(p)
+
+        src_vocab.from_dataset(
+            info.datasets['train'],
+            field_name=input_name,
+            no_create_entry_dataset=[ds for n, ds in info.datasets.items() if n != 'train'])
+        tgt_vocab.from_dataset(info.datasets['train'], field_name=target_name)
+
         src_vocab.index_dataset(
             *info.datasets.values(),
             field_name=input_name, new_field_name=input_name)
@@ -89,10 +96,5 @@ class SSTLoader(DataSetLoader):
             target_name: tgt_vocab
         }
 
-        if src_embed_op is not None:
-            src_embed_op.vocab = src_vocab
-            init_emb = EmbedLoader.load_with_vocab(**src_embed_op)
-            info.embeddings[input_name] = init_emb
-
         return info
 

fastNLP/io/utils.py

@@ -0,0 +1,69 @@
+import os
+
+from typing import Union, Dict
+
+
+def check_dataloader_paths(paths:Union[str, Dict[str, str]])->Dict[str, str]:
+    """
+    检查传入dataloader的文件的合法性。如果为合法路径，将返回至少包含'train'这个key的dict。类似于下面的结果
+    {
+        'train': '/some/path/to/', # 一定包含，建词表应该在这上面建立，剩下的其它文件应该只需要处理并index。
+        'test': 'xxx' # 可能有，也可能没有
+        ...
+    }
+    如果paths为不合法的，将直接进行raise相应的错误
+
+    :param paths: 路径. 可以为一个文件路径(则认为该文件就是train的文件); 可以为一个文件目录，将在该目录下寻找train(文件名
+        中包含train这个字段), test.txt, dev.txt; 可以为一个dict, 则key是用户自定义的某个文件的名称，value是这个文件的路径。
+    :return:
+    """
+    if isinstance(paths, str):
+        if os.path.isfile(paths):
+            return {'train': paths}
+        elif os.path.isdir(paths):
+            filenames = os.listdir(paths)
+            files = {}
+            for filename in filenames:
+                path_pair = None
+                if 'train' in filename:
+                    path_pair = ('train', filename)
+                if 'dev' in filename:
+                    if path_pair:
+                        raise Exception("File:{} in {} contains bot `{}` and `dev`.".format(filename, paths, path_pair[0]))
+                    path_pair = ('dev', filename)
+                if 'test' in filename:
+                    if path_pair:
+                        raise Exception("File:{} in {} contains bot `{}` and `test`.".format(filename, paths, path_pair[0]))
+                    path_pair = ('test', filename)
+                if path_pair:
+                    files[path_pair[0]] = os.path.join(paths, path_pair[1])
+            return files
+        else:
+            raise FileNotFoundError(f"{paths} is not a valid file path.")
+
+    elif isinstance(paths, dict):
+        if paths:
+            if 'train' not in paths:
+                raise KeyError("You have to include `train` in your dict.")
+            for key, value in paths.items():
+                if isinstance(key, str) and isinstance(value, str):
+                    if not os.path.isfile(value):
+                        raise TypeError(f"{value} is not a valid file.")
+                else:
+                    raise TypeError("All keys and values in paths should be str.")
+            return paths
+        else:
+            raise ValueError("Empty paths is not allowed.")
+    else:
+        raise TypeError(f"paths only supports str and dict. not {type(paths)}.")
+
+def get_tokenizer():
+    try:
+        import spacy
+        spacy.prefer_gpu()
+        en = spacy.load('en')
+        print('use spacy tokenizer')
+        return lambda x: [w.text for w in en.tokenizer(x)]
+    except Exception as e:
+        print('use raw tokenizer')
+        return lambda x: x.split()

reproduction/seqence_labelling/ner/model/dilated_cnn.py

@@ -8,16 +8,23 @@ from fastNLP.core.const import Const as C
 
 
 class IDCNN(nn.Module):
-    def __init__(self, init_embed, char_embed,
+    def __init__(self,
+                 init_embed,
+                 char_embed,
                  num_cls,
                  repeats, num_layers, num_filters, kernel_size,
                  use_crf=False, use_projection=False, block_loss=False,
                  input_dropout=0.3, hidden_dropout=0.2, inner_dropout=0.0):
         super(IDCNN, self).__init__()
         self.word_embeddings = Embedding(init_embed)
-        self.char_embeddings = Embedding(char_embed)
-        embedding_size = self.word_embeddings.embedding_dim + \
-            self.char_embeddings.embedding_dim
+
+        if char_embed is None:
+            self.char_embeddings = None
+            embedding_size = self.word_embeddings.embedding_dim
+        else:
+            self.char_embeddings = Embedding(char_embed)
+            embedding_size = self.word_embeddings.embedding_dim + \
+                self.char_embeddings.embedding_dim
 
         self.conv0 = nn.Sequential(
             nn.Conv1d(in_channels=embedding_size,
@@ -31,7 +38,7 @@ class IDCNN(nn.Module):
 
         block = []
         for layer_i in range(num_layers):
-            dilated = 2 ** layer_i
+            dilated = 2 ** layer_i if layer_i+1 < num_layers else 1
             block.append(nn.Conv1d(
                 in_channels=num_filters,
                 out_channels=num_filters,
@@ -67,11 +74,24 @@ class IDCNN(nn.Module):
         self.crf = ConditionalRandomField(
             num_tags=num_cls) if use_crf else None
         self.block_loss = block_loss
+        self.reset_parameters()
 
-    def forward(self, words, chars, seq_len, target=None):
-        e1 = self.word_embeddings(words)
-        e2 = self.char_embeddings(chars)
-        x = torch.cat((e1, e2), dim=-1) # b,l,h
+    def reset_parameters(self):
+        for m in self.modules():
+            if isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+                nn.init.xavier_normal_(m.weight, gain=1)
+                if m.bias is not None:
+                    nn.init.normal_(m.bias, mean=0, std=0.01)
+
+    def forward(self, words, seq_len, target=None, chars=None):
+        if self.char_embeddings is None:
+            x = self.word_embeddings(words)
+        else:
+            if chars is None:
+                raise ValueError('must provide chars for model with char embedding')
+            e1 = self.word_embeddings(words)
+            e2 = self.char_embeddings(chars)
+            x = torch.cat((e1, e2), dim=-1) # b,l,h
         mask = seq_len_to_mask(seq_len)
 
         x = x.transpose(1, 2) # b,h,l
@@ -84,21 +104,24 @@ class IDCNN(nn.Module):
 
         def compute_loss(y, t, mask):
             if self.crf is not None and target is not None:
-                loss = self.crf(y, t, mask)
+                loss = self.crf(y.transpose(1, 2), t, mask)
             else:
                 t.masked_fill_(mask == 0, -100)
                 loss = F.cross_entropy(y, t, ignore_index=-100)
             return loss
 
-        if self.block_loss:
-            losses = [compute_loss(o, target, mask) for o in output]
-            loss = sum(losses)
+        if target is not None:
+            if self.block_loss:
+                losses = [compute_loss(o, target, mask) for o in output]
+                loss = sum(losses)
+            else:
+                loss = compute_loss(output[-1], target, mask)
         else:
-            loss = compute_loss(output[-1], target, mask)
+            loss = None
 
         scores = output[-1]
         if self.crf is not None:
-            pred = self.crf.viterbi_decode(scores, target, mask)
+            pred, _ = self.crf.viterbi_decode(scores.transpose(1, 2), mask)
         else:
             pred = scores.max(1)[1] * mask.long()
 
@@ -107,5 +130,13 @@ class IDCNN(nn.Module):
             C.OUTPUT: pred,
         }
 
-    def predict(self, words, chars, seq_len):
-        return self.forward(words, chars, seq_len)[C.OUTPUT]
+    def predict(self, words, seq_len, chars=None):
+        res = self.forward(
+            words=words,
+            seq_len=seq_len,
+            chars=chars,
+            target=None
+        )[C.OUTPUT]
+        return {
+            C.OUTPUT: res
+        }

reproduction/seqence_labelling/ner/train_idcnn.py

@@ -0,0 +1,99 @@
+from reproduction.seqence_labelling.ner.data.OntoNoteLoader import OntoNoteNERDataLoader
+from fastNLP.core.callback import FitlogCallback, LRScheduler
+from fastNLP import GradientClipCallback
+from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR
+from torch.optim import SGD, Adam
+from fastNLP import Const
+from fastNLP import RandomSampler, BucketSampler
+from fastNLP import SpanFPreRecMetric
+from fastNLP import Trainer
+from reproduction.seqence_labelling.ner.model.dilated_cnn import IDCNN
+from fastNLP.core.utils import Option
+from fastNLP.modules.encoder.embedding import CNNCharEmbedding, StaticEmbedding
+from fastNLP.core.utils import cache_results
+import sys
+import torch.cuda
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+
+encoding_type = 'bioes'
+
+
+def get_path(path):
+    return os.path.join(os.environ['HOME'], path)
+
+data_path = get_path('workdir/datasets/ontonotes-v4')
+
+ops = Option(
+    batch_size=128,
+    num_epochs=100,
+    lr=3e-4,
+    repeats=3,
+    num_layers=3,
+    num_filters=400,
+    use_crf=True,
+    gradient_clip=5,
+)
+
+@cache_results('ontonotes-cache')
+def load_data():
+
+    data = OntoNoteNERDataLoader(encoding_type=encoding_type).process(data_path,
+                                                                  lower=True)
+
+    # char_embed = CNNCharEmbedding(vocab=data.vocabs['cap_words'], embed_size=30, char_emb_size=30, filter_nums=[30],
+    #                               kernel_sizes=[3])
+
+    word_embed = StaticEmbedding(vocab=data.vocabs[Const.INPUT],
+                                 model_dir_or_name='en-glove-840b-300',
+                                 requires_grad=True)
+    return data, [word_embed]
+
+data, embeds = load_data()
+print(data.datasets['train'][0])
+print(list(data.vocabs.keys()))
+
+for ds in data.datasets.values():
+    ds.rename_field('cap_words', 'chars')
+    ds.set_input('chars')
+
+word_embed = embeds[0]
+char_embed = CNNCharEmbedding(data.vocabs['cap_words'])
+# for ds in data.datasets:
+#     ds.rename_field('')
+
+print(data.vocabs[Const.TARGET].word2idx)
+
+model = IDCNN(init_embed=word_embed,
+              char_embed=char_embed,
+              num_cls=len(data.vocabs[Const.TARGET]),
+              repeats=ops.repeats,
+              num_layers=ops.num_layers,
+              num_filters=ops.num_filters,
+              kernel_size=3,
+              use_crf=ops.use_crf, use_projection=True,
+              block_loss=True,
+              input_dropout=0.33, hidden_dropout=0.2, inner_dropout=0.2)
+
+print(model)
+
+callbacks = [GradientClipCallback(clip_value=ops.gradient_clip, clip_type='norm'),]
+
+optimizer = Adam(model.parameters(), lr=ops.lr, weight_decay=0)
+# scheduler = LRScheduler(LambdaLR(optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch)))
+# callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 15)))
+# optimizer = SWATS(model.parameters(), verbose=True)
+# optimizer = Adam(model.parameters(), lr=0.005)
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+trainer = Trainer(train_data=data.datasets['train'], model=model, optimizer=optimizer,
+                  sampler=BucketSampler(num_buckets=50, batch_size=ops.batch_size),
+                  device=device, dev_data=data.datasets['dev'], batch_size=ops.batch_size,
+                  metrics=SpanFPreRecMetric(
+                      tag_vocab=data.vocabs[Const.TARGET], encoding_type=encoding_type),
+                  check_code_level=-1,
+                  callbacks=callbacks, num_workers=2, n_epochs=ops.num_epochs)
+trainer.train()

reproduction/text_classification/data/yelpLoader.py

@@ -8,18 +8,7 @@ from fastNLP.io.base_loader import DataInfo
 from fastNLP.io.embed_loader import EmbeddingOption
 from fastNLP.io.file_reader import _read_json
 from typing import Union, Dict
-from reproduction.utils import check_dataloader_paths
-
-
-def get_tokenizer():
-    try:
-        import spacy
-        en = spacy.load('en')
-        print('use spacy tokenizer')
-        return lambda x: [w.text for w in en.tokenizer(x)]
-    except Exception as e:
-        print('use raw tokenizer')
-        return lambda x: x.split()
+from reproduction.utils import check_dataloader_paths, get_tokenizer
 
 def clean_str(sentence, tokenizer, char_lower=False):
     """

reproduction/text_classification/train_dpcnn.py

@@ -9,6 +9,7 @@ from fastNLP import CrossEntropyLoss, AccuracyMetric
 from fastNLP.modules.encoder.embedding import StaticEmbedding, CNNCharEmbedding, StackEmbedding
 from reproduction.text_classification.model.dpcnn import DPCNN
 from data.yelpLoader import yelpLoader
+from fastNLP.core.sampler import BucketSampler
 import torch.nn as nn
 from fastNLP.core import LRScheduler
 from fastNLP.core.const import Const as C
@@ -28,19 +29,20 @@ class Config():
     embedding_grad = True
     train_epoch = 30
     batch_size = 100
-    num_classes = 2
     task = "yelp_p"
-    #datadir = '/remote-home/yfshao/workdir/datasets/SST'
-    datadir = '/remote-home/yfshao/workdir/datasets/yelp_polarity'
+    #datadir = 'workdir/datasets/SST'
+    datadir = 'workdir/datasets/yelp_polarity'
+    # datadir = 'workdir/datasets/yelp_full'
     #datafile = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"}
     datafile = {"train": "train.csv",  "test": "test.csv"}
     lr = 1e-3
-    src_vocab_op = VocabularyOption()
+    src_vocab_op = VocabularyOption(max_size=100000)
     embed_dropout = 0.3
     cls_dropout = 0.1
-    weight_decay = 1e-4
+    weight_decay = 1e-5
 
     def __init__(self):
+        self.datadir = os.path.join(os.environ['HOME'], self.datadir)
         self.datapath = {k: os.path.join(self.datadir, v)
                          for k, v in self.datafile.items()}
 
@@ -53,6 +55,8 @@ print('RNG SEED: {}'.format(ops.seed))
 # 1.task相关信息：利用dataloader载入dataInfo
 
 #datainfo=SSTLoader(fine_grained=True).process(paths=ops.datapath, train_ds=['train'])
+
+
 @cache_results(ops.model_dir_or_name+'-data-cache')
 def load_data():
     datainfo = yelpLoader(fine_grained=True, lower=True).process(
@@ -61,28 +65,23 @@ def load_data():
         ds.apply_field(len, C.INPUT, C.INPUT_LEN)
         ds.set_input(C.INPUT, C.INPUT_LEN)
         ds.set_target(C.TARGET)
-    return datainfo
+    embedding = StaticEmbedding(
+        datainfo.vocabs['words'], model_dir_or_name='en-glove-840b-300', requires_grad=ops.embedding_grad,
+        normalize=False
+    )
+    return datainfo, embedding
 
-datainfo = load_data()
+
+datainfo, embedding = load_data()
 
 # 2.或直接复用fastNLP的模型
 
-vocab = datainfo.vocabs['words']
 # embedding = StackEmbedding([StaticEmbedding(vocab), CNNCharEmbedding(vocab, 100)])
-#embedding = StaticEmbedding(vocab)
-embedding = StaticEmbedding(
-    vocab, model_dir_or_name='en-word2vec-300', requires_grad=ops.embedding_grad,
-    normalize=False
-)
 
-print(len(datainfo.datasets['train']))
-print(len(datainfo.datasets['test']))
+print(datainfo)
 print(datainfo.datasets['train'][0])
 
-print(len(vocab))
-print(len(datainfo.vocabs['target']))
-
-model = DPCNN(init_embed=embedding, num_cls=ops.num_classes,
+model = DPCNN(init_embed=embedding, num_cls=len(datainfo.vocabs[C.TARGET]),
               embed_dropout=ops.embed_dropout, cls_dropout=ops.cls_dropout)
 print(model)
 
@@ -93,11 +92,11 @@ optimizer = SGD([param for param in model.parameters() if param.requires_grad ==
                 lr=ops.lr, momentum=0.9, weight_decay=ops.weight_decay)
 
 callbacks = []
-callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5)))
-# callbacks.append
-#     LRScheduler(LambdaLR(optimizer, lambda epoch: ops.lr if epoch <
-#                          ops.train_epoch * 0.8 else ops.lr * 0.1))
-# )
+# callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 5)))
+callbacks.append(
+    LRScheduler(LambdaLR(optimizer, lambda epoch: ops.lr if epoch <
+                         ops.train_epoch * 0.8 else ops.lr * 0.1))
+)
 
 # callbacks.append(
 #     FitlogCallback(data=datainfo.datasets, verbose=1)
@@ -109,6 +108,7 @@ print(device)
 
 # 4.定义train方法
 trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                  sampler=BucketSampler(num_buckets=50, batch_size=ops.batch_size),
                   metrics=[metric],
                   dev_data=datainfo.datasets['test'], device=device,
                   check_code_level=-1, batch_size=ops.batch_size, callbacks=callbacks,

reproduction/utils.py

@@ -57,4 +57,13 @@ def check_dataloader_paths(paths:Union[str, Dict[str, str]])->Dict[str, str]:
     else:
         raise TypeError(f"paths only supports str and dict. not {type(paths)}.")
 
-
+def get_tokenizer():
+    try:
+        import spacy
+        spacy.prefer_gpu()
+        en = spacy.load('en')
+        print('use spacy tokenizer')
+        return lambda x: [w.text for w in en.tokenizer(x)]
+    except Exception as e:
+        print('use raw tokenizer')
+        return lambda x: x.split()