Merge branch 'dev'

# Conflicts:
#	setup.py

README.md

@@ -39,30 +39,30 @@ python -m spacy download en
 
 
 ## fastNLP教程
-中文[文档](https://fastnlp.readthedocs.io/)、[教程](https://fastnlp.readthedocs.io/zh/latest/user/tutorials.html)
+中文[文档](http://www.fastnlp.top/docs/fastNLP/)、 [教程](http://www.fastnlp.top/docs/fastNLP/user/quickstart.html)
 
 ### 快速入门
 
-- [0. 快速入门](https://fastnlp.readthedocs.io/zh/latest/user/quickstart.html)
+- [Quick-1. 文本分类](http://www.fastnlp.top/docs/fastNLP/tutorials/%E6%96%87%E6%9C%AC%E5%88%86%E7%B1%BB.html)
+- [Quick-2. 序列标注](http://www.fastnlp.top/docs/fastNLP/tutorials/%E5%BA%8F%E5%88%97%E6%A0%87%E6%B3%A8.html)
 
 ### 详细使用教程
 
-- [1. 使用DataSet预处理文本](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_1_data_preprocess.html)
-- [2. 使用Vocabulary转换文本与index](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_2_vocabulary.html)
-- [3. 使用Embedding模块将文本转成向量](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_3_embedding.html)
-- [4. 使用Loader和Pipe加载并处理数据集](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_4_load_dataset.html)
-- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_5_loss_optimizer.html)
-- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_6_datasetiter.html)
-- [7. 使用Metric快速评测你的模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_7_metrics.html)
-- [8. 使用Modules和Models快速搭建自定义模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_8_modules_models.html)
-- [9. 快速实现序列标注模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_9_seq_labeling.html)
-- [10. 使用Callback自定义你的训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_10_callback.html)
+- [1. 使用DataSet预处理文本](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_1_data_preprocess.html)
+- [2. 使用Vocabulary转换文本与index](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_2_vocabulary.html)
+- [3. 使用Embedding模块将文本转成向量](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_3_embedding.html)
+- [4. 使用Loader和Pipe加载并处理数据集](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_4_load_dataset.html)
+- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_5_loss_optimizer.html)
+- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_6_datasetiter.html)
+- [7. 使用Metric快速评测你的模型](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_7_metrics.html)
+- [8. 使用Modules和Models快速搭建自定义模型](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_8_modules_models.html)
+- [9. 使用Callback自定义你的训练过程](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_9_callback.html)
 
 ### 扩展教程
 
-- [Extend-1. BertEmbedding的各种用法](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_1_bert_embedding.html)
-- [Extend-2. 分布式训练简介](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_2_dist.html)
-- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_3_fitlog.html)
+- [Extend-1. BertEmbedding的各种用法](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_1_bert_embedding.html)
+- [Extend-2. 分布式训练简介](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_2_dist.html)
+- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_3_fitlog.html)
 
 
 ## 内置组件

docs/requirements.txt

@@ -1,8 +1,4 @@
-numpy>=1.14.2
-http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl
-torchvision>=0.1.8
-sphinx-rtd-theme==0.4.1
-tensorboardX>=1.4
-tqdm>=4.28.1
-ipython>=6.4.0
-ipython-genutils>=0.2.0
+sphinx==3.2.1
+docutils==0.16
+sphinx-rtd-theme==0.5.0
+readthedocs-sphinx-search==0.1.0rc3

docs/source/index.rst

@@ -4,6 +4,10 @@ fastNLP 中文文档
 `fastNLP <https://github.com/fastnlp/fastNLP/>`_ 是一款轻量级的自然语言处理（NLP）工具包。你既可以用它来快速地完成一个NLP任务，
 也可以用它在研究中快速构建更复杂的模型。
 
+.. hint::
+
+    如果你是从 readthedocs 访问的该文档，请跳转到我们的 `最新网站 <http://www.fastnlp.top/docs/fastNLP/>`_
+
 fastNLP具有如下的特性：
 
 - 统一的Tabular式数据容器，简化数据预处理过程;
@@ -41,7 +45,7 @@ API 文档
 fitlog文档
 ----------
 
-您可以 `点此 <https://fitlog.readthedocs.io/zh/latest/>`_  查看fitlog的文档。
+您可以 `点此 <http://www.fastnlp.top/docs/fitlog/>`_  查看fitlog的文档。
 fitlog 是由我们团队开发的日志记录+代码管理的工具。
 
 索引与搜索

docs/source/tutorials/extend_3_fitlog.rst

@@ -4,7 +4,7 @@
 
 本文介绍结合使用 fastNLP 和 fitlog 进行科研的方法。
 
-首先，我们需要安装 `fitlog <https://fitlog.readthedocs.io/>`_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。
+首先，我们需要安装 `fitlog <http://www.fastnlp.top/docs/fitlog/>`_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。
 
 我们从命令行中进入到一个文件夹，现在我们要在文件夹中创建我们的 fastNLP 项目。你可以在命令行输入 `fitlog init test1` ，
 然后你会看到如下提示::
@@ -15,7 +15,7 @@
     Fitlog project test1 is initialized.
 
 这表明你已经创建成功了项目文件夹，并且在项目文件夹中已经初始化了 Git。如果你不想初始化 Git，
-可以参考文档 `命令行工具 <https://fitlog.readthedocs.io/zh/latest/user/command_line.html>`_
+可以参考文档 `命令行工具 <http://www.fastnlp.top/docs/fitlog/user/command_line.html>`_
 
 现在我们进入你创建的项目文件夹 test1 中，可以看到有一个名为 logs 的文件夹，后面我们将会在里面存放你的实验记录。
 同时也有一个名为 main.py 的文件，这是我们推荐你使用的训练入口文件。文件的内容如下::
@@ -37,7 +37,7 @@
     fitlog.finish()                     # finish the logging
 
 我们推荐你保留除注释外的四行代码，它们有助于你的实验，
-他们的具体用处参见文档 `用户 API <https://fitlog.readthedocs.io/zh/latest/fitlog.html>`_
+他们的具体用处参见文档 `用户 API <http://www.fastnlp.top/docs/fitlog/>`_
 
 我们假定你要进行前两个教程中的实验，并已经把数据复制到了项目根目录下的 tutorial_sample_dataset.csv 文件中。
 现在我们编写如下的训练代码，使用 :class:`~fastNLP.core.callback.FitlogCallback` 进行实验记录保存::

docs/source/tutorials/文本分类.rst

@@ -291,7 +291,7 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所
 
 
 PS: 使用Bert进行文本分类
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code-block:: python
 
@@ -368,7 +368,7 @@ PS: 使用Bert进行文本分类
 
 
 PS: 基于词进行文本分类
-~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 由于汉字中没有显示的字与字的边界，一般需要通过分词器先将句子进行分词操作。
 下面的例子演示了如何不基于fastNLP已有的数据读取、预处理代码进行文本分类。

fastNLP/core/dataset.py

@@ -53,7 +53,7 @@ r"""
         from fastNLP import DataSet
         from fastNLP import Instance
         instances = []
-        winstances.append(Instance(sentence="This is the first instance",
+        instances.append(Instance(sentence="This is the first instance",
                             ords=['this', 'is', 'the', 'first', 'instance', '.'],
                             seq_len=6))
         instances.append(Instance(sentence="Second instance .",

fastNLP/core/tester.py

@@ -148,7 +148,7 @@ class Tester(object):
                 self._predict_func = self._model.predict
                 self._predict_func_wrapper = self._model.predict
         else:
-            if _model_contains_inner_module(model):
+            if _model_contains_inner_module(self._model):
                 self._predict_func_wrapper = self._model.forward
                 self._predict_func = self._model.module.forward
             else:

fastNLP/io/file_utils.py

@@ -103,6 +103,11 @@ DATASET_DIR = {
     "yelp-review-polarity": "yelp_review_polarity.tar.gz",
     "sst-2": "SST-2.zip",
     "sst": "SST.zip",
+    'mr': 'mr.zip',
+    "R8": "R8.zip",
+    "R52": "R52.zip",
+    "20ng": "20ng.zip",
+    "ohsumed": "ohsumed.zip",
 
     # Classification, Chinese
     "chn-senti-corp": "chn_senti_corp.zip",

fastNLP/io/pipe/__init__.py

@@ -23,15 +23,15 @@ __all__ = [
     "ChnSentiCorpPipe",
     "THUCNewsPipe",
     "WeiboSenti100kPipe",
-    "MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Loader",
-    
+    "MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Pipe",
+
     "Conll2003NERPipe",
     "OntoNotesNERPipe",
     "MsraNERPipe",
     "WeiboNERPipe",
     "PeopleDailyPipe",
     "Conll2003Pipe",
-    
+
     "MatchingBertPipe",
     "RTEBertPipe",
     "SNLIBertPipe",
@@ -53,14 +53,20 @@ __all__ = [
     "RenamePipe",
     "GranularizePipe",
     "MachingTruncatePipe",
-    
+
     "CoReferencePipe",
 
-    "CMRC2018BertPipe"
+    "CMRC2018BertPipe",
+
+    "R52PmiGraphPipe",
+    "R8PmiGraphPipe",
+    "OhsumedPmiGraphPipe",
+    "NG20PmiGraphPipe",
+    "MRPmiGraphPipe"
 ]
 
 from .classification import CLSBasePipe, YelpFullPipe, YelpPolarityPipe, SSTPipe, SST2Pipe, IMDBPipe, ChnSentiCorpPipe, THUCNewsPipe, \
-    WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Loader
+    WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Pipe
 from .conll import Conll2003NERPipe, OntoNotesNERPipe, MsraNERPipe, WeiboNERPipe, PeopleDailyPipe
 from .conll import Conll2003Pipe
 from .coreference import CoReferencePipe
@@ -70,3 +76,5 @@ from .matching import MatchingBertPipe, RTEBertPipe, SNLIBertPipe, QuoraBertPipe
     LCQMCPipe, BQCorpusPipe, LCQMCBertPipe, RenamePipe, GranularizePipe, MachingTruncatePipe
 from .pipe import Pipe
 from .qa import CMRC2018BertPipe
+
+from .construct_graph import MRPmiGraphPipe, R8PmiGraphPipe, R52PmiGraphPipe, NG20PmiGraphPipe, OhsumedPmiGraphPipe

fastNLP/io/pipe/construct_graph.py

@@ -0,0 +1,268 @@
+
+__all__ =[
+    'MRPmiGraphPipe',
+    'R8PmiGraphPipe',
+    'R52PmiGraphPipe',
+    'OhsumedPmiGraphPipe',
+    'NG20PmiGraphPipe'
+]
+try:
+    import networkx as nx
+    from sklearn.feature_extraction.text import CountVectorizer
+    from sklearn.feature_extraction.text import TfidfTransformer
+    from sklearn.pipeline import Pipeline
+except:
+    pass
+from collections import defaultdict
+import itertools
+import math
+from tqdm import tqdm
+import numpy as np
+
+from ..data_bundle import DataBundle
+from ...core.const import Const
+from ..loader.classification import MRLoader, OhsumedLoader, R52Loader, R8Loader, NG20Loader
+
+
+def _get_windows(content_lst: list, window_size:int):
+    r"""
+        滑动窗口处理文本，获取词频和共现词语的词频
+        :param content_lst:
+        :param window_size:
+        :return: 词频，共现词频，窗口化后文本段的数量
+    """
+    word_window_freq = defaultdict(int)  # w(i)  单词在窗口单位内出现的次数
+    word_pair_count = defaultdict(int)  # w(i, j)
+    windows_len = 0
+    for words in tqdm(content_lst, desc="Split by window"):
+        windows = list()
+
+        if isinstance(words, str):
+            words = words.split()
+        length = len(words)
+
+        if length <= window_size:
+            windows.append(words)
+        else:
+            for j in range(length - window_size + 1):
+                window = words[j: j + window_size]
+                windows.append(list(set(window)))
+
+        for window in windows:
+            for word in window:
+                word_window_freq[word] += 1
+
+            for word_pair in itertools.combinations(window, 2):
+                word_pair_count[word_pair] += 1
+
+        windows_len += len(windows)
+    return word_window_freq, word_pair_count, windows_len
+
+def _cal_pmi(W_ij, W, word_freq_i, word_freq_j):
+    r"""
+        params: w_ij:为词语i,j的共现词频
+                w:文本数量
+                word_freq_i: 词语i的词频
+                word_freq_j: 词语j的词频
+        return: 词语i，j的tfidf值
+    """
+    p_i = word_freq_i / W
+    p_j = word_freq_j / W
+    p_i_j = W_ij / W
+    pmi = math.log(p_i_j / (p_i * p_j))
+
+    return pmi
+
+def _count_pmi(windows_len, word_pair_count, word_window_freq, threshold):
+    r"""
+        params: windows_len: 文本段数量
+                word_pair_count: 词共现频率字典
+                word_window_freq: 词频率字典
+                threshold: 阈值
+        return 词语pmi的list列表,其中元素为[word1, word2, pmi]
+    """
+    word_pmi_lst = list()
+    for word_pair, W_i_j in tqdm(word_pair_count.items(), desc="Calculate pmi between words"):
+        word_freq_1 = word_window_freq[word_pair[0]]
+        word_freq_2 = word_window_freq[word_pair[1]]
+
+        pmi = _cal_pmi(W_i_j, windows_len, word_freq_1, word_freq_2)
+        if pmi <= threshold:
+            continue
+        word_pmi_lst.append([word_pair[0], word_pair[1], pmi])
+    return word_pmi_lst
+
+class GraphBuilderBase:
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        self.graph = nx.Graph()
+        self.word2id = dict()
+        self.graph_type = graph_type
+        self.window_size = widow_size
+        self.doc_node_num = 0
+        self.tr_doc_index = None
+        self.te_doc_index = None
+        self.dev_doc_index = None
+        self.doc = None
+        self.threshold = threshold
+
+    def _get_doc_edge(self, data_bundle: DataBundle):
+        r'''
+            对输入的DataBundle进行处理，然后生成文档-单词的tfidf值
+            ：param: data_bundle中的文本若为英文，形式为[ 'This is the first document.'],若为中文则为['他 喜欢 吃 苹果']
+            : return 返回带有具有tfidf边文档-单词稀疏矩阵
+        '''
+        tr_doc = list(data_bundle.get_dataset("train").get_field(Const.RAW_WORD))
+        val_doc = list(data_bundle.get_dataset("dev").get_field(Const.RAW_WORD))
+        te_doc = list(data_bundle.get_dataset("test").get_field(Const.RAW_WORD))
+        doc = tr_doc + val_doc + te_doc
+        self.doc = doc
+        self.tr_doc_index = [ind for ind in range(len(tr_doc))]
+        self.dev_doc_index = [ind+len(tr_doc) for ind in range(len(val_doc))]
+        self.te_doc_index = [ind+len(tr_doc)+len(val_doc) for ind in range(len(te_doc))]
+        text_tfidf = Pipeline([('count', CountVectorizer(token_pattern=r'\S+', min_df=1, max_df=1.0)),
+                               ('tfidf', TfidfTransformer(norm=None, use_idf=True, smooth_idf=False, sublinear_tf=False))])
+
+        tfidf_vec = text_tfidf.fit_transform(doc)
+        self.doc_node_num = tfidf_vec.shape[0]
+        vocab_lst = text_tfidf['count'].get_feature_names()
+        for ind, word in enumerate(vocab_lst):
+            self.word2id[word] = ind
+        for ind, row in enumerate(tfidf_vec):
+            for col_index, value in zip(row.indices, row.data):
+                self.graph.add_edge(ind, self.doc_node_num+col_index, weight=value)
+        return nx.to_scipy_sparse_matrix(self.graph)
+
+    def _get_word_edge(self):
+        word_window_freq, word_pair_count, windows_len = _get_windows(self.doc, self.window_size)
+        pmi_edge_lst = _count_pmi(windows_len, word_pair_count, word_window_freq, self.threshold)
+        for edge_item in pmi_edge_lst:
+            word_indx1 = self.doc_node_num + self.word2id[edge_item[0]]
+            word_indx2 = self.doc_node_num + self.word2id[edge_item[1]]
+            if word_indx1 == word_indx2:
+                continue
+            self.graph.add_edge(word_indx1, word_indx2, weight=edge_item[2])
+
+    def build_graph(self, data_bundle: DataBundle):
+        r"""
+            对输入的DataBundle进行处理，然后返回该scipy_sparse_matrix类型的邻接矩阵。
+
+            :param ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象
+            :return:
+        """
+        raise NotImplementedError
+
+    def build_graph_from_file(self, path: str):
+        r"""
+            传入文件路径，生成处理好的scipy_sparse_matrix对象。paths支持的路径形式可以参考 ：:meth:`fastNLP.io.Loader.load()`
+
+            :param paths:
+            :return: scipy_sparse_matrix
+        """
+        raise NotImplementedError
+
+
+class MRPmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = MRLoader().load(path)
+        return self.build_graph(data_bundle)
+
+class R8PmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = R8Loader().load(path)
+        return self.build_graph(data_bundle)
+
+class R52PmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = R52Loader().load(path)
+        return self.build_graph(data_bundle)
+
+class OhsumedPmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        data_bundle = OhsumedLoader().load(path)
+        return self.build_graph(data_bundle)
+
+
+class NG20PmiGraphPipe(GraphBuilderBase):
+
+    def __init__(self, graph_type='pmi', widow_size=10, threshold=0.):
+        super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold)
+
+    def build_graph(self, data_bundle: DataBundle):
+        r'''
+            params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象.
+            return 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        self._get_doc_edge(data_bundle)
+        self._get_word_edge()
+        return nx.to_scipy_sparse_matrix(self.graph,
+                                         nodelist=list(range(self.graph.number_of_nodes())),
+                                         weight='weight', dtype=np.float32, format='csr'), (
+               self.tr_doc_index, self.dev_doc_index, self.te_doc_index)
+
+    def build_graph_from_file(self, path: str):
+        r'''
+            param: path->数据集的路径.
+            return: 返回csr类型的稀疏矩阵图;训练集，验证集，测试集，在图中的index.
+        '''
+        data_bundle = NG20Loader().load(path)
+        return self.build_graph(data_bundle)

fastNLP/models/biaffine_parser.py

@@ -376,7 +376,7 @@ class BiaffineParser(GraphParser):
         if self.encoder_name.endswith('lstm'):
             sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True)
             x = x[sort_idx]
-            x = nn.utils.rnn.pack_padded_sequence(x, sort_lens, batch_first=True)
+            x = nn.utils.rnn.pack_padded_sequence(x, sort_lens.cpu(), batch_first=True)
             feat, _ = self.encoder(x)  # -> [N,L,C]
             feat, _ = nn.utils.rnn.pad_packed_sequence(feat, batch_first=True)
             _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False)

fastNLP/modules/encoder/_elmo.py

@@ -251,7 +251,7 @@ class LstmbiLm(nn.Module):
     def forward(self, inputs, seq_len):
         sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True)
         inputs = inputs[sort_idx]
-        inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=self.batch_first)
+        inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens.cpu(), batch_first=self.batch_first)
         output, hx = self.encoder(inputs, None)  # -> [N,L,C]
         output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=self.batch_first)
         _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False)
@@ -316,7 +316,7 @@ class ElmobiLm(torch.nn.Module):
         max_len = inputs.size(1)
         sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True)
         inputs = inputs[sort_idx]
-        inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=True)
+        inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens.cpu(), batch_first=True)
         output, _ = self._lstm_forward(inputs, None)
         _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False)
         output = output[:, unsort_idx]

readthedocs.yml

@@ -7,10 +7,11 @@ build:
   image: latest
 
 python:
-  version: 3.6
+  version: 3.8
   install:
+    - requirements: docs/requirements.txt
     - method: setuptools
       path: .
 
 formats:
-  - htmlzip
+  - htmlzip

setup.py

@@ -23,7 +23,7 @@ setup(
     long_description_content_type='text/markdown',
     license='Apache License',
     author='Fudan FastNLP Team',
-    python_requires='>=3.6',
+    python_requires='>=3.7',
     packages=pkgs,
     install_requires=reqs.strip().split('\n'),
 )