[Deprecation] Dataset Attributes (#4666)

* Update from master (#4584)

* [Example][Refactor] Refactor graphsage multigpu and full-graph example (#4430)

* Add refactors for multi-gpu and full-graph example

* Fix format

* Update

* Update

* Update

* [Cleanup] Remove async_transferer (#4505)

* Remove async_transferer

* remove test

* Remove AsyncTransferer
Co-authored-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Xin Yao <yaox12@outlook.com>

* [Cleanup] Remove duplicate entries of CUB submodule   (issue# 4395) (#4499)

* remove third_part/cub

* remove from third_party
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>

* [Bug] Enable turn on/off libxsmm at runtime (#4455)

* enable turn on/off libxsmm at runtime by adding a global config and related API
Co-authored-by: Ubuntu <ubuntu@ip-172-31-19-194.ap-northeast-1.compute.internal>

* [Feature] Unify the cuda stream used in core library (#4480)

* Use an internal cuda stream for CopyDataFromTo

* small fix white space

* Fix to compile

* Make stream optional in copydata for compile

* fix lint issue

* Update cub functions to use internal stream

* Lint check

* Update CopyTo/CopyFrom/CopyFromTo to use internal stream

* Address comments

* Fix backward CUDA stream

* Avoid overloading CopyFromTo()

* Minor comment update

* Overload copydatafromto in cuda device api
Co-authored-by: xiny <xiny@nvidia.com>

* [Feature] Added exclude_self and output_batch to knn graph construction (Issues #4323 #4316) (#4389)

* * Added "exclude_self" and "output_batch" options to knn_graph and segmented_knn_graph
* Updated out-of-date comments on remove_edges and remove_self_loop, since they now preserve batch information

* * Changed defaults on new knn_graph and segmented_knn_graph function parameters, for compatibility; pytorch/test_geometry.py was failing

* * Added test to ensure dgl.remove_self_loop function correctly updates batch information

* * Added new knn_graph and segmented_knn_graph parameters to dgl.nn.KNNGraph and dgl.nn.SegmentedKNNGraph

* * Formatting

* * Oops, I missed the one in segmented_knn_graph when I fixed the similar thing in knn_graph

* * Fixed edge case handling when invalid k specified, since it still needs to be handled consistently for tests to pass
* Fixed context of batch info, since it must match the context of the input position data for remove_self_loop to succeed

* * Fixed batch info resulting from knn_graph when output_batch is true, for case of 3D input tensor, representing multiple segments

* * Added testing of new exclude_self and output_batch parameters on knn_graph and segmented_knn_graph, and their wrappers, KNNGraph and SegmentedKNNGraph, into the test_knn_cuda test

* * Added doc comments for new parameters

* * Added correct handling for uncommon case of k or more coincident points when excluding self edges in knn_graph and segmented_knn_graph
* Added test cases for more than k coincident points

* * Updated doc comments for output_batch parameters for clarity

* * Linter formatting fixes

* * Extracted out common function for test_knn_cpu and test_knn_cuda, to add the new test cases to test_knn_cpu

* * Rewording in doc comments

* * Removed output_batch parameter from knn_graph and segmented_knn_graph, in favour of always setting the batch information, except in knn_graph if x is a 2D tensor
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* [CI] only known devs are authorized to trigger CI (#4518)

* [CI] only known devs are authorized to trigger CI

* fix if author is null

* add comments

* [Readability] Auto fix setup.py and update-version.py (#4446)

* Auto fix update-version

* Auto fix setup.py

* Auto fix update-version

* Auto fix setup.py

* [Doc] Change random.py to random_partition.py in guide on distributed partition pipeline (#4438)

* Update distributed-preprocessing.rst

* Update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-9-26.ap-northeast-1.compute.internal>

* fix unpinning when tensoradaptor is not available (#4450)

* [Doc] fix print issue in tutorial (#4459)

* [Example][Refactor] Refactor RGCN example (#4327)

* Refactor full graph entity classification

* Refactor rgcn with sampling

* README update

* Update

* Results update

* Respect default setting of self_loop=false in entity.py

* Update

* Update README

* Update for multi-gpu

* Update

* [doc] fix invalid link in user guide (#4468)

* [Example] directional_GSN for ogbg-molpcba (#4405)

* version-1

* version-2

* version-3

* update examples/README

* Update .gitignore

* update performance in README, delete scripts

* 1st approving review

* 2nd approving review
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* Clarify the message name, which is 'm'. (#4462)
Co-authored-by: Ubuntu <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
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* [Refactor] Auto fix view.py. (#4461)
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* [Example] SEAL for OGBL (#4291)

* [Example] SEAL for OGBL

* update index

* update

* fix readme typo

* add seal sampler

* modify set ops

* prefetch

* efficiency test

* update

* optimize

* fix ScatterAdd dtype issue

* update sampler style

* update
Co-authored-by: Quan Gan <coin2028@hotmail.com>

* [CI] use https instead of http (#4488)

* [BugFix] fix crash due to incorrect dtype in dgl.to_block() (#4487)

* [BugFix] fix crash due to incorrect dtype in dgl.to_block()

* fix test failure in TF

* [Feature] Make TensorAdapter Stream Aware (#4472)

* Allocate tensors in DGL's current stream

* make tensoradaptor stream-aware

* replace TAemtpy with cpu allocator

* fix typo

* try fix cpu allocation

* clean header

* redirect AllocDataSpace as well

* resolve comments

* [Build][Doc] Specify the sphinx version (#4465)
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* reformat

* reformat

* Auto fix update-version

* Auto fix setup.py

* reformat

* reformat
Co-authored-by: Ubuntu <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
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Co-authored-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Chang Liu <chang.liu@utexas.edu>
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* Move mock version of dgl_sparse library to DGL main repo (#4524)

* init

* Add api doc for sparse library

* support op btwn matrices with differnt sparsity

* Fixed docstring

* addresses comments

* lint check

* change keyword format to fmt
Co-authored-by: Israt Nisa <nisisrat@amazon.com>

* [DistPart] expose timeout config for process group (#4532)

* [DistPart] expose timeout config for process group

* refine code

* Update tools/distpartitioning/data_proc_pipeline.py
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* [Feature] Import PyTorch's CUDA stream management (#4503)

* add set_stream

* add .record_stream for NDArray and HeteroGraph

* refactor dgl stream Python APIs

* test record_stream

* add unit test for record stream

* use pytorch's stream

* fix lint

* fix cpu build

* address comments

* address comments

* add record stream tests for dgl.graph

* record frames and update dataloder

* add docstring

* update frame

* add backend check for record_stream

* remove CUDAThreadEntry::stream

* record stream for newly created formats

* fix bug

* fix cpp test

* fix None c_void_p to c_handle

* [examples]educe memory consumption (#4558)

* [examples]educe memory consumption

* reffine help message

* refine

* [Feature][REVIEW] Enable DGL cugaph nightly CI  (#4525)

* Added cugraph nightly scripts

* Removed nvcr.io//nvidia/pytorch:22.04-py3 reference
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

* Revert "[Feature][REVIEW] Enable DGL cugaph nightly CI  (#4525)" (#4563)

This reverts commit ec171c64

.

* [Misc] Add flake8 lint workflow. (#4566)

* Add pyproject.toml for autopep8.

* Add pyproject.toml for autopep8.

* Add flake8 annotation in workflow.

* remove

* add

* clean up
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* [Misc] Try use official pylint workflow. (#4568)

* polish update_version

* update pylint workflow.

* add

* revert.
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* [CI] refine stage logic (#4565)

* [CI] refine stage logic

* refine

* refine

* remove (#4570)
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* Add Pylint workflow for flake8. (#4571)

* remove

* Add pylint.
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* [Misc] Update the python version in Pylint workflow for flake8. (#4572)

* remove

* Add pylint.

* Change the python version for pylint.
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* Update pylint. (#4574)
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* [Misc] Use another workflow. (#4575)

* Update pylint.

* Use another workflow.
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* Update pylint. (#4576)
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* Update pylint.yml

* Update pylint.yml

* Delete pylint.yml

* [Misc]Add pyproject.toml for autopep8 & black. (#4543)

* Add pyproject.toml for autopep8.

* Add pyproject.toml for autopep8.
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* [Feature] Bump DLPack to v0.7 and decouple DLPack from the core library (#4454)

* rename `DLContext` to `DGLContext`

* rename `kDLGPU` to `kDLCUDA`

* replace DLTensor with DGLArray

* fix linting

* Unify DGLType and DLDataType to DGLDataType

* Fix FFI

* rename DLDeviceType to DGLDeviceType

* decouple dlpack from the core library

* fix bug

* fix lint

* fix merge

* fix build

* address comments

* rename dl_converter to dlpack_convert

* remove redundant comments
Co-authored-by: Chang Liu <chang.liu@utexas.edu>
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Xin Yao <yaox12@outlook.com>
Co-authored-by: Israt Nisa <neesha295@gmail.com>
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
Co-authored-by: peizhou001 <110809584+peizhou001@users.noreply.github.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-19-194.ap-northeast-1.compute.internal>
Co-authored-by: ndickson-nvidia <99772994+ndickson-nvidia@users.noreply.github.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>
Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-9-26.ap-northeast-1.compute.internal>
Co-authored-by: Zhiteng Li <55398076+ZHITENGLI@users.noreply.github.com>
Co-authored-by: rudongyu <ru_dongyu@outlook.com>
Co-authored-by: Quan Gan <coin2028@hotmail.com>
Co-authored-by: Vibhu Jawa <vibhujawa@gmail.com>

* [Deprecation] Dataset Attributes (#4546)

* Update

* CI

* CI

* Update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-9-26.ap-northeast-1.compute.internal>

* [Example] Bug Fix (#4665)

* Update

* CI

* CI

* Update

* Update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-9-26.ap-northeast-1.compute.internal>

* Update
Co-authored-by: Chang Liu <chang.liu@utexas.edu>
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Xin Yao <yaox12@outlook.com>
Co-authored-by: Israt Nisa <neesha295@gmail.com>
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
Co-authored-by: peizhou001 <110809584+peizhou001@users.noreply.github.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-19-194.ap-northeast-1.compute.internal>
Co-authored-by: ndickson-nvidia <99772994+ndickson-nvidia@users.noreply.github.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>
Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-9-26.ap-northeast-1.compute.internal>
Co-authored-by: Zhiteng Li <55398076+ZHITENGLI@users.noreply.github.com>
Co-authored-by: rudongyu <ru_dongyu@outlook.com>
Co-authored-by: Quan Gan <coin2028@hotmail.com>
Co-authored-by: Vibhu Jawa <vibhujawa@gmail.com>

examples/pytorch/dgi/train.py

@@ -23,18 +23,18 @@ def main(args):
     # load and preprocess dataset
     data = load_data(args)
     g = data[0]
-    features = torch.FloatTensor(data.features)
-    labels = torch.LongTensor(data.labels)
+    features = torch.FloatTensor(g.ndata['feat'])
+    labels = torch.LongTensor(g.ndata['label'])
     if hasattr(torch, 'BoolTensor'):
-        train_mask = torch.BoolTensor(data.train_mask)
-        val_mask = torch.BoolTensor(data.val_mask)
-        test_mask = torch.BoolTensor(data.test_mask)
+        train_mask = torch.BoolTensor(g.ndata['train_mask'])
+        val_mask = torch.BoolTensor(g.ndata['val_mask'])
+        test_mask = torch.BoolTensor(g.ndata['test_mask'])
     else:
-        train_mask = torch.ByteTensor(data.train_mask)
-        val_mask = torch.ByteTensor(data.val_mask)
-        test_mask = torch.ByteTensor(data.test_mask)
+        train_mask = torch.ByteTensor(g.ndata['train_mask'])
+        val_mask = torch.ByteTensor(g.ndata['val_mask'])
+        test_mask = torch.ByteTensor(g.ndata['test_mask'])
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = g.number_of_edges()
 
     if args.gpu < 0:
@@ -130,7 +130,7 @@ def main(args):
         loss = F.nll_loss(preds[train_mask], labels[train_mask])
         loss.backward()
         classifier_optimizer.step()
-        
+
         if epoch >= 3:
             dur.append(time.time() - t0)
 
@@ -171,5 +171,5 @@ if __name__ == '__main__':
     parser.set_defaults(self_loop=False)
     args = parser.parse_args()
     print(args)
-    
+
     main(args)

python/dgl/data/citation_graph.py

@@ -251,31 +251,6 @@ class CitationGraphDataset(DGLBuiltinDataset):
         We preserve these properties for compatability.
     """
 
-    @property
-    def train_mask(self):
-        deprecate_property('dataset.train_mask', 'g.ndata[\'train_mask\']')
-        return F.asnumpy(self._g.ndata['train_mask'])
-
-    @property
-    def val_mask(self):
-        deprecate_property('dataset.val_mask', 'g.ndata[\'val_mask\']')
-        return F.asnumpy(self._g.ndata['val_mask'])
-
-    @property
-    def test_mask(self):
-        deprecate_property('dataset.test_mask', 'g.ndata[\'test_mask\']')
-        return F.asnumpy(self._g.ndata['test_mask'])
-
-    @property
-    def labels(self):
-        deprecate_property('dataset.label', 'g.ndata[\'label\']')
-        return F.asnumpy(self._g.ndata['label'])
-
-    @property
-    def features(self):
-        deprecate_property('dataset.feat', 'g.ndata[\'feat\']')
-        return self._g.ndata['feat']
-
     @property
     def reverse_edge(self):
         return self._reverse_edge
@@ -306,43 +281,6 @@ def _sample_mask(idx, l):
 class CoraGraphDataset(CitationGraphDataset):
     r""" Cora citation network dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``graph`` is deprecated, it is replaced by:
-
-            >>> dataset = CoraGraphDataset()
-            >>> graph = dataset[0]
-
-        - ``train_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = CoraGraphDataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.ndata['train_mask']
-
-        - ``val_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = CoraGraphDataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.ndata['val_mask']
-
-        - ``test_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = CoraGraphDataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.ndata['test_mask']
-
-        - ``labels`` is deprecated, it is replaced by:
-
-            >>> dataset = CoraGraphDataset()
-            >>> graph = dataset[0]
-            >>> labels = graph.ndata['label']
-
-        - ``feat`` is deprecated, it is replaced by:
-
-            >>> dataset = CoraGraphDataset()
-            >>> graph = dataset[0]
-            >>> feat = graph.ndata['feat']
-
     Nodes mean paper and edges mean citation
     relationships. Each node has a predefined
     feature with 1433 dimensions. The dataset is
@@ -383,18 +321,6 @@ class CoraGraphDataset(CitationGraphDataset):
     ----------
     num_classes: int
         Number of label classes
-    graph: networkx.DiGraph
-        Graph structure
-    train_mask: numpy.ndarray
-        Mask of training nodes
-    val_mask: numpy.ndarray
-        Mask of validation nodes
-    test_mask: numpy.ndarray
-        Mask of test nodes
-    labels: numpy.ndarray
-        Ground truth labels of each node
-    features: Tensor
-        Node features
 
     Notes
     -----
@@ -454,43 +380,6 @@ class CoraGraphDataset(CitationGraphDataset):
 class CiteseerGraphDataset(CitationGraphDataset):
     r""" Citeseer citation network dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``graph`` is deprecated, it is replaced by:
-
-            >>> dataset = CiteseerGraphDataset()
-            >>> graph = dataset[0]
-
-        - ``train_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = CiteseerGraphDataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.ndata['train_mask']
-
-        - ``val_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = CiteseerGraphDataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.ndata['val_mask']
-
-        - ``test_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = CiteseerGraphDataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.ndata['test_mask']
-
-        - ``labels`` is deprecated, it is replaced by:
-
-            >>> dataset = CiteseerGraphDataset()
-            >>> graph = dataset[0]
-            >>> labels = graph.ndata['label']
-
-        - ``feat`` is deprecated, it is replaced by:
-
-            >>> dataset = CiteseerGraphDataset()
-            >>> graph = dataset[0]
-            >>> feat = graph.ndata['feat']
-
     Nodes mean scientific publications and edges
     mean citation relationships. Each node has a
     predefined feature with 3703 dimensions. The
@@ -531,18 +420,6 @@ class CiteseerGraphDataset(CitationGraphDataset):
     ----------
     num_classes: int
         Number of label classes
-    graph: networkx.DiGraph
-        Graph structure
-    train_mask: numpy.ndarray
-        Mask of training nodes
-    val_mask: numpy.ndarray
-        Mask of validation nodes
-    test_mask: numpy.ndarray
-        Mask of test nodes
-    labels: numpy.ndarray
-        Ground truth labels of each node
-    features: Tensor
-        Node features
 
     Notes
     -----
@@ -605,43 +482,6 @@ class CiteseerGraphDataset(CitationGraphDataset):
 class PubmedGraphDataset(CitationGraphDataset):
     r""" Pubmed citation network dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``graph`` is deprecated, it is replaced by:
-
-            >>> dataset = PubmedGraphDataset()
-            >>> graph = dataset[0]
-
-        - ``train_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = PubmedGraphDataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.ndata['train_mask']
-
-        - ``val_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = PubmedGraphDataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.ndata['val_mask']
-
-        - ``test_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = PubmedGraphDataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.ndata['test_mask']
-
-        - ``labels`` is deprecated, it is replaced by:
-
-            >>> dataset = PubmedGraphDataset()
-            >>> graph = dataset[0]
-            >>> labels = graph.ndata['label']
-
-        - ``feat`` is deprecated, it is replaced by:
-
-            >>> dataset = PubmedGraphDataset()
-            >>> graph = dataset[0]
-            >>> feat = graph.ndata['feat']
-
     Nodes mean scientific publications and edges
     mean citation relationships. Each node has a
     predefined feature with 500 dimensions. The
@@ -682,18 +522,6 @@ class PubmedGraphDataset(CitationGraphDataset):
     ----------
     num_classes: int
         Number of label classes
-    graph: networkx.DiGraph
-        Graph structure
-    train_mask: numpy.ndarray
-        Mask of training nodes
-    val_mask: numpy.ndarray
-        Mask of validation nodes
-    test_mask: numpy.ndarray
-        Mask of test nodes
-    labels: numpy.ndarray
-        Ground truth labels of each node
-    features: Tensor
-        Node features
 
     Notes
     -----

python/dgl/data/gnn_benchmark.py

@@ -106,11 +106,6 @@ class GNNBenchmarkDataset(DGLBuiltinDataset):
         """Number of classes."""
         raise NotImplementedError
 
-    @property
-    def data(self):
-        deprecate_property('dataset.data', 'dataset[0]')
-        return self._data
-
     def __getitem__(self, idx):
         r""" Get graph by index
 
@@ -142,13 +137,6 @@ class GNNBenchmarkDataset(DGLBuiltinDataset):
 class CoraFullDataset(GNNBenchmarkDataset):
     r"""CORA-Full dataset for node classification task.
 
-    .. deprecated:: 0.5.0
-
-        - ``data`` is deprecated, it is repalced by:
-
-        >>> dataset = CoraFullDataset()
-        >>> graph = dataset[0]
-
     Extended Cora dataset. Nodes represent paper and edges represent citations.
 
     Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
@@ -179,8 +167,6 @@ class CoraFullDataset(GNNBenchmarkDataset):
     ----------
     num_classes : int
         Number of classes for each node.
-    data : list
-        A list of DGLGraph objects
 
     Examples
     --------
@@ -211,13 +197,6 @@ class CoraFullDataset(GNNBenchmarkDataset):
 class CoauthorCSDataset(GNNBenchmarkDataset):
     r""" 'Computer Science (CS)' part of the Coauthor dataset for node classification task.
 
-    .. deprecated:: 0.5.0
-
-        - ``data`` is deprecated, it is repalced by:
-
-        >>> dataset = CoauthorCSDataset()
-        >>> graph = dataset[0]
-
     Coauthor CS and Coauthor Physics are co-authorship graphs based on the Microsoft Academic Graph
     from the KDD Cup 2016 challenge. Here, nodes are authors, that are connected by an edge if they
     co-authored a paper; node features represent paper keywords for each author’s papers, and class
@@ -251,8 +230,6 @@ class CoauthorCSDataset(GNNBenchmarkDataset):
     ----------
     num_classes : int
         Number of classes for each node.
-    data : list
-        A list of DGLGraph objects
 
     Examples
     --------
@@ -283,13 +260,6 @@ class CoauthorCSDataset(GNNBenchmarkDataset):
 class CoauthorPhysicsDataset(GNNBenchmarkDataset):
     r""" 'Physics' part of the Coauthor dataset for node classification task.
 
-    .. deprecated:: 0.5.0
-
-        - ``data`` is deprecated, it is repalced by:
-
-        >>> dataset = CoauthorPhysicsDataset()
-        >>> graph = dataset[0]
-
     Coauthor CS and Coauthor Physics are co-authorship graphs based on the Microsoft Academic Graph
     from the KDD Cup 2016 challenge. Here, nodes are authors, that are connected by an edge if they
     co-authored a paper; node features represent paper keywords for each author’s papers, and class
@@ -323,8 +293,6 @@ class CoauthorPhysicsDataset(GNNBenchmarkDataset):
     ----------
     num_classes : int
         Number of classes for each node.
-    data : list
-        A list of DGLGraph objects
 
     Examples
     --------
@@ -355,13 +323,6 @@ class CoauthorPhysicsDataset(GNNBenchmarkDataset):
 class AmazonCoBuyComputerDataset(GNNBenchmarkDataset):
     r""" 'Computer' part of the AmazonCoBuy dataset for node classification task.
 
-    .. deprecated:: 0.5.0
-
-        - ``data`` is deprecated, it is repalced by:
-
-        >>> dataset = AmazonCoBuyComputerDataset()
-        >>> graph = dataset[0]
-
     Amazon Computers and Amazon Photo are segments of the Amazon co-purchase graph [McAuley et al., 2015],
     where nodes represent goods, edges indicate that two goods are frequently bought together, node
     features are bag-of-words encoded product reviews, and class labels are given by the product category.
@@ -394,8 +355,6 @@ class AmazonCoBuyComputerDataset(GNNBenchmarkDataset):
     ----------
     num_classes : int
         Number of classes for each node.
-    data : list
-        A list of DGLGraph objects
 
     Examples
     --------
@@ -426,13 +385,6 @@ class AmazonCoBuyComputerDataset(GNNBenchmarkDataset):
 class AmazonCoBuyPhotoDataset(GNNBenchmarkDataset):
     r"""AmazonCoBuy dataset for node classification task.
 
-    .. deprecated:: 0.5.0
-
-        - ``data`` is deprecated, it is repalced by:
-
-        >>> dataset = AmazonCoBuyPhotoDataset()
-        >>> graph = dataset[0]
-
     Amazon Computers and Amazon Photo are segments of the Amazon co-purchase graph [McAuley et al., 2015],
     where nodes represent goods, edges indicate that two goods are frequently bought together, node
     features are bag-of-words encoded product reviews, and class labels are given by the product category.
@@ -465,8 +417,6 @@ class AmazonCoBuyPhotoDataset(GNNBenchmarkDataset):
     ----------
     num_classes : int
         Number of classes for each node.
-    data : list
-        A list of DGLGraph objects
 
     Examples
     --------

python/dgl/data/karate.py

@@ -14,13 +14,6 @@ __all__ = ['KarateClubDataset', 'KarateClub']
 class KarateClubDataset(DGLDataset):
     r""" Karate Club dataset for Node Classification
 
-    .. deprecated:: 0.5.0
-
-        - ``data`` is deprecated, it is replaced by:
-
-            >>> dataset = KarateClubDataset()
-            >>> g = dataset[0]
-
     Zachary's karate club is a social network of a university
     karate club, described in the paper "An Information Flow
     Model for Conflict and Fission in Small Groups" by Wayne W. Zachary.
@@ -45,8 +38,6 @@ class KarateClubDataset(DGLDataset):
     ----------
     num_classes : int
         Number of node classes
-    data : list
-        A list of :class:`dgl.DGLGraph` objects
 
     Examples
     --------
@@ -73,11 +64,6 @@ class KarateClubDataset(DGLDataset):
         """Number of classes."""
         return 2
 
-    @property
-    def data(self):
-        deprecate_property('dataset.data', 'dataset[0]')
-        return self._data
-
     def __getitem__(self, idx):
         r""" Get graph object
 

python/dgl/data/knowledge_graph.py

@@ -191,21 +191,6 @@ class KnowledgeGraphDataset(DGLBuiltinDataset):
     def save_name(self):
         return self.name + '_dgl_graph'
 
-    @property
-    def train(self):
-        deprecate_property('dataset.train', 'g.edata[\'train_mask\']')
-        return self._train
-
-    @property
-    def valid(self):
-        deprecate_property('dataset.valid', 'g.edata[\'val_mask\']')
-        return self._valid
-
-    @property
-    def test(self):
-        deprecate_property('dataset.test', 'g.edata[\'test_mask\']')
-        return self._test
-
 def _read_dictionary(filename):
     d = {}
     with open(filename, 'r+') as f:
@@ -344,35 +329,6 @@ def build_knowledge_graph(num_nodes, num_rels, train, valid, test, reverse=True)
 class FB15k237Dataset(KnowledgeGraphDataset):
     r"""FB15k237 link prediction dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``train`` is deprecated, it is replaced by:
-
-            >>> dataset = FB15k237Dataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.edata['train_mask']
-            >>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.find_edges(train_idx)
-            >>> rel = graph.edata['etype'][train_idx]
-
-        - ``valid`` is deprecated, it is replaced by:
-
-            >>> dataset = FB15k237Dataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.edata['val_mask']
-            >>> val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.find_edges(val_idx)
-            >>> rel = graph.edata['etype'][val_idx]
-
-        - ``test`` is deprecated, it is replaced by:
-
-            >>> dataset = FB15k237Dataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.edata['test_mask']
-            >>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.find_edges(test_idx)
-            >>> rel = graph.edata['etype'][test_idx]
-
     FB15k-237 is a subset of FB15k where inverse
     relations are removed. When creating the dataset,
     a reverse edge with reversed relation types are
@@ -411,12 +367,6 @@ class FB15k237Dataset(KnowledgeGraphDataset):
         Number of nodes
     num_rels: int
         Number of relation types
-    train: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the training graph
-    valid: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the validation graph
-    test: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the test graph
 
     Examples
     ----------
@@ -484,35 +434,6 @@ class FB15k237Dataset(KnowledgeGraphDataset):
 class FB15kDataset(KnowledgeGraphDataset):
     r"""FB15k link prediction dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``train`` is deprecated, it is replaced by:
-
-            >>> dataset = FB15kDataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.edata['train_mask']
-            >>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.edges(train_idx)
-            >>> rel = graph.edata['etype'][train_idx]
-
-        - ``valid`` is deprecated, it is replaced by:
-
-            >>> dataset = FB15kDataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.edata['val_mask']
-            >>> val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.edges(val_idx)
-            >>> rel = graph.edata['etype'][val_idx]
-
-        - ``test`` is deprecated, it is replaced by:
-
-            >>> dataset = FB15kDataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.edata['test_mask']
-            >>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.edges(test_idx)
-            >>> rel = graph.edata['etype'][test_idx]
-
     The FB15K dataset was introduced in `Translating Embeddings for Modeling
     Multi-relational Data <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_.
     It is a subset of Freebase which contains about
@@ -554,12 +475,6 @@ class FB15kDataset(KnowledgeGraphDataset):
         Number of nodes
     num_rels: int
         Number of relation types
-    train: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the training graph
-    valid: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the validation graph
-    test: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the test graph
 
     Examples
     ----------
@@ -627,35 +542,6 @@ class FB15kDataset(KnowledgeGraphDataset):
 class WN18Dataset(KnowledgeGraphDataset):
     r""" WN18 link prediction dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``train`` is deprecated, it is replaced by:
-
-            >>> dataset = WN18Dataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.edata['train_mask']
-            >>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.edges(train_idx)
-            >>> rel = graph.edata['etype'][train_idx]
-
-        - ``valid`` is deprecated, it is replaced by:
-
-            >>> dataset = WN18Dataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.edata['val_mask']
-            >>> val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.edges(val_idx)
-            >>> rel = graph.edata['etype'][val_idx]
-
-        - ``test`` is deprecated, it is replaced by:
-
-            >>> dataset = WN18Dataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.edata['test_mask']
-            >>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
-            >>> src, dst = graph.edges(test_idx)
-            >>> rel = graph.edata['etype'][test_idx]
-
     The WN18 dataset was introduced in `Translating Embeddings for Modeling
     Multi-relational Data <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_.
     It included the full 18 relations scraped from
@@ -696,12 +582,6 @@ class WN18Dataset(KnowledgeGraphDataset):
         Number of nodes
     num_rels: int
         Number of relation types
-    train: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the training graph
-    valid: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the validation graph
-    test: numpy.ndarray
-        A numpy array of triplets (src, rel, dst) for the test graph
 
     Examples
     ----------

python/dgl/data/reddit.py

@@ -15,48 +15,6 @@ from ..transforms import reorder_graph
 class RedditDataset(DGLBuiltinDataset):
     r""" Reddit dataset for community detection (node classification)
 
-    .. deprecated:: 0.5.0
-
-        - ``graph`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> graph = dataset[0]
-
-        - ``num_labels`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> num_classes = dataset.num_classes
-
-        - ``train_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> graph = dataset[0]
-            >>> train_mask = graph.ndata['train_mask']
-
-        - ``val_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> graph = dataset[0]
-            >>> val_mask = graph.ndata['val_mask']
-
-        - ``test_mask`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> graph = dataset[0]
-            >>> test_mask = graph.ndata['test_mask']
-
-        - ``features`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> graph = dataset[0]
-            >>> features = graph.ndata['feat']
-
-        - ``labels`` is deprecated, it is replaced by:
-
-            >>> dataset = RedditDataset()
-            >>> graph = dataset[0]
-            >>> labels = graph.ndata['label']
-
     This is a graph dataset from Reddit posts made in the month of September, 2014.
     The node label in this case is the community, or “subreddit”, that a post belongs to.
     The authors sampled 50 large communities and built a post-to-post graph, connecting
@@ -95,20 +53,6 @@ class RedditDataset(DGLBuiltinDataset):
     ----------
     num_classes : int
         Number of classes for each node
-    graph : :class:`dgl.DGLGraph`
-        Graph of the dataset
-    num_labels : int
-        Number of classes for each node
-    train_mask: numpy.ndarray
-        Mask of training nodes
-    val_mask: numpy.ndarray
-        Mask of validation nodes
-    test_mask: numpy.ndarray
-        Mask of test nodes
-    features : Tensor
-        Node features
-    labels :  Tensor
-        Node labels
 
     Examples
     --------
@@ -202,41 +146,6 @@ class RedditDataset(DGLBuiltinDataset):
         r"""Number of classes for each node."""
         return 41
 
-    @property
-    def num_labels(self):
-        deprecate_property('dataset.num_labels', 'dataset.num_classes')
-        return self.num_classes
-
-    @property
-    def graph(self):
-        deprecate_property('dataset.graph', 'dataset[0]')
-        return self._graph
-
-    @property
-    def train_mask(self):
-        deprecate_property('dataset.train_mask', 'graph.ndata[\'train_mask\']')
-        return F.asnumpy(self._graph.ndata['train_mask'])
-
-    @property
-    def val_mask(self):
-        deprecate_property('dataset.val_mask', 'graph.ndata[\'val_mask\']')
-        return F.asnumpy(self._graph.ndata['val_mask'])
-
-    @property
-    def test_mask(self):
-        deprecate_property('dataset.test_mask', 'graph.ndata[\'test_mask\']')
-        return F.asnumpy(self._graph.ndata['test_mask'])
-
-    @property
-    def features(self):
-        deprecate_property('dataset.features', 'graph.ndata[\'feat\']')
-        return self._graph.ndata['feat']
-
-    @property
-    def labels(self):
-        deprecate_property('dataset.labels', 'graph.ndata[\'label\']')
-        return self._graph.ndata['label']
-
     def __getitem__(self, idx):
         r""" Get graph by index
 

python/dgl/data/tree.py

@@ -22,16 +22,6 @@ __all__ = ['SST', 'SSTDataset']
 class SSTDataset(DGLBuiltinDataset):
     r"""Stanford Sentiment Treebank dataset.
 
-    .. deprecated:: 0.5.0
-
-        - ``trees`` is deprecated, it is replaced by:
-
-            >>> dataset = SSTDataset()
-            >>> for tree in dataset:
-            ....    # your code here
-
-        - ``num_vocabs`` is deprecated, it is replaced by ``vocab_size``.
-
     Each sample is the constituency tree of a sentence. The leaf nodes
     represent words. The word is a int value stored in the ``x`` feature field.
     The non-leaf node has a special value ``PAD_WORD`` in the ``x`` field.
@@ -74,16 +64,12 @@ class SSTDataset(DGLBuiltinDataset):
     ----------
     vocab : OrderedDict
         Vocabulary of the dataset
-    trees : list
-        A list of DGLGraph objects
     num_classes : int
         Number of classes for each node
     pretrained_emb: Tensor
         Pretrained glove embedding with respect the vocabulary.
     vocab_size : int
         The size of the vocabulary
-    num_vocabs : int
-        The size of the vocabulary
 
     Notes
     -----
@@ -224,11 +210,6 @@ class SSTDataset(DGLBuiltinDataset):
         if os.path.exists(emb_path):
             self._pretrained_emb = load_info(emb_path)['embed']
 
-    @property
-    def trees(self):
-        deprecate_property('dataset.trees', '[dataset[i] for i in len(dataset)]')
-        return self._trees
-
     @property
     def vocab(self):
         r""" Vocabulary
@@ -270,11 +251,6 @@ class SSTDataset(DGLBuiltinDataset):
         r"""Number of graphs in the dataset."""
         return len(self._trees)
 
-    @property
-    def num_vocabs(self):
-        deprecate_property('dataset.num_vocabs', 'dataset.vocab_size')
-        return self.vocab_size
-
     @property
     def vocab_size(self):
         r"""Vocabulary size."""

src/array/cuda/ge_spmm.cuh

@@ -18,7 +18,7 @@ using namespace cuda;
 namespace aten {
 namespace cuda {
 
-/*! 
+/*!
  * \brief CUDA kernel of GE-SpMM on Csr.
  * \note GE-SpMM: https://arxiv.org/pdf/2007.03179.pdf
  *       The grid dimension x and y are reordered for better performance.

tutorials/models/1_gnn/6_line_graph.py

@@ -97,7 +97,7 @@ from dgl.data import citation_graph as citegrh
 data = citegrh.load_cora()
 
 G = data[0]
-labels = th.tensor(data.labels)
+labels = th.tensor(G.ndata['label'])
 
 # find all the nodes labeled with class 0
 label0_nodes = th.nonzero(labels == 0, as_tuple=False).squeeze()

tutorials/models/1_gnn/9_gat.py

@@ -303,11 +303,9 @@ import networkx as nx
 
 def load_cora_data():
     data = citegrh.load_cora()
-    features = torch.FloatTensor(data.features)
-    labels = torch.LongTensor(data.labels)
-    mask = torch.BoolTensor(data.train_mask)
     g = data[0]
-    return g, features, labels, mask
+    mask = torch.BoolTensor(g.ndata['train_mask'])
+    return g, g.ndata['feat'], g.ndata['label'], mask
 
 ##############################################################################
 # The training loop is exactly the same as in the GCN tutorial.

tutorials/models/2_small_graph/3_tree-lstm.py

@@ -16,28 +16,28 @@ Tree-LSTM in DGL
     examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
 
 """
- 
+
 ##############################################################################
 #
-# In this tutorial, you learn to use Tree-LSTM networks for sentiment analysis. 
+# In this tutorial, you learn to use Tree-LSTM networks for sentiment analysis.
 # The Tree-LSTM is a generalization of long short-term memory (LSTM) networks to tree-structured network topologies.
-# 
-# The Tree-LSTM structure was first introduced by Kai et. al in an ACL 2015 
+#
+# The Tree-LSTM structure was first introduced by Kai et. al in an ACL 2015
 # paper: `Improved Semantic Representations From Tree-Structured Long
 # Short-Term Memory Networks <https://arxiv.org/pdf/1503.00075.pdf>`__.
-# The core idea is to introduce syntactic information for language tasks by 
-# extending the chain-structured LSTM to a tree-structured LSTM. The dependency 
+# The core idea is to introduce syntactic information for language tasks by
+# extending the chain-structured LSTM to a tree-structured LSTM. The dependency
 # tree and constituency tree techniques are leveraged to obtain a ''latent tree''.
 #
-# The challenge in training Tree-LSTMs is batching --- a standard 
-# technique in machine learning to accelerate optimization. However, since trees 
-# generally have different shapes by nature, parallization is non-trivial. 
-# DGL offers an alternative. Pool all the trees into one single graph then 
+# The challenge in training Tree-LSTMs is batching --- a standard
+# technique in machine learning to accelerate optimization. However, since trees
+# generally have different shapes by nature, parallization is non-trivial.
+# DGL offers an alternative. Pool all the trees into one single graph then
 # induce the message passing over them, guided by the structure of each tree.
 #
 # The task and the dataset
 # ------------------------
-# 
+#
 # The steps here use the
 # `Stanford Sentiment Treebank <https://nlp.stanford.edu/sentiment/>`__ in
 # ``dgl.data``. The dataset provides a fine-grained, tree-level sentiment
@@ -48,7 +48,7 @@ Tree-LSTM in DGL
 # their embeddings would be masked to all-zero.
 #
 # .. figure:: https://i.loli.net/2018/11/08/5be3d4bfe031b.png
-#    :alt: 
+#    :alt:
 #
 # The figure displays one sample of the SST dataset, which is a
 # constituency parse tree with their nodes labeled with sentiment. To
@@ -69,8 +69,8 @@ SSTBatch = namedtuple('SSTBatch', ['graph', 'mask', 'wordid', 'label'])
 # The non-leaf nodes have a special word PAD_WORD. The sentiment
 # label is stored in the "y" feature field.
 trainset = SSTDataset(mode='tiny')  # the "tiny" set has only five trees
-tiny_sst = trainset.trees
-num_vocabs = trainset.num_vocabs
+tiny_sst = [tr for tr in trainset]
+num_vocabs = trainset.vocab_size
 num_classes = trainset.num_classes
 
 vocab = trainset.vocab # vocabulary dict: key -> id
@@ -108,25 +108,25 @@ plot_tree(graph.to_networkx())
 # .. note::
 #
 #    **Definition**: :func:`~dgl.batch` unions a list of :math:`B`
-#      :class:`~dgl.DGLGraph`\ s and returns a :class:`~dgl.DGLGraph` of batch 
-#      size :math:`B`. 
-#    
+#      :class:`~dgl.DGLGraph`\ s and returns a :class:`~dgl.DGLGraph` of batch
+#      size :math:`B`.
+#
 #    - The union includes all the nodes,
 #      edges, and their features. The order of nodes, edges, and features are
-#      preserved. 
-#     
+#      preserved.
+#
 #        - Given that you have :math:`V_i` nodes for graph
 #          :math:`\mathcal{G}_i`, the node ID :math:`j` in graph
 #          :math:`\mathcal{G}_i` correspond to node ID
-#          :math:`j + \sum_{k=1}^{i-1} V_k` in the batched graph. 
-#    
+#          :math:`j + \sum_{k=1}^{i-1} V_k` in the batched graph.
+#
 #        - Therefore, performing feature transformation and message passing on
 #          the batched graph is equivalent to doing those
-#          on all ``DGLGraph`` constituents in parallel. 
+#          on all ``DGLGraph`` constituents in parallel.
 #
 #    - Duplicate references to the same graph are
 #      treated as deep copies; the nodes, edges, and features are duplicated,
-#      and mutation on one reference does not affect the other. 
+#      and mutation on one reference does not affect the other.
 #    - The batched graph keeps track of the meta
 #      information of the constituents so it can be
 #      :func:`~dgl.batched_graph.unbatch`\ ed to list of ``DGLGraph``\ s.
@@ -135,9 +135,9 @@ plot_tree(graph.to_networkx())
 # ------------------------------------------------
 #
 # Researchers have proposed two types of Tree-LSTMs: Child-Sum
-# Tree-LSTMs, and :math:`N`-ary Tree-LSTMs. In this tutorial you focus 
-# on applying *Binary* Tree-LSTM to binarized constituency trees. This 
-# application is also known as *Constituency Tree-LSTM*. Use PyTorch 
+# Tree-LSTMs, and :math:`N`-ary Tree-LSTMs. In this tutorial you focus
+# on applying *Binary* Tree-LSTM to binarized constituency trees. This
+# application is also known as *Constituency Tree-LSTM*. Use PyTorch
 # as a backend framework to set up the network.
 #
 # In `N`-ary Tree-LSTM, each unit at node :math:`j` maintains a hidden
@@ -145,7 +145,7 @@ plot_tree(graph.to_networkx())
 # :math:`j` takes the input vector :math:`x_j` and the hidden
 # representations of the child units: :math:`h_{jl}, 1\leq l\leq N` as
 # input, then update its new hidden representation :math:`h_j` and memory
-# cell :math:`c_j` by: 
+# cell :math:`c_j` by:
 #
 # .. math::
 #
@@ -164,7 +164,7 @@ plot_tree(graph.to_networkx())
 #    ``apply_node_func``, a user specifies what to do with node features,
 #    without considering edge features and messages. In a Tree-LSTM case,
 #    ``apply_node_func`` is a must, since there exists (leaf) nodes with
-#    :math:`0` incoming edges, which would not be updated with 
+#    :math:`0` incoming edges, which would not be updated with
 #    ``reduce_func``.
 #
 
@@ -337,7 +337,7 @@ weight_decay = 1e-4
 epochs = 10
 
 # create the model
-model = TreeLSTM(trainset.num_vocabs,
+model = TreeLSTM(trainset.vocab_size,
                  x_size,
                  h_size,
                  trainset.num_classes,
@@ -373,7 +373,7 @@ for epoch in range(epochs):
         c = th.zeros((n, h_size))
         logits = model(batch, h, c)
         logp = F.log_softmax(logits, 1)
-        loss = F.nll_loss(logp, batch.label, reduction='sum') 
+        loss = F.nll_loss(logp, batch.label, reduction='sum')
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()