[DGL-Go][Doc] Update DGL-Go version to 0.0.2 and misc fix from bug bash (#4236)

* Update

* Update

* Update

* Update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-53-142.us-west-2.compute.internal>
Co-authored-by: Xin Yao <xiny@nvidia.com>

dglgo/tests/cfg.yml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cpu

python/dgl/data/citation_graph.py

@@ -439,7 +439,7 @@ class CoraGraphDataset(CitationGraphDataset):
 
             graph structure, node features and labels.
 
-            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['train_mask']``: mask for training node set
             - ``ndata['val_mask']``: mask for validation node set
             - ``ndata['test_mask']``: mask for test node set
             - ``ndata['feat']``: node feature
@@ -590,7 +590,7 @@ class CiteseerGraphDataset(CitationGraphDataset):
 
             graph structure, node features and labels.
 
-            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['train_mask']``: mask for training node set
             - ``ndata['val_mask']``: mask for validation node set
             - ``ndata['test_mask']``: mask for test node set
             - ``ndata['feat']``: node feature
@@ -738,7 +738,7 @@ class PubmedGraphDataset(CitationGraphDataset):
 
             graph structure, node features and labels.
 
-            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['train_mask']``: mask for training node set
             - ``ndata['val_mask']``: mask for validation node set
             - ``ndata['test_mask']``: mask for test node set
             - ``ndata['feat']``: node feature

python/dgl/data/dgl_dataset.py

@@ -8,7 +8,6 @@ import traceback
 import abc
 from .utils import download, extract_archive, get_download_dir, makedirs
 from ..utils import retry_method_with_fix
-from .._ffi.base import __version__
 
 class DGLDataset(object):
     r"""The basic DGL dataset for creating graph datasets.
@@ -238,17 +237,13 @@ class DGLDataset(object):
     def save_dir(self):
         r"""Directory to save the processed dataset.
         """
-        return self._save_dir + "_v{}".format(__version__)
+        return self._save_dir
 
     @property
     def save_path(self):
         r"""Path to save the processed dataset.
         """
-        if hasattr(self, '_reorder'):
-            path = 'reordered' if self._reorder else 'un_reordered'
-            return os.path.join(self._save_dir, self.name, path)
-        else:
-            return os.path.join(self._save_dir, self.name)
+        return os.path.join(self._save_dir, self.name)
 
     @property
     def verbose(self):

python/dgl/data/flickr.py

@@ -50,6 +50,7 @@ class FlickrDataset(DGLBuiltinDataset):
 
     Examples
     --------
+    >>> from dgl.data import FlickrDataset
     >>> dataset = FlickrDataset()
     >>> dataset.num_classes
     7
@@ -151,9 +152,9 @@ class FlickrDataset(DGLBuiltinDataset):
 
             - ``ndata['label']``: node label
             - ``ndata['feat']``: node feature
-            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['train_mask']``: mask for training node set
             - ``ndata['val_mask']``: mask for validation node set
-            - ``ndata['test_mask']:`` mask for test node set
+            - ``ndata['test_mask']``: mask for test node set
 
         """
         assert idx == 0, "This dataset has only one graph"

python/dgl/data/utils.py

@@ -17,7 +17,6 @@ from .graph_serialize import save_graphs, load_graphs, load_labels
 from .tensor_serialize import save_tensors, load_tensors
 
 from .. import backend as F
-from .._ffi.base import __version__
 
 __all__ = ['loadtxt','download', 'check_sha1', 'extract_archive',
         'get_download_dir', 'Subset', 'split_dataset', 'save_graphs',
@@ -241,7 +240,7 @@ def get_download_dir():
     dirname : str
         Path to the download directory
     """
-    default_dir = os.path.join(os.path.expanduser('~'), '.dgl_v{}'.format(__version__))
+    default_dir = os.path.join(os.path.expanduser('~'), '.dgl')
     dirname = os.environ.get('DGL_DOWNLOAD_DIR', default_dir)
     if not os.path.exists(dirname):
         os.makedirs(dirname)

python/dgl/data/wikics.py

@@ -50,6 +50,7 @@ class WikiCSDataset(DGLBuiltinDataset):
 
     Examples
     --------
+    >>> from dgl.data import WikiCSDataset
     >>> dataset = WikiCSDataset()
     >>> dataset.num_classes
     10

python/dgl/data/yelp.py

@@ -151,9 +151,9 @@ class YelpDataset(DGLBuiltinDataset):
 
             - ``ndata['label']``: node label
             - ``ndata['feat']``: node feature
-            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['train_mask']``: mask for training node set
             - ``ndata['val_mask']``: mask for validation node set
-            - ``ndata['test_mask']:`` mask for test node set
+            - ``ndata['test_mask']``: mask for test node set
 
         """
         assert idx == 0, "This dataset has only one graph"

python/dgl/nn/pytorch/conv/egatconv.py

@@ -45,7 +45,7 @@ class EGATConv(nn.Module):
     num_heads : int
         Number of attention heads.
     bias : bool, optional
-        If True, add bias term to :math: `f_{ij}^{\prime}`. Defaults: ``True``.
+        If True, add bias term to :math:`f_{ij}^{\prime}`. Defaults: ``True``.
 
     Examples
     ----------
@@ -170,16 +170,16 @@ class EGATConv(nn.Module):
         Returns
         -------
         pair of torch.Tensor
-            node output features followed by edge output features
-            The node output feature of shape :math:`(N, H, D_{out})`
-            The edge output feature of shape :math:`(F, H, F_{out})`
+            node output features followed by edge output features.
+            The node output feature is of shape :math:`(N, H, D_{out})`
+            The edge output feature is of shape :math:`(F, H, F_{out})`
             where:
                 :math:`H` is the number of heads,
                 :math:`D_{out}` is size of output node feature,
                 :math:`F_{out}` is size of output edge feature.
         torch.Tensor, optional
             The attention values of shape :math:`(E, H, 1)`.
-            This is returned only when :attr: `get_attention` is ``True``.
+            This is returned only when :attr:`get_attention` is ``True``.
         """
 
         with graph.local_scope():

python/dgl/transforms/functional.py

@@ -2872,6 +2872,8 @@ def sort_csr_by_tag(g, tag, tag_offset_name='_TAG_OFFSET', tag_type='node'):
     ``tag_type`` is ``node``.
 
     >>> import dgl
+    >>> import torch
+
     >>> g = dgl.graph(([0,0,0,0,0,1,1,1],[0,1,2,3,4,0,1,2]))
     >>> g.adjacency_matrix(scipy_fmt='csr').nonzero()
     (array([0, 0, 0, 0, 0, 1, 1, 1], dtype=int32),
@@ -2890,11 +2892,10 @@ def sort_csr_by_tag(g, tag, tag_offset_name='_TAG_OFFSET', tag_type='node'):
 
     ``tag_type`` is ``edge``.
 
-    >>> from dgl import backend as F
     >>> g = dgl.graph(([0,0,0,0,0,1,1,1],[0,1,2,3,4,0,1,2]))
     >>> g.edges()
     (tensor([0, 0, 0, 0, 0, 1, 1, 1]), tensor([0, 1, 2, 3, 4, 0, 1, 2]))
-    >>> tag = F.tensor([1, 1, 0, 2, 0, 1, 1, 0])
+    >>> tag = torch.tensor([1, 1, 0, 2, 0, 1, 1, 0])
     >>> g_sorted = dgl.sort_csr_by_tag(g, tag, tag_type='edge')
     >>> g_sorted.adj(scipy_fmt='csr').nonzero()
     (array([0, 0, 0, 0, 0, 1, 1, 1], dtype=int32), array([2, 4, 0, 1, 3, 2, 0, 1], dtype=int32))
@@ -2995,6 +2996,7 @@ def sort_csc_by_tag(g, tag, tag_offset_name='_TAG_OFFSET', tag_type='node'):
     ``tag_type`` is ``node``.
 
     >>> import dgl
+    >>> import torch
     >>> g = dgl.graph(([0,1,2,3,4,0,1,2],[0,0,0,0,0,1,1,1]))
     >>> g.adjacency_matrix(scipy_fmt='csr', transpose=True).nonzero()
     (array([0, 0, 0, 0, 0, 1, 1, 1], dtype=int32),
@@ -3013,9 +3015,8 @@ def sort_csc_by_tag(g, tag, tag_offset_name='_TAG_OFFSET', tag_type='node'):
 
     ``tag_type`` is ``edge``.
 
-    >>> from dgl import backend as F
     >>> g = dgl.graph(([0,1,2,3,4,0,1,2],[0,0,0,0,0,1,1,1]))
-    >>> tag = F.tensor([1, 1, 0, 2, 0, 1, 1, 0])
+    >>> tag = torch.tensor([1, 1, 0, 2, 0, 1, 1, 0])
     >>> g_sorted = dgl.sort_csc_by_tag(g, tag, tag_type='edge')
     >>> g_sorted.adj(scipy_fmt='csr', transpose=True).nonzero()
     (array([0, 0, 0, 0, 0, 1, 1, 1], dtype=int32), array([2, 4, 0, 1, 3, 2, 0, 1], dtype=int32))