[Performance] Add a warning for ChebConv (#3099)

* add a warning for chebconv

* fix and docstrings

* update bgnn

* fix
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

examples/pytorch/bgnn/run.py

@@ -61,7 +61,12 @@ class GNNModelDGL(torch.nn.Module):
             h = self.l1(graph, h)
             h = self.l2(graph, h)
             logits = self.lin2(h)
-        elif self.name in ['gcn', 'cheb']:
+        elif self.name == 'che3b':
+            lambda_max = dgl.laplacian_lambda_max(graph)
+            h = self.drop(h)
+            h = self.l1(graph, h, lambda_max)
+            logits = self.l2(graph, h, lambda_max)
+        elif self.name == 'gcn':
             h = self.drop(h)
             h = self.l1(graph, h)
             logits = self.l2(graph, h)

python/dgl/nn/mxnet/conv/chebconv.py

@@ -5,7 +5,8 @@ import mxnet as mx
 from mxnet import nd
 from mxnet.gluon import nn
 
-from .... import laplacian_lambda_max, broadcast_nodes, function as fn
+from ....base import dgl_warning
+from .... import broadcast_nodes, function as fn
 
 
 class ChebConv(nn.Block):
@@ -50,7 +51,6 @@ class ChebConv(nn.Block):
     >>> import numpy as np
     >>> import mxnet as mx
     >>> from dgl.nn import ChebConv
-    >>>
     >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
     >>> feat = mx.nd.ones((6, 10))
     >>> conv = ChebConv(10, 2, 2)
@@ -106,8 +106,9 @@ class ChebConv(nn.Block):
             A list(tensor) with length :math:`B`, stores the largest eigenvalue
             of the normalized laplacian of each individual graph in ``graph``,
             where :math:`B` is the batch size of the input graph. Default: None.
-            If None, this method would compute the list by calling
-            ``dgl.laplacian_lambda_max``.
+
+            If None, this method would set the default value to 2.
+            One can use :func:`dgl.laplacian_lambda_max` to compute this value.
 
         Returns
         -------
@@ -119,8 +120,13 @@ class ChebConv(nn.Block):
             degs = graph.in_degrees().astype('float32')
             norm = mx.nd.power(mx.nd.clip(degs, a_min=1, a_max=float("inf")), -0.5)
             norm = norm.expand_dims(-1).as_in_context(feat.context)
+
             if lambda_max is None:
-                lambda_max = laplacian_lambda_max(graph)
+                dgl_warning(
+                    "lambda_max is not provided, using default value of 2.  "
+                    "Please use dgl.laplacian_lambda_max to compute the eigenvalues.")
+                lambda_max = [2] * graph.batch_size
+
             if isinstance(lambda_max, list):
                 lambda_max = nd.array(lambda_max).as_in_context(feat.context)
             if lambda_max.ndim == 1:

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

@@ -5,7 +5,7 @@ from torch import nn
 import torch.nn.functional as F
 
 from ....base import dgl_warning
-from .... import laplacian_lambda_max, broadcast_nodes, function as fn
+from .... import broadcast_nodes, function as fn
 
 
 class ChebConv(nn.Module):
@@ -95,8 +95,9 @@ class ChebConv(nn.Module):
             A list(tensor) with length :math:`B`, stores the largest eigenvalue
             of the normalized laplacian of each individual graph in ``graph``,
             where :math:`B` is the batch size of the input graph. Default: None.
-            If None, this method would compute the list by calling
-            ``dgl.laplacian_lambda_max``.
+
+            If None, this method would set the default value to 2.
+            One can use :func:`dgl.laplacian_lambda_max` to compute this value.
 
         Returns
         -------
@@ -115,17 +116,13 @@ class ChebConv(nn.Module):
                 min=1), -0.5).unsqueeze(-1).to(feat.device)
 
             if lambda_max is None:
-                try:
-                    lambda_max = laplacian_lambda_max(graph)
-                except BaseException:
-                    # if the largest eigenvalue is not found
                 dgl_warning(
-                        "Largest eigonvalue not found, using default value 2 for lambda_max",
-                        RuntimeWarning)
-                    lambda_max = th.Tensor(2).to(feat.device)
+                    "lambda_max is not provided, using default value of 2.  "
+                    "Please use dgl.laplacian_lambda_max to compute the eigenvalues.")
+                lambda_max = [2] * graph.batch_size
 
             if isinstance(lambda_max, list):
-                lambda_max = th.Tensor(lambda_max).to(feat.device)
+                lambda_max = th.Tensor(lambda_max).to(feat)
             if lambda_max.dim() == 1:
                 lambda_max = lambda_max.unsqueeze(-1)  # (B,) to (B, 1)
 

python/dgl/nn/tensorflow/conv/chebconv.py

@@ -5,7 +5,7 @@ from tensorflow.keras import layers
 import numpy as np
 
 from ....base import dgl_warning
-from .... import laplacian_lambda_max, broadcast_nodes, function as fn
+from .... import broadcast_nodes, function as fn
 
 
 class ChebConv(layers.Layer):
@@ -50,13 +50,12 @@ class ChebConv(layers.Layer):
     >>> import numpy as np
     >>> import tensorflow as tf
     >>> from dgl.nn import ChebConv
-    >>>
     >>> with tf.device("CPU:0"):
-    >>>     g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
-    >>>     feat = tf.ones((6, 10))
-    >>>     conv = ChebConv(10, 2, 2)
-    >>>     res = conv(g, feat)
-    >>>     res
+    ...     g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
+    ...     feat = tf.ones((6, 10))
+    ...     conv = ChebConv(10, 2, 2)
+    ...     res = conv(g, feat)
+    ...     res
     <tf.Tensor: shape=(6, 2), dtype=float32, numpy=
     array([[ 0.6163, -0.1809],
             [ 0.6163, -0.1809],
@@ -97,8 +96,9 @@ class ChebConv(layers.Layer):
             A list(tensor) with length :math:`B`, stores the largest eigenvalue
             of the normalized laplacian of each individual graph in ``graph``,
             where :math:`B` is the batch size of the input graph. Default: None.
-            If None, this method would compute the list by calling
-            ``dgl.laplacian_lambda_max``.
+
+            If None, this method would set the default value to 2.
+            One can use :func:`dgl.laplacian_lambda_max` to compute this value.
 
         Returns
         -------
@@ -117,15 +117,12 @@ class ChebConv(layers.Layer):
                                           clip_value_min=1,
                                           clip_value_max=np.inf)
             D_invsqrt = tf.expand_dims(tf.pow(in_degrees, -0.5), axis=-1)
+
             if lambda_max is None:
-                try:
-                    lambda_max = laplacian_lambda_max(graph)
-                except BaseException:
-                    # if the largest eigenvalue is not found
                 dgl_warning(
-                        "Largest eigonvalue not found, using default value 2 for lambda_max",
-                        RuntimeWarning)
-                    lambda_max = tf.constant(2, dtype=tf.float32)
+                    "lambda_max is not provided, using default value of 2.  "
+                    "Please use dgl.laplacian_lambda_max to compute the eigenvalues.")
+                lambda_max = [2] * graph.batch_size
 
             if isinstance(lambda_max, list):
                 lambda_max = tf.constant(lambda_max, dtype=tf.float32)