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Unverified Commit 61139302 authored by peizhou001's avatar peizhou001 Committed by GitHub
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[API Deprecation] Remove candidates in DGLGraph (#4946)

parent e088acac
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Showing with 75 additions and 401 deletions
examples/pytorch/transformer/modules/act.py
View file @ 61139302
......@@ -117,7 +117,7 @@ class UTransformer(nn.Module):
g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)), eids)
# Send weighted values to target nodes
g.send_and_recv(eids,
[fn.src_mul_edge('v', 'score', 'v'), fn.copy_edge('score', 'score')],
[fn.u_mul_e('v', 'score', 'v'), fn.copy_e('score', 'score')],
[fn.sum('v', 'wv'), fn.sum('score', 'z')])
def update_graph(self, g, eids, pre_pairs, post_pairs):
......
examples/pytorch/transformer/modules/models.py
View file @ 61139302
......@@ -79,8 +79,8 @@ class Transformer(nn.Module):
g.apply_edges(src_dot_dst('k', 'q', 'score'), eids)
g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)), eids)
# Send weighted values to target nodes
g.send_and_recv(eids, fn.src_mul_edge('v', 'score', 'v'), fn.sum('v', 'wv'))
g.send_and_recv(eids, fn.copy_edge('score', 'score'), fn.sum('score', 'z'))
g.send_and_recv(eids, fn.u_mul_e('v', 'score', 'v'), fn.sum('v', 'wv'))
g.send_and_recv(eids, fn.copy_e('score', 'score'), fn.sum('score', 'z'))
def update_graph(self, g, eids, pre_pairs, post_pairs):
"Update the node states and edge states of the graph."
......
examples/pytorch/transformer/modules/viz.py
View file @ 61139302
......@@ -17,7 +17,7 @@ def get_attention_map(g, src_nodes, dst_nodes, h):
for j, dst in enumerate(dst_nodes.tolist()):
if not g.has_edge_between(src, dst):
continue
eid = g.edge_id(src, dst)
eid = g.edge_ids(src, dst)
weight[i][j] = g.edata['score'][eid].squeeze(-1).cpu().detach()
weight = weight.transpose(0, 2)
......
examples/pytorch/tree_lstm/train.py
View file @ 61139302
......@@ -131,7 +131,7 @@ def main(args):
root_ids = [
i
for i in range(g.number_of_nodes())
if g.out_degree(i) == 0
if g.out_degrees(i) == 0
]
root_acc = np.sum(
batch.label.cpu().data.numpy()[root_ids]
......@@ -170,7 +170,7 @@ def main(args):
acc = th.sum(th.eq(batch.label, pred)).item()
accs.append([acc, len(batch.label)])
root_ids = [
i for i in range(g.number_of_nodes()) if g.out_degree(i) == 0
i for i in range(g.number_of_nodes()) if g.out_degrees(i) == 0
]
root_acc = np.sum(
batch.label.cpu().data.numpy()[root_ids]
......@@ -222,7 +222,7 @@ def main(args):
acc = th.sum(th.eq(batch.label, pred)).item()
accs.append([acc, len(batch.label)])
root_ids = [
i for i in range(g.number_of_nodes()) if g.out_degree(i) == 0
i for i in range(g.number_of_nodes()) if g.out_degrees(i) == 0
]
root_acc = np.sum(
batch.label.cpu().data.numpy()[root_ids]
......
examples/tensorflow/gcn/gcn_builtin.py
View file @ 61139302
......@@ -45,7 +45,7 @@ class GCNLayer(layers.Layer):
h = self.dropout(h)
self.g.ndata['h'] = tf.matmul(h, self.weight)
self.g.ndata['norm_h'] = self.g.ndata['h'] * self.g.ndata['norm']
self.g.update_all(fn.copy_src('norm_h', 'm'),
self.g.update_all(fn.copy_u('norm_h', 'm'),
fn.sum('m', 'h'))
h = self.g.ndata['h']
if self.bias is not None:
......
python/dgl/_deprecate/graph.py
View file @ 61139302
......@@ -3083,10 +3083,10 @@ class DGLGraph(DGLBaseGraph):
>>> g.add_nodes(3)
>>> g.ndata['x'] = th.tensor([[0.], [1.], [2.]])
Use the built-in message function :func:`~dgl.function.copy_src` for copying
Use the built-in message function :func:`~dgl.function.copy_u` for copying
node features as the message.
>>> m_func = dgl.function.copy_src('x', 'm')
>>> m_func = dgl.function.copy_u('x', 'm')
>>> g.register_message_func(m_func)
Use the built-int message reducing function :func:`~dgl.function.sum`, which
......@@ -3180,10 +3180,10 @@ class DGLGraph(DGLBaseGraph):
>>> g.add_nodes(3)
>>> g.ndata['x'] = th.tensor([[1.], [2.], [3.]])
Use the built-in message function :func:`~dgl.function.copy_src` for copying
Use the built-in message function :func:`~dgl.function.copy_u` for copying
node features as the message.
>>> m_func = dgl.function.copy_src('x', 'm')
>>> m_func = dgl.function.copy_u('x', 'm')
>>> g.register_message_func(m_func)
Use the built-int message reducing function :func:`~dgl.function.sum`, which
......
python/dgl/batch.py
View file @ 61139302
......@@ -2,7 +2,7 @@
from collections.abc import Mapping
from . import backend as F
from .base import ALL, is_all, DGLError, dgl_warning, NID, EID
from .base import ALL, is_all, DGLError, NID, EID
from .heterograph_index import disjoint_union, slice_gidx
from .heterograph import DGLGraph
from . import convert
......@@ -11,8 +11,7 @@ from . import utils
__all__ = ['batch', 'unbatch', 'slice_batch']
def batch(graphs, ndata=ALL, edata=ALL, *,
node_attrs=None, edge_attrs=None):
def batch(graphs, ndata=ALL, edata=ALL):
r"""Batch a collection of :class:`DGLGraph` s into one graph for more efficient
graph computation.
......@@ -151,14 +150,6 @@ def batch(graphs, ndata=ALL, edata=ALL, *,
"""
if len(graphs) == 0:
raise DGLError('The input list of graphs cannot be empty.')
if node_attrs is not None:
dgl_warning('Arguments node_attrs has been deprecated. Please use'
' ndata instead.')
ndata = node_attrs
if edge_attrs is not None:
dgl_warning('Arguments edge_attrs has been deprecated. Please use'
' edata instead.')
edata = edge_attrs
if not (is_all(ndata) or isinstance(ndata, list) or ndata is None):
raise DGLError('Invalid argument ndata: must be a string list but got {}.'.format(
type(ndata)))
......
python/dgl/function/message.py
View file @ 61139302
......@@ -9,7 +9,7 @@ from .._deprecate.runtime import ir
from .._deprecate.runtime.ir import var
__all__ = ["src_mul_edge", "copy_src", "copy_edge", "copy_u", "copy_e",
__all__ = ["copy_u", "copy_e",
"BinaryMessageFunction", "CopyMessageFunction"]
......@@ -34,7 +34,7 @@ class BinaryMessageFunction(MessageFunction):
See Also
--------
src_mul_edge
u_mul_e
"""
def __init__(self, binary_op, lhs, rhs, lhs_field, rhs_field, out_field):
self.binary_op = binary_op
......@@ -73,7 +73,7 @@ class CopyMessageFunction(MessageFunction):
See Also
--------
copy_src
copy_u
"""
def __init__(self, target, in_field, out_field):
self.target = target
......@@ -218,86 +218,3 @@ def _register_builtin_message_func():
__all__.append(func.__name__)
_register_builtin_message_func()
##############################################################################
# For backward compatibility
def src_mul_edge(src, edge, out):
"""Builtin message function that computes message by performing
binary operation mul between src feature and edge feature.
Notes
-----
This function is deprecated. Please use :func:`~dgl.function.u_mul_e` instead.
Parameters
----------
src : str
The source feature field.
edge : str
The edge feature field.
out : str
The output message field.
Examples
--------
>>> import dgl
>>> message_func = dgl.function.src_mul_edge('h', 'e', 'm')
"""
return getattr(sys.modules[__name__], "u_mul_e")(src, edge, out)
def copy_src(src, out):
"""Builtin message function that computes message using source node
feature.
Notes
-----
This function is deprecated. Please use :func:`~dgl.function.copy_u` instead.
Parameters
----------
src : str
The source feature field.
out : str
The output message field.
Examples
--------
>>> import dgl
>>> message_func = dgl.function.copy_src('h', 'm')
The above example is equivalent to the following user defined function:
>>> def message_func(edges):
>>> return {'m': edges.src['h']}
"""
return copy_u(src, out)
def copy_edge(edge, out):
"""Builtin message function that computes message using edge feature.
Notes
-----
This function is deprecated. Please use :func:`~dgl.function.copy_e` instead.
Parameters
----------
edge : str
The edge feature field.
out : str
The output message field.
Examples
--------
>>> import dgl
>>> message_func = dgl.function.copy_edge('h', 'm')
The above example is equivalent to the following user defined function:
>>> def message_func(edges):
>>> return {'m': edges.data['h']}
"""
return copy_e(edge, out)
python/dgl/heterograph.py
View file @ 61139302
......@@ -346,14 +346,6 @@ class DGLGraph(object):
self._node_frames[ntid].append(data)
self._reset_cached_info()
def add_edge(self, u, v, data=None, etype=None):
"""Add one edge to the graph.
DEPRECATED: please use ``add_edges``.
"""
dgl_warning("DGLGraph.add_edge is deprecated. Please use DGLGraph.add_edges")
self.add_edges(u, v, data, etype)
def add_edges(self, u, v, data=None, etype=None):
r"""Add multiple new edges for the specified edge type
......@@ -2623,20 +2615,6 @@ class DGLGraph(object):
"""
return len(self.ntypes) == 1 and len(self.etypes) == 1
@property
def is_readonly(self):
"""**DEPRECATED**: DGLGraph will always be mutable.
Returns
-------
bool
True if the graph is readonly, False otherwise.
"""
dgl_warning('DGLGraph.is_readonly is deprecated in v0.5.\n'
'DGLGraph now always supports mutable operations like add_nodes'
' and add_edges.')
return False
@property
def idtype(self):
"""The data type for storing the structure-related graph information
......@@ -2682,12 +2660,6 @@ class DGLGraph(object):
"""
return self._graph.dtype
def __contains__(self, vid):
"""**DEPRECATED**: please directly call :func:`has_nodes`."""
dgl_warning('DGLGraph.__contains__ is deprecated.'
' Please directly call has_nodes.')
return self.has_nodes(vid)
def has_nodes(self, vid, ntype=None):
"""Return whether the graph contains the given nodes.
......@@ -2745,14 +2717,6 @@ class DGLGraph(object):
else:
return F.astype(ret, F.bool)
def has_node(self, vid, ntype=None):
"""Whether the graph has a particular node of a given type.
**DEPRECATED**: see :func:`~DGLGraph.has_nodes`
"""
dgl_warning("DGLGraph.has_node is deprecated. Please use DGLGraph.has_nodes")
return self.has_nodes(vid, ntype)
def has_edges_between(self, u, v, etype=None):
"""Return whether the graph contains the given edges.
......@@ -2843,15 +2807,6 @@ class DGLGraph(object):
else:
return F.astype(ret, F.bool)
def has_edge_between(self, u, v, etype=None):
"""Whether the graph has edges of type ``etype``.
**DEPRECATED**: please use :func:`~DGLGraph.has_edge_between`.
"""
dgl_warning("DGLGraph.has_edge_between is deprecated. "
"Please use DGLGraph.has_edges_between")
return self.has_edges_between(u, v, etype)
def predecessors(self, v, etype=None):
"""Return the predecessor(s) of a particular node with the specified edge type.
......@@ -2969,17 +2924,7 @@ class DGLGraph(object):
raise DGLError('Non-existing node ID {}'.format(v))
return self._graph.successors(self.get_etype_id(etype), v)
def edge_id(self, u, v, force_multi=None, return_uv=False, etype=None):
"""Return the edge ID, or an array of edge IDs, between source node
`u` and destination node `v`, with the specified edge type
**DEPRECATED**: See edge_ids
"""
dgl_warning("DGLGraph.edge_id is deprecated. Please use DGLGraph.edge_ids.")
return self.edge_ids(u, v, force_multi=force_multi,
return_uv=return_uv, etype=etype)
def edge_ids(self, u, v, force_multi=None, return_uv=False, etype=None):
def edge_ids(self, u, v, return_uv=False, etype=None):
"""Return the edge ID(s) given the two endpoints of the edge(s).
Parameters
......@@ -2999,9 +2944,6 @@ class DGLGraph(object):
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
force_multi : bool, optional
**DEPRECATED**, use :attr:`return_uv` instead. Whether to allow the graph to be a
multigraph, i.e. there can be multiple edges from one node to another.
return_uv : bool, optional
Whether to return the source and destination node IDs along with the edges. If
False (default), it assumes that the graph is a simple graph and there is only
......@@ -3084,10 +3026,6 @@ class DGLGraph(object):
v = utils.prepare_tensor(self, v, 'v')
if F.as_scalar(F.sum(self.has_nodes(v, ntype=dsttype), dim=0)) != len(v):
raise DGLError('v contains invalid node IDs')
if force_multi is not None:
dgl_warning("force_multi will be deprecated, " \
"Please use return_uv instead")
return_uv = force_multi
if return_uv:
return self._graph.edge_ids_all(self.get_etype_id(etype), u, v)
......@@ -3424,14 +3362,6 @@ class DGLGraph(object):
else:
raise DGLError('Invalid form: {}. Must be "all", "uv" or "eid".'.format(form))
def in_degree(self, v, etype=None):
"""Return the in-degree of node ``v`` with edges of type ``etype``.
**DEPRECATED**: Please use in_degrees
"""
dgl_warning("DGLGraph.in_degree is deprecated. Please use DGLGraph.in_degrees")
return self.in_degrees(v, etype)
def in_degrees(self, v=ALL, etype=None):
"""Return the in-degree(s) of the given nodes.
......@@ -3508,14 +3438,6 @@ class DGLGraph(object):
else:
return deg
def out_degree(self, u, etype=None):
"""Return the out-degree of node `u` with edges of type ``etype``.
DEPRECATED: please use DGL.out_degrees
"""
dgl_warning("DGLGraph.out_degree is deprecated. Please use DGLGraph.out_degrees")
return self.out_degrees(u, etype)
def out_degrees(self, u=ALL, etype=None):
"""Return the out-degree(s) of the given nodes.
......@@ -3713,15 +3635,6 @@ class DGLGraph(object):
else:
return self._graph.adjacency_matrix_tensors(etid, False, fmt)[2:]
def adjacency_matrix_scipy(self, transpose=False, fmt='csr', return_edge_ids=None):
"""DEPRECATED: please use ``dgl.adjacency_matrix(transpose, scipy_fmt=fmt)``.
"""
dgl_warning('DGLGraph.adjacency_matrix_scipy is deprecated. '
'Please replace it with:\n\n\t'
'DGLGraph.adjacency_matrix(transpose, scipy_fmt="{}").\n'.format(fmt))
return self.adjacency_matrix(transpose=transpose, scipy_fmt=fmt)
def inc(self, typestr, ctx=F.cpu(), etype=None):
"""Return the incidence matrix representation of edges with the given
edge type.
......@@ -4283,7 +4196,7 @@ class DGLGraph(object):
# Message passing
#################################################################
def apply_nodes(self, func, v=ALL, ntype=None, inplace=False):
def apply_nodes(self, func, v=ALL, ntype=None):
"""Update the features of the specified nodes by the provided function.
Parameters
......@@ -4303,8 +4216,6 @@ class DGLGraph(object):
ntype : str, optional
The node type name. Can be omitted if there is
only one type of nodes in the graph.
inplace : bool, optional
**DEPRECATED**.
Examples
--------
......@@ -4340,8 +4251,6 @@ class DGLGraph(object):
--------
apply_edges
"""
if inplace:
raise DGLError('The `inplace` option is removed in v0.5.')
ntid = self.get_ntype_id(ntype)
ntype = self.ntypes[ntid]
if is_all(v):
......@@ -4351,7 +4260,7 @@ class DGLGraph(object):
ndata = core.invoke_node_udf(self, v_id, ntype, func, orig_nid=v_id)
self._set_n_repr(ntid, v, ndata)
def apply_edges(self, func, edges=ALL, etype=None, inplace=False):
def apply_edges(self, func, edges=ALL, etype=None):
"""Update the features of the specified edges by the provided function.
Parameters
......@@ -4382,9 +4291,6 @@ class DGLGraph(object):
Can be omitted if the graph has only one type of edges.
inplace: bool, optional
**DEPRECATED**.
Notes
-----
DGL recommends using DGL's bulit-in function for the :attr:`func` argument,
......@@ -4435,8 +4341,6 @@ class DGLGraph(object):
--------
apply_nodes
"""
if inplace:
raise DGLError('The `inplace` option is removed in v0.5.')
# Graph with one relation type
if self._graph.number_of_etypes() == 1 or etype is not None:
etid = self.get_etype_id(etype)
......@@ -4476,8 +4380,7 @@ class DGLGraph(object):
message_func,
reduce_func,
apply_node_func=None,
etype=None,
inplace=False):
etype=None):
"""Send messages along the specified edges and reduce them on
the destination nodes to update their features.
......@@ -4513,9 +4416,6 @@ class DGLGraph(object):
Can be omitted if the graph has only one type of edges.
inplace: bool, optional
**DEPRECATED**.
Notes
-----
DGL recommends using DGL's bulit-in function for the :attr:`message_func`
......@@ -4558,7 +4458,7 @@ class DGLGraph(object):
... ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1])
... })
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
>>> g.send_and_recv(g['follows'].edges(), fn.copy_src('h', 'm'),
>>> g.send_and_recv(g['follows'].edges(), fn.copy_u('h', 'm'),
... fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
......@@ -4588,8 +4488,6 @@ class DGLGraph(object):
Note that the feature of node 0 remains the same as it has no incoming edges.
"""
if inplace:
raise DGLError('The `inplace` option is removed in v0.5.')
# edge type
etid = self.get_etype_id(etype)
_, dtid = self._graph.metagraph.find_edge(etid)
......@@ -4612,8 +4510,7 @@ class DGLGraph(object):
message_func,
reduce_func,
apply_node_func=None,
etype=None,
inplace=False):
etype=None):
"""Pull messages from the specified node(s)' predecessors along the
specified edge type, aggregate them to update the node features.
......@@ -4645,9 +4542,6 @@ class DGLGraph(object):
Can be omitted if the graph has only one type of edges.
inplace: bool, optional
**DEPRECATED**.
Notes
-----
* If some of the given nodes :attr:`v` has no in-edges, DGL does not invoke
......@@ -4688,14 +4582,12 @@ class DGLGraph(object):
Pull.
>>> g['follows'].pull(2, fn.copy_src('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g['follows'].pull(2, fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[1.],
[1.]])
"""
if inplace:
raise DGLError('The `inplace` option is removed in v0.5.')
v = utils.prepare_tensor(self, v, 'v')
if len(v) == 0:
# no computation
......@@ -4716,8 +4608,7 @@ class DGLGraph(object):
message_func,
reduce_func,
apply_node_func=None,
etype=None,
inplace=False):
etype=None):
"""Send message from the specified node(s) to their successors
along the specified edge type and update their node features.
......@@ -4749,9 +4640,6 @@ class DGLGraph(object):
Can be omitted if the graph has only one type of edges.
inplace: bool, optional
**DEPRECATED**.
Notes
-----
DGL recommends using DGL's bulit-in function for the :attr:`message_func`
......@@ -4785,14 +4673,12 @@ class DGLGraph(object):
Push.
>>> g['follows'].push(0, fn.copy_src('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g['follows'].push(0, fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[0.],
[0.]])
"""
if inplace:
raise DGLError('The `inplace` option is removed in v0.5.')
edges = self.out_edges(u, form='eid', etype=etype)
self.send_and_recv(edges, message_func, reduce_func, apply_node_func, etype=etype)
......@@ -4864,7 +4750,7 @@ class DGLGraph(object):
Update all.
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
>>> g['follows'].update_all(fn.copy_src('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g['follows'].update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[0.],
......@@ -4881,7 +4767,7 @@ class DGLGraph(object):
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.]])
>>> g.nodes['game'].data['h'] = torch.tensor([[1.]])
>>> g.update_all(fn.copy_src('h', 'm'), fn.sum('m', 'h'))
>>> g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
>>> g.nodes['user'].data['h']
tensor([[0.],
[4.]])
......@@ -4989,8 +4875,8 @@ class DGLGraph(object):
Update all.
>>> g.multi_update_all(
... {'follows': (fn.copy_src('h', 'm'), fn.sum('m', 'h')),
... 'attracts': (fn.copy_src('h', 'm'), fn.sum('m', 'h'))},
... {'follows': (fn.copy_u('h', 'm'), fn.sum('m', 'h')),
... 'attracts': (fn.copy_u('h', 'm'), fn.sum('m', 'h'))},
... "sum")
>>> g.nodes['user'].data['h']
tensor([[0.],
......@@ -5004,8 +4890,8 @@ class DGLGraph(object):
Use the user-defined cross reducer.
>>> g.multi_update_all(
... {'follows': (fn.copy_src('h', 'm'), fn.sum('m', 'h')),
... 'attracts': (fn.copy_src('h', 'm'), fn.sum('m', 'h'))},
... {'follows': (fn.copy_u('h', 'm'), fn.sum('m', 'h')),
... 'attracts': (fn.copy_u('h', 'm'), fn.sum('m', 'h'))},
... cross_sum)
"""
all_out = defaultdict(list)
......@@ -5088,7 +4974,7 @@ class DGLGraph(object):
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2, 3], [2, 3, 4, 4])})
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.], [3.], [4.], [5.]])
>>> g['follows'].prop_nodes([[2, 3], [4]], fn.copy_src('h', 'm'),
>>> g['follows'].prop_nodes([[2, 3], [4]], fn.copy_u('h', 'm'),
... fn.sum('m', 'h'), etype='follows')
tensor([[1.],
[2.],
......@@ -5151,7 +5037,7 @@ class DGLGraph(object):
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2, 3], [2, 3, 4, 4])})
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.], [3.], [4.], [5.]])
>>> g['follows'].prop_edges([[0, 1], [2, 3]], fn.copy_src('h', 'm'),
>>> g['follows'].prop_edges([[0, 1], [2, 3]], fn.copy_u('h', 'm'),
... fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[1.],
......@@ -6062,113 +5948,6 @@ class DGLGraph(object):
"""
return self.astype(F.int32)
#################################################################
# DEPRECATED: from the old DGLGraph
#################################################################
def from_networkx(self, nx_graph, node_attrs=None, edge_attrs=None):
"""DEPRECATED: please use
``dgl.from_networkx(nx_graph, node_attrs, edge_attrs)``
which will return a new graph created from the networkx graph.
"""
raise DGLError('DGLGraph.from_networkx is deprecated. Please call the following\n\n'
'\t dgl.from_networkx(nx_graph, node_attrs, edge_attrs)\n\n'
', which creates a new DGLGraph from the networkx graph.')
def from_scipy_sparse_matrix(self, spmat, multigraph=None):
"""DEPRECATED: please use
``dgl.from_scipy(spmat)``
which will return a new graph created from the scipy matrix.
"""
raise DGLError('DGLGraph.from_scipy_sparse_matrix is deprecated. '
'Please call the following\n\n'
'\t dgl.from_scipy(spmat)\n\n'
', which creates a new DGLGraph from the scipy matrix.')
def register_apply_node_func(self, func):
"""Deprecated: please directly call :func:`apply_nodes` with ``func``
as argument.
"""
raise DGLError('DGLGraph.register_apply_node_func is deprecated.'
' Please directly call apply_nodes with func as the argument.')
def register_apply_edge_func(self, func):
"""Deprecated: please directly call :func:`apply_edges` with ``func``
as argument.
"""
raise DGLError('DGLGraph.register_apply_edge_func is deprecated.'
' Please directly call apply_edges with func as the argument.')
def register_message_func(self, func):
"""Deprecated: please directly call :func:`update_all` with ``func``
as argument.
"""
raise DGLError('DGLGraph.register_message_func is deprecated.'
' Please directly call update_all with func as the argument.')
def register_reduce_func(self, func):
"""Deprecated: please directly call :func:`update_all` with ``func``
as argument.
"""
raise DGLError('DGLGraph.register_reduce_func is deprecated.'
' Please directly call update_all with func as the argument.')
def group_apply_edges(self, group_by, func, edges=ALL, etype=None, inplace=False):
"""**DEPRECATED**: The API is removed in 0.5."""
raise DGLError('DGLGraph.group_apply_edges is removed in 0.5.')
def send(self, edges, message_func, etype=None):
"""Send messages along the given edges with the same edge type.
DEPRECATE: please use send_and_recv, update_all.
"""
raise DGLError('DGLGraph.send is deprecated. As a replacement, use DGLGraph.apply_edges\n'
' API to compute messages as edge data. Then use DGLGraph.send_and_recv\n'
' and set the message function as dgl.function.copy_e to conduct message\n'
' aggregation.')
def recv(self, v, reduce_func, apply_node_func=None, etype=None, inplace=False):
r"""Receive and reduce incoming messages and update the features of node(s) :math:`v`.
DEPRECATE: please use send_and_recv, update_all.
"""
raise DGLError('DGLGraph.recv is deprecated. As a replacement, use DGLGraph.apply_edges\n'
' API to compute messages as edge data. Then use DGLGraph.send_and_recv\n'
' and set the message function as dgl.function.copy_e to conduct message\n'
' aggregation.')
def multi_recv(self, v, reducer_dict, cross_reducer, apply_node_func=None, inplace=False):
r"""Receive messages from multiple edge types and perform aggregation.
DEPRECATE: please use multi_send_and_recv, multi_update_all.
"""
raise DGLError('DGLGraph.multi_recv is deprecated. As a replacement,\n'
' use DGLGraph.apply_edges API to compute messages as edge data.\n'
' Then use DGLGraph.multi_send_and_recv and set the message function\n'
' as dgl.function.copy_e to conduct message aggregation.')
def multi_send_and_recv(self, etype_dict, cross_reducer, apply_node_func=None, inplace=False):
r"""**DEPRECATED**: The API is removed in v0.5."""
raise DGLError('DGLGraph.multi_pull is removed in v0.5. As a replacement,\n'
' use DGLGraph.edge_subgraph to extract the subgraph first \n'
' and then call DGLGraph.multi_update_all.')
def multi_pull(self, v, etype_dict, cross_reducer, apply_node_func=None, inplace=False):
r"""**DEPRECATED**: The API is removed in v0.5."""
raise DGLError('DGLGraph.multi_pull is removed in v0.5. As a replacement,\n'
' use DGLGraph.edge_subgraph to extract the subgraph first \n'
' and then call DGLGraph.multi_update_all.')
def readonly(self, readonly_state=True):
"""Deprecated: DGLGraph will always be mutable."""
dgl_warning('DGLGraph.readonly is deprecated in v0.5.\n'
'DGLGraph now always supports mutable operations like add_nodes'
' and add_edges.')
############################################################
# Internal APIs
############################################################
......
python/dgl/nn/mxnet/conv/graphconv.py
View file @ 61139302
......@@ -261,13 +261,13 @@ class GraphConv(gluon.Block):
if weight is not None:
feat_src = mx.nd.dot(feat_src, weight)
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_src(src='h', out='m'),
graph.update_all(fn.copy_u(u='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.dstdata.pop('h')
else:
# aggregate first then mult W
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_src(src='h', out='m'),
graph.update_all(fn.copy_u(u='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.dstdata.pop('h')
if weight is not None:
......
python/dgl/nn/mxnet/conv/tagconv.py
View file @ 61139302
......@@ -114,7 +114,7 @@ class TAGConv(gluon.Block):
rst = rst * norm
graph.ndata['h'] = rst
graph.update_all(fn.copy_src(src='h', out='m'),
graph.update_all(fn.copy_u(u='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.ndata['h']
rst = rst * norm
......
python/dgl/nn/pytorch/conv/ginconv.py
View file @ 61139302
......@@ -136,7 +136,7 @@ class GINConv(nn.Module):
"""
_reducer = getattr(fn, self._aggregator_type)
with graph.local_scope():
aggregate_fn = fn.copy_src('h', 'm')
aggregate_fn = fn.copy_u('h', 'm')
if edge_weight is not None:
assert edge_weight.shape[0] == graph.number_of_edges()
graph.edata['_edge_weight'] = edge_weight
......
python/dgl/nn/pytorch/conv/graphconv.py
View file @ 61139302
......@@ -114,13 +114,13 @@ class EdgeWeightNorm(nn.Module):
if self._norm == 'both':
reversed_g = reverse(graph)
reversed_g.edata['_edge_w'] = edge_weight
reversed_g.update_all(fn.copy_edge('_edge_w', 'm'), fn.sum('m', 'out_weight'))
reversed_g.update_all(fn.copy_e('_edge_w', 'm'), fn.sum('m', 'out_weight'))
degs = reversed_g.dstdata['out_weight'] + self._eps
norm = th.pow(degs, -0.5)
graph.srcdata['_src_out_w'] = norm
if self._norm != 'none':
graph.update_all(fn.copy_edge('_edge_w', 'm'), fn.sum('m', 'in_weight'))
graph.update_all(fn.copy_e('_edge_w', 'm'), fn.sum('m', 'in_weight'))
degs = graph.dstdata['in_weight'] + self._eps
if self._norm == 'both':
norm = th.pow(degs, -0.5)
......@@ -389,7 +389,7 @@ class GraphConv(nn.Module):
'the issue. Setting ``allow_zero_in_degree`` '
'to be `True` when constructing this module will '
'suppress the check and let the code run.')
aggregate_fn = fn.copy_src('h', 'm')
aggregate_fn = fn.copy_u('h', 'm')
if edge_weight is not None:
assert edge_weight.shape[0] == graph.number_of_edges()
graph.edata['_edge_weight'] = edge_weight
......
python/dgl/nn/pytorch/conv/sageconv.py
View file @ 61139302
......@@ -213,7 +213,7 @@ class SAGEConv(nn.Module):
feat_src = feat_dst = self.feat_drop(feat)
if graph.is_block:
feat_dst = feat_src[:graph.number_of_dst_nodes()]
msg_fn = fn.copy_src('h', 'm')
msg_fn = fn.copy_u('h', 'm')
if edge_weight is not None:
assert edge_weight.shape[0] == graph.number_of_edges()
graph.edata['_edge_weight'] = edge_weight
......
python/dgl/nn/pytorch/sparse_emb.py
View file @ 61139302
......@@ -334,19 +334,6 @@ class NodeEmbedding: # NodeEmbedding
"""
self._trace = []
@property
def emb_tensor(self):
"""Return the tensor storing the node embeddings
DEPRECATED: renamed weight
Returns
-------
torch.Tensor
The tensor storing the node embeddings
"""
return self._tensor
@property
def weight(self):
"""Return the tensor storing the node embeddings
......
python/dgl/nn/tensorflow/conv/graphconv.py
View file @ 61139302
......@@ -253,13 +253,13 @@ class GraphConv(layers.Layer):
if weight is not None:
feat_src = tf.matmul(feat_src, weight)
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_src(src='h', out='m'),
graph.update_all(fn.copy_u(u='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.dstdata['h']
else:
# aggregate first then mult W
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_src(src='h', out='m'),
graph.update_all(fn.copy_u(u='h', out='m'),
fn.sum(msg='m', out='h'))
rst = graph.dstdata['h']
if weight is not None:
......
python/dgl/nn/tensorflow/conv/sageconv.py
View file @ 61139302
......@@ -166,24 +166,24 @@ class SAGEConv(layers.Layer):
if self._aggre_type == 'mean':
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_src('h', 'm'), fn.mean('m', 'neigh'))
graph.update_all(fn.copy_u('h', 'm'), fn.mean('m', 'neigh'))
h_neigh = graph.dstdata['neigh']
elif self._aggre_type == 'gcn':
check_eq_shape(feat)
graph.srcdata['h'] = feat_src
graph.dstdata['h'] = feat_dst # same as above if homogeneous
graph.update_all(fn.copy_src('h', 'm'), fn.sum('m', 'neigh'))
graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'neigh'))
# divide in_degrees
degs = tf.cast(graph.in_degrees(), tf.float32)
h_neigh = (graph.dstdata['neigh'] + graph.dstdata['h']
) / (tf.expand_dims(degs, -1) + 1)
elif self._aggre_type == 'pool':
graph.srcdata['h'] = tf.nn.relu(self.fc_pool(feat_src))
graph.update_all(fn.copy_src('h', 'm'), fn.max('m', 'neigh'))
graph.update_all(fn.copy_u('h', 'm'), fn.max('m', 'neigh'))
h_neigh = graph.dstdata['neigh']
elif self._aggre_type == 'lstm':
graph.srcdata['h'] = feat_src
graph.update_all(fn.copy_src('h', 'm'), self._lstm_reducer)
graph.update_all(fn.copy_u('h', 'm'), self._lstm_reducer)
h_neigh = graph.dstdata['neigh']
else:
raise KeyError(
......
python/dgl/optim/pytorch/sparse_optim.py
View file @ 61139302
......@@ -526,7 +526,7 @@ class SparseAdagrad(SparseGradOptimizer):
), "SparseAdagrad only supports dgl.nn.NodeEmbedding"
emb_name = emb.name
if th.device(emb.emb_tensor.device) == th.device("cpu"):
if th.device(emb.weight.device) == th.device("cpu"):
# if our embedding is on the CPU, our state also has to be
if self._rank < 0:
state = th.empty(
......@@ -550,9 +550,9 @@ class SparseAdagrad(SparseGradOptimizer):
else:
# distributed state on on gpu
state = th.empty(
emb.emb_tensor.shape,
emb.weight.shape,
dtype=th.float32,
device=emb.emb_tensor.device,
device=emb.weight.device,
).zero_()
emb.set_optm_state(state)
......@@ -689,7 +689,7 @@ class SparseAdam(SparseGradOptimizer):
), "SparseAdam only supports dgl.nn.NodeEmbedding"
emb_name = emb.name
self._is_using_uva[emb_name] = self._use_uva
if th.device(emb.emb_tensor.device) == th.device("cpu"):
if th.device(emb.weight.device) == th.device("cpu"):
# if our embedding is on the CPU, our state also has to be
if self._rank < 0:
state_step = th.empty(
......@@ -743,19 +743,19 @@ class SparseAdam(SparseGradOptimizer):
# distributed state on on gpu
state_step = th.empty(
[emb.emb_tensor.shape[0]],
[emb.weight.shape[0]],
dtype=th.int32,
device=emb.emb_tensor.device,
device=emb.weight.device,
).zero_()
state_mem = th.empty(
emb.emb_tensor.shape,
emb.weight.shape,
dtype=self._dtype,
device=emb.emb_tensor.device,
device=emb.weight.device,
).zero_()
state_power = th.empty(
emb.emb_tensor.shape,
emb.weight.shape,
dtype=self._dtype,
device=emb.emb_tensor.device,
device=emb.weight.device,
).zero_()
state = (state_step, state_mem, state_power)
emb.set_optm_state(state)
......
tests/compute/test_basics.py
View file @ 61139302
......@@ -32,10 +32,10 @@ def generate_graph_old(grad=False):
# create a graph where 0 is the source and 9 is the sink
# 17 edges
for i in range(1, 9):
g.add_edge(0, i)
g.add_edge(i, 9)
g.add_edges(0, i)
g.add_edges(i, 9)
# add a back flow from 9 to 0
g.add_edge(9, 0)
g.add_edges(9, 0)
g = g.to(F.ctx())
ncol = F.randn((10, D))
ecol = F.randn((17, D))
......@@ -431,8 +431,8 @@ def test_dynamic_addition():
assert g.ndata['h1'].shape[0] == g.ndata['h2'].shape[0] == N + 3
# Test edge addition
g.add_edge(0, 1)
g.add_edge(1, 0)
g.add_edges(0, 1)
g.add_edges(1, 0)
g.edata.update({'h1': F.randn((2, D)),
'h2': F.randn((2, D))})
assert g.edata['h1'].shape[0] == g.edata['h2'].shape[0] == 2
......@@ -441,12 +441,12 @@ def test_dynamic_addition():
g.edata['h1'] = F.randn((4, D))
assert g.edata['h1'].shape[0] == g.edata['h2'].shape[0] == 4
g.add_edge(1, 2)
g.add_edges(1, 2)
g.edges[4].data['h1'] = F.randn((1, D))
assert g.edata['h1'].shape[0] == g.edata['h2'].shape[0] == 5
# test add edge with part of the features
g.add_edge(2, 1, {'h1': F.randn((1, D))})
g.add_edges(2, 1, {'h1': F.randn((1, D))})
assert len(g.edata['h1']) == len(g.edata['h2'])
......
tests/compute/test_batched_graph.py
View file @ 61139302
......@@ -15,10 +15,10 @@ def tree1(idtype):
"""
g = dgl.graph(([], [])).astype(idtype).to(F.ctx())
g.add_nodes(5)
g.add_edge(3, 1)
g.add_edge(4, 1)
g.add_edge(1, 0)
g.add_edge(2, 0)
g.add_edges(3, 1)
g.add_edges(4, 1)
g.add_edges(1, 0)
g.add_edges(2, 0)
g.ndata['h'] = F.tensor([0, 1, 2, 3, 4])
g.edata['h'] = F.randn((4, 10))
return g
......@@ -34,10 +34,10 @@ def tree2(idtype):
"""
g = dgl.graph(([], [])).astype(idtype).to(F.ctx())
g.add_nodes(5)
g.add_edge(2, 4)
g.add_edge(0, 4)
g.add_edge(4, 1)
g.add_edge(3, 1)
g.add_edges(2, 4)
g.add_edges(0, 4)
g.add_edges(4, 1)
g.add_edges(3, 1)
g.ndata['h'] = F.tensor([0, 1, 2, 3, 4])
g.edata['h'] = F.randn((4, 10))
return g
......@@ -191,8 +191,8 @@ def test_batched_edge_ordering(idtype):
e2 = F.randn((6, 10))
g2.edata['h'] = e2
g = dgl.batch([g1, g2])
r1 = g.edata['h'][g.edge_id(4, 5)]
r2 = g1.edata['h'][g1.edge_id(4, 5)]
r1 = g.edata['h'][g.edge_ids(4, 5)]
r2 = g1.edata['h'][g1.edge_ids(4, 5)]
assert F.array_equal(r1, r2)
@parametrize_idtype
......
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