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Unverified Commit 9c135fd5 authored by VoVAllen's avatar VoVAllen Committed by GitHub
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Merge pull request #4 from jermainewang/master 

Sync with latest commit
parents 9d3f299d 00add9f2
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python/dgl/frame.py
View file @ 9c135fd5
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python/dgl/function/message.py
View file @ 9c135fd5
......@@ -4,17 +4,25 @@ from __future__ import absolute_import
import operator
import dgl.backend as F
__all__ = ["MessageFunction", "src_mul_edge", "copy_src", "copy_edge"]
__all__ = ["src_mul_edge", "copy_src", "copy_edge"]
class MessageFunction(object):
"""Base builtin message function class."""
def __call__(self, src, edge):
"""Regular computation of this builtin.
This will be used when optimization is not available.
"""
raise NotImplementedError
def name(self):
"""Return the name of this builtin function."""
raise NotImplementedError
def is_spmv_supported(self, g):
"""Return whether the SPMV optimization is supported."""
raise NotImplementedError
......@@ -22,12 +30,6 @@ class BundledMessageFunction(MessageFunction):
def __init__(self, fn_list):
if not isinstance(fn_list, (list, tuple)):
fn_list = [fn_list]
else:
# sanity check on out field
for fn in fn_list:
# cannot perform check for udf
if isinstance(fn, MessageFunction) and fn.out_field is None:
raise RuntimeError("Not specifying out field for multiple message is ambiguous")
self.fn_list = fn_list
def is_spmv_supported(self, g):
......@@ -43,11 +45,8 @@ class BundledMessageFunction(MessageFunction):
if ret is None:
ret = msg
else:
try:
# ret and msg must be dict
ret.update(msg)
except:
raise RuntimeError("Must specify out field for multiple message")
return ret
def name(self):
......@@ -55,25 +54,26 @@ class BundledMessageFunction(MessageFunction):
def _is_spmv_supported_node_feat(g, field):
if field is None:
feat = g.get_n_repr()
else:
"""Return whether the node feature shape supports SPMV optimization.
Only scalar and vector features are supported currently.
"""
feat = g.get_n_repr()[field]
shape = F.shape(feat)
return len(shape) == 1 or len(shape) == 2
def _is_spmv_supported_edge_feat(g, field):
# check shape, only scalar edge feature can be optimized at the moment
if field is None:
feat = g.get_e_repr()
else:
"""Return whether the edge feature shape supports SPMV optimization.
Only scalar feature is supported currently.
"""
feat = g.get_e_repr()[field]
shape = F.shape(feat)
return len(shape) == 1 or (len(shape) == 2 and shape[1] == 1)
class SrcMulEdgeMessageFunction(MessageFunction):
def __init__(self, mul_op, src_field=None, edge_field=None, out_field=None):
def __init__(self, mul_op, src_field, edge_field, out_field):
self.mul_op = mul_op
self.src_field = src_field
self.edge_field = edge_field
......@@ -84,21 +84,14 @@ class SrcMulEdgeMessageFunction(MessageFunction):
and _is_spmv_supported_edge_feat(g, self.edge_field)
def __call__(self, src, edge):
if self.src_field is not None:
src = src[self.src_field]
if self.edge_field is not None:
edge = edge[self.edge_field]
ret = self.mul_op(src, edge)
if self.out_field is None:
return ret
else:
ret = self.mul_op(src[self.src_field], edge[self.edge_field])
return {self.out_field : ret}
def name(self):
return "src_mul_edge"
class CopySrcMessageFunction(MessageFunction):
def __init__(self, src_field=None, out_field=None):
def __init__(self, src_field, out_field):
self.src_field = src_field
self.out_field = out_field
......@@ -106,14 +99,7 @@ class CopySrcMessageFunction(MessageFunction):
return _is_spmv_supported_node_feat(g, self.src_field)
def __call__(self, src, edge):
if self.src_field is not None:
ret = src[self.src_field]
else:
ret = src
if self.out_field is None:
return ret
else:
return {self.out_field : ret}
return {self.out_field : src[self.src_field]}
def name(self):
return "copy_src"
......@@ -142,14 +128,41 @@ class CopyEdgeMessageFunction(MessageFunction):
return "copy_edge"
def src_mul_edge(src=None, edge=None, out=None):
"""TODO(minjie): docstring """
def src_mul_edge(src, edge, out):
"""Builtin message function that computes message by multiplying source node features
with edge features.
Parameters
----------
src : str
The source feature name.
edge : str
The edge feature name.
out : str
The output message name.
"""
return SrcMulEdgeMessageFunction(operator.mul, src, edge, out)
def copy_src(src=None, out=None):
"""TODO(minjie): docstring """
def copy_src(src, out):
"""Builtin message function that computes message using source node feature.
Parameters
----------
src : str
The source feature name.
out : str
The output message name.
"""
return CopySrcMessageFunction(src, out)
def copy_edge(edge=None, out=None):
"""TODO(minjie): docstring """
def copy_edge(edge, out):
"""Builtin message function that computes message using edge feature.
Parameters
----------
edge : str
The edge feature name.
out : str
The output message name.
"""
return CopyEdgeMessageFunction(edge, out)
python/dgl/function/reducer.py
View file @ 9c135fd5
......@@ -3,27 +3,30 @@ from __future__ import absolute_import
from .. import backend as F
__all__ = ["ReduceFunction", "sum", "max"]
__all__ = ["sum", "max"]
class ReduceFunction(object):
"""Base builtin reduce function class."""
def __call__(self, node, msgs):
"""Regular computation of this builtin.
This will be used when optimization is not available.
"""
raise NotImplementedError
def name(self):
"""Return the name of this builtin function."""
raise NotImplementedError
def is_spmv_supported(self):
"""Return whether the SPMV optimization is supported."""
raise NotImplementedError
class BundledReduceFunction(ReduceFunction):
def __init__(self, fn_list):
if not isinstance(fn_list, (list, tuple)):
fn_list = [fn_list]
else:
# sanity check on out field
for fn in fn_list:
if isinstance(fn, ReduceFunction) and fn.out_field is None:
raise RuntimeError("Not specifying out field for multiple reduce is ambiguous")
self.fn_list = fn_list
def is_spmv_supported(self):
......@@ -39,51 +42,50 @@ class BundledReduceFunction(ReduceFunction):
if ret is None:
ret = rpr
else:
try:
# ret and rpr must be dict
ret.update(rpr)
except:
raise RuntimeError("Must specify out field for multiple reudce")
return ret
def name(self):
return "bundled"
class ReducerFunctionTemplate(ReduceFunction):
def __init__(self, name, batch_op, nonbatch_op, msg_field=None, out_field=None):
def __init__(self, name, op, msg_field, out_field):
self.name = name
self.batch_op = batch_op
self.nonbatch_op = nonbatch_op
self.op = op
self.msg_field = msg_field
self.out_field = out_field
def is_spmv_supported(self):
# TODO: support max
# NOTE: only sum is supported right now.
return self.name == "sum"
def __call__(self, node, msgs):
if isinstance(msgs, list):
if self.msg_field is None:
ret = self.nonbatch_op(msgs)
else:
ret = self.nonbatch_op([msg[self.msg_field] for msg in msgs])
else:
if self.msg_field is None:
ret = self.batch_op(msgs, 1)
else:
ret = self.batch_op(msgs[self.msg_field], 1)
if self.out_field is None:
return ret
else:
return {self.out_field : ret}
return {self.out_field : self.op(msgs[self.msg_field], 1)}
def name(self):
return self.name
_python_sum = sum
def sum(msgs=None, out=None):
return ReducerFunctionTemplate("sum", F.sum, _python_sum, msgs, out)
def sum(msg, out):
"""Builtin reduce function that aggregates messages by sum.
Parameters
----------
msg : str
The message name.
out : str
The output node feature name.
"""
return ReducerFunctionTemplate("sum", F.sum, msg, out)
def max(msg, out):
"""Builtin reduce function that aggregates messages by max.
_python_max = max
def max(msgs=None, out=None):
return ReducerFunctionTemplate("max", F.max, _python_max, msgs, out)
Parameters
----------
msg : str
The message name.
out : str
The output node feature name.
"""
return ReducerFunctionTemplate("max", F.max, msg, out)
python/dgl/generator/__init__.py deleted 100644 → 0
View file @ 9d3f299d
"""Package for graph generators"""
from __future__ import absolute_import
from .line import *
python/dgl/generator/line.py deleted 100644 → 0
View file @ 9d3f299d
"""Line graph generator."""
from __future__ import absolute_import
import networkx as nx
import numpy as np
from .. import backend as F
from ..graph import DGLGraph
from ..frame import FrameRef
def line_graph(G, no_backtracking=False):
"""Create the line graph that shares the underlying features.
The node features of the result line graph will share the edge features
of the given graph.
Parameters
----------
G : DGLGraph
The input graph.
no_backtracking : bool
Whether the backtracking edges are included in the line graph.
If i~j and j~i are two edges in original graph G, then
(i,j)~(j,i) and (j,i)~(i,j) are the "backtracking" edges on
the line graph.
"""
L = nx.DiGraph()
for eid, from_node in enumerate(G.edge_list):
L.add_node(from_node)
for to_node in G.edges(from_node[1]):
if no_backtracking and to_node[1] == from_node[0]:
continue
L.add_edge(from_node, to_node)
relabel_map = {}
for i, e in enumerate(G.edge_list):
relabel_map[e] = i
nx.relabel.relabel_nodes(L, relabel_map, copy=False)
return DGLGraph(L, node_frame=G._edge_frame)
python/dgl/graph.py
View file @ 9c135fd5
......@@ -6,7 +6,7 @@ import networkx as nx
import numpy as np
import dgl
from .base import ALL, is_all, __MSG__, __REPR__
from .base import ALL, is_all, DGLError, dgl_warning
from . import backend as F
from .backend import Tensor
from .frame import FrameRef, merge_frames
......@@ -22,7 +22,6 @@ class DGLGraph(object):
"""Base graph class specialized for neural networks on graphs.
TODO(minjie): document of batching semantics
TODO(minjie): document of __REPR__ semantics
Parameters
----------
......@@ -448,7 +447,9 @@ class DGLGraph(object):
The nx graph
"""
nx_graph = self._graph.to_networkx()
#TODO: attributes
#TODO(minjie): attributes
dgl_warning('to_networkx currently does not support converting'
' node/edge features automatically.')
return nx_graph
def from_networkx(self, nx_graph, node_attrs=None, edge_attrs=None):
......@@ -504,70 +505,95 @@ class DGLGraph(object):
self._msg_graph.add_nodes(self._graph.number_of_nodes())
def node_attr_schemes(self):
"""Return the node attribute schemes.
"""Return the node feature schemes.
Returns
-------
iterable
The set of attribute names
dict of str to schemes
The schemes of node feature columns.
"""
return self._node_frame.schemes
def edge_attr_schemes(self):
"""Return the edge attribute schemes.
"""Return the edge feature schemes.
Returns
-------
iterable
The set of attribute names
dict of str to schemes
The schemes of edge feature columns.
"""
return self._edge_frame.schemes
def set_n_initializer(self, initializer):
"""Set the initializer for empty node features.
Initializer is a callable that returns a tensor given the shape and data type.
Parameters
----------
initializer : callable
The initializer.
"""
self._node_frame.set_initializer(initializer)
def set_e_initializer(self, initializer):
"""Set the initializer for empty edge features.
Initializer is a callable that returns a tensor given the shape and data type.
Parameters
----------
initializer : callable
The initializer.
"""
self._edge_frame.set_initializer(initializer)
def set_n_repr(self, hu, u=ALL, inplace=False):
"""Set node(s) representation.
To set multiple node representations at once, pass `u` with a tensor or
a supported container of node ids. In this case, `hu` must be a tensor
of shape (B, D1, D2, ...), where B is the number of the nodes and
(D1, D2, ...) is the shape of the node representation tensor.
`hu` is a dictionary from the feature name to feature tensor. Each tensor
is of shape (B, D1, D2, ...), where B is the number of nodes to be updated,
and (D1, D2, ...) be the shape of the node representation tensor. The
length of the given node ids must match B (i.e, len(u) == B).
Dictionary type is also supported for `hu`. In this case, each item
will be treated as separate attribute of the nodes.
All update will be done out-placely to work with autograd unless the inplace
flag is true.
Parameters
----------
hu : tensor or dict of tensor
hu : dict of tensor
Node representation.
u : node, container or tensor
The node(s).
inplace : bool
True if the update is done inplacely
"""
# sanity check
if not utils.is_dict_like(hu):
raise DGLError('Expect dictionary type for feature data.'
' Got "%s" instead.' % type(hu))
if is_all(u):
num_nodes = self.number_of_nodes()
else:
u = utils.toindex(u)
num_nodes = len(u)
if utils.is_dict_like(hu):
for key, val in hu.items():
assert F.shape(val)[0] == num_nodes
else:
assert F.shape(hu)[0] == num_nodes
nfeats = F.shape(val)[0]
if nfeats != num_nodes:
raise DGLError('Expect number of features to match number of nodes (len(u)).'
' Got %d and %d instead.' % (nfeats, num_nodes))
# set
if is_all(u):
if utils.is_dict_like(hu):
for key, val in hu.items():
self._node_frame[key] = val
else:
self._node_frame[__REPR__] = hu
else:
if utils.is_dict_like(hu):
self._node_frame.update_rows(u, hu, inplace=inplace)
else:
self._node_frame.update_rows(u, {__REPR__ : hu}, inplace=inplace)
def get_n_repr(self, u=ALL):
"""Get node(s) representation.
The returned feature tensor batches multiple node features on the first dimension.
Parameters
----------
u : node, container or tensor
......@@ -576,23 +602,17 @@ class DGLGraph(object):
Returns
-------
dict
Representation dict
Representation dict from feature name to feature tensor.
"""
if len(self.node_attr_schemes()) == 0:
return dict()
if is_all(u):
if len(self._node_frame) == 1 and __REPR__ in self._node_frame:
return self._node_frame[__REPR__]
else:
return dict(self._node_frame)
else:
u = utils.toindex(u)
if len(self._node_frame) == 1 and __REPR__ in self._node_frame:
return self._node_frame.select_rows(u)[__REPR__]
else:
return self._node_frame.select_rows(u)
def pop_n_repr(self, key=__REPR__):
def pop_n_repr(self, key):
"""Get and remove the specified node repr.
Parameters
......@@ -607,71 +627,83 @@ class DGLGraph(object):
"""
return self._node_frame.pop(key)
def set_e_repr(self, h_uv, u=ALL, v=ALL):
def set_e_repr(self, he, u=ALL, v=ALL, inplace=False):
"""Set edge(s) representation.
To set multiple edge representations at once, pass `u` and `v` with tensors or
supported containers of node ids. In this case, `h_uv` must be a tensor
of shape (B, D1, D2, ...), where B is the number of the edges and
(D1, D2, ...) is the shape of the edge representation tensor.
`he` is a dictionary from the feature name to feature tensor. Each tensor
is of shape (B, D1, D2, ...), where B is the number of edges to be updated,
and (D1, D2, ...) be the shape of the edge representation tensor.
Dictionary type is also supported for `h_uv`. In this case, each item
will be treated as separate attribute of the edges.
All update will be done out-placely to work with autograd unless the inplace
flag is true.
Parameters
----------
h_uv : tensor or dict of tensor
he : tensor or dict of tensor
Edge representation.
u : node, container or tensor
The source node(s).
v : node, container or tensor
The destination node(s).
inplace : bool
True if the update is done inplacely
"""
# sanity check
if not utils.is_dict_like(he):
raise DGLError('Expect dictionary type for feature data.'
' Got "%s" instead.' % type(he))
u_is_all = is_all(u)
v_is_all = is_all(v)
assert u_is_all == v_is_all
if u_is_all:
self.set_e_repr_by_id(h_uv, eid=ALL)
self.set_e_repr_by_id(he, eid=ALL, inplace=inplace)
else:
u = utils.toindex(u)
v = utils.toindex(v)
_, _, eid = self._graph.edge_ids(u, v)
self.set_e_repr_by_id(h_uv, eid=eid)
self.set_e_repr_by_id(he, eid=eid, inplace=inplace)
def set_e_repr_by_id(self, h_uv, eid=ALL):
def set_e_repr_by_id(self, he, eid=ALL, inplace=False):
"""Set edge(s) representation by edge id.
`he` is a dictionary from the feature name to feature tensor. Each tensor
is of shape (B, D1, D2, ...), where B is the number of edges to be updated,
and (D1, D2, ...) be the shape of the edge representation tensor.
All update will be done out-placely to work with autograd unless the inplace
flag is true.
Parameters
----------
h_uv : tensor or dict of tensor
he : tensor or dict of tensor
Edge representation.
eid : int, container or tensor
The edge id(s).
inplace : bool
True if the update is done inplacely
"""
# sanity check
if not utils.is_dict_like(he):
raise DGLError('Expect dictionary type for feature data.'
' Got "%s" instead.' % type(he))
if is_all(eid):
num_edges = self.number_of_edges()
else:
eid = utils.toindex(eid)
num_edges = len(eid)
if utils.is_dict_like(h_uv):
for key, val in h_uv.items():
assert F.shape(val)[0] == num_edges
else:
assert F.shape(h_uv)[0] == num_edges
for key, val in he.items():
nfeats = F.shape(val)[0]
if nfeats != num_edges:
raise DGLError('Expect number of features to match number of edges.'
' Got %d and %d instead.' % (nfeats, num_edges))
# set
if is_all(eid):
if utils.is_dict_like(h_uv):
for key, val in h_uv.items():
# update column
for key, val in he.items():
self._edge_frame[key] = val
else:
self._edge_frame[__REPR__] = h_uv
else:
if utils.is_dict_like(h_uv):
self._edge_frame[eid] = h_uv
else:
self._edge_frame[eid] = {__REPR__ : h_uv}
# update row
self._edge_frame.update_rows(eid, he, inplace=inplace)
def get_e_repr(self, u=ALL, v=ALL):
"""Get node(s) representation.
......@@ -701,7 +733,7 @@ class DGLGraph(object):
_, _, eid = self._graph.edge_ids(u, v)
return self.get_e_repr_by_id(eid=eid)
def pop_e_repr(self, key=__REPR__):
def pop_e_repr(self, key):
"""Get and remove the specified edge repr.
Parameters
......@@ -727,20 +759,14 @@ class DGLGraph(object):
Returns
-------
dict
Representation dict
Representation dict from feature name to feature tensor.
"""
if len(self.edge_attr_schemes()) == 0:
return dict()
if is_all(eid):
if len(self._edge_frame) == 1 and __REPR__ in self._edge_frame:
return self._edge_frame[__REPR__]
else:
return dict(self._edge_frame)
else:
eid = utils.toindex(eid)
if len(self._edge_frame) == 1 and __REPR__ in self._edge_frame:
return self._edge_frame.select_rows(eid)[__REPR__]
else:
return self._edge_frame.select_rows(eid)
def register_edge_func(self, edge_func):
......@@ -793,12 +819,14 @@ class DGLGraph(object):
"""
self._apply_edge_func = apply_edge_func
def apply_nodes(self, v, apply_node_func="default"):
def apply_nodes(self, v=ALL, apply_node_func="default"):
"""Apply the function on node representations.
Applying a None function will be ignored.
Parameters
----------
v : int, iterable of int, tensor
v : int, iterable of int, tensor, optional
The node id(s).
apply_node_func : callable
The apply node function.
......@@ -827,7 +855,7 @@ class DGLGraph(object):
# merge current node_repr with reduce output
curr_repr = utils.HybridDict(reduce_accum, curr_repr)
new_repr = apply_node_func(curr_repr)
if reduce_accum is not None and utils.is_dict_like(new_repr) :
if reduce_accum is not None:
# merge new node_repr with reduce output
reduce_accum.update(new_repr)
new_repr = reduce_accum
......@@ -836,6 +864,8 @@ class DGLGraph(object):
def apply_edges(self, u=None, v=None, apply_edge_func="default", eid=None):
"""Apply the function on edge representations.
Applying a None function will be ignored.
Parameters
----------
u : optional, int, iterable of int, tensor
......@@ -852,7 +882,6 @@ class DGLGraph(object):
if not apply_edge_func:
# Skip none function call.
return
if eid is None:
new_repr = apply_edge_func(self.get_e_repr(u, v))
self.set_e_repr(new_repr, u, v)
......@@ -873,9 +902,8 @@ class DGLGraph(object):
The message function can be any of the pre-defined functions
('from_src').
Currently, we require the message functions of consecutive send's and
send_on's to return the same keys. Otherwise the behavior will be
undefined.
Currently, we require the message functions of consecutive send's to
return the same keys. Otherwise the behavior will be undefined.
Parameters
----------
......@@ -922,7 +950,11 @@ class DGLGraph(object):
src_reprs = self.get_n_repr(u)
edge_reprs = self.get_e_repr_by_id(eid)
msgs = message_func(src_reprs, edge_reprs)
self._msg_graph.add_edges(u, v)
self._msg_frame.append(msgs)
# TODO(minjie): Fix these codes in next PR.
"""
new_uv = []
msg_target_rows = []
msg_update_rows = []
......@@ -945,8 +977,8 @@ class DGLGraph(object):
self._msg_frame.update_rows(
msg_target_rows,
{k: F.gather_row(msgs[k], msg_update_rows.tousertensor())
for k in msgs}
)
for k in msgs},
inplace=False)
if len(msg_append_rows) > 0:
new_u, new_v = zip(*new_uv)
new_u = utils.toindex(new_u)
......@@ -954,14 +986,13 @@ class DGLGraph(object):
self._msg_graph.add_edges(new_u, new_v)
self._msg_frame.append(
{k: F.gather_row(msgs[k], msg_append_rows.tousertensor())
for k in msgs}
)
for k in msgs})
else:
if len(msg_target_rows) > 0:
self._msg_frame.update_rows(
msg_target_rows,
{__MSG__: F.gather_row(msgs, msg_update_rows.tousertensor())}
)
{__MSG__: F.gather_row(msgs, msg_update_rows.tousertensor())},
inplace=False)
if len(msg_append_rows) > 0:
new_u, new_v = zip(*new_uv)
new_u = utils.toindex(new_u)
......@@ -970,6 +1001,7 @@ class DGLGraph(object):
self._msg_frame.append(
{__MSG__: F.gather_row(msgs, msg_append_rows.tousertensor())}
)
"""
def update_edge(self, u=ALL, v=ALL, edge_func="default", eid=None):
"""Update representation on edge u->v
......@@ -1013,7 +1045,6 @@ class DGLGraph(object):
v = utils.toindex(v)
u, v = utils.edge_broadcasting(u, v)
_, _, eid = self._graph.edge_ids(u, v)
# call the UDF
src_reprs = self.get_n_repr(u)
dst_reprs = self.get_n_repr(v)
......@@ -1100,25 +1131,19 @@ class DGLGraph(object):
msg_shape = F.shape(msg)
new_shape = (bkt_len, deg) + msg_shape[1:]
return F.reshape(msg, new_shape)
if len(in_msgs) == 1 and __MSG__ in in_msgs:
reshaped_in_msgs = _reshape_fn(in_msgs[__MSG__])
else:
reshaped_in_msgs = utils.LazyDict(
lambda key: _reshape_fn(in_msgs[key]), self._msg_frame.schemes)
reordered_v.append(v_bkt.tousertensor())
new_reprs.append(reduce_func(dst_reprs, reshaped_in_msgs))
# TODO: clear partial messages
# TODO(minjie): clear partial messages
self.reset_messages()
# Pack all reducer results together
reordered_v = F.pack(reordered_v)
if utils.is_dict_like(new_reprs[0]):
keys = new_reprs[0].keys()
new_reprs = {key : F.pack([repr[key] for repr in new_reprs])
for key in keys}
else:
new_reprs = {__REPR__ : F.pack(new_reprs)}
if v_is_all and not has_zero_degree:
# First do reorder and then replace the whole column.
......@@ -1189,15 +1214,13 @@ class DGLGraph(object):
if executor:
new_reprs = executor.run()
if not utils.is_dict_like(new_reprs):
new_reprs = {__REPR__: new_reprs}
unique_v = executor.recv_nodes
self._apply_nodes(unique_v, apply_node_func, reduce_accum=new_reprs)
elif eid is not None:
_, v, _ = self._graph.find_edges(eid)
unique_v = utils.toindex(F.unique(v.tousertensor()))
# TODO: replace with the new DegreeBucketingScheduler
# TODO(quan): replace with the new DegreeBucketingScheduler
self.send(eid=eid, message_func=message_func)
self.recv(unique_v, reduce_func, apply_node_func)
else:
......@@ -1213,10 +1236,7 @@ class DGLGraph(object):
edge_reprs = self.get_e_repr(u, v)
msgs = message_func(src_reprs, edge_reprs)
msg_frame = FrameRef()
if utils.is_dict_like(msgs):
msg_frame.append(msgs)
else:
msg_frame.append({__MSG__: msgs})
# recv with degree bucketing
executor = scheduler.get_recv_executor(graph=self,
......@@ -1305,8 +1325,6 @@ class DGLGraph(object):
"update_all", self, message_func=message_func, reduce_func=reduce_func)
if executor:
new_reprs = executor.run()
if not utils.is_dict_like(new_reprs):
new_reprs = {__REPR__: new_reprs}
self._apply_nodes(ALL, apply_node_func, reduce_accum=new_reprs)
else:
self.send(ALL, ALL, message_func)
......@@ -1339,7 +1357,7 @@ class DGLGraph(object):
Arguments for pre-defined iterators.
"""
if isinstance(traverser, str):
# TODO Call pre-defined routine to unroll the computation.
# TODO(minjie): Call pre-defined routine to unroll the computation.
raise RuntimeError('Not implemented.')
else:
# NOTE: the iteration can return multiple edges at each step.
......
python/dgl/graph_index.py
View file @ 9c135fd5
......@@ -3,7 +3,7 @@ from __future__ import absolute_import
import ctypes
import numpy as np
import networkx as nx
import scipy.sparse as sp
import scipy
from ._ffi.base import c_array
from ._ffi.function import _init_api
......@@ -600,30 +600,59 @@ class GraphIndex(object):
return GraphIndex(handle)
class SubgraphIndex(GraphIndex):
def __init__(self, handle, parent, induced_nodes, induced_edges):
super().__init__(handle)
"""Graph index for subgraph.
Parameters
----------
handle : GraphIndexHandle
The capi handle.
paranet : GraphIndex
The parent graph index.
induced_nodes : utils.Index
The parent node ids in this subgraph.
induced_edges : utils.Index
The parent edge ids in this subgraph.
"""
def __init__(self, handle, parent, induced_nodes, induced_edges):
super(SubgraphIndex, self).__init__(handle)
self._parent = parent
self._induced_nodes = induced_nodes
self._induced_edges = induced_edges
def add_nodes(self, num):
"""Add nodes. Disabled because SubgraphIndex is read-only."""
raise RuntimeError('Readonly graph. Mutation is not allowed.')
def add_edge(self, u, v):
"""Add edges. Disabled because SubgraphIndex is read-only."""
raise RuntimeError('Readonly graph. Mutation is not allowed.')
def add_edges(self, u, v):
"""Add edges. Disabled because SubgraphIndex is read-only."""
raise RuntimeError('Readonly graph. Mutation is not allowed.')
@property
def induced_edges(self):
return self._induced_edges
@property
def induced_nodes(self):
"""Return parent node ids.
Returns
-------
utils.Index
The parent node ids.
"""
return self._induced_nodes
@property
def induced_edges(self):
"""Return parent edge ids.
Returns
-------
utils.Index
The parent edge ids.
"""
return self._induced_edges
def disjoint_union(graphs):
"""Return a disjoint union of the input graphs.
......@@ -697,8 +726,25 @@ def create_graph_index(graph_data=None, multigraph=False):
handle = _CAPI_DGLGraphCreate(multigraph)
gi = GraphIndex(handle)
if graph_data is not None:
if graph_data is None:
return gi
# scipy format
if isinstance(graph_data, scipy.sparse.spmatrix):
try:
gi.from_scipy_sparse_matrix(graph_data)
return gi
except:
raise Exception('Graph data is not a valid scipy sparse matrix.')
# networkx - any format
try:
gi.from_networkx(graph_data)
except:
raise Exception('Error while creating graph from input of type "%s".'
% type(graph_data))
return gi
_init_api("dgl.graph_index")
python/dgl/scheduler.py
View file @ 9c135fd5
......@@ -3,7 +3,7 @@ from __future__ import absolute_import
import numpy as np
from .base import ALL, __MSG__, __REPR__
from .base import ALL, DGLError
from . import backend as F
from .function import message as fmsg
from .function import reducer as fred
......@@ -111,7 +111,15 @@ def light_degree_bucketing_for_graph(graph):
class Executor(object):
"""Base class for executing graph computation."""
def run(self):
"""Run this executor.
This should return the new node features.
TODO(minjie): extend this to support computation on edges.
"""
raise NotImplementedError
class SPMVOperator(Executor):
......@@ -126,9 +134,6 @@ class SPMVOperator(Executor):
def run(self):
# get src col
if self.src_field is None:
srccol = self.node_repr
else:
srccol = self.node_repr[self.src_field]
ctx = F.get_context(srccol)
......@@ -142,9 +147,6 @@ class SPMVOperator(Executor):
dstcol = F.squeeze(dstcol)
else:
dstcol = F.spmm(adjmat, srccol)
if self.dst_field is None:
return dstcol
else:
return {self.dst_field : dstcol}
......@@ -180,20 +182,14 @@ class DegreeBucketingExecutor(Executor):
msg_shape = F.shape(msg)
new_shape = (len(vv), deg) + msg_shape[1:]
return F.reshape(msg, new_shape)
if len(in_msgs) == 1 and __MSG__ in in_msgs:
reshaped_in_msgs = _reshape_fn(in_msgs[__MSG__])
else:
reshaped_in_msgs = utils.LazyDict(
lambda key: _reshape_fn(in_msgs[key]), self.msg_frame.schemes)
new_reprs.append(self.rfunc(dst_reprs, reshaped_in_msgs))
# Pack all reducer results together
if utils.is_dict_like(new_reprs[0]):
keys = new_reprs[0].keys()
new_reprs = {key : F.pack([repr[key] for repr in new_reprs])
for key in keys}
else:
new_reprs = {__REPR__ : F.pack(new_reprs)}
return new_reprs
......@@ -249,12 +245,6 @@ class UpdateAllExecutor(BasicExecutor):
self._graph_shape = None
self._recv_nodes = None
@property
def graph_idx(self):
if self._graph_idx is None:
self._graph_idx = self.g._graph.adjacency_matrix()
return self._graph_idx
@property
def graph_shape(self):
if self._graph_shape is None:
......@@ -280,16 +270,13 @@ class UpdateAllExecutor(BasicExecutor):
def _adj_build_fn(self, edge_field, ctx, use_edge_feat):
if use_edge_feat:
if edge_field is None:
dat = self.edge_repr
else:
dat = self.edge_repr[edge_field]
dat = F.squeeze(dat)
# TODO(minjie): should not directly use _indices
idx = self.graph_idx.get(ctx)._indices()
idx = self.g.adjacency_matrix(ctx)._indices()
adjmat = F.sparse_tensor(idx, dat, self.graph_shape)
else:
adjmat = self.graph_idx.get(ctx)
adjmat = self.g.adjacency_matrix(ctx)
return adjmat
......@@ -351,9 +338,6 @@ class SendRecvExecutor(BasicExecutor):
def _adj_build_fn(self, edge_field, ctx, use_edge_feat):
if use_edge_feat:
if edge_field is None:
dat = self.edge_repr
else:
dat = self.edge_repr[edge_field]
dat = F.squeeze(dat)
else:
......@@ -386,9 +370,8 @@ class BundledExecutor(BasicExecutor):
func_pairs = []
for rfn in rfunc.fn_list:
mfn = out2mfunc.get(rfn.msg_field, None)
# field check
assert mfn is not None, \
"cannot find message func for reduce func in-field {}".format(rfn.msg_field)
if mfn is None:
raise DGLError('Cannot find message field "%s".' % rfn.msg_field)
func_pairs.append((mfn, rfn))
return func_pairs
......@@ -409,7 +392,6 @@ class BundledUpdateAllExecutor(BundledExecutor, UpdateAllExecutor):
self._init_state()
BundledExecutor.__init__(self, graph, mfunc, rfunc)
class BundledSendRecvExecutor(BundledExecutor, SendRecvExecutor):
def __init__(self, graph, src, dst, mfunc, rfunc):
self._init_state(src, dst)
......
src/c_api_common.cc
View file @ 9c135fd5
/*!
* Copyright (c) 2018 by Contributors
* \file c_runtime_api.cc
* \brief DGL C API common implementations
*/
#include "c_api_common.h"
using tvm::runtime::TVMArgs;
......
src/c_api_common.h
View file @ 9c135fd5
// DGL C API common util functions
/*!
* Copyright (c) 2018 by Contributors
* \file c_api_common.h
* \brief DGL C API common util functions
*/
#ifndef DGL_C_API_COMMON_H_
#define DGL_C_API_COMMON_H_
......@@ -12,11 +16,19 @@ namespace dgl {
// Graph handler type
typedef void* GraphHandle;
// Convert the given DLTensor to a temporary DLManagedTensor that does not own memory.
DLManagedTensor* CreateTmpDLManagedTensor(const tvm::runtime::TVMArgValue& arg);
/*!
* \brief Convert the given DLTensor to DLManagedTensor.
*
* Return a temporary DLManagedTensor that does not own memory.
*/
DLManagedTensor* CreateTmpDLManagedTensor(
const tvm::runtime::TVMArgValue& arg);
// Convert a vector of NDArray to PackedFunc
tvm::runtime::PackedFunc ConvertNDArrayVectorToPackedFunc(const std::vector<tvm::runtime::NDArray>& vec);
/*!
* \brief Convert a vector of NDArray to PackedFunc.
*/
tvm::runtime::PackedFunc ConvertNDArrayVectorToPackedFunc(
const std::vector<tvm::runtime::NDArray>& vec);
} // namespace dgl
......
src/graph/graph.cc
View file @ 9c135fd5
// Graph class implementation
/*!
* Copyright (c) 2018 by Contributors
* \file graph/graph.cc
* \brief DGL graph index implementation
*/
#include <dgl/graph.h>
#include <algorithm>
#include <unordered_map>
#include <set>
#include <functional>
#include <dgl/graph.h>
namespace dgl {
namespace {
......@@ -461,7 +465,8 @@ Subgraph Graph::EdgeSubgraph(IdArray eids) const {
rst.graph.AddEdge(oldv2newv[src_id], oldv2newv[dst_id]);
}
rst.induced_vertices = IdArray::Empty({static_cast<int64_t>(nodes.size())}, eids->dtype, eids->ctx);
rst.induced_vertices = IdArray::Empty(
{static_cast<int64_t>(nodes.size())}, eids->dtype, eids->ctx);
std::copy(nodes.begin(), nodes.end(), static_cast<int64_t*>(rst.induced_vertices->data));
return rst;
......
src/graph/graph_apis.cc
View file @ 9c135fd5
/*!
* Copyright (c) 2018 by Contributors
* \file graph/graph.cc
* \brief DGL graph index APIs
*/
#include <dgl/graph.h>
#include <dgl/graph_op.h>
#include "../c_api_common.h"
......
src/graph/graph_op.cc
View file @ 9c135fd5
// Graph operation implementation
/*!
* Copyright (c) 2018 by Contributors
* \file graph/graph.cc
* \brief Graph operation implementation
*/
#include <dgl/graph_op.h>
#include <algorithm>
namespace dgl {
Graph GraphOp::LineGraph(const Graph* g, bool backtracking){
Graph GraphOp::LineGraph(const Graph* g, bool backtracking) {
typedef std::pair<dgl_id_t, dgl_id_t> entry;
typedef std::map<dgl_id_t, std::vector<entry>> csm; // Compressed Sparse Matrix
......@@ -117,32 +121,6 @@ std::vector<Graph> GraphOp::DisjointPartitionBySizes(const Graph* graph, IdArray
node_offset += sizes_data[i];
edge_offset += num_edges;
}
/*for (int64_t i = 0; i < len; ++i) {
rst[i].AddVertices(sizes_data[i]);
}
for (dgl_id_t eid = 0; eid < graph->num_edges_; ++eid) {
const dgl_id_t src = graph->all_edges_src_[eid];
const dgl_id_t dst = graph->all_edges_dst_[eid];
size_t src_select = 0, dst_select = 0;
for (size_t i = 1; i < cumsum.size(); ++i) { // TODO: replace with binary search
if (cumsum[i] > src) {
src_select = i;
break;
}
}
for (size_t i = 1; i < cumsum.size(); ++i) { // TODO: replace with binary search
if (cumsum[i] > dst) {
dst_select = i;
break;
}
}
if (src_select != dst_select) {
// the edge is ignored if across two partitions
continue;
}
const int64_t offset = cumsum[src_select - 1];
rst[src_select - 1].AddEdge(src - offset, dst - offset);
}*/
return rst;
}
......
src/runtime/README.md deleted 100644 → 0
View file @ 9d3f299d
# C API and runtime
Borrowed and adapted from TVM project.
src/runtime/file_util.h
View file @ 9c135fd5
......@@ -3,8 +3,8 @@
* \file file_util.h
* \brief Minimum file manipulation util for runtime.
*/
#ifndef TVM_RUNTIME_FILE_UTIL_H_
#define TVM_RUNTIME_FILE_UTIL_H_
#ifndef DGL_RUNTIME_FILE_UTIL_H_
#define DGL_RUNTIME_FILE_UTIL_H_
#include <string>
#include "meta_data.h"
......@@ -73,4 +73,4 @@ void LoadMetaDataFromFile(
std::unordered_map<std::string, FunctionInfo>* fmap);
} // namespace runtime
} // namespace tvm
#endif // TVM_RUNTIME_FILE_UTIL_H_
#endif // DGL_RUNTIME_FILE_UTIL_H_
src/runtime/meta_data.h
View file @ 9c135fd5
......@@ -3,8 +3,8 @@
* \file meta_data.h
* \brief Meta data related utilities
*/
#ifndef TVM_RUNTIME_META_DATA_H_
#define TVM_RUNTIME_META_DATA_H_
#ifndef DGL_RUNTIME_META_DATA_H_
#define DGL_RUNTIME_META_DATA_H_
#include <dmlc/json.h>
#include <dmlc/io.h>
......@@ -33,4 +33,4 @@ struct FunctionInfo {
namespace dmlc {
DMLC_DECLARE_TRAITS(has_saveload, ::tvm::runtime::FunctionInfo, true);
} // namespace dmlc
#endif // TVM_RUNTIME_META_DATA_H_
#endif // DGL_RUNTIME_META_DATA_H_
src/runtime/module_util.h
View file @ 9c135fd5
......@@ -3,8 +3,8 @@
* \file module_util.h
* \brief Helper utilities for module building
*/
#ifndef TVM_RUNTIME_MODULE_UTIL_H_
#define TVM_RUNTIME_MODULE_UTIL_H_
#ifndef DGL_RUNTIME_MODULE_UTIL_H_
#define DGL_RUNTIME_MODULE_UTIL_H_
#include <dgl/runtime/module.h>
#include <dgl/runtime/c_runtime_api.h>
......@@ -58,4 +58,4 @@ void InitContextFunctions(FLookup flookup) {
}
} // namespace runtime
} // namespace tvm
#endif // TVM_RUNTIME_MODULE_UTIL_H_
#endif // DGL_RUNTIME_MODULE_UTIL_H_
src/runtime/pack_args.h
View file @ 9c135fd5
......@@ -10,8 +10,8 @@
* union_32bit args[N], int num_args);
* - Pack buffer by address, pack rest parameter into 32bit union buffer.
*/
#ifndef TVM_RUNTIME_PACK_ARGS_H_
#define TVM_RUNTIME_PACK_ARGS_H_
#ifndef DGL_RUNTIME_PACK_ARGS_H_
#define DGL_RUNTIME_PACK_ARGS_H_
#include <dgl/runtime/c_runtime_api.h>
#include <vector>
......@@ -307,4 +307,4 @@ inline PackedFunc PackFuncPackedArg(F f, const std::vector<TVMType>& arg_types)
}
} // namespace runtime
} // namespace tvm
#endif // TVM_RUNTIME_PACK_ARGS_H_
#endif // DGL_RUNTIME_PACK_ARGS_H_
src/runtime/runtime_base.h
View file @ 9c135fd5
......@@ -3,8 +3,8 @@
* \file runtime_base.h
* \brief Base of all C APIs
*/
#ifndef TVM_RUNTIME_RUNTIME_BASE_H_
#define TVM_RUNTIME_RUNTIME_BASE_H_
#ifndef DGL_RUNTIME_RUNTIME_BASE_H_
#define DGL_RUNTIME_RUNTIME_BASE_H_
#include <dgl/runtime/c_runtime_api.h>
#include <stdexcept>
......@@ -31,4 +31,4 @@ inline int TVMAPIHandleException(const std::runtime_error &e) {
return -1;
}
#endif // TVM_RUNTIME_RUNTIME_BASE_H_
#endif // DGL_RUNTIME_RUNTIME_BASE_H_
src/runtime/thread_storage_scope.h
View file @ 9c135fd5
......@@ -3,8 +3,8 @@
* \file thread_storage_scope.h
* \brief Extract thread axis configuration from TVMArgs.
*/
#ifndef TVM_RUNTIME_THREAD_STORAGE_SCOPE_H_
#define TVM_RUNTIME_THREAD_STORAGE_SCOPE_H_
#ifndef DGL_RUNTIME_THREAD_STORAGE_SCOPE_H_
#define DGL_RUNTIME_THREAD_STORAGE_SCOPE_H_
#include <dgl/runtime/packed_func.h>
#include <string>
......@@ -204,4 +204,4 @@ struct hash<::tvm::runtime::StorageScope> {
}
};
} // namespace std
#endif // TVM_RUNTIME_THREAD_STORAGE_SCOPE_H_
#endif // DGL_RUNTIME_THREAD_STORAGE_SCOPE_H_
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