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Commit 15a2c22c authored by Minjie Wang's avatar Minjie Wang
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Reorg folders; basic impl of the APIs

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.gitignore
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*.pyc # Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
*.swp
*.swo
LICENSE 0 → 100644
View file @ 15a2c22c
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graph.py deleted 100644 → 0
View file @ e735c20d
import networkx as nx
from networkx.classes.digraph import DiGraph
class dgl_Graph(DiGraph):
'''
Functions:
- m_func: per edge (u, v), default is u['state']
- u_func: per node u, default is RNN(m, u['state'])
'''
def __init__(self, *args, **kargs):
super(dgl_Graph, self).__init__(*args, **kargs)
self.m_func = DefaultMessageModule()
self.u_func = DefaultUpdateModule()
self.readout_func = DefaultReadoutModule()
self.init_reprs()
def init_reprs(self, h_init=None):
for n in self.nodes:
self.set_repr(n, h_init)
def set_repr(self, u, h_u, name='state'):
assert u in self.nodes
kwarg = {name: h_u}
self.add_node(u, **kwarg)
def get_repr(self, u, name='state'):
assert u in self.nodes
return self.nodes[u][name]
def _nodes_or_all(self, nodes='all'):
return self.nodes() if nodes == 'all' else nodes
def _edges_or_all(self, edges='all'):
return self.edges() if edges == 'all' else edges
def register_message_func(self, message_func, edges='all', batched=False):
if edges == 'all':
self.m_func = message_func
else:
for e in self.edges:
self.edges[e]['m_func'] = message_func
def register_update_func(self, update_func, nodes='all', batched=False):
if nodes == 'all':
self.u_func = update_func
else:
for n in nodes:
self.node[n]['u_func'] = update_func
def register_readout_func(self, readout_func):
self.readout_func = readout_func
def readout(self, nodes='all', **kwargs):
nodes_state = []
nodes = self._nodes_or_all(nodes)
for n in nodes:
nodes_state.append(self.get_repr(n))
return self.readout_func(nodes_state, **kwargs)
def sendto(self, u, v):
"""Compute message on edge u->v
Args:
u: source node
v: destination node
"""
f_msg = self.edges[(u, v)].get('m_func', self.m_func)
m = f_msg(self.get_repr(u))
self.edges[(u, v)]['msg'] = m
def sendto_ebunch(self, ebunch):
"""Compute message on edge u->v
Args:
ebunch: a bunch of edges
"""
#TODO: simplify the logics
for u, v in ebunch:
f_msg = self.edges[(u, v)].get('m_func', self.m_func)
m = f_msg(self.get_repr(u))
self.edges[(u, v)]['msg'] = m
def recvfrom(self, u, nodes):
"""Update u by nodes
Args:
u: node to be updated
nodes: nodes with pre-computed messages to u
"""
m = [self.edges[(v, u)]['msg'] for v in nodes]
f_update = self.nodes[u].get('u_func', self.u_func)
x_new = f_update(self.get_repr(u), m)
self.set_repr(u, x_new)
def update_by_edge(self, e):
u, v = e
self.sendto(u, v)
self.recvfrom(v, [u])
def update_to(self, u):
"""Pull messages from 1-step away neighbors of u"""
assert u in self.nodes
for v in self.pred[u]:
self.sendto(v, u)
self.recvfrom(u, list(self.pred[u]))
def update_from(self, u):
"""Update u's 1-step away neighbors"""
assert u in self.nodes
for v in self.succ[u]:
self.update_to(v)
def update_all_step(self):
self.sendto_ebunch(self.edges)
for u in self.nodes:
self.recvfrom(u, list(self.pred[u]))
def draw(self):
from networkx.drawing.nx_agraph import graphviz_layout
pos = graphviz_layout(self, prog='dot')
nx.draw(self, pos, with_labels=True)
def print_all(self):
for n in self.nodes:
print(n, self.nodes[n])
print()
python/dgl/backend/__init__.py 0 → 100644
View file @ 15a2c22c
__backend__ = 'numpy'
from dgl.backend.numpy import *
python/dgl/backend/numpy.py 0 → 100644
View file @ 15a2c22c
from __future__ import absolute_import
import numpy as np
import scipy as sp
Tensor = np.ndarray
SparseTensor = sp.sparse.spmatrix
def asnumpy(a):
return a
python/dgl/graph.py 0 → 100644
View file @ 15a2c22c
"""Base graph class specialized for neural networks on graphs.
"""
import networkx as nx
from networkx.classes.digraph import DiGraph
import dgl.backend as F
from dgl.backend import Tensor
import dgl.utils as utils
__MSG__ = "__msg__"
__REPR__ = "__repr__"
__MFUNC__ = "__mfunc__"
__UFUNC__ = "__ufunc__"
class DGLGraph(DiGraph):
"""Base graph class specialized for neural networks on graphs.
TODO(minjie): document of multi-node and multi-edge syntax.
Parameters
----------
data : graph data
Data to initialize graph. Same as networkx's semantics.
attr : keyword arguments, optional
Attributes to add to graph as key=value pairs.
"""
def __init__(self, graph_data=None, **attr):
super(DGLGraph, self).__init__(graph_data, **attr)
self.m_func = None
self.u_func = None
self.readout_func = None
def init_reprs(self, h_init=None):
print("[DEPRECATED]: please directly set node attrs "
"(e.g. g.nodes[node]['x'] = val).")
for n in self.nodes:
self.set_repr(n, h_init)
def set_repr(self, u, h_u, name=__REPR__):
print("[DEPRECATED]: please directly set node attrs "
"(e.g. g.nodes[node]['x'] = val).")
assert u in self.nodes
kwarg = {name: h_u}
self.add_node(u, **kwarg)
def get_repr(self, u, name=__REPR__):
print("[DEPRECATED]: please directly get node attrs "
"(e.g. g.nodes[node]['x']).")
assert u in self.nodes
return self.nodes[u][name]
def register_message_func(self, message_func, edges='all', batchable=False):
"""Register computation on edges.
The message function should be compatible with following signature:
(node_reprs, node_reprs, edge_reprs) -> edge_reprs
It computes the new edge representations (the same concept as messages)
using the representations of the source node, target node and the edge
itself. All node_reprs and edge_reprs are dictionaries.
Parameters
----------
message_func : callable
Message function on the edge.
edges : str, pair of nodes, pair of containers, pair of tensors
The edges for which the message function is registered. Default is
registering for all the edges. Registering for multiple edges is
supported.
batchable : bool
Whether the provided message function allows batch computing.
Examples
--------
Register for all edges.
>>> g.register_message_func(mfunc)
Register for a specific edge.
>>> g.register_message_func(mfunc, (u, v))
Register for multiple edges.
>>> u = [u1, u2, u3, ...]
>>> v = [v1, v2, v3, ...]
>>> g.register_message_func(mfunc, (u, v))
"""
if edges == 'all':
self.m_func = message_func
else:
for e in edges:
self.edges[e][__MFUNC__] = message_func
def register_update_func(self, update_func, nodes='all', batchable=False):
"""Register computation on nodes.
The update function should be compatible with following signature:
(edge_reprs, node_reprs) -> node_reprs
It computes the new node representations using the representations
of the in-coming edges (the same concept as messages) and the node
itself. All node_reprs and edge_reprs are dictionaries.
Parameters
----------
update_func : callable
Update function on the node.
nodes : str, node, container or tensor
The nodes for which the update function is registered. Default is
registering for all the nodes. Registering for multiple nodes is
supported.
batchable : bool
Whether the provided update function allows batch computing.
Examples
--------
Register for all nodes.
>>> g.register_update_func(ufunc)
Register for a specific node.
>>> g.register_update_func(ufunc, u)
Register for multiple nodes.
>>> u = [u1, u2, u3, ...]
>>> g.register_update_func(ufunc, u)
"""
if nodes == 'all':
self.u_func = update_func
else:
for n in nodes:
self.nodes[n][__UFUNC__] = update_func
def register_readout_func(self, readout_func):
"""Register computation on the whole graph.
The readout_func should be compatible with following signature:
(node_reprs, edge_reprs) -> any
It takes the representations of selected nodes and edges and
returns readout values.
NOTE: readout function can be implemented outside of DGLGraph.
One can simple get the node/edge reprs of the graph and perform
arbitrary computation.
Parameters
----------
readout_func : callable
The readout function.
See Also
--------
readout
"""
self.readout_func = readout_func
def readout(self, nodes='all', edges='all'):
"""Trigger the readout function on the specified nodes/edges.
Parameters
----------
nodes : str, node, container or tensor
The nodes to get reprs from.
edges : str, pair of nodes, pair of containers or pair of tensors
The edges to get reprs from.
"""
nodes = self._nodes_or_all(nodes)
edges = self._nodes_or_all(nodes)
assert self.readout_func is not None,
"Readout function is not registered."
# TODO(minjie): tensorize following loop.
nstates = [self.nodes[n] for n in nodes]
estates = [self.edges[e] for e in edges]
return self.readout_func(nstates, estates)
def sendto(self, u, v):
"""Trigger the message function on edge u->v
Parameters
----------
u : node, container or tensor
The source node(s).
v : node, container or tensor
The destination node(s).
"""
# TODO(minjie): tensorize the loop.
for uu, vv in utils.edge_iter(u, v):
f_msg = self.edges[uu, vv].get(__MFUNC__, self.m_func)
assert f_msg is not None,
"message function not registered for edge (%s->%s)" % (uu, vv)
m = f_msg(self.nodes[uu], self.nodes[vv], self.edges[uu, vv])
self.edges[uu, vv][__MSG__] = m
def recvfrom(self, u, preds=None):
"""Trigger the update function on node u.
It computes the new node state using the messages and edge
states from preds->u. If `u` is one node, `preds` is a list
of predecessors. If `u` is a container or tensor of nodes,
then `preds[i]` should be the predecessors of `u[i]`.
Parameters
----------
u : node, container or tensor
The node to be updated.
preds : container
Nodes with pre-computed messages to u. Default is all
the predecessors.
"""
u_is_container = type(u) in (list, tuple)
u_is_tensor = isinstance(u, Tensor)
# TODO(minjie): tensorize the loop.
for i, uu in enumerate(utils.node_iter(u)):
if preds is None:
v = list(self.pred[uu])
elif u_is_container or u_is_tensor:
v = preds[i]
else:
v = preds
# TODO(minjie): tensorize the message batching
m = [self.edges[vv, uu][__MSG__] for vv in v]
f_update = self.nodes[uu].get(__UFUNC__, self.u_func)
assert f_update is not None,
"Update function not registered for node %s" % uu
self.nodes[uu] = f_update(self.nodes[uu], m)
def update_by_edge(self, u, v):
"""Trigger the message function on u->v and update v.
Parameters
----------
u : node, container or tensor
The source node(s).
v : node, container or tensor
The destination node(s).
"""
self.sendto(u, v)
# TODO(minjie): tensorize the following loops.
preds = defaultdict(list)
for uu, vv in utils.edge_iter(u, v):
preds[vv].append(uu)
dst = preds.keys()
src = [preds[d] for d in dst]
self.recvfrom(dst, src)
def update_to(self, u):
"""Pull messages from the node's predecessors and then update it.
Parameters
----------
u : node, container or tensor
The node to be updated.
"""
# TODO(minjie): tensorize the following code.
for uu in utils.node_iter(u):
assert uu in self.nodes
preds = list(self.pred[uu])
self.sendto(preds, uu)
self.recvfrom(uu, preds)
def update_from(self, u):
"""Send message from the node to its successors and update them.
Parameters
----------
u : node, container or tensor
The node that sends out messages.
"""
# TODO(minjie): tensorize the following code.
for uu in utils.node_iter(u):
assert uu in self.nodes
for v in self.succ[uu]:
self.update_by_edge(uu, v)
def update_all(self):
"""Send messages through all the edges and update all nodes.
"""
# TODO(minjie): tensorize the following code.
u = [uu for uu, _ in self.edges]
v = [vv for _, vv in self.edges]
self.sendto(u, v)
self.recvfrom(v)
def propagate(self, iterator='bfs'):
"""Propagate messages and update nodes using iterator.
A convenient function for passing messages and updating
nodes according to the iterator. The iterator can be
any of the pre-defined iterators ('bfs', 'dfs', 'pre-order',
'mid-order', 'post-order'). The computation will be unrolled
in the backend efficiently. User can also provide custom
iterator that generates the edges and nodes.
Parameters
----------
iterator : str or generator of steps.
The iterator of the graph.
"""
if isinstance(iterator, str):
# TODO Call pre-defined routine to unroll the computation.
raise RuntimeError('Not implemented.')
else:
# NOTE: the iteration can return multiple edges at each step.
for u, v in iterator:
self.update_by_edge(u, v)
def draw(self):
"""Plot the graph using dot."""
from networkx.drawing.nx_agraph import graphviz_layout
pos = graphviz_layout(self, prog='dot')
nx.draw(self, pos, with_labels=True)
def _nodes_or_all(self, nodes='all'):
return self.nodes() if nodes == 'all' else nodes
def _edges_or_all(self, edges='all'):
return self.edges() if edges == 'all' else edges
python/dgl/utils.py 0 → 100644
View file @ 15a2c22c
import dgl.backend as F
from dgl.backend import Tensor
def node_iter(n):
n_is_container = type(n) in (list, tuple)
n_is_tensor = isinstance(n, Tensor)
if n_is_tensor:
n = F.asnumpy(n)
n_is_tensor = False
n_is_container = True
if n_is_container:
for nn in n:
yield nn
else:
yield n
def edge_iter(u, v):
u_is_container = type(u) in (list, tuple)
v_is_container = type(v) in (list, tuple)
u_is_tensor = isinstance(u, Tensor)
v_is_tensor = isinstance(v, Tensor)
if u_is_tensor:
u = F.asnumpy(u)
u_is_tensor = False
u_is_container = True
if v_is_tensor:
v = F.asnumpy(v)
v_is_tensor = False
v_is_container = True
if u_is_container and v_is_container:
# many-many
for uu, vv in zip(u, v):
yield uu, vv
elif u_is_container and not v_is_container:
# many-one
for uu in u:
yield uu, v
elif not u_is_container and v_is_container:
# one-many
for vv in v:
yield u, vv
else:
yield u, v
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