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Commit 3683a774 authored by Gan Quan's avatar Gan Quan
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Merge branch 'cpp' of github.com:jermainewang/dgl into cpp

parents f1ede61f c9e3c658
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Showing with 232 additions and 469 deletions
python/dgl/graph.py
View file @ 3683a774
......@@ -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):
......@@ -550,20 +551,17 @@ class DGLGraph(object):
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.
Dictionary type is also supported for `hu`. In this case, each item
will be treated as separate attribute of the nodes.
`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).
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).
......@@ -571,32 +569,31 @@ class DGLGraph(object):
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
for key, val in hu.items():
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
for key, val in hu.items():
self._node_frame[key] = val
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)
self._node_frame.update_rows(u, 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
......@@ -605,23 +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)
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)
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
......@@ -636,23 +627,19 @@ class DGLGraph(object):
"""
return self._node_frame.pop(key)
def set_e_repr(self, h_uv, u=ALL, v=ALL, inplace=False):
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.
Dictionary type is also supported for `h_uv`. In this case, each item
will be treated as separate attribute of the edges.
`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.
u : node, container or tensor
The source node(s).
......@@ -662,26 +649,33 @@ class DGLGraph(object):
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, inplace=inplace)
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, inplace=inplace)
self.set_e_repr_by_id(he, eid=eid, inplace=inplace)
def set_e_repr_by_id(self, h_uv, eid=ALL, inplace=False):
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).
......@@ -689,30 +683,27 @@ class DGLGraph(object):
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):
# update column
if utils.is_dict_like(h_uv):
for key, val in h_uv.items():
self._edge_frame[key] = val
else:
self._edge_frame[__REPR__] = h_uv
for key, val in he.items():
self._edge_frame[key] = val
else:
# update row
if utils.is_dict_like(h_uv):
self._edge_frame.update_rows(eid, h_uv, inplace=inplace)
else:
self._edge_frame.update_rows(eid, {__REPR__ : h_uv}, inplace=inplace)
self._edge_frame.update_rows(eid, he, inplace=inplace)
def get_e_repr(self, u=ALL, v=ALL):
"""Get node(s) representation.
......@@ -742,7 +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
......@@ -768,21 +759,15 @@ 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)
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)
return self._edge_frame.select_rows(eid)
def register_edge_func(self, edge_func):
"""Register global edge update function.
......@@ -837,6 +822,8 @@ class DGLGraph(object):
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, optional
......@@ -868,7 +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
......@@ -877,6 +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
......@@ -893,7 +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)
......@@ -914,9 +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
----------
......@@ -964,10 +951,7 @@ class DGLGraph(object):
edge_reprs = self.get_e_repr_by_id(eid)
msgs = message_func(src_reprs, edge_reprs)
self._msg_graph.add_edges(u, v)
if utils.is_dict_like(msgs):
self._msg_frame.append(msgs)
else:
self._msg_frame.append({__MSG__ : msgs})
self._msg_frame.append(msgs)
# TODO(minjie): Fix these codes in next PR.
"""
......@@ -1061,7 +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)
......@@ -1148,25 +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)
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)}
keys = new_reprs[0].keys()
new_reprs = {key : F.pack([repr[key] for repr in new_reprs])
for key in keys}
if v_is_all and not has_zero_degree:
# First do reorder and then replace the whole column.
......@@ -1237,15 +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:
......@@ -1261,10 +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})
msg_frame.append(msgs)
# recv with degree bucketing
executor = scheduler.get_recv_executor(graph=self,
......@@ -1353,8 +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)
......@@ -1387,7 +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/scheduler.py
View file @ 3683a774
......@@ -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):
......@@ -126,10 +134,7 @@ 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]
srccol = self.node_repr[self.src_field]
ctx = F.get_context(srccol)
# build adjmat
......@@ -142,10 +147,7 @@ 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}
return {self.dst_field : dstcol}
# FIXME: refactorize in scheduler/executor redesign
......@@ -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)
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)}
keys = new_reprs[0].keys()
new_reprs = {key : F.pack([repr[key] for repr in new_reprs])
for key in keys}
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 = 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
......@@ -351,10 +338,7 @@ 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 = self.edge_repr[edge_field]
dat = F.squeeze(dat)
else:
dat = F.ones((len(self.u), ))
......@@ -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 @ 3683a774
/*!
* 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;
......@@ -29,5 +34,5 @@ PackedFunc ConvertNDArrayVectorToPackedFunc(const std::vector<NDArray>& vec) {
return PackedFunc(body);
}
} // namespace dgl
} // namespace dgl
src/c_api_common.h
View file @ 3683a774
// 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_
......@@ -12,12 +16,20 @@ 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
} // namespace dgl
#endif // DGL_C_API_COMMON_H_
#endif // DGL_C_API_COMMON_H_
src/graph/graph.cc
View file @ 3683a774
// 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 {
......@@ -193,9 +197,9 @@ Graph::EdgeArray Graph::EdgeIds(IdArray src_ids, IdArray dst_ids) const {
const auto& succ = adjlist_[src_id].succ;
for (size_t k = 0; k < succ.size(); ++k) {
if (succ[k] == dst_id) {
src.push_back(src_id);
dst.push_back(dst_id);
eid.push_back(adjlist_[src_id].edge_id[k]);
src.push_back(src_id);
dst.push_back(dst_id);
eid.push_back(adjlist_[src_id].edge_id[k]);
}
}
}
......@@ -351,7 +355,7 @@ Graph::EdgeArray Graph::Edges(bool sorted) const {
return std::get<0>(t1) < std::get<0>(t2)
|| (std::get<0>(t1) == std::get<0>(t2) && std::get<1>(t1) < std::get<1>(t2));
});
// make return arrays
int64_t* src_ptr = static_cast<int64_t*>(src->data);
int64_t* dst_ptr = static_cast<int64_t*>(dst->data);
......@@ -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 @ 3683a774
/*!
* 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 @ 3683a774
// 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
typedef std::map<dgl_id_t, std::vector<entry>> csm; // Compressed Sparse Matrix
csm adj;
std::vector<entry> vec;
......@@ -67,7 +71,7 @@ std::vector<Graph> GraphOp::DisjointPartitionByNum(const Graph* graph, int64_t n
std::fill(sizes_data, sizes_data + num, graph->NumVertices() / num);
return DisjointPartitionBySizes(graph, sizes);
}
std::vector<Graph> GraphOp::DisjointPartitionBySizes(const Graph* graph, IdArray sizes) {
const int64_t len = sizes->shape[0];
const int64_t* sizes_data = static_cast<int64_t*>(sizes->data);
......@@ -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 @ f1ede61f
# C API and runtime
Borrowed and adapted from TVM project.
src/runtime/file_util.h
View file @ 3683a774
......@@ -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 @ 3683a774
......@@ -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 @ 3683a774
......@@ -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 @ 3683a774
......@@ -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 @ 3683a774
......@@ -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 @ 3683a774
......@@ -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_
src/runtime/workspace_pool.h
View file @ 3683a774
......@@ -3,8 +3,8 @@
* \file workspace_pool.h
* \brief Workspace pool utility.
*/
#ifndef TVM_RUNTIME_WORKSPACE_POOL_H_
#define TVM_RUNTIME_WORKSPACE_POOL_H_
#ifndef DGL_RUNTIME_WORKSPACE_POOL_H_
#define DGL_RUNTIME_WORKSPACE_POOL_H_
#include <dgl/runtime/device_api.h>
#include <vector>
......@@ -58,4 +58,4 @@ class WorkspacePool {
} // namespace runtime
} // namespace tvm
#endif // TVM_RUNTIME_WORKSPACE_POOL_H_
#endif // DGL_RUNTIME_WORKSPACE_POOL_H_
src/scheduler/scheduler.cc
View file @ 3683a774
// DGL Scheduler implementation
/*!
* Copyright (c) 2018 by Contributors
* \file scheduler/scheduler.cc
* \brief DGL Scheduler implementation
*/
#include <dgl/scheduler.h>
#include <unordered_map>
#include <vector>
#include <dgl/scheduler.h>
namespace dgl {
namespace sched {
......@@ -19,7 +22,7 @@ std::vector<IdArray> DegreeBucketing(const IdArray& vids) {
// bkt: deg->dsts
std::unordered_map<int64_t, std::vector<int64_t>> bkt;
for (auto& it: in_edges) {
for (const auto& it : in_edges) {
bkt[it.second.size()].push_back(it.first);
}
......@@ -38,15 +41,15 @@ std::vector<IdArray> DegreeBucketing(const IdArray& vids) {
int64_t* msec_ptr = static_cast<int64_t*>(mid_section->data);
// fill in bucketing ordering
for (auto& it: bkt) { // for each bucket
int64_t deg = it.first;
int64_t n_dst = it.second.size();
for (const auto& it : bkt) { // for each bucket
const int64_t deg = it.first;
const int64_t n_dst = it.second.size();
*deg_ptr++ = deg;
*nsec_ptr++ = n_dst;
*msec_ptr++ = deg * n_dst;
for (auto dst: it.second) { // for each dst in this bucket
for (const auto dst : it.second) { // for each dst in this bucket
*nid_ptr++ = dst;
for (auto mid: in_edges[dst]) { // for each in edge of dst
for (const auto mid : in_edges[dst]) { // for each in edge of dst
*mid_ptr++ = mid;
}
}
......@@ -62,6 +65,6 @@ std::vector<IdArray> DegreeBucketing(const IdArray& vids) {
return std::move(ret);
}
} // namespace sched
} // namespace sched
} // namespace dgl
} // namespace dgl
src/scheduler/scheduler_apis.cc
View file @ 3683a774
#include "../c_api_common.h"
/*!
* Copyright (c) 2018 by Contributors
* \file scheduler/scheduler_apis.cc
* \brief DGL scheduler APIs
*/
#include <dgl/graph.h>
#include <dgl/scheduler.h>
#include "../c_api_common.h"
using tvm::runtime::TVMArgs;
using tvm::runtime::TVMRetValue;
......@@ -18,8 +23,8 @@ TVM_REGISTER_GLOBAL("scheduler._CAPI_DGLDegreeBucketingFromGraph")
.set_body([] (TVMArgs args, TVMRetValue* rv) {
GraphHandle ghandle = args[0];
const Graph* gptr = static_cast<Graph*>(ghandle);
auto edges = gptr->Edges(false);
const auto& edges = gptr->Edges(false);
*rv = ConvertNDArrayVectorToPackedFunc(sched::DegreeBucketing(edges.dst));
});
} // namespace dgl
} // namespace dgl
tests/pytorch/test_basics.py
View file @ 3683a774
......@@ -209,14 +209,13 @@ def test_reduce_0deg():
g.add_edge(3, 0)
g.add_edge(4, 0)
def _message(src, edge):
return src
return {'m' : src['h']}
def _reduce(node, msgs):
assert msgs is not None
return node + msgs.sum(1)
return {'h' : node['h'] + msgs['m'].sum(1)}
old_repr = th.randn(5, 5)
g.set_n_repr(old_repr)
g.set_n_repr({'h' : old_repr})
g.update_all(_message, _reduce)
new_repr = g.get_n_repr()
new_repr = g.get_n_repr()['h']
assert th.allclose(new_repr[1:], old_repr[1:])
assert th.allclose(new_repr[0], old_repr.sum(0))
......@@ -226,25 +225,25 @@ def test_pull_0deg():
g.add_nodes(2)
g.add_edge(0, 1)
def _message(src, edge):
return src
return {'m' : src['h']}
def _reduce(node, msgs):
assert msgs is not None
return msgs.sum(1)
return {'h' : msgs['m'].sum(1)}
old_repr = th.randn(2, 5)
g.set_n_repr(old_repr)
g.set_n_repr({'h' : old_repr})
g.pull(0, _message, _reduce)
new_repr = g.get_n_repr()
new_repr = g.get_n_repr()['h']
assert th.allclose(new_repr[0], old_repr[0])
assert th.allclose(new_repr[1], old_repr[1])
g.pull(1, _message, _reduce)
new_repr = g.get_n_repr()
new_repr = g.get_n_repr()['h']
assert th.allclose(new_repr[1], old_repr[0])
old_repr = th.randn(2, 5)
g.set_n_repr(old_repr)
g.set_n_repr({'h' : old_repr})
g.pull([0, 1], _message, _reduce)
new_repr = g.get_n_repr()
new_repr = g.get_n_repr()['h']
assert th.allclose(new_repr[0], old_repr[0])
assert th.allclose(new_repr[1], old_repr[0])
......
tests/pytorch/test_basics_anonymous.py deleted 100644 → 0
View file @ f1ede61f
import torch as th
from torch.autograd import Variable
import numpy as np
from dgl.graph import DGLGraph, __REPR__
D = 32
reduce_msg_shapes = set()
def check_eq(a, b):
assert a.shape == b.shape
assert th.sum(a == b) == int(np.prod(list(a.shape)))
def message_func(hu, e_uv):
assert len(hu.shape) == 2
assert hu.shape[1] == D
return hu
def reduce_func(hv, msgs):
reduce_msg_shapes.add(tuple(msgs.shape))
assert len(msgs.shape) == 3
assert msgs.shape[2] == D
return hv + th.sum(msgs, 1)
def generate_graph(grad=False):
g = DGLGraph()
g.add_nodes(10)
# create a graph where 0 is the source and 9 is the sink
for i in range(1, 9):
g.add_edge(0, i)
g.add_edge(i, 9)
# add a back flow from 9 to 0
g.add_edge(9, 0)
ncol = Variable(th.randn(10, D), requires_grad=grad)
ecol = Variable(th.randn(17, D), requires_grad=grad)
g.set_n_repr(ncol)
g.set_e_repr(ecol)
return g
def test_batch_setter_getter():
def _pfc(x):
return list(x.numpy()[:,0])
g = generate_graph()
# set all nodes
g.set_n_repr(th.zeros((10, D)))
assert _pfc(g.get_n_repr()) == [0.] * 10
# pop nodes
assert _pfc(g.pop_n_repr()) == [0.] * 10
assert len(g.get_n_repr()) == 0
g.set_n_repr(th.zeros((10, D)))
# set partial nodes
u = th.tensor([1, 3, 5])
g.set_n_repr(th.ones((3, D)), u)
assert _pfc(g.get_n_repr()) == [0., 1., 0., 1., 0., 1., 0., 0., 0., 0.]
# get partial nodes
u = th.tensor([1, 2, 3])
assert _pfc(g.get_n_repr(u)) == [1., 0., 1.]
'''
s, d, eid
0, 1, 0
1, 9, 1
0, 2, 2
2, 9, 3
0, 3, 4
3, 9, 5
0, 4, 6
4, 9, 7
0, 5, 8
5, 9, 9
0, 6, 10
6, 9, 11
0, 7, 12
7, 9, 13
0, 8, 14
8, 9, 15
9, 0, 16
'''
# set all edges
g.set_e_repr(th.zeros((17, D)))
assert _pfc(g.get_e_repr()) == [0.] * 17
# pop edges
assert _pfc(g.pop_e_repr()) == [0.] * 17
assert len(g.get_e_repr()) == 0
g.set_e_repr(th.zeros((17, D)))
# set partial edges (many-many)
u = th.tensor([0, 0, 2, 5, 9])
v = th.tensor([1, 3, 9, 9, 0])
g.set_e_repr(th.ones((5, D)), u, v)
truth = [0.] * 17
truth[0] = truth[4] = truth[3] = truth[9] = truth[16] = 1.
assert _pfc(g.get_e_repr()) == truth
# set partial edges (many-one)
u = th.tensor([3, 4, 6])
v = th.tensor([9])
g.set_e_repr(th.ones((3, D)), u, v)
truth[5] = truth[7] = truth[11] = 1.
assert _pfc(g.get_e_repr()) == truth
# set partial edges (one-many)
u = th.tensor([0])
v = th.tensor([4, 5, 6])
g.set_e_repr(th.ones((3, D)), u, v)
truth[6] = truth[8] = truth[10] = 1.
assert _pfc(g.get_e_repr()) == truth
# get partial edges (many-many)
u = th.tensor([0, 6, 0])
v = th.tensor([6, 9, 7])
assert _pfc(g.get_e_repr(u, v)) == [1., 1., 0.]
# get partial edges (many-one)
u = th.tensor([5, 6, 7])
v = th.tensor([9])
assert _pfc(g.get_e_repr(u, v)) == [1., 1., 0.]
# get partial edges (one-many)
u = th.tensor([0])
v = th.tensor([3, 4, 5])
assert _pfc(g.get_e_repr(u, v)) == [1., 1., 1.]
def test_batch_setter_autograd():
g = generate_graph(grad=True)
h1 = g.get_n_repr()
# partial set
v = th.tensor([1, 2, 8])
hh = Variable(th.zeros((len(v), D)), requires_grad=True)
g.set_n_repr(hh, v)
h2 = g.get_n_repr()
h2.backward(th.ones((10, D)) * 2)
check_eq(h1.grad[:,0], th.tensor([2., 0., 0., 2., 2., 2., 2., 2., 0., 2.]))
check_eq(hh.grad[:,0], th.tensor([2., 2., 2.]))
def test_batch_send():
g = generate_graph()
def _fmsg(hu, edge):
assert hu.shape == (5, D)
return hu
g.register_message_func(_fmsg)
# many-many send
u = th.tensor([0, 0, 0, 0, 0])
v = th.tensor([1, 2, 3, 4, 5])
g.send(u, v)
# one-many send
u = th.tensor([0])
v = th.tensor([1, 2, 3, 4, 5])
g.send(u, v)
# many-one send
u = th.tensor([1, 2, 3, 4, 5])
v = th.tensor([9])
g.send(u, v)
def test_batch_recv():
g = generate_graph()
g.register_message_func(message_func)
g.register_reduce_func(reduce_func)
u = th.tensor([0, 0, 0, 4, 5, 6])
v = th.tensor([1, 2, 3, 9, 9, 9])
reduce_msg_shapes.clear()
g.send(u, v)
g.recv(th.unique(v))
assert(reduce_msg_shapes == {(1, 3, D), (3, 1, D)})
reduce_msg_shapes.clear()
def test_update_routines():
g = generate_graph()
g.register_message_func(message_func)
g.register_reduce_func(reduce_func)
# send_and_recv
reduce_msg_shapes.clear()
u = th.tensor([0, 0, 0, 4, 5, 6])
v = th.tensor([1, 2, 3, 9, 9, 9])
g.send_and_recv(u, v)
assert(reduce_msg_shapes == {(1, 3, D), (3, 1, D)})
reduce_msg_shapes.clear()
# pull
v = th.tensor([1, 2, 3, 9])
reduce_msg_shapes.clear()
g.pull(v)
assert(reduce_msg_shapes == {(1, 8, D), (3, 1, D)})
reduce_msg_shapes.clear()
# push
v = th.tensor([0, 1, 2, 3])
reduce_msg_shapes.clear()
g.push(v)
assert(reduce_msg_shapes == {(1, 3, D), (8, 1, D)})
reduce_msg_shapes.clear()
# update_all
reduce_msg_shapes.clear()
g.update_all()
assert(reduce_msg_shapes == {(1, 8, D), (9, 1, D)})
reduce_msg_shapes.clear()
if __name__ == '__main__':
test_batch_setter_getter()
test_batch_setter_autograd()
test_batch_send()
test_batch_recv()
test_update_routines()
tests/pytorch/test_batched_graph.py
View file @ 3683a774
......@@ -18,8 +18,8 @@ def tree1():
g.add_edge(4, 1)
g.add_edge(1, 0)
g.add_edge(2, 0)
g.set_n_repr(th.Tensor([0, 1, 2, 3, 4]))
g.set_e_repr(th.randn(4, 10))
g.set_n_repr({'h' : th.Tensor([0, 1, 2, 3, 4])})
g.set_e_repr({'h' : th.randn(4, 10)})
return g
def tree2():
......@@ -37,17 +37,17 @@ def tree2():
g.add_edge(0, 4)
g.add_edge(4, 1)
g.add_edge(3, 1)
g.set_n_repr(th.Tensor([0, 1, 2, 3, 4]))
g.set_e_repr(th.randn(4, 10))
g.set_n_repr({'h' : th.Tensor([0, 1, 2, 3, 4])})
g.set_e_repr({'h' : th.randn(4, 10)})
return g
def test_batch_unbatch():
t1 = tree1()
t2 = tree2()
n1 = t1.get_n_repr()
n2 = t2.get_n_repr()
e1 = t1.get_e_repr()
e2 = t2.get_e_repr()
n1 = t1.get_n_repr()['h']
n2 = t2.get_n_repr()['h']
e1 = t1.get_e_repr()['h']
e2 = t2.get_e_repr()['h']
bg = dgl.batch([t1, t2])
assert bg.number_of_nodes() == 10
......@@ -57,10 +57,10 @@ def test_batch_unbatch():
assert bg.batch_num_edges == [4, 4]
tt1, tt2 = dgl.unbatch(bg)
assert th.allclose(t1.get_n_repr(), tt1.get_n_repr())
assert th.allclose(t1.get_e_repr(), tt1.get_e_repr())
assert th.allclose(t2.get_n_repr(), tt2.get_n_repr())
assert th.allclose(t2.get_e_repr(), tt2.get_e_repr())
assert th.allclose(t1.get_n_repr()['h'], tt1.get_n_repr()['h'])
assert th.allclose(t1.get_e_repr()['h'], tt1.get_e_repr()['h'])
assert th.allclose(t2.get_n_repr()['h'], tt2.get_n_repr()['h'])
assert th.allclose(t2.get_e_repr()['h'], tt2.get_e_repr()['h'])
def test_batch_unbatch1():
t1 = tree1()
......@@ -74,20 +74,20 @@ def test_batch_unbatch1():
assert b2.batch_num_edges == [4, 4, 4]
s1, s2, s3 = dgl.unbatch(b2)
assert th.allclose(t2.get_n_repr(), s1.get_n_repr())
assert th.allclose(t2.get_e_repr(), s1.get_e_repr())
assert th.allclose(t1.get_n_repr(), s2.get_n_repr())
assert th.allclose(t1.get_e_repr(), s2.get_e_repr())
assert th.allclose(t2.get_n_repr(), s3.get_n_repr())
assert th.allclose(t2.get_e_repr(), s3.get_e_repr())
assert th.allclose(t2.get_n_repr()['h'], s1.get_n_repr()['h'])
assert th.allclose(t2.get_e_repr()['h'], s1.get_e_repr()['h'])
assert th.allclose(t1.get_n_repr()['h'], s2.get_n_repr()['h'])
assert th.allclose(t1.get_e_repr()['h'], s2.get_e_repr()['h'])
assert th.allclose(t2.get_n_repr()['h'], s3.get_n_repr()['h'])
assert th.allclose(t2.get_e_repr()['h'], s3.get_e_repr()['h'])
def test_batch_sendrecv():
t1 = tree1()
t2 = tree2()
bg = dgl.batch([t1, t2])
bg.register_message_func(lambda src, edge: src)
bg.register_reduce_func(lambda node, msgs: th.sum(msgs, 1))
bg.register_message_func(lambda src, edge: {'m' : src['h']})
bg.register_reduce_func(lambda node, msgs: {'h' : th.sum(msgs['m'], 1)})
u = [3, 4, 2 + 5, 0 + 5]
v = [1, 1, 4 + 5, 4 + 5]
......@@ -95,8 +95,8 @@ def test_batch_sendrecv():
bg.recv(v)
t1, t2 = dgl.unbatch(bg)
assert t1.get_n_repr()[1] == 7
assert t2.get_n_repr()[4] == 2
assert t1.get_n_repr()['h'][1] == 7
assert t2.get_n_repr()['h'][4] == 2
def test_batch_propagate():
......@@ -104,8 +104,8 @@ def test_batch_propagate():
t2 = tree2()
bg = dgl.batch([t1, t2])
bg.register_message_func(lambda src, edge: src)
bg.register_reduce_func(lambda node, msgs: th.sum(msgs, 1))
bg.register_message_func(lambda src, edge: {'m' : src['h']})
bg.register_reduce_func(lambda node, msgs: {'h' : th.sum(msgs['m'], 1)})
# get leaves.
order = []
......@@ -123,23 +123,23 @@ def test_batch_propagate():
bg.propagate(traverser=order)
t1, t2 = dgl.unbatch(bg)
assert t1.get_n_repr()[0] == 9
assert t2.get_n_repr()[1] == 5
assert t1.get_n_repr()['h'][0] == 9
assert t2.get_n_repr()['h'][1] == 5
def test_batched_edge_ordering():
g1 = dgl.DGLGraph()
g1.add_nodes(6)
g1.add_edges([4, 4, 2, 2, 0], [5, 3, 3, 1, 1])
e1 = th.randn(5, 10)
g1.set_e_repr(e1)
g1.set_e_repr({'h' : e1})
g2 = dgl.DGLGraph()
g2.add_nodes(6)
g2.add_edges([0, 1 ,2 ,5, 4 ,5], [1, 2, 3, 4, 3, 0])
e2 = th.randn(6, 10)
g2.set_e_repr(e2)
g2.set_e_repr({'h' : e2})
g = dgl.batch([g1, g2])
r1 = g.get_e_repr()[g.edge_id(4, 5)]
r2 = g1.get_e_repr()[g1.edge_id(4, 5)]
r1 = g.get_e_repr()['h'][g.edge_id(4, 5)]
r2 = g1.get_e_repr()['h'][g1.edge_id(4, 5)]
assert th.equal(r1, r2)
def test_batch_no_edge():
......
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