Merge branch 'master' into cpp

python/dgl/function/message.py

@@ -2,9 +2,11 @@
 from __future__ import absolute_import
 
 import operator
+import dgl.backend as F
 
 __all__ = ["MessageFunction", "src_mul_edge", "copy_src", "copy_edge"]
 
+
 class MessageFunction(object):
     def __call__(self, src, edge):
         raise NotImplementedError
@@ -12,10 +14,28 @@ class MessageFunction(object):
     def name(self):
         raise NotImplementedError
 
+    def is_spmv_supported(self, g):
+        raise NotImplementedError
+
+
 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):
+        for fn in self.fn_list:
+            if not isinstance(fn, MessageFunction) or not fn.is_spmv_supported(g):
+                return False
+        return True
+
     def __call__(self, src, edge):
         ret = None
         for fn in self.fn_list:
@@ -24,16 +44,34 @@ class BundledMessageFunction(MessageFunction):
                 ret = msg
             else:
                 try:
+                    # ret and msg must be dict
                     ret.update(msg)
-                except e:
-                    raise RuntimeError("Failed to merge results of two builtin"
-                                       " message functions. Please specify out_field"
-                                       " for the builtin message function.")
+                except:
+                    raise RuntimeError("Must specify out field for multiple message")
         return ret
 
     def name(self):
         return "bundled"
 
+
+def _is_spmv_supported_node_feat(g, field):
+    if field is None:
+        feat = g.get_n_repr()
+    else:
+        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:
+        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):
         self.mul_op = mul_op
@@ -41,6 +79,10 @@ class SrcMulEdgeMessageFunction(MessageFunction):
         self.edge_field = edge_field
         self.out_field = out_field
 
+    def is_spmv_supported(self, g):
+        return _is_spmv_supported_node_feat(g, self.src_field) \
+                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]
@@ -60,6 +102,9 @@ class CopySrcMessageFunction(MessageFunction):
         self.src_field = src_field
         self.out_field = out_field
 
+    def is_spmv_supported(self, g):
+        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]
@@ -78,6 +123,11 @@ class CopyEdgeMessageFunction(MessageFunction):
         self.edge_field = edge_field
         self.out_field = out_field
 
+    def is_spmv_supported(self, g):
+        # TODO: support this with g-spmv
+        return False
+        # return _is_spmv_supported_edge_feat(g, self.edge_field)
+
     def __call__(self, src, edge):
         if self.edge_field is not None:
             ret = edge[self.edge_field]
@@ -91,6 +141,7 @@ class CopyEdgeMessageFunction(MessageFunction):
     def name(self):
         return "copy_edge"
 
+
 def src_mul_edge(src=None, edge=None, out=None):
     """TODO(minjie): docstring """
     return SrcMulEdgeMessageFunction(operator.mul, src, edge, out)

python/dgl/function/reducer.py

@@ -12,10 +12,26 @@ class ReduceFunction(object):
     def name(self):
         raise NotImplementedError
 
+    def is_spmv_supported(self):
+        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):
+        for fn in self.fn_list:
+            if not isinstance(fn, ReduceFunction) or not fn.is_spmv_supported():
+                return False
+        return True
+
     def __call__(self, node, msgs):
         ret = None
         for fn in self.fn_list:
@@ -24,46 +40,50 @@ class BundledReduceFunction(ReduceFunction):
                 ret = rpr
             else:
                 try:
+                    # ret and rpr must be dict
                     ret.update(rpr)
-                except e:
-                    raise RuntimeError("Failed to merge results of two builtin"
-                                       " reduce functions. Please specify out_field"
-                                       " for the builtin reduce function.")
+                except:
+                    raise RuntimeError("Must specify out field for multiple reudce")
         return ret
 
     def name(self):
         return "bundled"
 
-class SumReducerFunction(ReduceFunction):
-    def __init__(self, batch_sum_op, nonbatch_sum_op, msg_field=None, out_field=None):
-        self.batch_sum_op = batch_sum_op
-        self.nonbatch_sum_op = nonbatch_sum_op
+class ReducerFunctionTemplate(ReduceFunction):
+    def __init__(self, name, batch_op, nonbatch_op, msg_field=None, out_field=None):
+        self.name = name
+        self.batch_op = batch_op
+        self.nonbatch_op = nonbatch_op
         self.msg_field = msg_field
         self.out_field = out_field
 
+    def is_spmv_supported(self):
+        # TODO: support max
+        return self.name == "sum"
+
     def __call__(self, node, msgs):
         if isinstance(msgs, list):
             if self.msg_field is None:
-                ret = self.nonbatch_sum_op(msgs)
+                ret = self.nonbatch_op(msgs)
             else:
-                ret = self.nonbatch_sum_op([msg[self.msg_field] for msg in msgs])
+                ret = self.nonbatch_op([msg[self.msg_field] for msg in msgs])
         else:
             if self.msg_field is None:
-                ret = self.batch_sum_op(msgs, 1)
+                ret = self.batch_op(msgs, 1)
             else:
-                ret = self.batch_sum_op(msgs[self.msg_field], 1)
+                ret = self.batch_op(msgs[self.msg_field], 1)
         if self.out_field is None:
             return ret
         else:
             return {self.out_field : ret}
 
     def name(self):
-        return "sum"
+        return self.name
 
 _python_sum = sum
 def sum(msgs=None, out=None):
-    return SumReducerFunction(F.sum, _python_sum, msgs, out)
+    return ReducerFunctionTemplate("sum", F.sum, _python_sum, msgs, out)
 
 _python_max = max
 def max(msgs=None, out=None):
-    return SumReducerFunction(F.max, _python_max, msgs, out)
+    return ReducerFunctionTemplate("max", F.max, _python_max, msgs, out)

python/dgl/graph.py

@@ -12,6 +12,8 @@ from .graph_index import GraphIndex
 from .frame import FrameRef, merge_frames
 from . import scheduler
 from . import utils
+from .function.message import BundledMessageFunction
+from .function.reducer import BundledReduceFunction
 
 class DGLGraph(object):
     """Base graph class specialized for neural networks on graphs.
@@ -431,6 +433,8 @@ class DGLGraph(object):
         if message_func == "default":
             message_func, batchable = self._message_func
         assert message_func is not None
+        if isinstance(message_func, (tuple, list)):
+            message_func = BundledMessageFunction(message_func)
         if batchable:
             self._batch_send(u, v, message_func)
         else:
@@ -470,7 +474,7 @@ class DGLGraph(object):
         else:
             self._msg_frame.append({__MSG__ : msgs})
 
-    def update_edge(self, u, v, edge_func="default", batchable=False):
+    def update_edge(self, u=ALL, v=ALL, edge_func="default", batchable=False):
         """Update representation on edge u->v
 
         The edge function should be compatible with following signature:
@@ -573,6 +577,8 @@ class DGLGraph(object):
         if reduce_func == "default":
             reduce_func, batchable = self._reduce_func
         assert reduce_func is not None
+        if isinstance(reduce_func, (list, tuple)):
+            reduce_func = BundledReduceFunction(reduce_func)
         if batchable:
             self._batch_recv(u, reduce_func)
         else:

python/dgl/scheduler.py

@@ -3,6 +3,7 @@ from __future__ import absolute_import
 
 import numpy as np
 
+from .base import ALL
 from . import backend as F
 from .function import message as fmsg
 from .function import reducer as fred
@@ -38,37 +39,32 @@ def degree_bucketing(cached_graph, v):
     #print('degree-bucketing:', unique_degrees, [len(b) for b in v_bkt])
     return unique_degrees, v_bkt
 
+
 class Executor(object):
-    def run(self, graph):
+    def run(self):
         raise NotImplementedError
 
-class UpdateAllSPMVExecutor(Executor):
-    def __init__(self, graph, src_field, dst_field, edge_field, use_adj):
-        self.graph = graph
+class SPMVOperator(Executor):
+    def __init__(self, src_field, edge_field, dst_field, use_edge_feat,
+                 node_repr, adj_build_fn):
         self.src_field = src_field
-        self.dst_field = dst_field
         self.edge_field = edge_field
-        self.use_adj = use_adj
+        self.dst_field = dst_field
+        self.use_edge_feat = use_edge_feat
+        self.node_repr = node_repr
+        self.adj_build_fn = adj_build_fn
 
     def run(self):
-        g = self.graph
+        # get src col
         if self.src_field is None:
-            srccol = g.get_n_repr()
+            srccol = self.node_repr
         else:
-            srccol = g.get_n_repr()[self.src_field]
+            srccol = self.node_repr[self.src_field]
         ctx = F.get_context(srccol)
-        if self.use_adj:
-            adjmat = g.cached_graph.adjmat().get(ctx)
-        else:
-            if self.edge_field is None:
-                dat = g.get_e_repr()
-            else:
-                dat = g.get_e_repr()[self.edge_field]
-            dat = F.squeeze(dat)
-            # TODO(minjie): should not directly use _indices
-            idx = g.cached_graph.adjmat().get(ctx)._indices()
-            n = g.number_of_nodes()
-            adjmat = F.sparse_tensor(idx, dat, [n, n])
+
+        # build adjmat
+        adjmat = self.adj_build_fn(self.edge_field, ctx, self.use_edge_feat)
+
         # spmm
         if len(F.shape(srccol)) == 1:
             srccol = F.unsqueeze(srccol, 1)
@@ -77,104 +73,249 @@ class UpdateAllSPMVExecutor(Executor):
         else:
             dstcol = F.spmm(adjmat, srccol)
         if self.dst_field is None:
-            g.set_n_repr(dstcol)
+            return dstcol
         else:
-            g.set_n_repr({self.dst_field : dstcol})
+            return {self.dst_field : dstcol}
 
-class SendRecvSPMVExecutor(Executor):
-    def __init__(self, graph, src, dst, src_field, dst_field, edge_field, use_edge_dat):
-        self.graph = graph
-        self.src = src
-        self.dst = dst
-        self.src_field = src_field
-        self.dst_field = dst_field
-        self.edge_field = edge_field
-        self.use_edge_dat = use_edge_dat
 
-    def run(self):
-        # get src col
-        g = self.graph
-        if self.src_field is None:
-            srccol = g.get_n_repr()
+class BasicExecutor(Executor):
+    def __init__(self, graph, mfunc, rfunc):
+        self.g = graph
+        self.exe = self._build_exec(mfunc, rfunc)
+
+    @property
+    def node_repr(self):
+        raise NotImplementedError
+
+    @property
+    def edge_repr(self):
+        raise NotImplementedError
+
+    @property
+    def graph_mapping(self):
+        raise NotImplementedError
+
+    def _build_exec(self, mfunc, rfunc):
+        if isinstance(mfunc, fmsg.CopySrcMessageFunction):
+            exe = SPMVOperator(src_field=mfunc.src_field,
+                               edge_field=None,
+                               dst_field=rfunc.out_field,
+                               use_edge_feat=False,
+                               node_repr=self.node_repr,
+                               adj_build_fn=self._adj_build_fn)
+        elif isinstance(mfunc, fmsg.SrcMulEdgeMessageFunction):
+            exe = SPMVOperator(src_field=mfunc.src_field,
+                               edge_field=mfunc.edge_field,
+                               dst_field=rfunc.out_field,
+                               use_edge_feat=True,
+                               node_repr=self.node_repr,
+                               adj_build_fn=self._adj_build_fn)
         else:
-            srccol = g.get_n_repr()[self.src_field]
-        ctx = F.get_context(srccol)
+            raise NotImplementedError("message func type {}".format(type(mfunc)))
+        return exe
 
-        # build adjmat
-        # build adjmat dat
-        u, v = utils.edge_broadcasting(self.src, self.dst)
-        if self.use_edge_dat:
-            if self.edge_field is None:
-                dat = g.get_e_repr(u, v)
+    def run(self):
+        attr = self.exe.run()
+        self.g.set_n_repr(attr, self.graph_mapping)
+
+
+class UpdateAllExecutor(BasicExecutor):
+    def __init__(self, graph, mfunc, rfunc):
+        self._init_state()
+        super(UpdateAllExecutor, self).__init__(graph, mfunc, rfunc)
+
+    def _init_state(self):
+        self._node_repr = None
+        self._edge_repr = None
+        self._graph_idx = None
+        self._graph_shape = None
+        self._graph_mapping = None
+
+    @property
+    def graph_idx(self):
+        if self._graph_idx is None:
+            self._graph_idx = self.g.cached_graph.adjmat()
+        return self._graph_idx
+
+    @property
+    def graph_shape(self):
+        if self._graph_shape is None:
+            n = self.g.number_of_nodes()
+            self._graph_shape = [n, n]
+        return self._graph_shape
+
+    @property
+    def graph_mapping(self):
+        return ALL
+
+    @property
+    def node_repr(self):
+        if self._node_repr is None:
+            self._node_repr = self.g.get_n_repr()
+        return self._node_repr
+
+    @property
+    def edge_repr(self):
+        if self._edge_repr is None:
+            self._edge_repr = self.g.get_e_repr()
+        return self._edge_repr
+
+    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 = g.get_e_repr(u, v)[self.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()
+            adjmat = F.sparse_tensor(idx, dat, self.graph_shape)
         else:
-            dat = F.ones((len(u),))
-        # build adjmat index
-        new2old, old2new = utils.build_relabel_map(v)
-        u = u.totensor()
-        v = v.totensor()
+            adjmat = self.graph_idx.get(ctx)
+        return adjmat
+
+
+class SendRecvExecutor(BasicExecutor):
+    def __init__(self, graph, src, dst, mfunc, rfunc):
+        self._init_state(src, dst)
+        super(SendRecvExecutor, self).__init__(graph, mfunc, rfunc)
+
+    def _init_state(self, src, dst):
+        self.u, self.v = utils.edge_broadcasting(src, dst)
+        self._node_repr = None
+        self._edge_repr = None
+        self._graph_idx = None
+        self._graph_shape = None
+        self._graph_mapping = None
+
+    @property
+    def graph_idx(self):
+        if self._graph_idx is None:
+            self._build_adjmat()
+        return self._graph_idx
+
+    @property
+    def graph_shape(self):
+        if self._graph_shape is None:
+            self._build_adjmat()
+        return self._graph_shape
+
+    @property
+    def graph_mapping(self):
+        if self._graph_mapping is None:
+            self._build_adjmat()
+        return self._graph_mapping
+
+    @property
+    def node_repr(self):
+        if self._node_repr is None:
+            self._node_repr = self.g.get_n_repr()
+        return self._node_repr
+
+    @property
+    def edge_repr(self):
+        if self._edge_repr is None:
+            self._edge_repr = self.g.get_e_repr(self.u, self.v)
+        return self._edge_repr
+
+    def _build_adjmat(self):
+        # handle graph index
+        new2old, old2new = utils.build_relabel_map(self.v)
+        u = self.u.totensor()
+        v = self.v.totensor()
         # TODO(minjie): should not directly use []
         new_v = old2new[v]
-        idx = F.pack([F.unsqueeze(new_v, 0), F.unsqueeze(u, 0)])
-        n = g.number_of_nodes()
+        n = self.g.number_of_nodes()
         m = len(new2old)
-        adjmat = F.sparse_tensor(idx, dat, [m, n])
-        adjmat = F.to_context(adjmat, ctx)
-        # spmm
-        if len(F.shape(srccol)) == 1:
-            srccol = F.unsqueeze(srccol, 1)
-            dstcol = F.spmm(adjmat, srccol)
-            dstcol = F.squeeze(dstcol)
+        self._graph_idx = F.pack([F.unsqueeze(new_v, 0), F.unsqueeze(u, 0)])
+        self._graph_shape = [m, n]
+        self._graph_mapping = new2old
+
+    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:
-            dstcol = F.spmm(adjmat, srccol)
-        if self.dst_field is None:
-            g.set_n_repr(dstcol, new2old)
+                dat = self.edge_repr[edge_field]
+            dat = F.squeeze(dat)
         else:
-            g.set_n_repr({self.dst_field : dstcol}, new2old)
+            dat = F.ones((len(self.u), ))
+        adjmat = F.sparse_tensor(self.graph_idx, dat, self.graph_shape)
+        return F.to_context(adjmat, ctx)
+
 
-def _is_spmv_supported_node_feat(g, field):
-    if field is None:
-        feat = g.get_n_repr()
+class BundledExecutor(BasicExecutor):
+    """
+    Base class for Bundled execution
+    All shared structure like graph index should be cached in this class or its subclass
+    BundledUpdateAllExecutor and BundledSendRecvExecutor should subclass BundledExecutor
+    """
+    def __init__(self, graph, mfunc, rfunc):
+        self.g = graph
+        func_pairs = self._match_message_with_reduce(mfunc, rfunc)
+        # create all executors
+        self.executors = self._build_executors(func_pairs)
+
+    def _build_executors(self, func_pairs):
+        executors = []
+        for mfunc, rfunc in func_pairs:
+            exe = self._build_exec(mfunc, rfunc)
+            executors.append(exe)
+        return executors
+
+    def _match_message_with_reduce(self, mfunc, rfunc):
+        out2mfunc = {fn.out_field: fn for fn in mfunc.fn_list}
+        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)
+            func_pairs.append((mfn, rfn))
+        return func_pairs
+
+    def run(self):
+        attr = None
+        for exe in self.executors:
+            res = exe.run()
+            if attr is None:
+                attr = res
             else:
-        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()
+                # attr and res must be dict
+                attr.update(res)
+        self.g.set_n_repr(attr, self.graph_mapping)
+
+
+class BundledUpdateAllExecutor(BundledExecutor, UpdateAllExecutor):
+    def __init__(self, graph, mfunc, rfunc):
+        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)
+        BundledExecutor.__init__(self, graph, mfunc, rfunc)
+
+def _is_spmv_supported(fn, graph=None):
+    if isinstance(fn, fmsg.MessageFunction):
+        return fn.is_spmv_supported(graph)
+    elif isinstance(fn, fred.ReduceFunction):
+        return fn.is_spmv_supported()
     else:
-        feat = g.get_e_repr()[field]
-    shape = F.shape(feat)
-    return len(shape) == 1 or (len(shape) == 2 and shape[1] == 1)
+        return False
 
 def _create_update_all_exec(graph, **kwargs):
     mfunc = kwargs.pop('message_func')
     rfunc = kwargs.pop('reduce_func')
-    if (isinstance(mfunc, fmsg.CopySrcMessageFunction)
-            and isinstance(rfunc, fred.SumReducerFunction)
-            and _is_spmv_supported_node_feat(graph, mfunc.src_field)):
-        # TODO(minjie): more sanity check on field names
-        return UpdateAllSPMVExecutor(graph,
-                                     src_field=mfunc.src_field,
-                                     dst_field=rfunc.out_field,
-                                     edge_field=None,
-                                     use_adj=True)
-    elif (isinstance(mfunc, fmsg.SrcMulEdgeMessageFunction)
-            and isinstance(rfunc, fred.SumReducerFunction)
-            and _is_spmv_supported_node_feat(graph, mfunc.src_field)
-            and _is_spmv_supported_edge_feat(graph, mfunc.edge_field)):
-        return UpdateAllSPMVExecutor(graph,
-                                     src_field=mfunc.src_field,
-                                     dst_field=rfunc.out_field,
-                                     edge_field=mfunc.edge_field,
-                                     use_adj=False)
-    elif (isinstance(mfunc, fmsg.CopyEdgeMessageFunction)
-            and isinstance(rfunc, fred.SumReducerFunction)):
-        return None
+    if isinstance(mfunc, (list, tuple)) or isinstance(rfunc, (list, tuple)):
+        mfunc = fmsg.BundledMessageFunction(mfunc)
+        rfunc = fred.BundledReduceFunction(rfunc)
+        exec_cls = BundledUpdateAllExecutor
+    else:
+        exec_cls = UpdateAllExecutor
+    if _is_spmv_supported(mfunc, graph) and _is_spmv_supported(rfunc):
+        return exec_cls(graph, mfunc=mfunc, rfunc=rfunc)
     else:
         return None
 
@@ -183,28 +324,14 @@ def _create_send_and_recv_exec(graph, **kwargs):
     dst = kwargs.pop('dst')
     mfunc = kwargs.pop('message_func')
     rfunc = kwargs.pop('reduce_func')
-    if (isinstance(mfunc, fmsg.CopySrcMessageFunction)
-            and isinstance(rfunc, fred.SumReducerFunction)
-            and _is_spmv_supported_node_feat(graph, mfunc.src_field)):
-        # TODO(minjie): more sanity check on field names
-        return SendRecvSPMVExecutor(graph,
-                                    src=src,
-                                    dst=dst,
-                                    src_field=mfunc.src_field,
-                                    dst_field=rfunc.out_field,
-                                    edge_field=None,
-                                    use_edge_dat=False)
-    elif (isinstance(mfunc, fmsg.SrcMulEdgeMessageFunction)
-            and isinstance(rfunc, fred.SumReducerFunction)
-            and _is_spmv_supported_node_feat(graph, mfunc.src_field)
-            and _is_spmv_supported_edge_feat(graph, mfunc.edge_field)):
-        return SendRecvSPMVExecutor(graph,
-                                    src=src,
-                                    dst=dst,
-                                    src_field=mfunc.src_field,
-                                    dst_field=rfunc.out_field,
-                                    edge_field=mfunc.edge_field,
-                                    use_edge_dat=True)
+    if isinstance(mfunc, (list, tuple)) or isinstance(rfunc, (list, tuple)):
+        mfunc = fmsg.BundledMessageFunction(mfunc)
+        rfunc = fred.BundledReduceFunction(rfunc)
+        exec_cls = BundledSendRecvExecutor
+    else:
+        exec_cls = SendRecvExecutor
+    if _is_spmv_supported(mfunc, graph) and _is_spmv_supported(rfunc):
+        return exec_cls(graph, src=src, dst=dst, mfunc=mfunc, rfunc=rfunc)
     else:
         return None
 

tests/pytorch/test_specialization.py

@@ -113,6 +113,110 @@ def test_send_and_recv():
     # test 2d node features
     _test('f2')
 
+def test_update_all_multi_fn():
+    def message_func(hu, edge):
+        return {'m2': hu['f2']}
+
+    def message_func_edge(hu, edge):
+        return {'m2': hu['f2'] * edge['e2']}
+
+    def reduce_func(hv, msgs):
+        return {'v2': th.sum(msgs['m2'], 1)}
+
+    g = generate_graph()
+    fld = 'f2'
+    # update all, mix of builtin and UDF
+    g.update_all([fn.copy_src(src=fld, out='m1'), message_func],
+                 [fn.sum(msgs='m1', out='v1'), reduce_func],
+                 None, batchable=True)
+    v1 = g.get_n_repr()['v1']
+    v2 = g.get_n_repr()['v2']
+    assert th.allclose(v1, v2)
+
+    # run builtin with single message and reduce
+    g.update_all(fn.copy_src(src=fld), fn.sum(out='v1'), None, batchable=True)
+    v1 = g.get_n_repr()['v1']
+    assert th.allclose(v1, v2)
+
+    # 1 message, 2 reduces, using anonymous repr
+    g.update_all(fn.copy_src(src=fld), [fn.sum(out='v2'), fn.sum(out='v3')], None, batchable=True)
+    v2 = g.get_n_repr()['v2']
+    v3 = g.get_n_repr()['v3']
+    assert th.allclose(v1, v2)
+    assert th.allclose(v1, v3)
+
+    # update all with edge weights, 2 message, 3 reduces
+    g.update_all([fn.src_mul_edge(src=fld, edge='e1', out='m1'), fn.src_mul_edge(src=fld, edge='e2', out='m2')],
+                 [fn.sum(msgs='m1', out='v1'), fn.sum(msgs='m2', out='v2'), fn.sum(msgs='m1', out='v3')],
+                 None, batchable=True)
+    v1 = g.get_n_repr()['v1']
+    v2 = g.get_n_repr()['v2']
+    v3 = g.get_n_repr()['v3']
+    assert th.allclose(v1, v2)
+    assert th.allclose(v1, v3)
+
+    # run UDF with single message and reduce
+    g.update_all(message_func_edge, reduce_func, None, batchable=True)
+    v2 = g.get_n_repr()['v2']
+    assert th.allclose(v1, v2)
+
+def test_send_and_recv_multi_fn():
+    u = th.tensor([0, 0, 0, 3, 4, 9])
+    v = th.tensor([1, 2, 3, 9, 9, 0])
+
+    def message_func(hu, edge):
+        return {'m2': hu['f2']}
+
+    def message_func_edge(hu, edge):
+        return {'m2': hu['f2'] * edge['e2']}
+
+    def reduce_func(hv, msgs):
+        return {'v2' : th.sum(msgs['m2'], 1)}
+
+    g = generate_graph()
+    fld = 'f2'
+
+    # send and recv, mix of builtin and UDF
+    g.send_and_recv(u, v,
+                    [fn.copy_src(src=fld, out='m1'), message_func],
+                    [fn.sum(msgs='m1', out='v1'), reduce_func],
+                    None, batchable=True)
+    v1 = g.get_n_repr()['v1']
+    v2 = g.get_n_repr()['v2']
+    assert th.allclose(v1, v2)
+
+    # run builtin with single message and reduce
+    g.send_and_recv(u, v, fn.copy_src(src=fld), fn.sum(out='v1'),
+                    None, batchable=True)
+    v1 = g.get_n_repr()['v1']
+    assert th.allclose(v1, v2)
+
+    # 1 message, 2 reduces, using anonymous repr
+    g.send_and_recv(u, v, fn.copy_src(src=fld), [fn.sum(out='v2'), fn.sum(out='v3')], None, batchable=True)
+    v2 = g.get_n_repr()['v2']
+    v3 = g.get_n_repr()['v3']
+    assert th.allclose(v1, v2)
+    assert th.allclose(v1, v3)
+
+    # send and recv with edge weights, 2 message, 3 reduces
+    g.send_and_recv(u, v,
+                    [fn.src_mul_edge(src=fld, edge='e1', out='m1'), fn.src_mul_edge(src=fld, edge='e2', out='m2')],
+                    [fn.sum(msgs='m1', out='v1'), fn.sum(msgs='m2', out='v2'), fn.sum(msgs='m1', out='v3')],
+                    None, batchable=True)
+    v1 = g.get_n_repr()['v1']
+    v2 = g.get_n_repr()['v2']
+    v3 = g.get_n_repr()['v3']
+    assert th.allclose(v1, v2)
+    assert th.allclose(v1, v3)
+
+    # run UDF with single message and reduce
+    g.send_and_recv(u, v, message_func_edge,
+            reduce_func, None, batchable=True)
+    v2 = g.get_n_repr()['v2']
+    assert th.allclose(v1, v2)
+
 if __name__ == '__main__':
-    #test_update_all()
+    test_update_all()
     test_send_and_recv()
+    test_update_all_multi_fn()
+    test_send_and_recv_multi_fn()