send_recv

'''
Send messages through all edges >>> update all nodes.
DGLGraph.update_all(message_func='default', reduce_func='default', apply_node_func='default')

message_func --message function on the edges
reduce_func--reduce function on the node
apply_node_func:apply function on the nodes
'''


'''
DGLGraph.send(edges='__ALL__', message_func='default')
edges:
      int:one edge using edge id
      pair of int :one edge using its endpoints
      int iterable/tensor :multiple edges using edge id
      pair of int iterable/pair of tensor :multiple edges using  their endpoints 

returns messages on the edges and can be later fetched in the destination node’s mailbox

'''


'''
DGLGraph.recv(v='__ALL__', reduce_func='default', apply_node_func='default', inplace=False)

'''

import warnings
warnings.filterwarnings("ignore")
import torch as th
import dgl
g=dgl.DGLGraph()

g.add_nodes(3)
g.ndata["x"]=th.tensor([[5.],[6.],[7.]])
g.add_edges([0,1],[1,2])
src=th.tensor([0])
dst=th.tensor([2])
g.add_edges(src,dst)
print("ndata",g.ndata["x"])


def send_source(edges):

    print("src",edges.src["x"].shape,edges.src["x"])  #源节点特征  ([2, 1])
    print("dst",edges.dst["x"].shape,edges.dst["x"])  #目标节点特征 ([2, 1])


    return {"m":edges.src["x"]}

g.register_message_func(send_source)

'''
Tensor Ndata ([[. 5.], 
        [6. The], 
        [7. The]]) 
the src torch.Size ([. 3,. 1]) Tensor ([[. 5.], 
        [6. The], 
        [5. The]]) 
torch.Size DST ([. 3,. 1]) Tensor ([[. 6],. 
        [7. The], 
        [7. The]]) 

'' ' 




DEF simple_reduce (Nodes): 
    Print ( "data_nodes", nodes.data [ " x "]) # node wherein 
    all incoming message characteristics print (" mailbox ", nodes.mailbox [ " m "]. shape, nodes.mailbox [" m "]) #mailbox comprising a stack along the second dimension of [2, . 1,. 1] 
    Print ( "SUM", nodes.mailbox [ "m"] SUM (. 1).) 



    return { "X": nodes.mailbox [ "m"] SUM (. 1).} # row summation 

g .register_reduce_func (simple_reduce) 



g.send (g.edges ()) 
g.recv (g.nodes ()) 
Print ( "Ndata", G.ndata["x"])


'''

data_nodes tensor([[6.]])
mailbox torch.Size([1, 1, 1]) tensor([[[5.]]])
sum tensor([[5.]])
data_nodes tensor([[7.]])
mailbox torch.Size([1, 2, 1]) tensor([[[6.],
         [5.]]])
sum tensor([[11.]])
ndata tensor([[ 0.],
        [ 5.],
        [11.]])

'''