def mfunc(edges):
op = getattr(F, binary_op)
lhs_data = target_switch(edges, lhs)[lhs]
rhs_data = target_switch(edges, rhs)[rhs]
# NOTE(zihao): we need to do batched broadcast
# e.g. (68, 3, 1) op (68, 5, 3, 4)
while F.ndim(lhs_data) < F.ndim(rhs_data):
lhs_data = F.unsqueeze(lhs_data, 1)
while F.ndim(rhs_data) < F.ndim(lhs_data):
rhs_data = F.unsqueeze(rhs_data, 1)
return {"m": op(lhs_data, rhs_data)}
def __call__(self, edges):
sdata = edges.src[self.src_field]
edata = edges.data[self.edge_field]
# Due to the different broadcasting semantics of different backends,
# we need to broadcast the sdata and edata to be of the same rank.
rank = max(F.ndim(sdata), F.ndim(edata))
sshape = F.shape(sdata)
eshape = F.shape(edata)
sdata = F.reshape(sdata, sshape + (1, ) * (rank - F.ndim(sdata)))
edata = F.reshape(edata, eshape + (1, ) * (rank - F.ndim(edata)))
ret = self.mul_op(sdata, edata)
return {self.out_field: ret}
def softmax(x):
ndim = K.ndim(x)
if ndim == 2:
return K.softmax(x)
elif ndim == 3:
e = K.exp(x - K.max(x, axis=-1, keepdims=True))
s = K.sum(e, axis=-1, keepdims=True)
return e / s
else:
raise ValueError('Cannot apply softmax to a tensor '
'that is not 2D or 3D. '
'Here, ndim=' + str(ndim))
def reduce_func(nodes):
msgs = nodes.mailbox['m']
reduce_msg_shapes.add(tuple(msgs.shape))
assert F.ndim(msgs) == 3
assert F.shape(msgs)[2] == D
return {'accum': F.sum(msgs, 1)}
def message_func(edges):
assert F.ndim(edges.src['h']) == 2
assert F.shape(edges.src['h'])[1] == D
return {'m': edges.src['h']}
