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test_apply_edges_hetero.py
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import itertools
import unittest
from collections import Counter
from itertools import product
import backend as F
import networkx as nx
import numpy as np
import pytest
import scipy.sparse as ssp
import test_utils
from scipy.sparse import rand
from test_utils import get_cases, parametrize_idtype
import dgl
import dgl.function as fn
from dgl import DGLError
rfuncs = {"sum": fn.sum, "max": fn.max, "min": fn.min, "mean": fn.mean}
fill_value = {"sum": 0, "max": float("-inf")}
feat_size = 2
@unittest.skipIf(
dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
)
def create_test_heterograph(idtype):
# test heterograph from the docstring, plus a user -- wishes -- game relation
# 3 users, 2 games, 2 developers
# metagraph:
# ('user', 'follows', 'user'),
# ('user', 'plays', 'game'),
# ('user', 'wishes', 'game'),
# ('developer', 'develops', 'game')])
g = dgl.heterograph(
{
("user", "follows", "user"): ([0, 1, 2, 1], [0, 0, 1, 1]),
("user", "plays", "game"): ([0, 1, 2, 1], [0, 0, 1, 1]),
("user", "wishes", "game"): ([0, 1, 1], [0, 0, 1]),
("developer", "develops", "game"): ([0, 1, 0], [0, 1, 1]),
},
idtype=idtype,
device=F.ctx(),
)
assert g.idtype == idtype
assert g.device == F.ctx()
return g
@parametrize_idtype
def test_unary_copy_u(idtype):
def _test(mfunc):
g = create_test_heterograph(idtype)
x1 = F.randn((g.num_nodes("user"), feat_size))
x2 = F.randn((g.num_nodes("developer"), feat_size))
F.attach_grad(x1)
F.attach_grad(x2)
g.nodes["user"].data["h"] = x1
g.nodes["developer"].data["h"] = x2
#################################################################
# apply_edges() is called on each relation type separately
#################################################################
with F.record_grad():
[
g.apply_edges(fn.copy_u("h", "m"), etype=rel)
for rel in g.canonical_etypes
]
r1 = g["plays"].edata["m"]
F.backward(r1, F.ones(r1.shape))
n_grad1 = F.grad(g.ndata["h"]["user"])
# TODO (Israt): clear not working
g.edata["m"].clear()
#################################################################
# apply_edges() is called on all relation types
#################################################################
g.apply_edges(fn.copy_u("h", "m"))
r2 = g["plays"].edata["m"]
F.backward(r2, F.ones(r2.shape))
n_grad2 = F.grad(g.nodes["user"].data["h"])
# correctness check
def _print_error(a, b):
for i, (x, y) in enumerate(
zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
):
if not np.allclose(x, y):
print("@{} {} v.s. {}".format(i, x, y))
if not F.allclose(r1, r2):
_print_error(r1, r2)
assert F.allclose(r1, r2)
if not F.allclose(n_grad1, n_grad2):
print("node grad")
_print_error(n_grad1, n_grad2)
assert F.allclose(n_grad1, n_grad2)
_test(fn.copy_u)
@parametrize_idtype
def test_unary_copy_e(idtype):
def _test(mfunc):
g = create_test_heterograph(idtype)
feat_size = 2
x1 = F.randn((4, feat_size))
x2 = F.randn((4, feat_size))
x3 = F.randn((3, feat_size))
x4 = F.randn((3, feat_size))
F.attach_grad(x1)
F.attach_grad(x2)
F.attach_grad(x3)
F.attach_grad(x4)
g["plays"].edata["eid"] = x1
g["follows"].edata["eid"] = x2
g["develops"].edata["eid"] = x3
g["wishes"].edata["eid"] = x4
#################################################################
# apply_edges() is called on each relation type separately
#################################################################
with F.record_grad():
[
g.apply_edges(fn.copy_e("eid", "m"), etype=rel)
for rel in g.canonical_etypes
]
r1 = g["develops"].edata["m"]
F.backward(r1, F.ones(r1.shape))
e_grad1 = F.grad(g["develops"].edata["eid"])
#################################################################
# apply_edges() is called on all relation types
#################################################################
g.apply_edges(fn.copy_e("eid", "m"))
r2 = g["develops"].edata["m"]
F.backward(r2, F.ones(r2.shape))
e_grad2 = F.grad(g["develops"].edata["eid"])
# # correctness check
def _print_error(a, b):
for i, (x, y) in enumerate(
zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
):
if not np.allclose(x, y):
print("@{} {} v.s. {}".format(i, x, y))
if not F.allclose(r1, r2):
_print_error(r1, r2)
assert F.allclose(r1, r2)
if not F.allclose(e_grad1, e_grad2):
print("edge grad")
_print_error(e_grad1, e_grad2)
assert F.allclose(e_grad1, e_grad2)
_test(fn.copy_e)
@parametrize_idtype
def test_binary_op(idtype):
def _test(lhs, rhs, binary_op):
g = create_test_heterograph(idtype)
n1 = F.randn((g.num_nodes("user"), feat_size))
n2 = F.randn((g.num_nodes("developer"), feat_size))
n3 = F.randn((g.num_nodes("game"), feat_size))
x1 = F.randn((g.num_edges("plays"), feat_size))
x2 = F.randn((g.num_edges("follows"), feat_size))
x3 = F.randn((g.num_edges("develops"), feat_size))
x4 = F.randn((g.num_edges("wishes"), feat_size))
builtin_msg_name = "{}_{}_{}".format(lhs, binary_op, rhs)
builtin_msg = getattr(fn, builtin_msg_name)
#################################################################
# apply_edges() is called on each relation type separately
#################################################################
F.attach_grad(n1)
F.attach_grad(n2)
F.attach_grad(n3)
g.nodes["user"].data["h"] = n1
g.nodes["developer"].data["h"] = n2
g.nodes["game"].data["h"] = n3
F.attach_grad(x1)
F.attach_grad(x2)
F.attach_grad(x3)
F.attach_grad(x4)
g["plays"].edata["h"] = x1
g["follows"].edata["h"] = x2
g["develops"].edata["h"] = x3
g["wishes"].edata["h"] = x4
with F.record_grad():
[
g.apply_edges(builtin_msg("h", "h", "m"), etype=rel)
for rel in g.canonical_etypes
]
r1 = g["plays"].edata["m"]
loss = F.sum(r1.view(-1), 0)
F.backward(loss)
n_grad1 = F.grad(g.nodes["game"].data["h"])
#################################################################
# apply_edges() is called on all relation types
#################################################################
F.attach_grad(n1)
F.attach_grad(n2)
F.attach_grad(n3)
g.nodes["user"].data["h"] = n1
g.nodes["developer"].data["h"] = n2
g.nodes["game"].data["h"] = n3
F.attach_grad(x1)
F.attach_grad(x2)
F.attach_grad(x3)
F.attach_grad(x4)
g["plays"].edata["h"] = x1
g["follows"].edata["h"] = x2
g["develops"].edata["h"] = x3
g["wishes"].edata["h"] = x4
with F.record_grad():
g.apply_edges(builtin_msg("h", "h", "m"))
r2 = g["plays"].edata["m"]
loss = F.sum(r2.view(-1), 0)
F.backward(loss)
n_grad2 = F.grad(g.nodes["game"].data["h"])
# correctness check
def _print_error(a, b):
for i, (x, y) in enumerate(
zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
):
if not np.allclose(x, y):
print("@{} {} v.s. {}".format(i, x, y))
if not F.allclose(r1, r2):
_print_error(r1, r2)
assert F.allclose(r1, r2)
if n_grad1 is not None or n_grad2 is not None:
if not F.allclose(n_grad1, n_grad2):
print("node grad")
_print_error(n_grad1, n_grad2)
assert F.allclose(n_grad1, n_grad2)
target = ["u", "v", "e"]
for lhs, rhs in product(target, target):
if lhs == rhs:
continue
for binary_op in ["add", "sub", "mul", "div", "dot"]:
print(lhs, rhs, binary_op)
_test(lhs, rhs, binary_op)
if __name__ == "__main__":
test_unary_copy_u()
test_unary_copy_e()