chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import itertools
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import unittest
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from collections import Counter
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from itertools import product
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import backend as F
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import dgl
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import dgl.function as fn
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import networkx as nx
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import numpy as np
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import pytest
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import scipy.sparse as ssp
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from dgl import DGLError
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from scipy.sparse import rand
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from utils import get_cases, parametrize_idtype
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rfuncs = {"sum": fn.sum, "max": fn.max, "min": fn.min, "mean": fn.mean}
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feat_size = 2
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@unittest.skipIf(
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dgl.backend.backend_name != "pytorch", reason="Only support PyTorch for now"
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)
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def create_test_heterograph(idtype):
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# test heterograph from the docstring, plus a user -- wishes -- game relation
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# 3 users, 2 games, 2 developers
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# metagraph:
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# ('user', 'follows', 'user'),
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# ('user', 'plays', 'game'),
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# ('user', 'wishes', 'game'),
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# ('developer', 'develops', 'game')])
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g = dgl.heterograph(
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{
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("user", "follows", "user"): ([0, 1, 2, 1], [0, 0, 1, 1]),
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("user", "plays", "game"): ([0, 1, 2, 1], [0, 0, 1, 1]),
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("user", "wishes", "game"): ([0, 1, 1], [0, 0, 1]),
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("developer", "develops", "game"): ([0, 1, 0], [0, 1, 1]),
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},
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idtype=idtype,
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device=F.ctx(),
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)
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assert g.idtype == idtype
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assert g.device == F.ctx()
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return g
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def create_test_heterograph_2(idtype):
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src = np.random.randint(0, 50, 25)
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dst = np.random.randint(0, 50, 25)
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src1 = np.random.randint(0, 25, 10)
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dst1 = np.random.randint(0, 25, 10)
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src2 = np.random.randint(0, 100, 1000)
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dst2 = np.random.randint(0, 100, 1000)
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g = dgl.heterograph(
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{
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("user", "becomes", "player"): (src, dst),
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("user", "follows", "user"): (src, dst),
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("user", "plays", "game"): (src, dst),
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("user", "wishes", "game"): (src1, dst1),
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("developer", "develops", "game"): (src2, dst2),
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},
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idtype=idtype,
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device=F.ctx(),
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)
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assert g.idtype == idtype
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assert g.device == F.ctx()
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return g
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def create_test_heterograph_large(idtype):
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src = np.random.randint(0, 50, 2500)
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dst = np.random.randint(0, 50, 2500)
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g = dgl.heterograph(
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{
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("user", "follows", "user"): (src, dst),
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("user", "plays", "game"): (src, dst),
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("user", "wishes", "game"): (src, dst),
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("developer", "develops", "game"): (src, dst),
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},
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idtype=idtype,
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device=F.ctx(),
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)
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assert g.idtype == idtype
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assert g.device == F.ctx()
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return g
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@parametrize_idtype
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def test_unary_copy_u(idtype):
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def _test(mfunc, rfunc):
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g = create_test_heterograph_2(idtype)
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g0 = create_test_heterograph(idtype)
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g1 = create_test_heterograph_large(idtype)
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cross_reducer = rfunc.__name__
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x1 = F.randn((g.num_nodes("user"), feat_size))
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x2 = F.randn((g.num_nodes("developer"), feat_size))
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F.attach_grad(x1)
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F.attach_grad(x2)
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g.nodes["user"].data["h"] = x1
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g.nodes["developer"].data["h"] = x2
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#################################################################
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# multi_update_all(): call msg_passing separately for each etype
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#################################################################
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with F.record_grad():
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g.multi_update_all(
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{
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etype: (mfunc("h", "m"), rfunc("m", "y"))
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for etype in g.canonical_etypes
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},
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cross_reducer,
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)
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r1 = g.nodes["game"].data["y"].clone()
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r2 = g.nodes["user"].data["y"].clone()
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r3 = g.nodes["player"].data["y"].clone()
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loss = r1.sum() + r2.sum() + r3.sum()
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F.backward(loss)
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n_grad1 = F.grad(g.nodes["user"].data["h"]).clone()
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n_grad2 = F.grad(g.nodes["developer"].data["h"]).clone()
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g.nodes["user"].data.clear()
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g.nodes["developer"].data.clear()
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g.nodes["game"].data.clear()
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g.nodes["player"].data.clear()
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#################################################################
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# update_all(): call msg_passing for all etypes
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#################################################################
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F.attach_grad(x1)
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F.attach_grad(x2)
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g.nodes["user"].data["h"] = x1
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g.nodes["developer"].data["h"] = x2
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with F.record_grad():
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g.update_all(mfunc("h", "m"), rfunc("m", "y"))
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r4 = g.nodes["game"].data["y"]
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r5 = g.nodes["user"].data["y"]
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r6 = g.nodes["player"].data["y"]
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loss = r4.sum() + r5.sum() + r6.sum()
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F.backward(loss)
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n_grad3 = F.grad(g.nodes["user"].data["h"])
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n_grad4 = F.grad(g.nodes["developer"].data["h"])
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assert F.allclose(r1, r4)
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assert F.allclose(r2, r5)
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assert F.allclose(r3, r6)
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assert F.allclose(n_grad1, n_grad3)
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assert F.allclose(n_grad2, n_grad4)
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_test(fn.copy_u, fn.sum)
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_test(fn.copy_u, fn.max)
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_test(fn.copy_u, fn.min)
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# _test('copy_u', 'mean')
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@parametrize_idtype
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def test_unary_copy_e(idtype):
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def _test(mfunc, rfunc):
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g = create_test_heterograph_large(idtype)
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g0 = create_test_heterograph_2(idtype)
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g1 = create_test_heterograph(idtype)
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cross_reducer = rfunc.__name__
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x1 = F.randn((g.num_edges("plays"), feat_size))
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x2 = F.randn((g.num_edges("follows"), feat_size))
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x3 = F.randn((g.num_edges("develops"), feat_size))
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x4 = F.randn((g.num_edges("wishes"), feat_size))
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F.attach_grad(x1)
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F.attach_grad(x2)
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F.attach_grad(x3)
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F.attach_grad(x4)
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g["plays"].edata["eid"] = x1
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g["follows"].edata["eid"] = x2
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g["develops"].edata["eid"] = x3
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g["wishes"].edata["eid"] = x4
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#################################################################
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# multi_update_all(): call msg_passing separately for each etype
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#################################################################
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with F.record_grad():
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g.multi_update_all(
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{
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"plays": (mfunc("eid", "m"), rfunc("m", "y")),
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"follows": (mfunc("eid", "m"), rfunc("m", "y")),
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"develops": (mfunc("eid", "m"), rfunc("m", "y")),
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"wishes": (mfunc("eid", "m"), rfunc("m", "y")),
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},
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cross_reducer,
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)
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r1 = g.nodes["game"].data["y"].clone()
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r2 = g.nodes["user"].data["y"].clone()
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loss = r1.sum() + r2.sum()
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F.backward(loss)
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e_grad1 = F.grad(g["develops"].edata["eid"]).clone()
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e_grad2 = F.grad(g["plays"].edata["eid"]).clone()
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e_grad3 = F.grad(g["wishes"].edata["eid"]).clone()
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e_grad4 = F.grad(g["follows"].edata["eid"]).clone()
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{etype: (g[etype].edata.clear()) for _, etype, _ in g.canonical_etypes},
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#################################################################
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# update_all(): call msg_passing for all etypes
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#################################################################
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# TODO(Israt): output type can be None in multi_update and empty
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F.attach_grad(x1)
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F.attach_grad(x2)
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F.attach_grad(x3)
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F.attach_grad(x4)
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g["plays"].edata["eid"] = x1
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g["follows"].edata["eid"] = x2
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g["develops"].edata["eid"] = x3
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g["wishes"].edata["eid"] = x4
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with F.record_grad():
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g.update_all(mfunc("eid", "m"), rfunc("m", "y"))
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r3 = g.nodes["game"].data["y"]
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r4 = g.nodes["user"].data["y"]
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loss = r3.sum() + r4.sum()
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F.backward(loss)
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e_grad5 = F.grad(g["develops"].edata["eid"])
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e_grad6 = F.grad(g["plays"].edata["eid"])
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e_grad7 = F.grad(g["wishes"].edata["eid"])
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e_grad8 = F.grad(g["follows"].edata["eid"])
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# # correctness check
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def _print_error(a, b):
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for i, (x, y) in enumerate(
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zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
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):
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if not np.allclose(x, y):
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print("@{} {} v.s. {}".format(i, x, y))
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assert F.allclose(r1, r3)
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assert F.allclose(r2, r4)
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assert F.allclose(e_grad1, e_grad5)
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assert F.allclose(e_grad2, e_grad6)
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assert F.allclose(e_grad3, e_grad7)
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assert F.allclose(e_grad4, e_grad8)
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_test(fn.copy_e, fn.sum)
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_test(fn.copy_e, fn.max)
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_test(fn.copy_e, fn.min)
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# _test('copy_e', 'mean')
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@parametrize_idtype
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def test_binary_op(idtype):
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def _test(lhs, rhs, binary_op, reducer):
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g = create_test_heterograph(idtype)
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x1 = F.randn((g.num_nodes("user"), feat_size))
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x2 = F.randn((g.num_nodes("developer"), feat_size))
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x3 = F.randn((g.num_nodes("game"), feat_size))
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F.attach_grad(x1)
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F.attach_grad(x2)
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F.attach_grad(x3)
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g.nodes["user"].data["h"] = x1
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g.nodes["developer"].data["h"] = x2
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g.nodes["game"].data["h"] = x3
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x1 = F.randn((4, feat_size))
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x2 = F.randn((4, feat_size))
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x3 = F.randn((3, feat_size))
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x4 = F.randn((3, feat_size))
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F.attach_grad(x1)
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F.attach_grad(x2)
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F.attach_grad(x3)
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F.attach_grad(x4)
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g["plays"].edata["h"] = x1
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g["follows"].edata["h"] = x2
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g["develops"].edata["h"] = x3
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g["wishes"].edata["h"] = x4
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builtin_msg_name = "{}_{}_{}".format(lhs, binary_op, rhs)
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builtin_msg = getattr(fn, builtin_msg_name)
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builtin_red = getattr(fn, reducer)
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#################################################################
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# multi_update_all(): call msg_passing separately for each etype
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#################################################################
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with F.record_grad():
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g.multi_update_all(
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{
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etype: (builtin_msg("h", "h", "m"), builtin_red("m", "y"))
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for etype in g.canonical_etypes
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},
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"sum",
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)
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r1 = g.nodes["game"].data["y"]
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F.backward(r1, F.ones(r1.shape))
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n_grad1 = F.grad(r1)
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#################################################################
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# update_all(): call msg_passing for all etypes
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#################################################################
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g.update_all(builtin_msg("h", "h", "m"), builtin_red("m", "y"))
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r2 = g.nodes["game"].data["y"]
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F.backward(r2, F.ones(r2.shape))
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n_grad2 = F.grad(r2)
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# correctness check
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def _print_error(a, b):
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for i, (x, y) in enumerate(
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zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())
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):
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if not np.allclose(x, y):
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print("@{} {} v.s. {}".format(i, x, y))
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if not F.allclose(r1, r2):
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_print_error(r1, r2)
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assert F.allclose(r1, r2)
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# TODO (Israt): r1 and r2 have different frad func associated with
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# if not F.allclose(n_grad1, n_grad2):
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# print('node grad')
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# _print_error(n_grad1, n_grad2)
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# assert(F.allclose(n_grad1, n_grad2))
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target = ["u", "v", "e"]
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for lhs, rhs in product(target, target):
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if lhs == rhs:
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continue
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for binary_op in ["add", "sub", "mul", "div"]:
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# TODO(Israt) :Add support for reduce func "max", "min", "mean"
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for reducer in ["sum"]:
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print(lhs, rhs, binary_op, reducer)
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_test(lhs, rhs, binary_op, reducer)
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# Issue #5873
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def test_multi_update_all_minmax_reduce_with_isolated_nodes():
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g = dgl.heterograph(
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{
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("A", "AB", "B"): ([0, 1, 2, 3], [0, 0, 1, 1]),
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("C", "CB", "B"): ([0, 1, 2, 3], [2, 2, 3, 3]),
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},
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device=F.ctx(),
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)
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g.nodes["A"].data["x"] = F.randn((4, 16))
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g.nodes["C"].data["x"] = F.randn((4, 16))
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g.multi_update_all(
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{
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"AB": (dgl.function.copy_u("x", "m"), dgl.function.min("m", "a1")),
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"CB": (dgl.function.copy_u("x", "m"), dgl.function.min("m", "a2")),
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},
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cross_reducer="min",
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)
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assert not np.isinf(F.asnumpy(g.nodes["B"].data["a1"])).any()
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assert not np.isinf(F.asnumpy(g.nodes["B"].data["a2"])).any()
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g.multi_update_all(
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{
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"AB": (dgl.function.copy_u("x", "m"), dgl.function.max("m", "a1")),
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"CB": (dgl.function.copy_u("x", "m"), dgl.function.max("m", "a2")),
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},
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cross_reducer="max",
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)
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assert not np.isinf(F.asnumpy(g.nodes["B"].data["a1"])).any()
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assert not np.isinf(F.asnumpy(g.nodes["B"].data["a2"])).any()
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if __name__ == "__main__":
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test_unary_copy_u()
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test_unary_copy_e()
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test_binary_op()
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Block a user