dmlc--dgl
nv-dlasalle
308 行
11 KiB
Python
308 行
11 KiB
Python
import dgl
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import dgl.function as fn
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from collections import Counter
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import numpy as np
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import scipy.sparse as ssp
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import itertools
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from itertools import product
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import backend as F
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import networkx as nx
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import unittest, pytest
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from dgl import DGLError
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import test_utils
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from test_utils import parametrize_idtype, get_cases
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from scipy.sparse import rand
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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(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
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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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('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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}, idtype=idtype, device=F.ctx())
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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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('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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}, idtype=idtype, device=F.ctx())
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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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('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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}, idtype=idtype, device=F.ctx())
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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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{etype : (mfunc('h', 'm'), rfunc('m', 'y'))
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for etype in g.canonical_etypes},
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cross_reducer)
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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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{'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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cross_reducer)
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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())
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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(zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())):
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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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{etype : (builtin_msg('h', 'h', 'm'), builtin_red('m', 'y'))
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for etype in g.canonical_etypes},
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'sum')
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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(zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())):
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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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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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