def test_segment_reduce(reducer):
ctx = F.ctx()
value = F.tensor(np.random.rand(10, 5))
v1 = F.attach_grad(F.clone(value))
v2 = F.attach_grad(F.clone(value))
seglen = F.tensor([2, 3, 0, 4, 1, 0, 0])
u = F.copy_to(F.arange(0, F.shape(value)[0], F.int32), ctx)
v = F.repeat(F.copy_to(F.arange(0, len(seglen), F.int32), ctx),
seglen,
dim=0)
num_nodes = {'_U': len(u), '_V': len(seglen)}
g = dgl.convert.heterograph({('_U', '_E', '_V'): (u, v)},
num_nodes_dict=num_nodes)
with F.record_grad():
rst1 = gspmm(g, 'copy_lhs', reducer, v1, None)
if reducer in ['max', 'min']:
rst1 = F.replace_inf_with_zero(rst1)
F.backward(F.reduce_sum(rst1))
grad1 = F.grad(v1)
with F.record_grad():
rst2 = segment_reduce(seglen, v2, reducer=reducer)
F.backward(F.reduce_sum(rst2))
assert F.allclose(rst1, rst2)
print('forward passed')
grad2 = F.grad(v2)
assert F.allclose(grad1, grad2)
print('backward passed')
def test_spmm(idtype, g, shp, msg, reducer):
g = g.astype(idtype).to(F.ctx())
if dgl.backend.backend_name == 'tensorflow' and (reducer in ['min', 'max']):
pytest.skip() # tensorflow dlpack has problem writing into int32 arrays on GPU.
print(g)
print(g.idtype)
hu = F.tensor(np.random.rand(*((g.number_of_src_nodes(),) + shp[0])) + 1)
he = F.tensor(np.random.rand(*((g.number_of_edges(),) + shp[1])) + 1)
print('u shape: {}, e shape: {}'.format(F.shape(hu), F.shape(he)))
g.srcdata['x'] = F.attach_grad(F.clone(hu))
g.edata['w'] = F.attach_grad(F.clone(he))
print('SpMM(message func: {}, reduce func: {})'.format(msg, reducer))
u = F.attach_grad(F.clone(hu))
e = F.attach_grad(F.clone(he))
with F.record_grad():
v = gspmm(g, msg, reducer, u, e)
non_degree_indices = F.tensor(
np.nonzero(F.asnumpy(g.in_degrees()) != 0)[0])
v = F.gather_row(v, non_degree_indices)
if g.number_of_edges() > 0:
F.backward(F.reduce_sum(v))
if msg != 'copy_rhs':
grad_u = F.grad(u)
if msg != 'copy_lhs':
grad_e = F.grad(e)
with F.record_grad():
g.update_all(udf_msg[msg], udf_reduce[reducer])
if g.number_of_edges() > 0:
v1 = F.gather_row(g.dstdata['v'], non_degree_indices)
assert F.allclose(v, v1)
print('forward passed')
F.backward(F.reduce_sum(v1))
if msg != 'copy_rhs':
if reducer in ['min', 'max']: # there might be some numerical errors
rate = F.reduce_sum(F.abs(F.grad(g.srcdata['x']) - grad_u)) /\
F.reduce_sum(F.abs(grad_u))
assert F.as_scalar(rate) < 1e-2, rate
else:
assert F.allclose(F.grad(g.srcdata['x']), grad_u)
if msg != 'copy_lhs':
if reducer in ['min', 'max']:
rate = F.reduce_sum(F.abs(F.grad(g.edata['w']) - grad_e)) /\
F.reduce_sum(F.abs(grad_e))
assert F.as_scalar(rate) < 1e-2, rate
else:
assert F.allclose(F.grad(g.edata['w']), grad_e)
print('backward passed')
g.srcdata.pop('x')
g.edata.pop('w')
if 'v' in g.dstdata: g.dstdata.pop('v')
def test_spmm(idtype, g, shp, msg, reducer):
g = g.astype(idtype).to(F.ctx())
print(g)
print(g.idtype)
hu = F.tensor(np.random.rand(*((g.number_of_src_nodes(), ) + shp[0])) + 1)
he = F.tensor(np.random.rand(*((g.number_of_edges(), ) + shp[1])) + 1)
print('u shape: {}, e shape: {}'.format(F.shape(hu), F.shape(he)))
g.srcdata['x'] = F.attach_grad(F.clone(hu))
g.edata['w'] = F.attach_grad(F.clone(he))
print('SpMM(message func: {}, reduce func: {})'.format(msg, reducer))
u = F.attach_grad(F.clone(hu))
e = F.attach_grad(F.clone(he))
with F.record_grad():
v = gspmm(g, msg, reducer, u, e)
if reducer in ['max', 'min']:
v = F.replace_inf_with_zero(v)
if g.number_of_edges() > 0:
F.backward(F.reduce_sum(v))
if msg != 'copy_rhs':
grad_u = F.grad(u)
if msg != 'copy_lhs':
grad_e = F.grad(e)
with F.record_grad():
g.update_all(udf_msg[msg], udf_reduce[reducer])
if g.number_of_edges() > 0:
v1 = g.dstdata['v']
assert F.allclose(v, v1)
print('forward passed')
F.backward(F.reduce_sum(v1))
if msg != 'copy_rhs':
if reducer in ['min',
'max']: # there might be some numerical errors
rate = F.reduce_sum(F.abs(F.grad(g.srcdata['x']) - grad_u)) /\
F.reduce_sum(F.abs(grad_u))
assert F.as_scalar(rate) < 1e-2, rate
else:
assert F.allclose(F.grad(g.srcdata['x']), grad_u)
if msg != 'copy_lhs':
if reducer in ['min', 'max']:
rate = F.reduce_sum(F.abs(F.grad(g.edata['w']) - grad_e)) /\
F.reduce_sum(F.abs(grad_e))
assert F.as_scalar(rate) < 1e-2, rate
else:
assert F.allclose(F.grad(g.edata['w']), grad_e)
print('backward passed')
g.srcdata.pop('x')
g.edata.pop('w')
if 'v' in g.dstdata: g.dstdata.pop('v')
