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qr and ormqr tests and bugfix #119

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Oct 14, 2016
Merged

qr and ormqr tests and bugfix #119

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43 changes: 32 additions & 11 deletions test/test_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,18 @@ def small_3d_positive(t):
def small_3d_unique(t):
return t(S, S, S).copy_(torch.range(1, S*S*S))

def small_1d_lapack(t):
return torch.range(1, 3).view(3)

def small_2d_lapack(t):
return torch.range(1, 9).view(3, 3)

def small_2d_lapack_skinny(t):
return torch.range(1, 12).view(3, 4)

def small_2d_lapack_fat(t):
return torch.range(1, 12).view(4, 3)

def new_t(*sizes):
def tmp(t):
return t(*sizes).copy_(torch.randn(*sizes))
Expand Down Expand Up @@ -92,13 +104,9 @@ def tmp(t):
('addmv', medium_1d, lambda t: [medium_2d(t), medium_1d(t)], ),
('addmv', medium_1d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'scalar' ),
('addmv', medium_1d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'two_scalars' ),
('addmv', medium_1d, lambda t: [medium_2d(t), medium_1d(t)], ),
('addmv', medium_1d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'scalar' ),
('addmv', medium_1d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'two_scalars' ),
('addr', medium_2d, lambda t: [medium_1d(t), medium_1d(t)], ),
('addr', medium_2d, lambda t: [number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'scalar' ),
('addr', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'two_scalars' ),
('addr', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'two_scalars' ),
('atan2', medium_2d, lambda t: [medium_2d(t)], ),
('chunk', medium_2d, lambda t: [4], ),
('chunk', medium_2d, lambda t: [4, 1], 'dim' ),
Expand Down Expand Up @@ -195,6 +203,11 @@ def tmp(t):
('rsqrt', lambda t: small_3d(t) + 1, lambda t: [], ),
('sinh', lambda t: small_3d(t).clamp(-1, 1), lambda t: [], ),
('tan', lambda t: small_3d(t).clamp(-1, 1), lambda t: [], ),
# lapack tests
('qr', small_2d_lapack, lambda t: [], 'square' ),
('qr', small_2d_lapack_skinny, lambda t: [], 'skinny' ),
('qr', small_2d_lapack_fat, lambda t: [], 'fat' ),

]

# TODO: random functions, cat, gather, scatter, index*, masked*, resize, resizeAs, storage_offset, storage, stride, unfold
Expand Down Expand Up @@ -251,6 +264,11 @@ def tmp(self):
if 'unimplemented data type' in reason:
raise unittest.SkipTest('unimplemented data type')
raise
except AttributeError as e:
reason = e.args[0]
if 'object has no attribute' in reason:
raise unittest.SkipTest('unimplemented data type')
raise
# If one changes, another should change as well
self.assertEqual(cpu_tensor, gpu_tensor, precision)
self.assertEqual(cpu_args, gpu_args, precision)
Expand Down Expand Up @@ -482,19 +500,22 @@ def test_multigpu_serialization_remap_dict(self):
precision = custom_precision.get(name, TestCuda.precision)
for inplace in (True, False):
if inplace:
name = name + '_'
if not hasattr(tensor, name):
name_inner = name + '_'
else:
name_inner = name
if not hasattr(tensor, name_inner):
print("Ignoring {}, because it's not implemented by torch.{}".format(name_inner, tensor.__class__.__name__))
continue
if not hasattr(gpu_tensor, name):
print("Ignoring {}, because it's not implemented by torch.cuda.{}".format(name, gpu_tensor.__class__.__name__))
if not hasattr(gpu_tensor, name_inner):
print("Ignoring {}, because it's not implemented by torch.cuda.{}".format(name_inner, gpu_tensor.__class__.__name__))
continue

test_name = 'test_' + t.__name__ + '_' + name
test_name = 'test_' + t.__name__ + '_' + name_inner
if desc:
test_name += '_' + desc

assert not hasattr(TestCase, test_name)
setattr(TestCuda, test_name, compare_cpu_gpu(constr, arg_constr, name, t, precision))
assert not hasattr(TestCuda, test_name), "Duplicated test name: " + test_name
setattr(TestCuda, test_name, compare_cpu_gpu(constr, arg_constr, name_inner, t, precision))

if __name__ == '__main__':
unittest.main()
117 changes: 117 additions & 0 deletions test/test_torch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1113,6 +1113,123 @@ def test_gesv(self):
torch.gesv(tb, ta, b, a)[0]
self.assertEqual(res1, tb)

@skipIfNoLapack
def test_qr(self):

# Since the QR decomposition is unique only up to the signs of the rows of
# R, we must ensure these are positive before doing the comparison.
def canonicalize(q, r):
d = r.diag().sign().diag()
return torch.mm(q, d), torch.mm(d, r)

def canon_and_check(q, r, expected_q, expected_r):
q_canon, r_canon = canonicalize(q, r)
expected_q_canon, expected_r_canon = canonicalize(expected_q, expected_r)
self.assertEqual(q_canon, expected_q_canon)
self.assertEqual(r_canon, expected_r_canon)

def check_qr(a, expected_q, expected_r):
# standard invocation
q, r = torch.qr(a)
canon_and_check(q, r, expected_q, expected_r)

# in-place
q, r = torch.Tensor(), torch.Tensor()
torch.qr(q, r, a)
canon_and_check(q, r, expected_q, expected_r)

# manually calculate qr using geqrf and orgqr
m = a.size(0)
n = a.size(1)
k = min(m, n)
result, tau = torch.geqrf(a)
self.assertEqual(result.size(0), m)
self.assertEqual(result.size(1), n)
self.assertEqual(tau.size(0), k)
r = torch.triu(result.narrow(0, 0, k))
q, _ = torch.orgqr(result, tau)
q, r = q.narrow(1, 0, k), r
canon_and_check(q, r, expected_q, expected_r)

# check square case
a = torch.Tensor(((1, 2, 3), (4, 5, 6), (7, 8, 10)))

expected_q = torch.Tensor((
(-1.230914909793328e-01, 9.045340337332914e-01, 4.082482904638621e-01),
(-4.923659639173310e-01, 3.015113445777629e-01, -8.164965809277264e-01),
(-8.616404368553292e-01, -3.015113445777631e-01, 4.082482904638634e-01)))
expected_r = torch.Tensor((
(-8.124038404635959e+00, -9.601136296387955e+00, -1.193987e+01),
( 0.000000000000000e+00, 9.045340337332926e-01, 1.507557e+00),
( 0.000000000000000e+00, 0.000000000000000e+00, 4.082483e-01)))

check_qr(a, expected_q, expected_r)

# check rectangular thin
a = torch.Tensor((
( 1, 2, 3),
( 4, 5, 6),
( 7, 8, 9),
(10, 11, 13),
))
expected_q = torch.Tensor((
(-0.0776150525706334, -0.833052161400748 , 0.3651483716701106),
(-0.3104602102825332, -0.4512365874254053, -0.1825741858350556),
(-0.5433053679944331, -0.0694210134500621, -0.7302967433402217),
(-0.7761505257063329, 0.3123945605252804, 0.5477225575051663)
))
expected_r = torch.Tensor((
(-12.8840987267251261, -14.5916298832790581, -17.0753115655393231),
( 0, -1.0413152017509357, -1.770235842976589 ),
( 0, 0, 0.5477225575051664)
))

check_qr(a, expected_q, expected_r)

# check rectangular fat
a = torch.Tensor((
(1, 2, 3, 4),
(5, 6, 7, 8),
(9, 10, 11, 13)
))
expected_q = torch.Tensor((
(-0.0966736489045663, 0.907737593658436 , 0.4082482904638653),
(-0.4833682445228317, 0.3157348151855452, -0.8164965809277254),
(-0.870062840141097 , -0.2762679632873518, 0.4082482904638621)
))
expected_r = torch.Tensor((
( -1.0344080432788603e+01, -1.1794185166357092e+01,
-1.3244289899925587e+01, -1.5564457473635180e+01),
( 0.0000000000000000e+00, 9.4720444555662542e-01,
1.8944088911132546e+00, 2.5653453733825331e+00),
( 0.0000000000000000e+00, 0.0000000000000000e+00,
1.5543122344752192e-15, 4.0824829046386757e-01)
))
check_qr(a, expected_q, expected_r)

@skipIfNoLapack
def test_ormqr(self):
mat1 = torch.randn(10, 10)
mat2 = torch.randn(10, 10)
q, r = torch.qr(mat1)
m, tau = torch.geqrf(mat1)

res1 = torch.mm(q, mat2)
res2, _ = torch.ormqr(m, tau, mat2)
self.assertEqual(res1, res2)

res1 = torch.mm(mat2, q)
res2, _ = torch.ormqr(m, tau, mat2, False)
self.assertEqual(res1, res2)

res1 = torch.mm(q.t(), mat2)
res2, _ = torch.ormqr(m, tau, mat2, True, True)
self.assertEqual(res1, res2)

res1 = torch.mm(mat2, q.t())
res2, _ = torch.ormqr(m, tau, mat2, False, True)
self.assertEqual(res1, res2)

@skipIfNoLapack
def test_trtrs(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
Expand Down
2 changes: 1 addition & 1 deletion torch/csrc/Module.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -715,7 +715,7 @@ static PyMethodDef TorchMethods[] = {
{"potrs", (PyCFunction)THPModule_potrs, METH_VARARGS | METH_KEYWORDS, NULL},
{"potri", (PyCFunction)THPModule_potri, METH_VARARGS | METH_KEYWORDS, NULL},
{"pstrf", (PyCFunction)THPModule_pstrf, METH_VARARGS | METH_KEYWORDS, NULL},
{"qe", (PyCFunction)THPModule_qr, METH_VARARGS | METH_KEYWORDS, NULL},
{"qr", (PyCFunction)THPModule_qr, METH_VARARGS | METH_KEYWORDS, NULL},
{"geqrf", (PyCFunction)THPModule_geqrf, METH_VARARGS | METH_KEYWORDS, NULL},
{"orgqr", (PyCFunction)THPModule_orgqr, METH_VARARGS | METH_KEYWORDS, NULL},
{"ormqr", (PyCFunction)THPModule_ormqr, METH_VARARGS | METH_KEYWORDS, NULL},
Expand Down
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