class EquilLibsTestCase(OptkitCOperatorTestCase):
"""
Equilibrate input A_in as
D * A_equil * E
with D, E, diagonal.
Test that
D * A_equil * E == A_in,
or that
D^-1 * A_in * E^-1 == A_equil.
"""
@classmethod
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = EquilibrationLibs()
self.A_test = self.A_test_gen
self.A_test_sparse = self.A_test_sparse_gen
@classmethod
def tearDownClass(self):
os.environ['OPTKIT_USE_LOCALLIBS'] = self.env_orig
def setUp(self):
self.x_test = np.random.rand(self.shape[1])
def tearDown(self):
self.free_all_vars()
self.exit_call()
def test_libs_exist(self):
libs = []
for (gpu, single_precision) in self.CONDITIONS:
libs.append(
self.libs.get(single_precision=single_precision, gpu=gpu))
self.assertTrue(any(libs))
def equilibrate(self, lib, order, A_test):
m, n = A_test.shape
DIGITS = 7 - 2 * lib.FLOAT - 2 * lib.GPU
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
pyorder = 'C' if order == lib.enums.CblasRowMajor else 'F'
hdl = self.register_blas_handle(lib, 'hdl')
A, A_py, A_ptr = self.register_matrix(lib, m, n, order, 'A')
d, d_py, d_ptr = self.register_vector(lib, m, 'd')
e, e_py, e_ptr = self.register_vector(lib, n, 'e')
A_in_py = A_test.astype(lib.pyfloat)
A_in_ptr = A_in_py.ctypes.data_as(lib.ok_float_p)
order_in = lib.enums.CblasRowMajor if A_in_py.flags.c_contiguous else \
lib.enums.CblasColMajor
self.assertCall(
lib.regularized_sinkhorn_knopp(hdl, A_in_ptr, A, d, e, order_in))
self.assertCall(lib.matrix_memcpy_am(A_ptr, A, order))
self.assertCall(lib.vector_memcpy_av(d_ptr, d, 1))
self.assertCall(lib.vector_memcpy_av(e_ptr, e, 1))
self.free_vars('A', 'd', 'e', 'hdl')
self.assertCall(lib.ok_device_reset())
A_eqx = A_py.dot(self.x_test)
DAEx = d_py * A_test.dot(e_py * self.x_test)
self.assertVecEqual(A_eqx, DAEx, ATOLM, RTOL)
def test_regularized_sinkhorn_knopp(self):
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
for order in (lib.enums.CblasRowMajor, lib.enums.CblasColMajor):
print "regularized sinkhorn, CBLAS layout:", order
self.equilibrate(lib, order, self.A_test)
A_rowmissing = np.zeros_like(self.A_test)
A_rowmissing += self.A_test
A_rowmissing[self.shape[0] / 2, :] *= 0
self.equilibrate(lib, order, A_rowmissing)
A_colmissing = np.zeros_like(self.A_test)
A_colmissing += self.A_test
A_colmissing[:, self.shape[1] / 2] *= 0
self.equilibrate(lib, order, A_colmissing)
self.assertCall(lib.ok_device_reset())
def test_operator_sinkhorn_knopp(self):
m, n = self.shape
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
DIGITS = 7 - 2 * single_precision - 2 * gpu
RTOL = 10**(-DIGITS)
ATOLN = RTOL * n**0.5
# -----------------------------------------
# test equilibration for each operator type defined in
# self.op_keys
for op_ in self.op_keys:
print "operator sinkhorn, operator type:", op_
hdl = self.register_blas_handle(lib, 'hdl')
x, x_py, x_ptr = self.register_vector(lib, n, 'x')
y, y_py, y_ptr = self.register_vector(lib, m, 'y')
d, d_py, d_ptr = self.register_vector(lib, m, 'd')
e, e_py, e_ptr = self.register_vector(lib, n, 'e')
x_py += self.x_test
A_, A, o = self.register_operator(lib, op_)
# equilibrate operator
self.assertCall(
lib.operator_regularized_sinkhorn(hdl, o, d, e, 1.))
# extract results
self.assertCall(lib.vector_memcpy_av(d_ptr, d, 1))
self.assertCall(lib.vector_memcpy_av(e_ptr, e, 1))
DAEx = d_py * A_.dot(e_py * self.x_test)
self.assertCall(lib.vector_memcpy_va(x, x_ptr, 1))
self.assertCall(o.contents.apply(o.contents.data, x, y))
self.assertCall(lib.vector_memcpy_av(y_ptr, y, 1))
A_eqx = y_py
self.assertVecEqual(A_eqx, DAEx, ATOLN, RTOL)
self.free_vars('A', 'o', 'x', 'y', 'd', 'e', 'hdl')
self.assertCall(lib.ok_device_reset())
def test_operator_equil(self):
m, n = self.shape
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
DIGITS = 7 - 2 * single_precision - 2 * gpu
RTOL = 10**(-DIGITS)
ATOLN = RTOL * n**0.5
# -----------------------------------------
# test equilibration for each operator type defined in
# self.op_keys
for op_ in self.op_keys:
print "operator equil generic operator type:", op_
hdl = self.register_blas_handle(lib, 'hdl')
x, x_py, x_ptr = self.register_vector(lib, n, 'x')
y, y_py, y_ptr = self.register_vector(lib, m, 'y')
d, d_py, d_ptr = self.register_vector(lib, m, 'd')
e, e_py, e_ptr = self.register_vector(lib, n, 'e')
x_py += self.x_test
A_, A, o = self.register_operator(lib, op_)
# equilibrate operator
status = lib.operator_equilibrate(hdl, o, d, e, 1.)
# extract results
self.assertCall(lib.vector_memcpy_av(d_ptr, d, 1))
self.assertCall(lib.vector_memcpy_av(e_ptr, e, 1))
DAEx = d_py * A_.dot(e_py * self.x_test)
self.assertCall(lib.vector_memcpy_va(x, x_ptr, 1))
o.contents.apply(o.contents.data, x, y)
self.assertCall(lib.vector_memcpy_av(y_ptr, y, 1))
A_eqx = y_py
# TODO: REPLACE THIS WITH THE REAL TEST BELOW
self.assertEqual(status, 1)
# REAL TEST:
# self.assertEqual( status, 0 )
# self.assertVecEqual( A_eqx, DAEx, ATOLN, RTOL )
self.free_vars('A', 'o', 'x', 'y', 'd', 'e', 'hdl')
self.assertCall(lib.ok_device_reset())
def test_operator_norm(self):
m, n = self.shape
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
RTOL = 5e-2
ATOL = 5e-3 * (m * n)**0.5
# -----------------------------------------
# test norm estimation for each operator type defined in
# self.op_keys
for op_ in self.op_keys:
print "operator norm, operator type:", op_
hdl = self.register_blas_handle(lib, 'hdl')
A_, A, o = self.register_operator(lib, op_)
# estimate operator norm
normest_p = lib.ok_float_p()
normest_p.contents = lib.ok_float(0.)
pynorm = np.linalg.norm(A_)
self.assertCall(lib.operator_estimate_norm(hdl, o, normest_p))
cnorm = normest_p.contents
if self.VERBOSE_TEST:
print "operator norm, Python: ", pynorm
print "norm estimate, C: ", cnorm
self.assertTrue(cnorm >= ATOL + RTOL * pynorm
or pynorm >= ATOL + RTOL * cnorm)
self.free_vars('A', 'o', 'hdl')
self.assertCall(lib.ok_device_reset())
class EquilLibsTestCase(OptkitCOperatorTestCase):
"""
Equilibrate input A_in as
D * A_equil * E
with D, E, diagonal.
Test that
D * A_equil * E == A_in,
or that
D^-1 * A_in * E^-1 == A_equil.
"""
@classmethod
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = EquilibrationLibs()
self.A_test = self.A_test_gen
self.A_test_sparse = self.A_test_sparse_gen
@classmethod
def tearDownClass(self):
os.environ['OPTKIT_USE_LOCALLIBS'] = self.env_orig
def setUp(self):
self.x_test = np.random.rand(self.shape[1])
def tearDown(self):
self.free_all_vars()
self.exit_call()
def test_libs_exist(self):
libs = []
for (gpu, single_precision) in self.CONDITIONS:
libs.append(self.libs.get(single_precision=single_precision,
gpu=gpu))
self.assertTrue(any(libs))
def equilibrate(self, lib, order, A_test):
m, n = A_test.shape
DIGITS = 7 - 2 * lib.FLOAT - 2 * lib.GPU
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
pyorder = 'C' if order == lib.enums.CblasRowMajor else 'F'
hdl = self.register_blas_handle(lib, 'hdl')
A, A_py, A_ptr = self.register_matrix(lib, m, n, order, 'A')
d, d_py, d_ptr = self.register_vector(lib, m, 'd')
e, e_py, e_ptr = self.register_vector(lib, n, 'e')
A_in_py = A_test.astype(lib.pyfloat)
A_in_ptr = A_in_py.ctypes.data_as(lib.ok_float_p)
order_in = lib.enums.CblasRowMajor if A_in_py.flags.c_contiguous else \
lib.enums.CblasColMajor
self.assertCall( lib.regularized_sinkhorn_knopp(
hdl, A_in_ptr, A, d, e, order_in) )
self.assertCall( lib.matrix_memcpy_am(A_ptr, A, order) )
self.assertCall( lib.vector_memcpy_av(d_ptr, d, 1) )
self.assertCall( lib.vector_memcpy_av(e_ptr, e, 1) )
self.free_vars('A', 'd', 'e', 'hdl')
self.assertCall( lib.ok_device_reset() )
A_eqx = A_py.dot(self.x_test)
DAEx = d_py * A_test.dot(e_py * self.x_test)
self.assertVecEqual( A_eqx, DAEx, ATOLM, RTOL )
def test_regularized_sinkhorn_knopp(self):
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
for order in (lib.enums.CblasRowMajor, lib.enums.CblasColMajor):
print "regularized sinkhorn, CBLAS layout:", order
self.equilibrate(lib, order, self.A_test)
A_rowmissing = np.zeros_like(self.A_test)
A_rowmissing += self.A_test
A_rowmissing[self.shape[0]/2, :] *= 0
self.equilibrate(lib, order, A_rowmissing)
A_colmissing = np.zeros_like(self.A_test)
A_colmissing += self.A_test
A_colmissing[:, self.shape[1]/2] *= 0
self.equilibrate(lib, order, A_colmissing)
self.assertCall( lib.ok_device_reset() )
def test_operator_sinkhorn_knopp(self):
m, n = self.shape
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
DIGITS = 7 - 2 * single_precision - 2 * gpu
RTOL = 10**(-DIGITS)
ATOLN = RTOL * n**0.5
# -----------------------------------------
# test equilibration for each operator type defined in
# self.op_keys
for op_ in self.op_keys:
print "operator sinkhorn, operator type:", op_
hdl = self.register_blas_handle(lib, 'hdl')
x, x_py, x_ptr = self.register_vector(lib, n, 'x')
y, y_py, y_ptr = self.register_vector(lib, m, 'y')
d, d_py, d_ptr = self.register_vector(lib, m, 'd')
e, e_py, e_ptr = self.register_vector(lib, n, 'e')
x_py += self.x_test
A_, A, o = self.register_operator(lib, op_)
# equilibrate operator
self.assertCall( lib.operator_regularized_sinkhorn(hdl, o, d,
e, 1.) )
# extract results
self.assertCall( lib.vector_memcpy_av(d_ptr, d, 1) )
self.assertCall( lib.vector_memcpy_av(e_ptr, e, 1) )
DAEx = d_py * A_.dot(e_py * self.x_test)
self.assertCall( lib.vector_memcpy_va(x, x_ptr, 1) )
self.assertCall( o.contents.apply(o.contents.data, x, y) )
self.assertCall( lib.vector_memcpy_av(y_ptr, y, 1) )
A_eqx = y_py
self.assertVecEqual( A_eqx, DAEx, ATOLN, RTOL )
self.free_vars('A', 'o', 'x', 'y', 'd', 'e', 'hdl')
self.assertCall( lib.ok_device_reset() )
def test_operator_equil(self):
m, n = self.shape
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
DIGITS = 7 - 2 * single_precision - 2 * gpu
RTOL = 10**(-DIGITS)
ATOLN = RTOL * n**0.5
# -----------------------------------------
# test equilibration for each operator type defined in
# self.op_keys
for op_ in self.op_keys:
print "operator equil generic operator type:", op_
hdl = self.register_blas_handle(lib, 'hdl')
x, x_py, x_ptr = self.register_vector(lib, n, 'x')
y, y_py, y_ptr = self.register_vector(lib, m, 'y')
d, d_py, d_ptr = self.register_vector(lib, m, 'd')
e, e_py, e_ptr = self.register_vector(lib, n, 'e')
x_py += self.x_test
A_, A, o = self.register_operator(lib, op_)
# equilibrate operator
status = lib.operator_equilibrate(hdl, o, d, e, 1.)
# extract results
self.assertCall( lib.vector_memcpy_av(d_ptr, d, 1) )
self.assertCall( lib.vector_memcpy_av(e_ptr, e, 1) )
DAEx = d_py * A_.dot(e_py * self.x_test)
self.assertCall( lib.vector_memcpy_va(x, x_ptr, 1) )
o.contents.apply(o.contents.data, x, y)
self.assertCall( lib.vector_memcpy_av(y_ptr, y, 1) )
A_eqx = y_py
# TODO: REPLACE THIS WITH THE REAL TEST BELOW
self.assertEqual(status, 1)
# REAL TEST:
# self.assertEqual( status, 0 )
# self.assertVecEqual( A_eqx, DAEx, ATOLN, RTOL )
self.free_vars('A', 'o', 'x', 'y', 'd', 'e', 'hdl')
self.assertCall( lib.ok_device_reset() )
def test_operator_norm(self):
m, n = self.shape
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
RTOL = 5e-2
ATOL = 5e-3 * (m * n)**0.5
# -----------------------------------------
# test norm estimation for each operator type defined in
# self.op_keys
for op_ in self.op_keys:
print "operator norm, operator type:", op_
hdl = self.register_blas_handle(lib, 'hdl')
A_, A, o = self.register_operator(lib, op_)
# estimate operator norm
normest_p = lib.ok_float_p()
normest_p.contents = lib.ok_float(0.)
pynorm = np.linalg.norm(A_)
self.assertCall( lib.operator_estimate_norm(hdl, o,
normest_p) )
cnorm = normest_p.contents
if self.VERBOSE_TEST:
print "operator norm, Python: ", pynorm
print "norm estimate, C: ", cnorm
self.assertTrue(
cnorm >= ATOL + RTOL * pynorm or
pynorm >= ATOL + RTOL * cnorm )
self.free_vars('A', 'o','hdl')
self.assertCall( lib.ok_device_reset() )
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = EquilibrationLibs()
self.A_test = self.A_test_gen
self.A_test_sparse = self.A_test_sparse_gen
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = EquilibrationLibs()
self.A_test = self.A_test_gen
self.A_test_sparse = self.A_test_sparse_gen
