def __init__(self, gpu=False, single_precision=False):
self.version = None
self.__device = None
self.__precision = None
self.__config = "(No libraries selected)"
self.lowtypes = None
self.dimcheck = getenv('OPTKIT_CHECKDIM', 0)
self.typecheck = getenv('OPTKIT_CHECKTYPE', 0)
self.devicecheck = getenv('OPTKIT_CHECKDEVICE', 0)
# library loaders
# self.dense_lib_loader = DenseLinsysLibs()
# self.sparse_lib_loader = SparseLinsysLibs()
# self.prox_lib_loader = ProxLibs()
self.pogs_lib_loader = PogsLibs()
self.cluster_lib_loader = ClusteringLibs()
# library instances
# self.dense = None
# self.sparse = None
# self.prox = None
self.pogs = None
self.cluster = None
self.__LIBGUARD_ON = False
self.__COBJECT_COUNT = 0
self.__set_lib()
def __init__(self, gpu=False, single_precision=False):
self.version = None
self.__device = None
self.__precision = None
self.__config = "(No libraries selected)"
self.lowtypes = None
self.dimcheck = getenv('OPTKIT_CHECKDIM', 0)
self.typecheck = getenv('OPTKIT_CHECKTYPE', 0)
self.devicecheck = getenv('OPTKIT_CHECKDEVICE', 0)
# library loaders
# self.dense_lib_loader = DenseLinsysLibs()
# self.sparse_lib_loader = SparseLinsysLibs()
# self.prox_lib_loader = ProxLibs()
self.pogs_lib_loader = PogsLibs()
self.cluster_lib_loader = ClusteringLibs()
# library instances
# self.dense = None
# self.sparse = None
# self.prox = None
self.pogs = None
self.cluster = None
self.__LIBGUARD_ON = False
self.__COBJECT_COUNT = 0
self.__set_lib()
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = ClusteringLibs()
class ClusterLibsTestCase(OptkitCTestCase):
@classmethod
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = ClusteringLibs()
# self.A_test = self.A_test_gen
@classmethod
def tearDownClass(self):
os.environ['OPTKIT_USE_LOCALLIBS'] = self.env_orig
def setUp(self):
self.k = int(self.shape[0]**0.5)
self.C_test = np.random.rand(self.k, self.shape[1])
self.A_test = np.random.rand(*self.shape)
# construct A_test as a noisy upsampled version of C
percent_noise = 0.05
for i in xrange(self.shape[0]):
self.A_test[i, :] *= percent_noise
self.A_test[i, :] += self.C_test[i % self.k, :]
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))
@staticmethod
def upsamplingvec_mul(tU, tA, tB, alpha, u, A, beta, B):
if not bool(isinstance(u, ndarray) and isinstance(A, ndarray)
and isinstance(B, ndarray)):
raise TypeError('u, A, and B must be of type {}'.format(ndarray))
if not (len(A.shape) == 2 and len(B.shape) == 2):
raise ValueError('A and B must be 2-d {}s'.format(ndarray))
umax = int(u.max()) + 1
u_dim1 = umax if tU == 'T' else len(u)
u_dim2 = len(u) if tU == 'T' else umax
A = A.T if tA == 'T' else A
B = B.T if tB == 'T' else B
if not bool(A.shape[1] == B.shape[1] and B.shape[0] == u_dim1 and
u_dim2 <= A.shape[0]):
raise ValueError('incompatible dimensions')
B *= beta
if tU == 'T':
for (row_a, row_b) in enumerate(u):
B[row_b, :] += alpha * A[row_a, :]
else:
for (row_b, row_a) in enumerate(u):
B[row_b, :] += alpha * A[row_a, :]
@staticmethod
def cluster(A, C, a2c, maxdist):
if not bool(isinstance(a2c, ndarray) and isinstance(A, ndarray)
and isinstance(C, ndarray)):
raise TypeError('a2c, A, and C must be of type {}'.format(ndarray))
if not (len(A.shape) == 2 and len(C.shape) == 2):
raise ValueError('A and C must be 2-d {}s'.format(ndarray))
if not bool(A.shape[1] == C.shape[1] and A.shape[0] == len(a2c) and
a2c.max() <= C.shape[0]):
raise ValueError('incompatible dimensions')
m, n = A.shape
k = C.shape[0]
c_squared = np.zeros(k)
for row, c in enumerate(C):
c_squared[row] = c.dot(c)
D = - 2 * C.dot(A.T)
for i in xrange(m):
D[:, i] += c_squared
indmin = D.argmin(0)
dmin = D.min(0)
reassigned = 0
if maxdist == np.inf:
reassigned = sum(a2c != indmin)
a2c[:] = indmin[:]
else:
for i in xrange(m):
if a2c[i] == indmin[i]:
continue
dist_inf = np.abs(A[i, :] - C[indmin[i], :]).max()
if dist_inf <= maxdist:
a2c[i] = indmin[i]
reassigned += 1
return D, dmin, reassigned
def gen_py_upsamplingvec(self, lib, size1, size2, random=False):
if 'c_size_t_p' not in lib.__dict__:
raise ValueError('symbol "c_size_t_p" undefined in '
'library {}'.format(lib))
u_py = np.zeros(size1).astype(c_size_t)
u_ptr = u_py.ctypes.data_as(lib.c_size_t_p)
if random:
u_py += (size2 * np.random.rand(size1)).astype(c_size_t)
u_py[-1] = size2 - 1
return u_py, u_ptr
def register_upsamplingvec(self, lib, size1, size2, name, random=False):
if not 'upsamplingvec_alloc' in lib.__dict__:
raise ValueError('library {} cannot allocate an '
'upsamplingvec'.format(lib))
u = lib.upsamplingvec()
self.assertCall( lib.upsamplingvec_alloc(u, size1, size2) )
self.register_var(name, u, lib.upsamplingvec_free)
u_py, u_ptr = self.gen_py_upsamplingvec(lib, size1, size2, random)
if random:
self.assertCall( lib.indvector_memcpy_va(u.vec, u_ptr, 1) )
return u, u_py, u_ptr
def register_cluster_aid(self, lib, n_vectors, n_clusters, order, name):
if not 'cluster_aid_alloc' in lib.__dict__:
raise ValueError('library {} cannot allocate a cluster '
'aid'.format(lib))
h = lib.cluster_aid()
self.assertCall( lib.cluster_aid_alloc(h, n_vectors, n_clusters, order) )
self.register_var('h', h, lib.cluster_aid_free)
return h
def test_upsamplingvec_alloc_free(self):
m, n = self.shape
k = self.k
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)
u = lib.upsamplingvec()
self.assertCall( lib.upsamplingvec_alloc(u, m, k) )
self.register_var('u', u, lib.upsamplingvec_free)
self.assertEqual( u.size1, m )
self.assertEqual( u.size2, k )
self.assertCall( lib.upsamplingvec_free(u) )
self.unregister_var('u')
self.assertCall( lib.ok_device_reset() )
def test_upsamplingvec_check_bounds(self):
m, n = self.shape
k = self.k
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)
u, u_py, u_ptr = self.register_upsamplingvec(lib, m, k, 'u',
random=True)
self.assertCall( lib.upsamplingvec_check_bounds(u) )
u_py += 2 * k
self.assertCall( lib.indvector_memcpy_va(u.vec, u_ptr, 1) )
# expect error
print '\nexpect dimension mismatch error:'
err = lib.upsamplingvec_check_bounds(u)
self.assertEqual( err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH )
self.assertCall( lib.upsamplingvec_free(u) )
self.unregister_var('u')
self.assertCall( lib.ok_device_reset() )
def test_upsamplingvec_update_size(self):
m, n = self.shape
k = self.k
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)
u, u_py, u_ptr = self.register_upsamplingvec(lib, m, k, 'u',
random=True)
self.assertCall( lib.upsamplingvec_check_bounds(u) )
# incorrect size
print '\nexpect dimension mismatch error:'
u.size2 = k / 2
err = lib.upsamplingvec_check_bounds(u)
self.assertEqual( err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH )
self.assertCall( lib.upsamplingvec_update_size(u) )
self.assertEqual( u.size2, k )
self.assertCall( lib.upsamplingvec_check_bounds(u) )
self.assertCall( lib.upsamplingvec_free(u) )
self.unregister_var('u')
self.assertCall( lib.ok_device_reset() )
def test_upsamplingvec_subvector(self):
m, n = self.shape
k = self.k
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)
offset = m / 4
msub = m / 2
ksub = k / 2
u, u_py, u_ptr = self.register_upsamplingvec(lib, m, k, 'u',
random=True)
usub = lib.upsamplingvec()
usub_py, usub_ptr = self.gen_py_upsamplingvec(lib, msub, k)
self.assertCall( lib.upsamplingvec_subvector(usub, u, offset, msub,
k) )
self.assertCall( lib.indvector_memcpy_av(usub_ptr, usub.vec, 1) )
self.assertTrue( usub.size1 == msub )
self.assertTrue( usub.size2 == k )
self.assertTrue( sum(usub_py - u_py[offset : offset + msub]) == 0 )
self.assertCall( lib.upsamplingvec_subvector(usub, u, offset, msub,
ksub) )
self.assertTrue( usub.size1 == msub )
self.assertTrue( usub.size2 == ksub )
self.assertCall( lib.upsamplingvec_free(u) )
self.unregister_var('u')
self.assertCall( lib.ok_device_reset() )
def test_upsamplingvec_mul_matrix(self):
m, n = self.shape
k = self.k
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 - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
ATOLK = RTOL * k**0.5
rowmajor = lib.enums.CblasRowMajor
hdl = self.register_blas_handle(lib, 'hdl')
A, A_py, A_ptr = self.register_matrix(lib, m, n, rowmajor, 'A',
random=True)
B, B_py, B_ptr = self.register_matrix(lib, n, m, rowmajor, 'B',
random=True)
C, C_py, C_ptr = self.register_matrix(lib, k, n, rowmajor, 'C',
random=True)
D, D_py, D_ptr = self.register_matrix(lib, n, k, rowmajor, 'D',
random=True)
E = lib.matrix(0, 0, 0, None, 0)
mvec, mvec_py, mvec_ptr = self.register_vector(lib, m, 'mvec',
random=True)
nvec, nvec_py, nvec_ptr = self.register_vector(lib, n, 'nvec',
random=True)
kvec, kvec_py, kvec_ptr = self.register_vector(lib, k, 'kvec',
random=True)
u, u_py, u_ptr = self.register_upsamplingvec(lib, m, k, 'u',
random=True)
T = lib.enums.CblasTrans
N = lib.enums.CblasNoTrans
alpha = np.random.rand()
beta = np.random.rand()
# functioning cases
self.upsamplingvec_mul(
'N', 'N', 'N', alpha, u_py, C_py, beta, A_py)
result_py = A_py.dot(nvec_py)
self.assertCall(lib.upsamplingvec_mul_matrix(
hdl, N, N, N, alpha, u, C, beta, A) )
self.assertCall( lib.blas_gemv(hdl, N, 1, A, nvec, 0, mvec) )
self.assertCall( lib.vector_memcpy_av(mvec_ptr, mvec, 1) )
self.assertVecEqual( mvec_py, result_py, ATOLM, RTOL )
self.upsamplingvec_mul(
'T', 'N', 'N', alpha, u_py, A_py, beta, C_py)
result_py = C_py.dot(nvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, T, N, N, alpha, u, A, beta, C) )
self.assertCall( lib.blas_gemv(hdl, N, 1, C, nvec, 0, kvec) )
self.assertCall( lib.vector_memcpy_av(kvec_ptr, kvec, 1) )
self.assertVecEqual( kvec_py, result_py, ATOLK, RTOL )
self.upsamplingvec_mul(
'N', 'N', 'T', alpha, u_py, C_py, beta, B_py)
result_py = B_py.dot(mvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, N, N, T, alpha, u, C, beta, B) )
self.assertCall( lib.blas_gemv(hdl, N, 1, B, mvec, 0, nvec) )
self.assertCall( lib.vector_memcpy_av(nvec_ptr, nvec, 1) )
self.assertVecEqual( nvec_py, result_py, ATOLN, RTOL )
self.upsamplingvec_mul(
'T', 'N', 'T', alpha, u_py, A_py, beta, D_py)
result_py = D_py.dot(kvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, T, N, T, alpha, u, A, beta, D) )
self.assertCall( lib.blas_gemv(hdl, N, 1, D, kvec, 0, nvec) )
self.assertCall( lib.vector_memcpy_av(nvec_ptr, nvec, 1) )
self.assertVecEqual( nvec_py, result_py, ATOLN, RTOL )
self.upsamplingvec_mul(
'N', 'T', 'N', alpha, u_py, D_py, beta, A_py)
result_py = A_py.dot(nvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, N, T, N, alpha, u, D, beta, A) )
self.assertCall( lib.blas_gemv(hdl, N, 1, A, nvec, 0, mvec) )
self.assertCall( lib.vector_memcpy_av(mvec_ptr, mvec, 1) )
self.assertVecEqual( mvec_py, result_py, ATOLM, RTOL )
self.upsamplingvec_mul(
'T', 'T', 'N', alpha, u_py, B_py, beta, C_py)
result_py = C_py.dot(nvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, T, T, N, alpha, u, B, beta, C) )
self.assertCall( lib.blas_gemv(hdl, N, 1, C, nvec, 0, kvec) )
self.assertCall( lib.vector_memcpy_av(kvec_ptr, kvec, 1) )
self.assertVecEqual( kvec_py, result_py, ATOLK, RTOL)
self.upsamplingvec_mul(
'N', 'T', 'T', alpha, u_py, D_py, beta, B_py)
result_py = B_py.dot(mvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, N, T, T, alpha, u, D, beta, B) )
self.assertCall( lib.blas_gemv(hdl, N, 1, B, mvec, 0, nvec) )
self.assertCall( lib.vector_memcpy_av(nvec_ptr, nvec, 1) )
self.assertVecEqual( nvec_py, result_py, ATOLN, RTOL)
self.upsamplingvec_mul(
'T', 'T', 'T', alpha, u_py, B_py, beta, D_py)
result_py = D_py.dot(kvec_py)
self.assertCall( lib.upsamplingvec_mul_matrix(
hdl, T, T, T, alpha, u, B, beta, D) )
self.assertCall( lib.blas_gemv(hdl, N, 1, D, kvec, 0, nvec) )
self.assertCall( lib.vector_memcpy_av(nvec_ptr, nvec, 1) )
self.assertVecEqual( nvec_py, result_py, ATOLN, RTOL)
# reject: dimension mismatch
print '\nexpect dimension mismatch error:'
err = lib.upsamplingvec_mul_matrix(
hdl, N, N, N, alpha, u, C, beta, B)
self.assertEqual( err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH )
print '\nexpect dimension mismatch error:'
err = lib.upsamplingvec_mul_matrix(
hdl, T, N, N, alpha, u, A, beta, D)
self.assertEqual( err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH )
# reject: unallocated
print '\nexpect unallocated error:'
err = lib.upsamplingvec_mul_matrix(
hdl, T, N, N, alpha, u, E, beta, C)
self.assertEqual( err, lib.enums.OPTKIT_ERROR_UNALLOCATED )
self.free_vars('A', 'B', 'C', 'D', 'mvec', 'nvec', 'kvec', 'u')
self.free_var('hdl')
self.assertCall( lib.ok_device_reset() )
def test_upsamplingvec_count(self):
m, n = self.shape
k = self.k
hdl = c_void_p()
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)
u, u_py, u_ptr = self.register_upsamplingvec(lib, m, k, 'u',
random=True)
counts, counts_py, counts_ptr = self.register_vector(lib, k,
'counts')
self.assertCall( lib.upsamplingvec_count(u, counts) )
self.assertCall( lib.vector_memcpy_av(counts_ptr, counts, 1) )
counts_host = np.zeros(k)
for idx in u_py:
counts_host[idx] += 1
self.assertTrue( sum(counts_py - counts_host) < 1e-7 * k**0.5 )
self.free_vars('u', 'counts')
self.assertCall( lib.ok_device_reset() )
def test_cluster_aid_alloc_free(self):
m, n = self.shape
k = self.k
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)
h = lib.cluster_aid()
self.assertCall( lib.cluster_aid_alloc(h, m, k,
lib.enums.CblasRowMajor) )
self.register_var('h', h, lib.cluster_aid_free)
self.assertEqual( h.a2c_tentative_full.size1, m )
self.assertEqual( h.a2c_tentative_full.size2, k )
self.assertEqual( h.a2c_tentative.size1, m )
self.assertEqual( h.a2c_tentative.size2, k )
self.assertEqual( h.d_min_full.size, m )
self.assertEqual( h.d_min.size, m )
self.assertEqual( h.c_squared_full.size, k )
self.assertEqual( h.c_squared.size, k )
self.assertEqual( h.D_full.size1, k )
self.assertEqual( h.D_full.size2, m )
self.assertEqual( h.D.size1, k )
self.assertEqual( h.D.size2, m )
self.assertEqual( h.reassigned, 0 )
self.assertCall( lib.cluster_aid_free(h) )
self.unregister_var('h')
self.assertEqual( h.a2c_tentative_full.size1, 0 )
self.assertEqual( h.a2c_tentative_full.size2, 0 )
self.assertEqual( h.d_min_full.size, 0 )
self.assertEqual( h.c_squared_full.size, 0 )
self.assertEqual( h.D_full.size1, 0 )
self.assertEqual( h.D_full.size2, 0 )
self.assertCall( lib.ok_device_reset() )
def test_cluster_aid_resize(self):
m, n = self.shape
k = self.k
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
def test_kmeans_work_alloc_free(self):
m, n = self.shape
k = self.k
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)
w = lib.kmeans_work()
self.assertCall( lib.kmeans_work_alloc(w, m, k, n) )
self.register_var('w', w, lib.kmeans_work_free)
self.assertNotEqual( w.indicator, 0 )
self.assertEqual( w.n_vectors, m )
self.assertEqual( w.n_clusters, k )
self.assertEqual( w.vec_length, n )
self.assertEqual( w.A.size1, m )
self.assertEqual( w.A.size2, n )
self.assertNotEqual( w.A.data, 0 )
self.assertEqual( w.C.size1, k )
self.assertEqual( w.C.size2, n )
self.assertNotEqual( w.C.data, 0 )
self.assertEqual( w.counts.size, k )
self.assertNotEqual( w.counts.data, 0 )
self.assertEqual( w.a2c.size1, m )
self.assertEqual( w.a2c.size2, k )
self.assertNotEqual( w.a2c.indices, 0 )
self.assertNotEqual( w.h.indicator, 0 )
self.assertCall( lib.kmeans_work_free(w) )
self.unregister_var('w')
self.assertEqual( w.n_vectors, 0 )
self.assertEqual( w.n_clusters, 0 )
self.assertEqual( w.vec_length, 0 )
self.assertCall( lib.ok_device_reset() )
def test_kmeans_work_resize(self):
m, n = self.shape
k = self.k
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)
w = lib.kmeans_work()
self.assertCall( lib.kmeans_work_alloc(w, m, k, n) )
self.register_var('w', w, lib.kmeans_work_free)
self.assertEqual( w.n_vectors, m )
self.assertEqual( w.n_clusters, k )
self.assertEqual( w.vec_length, n )
self.assertEqual( w.A.size1, m )
self.assertEqual( w.A.size2, n )
self.assertEqual( w.C.size1, k )
self.assertEqual( w.C.size2, n )
self.assertEqual( w.counts.size, k )
self.assertEqual( w.a2c.size1, m )
self.assertEqual( w.a2c.size2, k )
self.assertCall( lib.kmeans_work_subselect(w, m/2, k/2, n/2) )
self.assertEqual( w.n_vectors, m )
self.assertEqual( w.n_clusters, k )
self.assertEqual( w.vec_length, n )
self.assertEqual( w.A.size1, m / 2 )
self.assertEqual( w.A.size2, n / 2 )
self.assertEqual( w.C.size1, k / 2 )
self.assertEqual( w.C.size2, n / 2 )
self.assertEqual( w.counts.size, k / 2 )
self.assertEqual( w.a2c.size1, m / 2 )
self.assertEqual( w.a2c.size2, k / 2 )
self.free_var('w')
self.assertCall( lib.ok_device_reset() )
def test_kmeans_work_load_extract(self):
m, n = self.shape
k = self.k
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 - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLMN = RTOL * (m * n)**0.5
ATOLKN = RTOL * (k * n)**0.5
ATOLM = RTOL * m**0.5
ATOLK = RTOL * k**0.5
orderA = orderC = lib.enums.CblasRowMajor
A, A_ptr = self.gen_py_matrix(lib, m, n, orderA, random=True)
C, C_ptr = self.gen_py_matrix(lib, k, n, orderC, random=True)
a2c, a2c_ptr = self.gen_py_upsamplingvec(lib, m, k, random=True)
counts, counts_ptr = self.gen_py_vector(lib, k)
A_orig = np.zeros((m, n))
A_orig += A
C_orig = np.zeros((k, n))
C_orig += C
a2c_orig = np.zeros(m)
a2c_orig += a2c
counts_orig = np.zeros(k)
counts_orig += counts
SCALING = np.random.rand()
C_orig *= SCALING
counts_orig *= SCALING
w = lib.kmeans_work()
self.assertCall( lib.kmeans_work_alloc(w, m, k, n) )
self.register_var('w', w, lib.kmeans_work_free)
self.assertCall( lib.kmeans_work_load(w, A_ptr, orderA, C_ptr,
orderC, a2c_ptr, 1,
counts_ptr, 1) )
lib.matrix_scale(w.C, SCALING)
lib.vector_scale(w.counts, SCALING)
self.assertCall( lib.kmeans_work_extract(C_ptr, orderC, a2c_ptr, 1,
counts_ptr, 1, w) )
self.assertVecEqual( A_orig, A, ATOLKN, RTOL )
self.assertVecEqual( C_orig, C, ATOLKN, RTOL )
self.assertVecEqual( a2c_orig, a2c, ATOLM, RTOL )
self.assertVecEqual( counts_orig, counts, ATOLK, RTOL )
self.free_var('w')
self.assertCall( lib.ok_device_reset() )
def test_cluster(self):
""" cluster
given matrix A, centroid matrix C, upsampling vector a2c,
update cluster assignments in vector a2c, based on pairwise
euclidean distances between rows of A and C
compare # of reassignments
let D be the matrix of pairwise distances and dmin be the
column-wise minima of D:
-compare D * xrand in Python vs. C
-compare dmin in Python vs. C
"""
m, n = self.shape
k = self.k
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 MAXDIST in [1e3, 0.2]:
DIGITS = 7 - 2 * single_precision - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
ATOLK = RTOL * k**0.5
hdl = self.register_blas_handle(lib, 'hdl')
orderA = orderC = lib.enums.CblasRowMajor
A, A_py, A_ptr = self.register_matrix(lib, m, n, orderA, 'A')
C, C_py, C_ptr = self.register_matrix(lib, k, n, orderC, 'C',
random=True)
a2c, a2c_py, a2c_ptr = self.register_upsamplingvec(
lib, m, k, 'a2c', random=True)
a2c_orig = np.zeros(m).astype(c_size_t)
a2c_orig += a2c_py
A_py += self.A_test
self.assertCall( lib.matrix_memcpy_ma(A, A_ptr, orderA) )
D, dmin, reassigned = self.cluster(A_py, C_py, a2c_py, MAXDIST)
h = self.register_cluster_aid(lib, m, k, orderA, 'h')
self.assertCall( lib.cluster(A, C, a2c, h, MAXDIST) )
# compare number of reassignments, C vs Py
ATOL_REASSIGN = int(1 + 0.1 * m)
RTOL_REASSIGN = int(1 + 0.1 * reassigned)
self.assertTrue( abs(h.reassigned - reassigned) <=
ATOL_REASSIGN + RTOL_REASSIGN )
# verify number reassigned
self.assertCall( lib.indvector_memcpy_av(a2c_ptr, a2c.vec, 1) )
self.assertEqual( h.reassigned, sum(a2c_py != a2c_orig) )
# verify all distances
mvec, mvec_py, mvec_ptr = self.register_vector(lib, m, 'mvec')
kvec, kvec_py, kvec_ptr = self.register_vector(lib, k, 'kvec')
mvec_py += np.random.rand(m)
self.assertCall( lib.vector_memcpy_va(mvec, mvec_ptr, 1) )
self.assertCall( lib.blas_gemv(hdl, lib.enums.CblasNoTrans, 1,
h.D_full, mvec, 0, kvec) )
self.assertCall( lib.vector_memcpy_av(kvec_ptr, kvec, 1) )
Dmvec = D.dot(mvec_py)
self.assertVecEqual( Dmvec, kvec_py, ATOLK, RTOL)
# verify min distances
dmin_py = np.zeros(m).astype(lib.pyfloat)
dmin_ptr = dmin_py.ctypes.data_as(lib.ok_float_p)
self.assertCall( lib.vector_memcpy_av(
dmin_ptr, h.d_min_full, 1) )
self.assertVecEqual( dmin_py, dmin, ATOLM, RTOL )
self.assertCall( lib.cluster_aid_free(h) )
self.unregister_var('h')
self.free_vars('A', 'C', 'a2c', 'mvec', 'kvec', 'hdl')
self.assertCall( lib.ok_device_reset() )
def test_calculate_centroids(self):
""" calculate centroids
given matrix A, upsampling vector a2c,
calculate centroid matrix C
compare C * xrand in Python vs. C
"""
m, n = self.shape
k = self.k
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)
hdl = self.register_blas_handle(lib, 'hdl')
DIGITS = 7 - 2 * single_precision - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
ATOLK = RTOL * k**0.5
orderA = orderC = lib.enums.CblasRowMajor
A, A_py, A_ptr = self.register_matrix(lib, m, n, orderA, 'A')
C, C_py, C_ptr = self.register_matrix(lib, k, n, orderC, 'C',
random=True)
a2c, a2c_py, a2c_ptr = self.register_upsamplingvec(
lib, m, k, 'a2c', random=True)
A_py += self.A_test
self.assertCall( lib.matrix_memcpy_ma(A, A_ptr, orderA) )
counts, _, _ = self.register_vector(lib, k, 'counts')
nvec, nvec_py, nvec_ptr = self.register_vector(lib, n, 'nvec')
kvec, kvec_py, kvec_ptr = self.register_vector(lib, k, 'kvec')
# C: build centroids
h = self.register_cluster_aid(lib, m, k, orderA, 'h')
self.assertCall( lib.calculate_centroids(A, C, a2c, counts, h) )
# Py: build centroids
self.upsamplingvec_mul('T', 'N', 'N', 1, a2c_py, A_py, 0, C_py)
counts_local = np.zeros(k)
for c in a2c_py:
counts_local[c] += 1
for idx, c in enumerate(counts_local):
counts_local[idx] = (1. / c) if c > 0 else 0.
C_py[idx, :] *= counts_local[idx]
# C: kvec = C * nvec
self.assertCall( lib.vector_memcpy_va(nvec, nvec_ptr, 1) )
self.assertCall( lib.blas_gemv(hdl, lib.enums.CblasNoTrans, 1, C,
nvec, 0, kvec) )
self.assertCall( lib.vector_memcpy_av(kvec_ptr, kvec, 1) )
# Py: kvec = C * nvec
Cnvec = C_py.dot(nvec_py)
# compare C vs. Py
self.assertVecEqual( kvec_py, Cnvec, ATOLK, RTOL)
self.assertCall( lib.cluster_aid_free(h) )
self.unregister_var('h')
self.free_vars('A', 'C', 'a2c', 'counts', 'nvec', 'kvec', 'hdl')
self.assertCall( lib.ok_device_reset() )
def test_kmeans(self):
""" k-means
given matrix A, cluster # k
cluster A by kmeans algorithm. compare Python vs. C
"""
m, n = self.shape
k = self.k
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)
hdl = self.register_blas_handle(lib, 'hdl')
orderA = orderC = lib.enums.CblasRowMajor
A, A_py, A_ptr = self.register_matrix(lib, m, n, orderA, 'A')
C, C_py, C_ptr = self.register_matrix(lib, k, n, orderC, 'C',
random=True)
a2c, a2c_py, a2c_ptr = self.register_upsamplingvec(
lib, m, k, 'a2c', random=True)
counts, _, _ = self.register_vector(lib, k, 'counts')
A_py += self.A_test
self.assertCall( lib.matrix_memcpy_ma(A, A_ptr, orderA) )
DIST_RELTOL = 0.1
CHANGE_TOL = int(1 + 0.01 * m)
MAXITER = 500
VERBOSE = 1
h = self.register_cluster_aid(lib, m, k, orderA, 'h')
self.assertCall( lib.k_means(A, C, a2c, counts, h, DIST_RELTOL,
CHANGE_TOL, MAXITER, VERBOSE) )
self.free_vars('h', 'A', 'C', 'a2c', 'counts', 'nvec', 'kvec',
'hdl')
self.assertCall( lib.ok_device_reset() )
def test_kmeans_easy_init_free(self):
m, n = self.shape
k = self.k
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)
w_int = lib.kmeans_easy_init(m, k, n)
self.assertNotEqual(w_int, 0 )
work = c_void_p(w_int)
self.register_var('work', work, lib.kmeans_easy_finish)
cwork = cast(work, lib.kmeans_work_p)
self.assertEqual( cwork.contents.n_vectors, m )
self.assertEqual( cwork.contents.n_clusters, k )
self.assertEqual( cwork.contents.vec_length, n )
self.assertCall( lib.kmeans_easy_finish(work) )
self.unregister_var('work')
self.assertCall( lib.ok_device_reset() )
def test_kmeans_easy_resize(self):
m, n = self.shape
k = self.k
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)
work = lib.kmeans_easy_init(m, k, n)
self.register_var('work', work, lib.kmeans_easy_finish)
self.assertCall( lib.kmeans_easy_resize(work, m/2, k/2, n/2) )
cwork = cast(work, lib.kmeans_work_p)
self.assertEqual( cwork.contents.A.size1, m / 2 )
self.assertEqual( cwork.contents.C.size1, k / 2 )
self.assertEqual( cwork.contents.A.size2, n / 2 )
self.free_var('work')
self.assertCall( lib.ok_device_reset() )
def test_kmeans_easy_run(self):
""" k-means (unified call)
given matrix A, cluster # k
cluster A by kmeans algorithm. compare Python vs. C
"""
m, n = self.shape
k = self.k
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)
orderA = orderC = lib.enums.CblasRowMajor
A, A_ptr = self.gen_py_matrix(lib, m, n, orderA)
C, C_ptr = self.gen_py_matrix(lib, k, n, orderC, random=True)
a2c, a2c_ptr = self.gen_py_upsamplingvec(lib, m, k, random=True)
counts, counts_ptr = self.gen_py_vector(lib, k)
A += self.A_test
work = lib.kmeans_easy_init(m, k, n)
self.register_var('work', work, lib.kmeans_easy_finish)
io = lib.kmeans_io(A_ptr, C_ptr, counts_ptr, a2c_ptr, orderA,
orderC, 1, 1)
DIST_RELTOL = 0.1
CHANGE_TOL = int(1 + 0.01 * m)
MAXITER = 500
VERBOSE = 1
settings = lib.kmeans_settings(DIST_RELTOL, CHANGE_TOL, MAXITER,
VERBOSE)
self.assertCall( lib.kmeans_easy_run(work, settings, io) )
self.free_var('work')
self.assertCall( lib.ok_device_reset() )
class ClusterLibsTestCase(OptkitCTestCase):
@classmethod
def setUpClass(self):
self.env_orig = os.getenv('OPTKIT_USE_LOCALLIBS', '0')
os.environ['OPTKIT_USE_LOCALLIBS'] = '1'
self.libs = ClusteringLibs()
# self.A_test = self.A_test_gen
@classmethod
def tearDownClass(self):
os.environ['OPTKIT_USE_LOCALLIBS'] = self.env_orig
def setUp(self):
self.k = int(self.shape[0]**0.5)
self.C_test = np.random.rand(self.k, self.shape[1])
self.A_test = np.random.rand(*self.shape)
# construct A_test as a noisy upsampled version of C
percent_noise = 0.05
for i in xrange(self.shape[0]):
self.A_test[i, :] *= percent_noise
self.A_test[i, :] += self.C_test[i % self.k, :]
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))
@staticmethod
def upsamplingvec_mul(tU, tA, tB, alpha, u, A, beta, B):
if not bool(
isinstance(u, ndarray) and isinstance(A, ndarray)
and isinstance(B, ndarray)):
raise TypeError('u, A, and B must be of type {}'.format(ndarray))
if not (len(A.shape) == 2 and len(B.shape) == 2):
raise ValueError('A and B must be 2-d {}s'.format(ndarray))
umax = int(u.max()) + 1
u_dim1 = umax if tU == 'T' else len(u)
u_dim2 = len(u) if tU == 'T' else umax
A = A.T if tA == 'T' else A
B = B.T if tB == 'T' else B
if not bool(A.shape[1] == B.shape[1] and B.shape[0] == u_dim1
and u_dim2 <= A.shape[0]):
raise ValueError('incompatible dimensions')
B *= beta
if tU == 'T':
for (row_a, row_b) in enumerate(u):
B[row_b, :] += alpha * A[row_a, :]
else:
for (row_b, row_a) in enumerate(u):
B[row_b, :] += alpha * A[row_a, :]
@staticmethod
def cluster(A, C, a2c, maxdist):
if not bool(
isinstance(a2c, ndarray) and isinstance(A, ndarray)
and isinstance(C, ndarray)):
raise TypeError('a2c, A, and C must be of type {}'.format(ndarray))
if not (len(A.shape) == 2 and len(C.shape) == 2):
raise ValueError('A and C must be 2-d {}s'.format(ndarray))
if not bool(A.shape[1] == C.shape[1] and A.shape[0] == len(a2c)
and a2c.max() <= C.shape[0]):
raise ValueError('incompatible dimensions')
m, n = A.shape
k = C.shape[0]
c_squared = np.zeros(k)
for row, c in enumerate(C):
c_squared[row] = c.dot(c)
D = -2 * C.dot(A.T)
for i in xrange(m):
D[:, i] += c_squared
indmin = D.argmin(0)
dmin = D.min(0)
reassigned = 0
if maxdist == np.inf:
reassigned = sum(a2c != indmin)
a2c[:] = indmin[:]
else:
for i in xrange(m):
if a2c[i] == indmin[i]:
continue
dist_inf = np.abs(A[i, :] - C[indmin[i], :]).max()
if dist_inf <= maxdist:
a2c[i] = indmin[i]
reassigned += 1
return D, dmin, reassigned
def gen_py_upsamplingvec(self, lib, size1, size2, random=False):
if 'c_size_t_p' not in lib.__dict__:
raise ValueError('symbol "c_size_t_p" undefined in '
'library {}'.format(lib))
u_py = np.zeros(size1).astype(c_size_t)
u_ptr = u_py.ctypes.data_as(lib.c_size_t_p)
if random:
u_py += (size2 * np.random.rand(size1)).astype(c_size_t)
u_py[-1] = size2 - 1
return u_py, u_ptr
def register_upsamplingvec(self, lib, size1, size2, name, random=False):
if not 'upsamplingvec_alloc' in lib.__dict__:
raise ValueError('library {} cannot allocate an '
'upsamplingvec'.format(lib))
u = lib.upsamplingvec()
self.assertCall(lib.upsamplingvec_alloc(u, size1, size2))
self.register_var(name, u, lib.upsamplingvec_free)
u_py, u_ptr = self.gen_py_upsamplingvec(lib, size1, size2, random)
if random:
self.assertCall(lib.indvector_memcpy_va(u.vec, u_ptr, 1))
return u, u_py, u_ptr
def register_cluster_aid(self, lib, n_vectors, n_clusters, order, name):
if not 'cluster_aid_alloc' in lib.__dict__:
raise ValueError('library {} cannot allocate a cluster '
'aid'.format(lib))
h = lib.cluster_aid()
self.assertCall(lib.cluster_aid_alloc(h, n_vectors, n_clusters, order))
self.register_var('h', h, lib.cluster_aid_free)
return h
def test_upsamplingvec_alloc_free(self):
m, n = self.shape
k = self.k
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)
u = lib.upsamplingvec()
self.assertCall(lib.upsamplingvec_alloc(u, m, k))
self.register_var('u', u, lib.upsamplingvec_free)
self.assertEqual(u.size1, m)
self.assertEqual(u.size2, k)
self.assertCall(lib.upsamplingvec_free(u))
self.unregister_var('u')
self.assertCall(lib.ok_device_reset())
def test_upsamplingvec_check_bounds(self):
m, n = self.shape
k = self.k
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)
u, u_py, u_ptr = self.register_upsamplingvec(lib,
m,
k,
'u',
random=True)
self.assertCall(lib.upsamplingvec_check_bounds(u))
u_py += 2 * k
self.assertCall(lib.indvector_memcpy_va(u.vec, u_ptr, 1))
# expect error
print '\nexpect dimension mismatch error:'
err = lib.upsamplingvec_check_bounds(u)
self.assertEqual(err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH)
self.assertCall(lib.upsamplingvec_free(u))
self.unregister_var('u')
self.assertCall(lib.ok_device_reset())
def test_upsamplingvec_update_size(self):
m, n = self.shape
k = self.k
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)
u, u_py, u_ptr = self.register_upsamplingvec(lib,
m,
k,
'u',
random=True)
self.assertCall(lib.upsamplingvec_check_bounds(u))
# incorrect size
print '\nexpect dimension mismatch error:'
u.size2 = k / 2
err = lib.upsamplingvec_check_bounds(u)
self.assertEqual(err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH)
self.assertCall(lib.upsamplingvec_update_size(u))
self.assertEqual(u.size2, k)
self.assertCall(lib.upsamplingvec_check_bounds(u))
self.assertCall(lib.upsamplingvec_free(u))
self.unregister_var('u')
self.assertCall(lib.ok_device_reset())
def test_upsamplingvec_subvector(self):
m, n = self.shape
k = self.k
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)
offset = m / 4
msub = m / 2
ksub = k / 2
u, u_py, u_ptr = self.register_upsamplingvec(lib,
m,
k,
'u',
random=True)
usub = lib.upsamplingvec()
usub_py, usub_ptr = self.gen_py_upsamplingvec(lib, msub, k)
self.assertCall(
lib.upsamplingvec_subvector(usub, u, offset, msub, k))
self.assertCall(lib.indvector_memcpy_av(usub_ptr, usub.vec, 1))
self.assertTrue(usub.size1 == msub)
self.assertTrue(usub.size2 == k)
self.assertTrue(sum(usub_py - u_py[offset:offset + msub]) == 0)
self.assertCall(
lib.upsamplingvec_subvector(usub, u, offset, msub, ksub))
self.assertTrue(usub.size1 == msub)
self.assertTrue(usub.size2 == ksub)
self.assertCall(lib.upsamplingvec_free(u))
self.unregister_var('u')
self.assertCall(lib.ok_device_reset())
def test_upsamplingvec_mul_matrix(self):
m, n = self.shape
k = self.k
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 - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
ATOLK = RTOL * k**0.5
rowmajor = lib.enums.CblasRowMajor
hdl = self.register_blas_handle(lib, 'hdl')
A, A_py, A_ptr = self.register_matrix(lib,
m,
n,
rowmajor,
'A',
random=True)
B, B_py, B_ptr = self.register_matrix(lib,
n,
m,
rowmajor,
'B',
random=True)
C, C_py, C_ptr = self.register_matrix(lib,
k,
n,
rowmajor,
'C',
random=True)
D, D_py, D_ptr = self.register_matrix(lib,
n,
k,
rowmajor,
'D',
random=True)
E = lib.matrix(0, 0, 0, None, 0)
mvec, mvec_py, mvec_ptr = self.register_vector(lib,
m,
'mvec',
random=True)
nvec, nvec_py, nvec_ptr = self.register_vector(lib,
n,
'nvec',
random=True)
kvec, kvec_py, kvec_ptr = self.register_vector(lib,
k,
'kvec',
random=True)
u, u_py, u_ptr = self.register_upsamplingvec(lib,
m,
k,
'u',
random=True)
T = lib.enums.CblasTrans
N = lib.enums.CblasNoTrans
alpha = np.random.rand()
beta = np.random.rand()
# functioning cases
self.upsamplingvec_mul('N', 'N', 'N', alpha, u_py, C_py, beta,
A_py)
result_py = A_py.dot(nvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, N, N, N, alpha, u, C, beta,
A))
self.assertCall(lib.blas_gemv(hdl, N, 1, A, nvec, 0, mvec))
self.assertCall(lib.vector_memcpy_av(mvec_ptr, mvec, 1))
self.assertVecEqual(mvec_py, result_py, ATOLM, RTOL)
self.upsamplingvec_mul('T', 'N', 'N', alpha, u_py, A_py, beta,
C_py)
result_py = C_py.dot(nvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, T, N, N, alpha, u, A, beta,
C))
self.assertCall(lib.blas_gemv(hdl, N, 1, C, nvec, 0, kvec))
self.assertCall(lib.vector_memcpy_av(kvec_ptr, kvec, 1))
self.assertVecEqual(kvec_py, result_py, ATOLK, RTOL)
self.upsamplingvec_mul('N', 'N', 'T', alpha, u_py, C_py, beta,
B_py)
result_py = B_py.dot(mvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, N, N, T, alpha, u, C, beta,
B))
self.assertCall(lib.blas_gemv(hdl, N, 1, B, mvec, 0, nvec))
self.assertCall(lib.vector_memcpy_av(nvec_ptr, nvec, 1))
self.assertVecEqual(nvec_py, result_py, ATOLN, RTOL)
self.upsamplingvec_mul('T', 'N', 'T', alpha, u_py, A_py, beta,
D_py)
result_py = D_py.dot(kvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, T, N, T, alpha, u, A, beta,
D))
self.assertCall(lib.blas_gemv(hdl, N, 1, D, kvec, 0, nvec))
self.assertCall(lib.vector_memcpy_av(nvec_ptr, nvec, 1))
self.assertVecEqual(nvec_py, result_py, ATOLN, RTOL)
self.upsamplingvec_mul('N', 'T', 'N', alpha, u_py, D_py, beta,
A_py)
result_py = A_py.dot(nvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, N, T, N, alpha, u, D, beta,
A))
self.assertCall(lib.blas_gemv(hdl, N, 1, A, nvec, 0, mvec))
self.assertCall(lib.vector_memcpy_av(mvec_ptr, mvec, 1))
self.assertVecEqual(mvec_py, result_py, ATOLM, RTOL)
self.upsamplingvec_mul('T', 'T', 'N', alpha, u_py, B_py, beta,
C_py)
result_py = C_py.dot(nvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, T, T, N, alpha, u, B, beta,
C))
self.assertCall(lib.blas_gemv(hdl, N, 1, C, nvec, 0, kvec))
self.assertCall(lib.vector_memcpy_av(kvec_ptr, kvec, 1))
self.assertVecEqual(kvec_py, result_py, ATOLK, RTOL)
self.upsamplingvec_mul('N', 'T', 'T', alpha, u_py, D_py, beta,
B_py)
result_py = B_py.dot(mvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, N, T, T, alpha, u, D, beta,
B))
self.assertCall(lib.blas_gemv(hdl, N, 1, B, mvec, 0, nvec))
self.assertCall(lib.vector_memcpy_av(nvec_ptr, nvec, 1))
self.assertVecEqual(nvec_py, result_py, ATOLN, RTOL)
self.upsamplingvec_mul('T', 'T', 'T', alpha, u_py, B_py, beta,
D_py)
result_py = D_py.dot(kvec_py)
self.assertCall(
lib.upsamplingvec_mul_matrix(hdl, T, T, T, alpha, u, B, beta,
D))
self.assertCall(lib.blas_gemv(hdl, N, 1, D, kvec, 0, nvec))
self.assertCall(lib.vector_memcpy_av(nvec_ptr, nvec, 1))
self.assertVecEqual(nvec_py, result_py, ATOLN, RTOL)
# reject: dimension mismatch
print '\nexpect dimension mismatch error:'
err = lib.upsamplingvec_mul_matrix(hdl, N, N, N, alpha, u, C, beta,
B)
self.assertEqual(err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH)
print '\nexpect dimension mismatch error:'
err = lib.upsamplingvec_mul_matrix(hdl, T, N, N, alpha, u, A, beta,
D)
self.assertEqual(err, lib.enums.OPTKIT_ERROR_DIMENSION_MISMATCH)
# reject: unallocated
print '\nexpect unallocated error:'
err = lib.upsamplingvec_mul_matrix(hdl, T, N, N, alpha, u, E, beta,
C)
self.assertEqual(err, lib.enums.OPTKIT_ERROR_UNALLOCATED)
self.free_vars('A', 'B', 'C', 'D', 'mvec', 'nvec', 'kvec', 'u')
self.free_var('hdl')
self.assertCall(lib.ok_device_reset())
def test_upsamplingvec_count(self):
m, n = self.shape
k = self.k
hdl = c_void_p()
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)
u, u_py, u_ptr = self.register_upsamplingvec(lib,
m,
k,
'u',
random=True)
counts, counts_py, counts_ptr = self.register_vector(
lib, k, 'counts')
self.assertCall(lib.upsamplingvec_count(u, counts))
self.assertCall(lib.vector_memcpy_av(counts_ptr, counts, 1))
counts_host = np.zeros(k)
for idx in u_py:
counts_host[idx] += 1
self.assertTrue(sum(counts_py - counts_host) < 1e-7 * k**0.5)
self.free_vars('u', 'counts')
self.assertCall(lib.ok_device_reset())
def test_cluster_aid_alloc_free(self):
m, n = self.shape
k = self.k
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)
h = lib.cluster_aid()
self.assertCall(
lib.cluster_aid_alloc(h, m, k, lib.enums.CblasRowMajor))
self.register_var('h', h, lib.cluster_aid_free)
self.assertEqual(h.a2c_tentative_full.size1, m)
self.assertEqual(h.a2c_tentative_full.size2, k)
self.assertEqual(h.a2c_tentative.size1, m)
self.assertEqual(h.a2c_tentative.size2, k)
self.assertEqual(h.d_min_full.size, m)
self.assertEqual(h.d_min.size, m)
self.assertEqual(h.c_squared_full.size, k)
self.assertEqual(h.c_squared.size, k)
self.assertEqual(h.D_full.size1, k)
self.assertEqual(h.D_full.size2, m)
self.assertEqual(h.D.size1, k)
self.assertEqual(h.D.size2, m)
self.assertEqual(h.reassigned, 0)
self.assertCall(lib.cluster_aid_free(h))
self.unregister_var('h')
self.assertEqual(h.a2c_tentative_full.size1, 0)
self.assertEqual(h.a2c_tentative_full.size2, 0)
self.assertEqual(h.d_min_full.size, 0)
self.assertEqual(h.c_squared_full.size, 0)
self.assertEqual(h.D_full.size1, 0)
self.assertEqual(h.D_full.size2, 0)
self.assertCall(lib.ok_device_reset())
def test_cluster_aid_resize(self):
m, n = self.shape
k = self.k
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
def test_kmeans_work_alloc_free(self):
m, n = self.shape
k = self.k
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)
w = lib.kmeans_work()
self.assertCall(lib.kmeans_work_alloc(w, m, k, n))
self.register_var('w', w, lib.kmeans_work_free)
self.assertNotEqual(w.indicator, 0)
self.assertEqual(w.n_vectors, m)
self.assertEqual(w.n_clusters, k)
self.assertEqual(w.vec_length, n)
self.assertEqual(w.A.size1, m)
self.assertEqual(w.A.size2, n)
self.assertNotEqual(w.A.data, 0)
self.assertEqual(w.C.size1, k)
self.assertEqual(w.C.size2, n)
self.assertNotEqual(w.C.data, 0)
self.assertEqual(w.counts.size, k)
self.assertNotEqual(w.counts.data, 0)
self.assertEqual(w.a2c.size1, m)
self.assertEqual(w.a2c.size2, k)
self.assertNotEqual(w.a2c.indices, 0)
self.assertNotEqual(w.h.indicator, 0)
self.assertCall(lib.kmeans_work_free(w))
self.unregister_var('w')
self.assertEqual(w.n_vectors, 0)
self.assertEqual(w.n_clusters, 0)
self.assertEqual(w.vec_length, 0)
self.assertCall(lib.ok_device_reset())
def test_kmeans_work_resize(self):
m, n = self.shape
k = self.k
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)
w = lib.kmeans_work()
self.assertCall(lib.kmeans_work_alloc(w, m, k, n))
self.register_var('w', w, lib.kmeans_work_free)
self.assertEqual(w.n_vectors, m)
self.assertEqual(w.n_clusters, k)
self.assertEqual(w.vec_length, n)
self.assertEqual(w.A.size1, m)
self.assertEqual(w.A.size2, n)
self.assertEqual(w.C.size1, k)
self.assertEqual(w.C.size2, n)
self.assertEqual(w.counts.size, k)
self.assertEqual(w.a2c.size1, m)
self.assertEqual(w.a2c.size2, k)
self.assertCall(lib.kmeans_work_subselect(w, m / 2, k / 2, n / 2))
self.assertEqual(w.n_vectors, m)
self.assertEqual(w.n_clusters, k)
self.assertEqual(w.vec_length, n)
self.assertEqual(w.A.size1, m / 2)
self.assertEqual(w.A.size2, n / 2)
self.assertEqual(w.C.size1, k / 2)
self.assertEqual(w.C.size2, n / 2)
self.assertEqual(w.counts.size, k / 2)
self.assertEqual(w.a2c.size1, m / 2)
self.assertEqual(w.a2c.size2, k / 2)
self.free_var('w')
self.assertCall(lib.ok_device_reset())
def test_kmeans_work_load_extract(self):
m, n = self.shape
k = self.k
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 - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLMN = RTOL * (m * n)**0.5
ATOLKN = RTOL * (k * n)**0.5
ATOLM = RTOL * m**0.5
ATOLK = RTOL * k**0.5
orderA = orderC = lib.enums.CblasRowMajor
A, A_ptr = self.gen_py_matrix(lib, m, n, orderA, random=True)
C, C_ptr = self.gen_py_matrix(lib, k, n, orderC, random=True)
a2c, a2c_ptr = self.gen_py_upsamplingvec(lib, m, k, random=True)
counts, counts_ptr = self.gen_py_vector(lib, k)
A_orig = np.zeros((m, n))
A_orig += A
C_orig = np.zeros((k, n))
C_orig += C
a2c_orig = np.zeros(m)
a2c_orig += a2c
counts_orig = np.zeros(k)
counts_orig += counts
SCALING = np.random.rand()
C_orig *= SCALING
counts_orig *= SCALING
w = lib.kmeans_work()
self.assertCall(lib.kmeans_work_alloc(w, m, k, n))
self.register_var('w', w, lib.kmeans_work_free)
self.assertCall(
lib.kmeans_work_load(w, A_ptr, orderA, C_ptr, orderC, a2c_ptr,
1, counts_ptr, 1))
lib.matrix_scale(w.C, SCALING)
lib.vector_scale(w.counts, SCALING)
self.assertCall(
lib.kmeans_work_extract(C_ptr, orderC, a2c_ptr, 1, counts_ptr,
1, w))
self.assertVecEqual(A_orig, A, ATOLKN, RTOL)
self.assertVecEqual(C_orig, C, ATOLKN, RTOL)
self.assertVecEqual(a2c_orig, a2c, ATOLM, RTOL)
self.assertVecEqual(counts_orig, counts, ATOLK, RTOL)
self.free_var('w')
self.assertCall(lib.ok_device_reset())
def test_cluster(self):
""" cluster
given matrix A, centroid matrix C, upsampling vector a2c,
update cluster assignments in vector a2c, based on pairwise
euclidean distances between rows of A and C
compare # of reassignments
let D be the matrix of pairwise distances and dmin be the
column-wise minima of D:
-compare D * xrand in Python vs. C
-compare dmin in Python vs. C
"""
m, n = self.shape
k = self.k
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 MAXDIST in [1e3, 0.2]:
DIGITS = 7 - 2 * single_precision - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
ATOLK = RTOL * k**0.5
hdl = self.register_blas_handle(lib, 'hdl')
orderA = orderC = lib.enums.CblasRowMajor
A, A_py, A_ptr = self.register_matrix(lib, m, n, orderA, 'A')
C, C_py, C_ptr = self.register_matrix(lib,
k,
n,
orderC,
'C',
random=True)
a2c, a2c_py, a2c_ptr = self.register_upsamplingvec(lib,
m,
k,
'a2c',
random=True)
a2c_orig = np.zeros(m).astype(c_size_t)
a2c_orig += a2c_py
A_py += self.A_test
self.assertCall(lib.matrix_memcpy_ma(A, A_ptr, orderA))
D, dmin, reassigned = self.cluster(A_py, C_py, a2c_py, MAXDIST)
h = self.register_cluster_aid(lib, m, k, orderA, 'h')
self.assertCall(lib.cluster(A, C, a2c, h, MAXDIST))
# compare number of reassignments, C vs Py
ATOL_REASSIGN = int(1 + 0.1 * m)
RTOL_REASSIGN = int(1 + 0.1 * reassigned)
self.assertTrue(
abs(h.reassigned - reassigned) <= ATOL_REASSIGN +
RTOL_REASSIGN)
# verify number reassigned
self.assertCall(lib.indvector_memcpy_av(a2c_ptr, a2c.vec, 1))
self.assertEqual(h.reassigned, sum(a2c_py != a2c_orig))
# verify all distances
mvec, mvec_py, mvec_ptr = self.register_vector(lib, m, 'mvec')
kvec, kvec_py, kvec_ptr = self.register_vector(lib, k, 'kvec')
mvec_py += np.random.rand(m)
self.assertCall(lib.vector_memcpy_va(mvec, mvec_ptr, 1))
self.assertCall(
lib.blas_gemv(hdl, lib.enums.CblasNoTrans, 1, h.D_full,
mvec, 0, kvec))
self.assertCall(lib.vector_memcpy_av(kvec_ptr, kvec, 1))
Dmvec = D.dot(mvec_py)
self.assertVecEqual(Dmvec, kvec_py, ATOLK, RTOL)
# verify min distances
dmin_py = np.zeros(m).astype(lib.pyfloat)
dmin_ptr = dmin_py.ctypes.data_as(lib.ok_float_p)
self.assertCall(lib.vector_memcpy_av(dmin_ptr, h.d_min_full,
1))
self.assertVecEqual(dmin_py, dmin, ATOLM, RTOL)
self.assertCall(lib.cluster_aid_free(h))
self.unregister_var('h')
self.free_vars('A', 'C', 'a2c', 'mvec', 'kvec', 'hdl')
self.assertCall(lib.ok_device_reset())
def test_calculate_centroids(self):
""" calculate centroids
given matrix A, upsampling vector a2c,
calculate centroid matrix C
compare C * xrand in Python vs. C
"""
m, n = self.shape
k = self.k
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)
hdl = self.register_blas_handle(lib, 'hdl')
DIGITS = 7 - 2 * single_precision - 1 * gpu
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
ATOLK = RTOL * k**0.5
orderA = orderC = lib.enums.CblasRowMajor
A, A_py, A_ptr = self.register_matrix(lib, m, n, orderA, 'A')
C, C_py, C_ptr = self.register_matrix(lib,
k,
n,
orderC,
'C',
random=True)
a2c, a2c_py, a2c_ptr = self.register_upsamplingvec(lib,
m,
k,
'a2c',
random=True)
A_py += self.A_test
self.assertCall(lib.matrix_memcpy_ma(A, A_ptr, orderA))
counts, _, _ = self.register_vector(lib, k, 'counts')
nvec, nvec_py, nvec_ptr = self.register_vector(lib, n, 'nvec')
kvec, kvec_py, kvec_ptr = self.register_vector(lib, k, 'kvec')
# C: build centroids
h = self.register_cluster_aid(lib, m, k, orderA, 'h')
self.assertCall(lib.calculate_centroids(A, C, a2c, counts, h))
# Py: build centroids
self.upsamplingvec_mul('T', 'N', 'N', 1, a2c_py, A_py, 0, C_py)
counts_local = np.zeros(k)
for c in a2c_py:
counts_local[c] += 1
for idx, c in enumerate(counts_local):
counts_local[idx] = (1. / c) if c > 0 else 0.
C_py[idx, :] *= counts_local[idx]
# C: kvec = C * nvec
self.assertCall(lib.vector_memcpy_va(nvec, nvec_ptr, 1))
self.assertCall(
lib.blas_gemv(hdl, lib.enums.CblasNoTrans, 1, C, nvec, 0,
kvec))
self.assertCall(lib.vector_memcpy_av(kvec_ptr, kvec, 1))
# Py: kvec = C * nvec
Cnvec = C_py.dot(nvec_py)
# compare C vs. Py
self.assertVecEqual(kvec_py, Cnvec, ATOLK, RTOL)
self.assertCall(lib.cluster_aid_free(h))
self.unregister_var('h')
self.free_vars('A', 'C', 'a2c', 'counts', 'nvec', 'kvec', 'hdl')
self.assertCall(lib.ok_device_reset())
def test_kmeans(self):
""" k-means
given matrix A, cluster # k
cluster A by kmeans algorithm. compare Python vs. C
"""
m, n = self.shape
k = self.k
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)
hdl = self.register_blas_handle(lib, 'hdl')
orderA = orderC = lib.enums.CblasRowMajor
A, A_py, A_ptr = self.register_matrix(lib, m, n, orderA, 'A')
C, C_py, C_ptr = self.register_matrix(lib,
k,
n,
orderC,
'C',
random=True)
a2c, a2c_py, a2c_ptr = self.register_upsamplingvec(lib,
m,
k,
'a2c',
random=True)
counts, _, _ = self.register_vector(lib, k, 'counts')
A_py += self.A_test
self.assertCall(lib.matrix_memcpy_ma(A, A_ptr, orderA))
DIST_RELTOL = 0.1
CHANGE_TOL = int(1 + 0.01 * m)
MAXITER = 500
VERBOSE = 1
h = self.register_cluster_aid(lib, m, k, orderA, 'h')
self.assertCall(
lib.k_means(A, C, a2c, counts, h, DIST_RELTOL, CHANGE_TOL,
MAXITER, VERBOSE))
self.free_vars('h', 'A', 'C', 'a2c', 'counts', 'nvec', 'kvec',
'hdl')
self.assertCall(lib.ok_device_reset())
def test_kmeans_easy_init_free(self):
m, n = self.shape
k = self.k
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)
w_int = lib.kmeans_easy_init(m, k, n)
self.assertNotEqual(w_int, 0)
work = c_void_p(w_int)
self.register_var('work', work, lib.kmeans_easy_finish)
cwork = cast(work, lib.kmeans_work_p)
self.assertEqual(cwork.contents.n_vectors, m)
self.assertEqual(cwork.contents.n_clusters, k)
self.assertEqual(cwork.contents.vec_length, n)
self.assertCall(lib.kmeans_easy_finish(work))
self.unregister_var('work')
self.assertCall(lib.ok_device_reset())
def test_kmeans_easy_resize(self):
m, n = self.shape
k = self.k
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)
work = lib.kmeans_easy_init(m, k, n)
self.register_var('work', work, lib.kmeans_easy_finish)
self.assertCall(lib.kmeans_easy_resize(work, m / 2, k / 2, n / 2))
cwork = cast(work, lib.kmeans_work_p)
self.assertEqual(cwork.contents.A.size1, m / 2)
self.assertEqual(cwork.contents.C.size1, k / 2)
self.assertEqual(cwork.contents.A.size2, n / 2)
self.free_var('work')
self.assertCall(lib.ok_device_reset())
def test_kmeans_easy_run(self):
""" k-means (unified call)
given matrix A, cluster # k
cluster A by kmeans algorithm. compare Python vs. C
"""
m, n = self.shape
k = self.k
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)
orderA = orderC = lib.enums.CblasRowMajor
A, A_ptr = self.gen_py_matrix(lib, m, n, orderA)
C, C_ptr = self.gen_py_matrix(lib, k, n, orderC, random=True)
a2c, a2c_ptr = self.gen_py_upsamplingvec(lib, m, k, random=True)
counts, counts_ptr = self.gen_py_vector(lib, k)
A += self.A_test
work = lib.kmeans_easy_init(m, k, n)
self.register_var('work', work, lib.kmeans_easy_finish)
io = lib.kmeans_io(A_ptr, C_ptr, counts_ptr, a2c_ptr, orderA,
orderC, 1, 1)
DIST_RELTOL = 0.1
CHANGE_TOL = int(1 + 0.01 * m)
MAXITER = 500
VERBOSE = 1
settings = lib.kmeans_settings(DIST_RELTOL, CHANGE_TOL, MAXITER,
VERBOSE)
self.assertCall(lib.kmeans_easy_run(work, settings, io))
self.free_var('work')
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 = ClusteringLibs()
class OKBackend(object):
def __init__(self, gpu=False, single_precision=False):
self.version = None
self.__device = None
self.__precision = None
self.__config = "(No libraries selected)"
self.lowtypes = None
self.dimcheck = getenv('OPTKIT_CHECKDIM', 0)
self.typecheck = getenv('OPTKIT_CHECKTYPE', 0)
self.devicecheck = getenv('OPTKIT_CHECKDEVICE', 0)
# library loaders
# self.dense_lib_loader = DenseLinsysLibs()
# self.sparse_lib_loader = SparseLinsysLibs()
# self.prox_lib_loader = ProxLibs()
self.pogs_lib_loader = PogsLibs()
self.cluster_lib_loader = ClusteringLibs()
# library instances
# self.dense = None
# self.sparse = None
# self.prox = None
self.pogs = None
self.cluster = None
self.__LIBGUARD_ON = False
self.__COBJECT_COUNT = 0
self.__set_lib()
@property
def config(self):
return self.__config
@property
def precision(self):
return self.__precision
@property
def precision_is_32bit(self):
return self.__precision == '32'
@property
def device(self):
return self.__device
@property
def device_is_gpu(self):
return self.__device == 'gpu'
@property
def device_reset_allowed(self):
return self.__COBJECT_COUNT == 0
@property
def libguard_active(self):
return self.__LIBGUARD_ON
def increment_cobject_count(self):
self.__COBJECT_COUNT += 1
self.__LIBGUARD_ON = True
def decrement_cobject_count(self):
self.__COBJECT_COUNT -= 1
self.__LIBGUARD_ON = self.__COBJECT_COUNT == 0
def __get_version(self):
major = c_int()
minor = c_int()
change = c_int()
status = c_int()
try:
self.pogs.optkit_version(byref(major), byref(minor), byref(change),
byref(status))
self.version = "Optkit v{}".format(
version_string(major.value, minor.value, change.value,
status.value))
except:
self.version = "Optkit: version unknown"
def load_lib(self, name, override=False):
if name not in ['pogs', 'cluster']:
raise ValueError('invalid library name')
elif self.__dict__[name] is not None:
if not override:
print str(
'\nlibrary {} already loaded; call with keyword arg '
'"override"=True to bypass this check\n'.format(name))
if name == 'pogs':
self.pogs = self.pogs_lib_loader.get(
single_precision=self.precision_is_32bit,
gpu=self.device_is_gpu)
elif name == 'cluster':
self.cluster = self.cluster_lib_loader.get(
single_precision=self.precision_is_32bit,
gpu=self.device_is_gpu)
def load_libs(self, *names):
for name in names:
self.load_lib(name)
def __set_lib(self, device=None, precision=None, order=None):
devices = ['gpu', 'cpu'] if device == 'gpu' else ['cpu', 'gpu']
precisions = ['32', '64'] if precision == '32' else ['64', '32']
for dev in devices:
for prec in precisions:
lib_key = '{}{}'.format(dev, prec)
# lib_key_prox = '{}{}'.format(dev, prec)
# valid = self.dense_lib_loader.libs[lib_key] is not None
# valid &= self.prox_lib_loader.libs[lib_key] is not None
# valid &= self.sparse_lib_loader.libs[lib_key] is not None
valid = self.pogs_lib_loader.libs[lib_key] is not None
if valid:
self.__device = dev
self.__precision = prec
self.__config = lib_key
self.load_lib('pogs', override=True)
self.load_lib('cluster', override=True)
# self.dense = self.dense_lib_loader.get(
# single_precision=single, gpu=gpu)
# self.prox = self.prox_lib_loader.get(
# single_precision=single, gpu=gpu)
# self.sparse = self.sparse_lib_loader.get(
# single_precision=single, gpu=gpu)
return
else:
print str('Libraries for configuration {} '
'not found. Trying next configuration.'.format(
lib_key))
raise RuntimeError('No libraries found for backend.')
def change(self,
gpu=False,
double=True,
checktypes=None,
checkdims=None,
checkdevices=None):
if self.__LIBGUARD_ON:
print str('Backend cannot be changed once C objects have been '
'created.\n')
return
precision = '64' if double else '32'
device = 'gpu' if gpu else 'cpu'
self.__set_lib(device=device, precision=precision)
self.__get_version()
if checktypes is not None: self.typecheck = checktypes
if checkdims is not None: self.dimcheck = checkdims
if checkdevices is not None: self.devicecheck = checkdevices
def reset_device(self):
if self.device_reset_allowed:
for item in self.__dict__:
if isinstance(item, CDLL):
if 'ok_device_reset' in item.__dict__:
if self.pogs.ok_device_reset():
raise RuntimeError('device reset failed')
return
raise RuntimeError('device reset not possible: '
'no libraries loaded')
else:
raise RuntimeError('device reset not allowed: '
'C objects allocated')
class OKBackend(object):
def __init__(self, gpu=False, single_precision=False):
self.version = None
self.__device = None
self.__precision = None
self.__config = "(No libraries selected)"
self.lowtypes = None
self.dimcheck = getenv('OPTKIT_CHECKDIM', 0)
self.typecheck = getenv('OPTKIT_CHECKTYPE', 0)
self.devicecheck = getenv('OPTKIT_CHECKDEVICE', 0)
# library loaders
# self.dense_lib_loader = DenseLinsysLibs()
# self.sparse_lib_loader = SparseLinsysLibs()
# self.prox_lib_loader = ProxLibs()
self.pogs_lib_loader = PogsLibs()
self.cluster_lib_loader = ClusteringLibs()
# library instances
# self.dense = None
# self.sparse = None
# self.prox = None
self.pogs = None
self.cluster = None
self.__LIBGUARD_ON = False
self.__COBJECT_COUNT = 0
self.__set_lib()
@property
def config(self):
return self.__config
@property
def precision(self):
return self.__precision
@property
def precision_is_32bit(self):
return self.__precision == '32'
@property
def device(self):
return self.__device
@property
def device_is_gpu(self):
return self.__device == 'gpu'
@property
def device_reset_allowed(self):
return self.__COBJECT_COUNT == 0
@property
def libguard_active(self):
return self.__LIBGUARD_ON
def increment_cobject_count(self):
self.__COBJECT_COUNT += 1
self.__LIBGUARD_ON = True
def decrement_cobject_count(self):
self.__COBJECT_COUNT -= 1
self.__LIBGUARD_ON = self.__COBJECT_COUNT == 0
def __get_version(self):
major = c_int()
minor = c_int()
change = c_int()
status = c_int()
try:
self.pogs.optkit_version(byref(major), byref(minor), byref(change),
byref(status))
self.version = "Optkit v{}".format(
version_string(major.value, minor.value, change.value,
status.value))
except:
self.version = "Optkit: version unknown"
def load_lib(self, name, override=False):
if name not in ['pogs', 'cluster']:
raise ValueError('invalid library name')
elif self.__dict__[name] is not None:
if not override:
print str('\nlibrary {} already loaded; call with keyword arg '
'"override"=True to bypass this check\n'.format(name))
if name == 'pogs':
self.pogs = self.pogs_lib_loader.get(
single_precision=self.precision_is_32bit,
gpu=self.device_is_gpu)
elif name == 'cluster':
self.cluster = self.cluster_lib_loader.get(
single_precision=self.precision_is_32bit,
gpu=self.device_is_gpu)
def load_libs(self, *names):
for name in names:
self.load_lib(name)
def __set_lib(self, device=None, precision=None, order=None):
devices = ['gpu', 'cpu'] if device == 'gpu' else ['cpu', 'gpu']
precisions = ['32', '64'] if precision == '32' else ['64', '32']
for dev in devices:
for prec in precisions:
lib_key ='{}{}'.format(dev, prec)
# lib_key_prox = '{}{}'.format(dev, prec)
# valid = self.dense_lib_loader.libs[lib_key] is not None
# valid &= self.prox_lib_loader.libs[lib_key] is not None
# valid &= self.sparse_lib_loader.libs[lib_key] is not None
valid = self.pogs_lib_loader.libs[lib_key] is not None
if valid:
self.__device = dev
self.__precision = prec
self.__config = lib_key
self.load_lib('pogs', override=True)
self.load_lib('cluster', override=True)
# self.dense = self.dense_lib_loader.get(
# single_precision=single, gpu=gpu)
# self.prox = self.prox_lib_loader.get(
# single_precision=single, gpu=gpu)
# self.sparse = self.sparse_lib_loader.get(
# single_precision=single, gpu=gpu)
return
else:
print str('Libraries for configuration {} '
'not found. Trying next configuration.'.format(
lib_key))
raise RuntimeError('No libraries found for backend.')
def change(self, gpu=False, double=True, checktypes=None, checkdims=None,
checkdevices=None):
if self.__LIBGUARD_ON:
print str('Backend cannot be changed once C objects have been '
'created.\n')
return
precision = '64' if double else '32'
device = 'gpu' if gpu else 'cpu'
self.__set_lib(device=device, precision=precision)
self.__get_version()
if checktypes is not None: self.typecheck = checktypes
if checkdims is not None: self.dimcheck = checkdims
if checkdevices is not None: self.devicecheck = checkdevices
def reset_device(self):
if self.device_reset_allowed:
for item in self.__dict__:
if isinstance(item, CDLL):
if 'ok_device_reset' in item.__dict__:
if self.pogs.ok_device_reset():
raise RuntimeError('device reset failed')
return
raise RuntimeError('device reset not possible: '
'no libraries loaded')
else:
raise RuntimeError('device reset not allowed: '
'C objects allocated')