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Changes in Spectral Layout
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@Sakshi-2797
Sakshi-2797 committed Apr 5, 2023
1 parent 27a8912 commit 8c9b0ab
Showing 1 changed file with 294 additions and 4 deletions.
298 changes: 294 additions & 4 deletions umap/spectral.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
import scipy.sparse
import scipy.sparse.csgraph
import sklearn.decomposition
import os

from sklearn.manifold import SpectralEmbedding
from sklearn.metrics import pairwise_distances
Expand Down Expand Up @@ -278,6 +279,287 @@ def multi_component_layout(
return result


def mkl_eigenvalue(*args, **kwargs):
print("Inside mkl_eigenvalue")
use_fp32 = os.environ.get("UMAP_MKL_FP32", "1") != "0"
if use_fp32:
return mkl_eigenvalue_s(*args, **kwargs)
return mkl_eigenvalue_d(*args, **kwargs)


def mkl_eigenvalue_d(spM, k0, whichE, ncv, tol, maxiter):
import numpy.ctypeslib as npct
import ctypes

mkl_rt = ctypes.cdll.LoadLibrary("libmkl_rt.so")

# types
# MKL_INT is np.intc if MKL_INTERFACE is LP64
array_1d_mkl_int_t = npct.ndpointer(dtype=np.intc, ndim=1, flags="CONTIGUOUS")
array_1d_double_t = npct.ndpointer(dtype=np.double, ndim=1, flags="CONTIGUOUS")

# set up function mkl_sparse_ee_init
py_mkl_sparse_ee_init = mkl_rt.mkl_sparse_ee_init
py_mkl_sparse_ee_init.restupe = None
py_mkl_sparse_ee_init.argtypes = [array_1d_mkl_int_t]

sparse_matrix_t = ctypes.c_void_p
sparse_status_t = ctypes.c_int # enum
sparse_operation_t = ctypes.c_int # enum
sparse_matrix_type_t = ctypes.c_int # enum
sparse_index_base_t = ctypes.c_int # enum
sparse_fill_mode_t = ctypes.c_int # enum
sparse_diag_type_t = ctypes.c_int # enum
sparse_layout_t = ctypes.c_int # enum
verbose_mode_t = ctypes.c_int # enum
sparse_memory_usage = ctypes.c_int # enum
sparse_request_t = ctypes.c_int # enum

class SparseMatrixDescr(ctypes.Structure):
_fields_ = [
("type", sparse_matrix_type_t),
("mode", sparse_fill_mode_t),
("diag", sparse_diag_type_t),
]

# setup mkl_sparse_d_create_csr
py_mkl_sparse_d_create_csr = mkl_rt.mkl_sparse_d_create_csr
py_mkl_sparse_d_create_csr.restype = sparse_status_t
py_mkl_sparse_d_create_csr.argtypes = [
ctypes.POINTER(ctypes.c_void_p), # sparse_matrix_t *A
sparse_index_base_t, # enum
ctypes.c_int, # MKL_INT rows
ctypes.c_int, # MKL_INT cols
array_1d_mkl_int_t, # MKL_INT *row_start
array_1d_mkl_int_t, # MKL_INT *row_end
array_1d_mkl_int_t, # MKL_INT *col_indx
array_1d_double_t, # double * vals
]

py_mkl_sparse_d_ev = mkl_rt.mkl_sparse_d_ev
py_mkl_sparse_d_ev.restype = ctypes.c_int
py_mkl_sparse_d_ev.argtypes = [
ctypes.c_char_p, # char * whichE
array_1d_mkl_int_t, # MKL_INT * pm
sparse_matrix_t, # sparse_matrix_t A,
SparseMatrixDescr, # matrix_desc
ctypes.c_int, # k0
ctypes.POINTER(ctypes.c_int), # MKL_INT *k,
array_1d_double_t, # double * E
array_1d_double_t, # double * X
array_1d_double_t, # double * res
]

py_mkl_sparse_destroy = mkl_rt.mkl_sparse_destroy
py_mkl_sparse_destroy.returntype = None
py_mkl_sparse_destroy.argtypes = [sparse_matrix_t]

one = ctypes.c_double(1.0)
zero = ctypes.c_double(0.0)
tol = ctypes.c_int(int(tol))
compute_vectors = ctypes.c_int(1)
xgemmC = ctypes.c_char(b"T")
xgemmN = ctypes.c_char(b"N")
# whichE = ctypes.c_char(b'S')
whichE = ctypes.c_char(whichE)
whichV = ctypes.c_char(b"L")
num_lanczos_vectors = ctypes.c_int(int(ncv))
maxiter = ctypes.c_int(int(maxiter))

k = ctypes.c_int(0) # to be computed by MKL
SPARSE_MATRIX_TYPE_GENERAL = 20
descr = SparseMatrixDescr(type=SPARSE_MATRIX_TYPE_GENERAL)
mkl_csrA = sparse_matrix_t()

pm = np.zeros((128,), dtype=np.intc)
py_mkl_sparse_ee_init(pm)
pm[1] = tol.value
pm[2] = 1
pm[3] = num_lanczos_vectors.value
pm[4] = maxiter.value
pm[6] = compute_vectors.value
pm[
7
] = 1 # 0 = relative, 1 = Use absolute stopping critertia. Iteration is stopped if norm(Ax - lambda*x) < 10^(-pm[1])

k0 = ctypes.c_int(int(k0)) # how many eigenvalues to compute

SPARSE_INDEX_BASE_ZERO = 0
# mkl_sparse_d_create_csr ( &mkl_csrA, SPARSE_INDEX_BASE_ONE, N, M, ia, ia+1, ja, a );
csr_create_status = py_mkl_sparse_d_create_csr(
ctypes.byref(mkl_csrA),
SPARSE_INDEX_BASE_ZERO,
spM.shape[0],
spM.shape[1],
spM.indptr[:-1],
spM.indptr[1:],
spM.indices,
spM.data,
)

eigenvals = np.zeros((np.max(k0.value),), dtype=np.float64)
resid = np.empty_like(eigenvals)
X = np.empty((k0.value, spM.shape[0]), dtype=np.float64)
X_flat = X.reshape((-1))
# X_flat = np.ones((k0.value * spM.shape[0]), dtype=np.double)

info = py_mkl_sparse_d_ev(
ctypes.byref(whichE), # 1
pm, # 3
mkl_csrA, # 4
descr,
k0,
ctypes.byref(k),
eigenvals,
X_flat,
resid,
)

X = X_flat.reshape((k0.value, int(X_flat.shape[0] / k0.value)))
X = X.T
X[:, 0] = -X[:, 0]
X[:, 1] = -X[:, 1]
return eigenvals, X


def mkl_eigenvalue_s(spM, k0, whichE, ncv, tol, maxiter): # single-precision version
import numpy.ctypeslib as npct
import ctypes

mkl_rt = ctypes.cdll.LoadLibrary("libmkl_rt.so")

# types
# MKL_INT is np.intc if MKL_INTERFACE is LP64
array_1d_mkl_int_t = npct.ndpointer(dtype=np.intc, ndim=1, flags="CONTIGUOUS")
array_1d_float_t = npct.ndpointer(dtype=np.float32, ndim=1, flags="CONTIGUOUS")

# set up function mkl_sparse_ee_init
py_mkl_sparse_ee_init = mkl_rt.mkl_sparse_ee_init
py_mkl_sparse_ee_init.restupe = None
py_mkl_sparse_ee_init.argtypes = [array_1d_mkl_int_t]

sparse_matrix_t = ctypes.c_void_p
sparse_status_t = ctypes.c_int # enum
sparse_operation_t = ctypes.c_int # enum
sparse_matrix_type_t = ctypes.c_int # enum
sparse_index_base_t = ctypes.c_int # enum
sparse_fill_mode_t = ctypes.c_int # enum
sparse_diag_type_t = ctypes.c_int # enum
sparse_layout_t = ctypes.c_int # enum
verbose_mode_t = ctypes.c_int # enum
sparse_memory_usage = ctypes.c_int # enum
sparse_request_t = ctypes.c_int # enum

class SparseMatrixDescr(ctypes.Structure):
_fields_ = [
("type", sparse_matrix_type_t),
("mode", sparse_fill_mode_t),
("diag", sparse_diag_type_t),
]

# setup mkl_sparse_d_create_csr
py_mkl_sparse_s_create_csr = mkl_rt.mkl_sparse_s_create_csr
py_mkl_sparse_s_create_csr.restype = sparse_status_t
py_mkl_sparse_s_create_csr.argtypes = [
ctypes.POINTER(ctypes.c_void_p), # sparse_matrix_t *A
sparse_index_base_t, # enum
ctypes.c_int, # MKL_INT rows
ctypes.c_int, # MKL_INT cols
array_1d_mkl_int_t, # MKL_INT *row_start
array_1d_mkl_int_t, # MKL_INT *row_end
array_1d_mkl_int_t, # MKL_INT *col_indx
array_1d_float_t, # float * vals
]

py_mkl_sparse_s_ev = mkl_rt.mkl_sparse_s_ev
py_mkl_sparse_s_ev.restype = ctypes.c_int
py_mkl_sparse_s_ev.argtypes = [
ctypes.c_char_p, # char * whichE
array_1d_mkl_int_t, # MKL_INT * pm
sparse_matrix_t, # sparse_matrix_t A,
SparseMatrixDescr, # matrix_desc
ctypes.c_int, # k0
ctypes.POINTER(ctypes.c_int), # MKL_INT *k,
array_1d_float_t, # float * E
array_1d_float_t, # float * X
array_1d_float_t, # float * res
]

py_mkl_sparse_destroy = mkl_rt.mkl_sparse_destroy
py_mkl_sparse_destroy.returntype = None
py_mkl_sparse_destroy.argtypes = [sparse_matrix_t]

one = ctypes.c_float(1.0)
zero = ctypes.c_float(0.0)
tol = ctypes.c_int(int(tol))
compute_vectors = ctypes.c_int(1)
xgemmC = ctypes.c_char(b"T")
xgemmN = ctypes.c_char(b"N")
# whichE = ctypes.c_char(b'S')
whichE = ctypes.c_char(whichE)
whichV = ctypes.c_char(b"L")
num_lanczos_vectors = ctypes.c_int(int(ncv))
maxiter = ctypes.c_int(int(maxiter))

k = ctypes.c_int(0) # to be computed by MKL
SPARSE_MATRIX_TYPE_GENERAL = 20
descr = SparseMatrixDescr(type=SPARSE_MATRIX_TYPE_GENERAL)
mkl_csrA = sparse_matrix_t()

pm = np.zeros((128,), dtype=np.intc)
py_mkl_sparse_ee_init(pm)
pm[1] = tol.value
pm[2] = 1
pm[3] = num_lanczos_vectors.value
pm[4] = maxiter.value
pm[6] = compute_vectors.value
pm[
7
] = 1 # 0 = relative, 1 = Use absolute stopping critertia. Iteration is stopped if norm(Ax - lambda*x) < 10^(-pm[1])

k0 = ctypes.c_int(int(k0)) # how many eigenvalues to compute

tmp = np.empty(spM.data.shape, dtype=np.float32)
tmp[:] = spM.data.astype(np.float32)

SPARSE_INDEX_BASE_ZERO = 0
# mkl_sparse_d_create_csr ( &mkl_csrA, SPARSE_INDEX_BASE_ONE, N, M, ia, ia+1, ja, a );
csr_create_status = py_mkl_sparse_s_create_csr(
ctypes.byref(mkl_csrA),
SPARSE_INDEX_BASE_ZERO,
spM.shape[0],
spM.shape[1],
spM.indptr[:-1],
spM.indptr[1:],
spM.indices,
tmp, # spM.data.astype(np.float32)
)

eigenvals = np.zeros((np.max(k0.value),), dtype=np.float32)
resid = np.empty_like(eigenvals)
X = np.empty((k0.value, spM.shape[0]), dtype=np.float32)
X_flat = X.reshape((-1))
# X_flat = np.ones((k0.value * spM.shape[0]), dtype=np.double)

info = py_mkl_sparse_s_ev(
ctypes.byref(whichE), # 1
pm, # 3
mkl_csrA, # 4
descr,
k0,
ctypes.byref(k),
eigenvals,
X_flat,
resid,
)

X = X_flat.reshape((k0.value, int(X_flat.shape[0] / k0.value)))
X = X.T
X[:, 0] = -X[:, 0]
X[:, 1] = -X[:, 1]
return eigenvals, X


def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_kwds={}):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
Expand Down Expand Up @@ -332,13 +614,21 @@ def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_k
num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(graph.shape[0])))
try:
if L.shape[0] < 2000000:
eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
# eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
# L,
# k,
# which="SM",
# ncv=num_lanczos_vectors,
# tol=1e-4,
# v0=np.ones(L.shape[0]),
# maxiter=graph.shape[0] * 5,
# )
eigenvalues, eigenvectors = mkl_eigenvalue(
L,
k,
which="SM",
whichE=b"S",
ncv=num_lanczos_vectors,
tol=1e-4,
v0=np.ones(L.shape[0]),
tol=5,
maxiter=graph.shape[0] * 5,
)
else:
Expand Down

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