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_lobpcg.py
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_lobpcg.py
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import warnings
import numpy
import cupy
import cupy.linalg as linalg
# waiting implementation of the following modules in PR #4172
# from cupyx.scipy.linalg import (cho_factor, cho_solve)
from cupyx.scipy.sparse import linalg as splinalg
def _cholesky(B):
"""
Wrapper around `cupy.linalg.cholesky` that raises LinAlgError if there are
NaNs in the output
"""
R = cupy.linalg.cholesky(B)
if cupy.any(cupy.isnan(R)):
raise numpy.linalg.LinAlgError
return R
# TODO: This helper function can be replaced after cupy.block is supported
def _bmat(list_obj):
"""
Helper function to create a block matrix in cupy from a list
of smaller 2D dense arrays
"""
n_rows = len(list_obj)
n_cols = len(list_obj[0])
final_shape = [0, 0]
# calculating expected size of output
for i in range(n_rows):
final_shape[0] += list_obj[i][0].shape[0]
for j in range(n_cols):
final_shape[1] += list_obj[0][j].shape[1]
# obtaining result's datatype
dtype = cupy.result_type(*[arr.dtype for
list_iter in list_obj for arr in list_iter])
# checking order
F_order = all(arr.flags['F_CONTIGUOUS'] for list_iter
in list_obj for arr in list_iter)
C_order = all(arr.flags['C_CONTIGUOUS'] for list_iter
in list_obj for arr in list_iter)
order = 'F' if F_order and not C_order else 'C'
result = cupy.empty(tuple(final_shape), dtype=dtype, order=order)
start_idx_row = 0
start_idx_col = 0
end_idx_row = 0
end_idx_col = 0
for i in range(n_rows):
end_idx_row = start_idx_row + list_obj[i][0].shape[0]
start_idx_col = 0
for j in range(n_cols):
end_idx_col = start_idx_col + list_obj[i][j].shape[1]
result[start_idx_row:end_idx_row,
start_idx_col: end_idx_col] = list_obj[i][j]
start_idx_col = end_idx_col
start_idx_row = end_idx_row
return result
def _report_nonhermitian(M, name):
"""
Report if `M` is not a hermitian matrix given its type.
"""
md = M - M.T.conj()
nmd = linalg.norm(md, 1)
tol = 10 * cupy.finfo(M.dtype).eps
tol *= max(1, float(linalg.norm(M, 1)))
if nmd > tol:
warnings.warn(
f'Matrix {name} of the type {M.dtype} is not Hermitian: '
f'condition: {nmd} < {tol} fails.',
UserWarning, stacklevel=4)
def _as2d(ar):
"""
If the input array is 2D return it, if it is 1D, append a dimension,
making it a column vector.
"""
if ar.ndim == 2:
return ar
else: # Assume 1!
aux = cupy.array(ar, copy=False)
aux.shape = (ar.shape[0], 1)
return aux
def _makeOperator(operatorInput, expectedShape):
"""Takes a dense numpy array or a sparse matrix or
a function and makes an operator performing matrix * blockvector
products.
"""
if operatorInput is None:
return None
else:
operator = splinalg.aslinearoperator(operatorInput)
if operator.shape != expectedShape:
raise ValueError('operator has invalid shape')
return operator
def _applyConstraints(blockVectorV, YBY, blockVectorBY, blockVectorY):
"""Changes blockVectorV in place."""
YBV = cupy.dot(blockVectorBY.T.conj(), blockVectorV)
# awaiting the implementation of cho_solve in PR #4172
# tmp = cho_solve(factYBY, YBV)
tmp = linalg.solve(YBY, YBV)
blockVectorV -= cupy.dot(blockVectorY, tmp)
def _b_orthonormalize(B, blockVectorV, blockVectorBV=None, retInvR=False):
"""B-orthonormalize the given block vector using Cholesky."""
normalization = blockVectorV.max(
axis=0) + cupy.finfo(blockVectorV.dtype).eps
blockVectorV = blockVectorV / normalization
if blockVectorBV is None:
if B is not None:
blockVectorBV = B(blockVectorV)
else:
blockVectorBV = blockVectorV
else:
blockVectorBV = blockVectorBV / normalization
VBV = cupy.matmul(blockVectorV.T.conj(), blockVectorBV)
try:
# VBV is a Cholesky factor
VBV = _cholesky(VBV)
VBV = linalg.inv(VBV.T)
blockVectorV = cupy.matmul(blockVectorV, VBV)
if B is not None:
blockVectorBV = cupy.matmul(blockVectorBV, VBV)
else:
blockVectorBV = None
except numpy.linalg.LinAlgError:
# LinAlg Error: cholesky transformation might fail in rare cases
# raise ValueError("cholesky has failed")
blockVectorV = None
blockVectorBV = None
VBV = None
if retInvR:
return blockVectorV, blockVectorBV, VBV, normalization
else:
return blockVectorV, blockVectorBV
def _get_indx(_lambda, num, largest):
"""Get `num` indices into `_lambda` depending on `largest` option."""
ii = cupy.argsort(_lambda)
if largest:
ii = ii[:-num - 1:-1]
else:
ii = ii[:num]
return ii
# TODO: This helper function can be replaced after cupy.eigh
# supports generalized eigen value problems.
def _eigh(A, B=None):
"""
Helper function for converting a generalized eigenvalue problem
A(X) = lambda(B(X)) to standard eigen value problem using cholesky
transformation
"""
if(B is None): # use cupy's eigh in standard case
vals, vecs = linalg.eigh(A)
return vals, vecs
R = _cholesky(B)
RTi = linalg.inv(R)
Ri = linalg.inv(R.T)
F = cupy.matmul(RTi, cupy.matmul(A, Ri))
vals, vecs = linalg.eigh(F)
eigVec = cupy.matmul(Ri, vecs)
return vals, eigVec
def lobpcg(A, X,
B=None, M=None, Y=None,
tol=None, maxiter=None,
largest=True, verbosityLevel=0,
retLambdaHistory=False, retResidualNormsHistory=False):
"""Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)
LOBPCG is a preconditioned eigensolver for large symmetric positive
definite (SPD) generalized eigenproblems.
Args:
A (array-like): The symmetric linear operator of the problem,
usually a sparse matrix. Can be of the following types
- cupy.ndarray
- cupyx.scipy.sparse.csr_matrix
- cupy.scipy.sparse.linalg.LinearOperator
X (cupy.ndarray): Initial approximation to the ``k``
eigenvectors (non-sparse). If `A` has ``shape=(n,n)``
then `X` should have shape ``shape=(n,k)``.
B (array-like): The right hand side operator in a generalized
eigenproblem. By default, ``B = Identity``.
Can be of following types:
- cupy.ndarray
- cupyx.scipy.sparse.csr_matrix
- cupy.scipy.sparse.linalg.LinearOperator
M (array-like): Preconditioner to `A`; by default ``M = Identity``.
`M` should approximate the inverse of `A`.
Can be of the following types:
- cupy.ndarray
- cupyx.scipy.sparse.csr_matrix
- cupy.scipy.sparse.linalg.LinearOperator
Y (cupy.ndarray):
`n-by-sizeY` matrix of constraints (non-sparse), `sizeY < n`
The iterations will be performed in the B-orthogonal complement
of the column-space of Y. Y must be full rank.
tol (float):
Solver tolerance (stopping criterion).
The default is ``tol=n*sqrt(eps)``.
maxiter (int):
Maximum number of iterations. The default is ``maxiter = 20``.
largest (bool):
When True, solve for the largest eigenvalues,
otherwise the smallest.
verbosityLevel (int):
Controls solver output. The default is ``verbosityLevel=0``.
retLambdaHistory (bool):
Whether to return eigenvalue history. Default is False.
retResidualNormsHistory (bool):
Whether to return history of residual norms. Default is False.
Returns:
tuple:
- `w` (cupy.ndarray): Array of ``k`` eigenvalues
- `v` (cupy.ndarray) An array of ``k`` eigenvectors.
`v` has the same shape as `X`.
- `lambdas` (list of cupy.ndarray): The eigenvalue history,
if `retLambdaHistory` is True.
- `rnorms` (list of cupy.ndarray): The history of residual norms,
if `retResidualNormsHistory` is True.
.. seealso:: :func:`scipy.sparse.linalg.lobpcg`
.. note::
If both ``retLambdaHistory`` and ``retResidualNormsHistory`` are `True`
the return tuple has the following format
``(lambda, V, lambda history, residual norms history)``.
"""
blockVectorX = X
blockVectorY = Y
residualTolerance = tol
if maxiter is None:
maxiter = 20
if blockVectorY is not None:
sizeY = blockVectorY.shape[1]
else:
sizeY = 0
if len(blockVectorX.shape) != 2:
raise ValueError('expected rank-2 array for argument X')
n, sizeX = blockVectorX.shape
if verbosityLevel:
aux = "Solving "
if B is None:
aux += "standard"
else:
aux += "generalized"
aux += " eigenvalue problem with"
if M is None:
aux += "out"
aux += " preconditioning\n\n"
aux += "matrix size %d\n" % n
aux += "block size %d\n\n" % sizeX
if blockVectorY is None:
aux += "No constraints\n\n"
else:
if sizeY > 1:
aux += "%d constraints\n\n" % sizeY
else:
aux += "%d constraint\n\n" % sizeY
print(aux)
A = _makeOperator(A, (n, n))
B = _makeOperator(B, (n, n))
M = _makeOperator(M, (n, n))
if (n - sizeY) < (5 * sizeX):
# The problem size is small compared to the block size.
# Using dense general eigensolver instead of LOBPCG.
sizeX = min(sizeX, n)
if blockVectorY is not None:
raise NotImplementedError('The dense eigensolver '
'does not support constraints.')
A_dense = A(cupy.eye(n, dtype=A.dtype))
B_dense = None if B is None else B(cupy.eye(n, dtype=B.dtype))
# call numerically unstable general eigen solver
vals, vecs = _eigh(A_dense, B_dense)
if largest:
# Reverse order to be compatible with eigs() in 'LM' mode.
vals = vals[::-1]
vecs = vecs[:, ::-1]
vals = vals[:sizeX]
vecs = vecs[:, :sizeX]
return vals, vecs
if (residualTolerance is None) or (residualTolerance <= 0.0):
residualTolerance = cupy.sqrt(1e-15) * n
# Apply constraints to X.
if blockVectorY is not None:
if B is not None:
blockVectorBY = B(blockVectorY)
else:
blockVectorBY = blockVectorY
# gramYBY is a dense array.
gramYBY = cupy.dot(blockVectorY.T.conj(), blockVectorBY)
# awaiting implementation of cho_factor in PR #4172
# try:
# gramYBY is a Cholesky factor from now on...
# gramYBY = cho_factor(gramYBY)
# except numpy.linalg.LinAlgError:
# raise ValueError("cannot handle linearly dependent constraints")
_applyConstraints(blockVectorX, gramYBY, blockVectorBY, blockVectorY)
# B-orthonormalize X.
blockVectorX, blockVectorBX = _b_orthonormalize(B, blockVectorX)
# Compute the initial Ritz vectors: solve the eigenproblem.
blockVectorAX = A(blockVectorX)
gramXAX = cupy.dot(blockVectorX.T.conj(), blockVectorAX)
_lambda, eigBlockVector = _eigh(gramXAX)
ii = _get_indx(_lambda, sizeX, largest)
_lambda = _lambda[ii]
eigBlockVector = cupy.asarray(eigBlockVector[:, ii])
blockVectorX = cupy.dot(blockVectorX, eigBlockVector)
blockVectorAX = cupy.dot(blockVectorAX, eigBlockVector)
if B is not None:
blockVectorBX = cupy.dot(blockVectorBX, eigBlockVector)
# Active index set.
activeMask = cupy.ones((sizeX,), dtype=bool)
lambdaHistory = [_lambda]
residualNormsHistory = []
previousBlockSize = sizeX
ident = cupy.eye(sizeX, dtype=A.dtype)
ident0 = cupy.eye(sizeX, dtype=A.dtype)
##
# Main iteration loop.
blockVectorP = None # set during iteration
blockVectorAP = None
blockVectorBP = None
iterationNumber = -1
restart = True
explicitGramFlag = False
while iterationNumber < maxiter:
iterationNumber += 1
if verbosityLevel > 0:
print('-' * 50)
print('iteration %d' % iterationNumber)
if B is not None:
aux = blockVectorBX * _lambda[cupy.newaxis, :]
else:
aux = blockVectorX * _lambda[cupy.newaxis, :]
blockVectorR = blockVectorAX - aux
aux = cupy.sum(blockVectorR.conj() * blockVectorR, 0)
residualNorms = cupy.sqrt(aux)
residualNormsHistory.append(residualNorms)
ii = cupy.where(residualNorms > residualTolerance, True, False)
activeMask = activeMask & ii
if verbosityLevel > 2:
print(activeMask)
currentBlockSize = int(activeMask.sum())
if currentBlockSize != previousBlockSize:
previousBlockSize = currentBlockSize
ident = cupy.eye(currentBlockSize, dtype=A.dtype)
if currentBlockSize == 0:
break
if verbosityLevel > 0:
print(f'current block size: {currentBlockSize}')
print(f'eigenvalue(s):\n{_lambda}')
print(f'residual norm(s):\n{residualNorms}')
if verbosityLevel > 10:
print(eigBlockVector)
activeBlockVectorR = _as2d(blockVectorR[:, activeMask])
if iterationNumber > 0:
activeBlockVectorP = _as2d(blockVectorP[:, activeMask])
activeBlockVectorAP = _as2d(blockVectorAP[:, activeMask])
if B is not None:
activeBlockVectorBP = _as2d(blockVectorBP[:, activeMask])
if M is not None:
# Apply preconditioner T to the active residuals.
activeBlockVectorR = M(activeBlockVectorR)
# Apply constraints to the preconditioned residuals.
if blockVectorY is not None:
_applyConstraints(activeBlockVectorR,
gramYBY, blockVectorBY, blockVectorY)
# B-orthogonalize the preconditioned residuals to X.
if B is not None:
activeBlockVectorR = activeBlockVectorR\
- cupy.matmul(blockVectorX,
cupy
.matmul(blockVectorBX.T.conj(),
activeBlockVectorR))
else:
activeBlockVectorR = activeBlockVectorR - \
cupy.matmul(blockVectorX,
cupy.matmul(blockVectorX.T.conj(),
activeBlockVectorR))
##
# B-orthonormalize the preconditioned residuals.
aux = _b_orthonormalize(B, activeBlockVectorR)
activeBlockVectorR, activeBlockVectorBR = aux
activeBlockVectorAR = A(activeBlockVectorR)
if iterationNumber > 0:
if B is not None:
aux = _b_orthonormalize(B, activeBlockVectorP,
activeBlockVectorBP, retInvR=True)
activeBlockVectorP, activeBlockVectorBP, invR, normal = aux
else:
aux = _b_orthonormalize(B, activeBlockVectorP, retInvR=True)
activeBlockVectorP, _, invR, normal = aux
# Function _b_orthonormalize returns None if Cholesky fails
if activeBlockVectorP is not None:
activeBlockVectorAP = activeBlockVectorAP / normal
activeBlockVectorAP = cupy.dot(activeBlockVectorAP, invR)
restart = False
else:
restart = True
##
# Perform the Rayleigh Ritz Procedure:
# Compute symmetric Gram matrices:
if activeBlockVectorAR.dtype == 'float32':
myeps = 1
elif activeBlockVectorR.dtype == 'float32':
myeps = 1e-4
else:
myeps = 1e-8
if residualNorms.max() > myeps and not explicitGramFlag:
explicitGramFlag = False
else:
# Once explicitGramFlag, forever explicitGramFlag.
explicitGramFlag = True
# Shared memory assingments to simplify the code
if B is None:
blockVectorBX = blockVectorX
activeBlockVectorBR = activeBlockVectorR
if not restart:
activeBlockVectorBP = activeBlockVectorP
# Common submatrices:
gramXAR = cupy.dot(blockVectorX.T.conj(), activeBlockVectorAR)
gramRAR = cupy.dot(activeBlockVectorR.T.conj(), activeBlockVectorAR)
if explicitGramFlag:
gramRAR = (gramRAR + gramRAR.T.conj()) / 2
gramXAX = cupy.dot(blockVectorX.T.conj(), blockVectorAX)
gramXAX = (gramXAX + gramXAX.T.conj()) / 2
gramXBX = cupy.dot(blockVectorX.T.conj(), blockVectorBX)
gramRBR = cupy.dot(activeBlockVectorR.T.conj(),
activeBlockVectorBR)
gramXBR = cupy.dot(blockVectorX.T.conj(), activeBlockVectorBR)
else:
gramXAX = cupy.diag(_lambda)
gramXBX = ident0
gramRBR = ident
gramXBR = cupy.zeros((int(sizeX), int(currentBlockSize)),
dtype=A.dtype)
def _handle_gramA_gramB_verbosity(gramA, gramB):
if verbosityLevel > 0:
_report_nonhermitian(gramA, 'gramA')
_report_nonhermitian(gramB, 'gramB')
if verbosityLevel > 10:
# Note: not documented, but leave it in here for now
numpy.savetxt('gramA.txt', cupy.asnumpy(gramA))
numpy.savetxt('gramB.txt', cupy.asnumpy(gramB))
if not restart:
gramXAP = cupy.dot(blockVectorX.T.conj(), activeBlockVectorAP)
gramRAP = cupy.dot(activeBlockVectorR.T.conj(),
activeBlockVectorAP)
gramPAP = cupy.dot(activeBlockVectorP.T.conj(),
activeBlockVectorAP)
gramXBP = cupy.dot(blockVectorX.T.conj(), activeBlockVectorBP)
gramRBP = cupy.dot(activeBlockVectorR.T.conj(),
activeBlockVectorBP)
if explicitGramFlag:
gramPAP = (gramPAP + gramPAP.T.conj()) / 2
gramPBP = cupy.dot(activeBlockVectorP.T.conj(),
activeBlockVectorBP)
else:
gramPBP = ident
gramA = _bmat([[gramXAX, gramXAR, gramXAP],
[gramXAR.T.conj(), gramRAR, gramRAP],
[gramXAP.T.conj(), gramRAP.T.conj(), gramPAP]])
gramB = _bmat([[gramXBX, gramXBR, gramXBP],
[gramXBR.T.conj(), gramRBR, gramRBP],
[gramXBP.T.conj(), gramRBP.T.conj(), gramPBP]])
_handle_gramA_gramB_verbosity(gramA, gramB)
try:
_lambda, eigBlockVector = _eigh(gramA, gramB)
except numpy.linalg.LinAlgError:
# try again after dropping the direction vectors P from RR
restart = True
if restart:
gramA = _bmat([[gramXAX, gramXAR],
[gramXAR.T.conj(), gramRAR]])
gramB = _bmat([[gramXBX, gramXBR],
[gramXBR.T.conj(), gramRBR]])
_handle_gramA_gramB_verbosity(gramA, gramB)
try:
_lambda, eigBlockVector = _eigh(gramA, gramB)
except numpy.linalg.LinAlgError:
raise ValueError('eigh has failed in lobpcg iterations')
ii = _get_indx(_lambda, sizeX, largest)
if verbosityLevel > 10:
print(ii)
print(_lambda)
_lambda = _lambda[ii]
eigBlockVector = eigBlockVector[:, ii]
lambdaHistory.append(_lambda)
if verbosityLevel > 10:
print('lambda:', _lambda)
if verbosityLevel > 10:
print(eigBlockVector)
# Compute Ritz vectors.
if B is not None:
if not restart:
eigBlockVectorX = eigBlockVector[:sizeX]
eigBlockVectorR = eigBlockVector[sizeX:sizeX +
currentBlockSize]
eigBlockVectorP = eigBlockVector[sizeX + currentBlockSize:]
pp = cupy.dot(activeBlockVectorR, eigBlockVectorR)
pp += cupy.dot(activeBlockVectorP, eigBlockVectorP)
app = cupy.dot(activeBlockVectorAR, eigBlockVectorR)
app += cupy.dot(activeBlockVectorAP, eigBlockVectorP)
bpp = cupy.dot(activeBlockVectorBR, eigBlockVectorR)
bpp += cupy.dot(activeBlockVectorBP, eigBlockVectorP)
else:
eigBlockVectorX = eigBlockVector[:sizeX]
eigBlockVectorR = eigBlockVector[sizeX:]
pp = cupy.dot(activeBlockVectorR, eigBlockVectorR)
app = cupy.dot(activeBlockVectorAR, eigBlockVectorR)
bpp = cupy.dot(activeBlockVectorBR, eigBlockVectorR)
if verbosityLevel > 10:
print(pp)
print(app)
print(bpp)
blockVectorX = cupy.dot(blockVectorX, eigBlockVectorX) + pp
blockVectorAX = cupy.dot(blockVectorAX, eigBlockVectorX) + app
blockVectorBX = cupy.dot(blockVectorBX, eigBlockVectorX) + bpp
blockVectorP, blockVectorAP, blockVectorBP = pp, app, bpp
else:
if not restart:
eigBlockVectorX = eigBlockVector[:sizeX]
eigBlockVectorR = eigBlockVector[sizeX:sizeX +
currentBlockSize]
eigBlockVectorP = eigBlockVector[sizeX + currentBlockSize:]
pp = cupy.dot(activeBlockVectorR, eigBlockVectorR)
pp += cupy.dot(activeBlockVectorP, eigBlockVectorP)
app = cupy.dot(activeBlockVectorAR, eigBlockVectorR)
app += cupy.dot(activeBlockVectorAP, eigBlockVectorP)
else:
eigBlockVectorX = eigBlockVector[:sizeX]
eigBlockVectorR = eigBlockVector[sizeX:]
pp = cupy.dot(activeBlockVectorR, eigBlockVectorR)
app = cupy.dot(activeBlockVectorAR, eigBlockVectorR)
if verbosityLevel > 10:
print(pp)
print(app)
blockVectorX = cupy.dot(blockVectorX, eigBlockVectorX) + pp
blockVectorAX = cupy.dot(blockVectorAX, eigBlockVectorX) + app
blockVectorP, blockVectorAP = pp, app
if B is not None:
aux = blockVectorBX * _lambda[cupy.newaxis, :]
else:
aux = blockVectorX * _lambda[cupy.newaxis, :]
blockVectorR = blockVectorAX - aux
aux = cupy.sum(blockVectorR.conj() * blockVectorR, 0)
residualNorms = cupy.sqrt(aux)
# Future work:
# Generalized eigen value solver like `scipy.linalg.eigh`
# that takes in `B` matrix as input
# `cupy.linalg.cholesky` is more unstable than `scipy.linalg.cholesky`
# Making sure eigenvectors "exactly" satisfy the blockVectorY constrains?
# Making sure eigenvecotrs are "exactly" othonormalized by final "exact" RR
# Computing the actual true residuals
if verbosityLevel > 0:
print(f'Final eigenvalue(s):\n{_lambda}')
print(f'Final residual norm(s):\n{residualNorms}')
if retLambdaHistory:
if retResidualNormsHistory:
return _lambda, blockVectorX, lambdaHistory, residualNormsHistory
else:
return _lambda, blockVectorX, lambdaHistory
else:
if retResidualNormsHistory:
return _lambda, blockVectorX, residualNormsHistory
else:
return _lambda, blockVectorX