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tcw_fstat_map_funcs.py 17.11 KiB
""" Additional helper functions dealing with transient-CW F(t0,tau) maps """
import numpy as np
import os
import sys
import logging
# optional imports
import importlib as imp
def optional_import ( modulename, shorthand=None ):
'''
Import a module/submodule only if it's available.
using importlib instead of __import__
because the latter doesn't handle sub.modules
Also including a special check to fail more gracefully
when CUDA_DEVICE is set to too high a number.
'''
if shorthand is None:
shorthand = modulename
shorthandbit = ''
else:
shorthandbit = ' as '+shorthand
if('pycuda' in sys.modules):
try:
globals()[shorthand] = imp.import_module(modulename)
logging.debug('Successfully imported module %s%s.' % (modulename, shorthandbit))
success = True
except pycuda._driver.LogicError, e:
if e.message == 'cuDeviceGet failed: invalid device ordinal':
devn = int(os.environ['CUDA_DEVICE'])
raise RuntimeError('Requested CUDA device number {} exceeds number of available devices! Please change through environment variable $CUDA_DEVICE.'.format(devn))
else:
raise pycuda._driver.LogicError(e.message)
except ImportError, e:
if e.message == 'No module named '+modulename:
logging.debug('No module {:s} found.'.format(modulename))
success = False
else:
raise
else:
try:
globals()[shorthand] = imp.import_module(modulename)
logging.debug('Successfully imported module %s%s.' % (modulename, shorthandbit))
success = True
except ImportError, e:
if e.message == 'No module named '+modulename:
logging.debug('No module {:s} found.'.format(modulename))
success = False
else:
raise
return success
class pyTransientFstatMap(object):
'''
simplified object class for a F(t0,tau) F-stat map (not 2F!)
based on LALSuite's transientFstatMap_t type
replacing the gsl matrix with a numpy array
F_mn: 2D array of 2F values
maxF: maximum of F (not 2F!)
t0_ML: maximum likelihood transient start time t0 estimate
tau_ML: maximum likelihood transient duration tau estimate
'''
def __init__(self, N_t0Range, N_tauRange):
self.F_mn = np.zeros((N_t0Range, N_tauRange), dtype=np.float32)
self.maxF = float(-1.0) # Initializing to a negative value ensures that we always update at least once and hence return sane t0_d_ML, tau_d_ML even if there is only a single bin where F=0 happens.
self.t0_ML = float(0.0)
self.tau_ML = float(0.0)
# dictionary of the actual callable F-stat map functions we support,
# if the corresponding modules are available.
fstatmap_versions = {
'lal': lambda multiFstatAtoms, windowRange:
getattr(lalpulsar,'ComputeTransientFstatMap')
( multiFstatAtoms, windowRange, False ),
'pycuda': lambda multiFstatAtoms, windowRange:
pycuda_compute_transient_fstat_map
( multiFstatAtoms, windowRange )
}
def init_transient_fstat_map_features ( ):
'''
Initialization of available modules (or "features") for F-stat maps.
Returns a dictionary of method names, to match fstatmap_versions
each key's value set to True only if
all required modules are importable on this system.
'''
features = {}
have_lal = optional_import('lal')
have_lalpulsar = optional_import('lalpulsar')
features['lal'] = have_lal and have_lalpulsar
# import GPU features
have_pycuda = optional_import('pycuda')
have_pycuda_init = optional_import('pycuda.autoinit', 'autoinit')
have_pycuda_drv = optional_import('pycuda.driver', 'drv')
have_pycuda_gpuarray = optional_import('pycuda.gpuarray', 'gpuarray')
have_pycuda_tools = optional_import('pycuda.tools', 'cudatools')
have_pycuda_compiler = optional_import('pycuda.compiler', 'cudacomp')
features['pycuda'] = have_pycuda_drv and have_pycuda_init and have_pycuda_gpuarray and have_pycuda_tools and have_pycuda_compiler
logging.debug('Got the following features for transient F-stat maps:')
logging.debug(features)
if features['pycuda']:
logging.debug('CUDA version: {}'.format(drv.get_version()))
num_gpus = drv.Device.count()
logging.debug('Found {} CUDA device(s).'.format(num_gpus))
devices = []
for n in range(num_gpus):
devices.append(drv.Device(n))
for n, devn in enumerate(devices):
logging.debug('device {} model: {}, RAM: {}MB'.format(n,devn.name(),devn.total_memory()/(2.**20) ))
devnum = int(os.environ['CUDA_DEVICE'])
devn = drv.Device(devnum)
logging.info('Choosing CUDA device {}, of {} devices present: {}... (Can be changed through environment variable $CUDA_DEVICE.)'.format(devnum,num_gpus,devn.name()))
logging.debug('Available GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
return features
def call_compute_transient_fstat_map ( version, features, multiFstatAtoms=None, windowRange=None ):
'''Choose which version of the ComputeTransientFstatMap function to call.'''
if version in fstatmap_versions:
if features[version]:
FstatMap = fstatmap_versions[version](multiFstatAtoms, windowRange)
else:
raise Exception('Required module(s) for transient F-stat map method "{}" not available!'.format(version))
else:
raise Exception('Transient F-stat map method "{}" not implemented!'.format(version))
return FstatMap
def reshape_FstatAtomsVector ( atomsVector ):
'''
Make a dictionary of ndarrays out of a atoms "vector" structure.
The input is a "vector"-like structure with times as the higher hierarchical
level and a set of "atoms" quantities defined at each timestamp.
The output is a dictionary with an entry for each quantity,
which is a 1D ndarray over timestamps for that one quantity.
'''
numAtoms = atomsVector.length
atomsDict = {}
atom_fieldnames = ['timestamp', 'Fa_alpha', 'Fb_alpha',
'a2_alpha', 'ab_alpha', 'b2_alpha']
atom_dtypes = [np.uint32, complex, complex,
np.float32, np.float32, np.float32]
for f, field in enumerate(atom_fieldnames):
atomsDict[field] = np.ndarray(numAtoms,dtype=atom_dtypes[f])
for n,atom in enumerate(atomsVector.data):
for field in atom_fieldnames:
atomsDict[field][n] = atom.__getattribute__(field)
atomsDict['Fa_alpha_re'] = np.float32(atomsDict['Fa_alpha'].real)
atomsDict['Fa_alpha_im'] = np.float32(atomsDict['Fa_alpha'].imag)
atomsDict['Fb_alpha_re'] = np.float32(atomsDict['Fb_alpha'].real)
atomsDict['Fb_alpha_im'] = np.float32(atomsDict['Fb_alpha'].imag)
return atomsDict
def get_absolute_kernel_path ( kernel ):
pyfstatdir = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
kernelfile = kernel + '.cu'
return os.path.join(pyfstatdir,'pyCUDAkernels',kernelfile)
def pycuda_compute_transient_fstat_map ( multiFstatAtoms, windowRange ):
'''
GPU version of the function to compute transient-window "F-statistic map"
over start-time and timescale {t0, tau}.
Based on XLALComputeTransientFstatMap from LALSuite,
(C) 2009 Reinhard Prix, licensed under GPL
Returns a 2D matrix F_mn,
with m = index over start-times t0,
and n = index over timescales tau,
in steps of dt0 in [t0, t0+t0Band],
and dtau in [tau, tau+tauBand]
as defined in windowRange input.
'''
if ( windowRange.type >= lalpulsar.TRANSIENT_LAST ):
raise ValueError ('Unknown window-type ({}) passed as input. Allowed are [0,{}].'.format(windowRange.type, lalpulsar.TRANSIENT_LAST-1))
# internal dict for search/setup parameters
tCWparams = {}
# first combine all multi-atoms
# into a single atoms-vector with *unique* timestamps
tCWparams['TAtom'] = multiFstatAtoms.data[0].TAtom
TAtomHalf = int(tCWparams['TAtom']/2) # integer division
atoms = lalpulsar.mergeMultiFstatAtomsBinned ( multiFstatAtoms,
tCWparams['TAtom'] )
# make a combined input matrix of all atoms vectors, for transfer to GPU
tCWparams['numAtoms'] = atoms.length
atomsDict = reshape_FstatAtomsVector(atoms)
atomsInputMatrix = np.column_stack ( (atomsDict['a2_alpha'],
atomsDict['b2_alpha'],
atomsDict['ab_alpha'],
atomsDict['Fa_alpha_re'],
atomsDict['Fa_alpha_im'],
atomsDict['Fb_alpha_re'],
atomsDict['Fb_alpha_im'])
)
# actual data spans [t0_data, t0_data + tCWparams['numAtoms'] * TAtom]
# in steps of TAtom
tCWparams['t0_data'] = int(atoms.data[0].timestamp)
tCWparams['t1_data'] = int(atoms.data[tCWparams['numAtoms']-1].timestamp + tCWparams['TAtom'])
logging.debug('t0_data={:d}, t1_data={:d}'.format(tCWparams['t0_data'],
tCWparams['t1_data']))
logging.debug('numAtoms={:d}, TAtom={:d}, TAtomHalf={:d}'.format(tCWparams['numAtoms'],
tCWparams['TAtom'],
TAtomHalf))
# special treatment of window_type = none
# ==> replace by rectangular window spanning all the data
if ( windowRange.type == lalpulsar.TRANSIENT_NONE ):
windowRange.type = lalpulsar.TRANSIENT_RECTANGULAR
windowRange.t0 = tCWparams['t0_data']
windowRange.t0Band = 0
windowRange.dt0 = tCWparams['TAtom'] # irrelevant
windowRange.tau = tCWparams['numAtoms'] * tCWparams['TAtom']
windowRange.tauBand = 0;
windowRange.dtau = tCWparams['TAtom'] # irrelevant
""" NOTE: indices {i,j} enumerate *actual* atoms and their timestamps t_i, while the
* indices {m,n} enumerate the full grid of values in [t0_min, t0_max]x[Tcoh_min, Tcoh_max] in
* steps of deltaT. This allows us to deal with gaps in the data in a transparent way.
*
* NOTE2: we operate on the 'binned' atoms returned from XLALmergeMultiFstatAtomsBinned(),
* which means we can safely assume all atoms to be lined up perfectly on a 'deltaT' binned grid.
*
* The mapping used will therefore be {i,j} -> {m,n}:
* m = offs_i / deltaT = start-time offset from t0_min measured in deltaT
* n = Tcoh_ij / deltaT = duration Tcoh_ij measured in deltaT,
*
* where
* offs_i = t_i - t0_min
* Tcoh_ij = t_j - t_i + deltaT
*
"""
# We allocate a matrix {m x n} = t0Range * TcohRange elements
# covering the full timerange the transient window-range [t0,t0+t0Band]x[tau,tau+tauBand]
tCWparams['N_t0Range'] = int(np.floor( 1.0*windowRange.t0Band / windowRange.dt0 ) + 1)
tCWparams['N_tauRange'] = int(np.floor( 1.0*windowRange.tauBand / windowRange.dtau ) + 1)
FstatMap = pyTransientFstatMap ( tCWparams['N_t0Range'], tCWparams['N_tauRange'] )
logging.debug('N_t0Range={:d}, N_tauRange={:d}'.format(tCWparams['N_t0Range'],
tCWparams['N_tauRange']))
if ( windowRange.type == lalpulsar.TRANSIENT_RECTANGULAR ):
FstatMap.F_mn = pycuda_compute_transient_fstat_map_rect ( atomsInputMatrix,
windowRange,
tCWparams )
elif ( windowRange.type == lalpulsar.TRANSIENT_EXPONENTIAL ):
FstatMap.F_mn = pycuda_compute_transient_fstat_map_exp ( atomsInputMatrix,
windowRange,
tCWparams )
else:
raise ValueError('Invalid transient window type {} not in [{}, {}].'.format(windowRange.type, lalpulsar.TRANSIENT_NONE, lalpulsar.TRANSIENT_LAST-1))
# out of loop: get max2F and ML estimates over the m x n matrix
FstatMap.maxF = FstatMap.F_mn.max()
maxidx = np.unravel_index ( FstatMap.F_mn.argmax(),
(tCWparams['N_t0Range'],
tCWparams['N_tauRange']))
FstatMap.t0_ML = windowRange.t0 + maxidx[0] * windowRange.dt0
FstatMap.tau_ML = windowRange.tau + maxidx[1] * windowRange.dtau
logging.debug('Done computing transient F-stat map. maxF={}, t0_ML={}, tau_ML={}'.format(FstatMap.maxF , FstatMap.t0_ML, FstatMap.tau_ML))
return FstatMap
def pycuda_compute_transient_fstat_map_rect ( atomsInputMatrix, windowRange, tCWparams ):
'''only GPU-parallizing outer loop, keeping partial sums with memory in kernel'''
# gpu data setup and transfer
logging.debug('Initial GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
input_gpu = gpuarray.to_gpu ( atomsInputMatrix )
Fmn_gpu = gpuarray.GPUArray ( (tCWparams['N_t0Range'],tCWparams['N_tauRange']), dtype=np.float32 )
logging.debug('GPU memory with input+output allocated: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20), drv.mem_get_info()[1]/(2.**20)))
# GPU kernel
kernel = 'cudaTransientFstatRectWindow'
kernelfile = get_absolute_kernel_path ( kernel )
partial_Fstat_cuda_code = cudacomp.SourceModule(open(kernelfile,'r').read())
partial_Fstat_cuda = partial_Fstat_cuda_code.get_function(kernel)
partial_Fstat_cuda.prepare('PIIIIIIIIP')
# GPU grid setup
blockRows = min(1024,tCWparams['N_t0Range'])
blockCols = 1
gridRows = int(np.ceil(1.0*tCWparams['N_t0Range']/blockRows))
gridCols = 1
logging.debug('Looking to compute transient F-stat map over a N={}*{}={} (t0,tau) grid...'.format(tCWparams['N_t0Range'],tCWparams['N_tauRange'],tCWparams['N_t0Range']*tCWparams['N_tauRange']))
# running the kernel
logging.debug('Calling kernel with a grid of {}*{}={} blocks of {}*{}={} threads each: {} total threads...'.format(gridRows, gridCols, gridRows*gridCols, blockRows, blockCols, blockRows*blockCols, gridRows*gridCols*blockRows*blockCols))
partial_Fstat_cuda.prepared_call ( (gridRows,gridCols), (blockRows,blockCols,1), input_gpu.gpudata, tCWparams['numAtoms'], tCWparams['TAtom'], tCWparams['t0_data'], windowRange.t0, windowRange.dt0, windowRange.tau, windowRange.dtau, tCWparams['N_tauRange'], Fmn_gpu.gpudata )
# return results to host
F_mn = Fmn_gpu.get()
logging.debug('Final GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
return F_mn
def pycuda_compute_transient_fstat_map_exp ( atomsInputMatrix, windowRange, tCWparams ):
'''exponential window, inner and outer loop GPU-parallelized'''
# gpu data setup and transfer
logging.debug('Initial GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
input_gpu = gpuarray.to_gpu ( atomsInputMatrix )
Fmn_gpu = gpuarray.GPUArray ( (tCWparams['N_t0Range'],tCWparams['N_tauRange']), dtype=np.float32 )
logging.debug('GPU memory with input+output allocated: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20), drv.mem_get_info()[1]/(2.**20)))
# GPU kernel
kernel = 'cudaTransientFstatExpWindow'
kernelfile = get_absolute_kernel_path ( kernel )
partial_Fstat_cuda_code = cudacomp.SourceModule(open(kernelfile,'r').read())
partial_Fstat_cuda = partial_Fstat_cuda_code.get_function(kernel)
partial_Fstat_cuda.prepare('PIIIIIIIIIP')
# GPU grid setup
blockRows = min(32,tCWparams['N_t0Range'])
blockCols = min(32,tCWparams['N_tauRange'])
gridRows = int(np.ceil(1.0*tCWparams['N_t0Range']/blockRows))
gridCols = int(np.ceil(1.0*tCWparams['N_tauRange']/blockCols))
logging.debug('Looking to compute transient F-stat map over a N={}*{}={} (t0,tau) grid...'.format(tCWparams['N_t0Range'], tCWparams['N_tauRange'], tCWparams['N_t0Range']*tCWparams['N_tauRange']))
# running the kernel
logging.debug('Calling kernel with a grid of {}*{}={} blocks of {}*{}={} threads each: {} total threads...'.format(gridRows, gridCols, gridRows*gridCols, blockRows, blockCols, blockRows*blockCols, gridRows*gridCols*blockRows*blockCols))
partial_Fstat_cuda.prepared_call ( (gridRows,gridCols), (blockRows,blockCols,1), input_gpu.gpudata, tCWparams['numAtoms'], tCWparams['TAtom'], tCWparams['t0_data'], windowRange.t0, windowRange.dt0, windowRange.tau, windowRange.dtau, tCWparams['N_t0Range'], tCWparams['N_tauRange'], Fmn_gpu.gpudata )
# return results to host
F_mn = Fmn_gpu.get()
logging.debug('Final GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
return F_mn