use logging package for output, and remove 'verbose' options
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@ -16,6 +16,7 @@ satisfy the constraints for the 'conjugate gradient' algorithm
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from typing import Dict, Any
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import time
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import logging
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import numpy
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from numpy.linalg import norm
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@ -27,6 +28,11 @@ import fdfd_tools.solvers
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from . import ops
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__author__ = 'Jan Petykiewicz'
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logger = logging.getLogger(__name__)
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class CSRMatrix(object):
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"""
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Matrix stored in Compressed Sparse Row format, in GPU RAM.
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@ -49,7 +55,6 @@ def cg(A: 'scipy.sparse.csr_matrix',
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err_threshold: float = 1e-6,
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context: pyopencl.Context = None,
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queue: pyopencl.CommandQueue = None,
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verbose: bool = False,
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) -> numpy.ndarray:
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"""
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General conjugate-gradient solver for sparse matrices, where A @ x = b.
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@ -60,7 +65,6 @@ def cg(A: 'scipy.sparse.csr_matrix',
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:param err_threshold: Error threshold for successful solve, relative to norm(b)
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:param context: PyOpenCL context. Will be created if not given.
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:param queue: PyOpenCL command queue. Will be created if not given.
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:param verbose: Whether to print statistics to screen.
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:return: Solution vector x; returned even if solve doesn't converge.
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"""
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@ -102,13 +106,11 @@ def cg(A: 'scipy.sparse.csr_matrix',
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_, err2 = rhoerr_step(r, [])
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b_norm = numpy.sqrt(err2)
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if verbose:
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print('b_norm check: ', b_norm)
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logging.debug('b_norm check: ', b_norm)
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success = False
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for k in range(max_iters):
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if verbose:
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print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
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logging.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
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rho_prev = rho
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e = xr_step(x, p, r, v, alpha, [])
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@ -116,8 +118,7 @@ def cg(A: 'scipy.sparse.csr_matrix',
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errs += [numpy.sqrt(err2) / b_norm]
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if verbose:
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print('err', errs[-1])
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logging.debug('err {}'.format(errs[-1]))
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if errs[-1] < err_threshold:
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success = True
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@ -128,7 +129,7 @@ def cg(A: 'scipy.sparse.csr_matrix',
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alpha = rho / dot(p, v, e)
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if verbose and k % 1000 == 0:
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print(k)
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logging.info('iteration {}'.format(k))
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'''
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Done solving
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@ -137,17 +138,16 @@ def cg(A: 'scipy.sparse.csr_matrix',
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x = x.get()
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if verbose:
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if success:
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print('Success', end='')
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logging.info('Solve success')
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else:
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print('Failure', end=', ')
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print(', {} iterations in {} sec: {} iterations/sec \
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logging.warning('Solve failure')
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logging.info('{} iterations in {} sec: {} iterations/sec \
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'.format(k, time_elapsed, k / time_elapsed))
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print('final error', errs[-1])
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print('overhead {} sec'.format(start_time2 - start_time))
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logging.debug('final error {}'.format(errs[-1]))
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logging.debug('overhead {} sec'.format(start_time2 - start_time))
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print('Final residual:', norm(A @ x - b) / norm(b))
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logging.info('Final residual: {}'.format(norm(A @ x - b) / norm(b)))
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return x
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@ -8,6 +8,7 @@ a matrix).
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from typing import List
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import time
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import logging
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import numpy
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from numpy.linalg import norm
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@ -18,8 +19,11 @@ import fdfd_tools.operators
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from . import ops
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__author__ = 'Jan Petykiewicz'
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logger = logging.getLogger(__name__)
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def cg_solver(omega: complex,
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dxes: List[List[numpy.ndarray]],
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@ -32,7 +36,6 @@ def cg_solver(omega: complex,
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max_iters: int = 40000,
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err_threshold: float = 1e-6,
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context: pyopencl.Context = None,
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verbose: bool = False,
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) -> numpy.ndarray:
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"""
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OpenCL FDFD solver using the iterative conjugate gradient (cg) method
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@ -57,7 +60,6 @@ def cg_solver(omega: complex,
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:param err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
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Default 1e-6.
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:param context: PyOpenCL context to run in. If not given, construct a new context.
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:param verbose: If True, print progress to stdout. Default False.
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:return: E-field which solves the system. Returned even if we did not converge.
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"""
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@ -171,12 +173,13 @@ def cg_solver(omega: complex,
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_, err2 = rhoerr_step(r, [])
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b_norm = numpy.sqrt(err2)
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print('b_norm check: ', b_norm)
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logging.debug('b_norm check: {}'.format(b_norm))
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success = False
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for k in range(max_iters):
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if verbose:
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print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
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do_print = (k % 100 == 0)
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if do_print:
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logger.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
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rho_prev = rho
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e = xr_step(x, p, r, v, alpha, [])
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@ -184,8 +187,8 @@ def cg_solver(omega: complex,
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errs += [numpy.sqrt(err2) / b_norm]
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if verbose:
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print('err', errs[-1])
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if do_print:
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logger.debug('err {}'.format(errs[-1]))
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if errs[-1] < err_threshold:
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success = True
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@ -196,7 +199,7 @@ def cg_solver(omega: complex,
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alpha = rho / dot(p, v, e)
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if k % 1000 == 0:
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print(k)
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logger.info('iteration {}'.format(k))
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'''
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Done solving
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@ -210,18 +213,18 @@ def cg_solver(omega: complex,
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x = (Pr * x).get()
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if success:
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print('Success', end='')
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logger.info('Solve success')
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else:
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print('Failure', end=', ')
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print(', {} iterations in {} sec: {} iterations/sec \
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logger.warning('Solve failure')
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logger.info('{} iterations in {} sec: {} iterations/sec \
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'.format(k, time_elapsed, k / time_elapsed))
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print('final error', errs[-1])
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print('overhead {} sec'.format(start_time2 - start_time))
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logger.debug('final error {}'.format(errs[-1]))
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logger.debug('overhead {} sec'.format(start_time2 - start_time))
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A0 = fdfd_tools.operators.e_full(omega, dxes, epsilon, mu).tocsr()
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if adjoint:
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# Remember we conjugated all the contents of A earlier
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A0 = A0.T
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print('Post-everything residual:', norm(A0 @ x - b) / norm(b))
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logger.info('Post-everything residual: {}'.format(norm(A0 @ x - b) / norm(b)))
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return x
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@ -8,6 +8,7 @@ See kernels/ for any of the .cl files loaded in this file.
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"""
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from typing import List, Callable
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import logging
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import numpy
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import jinja2
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@ -18,6 +19,8 @@ from pyopencl.elementwise import ElementwiseKernel
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from pyopencl.reduction import ReductionKernel
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logger = logging.getLogger(__name__)
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# Create jinja2 env on module load
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jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
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@ -145,6 +148,11 @@ def create_a(context: pyopencl.Context,
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e2 = H2E_kernel(E, H, oeps, Pl, pec, *idxes[1], wait_for=[e2])
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return [e2]
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logger.debug('Preamble: \n{}'.format(preamble))
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logger.debug('p2e: \n{}'.format(p2e_source))
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logger.debug('e2h: \n{}'.format(e2h_source))
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logger.debug('h2e: \n{}'.format(h2e_source))
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return spmv
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