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3
.gitignore vendored
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@ -60,6 +60,3 @@ target/
# PyCharm
.idea/
.mypy_cache/
.pytest_cache/

24
README.md
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@ -6,10 +6,10 @@ electromagnetic solver implemented in Python and OpenCL.
**Capabilities:**
* Arbitrary distributions of the following:
* Dielectric constant (`epsilon`)
* Magnetic permeabilty (`mu`)
* Perfect electric conductor (`PEC`)
* Perfect magnetic conductor (`PMC`)
* Dielectric constant (```epsilon```)
* Magnetic permeabilty (```mu```)
* Perfect electric conductor (```PEC```)
* Perfect magnetic conductor (```PMC```)
* Variable-sized rectangular grids
* Stretched-coordinate PMLs (complex cell sizes allowed)
@ -17,10 +17,10 @@ Currently, only periodic boundary conditions are included.
PEC/PMC boundaries can be implemented by drawing PEC/PMC cells near the edges.
Bloch boundary conditions are not included but wouldn't be very hard to add.
The default solver `opencl_fdfd.cg_solver(...)` located in main.py
The default solver ```opencl_fdfd.cg_solver(...)``` located in main.py
implements the E-field wave operator directly (ie, as a list of OpenCL
instructions rather than a matrix). Additionally, there is a slower
(and slightly more versatile) solver in `csr.py` which attempts to solve
(and slightly more versatile) solver in ```csr.py``` which attempts to solve
an arbitrary sparse matrix in compressed sparse row (CSR) format using
the same conjugate gradient method as the default solver. The CSR solver
is significantly slower, but can be very useful for testing alternative
@ -34,11 +34,11 @@ generalization to multiple GPUs should be pretty straightforward
## Installation
**Dependencies:**
* python 3 (written and tested with 3.7)
* python 3 (written and tested with 3.5)
* numpy
* pyopencl
* jinja2
* [meanas](https://mpxd.net/code/jan/meanas) (>=0.5)
* [fdfd_tools](https://mpxd.net/code/jan/fdfd_tools) (>=0.2)
Install with pip, via git:
@ -49,14 +49,14 @@ pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release
## Use
See the documentation for `opencl_fdfd.cg_solver(...)`
See the documentation for ```opencl_fdfd.cg_solver(...)```
(located in ```main.py```) for details about how to call the solver.
The FDFD arguments are identical to those in
`meanas.solvers.generic(...)`, and a few solver-specific
```fdfd_tools.solvers.generic(...)```, and a few solver-specific
arguments are available.
An alternate (slower) FDFD solver and a general gpu-based sparse matrix
solver is available in `csr.py`. These aren't particularly
solver is available in ```csr.py```. These aren't particularly
well-optimized, and something like
[MAGMA](http://icl.cs.utk.edu/magma/index.html) would probably be a
better choice if you absolutely need to solve arbitrary sparse matrices

1
opencl_fdfd/LICENSE.md
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@ -1 +0,0 @@
../LICENSE.md

1
opencl_fdfd/README.md
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@ -1 +0,0 @@
../README.md

8
opencl_fdfd/__init__.py
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@ -31,14 +31,14 @@
Dependencies:
- meanas ( https://mpxd.net/code/jan/meanas )
- fdfd_tools ( https://mpxd.net/code/jan/fdfd_tools )
- numpy
- pyopencl
- jinja2
"""
from .main import cg_solver as cg_solver
from .main import cg_solver
__author__ = 'Jan Petykiewicz'
__version__ = '0.4'
version = __version__
version = '0.3'

138
opencl_fdfd/csr.py
View File

@ -6,7 +6,7 @@ CSRMatrix sparse matrix representation.
The FDFD solver (fdfd_cg_solver()) solves an FDFD problem by
creating a sparse matrix representing the problem (using
meanas) and then passing it to cg(), which performs a
fdfd_tools) and then passing it to cg(), which performs a
conjugate gradient solve.
cg() is capable of solving arbitrary sparse matrices which
@ -14,66 +14,58 @@ satisfy the constraints for the 'conjugate gradient' algorithm
(positive definite, symmetric) and some that don't.
"""
from typing import Any, TYPE_CHECKING
from typing import Dict, Any
import time
import logging
import numpy
from numpy.typing import NDArray, ArrayLike
from numpy.linalg import norm
from numpy import complexfloating
import pyopencl
import pyopencl.array
import meanas.fdfd.solvers
import fdfd_tools.solvers
from . import ops
if TYPE_CHECKING:
import scipy
__author__ = 'Jan Petykiewicz'
logger = logging.getLogger(__name__)
class CSRMatrix:
class CSRMatrix(object):
"""
Matrix stored in Compressed Sparse Row format, in GPU RAM.
"""
row_ptr: pyopencl.array.Array
col_ind: pyopencl.array.Array
data: pyopencl.array.Array
row_ptr = None # type: pyopencl.array.Array
col_ind = None # type: pyopencl.array.Array
data = None # type: pyopencl.array.Array
def __init__(
self,
queue: pyopencl.CommandQueue,
m: 'scipy.sparse.csr_matrix',
) -> None:
def __init__(self,
queue: pyopencl.CommandQueue,
m: 'scipy.sparse.csr_matrix'):
self.row_ptr = pyopencl.array.to_device(queue, m.indptr)
self.col_ind = pyopencl.array.to_device(queue, m.indices)
self.data = pyopencl.array.to_device(queue, m.data.astype(numpy.complex128))
def cg(
A: 'scipy.sparse.csr_matrix',
b: ArrayLike,
max_iters: int = 10000,
err_threshold: float = 1e-6,
context: pyopencl.Context | None = None,
queue: pyopencl.CommandQueue | None = None,
) -> NDArray[complexfloating]:
def cg(A: 'scipy.sparse.csr_matrix',
b: numpy.ndarray,
max_iters: int = 10000,
err_threshold: float = 1e-6,
context: pyopencl.Context = None,
queue: pyopencl.CommandQueue = None,
) -> numpy.ndarray:
"""
General conjugate-gradient solver for sparse matrices, where A @ x = b.
Args:
A: Matrix to solve (CSR format)
b: Right-hand side vector (dense ndarray)
max_iters: Maximum number of iterations
err_threshold: Error threshold for successful solve, relative to norm(b)
context: PyOpenCL context. Will be created if not given.
queue: PyOpenCL command queue. Will be created if not given.
Returns:
Solution vector x; returned even if solve doesn't converge.
:param A: Matrix to solve (CSR format)
:param b: Right-hand side vector (dense ndarray)
:param max_iters: Maximum number of iterations
:param err_threshold: Error threshold for successful solve, relative to norm(b)
:param context: PyOpenCL context. Will be created if not given.
:param queue: PyOpenCL command queue. Will be created if not given.
:return: Solution vector x; returned even if solve doesn't converge.
"""
start_time = time.perf_counter()
@ -84,10 +76,10 @@ def cg(
if queue is None:
queue = pyopencl.CommandQueue(context)
def load_field(v: NDArray[numpy.complexfloating], dtype: type = numpy.complex128) -> pyopencl.array.Array:
def load_field(v, dtype=numpy.complex128):
return pyopencl.array.to_device(queue, v.astype(dtype))
r = load_field(numpy.asarray(b))
r = load_field(b)
x = pyopencl.array.zeros_like(r)
v = pyopencl.array.zeros_like(r)
p = pyopencl.array.zeros_like(r)
@ -98,27 +90,27 @@ def cg(
m = CSRMatrix(queue, A)
#
# Generate OpenCL kernels
#
'''
Generate OpenCL kernels
'''
a_step = ops.create_a_csr(context)
xr_step = ops.create_xr_step(context)
rhoerr_step = ops.create_rhoerr_step(context)
p_step = ops.create_p_step(context)
dot = ops.create_dot(context)
#
# Start the solve
#
'''
Start the solve
'''
start_time2 = time.perf_counter()
_, err2 = rhoerr_step(r, [])
b_norm = numpy.sqrt(err2)
logging.debug(f'b_norm check: {b_norm}')
logging.debug('b_norm check: ', b_norm)
success = False
for k in range(max_iters):
logging.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
logging.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
rho_prev = rho
e = xr_step(x, p, r, v, alpha, [])
@ -126,7 +118,7 @@ def cg(
errs += [numpy.sqrt(err2) / b_norm]
logging.debug(f'err {errs[-1]}')
logging.debug('err {}'.format(errs[-1]))
if errs[-1] < err_threshold:
success = True
@ -136,12 +128,12 @@ def cg(
e = a_step(v, m, p, e)
alpha = rho / dot(p, v, e)
if k % 1000 == 0:
logger.info(f'iteration {k}')
if verbose and k % 1000 == 0:
logging.info('iteration {}'.format(k))
#
# Done solving
#
'''
Done solving
'''
time_elapsed = time.perf_counter() - start_time
x = x.get()
@ -150,46 +142,38 @@ def cg(
logging.info('Solve success')
else:
logging.warning('Solve failure')
logging.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
logging.debug(f'final error {errs[-1]}')
logging.debug(f'overhead {start_time2 - start_time} sec')
logging.info('{} iterations in {} sec: {} iterations/sec \
'.format(k, time_elapsed, k / time_elapsed))
logging.debug('final error {}'.format(errs[-1]))
logging.debug('overhead {} sec'.format(start_time2 - start_time))
residual = norm(A @ x - b) / norm(b)
logging.info(f'Final residual: {residual}')
logging.info('Final residual: {}'.format(norm(A @ x - b) / norm(b)))
return x
def fdfd_cg_solver(
solver_opts: dict[str, Any] | None = None,
**fdfd_args,
) -> NDArray[complexfloating]:
def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
**fdfd_args
) -> numpy.ndarray:
"""
Conjugate gradient FDFD solver using CSR sparse matrices, mainly for
testing and development since it's much slower than the solver in main.py.
Calls meanas.fdfd.solvers.generic(
**fdfd_args,
matrix_solver=opencl_fdfd.csr.cg,
matrix_solver_opts=solver_opts,
)
Calls fdfd_tools.solvers.generic(**fdfd_args,
matrix_solver=opencl_fdfd.csr.cg,
matrix_solver_opts=solver_opts)
Args:
solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
Default {}.
fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
Should include all of the arguments **except** matrix_solver and matrix_solver_opts
Returns:
E-field which solves the system.
:param solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
Default {}.
:param fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
Should include all of the arguments **except** matrix_solver and matrix_solver_opts
:return: E-field which solves the system.
"""
if solver_opts is None:
solver_opts = dict()
x = meanas.fdfd.solvers.generic(
matrix_solver=cg,
matrix_solver_opts=solver_opts,
**fdfd_args,
)
x = fdfd_tools.solvers.generic(matrix_solver=cg,
matrix_solver_opts=solver_opts,
**fdfd_args)
return x

158
opencl_fdfd/main.py
View File

@ -5,70 +5,69 @@ This file holds the default FDFD solver, which uses an E-field wave
operator implemented directly as OpenCL arithmetic (rather than as
a matrix).
"""
from typing import List
import time
import logging
import numpy
from numpy.typing import NDArray, ArrayLike
from numpy.linalg import norm
from numpy import floating, complexfloating
import pyopencl
import pyopencl.array
import meanas.fdfd.operators
import fdfd_tools.operators
from . import ops
__author__ = 'Jan Petykiewicz'
logger = logging.getLogger(__name__)
def cg_solver(
omega: complex,
dxes: list[list[NDArray[floating | complexfloating]]],
J: ArrayLike,
epsilon: ArrayLike,
mu: ArrayLike | None = None,
pec: ArrayLike | None = None,
pmc: ArrayLike | None = None,
adjoint: bool = False,
max_iters: int = 40000,
err_threshold: float = 1e-6,
context: pyopencl.Context | None = None,
) -> NDArray:
def cg_solver(omega: complex,
dxes: List[List[numpy.ndarray]],
J: numpy.ndarray,
epsilon: numpy.ndarray,
mu: numpy.ndarray = None,
pec: numpy.ndarray = None,
pmc: numpy.ndarray = None,
adjoint: bool = False,
max_iters: int = 40000,
err_threshold: float = 1e-6,
context: pyopencl.Context = None,
) -> numpy.ndarray:
"""
OpenCL FDFD solver using the iterative conjugate gradient (cg) method
and implementing the diagonalized E-field wave operator directly in
OpenCL.
All ndarray arguments should be 1D arrays. To linearize a list of 3 3D ndarrays,
either use meanas.fdmath.vec() or numpy:
either use fdfd_tools.vec() or numpy:
f_1D = numpy.hstack(tuple((fi.flatten(order='F') for fi in [f_x, f_y, f_z])))
Args:
omega: Complex frequency to solve at.
dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
J: Electric current distribution (at E-field locations)
epsilon: Dielectric constant distribution (at E-field locations)
mu: Magnetic permeability distribution (at H-field locations)
pec: Perfect electric conductor distribution
(at E-field locations; non-zero value indicates PEC is present)
pmc: Perfect magnetic conductor distribution
(at H-field locations; non-zero value indicates PMC is present)
adjoint: If true, solves the adjoint problem.
max_iters: Maximum number of iterations. Default 40,000.
err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
Default 1e-6.
context: PyOpenCL context to run in. If not given, construct a new context.
Returns:
E-field which solves the system. Returned even if we did not converge.
:param omega: Complex frequency to solve at.
:param dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
:param J: Electric current distribution (at E-field locations)
:param epsilon: Dielectric constant distribution (at E-field locations)
:param mu: Magnetic permeability distribution (at H-field locations)
:param pec: Perfect electric conductor distribution
(at E-field locations; non-zero value indicates PEC is present)
:param pmc: Perfect magnetic conductor distribution
(at H-field locations; non-zero value indicates PMC is present)
:param adjoint: If true, solves the adjoint problem.
:param max_iters: Maximum number of iterations. Default 40,000.
:param err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
Default 1e-6.
:param context: PyOpenCL context to run in. If not given, construct a new context.
:return: E-field which solves the system. Returned even if we did not converge.
"""
start_time = time.perf_counter()
shape = [dd.size for dd in dxes[0]]
b = -1j * omega * J
b = -1j * omega * numpy.asarray(J)
shape = [d.size for d in dxes[0]]
'''
** In this comment, I use the following notation:
@ -97,29 +96,30 @@ def cg_solver(
We can accomplish all this simply by conjugating everything (except J) and
reversing the order of L and R
'''
epsilon = numpy.asarray(epsilon)
if adjoint:
# Conjugate everything
dxes = [[numpy.conj(dd) for dd in dds] for dds in dxes]
dxes = [[numpy.conj(d) for d in dd] for dd in dxes]
omega = numpy.conj(omega)
epsilon = numpy.conj(epsilon)
if mu is not None:
mu = numpy.conj(mu)
assert isinstance(epsilon, NDArray[floating] | NDArray[complexfloating])
L, R = meanas.fdfd.operators.e_full_preconditioners(dxes)
b_preconditioned = (R if adjoint else L) @ b
L, R = fdfd_tools.operators.e_full_preconditioners(dxes)
#
# Allocate GPU memory and load in data
#
if adjoint:
b_preconditioned = R @ b
else:
b_preconditioned = L @ b
'''
Allocate GPU memory and load in data
'''
if context is None:
context = pyopencl.create_some_context(interactive=True)
queue = pyopencl.CommandQueue(context)
def load_field(v: NDArray[complexfloating | floating], dtype: type = numpy.complex128) -> pyopencl.array.Array:
def load_field(v, dtype=numpy.complex128):
return pyopencl.array.to_device(queue, v.astype(dtype))
r = load_field(b_preconditioned) # load preconditioned b into r
@ -132,31 +132,30 @@ def cg_solver(
rho = 1.0 + 0j
errs = []
inv_dxes = [[load_field(1 / numpy.asarray(dd)) for dd in dds] for dds in dxes]
oeps = load_field(-omega * omega * epsilon)
inv_dxes = [[load_field(1 / d) for d in dd] for dd in dxes]
oeps = load_field(-omega ** 2 * epsilon)
Pl = load_field(L.diagonal())
Pr = load_field(R.diagonal())
if mu is None:
invm = load_field(numpy.array([]))
else:
invm = load_field(1 / numpy.asarray(mu))
mu = numpy.asarray(mu)
invm = load_field(1 / mu)
if pec is None:
gpec = load_field(numpy.array([]), dtype=numpy.int8)
else:
gpec = load_field(numpy.asarray(pec, dtype=bool), dtype=numpy.int8)
gpec = load_field(pec.astype(bool), dtype=numpy.int8)
if pmc is None:
gpmc = load_field(numpy.array([]), dtype=numpy.int8)
else:
gpmc = load_field(numpy.asarray(pmc, dtype=bool), dtype=numpy.int8)
gpmc = load_field(pmc.astype(bool), dtype=numpy.int8)
#
# Generate OpenCL kernels
#
has_mu, has_pec, has_pmc = (qq is not None for qq in (mu, pec, pmc))
'''
Generate OpenCL kernels
'''
has_mu, has_pec, has_pmc = [q is not None for q in (mu, pec, pmc)]
a_step_full = ops.create_a(context, shape, has_mu, has_pec, has_pmc)
xr_step = ops.create_xr_step(context)
@ -164,28 +163,23 @@ def cg_solver(
p_step = ops.create_p_step(context)
dot = ops.create_dot(context)
def a_step(
E: pyopencl.array.Array,
H: pyopencl.array.Array,
p: pyopencl.array.Array,
events: list[pyopencl.Event],
) -> list[pyopencl.Event]:
def a_step(E, H, p, events):
return a_step_full(E, H, p, inv_dxes, oeps, invm, gpec, gpmc, Pl, Pr, events)
#
# Start the solve
#
'''
Start the solve
'''
start_time2 = time.perf_counter()
_, err2 = rhoerr_step(r, [])
b_norm = numpy.sqrt(err2)
logging.debug(f'b_norm check: {b_norm}')
logging.debug('b_norm check: {}'.format(b_norm))
success = False
for k in range(max_iters):
do_print = (k % 100 == 0)
if do_print:
logger.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
logger.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
rho_prev = rho
e = xr_step(x, p, r, v, alpha, [])
@ -194,7 +188,7 @@ def cg_solver(
errs += [numpy.sqrt(err2) / b_norm]
if do_print:
logger.debug(f'err {errs[-1]}')
logger.debug('err {}'.format(errs[-1]))
if errs[-1] < err_threshold:
success = True
@ -205,30 +199,32 @@ def cg_solver(
alpha = rho / dot(p, v, e)
if k % 1000 == 0:
logger.info(f'iteration {k}')
logger.info('iteration {}'.format(k))
#
# Done solving
#
'''
Done solving
'''
time_elapsed = time.perf_counter() - start_time
# Undo preconditioners
x = ((Pl if adjoint else Pr) * x).get()
if adjoint:
x = (Pl * x).get()
else:
x = (Pr * x).get()
if success:
logger.info('Solve success')
else:
logger.warning('Solve failure')
logger.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
logger.debug(f'final error {errs[-1]}')
logger.debug(f'overhead {start_time2 - start_time} sec')
logger.info('{} iterations in {} sec: {} iterations/sec \
'.format(k, time_elapsed, k / time_elapsed))
logger.debug('final error {}'.format(errs[-1]))
logger.debug('overhead {} sec'.format(start_time2 - start_time))
A0 = meanas.fdfd.operators.e_full(omega, dxes, epsilon, mu).tocsr()
A0 = fdfd_tools.operators.e_full(omega, dxes, epsilon, mu).tocsr()
if adjoint:
# Remember we conjugated all the contents of A earlier
A0 = A0.T
residual = norm(A0 @ x - b) / norm(b)
logger.info(f'Post-everything residual: {residual}')
logger.info('Post-everything residual: {}'.format(norm(A0 @ x - b) / norm(b)))
return x

307
opencl_fdfd/ops.py
View File

@ -7,11 +7,10 @@ kernels for use by the other solvers.
See kernels/ for any of the .cl files loaded in this file.
"""
from collections.abc import Callable, Sequence
from typing import List, Callable
import logging
import numpy
from numpy.typing import ArrayLike
import jinja2
import pyopencl
@ -20,77 +19,61 @@ from pyopencl.elementwise import ElementwiseKernel
from pyopencl.reduction import ReductionKernel
from .csr import CSRMatrix
logger = logging.getLogger(__name__)
class FDFDError(Exception):
""" Custom error for opencl_fdfd """
pass
# Create jinja2 env on module load
jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
# Return type for the create_opname(...) functions
operation = Callable[..., list[pyopencl.Event]]
operation = Callable[..., List[pyopencl.Event]]
def type_to_C(
float_type: type[numpy.floating | numpy.complexfloating],
) -> str:
def type_to_C(float_type: numpy.float32 or numpy.float64) -> str:
"""
Returns a string corresponding to the C equivalent of a numpy type.
Args:
float_type: numpy type: float32, float64, complex64, complex128
Returns:
string containing the corresponding C type (eg. 'double')
:param float_type: numpy type: float32, float64, complex64, complex128
:return: string containing the corresponding C type (eg. 'double')
"""
types = {
numpy.float16: 'half',
numpy.float32: 'float',
numpy.float64: 'double',
numpy.complex64: 'cfloat_t',
numpy.complex128: 'cdouble_t',
}
if float_type not in types:
raise FDFDError('Unsupported type')
raise Exception('Unsupported type')
return types[float_type]
# Type names
ctype = type_to_C(numpy.complex128)
ctype_bare = 'cdouble'
# Preamble for all OpenCL code
preamble = f'''
preamble = '''
#define PYOPENCL_DEFINE_CDOUBLE
#include <pyopencl-complex.h>
//Defines to clean up operation and type names
#define ctype {ctype_bare}_t
#define zero {ctype_bare}_new(0.0, 0.0)
#define add {ctype_bare}_add
#define sub {ctype_bare}_sub
#define mul {ctype_bare}_mul
'''
#define ctype {ctype}_t
#define zero {ctype}_new(0.0, 0.0)
#define add {ctype}_add
#define sub {ctype}_sub
#define mul {ctype}_mul
'''.format(ctype=ctype_bare)
def ptrs(*args: str) -> list[str]:
def ptrs(*args: str) -> List[str]:
return [ctype + ' *' + s for s in args]
def create_a(
context: pyopencl.Context,
shape: ArrayLike,
mu: bool = False,
pec: bool = False,
pmc: bool = False,
) -> operation:
def create_a(context: pyopencl.Context,
shape: numpy.ndarray,
mu: bool = False,
pec: bool = False,
pmc: bool = False,
) -> operation:
"""
Return a function which performs (A @ p), where A is the FDFD wave equation for E-field.
@ -111,15 +94,12 @@ def create_a(
and returns a list of pyopencl.Event.
Args:
context: PyOpenCL context
shape: Dimensions of the E-field
mu: False iff (mu == 1) everywhere
pec: False iff no PEC anywhere
pmc: False iff no PMC anywhere
Returns:
Function for computing (A @ p)
:param context: PyOpenCL context
:param shape: Dimensions of the E-field
:param mu: False iff (mu == 1) everywhere
:param pec: False iff no PEC anywhere
:param pmc: False iff no PMC anywhere
:return: Function for computing (A @ p)
"""
common_source = jinja_env.get_template('common.cl').render(shape=shape)
@ -129,71 +109,49 @@ def create_a(
des = [ctype + ' *inv_de' + a for a in 'xyz']
dhs = [ctype + ' *inv_dh' + a for a in 'xyz']
#
# Convert p to initial E (ie, apply right preconditioner and PEC)
#
'''
Convert p to initial E (ie, apply right preconditioner and PEC)
'''
p2e_source = jinja_env.get_template('p2e.cl').render(pec=pec)
P2E_kernel = ElementwiseKernel(
context,
name='P2E',
preamble=preamble,
operation=p2e_source,
arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg),
)
P2E_kernel = ElementwiseKernel(context,
name='P2E',
preamble=preamble,
operation=p2e_source,
arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg))
#
# Calculate intermediate H from intermediate E
#
e2h_source = jinja_env.get_template('e2h.cl').render(
mu=mu,
pmc=pmc,
common_cl=common_source,
)
E2H_kernel = ElementwiseKernel(
context,
name='E2H',
preamble=preamble,
operation=e2h_source,
arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des),
)
'''
Calculate intermediate H from intermediate E
'''
e2h_source = jinja_env.get_template('e2h.cl').render(mu=mu,
pmc=pmc,
common_cl=common_source)
E2H_kernel = ElementwiseKernel(context,
name='E2H',
preamble=preamble,
operation=e2h_source,
arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des))
#
# Calculate final E (including left preconditioner)
#
h2e_source = jinja_env.get_template('h2e.cl').render(
pec=pec,
common_cl=common_source,
)
H2E_kernel = ElementwiseKernel(
context,
name='H2E',
preamble=preamble,
operation=h2e_source,
arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs),
)
'''
Calculate final E (including left preconditioner)
'''
h2e_source = jinja_env.get_template('h2e.cl').render(pec=pec,
common_cl=common_source)
H2E_kernel = ElementwiseKernel(context,
name='H2E',
preamble=preamble,
operation=h2e_source,
arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs))
def spmv(
E: pyopencl.array.Array,
H: pyopencl.array.Array,
p: pyopencl.array.Array,
idxes: Sequence[Sequence[pyopencl.array.Array]],
oeps: pyopencl.array.Array,
inv_mu: pyopencl.array.Array | None,
pec: pyopencl.array.Array | None,
pmc: pyopencl.array.Array | None,
Pl: pyopencl.array.Array,
Pr: pyopencl.array.Array,
e: list[pyopencl.Event],
) -> list[pyopencl.Event]:
def spmv(E, H, p, idxes, oeps, inv_mu, pec, pmc, Pl, Pr, e):
e2 = P2E_kernel(E, p, Pr, pec, wait_for=e)
e2 = E2H_kernel(E, H, inv_mu, pmc, *idxes[0], wait_for=[e2])
e2 = H2E_kernel(E, H, oeps, Pl, pec, *idxes[1], wait_for=[e2])
return [e2]
logger.debug(f'Preamble: \n{preamble}')
logger.debug(f'p2e: \n{p2e_source}')
logger.debug(f'e2h: \n{e2h_source}')
logger.debug(f'h2e: \n{h2e_source}')
logger.debug('Preamble: \n{}'.format(preamble))
logger.debug('p2e: \n{}'.format(p2e_source))
logger.debug('e2h: \n{}'.format(e2h_source))
logger.debug('h2e: \n{}'.format(h2e_source))
return spmv
@ -209,11 +167,8 @@ def create_xr_step(context: pyopencl.Context) -> operation:
after waiting for all in the list e
and returns a list of pyopencl.Event
Args:
context: PyOpenCL context
Returns:
Function for performing x and r updates
:param context: PyOpenCL context
:return: Function for performing x and r updates
"""
update_xr_source = '''
x[i] = add(x[i], mul(alpha, p[i]));
@ -222,28 +177,19 @@ def create_xr_step(context: pyopencl.Context) -> operation:
xr_args = ', '.join(ptrs('x', 'p', 'r', 'v') + [ctype + ' alpha'])
xr_kernel = ElementwiseKernel(
context,
name='XR',
preamble=preamble,
operation=update_xr_source,
arguments=xr_args,
)
xr_kernel = ElementwiseKernel(context,
name='XR',
preamble=preamble,
operation=update_xr_source,
arguments=xr_args)
def xr_update(
x: pyopencl.array.Array,
p: pyopencl.array.Array,
r: pyopencl.array.Array,
v: pyopencl.array.Array,
alpha: complex,
e: list[pyopencl.Event],
) -> list[pyopencl.Event]:
def xr_update(x, p, r, v, alpha, e):
return [xr_kernel(x, p, r, v, alpha, wait_for=e)]
return xr_update
def create_rhoerr_step(context: pyopencl.Context) -> Callable[..., tuple[complex, complex]]:
def create_rhoerr_step(context: pyopencl.Context) -> operation:
"""
Return a function
ri_update(r, e)
@ -254,11 +200,8 @@ def create_rhoerr_step(context: pyopencl.Context) -> Callable[..., tuple[complex
after waiting for all pyopencl.Event in the list e
and returns a list of pyopencl.Event
Args:
context: PyOpenCL context
Returns:
Function for performing x and r updates
:param context: PyOpenCL context
:return: Function for performing x and r updates
"""
update_ri_source = '''
@ -270,20 +213,18 @@ def create_rhoerr_step(context: pyopencl.Context) -> Callable[..., tuple[complex
# Use a vector type (double3) to make the reduction simpler
ri_dtype = pyopencl.array.vec.double3
ri_kernel = ReductionKernel(
context,
name='RHOERR',
preamble=preamble,
dtype_out=ri_dtype,
neutral='(double3)(0.0, 0.0, 0.0)',
map_expr=update_ri_source,
reduce_expr='a+b',
arguments=ctype + ' *r',
)
ri_kernel = ReductionKernel(context,
name='RHOERR',
preamble=preamble,
dtype_out=ri_dtype,
neutral='(double3)(0.0, 0.0, 0.0)',
map_expr=update_ri_source,
reduce_expr='a+b',
arguments=ctype + ' *r')
def ri_update(r: pyopencl.array.Array, e: list[pyopencl.Event]) -> tuple[complex, complex]:
def ri_update(r, e):
g = ri_kernel(r, wait_for=e).astype(ri_dtype).get()
rr, ri, ii = (g[qq] for qq in 'xyz')
rr, ri, ii = [g[q] for q in 'xyz']
rho = rr + 2j * ri - ii
err = rr + ii
return rho, err
@ -301,66 +242,48 @@ def create_p_step(context: pyopencl.Context) -> operation:
after waiting for all pyopencl.Event in the list e
and returns a list of pyopencl.Event
Args:
context: PyOpenCL context
Returns:
Function for performing the p update
:param context: PyOpenCL context
:return: Function for performing the p update
"""
update_p_source = '''
p[i] = add(r[i], mul(beta, p[i]));
'''
p_args = ptrs('p', 'r') + [ctype + ' beta']
p_kernel = ElementwiseKernel(
context,
name='P',
preamble=preamble,
operation=update_p_source,
arguments=', '.join(p_args),
)
p_kernel = ElementwiseKernel(context,
name='P',
preamble=preamble,
operation=update_p_source,
arguments=', '.join(p_args))
def p_update(
p: pyopencl.array.Array,
r: pyopencl.array.Array,
beta: complex,
e: list[pyopencl.Event]) -> list[pyopencl.Event]:
def p_update(p, r, beta, e):
return [p_kernel(p, r, beta, wait_for=e)]
return p_update
def create_dot(context: pyopencl.Context) -> Callable[..., complex]:
def create_dot(context: pyopencl.Context) -> operation:
"""
Return a function for performing the dot product
p @ v
with the signature
dot(p, v, e) -> complex
dot(p, v, e) -> float
Args:
context: PyOpenCL context
Returns:
Function for performing the dot product
:param context: PyOpenCL context
:return: Function for performing the dot product
"""
dot_dtype = numpy.complex128
dot_kernel = ReductionKernel(
context,
name='dot',
preamble=preamble,
dtype_out=dot_dtype,
neutral='zero',
map_expr='mul(p[i], v[i])',
reduce_expr='add(a, b)',
arguments=ptrs('p', 'v'),
)
dot_kernel = ReductionKernel(context,
name='dot',
preamble=preamble,
dtype_out=dot_dtype,
neutral='zero',
map_expr='mul(p[i], v[i])',
reduce_expr='add(a, b)',
arguments=ptrs('p', 'v'))
def dot(
p: pyopencl.array.Array,
v: pyopencl.array.Array,
e: list[pyopencl.Event],
) -> complex:
def dot(p, v, e):
g = dot_kernel(p, v, wait_for=e)
return g.get()
@ -381,11 +304,8 @@ def create_a_csr(context: pyopencl.Context) -> operation:
The function waits on all the pyopencl.Event in e before running, and returns
a list of pyopencl.Event.
Args:
context: PyOpenCL context
Returns:
Function for sparse (M @ v) operation where M is in CSR format
:param context: PyOpenCL context
:return: Function for sparse (M @ v) operation where M is in CSR format
"""
spmv_source = '''
int start = m_row_ptr[i];
@ -406,20 +326,13 @@ def create_a_csr(context: pyopencl.Context) -> operation:
m_args = 'int *m_row_ptr, int *m_col_ind, ' + ctype + ' *m_data'
v_in_args = ctype + ' *v_in'
spmv_kernel = ElementwiseKernel(
context,
name='csr_spmv',
preamble=preamble,
operation=spmv_source,
arguments=', '.join((v_out_args, m_args, v_in_args)),
)
spmv_kernel = ElementwiseKernel(context,
name='csr_spmv',
preamble=preamble,
operation=spmv_source,
arguments=', '.join((v_out_args, m_args, v_in_args)))
def spmv(
v_out: pyopencl.array.Array,
m: CSRMatrix,
v_in: pyopencl.array.Array,
e: list[pyopencl.Event],
) -> list[pyopencl.Event]:
def spmv(v_out, m, v_in, e):
return [spmv_kernel(v_out, m.row_ptr, m.col_ind, m.data, v_in, wait_for=e)]
return spmv

0
opencl_fdfd/py.typed
View File

96
pyproject.toml
View File

@ -1,96 +0,0 @@
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "opencl_fdfd"
description = "OpenCL FDFD solver"
readme = "README.md"
license = { file = "LICENSE.md" }
authors = [
{ name="Jan Petykiewicz", email="jan@mpxd.net" },
]
homepage = "https://mpxd.net/code/jan/opencl_fdfd"
repository = "https://mpxd.net/code/jan/opencl_fdfd"
keywords = [
"FDFD",
"finite",
"difference",
"frequency",
"domain",
"simulation",
"optics",
"electromagnetic",
"dielectric",
"PML",
"solver",
"FDTD",
]
classifiers = [
"Programming Language :: Python :: 3",
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Manufacturing",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU Affero General Public License v3",
"Topic :: Scientific/Engineering",
]
requires-python = ">=3.11"
dynamic = ["version"]
dependencies = [
"numpy>=1.26",
"pyopencl",
"jinja2",
"meanas>=0.5",
]
[tool.hatch.version]
path = "opencl_fdfd/__init__.py"
[tool.ruff]
exclude = [
".git",
"dist",
]
line-length = 145
indent-width = 4
lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
lint.select = [
"NPY", "E", "F", "W", "B", "ANN", "UP", "SLOT", "SIM", "LOG",
"C4", "ISC", "PIE", "PT", "RET", "TCH", "PTH", "INT",
"ARG", "PL", "R", "TRY",
"G010", "G101", "G201", "G202",
"Q002", "Q003", "Q004",
]
lint.ignore = [
#"ANN001", # No annotation
"ANN002", # *args
"ANN003", # **kwargs
"ANN401", # Any
"ANN101", # self: Self
"SIM108", # single-line if / else assignment
"RET504", # x=y+z; return x
"PIE790", # unnecessary pass
"ISC003", # non-implicit string concatenation
"C408", # dict(x=y) instead of {'x': y}
"PLR09", # Too many xxx
"PLR2004", # magic number
"PLC0414", # import x as x
"TRY003", # Long exception message
]
[[tool.mypy.overrides]]
module = [
"scipy",
"scipy.optimize",
"scipy.linalg",
"scipy.sparse",
"scipy.sparse.linalg",
"pyopencl",
"pyopencl.array",
"pyopencl.elementwise",
"pyopencl.reduction",
]
ignore_missing_imports = true

30
setup.py Normal file
View File

@ -0,0 +1,30 @@
#!/usr/bin/env python3
from setuptools import setup, find_packages
import opencl_fdfd
with open('README.md', 'r') as f:
long_description = f.read()
setup(name='opencl_fdfd',
version=opencl_fdfd.version,
description='OpenCL FDFD solver',
long_description=long_description,
long_description_content_type='text/markdown',
author='Jan Petykiewicz',
author_email='anewusername@gmail.com',
url='https://mpxd.net/code/jan/opencl_fdfd',
packages=find_packages(),
package_data={
'opencl_fdfd': ['kernels/*']
},
install_requires=[
'numpy',
'pyopencl',
'jinja2',
'fdfd_tools>=0.3',
],
extras_require={
},
)