Use python3 for setup

README.md

@@ -38,12 +38,12 @@ generalization to multiple GPUs should be pretty straightforward
 * numpy
 * pyopencl
 * jinja2
-* [fdfd_tools](https://mpxd.net/gogs/jan/fdfd_tools) (>=0.2)
+* [fdfd_tools](https://mpxd.net/code/jan/fdfd_tools) (>=0.2)
 
 
 Install with pip, via git:
 ```bash
-pip install git+https://mpxd.net/gogs/jan/opencl_fdfd.git@release
+pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release
 ```
 
 

opencl_fdfd/__init__.py

@@ -31,7 +31,7 @@
 
 
   Dependencies:
-    - fdfd_tools    ( https://mpxd.net/gogs/jan/fdfd_tools )
+    - fdfd_tools    ( https://mpxd.net/code/jan/fdfd_tools )
     - numpy
     - pyopencl
     - jinja2
@@ -41,3 +41,4 @@ from .main import cg_solver
 
 __author__ = 'Jan Petykiewicz'
 
+version = '0.3'

opencl_fdfd/csr.py

@@ -16,6 +16,7 @@ satisfy the constraints for the 'conjugate gradient' algorithm
 
 from typing import Dict, Any
 import time
+import logging
 
 import numpy
 from numpy.linalg import norm
@@ -27,6 +28,11 @@ import fdfd_tools.solvers
 from . import ops
 
 
+__author__ = 'Jan Petykiewicz'
+
+logger = logging.getLogger(__name__)
+
+
 class CSRMatrix(object):
     """
     Matrix stored in Compressed Sparse Row format, in GPU RAM.
@@ -49,7 +55,6 @@ def cg(A: 'scipy.sparse.csr_matrix',
        err_threshold: float = 1e-6,
        context: pyopencl.Context = None,
        queue: pyopencl.CommandQueue = None,
-       verbose: bool = False,
        ) -> numpy.ndarray:
     """
     General conjugate-gradient solver for sparse matrices, where A @ x = b.
@@ -60,7 +65,6 @@ def cg(A: 'scipy.sparse.csr_matrix',
     :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.
-    :param verbose: Whether to print statistics to screen.
     :return: Solution vector x; returned even if solve doesn't converge.
     """
 
@@ -102,13 +106,11 @@ def cg(A: 'scipy.sparse.csr_matrix',
 
     _, err2 = rhoerr_step(r, [])
     b_norm = numpy.sqrt(err2)
-    if verbose:
-        print('b_norm check: ', b_norm)
+    logging.debug('b_norm check: ', b_norm)
 
     success = False
     for k in range(max_iters):
-        if verbose:
-            print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
+        logging.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
 
         rho_prev = rho
         e = xr_step(x, p, r, v, alpha, [])
@@ -116,8 +118,7 @@ def cg(A: 'scipy.sparse.csr_matrix',
 
         errs += [numpy.sqrt(err2) / b_norm]
 
-        if verbose:
-            print('err', errs[-1])
+        logging.debug('err {}'.format(errs[-1]))
 
         if errs[-1] < err_threshold:
             success = True
@@ -128,7 +129,7 @@ def cg(A: 'scipy.sparse.csr_matrix',
         alpha = rho / dot(p, v, e)
 
         if verbose and k % 1000 == 0:
-            print(k)
+            logging.info('iteration {}'.format(k))
 
     '''
     Done solving
@@ -137,17 +138,16 @@ def cg(A: 'scipy.sparse.csr_matrix',
 
     x = x.get()
 
-    if verbose:
-        if success:
-            print('Success', end='')
-        else:
-            print('Failure', end=', ')
-        print(', {} iterations in {} sec: {} iterations/sec \
-                      '.format(k, time_elapsed, k / time_elapsed))
-        print('final error', errs[-1])
-        print('overhead {} sec'.format(start_time2 - start_time))
+    if success:
+        logging.info('Solve success')
+    else:
+        logging.warning('Solve failure')
+    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))
 
-        print('Final residual:', norm(A @ x - b) / norm(b))
+    logging.info('Final residual: {}'.format(norm(A @ x - b) / norm(b)))
     return x
 
 

opencl_fdfd/kernels/e2h.cl

@@ -31,7 +31,7 @@ __global char *pmc_z = pmc + ZZ;
 
 
 //Update H components; set them to 0 if PMC is enabled at that location.
-//Mu division and PMC conditional are only included if {{mu}} and {{pmc}} are true
+//Mu division and PMC conditional are only included if {mu} and {pmc} are true
 {% if pmc -%}
 if (pmc_x[i] != 0) {
     Hx[i] = zero;
@@ -42,9 +42,9 @@ if (pmc_x[i] != 0) {
     ctype Dyz = mul(sub(Ey[i + pz], Ey[i]), inv_dez[z]);
     ctype x_curl = sub(Dzy, Dyz);
 
-    {%- if mu -%}
+    {%- if mu %}
     Hx[i] = mul(inv_mu_x[i], x_curl);
-    {%- else -%}
+    {%- else %}
     Hx[i] = x_curl;
     {%- endif %}
 }
@@ -59,9 +59,9 @@ if (pmc_y[i] != 0) {
     ctype Dzx = mul(sub(Ez[i + px], Ez[i]), inv_dex[x]);
     ctype y_curl = sub(Dxz, Dzx);
 
-    {%- if mu -%}
+    {%- if mu %}
     Hy[i] = mul(inv_mu_y[i], y_curl);
-    {%- else -%}
+    {%- else %}
     Hy[i] = y_curl;
     {%- endif %}
 }
@@ -76,9 +76,9 @@ if (pmc_z[i] != 0) {
     ctype Dxy = mul(sub(Ex[i + py], Ex[i]), inv_dey[y]);
     ctype z_curl = sub(Dyx, Dxy);
 
-    {%- if mu -%}
+    {%- if mu %}
     Hz[i] = mul(inv_mu_z[i], z_curl);
-    {%- else -%}
+    {%- else %}
     Hz[i] = z_curl;
     {%- endif %}
 }

opencl_fdfd/main.py

@@ -8,6 +8,7 @@ a matrix).
 
 from typing import List
 import time
+import logging
 
 import numpy
 from numpy.linalg import norm
@@ -18,8 +19,11 @@ import fdfd_tools.operators
 
 from . import ops
 
+
 __author__ = 'Jan Petykiewicz'
 
+logger = logging.getLogger(__name__)
+
 
 def cg_solver(omega: complex,
               dxes: List[List[numpy.ndarray]],
@@ -32,7 +36,6 @@ def cg_solver(omega: complex,
               max_iters: int = 40000,
               err_threshold: float = 1e-6,
               context: pyopencl.Context = None,
-              verbose: bool = False,
               ) -> numpy.ndarray:
     """
     OpenCL FDFD solver using the iterative conjugate gradient (cg) method
@@ -57,7 +60,6 @@ def cg_solver(omega: complex,
     :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.
-    :param verbose: If True, print progress to stdout. Default False.
     :return: E-field which solves the system. Returned even if we did not converge.
     """
 
@@ -171,12 +173,13 @@ def cg_solver(omega: complex,
 
     _, err2 = rhoerr_step(r, [])
     b_norm = numpy.sqrt(err2)
-    print('b_norm check: ', b_norm)
+    logging.debug('b_norm check: {}'.format(b_norm))
 
     success = False
     for k in range(max_iters):
-        if verbose:
-            print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
+        do_print = (k % 100 == 0)
+        if do_print:
+            logger.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
 
         rho_prev = rho
         e = xr_step(x, p, r, v, alpha, [])
@@ -184,8 +187,8 @@ def cg_solver(omega: complex,
 
         errs += [numpy.sqrt(err2) / b_norm]
 
-        if verbose:
-            print('err', errs[-1])
+        if do_print:
+            logger.debug('err {}'.format(errs[-1]))
 
         if errs[-1] < err_threshold:
             success = True
@@ -196,7 +199,7 @@ def cg_solver(omega: complex,
         alpha = rho / dot(p, v, e)
 
         if k % 1000 == 0:
-            print(k)
+            logger.info('iteration {}'.format(k))
 
     '''
     Done solving
@@ -210,18 +213,18 @@ def cg_solver(omega: complex,
         x = (Pr * x).get()
 
     if success:
-        print('Success', end='')
+        logger.info('Solve success')
     else:
-        print('Failure', end=', ')
-    print(', {} iterations in {} sec: {} iterations/sec \
+        logger.warning('Solve failure')
+    logger.info('{} iterations in {} sec: {} iterations/sec \
                   '.format(k, time_elapsed, k / time_elapsed))
-    print('final error', errs[-1])
-    print('overhead {} sec'.format(start_time2 - start_time))
+    logger.debug('final error {}'.format(errs[-1]))
+    logger.debug('overhead {} sec'.format(start_time2 - start_time))
 
     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
-    print('Post-everything residual:', norm(A0 @ x - b) / norm(b))
+    logger.info('Post-everything residual: {}'.format(norm(A0 @ x - b) / norm(b)))
     return x
 

opencl_fdfd/ops.py

@@ -8,6 +8,7 @@ See kernels/ for any of the .cl files loaded in this file.
 """
 
 from typing import List, Callable
+import logging
 
 import numpy
 import jinja2
@@ -18,6 +19,8 @@ from pyopencl.elementwise import ElementwiseKernel
 from pyopencl.reduction import ReductionKernel
 
 
+logger = logging.getLogger(__name__)
+
 # Create jinja2 env on module load
 jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
 
@@ -145,6 +148,11 @@ def create_a(context: pyopencl.Context,
         e2 = H2E_kernel(E, H, oeps, Pl, pec, *idxes[1], wait_for=[e2])
         return [e2]
 
+    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
 
 

setup.py

@@ -1,14 +1,23 @@
-#!/usr/bin/env python
+#!/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='0.3',
-      description='Opencl FDFD solver',
+      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/gogs/jan/opencl_fdfd',
+      url='https://mpxd.net/code/jan/opencl_fdfd',
       packages=find_packages(),
+      package_data={
+          'opencl_fdfd': ['kernels/*']
+      },
       install_requires=[
             'numpy',
             'pyopencl',