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diff --git a/silx/opencl/test/test_doubleword.py b/silx/opencl/test/test_doubleword.py
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-#!/usr/bin/env python
-# coding: utf-8
-#
-# Project: The silx project
-# https://github.com/silx-kit/silx
-#
-# Copyright (C) 2021-2021 European Synchrotron Radiation Facility, Grenoble, France
-#
-# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-# THE SOFTWARE.
-
-"test suite for OpenCL code"
-
-__author__ = "Jérôme Kieffer"
-__contact__ = "Jerome.Kieffer@ESRF.eu"
-__license__ = "MIT"
-__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "31/05/2021"
-
-import unittest
-import numpy
-import logging
-import platform
-
-logger = logging.getLogger(__name__)
-try:
- import pyopencl
-except ImportError as error:
- logger.warning("OpenCL module (pyopencl) is not present, skip tests. %s.", error)
- pyopencl = None
-
-from .. import ocl
-if ocl is not None:
- from ..utils import read_cl_file
- from .. import pyopencl
- import pyopencl.array
- from pyopencl.elementwise import ElementwiseKernel
-from ...test.utils import test_options
-
-EPS32 = numpy.finfo("float32").eps
-EPS64 = numpy.finfo("float64").eps
-
-
-class TestDoubleWord(unittest.TestCase):
- """
- Test the kernels for compensated math in OpenCL
- """
-
- @classmethod
- def setUpClass(cls):
- if not test_options.WITH_OPENCL_TEST:
- raise unittest.SkipTest("User request to skip OpenCL tests")
- if pyopencl is None or ocl is None:
- raise unittest.SkipTest("OpenCL module (pyopencl) is not present or no device available")
-
- cls.ctx = ocl.create_context(devicetype="GPU")
- cls.queue = pyopencl.CommandQueue(cls.ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
-
- # this is running 32 bits OpenCL woth POCL
- if (platform.machine() in ("i386", "i686", "x86_64") and (tuple.__itemsize__ == 4) and
- cls.ctx.devices[0].platform.name == 'Portable Computing Language'):
- cls.args = "-DX87_VOLATILE=volatile"
- else:
- cls.args = ""
- size = 1024
- cls.a = 1.0 + numpy.random.random(size)
- cls.b = 1.0 + numpy.random.random(size)
- cls.ah = cls.a.astype(numpy.float32)
- cls.bh = cls.b.astype(numpy.float32)
- cls.al = (cls.a - cls.ah).astype(numpy.float32)
- cls.bl = (cls.b - cls.bh).astype(numpy.float32)
- cls.doubleword = read_cl_file("doubleword.cl")
-
- @classmethod
- def tearDownClass(cls):
- cls.queue = None
- cls.ctx = None
- cls.a = cls.al = cls.ah = None
- cls.b = cls.bl = cls.bh = None
- cls.doubleword = None
-
- def test_fast_sum2(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *a, float *b, float *res_h, float *res_l",
- "float2 tmp = fast_fp_plus_fp(a[i], b[i]); res_h[i] = tmp.s0; res_l[i] = tmp.s1",
- preamble=self.doubleword)
- a_g = pyopencl.array.to_device(self.queue, self.ah)
- b_g = pyopencl.array.to_device(self.queue, self.bl)
- res_l = pyopencl.array.empty_like(a_g)
- res_h = pyopencl.array.empty_like(a_g)
- test_kernel(a_g, b_g, res_h, res_l)
- self.assertEqual(abs(self.ah + self.bl - res_h.get()).max(), 0, "Major matches")
- self.assertGreater(abs(self.ah.astype(numpy.float64) + self.bl - res_h.get()).max(), 0, "Exact mismatches")
- self.assertEqual(abs(self.ah.astype(numpy.float64) + self.bl - (res_h.get().astype(numpy.float64) + res_l.get())).max(), 0, "Exact matches")
-
- def test_sum2(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *a, float *b, float *res_h, float *res_l",
- "float2 tmp = fp_plus_fp(a[i],b[i]); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- a_g = pyopencl.array.to_device(self.queue, self.ah)
- b_g = pyopencl.array.to_device(self.queue, self.bh)
- res_l = pyopencl.array.empty_like(a_g)
- res_h = pyopencl.array.empty_like(a_g)
- test_kernel(a_g, b_g, res_h, res_l)
- self.assertEqual(abs(self.ah + self.bh - res_h.get()).max(), 0, "Major matches")
- self.assertGreater(abs(self.ah.astype(numpy.float64) + self.bh - res_h.get()).max(), 0, "Exact mismatches")
- self.assertEqual(abs(self.ah.astype(numpy.float64) + self.bh - (res_h.get().astype(numpy.float64) + res_l.get())).max(), 0, "Exact matches")
-
- def test_prod2(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *a, float *b, float *res_h, float *res_l",
- "float2 tmp = fp_times_fp(a[i],b[i]); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- a_g = pyopencl.array.to_device(self.queue, self.ah)
- b_g = pyopencl.array.to_device(self.queue, self.bh)
- res_l = pyopencl.array.empty_like(a_g)
- res_h = pyopencl.array.empty_like(a_g)
- test_kernel(a_g, b_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertEqual(abs(self.ah * self.bh - res_m).max(), 0, "Major matches")
- self.assertGreater(abs(self.ah.astype(numpy.float64) * self.bh - res_m).max(), 0, "Exact mismatches")
- self.assertEqual(abs(self.ah.astype(numpy.float64) * self.bh - res).max(), 0, "Exact matches")
-
- def test_dw_plus_fp(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *ah, float *al, float *b, float *res_h, float *res_l",
- "float2 tmp = dw_plus_fp((float2)(ah[i], al[i]),b[i]); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- ah_g = pyopencl.array.to_device(self.queue, self.ah)
- al_g = pyopencl.array.to_device(self.queue, self.al)
- b_g = pyopencl.array.to_device(self.queue, self.bh)
- res_l = pyopencl.array.empty_like(b_g)
- res_h = pyopencl.array.empty_like(b_g)
- test_kernel(ah_g, al_g, b_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertLess(abs(self.a + self.bh - res_m).max(), EPS32, "Major matches")
- self.assertGreater(abs(self.a + self.bh - res_m).max(), EPS64, "Exact mismatches")
- self.assertLess(abs(self.ah.astype(numpy.float64) + self.al + self.bh - res).max(), 2 * EPS32 ** 2, "Exact matches")
-
- def test_dw_plus_dw(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *ah, float *al, float *bh, float *bl, float *res_h, float *res_l",
- "float2 tmp = dw_plus_dw((float2)(ah[i], al[i]),(float2)(bh[i], bl[i])); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- ah_g = pyopencl.array.to_device(self.queue, self.ah)
- al_g = pyopencl.array.to_device(self.queue, self.al)
- bh_g = pyopencl.array.to_device(self.queue, self.bh)
- bl_g = pyopencl.array.to_device(self.queue, self.bl)
- res_l = pyopencl.array.empty_like(bh_g)
- res_h = pyopencl.array.empty_like(bh_g)
- test_kernel(ah_g, al_g, bh_g, bl_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertLess(abs(self.a + self.b - res_m).max(), EPS32, "Major matches")
- self.assertGreater(abs(self.a + self.b - res_m).max(), EPS64, "Exact mismatches")
- self.assertLess(abs(self.a + self.b - res).max(), 3 * EPS32 ** 2, "Exact matches")
-
- def test_dw_times_fp(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *ah, float *al, float *b, float *res_h, float *res_l",
- "float2 tmp = dw_times_fp((float2)(ah[i], al[i]),b[i]); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- ah_g = pyopencl.array.to_device(self.queue, self.ah)
- al_g = pyopencl.array.to_device(self.queue, self.al)
- b_g = pyopencl.array.to_device(self.queue, self.bh)
- res_l = pyopencl.array.empty_like(b_g)
- res_h = pyopencl.array.empty_like(b_g)
- test_kernel(ah_g, al_g, b_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertLess(abs(self.a * self.bh - res_m).max(), EPS32, "Major matches")
- self.assertGreater(abs(self.a * self.bh - res_m).max(), EPS64, "Exact mismatches")
- self.assertLess(abs(self.a * self.bh - res).max(), 2 * EPS32 ** 2, "Exact matches")
-
- def test_dw_times_dw(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *ah, float *al, float *bh, float *bl, float *res_h, float *res_l",
- "float2 tmp = dw_times_dw((float2)(ah[i], al[i]),(float2)(bh[i], bl[i])); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- ah_g = pyopencl.array.to_device(self.queue, self.ah)
- al_g = pyopencl.array.to_device(self.queue, self.al)
- bh_g = pyopencl.array.to_device(self.queue, self.bh)
- bl_g = pyopencl.array.to_device(self.queue, self.bl)
- res_l = pyopencl.array.empty_like(bh_g)
- res_h = pyopencl.array.empty_like(bh_g)
- test_kernel(ah_g, al_g, bh_g, bl_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertLess(abs(self.a * self.b - res_m).max(), EPS32, "Major matches")
- self.assertGreater(abs(self.a * self.b - res_m).max(), EPS64, "Exact mismatches")
- self.assertLess(abs(self.a * self.b - res).max(), 5 * EPS32 ** 2, "Exact matches")
-
- def test_dw_div_fp(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *ah, float *al, float *b, float *res_h, float *res_l",
- "float2 tmp = dw_div_fp((float2)(ah[i], al[i]),b[i]); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- ah_g = pyopencl.array.to_device(self.queue, self.ah)
- al_g = pyopencl.array.to_device(self.queue, self.al)
- b_g = pyopencl.array.to_device(self.queue, self.bh)
- res_l = pyopencl.array.empty_like(b_g)
- res_h = pyopencl.array.empty_like(b_g)
- test_kernel(ah_g, al_g, b_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertLess(abs(self.a / self.bh - res_m).max(), EPS32, "Major matches")
- self.assertGreater(abs(self.a / self.bh - res_m).max(), EPS64, "Exact mismatches")
- self.assertLess(abs(self.a / self.bh - res).max(), 3 * EPS32 ** 2, "Exact matches")
-
- def test_dw_div_dw(self):
- test_kernel = ElementwiseKernel(self.ctx,
- "float *ah, float *al, float *bh, float *bl, float *res_h, float *res_l",
- "float2 tmp = dw_div_dw((float2)(ah[i], al[i]),(float2)(bh[i], bl[i])); res_h[i]=tmp.s0; res_l[i]=tmp.s1;",
- preamble=self.doubleword)
- ah_g = pyopencl.array.to_device(self.queue, self.ah)
- al_g = pyopencl.array.to_device(self.queue, self.al)
- bh_g = pyopencl.array.to_device(self.queue, self.bh)
- bl_g = pyopencl.array.to_device(self.queue, self.bl)
- res_l = pyopencl.array.empty_like(bh_g)
- res_h = pyopencl.array.empty_like(bh_g)
- test_kernel(ah_g, al_g, bh_g, bl_g, res_h, res_l)
- res_m = res_h.get()
- res = res_h.get().astype(numpy.float64) + res_l.get()
- self.assertLess(abs(self.a / self.b - res_m).max(), EPS32, "Major matches")
- self.assertGreater(abs(self.a / self.b - res_m).max(), EPS64, "Exact mismatches")
- self.assertLess(abs(self.a / self.b - res).max(), 6 * EPS32 ** 2, "Exact matches")
-
-
-def suite():
- testsuite = unittest.TestSuite()
- loader = unittest.defaultTestLoader.loadTestsFromTestCase
- testsuite.addTest(loader(TestDoubleWord))
- return testsuite
-
-
-if __name__ == '__main__':
- runner = unittest.TextTestRunner()
- runner.run(suite())
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