/usr/lib/python2.7/dist-packages/pyFAI/ocl_azim_lut.py is in pyfai 0.10.2-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 | # -*- coding: utf-8 -*-
#
# Project: Azimuthal integration
# https://github.com/kif/pyFAI
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
__author__ = "Jerome Kieffer"
__license__ = "GPLv3"
__date__ = "10/10/2014"
__copyright__ = "2012, ESRF, Grenoble"
__contact__ = "jerome.kieffer@esrf.fr"
import os, gc, logging
import threading
import hashlib
import numpy
from .opencl import ocl, pyopencl, allocate_cl_buffers, release_cl_buffers
from .splitBBoxLUT import HistoBBox1d
from .utils import concatenate_cl_kernel
if pyopencl:
mf = pyopencl.mem_flags
else:
raise ImportError("pyopencl is not installed")
try:
from .fastcrc import crc32
except:
from zlib import crc32
logger = logging.getLogger("pyFAI.ocl_azim_lut")
class OCL_LUT_Integrator(object):
BLOCK_SIZE = 16
def __init__(self, lut, image_size, devicetype="all",
platformid=None, deviceid=None,
checksum=None, profile=False):
"""
@param lut: array of int32 - float32 with shape (nbins, lut_size) with indexes and coefficients
@param image_size: Expected image size: image.shape.prod()
@param devicetype: can be "cpu","gpu","acc" or "all"
@param platformid: number of the platform as given by clinfo
@type platformid: int
@param deviceid: number of the device as given by clinfo
@type deviceid: int
@param checksum: pre - calculated checksum to prevent re - calculating it :)
@param profile: store profiling elements
"""
self._sem = threading.Semaphore()
self._lut = lut
self.nbytes = lut.nbytes
self.bins, self.lut_size = lut.shape
self.size = image_size
self.profile = profile
if not checksum:
checksum = crc32(self._lut)
self.on_device = {"lut":checksum, "dark":None, "flat":None, "polarization":None, "solidangle":None}
self._cl_kernel_args = {}
self._cl_mem = {}
self.events = []
if (platformid is None) and (deviceid is None):
res = ocl.select_device(devicetype)
if res:
platformid, deviceid = res
else:
logger.warning("No such devicetype %s"%devicetype)
platformid, deviceid = ocl.select_device()
elif platformid is None:
platformid = 0
elif deviceid is None:
deviceid = 0
self.platform = ocl.platforms[platformid]
self.device = self.platform.devices[deviceid]
self.device_type = self.device.type
self.BLOCK_SIZE = min(self.BLOCK_SIZE, self.device.max_work_group_size)
self.workgroup_size = self.BLOCK_SIZE, # Note this is a tuple
self.wdim_bins = (self.bins + self.BLOCK_SIZE - 1) & ~(self.BLOCK_SIZE - 1),
self.wdim_data = (self.size + self.BLOCK_SIZE - 1) & ~(self.BLOCK_SIZE - 1),
try:
self._ctx = pyopencl.Context(devices=[pyopencl.get_platforms()[platformid].get_devices()[deviceid]])
if self.profile:
self._queue = pyopencl.CommandQueue(self._ctx, properties=pyopencl.command_queue_properties.PROFILING_ENABLE)
else:
self._queue = pyopencl.CommandQueue(self._ctx)
self._allocate_buffers()
self._compile_kernels()
self._set_kernel_arguments()
except (pyopencl.MemoryError, pyopencl.LogicError) as error:
raise MemoryError(error)
if self.device_type == "CPU":
ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["lut"], lut)
else:
ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["lut"], lut.T.copy())
if self.profile: self.events.append(("copy LUT", ev))
def __del__(self):
"""
Destructor: release all buffers
"""
self._free_kernels()
self._free_buffers()
self._queue = None
self._ctx = None
gc.collect()
def _allocate_buffers(self):
"""
Allocate OpenCL buffers required for a specific configuration
Note that an OpenCL context also requires some memory, as well
as Event and other OpenCL functionalities which cannot and are
not taken into account here. The memory required by a context
varies depending on the device. Typical for GTX580 is 65Mb but
for a 9300m is ~15Mb In addition, a GPU will always have at
least 3-5Mb of memory in use. Unfortunately, OpenCL does NOT
have a built-in way to check the actual free memory on a
device, only the total memory.
"""
buffers = [
("lut", mf.READ_WRITE, [("bins", numpy.float32), ("lut_size", numpy.int32)], self.bins * self.lut_size), # noqa
("outData", mf.WRITE_ONLY, numpy.float32, self.bins),
("outCount", mf.WRITE_ONLY, numpy.float32, self.bins),
("outMerge", mf.WRITE_ONLY, numpy.float32, self.bins),
("image_raw", mf.READ_ONLY, numpy.float32, self.size),
("image", mf.READ_WRITE, numpy.float32, self.size),
("dark", mf.READ_ONLY, numpy.float32, self.size),
("flat", mf.READ_ONLY, numpy.float32, self.size),
("polarization", mf.READ_ONLY, numpy.float32, self.size),
("solidangle", mf.READ_ONLY, numpy.float32, self.size),
]
if self.size < self.BLOCK_SIZE:
raise RuntimeError("Fatal error in _allocate_buffers. size (%d) must be >= BLOCK_SIZE (%d)\n", self.size, self.BLOCK_SIZE)
self._cl_mem = allocate_cl_buffers(buffers, self.device, self._ctx)
def _free_buffers(self):
"""
free all memory allocated on the device
"""
self._cl_mem = release_cl_buffers(self._cl_mem)
def _compile_kernels(self, kernel_file=None):
"""
Call the OpenCL compiler
@param kernel_file: path to the kernel (by default use the one in the src directory)
"""
# concatenate all needed source files into a single openCL module
kernel_file = kernel_file or "ocl_azim_LUT.cl"
kernel_src = concatenate_cl_kernel(["preprocess.cl", kernel_file])
compile_options = "-D NBINS=%i -D NIMAGE=%i -D NLUT=%i -D ON_CPU=%i" % \
(self.bins, self.size, self.lut_size, int(self.device_type == "CPU"))
logger.info("Compiling file %s with options %s" % (kernel_file, compile_options))
try:
self._program = pyopencl.Program(self._ctx, kernel_src).build(options=compile_options)
except (pyopencl.MemoryError, pyopencl.LogicError) as error:
raise MemoryError(error)
def _free_kernels(self):
"""
free all kernels
"""
for kernel in self._cl_kernel_args:
self._cl_kernel_args[kernel] = []
self._program = None
def _set_kernel_arguments(self):
"""Tie arguments of OpenCL kernel-functions to the actual kernels
set_kernel_arguments() is a private method, called by configure().
It uses the dictionary _cl_kernel_args.
Note that by default, since TthRange is disabled, the integration kernels have tth_min_max tied to the tthRange argument slot.
When setRange is called it replaces that argument with tthRange low and upper bounds. When unsetRange is called, the argument slot
is reset to tth_min_max.
"""
self._cl_kernel_args["corrections"] = [self._cl_mem["image"], numpy.int32(0), self._cl_mem["dark"], numpy.int32(0), self._cl_mem["flat"], \
numpy.int32(0), self._cl_mem["solidangle"], numpy.int32(0), self._cl_mem["polarization"], \
numpy.int32(0), numpy.float32(0), numpy.float32(0)]
self._cl_kernel_args["lut_integrate"] = [self._cl_mem["image"], self._cl_mem["lut"], numpy.int32(0), numpy.float32(0), \
self._cl_mem["outData"], self._cl_mem["outCount"], self._cl_mem["outMerge"]]
self._cl_kernel_args["memset_out"] = [self._cl_mem[i] for i in ["outData", "outCount", "outMerge"]]
self._cl_kernel_args["u8_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]
self._cl_kernel_args["s8_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]
self._cl_kernel_args["u16_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]
self._cl_kernel_args["s16_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]
self._cl_kernel_args["u32_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]
self._cl_kernel_args["s32_to_float"] = [self._cl_mem[i] for i in ["image_raw", "image"]]
def integrate(self, data, dummy=None, delta_dummy=None, dark=None, flat=None, solidAngle=None, polarization=None,
dark_checksum=None, flat_checksum=None, solidAngle_checksum=None, polarization_checksum=None,
preprocess_only=False):
events = []
with self._sem:
if data.dtype == numpy.uint8:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))
cast_to_float = self._program.u8_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["u8_to_float"])
events += [("copy image", copy_image), ("cast", cast_to_float)]
elif data.dtype == numpy.int8:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))
cast_to_float = self._program.s8_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["s8_to_float"])
events += [("copy image", copy_image), ("cast", cast_to_float)]
elif data.dtype == numpy.uint16:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))
cast_to_float = self._program.u16_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["u16_to_float"])
events += [("copy image", copy_image), ("cast", cast_to_float)]
elif data.dtype == numpy.int16:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))
cast_to_float = self._program.s16_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["s16_to_float"])
events += [("copy image", copy_image), ("cast", cast_to_float)]
elif data.dtype == numpy.uint32:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))
cast_to_float = self._program.u32_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["u32_to_float"])
events += [("copy image", copy_image), ("cast", cast_to_float)]
elif data.dtype == numpy.int32:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image_raw"], numpy.ascontiguousarray(data))
cast_to_float = self._program.s32_to_float(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["s32_to_float"])
events += [("copy image", copy_image), ("cast", cast_to_float)]
else:
copy_image = pyopencl.enqueue_copy(self._queue, self._cl_mem["image"], numpy.ascontiguousarray(data, dtype=numpy.float32))
events += [("copy image", copy_image)]
memset = self._program.memset_out(self._queue, self.wdim_bins, self.workgroup_size, *self._cl_kernel_args["memset_out"])
events.append(("memset", memset))
if dummy is not None:
do_dummy = numpy.int32(1)
dummy = numpy.float32(dummy)
if delta_dummy == None:
delta_dummy = numpy.float32(0)
else:
delta_dummy = numpy.float32(abs(delta_dummy))
else:
do_dummy = numpy.int32(0)
dummy = numpy.float32(0)
delta_dummy = numpy.float32(0)
self._cl_kernel_args["corrections"][9] = do_dummy
self._cl_kernel_args["corrections"][10] = dummy
self._cl_kernel_args["corrections"][11] = delta_dummy
self._cl_kernel_args["lut_integrate"][2] = do_dummy
self._cl_kernel_args["lut_integrate"][3] = dummy
if dark is not None:
do_dark = numpy.int32(1)
if not dark_checksum:
dark_checksum = crc32(dark)
if dark_checksum != self.on_device["dark"]:
ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["dark"], numpy.ascontiguousarray(dark, dtype=numpy.float32))
events.append(("copy dark", ev))
self.on_device["dark"] = dark_checksum
else:
do_dark = numpy.int32(0)
self._cl_kernel_args["corrections"][1] = do_dark
if flat is not None:
do_flat = numpy.int32(1)
if not flat_checksum:
flat_checksum = crc32(flat)
if self.on_device["flat"] != flat_checksum:
ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["flat"], numpy.ascontiguousarray(flat, dtype=numpy.float32))
events.append(("copy flat", ev))
self.on_device["flat"] = flat_checksum
else:
do_flat = numpy.int32(0)
self._cl_kernel_args["corrections"][3] = do_flat
if solidAngle is not None:
do_solidAngle = numpy.int32(1)
if not solidAngle_checksum:
solidAngle_checksum = crc32(solidAngle)
if solidAngle_checksum != self.on_device["solidangle"]:
ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["solidangle"], numpy.ascontiguousarray(solidAngle, dtype=numpy.float32))
events.append(("copy solidangle", ev))
self.on_device["solidangle"] = solidAngle_checksum
else:
do_solidAngle = numpy.int32(0)
self._cl_kernel_args["corrections"][5] = do_solidAngle
if polarization is not None:
do_polarization = numpy.int32(1)
if not polarization_checksum:
polarization_checksum = crc32(polarization)
if polarization_checksum != self.on_device["polarization"]:
ev = pyopencl.enqueue_copy(self._queue, self._cl_mem["polarization"], numpy.ascontiguousarray(polarization, dtype=numpy.float32))
events.append(("copy polarization", ev))
self.on_device["polarization"] = polarization_checksum
else:
do_polarization = numpy.int32(0)
self._cl_kernel_args["corrections"][7] = do_polarization
copy_image.wait()
if do_dummy + do_polarization + do_solidAngle + do_flat + do_dark > 0:
ev = self._program.corrections(self._queue, self.wdim_data, self.workgroup_size, *self._cl_kernel_args["corrections"])
events.append(("corrections", ev))
if preprocess_only:
image = numpy.empty(data.shape, dtype=numpy.float32)
ev = pyopencl.enqueue_copy(self._queue, image, self._cl_mem["image"])
events.append(("copy D->H image", ev))
ev.wait()
return image
integrate = self._program.lut_integrate(self._queue, self.wdim_bins, self.workgroup_size, *self._cl_kernel_args["lut_integrate"])
events.append(("integrate", integrate))
outMerge = numpy.empty(self.bins, dtype=numpy.float32)
outData = numpy.empty(self.bins, dtype=numpy.float32)
outCount = numpy.empty(self.bins, dtype=numpy.float32)
ev = pyopencl.enqueue_copy(self._queue, outMerge, self._cl_mem["outMerge"])
events.append(("copy D->H outMerge", ev))
ev = pyopencl.enqueue_copy(self._queue, outData, self._cl_mem["outData"])
events.append(("copy D->H outData", ev))
ev = pyopencl.enqueue_copy(self._queue, outCount, self._cl_mem["outCount"])
events.append(("copy D->H outCount", ev))
ev.wait()
if self.profile:
self.events += events
return outMerge, outData, outCount
def log_profile(self):
"""
If we are in profiling mode, prints out all timing for every single OpenCL call
"""
t = 0.0
if self.profile:
for e in self.events:
if "__len__" in dir(e) and len(e) >= 2:
et = 1e-6 * (e[1].profile.end - e[1].profile.start)
print("%50s:\t%.3fms" % (e[0], et))
t += et
print("_"*80)
print("%50s:\t%.3fms" % ("Total execution time", t))
|