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Diffstat (limited to 'src/silx/opencl/processing.py')
| -rw-r--r-- | src/silx/opencl/processing.py | 447 |
1 files changed, 447 insertions, 0 deletions
diff --git a/src/silx/opencl/processing.py b/src/silx/opencl/processing.py new file mode 100644 index 0000000..8b81f7f --- /dev/null +++ b/src/silx/opencl/processing.py @@ -0,0 +1,447 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Project: S I L X project +# https://github.com/silx-kit/silx +# +# Copyright (C) 2012-2018 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. +# + +""" +Common OpenCL abstract base classe for different processing +""" + +__author__ = "Jerome Kieffer" +__contact__ = "Jerome.Kieffer@ESRF.eu" +__license__ = "MIT" +__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" +__date__ = "02/03/2021" +__status__ = "stable" + +import sys +import os +import logging +import gc +from collections import namedtuple, OrderedDict +import numpy +import threading +from .common import ocl, pyopencl, release_cl_buffers, query_kernel_info, allocate_texture, check_textures_availability +from .utils import concatenate_cl_kernel +import platform + +BufferDescription = namedtuple("BufferDescription", ["name", "size", "dtype", "flags"]) +EventDescription = namedtuple("EventDescription", ["name", "event"]) + +logger = logging.getLogger(__name__) + + +class KernelContainer(object): + """Those object holds a copy of all kernels accessible as attributes""" + + def __init__(self, program): + """Constructor of the class + + :param program: the OpenCL program as generated by PyOpenCL + """ + self._program = program + for kernel in program.all_kernels(): + self.__setattr__(kernel.function_name, kernel) + + def get_kernels(self): + "return the dictionary with all kernels" + return dict(item for item in self.__dict__.items() + if not item[0].startswith("_")) + + def get_kernel(self, name): + "get a kernel from its name" + logger.debug("KernelContainer.get_kernel(%s)", name) + return self.__dict__.get(name) + + def max_workgroup_size(self, kernel_name): + "Retrieve the compile time WORK_GROUP_SIZE for a given kernel" + if isinstance(kernel_name, pyopencl.Kernel): + kernel = kernel_name + else: + kernel = self.get_kernel(kernel_name) + + return query_kernel_info(self._program, kernel, "WORK_GROUP_SIZE") + + def min_workgroup_size(self, kernel_name): + "Retrieve the compile time PREFERRED_WORK_GROUP_SIZE_MULTIPLE for a given kernel" + if isinstance(kernel_name, pyopencl.Kernel): + kernel = kernel_name + else: + kernel = self.get_kernel(kernel_name) + + return query_kernel_info(self._program, kernel, "PREFERRED_WORK_GROUP_SIZE_MULTIPLE") + + +class OpenclProcessing(object): + """Abstract class for different types of OpenCL processing. + + This class provides: + * Generation of the context, queues, profiling mode + * Additional function to allocate/free all buffers declared as static attributes of the class + * Functions to compile kernels, cache them and clean them + * helper functions to clone the object + """ + # Example of how to create an output buffer of 10 floats + buffers = [BufferDescription("output", 10, numpy.float32, None), + ] + # list of kernel source files to be concatenated before compilation of the program + kernel_files = [] + + def __init__(self, ctx=None, devicetype="all", platformid=None, deviceid=None, + block_size=None, memory=None, profile=False): + """Constructor of the abstract OpenCL processing class + + :param ctx: actual working context, left to None for automatic + initialization from device type or platformid/deviceid + :param devicetype: type of device, can be "CPU", "GPU", "ACC" or "ALL" + :param platformid: integer with the platform_identifier, as given by clinfo + :param deviceid: Integer with the device identifier, as given by clinfo + :param block_size: preferred workgroup size, may vary depending on the + out come of the compilation + :param memory: minimum memory available on device + :param profile: switch on profiling to be able to profile at the kernel + level, store profiling elements (makes code slightly slower) + """ + self.sem = threading.Semaphore() + self._X87_VOLATILE = None + self.profile = None + self.events = [] # List with of EventDescription, kept for profiling + self.cl_mem = {} # dict with all buffer allocated + self.cl_program = None # The actual OpenCL program + self.cl_kernel_args = {} # dict with all kernel arguments + self.queue = None + if ctx: + self.ctx = ctx + else: + self.ctx = ocl.create_context(devicetype=devicetype, + platformid=platformid, deviceid=deviceid, + memory=memory) + device_name = self.ctx.devices[0].name.strip() + platform_name = self.ctx.devices[0].platform.name.strip() + platform = ocl.get_platform(platform_name) + self.device = platform.get_device(device_name) + self.cl_kernel_args = {} # dict with all kernel arguments + + self.set_profiling(profile) + self.block_size = block_size + self.program = None + self.kernels = None + + def check_textures_availability(self): + return check_textures_availability(self.ctx) + + def __del__(self): + """Destructor: release all buffers and programs + """ + try: + self.reset_log() + self.free_kernels() + self.free_buffers() + if self.queue is not None: + self.queue.finish() + except Exception as err: + logger.warning("%s: %s", type(err), err) + self.queue = None + self.device = None + self.ctx = None + gc.collect() + + def allocate_buffers(self, buffers=None, use_array=False): + """ + Allocate OpenCL buffers required for a specific configuration + + :param buffers: a list of BufferDescriptions, leave to None for + paramatrized buffers. + :param use_array: allocate memory as pyopencl.array.Array + instead of pyopencl.Buffer + + 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. + """ + if buffers is None: + buffers = self.buffers + + with self.sem: + mem = {} + + # check if enough memory is available on the device + ualloc = 0 + for buf in buffers: + ualloc += numpy.dtype(buf.dtype).itemsize * numpy.prod(buf.size) + logger.info("%.3fMB are needed on device: %s, which has %.3fMB", + ualloc / 1.0e6, self.device, self.device.memory / 1.0e6) + + if ualloc >= self.device.memory: + raise MemoryError("Fatal error in allocate_buffers. Not enough " + " device memory for buffers (%lu requested, %lu available)" + % (ualloc, self.device.memory)) + + # do the allocation + try: + if use_array: + for buf in buffers: + mem[buf.name] = pyopencl.array.empty(self.queue, buf.size, buf.dtype) + else: + for buf in buffers: + size = numpy.dtype(buf.dtype).itemsize * numpy.prod(buf.size) + mem[buf.name] = pyopencl.Buffer(self.ctx, buf.flags, int(size)) + except pyopencl.MemoryError as error: + release_cl_buffers(mem) + raise MemoryError(error) + + self.cl_mem.update(mem) + + def add_to_cl_mem(self, parrays): + """ + Add pyopencl.array, which are allocated by pyopencl, to self.cl_mem. + This should be used before calling allocate_buffers(). + + :param parrays: a dictionary of `pyopencl.array.Array` or `pyopencl.Buffer` + """ + mem = self.cl_mem + for name, parr in parrays.items(): + mem[name] = parr + self.cl_mem.update(mem) + + def check_workgroup_size(self, kernel_name): + "Calculate the maximum workgroup size from given kernel after compilation" + return self.kernels.max_workgroup_size(kernel_name) + + def free_buffers(self): + """free all device.memory allocated on the device + """ + with self.sem: + for key, buf in list(self.cl_mem.items()): + if buf is not None: + if isinstance(buf, pyopencl.array.Array): + try: + buf.data.release() + except pyopencl.LogicError: + logger.error("Error while freeing buffer %s", key) + else: + try: + buf.release() + except pyopencl.LogicError: + logger.error("Error while freeing buffer %s", key) + self.cl_mem[key] = None + + def compile_kernels(self, kernel_files=None, compile_options=None): + """Call the OpenCL compiler + + :param kernel_files: list of path to the kernel + (by default use the one declared in the class) + :param compile_options: string of compile options + """ + # concatenate all needed source files into a single openCL module + kernel_files = kernel_files or self.kernel_files + kernel_src = concatenate_cl_kernel(kernel_files) + + compile_options = compile_options or self.get_compiler_options() + logger.info("Compiling file %s with options %s", kernel_files, 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) + else: + self.kernels = KernelContainer(self.program) + + def free_kernels(self): + """Free all kernels + """ + for kernel in self.cl_kernel_args: + self.cl_kernel_args[kernel] = [] + self.kernels = None + self.program = None + + def set_profiling(self, value=True): + """Switch On/Off the profiling flag of the command queue to allow debugging + + :param value: set to True to enable profiling, or to False to disable it. + Without profiling, the processing is marginally faster + + Profiling information can then be retrieved with the 'log_profile' method + """ + if bool(value) != self.profile: + with self.sem: + self.profile = bool(value) + if self.queue is not None: + self.queue.finish() + if self.profile: + self.queue = pyopencl.CommandQueue(self.ctx, + properties=pyopencl.command_queue_properties.PROFILING_ENABLE) + else: + self.queue = pyopencl.CommandQueue(self.ctx) + + def profile_add(self, event, desc): + """ + Add an OpenCL event to the events lists, if profiling is enabled. + + :param event: silx.opencl.processing.EventDescription. + :param desc: event description + """ + if self.profile: + self.events.append(EventDescription(desc, event)) + + def allocate_texture(self, shape, hostbuf=None, support_1D=False): + return allocate_texture(self.ctx, shape, hostbuf=hostbuf, support_1D=support_1D) + + def transfer_to_texture(self, arr, tex_ref): + """ + Transfer an array to a texture. + + :param arr: Input array. Can be a numpy array or a pyopencl array. + :param tex_ref: texture reference (pyopencl._cl.Image). + """ + copy_args = [self.queue, tex_ref, arr] + shp = arr.shape + ndim = arr.ndim + if ndim == 1: + # pyopencl and OpenCL < 1.2 do not support image1d_t + # force 2D with one row in this case + # ~ ndim = 2 + shp = (1,) + shp + copy_kwargs = {"origin":(0,) * ndim, "region": shp[::-1]} + if not(isinstance(arr, numpy.ndarray)): # assuming pyopencl.array.Array + # D->D copy + copy_args[2] = arr.data + copy_kwargs["offset"] = 0 + ev = pyopencl.enqueue_copy(*copy_args, **copy_kwargs) + self.profile_add(ev, "Transfer to texture") + + def log_profile(self, stats=False): + """If we are in profiling mode, prints out all timing for every single OpenCL call + + :param stats: if True, prints the statistics on each kernel instead of all execution timings + :return: list of lines to print + """ + total_time = 0.0 + out = [""] + if stats: + stats = OrderedDict() + out.append(f"OpenCL kernel profiling statistics in milliseconds for: {self.__class__.__name__}") + out.append(f"{'Kernel name':>50} (count): min median max mean std") + else: + stats = None + out.append(f"Profiling info for OpenCL: {self.__class__.__name__}") + + if self.profile: + for e in self.events: + if "__len__" in dir(e) and len(e) >= 2: + name = e[0] + pr = e[1].profile + t0 = pr.start + t1 = pr.end + et = 1e-6 * (t1 - t0) + total_time += et + if stats is None: + out.append(f"{name:>50} : {et:.3f}ms") + else: + if name in stats: + stats[name].append(et) + else: + stats[name] = [et] + if stats is not None: + for k, v in stats.items(): + n = numpy.array(v) + out.append(f"{k:>50} ({len(v):5}): {n.min():8.3f} {numpy.median(n):8.3f} {n.max():8.3f} {n.mean():8.3f} {n.std():8.3f}") + out.append("_" * 80) + out.append(f"{'Total OpenCL execution time':>50} : {total_time:.3f}ms") + + logger.info(os.linesep.join(out)) + return out + + def reset_log(self): + """ + Resets the profiling timers + """ + with self.sem: + self.events = [] + + @property + def x87_volatile_option(self): + # this is running 32 bits OpenCL woth POCL + if self._X87_VOLATILE is None: + if (platform.machine() in ("i386", "i686", "x86_64", "AMD64") and + (tuple.__itemsize__ == 4) and + self.ctx.devices[0].platform.name == 'Portable Computing Language'): + self._X87_VOLATILE = "-DX87_VOLATILE=volatile" + else: + self._X87_VOLATILE = "" + return self._X87_VOLATILE + + def get_compiler_options(self, x87_volatile=False): + """Provide the default OpenCL compiler options + + :param x87_volatile: needed for Kahan summation + :return: string with compiler option + """ + option_list = [] + if x87_volatile: + option_list.append(self.x87_volatile_option) + return " ".join(i for i in option_list if i) + +# This should be implemented by concrete class +# def __copy__(self): +# """Shallow copy of the object +# +# :return: copy of the object +# """ +# return self.__class__((self._data, self._indices, self._indptr), +# self.size, block_size=self.BLOCK_SIZE, +# platformid=self.platform.id, +# deviceid=self.device.id, +# checksum=self.on_device.get("data"), +# profile=self.profile, empty=self.empty) +# +# def __deepcopy__(self, memo=None): +# """deep copy of the object +# +# :return: deepcopy of the object +# """ +# if memo is None: +# memo = {} +# new_csr = self._data.copy(), self._indices.copy(), self._indptr.copy() +# memo[id(self._data)] = new_csr[0] +# memo[id(self._indices)] = new_csr[1] +# memo[id(self._indptr)] = new_csr[2] +# new_obj = self.__class__(new_csr, self.size, +# block_size=self.BLOCK_SIZE, +# platformid=self.platform.id, +# deviceid=self.device.id, +# checksum=self.on_device.get("data"), +# profile=self.profile, empty=self.empty) +# memo[id(self)] = new_obj +# return new_obj |