Source code for silx.opencl.processing

#!/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)
#
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"""
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__)


[docs]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)
[docs] def get_kernels(self): "return the dictionary with all kernels" return dict(item for item in self.__dict__.items() if not item[0].startswith("_"))
[docs] def get_kernel(self, name): "get a kernel from its name" logger.debug("KernelContainer.get_kernel(%s)", name) return self.__dict__.get(name)
[docs] 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")
[docs] 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")
[docs]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
[docs] 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()
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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
[docs] 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)
[docs] 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
[docs] 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)
[docs] 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))
[docs] def allocate_texture(self, shape, hostbuf=None, support_1D=False): return allocate_texture(self.ctx, shape, hostbuf=hostbuf, support_1D=support_1D)
[docs] 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")
[docs] 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
[docs] 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
[docs] 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