""" Module implementing the C++ "standalone" device. """ import inspect import itertools import numbers import os import shutil import subprocess import sys import tempfile import time import zlib from collections import Counter, defaultdict from collections.abc import Mapping from distutils import ccompiler from hashlib import md5 import numpy as np from brian2.codegen.codeobject import check_compiler_kwds from brian2.codegen.cpp_prefs import get_compiler_and_args, get_msvc_env from brian2.codegen.generators.cpp_generator import c_data_type from brian2.core.functions import Function from brian2.core.namespace import get_local_namespace from brian2.core.network import Network from brian2.core.preferences import BrianPreference, prefs from brian2.core.variables import ( ArrayVariable, Constant, DynamicArrayVariable, Variable, VariableView, ) from brian2.devices.device import Device, all_devices, reset_device, set_device from brian2.groups.group import Group from brian2.input import TimedArray from brian2.parsing.rendering import CPPNodeRenderer from brian2.synapses.synapses import Synapses from brian2.units import second from brian2.units.fundamentalunits import Quantity, fail_for_dimension_mismatch from brian2.utils.filelock import FileLock from brian2.utils.filetools import copy_directory, ensure_directory, in_directory from brian2.utils.logger import get_logger, std_silent from brian2.utils.stringtools import word_substitute from .codeobject import CPPStandaloneCodeObject, openmp_pragma __all__ = [] logger = get_logger(__name__) # Preferences prefs.register_preferences( "devices.cpp_standalone", "C++ standalone preferences ", openmp_threads=BrianPreference( default=0, docs=""" The number of threads to use if OpenMP is turned on. By default, this value is set to 0 and the C++ code is generated without any reference to OpenMP. If greater than 0, then the corresponding number of threads are used to launch the simulation. """, ), openmp_spatialneuron_strategy=BrianPreference( default=None, validator=lambda val: val in [None, "branches", "systems"], docs=""" DEPRECATED. Previously used to chose the strategy to parallelize the solution of the three tridiagonal systems for multicompartmental neurons. Now, its value is ignored. """, ), make_cmd_unix=BrianPreference( default="make", docs=""" The make command used to compile the standalone project. Defaults to the standard GNU make commane "make".""", ), run_cmd_unix=BrianPreference( default="./main", validator=lambda val: isinstance(val, str) or isinstance(val, list), docs=""" The command used to run the compiled standalone project. Defaults to executing the compiled binary with "./main". Must be a single binary as string or a list of command arguments (e.g. ["./binary", "--key", "value"]). """, ), extra_make_args_unix=BrianPreference( default=["-j"], docs=""" Additional flags to pass to the GNU make command on Linux/OS-X. Defaults to "-j" for parallel compilation.""", ), extra_make_args_windows=BrianPreference( default=[], docs=""" Additional flags to pass to the nmake command on Windows. By default, no additional flags are passed. """, ), run_environment_variables=BrianPreference( default={"LD_BIND_NOW": "1"}, docs=""" Dictionary of environment variables and their values that will be set during the execution of the standalone code. """, ), ) class CPPWriter: def __init__(self, project_dir): self.project_dir = project_dir self.source_files = set() self.header_files = set() def write(self, filename, contents): logger.diagnostic(f"Writing file {filename}:\n{contents}") if filename.lower().endswith(".cpp") or filename.lower().endswith(".c"): self.source_files.add(filename) elif filename.lower().endswith(".h"): self.header_files.add(filename) elif filename.endswith(".*"): self.write(f"{filename[:-1]}cpp", contents.cpp_file) self.write(f"{filename[:-1]}h", contents.h_file) return fullfilename = os.path.join(self.project_dir, filename) if os.path.exists(fullfilename): with open(fullfilename) as f: if f.read() == contents: return with open(fullfilename, "w") as f: f.write(contents) def invert_dict(x): return {v: k for k, v in x.items()} class CPPStandaloneDevice(Device): """ The `Device` used for C++ standalone simulations. """ def __init__(self): super().__init__() #: Dictionary mapping `ArrayVariable` objects to their globally #: unique name self.arrays = {} #: Dictionary mapping `ArrayVariable` objects to their value or to #: ``None`` if the value (potentially) depends on executed code. This #: mechanism allows to access state variables in standalone mode if #: their value is known at run time self.array_cache = {} #: List of all dynamic arrays #: Dictionary mapping `DynamicArrayVariable` objects with 1 dimension to #: their globally unique name self.dynamic_arrays = {} #: Dictionary mapping `DynamicArrayVariable` objects with 2 dimensions #: to their globally unique name self.dynamic_arrays_2d = {} #: List of all arrays to be filled with zeros (list of (var, varname) ) self.zero_arrays = [] #: List of all arrays to be filled with numbers (list of #: (var, varname, start) tuples self.arange_arrays = [] #: Set of all existing synapses self.synapses = set() #: Whether the simulation has been run self.has_been_run = False #: Whether apply_run_args has been called self.run_args_applied = False #: Whether a run should trigger a build self.build_on_run = False #: build options self.build_options = None #: The directory which contains the generated code and results self.project_dir = None #: The directory which contains the results (relative to `project_dir``) self.results_dir = None #: Whether to generate profiling information (stored in an instance #: variable to be accessible during CodeObject generation) self.enable_profiling = False #: CodeObjects that use profiling (users can potentially enable #: profiling only for a subset of runs) self.profiled_codeobjects = [] #: Dict of all static saved arrays self.static_arrays = {} #: Names of static arrays used for run_args given as lists of values self.run_args_arrays = [] #: Dict of all TimedArray objects self.timed_arrays = {} self.code_objects = {} self.main_queue = [] self.runfuncs = {} self.networks = set() self.static_array_specs = [] self.report_func = "" #: Code lines that have been manually added with `device.insert_code` #: Dictionary mapping slot names to lists of lines. #: Note that the main slot is handled separately as part of `main_queue` self.code_lines = { "before_start": [], "after_start": [], "before_network_run": [], "after_network_run": [], "before_end": [], "after_end": [], } #: Dictionary storing compile and binary execution times self.timers = {"run_binary": None, "compile": {"clean": None, "make": None}} self.clocks = set() self.extra_compile_args = [] self.define_macros = [] self.headers = [] self.include_dirs = [] self.library_dirs = [] self.runtime_library_dirs = [] self.run_environment_variables = {} if sys.platform.startswith("darwin"): if "DYLD_LIBRARY_PATH" in os.environ: dyld_library_path = f"{os.environ['DYLD_LIBRARY_PATH']}:{os.path.join(sys.prefix, 'lib')}" else: dyld_library_path = os.path.join(sys.prefix, "lib") self.run_environment_variables["DYLD_LIBRARY_PATH"] = dyld_library_path self.libraries = [] if sys.platform == "win32": self.libraries += ["advapi32"] self.extra_link_args = [] self.writer = None def reinit(self): # Remember the build_on_run setting and its options -- important during # testing build_on_run = self.build_on_run build_options = self.build_options self.__init__() super().reinit() self.build_on_run = build_on_run self.build_options = build_options def spike_queue(self, source_start, source_end): return None # handled differently def freeze(self, code, ns): # TODO: Remove this function at some point logger.warn( "The CPPStandaloneDevice.freeze function should no longer " "be used, add constant definitions directly to the " 'code in the "CONSTANTS" section instead.', name_suffix="deprecated_freeze_use", once=True, ) # this is a bit of a hack, it should be passed to the template somehow for k, v in ns.items(): if isinstance(v, Variable) and v.scalar and v.constant and v.read_only: try: v = v.get_value() except NotImplementedError: continue if isinstance(v, str): code = word_substitute(code, {k: v}) elif isinstance(v, numbers.Number): # Use a renderer to correctly transform constants such as True or inf renderer = CPPNodeRenderer() string_value = renderer.render_expr(repr(v)) if prefs.core.default_float_dtype == np.float32 and isinstance( v, (float, np.float32, np.float64) ): string_value += "f" if v < 0: string_value = f"({string_value})" code = word_substitute(code, {k: string_value}) else: pass # don't deal with this object return code def insert_code(self, slot, code): """ Insert code directly into main.cpp """ if slot == "main": self.main_queue.append(("insert_code", code)) elif slot in self.code_lines: self.code_lines[slot].append(code) else: logger.warn(f"Ignoring device code, unknown slot: {slot}, code: {code}") def apply_run_args(self): if self.run_args_applied: raise RuntimeError( "The 'apply_run_args()' function can only be called once." ) self.insert_code("main", "set_from_command_line(args);") self.run_args_applied = True def static_array(self, name, arr): arr = np.atleast_1d(arr) assert len(arr), f"length for {name}: {len(arr)}" name = f"_static_array_{name}" basename = name i = 0 while name in self.static_arrays: i += 1 name = f"{basename}_{str(i)}" self.static_arrays[name] = arr.copy() return name def get_array_name(self, var, access_data=True): """ Return a globally unique name for `var`. Parameters ---------- access_data : bool, optional For `DynamicArrayVariable` objects, specifying `True` here means the name for the underlying data is returned. If specifying `False`, the name of object itself is returned (e.g. to allow resizing). """ if isinstance(var, DynamicArrayVariable): if access_data: return self.arrays[var] elif var.ndim == 1: return self.dynamic_arrays[var] else: return self.dynamic_arrays_2d[var] elif isinstance(var, ArrayVariable): return self.arrays[var] else: raise TypeError(f"Do not have a name for variable of type {type(var)}.") def get_array_filename(self, var, basedir=None): """ Return a file name for a variable. Parameters ---------- var : `ArrayVariable` The variable to get a filename for. basedir : str The base directory for the filename, defaults to ``'results'``. DEPRECATED: Will raise an error if specified. Returns ------- filename : str A filename of the form ``varname+'_'+str(zlib.crc32(varname))``, where varname is the name returned by `get_array_name`. Notes ----- The reason that the filename is not simply ``varname`` is that this could lead to file names that are not unique in file systems that are not case sensitive (e.g. on Windows). """ if basedir is not None: raise ValueError("Specifying 'basedir' is no longer supported.") varname = self.get_array_name(var, access_data=False) return f"{varname}_{str(zlib.crc32(varname.encode('utf-8')))}" def add_array(self, var): # Note that a dynamic array variable is added to both the arrays and # the _dynamic_array dictionary if isinstance(var, DynamicArrayVariable): # The code below is slightly more complicated than just looking # for a unique name as above for static_array, the name has # potentially to be unique for more than one dictionary, with # different prefixes. This is because dynamic arrays are added to # a ``dynamic_arrays`` dictionary (with a `_dynamic` prefix) and to # the general ``arrays`` dictionary. We want to make sure that we # use the same name in the two dictionaries, not for example # ``_dynamic_array_source_name_2`` and ``_array_source_name_1`` # (this would work fine, but it would make the code harder to read). orig_dynamic_name = dynamic_name = ( f"_dynamic_array_{var.owner.name}_{var.name}" ) orig_array_name = array_name = f"_array_{var.owner.name}_{var.name}" suffix = 0 if var.ndim == 1: dynamic_dict = self.dynamic_arrays elif var.ndim == 2: dynamic_dict = self.dynamic_arrays_2d else: raise AssertionError( "Did not expect a dynamic array with {var.ndim} dimensions." ) while ( dynamic_name in dynamic_dict.values() or array_name in self.arrays.values() ): suffix += 1 dynamic_name = f"{orig_dynamic_name}_{int(suffix)}" array_name = f"{orig_array_name}_{int(suffix)}" dynamic_dict[var] = dynamic_name self.arrays[var] = array_name else: orig_array_name = array_name = f"_array_{var.owner.name}_{var.name}" suffix = 0 while array_name in self.arrays.values(): suffix += 1 array_name = f"{orig_array_name}_{int(suffix)}" self.arrays[var] = array_name def init_with_zeros(self, var, dtype): if isinstance(var, DynamicArrayVariable): varname = f"_dynamic{self.arrays[var]}" else: varname = self.arrays[var] self.zero_arrays.append((var, varname)) self.array_cache[var] = np.zeros(var.size, dtype=dtype) def init_with_arange(self, var, start, dtype): if isinstance(var, DynamicArrayVariable): varname = f"_dynamic{self.arrays[var]}" else: varname = self.arrays[var] self.arange_arrays.append((var, varname, start)) self.array_cache[var] = np.arange(0, var.size, dtype=dtype) + start def fill_with_array(self, var, arr): arr = np.asarray(arr) if arr.size == 0: return # nothing to do array_name = self.get_array_name(var, access_data=False) if isinstance(var, DynamicArrayVariable): # We can never be sure about the size of a dynamic array, so # we can't do correct broadcasting. Therefore, we do not cache # them at all for now. self.array_cache[var] = None else: new_arr = np.empty(var.size, dtype=var.dtype) new_arr[:] = arr self.array_cache[var] = new_arr if arr.size == 1: if var.size == 1: value = CPPNodeRenderer().render_expr(repr(arr.item(0))) # For a single assignment, generate a code line instead of storing the array self.main_queue.append(("set_by_single_value", (array_name, 0, value))) else: self.main_queue.append( ( "set_by_constant", (array_name, arr.item(), isinstance(var, DynamicArrayVariable)), ) ) else: # Using the std::vector instead of a pointer to the underlying # data for dynamic arrays is fast enough here and it saves us some # additional work to set up the pointer static_array_name = self.static_array(array_name, arr) self.main_queue.append( ( "set_by_array", ( array_name, static_array_name, isinstance(var, DynamicArrayVariable), ), ) ) def resize(self, var, new_size): array_name = self.get_array_name(var, access_data=False) self.main_queue.append(("resize_array", (array_name, new_size))) def variableview_set_with_index_array(self, variableview, item, value, check_units): if isinstance(item, slice) and item == slice(None): item = "True" value = Quantity(value) if ( isinstance(item, int) or (isinstance(item, np.ndarray) and item.shape == ()) ) and value.size == 1: array_name = self.get_array_name(variableview.variable, access_data=False) value_str = CPPNodeRenderer().render_expr(repr(np.asarray(value).item(0))) if self.array_cache.get(variableview.variable, None) is not None: self.array_cache[variableview.variable][item] = value # For a single assignment, generate a code line instead of storing the array self.main_queue.append( ("set_by_single_value", (array_name, item, value_str)) ) # Simple case where we don't have to do any indexing elif item == "True" and variableview.index_var in ("_idx", "0"): self.fill_with_array(variableview.variable, value) else: # We have to calculate indices. This will not work for synaptic # variables try: indices = np.asarray( variableview.indexing(item, index_var=variableview.index_var) ) except NotImplementedError: raise NotImplementedError( f"Cannot set variable '{variableview.name}' " "this way in standalone, try using " "string expressions." ) # Using the std::vector instead of a pointer to the underlying # data for dynamic arrays is fast enough here and it saves us some # additional work to set up the pointer arrayname = self.get_array_name(variableview.variable, access_data=False) if indices.shape != () and ( value.shape == () or (value.size == 1 and indices.size > 1) ): value = np.repeat(value, indices.size) elif value.shape != indices.shape and len(value) != len(indices): raise ValueError( "Provided values do not match the size " "of the indices, " f"{len(value)} != len(indices)." ) staticarrayname_index = self.static_array(f"_index_{arrayname}", indices) staticarrayname_value = self.static_array(f"_value_{arrayname}", value) self.array_cache[variableview.variable] = None self.main_queue.append( ( "set_array_by_array", (arrayname, staticarrayname_index, staticarrayname_value), ) ) def get_value(self, var, access_data=True): # Usually, we cannot retrieve the values of state variables in # standalone scripts since their values might depend on the evaluation # of expressions at runtime. For some variables we do know the value # however (values that have been set with explicit values and not # changed in code objects) if self.array_cache.get(var, None) is not None: return self.array_cache[var] else: # After the network has been run, we can retrieve the values from # disk if self.has_been_run: dtype = var.dtype fname = os.path.join(self.results_dir, self.get_array_filename(var)) with open(fname, "rb") as f: data = np.fromfile(f, dtype=dtype) # This is a bit of an heuristic, but our 2d dynamic arrays are # only expanding in one dimension, we assume here that the # other dimension has size 0 at the beginning if isinstance(var.size, tuple) and len(var.size) == 2: if var.size[0] * var.size[1] == len(data): size = var.size elif var.size[0] == 0: size = (len(data) // var.size[1], var.size[1]) elif var.size[1] == 0: size = (var.size[0], len(data) // var.size[0]) else: raise IndexError( "Do not now how to deal with 2d " f"array of size {var.size!s}, the array on " f"disk has length {len(data)}." ) var.size = size return data.reshape(var.size) var.size = len(data) return data raise NotImplementedError( "Cannot retrieve the values of state " "variables in standalone code before the " "simulation has been run." ) def variableview_get_subexpression_with_index_array( self, variableview, item, run_namespace=None ): if not self.has_been_run: raise NotImplementedError( "Cannot retrieve the values of state " "variables in standalone code before the " "simulation has been run." ) # Temporarily switch to the runtime device to evaluate the subexpression # (based on the values stored on disk) set_device("runtime") result = VariableView.get_subexpression_with_index_array( variableview, item, run_namespace=run_namespace ) reset_device() return result def variableview_get_with_expression(self, variableview, code, run_namespace=None): raise NotImplementedError( "Cannot retrieve the values of state " "variables with string expressions in " "standalone scripts." ) def code_object_class(self, codeobj_class=None, fallback_pref=None): """ Return `CodeObject` class (either `CPPStandaloneCodeObject` class or input) Parameters ---------- codeobj_class : a `CodeObject` class, optional If this is keyword is set to None or no arguments are given, this method will return the default (`CPPStandaloneCodeObject` class). fallback_pref : str, optional For the cpp_standalone device this option is ignored. Returns ------- codeobj_class : class The `CodeObject` class that should be used """ # Ignore the requested pref (used for optimization in runtime) if codeobj_class is None: return CPPStandaloneCodeObject else: return codeobj_class def code_object( self, owner, name, abstract_code, variables, template_name, variable_indices, codeobj_class=None, template_kwds=None, override_conditional_write=None, compiler_kwds=None, ): if compiler_kwds is None: compiler_kwds = {} check_compiler_kwds( compiler_kwds, [ "headers", "sources", "define_macros", "libraries", "include_dirs", "library_dirs", "runtime_library_dirs", ], "C++ standalone", ) if template_kwds is None: template_kwds = dict() else: template_kwds = dict(template_kwds) # In standalone mode, the only place where we use additional header # files is by inserting them into the template codeobj_headers = compiler_kwds.get("headers", []) template_kwds["user_headers"] = ( self.headers + prefs["codegen.cpp.headers"] + codeobj_headers ) template_kwds["profiled"] = self.enable_profiling do_not_invalidate = set() if template_name == "synapses_create_array": cache = self.array_cache if ( cache[variables["N"]] is None ): # synapses have been previously created with code # Nothing we can do logger.debug( f"Synapses for '{owner.name}' have previously been created with " "code, we therefore cannot cache the synapses created with arrays " f"via '{name}'", name_suffix="code_created_synapses_exist", ) else: # first time we create synapses, or all previous connect calls were with arrays cache[variables["N"]][0] += variables["sources"].size do_not_invalidate.add(variables["N"]) for var, value in [ ( variables["_synaptic_pre"], variables["sources"].get_value() + variables["_source_offset"].get_value(), ), ( variables["_synaptic_post"], variables["targets"].get_value() + variables["_target_offset"].get_value(), ), ]: cache[var] = np.append( cache.get(var, np.empty(0, dtype=int)), value ) do_not_invalidate.add(var) codeobj = super().code_object( owner, name, abstract_code, variables, template_name, variable_indices, codeobj_class=codeobj_class, template_kwds=template_kwds, override_conditional_write=override_conditional_write, compiler_kwds=compiler_kwds, ) self.code_objects[codeobj.name] = codeobj if self.enable_profiling: self.profiled_codeobjects.append(codeobj.name) for var in codeobj.variables.values(): if isinstance(var, TimedArray): self.timed_arrays[var] = var.name # We mark all writeable (i.e. not read-only) variables used by the code # as "dirty" to avoid that the cache contains incorrect values. This # might remove a number of variables from the cache unnecessarily, # since many variables are only read in the code. # On the other hand, there are also *read-only* variables that can be # changed by code (the "read-only" attribute only refers to the user # being able to change values directly). For example, synapse creation # write source and target indices, and monitors write the shared values. # To correctly mark these values as changed, templates can include a # "WRITES_TO_READ_ONLY_VARIABLES" comment, stating the name of the # changed variables. For a monitor, this would for example state that # the number of recorded values "N" changes. For the recorded variables, # however, this information cannot be included in the template because # it is up to the user to define which variables are recorded. For such # cases, the "owner" object (e.g. a SpikeMonitor) can define a # "written_readonly_vars" attribute, storing a set of `Variable` objects # that will be changed by the owner's code objects. template = getattr(codeobj.templater, template_name) written_readonly_vars = { codeobj.variables[varname] for varname in template.writes_read_only } | getattr(owner, "written_readonly_vars", set()) for var in codeobj.variables.values(): if ( isinstance(var, ArrayVariable) and var not in do_not_invalidate and (not var.read_only or var in written_readonly_vars) ): self.array_cache[var] = None return codeobj def check_openmp_compatible(self, nb_threads): if nb_threads > 0: logger.warn( "OpenMP code is not yet well tested, and may be inaccurate.", "openmp", once=True, ) logger.diagnostic(f"Using OpenMP with {int(nb_threads)} threads ") if prefs.devices.cpp_standalone.openmp_spatialneuron_strategy is not None: logger.warn( "The devices.cpp_standalone.openmp_spatialneuron_strategy " "preference is no longer used and will be removed in " "future versions of Brian.", "openmp_spatialneuron_strategy", once=True, ) def generate_objects_source( self, writer, arange_arrays, synapses, static_array_specs, networks, timed_arrays, ): arr_tmp = self.code_object_class().templater.objects( None, None, array_specs=self.arrays, dynamic_array_specs=self.dynamic_arrays, dynamic_array_2d_specs=self.dynamic_arrays_2d, zero_arrays=self.zero_arrays, arange_arrays=arange_arrays, synapses=synapses, clocks=self.clocks, static_array_specs=static_array_specs, networks=networks, get_array_filename=self.get_array_filename, get_array_name=self.get_array_name, profiled_codeobjects=self.profiled_codeobjects, code_objects=list(self.code_objects.values()), timed_arrays=timed_arrays, ) writer.write("objects.*", arr_tmp) def generate_main_source(self, writer): main_lines = [] procedures = [("", main_lines)] runfuncs = {} for func, args in self.main_queue: if func == "before_run_code_object": (codeobj,) = args main_lines.append(f"_before_run_{codeobj.name}();") elif func == "run_code_object": (codeobj,) = args main_lines.append(f"_run_{codeobj.name}();") elif func == "after_run_code_object": (codeobj,) = args main_lines.append(f"_after_run_{codeobj.name}();") elif func == "run_network": net, netcode = args main_lines.extend(netcode) elif func == "set_by_constant": arrayname, value, is_dynamic = args size_str = f"{arrayname}.size()" if is_dynamic else f"_num_{arrayname}" code = f""" {openmp_pragma('static')} for(int i=0; i<{size_str}; i++) {{ {arrayname}[i] = {CPPNodeRenderer().render_expr(repr(value))}; }} """ main_lines.extend(code.split("\n")) elif func == "set_by_array": arrayname, staticarrayname, is_dynamic = args size_str = f"{arrayname}.size()" if is_dynamic else f"_num_{arrayname}" code = f""" {openmp_pragma('static')} for(int i=0; i<{size_str}; i++) {{ {arrayname}[i] = {staticarrayname}[i]; }} """ main_lines.extend(code.split("\n")) elif func == "set_by_single_value": arrayname, item, value = args code = f"{arrayname}[{item}] = {value};" main_lines.extend([code]) elif func == "set_array_by_array": arrayname, staticarrayname_index, staticarrayname_value = args code = f""" {openmp_pragma('static')} for(int i=0; i<_num_{staticarrayname_index}; i++) {{ {arrayname}[{staticarrayname_index}[i]] = {staticarrayname_value}[i]; }} """ main_lines.extend(code.split("\n")) elif func == "resize_array": array_name, new_size = args main_lines.append(f"{array_name}.resize({new_size});") elif func == "insert_code": main_lines.append(args) elif func == "start_run_func": name, include_in_parent = args if include_in_parent: main_lines.append(f"{name}();") main_lines = [] procedures.append((name, main_lines)) elif func == "end_run_func": name, include_in_parent = args name, main_lines = procedures.pop(-1) runfuncs[name] = main_lines name, main_lines = procedures[-1] elif func == "seed": seed = args nb_threads = prefs.devices.cpp_standalone.openmp_threads if nb_threads == 0: # no OpenMP nb_threads = 1 main_lines.append(f"for (int _i=0; _i<{nb_threads}; _i++)") if seed is None: # random main_lines.append(" brian::_random_generators[_i].seed();") else: main_lines.append( f" brian::_random_generators[_i].seed({seed!r}L + _i);" ) else: raise NotImplementedError(f"Unknown main queue function type {func}") self.runfuncs = runfuncs # generate the finalisations for codeobj in self.code_objects.values(): if hasattr(codeobj.code, "main_finalise"): main_lines.append(codeobj.code.main_finalise) user_headers = self.headers + prefs["codegen.cpp.headers"] main_tmp = self.code_object_class().templater.main( None, None, main_lines=main_lines, code_lines=self.code_lines, code_objects=list(self.code_objects.values()), report_func=self.report_func, dt=float(self.defaultclock.dt), user_headers=user_headers, ) writer.write("main.cpp", main_tmp) def generate_codeobj_source(self, writer): # Generate data for non-constant values renderer = CPPNodeRenderer() code_object_defs = defaultdict(list) for codeobj in self.code_objects.values(): lines = [] for k, v in codeobj.variables.items(): if isinstance(v, ArrayVariable): try: if isinstance(v, DynamicArrayVariable): if v.ndim == 1: dyn_array_name = self.dynamic_arrays[v] array_name = self.arrays[v] c_type = c_data_type(v.dtype) line = ( f"{c_type}* const {array_name} =" f" {dyn_array_name}.empty()? 0 :" f" &{dyn_array_name}[0];" ) lines.append(line) line = ( f"const size_t _num{k} = {dyn_array_name}.size();" ) lines.append(line) else: lines.append(f"const size_t _num{k} = {v.size};") except TypeError: pass elif isinstance(v, Constant): value = renderer.render_expr(repr(v.value)) c_type = c_data_type(v.dtype) line = f"const {c_type} {k} = {value};" lines.append(line) for line in lines: # Sometimes an array is referred to by to different keys in our # dictionary -- make sure to never add a line twice if line not in code_object_defs[codeobj.name]: code_object_defs[codeobj.name].append(line) # Generate the code objects for codeobj in self.code_objects.values(): # Before/after run code for block in codeobj.before_after_blocks: cpp_code = getattr(codeobj.code, f"{block}_cpp_file") cpp_code = cpp_code.replace( "%CONSTANTS%", "\n".join(code_object_defs[codeobj.name]) ) h_code = getattr(codeobj.code, f"{block}_h_file") writer.write(f"code_objects/{block}_{codeobj.name}.cpp", cpp_code) writer.write(f"code_objects/{block}_{codeobj.name}.h", h_code) # Main code code = codeobj.code.cpp_file code = code.replace( "%CONSTANTS%", "\n".join(code_object_defs[codeobj.name]) ) writer.write(f"code_objects/{codeobj.name}.cpp", code) writer.write(f"code_objects/{codeobj.name}.h", codeobj.code.h_file) def generate_network_source(self, writer, compiler): maximum_run_time = self._maximum_run_time if maximum_run_time is not None: maximum_run_time = float(maximum_run_time) network_tmp = self.code_object_class().templater.network( None, None, maximum_run_time=maximum_run_time ) writer.write("network.*", network_tmp) def generate_synapses_classes_source(self, writer): synapses_classes_tmp = self.code_object_class().templater.synapses_classes( None, None ) writer.write("synapses_classes.*", synapses_classes_tmp) def generate_run_source(self, writer): run_tmp = self.code_object_class().templater.run( None, None, run_funcs=self.runfuncs, code_objects=list(self.code_objects.values()), user_headers=self.headers, array_specs=self.arrays, clocks=self.clocks, ) writer.write("run.*", run_tmp) def generate_makefile( self, writer, compiler, compiler_flags, linker_flags, nb_threads, debug ): if compiler == "msvc": if nb_threads > 1: openmp_flag = "/openmp" else: openmp_flag = "" if debug: compiler_debug_flags = "/DEBUG /DDEBUG" linker_debug_flags = "/DEBUG" else: compiler_debug_flags = "" linker_debug_flags = "" # Generate the visual studio makefile source_bases = [ fname.replace(".cpp", "").replace(".c", "").replace("/", "\\") for fname in sorted(writer.source_files) ] win_makefile_tmp = self.code_object_class().templater.win_makefile( None, None, source_files=sorted(writer.source_files), source_bases=source_bases, compiler_flags=compiler_flags, compiler_debug_flags=compiler_debug_flags, linker_flags=linker_flags, linker_debug_flags=linker_debug_flags, openmp_flag=openmp_flag, ) writer.write("win_makefile", win_makefile_tmp) # write the list of sources source_list = " ".join(source_bases) source_list_fname = os.path.join(self.project_dir, "sourcefiles.txt") if os.path.exists(source_list_fname): with open(source_list_fname) as f: if f.read() == source_list: return with open(source_list_fname, "w") as f: f.write(source_list) else: # Generate the makefile if os.name == "nt": rm_cmd = "del *.o /s\n\tdel main.exe $(DEPS)" else: rm_cmd = "rm $(OBJS) $(PROGRAM) $(DEPS)" if debug: compiler_debug_flags = "-g -DDEBUG" linker_debug_flags = "-g" else: compiler_debug_flags = "" linker_debug_flags = "" makefile_tmp = self.code_object_class().templater.makefile( None, None, source_files=" ".join(sorted(writer.source_files)), header_files=" ".join(sorted(writer.header_files)), compiler_flags=compiler_flags, compiler_debug_flags=compiler_debug_flags, linker_debug_flags=linker_debug_flags, linker_flags=linker_flags, rm_cmd=rm_cmd, ) writer.write("makefile", makefile_tmp) def copy_source_files(self, writer, directory): # Copy the brianlibdirectory brianlib_dir = os.path.join( os.path.split(inspect.getsourcefile(CPPStandaloneCodeObject))[0], "brianlib" ) brianlib_files = copy_directory( brianlib_dir, os.path.join(directory, "brianlib") ) for file in brianlib_files: if file.lower().endswith(".cpp"): writer.source_files.add(f"brianlib/{file}") elif file.lower().endswith(".h"): writer.header_files.add(f"brianlib/{file}") # Copy the CSpikeQueue implementation shutil.copy2( os.path.join( os.path.split(inspect.getsourcefile(Synapses))[0], "cspikequeue.cpp" ), os.path.join(directory, "brianlib", "spikequeue.h"), ) shutil.copy2( os.path.join( os.path.split(inspect.getsourcefile(Synapses))[0], "stdint_compat.h" ), os.path.join(directory, "brianlib", "stdint_compat.h"), ) def _insert_func_namespace(self, func, code_object, namespace): impl = func.implementations[self.code_object_class()] func_namespace = impl.get_namespace(code_object.owner) if func_namespace is not None: namespace.update(func_namespace) if impl.dependencies is not None: for dep in impl.dependencies.values(): self._insert_func_namespace(dep, code_object, namespace) def write_static_arrays(self, directory): # Write Function namespaces as static arrays for code_object in self.code_objects.values(): for var in code_object.variables.values(): if isinstance(var, Function): self._insert_func_namespace(var, code_object, self.static_arrays) logger.diagnostic(f"static arrays: {str(sorted(self.static_arrays.keys()))}") static_array_specs = [] for name, arr in sorted(self.static_arrays.items()): arr.tofile(os.path.join(directory, "static_arrays", name)) static_array_specs.append((name, c_data_type(arr.dtype), arr.size, name)) self.static_array_specs = static_array_specs def compile_source(self, directory, compiler, debug, clean): with in_directory(directory): if compiler == "msvc": msvc_env, vcvars_cmd = get_msvc_env() make_cmd = "nmake /f win_makefile" make_args = " ".join( prefs.devices.cpp_standalone.extra_make_args_windows ) if os.path.exists("winmake.log"): os.remove("winmake.log") if vcvars_cmd: with open("winmake.log", "w") as f: f.write(f"{vcvars_cmd}\n") else: with open("winmake.log", "w") as f: f.write("MSVC environment: \n") for key, value in msvc_env.items(): f.write(f"{key}={value}\n") with std_silent(debug): if vcvars_cmd: if clean: start_time = time.time() os.system( f"{vcvars_cmd} >>winmake.log 2>&1 && {make_cmd} clean >" " NUL 2>&1" ) self.timers["compile"]["clean"] = time.time() - start_time start_time = time.time() x = os.system( f"{vcvars_cmd} >>winmake.log 2>&1 &&" f" {make_cmd} {make_args}>>winmake.log 2>&1" ) self.timers["compile"]["make"] = time.time() - start_time else: os.environ.update(msvc_env) if clean: start_time = time.time() os.system(f"{make_cmd} clean > NUL 2>&1") self.timers["compile"]["clean"] = time.time() - start_time start_time = time.time() x = os.system(f"{make_cmd} {make_args}>>winmake.log 2>&1") self.timers["compile"]["make"] = time.time() - start_time if x != 0: error_message = f"Project compilation failed (error code: {x}), consider having a look at 'winmake.log'." if not clean: error_message += ( " Consider running with " '"clean=True" to force a complete ' "rebuild." ) raise RuntimeError(error_message) else: with std_silent(debug): if clean: start_time = time.time() os.system("make clean >/dev/null 2>&1") self.timers["compile"]["clean"] = time.time() - start_time make_cmd = prefs.devices.cpp_standalone.make_cmd_unix make_args = " ".join( prefs.devices.cpp_standalone.extra_make_args_unix ) start_time = time.time() x = os.system(f"{make_cmd} {make_args}") self.timers["compile"]["make"] = time.time() - start_time if x != 0: error_message = ( "Project compilation failed (error code: %u)." % x ) if not clean: error_message += ( " Consider running with " '"clean=True" to force a complete ' "rebuild." ) raise RuntimeError(error_message) def seed(self, seed=None): """ Set the seed for the random number generator. Parameters ---------- seed : int, optional The seed value for the random number generator, or ``None`` (the default) to set a random seed. """ self.main_queue.append(("seed", seed)) def run( self, directory=None, results_directory=None, with_output=True, run_args=None ): if directory is None: directory = self.project_dir if results_directory is None: results_directory = self.results_dir else: if os.path.isabs(results_directory): raise TypeError( "The 'results_directory' argument needs to be a relative path but" f" was '{results_directory}'." ) # Translate path to absolute path which ends with / self.results_dir = os.path.join( os.path.abspath(os.path.join(directory, results_directory)), "" ) ensure_directory(self.results_dir) self.run_args_arrays.clear() # forget about arrays from previous runs if run_args is None: run_args = [] elif isinstance(run_args, Mapping): list_rep = [] for key, value in run_args.items(): if isinstance(key, VariableView): ( name, string_value, value_name, value_ar, ) = self._prepare_variableview_run_arg(key, value) elif isinstance(key, TimedArray): ( name, string_value, value_name, value_ar, ) = self._prepare_timed_array_run_arg(key, value) else: raise TypeError( "The keys for 'run_args' need to be 'VariableView' objects," " i.e. attributes of groups such as 'neurongroup.v', or a" f" 'TimedArray'. Key has type '{type(key)}' instead." ) if value_name: fname = os.path.join(self.project_dir, "static_arrays", value_name) # Make sure processes trying to write the same file don't clash with FileLock(fname + ".lock"): if not os.path.exists(fname): value_ar.tofile(fname) self.run_args_arrays.append(value_name) list_rep.append(f"{name}={string_value}") run_args = list_rep # Invalidate array cache for all variables set on the command line for arg in run_args: s = arg.split("=") if len(s) == 2: for var in self.array_cache: if ( hasattr(var.owner, "name") and var.owner.name + "." + var.name == s[0] ): self.array_cache[var] = None run_args = ["--results_dir", self.results_dir] + run_args # Invalidate the cached end time of the clock and network, to deal with stopped simulations for clock in self.clocks: self.array_cache[clock.variables["t"]] = None with in_directory(directory): # Set environment variables for key, value in itertools.chain( prefs["devices.cpp_standalone.run_environment_variables"].items(), self.run_environment_variables.items(), ): if key in os.environ and os.environ[key] != value: logger.info( f'Overwriting environment variable "{key}"', name_suffix="overwritten_env_var", once=True, ) os.environ[key] = value if not with_output: stdout = open(os.path.join(self.results_dir, "stdout.txt"), "w") else: stdout = None if os.name == "nt": start_time = time.time() Network._globally_running = True x = subprocess.call(["main"] + run_args, stdout=stdout) self.timers["run_binary"] = time.time() - start_time Network._globally_running = False else: run_cmd = prefs.devices.cpp_standalone.run_cmd_unix if isinstance(run_cmd, str): run_cmd = [run_cmd] start_time = time.time() Network._globally_running = True x = subprocess.call(run_cmd + run_args, stdout=stdout) self.timers["run_binary"] = time.time() - start_time Network._globally_running = False if stdout is not None: stdout.close() if x: stdout_fname = os.path.join(self.results_dir, "stdout.txt") if os.path.exists(stdout_fname): with open(stdout_fname) as f: print(f.read()) raise RuntimeError( "Project run failed (project directory:" f" {os.path.abspath(directory)})" ) self.has_been_run = True run_info_fname = os.path.join(self.results_dir, "last_run_info.txt") if os.path.isfile(run_info_fname): with open(run_info_fname) as f: last_run_info = f.read() run_time, completed_fraction = last_run_info.split() self._last_run_time = float(run_time) self._last_run_completed_fraction = float(completed_fraction) # Make sure that integration did not create NaN or very large values owners = [var.owner for var in self.arrays] # We don't want to check the same owner twice but var.owner is a # weakproxy which we can't put into a set. We therefore store the name # of all objects we already checked. Furthermore, under some specific # instances a variable might have been created whose owner no longer # exists (e.g. a `_sub_idx` variable for a subgroup) -- we ignore the # resulting reference error. already_checked = set() for owner in owners: try: if not hasattr(owner, "name") or owner.name in already_checked: continue if isinstance(owner, Group): owner._check_for_invalid_states() already_checked.add(owner.name) except ReferenceError: pass def _prepare_variableview_run_arg(self, key, value): fail_for_dimension_mismatch(key.dim, value) # TODO: Give name of variable value_ar = np.asarray(value, dtype=key.dtype) if value_ar.ndim == 0 or value_ar.size == 1: # single value, give value directly on command line string_value = repr(value_ar.item()) value_name = None else: if value_ar.ndim != 1 or ( not key.variable.dynamic and value_ar.size != key.shape[0] ): raise TypeError( "Incorrect size for variable" f" '{key.group_name}.{key.name}'. Shape {key.shape} ≠" f" {value_ar.shape}." ) value_name = ( f"init_{key.group_name}_{key.name}_{md5(value_ar.data).hexdigest()}.dat" ) string_value = os.path.join("static_arrays", value_name) name = f"{key.group_name}.{key.name}" return name, string_value, value_name, value_ar def _prepare_timed_array_run_arg(self, key, value): fail_for_dimension_mismatch(key.dim, value) # TODO: Give name of variable value_ar = np.asarray(value, dtype=key.values.dtype) if value_ar.ndim == 0 or value_ar.size == 1: # single value, give value directly on command line string_value = repr(value_ar.item()) value_name = None elif value_ar.shape == key.values.shape: value_name = f"init_{key.name}_values_{md5(value_ar.data).hexdigest()}.dat" string_value = os.path.join("static_arrays", value_name) else: raise TypeError( "Incorrect size for variable" f" '{key.name}.values'. Shape {key.values.shape} ≠" f" {value_ar.shape}." ) name = f"{key.name}.values" return name, string_value, value_name, value_ar def build( self, directory="output", results_directory="results", compile=True, run=True, debug=False, clean=False, with_output=True, additional_source_files=None, run_args=None, direct_call=True, **kwds, ): """ Build the project TODO: more details Parameters ---------- directory : str, optional The output directory to write the project to, any existing files will be overwritten. If the given directory name is ``None``, then a temporary directory will be used (used in the test suite to avoid problems when running several tests in parallel). Defaults to ``'output'``. compile : bool, optional Whether or not to attempt to compile the project. Defaults to ``True``. run : bool, optional Whether or not to attempt to run the built project if it successfully builds. Defaults to ``True``. debug : bool, optional Whether to compile in debug mode. Defaults to ``False``. with_output : bool, optional Whether or not to show the ``stdout`` of the built program when run. Output will be shown in case of compilation or runtime error. Defaults to ``True``. clean : bool, optional Whether or not to clean the project before building. Defaults to ``False``. additional_source_files : list of str, optional A list of additional ``.cpp`` files to include in the build. direct_call : bool, optional Whether this function was called directly. Is used internally to distinguish an automatic build due to the ``build_on_run`` option from a manual ``device.build`` call. """ if self.build_on_run and direct_call: raise RuntimeError( "You used set_device with build_on_run=True " "(the default option), which will automatically " "build the simulation at the first encountered " "run call - do not call device.build manually " "in this case. If you want to call it manually, " "e.g. because you have multiple run calls, use " "set_device with build_on_run=False." ) if self.has_been_run: raise RuntimeError( "The network has already been built and run " "before. To build several simulations in " 'the same script, call "device.reinit()" ' 'and "device.activate()". Note that you ' "will have to set build options (e.g. the " "directory) and defaultclock.dt again." ) renames = { "project_dir": "directory", "compile_project": "compile", "run_project": "run", } if len(kwds): msg = "" for kwd in kwds: if kwd in renames: msg += ( f"Keyword argument '{kwd}' has been renamed to " f"'{renames[kwd]}'. " ) else: msg += f"Unknown keyword argument '{kwd}'. " raise TypeError(msg) if additional_source_files is None: additional_source_files = [] if run_args is None: run_args = [] if directory is None: directory = tempfile.mkdtemp(prefix="brian_standalone_") self.project_dir = directory ensure_directory(directory) if os.path.isabs(results_directory): raise TypeError( "The 'results_directory' argument needs to be a relative path but was " f"'{results_directory}'." ) # Translate path to absolute path which ends with / self.results_dir = os.path.join( os.path.abspath(os.path.join(directory, results_directory)), "" ) # Determine compiler flags and directories compiler, default_extra_compile_args = get_compiler_and_args() extra_compile_args = self.extra_compile_args + default_extra_compile_args extra_link_args = self.extra_link_args + prefs["codegen.cpp.extra_link_args"] codeobj_define_macros = [ macro for codeobj in self.code_objects.values() for macro in codeobj.compiler_kwds.get("define_macros", []) ] define_macros = ( self.define_macros + prefs["codegen.cpp.define_macros"] + codeobj_define_macros ) codeobj_include_dirs = [ include_dir for codeobj in self.code_objects.values() for include_dir in codeobj.compiler_kwds.get("include_dirs", []) ] include_dirs = ( self.include_dirs + prefs["codegen.cpp.include_dirs"] + codeobj_include_dirs ) codeobj_library_dirs = [ library_dir for codeobj in self.code_objects.values() for library_dir in codeobj.compiler_kwds.get("library_dirs", []) ] library_dirs = ( self.library_dirs + prefs["codegen.cpp.library_dirs"] + codeobj_library_dirs ) codeobj_runtime_dirs = [ runtime_dir for codeobj in self.code_objects.values() for runtime_dir in codeobj.compiler_kwds.get("runtime_library_dirs", []) ] runtime_library_dirs = ( self.runtime_library_dirs + prefs["codegen.cpp.runtime_library_dirs"] + codeobj_runtime_dirs ) codeobj_libraries = [ library for codeobj in self.code_objects.values() for library in codeobj.compiler_kwds.get("libraries", []) ] libraries = self.libraries + prefs["codegen.cpp.libraries"] + codeobj_libraries compiler_obj = ccompiler.new_compiler(compiler=compiler) # Distutils does not use the shell, so it does not need to quote filenames/paths # Since we include the compiler flags in the makefile, we need to quote them include_dirs = [f'"{include_dir}"' for include_dir in include_dirs] library_dirs = [f'"{library_dir}"' for library_dir in library_dirs] runtime_library_dirs = [ f'"{runtime_dir}"' for runtime_dir in runtime_library_dirs ] compiler_flags = ( ccompiler.gen_preprocess_options(define_macros, include_dirs) + extra_compile_args ) linker_flags = ( ccompiler.gen_lib_options( compiler_obj, library_dirs=library_dirs, runtime_library_dirs=runtime_library_dirs, libraries=libraries, ) + extra_link_args ) codeobj_source_files = [ source_file for codeobj in self.code_objects.values() for source_file in codeobj.compiler_kwds.get("sources", []) ] additional_source_files += codeobj_source_files for d in ["code_objects", "results", "static_arrays"]: ensure_directory(os.path.join(directory, d)) self.writer = CPPWriter(directory) # Get the number of threads if specified in an openmp context nb_threads = prefs.devices.cpp_standalone.openmp_threads # If the number is negative, we need to throw an error if nb_threads < 0: raise ValueError("The number of OpenMP threads can not be negative !") logger.diagnostic( "Writing C++ standalone project to directory " f"'{os.path.normpath(directory)}'." ) self.check_openmp_compatible(nb_threads) self.write_static_arrays(directory) # Check that all names are globally unique names = [obj.name for net in self.networks for obj in net.sorted_objects] non_unique_names = [name for name, count in Counter(names).items() if count > 1] if len(non_unique_names): formatted_names = ", ".join(f"'{name}'" for name in non_unique_names) raise ValueError( "All objects need to have unique names in " "standalone mode, the following name(s) were used " f"more than once: {formatted_names}" ) self.generate_objects_source( self.writer, self.arange_arrays, self.synapses, self.static_array_specs, self.networks, self.timed_arrays, ) self.generate_main_source(self.writer) self.generate_codeobj_source(self.writer) self.generate_network_source(self.writer, compiler) self.generate_synapses_classes_source(self.writer) self.generate_run_source(self.writer) self.copy_source_files(self.writer, directory) self.writer.source_files.update(additional_source_files) self.generate_makefile( self.writer, compiler, compiler_flags=" ".join(compiler_flags), linker_flags=" ".join(linker_flags), nb_threads=nb_threads, debug=debug, ) if compile: self.compile_source(directory, compiler, debug, clean) if run: self.run(directory, results_directory, with_output, run_args) time_measurements = { "'make clean'": self.timers["compile"]["clean"], "'make'": self.timers["compile"]["make"], "running 'main'": self.timers["run_binary"], } logged_times = [ f"{task}: {measurement:.2f}s" for task, measurement in time_measurements.items() if measurement is not None ] logger.debug(f"Time measurements: {', '.join(logged_times)}") def delete(self, code=True, data=True, run_args=True, directory=True, force=False): if self.project_dir is None: return # Nothing to delete if directory and not all([code, data, run_args]): raise ValueError( "When deleting the directory, code and data will" "be deleted as well. Set the corresponding " "parameters to True." ) fnames = [] # Delete data if data: results_dir = self.results_dir logger.debug(f"Deleting data files in '{results_dir}'") fnames.append(os.path.join(results_dir, "last_run_info.txt")) if self.profiled_codeobjects: fnames.append(os.path.join(results_dir, "profiling_info.txt")) for var in self.arrays: fnames.append(os.path.join(results_dir, self.get_array_filename(var))) # Delete code if code: logger.debug(f"Deleting code files in '{self.project_dir}'") if sys.platform == "win32": fnames.extend( [ "sourcefiles.txt", "win_makefile", "main.exe", "main.ilk", "main.pdb", "winmake.log", ] ) else: fnames.extend(["make.deps", "makefile", "main"]) fnames.extend( [ os.path.join("brianlib", "spikequeue.h"), os.path.join("brianlib", "stdint_compat.h"), ] ) fnames.extend(self.writer.header_files) for source_file in self.writer.source_files: fnames.append(source_file) base_name, _ = os.path.splitext(source_file) if sys.platform == "win32": fnames.append(f"{base_name}.obj") else: fnames.append(f"{base_name}.o") for static_array_name in self.static_arrays: fnames.append(os.path.join("static_arrays", static_array_name)) if run_args: for fname in self.run_args_arrays: fnames.append(os.path.join("static_arrays", fname)) for fname in fnames: full_fname = os.path.join(self.project_dir, fname) try: os.remove(full_fname) except OSError as ex: logger.warn(f'File "{full_fname}" could not be deleted: {str(ex)}') # Delete directories if directory: directories = [ "brianlib", "code_objects", "results", "static_arrays", "", ] full_directories = [ os.path.join(self.project_dir, directory) for directory in directories ] for full_directory in full_directories: try: os.rmdir(full_directory) except OSError: if not os.path.exists(full_directory): continue # The directory is not empty: if force: logger.debug( 'Directory "{}" is not empty, but ' "deleting it due to the use of the force " "option." ) shutil.rmtree(full_directory) else: # We only give a warning if there is a file or a # directory we do not know about. We do not want to e.g. # complain about an unknown file in the results # directory and then again complain about the results # directory when deleting the main directory still_present = [ name for name in os.listdir(full_directory) if os.path.isfile(name) or os.path.join(full_directory, name) not in full_directories ] if len(still_present): still_present = ", ".join( f'"{name}"' for name in still_present ) logger.warn( f"Not deleting the '{full_directory}' directory, " "because it contains files/directories " f"not added by Brian: {still_present}", name_suffix="delete_skips_directory", ) def network_run( self, net, duration, report=None, report_period=10 * second, namespace=None, profile=None, level=0, **kwds, ): if duration < 0: raise ValueError( f"Function 'run' expected a non-negative duration but got '{duration}'" ) self.networks.add(net) if kwds: logger.warn( "Unsupported keyword argument(s) provided for run: %s" % ", ".join(kwds.keys()) ) # We store this as an instance variable for later access by the # `code_object` method self.enable_profiling = profile # Allow setting `profile` in the `set_device` call (used e.g. in brian2cuda # SpeedTest configurations) if profile is None: self.enable_profiling = self.build_options.get("profile", False) all_objects = net.sorted_objects net._clocks = {obj.clock for obj in all_objects} t_end = net.t + duration for clock in net._clocks: clock.set_interval(net.t, t_end) # Get the local namespace if namespace is None: namespace = get_local_namespace(level=level + 2) net.before_run(namespace) self.synapses |= {s for s in net.objects if isinstance(s, Synapses)} self.clocks.update(net._clocks) net.t_ = float(t_end) # TODO: remove this horrible hack for clock in self.clocks: if clock.name == "clock": clock._name = "_clock" # Extract all the CodeObjects # Note that since we ran the Network object, these CodeObjects will be sorted into the right # running order, assuming that there is only one clock code_objects = [] for obj in all_objects: if obj.active: for codeobj in obj._code_objects: code_objects.append((obj.clock, codeobj)) # Code for a progress reporting function standard_code = """ std::string _format_time(float time_in_s) { float divisors[] = {24*60*60, 60*60, 60, 1}; char letters[] = {'d', 'h', 'm', 's'}; float remaining = time_in_s; std::string text = ""; int time_to_represent; for (int i =0; i < sizeof(divisors)/sizeof(float); i++) { time_to_represent = int(remaining / divisors[i]); remaining -= time_to_represent * divisors[i]; if (time_to_represent > 0 || text.length()) { if(text.length() > 0) { text += " "; } text += (std::to_string(time_to_represent)+letters[i]); } } //less than one second if(text.length() == 0) { text = "< 1s"; } return text; } void report_progress(const double elapsed, const double completed, const double start, const double duration) { if (completed == 0.0) { %STREAMNAME% << "Starting simulation at t=" << start << " s for duration " << duration << " s"; } else { %STREAMNAME% << completed*duration << " s (" << (int)(completed*100.) << "%) simulated in " << _format_time(elapsed); if (completed < 1.0) { const int remaining = (int)((1-completed)/completed*elapsed+0.5); %STREAMNAME% << ", estimated " << _format_time(remaining) << " remaining."; } } %STREAMNAME% << std::endl << std::flush; } """ if report is None: report_func = "" elif report == "text" or report == "stdout": report_func = standard_code.replace("%STREAMNAME%", "std::cout") elif report == "stderr": report_func = standard_code.replace("%STREAMNAME%", "std::cerr") elif isinstance(report, str): report_func = """ void report_progress(const double elapsed, const double completed, const double start, const double duration) { %REPORT% } """.replace( "%REPORT%", report ) else: raise TypeError( "report argument has to be either 'text', " "'stdout', 'stderr', or the code for a report " "function" ) if report_func != "": if self.report_func != "" and report_func != self.report_func: raise NotImplementedError( "The C++ standalone device does not " "support multiple report functions, " "each run has to use the same (or " "none)." ) self.report_func = report_func if report is not None: report_call = "report_progress" else: report_call = "NULL" # Generate the updaters run_lines = [f"{net.name}.clear();"] all_clocks = set() for clock, codeobj in code_objects: run_lines.append(f"{net.name}.add(&{clock.name}, _run_{codeobj.name});") all_clocks.add(clock) # Under some rare circumstances (e.g. a NeuronGroup only defining a # subexpression that is used by other groups (via linking, or recorded # by a StateMonitor) *and* not calculating anything itself *and* using a # different clock than all other objects) a clock that is not used by # any code object should nevertheless advance during the run. We include # such clocks without a code function in the network. for clock in net._clocks: if clock not in all_clocks: run_lines.append(f"{net.name}.add(&{clock.name}, NULL);") run_lines.extend(self.code_lines["before_network_run"]) if not self.run_args_applied: run_lines.append("set_from_command_line(args);") self.run_args_applied = True run_lines.append( f"{net.name}.run({float(duration)!r}, {report_call}," f" {float(report_period)!r});" ) run_lines.extend(self.code_lines["after_network_run"]) self.main_queue.append(("run_network", (net, run_lines))) net.after_run() # Manually set the cache for the clocks, simulation scripts might # want to access the time (which has been set in code and is therefore # not accessible by the normal means until the code has been built and # run) for clock in net._clocks: self.array_cache[clock.variables["timestep"]] = np.array([clock._i_end]) self.array_cache[clock.variables["t"]] = np.array( [clock._i_end * clock.dt_] ) if self.build_on_run: if self.has_been_run: raise RuntimeError( "The network has already been built and run " "before. Use set_device with " "build_on_run=False and an explicit " "device.build call to use multiple run " "statements with this device." ) self.build(direct_call=False, **self.build_options) def network_store(self, net, *args, **kwds): raise NotImplementedError( "The store/restore mechanism is not supported in the C++ standalone" ) def network_restore(self, net, *args, **kwds): raise NotImplementedError( "The store/restore mechanism is not supported in the C++ standalone" ) def network_get_profiling_info(self, net): fname = os.path.join(self.project_dir, "results", "profiling_info.txt") if not os.path.exists(fname): raise ValueError( "No profiling info collected (did you run with 'profile=True'?)" ) net._profiling_info = [] with open(fname) as f: for line in f: (key, val) = line.split() net._profiling_info.append((key, float(val) * second)) return sorted(net._profiling_info, key=lambda item: item[1], reverse=True) def run_function(self, name, include_in_parent=True): """ Context manager to divert code into a function Code that happens within the scope of this context manager will go into the named function. Parameters ---------- name : str The name of the function to divert code into. include_in_parent : bool Whether or not to include a call to the newly defined function in the parent context. """ return RunFunctionContext(name, include_in_parent) class RunFunctionContext: def __init__(self, name, include_in_parent): self.name = name self.include_in_parent = include_in_parent def __enter__(self): cpp_standalone_device.main_queue.append( ("start_run_func", (self.name, self.include_in_parent)) ) def __exit__(self, type, value, traceback): cpp_standalone_device.main_queue.append( ("end_run_func", (self.name, self.include_in_parent)) ) cpp_standalone_device = CPPStandaloneDevice() all_devices["cpp_standalone"] = cpp_standalone_device