'''
Module implementing the bulk of the brian2genn interface by defining the "genn" device.
'''
import os
import shutil
import sys
import platform
from subprocess import call, check_call, CalledProcessError
import inspect
from collections import defaultdict
import tempfile
import itertools
import numpy
import numbers
from brian2.codegen.translation import make_statements
from brian2.input.poissoninput import PoissonInput
from brian2.spatialneuron.spatialneuron import (SpatialNeuron,
SpatialStateUpdater)
from brian2.units import second
from brian2.codegen.generators.cpp_generator import (c_data_type,
CPPCodeGenerator)
from brian2.codegen.templates import MultiTemplate
from brian2.core.clocks import defaultclock
from brian2.core.variables import *
from brian2.core.network import Network
from brian2.devices.device import all_devices
from brian2.devices.cpp_standalone.device import CPPStandaloneDevice
from brian2.parsing.rendering import CPPNodeRenderer
from brian2.synapses.synapses import Synapses, SynapticPathway
from brian2.monitors.spikemonitor import SpikeMonitor
from brian2.monitors.ratemonitor import PopulationRateMonitor
from brian2.monitors.statemonitor import StateMonitor
from brian2.utils.filetools import copy_directory, ensure_directory
from brian2.utils.stringtools import word_substitute, get_identifiers
from brian2.groups.group import Group, CodeRunner
from brian2.groups.neurongroup import (NeuronGroup, StateUpdater, Resetter,
Thresholder, SubexpressionUpdater)
from brian2.groups.subgroup import Subgroup
from brian2.input.poissongroup import PoissonGroup
from brian2.input.spikegeneratorgroup import *
from brian2.synapses.synapses import StateUpdater as SynapsesStateUpdater
from brian2.utils.logger import get_logger, std_silent
from brian2.devices.cpp_standalone.codeobject import CPPStandaloneCodeObject
from brian2 import prefs
from .codeobject import GeNNCodeObject, GeNNUserCodeObject
from .genn_generator import get_var_ndim
__all__ = ['GeNNDevice']
logger = get_logger('brian2.devices.genn')
[docs]def stringify(code):
'''
Helper function to prepare multiline strings (potentially including
quotation marks) to be included in strings.
Parameters
----------
code : str
The code to convert.
'''
code = code.replace('\n', '\\n\\\n')
code = code.replace('"', '\\"')
return code
[docs]def freeze(code, ns):
'''
Support function for substituting constant values.
'''
# 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, basestring):
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 v < 0:
string_value = '(%s)' % string_value
code = word_substitute(code, {k: string_value})
else:
pass # don't deal with this object
return code
[docs]def get_compile_args():
'''
Get the compile args based on the users preferences. Uses Brian's
preferences for the C++ compilation (either `codegen.cpp.extra_compile_args`
for both Windows and UNIX, or `codegen.cpp.extra_compile_args_gcc` for UNIX
and `codegen.cpp.extra_compile_args_msvc` for Windows), and the Brian2GeNN
preference `devices.genn.extra_compile_args_nvcc` for the CUDA compilation
with nvcc.
Returns
-------
(compile_args_gcc, compile_args_msvc, compile_args_nvcc) : (str, str, str)
Tuple with the respective compiler arguments (as strings).
'''
if prefs.codegen.cpp.extra_compile_args is not None:
args = ' '.join(prefs.codegen.cpp.extra_compile_args)
compile_args_gcc = args
compile_args_msvc = args
else:
compile_args_gcc = ' '.join(prefs.codegen.cpp.extra_compile_args_gcc)
compile_args_msvc = ' '.join(prefs.codegen.cpp.extra_compile_args_msvc)
compile_args_nvcc = ' '.join(prefs.devices.genn.extra_compile_args_nvcc)
return compile_args_gcc, compile_args_msvc, compile_args_nvcc
[docs]def get_genn_prefs():
'''
Get the GeNN preferences that are exposed in brian2genn user preferences.
Uses the Brian2GeNN preferences `devices.genn.auto_choose_device` and
`devices.genn.default_device`
Returns
-------
(genn_auto_choose_device, genn_default_device) : (int, int)
Tuple with the genn preference settings.
'''
if prefs.devices.genn.auto_choose_device:
genn_auto_choose_device= 1
else:
genn_auto_choose_device= 0
genn_default_device= prefs.devices.genn.default_device
return genn_auto_choose_device, genn_default_device
[docs]def decorate(code, variables, shared_variables, parameters, do_final=True):
'''
Support function for inserting GeNN-specific "decorations" for variables and
parameters, such as $(.).
'''
# this is a bit of a hack, it should be part of the language probably
for v in itertools.chain(variables, shared_variables, parameters):
code = word_substitute(code, {v: '$(' + v + ')'})
code = word_substitute(code, {'dt': 'DT'}).strip()
if do_final:
code = stringify(code)
code = word_substitute(code, {'addtoinSyn': '$(addtoinSyn)'})
code = word_substitute(code, {'_hidden_weightmatrix': '$(_hidden_weightmatrix)'})
return code
[docs]class DelayedCodeObject(object):
'''
Dummy class used for delaying the CodeObject creation of stateupdater,
thresholder, and resetter of a NeuronGroup (which will all be merged into a
single code object).
'''
def __init__(self, owner, name, abstract_code, variables, variable_indices,
override_conditional_write):
self.owner = owner
self.name = name
self.abstract_code = abstract_code
self.variables = variables
self.variable_indices = variable_indices
self.override_conditional_write = override_conditional_write
[docs]class neuronModel(object):
'''
Class that contains all relevant information of a neuron model.
'''
def __init__(self):
self.name = ''
self.clock = None
self.N = 0
self.variables = []
self.variabletypes = []
self.variablescope = dict()
self.shared_variables = []
self.shared_variabletypes = []
self.parameters = []
self.pvalue = []
self.code_lines = []
self.thresh_cond_lines = []
self.reset_code_lines = []
self.support_code_lines = []
self.run_regularly_object = None
self.run_regularly_step = None
self.run_regularly_read = None
self.run_regularly_write = None
[docs]class spikegeneratorModel(object):
'''
Class that contains all relevant information of a spike generator group.
'''
def __init__(self):
self.name = ''
self.N = 0
[docs]class synapseModel(object):
'''
Class that contains all relevant information about a synapse model.
'''
def __init__(self):
self.name = ''
self.srcname = ''
self.srcN = 0
self.trgname = ''
self.trgN = 0
self.N = 0
self.variables = []
self.variabletypes = []
self.shared_variables = []
self.shared_variabletypes = []
self.variablescope = dict()
self.external_variables = []
self.parameters = []
self.pvalue = []
self.postSyntoCurrent = []
# The following dictionaries contain keys "pre"/"post" for the pre-
# and post-synaptic pathway and "dynamics" for the synaptic dynamics
self.main_code_lines = defaultdict(str)
self.support_code_lines = defaultdict(str)
self.connectivity = ''
self.delay = 0
[docs]class spikeMonitorModel(object):
'''
Class the contains all relevant information about a spike monitor.
'''
def __init__(self):
self.name = ''
self.neuronGroup = ''
self.notSpikeGeneratorGroup = True
[docs]class rateMonitorModel(object):
'''
CLass that contains all relevant information about a rate monitor.
'''
def __init__(self):
self.name = ''
self.neuronGroup = ''
self.notSpikeGeneratorGroup = True
[docs]class stateMonitorModel(object):
'''
Class that contains all relvant information about a state monitor.
'''
def __init__(self):
self.name = ''
self.monitored = ''
self.isSynaptic = False
self.variables = []
self.srcN = 0
self.trgN = 0
self.when = ''
self.connectivity = ''
[docs]class CPPWriter(object):
'''
Class that provides the method for writing C++ files from a string of code.
'''
def __init__(self, project_dir):
self.project_dir = project_dir
self.source_files = []
self.header_files = []
[docs] def write(self, filename, contents):
logger.diagnostic('Writing file %s:\n%s' % (filename, contents))
if filename.lower().endswith('.cpp'):
self.source_files.append(filename)
elif filename.lower().endswith('.h'):
self.header_files.append(filename)
elif filename.endswith('.*'):
self.write(filename[:-1] + 'cpp', contents.cpp_file)
self.write(filename[:-1] + 'h', contents.h_file)
return
fullfilename = os.path.join(self.project_dir, filename)
if os.path.exists(fullfilename):
if open(fullfilename, 'r').read() == contents:
return
open(fullfilename, 'w').write(contents)
# ------------------------------------------------------------------------------
# Start of GeNNDevice
# ------------------------------------------------------------------------------
[docs]class GeNNDevice(CPPStandaloneDevice):
'''
The main "genn" device. This does most of the translation work from Brian 2
generated code to functional GeNN code, assisted by the "GeNN language".
'''
def __init__(self):
super(GeNNDevice, self).__init__()
# Remember whether we have already passed the "run" statement
self.run_statement_used = False
self.network_schedule = ['start', 'synapses', 'groups', 'thresholds',
'resets', 'end']
self.neuron_models = []
self.spikegenerator_models = []
self.synapse_models = []
self.delays = {}
self.spike_monitor_models = []
self.rate_monitor_models = []
self.state_monitor_models = []
self.run_duration = None
self.simple_code_objects = {}
self.report_func = ''
#: List of all source and header files (to be included in runner)
self.source_files = []
self.header_files = []
self.connectivityDict = dict()
self.groupDict = dict()
[docs] def activate(self, build_on_run=True, **kwargs):
new_prefs = {'codegen.generators.cpp.restrict_keyword': '__restrict',
'codegen.loop_invariant_optimisations': False,
'core.network.default_schedule': ['start', 'synapses',
'groups', 'thresholds',
'resets', 'end']}
changed = []
for new_pref, new_value in new_prefs.iteritems():
if prefs[new_pref] != new_value:
changed.append(new_pref)
prefs[new_pref] = new_value
if changed:
logger.info('The following preferences have been changed for '
'Brian2GeNN, reset them manually if you use a '
'different device later in the same script: '
'{}'.format(', '.join(changed)), once=True)
prefs._backup()
super(GeNNDevice, self).activate(build_on_run, **kwargs)
[docs] def code_object_class(self, codeobj_class=None, *args, **kwds):
if codeobj_class is GeNNCodeObject:
return codeobj_class
else:
return GeNNUserCodeObject
[docs] def code_object(self, owner, name, abstract_code, variables, template_name,
variable_indices, codeobj_class=None, template_kwds=None,
override_conditional_write=None):
'''
Processes abstract code into code objects and stores them in different
arrays for `GeNNCodeObjects` and `GeNNUserCodeObjects`.
'''
if (template_name in ['stateupdate', 'threshold', 'reset'] and
isinstance(owner, NeuronGroup)):
# Delay the code generation process, we want to merge them into one
# code object later
codeobj = DelayedCodeObject(owner=owner,
name=name,
abstract_code=abstract_code,
variables=variables,
variable_indices=variable_indices,
override_conditional_write=override_conditional_write)
self.simple_code_objects[name] = codeobj
elif template_name in ['reset', 'synapses', 'stateupdate', 'threshold']:
codeobj_class = GeNNCodeObject
codeobj = super(GeNNDevice, self).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,
)
self.simple_code_objects[codeobj.name] = codeobj
else:
codeobj_class = GeNNUserCodeObject
codeobj = super(GeNNDevice, self).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,
)
self.code_objects[codeobj.name] = codeobj
return codeobj
# The following two methods are only overwritten to catch assignments to the
# delay variable -- GeNN does not support heterogeneous delays
[docs] def fill_with_array(self, var, arr):
if isinstance(var.owner, Synapses) and var.name == 'delay':
# Assigning is only allowed if the variable has been declared in the
# Synapse constructor and is therefore scalar
if not var.scalar:
raise NotImplementedError(
'GeNN does not support assigning to the '
'delay variable -- set the delay for all'
'synapses (heterogeneous delays are not '
'supported) as an argument to the '
'Synapses initializer.')
else:
# We store the delay so that we can later access it
self.delays[var.owner.name] = numpy.asarray(arr).item()
super(GeNNDevice, self).fill_with_array(var, arr)
[docs] def variableview_set_with_index_array(self, variableview, item,
value, check_units):
var = variableview.variable
if isinstance(var.owner, Synapses) and var.name == 'delay':
raise NotImplementedError('GeNN does not support assigning to the '
'delay variable -- set the delay for all '
'synapses (heterogeneous delays are not '
'supported) as an argument to the '
'Synapses initializer.')
super(GeNNDevice, self).variableview_set_with_index_array(variableview,
item,
value,
check_units)
[docs] def variableview_set_with_expression(self, variableview, item, code, run_namespace, check_units=True):
var = variableview.variable
if isinstance(var.owner, Synapses) and var.name == 'delay':
raise NotImplementedError('GeNN does not support assigning to the '
'delay variable -- set the delay for all '
'synapses (heterogeneous delays are not '
'supported) as an argument to the '
'Synapses initializer.')
variableview.set_with_expression.original_function(variableview,
item,
code,
run_namespace,
check_units)
[docs] def variableview_set_with_expression_conditional(self, variableview, cond,
code, run_namespace,
check_units=True):
var = variableview.variable
if isinstance(var.owner, Synapses) and var.name == 'delay':
raise NotImplementedError('GeNN does not support assigning to the '
'delay variable -- set the delay for all '
'synapses (heterogeneous delays are not '
'supported) as an argument to the '
'Synapses initializer.')
variableview.set_with_expression_conditional.original_function(variableview,
cond,
code,
run_namespace,
check_units)
# --------------------------------------------------------------------------
[docs] def make_main_lines(self):
'''
Generates the code lines that handle initialisation of Brian 2
cpp_standalone type arrays. These are then translated into the
appropriate GeNN data structures in separately generated code.
'''
main_lines = []
procedures = [('', main_lines)]
runfuncs = {}
for func, args in self.main_queue:
if func == 'run_code_object':
codeobj, = args
if self.run_statement_used:
raise NotImplementedError('Cannot execute code after the '
'run statement '
'(CodeObject: %s)' % codeobj.name)
# explicitly exclude spike queue related code objects here:
if not (codeobj.name.endswith('_initialise_queue') or
(codeobj.name.endswith('_push_spikes'))):
main_lines.append('_run_%s();' % codeobj.name)
elif func == 'run_network':
net, netcode = args
# do nothing
elif func == 'set_by_constant':
arrayname, value, is_dynamic = args
size_str = arrayname + '.size()' if is_dynamic else '_num_' + arrayname
code = '''
for(int i=0; i<{size_str}; i++)
{{
{arrayname}[i] = {value};
}}
'''.format(arrayname=arrayname, size_str=size_str,
value=CPPNodeRenderer().render_expr(repr(value)))
main_lines.extend(code.split('\n'))
elif func == 'set_by_array':
arrayname, staticarrayname, is_dynamic = args
size_str = arrayname + '.size()' if is_dynamic else '_num_' + arrayname
code = '''
for(int i=0; i<{size_str}; i++)
{{
{arrayname}[i] = {staticarrayname}[i];
}}
'''.format(arrayname=arrayname, size_str=size_str,
staticarrayname=staticarrayname)
main_lines.extend(code.split('\n'))
elif func == 'set_by_single_value':
arrayname, item, value = args
code = '{arrayname}[{item}] = {value};'.format(
arrayname=arrayname,
item=item,
value=value)
main_lines.extend([code])
elif func == 'set_array_by_array':
arrayname, staticarrayname_index, staticarrayname_value = args
code = '''
for(int i=0; i<_num_{staticarrayname_index}; i++)
{{
{arrayname}[{staticarrayname_index}[i]] = {staticarrayname_value}[i];
}}
'''.format(arrayname=arrayname,
staticarrayname_index=staticarrayname_index,
staticarrayname_value=staticarrayname_value)
main_lines.extend(code.split('\n'))
elif func == 'resize_array':
array_name, new_size = args
main_lines.append("{array_name}.resize({new_size});".format(
array_name=array_name,
new_size=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('%s();' % 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]
else:
raise NotImplementedError(
"Unknown main queue function type " + func)
# generate the finalisations
for codeobj in self.code_objects.itervalues():
if hasattr(codeobj.code, 'main_finalise'):
main_lines.append(codeobj.code.main_finalise)
return main_lines
[docs] def fix_random_generators(self, model, code):
'''
Translates cpp_standalone style random number generator calls into
GeNN- compatible calls by replacing the cpp_standalone
`_vectorisation_idx` argument with the GeNN `_seed` argument.
'''
# TODO: In principle, _vectorisation_idx is an argument to any
# function that does not take any arguments -- in practice, random
# number generators are the only argument-less functions that are
# commonly used. We cannot check for explicit names `_rand`, etc.,
# since multiple uses of binomial or PoissonInput will need to names
# that we cannot easily predict (poissoninput_binomial_2, etc.)
if '(_vectorisation_idx)' in code:
code = code.replace('(_vectorisation_idx)',
'(_seed)')
if not '_seed' in model.variables:
model.variables.append('_seed')
model.variabletypes.append('uint64_t')
model.variablescope['_seed'] = 'genn'
return code
# --------------------------------------------------------------------------
[docs] def build(self, directory='GeNNworkspace', compile=True, run=True,
use_GPU=True,
debug=False, with_output=True, direct_call=True):
'''
This function does the main post-translation work for the genn device.
It uses the code generated during/before run() and extracts information
about neuron groups, synapse groups, monitors, etc. that is then
formatted for use in GeNN-specific templates. The overarching strategy
of the brian2genn interface is to use cpp_standalone code generation
and templates for most of the "user-side code" (in the meaning defined
in GeNN) and have GeNN-specific templates for the model definition and
the main code for the executable that pulls everything together (in
main.cpp and engine.cpp templates). The handling of input/output
arrays for everything is lent from cpp_standalone and the
cpp_standalone arrays are then translated into GeNN-suitable data
structures using the static (not code-generated) b2glib library
functions. This means that the GeNN specific cod only has to be
concerned about executing the correct model and feeding back results
into the appropriate cpp_standalone data structures.
'''
print 'building genn executable ...'
# Check for GeNN compatibility
if directory is None: # used during testing
directory = tempfile.mkdtemp()
# Start building the project
self.project_dir = directory
ensure_directory(directory)
for d in ['code_objects', 'results', 'static_arrays']:
ensure_directory(os.path.join(directory, d))
writer = CPPWriter(directory)
logger.debug(
"Writing GeNN project to directory " + os.path.normpath(directory))
# FIXME: This is only needed to keep Brian2GeNN compatible with Brian2 2.0.1 and earlier
if isinstance(self.arange_arrays, dict):
arange_arrays = sorted([(var, start)
for var, start in
self.arange_arrays.iteritems()],
key=lambda (var, start): var.name)
else:
arange_arrays = self.arange_arrays
# write the static arrays
logger.debug("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))
networks = [net() for net in Network.__instances__() if
net().name != '_fake_network']
if len(networks) != 1:
raise NotImplementedError("GeNN only supports a single network")
net = networks[0]
synapses = []
synapses.extend(s for s in net.objects if isinstance(s, Synapses))
main_lines = self.make_main_lines()
# assemble the model descriptions:
objects = dict((obj.name, obj) for obj in net.objects)
neuron_groups = [obj for obj in net.objects if
isinstance(obj, NeuronGroup)]
poisson_groups = [obj for obj in net.objects if
isinstance(obj, PoissonGroup)]
spikegenerator_groups = [obj for obj in net.objects if
isinstance(obj, SpikeGeneratorGroup)]
synapse_groups = [obj for obj in net.objects if
isinstance(obj, Synapses)]
spike_monitors = [obj for obj in net.objects if
isinstance(obj, SpikeMonitor)]
rate_monitors = [obj for obj in net.objects if
isinstance(obj, PopulationRateMonitor)]
state_monitors = [obj for obj in net.objects if
isinstance(obj, StateMonitor)]
for obj in net.objects:
if isinstance(obj, (SpatialNeuron, SpatialStateUpdater)):
raise NotImplementedError(
'Brian2GeNN does not support multicompartmental neurons')
if not isinstance(obj, (
NeuronGroup, PoissonGroup, SpikeGeneratorGroup, Synapses,
SpikeMonitor, PopulationRateMonitor, StateMonitor,
StateUpdater, SynapsesStateUpdater, Resetter,
Thresholder, SynapticPathway, CodeRunner)):
raise NotImplementedError(
"Brian2GeNN does not support objects of type "
"'%s'" % obj.__class__.__name__)
# We only support run_regularly and "constant over dt"
# subexpressions for neurons
if (isinstance(obj, SubexpressionUpdater) and
not isinstance(obj.group, NeuronGroup)):
raise NotImplementedError(
'Subexpressions with the flag "constant over dt" are only '
'supported for NeuronGroup (not for objects of type '
'"%s").' % obj.group.__class__.__name__
)
if (obj.__class__ == CodeRunner and
not isinstance(obj.group, NeuronGroup)):
raise NotImplementedError(
'CodeRunner objects (most commonly created with the '
'"run_regularly" method) are only supported for '
'NeuronGroup (not for objects of type '
'"%s").' % obj.group.__class__.__name__
)
self.model_name = net.name + '_model'
self.dtDef = 'model.setDT(' + repr(float(defaultclock.dt)) + ');'
# Process groups
self.process_neuron_groups(neuron_groups, objects)
self.process_poisson_groups(objects, poisson_groups)
self.process_spikegenerators(spikegenerator_groups)
self.process_synapses(synapse_groups)
# Process monitors
self.process_spike_monitors(spike_monitors)
self.process_rate_monitors(rate_monitors)
self.process_state_monitors(directory, state_monitors, writer)
# Write files from templates
# Create an empty network.h file, this allows us to use Brian2's
# objects.cpp template unchanged
writer.write('network.*', GeNNUserCodeObject.templater.network(None, None))
self.header_files.append('network.h')
self.generate_objects_source(arange_arrays, net,
static_array_specs,
synapses, writer)
self.copy_source_files(writer, directory)
# Rename randomkit.c so that it gets compiled by an explicit rule in
# GeNN's makefile template, otherwise optimization flags will not be
# used.
randomkit_dir = os.path.join(directory, 'brianlib', 'randomkit')
shutil.move(os.path.join(randomkit_dir, 'randomkit.c'),
os.path.join(randomkit_dir, 'randomkit.cc'))
self.generate_code_objects(writer)
self.generate_model_source(writer)
self.generate_main_source(writer, main_lines)
self.generate_engine_source(writer)
self.generate_makefile(directory, use_GPU)
# Compile and run
if compile:
try:
self.compile_source(debug, directory, use_GPU)
except CalledProcessError as ex:
raise RuntimeError(('Project compilation failed (Command {cmd} '
'failed with error code {returncode}).\n'
'See the output above (if any) for more '
'details.').format(cmd=ex.cmd,
returncode=ex.returncode)
)
if run:
try:
self.run(directory, use_GPU, with_output)
except CalledProcessError as ex:
if ex.returncode == 222:
raise NotImplementedError('GeNN does not support multiple '
'synapses per neuron pair (use '
'multiple Synapses objects).')
else:
raise RuntimeError(('Project run failed (Command {cmd} '
'failed with error code {returncode}).\n'
'See the output above (if any) for more '
'details.').format(cmd=ex.cmd,
returncode=ex.returncode)
)
[docs] def generate_code_objects(self, writer):
# Generate data for non-constant values
code_object_defs = defaultdict(list)
for codeobj in self.code_objects.itervalues():
lines = []
for k, v in codeobj.variables.iteritems():
if isinstance(v, ArrayVariable):
try:
if isinstance(v, DynamicArrayVariable):
if get_var_ndim(v) == 1:
dyn_array_name = self.dynamic_arrays[v]
array_name = self.arrays[v]
line = '{c_type}* const {array_name} = &{dyn_array_name}[0];'
line = line.format(c_type=c_data_type(v.dtype),
array_name=array_name,
dyn_array_name=dyn_array_name)
lines.append(line)
line = 'const int _num{k} = {dyn_array_name}.size();'
line = line.format(k=k,
dyn_array_name=dyn_array_name)
lines.append(line)
else:
lines.append('const int _num%s = %s;' % (k, v.size))
except TypeError:
pass
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 not line in code_object_defs[codeobj.name]:
code_object_defs[codeobj.name].append(line)
# Generate the code objects
for codeobj in self.code_objects.itervalues():
ns = codeobj.variables
# TODO: fix these freeze/CONSTANTS hacks somehow - they work but not elegant.
if not codeobj.template_name in ['stateupdate', 'threshold',
'reset', 'synapses']:
if isinstance(codeobj.code, MultiTemplate):
code = freeze(codeobj.code.cpp_file, ns)
code = code.replace('%CONSTANTS%', '\n'.join(
code_object_defs[codeobj.name]))
code = '#include "objects.h"\n' + code
writer.write('code_objects/' + codeobj.name + '.cpp', code)
self.source_files.append(
'code_objects/' + codeobj.name + '.cpp')
writer.write('code_objects/' + codeobj.name + '.h',
codeobj.code.h_file)
self.header_files.append(
'code_objects/' + codeobj.name + '.h')
[docs] def run(self, directory, use_GPU, with_output):
gpu_arg = "1" if use_GPU else "0"
if gpu_arg == "1":
where = 'on GPU'
else:
where = 'on CPU'
print 'executing genn binary %s ...' % where
pref_vars = prefs['devices.cpp_standalone.run_environment_variables']
for key, value in itertools.chain(pref_vars.iteritems(),
self.run_environment_variables.iteritems()):
if key in os.environ and os.environ[key] != value:
logger.info('Overwriting environment variable '
'"{key}"'.format(key=key),
name_suffix='overwritten_env_var', once=True)
os.environ[key] = value
with std_silent(with_output):
if os.sys.platform == 'win32':
cmd = directory + "\\main.exe test " + str(
self.run_duration) + " " + gpu_arg
check_call(cmd, cwd=directory)
else:
# print ["./main", "test", str(self.run_duration), gpu_arg]
check_call(["./main", "test", str(self.run_duration), gpu_arg],
cwd=directory)
self.has_been_run = True
last_run_info = open(
os.path.join(directory, 'results/last_run_info.txt'), 'r').read()
self._last_run_time, self._last_run_completed_fraction = map(float,
last_run_info.split())
# The following is a verbatim copy of the respective code in
# CPPStandaloneDevice.run. In the long run, we can hopefully implement
# this on the device-independent level, see #761 and discussion in
# #750.
# 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 owner.name in already_checked:
continue
if isinstance(owner, Group):
owner._check_for_invalid_states()
already_checked.add(owner.name)
except ReferenceError:
pass
[docs] def compile_source(self, debug, directory, use_GPU):
if prefs.devices.genn.path is not None:
genn_path = prefs.devices.genn.path
logger.debug('Using GeNN path from preference: '
'"{}"'.format(genn_path))
elif os.path.isdir(os.path.join(sys.prefix, 'opt', 'genn')):
genn_path = os.path.join(sys.prefix, 'opt', 'genn')
logger.debug('Using GeNN path from installation: '
'"{}"'.format(genn_path))
if 'GENN_PATH' in os.environ:
logger.debug('Unsetting the GENN_PATH environment variable to '
'avoid conflicts with the installed GeNN version.')
del os.environ['GENN_PATH']
elif 'GENN_PATH' in os.environ:
genn_path = os.environ['GENN_PATH']
logger.debug('Using GeNN path from environment variable: '
'"{}"'.format(genn_path))
else:
raise RuntimeError('Set the GENN_PATH environment variable or '
'the devices.genn.path preference.')
env = os.environ.copy()
env['GENN_PATH'] = genn_path
if use_GPU:
if prefs.devices.genn.cuda_path is not None:
cuda_path = prefs.devices.genn.cuda_path
env['CUDA_PATH'] = cuda_path
logger.debug('Using CUDA path from preference: '
'"{}"'.format(cuda_path))
elif 'CUDA_PATH' in env:
cuda_path = env['CUDA_PATH']
logger.debug('Using CUDA path from environment variable: '
'"{}"'.format(cuda_path))
else:
raise RuntimeError('Set the CUDA_PATH environment variable or '
'the devices.genn.cuda_path preference.')
if prefs['codegen.cpp.extra_link_args']:
# declare the link flags as an environment variable so that GeNN's
# generateALL can pick it up
env['LINK_FLAGS'] = ' '.join(prefs['codegen.cpp.extra_link_args'])
with std_silent(debug):
if os.sys.platform == 'win32':
vcvars_loc = prefs['codegen.cpp.msvc_vars_location']
if vcvars_loc == '':
from distutils import msvc9compiler
for version in xrange(16, 8, -1):
fname = msvc9compiler.find_vcvarsall(version)
if fname:
vcvars_loc = fname
break
if vcvars_loc == '':
raise IOError("Cannot find vcvarsall.bat on standard "
"search path. Set the "
"codegen.cpp.msvc_vars_location preference "
"explicitly.")
arch_name = prefs['codegen.cpp.msvc_architecture']
if arch_name == '':
mach = platform.machine()
if mach == 'AMD64':
arch_name = 'x86_amd64'
else:
arch_name = 'x86'
vcvars_cmd = '"{vcvars_loc}" {arch_name}'.format(
vcvars_loc=vcvars_loc, arch_name=arch_name)
buildmodel_cmd = os.path.join(genn_path, 'lib', 'bin',
'genn-buildmodel.bat')
cmd = vcvars_cmd + ' && ' + buildmodel_cmd + " " + self.model_name + ".cpp"
if not use_GPU:
cmd += ' -c'
cmd += ' && nmake /f WINmakefile clean && nmake /f WINmakefile'
check_call(cmd.format(genn_path=genn_path), cwd=directory, env=env)
else:
buildmodel_cmd = os.path.join(genn_path, 'lib', 'bin', 'genn-buildmodel.sh')
args = [buildmodel_cmd, self.model_name + '.cpp']
if not use_GPU:
args += ['-c']
check_call(args, cwd=directory, env=env)
call(["make", "clean"], cwd=directory, env=env)
check_call(["make"], cwd=directory, env=env)
[docs] def add_parameter(self, model, varname, variable):
model.parameters.append(varname)
model.pvalue.append(CPPNodeRenderer().render_expr(repr(variable.value)))
[docs] def add_array_variable(self, model, varname, variable):
if variable.scalar:
model.shared_variables.append(varname)
model.shared_variabletypes.append(c_data_type(variable.dtype))
else:
model.variables.append(varname)
model.variabletypes.append(c_data_type(variable.dtype))
model.variablescope[varname] = 'brian'
[docs] def add_array_variables(self, model, owner):
for varname, variable in owner.variables.iteritems():
if varname in ['_spikespace', 't', 'dt']:
pass
elif getattr(variable.owner, 'name', None) != owner.name:
pass
elif isinstance(variable, ArrayVariable):
self.add_array_variable(model, varname, variable)
[docs] def process_poisson_groups(self, objects, poisson_groups):
for obj in poisson_groups:
# throw error if events other than spikes are used
if len(obj.events.keys()) > 1 or (len(
obj.events.keys()) == 1 and not obj.events.iterkeys().next() == 'spike'):
raise NotImplementedError(
'Brian2GeNN does not support events that are not spikes')
# Extract the variables
neuron_model = neuronModel()
neuron_model.name = obj.name
neuron_model.clock = obj.clock
neuron_model.N = obj.N
self.add_array_variables(neuron_model, obj)
support_lines = []
suffix = '_thresholder';
lines = neuron_model.thresh_cond_lines;
codeobj = objects[obj.name + suffix].codeobj
for k, v in codeobj.variables.iteritems():
if k != 'dt' and isinstance(v, Constant):
if k not in neuron_model.parameters:
self.add_parameter(neuron_model, k, v)
code = codeobj.code.cpp_file
code = self.fix_random_generators(neuron_model, code)
code = decorate(code, neuron_model.variables,
neuron_model.shared_variables,
neuron_model.parameters).strip()
lines.append(code)
code = stringify(codeobj.code.h_file)
support_lines.append(code)
neuron_model.support_code_lines = support_lines
self.neuron_models.append(neuron_model)
self.groupDict[neuron_model.name] = neuron_model
[docs] def process_neuron_groups(self, neuron_groups, objects):
for obj in neuron_groups:
# throw error if events other than spikes are used
if len(obj.events.keys()) > 1 or (len(
obj.events.keys()) == 1 and not obj.events.iterkeys().next() == 'spike'):
raise NotImplementedError(
'Brian2GeNN does not support events that are not spikes')
# Extract the variables
neuron_model = neuronModel()
neuron_model.name = obj.name
neuron_model.clock = obj.clock
neuron_model.N = obj.N
self.add_array_variables(neuron_model, obj)
# We have previously only created "dummy code objects" for the
# state update, threshold, and reset of a NeuronGroup. We will now
# generate a single code object for all of them, adding the
# threshold calculation code to the end of the state update. When
# using subexpressions, the threshold condition code could consist
# of multiple lines, and GeNN only supports a threshold condition
# that is directly used as an if condition. We therefore store the
# result in a boolean variable and only pass this variable as the
# threshold condition to GeNN.
# It is also important that stateupdate/threshold share the same
# code object with the reset, as in GeNN both codes have the same
# support code. If they used two separate code objects, adding the
# two support codes might lead to duplicate definitions of
# functions.
combined_abstract_code = {'stateupdate': [], 'reset': [],
'subexpression_update': [],
'run_regularly': [],
'poisson_input': []}
combined_variables = {}
combined_variable_indices = defaultdict(lambda: '_idx')
combined_override_conditional_write = set()
thresholder_codeobj = getattr(objects.get(obj.name + '_thresholder', None), 'codeobj', None)
if thresholder_codeobj is not None:
neuron_model.thresh_cond_lines = '_cond'
else:
neuron_model.thresh_cond_lines = '0'
run_regularly_objects = [o for o in objects
if o.startswith(obj.name + '_run_regularly')]
has_run_regularly = len(run_regularly_objects) > 0
if len(run_regularly_objects) > 1:
raise NotImplementedError('Brian2GeNN supports only a '
'single run_regularly operation '
'per NeuronGroup.')
for suffix, code_slot in [('_stateupdater', 'stateupdate'),
('_thresholder', 'stateupdate'),
('_resetter', 'reset'),
('_subexpression_update', 'subexpression_update'),
('_run_regularly', 'run_regularly')]:
full_name = obj.name + suffix
if full_name in objects and objects[full_name].codeobj is not None:
codeobj = objects[full_name].codeobj
combined_abstract_code[code_slot] += [codeobj.abstract_code[None]]
combined_variables.update(codeobj.variables)
combined_variable_indices.update(codeobj.variable_indices)
# The resetter includes "not_refractory" as an override_conditional_write
# variable, meaning that it removes the write-protection based on that
# variable that would otherwise apply to "unless refractory" variables,
# e.g. the membrane potential. This is not strictly necessary, it will just
# introduce an unnecessary check, because a neuron that spiked is by
# definition not in its refractory period. However, if we included it as
# a override_conditional_write variable for the whole code object here,
# this would apply also to the state updater, and therefore
# remove the write-protection from "unless refractory" variables in the
# state update code.
if suffix != '_resetter':
combined_override_conditional_write.update(codeobj.override_conditional_write)
if suffix == '_run_regularly':
if objects[full_name].when != 'start':
raise NotImplementedError('Brian2GeNN does not support changing '
'the scheduling slot for "run_regularly" '
'operations.')
neuron_model.parameters.append('_run_regularly_steps')
run_regularly_dt = objects[full_name].clock.dt_
neurongroup_dt = objects[full_name].group.dt_[:]
if run_regularly_dt < neurongroup_dt:
raise NotImplementedError('Brian2GeNN does not support run_regularly '
'operations with a dt smaller than the dt '
'used by the NeuronGroup.')
dt_mismatch = abs(((run_regularly_dt + neurongroup_dt/2) % neurongroup_dt) - neurongroup_dt/2)
if dt_mismatch > 1e-4*neurongroup_dt:
raise NotImplementedError('Brian2GeNN does not support run_regularly '
'operations where the dt is not a multiple of '
'the dt used by the NeuronGroup.')
step_value = int(run_regularly_dt/neurongroup_dt + 0.5)
neuron_model.run_regularly_step = step_value
neuron_model.pvalue.append(step_value)
if obj._refractory is not False:
combined_abstract_code['reset'] += ['lastspike = t',
'not_refractory = False']
# Find PoissonInputs targetting this NeuronGroup
poisson_inputs = [o for o in objects.itervalues()
if isinstance(o, PoissonInput) and
o.group.name == obj.name]
for poisson_input in poisson_inputs:
if poisson_input.when != 'synapses':
raise NotImplementedError('Brian2GeNN does not support '
'changing the scheduling slot '
'of PoissonInput objects.')
codeobj = poisson_input.codeobj
combined_abstract_code['poisson_input'] += [codeobj.abstract_code[None]]
combined_variables.update(codeobj.variables)
combined_variable_indices.update(codeobj.variable_indices)
for code_block in combined_abstract_code.iterkeys():
combined_abstract_code[code_block] = '\n'.join(combined_abstract_code[code_block])
if any(len(ac) for ac in combined_abstract_code.itervalues()):
codeobj = super(GeNNDevice, self).code_object(obj, obj.name + '_stateupdater',
combined_abstract_code,
combined_variables.copy(),
'neuron_code',
combined_variable_indices,
codeobj_class=GeNNCodeObject,
template_kwds={'has_run_regularly': has_run_regularly},
override_conditional_write=combined_override_conditional_write,
)
# Remove the code object from the code_objects dictionary, we
# take care of it manually and do not want it to be generated as
# part of `generate_code_objects`.
del self.code_objects[codeobj.name]
if has_run_regularly:
# Add an extra code object that executes the scalar code of
# the run_regularly operation (will be directly called from
# engine.cpp)
abstract_code = {'None': combined_abstract_code['run_regularly']}
rr_codeobject = self.code_object(obj, obj.name + '_run_regularly',
abstract_code,
combined_variables.copy(),
'run_regularly_scalar_code',
combined_variable_indices,
codeobj_class=GeNNUserCodeObject,
override_conditional_write=combined_override_conditional_write)
# FIXME: The following is redundant with what is done during
# the code object creation above. At the moment, the code
# object does not allow us to access the information we
# need (variables that are read/written by the run_regularly
# code), though.
generator = CPPCodeGenerator(combined_variables.copy(),
combined_variable_indices, owner=obj,
iterate_all=False,
codeobj_class=GeNNUserCodeObject,
name=obj.name + '_run_regularly',
template_name='run_regularly_scalar_code',
override_conditional_write=combined_override_conditional_write,
allows_scalar_write=True)
scalar_statements, _ = make_statements(abstract_code['None'], combined_variables, numpy.float64)
read, write, _ = generator.array_read_write(scalar_statements)
neuron_model.run_regularly_object = rr_codeobject
# Store which variables are read/written by the
# run_regularly scalar code. We have to copy these variables
# between Brian and GeNN.
neuron_model.run_regularly_read = read
neuron_model.run_regularly_write = write
for k, v in codeobj.variables.iteritems():
if k != 'dt' and isinstance(v, Constant):
if k not in neuron_model.parameters:
self.add_parameter(neuron_model, k, v)
update_code = codeobj.code.stateupdate_code
reset_code = codeobj.code.reset_code
for code, lines in [(update_code, neuron_model.code_lines),
(reset_code, neuron_model.reset_code_lines)]:
code = self.fix_random_generators(neuron_model, code)
code = decorate(code, neuron_model.variables,
neuron_model.shared_variables,
neuron_model.parameters).strip()
lines.append(code)
support_code = stringify(codeobj.code.h_file)
neuron_model.support_code_lines = support_code
self.neuron_models.append(neuron_model)
self.groupDict[neuron_model.name] = neuron_model
[docs] def process_spikegenerators(self, spikegenerator_groups):
for obj in spikegenerator_groups:
spikegenerator_model = spikegeneratorModel()
spikegenerator_model.name = obj.name
spikegenerator_model.N = obj.N
self.spikegenerator_models.append(spikegenerator_model)
[docs] def process_synapses(self, synapse_groups):
for obj in synapse_groups:
synapse_model = synapseModel()
synapse_model.name = obj.name
if isinstance(obj.source, Subgroup):
synapse_model.srcname = obj.source.source.name
synapse_model.srcN = obj.source.source.variables['N'].get_value()
else:
synapse_model.srcname = obj.source.name
synapse_model.srcN = obj.source.variables['N'].get_value()
if isinstance(obj.target, Subgroup):
synapse_model.trgname = obj.target.source.name
synapse_model.trgN = obj.target.source.variables['N'].get_value()
else:
synapse_model.trgname = obj.target.name
synapse_model.trgN = obj.target.variables['N'].get_value()
synapse_model.connectivity = prefs.devices.genn.connectivity
self.connectivityDict[obj.name] = synapse_model.connectivity
for pathway in obj._synaptic_updaters:
if pathway not in ['pre', 'post']:
raise NotImplementedError("brian2genn only supports a "
"single synaptic pre and post "
"pathway, cannot use pathway "
"'%s'." % pathway)
for pathway in ['pre', 'post']:
if hasattr(obj, pathway):
codeobj = getattr(obj, pathway).codeobj
# A little hack to support "write-protection" for refractory
# variables -- brian2genn currently requires that
# post-synaptic variables end with "_post"
if pathway == 'pre' and 'not_refractory' in codeobj.variables:
codeobj.variables['not_refractory_post'] = \
codeobj.variables['not_refractory']
codeobj.variable_indices['not_refractory_post'] = \
codeobj.variable_indices['not_refractory']
del codeobj.variables['not_refractory']
del codeobj.variable_indices['not_refractory']
self.collect_synapses_variables(synapse_model, pathway,
codeobj)
if pathway == 'pre':
# Use the stored scalar delay (if any) for these synapses
synapse_model.delay = int(
self.delays.get(obj.name,
0.0) / defaultclock.dt_ + 0.5)
code = codeobj.code.cpp_file
code_lines = [line.strip() for line in code.split('\n')]
new_code_lines = []
if pathway == 'pre':
for line in code_lines:
if line.startswith('addtoinSyn'):
if synapse_model.connectivity == 'SPARSE':
line = line.replace('_hidden_weightmatrix*',
'')
line = line.replace(
'_hidden_weightmatrix *', '')
new_code_lines.append(line)
if line.startswith('addtoinSyn'):
new_code_lines.append('$(updatelinsyn);')
code = '\n'.join(new_code_lines)
self.fix_synapses_code(synapse_model, pathway, codeobj,
code)
if obj.state_updater != None:
codeobj = obj.state_updater.codeobj
code = codeobj.code.cpp_file
self.collect_synapses_variables(synapse_model, 'dynamics',
codeobj)
self.fix_synapses_code(synapse_model, 'dynamics', codeobj,
code)
if (hasattr(obj, '_genn_post_write_var')):
synapse_model.postSyntoCurrent = '0; $(' + obj._genn_post_write_var.replace(
'_post', '') + ') += $(inSyn); $(inSyn)= 0'
else:
synapse_model.postSyntoCurrent = '0'
self.synapse_models.append(synapse_model)
self.groupDict[synapse_model.name] = synapse_model
[docs] def collect_synapses_variables(self, synapse_model, pathway, codeobj):
identifiers = set()
for code in codeobj.code.values():
identifiers |= get_identifiers(code)
indices = codeobj.variable_indices
for k, v in codeobj.variables.iteritems():
if k in ['_spikespace', 't', 'dt'] or k not in identifiers:
pass
elif isinstance(v, Constant):
if k not in synapse_model.parameters:
self.add_parameter(synapse_model, k, v)
elif isinstance(v, ArrayVariable):
if indices[k] == '_idx':
if k not in synapse_model.variables:
self.add_array_variable(synapse_model, k, v)
elif indices[k] == '0':
if k not in synapse_model.shared_variables:
self.add_array_variable(synapse_model, k, v)
else:
index = indices[k]
if (pathway in ['pre', 'post'] and
index == '_{}synaptic_idx'.format(pathway)):
raise NotImplementedError('brian2genn does not support '
'references to {pathway}-'
'synaptic variables in '
'on_{pathway} '
'statements.'.format(
pathway=pathway))
if k not in synapse_model.external_variables:
synapse_model.external_variables.append(k)
elif isinstance(v, Subexpression):
raise NotImplementedError(
'Brian2genn does not support the use of '
'subexpressions in synaptic statements')
[docs] def fix_synapses_code(self, synapse_model, pathway, codeobj, code):
if synapse_model.connectivity == 'DENSE':
code = 'if (_hidden_weightmatrix != 0.0) {' + code + '}'
code = self.fix_random_generators(synapse_model, code)
thecode = decorate(code, synapse_model.variables,
synapse_model.shared_variables,
synapse_model.parameters, False).strip()
thecode = decorate(thecode, synapse_model.external_variables, [],
[], True).strip()
synapse_model.main_code_lines[pathway] = thecode
code = stringify(codeobj.code.h_file)
synapse_model.support_code_lines[pathway] = code
[docs] def process_spike_monitors(self, spike_monitors):
for obj in spike_monitors:
if obj.event != 'spike':
raise NotImplementedError(
'GeNN does not yet support event monitors for non-spike events.');
sm = spikeMonitorModel()
sm.name = obj.name
if (hasattr(obj, 'when')):
if (not obj.when in ['end', 'thresholds']):
# GeNN always records in the end slot but this should
# almost never make a difference and we therefore do not
# raise a warning if the SpikeMonitor records in the default
# thresholds slot. We do raise a NotImplementedError if the
# user manually changed the time slot to something else --
# there was probably a reason for doing it.
raise NotImplementedError(
"Spike monitor {!s} has 'when' property '{!s}' which "
"is not supported in GeNN, defaulting to 'end'.".format(
sm.name, obj.when))
src = obj.source
if isinstance(src, Subgroup):
src = src.source
sm.neuronGroup = src.name
if isinstance(src, SpikeGeneratorGroup):
sm.notSpikeGeneratorGroup = False
self.spike_monitor_models.append(sm)
self.header_files.append('code_objects/' + sm.name + '_codeobject.h')
# ------------------------------------------------------------------------------
# Process rate monitors
[docs] def process_rate_monitors(self, rate_monitors):
for obj in rate_monitors:
sm = rateMonitorModel()
sm.name = obj.name
if obj.when != 'end':
logger.warn("Rate monitor {!s} has 'when' property '{!s}' which"
"is not supported in GeNN, defaulting to"
"'end'.".format(sm.name, obj.when))
src = obj.source
if isinstance(src, Subgroup):
src = src.source
sm.neuronGroup = src.name
if isinstance(src, SpikeGeneratorGroup):
sm.notSpikeGeneratorGroup = False
self.rate_monitor_models.append(sm)
self.header_files.append(
'code_objects/' + sm.name + '_codeobject.h')
[docs] def process_state_monitors(self, directory, state_monitors, writer):
for obj in state_monitors:
sm = stateMonitorModel()
sm.name = obj.name
src = obj.source
if isinstance(src, Subgroup):
src = src.source
sm.monitored = src.name
sm.when = obj.when
if sm.when not in ['start', 'end']:
logger.warn("State monitor {!s} has 'when' property '{!s}'"
"which is not supported in GeNN, defaulting to"
"'end'.".format(sm.name, sm.when))
sm.when = 'end'
if isinstance(src, Synapses):
sm.isSynaptic = True
sm.srcN = src.source.variables['N'].get_value()
sm.trgN = src.target.variables['N'].get_value()
sm.connectivity = self.connectivityDict[src.name]
else:
sm.isSynaptic = False
sm.N = src.variables['N'].get_value()
for varname in obj.record_variables:
if isinstance(src.variables[varname], Subexpression):
extract_source_variables(src.variables, varname,
sm.variables)
elif isinstance(src.variables[varname], Constant):
logger.warn(
"variable '%s' is a constant - not monitoring" % varname)
elif varname not in self.groupDict[sm.monitored].variables:
logger.warn(
"variable '%s' is unused - not monitoring" % varname)
else:
sm.variables.append(varname)
self.state_monitor_models.append(sm)
self.header_files.append(
'code_objects/' + sm.name + '_codeobject.h')
[docs] def generate_model_source(self, writer):
synapses_classes_tmp = CPPStandaloneCodeObject.templater.synapses_classes(None, None)
writer.write('synapses_classes.*', synapses_classes_tmp)
compile_args_gcc, compile_args_msvc, compile_args_nvcc = get_compile_args()
genn_auto_choose_device, genn_default_device = get_genn_prefs()
model_tmp = GeNNCodeObject.templater.model(None, None,
neuron_models=self.neuron_models,
spikegenerator_models=self.spikegenerator_models,
synapse_models=self.synapse_models,
dtDef=self.dtDef,
model_name=self.model_name,
compile_args_gcc=compile_args_gcc,
compile_args_msvc=compile_args_msvc,
compile_args_nvcc=compile_args_nvcc,
genn_auto_choose_device=genn_auto_choose_device,
genn_default_device=genn_default_device
)
writer.write(self.model_name + '.cpp', model_tmp)
[docs] def generate_main_source(self, writer, main_lines):
runner_tmp = GeNNCodeObject.templater.main(None, None,
neuron_models=self.neuron_models,
synapse_models=self.synapse_models,
model_name=self.model_name,
main_lines=main_lines,
header_files=self.header_files,
source_files=self.source_files,
)
writer.write('main.*', runner_tmp)
[docs] def generate_engine_source(self, writer):
maximum_run_time = self._maximum_run_time
if maximum_run_time is not None:
maximum_run_time = float(maximum_run_time)
engine_tmp = GeNNCodeObject.templater.engine(None, None,
neuron_models=self.neuron_models,
spikegenerator_models=self.spikegenerator_models,
synapse_models=self.synapse_models,
spike_monitor_models=self.spike_monitor_models,
rate_monitor_models=self.rate_monitor_models,
state_monitor_models=self.state_monitor_models,
model_name=self.model_name,
maximum_run_time=maximum_run_time
)
writer.write('engine.*', engine_tmp)
[docs] def generate_makefile(self, directory, use_GPU):
compile_args_gcc, compile_args_msvc, compile_args_nvcc = get_compile_args()
linker_flags = ' '.join(prefs.codegen.cpp.extra_link_args)
if os.sys.platform == 'win32':
makefile_tmp = GeNNCodeObject.templater.WINmakefile(None, None,
neuron_models=self.neuron_models,
model_name=self.model_name,
ROOTDIR=os.path.abspath(
directory),
source_files=self.source_files,
use_GPU=use_GPU,
compiler_flags=compile_args_msvc,
linker_flags=linker_flags,
nvcc_compiler_flags=compile_args_nvcc
)
open(os.path.join(directory, 'WINmakefile'), 'w').write(
makefile_tmp)
else:
makefile_tmp = GeNNCodeObject.templater.GNUmakefile(None, None,
neuron_models=self.neuron_models,
model_name=self.model_name,
ROOTDIR=os.path.abspath(
directory),
source_files=self.source_files,
use_GPU=use_GPU,
compiler_flags=compile_args_gcc,
linker_flags=linker_flags,
nvcc_compiler_flags=compile_args_nvcc
)
open(os.path.join(directory, 'GNUmakefile'), 'w').write(makefile_tmp)
[docs] def generate_objects_source(self, arange_arrays, net, static_array_specs,
synapses, writer):
# ------------------------------------------------------------------------------
# create the objects.cpp and objects.h code
the_objects = self.code_objects.values()
arr_tmp = GeNNUserCodeObject.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=[], # We don't want to create any networks
get_array_filename=self.get_array_filename,
get_array_name=self.get_array_name,
code_objects=the_objects
)
writer.write('objects.*', arr_tmp)
self.header_files.append('objects.h')
self.source_files.append('objects.cpp')
[docs] def copy_source_files(self, writer, directory):
# Copies brianlib, spikequeue and randomkit
super(GeNNDevice, self).copy_source_files(writer, directory)
# Copy the b2glib directory
b2glib_dir = os.path.join(
os.path.split(inspect.getsourcefile(GeNNCodeObject))[0],
'b2glib')
b2glib_files = copy_directory(b2glib_dir,
os.path.join(directory, 'b2glib'))
for file in b2glib_files:
if file.lower().endswith('.cpp'):
self.source_files.append('b2glib/' + file)
elif file.lower().endswith('.h'):
self.header_files.append('b2glib/' + file)
[docs] def network_run(self, net, duration, report=None, report_period=10 * second,
namespace=None, profile=False, level=0, **kwds):
# We quietly ignore the profile argument instead of raising a warning
# every time...
if kwds:
logger.warn(('Unsupported keyword argument(s) provided for run: '
+ '%s') % ', '.join(kwds.keys()))
if self.run_duration is not None:
raise NotImplementedError(
'Only a single run statement is supported for the genn device.')
self.run_duration = float(duration)
for obj in net.objects:
if obj.clock.name is not 'defaultclock' and not (obj.__class__ == CodeRunner):
raise NotImplementedError(
'Multiple clocks are not supported for the genn device')
for obj in net.objects:
if hasattr(obj, '_linked_variables'):
if len(obj._linked_variables) > 0:
raise NotImplementedError(
'The genn device does not support linked variables')
for obj in net.objects:
if hasattr(obj, 'template'):
if obj.template in ['summed_variable']:
raise NotImplementedError(
'The function of %s is not yet supported in GeNN.' % obj.template)
print 'running brian code generation ...'
super(GeNNDevice, self).network_run(net=net, duration=duration,
report=report,
report_period=report_period,
namespace=namespace,
level=level + 1)
self.run_statement_used = True
# ------------------------------------------------------------------------------
# End of GeNNDevice
# ------------------------------------------------------------------------------
genn_device = GeNNDevice()
all_devices['genn'] = genn_device