def _generate(self):
"""
Overrides default code generation. This function is called during the code generation procedure.
"""
# Filter definition
filter_definition, filter_pyx_definition = self._filter_definition()
# Convolve_code
if not self.multiple:
convolve_code, sum_code = self._generate_convolve_code()
else:
convolve_code, sum_code = self._generate_bank_code()
if Global._check_paradigm("openmp"):
self._generate_omp(filter_definition, filter_pyx_definition,
convolve_code, sum_code)
elif Global._check_paradigm("cuda"):
raise Global.ANNarchyException(
"Convolution is not available on CUDA devices yet.", True)
else:
raise NotImplementedError
def _check_storage_formats(projections):
"""
ANNarchy 4.7 introduced a set of sparse matrix formats. Some of them are not implemented for
all paradigms or might not support specific optimizations.
"""
for proj in projections:
# Most of the sparse matrix formats are not trivially invertable and therefore we can not implement
# spiking models with them
if proj.synapse_type.type == "spike" and proj._storage_format in [
"ell", "ellr", "coo", "hyb"
]:
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is not allowed for spiking synapses.", True)
# Dense format is not implemented for GPUs and spiking models
if proj._storage_format == "dense" and proj.synapse_type.type == "spike" and Global._check_paradigm(
"cuda"):
raise Global.ANNarchyException(
"Dense representation is not available for spiking models on GPUs yet.",
True)
# For some of the sparse matrix formats we don't implemented plasticity yet.
if proj.synapse_type.type == "spike" and proj._storage_format in [
"dense"
] and not isinstance(proj.synapse_type, DefaultSpikingSynapse):
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is only allowed for default spiking synapses yet.", True)
# For some of the sparse matrix formats we don't implemented plasticity yet.
if proj.synapse_type.type == "rate" and proj._storage_format in [
"coo", "hyb"
] and not isinstance(proj.synapse_type, DefaultRateCodedSynapse):
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is only allowed for default rate-coded synapses yet.", True)
# OpenMP disabled?
if proj._storage_format in ["bsr"
] and Global.config["num_threads"] > 1:
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is not available for OpenMP yet.", True)
# Single weight optimization available?
if proj._has_single_weight() and proj._storage_format in ["dense"]:
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is not allowed for single weight projections.", True)
# Slicing available?
if isinstance(proj.post,
PopulationView) and proj._storage_format in ["dense"]:
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is not allowed for PopulationViews as target.", True)
# In some cases we don't allow the usage of non-unifom delay
if (proj.max_delay > 1 and proj.uniform_delay == -1):
if Global._check_paradigm("cuda"):
raise Global.ANNarchyException(
"Using non-uniform delays is not available for CUDA devices.",
True)
else:
if proj._storage_format == "ellr":
raise Global.ANNarchyException(
"Using 'storage_format=" + proj._storage_format +
"' is and non-uniform delays is not implemented.",
True)
if Global._check_paradigm("cuda") and proj._storage_format == "lil":
proj._storage_format = "csr"
Global._info(
"LIL-type projections are not available for GPU devices ... default to CSR"
)
if Global._check_paradigm("cuda") and proj._storage_format == "ell":
Global._info(
"We would recommend to use ELLPACK-R (format=ellr) on GPUs.")
def _generate_cuda(self):
raise Global.ANNarchyException(
"The DecodingProjection is not available on CUDA devices.", True)
