def __init__(self, nickname=None):
parameters = _resolve_parameters(template.parameters,
template.name)
dependencies = _resolve_dependencies(template.dependencies)
if template.cl_extra:
extra_code = '''
#ifndef {inclusion_guard_name}
#define {inclusion_guard_name}
{cl_extra}
#endif // {inclusion_guard_name}
'''.format(
inclusion_guard_name='INCLUDE_GUARD_CL_EXTRA_{}'.
format(template.name),
cl_extra=template.cl_extra)
dependencies.append(SimpleCLCodeObject(extra_code))
super().__init__(
template.return_type,
template.name,
parameters,
template.cl_code,
dependencies=dependencies,
constraints_func=_resolve_constraints(
template.constraints, template.name, parameters,
dependencies),
model_function_priors=_resolve_prior(
template.extra_prior, template.name,
[p.name for p in parameters]),
extra_optimization_maps_funcs=builder.
_get_extra_optimization_map_funcs(template, parameters),
extra_sampling_maps_funcs=builder.
_get_extra_sampling_map_funcs(template, parameters),
nickname=nickname,
cache_info=builder._get_cache_info(template))
def __init__(self):
dependencies = _resolve_dependencies(template.dependencies)
if template.cl_extra:
extra_code = '''
#ifndef {inclusion_guard_name}
#define {inclusion_guard_name}
{cl_extra}
#endif // {inclusion_guard_name}
'''.format(inclusion_guard_name='INCLUDE_GUARD_{}_EXTRA'.format(template.name),
cl_extra=template.cl_extra)
dependencies.append(SimpleCLCodeObject(extra_code))
super().__init__(
template.return_type, template.name,
_resolve_parameters(template.parameters), template.cl_code,
dependencies=dependencies)
def _get_compute_func(self, nmr_samples, thinning, return_output):
"""Get the MCMC algorithm as a computable function.
Args:
nmr_samples (int): the number of samples we will draw
thinning (int): the thinning factor we want to use
return_output (boolean): if the kernel should return output
Returns:
mot.lib.cl_function.CLFunction: the compute function
"""
cl_func = '''
void compute(global uint* rng_state,
global mot_float_type* current_chain_position,
global mot_float_type* current_log_likelihood,
global mot_float_type* current_log_prior,
ulong iteration_offset,
ulong nmr_iterations,
''' + ('''global mot_float_type* samples,
global mot_float_type* log_likelihoods,
global mot_float_type* log_priors,'''
if return_output else '') + '''
void* method_data,
void* data){
bool is_first_work_item = get_local_id(0) == 0;
rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
rng_state[0], rng_state[1], rng_state[2], rng_state[3],
rng_state[4], rng_state[5], 0, 0});
void* rng_data = (void*)&rand123_rng_data;
for(ulong i = 0; i < nmr_iterations; i++){
'''
if return_output:
cl_func += '''
if(is_first_work_item){
if(i % ''' + str(thinning) + ''' == 0){
log_likelihoods[i / ''' + str(
thinning) + '''] = *current_log_likelihood;
log_priors[i / ''' + str(
thinning) + '''] = *current_log_prior;
for(uint j = 0; j < ''' + str(
self._nmr_params) + '''; j++){
samples[(ulong)(i / ''' + str(
thinning) + ''') // remove the interval
+ j * ''' + str(
nmr_samples) + ''' // parameter index
] = current_chain_position[j];
}
}
}
'''
cl_func += '''
_advanceSampler(method_data, data, i + iteration_offset, rng_data,
current_chain_position, current_log_likelihood, current_log_prior);
}
if(is_first_work_item){
uint state[8];
rand123_data_to_array(rand123_rng_data, state);
for(uint i = 0; i < 6; i++){
rng_state[i] = state[i];
}
}
}
'''
return SimpleCLFunction.from_string(
cl_func,
dependencies=[
Rand123(),
self._get_log_prior_cl_func(),
self._get_log_likelihood_cl_func(),
SimpleCLCodeObject(
self._get_state_update_cl_func(nmr_samples, thinning,
return_output))
])