def compute_se_cpp(job):
# setup input for the script
data = job.statepoint()
rep = SphericalExpansion(**data['representation'])
data['calculator'] = rep.hypers
tojson(job.fn(group['fn_in']), data)
# look at memory footprint
p = Popen([
group['executable'][data['nl_type']],
job.fn(group['fn_in']),
job.fn(group['fn_out'])
],
stdout=PIPE,
stderr=PIPE)
max_mem = memory_usage(p, interval=0.1, max_usage=True)
# look at timings
p = Popen([
group['executable'][data['nl_type']],
job.fn(group['fn_in']),
job.fn(group['fn_out'])
],
stdout=PIPE,
stderr=PIPE)
if p.stderr.read(): print(p.stderr.read())
data = fromjson(job.fn(group['fn_out']))
data = data['results']
data['mem_max'] = max_mem
data['mem_unit'] = 'MiB'
tojson(job.fn(group['fn_res']), data)
def compute_si_cpp(job):
# setup input for the script
data = job.statepoint()
np.random.seed(data['seed'])
if data['representation']['coefficient_subselection'] is not None:
rpr = deepcopy(data['representation'])
rpr.pop('coefficient_subselection')
rep = SphericalInvariants(**rpr)
rep,n_feat = get_randomly_sparsified_soap(data, rep)
else:
n_feat = None
rep = SphericalInvariants(**data['representation'])
data['calculator'] = rep.hypers
tojson(job.fn(group['fn_in']), data)
# look at memory footprint
p = Popen([group['executable'][data['nl_type']], job.fn(group['fn_in']), job.fn(group['fn_out'])], stdout=PIPE, stderr=PIPE)
max_mem = memory_usage(p, interval=0.1, max_usage=True)
# look at timings
p = Popen([group['executable'][data['nl_type']], job.fn(group['fn_in']), job.fn(group['fn_out'])], stdout=PIPE, stderr=PIPE)
if p.stderr.read(): print(p.stderr.read())
data = fromjson(job.fn(group['fn_out']))
data = data['results']
data['n_features'] = n_feat
data['mem_max'] = max_mem
data['mem_unit'] = 'MiB'
tojson(job.fn(group['fn_res']), data)
def compute_feature_selection(job):
sp = _decode(job.statepoint())
st, lg = job.sp.start_structure, job.sp.n_structures
frames = fromfile(job.sp.filename)[st:st + lg]
soap = SphericalInvariants(**sp['representation'])
managers = soap.transform(frames)
compressor = RandomFilter(soap, **sp['feature_subselection'])
feature_subselection = compressor.select_and_filter(managers)
# if sp['feature_subselection']['Nselect'] is None:
# feature_subselection['coefficient_subselection'] = None
tojson(job.fn(group['feature_fn']), feature_subselection)
def compute_nl_cpp(job):
# setup input for the script
data = job.statepoint()
tojson(job.fn(group['fn_in']), data)
# look at timings
p = Popen([group['executable'], job.fn(group['fn_in']), job.fn(group['fn_out'])], stdout=PIPE, stderr=PIPE)
if p.stderr.read(): print(p.stderr.read())
data = fromjson(job.fn(group['fn_out']))
data = data['results']
tojson(job.fn(group['fn_res']), data)
def run(job, N_ITERATION, max_mem):
data = job.statepoint()
data['N_ITERATIONS'] = N_ITERATION
tojson(job.fn(group['fn_in']), data)
p = Popen(
[group['executable'],
job.fn(group['fn_in']),
job.fn(group['fn_out'])],
stdout=PIPE,
stderr=PIPE)
# if p.stderr.read(): print(p.stderr.read())
data = fromjson(job.fn(group['fn_out']))
data = data['results']
data['mem_max'] = max_mem
data['mem_unit'] = 'MiB'
tojson(job.fn(group['fn_res']), data)
# job.document.elapsed_mean < 3*job.document.elapsed_std
return data['elapsed_mean'] > 4 * data['elapsed_std']
def compute_ri_cpp(job):
# setup input for the script
data = job.statepoint()
rep = SphericalExpansion(**data['representation'])
data['calculator'] = rep.hypers
tojson(job.fn(group['fn_in']), data)
# look at memory footprint
p = Popen(
[group['executable'],
job.fn(group['fn_in']),
job.fn(group['fn_out'])],
stdout=PIPE,
stderr=PIPE)
max_mem = memory_usage(p, interval=0.001, max_usage=True)
# look at timings
carry_on = True
N_ITERATIONS = [
int(data['N_ITERATIONS'] * (1 + f))
for f in [0., 0.5, 1, 2, 3, 5, 10, 20, 30, 50, 100]
]
for N_ITERATION in N_ITERATIONS:
if run(job, N_ITERATION, max_mem):
break
def compute_benchmark(job):
from rascal.models.krr import compute_sparse_kernel_gradients
sp = _decode(job.statepoint())
st, lg = job.sp.start_structure, job.sp.n_structures
frames = fromfile(job.sp.filename)[st:st + lg]
model = load_obj(job.fn(group['model_fn']))
soap = model.get_representation_calculator()
grads_timing = job.sp.grads_timing
hypers = soap._get_init_params()
hypers['compute_gradients'] = grads_timing
soap = SphericalInvariants(**hypers)
rc = sp['representation']['interaction_cutoff']
nl_options = [
dict(name='centers', args=[]),
dict(name='neighbourlist', args=dict(cutoff=rc)),
# dict(name='halflist', args=dict()),
dict(name="centercontribution", args=dict()),
dict(name='strict', args=dict(cutoff=rc))
]
kernel = Kernel(soap, **sp['kernel'])
N_ITERATIONS = sp['N_ITERATIONS']
if grads_timing:
tags = ['NL', 'rep with grad', 'pred energy', 'pred forces']
else:
tags = ['NL', 'rep', 'pred energy']
timers = {k: Timer(tag=k, logger=None) for k in tags}
if job.sp.name != 'qm9':
frames = [
make_supercell(frames[0],
job.sp.n_replication * np.eye(3),
wrap=True,
tol=1e-11)
]
else:
frames = frames[:100]
if grads_timing:
for ii in range(N_ITERATIONS):
with timers['NL']:
managers = AtomsList(frames, nl_options)
sleep(0.1)
with timers['rep with grad']:
managers = soap.transform(managers)
sleep(0.1)
Y0 = model._get_property_baseline(managers)
with timers['pred energy']:
KNM = kernel(managers, model.X_train, (False, False))
Y0 + np.dot(KNM, model.weights).reshape((-1))
sleep(0.1)
with timers['pred forces']:
rep = soap._representation
forces = -compute_sparse_kernel_gradients(
rep, model.kernel._kernel, managers.managers,
model.X_train._sparse_points, model.weights.reshape(
(1, -1)))
sleep(0.1)
managers, KNM = [], []
del managers, KNM
sleep(0.3)
else:
for ii in range(N_ITERATIONS):
with timers['NL']:
managers = AtomsList(frames, nl_options)
sleep(0.1)
with timers['rep']:
managers = soap.transform(managers)
sleep(0.1)
Y0 = model._get_property_baseline(managers)
with timers['pred energy']:
KNM = kernel(managers, model.X_train, (False, False))
Y0 + np.dot(KNM, model.weights).reshape((-1))
sleep(0.1)
managers, KNM = [], []
del managers, KNM
sleep(0.3)
n_atoms = 0
for frame in frames:
n_atoms += len(frame)
timings = []
for tag in tags:
data = timers[tag].dumps()
data.update({'name': job.sp.name, 'n_atoms': n_atoms})
timings.append(data)
tojson(job.fn(group['benchmark_fn']), timings)