def main():
bm = Benchmark(
datasets=[
Dataset(
factories=[lambda n: make_vec_dataset(n, dtype=np.float16)],
title='16bits',
extra_args=dict(dtype=np.uint16, nbits=4)),
Dataset(
factories=[lambda n: make_vec_dataset(n, dtype=np.float32)],
title='32bits',
extra_args=dict(dtype=np.uint32, nbits=10)),
Dataset(
factories=[lambda n: make_vec_dataset(n, dtype=np.float64)],
title='64bits',
extra_args=dict(dtype=np.uint64, nbits=20))
],
dataset_sizes=[2**n for n in range(25)],
kernels=[
Kernel(
stmt=
"vector.encode_vec_to_uint(DATASET, dtype=EXTRA_ARGS['dtype'], nbits=EXTRA_ARGS['nbits'], encoder=fp.encode_fp_to_std_snorm)",
setup='from fpq import vector\nfrom fpq import fp',
label='encode_vec_to_uint(std)'),
Kernel(
stmt=
"vector.encode_vec_to_uint(DATASET, dtype=EXTRA_ARGS['dtype'], nbits=EXTRA_ARGS['nbits'], encoder=fp.encode_fp_to_ogl_snorm)",
setup='from fpq import vector\nfrom fpq import fp',
label='encode_vec_to_uint(ogl)'),
],
xlabel='dataset sizes',
title='encode_vec_to_uint')
bm.run()
script_name, _ = os.path.splitext(os.path.basename(__file__))
bm.save_as_html(filepath=script_name + '.html')
def make_dataset(n, *, dtype):
dataset = make_vec_dataset(n, dtype=dtype)
max_abs_inds = fpq.utils.get_max_component_indices(np.absolute(dataset))
sign = np.sign(dataset[max_abs_inds])
rest_components = fpq.utils.remove_component(dataset, indices=max_abs_inds)
rest_components *= sign[..., None]
return rest_components.transpose()
def main():
dataset_size = 100000
src_dtype = np.float64
dst_dtype = np.uint64
nbits = 20
dataset = make_vec_dataset(dataset_size, dtype=src_dtype)
mt.trace(
vector.encode_vec_to_uint,
target_args=dict(v=dataset, dtype=dst_dtype, nbits=nbits),
related_traces_output_mode=mt.RelatedTracesOutputMode.FOR_EACH_FILE,
include_patterns={'*/fpq/*'},
exclude_patterns=None)
def make_dataset(n, *, src_dtype, dst_dtype, nbits, encoder):
dataset = make_vec_dataset(n, dtype=src_dtype)
return vector.encode_vec_to_uint(dataset, dtype=dst_dtype, nbits=nbits, encoder=encoder)