def run(self):
if self.data_generation != 'dataset':
dataset_dict_output = dp.generate_random_output_data(
self.data_size,
self.num_batches,
self.mini_batch_size,
round_targets=self.round_targets)
else:
raise NotImplementedError(
f'data_generation {self.data_generation} not implemented')
self.save(dataset_dict_output)
args.num_indices_per_lookup,
args.num_indices_per_lookup_fixed,
m_den,
ln_emb,
args.data_trace_file,
args.data_trace_enable_padding,
)
else:
sys.exit(
"ERROR: --data-generation=" + args.data_generation + " is not supported"
)
# target data
(nbatches, lT) = dp.generate_random_output_data(
args.data_size,
args.num_batches,
args.mini_batch_size,
round_targets=args.round_targets,
)
### parse command line arguments ###
m_spa = args.arch_sparse_feature_size
num_fea = ln_emb.size + 1 # num sparse + num dense features
m_den_out = ln_bot[ln_bot.size - 1]
if args.arch_interaction_op == "dot":
# approach 1: all
# num_int = num_fea * num_fea + m_den_out
# approach 2: unique
if args.arch_interaction_itself:
num_int = (num_fea * (num_fea + 1)) // 2 + m_den_out
else:
num_int = (num_fea * (num_fea - 1)) // 2 + m_den_out