Released v1.0.12! Provides N-Step RNN/LSTM/GRU & Resolved Memory Leak Problem.

Tuesday, February 19, 2019


Posted by shin

This week, we released nnabla packages v1.0.12! The main features updated from the previous version are described below:



Recurrent networks can now be trained in sequences more easily and faster with N-Step RNN/LSTM/GRU empowered by cuDNN. It is expected to be several times faster than our previous implementation of recurrent networks.

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

x = nn.Variable((seq_len, batch_size, input_size))
h = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
c = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
y, hn, cn = PF.lstm(x, h, c)

N-Step recurrent models are currently available for GPUs only, and we are now working on implementing CPU versions.

Important Bug Fix

Resolved Memory Leak for variable.forward()

Previous release of v1.0.11 introduced forward_all, which turned out to cause memory leak problems. We quickly noticed the problem and reacted to it immediately. If you have upgraded to v1.0.11, make sure to upgrade to v1.0.12 to avoid memory leakage.

Other Features