How to use MXNet as a (almost) full function Torch front-end¶

Compile with Torch¶

• First install Torch following official guide.
• Then, in config.mk (if you haven’t already, copy make/config.mk (Linux) or make/osx.mk (Mac) into MXNet root folder as config.mk) uncomment the lines TORCH_PATH = $(HOME)/torch and MXNET_PLUGINS += plugin/torch/torch.mk. By default Torch should be installed in your home folder (so TORCH_PATH = $(HOME)/torch). Modify TORCH_PATH to point to your torch installation if necessary.
• Run make clean && make to build with torch support.

Tensor Mathematics¶

mxnet.th module supports calling Torch’s tensor mathematical functions with mxnet.nd.NDArray. For example (full code):

import mxnet as mx
x = mx.th.randn(2, 2, ctx=mx.cpu(0))
print x.asnumpy()
y = mx.th.abs(x)
print y.asnumpy()

x = mx.th.randn(2, 2, ctx=mx.cpu(0))
print x.asnumpy()
mx.th.abs(x, x) # in-place
print x.asnumpy()

Help can be found with help(mx.th). We already added support for most common functions listed on Torch’s doc page. If you find that the function you need is not supported, you can easily register it in mxnet_root/plugin/torch/torch_function.cc following existing registrations.

Torch Modules (Layers)¶

Torch’s neural network modules is also supported by MXNet through mxnet.symbol.TorchModule symbol. For example, the following code defines a 3 layer DNN for classifying MNIST digits (full code):

data = mx.symbol.Variable('data')
fc1 = mx.symbol.TorchModule(data_0=data, lua_string='nn.Linear(784, 128)', num_data=1, num_params=2, num_outputs=1, name='fc1')
act1 = mx.symbol.TorchModule(data_0=fc1, lua_string='nn.ReLU(false)', num_data=1, num_params=0, num_outputs=1, name='relu1')
fc2 = mx.symbol.TorchModule(data_0=act1, lua_string='nn.Linear(128, 64)', num_data=1, num_params=2, num_outputs=1, name='fc2')
act2 = mx.symbol.TorchModule(data_0=fc2, lua_string='nn.ReLU(false)', num_data=1, num_params=0, num_outputs=1, name='relu2')
fc3 = mx.symbol.TorchModule(data_0=act2, lua_string='nn.Linear(64, 10)', num_data=1, num_params=2, num_outputs=1, name='fc3')
mlp = mx.symbol.SoftmaxOutput(data=fc3, name='softmax')

Let’s break it down. First data = mx.symbol.Variable('data') defines a Variable as placeholder for input. Then it’s fed through Torch’s nn modules with fc1 = mx.symbol.TorchModule(data_0=data, lua_string='nn.Linear(784, 128)', num_data=1, num_params=2, num_outputs=1, name='fc1'). We can also replace the last line with:

logsoftmax = mx.symbol.TorchModule(data_0=fc3, lua_string='nn.LogSoftMax()', num_data=1, num_params=0, num_outputs=1, name='logsoftmax')
# Torch's label starts from 1
label = mx.symbol.Variable('softmax_label') + 1
mlp = mx.symbol.TorchCriterion(data=logsoftmax, label=label, lua_string='nn.ClassNLLCriterion()', name='softmax')

to use Torch’s criterion as loss functions. The input to nn module is named as data_i for i = 0 ... num_data-1. lua_string is a single Lua statement that creates the module object. For Torch’s built-in module this is simply nn.module_name(arguments). If you are using custom module, place it in a .lua script file and load it with require 'module_file.lua' if your script returns an torch.nn object, or (require 'module_file.lua')() if your script returns a torch.nn class.