Overview of Chainer
and Its Features
Deep Learning Tokyo 2016 at Yahoo! JAPAN
Seiya Tokui, Preferred Networks, Inc.
Mar. 20, 2016

This talk aims at providing
 The basics of deep learning frameworks
 The concept and characteristics of Chainer among them
 What you can do with Chainer
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Typical flow of using DL frameworks
3
objective
training data
function
function
function
parameters
1. Build a neural network (as a computational graph)
2. Feed it to a gradient-based
numerical optimizer
Numerical
Optimizer
3. The optimizer runs iterations
over the training dataset
4. Extract the resulting
parameters for some applications

Elements of Neural Network Implementations
 Multi-dimensional array
 Differentiable functions
– Called by various names (layers, modules, operators, primitives, etc.)
 Computational graphs
– DAG structure with executors (compiler or interpreter)
– Should support backpropagation
– May be optimized after the construction
 Gradient-based numerical optimizers (SGD, Adam, etc.)
 Data loaders, training loops, etc.
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Common goals of deep learning frameworks
 Making it easy to write codes involving neural networks and running
them efficiently
 Four perspectives of DL frameworks:
– API to let users concentrate on the essential parts of NN models
 Automatic differentiation (backprop)
 Intuitive coding
– Extensibility to write a wide range of NN models
– Performance of executing the computational flow
 GPU support, parallelization
 Automatic optimization
– Portability of the network implementation (training and deploying phases)
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Goals of Chainer
 Making it easy to write a wide range of codes involving neural networks
and running them efficiently enough for most researches
 What Chainer provides:
– API to let users concentrate on the essential parts of NN models
 Automatic differentiation (backprop)
 Intuitive coding: allow any Python control flows to appear in NNs
– Extensibility to write a wide range of NN models
– Performance of executing the computational flow
 GPU support, parallelization (multi-GPU support)
 Automatic optimization of computation (future work)
– Portability of the network implementation (training and deploying phases)
(Future work. Current Chainer heavily depends on CPython, and deployment
to environments without CPython might be done by other frameworks)
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Basic information
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Chainer
 Python-based framework of neural nets
 Open sourced: June 2015
 Core development:
Preferred Networks / Preferred Infrastructure
 Current version: v1.7.1
 Mainly designed for fast research and prototyping
Important URLs
 http://chainer.org/
 https://github.com/pfnet/chainer

Overall structure of Chainer
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CuPy
CPU NVIDIA GPU
CUDA
cuDNN
BLAS
NumPy
Chainer

Backpropagation in Chainer
 Consider an objective L = f(x * w + b)
 This code computes the value of L (i.e. forward prop), and
simultaneously builds the following “backward graph”
– is Variable, and is Function
 Using this graph, one can compute the gradient of L with respect to any
variables by backpropagation
 Optimizer optimizes the parameters by backprop
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f* +x
w b
L

Paradigms of BP: Define and Run vs Define by Run
 Define and Run (most DL frameworks)
– Computational graphs are constructed beforehand of any forward/backward
propagations (i.e. it defines graphs AND runs them)
– Pros: easy to optimize, high portability (definition of forward/backward prop
can be serialized to static data structure)
– Cons: hard to write graphs whose shapes depend on data, require special
treatment on control flows in the graphs
 Define by Run (Chainer and autograd)
– Graphs are constructed during the forward computation (i.e. it defines graphs
BY runs forward computations)
– Pros: shapes of graphs can be changed for different iterations, any control
flows of the host language can be used to define the forward computation
– Cons: hard to optimize the forward computation
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Control flows in writing NNs: a case of RNN
rnn = RNN()
xs = [list of arrays] # The length can be changed for every
ys = [list of arrays] # iteration
loss = 0
for x, y in zip(xs, ys): # You can use for loop with
x_var = Variable(x) # arbitrary loop conditions
y_var = Variable(y) # (you can even use the results of
y_pred = rnn(x_var) # forward computations here)
loss += L(y_pred, y_var)
loss.backward() # backward through the dynamically
# constructed graph
optimizer.update()
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Debug NNs just like programs
 In Chainer, NN is juat a fragment of Python program
– Functions applied to variables are used for later backprop
 Errors in forward computation occurs right at the execution of user code
– They can be debugged just as usual Python programs
(using appropriate stacktraces, pdb, etc.)
– Easy to print-debug (no need to add an auxiliary function)
– Easy to execute a part of NN in debug mode
 Just by switching the mode before and after the execution of the part
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Extensibility – built-in Functions (differentiable!)
 Mathematics
Arithemetics, common elementwise maths, matrix product and inversion, sum
along axes
 Activation functions
Most of popular activations (sigmoid, tanh, relu family, maxout, lstm family)
 Array routines
Useful routines, most of which borrowed from NumPy API
(reshape, broadcast, concat/split_axis, transpose, where, etc.)
 Neural net connections
To implement trainable layers (linear, 2d convolution, word embedding, etc.)
 Loss functions
Typical loss functions over minibatch (softmax cross entropy, elementwise
sigmoid cross entropy, hinge loss, MSE, Negative Sampling, Hierarchical SoftMax,
CTC, etc.)
 Many others (dropout, batch_normalization, pooling, SPP, unpooling, LRN, etc.)
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Extensibility – writing custom Functions (1)
 Function consists of two methods: forward and backward
class MulAdd(Function):
def forward(self, inputs):
x, y, z = inputs
w = x * y + z
return w,
def backward(self, inputs, grad_outputs):
x, y, z = inputs
gw = grad_outputs[0]
gx = y * gw
gy = x * gw
gz = gw
return gx, gy, gz
 This Function implements an elementwise expression x * y + z
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Extensibility – writing custom Functions (2)
 Using NumPy/CuPy, you can write “device-agnostic codes” to implement
Functions
 Consider x and y are arrays either on CPU or on GPU
xp = cuda.get_array_module(x, y)
z = xp.exp(x) + xp.exp(y)
 This code executes exp(x) + exp(y) regardless of the type of x and y
(numpy.ndarray or cupy.ndarray)
– xp refers to either numpy or cupy
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CuPy – NumPy-like GPU array
 CuPy is a multi-dimensional array library for CUDA
 It implements many interface compatible to NumPy
– Ndarray type
– Elementwise operations (including ufuncs) and reduction operations
– Full support of basic indexing
 It also supports multiple GPUs
– copy and copyto can be applied to arrays on different devices
 Chainer uses a memory pool to avoid calling cudaMalloc during iterations
(it syncs everything and stops hiding Python overhead!!)
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CuPy – customized kernels
 It also supports easy-to-write custom kernels
 Example: muladd in one kernel
w = cuda.elementwise(
‘T x, T y, T z’, # argument list (T: variadic type)
‘T w’, # output
‘w = x * y + z’, # code applied to every element
‘muladd_forward’ # kernel name
)(x, y, z) # invocation
 Kernels are compiled on-the-fly
– Compiled kernels are cached to the disk and reused in later uses
– It also caches the kernels sent to each device and reuses them in the same
process
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Extensibility – Link for binding params to Functions
 You can think of it as a “layer” in classic NN definitions
 Example: a simple fully-connected layer
class FullyConnected(Link):
def __init__(self, n_in, n_out):
super(FullyConnected, self).__init__()
self.add_param(‘W’, (n_out, n_in))
self.add_param(‘b’, n_out)
def __call__(self, x):
a = dot(x, transpose(self.W))
a, b = broadcast(a, self.b)
return a + b
 Note that equivalent (and more feature-rich) Link is also provided as
chainer.links.Linear
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Extensibility – Chain as a reusable NN component
 Chain is a kind of Link having ability to combine one or more child links
 Examples: Multi-Layer Perceptron and AutoEncoder
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class MLP(Chain):
def __init__(self):
super(MLP, self).__init__(
l1=Linear(784, 100),
l2=Linear(100, 10),
)
def __call__(self, x):
h = relu(self.l1(x))
return self.l2(h)
class AE(Chain):
def __init__(self, enc, dec):
super(AE, self).__init__(
encoder=enc, # child chain
decoder=dec, # child chain
)
def __call__(self, x):
h = self.encoder(x)
x_hat = self.decoder(h)
return mean_squared_error(
x, x_hat)

Features of Link and Chain
 You can collect parameters from Link/Chain
 Link/Chain are easy to serialize
– Just passing them to Serializer
– Chainer currently supports serialization to NPZ (NumPy) and HDF5
– It only serializes parameters (and specifically registered “persistent values”)
 There is another kind of chain called ChainList to define a chain with
arbitrary number of child links
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Summary
 Chainer is a deep learning framework for researchers with high flexibility
and easiness to write NNs
– Computational graphs are only constructed for backprop, and are built on-
the-fly during the forward computations
– It enables us to build a different graph for every iteration
– It also makes it easy to debug the NNs
 You can write device-agnostic codes using NumPy and CuPy
– Not only that, CuPy also makes it easy to write custom kernels without
writing boilerplate codes
 Link/Chain is a convenient tool to write fragments of NNs as reusable
components, with capability of serialization etc.
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