In this section, you will learn about the following things:

• How to define a non-parameterized function
• Useful tools to write a function using a GPU
• How to define a parameterized function
• How to test the function definition

After reading this section, you will be able to:

• Write your own non-parameterized function
• Define simple kernels in the function definition
• Write your own parameterized function

## Non-parameterized Functions¶

Chainer provides a collection of functions in the functions module. It covers typical use cases in deep learning, so many existing works can be implemented with them. On the other hand, deep learning is evolving rapidly and we cannot cover all possible functions to define unseen architectures. So it is important to learn how to define your own functions.

Since they are simpler, we first show how to define non-parameterized functions. First, suppose we want to define an elementwise function $$f(x, y, z) = x * y + z$$. While it is possible to implement this equation using a combination of the * and + functions, defining it as a single function may reduce memory consumption, so it is not only a toy example. Here we call this function MulAdd.

Let’s start with defining MulAdd working on the CPU. Any function must inherit the Function class. The skeleton of a non-parameterized function looks like:

class MulAdd(Function):
def forward_cpu(self, inputs):
# do forward computation on CPU
return some_tuple

# do backward computation on CPU
return some_tuple


We must implement forward_cpu() and backward_cpu() methods. The non-self arguments of these functions are tuples of array(s), and these functions must return a tuple of array(s).

Warning

Be careful to return a tuple of arrays even if you have just one array to return.

MulAdd is simple and implemented as follows:

class MulAdd(Function):
def forward_cpu(self, inputs):
x, y, z = inputs
w = x * y + z
return w,

x, y, z = inputs

gx = y * gw
gy = x * gw
gz = gw
return gx, gy, gz


As per the warning above, forward_cpu function returns a tuple of single element. Note that all arrays appearing in CPU functions are numpy.ndarray. The forward function is straightforward: It unpacks the input tuple, computes the output, and packs it into a tuple. The backward function is a bit more complicated. Recall the rule of differentiation of multiplication. This example just implements the rule. Look at the return values, the function just packs the gradient of each input in same order and returns them.

By just defining the core computation of forward and backward, Function class provides a chaining logic on it (i.e. storing the history of computation, etc.).

Now let’s define the corresponding GPU methods. You can easily predict that the methods we have to write are named forward_gpu() and backward_gpu():

class MulAdd(Function):
def forward_cpu(self, inputs):
...

...

def forward_gpu(self, inputs):
x, y, z = inputs
w = x * y + z
return w,

x, y, z = inputs

gx = y * gw
gy = x * gw
gz = gw
return gx, gy, gz


In GPU methods, arrays are of type pycuda.gpuarray.GPUArray We use arithmetic operators defined for GPUArray. These operators implement the basic elementwise arithmetics.

You maybe find that the definitions of GPU methods are exactly same as those of CPU methods. In that case, we can reduce them to forward() and backward() methods:

class MulAdd(Function):
def forward(self, inputs):
x, y, z = inputs
w = x * y + z
return w,

x, y, z = inputs

gx = y * gw
gy = x * gw
gz = gw
return gx, gy, gz


Note that this is a very rare case, since GPUArray does not implement most features of numpy.ndarray.

## Write an Elementwise Kernel Function¶

The GPU implementation of MulAdd as shown above is already fast and parallelized on GPU cores. However, it invokes two kernels during each of forward and backward computations, which may hurt performance. We can reduce the number of invocations by defining our own kernel.

Most functions only require elementwise operations like MulAdd. PyCUDA provides a useful tool to define elementwise kernels, the pycuda.elementwise.ElementwiseKernel class, and Chainer wraps it by cuda.elementwise() function. Our MulAdd implementation can be improved as follows:

class MulAdd(Function):
def forward_cpu(self, inputs):
...

...

def forward_gpu(self, inputs):
x, y, z = inputs
w = cuda.empty_like(x)
cuda.elementwise(
'float* w, const float* x, const float* y, const float* z',
'w[i] = x[i] * y[i] + z[i]',
return w,

x, y, z = inputs

gx = cuda.empty_like(x)
gy = cuda.empty_like(y)
cuda.elementwise(
'''
float* gx, float* gy,
const float* x, const float* y, const float* gw
''', '''
gx[i] = gy[i] * gw[i];
gy[i] = gx[i] * gw[i];
''', 'muladd_bwd')(gx, gy, x, y, gw)

gz = gw  # no copy
return gx, gy, gz


cuda.elementwise() function accepts the essential implentation of the kernel function, and returns a kernel invokation function (actually, it returns ElementwiseKernel object, which is callable). In typical usage, we pass three arguments to this function. The first is an argument list of the kernel function. The second is a body of parallel loop, where the variable i indicates the index in the loop. Note that i runs through all indexes of the first array argument by default. The third is the name of the kernel function, which is shown in debugger and profilers.

Above code is not compiled on every forward/backward computation thanks to two caching mechanisms provided by cuda.elementwise().

The first one is binary caching: cuda.elementwise() function caches the compiled binary in the /tmp directory with a hash value of the CUDA code, and reuses it if the given code matches the hash value. This caching mechanism is actually implemented in PyCUDA.

The second one is upload caching: Given a compiled binary code, we have to upload it to the current GPU in order to execute it. cuda.elementwise() function memoizes the arguments and the curent context, and if it is called with the same arguments and the same context, it reuses the previously uploaded kernel code.

## Parameterized Functions¶

Next we show how to define a parameterized function. At this time, suppose that we want to implement elementwise product function between the input array and the parameter array.

Note

Note that the elementwise product between a variable and parameters can be simply implemented by functions.Parameter function:

p = F.Parameter(np.random.rand((4, 3), dtype=np.float32))
x = Variable(...)
y = p() * x


The Parameter function takes no arguments and just returns a variable holding the parameter array. The example in this subsection may be slightly more efficient with respect to memory consumption, though.

There are two differences between parameterized functions and non-parameterized functions:

Note that gradient arrays are automatically zeroed by an optimizer, so function implementation only need to initialize their shapes. Then, the implementation of elementwise product may be as following:

class EltwiseParamProduct(Function):
parameter_names = 'w',

def __init__(self, shape):
self.w  = np.random.randn(shape).astype(np.float32)
self.gw = np.empty_like(self.w)

def forward(self, inputs):
x = inputs[0]
y = self.w * x
return y,

x  = inputs[0]

self.gw += gy * x
gx       = gy * self.w

return gx,


Note

An advanced tip to implement functions: if you want to preserve some information between forward and backward computations (e.g. to cache some arrays), you can store it as attributes. It does not make any trouble even if the function object is used more than once in the same network, since Function.__call__() operator copies itself before the forward computation.

Warning

You should not assume a one-to-one match of calls of forward and backward. Some users may call backward more than once after one forward call.

## Testing Function¶

In order to isolate the cause of learning failure from implementation bugs, it is important to test function implementations. Chainer provides simple utilities to help writing unit tests. They are defined in the gradient_check module.

The most important test utility is the numerical_grad() function. This function computes the numerical gradient of given function using finite differences. It can be used as follows:

x  = np.random.randn(4, 3).astype(np.float32)
gy = np.ones((4, 3), dtype=np.float32)
f  = lambda: (x * x,)


f is a closure that returns a tuple of array(s) computed from input arrays. The second and third arguments of numerical_grad() are tuples of input arrays and output gradient arrays, respectively. The code above computes the numerical gradients of sum(f(x)), where sum indicates the summation over all elements. The summation can be weighted by changing gy. numerical_grad() function also accepts additional eps argument, which indicates the quantization width of finite differences.

Note

numerical_grad() function accepts both CPU and GPU arrays. Note that we cannot mix CPU and GPU arrays.

Another utility is assert_allclose() function. This is similar to numpy.testing.assert_allclose() function. The difference is that Chainer’s version accepts CPU and GPU arrays as inputs. We can mix them in one invocation of assert_allclose. The default values of optional arguments are also different.

Here is a typical usage of gradient checking utilities. This is a test example of functions.relu() function:

class TestReLU(TestCase):
def test_backward_cpu(self):
x = Variable(np.random.randn(3, 2).astype(np.float32))
y = F.relu(x)

We used Variable.creator to extract creator function object of a variable. The first four lines of the test code are simple forward and backward computation of ReLU function. The next three lines compute numerical gradient using the same forward function without backward routine. And at last, we compare these two results elementwise. Note that above test code can be easily modified to test GPU version just by replacing CPU arrays to GPU arrays.
You can find many examples of function tests under tests/function_tests directory.