# 2.3. Automatic Differentiation¶

In machine learning, we *train* models, updating them successively so
that they get better and better as they see more and more data. Usually,
*getting better* means minimizing a *loss function*, a score that
answers the question “how *bad* is our model?” With neural networks, we
typically choose loss functions that are differentiable with respect to
our parameters. Put simply, this means that for each of the model’s
parameters, we can determine how much *increasing* or *decreasing* it
might affect the loss. While the calculations for taking these
derivatives are straightforward, requiring only some basic calculus, for
complex models, working out the updates by hand can be a pain (and often
error-prone).

The autograd package expedites this work by automatically calculating
derivatives. And while many other libraries require that we compile a
symbolic graph to take automatic derivatives, `autograd`

allows us to
take derivatives while writing ordinary imperative code. Every time we
pass data through our model, `autograd`

builds a graph on the fly,
tracking which data combined through which operations to produce the
output. This graph enables `autograd`

to subsequently backpropagate
gradients on command. Here *backpropagate* simply means to trace through
the compute graph, filling in the partial derivatives with respect to
each parameter. If you are unfamiliar with some of the math,
e.g. gradients, please refer to Section 16.2.

```
from mxnet import autograd, np, npx
npx.set_np()
```

## 2.3.1. A Simple Example¶

As a toy example, say that we are interested in differentiating the
mapping \(y = 2\mathbf{x}^{\top}\mathbf{x}\) with respect to the
column vector \(\mathbf{x}\). To start, let’s create the variable
`x`

and assign it an initial value.

```
x = np.arange(4)
x
```

```
array([0., 1., 2., 3.])
```

Once we compute the gradient of `y`

with respect to `x`

, we will
need a place to store it. We can tell an ndarray to store a gradient by
invoking its `attach_grad()`

method.

```
x.attach_grad()
```

Now we are going to compute `y`

and MXNet will generate a computation
graph on the fly. It is as if MXNet turned on a recording device and
captured the exact path by which each variable was generated.

Note that building the computation graph requires a nontrivial amount of
computation. So MXNet will only build the graph when explicitly told to
do so. This happens by placing code inside a `with autograd.record():`

block.

```
with autograd.record():
y = 2.0 * np.dot(x, x)
y
```

```
array(28.)
```

Since `x`

is a vector of length 4, `np.dot`

will perform inner
product and therefore `y`

is a scalar. Next, we can automatically find
the gradient of all the inputs by calling the `backward`

function.

```
y.backward()
```

The gradient of the function \(y = 2\mathbf{x}^{\top}\mathbf{x}\) with respect to \(\mathbf{x}\) should be \(4\mathbf{x}\). Now let’s verify that the gradient produced is correct.

```
x.grad - 4 * x
```

```
array([0., 0., 0., 0.])
```

If \(x\) is used in another computation to compute the gradient,
previous `x.grad`

contents will be overwritten.

```
with autograd.record():
y = x.sum()
y.backward()
x.grad
```

```
array([1., 1., 1., 1.])
```

## 2.3.2. Backward for Non-scalar Variable¶

When `y`

is not a scalar, the gradients could be high order tensor and
complex to compute (refer to Section 16.2). In both machine
learning and deep learning, luckily we only compute gradients for loss
functions, whose values are often scalars. So MXNet will sum the
elements in `y`

to get the new variable by default, and then find the
analytical gradient of the variable with respect to `x`

evaluated at
its current value \(\frac{dy}{dx}\).

```
with autograd.record(): # y is a vector
y = x * x
y.backward()
u = x.copy()
u.attach_grad()
with autograd.record(): # v is scalar
v = (u * u).sum()
v.backward()
x.grad - u.grad
```

```
array([0., 0., 0., 0.])
```

## 2.3.3. Detach Computations¶

We could move some parts of computations out of the computation graph.
Assume \(y=f(x)\) and \(z=g(y)\). Then `u = y.detach()`

will
return a new variable has the same values as \(y\) but forgets how
\(u\) is computed. It equals to compute \(u=f(x)\) not within a
`autograd.record`

scope, namely \(u\) is treated as constant. The
following backward computes \(\partial u^2 x/\partial x\) with
\(u=x\) instead of \(\partial x^3/\partial x\).

```
with autograd.record():
y = x * x
u = y.detach()
z = u * x
z.backward()
x.grad - u
```

```
array([0., 0., 0., 0.])
```

Since the computation of \(y\) is still recorded, we can call
`y.backward()`

to get \(\partial y/\partial x = 2x\).

```
y.backward()
x.grad - 2*x
```

```
array([0., 0., 0., 0.])
```

## 2.3.4. Attach Gradients to Internal Variables¶

Attaching gradients to a variable `x`

implicitly calls
`x=x.detach()`

. If `x`

is computed based on other variables, this
part of computation will not be used in the backward function.

```
y = np.ones(4) * 2
y.attach_grad()
with autograd.record():
u = x * y
u.attach_grad() # implicitly run u = u.detach()
z = u + x
z.backward()
print(x.grad, '\n', u.grad, '\n', y.grad)
```

```
[1. 1. 1. 1.]
[1. 1. 1. 1.]
[0. 0. 0. 0.]
```

## 2.3.5. Head gradients¶

Detaching allows to breaks the computation into several parts. We could
use chain rule Section 16.2 to compute the gradient for the
whole computation. Assume \(u = f(x)\) and \(z = g(u)\), by
chain rule we have \(\frac{dz}{dx} = \frac{dz}{du} \frac{du}{dx}.\)
To compute \(\frac{dz}{du}\), we can first detach \(u\) from the
computation and then call `z.backward()`

to compute the first term.

```
y = np.ones(4) * 2
y.attach_grad()
with autograd.record():
u = x * y
v = u.detach() # u still keeps the computation graph
v.attach_grad()
z = v + x
z.backward()
print(x.grad, '\n', y.grad)
```

```
[1. 1. 1. 1.]
[0. 0. 0. 0.]
```

Later on we call `u.backward()`

to compute the second term, but pass
the first term as the head gradients to multiply both terms so that
`x.grad`

will contains \(\frac{dz}{dx}\) instead of
\(\frac{du}{dx}\).

```
u.backward(v.grad)
print(x.grad, '\n', y.grad)
```

```
[2. 2. 2. 2.]
[0. 1. 2. 3.]
```

## 2.3.6. Computing the Gradient of Python Control Flow¶

One benefit of using automatic differentiation is that even if the
computational graph of the function contains Python’s control flow (such
as conditional and loop control), we may still be able to find the
gradient of a variable. Consider the following program: It should be
emphasized that the number of iterations of the loop (while loop) and
the execution of the conditional statement (if statement) depend on the
value of the input `b`

.

```
def f(a):
b = a * 2
while np.abs(b).sum() < 1000:
b = b * 2
if b.sum() > 0:
c = b
else:
c = 100 * b
return c
```

Note that the number of iterations of the while loop and the execution
of the conditional statement (if then else) depend on the value of
`a`

. To compute gradients, we need to `record`

the calculation, and
then call the `backward`

function to calculate the gradient.

```
a = np.random.normal()
a.attach_grad()
with autograd.record():
d = f(a)
d.backward()
```

Let’s analyze the `f`

function defined above. As you can see, it is
piecewise linear in its input `a`

. In other words, for any `a`

there
exists some constant such that for a given range `f(a) = g * a`

.
Consequently `d / a`

allows us to verify that the gradient is correct:

```
print(a.grad == (d / a))
```

```
1.0
```

## 2.3.7. Training Mode and Prediction Mode¶

As you can see from the above, after calling the `record`

function,
MXNet will record the operations and calculate the gradient. In
addition, `autograd`

will also change the running mode from the
prediction mode to the training mode by default. This can be viewed by
calling the `is_training`

function.

```
print(autograd.is_training())
with autograd.record():
print(autograd.is_training())
```

```
False
True
```

In some cases, the same model behaves differently in the training and prediction modes (e.g. when using neural techniques such as dropout Section 4.6 and batch normalization Section 7.5). In other cases, some models may store more auxiliary variables to make computing gradients easier. We will cover these differences in detail in later chapters. For now, you do not need to worry about them.

## 2.3.8. Summary¶

- MXNet provides an
`autograd`

package to automate the derivation process. To do so, we first attach gradients to variables, record the computation, and then run the backward function. - We can detach gradients and pass head gradients to the backward function to control the part of the computation will be used in the backward function.
- The running modes of MXNet include the training mode and the
prediction mode. We can determine the running mode by
`autograd.is_training()`

.

## 2.3.9. Exercises¶

- Try to run
`y.backward()`

twice. - In the control flow example where we calculate the derivative of
`d`

with respect to`a`

, what would happen if we changed the variable`a`

to a random vector or matrix. At this point, the result of the calculation`f(a)`

is no longer a scalar. What happens to the result? How do we analyze this? - Redesign an example of finding the gradient of the control flow. Run and analyze the result.
- In a second-price auction (such as in eBay or in computational
advertising), the winning bidder pays the second-highest price.
Compute the gradient of the final price with respect to the winning
bidder’s bid using
`autograd`

. What does the result tell you about the mechanism? If you are curious to learn more about second-price auctions, check out this paper by Edelman, Ostrovski and Schwartz, 2005. - Why is the second derivative much more expensive to compute than the first derivative?
- Derive the head gradient relationship for the chain rule. If you get stuck, use the “Chain rule” article on Wikipedia.
- Assume \(f(x) = \sin(x)\). Plot \(f(x)\) and \(\frac{df(x)}{dx}\) on a graph, where you computed the latter without any symbolic calculations, i.e. without exploiting that \(f'(x) = \cos(x)\).