2.5. Automatic Differentiation¶ Open the notebook in SageMaker Studio Lab
Recall from Section 2.4 that calculating derivatives is the crucial step in all of the optimization algorithms that we will use to train deep networks. While the calculations are straightforward, working them out by hand can be tedious and error-prone, and this problem only grows as our models become more complex.
Fortunately all modern deep learning frameworks take this work off of our plates by offering automatic differentiation (often shortened to autograd). As we pass data through each successive function, the framework builds a computational graph that tracks how each value depends on others. To calculate derivatives, automatic differentiation works backwards through this graph applying the chain rule. The computational algorithm for applying the chain rule in this fashion is called backpropagation.
While autograd libraries have become a hot concern over the past decade, they have a long history. In fact the earliest references to autograd date back over half of a century (Wengert, 1964). The core ideas behind modern backpropagation date to a PhD thesis from 1980 (Speelpenning, 1980) and were further developed in the late 1980s (Griewank, 1989). While backpropagation has become the default method for computing gradients, it is not the only option. For instance, the Julia programming language employs forward propagation (Revels et al., 2016). Before exploring methods, let’s first master the autograd package.
import torch
from mxnet import autograd, np, npx
npx.set_np()
from jax import numpy as jnp
import tensorflow as tf
2.5.1. A Simple Function¶
Let’s assume that we are interested in differentiating the function
\(y = 2\mathbf{x}^{\top}\mathbf{x}\) with respect to the column
vector \(\mathbf{x}\). To start, we assign x an initial value.
x = torch.arange(4.0)
x
tensor([0., 1., 2., 3.])
Before we calculate the gradient of \(y\) with respect to \(\mathbf{x}\), we need a place to store it. In general, we avoid allocating new memory every time we take a derivative because deep learning requires successively computing derivatives with respect to the same parameters thousands or millions of times, and we might risk running out of memory. Note that the gradient of a scalar-valued function with respect to a vector \(\mathbf{x}\) is vector-valued and has the same shape as \(\mathbf{x}\).
# Can also create x = torch.arange(4.0, requires_grad=True)
x.requires_grad_(True)
x.grad # The gradient is None by default
x = np.arange(4.0)
x
array([0., 1., 2., 3.])
Before we calculate the gradient of \(y\) with respect to \(\mathbf{x}\), we need a place to store it. In general, we avoid allocating new memory every time we take a derivative because deep learning requires successively computing derivatives with respect to the same parameters thousands or millions of times, and we might risk running out of memory. Note that the gradient of a scalar-valued function with respect to a vector \(\mathbf{x}\) is vector-valued and has the same shape as \(\mathbf{x}\).
# We allocate memory for a tensor's gradient by invoking `attach_grad`
x.attach_grad()
# After we calculate a gradient taken with respect to `x`, we will be able to
# access it via the `grad` attribute, whose values are initialized with 0s
x.grad
array([0., 0., 0., 0.])
x = jnp.arange(4.0)
x
No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
Array([0., 1., 2., 3.], dtype=float32)
x = tf.range(4, dtype=tf.float32)
x
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 1., 2., 3.], dtype=float32)>
Before we calculate the gradient of \(y\) with respect to \(\mathbf{x}\), we need a place to store it. In general, we avoid allocating new memory every time we take a derivative because deep learning requires successively computing derivatives with respect to the same parameters thousands or millions of times, and we might risk running out of memory. Note that the gradient of a scalar-valued function with respect to a vector \(\mathbf{x}\) is vector-valued and has the same shape as \(\mathbf{x}\).
x = tf.Variable(x)
We now calculate our function of x and assign the result to y.
y = 2 * torch.dot(x, x)
y
tensor(28., grad_fn=<MulBackward0>)
We can now take the gradient of y with respect to x by calling
its backward method. Next, we can access the gradient via x’s
grad attribute.
y.backward()
x.grad
tensor([ 0., 4., 8., 12.])
# Our code is inside an `autograd.record` scope to build the computational
# graph
with autograd.record():
y = 2 * np.dot(x, x)
y
array(28.)
We can now take the gradient of y with respect to x by calling
its backward method. Next, we can access the gradient via x’s
grad attribute.
y.backward()
x.grad
[09:38:36] src/base.cc:49: GPU context requested, but no GPUs found.
array([ 0., 4., 8., 12.])
y = lambda x: 2 * jnp.dot(x, x)
y(x)
Array(28., dtype=float32)
We can now take the gradient of y with respect to x by passing
through the grad transform.
from jax import grad
# The `grad` transform returns a Python function that
# computes the gradient of the original function
x_grad = grad(y)(x)
x_grad
Array([ 0., 4., 8., 12.], dtype=float32)
# Record all computations onto a tape
with tf.GradientTape() as t:
y = 2 * tf.tensordot(x, x, axes=1)
y
<tf.Tensor: shape=(), dtype=float32, numpy=28.0>
We can now calculate the gradient of y with respect to x by
calling the gradient method.
x_grad = t.gradient(y, x)
x_grad
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0., 4., 8., 12.], dtype=float32)>
We already know that the gradient of the function \(y = 2\mathbf{x}^{\top}\mathbf{x}\) with respect to \(\mathbf{x}\) should be \(4\mathbf{x}\). We can now verify that the automatic gradient computation and the expected result are identical.
x.grad == 4 * x
tensor([True, True, True, True])
Now let’s calculate another function of x and take its gradient.
Note that PyTorch does not automatically reset the gradient buffer when
we record a new gradient. Instead, the new gradient is added to the
already-stored gradient. This behavior comes in handy when we want to
optimize the sum of multiple objective functions. To reset the gradient
buffer, we can call x.grad.zero() as follows:
x.grad.zero_() # Reset the gradient
y = x.sum()
y.backward()
x.grad
tensor([1., 1., 1., 1.])
x.grad == 4 * x
array([ True, True, True, True])
Now let’s calculate another function of x and take its gradient.
Note that MXNet resets the gradient buffer whenever we record a new
gradient.
with autograd.record():
y = x.sum()
y.backward()
x.grad # Overwritten by the newly calculated gradient
array([1., 1., 1., 1.])
x_grad == 4 * x
Array([ True, True, True, True], dtype=bool)
y = lambda x: x.sum()
grad(y)(x)
Array([1., 1., 1., 1.], dtype=float32)
x_grad == 4 * x
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([ True, True, True, True])>
Now let’s calculate another function of x and take its gradient.
Note that TensorFlow resets the gradient buffer whenever we record a new
gradient.
with tf.GradientTape() as t:
y = tf.reduce_sum(x)
t.gradient(y, x) # Overwritten by the newly calculated gradient
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([1., 1., 1., 1.], dtype=float32)>
2.5.2. Backward for Non-Scalar Variables¶
When y is a vector, the most natural interpretation of the
derivative of y with respect to a vector x is a matrix called
the Jacobian that contains the partial derivatives of each component
of y with respect to each component of x. Likewise, for
higher-order y and x, the differentiation result could be an
even higher-order tensor.
While Jacobians do show up in some advanced machine learning techniques,
more commonly we want to sum up the gradients of each component of y
with respect to the full vector x, yielding a vector of the same
shape as x. For example, we often have a vector representing the
value of our loss function calculated separately for each example among
a batch of training examples. Here, we just want to sum up the
gradients computed individually for each example.
Because deep learning frameworks vary in how they interpret gradients of
non-scalar tensors, PyTorch takes some steps to avoid confusion.
Invoking backward on a non-scalar elicits an error unless we tell
PyTorch how to reduce the object to a scalar. More formally, we need to
provide some vector \(\mathbf{v}\) such that backward will
compute \(\mathbf{v}^\top \partial_{\mathbf{x}} \mathbf{y}\) rather
than \(\partial_{\mathbf{x}} \mathbf{y}\). This next part may be
confusing, but for reasons that will become clear later, this argument
(representing \(\mathbf{v}\)) is named gradient. For a more
detailed description, see Yang Zhang’s Medium
post.
x.grad.zero_()
y = x * x
y.backward(gradient=torch.ones(len(y))) # Faster: y.sum().backward()
x.grad
tensor([0., 2., 4., 6.])
MXNet handles this problem by reducing all tensors to scalars by summing before computing a gradient. In other words, rather than returning the Jacobian \(\partial_{\mathbf{x}} \mathbf{y}\), it returns the gradient of the sum \(\partial_{\mathbf{x}} \sum_i y_i\).
with autograd.record():
y = x * x
y.backward()
x.grad # Equals the gradient of y = sum(x * x)
array([0., 2., 4., 6.])
y = lambda x: x * x
# grad is only defined for scalar output functions
grad(lambda x: y(x).sum())(x)
Array([0., 2., 4., 6.], dtype=float32)
By default, TensorFlow returns the gradient of the sum. In other words, rather than returning the Jacobian \(\partial_{\mathbf{x}} \mathbf{y}\), it returns the gradient of the sum \(\partial_{\mathbf{x}} \sum_i y_i\).
with tf.GradientTape() as t:
y = x * x
t.gradient(y, x) # Same as y = tf.reduce_sum(x * x)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 2., 4., 6.], dtype=float32)>
2.5.3. Detaching Computation¶
Sometimes, we wish to move some calculations outside of the recorded
computational graph. For example, say that we use the input to create
some auxiliary intermediate terms for which we do not want to compute a
gradient. In this case, we need to detach the respective computational
graph from the final result. The following toy example makes this
clearer: suppose we have z = x * y and y = x * x but we want to
focus on the direct influence of x on z rather than the
influence conveyed via y. In this case, we can create a new variable
u that takes the same value as y but whose provenance (how it
was created) has been wiped out. Thus u has no ancestors in the
graph and gradients do not flow through u to x. For example,
taking the gradient of z = x * u will yield the result x, (not
3 * x * x as you might have expected since z = x * x * x).
x.grad.zero_()
y = x * x
u = y.detach()
z = u * x
z.sum().backward()
x.grad == u
tensor([True, True, True, True])
with autograd.record():
y = x * x
u = y.detach()
z = u * x
z.backward()
x.grad == u
array([ True, True, True, True])
import jax
y = lambda x: x * x
# jax.lax primitives are Python wrappers around XLA operations
u = jax.lax.stop_gradient(y(x))
z = lambda x: u * x
grad(lambda x: z(x).sum())(x) == y(x)
Array([ True, True, True, True], dtype=bool)
# Set persistent=True to preserve the compute graph.
# This lets us run t.gradient more than once
with tf.GradientTape(persistent=True) as t:
y = x * x
u = tf.stop_gradient(y)
z = u * x
x_grad = t.gradient(z, x)
x_grad == u
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([ True, True, True, True])>
Note that while this procedure detaches y’s ancestors from the graph
leading to z, the computational graph leading to y persists and
thus we can calculate the gradient of y with respect to x.
x.grad.zero_()
y.sum().backward()
x.grad == 2 * x
tensor([True, True, True, True])
y.backward()
x.grad == 2 * x
array([ True, True, True, True])
grad(lambda x: y(x).sum())(x) == 2 * x
Array([ True, True, True, True], dtype=bool)
t.gradient(y, x) == 2 * x
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([ True, True, True, True])>
2.5.4. Gradients and Python Control Flow¶
So far we reviewed cases where the path from input to output was
well-defined via a function such as z = x * x * x. Programming
offers us a lot more freedom in how we compute results. For instance, we
can make them depend on auxiliary variables or condition choices on
intermediate results. One benefit of using automatic differentiation is
that even if building the computational graph of a function required
passing through a maze of Python control flow (e.g., conditionals,
loops, and arbitrary function calls), we can still calculate the
gradient of the resulting variable. To illustrate this, consider the
following code snippet where the number of iterations of the while
loop and the evaluation of the if statement both depend on the value
of the input a.
def f(a):
b = a * 2
while b.norm() < 1000:
b = b * 2
if b.sum() > 0:
c = b
else:
c = 100 * b
return c
def f(a):
b = a * 2
while np.linalg.norm(b) < 1000:
b = b * 2
if b.sum() > 0:
c = b
else:
c = 100 * b
return c
def f(a):
b = a * 2
while jnp.linalg.norm(b) < 1000:
b = b * 2
if b.sum() > 0:
c = b
else:
c = 100 * b
return c
def f(a):
b = a * 2
while tf.norm(b) < 1000:
b = b * 2
if tf.reduce_sum(b) > 0:
c = b
else:
c = 100 * b
return c
Below, we call this function, passing in a random value as input. Since
the input is a random variable, we do not know what form the
computational graph will take. However, whenever we execute f(a) on
a specific input, we realize a specific computational graph and can
subsequently run backward.
a = torch.randn(size=(), requires_grad=True)
d = f(a)
d.backward()
a = np.random.normal()
a.attach_grad()
with autograd.record():
d = f(a)
d.backward()
from jax import random
a = random.normal(random.PRNGKey(1), ())
d = f(a)
d_grad = grad(f)(a)
a = tf.Variable(tf.random.normal(shape=()))
with tf.GradientTape() as t:
d = f(a)
d_grad = t.gradient(d, a)
d_grad
<tf.Tensor: shape=(), dtype=float32, numpy=204800.0>
Even though our function f is a bit contrived for demonstration
purposes, its dependence on the input is quite simple: it is a linear
function of a with piecewise defined scale. As such, f(a) / a is
a vector of constant entries and, moreover, f(a) / a needs to match
the gradient of f(a) with respect to a.
a.grad == d / a
tensor(True)
a.grad == d / a
array(True)
d_grad == d / a
Array(True, dtype=bool)
d_grad == d / a
<tf.Tensor: shape=(), dtype=bool, numpy=True>
Dynamic control flow is very common in deep learning. For instance, when processing text, the computational graph depends on the length of the input. In these cases, automatic differentiation becomes vital for statistical modeling since it is impossible to compute the gradient a priori.
2.5.5. Discussion¶
You have now gotten a taste of the power of automatic differentiation. The development of libraries for calculating derivatives both automatically and efficiently has been a massive productivity booster for deep learning practitioners, liberating them to focus on loftier concerns. Moreover, autograd permits us to design massive models for which pen and paper gradient computations would be prohibitively time consuming. Interestingly, while we use autograd to optimize models (in a statistical sense) the optimization of autograd libraries themselves (in a computational sense) is a rich subject of vital interest to framework designers. Here, tools from compilers and graph manipulation are leveraged to compute results in the most expedient and memory-efficient manner.
For now, try to remember these basics: (i) attach gradients to those variables with respect to which we desire derivatives; (ii) record the computation of the target value; (iii) execute the backpropagation function; and (iv) access the resulting gradient.
2.5.6. Exercises¶
Why is the second derivative much more expensive to compute than the first derivative?
After running the function for backpropagation, immediately run it again and see what happens. Why?
In the control flow example where we calculate the derivative of
dwith respect toa, what would happen if we changed the variableato a random vector or a matrix? At this point, the result of the calculationf(a)is no longer a scalar. What happens to the result? How do we analyze this?Let \(f(x) = \sin(x)\). Plot the graph of \(f\) and of its derivative \(f'\). Do not exploit the fact that \(f'(x) = \cos(x)\) but rather use automatic differentiation to get the result.
Let \(f(x) = ((\log x^2) \cdot \sin x) + x^{-1}\). Write out a dependency graph tracing results from \(x\) to \(f(x)\).
Use the chain rule to compute the derivative \(\frac{df}{dx}\) of the aforementioned function, placing each term on the dependency graph that you constructed previously.
Given the graph and the intermediate derivative results, you have a number of options when computing the gradient. Evaluate the result once starting from \(x\) to \(f\) and once from \(f\) tracing back to \(x\). The path from \(x\) to \(f\) is commonly known as forward differentiation, whereas the path from \(f\) to \(x\) is known as backward differentiation.
When might you want to use forward differentiation and when backward differentiation? Hint: consider the amount of intermediate data needed, the ability to parallelize steps, and the size of matrices and vectors involved.