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(numba_lecture)=
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
<img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">
</a>
</div>
In addition to what's in Anaconda, this lecture will need the following libraries:
:tags: [hide-output]
!pip install quantecon
Please also make sure that you have the latest version of Anaconda, since old
versions are a {doc}common source of errors <troubleshooting>.
Let's start with some imports:
import numpy as np
import quantecon as qe
import matplotlib.pyplot as plt
In an {doc}earlier lecture <need_for_speed> we discussed vectorization,
which can improve execution speed by sending array processing operations in batch to efficient low-level code.
However, as {ref}discussed in that lecture <numba-p_c_vectorization>,
traditional vectorization schemes have weaknesses:
- Highly memory-intensive for compound array operations
- Ineffective or impossible for some algorithms
One way to circumvent these problems is by using Numba, a just in time (JIT) compiler for Python.
Numba compiles functions to native machine code instructions at runtime.
When it succeeds, the result is performance comparable to compiled C or Fortran.
In addition, Numba can do useful tricks such as {ref}multithreading <multithreading>.
This lecture introduces the core ideas.
Some readers might be curious about the relationship between Numba and [Julia](https://julialang.org/),
which contains its own JIT compiler. While the two compilers are similar in
many ways, Numba is less ambitious, attempting only to compile a small subset of
the Python language. Although this might sound like a deficiency, it is also a
strength: the more restrictive nature of Numba makes it easy to use well and
good at what it does.
(numba_link)=
(quad_map_eg)=
Let's consider a problem that's difficult to vectorize (i.e., hand off to array processing operations).
The problem involves generating the trajectory via the quadratic map
In what follows we set
Here's the plot of a typical trajectory, starting from
def qm(x0, n, α=4.0):
x = np.empty(n+1)
x[0] = x0
for t in range(n):
x[t+1] = α * x[t] * (1 - x[t])
return x
x = qm(0.1, 250)
fig, ax = plt.subplots()
ax.plot(x, 'b-', lw=2, alpha=0.8)
ax.set_xlabel('$t$', fontsize=12)
ax.set_ylabel('$x_{t}$', fontsize = 12)
plt.show()
Let's see how long this takes to run for large
n = 10_000_000
with qe.Timer() as timer1:
# Time Python base version
x = qm(0.1, n)
To speed the function qm up using Numba, we first import the jit function
from numba import jit
Now we apply it to qm, producing a new function:
qm_numba = jit(qm)
The function qm_numba is a version of qm that is "targeted" for
JIT-compilation.
We will explain what this means momentarily.
Let's time this new version:
with qe.Timer() as timer2:
# Time jitted version
x = qm_numba(0.1, n)
This is a large speed gain.
In fact, the next time and all subsequent times it runs even faster as the function has been compiled and is in memory:
(qm_numba_result)=
with qe.Timer() as timer3:
# Second run
x = qm_numba(0.1, n)
Here's the speed gain
timer1.elapsed / timer3.elapsed
This is a big boost for a small modification to our original code.
Let's discuss how this works.
Numba attempts to generate fast machine code using the infrastructure provided by the LLVM Project.
It does this by inferring type information on the fly.
(See our {doc}earlier lecture <need_for_speed> on scientific computing for a discussion of types.)
The basic idea is this:
- Python is very flexible and hence we could call the function qm with many types.
- e.g.,
x0could be a NumPy array or a list,ncould be an integer or a float, etc.
- e.g.,
- This makes it very difficult to generate efficient machine code ahead of time (i.e., before runtime).
- However, when we do actually call the function, say by running
qm(0.5, 10), the types ofx0,αandnare determined. - Moreover, the types of other variables in
qmcan be inferred once the input types are known. - So the strategy of Numba and other JIT compilers is to wait until the function is called, and then compile.
That is called "just-in-time" compilation.
Note that, if you make the call qm_numba(0.5, 10) and then follow it with qm_numba(0.9, 20), compilation only takes place on the first call.
This is because compiled code is cached and reused as required.
This is why, in the code above, the second run of qm_numba is faster.
In practice, rather than writing `qm_numba = jit(qm)`, we typically use
*decorator* syntax and put `@jit` before the function definition. This is
equivalent to adding `qm = jit(qm)` after the definition.
Numba is relatively easy to use but not always seamless.
Let's review some of the issues users run into.
Successful type inference is the key to JIT compilation.
In an ideal setting, Numba can infer all necessary type information.
When Numba cannot infer all type information, it will raise an error.
For example, in the setting below, Numba is unable to determine the type of the
function g when compiling iterate
@jit
def iterate(f, x0, n):
x = x0
for t in range(n):
x = f(x)
return x
# Not jitted
def g(x):
return np.cos(x) - 2 * np.sin(x)
# This code throws an error
try:
iterate(g, 0.5, 100)
except Exception as e:
print(e)
In the present case, we can fix this easily by compiling g.
@jit
def g(x):
return np.cos(x) - 2 * np.sin(x)
iterate(g, 0.5, 100)
In other cases, such as when we want to use functions from external libaries
such as SciPy, there might not be any easy workaround.
Another thing to be careful about when using Numba is handling of global variables.
For example, consider the following code
a = 1
@jit
def add_a(x):
return a + x
print(add_a(10))
a = 2
print(add_a(10))
Notice that changing the global had no effect on the value returned by the function 😱.
When Numba compiles machine code for functions, it treats global variables as constants to ensure type stability.
To avoid this, pass values as function arguments rather than relying on globals.
(multithreading)=
In addition to JIT compilation, Numba provides support for parallel computing on CPUs and GPUs.
The key tool for parallelization on CPUs in Numba is the prange function, which tells
Numba to execute loop iterations in parallel across available cores.
To illustrate, let's look first at a simple, single-threaded (i.e., non-parallelized) piece of code.
The code simulates updating the wealth
Here
-
$R$ is the gross rate of return on assets -
$s$ is the savings rate of the household and -
$y$ is labor income.
We model both
Here's the code:
@jit
def update(w, r=0.1, s=0.3, v1=0.1, v2=1.0):
" Updates household wealth. "
# Draw shocks
R = np.exp(v1 * np.random.randn()) * (1 + r)
y = np.exp(v2 * np.random.randn())
# Update wealth
w = R * s * w + y
return w
Let's have a look at how wealth evolves under this rule.
fig, ax = plt.subplots()
T = 100
w = np.empty(T)
w[0] = 5
for t in range(T-1):
w[t+1] = update(w[t])
ax.plot(w)
ax.set_xlabel('$t$', fontsize=12)
ax.set_ylabel('$w_{t}$', fontsize=12)
plt.show()
Now let's suppose that we have a large population of households and we want to know what median wealth will be.
This is not easy to solve with pencil and paper, so we will use simulation instead:
- Simulate a large number of households forward in time
- Calculate median wealth
Here's the code:
@jit
def compute_long_run_median(w0=1, T=1000, num_reps=50_000):
obs = np.empty(num_reps)
# For each household
for i in range(num_reps):
# Set the initial condition and run forward in time
w = w0
for t in range(T):
w = update(w)
# Record the final value
obs[i] = w
# Take the median of all final values
return np.median(obs)
Let's see how fast this runs:
with qe.Timer():
# Warm up
compute_long_run_median()
with qe.Timer():
# Second run
compute_long_run_median()
To speed this up, we're going to parallelize it via multithreading.
To do so, we add the parallel=True flag and change range to prange:
from numba import prange
@jit(parallel=True)
def compute_long_run_median_parallel(
w0=1, T=1000, num_reps=50_000
):
obs = np.empty(num_reps)
for i in prange(num_reps): # Parallelize over households
w = w0
for t in range(T):
w = update(w)
obs[i] = w
return np.median(obs)
Let's look at the timing:
with qe.Timer():
# Warm up
compute_long_run_median_parallel()
with qe.Timer():
# Second run
compute_long_run_median_parallel()
The speed-up is significant.
Notice that we parallelize across households rather than over time -- updates of an individual household across time periods are inherently sequential.
For GPU-based parallelization, see our {doc}lectures on JAX <jax_intro>.
{ref}speed_ex1 and {ref}numba_ex3 both estimate
We generate them here and store them in u_draws and v_draws so that we can use them in both exercises and compare results
n = 1_000_000
rng = np.random.default_rng()
u_draws = rng.uniform(size=n)
v_draws = rng.uniform(size=n)
:label: speed_ex1
{ref}`Previously <pbe_ex5>` we considered how to approximate $\pi$ by
Monte Carlo.
Use the same idea here, but make the code efficient using Numba.
Compare speed with and without Numba when the sample size is large.
:class: dropdown
Here is one solution:
@jit
def calculate_pi(u_draws, v_draws):
n = len(u_draws)
count = 0
for i in range(n):
u, v = u_draws[i], v_draws[i]
d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)
if d < 0.5:
count += 1
area_estimate = count / n
return area_estimate * 4 # dividing by radius**2
Now let's see how fast it runs:
with qe.Timer():
calculate_pi(u_draws, v_draws)
with qe.Timer():
calculate_pi(u_draws, v_draws)
If we switch off JIT compilation by removing @jit, the code takes around
150 times as long on our machine.
So we get a speed gain of 2 orders of magnitude by adding four characters.
The solution above takes one of two natural approaches: it draws all the
random points first, stores them in u_draws and v_draws, and then lets the
jitted function loop over them.
The other approach is to draw each point inside the loop.
To do this with a NumPy Generator, we pass rng in as an argument and call rng.uniform() inside the loop body
@jit
def calculate_pi_in_loop(rng, n):
count = 0
for i in range(n):
u, v = rng.uniform(), rng.uniform()
d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)
if d < 0.5:
count += 1
return (count / n) * 4
with qe.Timer():
calculate_pi_in_loop(rng, n)
In this serial setting the two approaches give equally good estimates and run at similar speed, but they are not equivalent in memory use.
The first approach
must hold all n floating point
numbers, or about 16n bytes (around n = 100_000_000).
The
second draws each point on demand and discards it, so its memory footprint does
not grow with n.
This might suggest that drawing inside the loop is the better default.
But as we
will see in {ref}numba_ex_race, drawing inside the loop interacts
badly with parallelization.
:label: speed_ex2
In the Introduction to Quantitative Economics with Python lecture series you can learn all about finite-state Markov chains.
For now, let's just concentrate on simulating a very simple example of such a chain.
Suppose that the volatility of returns on an asset can be in one of two regimes --- high or low.
The transition probabilities across states are as follows
:align: center
For example, let the period length be one day, and suppose the current state is high.
We see from the graph that the state tomorrow will be
- high with probability 0.8
- low with probability 0.2
Your task is to simulate a sequence of daily volatility states according to this rule.
Set the length of the sequence to n = 1_000_000 and start in the high state.
Implement a pure Python version and a Numba version, and compare speeds.
To test your code, evaluate the fraction of time that the chain spends in the low state.
If your code is correct, it should be about 2/3.
:class: dropdown
* Represent the low state as 0 and the high state as 1.
* If you want to store integers in a NumPy array and then apply JIT compilation, use `x = np.empty(n, dtype=np.int64)`.
:class: dropdown
We let
- 0 represent "low"
- 1 represent "high"
p, q = 0.1, 0.2 # Prob of leaving low and high state respectively
Here's a pure Python version of the function
n = 1_000_000
rng = np.random.default_rng()
U = rng.uniform(0, 1, size=n)
def compute_series(n, U):
x = np.empty(n, dtype=np.int64)
x[0] = 1 # Start in state 1
for t in range(1, n):
current_x = x[t-1]
if current_x == 0:
x[t] = U[t] < p
else:
x[t] = U[t] > q
return x
Let's run this code and check that the fraction of time spent in the low state is about 0.666
x = compute_series(n, U)
print(np.mean(x == 0)) # Fraction of time x is in state 0
This is (approximately) the right output.
Now let's time it:
with qe.Timer():
compute_series(n, U)
Next let's implement a Numba version, which is easy
compute_series_numba = jit(compute_series)
Let's check we still get the right numbers
x = compute_series_numba(n, U)
print(np.mean(x == 0))
Let's see the time
with qe.Timer():
compute_series_numba(n, U)
This is a nice speed improvement for one line of code!
:label: numba_ex3
In {ref}`an earlier exercise <speed_ex1>`, we used Numba to accelerate an
effort to compute the constant $\pi$ by Monte Carlo.
Now try adding parallelization and see if you get further speed gains.
You should not expect huge gains here because, while there are many
independent tasks (draw point and test if in circle), each one has low
execution time.
Generally speaking, parallelization is less effective when the individual
tasks to be parallelized are very small relative to total execution time.
This is due to overheads associated with spreading all of these small tasks across multiple CPUs.
Nevertheless, with suitable hardware, it is possible to get nontrivial speed gains in this exercise.
For the size of the Monte Carlo simulation, use something substantial, such as
`n = 100_000_000`.
:class: dropdown
Here is one solution:
@jit(parallel=True)
def calculate_pi(u_draws, v_draws):
n = len(u_draws)
count = 0
for i in prange(n):
u, v = u_draws[i], v_draws[i]
d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)
if d < 0.5:
count += 1
area_estimate = count / n
return area_estimate * 4 # dividing by radius**2
Now let's see how fast it runs:
with qe.Timer():
calculate_pi(u_draws, v_draws)
with qe.Timer():
calculate_pi(u_draws, v_draws)
By switching parallelization on and off (selecting True or
False in the @jit annotation), we can test the speed gain that
multithreading provides on top of JIT compilation.
On our workstation, we find that parallelization increases execution speed by a factor of 2 or 3.
(If you are executing locally, you will get different numbers, depending mainly on the number of CPUs on your machine.)
Notice that we drew all of the random points before the loop and passed them in as arrays, so the parallel loop only reads from memory.
Drawing the points inside the parallel loop instead is surprisingly delicate.
We investigate why, and how to do it safely, in
{ref}numba_ex_race.
:label: numba_ex_race
In {ref}`numba_ex3` we drew all of the random points *before* the parallel loop.
It is tempting to instead draw each point *inside* the `prange` loop, by passing a generator `rng` in as an argument and calling `rng.uniform()` in the loop body.
Try it: the code should run and return a number close to $\pi$, yet there is a subtle bug in this approach.
Investigate as follows:
1. Call your function a few times with the *same* seed and check whether the result is reproducible.
2. Repeat the estimate many times across a range of sample sizes and compare its spread against a correct parallel version.
Then explain what goes wrong and give a correct way to draw inside a parallel loop.
Hint: try to use a legacy random function such as `np.random.uniform()` instead of a `Generator` and see what happens.
:class: dropdown
Here is the tempting version.
We pass rng in as an argument and call it inside the prange loop.
n = 1_000_000
rng = np.random.default_rng()
@jit(parallel=True)
def calculate_pi_in_loop(rng, n):
count = 0
for i in prange(n):
u, v = rng.uniform(), rng.uniform()
d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)
if d < 0.5:
count += 1
return (count / n) * 4
calculate_pi_in_loop(rng, n)
The code runs without error and returns something close to
But something is silently wrong with the results.
Here, every thread draws from the same generator rng.
A generator produces each number by updating an internal state.
Under prange, many threads read and update that single state at once, with no coordination between them.
This is a data race.
It creates correlations across the draws and can even cause some draws to be duplicated in an unpredictable way.
Two symptoms reveal the problem.
Symptom 1: the result is no longer reproducible.
A correct generator returns the same answer whenever it is given the same seed.
Because of the data race, the order in which threads happen to touch the shared state affects the stream of draws, so the answer is not reproducible even when the seed is fixed.
for seed in (1, 1, 1):
print(calculate_pi_in_loop(np.random.default_rng(seed), n))
Each call uses the same seed, yet the answers differ.
Symptom 2: the estimator is far noisier than it should be.
The duplicated and correlated draws carry less information than
The fix is to give each thread its own random state, which NumPy's legacy functions such as np.random.uniform() do automatically under Numba.
@jit(parallel=True)
def calculate_pi_legacy(n):
count = 0
for i in prange(n):
u, v = np.random.uniform(0, 1), np.random.uniform(0, 1)
d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)
if d < 0.5:
count += 1
return (count / n) * 4
To see the cost of the race, we repeat each estimate many times and plot its spread against the correct version as the sample size grows.
sample_sizes = np.logspace(3, 6, 10).astype(int)
num_reps = 20
methods = [("per-thread state (correct)",
lambda n: calculate_pi_legacy(n), 'C0'),
("shared generator in prange (data race)",
lambda n: calculate_pi_in_loop(np.random.default_rng(), n), 'C1')]
fig, ax = plt.subplots()
for label, estimate, color in methods:
draws = np.array([[estimate(int(m)) for _ in range(num_reps)]
for m in sample_sizes])
means, stds = draws.mean(axis=1), draws.std(axis=1)
ax.plot(sample_sizes, means, color=color, marker='o', ms=3, label=label)
ax.fill_between(sample_sizes, means - 2 * stds, means + 2 * stds,
color=color, alpha=0.2)
ax.axhline(np.pi, color='k', lw=0.8, ls='--', label=r'$\pi$')
ax.set_xscale('log')
ax.set_xlabel('number of samples')
ax.set_ylabel(r'estimate of $\pi$')
ax.legend()
plt.show()
Both bands are centered on
The other safe option is the one from {ref}numba_ex3: draw the points before the loop so that the parallel loop only reads from memory.
:label: numba_ex_draw_speed
We now have two correct ways to estimate $\pi$ in parallel.
One draws all the points *before* the loop, as in {ref}`numba_ex3`.
The other draws them *inside* the loop with legacy functions, as in {ref}`numba_ex_race`.
Compare their speed at `n = 100_000_000`, including the time spent generating the random points.
:class: dropdown
We time each approach from start to finish, so the pre-draw version pays for building its arrays.
n = 100_000_000
rng = np.random.default_rng()
with qe.Timer():
u_draws = rng.uniform(size=n)
v_draws = rng.uniform(size=n)
calculate_pi(u_draws, v_draws)
with qe.Timer():
calculate_pi_legacy(n)
Drawing inside the loop is much faster.
The pre-draw version generates its two arrays on a single thread before the loop begins.
The in-loop version spreads the random number generation across all threads instead.
It also avoids allocating two arrays of n numbers, so it saves both time and memory.
:label: numba_ex4
In {doc}`our lecture on SciPy<scipy>`, we discussed pricing a call option in a
setting where the underlying stock price had a simple and well-known
distribution.
Here we discuss a more realistic setting.
We recall that the price of the option obeys
$$
P = \beta^n \mathbb E \max\{ S_n - K, 0 \}
$$
where
1. $\beta$ is a discount factor,
2. $n$ is the expiry date,
3. $K$ is the strike price and
4. $\{S_t\}$ is the price of the underlying asset at each time $t$.
Suppose that `n, β, K = 20, 0.99, 100`.
Assume that the stock price obeys
$$
\ln \frac{S_{t+1}}{S_t} = \mu + \sigma_t \xi_{t+1}
$$
where
$$
\sigma_t = \exp(h_t),
\quad
h_{t+1} = \rho h_t + \nu \eta_{t+1}
$$
Here $\{\xi_t\}$ and $\{\eta_t\}$ are IID and standard normal.
(This is a **stochastic volatility** model, where the volatility $\sigma_t$
varies over time.)
Use the defaults `μ, ρ, ν, S0, h0 = 0.0001, 0.1, 0.001, 10, 0`.
(Here `S0` is $S_0$ and `h0` is $h_0$.)
By generating $M$ paths $s_0, \ldots, s_n$, compute the Monte Carlo estimate
$$
\hat P_M
:= \beta^n \mathbb E \max\{ S_n - K, 0 \}
\approx
\frac{1}{M} \sum_{m=1}^M \max \{S_n^m - K, 0 \}
$$
of the price, applying Numba and parallelization.
:class: dropdown
With
Using this fact, the solution can be written as follows.
Note that random draws are kept inside the inner loop rather than pre-allocated,
to avoid creating large shock arrays of size M * n.
M = 10_000_000
n, β, K = 20, 0.99, 100
μ, ρ, ν, S0, h0 = 0.0001, 0.1, 0.001, 10, 0
@jit(parallel=True)
def compute_call_price_parallel(β=β,
μ=μ,
S0=S0,
h0=h0,
K=K,
n=n,
ρ=ρ,
ν=ν,
M=M):
current_sum = 0.0
# For each sample path
for m in prange(M):
s = np.log(S0)
h = h0
# Simulate forward in time
for t in range(n):
s = s + μ + np.exp(h) * np.random.randn()
h = ρ * h + ν * np.random.randn()
# And add the value max{S_n - K, 0} to current_sum
current_sum += max(np.exp(s) - K, 0)
return β**n * current_sum / M
Try swapping between parallel=True and parallel=False and noting the run time.
If you are on a machine with many CPUs, the difference should be significant.