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Numba
{:.no_toc}
* TOC {:toc}The goal
Questions to David Rotermund
pip install numba
Numba basic types
For the example that will show you the options of optimization we need to understand the numba naming schema.
Numbers
For the function signatures we need to be able to translate the usual np.dtype into numpy.types.
For doing so we just replace np. by numba.types. .
| Type name(s) | Shorthand | Comments |
|---|---|---|
| numba.types.boolean | b1 | represented as a byte |
| numba.types.uint8, byte | u1 | 8-bit unsigned byte |
| numba.types.uint16 | u2 | 16-bit unsigned integer |
| numba.types.uint32 | u4 | 32-bit unsigned integer |
| numba.types.uint64 | u8 | 64-bit unsigned integer |
| numba.types.int8, char | i1 | 8-bit signed byte |
| numba.types.int16 | i2 | 16-bit signed integer |
| numba.types.int32 | i4 | 32-bit signed integer |
| numba.types.int64 | i8 | 64-bit signed integer |
| numba.types.intc | – | C int-sized integer |
| numba.types.uintc | – | C int-sized unsigned integer |
| numba.types.intp | – | pointer-sized integer |
| numba.types.uintp | – | pointer-sized unsigned integer |
| numba.types.float32 | f4 | single-precision floating-point number |
| numba.types.float64, double | f8 | double-precision floating-point number |
| numba.types.complex64 | c8 | single-precision complex number |
| numba.types.complex128 | c16 | double-precision complex number |
Arrays
If we have arrays in the function signature, which is a very likely senario, we might want to give as much information to numpy as possible about the numpy.ndarray. In some cases it is very benificial to make a np.ndarray an array with C memory layout and tell numba about it.
We can use the numpy function numpy.ascontiguousarray for converting a numpy array into a C memory layout.
We can also check a numpy array, let's call it X, if it is already in the C memory layout. This is done by looking at X.flags['C_CONTIGUOUS'].
Some example for array signatures are:
| numba.types.float32[:] | 1d array of float32 with no particular memory layout |
| numba.types.float32[:,:] | 2d array of float32 with no particular memory layout |
| numba.types.float32[:,:,:] | 3d array of float32 with no particular memory layout |
| numba.types.float32[::1] | 1d array of float32 with C memory layout |
| numba.types.float32[:,::1] | 2d array of float32 with C memory layout |
| numba.types.float32[:,:,::1] | 3d array of float32 with C memory layout |
| numba.types.float32[::1,:] | 2d array of float32 with Fortran memory layout |
| numba.types.float32[::1,:,:] | 3d array of float32 with Fortran memory layout |
An example (up to 437x faster)
For measuring the time used by the program I ran everything twice and took the second time. I did this is because the just-in-time compilation takes a moment for the first call of a function.
Basis code (7.76 sec)
This is the basic code without any optimizations.
import time
import numpy as np
def get_spike(
h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float64
) -> np.uint64:
summation: np.float64 = np.float64(0.0)
output: np.uint64 = np.uint64(number_of_neurons - 1)
for i in range(0, np.uint64(number_of_neurons - 1)):
summation += h[i]
if random_number <= summation:
output = np.uint64(i)
return output
return output
def main(
number_of_iterations: np.uint64,
number_of_neurons: np.uint64,
random_number_spikes: np.ndarray,
random_number_h: np.ndarray,
) -> np.ndarray:
results = np.zeros((number_of_iterations), dtype=np.uint64)
for i in range(0, number_of_iterations):
h = random_number_h[i, :]
h /= h.sum()
results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
return results
if __name__ == "__main__":
number_of_iterations: np.uint64 = np.uint64(10000)
number_of_neurons: np.uint64 = np.uint64(10000)
myrng = np.random.default_rng()
random_number_spikes = myrng.random((number_of_iterations), dtype=np.float64)
random_number_h = myrng.random(
(number_of_iterations, number_of_neurons), dtype=np.float64
)
start_time = time.perf_counter()
results = main(
number_of_iterations=number_of_iterations,
number_of_neurons=number_of_neurons,
random_number_spikes=random_number_spikes,
random_number_h=random_number_h,
)
end_time = time.perf_counter()
check_for_errors = np.sum([results >= number_of_neurons])
if check_for_errors > 0:
print("Something went really wrong! Panic!")
print(f"{end_time-start_time:.5f} sec")
print(results[0:10])
Optimization 1 (0.482sec)
We add just-in-time compilation to the function get_spike with @njit(cache=True). "To avoid compilation times each time you invoke a Python program, you can instruct Numba to write the result of function compilation into a file-based cache."
import time
import numpy as np
from numba import njit
@njit(cache=True)
def get_spike(
h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float64
) -> np.uint64:
summation: np.float64 = np.float64(0.0)
output: np.uint64 = np.uint64(number_of_neurons - 1)
for i in range(0, np.uint64(number_of_neurons - 1)):
summation += h[i]
if random_number <= summation:
output = np.uint64(i)
return output
return output
def main(
number_of_iterations: np.uint64,
number_of_neurons: np.uint64,
random_number_spikes: np.ndarray,
random_number_h: np.ndarray,
) -> np.ndarray:
results = np.zeros((number_of_iterations), dtype=np.uint64)
for i in range(0, number_of_iterations):
h = random_number_h[i, :]
h /= h.sum()
results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
return results
if __name__ == "__main__":
number_of_iterations: np.uint64 = np.uint64(10000)
number_of_neurons: np.uint64 = np.uint64(10000)
myrng = np.random.default_rng()
random_number_spikes = myrng.random((number_of_iterations), dtype=np.float64)
random_number_h = myrng.random(
(number_of_iterations, number_of_neurons), dtype=np.float64
)
start_time = time.perf_counter()
results = main(
number_of_iterations=number_of_iterations,
number_of_neurons=number_of_neurons,
random_number_spikes=random_number_spikes,
random_number_h=random_number_h,
)
end_time = time.perf_counter()
check_for_errors = np.sum([results >= number_of_neurons])
if check_for_errors > 0:
print("Something went really wrong! Panic!")
print(f"{end_time-start_time:.5f} sec")
print(results[0:10])
Optimization 2 (0.627sec)
We also add just-in-time compilation to the function main with @njit(cache=True).
import time
import numpy as np
from numba import njit
@njit(cache=True)
def get_spike(
h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float64
) -> np.uint64:
summation: np.float64 = np.float64(0.0)
output: np.uint64 = np.uint64(number_of_neurons - 1)
for i in range(0, np.uint64(number_of_neurons - 1)):
summation += h[i]
if random_number <= summation:
output = np.uint64(i)
return output
return output
@njit(cache=True)
def main(
number_of_iterations: np.uint64,
number_of_neurons: np.uint64,
random_number_spikes: np.ndarray,
random_number_h: np.ndarray,
) -> np.ndarray:
results = np.zeros((number_of_iterations), dtype=np.uint64)
for i in range(0, number_of_iterations):
h = random_number_h[i, :]
h /= h.sum()
results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
return results
if __name__ == "__main__":
number_of_iterations: np.uint64 = np.uint64(10000)
number_of_neurons: np.uint64 = np.uint64(10000)
myrng = np.random.default_rng()
random_number_spikes = myrng.random((number_of_iterations), dtype=np.float64)
random_number_h = myrng.random(
(number_of_iterations, number_of_neurons), dtype=np.float64
)
start_time = time.perf_counter()
results = main(
number_of_iterations=number_of_iterations,
number_of_neurons=number_of_neurons,
random_number_spikes=random_number_spikes,
random_number_h=random_number_h,
)
end_time = time.perf_counter()
check_for_errors = np.sum([results >= number_of_neurons])
if check_for_errors > 0:
print("Something went really wrong! Panic!")
print(f"{end_time-start_time:.5f} sec")
print(results[0:10])
Optimization 3 (0.619sec)
We add function signatures to the code with:
@njit(
numba.types.uint64(numba.types.float64[:], numba.types.uint64, numba.types.float64),
cache=True,
)
def get_spike(
h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float64
) -> np.uint64:
[...]
@njit(
numba.types.uint64[:](
numba.types.uint64,
numba.types.uint64,
numba.types.float64[:],
numba.types.float64[:, :],
),
cache=True,
)
def main(
number_of_iterations: np.uint64,
number_of_neurons: np.uint64,
random_number_spikes: np.ndarray,
random_number_h: np.ndarray,
) -> np.ndarray:
import time
import numpy as np
from numba import njit
import numba
@njit(
numba.types.uint64(numba.types.float64[:], numba.types.uint64, numba.types.float64),
cache=True,
)
def get_spike(
h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float64
) -> np.uint64:
summation: np.float64 = np.float64(0.0)
output: np.uint64 = np.uint64(number_of_neurons - 1)
for i in range(0, np.uint64(number_of_neurons - 1)):
summation += h[i]
if random_number <= summation:
output = np.uint64(i)
return output
return output
@njit(
numba.types.uint64[:](
numba.types.uint64,
numba.types.uint64,
numba.types.float64[:],
numba.types.float64[:, :],
),
cache=True,
)
def main(
number_of_iterations: np.uint64,
number_of_neurons: np.uint64,
random_number_spikes: np.ndarray,
random_number_h: np.ndarray,
) -> np.ndarray:
results = np.zeros((number_of_iterations), dtype=np.uint64)
for i in range(0, number_of_iterations):
h = random_number_h[i, :]
h /= h.sum()
results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
return results
if __name__ == "__main__":
number_of_iterations: np.uint64 = np.uint64(10000)
number_of_neurons: np.uint64 = np.uint64(10000)
myrng = np.random.default_rng()
random_number_spikes = myrng.random((number_of_iterations), dtype=np.float64)
random_number_h = myrng.random(
(number_of_iterations, number_of_neurons), dtype=np.float64
)
start_time = time.perf_counter()
results = main(
number_of_iterations=number_of_iterations,
number_of_neurons=number_of_neurons,
random_number_spikes=random_number_spikes,
random_number_h=random_number_h,
)
end_time = time.perf_counter()
check_for_errors = np.sum([results >= number_of_neurons])
if check_for_errors > 0:
print("Something went really wrong! Panic!")
print(f"{end_time-start_time:.5f} sec")
print(results[0:10])
Optimization 4 (0.419sec)
We tell numba about the C memory layout of the arrays with refining the function signature:
@njit(
numba.types.uint64[::1](
numba.types.uint64,
numba.types.uint64,
numba.types.float64[::1],
numba.types.float64[:, ::1],
),
cache=True,
)
import time
import numpy as np
from numba import njit
import numba
@njit(
numba.types.uint64(numba.types.float64[:], numba.types.uint64, numba.types.float64),
cache=True,
)
def get_spike(
h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float64
) -> np.uint64:
summation: np.float64 = np.float64(0.0)
output: np.uint64 = np.uint64(number_of_neurons - 1)
for i in range(0, np.uint64(number_of_neurons - 1)):
summation += h[i]
if random_number <= summation:
output = np.uint64(i)
return output
return output
@njit(
numba.types.uint64[::1](
numba.types.uint64,
numba.types.uint64,
numba.types.float64[::1],
numba.types.float64[:, ::1],
),
cache=True,
)
def main(
number_of_iterations: np.uint64,
number_of_neurons: np.uint64,
random_number_spikes: np.ndarray,
random_number_h: np.ndarray,
) -> np.ndarray:
results = np.zeros((number_of_iterations), dtype=np.uint64)
for i in range(0, number_of_iterations):
h = random_number_h[i, :]
h /= h.sum()
results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
return results
if __name__ == "__main__":
number_of_iterations: np.uint64 = np.uint64(10000)
number_of_neurons: np.uint64 = np.uint64(10000)
myrng = np.random.default_rng()
random_number_spikes = myrng.random((number_of_iterations), dtype=np.float64)
random_number_h = myrng.random(
(number_of_iterations, number_of_neurons), dtype=np.float64
)
start_time = time.perf_counter()
results = main(
number_of_iterations=number_of_iterations,
number_of_neurons=number_of_neurons,
random_number_spikes=random_number_spikes,
random_number_h=random_number_h,
)
end_time = time.perf_counter()
check_for_errors = np.sum([results >= number_of_neurons])
if check_for_errors > 0:
print("Something went really wrong! Panic!")
print(f"{end_time-start_time:.5f} sec")
print(results[0:10])