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Figuring out how to do speedy evaluation of lists of SymPy expressions.
Testing results.
Timing the functions.
Timing: cython
cython time: 0.00288254904747 s
Timing: numpy_broadcast
numpy_broadcast time: 0.00597401690483 s
Timing: lambdify_individual
lambdify_individual time: 0.00303873364131 s
Timing: ufuncify_f2py
ufuncify_f2py time: 0.00201614236832 s
Timing: python
python time: 0.119000189304 s
Timing: ufuncify_cython
ufuncify_cython time: 0.00293522365888 s
Timing: numpy
numpy time: 0.00303197193146 s
Timing: c
c time: 0.0029081483682 s
Timing: lambdify
lambdify time: 0.00599523711205 s
Timing: ufuncify_matrix_cython
ufuncify_matrix_cython time: 0.00292766968409 s
Benchmark time: 0.119000189304 s
Ratios of the timings to the benchmark time:
--------------------------------------------
ufuncify_f2py ratio: 59.0237034717
cython ratio: 41.2829711983
c ratio: 40.9195729508
ufuncify_matrix_cython ratio: 40.6467266273
ufuncify_cython ratio: 40.5421198294
numpy ratio: 39.2484468836
lambdify_individual ratio: 39.1611122761
numpy_broadcast ratio: 19.9196271454
lambdify ratio: 19.8491214076
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include "eval_matrix_h.h"
/* This just tests out the C functions in eval_matrix_c.c. */
int main()
{
// generate the a, b and c arrays of doubles
int n = 10000;
double a[n];
double b[n];
double c[n];
srand(time(NULL));
int i;
for (i = 0; i < n; i++){
a[i] = (rand() % 100);
b[i] = (rand() % 100);
c[i] = (rand() % 100);
}
// for (i = 0; i < n; i++){
// printf("a%d: %f\n", i, a[i]);
// printf("b%d: %f\n", i, b[i]);
// printf("c%d: %f\n", i, c[i]);
// }
double matrix[n * 4];
eval_matrix_loop(n, a, b, c, matrix);
double mat[4];
for (i = 0; i < n; i++){
mat[0] = matrix[i * 4];
mat[1] = matrix[i * 4 + 1];
mat[2] = matrix[i * 4 + 2];
mat[3] = matrix[i * 4 + 3];
printf("matrix[%d] = [%f %f %f %f]\n", i, mat[0], mat[1], mat[2], mat[3]);
}
return 0;
}
"""This is a small rewrite of the ufuncify function in sympy so it supports
array arguments for all values."""
import importlib
import subprocess
import sympy as sy
from sympy.utilities.lambdify import implemented_function
from sympy.utilities.autowrap import autowrap
_c_template = """\
#include <math.h>
#include "{file_prefix}_h.h"
void {routine_name}(double matrix[{matrix_output_size}], {input_args})
{{
{eval_code}
}}
"""
_h_template = """\
void {routine_name}(double matrix[{matrix_output_size}], {input_args});
"""
_cython_template = """\
import numpy as np
cimport numpy as np
cimport cython
cdef extern from "{file_prefix}_h.h":
void {routine_name}(double matrix[{matrix_output_size}], {input_args})
@cython.boundscheck(False)
@cython.wraparound(False)
def {routine_name}_loop(np.ndarray[np.double_t, ndim=2] matrix, {numpy_typed_input_args}):
cdef int n = matrix.shape[0]
cdef int i
for i in range(n):
{routine_name}(&matrix[i, 0], {indexed_input_args})
return matrix.reshape(n, {num_rows}, {num_cols})
"""
_setup_template = """\
import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
extension = Extension(name="{file_prefix}",
sources=["{file_prefix}.pyx",
"{file_prefix}_c.c"],
extra_compile_args=[],
include_dirs=[numpy.get_include()])
setup(name="{routine_name}",
cmdclass={{'build_ext': build_ext}},
ext_modules=[extension])
"""
def ufuncify_matrix(args, expr, cse=True):
matrix_size = expr.shape[0] * expr.shape[1]
file_prefix_base = 'ufuncify_matrix'
file_prefix = '{}_{}'.format(file_prefix_base, 0)
taken = False
i = 1
while not taken:
try:
open(file_prefix + '.pyx', 'r')
except IOError:
taken = True
else:
file_prefix = '{}_{}'.format(file_prefix_base, i)
i += 1
d = {'routine_name': 'eval_matrix',
'file_prefix': file_prefix,
'matrix_output_size': matrix_size,
'num_rows': expr.shape[0],
'num_cols': expr.shape[1]}
matrix_sym = sy.MatrixSymbol('matrix', expr.shape[0], expr.shape[1])
sub_exprs, simple_mat = sy.cse(expr, sy.numbered_symbols('z_'))
sub_expr_code = '\n'.join(['double ' + sy.ccode(sub_expr[1], sub_expr[0])
for sub_expr in sub_exprs])
matrix_code = sy.ccode(simple_mat[0], matrix_sym)
d['eval_code'] = ' ' + '\n '.join((sub_expr_code + '\n' + matrix_code).split('\n'))
c_indent = len('void {routine_name}('.format(**d))
c_arg_spacer = ',\n' + ' ' * c_indent
d['input_args'] = c_arg_spacer.join(['double {}'.format(a) for a in args])
cython_indent = len('def {routine_name}_loop('.format(**d))
cython_arg_spacer = ',\n' + ' ' * cython_indent
d['numpy_typed_input_args'] = cython_arg_spacer.join(['np.ndarray[np.double_t, ndim=1] {}'.format(a) for a in args])
d['indexed_input_args'] = ',\n'.join(['{}[i]'.format(a) for a in args])
files = {}
files[d['file_prefix'] + '_c.c'] = _c_template.format(**d)
files[d['file_prefix'] + '_h.h'] = _h_template.format(**d)
files[d['file_prefix'] + '.pyx'] = _cython_template.format(**d)
files[d['file_prefix'] + '_setup.py'] = _setup_template.format(**d)
for filename, code in files.items():
with open(filename, 'w') as f:
f.write(code)
cmd = ['python', d['file_prefix'] + '_setup.py', 'build_ext', '--inplace']
subprocess.call(cmd, stderr=subprocess.STDOUT, stdout=subprocess.PIPE)
cython_module = importlib.import_module(d['file_prefix'])
return getattr(cython_module, d['routine_name'] + '_loop')
def ufuncify(args, expr, **kwargs):
"""
Generates a binary ufunc-like lambda function for numpy arrays
``args``
Either a Symbol or a tuple of symbols. Specifies the argument sequence
for the ufunc-like function.
``expr``
A SymPy expression that defines the element wise operation
``kwargs``
Optional keyword arguments are forwarded to autowrap().
The returned function can only act on one array at a time, as only the
first argument accept arrays as input.
.. Note:: a *proper* numpy ufunc is required to support broadcasting, type
casting and more. The function returned here, may not qualify for
numpy's definition of a ufunc. That why we use the term ufunc-like.
References
==========
[1] http://docs.scipy.org/doc/numpy/reference/ufuncs.html
Examples
========
>>> from sympy.utilities.autowrap import ufuncify
>>> from sympy.abc import x, y
>>> import numpy as np
>>> f = ufuncify([x, y], y + x**2)
>>> f([1, 2, 3], [2, 2, 2])
[ 3. 6. 11.]
>>> a = f(np.arange(5), 3 * np.ones(5))
>>> isinstance(a, np.ndarray)
True
>>> print a
[ 3. 4. 7. 12. 19.]
"""
y = sy.IndexedBase(sy.Dummy('y')) # result array
i = sy.Dummy('i', integer=True) # index of the array
m = sy.Dummy('m', integer=True) # length of array (dimension)
i = sy.Idx(i, m) # index of the array
l = sy.Lambda(args, expr) # A lambda function that represents the inputs and outputs of the expression
f = implemented_function('f', l) # maps an UndefinedFunction('f') to the lambda function
if isinstance(args, sy.Symbol):
args = [args]
else:
args = list(args)
# For each of the args create an indexed version.
indexed_args = [sy.IndexedBase(sy.Dummy(str(a))) for a in args]
# ensure correct order of arguments
kwargs['args'] = [y] + indexed_args + [m]
args_with_indices = [a[i] for a in indexed_args]
return autowrap(sy.Eq(y[i], f(*args_with_indices)), **kwargs)
if __name__ == '__main__':
result_array = sy.IndexedBase('y')
arg_1 = sy.IndexedBase('a')
arg_2 = sy.IndexedBase('b')
array_length = sy.symbols('n', integer=True)
index = sy.symbols('i', integer=True)
index = sy.Idx(index, array_length)
a, b = sy.symbols('a, b')
expr = a**2 + b / 2.0
lam = sy.Lambda((a, b), expr)
sym_func = implemented_function('f', lam)
code_args = [result_array, arg_1, arg_2, array_length]
equation = sy.Eq(result_array[index], sym_func(arg_1[index], arg_2[index]))
f_for = autowrap(equation, tempdir='multi-fortran', args=code_args)
f_cyt = autowrap(equation, language='C', backend='cython', tempdir='multi-cython', args=code_args)
"""This file builds the Cython extensions."""
import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
extension = Extension(name="eval_matrix",
sources=["eval_matrix.pyx",
"eval_matrix_c.c"],
include_dirs=[numpy.get_include()])
setup(name="eval_matrix",
cmdclass={'build_ext': build_ext},
ext_modules=[extension])
function test_matrix_eval()
n = 10000;
a_vals = rand(n, 1);
b_vals = rand(n, 1);
c_vals = rand(n, 1);
f_loop = @() eval_matrices_loop(n, a_vals, b_vals, c_vals);
f_vectorized = @() eval_matrices_vectorized(n, a_vals, b_vals, c_vals);
% These are taken manually from the Python results.
python_loop_time= 0.136805589199;
python_vectorized_time = 0.00317755591869;
t_loop = timeit(f_loop);
t_vectorized = timeit(f_vectorized);
display('------------------------------------')
display(sprintf('Mean time to evaluate the loop %1.4f s', t_loop))
display(sprintf('Ratio to the Python loop benchmark time is %1.2f', ...
python_loop_time / t_loop))
display(sprintf('Ratio to the Python vectorized time is %1.2f', ...
python_vectorized_time / t_loop))
display('------------------------------------')
display(sprintf('Mean time to evaluate the vectorized loop %1.4f s', ...
t_vectorized))
display(sprintf('Ratio to the Python loop benchmark time is %1.2f', ...
python_loop_time / t_vectorized))
display(sprintf('Ratio to the Python vectorized time is %1.2f', ...
python_vectorized_time / t_vectorized))
display('------------------------------------')
end
function result = eval_matrices_loop(n, a_vals, b_vals, c_vals)
result = zeros(n, 2, 2);
for i = 1:n
result(i, 1, 1) = a_vals(i).^2 .* cos(b_vals(i)).^c_vals(i);
result(i, 1, 2) = tan(b_vals(i)) ./ sin(a_vals(i) + b_vals(i)) + c_vals(i).^4;
result(i, 2, 1) = a_vals(i).^2 + b_vals(i).^2 - sqrt(c_vals(i));
result(i, 2, 2) = (((a_vals(i) + b_vals(i) + c_vals(i)) .* (a_vals(i) + b_vals(i))) ./ a_vals(i) .* sin(b_vals(i)));
end
end
function result = eval_matrices_vectorized(n, a_vals, b_vals, c_vals)
result = zeros(n, 2, 2);
result(:, 1, 1) = a_vals.^2 .* cos(b_vals).^c_vals;
result(:, 1, 2) = tan(b_vals) ./ sin(a_vals + b_vals) + c_vals.^4;
result(:, 2, 1) = a_vals.^2 + b_vals.^2 - sqrt(c_vals);
result(:, 2, 2) = (((a_vals + b_vals + c_vals) .* (a_vals + b_vals)) ./ a_vals .* sin(b_vals));
end
#!/usr/bin/env python
"""A common need in my dynamics work is the to evaluate a small list of long
mathematical expressions (typically less than 100 expressions) a large
number of times (up to millions) with different scalar arguments each time.
The number of evaluations is always much greater than the number of
expressions. The expressions are generally all a function of the same
scalars but not necessarily and sometimes the expressions have scalars that
are constant with respect to each evaluations.
This set of Python, Cython, and C programs explore how to do this as fast as
possible.
This requires the current master of SymPy.
You will need to build the Cython extension modules before running this
module.
$ python setup.py build_exit --inplace
"""
import math
import timeit
import numpy as np
import sympy as sp
from eval_matrix import eval_matrix_loop_cython as cython_loop
from eval_matrix import eval_matrix_loop_c as c_loop
from multiindex import ufuncify, ufuncify_matrix
"""First I create the symbolic form of some sample expressions (these are
much shorter than my real problems)."""
a, b, c = sp.symbols('a, b, c')
expr_00 = a**2 * sp.cos(b)**c
expr_01 = sp.tan(b) / sp.sin(a + b) + c**4
expr_10 = a**2 + b**2 - sp.sqrt(c)
expr_11 = ((a + b + c) * (a + b)) / a * sp.sin(b)
sym_mat = sp.Matrix([[expr_00, expr_01],
[expr_10, expr_11]])
# These simply set up some large one dimensional arrays that will be used in
# the expression evaluation.
n = 10000
a_vals = np.random.random(n)
b_vals = np.random.random(n)
c_vals = np.random.random(n)
"""The mathematical expressions are generally generated symbolically with
SymPy. The easiet method available in SymPy is to use the lambdify function
to generate a NumPy backed numerical function.
I'll try two methods:
1. lamdify the matrix and rely on numpy's underlying broadcasting
2. lambdify each expression individually
"""
f = sp.lambdify((a, b, c), sym_mat, modules='numpy', default_array=True)
f00 = sp.lambdify((a, b, c), expr_00, modules='numpy', default_array=True)
f01 = sp.lambdify((a, b, c), expr_01, modules='numpy', default_array=True)
f10 = sp.lambdify((a, b, c), expr_10, modules='numpy', default_array=True)
f11 = sp.lambdify((a, b, c), expr_11, modules='numpy', default_array=True)
def eval_matrix_loop_lambdify():
"""This evaluates the lambdified matrix of expressions all in one
shot."""
return f(a_vals, b_vals, c_vals)
def eval_matrix_loop_lambdify_individual():
"""This allocates a 3D array inserts the evaluated lambdified
expressions in the correct entries."""
result = np.empty((n, 2, 2))
result[:, 0, 0] = f00(a_vals, b_vals, c_vals)
result[:, 0, 1] = f01(a_vals, b_vals, c_vals)
result[:, 1, 0] = f10(a_vals, b_vals, c_vals)
result[:, 1, 1] = f11(a_vals, b_vals, c_vals)
return result
"""These two methods are explicit Python functions that use NumPy to do
exactly what lambdify does under the hood."""
def eval_matrix_loop_numpy_broadcast():
"""This is the same thing as lambdifying the SymPy matrix."""
result = np.array(
[[a_vals**2 * np.cos(b_vals)**c_vals,
np.tan(b_vals) / np.sin(a_vals + b_vals) + c_vals**4],
[a_vals**2 + b_vals**2 - np.sqrt(c_vals),
((a_vals + b_vals + c_vals) * (a_vals + b_vals)) / a_vals *
np.sin(b_vals)]])
return result
def eval_matrix_loop_numpy():
"""Since the number of matrix elements are typically much smaller than
the number of evaluations, NumPy can be used to compute each of the
Matrix expressions. This is equivalent to the individual lambdified
expressions above."""
result = np.empty((n, 2, 2))
result[:, 0, 0] = a_vals**2 * np.cos(b_vals)**c_vals
result[:, 0, 1] = np.tan(b_vals) / np.sin(a_vals + b_vals) + c_vals**4
result[:, 1, 0] = a_vals**2 + b_vals**2 - np.sqrt(c_vals)
result[:, 1, 1] = (((a_vals + b_vals + c_vals) * (a_vals + b_vals)) /
a_vals * np.sin(b_vals))
return result
"""The most basic method of building the result array is a simple loop in
Python. But this will definitely be the slowest due to Python's overhead.
But this is what we ultimately want to improve with all these methods that
rely on fast low level code for the loop (vectorizing). This is the speed
benchmark. All other method will be compared against it."""
def eval_matrix_loop_python():
"""This is the standard Python method, i.e. loop through each array and
compute the four matrix entries."""
result = np.empty((n, 2, 2))
for i in range(n):
result[i, 0, 0] = a_vals[i]**2 * math.cos(b_vals[i])**c_vals[i]
result[i, 0, 1] = (math.tan(b_vals[i]) / math.sin(a_vals[i] +
b_vals[i]) + c_vals[i]**4)
result[i, 1, 0] = a_vals[i]**2 + b_vals[i]**2 - math.sqrt(c_vals[i])
result[i, 1, 1] = (((a_vals[i] + b_vals[i] + c_vals[i]) * (a_vals[i]
+ b_vals[i])) / a_vals[i] * math.sin(b_vals[i]))
return result
"""The next methods utilized hand written C functions and some Cython
wrappers. I have two flavors. In the cython one the loop is in Cython and
the expression eval is in C. In the second one, _c, both the loop and the
expression evals are in C, with just a light Cython wrapper."""
def eval_matrix_loop_cython():
"""This is equivalent to the naive Python loop but is implemented in a
lower level as a combination of Cython and C. The loop is in Cython and
the expression eval is in C."""
result = np.empty((n, 4))
return cython_loop(a_vals, b_vals, c_vals, result)
def eval_matrix_loop_c():
"""This is equivalent to the naive Python loop but is implemented in a
lower level as a combination of Cython and C. The loop and expression
evals are all in C."""
result = np.empty((n * 4))
return c_loop(a_vals, b_vals, c_vals, result)
"""sympy.utilities.ufuncify automatically generates the broadcasting loop in
the low level. The default settings use Fortran and f2py. Currently,
ufuncify only supports scalar expressions and an array for the first
argument. But I've included a modified version in multiindex.py that
requires all of the arguments to the function to be arrays of equal length.
ufuncify currently doesn't support a list of expressions (or sympy matrices)
so I ufuncify each expression. If all of the expressions were in the low
level loop then things will likely be faster especially if cse is used and
other optimizations."""
g00 = ufuncify((a, b, c), expr_00, language='F95', backend='f2py',
tempdir='ufunc-fortran-code')
g01 = ufuncify((a, b, c), expr_01, language='F95', backend='f2py')
g10 = ufuncify((a, b, c), expr_10, language='F95', backend='f2py')
g11 = ufuncify((a, b, c), expr_11, language='F95', backend='f2py')
def eval_matrix_loop_ufuncify_f2py():
"""This creates the result using the Fortran backend."""
result = np.empty((n, 2, 2))
result[:, 0, 0] = g00(a_vals, b_vals, c_vals)
result[:, 0, 1] = g01(a_vals, b_vals, c_vals)
result[:, 1, 0] = g10(a_vals, b_vals, c_vals)
result[:, 1, 1] = g11(a_vals, b_vals, c_vals)
return result
h00 = ufuncify((a, b, c), expr_00, language='C', backend='Cython',
tempdir='ufunc-cython-code')
h01 = ufuncify((a, b, c), expr_01, language='C', backend='Cython')
h10 = ufuncify((a, b, c), expr_10, language='C', backend='Cython')
h11 = ufuncify((a, b, c), expr_11, language='C', backend='Cython')
def eval_matrix_loop_ufuncify_cython():
"""This creates the result using the C/Cython backend."""
result = np.empty((n, 2, 2))
result[:, 0, 0] = h00(a_vals, b_vals, c_vals)
result[:, 0, 1] = h01(a_vals, b_vals, c_vals)
result[:, 1, 0] = h10(a_vals, b_vals, c_vals)
result[:, 1, 1] = h11(a_vals, b_vals, c_vals)
return result
r = ufuncify_matrix((a, b, c), sym_mat)
def eval_matrix_loop_ufuncify_matrix_cython():
result = np.empty((n, 4))
return r(result, a_vals, b_vals, c_vals)
def test_results():
"""This makes sure all of the methods return the same result. I opted to
put the reshaping here which may or may not be a good idea."""
print('Testing results.\n')
gold_standard = eval_matrix_loop_python()
np.testing.assert_allclose(np.transpose(eval_matrix_loop_lambdify(),
(2, 0, 1)),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_lambdify_individual(),
gold_standard)
np.testing.assert_allclose(np.transpose(eval_matrix_loop_numpy_broadcast(),
(2, 0, 1)),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_numpy(),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_cython().reshape(n, 2, 2),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_c().reshape(n, 2, 2),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_ufuncify_f2py(),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_ufuncify_cython(),
gold_standard)
np.testing.assert_allclose(eval_matrix_loop_ufuncify_matrix_cython(),
gold_standard)
def time_functions():
print('Timing the functions.\n')
func_suffixes = {'python': 100,
'lambdify': 1000,
'lambdify_individual': 3000,
'numpy_broadcast': 1000,
'numpy': 2000,
'cython': 3000,
'c': 3000,
'ufuncify_f2py': 3000,
'ufuncify_cython': 3000,
'ufuncify_matrix_cython': 3000}
times = {}
eval_stmt = 'eval_matrix_loop_{}()'
import_stmt = 'from __main__ import eval_matrix_loop_{}'
for suffix, n in func_suffixes.items():
print('Timing: {}'.format(suffix))
times[suffix] = (timeit.timeit(eval_stmt.format(suffix),
setup=import_stmt.format(suffix),
number=n) / float(n))
print('{} time: {} s\n'.format(suffix, times[suffix]))
benchmark_time = times.pop('python')
print('Benchmark time: {} s\n'.format(benchmark_time))
print('Ratios of the timings to the benchmark time:')
print('--------------------------------------------\n')
ratios = {}
for k, v in times.items():
ratios[k] = benchmark_time / v
for k in sorted(ratios, key=ratios.__getitem__, reverse=True):
print('{} ratio: {}'.format(k, ratios[k]))
if __name__ == "__main__":
test_results()
time_functions()
import math
import random
n = 10000
a_vals = random.sample(xrange(1, n + 1), n)
b_vals = random.sample(xrange(1, n + 1), n)
c_vals = random.sample(xrange(1, n + 1), n)
def eval_matrix_loop_pypy():
"""This is the standard Python method, i.e. loop through each array and
compute the four matrix entries."""
result = []
for i in range(n):
mat = []
mat.append(a_vals[i]**2 * math.cos(b_vals[i])**c_vals[i])
mat.append(math.tan(b_vals[i]) / math.sin(a_vals[i] + b_vals[i]) +
c_vals[i]**4)
mat.append(a_vals[i]**2 + b_vals[i]**2 - math.sqrt(c_vals[i]))
mat.append(((a_vals[i] + b_vals[i] + c_vals[i]) * (a_vals[i] +
b_vals[i])) /
a_vals[i] * math.sin(b_vals[i]))
result.append(mat)
return result
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