- do 28 augustus 2014
- misc
- Jason K. Moore
- #sympy, #cython, #fortran, #c, #matrices, #code generation, #python

I'm working on using direct collocation and nonlinear programming for system/parameter identification. This requires evaluating a vector of constraint equations and it's sparse Jacobian. When there are thousands of collocation nodes and a fair number of model states the equations need to be evaluated on the order of a million times at each optimization step. I've been generating the constraints equations and non-sparse Jacobian entries with SymPy and then generating code to evaluate the equations.

SymPy has a few facilities for generating fast code:

`lambdify`: generates Python code and can utilize NumPy`autowrap/codegen`: generates and wraps Fortran 95 and C code`ufuncify`: same as`autowrap`but "vectorizes" in the inputs and outputs

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.ImmutableDenseMatrix([[expr_00, expr_01],
[expr_10, expr_11]])
```

Then 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 easiest method available in SymPy is to use the lambdify function to generate a NumPy backed numerical function.

I'll try two methods:

- lambdify the matrix and rely on numpy's underlying broadcasting
- 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
```

So these the program is run as so:

$ python test_eval_matrix.py

And it prints these results (example timings on my machine):

Testing results. Timing the functions. Timing: cython cython time: 0.00300521969795 s Timing: numpy_broadcast numpy_broadcast time: 0.00657413101196 s Timing: lambdify_individual lambdify_individual time: 0.00323091069857 s Timing: ufuncify_f2py ufuncify_f2py time: 0.0021202070713 s Timing: python python time: 0.136805589199 s Timing: ufuncify_cython ufuncify_cython time: 0.00302646199862 s Timing: numpy numpy time: 0.00317755591869 s Timing: c c time: 0.00297607461611 s Timing: lambdify lambdify time: 0.00649729514122 s Benchmark time: 0.136805589199 s Ratios of the timings to the benchmark time: -------------------------------------------- ufuncify_f2py ratio: 64.5246358484 c ratio: 45.9684674767 cython ratio: 45.5226582244 ufuncify_cython ratio: 45.2031412459 numpy ratio: 43.0537157172 lambdify_individual ratio: 42.3427330441 lambdify ratio: 21.0557757075 numpy_broadcast ratio: 20.8096840404

I'm actually using the `python` loop in my Jacobian evaluation currently so I
can get ~60X speedup using ufuncify with Fortran 95 code. And I can get a 3X
speedup on my lambify code for the constraints.

Other notes of interest:

- Assuming the number of expressions is much greater than the number of
evaluations, the loop on the expressions with NumPy expression evaluations,
`numpy`, is pretty fast and is 2X faster than the default lambdify method. You can even speed up lambdify by simply computing each expression in the matrix seperately. - The three Cython/C based methods all give about the same speed.
- I don't know why the Fortran backend is faster. But I've seen a number of other benchmarks that show Fortran is generally faster than C for these kinds of things.
- I'd like to get the ufuncify_f2py version working for evaluating all the matrix entries in the same loop. Common sub expressions may help there too depending on whether the Fortran compiler does this or not.

The working code is avaiable in this gist:

https://gist.github.com/moorepants/6ef8ab450252789a1411

## Update (September 11, 2014)

My PI was curious how these speeds compare to Matlab, so I wrote two Matlab
functions that mirror `eval_matrix_loop_python` and
`eval_matrix_loop_numpy`. The code is in the gist and these are the results:

>> version ans = 8.3.0.532 (R2014a) >> test_matrix_eval ------------------------------------ Mean time to evaluate the loop 0.1158 s Ratio to the Python loop benchmark time is 1.18 Ratio to the Python vectorized time is 0.03 ------------------------------------ Mean time to evaluate the vectorized loop 0.0026 s Ratio to the Python loop benchmark time is 53.60 Ratio to the Python vectorized time is 1.24 ------------------------------------

Matlab beats Python on both functions in this case but not by leaps and bounds. Matlab as a JIT since version 6.5 that helps speed up loops and Pure python doesn't. There are several JITs for Python (pypy, numba, parakeet, etc). I tried a version that grows lists in Python and PyPy and get these results:

$ python -mtimeit -s "import test_pypy" "test_pypy.eval_matrix_loop_pypy()" 10 loops, best of 3: 36.2 msec per loop $ pypy -mtimeit -s "import test_pypy" "test_pypy.eval_matrix_loop_pypy()" 100 loops, best of 3: 7.2 msec per loop

This gives an improvement but Matlab still beats PyPy. This isn't a good comparison though, as the arrays are preallocated in Matlab and not in the PyPy version.

Matlab's vectorized version is closer in speed to my generated Fortran code.

Also I created a basic function that ufuncifies a SymPy matrix all in one shot. It even uses CSE to improve things. It automatically creates what I did manually for the Cython files. New timings show the obvious, that it gives the same results and the manual one. But for large matrices, the compile times are significantly reduced. Now I need to make that function generate Fortran code and I think that will be the fastest option.

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

After all this, I'm not sure this is the best benchmark. I really need a benchmark that includes varying the size of the matrices and the expression length and complexity to find the best solution.