Posted on August 20, 2014
The GSoC program officially ended this Monday, and so my work for SymPy has concluded. I got a lot done in these last two weeks though. Here's what's new. In order of completion:
I wasn't happy with the way the codeprinters were done previously. There was a
lot of redundant code throughout ccode
, fcode
and jscode
(the main three
printers). They also had a lot of special case code in the doprint
method
for handling multiline statements, which I felt could be better accomplished
using the visitor pattern that
is used by all the other printers. The issue is that some nodes need to know if
they are part of a larger expression, or part of an assignment. For example, in
C
Piecewise
are printed as if statements if they contain an assignment, or
inline using the ternary
operator if they don't.
After some thought, this was solved by adding an Assignment
node that
contains this information, and then dispatching to it in the printer just like
any other node. Less special case code, and allowed the base CodePrinter
class to contain a lot of the redundancies. For those implementing a new code
printer (perhaps for Octave?) all you'd need to do is add how to print certain
operators, and a dictionary of function translations. Everything else should
just work. I may add little cleanups here and there, but I'm pretty happy with
the refactor.
This was the original goal, but got put aside to do the previously described refactor. The codeprinters now support matrices - both as inputs and outputs. For example, the following now works:
# Expressions inside a matrix
x, y, z = symbols('x, y, z')
mat = Matrix([x*y, Piecewise((2 + x, y>0), (y, True)), sin(z)])
A = MatrixSymbol('A', 3, 1)
print(ccode(mat, A))
A[0] = x*y;
if (y > 0) {
A[1] = x + 2;
}
else {
A[1] = y;
}
A[2] = sin(z);
# Matrix elements inside expressions
expr = Piecewise((2*A[2, 0], x > 0), (A[2, 0], True)) + sin(A[1, 0]) + A[0, 0]
print(ccode(expr))
((x > 0) ? (
2*A[2]
)
: (
A[2]
)) + sin(A[1]) + A[0]
# Matrix elemnts in expressions inside a matrix
q = MatrixSymbol('q', 5, 1)
M = MatrixSymbol('M', 3, 3)
m = Matrix([[sin(q[1,0]), 0, cos(q[2,0])],
[q[1,0] + q[2,0], q[3, 0], 5],
[2*q[4, 0]/q[1,0], sqrt(q[0,0]) + 4, 0]])
print(ccode(m, M))
M[0] = sin(q[1]);
M[1] = 0;
M[2] = cos(q[2]);
M[3] = q[1] + q[2];
M[4] = q[3];
M[5] = 5;
M[6] = 2*q[4]*1.0/q[1];
M[7] = 4 + sqrt(q[0]);
M[8] = 0;
There even was a Piecewise
inside a Matrix
in there. As long as there is an
assignment between two compatible types (matrix -> matrix, scalar -> scalar),
the new codeprinters should print out valid expressions.
codegen
now supports matricesThis is more of a continuation of the above. The code generators have been
modified to recognize instances of MatrixSymbol
as array variables and act
accordingly. There actually wasn't that much to change here to make this work.
The biggest change that happened is that all C
functions that have a return
value (non void
functions) allocate a local variable of the same type. This
is to cover a larger set of expressions, while still generating valid code. So
now, when performing codegen on "sin(x)
" you won't get "return sin(x)
",
you'll get:
result = codegen(('sin_c', sin(x)), "C", "file", header=False)
print(result)
double sin_c(double x) {
double sin_c_result;
sin_c_result = sin(x);
return sin_c_result;
}
This isn't as pretty, but handling return inside expressions is a tricky problem, and this solves it without much work. Modern compilers should remove the variable assignment if it's unnecessary, so there shouldn't be a resulting speed loss in the code.
Cython
wrapper for autowrap
now worksThere was a code wrapper for Cython
in the codebase, but I don't think it has
ever worked. It does now:) It can do everything f2py
can do, and I plan on
adding more useful features. In it's current state it can:
The last thing I want to do to make this really nice is to add support for informative docstrings. Even so, this is already usable:
x, y, z = symbols('x, y, z')
mat = Matrix([x*y, Piecewise((2 + x, y>0), (y, True)), sin(z)])
func = autowrap(mat, 'c', 'cython')
func(1, 2, 3)
array([[ 2. ],
[ 3. ],
[ 0.14112001]])
For some reason the Fortran
/f2py
has around a 2 microseconds faster than
the C
/Cython
code. I think this has something to do with array allocations,
but I'm not sure. For larger expressions they're pretty equal, so this
shouldn't be that big of a deal. I still plan to look into code optimizations I
could make in the Cython wrapper.
Overall, I accomplished most of what I set out to do this summer. Some things (pre-solution linearization) were nixed from the project due to changing goals. Here's a short list of what was done:
General linearization methods added for both KanesMethod
and LagrangesMethod
.
Code cleanup and speedup for KanesMethod
and LagrangesMethod
.
Creation of msubs
- a specialized subs
function for mechanics
expressions. This runs significantly faster than the default subs
, while
adding some niceities (selective simplification).
Complete overhaul of codeprinters. Fixed a lot of bugs.
Addition of support for matrices in code printers, code generators, and autowrap
.
Overhaul of Cython
codewrapper. It works now, and does some nice things to
make the wrapped functions more pythonic.
Documentation for the above.
I had an excellent summer working for SymPy, and I plan on continuing to contribute. I have some code for discretization that I've been using for my research that may be of interest to the mechanics group. I also want to get common sub-expression elimination added to the code generators, as this kind of optimization may result in speedups for the large expressions we see in mechanics. My contributions will unfortunately be less frequent, as I need to really focus on research and finishing my degree, but I still hope to help out.
I plan on writing another post in the next few days about the GSoC experience as a whole, so I won't touch on that here. Let me just say thank you to Jason, Luke, Oliver, Sachin, Tarun, Aaron, and all the other wonderful people that have offered me guidance and support throughout the summer. You guys are awesome.