Update:
I have basic code generation completed. I found that the Routine class
didn't meet my needs to I built something new. There is some duplication.
An example:
# Mathematical Problem
A = MatrixSymbol('A', n, n)
B = MatrixSymbol('B', n, n)
C = MatrixSymbol('C', n, n)
x = MatrixSymbol('x', n, 1)
context = (Q.lower_triangular(A) &
Q.lower_triangular(B) &
Q.lower_triangular(C))
expr = (alpha*A*B + beta*C).I*x
# Build a computation object from this expression
# This should be replaced by a clever pattern matching/rules system
gemm = GEMM(alpha, A, B, beta, C) # produces alpha*A*B + beta*C and
stores result in C
trsv = TRSV(alpha*A*B + beta*C, x) # computes (alpha*A*B+beta*C).I*x
and stores result in x
comp = MatrixRoutine((gemm, trsv), (alpha, A, B, beta, C, x), (expr,))
# a computation to do both gemm and trsv
# build callable object
f = comp.build(str, context) # Write fortran code (below) and compile
with f2py
# Use f
import numpy as np
nalpha, nbeta = 2.0, 3.0
nA,nB,nC = [np.asarray([[1,0],[3,4]], order='F', dtype='float64')
for i in range(3)]
nx = np.asarray((2.3, 4.5), order='F', dtype='float64')
f(nalpha, nA, nB, nbeta, nC, nx)
On Thu, Oct 25, 2012 at 1:09 PM, Matthew Rocklin <[email protected]> wrote:
> For those interested in following along I'm working off of my blas branch.
> Here is a link to the comparison master->blas
>
> https://github.com/mrocklin/sympy/compare/blas
>
> In particular I recommend looking at matrices/expressions/blas.py. This
> branch also includes my work on matrix assumptions (separate PR).
>
> The goal is to create Fortran code from MatrixExprs. The work left to do
> can be decomposed into the following tasks
>
> 1. Write a good Computation class (the current structure isn't yet ideal)
> 2. Write down enough of BLAS to get going (close!)
> 3. Write down rules that translate MatrixExprs into BLAS computations
> (this is easier if we have good pattern matching)
> 4. Write down strategies to handle non-confluent rules (there are lots of
> possible solutions)
> 5. Use f2py to reconnect the Fortran code to numpy interface
> 6. Compare SymPy MatExpr times to NumPy times. I expect we'll be able to
> generate code that is several times faster than NumPy while still
> maintaining a nice interface.
>
>
> On Thu, Oct 25, 2012 at 12:58 PM, Øyvind Jensen
> <[email protected]>wrote:
>
>>
>> >> If you don't mind, maybe we should CC the mailing list?
>> >
>> > Go for it. I've been flooding the list recently which is why I didn't do
>> > this earlier.
>> >
>> > I've recently been working on a Theano-like graph for SymPy (a
>> Computation
>> > object). I'm may be reinventing something. I'm working off of this
>> branch
>> > https://github.com/mrocklin/sympy/tree/blas . What is there now will
>> soon
>> > change however. I'm still trying to figure out the best way to do this.
>> >
>> > Question about codegen. I have to compose routines that have multiple
>> > out-params and inplace operations. Is SymPy codegen equipped to handle
>> this?
>> > Does it have tools to reason about these sorts of problems? Maybe this
>> will
>> > become clearer to me when I do the mat-vec multiply example.
>>
>> Multiple output has at least some support. In test_codegen.py you 'll
>> find test_multiple_results_f(). What kind of inplace operations are you
>> referring to?
>>
>> I took a quick glance at your blas branch. I think that your Computation
>> class is somewhat comparable with codegen.Routine, except that you are
>> doing it properly. The Routine class aims to describe how a given
>> mathematical expression can be programmed as a function or subroutine. But
>> with the Computation class, you aim to describe also the relationship
>> between several computations, isn't that right? It might be a good idea to
>> remove or at least shrink the Routine class in favor of your stuff.
>>
>> If you want codegen to support MatrixExpr objects, you would need to
>> implement them in the code printers, and I guess you will also need to hack
>> the constructor of Routine.
>>
>>
>> Cheers,
>> Øyvind
>>
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>
>
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