Guy, I don't know enough about the details of the codegen module, but theoretically there should be a mapping from sympy functions to a c-code equivalent that you can add and/or override. It is just a matter of getting real familiar with the code gen modules.
You may want to check out https://github.com/IgnitionProject/ignition. Andy showed me this at SciPy and setting up your own mappings for various things seemed more clean than what is in SymPy. Jason moorepants.info +01 530-601-9791 On Tue, Aug 6, 2013 at 11:30 AM, Guy Parsey <guy.par...@gmail.com> wrote: > Addon to the Codegen section: > With the example above of having an implemented function in some > expression in sympy, assuming one can write the c-code equivalent, how > would one link the c-code to the codegen process as opposed to having > codegen generate it? > Cheers, > Guy > > On Tuesday, August 6, 2013 11:04:33 AM UTC-4, Guy Parsey wrote: >> >> Hello, >> >> Theano/Sympy questions >> Fred, in terms of replacing sympy.Pieceiwse with a Theano equivalent, >> since sympy.Piecewise attempts each condition (from ExprCondPair) until one >> is valid, I would think that the closest equivalent would be a recursive >> theano ifelse (ie. sympy.Piecewise((expr1,cond1),**(expr2,cond2)) ~ >> theano.ifelse(cond1,expr1,**theano.ifelse(cond2,expr2,**None))) >> statement so that the expressions are not evaluated until the condition is >> achieved. This might be too much of a patch though and I am not sure how to >> implement it with the same argument structure as sympy.Pieceiwse. >> >> With regards to using an externally defined theano graph or op (theano >> wrapped sympy implementation), how would one pass them to theano_function? >> It feels as though it would be analogous to the autowrap helpers argument, >> or would this be an issue of merging graphs before calling theano.function? >> If I can understand this step, I feel as though both of my problems would >> be solvable (a wrapping of a piecewise along with a wrapping of an >> interpolated function) >> >> In all of my simplified test cases (no pieceiwse or interpolations, 9 >> heavily-linked non-linear ODEs), I also show that the lambdify function is >> faster to evaluate than my created theano function. When passing a list of >> expressions, if there is a commons subexpression that exists in two >> expressions (not twice in the same expression), is it treated as such >> during theano compilation or is each expression handled separately? >> >> Codegen questions (maybe a different topic): >> Jason, I have started playing with the sympy.printing.codegen (C for now, >> Fortran later) as an alternative to theano (I would very much like to get >> multiple methods working, and GPU acceleration makes me want to keep theano >> implementation). There is a size-able speedup relative to lambdify or >> theano_function, which is to be expected, but I arrive at the same problem >> as above with regards to interpolated or piecewise functions. The initial >> crash from using Piecewise functions comes from Routine calling >> sympy.tensor.index_methods get_contraction_structure which explicitly >> states no support for Piecewise function types. In principle, if one made a >> symbolic implemented function (preferably without an analytical >> representation), could one link the python object to the compiled code? >> What attributes would said implemented_function need to have? >> >> Thank you for your responses, I am glad to hear that at least I am not >> completely missing something. >> Cheers, >> Guy >> >> On Tuesday, August 6, 2013 9:37:41 AM UTC-4, Jason Moore wrote: >>> >>> Fred, >>> >>> I think on_used_input=ignore should be a default in Matthew's theano >>> printing code or that arg needs to be pushed up to his layer. I hit that >>> issue too. >>> >>> The code I have is here: https://github.com/** >>> PythonDynamics/pydy-code-gen<https://github.com/PythonDynamics/pydy-code-gen> >>> >>> See the results.txt file for basic speed comparisons. I'm generating the >>> ODEs for an n-link pendulum with mechanics and then see how fast it >>> generates and simulates with scipy.odeint. >>> >>> The code doesn't work at the moment. I haven't touched it in a month and >>> looks like some things have changed in sympy and/or theano. I'll work on >>> the bugs now. >>> >>> But "python benchmark.py" should run it with sympy master and ?some? >>> version of Theano. >>> >>> >>> Jason >>> moorepants.info >>> +01 530-601-9791 >>> >>> >>> On Tue, Aug 6, 2013 at 9:25 AM, Frédéric Bastien <no...@nouiz.org>wrote: >>> >>>> Hi, >>>> >>>> I don't know what is sympy.functions.elementary.**piecewise. Do Jason >>>> answered that part? If not, I'll look into it to know how to make Theano >>>> reproduce it. About converting any Sympy symbol to Theano symbol, when >>>> there isn't a one to one matching, you can create a one to a full Theano >>>> graph conversion. When this is possible, it is probably the best, as if you >>>> make a new Theano op, it work work on the GPU. But if you make a Theano >>>> graph, there is good change that the graph will already work on the GPU. >>>> >>>> If it is not possible to make a Theano graph for a sympy symbol, it is >>>> possible to make a new Theano op that just wrap the sympy implementation. >>>> Also, if this is a bottleneck, I recently added an example that show how to >>>> use numba with Theano so speed up the python code in a Theano op. >>>> >>>> >>>> Theano do not parallelize on the CPU, except for the call to BLAS, when >>>> the BLAS library is parallel. On the GPU, it is parallel. >>>> >>>> Having on_unused_input=ignore is normal for complicated generated >>>> code. That is why it was added. But when the code is simpler and not >>>> generated, but all user manually coded, most of time it mean the user >>>> didn't do what he wanted. If you know it is normal that you have unused >>>> input, there is no problem to use that flag. >>>> >>>> >>>> Jason, about the case where Theano is slower, can you send me the >>>> Theano code? I would like to look at it. I'm very surprised that Theano is >>>> slower then Sympy in this case and would like to know why it is like this. >>>> >>>> Fred >>>> >>>> >>>> On Mon, Aug 5, 2013 at 9:03 PM, Jason Moore <moore...@gmail.com> wrote: >>>> >>>>> Guy, >>>>> >>>>> We're working on the same problem for sympy.physics.mechanics. Matthew >>>>> Rocklin added support for matrix conversions in the theano code that is in >>>>> SymPy and I used that, but found that theano was slower that lambdify for >>>>> most of my cases (I only have two cores, so I'm not taking advantage of >>>>> the >>>>> Theano parallel stuff). I think writing specific code gen for ode >>>>> integration is going to be the best bet. I'm happy to collaborate on this >>>>> with you. >>>>> >>>>> >>>>> Jason >>>>> moorepants.info >>>>> +01 530-601-9791 >>>>> >>>>> >>>>> On Mon, Aug 5, 2013 at 3:13 PM, Guy Parsey <guy.p...@gmail.com> wrote: >>>>> >>>>>> Hello Everyone, >>>>>> Thank you in advance for reading through my problem and for any input >>>>>> you may have. I must say that I still feel like a novice programmer and >>>>>> my >>>>>> problems may be easily solvable from a different mindset. My present >>>>>> project entails time-integration of extremely stiff and non-linear ODEs >>>>>> with regards to chemical kinetics (one derivative equation for each >>>>>> variable species) and energy equations. Initially we were planning on >>>>>> using >>>>>> the sympy.lambdify function to create callable functions for the main >>>>>> function along with the jacobian and passing said functions to >>>>>> scipy.integrate.odeint, however this method only works for easier test >>>>>> cases (fewer species and/or no energy equations) before being limited by >>>>>> either the list recursion limit or segfaulting due to the limited stack >>>>>> size. I know that both of these limits can be edited, but that fact that >>>>>> I >>>>>> am reaching them makes me feel as though I am doing something extremely >>>>>> inefficiently. Outside of the documentation of SymPy and Theano, I have >>>>>> also been heavily using the BlogPost by Matthew Rocklin >>>>>> http://matthewrocklin.**com/blog/work/2013/03/19/** >>>>>> SymPy-Theano-part-1/<http://matthewrocklin.com/blog/work/2013/03/19/SymPy-Theano-part-1/>. >>>>>> >>>>>> Presently I am trying to use the mapping between Theano and SymPy >>>>>> (sympy.printing.theanocode theano_function) to make my callable functions >>>>>> and take advantage of the optimization routines. I have two major >>>>>> problems >>>>>> and a few questions: >>>>>> >>>>>> 1st major problem: Though piecewise functions exist in SymPy >>>>>> (sympy.functions.elementary.**piecewise) there is no counterpart in >>>>>> Theano. Looking at the source of the inspiration for theanocode ( >>>>>> https://github.com/nouiz/**theano_sympy/<https://github.com/nouiz/theano_sympy/> >>>>>> graph_translation.py) I see that some of the SymPy equivalents were >>>>>> defined as lambda functions. Is there an equivalent way to add Theano >>>>>> conditional expressions wrapped into a function to add to the mapping >>>>>> dictionary in theanocode.py? >>>>>> >>>>>> 2nd major problem: Similar to the problem above in that I am not sure >>>>>> that the Theano counterpart is; some of the terms that I use are >>>>>> interpolated functions (with one ODE variable as input) that we have >>>>>> wrapped symbolically while providing a numerical implementation (so that >>>>>> symbolic derivatives can be made, resulting in their own interpolations). >>>>>> Is it possible to recreate the interpolation function as a Theano >>>>>> operation >>>>>> for use within the system of ODEs? >>>>>> >>>>>> Remain questions: >>>>>> I presently have to flatten my input to theano_function to a list of >>>>>> expressions and then wrap to return to a form (Jacobian is a matrix not a >>>>>> vector); is it possible to have a matrix of different expressions as an >>>>>> input to theano_function with a vector output? >>>>>> >>>>>> I know that a huge amount of Theano speed up is due to >>>>>> parallelization of matrix operations (which I do not have), should I be >>>>>> focusing on SymPy Autowrap/Ufuncify or my own code generation instead of >>>>>> trying to get Theano to play nicely? >>>>>> >>>>>> Stupid questions: >>>>>> Does sympy.printing.theanocode.**theano_function automatically >>>>>> optimize the compiled graph? >>>>>> >>>>>> Minor comment: >>>>>> Perhaps unnecessary for most uses of the theano_function, but I >>>>>> needed to modify function inputs so as to be able to use the keyword >>>>>> argument 'on_unused_input=ignore' as opposed to 'raise' so that I did not >>>>>> need to have all symbols in all equations. This may be avoided by having >>>>>> the unused symbols somehow (I don't know how) included in each >>>>>> expression. >>>>>> >>>>>> Thank you again for your time in reading my problems and any >>>>>> potential help you may think of. I can attach code if necessary, I just >>>>>> didn't want to make my post more confusing. >>>>>> Have an excellent day. >>>>>> Sincerely, >>>>>> Guy Parsey >>>>>> >>>>>> -- >>>>>> You received this message because you are subscribed to the Google >>>>>> Groups "sympy" group. >>>>>> To unsubscribe from this group and stop receiving emails from it, >>>>>> send an email to sympy+un...@googlegroups.com. >>>>>> To post to this group, send email to sy...@googlegroups.com. >>>>>> Visit this group at >>>>>> http://groups.google.com/**group/sympy<http://groups.google.com/group/sympy> >>>>>> . >>>>>> For more options, visit >>>>>> https://groups.google.com/**groups/opt_out<https://groups.google.com/groups/opt_out> >>>>>> . >>>>>> >>>>>> >>>>>> >>>>> >>>>> -- >>>>> You received this message because you are subscribed to the Google >>>>> Groups "sympy" group. >>>>> To unsubscribe from this group and stop receiving emails from it, send >>>>> an email to sympy+un...@googlegroups.com. >>>>> To post to this group, send email to sy...@googlegroups.com. >>>>> Visit this group at >>>>> http://groups.google.com/**group/sympy<http://groups.google.com/group/sympy> >>>>> . >>>>> For more options, visit >>>>> https://groups.google.com/**groups/opt_out<https://groups.google.com/groups/opt_out> >>>>> . >>>>> >>>>> >>>>> >>>> >>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "sympy" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to sympy+un...@googlegroups.com. >>>> To post to this group, send email to sy...@googlegroups.com. >>>> Visit this group at >>>> http://groups.google.com/**group/sympy<http://groups.google.com/group/sympy> >>>> . >>>> For more options, visit >>>> https://groups.google.com/**groups/opt_out<https://groups.google.com/groups/opt_out> >>>> . >>>> >>>> >>>> >>> >>> -- > You received this message because you are subscribed to the Google Groups > "sympy" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to sympy+unsubscr...@googlegroups.com. > To post to this group, send email to sympy@googlegroups.com. > Visit this group at http://groups.google.com/group/sympy. > For more options, visit https://groups.google.com/groups/opt_out. > > > -- You received this message because you are subscribed to the Google Groups "sympy" group. 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