How do you specify which values are constants and which aren't? I do pass in a lot of constants and they should be hard coded in C if possible (or some other way to make them available).
Jason moorepants.info +01 530-601-9791 On Tue, Aug 6, 2013 at 11:53 AM, Frédéric Bastien <[email protected]> wrote: > About the benchmark, the problem is a known one, Theano isn't very fast to > parse Theano input. In your case, it is this parsing that take the most > time in theano when n is small. But when n increase, Theano is faster > then lambdify: > > n = 1 > The derivation took 0.154258966446 seconds. > Running with theano method. > It took 0.515482187271 seconds to compute M and F with theano 1000 times > at an average of 0.000515482187271 seconds per computation. > Running with lambdify method. > It took 0.0773530006409 seconds to compute M and F with lambdify 1000 > times at an average of 7.73530006409e-05 seconds per computation. > n = 6 > The derivation took 6.48748397827 seconds. > Running with theano method. > It took 1.83713889122 seconds to compute M and F with theano 1000 times at > an average of 0.00183713889122 seconds per computation. > Running with lambdify method. > It took 0.807645082474 seconds to compute M and F with lambdify 1000 times > at an average of 0.000807645082474 seconds per computation. > n = 11 > The derivation took 33.0718569756 seconds. > Running with theano method. > It took 3.446434021 seconds to compute M and F with theano 1000 times at > an average of 0.003446434021 seconds per computation. > Running with lambdify method. > It took 3.89024996758 seconds to compute M and F with lambdify 1000 times > at an average of 0.00389024996758 seconds per computation. > n = 16 > The derivation took 104.534656048 seconds. > Running with theano method. > It took 5.41041302681 seconds to compute M and F with theano 1000 times at > an average of 0.00541041302681 seconds per computation. > Running with lambdify method. > It took 11.5178751945 seconds to compute M and F with lambdify 1000 times > at an average of 0.0115178751945 seconds per computation. > > > The option to make it faster would be to give less inputs to the theano > function. The first step would be to make the constants as theano constant > instead of input to the theano function. That will make put the constant in > the c code, so it could also speed up even the computation itself. > > > On Tue, Aug 6, 2013 at 11:34 AM, Jason Moore <[email protected]> wrote: > >> 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 <[email protected]> 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 <[email protected]>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 <[email protected]>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 <[email protected]>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 [email protected]. >>>>>>>> To post to this group, send email to [email protected]. >>>>>>>> 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 [email protected]. >>>>>>> To post to this group, send email to [email protected]. >>>>>>> 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 [email protected]. >>>>>> To post to this group, send email to [email protected]. >>>>>> 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 [email protected]. >>> To post to this group, send email to [email protected]. >>> 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. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To post to this group, send email to [email protected]. >> 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. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > 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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