,Vh8dZddlZddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddlmZmZmZmZmZmZmZmZddlmZmZdd lmZmZmZgd ZGd d ej>Z d d Z!dedeededee"e#fdee#e#ff dZ$de#deededee"e#fdededeee#e#ffdZ%Gdde Z& d!deededee"e#fdee#dedef dZ'ejPe'dZ)y)"z@Support for random optimizers, including the random-greedy path.N)deque)Decimal)choices)seed)AnyDict GeneratorIterableListOptionalTupleUnion)helperspaths)ArrayIndexType ArrayTypePathType) RandomGreedy random_greedyrandom_greedy_128c \eZdZdZ ddedeededee e efdef dZ e d e fd Ze d ee e effd Zej dee e efd dfd Zd eededed eeddffdZddZdeededeeefd eeeffdZ ddeededeeefdeed e f dZdZy)RandomOptimizeraBase class for running any random path finder that benefits from repeated calling, possibly in a parallel fashion. Custom random optimizers should subclass this, and the `setup` method should be implemented with the following signature: ```python def setup(self, inputs, output, size_dict): # custom preparation here ... return trial_fn, trial_args ``` Where `trial_fn` itself should have the signature:: ```python def trial_fn(r, *trial_args): # custom computation of path here return ssa_path, cost, size ``` Where `r` is the run number and could for example be used to seed a random number generator. See `RandomGreedy` for an example. Parameters: max_repeats: The maximum number of repeat trials to have. max_time: The maximum amount of time to run the algorithm for. minimize: Whether to favour paths that minimize the total estimated flop-count or the size of the largest intermediate created. parallel: Whether to parallelize the random trials, by default `False`. If `True`, use a `concurrent.futures.ProcessPoolExecutor` with the same number of processes as cores. If an integer is specified, use that many processes instead. Finally, you can supply a custom executor-pool which should have an API matching that of the python 3 standard library module `concurrent.futures`. Namely, a `submit` method that returns `Future` objects, themselves with `result` and `cancel` methods. pre_dispatch: If running in parallel, how many jobs to pre-dispatch so as to avoid submitting all jobs at once. Should also be more than twice the number of workers to avoid under-subscription. Default: 128. Attributes: path: The best path found so far. costs: The list of each trial's costs found so far. sizes: The list of each trial's largest intermediate size so far. N max_repeatsmax_timeminimizeparallel pre_dispatchc|dvr td||_||_||_t j ||_d|_||_||_ g|_ g|_ tdtdd|_ d|_||y)N)flopssizez.`minimize` should be one of {'flops', 'size'}.Finfr) ValueErrorrrrr get_better_fnbetter _parallelrrcostssizesfloatbest_repeats_start)selfrrrrrs F/home/dcms/DCMS/lib/python3.12/site-packages/opt_einsum/path_random.py__init__zRandomOptimizer.__init__As , ,MN N&    ))(3 49  ( " " .3ElE%L$Q   returncFtj|jdS)zThe best path found so far.ssa_path)r ssa_to_linearr)r+s r,pathzRandomOptimizer.path\s""499Z#899r.c|jSN)r%r3s r,rzRandomOptimizer.parallelas ~~r.cDt|ddr|jj||_d|_|durd|_y|durddlm}||_d|_yt|ttfr$ddlm}|t||_d|_y||_y)N_managing_executorFTr)ProcessPoolExecutor) getattr _executorshutdownr%r8concurrent.futuresr9 isinstanceintr)r+rr9s r,rzRandomOptimizer.paralleles 4-u 5 NN # # %!"' u !DN  t  >02DN&*D #  hg / >0X?DN&*D # "r.repeatstrial_fnargsc#Kt|_|D]}t|j|jkr8|jj |j j ||g|]|jjj|jr8|jjj|jr7yyw)zMLazily generate results from an executor without submitting all jobs at once.N) r_futureslenrappendr;submitpopleftresult)r+r@rArBrs r,_gen_results_parallelz%RandomOptimizer._gen_results_parallels  3A4==!D$5$55 $$%:T^^%:%:8Q%N%NO--'')002 2  3 mm--'')002 2mms CC" C"c`|j"|jD]}|jyyr6)r;rDcancel)r+fs r,_cancel_futureszRandomOptimizer._cancel_futuress- >> %]]    &r.inputsoutput size_dictctr6)NotImplementedError)r+rPrQrRs r,setupzRandomOptimizer.setups "!r. memory_limitcD|j||||jtj}|j|||\|jt |j z}||jz}t||}|j|j|} n fd|D} | D]\} } } |j j| |jj| |j| | |jd|jd} | r-| |jd<| |jd<| |jd<|jtj|jzkDsn|j|j S)Nc30K|] }|gywr6).0rJ trial_argsrAs r, z+RandomOptimizer.__call__..s@1hq.:.@srr r1)_check_args_against_first_callrtimerUr*rEr&rranger;rKrFr'r$r)rOr4)r+rPrQrRrVt0r_startr_stopr@trialsr1costr found_new_bestr[rAs @@r,__call__zRandomOptimizer.__call__sr ++FFIF == $B#zz&&)D*%%DJJ74+++( >> %//:NF@@F%+  HdD JJ  d # JJ  d #"[[tTYYw5GSYIZ[N%) '"$( &!(0 *% ) b4==>P0P " yyr.cTt|ddr|jjyy)Nr8F)r:r;r<r3s r,__del__zRandomOptimizer.__del__s$ 4-u 5 NN # # % 6r.) NrF)r/Nr6)__name__ __module__ __qualname____doc__r?r r(strrboolrr-propertyrr4rsetterr rr rKrOr rrr rUrfrhrYr.r,rrs+^$(.3 5/  gs*+   6:h::%gs 23__"tWc'9!:"t"": 3Xc] 3c 3QT 3YbcfhlnrcrYs 3 "^$""S> " sCx "'+ ,^$,,S> , sm ,  ,\&r.rctd}g}|rJ||krEtj|\}}} } ||vs| |vr*|j||| | f|dz }|r||krE|dk(ry|dk(r|dS|D cgc] } | dd } } | d} |r|tdt | z}|dk(r| Dcgc] }|| k(rdnd }}n)| Dcgc]}t j || z |z  }}tt||\}|j|\}}} } |D]}tj||||| | fScc} wcc}wcc}w)a^A contraction 'chooser' that weights possible contractions using a Boltzmann distribution. Explicitly, given costs `c_i` (with `c_0` the smallest), the relative weights, `w_i`, are computed as: $$w_i = exp( -(c_i - c_0) / temperature)$$ Additionally, if `rel_temperature` is set, scale `temperature` by `abs(c_0)` to account for likely fluctuating cost magnitudes during the course of a contraction. Parameters: queue: The heapified list of candidate contractions. remaining: Mapping of remaining inputs' indices to the ssa id. temperature: When choosing a possible contraction, its relative probability will be proportional to `exp(-cost / temperature)`. Thus the larger `temperature` is, the further random paths will stray from the normal 'greedy' path. Conversely, if set to zero, only paths with exactly the same cost as the best at each step will be explored. rel_temperature: Whether to normalize the `temperature` at each step to the scale of the best cost. This is generally beneficial as the magnitude of costs can vary significantly throughout a contraction. nbranch: How many potential paths to calculate probability for and choose from at each step. Returns: cost k1 k2 k3 rNg)weights) heapqheappoprFmaxabsmathexprandom_choicesr_popheappush)queue remainingnbranch temperaturerel_temperaturenrrdk1k2k12choicer&cmincenergieschosenothers r,thermal_chooserrs< AG AK!MM%0b"c Y "I"5 b"c*+ Q AK AvAvqz(/ 0fVAYq\ 0E 0 8Ds1c$i(( c389adA)99BGGADHHq4x[;67GGuQx:IV F+D"b#% ue$% R - 1:Hs%D+D05#D5r1rPrQrRr/c ttt|}t|}tt t |}d}d}|D]\}}t j||||||\} } |j||j||jt ||j| || z }t|tj| |}||fS)z.Compute the flops and max size of an ssa path.r)listmap frozensetsetr_rErcalc_k12_flopsdiscardaddrFrxrcompute_size_by_dict) r1rPrQrRr total_costmax_sizeijrflops12s r,ssa_path_compute_costrs#i( )F v FE#f+&'IJHO1++FFIq!YW W!! c&k" cg x!=!=c9!MNO x r.rJ choose_fncost_fnc|dk(rd}t|tj|||||}t||||\}}|||fS)zKA single, repeatable, greedy trial run. **Returns:** ``ssa_path`` and cost.rN) random_seedrssa_greedy_optimizer) rJrPrQrRrrr1rdr s r,_trial_greedy_ssa_path_and_costr1sP Av N((IwWH&xKJD$ T4 r.c eZdZ d dededededef fd Ze defdZ d e e d e d e eefdeeeffd ZxZS)rrrrrkwargsc \||_||_||_||_t |di|y)aParameters: cost_fn: A function that returns a heuristic 'cost' of a potential contraction with which to sort candidates. Should have signature `cost_fn(size12, size1, size2, k12, k1, k2)`. temperature: When choosing a possible contraction, its relative probability will be proportional to `exp(-cost / temperature)`. Thus the larger `temperature` is, the further random paths will stray from the normal 'greedy' path. Conversely, if set to zero, only paths with exactly the same cost as the best at each step will be explored. rel_temperature: Whether to normalize the ``temperature`` at each step to the scale of the best cost. This is generally beneficial as the magnitude of costs can vary significantly throughout a contraction. If False, the algorithm will end up branching when the absolute cost is low, but stick to the 'greedy' path when the cost is high - this can also be beneficial. nbranch: How many potential paths to calculate probability for and choose from at each step. kwargs: Supplied to RandomOptimizer. NrY)rrrrsuperr-)r+rrrrr __class__s r,r-zRandomGreedy.__init__Gs34 &.  "6"r.r/c|jdk(rytjt|j|j|j S)zThe function that chooses which contraction to take - make this a property so that ``temperature`` and ``nbranch`` etc. can be updated between runs. rtN)rrr)r functoolspartialrrrr3s r,rzRandomGreedy.choose_fngsB <<1   ((LL 00   r.rPrQrRcLt}||||j|jf}||fSr6)rrr)r+rPrQrRfnrBs r,rUzRandomGreedy.setupws* - 4>>4<<H4xr.)zmemory-removed-jitterg?T)rkrlrmror(rpr?rr-rqrr rrr rU __classcell__)rs@r,rrFs/ $ ### #  #  #@  3    ^$S>  sCx r.ridx_dictrVoptimizer_kwargsc .tdi|}|||||S)z5A simple wrapper around the `RandomGreedy` optimizer.rY)r)rPrQrrVr optimizers r,rrs#0/0I VVX| <rsrF ,&OOO%AA @}&e))}&@BJ      CH~   38_  2       CH~       8S#  *9?9@#' =   =  =38n =3- =  =  =&I%%mEr.