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W,,U3 ' > -335LD&#&&"($")"5"5fbhhWcNd?e>f"g  6 $$$+ 3rc[SSS9nU[SSS9- nUHanSHXnSU;n[R"/SQUS9nURU5nUR U5U:XaMGX!UR 45e Mc g)NFrVT)int32int64float32float64float)r.rr/)dtype)r r rrc is_float_typer)r%r&ris_floatinputs rtest_is_float_typers$eEBH %uTBBH=E%'HHHYe4E&&u-E((/8; Zeekk=Z Z; >rc ^ ^ ^ ^^[S5(d[R"5 SSKnSSKJn SSKJm Jm Jm J mJ m SSK J n U"5 "U U U UU4SjSUR5nU"5nURUS S 9nS HmnURXfS -US -/5nSHKnU"Xx5n U"UR!5U5R#5n UR$R'XSSS9 MM Mo g)a Test ensures that allow_ops_in_compiled_graph allows compiling in a single graph Additionally we ensure that after compilation cache works properly (by changing shapes and patterns) We additionally check that pack/unpack still can be handled despite variable number of inputs/outputs torchrNnn)einsumpackreducerepeatunpack)allow_ops_in_compiled_graphcF>^\rSrSrSU4SjjrSUUUUU4SjjrSrU=r$)Ctest_torch_compile_for_functions..TorchModuleWithOperationsic">[TU]5 g)N)super__init__)self __class__s rrLtest_torch_compile_for_functions..TorchModuleWithOperations.__init__s G  rcL>^URup4nU4SjnT"X"S55nT"Xv"S5S5nT"U/S[T5--U"S55upT"[X"S55U S5n U S [U 5-n [X"S 5US 9n T "XU"S 55S n X-$) Nc>UR5nSRUVs/sHo"SS;aUOUT-PM sn5$s snf)N r4acd)splitjoin)rpartspsuffixs rsufXtest_torch_compile_for_functions..TorchModuleWithOperations.forward..sufsD xxRW XRWQe5&8a&j!HRW XYY XsAza b c -> a b c 4za b c d -> a b cminr,za b * cza b d c -> (a b ) 1 c dz ab one1 c *rz(a b ) 1 c -> a b c)rTza b c , a b c d -> d)rCr!r)rx_abcrrSrTr_rx_abcdx_abdcpsx_arrayx1additionrrrrrs ` rforwardKtest_torch_compile_for_functions..TorchModuleWithOperations.forwardskkGA! Z E3'9#:;F63'9#:EBEugS[93y>JJFYvs3L/MNPRTabGc'l*B2s#89Q?BeS1G-HI!LH= rr)returnN)r)__name__ __module__ __qualname____firstlineno__rr__static_attributes__ __classcell__)rrrrrrs@rTorchModuleWithOperationsrs  ! ! !rrT fullgraph)rYrZr\rr,)rsuf1 other_suffixgh㈵>)atolrtol)r rsskiprrrrrrrreinops._torch_specificrModulecompileranddoublertesting assert_close)rrrroriginalcompiledr3rErrPrQrrrrrs @@@@@r test_torch_compile_for_functionsrs W % % ;;B!!!BII!,)*H}}X}6H JJQhq1 22Fq)Gqxxz6288:G MM & &wd & N 3rc j[S5(d[R"5 SSKnSSKJn SSKJnJnJn URU"SSS9U"S S S S SS S9U"SSS S95nURUSS9nSH=nURXw/5nU"U5n U"U5n URX5(aM=e g)z{ Einops layers are in general very friendly towards tracing/compiling, but we still want to make sure we can compile them. rrNr)EinMix RearrangeReducezb (t c) -> b t c)r_zb t c -> qkv b t coutz qkv c coutzqkv coutr.) weight_shape bias_shapeqkvr_coutzqkv b t cout -> b t qkvr)rTr)r @) r rsrrreinops.layers.torchrrr Sequentialrrr) rrrrrrrr3rErPrQs rtest_torch_compile_for_layersr4s W % % ==}}$+&\j^_celmn(%a8H }}X}6H JJ| $1+1+~~g//// raS import einops import numpy as np from concurrent.futures import ThreadPoolExecutor import torch def f(): return einops.rearrange(np.ndarray((20, 150, 150)), "... i j -> ... j i") with ThreadPoolExecutor(max_workers=2) as ex: fs = [] for i in range(20): fs.append(ex.submit(f)) for fut in fs: fut.result() c[S5(d[R"5 [S5(d[R"5 [R"5n[ U5R S5nUR[5 [R"SUR5R5/SS9 SSS5 g!,(df  g=f)Nrnumpyztest.pypythonT)check) r rsrtempfileTemporaryDirectoryrjoinpath write_textsrc subprocessrunabsoluteas_posix)r`testfiles rtest_einops_threadingr as W % % W % %  $ $ &!7##I.C ("3"3"5">">"@AN ' & &s A#C  C)1rrdoctestrpathlibrrr rsr einops.layerseinops._backendsr einops.einopsrrr einops.testsr r __author__random default_rngr8rr)rUrarrbrgrjrmrqrwrrr_SYMBOLIC_BACKENDSrmark parametrizerrrrrrr rvs0rrsW ,JJA  iiH h<,@-eEJ xx !A,4==2* 2 < D7D6.@p-?'CYCY]oCog-?p$67&8&6$67%8%2[0Of08" OIqs )DD