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Z E3'9#:;F63'9#:EBEugS[93y>JJFBYvs3L/MNPRTabGc'l*B2s#89Q?BeVS1G-HI!LH= r)returnN)r)__name__ __module__ __qualname__rr __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}6HO JJdQhq1 22 OFq&)Gqxxz6288:G MM & &wd & N  OOrc ltdstjddl}ddlm}ddlm}m}m}|j|dd|d d d d dd |ddd }|j|d}dD]9}|j||g}||} ||} |j| | r9Jy)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}6H0 JJd| $1+1+~~gw/// 0raS 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() ctdstjtdstjtj5}t |j d}|jttjd|jjgddddy#1swYyxYw)Nrnumpyztest.pypythonT)check) r rsrtempfileTemporaryDirectoryrjoinpath write_textsrc subprocessrunabsoluteas_posix)r`testfiles rtest_einops_threadingras W % W %  $ $ &O!7##I.C ("3"3"5">">"@ANOOOs A%CC )1rrdoctestrpathlibrrr rsr einops.layerseinops._backendsr einops.einopsrrr einops.testsr r __author__random default_rngr8rr)rUrarrbrgrjrmrqrwrrr_SYMBOLIC_BACKENDSrmark parametrizerrrrrrrrvs0rrsU ,JJA  iiH h<,@-eEJrxx !A,4==2* 2 < D7D6.@p'7CYCY]oCogp$67&8&6$67%8%2[0Of08" OIqs 2D!D!