U Kc @sddlZddlmZmZmZddlZddlmZddlm Z ddlm Z m Z m Z m Z mZddlmZddlmZddlmZejjZejdd d ZiZdd d ZddZeejjddZeej jddZ!eej"j#ddddZ$eej%jej%j&geddZ'eej(jdddddZ)eej*jddZ+eej*j&dd Z,eej-jej.jgd!d"Z/eej0jd#d$Z1eej0j&d%d&Z2d'd(Z3e4d)d*d+Z5dd-d.Z6eej7jd/d0Z8d1d2Z9eej:jd3d4Z;eejeej?j&ddd9d:Z@eejAjej ej ej eeBeeBeeBeCeeBeBd; dd?ZEeejFjddddBdCZGdDdEZHeejIjdFdGZJeejKjdHdIZLeejMj ddJdKZNeejOjejOj&gedLdMZPeejQjdNdOZQeejRj dddPdQZSeejTjejTj&gedRdRdSdTdUZUeejVjdVdWZWeejXjddYdZZYeejZjejZj&gedd[d\Z[eej\jd]d^Z]dd_d`Z^eej_jej_j&gedddadbdcZ`eejajdddeZbeejajcejajdgedfdgddhdiZeeejfjdjdkZgeejhjdldmZieejjjdddndoZkeejlj ejljmejnj ejnjmgdddpdqZoeejpjdddrdsZqeejrjddddtduZseejtjmdddvdwZueejvjddddxdyZweejxjdddzd{Zydd|d}Zzeej{jddd~dZ|ddZ}ddZ~ddZddZeejjdddddZeejjgddZeejjejjejjejjejjejjejjgddZeejjddZeejjddZddlZddlZddlZdS)N)ListOptionalUnion)Tensor)checkcorresponding_complex_dtypecorresponding_real_dtypeelementwise_dtypesELEMENTWISE_TYPE_PROMOTION_KIND) out_wrapper)_broadcast_shapestree_mapatenZIMPLMetaTcsfdd}|S)Ncsfdd}t|S)NcsHt|<r0|jdkr|jn|jj}t||tjj j dS)Ndefault) meta_tableZ _overloadname__name__Zoverloadpacket'_meta_lib_dont_use_me_use_register_metaimplZpy_impltorchZ_CZ DispatchKeyr)opname)fregister_dispatcher=/tmp/pip-unpacked-wheel-gikjz4vx/torch/_meta_registrations.pyadd_funcs z0register_meta..wrapper..add_funcr )rrrr)rrwrappers zregister_meta..wrapperr)rrrrrr register_metasr cCs(tjtjtjtjtjtji}|||SN)rZ complex32halfZcfloatfloatZcdoubledoubleget)dtypeZ from_complexrrrtoRealValueType.sr'cCs|jjs t||Sr!)r& is_complexAssertionError new_emptysize)selfdim normalizationZforwardrrr meta_fft_c2c7s r/cCsR|jjs tt|}|r<|d}||dd}|||<|j|t|jdS)Nr&)r&is_floating_pointr)listr+r*utilsr)r,r-r.Zonesided output_sizesZlast_dimZlast_dim_halfsizerrr meta_fft_r2c=s   r8) generatorcCs |jdkr|d|kst|SNr2r)ndimr+r))nr9outrrr meta_randpermLsr>cCs8|jjs tt|}|||d<|j|t|jdS)Nr0r3)r&r(r)r5r+r*r')r,r-r.Zlastdimr7rrr meta_fft_c2rRs   r?F)rcCs|Sr!r)r,srcZ non_blockingrrr meta_copy_[srAcCs.t|}|dkr$|||<||S)Nr)r5r+r-numelr*)r,r-indexZ result_sizerrrmeta_index_selectas   rDcCs(t|||j|t|||Sr!)r_resize_output_r+devicecopy_ index_select)r,r-rCr=rrrmeta_index_select_outisrIcCs |dSNr)r*r,rrrmeta_maxosrLcCs8|rt|j}nt|tjd\}}|j||dS)N)Ztype_promotion_kindr3)r(rr&r r Z INT_TO_FLOATr*r+)r,Z result_dtype_rrr meta_anglets  rNcCs$t|||j|t|Sr!)rrEr+rFrGangle)r,r=rrrmeta_angle_outsrPcCsX|dkst|d|d|dksTt|d|dd|dddS)Nr1z3: The input tensor must have at least 2 dimensions.r0z5: A must be batches of square matrices, but they are z by z matrices)r-r)r+)r,f_namerrrsquareCheckInputss   rS)uplocCs6|}t|dkr|dks2|dks2td|dS)Nr2ULz1Expected UPLO argument to be 'L' or 'U', but got )upperlenr))rTZuplo_uppercaserrr checkUplos rYrVcCsdt|dt|t|j}|dks,t|j|j|d}|dd||jdd}||fS)NZ linalg_eighr1r3rQr0) rSrYr'r&r-r)r*shapeZ transpose_)r,rTZ real_dtypevaluesZvectorsrrrmeta_linalg_eighs   r\c sddkoddk}tjdkr,|pFjdkoF|oFddkfddjdkrnj\}}}}nd}j\}}}|\}}} } || | } |||} jdkr|| | fS||| | fSdS)Nr2rr1cs dS)Nz:3D or 4D (batch mode) tensor expected for input, but got: rrrKrrzmeta_pad2d..)r+rr;rZr*) r,padding valid_dimsnbatchZnplaneZinput_hZinput_wpad_lpad_rZpad_tZpad_bZoutput_hZoutput_wrrKr meta_pad2ds        rfcs,tdkodkfdddS)Nr2csdddS)Nz1D tensors expected, but got zD and z D tensorsr-rotherr,rrr_r`zdot_check..)rr-r,rirrhr dot_checks rkcCst|||dSrJ)rkr*)r,tensorrrrmeta_dots rmcCs^t|dkddt|dkdd|j\}}|j\}}t||kdd|||S)Nr1cSsdS)Nz a must be 2Drrrrrr_r`zmeta_mm..cSsdS)Nz b must be 2Drrrrrr_r`cSsdS)Nz$a and b must have same reduction dimrrrrrr_r`)rr-rZr*)abNZM1ZM2Prrrmeta_mms   rrcs0|r"tfddtjDStjS)Nc3s$|]}|krj|ndVqdS)r2NrZ.0idimsr,rr sz+_compute_reduction_shape..)tupleranger;r6compute_reduction_output_shaperZ)r,rxkeepdimrrwr_compute_reduction_shapesr~cCst|||j|Sr!)rrEr+rF)r,r9r=rrrmeta_bernoullisr) input_tensorweightbiasstrideradilation is_transposedoutput_paddinggroupsc  svttttttdddtttttttddddtjtjttttfttttfttttftttttfdfdd } d d fd d } jdd} jdd} |r|jd} | | | ||||}n<jd} jd|jdkrtd| | | |||}jd| f|}ddl m }t |rZj j }nd}| |}|j|d}|S)N)lnpdksreturncSs$|d|||dd|dS)a Formula to apply to calculate the length of some dimension of the output See: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html Args: ln: length of the dimension p: padding in that dim d: dilation in that dim k: kernel size in that dim s: stride in that dim Returns: The output length r1r2r)rrrrrrrr_formulaszmeta_conv.._formula)rrrrrrrcSs(|d|d|||d|dS)a Formula to apply to calculate the length of some dimension of the output if transposed convolution is used. See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html Args: ln: length of the dimension p: padding in that dim d: dilation in that dim k: kernel size in that dim s: stride in that dim op: output padding in that dim Returns: The output length r2r1r)rrrrrrrrr_formula_transposedsz&meta_conv.._formula_transposed)rx kernel_sizerrarrc sjg}t|tr|gt|}nt|dkr<|dgt|}t|trV|gt|}nt|dkrt|dgt|}t|tr|gt|}nt|dkr|dgt|}d}|rt|tr|gt|}n$t|dkr|dgt|}n|}tt|D]f}|r:|||||||||||||q|||||||||||q|Sr:) isinstanceintrXr{append) rxrrrarrZ ret_shapeZoutput_padding_listrv)rrrrcalc_conv_nd_return_shapesT         z,meta_conv..calc_conv_nd_return_shapecSstj|tjkSr!)r _prims_commonsuggest_memory_format channels_last)Ztenrrris_channels_lastDsz#meta_conv..is_channels_lastcsZ|dkr sr.tjSnr.tjSjtjdrBtjSjtjdrVtjSdS)Ncuda memory_format)rrZ is_contiguouscontiguous_formatZpreserve_format) device_hint)rrrrrpick_memory_formatGsz%meta_conv..pick_memory_formatr1r2rzInvalid channel dimensions) FakeTensorrr)N)rrSizerrrrZ RuntimeErrorr*Ztorch._subclasses.fake_tensorrrZ fake_devicetypeto)rrrrrarrrrrrrrxZ out_channelsZ shape_outr=rrZmem_fmtr)rrrrrr meta_convsV 6      rcs2tkojkfdddS)Ncs8dddddddjS)NzExpected a tensor of dimension z and tensor.size[z] == , zbut got : dimension z] = )r-rZrr-dim_sizer+rlrrr_zsz check_dim_size..)rr-rZ)rlr-rr+rrrcheck_dim_sizewsrrrcCs`dd}|d|\}} tt|dkddt|dkrD|| } } n.t|dkrd|d|d} } n|d |\} } |d |\} } t|dkp|dkd d|d kr|d nd}|d}|d}|d}t||| | d|}t|| | | d|}t|}t||| | | | | dd|||||||dkr>|||g}n ||||g}tj ||j |j |dS)NcsBtt|dkfdd|d}t|dkr2|n|d}||fS)Nr2r1cs ddS)Nz avg_pool2d: 4 must either be a single int, or a tuple of two intsrrrrrr_r`z1meta_avg_pool2d..unpack..rr2rrXrvalHWrrrunpacks  zmeta_avg_pool2d..unpackrrr2r1cSsdS)NzOavg_pool2d: stride must either be omitted, a single int, or a tuple of two intsrrrrrr_r`z!meta_avg_pool2d..rr2rracSsdS)Nzdivisor must be not zerorrrrrr_r`r^rQr0r]r&rFr) rrXr-r+pooling_output_shaper6rpool2d_shape_checkremptyr&rF)inputrrra ceil_modeZcount_include_padZdivisor_overriderkHkWdHdWpadHpadWrc nInputPlane inputHeight inputWidth outputHeight outputWidthrr+rrrmeta_avg_pool2dsb            rcCsjt|||||||dd| | | | | ||}| }t|||d|t|||d| t|||d| dS)Nr2r]r1)rr-r)rZ gradOutputrcrrrrrrrrrrrZ mem_formatr; nOutputPlanerrravg_pool2d_backward_shape_checks,rcs>tjdkpjdkfddjddt|S)Nr]r^cs djS)Nz"Expected 3D or 4D tensor, but got rsrrKrrr_r`z*meta_adaptive_avg_pool2d..rQrr;r*rZrzr, output_sizerrKrmeta_adaptive_avg_pool2ds  rcs>tjdkpjdkfddjddt|S)Nr^cs djS)Nz"Expected 4D or 5D tensor, but got rsrrKrrr_r`z*meta_adaptive_avg_pool3d..rrrrrKrmeta_adaptive_avg_pool3ds  rcCs|dkrtd||S)Nz:cannot repeat_interleave a meta tensor without output_size)rr*)repeatsrrrrmeta_repeat_interleave_TensorsrcCs:|jjs t|jjstt|j|j}|j|t|jdSNr3)r&r4r)r rZr*r)realimagZ out_shaperrr meta_complexs  rcCsx|jst||S|rH|r6t||St||Sn|rdt||St|||dSrJ)r(rdotZis_conjvdotZconjrkr*rjrrrrs  rc stddg}tD]\dk rtjtjtjtjfkddjtjtjfkr}t|tj j kfddt t j D]Ftj j kfddt | |dqq| q| q|ttj kfddddlm}t|jtj krR dq4d}d }D]J|dkr|dk rd}n*|dkrdkrd }ndk r^qq^d }|s.g}g}tD](\dk r| | qtD](\dkr| | q||g} g} g} tD]H\} dkr~| rl| j | n| j | n tj } qB| | | S) NcSsdS)Nz#at least one index must be providedrrrrrr_.r`z#meta_index_Tensor..cSsdS)Nz:tensors used as indices must be long, byte or bool tensorsrrrrrr_6r`cs djS)N)too many indices for tensor of dimension r;rrKrrr_=r`c s$djddjdS)NzThe shape of the mask z at index z0 does not match the shape of the indexed tensor rsr)rvrCjrr,rrr_Cr`r2csdjdtdS)Nrz (got ))r;rXr)indicesr,rrr_Or`rFr1T)r enumerater&rlongZint8boolnonzerorXr; IndexErrorr{rZrselect torch._refsZ_refsr5Z_maybe_broadcastZpermuter*) r,rresultrrefsstateZhas_contiguous_subspacerxZtransposed_indicesZ before_shapeZ after_shapeZreplacement_shaper-r)rvrCrrrr,rmeta_index_Tensor,s                  rr2)betaalphacsd}d}|||f}tdkddtdkddtddkfddtddkfd dt|d|ko|d|kd d||S) Nr2r1r]cSsdSNzbatch1 must be a 3D tensorrrrrrr_r`zmeta_addbmm..cSsdSNzbatch2 must be a 3D tensorrrrrrr_r`rcsddddS)Nz8batch1 and batch2 must have same number of batches, got r and r+rbatch1batch2rrr_r`c s6ddddddddd S)Nz#Incompatible matrix sizes for bmm (r2xr1rrrrrrrr_scSsdS)Nz.self tensor does not match matmul output shaperrrrrr_r`)r+expandrr-r*)r,rrrrZdim1Zdim2rrr meta_addbmms$    rc s tdkfddtdkfddtddkfddttjddttjddt|d kd dtd kodkfd dd }d }jdd }jdd }tt ||}| ||g |S) Nr1csddS)Nz1cdist only supports at least 2D tensors, X1 got: Drgr)x1rrr_r`z$meta_cdist_forward..csddS)Nz1cdist only supports at least 2D tensors, X2 got: rrgr)x2rrr_r`r0csddddS)Nz4X1 and X2 must have the same number of columns. X1: r0z X2: rr)rrrrr_r`cSsdS)Nz=cdist only supports floating-point dtypes, X1 got: {x1.dtype}rrrrrr_r`cSsdS)Nz=cdist only supports floating-point dtypes, X2 got: {x2.dtype}rrrrrr_r`rcSsdS)Nz)cdist only supports non-negative p valuesrrrrrr_r`cs dS)Nz"possible modes: 0, 1, 2, but was: rr) compute_moderrr_r`rQ) rr-r+r6is_float_dtyper&rZr5rZbroadcast_shapesextendr*) rrrrZr1Zr2Z batch_tensor1Z batch_tensor2 output_shaper)rrrrmeta_cdist_forwards@          rr0c stjtjtjfkfddtjtjtjfkfddttjfddd} |rt| dkdd| d8} | d} t d\} } } dk rt|| kd dtjjkfd dtj dkfd dt kfd dfd dddfdd}j j dkrd}}|| kr~| d}n d}n\|| |}|| ks|| ks|sĈd}n d}| }|}| |||fS)Ncs djS)Nz(expected indices to be long or int, got r3r)rrrr_r`z$meta_embedding_bag..cs djS)Nz(expected offsets to be long or int, got r3r)offsetsrrr_r`cs djS)Nz/expected weight to be floating point type, got r3r)rrrr_r`rr2cSsdS)Nz1include_last_offset: numBags should be at least 1rrrrrr_r`r]cSsdS)Nz@embedding_bag: per_sample_weights only supported with mode='sum'rrrrrr_r`csdjdjdS)Nzexpected weight (z) and per_sample_weights (z) to have same dtyper3r)per_sample_weightsrrrr_r`csdjdS)Nz1expected per_sample_weights to be 1D tensor, got rrr)rrrr_r`csdddS)Nz%expected per_sample_weights.numel() (z$ to be the same as indices.numel() (r)rBr)rrrrr_scs|||o|ddkSNrr2)rr@Zscaleoutput padding_idx)is_fast_path_index_selectrris_fast_path_index_select_scalesz;meta_embedding_bag..is_fast_path_index_select_scalecSs<|jtjks|jtjko:|ddko:|ddko:|dkSr:)r&rr#r"r)r@rrrrrr s  z5meta_embedding_bag..is_fast_path_index_selectcs&|dk r||||S|||SdSr!rr)rrrr is_fast_pathsz(meta_embedding_bag..is_fast_pathcpu)rr&rrrr6rr+r*r{r;rBrFr)rrrZscale_grad_by_freqmodesparserZinclude_last_offsetrZnum_bagsrZMODE_SUMZ MODE_MEANZMODE_MAXr offset2bagbag_size max_indicesZ fast_path_sumr)rrrrrrrmeta_embedding_bagsl                r cCst|dkdd|dkrB|dt|}|||fS|dkrft|d|d|}nt|d||d}||fS)NrcSsdS)Nzmatrix or a vector expectedrrrrrr_0r`zmeta_diag..r2r)rr-r+absr*min)r,r-szrrr meta_diag-s rcGs@t|||f|\}}}}|jjdkr4||}||||fS)Nr)r rFrr*r+)rrrargsrrr r rrrmeta_embedding_bag_forward_only=s rcCs.|r|S|jjs|jjr|jS|r(tjS|jSr!)r&r4r(rr)rr&promote_int_to_longrrr_get_reduction_dtypeGsrr3cCs6t||dd}t|j|}t|||}|j||dS)NT)rr3)rr6reduction_dimsrZr~r*)rrxr}r&Z output_dtyperrrr meta_nansumTs rcCs$t|jtt|}||Sr!)r6r|rZrzr{r-r*)rrrrrmeta_nanmedian]s rr[rcCs6t|j|f}t|||}|||j|tjdfSr)r6rrZr~r*rr)rr-r}rrrrmeta_nanmedian_dimes  rcCs|Sr!rrKrrrmeta_logical_not_psrcsbtt|kddt|}d|t|jfddttD}||S)NcSsdS)NzZNumber of dimensions of repeat dims can not be smaller than number of dimensions of tensorrrrrrr_yr`zmeta_repeat..r2csg|]}||qSrrrtZ padded_sizerrr szmeta_repeat..)rrXr-rzrZr{r*)r,rZnum_new_dimensionsZ target_sizerrr meta_repeatusrcCs|Sr!rrKrrr meta_zero_srcCs|Sr!r)r,rrrr meta_fill_srcCs|Sr!rrKrrr meta_relu_srcCs||Sr!)r*r+r,rr[ accumulaterrrmeta_index_putsr"cCs|Sr!r)r,maskvaluerrrmeta_masked_fill_sr%cCs|Sr!rr rrrmeta_index_put_sr&cCs ||jSr!)viewrZrKrrr meta_aliassr(c st|dkddt|dkdd|}||d|d|d}d}||f}tdko~dkfdd||}|s|dk rt|dkd dt||kd d|S) Nr]cSsdSrrrrrrr_r`z)common_meta_baddbmm_bmm..cSsdSrrrrrrr_r`rr1r2c s&ddddddd S)Nz@Expected size for first two dimensions of batch2 tensor to be: [rz ] but got: [rr2z].rrZ batch2_sizesbsZcontraction_sizerrr_r`cSsdS)Nzself must be a 3D tensorrrrrrr_r`cSsdS)NzTExpected an input tensor shape with shape {output_size} but got shape: {self.size()}rrrrrr_r`)rr-r+r*) rrZis_bmmZ self_baddbmmZ batch1_sizesZres_rowsZres_colsrrrr)rcommon_meta_baddbmm_bmms*    r+cCs t||dS)NT)r+)r,Zmat2rrrmeta_bmmsr,cCs<||}||}|dkr8t|dkt|dkkr8|d8}|Sr)r)ryqrrrrdiv_rtns  r0cCsZt|||||dd|r(|dnd|d}|rV|d|||krV|d8}|Sr:)r0) inputSize kernelSizerdrerrrZ outputSizerrrpooling_output_shape_pad_lrs*   r3csXt|dkddtdkfddtdkfddt||||S)NrcSsdS)Nzstride should not be zerorrrrrr_r`z&pooling_output_shape..cs dS)Nz'pad must be non-negative, but got pad: rr)padrrr_r`r1csddS)Nz7pad should be at most half of kernel size, but got pad=z and kernel_size=rrr2r4rrr_r`)rr3)r1r2r4rrrrr5rrs  rcsD}tdkodkddt|dko6|dkddt|dkoP|dkddddkotddk}|tjkrt|dko|od dkd dnBt|d krˆddkr|p|dko|oڈd dkfd dtd kod k fd dtdko(dkfd ddS)NrcSsdS)NzCkernel size should be greater than zero, but got kH: {kH}, kW: {kW}rrrrrr_r`z$pool2d_shape_check..cSsdS)Nz>stride should be greater than zero, but got dH: {dH}, dW: {dW}rrrrrr_r`cSsdS)Nz\dilation should be greater than zero, but got dilationH: {dilationH}, dilationW: {dilationW}rrrrrr_r`r2r1r^r]cSsdS)NzExpected 4D (batch mode) tensor expected for input with channels_last layout with optional 0 dim batch size for input, but got: {input.size()}rrrrrr_!r`csdS)NzYExpected 3D or 4D (batch mode) tensor with optional 0 dim batch size for input, but got: rr)rrrr_(r`csddddS)NzKpad should be smaller than or equal to half of kernel size, but got padW = z , padH = z, kW = z, kH = rr)rrrrrrr_-r`c s*ddddddd S)NzGiven input size: (rz). Calculated output size: (z). Output size is too smallrr)rrrrrrrrr_3r`)r-rr+rr)rrrrrrr dilationH dilationWrrrrrrr;rbr) rrrrrrrrrrrrrsB  rrcCsdd}|d|\}}tt|dkddt|dkrD||} } n|d|\} } |d |\} } |d |\} }t|}|tjkrt|d kd dn0|tjkrt|d kddntddd|d kr|dnd}|d}|d}|d}t ||| | | |}t ||| | ||}t |||| | | | | ||||||||dkrd|||g}n ||||g}tj ||j |j |dtj |tj|j |dfS)NcsBtt|dkfdd|d}t|dkr2|n|d}||fS)Nrcs ddS)Nz max_pool2d: rrrrrrr_Ar`z>meta_max_pool2d_with_indices..unpack..rr2rrrrrr>s  z,meta_max_pool2d_with_indices..unpackrrcSsdS)NzOmax_pool2d: stride must either be omitted, a single int, or a tuple of two intsrrrrrr_Kr`z.meta_max_pool2d_with_indices..rrrarr^cSsdS)NzMnon-empty 4D (batch mode) tensor expected for input with channels_last layoutrrrrrr_Yr`)r]r^cSsdS)Nz9non-empty 3D or 4D (batch mode) tensor expected for inputrrrrrr_^r`FcSsdS)Nz?Unsupport memory format. Supports only ChannelsLast, Contiguousrrrrrr_cr`rr2rrQr0r]r)rrXr6rrrr-rr+rrrr&rFint64)rrrrarrrrrrrrrr6r7rrcrrrrrr+rrrmeta_max_pool2d_with_indices9s              r9cOstj|f||Sr!)rr)r+Z fill_valuerkwargsrrrfullsr;cOstjj|f|Sr!)rZ empty_liker)r,rr:rrr meta_likes r<cKsRt|trt|}dd}|dddko2||}|rBtj|d<tj|gf|S)NcSst|tpt|tSr!)rrr)rrrr is_integralszarange..is_integralr&) rr#mathceilr%rr8rr)endr:r=Zset_to_integral_dtyperrraranges   rAcKstj||f|Sr!)rrA)startr@r:rrr arange_startsrC)T)F)rV)rrFTN)N)FrFNFr0)r)T)NF)r0F)F)F)N)rrrF)r>typingrrrrZtorch._prims_commonrr6rrrrr r Ztorch._prims_common.wrappersr rr Ztorch.utils._pytreeropsrZlibraryLibraryrrr r'Z_fft_c2crr/Z_fft_r2cr8ZrandpermZ generator_outr>Z_fft_c2rr=r?rGrArHrDrImaxr rLrOrNrPrSstrrYr\Zreflection_pad2drfrkrrmmmrrr~Z bernoullirZ convolutionrrrrZ avg_pool2drrZ_adaptive_avg_pool2drZ_adaptive_avg_pool3drZrepeat_interleavercomplexrrrCrZaddbmmrZ_cdist_forwardrZ_embedding_bagr ZdiagrZ_embedding_bag_forward_onlyrrZnansumrZ nanmedianrr-Z dim_valuesrZ logical_not_rrepeatrZzero_rfillZScalarZfill_rZrelu_rZ index_putr"Z masked_fill_r%Z index_put_r&aliasr(r+Zbmmr,r0r3rrZmax_pool2d_with_indicesr9r;Z randint_likeZ low_dtypeZ randn_likeZ rand_likeZ full_likeZ zeros_likeZ ones_liker<rArBrCZtorch._refs.nn.functionalZtorch._refs.specialrrrrsD                              J+          e  #  \                !   > T