g^(hddlZddlmZddlZddlmZddlmZmZddl m Z m Z e ddZ ejejejej ej"gZej&ej(gZeDcic];}|ej,|j.ej,|j0f=c}Zej5eDcic]G}|eej8|j.eej8|j0fIc}dZe j=d e e d d d ej>d e dedededejBdej>fdZ"e e d dd ej>d e dedededejBdej>fdZ#e j=de e dd d ej>d ej>dej>dededejBdej>fdZ$e e ddd ej>d ej>dej>dededejBdej>fdZ%e j=de e dd d ej>d ej>dej>dej>dej>dejBdej>fdZ&e e ddd ej>d ej>dej>dej>dej>dejBdej>fdZ'e j=de e dd dd d ej>d e dedededejBd!eejBdej>fd"Z(e e dddd d ej>d ej>dej>dededejBd!eejBdej>fd#Z)e j=d$e e d%d dd d ej>d ej>dej>dededejBd!eejBdej>fd&Z*e e d%ddd d ej>d ej>dej>dededejBd!eejBdej>fd'Z+e j=d(e e d)d dd d ej>d ej>dej>dej>dej>dejBd!eejBdej>fd*Z,e e d)ddd d!eejBdej>fd+Z-e j=d,e e d-d d ej>d.ed/ed0e dejBde.ej>ej>ff d1Z/e j=d2e e d3d d ej>d.ed/ed0e dejBde.ej>ej>ff d4Z0e e d-dd ej>deded0e dejBde.ej>ej>ff d5Z1e e d3dd ej>deded0e dejBde.ej>ej>ff d6Z2d7Z3e j=d8e e d9d d ej>d:ej>d;ej>dfd=Z4e e d9dd ej>d:ej>d;ej>dfd>Z5e j=d?e e d@d dd d ej>d:ej>d;eej>dfdAZ6e e d@ddd d ej>d:ej>d;eej>dfdBZ7e j=dCe e dDd d ej>dejBde.ej>ej>ffdEZ8e e dDdd ej>dejBde.ej>ej>ffdFZ9e j=dGe e dHdId ej>dejBde.ej>ej>ffdJZ:e j=dKe e dLd d ej>dejBde.ej>ej>ffdMZ;e e dLdd ej>dejBde.ej>ej>ffdNZd:ej>d;ej>dededejBf dRZ>e e dQdd ej>d:ej>d;ej>dededejBf dSZ?e j=dTe e dUd ejfd ej>d:ej>d;ej>dededejBdVejBfdWZAe e dUdejfd ej>d:ej>d;ej>dededejBdVejBfdXZBe j=dYe e dZd  dnd ej>d:ej>d;ej>dededejBf d\ZCe e dZd dnd ej>d:ej>d;ej>dededejBf d]ZDe j=d^e e d_d d[ejfd`ej>d:ej>d;eej>dededejBdaedVejBfdbZEe j=dcGdddeejjZHe e dfdgd ej>d:ej>d;ej>dfdhZIe e dfdd ej>d:ej>d;ej>dfdiZJe j=dje e dkd d ej>dejBdej>fdlZKe e dkdd ej>dejBdej>fdmZLycc}wcc}w)oN)Optional)_unsqueeze_multiple)determine_qparamsvalidate_qmin_qmax)implLibraryquantized_decomposedDEFc|tvrtd|t|\}}||k\s Jd|d|||ks Jd|d|y)NzUnsupported dtype: z9quant_min out of bound for dtype, quant_min_lower_bound: z quant_min: z9quant_max out of bound for dtype, quant_max_upper_bound: z quant_max: )_DTYPE_TO_QVALUE_BOUNDS ValueError) quant_min quant_maxdtypequant_min_lower_boundquant_max_upper_bounds b/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/ao/quantization/fx/_decomposed.py_quant_min_max_bounds_checkrs ++.ug6773J53Q00 - - ""7!8 YK Q - - - ""7!8 YK Q -zxquantize_per_tensor(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype) -> Tensorquantize_per_tensorCompositeExplicitAutogradinputscale zero_pointrrrreturnc|jtjtjfvr|j tj }|jtj k(sJd|jt |||d|z }tjtj||z|z||j |S)aAffine quantization for the Tensor using the same quantization parameters to map from floating point to quantized values Args: input (torch.Tensor): original float32 or bfloat16 Tensor scale (float): quantization parameter for affine quantization zero_point (int): quantization parameter for affine quantization quant_min (int): minimum quantized value for output Tensor quant_max (int): maximum quantized value for output Tensor dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor Returns: Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters are not stored in the Tensor, we are storing them in function arguments instead Tensorzquantize_per_tensor.tensorc|jdk(sJd|j|jdk(sJd|jt||j|j|||SzAffine quantization for the Tensor using the same quantization parameters to map from floating point to quantized values Same as `quantize_per_tensor` but scale and zero_point are Scalar Tensor instead of scalar values >Expecting zero_point tensor to be one element, but received : 9Expecting scale tensor to be one element, but received : numelritemr+s rquantize_per_tensor_tensorr5ms" a] G HXHXHZG[\]  S B5;;=/RS  uzz|Z__. 9e rc|jtjtjfvr|j tj }|j dk(sJd|j |j dk(sJd|j |jtj k(sJd|jtj||S)Nr/r0r1rr))rrr r!r"r#r3r*r+s rquantize_per_tensor_tensor_metar7s {{u}}enn55'a] G HXHXHZG[\]  S B5;;=/RS  u}}$T Eekk]ST$   E //rzquantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, Tensor quant_min, Tensor quant_max, ScalarType dtype) -> Tensorzquantize_per_tensor.tensor2c>|jdk(sJd|j|jdk(sJd|jt||j|j|j|j|Sr.r2r+s rquantize_per_tensor_tensor2r9s" a] G HXHXHZG[\]  S B5;;=/RS     rc"t||||||SN)r7r+s r quantize_per_tensor_tensor2_metar<s + uj)Y rzdequantize_per_tensor(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensordequantize_per_tensor out_dtyper?c|j|k(sJd|d|j|tj}|tvr|j ||z |zSt d|)aAffine dequantization for the Tensor using the same quantization parameters to map from quantized values to floating point values Args: input (torch.Tensor): Tensor with dtype matching `dtype` argument, e.g. (`torch.uint8`), it is a per tensor quantized Tensor if combined with quantization parameters in the argument of this function (scale/zero_point) scale (float): quantization parameter for affine quantization zero_point (int): quantization parameter for affine quantization quant_min (int): minimum quantized value for input Tensor (not used in computation, reserved for pattern matching) quant_max (int): maximum quantized value for input Tensor (not used in computation, reserved for pattern matching) dtype (torch.dtype): dtype for input Tensor (not used in computation, reserved for pattern matching) out_dtype (torch.dtype?): optional dtype for output Tensor Returns: dequantized float32 Tensor Expecting input to have dtype: z , but got ,Unsupported dtype in dequantize_per_tensor: )rrr#r r"r rrrrrrr?s rr=r=szL  uH (z%++GHMM  ''#j0E99GwOPPrcT|tj}tj||SNr))rr#r*rCs rdequantize_per_tensor_metarFs&MM   E 33rzdequantize_per_tensor.tensor(Tensor input, Tensor scale, Tensor zero_point, int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensorzdequantize_per_tensor.tensorc  |jdk(sJd|j|jdk(sJd|jt||j|j||||SzAffine dequantization for the Tensor using the same quantization parameters to map from quantized values to floating point values Same as `dequantize_per_tensor` but scale and zero_point are Scalar Tensor instead of scalar values r/r0r1r>r3r=r4rCs rdequantize_per_tensor_tensorrJ's* a] G HXHXHZG[\]  S B5;;=/RS     rc^|tj}|jdk(sJd|j|jdk(sJd|j|j|k(s Jd||tvrtj ||St d|)Nr/r0r1rAr)rB)rr#r3rr r*r rCs r!dequantize_per_tensor_tensor_metarLLsMM a] G HXHXHZG[\]  S B5;;=/RS ;;% J#B5'!JJ  ''Y77GwOPPrzdequantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, Tensor quant_min, Tensor quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensorzdequantize_per_tensor.tensor2c B|jdk(sJd|j|jdk(sJd|jt||j|j|j|j||SrHrIrCs rdequantize_per_tensor_tensor2rNms* a] G HXHXHZG[\]  S B5;;=/RS     rc &t|||||||S)Nr>)rLrCs r"dequantize_per_tensor_tensor2_metarPs  - uj)Y rzrchoose_qparams.tensor(Tensor input, int quant_min, int quant_max, float eps, ScalarType dtype) -> (Tensor, Tensor)zchoose_qparams.tensorqminqmaxepsc |jtjtjtjfvsJd|j|t vsJdt j d|t||tj|\}}t|||||tj|gdS)[Given an input Tensor, derive the per tensor affine quantization parameter (scale and zero_point) for target quantized Tensor from the Tensor Args: input (torch.Tensor): floating point input Tensor quant_min (int): minimum quantized value for target quantized Tensor quant_max (int): maximum quantized value for target quantized Tensor dtype (torch.dtype): dtype for target quantized Tensor Returns: scale (float): quantization parameter for the target quantized Tensor zero_point (int): quantization parameter for the target quantized Tensor CExpecting input to have dtype torch.float32/16/b16, but got dtype: $Expecting target dtype to be one of , but got: F)has_customized_qrange) rrr#r r!r keysraminmaxrTensorrrQrRrSrmin_valmax_vals rchoose_qparams_tensorr`s" ;;    [ MU[[MZ [  ((a -.E.J.J.L-M[Y^X_`a(tT"}}U+GW     cU# rz|choose_qparams_symmetric.tensor(Tensor input, int quant_min, int quant_max, float eps, ScalarType dtype) -> (Tensor, Tensor)zchoose_qparams_symmetric.tensorc |jtjtjtjfvsJd|j|t vsJdt j d|t||tj|\}}t|||||tj|gdtjS)rUrVrWrXF)rYqscheme) rrr#r r!r rZrr[rr\per_tensor_symmetricr]s rchoose_qparams_symmetric_tensorrds* ;;    [ MU[[MZ [  ((a -.E.J.J.L-M[Y^X_`a(tT"}}U+GW     cU#**  rc|jtjtjtjfvsJd|j||ks Jd|d|tj dtj |jtj dtj|jfS)NrVzKExpecting quant_min to be smaller than quant_max but received min: z max: r/rdevice) rrr#r r!emptydoublergint64rrrrSrs rchoose_qparams_tensor_metarls ;;    [ MU[[MZ [  I&  6)&& ;;q U\\ BEKK U\\E rctjdtj|jtjdtj|jfS)Nr/rf)rrhrirgrjrks r$choose_qparams_symmetric_tensor_metarnsA ;;q U\\ BEKK U\\E rctt|j}d||<||d<|jt |}||fS)Nr)listrangedimpermutetuple)xaxis new_axis_listys r_permute_to_axis_zerorysHquuw(MM$M! % &'A m rzquantize_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, int quant_min, int quant_max, ScalarType dtype) -> Tensorquantize_per_channelscales zero_pointsrvc|jtjtjfvr|j tj }|jtj k(sJd|j||j ksJd|j t|||t||\}}dg|j z}|jd|d<|j|}|j|}tjtj|d|z z|z||} | jt|} | j |S)atAffine per channel quantization for the Tensor using the same quantization parameters for each channel/axis to map from floating point to quantized values Args: input (torch.Tensor): original float32 or bfloat16 Tensor scales (torch.Tensor): a list of scale quantization parameter for affine quantization, one per channel zero_point (torch.Tensor): a list of zero_point quantization parameter for affine quantization, one per channel quant_min (int): minimum quantized value for output Tensor quant_max (int): maximum quantized value for output Tensor dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor Returns: Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters are not stored in the Tensor, we are storing them in function arguments instead rExpecting axis to be < r/rr)rrr r!r"r#rrrryshapeviewr$r%rsrt) rr{r|rvrrrpermute_axis_list new_shaperesouts rrzrz,s26 {{u}}enn55' u}}$T Eekk]ST$ %))+ F!8 FF  9e<4UDAE eiik!I<<?IaL [[ #F""9-K ++ ES6\*+k99i C ++e-. /C 66%=rc|jtjtjfvr|j tj }|jtj k(sJd|j||j ksJd|j t|||tj||S)Nrr~r)) rrr r!r"r#rrrr*)rr{r|rvrrrs rquantize_per_channel_metar\s {{u}}enn55' u}}$T Eekk]ST$ %))+ F!8 FF  9e<   E //rzdequantize_per_channel(Tensor input, Tensor scales, Tensor? zero_points, int axis, int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensordequantize_per_channelc|j|k(sJd|d|j|tj}||jksJd|jt |||t ||\}}dg|jz} |j d| d<|j| }|||j| z |z} n||z} | j|} | jt|} | S)aAffine per channel dequantization for the Tensor using the same quantization parameters for each channel/axis to map from quantized values to floating point values Args: input (torch.Tensor): Tensor with dtype matching `dtype` argument, e.g. (`torch.uint8`), it is a per channel quantized Tensor if combined with quantization parameter in the argument of this function (scales/zero_points/axis) scales (torch.Tensor): a list of scale quantization parameter for affine quantization, one per channel zero_points (torch.Tensor): a list of zero_point quantization parameter for affine quantization, one per channel quant_min (int): minimum quantized value for output Tensor (not used in computation, reserved for pattern matching) quant_max (int): maximum quantized value for output Tensor (not used in computation, reserved for pattern matching) dtype (torch.dtype): requested dtype for output Tensor (not used in computation, reserved for pattern matching) out_dtype (torch.dtype?): optional dtype for output Tensor Returns: dequantized float32 Tensor Expecting input to have dtype , but got dtype: r~r/r) rrr#rrrryrrr"rsrt) rr{r|rvrrrr?rrrrs rrrzs R  uN 'w.? }MNMM %))+ F!8 FF  9e<4UDAE eiik!I<<?IaL [[ #F{'' 22f<fn && C ++e-. /C Jrc|j|k(sJd|d|j|tj}||jksJd|jt |||tj ||S)Nrrr~r))rrr#rrrr*)rr{r|rvrrrr?s rdequantize_per_channel_metars  uN 'w.? }MNMM %))+ F!8 FF  9e<   E 33rzLchoose_qparams_per_token(Tensor input, ScalarType dtype) -> (Tensor, Tensor)choose_qparams_per_tokencp|jjdd}|jtjk(r|j }|tj k(rd}d|dz zdz }ntd||jd j|}tj|}||fS) Choose quantization parameters for per token quantization. This means for a N dimension Tensor (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize every N elements with the same quantization parameter. The dimension for scales/zero_points will be (M1 * M2 ... * Mn) Args: input (torch.Tensor): original float32/float16 Tensor dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor Returns: scales and zero_points, both float32 Tensors Trrkeepdimr/z/unsupported dtype in choose_qparams_per_token: gh㈵>min) absamaxrrr floatint8 Exceptionr$div zeros_like)rrr{n_bitsrr|s rrrs,YY[  "d  3F ||u}}$ LLN   &1*%) =eW E  \\d\ # ' ' 2F""6*K ; rct|jdddgz}tj|tj|j tj|tj |j fSNrr/rfrprrrhrirgrjrrsizes rchoose_qparams_per_token_metar]  CR !QC 'D ;;t5<< Eu{{ EKK H rz]_choose_qparams_per_token_asymmetric_impl(Tensor input, ScalarType dtype) -> (Tensor, Tensor))_choose_qparams_per_token_asymmetric_implCompositeImplicitAutogradcd\}}tj|dd}tj|dd}tj|tj|}tj |tj|}tj tjj}||z t||z z } | j|} || z } || z } || z} || z} tj| | zdkD|| z || z }tj|||j}| jtj|jtjfS)r)irTrrr)raminrrrmaxfinfor#rSrr$wherer%r"float64rj)rrrQrRr^r_ min_val_neg max_val_posrSr descaled_min descaled_maxzero_point_from_min_errorzero_point_from_max_errorrs rrrsC,JD$jjB5GjjB5G))GU%5%5g%>?K))GU%5%5g%>?K ++emm $ ( (C; &%t *< >>rzWchoose_qparams_per_token_asymmetric(Tensor input, ScalarType dtype) -> (Tensor, Tensor)#choose_qparams_per_token_asymmetricct||Sr;)rrrs rrrEs 5UE BBrct|jdddgz}tj|tj|j tj|tj |j fSrrrs r(choose_qparams_per_token_asymmetric_metarQrrctjt|jdd}||j k(sJd|d|j||j k(sJd|d|jy)Nrz num_tokens: z scales: z zero_points: )mathprodrprr3)rr{r| num_tokenss r!_per_token_quant_qparam_dim_checkr`s4 -cr23Jflln$; j\6;;=/:;$ k''))E j\ 0@0@0B/CDE)rz}quantize_per_token(Tensor input, Tensor scales, Tensor zero_points, int quant_min, int quant_max, ScalarType dtype) -> Tensorquantize_per_tokenct|||t||||jd|z j|j j ||j |}|S)aPer token quantization for the Tensor using the quantization parameters to map from floating point to quantized values. This means for a N dimension Tensor (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize every N elements with the same quantization parameter. The dimension for scales/zero_points will be (M1 * M2 ... * Mn) Args: input (torch.Tensor): original float32 or bfloat16 Tensor scales (float32 torch.Tensor): quantization parameter for per token affine quantization zero_points (int32 torch.Tensor): quantization parameter for per token affine quantization quant_min (int): minimum quantized value for output Tensor quant_max (int): maximum quantized value for output Tensor dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor Returns: Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters are not stored in the Tensor, we are storing them in function arguments instead r)rrmuladdr%r$r"rr{r|rrrs rrrps^6 9e<%eV[A #, [   y) $ E   LrcJt|||tj||SrErrr*rs rquantize_per_token_metars# 9e<   E //rzdequantize_per_token(Tensor input, Tensor scales, Tensor zero_points, int quant_min, int quant_max, ScalarType dtype, ScalarType output_dtype) -> Tensordequantize_per_token output_dtypec8||z }||z}|j|S)aPer token dequantization for the Tensor using the quantization parameters to map from floating point to quantized values. This means for a N dimension Tensor (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize every N elements with the same quantization parameter. The dimension for scales/zero_points will be (M1 * M2 ... * Mn) Args: input (torch.Tensor): quantized Tensor (uint8, int8 etc.) scales (float64 torch.Tensor): quantization parameter for per token affine quantization zero_points (int64 torch.Tensor): quantization parameter for per token affine quantization quant_min (int): minimum quantized value for input Tensor quant_max (int): maximum quantized value for input Tensor dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor output_dtype (torch.dtype): dtype (e.g. torch.float32) for output Tensor Returns: dequantized Tensor with dtype `output_dtype` )r"rr{r|rrrrs rrrs&8 K E FNE 88L !!rcJt|||tj||SrErrs rdequantize_per_token_metars# 9e<   E 66rzquantize_per_channel_group(Tensor input, Tensor scales, Tensor zero_points, int quant_min, int quant_max, ScalarType dtype, int group_size) -> Tensorquantize_per_channel_groupcL|dkDsJ||jdkDr!|jddk(r|jd}|jd|zdk(sJ|jdk(sJ|jd|}tj|j dk(sJ|jdd}|jdd}|j d|z j|jj||j|j|}|S)Nr/rrrr) rrrreshaperisnansumrrr%clamp_r" reshape_as) rr{r|rrr group_sizeto_quant input_int8s rrrs >>EKKO# R(8A(=[[_ ;;r?Z '1 ,, , 99;!  }}R,H ;;x $ $ &! ++ + ^^B "F%%b!,K  S6\" [    9 % E E   rc|dkDsJ||jdkDr!|jddk(r|jd}|jd|zdk(sJ|jdk(sJtj||S)aXGroupwise quantization within each channel for an 2-d Tensor using the quantization parameters to map from floating point to quantized values. This means for each row of a 2-d Tensor (M, N), we calculate scales/zero_points for each `group_size` elements and quantize every `group_size` elements with the same quantization parameter. The dimension for scales/zero_points will be (M * ceil(N, group_size),) Args: input (torch.Tensor): original float32 or bfloat16 Tensor scales (float32 torch.Tensor): quantization parameter for per channel group affine quantization zero_points (int32 torch.Tensor): quantization parameter for per channel group affine quantization quant_min (int): minimum quantized value for output Tensor quant_max (int): maximum quantized value for output Tensor dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor Returns: Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters are not stored in the Tensor, we are storing them in function arguments instead r/rrrr))rrrrr*)rr{r|rrrrs rquantize_per_channel_group_metar s8 >>EKKO# R(8A(=[[_ ;;r?Z '1 ,, , 99;!     E //rzdequantize_per_channel_group(Tensor input, Tensor scales, Tensor? zero_points, int quant_min, int quant_max, ScalarType dtype, int group_size, ScalarType output_dtype) -> Tensordequantize_per_channel_groupw_int8rc |dkDsJ||jdkDr!|jddk(r|jd}|jd|zdk(sJ|jdk(sJ|jd|}|jdd}||jdd} n0tjgtj |j } |j| j|j|j|} | S)a!Groupwise dequantization within each channel for an 2-d Tensor using the quantization parameters to map from floating point to quantized values. This means for each row of a 2-d Tensor (M, N), we calculate scales/zero_points for each `group_size` elements and quantize every `group_size` elements with the same quantization parameter. The dimension for scales/zero_points will be (M * ceil(N, group_size),) Args: input (torch.Tensor): quantized Tensor (uint8/int8 etc.) scales (float32 torch.Tensor): quantization parameter for per channel group affine quantization zero_points (int32 torch.Tensor): quantization parameter for per channel group affine quantization quant_min (int): minimum quantized value for input Tensor quant_max (int): maximum quantized value for input Tensor dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor output_dtype (torch.dtype): dtype (e.g. torch.float32) for output Tensor Returns: dequantized Tensor with dtype `output_dtype` r/rrrrf) rrrrrzerosint32rgsubrrr") rr{r|rrrrrw_int8_groupedzpw_dqs rrr5sD >>FLL$$b)9Q)>\\"% << j (A -- - ::<1  ^^B 3N ^^B "F  Q ' [[5;;v}} E   b ! % %f - 8 8 @ C CL QD Krzyfake_quant_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, int quant_min, int quant_max) -> Tensorc,eZdZedZedZy)FakeQuantPerChannelc|jtjk7r|jtj}|jtjk7r|jtj}|jtjk(sJd|j||j ksJd|j t td|t t|dz|jz}t||}t||} tj|d|z z| z} tj| ||| z |z} tj| |k\| |k} |j| | S)Nrr~rr/r)rrr#r"rrrrprqndimrr%r$ logical_andsave_for_backward) ctxrr{r|rvrrbroadcast_dimsunsqueeze_scalesunsqueeze_zero_pointstemprmasks rforwardzFakeQuantPerChannel.forwardosO <<5== (YYu}}-F    +%..5K KK5== ( X I%++ W X (eiik!J% Tensorzconvert_element_type.no_fusecjtjjjj ||Sr;)ropsprimsconvert_element_typedefaultrs rrrs% 99?? / / 7 7u EErc0tj||SrErrs rconvert_element_type_metars   E //r)r)Mrtypingrr torch._refsrtorch.ao.quantization.utilsrr torch.libraryrrquantized_decomposed_libuint8ruint16int16r_INTEGER_DTYPES float8_e5m2 float8_e4m3fn _FLOAT_DTYPESiinforrr updateintrrdefiner\rrrr,r5r7r9r<r=rFrJrLrNrPrtr`rdrlrnryrzrrrrrrrrrrrr#rrrrrautogradFunctionrrrrr)ks0rrs  +M' ##95A;; ELL%++u{{S""E$7$78 :I45A  AKEKKN..//DQRqQ[U[[^   #kekk!n&8&8"9::R  @  57RS! <<! !! !  ! ;; ! \\!T!H 5v> 0 << 0  0 0 0  0 ;; 0 \\ 0? 0 @ :G(,Q <<Q <<Q Q Q  Q ;; Q $Q \\QHQ4e #(, << << ||  ||  ;;  $ \\  @ ?H(,  $  \\ I 7  79TU$ <<$"$*-$49$BG++$ 5<< %&$V$N7 % $ <<$"$*-$49$BG++$ 5<< %&$  $N 7@ <<$'47>CLQKK 5<< %&A" A6J <<$'47>CLQKK 5<< %&K@  68ST, <<, LL,,  ,  ,  , ;;, \\,U,^ 6?0 <<0 LL00  0  0  0 ;;0 \\0@0._  8:UV(,; <<; LL;%,,';  ;  ;  ; ;;; $; \\;W;| 8&A(,4 <<4 LL4%,,'4  4  4  4 ;;4 $4 \\4B4*R   <<  ;;  5<< %&   F   << ;; 5<< %&  c / (? <<(? ;;(? 5<< %&(?  (?V] ) C <<C ;;C 5<< %&C  C)   << ;; 5<< %&  E@  46QR# <<# LL## #  # ;; #S#L 4f= 0 << 0 LL 0 0 0  0 ;; 0> 0Y  68ST!& " <<" LL"" "  " ;; "++"U"B 6?!&  7 << 7 LL 7 7 7  7 ;; 7++ 7@ 7A:G0U\\0%++0%,,0H0E%SsAv7A v<