qivddlZddlZddlmZddlmZmZddlmZm Z e ddZ ejejejejejgZej"ej$gZeDcic];}|ej(|j*ej(|j,f=c}Zej1eDcic]G}|eej4|j*eej4|j,fIc}dZe j9dee d d d ej:d ed edededej>dej:fdZ ee d dd ej:d ed edededej>dej:fdZ!e j9dee dd d ej:d ej:d ej:dededej>dej:fdZ"ee ddd ej:d ej:d ej:dededej>dej:fdZ#e j9dee dd d ej:d ej:d ej:dej:dej:dej>dej:fdZ$ee ddd ej:d ej:d ej:dej:dej:dej>dej:fdZ%e j9dee dd ddd ej:d ed edededej>d ej>dzdej:fd!Z&ee ddddd ej:d ej:d ej:dededej>d ej>dzdej:fd"Z'e j9d#ee d$d ddd ej:d ej:d ej:dededej>d ej>dzdej:fd%Z(ee d$dddd ej:d ej:d ej:dededej>d ej>dzdej:fd&Z)e j9d'ee d(d ddd ej:d ej:d ej:dej:dej:dej>d ej>dzdej:fd)Z*ee d(dddd ej>dzdej:fd*Z+e j9d+ee d,d d ej:d-ed.ed/edej>de,ej:ej:ff d0Z-e j9d1ee d2d d ej:d-ed.ed/edej>de,ej:ej:ff d3Z.ee d,dd ej:deded/edej>de,ej:ej:ff d4Z/ee d2dd ej:deded/edej>de,ej:ej:ff d5Z0d6Z1e j9d7ee d8d d ej:d9ej:d:ej:d;edededej>dej:fd<Z2ee d8dd ej:d9ej:d:ej:d;edededej>dej:fd=Z3e j9d>ee d?d ddd ej:d9ej:d:ej:dzd;edededej>d ej>dzdej:fd@Z4ee d?dddd ej:d9ej:d:ej:dzd;edededej>d ej>dzdej:fdAZ5e j9dBee dCd d ej:dej>de,ej:ej:ffdDZ6ee dCdd ej:dej>de,ej:ej:ffdEZ7e j9dFee dGdHd ej:dej>de,ej:ej:ffdIZ8e j9dJee dKd d ej:dej>de,ej:ej:ffdLZ9ee dKdd ej:dej>de,ej:ej:ffdMZ:dNZ;e j9dOee dPd d ej:d9ej:d:ej:dededej>f dQZ<ee dPdd ej:d9ej:d:ej:dededej>f dRZ=e j9dSee dTd ej|fd ej:d9ej:d:ej:dededej>dUej>fdVZ?ee dTdej|fd ej:d9ej:d:ej:dededej>dUej>fdWZ@e j9dXee dYd  dmd ej:d9ej:d:ej:dededej>f d[ZAee dYd dmd ej:d9ej:d:ej:dededej>f d\ZBe j9d]ee d^d dZej|fd_ej:d9ej:d:ej:dzdededej>d`edUej>fdaZCe j9dbGdcddejjZFee dedfd ej:d9ej:d:ej:d;edededej:fdgZGee dedd ej:d9ej:d:ej:d;edededej:fdhZHe j9diee djd d ej:dej>dej:fdkZIee djdd ej:dej>dej:fdlZJycc}wcc}w)nN)_unsqueeze_multiple)determine_qparamsvalidate_qmin_qmax)implLibraryquantized_decomposedDEFc|tvrtd|t|\}}||krtd|d|||kDrtd|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 ValueErrorAssertionError) quant_min quant_maxdtypequant_min_lower_boundquant_max_upper_bounds j/home/ubuntu/crypto_trading_bot/.venv/lib/python3.12/site-packages/torch/ao/quantization/fx/_decomposed.py_quant_min_max_bounds_checkrs ++.ug6773J53Q00(( &&;% Tensorquantize_per_tensorCompositeExplicitAutogradinputscale zero_pointrrrreturnc|jtjtjfvr|j tj }|jtj k7rt d|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|jdk7rtd|j|jdk7rtd|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 : numelr ritemr+s rquantize_per_tensor_tensorr5ps QLZM]M]M_L` a   {{}G  W       rc|jtjtjfvr|j tj }|j dk7rtd|j |j dk7rtd|j |jtj k7rtd|jtj||S)Nr/r0r1rr)) rrr r!r"r#r3r r*r+s rquantize_per_tensor_tensor_metar7s {{u}}enn55'QLZM]M]M_L` a   {{}G  W   {{emm#J5;;- X     E //rzquantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, Tensor quant_min, Tensor quant_max, ScalarType dtype) -> Tensorzquantize_per_tensor.tensor2cN|jdk7rtd|j|jdk7rtd|jt||j|j|j|j|Sr.r2r+s rquantize_per_tensor_tensor2r9s QLZM]M]M_L` a   {{}G  W       rc"t||||||SN)r7r+s r quantize_per_tensor_tensor2_metar<s# +    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|k7rtd|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: , but got ,Unsupported dtype in dequantize_per_tensor: )rr rr#r r"r rrrrrrr?s rr=r=szJ {{e-eWJu{{m L  MM  ''#j0E99GwOPPrcT|tj}tj||SNr))rr#r*rDs rdequantize_per_tensor_metarG&s&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 |jdk7rtd|j|jdk7rtd|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 r=r4rDs rdequantize_per_tensor_tensorrK<s(QLZM]M]M_L` a   {{}G  W   !    rc|tj}|jdk7rtd|j|jdk7rtd|j|j|k7rtd|d|j|t vrtj ||Std|)Nr/r0r1rArBr)rC)rr#r3r rr r*r rDs r!dequantize_per_tensor_tensor_metarMcsMM QLZM]M]M_L` a   {{}G  W   {{e-eWJu{{m L   ''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 R|jdk7rtd|j|jdk7rtd|jt||j|j|j|j||SrIrJrDs rdequantize_per_tensor_tensor2rOs(QLZM]M]M_L` a   {{}G  W   !    rc &t|||||||S)Nr>)rMrDs r"dequantize_per_tensor_tensor2_metarQs  - uj)Y rzrchoose_qparams.tensor(Tensor input, int quant_min, int quant_max, float eps, ScalarType dtype) -> (Tensor, Tensor)zchoose_qparams.tensorqminqmaxepsc |jtjtjtjfvrt d|j|t vr#t dt jd|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: $Expecting target dtype to be one of , but got: F)has_customized_qrange) rrr#r r!r r keysraminmaxrTensorrrRrSrTrmin_valmax_vals rchoose_qparams_tensorras" {{    QRWR]R]Q^ _   ++23J3O3O3Q2RR]^c]d e  tT"}}U+GW     cU# rz|choose_qparams_symmetric.tensor(Tensor input, int quant_min, int quant_max, float eps, ScalarType dtype) -> (Tensor, Tensor)zchoose_qparams_symmetric.tensorc |jtjtjtjfvrt d|j|t vr#t dt jd|t||tj|\}}t|||||tj|gdtjS)rVrWrXrYF)rZqscheme) rrr#r r!r r r[rr\rr]per_tensor_symmetricr^s rchoose_qparams_symmetric_tensorres* {{    QRWR]R]Q^ _   ++23J3O3O3Q2RR]^c]d e  tT"}}U+GW     cU#**  rc|jtjtjtjfvrt d|j||k\rt d|d|tj dtj|jtj dtj|jfS)NrWzCExpecting quant_min to be smaller than quant_max but received min: z max: r/rdevice) rrr#r r!r emptydoublerhint64rrrrTrs rchoose_qparams_tensor_metarm(s {{    QRWR]R]Q^ _  IQR[Q\\bclbm n   ;;q U\\ BEKK U\\E rctjdtj|jtjdtj|jfS)Nr/rg)rrirjrhrkrls r$choose_qparams_symmetric_tensor_metaro=sA ;;q U\\ BEKK U\\E rctt|j}d||<||d<|jt |}||fS)Nr)listrangedimpermutetuple)xaxis new_axis_listys r_permute_to_axis_zerorzGsHquuw(MM$M! % &'A m rzquantize_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, int quant_min, int quant_max, ScalarType dtype) -> Tensorquantize_per_channelscales zero_pointsrwc|jtjtjfvr|j tj }|jtj k7rt d|j||jk\rt d|jt|||t||\}}dg|jz}|jd|d<|j|}|j|}tjtj|d|z z|z||} | jt|} | j |S)atAffine per channel quantization for the Tensor using the same quantization parameters for each channel/axis to map from floating point to quantized values Args: input (torch.Tensor): original float32 or bfloat16 Tensor scales (torch.Tensor): a list of scale quantization parameter for affine quantization, one per channel zero_point (torch.Tensor): a list of zero_point quantization parameter for affine quantization, one per channel quant_min (int): minimum quantized value for output Tensor quant_max (int): maximum quantized value for output Tensor dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor Returns: Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters are not stored in the Tensor, we are storing them in function arguments instead rExpecting axis to be < r/rr)rrr r!r"r#r rsrrzshapeviewr$r%rtru) rr|r}rwrrrpermute_axis_list new_shaperesouts rr{r{Us26 {{u}}enn55' {{emm#J5;;- X   uyy{6uyy{mDEE 9e<4UDAE eiik!I<<?IaL [[ #F""9-K ++ ES6\*+k99i C ++e-. /C 66%=rc|jtjtjfvr|j tj }|jtj k7rt d|j||jk\rt d|jt|||tj||S)Nrrr)) rrr r!r"r#r rsrr*)rr|r}rwrrrs rquantize_per_channel_metars {{u}}enn55' {{emm#J5;;- X   uyy{6uyy{mDEE 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|k7rtd|d|j|tj}||j k\rtd|j t |||t ||\}}dg|j z} |jd| 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 rA, but got dtype: rr/r) rr rr#rsrrzrrr"rtru) rr|r}rwrrrr?rrrrs rrrs P {{e-eW4Eekk] S  MM  uyy{6uyy{mDEE 9e<4UDAE eiik!I<<?IaL [[ #F{'' 22f<fn && C ++e-. /C Jrc |j|k7rtd|d|j|tj}||j k\rtd|j t |||tj ||S)NzExpecting input to have dtype rrr))rr rr#rsrr*)rr|r}rwrrrr?s rdequantize_per_channel_metars {{e,UG3DU[[M R  MM  uyy{6uyy{mDEE 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 Trskeepdimr/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/rgrqrrrirjrhrkrrsizes 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#rTrr$wherer%r"float64rk)rrrRrSr_r` min_val_neg max_val_posrTr descaled_min descaled_maxzero_point_from_min_errorzero_point_from_max_errorrs rrrAsC,JD$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 rrrvs 5UE BBrct|jdddgz}tj|tj|j tj|tj |j fSrrrs r(choose_qparams_per_token_asymmetric_metarrrc*tjt|jdd}||j k7rt d|d|j||j k7rt d|d|jy)Nrz num_tokens: z scales: z zero_points: )mathprodrqrr3r )rr|r} num_tokenss r!_per_token_quant_qparam_dim_checkrs4 -cr23JV\\^#|J 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 rrrs^6 9e<%eV[A #, [   y) $ E   LrcJt|||tj||SrFrrr*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||SrFrrs 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_groupc|dkr td||jdkDr!|jddk(r|jd}|jd|zdk7r td|jdk7r td|jd|}t j |j dk7r td|jdd}|jdd}|jd |z j|jj||j|j|}|S) Nr/group_size must be > 1rr/input.shape[-1] must be divisible by group_sizerinput must be 2-dimensionalzto_quant must not contain NaNsr) r rrsreshaperisnansumrrr%clamp_r" reshape_as) rr|r}rrr group_sizeto_quant input_int8s rrrs(Q566EKKO# R(8A(=[[_  {{2#q(NOO yy{a:;;}}R,H {{8  "a'=>> ^^B "F%%b!,K  S6\" [    9 % E E   rc2|dkr td||jdkDr!|jddk(r|jd}|jd|zdk7r td|jdk7r tdtj||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/rrrrrrr))r rrsrr*)rr|r}rrrrs rquantize_per_channel_group_metar>s8Q566EKKO# R(8A(=[[_  {{2#q(NOO yy{a:;;   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_int8rcV|dkr td||jdkDr!|jddk(r|jd}|jd|zdk7r td|jdk7r td|jd|}|jdd}||jdd} n0t j gtj |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/rrrz0w_int8.shape[-1] must be divisible by group_sizerzw_int8 must be 2-dimensionalrg) r rrsrrzerosint32rhsubrrr") rr|r}rrrrrw_int8_groupedzpw_dqs rrrms DQ566FLL$$b)9Q)>\\"%  ||B*$)OPP zz|q;<<^^B 3N ^^B "F  Q ' [[5;;v}} E   b ! % %f - 8 8 @ C CL QD Krzyfake_quant_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, int quant_min, int quant_max) -> Tensorc,eZdZedZedZy)FakeQuantPerChannelc,|jtjk7r|jtj}|jtjk7r|jtj}|jtjk7rt d|j||j k\rt d|j tt|tt|dz|jz}t||}t||} tj|d|z z| z} tj| ||| z |z} tj| |k\| |k} |j| | S)Nrrr/r)rrr#r"rr rsrqrrndimrr%r$ logical_andsave_for_backward) ctxrr|r}rwrrbroadcast_dimsunsqueeze_scalesunsqueeze_zero_pointstemprmasks rforwardzFakeQuantPerChannel.forwardsO <<5== (YYu}}-F    +%..5K ;;%-- ' Nu{{m\  599;  #:599;-!HI IeDk*T%q%**2M-NN.v~F 3K P{{5C*:$:; Tensorzconvert_element_type.no_fusecjtjjjj ||Sr;)ropsprimsconvert_element_typedefaultrs rrrs% 99?? / / 7 7u EErc0tj||SrFrrs rconvert_element_type_metar s   E //r)r)Krr torch._refsrtorch.ao.quantization.utilsrr torch.libraryrrquantized_decomposed_libuint8ruint16int16r_INTEGER_DTYPES float8_e5m2 float8_e4m3fn _FLOAT_DTYPESiinforrr updateintrrdefiner]rrrr,r5r7r9r<r=rGrKrMrOrQrurarermrorzr{rrrrrrrrrrrr#rrrrrautogradFunctionrrrrr )ks0rrs +M' ##95A;; ELL%++u{{S""E$7$78 :I45A  AKEKKN..//DQRqQ[U[[^   #kekk!n&8&8"9::R  $@  57RS" <<" "" "  " ;; " \\"T"J 5v>0 <<0 00 0  0 ;; 0 \\0?0"@ :  =vF << << ||  ||  ;;  \\G,_  79TU%)0Q <<0Q 0Q0Q 0Q  0Q ;; 0Q{{T!0Q \\0QV0Qf 7@%) 4 << 4 << 4  4 4  4 ;; 4{{T! 4 \\ 4A 4_ "%) << <<     ;; {{T! \\  D >G%)Q <<Q <<Q Q Q  Q ;; Q{{T!Q \\QHQ>e #%) << << ||  ||  ;; {{T! \\  D ?H%) {{T!  \\ I 7  79TU( <<("(*-(49(BG++( 5<< %&(V(V7 % ( <<("(*-(49(BG++( 5<< %&(  (V 7@ <<$'47>CLQKK 5<< %&A( A6J <<$'47>CLQKK 5<< %&K@  68ST. <<. LL..  .  .  . ;;. \\.U.b 6?0 <<0 LL00  0  0  0 ;;0 \\0@02_  8:UV%)= <<= LL=$=  =  =  = ;;={{T!= \\=W=@ 8&A%)4 <<4 LL4$4  4  4  4 ;;4{{T!4 \\4B4.R   <<  ;;  5<< %&   F   << ;; 5<< %&  c / (? <<(? ;;(? 5<< %&(?  (?V] ) C <<C ;;C 5<< %&C  C)   << ;; 5<< %&   @  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'Ss=Av1A v6