h'lSrSSKrSSKJs Jr SSKJrJr /SQr SSjr "SS\RR5r "S S \ \RR5r\RR\\RRR R"\0rSSS jjrSSS jjrS rg)a( Testing out accuracy-only implementation of SmoothQuant (https://arxiv.org/pdf/2211.10438.pdf) Note: this is an application of input-weight equalization, with the addition that the multiplication by scale is fused into the preceding layer, specifically for relevant parts of transformer blocks. N)$_quant_int8_dynamic_per_token_linear dynamically_quantize_per_channel) get_scaleSmoothFakeDynQuantMixin$SmoothFakeDynamicallyQuantizedLinear!swap_linear_with_smooth_fq_linearsmooth_fq_linear_to_inferenceset_smooth_fq_attributec[R"X5n[R"USU- 5nX4- nURS5$)a Calculate the scale based on abs(max(X)), abs(max(W)), and alpha. Args: X_absmax (torch.Tensor): Absolute maximum values of the input tensor X. W_absmax (torch.Tensor): Absolute maximum values of the weight tensor W. alpha (float, optional): Scaling factor. Defaults to 0.5. Returns: torch.Tensor: The calculated scale of dimension `k` if X is of dimension `b*n*k` and W is of dimension `k*m`. g?)torchpowreshape)X_absmaxW_absmaxalphaX_powW_powdivs Z/home/james-whalen/.local/lib/python3.13/site-packages/torchao/quantization/smoothquant.pyrr!s; IIh &E IIhe ,E -C ;;r?c8\rSrSrSrSrSrSrSrSr Sr g ) r3clSUlSUlURSS5 XlSUlSUlg)NT smooth_scaleF) calibratingx_running_abs_maxregister_bufferrdebug_skip_scalingstore_w_int_repr_t)selfrs rinit_smoothquant_variables2SmoothFakeDynQuantMixin.init_smoothquant_variables4s9!% ^T2 "'#(rc[[[UR5S- 55n[R "[R "U5US9nURcX0lg[R"X0R5Ulg)Nrdim) tuplerangelenshaperamaxabsrmax)r"Xall_dims_except_last cur_abs_maxs rupdate_x_running_abs_max0SmoothFakeDynQuantMixin.update_x_running_abs_maxHsa$U3qwwr!rr9r;r6)r"r=_W_zpss r fold_weight#SmoothFakeDynQuantMixin.fold_weightmsq-1,G,G,I) M6  " "  z/C/CAq/I/T/T/V W K  z/D/D/F G Krc[5e)z Sets `self.x_running_abs_max` to a value which will lead to smooth scale of all ones if `alpha=0.5`, to enable performance benchmarking without calibration. rDrFs rset_debug_x_absmax*SmoothFakeDynQuantMixin.set_debug_x_absmaxys "##r)r>rrr r!rN) __name__ __module__ __qualname____firstlineno__r#r2r@rGrKrN__static_attributes__rrrr3s!((T+2$ $rrcR^\rSrSrSrU4SjrSr\S Sj5rSr Sr Sr U=r $) rz This is a replacement for `torch.nn.Linear` which implements dynamic per-token activation quantization and dynamic per-channel weight quantization based on Smoothquant scaling. cj>URS5n[TU]"U0UD6 URU5 g)Nr)popsuper__init__r#)r"argskwargsr __class__s rr[-SmoothFakeDynamicallyQuantizedLinear.__init__s1 7# $)&) ''.rcUR(a>URU5 [R"XRUR 5nU$UR (dXR- nUR(a UROURR5n[XURUR UR5nU$rC)rr2Flinearr6biasr rr!r=trr>dtype)r"r/r\r]Y W_int_repr_ts rforward,SmoothFakeDynamicallyQuantizedLinear.forwards     ) )! ,KK3A**)))#'#:#:@Q@Q@S 5 177Arc&Sup4U"X4URSLUS9nURUlURUlURUlURUl[ UR 55R nURU5 U$)zv Converts a `mod` of class `torch.nn.Linear` to the smooth fake quantized version of it. Note: requires calibration. )rkN)rcr)rc in_features out_featuresr6next parametersdeviceto)clsmodrfake_in_featuresfake_out_featuresnew_mod device_to_uses r from_float/SmoothFakeDynamicallyQuantizedLinear.from_floats/3+ chhd6JRW "oo"//xx S^^-.55  =!rc 8URcS5eSUl[UR[R"[R "UR RSS55SS9RURS9Ul UR5 g)zT Calculates the smoothquant scale based on calibration in preparation for inference Nzno calibration data foundFrrr&r) rrrrr.r-r6r9valuesrrrKrFs rrG1SmoothFakeDynamicallyQuantizedLinear.to_inferences} %%1N3NN1 %  " " IIeii 5 5a ;J"--e-AI E + -eG S-rcUR5HZup#[U[[R 555(dM3U(aUR 5 UR 5 M\ g)a Prepares the model for inference by calculating the smoothquant scale for each SmoothFakeDynamicallyQuantizedLinear layer. Args: model (torch.nn.Module): The model containing SmoothFakeDynamicallyQuantizedLinear layers. debug_skip_calibration (bool, optional): If True, sets the running maximum of activations to a debug value for performance benchmarking. Defaults to False. Returns: None N) named_modules isinstancer(rr|rNrG)rdebug_skip_calibration_rss rr r sP%%' c5!9!@!@!BC D D%&&(     (rcUR5HQup4[U[[R 555(dM3[ XA5(dME[ XAU5 MS grC)rrr(rr|hasattrr)rattribute_namenew_attribute_valrrss rr r sI%%' c5!9!@!@!BC D Ds++->?(rr)Nrr)returnN)F)rrtorch.nn.functionalnn functionalrautilsrr__all__rModulerLinearrmodulesrbNonDynamicallyQuantizableLinearrr r r rUrrrs   $L$ehhooL$^B*+BEHHOOB*T HHOO9 HH;;=a25T T@*@r