+ NV-j^RIt^RIHtHt^RIHt^RIHt^RI H t H t !RR]4t !RR]4t !R R ]4tR#) N)AnyOptional)Module)QQLinearQuantizedLinearcTaa]tRt^ toRtV3RlV3RlltV3RlRltRtVtV;t #)IdentityzA placeholder identity operator that is argument-insensitive. Args: args: any argument (unused) kwargs: any keyword argument (unused) c*<V^8dQhRS[RS[RR/#)argskwargsreturnN)r)format __classdict__s"e/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/mlx/nn/layers/linear.py __annotate__Identity.__annotate__s"cSTc$<\SV`4R#N)super__init__)selfr r __class__s&*,rrIdentity.__init__s rcN<V^8dQhRS[PRS[P/#r xrmxarray)rrs"rrrs#"((rxxrcV#rrrs&&r__call__Identity.__call__srr#) __name__ __module__ __qualname____firstlineno____doc__rr%__static_attributes____classdictcell__ __classcell__rrs@@rr r s#rr caa]tRt^toRtR V3RlV3RllltV3RlRltV3RlRltR V3RlR lltR t Vt V;t #) LinearajApplies an affine transformation to the input. Concretely: .. math:: y = x W^\top + b where: where :math:`W` has shape ``[output_dims, input_dims]`` and :math:`b` has shape ``[output_dims]``. The values are initialized from the uniform distribution :math:`\mathcal{U}(-{k}, {k})`, where :math:`k = \frac{1}{\sqrt{D_i}}` and :math:`D_i` is equal to ``input_dims``. Args: input_dims (int): The dimensionality of the input features output_dims (int): The dimensionality of the output features bias (bool, optional): If set to ``False`` then the layer will not use a bias. Default is ``True``. c0<V^8dQhRS[RS[RS[RR/#)r input_dims output_dimsbiasrNintbool)rrs"rrLinear.__annotate__0s*  3 S  PT rc<\SV`4\P!RV, 4p\P P V)VW!3R7VnV'd,\P P V)VV3R7VnR#R#g?)lowhighshapeN rrmathsqrtr randomuniformweightr5)rr3r4r5scalers&&&& rrLinear.__init__0s|  # *+ii''+(   ))F"n*DI rc <V^8dQhRS[/#r rstr)rrs"rrr9?sooSorcRVPP^, RVPP^, RRV9 2#)z input_dims=, output_dims=, bias=r5rDr>)rs&r _extra_reprLinear._extra_repr?sET[[..q12.ARARSTAU@VV]^dhl^l]mnnrcN<V^8dQhRS[PRS[P/#rr)rrs"rrr9Bs#"((rxxrcRV9d2\P!VR,WR,P4pV#WR,P,pV#r5rD)r addmmTr$s&&rr%Linear.__call__BsG T>fqx.*:*:;AN$$$ArcR<V^8dQhRS[S[,RS[S[,RS[RS[/#)r group_sizebitsmodequantize_input)rr7rJr8)rrs"rrr9IsA#I#ISM#Ism#I #I  #Irc V'd.VR9d\RV R24h\P!WW#4#\P!WW#4#)aKReturn a quantized approximation of this layer. If ``quantize_input`` is ``False``, returns a :obj:`QuantizedLinear` (weights are quantized). If ``quantize_input`` is ``True``, returns a :obj:`QQLinear` (weights and activations are quantized). Args: group_size (Optional[int]): The quantization group size (see :func:`mlx.core.quantize`). Default: ``None``. bits (Optional[int]): The number of bits per parameter (see :func:`mlx.core.quantize`). Default: ``None``. mode (str): The quantization method to use (see :func:`mlx.core.quantize`). Default: ``"affine"``. quantize_input (bool): Whether to quantize input. Default: ``False``. Returns: QuantizedLinear or QQLinear: A quantized version of this layer. Notes: Quantized input is only supported for ``"nvfp4"`` and ``"mxfp8"`` modes. zLQuantized activations are only supported for 'nvfp4' and 'mxfp8' modes, got .)nvfp4mxfp8) ValueErrorr from_linearr)rrXrYrZr[s&&&&&r to_quantizedLinear.to_quantizedIsT: -- bcgbhhij''$E E**4THHrrST)NNaffineF) r'r(r)r*r+rrOr%rbr,r-r.r/s@@rr1r1s<*  oo#I#I#Irr1cjaa]tRt^otoRtR V3RlV3RllltV3RlRltV3RlRltRtVt V;t #) BilinearaApplies a bilinear transformation to the inputs. Concretely: .. math:: y_i = x_1^\top W_i x_2 + b_i where: :math:`W` has shape ``[output_dims, input1_dims, input2_dims]``, :math:`b` has shape ``[output_dims ]``, and :math:`i` indexes the output dimension. The values are initialized from the uniform distribution :math:`\mathcal{U}(-{k}, {k})`, where :math:`k = \frac{1}{\sqrt{D_1}}` and :math:`D_1` is ``input1_dims``. Args: input1_dims (int): The dimensionality of the input1 features input2_dims (int): The dimensionality of the input2 features output_dims (int): The dimensionality of the output features bias (bool, optional): If set to ``False`` then the layer will not use a bias. Default is ``True``. c 6<V^8dQhRS[RS[RS[RS[RR/#)r input1_dims input2_dimsr4r5rNr6)rrs"rrBilinear.__annotate__s5-0?BJN rc<\SV`4\P!RV, 4p\P P V)VW2V3R7VnV'd,\P P V)VV3R7VnR#R#r;r?)rrirjr4r5rErs&&&&& rrBilinear.__init__s  # +,ii''[9(   ))F"n*DI rc <V^8dQhRS[/#rHrI)rrs"rrrks  S rc VVPPwrpRV RV RV RRV9 2#)z input1_dims=z, input2_dims=rLrMr5rN)routin2in1s& rrOBilinear._extra_reprsB )) #3%~cU.FdN# % rch<V^8dQhRS[PRS[PRS[P/#)r x1x2rr)rrs"rrrks.288bhhrcVPPwr4pVPRRpVPRV4pVPR^V4pVPPW4,V4pWP,pVPRW44P RR4pW(,pVP ^4pVP!.VOVN5!pRV9dWP ,pV#)Nr5)rDr>reshaperUswapaxessqueezer5) rrurvrprqrrxshapewys &&& rr%Bilinear.__call__s )) ##2 ZZC  ZZAs # KK   3 / H IIb# # , ,R 4 F IIaL II #v #s # T>II ArrSrd) r'r(r)r*r+rrOr%r,r-r.r/s@@rrgrgos-."  rrg)r@typingrrmlx.corecorer mlx.nn.layers.basermlx.nn.layers.quantizedrrr r1rgr#rrrsA %= v RIVRIjEvEr