tjX0ddlZddlmZddlZddlmZddlmZmZddl m Z m Z ddl m Z ddlmZgd ZGd d eZGd d eZGddeZGddeZGdde eZdS)N)Any)Tensor) functionalinit) ParameterUninitializedParameter)LazyModuleMixin)Module)BilinearIdentity LazyLinearLinearc@eZdZdZdededdffd ZdedefdZxZS) r aA placeholder identity operator that is argument-insensitive. Args: args: any argument (unused) kwargs: any keyword argument (unused) Shape: - Input: :math:`(*)`, where :math:`*` means any number of dimensions. - Output: :math:`(*)`, same shape as the input. Examples:: >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 20]) argskwargsreturnNcHtdS)Nsuper__init__)selfrr __class__s U/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/nn/modules/linear.pyrzIdentity.__init__+s inputc|Sz( Runs the forward pass. rrs rforwardzIdentity.forward.s  r) __name__ __module__ __qualname____doc__rrrr! __classcell__rs@rr r s~(cSTVrr c eZdZUdZddgZeed<eed<eed< ddedededdffd Z dd Z d edefd Z de fd Z xZS)raCApplies an affine linear transformation to the incoming data: :math:`y = xA^T + b`. This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. Args: in_features: size of each input sample out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input: :math:`(*, H_\text{in})` where :math:`*` means any number of dimensions including none and :math:`H_\text{in} = \text{in\_features}`. - Output: :math:`(*, H_\text{out})` where all but the last dimension are the same shape as the input and :math:`H_\text{out} = \text{out\_features}`. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` Examples:: >>> m = nn.Linear(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) in_features out_featuresweightTNbiasrc\||d}t||_||_t t j||ffi||_|r%t t j|fi||_n| dd| dSNdevicedtyper,) rrr)r*rtorchemptyr+r,register_parameterreset_parameters)rr)r*r,r0r1factory_kwargsrs rrzLinear.__init__`s%+U;; &( K{3 F F~ F F     2!%+l"M"Mn"M"MNNDII  # #FD 1 1 1 rc tj|jtjd|jYtj|j\}}|dkrdtj|z nd}tj|j| |dSdS)W Resets parameters based on their initialization used in ``__init__``. )aNrr )rkaiming_uniform_r+mathsqrtr,_calculate_fan_in_and_fan_outuniform_)rfan_in_bounds rr5zLinear.reset_parametersus dkTYq\\:::: 9 :4;GGIFA-3aZZA &))))QE M$)eVU 3 3 3 3 3 ! rrcBtj||j|jSr)Flinearr+r,r s rr!zLinear.forwardsxt{DI666rc:d|jd|jd|jduS)@ Return the extra representation of the module. z in_features=, out_features=, bias=N)r)r*r,rs r extra_reprzLinear.extra_reprs3qd.ppt?PppY]YbjnYnppprTNNrNr"r#r$r% __constants__int__annotations__rboolrr5r!strrKr&r's@rrr5s##J#N3M NNN               * 4 4 4 47V77777 qCqqqqqqqqrrc 8eZdZ ddedededdffd ZxZS) NonDynamicallyQuantizableLinearTNr)r*r,rcTt|||||dS)N)r,r0r1r)rr)r*r,r0r1rs rrz(NonDynamicallyQuantizableLinear.__init__s;  Du      rrL)r"r#r$rPrRrr&r's@rrUrUss                      rrUc eZdZUdZgdZeed<eed<eed<eed< ddededed ed df fd Z dd Z d eded efdZ d e fdZ xZS)r aApplies a bilinear transformation to the incoming data: :math:`y = x_1^T A x_2 + b`. Args: in1_features: size of each first input sample, must be > 0 in2_features: size of each second input sample, must be > 0 out_features: size of each output sample, must be > 0 bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input1: :math:`(*, H_\text{in1})` where :math:`H_\text{in1}=\text{in1\_features}` and :math:`*` means any number of additional dimensions including none. All but the last dimension of the inputs should be the same. - Input2: :math:`(*, H_\text{in2})` where :math:`H_\text{in2}=\text{in2\_features}`. - Output: :math:`(*, H_\text{out})` where :math:`H_\text{out}=\text{out\_features}` and all but the last dimension are the same shape as the input. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` Examples:: >>> m = nn.Bilinear(20, 30, 40) >>> input1 = torch.randn(128, 20) >>> input2 = torch.randn(128, 30) >>> output = m(input1, input2) >>> print(output.size()) torch.Size([128, 40]) ) in1_features in2_featuresr*rXrYr*r+TNr,rcl||d}t||_||_||_t t j|||ffi||_|r%t t j|fi||_ n| dd| dSr.) rrrXrYr*rr2r3r+r,r4r5) rrXrYr*r,r0r1r6rs rrzBilinear.__init__s%+U;; ((( K|\B U Un U U     2!%+l"M"Mn"M"MNNDII  # #FD 1 1 1 rc,|jdkrtd|jddtj|jdz }t j|j| ||jt j|j| |dSdS)r8rz0in1_features must be > 0, but got (in1_features=)r N) rX ValueErrorr<r=r+sizerr?r,)rrBs rr5zBilinear.reset_parameterss   ! !W4CTWWW DIdk..q11222 dkE65111 9 M$)eVU 3 3 3 3 3 ! rinput1input2cDtj|||j|jSr)rDbilinearr+r,)rr_r`s rr!zBilinear.forwardsz&&$+tyAAArc Jd|jd|jd|jd|jduS)rGz in1_features=z, in2_features=rHrIN)rXrYr*r,rJs rrKzBilinear.extra_reprsW  ND- N Nd>O N N - N N6:it6K N N rrLrMrNr's@rr r s$##JEDDM NNN               0 4 4 4 4BfBfBBBBB  C        rr cbeZdZUdZeZeed<eed< d dede ddffd Z d fd Z d d Z xZ S) raA :class:`torch.nn.Linear` module where `in_features` is inferred. In this module, the `weight` and `bias` are of :class:`torch.nn.UninitializedParameter` class. They will be initialized after the first call to ``forward`` is done and the module will become a regular :class:`torch.nn.Linear` module. The ``in_features`` argument of the :class:`Linear` is inferred from the ``input.shape[-1]``. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` r+r,TNr*rc||d}tdddtdi||_||_|rtdi||_dSdS)Nr/rFr)rrrr+r*r,)rr*r,r0r1r6rs rrzLazyLinear.__init__%s{%+U;; Au%%%,>>~>> (  A.@@@@DIII A Arc|s-|jdkr$tdSdSdS)r8rN)has_uninitialized_paramsr)rr5)rrs rr5zLazyLinear.reset_parameters4sR ,,.. '43Cq3H3H GG $ $ & & & & & ' '3H3HrcJ|rtj5|jd|_|j|j|jf|j |j|jf| dddn #1swxYwY|jdkre|jd|jjdkr0td|jdd|jjd|jd|_dSdS)zW Infers ``in_features`` based on ``input`` and initializes parameters. Nrz%The in_features inferred from input: z/ is not equal to in_features from self.weight: ) rgr2no_gradshaper)r+ materializer*r,r5AssertionErrorr s rinitialize_parametersz LazyLinear.initialize_parameters<sg  ( ( * * ( ( (#(;r?  ''):D???%%'''  ( ( ( ( ( ( ( ( ( ( ( ( ( ( (  q {2$+"3B"777$/EKO//{(,// %{2D    ! sA4B((B,/B,rLrM)r"r#r$r%r cls_to_becomerrQrPrRrr5rnr&r's@rrrs8M """"    HL A A A'+ A  A A A A A A''''''////////rr)r<typingrr2rtorch.nnrrDrtorch.nn.parameterrrlazyr moduler __all__r rrUr rrrrrvs  ********@@@@@@@@!!!!!!   v>WqWqWqWqWqVWqWqWq~      f    ^ ^ ^ ^ ^ v^ ^ ^ BL/L/L/L/L/&L/L/L/L/L/r