[^(hm:ddlZddlmZmZddlZddlmZmZddlmZ m Z ddl m Z ddl mZddlmZgd ZGd d eZGd d eZeeeeefZGddeZGddeZGddeZy)N)OptionalUnion)SizeTensor) functionalinit) Parameter) CrossMapLRN2d)Module)LocalResponseNormr LayerNorm GroupNormRMSNormc eZdZUdZgdZeed<eed<eed<eed< d dededededdf fd Zd e de fd Z d Z xZ S)r aApplies local response normalization over an input signal. The input signal is composed of several input planes, where channels occupy the second dimension. Applies normalization across channels. .. math:: b_{c} = a_{c}\left(k + \frac{\alpha}{n} \sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta} Args: size: amount of neighbouring channels used for normalization alpha: multiplicative factor. Default: 0.0001 beta: exponent. Default: 0.75 k: additive factor. Default: 1 Shape: - Input: :math:`(N, C, *)` - Output: :math:`(N, C, *)` (same shape as input) Examples:: >>> lrn = nn.LocalResponseNorm(2) >>> signal_2d = torch.randn(32, 5, 24, 24) >>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7) >>> output_2d = lrn(signal_2d) >>> output_4d = lrn(signal_4d) )sizealphabetakrrrrreturnNcZt|||_||_||_||_yNsuper__init__rrrrselfrrrr __class__s \/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/nn/modules/normalization.pyrzLocalResponseNorm.__init__5,    inputctj||j|j|j|j Sr)Flocal_response_normrrrrrr"s rforwardzLocalResponseNorm.forward>s+$$UDIItzz499dffUUr!c:djdi|jSNz){size}, alpha={alpha}, beta={beta}, k={k}format__dict__rs r extra_reprzLocalResponseNorm.extra_reprAA:AARDMMRRr!)-C6??g?) __name__ __module__ __qualname____doc__ __constants__int__annotations__floatrrr'r/ __classcell__rs@rr r ss:3M I L K HNQ %49EJ VVVVSr!r c ~eZdZUeed<eed<eed<eed< d dededededdf fd Zdedefd Zde fd Z xZ S) r rrrrrNcZt|||_||_||_||_yrrrs rrzCrossMapLRN2d.__init__Kr r!r"ctj||j|j|j|j Sr)_cross_map_lrn2dapplyrrrrr&s rr'zCrossMapLRN2d.forwardTs+%%eTYY DIItvvVVr!c:djdi|jSr)r+r.s rr/zCrossMapLRN2d.extra_reprWr0r!)r1r2r ) r3r4r5r8r9r:rrr'strr/r;r<s@rr r Eso I L K HNO %49EJ WVWWSCSr!r c eZdZUdZgdZeedfed<eed<e ed< dde dede de d df fd Z dd Z d e d e fd Zd efdZxZS)ra Applies Layer Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Layer Normalization `__ .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated over the last `D` dimensions, where `D` is the dimension of :attr:`normalized_shape`. For example, if :attr:`normalized_shape` is ``(3, 5)`` (a 2-dimensional shape), the mean and standard-deviation are computed over the last 2 dimensions of the input (i.e. ``input.mean((-2, -1))``). :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``. The variance is calculated via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. .. note:: Unlike Batch Normalization and Instance Normalization, which applies scalar scale and bias for each entire channel/plane with the :attr:`affine` option, Layer Normalization applies per-element scale and bias with :attr:`elementwise_affine`. This layer uses statistics computed from input data in both training and evaluation modes. Args: normalized_shape (int or list or torch.Size): input shape from an expected input of size .. math:: [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1] \times \ldots \times \text{normalized\_shape}[-1]] If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size. eps: a value added to the denominator for numerical stability. Default: 1e-5 elementwise_affine: a boolean value that when set to ``True``, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases). Default: ``True``. bias: If set to ``False``, the layer will not learn an additive bias (only relevant if :attr:`elementwise_affine` is ``True``). Default: ``True``. Attributes: weight: the learnable weights of the module of shape :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. The values are initialized to 1. bias: the learnable bias of the module of shape :math:`\text{normalized\_shape}` when :attr:`elementwise_affine` is set to ``True``. The values are initialized to 0. Shape: - Input: :math:`(N, *)` - Output: :math:`(N, *)` (same shape as input) Examples:: >>> # NLP Example >>> batch, sentence_length, embedding_dim = 20, 5, 10 >>> embedding = torch.randn(batch, sentence_length, embedding_dim) >>> layer_norm = nn.LayerNorm(embedding_dim) >>> # Activate module >>> layer_norm(embedding) >>> >>> # Image Example >>> N, C, H, W = 20, 5, 10, 10 >>> input = torch.randn(N, C, H, W) >>> # Normalize over the last three dimensions (i.e. the channel and spatial dimensions) >>> # as shown in the image below >>> layer_norm = nn.LayerNorm([C, H, W]) >>> output = layer_norm(input) .. image:: ../_static/img/nn/layer_norm.jpg :scale: 50 % normalized_shapeepselementwise_affine.rFrGrHNbiasrc||d}t|t|tjr|f}t ||_||_||_|jrrttj|j fi||_ |r/ttj|j fi||_ n7|jddn$|jdd|jdd|jy)NdevicedtyperIweight)rr isinstancenumbersIntegraltuplerFrGrHr torchemptyrNrIregister_parameterreset_parameters) rrFrGrHrIrLrMfactory_kwargsrs rrzLayerNorm.__init__s%+U;  &(8(8 9 02  %&6 7"4  " "# D11D^DDK%KK 5 5HH ''5  # #Hd 3  # #FD 1 r!c|jrLtj|j|j tj |jyyyr)rHrones_rNrIzeros_r.s rrVzLayerNorm.reset_parameterss?  " " JJt{{ #yy$ DII&% #r!r"ctj||j|j|j|j Sr)r$ layer_normrFrNrIrGr&s rr'zLayerNorm.forwards0|| 4(($++tyy$((  r!c:djdi|jS)NF{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}r*r+r.s rr/zLayerNorm.extra_reprs) = 66TTNNrN)r3r4r5r6r7rRr8r9r:bool_shape_trrVrr'rCr/r;r<s@rrr^sKZFMCHo% J #' "  !      B'  V   C r!rc eZdZUdZgdZeed<eed<eed<eed< ddededededdf fd Z dd Z d e de fd Z de fd ZxZS)raApplies Group Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Group Normalization `__ .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The input channels are separated into :attr:`num_groups` groups, each containing ``num_channels / num_groups`` channels. :attr:`num_channels` must be divisible by :attr:`num_groups`. The mean and standard-deviation are calculated separately over the each group. :math:`\gamma` and :math:`\beta` are learnable per-channel affine transform parameter vectors of size :attr:`num_channels` if :attr:`affine` is ``True``. The variance is calculated via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. This layer uses statistics computed from input data in both training and evaluation modes. Args: num_groups (int): number of groups to separate the channels into num_channels (int): number of channels expected in input eps: a value added to the denominator for numerical stability. Default: 1e-5 affine: a boolean value that when set to ``True``, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: ``True``. Shape: - Input: :math:`(N, C, *)` where :math:`C=\text{num\_channels}` - Output: :math:`(N, C, *)` (same shape as input) Examples:: >>> input = torch.randn(20, 6, 10, 10) >>> # Separate 6 channels into 3 groups >>> m = nn.GroupNorm(3, 6) >>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm) >>> m = nn.GroupNorm(6, 6) >>> # Put all 6 channels into a single group (equivalent with LayerNorm) >>> m = nn.GroupNorm(1, 6) >>> # Activating the module >>> output = m(input) ) num_groups num_channelsrGaffinerdrerGrfNrc||d}t|||zdk7r td||_||_||_||_|j rIttj|fi||_ ttj|fi||_ n$|jdd|jdd|jy)NrKrz,num_channels must be divisible by num_groupsrNrI)rr ValueErrorrdrerGrfr rSrTrNrIrUrV) rrdrerGrfrLrMrWrs rrzGroupNorm.__init__s%+U;  * $ )KL L$( ;;#EKK $O$OPDK!%++l"Mn"MNDI  # #Hd 3  # #FD 1 r!c|jr?tj|jtj|j yyr)rfrrYrNrZrIr.s rrVzGroupNorm.reset_parameters3s. ;; JJt{{ # KK " r!r"ctj||j|j|j|j Sr)r$ group_normrdrNrIrGr&s rr'zGroupNorm.forward8s)||E4??DKKDHHUUr!c:djdi|jS)Nz8{num_groups}, {num_channels}, eps={eps}, affine={affine}r*r+r.s rr/zGroupNorm.extra_repr;s$PIPP mm  r!)r_TNNr`)r3r4r5r6r7r8r9r:rarrVrr'rCr/r;r<s@rrrs+ZDMO J L          6# VVVV C r!rc eZdZUdZgdZeedfed<ee ed<e ed< dde dee de ddffd Z dd Z d ejdejfd Zdefd ZxZS)raApplies Root Mean Square Layer Normalization over a mini-batch of inputs. This layer implements the operation as described in the paper `Root Mean Square Layer Normalization `__ .. math:: y_i = \frac{x_i}{\mathrm{RMS}(x)} * \gamma_i, \quad \text{where} \quad \text{RMS}(x) = \sqrt{\epsilon + \frac{1}{n} \sum_{i=1}^{n} x_i^2} The RMS is taken over the last ``D`` dimensions, where ``D`` is the dimension of :attr:`normalized_shape`. For example, if :attr:`normalized_shape` is ``(3, 5)`` (a 2-dimensional shape), the RMS is computed over the last 2 dimensions of the input. Args: normalized_shape (int or list or torch.Size): input shape from an expected input of size .. math:: [* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1] \times \ldots \times \text{normalized\_shape}[-1]] If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size. eps: a value added to the denominator for numerical stability. Default: :func:`torch.finfo(x.dtype).eps` elementwise_affine: a boolean value that when set to ``True``, this module has learnable per-element affine parameters initialized to ones (for weights). Default: ``True``. Shape: - Input: :math:`(N, *)` - Output: :math:`(N, *)` (same shape as input) Examples:: >>> rms_norm = nn.RMSNorm([2, 3]) >>> input = torch.randn(2, 2, 3) >>> rms_norm(input) rE.rFrGrHNrc\||d}t|t|tjr|f}t ||_||_||_|jr/ttj|j fi||_ n|jdd|jy)NrKrN)rrrOrPrQrRrFrGrHr rSrTrNrUrV)rrFrGrHrLrMrWrs rrzRMSNorm.__init__ns%+U;  &(8(8 9 02  %&6 7"4  " "# D11D^DDK  # #Hd 3 r!c\|jr tj|jyy)zS Resets parameters based on their initialization used in __init__. N)rHrrYrNr.s rrVzRMSNorm.reset_parameterss"  " " JJt{{ # #r!xcntj||j|j|jS)z$ Runs forward pass. )r$rms_normrFrNrG)rrps rr'zRMSNorm.forwards'zz!T22DKKJJr!c:djdi|jS)z5 Extra information about the module. r^r*r+r.s rr/zRMSNorm.extra_reprs)  = 66r{s" *(9 V1S1ShSFS, d3i% &C C LZ Z zY fY r!