P/jy%ddlmZmZmZddlZddlmZddlmZddl m Z m Z m Z ddl mZmZmZgdZedej$j&j(ZGd d ej$j&j(ZGd d eej,ZGd deej.ZGddeej0Zy))TupleTypeVarUnionN) _FusedModule) _size_1_t _size_2_t _size_3_t)_pair_single_triple)Conv1dConv2dConv3dMOD)boundceZdZeZ ddededeedfdeedfdeedfdeedfd ed eedfd ed ed eddfdZ dZ e ddZ dZ y)_ConvNdN in_channels out_channels kernel_size.stridepaddingdilation transposedoutput_paddinggroupsbias padding_modereturnc| |d}tjjjj|||||||||| | | f i|| sJd| |_| j ||_y)N)devicedtypez'qconfig must be provided for QAT module)factory_kwargs)nnmodulesconvr__init__qconfigweightweight_fake_quant)selfrrrrrrrrrrrr(r!r"r#s _/root/repos/pocs/video-evals/.venv/lib/python3.12/site-packages/torch/ao/nn/qat/modules/conv.pyr'z_ConvNd.__init__s"%+U; ((              AAAw !(~!Ncn|j||j|j|jSN _conv_forwardr*r)rr+inputs r,forwardz_ConvNd.forward8*!!%)?)? )LdiiXXr-c Rt||jk(s.Jd|jzdz|jjzt|dsJd|jsJdt t|t r|d}|j}||j|j|j|j|j|j|j|jdu|j| }|j |_|j|_|S) zCreate a qat module from a float module Args: `mod`: a float module, either produced by torch.ao.quantization utilities or directly from user zqat.z.from_float only works for r(z,Input float module must have qconfig definedz,Input float module must have a valid qconfigrN)rrrrrrr()type _FLOAT_MODULE__name__hasattrr( issubclassrrrrrrrrrrr))clsmoduse_precomputed_fake_quantr(qat_convs r, from_floatz_ConvNd.from_float;s CyC--- ll + ,(( ) - sI&V(VV&{{JJJ{ d3i .a&C++ OO    OO::KK\\::%))  ** r-c t|}|j|j|j|j|j |j |j|j|jdu|j }tjj|jj|_|j 99 **499+;+;+=>DI c< (fG3 45 55))+D NN4 %C%%w/E KK &LKr-)NNNF)r9 __module__ __qualname__rr8intrboolstrr'r4 staticmethodr@rLr-r,rrsM#O#O#O38_ #O c3h #O sCx #OS/#O#Oc3h#O#O#O#O !#OJY  Dr-rceZdZdZej Zej Z ddedede de de e e fde d ed e d e d dffd Z edfd ZxZS)r aZ A Conv1d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as :class:`~torch.nn.Conv1d` Similar to :class:`~torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight Nrrrrrrrrrrc t|} t|}t|tr|n t|}t|}t|||| |||dtd||| | | | yNFr) rrrrrrrrr(r!r")r isinstancerRsuperr'r+rrrrrrrrrr(r!r" kernel_size_stride_padding_ dilation_ __class__s r,r'zConv1d.__init__w{+ &/(#67GGrYr@r<r=r>r_s r,r@zConv1d.from_float"w! 1K"  r- rrgrgTzerosNNNrM)r9rNrO__doc__r$r r8rCrPrrrRrQr' classmethodr@ __classcell__r_s@r,r r ~s IIM)*#" " "  "  " sI~& " " " " "  " H  r-r ceZdZdZej Zej Z ddedede de de e e fde d ed e d e d dffd Z dZedfd ZxZS)ra A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv2d`, please see https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d for documentation. Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight Nrrrrrrrrrrc t|} t|}t|tr|n t|}t|}t|||| |||dtd||| | | | yrW)r rXrRrYr'rZs r,r'zConv2d.__init__su[) -(#67E'N(O      8%  r-cn|j||j|j|jSr/r0r2s r,r4zConv2d.forwardr5r-c(t||||Srbrcrds r,r@zConv2d.from_floatrer-rfrM)r9rNrOrir$rr8rCrPrrrRrQr'r4rjr@rkrls@r,rr IIM)*#" " "  "  " sI~& " " " " "  " HY  r-rceZdZdZej Zej Z ddedede de de e e fde d ed e d e d dffd Z dZedfd ZxZS)ra A Conv3d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv3d`, please see https://pytorch.org/docs/stable/nn.html?highlight=conv3d#torch.nn.Conv3d for documentation. Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight Nrrrrrrrrrrc t|} t|}t|tr|n t|}t|}t|||| |||dtd||| | | | yrW)r rXrRrYr'rZs r,r'zConv3d.__init__ r`r-cn|j||j|j|jSr/r0r2s r,r4zConv3d.forward/r5r-c(t||||Srbrcrds r,r@zConv3d.from_float2rer-rfrM)r9rNrOrir$rr8rCrPr rrRrQr'r4rjr@rkrls@r,rrrqr-r)typingrrrrDtorch.nnr$torch.ao.nn.intrinsicrtorch.nn.common_typesrrr torch.nn.modules.utilsr r r __all__r%r&rrr rrrTr-r,r|s(( .AA:: ) e2::??223kbjjoo%%k\8 Wbii8 v= Wbii= @= Wbii= r-