U Kc4@sddlZddlmZddlmmZddlmZmZm Z m Z ddl m Z ddl mZdddd d d gZGd d d ejjjjeZGdddeejZGdddeejZGdddeejZGdddeejjjjZGdd d eejZGdd d eejZGdd d eejZdS)N)OptionalDictAnyList) _size_1_t)ReferenceQuantizedModuleConv1dConv2dConv3dConvTranspose1dConvTranspose2dConvTranspose3dc@s.eZdZdZdeejiZdZe ddZ dS)_ConvNda, A reference version of nn.quantized.Conv2d we will not pack the parameters in this module, since weight packing is an optimization for quantized backends supported in PyTorch (fbgemm/qnnpack), this is useful when user want to use this module in other backends like Glow. biasTcCst||j|j|j|j|j|j|j|jdk |j|j j |j j |d }t j |j |_ |jdk rpt j |j|_|SN)devicedtypeweight_qparams) in_channels out_channels kernel_sizestridepaddingdilationgroupsr padding_modeweightrrtorchnn Parameterdetachcls float_convrZ qref_convr%P/tmp/pip-unpacked-wheel-gikjz4vx/torch/ao/nn/quantized/reference/modules/conv.py from_floats$  z_ConvNd.from_floatN) __name__ __module__ __qualname____doc__rrTensor__annotations__Z _IS_REFERENCE staticmethodr'r%r%r%r&r s rc@sbeZdZdeeeeeeeeeeeee fd ddZ e j e j d d d Z d d ZeddZdS)r rrTzerosN) rrrrrrrrrrc Cs2tj|||||||||| | | || | dSN)rr __init___init_weight_qparams selfrrrrrrrrrrrrr%r%r&r1(s zConv1d.__init__xreturnc Cs,|}t|||j|j|j|j|j}|S)aR we have: w(float) -- quant - dequant x(float) ------------- F.conv1d --- In the full model, we will see w(float) -- quant - *dequant x -- quant --- *dequant -- *F.conv1d --- *quant - dequant and the backend should be able to fuse the ops with `*` into a quantized conv1d ) get_weightFZconv1drrrrrr4r6weight_quant_dequantresultr%r%r&forward:s zConv1d.forwardcCsdS)NzQuantizedConv1d(Reference)r%r4r%r%r& _get_nameKszConv1d._get_namecCst|||Sr0rr'r#r$rr%r%r&r'NszConv1d.from_float) rrrrTr/NNN)r(r)r*intrboolstrrrrr1rr,r=r? classmethodr'r%r%r%r&r 's2 c @sPeZdZdeeeefdddZej ej d d d Z d d Z e ddZ dS)r rrTr/Nrc Cs2tj|||||||||| | | || | dSr0)rr r1r2r3r%r%r&r1SszConv2d.__init__r5c Cs,|}t|||j|j|j|j|j}|S)aR we have: w(float) -- quant - dequant x(float) ------------- F.conv2d --- In the full model, we will see w(float) -- quant - *dequant x -- quant --- *dequant -- *F.conv2d --- *quant - dequant and the backend should be able to fuse the ops with `*` into a quantized conv2d )r8r9Zconv2drrrrrr:r%r%r&r=^s zConv2d.forwardcCsdS)NzQuantizedConv2d(Reference)r%r>r%r%r&r?oszConv2d._get_namecCst|||Sr0r@rAr%r%r&r'rszConv2d.from_float) rrrrTr/NNNr(r)r*rrrDrr1rr,r=r?rEr'r%r%r%r&r Rs  c @sPeZdZdeeeefdddZej ej d d d Z d d Z e ddZ dS)r rrTr/NrFc Cs2tj|||||||||| | | || | dSr0)rr r1r2r3r%r%r&r1wszConv3d.__init__r5c Cs,|}t|||j|j|j|j|j}|S)aR we have: w(float) -- quant - dequant x(float) ------------- F.conv3d --- In the full model, we will see w(float) -- quant - *dequant x -- quant --- *dequant -- *F.conv3d --- *quant - dequant and the backend should be able to fuse the ops with `*` into a quantized conv3d )r8r9Zconv3drrrrrr:r%r%r&r=s zConv3d.forwardcCsdS)NzQuantizedConv3d(Reference)r%r>r%r%r&r?szConv3d._get_namecCst|||Sr0r@rAr%r%r&r'szConv3d.from_float) rrrrTr/NNNrGr%r%r%r&r vs  c@seZdZdZeddZdS)_ConvTransposeNda5 A reference version of nn.quantized.ConvTranspose2d we will not pack the parameters in this module, since weight packing is an optimization for quantized backends supported in PyTorch (fbgemm/qnnpack), this is useful when user want to use this module in other backends like Glow. cCsx||j|j|j|j|j|j|j|jdk |j|j |j j |j j |d }t j|j |_ |jdk rtt j|j|_|Sr)rrrrroutput_paddingrrrrrrrrrr r!r"r%r%r&r's& z_ConvTransposeNd.from_floatN)r(r)r*r+r.r'r%r%r%r&rHsrHc@speZdZdeeeeeeeeeeeeee fd ddZ de j ee ee j d d d Zd d ZeddZdS)r rrTr/N) rrrrrrIrrrrrcCs4tj|||||||||| | | | || | dSr0)rr r1r2r4rrrrrrIrrrrrrrr%r%r&r1s zConvTranspose1d.__init__r6 output_sizer7c CsZt|jtst|t||j|j|j|j}| }t |||j |j|j||j |j}|S)ac we have: w(float) -- quant - dequant x(float) ------------- F.convTranspose1d --- In the full model, we will see w(float) -- quant - *dequant x -- quant --- *dequant -- *F.convTranspose1d --- *quant - dequant and the backend should be able to fuse the ops with `*` into a quantized conv1d ) isinstancertupleAssertionError_output_paddinginputrrrr8r9Zconv_transpose1drrr4r6rLrIr;r<r%r%r&r=s* zConvTranspose1d.forwardcCsdS)Nz#QuantizedConvTranspose1d(Reference)r%r>r%r%r&r?szConvTranspose1d._get_namecCst|||Sr0rHr'rAr%r%r&r'szConvTranspose1d.from_float) rrrrTrr/NNN)N)r(r)r*rBrrCrDrrrr1rr,rr=r?rEr'r%r%r%r&r s6  c @s\eZdZdeeeefdddZdej ee e ej d d d Z d d Z eddZdS)r rrTr/NrFcCs4tj|||||||||| | | | || | dSr0)rr r1r2rJr%r%r&r1s zConvTranspose2d.__init__rKc CsZt|jtst|t||j|j|j|j}| }t |||j |j|j||j |j}|S)ac we have: w(float) -- quant - dequant x(float) ------------- F.convTranspose2d --- In the full model, we will see w(float) -- quant - *dequant x -- quant --- *dequant -- *F.convTranspose2d --- *quant - dequant and the backend should be able to fuse the ops with `*` into a quantized conv2d )rMrrNrOrPrQrrrr8r9Zconv_transpose2drrrRr%r%r&r=s* zConvTranspose2d.forwardcCsdS)Nz#QuantizedConvTranspose2d(Reference)r%r>r%r%r&r?szConvTranspose2d._get_namecCst|||Sr0rSrAr%r%r&r'szConvTranspose2d.from_float) rrrrTrr/NNN)Nr(r)r*rrrDrr1rr,rrBr=r?rEr'r%r%r%r&r s" c @s\eZdZdeeeefdddZdej ee e ej d d d Z d d Z eddZdS)rrrTr/NrFcCs4tj|||||||||| | | | || | dSr0)rrr1r2rJr%r%r&r1s zConvTranspose3d.__init__rKc CsZt|jtst|t||j|j|j|j}| }t |||j |j|j||j |j}|S)ac we have: w(float) -- quant - dequant x(float) ------------- F.convTranspose3d --- In the full model, we will see w(float) -- quant - *dequant x -- quant --- *dequant -- *F.convTranspose3d --- *quant - dequant and the backend should be able to fuse the ops with `*` into a quantized conv3d )rMrrNrOrPrQrrrr8r9Zconv_transpose3drrrRr%r%r&r="s* zConvTranspose3d.forwardcCsdS)Nz#QuantizedConvTranspose3d(Reference)r%r>r%r%r&r?9szConvTranspose3d._get_namecCst|||Sr0rSrAr%r%r&r'<szConvTranspose3d.from_float) rrrrTrr/NNN)NrTr%r%r%r&rs" )rZtorch.nnrZtorch.nn.functionalZ functionalr9typingrrrrZtorch.nn.common_typesrutilsr__all__modulesconvrr r r rHr r rr%r%r%r&s   +$$2-