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 d dlmZmZ d dlmZmZmZmZmZ d dlZd dlZd dlmZ d dlmZ d dlm  mZ d dlmZm Z m!Z! d dl"m#Z# d d	l$m%Z% d d
lm&Z&m'Z'm(Z(m)Z)m*Z* d dl+m,Z,m-Z-m.Z.m/Z/ d dl0m1Z2 d dl3m4Z4 ej5j6Z6g Z7e8e9 e:d< ej;j<j=Z=G dd deZ>	d|dedej?de@fddZAe	eAej?jBddZCe	eAej?jBdZDe	eAej?jEdZFde!deGde!fddZHe#e=jIe/deDd e!d!e!fd"d#ZIe#e=jJe/deDd e!d!e!fd$d%ZJe#e=jKe/deDd e!de!d&eLd'eLfd(d)ZKe#e=jMe/deDd*e!d+eLd,eLd-eLd.e@d/e!fd0d1ZMe#e=jNjOgd2d3 ZPe#e=jNj!gd4e!fd5d6ZQe#e=jRe/ eDd7e!de!fd8d9ZRe#e=jSe/deDd*e!d7e!fd:d;ZSe#e=jTe/dd*e!d7e!d<eLd=eLfd>d?ZTe#e=jUe/ eDd7e!de!fd@dAZUe#e=jVe/ eDd*e!d7e!de!fdBdCZVe#e=jWe/dd*e!d7e!d'eLfdDdEZWe#e=jXe/deDd*e!d7e!dFeLdGe@fdHdIZXe#e=jYe/deDd}dKe!d7e!dLe9fdMdNZYe#e=jZeDd*e!dOe!fdPdQZZe#e=j[e/ eDd7e!de!fdRdSZ[e#e=j\e/deDd*e!d7e!de!fdTdUZ\e#e=j]d7e!dVe!de!fdWdXZ]e#e=j^d*e!d7e!dVe!de_e!e!f fdYdZZ^e#e=j`e/ eDd*e!d7e!d[e!d\eLd]eLd^e@dGe@de!fd_d`Z`e#e=jae/deDd*e!d7e!dae!de!fdbdcZadde!deeGfdfdgZbdhejcfdidjZde#e=jee/ eDe>jfjgfd7e!dke!deeGde!fdldmZee#e=jhe/deDd*e!dOe!dke!deeGfdndoZhe#e=jid~dpdqZje#e=jke/ eDe>jfjgdrfd7e!dke!deeGd&eLfdsdtZke#e=jljmeDd*e!d7e!dke!deeGd&eLf
dudvZle#e=jljneDd*e!d7e!dke!deeGd&eLde!fdwdxZoe#e=jpjmeDd*e!d7e!dke!deeGdyeLf
dzd{Zpe#e=jpjqeDd*e!d7e!dke!deeGdyeLde!fd|d}Zrd*e!d7e!dke!dVee! deeGd~eGde!de!fddZse#e=jte/deDd*e!d7e!deGde!fddZte#e=jue/dd*e!d7e!dke!dVee! deeGd~eGde!de!fddZue#e=jve/dd*e!d7e!dke!dVee! deeGd~eGde!de!fddZve#e=jwe/ eDde>jfjgfd7e!dke!dVee! deeGde!f
ddZwe#e=jxe/deDde>jfjgfd*e!d7e!dke!dVee! deeGde!fddZxe#e=jye/ eDe>jfjgfdOe!dke!deeGde!fddZye#e=jze/deDe>jfjgfd*e!d7e!dke!deeGde!f
ddZze#e=j{e/ ddOe!de!deLfddZ{e#e=j|e/ de!de!de!fddZ|e#e=j}e/ d*e!de8eG deGdeGdeGdeGfddZ}e#e=j~j!	 			dd7e!deGdeeG deeG deGf
ddZde!deGdeeG deeG de_eGeGf f
ddZe#e=je/ 	 			ddOe!de!deGdeeG deeG deGfddZe#e=je/ d*e!de8eG deGdeGfddZe#e=je/ d*e!de8eG deGdeGdeGf
ddZd*e!de!dejcfddZe#e=je/deCd*e!de!deGdejcfddZe#e=je/ eCd*e!de!deGdejcfddZdd Ze#e=je/ dOe!de8eG de8eG de8eG de8eG de!fddZe#e=je/ eDdOe!de8eG de8eG de8eG de8eG de8eG de!fddZe#e=je/ d*e!de!d,eLfddĄZe#e=je/ dKe!de8eG deGdeGdeGde!fddɄZe#e=jjmeD	d~d*e!d7e!deeL de!fdd̄Ze#e=je=jjme6je=jjme6jdOe!deLdee@ fddτZe#e=je/ddуdOe!deLdee@ fddӄZe#e=je/ de!deGde@fddքZe#e=je/dd׍de!deGde@fddلZe#e=je/ 			ddVe!de!deGde@de@de!fddZe#e=je/ d*e!de!deGdeGde@f
ddZde8eG fddZde8e! deGdeGde8e! fddZde8e! fddZde8e! deGfddZde8e! deGdeGfddZe#e=jjme=jjqg	d~de8e! deGdeGdee! de!f
ddZe#e=jjme=jjqg	 	dd7e!de8eG deGdee8e!  dee8e!  f
ddZe#e=jj!ddOe!deGdeGde_e!df fddZe#e=jjm	 ddOe!de8eG deGde_e!df fddZe#e=jj!dd7e!deGdeGde_e!df fddZe=jje6j	 dd7e!de!deGde_e!df fdd Ze#e=je/dd׍eDdd7e!de!de!d&eGd+eGf
ddZe#e=je/ eD			dd7e!de!de!d&eGd+eGde@fddZe#e=je/ eDdd7e!de!de!d&eGd+eGf
d	d
Ze#e=jjmeDd*e!dOe!de!de!dee! deGdeGdeGdeGde8e@ de_ee! ee! ee! f fddZe#e=jjqd*e!dOe!de!de!dee! deGdeGdeGdeGde8e@ dej!dej!dej!de_ee! ee! ee! f fddZdee! dee! fddZe#e=jjmde!dOe!de8eG de!de!dVee! dee! de8e@ de_ee! ee! ee! f fddZe#e=jjqde!dOe!de8eG de!de!dVee! dee! de8e@ dej!dej!dej!de_ee! ee! ee! f fdd ZdOe!dVee! dee! d!ee! d"ee! d^e@d#eLdeLd$e@de_e!e!e!ee! ee! f fd%d&Ze#e=je/dd'd(dOe!dVee! dee! d!ee! d"ee! d^e@d#eLdeLde_e!e!e!f fd)d*Ze=jjme6je=jjme6jdOe!dVee! dee! d!ee! d"ee! d^e@d#eLdeLde_e!e!e!f fd+d,Ze=jjme6jdde8e! fd-d.Ze#e=jjmdOe!dVee! dee! d!e!d"e!d#eLdeLde_e!e!e!f fd/d0Ze#e=jjmdOe!dVee! dee! d!e!d"e!d^e@d#eLdeLde_e!e!e!f fd1d2Ze#e=jjdOe!dVee! dee! d^e@d#eLdeLde_e!e!e!f fd3d4Ze#e=jjmdOe!dVee! dee! d!e!d"e!d^e@d#eLdeLde_e!e!e!e!e!f fd5d6ZdOe!dVee! dee! d!e!d"e!deLd^e@de!fd7d8Ze#e=jjmdOe!dVee! dee! d!e!d"e!d#eLdeLde_e!e!e!e!f fd9d:Ze#e=jjmdOe!dVee! dee! d!e!d"e!d#eLdeLde_e!e!e!e!e!e!f fd;d<Ze#e=jjmdOe!dVee! dee! d!e!d"e!d#eLdeLde_e!e!e!e!f fd=d>Ze#e=je/ddуeDd~d?d@Ze#e=je/ dddddddAdee!e'f dheejc dBeej dCe@dDe@dEeej fdFdGZe#e=je=je=jge/ dHdI Ze=jjme6je#e=je/ddѐddJdOe!dVe!dee! d!ee! d"ee! d^e@dKeLdLeLfdMdNZdOdP Ze#e=jjmde!dOe!dVee! d!ee! d"ee! d'ee! d(ee! de@deLde8e@ dQe!de_e!ee! ee! f fdRdSZe#e=jjmde!dOe!dVee! d!ee! d"ee! d'ee! d(ee! de@deLde8e@ de_e!ee! ee! f fdTdUZe#e=jjqde!dOe!dVee! d!ee! d"ee! d'ee! d(ee! de@deLde8e@ dej!dej!dej!de_e!ee! ee! f fdVdWZe#e=jǃe/ddѐddOe!d*e!dVe!d!ee! d"ee! d'ee! dXee! dLeLfdYdZZe#e=jȃe/ddѐddOe!d*e!dVe!d!ee! d"ee! d'ee! dXee! dLeLd[e!fd\d]Ze#e=jɃe/ eDdOe!de_eGeGf fd^d_Zd7e)de)de8eG deGfd`daZe#e=j̃e/ d7e)de)de8eG fdbdcZe#e=j̓e/ dOe)de)de8eG de8eG de8eG f
dddeZe#e=j΃ddfde)deGde)dge)d+e'f
dhdiZe#e=jσe/ ddfde)deGde)dge)d+e'f
djdkZddfde)deGde)dge)dle@d+e'fdmdnZe#e=jjme=jjme6jddpdqZe#e=j҃de)deGde)dge)fdrdsZe#e=jӃe/ de)deGde)dge)fdtduZde)deGde)dge)dle@f
dvdwZe#e=jՃe/ddaeDd7e!de_e!e!f fdxdyZe#e=jփe/ 	o	r	dde!dzee@eGeLf d{ee@eGeLf d|eej fd}d~Ze#e=j؃dddZؐdd Zِdd Ze#e=jj܃e#e=jj܃e#e=jj܃e=jjܠe6je=jjܠe6je=jjܠe6je=jjܠe6je=jjܠe6je=jjܠe6jdOe!dee8eG  dee8eL  de!fddZe#e=jj܃e#e=jj܃e#e=jj܃e=jjܠe6je=jjܠe6je=jjܠe6je=jjܠe6je=jjܠe6je=jjܠe6jdOe!dee8eG  dee8eL  de!fddZd|ddZe#e=jjme=jjqge=jjme6je=jjme6je/ddd	d~dOe!de8eG deeL de!fddZe#e=jjme=jjqge=jjme6je=jjme6je/ddd	d~dOe!de8eG deeL de!fddZe#e=jjme=jjqge=jjme6je=jjme6je/ddd		ddOe!de8eG deeL deeL de!f
ddZe#e=jjme=jjqge=jjme6je=jjme6je/ddd		ddOe!de8eG deeL deeL de!f
ddZe#e=jjme=jjqge=jjme6je=jjme6je/ddd			ddOe!de8eG deeL deeL deeL de!fddZe#e=jjme=jjqge=jjme6je=jjme6je/ddd			ddOe!de8eG deeL deeL deeL de!fddZeD	d|dOe!de8eG de8eeL  de@de!f
ddZdd Zdd Zdd Zdd Z	d|ddZdd Zdd Zd|ddZd|ddZdd Ze#e=jje=jje6je=jje6jdd Ze#e=jje=jje6je=jje6jdd Ze#e=jje=jje6je=jje6jdd Ze#e=jje=jje6je=jje6jdd Zdd Zd|ddZd|ddZddÄ Ze#e=jje=jje6je=jje6jdĐdń Ze#e=jje=jje6je=jje6jdƐdǄ ZdȐdɄ Z dʐd˄ Ze#e=jje=jje6je=jje6jd̐d̈́ Ze#e=jje=jje6je=jje6jdΐdτ Ze#e=jj܃e=jjܠe6je=jjܠe6jdАdф Ze#e=jj܃e=jjܠe6je=jjܠe6jdҐdӄ Ze#e=j	j܃e#e=j
j܃e=jjܠe6je=jjܠe6je=j	jܠe6je=j	jܠe6je=j
jܠe6je=j
jܠe6jdԐdՄ Ze#e=jjme=jjqge/ 	d~dOe!de8eG de@deeL de!f
dאd؄Ze#e=j	jme=j	jqge=j	jme6je/ 		ddOe!de8eG de@deeL deeL de!fdِdڄZ	e#e=j
jme=j
jqge/ 			ddOe!de8eG de@deeL deeL deeL de!fdېd܄Z
d~dݐdބZdߐd Zdee! dee! de!de!fddZde*de!fddZeDdOe!de8eG de@de8eeL  de!f
ddZe#e=jjmde!de!de@fddZe#e=je=jge/ dd Ze#e=jgdd Ze#e=jgd|ddZe#e=jgdd Ze#e=jgdd Zd7e!dke!dVee! deeGd~eGde_e!e!f fddZe#e=je/ddd7e!dke!dVee! deeGd~eGde_e!e!f fddZe#e=je/ddd7e!dke!dVee! deeGd~eGde_e!e!f fddZde!deLde!fddZde!deLde!fd dZde!de*fddZde*de!de!fddZdee! de!fd	d
Z deGde@dhejcdBejfddZ!de!deGdeGde@fddZ"de!deGdeGdeGde@f
ddZ#de!de8eG de@fddZ$de!de8eG de@fddZ%e#e=j&e/ eDde!de8eG de@fddZ&	 	 		dde!de!deGdeGde@de@de!fd d!Z'e#e=j(e/ eD	 	 	dde!de!deGdeGde@de!fd"d#Z(e#e=j)e/ eDd$d% Z)e#e=j*e/ dde>jfjgfd&d'Z*d(ej!d)ej!d*e@de@fd+d,Z+e=j,jme6je=j,jqe6je/dd-dd.d/d0Z,e#e=j-jme=j-jqge=j-jme6je/ eD		ddOe!de_eGeGf de@d1eeL d2eeL de!fd3d4Z.e#e=j-j܃e=j-jܠe6je=j-jܠe6je/ eD	d~de!dee_eGeGf  de@dee_eLeLf  de!f
d5d6Z/e#e=j0e#e=j1e#e=j2eDe/ de!de_eGdf de!fd7d8Z3e#e=j4e#e=j5e#e=j6eDe/ de!de_eGdf de!fd9d:Z7de!de_eGdf d;eeGeGeGge!f de!fd<d=Z8e#e=j9e#e=j:e#e=j;e/dd>d? Z<e#e=j=e/d@dAdddBdCdDZ=e#e=j>e/ dddEdFdGZ>e#e=j?jme=j?jqge/ dej@dddHde'dheejc dIejAdBeej dCe@f
dJdKZBe#e=j?jCgdej@dddHde'de'dheejc dIejAdBeej dCe@fdLdMZDe#e%dNdO ZEe#e=jFe=jFjme6je/ ddde>jfjgfdOe!dke!de'dPe'dVee! deeGde!fdQdRZFe#e=jGe=jGjme6je/ddSdOe!dke!deeGde_e!e!f fdTdUZGe#e=jHjm	o	ddddVdWe!dXe!d4e!dYeLdZe@d[ee! d,eeL de_e!e!f fd\d]ZId^d_ ZJe#e=jKge/ eDdd`daZKe#e=jLe/ dbdc ZLe#e=jMddde ZMe#e=jNjme=jNjqgdddfd7e!dheejc dee! de!fdgdhZOe#e=jPjme=jPjQgd~d7e!deeG fdidjZRe#ej<j=jSddkdlZSe#e=jTe/ dddmdndoZTe#e=jUjmddpd7ej!d|eej dej!fdqdrZUddsdtduZVdddmdvdwZWe#e=jXe/ dxdy ZXe#e=jYd~dzd{ZYeJe=jZe=j[ eJe=j\e=j eJe=j]e=j eJe=j^e=jK eJe=j_e=jN eJe=j`e=ja eJe=jbe=jU eJe=jce=jd eJe=jee=jR eJe=jfe=jg eJe=jhe=ji eJe=jje=jk eJe=jle=jm eJe=jne=jo eJe=jpe=jq eJe=jre=js eJe=jte=ju eJe=jve=jw eJe=jxe=jy eJe=jze=j{ eJe=j|e=j} eJe=j~e=j eJe=je=j eJe=je=j eJe=je=j[ dS (      N)Iterable)Enum)partialreduce)chainproduct)AnyCallablecastOptionalUnion)	sym_floatsym_intTensorregister_decomposition)	out_dtype)IntLike
NumberTypesuggest_memory_format
TensorLikeTensorSequenceType)_maybe_convert_to_dtype_maybe_resize_out_safe_copy_outout_wrapper)_pytree)tree_map__all__c                   @   s   e Zd ZdZdZdZdS )	Reductionr         N)__name__
__module____qualname__NONEMEANSUM r(   r(   p/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/_decomp/decompositions.pyr   0   s    r   Fftype_promotioncompute_dtype_onlyc                    s   t  fdd}|S )Nc                     sr   dd t j| i |D }tj|di\  fdd}fdd}t|| i t||}r4|S t||S )Nc                 S   s   g | ]	}t |tr|qS r(   )
isinstancer   .0xr(   r(   r)   
<listcomp>@   s
    
z-type_casts.<locals>.inner.<locals>.<listcomp>type_promotion_kindc                       t | tr
|  S | S Nr-   r   tor0   computation_dtyper(   r)   increase_precH      

z0type_casts.<locals>.inner.<locals>.increase_precc                    r3   r4   r5   r7   )result_dtyper(   r)   decrease_precN   r;   z0type_casts.<locals>.inner.<locals>.decrease_prec)pytreearg_tree_leavesutilselementwise_dtypesr   )argskwargs	flat_argsr:   r=   rr,   r*   r+   )r9   r<   r)   inner>   s   

ztype_casts.<locals>.inner)	functoolswraps)r*   r+   r,   rG   r(   rF   r)   
type_casts9   s   rJ   T)r+   r,   )r+   r0   dimreturnc                 C   s$   t ||   D ]}| d} q| S N)rangerK   	unsqueeze)r0   rK   _r(   r(   r)   _unsqueeze_to_dimk   s   rR   
grad_inputout_gradyc                 C   s   | d||     S Nr    conj_physicalrT   rU   r(   r(   r)   tanh_backwardq      rZ   c                 C   s   | |d|     S rV   rW   rY   r(   r(   r)   sigmoid_backwardx   r[   r\   beta	thresholdc                 C   s.   ||   }t|| |k| | | |d  S N      ?)exptorchwhere)rT   r0   r]   r^   zr(   r(   r)   softplus_backward   s   "re   grad_outputalphascaleinput_scale	is_resultself_or_resultc           	      C   sb   || }|}|}|rt |dk| | ||  | | S t |dk| | | t ||  | | S Nr   )rb   rc   ra   )	rf   rg   rh   ri   rj   rk   negcoefposcoef
negiptcoefr(   r(   r)   elu_backward   s   rp   c                 C      t | |S r4   )rb   	full_likeselfvaluer(   r(   r)   fill_scalar      rv   ru   c                    s(   t   dk fdd t|  S )Nr   c                      s   d    dS )Nz@fill only supports 0-dimension value tensor but got tensor with z dimensionsrK   r(   ru   r(   r)   <lambda>       zfill_tensor.<locals>.<lambda>)rb   _checkrK   atencopyrs   r(   ry   r)   fill_tensor   s
   

r   rt   c                 C   s    t jt j| d ddddd S N   r   min   maxrb   clamprt   r(   r(   r)   hardsigmoid   s    r   c                 C   s   t |dk|dk @ | d dS )Ng      g      @gUUUUUU?        rb   rc   rf   rt   r(   r(   r)   hardsigmoid_backward   s
   r   min_valmax_valc                 C   s   t ||k||kB d| S )Nr   r   )rf   rt   r   r   r(   r(   r)   hardtanh_backward   s   r   c                 C   s$   | t jt j| d dddd d S r   r   r   r(   r(   r)   	hardswish   s   $r   c              
   C   s,   t |dk dt |dk| |d d  | S )Nr   r         ?r   r   r(   r(   r)   hardswish_backward   s
   r   c                 C   s   t ||kd| S rl   r   )rf   rt   r^   r(   r(   r)   threshold_backward      r   negative_slopeself_is_resultc                 C   s   t |dk| | | S rl   r   )rf   rt   r   r   r(   r(   r)   leaky_relu_backward   s   r   nonegradapproximatec                 C   s   d}d}d}|dkrO|| d }d}|| }|| }	||||	   }
t |
}d| }d| }d| }d||  }|dd| |   }|| | }| ||  S |}|| d }ddt ||   }|t || d	  }| |||   S )
Ng;f?g;f?gmBP?tanhr   gHm?r    r   g      )rb   r   erfra   )r   rt   r   M_SQRT2	M_SQRT1_2
M_2_SQRTPIkBetakKappax_sqx_cuberG   
tanh_innerleftrightleft_derivativetanh_derivativeinner_derivativeright_derivativekAlphacdfpdfr(   r(   r)   gelu_backward   s,   
r   inputc                 C   s:   t t|}t |}|| d||   }| ||  S rV   )rb   r   Fsoftplussigmoid)rf   r   input_tanh_softplusinput_sigmoidoutr(   r(   r)   mish_backward  s   
r   c                 C   s   | t |  S r4   )rb   r   r   r(   r(   r)   silu  s   r   c                 C   s,   ddt |   }| | d|d|    S rV   )rb   ra   )rf   rt   r   r(   r(   r)   silu_backward  s   r   weightc                 C   s   t | dk| ||  S rl   r   )rt   r   r(   r(   r)   _prelu_kernel%  s   r   c                 C   s4   t |dk| ||  }t |dkd||  }||fS )Nr   r   r   )rf   rt   r   
input_gradweight_gradr(   r(   r)   _prelu_kernel_backward*  s   r   noiseloweruppertrainingc                 C   s6   |r|| dkr|  |S || d }t| |||S )Ngư>r!   )mulr}   r   )rf   rt   r   r   r   r   r   r   r(   r(   r)   rrelu_with_noise_backward5  s   
r   bufferc                 C   sN   |dk }t |dd}t |dd}t t | }| |||d|     S )Nr   r    rN   )rb   rc   ra   abs)rf   rt   r   in_negative	max_derivsignrd   r(   r(   r)   log_sigmoid_backwardJ  s
   r   loss	reductionc                 C   s0   |t jjkrt| S |t jjkrt| S | S r4   )r   r&   ru   rb   meanr'   sum)r   r   r(   r(   r)   apply_loss_reductionW  s
   

r   dtypec                 C   s4   | t jkrt jS | t jkrt jS | t jkrt jS d S r4   )rb   	complex32float16	complex64float32
complex128float64r   r(   r(   r)   to_real_dtype`  s   


r   targetc                 C   s   | | d }t ||S )Nr!   )r   )rt   r   r   r   r(   r(   r)   mse_losso  s   
r   c                 C   s,   |t jjkrd|  nd}|||  |  S )N       @)r   r&   ru   numel)rf   r   r   r   normr(   r(   r)   mse_loss_backwardy  s   r   c                 C   sF   t j| ||d}| td}t j||dd}t |}t |||S )N)rK   r   z-infTrK   keepdim)rb   softmaxeqfloatall
zeros_likerc   )rt   rK   r   r   maskedmasked_rowszerosr(   r(   r)   safe_softmax  s
   
r   r`   c                 C   s<   | |   }t||k d|d  | |d|  }t||S )Nr   r!   )r   rb   rc   r   )rt   r   r   r]   r   r(   r(   r)   smooth_l1_loss  s   	&
r   c           	      C   sZ   |t jjkrd|  nd}|| }t|}||  }t||k || | |t| S r_   )r   r&   ru   r   rb   r   rc   r   )	rf   rt   r   r   r]   r   r0   abs_x	norm_gradr(   r(   r)   smooth_l1_loss_backward  s   

r   c                 C   *   t | ||||}t||j t||ddS NT	copy_fromcopy_toexact_dtype)r   r   shaper   )rf   rt   r   r   r]   rS   resultr(   r(   r)   smooth_l1_loss_backward_out     
r   deltac              
   C   s`   |t jjkrd|  nd}|| }t|| k | |  | t||k||  | || |  S r_   )r   r&   ru   r   rb   rc   )rf   rt   r   r   r   r   r0   r(   r(   r)   huber_loss_backward  s    r   c                 C   r   r   )r   r   r   r   )rf   rt   r   r   r   rS   r   r(   r(   r)   huber_loss_backward_out  r   r   ignore_indextotal_weightc                 C   s   |  dk rdnd}|tjjkr| | } ||}t||k|d}t|}	t|	||d}	|	  |     kr=dkrDn n| |} |d urcdd t	|  D }
|j
d |
|< ||
}| | } t||k| d} |	|  S )Nr!   r   r    g      c                 S   s   g | ]}d qS r    r(   r/   rQ   r(   r(   r)   r1     r{   z&_nll_loss_backward.<locals>.<listcomp>)rK   r   r&   ru   rP   rb   rc   r   scatterrO   r   reshape)rf   rt   r   r   r   r   r   channel_dimsafe_targetrS   	new_shaper(   r(   r)   _nll_loss_backward  s    	

 

r  c           
      C   s   |  dks
J dt|  |}||}|d dks'J d| d| |d }||d|}||||}t|}d| | | |  }	||  }tj||	g|dS )Nr   z*glu does not support 0-dimensional tensorsr!   z.Halving dimension must be even, but dimension z	 is size r`   rx   )rK   r@   canonicalize_dimsizenarrowrb   r   cat)
rf   rt   rK   wrap_dimnIn	inputSize	firstHalf
secondHalfgradInputFirstHalfgradInputSecondHalfr(   r(   r)   glu_backward  s   

r  c                 C   sr  d|    krdksJ d J d|  dksJ d|  dko)|  dk}|sC|jd |jd ksCJ d|j d|j d| dksXJ d	|j d
|  df|d u si| |jd ksiJ d|tjjkr|  dkr|   dkr| jd |jd ksJ d|jd  d|    d| jd  n|   dkr|  dksJ d| j t| ||||||S )Nr   r!   input tensor should be 1D or 2Dr    ;0D or 1D target tensor expected, multi-target not supportedsize mismatch (got input: 
, target: ):expected total_weight to be a single element tensor, got: z (z
 elements)rN   z<weight tensor should be defined either for all or no classesz7Expected a tensor of dimension 1 and tensor.size[0] == z but got: dimension z and tensor.size[0] == z7Expected a single element grad_output tensor, but got: )rK   r   r   r   r%   ru   r  )rf   rt   r   r   r   r   r   no_batch_dimr(   r(   r)   nll_loss_backward  s:   ("
r  c                 C   s   |  dksJ d|   |  dksJ d|   |jd |jd kr<|jd |jd kr<|jd |jd ksHJ d|j d	|j | dks\J d
|j d|  dt| ||||||S )N   zSonly batches of spatial inputs supported (4D tensors), but got input of dimension: r   zUonly batches of spatial targets supported (3D tensors) but got targets of dimension: r   r!   r    r  r  r  z ( z, elements))rK   r   r   r  )rf   rt   r   r   r   r   r   r(   r(   r)   nll_loss2d_backward8  s*   r  c              	   C   s\   |d t t |  | dd |t t | | dd  }|d ur)|| }t||S )Nr    r(   i)rb   maximumlog1pnew_fulllogr   )rt   r   r   r   r   r(   r(   r)   binary_cross_entropy[  s   

r!  c                 C   sR   d}| ||  t j|d|  |d }|d ur|| }|tjjkr'||  }|S )Ng-q=r    r   )rb   r   r   r&   ru   r   )rf   rt   r   r   r   EPSILONr   r(   r(   r)   binary_cross_entropy_backwardq  s   
"r#  c                 C   s    t t |  | }t||S r4   )rb   r  ra   r   )r   r   r   r   r(   r(   r)   soft_margin_loss  s   
r$  c                 C   s6   ||  t || d  }|tjjkr||  }|S rV   )rb   r   r   r&   ru   r   )rf   rt   r   r   rS   r(   r(   r)   soft_margin_loss_backward  s   	r%  r!   otherpc                 C   s   t j| | |dS )N)r'  )r}   r   )r   r&  r'  r(   r(   r)   dist  r   r(  x1x2c           	      C   s   |  ddd}tj|tjd}| ddd}tj|tjd}t| d||gd}t|||gd}||j}|	d
 S )Nr!   rN   Tmemory_formatr   )powr   rb   	ones_likecontiguous_formatr
  r   matmulmT	clamp_minsqrt)	r)  r*  x1_normx1_padx2_normx2_padx1_x2_r   r(   r(   r)   _euclidean_dist  s   r;  input_sizesstartendstepc                 C   s   |  |}t|| ||||S r4   )	new_zerosrb   slice_scatter)rf   r<  rK   r=  r>  r?  rS   r(   r(   r)   slice_backward  s   

rB  r    c                 C   sp  ddl m}m} |  }|dkrtdt|  |}t|  }t| 	 }	|dkr0td|d ur6|nd}
|d ur>|nt
j}||
dk rM|
|| 7 }
||dk rY||| 7 }||
dk rbd}
n||
|| krn|| }
|||
k rw|
}n||t
jks|||| kr|| }|  |
|	|   }||
 }|| d | ||< |	|  |9  < | jrtd| ||	|S )Nr   )guard_size_obliviousstatically_known_truez,slice() cannot be applied to a 0-dim tensor.zslice step must be positiver    z<Slice decomposition for quantized tensors aren't implemented)%torch.fx.experimental.symbolic_shapesrC  rD  rK   RuntimeErrorr@   r  listr  stridesysmaxsizestorage_offsetis_quantizedNotImplementedError
as_strided)rt   rK   r=  r>  r?  rC  rD  ndimsizesstrides	start_valend_valrK  lenr(   r(   r)   slice_forward  sD   	
rU  c                    s@   | j |  dtf fdd}||d d}|||  }||fS )zn
    Normalize start and end such that both are in the range
    [0, x.get_size()[dim]] and start <= end.
    rL   c                    s,   | d u r|S | dk r|   } t t| ||S rl   r   r   )valr   r   defaultdim_sizer(   r)   
clamp_wrap  s
   z(_normalize_start_end.<locals>.clamp_wrapr   )r   int)r0   rK   r=  r>  r[  r(   rY  r)   _normalize_start_end  s
   
r]  srcc              	   C   sB  t | j|}| j| }t| |||\}}t| j}|| |d  | ||< ||}|dkr;||kr;|dkr;| S d g|   }t	j
|| jd}	|	| | ||< t	j|| jt	jd}
|dkrht	|
|	|k}
||krtt	|
|	|k }
|dkrt	|
|	| | dk}
dg|   }d||< |
|}
t|
t||
|d| S )Nr    r   devicer`  r   rN   )r@   r  rO  r   r]  rG  expandclonerK   rb   aranger`  onesboollogical_andviewr}   rc   _unsafe_masked_index)r   r^  rK   r=  r>  r?  rZ  src_sizeindicesidxmask
mask_shaper(   r(   r)   rA    s,   




rA  indexc                 C   s   |  |}t|| ||S r4   )r@  rb   select_scatter)rf   r<  rK   ro  rS   r(   r(   r)   select_backward:  s   
rq  offsetdim1dim2c                 C   s   |  |}t|| |||S r4   )r@  rb   diagonal_scatter)rf   r<  rr  rs  rt  rS   r(   r(   r)   diagonal_backwardA  s   
rv  input_dtypec                 C   s   | j |kr
||}|S r4   )r   r6   )rf   rS   rw  r(   r(   r)   _cast_grad_to_input_dtypeJ  s   

rx  outputc                 C   s0   | | }||t j||dd  }t| || S NTr   )rb   r   rx  
contiguous)rf   ry  rK   rw  new_grad_outputrS   r(   r(   r)   _softmax_backward_dataR  s
   
r}  c                 C   s*   | t |t j| |dd  }t| ||S rz  )rb   ra   r   rx  )rf   ry  rK   rw  rS   r(   r(   r)   _log_softmax_backward_datad  s   
r~  c           
      C   sZ   | |d  ||d   }t tjtj|d}|d||d}|d|| |d}	||	 S )z/Utility function to implement im2col and col2imr!   r    r   r`  r   rN   )r   rb   rd  int64rP   )
input_dkernel_d
dilation_d	padding_dstride_dr`  blocks_d	arange_kwblocks_d_indiceskernel_gridr(   r(   r)    _im2col_col2im_indices_along_dimp  s
   r  kernel_sizedilationpaddingrH  c              	      s&  t tdkdd  t t dkdd  t tdkdd  t tdkdd  ddd	}|d
 | d | ddd |d | jt}t |dv odtdd dd  D fdd tdd tdd   D t tdd D  fdd |dk}|s| d} | j\}}	}
}\}}\}} \}}\}}t|
||||| j	}t|||||| j	}t
| ||||f}|dd}|d d d d ||f }|dddddd}|d}|d}|||	| | || }|s|d}|S ) Nr!   c                   S      dS )Nz"im2col(): only 2D kernel supportedr(   r(   r(   r(   r)   rz         zim2col.<locals>.<lambda>c                   S   r  )Nz$im2col(): only 2D dilation supportedr(   r(   r(   r(   r)   rz     r  c                   S   r  )Nz#im2col(): only 2D padding supportedr(   r(   r(   r(   r)   rz     r  c                   S   r  )Nz"im2col(): only 2D stride supportedr(   r(   r(   r(   r)   rz     r  Tc                 S   <   |rt dd | D nt dd | D }t|dd  d S )Nc                 s       | ]}|d kV  qdS r   Nr(   r/   r'  r(   r(   r)   	<genexpr>      z1im2col.<locals>.check_positive.<locals>.<genexpr>c                 s       | ]}|d kV  qdS r  r(   r  r(   r(   r)   r    r  c                   S   r  )Nz<{param_name} should be greater {'than' zero, but got {param}r(   r(   r(   r(   r)   rz     r  z0im2col.<locals>.check_positive.<locals>.<lambda>r   rb   r|   param
param_namestrictcondr(   r(   r)   check_positive     (zim2col.<locals>.check_positiver  r  r  Fr  rH  r   r  c                 s       | ]}|d kV  qdS r  r(   r/   dr(   r(   r)   r    r  zim2col.<locals>.<genexpr>r   c                         dt   S )NzmExpected 3D or 4D (batch mode) tensor for input with possible 0 batch size and non-zero dimensions, but got: tupler(   r   r(   r)   rz         c                 s   s>    | ]\}}}}}d |d|  ||d    d  |  V  qdS )r    r!   Nr(   r/   r   paddilkerstr(   r(   r)   r    s
    "
r-  c                 s   r  r  r(   )r/   cr(   r(   r)   r    r  c                      s6   dt dd   d d  d d d dS )	Nz!Given an input with spacial size r-  , kernel_size=, dilation=
, padding=	, stride=z9, the calculated shape of the array of sliding blocks is z*, but its components must be at least one.r  r(   r  r  output_sizer  r   rH  r(   r)   rz     s    r  r   rN   r    r      T)rb   r|   rT  r   r   r  ziprP   r  r`  r   r  permuter  r  squeeze)r   r  r  r  rH  r  rO  batched_input	batch_dimr  input_hinput_wstride_hstride_w	padding_h	padding_w
dilation_h
dilation_wkernel_hkernel_wblocks_row_indicesblocks_col_indicespadded_inputry  num_blocks_rownum_blocks_colr(   r  r)   im2col  sd   	



 




r  r  c              
      s  t tdkdd  t tdkdd  t tdkdd  t tdkdd  t tdkdd  d$d	d
}|d |d |ddd |d |d | jt}t |dv outdd dd  D fdd d d  }t d | dkfdd dd tD }	|	d |	d   t d  k fdd t  dk fdd |dk}
|
s| d} | j\}}\}}\}}\}}\}}| d d | g |	 } | dddd dd!} t	|||||| j
}t|d }t	|||||| j
}d"d tD }| d d t g| }d d ||f}tj||| dd#}t|| | | | f}|
sf|d}|S )%Nr!   c                   S   r  )Nzonly 2D output_size supportedr(   r(   r(   r(   r)   rz     r  zcol2im.<locals>.<lambda>c                   S   r  )Nzonly 2D kernel supportedr(   r(   r(   r(   r)   rz     r  c                   S   r  )Nzonly 2D dilation supportedr(   r(   r(   r(   r)   rz     r  c                   S   r  )Nzonly 2D padding supportedr(   r(   r(   r(   r)   rz     r  c                   S   r  )Nzonly 2D stride supportedr(   r(   r(   r(   r)   rz     r  Tc                 S   r  )Nc                 s   r  r  r(   r  r(   r(   r)   r    r  z1col2im.<locals>.check_positive.<locals>.<genexpr>c                 s   r  r  r(   r  r(   r(   r)   r    r  c                   S   r  )Nz9{param_name} should be greater than zero, but got {param}r(   r(   r(   r(   r)   rz     r  z0col2im.<locals>.check_positive.<locals>.<lambda>r  r  r(   r(   r)   r    r  zcol2im.<locals>.check_positiver  r  r  Fr  rH  r  )r!   r   c                 s   r  r  r(   r  r(   r(   r)   r    r  zcol2im.<locals>.<genexpr>r-  c                      r  )NzmExpected 2D or 3D (batch mode) tensor for input with possible 0 batch size and non-zero dimensions, but got: r  r(   r  r(   r)   rz     r  r   r    c                      s   dd  d  S )Nz|Expected size of input's first non-batch dimension to be divisible by the product of kernel_size, but got input.shape[-2] = r-  z and kernel_size=r(   r(   )r  r   r(   r)   rz     s
    c                 S   s:   g | ]\}}}}}d |d|  ||d    d  |  qS r    r!   r(   r  r(   r(   r)   r1      s    "zcol2im.<locals>.<listcomp>rN   c                      4   d d d d d d  dd  d	S 
NzGiven output_size=r  r  r  r  z , expected input.size(-1) to be 	 but got rN   .r(   r(   Lr  r  r  r  r   rH  r(   r)   rz   	      c                      r  r  r(   r(   r  r(   r)   rz     r  r   r  r  c                 S   s   g | ]
\}}|d |  qS r!   r(   )r/   or'  r(   r(   r)   r1   +      
accumulater  )rb   r|   rT  r   r   r  rP   r  r  r  r`  rR   r@  prodr}   _unsafe_index_putr   r  r  )r   r  r  r  r  rH  r  rO  prod_kernel_sizecolr  out_hout_wr  r  r  r  r  r  r  r  indices_rowindices_coloutput_padded_sizery  rl  r(   r  r)   col2im  s   




 



"

r  rm  c                 C   s$   | | | |  jt| d}|S Nr+  )type_asrc  r@   r   )rf   rm  rh   rE   r(   r(   r)   native_dropout_backward8  s   	r  
input_size	dimensionr  c           	      C   s   t |dkrt| dS tt ||}tj|| | jtjd}|d||	 }| 
d|d 	||d } | |}d| |f }tj||| dd S )Nr   ra  rN   r    r4   Tr  )rT  rb   squeeze_copyr@   r  rd  r`  int32unfoldflattenmovedimr@  r}   r  r{  )	r   r  r  r  r?  rK   rl  rS   ro  r(   r(   r)   unfold_backwardG  s   
r  epsc              	   C   st   |d ur|}d| }t t ||k||k| |d|   dS t t |dk|dk| |d|   |dtdS )Nr`   r   r(   nan)rb   rc   rg  r  r   )rf   rt   r  lohir(   r(   r)   logit_backwardZ  s   r  trainc                 C   s&   |r|dkrt | ||d S |  S rl   )r}   native_dropoutrc  )r   r'  r  r(   r(   r)   dropouto  s   r  out0out1c                 C   s   |r6|dkr6|dkrt | t j| t jdfS | jjstdt | |k}||  tdd|   }||fS | t j| t jdfS )Nr   r    r   z?result type Float can't be cast to the desired output type Longr`   )	rb   r   rf  r   is_floating_pointrF  	rand_liker   r/  )r   r'  r  	bool_maskresr(   r(   r)   r  y  s   r  half_to_floatc                 C   s   |   } |r| jtjksJ tj| tjjd\}}| |} | 	 dkr*t
| }ntj| |dd}t
| | }|tj||dd }|sJ||}|S Nr2   r   T)r   )r{  r   rb   halfr@   rA   ELEMENTWISE_TYPE_PROMOTION_KINDDEFAULTr6   r   ra   amaxr   )r0   rK   r  r9   r<   unnormalizedx_maxr   r(   r(   r)   _softmax  s   


r  )r   c           	      C   s   |   } |r| jtjksJ tj| tjjd\}}| |} | 	 dkr'| }ntj
| |dd}| | }ttjt||dd}|| }|sL||}|S r  )r{  r   rb   r  r@   rA   r   r  r6   r   r  r   r   ra   )	r0   rK   r  r9   r<   shiftedr  shifted_logsumexpr   r(   r(   r)   _log_softmax  s    


r  rN   rk  padding_idxscale_grad_by_freqsparsec                 C   sJ   |   dks
J d|jdkr!| d|}|jdkr|d}|S | | S )Nr!   z'weight' must be 2-Dr    r   )rK   rO  index_selectr  )r   rk  r	  r
  r  r   r(   r(   r)   	embedding  s   	


r  num_weightsc                 C   s   t j| t jjd\}}| |} t|tj}|r8||f}t	|}t
j||g|dd}|| }	| |	d } t||k| j}
| |
d}| |f| j|jd   }t
j||g|dd|S )Nr  Tr  rN   r   )r@   rA   r   r  r6   r   rb   longr@  r/  r}   r  rP   rR   rO  masked_fillr   )rf   rk  r  r	  r
  r9   r<   countsre  grad_weights_scalerm  r   grad_weightr(   r(   r)   embedding_dense_backward  s&   	


r  c                 C   s   d}| D ]}||9 }q|S rV   r(   )r0   rE   ir(   r(   r)   r    s   
r  tensors
num_chunksc           	      C   s   g }| D ]H}|  }|| | d | | }||| kr7dgd |j| d  d|||  g }t||d}|d | t|dg }||| q|S )Nr    r   r!   rN   )r  rO  r}   constant_pad_ndrb   Sizeappendrh  )	r  rK   r  padded_tensorstensortensor_sizepad_along_dimr  	view_sizer(   r(   r)   
_pad_chunk  s   
r   c                 C   s(   | d j }| D ]
}|j |kr dS qdS )Nr   FTrO  )r  rO  r  r(   r(   r)   have_same_ndims	  s   

r"  c                 C   sB   | d   d | }| D ]}t|  d | |kdd  qd S )Nr   c                   S   r  )NzG_chunk_cat expects same sizes of 0,...,dim-1 dimensions for all tensorsr(   r(   r(   r(   r)   rz     r  z+leading_dimension_matches.<locals>.<lambda>)r  rb   r|   )r  rK   leading_dim_sizesr  r(   r(   r)   leading_dimension_matches  s   r$  c                 C   s   t |dkdd  t t| dkdd  | d j}| d j}| D ]$}t | dkdd  t |j|kdd  t |j|kdd  q"t| rVt| d 	 |}nt |dkd	d  | D ]}t ||j
k d
d  qbt| | |S )Nr    c                   S   r  )Nz&_chunk_cat expects positive num_chunksr(   r(   r(   r(   r)   rz     r  z._preprocess_chunk_cat_inputs.<locals>.<lambda>r   c                   S   r  )Nz0_chunk_cat expects a non-empty input tensor listr(   r(   r(   r(   r)   rz   !  r  c                   S   r  )Nz#_chunk_cat expects non-empty tensorr(   r(   r(   r(   r)   rz   &  r  c                   S   r  )Nz8_chunk_cat expects all input tensors with the same dtyper(   r(   r(   r(   r)   rz   )  r  c                   S   r  )Nz8_chunk_cat expects all inputs tensors on the same devicer(   r(   r(   r(   r)   rz   -  r  c                   S   r  )NzK_chunk_cat expects non-negative dim when input tensors have different ndimsr(   r(   r(   r(   r)   rz   4  r  c                   S   r  )Nz3_chunk_cat expects dim < ndim for all input tensorsr(   r(   r(   r(   r)   rz   9  r  )rb   r|   rT  r   r`  r   r"  r@   r  rK   rO  r$  )r  rK   r  expected_dtypeexpected_devicer  r(   r(   r)   _preprocess_chunk_cat_inputs  s:   


r'  r   c                 C   sH   t | ||}t| ||}|d u rt||d S tj||d |d |S )Nr    )r   )r'  r   rb   r
  )r  rK   r  r   r  r(   r(   r)   
_chunk_cat?  s   r(  split_sizesc                 C   sX   t j| ||d}|d u rdd |D S t||D ]\}}t||j t||dd qd S )Nrx   c                 S   s   g | ]	}|j tjd qS )r+  )rc  rb   r0  )r/   sr(   r(   r)   r1   [  s    z)split_with_sizes_copy.<locals>.<listcomp>Tr   )r}   split_with_sizesr  r   r   r   )rt   r)  rK   r   splitsry  splitr(   r(   r)   split_with_sizes_copyP  s   	r.  
split_size.c                 C      t j| ||S r4   )r}   r-  r   )r   r/  rK   r(   r(   r)   unsafe_splitc     r1  c                 C   r0  r4   )r}   r+  rX  )r   r)  rK   r(   r(   r)   unsafe_split_with_sizesh  s   r3  c                    s   | j }|| } dkr|dksJ |  fS |  d   }ddlm} ||} fddt|D }  | |  |d< t| ||S )Nr   r    )	guard_intc                       g | ]} qS r(   r(   r/   r  r/  r(   r)   r1   |  r{   zsplit.<locals>.<listcomp>rN   )r   detachrE  r4  rO   rb   r-  )rt   r/  rK   r<  rZ  chunksr4  r)  r(   r7  r)   r-  o  s   
r-  tensor_indices_or_sectionsc                    s   |j jdksJ |jtjksJ |  t dkp dk fdd  dkr9| }t|t	s3J | 
||S dd |D }| 
||S )Ncpur    r   c                      s   d  dS )Nz{tensor_split expected tensor_indices_or_sections to be a zero-dimensional or one-dimensional tensor, but got a tensor with z dimsr(   r(   	split_dimr(   r)   rz     s    zAtensor_split_tensor_indices_or_sections_py_impl.<locals>.<lambda>c                 S   s   g | ]}|  qS r(   )itemr6  r(   r(   r)   r1         zCtensor_split_tensor_indices_or_sections_py_impl.<locals>.<listcomp>)r`  typer   rb   r  rK   r|   r>  r-   r   tensor_split)rt   r:  rK   sectionsrk  r(   r<  r)   /tensor_split_tensor_indices_or_sections_py_impl  s   

rC  mat1mat2c                 C   sH   |   s|  st|}t|}|t|| }|dkr|S |||   S rl   )r  
is_complexr\  rb   mm)rt   rD  rE  r]   rg   r   r(   r(   r)   addmm  s   rH  use_geluc                 C   s<   t | ||||}|r| jrtj|ddS t|S t|S )Nr   )r   )rH  is_cudar}   gelurelu)rt   rD  rE  r]   rg   rI  r   r(   r(   r)   _addmm_activation  s   

rM  vecc                 C   s\   |   s|  st|}t|}|t|| }|dkr|S | dkr(||  S |||   S rl   )r  rF  r\  rb   mvr   )rt   rD  rN  r]   rg   r   r(   r(   r)   addmv  s   rP  r   rstdgammaNCHxWgroupoutput_maskc
              	      s  t j| ||dd t j|| dd t j|dd t|    k fdd tjfkfdd td u pJ  k fdd t \}
}t|dk fdd t| |	 j
d	gd
}| 	 j
d	gd
}d }d }d }|	d r:d|
  }d urt|d|

d	}t|d|

d	}t|dd|
}n&||

d	}||

d	}t|dtjd|
f|jd}| | | | | | }|  || |  }|d}t|d}t|d}t| |
|t||
| | }||j|j}|	d r_|	|
|	|
d  |d j
dgd
 }|	d	 rk|j
dgd
}|||fS )NF)allow_cpu_scalar_tensorsc                      s   d    dS )NzExpect input to have z	 elementsr(   r(   )rT  rU  rS  r(   r)   rz     r?  z,native_group_norm_backward.<locals>.<lambda>c                      s   d  d dj  S )NzExpect mean to have shape (, z
, but got r  r(   )rS  rV  r   r(   r)   rz         c                      s$   d  dd ur   S d S )NzExpect gamma to have z elements but got rN   )r   r(   )rT  rR  r(   r)   rz        $ r   c                      s   d  d S )NzExpect number of channels z, to be evenly-divisible by number of groups r(   r(   )rT  rV  r(   r)   rz     r{   r!   rx   r`   rN   r    r_  r  )r@   check_same_devicecheck_same_shaperb   r|   r   r   divmodr   rh  r   rP   r  re  r`  rR   r6   r   )rf   r   r   rQ  rR  rS  rT  rU  rV  rW  cpg_remdsdbd_inputd_gammad_biasr*  ds_valdb_valc1c2c3r(   )rT  rU  rS  rR  rV  r   r)   native_group_norm_backward  s   
 
""



$

rk  out2c
                C   d   t | |||||||||	
}|
||f}t|D ]\}}|d ur/t|| |j t||| dd q|S r   )rk  	enumerater   r   r   )rf   r   r   rQ  rR  rS  rT  rU  rV  rW  r  r  rl  r   rS   r  rE   r(   r(   r)   native_group_norm_backward_out4  s   
ro  c                 C   s   | d ur	|  |S | S r4   r6   )r0   r   r(   r(   r)   _maybe_castQ  s   
rq  grad_outnormalized_shapebiasc           "         sf  |j }| }	t|j  fdd| |||fD \}
}}}|
d us$J |	t| }||d  }|d | }g }g }t|	D ]}||krJ|| q>|| q>t|}t|}ddl	m
} ||dksj||dkr|d rs||nd |d r|||d  nd |d r|||d  fS d fS t|| }t|| }|| | }|d ur|
| }n|
}|| }t||d}t||}t||d}t||}|| | }d }d } d }!|d r|| | }|d r|d urt|dkrt|
| |d} n|
| } |d r"|d ur"t|dkrt|
|d}!n|
 }!t||jt| |jt|!|jfS )	Nc                 3   s,    | ]}|d ur|j  tjdn|V  qd S r  )r6   rb   r0  r.   r8   r(   r)   r  f  s    
z-native_layer_norm_backward.<locals>.<genexpr>r   rC  r    r!   TF)r   rK   r@   get_computation_dtyper   rT  rO   r  r  rE  rC  r@  rR   rb   r   r   rc  rq  )"rr  r   rs  r   rQ  r   rt  rW  input_shape
input_ndimgrad_out_cast
input_castweight_cast	bias_castaxis
inner_dims
outer_dimsinner_dim_indicesouter_dim_indicesr  rS  MrC  x_hat
grad_x_hatabrh  ri  rj  rG   rc  d_weightre  r(   r8   r)   native_layer_norm_backwardX  sn   





r  c             	   C   s`   t | |||||||}||	|
f}t|D ]\}}|d ur-t|| |j t||| dd q|S r   )r  rn  r   r   r   )rr  r   rs  r   rQ  r   rt  rW  r  r  rl  r   rS   r  rE   r(   r(   r)   native_layer_norm_backward_out  s   
r  running_meanrunning_varmomentum
functionalc	                 C   sT  dgt td|   }	t| j}
|}|}|rt| j}
| j|
d}tj||	ddd\}}t	|| }| | | }t
||	}t
||	}|d ur]|| d| |  }|s]|| |d ur|  | jd  }t
||	}|||d   }|| d| |  }|s|| nT|d ur|d usJ |j|
dd}|}|j|
dd}|}|}dt||  }| jjdkr|}|}n
| d	}| d	}t||  d }t||  d }| | | }|d ur| }t||  d }|| }|d ur	| }t||  d }|| }| jjdkr|j| jd}|j| jd}|j| jd||||fS )
Nr   r!   r   T)rK   
correctionr   r    )r   r~   r;  r   )rG  rO   rK   r@   rv  r   r6   rb   var_meanrsqrtr  copy_r   r   r4  r`  r@  r@  rR   r  )r   r   rt  r  r  r   r  r  r  reduction_dimsr9   new_running_meannew_running_var	input_acc
biased_varr   rQ  ry  	save_mean	save_rstdnsqueezed_varunbiased_varinvstdr(   r(   r)   native_batch_norm_helper  st   





r  r  save_invstdc              
   C   ,   t | |||||||d	\}}	}
}}||	|
fS NFr  r   r   rt  r  r  r   r  r  ry  r  r  rQ   r(   r(   r)   native_batch_norm  s   
r  c              
   C   sv   |d u r|d u rt | |||||S |d u rtd|d u r"td|r0t | |||||||S t | ||||||S )Nz`running_mean is None, but running_var is provided. They should both be None or both be provided.z`running_var is None, but running_mean is provided. They should both be None or both be provided.)r}   _native_batch_norm_legitrF  $_native_batch_norm_legit_no_training)r   r   rt  r  r  r   r  r  r(   r(   r)   native_batch_norm_decomposition4  s&   r  c                    s|   |  |}|| d |   dkr4|dkr4 fdd|D }  | |  ||d < tjjj| ||S tjjj|  |S )Nr    r   c                    r5  r(   r(   r   r7  r(   r)   r1   _  r{   z(unsafe_chunk_py_impl.<locals>.<listcomp>)r  rb   opsr}   r3  rX  r1  r   )r  r9  rK   rZ  r)  r(   r7  r)   unsafe_chunk_py_implY  s   
r  c              
   C   s   t j| ||||d||S r  )r}   r  rX  )r   r   rt  r  r  r  r  r(   r(   r)   r  e  s   
r  c              
   C   r  r  r  r  r(   r(   r)   r  {  s   
r  c           
   
   C   s,   t | ||d d |||d	\}}}}	}	|||fS r  r  )
r   r   rt  r   r  r  ry  r  r  rQ   r(   r(   r)   !_native_batch_norm_legit_no_stats  s   	
r  c              
   C   sP   t | |||||||d	\}}	}
}}|d usJ d|d us!J d||	|
||fS )NT#new_running_mean should not be None"new_running_var should not be Noner  )r   r   rt  r  r  r   r  r  ry  r  r  r  r  r(   r(   r)   #_native_batch_norm_legit_functional  s   r  c           	   	   C   sP   t j| ||||d|}d}|t jjjkrt j| |}t j|t j| j| j	dS )a  
    Return a reserve tensor for batch norm, used only by cudnn to pass forward state to the
    backward pass. This is needed for `_batch_norm_with_update` and `_batch_norm_no_update`,
    which support a variety of backends including cudnn. We create this tensor here to get
    the correct shape in the traced graph if we detect that will call the cudnn kernel,
    and rely on DCE to avoid materializing this tensor.
    Tr   )r   layoutr`  )
rb   _C_select_batch_norm_backend_BatchNormBackendCudnn(_get_cudnn_batch_norm_reserve_space_sizeemptyuint8r  r`  )	r   r   rt  r  r  r  r   backendreserve_sizer(   r(   r)   _get_batch_norm_reserve_tensor  s   r  c              
   C   sD   t | ||||d||d	\}}}	}
}
t| |||||dd}|||	|fS )NTFr   r  r  r   r   rt  r  r  r  r  ry  r  r  rQ   reserver(   r(   r)   _batch_norm_with_update     
r  c              
   C   sh   t | ||||d||d	\}}}	}
}t| |||||dd}|
d us$J d|d us,J d|||	||
|fS )NTr  r  r  r  )r   r   rt  r  r  r  r  ry  r  r  new_rmnew_rvr  r(   r(   r)   "_batch_norm_with_update_functional  s   r  c              
   C   sD   t | ||||d||d	\}}}	}
}
t| |||||dd}|||	|fS )NFr  r  r  r(   r(   r)   _batch_norm_no_update  r  r  c                 C   sB   |d u sJ t | |k jt jd}|| |  d|  }||fS )Nr   r`   )rb   r  r6   r  r  )r   r'  	generatorrm  r  r(   r(   r)   _fused_dropout_decomposition"  s   r  )r   r  r`  
pin_memorynon_blockingr,  r`  r  r  r,  c          	      C   s
  |r|t jksJ d|rJ dt| t jttttfsJ |d u r6|d u r6|d u r6t| t jr4|  S | S d}t| t jrA| }nt 	| }|d uri||j
kri|d ura|jdkrat j||}d}t j|||}|d urx|sxt j||}d}|d urt j||dS |S )NTODOFr;  Tr+  )rb   stridedr-   r   r\  r   rf  complexrc  scalar_tensorr`  r@  _primsconvert_element_type
device_put)	r0   r   r  r`  r  r  r,  dtype_convertedx_tensorr(   r(   r)   _to_copy,  s,   
r  c                 C   s
   t | S r4   )r}   aliasr7   r(   r(   r)   nop_decompositionZ  s   
r  out3exponential_average_factorepsilonc              
   C   s^   t | |||||||\}}	}
|r||	|
| jdtjdfS ||d|d| jdtjdfS )Nr  r   )r}   r  r@  rb   r  )r   r   rt  r  r  r   r  r  r  r  r  r(   r(   r)   cudnn_batch_normb  s"   
r  c                 C   s@   t |D ]\}}|dkr|| jk r| j| |ks| |} q| S rV   )rn  rO  r   rP   )r0   broadcast_maskr}  rm  r(   r(   r)   _broadcast_batch_norm_backward  s
    
r  r  c                 C   s   t | |||||||||	
S r4   )native_batch_norm_backward)rr  r   r   r  r  r  r  r  r  rW  r  r(   r(   r)   batch_norm_backward  s   r  c
           &         s  |j }
|d ur|j }n|
}t|j   fdd| ||||||fD \}}}}}}}|j}| }|dks9J dd}tt|||  }|}|}|rV|d urS|d usUJ n|d ur^|d us`J |}t|| }dg| }|| ||< g }t	|D ]}||kr|
| qzt||}d| }t||}t|||  |}t|| |}tt|| || |} |d u rt||d }!nt|| |}!|r|| |  }"||" | |! }#n||! }#|	d r|| }$nd }$|	d r|}%nd }%|#|
t|$|t|%|fS )Nc                 3   s&    | ]}|d ur|  n|V  qd S r4   rp  r.   r8   r(   r)   r    s
    
z-native_batch_norm_backward.<locals>.<genexpr>r!   z$rank of the input must be at least 2r    r`   )r   r@   rv  r   rK   r  rG  rb   r  rO   r  r  r   r   r6   rq  )&rr  r   r   r  r  r  r  r  r  rW  rw  weight_dtypery  rz  r{  running_mean_castrunning_var_castsave_mean_castsave_invstd_castrw  
input_rankr}  num_featuresr   r  r  reduction_axesr  r   grad_output_sumdot_p	grad_mean
proj_scale
grad_scaleprojrS   r  	grad_biasr(   r8   r)   r    s   
	



r  c
                C   rm  r   )r  rn  r   r   r   )rr  r   r   r  r  r  r  r  r  rW  r  r  rl  r   rS   r  rE   r(   r(   r)   native_batch_norm_backward_out	  s&   
r  save_varc                 C       t || |||||d|g d
S NT)TTTr}   r  )r   rf   r   r  r  r  r  r  r(   r(   r)   miopen_batch_norm_backward6	  s   r  reserveSpacec	           	      C   r  r  r  )	r   rf   r   r  r  r  r  r  r  r(   r(   r)   cudnn_batch_norm_backwardP	  s   r  c                    s  | j  | jttdv fdd | jdd  D ]}t|dkfdd qd |d  dkrjd |d  dkrjtdd	 tdd  |D }td
d	 tdd  ||D }tjj	| ||S dd dd  fdd}|d |d \}}}}	|d |d \}
}}}| dt
|d|
f }|	s|stj|ddS dd }|||||	dd\}}|||||dd\}}d }tt|jd t|jd D ]\}}|d u r|d|d d |f }q||d|d d |f  }q|||  S )Nr  c                      
   d  S )Nz9adaptive_avg_pool2d(): Expected 3D or 4D tensor, but got r(   r(   r!  r(   r)   rz   u	     
 z%adaptive_avg_pool2d.<locals>.<lambda>r-  r   c                         dt   dS )Nzjadaptive_avg_pool2d(): Expected input to have non-zero size for non-batch dimensions, but input has shape r  r  r(   r  r(   r)   rz   z	  s    rN   c                 s   s    | ]	\}}|| V  qd S r4   r(   )r/   r  r  r(   r(   r)   r  	      z&adaptive_avg_pool2d.<locals>.<genexpr>c                 s   s&    | ]\}}}||d  |  V  qdS r    Nr(   )r/   r  r  r*  r(   r(   r)   r  	  s    
c                 S   s   t j| | |ddS )Ntruncrounding_moderb   divr  r  r  r(   r(   r)   start_index	  s   z(adaptive_avg_pool2d.<locals>.start_indexc                 S   s    t j| d | | d |ddS )Nr    r  r  r  r  r(   r(   r)   	end_index	      z&adaptive_avg_pool2d.<locals>.end_indexc                    s   t j| t jd}||| }| | d }| | }|dkp"|| dk }|r+|d7 }n|dkr3|d8 }t j| t jd}|d| }|rbt j| d |j|jd}	t ||	}||| }
|
| }n|}||||fS )Nra  r    r   rN   r  )rb   rd  r  rP   r  r   r`  minimum)in_sizeout_sizeorangei0	maxlengthin_size_modadaptive	range_maxrl  maxvali1length)r`  r  r  r(   r)   compute_idx	  s(   

z(adaptive_avg_pool2d.<locals>.compute_idx.r  )r   rN   rx   c                 S   s`   t |tr	| |fS |dk sJ ||dk}|dkrt|d}t| |d} t|| }| |fS )Nr   rN   r-  r  r   )r-   r   rP   rR   rb   r  )valsr  r  r  rK   rm  r(   r(   r)   
maybe_mask	  s   

z'adaptive_avg_pool2d.<locals>.maybe_mask)r  rK   r   )r`  r   rT  rb   r|   r  r  nnr  
avg_pool2drR   r   r   rO   )r   r  r  rH  kernelr  idxhlength_hrange_max_h
adaptive_hidxwlength_wrange_max_w
adaptive_wr  r  retr  jr(   )r`  r  rO  r   r  r)   adaptive_avg_pool2dk	  sN   

(  



&r  c           	      C   s   t d| d ttj| jd |  }ttj|}dg| j }| jd |  |d | < |tj|| j	d
||  d}| t| jd |  t| }tj|d|g| ddd
|jS )Nmax_unpoolingd_forward_outr    r_  rN   Fr  )r@   alert_not_deterministicr   operatorr   r   rO  r}   rd  r`  rh  r  r@  rG  r  )	rt   rk  r  rK   nchwindices_nc_shapeindices_flatry  r(   r(   r)   _max_unpoolnd	  s   	"r%  c                    s   t jt jkfdd t tdkfdd t jdv fdd t jjkfdd tdjD ] t  d	k fd
d q>t	dS )Nc                         d j  S )Nz2elements in indices should be type int64 but got: r   r(   )rk  r(   r)   rz   	      zmax_unpool2d.<locals>.<lambda>r!   c                      r  )NzMThere should be exactly two elements (height, width) in output_size, but got 
 elements.rT  r(   r  r(   r)   rz   	     r  c                         d j  dS )NzLInput to max_unpooling2d should be a 3d or 4d Tensor, but got a tensor with  dimensions.r!  r(   r   r(   r)   rz   
  s   c                         dj  d j  S NzBExpected shape of indices to be same as that of the input tensor (z%) but got indices tensor with shape: r  r(   )rk  rt   r(   r)   rz   
     
r    r   c                         dj  d  dS )NzZmax_unpooling2d(): Expected input to have non-zero size for non-batch dimensions, but got  with dimension  being empty.r  r(   )r  rt   r(   r)   rz   
  
   )
rb   r|   r   r  rT  rO  r   rO   r  r%  )rt   rk  r  r(   )r  rk  r  rt   r)   max_unpool2d	  s,   





	r5  c                    s  t jt jkdd  t jdv fdd t tdkfdd t tdkfdd t tdkfdd t jjkfd	d td
jD ] t  dk fdd qXt d dko~d
 dko~d dkfdd t	dS )Nc                   S   r  )Nz(elements in indices should be type int64r(   r(   r(   r(   r)   rz   %
  r  zmax_unpool3d.<locals>.<lambda>r  r  c                      r,  )NzLInput to max_unpooling3d should be a 4d or 5d Tensor, but got a tensor with r-  r!  r(   r   r(   r)   rz   )
      r   c                      r  )NzVThere should be exactly three elements (depth, height, width) in output_size, but got r(  r)  r(   r*  r(   r)   rz   -
  r+  c                      r  )NzRThere should be exactly three elements (depth, height, width) in stride, but got: r(  r)  r(   rH  r(   r)   rz   4
  r{   c                      r  )NzSThere should be exactly three elements (depth, height, width) in padding, but got: r(  r)  r(   )r  r(   r)   rz   8
  r{   c                      r.  r/  r  r(   )rk  r   r(   r)   rz   <
  r0  r    r   c                      r1  )NzZmax_unpooling3d(): Expected input to have non-zero size for non-batch dimensions, but got r2  r3  r  r(   )r  r   r(   r)   rz   E
  r4  r!   c                      r  )Nz5strides should be greater than zero, but got stride: r(   r(   r9  r(   r)   rz   N
  r  )
rb   r|   r   r  rO  rT  r   rO   r  r%  )r   rk  r  rH  r  r(   )r  rk  r   r  r  rH  r)   max_unpool3d
  sB   	







	"
r:  )rg   r  c                C      t | |||d|dS )NTinplacerg   
_index_addr0   rK   ro  r  rg   r(   r(   r)   
index_add_T
  s   	rA  c                C   r;  )NFr<  r>  r@  r(   r(   r)   	index_add`
  s   
rB  r=  c                   s"  t | jtjdkfdd jdkrdnd|jdkr*|ndtkfdd  dkr]t | jttkpQt 	t
  fdd |  }| jdk}|ri| dn| }d f }|rwtjntj}	|	|||dd	}
|r| S |r|
dS |
 S )
Nr    c                      r,  Nz(Index should have dimension 1 or 0 (got r  r!  r(   ro  r(   r)   rz   y
  r8  z_index_add.<locals>.<lambda>r   c                      s   d d d S )NzNumber of indices (z') should be equal to tensor.size(dim) (z), for dim=r(   r(   )rK   
index_sizer  r(   r)   rz   
      c                      s   dt   d dS )Nzalpha argument of type z cannot be safely cast to type !)r@  r(   )rg   python_typer(   r)   rz   
  rF  r4   Tr  )r@   canonicalize_dimsrO  rb   r|   r  dtype_to_typer   rf  is_weakly_lesser_typer@  rP   r}   
index_put_	index_putr  r{  )r0   rK   ro  r  r=  rg   zero_dimr)  rl  rM  r   r(   )rg   rK   ro  rE  rH  r  r)   r?  m
  s6   	

r?  r   c              
   C   s   t t| dkdd  t| }| d  }|dd  }tdd | D }|r,||f}n||f}|| }| d ||}dt| }	t|D ]+}
| |
 }t||	d||d f |}|rhtj	||d|
d}qFtj	||d|
d}qF|S )	Nr   c                   S   r  )Nz#received an empty list of sequencesr(   r(   r(   r(   r)   rz   
  r  zpad_sequence.<locals>.<lambda>r    c                 s   s    | ]}| d V  qdS r  r  r.   r(   r(   r)   r  
      zpad_sequence.<locals>.<genexpr>)r   r   rK   ro  )
rb   r|   rT  r  r   r  rO   r}   r  rp  )	sequencesbatch_firstpadding_valuesequences_sizemax_sizetrailing_dimsmax_lenout_dimsr   dim_paddingsr  currseqrowr(   r(   r)   pad_sequence
  s(   
r]  c                 C      t | |||ddS )NTr=  _index_copyr0   rK   ro  r  r(   r(   r)   index_copy_
     rc  c                 C   r^  )NFr_  r`  rb  r(   r(   r)   
index_copy
  r   re  c          
         s   t | j|}t jdk fdd | jdk}|r | dn| } jdkr, dn  d|  f }|r:tjntj}||||}	|rG| S |rN|		dS |	
 S )Nr    c                      r,  rC  r!  r(   rD  r(   r)   rz   
  r8  z_index_copy.<locals>.<lambda>r   r4   )r@   rI  rO  rb   r|   rP   r}   rL  rM  r  r{  )
r0   rK   ro  r  r=  rN  r)  rl  rM  r   r(   rD  r)   ra  
  s   

ra  c                 C   sR   t | d| }t t |  }| js| jr| d}n|}|t | |fS )Nr(   r  )rb   r   r@  ra   r   rJ  is_xpur  )rt   r   rd   r   r(   r(   r)   log_sigmoid_forward
  s   rg  lowhighr  c                 C   s$   t j| jt|t|| j| j|dS )N)rh  ri  r   r`  r  )prims_uniform_helperr   r   r   r`  )r0   rh  ri  r  r(   r(   r)   uniform
  s   rl  c                 C   s   |  t| |||S r4   )r  rl  )rt   rh  ri  r  r(   r(   r)   uniform_
  s   rm  c                 C   s   t | d }|d ur"t|d u dd  tt ||kdd  |S |d urjt|d u dd  tt ||kdd  g }t|D ]%\}}t||krZ|| |d  t|  qB|t| |d  |  qB|S tddd  d S )	Nr!   c                   S   r  Nz9Must specify exactly one of output_size and scale_factorsr(   r(   r(   r(   r)   rz   
  r  z.upsample_compute_output_size.<locals>.<lambda>c                   S   r  N r(   r(   r(   r(   r)   rz   
  r  c                   S   r  rn  r(   r(   r(   r(   r)   rz     r  c                   S   r  ro  r(   r(   r(   r(   r)   rz     r  Fc                   S   r  rn  r(   r(   r(   r(   r)   rz     r  )rT  rb   r|   rn  r\  r  r   )r  r  scale_factorsspatial_dimensionsr  r*  r(   r(   r)   upsample_compute_output_size
  s.   rs  c                 C   s   | d u rd S | | S r4   r(   )scalesrl  r(   r(   r)   get_scale_value  s   ru  rq  c                 C   s2   t |  ||}|r|nd gt| }t| ||S r4   rs  r  rT  _upsample_nearestr   r  rq  osizert  r(   r(   r)   _upsample_nearest_vec  s   rz  c                 C   s6   t |  ||}|r|nd gt| }t| ||ddS NTexactrv  rx  r(   r(   r)   _upsample_nearest_exact_vec-  s   r~  c                 C   s   g }t |}|r
dnd}t|D ]I}|| }| j| |  }	|| d ur,|	|	||   n|	| }
tj|tj| jd}|| |
 tj}t|d | D ]}|	d}qL|
| q|S )Nr   r   r  r    rN   )rT  rO   r   rb   rd  r   r`  r6   r  rP   r  )r   r  rt  r}  rk  num_spatial_dimsrr  r  ry  isizerh   output_indicesinput_indicesrQ   r(   r(   r)   !_compute_upsample_nearest_indicesB  s   $r  )preserve_memory_formatr   rt  c                 C   s   t | ||gS r4   rw  r   r  rt  r(   r(   r)   upsample_nearest1db  s   	r  c                 C   s   t | ||gddS r{  r  r  r(   r(   r)   upsample_nearest_exact1dn     r  scales_hscales_wc                 C   s   t | |||gS r4   r  r   r  r  r  r(   r(   r)   upsample_nearest2d|  s   
r  c                 C   s   t | |||gddS r{  r  r  r(   r(   r)   _upsample_nearest_exact2d  s   r  scales_dc                 C   s   t | ||||gS r4   r  r   r  r  r  r  r(   r(   r)   upsample_nearest3d  r  r  c                 C   s   t | ||||gddS r{  r  r  r(   r(   r)   _upsample_nearest_exact3d  s   r  r}  c           	      C   sp   t | |||d}d d g| }t| |}|jdkr6t| }| jd }| jjdkr0|dk r0t	j
}|j|d}|S )Nr|  r  r    cudar+  )r  r}   _unsafe_indexrO  r@   r   r   r`  r@  rb   r0  r{  )	r   r  rt  r}  spatial_indicesrk  r   r,  
n_channelsr(   r(   r)   rw    s   


rw  c                    sb   |r|rd n|rd n|rd nd t   dks!J t  fddtdt  D S )Nr  r  r   r!   r   c                    s    g | ]}t ||   qS r(   r  r6  
group_sizeparamsr(   r)   r1     s    z!gather_params.<locals>.<listcomp>)rT  rO   )r  
has_biaseshas_projectionsr(   r  r)   gather_params  s   r  c                 C   sh   |r!| d|  |d|  }}| d| d  |d| d  }}n| | || }}d\}}||||fS )Nr!   r    NNr(   )r  hiddensr  bidirectional
cur_params
cur_hiddenbidir_paramsbidir_hiddenr(   r(   r)   params_hiddens  s   $r  c                 C   s2   ||ksJ | | d|||  | dd|S rl   )r  r	  )r  last_batch_size
batch_sizer  r(   r(   r)   update_hidden_for_packed  s   r  c              	   C   s4   ||kr| S ||k sJ t | |d||| fS rl   )rb   concatr	  )r  r  r  
inp_hiddenr(   r(   r)    update_hidden_for_packed_reverse  s   r  c                 C   s$  |d }|d }|r|d nd }	|r|d nd }
g }g }|r"|d n|d }| dd|}t| t|}|r>|d d d }|D ]-} | jd }||krLn|rVt||||}nt||||}|| |||	||
}|}|| q@|ru|  n	|| |  t	|d}|st	|dn|}||fS )Nr   r    r!   r   rN   )
r	  rb   r-  rG  r   r  r  r  reverser
  )inphiddenr  r  	hidden_fnbatch_sizesr  	ih_weight	hh_weightih_biashh_biasstep_outputr  r  r  	split_inpr  r   
hidden_outr(   r(   r)   one_layer_rnn_data  s@   


r  c                        fdd}|S )Nc                    s    t ||||  S r4   r   linearr  r  r  r  r  r  nonlinearityr(   r)   rG   1  s   zrnn_cell.<locals>.innerr(   r  rG   r(   r  r)   rnn_cell0  s   r  c                    r  )Nc                    s$   t | ||}  t ||||  S r4   r  r  r  r(   r)   rG   8  s   zrnn_cell_data.<locals>.innerr(   r  r(   r  r)   rnn_cell_data7  s   r  c                 C   s   |d }|d }|r|d nd }|r|d nd }	t | ||}
|r&|
dn|
}
|d}g }|
D ]}|||||||	}|| q1|rH|  t|d}||dfS )Nr   r    r!   r   )	r   r  fliprP   r  r  rb   r
  r  )r  r  r  r  r  r  r  r  r  r  precomputed_inputr  r  r  r   r(   r(   r)   one_layer_rnn?  s   
r  c                 C   s   |d }|d }|r|d }|d }nt | }t | }|d d}	|d d}
g }d}|	d}d}d}d}d}|  } |	 }	|
 }
t jjj| |||||	|
|||||||||}|d |d |d }}}||	d|	dffS )Nr   r    r!   r   F)
rb   r   r  rP   r{  r  r}   mkldnn_rnn_layerrX  r  )r  r  r  r  r  w0w1w2w3hxcxr  modehidden_size
num_layersr  rS  r  outputsrU   hycyr(   r(   r)   mkldnn_one_layer_lstmU  sN   


r  c
                 C   s   |r|  ddn| } g }
t|D ]^}t||||\}}}}|r'||d k r'|nd}|	| |||\}}|
| |rI|	| |||dd\}}|
| |rXt||g| d } n|} |dkrn|rn||d k rntj| |dd} q|rw|  ddn| } | |
fS )Nr   r    r   T)r  )r  )	transposerO   r  r  rb   r
  rK   r  )r   r  r  r  r  r  r  r  rS  layer_fnfinal_hiddensr  r  r  r  r  fwd_inp
fwd_hiddenbwd_inp
bwd_hiddenr(   r(   r)   _rnn_helper  s,   



r  c	                 C   R   | d}	t||d}t| |	|||||||ttttjd
\}
}|
t|dfS Nr   Fr  )	unbindr  r  r   r  r  rb   r   stackr   r  r  r  r  r  r  r  rS  r  r   r  r(   r(   r)   rnn_tanh_input     
r  c	                 C   r  r  )	r  r  r  r   r  r  rb   rL  r  r  r(   r(   r)   rnn_relu_input  r  r  c	                 C   T   | d}	t||d}t| |	||||||dtt|ttjd
\}
}|
t|dfS Nr   Fr  r  )	r  r  r  r   r  r  rb   rL  r  datar  r  r  r  r  r  r  r  r  r   r  r(   r(   r)   rnn_relu_data  &   
r  c	                 C   r  r  )	r  r  r  r   r  r  rb   r   r  r  r(   r(   r)   rnn_tanh_data  r  r  c                 C   s   t ||||  }|d|}|d  }	|d  }
|d  }|d  }|
| |	|  }||  }|d u r;|nt ||d }||fS )Nr  r   r    r!   r   r   r  chunkr   r   )r  r  r  r  r  	hr_weight	chunk_dimgateschunked_gatesin_gateforget_gate	cell_gateout_gater  r  r(   r(   r)   	lstm_cell4  s   r  c              
   C   s   |d }|d }|r|d nd }|r|d nd }t |dkr"|d nt |dkr,|d nd }	|d d}
|d d}t| ||}|rJ|dn|}g }|D ]} t| |
||||	dd\}
}||
 qP|rk|  t	|d}||

d|
dffS )Nr   r    r!   r   r  r  r  )rT  rP   r   r  r  r  r  r  rb   r
  r  )r  r  r  r  r  r  r  r  r  r  r  r  r  r  r   r(   r(   r)   one_layer_lstmB  s$   *r  c              
   C   s
  |d }|d }|r|d nd }|r|d nd }	t |dkr"|d nt |dkr,|d nd }
g }g }|r8|d n|d }t| t|}|rM|d d d }|d }|d }|dd||dd|}}|D ]l} | jd }t| ||} ||k r||d||| |d||| f |dd||dd|}}||krt	||d||| fd}t	||d||| fd}t
| ||||	|
dd\}}|}|| qf|r|  ||f}n|||f |  t| \}}t|dt|df}t|d}||fS )	Nr   r    r!   r   r  r  rN   r  )rT  rb   r-  rG  r	  r   r   r  r  r  r  r  r  r
  )r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  orig_hxorig_cxr  r  r  r  hidden0hidden1r   r(   r(   r)   one_layer_lstm_data]  s\   *

r  c                 C   s   dd }|| ||rt S tS )a*  Check whether we could use decompose lstm with mkldnn_rnn_layer.
    All the below conditions need to be met:
        * ``torch._C._get_mkldnn_enabled()`` returns ``True``.
        * All the input args are on CPU.
        * The dtypes of args are either torch.float or torch.bfloat16.
        * Inference.
        * ``has_projections`` returns ``False``.

    Args:
        * input: the input sequence to LSTM
        * hx: a tuple of the input hidden state and cell state ``(h_0, c_0)`` to LSTM
        * params: the weight and bias tensors of LSTM
    c           	      S   s   t j sdS | gt| tt| }dd |D }t|dkr$dS | }|t dkr1dS dd |D }|D ]}|t j	t j
fvrG dS q:| jrMdS |d d|d dk}|r_dS d	S )
NFc                 S      h | ]}|j qS r(   r_  r/   tr(   r(   r)   	<setcomp>      zEselect_one_layer_lstm_function.<locals>.use_mkldnn.<locals>.<setcomp>r    r;  c                 S   r  r(   r   r  r(   r(   r)   r     r  r   r!   T)rb   r  _get_mkldnn_enabledrG  r   from_iterablerT  popr`  r   bfloat16requires_gradr  )	r   r  r  r  devicesr`  dtypesr   r  r(   r(   r)   
use_mkldnn  s(   
z2select_one_layer_lstm_function.<locals>.use_mkldnn)r  r  )r   r  r  r	  r(   r(   r)   select_one_layer_lstm_function  s   r
  c	                 C   s   t |dks
J dt|||d d|d dk}tt|d |d }	t| ||}
t| |	||||||||

\}}tt| }|t|d dt|d dfS )Nr!   lstm expects two hidden statesr   r    )	rT  r  r  rG  r  r
  r  rb   r  )r   r  r  r  r  r  r  r  rS  r  r  r   r  r(   r(   r)   	lstm_impl  s$   $"r  c	                 C   s   t |dks
J dt|||d d|d dk}tt|d |d }	t| |	||||||dtt|d
\}
}tt| }|
t	|d dt	|d dfS )Nr!   r  r   r    F)r  )
rT  r  r  rG  r  r  r   r  rb   r  r  r(   r(   r)   lstm_data_impl  s"   $
"r  c                 C   sr   |  dd}t||| dd}|d |d   }|d |d   }	|d |d |   }
||
 |	 |
 S )Nr   r    r!   r   )r  r   r  r   r   r  r  r  r  r  r  chunked_igateschunked_hgates
reset_gate
input_gatenew_gater(   r(   r)   gru_cell  s   r  c                 C   s|   t | ||dd}t |||dd}|d |d   }|d |d   }	|d |d |   }
||
 |	 |
 S )Nr   r    r   r!   r  r  r(   r(   r)   gru_cell_data  s   r  c	                 C   sJ   t ||d}t| |d||||||dtt|td
\}	}
|	t|
dfS )NFr   r  )r  r  r  r   r  r  rb   r  )r  r  r  r  r  r  r  r  r  r   r  r(   r(   r)   gru_impl_data$  s   r  c	                 C   sH   t ||d}t| |d|||||||tttd
\}	}
|	t|
dfS )NFr   r  )r  r  r  r   r  r  rb   r  )r   r  r  r  r  r  r  r  rS  r   r  r(   r(   r)   gru_implB  s   
r  c                 C   :   t |  ||}t|d}t|d}tjj| ||||S Nr   r    )rs  r  ru  rb   r  r}   _upsample_bilinear2d_aar   r  align_cornersrq  ry  scale_hscale_wr(   r(   r)   upsample_bilinear2d_aa_vec`     


r  c                 C   r  r  )rs  r  ru  rb   r  r}   _upsample_bicubic2d_aar  r(   r(   r)   upsample_bicubic2d_aa_vecl  r   r"  c                 C   s4   t |  ||}|r|nd gt| }t| |||S r4   )rs  r  rT  _upsample_linear)r   r  r  rq  ry  rt  r(   r(   r)   _upsample_linear_vecx  s   	r$  r  c                 C   s   t | |||gS r4   r#  )r   r  r  r  r(   r(   r)   upsample_linear1d  s   r&  c                 C   s   t | ||||gS r4   r%  )r   r  r  r  r  r(   r(   r)   upsample_bilinear2d  s   r'  c                 C   s   t | |||||gS r4   r%  )r   r  r  r  r  r  r(   r(   r)   upsample_trilinear3d  s   r(  c                 C   s@   |r|dkr| d |d  S dS |d ur|dkrd| S | | S )Nr    r`   r   r(   )r  r  r  rh   r(   r(   r)   _compute_scale  s    r)  c                 C   s   |r| | S | |d  d S Nr   r(   )rh   	dst_indexr  r(   r(   r)   _compute_source_index  s   r,  weightsweights_precisionc                 C   sB   t dd t| |D d|d >  }||? }t|ddtjS )Nc                 s   s,    | ]\}}| tj| tj V  qd S r4   )r6   rb   r  )r/   r*  r  r(   r(   r)   r    s    
z%_sum_tensors_uint8.<locals>.<genexpr>r    r      )_sum_tensorsr  rb   r   r6   r  )r^  r-  r.  ry  r(   r(   r)   _sum_tensors_uint8  s   
r1  c                 C   sJ   t |  }d}t j||jd}d|d|d >   }|dk}||  S )N   r_  r   r    i   )rb   r  r   rd  r`  r   )r-  
max_weightmax_weight_precision
precisionsvaluesrm  r(   r(   r)   _compute_weight_precision  s   r7  c                    s  j d }j dd  }t|tjtjjd\}fddfddtt|||D }tt| \}g }	t	ddgg  D ]# d d g fd	dt
D  }
t|
}t|}|	| qGtt
D ]'}|| |  d
dfddt|	d d d |	dd d D }	qqt|	dksJ |	d }t}jjdkr|dk rtj}t|tjsJ |j|d} s| }|S )Nr    r!   r  c           	         s   t | | |}tj|jdjd}t|| jdd}|j|jd gdg| R  }|tj	}|d j| d d}|||fS )Nr_  r   r   r   r   r    r   )
r)  rb   rd  r`  r6   r,  r   r  r   r  )	inp_sizer  rt  nsqueezescale_factorr  x_f32r0   xp1)r  r   r   r(   r)   
get_values  s   
z$_upsample_linear.<locals>.get_valuesc                    s,   g | ]\}\}}} |||d  | qS r   r(   )r/   r  r8  r  rt  )r=  n_dimsr(   r)   r1     s    z$_upsample_linear.<locals>.<listcomp>r   c                    s(   g | ]} | d kr| n| qS r  r(   )r/   k)r  xp1sxsr(   r)   r1        ( r   r`   c                    s$   g | ]\}}|t ||   qS r(   )rb   r   )r/   v1v2)xscaler(   r)   r1     s    r     r+  )r   rT  r@   rA   r   INT_TO_FLOATrn  r  rG  r   rO   r}   r  r   r  reversedr   r6   r   r`  r@  rb   r0  r-   r   r{  r  round)r   r  r  rt  r  	inp_sizesrQ   r6  xs_f32vsrl  vr  r   r,  r(   )	r  r  r   r=  r   r>  r@  rA  rE  r)   r#    sF   


"


r#  r  r  c                 C   s   | j |j kS r4   r  )r  r  r(   r(   r)   is_same_size  rw   rN  c                 G   rq   r4   )r}   rh  )r0   r   rB   r(   r(   r)   _reshape_alias!  s   rO  c                 C   rq   r4   )r}   ro  )r0   rk  r(   r(   r)   r  '  rw   r  c                 C   s   t | |||S r4   )r}   rM  )r0   rk  ru   r  r(   r(   r)   r  ,  r2  r  c                 C   s   |D ]}|d urt |jt jt jfv dd  qt |jt jkdd  ddlm} ||  dkr@t j	
| |}| |j|S tt|D ]}|| }|d ur^|jd| |d d||< qFt| || |S )Nc                   S   r  Nz3tensors used as indices must be long or int tensorsr(   r(   r(   r(   r)   rz   7  r  z&_unsafe_masked_index.<locals>.<lambda>c                   S   r  Nz*tensors used as masks must be bool tensorsr(   r(   r(   r(   r)   rz   <  r  r   ru  r    rV  )rb   r|   r   r  r\  rf  rE  rC  r   _meta_registrationsmeta_index_Tensorr  r   rO   rT  r   r  r}   r  r  )r0   rm  rk  fillro  rC  meta_resultr  r(   r(   r)   ri  1  s*   
ri  c                 C   s   |D ]}|d urt |jt jt jfv dd  qt |jt jkdd  |  dkr.|  S tt	|D ]}|| }|d urP|j
| | | |d d||< q4|| d}tj| ||ddS )	Nc                   S   r  rP  r(   r(   r(   r(   r)   rz   S  r  z5_unsafe_masked_index_put_accumulate.<locals>.<lambda>c                   S   r  rQ  r(   r(   r(   r(   r)   rz   X  r  r   r    rV  Tr  )rb   r|   r   r  r\  rf  r   rc  rO   rT  r   r  r  r}   r  )r0   rm  rk  r6  ro  r  masked_valuer(   r(   r)   #_unsafe_masked_index_put_accumulateM  s(   
$rW  c                 C   sV  |   }d}|dk rd}|d ur,|dkr&dg| }|jd ||< ||}n|}| | } t||k|d}	|	|}
t| ||
| }t||k|d}|tj	j
krb|dkrb| dd}||fS |d ur|| j}t|||
|}t||k|d}| }n	||k | }|tjj
kr| }||fS |tjj
kr| | }||fS )Nr    r!   r   r(   r   )rK   r   rh  rb   rc   rP   gatherr  r   r%   ru   r  rb  r   r6   r'   r&   )rt   r   r   r   r   r>  r  r   wr  safe_target_r   r   wsumr(   r(   r)   _nll_loss_forwardg  sB   


r\  c                 C   s   |   dkr|   dksJ d|  dksJ d|   dko%|  dk}|s?| jd |jd ks?J d| j d|j d| jd	 }|d u s_|  dkrT| |ks_J d
| d|j t| ||||S )Nr   r!   r  r    r  r  r  r  rN   z/weight tensor should be defined either for all z7 classes or no classes but got weight tensor of shape: )rK   r   r   r\  )rt   r   r   r   r   r  	n_classesr(   r(   r)   nll_loss_forward  s    	
"r^  c                 C   s   t | ||||S r4   )r\  )rt   r   r   r   r   r(   r(   r)   nll_loss2d_forward  s   	r_  Ac                 C   s    |d |  |d  |  |  d S )Nr!   r   r    r(   r0   r`  r(   r(   r)   _upsample_cubic_convolution1  r  rb  c                 C   s(   ||  d|  |  d|  |  d|  S )Nr     r  r(   ra  r(   r(   r)   _upsample_cubic_convolution2  s   (rd  r  c           
      C   s   d}| j t dkrDtj| d|  gdd}tj| d d|  gdd}t||}t||}tj|dd\}}tj|dd\}}	|||	|fS t| d |t| |td|  |td|  |fS )Ng      r;  r`   r   rx   r   )r`  rb   r  rd  rb  r  )
r  r`  tt1tt2w03w12r  r  r  r  r(   r(   r)    _upsample_get_cubic_coefficients  s   

ri  coeffstsc                 C   s    t |}tdd t| |D S )Nc                 s       | ]	\}}|| V  qd S r4   r(   r/   rh  ri  r(   r(   r)   r    r  z+_upsample_cubic_interp1d.<locals>.<genexpr>)ri  r0  r  )rj  rk  coeffs2r(   r(   r)   _upsample_cubic_interp1d  s   ro  c                 C   s   t tj| S r4   )r   rb   add)rk  r(   r(   r)   r0    s   r0  	num_stepsc                 C   sB   | dkrt jd||dS |s| d |  nd}t j| || ||dS )Nr    r   ra  )stepsr`  r   )rb   r  linspace)rq  r  r   r`  r  r(   r(   r)   _linspace_from_neg_one  s   rt  thetahrY  c           	      C   s   | j }| j}t||||d|d}t|||||dd}tjd||d}tjjj|dddd}tjjj|dddd}tjjj|d	ddd}|| | S )
Nr    )r    r    r    r  )r   r!   constantr   r  r  ru   r    r    )r!   r   	r   r`  rt  rh  rb   re  r  r  r  )	ru  rv  rY  r  r   r`  grid_xgrid_ygrid_oner(   r(   r)   _make_base_grid_4d  s   r~  r  c                 C   s   | j }| j}t||||dd|d}t||||d|dd}t|||||ddd}	tjd||d}
tjjj|dddd}tjjj|dddd}tjjj|	d	ddd}	tjjj|
d
ddd}
|| |	 |
 S )Nr    )r    r    r    r    r  )r   r   rw  r   rx  r  r!   r    )r   r   rz  )ru  r  rv  rY  r  r   r`  r{  r|  grid_zr}  r(   r(   r)   _make_base_grid_5d  s   r  c           	      C   sL   |\}}}}t | |||d}|ddd| jd d}||||dS )Nr  rN   r   r    r-  r!   )r~  rh  r2  rP   r   )	ru  r  r  r  rQ   rv  rY  	base_gridgridr(   r(   r)   _affine_grid_generator_4d  s    r  c           
      C   sR   |\}}}}}t | ||||d}|ddd| jd d}	|	||||dS )Nr  rN   r  r    r-  r   )r  rh  r2  rP   r   )
ru  r  r  r  rQ   r  rv  rY  r  r  r(   r(   r)   _affine_grid_generator_5d#  s    r  c                 C   s@   t t|dv dd  t|dkrt| ||dS t| ||dS )Nr6  c                   S   r  )NzCaffine_grid_generator needs 4d (spatial) or 5d (volumetric) inputs.r(   r(   r(   r(   r)   rz   3  r  z'affine_grid_generator.<locals>.<lambda>r  r  )rb   r|   rT  r  r  )ru  r  r  r(   r(   r)   affine_grid_generator-  s   
r  r  interpolation_modepadding_mode_expand_gridc                    sJ  t dv fdd t dv fdd dtdtdtffdd	dtd
tdtdtfdddtdtdtffdddtdtdtffdd}j\ |j\}}|dkscJ ru|d| d}dtdtdtffddt jjddddt j jdd dddtdtdtdt	f fdddtdtdtffdd
|d  }	|d! }
d"kr1||	}||
}|
 |
 d }}d }}||}}|| ||  }|| ||  }|| ||  }| |  }t
fd#d$|f|||f|||f|||ffD S dkrN||	}||
}| }| }
||dS |	}|
}|
 |
 | | }sud|d}dtdtdtf
fd%d&d'tdtffd(d)	t	fd*d$td+D }t||S ),N)r   r    r!   c                      r  )NzInvalid interpolation mode r(   r(   )r  r(   r)   rz   L  r  z"_grid_sampler_2d.<locals>.<lambda>c                      r  )NzInvalid padding mode r(   r(   )r  r(   r)   rz   O  r  coordsr  rL   c                    s0    r|d d n|d }|d d }| | | S r*  r(   )r  r  r   ofsr  r(   r)   unnormalizeR  s   z%_grid_sampler_2d.<locals>.unnormalize	twice_low
twice_highc                 S   sv   ||kr	t | S |d }|| d }| |  }t ||}||  jt jd}t |d@ dk|| || | S )Nr!   r   r    r   )rb   r   r   fmodfloorr6   int8rc   )r  r  r  
coords_mincoords_spancoords2extraflipsr(   r(   r)   reflect_coordinates]  s   
z-_grid_sampler_2d.<locals>.reflect_coordinatesc                    sf   dkr| S dkrt | d|d S  r | dd|d  }n
| dd| d }t |d|d S )Nr   r    r!   rN   r   )r  r  coords_reflected)r  r  r  r(   r)   compute_coordinatesi  s   z-_grid_sampler_2d.<locals>.compute_coordinatesc                    s   | |} ||S r4   r(   )r  r  	coords_un)r  r  r(   r)   compute_source_indexu  s   

z._grid_sampler_2d.<locals>.compute_source_indexr!   r    rA  ysc                    s,   t d| kt | k t d|k| k S rl   rb   rg  )rA  r  )iHiWr(   r)   in_bounds_cond  s   $z(_grid_sampler_2d.<locals>.in_bounds_condr_  wsc                    sN   | |r	nd t  fdd| jtjd|jtjd|fD S )Nr    c                 3   s*    | ]}t |d  V  qdS r  )rb   rc   rh  r  )rS  r  r  oHoWr(   r)   r    s
    
z1_grid_sampler_2d.<locals>.clip.<locals>.<genexpr>r   )r  r6   rb   r  )rA  r  r  )rT  rS  r  r  r  r  )r  r  r)   clip  s
   
z_grid_sampler_2d.<locals>.clipixiyc                    s&   | ||\}}} ||f | S r4   r(   )r  r  rY  idx_xidx_yw_)C_idxN_idxr  r  r(   r)   get_summand  s   z%_grid_sampler_2d.<locals>.get_summand).r   ).r    r   c                 3   s"    | ]\}}} |||V  qd S r4   r(   )r/   r  r  rY  )r  r(   r)   r    s
    

z#_grid_sampler_2d.<locals>.<genexpr>c                    s     | } |}||dS rV   r(   )r  r  r0   rU   )r  r  r  r  r(   r)   get_value_bounded  s   

z+_grid_sampler_2d.<locals>.get_value_boundedr  c                    sF   | d  } d | | d | d |f}t |S )Nr    r!   )ro  )r  iy_ofscs)r  ix_nwiy_nwtxr(   r)   	get_coeff  s   
z#_grid_sampler_2d.<locals>.get_coeffc                 3       | ]} |V  qd S r4   r(   )r/   r  )r  r(   r)   r    r  r  )rb   r|   r   r\  r   rh  rb  rd  r`  r   r  r0  rI  rP   r  rO   ro  )r  r  r  r  r  r  r  rQ   twor0   rU   r  r  ix_neiy_neix_swiy_swix_seiy_sew_nww_new_sww_se
ix_nearest
iy_nearesttyrj  r(   )rT  r  rS  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r)   _grid_sampler_2d;  sx   
 ( 




	





 

r  c                 C   s   t | ||||dS )N)r  r  r  r  )r  )r  r  r  r  r  r(   r(   r)   grid_sampler_2d  s   
r  c                    s`   t   dko dk fdd t  ddk fdd   jddS )Nr!   r    c                      s   d    d   S )Nzmatrix @ vector expected, got rY  rx   r(   rt   rN  r(   r)   rz     rZ  zmv.<locals>.<lambda>r   c                      s*   d  d d  d d d dS )Nzsize mismatch, got input (r   r0   r    z), vec (r  rO  r(   r  r(   r)   rz     s   * rx   )rb   r|   rK   r  r   r  r(   r  r)   rO    s   rO  c                 C   sd   |d ur|d | d }d| |  |t |   }nd| |  t |  }|d ur-|| }t||S rV   )r   
logsigmoidr   )rt   r   r   
pos_weightr   
log_weightr   r(   r(   r)    binary_cross_entropy_with_logits  s   
r  tensor1tensor2is_outc           	         s   | j |j kr
| |fn|| f\}}ddlm  |j dkr |j dks"dS |jr)|s)dS | j dkr0dS  | dkr:dS |j}| }dg}t|dd  D ]}|||d   qLt	 fd	d
t
|tt||D S )Nr   ru  r   r!   FTr    rN   c                 3   s*    | ]\}}} |d kp||kV  qdS r  r(   )r/   r   r   r  ru  r(   r)   r  -  s
    
zshould_fold.<locals>.<genexpr>)rO  rE  rC  r  r   r   rH  rH  r  r   r  rG  )	r  r  r  t1t2t1_shape	t1_strideexpected_strider  r(   ru  r)   should_fold  s(    

r  )pass_is_out)r  c                C   sx  |   }|  }|dkr|dksJ |dkr |dkr t| |S |dkr.|dkr.t| |S |dkrD|dkrDttt| d|dS |dkrR|dkrRt| |S t| ||r||k}|ra|jn| }|sg|n	|dkro| 	 n| }|j
}t|d d }	ttj|	}
|  dk}|r|	|j
d  ||
|d }|rtjj|||	}|r|j S |S tjj|||	S |dkr|dkr|dkr| dnd}| d}| j
d d }|dkr|dn|d}|dkr|dnd}g }t|d D ]
}||| q|dkrA|dkrA|d |d krA|d dkr.| jr.t| d|S |d dkrA|jrAt| |dS tt||}|||g }t|}| ||||}|dk}|rv||g }||||d}n|||g }|||||}|}	|dkr|	| |dkr|	| |r||d|	S |||	S tddd	  d S )
Nr   r    r!   rN   r-  r   Fc                   S   r  )Nz/both arguments to matmul need to be at least 1Dr(   r(   r(   r(   r)   rz     r  zmatmul.<locals>.<lambda>)rK   rb   dotrO  r  rG  rP   r  r2  r  r   rG  r   r   r   r  r  r  r}   _unsafe_viewr{  r  rO   r  r1  broadcast_shapesr  rb  bmmrh  r|   )r  r  r  dim_tensor1dim_tensor2r  r  r  sizes_1output_shapefolded_dim1t2_is_matrix	t1_foldedry  r  m1batch_tensor1m2r'  batch_tensor2r  expand_batch_portiontensor1_expand_sizeexpand_batch_producttensor1_expanded
vector_rhstensor2_expand_sizetensor2_expandedr(   r(   r)   r1  5  s   	










r1  r  r  c                    s  j \}}t|d ||}t|d ||}tjtjjd\}}tj|d jdj	|d}	tj|d jdj	|d}
t
||
|}t
||	|}|d}| }| }|| dd}|| dd}|	tj}|	tj}|d ||d |d	 f}|d ||d |d	 ft|t|}d
\jtjkrtt|fddD fdd|D }fddfdd t fdd|D }jtjkrd usJ t||}ntdd t||D }t}|j|d}|S )Nr   r    r  r_  r   rN   r   r`   r!   r  c                    .   g | ]}|d  >  t |d  t jqS r    r   rb   r   r6   int16r/   rY  )weights_precision_xr(   r)   r1          z.upsample_bicubic2d_default.<locals>.<listcomp>c                    r  r  r  r  )weights_precision_yr(   r)   r1     r  c                    s<   t | d d }t |dd }td d ||g}|S r  )rb   r   r}   r  )r  rA  y_idxx_idxrM  )in_hin_wr   r(   r)   load_bounded  s   z0upsample_bicubic2d_default.<locals>.load_boundedc                    sT   t  fddD }jtjkrd usJ t|S tdd t|D S )Nc                 3   s    | ]} |V  qd S r4   r(   )r/   x_ofs)r  rU   r(   r)   r    rP  zCupsample_bicubic2d_default.<locals>.get_x_interp.<locals>.<genexpr>c                 s   rl  r4   r(   rm  r(   r(   r)   r    r  )r  r   rb   r  r1  r0  r  )rU   src_x)r   ixs_ofsr  r  	weights_x)rU   r)   get_x_interp  s
   z0upsample_bicubic2d_default.<locals>.get_x_interpc                 3   r  r4   r(   )r/   y_ofs)r  r(   r)   r    r  z-upsample_bicubic2d_default.<locals>.<genexpr>c                 s   rl  r4   r(   rm  r(   r(   r)   r    r  r+  )r   r)  r@   rA   r   rG  rb   rd  r`  r6   r,  rP   r  r   r  ri  r   r  r7  r  r1  r0  r  r   r{  )r   r  r  r  r  rQ   h_scale_factorw_scale_factorr   r  r  x_floaty_floatr0   rU   yscalerE  iys_ofs	weights_ysrc_yr   r,  r(   )	r  r  r  r   r  r  r  r  r  r)   upsample_bicubic2d_default  sR   




r  c                 C   s   t t|t| dkdd  |d u r2|d usJ ttttf tdd t| jdd  |D }|r6|nd\}}t| ||||S )Nr    c                   S   r  )Nz:Must specify exactly one of output_size and scale_factors.r(   r(   r(   r(   r)   rz     r  z(upsample_bicubic2d_vec.<locals>.<lambda>c                 s   s$    | ]\}}t t|| V  qd S r4   )r   r   )r/   rY  rh   r(   r(   r)   r    s
    
z)upsample_bicubic2d_vec.<locals>.<genexpr>r!   r  )	rb   r|   rf  r
   r  r\  r  r   r  )r  r  r  rq  r  r  r(   r(   r)   upsample_bicubic2d_vec  s   
r  c                        fdd}t  ||S )Nc                    s4   t j|  ||  jd}|d |d |    S )Nr_  r    )rb   rd  r`  r   r   middler   dim_idxr  r(   r)   rl  (  s   z_reflection_pad.<locals>.idx_reflection_or_replication_padr  r  rl  r(   r  r)   _reflection_pad"     r  c                    r  )Nc                    s*   t j|  ||  jd}t |d|d S )Nr_  r   r    )rb   rd  r`  r   r	  r  r(   r)   rl  9  s   z_replication_pad.<locals>.idxr  r  r(   r  r)   _replication_pad3  r  r  idx_fnc                    s   t d  t|   d  d fv  fdd | j  d  }|    } fddt D } fddt D }| }t D ]}d g|  }	||| || || |	|| < t||	}qFt	|}
|j
|
d}|S )	Nr!   r    c                      s    d  d d  d d  dS )Nreflection_padzd requires r    zD or r!   zD inputr(   r(   rx   r(   r)   rz   L       z0_reflection_or_replication_pad.<locals>.<lambda>c                        g | ]}d  d |   qS r  r(   r6  rK   r  r(   r)   r1   Q  r  z2_reflection_or_replication_pad.<locals>.<listcomp>c                    $   g | ]}d  d |  d  qS r  r(   r6  r  r(   r)   r1   R  r[  r+  )rT  rb   r|   rK   r   rO   r}   r  r@   r   r{  )r  r  r  	inp_shapenc_dimpadding_leftpadding_rightr   r  rl  r,  r(   r  r)   r  D  s"   
 
r  c                    s\  t d dd |j d  D fddtD fddtD g }t|jD ]}dg|j }d||< |tj|j| |jd| q2|d    | d  
d	d
 
fddtD 
fddtD }
fddtD }fddtD 	t	
tj	fddtD }t|  d}	 fdd}
tjdd tD  D ]f}|tdg krqg }g }tD ]K}|| dkr| }	| }n0|| dkr|| }
| d| f}n|| dkr|| }
| | |  | d f}|| || q|
|	||}	q|	S )Nr!   c                 S   s   g | ]}|d  qS r   r(   )r/   rv  r(   r(   r)   r1   g  r?  z,_reflection_pad_backward.<locals>.<listcomp>c                    r  r  r(   r6  r  r(   r)   r1   i  r  c                    r  r  r(   r6  r  r(   r)   r1   j  r[  r    rN   r_  c                 S   s   | \}}}t ||k||kS r4   r  )index_ranger  lbubr(   r(   r)   index_range_conditionu  s   
z7_reflection_pad_backward.<locals>.index_range_conditionc                    s   g | ]
}|  |  qS r(   r(   r6  r  xyzr(   r)   r1     r  c                    s   g | ]
} | |  qS r(   r(   r6  r!  r(   r)   r1     r  c                    s(   g | ]}d  |  |  |  qS r  r(   r6  )dhwr  r"  r(   r)   r1     rB  c                    s.   g | ]} | d | |  |  fqS r  r(   r6  )centerr#  r  r  r(   r)   r1     s    "c                    s   g | ]} | qS r(   r(   r6  )r   range_cr(   r)   r1     rZ  r   c                    st   t D ]}|| d || d k }t|tr|r|   S qttjfdd|D }t| | d}| | S )Nr!   r    c                    s   g | ]} |qS r(   r(   )r/   r  )r   r(   r)   r1     r?  z@_reflection_pad_backward.<locals>.accumulate.<locals>.<listcomp>r   )rO   r-   rf  rH   r   r}   rg  ri  )r   r   index_rangesr  upper_less_than_lowerr  g)r  rK   rf   r   r(   r)   r    s   z,_reflection_pad_backward.<locals>.accumulatec                 S   s   g | ]}g d qS ))rN   r   r    r(   r   r(   r(   r)   r1     r?  r   )rT  r   rO   rO  r  rb   rd  r`  rh  rH   r   r}   rg  ri  	itertoolsr   r  )rf   r0   r  rk  r  
view_shapeleft_reflectright_reflectr  r   r  areaoutsr&  r   r  r(   )r  r$  r#  rK   rf   r   r  r  r  r%  r"  r)   _reflection_pad_backward`  sT   $
"
r/  r   r   r   c                C   s(   t j| ||d}t j| ||d}||fS )Nr   )rb   aminr  )rt   rK   r   r0  r  r(   r(   r)   aminmax  s   r1  r   c                C   s"   t jtt| d| |||dS )Nr   r   )r}   r   rb   rc   isnan)rt   rK   r   r   r(   r(   r)   nansum  s   "r3  r   r  r`  r  r  c             	   C   s   t jjd| d||||dS )Nr   r    r4  r}   rd  
start_step)r>  r   r  r`  r  r(   r(   r)   arange_default     
r7  c             	   C   s   t jj| |d||||dS )Nr    r4  r5  )r=  r>  r   r  r`  r  r(   r(   r)   arange_start  r8  r9  c                  O   s   ddl m} || i |S )Nr   )out_dtype_dense)!torch._higher_order_ops.out_dtyper:  )rB   rC   r:  r(   r(   r)   out_dtype_decomp  s   r<  marginc           	         s  t t jd jd  t |dkp|dkdd  t jdko, dkfdd t jdko? kfdd d urdt t jdko\  k fdd dt jdd	}||  }|	d}|dkr|n|| }d ur|  }t j
 jd
}t |k|d}|tjjkr| S |tjjkr| |jd  S |jddS )Nr   r    r!   c                   S   r  )Nz only p == 1 and p == 2 supportedr(   r(   r(   r(   r)   rz     r  z#multi_margin_loss.<locals>.<lambda>c                      r&  NzMExpected non-empty vector or matrix with optional 0-dim batch size, but got: r  r(   r7  r(   r)   rz      r'  c                         d  dj  S )Nz#inconsistent target size, expected r  r  r(   )nframer   r(   r)   rz     r  c                      r?  )Nz#inconsistent weight size, expected r  r  r(   )rK   r   r(   r)   rz   
  r  rQ  r_  rx   )rb   
atleast_2d
atleast_1dr   r|   rO  r   rP   rX  r3  rd  r`  rc   r   r&   ru   r   r'   r   )	r   r   r'  r=  r   r   urd   rl  r(   )rK   r   r@  r   r   r)   multi_margin_loss  sB   







rD  	is_targetc                    s  | j  |j t| } t|}| j d }tt dko |dk fdd ttdko2 k fdd tj||jd}|dk}tjt|||dd	d
}||k }t||d}tj	| d|d}	t||d}
tj
||
jddkdd}d|	jjdd |  }|d}|| }t|d|}|tjjkr|jdd }n|tjjkr| }n|jdd}|| j}||fS )Nr    r!   r   c                      r  r>  r(   r(   )orig_input_shaper(   r)   rz   ,  r  z0multilabel_margin_loss_forward.<locals>.<lambda>c                      s   d d  S )Nzinconsistent target size: z for input of size: r(   r(   rF  orig_target_shaper(   r)   rz   0  r{   r_  rN   Tr   rQ  rx   r`   )r   rN   )r   rb   rA  r|   rT  rd  r`  r0  rc   rX  anyrP   Tr3  r   r&   ru   r   r   r'   r6   r   r  )r   r   r   rK   rl  is_endend_idxtarget_masktidx0rC  tidx1rE  rd   r(   rG  r)   multilabel_margin_loss_forward  s@   





rP  )	attn_maskrh   querykey	dropout_p	is_causalrQ  c          	   
      s   t t fdd t  dko  dko  dkfdd t  dk fdd t jd jd koJjd jd kdd  tjj| |d |d	\}}|d
dddj	t j
ddd
dd}||fS )Nc                      r&  )Nz-query must be FP32, FP64, BF16, FP16 but got r   r(   )rR  r(   r)   rz   i  r'  z<scaled_dot_product_flash_attention_for_cpu.<locals>.<lambda>r  c                      s"   d   d    d   S )Nz,q, k, v must be a 4 dimensional tensor, got rY  rx   r(   )rS  rR  ru   r(   r)   rz   m  s   " r   c                      r  )Nz&dropout probability must be zero, got r(   r(   )rT  r(   r)   rz   p  r  r   c                   S   r  )Nz&q, k, v should have the same head sizer(   r(   r(   r(   r)   rz   t  r  )rQ  rT  rU  dropout_maskrh   r!   r   r    r+  )rb   r|   r  rK   r   r}   "_scaled_dot_product_attention_mathrX  r  r{  r0  )	rR  rS  ru   rT  rU  rQ  rh   ry  attnr(   )rT  rS  rR  ru   r)   *scaled_dot_product_flash_attention_for_cpu\  s>   
"&
"rY  c                    s   t |  fdd}|S )Nc                     s    | i |}| d  |S rl   )r  )rB   rC   r   outplace_opr(   r)   
inplace_op  s   z$register_inplace.<locals>.inplace_opr   )aten_opr[  r\  r(   rZ  r)   register_inplace  s   r^  c                 C   sx   |   s|  st|}t|}t||}t|tjr |dkr$|| }|dkr*|S t|tjr4|dkr8| | } | | S )Nr    r   )r  rF  r\  rb   r  r-   numbersNumber)rt   batch1batch2r]   rg   r   r(   r(   r)   baddbmm  s   rc  c                 C   s   t j| |ddS )Nr  r  r  )rt   r&  r(   r(   r)   floor_divide  s   rd  c                 C   s   t tj| jdS rV   )rH   r   r   r   r   )r  r(   r(   r)   	sym_numel  rd  re  r   r   c                C   s.   |d u rt jj| g |dS t jj| g ||dS )Nr   rf  )r}   r   dim_IntListIntList_out)rt   r   r   r(   r(   r)   sum_default  s   ri  c                 C   sB   t | tjs| S |d u rtj| tt|  S tj| |gS r4   )	r-   rb   r   r}   r  dimsrG  rO   rK   )rt   rK   r(   r(   r)   squeeze_default  s
   rk  c                    s`   t  fddtt| jD }|jtjkrtjnd }| jd|d|d}| ||	|j  |fS )Nc                 3   s    | ]	}| kr|V  qd S r4   r(   r6  rx   r(   r)   r    r  z)_weight_norm_interface.<locals>.<genexpr>r!   T)r   r   )
r  rO   rT  r   r   rb   r  r   r   r6   )rM  r(  rK   keep_dim
norm_dtyper   r(   rx   r)   _weight_norm_interface  s    rn  assume_uniqueinvertc                C   s|   t | tjstj| |jd} t |tjs"|rt| |S t| |S | dt|  d k r6t	| ||dS t
| |||dS )Nr_  g      $@g(\?rq  ro  )r-   rb   r   r  r`  ner   r   r.  isin_defaultisin_sorting)elementstest_elementsrp  rq  r(   r(   r)   isin  s   rx  )r  c                C   sP   |d u rt j|  t j| jd}nt j|  |t j| jd}|| k | j}|S )Nr  )r  r   r`  )rb   randr  r   r`  r6   r   )rt   r  raw_pr'  r(   r(   r)   	bernoulli  s   r{  rr  c                C   sn   |   dkrtj| tjdS | jg | jd|j R  }ttd|j d d}||kj	|d}|r5| S |S )Nr   r   r   rN   r    rx   )
r   rb   
empty_likerf  rh  r   rO  r  rO   rI  )rv  rw  rq  r0   rK   r  r(   r(   r)   rt    s   rt  c                C   s   |   }|  }|rIt||g}tj|dd\}}|dd  |d d k}	t|	ddgd}	|r5|	 }	t|	}
|
d||	}
|
d|   S t|\}}t	||}t
|| k |d}|| |k}|rm| n|}|| jS )NT)stabler    rN   r   F)r  rb   r
  sortr  logical_notr|  re  r   searchsortedrc   r  r   )rv  rw  rp  rq  elements_flattest_elements_flatall_elementssorted_elementssorted_orderduplicate_maskrm  sorted_test_elementsrQ   rl  test_idxcmpr(   r(   r)   ru    s$   
ru  c                 C   s   |  d}|| S rM   )r  )rt   ro  	flattenedr(   r(   r)   take5  s   
r  c                 C   s2   |d u rt j}|t jkrt|}tj| |j|dS r  )rb   r0  preserve_formatr   r}   resizer   )rt   r&  r,  r(   r(   r)   	resize_as<  s
   
r  )F)r   r4   r  )r   NNr    )rN   FFr  r  ry  )r    r    F)Fr   )r   r`   N)r   r    Nr  )NNN)r   r   FT)r   r   Fr  )r   F(  rH   r)  r_  r   rI  collections.abcr   enumr   r   r   r   r   typingr   r	   r
   r   r   rb   torch._meta_registrationstorch._primsr  rj  torch._prims_common_prims_commonr@   torch.nn.functionalr  r  r   r   r   r   torch._decompr   r;  r   r   r   r   r   r   torch._prims_common.wrappersr   r   r   r   torch.utilsr   r>   torch.utils._pytreer   r  DispatchKeyr   rG  str__annotations___opsr  r}   r   r   rf  rJ   r  compute_only_pw_cast_for_opmathpw_cast_for_opmathrG  pw_cast_for_int_to_realr\  rR   rZ   r\   re   r   rp   rT  Scalarrv   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r  r   r   r   r   r   r   r&   ru   r   _safe_softmaxr   r   r   rX  rS   r   r   r   r   r  r  r  r  r!  r#  r$  r%  r(  r;  rB  slicerU  r]  rA  rq  rv  rx  r}  r~  r  r  r  r  r  r  r  py_implCompositeImplicitAutogradAutogradr  r  r  r  r  r  r   r"  r$  r'  r(  r.  r1  r3  r-  rA  r:  rC  rH  rM  rP  rk  ro  rq  r  r  r  r  r  unsafe_chunkr  r  r  no_statsr  r  r  r  r  r  _fused_dropoutr  r  r`  r,  r8  lift
lift_freshr  r  r  r  r  r  r  r  _adaptive_avg_pool2dr  r%  r5  r:  rA  rB  r?  r]  rc  re  ra  rg  rl  	Generatorrm  rs  ru  r  rN  r  r  rz  _upsample_nearest_exact1dr  r  r~  r  r  rw  r  r  r  r  r  r  r  r  r  r  rnn_tanhr   r  rnn_relur  r  r  r  r  r  r  r
  lstmr  r  r  r  grur  r  r  r  r!  r"  r'  r(  r&  r$  r)  r,  r1  r7  r#  rN  rO  r  r  r  ri  rW  r\  r^  r_  rb  rd  ri  ro  r0  rt  r~  r  r  r  r  r  r  rO  r  r  r1  upsample_bicubic2dr  r  reflection_pad1dreflection_pad2dreflection_pad3dr  replication_pad1dreplication_pad2dreplication_pad3dr  r  reflection_pad1d_backwardreflection_pad2d_backwardreflection_pad3d_backwardr/  r1  r3  rd  r  r  r7  r=  r9  r<  rD  rP  +_scaled_dot_product_flash_attention_for_cpurY  r^  rc  rd  re  r   ri  r  rK   rk  rn  rx  r{  rt  ru  r  r  addbmm_addbmmaddmm_addmv_baddbmm_fill_gelu_rK  
hardswish_	hardtanh_hardtanhhardsigmoid___iand____and____ilshift__
__lshift__rL  rM  index_reduce_index_reduce__ior____or____irshift__
__rshift____ixor____xor__leaky_relu_
leaky_relulogit_logitrelu_rL  renorm_renormround_rI  scatter_r  scatter_add_scatter_addscatter_reduce_scatter_reducesilu_r(   r(   r(   r)   <module>   sx  


$ 
 

 
	




  *!	
9

'"
	P`
 
	
%


(


(
 00
	

W	

	
R
	
R		#

	
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