§
    ŠŠtjá ã                   óŽ  — U d Z ddlZddlZddlZddlZddlZddl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ZddlZddlmZmZmZ ddlmZ dd	lmZmZ dd
lmZ ddlmZm Z m!Z!m"Z"m#Z# ddl$m%Z% ddl&m'Z'm(Z(m)Z) 	 ddlm*Z* n# e+$ r dZ*Y nw xY wda, ej-        ¦   «         Z. ej/        ¦   «         Z0g a1e2e3eg df         e2e4         f                  e5d<    e6ej7        dd„ ¦  «        Z8dZ9dZ:dZ;dZ<	 ddlm=Z> 	 e>j?        sddl@Z@nQddlAZAddlBmCZC  G d„ d¦  «        ZD	  eD¦   «         5  ddlEZEddd¦  «         n# 1 swxY w Y   dZ:n# eF$ rZGeGZ<‚ dZG[Gww xY wdZ9n# eF$ r Y nw xY w[>n# [>w xY wn# e+$ rZGeGZ;Y dZG[GndZG[Gww xY w e¦   «         aH eIej7        d¦  «        rej7        jJ        ZJn ed¦  «        ZJ eIej7        d¦  «        rej7        jK        ZLndeMdeMfd„ZL eIej7        d¦  «        rej7        jN        ZOndeMdeMfd„ZOdZPeQe5d<   ej7        jR        ZSeQe5d <   d!ZTe3ej7        jU                 e5d"<   deQfd#„ZVdeQfd$„ZWdeQfd%„ZXdØd&eQfd'„ZY e
d(¬)¦  «        defd*„¦   «         ZZdeQfd+„Z[d,„ Z\d-e4deMfd.„Z] G d/„ d0¦  «        Z^ G d1„ d2¦  «        Z_i d3 e^d3d4h¬5¦  «        “d6 e^d6d4h¬5¦  «        “d4 e_d4h¦  «        “d7 e^d7d8h¬5¦  «        “d9 e^d9d8h¬5¦  «        “d8 e_d8h¦  «        “d: e^d:d;h¬5¦  «        “d; e_d;h¦  «        “d< e^d<¬=¦  «        “d> e^d>d?h¬5¦  «        “d@ e^d@d?h¬5¦  «        “d? e_d?h¦  «        “dA e^dA¬=¦  «        “dB e^dB¬=¦  «        “dC e^dCdDh¬5¦  «        “dD e_dDdEh¦  «        “dF e^dF¬=¦  «        “ e^dE¬=¦  «         e^dG¬=¦  «         e^dH¬=¦  «        dIœ¥Z`eaeMe_e^z  f         e5dJ<   h dK£d>dBhdLœh dM£h dN£dLœh dM£h dO£dLœh dM£h dO£dLœdPœZbeae4eae4eceM         f         f         e5dQ<   de4fdR„ZddSeMdTeMfdU„ZedVeMdSeMdWe2eM         fdX„ZfdY„ ZgdZ„ Zhd[„ Zid\„ Zj ejeg¦  «          ejeh¦  «          G d]„ d^ek¦  «        Zlej7        jm        Zmej7        jn        Znd_„ Zod`„ Zpda„ Zq G db„ dc¦  «        Zr G dd„ dees¦  «        ZtdfeMddfdg„Zu G dh„ di¦  «        Zv G dj„ d¦  «        Zw G dk„ dlew¦  «        Zxdeddfdm„ZydÙdede4fdn„ZzdÙdede3eMeMf         fdo„Z{dÙdedeJfdp„Z|dedqedeQfdr„Z} G ds„ dt¦  «        Z~duedv         de~fdw„Zdx„ Z€due)fdy„Z�de2eM         e2e4         z  fdz„Z‚deMfd{„ZƒdeMfd|„Z„de2e4         dz  fd}„Z…de2e4         dz  fd~„Z†de2e4         d€e2e4         de2eM         fd�„Z‡deMfd‚„ZˆdeMfdƒ„Z‰dedeMfd„„ZŠda‹eMdz  e5d…<   deMfd†„ZŒde2e4         fd‡„Z�de4fdˆ„ZŽdeMfd‰„Z�dÙdeddfdŠ„Z�d‹„ Z‘dÙdede)fdŒ„Z’dÙdede)fd�„Z“dÙdŽeMdede)fd�„Z”d�„ Z•d‘„ Z–d’eMe4z  ddfd“„Z—deMfd”„Z˜dÙdefd•„Z™dÙdefd–„Zšda›eaeMeMf         dz  e5d—<   deMdeMfd˜„ZœdedeMfd™„Z�dÙdedeMfdš„ZždÙdedeMfd›„ZŸdÙdedeMfdœ„Z dÙdedeMfd�„Z¡dÙdedeMfdž„Z¢dÙdedeMfdŸ„Z£dÙdedeMfd „Z¤dÙdedeMfd¡„Z¥dÙdedeMfd¢„Z¦dÙdedeMfd£„Z§dÙdedeMfd¤„Z¨dÙdedeMfd¥„Z©deMe4z  ejw        z  dejw        fd¦„Zªdejw        dej7        jU        fd§„Z«	 dÚd©eMdeMe4z  ejw        z  ddfdª„Z¬dÚdeMe4z  ejw        z  deMfd«„Z­dd¬l®T dd­l®m¯Z¯ dd¬l°T e±d®„ ¦   «         Z² G d¯„ d°¦  «        Z³dd±l´mµZµm¶Z¶  G d²„ d³eµ¦  «        Z· G d´„ dµe·¦  «        Z¸ G d¶„ d·e·¦  «        Z¹ G d¸„ d¹e·¦  «        Zº G dº„ d»e·¦  «        Z» G d¼„ d½e·¦  «        Z¼ G d¾„ d¿e·¦  «        Z½ G dÀ„ dÁe·¦  «        Z¾ G dÂ„ dÃe·¦  «        Z¿ G dÄ„ dÅe·¦  «        ZÀ G dÆ„ dÇe·¦  «        ZÁ G dÈ„ dÉe·¦  «        ZÂ G dÊ„ dËe·¦  «        ZÃ[µ[·ejÄ         Å                    e¹¦  «         ejÄ         Å                    eº¦  «         ejÄ         Å                    e¼¦  «         ejÄ         Å                    e½¦  «         ejÄ         Å                    e¾¦  «         ejÄ         Å                    e¿¦  «         ejÄ         Å                    e¸¦  «         ejÄ         Å                    e»¦  «         ejÄ         Å                    eÀ¦  «         ejÄ         Å                    eÁ¦  «         ejÄ         Å                    eÂ¦  «         ejÄ         Å                    eÃ¦  «          G dÌ„ dÍ¦  «        ZÆdÎ„ ZÇ ejeÇ¦  «         	 	 	 dÛdÏe4dÐe4dÑe4dz  dÒe2dz  dÓe2dz  f
dÔ„ZÈddÕlmÉZÉmÊZÊmËZËmÌZÌmÍZÍmÎZÎ  edÖe3eMeMf         ¦  «        ZÏg d×¢ZÐdS )ÜaM  
This package adds support for CUDA tensor types.

It implements the same function as CPU tensors, but they utilize
GPUs for computation.

It is lazily initialized, so you can always import it, and use
:func:`is_available()` to determine if your system supports CUDA.

:ref:`cuda-semantics` has more details about working with CUDA.
é    N)ÚCallable)Ú	lru_cache)ÚAnyÚcastÚNewTypeÚOptionalÚTYPE_CHECKING)Ú_dummy_typeÚ_LazySeedTrackerÚclassproperty)ÚDeviceé   )Ú_device_limitsÚgds)Ú_get_device_index)Ú	CUDAGraphÚgraphÚgraph_pool_handleÚis_current_stream_capturingÚmake_graphed_callables)ÚGreenContext)ÚEventÚExternalStreamÚStream)Ú_cudartFÚ_queued_callsÚ_cuda_isInBadForkc                  ó   — dS ©NF© r    ó    úQ/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/cuda/__init__.pyú<lambda>r#   5   s   € À€ r!   )Úversion)ÚPathc                   ód   — e Zd Zdd„Zdeez  dz  dededej        fd„Z	dd„Z
d	ed
ededdfd„ZdS )Ú_amdsmi_cdll_hookÚreturnNc                 óÖ   — t           j        | _        dg}t          j        dt          j        d¦  «        ¦  «        x}r$t          j                             |d¦  «        g|z   }|| _        d S )Núlibamd_smi.soÚ	ROCM_HOMEÚ	ROCM_PATHzlib/libamd_smi.so)ÚctypesÚCDLLÚoriginal_CDLLÚosÚgetenvÚpathÚjoinÚpaths)Úselfr4   Ú	rocm_homes      r"   Ú__init__z_amdsmi_cdll_hook.__init__V   sb   € Ý)/¬�DÔ&Ø,Ð-�EÝ$&¤I¨k½2¼9À[Ñ;QÔ;QÑ$RÔ$RÐR�yð WÝ!#¤§¢¨iÐ9LÑ!MÔ!MÐ NÐQVÑ V˜Ø,1�D”J�J�Jr!   ÚnameÚargsÚkwargsc                 ó²   — |rEt          |¦  «        j        dk    r-| j        D ]%}	  | j        |g|¢R i |¤Žc S # t          $ r Y Œ"w xY w | j        |g|¢R i |¤ŽS )Nr*   )r%   r8   r4   r/   ÚOSError)r5   r8   r9   r:   r2   s        r"   Úhooked_CDLLz_amdsmi_cdll_hook.hooked_CDLL]   s    € ð ð %¥ T¡
¤
¤°?Ò BÐ BØ$(¤Jð %ð %˜Dð%Ø'9 tÔ'9¸$Ð'PÀÐ'PÐ'PÐ'PÈÐ'PÐ'PÐ PÐ PÐ PøÝ#*ð %ð %ð %Ø $ ð%øøøà-˜4Ô-¨dÐD°TÐDÐDÐD¸VÐDÐDÐDs   ¥8¸
AÁAc                 ó(   — | j         t          _        d S ©N)r=   r-   r.   ©r5   s    r"   Ú	__enter__z_amdsmi_cdll_hook.__enter__h   s   € Ø"&Ô"2•F”K�K�Kr!   ÚtypeÚvalueÚ	tracebackc                 ó(   — | j         t          _        d S r?   )r/   r-   r.   ©r5   rB   rC   rD   s       r"   Ú__exit__z_amdsmi_cdll_hook.__exit__k   s   € Ø"&Ô"4•F”K�K�Kr!   )r(   N)Ú__name__Ú
__module__Ú__qualname__r7   Ústrr%   r   r-   r.   r=   rA   rG   r    r!   r"   r'   r'   U   s³   € € € € € ð2ð 2ð 2ð 2ð	EØ # d¡
¨TÑ 1ð	EØ:=ð	EØILð	Eà”[ð	Eð 	Eð 	Eð 	Eð3ð 3ð 3ð 3ð5¨ð 5°Sð 5ÀSð 5ÈTð 5ð 5ð 5ð 5ð 5ð 5r!   r'   TÚ_CudaDevicePropertiesÚ_cuda_exchangeDeviceÚdevicer(   c                 ó0   — | dk     rdS t          d¦  «        ‚©Nr   éÿÿÿÿz)PyTorch was compiled without CUDA support©ÚRuntimeError©rN   s    r"   Ú_exchange_devicerU   Š   ó   € Ø�AŠ:ˆ:Ø�2ÝÐFÑGÔGÐGr!   Ú_cuda_maybeExchangeDevicec                 ó0   — | dk     rdS t          d¦  «        ‚rP   rR   rT   s    r"   Ú_maybe_exchange_devicerY   ”   rV   r!   Úhas_halfÚ	has_magmar    Údefault_generatorsc                  ó6   — t          t          j        d¦  «        S )z)Return true if compile with CUDA support.Ú_cuda_getDeviceCount)ÚhasattrÚtorchÚ_Cr    r!   r"   Ú_is_compiledrb       s   € å•5”8Ð3Ñ4Ô4Ð4r!   c                  ó2   — t          j        d¦  «        dk    S )NÚPYTORCH_NVML_BASED_CUDA_CHECKÚ1)r0   r1   r    r!   r"   Ú_nvml_based_availrf   ¥   s   € ÝŒ9Ð4Ñ5Ô5¸Ò<Ð<r!   c                  ó¦   — t          ¦   «         sdS t          ¦   «         rt          ¦   «         dk    S t          j                             ¦   «         dk    S )a  
    Return a bool indicating if CUDA is currently available.

    .. note:: This function will NOT poison fork if the environment variable
        ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set. For more details, see
        :ref:`multiprocessing-poison-fork-note`.
    Fr   )rb   rf   Údevice_countr`   ra   r^   r    r!   r"   Úis_availableri   ©   sO   € õ ‰>Œ>ð ØˆuÝÑÔð 	3õ ‰~Œ~ Ò!Ð!õ
 Œx×,Ò,Ñ.Ô.°Ò2Ð2r!   Úincluding_emulationc                 óþ   — t           j        j        rdS t          ¦   «         sdS t           j                             ¦   «         }t           j                             |¦  «        j        dk    rdS | sdS t          |¦  «        S )zQReturn a bool indicating if the current CUDA/ROCm device supports dtype bfloat16.TFé   )	r`   r$   Úhipri   ÚcudaÚcurrent_deviceÚget_device_propertiesÚmajorÚ_check_bf16_tensor_supported)rj   rN   s     r"   Úis_bf16_supportedrs   ¿   s|   € õ „}Ôð Øˆtõ ‰>Œ>ð ØˆuåŒZ×&Ò&Ñ(Ô(€Få„z×'Ò'¨Ñ/Ô/Ô5¸Ò:Ð:Øˆtàð Øˆuõ (¨Ñ/Ô/Ð/r!   é   )Úmaxsizec                 ón   — 	 t          j        dgt           j        | ¬¦  «         dS # t          $ r Y dS w xY w)Ng      ð?)ÚdtyperN   TF)r`   ÚtensorÚbfloat16Ú	ExceptionrT   s    r"   rr   rr   Ö   sI   € ðÝŒ�c�U¥%¤.¸Ð@Ñ@Ô@Ð@ØˆtøÝð ð ð Øˆuˆuðøøøs   ‚"& ¦
4³4c                  óª   — t           j        j        r3t           j                             ¦   «         j        } d}|D ]	}|| v r dS Œ
dS t          d¬¦  «        S )zMReturn a bool indicating if the current CUDA/ROCm device supports dtype tf32.)Úgfx94Úgfx95TF)rj   )r`   r$   rm   rn   rp   ÚgcnArchNamers   )Ú	prop_nameÚarchsÚarchs      r"   Úis_tf32_supportedr‚   ß   sh   € å„}Ôð Ý”J×4Ò4Ñ6Ô6ÔBˆ	Ø"ˆØð 	ð 	ˆDØ�yÐ Ð Ø�t�tð !àˆuõ °Ð7Ñ7Ô7Ð7r!   c                 óD   — t           j                             | ¦  «         d S r?   )r`   ra   Ú_cuda_sleep)Úcycless    r"   Ú_sleepr†   î   s   € Ý	„H×Ò˜Ñ Ô Ð Ð Ð r!   Úarch_stringc                 óª   — |                       dd¬¦  «        d         }|                     d¦  «                             d¦  «        }t          |¦  «        S )z4Extracts the architecture string from a CUDA versionÚ_é   )Úmaxsplitr   ÚaÚf)ÚsplitÚremovesuffixÚint)r‡   Úbases     r"   Ú_extract_arch_versionr’   ò   sL   € à×Ò˜S¨1ÐÑ-Ô-¨aÔ0€DØ×Ò˜SÑ!Ô!×.Ò.¨sÑ3Ô3€DÝˆt‰9Œ9Ðr!   c                   ó>   — e Zd ZdZddee         dz  fd„Zd„ Zd„ ZdS )Ú_CompatIntervalzÁ
    Defines a range of compute capabilities starting at a given
    version and going up to the end of that major version. This
    also allows excluding specific versions from the range.
    NÚexcludec                 ó^   — |dz  |dz  c| _         | _        |€t          ¦   «         n|| _        d S )Né
   )rq   ÚminorÚsetr•   )r5   Ústartr•   s      r"   r7   z_CompatInterval.__init__   s1   € Ø!&¨"¡¨e°b©jÐˆŒ
�D”JØ ' •s‘u”u�u°WˆŒˆˆr!   c                 óX   — || j         v rdS |dz  |dz  }}|| j        k    o
|| j        k    S )NFr—   )r•   rq   r˜   )r5   ÚxÚx_majorÚx_minors       r"   Ú__contains__z_CompatInterval.__contains__  s>   € Ø�”ÐÐØ�5Ø ™7 A¨¡F�ˆØ˜$œ*Ò$Ð>¨°D´JÒ)>Ð>r!   c                 óÌ   — d| j         › d| j        › d| j         dz   › d�}t          | j        ¦  «        dk    r-d                     d„ | j        D ¦   «         ¦  «        }|d	|› d
�z  }|S )Nz>=ú.z,<r   z.0r   ú, c              3   ó0   K  — | ]}|d z  › d|d z  › �V — ŒdS ©r—   r¡   Nr    )Ú.0rœ   s     r"   ú	<genexpr>z*_CompatInterval.__str__.<locals>.<genexpr>  s8   è è € Ð"OÐ"O¸Q a¨2¡gÐ#8Ð#8°°B±Ð#8Ð#8Ð"OÐ"OÐ"OÐ"OÐ"OÐ"Or!   z	 except {Ú})rq   r˜   Úlenr•   r3   )r5   ÚresultÚ
exceptionss      r"   Ú__str__z_CompatInterval.__str__
  s~   € ØC�d”jÐCÐC 4¤:ÐCÐC°´¸a±ÐCÐCÐCˆÝˆtŒ|ÑÔ˜qÒ Ð ØŸšÐ"OÐ"OÀ$Ä,Ð"OÑ"OÔ"OÑOÔOˆJØÐ1 :Ð1Ð1Ð1Ñ1ˆFØˆr!   r?   ©	rH   rI   rJ   Ú__doc__r™   r�   r7   rŸ   r«   r    r!   r"   r”   r”   ù   sd   € € € € € ðð ð=ð = s¨3¤x°$¡ð =ð =ð =ð =ð?ð ?ð ?ðð ð ð ð r!   r”   c                   ó6   — e Zd ZdZdee         fd„Zd„ Zd„ ZdS )Ú
_CompatSetz–
    A set of compute capabilities. It exists primarily to support custom
    printing logic and is otherwise equivalent to a plain python set().
    Úvaluesc                 ó   — || _         d S r?   ©r°   )r5   r°   s     r"   r7   z_CompatSet.__init__  s   € ØˆŒˆˆr!   c                 ó   — || j         v S r?   r²   )r5   rœ   s     r"   rŸ   z_CompatSet.__contains__  s   € Ø�D”KÐÐr!   c                 óV   — dd                      d„ | j        D ¦   «         ¦  «        z   dz   S )NÚ{r¢   c              3   ó0   K  — | ]}|d z  › d|d z  › �V — ŒdS r¤   r    )r¥   Úvs     r"   r¦   z%_CompatSet.__str__.<locals>.<genexpr>  s8   è è € ÐJÐJ¸ ! r¡'Ð4Ð4¨A°©FÐ4Ð4ÐJÐJÐJÐJÐJÐJr!   r§   )r3   r°   r@   s    r"   r«   z_CompatSet.__str__  s/   € Ø�T—Y’YÐJÐJ¸d¼kÐJÑJÔJÑJÔJÑJÈSÑPÐPr!   Nr¬   r    r!   r"   r¯   r¯     s`   € € € € € ðð ð
˜s 3œxð ð ð ð ð ð  ð  ðQð Qð Qð Qð Qr!   r¯   é2   é5   )rš   r•   é4   é<   é>   é=   éF   éH   éK   ©rš   éP   éW   éV   éY   éZ   éd   ée   én   ég   éx   éy   )rÉ   rË   rÌ   ÚDEVICE_REQUIREMENT>   r¸   r»   r¾   rÀ   rÂ   rÄ   rÆ   )Úx86_64Úaarch64>   rÀ   rÂ   rÄ   rÆ   rÇ   rË   >   rÂ   rÆ   rÇ   rË   >   rÂ   rÆ   rÇ   rÉ   rË   )z12.6z12.9z13.0z13.2ÚPYTORCH_RELEASES_CODE_CCc                  ó`   — t          j        ¦   «                              ¦   «         } | dk    rdndS )NrÏ   rÎ   )ÚplatformÚmachineÚlower)rÓ   s    r"   Ú_host_arch_keyrÕ   Z  s0   € ÝÔÑ Ô ×&Ò&Ñ(Ô(€GØ 9Ò,Ð,ˆ9ˆ9°(Ð:r!   Ú	device_ccÚcode_ccc                 ó¢   — |t           vr8t          j        d|dz  › d|dz  › d�dz   d¬¦  «         | t          |¬¦  «        v S | t           |         v S )	Nz8PyTorch was compiled with an unknown compute capability r—   r¡   z. zH Please create an issue on Github if this is a valid compute capability.rŠ   ©Ú
stacklevelrÁ   )rÍ   ÚwarningsÚwarnr”   )rÖ   r×   s     r"   Ú_code_compatible_with_devicerÝ   _  s~   € ØÕ(Ð(Ð(ÝŒØgÀwÐRTÁ}ÐgÐgÐW^ÐacÑWcÐgÐgÐgØXñYàð	
ñ 	
ô 	
ð 	
ð
 �O°'Ð:Ñ:Ô:Ð:Ð:ØÕ*¨7Ô3Ð3Ð3r!   Údevice_indexÚcode_ccsc                 óB  ‡— t          | ¦  «        }t          ¦   «         }g }t                               ¦   «         D ]W\  }}|                     |t          ¦   «         ¦  «        }t          ˆfd„|D ¦   «         ¦  «        r|                     |¦  «         ŒXd| › d|› d‰dz  › d‰dz  › d�	dgd„ |D ¦   «         z   }	t          |¦  «        d	k    rŠt          j
        }
|
                     d
¦  «        d	         }d|v }|rdnd}|	                     d|
› d�¦  «         |D ]<}d|                     dd¦  «        z   }|	                     d|› d|› d|› d|› �¦  «         Œ=n#|	                     dt          j
        › d�¦  «         t          j        d                     |	¦  «        d¬¦  «         d S )Nc              3   ó8   •K  — | ]}t          ‰|¦  «        V — Œd S r?   ©rÝ   )r¥   ÚccrÖ   s     €r"   r¦   z)_warn_unsupported_code.<locals>.<genexpr>q  s.   øè è € ÐOÐO¸rÕ+¨I°rÑ:Ô:ÐOÐOÐOÐOÐOÐOr!   z	Found GPUú z% which is of compute capability (CC) r—   r¡   zhThe following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:c           	      óF   — g | ]}d |dz  › d|dz  › dt           |         › �‘ŒS )z- r—   r¡   z which supports hardware CC )rÍ   ©r¥   rã   s     r"   ú
<listcomp>z*_warn_unsupported_code.<locals>.<listcomp>w  sP   € ð 	ð 	ð 	àð 	VˆR�2‰XÐUÐU˜˜R™ÐUÐUÕ=OÐPRÔ=SÐUÐUð	ð 	ð 	r!   r   ú+Údevz(https://download.pytorch.org/whl/nightlyz https://download.pytorch.org/whlzYour installed torch==zh does not include kernels for this GPU. Reinstall the same version against a CUDA build that does, e.g.:ÚcuÚ z  For CUDA z use pip install torch==z --index-url ú/z-No published PyTorch CUDA builds for release z_ support this GPU. Visit https://pytorch.org/get-started/locally/ to find a compatible release.ú
rŠ   rÙ   )Úget_device_namerÕ   rÐ   ÚitemsÚgetr™   ÚanyÚappendr¨   r`   Ú__version__rŽ   ÚreplacerÛ   rÜ   r3   )rÞ   rÖ   rß   r8   r�   Úcompatible_releasesrn   Úby_archÚ	build_ccsÚlinesr$   Úbase_versionÚ
is_nightlyÚ
index_rootÚcu_tags    `             r"   Ú_warn_unsupported_coderý   j  sE  ø€ Ý˜<Ñ(Ô(€DåÑÔ€DØ%'ÐÝ1×7Ò7Ñ9Ô9ð -ð -‰ˆˆgØ—K’K ¥c¡e¤eÑ,Ô,ˆ	ÝÐOÐOÐOÐOÀYÐOÑOÔOÑOÔOð 	-Ø×&Ò& tÑ,Ô,Ð,øð 	r�LÐqÐq 4ÐqÐqÈiÐ[]ÉoÐqÐqÐ`iÐlnÑ`nÐqÐqÐqØrðð	ð 	àð	ñ 	ô 	ñ€Eõ ÐÑÔ !Ò#Ð#ÝÔ#ˆØ—}’} SÑ)Ô)¨!Ô,ˆØ˜lÐ*ˆ
ð ð4Ð6Ð6à3ð 	ð
 	�ŠðO Wð Oð Oð Oñ	
ô 	
ð 	
ð (ð 	ð 	ˆDØ˜DŸLšL¨¨bÑ1Ô1Ñ1ˆFØ�LŠLØl˜dÐlÐl¸LÐlÐlÐWaÐlÐlÐdjÐlÐlñô ð ð ð	ð 	�Šð[½EÔ<Mð [ð [ð [ñ	
ô 	
ð 	
õ
 „M�$—)’)˜EÑ"Ô"¨qÐ1Ñ1Ô1Ð1Ð1Ð1r!   c                  óT  ‡— t           j        j        €d S t          ¦   «         } t	          | ¦  «        dk    rd S d„ | D ¦   «         }t          t          ¦   «         ¦  «        D ]H}t          |¦  «        \  }}d|z  |z   Št          ˆfd„|D ¦   «         ¦  «        st          |‰|¦  «         ŒId S )Nr   c                 ó,   — g | ]}t          |¦  «        ‘ŒS r    ©r’   ræ   s     r"   rç   z%_check_capability.<locals>.<listcomp>Ÿ  s!   € Ð>Ð>Ð>¨bÕ% bÑ)Ô)Ð>Ð>Ð>r!   r—   c              3   ó8   •K  — | ]}t          ‰|¦  «        V — Œd S r?   râ   )r¥   r×   rÖ   s     €r"   r¦   z$_check_capability.<locals>.<genexpr>£  s?   øè è € ð 
ð 
ØAHÕ(¨°GÑ<Ô<ð
ð 
ð 
ð 
ð 
ð 
r!   )
r`   r$   rn   Úget_arch_listr¨   Úrangerh   Úget_device_capabilityrñ   rý   )Ú	arch_listrß   Údrq   r˜   rÖ   s        @r"   Ú_check_capabilityr  —  sÖ   ø€ Ý„}ÔÐ!Øˆå‘”€IÝ
ˆ9�~„~˜ÒÐØˆà>Ð>°IÐ>Ñ>Ô>€HÝ•<‘>”>Ñ"Ô"ð ;ð ;ˆÝ,¨QÑ/Ô/‰ˆˆuØ˜‘J Ñ&ˆ	Ýð 
ð 
ð 
ð 
ØLTð
ñ 
ô 
ñ 
ô 
ð 	;õ # 1 i°Ñ:Ô:Ð:øð;ð ;r!   c            
      óÖ  ‡— d} t           j        j        €d S t          ¦   «         }t	          |¦  «        dk    rd S d„ |D ¦   «         }t          t          ¦   «         ¦  «        D ]‡}t          |¦  «        \  Š}t          ˆfd„|D ¦   «         ¦  «        }|sVt          |¦  «        }‰dz  |z   }t          j        |                      ||d                     |¦  «        |¦  «        d¬¦  «         Œˆd S )	Na	  
{} with CUDA capability sm_{} is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities {}.
If you want to use the {} GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
r   c                 ó4   — g | ]}d |v ¯t          |¦  «        ‘ŒS )Úsm_r   ©r¥   r�   s     r"   rç   z!_check_cubins.<locals>.<listcomp>´  s'   € ÐWÐWÐW°DÈÐRVÈÈÕ)¨$Ñ/Ô/ÈÈÈr!   c              3   ó*   •K  — | ]}|d z  ‰k    V — ŒdS )r—   Nr    )r¥   ÚsmÚ	cap_majors     €r"   r¦   z _check_cubins.<locals>.<genexpr>¸  s,   øè è € ÐEÐE°"˜˜b™ IÒ-ÐEÐEÐEÐEÐEÐEr!   r—   rä   rŠ   rÙ   )r`   r$   rn   r  r¨   r  rh   r  rñ   rî   rÛ   rÜ   Úformatr3   )	Úincompatible_device_warnr  Úsupported_smÚidxÚ	cap_minorÚ	supportedÚdevice_nameÚ
capabilityr  s	           @r"   Ú_check_cubinsr  ©  s  ø€ ð Ðõ
 „}ÔÐ!ØˆÝ‘”€IÝ
ˆ9�~„~˜ÒÐØˆØWÐW¸IÐWÑWÔW€LÝ•\‘^”^Ñ$Ô$ð ð ˆÝ4°SÑ9Ô9Ñˆ	�9åÐEÐEÐEÐE¸ÐEÑEÔEÑEÔEˆ	Øð 	Ý)¨#Ñ.Ô.ˆKØ" R™¨)Ñ3ˆJÝŒMØ(×/Ò/Ø ¨S¯XªX°iÑ-@Ô-@À+ñô ð ð	ñ ô ð øðð r!   c                  ó.   — t           ot          ¦   «          S )z9Return whether PyTorch's CUDA state has been initialized.)Ú_initializedÚ_is_in_bad_forkr    r!   r"   Úis_initializedr  Ä  s   € åÐ1¥Ñ 1Ô 1Ð1Ð1r!   c                 óÞ  — t           5  t          ¦   «         r | ¦   «          n³|                     dd¦  «        r-t                               | t          j        ¦   «         ¦  «         np|                     dd¦  «        r-t                               | t          j        ¦   «         ¦  «         n-t           	                    | t          j        ¦   «         f¦  «         d d d ¦  «         d S # 1 swxY w Y   d S )NÚseed_allFÚseed)
Ú_initialization_lockr  rð   Ú_lazy_seed_trackerÚqueue_seed_allrD   Úformat_stackÚ
queue_seedr   rò   )Úcallabler:   s     r"   Ú
_lazy_callr%  É  s4  € Ý	ð Kð KÝÑÔð 	KØˆH‰JŒJˆJˆJð �zŠz˜* eÑ,Ô,ð KÝ"×1Ò1°(½IÔ<RÑ<TÔ<TÑUÔUÐUÐUØ—’˜F EÑ*Ô*ð KÝ"×-Ò-¨h½	Ô8NÑ8PÔ8PÑQÔQÐQÐQõ ×$Ò$ hµ	Ô0FÑ0HÔ0HÐ%IÑJÔJÐJðKð Kð Kñ Kô Kð Kð Kð Kð Kð Kð Kð Køøøð Kð Kð Kð Kð Kð Ks   ˆCC"Ã"C&Ã)C&c                   ó   — e Zd ZdS )ÚDeferredCudaCallErrorN)rH   rI   rJ   r    r!   r"   r'  r'  ß  s   € € € € € Ø€Dr!   r'  c                  ó"   — t          ¦   «          dS )aª  Initialize PyTorch's CUDA state.

    You may need to call this explicitly if you are interacting with
    PyTorch via its C API, as Python bindings for CUDA functionality
    will not be available until this initialization takes place.
    Ordinary users should not need this, as all of PyTorch's CUDA methods
    automatically initialize CUDA state on-demand.

    Does nothing if the CUDA state is already initialized.
    N)Ú
_lazy_initr    r!   r"   Úinitr*  ç  s   € õ �L„L€L€L€Lr!   c            	      óv  — t          ¦   «         st          t          d¦  «        rd S t          5  t          ¦   «         r	 d d d ¦  «         d S t	          ¦   «         rt          d¦  «        ‚t          t          j        d¦  «        st          d¦  «        ‚t          €t          d¦  «        ‚t          j         
                    ¦   «          dt          _        t                               d„ t                               ¦   «         D ¦   «         ¦  «         	 t          D ]Z\  } }	  | ¦   «          Œ# t           $ r=}dt#          |¦  «        › d	d
                     |¦  «        › �}t'          |¦  «        |‚d }~ww xY w	 t)          t          d¦  «         n# t)          t          d¦  «         w xY wdad d d ¦  «         d S # 1 swxY w Y   d S )NÚis_initializingzwCannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start methodr^   z$Torch not compiled with CUDA enabledzGlibcudart functions unavailable. It looks like you have a broken build?Tc              3   ó   K  — | ]}|¯|V — Œ	d S r?   r    )r¥   Úcallss     r"   r¦   z_lazy_init.<locals>.<genexpr>  s(   è è € ÐXÐX uÐRWÐX˜UÐXÐXÐXÐXÐXÐXr!   z6CUDA call failed lazily at initialization with error: z(

CUDA call was originally invoked at:

rë   )r  r_   Ú_tlsr  r  rS   r`   ra   ÚAssertionErrorr   Ú
_cuda_initr,  r   Úextendr   Ú	get_callsrz   rK   r3   r'  Údelattrr  )Úqueued_callÚorig_tracebackÚeÚmsgs       r"   r)  r)  õ  sW  € åÑÔð �7¥4Ð):Ñ;Ô;ð ØˆÝ	ð ,ð ,õ ÑÔð 	Øð,ð ,ð ,ñ ,ô ,ð ,ð ,ð ,õ ÑÔð 	ÝðIñô ð õ •u”xÐ!7Ñ8Ô8ð 	IÝ Ð!GÑHÔHÐHÝˆ?Ý ØYñô ð õ
 	Œ×ÒÑÔÐð  $�Ôå×ÒÐXÐXÕ0B×0LÒ0LÑ0NÔ0NÐXÑXÔXÑXÔXÐXð	-Ý/<ð <ð <Ñ+�˜^ð<Ø�K‘M”M�M�MøÝ ð <ð <ð <ð]ÕQTÐUVÑQWÔQWð ]ð ]ØCEÇ7Â7È>ÑCZÔCZð]ð ]ð õ 0°Ñ4Ô4¸!Ð;øøøøð<øøøð<õ •DÐ+Ñ,Ô,Ð,Ð,ø�G•DÐ+Ñ,Ô,Ð,Ð,øøøØˆðY,ð ,ð ,ñ ,ô ,ð ,ð ,ð ,ð ,ð ,ð ,ð ,øøøð ,ð ,ð ,ð ,ð ,ð ,sT   ­F.Á
CF.ÄFÄ
D$Ä#FÄ$
E+Ä.8E&Å&E+Å+FÅ/F.ÆFÆF.Æ.F2Æ5F2c                  ó,   — t          ¦   «          t          S )a 	  Retrieves the CUDA runtime API module.


    This function initializes the CUDA runtime environment if it is not already
    initialized and returns the CUDA runtime API module (_cudart). The CUDA
    runtime API module provides access to various CUDA runtime functions.

    Args:
        ``None``

    Returns:
        module: The CUDA runtime API module (_cudart).

    Raises:
        RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
        AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.

    Example of CUDA operations with profiling:
        >>> import torch
        >>> from torch.cuda import cudart, check_error
        >>> import os
        >>>
        >>> os.environ["CUDA_PROFILE"] = "1"
        >>>
        >>> def perform_cuda_operations_with_streams():
        >>>     stream = torch.cuda.Stream()
        >>>     with torch.cuda.stream(stream):
        >>>         x = torch.randn(100, 100, device='cuda')
        >>>         y = torch.randn(100, 100, device='cuda')
        >>>         z = torch.mul(x, y)
        >>>     return z
        >>>
        >>> torch.cuda.synchronize()
        >>> print("====== Start nsys profiling ======")
        >>> check_error(cudart().cudaProfilerStart())
        >>> with torch.autograd.profiler.emit_nvtx():
        >>>     result = perform_cuda_operations_with_streams()
        >>>     print("CUDA operations completed.")
        >>> check_error(torch.cuda.cudart().cudaProfilerStop())
        >>> print("====== End nsys profiling ======")

    To run this example and save the profiling information, execute:
        >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py

    This command profiles the CUDA operations in the provided script and saves
    the profiling information to a file named `trace_name.prof`.
    The `--profile-from-start off` option ensures that profiling starts only
    after the `cudaProfilerStart` call in the script.
    The `--csv` and `--print-summary` options format the profiling output as a
    CSV file and print a summary, respectively.
    The `-o` option specifies the output file name, and the `-f` option forces the
    overwrite of the output file if it already exists.
    )r)  r   r    r!   r"   Úcudartr:  (  s   € õl �L„L€LÝ€Nr!   c                   ó,   — e Zd ZU dZeed<   dZeed<   dS )Ú
cudaStatusr   ÚSUCCESSé"   ÚERROR_NOT_READYN)rH   rI   rJ   r=  r�   Ú__annotations__r?  r    r!   r"   r<  r<  b  s2   € € € € € € Ø€GˆSÐÐÑØ€O�SÐÐÑÐÐr!   r<  c                   ó(   ‡ — e Zd Zdeddfˆ fd„Zˆ xZS )Ú	CudaErrorÚcoder(   Nc                 ó¢   •— t          j        t          j        |¦  «        ¦  «        }t          ¦   «                              |› d|› d�¦  «         d S )Nz (ú))r   ÚcudaGetErrorStringÚ	cudaErrorÚsuperr7   )r5   rC  r8  Ú	__class__s      €r"   r7   zCudaError.__init__h  sM   ø€ åÔ(­Ô):¸4Ñ)@Ô)@ÑAÔAˆÝ‰Œ×Ò˜CÐ*Ð* 4Ð*Ð*Ð*Ñ+Ô+Ð+Ð+Ð+r!   )rH   rI   rJ   r�   r7   Ú__classcell__©rI  s   @r"   rB  rB  g  sK   ø€ € € € € ð,˜Sð , Tð ,ð ,ð ,ð ,ð ,ð ,ð ,ð ,ð ,ð ,r!   rB  Úresc                 óN   — | t           j        j        k    rt          | ¦  «        ‚dS )zGRaise an error if the result of a CUDA runtime API call is not success.N)r   rG  ÚsuccessrB  )rL  s    r"   Úcheck_errorrO  n  s)   € ð �gÔÔ'Ò'Ð'Ý˜‰nŒnÐð (Ð'r!   c                   ó4   — e Zd Zdefd„Zd„ Zdededefd„ZdS )	Ú_DeviceGuardÚindexc                 ó"   — || _         d| _        d S ©NrQ   )r  Úprev_idx)r5   rR  s     r"   r7   z_DeviceGuard.__init__v  s   € ØˆŒØˆŒˆˆr!   c                 óX   — t           j                             | j        ¦  «        | _        d S r?   ©r`   rn   rU   r  rU  r@   s    r"   rA   z_DeviceGuard.__enter__z  ó   € Ýœ
×3Ò3°D´HÑ=Ô=ˆŒˆˆr!   rB   rC   rD   c                 óX   — t           j                             | j        ¦  «        | _        dS r   ©r`   rn   rY   rU  r  rF   s       r"   rG   z_DeviceGuard.__exit__}  ó    € Ý”:×4Ò4°T´]ÑCÔCˆŒØˆur!   N)rH   rI   rJ   r�   r7   rA   r   rG   r    r!   r"   rQ  rQ  u  sg   € € € € € ð˜cð ð ð ð ð>ð >ð >ð˜Sð ¨ð ¸ð ð ð ð ð ð r!   rQ  c                   ó8   — e Zd ZdZd efd„Zd„ Zdededefd„ZdS )	rN   zÌContext-manager that changes the selected device.

    Args:
        device (torch.device or int): device index to select. It's a no-op if
            this argument is a negative integer or ``None``.
    c                 ó@   — t          |d¬¦  «        | _        d| _        d S )NT©ÚoptionalrQ   )r   r  rU  )r5   rN   s     r"   r7   zdevice.__init__Š  s!   € Ý$ V°dÐ;Ñ;Ô;ˆŒØˆŒˆˆr!   c                 óX   — t           j                             | j        ¦  «        | _        d S r?   rW  r@   s    r"   rA   zdevice.__enter__Ž  rX  r!   rB   rC   rD   c                 óX   — t           j                             | j        ¦  «        | _        dS r   rZ  rF   s       r"   rG   zdevice.__exit__‘  r[  r!   N)rH   rI   rJ   r­   r   r7   rA   rG   r    r!   r"   rN   rN   ‚  sq   € € € € € ðð ð˜sð ð ð ð ð>ð >ð >ð˜Sð ¨ð ¸ð ð ð ð ð ð r!   c                   ó"   ‡ — e Zd ZdZˆ fd„Zˆ xZS )Ú	device_ofa  Context-manager that changes the current device to that of given object.

    You can use both tensors and storages as arguments. If a given object is
    not allocated on a GPU, this is a no-op.

    Args:
        obj (Tensor or Storage): object allocated on the selected device.
    c                 ó„   •— |j         r|                     ¦   «         nd}t          ¦   «                              |¦  «         d S rT  )Úis_cudaÚ
get_devicerH  r7   )r5   Úobjr  rI  s      €r"   r7   zdevice_of.__init__   s<   ø€ Ø"%¤+Ð5ˆc�nŠnÑÔÐ°2ˆÝ‰Œ×Ò˜ÑÔÐÐÐr!   )rH   rI   rJ   r­   r7   rJ  rK  s   @r"   rc  rc  –  sB   ø€ € € € € ðð ðð ð ð ð ð ð ð ð r!   rc  c                 ór   — t          | ¦  «        } | dk    r!t          j                             | ¦  «         dS dS )a=  Set the current device.

    Usage of this function is discouraged in favor of :any:`device`. In most
    cases it's better to use ``CUDA_VISIBLE_DEVICES`` environmental variable.

    Args:
        device (torch.device or int): selected device. This function is a no-op
            if this argument is negative.
    r   N)r   r`   ra   Ú_cuda_setDevicerT   s    r"   Ú
set_devicerj  ¥  s=   € õ ˜vÑ&Ô&€FØ�‚{€{ÝŒ× Ò  Ñ(Ô(Ð(Ð(Ð(ð €{r!   c                 ó*   — t          | ¦  «        j        S )aŽ  Get the name of a device.

    Args:
        device (torch.device or int or str, optional): device for which to return the
            name. This function is a no-op if this argument is a negative
            integer. It uses the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    Returns:
        str: the name of the device
    )rp   r8   rT   s    r"   rî   rî   ´  s   € õ ! Ñ(Ô(Ô-Ð-r!   c                 ó<   — t          | ¦  «        }|j        |j        fS )aÙ  Get the cuda capability of a device.

    Args:
        device (torch.device or int or str, optional): device for which to return the
            device capability. This function is a no-op if this argument is
            a negative integer. It uses the current device, given by
            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
            (default).

    Returns:
        tuple(int, int): the major and minor cuda capability of the device
    )rp   rq   r˜   )rN   Úprops     r"   r  r  Ã  s    € õ ! Ñ(Ô(€DØŒ:�t”zÐ!Ð!r!   c                 ó¬   — t          ¦   «          t          | d¬¦  «        } | dk     s| t          ¦   «         k    rt          d¦  «        ‚t	          | ¦  «        S )a€  Get the properties of a device.

    Args:
        device (torch.device or int or str, optional): device for which to return the
            properties of the device.  It uses the current device, given by
            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
            (default).

    Returns:
        _CudaDeviceProperties: the properties of the device
    Tr^  r   úInvalid device id)r)  r   rh   r0  Ú_get_device_propertiesrT   s    r"   rp   rp   Õ  sS   € õ �L„L€LÝ˜v°Ð5Ñ5Ô5€FØ�‚z€z�V�|™~œ~Ò-Ð-ÝÐ0Ñ1Ô1Ð1Ý! &Ñ)Ô)Ð)r!   Úpeer_devicec                 ó:  — t          ¦   «          t          | d¬¦  «        } t          |¦  «        }| dk     s| t          ¦   «         k    rt          d¦  «        ‚|dk     s|t          ¦   «         k    rt          d¦  «        ‚t          j                             | |¦  «        S )z5Check if peer access between two devices is possible.Tr^  r   ro  zInvalid peer device id)r)  r   rh   r0  r`   ra   Ú_cuda_canDeviceAccessPeer)rN   rq  s     r"   Úcan_device_access_peerrt  è  sŽ   € å�L„L€LÝ˜v°Ð5Ñ5Ô5€FÝ# KÑ0Ô0€KØ�‚z€z�V�|™~œ~Ò-Ð-ÝÐ0Ñ1Ô1Ð1Ø�Q‚€˜+­©¬Ò7Ð7ÝÐ5Ñ6Ô6Ð6ÝŒ8×-Ò-¨f°kÑBÔBÐBr!   c                   ó\   — e Zd ZU dZed         ed<   ded         fd„Zd„ Zdeded	efd
„Z	dS )ÚStreamContexta  Context-manager that selects a given stream.

    All CUDA kernels queued within its context will be enqueued on a selected
    stream.

    Args:
        Stream (Stream): selected stream. This manager is a no-op if it's
            ``None``.
    .. note:: Streams are per-device.
    útorch.cuda.StreamÚ
cur_streamÚstreamc                 ó¦  — || _         t          d d¦  «        | _        t          j                             ¦   «         s| j        €d| _        t          j                             ¦   «         sd nt          j                             d ¦  «        | _        t          j                             ¦   «         sd nt          j                             d ¦  «        | _	        d S )NTrQ   )
ry  r   r  r`   ÚjitÚis_scriptingrn   Údefault_streamÚsrc_prev_streamÚdst_prev_stream)r5   ry  s     r"   r7   zStreamContext.__init__  s¯   € ØˆŒÝ$ T¨4Ñ0Ô0ˆŒÝŒy×%Ò%Ñ'Ô'ð 	ØŒxÐà�”õ œ	×.Ò.Ñ0Ô0ÐUˆDˆDµe´j×6OÒ6OÐPTÑ6UÔ6Uð 	Ôõ œ	×.Ò.Ñ0Ô0ÐUˆDˆDµe´j×6OÒ6OÐPTÑ6UÔ6Uð 	ÔÐÐr!   c                 óŒ  — | j         }|�| j        dk    rd S t          j                             d ¦  «        | _        | j        j        |j        k    rUt          |j        ¦  «        5  t          j                             |j        ¦  «        | _        d d d ¦  «         n# 1 swxY w Y   t          j                             |¦  «         d S rT  )	ry  r  r`   rn   Úcurrent_streamr~  rN   r  Ú
set_stream)r5   rx  s     r"   rA   zStreamContext.__enter__  sú   € à”[ˆ
àÐ ¤¨R¢ ØˆFÝ$œz×8Ò8¸Ñ>Ô>ˆÔð ÔÔ&¨*Ô*;Ò;Ð;Ý˜
Ô)Ñ*Ô*ð Tð TÝ',¤z×'@Ò'@ÀÔARÑ'SÔ'S�Ô$ðTð Tð Tñ Tô Tð Tð Tð Tð Tð Tð Tøøøð Tð Tð Tð TåŒ
×Ò˜jÑ)Ô)Ð)Ð)Ð)s   Á$*BÂBÂ!BrB   rC   rD   c                 óì   — | j         }|�| j        dk    rd S | j        j        |j        k    r$t          j                             | j        ¦  «         t          j                             | j        ¦  «         d S rT  )ry  r  r~  rN   r`   rn   r‚  r  )r5   rB   rC   rD   rx  s        r"   rG   zStreamContext.__exit__   sl   € à”[ˆ
àÐ ¤¨R¢ ØˆFð ÔÔ&¨*Ô*;Ò;Ð;ÝŒJ×!Ò! $Ô"6Ñ7Ô7Ð7ÝŒ
×Ò˜dÔ2Ñ3Ô3Ð3Ð3Ð3r!   N)
rH   rI   rJ   r­   r   r@  r7   rA   r   rG   r    r!   r"   rv  rv  ô  s�   € € € € € € ð	ð 	ð Ð,Ô-Ð-Ð-Ñ-ð
˜xÐ(;Ô<ð 
ð 
ð 
ð 
ð*ð *ð *ð4˜Sð 4¨ð 4¸ð 4ð 4ð 4ð 4ð 4ð 4r!   rv  ry  rw  c                 ó    — t          | ¦  «        S )aZ  Wrap around the Context-manager StreamContext that selects a given stream.

    Arguments:
        stream (Stream): selected stream. This manager is a no-op if it's
            ``None``.
    .. note::
        In eager mode stream is of type Stream class while in JIT it is
        an object of the custom class ``torch.classes.cuda.Stream``.
    )rv  ©ry  s    r"   ry  ry  .  s   € õ ˜Ñ Ô Ð r!   c                 óJ   — t           j                             | ||¬¦  «         dS )zæset stream specified by the stream id, device index and
        device type

    Args: stream_id (int): stream id in stream pool
          device_index (int): device index in topo
          device_type (int): enum device type
    ©Ú	stream_idrÞ   Údevice_typeN)r`   ra   Ú_cuda_setStreamr‡  s      r"   Ú_set_stream_by_idr‹  ;  s6   € õ 
„H×ÒØØ!Øð ñ ô ð ð ð r!   c                 óP   — | €dS t          | j        | j        | j        ¬¦  «         dS )a  Set the current stream. This is a wrapper API to set the stream.
        Usage of this function is discouraged in favor of the ``stream``
        context manager.

    Args:
        stream (Stream): selected stream. This function is a no-op
            if this argument is ``None``.
    Nr‡  )r‹  rˆ  rÞ   r‰  r…  s    r"   r‚  r‚  J  sA   € ð €~ØˆÝØÔ"ØÔ(ØÔ&ðñ ô ð ð ð r!   c                  ó¼  — t          j        d¦  «        } t          j        j        r¦t          j        d¦  «        }t          j        d¦  «        }|�xt          |                     d¦  «        ¦  «        }|�8t          |                     d¦  «        ¦  «        |k    rt          d¦  «        ‚|} n t          t          |¦  «        ¦  «        S |�|} | €t          t          d¦  «        ¦  «        S dt          d	t          fd
„}dt          dt          d	t          t                   fd„}|                      d¦  «        r || d¦  «        S |                      d¦  «        r || d¦  «        S g }|                      d¦  «        D ]b} ||                     ¦   «         ¦  «        }||v r"t          t          t                   g ¦  «        c S |dk     r n|                     |¦  «         Œc|S )z0Parse CUDA_VISIBLE_DEVICES environment variable.ÚCUDA_VISIBLE_DEVICESÚHIP_VISIBLE_DEVICESÚROCR_VISIBLE_DEVICESNú,zCHIP_VISIBLE_DEVICES contains more devices than ROCR_VISIBLE_DEVICESé@   Úsr(   c                 óè   — | sdS t          | ¦  «        D ]@\  }}|                     ¦   «         s|dk    r|dv s n|dz   t          | ¦  «        k    r|dz  }ŒA|dk    rt          | d|…         ¦  «        ndS )z:Return -1 or positive integer sequence string starts with.rQ   r   z+-r   N)Ú	enumerateÚisdigitr¨   r�   )r“  r  Úcs      r"   Ú_strtoulz(_parse_visible_devices.<locals>._strtoulz  sŒ   € àð 	Ø�2Ý ‘l”lð 	ð 	‰FˆC�Ø—I’I‘K”Kð  C¨1¢H H°°d°°Ø�Ø�Q‰w�#˜a™&œ&Ò Ð Ø�q‘�øØ" Qšw˜w�s�1�T�c�T”7‰|Œ|ˆ|¨BÐ.r!   ÚlstÚprefixc                 óÞ   — g }|                       d¦  «        D ]T}||v r"t          t          t                   g ¦  «        c S |                     |¦  «        s n|                     |¦  «         ŒU|S )Nr‘  )rŽ   r   ÚlistrK   Ú
startswithrò   )r™  rš  ÚrcsÚelems       r"   Úparse_list_with_prefixz6_parse_visible_devices.<locals>.parse_list_with_prefix…  sv   € ØˆØ—I’I˜c‘N”Nð 	ð 	ˆDà�sˆ{ˆ{Ý�D¥œI rÑ*Ô*Ð*Ð*Ð*à—?’? 6Ñ*Ô*ð Ø�Ø�JŠJ�tÑÔÐÐØˆ
r!   úGPU-úMIG-r   )r0   r1   r`   r$   rm   r¨   rŽ   rS   rœ  r  rK   r�   r�  Ústripr   rò   )	ÚvarÚhip_devicesÚrocr_devicesÚ
rocr_countr˜  r   ÚrcrŸ  rœ   s	            r"   Ú_parse_visible_devicesr©  \  sü  € å
Œ)Ð*Ñ
+Ô
+€Cå„}Ôð Ý”iÐ 5Ñ6Ô6ˆÝ”yÐ!7Ñ8Ô8ˆð Ð#Ý˜\×/Ò/°Ñ4Ô4Ñ5Ô5ˆJØÐ&å�{×(Ò(¨Ñ-Ô-Ñ.Ô.°Ò;Ð;Ý&Ø]ñô ð ð "��å�E *Ñ-Ô-Ñ.Ô.Ð.ØÐ$ØˆCà
€{Ý•E˜"‘I”I‰ŒÐð	/•Cð 	/�Cð 	/ð 	/ð 	/ð 	/ð
¥Cð 
µð 
½½c¼ð 
ð 
ð 
ð 
ð ‡~‚~�fÑÔð 3Ø%Ð% c¨6Ñ2Ô2Ð2Ø
‡~‚~�fÑÔð 3Ø%Ð% c¨6Ñ2Ô2Ð2ð €BØ—	’	˜#‘”ð ð ˆØˆH�T—Z’Z‘\”\Ñ"Ô"ˆà�ˆ7ˆ7Ý��Sœ	 2Ñ&Ô&Ð&Ð&Ð&àˆqŠ5ˆ5ØˆEØ
�	Š	�!‰ŒˆˆØ€Ir!   c                  óø   — t           sdS 	 t          j        ¦   «          n;# t          j        $ r)} t	          j        d| j        › �d¬¦  «         Y d } ~ dS d } ~ ww xY wt          j        ¦   «         }t          |¦  «        S )NrQ   z&Can't initialize amdsmi - Error code: rŠ   rÙ   )	Ú_HAS_AMDSMIÚamdsmiÚamdsmi_initÚAmdSmiExceptionrÛ   rÜ   Úerr_codeÚamdsmi_get_processor_handlesr¨   )r7  Úsocket_handless     r"   Ú_raw_device_count_amdsmir²  ¤  s�   € Ýð ØˆrðÝÔÑÔÐÐøÝÔ!ð ð ð ÝŒØA°Q´ZÐAÐAÈað	
ñ 	
ô 	
ð 	
ð ˆrˆrˆrˆrˆrøøøøð	øøøõ
 Ô8Ñ:Ô:€NÝˆ~ÑÔÐs   ‹ ŸA®AÁAc                  ó.  — ddl m} m}m}  |d¦  «        }|                     ¦   «         }|dk    rt          j        dd¬¦  «         dS  |d¦  «        }|                      | |¦  «        ¦  «        }|dk    rt          j        dd¬¦  «         dS ~|j        S )	zgReturn number of devices as reported by NVML or negative value if NVML discovery/initialization failed.r   )ÚbyrefÚc_intr.   úlibnvidia-ml.so.1úCan't initialize NVMLrŠ   rÙ   rQ   úCan't get nvml device count)	r-   r´  rµ  r.   ÚnvmlInitrÛ   rÜ   ÚnvmlDeviceGetCount_v2rC   )r´  rµ  r.   Únvml_hr¨  Ú	dev_counts         r"   Ú_raw_device_count_nvmlr½  ²  s¼   € à)Ð)Ð)Ð)Ð)Ð)Ð)Ð)Ð)Ð)àˆTÐ%Ñ&Ô&€FØ	�ŠÑ	Ô	€BØ	ˆQ‚w€wÝŒÐ-¸!Ð<Ñ<Ô<Ð<ØˆrØ��b‘	”	€IØ	×	%Ò	% e e¨IÑ&6Ô&6Ñ	7Ô	7€BØ	ˆQ‚w€wÝŒÐ3ÀÐBÑBÔBÐBØˆrØØŒ?Ðr!   c                  ó  — ddl m} m}m}m}m} t          sd S 	 t          j        ¦   «          n,# t          j	        $ r t          j        dd¬¦  «         Y d S w xY w	 t          j        ¦   «         }t          |¦  «        }n,# t          j	        $ r t          j        dd¬¦  «         Y d S w xY wg }t          |¦  «        D ]Ï}	 t          j        ¦   «         |         }	n-# t          j	        $ r t          j        dd¬¦  «         Y  d S w xY w	 t          j        |	¦  «        d         dd …         }
n-# t          j	        $ r t          j        d	d¬¦  «         Y  d S w xY w|                     t#          |
¦  «                             ¦   «         ¦  «         ŒÐ|S )
Nr   ©r´  rµ  Úc_void_pr.   Úcreate_string_bufferzCan't initialize amdsmirŠ   rÙ   zCan't get amdsmi device countzCannot get amd device handlerÚasic_serialzCannot get uuid for amd device)r-   r´  rµ  rÀ  r.   rÁ  r«  r¬  r­  r®  rÛ   rÜ   r°  r¨   r  Úamdsmi_get_gpu_asic_inforò   rK   rÔ   )r´  rµ  rÀ  r.   rÁ  r±  r¼  Úuuidsr  ÚhandlerÚuuids              r"   Ú_raw_device_uuid_amdsmirÇ  Ä  sû  € ØIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIåð ØˆtðÝÔÑÔÐÐøÝÔ!ð ð ð ÝŒÐ/¸AÐ>Ñ>Ô>Ð>ØˆtˆtðøøøðÝÔ<Ñ>Ô>ˆÝ˜Ñ'Ô'ˆ	ˆ	øÝÔ!ð ð ð ÝŒÐ5À!ÐDÑDÔDÐDØˆtˆtðøøøð €EÝ�YÑÔð 
ð 
ˆð	ÝÔ9Ñ;Ô;¸CÔ@ˆGˆGøÝÔ%ð 	ð 	ð 	ÝŒMÐ9ÀaÐHÑHÔHÐHØ�4�4�4ð	øøøð	ÝÔ2°7Ñ;Ô;¸MÔJØ��ôˆDˆDøõ Ô%ð 	ð 	ð 	ÝŒMÐ:ÀqÐIÑIÔIÐIØ�4�4�4ð	øøøð 	�ŠÝ�‰IŒI�OŠOÑÔñ	
ô 	
ð 	
ð 	
ð €LsE   ™- ­%AÁAÁ"A= Á=%B&Â%B&Â=CÃ%DÄ DÄ"D(Ä(%EÅEc                  óô  — ddl m} m}m}m}m}  |d¦  «        }|                     ¦   «         }|dk    rt          j        dd¬¦  «         dS  |d¦  «        }| 	                     | |¦  «        ¦  «        }|dk    rt          j        d	d¬¦  «         dS g }t          |j        ¦  «        D ]Í}	 |¦   «         }
|                     |	 | |
¦  «        ¦  «        }|dk    rt          j        d
d¬¦  «          dS d} ||¦  «        }|                     |
||¦  «        }|dk    rt          j        dd¬¦  «          dS |                     |j                             d¦  «                             d¦  «        ¦  «         ŒÎ~|S )z^Return list of device UUID as reported by NVML or None if NVM discovery/initialization failed.r   r¿  r¶  r·  rŠ   rÙ   NrQ   r¸  zCan't get device handleé`   zCan't get device UUIDÚasciiú )r-   r´  rµ  rÀ  r.   rÁ  r¹  rÛ   rÜ   rº  r  rC   ÚnvmlDeviceGetHandleByIndex_v2ÚnvmlDeviceGetUUIDrò   ÚrawÚdecoder£  )r´  rµ  rÀ  r.   rÁ  r»  r¨  r¼  rÄ  r  Údev_idÚbuf_lenÚbufs                r"   Ú_raw_device_uuid_nvmlrÓ  è  s´  € àIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIÐIàˆTÐ%Ñ&Ô&€FØ	�ŠÑ	Ô	€BØ	ˆQ‚w€wÝŒÐ-¸!Ð<Ñ<Ô<Ð<ØˆtØ��b‘	”	€IØ	×	%Ò	% e e¨IÑ&6Ô&6Ñ	7Ô	7€BØ	ˆQ‚w€wÝŒÐ3ÀÐBÑBÔBÐBØˆtØ€EÝ�Y”_Ñ%Ô%ð :ð :ˆØ�‘”ˆØ×1Ò1°#°u°u¸V±}´}ÑEÔEˆØ�Š7ˆ7ÝŒMÐ3ÀÐBÑBÔBÐBØ�4�4ØˆØ"Ð" 7Ñ+Ô+ˆØ×%Ò% f¨c°7Ñ;Ô;ˆØ�Š7ˆ7ÝŒMÐ1¸aÐ@Ñ@Ô@Ð@Ø�4�4Ø�Š�S”W—^’^ GÑ,Ô,×2Ò2°4Ñ8Ô8Ñ9Ô9Ð9Ð9ØØ€Lr!   Ú
candidatesrÄ  c                 óJ  — dt           dt          t                    dt          fd„}g }| D ]y}t          j        j        r|                     ddd¦  «        } |||¦  «        }|dk     r n<||v r"t          t          t                   g ¦  «        c S |                     |¦  «         Œz|S )	zqGiven the set of partial uuids and list of known uuids builds a set of ordinals excluding ambiguous partials IDs.Ú	candidaterÄ  r(   c                 óv   — d}t          |¦  «        D ]&\  }}|                     | ¦  «        sŒ|dk    r dS |}Œ'|S rT  )r•  r�  )rÖ  rÄ  Ú
best_matchr  rÆ  s        r"   Úuuid_to_ordinalz4_transform_uuid_to_ordinals.<locals>.uuid_to_ordinal  sX   € Øˆ
Ý" 5Ñ)Ô)ð 	ð 	‰IˆC�Ø—?’? 9Ñ-Ô-ð Øà˜RÒÐØ�r�rØˆJˆJØÐr!   r¡  rë   r   r   )	rK   rœ  r�   r`   r$   rm   rô   r   rò   )rÔ  rÄ  rÙ  r¨  rÖ  r  s         r"   Ú_transform_uuid_to_ordinalsrÚ    sÈ   € ð	¥3ð 	­tµC¬yð 	½Sð 	ð 	ð 	ð 	ð €BØð ð ˆ	ÝŒ=Ôð 	Ø!×)Ò)Ø˜˜Añô ˆIð ˆo˜i¨Ñ/Ô/ˆà�Š7ˆ7ØˆEà�"ˆ9ˆ9Ý��Sœ	 2Ñ&Ô&Ð&Ð&Ð&Ø
�	Š	�#‰ŒˆˆØ€Ir!   c                  óÐ  — t          ¦   «         } | sdS 	 t          | d         ¦  «        t          u rCt          ¦   «         }|€dS t	          t
          t                   | ¦  «        }t          ||¦  «        } nHt          ¦   «         }|dk    r|S t          | ¦  «        D ]"\  }}t	          t          |¦  «        |k    r|c S Œ#n# t          $ r Y dS t          $ r Y dS w xY wt          | ¦  «        S )Nr   rQ   )r©  rB   rK   rÇ  r   rœ  rÚ  r²  r•  r�   r<   ÚAttributeErrorr¨   )Úvisible_devicesrÄ  Úvisible_device_strÚraw_cntr  Úvals         r"   Ú_device_count_amdsmirá  '  s  € Ý,Ñ.Ô.€OØð ØˆqðÝ� Ô"Ñ#Ô#¥sÐ*Ð*Ý+Ñ-Ô-ˆEØˆ}Ø�rå!%¥d­3¤i°Ñ!AÔ!AÐÝ9Ð:LÈeÑTÔTˆOˆOå.Ñ0Ô0ˆGØ˜!Š|ˆ|Ø�õ & oÑ6Ô6ð ð ‘��Så�˜S‘>”> WÒ,Ð,Ø�J�J�Jð -øøåð ð ð ØˆrˆrÝð ð ð ØˆrˆrðøøøåˆÑÔÐs*   ”,B< ÁAB< Â	0B< Â:B< Â<
CÃ		CÃCc                  ó  — t          ¦   «         } | sdS 	 t          | d         ¦  «        t          u r^| d                              d¦  «        rdS t	          ¦   «         }|€dS t          t          t          t                   | ¦  «        |¦  «        } nHt          ¦   «         }|dk    r|S t          | ¦  «        D ]"\  }}t          t          |¦  «        |k    r|c S Œ#n# t          $ r Y dS t          $ r Y dS w xY wt          | ¦  «        S )z«Return number of devices as reported by NVML taking CUDA_VISIBLE_DEVICES into account.

    Negative value is returned if NVML discovery or initialization has failed.
    r   r¢  rQ   )r©  rB   rK   r�  rÓ  rÚ  r   rœ  r½  r•  r�   r<   rÜ  r¨   )rÝ  rÄ  rß  r  rà  s        r"   Ú_device_count_nvmlrã  D  s5  € õ
 -Ñ.Ô.€OØð ØˆqðÝ� Ô"Ñ#Ô#¥sÐ*Ð*à˜qÔ!×,Ò,¨VÑ4Ô4ð Ø�rÝ)Ñ+Ô+ˆEØˆ}Ø�rÝ9Ý•T�#”Y Ñ0Ô0°%ñô ˆOˆOõ -Ñ.Ô.ˆGØ˜!Š|ˆ|Ø�õ & oÑ6Ô6ð ð ‘��Så�˜S‘>”> WÒ,Ð,Ø�J�J�Jð -øøåð ð ð ØˆrˆrÝð ð ð ØˆrˆrðøøøåˆÑÔÐs0   ”7C ÁC ÁAC Â$0C ÃC Ã
C1Ã$	C1Ã0C1c                 óÀ  — t          | d¬¦  «        }t          ¦   «         }t          |d         ¦  «        t          u rMt	          ¦   «         }|€t          d¦  «        ‚t          t          t          t                   |¦  «        |¦  «        }t          t          t                   |¦  «        }|dk     s|t          |¦  «        k    rt          d|› d|› d�¦  «        ‚||         S )	zNReturn the NVML index of the device, taking CUDA_VISIBLE_DEVICES into account.Tr^  r   NúCan't get device UUIDsúdevice z& is not visible (CUDA_VISIBLE_DEVICES=rE  )r   r©  rB   rK   rÓ  rS   rÚ  r   rœ  r�   r¨   )rN   r  rÝ  rÄ  s       r"   Ú_get_nvml_device_indexrç  h  sÝ   € å
˜F¨TÐ
2Ñ
2Ô
2€CÝ,Ñ.Ô.€OÝˆO˜AÔÑÔ¥3Ð&Ð&Ý%Ñ'Ô'ˆØˆ=ÝÐ7Ñ8Ô8Ð8Ý5Ý••c”˜OÑ,Ô,¨eñ
ô 
ˆõ �4¥œ9 oÑ6Ô6€OØ
ˆQ‚w€w�#�˜_Ñ-Ô-Ò-Ð-ÝØS�cÐSÐSÀÐSÐSÐSñ
ô 
ð 	
ð ˜3ÔÐr!   Ú_cached_device_countc                  ór  — t          ¦   «         sdS t          �t          S t          st          t          d¦  «        rt
          j                             ¦   «         } nTt
          j        j	        rt          ¦   «         }nt          ¦   «         }|dk     rt
          j                             ¦   «         n|} t          r| a| S )z½
    Return the number of GPUs available.

    .. note:: This API will NOT poison fork if NVML discovery succeeds.
        See :ref:`multiprocessing-poison-fork-note` for more details.
    r   Nr,  )rb   rè  r  r_   r/  r`   ra   r^   r$   rm   rá  rã  )ÚrÚ
nvml_counts     r"   rh   rh   ~  s®   € õ ‰>Œ>ð ØˆqÝÐ'Ý#Ð#Ýð N•w�tÐ%6Ñ7Ô7ð NÝŒH×)Ò)Ñ+Ô+ˆˆõ Œ=Ôð 	.Ý-Ñ/Ô/ˆJˆJå+Ñ-Ô-ˆJØ/9¸Aª~¨~�EŒH×)Ò)Ñ+Ô+Ð+À:ˆõ ð !Ø ÐØ€Hr!   c                  óŽ   — t          ¦   «         sg S t          j                             ¦   «         } | €g S |                      ¦   «         S )z=Return list CUDA architectures this library was compiled for.)rb   r`   ra   Ú_cuda_getArchFlagsrŽ   )Ú
arch_flagss    r"   r  r  ›  sC   € å‰>Œ>ð Øˆ	Ý”×,Ò,Ñ.Ô.€JØÐØˆ	Ø×ÒÑÔÐr!   c                  óž   — t          ¦   «         } t          | ¦  «        dk    rdS d„ | D ¦   «         }d                     d„ |D ¦   «         ¦  «        S )z9Return NVCC gencode flags this library was compiled with.r   rë   c                 ó8   — g | ]}|                      d ¦  «        ‘ŒS )r‰   )rŽ   r  s     r"   rç   z%get_gencode_flags.<locals>.<listcomp>ª  s"   € Ð8Ð8Ð8 d�$—*’*˜S‘/”/Ð8Ð8Ð8r!   rä   c                 ó*   — g | ]\  }}d |› d|› d|› �‘ŒS )z-gencode compute=compute_z,code=r‰   r    )r¥   Úkindr�   s      r"   rç   z%get_gencode_flags.<locals>.<listcomp>¬  sE   € ð 	
ð 	
ð 	
á��tð B¨ÐAÐA°DÐAÐA¸4ÐAÐAð	
ð 	
ð 	
r!   )r  r¨   r3   )r  Ú
arch_list_s     r"   Úget_gencode_flagsrô  ¥  sf   € å‘”€IÝ
ˆ9�~„~˜ÒÐØˆrØ8Ð8¨iÐ8Ñ8Ô8€JØ�8Š8ð	
ð 	
à *ð	
ñ 	
ô 	
ñô ð r!   c                  óZ   — t          ¦   «          t          j                             ¦   «         S )z0Return the index of a currently selected device.)r)  r`   ra   Ú_cuda_getDevicer    r!   r"   ro   ro   ³  s   € å�L„L€LÝŒ8×#Ò#Ñ%Ô%Ð%r!   c                 óÌ   — t          ¦   «          t          j                             | ¦  «        5  t          j                             ¦   «         cddd¦  «         S # 1 swxY w Y   dS )a,  Wait for all kernels in all streams on a CUDA device to complete.

    Args:
        device (torch.device or int, optional): device for which to synchronize.
            It uses the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).
    N)r)  r`   rn   rN   ra   Ú_cuda_synchronizerT   s    r"   Úsynchronizerù  ¹  s˜   € õ �L„L€LÝ	Œ×	Ò	˜6Ñ	"Ô	"ð ,ð ,ÝŒx×)Ò)Ñ+Ô+ð,ð ,ð ,ð ,ñ ,ô ,ð ,ð ,ð ,ð ,ð ,ð ,øøøð ,ð ,ð ,ð ,ð ,ð ,s   ®AÁAÁ Ac                  óZ   — t          ¦   «          t          j                             ¦   «         S )ax  Force collects GPU memory after it has been released by CUDA IPC.

    .. note::
        Checks if any sent CUDA tensors could be cleaned from the memory. Force
        closes shared memory file used for reference counting if there is no
        active counters. Useful when the producer process stopped actively sending
        tensors and want to release unused memory.
    )r)  r`   ra   Ú_cuda_ipc_collectr    r!   r"   Úipc_collectrü  Æ  s!   € õ �L„L€LÝŒ8×%Ò%Ñ'Ô'Ð'r!   c                 óÂ   — t          ¦   «          t          j                             t	          | d¬¦  «        ¦  «        }t          |d         |d         |d         ¬¦  «        S )aS  Return the currently selected :class:`Stream` for a given device.

    Args:
        device (torch.device or int, optional): selected device. Returns
            the currently selected :class:`Stream` for the current device, given
            by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
            (default).
    Tr^  r   r   rŠ   r‡  )r)  r`   ra   Ú_cuda_getCurrentStreamr   r   ©rN   Ú
streamdatas     r"   r�  r�  Ó  óa   € õ �L„L€LÝ”×0Ò0Ý˜&¨4Ð0Ñ0Ô0ñô €Jõ Ø˜Q”-¨j¸¬mÈÐTUÌðñ ô ð r!   c                 óÂ   — t          ¦   «          t          j                             t	          | d¬¦  «        ¦  «        }t          |d         |d         |d         ¬¦  «        S )a=  Return the default :class:`Stream` for a given device.

    Args:
        device (torch.device or int, optional): selected device. Returns
            the default :class:`Stream` for the current device, given by
            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
            (default).
    Tr^  r   r   rŠ   r‡  )r)  r`   ra   Ú_cuda_getDefaultStreamr   r   rÿ  s     r"   r}  r}  å  r  r!   Údata_ptrc                 óÄ   — t          ¦   «          t          j                             | t	          |d¬¦  «        ¦  «        }t          |d         |d         |d         ¬¦  «        S )aå  Return a :class:`Stream` from an externally allocated CUDA stream.

    This function is used to wrap streams allocated in other libraries in order
    to facilitate data exchange and multi-library interactions.

    .. note:: This function doesn't manage the stream life-cycle, it is the user
       responsibility to keep the referenced stream alive while this returned
       stream is being used.

    Args:
        data_ptr(int): Integer representation of the `cudaStream_t` value that
            is allocated externally.
        device(torch.device or int, optional): the device where the stream
            was originally allocated. If device is specified incorrectly,
            subsequent launches using this stream may fail.
    Tr^  r   r   rŠ   r‡  )r)  r`   ra   Ú_cuda_getStreamFromExternalr   r   )r  rN   r   s      r"   Úget_stream_from_externalr  ÷  sd   € õ" �L„L€LÝ”×5Ò5ØÕ# F°TÐ:Ñ:Ô:ñô €Jõ Ø˜Q”-¨j¸¬mÈÐTUÌðñ ô ð r!   c                  óZ   — t          ¦   «          t          j                             ¦   «         S )z6Return cublasHandle_t pointer to current cuBLAS handle)r)  r`   ra   Ú_cuda_getCurrentBlasHandler    r!   r"   Úcurrent_blas_handler
    s   € å�L„L€LÝŒ8×.Ò.Ñ0Ô0Ð0r!   c                  óZ   — t          ¦   «          t          j                             ¦   «         S )z<Return cusolverDnHandle_t pointer to current cuSOLVER handle)r)  r`   ra   Ú_cuda_getCurrentSolverHandler    r!   r"   Úcurrent_solver_handler    s   € å�L„L€LÝŒ8×0Ò0Ñ2Ô2Ð2r!   Ú
debug_modec                 óÞ   — t          ¦   «          t          | t          ¦  «        r*| dk    rd} n!| dk    rd} n| dk    rd} nt          d¦  «        ‚t          j                             | ¦  «         dS )	aÿ  Set the debug mode for cuda synchronizing operations.

    Args:
        debug_mode(str or int): if "default" or 0, don't error or warn on synchronizing operations,
            if "warn" or 1, warn on synchronizing operations, if "error" or 2, error out synchronizing operations.

    Warning:
        This is an experimental feature, and not all synchronizing operations will trigger warning or error. In
        particular, operations in torch.distributed and torch.sparse namespaces are not covered yet.
    Údefaultr   rÜ   r   ÚerrorrŠ   zGinvalid value of debug_mode, expected one of `default`, `warn`, `error`N)r)  Ú
isinstancerK   rS   r`   ra   Ú_cuda_set_sync_debug_mode)r  s    r"   Úset_sync_debug_moder    sˆ   € õ �L„L€LÝ�*�cÑ"Ô"ð 
Ø˜Ò"Ð"ØˆJˆJØ˜6Ò!Ð!ØˆJˆJØ˜7Ò"Ð"ØˆJˆJåØYñô ð õ 
„H×&Ò& zÑ2Ô2Ð2Ð2Ð2r!   c                  óZ   — t          ¦   «          t          j                             ¦   «         S )zEReturn current value of debug mode for cuda synchronizing operations.)r)  r`   ra   Ú_cuda_get_sync_debug_moder    r!   r"   Úget_sync_debug_moder  8  s   € å�L„L€LÝŒ8×-Ò-Ñ/Ô/Ð/r!   c                 óô   — t           st          d¦  «        t          ‚ddlm} 	 t          j        ¦   «          n# |$ r}t          d¦  «        |‚d }~ww xY wt          | ¦  «        } t          j        | ¦  «        }|S )NzCnvidia-ml-py does not seem to be installed or it can't be imported.r   )ÚNVMLError_DriverNotLoadedz-cuda driver can't be loaded, is cuda enabled?)	Ú_HAS_PYNVMLÚModuleNotFoundErrorÚ_PYNVML_ERRÚpynvmlr  r¹  rS   rç  ÚnvmlDeviceGetHandleByIndex)rN   r  r7  Úhandles       r"   Ú_get_pynvml_handlerr   >  s¨   € Ýð Ý!ØQñ
ô 
õ ð	ð
 1Ð0Ð0Ð0Ð0Ð0ðSÝŒÑÔÐÐøØ$ð Sð Sð SÝÐJÑKÔKÐQRÐRøøøøðSøøøõ $ FÑ+Ô+€FÝÔ.¨vÑ6Ô6€FØ€Ms   ¤8 ¸A½AÁAc                 ó  — t           st          d¦  «        t          ‚	 t          j        ¦   «          n'# t          j        $ r}t          d¦  «        |‚d }~ww xY wt          | ¦  «        } t          j        ¦   «         |          }|S )Nz=amdsmi does not seem to be installed or it can't be imported.z>amdsmi driver can't be loaded, requires >=ROCm6.0 installation)	r«  r  Ú_AMDSMI_ERRr¬  r­  r®  rS   Ú_get_amdsmi_device_indexr°  )rN   r7  r  s      r"   Ú_get_amdsmi_handlerr$  Q  sš   € Ýð Ý!ØKñ
ô 
åð	ðÝÔÑÔÐÐøÝÔ!ð ð ð ÝØLñ
ô 
àð	øøøøðøøøõ & fÑ-Ô-€FÝÔ0Ñ2Ô2°6Ô:€FØ€Ms   ž2 ²AÁAÁAÚ_cached_hip_to_amdsmic           	      ó’  ‡— t           €]t          j        ¦   «         Šˆfd„}t           |¦   «         ¦  «        a t           s't	          ‰¦  «        dk    rt          j        d¦  «         | t           vr?t          j        d| › dt          t                                ¦   «         ¦  «        › d�¦  «         t            	                    | | ¦  «        S )z‚Return amdsmi index from HIP device index. They are not always the same.

    Assume amdsmi_init() already completes successfully.Nc               3   ó~   •K  — t          ‰¦  «        D ])\  } }t          j        |¦  «        }d|v r|d         | fV — Œ*d S )NÚhip_id)r•  r¬  Úamdsmi_get_gpu_enumeration_info)Ú
amdsmi_idxr  ÚinfoÚamdsmi_handless      €r"   Úgenz4_get_amdsmi_device_index_from_hip_index.<locals>.genl  s^   øè è € Ý&/°Ñ&?Ô&?ð 5ð 5Ñ"�
˜FÝÔ=¸fÑEÔE�Ø˜tÐ#Ð#Ø˜xœ.¨*Ð4Ð4Ð4Ð4øð5ð 5r!   r   z°Cannot translate HIP ID to AMD SMI ID due to lack of translation information prior to ROCm 6.4. Functions that rely on amdsmi (e.g. temperature()) may operate on wrong devices.zCannot translate HIP ID zq to AMD SMI ID due to undetected HIP ID from amdsmi. amdsmi_get_gpu_enumeration_info() only report these HIP IDs zR. Functions that rely on amdsmi (e.g. temperature()) may operate on wrong devices.)
r%  r¬  r°  Údictr¨   rÛ   rÜ   rœ  Úkeysrð   )rN   r-  r,  s     @r"   Ú'_get_amdsmi_device_index_from_hip_indexr0  d  sô   ø€ õ
 Ð$ÝÔ<Ñ>Ô>ˆð	5ð 	5ð 	5ð 	5ð 	5õ !% S S¡U¤U¡¤ÐÝ$ð 	­¨^Ñ)<Ô)<¸qÒ)@Ð)@ÝŒMðFñô ð ð Õ*Ð*Ð*ÝŒðB vð Bð Bõ Õ*×/Ò/Ñ1Ô1Ñ2Ô2ðBð Bð Bñ	
ô 	
ð 	
õ !×$Ò$ V¨VÑ4Ô4Ð4r!   c                 ó   — t          | d¬¦  «        }t          ¦   «         }t          |d         ¦  «        t          u }|rOt	          ¦   «         }|€t          d¦  «        ‚t          t          t                   |¦  «        }t          ||¦  «        }t          t          t          t          t                   |¦  «        ¦  «        ¦  «        }||vrt          d|› d|› d�¦  «        ‚|r||         S t          ||         ¦  «        S )	zKReturn the amdsmi index of the device, taking visible_devices into account.Tr^  r   Nrå  ræ  z% is not visible (HIP_VISIBLE_DEVICES=rE  )r   r©  rB   rK   rÇ  rS   r   rœ  rÚ  r.  r•  r�   r0  )rN   r  rÝ  Úvisible_device_is_strrÄ  Úvisible_devices_strÚidx_maps          r"   r#  r#  †  s  € å
˜F¨TÐ
2Ñ
2Ô
2€CÝ,Ñ.Ô.€OÝ  °Ô!3Ñ4Ô4½Ð;ÐØð RÝ'Ñ)Ô)ˆØˆ=ÝÐ7Ñ8Ô8Ð8Ý"Ý•ŒI�ñ
ô 
Ðõ 6Ð6IÈ5ÑQÔQˆÝ•9�T¥$¥s¤)¨_Ñ=Ô=Ñ>Ô>Ñ?Ô?€GØ
�'ÐÐÝØR�cÐRÐRÀÐRÐRÐRñ
ô 
ð 	
ð ð EØ�sŒ|Ðå6°w¸s´|ÑDÔDÐDr!   c                 óh   — t          | ¦  «        }t          j        |¦  «        d         }|dz  dz  }|S )NÚ	vram_usedi   )r$  r¬  Úamdsmi_get_gpu_vram_usage)rN   r  Úmem_mega_bytesÚ	mem_bytess       r"   Ú_get_amdsmi_device_memory_usedr:  ž  s9   € Ý  Ñ(Ô(€FåÔ5°fÑ=Ô=¸kÔJ€NØ Ñ%¨Ñ,€IØÐr!   c                 óT   — t          | ¦  «        }t          j        |¦  «        d         S )NÚumc_activity©r$  r¬  Úamdsmi_get_gpu_activity©rN   r  s     r"   Ú_get_amdsmi_memory_usager@  ¦  ó%   € Ý  Ñ(Ô(€FÝÔ)¨&Ñ1Ô1°.ÔAÐAr!   c                 óT   — t          | ¦  «        }t          j        |¦  «        d         S )NÚgfx_activityr=  r?  s     r"   Ú_get_amdsmi_utilizationrD  «  rA  r!   c                 óˆ   — t          | ¦  «        }t          j        |t          j        j        t          j        j        ¦  «        S r?   )r$  r¬  Úamdsmi_get_temp_metricÚAmdSmiTemperatureTypeÚJUNCTIONÚAmdSmiTemperatureMetricÚCURRENTr?  s     r"   Ú_get_amdsmi_temperaturerK  °  s9   € Ý  Ñ(Ô(€FÝÔ(ØÝÔ$Ô-ÝÔ&Ô.ñô ð r!   c                 ó¬   — t          | ¦  «        }t          j        |¦  «        d         }|dk    r|S t          j        |¦  «        d         }|dk    r|S dS )NÚaverage_socket_powerúN/AÚcurrent_socket_powerr   )r$  r¬  Úamdsmi_get_power_info)rN   r  Úsocket_powers      r"   Ú_get_amdsmi_power_drawrR  ¹  sb   € Ý  Ñ(Ô(€FÝÔ/°Ñ7Ô7Ð8NÔO€LØ�uÒÐØÐåÔ3°FÑ;Ô;Ð<RÔSˆØ˜5Ò Ð ØÐà�1r!   c                 ó¦   — t          | ¦  «        }t          j        |t          j        j        ¦  «        }d|v r	|d         }n|d         }|dk    r|S dS )NÚcur_clkÚclkrN  r   )r$  r¬  Úamdsmi_get_clock_infoÚAmdSmiClkTypeÚGFX)rN   r  Ú
clock_infoÚ
clock_rates       r"   Ú_get_amdsmi_clock_rater[  Æ  s^   € Ý  Ñ(Ô(€FÝÔ-¨fµfÔ6JÔ6NÑOÔO€JØ�JÐÐØ 	Ô*ˆ
ˆ
à Ô&ˆ
Ø�UÒÐØÐàˆqr!   c                 óÖ   — t           j        j        sJt          ¦   «         }t	          | ¦  «        } t          j        | ¦  «        }t          j        |¦  «        j        S t          | ¦  «        S )a<  Return used global (device) memory in bytes as given by `nvidia-smi` or `amd-smi`.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistic for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    )
r`   r$   rm   r   rç  r  r  ÚnvmlDeviceGetMemoryInfoÚusedr:  r?  s     r"   Údevice_memory_usedr_  Ó  s[   € õ Œ=Ôð 6Ý$Ñ&Ô&ˆÝ'¨Ñ/Ô/ˆÝÔ2°6Ñ:Ô:ˆÝÔ-¨fÑ5Ô5Ô:Ð:å-¨fÑ5Ô5Ð5r!   c                 óÖ   — t           j        j        sJt          ¦   «         }t	          | ¦  «        } t          j        | ¦  «        }t          j        |¦  «        j        S t          | ¦  «        S )að  Return the percent of time over the past sample period during which global (device)
    memory was being read or written as given by `nvidia-smi`.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistic for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    Warning: Each sample period may be between 1 second and 1/6 second,
    depending on the product being queried.
    )
r`   r$   rm   r   rç  r  r  ÚnvmlDeviceGetUtilizationRatesÚmemoryr@  r?  s     r"   Úmemory_usagerc  å  s[   € õ Œ=Ôð 0Ý$Ñ&Ô&ˆÝ'¨Ñ/Ô/ˆÝÔ2°6Ñ:Ô:ˆÝÔ3°FÑ;Ô;ÔBÐBå'¨Ñ/Ô/Ð/r!   c                 óØ   — t           j        j        sKt          | ¦  «        }t	          | ¦  «        } t          j        | ¦  «        }t          j        |¦  «        j        S t          | ¦  «        S )aì  Return the percent of time over the past sample period during which one or
    more kernels was executing on the GPU as given by `nvidia-smi`.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistic for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    Warning: Each sample period may be between 1 second and 1/6 second,
    depending on the product being queried.
    )
r`   r$   rm   r   rç  r  r  ra  ÚgpurD  r?  s     r"   Úutilizationrf  ú  s]   € õ Œ=Ôð /Ý$ VÑ,Ô,ˆÝ'¨Ñ/Ô/ˆÝÔ2°6Ñ:Ô:ˆÝÔ3°FÑ;Ô;Ô?Ð?å& vÑ.Ô.Ð.r!   c                 óŠ   — t           j        j        s$t          | ¦  «        }t	          j        |d¦  «        S t          | ¦  «        S )a	  Return the average temperature of the GPU sensor in Degrees C (Centigrades).

    The average temperature is computed based on past sample period as given by `nvidia-smi`.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistic for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    Warning: Each sample period may be between 1 second and 1/6 second,
    depending on the product being queried.
    r   )r`   r$   rm   r   r  ÚnvmlDeviceGetTemperaturerK  r?  s     r"   Útemperatureri    s?   € õ Œ=Ôð /Ý$ VÑ,Ô,ˆåÔ.¨v°qÑ9Ô9Ð9å& vÑ.Ô.Ð.r!   c                 óˆ   — t           j        j        s#t          | ¦  «        }t	          j        |¦  «        S t          | ¦  «        S )a	  Return the average power draw of the GPU sensor in mW (MilliWatts)
        over the past sample period as given by `nvidia-smi` for Fermi or newer fully supported devices.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistic for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    Warning: Each sample period may be between 1 second and 1/6 second,
    depending on the product being queried.
    )r`   r$   rm   r   r  ÚnvmlDeviceGetPowerUsagerR  r?  s     r"   Ú
power_drawrl  $  s=   € õ Œ=Ôð .Ý$ VÑ,Ô,ˆÝÔ-¨fÑ5Ô5Ð5å% fÑ-Ô-Ð-r!   c                 óŠ   — t           j        j        s$t          | ¦  «        }t	          j        |d¦  «        S t          | ¦  «        S )aË  Return the clock speed of the GPU SM in MHz (megahertz) over the past sample period as given by `nvidia-smi`.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistic for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    Warning: Each sample period may be between 1 second and 1/6 second,
    depending on the product being queried.
    r   )r`   r$   rm   r   r  ÚnvmlDeviceGetClockInfor[  r?  s     r"   rZ  rZ  7  s?   € õ Œ=Ôð .Ý$ VÑ,Ô,ˆÝÔ,¨V°QÑ7Ô7Ð7å% fÑ-Ô-Ð-r!   c                 ó®   — t          | t          ¦  «        rt          j        | ¦  «        } n*t          | t          ¦  «        rt          j        d| ¦  «        } | S )z…Return the torch.device type object from the passed in device.

    Args:
        device (torch.device or int): selected device.
    rn   )r  rK   r`   rN   r�   rT   s    r"   Ú_get_devicerp  I  sO   € õ �&�#ÑÔð .Ý”˜fÑ%Ô%ˆˆÝ	�F�CÑ	 Ô	 ð .Ý”˜f fÑ-Ô-ˆØ€Mr!   c                 ó^   — | j         }|€t          ¦   «         }t          j        j        |         S )zvReturn the CUDA Generator object for the given device.

    Args:
        device (torch.device): selected device.
    )rR  ro   r`   rn   r\   )rN   r  s     r"   Ú_get_generatorrr  V  s+   € ð Œ,€CØ
€{ÝÑÔˆÝŒ:Ô(¨Ô-Ð-r!   rn   Úoffsetc                 óR   ‡ ‡— t          |¦  «        Šˆˆ fd„}t          |¦  «         dS )a'  Set the random number generator state offset of the specified GPU.

    Args:
        offset (int): The desired offset
        device (torch.device or int, optional): The device to set the RNG state.
            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
    c                  óP   •— t          ‰¦  «        } |                      ‰¦  «         d S r?   )rr  Ú
set_offset)Údefault_generatorÚfinal_devicers  s    €€r"   Úcbz!_set_rng_state_offset.<locals>.cbn  s+   ø€ Ý*¨<Ñ8Ô8ÐØ×$Ò$ VÑ,Ô,Ð,Ð,Ð,r!   N)rp  r%  )rs  rN   ry  rx  s   `  @r"   Ú_set_rng_state_offsetrz  b  sB   øø€ õ ˜vÑ&Ô&€Lð-ð -ð -ð -ð -ð -õ ˆr�N„N€N€N€Nr!   c                 ó‚   — t          ¦   «          t          | ¦  «        }t          |¦  «        }|                     ¦   «         S )aP  Return the random number generator state offset of the specified GPU.

    Args:
        device (torch.device or int, optional): The device to return the RNG state offset of.
            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).

    .. warning::
        This function eagerly initializes CUDA.
    )r)  rp  rr  Ú
get_offset)rN   rx  rw  s      r"   Ú_get_rng_state_offsetr}  u  s:   € õ �L„L€LÝ˜vÑ&Ô&€LÝ& |Ñ4Ô4ÐØ×'Ò'Ñ)Ô)Ð)r!   )Ú*)Ú_use_uvmc                 óf   — t          ¦   «           t          t          | ¦  «        j        | g|¢R i |¤ŽS r?   )r)  rH  Ú	_CudaBaseÚ__new__©Úclsr9   r:   s      r"   Ú	_lazy_newr…  �  s:   € å�L„L€Lð )�5•˜CÑ Ô Ô(¨Ð>¨tÐ>Ð>Ð>°vÐ>Ð>Ð>r!   c                   ó*   ‡ — e Zd ZdZdZˆ fd„ZeZˆ xZS )r�  TFc                 ó¬   •— t          |                      ¦   «         ¦  «        5   t          ¦   «         j        |i |¤Žcd d d ¦  «         S # 1 swxY w Y   d S r?   )rN   rf  rH  rB   )r5   r9   r:   rI  s      €r"   rB   z_CudaBase.typeœ  s™   ø€ õ �D—O’OÑ%Ô%Ñ&Ô&ð 	1ð 	1Ø•5‘7”7”< Ð0¨Ð0Ð0ð	1ð 	1ð 	1ð 	1ñ 	1ô 	1ð 	1ð 	1ð 	1ð 	1ð 	1ð 	1øøøð 	1ð 	1ð 	1ð 	1ð 	1ð 	1s   £A	Á	AÁA)	rH   rI   rJ   re  Ú	is_sparserB   r…  r‚  rJ  rK  s   @r"   r�  r�  ˜  s=   ø€ € € € € Ø€GØ€Ið1ð 1ð 1ð 1ð 1ð €G€G€G€G€Gr!   r�  )Ú_LegacyStorageÚ_warn_typed_storage_removalc                   óX   — e Zd Zed„ ¦   «         Zed„ ¦   «         Zedddœd„¦   «         ZdS )Ú_CudaLegacyStoragec                 ó<   — t          ¦   «          t          d¦  «        ‚)Nz+from_buffer: Not available for CUDA storage)rŠ  rS   rƒ  s      r"   Úfrom_bufferz_CudaLegacyStorage.from_bufferª  s   € å#Ñ%Ô%Ð%ÝÐHÑIÔIÐIr!   c                 ó    — t          d¦  «        ‚)Nz2_new_with_weak_ptr: Not available for CUDA storagerR   rƒ  s      r"   Ú_new_with_weak_ptrz%_CudaLegacyStorage._new_with_weak_ptr¯  s   € åÐOÑPÔPÐPr!   N)rN   rw   c                ó    — t          d¦  «        ‚)Nz4_new_shared_filename: Not available for CUDA storagerR   )r„  Úmanagerrg  ÚsizerN   rw   s         r"   Ú_new_shared_filenamez'_CudaLegacyStorage._new_shared_filename³  s   € åÐQÑRÔRÐRr!   )rH   rI   rJ   ÚclassmethodrŽ  r�  r”  r    r!   r"   rŒ  rŒ  ©  s   € € € € € ØðJð Jñ „[ðJð ðQð Qñ „[ðQð Ø@DÈDð Sð Sð Sð Sñ „[ðSð Sð Sr!   rŒ  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚByteStoragec                 ó,   — t          ¦   «          | j        S r?   ©rŠ  Ú_dtyper@   s    r"   rw   zByteStorage.dtype¹  ó   € å#Ñ%Ô%Ð%ØŒ{Ðr!   c                 ó   — t           j        S r?   )r`   Úuint8r@   s    r"   rš  zByteStorage._dtype¾  ó
   € åŒ{Ðr!   N©rH   rI   rJ   r   rw   rš  r    r!   r"   r—  r—  ¸  óH   € € € € € Øðð ñ „]ðð ðð ñ „]ðð ð r!   r—  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚDoubleStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zDoubleStorage.dtypeÄ  r›  r!   c                 ó   — t           j        S r?   )r`   Údoubler@   s    r"   rš  zDoubleStorage._dtypeÉ  ó
   € åŒ|Ðr!   NrŸ  r    r!   r"   r¢  r¢  Ã  óH   € € € € € Øðð ñ „]ðð ðð ñ „]ðð ð r!   r¢  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚFloatStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zFloatStorage.dtypeÏ  r›  r!   c                 ó   — t           j        S r?   )r`   Úfloatr@   s    r"   rš  zFloatStorage._dtypeÔ  rž  r!   NrŸ  r    r!   r"   r©  r©  Î  r   r!   r©  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚHalfStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zHalfStorage.dtypeÚ  r›  r!   c                 ó   — t           j        S r?   )r`   Úhalfr@   s    r"   rš  zHalfStorage._dtypeß  ó
   € åŒzÐr!   NrŸ  r    r!   r"   r®  r®  Ù  óH   € € € € € Øðð ñ „]ðð ðð ñ „]ðð ð r!   r®  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚLongStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zLongStorage.dtypeå  r›  r!   c                 ó   — t           j        S r?   )r`   Úlongr@   s    r"   rš  zLongStorage._dtypeê  r²  r!   NrŸ  r    r!   r"   rµ  rµ  ä  r³  r!   rµ  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )Ú
IntStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zIntStorage.dtypeð  r›  r!   c                 ó   — t           j        S r?   )r`   r�   r@   s    r"   rš  zIntStorage._dtypeõ  s
   € åŒyÐr!   NrŸ  r    r!   r"   rº  rº  ï  sH   € € € € € Øðð ñ „]ðð ðð ñ „]ðð ð r!   rº  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚShortStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zShortStorage.dtypeû  r›  r!   c                 ó   — t           j        S r?   )r`   Úshortr@   s    r"   rš  zShortStorage._dtype   rž  r!   NrŸ  r    r!   r"   r¾  r¾  ú  r   r!   r¾  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚCharStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zCharStorage.dtype  r›  r!   c                 ó   — t           j        S r?   )r`   Úint8r@   s    r"   rš  zCharStorage._dtype  r²  r!   NrŸ  r    r!   r"   rÃ  rÃ    r³  r!   rÃ  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚBoolStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zBoolStorage.dtype  r›  r!   c                 ó   — t           j        S r?   )r`   Úboolr@   s    r"   rš  zBoolStorage._dtype  r²  r!   NrŸ  r    r!   r"   rÈ  rÈ    r³  r!   rÈ  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚBFloat16Storagec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zBFloat16Storage.dtype  r›  r!   c                 ó   — t           j        S r?   )r`   ry   r@   s    r"   rš  zBFloat16Storage._dtype!  s
   € åŒ~Ðr!   NrŸ  r    r!   r"   rÍ  rÍ    sH   € € € € € Øðð ñ „]ðð ðð ñ „]ðð ð r!   rÍ  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚComplexDoubleStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zComplexDoubleStorage.dtype'  r›  r!   c                 ó   — t           j        S r?   )r`   Úcdoubler@   s    r"   rš  zComplexDoubleStorage._dtype,  s
   € åŒ}Ðr!   NrŸ  r    r!   r"   rÑ  rÑ  &  sH   € € € € € Øðð ñ „]ðð ðð ñ „]ðð ð r!   rÑ  c                   ó:   — e Zd Zed„ ¦   «         Zed„ ¦   «         ZdS )ÚComplexFloatStoragec                 ó,   — t          ¦   «          | j        S r?   r™  r@   s    r"   rw   zComplexFloatStorage.dtype2  r›  r!   c                 ó   — t           j        S r?   )r`   Úcfloatr@   s    r"   rš  zComplexFloatStorage._dtype7  r¦  r!   NrŸ  r    r!   r"   rÖ  rÖ  1  r§  r!   rÖ  c                   ó   — e Zd ZdZd„ Zd„ ZdS )Ú_WrappedTritonKernelzBJust a simple wrapper to store some metadata for testing purposes.c                 ó"   — || _         d| _        d S r   ©ÚkernelÚkernel_invoked)r5   rÞ  s     r"   r7   z_WrappedTritonKernel.__init__P  s   € ØˆŒØ#ˆÔÐÐr!   c                 ó.   —  | j         |i |¤Ž}d| _        |S )NTrÝ  )r5   r9   r:   rL  s       r"   Ú__call__z_WrappedTritonKernel.__call__T  s%   € ØˆdŒk˜4Ð* 6Ð*Ð*ˆØ"ˆÔØˆ
r!   N)rH   rI   rJ   r­   r7   rá  r    r!   r"   rÛ  rÛ  M  s8   € € € € € ØLÐLð$ð $ð $ðð ð ð ð r!   rÛ  c                  ó  — t           d„ ¦   «         } t           d„ ¦   «         }t          j                             d¦  «        d u}|rFt          j                             dd| d¦  «         t          j                             dd|d¦  «         d S d S )	Nc                  ó"   — ddl m}  || ddi|¤ŽS )Nr   )Úbsr_dense_mmÚskip_checksT)Útorch.sparse._triton_opsrä  )r9   r:   rä  s      r"   Úkernel_implz-_register_triton_kernels.<locals>.kernel_impl[  s0   € à9Ð9Ð9Ð9Ð9Ð9ð ˆ|˜TÐ>¨tÐ>°vÐ>Ð>Ð>r!   c                  ó"   — ddl m}  || ddi|¤ŽS )Nr   )Úbsr_dense_addmmrå  T)ræ  ré  )r9   r:   ré  s      r"   Úaddmm_kernel_implz3_register_triton_kernels.<locals>.addmm_kernel_implb  s.   € à<Ð<Ð<Ð<Ð<Ð<àˆ ÐA°$ÐA¸&ÐAÐAÐAr!   ÚtritonÚ_triton_bsr_dense_mm_outzS_triton_bsr_dense_mm_out(Tensor bsr, Tensor dense, *, Tensor(a!) out) -> Tensor(a!)ÚSparseCsrCUDAÚ_triton_bsr_dense_addmm_outz_triton_bsr_dense_addmm_out(Tensor input, Tensor bsr, Tensor dense, *, Scalar beta, Scalar alpha, Tensor(a!) out) -> Tensor(a!))rÛ  Ú	importlibÚutilÚ	find_specr`   Ú_TritonLibraryÚ
registerOp)rç  rê  Ú
has_tritons      r"   Ú_register_triton_kernelsrõ  Z  sÈ   € Ýð?ð ?ñ Ôð?õ ðBð Bñ ÔðBõ
 ”×)Ò)¨(Ñ3Ô3¸4Ð?€JØð 
ÝÔ×'Ò'Ø&ØaØØñ		
ô 	
ð 	
õ 	Ô×'Ò'Ø)ðOð Øñ	
ô 	
ð 	
ð 	
ð 	
ð
ð 
r!   Úkernel_sourceÚkernel_nameÚcompute_capabilityÚcuda_include_dirsÚnvcc_optionsc                 óª   — ddl m}m}  || ||||¦  «        \  }} |||g¦  «        }	t          |	t          ¦  «        r|	|         S t          |	|¦  «        S )ab  
    Compiles a CUDA kernel using NVRTC and returns a callable function.

    This function is a wrapper for NVRTC that enables runtime compilation of CUDA kernels.
    Note that this returns a raw CUDA kernel that operates on raw memory pointers.
    To use this kernel as a proper PyTorch operator, you should wrap it following the guide at:
    pytorch.org/tutorials/advanced/python_custom_ops.html

    Args:
        kernel_source (str): The CUDA kernel source code as a string
        kernel_name (str): The name of the kernel function to compile
        compute_capability (str, optional): The compute capability to target (e.g., "86").
                                           If None, will detect from current device.
        cuda_include_dirs (list, optional): List of directories containing CUDA headers
        nvcc_options (list, optional): Additional options to pass to NVRTC

    Returns:
        callable: A Python function that can be called with PyTorch tensor arguments to execute the kernel

    Example:
        >>> # xdoctest: +SKIP
        >>> kernel_code = '''
        extern "C"
        __global__ void add_tensors(const float* a, const float* b, float* c, int n) {
            int i = threadIdx.x + blockIdx.x * blockDim.x;
            if (i < n)
                c[i] = a[i] + b[i];
        }
        '''
        >>> add_kernel = torch.cuda.compile_kernel(kernel_code, "add_tensors")
        >>> a = torch.randn(1024, device="cuda")
        >>> b = torch.randn(1024, device="cuda")
        >>> c = torch.empty_like(a)
        >>> add_kernel(grid=(4, 1, 1), block=(256, 1, 1), args=[a, b, c, a.numel()])
    r   )Ú_cuda_load_moduleÚ_nvrtc_compile)Útorch.cuda._utilsrü  rý  r  r.  Úgetattr)
rö  r÷  rø  rù  rú  rü  rý  ÚptxÚmangled_namer©   s
             r"   Ú_compile_kernelr    sŠ   € ðT DÐCÐCÐCÐCÐCÐCÐCð '˜ØØØØØñô Ñ€Cˆð Ð˜s \ NÑ3Ô3€Få�&�$ÑÔð -Ø�lÔ#Ð#õ �v˜|Ñ,Ô,Ð,r!   )ÚampÚ	jiteratorÚnvtxÚprofilerÚsparseÚtunableÚ_POOL_HANDLE)}rÍ  ÚBFloat16TensorrÈ  Ú
BoolTensorr—  Ú
ByteTensorrÃ  Ú
CharTensorrÑ  rÖ  r¢  ÚDoubleTensorr©  ÚFloatTensorr®  Ú
HalfTensorrº  Ú	IntTensorrµ  Ú
LongTensorr¾  ÚShortTensorr   rB  r'  r   r   r   rv  r   r  Úcaching_allocator_allocÚcaching_allocator_deleteÚcaching_allocator_disabledÚcaching_allocator_enablert  rO  r<  r:  r
  r  ro   r�  r\   r}  rN   rh   r_  rc  Úempty_cacheÚget_allocator_backendÚCUDAPluggableAllocatorÚchange_current_allocatorr  r  rî   rp   rô  Úget_per_process_memory_fractionÚget_rng_stateÚget_rng_state_allr  r  r   r   ÚgraphsrZ   r[   Úhost_memory_statsÚ host_memory_stats_as_nested_dictr*  Úinitial_seedrü  ri   rs   r   r  r‚   r  Úlist_gpu_processesr   Úmanual_seedÚmanual_seed_allÚmax_memory_allocatedÚmax_memory_cachedÚmax_memory_reservedÚmem_get_inforb  Úmemory_allocatedÚmemory_cachedÚmemory_reservedÚmemory_snapshotÚmemory_statsÚmemory_stats_as_nested_dictÚmemory_summaryrc  ÚMemPoolÚuse_mem_poolri  rl  rZ  Úncclr  r  ÚrandomÚ#reset_accumulated_host_memory_statsÚreset_accumulated_memory_statsÚreset_max_memory_allocatedÚreset_max_memory_cachedÚreset_peak_host_memory_statsÚreset_peak_memory_statsr  r  rj  Úset_per_process_memory_fractionÚset_rng_stateÚset_rng_state_allr‚  r  r  ry  Ústreamsrù  r  rf  )Tr?   )rn   )NNN)Ñr­   rï  r0   rÒ   Ú	threadingrD   rÛ   Úcollections.abcr   Ú	functoolsr   Útypingr   r   r   r   r	   r`   Útorch._CÚtorch._utilsr
   r   r   Útorch.typesr   rë   r   r   Ú_utilsr   r  r   r   r   r   r   Úgreen_contextsr   r>  r   r   r   r   ÚImportErrorr  Úlocalr/  ÚLockr  r   rœ  ÚtuplerK   r@  rÿ  ra   r  r  r«  r  r"  r$   Ú_versionrm   r  r-   Úpathlibr%   r'   r¬  r  Úerrr   r_   rL   rM   rU   r�   rW   rY   rZ   rË  Ú
_has_magmar[   r\   Ú	Generatorrb   rf   ri   rs   rr   r‚   r†   r’   r”   r¯   rÍ   r.  rÐ   r™   rÕ   rÝ   rý   r  r  r  r%  rz   r'  ÚAcceleratorErrorÚOutOfMemoryErrorr*  r)  r:  r<  rS   rB  rO  rQ  rN   rc  rj  rî   r  rp   rt  rv  ry  r‹  r‚  r©  r²  r½  rÇ  rÓ  rÚ  rá  rã  rç  rè  rh   r  rô  ro   rù  rü  r�  r}  r  r
  r  r  r  r   r$  r%  r0  r#  r:  r@  rD  rK  rR  r[  r_  rc  rf  ri  rl  rZ  rp  rr  rz  r}  rb  r  r4  Ústaticmethodr…  r�  Útorch.storager‰  rŠ  rŒ  r—  r¢  r©  r®  rµ  rº  r¾  rÃ  rÈ  rÍ  rÑ  rÖ  Ú_storage_classesÚaddrÛ  rõ  r  r  r  r  r  r  r  r	  Ú__all__r    r!   r"   ú<module>rX     s®  ðð
ð 
ð 
ð Ð Ð Ð Ø 	€	€	€	Ø €€€Ø Ð Ð Ð Ø Ð Ð Ð Ø €€€Ø $Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð Ø >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >à €€€Ø €€€Ø EÐ EÐ EÐ EÐ EÐ EÐ EÐ EÐ EÐ EØ Ð Ð Ð Ð Ð à !Ð !Ð !Ð !Ð !Ð !Ð !Ð !Ø %Ð %Ð %Ð %Ð %Ð %ðð ð ð ð ð ð ð ð ð ð ð ð ð ð )Ð (Ð (Ð (Ð (Ð (Ø 2Ð 2Ð 2Ð 2Ð 2Ð 2Ð 2Ð 2Ð 2Ð 2ðØ Ð Ð Ð Ð Ð Ð øØð ð ð Ø€G€G€Gðøøøð €Ø€y„ÑÔ€Ø%�y”~Ñ'Ô'Ð ð ð ˆtØ	ˆ(�2�t�8Ô
˜d 3œiÐ
'Ô(ôð ð ñ ð �'˜%œ(Ð$7¸¸ÑGÔG€à€Ø€Ø€Ø€ðAØ)Ð)Ð)Ð)Ð)Ð)ð<ØŒ|ð 5	ØˆMˆMˆMˆMàˆMˆMˆMØ$Ð$Ð$Ð$Ð$Ð$ð$5ð 5ð 5ð 5ð 5ñ 5ô 5ð 5ð2Ø&Ð&Ñ(Ô(ð "ð "Ø!�M�M�Mð"ð "ð "ñ "ô "ð "ð "ð "ð "ð "ð "øøøð "ð "ð "ð "à"��øØ&ð ð ð Ø!�Øøøøøðøøøð ˆˆøØð ð ð Øˆðøøøð ˆHøˆHˆˆˆˆˆHøØð ð ð Ø€K€K€K€K€K€Køøøøðøøøð &Ð%Ñ'Ô'Ð ð €7ˆ5Œ8Ð,Ñ-Ô-ð AØ!œHÔ:ÐÐà'˜KÐ(?Ñ@Ô@Ðà
€7ˆ5Œ8Ð+Ñ,Ô,ð HØ”xÔ4ÐÐðH ð H¨ð Hð Hð Hð Hð €7ˆ5Œ8Ð0Ñ1Ô1ð HØ"œXÔ?ÐÐðH sð H¨sð Hð Hð Hð Hð €ˆ$Ð Ð Ñ Ø”(Ô%€	ˆ4Ð %Ð %Ñ %à02Ð �E˜%œ(Ô,Ô-Ð 2Ð 2Ñ 2ð5�dð 5ð 5ð 5ð 5ð
=˜4ð =ð =ð =ð =ð3�dð 3ð 3ð 3ð 3ð,0ð 0¨4ð 0ð 0ð 0ð 0ð. €�2ÐÑÔð¨ð ð ð ñ Ôðð8˜4ð 8ð 8ð 8ð 8ð!ð !ð !ð sð ¨sð ð ð ð ðð ð ð ð ñ ô ð ð2Qð Qð Qð Qð Qñ Qô Qð Qð2?Øˆˆ˜b¨2¨$Ð/Ñ/Ô/ð?àˆˆ˜b¨2¨$Ð/Ñ/Ô/ð?ð ˆ
ˆ
�B�4ÑÔð?ð ˆˆ˜b¨2¨$Ð/Ñ/Ô/ð	?ð
 ˆˆ˜b¨2¨$Ð/Ñ/Ô/ð?ð ˆ
ˆ
�B�4ÑÔð?ð ˆˆ˜b¨2¨$Ð/Ñ/Ô/ð?ð ˆ
ˆ
�B�4ÑÔð?ð ˆˆ˜bÐ!Ñ!Ô!ð?ð ˆˆ˜b¨2¨$Ð/Ñ/Ô/ð?ð ˆˆ˜b¨2¨$Ð/Ñ/Ô/ð?ð ˆ
ˆ
�B�4ÑÔð?ð ˆˆ˜bÐ!Ñ!Ô!ð?ð ˆˆ˜bÐ!Ñ!Ô!ð?ð ˆˆ˜s¨S¨EÐ	2Ñ	2Ô	2ð?ð  ˆˆ�S˜#�JÑ	Ô	ð!?ð" ˆˆ˜sÐ	#Ñ	#Ô	#ð#?ð$ 
ˆ˜sÐ	#Ñ	#Ô	#Ø	ˆ˜sÐ	#Ñ	#Ô	#Ø	ˆ˜sÐ	#Ñ	#Ô	#ð)?ð ?ð ?Ð �D˜˜j¨?Ñ:Ð:Ô;ð ð ñ ð: /Ð.Ð.Ø˜�8ðð ð
 -Ð,Ð,Ø%Ð%Ð%ðð ð
 -Ð,Ð,Ø*Ð*Ð*ðð ð
 -Ð,Ð,Ø*Ð*Ð*ðð ð<ð <Ð ˜$˜s D¨¨c°#¬h¨Ô$7Ð7Ô8ð ð ñ ð(;˜ð ;ð ;ð ;ð ;ð
4¨Cð 4¸#ð 4ð 4ð 4ð 4ð*2¨ð *2¸ð *2ÈÈSÌ	ð *2ð *2ð *2ð *2ðZ;ð ;ð ;ð$ð ð ð62ð 2ð 2ð
Kð Kð Kð$ €
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ð=-ð ˜d‘{ð	=-ð
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