o 0ñh‡/ã @sUdZddlZddlZddlmZmZmZmZddlZddlm Z mZddlm Z mZmZddlmZddlmZee eefZejZejZd agaeeegdfeefed <e ¡Ze ¡Z!eƒZ"dd„Z#d d„Z$de%fdd„Z&dDdd„Z'Gdd„de(ƒZ)de%fdd„Z*de%fdd„Z+dEdeeddfdd„Z,defdd„Z-defdd „Z.dEdeedefd!d"„Z/dEdeedefd#d$„Z0 dFd'eed(ed)eddfd*d+„Z1de2eeffd,d-„Z3dEdeedeeeffd.d/„Z4dDd0d1„Z5d2efd3d4„Z6deddfd5d6„Z7Gd7d„dƒZ Gd8d9„d9ƒZ8d2ed:de8fd;d2„Z9dGdeeeej fdefd=d>„Z: tye}zdt|ƒ›dd |¡›}t|ƒ|‚d}~wwWttdƒnttdƒwdaWdƒdS1s€wYdS) NÚis_initializingzwCannot re-initialize MTIA in forked subprocess. To use MTIA with multiprocessing, you must use the 'spawn' start methodzÖTorch not compiled with MTIA enabled. Ensure you have `import mtia.host_runtime.torch_mtia.dynamic_library` in your python src file and include `//mtia/host_runtime/torch_mtia:torch_mtia` as your target dependency!Tcss|]}|r|VqdSrr)Ú.0ÚcallsrrrÚ Ps€z_lazy_init..z6MTIA call failed lazily at initialization with error: z( MTIA call was originally invoked at: Ú)rÚhasattrÚ_tlsÚ_initialization_lockrÚRuntimeErrorÚ_is_compiledÚAssertionErrorrrÚ _mtia_initrrÚextendÚ_lazy_seed_trackerÚ get_callsÚ ExceptionÚstrÚjoinÚDeferredMtiaCallErrorÚdelattrr)Úqueued_callÚorig_tracebackÚeÚmsgrrrr.sBùÿÿ ÿÿ €ûý "Ôrc@seZdZdS)r/N)Ú__name__Ú __module__Ú__qualname__rrrrr/asr/cCr)z*Return true if compiled with MTIA support.)rrÚ _mtia_isBuiltrrrrr&eó r&cCstƒsdStƒdkS)z'Return true if MTIA device is availableFr)r&Údevice_countrrrrÚis_availablejs r;rcCs:tj |¡ tj ¡WdƒS1swYdS)z?Waits for all jobs in all streams on a MTIA device to complete.N)rÚmtiarrÚ_mtia_deviceSynchronize©rrrrÚsynchronizers$ÿr?cCr)z,Return the number of MTIA devices available.)rrÚ_mtia_getDeviceCountrrrrr:xó r:cCr)z0Return the index of a currently selected device.)rrÚ%_accelerator_hooks_get_current_devicerrrrÚcurrent_device~r9rCcCótj t|dd¡S)aSReturn the currently selected :class:`Stream` for a given device. 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Maximum number of entries to record. N)rrrÚ_mtia_recordMemoryHistory)rNrOrPrrrÚrecord_memory_history›srRcCr)z4Return a dictionary of MTIA memory allocator history)rrÚ_mtia_memorySnapshotrrrrÚsnapshot®rArTcCrD)aReturn capability of a given device as a tuple of (major version, minor version). Args: device (torch.device or int, optional) selected device. Returns statistics for the current device, given by current_device(), if device is None (default). TrE)rrÚ_mtia_getDeviceCapabilityr r>rrrÚget_device_capability´srVcCr)zEmpty the MTIA device cache.)rrÚ_mtia_emptyCacherrrrÚempty_cache¿r9rXÚstreamcCs|durdStj |¡dS)aSet the current stream.This is a wrapper API to set the stream. Usage of this function is discouraged in favor of the ``stream`` context manager. Args: stream (Stream): selected stream. This function is a no-op if this argument is ``None``. N)rrÚ_mtia_setCurrentStream©rYrrrÚ set_streamÄs r\cCs$t|ƒ}|dkrtj |¡dSdS)z¡Set the current device. Args: device (torch.device or int): selected device. This function is a no-op if this argument is negative. rN)r rrÚ%_accelerator_hooks_set_current_devicer>rrrÚ set_deviceÒsÿr^c@s<eZdZdZdefdd„Zdd„Zdededefd d „ZdS)rzÌContext-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``. cCst|dd|_d|_dS)NTrEéÿÿÿÿ)r ÚidxÚprev_idx)ÚselfrrrrÚ__init__æs zdevice.__init__cCstj |j¡|_dSr)rrÚ(_accelerator_hooks_maybe_exchange_devicer`ra)rbrrrÚ __enter__êszdevice.__enter__ÚtypeÚvalueÚ tracebackcCstj |j¡|_dS)NF)rrrdrar`)rbrfrgrhrrrÚ__exit__íszdevice.__exit__N)r5r6r7Ú__doc__rrcrerirrrrrÞs c@sNeZdZUdZeded<dedfdd„Zdd„Zd ed edefdd „Z dS)Ú StreamContextaContext-manager that selects a given stream. All MTIA 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. útorch.mtia.StreamÚ cur_streamrYcCstd|_||_tddƒ|_tj ¡s|jdurd|_tj ¡s dntj d¡|_ tj ¡s1d|_ dStj d¡|_ dS)NTr_)rmrYr r`rÚjitÚis_scriptingr<rKÚsrc_prev_streamÚdst_prev_stream)rbrYrrrrcs ÿÿ ÿzStreamContext.__init__cCs‚|j}|dus|jdkrdStj d¡|_|jj|jkr9t|jƒtj |j¡|_Wdƒn1s4wYtj |¡dS©Nr_) rYr`rr<rHrprrqr\)rbrmrrrresÿzStreamContext.__enter__rfrgrhcCsJ|j}|dus|jdkrdS|jj|jkrtj |j¡tj |j¡dSrr)rYr`rprrr<r\rq)rbrfrgrhrmrrrriszStreamContext.__exit__N) r5r6r7rjrÚ__annotations__rcrerrirrrrrkòs rkrlcCst|ƒS)a,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 doesn't support torch.mtia.stream )rkr[rrrrY,sr<cCs$tjdtddtjdgtj|dS)züReturns the random number generator state as a ByteTensor. Args: device (torch.device or int, optional): The device to return the RNG state of. Default: ``'mtia'`` (i.e., ``torch.device('mtia')``, the current mtia device). z.get_rng_state is not implemented in torch.mtiaé©Ú stacklevelr)Údtyper)ÚwarningsÚwarnÚUserWarningrÚzerosÚuint8r>rrrÚ get_rng_state7sýr}Ú new_statecCstjdtdddS)aSets the random number generator state. Args: new_state (torch.ByteTensor): The desired state device (torch.device or int, optional): The device to set the RNG state. Default: ``'mtia'`` (i.e., ``torch.device('mtia')``, the current mtia device). z.set_rng_state is not implemented in torch.mtiartruN)rxryrz)r~rrrrÚ set_rng_stateFs ýr)Ú*)rr;rr?r:rCrHrKÚmemory_statsÚmax_memory_allocatedÚreset_peak_memory_statsrVrRrTrXr^r\rYrrr})rNr)rLrMr)r<)>rjÚ threadingrxÚtypingrrrrrrÚ_devicerÚtorch._utilsrr r Útorch.typesrÚ_utilsr r-ÚintÚ _device_tÚEventÚStreamrrÚlistÚtuplersÚlocalr#ÚLockr$r*rrÚboolrrr,r/r&r;r?r:rCrHrKrRÚdictrTrVrXr\r^rkrYr}rÚmemoryÚ__all__rrrrÚszþÿ 3 ÿÿÿÿ þ : ÿÿÿ þ