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False otherwise.)rMAXMINPRODUCTBANDBORBXOR)rudenyListrkrkrlr]sr]c @s4eZdZUdZdZdZdZdZdZdZ e dd d gZ iZ e ee fed <eeee eegZeee d Ze eefed <eddgedge dgeddgeddgiZe eeefed<eejjeejjeejje ejj eejjeejjiZe eejfed<defddZe  ddeeeeefddfddZdS)r-a An enum-like class for backends. Available backends: GLOO, NCCL, UCC, MPI, XCCL, and other registered backends. The values of this class are lowercase strings, e.g., ``"gloo"``. They can be accessed as attributes, e.g., ``Backend.NCCL``. This class can be directly called to parse the string, e.g., ``Backend(backend_str)`` will check if ``backend_str`` is valid, and return the parsed lowercase string if so. It also accepts uppercase strings, e.g., ``Backend("GLOO")`` returns ``"gloo"``. .. note:: The entry ``Backend.UNDEFINED`` is present but only used as initial value of some fields. 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NroptensorpeerrGtag group_peercCsD||_||_t||_t|j||dd|_||_t|j|||_dS)rT return_globalN)rr_group_or_default_grouprG_canonicalize_group_rankrrr)rrrrrGrrrkrkrlrs   zP2POp.__init__cCst|t|dt|S)rr) _check_op_check_single_tensorrr)rrrrrGrrrkrkrlrs  z P2POp.__new__c st|j}|jj}|jr|jjnd}d|vr|}|j}nd|vr&|j}|}ntSd|d|d|d|d|jj d |jj d S) N default_pgr\rWzP2POp(z pg=z , group_src=z , group_dst=z, z, )) rDrGrr group_namersuperrrshapedtype)r my_group_rankop_namersd __class__rkrlrs  2zP2POp.__repr__NNrN)rrhrrrrTensorrintrrrr __classcell__rkrkrrlr0sJ  r0c @sFeZdZdZ   d dedejdeejdeedee f dd Z dS) _CollOpa A class to capture collective operations. Args: op (Callable): A collective function, e.g. ``torch.distributed.all_reduce``. tensor (Tensor): Tensor to operate on. dst_tensor (Tensor, optional): Provided when source and destinaton tensors are not the same. redop (ReduceOp, optional): reduce operation. root (int, optional): root of broadcast or reduce. Nrr dst_tensorredoprootcCs"||_||_||_||_||_dSr)rrrrr)rrrrrrrkrkrlr*s  z_CollOp.__init__)NNN) rrhrrrrrrrrrrkrkrkrlrs r_pg_map _pg_names_pg_group_ranks_pg_backend_config _tags_to_pg _pg_to_tag_backendc@sVeZdZdZdddZedeefddZej dddZede ee e e fffd d Zede ee ffd d Zede ee eefffd dZede ee ffddZedefddZej deddfddZede e eeffddZede ee ffddZede eeeffddZedee e effddZdS)_Worldz Container class for c10d process group state. This is used during registration and lookup of PG state. .. warning:: This is an experimental API intended to expose the inner workings of c10d and is subject to change.. rfNcCsd|_i|_dSr) _default_pg_pg_coalesce_staterrkrkrlrPs z_World.__init__cCr)z Process group that includes all ranks of the cluster. This default ProcessGroup is used by c10d APIs when a ProcessGroup is needed but None is provided. rrrkrkrlrTsz_World.default_pgcCs ||_dSrr rrrkrkrlr^ cCtS)a  Provide Mapping from ProcessGroup to backend name and store. For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store) For MPI pg, it is a map from ProcessGroup to (Backend, None) TODO don't expose the map, expose fine grained ops )rrrkrkrlpg_mapbs z _World.pg_mapcCr )z Process group's names, map from ProcessGroup to str. TODO don't expose the map, expose fine grained ops )rrrkrkrlpg_namesoz_World.pg_namescCr )z Process group's global rank to local rank mapping. TODO don't expose the map, expose fine grained ops )rrrkrkrlpg_group_ranksyrz_World.pg_group_rankscCr )zm Process group's backend config. TODO don't expose the map, expose fine grained ops )rrrkrkrlpg_backend_configrz_World.pg_backend_configcCr )z| Process group count for default naming. 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Allows them to have the class property ``WORLD``. rfcCtjSr_worldr)rrkrkrlWORLDrz_WorldMeta.WORLDr$cCs |t_dSrr+)rr$rkrkrlr-r N) rrhrrr&rrr-r'rkrkrkrlr)s r)c@seZdZdZdS)rGzGroup class. Placeholder.N)rrhrrrkrkrkrlrGsrG) metaclassc@seZdZdZdZdS)r/zGroup member class.iN)rrhrrNON_GROUP_MEMBERrkrkrkrlr/sr/rcCs*|tjkrtttstdtStStS)NzSAttempted to get default timeout for nccl backend, but NCCL support is not compiled)r-rrr)rrrr*rrkrkrl_get_default_timeouts  r1timeoutcCst|ts td|dS)Nz@Expected timeout argument to be of type datetime.timedelta, got )rr TypeErrorr2rkrkrl_check_valid_timeouts r5_default_pg_init_methodstore_based_barrier_keycCs|pt}t|ts&tdt|dt|trdStdt|d |j}t |dkr5|djSt |dkr=dSt d|vrFdS|djS)aB .. note:: This is an internal helper and does not have backward compatibility, please use with caution. Return the device type to use with ``group`` for object collectives or barrier. There are selection rules: 1. If user specifies exactly one backend in ``init_process_group`` call: use that backend 2. Else if user specifies multiple "device:backend" pairs in init_process_group: If "cpu" is among those pairs, use "cpu" (because the object is in cpu memory); Otherwise, use the first backend (sort of a random pick). Args: group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Returns: str: The device type to use for object collective with ``group``. You are using a Backend w as a ProcessGroup. This usage is deprecated since PyTorch 2.0. Please use a public API of PyTorch Distributed instead.rz$Expecting a ProcessGroup, but got a .r&r) _get_default_grouprrrrrjrqr _device_typesrrr)rGrrkrkrl_get_object_coll_devices"       r=cCstd|p t}t|ts"tjdt|dtddtdS |j }t |dkr0|dSt |dkr:t d td|vrGtd}n|d}td |d |S) a$ .. note:: This method will be deprecated, it only stays for backward-compatiblity reason. Alternatives: - If you need to find a device for object collectives, please use `_get_object_coll_device(group)`. - If you need to query the device types supported by group, please use `_device_capability(group)`. Return the device type registered with ``group``. For example, if `init_process_group("nccl", ...)` was called, the returned value would be `torch.device("cuda")`. Errors out if no device has been registered. Args: group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Returns: torch.device: The device type registered with ``group``. a`_get_pg_default_device` will be deprecated, it only stays for backward-compatiblity reason. If you need to find a device for object collectives, please use `_get_object_coll_device`. If you need to query the device types supported by group, please use `_device_capability(group)`. r8r9) stacklevelrr&rzXDefault device not found, because no backend has been registered with this ProcessGroup.zqMultiple backends are registered with this ProcessGroup. We cannot determine which one is the default. Returning z#. Please consider using other APIs.) rrr;rrrjrrrr<r RuntimeError)rGrrvrkrkrl_get_pg_default_deviceAs8      rBcCs|pt}dd|jDS)a Return the device type(s) supported by ``group``. Args: group (ProcessGroup, optional): The process group to query. If None, the default process group will be used. Returns: List[str]: A list of device types supported by ``group``. cSg|]}|jqSrkrj)rrrkrkrl z&_device_capability..)r;r<rGrkrkrl_device_capabilitys rH secondsc Cs6td|}||dtd|||}||d}|d} ||kr+|| dt|td|dd }t} z || g|WnKt y} z>||d}td t| |||||| tt| d |krt d |d |d|d|d|d| d WYd} ~ nd} ~ wwq.z with elements of type cSg|]}t|qSrkrDrxrkrkrlrEjz&_check_tensor_list..r:N)rrr3rjallrurkrkrl_check_tensor_list`s  r}cCs|dus |tjur t}|Sr)r/r-r;rGrkrkrlrnsrrcCsJ|dur|dur tdt||}n |durtdt||}|r#|S|S)z Helper method to take _either_ a global rank or a group rank and produce a group rank. If 'return_global' is true, produce a global rank instead of a group rank. Nz-Can't specify both group_rank and global_rankz&Must specify global_rank or group_rank)rr_r^)rGrbrirrkrkrlrts    rrU rank_typecCs&||krtd|d|ddS)NzInvalid z rank: z@ rank should not be the same as the rank of the current process.)rUr)rGrUr~rkrkrl_check_not_self_ranks rcCst|tr|S|fSr)rr)objrkrkrl _as_iterablesrcGsnd}tjtt|D])}|j}|jr|tjkrtj ntj }|dur%|}q ||kr4t d|d|jq dS)Nz4Invalid usage of tensors with different dtypesFound z and ) itertoolschain from_iterablemaprr is_complexr complex64float32 complex128r)tensors last_dtyper tensor_dtyperkrkrl_ensure_all_tensors_same_dtypes$rcCs|ttfvr tddS)z/Check that the ``op`` is either isend or irecv.ziInvalid ``op``. Expected ``op`` to be of type ``torch.distributed.isend`` or ``torch.distributed.irecv``.N)rRrIr)rrkrkrlrs rcsPt|trtdd|Dstd|djtfdd|Ds&tddS)zx Check that the ``p2p_op_list`` is a list of P2POp instances. Also, check that all ops use the same group. css|]}t|tVqdSr)rr0rp2p_oprkrkrlrs  z%_check_p2p_op_list..zZInvalid ``p2p_op_list``. Each op is expected to to be of type ``torch.distributed.P2POp``.rc3s|]}|jkVqdSrrGrrGrkrlrsz#All ops need to use the same group.N)rrr|rrG) p2p_op_listrkrGrl_check_p2p_op_lists rcCr )z&Check if the MPI backend is available.)_MPI_AVAILABLErkrkrkrlrLrLcCr )z'Check if the NCCL backend is available.)_NCCL_AVAILABLErkrkrkrlrNrrNcCr )z'Check if the Gloo backend is available.)_GLOO_AVAILABLErkrkrkrlrJrrJcCr )z&Check if the UCC backend is available.)_UCC_AVAILABLErkrkrkrlrPrrPcCr )z'Check if the XCCL backend is available.)_XCCL_AVAILABLErkrkrkrlrQrrQcCs2ttjd|dd}|r|S|tjvS)aF Check backend availability. Checks if the given backend is available and supports the built-in backends or third-party backends through function ``Backend.register_backend``. Args: backend (str): Backend name. Returns: bool: Returns true if the backend is available otherwise false. is_ _availableN)rr distributedrr-r)ravailable_funcrkrkrlrMs rMcCs tjduS)z8Check if the default process group has been initialized.N)r/r-rkrkrkrlrKr rKcCstdduS)a Check whether this process was launched with ``torch.distributed.elastic`` (aka torchelastic). The existence of ``TORCHELASTIC_RUN_ID`` environment variable is used as a proxy to determine whether the current process was launched with torchelastic. This is a reasonable proxy since ``TORCHELASTIC_RUN_ID`` maps to the rendezvous id which is always a non-null value indicating the job id for peer discovery purposes.. TORCHELASTIC_RUN_IDN)osgetenvrkrkrkrlrOs rOcCsttddS)NTORCH_DIST_INIT_BARRIER0)rrrrkrkrkrl_is_barrier_after_initsrcCs"tstdtrttjStjS)z.)r,rr )r"rkrkrl_get_all_pg_configssrcCr*)z/ Return the number of process groups. )r,rrkrkrkrlrFsrF fallback_rankcCs0dtjvr ttjdS|durt|Std)a: Return the local rank of the current process relative to the node. Semantically, this is a useful concept for mapping processes to devices. For example, on a node with 8 accelerator you could use the node local rank to decide which accelerator device to bind the process to. In practice, the actual assignment of node local ranks is handled by the process launcher outside of pytorch, and communicated via the `LOCAL_RANK` environment variable. Torchrun will automatically populate `LOCAL_RANK`, but other launchers may not. If `LOCAL_RANK` is unspecified, this API will fall back to the provided kwarg 'fallback_rank' if specified, otherwise it will raise an error. The intent is to allow writing an application that runs either in single or multi device contexts without error. LOCAL_RANKNzLOCAL_RANK is not in the environment. Consider passing fallback_rank to allow `get_node_local_rank` to work, assuming you are not running in a multi-device context and want the code to run locally instead.)renvironrr@)rrkrkrlrds rdcCsRtjD]!}|j}td|vr&|td}tr&t|t r&| |qdS)a This API adds an ephemeral timeout extension for all PGs locally on one rank. The timeout gets reset when the first collective issued after API called finished. NOTE: We only support to set timeout for cuda backends for now. NOTE: While this feature provides flexibility in specific scenarios, it introduces statefulness to timeout setting. Therefore, it is advisable to use this API sparingly and consider alternative approaches, such as directly setting the timeout or utilizing a barrier collective (one can set any timeout to the barrier), whenever feasible. Args: timeout (timedelta): The delta of timeout to extend. Returns: None. rN) r,rr r<rrrrNrro_add_ephemeral_timeout)r2r$rrrkrkrl"_add_ephemeral_timeout_for_all_pgss rcCs|durt}t|rtdt|tsJ|j}t}td|vr8t r8| td}t|t r8| |td|vrb| td}t rUt|trU| |n t rbt|t rb| |t|dkrmtd|D]}||qodS)a Set the timeout for the given process group when users want to use a different timeout instead of default values. Args: timeout (timedelta): Timeout for operations executed against the process group which users want to set. Default value is 10 minutes for NCCL and 30 minutes for other backends. This is the duration after which collectives will be aborted asynchronously and the process will crash. This is done since CUDA execution is async and it is no longer safe to continue executing user code since failed async NCCL operations might result in subsequent CUDA operations running on corrupted data. When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout. group (ProcessGroup, optional): The process group to work on. The default is the general main process group. If another specific group is specified, the calling process must be part of :attr:`group`. Returns: None Nrrrrz:Set timeout is now only supported for either nccl or gloo.)r;r`rrrr<rPrrrJrrqrNrNrorrr_set_default_timeout)r2rGrbackendsrrkrkrl_set_pg_timeouts,       rrarp init_methodrZrV pg_options device_idc s8tjdur tdtdtjvrtjj |dus#|dus#Jd|dur8|dks/Jd|dks7Jdn|dur>d}|durM|durMt j |j }|durSd }t |}|dur_t|}t| tgd d }|t jkr|d ksw|d krtd |d|dtd d g|t||dd\} } t| n3|durtt||||d} t| \}}}||td|}t||g||||||dd \} } t| ddttjDtjtj<tj ttjda!|a"tj#dt$dfdd} | t_#t%dkrt&'d|t jkrt(dSt)|||||dSdS)a Initialize the default distributed process group. This will also initialize the distributed package. There are 2 main ways to initialize a process group: 1. Specify ``store``, ``rank``, and ``world_size`` explicitly. 2. Specify ``init_method`` (a URL string) which indicates where/how to discover peers. Optionally specify ``rank`` and ``world_size``, or encode all required parameters in the URL and omit them. If neither is specified, ``init_method`` is assumed to be "env://". Args: backend (str or Backend, optional): The backend to use. Depending on build-time configurations, valid values include ``mpi``, ``gloo``, ``nccl``, ``ucc``, or one that is registered by a third-party plugin. Since 2.6, if ``backend`` is not provided, c10d will use a backend registered for the device type indicated by the `device_id` kwarg (if provided). The known default registrations today are: ``nccl`` for ``cuda``, ``gloo`` for ``cpu``. If neither ``backend`` nor ``device_id`` is provided, c10d will detect the accelerator on the run-time machine and use a backend registered for that detected accelerator (or ``cpu``). This field can be given as a lowercase string (e.g., ``"gloo"``), which can also be accessed via :class:`Backend` attributes (e.g., ``Backend.GLOO``). If using multiple processes per machine with ``nccl`` backend, each process must have exclusive access to every GPU it uses, as sharing GPUs between processes can result in deadlock or NCCL invalid usage. ``ucc`` backend is experimental. init_method (str, optional): URL specifying how to initialize the process group. Default is "env://" if no ``init_method`` or ``store`` is specified. Mutually exclusive with ``store``. world_size (int, optional): Number of processes participating in the job. Required if ``store`` is specified. rank (int, optional): Rank of the current process (it should be a number between 0 and ``world_size``-1). Required if ``store`` is specified. store(Store, optional): Key/value store accessible to all workers, used to exchange connection/address information. Mutually exclusive with ``init_method``. timeout (timedelta, optional): Timeout for operations executed against the process group. Default value is 10 minutes for NCCL and 30 minutes for other backends. This is the duration after which collectives will be aborted asynchronously and the process will crash. This is done since CUDA execution is async and it is no longer safe to continue executing user code since failed async NCCL operations might result in subsequent CUDA operations running on corrupted data. When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout. group_name (str, optional, deprecated): Group name. This argument is ignored pg_options (ProcessGroupOptions, optional): process group options specifying what additional options need to be passed in during the construction of specific process groups. As of now, the only options we support is ``ProcessGroupNCCL.Options`` for the ``nccl`` backend, ``is_high_priority_stream`` can be specified so that the nccl backend can pick up high priority cuda streams when there're compute kernels waiting. For other availble options to config nccl, See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t device_id (torch.device, optional): a single, specific device to "bind" this process to, allowing for backend-specific optimizations. Currently this has two effects, only under NCCL: the communicator is immediately formed (calling ``ncclCommInit*`` immediately rather than the normal lazy call) and sub-groups will use ``ncclCommSplit`` when possible to avoid unnecessary overhead of group creation. If you want to know NCCL initialization error early, you can also use this field. .. note:: To enable ``backend == Backend.MPI``, PyTorch needs to be built from source on a system that supports MPI. .. note:: Support for multiple backends is experimental. Currently when no backend is specified, both ``gloo`` and ``nccl`` backends will be created. The ``gloo`` backend will be used for collectives with CPU tensors and the ``nccl`` backend will be used for collectives with CUDA tensors. A custom backend can be specified by passing in a string with format ":,:", e.g. "cpu:gloo,cuda:custom_backend". Nz5trying to initialize the default process group twice!z torch._dynamoz*Cannot specify both init_method and store.rz*world_size must be positive if using storez(rank must be non-negative if using storezenv://r}Fuse_hashed_namerazFor MPI backend, world_size (z ) and rank (z9) are ignored since they are assigned by the MPI runtime.r)r2r!r4)backend_optionsr2rr!cSsi|]}||qSrkrkrirkrkrl sz&init_process_group..z[rank]csptj}tt_}z |W|t_n|t_w|}dfdd|dD}tj|tjdS)N c3s*|]}|dkrd|ndVqdS)rpz: Nrk)rr)excepthook_prefixrkrlrs zFinit_process_group.._distributed_excepthook..) sysstderrioStringIOgetvaluerrwriteflush)args old_stderrbufmsgrold_hookrkrl_distributed_excepthooks  z3init_process_group.._distributed_excepthookr&VPerforming barrier after ProcessGroup initialization since TORCH_DIST_INIT_BARRIER = 1)*r/r-rr!rmodulesr_dynamo trace_rulesclear_lru_cacher-rrrjr1r5_process_group_namerrr_new_process_group_helperrrr,r%next set_timeoutrrangerlr,rrrr6 excepthookrDrrrOr8r_) rrr2rZrUrVrrrrrrendezvous_iteratorrrkrrlrHs e              rHcCs~d}|jr ||j}n|tjur%z |td}Wn ty$Ynw|r*|js,dStr=t |t r=|j }tr=t |t s3|S)Nr) bound_device_idrr,rrrr@supports_splittingrrrp wrapped_pg)r$ split_fromrkrkrl_get_split_sources   rc  CsB|tjvr td| dur| jdus| jdkrtdt||dvr7t||} | r7tj| \} } | | fS| dur=dn| } t |dk}t rRt j rRt t }nd}|smt }||vrm|rh|t j tjdfSt|d|} t| ||}t|}d t|vrd t|vr|tjvsJd ||tjkrtj|vr|tjjnE|tjjn=|tj|n4tj|jvr|tjjn$tj rt!t"tj }||jvr|tjj#n|tjj| r| |_ |$D]\}}t|d| }|tj%krIt&st'd t()|}tjj%}|s+tjdfS|d krH|*d krHt|||*}||n|tjkr\t+||||d}tjj}n|tjkrt,sjt'd|durt-|t.j/szJd|j0|krt12dnt./}d|_3||_0|r||_4t5||_6||_7||_8t.||||}tjj}nw|tj9krt:rt;||||d}tjj9}n`|tj|||}tjj<}nG|?tjvsJd|?tj|?}|j@}|jA}tjj#}|s |||||}ntB}||_C||_D||_E||_F||_G||_7|||}|tjkr7t-|t+s2J|Hn|tjkrIt-|t.sEJ|HtIt|trU|}n[|tjtjtj9fvsh|?tjvrtJtKjLkrtMsxtNOdn tP||||||d}t tQ|dkr| D] }|RtST|||qn |RtST|||q|dusJ| dusJ|U||V| | r|W| jXr|W| }|Y| || ftj|<|tj|<tZ||t|tj[|<|dvr d|}tj\]dg^|nd|}tj\]|g^||tj_|<|| fS)a` Create a new distributed process group. This function must be called by ALL processes in the global group, even if the calling process is not part of the newly created group. In that case, this function returns GroupMember.NON_GROUP_MEMBER. This function is called with ``global_ranks_in_group == []`` for the default group. zTThe specified group name has already been created, please use a different group nameNrzKinit_process_group device_id parameter must be an accelerator with an indexror}r/rrzUnknown backend type zDistributed package doesn't have MPI built in. MPI is only included if you build PyTorch from source on a host that has MPI installed.rar4z.Distributed package doesn't have NCCL built inzHExpected backend_options argument to be of type ProcessGroupNCCL.Optionszlbackend_options._timeout was specified, but timeout kwarg has a default value that will always override it. Fz.Distributed package doesn't have XCCL built inzUnknown c10d backend type aTORCH_DISTRIBUTED_DEBUG was set to DETAIL, but GLOO is not available. Build with Gloo to create a wrapper process group in debug mode to aid collective desynchronization debugging.)r store_prefixrVrUrZr2r&ptd:rpuser:)`r,rvaluesrindexrjr5rqrrrKr;rrrUperform_nocolor_splitr/r/rrr.rr-rrrr_set_default_backendrrrrr riterrrrrLr@rncreaterlrqrNrroOptions_timeoutrris_high_priority_streamr_process_group_color split_colorglobal_ranks_in_grouprrrPrrrrQrsrrrr rVrirr2group_id_set_sequence_number_for_group issubclassrrDETAILrrr_create_process_group_wrapperrP_register_backendrr_set_group_name_set_group_descrreager_connect_single_devicer rr setdefaultrr)rrirrrVrrr2pg_tagrr!existing_groupr prefix_storeis_default_grouprrbr$rcustom_backendrrbackend_prefix_store backend_class backend_typebackend_pluginrrdist_backend_opts eager_backendrkrkrlr sb                                   rcCs|tjkrdS|durtj}n|}|dusJtj|ddur$tdt|tkr2| r2| |dus;|tjkr{t tj ddddD]}| qEtdtjtj tjtjtjtjtjtdt_dS| tj|=tj |=tj|=tj|=|tjvrtdtj|=tj|}tj|=|durztj|||d rtjd |Wn tyYnwt|jdS) ay Destroy a given process group, and deinitialize the distributed package. Args: group (ProcessGroup, optional): The process group to be destroyed, if group.WORLD is given, all process groups including the default one will be destroyed. NrcS tj|Srr,rxrkrkrlU z'destroy_process_group..TrreverserzfSome coalesced collectives haven't been launched when ProcessGroup is destroyed. They will be cleaned.rrp)r/r/r-r,rrrrjr _has_hooks_wait_for_pending_workssortedrshutdownrclearrrrrrrrr rrremove startswith Exceptionrr)rGr$pg_to_shutdownrrkrkrlr>.s^                r>cCs|tjkrdS|p tj}|dusJtj|ddurtdz |t d}Wn t y4d}Ynw|dus>|tjkrt rJt |t rJ|ttjddddD]}|qTt rgt |t rg|tdtjtjtjtjtjtjtjtdt_dS|tj|=tj|=tj|=tj|=|tjvrtd tj|=tj|}tj|=|durztj||| d rtjd |Wn t!yYnwt"|j#dS) aE Abort a given process group. If group.WORLD (i.e. `None`) is given, all process groups including the default one will be aborted. Args: group (ProcessGroup, optional): The process group to be aborted. .. note:: this API is experimental and currently only works with the NCCL backend. .. note:: this API should be used with `TORCH_NCCL_ASYNC_ERROR_HANDLING` turned off (i.e. set to 0). Otherwise, ProcessGroupNCCL's watchdog may automatically handle errors or timeouts for you including aborting the ProcessGroup. Nz6Invalid process group specified or has been destroyed.rcSr rr rrkrkrlrrz&_abort_process_group..TrrzdSome coalesced collectives haven't been launched when ProcessGroup is aborted. They will be cleaned.rrp)$r/r/r-r,rrrrrrr@rNrro _group_startrrabort _group_endrrrrrrrrrr rrrrrrr)rGr$r pg_to_abortrrkrkrl_abort_process_groupsh                 r!cCs:t|rdSt}|dus|tjur|St||S)a Return the rank of the current process in the provided ``group``, default otherwise. Rank is a unique identifier assigned to each process within a distributed process group. They are always consecutive integers ranging from 0 to ``world_size``. Args: group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Returns: The rank of the process group -1, if not part of the group raN)r`r;r/r-rUr^rmrkrkrlrDs rDcCst|rdSt|S)a4 Return the number of processes in the current process group. Args: group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Returns: The world size of the process group -1, if not part of the group ra)r`rrGrkrkrlrEs rErdst group_dstcCsTt|}t|||}t|dt|rtddS|r"t|}||g||S)a] Send a tensor asynchronously. .. warning:: Modifying ``tensor`` before the request completes causes undefined behavior. .. warning:: ``tag`` is not supported with the NCCL backend. Unlike send, which is blocking, isend allows src == dst rank, i.e. send to self. Args: tensor (Tensor): Tensor to send. dst (int): Destination rank on global process group (regardless of ``group`` argument) group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. tag (int, optional): Tag to match send with remote recv group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` Returns: A distributed request object. None, if not part of the group rrRN) rrrr`rcrr view_as_realr\)rr"rGrr#rkrkrlrR s   rRsrc group_srccCsrt|dt|rtddS|rt|}t|}|dur+|dur+||g|St|||}| |g||S)aH Receives a tensor asynchronously. .. warning:: ``tag`` is not supported with the NCCL backend. Unlike recv, which is blocking, irecv allows src == dst rank, i.e. recv from self. Args: tensor (Tensor): Tensor to fill with received data. src (int, optional): Source rank on global process group (regardless of ``group`` argument). Will receive from any process if unspecified. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. tag (int, optional): Tag to match recv with remote send group_src (int, optional): Destination rank on ``group``. Invalid to specify both ``src`` and ``group_src``. Returns: A distributed request object. None, if not part of the group rrIN) rr`rcrrr$rrecv_anysourcerrW)rr%rGrr&rkrkrlrI: s   rIcCsHt|}t|||}t||dt||||d}|dur"|dSdS)a Send a tensor synchronously. .. warning:: ``tag`` is not supported with the NCCL backend. Args: tensor (Tensor): Tensor to send. dst (int): Destination rank on global process group (regardless of ``group`` argument). Destination rank should not be the same as the rank of the current process. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. tag (int, optional): Tag to match send with remote recv group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst``. destination)rGrr#N)rrrrRrS)rr"rGrr#workrkrkrlr\g s   r\cCs`t|||||d}|durdS||dur.|dur|}t|}t||dt||}|S)a Receives a tensor synchronously. .. warning:: ``tag`` is not supported with the NCCL backend. Args: tensor (Tensor): Tensor to fill with received data. src (int, optional): Source rank on global process group (regardless of ``group`` argument). Will receive from any process if unspecified. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. tag (int, optional): Tag to match recv with remote send group_src (int, optional): Destination rank on ``group``. Invalid to specify both ``src`` and ``group_src``. Returns: Sender rank -1, if not part of the group )r%rGrr&Nrasource)rIrS _source_rankrrr_)rr%rGrr&r)rkrkrlrW s  rWc@seZdZddZdS) _IllegalWorkcCs|dvr td|ddS)N) is_success exceptionrS source_rankr+result synchronizezIllegal to call z on IllegalWork objectr)rrrkrkrlr s z_IllegalWork.__getattribute__N)rrhrrrkrkrkrlr, s r,c@s,eZdZd ddZdefddZdd ZdS) _CoalescingManagerrfNcCs g|_dSr)worksrrkrkrlr s z_CoalescingManager.__init__r)cCs|r |j|dSdSr)r4rrr)rkrkrlr sz_CoalescingManager.appendcCs|jD]}|qdSr)r4rSr5rkrkrlrS s  z_CoalescingManager.waitr)rrhrrr rrSrkrkrkrlr3 s  r3 async_opsc csv|pt}tj|g}|rtd|r||t}|Vtj|}|r|dj}|t krJdd|D}t }t |dj |_ |||}n[|tkrlg} g} |D]} | | j| t | jqT|| | }n9|tkrg} g} |D]} | | j| t | jqvt} t |dj | _ || | | }n td|d|d|r||}|r||dS|dS) a: Context manager used to coalesce collectives or P2P operations when possible. Args: group (`ProcessGroup`, optional): The process group to work on. If None, the default process group will be used. device (`torch.device`, optional): Default is None, set to a device if there isn't a `**_coalesced` implementation by the backend. async_ops (`bool`, optional): whether the coalesced ops are async ops. Examples: >>> # xdoctest: +SKIP("no rank") >>> # Synchronous ops >>> with _coalescing_manager(): >>> for i in range(num_colls): >>> dist.all_reduce(tensors[i]) >>> # Asynchronous ops >>> with _coalescing_manager(async_ops=True) as cm: >>> for i in range(num_colls): >>> dist.all_reduce(tensors[i]) >>> cm.wait() .. warning:: :func:`_coalescing_manager` currently do not support coalescing all-reduces with different reduce operators, e.g. `ReduceOp.SUM` mixed with `ReduceOp.PRODUCT`. z=ProcessGroup has non-empty op list at the start of coalescingrcSrCrk)r)rrrkrkrlrE rFz'_coalescing_manager..z>> # xdoctest: +SKIP("no rank") >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank >>> recv_tensor = torch.randn(2, dtype=torch.float32) >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) >>> recv_op = dist.P2POp( ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size ... ) >>> reqs = batch_isend_irecv([send_op, recv_op]) >>> for req in reqs: >>> req.wait() >>> recv_tensor tensor([2, 3]) # Rank 0 tensor([0, 1]) # Rank 1 .. note:: Note that when this API is used with the NCCL PG backend, users must set the current GPU device with `torch.cuda.set_device`, otherwise it will lead to unexpected hang issues. In addition, if this API is the first collective call in the ``group`` passed to ``dist.P2POp``, all ranks of the ``group`` must participate in this API call; otherwise, the behavior is undefined. If this API call is not the first collective call in the ``group``, batched P2P operations involving only a subset of ranks of the ``group`` are allowed. rNrrfcSs|jtkrdnd}||jiS)Nr#r&)rrRr)rrrkrkrl peer_kwargS s z%batch_isend_irecv..peer_kwargT)r6)rGr)rrGr;rrr0rrrrjrrsupports_coalescingrErrr4r)rrGrrFr?rreqsr)rkrkrlr9# sH*      r9async_opcCspt|}t|||dd}t|dt|rtddSt}||_d|_||_| |g|}|r2|S| dS)ad Broadcasts the tensor to the whole group. ``tensor`` must have the same number of elements in all processes participating in the collective. Args: tensor (Tensor): Data to be sent if ``src`` is the rank of current process, and tensor to be used to save received data otherwise. src (int): Source rank on global process group (regardless of ``group`` argument). group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op group_src (int): Source rank on ``group``. Must specify one of ``group_src`` and ``src`` but not both. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group Frrr:Nr) rrrr`rcrrootRank rootTensorasyncOpr:rS)rr%rGrIr&optsr)rkrkrlr:r s  r:cCs|f}t|rtt|||||dSt|dt|r tddS|r5t|s0td|dt |}t }||_ |durBt }|tjvr`tt|d|d}tj|||r^tSdS||g|}|rk|S|dS)a Reduces the tensor data across all machines in a way that all get the final result. After the call ``tensor`` is going to be bitwise identical in all processes. Complex tensors are supported. Args: tensor (Tensor): Input and output of the collective. The function operates in-place. op (optional): One of the values from ``torch.distributed.ReduceOp`` enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group Examples: >>> # xdoctest: +SKIP("no rank") >>> # All tensors below are of torch.int64 type. >>> # We have 2 process groups, 2 ranks. >>> device = torch.device(f"cuda:{rank}") >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank >>> tensor tensor([1, 2], device='cuda:0') # Rank 0 tensor([3, 4], device='cuda:1') # Rank 1 >>> dist.all_reduce(tensor, op=ReduceOp.SUM) >>> tensor tensor([4, 6], device='cuda:0') # Rank 0 tensor([4, 6], device='cuda:1') # Rank 1 >>> # All tensors below are of torch.cfloat type. >>> # We have 2 process groups, 2 ranks. >>> tensor = torch.tensor( ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device ... ) + 2 * rank * (1 + 1j) >>> tensor tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 >>> dist.all_reduce(tensor, op=ReduceOp.SUM) >>> tensor tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 rrGrIrr4Nall_reduce does not support  on complex tensors)r$r#r4rr`rcrr]rrr$rrur;r,rr rrr, allreducerS)rrrGrI relevant_argsrMcollr)rkrkrlr4 s@6   r4z`torch.distributed.all_reduce_coalesced` will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/main/distributed.html#collective-functionscCst|tjr |g}t|dt|t|rtddStdd|Dr1t|s1t d|ddd |D}t }||_ |pBt }| ||}|rO|S|dS) a WARNING: at this time individual shape checking is not implemented across nodes. For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce operation will proceed without complaint and return erroneous outputs. This lack of shape checking results in significant performance improvements but users of this function should take extra care to ensure that each node passes in tensors whose shapes match across nodes. Reduces each tensor in tensors (residing on the same device) across all machines in such a way that all get the final result. After the call each tensor in tensors is going to bitwise identical in all processes. Complex tensors are supported. Args: tensors (Union[List[Tensor], Tensor]): Input and output of the collective. The function operates in-place. op (Optional[ReduceOp]): One of the values from ``torch.distributed.ReduceOp`` enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (Optional[bool]): Whether this op should be an async op. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group. rr5Ncss|]}|VqdSr)rrtrkrkrlr3 z'all_reduce_coalesced..rOrPcS"g|] }|s |nt|qSrkrrr$rTrkrkrlrE6 "z(all_reduce_coalesced..)rrrr}rr`rcanyr]rrrur;r9 get_futurerS)rrrGrIrMr)rkrkrlr5 s" )    r5cCsjt|}t|||dd}t|dt|rtddSt}||_||_||g|}|r/|S| dS)a Reduces the tensor data across all machines. Only the process with rank ``dst`` is going to receive the final result. Args: tensor (Tensor): Input and output of the collective. The function operates in-place. dst (int): Destination rank on global process group (regardless of ``group`` argument) op (optional): One of the values from ``torch.distributed.ReduceOp`` enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op group_dst (int): Destination rank on ``group``. Must specify one of ``group_dst`` and ``dst`` but not both. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group FrrrXN) rrrr`rcrrurJrXrS)rr"rrGrIr#rMr)rkrkrlrXC s   rXc CstdUt}t||tj| }t | |}t t jkrEtrEt|}|tjkrEtjj|g}td||t|g |}||fWdS1s^wYdS)Nz+pytorch.wait_counter.c10d._object_to_tensorz)_object_to_tensor size: %s hash value: %s)r"guardrBytesIO_picklerdumpr ByteStorage _from_bufferr ByteTensortorrrrNrBr-rrr _hash_tensorsrwarningnumel LongTensor) rrrGf byte_storage byte_tensorrhash local_sizerkrkrl_object_to_tensort s" $rmcCstdBttjkr*tr*t|}|tjkr*t j j |g}t d|||}|d|}tt|WdS1sKwYdS)Nz+pytorch.wait_counter.c10d._tensor_to_objectz)_tensor_to_object size: %s hash value: %s)r"r\rrrrNrBr-rrrrrdrrerfrnumpytobytes _unpicklerrr]load)r tensor_sizerGrrkrrkrkrl_tensor_to_object s  $rsc st|r tddSt|}t|||\}}t|d}tj|tj|dfddt|D}t |||dt t | | tj|tj|dfddt|D}t |||dt|D]\} } | tj} || } t| | ||| <qedS)a Gathers picklable objects from the whole group into a list. Similar to :func:`all_gather`, but Python objects can be passed in. Note that the object must be picklable in order to be gathered. Args: object_list (list[Any]): Output list. It should be correctly sized as the size of the group for this collective and will contain the output. obj (Any): Pickable Python object to be broadcast from current process. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. Returns: None. If the calling rank is part of this group, the output of the collective will be populated into the input ``object_list``. If the calling rank is not part of the group, the passed in ``object_list`` will be unmodified. .. note:: Note that this API differs slightly from the :func:`all_gather` collective since it does not provide an ``async_op`` handle and thus will be a blocking call. .. note:: For NCCL-based processed groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsiblity to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()``. .. warning:: :func:`all_gather_object` uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust. .. warning:: Calling :func:`all_gather_object` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using :func:`all_gather` instead. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> # Assumes world_size of 3. >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object >>> output = [None for _ in gather_objects] >>> dist.all_gather_object(output, gather_objects[dist.get_rank()]) >>> output ['foo', 12, {1: 2}] r3NrGrrcg|] }|jddqSr)dim unsqueezerobject_sizes_tensorrkrlrE z%all_gather_object..c$g|]}||dqSr&rkrcoalesced_output_tensormax_object_sizerkrlrE )r`rcr=rmrErzeroslongrr1rrQitemresize_emptyuint8 enumeraterjrs) object_listrrGcurrent_device input_tensorrlrobject_size_listoutput_tensorsrrrrrkrrr{rlr3 s66      r3robject_gather_listcslt|}|dur|durd}t|||dd}t|r tddSt}t|||t|}t|||\}} t|d} t j | t j |dfdd t | D} t | | |dtt| |||kr}t j| t j|dfd d t | D} t|||kr| nd||d ||krdS|dusJd t| D]\} }|t j}| | }t||||| <qdS) a Gathers picklable objects from the whole group in a single process. Similar to :func:`gather`, but Python objects can be passed in. Note that the object must be picklable in order to be gathered. Args: obj (Any): Input object. Must be picklable. object_gather_list (list[Any]): Output list. On the ``dst`` rank, it should be correctly sized as the size of the group for this collective and will contain the output. Must be ``None`` on non-dst ranks. (default is ``None``) dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (If both ``dst`` and ``group_dst`` are None, default is global rank 0) group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` Returns: None. On the ``dst`` rank, ``object_gather_list`` will contain the output of the collective. .. note:: Note that this API differs slightly from the gather collective since it does not provide an async_op handle and thus will be a blocking call. .. note:: For NCCL-based processed groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsiblity to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()``. .. warning:: :func:`gather_object` uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust. .. warning:: Calling :func:`gather_object` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using :func:`gather` instead. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> # Assumes world_size of 3. >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object >>> output = [None for _ in gather_objects] >>> dist.gather_object( ... gather_objects[dist.get_rank()], ... output if dist.get_rank() == 0 else None, ... dst=0 ... ) >>> # On rank 0 >>> output ['foo', 12, {1: 2}] NrTrr@rGrtcrurvrxrrzrkrlrEK r|z!gather_object..cr}r~rkrrrkrlrE[ r) gather_listr"rGz+Must provide object_gather_list on dst rank)rrr`rcrD_validate_output_list_for_rankr=rmrErrrrr1rrQrrrrr?rrjrs)rrr"rGr# global_dstmy_global_rankrrrlrrrrrrrrkrrlr@ sTD       r@rc stt||}t|dtrtddS|pttfdd|D\}}t|}t ||dt |dkrE|d}nt|}t ||ddS) ar Sends picklable objects in ``object_list`` synchronously. Similar to :func:`send`, but Python objects can be passed in. Note that all objects in ``object_list`` must be picklable in order to be sent. Args: object_list (List[Any]): List of input objects to sent. Each object must be picklable. Receiver must provide lists of equal sizes. dst (int): Destination rank to send ``object_list`` to. Destination rank is based on global process group (regardless of ``group`` argument) group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. device (``torch.device``, optional): If not None, the objects are serialized and converted to tensors which are moved to the ``device`` before sending. Default is ``None``. group_dst (int, optional): Destination rank on ``group``. Must specify one of ``dst`` and ``group_dst`` but not both Returns: ``None``. .. note:: For NCCL-based process groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsibility to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()``. .. warning:: :func:`send_object_list` uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust. .. warning:: Calling :func:`send_object_list` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using :func:`send` instead. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> # Assumes backend is not NCCL >>> device = torch.device("cpu") >>> if dist.get_rank() == 0: >>> # Assumes world_size of 2. >>> objects = ["foo", 12, {1: 2}] # any picklable object >>> dist.send_object_list(objects, dst=1, device=device) >>> else: >>> objects = [None, None, None] >>> dist.recv_object_list(objects, src=0, device=device) >>> objects ['foo', 12, {1: 2}] r(r;Ncg|]}t|qSrkrmrrrrGrkrlrE z$send_object_list..)r#rGr&r) rrrr`rcr=ziprcatr\r) rr"rGrr# tensor_list size_listr{ object_tensorrkrrlr;p s @       r;cCst|r tddS|pt|}tjt|tj|d}t||||d}tjt| tj |d}t||||d} || ksAJdd} t |D]\} } || | | } | tj } | | 7} t | | ||| <qG| S)a Receives picklable objects in ``object_list`` synchronously. Similar to :func:`recv`, but can receive Python objects. Args: object_list (List[Any]): List of objects to receive into. Must provide a list of sizes equal to the size of the list being sent. src (int, optional): Source rank from which to recv ``object_list``. Source rank is based on global process group (regardless of ``group`` argument) Will receive from any rank if set to None. Default is ``None``. group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. device (``torch.device``, optional): If not None, receives on this device. Default is ``None``. group_src (int, optional): Destination rank on ``group``. Invalid to specify both ``src`` and ``group_src``. Returns: Sender rank. -1 if rank is not part of the group. If rank is part of the group, ``object_list`` will contain the sent objects from ``src`` rank. .. note:: For NCCL-based process groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsibility to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()``. .. warning:: :func:`recv_object_list` uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust. .. warning:: Calling :func:`recv_object_list` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using :func:`recv` instead. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> # Assumes backend is not NCCL >>> device = torch.device("cpu") >>> if dist.get_rank() == 0: >>> # Assumes world_size of 2. >>> objects = ["foo", 12, {1: 2}] # any picklable object >>> dist.send_object_list(objects, dst=1, device=device) >>> else: >>> objects = [None, None, None] >>> dist.recv_object_list(objects, src=0, device=device) >>> objects ['foo', 12, {1: 2}] r<rart)r%rGr&z6Mismatch in return ranks for object sizes and objects.r)r`rcr=rrrrrWsumrrrrjrs)rr%rGrr&rr{ rank_sizesr rank_objectsoffsetrobj_sizeobj_viewrkrkrlr< s0?     r<csTt|dur|durd}t||dd}tr tddS|p%tt}||krAtfdd|D\}}t|} n tj t |tj d} t | |d ||krht |d krb|d} nt|} ntj t | tjd} t | |d d} ||krt| D] \} } | | | | }|tj}| | 7} t|| || <qdSdS) a Broadcasts picklable objects in ``object_list`` to the whole group. Similar to :func:`broadcast`, but Python objects can be passed in. Note that all objects in ``object_list`` must be picklable in order to be broadcasted. Args: object_list (List[Any]): List of input objects to broadcast. Each object must be picklable. Only objects on the ``src`` rank will be broadcast, but each rank must provide lists of equal sizes. src (int): Source rank from which to broadcast ``object_list``. Source rank is based on global process group (regardless of ``group`` argument) group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. device (``torch.device``, optional): If not None, the objects are serialized and converted to tensors which are moved to the ``device`` before broadcasting. Default is ``None``. group_src (int): Source rank on ``group``. Must not specify one of ``group_src`` and ``src`` but not both. Returns: ``None``. If rank is part of the group, ``object_list`` will contain the broadcasted objects from ``src`` rank. .. note:: For NCCL-based process groups, internal tensor representations of objects must be moved to the GPU device before communication takes place. In this case, the device used is given by ``torch.cuda.current_device()`` and it is the user's responsibility to ensure that this is set so that each rank has an individual GPU, via ``torch.cuda.set_device()``. .. note:: Note that this API differs slightly from the :func:`broadcast` collective since it does not provide an ``async_op`` handle and thus will be a blocking call. .. warning:: :func:`broadcast_object_list` uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust. .. warning:: Calling :func:`broadcast_object_list` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using :func:`broadcast` instead. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> if dist.get_rank() == 0: >>> # Assumes world_size of 3. >>> objects = ["foo", 12, {1: 2}] # any picklable object >>> else: >>> objects = [None, None, None] >>> # Assumes backend is not NCCL >>> device = torch.device("cpu") >>> dist.broadcast_object_list(objects, src=0, device=device) >>> objects ['foo', 12, {1: 2}] NrTrr=crrkrrrrkrlrE rz)broadcast_object_list..rtr%rGr&)rrr`rcr=rDrrrrrrr:rrrrrjrs)rr%rGrr& global_srcrrrr{rrrrrrkrrlr=9 sJF        r=scatter_object_output_listscatter_object_input_listc sht|dur|durd}t||dd}tr tddSt|tr+t|dkr/tdt}t ||krh|durBtdt fd d |D\}}t|t|}}t |} |D]} | | q_n t jdgt jd } t| |d t j| t jd } t| ||krdn||d t jdgt jd } t| ||krdn||d t| | |d<dS)a3 Scatters picklable objects in ``scatter_object_input_list`` to the whole group. Similar to :func:`scatter`, but Python objects can be passed in. On each rank, the scattered object will be stored as the first element of ``scatter_object_output_list``. Note that all objects in ``scatter_object_input_list`` must be picklable in order to be scattered. Args: scatter_object_output_list (List[Any]): Non-empty list whose first element will store the object scattered to this rank. scatter_object_input_list (List[Any], optional): List of input objects to scatter. Each object must be picklable. Only objects on the ``src`` rank will be scattered, and the argument can be ``None`` for non-src ranks. src (int): Source rank from which to scatter ``scatter_object_input_list``. Source rank is based on global process group (regardless of ``group`` argument). (If both ``src`` and ``group_src`` are None, default is global rank 0) group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. group_src (int, optional): Source rank on ``group``. Invalid to specify both ``src`` and ``group_src`` Returns: ``None``. If rank is part of the group, ``scatter_object_output_list`` will have its first element set to the scattered object for this rank. .. note:: Note that this API differs slightly from the scatter collective since it does not provide an ``async_op`` handle and thus will be a blocking call. .. warning:: :func:`scatter_object_list` uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Only call this function with data you trust. .. warning:: Calling :func:`scatter_object_list` with GPU tensors is not well supported and inefficient as it incurs GPU -> CPU transfer since tensors would be pickled. Please consider using :func:`scatter` instead. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> if dist.get_rank() == 0: >>> # Assumes world_size of 3. >>> objects = ["foo", 12, {1: 2}] # any picklable object >>> else: >>> # Can be any list on non-src ranks, elements are not used. >>> objects = [None, None, None] >>> output_list = [None] >>> dist.scatter_object_list(output_list, objects, src=0) >>> # Rank i gets objects[i]. For example, on rank 2: >>> output_list [{1: 2}] NrTrr[r&zMExpected argument scatter_object_output_list to be a list of size at least 1.z;source rank must provide non-None scatter_object_input_listcsg|]}t|qSrkrrrG pg_devicerkrlrEs z'scatter_object_list..rtr) scatter_listr%rG)rrr`rcrrrrrDr=rrQrrrrr:rrrrZrs) rrr%rGr&rrr tensor_sizesmax_tensor_sizer output_tensorobj_tensor_sizerkrrlr[ sf@     r[cCs|f}t|rtt|||||dSt|dt|dt||t|r*tddSdd|D}|s7|nt |}|p@t }| |g|g}|rM|S| dS)a Gathers tensors from the whole group in a list. Complex and uneven sized tensors are supported. Args: tensor_list (list[Tensor]): Output list. It should contain correctly-sized tensors to be used for output of the collective. Uneven sized tensors are supported. tensor (Tensor): Tensor to be broadcast from current process. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group Examples: >>> # xdoctest: +SKIP("need process group init") >>> # All tensors below are of torch.int64 dtype. >>> # We have 2 process groups, 2 ranks. >>> device = torch.device(f"cuda:{rank}") >>> tensor_list = [ ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) ... ] >>> tensor_list [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank >>> tensor tensor([1, 2], device='cuda:0') # Rank 0 tensor([3, 4], device='cuda:1') # Rank 1 >>> dist.all_gather(tensor_list, tensor) >>> tensor_list [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 >>> # All tensors below are of torch.cfloat dtype. >>> # We have 2 process groups, 2 ranks. >>> tensor_list = [ ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) ... ] >>> tensor_list [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 >>> tensor = torch.tensor( ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device ... ) + 2 * rank * (1 + 1j) >>> tensor tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 >>> dist.all_gather(tensor_list, tensor) >>> tensor_list [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 rGrIrrr1NcSrWrkrXrTrkrkrlrEzall_gather..)r$r#r1r}rrr`rcrrr$r; allgatherrS)rrrGrIrRr)rkrkrlr19s2?    r1cCs|f}t|rtt|||||dSt|dt|dt|r%tddS|s+|nt|}|s6|nt|}t }||_ |pEt }|t j vrbtt||}t j |||r`tSdS||||}|rm|S|dS)a Gather tensors from all ranks and put them in a single output tensor. This function requires all tensors to be the same size on each process. Args: output_tensor (Tensor): Output tensor to accommodate tensor elements from all ranks. It must be correctly sized to have one of the following forms: (i) a concatenation of all the input tensors along the primary dimension; for definition of "concatenation", see ``torch.cat()``; (ii) a stack of all the input tensors along the primary dimension; for definition of "stack", see ``torch.stack()``. Examples below may better explain the supported output forms. input_tensor (Tensor): Tensor to be gathered from current rank. Different from the ``all_gather`` API, the input tensors in this API must have the same size across all ranks. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group Examples: >>> # xdoctest: +SKIP("need process group init") >>> # All tensors below are of torch.int64 dtype and on CUDA devices. >>> # We have two ranks. >>> device = torch.device(f"cuda:{rank}") >>> tensor_in = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank >>> tensor_in tensor([1, 2], device='cuda:0') # Rank 0 tensor([3, 4], device='cuda:1') # Rank 1 >>> # Output in concatenation form >>> tensor_out = torch.zeros(world_size * 2, dtype=torch.int64, device=device) >>> dist.all_gather_into_tensor(tensor_out, tensor_in) >>> tensor_out tensor([1, 2, 3, 4], device='cuda:0') # Rank 0 tensor([1, 2, 3, 4], device='cuda:1') # Rank 1 >>> # Output in stack form >>> tensor_out2 = torch.zeros(world_size, 2, dtype=torch.int64, device=device) >>> dist.all_gather_into_tensor(tensor_out2, tensor_in) >>> tensor_out2 tensor([[1, 2], [3, 4]], device='cuda:0') # Rank 0 tensor([[1, 2], [3, 4]], device='cuda:1') # Rank 1 .. warning:: The Gloo backend does not support this API. rrrrbN)r$r#rbrr`rcrrr$rrLr;r,rr rrr,_allgather_baserS)rrrGrIrRrMrSr)rkrkrlrbsH:    rbz`torch.distributed._all_gather_base` is a private function and will be deprecated. Please use `torch.distributed.all_gather_into_tensor` instead.cCst||||S)a$ Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor. Args: output_tensor (Tensor): Output tensor. It should contain correctly-sized tensors to be used for output of the collective. input_tensor (Tensor): Tensor to be broadcast from current process. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group .. warning:: `_all_gather_base` is a private function. Users should use `all_gather_into_tensor` instead. )rb)rrrGrIrkrkrl_all_gather_basesrz`torch.distributed.all_gather_coalesced` will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/main/distributed.html#collective-functionscCst|r tddSt|dt|t|tstd|D] }t|dt|qdd|D}dd|D}|p..rk)rlrkrkrlrEfs z(all_gather_coalesced..cSrWrkrXrTrkrkrlrEjr) r`rcr}rrrr3r;allgather_coalescedr[rS)rrrGrIoutput_tensor_listr)rkrkrlr2"s.7       r2cCs(||kr |s tddS|rtddS)Nz?Argument ``gather_list`` must be specified on destination rank.zHArgument ``gather_list`` must NOT be specified on non-destination ranks.r2)my_rankr"rrkrkrlrwsrrc Cst|d|r t|dng}t||t|}t|r"tddS|dur,|dur,d}t|||dd}t|||dd}t}t|||||krL|gng}|g} t } || _ | || | } |rb| S| dS) a Gathers a list of tensors in a single process. This function requires all tensors to be the same size on each process. Args: tensor (Tensor): Input tensor. gather_list (list[Tensor], optional): List of appropriately, same-sized tensors to use for gathered data (default is None, must be specified on the destination rank) dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (If both ``dst`` and ``group_dst`` are None, default is global rank 0) group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group .. note:: Note that all Tensors in gather_list must have the same size. Example:: >>> # xdoctest: +SKIP("no rank") >>> # We have 2 process groups, 2 ranks. >>> tensor_size = 2 >>> device = torch.device(f'cuda:{rank}') >>> tensor = torch.ones(tensor_size, device=device) + rank >>> if dist.get_rank() == 0: >>> gather_list = [torch.zeros_like(tensor, device=device) for i in range(2)] >>> else: >>> gather_list = None >>> dist.gather(tensor, gather_list, dst=0) >>> # Rank 0 gets gathered data. >>> gather_list [tensor([1., 1.], device='cuda:0'), tensor([2., 2.], device='cuda:0')] # Rank 0 None # Rank 1 rrr?NrTrF) rr}rrr`rcrrDrrrJr?rS) rrr"rGrIr#rrr input_tensorsrMr)rkrkrlr?s. 1    r?rc Cst|d|r t|dng}t||t|}|dur"|dur"d}t|||dd}t|||dd}t|r>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> tensor_size = 2 >>> device = torch.device(f'cuda:{rank}') >>> output_tensor = torch.zeros(tensor_size, device=device) >>> if dist.get_rank() == 0: >>> # Assumes world_size of 2. >>> # Only tensors, all of which must be the same size. >>> t_ones = torch.ones(tensor_size, device=device) >>> t_fives = torch.ones(tensor_size, device=device) * 5 >>> scatter_list = [t_ones, t_fives] >>> else: >>> scatter_list = None >>> dist.scatter(output_tensor, scatter_list, src=0) >>> # Rank i gets scatter_list[i]. >>> output_tensor tensor([1., 1.], device='cuda:0') # Rank 0 tensor([5., 5.], device='cuda:1') # Rank 1 rrNrTrFrZcSrWrkrXrTrkrkrlrErzscatter..z;Argument ``scatter_list`` must be specified on source rank.zDArgument ``scatter_list`` must NOT be specified on non-source ranks.)rr}rrrr`rcrrr$rDrrrJrLrZrS) rrr%rGrIr&rrrrrMr)rkrkrlrZsL 8   rZcCsnt|dt|dt||t|rtddSt}||_|p#t}||g|g|}|r1|S| dS)a Reduces, then scatters a list of tensors to all processes in a group. Args: output (Tensor): Output tensor. input_list (list[Tensor]): List of tensors to reduce and scatter. op (optional): One of the values from ``torch.distributed.ReduceOp`` enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group. output input_listrYN) rr}rr`rcrrur;rYrS)rrrrGrIrMr)rkrkrlrY:s     rYc Cs|f}t|rtt||||||dSt|dt|dt|r&tddSt}||_||_|p3t }|t j vrRt t|||d}t j |||rPtSdS||||}|r]|S|dS)a Reduces, then scatters a tensor to all ranks in a group. Args: output (Tensor): Output tensor. It should have the same size across all ranks. input (Tensor): Input tensor to be reduced and scattered. Its size should be output tensor size times the world size. The input tensor can have one of the following shapes: (i) a concatenation of the output tensors along the primary dimension, or (ii) a stack of the output tensors along the primary dimension. For definition of "concatenation", see ``torch.cat()``. For definition of "stack", see ``torch.stack()``. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group. Examples: >>> # xdoctest: +SKIP("need process group init") >>> # All tensors below are of torch.int64 dtype and on CUDA devices. >>> # We have two ranks. >>> device = torch.device(f"cuda:{rank}") >>> tensor_out = torch.zeros(2, dtype=torch.int64, device=device) >>> # Input in concatenation form >>> tensor_in = torch.arange(world_size * 2, dtype=torch.int64, device=device) >>> tensor_in tensor([0, 1, 2, 3], device='cuda:0') # Rank 0 tensor([0, 1, 2, 3], device='cuda:1') # Rank 1 >>> dist.reduce_scatter_tensor(tensor_out, tensor_in) >>> tensor_out tensor([0, 2], device='cuda:0') # Rank 0 tensor([4, 6], device='cuda:1') # Rank 1 >>> # Input in stack form >>> tensor_in = torch.reshape(tensor_in, (world_size, 2)) >>> tensor_in tensor([[0, 1], [2, 3]], device='cuda:0') # Rank 0 tensor([[0, 1], [2, 3]], device='cuda:1') # Rank 1 >>> dist.reduce_scatter_tensor(tensor_out, tensor_in) >>> tensor_out tensor([0, 2], device='cuda:0') # Rank 0 tensor([4, 6], device='cuda:1') # Rank 1 .. warning:: The Gloo backend does not support this API. rNrinputrcN)r$r#rcrr`rcrrurLr;r,rr rrr,_reduce_scatter_baserS) rrrrGrIrRrMrSr)rkrkrlrcas<:   rcz`torch.distributed._reduce_scatter_base` is a private function and will be deprecated. Please use `torch.distributed.reduce_scatter_tensor` instead.cCst|||||S)a Reduces, then scatters a flattened tensor to all processes in a group. Args: output (Tensor): Output tensor. input (Tensor): Input tensor that is of size output tensor size times world size group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group. .. warning:: `_reduce_scatter_base` is a private function. Users should use `reduce_scatter_tensor` instead. )rc)rrrrGrIrkrkrlrsrc Cs|f}t|rtt|||||||dSt|rtddSt}t|dt|dt|||r8t |}|rAt |}|durGgn|}|durOgn|}|pUt }| |||||}|rc|S| dS)a Split input tensor and then scatter the split list to all processes in a group. Later the received tensors are concatenated from all the processes in the group and returned as a single output tensor. Complex tensors are supported. Args: output (Tensor): Gathered concatenated output tensor. input (Tensor): Input tensor to scatter. output_split_sizes: (list[Int], optional): Output split sizes for dim 0 if specified None or empty, dim 0 of ``output`` tensor must divide equally by ``world_size``. input_split_sizes: (list[Int], optional): Input split sizes for dim 0 if specified None or empty, dim 0 of ``input`` tensor must divide equally by ``world_size``. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group. .. warning:: `all_to_all_single` is experimental and subject to change. Examples: >>> # xdoctest: +SKIP("Undefined rank") >>> input = torch.arange(4) + rank * 4 >>> input tensor([0, 1, 2, 3]) # Rank 0 tensor([4, 5, 6, 7]) # Rank 1 tensor([8, 9, 10, 11]) # Rank 2 tensor([12, 13, 14, 15]) # Rank 3 >>> output = torch.empty([4], dtype=torch.int64) >>> dist.all_to_all_single(output, input) >>> output tensor([0, 4, 8, 12]) # Rank 0 tensor([1, 5, 9, 13]) # Rank 1 tensor([2, 6, 10, 14]) # Rank 2 tensor([3, 7, 11, 15]) # Rank 3 >>> # Essentially, it is similar to following operation: >>> scatter_list = list(input.chunk(world_size)) >>> gather_list = list(output.chunk(world_size)) >>> for i in range(world_size): >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) >>> # Another example with uneven split >>> input tensor([0, 1, 2, 3, 4, 5]) # Rank 0 tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 tensor([20, 21, 22, 23, 24]) # Rank 2 tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 >>> input_splits [2, 2, 1, 1] # Rank 0 [3, 2, 2, 2] # Rank 1 [2, 1, 1, 1] # Rank 2 [2, 2, 2, 1] # Rank 3 >>> output_splits [2, 3, 2, 2] # Rank 0 [2, 2, 1, 2] # Rank 1 [1, 2, 1, 2] # Rank 2 [1, 2, 1, 1] # Rank 3 >>> output = ... >>> dist.all_to_all_single(output, input, output_splits, input_splits) >>> output tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 tensor([ 5, 17, 18, 24, 36]) # Rank 3 >>> # Another example with tensors of torch.cfloat type. >>> input = torch.tensor( ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat ... ) + 4 * rank * (1 + 1j) >>> input tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 >>> output = torch.empty([4], dtype=torch.int64) >>> dist.all_to_all_single(output, input) >>> output tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 )output_split_sizesinput_split_sizesrGrIr7Nrr)r$r#r7r`rcrrrrrr$r; alltoall_baserS) rrrrrGrIrRrMr)rkrkrlr7s@h         r7cCst|r tddSt}t|dt|dt||dd|D}dd|D}|p.t}||||}|r:|S|dS)a Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. Complex tensors are supported. Args: output_tensor_list (list[Tensor]): List of tensors to be gathered one per rank. input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group. .. warning:: `all_to_all` is experimental and subject to change. Examples: >>> # xdoctest: +SKIP("Undefined rank") >>> input = torch.arange(4) + rank * 4 >>> input = list(input.chunk(4)) >>> input [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) >>> dist.all_to_all(output, input) >>> output [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 >>> # Essentially, it is similar to following operation: >>> scatter_list = input >>> gather_list = output >>> for i in range(world_size): >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) >>> input tensor([0, 1, 2, 3, 4, 5]) # Rank 0 tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 tensor([20, 21, 22, 23, 24]) # Rank 2 tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 >>> input_splits [2, 2, 1, 1] # Rank 0 [3, 2, 2, 2] # Rank 1 [2, 1, 1, 1] # Rank 2 [2, 2, 2, 1] # Rank 3 >>> output_splits [2, 3, 2, 2] # Rank 0 [2, 2, 1, 2] # Rank 1 [1, 2, 1, 2] # Rank 2 [1, 2, 1, 1] # Rank 3 >>> input = list(input.split(input_splits)) >>> input [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 >>> output = ... >>> dist.all_to_all(output, input) >>> output [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 >>> # Another example with tensors of torch.cfloat type. >>> input = torch.tensor( ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat ... ) + 4 * rank * (1 + 1j) >>> input = list(input.chunk(4)) >>> input [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) >>> dist.all_to_all(output, input) >>> output [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 r6NrrcSrWrkrXrTrkrkrlrErzall_to_all..cSrWrkrXrTrkrkrlrEr)r`rcrr}rr;alltoallrS)rrrGrIrMr)rkrkrlr6ss$]     r6cCsvt|r tddSt}tt||_|dur&t|tr"||_nt d|p*t }|j |d}|r5|S| dS)a Synchronize all processes. This collective blocks processes until the whole group enters this function, if async_op is False, or if async work handle is called on wait(). Args: group (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. async_op (bool, optional): Whether this op should be an async op device_ids ([int], optional): List of device/GPU ids. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group .. note:: `ProcessGroupNCCL` now blocks the cpu thread till the completion of the barrier collective. r8Nz>Invalid function argument: device_ids type should be List[int])rM) r`rcrrrr=rr device_idsr3r;r8rS)rGrIrrMr)rkrkrlr8s     r8cCst|r tddSt|tjkrtd|dur tt|}nt|tr3t d|dt |d}t ||dur>t n|}|j||dS)a Synchronize processes similar to ``torch.distributed.barrier``, but consider a configurable timeout. It is able to report ranks that did not pass this barrier within the provided timeout. Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0. Rank 0 will block until all send /recv from other ranks are processed, and will report failures for ranks that failed to respond in time. Note that if one rank does not reach the monitored_barrier (for example due to a hang), all other ranks would fail in monitored_barrier. This collective will block all processes/ranks in the group, until the whole group exits the function successfully, making it useful for debugging and synchronizing. However, it can have a performance impact and should only be used for debugging or scenarios that require full synchronization points on the host-side. For debugging purposes, this barrier can be inserted before the application's collective calls to check if any ranks are desynchronized. .. note:: Note that this collective is only supported with the GLOO backend. Args: group (ProcessGroup, optional): The process group to work on. If ``None``, the default process group will be used. timeout (datetime.timedelta, optional): Timeout for monitored_barrier. If ``None``, the default process group timeout will be used. wait_all_ranks (bool, optional): Whether to collect all failed ranks or not. By default, this is ``False`` and ``monitored_barrier`` on rank 0 will throw on the first failed rank it encounters in order to fail fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will collect all failed ranks and throw an error containing information about all failed ranks. Returns: ``None``. Example:: >>> # xdoctest: +SKIP("need process group init") >>> # Note: Process group initialization omitted on each rank. >>> import torch.distributed as dist >>> if dist.get_rank() != 1: >>> dist.monitored_barrier() # Raises exception indicating that >>> # rank 1 did not call into monitored_barrier. >>> # Example with wait_all_ranks=True >>> if dist.get_rank() == 0: >>> dist.monitored_barrier(wait_all_ranks=True) # Raises exception >>> # indicating that ranks 1, 2, ... world_size - 1 did not call into >>> # monitored_barrier. rSNz7monitored_barrier is only implemented for GLOO backend.zGPlease specify timeout arg as a timedelta. Converting current value of z assuming it represents secondsrJ)wait_all_ranks)r`rcrBr-rrr1rfloatrrrr5r;rS)rGr2r group_to_userkrkrlrSs&6  rSrrcCsBtsJdtd|}t||}t||||d}t||}|S)Nz5ProcessGroupWrapper unsupported without GLOO backend.rr4)rPG_WRAPPER_STORE_PREFIXrrqrp)rrrVrUrZr2prefix helper_pgrkrkrlres   rcCs@dtt|}d|tttjg}tjt|ddd S)Nrzutf-8F)usedforsecurity) rrrrr,rhashlibsha1bytes hexdigest)r rank_join unique_strrkrkrl_hash_ranks_to_str|srcCs8t|}t|}dttjdd}t||}|S)Nrr&)r(rkctypessizeofc_intabs)r hash_value max_c_intcolorrkrkrlrs  rcCs,|rt|}|Sttj}tjd7_|SNr&)rrr,r)rrrrkrkrlrs  rcCs|stt}t|Sr)rBr;r-r0rkrkrl_get_backend_from_strs rcCstjdurdSdS)z Checks if it is safe to split the any process group in the world. This is only safe if the default pg has a bound device id, otherwise users must be aware that a pg is only splittable after the first collective is issued. NFT)r;rrkrkrkrl_is_safe_to_splitsr parent_pg split_ranksr!csB|durtdt}|j}|stdtj|\}}|} |} |s'|}|tjvr4td|dtj|} dd| D| | vrPtd| d || | } | t d } | rf| j rft| tsjtd |durx|jd | n|}tj|\}}t|}t|}|durt|tjsJd n| j}|durt|}t|d}d}|D]5}t|dkrtdt|| krtdt|tt|krtdt|}| |vr|}|| }nq|dus|dkr| |dSt|dd}fdd|D}t|d|}t||t|}tj j!}||_|"|||_#| |_$t%||_&||_'||_(t||t||}|)|*t d |||dusLJ|dusSJ|+||,|| |}|-|||ftj|<|tj.|<t/||t0|tj1|<d|}tj23|g4||tj5|<ddt6|Dtj|<|S)a4 Create a new process group splitted from the given parent process group. warning:: This is an experimental API and only the ``NCCL`` backend supports this API. Other backends will raise an error. Users of this API must gurantee that all ranks in the parent group enter this API call, and the split of the sub groups is the same across all ranks in the parent group. Args: parent_pg (ProcessGroup, optional): The parent process group. If None, the default process group will be used. Users need to gurantee that the parent group is fully initialized (e.g, communicators are initialized) split_ranks (list[list[int]]): the split ranks, which is a list of list of ranks. Users need to make sure the validity of the split ranks such that one split (represented by one inner list of ints) does not overlap with any other split. Note that the ranks in each split is the group rank (instead of global rank) in the parent pg. For example, if the parent group has 4 ranks, and split_ranks can be [[0, 1], [2, 3]]. Note [[0,1]] is also a valid split, in which case ranks 2, 3 would return a non-group member. timeout (timedelta, optional): see `init_process_group` for details and default value. pg_options (ProcessGroupOptions, optional): only ProcessGroupNCCLOptions is supported now. specifying what additional options need to be passed in during the construction of specific process groups. i.e.``is_high_priority_stream`` can be specified so that process group can pick up high priority cuda streams. For other availble options to config nccl, See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t group_desc (str, optional): a string to describe the process group. Returns: ProcessGroup if the current rank is within one split/subgroup given by split_ranks, or None if the current rank is not part of any split_ranks`. Nzsplit_ranks cannot be NonezNNo device associated with the default pg, not safe to split any process groupsrdz is not registeredcSi|]\}}||qSrkrk)rrbrirkrkrlrzsplit_group..rfz! is not part of the parent group rzQNo backend for the parent process group or its backend does not support splittingz:split:zCExpected pg_options argument to be of type ProcessGroupNCCL.Optionsrarzthe split group cannot be emptyzZthe split group's size should be less or equal to the world_size set by init_process_groupz+the split group cannot have duplicate ranksFrcsg|]}|qSrkrk)rrUparent_group_to_global_ranksrkrlrE8r{zsplit_group..rrcSrrkrkrrirbrkrkrlrer)7rr;rr@r,rrUrlrrrrrrrror!comm_split_countr-r.roptionsr1r5rrPrrrrrrrrrrrrrrrrrrrrrr rrrrrrr)rrr2rr!rr_default_backendrrbglobal_world_sizeparent_global_to_group_ranksparent_group_rankparent_backendparent_backend_strrrrmy_grouprirerglobal_ranks_in_my_grouprr$rrr rrkrrlres*                   rec Cst||||d|||dS)aA Create a new distributed group. This function requires that all processes in the main group (i.e. all processes that are part of the distributed job) enter this function, even if they are not going to be members of the group. Additionally, groups should be created in the same order in all processes. .. warning:: Safe concurrent usage: When using multiple process groups with the ``NCCL`` backend, the user must ensure a globally consistent execution order of collectives across ranks. If multiple threads within a process issue collectives, explicit synchronization is necessary to ensure consistent ordering. When using async variants of torch.distributed communication APIs, a work object is returned and the communication kernel is enqueued on a separate CUDA stream, allowing overlap of communication and computation. Once one or more async ops have been issued on one process group, they must be synchronized with other cuda streams by calling `work.wait()` before using another process group. See `Using multiple NCCL communicators concurrently `_ for more details. Args: ranks (list[int]): List of ranks of group members. If ``None``, will be set to all ranks. Default is ``None``. timeout (timedelta, optional): see `init_process_group` for details and default value. backend (str or Backend, optional): The backend to use. Depending on build-time configurations, valid values are ``gloo`` and ``nccl``. By default uses the same backend as the global group. This field should be given as a lowercase string (e.g., ``"gloo"``), which can also be accessed via :class:`Backend` attributes (e.g., ``Backend.GLOO``). If ``None`` is passed in, the backend corresponding to the default process group will be used. Default is ``None``. pg_options (ProcessGroupOptions, optional): process group options specifying what additional options need to be passed in during the construction of specific process groups. i.e. for the ``nccl`` backend, ``is_high_priority_stream`` can be specified so that process group can pick up high priority cuda streams. For other availble options to config nccl, See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t use_local_synchronization (bool, optional): perform a group-local barrier at the end of the process group creation. This is different in that non-member ranks don't need to call into API and don't join the barrier. group_desc (str, optional): a string to describe the process group. device_id (torch.device, optional): a single, specific device to "bind" this process to, The `new_group` call will try to initialize a communication backend immediately for the device if this field is given. Returns: A handle of distributed group that can be given to collective calls or GroupMember.NON_GROUP_MEMBER if the rank is not part of ``ranks``. N.B. use_local_synchronization doesn't work with MPI. N.B. While use_local_synchronization=True can be significantly faster with larger clusters and small process groups, care must be taken since it changes cluster behavior as non-member ranks don't join the group barrier(). N.B. use_local_synchronization=True can lead to deadlocks when each rank creates multiple overlaping process groups. To avoid that, make sure all ranks follow the same global creation order. N)use_local_synchronizationr!r)_new_group_with_tag)rr2rrrr!rrkrkrlrTmsOrTc Cst}|dur |j}n|jdur||jksJdtj|\} } |} |} |s,| }t|}|dur8t|}t||rR|tj krGt d|durRt |vrRdS|durt |}t |} | | krft d|D]}|dksr|| krvt dqh| |vr|| }n d}n tt| }| } | }t||d}t| |||| ||||||d \}}d d t|Dtj|<td krtd |tj krt|S|r|n| }|rt |nt}t| |||||S) z Variant of ``new_group`` that exposes tag creation. :: N.B. The mechanism is experimental and tied to the functional collectives effort, see ``torch.distributed._functional_collectives`` for reference on how to use it. Nz:Mismatched bound device between new pg and the default pg.z:MPI backend doesn't support use_local_synchronization=Truez^the new group's world size should be less or equal to the world size set by init_process_grouprzNThe new group's rank should be within the world_size set by init_process_groupr)rr2rrr!cSrrkrkrrkrkrlr%s z'_new_group_with_tag..r&r)r;rr,rrUrlr-r1r5rrrDrrrrrrrrrrrrr8rEr_)rr2rrrrr!rrdefault_backendrrbrgroup_world_sizerUrirr$r barrier_storerZrkrkrlrs            rcCs|durtjs tdtj}|dkrtd|dt}||kr0td|d|d||dkr:tdg}d}t||D]/} | |} | |} tt| | } t| ||||d } | | t }|| vrs| }t d || qD||fS) aS Create subgroups of equal size. By default, it creates intra-machine subgroups, where each of which contains all the ranks of a machine, based on the assumption that each machine has the same number of devices. This is a convenience API that calls ``new_group`` to generate multiple subgroups. It requires that all processes in the main group (i.e. all processes that are part of the distributed job) enter this function, even if they are not going to be members of the group. .. warning:: If ``group_size`` is passed in, the world size must be divisible by ``group_size``. If no ``group_size`` is passed in, it believe that you are creating a group based on CUDA and determining the group size by number of CUDA devices, and if not all the machines have the same number of devices, the subgroup division will be different across nodes and can cause unexpected behaviors. Therefore, if you are creating a subgroup that does not depend on CUDA (such as Gloo on CPU), please pass in ``group_size`` correctly. .. warning:: See warning `Safe concurrent usage` for `new_group` API for important details about using multiple process groups concurrently in a safe manner. Args: group_size (int, optional): The size of each subgroup. If ``None``, the default subgroup size is equal to the number of devices on each machine, based on the assumption that each machine has exactly the same number of devices. Default is ``None``. timeout (timedelta, optional): see `init_process_group` for details and default value. backend (str or Backend, optional): The backend to use. Depending on build-time configurations, valid values are ``gloo`` and ``nccl``. By default uses the same backend as the global group. This field should be given as a lowercase string (e.g., ``"gloo"``), which can also be accessed via :class:`Backend` attributes (e.g., ``Backend.GLOO``). If ``None`` is passed in, the backend corresponding to the default process group will be used. Default is ``None``. pg_options (ProcessGroupOptions, optional): process group options specifying what additional options need to be passed in during the construction of specific process groups. i.e. for the ``nccl`` backend, ``is_high_priority_stream`` can be specified so that process group can pick up high priority cuda streams. group_desc (str, optional): A string describing the group. Each subgroup will inherit its group_desc Returns: The subgroup containing the current rank, and all the subgroups used for cleanup. Examples: >>> # Create intra-machine subgroups. >>> # xdoctest: +SKIP("need process group init") >>> cur_subgroup, subgroups = dist.new_subgroups() >>> # Allreduce within the machine. >>> rank = dist.get_rank() >>> tensor = torch.ones(1, device=rank) * rank >>> dist.all_reduce(tensor, group=cur_subgroup) >>> tensor tensor([28]) # Assume 8 CUDA devices per machine. 28 is sum(range(8)). >>> # Cleanup. >>> for subgroup in subgroups: >>> dist.destroy_process_group(subgroup) NzDefault group size only takes effect when CUDA is available.If your subgroup using a backend that does not depend on CUDA,please pass in 'group_size' correctly.rzThe arg 'group_size' (z) must be positivez") must not exceed the world size (rz0The world size must be divisible by 'group_size'rr2rrr!"Rank %s is assigned to subgroup %s) rr is_availabler device_countrErrrTrrDrr)rrGr2rrr!rZ subgroups cur_subgroup subgroup_id start_rankend_rankranks_in_subgroupsubgrouprUrkrkrlrUEsFH    rUc Cs|dus t|dkrtdg}d}i}|D]<}t|||||d} || t} |D]&} | |vr@td| d|| d|||| <| | krQ| }td| |q+q||fS) a Create subgroups by dividing the global world. The division is specified by a nested list of ranks. The subgroups cannot have overlap, and some ranks may not have to be in any subgroup. This is a convenience API that calls ``new_group`` to generate multiple subgroups. It requires that all processes in the main group (i.e. all processes that are part of the distributed job) enter this function, even if they are not going to be members of the group. .. warning:: See warning `Safe concurrent usage` for `new_group` API for important details about using multiple process groups concurrently in a safe manner. Args: ranks_per_subgroup_list (list[list[int]]): A nested list of ranks of group members. timeout (timedelta, optional): see `init_process_group` for details and default value. backend (str or Backend, optional): The backend to use. Depending on build-time configurations, valid values are ``gloo`` and ``nccl``. By default uses the same backend as the global group. This field should be given as a lowercase string (e.g., ``"gloo"``), which can also be accessed via :class:`Backend` attributes (e.g., ``Backend.GLOO``). If ``None`` is passed in, the backend corresponding to the default process group will be used. Default is ``None``. pg_options (ProcessGroupOptions, optional): process group options specifying what additional options need to be passed in during the construction of specific process groups. i.e. for the ``nccl`` backend, ``is_high_priority_stream`` can be specified so that process group can pick up high priority cuda streams. group_desc (str, optional): A string describing the group. Each subgroup will inherit its group_desc. Returns: The subgroup containing the current rank, and all the subgroups used for cleanup. Examples: >>> # Create two subgroups, where each has 2 processes. >>> # xdoctest: +SKIP("need process group init") >>> cur_subgroup, subgroups = dist.new_subgroups(ranks=[[0, 2], [1, 3]]) >>> rank = dist.get_rank() >>> tensor = torch.ones(1, device=rank) * rank >>> dist.all_reduce(tensor, group=cur_subgroup) >>> tensor tensor([2]) # Subgroup 0: ranks 0 and 2 tensor([4]) # Subgroup 1: ranks 1 and 3 Nrz1The arg 'ranks_per_subgroup_list' cannot be emptyrzRank z has appeared in both subgroup z and r)rrrTrrDrr) ranks_per_subgroup_listr2rrr!rrrank_to_ranks_dictrrrrUrkrkrlrVs68  rVcs~t|dkr|ds|dsd|}tj|gD] }|t|kr'qt|tfdd|D}|r<|SqdS)Nrrrc3s|]}|vVqdSrrk)rrrhrkrlrrVz,_find_pg_by_ranks_and_tag..)rrr,rrrlr`r|)rrrGgoodrkrrlrqs  rqstridecCst||dksJdt|d|dt}d}|t|kr-|}||vs,Jdn!tdt||D]}||||}||vrE|}q5|dusNJdt|}t||}|dur]|S|dkretdt||dS) NrzRanks length (z) must be divisible by stride (rz(rankset doesn't include the current noderpz-Cannot automatically create PG with empty tag)r)rrDcopyrrrqrr)rrrrmy_ranksrrank_setr$rkrkrl#_find_or_create_pg_by_ranks_and_tags*   rcCstj|}|d}|S)z&Return the tag associated with ``pg``.r)r,r removeprefix)r$rrkrkrl_get_group_tag<s  r cCstj|dS)NNone)r,rrr$rkrkrlrCrcCstj|dSr)r,rr rkrkrl_get_process_group_storeGr rrr)NNF) NNNraraNrpNN)NNNNNr)NNFN)NNNN)NF)NNNFN)NNNF)NNNNFNN)NNNNNFNN)NNNNNN)rcollections.abc collections contextlibrrrrloggingrpicklerrRrrdatetimertypingrrrrrtyping_extensionsr rtorch._Cr rTtorch._C._distributed_c10dr r r rrrrrrrrrrrrrrrrrrr torch._utils_internalr! torch.monitorr"torch.overridesr#r$torch.utils._typing_utilsr% c10d_loggerr'r( constantsr)r*r,r+__all__rrrrrPicklerr^ Unpicklerrprmrnrh ImportErrorrorprqrrrs getLoggerrrrboolr]rr-r.rrar0rrrr(rrrrrrrrrrrr,rjr)rGr/r1r5r6rMr=rrBrHr_r`rcr^r_rrkr`rrnrrrr}rrrabcIterablerrrrrLrNrJrPrQrMrKrOrr;rrrArBrCrrrrFrdrrrHrrr>r!rDrErrRrIr\rWr,r3contextmanagerrEr9r:SUMr4r5rXrmrsr3r@r;r<r=r[r1rbrr2rr?rZrYrcrr7r6r-r8rSrrrrrrrrrerTrrUrVrqrr rrrkrkrkrls    `   M        PFAND         "  +  x 'WZ / -  '  XO .`: 0Z  | b e }  ^k N  O f&du-U   >[~u"V