a NSic@s<UddlZddlZddlZddlZddlZddlZddlZddlmZddl m Z m Z m Z m Z mZddlZddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddl m!Z!ddl"m#Z#ddl$m%Z%m$Z$d Z&d Z'd Z(ej)Z*ej+Z,zdd lm-Z-Wne.yd Z&Yn0zdd lm/Z/Wne.y@d Z'Yn0zdd lm0Z0ddlm1Z1Wne.yvd Z(Yn0e2e3Z4dZ5ee6dddZ7Gddde8Z9e9j:a;eGddde8Z?e?Z@Gddde8ZAGddde8ZBiaCe ee eZXd?d@ZYdAdBZZdCdDZ[ddEdFZ\de#dGdGddHdfdIdJZ]dde#fdKdLZ^ddMdNZ_ddOdPZ`ddQdRZaddSdTZbddUdVZcddWdXZdddYdZZeGd[d\d\e8Zfejgd]d^Zhd_d`ZiddadbZjddcddZkejldd fdedfZmejldd fdgdhZnejldd fdidjZoejldd dfdkdlZpejldd fdmdnZqddodpZrdqdrZsdsdtZtdudvZuddwdxZvddydzZwdd{d|Zxdd}d~ZydddZzdddZ{dddZ|ddZ}dddZ~dddZejldd fddZejldd fddZejldd fddZdddZdddZeBjd dfddZeBjdd fddZe#feetdn@|tjkrXtd|n&|tj kr~|tj kr~|tj kr~| }|S)Nz*Backend name must be a string, but got: {}ziTCP backend has been deprecated. Please use Gloo or MPI backend for collective operations on CPU tensors.zInvalid backend: '{}') isinstancer ValueErrorformatgetattrr,upper UNDEFINEDTCPGLOONCCLMPIlower)clsr/valuer)r)r*__new__ps   zBackend.__new__cCsftt|r J|d|tjvs@J|dtt|||tj|<dS)a Registers a new backend with the given name and instantiating function. This class method is used by 3rd party ``ProcessGroup`` extension to register new backends. Args: name (str): Backend name of the ``ProcessGroup`` extension. It should match the one in ``init_process_group()``. func (function): Function handler that instantiates the backend. 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Intended to be used only with those two methods and is not a generic alternative to ``barrier()``. z{}:{}rz#Added key: {} to store for rank: {}rg{Gz?)seconds z}Waiting in store based barrier to initialize process group for rank: {}, key: {} (world_size={}, worker_count={}, timeout={})zTimed out initializing process group in store based barrier on rank: {}, for key: {} (world_size={}, worker_count={}, timeout={})zRank z(: Completed store-based barrier for key:z with z nodes.N) r2STORE_BASED_BARRIER_PREFIX _group_countaddloggerinfoget_world_sizetimesleepr RuntimeError)rankstoretimeoutZ store_key world_sizeZ worker_countstartZlog_timer)r)r*_store_based_barriers4       rmrVcCs|dur dS|tjkS)zS Helper that checks if the current process's rank is not in a given group. NF)rXrYrnr)r)r*_rank_not_in_group srocCs4tjdurdntj}td|d|ddS)NzRunning z on global rank z* which does not belong to the given group.)rXrWrhrQrR)Zop_name global_rankr)r)r*_warn_not_in_groupsrrcCs`|tjurtd|tvr"tdzt||}Wn(tyZtd|d|dYn0|S)z` Helper that gets a given group's local rank in the group from a given global rank. ;group.WORLD does not have local rank to global rank mappingzThe given group does not existzThe global rank z is not part of the group N)rXrWrgr\KeyError)rVrh group_rankr)r)r*_get_group_ranks  rvcCsH|tjurtdt|}|D]\}}||kr"|Sq"tddS)z` Helper that gets a given group's global rank from a given local rank in the group. rsz'The group rank is not part of the groupN)rXrWrgr\rL)rVruZgroup_rank_maprhZgrp_rankr)r)r*_get_global_rank0s  rwcCs(|tjus|dur t}|S|S)z6 Helper that gets a given group's world size. N)rXrW_get_default_groupsizerV default_pgr)r)r*_get_group_size@sr|cCst|tjstd|dS)zO Helper to check that the parameter ``param_name`` is a single tensor. zNInvalid function argument. 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Each op is expected to to be of type ``torch.distributed.P2POp``.rc3s|]}t|jkVqdSr") get_backendrVrbackendr)r*r|z(All groups need to use the same backend.N)r0rrrgrrV) p2p_op_listr)rr*_check_p2p_op_listnsrcCstS)z1 Checks if the MPI backend is available. )_MPI_AVAILABLEr)r)r)r*is_mpi_availablesrcCstS)z2 Checks if the NCCL backend is available. )_NCCL_AVAILABLEr)r)r)r*is_nccl_availablesrcCstS)z2 Checks if the Gloo backend is available. )_GLOO_AVAILABLEr)r)r)r*is_gloo_availablesrcCs tjduS)zD Checking if the default process group has been initialized N)rXrWr)r)r)r*is_initializedsrcCstdduS)a Checks 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.. ZTORCHELASTIC_RUN_IDN)osgetenvr)r)r)r*is_torchelastic_launcheds rcCststdtjS)zI Getting the default process group created by init_process_group \Default process group has not been initialized, please make sure to call init_process_group.)rrgrXrWr)r)r)r*rxs rxcCs$tstdt}t|\}}|S)zA Getting the default store created by init_process_group r)rrgrxrZ)r{_ default_storer)r)r*_get_default_stores rcCs|t_t_dSr")rXrWrV)pgr)r)r*_update_default_pgsrcCsD|durt}n|}t|r$tdt|d}|dusfrz&init_process_group..)r0rrgrXrWr,r9rQrRr2_new_process_group_helperrrnext set_timeoutrrangeryr\rZrI_default_pg_init_methodbarrierrmrr7r8_set_sequence_number_for_group) rZ init_methodrjrkrhrirrr{Zrendezvous_iteratorr)r)r*init_process_groupslO          rc Cs0|stt}td7a|tvr(tdt|ts:tdt|dk}t|}|tj krt sftdt |} | szt jStj dft| <|t| <n|st} | |vrt jSt||} |tjkr2|durtdt| |||d} ttjkrtstd nt| |||||d } tj|ft| <|t| <n|tjkrtsNtd |durpt|tjsJd nt}d |_ ||_!t| |||} ttjkrtstd nt| |||||d } tj|ft| <|t| <nN|"tj#vsJd|"tj#|"| |||} ||ft| <|t| <| S)aV Create a new distributed process group. This function must be called by ALL processes in the global group, even if the calling process is not part of the newly created group. In that case, this function returns GroupMember.NON_GROUP_MEMBER. This function is called with ``group_ranks == []`` for the default group. rzTThe specified group name has already been created, please use a different group namerrzDistributed package doesn't have MPI built in. MPI is only included if you build PyTorch from source on a host that has MPI installed.NzGLOO options not supportedraTORCH_DISTRIBUTED_DEBUG was set to DETAIL, but GLOO is not available. Build with Gloo to create a wrapper process group in debug mode to aid collective desynchronization debugging. wrapped_pg store_prefixrirhrkrjz.Distributed package doesn't have NCCL built inzCExpected pg_options argument to be of type ProcessGroupNCCL.OptionsFzunknown c10d backend type )$rFr`r[valuesrgr0rlenr,r9rrcreaterXrYrZrxrhrr7rrrDETAILrrbrc_create_process_group_wrapperr8rrOptionsis_high_priority_stream_timeoutr4r.) rkrhZ group_ranksrrirrrjZis_default_grouprrqZ prefix_storer)r)r*rys                rcCs|tjkrdS|durtj}n|}|dus.Jt|ddurFtd|dusX|tjkrtddatt t da nt|=t |=t |=dS)ax 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. Nrr) rXrYrWrZrrgrrclearr[r\r`rVrr)r)r*destroy_process_groups$   rcCs:t|r dSt}|dus$|tjur,|St||S)a Returns the rank of the current process in the provided ``group`` or the default group if none was provided. 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 rpN)rorxrXrWrhrvrzr)r)r*get_rank7s rcCst|r dSt|S)a4 Returns 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 rp)ror|rnr)r)r*rdSs rdcCsdt|dt|rtddS|dus0|tjurFt}||g||St||}||g||SdS)a Sends a tensor asynchronously. .. warning:: Modifying ``tensor`` before the request completes causes undefined behavior. Args: tensor (Tensor): Tensor to send. dst (int): Destination rank. 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 Returns: A distributed request object. None, if not part of the group tensorrN)rrorrrXrWrxsendrvrdstrVtagr{group_dst_rankr)r)r*rfs  rcCst|dt|rtddS|dus0|tjur8t}n|}|durR||g|S|tjurl||g||St||}||g||SdS)a Receives a tensor asynchronously. Args: tensor (Tensor): Tensor to fill with received data. src (int, optional): Source rank. 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 Returns: A distributed request object. None, if not part of the group rrN) rrorrrXrWrxrecv_anysourcerecvrv)rsrcrVrrgroup_src_rankr)r)r*rs   rcCsnt|dt|rtddS|dus0|tjurLt}||g||nt||}||g||dS)aD Sends a tensor synchronously. Args: tensor (Tensor): Tensor to send. dst (int): Destination rank. 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 rrN) rrorrrXrWrxrwaitrvrr)r)r*rs  rcCst|dt|rtddS|dur.t}n|}|durz||g|}||}|dusj|tjurn|St ||SnJ|dus|tjur| |g||nt ||}| |g|||SdS)a Receives a tensor synchronously. Args: tensor (Tensor): Tensor to fill with received data. src (int, optional): Source rank. 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 Returns: Sender rank -1, if not part of the group rrrpN) rrorrrxrr _source_rankrXrWrwrrv)rrrVrrworkZsrc_rankrr)r)r*rs&   rc@s$eZdZdZdddZd ddZdS) ra A class to build point-to-point operations for ``batch_isend_irecv``. This class builds the type of P2P operation, communication buffer, peer rank, Process Group group, and tag. Instances of this class will be passed to ``batch_isend_irecv`` for point-to-point communications. Args: op (callable): A function to send data to or receive data from a peer process. The type of ``op`` is either ``torch.distributed.isend`` or ``torch.distributed.irecv``. tensor (Tensor): Tensor to send or receive. peer (int): Destination or source rank. 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 recv. NrcCs"||_||_||_||_||_dSr")rrpeerrVr)rMrrrrVrr)r)r*rPs zP2POp.__init__cCst|t|dt|S)Nr)rrrSr=)r;rrrrVrr)r)r*r= s z P2POp.__new__)Nr)Nr)rBrCrDrErPr=r)r)r)r*rs rccsH|tjkrtzdVW|tjkrDtn|tjkrBt0dSr")r,r8r _group_start _group_endrr)r)r*_batch_p2p_managers   rc Cst|t|dj}g}t|X|D]B}|j}|j}|j}|j}|j}|||||} | dur(|| q(Wdn1s0Y|S)a Send or Receive a batch of tensors asynchronously and return a list of requests. Process each of the operations in ``p2p_op_list`` and return the corresponding requests. NCCL and Gloo backend are currently supported. Args: p2p_op_list: A list of point-to-point operations(type of each operator is ``torch.distributed.P2POp``). The order of the isend/irecv in the list matters and it needs to match with corresponding isend/irecv on the remote end. Returns: A list of distributed request objects returned by calling the corresponding op in the op_list. Examples: >>> send_tensor = torch.arange(2) + 2 * rank >>> recv_tensor = torch.randn(2) >>> 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. rN) rrrVrrrrrappend) rrreqsrrrrZ curr_grouprretr)r)r*batch_isend_irecvs' *rc Cs|t|rtddSt}||_||_|dus8|tjurLt}|||}nt ||}||_|||}|rp|S| dS)aP Broadcasts the tensor to the whole group with multiple GPU tensors per node. ``tensor`` must have the same number of elements in all the GPUs from all processes participating in the collective. each tensor in the list must be on a different GPU Only nccl and gloo backend are currently supported tensors should only be GPU tensors Args: tensor_list (List[Tensor]): Tensors that participate in the collective operation. If ``src`` is the rank, then the specified ``src_tensor`` element of ``tensor_list`` (``tensor_list[src_tensor]``) will be broadcast to all other tensors (on different GPUs) in the src process and all tensors in ``tensor_list`` of other non-src processes. You also need to make sure that ``len(tensor_list)`` is the same for all the distributed processes calling this function. src (int): Source 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 src_tensor (int, optional): Source tensor rank within ``tensor_list`` Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group broadcast_multigpuN) rorrr rootRank rootTensorrXrWrx broadcastrvr) tensor_listrrVasync_opZ src_tensoroptsr{rrr)r)r*rUs   rcCst|dt|rtddSt}||_d|_|dusB|tjurXt}| |g|}nt ||}||_| |g|}|r~|S| dS)a 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. 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 rrNr) rrorrr rrrXrWrxrrvr)rrrVrrr{rrr)r)r*rs   rcCsbt|r dSdd|D}t}||_|durBt}|||}n |||}|rV|S|dS)a Reduces the tensor data across all machines in such a way that all get the final result. This function reduces a number of tensors on every node, while each tensor resides on different GPUs. Therefore, the input tensor in the tensor list needs to be GPU tensors. Also, each tensor in the tensor list needs to reside on a different GPU. After the call, all ``tensor`` in ``tensor_list`` is going to be bitwise identical in all processes. Complex tensors are supported. Only nccl and gloo backend is currently supported tensors should only be GPU tensors Args: tensor_list (List[Tensor]): List of input and output tensors of the collective. The function operates in-place and requires that each tensor to be a GPU tensor on different GPUs. You also need to make sure that ``len(tensor_list)`` is the same for all the distributed processes calling this function. 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 NcSs"g|]}|s|nt|qSr) is_complexr} view_as_realrtr)r)r* sz'all_reduce_multigpu..)ror r rx allreducer)rrrVrrr{rr)r)r*all_reduce_multigpus" rcCst|dt|rtddS|rHt|s>td|dt|}t}||_ |durrt }| |g|}n| |g|}|r|S| dS)a Reduces the tensor data across all machines in such 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: >>> # All tensors below are of torch.int64 type. >>> # We have 2 process groups, 2 ranks. >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank >>> tensor tensor([1, 2]) # Rank 0 tensor([3, 4]) # Rank 1 >>> dist.all_reduce(tensor, op=ReduceOp.SUM) >>> tensor tensor([4, 6]) # Rank 0 tensor([4, 6]) # 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) + 2 * rank * (1+1j) >>> tensor tensor([1.+1.j, 2.+2.j]) # Rank 0 tensor([3.+3.j, 4.+4.j]) # Rank 1 >>> dist.all_reduce(tensor, op=ReduceOp.SUM) >>> tensor tensor([4.+4.j, 6.+6.j]) # Rank 0 tensor([4.+4.j, 6.+6.j]) # Rank 1 r all_reduceNall_reduce does not support  on complex tensors) rrorrrr+rgr}rr r rxrr)rrrVrrr{rr)r)r*rs"/  rcCst|dt|rtddStdd|DrHt|sHtd|ddd|D}t}||_|dur~t}| ||}n | ||}|r| 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 (List[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. rall_reduce_coalescedNcSsg|] }|qSr))rrr)r)r*rXrz(all_reduce_coalesced..rrcSs"g|]}|s|nt|qSr)rrr)r)r*r[r) rrorranyr+rgrr rxallreduce_coalesced get_futurer)tensorsrrVrrr{rr)r)r*r2s !  rc Cst|rtddSt}||_||_||_|dus>|tjurRt}| ||}nt ||} | |_| ||}|rv|S| dS)a Reduces the tensor data on multiple GPUs across all machines. Each tensor in ``tensor_list`` should reside on a separate GPU Only the GPU of ``tensor_list[dst_tensor]`` on the process with rank ``dst`` is going to receive the final result. Only nccl backend is currently supported tensors should only be GPU tensors Args: tensor_list (List[Tensor]): Input and output GPU tensors of the collective. The function operates in-place. You also need to make sure that ``len(tensor_list)`` is the same for all the distributed processes calling this function. dst (int): Destination rank 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 dst_tensor (int, optional): Destination tensor rank within ``tensor_list`` Returns: Async work handle, if async_op is set to True. None, otherwise reduce_multigpuN) rorrrr rrrXrWrxreducervr) rrrrVrZ dst_tensorrr{rrr)r)r*rks !  rc Cst|dt|rtddSt}||_||_|dusB|tjurXt}| |g|}nt ||}||_| |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 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 rrN) rrorrrr rrXrWrxrrvr) rrrrVrrr{rrr)r)r*rs   rcCslt|rtddSdd|D}dd|D}|durLt}|||}n |||}|r`|S|dS)ak Gathers tensors from the whole group in a list. Each tensor in ``tensor_list`` should reside on a separate GPU Only nccl backend is currently supported tensors should only be GPU tensors Complex tensors are supported. Args: output_tensor_lists (List[List[Tensor]]): Output lists. It should contain correctly-sized tensors on each GPU to be used for output of the collective, e.g. ``output_tensor_lists[i]`` contains the all_gather result that resides on the GPU of ``input_tensor_list[i]``. Note that each element of ``output_tensor_lists`` has the size of ``world_size * len(input_tensor_list)``, since the function all gathers the result from every single GPU in the group. To interpret each element of ``output_tensor_lists[i]``, note that ``input_tensor_list[j]`` of rank k will be appear in ``output_tensor_lists[i][k * world_size + j]`` Also note that ``len(output_tensor_lists)``, and the size of each element in ``output_tensor_lists`` (each element is a list, therefore ``len(output_tensor_lists[i])``) need to be the same for all the distributed processes calling this function. input_tensor_list (List[Tensor]): List of tensors(on different GPUs) to be broadcast from current process. Note that ``len(input_tensor_list)`` needs to be the same for all the distributed processes calling this function. 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 all_gather_multigpuNcSsg|]}dd|DqS)cSs"g|]}|s|nt|qSr)rrr)r)r*rrz2all_gather_multigpu...r)rlr)r)r*rsz'all_gather_multigpu..cSs"g|]}|s|nt|qSr)rrr)r)r*rs)rorrrx allgatherr)output_tensor_listsinput_tensor_listrVrr{rr)r)r*rs - rcCsHt}t||tj|}t|}t | g}||fSr") ioBytesIO_picklerdumpr} ByteStorage from_buffergetvalue ByteTensor LongTensornumel)objfZ byte_storageZ byte_tensor local_sizer)r)r*_object_to_tensors  rcCs&|d|}tt|Sr")numpytobytes _unpicklerrrload)r tensor_sizebufr)r)r*_tensor_to_object!srcCs0|pt}tr t|tr |j}qto.t|tSr")rxrr0rrrrrr)r)r*_check_for_nccl_backend%s  rc sDt|rtddSt|\}}td}t|}|r\tdtj}||}||}t |d}tj |tj |dfddt |D}t |||dtt||tj|tj|dfd dt |D} t | ||dt| D]D\} } | tj} | jtdkr(| } || } t| | || <qdS) 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. object (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. Example:: >>> # 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}] all_gather_objectNcpucudarndtypedevicecsg|]}|jddqSr)dim unsqueezerobject_sizes_tensorr)r*rusz%all_gather_object..cs$g|]}||dqSrr)rcoalesced_output_tensormax_object_sizer)r*rs)rorrrr}rrrcurrent_devicetordzeroslongr all_gatherintmaxitemresize_emptyuint8 enumeratetyperr) object_listrrV input_tensorrr!is_nccl_backend group_sizeobject_size_listoutput_tensorsrrr r)rr rr*r3sB.          rcs~t|rtddSt}t|||t|\}}td}t|}|rntdtj }| |}| |}t |d} tj | tj |dfddt| D} t| ||dtt| |||krtj| tj|dfd dt| D} t|||kr| nd||d ||kr*dSt| D]F\} } | tj} | jtdkr`| } | | }t| ||| <q2dS) 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. (default is 0) group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. 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. Example:: >>> # 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}] gather_objectNrrrnrcsg|]}|jddqSrrrrr)r*rsz!gather_object..cs$g|]}||dqSrr)rrr)r*rs) gather_listrrV)rorrr_validate_output_list_for_rankrr}rrrr!r"rdr#r$rr%r&r'r(r)r*r+gatherr,r-rr)rZobject_gather_listrrVmy_rankr/rr!r0r1r2r3rrr r)r4r*r5sV4            r5cCst|rtddSt}||krDtdd|D\}}t|}ntjt|tjd}t |}d} |dur|r|j dkrt d|} n t d} |rt dtj } |r|| }t|||d ||krt|} ntjt|tjd} |r| | } t| ||d d } ||kr~t|D]V\} } | | | | }| tj}|j t dkrd|}| | 7} t|| || <q&dS) 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``. 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``. Returns: ``None``. If rank is part of the group, ``object_list`` will contain the broadcasted objects from ``src`` rank. .. 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()``. .. 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. .. 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. Example:: >>> # 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}] broadcast_object_listNcSsg|] }t|qSr)rrrr)r)r*r9rz)broadcast_object_list..rrz)device type must be cuda for nccl backendrrrVr)rorrrzipr}catr*rr$rr-r1rrr!r"rsumr(r+r,rr)r.rrVrr9r size_listrr0r!Z object_tensoroffsetrZobj_sizeZobj_viewr)r)r*r:sL5        r:c Cs"t|rtddSt|tr*t|dkr2tdt|}||krjtdd|D\}}t|t|}}||krt|}|D]}| |q~nt j dgt j d}t |||d t j|t jd} t| ||krdn|||d t j dgt j d} t| ||krdn|||d t| | |d<dS) a 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]): 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``. group: (ProcessGroup, optional): The process group to work on. If None, the default process group will be used. Default is ``None``. 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. .. note:: Note that this API does not support the NCCL backend, as the tensor-based scatter collective is not supported by ProcessGroupNCCL. .. 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. Example:: >>> # 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}] scatter_object_listNrzMExpected argument scatter_object_output_list to be a list of size at least 1.cSsg|] }t|qSr)r;r<r)r)r*rrz'scatter_object_list..rr=r>) scatter_listrrV)rorrr0rrrgrr?r'r)r}rr$rr*r(r+scatterr) Zscatter_object_output_listZscatter_object_input_listrrVr9rZ tensor_sizesZmax_tensor_sizer output_tensorZobj_tensor_sizer)r)r*rDosJ5  rDcCst|dt|dt|r(tddSdd|D}|sB|nt|}|durlt}||g|g}n||g|g}|r|S| dS)a Gathers tensors from the whole group in a list. Complex tensors are supported. Args: tensor_list (list[Tensor]): Output list. It should contain correctly-sized tensors to be used for output of the collective. 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: >>> # All tensors below are of torch.int64 dtype. >>> # We have 2 process groups, 2 ranks. >>> tensor_list = [torch.zeros(2, dtype=torch.int64) for _ in range(2)] >>> tensor_list [tensor([0, 0]), tensor([0, 0])] # Rank 0 and 1 >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank >>> tensor tensor([1, 2]) # Rank 0 tensor([3, 4]) # Rank 1 >>> dist.all_gather(tensor_list, tensor) >>> tensor_list [tensor([1, 2]), tensor([3, 4])] # Rank 0 [tensor([1, 2]), tensor([3, 4])] # 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) for _ in range(2)] >>> tensor_list [tensor([0.+0.j, 0.+0.j]), tensor([0.+0.j, 0.+0.j])] # Rank 0 and 1 >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j) >>> tensor tensor([1.+1.j, 2.+2.j]) # Rank 0 tensor([3.+3.j, 4.+4.j]) # Rank 1 >>> dist.all_gather(tensor_list, tensor) >>> tensor_list [tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 0 [tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 1 rrr%NcSs"g|]}|s|nt|qSr)rrr)r)r*r szall_gather..) rrrorrrr}rrxrr)rrrVrr{rr)r)r*r%s 0  r%cCst|dt|dt|r(tddS|s4|nt|}|sJ|nt|}|durpt}|||}n |||}|r|S|dS)au 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 Examples: >>> # All tensors below are of torch.int64 dtype. >>> # We have 2 process groups, 2 ranks. >>> output_tensor = torch.zeros(2, dtype=torch.int64) >>> output_tensor [tensor([0, 0])] # Rank 0 and 1 >>> tensor = torch.arange(1, dtype=torch.int64) + 1 + rank >>> tensor tensor([1]) # Rank 0 tensor([2]) # Rank 1 >>> dist.all_gather_base(output_tensor, tensor) >>> output_tensor tensor([1,2]) # Rank 0 tensor([1,2]) # Rank 1 .. warning:: `_all_gather_base` is experimental and subject to change. It is the caller's responsibility to ensure the output_tensor is correctly sized. r/rG_all_gather_baseN) rrorrrr}rrx_allgather_baser)rGr/rVrr{rr)r)r*rHs(%   rHcCst|rtddSt|dt|ts0td|D]}t|dq4dd|D}dd|D}|dur|t}|||}n |||}|r|S| dS) a Gathers input tensors from the whole group in a list in a coalesced manner. Complex tensors are supported. Args: output_tensor_lists (list[list[Tensor]]): Output list. It should contain correctly-sized tensors to be used for output of the collective. input_tensor_list (list[Tensor]): Tensors to be broadcast from current process. At least one tensor has to be non empty. 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 Example: we have 2 process groups, 2 ranks. rank 0 passes: input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]] output_tensor_lists = [[[[-1, -1], [-1, -1]], [-1], [-1, -1]], [[[-1, -1], [-1, -1]], [-1], [-1, -1]]] rank 1 passes: input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]] output_tensor_lists = [[[[-1, -1], [-1, -1]], [-1], [-1, -1]], [[[-1, -1], [-1, -1]], [-1], [-1, -1]]] both rank 0 and 1 get: output_tensor_lists = [[[1, 1], [1, 1]], [2], [3, 3]], [[3, 3], [3, 3]], [5], [1, 1]]]. 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 all_gather_coalesced 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. all_gather_coalescedNrz?Invalid function argument: output_tensor_lists should be a listrcSsg|]}dd|DqS)cSs"g|]}|s|nt|qSr)rrr)r)r*rrz3all_gather_coalesced...r)rr)r)r*rsz(all_gather_coalesced..cSs"g|]}|s|nt|qSr)rrr)r)r*rs) rorrrr0rrgrxallgather_coalescedrr)rrrVroutput_tensor_listr{rr)r)r*rJ`s.0    rJcCs&||kr|s"tdn |r"tddS)Nz?Argument ``gather_list`` must be specified on destination rank.zHArgument ``gather_list`` must NOT be specified on non-destination ranks.)r1)r9rr6r)r)r*r7sr7c Cst|d|rt|dng}t|r2tddSt}t|||||krR|gng}|g}t}||_|dusz|tj urt } | |||} nt ||} | |_| |||} |r| S| dS)a Gathers a list of tensors in a single process. Args: tensor (Tensor): Input tensor. gather_list (list[Tensor], optional): List of appropriately-sized tensors to use for gathered data (default is None, must be specified on the destination rank) dst (int, optional): Destination rank (default is 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 Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group rr6r8N)rrrorrrr7r rrXrWrxr8rvr) rr6rrVrr9r3 input_tensorsrr{rrr)r)r*r8s,    r8c Cst|d|rt|dng}t|r2tddSdd|D}|sL|nt|}t}||kr~|sptd|g}|g}n|rtdg}|g}t }||_ |dus|t j urt } | |||} nt||} | |_ ||||} |r| S| dS) a Scatters a list of tensors to all processes in a group. Each process will receive exactly one tensor and store its data in the ``tensor`` argument. Complex tensors are supported. Args: tensor (Tensor): Output tensor. scatter_list (list[Tensor]): List of tensors to scatter (default is None, must be specified on the source rank) src (int): Source rank (default is 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 Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group rrErFNcSs"g|]}|s|nt|qSr)rrr)r)r*r szscatter..z;Argument ``scatter_list`` must be specified on source rank.zDArgument ``scatter_list`` must NOT be specified on non-source ranks.)rrrorrrr}rrr1rrrXrWrxrFrvr) rrErrVrr9rMr3rr{rrr)r)r*rFsH   rFcCs`t|rtddSt}||_|dur>t}||||}n||||}|rT|S|dS)a Reduce and scatter a list of tensors to the whole group. Only nccl backend is currently supported. Each tensor in ``output_tensor_list`` should reside on a separate GPU, as should each list of tensors in ``input_tensor_lists``. Args: output_tensor_list (List[Tensor]): Output tensors (on different GPUs) to receive the result of the operation. Note that ``len(output_tensor_list)`` needs to be the same for all the distributed processes calling this function. input_tensor_lists (List[List[Tensor]]): Input lists. It should contain correctly-sized tensors on each GPU to be used for input of the collective, e.g. ``input_tensor_lists[i]`` contains the reduce_scatter input that resides on the GPU of ``output_tensor_list[i]``. Note that each element of ``input_tensor_lists`` has the size of ``world_size * len(output_tensor_list)``, since the function scatters the result from every single GPU in the group. To interpret each element of ``input_tensor_lists[i]``, note that ``output_tensor_list[j]`` of rank k receives the reduce-scattered result from ``input_tensor_lists[i][k * world_size + j]`` Also note that ``len(input_tensor_lists)``, and the size of each element in ``input_tensor_lists`` (each element is a list, therefore ``len(input_tensor_lists[i])``) need to be the same for all the distributed processes calling this function. 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. reduce_scatter_multigpuN)rorrrr rxreduce_scatterr)rLZinput_tensor_listsrrVrrr{rr)r)r*rN: s,rNcCs|t|dt|dt|r(tddSt}||_|durVt}||g|g|}n||g|g|}|rp|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. 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_listrON) rrrorrrr rxrOr)rPrQrrVrrr{rr)r)r*rOy s  rOcCstt|dt|dt|r(tddSt}||_|durRt}||||}n||||}|rh|S|dS)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. rPinput_reduce_scatter_baseN)rrorrrr rxrSr)rPrRrrVrrr{rr)r)r*rS s  rSc Cst|rtddSt}t|dt|d|r@t|}|rRt|}|dur^gn|}|durngn|}|durt}||||||}n||||||}|r|S| dS)a Each process splits input tensor and then scatters the split list to all processes in a group. Then concatenate the received tensors from all the processes in the group and return single output tensor. Complex tensors are supported. Args: output (Tensor): Gathered cancatenated 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: >>> 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 all_to_all_singleNrPrR) rorrr rrr}rrx alltoall_baser) rPrRZoutput_split_sizesZinput_split_sizesrVrrr{rr)r)r*rT s.`      rTcCst|rtddSt}t|dt|ddd|D}dd|D}|durht}||||}n||||}|r~|S|dS)aW Each process 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: >>> 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 all_to_allNrLrcSs"g|]}|s|nt|qSr)rrr)r)r*r szall_to_all..cSs"g|]}|s|nt|qSr)rrr)r)r*r s)rorrr rrxalltoallr)rLrrVrrr{rr)r)r*rVD s&Z  rVcCst|rtddSt}|dur\t|tjkrBtdt|t|t rT||_ ntd|durxt }|j |d}n |j |d}|r|S| dS)a Synchronizes 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. Valid only for NCCL backend. Returns: Async work handle, if async_op is set to True. None, if not async_op or if not part of the group rNzFFunction argument device_ids not supported for the selected backend {}z>Invalid function argument: device_ids type should be List[int])r)rorrr rr,r8rgr2r0r device_idsrxrr)rVrrXrr{rr)r)r*r s.  rcCsVt|rtddSt|tjkr*td|dur6t}|durDtn|}|j||dS)a Synchronizes all processes similar to ``torch.distributed.barrier``, but takes a configurable timeout and is able to report ranks that did not pass this barrier within that 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 purposees, 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:: >>> # 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. monitored_barrierNz7monitored_barrier is only implemented for GLOO backend.)wait_all_ranks) rorrrr,r7rgrrxrY)rVrjrZZ group_to_user)r)r*rY s3rYrcCs6td|}t||}t||||d}t||}|S)N:r)PG_WRAPPER_STORE_PREFIXrrr)rrrirhrkrjprefixZ helper_pgr)r)r*r+ s   rc Cs"t}t|\}}|}|}|s*|}|durt|}t|} | |krRtd|D]} | dksj| |krVtdqV||vr||} qd} ntt |}|} |} t |}t | | |||||d} ddt |Dt | <|t jkrtn6t|||| tjkrt| t jt jfvr| | S)a Creates 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:: Using multiple process groups with the ``NCCL`` backend concurrently is not safe and the user should perform explicit synchronization in their application to ensure only one process group is used at a time. This means collectives from one process group should have completed execution on the device (not just enqueued since CUDA execution is async) before collectives from another process group are enqueued. 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): Timeout for operations executed against the process group. Default value equals 30 minutes. This is applicable for the ``gloo`` backend. For ``nccl``, this is applicable only if the environment variable ``NCCL_BLOCKING_WAIT`` or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the process will block and wait for collectives to complete before throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set, this is the duration after which collectives will be aborted asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT`` will provide errors to the user which can be caught and handled, but due to its blocking nature, it has a performance overhead. On the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set. 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. Returns: A handle of distributed group that can be given to collective calls. Nz^the new group's world size should be less or equal to the world size set by init_process_grouprzRThe new group's rank should be within the the world_size set by init_process_group)rrjcSsi|]\}}||qSr)r))rrurqr)r)r*r sznew_group..)rxrZrhrysortedrrgindexrrr,rr,r\r9rrmrXrYrr7r8r) ranksrjrrr{Zdefault_backendrrqZglobal_world_sizeZgroup_world_sizerhrurr)r)r* new_group< s\=       racCstjstd|dur$tj}t}||kr:td||dkrNtdg}d}t||D]`}||} | |} tt| | } t| |||d} | | t } | | vrb| }t d | | qb||fS)a Creates GPU 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 CUDA 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:: This API only works when CUDA is available. .. warning:: If ``group_size`` is passed in, the world size must be divisible by ``group_size``. If no ``group_size`` is passed in, and not all the machines have the same number of devices, the subgroup division will be different across nodes and can cause unexpected behaviors. .. warning:: Using multiple process groups with the ``NCCL`` backend concurrently is not safe and the user should perform explicit synchronization in their application to ensure only one process group is used at a time. This means collectives from one process group should have completed execution on the device (not just enqueued since CUDA execution is async) before collectives from another process group are enqueued. See `Using multiple NCCL communicators concurrently `_ for more details. 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): Timeout for operations executed against the process group. Default value equals 30 minutes. This is applicable for the ``gloo`` backend. For ``nccl``, this is applicable only if the environment variable ``NCCL_BLOCKING_WAIT`` or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the process will block and wait for collectives to complete before throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set, this is the duration after which collectives will be aborted asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT`` will provide errors to the user which can be caught and handled, but due to its blocking nature, it has a performance overhead. On the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set. 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. Returns: The subgroup containing the current rank, and all the subgroups used for cleanup. Examples: >>> # Create intra-machine subgroups. >>> 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([8]) # Assume 8 is the number of CUDA devices per machine. >>> # Cleanup. >>> for subgroup in subgroups: >>> dist.destroy_process_group(subgroup) 4Subgroups can only be created when CUDA is availableNz3The arg 'group_size' must not exceed the world sizerz0The world size must be divisible by 'group_size'r`rjrr"Rank {} is assigned to subgroup {})r}r is_availabler1 device_countrdrrrarrrbrcr2)r1rVrjrrrk subgroups cur_subgroupZ subgroup_idZ start_rankZend_rankZranks_in_subgroupsubgrouprhr)r)r* new_subgroups s:Z     rjc Cstjstd|dus&t|dkr.tdt}g}d}i}|D]r}t||||d} || t} |D]H} | |vrtd | || |||| <| | krl| }t d | |qlqD||fS)a& Creates GPU subgroups by dividing the global world, where 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:: Using multiple process groups with the ``NCCL`` backend concurrently is not safe and the user should perform explicit synchronization in their application to ensure only one process group is used at a time. This means collectives from one process group should have completed execution on the device (not just enqueued since CUDA execution is async) before collectives from another process group are enqueued. See `Using multiple NCCL communicators concurrently `_ for more details. Args: ranks_per_subgroup_list (list[list[int]]): A nested list of ranks of group members. timeout (timedelta, optional): Timeout for operations executed against the process group. Default value equals 30 minutes. This is applicable for the ``gloo`` backend. For ``nccl``, this is applicable only if the environment variable ``NCCL_BLOCKING_WAIT`` or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the process will block and wait for collectives to complete before throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set, this is the duration after which collectives will be aborted asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT`` will provide errors to the user which can be caught and handled, but due to its blocking nature, it has a performance overhead. On the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set. 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. Returns: The subgroup containing the current rank, and all the subgroups used for cleanup. Examples: >>> # Create two subgroups, where each has 2 processes. >>> 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 rbNrz1The arg 'ranks_per_subgroup_list' cannot be emptyrcz/Rank {} has appeared in both subgroup {} and {}rd) r}rrer1rrdrarrr2rbrc) Zranks_per_subgroup_listrjrrrkrgrhZrank_to_ranks_dictr`rir9rhr)r)r*new_subgroups_by_enumeration? s:K   rk)N)N)N)N)Nr)NNr)Nr)NNr)NFr)NF)NF)N)NrN)rNN)rN)NF)NF)NF)NrNF)NrNF)NNNF)NF) contextlibrloggingrpicklererQdatetimertypingrrrrrr}torch._C._distributed_c10drr r r r r rrrrrrrrrZ torch._sixr constantsrrrrrrPicklerr Unpicklerr r ImportErrorrrr getLoggerrBrbr\boolr+rSr,r5rIrFrGZ dist_backendrJZ reduce_oprVrXrZr[r\r&rr`r_rmrorrrvrwr|rrrrrrrrrrxrrrrrrrrdrrrrrcontextmanagerrrrrSUMrrrrrrrrrrr5r:rDr%rHrJr7r8rFrNrOrSrTrVrWrrYrrarjrkr)r)r)r*s.  D         K /         "  2   ! %  ,  9 5 *7G: 8. E  \ n s h G C O 5 J ?%)  u1G