sg $dZddlZddlZddlZddlZddlZddlZddlmZddl m Z m Z m Z m Z mZmZmZmZddlZddlmZddlmZmZmZddlmZddlmZdgZej>e Z!d e d e"d ejFd e fd Z$dejJd e"fdZ&ejNjPejFdfde de)de*d ejFd e f dZ+GddeZ,GddZ-Gddej\Z/GddZ0GddeeZ1y)zZero Redundancy Optimizer.N)chain)AnyCallableDictListOptionalSetTypeUnion)JoinJoinableJoinHook)functional_optim_map) OptimizerZeroRedundancyOptimizervalue non_blockingdevicereturnc t|tjr|j||St|tt fr8|Dcgc]}t |||}}t|tr|St |St|tjjr0|jDcic]\}}|t |||c}}S|Scc}wcc}}w)aQ Recursively searches lists, tuples, dicts and copies tensors to device if possible. Non-tensor values are passed as-is in the result. .. note: These are all copies, so if there are two objects that reference the same object, then after this call, there will be two different objects referenced on the device. )rrr) isinstancetorchTensortolisttuple_recursive_copy_to_device collectionsabcMappingitems)rrrvalvalueskeys d/var/www/html/venv/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyrrs%&xx\x::%$'  &c V T  $E40vCeFmC%001 "KKM  S *,v    L  s C3Cparamc|jS)z]Return if a parameter is trainable, where trainability is equivalent to requiring a gradient.) requires_grad)r's r& _is_trainabler*As   cpuobjsrc_rankgroupctj|k(rtj}t j ||t |j}t jt|gj|}t j|j|}tj|||dtj|||d|St jdgj|}tj|||dt jt|jgtj |}tj|||dtj|j#j%}t j&||d}|S)a> Broadcasts an object to the given group. It will be sending the object if called from the source rank and receiving the object otherwise. Arguments: obj: object to broadcast; only used if called on the source rank. src_rank (int): source rank. group (``ProcessGroup``, optional): group used for the broadcast (default: ``dist.group.WORLD``). device (``torch.device``, optional): device to send from or receive to (default: ``torch.device("cpu")``). Returns: The broadcasted object. F)srcr/async_oprdtyper) map_location weights_only)distget_rankioBytesIOrsave bytearray getbuffer LongTensorlenr ByteTensor broadcastemptyintitemuint8r,numpyload) r-r.r/rbufferdata length_tensordata_send_tensordata_recv_tensors r&_broadcast_objectrMFsP. }}(" 3))+,((#d)588@  ++D144V< }(%%P 'XUUS J((!-008  }(%%P ;; ##% & 'u{{6  'XUUS,00288:;jjf5I Jr+c$eZdZfdZdZxZS) _ZeROJoinHookc^t|tsJd||_t|y)NzRZeRO join hook requires passing in a ZeroRedundancyOptimizer instance as the state)rrzerosuper__init__)selfrQ __class__s r&rSz_ZeROJoinHook.__init__ts4$ 78  $ 8  r+c8|jjy)z Perform an optimizer step. This step updates the joined process's shard of the parameters and broadcasts those parameters. N)rQsteprTs r& main_hookz_ZeROJoinHook.main_hook|s r+)__name__ __module__ __qualname__rSrY __classcell__rUs@r&rOrOss r+rOc>eZdZdZdedeejdefdZy)_DDPBucketAssignmenta Represent a :class:`DistributedDataParallel` bucket assignment. This means that a (possibly non-strict) subset of the parameters corresponding to a DDP bucket assigned to a rank to update. Attributes: bucket_index (int): index of the bucket determined by the DDP gradient bucket all-reduce order. parameters (List[torch.Tensor]): model parameters in the bucket assigned to this rank. offset (int): offset into the :class:`GradBucket` 's :meth:`parameters` giving the index of the first element in the passed-in ``parameters``; this equivalently indexes into the :class:`GradBucket` 's :meth:`gradients`. device (torch.device): device on which the parameters are stored. tensor (torch.Tensor): flattened tensor giving the data of the parameter subset assigned to the rank. bucket_index parametersoffsetc||_||_||_t|jdk(r t d|jdj |_d|_y)NrEmpty bucket assignment)rarbrcr? ValueErrorrtensor)rTrarbrcs r&rSz_DDPBucketAssignment.__init__sT )$ t 1 $67 7$(OOA$6$=$= .2 r+N) rZr[r\__doc__rCrrrrSr+r&r`r`s1( 3 3& 3 3r+r`ceZdZdZdZdZdZy)_OverlapStatusah Define possible statuses that :class:`ZeroRedundancyOptimizer` can be in when overlapping with :class:`DistributedDataParallel`. Attributes: ``UNINITIALIZED``: The ZeRO instance is effectively uninitialized and is waiting for DDP to finalize its bucketing. ``DDP_HAS_REBUILT_BUCKETS``: DDP has rebuilt its buckets, meaning that its bucketing is finalized. The ZeRO instance can now collect the necessary information about the DDP bucketing. ``INITIALIZED``: The ZeRO instance is fully initialized and can now optimize parameters. rN)rZr[r\rh UNINITIALIZEDDDP_HAS_REBUILT_BUCKETS INITIALIZEDrir+r&rkrks MKr+rkc(eZdZdZddZddZddZy) _OverlapInfoaP Information needed by :class:`ZeroRedundancyOptimizer` to overlap with :class:`DistributedDataParallel`. Arguments: world_size (int): world size of the process group being used. Attributes: shard_buckets (bool): if ``True``, then the assignment of each :class:`DistributedDataParallel` bucket is partitioned across possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e. across possibly multiple ranks) to approximate uniformity following a threshold given by the total parameter size divided by the world size; if ``False``, then each bucket is wholly assigned to a single :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank); this should be set to the value passed into the hook constructor. status (_OverlapStatus): current status; see :class:`_OverlapStatus` for more information. params_per_bucket (List[List[torch.Tensor]]): ``params_per_bucket[i]`` gives the model parameters in the ``i``th bucket. params_per_rank (List[List[torch.Tensor]]): ``params_per_rank[i]`` gives the model parameters assigned to the ``i``th rank, where the parameters are grouped by increasing bucket indices. offsets (Dict[int, int]): maps from bucket index to the offset in ``self.params_per_rank[rank]`` giving the index of the first parameter in that bucket, where ``rank`` is this process's own rank; the keys of this :class:`dict` are the bucket indices assigned to this rank. num_bucket_assignments (int): total number of bucket assignments across all ranks; this is equal to the number of :class:`DistributedDataParallel` gradient buckets if ``shard_buckets=False`` and possibly greater otherwise. total_size (int, optional): total size of all buckets (i.e. sum of ``param.numel()`` for all ``param`` across all buckets) if ``shard_buckets=True``; otherwise, ``None``. broadcast_handles (List[Work]): :class:`list` of async work handles for the parameter broadcasts. bucket_index_to_future (Dict[int, torch.futures.Future]): :class:`dict` mapping bucket index to the corresponding all-reduce future. bucket_index_to_bucket (Dict[int, dist.GradBucket]): :class:`dict` mapping bucket index to the corresponding bucket. bucket_indices_seen (List[int]): :class:`list` of the bucket indices seen on this iteration. Nctj|_d|_g|_t |Dcgc]}gc}|_i|_g|_d|_ d|_ g|_ g|_ i|_ i|_ycc}w)NFr)rkrnstatus shard_bucketsparams_per_bucketrangeparams_per_rankoffsetsassigned_ranks_per_bucketnum_bucket_assignments total_sizebroadcast_handlesbucket_indices_seenbucket_index_to_futurebucket_index_to_bucket)rT world_size_s r&rSz_OverlapInfo.__init__s&4&B&B #(<>FKJFW9X"9X') 9;&+,#)--/.0 GI#BD#:Ys A;c t|j|jk(sJdtj|jDcgc]}|j }}|jj ycc}w)a Wait for all parameter broadcasts. This function should be called once all broadcasts have been scheduled, meaning ``self.broadcast_handles`` is filled. This clears ``self.broadcast_handles`` in preparation for the next iteration. z.Missing at least one broadcast handle on rank N)r?r}r{r7r8waitclear)rTxrs r&wait_for_broadcastsz _OverlapInfo.wait_for_broadcastsss && '4+F+F F N ;DMMO;L M N F#55 6!QVVX 6 6 $$& 7s Bc|jj|jj|jjy)z Clear the data structures that are modified per-iteration. This function should be called at the end of an iteration. N)r~rrrrXs r&clear_per_iter_infoz _OverlapInfo.clear_per_iter_infos:   &&( ##))+ ##))+r+rN)rZr[r\rhrSrrrir+r&rrrrs+ZE( ',r+rrc eZdZdZ d=deedeedededef dZ d>d Z d e e efd dffd Z d?d ed dfdZdeeej$d dfdZd e e efdeeej$d dfdZ d@deeeej$d eee fdZed e ej$effdZed e ej$effdZed eej$fdZdefdZdZed e ej8eeej$ffdZ d@deedeeed efdZdedeej$ded ed!eeed df d"Z ed ee ee!ffd#Z" dAd$eeeej$d%ee#ge$fd&ed ee$fd'Z% d@d%ee#ge$fd&ed ee$fd(Z&d)Z'ed ej8fd*Z(ed efd+Z)d,e e efd dffd- Z*d e e efffd. Z+e,d/ee eefd0ee eefd dfd1Z-d>d2Z.d>d3Z/d4ed e0eej$ee1ffd5Z2d>d6Z3d eefd7Z4d>d8Z5d>d9Z6ded efd:Z7d;Z8ded efd<Z9xZ:S)Bra' Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. The sharing is done as described by ZeRO_. The local optimizer instance in each rank is only responsible for updating approximately ``1 / world_size`` parameters and hence only needs to keep ``1 / world_size`` optimizer states. After parameters are updated locally, each rank will broadcast its parameters to all other peers to keep all model replicas in the same state. ``ZeroRedundancyOptimizer`` can be used in conjunction with :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak memory consumption. ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number of parameters at each rank. Each parameter belongs to a single rank and is not divided among ranks. The partition is arbitrary and might not match the the parameter registration or usage order. Arguments: params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s or :class:`dict` s giving all parameters, which will be sharded across ranks. Keyword Args: optimizer_class (:class:`torch.nn.Optimizer`): the class of the local optimizer. process_group (``ProcessGroup``, optional): ``torch.distributed`` ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by :meth:`torch.distributed.init_process_group`). parameters_as_bucket_view (bool, optional): if ``True``, parameters are packed into buckets to speed up communication, and ``param.data`` fields point to bucket views at different offsets; if ``False``, each individual parameter is communicated separately, and each ``params.data`` stays intact (default: ``False``). overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is overlapped with :class:`DistributedDataParallel` 's gradient synchronization; this requires (1) either a functional optimizer for the ``optimizer_class`` argument or one with a functional equivalent and (2) registering a DDP communication hook constructed from one of the functions in ``ddp_zero_hook.py``; parameters are packed into buckets matching those in :class:`DistributedDataParallel`, meaning that the ``parameters_as_bucket_view`` argument is ignored. If ``False``, :meth:`step` runs disjointly after the backward pass (per normal). (default: ``False``) **defaults: any trailing arguments, which are forwarded to the local optimizer. Example:: >>> # xdoctest: +SKIP >>> import torch.nn as nn >>> from torch.distributed.optim import ZeroRedundancyOptimizer >>> from torch.nn.parallel import DistributedDataParallel as DDP >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) >>> ddp = DDP(model, device_ids=[rank]) >>> opt = ZeroRedundancyOptimizer( >>> ddp.parameters(), >>> optimizer_class=torch.optim.Adam, >>> lr=0.01 >>> ) >>> ddp(inputs).sum().backward() >>> opt.step() .. warning:: Currently, ``ZeroRedundancyOptimizer`` requires that all of the passed-in parameters are the same dense type. .. warning:: If you pass ``overlap_with_ddp=True``, be wary of the following: Given the way that overlapping :class:`DistributedDataParallel` with :class:`ZeroRedundancyOptimizer` is currently implemented, the first two or three training iterations do not perform parameter updates in the optimizer step, depending on if ``static_graph=False`` or ``static_graph=True``, respectively. This is because it needs information about the gradient bucketing strategy used by :class:`DistributedDataParallel`, which is not finalized until the second forward pass if ``static_graph=False`` or until the third forward pass if ``static_graph=True``. To adjust for this, one option is to prepend dummy inputs. .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. .. _ZeRO: https://arxiv.org/abs/1910.02054 Noptimizer_class process_groupparameters_as_bucket_viewoverlap_with_ddpdefaultsc |j|}|jd|_tj|||t j|i|_i|_g|_g|_ i|_ g|_ |j|_ |jdj|_||nt"j$j&|_t#j*|j(|_t#j.|j(|_t"j2j5|j(|j0|_||_||_|j=||_|s|jAn1tC|j,|_"|rtFjId||_%g|_&|jOg|_(d|_y)zInit.FrNz`parameters_as_bucket_view=True` will be ignored since `overlap_with_ddp=True`; instead, a different bucketing strategy will be usedT))_verify_and_init_params_verify_same_dense_param_type initializedrrSr _param_to_rank_cache_param_to_index_cache_partition_parameters_cache_index_to_param_cache _device_to_params_per_rank_cache"_bucket_assignments_per_rank_cache_get_is_trainable_mask_is_trainable_mask _all_paramsr_default_devicer7r/WORLDrget_world_sizerr8rankdistributed_c10dget_global_rank global_rank_overlap_with_ddp_optim_defaults_get_optimizer_constructor_optim_constructor_init_local_optimizerrr _overlap_infologgerwarningr_buckets_build_param_buckets_all_state_dicts)rTparamsrrrrrs r&rSz ZeroRedundancyOptimizer.__init__ss--f5 **, !42$ >@!>@"=?(9;"  -  /#'"="="? $//299+6MDJJClear the cached data structures giving partition information.N)rrrrrrrrXs r& _clear_cachez$ZeroRedundancyOptimizer._clear_cachesn ((..0 !!'') ""((* ""((* --335 //557r+ param_groupc|jr|jr tdt|||jr|j |j |j}t|t|jjdzk(r|jj |d|jr|jyyy)a% Add a parameter group to the :class:`Optimizer` 's ``param_groups``. This can be useful when fine tuning a pre-trained network, as frozen layers can be made trainable and added to the :class:`Optimizer` as training progresses. Arguments: param_group (dict): specifies the parameters to be optimized and group-specific optimization options. .. warning:: This method handles updating the shards on all partitions but needs to be called on all ranks. Calling this on a subset of the ranks will cause the training to hang because communication primitives are called depending on the managed parameters and expect all the ranks to participate on the same set of parameters. z[ZeroRedundancyOptimizer with `overlap_with_ddp=True` only supports a single parameter grouprlN) rr RuntimeErrorrRadd_param_groupr_partition_parametersrr?optim param_groupsrr)rTrrrUs r&rz'ZeroRedundancyOptimizer.add_param_groups$    6 64   ,       557 BL< C (?(?$@1$DD **<+;<--))+. r+rc |j|j|j|jjt j dgtj |j}g|_t|jD]|}tjj|j|}|j|k(r||jk(rS|jj!t#|jj%dt j&dt)|||j|j}|jj!t#|dt j&d||jk(rGt)|jj%|j*|j|j}S||k7sZt)|||j|j}y)aG Consolidate a list of ``state_dict`` s (one per rank) on the target rank. Arguments: to (int): the rank that receives the optimizer states (default: 0). Raises: RuntimeError: if ``overlap_with_ddp=True`` and this method is called before this :class:`ZeroRedundancyOptimizer` instance has been fully initialized, which happens once :class:`DistributedDataParallel` gradient buckets have been rebuilt. .. warning:: This needs to be called on all ranks. rr3Tr,r)r.r/rN)_check_overlap_initialized_sync_param_groupsrrrrgrErrrwrr7rrrrappendr state_dictrrMr)rTrempty_messengerrrlocal_state_dictrs r&consolidate_state_dictz.ZeroRedundancyOptimizer.consolidate_state_dicts '')  1 14::3J3JK ,, Cu{{4+?+? !#$//*0 D//??""DKyyB499$))001 JJ113)-#(<<#6(9'!,"00#33 ($ ))001,)-#(<<#6499$) --/!%!1!1"00#33 A RZ*'!,"00#33 AW0 r+rxct||jk7r tdt|j}|D]}|D]}||vstdy)a Verify ``params_per_rank`` for :meth:`_partition_parameters`. The verification is done by checking that ``params_per_rank`` has length equal to the world size and that it does not contain any parameters not passed into the :class:`ZeroRedundancyOptimizer` constructor. The parameters in ``params_per_rank`` being a strict subset of those passed into the constructor is valid since some parameters may be frozen. Raises: ValueError: if ``params_per_rank`` does not have length equal to the world size or if it contains a parameter that was not passed into the :class:`ZeroRedundancyOptimizer` constructor. z:`params_per_rank` must have length equal to the world sizezmPassing a new parameter in `params_per_rank` that was not passed into the ZeroRedundancyOptimizer constructorN)r?rrfsetr)rTrxall_params_setrr's r&_verify_params_per_rankz/ZeroRedundancyOptimizer._verify_params_per_rankJso(  4?? 2L T--.% F .$&  r+ct|D]=\}}tj|}||d<|j|j|?y)aR Partition the parameter group ``param_group`` according to ``params_per_rank``. The partition will modify the ``self._partition_parameters_cache``. This method should only be used as a subroutine for :meth:`_partition_parameters`. Arguments: param_group (dict[str, Any]): a parameter group as normally defined in an optimizer state. params_per_rank (list[list[torch.Tensor]]): a :class:`list` of length world size containing :class:`list` s of parameters to assign to each rank. rN) enumeratecopyrr)rTrrxrrrank_param_groups r&_partition_param_groupz.ZeroRedundancyOptimizer._partition_param_grouplsR &o6 LLD&#yy5 )/ X &  , ,T 2 9 9:J K Lr+ch|t|jdk(rt|jDcgc]}gc}|_dg|jz}|jD]}t|jDcgc]}g}}t |ddd}|D]B}|j |}||j|||xx|jz cc<D|j|||jSt|jdk(sJdt|jdk7r td|j|t|jDcgc]}gc}|_|jd}|j|||jScc}wcc}wcc}w) aw Partitions parameters across distributed data parallel ranks. Arguments: params_per_rank (list[list[torch.Tensor]], optional): a :class:`list` of length world size containing :class:`list` s of parameters to assign to each rank; this provides a way to specify a partition manually. If ``None``, the parameters are partitioned according to an internal algorithm. (default: ``None``) Returns: A :class:`list` where each element of the list contains the ``param_groups`` for a rank (which itself is a :class:`list` of :class:`dict`); element 0 corresponds to rank 0, etc.; each rank stores the ``param_groups`` for all ranks for the collective communication in :meth:`step`. Raises: ValueError: see :meth:`_validate_params_per_rank`. RuntimeError: if ``params_per_rank`` is not ``None`` and this :class:`ZeroRedundancyOptimizer` instance is using more than one parameter group. rrc"|jSNnumel)ts r&z?ZeroRedundancyOptimizer._partition_parameters..s QWWYr+T)r%reversezbSpecifying `params_per_rank` should only be done when the parameters have not been partitioned yetrlzCSpecifying `params_per_rank` only supports a single parameter group) r?rrwrrsorted_get_min_indexrrrrr) rTrxrsizesrparam_group_params_per_rank params_sortedr'rs r&rz-ZeroRedundancyOptimizer._partition_parameterss:  "43349@Edoo@V3W1B3W0doo-#'#4#4K$)$//$:? ?/?%+#H-3FPT%M"/5#22593D9@@Gd u{{}4 5 //#%@$33 343349  7 9 t  !Q &U  $$_58=doo8N+O1B+O(''*  ##KA///M4X?:,Ps F%9 F*' F/ct|jdk(rAt|jD]%\}}|D]}|dD]}||j|<'|jS)zW:class:`dict` mapping parameters to their assigned data parallel rank in the partition.rr)r?rrr)rTrrrr's r&_param_to_rankz&ZeroRedundancyOptimizer._param_to_ranks t(( )Q .&/0J0J0L&M @"l#/@K!,X!6@;?11%8@@ @(((r+ct|jdk(r;ttd|jDDcic]\}}|| c}}|_|jScc}}w)z :class:`dict` mapping parameters to their indices in the global optimizer state. NOTE: This assumes that the global optimizer state's indexing (in ``state_dict``) follows a linear ordering over the parameter groups. rc3&K|] }|d ywrNri.0gs r& z:ZeroRedundancyOptimizer._param_to_index..s-Uaak-U)r?rrrr)rTips r&_param_to_indexz'ZeroRedundancyOptimizer._param_to_indexse t)) *a /&e-U4CTCT-U&VW*Aq1*D &))) *s A ct|jdk(r'ttd|jD|_|jS)zSList mapping parameter indices in the global optimizer scheme to the actual params.rc3&K|] }|d ywrrirs r&rz:ZeroRedundancyOptimizer._index_to_param..s?( ?r)r?rrrrrXs r&_index_to_paramz'ZeroRedundancyOptimizer._index_to_paramsF t)) *a /)-?T->->?@*D &)))r+rc D|jrJdg}|jrt|jD]c}||}tjj |j |}|jtj|||j de|S|j|}tjj |j |}|D]H}|dD]>}|jtj|j||j d@J|S)a8 Broadcast the shard of parameters from a given rank to all other ranks asynchronously. Arguments: rank (int): the source rank. Returns: A :class:`list` of async work handles for the ``broadcast()`` s performed to synchronize the parameters. z`_broadcast_params_from_rank()` should not be used if `overlap_with_ddp=True`; instead, the broadcasting should happen in the DDP communication hookT)rgr1r/r2r) rrrr7rrrrrArrI) rTrhandles dev_i_bucketsbucketrrrr's r&_broadcast_params_from_rankz3ZeroRedundancyOptimizer._broadcast_params_from_ranks0))  3 )   ) )!%  &t,"33CC&& NN%'"00!%  8 557=L//??""DK ,  (2ENN#(:: +"&"4"4%)  r+cg}t|jD]"}|j|j|$|Dcgc]}|j }}ycc}w)ai Sync all parameter shards across the ranks. This rank sends its shard of the parameters to all other ranks and receives a shard from each other rank. This is done using ``broadcast()``. Parameters are sent bucket-by-bucket if ``parameters_as_bucket_view=True``and sent parameter-by-parameter otherwise. N)rwrextendrr)rTrrrrs r& _sync_paramsz$ZeroRedundancyOptimizer._sync_paramssV$//* CD NN4;;DA B C& '!QVVX ' ' 'sAc|jsJdt|jdk(rt|j D]\}}|D]w}|dD]m}|j }||jvr0t |jDcgc]}gc}|j|<|j||j|oy|jScc}w)au Return device parameters assigned per rank. :class:`dict` mapping each device to a :class:`list` of the per-rank parameter lists filtered to only include the parameters stored on that device. Each per-rank parameter list gives the parameters assigned to that rank to update. This is used for constructing the parameter buckets if ``parameters_as_bucket_view=True``. Let ``dev_i`` denote the ``i``th device for this rank. Then: ``dev_0`` maps to a list containing: rank 0's assigned parameters stored on ``dev_0``, rank 1's assigned parameters stored on ``dev_0``, ... ``dev_1`` maps to a list containing: rank 0's assigned parameters stored on ``dev_1``, rank 1's assigned parameters stored on ``dev_1``, ... ... zT`_device_to_params_per_rank` should only be used if `parameters_as_bucket_view=True`rr) rr?rrrrrwrr)rTrrrr'rrs r&_device_to_params_per_rankz2ZeroRedundancyOptimizer._device_to_params_per_rank*s4--  / - t44 5 :&/0J0J0L&M "l#/ K!,X!6!&!)N)NN,1$//,BM'(MDAA&I==fEdKRR!   444 Ms C r$disallowed_indicescd}td}t|D]\}}|r||vr ||ks|}|}|dk\sJd|S)aq Return ``values.index(min(values))``, except only uses one pass. It also excludes any indices in ``disallowed_indices`` if provided. Arguments: values: (List[int]): :class:`list` of values. disallowed_indices (Optional[Set[int]]): indices that are disallowed from being the returned min index. rinfrzAll indices are disallowed)floatr)rTr$r min_index min_valuerrs r&rz&ZeroRedundancyOptimizer._get_min_indexVsd %L !&) HAu!a+=&=y !    A~;;;~r+ra bucket_params bucket_offset assigned_rankrzc|j}t|dk(r td|j}|j}t ||||j ||<|j|k(rt||||<||j|||j||jxjdz c_ y)a Assign ``bucket_params`` to the rank with the least size assigned so far and collects relevant information. The model parameters given by ``bucket_params`` represents a (possibly non-strict) subset of the parameters corresponding to a :class:`DistributedDataParallel` bucket. Arguments: bucket_index (int): index of the :class:`DistributedDataParallel` gradient bucket. bucket_params (List[torch.Tensor]): subset of the parameters corresponding to the bucket to assign. bucket_offset (int): offset giving the index of the first element in ``bucket_params`` in the bucket's full parameter list. assigned_rank (int): group rank to assign to. assigned_ranks_per_bucket (List[Set[int]]): :class:`set` of group ranks assigned to each bucket. rrerlN) rr?rfrxryr`rrraddr{) rTrarrrrz overlap_inforxrys r&_assign_bucket_subset_to_rankz5ZeroRedundancyOptimizer._assign_bucket_subset_to_rankps2)) }  "67 7&66&& !}m L // >     } ,$' (F$GGL ! &--m<!,/33MB 11Q61r+c ,|jsJdt|jdkDr |jS|j}|jt j k(sJt|jDcgc]}ic}|_|j}|jrN|jJd|j|jz }t|jDcgc]}d}}t|}t|Dcgc] }tc}|_ |j}|jsZt|D]@\}} t| dkDsJd|j|} |j!|| d| |B|jSt#t|d} | D]\}} t| dkDsJdd} d} t| D]e\}}|j%}| |zk\rC|| kDr>|j'||} |j!|| | || | ||| xx| z cc<|} d} | |z } g|j'||} |j!|| | d| | ||| xx| z cc<|jScc}wcc}wcc}w)z Return DDP bucket parameters assigned per rank. :class:`list` of length world size consisting of :class:`dict` s mapping bucket indices to :class:`_DDPBucketAssignment` s for each rank. zF`_bucket_assignments_per_rank` only be used if `overlap_with_ddp=True`rNz`total_size` was not computed Empty bucketc,td|dDS)Nc3<K|]}|jywrrrrs r&rzYZeroRedundancyOptimizer._bucket_assignments_per_rank....s?Xa ?Xrl)sumrs r&rzFZeroRedundancyOptimizer._bucket_assignments_per_rank..sC?XSTUVSW?X?1Q?M?+, AF{AS1TA#%1T .$0$J$J!))/89J/K + m=)A-=~=- $ 7 7 E 22 !!-  z666W&,+,2X& "0F& @+ m=)A-=~=- ! "#*3M*B3&K"'++-K'+5B'-7(,(;(;)+D\+R) ::()- D))5 &m4G4(3 *+#{2O+30!% 3 3!#<\#J! 22 !-.1!!- m,?,M& @P666W3W@2Us: J& J  J gradientsclosurekwargsc tj||j}||jk7rC|jr t dt jd|j||_|j|j|jj|=||jjdi|n|jjdd|i|}n8|jsJd|Jd|jj|}|j|jj|j|S)a Perform a single optimizer step without syncing parameters across ranks. Arguments: gradients (list[Optional[torch.Tensor]], optional): a :class:`list` of length equal to the number of parameters assigned to this rank containing gradient tensors or ``None`` as its elements; a ``None`` in the :class:`list` indicates that the corresponding parameter should not be updated. If the argument itself is ``None``, then all parameters are updated, and the gradients are assumed to be already populated. (default: ``None``) closure (Callable): a closure that re-evaluates the model and returns the loss; optional for most optimizers and should be ``None`` if ``gradients`` is not ``None``; (default: ``None``) Returns: Optional loss depending on the underlying local optimizer. .. warning:: The argument ``gradients`` should only be specified (i.e. not ``None``) if ``overlap_with_ddp=True``, in which case :class:`ZeroRedundancyOptimizer` wraps a functional optimizer. zqZeroRedundancyOptimizer with `overlap_with_ddp=True` does not support changing parameter trainability at run timezsZeroRedundancyOptimizer detected that the trainable parameters changed; rebuilding the parameter buckets if enabledrzGSpecifying `gradients` should not be used when `overlap_with_ddp=False`zB`closure` is not supported when using a local functional optimizer)rri) r notify_join_contextrrrrrrrrrrrW)rTrrris_trainable_masklosss r& _local_stepz#ZeroRedundancyOptimizer._local_stepsK:   & 779  7 7 7%%" NN   % % '&7D #  1 14::3J3JK  ?  )&)$TZZ__?W??  )) 8 )  TS T::??Y?7D  7 79J9JK r+c |jrtjdy|jdd|i|}|j |S)a Perform a single optimizer step and syncs parameters across all ranks. Arguments: closure (Callable): a closure that re-evaluates the model and returns the loss; optional for most optimizers. Returns: Optional loss depending on the underlying local optimizer. .. note: Any extra parameters are passed to the base optimizer as-is. zQ`step()` should not be included in the training loop when `overlap_with_ddp=True`Nrri)rrrrr)rTrrrs r&rWzZeroRedundancyOptimizer.stepAsR  ! ! NN*  t::6:  r+c t|S)a~ Return the ZeRO join hook. It enables training on uneven inputs by shadowing the collective communications in the optimizer step. Gradients must be properly set before this hook is called. Arguments: kwargs (dict): a :class:`dict` containing any keyword arguments to modify the behavior of the join hook at run time; all :class:`Joinable` instances sharing the same join context manager are forwarded the same value for ``kwargs``. This hook does not support any keyword arguments; i.e. ``kwargs`` is unused. )rO)rTrs r& join_hookz!ZeroRedundancyOptimizer.join_hook`s$T""r+c|jS)zReturn default device.)rrXs r& join_devicez#ZeroRedundancyOptimizer.join_devicets###r+c|jS)zReturn process group.)rrXs r&join_process_groupz*ZeroRedundancyOptimizer.join_process_groupys!!!r+rc|j|djD]\}}|j|}|j||jk7r d|d|<:t |d|j |jj|<|jj|jD]Y\}}tj|s|jdk(s0|j|jj||<[t|9||j|d|j |j|j |jj y)ad Load the state pertaining to the given rank from the input ``state_dict``, updating the local optimizer as needed. Arguments: state_dict (dict): optimizer state; should be an object returned from a call to :meth:`state_dict`. Raises: RuntimeError: if ``overlap_with_ddp=True`` and this method is called before this :class:`ZeroRedundancyOptimizer` instance has been fully initialized, which happens once :class:`DistributedDataParallel` gradient buckets have been rebuilt. stateNTrrr)rr"rrrrrrr!r is_tensordimr,rRload_state_dictrr)rTrindexrr' state_name state_valuerUs r&r$z'ZeroRedundancyOptimizer.load_state_dict~sG '')&w/557 PLE5((/E""5)TYY6-1 7#E*+DU\\+   '04zz/?/?/F/L/L/NP+J {3 8IQ8N>Ioo>O ((/ ;P P  +  > :D))ENN W') '00E0E&F - "D"!1.!A "&"<"<">t"D )*c#/ NM N:="$7: -5!#5 '8&A# 28 < ./3!4_^_8;'8-3%|),? - - -:#6*W*=*C*C*E#FG 7r+src_param_groupsdst_param_groupsct|t|k(sJdt||D]-\}}td|jD] }||||< /y)a Sync the attributes from the source parameter groups to the destination parameter groups. Example attributes include learning rate or scheduler attributes. The two parameter groups should have the same length (i.e. same number of parameter groups). Arguments: src_param_groups (list[dict]): parameter groups giving the attribute settings to copy. dst_param_groups (list[dict]): parameter groups giving the attribute settings to set. zBMismatch between number of source and destination parameter groupsc |dk7S)Nrrirs r&rz.s hr+N)r?r)filterkeys)r4r5src_param_groupdst_param_groupattrs r&rz*ZeroRedundancyOptimizer._sync_param_groupss~$#$ )   P O P 144DFV0W > ,O_68L8L8NO >(7(=% > >r+c|jr |jryt|j}t |Dcgc]}gc}|_t |jjD]H\}\}}|D]9}d}d}g} |D]k} t| s.| jjj| _ n$|| jz }| j| | j}m|dk(rtj d|} ntj"|||} d} | D]f} | | jz} | | | j%| jj'| | | j)| j| _ | } h|j |j| <Kycc}w)ab Build parameter buckets if ``parameters_as_bucket_view=True``. For each device that stores this rank's parameters, there is a bucket (represented as a tensor) containing all of the parameters on that device that are assigned to a given rank in the parameter update partition. This method is called in the constructor and any time parameter trainability is changed. .. warning:: The current implementation assumes that all of the parameters in a bucket are of the same dense type when allocating the bucket's tensor. .. warning:: If the model parameters are stored across more than one device, then the storage partitioning must be the same across all processes in order for parameter synchronization to work. Nrrl)rr3)rrr?rrwrrr"r*rIdetachclonerrr4rzerosrBcopy_flattenview_as)rT num_devicesrdev_irrxr bucket_sizer4trainable_paramsr'rrc offset_nexts r&rz,ZeroRedundancyOptimizer._build_param_bucketss,--1G1G $99: %*;%788 09  + + 1 1 31  4 ,E,FO* 4 #% #(E(/&+ZZ%6%6%8%>%>%@ #u{{}4 (//6!KKE(!#"[[6:F#[[E&QFF!1-&,u{{}&< vk2889K9K9MN%+F;%?%G%G %S !, -  e$++F35 4 49s F=c(|jD]}|jD]}|j}d}d}|D]3}t|sJd||j z }|j }5|dkDsJdt j|||j}d}|D]f}||j z} ||| j|jj||| j|j|_ | }h||_ y)a/ Build the DDP bucket with parameters assigned to this rank. For each DDP bucket with parameters assigned to this rank, flattens the data of those parameters into a single tensor and saves the tensor to the ``tensor`` attribute in the corresponding :class:`_DDPBucketAssignment` instance stored in ``self._bucket_assignments_per_rank``. :class:`DistributedDataParallel` guarantees that the parameters corresponding to a gradient bucket have the same device and the same dtype. rNzUModel parameter corresponding to a gradient in a DDP bucket should require a gradientrr3)rr$rbr*rr4rrBrrArIrBrCrg) rTbucket_assignmentsbucket_assignmentrrFr4r'rgrcrHs r&_build_ddp_param_bucketsz0ZeroRedundancyOptimizer._build_ddp_param_buckets;s2#'"C"C 2 %7%>%>%@ 2!*55 #(E(/-/  5;;=0K!KKE(#Q66u5F5M5M#)E"(5;;="8K6+.44UZZ5G5G5IJ!'{!;!C!CEJJ!OEJ(F ) ,2!(1 2 2r+rcPt|tjr!tdtj| t |}t |dk(r tdd}d}|D]2}|t|tjz}|t|tz}4|s |s td|r ||_ |S|r;g|_ |D]/}d|vr td |jj|d1|S#t$r'}tdtj||d}~wwxYw) a Verify the type of ``params`` and initializes ``self._all_params`` as a :class:`list` of all parameters. The initializagtion will first make sure that provided ``params`` is valid. Arguments: params (Any): Candidate parameter list or parameter groups to verify. Raises: TypeError: ``params`` has an invalid type. ValueError: ``params`` is empty. Returns: The persistent form of ``params`` to be passed into the parent :class:`Optimizer` constructor -- i.e. returns ``params`` as a :class:`list` to ensure that it can be iterated over again. z<`params` argument should be an iterable of Tensors, but got zE`params` argument should be an iterable of Tensors or dicts, but got Nrz3ZeroRedundancyOptimizer got an empty parameter listTz;`params` argument should be an iterable of Tensors or dictsrzkEach parameter group passed-in via `params` must have a 'params' key mapping to the parameters in the group) rrr TypeErrortypenamerr?rfr*rr)rTr all_paramse all_tensors all_dictsr'rs r&rz/ZeroRedundancyOptimizer._verify_and_init_paramsdsa* fell +$$)NN6$:#;=  fJ z?a RS S   1E :eU\\: :K E40 0I 19M  )D !D ) ? ;.$$   '' H(=> ?= &&+nnV&<%=?  s C55 D%>"D  D%ctj|jd}|jdjrt d||jddD]-}tj|}||k7st d|d|y)a Verify that all parameters are of the same dense type. The method assumes that ``self._all_params`` has been initialized and is non-empty. Raises: ValueError: ``params`` contains sparse parameters or parameters of varying dense types. NOTE: This method can be removed once support for sparse parameters and varying parameter types is added. rz[ZeroRedundancyOptimizer only supports using the same dense type for all parameters but got rlNz`ZeroRedundancyOptimizer only supports using the same dense type for all parameters but got both z and )rrOr is_sparserf)rTrOr'other_typenames r&rz5ZeroRedundancyOptimizer._verify_same_dense_param_types>>$"2"21"56   A  ( (B*  %%ab) E"^^E2N) //7j%&( r+cHttt|jS)z[Return a boolean mask indicating if each parameter is trainable (``requires_grad``) or not.)rmapr*rrXs r&rz.ZeroRedundancyOptimizer._get_is_trainable_masksC t'7'7899r+c|jJd|j|j}|jrt |dk(sJd|dd}dt j |jjvr'|j|fi|jddi|_ nBtjd |j|j|fi|j|_ tjtjjk7rt!d |D}t |j"|j$}tj'd |j$|||j$dk(rktj'd t |j(j*|j(j,n"|j|fi|j|_ |jrYt/|jd sCt/|jdsJd|jj0g|j_|j5|jj2|j2y)z Initialize this rank's local optimizer, responsible for its subset of the parameters. The local optimizer is saved in ``self.optim``. Nz*The local optimizer class has not been setrlzNInitializing the local functional optimizer with more than one parameter grouprr_allow_empty_param_listTz|%s does not support the argument `_allow_empty_param_list`; ZeroRedundancyOptimizer may error due to an empty parameter listc3<K|]}|jywrrrs r&rz@ZeroRedundancyOptimizer._init_local_optimizer..s!<!'')!  ! !|$) J )"!_X.F*$$T%<%<=HHI#:$"9"9#"22#LP# ;++  #:$"9"9&"YDDXDX"Y ##%)<)<<!!,,d,,\OOTOb d5<<01   DLS>L<@ellAS &7 &7P\7d4=Q8Q3R.S\7\7@=A15HD%,,!789H(2u9-.H H % HX26(2u9-. % >#($U\\$$"C""$L$sCx.$LT$LL>DcN>@>tCH~.>tCH~.> >>2;4z'2R:: tELL!4:- .:x>:T :FLP % .s .s ..7#7#7r+)2rhrrenumr\r9logging itertoolsrtypingrrrrrr r r rtorch.distributed distributedr7!torch.distributed.algorithms.joinr r rtorch.distributed.optim.utilsr torch.optimr__all__ getLoggerrZrrlrrrr*r/rrCobjectrMrOr`IntEnumrkrrrrir+r&r}s6 "  HHH FF>! % %   8 $     LL  F$JJ$$'5<<. * ** * LL *  *ZH&"3"3JT\\&X,X,v[i[r+