VhBddlZddlZddlZddlZddlmZmZmZddlm Z m Z m Z m Z m Z mZddlZddlmZddlmcmcmZddlmcmcmZddlmcmcmZddlmZddlm Z ddl!m"Z"m#Z#ddl$m%Z%ddl&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-ddl.m/Z/m0Z0m1Z1m2Z2dd l3m4Z4dd l5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mm?Z?dd l@mAZAdd lBmCZCddlDmEZEe rddlFmGZGdZH ddlImJZJmKZKeMdZNdZOePejejfZRe eejeRfZSe;je2je;je2je;je2je;je2je;je2jiZYe;je;jgZZe;je;jfZ[e d_de(deSde;de e?de e#de(f dZ\e de(deSde#de(fdZ]e de de^fdZ_e de#de^fdZ`e deMdejfd Zae d!ejdeMdejfd"Zbd!ejdeMdePejejffd#Zce d_de(d$ejd%e eej<jd&ee eej<je eej<jfde(f d'Zfd&ege d(e^ddfd)Zhe de(d$ejd*eiejd+e eeMejfde(f d,Zke de(d$ejde(fd-Zle de(de e;d.e e:d/e e7d0e^d1e^d2eMd3eMde(fd4Zme de(de(fd5Zne de(d6e6d7e^de(fd8Zoe d_de(de#de(fd9Zpe de(de(fd:Zqd$ejd;egejddfd<Zre de(d=ejd+e eeMejfd>e e ejgdfd?e^de(f d@Zse de(d;egejd=ejfdAZtdBejdCe eej<jdeiejfdDZu d_dBej<jd%eiej<jdEe eej<jdeiej<jfdFZvdBej<jd%eiej<jdeiewfdGZxdBejdeiewfdHZyd$ejd*eiejd+e eeMejfddfdIZzd+e eeMejfdJeMdKe'de ejfdLZ{d$ejd*eiejd%eiejdePe^e^ffdMZ|dBejd>e ejgdfd%eiejddfdNZ}dBejdOe ejd%eiejdKe'fdPZ~dBejd%eiejdegejfdQZd$ejd*eiejdReiejdOe ejddf dSZd;egejdTegejdOe ejddfdUZdVZd$ejd*eiejdOe ejdJeMdKe'dejf dWZd$ejd;egejdejddfdXZdYegejddfdZZd$ejd*eiejdeejfd[Zd*eiejddfd\Zde;fd]Zdejde jfd^Zy#eL$rdZHYwxYw)`N) GeneratorIterableIterator)AnyCallable no_type_checkOptional TYPE_CHECKINGUnion) default_hooks)_mesh_resources DeviceMesh)_get_default_group)_FSDPDeviceHandle _FSDPState_get_module_fsdp_state_is_fsdp_flattened!_named_parameters_with_duplicatesclean_tensor_name TrainingState)_FSDP_USE_FULL_PREC_IN_EVAL FlatParameterFlatParamHandleHandleShardingStrategy)_FreeEventQueue)BackwardPrefetch CPUOffloadFullOptimStateDictConfigFullStateDictConfigMixedPrecisionShardingStrategyStateDictConfig StateDictType)_Policy)DTensorExtensions)_sync_params_and_buffers)is_traceable_wrapper_subclass)RemovableHandleT) deferred_initfakeFi _fsdp_syncedstate process_groupsharding_strategypolicy device_meshreturnc(| | td|tv}|r#|||td|dt|||}n4|r||_|j d|_n||n t |_|j j|_|j j|_ |j}|r||jjz}tjj||_||jz |_|S)NzcCannot pass both process_group and device_mesh at the same time. Please just pass only one of them.zManual wrapping with zA requires explicit specification of process group or device_mesh.rmesh_dim) ValueErrorHYBRID_SHARDING_STRATEGIES*_init_process_group_state_for_hybrid_shard _device_mesh get_groupr-rranksize world_size_inter_node_pgr DefaultState_get_gradient_predivide_factor_gradient_predivide_factor_gradient_postdivide_factor)r,r-r.r/r0is_hybrid_strategydata_parallel_world_sizes R/home/dcms/DCMS/lib/python3.12/site-packages/torch/distributed/fsdp/_init_utils.py_init_process_group_staterEYsI [%< <  +.HH  V^ 8K'(9':;SS  ?}kE !,E "-"7"7"7"CE "/!: @R@T  $$))+EJ**//1E$// E$8$8$=$=$?? ""AA $  $ !5#C#CC % Lc|rYt|r6||_|jd|_|jd|_nt d|j |@t}t||jj\}}||_||_n2t|r|\|_|_nt dt|t|j|_|S)Nrr3z,Expected device_mesh to have ndim=2 but got zmExpected process_group to be passed in as either None or Tuple[dist.ProcessGroup, dist.ProcessGroup] but got r-)"_is_valid_hybrid_shard_device_meshr8r9r=r-r5ndimr!_init_intra_and_inter_node_groups_device_handle device_count_is_valid_hybrid_shard_pg_typetype_get_default_comm_hook_state_inter_node_state)r,r-r0 default_groupintra_node_groupinter_node_groups rDr7r7s   -k :!,E $/#8#8!#8#DE "-"7"7"7"CE >{?O?O>PQ   *, -N 5//<<>. **// *- 89F 5E !5GGKMGZF[]  ;**E LrFcjt|txr"t|dk(xrtd|DS)Nc3PK|]}t|tj ywN) isinstancedist ProcessGroup).0pgs rD z1_is_valid_hybrid_shard_pg_type..sJb 2t001J$&)rZtuplelenallrIs rDrOrOs: =%( K  ! # K JMJ JrFcDt|txr|jdk(S)NrW)rZrrK)r0s rDrJrJs k: . H;3C3Cq3HHrFnum_devices_per_nodec6tj|\}}|S)aU Return a process group across the current node. For example, given each row is a distinct node: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 This API would return an intra-node subgroup across [0, 1, ..., 7] or [8, 9, ..., 15] depending on the process's rank. For example, rank 3 would get [0, 1, ..., 7]. )r[ new_subgroups)reintra_node_subgroup_s rD_init_intra_node_process_grouprjs!"//0DE rFglobal_process_groupcTd}tj|}tj|}||z}tj||z}t |D]?}t |Dcgc] }|||zz } }tj | |} ||k(s>| }A| J|d|Scc}w)a Return an inter-node process group where each contained rank has the same local rank. For example, given each row is a distinct node: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 This API would return inter-node process group [0, 8], [1, 9], [2, 10], and so forth depending on the process's rank. For example, rank 1 would get [1, 9], rank 5 would get [5, 13]. N)ranksbackendz. expected to assign inter-node pg, but did not)r[ get_backendget_world_sizeget_rankrange new_group) rkre inter_node_pgsharding_backendr< num_nodes my_local_rank local_rankiranks_for_inter_groupgrps rD_init_inter_node_process_groupr|s M''(<=$$%9:J22IMM"67:NNM01 =B9=M! 89J!22 3! ! nn#8BRS  &M   $ /GH $ ! s%B%c0t|t||fS)a Initialize intra and inter-node process groups and return the ones corresponding to this process's rank. This function can be used to initialize process groups for ``HYBRID_SHARD`` or ``_HYBRID_SHARD_ZERO2`` in FSDP. This function assumes each node has an equal number of CUDA-enabled devices. Returns: Tuple[dist.ProcessGroup, dist.ProcessGroup]: Intra and inter-node process group. )rjr|)rkres rDrLrLs# '';<&';=QR rFmoduleignored_modulesignored_statesc| | tdd}|du}|rt|}t|dng}t| t|ngdt|dkDr"t |dt j r|}n|}t|||_t||j||_ t||j|_ |S)NzfCannot pass both ignored_modules and ignored_states at the same time. Please just pass ignored_states.TFr) r5list_check_ignored_statesrbrZnn Parameter_get_ignored_modules_ignored_modules_get_ignored_params_ignored_params_get_ignored_buffer_names_ignored_buffer_names)r,r~rrignored_parameterspassed_as_ignored_statesignored_states_lists rD_init_ignored_module_statesrs"~'A :  -T9">2148 %4%@D !b%  !# )!,bll ;!4 1O1&/JE/ E #< #E LrFrct|dk(ry|r`td|D}td|D}|s9|s6t|Dchc] }t|c}t}t d|yytd|Ds6t|Dchc] }t|c}t}t d|ycc}wcc}w) z Check that the ignored states are uniformly parameters or uniformly modules. We may remove this check in the future if we permit mixing. rNc3PK|]}t|tj ywrY)rZrrr]r,s rDr_z(_check_ignored_states..IsUUE2<<8Ur`c3PK|]}t|tj ywrYrZrModulers rDr_z(_check_ignored_states..JsS5*UBII6Sr`)keyzUignored_states expects all nn.Parameter or all nn.Module list elements but got types c3PK|]}t|tj ywrYrrs rDr_z(_check_ignored_states..SsLE:eRYY/Lr`z>ignored_modules expects nn.Module list elements but got types )rbrcsortedrPreprr5)rr all_params all_modulesr, sorted_typess rDrr>s >aUnUU SNSS +!N"K54;"KQUVL**69 #.zL^LL!N"K54;"KQUVL%( M #L#Ls B; Cignored_params device_idc6d}|1t|tjr|ntj|}|t||D]|}|jjdvr| |j}+|jj|jk7sOt d|jd|jj|xstj j}|jdk(r t dtj||_ |S)a< Determine device handle used for initializing FSDP. If a device is specified by ``device_id``, then returns device handle corresponds to that device type. Otherwise, If the module is already on a non-CPU device, then the device type is that non-CPU device type. If the module is on CPU or meta, then the device type is the current accelerator device. See the :ref:`Accelerators` for details. This method will be called once ignored paramters was determined, as the device handle maybe needed for other initialization. N>cpumetazLFSDP does not support modules with different device types but got params on z and rzOFSDP needs a non-CPU accelerator device, but no accelerator device is detected.) rZtorchdevice_get_orig_paramsrP RuntimeError_C_get_acceleratorr from_devicerM)r,r~rrdetermined_deviceparams rD_init_device_handler[s()U\\2 i(   %fn= E||  O3 ($)LL!<<$$(9(>(>>&-->-C-C,DE%,,J[J[I\^ .L1J1J1L  ! !U *a -889JKE LrFct||_i}|jD]\}}t|}|j||<!||_|SrY)_get_buffer_names _buffer_names named_buffersrdtype_buffer_name_to_orig_dtype)r,r~r buffer_namebuffers rD_init_buffer_staters_ ,F3E :<%335? V' 4 28,,";/?(BE$ LrFmixed_precision cpu_offloadlimit_all_gathersuse_orig_paramsbackward_prefetch_limitforward_prefetch_limitc|jdk(rO|tjk7r+tjd|xstj dtj}n/|tjk(rtjdt d|xstj |_|xs t|_ |5tjjdt|jtjj!t"dd k(|_|xs t'|_||_||_t.j0|_d|_t7|_t;j<|_tAjB|j>|||_"d|_#i}||_$d} | |_%g} | |_&|S) NrHz/FSDP is switching to use `NO_SHARD` instead of z since the world size is 1.zoThe `NO_SHARD` sharding strategy is deprecated. If having issues, please use `DistributedDataParallel` instead.) stacklevelz'torch.distributed.fsdp.mixed_precision.1)'r<r!NO_SHARDwarningswarn FULL_SHARD FutureWarningr.r rrr_log_api_usage_oncestrosenvirongetr_use_full_prec_in_evalrrr_use_orig_paramsrIDLEtraining_state_is_rootr_free_event_queuer[get_debug_level _debug_levelexec_order_utils_ExecOrderData_exec_order_data_unshard_event_fully_sharded_module_to_handle_handleparams) r,r.rrrrrrrrrs rD_init_core_staters 1  0 9 9 9 MMA$C(8(C(CDE''  -55 .77 7  <   0N3C3N3NE+?~/?E" $$5c%:O:O6P5Q R  2B73>  $3z|E/E,E(--EEN-/E--/E-<< E  EIK#,KE)*.GEM"$FEL LrFcfg}||_g}||_g}||_d|_d|_d|_|S)NT)_root_pre_forward_handles_pre_forward_handles_post_forward_handles_sync_gradients _comm_hook_comm_hook_state)r,rrrs rD_init_runtime_statersK8:&?E#24!5E35"7E EE!E LrFbackward_prefetchforward_prefetchc"||_||_|SrY)rr)r,rrs rD_init_prefetching_staters 0E-E LrFctj|}|r+||jk7rt|j|_|Sd|_|SrY)r get_root_meshr8r%rM_fsdp_extension)r,r0 root_meshs rD_init_extensionrsO --k:IyE$6$66 1%2F2F G L!% LrFctj|_t}t |_||_i}||_|SrY)r#FULL_STATE_DICT_state_dict_typerr_optim_state_dict_config_state_dict_config_unshard_params_ctx)r,state_dict_configunshard_params_ctxs rD_init_state_dict_staters?*::E)<)>%=%?E"0E57 2E LrFrc|D]O}t|jdk(sd}|jD] \}}||us |}n|sJtd|dy)z Verify if the parameters are accepted by FSDP. The only restriction now is that the parameter cannot be a scalar tensor (param.shape == []). rrz/FSDP doesn't support scalar parameters. Change z& to a 1D tensor with numel equal to 1.N)rbshapenamed_parametersr5)r~rr param_namenameparam_s rD_verify_managed_paramsrs~   u{{ q J & 7 7 9  fF?!%J  :$%KM  rFfully_sharded_module param_init_fnsync_module_statescrt|j|t|jj}t |jj \}}|s|r|t||j nA|r#t||j jn|rtj|fdj D chc]}|jD]} | } }} t|j| |t|j|jj_tt!|j} t#|| |r@t%|| j&j(t*vrt%|| j,t/| |Scc} }w)zHInitialize a ``FlatParamHandle`` from a module ``fully_sharded_module``.c>t|duxr|jvSrY)rr) submoduler,s rDz0_init_param_handle_from_module..Ls('=i'HD'P(8!7!77rF)check_fn)_check_single_device_moduler_get_device_from_device_idr:rM_need_to_materialize_moduler_materialize_with_param_init_fn_materialize_meta_moduler)materialize_modulebuffers_move_module_to_device_get_compute_devicecompute_devicerrr_sync_module_params_and_buffersr-r.r6r=_init_param_handle_from_params) r,rrrrdevice_from_device_idis_meta_moduleis_torchdistX_deferred_initignored_modulerignored_buffersmanaged_paramss ` rD_init_param_handle_from_moduler-s 4e6K6KYW65::u333Ne33U5K5K3/N/ 5=;T' -1G1G     " "   %(( 8 $44 $,,.  O    /    E*+?AVAVWXN/@' .%2E2E   " "&@ @ +$ne6J6J #5.:NO L?s F3c t|dk(ryt|||jt|j|j j |jj|jj|jj|j|j|j }|j|jrJ|j j#|j$||_||j&|j(<t+j,d}|j j r,|j$j,|k7r|j/|yyy)Nr)fsdp_extensionr)rbrr SHARDING_STRATEGY_MAPr.roffload_paramsr param_dtype reduce_dtypekeep_low_precision_gradsr-rrshardrrappend flat_paramr_fully_sharded_modulerr flat_param_to)r,rrhandle cpu_devices rDrrrs%  6{a  e556 (( )) ** 66  ,, F LLN}}  LL))*EMJPE))&*F*FGe$J ''F,=,=,D,D ,RZ(-S'rF root_modulerc d} | t|n t}|D]V}t|tj j st|dt|zt|sMtd|jD])}tj|r|j|+|Dchc]3}|jD]}t|tjs| 5}}}||vrtj d|jD]9}t|} | t#| dsJ|j%| j&;|S#t$r }t|dt|z|d}~wwxYwcc}}w)ah Check that ``_ignored_modules`` is an iterable of ``nn.Module`` s without any FSDP instances. Return the modules contained in their module subtrees as a :class:`set`. Nested FSDP instances are excluded, but their already-computed ignored modules are included. ``_ignored_modules`` represents the argument passed by the user to FSDP. z>`ignored_modules` should be an iterable of `torch.nn.Module`s Nzbut got zbut got an iterable with z1`ignored_modules` should not include FSDP moduleszTrying to ignore the top-level module passed into the FSDP constructor itself will result in all parameters being ignored and is not well-supported: r)set TypeErrorrPrZrrrrr5modulestraversal_utils _composableadd fsdp_fileFullyShardedDataParallelrrhasattrupdater) r$r msg_prefixignored_root_moduleser~childrroptional_fsdp_states rDrrsRJQ%5%AC !su  'R&%((//2J+DT&\N)SST T !& )PQ Q R%%'-**62 $ $V ,-+ ^^% %!C!CD  O o%  228 ; !((*I 4Y?  *.0BC CC  " "#6#G#G H I I Q x5E0F/G%HHIqPQ$sE78F F%FFrct}|Dchc]%}|jD]}t|r|'}}}|j||,|Dchc]}t|r|}}|j||j D]9}t |} | t | dsJ|j| j;|Scc}}wcc}w)z Return the parameters of the modules in ``ignored_modules`` and the parameters in ``ignored_parameters``. :class:`FlatParameter` s are excluded from the result. r)r& parametersrr/r(rr.r) r$rrall_ignored_paramsmpparams_in_ignored_modulesparams_in_ignored_parametersrr4s rDrrs36%#!ALLN!'(BTUVBW! !!78%)( 1CA1FA( $(  !!">?!((*K 4Y?  *.0AB BB  % %&9&I&I J K '!( s#C C C%Cc t}|Dchc]}|jD]}|}}}|j|jDchc]\}}||vr t |c}}|j D]9}t |}|t|dsJ|j|j;|Scc}}wcc}}w)z6Return the cleaned buffer FQNs in ``ignored_modules``.r) r&r r/rrr(rr.r) r$rall_ignored_buffer_namesr8rbuffers_in_ignored_modulesrrr4s rDrrs *-("aiik",2"""##(3'@'@'B # V33 k * !((*W 4Y?  *.0GH HH $ + +,?,U,U V W $#'"  s B>C cf|jDchc]\}}t|c}}Scc}}w)zrReturn the fully prefixed names of all buffers in the module hierarchy rooted at ``root_module`` as a class:`set`.)rr)r$rris rDrr s5>I=V=V=X +9;+&  s-ct||Dchc]}|j}}t|dk(r%tjd|vr| t dyt|dkDrt d|ycc}w)z Raise an error if ``module`` has original parameters on multiple devices, ignoring the parameters in ``ignored_params``. Thus, after this method, the module must be either fully on the CPU or fully on a non-CPU device. rWrNzTTo support a module with both CPU and GPU params, please pass in device_id argument.rHz;FSDP only supports single device modules but got params on )rrrbrr)r~rrrdevicess rDrrs*:&.)QRu||RGR 7|qU\\%0G;  5   W I' S   SsA4r: device_handlec ^|yt|tjr|ntj|}|jdk7ri|j]t j d|d|d|jd|jd tj|j}|S)z Return a ``torch.device`` for the specified ``device_id``. Processes ``device_id`` and returns either the corresponding device or ``None`` if ``device_id`` is ``None``. Nrz"FSDP got the argument `device_id` z on rank zJ, which does not have an explicit index. FSDP will use the current device z6. If this is incorrect, please explicitly call `torch.zk.set_device()` before FSDP initialization or pass in the explicit device index as the `device_id` argument.)rZrrrPindexrrcurrent_device)rr:rBrs rDrr.s 5<<8 ell9>U {{e 4 0 9f00=0L0L0N/OPCCI;;-P1 1 m::<= MrFctt||}td|D}|jD]-}||vr|j dD]}||j z}/| xrt xrtd|D}||fS)z Return if ``module`` has parameters on meta device and if ``module`` is using torchdistX deferred initialization. At most of the returned bools can be ``True``. If either is ``True``, then ``module`` needs to be materialized. c34K|]}|jywrY)is_metar]rs rDr_z._need_to_materialize_module..XsC5CsFrecursec3FK|]}tj|ywrY)r*is_fakerIs rDr_z._need_to_materialize_module..ds@ U#@s!)rranyr(r rH_TORCHDISTX_AVAIL)r~rrrrrbufrs rDrrKs*6>BCNCNCCN^^%*  ' $$U$3 *C ckk )N **  A  A @@ @ 6 66rFct|std|dt|t||}|D] }|| y)Nz Expected z to be callable but got )callabler5rP_get_modules_to_materialize)r$rrmodules_to_materializer~s rDrrisV M " &>tM?R>S T  9oV(frFrc :|xs#tj|j}t||}d} tj5|D]u}t j |jd|jd}tt|dkD}|sS|j|d|jw dddy#1swYyxYw#t$r5} tjdt!| dt#|d| d} ~ wwxYw)NFrJr)rrKzIUnable to call `reset_parameters()` for module on meta device with error z(. Please ensure that your module oftype z* implements a `reset_parameters()` method.)rrrErSno_grad itertoolschainr6r rbrto_emptyreset_parameters BaseExceptionrrrrP) r$rrrBmaterialization_devicerTr~module_state_iterhas_module_statesr2s rDrrws3ell$$&79oV F]]_ .0 .%.OO%%e%4fnnUn6S%!%(->(?$@1$D!$OO+A5OQ++- . . . .   !!$Q)L>!K M  s<C AC!%CCCCC D%0DDc"g}tj|g}|h}|rq|j}|j||j D]:}||vst |||vs|j ||j|<|rq|SrY) collectionsdequepopleftrchildrenrr+)r$rrTqueuevisited_modulesr~ child_modules rDrSrSs /1   {m ,E'2mO %%f-"OO- +LO3*<8@ 7##L1 \* +  "!rFrc tjd | tj}|j |g}g}|r|j }|j fd|jdD|j fd|jdD|jD].}t|tjr|j |0|r|D cgc] } | |vs|  } } |D cgc] } | |vs|  } } t| | |ytt||d} | | j k(r t!yyycc} wcc} w)a Move ``module`` depending on ``device_from_device_id`` and its current device. This includes moving ignored modules' parameters. - If ``device_from_device_id`` is not ``None``, then this moves ``module`` to the device. - If ``device_from_device_id`` is ``None``, then this does not move ``module`` but warns the user if it is on CPU. Precondition: ``_check_single_device_module()``. rNc3@K|]}|jk(r|ywrYr)r]rr#s rDr_z)_move_module_to_device..s%<<:-FrJc3@K|]}|jk(r|ywrYri)r]rr#s rDr_z)_move_module_to_device..s%==J.rj)rrr`rarrbextendr6r rcrZr,r-_move_states_to_devicenextr_warn_cpu_init)r~rrrrdrr  curr_modulerr9params_to_move bufs_to_moverr#s @rDr r s\$e$J(/:.?.?.A V%'&(--/K MM(33E3B  NN)11%1@  )113 , !)Y-O-OPLL+ ,!&&,Gq/F!GG#*Gaa.FG G~|=RS !&.94 @E U\\Z78 HGs1 E;E EEr c6t|dk(rt|dk(ryt|dkDr|dj}nt|dkDr|dj}tjd}||D]k}tj5|j ||_|j *|j j ||j _dddm|D]}|j ||_y|k(r tyy#1swYxYw)z Move states to the specified device. Precondition: ``_check_single_device_module()`` and module's parameters and buffers have been materialized if needed. rNr)rbrrrVtodatagradro)rr rrEr#rrs rDrmrms  6{aCLA- 6{Q)) W  **e$J( KE K"XX&;< ::)&+jjmm4I&JEJJO K K K  ;F ))$9:FK ; : % &  K Ks A DD c.tjdy)NamThe passed-in `module` is on CPU and will thus have FSDP's sharding initialization run on CPU, which may be slower than on GPU. We recommend passing in the `device_id` argument for FSDP to move `module` to GPU for the sharding initialization. `module` must also be on GPU device to work with the `sync_module_states=True` flag since that requires GPU communication.)rrrFrDroros MM 1rFctt||d}|&|jjdk7r |j}n#t j|j }|||k7rt d|d|d||S)a) Determine and return this FSDP instance's compute device. If the module is already on a non-CPU device, then the compute device is that non-CPU device. If the module is on CPU, then the compute device is the current device. Since this method should be called after materializing the module, any non-CPU device should not be meta device. For now, the compute device is always a CUDA or CUDA-like device with its explicit index. Precondition: ``_check_single_device_module()`` and ``_move_module_to_device()``. Nrz4Inconsistent compute device and `device_id` on rank z: z vs )rnrrrPrrEr5)r~rrr:rBrr s rDr r s* !&.94 @E U\\..%7m&B&B&DE(^?T-TB4&d#8"9 ;   rFc~g}|jD]}t|tdrt|td|j }t |r>|j \}}|Dcgc]}t||} }|j| |j||D]l} | j } t | r>| j \}}|Dcgc]}t| |} }|j| \|j| nt|t||tdycc}wcc}w)z Synchronize module states (i.e. parameters ``params`` and all not-yet-synced buffers) by broadcasting from rank 0 to all ranks. Precondition: ``sync_module_states == True`` and ``self.process_group`` has been set. FTr)srcN) r getattr FSDP_SYNCEDsetattrdetachr'__tensor_flatten__rlr+_check_module_states_for_sync_module_statesr&PARAM_BROADCAST_BUCKET_SIZE) r~rr- module_statesrdetached_bufferattrsriattr inner_buffersrdetached_param inner_paramss rDr r 4s*)+M.." 6v{E2 FK .$mmoO,_=+==?qLQ RD$!? R R$$]3$$_5 61 ( 8%88:HE1FKLdGND9LLL   .   010 ># !SMs +D5D:rcD|rtd|Dr tdyy)Nc3`K|]&}|jtjdk((yw)rN)rr)r]tensors rDr_z>_check_module_states_for_sync_module_states..cs'17 e,,s,.zThe module has CPU parameters or buffers when `sync_module_states=True`, which requires them to be on GPU. Please specify the `device_id` argument or move the module to GPU before passing it to FSDP.)rNr5)rs rDrr`s7;H C  }rFc#K|j} t|}||vrt|s|#t$rYywxYww)aD Return an iterator over the original parameters in ``module``. The iterator does not return the parameters in ``ignored_params``, any ``FlatParameter`` s (which may be present due to nested FSDP wrapping), or any original parameters already flattened (only relevant when ``use_orig_params=True``). N)r6rnr StopIteration)r~r param_genrs rDrrmsT!!#I OEN*3Ee3L     sA 4 AAAAc t|D]A\}}||vs t|rtd|d|jd|jy)a5 Check that original parameters in ``fsdp_module`` have been flattened. The flattened parameters are made invisible to ``named_parameters()`` for the module hierarchy rooted at ``fsdp_module``. This should be called as a sanity check after flattening the wrapped module's parameters. z Found an unflattened parameter: z;  N)rrrr; __class__) fsdp_modulerrrs rD_check_orig_params_flattenedrs^?{K E  &/A%/H2:,b::<.%//!24 rFch|tjk(rtjStjSrY)r!rr allreduce_hookreduce_scatter_hook)r.s rD_get_default_comm_hookrs3  0 9 9 9 $$ . .rFc.tj|S)NrI)r r>rIs rDrQrQs  % %M BBrFrY)r`rWrrcollections.abcrrrtypingrrrr r r rtorch.distributed distributedr[(torch.distributed.fsdp._exec_order_utilsfsdp_exec_order_utilsr'torch.distributed.fsdp._traversal_utils_traversal_utilsr)2torch.distributed.fsdp.fully_sharded_data_parallelfully_sharded_data_parallelr,torch.nnr(torch.distributed.algorithms._comm_hooksr torch.distributed.device_meshr r"torch.distributed.distributed_c10dr$torch.distributed.fsdp._common_utilsrrrrrrr"torch.distributed.fsdp._flat_paramrrrr%torch.distributed.fsdp._limiter_utilsrtorch.distributed.fsdp.apirrrrr r!r"r#torch.distributed.fsdp.wrapr$&torch.distributed.tensor.parallel.fsdpr%torch.distributed.utilsr&torch.utils._python_dispatchr'torch.utils.hooksr(rO torchdistxr)r* ImportErrorintrr}rar\HybridShardProcessGroupTypeProcessGroupTyperr SHARD_GRAD_OP HYBRID_SHARD_HYBRID_SHARD_ZERO2rr6#NO_RESHARD_AFTER_FORWARD_STRATEGIESrEr7boolrOrJrjr|rLrrrrrr&rrrrrrrrrrrrrrrrrrrrrrSTensorr rmror r rrrrr>rQrxrFrDrs  99OO CCAAFFBEA B   0D<F1.""34 #D$5$5t7H7H$HIE$"3"35P"PQR 5>>!7!B!B""$:$H$H!!#9#F#F((*@*T*T !!(( ""(('# )- 0 0#0(0 W  0 *% 0  00f( (#(( ((V#$IJI4II  ARAR  !++!! !!H++ 4  d// /0& + + II+huxx78+%((,,-.%((//9R0SS +++\I9= :- - II- %-c5<</01 -  --`  II"> > 01>n->*% >  >  >!> >>>B         '     : J *  *299d2<<6HT&A A))Ac5<</01AHbii[$%678 A  A  AAH) )  )))))<66x896 ^6xBF)!%((*<*    D$$)$ X$:299S II  % c5<</01   <c5<</01 %ell :7 II7 %7^7 4: 7<  RYYK-. ^    #ELL1 ^ % F""-0^" "))_",3 II3 %3&3$ELL1 3  3l   %,, $ELL1 @ II %$ELL1   %   \\ F) II)  )$$) )X   %      II  % bll , % (.>C$$CCO#s^^^