
    sgX3                     v   d dl Z d dlmZmZmZmZmZmZmZ d dl	Z	d dl
mZ d dlmZ d dlmZ d dlmZ d dlmZ d dlmZ d dlmZ d d	lmZ d d
lmZmZmZm Z m!Z!mZm"Z" d dl#m$Z$m%Z% d dl&m'Z'm(Z( d dl)m*Z* d dl+m,Z, d dl-m.Z.m/Z/m0Z0 d dl1m2Z2 d dl3m4Z4 d dl5m6Z6 d dl7m8Z8 ee9eeee:      ee:   f   f   Z;dgZ<d)de:de9de9fdZ=	 d*deej|                     defdZ?de	j                  deAfdZB	 d)dedee:   de9de	j                  fdZCd e!dee;eej|                     f   fd!ZD G d" d#e      ZE	 d*d$e!d%e9d&e,d'ee%   de!f
d(ZFy)+    N)castDictListOptionalSequenceTupleUnion)_get_device_module)ShardedTensor)TensorProperties)Shard)ChunkShardingSpec)unflatten_state_dict)DefaultLoadPlanner)BytesStorageMetadataChunkStorageMetadataMetadataMetadataIndexSTATE_DICT_TYPEr   TensorStorageMetadata)LoadPlanLoadPlanner)_create_read_items create_read_items_for_chunk_list)load_state_dict)StorageReader)_element_wise_add_element_wise_sub_normalize_device_info)_get_default_group)_create_chunk_sharded_tensor)_remote_device)DTensor!load_sharded_optimizer_state_dictglobal_rankdevice_typereturnc                     |dk(  ryt        |      }|j                         rt        || |j                         z        S y)Ncpu)r
   is_availabler   device_count)r%   r&   device_modules      Y/var/www/html/venv/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py_gen_rank_devicer.   5   sH    e&{3M!!#%}'A'A'CC
 	
     pgc                    t         j                  j                  |       j                  }| ;t	        t        j
                               D cg c]  }d| dt        ||        }}nJt	        | j                               D cg c](  }d| dt        t        j                  | |      |       * }}t        dt        t        t        t        t        f      |            S c c}w c c}w )Nrank:/r   dim
placements)distdistributed_c10d_get_pg_default_devicetyperangeget_world_sizer.   sizeget_global_rankr   r   r   r	   r"   str)r0   pg_device_typeidxr6   s       r-   _create_colwise_specrB   @   s     **AA"EJJN	z T0023
 C5*3?@A

 
 RWWY'
 C5*4+?+?C+H.YZ[

 
 U>3#678*E 


s   C -C%valc                    t        |       t        u rt        | j                               dk(  ryt        | j                         d   j                        t        u ryt        | j                         d   j                        t
        u rt        d      yt        |       t
        u rAt        | j                        t
        u st        | j                        t        u rt        d      y)Nr   FTz2Cannot handle DTensor nested insided ShardedTensorzCannot handle nested DTensor)r:   r   lenlocal_shardstensorr#   
ValueError_local_tensor)rC   s    r-   _is_nested_tensorrJ   T   s    CyM!s!"a'  "1%,,->  "1%,,-8QRR
 	 
cg	S7*d33D3D.E.V788r/   propsr=   c                 P   |dk(  r2t        t        j                  t        |      j	                               }n-t        j                  |t        |      j	                               }t        j
                  || j                  | j                  | j                  | j                  |      S )Nr)   )r=   dtypelayoutrequires_grad
pin_memorydevice)
r   torchrQ   r
   current_deviceemptyrM   rN   rO   rP   )rK   r=   r&   rQ   s       r-   _alloc_tensorrU   c   s     eell$6{$C$R$R$TU+K8GGI
 ;;kk||))## r/   
state_dictc                    i }d}| j                         D ]  \  }}d|j                         f||<   t        |      s't        |j	                               dk(  sJ d       t        |t              sJ d       |j	                         d   }|j                  j                  |j                  j                  f||<   |j                  j                  } ||fS )a+  
    Load the right TP slice of the optimizer state.

    This is not easy since the per-tensor slicing can't be inferred from checkpoint metadata.
    We take advantage of the model state_dict producing a sliced ST to figure out what we need to load.
    This is pretty fragile and it might be easier for FSDP to compute this info for us.
    Returns a dictionary where keys are the same of the state_dict and the value is a tuple of
    (offset, size) for the current rank TP slice.
    N.B. The state_dict *MUST* come from FSDP.sharded_state_dict.
    N   z%Cannot handle ST with multiple shardsz$Can only handle nested ShardedTensorr   )itemsr=   rJ   rE   rF   
isinstancer   metadatashard_offsetsshard_sizesrG   _process_group)rV   specsdp_pgkeyvalueshards         r-   _get_state_dict_2d_layoutrd   w   s     #%E)-E &&( 0
UEJJL)c
U#E&&()Q.767.} 656  &&(+E,,**E#J LL//E0" 	 r/   c                        e Zd ZU eeef   ed<   eed<   eed<   deee	e
   f   ddf fdZdefdZd	edej                  f fd
Z xZS )_ReaderWithOffsettranslationrV   r[   fqn_to_offsetr'   Nc                 l    t         |           || _        t        i       | _        i | _        i | _        y N)super__init__rh   r   r[   rV   rg   )selfrh   	__class__s     r-   rl   z_ReaderWithOffset.__init__   s0    * r/   c           	         g }i | _         | j                  j                         D ]  \  }}| j                  j                  |   }t        |t              s|t        |||      z  }A|| j                  vr|t        |||      z  }`| j                  |   }t        |j                               dk(  sJ |j                         d   }t        t        j                  t        |j                  j                  |            t        j                  |j                  j                               g}t#        |t%        t&        |      |      }|D ]  }	|	j(                  j*                  J t-        |	j(                  j*                  |      }
t/        j0                  |	j(                  t        j                  |
            }|| j                   |	j(                  <    ||z  } t3        |      S )NrX   r   )offsetssizes)offset)rg   rV   rY   r[   state_dict_metadatarZ   r   r   rh   rE   rF   r   rR   Sizer   r\   r]   r   r   r   
dest_indexrr   r   dataclassesreplacer   )rm   requestsfqnobjmdrr   original_shardlocal_chunksreqsrioriginal_offsetoriginal_indexs               r-   create_local_planz#_ReaderWithOffset.create_local_plan   s   --/ $	HC2237Bc=1.sB<<$,,,.sB<<'',Fs'')*a/// --/2N$!JJ).*A*A*O*OQWX  **^%<%<%H%HI	L 4T/4lD
  A}}++777"3BMM4H4H&"Q!,!4!4MM%**_*E" 3A  /A HI$	J !!r/   indexc                 V    t         |   | j                  j                  ||            S rj   )rk   lookup_tensorrg   get)rm   r   rn   s     r-   r   z_ReaderWithOffset.lookup_tensor   s&    w$T%5%5%9%9%%GHHr/   )__name__
__module____qualname__r   r   __annotations__r   r   r?   r   intrl   r   r   rR   Tensorr   __classcell__)rn   s   @r-   rf   rf      sm    m]233d3+=&> 4 ("8 ("TI= IU\\ I Ir/   rf   model_state_dictoptimizer_keystorage_readerplannerc                 .   |j                         }t        |       \  }}t        j                  j	                  |      j
                  }t        |      }|fg }	t        t        j                               D ]6  }
t        ||
|j                         z        }|	j                  d|
 d|        8 t        d|	      }nt        |      }i }i }|j                  j                         D ]|  \  }}|j                   |   }|d   |k7  rt#        |t$              rd||<   5|j&                  j)                         dk(  r%t+        |j,                  |j&                  |      ||<   w|mt/        t+        |j,                  |j&                  |      t        j0                         t        j                         |j                         t3                     ||<   |d	   }|j5                  |d|j&                  f      d   }t7        |j,                  j8                  |j,                  j:                  |j,                  j<                  |j,                  j>                  |j,                  j@                  
      }|jC                  tE        jF                  |      |      }g }t        j0                  |      }|jH                  D ]i  }tK        tL        |jN                        jQ                         |k7  r/|j                  tS        t+        |j,                  |jT                  |      |             k tW        jX                  |||      }||v r(||   d    tK        tZ        t\           ||   d         ||<   |||<    t_        |||ta        |      n|       tc        ||j                         }|S )a  
    Load a state_dict in conjunction with FSDP sharded optimizer state.

    This is the current recommended way to checkpoint FSDP.
    >>> # xdoctest: +SKIP
    >>> import torch.distributed.checkpoint as dist_cp
    >>> # Save
    >>> model: torch.nn.Model
    >>> optim_params = model.parameters()
    >>> optim = torch.optim.SGD(optim_params, lr=0.01)
    >>> # Save
    >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
    >>>     state_dict = {
    >>>         "optimizer": FSDP.optim_state_dict(model, optim),
    >>>         "model": model.state_dict()
    >>>     }
    >>>     dist_cp.save_state_dict(
    >>>         state_dict=optim_state,
    >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
    >>>         planner=dist_cp.DefaultSavePlanner(),
    >>>     )
    >>>
    >>> # Load
    >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
    >>>     model_state_dict = model_tp.state_dict()
    >>>     checkpoint = {
    >>>         "model": model_state_dict
    >>>     }
    >>>     dist_cp.load_state_dict(
    >>>         state_dict=checkpoint,
    >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
    >>>         planner=dist_cp.DefaultLoadPlanner(),
    >>>     )
    >>>     model.load_state_dict(checkpoint["model_state"])
    >>>
    >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
    >>>         model_state_dict,
    >>>         optimizer_key="optimizer",
    >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
    >>>     )
    >>>
    >>>     flattened_osd = FSDP.optim_state_dict_to_load(
    >>>        model, optim, optim_state["optimizer"]
    >>>     )
    >>>
    >>>     optim.load_state_dict(flattened_osd)
    Nr2   r3   r   r4   z
<bytes_io>rX   )rank
world_sizenum_devices_per_noder0      )rM   rN   rO   memory_formatrP   )rG   r[   )process_group)rV   r   r   )2read_metadatard   r7   r8   r9   r:   r
   r;   r<   r   r+   appendr   rB   rs   rY   planner_datarZ   r   r=   numelrU   
propertiesr!   get_rankr    r   ShardTensorPropertiesrM   rN   rO   r   rP   build_metadatarR   rt   shards_metadatar   r"   	placementr   r   r]   r   +_init_from_local_shards_and_global_metadatar   r   r   rf   r   )r   r   r   r   r[   layout_specsr`   dp_pg_device_typer,   r6   idevice_infosharding_specrV   rh   ra   rb   key_pathspec_key
alloc_sizer   st_mdrF   current_rankshard_mdsts                             r-   r$   r$      sf   j ++-H34DEL%--DDUKPP&'89M}
t**,- 	9A0!1}'A'A'C#CK aS+78		9
 *aJG,U3 #%J.0M2288: 8!
U((-A;-'e12*JsO ::"+  %**.?JsO ]:e..

<MN]]_..0%2%?%?%A%'JsO  {H%))(T5::4FGJJ.&&,,''..#..<<#..<< ++66J "00J1GTEL==/L!11 
(:(:;@@BlR##,!,,h.B.BDU  "*	
 JJe5B <'L,B1,E,Q%)(3-h9OPQ9R%Sc" JsOq8!v %494E!-07	 &j(2G2GHJr/   )cudarj   )Grv   typingr   r   r   r   r   r   r	   rR   torch.distributeddistributedr7   torch._utilsr
   +torch.distributed._shard.sharded_tensor.apir   0torch.distributed._shard.sharded_tensor.metadatar   r   -torch.distributed._shard.sharded_tensor.shardr   :torch.distributed._shard.sharding_spec.chunk_sharding_specr   )torch.distributed.checkpoint._nested_dictr   ,torch.distributed.checkpoint.default_plannerr   %torch.distributed.checkpoint.metadatar   r   r   r   r   r   $torch.distributed.checkpoint.plannerr   r   ,torch.distributed.checkpoint.planner_helpersr   r   .torch.distributed.checkpoint.state_dict_loaderr   $torch.distributed.checkpoint.storager   "torch.distributed.checkpoint.utilsr   r   r   "torch.distributed.distributed_c10dr    #torch.distributed.fsdp._shard_utilsr!   torch.distributed.remote_devicer"   torch.distributed.tensorr#   r?   r   STATE_DICT_2D_LAYOUT__all__r.   ProcessGrouprB   r   boolrJ   rU   rd   rf   r$    r/   r-   <module>r      s    E E E    + E @ X J K   G K > 
 B L : , Cx'>'M!NNO 
 (
# C S  '+""#(5<< D   FL#+C=?B
\\(""
$*;*;!<<="J7I* 7I| &*	N%NN "N k"	N
 Nr/   