sg2 tUddlZddlmZmZmZmZmZddlZddlm Z ddl m Z ddl mZddlmZddlmZddlmZdd lmZmZmZmZmZmZmZdd lmZmZm Z m!Z!m"Z"m#Z#dd l$m%Z%m&Z&d gZ'ee(e)d <dejTdefdZ+dedefdZ,dedede!fdZ-de(dede"fdZ.de(dedede"fdZ/de(dejTde"fdZ0de(defdZ1dZ2dZ3de(dedeedeefdZ4d ede fd!Z5de(d"edee"fd#Z6dedefd$Z7dejTdeefd%Z8de(d&ed'edeefd(Z9d ee(efdefd)Z:d*ed+ed,ed-efd.Z;y)/N)AnyCallablecastDictList)_get_device_module) ShardMetadata) ShardedTensor)DTensor)%compute_local_shape_and_global_offset)BytesStorageMetadataChunkStorageMetadata MetadataIndexSTATE_DICT_TYPE STORAGE_TYPESTensorPropertiesTensorStorageMetadata) LoadItemTypeReadItemSavePlanTensorWriteData WriteItem WriteItemType)"_check_shard_metadata_pair_overlap+_shards_get_overlap_region_wrt_saved_tensor create_read_items_for_chunk_list__all__tensorreturnc ttjdgt|j z|j S)Nroffsetssizes)rtorchSizelensize)rs _/var/www/html/venv/lib/python3.12/site-packages/torch/distributed/checkpoint/planner_helpers.py_create_chunk_from_tensorr*'s5  A3V[[]!334FKKM shard_mdcttj|jtj|jSNr")rr%r& shard_offsets shard_sizes)r,s r)_chunk_for_shardr1-s3  8112jj--. r+sharded_tensorc|jj}t|j|j|j |j |j}tt|||jjS)N)dtypelayout requires_grad memory_format pin_memorychunk propertiesr() metadatatensor_propertiesrr4r5r6r7r8rr1r()r2r,shard_propertiesr;s r)_sharded_tensor_metadatar?4s|&..0BB!$$&&&44&44#.. J x(  $ $ & + + r+fqnc t|j|j|j\}}t j |t j |}}t t||tjtt||tj|j|jS)Nr"r9indextype tensor_data)r shape device_mesh placementsr%r&rrrSHARDrrrcreate_from_tensorto_localr()r@rr$r#s r)_create_write_items_for_dtensorrLHs: f((&*;*;NE7ZZ& 7(;7E C)  #&(::6??;LM    r+ctj|j}tt ||t j t||S)NrB)r%r&r/rrrrIr?)r@r2r,r#s r)_create_write_item_for_shardrN\sBjj//0G C)  ,^XF r+c :tjdgt|jz}t t ||t jtt||jtj||jS)Nrr"r9rB) r%r&r'r(rrrTENSORrrrrJ)r@rr#s r)_create_write_item_for_tensorrQgsqjj!s6;;=112G C)  ! !#&wfkkmL'::6B  r+bytescJtt|tjS)N)rCrD)rrrBYTE_IO)r@rRs r)_create_write_item_for_bytesiorUts C  " " r+c ttj|tj|f|tj|ftj|fSN)rD dest_index dest_offsets storage_indexstorage_offsetslengths)rrrTr%r&rX dest_offsetrZstorage_offsetlengths r)_create_read_item_for_byteiora{sK   ! !ZZ/# N#45 F9%  r+c ttj|tj||tj|tj|SrW)rrrPr%r&rXrYrZr[r\s r)_create_read_item_for_tensorrdsD   ZZ -# ?3 7#  r+ checkpoint_md local_chunksc g}t|D]\}}t|jD]\}}t||sg}g} g} t||D]:\} } } }|j | | j | | j |<|j t t ||j|| t ||j||| |S)aW Create a list of ``ReadItem`` based on the checkpoint and local chunks. This applies the resharding algorithm and computes the reads needed to satisfy ``local_chunks`` with a checkpoint described by ``checkpoint_md``. Args: fqn (str) : The state_dict FQN to pass to ``ReadItem``. checkpoint_md (TensorStorageMetadata): metadata for a given tensor from a checkpoint. local_chunks (List[ChunkStorageMetadata]): Local chunks that needs to be loaded. Returns: A list of ``ReadItem`` that will satisfy all input chunks. ) saved_shard current_shardrc) enumeratechunksrrappendrdrr#)r@rerf read_itemsidxshard storage_idx storage_mdr[rYr\dimoffset_for_saved_tensoroffset_for_current_tensorr`s r)rrs*J - U'01E1E'F  #K5eZH OLG =&e '') &&'>?##$=>v& '   ,,S%--E!-"/Z5G5G"U$3#  ' : r+ state_dictc g}|jD]\}}t|tr|jt ||2t|t r<|j jD]}|jt||| ~t|tjr|jt|||jt||t|S)N)items isinstancer rlrLr r<shards_metadatarNr%TensorrQrUr)rurequestsr@objr,s r)"_create_default_metadata_only_planr}sH$$& FS c7 # OO;CE F ] +LLN:: R .dtensor_func$s$/ U\\&) )//FFHMMK 0=LLNF %//0@P (( !-- ++jjl||~ GNLr+ct|dd}|tjdk(rtdt |d|S)NrrzFound unsupported type z for meta device loading.)rr%r RuntimeErrorrD)rrs r)sharded_tensor_funcz-_init_state_dict..sharded_tensor_func9sF$/ U\\&) ))$u+6OP Lr+c4t|dd}|tjdk(rrtjj j }ttjt|j}tj||}|S|S)Nrrr) rr%rrrrrDrrrr)rrrrs r) tensor_funcz%_init_state_dict..tensor_funcBs{$/ U\\&) )//FFHMMK 0=LLNF%%eF;FMLr+N)r rr%rz_iterate_state_dict)rurrrs r)_init_state_dictrs@ G*3 5<<  r+ iter_objectrrrc *t|tr||St|tr||St|tjr||St|t t tttjfs||St|tr+|jD]\}}t||||||<|St|ttfr8|Dcgc]}t||||}}t|tr t|}|Sycc}w)a$ Iterate through the state dict, applying the given functions to each tensor type and update the state dict in place. Args: iter_object (Any): the target state_dict. sharded_tensor_func (Callable): the function to apply to ShardedTensor dtensor_func (Callable): the function to apply to DTensor tensor_func (Callable): the function to apply to Tensor # TODO: let state_dict_util._iterate_state_dict() to support in place option so we don't need to have two versions of _iterate_state_dict. N)rxr r r%rzintfloatstrrRioBytesIOdictrwrlisttuple)rrrrkeyrvrets r)rrVs(+w'K(( K /";// K .;'';eS% DE   K &%++- JC2|%8+ K   K$ /!  <1Dk R   k5 )*C  0 sD)rs 22 +;A,Q 99c9ell7K }1E!-:(g)( +7D s ELL Y C  4 4(4+,4 (^ 4n ? x  =S =# =$y/ =w3Gu||5I0J& C ]  h .4c3h4C4n,,,", ,r+