U Jc!* @s"ddlZddlmZmZddlZddlmZddlm Z ddl mm m Z ddlmZmZddlmZmZddlmZddlmZed d d Zeje jeeeeeefd d dZejeje je je eeeeefdddZejeje je je eeeeefdddZdS)N)ListTuple) ProcessGroup)get_split_sizeget_chunked_dim_size) all_to_allall_to_all_single) ShardMetadata)Shard)returncCs2t|D]\}}||kr|SqtddS)a Return the position of the current rank in the given placements. Args: placements(List[Union[_remote_device, str]]): Specifies the placement of each shard of the Tensor. The size of the list represents the number of shards to be created. This could be a list of :class:`torch.distributed._remote_device`'s. This list could also contain a string which represents remote device as accepted by :class:`torch.distributed._remote_device` current_rank (int): number of current device. Returns: A int which contains the position of current device in the placement list. z"current_rank not in the placement.N) enumeraterank RuntimeError) placements current_rankidx placementrS/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/_shard/sharded_tensor/reshard.pyget_idx_from_placementss  r)st_size sharding_spec world_sizer c Cst|j}dg|}g}dgt|}t|||}t|jD]b\}} || t||||} t |} | | |<t t || | d|| <||| 7<q>||fS)a Based the given sharding spec, we calculate the offset and local shard size. We then build a ShardMetadata on top of the calculation result. Args: st_size (torch.Size): The size of the sharded tensor. sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification describing how the tensor is sharded. world_size (int): number of ranks. Returns: A Tuple of the followings: A List[`ShardMetadata`] which contains the metadata for the shard, including offsets, lengths and device placement. A List[int] which contains the ranks in the order of placement. Nr) shard_offsets shard_sizesr) intdimlenrr rappendrrlistr copydeepcopy) rrrZ shard_dimshards_metadataranksoffsets split_sizerrsharded_dim_sizeZlocal_tensor_sizerrrbuild_reshard_metadata/s"  r() local_shardrrresharding_specpgr cCs t|}t|}t|||\}}t|j} t|| |} dg|} t|j|} |j|  } | | | | <dg|}| |}t || | |}||| <| d| }t| }||d<tj||j|jd}t||| ||d}| d| }t|||g}||fS)a Reshuffle the local shard directly when the reshard dim is same as the original sharding dim. Logically we do this in two step: 1. To collect all shards based on original sharding spec. 2. Reshard the tensor based on the given resharding spec. In reality, we consolidate the two steps into one by sending the local tensor to the new shard directly based on the resharding spec. Args: local_tensor (Tensor): Local tensor stored in the current rank. st_size (torch.Size): The size of the sharded tensor. sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification describing how the tensor is sharded originally. resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification describing how the tensor will be resharded. pg (ProcessGroup): The process group to aggregate on. Returns: A Tuple of the followings: A List[`Shard`] which contains the local tensor and its metadata. A List[`ShardMetadata`] which contains the metadata for the shard, including offsets, lengths and device placement. rdevicedtype)input_split_sizesoutput_split_sizesgroup)distget_rankget_world_sizer(rrrrrrsizeindexrZ transpose contiguousr torchemptyr-r.rr )r)rrr*r+rrr#r$ reshard_dimr&r/rZnew_rankr0Znew_idxr'Zgathered_input_sizeZgathered_input local_tensor local_shardsrrrreshuffle_local_shardWs>        r=)r;rrr*r+r cst|}t|}t|j}t|j}t|||\} g} | D]} | | j|q@tfddt t dD} | rg} | D]*} | j |}| j|}| t |||7} q| |t j| |jd}t dg|t|||}d}g} t|jD]n\}}t||||}t|}|||<| |||<t j||j|jd|<| |||krd}qt j|| |d}d d |D}t||d |rfd d | Dt j|d}t|| |g}|| fS) a Reshard a sharded tensor given the ``resharding_spec``. When the reshard dim is different from the original sharding dim, we need to do two steps logically: 1. To collect all shards based on original sharding spec. 2. Reshard the tensor based on the given resharding spec. In reality, we consolidate the two steps into one by sending each rank the new shard based on the resharding spec. Args: local_tensor (Tensor): Local tensor stored in the current rank. st_size (torch.Size): The size of the sharded tensor. sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification describing how the tensor is sharded originally. resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification describing how the tensor will be resharded. pg (ProcessGroup): The process group to aggregate on. Returns: A Tuple of the followings: A List[`Shard`] which contains the local tensor and its metadata. A List[`ShardMetadata`] which contains the metadata for the shard, including offsets, lengths and device placement. c3s"|]}||dkVqdS)r Nr).0i)r$rr sz&reshard_local_shard..r )r-Fr,T)rcSsg|] }|qSr)r7)r>tensorrrr sz'reshard_local_shard..)r1csg|] }|qSrr)r>r)output_tensor_listrrrBs)r2r3r4rrr(rranyrangerrZ index_selectr8rAr-rr rrr r9r.rsplitrcatr )r;rrr*r+rrZcurrent_sharding_dimr:r#r/metadataZrearrange_inputindicesZoffset_start_idxr&Zrearrange_output_listrrr'Zoutput_tensor_sizeZinput_tensor_listr<r)rCr$rreshard_local_shardsv    "    rJ) r!typingrrr8Ztorch.distributedZ distributedr2Ztorch._C._distributed_c10drZ&torch.distributed._shard.sharding_specZ_shardrZ shard_specZ1torch.distributed._shard.sharding_spec._internalsrrZtorch.distributed.nn.functionalrrZ!torch.distributed._shard.metadatar Zshardr rrSizeZ ShardingSpecr(ZTensorr=rJrrrrs:     ) D