U d3 @s<ddlZddlZddlZddlmZmZmZmZmZddl Z ddl m Z ddl mmZddl mZddlmZddlmZmZmZeeeeedddZeeeeeeeeeeeefd d d Zeeeee jee je jd d dZdeee je jdddZdee efee jee efdddZ!dS)N)AnyDictListTupleOptional)distributed_c10d) ShardedTensor)ChunkShardingSpecEnumerableShardingSpec ShardingSpec) sharding_spec tensor_numel world_sizereturncsg}t|tr,|jD]}||jdqnt|tr|jdksDtt |dkrrfddt |D}qfddt |D}t t |}ntdt|d|S)z Translates the sharding spec to a list of offsets along dim 0. If the sharding spec is ChunkShardingSpec, only the ``dim`` is used and the placement is not used. rcsg|]}|kr|nqSr.0Zrank)r rF/tmp/pip-unpacked-wheel-ua33x9lu/torch/distributed/fsdp/shard_utils.py "sz-_sharding_spec_to_offsets..csg|]}|dkrndqSrrr) chunk_sizerrr'sz!Un-recognized sharding spec type .) isinstancer shardsappendZ shard_offsetsr ZdimAssertionErrormathceilrangelist itertools accumulate ValueErrortype)r r roffsetsZshardr)rr r_sharding_spec_to_offsetss    r&) input_offsetsoutput_offsetsr rmy_rankrcsjfddddfdd}ddtD}d dtD}||||||||||fS) z Given the shard offsets for each rank of the input tensor and output tensor, this API returns the corresponding split sizes that can be passed to all_to_all_single(). cs8dkr$||ddfS|dfSdSNrr)r%)r)r rrr _get_interval<s z._offsets_to_split_sizes.._get_intervalcSslg}t|D]Z\}}|t|dkr0||dn|d}|||t||dt||ddq |S)Nrr) enumeratelenrmax)r%beginendZsizesioffsetZ next_offsetrrr_offsets_to_sizesBs$ z2_offsets_to_split_sizes.._offsets_to_sizescsX|\}}t||d}t||d}|||d||}||||d<dSr*)bisect)Z from_offsetsZ to_offsetsZ split_sizesr/r0Z to_begin_rankZ to_end_rankZ _split_sizes)r+r3rr_convertMs z)_offsets_to_split_sizes.._convertcSsg|]}dqSrrr_rrrrVsz+_offsets_to_split_sizes..cSsg|]}dqSrrr6rrrrWs)r)r'r(r rr)r5input_split_sizesoutput_split_sizesr)r+r3r)r rr_offsets_to_split_sizes/s     r:) input_tensor output_specrr)device process_grouprcCs|}|}t|tr"td|}t|||} t|||} t| | |||\} } t| } t j | |j |d}t j ||dj| | |d|S)a Resharded a sharded flatten tensor, this is used by FSDP to do sharded state_dict. But the functionaility is not supported by ShardedTensor. This API is designed to be used for FSDP; therefore this API supports only 1-D ShardedTensor (hence the naming, reshard_flatten_tensor). This API uses the ChunkShardingSpec and EnumerableShardingSpec from torch.distributed.sharding_spec but ignores the placement field in ChunkShardingSpec, as the placement requires the callees understand the number of GPUs per node. The API simply uses the semantics of the sharding specs. Args: input_tensor (ShardedTensor): the original ShardedTensor. Must be 1D. output_spec (ShardingSpec): the sharding spect for the output tensor. world_size (int): total trainer count. my_rank (int): the rank for this trainer. Returns: The local shard for the new ShardedTensor. z#The input tensor has no dimensions.)dtyper=r)r8r9group)r sizerintr#numelr&r:sumtorchemptyr?distZall_to_all_single local_shardstensor)r;r<rr)r=r>Z input_specrAr r'r(r8r9Z output_sizeZ local_shardrrr_reshard_flatten_tensor^s0    rJ)sharded_tensorpgrc Cs|dkrt}t|}|}|dj}|d}|}t ||||}||}|dkrt |d|g}t j|||jd} tj| ||d| dd||S)Nr)r?)r@)rZ_get_default_grouprGZget_world_sizerHrIflattenrArCrrFpadrErFr?ZcudaZ_all_gather_baseZnarrowZreshape) rKrLrrZ local_tensorZ dim_0_sizer rZ num_paddingrIrrr_all_gather_sharded_tensors    rP) state_dictrLrcCs:i}|D](\}}t|tr,t||}|}|||<q |S)z Given a state_dict, this API gathers all the ShardedTensor in the state_dict to the output_rank, and creates a new state_dict which the values are either the gathered tensors (rank == output_rank) or None (rank != output_rank). )itemsrrrP)rQrLZnew_state_dictkeyrIZ output_tensorrrr_gather_state_dicts   rT)N)N)"r4r!rtypingrrrrrrEZtorch.distributedZ distributedrGZtorch.nn.functionalnnZ functionalrNrZ'torch.distributed._shard.sharded_tensorrZ&torch.distributed._shard.sharding_specr r r rBr&r:r=Z ProcessGroupZTensorrJrPstrrTrrrrsR     0 5