o Ti@sddlmZmZddlZddlmZmZefdedededed eeeff d d Z  dd eej dedededededed ej fddZ dS))OptionalTupleN) ShardingTypeDEFAULT_SHARD_GRANULARITYdim_size shard_rank num_shards granularityreturnc Csf||dks Jd||}||}||}||t||}||||kr(dnd}||||fS)a Given a dimension to shard with size dim_size, return the start and end indices of the slice that belong to the given rank. The typical use of this is as an internal helper function, so see if there is a higher level API that better suits the application. Args: dim_size (int): The size of the dimension to shard. shard_rank (int): The rank of the shard to return. num_shards (int): Total number of shards the dimension will be distributed across. granularity (int): The minimum alignment of the shard endpoints. This is used to support non-even head counts as well as align dimensions to cleaner GEMM boundaries. rz/Dimension size must be divisible by granularityr)min) rrr r total_chunksbase_chunks_per_rankremainder_chunksstart_chunk_id end_chunk_idro/home/ubuntu/.local/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/sharding/utils.pyget_shard_endpoints sr param shard_modenum_concatenated_matrices bias_dimsc sksJddurdSdkrS|tjkr\dtdtjffdd }j|krE|dd \}tjfd d |Ddd S|d d \}tjfdd |Dd d S|tjkrd} t | \tj dd }tjfdd |Ddd SdS)aj Utility for sharding a parameter. This will return the slice of the parameter that should exist on the given shard_rank given the sharding configuration. The workflow here is to find the minimum bounded Tensor to shard, get the slicing endpoints, and then concatenate as needed. The typical use of this is as an internal helper function, so see if there is a higher level API that better suits the application. Args: param (torch.Tensor): The parameter to shard. shard_mode (ShardingType): The type of sharding to apply. See ShardingType for more context. shard_rank (int): The rank of the shard to return. num_shards (int): Total number of shards the parameter will be distrbuted across. num_concatenated_matrices (int): The number of matrices that have been concatenated together in the original parameter. An example of this is a fused QKV projection matrix, where the `num_concatenated_matrices` argument would be 3. granularity (int): The minimum alignment of the shard endpoints. For attention projection matrices, this should be set to the head size to support non-even sharding. bias_dims (int): The number of dimensions that are considered bias dimensions. This is used to support sharding of MoE and non-MoE biases on the same codepath. z'Shard rank must be less than num_shardsNrdim_idxr cs6|}t|\}}tj|d||fS)Ndim)sizertorchchunk)rrstart_channel_idend_channel_id)r rr rrrr get_matricesUsz!shard_param..get_matrices)rcg|] }|dfqS.r.0matr!r rr ]zshard_param..rcs"g|] }|dddfqS).Nrr&r)rrr*bs"cr$r%rr&r)rrr*hr+) rOUTER_DIMENSIONintrTensorndimcatINNER_DIMENSIONrrr) rrrr rr rr"matricesrr)r!r rr rrr r shard_param+s$    r4)rrr) typingrrrtypesrrr.rr/r4rrrrs>  "