a hF? @sddlZddlZddlmZmZddlZddlmZddlm Z m Z gdZ e e ZdZGdddZGd d d eZeed ejZdZGd d d ZGdddZddZdeedfeeeefeeeedfeeeefeeeeefdddZeedddZdS)N)AnyOptional map_aggregate) tree_flattentree_unflatten)TensorChunkSpecsplit_args_kwargs_into_chunks merge_chunksFc@seZdZdZddZdS)_CustomReducera$ Custom reducer class that can be used to specify a custom operation that reduces losses of multiple microbatches into one value. Example: >>> # xdoctest: +SKIP >>> sum_reducer = _CustomReducer( >>> torch.tensor(0.0), >>> lambda a, b: a + b >>> ) cCs||_||_dSN) init_value reduce_fn)selfr rrU/var/www/auris/lib/python3.9/site-packages/torch/distributed/pipelining/microbatch.py__init__)sz_CustomReducer.__init__N)__name__ __module__ __qualname____doc__rrrrrr s r c@s eZdZdS) _LossReducerNrrrrrrrr.srgc@sfeZdZUdZddZeed<ddZddZe e ed fd d d Z e e e efd d dZdS)rz2 Class used to specify chunking of inputs cCs ||_dSr  split_dim)rrrrrr>szTensorChunkSpec.__init__rcCs |jjd|jjd|jdS)N.()) __class__rrrrrrr__repr__CszTensorChunkSpec.__repr__cCsd|jdS)NzTensorChunkSpec(rrrrrr__str__HszTensorChunkSpec.__str__.) chunk_dimscCst|dd}|S)a A helper for creating a tuple of `TensorChunkSpec` from a tuple of chunk dimensions (int's). 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