o "j4@sddlmZddlmZmZddlZddlZddlm Z ddl m Z m Z ddl mZddlmZdd lmZdd lmZmZmZd"d d Z d#d dZdaddZddZiZGdddZddZd$ddZd%ddZ da!ddZ"deee#e$ffd d!Z%dS)&)reduce)ListTupleN)core) ProcessMeshget_current_process_mesh)get_default_distributed_context)DistributedOperatorHelper)DistributedTensor)__no_shape_var_type__convert_to_dims_mappingverify_shard_speccCs2|durt|tjsJd|dn t}|dusJdt|ts+Jd|dt|tr?tj |}t |}nt |}|j }||j _|jtvrRg}n|j}|durqt|||sjJd|j|||t|||j _|dur{|j d|dur|j d t}||||}|||S) a Shard a tensor on a process mesh according to the shard specification. Args: x (Tensor): the tensor to be sharded. process_mesh (ProcessMesh, optional): An instance of ProcessMesh describes a mesh topology of the used logical processes where the tensor is sharded. If it is None, the found current process mesh will be used. And an error will be raised if the current process mesh cannot be found. Default: None. shard_spec (list, optional): a list to describe the sharding mapping between `x` and `process_mesh`, which means the dimension `i` of `x` is split across the dimension `shard_spec[i]` of `process_mesh`, where `None` means that tensor dimension is not split. For example, given a tensor wih the shape [6, 12] and a process mesh with the shape [2, 3] and the dimension names ["x", "y"]: If `shard_spec=["x", "y"]`, each shard of the tensor will have a shape [3, 4]; If `shard_spec=["y", "x"]`, each shard of the tensor will have a shape [2, 6]; If `shard_spec=["x", None]`, each shard of the tensor will have a shape [3, 12]; If `shard_spec=[None, "x"]`, each shard of the tensor will have a shape [6, 4]; If `shard_spec=["y", None]`, each shard of the tensor will have a shape [2, 12]; If `shard_spec=[None, "y"]`, each shard of the tensor will have a shape [6, 4]; If `shard_spec=[None, None]`, each shard of the tensor will have a shape [6, 12]; If the `shard_spec` is None, the tensor will be replicated across all the processes of `process_mesh`. In the above example, the `shard_spec=None` is same as 'shard_spec=[None, None]'. Defaults: None. Returns: Tensor: the tensor `x` annotated with sharding information. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> import paddle >>> from paddle.distributed.fleet import auto >>> mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"]) >>> x = paddle.ones([4, 6]) >>> shard_spec = ["x", "y"] >>> auto.shard_tensor(x, mesh, shard_spec) NArgument process_mesh " is not an instance of ProcessMeshKSpecify the process mesh argument or use ProcessMesh context manager first.zArgument shard_spec z is not an instance of listzQFor tensor {}, shard_spec {} is invalid with tensor_shape {} and process_mesh {}. process_mesh dims_mapping) isinstancerrrliststrpaddlestaticdefault_main_programZ global_blockZ_var_recursiver serial_tensorZ dist_attrrtyper shaperformatnamer rZmark_annotatedr Zadd_dist_tensor_for_programZget_dist_tensor_for_programZadd_process_mesh)xr shard_specZ dist_tensorrZ tensor_shapeZdefault_dist_ctxr!k/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/distributed/auto_parallel/interface.py shard_tensor"s^)            r#cKs|durt|tsJd|dn t}|dusJdg}|durKtdd|Ds4Jd|d|D]}|durE|t||q6|dq6g}|durytd d|DsbJd |d|D]}|durs|t||qd|dqdt|||||}|S) aj Shard an operation on a process mesh according to its input and output shard specification. Args: op (Callable): a callable operator or module to be sharded. process_mesh (ProcessMesh, optional): An instance of ProcessMesh describes a mesh topology of the used logical processes where the op is sharded. All of its inputs and outputs are sharded by this process mesh. If it is None, the found current process mesh will be used. And an error will be raised if the current process mesh cannot be found. Default: None. in_shard_specs (list of list, optional): a list of list to describe the sharding specifications for the inputs. Each item of `in_shard_specs` is a `shard_spec` between the corresponding input and `process_mesh`. If one item is None, the corresponding input is replicated across all processes If it is None, all inputs are replicated across all processes. Note that the length of the `in_shard_specs` should be equal to the actual number of inputs when calling this operation. Default: None. out_shard_specs (list of list, optional): a list of list to describe the sharding specifications for the outputs. Each item of `out_shard_specs` is a `shard_spec` between the corresponding output and `process_mesh`. If one item is None, the corresponding output is replicated across all processes If it is None, all outputs are replicated across all processes. Note that the length of the `in_shard_specs` should be equal to the actual number of inputs when calling this operation. Default: None. Default: None. Returns: Outputs of `op`, each of which is annotated with sharding information. Examples: .. code-block:: python >>> import paddle >>> from paddle.distributed.fleet import auto >>> x = paddle.ones([4, 6]) >>> y = paddle.zeros([4, 6]) >>> mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"]) >>> dist_add = auto.shard_op(paddle.add, ... mesh, ... in_shard_specs=[["x", "y"], ["y", None]], ... out_shard_specs=[[None, "x"]]) >>> dist_add(x, y) Nrrrcs"|] }t|tp |duVqdSNrr.0r r!r!r"  zshard_op..zin_shard_spec z is not a list of list or Nonecsr$r%r&r'r!r!r"r)r*zout_shard_spec )rrrallappendr r )oprZin_shard_specsZout_shard_specskwargsZin_dims_mappingsr Zout_dims_mappingsr!r!r"shard_opzsP.       r/cCstd7aGddd}||S)Nrc@eZdZddZddZdS)z$recompute..RecomputeOperatorcS ||_dSr%)_op)selfr-r!r!r"__init__ z-recompute..RecomputeOperator.__init__c _stj}|}t|j}|j|i|}t|j}t||D]*}|j|} | dr@d| dvr@| ddt t dq | ddt t q |S)N op_namescopezauto_parallel/exclude_rcz/auto_parallel/rc_Z _exclude_rc) rrr current_blocklenopsr3rangeZhas_attrattr _set_attrr_g_recompute_idx r4argsr.Z default_progZ cur_blockZop_sizeoutputZ new_op_sizeidxr-r!r!r"__call__s0     z-recompute..RecomputeOperator.__call__N__name__ __module__ __qualname__r5rCr!r!r!r"RecomputeOperator rH)r>)r-rHr!r!r" recomputesrJcCsGddd}||S)z Exclude some operators in recompute segements. 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