o * i2:@sddlmZddlmZddlZddlZddlmZddl m Z m Z ddl m Z ddlmZdd lmZdd lmZmZmZd)d d Z d*d dZdaddZddZiZGdddZddZd+ddZd,ddZda d-dd Z!d.d#d$Z"d/d'd(Z#dS)0) annotations)reduceN)core) ProcessMeshget_current_process_mesh)get_default_distributed_context)DistributedOperatorHelper)DistributedTensor)__no_shape_var_type__convert_to_dims_mappingverify_shard_specc Cs@|durt|tjsJd|dn t}|dusJdt|ts+Jd|dt|tr?tj |}t |}nt |}|j }||j _|jtvrRg}n|j}|durxt|||sqJd|jd|d |d |d 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 with 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 listz For tensor z , shard_spec z is invalid with tensor_shape z and process_mesh . process_mesh dims_mapping) isinstancerrrliststrpaddlestaticdefault_main_program global_blockZ_var_recursiver serial_tensorZ dist_attrrtyper shaper namer rZmark_annotatedrZadd_dist_tensor_for_programZget_dist_tensor_for_programZadd_process_mesh)xr shard_specZ dist_tensorrZ tensor_shapeZdefault_dist_ctxr!v/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/distributed/auto_parallel/interface.py shard_tensor"sN)            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_opxsL.        r/cCstd7aGddd}||S)Nrc@eZdZddZddZdS)z$recompute..RecomputeOperatorcS ||_dSr%)_op)selfr-r!r!r"__init__ z-recompute..RecomputeOperator.__init__c _stj}t|j}tjdt&tjj j r,tj j j |j|i|}n|j|i|}Wdn1s>wYtjrgtj}t|j}t||D] }|j|}|dtqY|S)Nz/auto_parallel/rc_Zfwd_recompute_id)rrrrlenops name_scope_g_recompute_idxbasedygraphin_to_static_modejit dy2staticconvert_call_func convert_callr3Z frameworkZ in_pir_moderangeZ set_int_attr) r4argsr.blockZ rc_begin_idoutputZ rc_end_ididxZrc_opr!r!r"__call__s0     z-recompute..RecomputeOperator.__call__N__name__ __module__ __qualname__r5rGr!r!r!r"RecomputeOperator rL)r:)r-rLr!r!r" recomputesrNcCsGddd}||S)z Exclude some operators in recompute segments. Args: run_function (callable): The callable function to be excluded. Returns: ExcludeOperator: The callable object. c@r1)z1exclude_ops_in_recompute..ExcludeOperatorcSr2r%) _run_function)r4 run_functionr!r!r"r5r6z:exclude_ops_in_recompute..ExcludeOperator.__init__c_s~tjd/tjjjrtjjj |j |i|}n|j |i|}Wd|SWd|S1s8wY|S)Nz /exclude_rc) rrr9r;r<r=r>r?r@rArO)r4rCr.rEr!r!r"rGs$      z:exclude_ops_in_recompute..ExcludeOperator.__call__NrHr!r!r!r"ExcludeOperatorrMrQr!)rPrQr!r!r"exclude_ops_in_recomputes rRc@seZdZdZdZdS)CollectionNamesZfetchesloggingN)rIrJrKFETCHESLOGGINGr!r!r!r"rSsrScCs(t|d}|durg}|t|<t|Sr%)_g_collectionsget)rZ collectionr!r!r"get_collections rYcCs|tvrgt|<|dur't|D] \}}||krdSqt|||fdSt|D] \}}||kr6dSq+t|d|fdSr%)rWr,)Zcollection_namevaluer_vr!r!r"add_to_collection%sr]FcCsbt|tjjr |j}nt|tr|}n tdt|dtt j |||r/tt j ||dSdS)NzDOnly support fetch `Variable` or `str`[`Variable`'s name], but got ``) rrrVariablerr TypeErrorrr]rSrUrV)ZtensorrrTr!r!r"fetch4s rareturnpaddle.distributed.ProcessMeshcCstS)a Get the global mesh set by set_mesh. Returns: mesh (paddle.distributed.ProcessMesh): the global mesh. Examples: .. code-block:: python >>> import paddle >>> import paddle.distributed as dist >>> mesh = dist.ProcessMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["dp", "mp", "pp"]) >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> dist.auto_parallel.set_mesh(mesh) >>> mesh = dist.auto_parallel.get_mesh() >>> # This case need to be executed in multi-card environment >>> # python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 {test_case}.py _g_meshr!r!r!r"get_meshEsrfmeshNonecCs|adS)a Set the global mesh. Args: mesh (paddle.distributed.ProcessMesh): global mesh to be set. Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.distributed as dist >>> mesh = dist.ProcessMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["dp", "mp", "pp"]) >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> dist.auto_parallel.set_mesh(mesh) >>> # This case need to be executed in multi-card environment >>> # python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 {test_case}.py Nrd)rgr!r!r"set_mesh\sri mesh_dimslist[tuple[str, int]]cCsHdd|D}dd|D}tdtdd|d|}t||atS)z Create a global process_mesh for auto parallel. Args: mesh_dims (list[tuple[str, int]]): A list of tuple, each element is (dim_name, dim_degree). cSg|]}|dqS)rr!r(Zmesh_dimr!r!r" }zcreate_mesh..cSrl)rr!rmr!r!r"rn~rorcSs||Sr%r!)ryr!r!r"szcreate_mesh..r)npZarangerZreshaperre)rjZ dim_namesZ mesh_shapeZmesh_arrr!r!r" create_meshus rs)NN)NNNr%)NF)rbrc)rgrcrbrh)rjrk)$ __future__r functoolsrnumpyrrrZpaddle.frameworkrrrrZstatic.dist_contextrZstatic.dist_opr Zstatic.dist_tensorr Z static.utilsr r r r#r/r:rNrRrWrSrYr]rarerfrirsr!r!r!r"s2      W W$