Oj ddlZddlmZddlZddlmZddlmZmZddlm Z dgZ d dddej dedzd e e e e fzdzd edzd ej f d ZdS)Nfnmatch)_mesh_resources DeviceMesh) ParallelStyleparallelize_module src_data_rankmodule device_meshparallelize_planr returnc v tjd|ptj}|t jdd|St|tr||_ | ||St|tr| D]\}}|dkrt||||d}|d tt! fd |}|s!t jd |d d |d d|d|D]E\}} |r+d|} t| || |i|2t| |||F|St)dt+|d)a Apply Tensor Parallelism in PyTorch by parallelizing modules or sub-modules based on a user-specified plan. We parallelize module or sub_modules based on a parallelize_plan. The parallelize_plan contains :class:`ParallelStyle`, which indicates how user wants the module or sub_module to be parallelized. User can also specify different parallel style per module fully qualified name (FQN). Note that ``parallelize_module`` only accepts a 1-D :class:`DeviceMesh`, if you have a 2-D or N-D :class:`DeviceMesh`, slice the DeviceMesh to a 1-D sub DeviceMesh first then pass to this API(i.e. ``device_mesh["tp"]``) Args: module (:class:`nn.Module`): Module to be parallelized. device_mesh (:class:`DeviceMesh`, optional): Object which describes the mesh topology of devices for the DTensor. If not specified, the call must be under a DeviceMesh context. parallelize_plan (Union[:class:`ParallelStyle`, Dict[str, :class:`ParallelStyle`]], optional): The plan used to parallelize the module. It can be either a :class:`ParallelStyle` object which contains how we prepare input/output for Tensor Parallelism or it can be a dict of module FQN and its corresponding :class:`ParallelStyle` object. If not specified, the call will do nothing at the moment. Keyword args: src_data_rank (int, optional): the rank of the source data for the logical/global tensor, it is used by :meth:`distribute_tensor` to scatter/broadcast the shards/replicas to other ranks. By default, we use ``group_rank=0`` on each DeviceMesh dimension as the source data to preserve the single-device semantic. If passing ``None`` explicitly, :meth:`parallelize_module` simply uses its local data instead of trying to preserve the single-device semantic via scatter/broadcast. Default: 0 Return: A :class:`nn.Module` object parallelized. Example:: >>> # xdoctest: +SKIP("distributed") >>> from torch.distributed.tensor.parallel import parallelize_module, ColwiseParallel >>> from torch.distributed.device_mesh import init_device_mesh >>> >>> # Define the module. >>> m = Model(...) >>> tp_mesh = init_device_mesh("cuda", (8,)) >>> m = parallelize_module(m, tp_mesh, {"w1": ColwiseParallel(), "w2": RowwiseParallel()}) >>> .. note:: For complex module architecture like Attention, MLP layers, we recommend composing different ParallelStyles together (i.e. ``ColwiseParallel`` and ``RowwiseParallel``) and pass as a parallelize_plan, to achieve the desired sharding computation. z4torch.distributed.tensor.parallel.parallelize_moduleNzNo parallelize_plan is provided and auto-parallel is not supported at the moment, so this parallelize_module call will do nothing.) stacklevel.rc0t|dS)Nrr)ttokens SC:\Projects\DSCons\.venv\Lib\site-packages\torch\distributed\tensor\parallel\api.pyz$parallelize_module..fsgadE22zParallelize plan key 'z6' could not be resolved: no submodule matching token 'z ' in module z, skipping this plan entry.r zLExpect Union[ParallelStyle, Dict[str, ParallelStyle]] for parallelize_plan, z found!)torch_C_log_api_usage_oncerget_current_meshwarningswarn isinstancerr _applydictitemsrsplitlistfilternamed_childrenpopjoin TypeErrortype) r r r r module_pathparallelize_style path_splitsmatched_children_ submodule leaf_pathrs @rrrsun H  !WXXXC!A!C!CK  N     "M22: )6&&&v{;;; $d + +7 .>.D.D.F.F0 0  *K*b  "6;8IJJJ%++C00K NE#2222))++   $  1[114911GM111 !   OOA    0   9 # 5 5I&!#"$56&3 '!#)&3  $  B"&'7"8"8 B B B   r)NN)rrrtorch.nnnntorch.distributed.device_meshrr'torch.distributed.tensor.parallel.styler__all__Moduler"strintrrrr<s EEEEEEEEAAAAAA  &*HL@ !" @ @ @ I@ d"@ $d3 +=&>>E@ : @ Y @ @ @ @ @ @ r