U
    *-e<                  	   @   s   d dl Z d dlmZmZmZmZmZ d dlZd dlm	Z	 d dl
mZ ddgZe	ddG d	d dZe	dddeejjeejjjgef eeeef  ee dddZdS )    N)AnyCallableDictListOptional)compatibility)GraphModule	Partitionsplit_moduleT)Zis_backward_compatiblec                   @   s(   e Zd ZedddZedddZdS )r	   namec                 C   sN   || _ d| | _g | _i | _i | _i | _i | _tjj	
 | _	i | _i | _d S )NZsubmod_)r   submod_name
node_namesinputsoutputspartitions_dependent_onpartition_dependentstorchfxgraphGraphenvironmenttargets)selfr    r   ]/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/torch/fx/passes/split_module.py__init__   s    zPartition.__init__)returnc                 C   s4   d| j  d| j d| j d| j d| j d| j S )Nzname: z
,
 nodes: z,
 inputs: z,
 outputs: z,
 partitions dependent on: z,
 partition dependents: )r   r   r   r   r   r   )r   r   r   r   __repr__   s    2zPartition.__repr__N)__name__
__module____qualname__strr   r   r   r   r   r   r	      s   F)mroot_msplit_callbackqualname_mapkeep_original_orderc           !   	      s  t jjjttt jjjf ttt jjjf dfdd}i i t jjjtt jjj dfdd	j	j
D ]j< jdkrqnjdkrt jj	jd	 	fd
d qnt|}|dkrt| |< jj |_t jj	j	fdd t jj	j	fdd qnt }g } D ] \}tjsF|| qFg }	|r| }
|	|
 |
 jD ],}| j|
 | js|| qqlt|	tkrtd|	D ]P}| jD ]:}j	j|| jd}| j ! |_ |j"| < qqވj	j
D ]6t#dr8j j"t jj	jfdd}t jj	jfdd}jdkrj$}n|j$%d}}|D ].}t#||st&dj$ dt'||}qd(|}|j)|< |dk	rj* d| }j$||< t+|t,s.t-t+|t.s>t-j	j/j|||jd}j ! |_ |j"< q8i i  t jj	0 i }|sj	j
D ]| |\ }qnj	j
D ]j< q|s|	n|}t1 }|D ]P}| t,fddj2D }t|}|dkr0j	3|d	  n|dkrFj	3| |rfddjD }|D ].|krvqd| |\ }|4 qdt jjj)j	|j*< 5j*t, fddjD }tj2}|dkrt jj67|}t8j2D ]\}} || j | < qn|dkr| tj2d	 < qj	j
D ]6jdkr@3t jj	jd	  fd d q@t jj|S )!a  
    Creates subgraphs out of main graph

    Args:
        m (GraphModule): Graph module to split
        root_m (torch.nn.Module): root nn module. Not currently used. Included
            because the root nn module is usually transformed via
            torch.fx._symbolic_trace.symbolic_trace (see example below)
        split_callback (Callable[[torch.fx.node.Node], int]): Callable function
            that maps a given Node instance to a numeric partition identifier.
            split_module will use this function as the policy for which operations
            appear in which partitions in the output Module.
        qualname_map: Optional[Dict[str, str]]: optional output parameter that returns a
            mapping from new target names in the module after split to old target
            names in the original module.
        keep_original_order: Optional[bool]: keep the original order of the GraphModule
            or use the Topological order of the new constructed GraphModule


    Returns:
        GraphModule: the module after split.

    Example:

        This is a sample setup:

            import torch
            from torch.fx.symbolic_trace import symbolic_trace
            from torch.fx.graph_module import GraphModule
            from torch.fx.node import Node
            from torch.fx.passes.split_module import split_module

            class MyModule(torch.nn.Module):
                def __init__(self):
                    super().__init__()
                    self.param = torch.nn.Parameter(torch.rand(3, 4))
                    self.linear = torch.nn.Linear(4, 5)

                def forward(self, x, y):
                    z = self.linear(x + self.param).clamp(min=0.0, max=1.0)
                    w = self.linear(y).clamp(min=0.0, max=1.0)
                    return z + w

            # symbolically trace model
            my_module = MyModule()
            my_module_traced = symbolic_trace(my_module)

            # random mod partitioning
            partition_counter = 0
            NPARTITIONS = 3

            def mod_partition(node: Node):
                global partition_counter
                partition = partition_counter % NPARTITIONS
                partition_counter = (partition_counter + 1) % NPARTITIONS
                return partition

            # split module in module with submodules
            module_with_submodules = split_module(
                my_module_traced, my_module, mod_partition
            )

        Output looks like this. Original graph is broken into partitions

            > print(module_with_submodules)
            GraphModule(
                (submod_0): GraphModule(
                    (linear): Linear(in_features=4, out_features=5, bias=True)
                )
                (submod_1): GraphModule(
                    (linear): Linear(in_features=4, out_features=5, bias=True)
                )
                (submod_2): GraphModule()
            )

            def forward(self, x, y):
                param = self.param
                submod_0 = self.submod_0(x, param, y);  x = param = y = None
                getitem = submod_0[0]
                getitem_1 = submod_0[1];  submod_0 = None
                submod_1 = self.submod_1(getitem, getitem_1);  getitem = getitem_1 = None
                getitem_2 = submod_1[0]
                getitem_3 = submod_1[1];  submod_1 = None
                submod_2 = self.submod_2(getitem_2, getitem_3);  getitem_2 = getitem_3 = None
                return submod_2

        Output of split module is the same as output of input traced module.
        This is an example within a test setting:

            > orig_out = my_module_traced(x, y)
            > submodules_out = module_with_submodules(x, y)
            > self.assertEqual(orig_out, submodules_out)
            True
    )nodebase_mod_envbase_mod_attrsc                    s   | j dkrXt| jdkr"| jd ntjj} j| j| j|d|| j	< | j
 || j	 _
nt| j dkr̈ | j|| j	< | j
 || j	 _
}| jdD ]*}t||std| j dt||}q||| j< ||fS )Nplaceholderr   )	type_exprdefault_valueget_attr.zNode target  not found!)oplenargsinspect	Signatureemptyr+   targettyper   metacopyr.   splithasattrAttributeErrorgetattr)r(   r)   r*   r-   Zattr_valatom)base_mod_graphr#   r   r   construct_graph   s&    
  


z%split_module.<locals>.construct_graph)def_nodeuse_nodec                    s   t | dd }t |dd }||kr|d k	rR | }|j| j |d k	rR|j| |d k	r | }|j| j |d k	r|j| d S )N_fx_partition)r>   r   
setdefaultr   r   r   r   )rB   rC   Zdef_partition_nameZuse_partition_nameZdef_partitionZuse_partition)
partitionsr   r   record_cross_partition_use   s    z0split_module.<locals>.record_cross_partition_use)r+   r.   outputr   c                    s
    | d S Nr   n)rG   r   r   <lambda>       zsplit_module.<locals>.<lambda>Nc                    s
   |  S rI   r   rB   r(   rG   r   r   rL      rM   c                    s
   |  S rI   r   rN   rO   r   r   rL      rM   z cycle exists between partitions!)r,   rD   c                    s    |  S rI   r   rJ   r   r   r   rL      rM   c                    s    |  S rI   r   rJ   rP   r   r   rL      rM   )call_moduler.   r/   zOperator target r0   _)r1   r7   r3   kwargsr,   c                 3   s   | ]}j  |  V  qd S rI   rP   .0r   )
orig_nodes	partitionr   r   	<genexpr>@  s    zsplit_module.<locals>.<genexpr>   c                    s   g | ]} | qS r   r   )rU   key)org_mod_envr   r   
<listcomp>M  s    z split_module.<locals>.<listcomp>c                 3   s   | ]} | V  qd S rI   r   rT   r)   r   r   rX   `  s     c                    s
    | j  S rI   r   rJ   r]   r   r   rL   o  rM   )9r   r   r(   Noder   r"   Zgraph_moduler   r   r   Znodesr   r1   Zmap_argr3   getr	   r   appendrD   rS   listkeysitemsr2   r   popr   RuntimeErrorr   r+   r8   r9   r:   r   r<   r7   r;   r=   r>   joinr   r   
isinstancetupleAssertionErrordictZcreate_noder   setr   rH   addrQ   proxyZProxy	enumerate)!r#   r$   r%   r&   r'   rA   Zpartition_nameZoriginal_partition_orderZroot_partitionsZsorted_partitionsZroot_partitionZ	dependentinputr+   Zgathered_argsZgathered_kwargsr7   Ztarget_atomsZtarget_attrr?   qualnamenew_noder*   Zconstruct_order_partitionsZalready_constructed_attr_nodesZoutput_valsZnum_output_valsZorg_mod_attr_nodesZ
output_valZnum_outputsZoutput_val_proxyiZoutput_namer   )
r)   r@   r   r#   r(   r[   rV   rW   rF   rG   r   r
   %   s,   h 


 

  


 




  	




   



)NF)r4   typingr   r   r   r   r   r   Ztorch.fx._compatibilityr   Ztorch.fx.graph_moduler   __all__r	   nnModuler   r(   r^   intr"   boolr
   r   r   r   r   <module>   s"     