o
    h(                     @   s   d dl Z d dlmZmZ d dlmZmZmZ d dlZ	d dl
mZ d dlmZ d dlmZmZ ddlmZ g d	Zed
ddd Zed
ddd Zed
deG dd dZed
dde	jjdee de	jjfddZdS )    N)	dataclassfield)DictListOptional)compatibility)map_arg)HolderModulelift_subgraph_as_module   )NodeList)getattr_recursivesetattr_recursive	Componentsplit_by_tagsF)is_backward_compatiblec                 C   s.   | dD ]}t| |rt| |} q d S | S )N.)splithasattrgetattr)objnamelayer r   Q/var/www/html/ai/venv/lib/python3.10/site-packages/torch/fx/passes/split_utils.pyr      s
   
r   c                 C   sJ   d|vrt | || d S |d}tt| |d d|dd  | d S )Nr   r   r   )setattrr   r   r   join)r   attrvaluer   r   r   r   r      s   
(r   c                   @   s   e Zd ZU dZejjed< eed< e	ed< e
edZeed< e
edZeed< e
edZeed< e
edZeejjejjf ed	< e
edZee	 ed
< dZeejj ed< dS )r   zX
    A component serves as a container for a subgraph we want to create afterwards.
    graphorderr   )default_factoryinput_placeholdersorig_inputsorig_outputsgetattr_mapsconstructor_argsNgm)__name__
__module____qualname____doc__torchfxGraph__annotations__intstrr   listr"   r   r#   r$   dictr%   r   Noder&   r'   r   GraphModuler   r   r   r   r   !   s   
 "r   r'   tagsreturnc                    s  dt jjjdtfdd}i i i }g }i t j }i }d}|D ]}tt j t||  |   ||< q!| j	j
D ]}	|	jdkrO|durLtd|	}q=|	jdkri|j|	j|	jd	||	< t|	j||	 _q=|	jd
kroq=t|	dsvJ fdd||	j||	j D }
||	j   |	< tdd |
D dd} j|ksJ  fdd} j	|	|}|	j|_||	<  |< q=|du rtd||jd D ]}|jd
kr|j|j|jd	||< qd|< qΈD ]}|jdkr| j| q|D ]X ttj j} j	t|dkr|d n| t|  j	 _ |j! jtt|j j"dd}t|dkr<|| jd < qt# jD ]\}}t j$|| j||< qAq|t%|jd |j t&dd |D }||jd D ]}|jd
krt'||jt(| |j) qot j*||S )a~  
    Splits a GraphModule using tags on its graph nodes. We honor the order of
    tags. For example, we have tags = ["a", "b", "c"], the function will create
    the initial submodules in the order of "a_0", "b_1", "c_2".

    To set a tag:
    gm.graph.nodes[idx].tag = "mytag"

    This will result in all nodes with the same tag being extracted and placed in their
    own submodule. For placeholder, output and get_attr node, the tag is ignored. placeholder
    and output nodes are created when needed while get_attr nodes get copied to submodules
    where they are used.

    Given the following module def:

    class SimpleModule(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.linear1 = torch.nn.Linear(...)
            self.linear2 = torch.nn.Linear(...)
            self.linear3 = torch.nn.Linear(...)

        def forward(self, in1, in2):
            r1 = self.linear1(in1)
            r2 = self.linear2(in2)
            r3 = torch.cat([r1, r2])
            return self.linear3(r3)

    Marking the node corresponding to in1 with the tag sc.REQUEST_ONLY.lower() results in the following split:

    ro_0:
    def forward(self, in1):
        self = self.root
        linear1 = self.linear1(in1)
        return linear1

    main_1:
    def forward(self, in2, linear1):
        self = self.root
        linear2 = self.linear2(in2)
        cat_1 = torch.cat([linear1, linear2])
        linear3 = self.linear3(cat_1)
        return linear3

    main_0:
    def forward(self, in1, in2):
        self = self.root
        ro_0 = self.ro_0(in1)
        main_1 = self.main_1(in2, ro_0)
        return main_1
    xr7   c                 S   s   g }t | |j |S )zC
        Stores nodes in x to a list and returns the list.
        )r   append)r8   rr   r   r   flattenq   s   zsplit_by_tags.<locals>.flattenNoutputzMultiple output nodes in graph!placeholder	type_exprget_attrtagc                    s   g | ]}|j d vr | qS )>   r@   r=   )op).0r8   )node_to_componentr   r   
<listcomp>   s
    
z!split_by_tags.<locals>.<listcomp>c                 s   s    | ]}|j V  qd S )N)r    )rC   cr   r   r   	<genexpr>   s    z split_by_tags.<locals>.<genexpr>r   )defaultc                    s   | j dkr|  jvr jj| j| jd j| <  j|  S | j dkr+|   kr+|  S |  jvrQ j|   jj| j	| jd}t

| j|_ j| d | <  j j|  S )Nr@   r>   r=   )rB   r%   r   r@   targettyper#   r9   r=   r   copymetar"   index)r8   r=   compnode_remappingrD   used_in_mainr   r   
remap_func   s"   



z!split_by_tags.<locals>.remap_funczGraph had no output node!r   )argskwargsc                 S   s   i | ]}|j |jqS r   )r   r'   )rC   rO   r   r   r   
<dictcomp>  s    z!split_by_tags.<locals>.<dictcomp>)+r,   r-   nodeArgumentr   r.   r   lenr9   r   nodesrB   RuntimeErrorr=   r   rJ   rK   rL   r   rS   rT   rA   maxr    	node_copyr@   r$   tuplemap__getitem__r<   r
   r'   call_moduler#   	enumerateProxyr   r	   r   r   rI   r5   )r'   r6   r;   tag_to_componentall_componentsmain_gmain_remappingoutput_noderA   rV   upstream_componentsmxrR   nr8   outs	main_nodeio	main_rootr   rN   r   r   ;   s   6	











"r   )rK   dataclassesr   r   typingr   r   r   torch.fxr,   torch.fx._compatibilityr   torch.fx.graphr   torch.fx.passes.utilsr	   r
   tools_commonr   __all__r   r   r   r-   r5   r1   r   r   r   r   r   <module>   s$    
	
(