o
    eiq                     @   s&  d dl 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	Z	d dl
mZ d dlmZ d dlmZ d dlmZ d	d
gZee ZZeddG dd	 d	Zde	jjdedefddZedd				ddddede	jjdeegef deeeef  dee dee dedee fdd
ZdS )    N)OrderedDict)Callable)AnyOptional)compatibility)lazy_format_graph_code)GraphModule)Node	Partitionsplit_moduleT)is_backward_compatiblec                   @   s(   e Zd ZdefddZdefddZdS )r
   namec                 C   sN   || _ d| | _g | _i | _i | _i | _i | _tjj	
 | _	i | _i | _d S )Nsubmod_)r   submod_name
node_namesinputsoutputsdependencies
dependentstorchfxgraphGraphenvironmenttargets)selfr    r   f/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/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 depended on: z,
 partition dependents: )r   r   r   r   r   r   )r   r   r   r   __repr__!   s   
zPartition.__repr__N)__name__
__module____qualname__strr   r    r   r   r   r   r
      s    modqualnamer   c                 C   s<   | }| dD ]}t||std| dt||}q|S )N.zNode target z not found!)splithasattrAttributeErrorgetattr)r%   r&   attr_valatomr   r   r   _get_attr_from_qualname,   s   
r.   F)partition_affixmroot_msplit_callbackqualname_mapkeep_original_orderkeep_original_node_namekeep_original_input_namer/   c          9   
      s
  t dtddd dtdtttf dtttjjj	f ffdd	}d
dl
}	i i i dtdtt ffddfdd}
tjjtjjtjjg}t }t }i }d}t }jjD ]		jd }durt|tjtjfrt|jj }|	jr|vr	|jj< 	jdv rqa|
	 	jdkr6	j|v r6	jtjju rt	j dkrt!dt	j  t	j d
 t"st!dt#	j d
  	}t	h||< n^	jtjju rt$dd 	j D st!d|%	 t	h|	< d|	< n5	jtjju r6t	j dkrt!dt	j  |	j d
  %	 |&	j d
  	|	j d
 < |durD|| %	 |D ]}|| %	 qFqat$dd |' D sdt!ddd |( D }d d |( D }t)*t+j,rt)d!| t)d"| t"|pt"|}d#}jjD ]a			j-< 	jd$v rq	jd%krtjj.	j d
 fd&d' q|rֈ	}||krt!d(| d)| |}	j|vrtjj.	j 	fd*d' tjj.	j/	fd+d' qt01 }g }( D ]\}tj2s|3| qg }|rD|4 }|3| | j5D ]}| j24| | j2s?|3| q*|st|tkrQt6d,||fD ]T}|( D ]L\	}t|d
krjt!d-	t|d
  j7	< |dd D ]*}t| jj8	j	jt9d.d 	j D i 	j#d/} 	j: | _| j7	< q{q[qU|D ]m}| i d
j;D ][ }!fd0d1}"|!jd2krt|!jtst!d3t#|!j t<|!j}#t|#tj=j>rj?|!j}$|#j@|!j< n|" }$n|" }$ j: |$_|$j7 < q_;qjjD ]	tA	d4r	jB j7tjj.	j fd5d'}%tjj.	j/fd6d'}&	jd7vrR	j}'n$t<	j}(	jCd8d9}'|(j@|'< |durvjD d8|' })	j||)< t|%t9st!d:t#|% t|&tst!d;t#|& r	j-nd}*jj8	j|'|%|&	j#|*d<} 	j: | _| j7	< q|fD ]O}tE|D ]G	|	 }t|d
krt!d=|dd# D ].}t| |	 }+|+du rt!d>jj8|+j|+jj7	 fi |+j#d/} |+j: | _qאqqi 
i  tjjF i },|s*jjD ]	|	 |,\ },qnjjD ]		
	j-< q.|s<|n|}-t }.d?d@ jjD |-D ]}| t9fdAdjGD }/t|/}0|0dkrqjH|/d
  n|0dkr}jH|/ njHdB |rŇ
fdCd@j;D }1D ]		|.v rq|	 |,\ }2|.%	 q|1D ]		|.v rq|	 |,\ },|.%	 qtjj	j@j|,jD< IjDt9 fdDdj;D }3tjG}4|4dkrtjjJK|3}5tLjGD ]\}6}7|5|6 j |7< qqL|4dkr|3 tMtNjG< qL|r. s.jjD ]	|	 |,\ },q"jjD ]		jd%krLHtjj.	j d
  fdEd' q2tjj	|,}8t dtdF|8dd |8S )Ga  
    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[[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
        keep_original_node_name: Optional[bool]: If the partitioned graphs should
            have the same node names as the original graph.
        keep_original_input_name: bool: If the partitioned graphs should
            have the same input names as the original graph.
        partition_affix: Optional[str]: If specified, the submodules' names will contain
            the affix, e.g. "submod_<affix>_<idx>".

    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) -> None:
                    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
    z%szpre split_moduleT)colorednodebase_mod_envbase_mod_attrsc                    s  | j dkrKt| jdkr| jd ntjj}r1|tjju rdn|f} jd| j|| jd|| j< n j	| j
| j|d|| j< | j || j _||fS | j dkr} | j
|| j< | j || j _t| j
tsrtdt| j
 t| j
}||| j
< ||fS )Nplaceholderr   r   )args	type_expr)r=   default_valueget_attrExpected str target, got )oplenr<   inspect	Signatureemptycreate_noder   typer;   targetmetacopyr?   
isinstancer$   AssertionErrorr.   )r8   r9   r:   r>   r<   r,   )base_mod_graphr5   r0   r   r   construct_graph   s6   


z%split_module.<locals>.construct_graphr   Ndef_nodeuse_nodec                    sR  ddl m} t| dd }t|dd }td| j||d ur|jnd| ||kr|d ur@ | }|j| j |d ur@|j| |d ur | }|j	| j | j
d }d urt||tdD ]5}| }	|j	|	j | jdkrt|	dd }
|
d ur |
 }|j|	j |j| |j|
 qa|d ur|j| d S d S d S d S )	Nr   )free_symbols_fx_partitionz*record_cross_partition_use %s (%s) %s (%s)-example_value)keyr;   )%torch.fx.experimental.symbolic_shapesrQ   r+   logdebugr   r   
setdefaultr   r   rI   getsortedr$   rA   r   )rO   rP   rQ   defineduseddef_partitionuse_partitiondef_valss_node	s_defineds_def_partition)
partitionssymbol_to_noder   r   record_cross_partition_use   sH   	z0split_module.<locals>.record_cross_partition_usec                    sp   | }t |} d urd |g}td| j| |}|d u r,t| |< }|j| j || _	d S )N_z*instantiate_node_partition_mapping %s (%s))
r$   joinrW   rX   r   rZ   r
   r   appendrR   )r8   partition_idxpartition_name	partition)r/   re   r2   r   r   "instantiate_node_partition_mapping  s   

z8split_module.<locals>.instantiate_node_partition_mappingrT   )r;   r?   outputcall_function   z*Expected 1 arg for _set_grad_enabled, got zExpected bool arg, got c                 s   s    | ]	}t |t V  qd S N)rK   r	   .0argr   r   r   	<genexpr>U  s    zsplit_module.<locals>.<genexpr>z3Expected all args to be python constants, not Nodesz'Expected 1 arg for _exit_autocast, got c                 s   s    | ]}|d uV  qd S rr   r   )rt   vr   r   r   rv   k      zautocast must exitc                 S      i | ]	\}}|t |qS r   r[   rt   krw   r   r   r   
<dictcomp>o      z split_module.<locals>.<dictcomp>c                 S   ry   r   rz   r{   r   r   r   r}   q  r~   zautocast_regions: %szgrad_regions: %s)r;   r?   ro   c                    s
    | d S rr   r   n)rg   r   r   <lambda>     
 zsplit_module.<locals>.<lambda>zSautocast or set_grad_enabled require monotonically increasing partitions: highest: z, this node's: c                    
   |  S rr   r   rO   r8   rg   r   r   r     r   c                    r   rr   r   r   r   r   r   r     r   z cycle exists between partitions!z%Expected at least one region for nodec                 s   s    | ]}|V  qd S rr   r   rs   r   r   r   rv     s    )rA   rH   r<   kwargsr=   c                     s>   r} n	d  }  d7  j j|  jd}d < |S )Narg_rq   )r=   )r   r;   rG   )r   r;   )counterinpr6   
new_inputs
orig_nodesrm   r   r   add_placeholder  s   
z%split_module.<locals>.add_placeholderr?   r@   rR   c                        |  S rr   r   r   r   r   r   r         c                    r   rr   r   r   r   r   r   r     r   )call_moduler?   r'   rh   z&Expected tuple for gathered_args, got z'Expected dict for gathered_kwargs, got )rA   rH   r<   r   r=   r   zExpected at least one regionzMissing exit nodec                 S   s   g | ]	}|j d kr|qS )r;   )rA   )rt   r8   r   r   r   
<listcomp>T  r~   z split_module.<locals>.<listcomp>c                 3   s    | ]
}j  |  V  qd S rr   r   rt   r   )r   rm   r   r   rv   Z  s    
r   c                    s   g | ]
}|vr | qS r   r   )rt   rU   )orig_mod_envoriginal_orderr   r   r   j  s
    c                 3   s    | ]} | V  qd S rr   r   r   r9   r   r   rv     rx   c                    s
    | j  S rr   )r   r   r   r   r   r     r   zpost split_module)OrW   rX   r   r	   dictr$   r   r   graph_moduler   sympyr   amp_enter_autocast_exit_autocast_C_set_grad_enabledr   setr   nodesrI   rZ   rK   SymIntSymFloatr8   exprSymbolrA   rH   rB   r<   rL   boolrG   alladdremovevaluesitems_LOGGERisEnabledForloggingDEBUGr   map_argr   listkeysr   rj   popr   RuntimeErrorr   rF   tuplerJ   r   r.   nnModuler?   r   r)   rR   replacer   reversedr   r   ro   r   proxyProxy	enumeratenextiter)9r0   r1   r2   r3   r4   r5   r6   r/   rN   r   rn   GLOBAL_STATE_NODESgrad_regionsautocast_regionsautocast_exitsactive_gradactive_autocastsvals0aassert_monotonically_increasinghighest_partitionpidoriginal_partition_orderroot_partitionsrl   sorted_partitionsroot_partition	dependentregions_mappingregionsrnew_node	orig_noder   	orig_attrr;   gathered_argsgathered_kwargsrH   target_attrr&   r   	exit_noder:   construct_order_partitionsalready_constructed_attr_nodesoutput_valsnum_output_valsorig_mod_attr_nodes_based_mod_attrs
output_valnum_outputsoutput_val_proxyioutput_nameretr   )r9   rM   r   r   r   r6   r5   r0   r   r8   r   r   r   rm   r/   re   rg   r2   rf   r   r   6   sT  p
#1
		


















	







)NFFT) rC   r   collectionsr   collections.abcr   typingr   r   r   torch.fx._compatibilityr   torch.fx._utilsr   torch.fx.graph_moduler   torch.fx.noder	   __all__	getLoggerr!   rW   r   r
   r   r   r$   r.   intr   r   r   r   r   r   r   <module>   sN   
		