U
    d@                     @   s  d dl mZ d dlmZmZ d dlmZ d dlmZm	Z	m
Z
mZmZmZmZmZ d dlZd dlmZ d dlm  mZ d dlmZ d dlZd dlmZ d dlm  mZ d dlZd dl Z d dl!Z!d dl"m#Z# e$ee$e$f dd	d
Z%ee ej&e	e$e
f dddZ'ej&e	e$e
f ejj(dddZ)d0ejj(ejj(dddZ*ej(ej(dddZ+ej(eej& eej& eej& dddZ,ej-ej.ej/ej0ej1ej2ej3ej4ej5ej6ej4ej7ej8gZ9ej:ej;gZ<ej-ej=ej.ej>ej/dd iZ?eej& e	e$ej(f dddZ@eej& e	e$ej(f e	ej(ej(f dd d!ZAG d"d# d#ZBd1d&d'ZCeBeDd(d)d*ZEG d+d, d,ZFdejGfejj(ee	e$e
f  eejG ejj(d-d.d/ZHdS )2    N)ArgumentTarget)fuse_conv_bn_eval)TypeDictAnyTupleIterableOptionalListcast)	ShapeProp)defaultdict)Enum)targetreturnc                 C   s&   |  dd^ }}|r|d nd|fS )zp
    Splits a qualname into parent path and last atom.
    For example, `foo.bar.baz` -> (`foo.bar`, `baz`)
    .   r    )rsplit)r   parentname r   F/tmp/pip-unpacked-wheel-ua33x9lu/torch/fx/experimental/optimization.py_parent_name   s    r   )patternnodemodulesc                 C   s   t |jdkrdS |jd |f}t| |D ]d\}}t|tjsD dS |jdkrT dS t|jtsf dS |j|krv dS t	||j |k	r* dS q*dS )Nr   Fcall_moduleT)
lenargszip
isinstancefxNodeopr   strtype)r   r   r   nodesZexpected_typeZcurrent_noder   r   r   matches_module_pattern   s    

r)   )r   r   
new_modulec                 C   s<   t | jtstt| j\}}||| j< t|| || d S N)r"   r   r&   AssertionErrorr   setattr)r   r   r*   parent_namer   r   r   r   replace_node_module,   s    
r/   F)modelr   c                 C   s   t jt jft jt jft jt jfg}|s0t| } t	
| }t| }t|j}|D ]}|jD ]~}t|||r`t|jd jdkrq`||jd j }||j }	|	jsq`t||	}
t|jd ||
 ||jd  || q`qVt	||S )z
    Fuses convolution/BN layers for inference purposes. Will deepcopy your
    model by default, but can modify the model inplace as well.
    r   r   )nnZConv1dZBatchNorm1dConv2dBatchNorm2dZConv3dZBatchNorm3dcopydeepcopyr#   symbolic_tracedictnamed_modulesgraphr(   r)   r   r    usersr   Ztrack_running_statsr   r/   replace_all_uses_with
erase_nodeGraphModule)r0   Zinplacepatternsfx_modelr   	new_graphr   r   convZbnZ
fused_convr   r   r   fuse2   s.    







rB   c                 C   s*   t | }G dd dtj j}|| S )z5
    Removes all dropout layers from the module.
    c                       s8   e Zd Zeeedf eeef ed fddZ	  Z
S )z&remove_dropout.<locals>.DropoutRemover.)r   r    kwargsr   c                    s>   t | j| tjr*t|dks"t|d S t |||S d S )Nr   r   )r"   Z
submodulesr1   ZDropoutr   r,   superr   )selfr   r    rC   	__class__r   r   r   V   s    z2remove_dropout.<locals>.DropoutRemover.call_module)__name__
__module____qualname__r   r   r   r   r&   r   r   __classcell__r   r   rF   r   DropoutRemoverU   s   rL   )r#   r6   torchZTransformerZ	transform)r0   r?   rL   r   r   r   remove_dropoutO   s    
rN   )orig_moduler(   inputsoutputsc                    s|   t  }i  |D ]}||j}| |< q|D ] }|| fdd}| |< q.| fdd|D  |  t | |S )z
    Given lists of nodes from an existing graph that represent a subgraph, returns a submodule that executes that subgraph.
    c                    s    |  S r+   r   )xenvr   r   <lambda>h       z"extract_subgraph.<locals>.<lambda>c                    s   g | ]} | qS r   r   ).0outputrS   r   r   
<listcomp>j   s     z$extract_subgraph.<locals>.<listcomp>)r#   Graphplaceholderr   Z	node_copyrX   lintr=   )rO   r(   rP   rQ   r@   inputZnew_noder   r   rS   r   extract_subgraph^   s    

r^   c                 C   s
   t | S r+   )	th_mkldnnZMkldnnBatchNorm)a_r   r   r   rU   {   rV   rU   )r(   r   c                 C   s   i }| D ]r}|j dkrt|jts&t||j }t|tkrtt| |tj}t|t	j
s`tt|||< t||| q|S )z
    For each node, if it's a module that can be preconverted into MKLDNN,
    then we do so and create a mapping to allow us to convert from the MKLDNN
    version of the module to the original.
    r   )r%   r"   r   r&   r,   r'   
mkldnn_maprM   floatr1   Moduler4   r5   r/   )r(   r   old_modulesr   
cur_moduler*   r   r   r   modules_to_mkldnn   s    

rg   )r(   r   re   c                 C   sJ   | D ]@}|j dkrt|jts"t||j }||krt||||  qdS )za
    Maps each module that's been changed with `modules_to_mkldnn` back to its
    original.
    r   N)r%   r"   r   r&   r,   r/   )r(   r   re   r   rf   r   r   r   reset_modules   s    

rh   c                   @   s   e Zd ZejdddZdS )MklSubgraphfx_graphc                 C   s   || _ g | _g | _g | _d S r+   )rk   r(   start_nodes	end_nodes)rE   rk   r   r   r   __init__   s    zMklSubgraph.__init__N)rH   rI   rJ   r#   rZ   rn   r   r   r   r   ri      s   ri   
   r   c                    s(   ddt td fdd}|S )aW  
    This generates a heuristic that can be passed into `optimize_for_inference` that
    determines whether a subgraph should be run in MKL by running it with the example_inputs.

    Example usage:
        heuristic = gen_mkl_autotuner(example_inputs, iters=10)
        fast_model = optimization.optimize_for_inference(model, heuristic)
    Nr9   r   c                    s   | j }d kr,| jj| jjt dd |D  tttj	 dd | j
D }t| j||fdd}| fdd}tjjt  | fdd}||k S )	Nc                 S   s   g | ]}t |jqS r   )rM   ZrandnshaperW   r   r   r   r   rY      s     z@gen_mkl_autotuner.<locals>.use_mkl_heuristic.<locals>.<listcomp>c                 S   s   g | ]}|j d  qS )r   )r    rr   r   r   r   rY      s     c                    s<   t D ]
}|   qt }t  D ]
}|  }q$t | S r+   )rangetime)fra   beginout)iterswarmupr   r   	benchmark   s    z?gen_mkl_autotuner.<locals>.use_mkl_heuristic.<locals>.benchmarkc                      s   dd dd  D  D S )Nc                 S   s   g | ]}|  qS r   to_denserW   ir   r   r   rY      s     zRgen_mkl_autotuner.<locals>.use_mkl_heuristic.<locals>.<lambda>.<locals>.<listcomp>c                 S   s   g | ]}|  qS r   )	to_mkldnnr}   r   r   r   rY      s     r   r   Zsample_inputs	submoduler   r   rU      rV   z>gen_mkl_autotuner.<locals>.use_mkl_heuristic.<locals>.<lambda>c                      s     S r+   r   r   r   r   r   rU      rV   )rl   rk   Zowning_modulere   r   	propagater   r   r#   r$   rm   r^   r(   rh   r9   r7   r8   )r9   Zinput_nodesZoutput_argsrz   Zmkl_timeZno_mkl_timeexample_inputsr?   rx   re   ry   r   r   use_mkl_heuristic   s    z,gen_mkl_autotuner.<locals>.use_mkl_heuristic)ri   bool)r   rx   ry   r   r   r   r   gen_mkl_autotuner   s    	r   rp   c                 C   s   t | jdkS )z
    This is a heuristic that can be passed into `optimize_for_inference` that
    determines whether a subgraph should be run in MKL by checking if there
    are more than 2 nodes in it
       )r   r(   )r9   r   r   r   use_mkl_length   s    r   c                   @   sB   e Zd Zdd ZedddZeedddZeed	d
dZdS )	UnionFindc                 C   s   d g| | _ dg| | _d S )Nr   r   size)rE   nr   r   r   rn      s    zUnionFind.__init__)vc                 C   s   || j |< d| j|< d S )Nr   r   )rE   r   r   r   r   make_set   s    
zUnionFind.make_set)r   r   c                 C   sB   | j | }||kr|S |d k	s"t| || j |< tt| j | S r+   )r   r,   findr   int)rE   r   parr   r   r   r      s    
zUnionFind.find)r`   bc                 C   sf   |  ||  | }}||kr"|S | j| | j| k r@|| }}|| j|< | j|  | j| 7  < d S r+   )r   r   r   )rE   r`   r   r   r   r   join   s    

zUnionFind.joinN)rH   rI   rJ   rn   r   r   r   r   r   r   r   r   r      s   r   )r0   pass_configtracerr   c              
      s*  dddt id}|dkri }|| |d r6t| } |d rFt| } |d dkrV| S t|d tsltd	d|d krtd
|d d }| }|t	|  t
|j }t|  }G dd dt}t jD ]V}	|j}
|	jdkrV||	j }t|tkr|j}
t| d}|dk	r|jtjks:td|jtdkstdn2|	jdkr|	jtkrv|j}
n|	jtkr|j}
|
|jkr|
|jkrt dd |	j!D sqֈ "|	 t
#|	j! fdd}W 5 Q R X t$t%t
j&j' ||	_! (|	(  )dd|	f}|	*| |	f|_!W 5 Q R X qt+t j|}| _, jD ]}	|	jdkrL|	jdkrL|	j!d }t|	j-}|D ]2}|jdkr|jdkr|*|  .| qt/|	j-dkrL .|	 qLt/ j}t0|fddt1 jD ]\}}	|	jdkr,|	jdkr,||	_23| n|	jdkrn|	jdkrn|	j!d dk	s\t|	j!d |	_4ntfdd|	j5D }t/|dkrqt dd |D rtt6|}|d |	_7|dd D ]}8|d | qʐqt9 fd d} jD ]r}	t:|	d!r$|;|	j7 j<|	 t:|	d"rH|;|	j2 j=<|	 t:|	d#r|;|	j4 j><|	 q|? D ]P}||sx|j=|j> D ]$}	|	j!d }|	*|  .|	 qt@|j|| qxd} jD ]&}	|	jdks|	jdkr|d7 }qtABtCDd$|   E  t
|  }|S )%a  
    Performs a set of optimization passes to optimize a model for the
    purposes of inference. Specifically, the passes that are run are:
    1. Conv/BN fusion
    2. Dropout removal
    3. MKL layout optimizations

    The third optimization takes a function `use_mkl_heuristic` that's used
    to determine whether a subgraph should be explicity run in MKL layout.

    Note: As FX does not currently handle aliasing, this pass currently
    assumes nothing aliases. If that isn't true, use at your own risk.
    T	heuristic)conv_bn_fuserN   mkldnn_layout_optimizeNr   rN   r   Fz+mkldnn_layout_optimize config is not a dictz4Heuristic not found in mkldnn_layout_optimize configc                   @   s   e Zd ZdZdZdZdS )z*optimize_for_inference.<locals>.MklSupportr   r      N)rH   rI   rJ   NOYESUNKNOWNr   r   r   r   
MklSupport  s   r   r   z)this pass is only for torch.float modulescpuz!this pass is only for CPU modulescall_functionc                 S   s   g | ]}|j d kqS r{   )r   )rW   argr   r   r   rY   3  s     z*optimize_for_inference.<locals>.<listcomp>c                    s     d| fS )Nr   )call_methodr   rj   r   r   rU   6  rV   z(optimize_for_inference.<locals>.<lambda>r   r|   r   r   c                    s0   t | dr | jS t | dr, | jS d S )Ncolorstart_color)hasattrr   r   r   r   )ufr   r   	get_colorS  s
    

z)optimize_for_inference.<locals>.get_colorc                    s,   g | ]$}t |tjr |d k	r |qS r+   )r"   r#   r$   r}   )r   r   r   rY   n  s       c                 s   s   | ]}|d kV  qd S r+   r   r}   r   r   r   	<genexpr>r  s     z)optimize_for_inference.<locals>.<genexpr>r   c                      s   t  S r+   )ri   r   rj   r   r   rU   y  rV   r   r   	end_colorzmkldnn conversions: )Fr   updaterB   rN   r"   r7   RuntimeErrortracer4   r5   r#   r=   rootr8   r   listr(   r   r%   r   r'   mkldnn_supportedr   next
parametersZdtyperM   rc   r,   Zdevicemkldnn_supported_unknownr   anyr    Zinserting_beforeZmap_argr   r   r   r   Zinserting_afterZcreate_noder;   rg   re   r:   r<   r   r   	enumerater   r   r   Zall_input_nodessortedr   r   r   r   r   appendrl   rm   valuesrh   logging	getLoggerrH   infor\   )r0   r   r   Zdefault_pass_configr   Z
cur_tracerr?   r   r   r   Zsupports_mkldnnrf   Zsample_parameterZmkldnn_argsZdense_xre   Zprv_noder:   userZ	num_nodesZcur_idxZ
cur_colorsZother_colorZmkldnn_graphsr9   ZprvZmkldnn_conversionsresultr   )rk   r   r   r   optimize_for_inference   s    
	


 











r   )F)ro   r   )IZtorch.fxr#   Ztorch.fx.noder   r   Ztorch.nn.utils.fusionr   typingr   r   r   r   r	   r
   r   r   rM   Ztorch.nnr1   Ztorch.nn.functionalZ
functionalFZtorch.fx.passes.shape_propr   r4   collectionsr   Ztorch.utils.mkldnnutilsZmkldnnr_   operatorrt   r   enumr   r&   r   r$   r)   rd   r/   rB   rN   r^   r2   ZLinearr3   ZReLUZ	MaxPool2dZ	AvgPool2dZAdaptiveAvgPool2dZreluZ	transposeZsigmoidZ
avg_pool2dZadaptive_avg_pool2dr   addmulr   ZMkldnnConv2dZMkldnnLinearrb   rg   rh   ri   r   r   r   r   ZTracerr   r   r   r   r   <module>   sp   (	  (          	    .
&