o
    hO                     @   s   d Z ddlZddlZddlmZ ddlmZ ddlZddlm	Z	 ej
G dd dZG d	d
 d
ZdefddZdee ddfddZG dd dZdejjdee defddZdS )aN  
Dynamo profiling implementation.

This module provides profiling functionality for Dynamo, including:
- ProfileMetrics: Class for collecting and aggregating performance metrics like
  execution time, operator counts, and fusion statistics
- ProfileResult: Class for analyzing and reporting profiling results
- Utilities for tracking missed/uncaptured operations
- Functions for instrumenting FX graphs with profiling capabilities

The profiler helps measure and optimize the performance of Dynamo-compiled code
by tracking both captured and total operations, timing, and graph statistics.
    N)Any)Self   )
print_oncec                   @   s   e Zd ZU dZeed< dZeed< dZeed< dZ	eed< de
de
fd	d
ZdddZdedd fddZdefddZdee fddZdS )ProfileMetricsg        microsecondsr   	operatorsfusionsgraphsotherreturnc                 C   4   |  j |j 7  _ |  j|j7  _|  j|j7  _| S Nr   r   r	   selfr    r   j/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/_dynamo/profiler.py__iadd__       zProfileMetrics.__iadd__c                 C   s2   t |tsJ t| j|j | j|j | j|j S r   )
isinstancer   r   r   r	   r   r   r   r   __add__&   s   


zProfileMetrics.__add__c                 C   sL   t |trt|||}t| jtd|j | jtd|j | jtd|j S )Nr   )r   intr   r   maxr   r	   r   r   r   r   __truediv__.   s   
zProfileMetrics.__truediv__c                 C   s   | j dd| jddS )Nz4.0%z ops z timer   r   r   r   r   r   __str__7   s   zProfileMetrics.__str__c                 C   s   | j | jgS r   r   r   r   r   r   tocsv:      zProfileMetrics.tocsvN)r   r   r   r   )__name__
__module____qualname__r   float__annotations__r   r   r	   r
   r   r   r   r   r   strr   listr   r   r   r   r   r      s   
 
	r   c                   @   sf   e Zd ZdedededdfddZdedefd	d
ZdefddZde	fddZ
dee fddZdS )ProfileResultcapturedtotalunique_graphsr   Nc                 C   s"   |pt  | _|p
t  | _|| _d S r   )r   r(   r)   r*   )r   r(   r)   r*   r   r   r   __init__?   s   
zProfileResult.__init__r   c                 C   r   r   r(   r)   r*   r   r   r   r   r   F   r   zProfileResult.__iadd__c                 C   s   | j | j S r   )r(   r)   r   r   r   r   percentL   r   zProfileResult.percentc                 C   s>   | j dd| jjdd| jjdd| jjddt|   S )N2z graphs z graph calls 4/z = )r*   r(   r
   r   r)   r%   r-   r   r   r   r   r   O   s   
zProfileResult.__str__c                 C   s&   | j | jj| jj| jjg|    S r   )r*   r(   r
   r   r)   r-   r   r   r   r   r   r   V   s   
zProfileResult.tocsv)r    r!   r"   r   r   r+   r   r   r-   r%   r   r&   r   r   r   r   r   r   r'   >   s    
r'   r   c                   C   s   t jddkS )NTORCHDYNAMO_PRINT_MISSING1)osenvirongetr   r   r   r   should_print_missing_   s   r6   stackc                 C   s@   t dd | D rd S dd | D } tdd| dd   d S )Nc                 s   s    | ]}d |v V  qdS )z/torch/autograd/profiler.pyNr   .0xr   r   r   	<genexpr>d   s    z print_missing.<locals>.<genexpr>c                 S   s    g | ]}d |vrd|vr|qS )z	<built-inzsite-packages/torch/r   r8   r   r   r   
<listcomp>f   s    z!print_missing.<locals>.<listcomp>MISSINGz >> )anyr   join)r7   r   r   r   print_missingc   s   rA   c                   @   s2   e Zd ZU dZeed< d	ddZdefddZdS )
Profilerr   r*   r   Nc                 C   s    t jjt jjjgt d| _d S )N)
activities
with_stack)torchprofilerprofileProfilerActivityCPUr6   profr   r   r   r   r+   o   s   
zProfiler.__init__c                 C   s  d}d}d}d}d}d}d}t | j dd d}|D ]E}	|	jdkr/|	jj}|d7 }|d8 }q|	jj|kr_|	jj}|	jj|krK|d7 }||	j 7 }nt rSt	|	j
 |d7 }||	j 7 }q	 qtj}
dt_|d8 }tt|||| |dt|||d d	|
d
S )Nr   c                 S   s   | j jS r   )
time_rangestart)r:   r   r   r   <lambda>~   s    z"Profiler.results.<locals>.<lambda>)keyTORCHDYNAMOr   )r   r   r	   r
   r   r,   )sortedrJ   eventsnamerL   endrM   
elapsed_usr6   rA   r7   rB   r*   r'   r   )r   captured_regionscaptured_opscaptured_microseconds	total_opstotal_microsecondslast_op_end_timecaptured_region_end_timerR   er*   r   r   r   resultsu   sP   


zProfiler.results)r   N)	r    r!   r"   r*   r   r$   r+   r'   r^   r   r   r   r   rB   l   s   
 
rB   gmexample_inputsc                    s(   dt dt f fdd}t jd7  _|S )Nargsr   c                     s:   t jd  j|  W  d    S 1 sw   Y  d S )NrP   )rE   rF   record_functionforward)ra   r_   r   r   _wrapped   s   $z%fx_insert_profiling.<locals>._wrappedr   )r   rB   r*   )r_   r`   re   r   rd   r   fx_insert_profiling   s   rf   )__doc__dataclassesr3   typingr   typing_extensionsr   rE   utilsr   	dataclassr   r'   boolr6   r&   r%   rA   rB   fxGraphModulerf   r   r   r   r   <module>   s    $!	":