o
    lhPN                     @   s   d dl Z d dlZd dlZd dlZd dlZd dlZd dlmZ d dlm	Z	m
Z
mZ ddlmZ ddlmZ dd Zd	d
 Zdd Zd*ddZd+ddZd,ddZG dd dZG dd dZdd Zd-ddZd-d d!Zd"d# Zed.d&d'Zd-d(d)ZdS )/    N)contextmanager)AnyDictList   )language)runtimec                 C   sL   d | } dddd|  dg}t|}|tjjd}dd |D }|S )	N,
nvidia-smi-i0z--query-gpu=z--format=csv,noheader,nounitsc                 S   s   g | ]}t |qS  )int.0xr   r   b/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/triton/testing.py
<listcomp>       znvsmi.<locals>.<listcomp>)join
subprocesscheck_outputdecodesysstdoutencodingsplit)attrscmdoutretr   r   r   nvsmi   s   

r!   c                    s0   t  t   fddfdd|D S )Nc                    sh   d|   krdkst d t d| d  }t|}t|}|| }d|  |  | |   S )Nr   r   z%Quantiles must be in the range [0, 1])
ValueErrormathfloorceil)qpointloweruppert)anr   r   get_quantile   s   

z_quantile.<locals>.get_quantilec                    s   g | ]} |qS r   r   )r   r&   )r-   r   r   r   '   r   z_quantile.<locals>.<listcomp>)lensorted)r+   r&   r   )r+   r-   r,   r   	_quantile   s   	r0   c                 C   s~   |d urt | |}t|dkr|d }|S |dkr| S |dkr#t| S |dkr+t| S |dkr4t| S |dkr=t| S d S )Nr   r   allminmaxmeanmedian)r0   r.   r2   r3   
statisticsr4   r5   )times	quantilesreturn_moder    r   r   r   _summarize_statistics*   s    


r:      r4   c              	   C   s  ddl }|dv s
J |j|j  |   |dur,|D ]}|  |d d|_q|jjdd}|jjdd}|  t	dD ]}	|   qB|  |j
  ||d }
tdt||
 }|j }|j| t	|D ]}	|dur|D ]}d|_qy|   qqW d   n1 sw   Y  |j
  g }d}t	|D ]+}	|jjdd}|jjdd}|  |  |  |j
  |||| g7 }qt|||W  d   S 1 sw   Y  dS )	a  
    Benchmark the runtime of the provided function.

    :param fn: Function to benchmark
    :type fn: Callable
    :param rep: Repetition time (in ms)
    :type rep: int
    :param grad_to_none: Reset the gradient of the provided tensor to None
    :type grad_to_none: torch.tensor, optional
    :param return_mode: The statistical measure to return. Options are "min", "max", "mean", "median", or "all". Default is "mean".
    :type return_mode: str
    r   Nr2   r3   r4   r5   r1   Tenable_timing   r   
   )torchcudastreamStreamdetach_requires_grad_gradEventrecordrangesynchronizeelapsed_timer3   r   	CUDAGraphgraphreplayr:   )fnrepgrad_to_noner8   r9   rA   r   start_event	end_event_estimate_msn_repeatgr    	n_retriesr   r   r   do_bench_cudagraph<   sP   





$rZ      d   c                    sp  |dv sJ t jj  |      t jj } jdd} jdd}|  tdD ]}	t jj	| |   q-|     |
|d }
tdt||
 }tdt||
 } fddt|D } fddt|D }t|D ]}	|   qut|D ]$}|d	ur|D ]}d	|_qt jj	| ||   |   ||   q   d
d t||D }t|||S )a  
    Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with
    the 20-th and 80-th performance percentile.

    :param fn: Function to benchmark
    :type fn: Callable
    :param warmup: Warmup time (in ms)
    :type warmup: int
    :param rep: Repetition time (in ms)
    :type rep: int
    :param grad_to_none: Reset the gradient of the provided tensor to None
    :type grad_to_none: torch.tensor, optional
    :param quantiles: Performance percentile to return in addition to the median.
    :type quantiles: list[float], optional
    :param return_mode: The statistical measure to return. Options are "min", "max", "mean", "median", or "all". Default is "mean".
    :type return_mode: str
    r<   Tr=   r?   r   c                       g | ]} j d dqS Tr=   rH   r   idir   r   r          zdo_bench.<locals>.<listcomp>c                    r]   r^   r_   r`   rb   r   r   r      rd   Nc                 S   s   g | ]	\}}| |qS r   )rL   )r   ser   r   r   r      s    )r   driveractiveget_device_interfacerK   get_empty_cache_for_benchmarkrH   rI   rJ   clear_cacherL   r3   r   rG   zipr:   )rP   warmuprQ   rR   r8   r9   cacherS   rT   rU   rV   n_warmuprW   ra   r   r7   r   rb   r   do_bench{   s>   rp    c                 C   sJ  ddl }ddl}t| |js|| } t||js||}|du r$d}t|r-|| jn|}|du r5d}t|r>|| jn|}t| |jrX| j|jkrP|  } | 	 
   } t||jrp|j|jkrh| }|	 
   }| jdksz|jdkr|jj| |||dd dS |j| |||dst| d	|  d
| d| d| d
dS )a  
    Asserts that two inputs are close within a certain tolerance.

    :param x: The first input.
    :type x: scala, list, numpy.ndarray, or torch.Tensor
    :param y: The second input.
    :type y: scala, list, numpy.ndarray, or torch.Tensor
    :param atol: The absolute tolerance. Default value is 1e-2.
    :type atol: float, optional
    :param rtol: The relative tolerance. Default value is 0.
    :type rtol: float, optional
    :param err_msg: The error message to use if the assertion fails.
    :type err_msg: str
    r   Ng{Gz?g        r   T)atolrtol	equal_nan)rr   rs    z is not close to z (atol=z, rtol=))numpyrA   
isinstanceTensortensorcallabledtypebfloat16floatcpudetachsizetestingassert_allcloseallcloseAssertionError)r   yrr   rs   err_msgnprA   r   r   r   assert_close   s4   

&r   c                   @   sj   e Zd ZdZ					ddee dee dedee d	ee d
edeeef dedededefddZ	dS )	Benchmarkzk
    This class is used by the :code:`perf_report` function to generate line plots with a concise API.
    rq   FNx_namesx_valsline_arg	line_vals
line_names	plot_nameargsxlabelylabelx_logy_logc                 C   sL   || _ || _|
| _|| _|| _|| _|| _|| _|| _|	| _	|| _
|| _dS )aq  
        Constructor.
        x_vals can be a list of scalars or a list of tuples/lists. If x_vals is a list
        of scalars and there are multiple x_names, all arguments will have the same value.
        If x_vals is a list of tuples/lists, each element should have the same length as
        x_names.

        :param x_names: Name of the arguments that should appear on the x axis of the plot.
        :type x_names: List[str]
        :param x_vals: List of values to use for the arguments in :code:`x_names`.
        :type x_vals: List[Any]
        :param line_arg: Argument name for which different values correspond to different lines in the plot.
        :type line_arg: str
        :param line_vals: List of values to use for the arguments in :code:`line_arg`.
        :type line_vals: List[Any]
        :param line_names: Label names for the different lines.
        :type line_names: List[str]
        :param plot_name: Name of the plot.
        :type plot_name: str
        :param args: Dictionary of keyword arguments to remain fixed throughout the benchmark.
        :type args: Dict[str, Any]
        :param xlabel: Label for the x axis of the plot.
        :type xlabel: str, optional
        :param ylabel: Label for the y axis of the plot.
        :type ylabel: str, optional
        :param x_log: Whether the x axis should be log scale.
        :type x_log: bool, optional
        :param y_log: Whether the y axis should be log scale.
        :type y_log: bool, optional
        :param styles: A list of tuples, where each tuple contains two elements: a color and a linestyle.
        :type styles: list[tuple[str, str]]
        N)r   r   r   r   r   r   r   stylesr   r   r   r   )selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   __init__   s   /
zBenchmark.__init__)rq   rq   FFN)
__name__
__module____qualname____doc__r   strr   r   boolr   r   r   r   r   r      s<    
	
r   c                	   @   s>   e Zd Zdd Z		ddedededefd	d
ZdddZdS )Markc                 C   s   || _ || _d S N)rP   
benchmarks)r   rP   r   r   r   r   r   5  s   
zMark.__init__F   bench	save_path
show_plots
print_datac              
      sr  dd l }dd lm}	 dd l}
|j}dd |jD }dd |jD }t|j}|
j|| | | d}|jD ] t	 tt
fsG fdd|D  t t|kr[tdt| d  tt| }g g g }}}|jD ]<}| jdi ||j|i|j|}z|\}}}W n ty   |d d }}}Y nw ||g7 }||g7 }||g7 }qmt | | | |jt|< q5|jrl|	  |	 }|d }t|jD ][\}}||d	  ||d
  }}|jr|j| d nd }|jr|j| d nd }|j|| || |||d |  s*|  s*|t}|t}|j|| ||d|d q|   |!|j"p6| |#|j$ |%|j&rFdnd |'|j(rQdnd |r[|	)  |rl|	*|j+,||j d |||j  }|r|j-d dkr|j./ \}}|| ||  |d< |rt0|jd  t0|1  |r|j2|j+,||j dd| ddd |S )Nr   c                 S      g | ]}| d qS )-minr   r   r   r   r   r   @      zMark._run.<locals>.<listcomp>c                 S   r   )-maxr   r   r   r   r   r   A  r   )columnsc                    s   g | ]} qS r   r   )r   rU   r   r   r   r   G  s    z	Expected z values, got r   r   r   )labelcolorlsg333333?)alphar   loglinearz.png   Diff:z.csvz%.fF)float_formatindexr   )3osmatplotlib.pyplotpyplotpandasr   listr   	DataFramer   rx   tupler.   r"   dictrl   r   rP   r   r   	TypeErrorlocr   figuresubplot	enumerater   plotisnullr1   astyper~   fill_betweenlegend
set_xlabelr   
set_ylabelr   
set_xscaler   
set_yscaler   showsavefigpathr   shaper   tolistprint	to_stringto_csv)r   r   r   r   r   diff_colsave_precisionkwragsr   pltpdy_meany_miny_maxr   dfx_argsrow_meanrow_minrow_maxr   r    axfirst_xra   colstycol0col1r   r   r   _run9  sz   


$

 

"z	Mark._runrq   c                 K   s   t | jt}|r| jgn| j}g }|r)tj|dd ttj|dd}	|	d |D ]}
|	| j
|
|||fi | |rH|	d|
j d q+|rT|	d |	  |r^|r\|d	 S |S d S )
NT)exist_okzresults.htmlwz<html><body>
z<image src="z.png"/>
z</body></html>
r   )rx   r   r   r   makedirsopenr   r   writeappendr   r   close)r   r   r   r   	return_dfkwargshas_single_benchr   
result_dfshtmlr   r   r   r   run~  s(   

zMark.runN)Fr   )FFrq   F)	r   r   r   r   r   r   r   r   r   r   r   r   r   r   3  s    Er   c                        fdd}|S )z
    Mark a function for benchmarking. The benchmark can then be executed by using the :code:`.run` method on the return value.

    :param benchmarks: Benchmarking configurations.
    :type benchmarks: List of :class:`Benchmark`
    c                    s
   t |  S r   )r   )rP   r   r   r   <lambda>  s   
 zperf_report.<locals>.<lambda>r   )r   wrapperr   r   r   perf_report  s   r   c                 C   s^   ddl }ddlm} | s|j } |jj| d }|jj| d }|| d d d	 }|S )
z return DRAM bandwidth in GB/s r   Nr   rg   mem_clock_ratemem_bus_widthr   g    .A   )rA   r   rg   rB   current_devicerh   utilsget_device_properties)devicerA   rg   mem_clock_khz	bus_widthbw_gbpsr   r   r   get_dram_gbps  s   
r  c           	      C   s   dd l }ddlm} |s|j }|jj|d d }|j|}|d dk r2| |j	ks/J d}n+| |j
|jfv r=d}n | |j	|j|jfv rJd}n| |jtjtjtjfv rYd	}ntd
|| | d }|S )Nr   r   r   multiprocessor_count   r      i   i   dtype not supported&.>)rA   r   rg   rB   r  rh   r  r  get_device_capabilityfloat16float32int32r}   int16int8tl
float8e4nvfloat8e4b15float8e5RuntimeError	r|   
clock_rater  rA   rg   num_subcores
capabilityops_per_sub_coretflopsr   r   r   get_max_tensorcore_tflops  s$   
r  c                     r   )Nc                    s   t   fdd}|S )Nc            
         s   dd l }|t  }  | k}|rg|dkrgtjjd }tj	d dd}d|v s4J d|d j
jj}| d	j d
| d}tjddd|gd|d}	|	jdks\J ddt|	jv seJ d S | i | d S )Nr   zcuda-memcheck__file__PATH1)r!  PYTORCH_NO_CUDA_MEMORY_CACHINGrequestz@memcheck'ed test must have a (possibly unused) `request` fixturez::[]pytestz-vsT)capture_outputenvz7cuda-memcheck returned an error: bounds checking failedzERROR SUMMARY: 0 errors)psutilProcessr   getppidnameitemsr   realpath__globals__environnodecallspecidr   r   r   
returncoder   r   )
r   r   r*  	ppid_namerun_cuda_memcheckr   r)  test_idr   r   )target_kwargstest_fnr   r   r     s   z1cuda_memcheck.<locals>.decorator.<locals>.wrapper)	functoolswraps)r:  r   r9  )r:  r   	decorator  s   z cuda_memcheck.<locals>.decoratorr   )r9  r>  r   r=  r   cuda_memcheck  s   r?  F    c              
   c   s$   zzt g d t dddd|  d|  g t dddd| d| g tdgd	 }td
gd	 }t||  dk sEJ d|  dt|| dk sUJ d| dd|  }d| d }||fV  W t g d t g d t g d d S t g d t g d t g d w )N)r
   r   r   -pmr"  r
   r   r   z--lock-gpu-clocks=r	   z--lock-memory-clocks=zclocks.current.smr   zclocks.current.memoryr@   zGPU SMs must run at z MHzg 3O?i   gMbP?)r
   r   r   rB  r   )r
   r   r   z-rgc)r
   r   r   z-rmc)r   r   r!   abs)ref_sm_clockref_mem_clockcur_sm_clockcur_mem_clockr  gbpsr   r   r   set_gpu_clock  s8     rI  c           	      C   s   dd l }ddlm} |s|j }|jj|d d }|j }|d dk r;| |j	kr/d}n#| |j
kr7d}ntd	| |j	krCd}n| |j
|jfv rNd}ntd	|| | d
 }|S )Nr   r   r   r	  r
  r       @   r  r  )rA   r   rg   rB   r  rh   r  r  r  r  r  r  r}   r  r   r   r   get_max_simd_tflops  s&   




rL  )r;   NNr4   )r[   r\   NNr4   )NNrq   r   )r@  rA  )r;  r#   r   r6   r   r   
contextlibr   typingr   r   r   rq   r   r  r   r!   r0   r:   rZ   rp   r   r   r   r   r  r  r?  rI  rL  r   r   r   r   <module>   s2    

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B3Cc

