
    Χgg                        U d dl Z d dlmZ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 d dlmZ d dlmZ d dlmZ d d	lmZ d dlZd
dgZe j        j        Zd Zi Ze
e	e	f         ed<   d Zd4dZ eej                   ddde!fd            Z" eej#                  d5de!fd            Z$ eej%                  d5de!fd            Z& eej'                  d5de!fd            Z(	 d4dee!         dee!         dee!         de)de!f
dZ* eej+        ej,        g          ddde!fd            Z- eej.                  de!fd            Z/d Z0 eej1        ej2        ej3        g          ddde!fd            Z4d  Z5dd!deeee!d"f         ee!d"f         ee!d"f         eee!d"f                  f                  fd#Z6dd!deeee!d"f         ee!d"f         ee!d"f         eee!d"f                  f                  fd$Z7 eej8        d%&          ddde!fd'            Z9 eej:        d%&          de!fd(            Z;d) Z< eej=        ej>        ej?        g          ddde!fd*            Z@ eejA        d%&          de!fd+            ZB eejC        d%&          de!fd,            ZDi ej         e"ej#        e$ej%        e&ej'        e(ej+        e-ej,        e-ej.        e/ej1        e4ej2        e4ej3        e4ej=        e@ej>        e@ej?        e@ej8        e9ej:        e;ejA        eBejC        eDZd- ZEg d.ZFd/ ZGd0 ZHd1 ZId2 ZJ G d3 d
e          ZKdS )6    N)tree_maptree_flattentree_unflatten   )ModuleTracker)ListAnyDictOptionalUnionTupleIterator)defaultdict)TorchDispatchModeprodwrapsFlopCounterModeregister_flop_formulac                 H    t          | t          j                  r| j        S | S N)
isinstancetorchTensorshape)is    T/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/utils/flop_counter.py	get_shaper      s"    !U\"" wH    flop_registryc                 B     t                     d d fd
            }|S )N)out_valc                 P    t          t          ||| f          \  }}} |d|i|S )N	out_shape)r   r   )r#   argskwargsr%   fs       r   nfzshape_wrapper.<locals>.nf   s:    "*9tVW6M"N"Nfiq$6)6v666r    r   r(   r)   s   ` r   shape_wrapperr+      s@    
1XX 7 7 7 7 7 7 X7 Ir    Fc                       fd}|S )Nc                      st                       fd}t          j        j                            |            S )Nc                     t          | t          j        j                  s"t	          d|  dt          |                      | t          v rt          d|            t          | <   d S )Nzlregister_flop_formula(targets): expected each target to be OpOverloadPacket (i.e. torch.ops.mylib.foo), got z which is of type zduplicate registrations for )r   r   _opsOpOverloadPacket
ValueErrortyper!   RuntimeError)targetflop_formulas    r   registerz=register_flop_formula.<locals>.register_fun.<locals>.register&   s    fej&ABB A @@ @15f@ @A A A &&"#J&#J#JKKK$0M&!!!r    )r+   r   utils_pytree	tree_map_)r5   r6   get_rawtargetss   ` r   register_funz+register_flop_formula.<locals>.register_fun"   sU     	7(66L	1 	1 	1 	1 	1 	%%h888r     )r;   r:   r<   s   `` r   r   r   !   s*         & r    )r%   returnc                <    | \  }}|\  }}||k    sJ ||z  dz  |z  S )zCount flops for matmul.   r=   )	a_shapeb_shaper%   r&   r'   mkk2ns	            r   mm_floprG   7   s5    
 DAqEB7777q519q=r    c                 "    t          ||          S )zCount flops for addmm.)rG   
self_shaperA   rB   r%   r'   s        r   
addmm_floprK   B   s     7G$$$r    c                 Z    | \  }}}|\  }}}	||k    sJ ||k    sJ ||z  |	z  dz  |z  }
|
S )z"Count flops for the bmm operation.r@   r=   )rA   rB   r%   r'   brC   rD   b2rE   rF   flops              r   bmm_floprP   G   sO    
 GAq!IBA77777777q519q=1DKr    c                 "    t          ||          S )z&Count flops for the baddbmm operation.rP   rI   s        r   baddbmm_floprS   T   s    
 GW%%%r    x_shapew_shaper%   
transposedc                     | d         }|r| n|dd         }|^}}}	 t          |          t          |          z  |z  |z  |z  dz  }	|	S )a  Count flops for convolution.

    Note only multiplication is
    counted. Computation for bias are ignored.
    Flops for a transposed convolution are calculated as
    flops = (x_shape[2:] * prod(w_shape) * batch_size).
    Args:
        x_shape (list(int)): The input shape before convolution.
        w_shape (list(int)): The filter shape.
        out_shape (list(int)): The output shape after convolution.
        transposed (bool): is the convolution transposed
    Returns:
        int: the number of flops
    r   r@   Nr   )
rT   rU   r%   rV   
batch_size
conv_shapec_outc_infilter_sizerO   s
             r   conv_flop_countr]   \   sj    * J'6''Y;J 'E4+ 
d;///*<uDtKaODKr    c                (    t          | |||          S )zCount flops for convolution.rV   )r]   )
rT   rU   _bias_stride_padding	_dilationrV   r%   r&   r'   s
             r   	conv_floprd      s     7GY:NNNNr    c                 |   d }d}	 |
d         r+t          |d                   }|t          | |||           z  }|
d         rzt          |d                   }|r2|t           ||            ||           ||          d          z  }n1|t           ||           ||            ||          d          z  }|S )Nc                 R    | d         | d         gt          | dd                    z   S )Nr   r   r@   )list)r   s    r   tzconv_backward_flop.<locals>.t   s(    a%(#d59oo55r    r   r   Fr_   )r   r]   )grad_out_shaperT   rU   r`   ra   rb   rc   rV   _output_padding_groupsoutput_maskr%   rh   
flop_countgrad_input_shapegrad_weight_shapes                   r   conv_backward_floprp      s    6 6 6JDL 1~ a$Yq\22ong?OU_Q_```
1~ q%il33 	q/!!N*;*;QQwZZK\I]I]joppppJJ /!!G**aa6G6GK\I]I]joppppJr    c                    | \  }}}}|\  }}}	}
|\  }}}}||cxk    r|k    r%n n"||cxk    r|k    rn n||
k    r|	|k    r||
k    sJ d}|t          ||z  ||f||z  ||	f          z  }|t          ||z  ||	f||z  |	|f          z  }|S )z^
    Count flops for self-attention.

    NB: We can assume that value_shape == key_shape
    r   rR   )query_shape	key_shapevalue_shaperM   hs_qd_q_b2_h2s_k_d2_b3_h3_s3d_vtotal_flopss                   r   sdpa_flop_countr      s     !NAq#s"Cc3$Cc3????s?????qC33#::#**QTX[Q[Q[Q[Q[K8QUC-AsC/@AAAK8QUC-AsC/@AAAKr    c                $    t          | ||          S )Count flops for self-attention.r   )rr   rs   rt   r%   r&   r'   s         r   	sdpa_flopr     s     ;	;???r    c                     ddl m} ddlm} t	          | ||f          s&|                                                                 S |g|                     d          dz
  z  S )z
    If the offsets tensor is fake, then we don't know the actual lengths.
    In that case, we can just assume the worst case; each batch has max length.
    r   )
FakeTensor)FunctionalTensorr   )torch._subclasses.fake_tensorr   #torch._subclasses.functional_tensorr   r   difftolistsize)offsetsmax_lenr   r   s       r   _offsets_to_lengthsr     sv    
 988888DDDDDDg
,<=>> '||~~$$&&&9Q!+,,r    )grad_out.c              #     K   |t          |j                  dk    sJ t          |j                  dk    sJ ||j        | j        k    sJ | j        \  }}	}
|j        \  }}}|j        \  }}}|J |J |j        |j        k    sJ t          ||          }t          ||          }t          ||          D ]%\  }}d|	||
f}d|||f}d|||f}||nd}||||fV  &dS | j        |j        |j        ||j        ndfV  dS )a;  
    Given inputs to a flash_attention_(forward|backward) kernel, this will handle behavior for
    NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for
    each batch element.

    In the case that this isn't a NestedTensor kernel, then it just yields the original shapes.
    N   r   lenr   r   zip)querykeyvaluer   	cum_seq_q	cum_seq_kmax_qmax_k_h_qrw   h_kd_kh_vr   seq_q_lengthsseq_k_lengths	seq_q_len	seq_k_lennew_query_shapenew_key_shapenew_value_shapenew_grad_out_shapes                          r   %_unpack_flash_attention_nested_shapesr     st     $  39~~""""5;1$$$$8>U[#@#@#@#@k3i3k3$$$$$$)/1111+Iu==+Iu==&)-&G&G 	V 	V"Y	 #y#6OY4M #y#6O4<4Hd!=/CUUUUUU
+sy%+AUx~~[_
______r    c              #   
  K   |t          |j                  dk    sJ t          |j                  dk    sJ ||j        | j        k    sJ | j        \  }}}	}
|j        \  }}}}|j        \  }}}}|J |J |j        |j        k    sJ t          ||          }t          ||          }t          ||          D ]%\  }}d|	||
f}d|||f}d|||f}||nd}||||fV  &dS | j        |j        |j        ||j        ndfV  dS )a?  
    Given inputs to a efficient_attention_(forward|backward) kernel, this will handle behavior for
    NestedTensor inputs by effectively unbinding the NestedTensor and yielding the shapes for
    each batch element.

    In the case that this isn't a NestedTensor kernel, then it just yields the original shapes.
    N   r   r   )r   r   r   r   cu_seqlens_qcu_seqlens_kmax_seqlen_qmax_seqlen_kr   r   rw   r   r   r   r   	seqlens_q	seqlens_klen_qlen_kr   r   r   r   s                          r   )_unpack_efficient_attention_nested_shapesr   F  s{     $  39~~""""5;1$$$$8>U[#@#@#@#@1c31c31c3''''''!\%77777'lCC	'lCC		955 	V 	VLE5 #uc2OUC0M #uc2O4<4Hd!=/CUUUUUU
+sy%+AUx~~[_
______r    T)r:   c          	      `    t          | ||||||          }
t          d |
D                       S )r   )r   r   r   r   r   r   r   c              3   B   K   | ]\  }}}}t          |||          V  d S r   r   .0rr   rs   rt   r   s        r   	<genexpr>z0_flash_attention_forward_flop.<locals>.<genexpr>  J        2KK 	Y<<     r    r   sum)r   r   r   r   r   r   r   r%   r&   r'   sizess              r   _flash_attention_forward_flopr   v  s]    " 2  E   6;     r    c           	      `    t          | ||||||          }
t          d |
D                       S )r   )r   r   r   r   r   r   r   c              3   B   K   | ]\  }}}}t          |||          V  d S r   r   r   s        r   r   z4_efficient_attention_forward_flop.<locals>.<genexpr>  r   r    r   r   )r   r   r   biasr   r   r   r   r&   r'   r   s              r   !_efficient_attention_forward_flopr     s]    " 6!!!!  E   6;     r    c                    d}|\  }}}}|\  }	}
}}|\  }}}}| \  }}}}||	cxk    r|cxk    r|k    r n n||
cxk    r|cxk    r|k    r	n n||k    sJ ||k    r||k    r||k    sJ d}|t          ||z  ||f||z  ||f          z  }|t          ||z  ||f||z  ||f          z  }|t          ||z  ||f||z  ||f          z  }|t          ||z  ||f||z  ||f          z  }|t          ||z  ||f||z  ||f          z  }|S )Nr   rR   )ri   rr   rs   rt   r   rM   ru   rv   rw   rx   ry   rz   r{   r|   r}   r~   r   _b4_h4_s4_d4s                        r   sdpa_backward_flop_countr     s   K NAq#s"Cc3$Cc3'Cc3!!!!s!!!!c!!!!!a3&<&<&<&<#&<&<&<&<&<&<&<&<&<#::#**K 8QUC-AsC/@AAAK 8QUC-AsC/@AAAK8QUC-AsC/@AAAK 8QUC-AsC/@AAAK8QUC-AsC/@AAAKr    c                &    t          | |||          S )z(Count flops for self-attention backward.r   )ri   rr   rs   rt   r%   r&   r'   s          r   sdpa_backward_flopr     s    
 $NKKXXXr    c
           
      b    t          |||| ||||	          }t          d |D                       S )N)r   r   r   r   r   r   r   r   c              3   D   K   | ]\  }}}}t          ||||          V  d S r   r   r   rr   rs   rt   ri   s        r   r   z1_flash_attention_backward_flop.<locals>.<genexpr>  L        ?KK 	!iUU     r    r   )r   r   r   r   out	logsumexpr   r   r   r   r&   r'   shapess                r   _flash_attention_backward_flopr     s`    " 3	 	 	F   CI     r    c
           
      b    t          |||| ||||	          }t          d |D                       S )N)r   r   r   r   r   r   r   r   c              3   D   K   | ]\  }}}}t          ||||          V  d S r   r   r   s        r   r   z5_efficient_attention_backward_flop.<locals>.<genexpr>  r   r    r   )r   r   r   r   r   r   r   r   r   r   r&   r'   r   s                r   "_efficient_attention_backward_flopr     s`    " 7!!!!	 	 	F   CI     r    c                 6    t          | t                    s| fS | S r   )r   tuple)xs    r   normalize_tupler   .  s     a tHr    ) KMBTc                     t          dt          t          t                    dz
  t          t	          |                     dz
  dz                      }t          |         S )Nr   r   r@   r   )maxminr   suffixesstr)numberindexs     r   get_suffix_strr   7  sJ     3s8}}q(3s6{{+;+;a+?A*EFFGGEE?r    c                 j    t                               |          }| d|z  z  d}|t           |         z   S )Ni  z.3f)r   r   )r   suffixr   r   s       r   convert_num_with_suffixr   >  s6    NN6""E%++E8E?""r    c                      |dk    rdS | |z  dS )Nr   0%z.2%r=   )numdenoms     r   convert_to_percent_strr   E  s     zztEkr    c                 <     t                      fd            }|S )Nc                 R    t          |           \  }} | }t          ||          S r   )r   r   )r&   	flat_argsspecr   r(   s       r   r)   z)_pytreeify_preserve_structure.<locals>.nfK  s/    &t,,	4amc4(((r    r   r*   s   ` r   _pytreeify_preserve_structurer   J  s3    
1XX) ) ) ) X)
 Ir    c                       e Zd ZdZ	 	 	 	 ddeeej        j        e	ej        j                 f                  de
dedeeeef                  f fd	Zd
e
fdZd
eeeee
f         f         fdZddZ fdZ fdZddZd Z xZS )r   a  
    ``FlopCounterMode`` is a context manager that counts the number of flops within its context.

    It does this using a ``TorchDispatchMode``.

    It also supports hierarchical output by passing a module (or list of
    modules) to FlopCounterMode on construction. If you do not need hierarchical
    output, you do not need to use it with a module.

    Example usage

    .. code-block:: python

        mod = ...
        with FlopCounterMode(mod) as flop_counter:
            mod.sum().backward()

    Nr@   Tmodsdepthdisplaycustom_mappingc                 D   t                                                       t          d           | _        || _        || _        |i }|t          j        dd           i t          d |	                                D             | _        t                      | _        d S )Nc                  *    t          t                    S r   )r   intr=   r    r   <lambda>z*FlopCounterMode.__init__.<locals>.<lambda>o  s    +VYJZJZ r    z<mods argument is not needed anymore, you can stop passing itr@   )
stacklevelc                 Z    i | ](\  }}|t          |d d          r|nt          |          )S )_get_rawF)getattrr+   r   rD   vs      r   
<dictcomp>z,FlopCounterMode.__init__.<locals>.<dictcomp>x  s<    nnntqRSqwq*e44J!!-:J:Jnnnr    )super__init__r   flop_countsr   r   warningswarnr!   itemsr   mod_tracker)selfr   r   r   r   	__class__s        r   r	  zFlopCounterMode.__init__h  s     	6ABZBZ6[6[
!NMXefgggg

nnWeWkWkWmWmnnn
 )??r    r>   c                 Z    t          | j        d                                                   S )NGlobal)r   r
  valuesr  s    r   get_total_flopszFlopCounterMode.get_total_flops|  s$    4#H-4466777r    c                 H    d | j                                         D             S )a  Return the flop counts as a dictionary of dictionaries.

        The outer
        dictionary is keyed by module name, and the inner dictionary is keyed by
        operation name.

        Returns:
            Dict[str, Dict[Any, int]]: The flop counts as a dictionary.
        c                 4    i | ]\  }}|t          |          S r=   )dictr  s      r   r  z3FlopCounterMode.get_flop_counts.<locals>.<dictcomp>  s$    @@@tq!477@@@r    )r
  r  r  s    r   get_flop_countszFlopCounterMode.get_flop_counts  s(     A@t'7'='='?'?@@@@r    c                    
 | j         }|d}dd l}d|_        g d}g }                                 
t	          
          d
 fd}t           j                                                  D ]L}|dk    r	|                    d          d	z   }||k    r( |||d	z
            }|	                    |           Md j        v rJsHt          t          |                    D ]}	d
||	         d         z   ||	         d<    |dd          |z   }t          |          dk    rg dg}|                    ||d          S )Ni?B r   T)ModuleFLOPz% TotalFc           	         t          
j        |                                                    }	|k    z  	d|z  }g }|                    || z   t	          |          t          |          g           
j        |                                          D ]L\  }}|                    |dz   t          |          z   t	          |          t          |          g           M|S )N z - )r   r
  r  appendr   r   r  r   )mod_namer   r   paddingr  rD   r  global_flopsglobal_suffixis_global_subsumedr  s          r   process_modz.FlopCounterMode.get_table.<locals>.process_mod  s     d.x8??AABBK+"==EkGFMM("']CC&{LAA   
 (288::  1eOc!ff,+A}==*1l;;    
 Mr    r  .r   r  )r  0r   )leftrightr)  )headerscolalign)r   tabulatePRESERVE_WHITESPACEr  r   sortedr
  keyscountextendranger   )r  r   r,  headerr  r%  mod	mod_depth
cur_valuesidxr"  r#  r$  s   `         @@@r   	get_tablezFlopCounterMode.get_table  s   =JE=E'+$...++--&|44"	 	 	 	 	 	 	 	, $*//1122 	& 	&Ch		#*I5  $S)a-88JMM*%%%%
 t'''0B'S[[)) 6 6!$vc{1~!5sA [1--6Fv;;!+++,F  B\ ]]]r    c                     | j                                          | j                                         t	                                                       | S r   )r
  clearr  	__enter__r  )r  r  s    r   r;  zFlopCounterMode.__enter__  sH       ""$$$r    c                      t                      j        |  | j                                         | j        r)t	          |                     | j                             d S d S r   )r  __exit__r  r   printr8  r   )r  r&   r  s     r   r=  zFlopCounterMode.__exit__  sb    $!!###< 	.$..,,-----	. 	.r    r=   c                 X    |r|ni } ||i |}|                      |j        |||          S r   )_count_flops_overloadpacket)r  functypesr&   r'   r   s         r   __torch_dispatch__z"FlopCounterMode.__torch_dispatch__  sA    !)rdD#F##  !5sD&IIIr    c                     || j         v rP| j         |         } ||i |d|i}t          | j        j                  D ]}| j        |         |xx         |z  cc<   |S )Nr#   )r!   setr  parentsr
  )r  func_packetr   r&   r'   flop_count_funcrm   pars           r   r@  zFlopCounterMode._count_flops  s    $,,,"0=O($F&FF#FFFJ4+344 A A %k222j@2222
r    )Nr@   TNr   )r=   N)__name__
__module____qualname____doc__r   r   r   nnr  r   r   boolr
   r	   r	  r  r   r  r8  r;  r=  rD  r@  __classcell__)r  s   @r   r   r   T  sj        * MQ 7;+ +5$ux2G!GHI+ + 	+
 %T#s(^4+ + + + + +(8 8 8 8 8
Ac4S>&9!: 
A 
A 
A 
A:^ :^ :^ :^x    . . . . .J J J J
      r    )Fr   )Lr   torch.utils._pytreer   r   r   module_trackerr   typingr   r	   r
   r   r   r   r   collectionsr   torch.utils._python_dispatchr   mathr   	functoolsr   r  __all__opsatenr   r!   __annotations__r+   r   mmr   rG   addmmrK   bmmrP   baddbmmrS   rP  r]   convolution_convolutionrd   convolution_backwardrp   r   '_scaled_dot_product_efficient_attention#_scaled_dot_product_flash_attention#_scaled_dot_product_cudnn_attentionr   r   r   r   _flash_attention_forwardr   _efficient_attention_forwardr   r   0_scaled_dot_product_efficient_attention_backward,_scaled_dot_product_flash_attention_backward,_scaled_dot_product_cudnn_attention_backwardr   _flash_attention_backwardr   _efficient_attention_backwardr   r   r   r   r   r   r   r   r=   r    r   <module>rn     s    F F F F F F F F F F ) ) ) ) ) ) D D D D D D D D D D D D D D D D D D # # # # # # : : : : : :              5
6y~  
 !#tCH~ " " "     , tw/3   #      tz""% %# % % % #"% tx  
 
C 
 
 
 ! 
 t|$$& &C & & & %$& 	% %#Y%#Y% Cy% 	%
 	% % % %N ($*;<==bf O O Oux O O O >=O
 t011e e e e 21eN  $ D@@B C C EI @ @ @WZ @ @ @C C@	- 	- 	-" +` +` +` eE#s(OU38_eCHoxPUVY[^V^P_G``ab+` +` +` +`f -` -` -` eE#s(OU38_eCHoxPUVY[^V^P_G``ab-` -` -` -`` t4dCCC    	   DC> t8$GGG 	   HG>  6 MIIK L L ^b Y Y Yps Y Y YL LY t5tDDD 	   ED@ t94HHH 	   IH@GWJ
 	Hh 	L,	
 	i 	y 	1 	0) 	,i 	,i 	9;M 	57I 	57I 	!#@ 	%'H  	"$B!" 	&(J#(   $##  # # #     
  K K K K K' K K K K Kr    