
    ¯wg$                       d Z ddlmZ ddlZddlZddlmZmZmZ ddl	m
Z
 ddlmZmZ g dZ ej                  ej                   d	      Z ed
       ej$                  d      dd              Z ed      d dd       Z ed      d dd       Z ed       ej,                  d       ej$                  dd      dd                     Z ed       ej$                  ddddddddd	      	 	 dd              Z ed      dd       Z ed       ej$                  dddddddd      	 	 	 	 	 d!	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d"d              Z	 	 	 	 	 	 d#dZ	 	 	 	 	 	 	 	 d$dZy)%a&  This file exports ONNX ops for opset 14.

Note [ONNX operators that are added/updated in opset 14]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
New operators:
    HardSwish, Trilu

Updated operators:
    Reshape
    Add, Sub, Mul, Div
    GRU, LSTM, RNN
    BatchNorm, Cumsum, Relu
    )annotationsN)
_constants_type_utilssymbolic_helper)GLOBALS)	jit_utilsregistration)	hardswishtriltriureshape
batch_normquantized_hardswishscaled_dot_product_attention   )opsetzaten::hardswishvc                &    | j                  d|      S )N	HardSwishop)gselfs     `/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/torch/onnx/symbolic_opset14.pyr
   r
   *   s     44T""    z
aten::trilc                ,    | j                  d||d      S )NTrilur   upper_ir   r   r   diagonalouts       r   r   r   0       44x433r   z
aten::triuc                ,    | j                  d||d      S )Nr      r   r   r    s       r   r   r   5   r#   r   zaten::reshapeTc                4    t        j                  | ||d      S )Nr   )	allowzero)r   _reshape_helper)r   r   shapes      r   r   r   :   s     **1dEQGGr   zaten::batch_normifc
                    t        j                         rFt        j                  |||||g      s,t        j
                  dk  rt        j                  dddd|      S t        j                  |d       t        j                  | |||||      \  }}}}| j                  d||||||d|z
  |sdnd|sdnd	
      }
|s|
S |
\  }}}|j                  |j                                |j                  |j                                |S )
N   BatchNormalizationr   zaAll input tensors must have the same `dtype`. Turn off Autocast or export using opset version 15.r   r%   r      )	epsilon_f
momentum_ftraining_mode_ioutputs)torchis_autocast_enabledr   args_have_same_dtyper   export_onnx_opset_version _onnx_opset_unsupported_detailedcheck_training_mode_batchnorm_helperr   setTypetype)r   inputweightbiasrunning_meanrunning_vartrainingmomentumepscudnn_enabledr"   resnew_running_meannew_running_vars                 r   r   r   C   s"    	!!#44FD,<
 --2?? C
 	
 '',?.=.O.O	5&$k/+FD, $$x<!)q!q  C 
14.  !2!2!45 0 0 23
r   zquantized::hardswishc                    t        j                  | |      \  }}}}t        | |      }t        j                  | |||      S N)r   dequantize_helperr
   quantize_helper)r   xop_scaleop_zero_point_outputs         r   r   r   z   s>     221a8JAq!Qq!_F**1fhNNr   z"aten::scaled_dot_product_attentionbc	                   |r|rt        j                  |      sJ d       |rJ d       t        j                  |      rt        | |      }|rt        | ||      }t        j                  |      }	t        t        |	            }
|
d   |
d   c|
d<   |
d<   | j                  d||
      }| j                  d|| j                  d|            }| j                  d|| j                  d|            }| j                  d	||      }t        j                  |      r|}net        j                  j                  |      t        j                  j                  k(  r| j                  d
t        j                  dg            }| j                  d
t        j                  t        d       g            }| j                  d|||      }| j                  d||      }nt        j                  j                  |      t        j                  j                  t        j                  j                   t        j                  j"                  fv r| j                  d||      }n+t%        dt        j                  j                  |             | j                  d|d      }|dk7  rG| j                  d|| j                  d
t        j                  |t        j                                    }| j                  d	||      S )Nz6is_causal and attn_mask cannot be set at the same timezPconversion of scaled_dot_product_attention not implemented if enable_gqa is True	Transpose)perm_iMulSqrtMatMulConstant        value_tinfWhereAddz Unsupported type for attn_mask: Softmaxaxis_ir   Dropoutdtype)r   _is_none_attention_scale_causal_attention_mask_get_tensor_ranklistranger   r   JitScalarType
from_valueBOOLr4   tensorfloatFLOATHALFBFLOAT16
ValueError)r   querykeyvalue	attn_mask	dropout_p	is_causalscale
enable_gqakey_shape_builtinkey_transposed_axeskey_transposedquery_scaledkey_transposed_scaledmul_qk
mul_qk_add
const_zeroconst_neg_infattn_weights                      r   r   r      s    o..y9@?@  mmm>& E**1eS9	
 (88=u%678BB 504 TT+s3FTGN 44uadd65&9:LDDVU8KLTT(L*?@F	*
!!,,Y7$$))	* TT*ellC5.ATB
Ze}o1NODD)ZG	TT%3
		"	"	-	-i	8!!''!!&&!!**= 

 TT%3
.{/H/H/S/ST]/^._`
 	
 $$y*R$8KA~ddDDU\\)5;;%ODP
 44+u--r   c                   | j                  d|      }| j                  d|| j                  dt        j                  dgt        j                              | j                  dt        j                  t        j
                  gt        j                                    }| j                  d|t        j                  j                  |      j                               }| j                  dt        j                  d	gt        j                              }| j                  d
|| j                  d|            }| j                  d|t        j                  j                  |      j                               }|S )zCalculate the scale factor for the attention result.

    Args:
        query: Tensor of shape [..., L, E]

    Returns:
        Scalar scale factor := 1 / math.sqrt(query.size(-1))
    ShapeSlicer[   rU   rf   r]   Cast)to_i      ?DivrY   )r   r4   rq   int64r   	INT64_MAXr   rn   ro   	onnx_typerr   )r   rw   query_shapequery_shape_lastembedding_size	const_oner}   s          r   ri   ri      s/    $$w&Ktt	Zrd%++!FG	j.B.B-C5;; W 	 	
	 TT&&11%8BBD  N
 Zse5;;)OPIDD	144#?@EDD&&11%8BBD  E
 Lr   c                :   | j                  d|      }| j                  d|      }| j                  dt        j                  dgt        j                              }| j                  dt        j                  dgt        j                              }| j                  d|||      }| j                  d|||      }| j                  d||d	
      }	| j                  dt        j                  dg            }
| j                  d|
|	      }| j                  d|d	      }| j                  dt        j                  dg            }| j                  dt        j                  t	        d       g            }| j                  d| j                  d||      ||      }|S )a  Create a causal mask for the given query and key tensors.

    Equivalent to::
        mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
        attn_mask = torch.zeros(L, S, dtype=torch.float)
        attn_mask = attn_mask.masked_fill(not mask, -float("inf"))

    Args:
        query: Tensor of shape [..., L, E]
        key: Tensor of shape [..., S, E]

    Returns:
        Tensor of shape [L, S]
    r   r[   rU   rf   r]   rT   r   Concatr   rc   r   Expandr   r   r\   r_   r`   Equal)r   r4   rq   r   rr   )r   rw   rx   r   	key_shapelast_idxsecond_last_idxtarget_lengthsource_lengthsizer   rz   r   r   s                 r   rj   rj      sW   $ $$w&KWc"IttJbT(MtNHdd:u||RD/TdUODD+IMDD)_hGM44-q4ADZse)<=IXy$/IWi3Ij%,,u*=>JDDU\\E%L=/-JDKMgy*5}jI r   )r   jit_utils.GraphContextrJ   )Nr\   FNF)r   r   rw   torch._C.Valuerx   r   ry   r   rz   torch._C.Value | Noner{   rr   r|   boolr}   r   r~   r   )r   r   rw   r   returnr   )r   r   rw   r   rx   r   r   r   )__doc__
__future__r   	functoolsr4   
torch.onnxr   r   r   torch.onnx._globalsr   torch.onnx._internalr   r	   __all__partialonnx_symbolic_onnx_symbolic
parse_argsr
   r   r   quantized_argsr   r   r   r   ri   rj    r   r   <module>r      sE    #   ? ? ' 8 #""<#=#=RH !"C # ! ## 4 4 4 4  %C%H & & !H "#Cc3S#sCH22 I $2j &'O (O 45Cc3S#sC (,#'F.F.F. 
F. 	F.
 %F. F. F. !F. F. D 6F.R!!&4!!H%%&4%;I%%r   