§
    Ÿ²ÅjÐ  ã                   ó4  — d dl Z d dlmZmZmZmZmZ d dlZd dlZd dl	m
Z
 d dlmZ d dlmZ d dlmZ d dlmZmZ dd	gZ ed
¬¦  «         G d„ de¦  «        ¦   «         Z	 ddej        defd„Z ed
¬¦  «         G d„ d	ej        j        ¦  «        ¦   «         ZdS )é    N)ÚAnyÚDictÚ
NamedTupleÚOptionalÚTuple)Úenable_python_dispatcher)Údetect_fake_mode)Úis_sparse_any)Úcompatibility)Úmap_aggregateÚNodeÚTensorMetadataÚ	ShapePropT)Úis_backward_compatiblec                   ó    — e Zd ZU ej        ed<   ej        ed<   eed<   ee	df         ed<   e
ej                 ed<   eed<   eeef         ed<   d	S )
r   ÚshapeÚdtypeÚrequires_grad.ÚstrideÚmemory_formatÚis_quantizedÚqparamsN)Ú__name__Ú
__module__Ú__qualname__ÚtorchÚSizeÚ__annotations__r   Úboolr   Úintr   r   r   Ústrr   © ó    úKC:\Projects\Historical\venv\Lib\site-packages\torch/fx/passes/shape_prop.pyr   r      s†   € € € € € € ð Œ:ÐÐÑØŒ;ÐÐÑØÐÐÑØ�#�s�(ŒOÐÐÑØ˜EÔ/Ô0Ð0Ð0Ñ0ð ÐÐÑØ�#�s�(Œ^ÐÐÑÐÐr#   ÚresultÚreturnc           	      ó@  — | j         }| j        }| j        }t          | ¦  «        s|                      ¦   «         nd}d}|rQt          | ¦  «        sBt
          j        t
          j        t
          j        h}|D ]}|  	                    |¬¦  «        r|} nŒ| j
        }	i }
|	rð|                      ¦   «         }||
d<   |t
          j        t
          j        hv r/|                      ¦   «         |
d<   |                      ¦   «         |
d<   nŽ|t
          j        t
          j        t
          j        hv ri|                      ¦   «                              ¦   «         |
d<   |                      ¦   «                              ¦   «         |
d<   |                      ¦   «         |
d<   t/          ||||||	|
¦  «        S )zB
    Extract a TensorMetadata NamedTuple describing `result`.
    r"   N)r   ÚqschemeÚscaleÚ
zero_pointÚaxis)r   r   r   r
   r   r   Úcontiguous_formatÚchannels_lastÚchannels_last_3dÚis_contiguousr   r(   Úper_tensor_affineÚper_tensor_symmetricÚq_scaleÚq_zero_pointÚper_channel_affineÚ per_channel_affine_float_qparamsÚper_channel_symmetricÚq_per_channel_scalesÚtolistÚq_per_channel_zero_pointsÚq_per_channel_axisr   )r%   Úinclude_contiguityr   r   r   r   r   Úmemory_formatsÚquery_formatr   r   r(   s               r$   Ú_extract_tensor_metadatar>   #   s¹  € ð ŒL€EØŒL€EØÔ(€MÝ$1°&Ñ$9Ô$9ÐAˆV�]Š]‰_Œ_ˆ_¸r€Fà€Màð 	¥-°Ñ"7Ô"7ð 	åÔ#ÝÔÝÔ"ð
ˆð
 +ð 	ð 	ˆLØ×#Ò#°,Ð#Ñ?Ô?ð Ø ,�Ø�ðð Ô&€LØ €GØð :Ø—.’.Ñ"Ô"ˆØ$ˆ�	ÑØ•uÔ.µÔ0JÐKÐKÐKØ%Ÿ~š~Ñ/Ô/ˆG�GÑØ$*×$7Ò$7Ñ$9Ô$9ˆG�LÑ!Ð!ØÝÔ$ÝÔ2ÝÔ'ð
ð 
ð 
ð  &×:Ò:Ñ<Ô<×CÒCÑEÔEˆG�GÑØ$*×$DÒ$DÑ$FÔ$F×$MÒ$MÑ$OÔ$OˆG�LÑ!Ø$×7Ò7Ñ9Ô9ˆG�F‰OåØˆu�m V¨]¸LÈ'ñô ð r#   c                   óB   ‡ — e Zd ZdZdˆ fd„	Zdedefˆ fd„Zˆ fd„Zˆ xZ	S )	r   aE  
    Execute an FX graph Node-by-Node and
    record the shape and type of the result
    into the corresponding node.

    Example:
         In this example, we record the shape
         and data type of a module given
         an example input ``torch.randn(50, D_in)``.
         We print the name, shape and dtype of each node.

        class TwoLayerNet(torch.nn.Module):
            def __init__(self, D_in, H, D_out):
                super().__init__()
                self.linear1 = torch.nn.Linear(D_in, H)
                self.linear2 = torch.nn.Linear(H, D_out)
            def forward(self, x):
                h_relu = self.linear1(x).clamp(min=0)
                y_pred = self.linear2(h_relu)
                return y_pred
        N, D_in, H, D_out = 64, 1000, 100, 10
        x = torch.randn(N, D_in)
        y = torch.randn(N, D_out)
        model = TwoLayerNet(D_in, H, D_out)
        gm = torch.fx.symbolic_trace(model)
        sample_input = torch.randn(50, D_in)
        ShapeProp(gm).propagate(sample_input)

        for node in gm.graph.nodes:
            print(node.name, node.meta['tensor_meta'].dtype,
                node.meta['tensor_meta'].shape)

        The output of this code is:

        x torch.float32 torch.Size([50, 1000])
        linear1 torch.float32 torch.Size([50, 100])
        clamp_1 torch.float32 torch.Size([50, 100])
        linear2 torch.float32 torch.Size([50, 10])
        output torch.float32 torch.Size([50, 10])

    Args:
         module (GraphModule): The module to be executed
         fake_mode (FakeTensorMode): A fake mode for copying the gm

    Nc                 óê   •— t          ¦   «                              |¦  «         |€t          ¦   «         }|�$ddlm}  || j        |¦  «        | _        || _        nd | _        d | _        | j        | _        d S )Nr   )Údeepcopy_to_fake_tensor)	ÚsuperÚ__init__r	   Útorch._dynamo.utilsrA   ÚmoduleÚfake_moduleÚ	fake_modeÚreal_module)ÚselfÚgmrG   rA   Ú	__class__s       €r$   rC   zShapeProp.__init__„   s…   ø€ Ý‰Œ×Ò˜ÑÔÐØÐÝ(Ñ*Ô*ˆIØÐ ØCÐCÐCÐCÐCÐCð  7Ð6°t´{ÀIÑNÔNˆDÔØ&ˆDŒNˆNà#ˆDÔØ!ˆDŒNàœ;ˆÔÐÐr#   Únr&   c                 ó¨  •‡— 	 | j         �| j         | _        	 | j        �g| j        5  t          ¦   «         5  t	          ¦   «                              |¦  «        }d d d ¦  «         n# 1 swxY w Y   d d d ¦  «         n# 1 swxY w Y   n!t	          ¦   «                              |¦  «        }| j        | _        n# | j        | _        w xY wnR# t          $ rE}t          j	        ¦   «          t          d|                     ¦   «         › d|j        › �¦  «        |‚d }~ww xY wdŠˆfd„}t          ||¦  «        }‰r
||j        d<   t          |¦  «        |j        d<   |S )NzShapeProp error for: node=z with meta=Fc                 ó^   •— t          | t          j        ¦  «        rdŠt          | ¦  «        S | S )NT)Ú
isinstancer   ÚTensorr>   )ÚobjÚfound_tensors    €r$   Úextract_tensor_metaz/ShapeProp.run_node.<locals>.extract_tensor_meta²   s/   ø€ Ý˜#�uœ|Ñ,Ô,ð à#�Ý/°Ñ4Ô4Ð4à�
r#   Útensor_metaÚtype)rF   rE   rG   r   rB   Úrun_noderH   Ú	ExceptionÚ	tracebackÚ	print_excÚRuntimeErrorÚformat_nodeÚmetar   rU   )rI   rL   r%   ÚerS   r\   rR   rK   s         @€r$   rV   zShapeProp.run_nodeœ   sú  øø€ ð	ØÔÐ+ð #Ô.�”ð/Ø”>Ð-Øœð 5ð 5Õ)AÑ)CÔ)Cð 5ð 5Ý!&¡¤×!1Ò!1°!Ñ!4Ô!4˜ð5ð 5ð 5ñ 5ô 5ð 5ð 5ð 5ð 5ð 5ð 5øøøð 5ð 5ð 5ð 5ð 5ð 5ð 5ñ 5ô 5ð 5ð 5ð 5ð 5ð 5ð 5øøøð 5ð 5ð 5ð 5øõ #™WœW×-Ò-¨aÑ0Ô0�Fà"Ô.�”�ø˜dÔ.�”Ð.Ð.Ð.Ð.�øÝð 	ð 	ð 	ÝÔÑ!Ô!Ð!ÝØU¨Q¯]ª]©_¬_ÐUÐUÈQÌVÐUÐUñô àðøøøøð	øøøð ˆð	ð 	ð 	ð 	ð 	õ ˜VÐ%8Ñ9Ô9ˆØð 	)Ø$(ˆAŒF�=Ñ!å˜f™œˆŒˆv‰Øˆsu   „C ˜B4 ¦A:µ"A#ÁA:Á#A'	Á'A:Á*A'	Á+A:Á.B4 Á:A>Á>B4 ÂA>Â%B4 Â'C Â4CÃC Ã
DÃA DÄDc                 ób   •‡ — ‰ j         �ˆ fd„|D ¦   «         }n|} t          ¦   «         j        |Ž S )a  
        Run `module` via interpretation and return the result and
        record the shape and type of each node.

        Args:
            *args (Tensor): the sample input.

        Returns:
            Any: The value returned from executing the Module
        Nc                 ó|   •— g | ]8}t          |t          j        ¦  «        r‰j                             |¦  «        n|‘Œ9S r"   )rO   r   rP   rG   Úfrom_tensor)Ú.0ÚtrI   s     €r$   ú
<listcomp>z'ShapeProp.propagate.<locals>.<listcomp>Í   sP   ø€ ð ð ð àõ 2<¸A½u¼|Ñ1LÔ1LÐS�”×*Ò*¨1Ñ-Ô-Ð-ÐRSðð ð r#   )rG   rB   Úrun)rI   ÚargsÚ	fake_argsrK   s   `  €r$   Ú	propagatezShapeProp.propagateÁ   sR   øø€ ð Œ>Ð%ðð ð ð àðñ ô ˆIˆIð
 ˆIØ�u‰wŒwŒ{˜IÐ&Ð&r#   )N)
r   r   r   Ú__doc__rC   r   r   rV   rg   Ú__classcell__)rK   s   @r$   r   r   T   sŽ   ø€ € € € € ð,ð ,ð\'ð 'ð 'ð 'ð 'ð 'ð0#˜$ð # 3ð #ð #ð #ð #ð #ð #ðJ'ð 'ð 'ð 'ð 'ð 'ð 'ð 'ð 'r#   )T)rX   Útypingr   r   r   r   r   r   Útorch.fxÚtorch._dispatch.pythonr   Útorch._guardsr	   Útorch._subclasses.meta_utilsr
   Útorch.fx._compatibilityr   Útorch.fx.noder   r   Ú__all__r   rP   r>   ÚfxÚInterpreterr   r"   r#   r$   ú<module>rt      s�  ðð Ð Ð Ð Ø 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9Ð 9à €€€Ø €€€Ø ;Ð ;Ð ;Ð ;Ð ;Ð ;Ø *Ð *Ð *Ð *Ð *Ð *Ø 6Ð 6Ð 6Ð 6Ð 6Ð 6Ø 1Ð 1Ð 1Ð 1Ð 1Ð 1Ø -Ð -Ð -Ð -Ð -Ð -Ð -Ð -ð ˜[Ð
)€ð € dÐ+Ñ+Ô+ðð ð ð ð �Zñ ô ñ ,Ô+ðð" .2ð.ð .ØŒLð.àð.ð .ð .ð .ðb € dÐ+Ñ+Ô+ð~'ð ~'ð ~'ð ~'ð ~'�”Ô$ñ ~'ô ~'ñ ,Ô+ð~'ð ~'ð ~'r#   