o
    Zh                     @   sd   d dl mZ d dlZd dlmZ d ZdZdZG dd dejj	Z
dddedfd	d
Z	dddZdS )    )OptionalN)_NnapiSerializer      c                       s   e Zd ZU dZeejjj e	d< e
ej e	d< e
ej e	d< dejjdejde
ej de
e de
e d	ed
ef fddZejjde
ej fddZde
ej de
ej fddZ  ZS )NnapiModulezTorch Module that wraps an NNAPI Compilation.

    This module handles preparing the weights, initializing the
    NNAPI TorchBind object, and adjusting the memory formats
    of all inputs and outputs.
    compweightsout_templatesshape_compute_module	ser_modelinp_mem_fmtsout_mem_fmtscompilation_preferencerelax_f32_to_f16c                    sD   t    || _|| _|| _|| _|| _g | _d | _|| _	|| _
d S N)super__init__r
   r   r   r   r   r	   r   r   r   )selfr
   r   r   r   r   r   r   	__class__ L/var/www/auris/lib/python3.10/site-packages/torch/backends/_nnapi/prepare.pyr      s   


zNnapiModule.__init__argsc                 C   s`   | j d u sJ | j| j|| _dd | jD | _tjj	 }|
| j| j| j| j || _ d S )Nc                 S   s   g | ]}|  qS r   )
contiguous).0wr   r   r   
<listcomp>4   s    z$NnapiModule.init.<locals>.<listcomp>)r   r
   preparer   r	   r   torchclasses_nnapiCompilationZinit2r   r   )r   r   r   r   r   r   init0   s   
zNnapiModule.initreturnc              	   C   s,  | j d u r
| | | j }|d usJ dd | jD }t|t| jks&J g }tt|D ],}| j| }|dkrC|||   q.|dkrW||| dddd  q.t	d|
|| t|t| jkslJ tt| jD ] }| j| }|dv rqs|dkr|| dddd||< qst	d|S )	Nc                 S   s   g | ]}t |qS r   )r   Z
empty_like)r   outr   r   r   r   D   s    z'NnapiModule.forward.<locals>.<listcomp>r   r   r      zInvalid mem_fmt)r   r   )r   r"   r	   lenr   rangeappendr   Zpermute
ValueErrorrunr   )r   r   r   ZoutsZ
fixed_argsidxfmtr   r   r   forward?   s0   


 
zNnapiModule.forward)__name__
__module____qualname____doc__r   r   r   r    r!   __annotations__listTensornnModuleintboolr   jitZexportr"   r-   __classcell__r   r   r   r   r      s.   
 &r   Fc              	   C   s   t | ||||\}}}	}
}}t|||	|
|||}G dd dtjj}||}tj|}ddd tt	|D }|dk rAd}ndd	d t|D }|
d
| d| d| d |S )Nc                       s    e Zd ZdZ fddZ  ZS )z5convert_model_to_nnapi.<locals>.NnapiInterfaceWrappera0  NNAPI list-ifying and de-list-ifying wrapper.

        NNAPI always expects a list of inputs and provides a list of outputs.
        This module allows us to accept inputs as separate arguments.
        It returns results as either a single tensor or tuple,
        matching the original module.
        c                    s   t    || _d S r   )r   r   mod)r   r;   r   r   r   r      s   

z>convert_model_to_nnapi.<locals>.NnapiInterfaceWrapper.__init__)r.   r/   r0   r1   r   r:   r   r   r   r   NnapiInterfaceWrapper   s    r<   z, c                 s   s    | ]}d | V  qdS )Zarg_Nr   r   r+   r   r   r   	<genexpr>   s    z)convert_model_to_nnapi.<locals>.<genexpr>r   z
retvals[0] c                 s   s    | ]	}d | dV  qdS )zretvals[z], Nr   r=   r   r   r   r>      s    zdef forward(self, z):
    retvals = self.mod([z])
    return 
)process_for_nnapir   r   r5   r6   r9   scriptjoinr'   r&   define)modelinputs
serializerreturn_shapesuse_int16_for_qint16r   r   r
   ser_model_tensorused_weightsr   r   retval_countZnnapi_modelr<   Zwrapper_model_pyZwrapper_modelarg_listZret_exprr   r   r   convert_model_to_nnapia   sD   

rN   c                 C   s   t j| } t|t jr|g}|ptd |d}|| ||\}}}}}	}
t j|t jd}G dd dt j	j
}t j| }dgdd |	D  }|d| ||||||
fS )	N)configrI   )Zdtypec                   @   s   e Zd ZdZdS )z-process_for_nnapi.<locals>.ShapeComputeModulezCode-gen-ed module for tensor shape computation.

        module.prepare will mutate ser_model according to the computed operand
        shapes, based on the shapes of args.  Returns a list of output templates.
        N)r.   r/   r0   r1   r   r   r   r   ShapeComputeModule   s    rP   z\def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]:
c                 S   s   g | ]}d | dqS )z    r@   r   )r   liner   r   r   r      s    z%process_for_nnapi.<locals>.<listcomp>r?   )r   r9   freeze
isinstancer4   r   Zserialize_modelZtensorZint32r5   r6   rB   rD   rC   )rE   rF   rG   rH   rI   r   rK   r   r   Zshape_compute_linesrL   rJ   rP   r
   Zreal_shape_compute_linesr   r   r   rA      s:   
rA   )NNF)typingr   r   Z torch.backends._nnapi.serializerr   Z ANEURALNETWORKS_PREFER_LOW_POWERZ)ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWERZ&ANEURALNETWORKS_PREFER_SUSTAINED_SPEEDr5   r6   r   rN   rA   r   r   r   r   <module>   s   V
<