a h@sbddlmZddlZddlmZdZdZdZGdddejj Z dddedfd d Z d d d Z dS))OptionalN)_NnapiSerializercseZdZUdZeejjje d<e ej e d<e ej e d<ej j ej e ej e ee eeedfdd Zejje ej dd d Ze ej e ej d d d ZZS) 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. compweights out_templates)shape_compute_module ser_modelr inp_mem_fmts out_mem_fmtscompilation_preferencerelax_f32_to_f16csDt||_||_||_||_||_g|_d|_||_ ||_ dSN) super__init__r r rr r r rrr)selfr r rr r rr __class__K/var/www/auris/lib/python3.9/site-packages/torch/backends/_nnapi/prepare.pyrs zNnapiModule.__init__)argscCs`|jdusJ|j|j||_dd|jD|_tjj }| |j|j|j |j ||_dS)NcSsg|] }|qSr) contiguous).0wrrr 4z$NnapiModule.init..) rr preparer r rtorchclasses_nnapi CompilationZinit2rr)rrrrrrinit0s zNnapiModule.init)rreturnc Cs4|jdur|||j}|dus&Jdd|jD}t|t|jksLJg}tt|D]Z}|j|}|dkr|||q\|dkr|||ddddq\t dq\| ||t|t|j ksJtt|jD]F}|j |}|dvrq|dkr&||dddd||<qt dq|S) NcSsg|]}t|qSr)rZ empty_like)routrrrrDrz'NnapiModule.forward..rrrzInvalid mem_fmt)rr) rr#r lenr rangeappendrZpermute ValueErrorrunr )rrrZoutsZ fixed_argsidxfmtrrrforward?s0           zNnapiModule.forward)__name__ __module__ __qualname____doc__rrr r!r"__annotations__listTensornnModuleintboolrjitexportr#r. __classcell__rrrrrs rFc Cst|||||\}}} } } } t||| | | ||} Gdddtjj}|| }tj|}dddtt |D}| dkrd}ndd dt| D}| d |d |d |d |S)Ncs eZdZdZfddZZS)z5convert_model_to_nnapi..NnapiInterfaceWrappera0NNAPI 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. cst||_dSr)rrmod)rr=rrrrs z>convert_model_to_nnapi..NnapiInterfaceWrapper.__init__)r/r0r1r2rr<rrrrNnapiInterfaceWrappersr>z, css|]}d|VqdS)Zarg_Nrrr,rrr rz)convert_model_to_nnapi..rz retvals[0]css|]}d|dVqdS)zretvals[z], Nrr?rrrr@rzdef forward(self, z): retvals = self.mod([z]) return  ) process_for_nnapirrr6r7r:scriptjoinr(r'define)modelinputs serializer return_shapesuse_int16_for_qint16rrr ser_model_tensor used_weightsr r retval_countZ nnapi_modelr>Zwrapper_model_pyZ wrapper_modelarg_listZret_exprrrrconvert_model_to_nnapiasD    rPcCstj|}t|tjr|g}|p,td|d}||||\}}}}} } tj|tjd} Gdddtj j } tj | } dgdd| D}| d || | |||| fS) N)configrK)Zdtypec@seZdZdZdS)z-process_for_nnapi..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/r0r1r2rrrrShapeComputeModulesrRz\def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]: cSsg|]}d|dqS)z rBr)rlinerrrrrz%process_for_nnapi..rA)rr:freeze isinstancer5rZserialize_modelZtensorint32r6r7rDrFrE)rGrHrIrJrKr rMr r Zshape_compute_linesrNrLrRr Zreal_shape_compute_linesrrrrCs:    rC)NNF) typingrrZ torch.backends._nnapi.serializerrZ ANEURALNETWORKS_PREFER_LOW_POWERZ)ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWERZ&ANEURALNETWORKS_PREFER_SUSTAINED_SPEEDr6r7rrPrCrrrrs  V <