o ei @s<ddlZddlZddlZddlZddlZddlZddlZddlmZm Z ddl Z e dZ GdddZ GdddZGdd d ZGd d d ZGd d d ejZd+ddZddZddZGdddeZGdddejZGdddeZddZddZdd Zd!d"Zd#d$ZGd%d&d&Zddd'd(d)d*ZdS),N) NamedTupleOptionalnnapi_serializec@s@eZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdS)NNAPI_OperandCoder N)__name__ __module__ __qualname__FLOAT32INT32UINT32TENSOR_FLOAT32 TENSOR_INT32TENSOR_QUANT8_ASYMMBOOLTENSOR_QUANT16_SYMMTENSOR_FLOAT16 TENSOR_BOOL8FLOAT16TENSOR_QUANT8_SYMM_PER_CHANNELTENSOR_QUANT16_ASYMMr"r"j/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/site-packages/torch/backends/_nnapi/serializer.pyrsrc@seZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZdZdZdZdZdZdZdZdZdZdZdZdZdZdZdZ dZ!d Z"d!Z#d"Z$d#Z%d$Z&d%Z'd&Z(d'Z)d(Z*d)Z+d*Z,d+Z-d,Z.d-Z/d.Z0d/Z1d0Z2d1Z3d2Z4d3Z5d4Z6d5Z7d6Z8d7Z9d8Z:d9Z;d:Zd=Z?d>Z@d?ZAd@ZBdAZCdBZDdCZEdDZFdEZGdFZHdGZIdHZJdIZKdJZLdKZMdLZNdMZOdNZPdOZQdPZRdQZSdRZTdSZUdTZVdUZWdVZXdWZYdXZZdYZ[dZZ\d[Z]d\Z^d]Z_d^Z`d_Zad`S)aNNAPI_OperationCoderrrrr r r r r rrrr  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ROI_POOLINGRSQRTSELECTSINSLICESPLITSQRTTILETOPK_V2TRANSPOSE_CONV_2DUNIDIRECTIONAL_SEQUENCE_LSTMUNIDIRECTIONAL_SEQUENCE_RNNRESIZE_NEAREST_NEIGHBORr"r"r"r#r$%sr$c@eZdZdZdZdZdZdS)NNAPI_FuseCoderrrrN)rrr FUSED_NONE FUSED_RELU FUSED_RELU1 FUSED_RELU6r"r"r"r#r rc@seZdZdZdZdZdS)OperandValueSourceTyperrrN)rrr IMMEDIATENUMBERED_BUFFERNUMBERED_MEMORYr"r"r"r#rsrc@seZdZdZdS)TorchScalarTypesr%N)rrrQUINT8r"r"r"r#rsrư>cCst|||t||kSN)absmin)lhsrhs tolerancer"r"r# approx_equalsrc Cs@tjdtjdtjdtjdtjdi}||}|D]}||9}q|S)Nr rr)rrrrrr!)op_typedims ITEM_SIZESsizedr"r"r# tensor_sizes rcCst|}|||<t|Sr)listtuple)tupindexvaluelsr"r"r#change_elementsrc@sjeZdZUdZeed<eed<eed<eed<eed<eed<eed<eed <eed <eed <eed <d S)ConvPoolArgs2dz*Configuration arguments for a convolution.kernel_hkernel_wstride_hstride_wpad_tpad_bpad_lpad_r dilation_h dilation_wgroupN)rrr__doc__int__annotations__r"r"r"r#rs  rc@r)DimOrderrrriN)rrrPRESUMED_CONTIGUOUS 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t|dgt|dd|dgS|tjurZ#dd?d@Z$dAdBZ%e&dCdDZ'idEdFdGdHdIdGdJdKdGdLdMdGdNdOdGdPdQdGdRdSdGdTdUdGdVdWdGdXdYdGdZd[dGd\d]dGd^d_dGd`dadGdbdcdGdddedGdfdgdGidhdidGdjdkdGdldmdGdndodGdpdqdGdrdsdGdtdudGdvdwdGdxdydGdzd{dGd|d}dGd~ddGdddGdddGdddGdddGdddGddGddGddGddGddGddGdZ(ddZ)ddZ*ddZ+ddZ,ddZ-ddZ.ddZ/ddZ0ddZ1ddZ2ddZ3ddZ4ddZ5ddZ6ddZ7ddZ8ddZ9d*dddZ:ddZ;ddZddZ?ddZ@ddÄZAddńZBddDŽZCddɄZDdd˄ZEdd̈́ZFddτZGddфZHdddӄZIddՄZJddׄZKddلZLdddۄZMdd݄ZNdd߄ZOd*S)_NnapiSerializerFcCstg|_g|_g|_g|_g|_g|_g|_g|_i|_i|_ i|_ i|_ i|_ g|_ d|_||_|dur8i}dSdSNr)operandsvalues operations value_dataoperation_argsinputsoutputs flexible_shape_computation_linesmodules constantstensor_sequencesjitval_operand_mapcached_immediates used_weights weight_offsetuse_int16_for_qint16)rconfigrIr"r"r#__init__Ms&z_NnapiSerializer.__init__cCs t|jSr)rr:rr"r"r#get_next_operand_idc z$_NnapiSerializer.get_next_operand_idcCsVt|tstdt|||jvrtd||}|j|||j|<|S)Nexpected Operand, got zDuplicate tensor: ) isinstancer rtyperErrLr:r)rjitvaloper operand_idr"r"r#add_tensor_operandis    z#_NnapiSerializer.add_tensor_operandcCs4t|tstdt||}|j||S)NrN)rOr rrPrLr:r)rrRrSr"r"r#add_anonymous_tensor_operandws  z-_NnapiSerializer.add_anonymous_tensor_operandc Cst|jdd}d}d}|dkrtj}nh|dkrtj}n`|dkr-tj}|}|}nP|dkrHtj}|}|}|dkrGt d |n5|d krt|j rpt |d d}tj tj f}||vrh|}|j}|j}ntd |d tdtd|jdtt|j||||dS)Nztorch.rfloat32int32quint8qint32z!qint32 zero_point must be 0, got int16 nnapi_dtypez `nnapi_type` needs to be one of z for `int16`y`int16` isn't supported. If you're trying to represent NNAPI qint16 with Pytorch int16, set `use_int16_for_qint16 = True`zCan't handle input with dtype '')r rrrr)strdtypereplacerrrrq_scale q_zero_pointrrIgetattrrr! nnapi_scalennapi_zero_pointrr rr ) rtensorrrarrrr]op_codesr"r"r#torch_tensor_to_operand~sX    z(_NnapiSerializer.torch_tensor_to_operandc Csxt|ddr tjntj}|||}|||}|j|t|j D]\}}|dkr9| ||d|d|dq#|S)N nnapi_nhwcFrzargs[z].shape[]) rerr rrjrTr?r enumerater compute_operand_shape) rarg_idxrQrhrtoperrSr6rr"r"r#add_tensor_operand_for_inputs    z-_NnapiSerializer.add_tensor_operand_for_inputcCs|||}t|j}|j|t|j|j}|j|tj ft|j }d}|j t d||||tjkrA|dddd}|j ||S)Nriiirrr)rjrr:rrrr r;rrrGr=structpackrr permute)rrhrrprStsizebuf_numoffsetr"r"r#add_tensor_operand_for_weights      z._NnapiSerializer.add_tensor_operand_for_weightcCst|tstdt|||f}||jvr=t|j}|jt||t j dd|j |t j f|j|||j|<|j|S)Nzdims must be a tuple, got rWr)rOrrrPrFrr:rr rr r;rrr=)rcoderr cache_keyrSr"r"r#add_immediate_operands      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||||fS|j|jf}|tjtjfkr$|||||fS|tjtjfkr6||f|||Std|jd|j)Nz2Automatic transpose not supported for dim_orders: z, )rrrr rr)rin0_idin0_operin1_idin1_operordersr"r"r#transpose_for_broadcasts   z(_NnapiSerializer.transpose_for_broadcastcCsZ||\}}|dkr"|dkr td||Std|d|d)NListTypeIntTypez"expected ListType of IntType, got zCan't handle size arg of type 'z' for 'r_)rrgetElementTyperrrr"r"r# get_size_args z_NnapiSerializer.get_size_argc Csdd|D}|ddkrtd|d|d|dg}|d|dg}|d |d g}|d |d g}|d }t|dkrItdt||ddgkrVtd|||||||S)NcSsg|]}|qSr")item.0r~r"r"r# szD_NnapiSerializer.get_conv_pool_args_2d_from_pack..rrzexpected pc[0] == 2, got rrr r r r r rrzexpected len(pc) == 11, got z'expected output_padding == [0, 0], got )rrget_conv_pool_args_2d_common) r kernel_size packed_configpcstridespaddings dilationsoutput_padding group_numr"r"r#get_conv_pool_args_2d_from_packs"    z0_NnapiSerializer.get_conv_pool_args_2d_from_packc Cs`||}||}|durddg}n||}|dur%||d\} } nd} |||||| S)Nrr)rrr) rrstridepaddingdilationrrrrr4rr"r"r#get_conv_pool_args_2d_from_jits     z/_NnapiSerializer.get_conv_pool_args_2d_from_jitc Cst|}t|dkrtdt|t|dkr"tdt|t|dkr1tdt|t|dkr@tdt||\}}||||g} t||| ||gS)Nrz expected len(kernels) == 2, got z expected len(strides) == 2, got z!expected len(paddings) == 2, got z"expected len(dilations) == 2, got )rrrr) rrrrrrkernelsphpw real_paddingsr"r"r#rs     z-_NnapiSerializer.get_conv_pool_args_2d_commonc Cs|d|dg}g}t|j}||||ttt|jdd|D]\}\}} | ||| } | |j | j j q.t|jD]\} } td| | || qM|j} | dkrrtd| | dkrtd| | d}dg}|d kr|g}d }n|d kr|j|}t|}n td ||durt|t|krtd t|dt|t|D]0\}}|j|} |j | | |j | j j |r||nd}| || |j | |dq| dg}d}td|t|j t|j t|j!t|jt|j}| ||"\}}|#dd|j D|#||#dd|j!Dd$|g}t|d}|ddkrdtd|t%|d}t|j D]G\} \}}}}}t&||}t|D]"\}}|dkrt'||}|j( d|dt)| ||d7}qt*dd|D}| |+|qo|#|| |+|j,| |+|j| |+|j|j(#|t--dd$||j.|||j(|fS)NFTrzProcessing node #%d: %rz%expected retn.inputsSize() == 1, got rz&expected retn.outputsSize() == 0, got zreturn [rr TupleTypezUnsupported return type: zreturn_shapes length z != return_values length rrliiiiiicss0|]\}}}}}td|t|||VqdS)iifiN)rsrtr)rtr_mrzr"r"r# 's  z3_NnapiSerializer.serialize_model..css"|] }tjdg|RVqdS)rrN)rsrt)rxr"r"r#r+s r z)model_offset must be divisible by 4, got z ser_model[z] = cs |] }|dkr |ndVqdS)rrNr"rrr"r"r#rDr~)/rnextgraphr?rrPrmrrrqrr:rrnodesrdebugadd_node return_node inputsSizer outputsSizeinputsAtrrDrrrEr@rrsrtr;r<serialize_valuesrrrr0r2rAr7rserialize_intsr>rrG) rmodelr? return_shapesinp_dim_ordersout_dim_orders self_jitvalro input_value input_tensorr5idxnoderetn retn_inputtemplate_return_lines return_values retval_countr~vr versionheaderserialized_valuesserialized_value_data model_offsetr4rrrrpt_dr"r"r#serialize_models                           z _NnapiSerializer.serialize_modelc Csg}g}t|jt|jkrtdt|jdt|jt|j|jD]*\\}}}t|}|ddBd}|d||}|td|||||q%||fS)Nzvalues length z != value_data length rrrrr)rr;r=rrrrsrt) rrrop_index source_typedata source_lengthphysical_length padded_datar"r"r#rWs 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aten::relucC||tjSr)add_pointwise_simple_unary_opr$rr r"r"r#r z aten::sigmoidcCr r)r!r$rr r"r"r#r r"z aten::softmaxcCrr) add_softmaxr r"r"r#r r zaten::hardtanhcCrr) add_hardtanhr r"r"r#r r zaten::avg_pool2dcCrr)add_avg_pool2dr r"r"r#r r zaten::max_pool2dcCr r)add_pool2d_noder$rr r"r"r#r r"zaten::adaptive_avg_pool2dcCrr)add_adaptive_avg_pool2dr r"r"r#r zaten::upsample_nearest2dcCrr)add_upsample_nearest2dr r"r"r#r r(z aten::prelucCrr) add_prelu_opr r"r"r#r r z aten::addmmcCrr) add_addmmr r"r"r#r r z aten::linearcCrr) add_linearr r"r"r#r r zaten::_convolutioncCrr)add_conv_underscorer r"r"r#r r z aten::conv2dcCrr) add_conv2dr r"r"r#r r zaten::log_softmaxcCrr)add_log_softmaxr r"r"r#r r zquantized::linearcCrr) add_qlinearr r"r"r#r r cCr r add_qconv2drrr r"r"r#r r"cCr r)r2rrr r"r"r#r r"cCs|j|tjddS)NT)r%r1r r"r"r#r scCrr)add_qaddr$rwrrr r"r"r#r rcCrr)r3r$rwrrr r"r"r#r rcCrr)r3r$rrrr r"r"r#r r)zquantized::conv2dzquantized::conv2d_reluzquantized::conv_transpose2dzquantized::addzquantized::add_reluzquantized::mulcCs:|j|}|std|d||||dS)NzUnsupported node kind (z ) in node ) ADDER_MAPrrr)rradderr"r"r#rs z_NnapiSerializer.add_nodecCs,||d\}}|d}||j|<dSr9)rr outputsAtrE)rrr_in_operrQr"r"r#rs z_NnapiSerializer._identitycCs|dkrtd||dkrtd|||d\}}t|ds6td||d}t||}| d}| }| |||dS)Nr%expected node.inputsSize() == 1, got &expected node.outputsSize() == 1, got rz __torch__.z3expected obj_ctype to start with '__torch__.', got name) rrrrrr` startswithrrer6rPr)rr obj_ctypeobjr:routputrr"r"r#r s$       z_NnapiSerializer.add_getattrcCsh|dkrtd||dkrtd||d}|}|}||||dS)Nrz%expected node.inputsSize() == 0, got rr9)rrrr6rPtoIValuer)rrr>rrr"r"r#r s     z"_NnapiSerializer.add_constant_nodec Cs|dkrtd||d}|}g}g}|D].}|dur8||jvr8||\}}||nd}|durL|dkrL||q d}q |durZ| ||||durd| |||durs|durut d|dSdS)Nrr9rrzMUnable to handle ListConstruct node. Neither all constants nor all tensors. ) rrr6rPr?rCrrrrrr) rrr>r const_valstensorsinpr4valr"r"r#rs2       z#_NnapiSerializer.add_list_constructcCsD|dkrtd||d}t|}|||dS)Nrr9r)rrr6rr?r)rrr>r;r"r"r#rs    z$_NnapiSerializer.add_tuple_constructc Cs|dkrtd||dkrtd|||d\}}||dd\}}|jtjkrAtd|j|dkrG|n|t |j d}t |j }| |dt |}|j|d} dgd} || d<||| d<dgd} ||d| | d<|tj| | dS) Nr%expected node.inputsSize() == 2, got rr9rrz,expected dim_order PRESUMED_CONTIGUOUS, got r )rrrrrrrrrrr rinsertrrrrTr6rr$r) rrrin_operr4r6real_dimout_shape_listr,rr?r@r"r"r#rs2           z_NnapiSerializer.add_unsqueezecCs||dSrrr r"r"r#r1sz_NnapiSerializer.add_toc CsV|dkrtd||dkrtd|||d\}}||d\}}|dkrAtd||dkrTtd |t|dko_|dd k}|j t j krl|slt d t d|j|j}|j|t j d }dgd} || d<||| d<dgd} ||d|| d<|tj| | dS) NrrDrr9rrz#expected shape_ctype ListType, got rz)expected shape element type IntType, got rzSCurrently, reshape is only supported on NHWC tensors if the target size is [X, -1].r/)rrrrrrrrrrrrrtorchzerosexpandr reshaperrrTr6rr$r) rrrrG shape_ctyper is_trivial_reshaper,rr?r@r"r"r#r5sB        z_NnapiSerializer.add_reshapec Cs@|dkrtd||dkrtd|||d\}}||dd\}}||dd\}}t|jdkoY|jddkpY|jddkoY|jddk}|jt j krf|sft d |dkrq|t|j7}|dkr||t|j7}|jd|t tj|j||df|j|dd} td d |j||dDrt d |jd||j|dd} | ddkrt d |j| t j d} ||d| } t| D]\} }|dkr|| | ||jdqtdd | D}dgd}||d<|||d<dgd}| |d<|tj||dS)Nr%expected node.inputsSize() == 3, got rr9rrrr zGCurrently, flatten is not supported on NHWC tensors unless C=1 or H=W=1css|]}|dkVqdS)rNr"rr6r"r"r#rsz/_NnapiSerializer.add_flatten..z-Flattening flexible dims is not supported yetzOnly 1 dim can be flexibler/csr)rrNr"rQr"r"r#rr)rrrrrrrr rrrr functoolsreduceoperatormulanycountrrTr6rmrrrrrr$r)rrrrG _start_ctype start_dim _end_ctypeend_dimis_trivial_flattenr,non_flattened_dimsrout_idrr6inputs_1r?r@r"r"r#r`sd    (     z_NnapiSerializer.add_flattenc s|dkrtd||dkrtd|||d\}}||d\}||d\}||d\}||d\}durVddur]tjdkri|j7ntjkrpddkrtjkr| |dS|jdkrt d dkr|j7n tjkr|jkrt d t fd d t |jD}| |d|j|d }d}t |D]\}} | dkr||||||d|>O}qdgd} || d<|fddtt|jD| d<|fddt |jD| d<|fddtt|jD| d<|d| d<||| d<|d| d<dgd} || d<|tj| | dS)Nr %expected node.inputsSize() == 5, got rr9rrrr z#Unable to slice with flexible shapez0Slice start value should be less than stop valuec3s$|] \}}|kr n|VqdSrr"rr~r6) dim_valueout_lenr"r#rs z-_NnapiSerializer.add_slice..rEr cg|] }|kr ndqS)rr"r)rb start_valuer"r#rz._NnapiSerializer.add_slice..cs g|] \}}|kr n|qSr"r"ra)rb stop_valuer"r#rscrd)rr"r)rb step_valuer"r#rrfr )rrrrrrsysmaxsizer rrrrmrTr6rrrrangerrrr$r) rrrrGr4r,r^end_maskrr6r?r@r")rbrcrerhrgr#rs             z_NnapiSerializer.add_slicecCs|dkrtd||dkrtd|||d\}}|j|d\}}|j|}|d}||| |dS)NrrDrr9r) rrrrrrCr r6rrP)rrr4rGrresr>r"r"r#rs      z_NnapiSerializer.add_sizec s||dkrtd||dkrtd||j|d}||dd\}t|dkr@tdt|g}d}d}|D]l}||\}} |durbt| j d} | j | d }| j |j krttd | j d |j | j |j krtd | j d |j t| j dt|j dkrtdt| j ddt|j d| ||| j 7}qH|durtd|j t|j |d }| j tjkrt|j dkrtdt|j gd} n} ||d|} t|j D],\} }|dkr"| krdfdd|D}|| | |q|| | |d| q||| g}dgd}| |d<|tj||dS)NrrDrr9rrzexpected len(tensors) > 0, got rrEzin_oper.op_type z != out_oper.op_type zin_oper.dim_order z != out_oper.dim_order zshape mismatch: z != zout_oper must not be Noner z9expected len(out_oper.shape) == 4 for CHANNELS_LAST, got rrrr + c3s|]}t|VqdSr)r7)rip_idr6r"r#r<z+_NnapiSerializer.add_cat..)rrrrDrrrrrr rrrrrr rTr6rmrrnrrrr$ry)rrrAr4in_idsr out_dim_sizerBrrGr, nnapi_dimr^rrr r?r@r"rqr#rsx        "      z_NnapiSerializer.add_catcCs&|dkrtd||dkrtd|||d\}}||d\}}|dkrAtd||dkrTtd |||d d \}}||d d |jt j krt |j dkrtdt |j dd|D}n|}t } |D]} | dkr| t |j 7} | | q|jt j kr|s| d d hstd| t j} n|j} g} t|j D]\} }| | vr| |q|r| dq|j| | d}dgd }||d<|||d<|||d <dgd}||d||d<|tj||dS)Nr %expected node.inputsSize() == 4, got rr9rrz!expected dim_ctype ListType, got rz'expected dim element type IntType, got rBoolTyperNoneTypez8expected len(in_oper.shape) == 4 for CHANNELS_LAST, got cSsg|]}gd|qS)rnr"rr"r"r#rcsz-_NnapiSerializer.add_mean..z/expected collapsed_dims to include {2, 3}, got r/)rrrrrrrrrrr rr setadd issupersetrrmrrrrrTr6rr$r)rrrrG dim_ctyper6r4keep_dimrucollapsed_dimsr out_dim_orderr,r~rrr?r@r"r"r#rHsh           z_NnapiSerializer.add_meanc Cs|dkrtd||dkrtd|||d\}}|jtjkr2td| |dd\}}| |dd \}}| |d d \}}|t j j kr]td t j}|j|||d } dgd} || d<dgd} ||d| | d<|tj| | dS) Nr rvrr9rzqMost hardware backends prefer NHWC quantized tensors. Try setting `t.nnapi_nhwc = True` on your tensor inputs. FloatTyperrrzKPyTorch NNAPI export only supports quantized tensors with the quint8 dtype.rrr)rrrrrrrr rrrrrrrrrTr6rr$r) rrrrGr4rr scalar_typerrr?r@r"r"r#rs>        z_NnapiSerializer.add_quantizecCs|dkrtd||dkrtd|||d\}}|jtjddd}dgd}||d<dgd}|| d||d<| t j ||dS)Nrr8r9rrWr) rrrrrrrrrTr6rr$r})rrrrGrr?r@r"r"r#rs&      z_NnapiSerializer.add_dequantizec Cs|dkrtd||dkrtd|||d\}}|}|tjkr<|jtj kr<|j ddd}| | d|}t |jD]\}}|dkrZ|||||qJdgd} || d<dgd} || d<||| | dS)Nrr8r9rgp?)rr)rrrrrr$rrrrrrTr6rmr rr) rrrrrGrr^rr6r?r@r"r"r#r!s.        z._NnapiSerializer.add_pointwise_simple_unary_opqparamsc Cs|dkrtd||ddkr(td|d|ddkrAtd|d||dr`||d\}}||d|j\}}n'||dr||d\}}||d|j\}}nt d|d|j |j krtd |j d |j | ||||\}}}}t |j |j } |j| d } |d ur|\} } | j| | d } ||d| } tt|j |j D]L\}\}}|dkr|dkr|| |||q|dkr|dkr|| |||q|dkr|dkr|jdt||dt|||| |||qd gd}||d<||d<|||d<d gd}| |d<||||d S)zFHelper for pointwise binary broadcast ops with superfluous extra args.rr9rr%expected inputsAt(0) TensorType, got %expected inputsAt(1) TensorType, got zCan't do a NNAPI binary op: z on two constantszin0_oper.op_type z != in1_oper.op_type rENrrzassert z == rr)rrrrPrrrrrrrrr!r rrTr6rmrrrArr7rr)rrr fuse_coderrrrrr,rrzpr^rd0rr?r@r"r"r#_do_add_binarysp            z_NnapiSerializer._do_add_binarycCs0|dkrtd|||||dS)NrrD)rrr)rrrrr"r"r#r/s  z9_NnapiSerializer.add_pointwise_simple_binary_broadcast_opcCsV|dkrtd|||dd\}}|dkr"td||||dS)NrrPrrrz*NNAPI does not support add/sub with alpha.)rrrrrr)rrrrr4alphar"r"r#r6s  z_NnapiSerializer.add_add_sub_opcCsd|dkrtd|||dd\}}||dd\}}|j|||||fddS)Nr rvrrrrr)rrrrr)rrrrr4rrr"r"r#r3Ds  z_NnapiSerializer.add_qaddc Cs|dkrtd|||d\}}||dd\}}||d|}t|jD]\}}|dkrB| ||||q2dgd} || d<| d| d<| || d<dgd} || d<| t j| | dS)NrrPrrrg?r)rrrrrrTr6rmr rrrrr$r) rrrrGr4 softmax_dimr^r6rr?r@r"r"r#r#Os(    z_NnapiSerializer.add_softmaxc Cs|dkrtd||dkrtd|||d\}}||dd\}}||dd\}}tjtjd}| ||f}|durTt d dgd} || d<dgd} | | d|| d<| || | dS) NrrPrr9rrr))rr)rr z9NNAPI only supports hardtanh with args (-1, 1) or (0, 6).)rrrrrrr$rrrrrTr6r) rrrrGr4min_valmax_valop_maprr?r@r"r"r#r$is0      z_NnapiSerializer.add_hardtanhc Cs|dkrtd||dkrtd||ddkr7td|d|ddkrPtd|d||d\}}||d\}}t|j dkrutd t|j |j ddkrtd |j d|j ddkr| rt d | | d|}t|j D]\}}|dkrq|dkrt d |||||qdgd} || d<|| d<dgd} || d<|tj| | dS) NrrDrr9rrrrz%expected len(w_oper.shape) == 1, got z"expected w_oper.shape[0] > 0, got z8Per-channel PReLU only supports channels_last right now.z.PReLU requires fixed size for dim 0 and dim 1.)rrrrrPrrrrr rrrTr6rmrrr$r) rrrrGw_idw_operr^r6rr?r@r"r"r#r*sX      z_NnapiSerializer.add_prelu_opcCs|dkrtd||dkrtd||\}}}}}}|p+|}||||||} | jdksA| jdkrEtd| |\} } t | j dkr]tdt | j t | j | | j dd} | } dgd }| |d <|| j|d<|| j|d <|| j|d <|| j|d<|| j|d <|| j|d<|| j|d<|| j|d<|tj|d<|| |d<dgd}||d | j| d|d <||||dS)Nr z%expected node.inputsSize() == 6, got rr9z'NNAPI does not support dilated pooling.r )expected len(image_oper.shape) == 4, got Frrrrr r r rrrE)rrrr?rrrrrrrr r-rrrrrrrrrrrrrrTr6rr)rrrimagekernelrrr _ceil_moder#image_id image_operr,rr?r@r"r"r#r&sR      z _NnapiSerializer.add_pool2d_nodecCs|dkrtd||dkrtd||\}}}}}}}||\} } ||\} } | r;| r?td|||||} ||\} }t |j dkratdt |j t |j | |j dd}| }dgd }| |d <| | j|d<| | j|d <| | j|d <| | j|d<| | j|d <| | j|d<| | j|d<| | j|d<| tj|d<|||d<dgd}||d |j|d}|||| d||d <|tj||dS)Nr %expected node.inputsSize() == 7, got rr9zANNAPI doesn't support count_include_pad=False or divisor_overrider rFrrrrr r r rrrE) rrrr?rrrrrrr r-rrrrrrrrrrrrrrTr6r _handle_conv_pool_flexible_inputrr$rx)rrrrrrrcount_include_paddivisor_overrider4count_include_pad_valuedivisor_override_valuer#rrr,rr?r@r^r"r"r#r%sl        z_NnapiSerializer.add_avg_pool2dc Cs|dkrtd||dkrtd|||d\}}t|jdkr9tdt|j||d\}}|dkrRtd || d kretd | |ddgkrot d |jddt |}| }dgd }||d<| d|d<| d|d<| d|d<| d|d<| d|d<| d|d<| |jd|d<| |jd|d<| tj|d<|||d<dgd} ||d|j|d| d<|tj|| dS)NrrDrr9rr rr"expected size_ctype ListType, got r(expected size element type IntType, got z@NNAPI only supports adaptive_avg_pool2d with output size (1, 1).rrr r r r rrrE)rrrrrrr rrrrrrrrrrrTr6rrr$rx) rrrr size_ctypesize_argr,rr?r@r"r"r#r'3s^         z(_NnapiSerializer.add_adaptive_avg_pool2dc Cs|dks|dkstd||dkr$td||dkr2|\}}}n|\}}}}||\}}|dkrO||\} } n0||\} } ||\} }| dkrltd| | dkr{td| | } | } ||\}}t|jdkrtd t|j|dkr| dkrt d |dkr:|d krtd || d krtd| | dkrtd| | durtd| t |t stdt ||stdtdd|Ds tdt|dkr|d}t|dkr'tdt||d}|d}||}||}n| dkr| d krQtd| | dkretd| |dkrutd||durtd|t | t stdt | | stdtd d| Dstd!t| dkr| d} t| dkrtd"t| t| d|jd}t| d|jd}|| d}|| d}nt d#|jd|jd||f}|}||d|j|d$}|jddks|jddkrt d%d&D]A}|j|dkr\|dkr:|||||dq| dkrX|||d'| |dd(t||d)qt d#qdgd}||d<||d<||d<|||d<dgd}||d<|tj||dS)*Nrr z*expected node.inputsSize() == 3 or 4, got rr9rxz%expected scale_h_ctype NoneType, got z%expected scale_w_ctype NoneType, got rz'Size and scale cannot both be non-None.rrrrz#expected scale_ctype NoneType, got zexpected scale_arg None, got z"expected size_arg to be list, got z!expected size_arg to be non-emptycs|]}t|tVqdSr)rOrrrCr"r"r#rrrz:_NnapiSerializer.add_upsample_nearest2d..z&expected all size_arg values to be intrz!expected len(size_arg) == 2, got rz#expected scale_ctype ListType, got rz+expected scale element type FloatType, got z"expected size_ctype NoneType, got zexpected size_arg None, got z#expected scale_arg to be list, got z"expected scale_arg to be non-emptycsrr)rOrrr"r"r#rrrz)expected all scale_arg values to be floatz"expected len(scale_arg) == 2, got z#Size and scale cannot both be None.rEz(Flexible batch or channels not supported)rrzint(z * r)rrrr?rrrrr rrrOrrPallrrrrrTr6rrnr7rrr$r)rrrsize_jit scale_jit scale_h_jit scale_w_jitrr scale_ctype scale_arg scale_h_ctype scale_h_arg scale_w_ctype _scale_w_argrrr*r+arg_harg_wr,rr^r6r?r@r"r"r#r)js                           z'_NnapiSerializer.add_upsample_nearest2dc Cs|dkrtd||dkrtd||\}}}}}||fD] }||\}} |dvrCtd|| dkrKtdq+||d|||dS) Nr r`rr9)rrz/expected scale_ctype IntType or FloatType, got z6NNAPI Fully-Connected does not support alpha and beta.T)rrrr?rrradd_addmm_or_linear) rrjit_bias jit_input jit_weightjit_beta jit_alpharQr scale_valuer"r"r#r+s*       z_NnapiSerializer.add_addmmcCs`|dkrtd||dkrtd||\}}}||d|||dS)NrrPrr9F)rrrr?r)rrrrrr"r"r#r,s    z_NnapiSerializer.add_linearcCsd||\}}||\}} t|jdkrtdt|jt| jdkr0tdt| j||d\} } t| jdkrItdt| j|rR| } n| } || } |j | }|jd|jdf}| | d|j |d}|jddkr| |d|ddgd }||d<| |d<||d<|tj|d <dgd}||d<|tj||dS) Nr)expected len(input_oper.shape) == 2, got r(expected len(bias_oper.shape) == 1, got rz,expected len(weight_tensor.shape) == 2, got rrEr r)rrrr rrr contiguousryr:rTr6rrrrrrr$r)rrtranspose_weightrrrinput_id input_operbias_id bias_operr4 weight_tensornnapi_weight_tensor weight_id weight_operr,r^r?r@r"r"r#r&sD    z$_NnapiSerializer.add_addmm_or_linearcCs|dkrtd||dkrtd||\}}}}||\}}t|jdkr>tdt|j||d\}} ||d\}} ||\} } | d krdtd | | d \} }|durttd t| jdkrtd t| jt|jdkrtdt|j|jd | jd krtd|jd d| jd | jd|jdkrtd| jdd|jd| t j krtd| | j t jkr| }n&| j t jkrtd| j t j| dt j| | dd}|}|j|}t ||d t j}||}|j|| }|d kr3td||dkr 0, got Quantized convolution multiplier is greater than 1. This is supported by NNAPI, but not by most hardware backends. Try training a model without quantization-aware training. r rrr)'rrrr?rrr rr: __getstate__qschemerJper_tensor_affinerarZqint8!_make_per_tensor_quantized_tensorint_reprrtouint8rcrdrquantize_per_tensorr[ryrrr:rrrrrTr6rr$r)rrrjit_packed_weight jit_scalejit_zero_pointrrr4 out_scaleout_zero_point weight_ctype packed_weight raw_weightraw_biasunsigned_weight weight_scale bias_scaleint_biasr multiplierrrrr,rr?r@r"r"r#r0Us                   z_NnapiSerializer.add_qlinearc Csd||\}}|dkr-|rdnd}tj|||jd}||}|j|} || fS||S)Nrxrr)ra) rrrJrKrraryr:r) rrrr%r_valuebias_idxnnapi_bias_tensorrrr"r"r#get_optional_biass     z"_NnapiSerializer.get_optional_biasc Cs|dkrtd||dkrtd||\}}}}}}}||d\} } ||| \} } || jdd||||} || dd d|| | | d t j S) Nr rrr9rrr rrWF rrrr?rrrr add_conv2d_commonr6rr)rr jit_imagerr jit_stridejit_pad jit_dilation jit_groupsr4rr _bias_operr#r"r"r#r.sB     z_NnapiSerializer.add_conv2dc Cs|dkrtd||dkrtd||\ }}}}}}}} } } } } } ||d\} } ||\} } ||| | \} }|| jdd|||| }|| dd d|| | || t j S) Nr%z&expected node.inputsSize() == 13, got rr9rrr rrWr)rrrrrrrr jit_transposer4rrr%rrr#r"r"r#r-sP    z$_NnapiSerializer.add_conv_underscorec Cs|dkrtd||dkrtd||\}}}||\}}||d\}}|j} dgd} || d<|d| d<||| d<dgd} | | d|j | d| d<| t j| | dS) NrrPrr9rrrrE)rrrr?rrr rrrTr6rrr$r) rrrjit_dim_jit_half_to_floatrrr4r6r,r?r@r"r"r#r/ s*      z _NnapiSerializer.add_log_softmaxc Cs|dkrtd||dkrtd||\}}}}||d\}} ||d\}} ||\} } | dkrLtd| | d \} }}| d kr`td | |\}}|\}|durotd ||jd d|}| t j krtd| |j t j kr|}n&|j t jkrtd|j t j|dt j||dd}|}||\}}|j|}t ||d t j}||}|j|| }|d krtd||dkrtd||d | | |||||| S)Nr rvrr9rrConv2dPackedParamsBasez2expected weight_ctype Conv2dPackedParamsBase, got r2zexpected pack_version '2', got rrrrrrrr)rrrr?rr:rrr rrJrrarZrrrrrrrcrdrrrr[ryrrr6)rrrr%rrrrr4rrrr pack_versionrA opt_tensorsrrrr#rrrrrrrr"r"r#r2: s               z_NnapiSerializer.add_qconv2dc !Cs||\} } | jd} |jdkrd} |rd}nd}n|j| kr%d} d}ntd|j|}||}|j|}|j|}| jt j krb|jt j krSt d|j|jt j krat d|jnQ| jt j kr|jt j krvt d |j|jt j krt d |jt| j|j|jst d | j|jd |j|jd krt d|jntd| jt| jdkrt dt| jt|jdkrt dt|jt|jdkrt dt|j| r,|j\}}}}|dkrt d||| d kr t d|d| || }|dkrt d||| kr+t d|d| n|j\}}}}|| krBt d|d| ||jd krWt d|d|jd | }| rdd}tj}n d}|rmtj}ntj}dg|}| |d <||d<||d<||j|d<||j|d<||j|d<||j|d <||j|d!<||j|d"<| r|d|d#<|| |d$<|||d<n|| |d#<|||d$<dgd}t| j|||}| j |||d%}|!||} |"| |||| |d <|#|||dS)&NrF)rrrrr1Tz$Group convolution not supported yet.z)expected weight_oper TENSOR_FLOAT32, got z'expected bias_oper TENSOR_FLOAT32, got z.expected weight_oper TENSOR_QUANT8_ASYMM, got z%expected bias_oper TENSOR_INT32, got z#scale mismatch: image*weight scale z != bias scale rz(expected bias_oper.zero_point == 0, got z#Unsupported input type for conv2d: r rz*expected len(weight_oper.shape) == 4, got rz(expected weight_oper.shape[0] == 1, got zout_c z must be divisible by in_c z"channel_multiplier must be 1, got z != in_c zkern_d z != bias_oper.shape[0] rrrrr r r r rrr)$rr rrrurryr:rrrrrrrrrrrr$r{rrzrrrrrrrrr-rrTrr)!rjit_outrrrrrr#r%rrrin_c depthwiseweight_permutationrrrrone_kern_h_kern_wout_cchannel_multiplierkern_drnum_argsrr?r@r,rr^r"r"r#r s                         z"_NnapiSerializer.add_conv2d_commonc Csd||\}}|j\}}} } |dkr||d|d|dkr"td|rj| dkrE||ddt|dd|jd|jd|jd|j | dkrh||ddt|dd|j d|j d|j d|j dSdS| dkr||ddt|dd|jd|jd|j d |jd | dkr||ddt|dd|j d|j d|j d |j d dSdS) Nrz Input channels can't be flexiblerrz - 1) * roz - rz) // z + 1)rr rrrnr7rrrrrrrr) rr^rr#r%rrr&in_chr(r)r"r"r#r! sB..00z1_NnapiSerializer._handle_conv_pool_flexible_input)Fr)NN)PrrrrKrLrTrUrjrqrr ryr|rrrrrrrrrrrrrrrrrnrrrrrrrr staticmethodrr4rrr r rrrrrrrrrrrrr!rrrr3r#r$r*r&r%r'r)r+r,rr0rr.r-r/r2rrr"r"r"r#r8Ls6 3      6   |      "#$%(+./012345I !+>QIA("C !64?7 / i &- S r8F)rJrrIcCst|||||S)aConvert to NNAPI and serialize torchscript module. Parameters: module: Torchscript module to convert inputs: Tensors used to specify input details for NNAPI config (optional): Optional config to attach to module return_shapes (optional): Specify shape of outputs if your module uses runtime flexible shapes to set output buffer size for NNAPI use_int16_for_qint16 (optional): Use Pytorch int16 to represent NNAPI qint16 values )r8r)moduler?rJrrIr"r"r#rF s r)r) renumrRloggingrTrsritypingrrrJ getLoggerrrr$rrEnumrrrrrrr r!r-r0r2r7r8rr"r"r"r#s^ b  #