a 8Sic@sUddlmZddlZddlZddlZddlZddlZddlmZm Z m Z m Z m Z m Z mZmZddlZddlmmZddlZddlmZddlmZddlmZdddZd d Zd d Zd dZdddddZddZddZddZ ddddddddd Z!d!d"Z"d#d$d%d&Z#d'd(Z$d)d*Z%d+d,Z&d-d.Z'd/d0Z(d1d2Z)d3d4Z*d5d6Z+d7d8Z,dd:d;Z-dd?Z/d@dAZ0dBdCZ1dDdEZ2dFdGZ3dHdIZ4dJdKZ5ddLdMZ6ddOdPZ7dQdRZ8dSdTZ9ddUdVZ:ddWdXZ;dYdZZd_d`Z?ddbdcZ@dddeZAdfdgZBdhdiZCdjdkZDdldmZEdndoZFdpdqZGdrdsZHdtduZIdvdwZJddxdyZKdzd{ZLd|d}ZMd~dZNdddZOddZPddddddZQddZRddZSddZTddZUddZVdddddddZWdddddddddZXdddZYddZZddddZ[ddZ\ddZ]ddddZ^ej_j`ej_jaej_jbej_jcej_jdej_jeej_jfej_jgej_jhej_jiej_jjej_jkej_jld ZmddddddddddddddddZnGdddejoZpejqejrejsejtejuejvejwejxejyejzej{ej|ej}ej~ejejgZejqejrejxejwejvejtejuejsej|ejzej{ej}ej~ejejdZddZemdemdemdemdemdemdemdemdemdemdemdemdemdemdemdemdgZeZded<dS)) annotationsN)AnyCallableListOptionalSequenceSetTupleUnion)_C) _patch_torch)GLOBALScCs|dkr |S|dkst|s |S|r0dS|dkr|d}|dkr\t|S|dkrlt|S|dkr|t|S|dkrt|S|d kr|S|d krd d |DS|d krdd |DStdnx|dkrF|d kr>| D]0}|dkrtd|dqdd | DStd|dusZ|durrtd |ntd |||dS)Nnonevonnx::ConstantvalueifbstiscSsg|] }t|qS)int.0rrrV/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/onnx/symbolic_helper.py Xz_parse_arg..fscSsg|] }t|qSr)floatrrrrrZrz5ONNX symbolic doesn't know to interpret Constant nodeprim::ListConstructz$Failed to export an ONNX attribute 'z\', since it's not constant, please try to make things (e.g., kernel size) static if possiblecSsg|]}t|dqSr)rnoderrrrrgrz:ONNX symbolic doesn't know to interpret ListConstruct nodez.Expected node type 'onnx::Constant', got '{}'.zMExpected node type 'onnx::Constant' for argument '{}' of node '{}', got '{}'.) _is_valuer# mustBeNonekindrr boolstr RuntimeErrorinputsformat)rdescarg_name node_nameZtvalrrrr _parse_argDs^     r/cCs&t|r"|dkr"t||S|S)Nr)r$r#r&r/)rr,rrr_maybe_get_constzs r0cCs(t|d}t|tjr$|jdkr$|S|S)Nrr)r0 isinstancetorchTensorshape)rvalue_trrr_maybe_get_scalars r6cCs"t|std||t||S)NzDONNX symbolic expected a constant value of the {} argument, got `{}`) _is_constantr)r+r/)rr,r-rrr _get_constsr8z_C.ValuezList[_C.Value]) list_valuereturncCs$|}|dksJt|SNr!)r#r&listr*)r9Z list_noderrr _unpack_listsr=cCs.|}|dkr"td|t|S)Nprim::TupleConstructzAONNX symbolic expected node type `prim::TupleConstruct`, got `{}`)r#r&r)r+r<r*) tuple_valueZ tuple_noderrr _unpack_tuples r@cCst|o|dkSr;r$r#r&)r9rrr_is_packed_listsrBcsfdd}|S)aA decorator which converts args from torch._C.Value to built-in types. 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"i": int "is": list of int "f": float "fs": list of float "b": bool "s": str "t": torch.Tensor cs"_tfdd}|S)Nc s d}tt|ks>Jdt|dtdjd|z*t}t|jdd}jWn$tydgt|}dYn0fddt||D}t|dksJd jd |t|dkrd |vsJd jd ||g|Ri|S) NzIf you believe this is not due to custom symbolic implementation within your code or an external library, please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml to report this bug.z,A mismatch between the number of arguments (z) and their descriptors (z") was found at symbolic function 'z'. cs g|]\}}}t|||qSr)r/)rargZarg_descr-fn_namerrrszBparse_args..decorator..wrapper..zSymbolic function z4's '**kwargs' can contain a single key/value entry. _outputsz='s '**kwargs' can only contain '_outputs' key at '**kwargs'. ) len__name__inspect signaturer< parameterskeys Exceptionzip)gargskwargsZ FILE_BUG_MSGsig arg_names)arg_descriptorsfnrErwrappersB           z.parse_args..decorator..wrapper)Z_arg_descriptors functoolswrapsrVrWrUrVr decorators%zparse_args..decoratorr)rUr]rr[r parse_argss +r^)scale zero_pointr'zOptional[float]z Optional[int]arg_q_descriptorsr_r`csfdd}|S)aA decorator which extends support for quantized version of the base operator. 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Args: g: Graph, the ONNX IR graph that is under construction. qtensor: torch._C.Value, either a tuple of (quantized_tensor, scale, zero_point) for per tensor quantization, or (quantized_tensor, scale, zero_point, axis) for per channel quantization. Representing the quantized tensor. qdtype: torch.onnx.TensorProtoDataType default None, if not None, represents the data type of quantized tensor. It must be either torch.onnx.TensorProtoDataType.UINT8 or torch.onnx.TensorProtoDataType.INT8. NrraxisrrrZDequantizeLinear Attribute axis is not supported.r)r@rHr8rryrzrrUINT8rhFLOATr rr) rPrrrsZunpacked_qtensorsrir_r`rurZ input_qdtyperrrrrms0 rmzOptional[_C.Value])rir_r`rur:c Cs|dur*t|s*tjdkr*tdtjdd|dus6J|dkrZ|jd|tjj d}|dusfJ|dvr|jd|tjj d}|jd|||t |d d d }|||g}|durt|s| ||jd g|RS) aAppends to graph `g` ONNX nodes that quantizes `tensor` based on `scale`, `zero_point` and `axis`. Args: g: Graph, the ONNX IR graph that is under construction. tensor: torch._C.Value, representing the tensor to be quantized. scale: torch._C.Value, quantized scale. zero_point: torch._C.Value, quantized zero point. axis: Optional[torch._C.Value] default None, if None, represents per tensor quantization. Otherwise, represents per channel quantization, along given axis. NrZQuantizeLinearrvrrr)ByteCharrrurr>) rr rrryrzrhrrrxrwr8rn)rPrir_r`rurqrQrrrros:     roc Cs|d||}|d|}|jd|tjdgtjdd}|jd|d||tjjd }g} |d urtt|st| ||jd |||g| RS) adIn PyTorch, bias is float and is quantized to int32 implicitly inside the quantized ATen op kernel. In ONNX we need to make the quantization explicit because operators expect all of their inputs to be quantized. Since int32 is not a supported output type by ONNX operator `QuantizeLinear`, quantization is exported using regular operators. 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