U qh/i@sddlmZddlZddlmZmZmZddlZddl m Z ddl m Z m Z mZmZmZmZmZddlmZmZmZddlmZmZmZddlmZdd lmZmZm Z m!Z!d d Z"Gd d d e#Z$Gddde#Z%Gddde&Z'GdddZ(GdddZ)ddddZ*e*Z+GdddZ,GdddZ-dddd d!Z.d"d#d$d%d&Z/Gd'd(d(Z0Gd)d*d*ej1Z2Gd+d,d,e2Z3Gd-d.d.e2Z4Gd/d0d0e4Z5dS)1) annotationsN)AnyClassVarIterable) TensorProto)bfloat16 float8e4m3fnfloat8e4m3fnuz float8e5m2float8e5m2fnuzint4uint4)get_all_schemas_with_history get_schemaonnx_opset_version) make_nodemake_tensor_type_protonp_dtype_to_tensor_dtypeto_array)AttributeProto GraphProto NodeProto TypeProtocCsTd|krL|jddd\}}z t|}Wntk rB|dfYSX||fS|dfS)N_)maxsplit)rsplitint ValueError)nameprefixversvr$9/tmp/pip-unpacked-wheel-xnis5xre/onnx/reference/op_run.py_split_class_names r&c@seZdZdZdS)RuntimeTypeErrorz/Raised when a type of a variable is unexpected.N__name__ __module__ __qualname____doc__r$r$r$r%r'&sr'c@seZdZdZdS)RuntimeContextErrorzfRaised when the context is missing but an context dependent implementation is defined for an operator.Nr(r$r$r$r%r-*sr-c@seZdZdZdS)RuntimeImplementationErrorz8Raised when no implementation was found for an operator.Nr(r$r$r$r%r..sr.c@seZdZdZdS) DefaultNonezjDefault value for parameters when the parameter is not set but the operator has a default behavior for it.Nr(r$r$r$r%r/2sr/c@s,eZdZdZddddZddddZd S) RefAttrNamez}Implements a link between a parameter of a function and an attribute in node. Args: name: name of the input strr cCs ||_dSNr2)selfr r$r$r%__init__=szRefAttrName.__init__returncCs|jjd|jdS)N()) __class__r)r r4r$r$r%__repr__@szRefAttrName.__repr__N)r)r*r+r,r5r<r$r$r$r%r06sr0zdict[str, type]r6c Csi}tD]}|j|krp|j||jjkrRtd|jd|jd||jjd|j||jjkrz|||j<n |||j<|||jdt|j<q |S)Nz7This function assumes every operator has a unique name z even accross multiple domains z and .r)rr domainNotImplementedErrorZ since_versionr1)resschemar$r$r%_build_schemasDs  "  rBc@s(eZdZddddZddddZd S) OnnxTyper type_protocCs(t|tstdt|d||_dS)Nz type_proto z must be of type TypeProto.) isinstancer TypeErrortyperE)r4rEr$r$r%r5[s zOnnxType.__init__r1r6cCsd|jdS)Nz OnnxType(r9rDr;r$r$r%r<`szOnnxType.__repr__N)r)r*r+r5r<r$r$r$r%rCZsrCc@s6eZdZdZdddddddZedd d d Zd S) SparseTensorzcSimple representation of a sparse tensor. It is based on numpy but does not require scipy. np.ndarrayz tuple[int]None)valuesindicesshaper7cCs||_||_||_dSr3)rLrMrN)r4rLrMrNr$r$r%r5iszSparseTensor.__init__rr6cCs|jjSr3)rLdtyper;r$r$r%rOpszSparseTensor.dtypeN)r)r*r+r,r5propertyrOr$r$r$r%rIdsrIr)attr7cCs,tdd|jD}tt|jt|j|S)zHosts a sparse tensor.css|] }|VqdSr3r$).0dr$r$r% wsz#to_sparse_tensor..)tupleZdimsrIrrLrM)rQrNr$r$r%to_sparse_tensorusrVrrJ)tensorr7cCst|S)zAlias for :func:`to_array`.r)rWr$r$r%to_array_extended{srXc@s eZdZdZdddddZdS)GraphgrrK)r[r7cCs ||_dSr3rZ)r4r[r$r$r%r5szGraph.__init__N)r)r*r+ __slots__r5r$r$r$r%rYsrYc@seZdZUdZdZejddejddejddej ddej ddej d dej d dej d dejd dejd dejddejddejddejddiZded<dUddddddZddZdVddddd d!Zed"d#Zd$d%d&d'Zed(d)d*d+d,Zed-d%d.d/Zed-d%d0d1Zed2d%d3d4Z ed2d%d5d6Z!d7d%d8d9Z"d2d%d:d;Z#e$j%dd>d?d@dAZ'dddBdCdDZ(e)dEdFZ*e)dWdGdGdddHdIdJZ+e)dXdGdGdLdddMdNdOZ,e)ddKdPdQdGdLdddRdSdTZ-dS)YOpRuna0Ancestor to all operators in this subfolder. Args: onnx_node: `onnx` node run_params: additional parameters such as `verbose`, `opsets` (it can be more than one if the operator has a subgraph), `log` for a logging function schema: operator schema cCs t|jSr3)npfloat32frQr$r$r%zOpRun.cCsdd|jDS)NcSsg|]}t|qSr$)r_r`)rRrar$r$r% s"OpRun...)Zfloatsrbr$r$r%rcrdcCs t|jSr3)rYr[rbr$r$r%rcrdcCsdd|jDS)NcSsg|] }t|qSr$)rY)rRr[r$r$r%resrf)Zgraphsrbr$r$r%rcrdcCs t|jSr3)rirbr$r$r%rcrdcCsdd|jDS)NcSsg|] }t|qSr$)rrRrgr$r$r%resrf)Zintsrbr$r$r%rcrdcCs t|jSr3)rVZ sparse_tensorrbr$r$r%rcrdcCsdd|jDS)NcSsg|] }t|qSr$)rVrRtr$r$r%resrf)Zsparse_tensorsrbr$r$r%rcscCs |jdS)Nutf-8)sdecoderbr$r$r%rcrdcCsdd|jDS)NcSsg|]}|dqS)rk)rm)rRrlr$r$r%resrf)stringsrbr$r$r%rcrdcCs t|jSr3)rXrjrbr$r$r%rcrdcCsdd|jDS)NcSsg|] }t|qSr$)rXrir$r$r%resrf)Ztensorsrbr$r$r%rcrdcCs t|jSr3)rCtprbr$r$r%rcrdcCsdd|jDS)NcSsg|] }t|qSr$)rCrir$r$r%resrf)Z type_protosrbr$r$r%rcrdzClassVar[dict[Any, Any]]_attribute_conversion_functionsNrdict[str, Any]r) onnx_node run_paramsrAcCst|tstdt|ddD]&}||kr"td|dt|dq"d|krZtd||_||_|dkrt |j d r|j j |_ q|j j tkrt|j j |_ q|jtkrt|j|_ qd|_ n||_ d |_|dS) Nz$run_params must be a dictionary not r=)opsetsnew_ops Attribute z must be in run_params, only z was found.logz#run_params must contains key 'log'. op_schemaF)rFdictrGrH RuntimeErrorsortedKeyErrorrrrshasattrr:rx_schemar)_schemasop_type has_subgraph_load_attributes)r4rrrsrArQr$r$r%r5s,     zOpRun.__init__cGs|jd|f|dS)Nrw)rs)r4patternargsr$r$r%_logsz OpRun._logrzAttributeProto | None)rQref_attr7cCs8|jtjkr|jdd}d|jkr8t|jd}nd}|jdd}|dk s`td|||j|jdt d|jddd |dkrdn t||d S|jt j krt j |j|S|dkrt d |j d |jd |jjd|jjd|d t d|j d |jd |jjd|jjd|d|d dS)z0Converts an attribute value into a python value.ruNZexisting_functions evaluator_clsz0evaluator_cls must be specified to evaluate att=rtrverbose)rtrru functionszUnable to convert attribute z type z from node type z , domain  r=z$Unable to convert default value for )rHrGRAPHrsgetlistrLAssertionErrorr[maxr]rpAttributeErrorr rrrr>)r4rQrrurrr$r$r%_extract_attribute_values4   .4zOpRun._extract_attribute_valuecCs|jd|p i|dS)N attributes)runcontextvaluerr$r$r%_evaluate_subgraphszOpRun._evaluate_subgraphrKr6cCs`d|_g}|jjD]}|j}|jr4t|j}d|_n ||}t||||||j t j krd|_ |j|jO_t|d|j|dfddq|j rR|jjdkrR|j jD]\}}t||st|ddrtd |d |jjd t|d r|jj d ks&|jj dkr,|jjjd kr,d}n||j|}t|||||qt||_dS)zChecks and loads attributes.FTZ_run_NcSst|||Sr3)r]rrr$r$r%rcsz(OpRun._load_attributes..>Constantrequiredrvz3 is expected based on ONNX specifications for node r= default_valuer)has_linked_attributerr attributer Z ref_attr_namer0rsetattrappendrHrrrr~rritemsr}getattrrzrrjZ data_typesetattributes_names_)r4Zadded_attributesrQr rkr#r$r$r%rsH               zOpRun._load_attributesrz list[str])graphr7c Cst|tstdt|dt}t}|jD]}||jq0|jD]}||jqH|j D]}||jq`|j D]8}|j D]}||q|j D]}||kr||qqxt |S)zReturns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part of the context existing in the graph calling this one. zUnexpected type r=) rFrrGrHrZ initializeraddr Zsparse_initializerinputnodeoutputr) rlocalZknowninitZ sparse_initinprorgr$r$r%implicit_inputss"        zOpRun.implicit_inputsz Iterable[str]cCs|jjS)zReturns node attribute `input`.)rrrr;r$r$r%r3sz OpRun.inputcCs|jjS)z Returns node attribute `output`.)rrrr;r$r$r%r8sz OpRun.outputr1cCs|jjS)z!Returns node attribute `op_type`.)rrrr;r$r$r%r=sz OpRun.op_typecCs|jjS)z Returns node attribute `domain`.)rrr>r;r$r$r%r>Bsz OpRun.domainboolcCsdS)zTells the runtime if this node needs the context (all the results produced so far) as it may silently access one of them (operator Scan, If, Loop). The default answer is `False`. Fr$r;r$r$r% need_contextGszOpRun.need_contextcCs|jjdd|jjg}t|jD]R\}}|dkr:q(d|dkrRdkr(nq(|ddkr(|d |d |d q(|d d |S)Nr8z op_type=>descrrarzrz =,r9r) r:r)rrrr{__dict__rrjoin)r4Zattsrr#r$r$r%__str__Os( z OpRun.__str__cOstd|jjddS)aShould be overwritten. Args: *args: operator inputs **kwargs: optional inputs and overriden attributes, an attribute may be overridden if it belongs to a function, in this case, the same instance of OpRun can be called with different values of the same attribute. Returns: outputs z/Method '_run' must be overwritten for operator r=N)r?r:r))r4rkwargsr$r$r%_runYsz OpRun._runztuple[Any, ...])r@r7cCst|ts(td|jjdt|d|s@td|jjdtdd|Drzdd|D}td |jjd |d td d|D}td d|Drdd|D}td |jjd|d |S)z-Checks the output are from the expected type.zMethod '_run' of class z does not return a tuple but 'z'.z does not return any result.css|]}t|tVqdSr3)rFrUrir$r$r%rTusz/OpRun._check_and_fix_outputs..cSsg|] }t|qSr$rHrir$r$r%revsz0OpRun._check_and_fix_outputs..z6One of the results returned by method '_run' of class zg is a tuple, this is no ONNX corresponding type (Map, List, Tensor, SparseTensor). All returned types: r=css&|]}t|rt|n|VqdSr3)r_Zisscalararray)rRxr$r$r%rT|scss,|]$}t|tjttfp t|d VqdS)ZtodenseN)rFr_Zndarrayrryr}rir$r$r%rTscSsg|] }t|qSr$rrir$r$r%reszr has an unexpected type, this is no ONNX correponding type (Map, List, Tensor, SparseTensor). All returned types: )rFrUrGr:r)rHrany)r4r@Zdtypesr$r$r%_check_and_fix_outputsks0 zOpRun._check_and_fix_outputs)linked_attributesrc Gsz|r&|dkrBtdt|dn|dk rBtdt|d|jrd|dkrdtdt|d|js|dk rtdt|dt|di}|jr|dkrtd t|d|jD]R}t||}t |t r|j |krtd |d |d t|d||j ||<q| d |j jt|i}|jD]H}||krDq2t||sjtd|d|j jdt||||<q2|jr|jr|stdt|d||d<|dk r||d<z(|r|j|||}n |j||}Wnbttfk rJ} z>tddd|Ddt|dt|d|j jd | W5d} ~ XYnX| d|j j|dk rjt|nd||S)aCalls method ``_run``, catches exceptions, displays a longer error message. Args: *args: inputs linked_attributes: used if this has an attriute linked to the attribute of the function it belongs to context: if this node is part of the subgraph, `context` is a dictionary with the values this node may use Returns: tuple of results NzThis node if type z needs context to be filled.z+ does not need any contextbut one is given.z This node zK has linked attributes but None are given in parameter 'linked_attributes'.zM has no linked attribute but some are given in parameter 'linked_attributes' r=zBOne attribute is linked but no linked value is provided, in class z,Unable to find a value for linked attribute z in z in node z-- begin %s.run(%d inputs)rvz is missing in operator z6A subgraph has linked attribute but none was given to rrzIssues with types cSsg|] }t|qSr$r)rRrr$r$r%reszOpRun.run..z and attributes z and linked attributes=z (operator ).z-- done %s.run -> %d outputsr)rrzrHrrrrrrrFr0r rr:r)lenr} NameErrorrrrGr{r) r4rrrZoverridden_attributesrQr#rr@er$r$r%rs        2z OpRun.runcCs^|j}d|kr|tfS|dd\}}z t|}Wn tk rT|jtfYSX||fS)Nrr)r)rrrr)clsr r"Zi_versr$r$r% infer_names  zOpRun.infer_namez int | None)n_inputs n_outputsrr7c Ks|\}}|j}d}|dkr8|dkr2t|||}|j}|dkrZ|dkrTt|||}|j}ddt|D}ddt|D} t||| f|} | S)aCreates an ONNX node for this class based on the given information. Args: n_inputs: number of inputs (default is defined by the operator schema) n_outputs: number of outputs (default is defined by the operator schema) verbose: verbosity **kwargs: node attributes Returns: NodeProto Method :meth:`eval ` creates an onnx node returned by method :meth:`make_node `. .. exec_code:: import numpy as np from onnx.reference.ops._op_list import Celu onnx_node = Celu.make_node(alpha=0.5) print(onnx_node) NcSsg|]}d|qS)rr$rhr$r$r%resz#OpRun.make_node..cSsg|]}d|qS)yr$rhr$r$r%res)r op_domainrZ min_inputZ min_outputranger) rrrrrZopsetr>rAZnames_inZ names_outrr$r$r%rs   zOpRun.make_noderr)rrrrr7c  sHddddfdd }|j||f|}|ddtid }|||}|S) aInstantiates this class based on the given information. Args: n_inputs: number of inputs (default is defined by the operator schema) n_outputs: number of outputs (default is defined by the operator schema) verbose: verbosity **kwargs: node attributes Returns: NodeProto r1rrK)rrr7csdkrt|t|dS)Nr)printrU)rrrr$r% log_function2sz"OpRun.create..log_functionNr^)rrwrurt)rr) rrrrrrrrsclr$rr%creates z OpRun.createrrz list[Any])rrrrr7cOs>|jt|f||d|}|j|}t|dkr:|dS|S)aEvaluates this operator. Args: *args: inputs n_outputs: number of outputs (default is defined by the operator schema) verbose: verbosity **kwargs: node attributes Returns: NodeProto rrr)rrr)rrrrrinstr@r$r$r%eval@s   z OpRun.eval)N)N)NN)NNr).r)r*r+r,rrFLOATZFLOATSrZGRAPHSINTZINTSZ SPARSE_TENSORZSPARSE_TENSORSSTRINGZSTRINGSZTENSORZTENSORSZ TYPE_PROTOZ TYPE_PROTOSrp__annotations__r5rr staticmethodrrrrPrrrr>rrabcabstractmethodrrr classmethodrrrrr$r$r$r%r]s   # -   U /#r]c@s,eZdZdZd ddddddZd d ZdS) OpRunExpandz.Class any operator to avoid must inherit from.Nrrqr)rrrsimplcCstdt|ddSNz5The reference implementation must not use this node (rrzrH)r4rrrsrr$r$r%r5^szOpRunExpand.__init__cOstdt|ddSrrr4inputsrr$r$r%rhszOpRunExpand._run)Nr)r*r+r,r5rr$r$r$r%r[s rc@s6eZdZdZd dddddddZd d Zd d ZdS) OpFunctionzRuns a custom function.Nrdict[str, Any] | Noner)rrrsrrcsX|dkr"td|jd|jdt|||_fddtjd|D_dS)Nz"impl cannot be None for node type from domain r=csi|]}|t|qSr$)r)rRr r;r$r% sz'OpFunction.__init__.. attributes_)rzrr>r]r5impl_rr)r4rrrsrrr$r;r%r5qs  zOpFunction.__init__cOs|j|jf||Sr3) _run_implrrr$r$r%rszOpFunction._runc Ost|jt|krBtdt|dt|jd|jd|jd tt|j|}|j }| ||j d||d}t|j t|krtdt|dt|j d|jd|jd t |S) Nz.Mismatch lengths between the number of inputs z# and the expected number of inputs z for node rr=rz/Mismatch lengths between the number of outputs z$ and the expected number of outputs )rZ input_namesrzrrr>ryziprcopyupdaterZ output_namesrU)r4rrrZfeedsrresultsr$r$r%rs*  *zOpFunction._run_impl)NN)r)r*r+r,r5rrr$r$r$r%rns rc@s,eZdZdZd ddddddZd d ZdS) OpFunctionContextDependantzThe function can be instantiated but only at execution time. An instance of OpFunction is created everytime to node is executed. This is needed when the schema of an operator defines a context dependant function. Nrrr)rrrsparentcCs<tj||||id||_|j|j}t|j||j|_dS)N)rr)rr5rrtr>rrZschema_)r4rrrsrversionr$r$r%r5s z#OpFunctionContextDependant.__init__c Osg}|D]}zt|j}Wntk r|jtkrs:  $    W/