BVhQdZddlZddlZddlZddlZddlZddlmZmZddl m Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddl m!Z" ddl#m$Z$dZ%dZ*dZ+dZ,dZ-dZ.dZ/dZ0dZ1dZ2de)de jfde)d e)d!ee)d"ee)e)ff d#Z4 d(d$Z5d%Z6d&Z7d'Z8y#e&$r'Z'e&djQe)e'Z%YdZ'['qdZ'['wwxYw))z%Helper utilities for AOT compilation.N)ListTuple) config_pb2)meta_graph_pb2)session)convert_to_constants)ops) tensor_shape)versions) tf_optimizer)file_io) array_ops) sysconfig)test) tf_logging)saver)_pywrap_tfcompilezUnable to import _pywrap_tfcompile; you must build TensorFlow with XLA. You may need to build tensorflow with flag --define=with_xla_support=true. Original error: {})ReadVariableOpIsVariableInitializedOpResourceGatherResourceGatherNd VariableShape)Identity IdentityNc,tj|SN)shlexquote)ss _/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/tools/saved_model_aot_compile.py _shlex_quoter!=s Qcb tj}tS#ttf$rYywxYw)zHLoad tf.sysconfig if available and working (i.e., inside a pip package).N) sysconfig_lib get_include ImportError ValueError)_s r _sysconfig_moduler)As8!!#A  z " s ..cd|vrI|jds8|d|jd}t||jddzd}||fS|dfS)zAConvert a tensor name like 'tensor:0' into a tuple ('tensor', 0).:N)endswithrfindint)name node_name output_slots r _parse_tensor_namer3Ls]D[s+%djjo&Id4::c?Q./01K k !! :r"zM INC = -I{tensorflow_includes} LIB = -L{compiled_dir} CXXFLAGS = {cxx_flags} ct}tjj|\}}|rt |j }nrtjj trt}tjj |r5tj|}tjj |r5tjjtjjtjj|gtjjgdz}n tjd}tjj|dddd}tjj!|s$t#j$dj'||}t(j'|t |dj't*j, S#t$r~tjjtjjtjjtgtjjgdz}Y,wxYw) aReturns a Makefile string with variables for using XLA binary object files. Attempts to identify the right include header paths when run from either an installed TensorFlow pip package, or from bazel run. Args: output_prefix: A string containing the output prefix for the XLA AOT compiled header + object files. Returns: A string containing a filled out `_XLA_MAKEFILE_TEMPLATE`.  tensorflowcompilertf2xlazxla_compiled_cpu_function.hz/Could not find includes path. Missing file: {}z-D_GLIBCXX_USE_CXX11_ABI={})tensorflow_includes compiled_dir cxx_flags)r)ospathsplitr!r%islink__file__readlinkrealpathjoindirnamepardirrtest_src_dir_pathKeyErrorexistsloggingerrorformat_XLA_MAKEFILE_TEMPLATEr CXX11_ABI_FLAG) output_prefixr output_dirr(r: this_filebaseexpected_headers r _xla_makefile_stringrT]s !)''-- .-*a&y'<'<'>? ww~~hi GGNN9 %KK *  GGNN9 % WW   '',,rwwy1 Kbggnn5E5I KMdN%%b)ggll lJ2OQO 77>>/ * mm ; 6/ "$  & &- +-44  ! !# ' $$Nww GGLL2 Lrww~~6F6J LNNs?G::BJJc|jDcgc]}|jdk(s|}}td|D}tjd}|jD]9}|j D](}|j dr||j|*;i}|jD]\}}t||}tg} |r|j} | j| vr!| j| j| jtvr.|j| jg} |j!| n| jt"vr |df||<g}|r||vs|df||<|Scc}w)aGet the list of Variable nodes from `graph_def`. Args: graph_def: An instance of `GraphDef`. This GraphDef *must* have already been optimized by Grappler. In particular, function inlining must have already happened. Returns: A dict mapping string names of variables to tuples `(node_def, modified)`, where `node_def` is the `NodeDef` corresponding to variable, and `modified` is a python bool describing whether the variable is modified during runtime. VarHandleOpc38K|]}|j|fywrr0.0ns r z5_get_variable_nodes_from_graph_def..s:1AFFA;:cgSrr_r"r z4_get_variable_nodes_from_graph_def..sbr"^TF)nodeopdict collections defaultdictinput startswithappenditemslistsetpopr0add_PASS_THROUGH_VARIABLE_OPSgetextend_READ_ONLY_VARIABLE_OPS) graph_defr[ variablesvariable_name_map child_mapinpv_namev_nodequeue processed n_currentchildrens r "_get_variable_nodes_from_graph_defr~sl$..BQADDM,AqB)B: ::%%j1) >>!aww! ^^C #a !!)+113*vv 6" #EBI ))+i 9 $mmINN# 3 3==4 X <<6 6#TN & Y!5/i*" 3Cs E5E5ct|jDcgc]}|jc}}tj}|j ||j jD]T\}}t|j\}}||vs#tjdj|||j |=V|Scc}w)zIdentify the inputs in the signature no longer in graph_def, prune them. Args: signature_def: A `SignatureDef` instance. graph_def: A `GraphDef` instance. Returns: A new pruned `SignatureDef`. zSignature input key '{}', tensor name '{}', has been pruned while freezing the graph. Removing it from the compiled signatures.) rlrbr0r SignatureDefCopyFrominputsrjr3rJwarnrL) signature_defrsr[ node_namesnew_signature_defkv tensor_namer(s r _prune_removed_feed_nodesrsINN3qAFF34*$113]+$$**,&fq!'/NK*$ ll Q 6![ !#  " "1 %& 4sCcheckpoint_pathmeta_graph_defrOsignature_def_keyvariables_to_feedreturnc|j}||vr(td|dt|j||}|jstd|dt |t j|tjtjk\rhtjj|d}t j|d5}|j|j j#dddt%|j |t'||} t)| } |t| j+} nQt-|j/t| } | rtd| d t| |D cgc]} | |  } } tjtjk\r^tjj|d }t j|d5}|j| j#dddt1j2t5j6 5}t9j:|d }||j=||| j?tAjB|| |jj+Dcgc]}tE|jFdc}| Dcgc]\}}|jFc}}dddtI|| }tjj|d}tjj|d}tjJdjM|t j|d5}|j| j#dddtO|| }tjJdjM|t j|d5}|jt |ddd||fS#1swY!xYwcc} w#1swYxYwcc}wcc}}w#1swYQxYw#1swYxYw#1swY||fSxYw)anFreeze a `MetaGraphDef` in preparation for tfcompile`. The graph is always optimized with grappler, and optionally (by default) variables are frozen as constants, before compilation happens. Args: checkpoint_path: Python string. Path to checkpoints/variables. meta_graph_def: Instance of `MetaGraphDef`. output_prefix: Python string. Path prefix for outputs. signature_def_key: String, the signature_def to use in the SavedModel. variables_to_feed: A list of strings, the variables that will be fed by the user; these won't be frozen. If `None`, then we will extract all the variables in the graph and mark them as to-feed. The default behavior is an empty tuple: all variables must be frozen. Returns: a pair containing the path to the frozen model and the path to the config. Raises: RuntimeError: If tensorflow was not built with XLA. ImportError: If tensorflow was built with XLA but there was another issue importing the tfcompile python wrapper. ValueError: If `meta_graph_def.signature_def[signature_def_key]` is missing or has empty outputs. z"Unable to find signature_def_key 'z<' in signature def map of `meta_graph_def`. Available keys: zSignature key z" must have outputs, but saw none: zoriginal_graph.pbwbNz6Asked to feed variables that were not found in graph: z$. Variables contained in the graph: zprefrozen_graph.pb)graphT) clear_devicesr)output_node_namesvariable_names_blacklistzfrozen_graph.pbz config.pbtxtzWriting graph def to: {})variable_nodes_to_feedzWriting config_pbtxt to: {}wmode)(rr'rkkeysoutputsstrr recursive_create_dirrJ get_verbosityINFOr=r>rDFileIOwritersSerializeToString/_replace_input_placeholders_with_default_values_optimize_graphr~valuesrl differencerSessionops_libGraph saver_libimport_meta_graphrestorerrconvert_variables_to_constantsr3r0rinforL_signature_to_tf2xla_config)rrrOrrsignature_def_maproriginal_graph_def_location graph_writerrs all_variablesr not_in_graphr0prefrozen_graph_def_locationsessrestorerr[r(frozen_graph_def_locationconfig_pbtxt_locationconfig config_writers r freeze_modelrs6%22//  ,->,?@8 !&&( ) * , --$$56-     *++N }    !! }-  ,"$'',,}/B#D 3T :Gl11CCEFG2 /nm<)4Y?-!-"6"6"89()44T-5HIL O&'K}-.0 11): $ d  ,#%77<< 0D$F 4d ;8|44678W]]_- **>NHt_- ;;  '..557#166*1- $:&1a&    ",M9E- ggll=:KL'',,}nE ,,)001JKL~~/66,y22456 &,B D& ,,,334IJK~~+#6%-F $% "$9 99uGG$88&  ,66 % "$9 99sa*P P*; P/ A$Q/P<QQ+ Q Q6Q P'/P9< QQQ Q,c trttjjd} | sdj |rdnd} n| dj |rdndz } | tjd<t j } tj| t||| ||\} } tjj|} tj| tjddd |zd z|z}tjd j | d j |}tj |d 5}|j#t%|dddt'|}t)j*| | ||||dj |dj |dj |dd y#1swYbxYw)a"Compile a `MetaGraphDef` to header+object files in `output_prefix`. Use XLA AOT (`tfcompile`) to convert the given meta graph and signature into a header + object files. Also create an include makefile that helps identify the appropriate necessary include and library paths to incorporate these files into your C++ program. Freezing a graph entails restoring the checkpoint and replacing any inputs and variables with constants. If values are feed, those are used, else inputs are replaced with default all-zero constants. Finally, the graph is pruned and then optimized with grappler. If the `freeze_graph` is `True`, all variables are embedded as constants into the graph and binary objects. If it is `False`, then the variable values become inputs and outputs of the compiled class and the C++ caller must set these values manually. Args: checkpoint_path: Python string. Path to checkpoints/variables. meta_graph_def: Instance of `MetaGraphDef`. output_prefix: Python string. Path prefix for outputs. signature_def_key: String, the signature_def to use in the SavedModel. cpp_class: String, Name of output C++ class. target_triple: String, LLVM target triple. target_cpu: String, LLVM target cpu name. variables_to_feed: A list of strings, the variables that will be fed by the user; these won't be frozen. If `None`, then we will extract all the variables in the graph and mark them as to-feed. The default behavior is an empty tuple: all variables must be frozen. multithreading: Whether to enable multithreading in the compiled computation. Note that if using this option, the resulting object files may have external dependencies on multithreading libraries like Abseil. Raises: RuntimeError: If tensorflow was not built with XLA. ImportError: If tensorflow was built with XLA but there was another issue importing the tfcompile python wrapper. ValueError: If `meta_graph_def.signature_def[signature_def_key]` is missing or has empty outputs. XLA_FLAGSz--xla_cpu_multi_thread_eigen={}truefalsez --xla_cpu_multi_thread_eigen={})rrrOrrz [^0-9a-zA-Z]+r(__xla___z#Generating XLA AOT artifacts in: {}z{}_makefile.incrrNz{}.oz{}.hz {}_metadata.oTF) rr cpp_class target_triple target_cpu entry_pointout_function_object out_headerout_metadata_objectgen_name_to_indexgen_program_shape)_pywrap_tfcompile_import_errorr=environrprLr get_temp_dirr rrr>rEresubrJrrrrTr!rCompile)rrrOrrrrrmultithreading xla_flagstemp_dirrrrPrmakefile_inc_locationmakefile_writers r aot_compile_cpu_meta_graph_defr4sb$ ((  {+I 3::"&1i5<<"&11i'BJJ{    ( x(5A%#)) 6+22 ww}-* z*s% 13+ ,,4;;JGH+22=A~~+#6?/.}=>?}-- % "! -- 6}-)00?  ??s GGc6tj|}tj}t |j j t |jj zD]1}|jjj|j3|jdj||jdtj }|j"j$}d|_t)j*||S)z@Optimize `meta_graph_def` using grappler. Returns a `GraphDef`.train_op saver_def)copydeepcopyr CollectionDefrkrrr node_listvaluerir0collection_defr ClearFieldr ConfigProto graph_optionsrewrite_optionsmin_graph_nodesr OptimizeGraph)rrnew_meta_graph_deffetch_collection tensor_inforrs r rrs}}^4#113 =   & & () = ' ' )*+>k$$++K,<,<=> ##J/889IJ ,  ! ! #&((88/$&/!  # #F,> ??r"c Jtd|jD}tg}|jj D]\}}t |j \}}||vr$|j|||vr(td|dt|j||}|jdvr&tjdj||tj |j} | j#st%d|d|d| d t'j(} | j+5t-j.| |j0| } d d d |j3 jj4| j7D]n} | j | jj k(r'|jj9| j4| j4|| j4j <py #1swYxYw) zFReplace graphdef's `tf.placeholder` input ops with all-zero constants.c38K|]}|j|fywrrXrYs r r\zB_replace_input_placeholders_with_default_values..s>!1661+>r]z'Unable to find input signature tensor 'z*' in optimized GraphDef. Graph nodes are: ) Placeholder PlaceholderV2zjTried to convert SavedModel input node '{}' from a placeholder, but it doesn't look like a placeholder: {}z6Expected fully defined input shape for signature_def 'z', tensor name: 'z'; but shape is: .)dtyper0N)rdrbrlrrjr3r0rn RuntimeErrorrkrrcrJrrLr TensorShapeis_fully_definedr'rr as_defaultrzerosrrnode_defget_operationsri) rsrname_to_node_mapprocessed_nodesr0input_rr(rbshape temp_graphconstrcs r rrs>y~~>>G/#**0027ldF' 4NKo% $**  3K=A((,-=-B-B-D(E'F H II K (D ww66 ll 88>{?C9EF  $ $V%8%8 9E  ! ! #  B4&I&-'8q B CCJ    7oo v||+7e7 MM%((##$'')7 EHHMM !nnBKK(+-;;r{{''( 777,77s #HH" c Pddlm}|j}|j}|jj D]\}}|j dd}dj|}t|j\}}t|}|jj|j|||||j|j|j j D]\}} |j dd}dj|}t| j\}}t|}|j"j|j%||||| j| j|D]\} } | jj dd}d j|}|j&j|j)| j|| j*d j,| j*d j.| |S) aConvert `signature_def` to tf2xla config. Returns a `tf2xla.Config` proto. Args: signature_def: Instance of `SignatureDef`. variable_nodes_to_feed: List of tuples of form `(node_def, modified)` corresponding to VarHandleOp, and a boolean `modified` that describes whether the variable was modified during execution. Returns: An instance of `tf2xla.Config` proto. Raises: RuntimeError: If TensorFlow was not compiled with XLA. r) tf2xla_pb2/r(zfeed_{})r1 output_index)idr0typerzfetch_{}zparam_{}rr)r1r0rrreadonly)tensorflow.compiler.tf2xlarConfigTensorIdrrjreplacerLr3r0r/feedriFeedrr rfetchFetchvariableVariableattrrr) rrrr tensor_idr0rr1routput_rbmodifieds r rrs4    &!!)#**002 (ldF <<S !D   D !D 26;; ?Y |$L KK9<H%%  '( (%,,224 )mdG <<S !D   T "D 27<< @Y |$L LL9<H&&  () )1 $tX 99  S# &D   T "D OOii7#(())G$**!\  #$ $ -r")r_F)9__doc__rerr=rrtypingrrtensorflow.core.protobufrrtensorflow.python.clientrtensorflow.python.frameworkrr rr r tensorflow.python.grapplerr tensorflow.python.lib.ior tensorflow.python.opsrtensorflow.python.platformrr$rrrJtensorflow.python.trainingrrtensorflow.pythonrrr&erLrrrror!r)r3rMrTr~r MetaGraphDefrrrrrr_r"r r s3, /3,<6403,+A+<9(1$( 7+$\&R0e:#e:!/!