͑i SSKJr SSKrSSKrSSKrSSKrSSKrSSKJr SSK J r J r J r J r SSKrSSKJr SSKJr SSKJr SS KJrJrJrJr S S KJrJrJrJr S S K J!r! S S KJ"r"JrJ#r#J$r$J%r%J&r&J'r'J(r(J)r)J*r* S SK+J,r- S SK.J/r/J0r0 S SK1J2r2 \ (a2SSK3J4r4J5r5 SSK6J r7 SSKJ8r8 SSK9J:r: SSK;Jr> SSK?J>r@ SSKAJBrB /rC\R"5qE\RrG\RrIS7SjrJS8SjrK\2S9Sj5rLS:SjrMSrNSrOS;SjrPS;SjrQSrRSS&jrYS'rZS(r[S)r\S*r]S+r^S?S,jr_S-r`\S.5ra"S/S05rb"S1S25rc"S3S45rd"S5S65reg)@) annotationsN) lru_cache) TYPE_CHECKINGAnyLiteraloverload)pir) in_cinn_mode)apply_cinn_pass)ProgramValuetranslate_to_pirtranslate_to_pir_with_param_map)compilercore framework unique_name) convert_dtype) Operatorr Variableconvert_np_dtype_to_dtype_default_main_program get_flags in_pir_modepaddle_type_to_proto_typeprocess_type_promotion set_flags)auto_checkpoint)FetchHandlerMonitorTrainerFactory)signature_safe_contextmanager) GeneratorSequence)Tensor) PlaceLike)_Place) DatasetBase)CompiledProgramc[$)a :api_attr: Static Graph Get the global/default scope instance. There are a lot of APIs use :code:`global_scope` as its default value, e.g., :code:`Executor.run` Returns: Scope: The global/default scope instance. Examples: .. code-block:: python >>> import paddle >>> import numpy >>> paddle.static.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), paddle.CPUPlace()) >>> numpy.array(paddle.static.global_scope().find_var("data").get_tensor()) g_scopeT/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/base/executor.py global_scoper1Ls & Nr/c[nUqU$Nr,scopeexs r0 _switch_scoper7bs BG Ir/c#b# [U5nSv [U5 g![U5 f=f7f)at This function switches scope through python `with` statement. Scope records the mapping between variable names and variables ( :ref:`api_guide_Variable` ), similar to brackets in programming languages. If this function is not invoked, all variables and variable names are recorded in the default global scope. When users need to create variables with the same name, they need to switch scopes through this function if they do not want the mapping of variables with the same name to be overwritten. After switching through the `with` statement, all variables created in the `with` block will be assigned to a new scope. Parameters: scope: The new scope. Returns: None Examples: .. code-block:: python >>> import paddle >>> import numpy >>> paddle.enable_static() >>> new_scope = paddle.static.Scope() >>> with paddle.static.scope_guard(new_scope): ... paddle.static.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), paddle.CPUPlace()) >>> numpy.array(new_scope.find_var("data").get_tensor()) array([[1., 1.], [1., 1.]]) N)r7r4s r0 scope_guardr9is*H u B b bs / / ,/c[U[R5(aUVs/sHn[X!5PM sn$[U[5(aUVs/sHn[X!5PM sn$[U[R 5(deUR 5n[U5S:a [S5eUR5(a3U(a[R"U5$[R"U5$gs snfs snf)a Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information. For higher dimensional sequence data, please use DenseTensor directly. Examples: .. code-block:: python >>> import paddle.base as base >>> import numpy >>> new_scope = base.Scope() >>> with base.scope_guard(new_scope): ... base.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), base.CPUPlace()) >>> tensor = new_scope.find_var("data").get_tensor() >>> base.executor.as_numpy(tensor) # or numpy.array(new_scope.find_var("data").get_tensor()) Args: tensor(Variable): a instance of Tensor copy(bool, optional): Whether to use deep copy. Returns: numpy.ndarray rzSome of your fetched tensors hold LoD information. They can not be completely cast to Python ndarray. Please set the parameter 'return_numpy' as 'False' to return DenseTensor itself directly.N) isinstancerDenseTensorArrayas_numpylist DenseTensorlodlen RuntimeError_is_initializednparrayasarray)tensorcopytr@s r0r=r=s0&$//00+126a!622&$+126a!622 fd.. / // / **,C 3x!| 1   88F# #::f% %%32s DDc[U[RR5(d [ U5n[U[RR5(d [ U5nX:H$)z Returns True if the first dtype can be compatible the second one. Currently, we require the two dtype's have to be same. Args: dtype (np.dtype|VarType|str): The type of data: float32, int64, etc. Returns: True if the two types are same. )r;rVarDescVarTyper)firstseconds r0dtype_is_compatible_withrOsN eT\\11 2 2*51 fdll22 3 3+F3 ?r/c[U5nU[U5:wag[U5H-nXbXS:aMXbXS:aM!XX:wdM- g g)a? Returns True if the two dimensions are compatible. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimensions are same. 3. For negative number or 'None' in a dimension, it means unknown so it is compatible with any number. Args: first (list/tuple): integers representing shape. "None" or negative number means unknown. second (list/tuple): integers representing shape. "None" or negative number means unknown. Returns: True if the two dimensions are compatible. FrT)rArange)rMrNdim_lenis r0dimension_is_compatible_withrTsd(%jG#f+ 7^ 8 ux!|  9  A  8vy   r/c  URR5(Gad[R"XRU5nUbB[ SUR <S[ UR5SUR<SU<S3 5e[UR5UR5(d[UR[RR5(a[UR5O URn[UR5[RR5(a[UR55OUR5n[ SUR <SU<SU<35eg ) a@ Returns True if the variable doesn't require feed check or it is compatible with the shape and have same dtype as the fed value. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimensions are same. 3. For negative number or 'None' in a dimension, it means unknown so it is compatible with any number. Args: var (Variable): the Variable object feed (DenseTensor): the fed value, which must be a DenseTensor num_places: an integer value indicating the number of places. ParallelExecutor will divide data into devices (CPU/GPU) evenly. Returns: True if the shape and dtype of variable is compatible with the feed value Raises: ValueError: if the shape or dtype of the variable is not compatible with the feed value The fed Variable  should have dimensions = , shape = , but received fed shape  on each deviceThe data type of fed Variable must be , but received T)descneed_check_feedrdiff_tensor_shape ValueErrornamerAshaperO_dtypedtyper;rKrLr)varfeed num_places diff_shapevar_dtype_formatfeed_dtype_formats r0check_feed_shape_typerls6, xx!!++D((JG  !#CHH>cii(YY dkkmT\\-A-ABBdkkm,[[]  0 IFVEYYhizh}~  r/c D[R"XU5nUb/[R"SU<S[ U5SU<SU<S3 5 [ UR 5U5(d[U[RR5(a [U5OUn[UR 5[RR5(a[UR 55OUR 5n[R"SU<SU<SU<35 g ) a Returns True if the variable doesn't require feed check or it is compatible with the shape and have same dtype as the fed value. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimensions are same. 3. For negative number or 'None' in a dimension, it means unknown so it is compatible with any number. Args: feed (DenseTensor): the fed value, which must be a DenseTensor name (str): name of the variable target_shape (list): the shape that will be compared with feed dtype (core.VarDesc.VarType): the dtype that will be compared with feed num_places: an integer value indicating the number of places. ParallelExecutor will divide data into devices (CPU/GPU) evenly. Returns: True if the shape and dtype of variable is compatible with the feed value Raises: ValueError: if the shape or dtype of the variable is not compatible with the feed value rVrWrXrYrZr[r\r]T) rr`warningswarnrArOrdr;rKrLr)rgrb target_shapererhrirjrks r0pir_check_feed_shape_typerq%s0''JGJ x'A#lBSATT^7 ~_ V  $DKKM5 9 9%!5!566 %  $++-)=)=>> $++- (   ,THI>N=QQ`ar`u v  r/clSnURH~nURR5S:Xa]US- nURRS5SU:XdeURR S5SnXQ;a[ SUS35eM~ O US:aU[ U5:wa [ S5eUS:$) apCheck whether the block already has feed operators. Return false if the block does not have any feed operators. If some feed operators have been prepended to the block, check that the info contained in these feed operators matches the feed_targets and feed_holder_name. Raise exception when any mismatch is found. Return true when the block has feed operators with matching info. Args: block: a block instance (typically global block of a program) feed_targets: a dictionary of {feed_target_name: feed_target_data} feed_holder_name: the name of the variable that holds the data of all feed targets. The type of this feed_holder variable is FEED_MINIBATCH, which is essentially vector. Returns: A boolean value that indicates whether a block has feed operators that match the info contained in feed_targets and feed_holder_name. rrgrXOutz'feed_targets' does not have variablez:Feed operators in program desc do not match 'feed_targets')opsr^typeinputoutput ExceptionrA)block feed_targetsfeed_holder_name feed_countopfeed_target_names r0has_feed_operatorsrTs*Jii 77<<>V # !OJ77==%a(,<< <<!ww~~e4Q7 334D3EYO4 A~*L(99 H   >r/cBSnURHnURR5U:XdM#US- nURRS5SU:XdeURR S5SnUUVs/sHowRR 5PM sn;a[ SUS35eURRS5nXaURR 5:XaMe US:aU[U5:wa [ S5eUS:$s snf) aCheck whether the block already has fetch operators. Return false if the block does not have any fetch operators. If some fetch operators have been appended to the block, check that the info contained in these fetch operators matches the fetch_targets and fetch_holder_name. Raise exception when any mismatch is found. Return true when the block has fetch operators with matching info. Args: block: a block instance (typically global block of a program) fetch_targets: a dictionary of {fetch_target_name: fetch_target_data} fetch_holder_name: the name of the variable that holds the data of all fetch targets. The type of this fetch_holder variable is FETCH_LIST, which is essentially vector. fetch_op: the operator name of fetch Return: A boolean value that indicates whether a block has fetch operators that match the info contained in fetch_targets and fetch_holder_name. rrrtrsz'fetch_targets' does not have rucolzX % 1 K77>>%(+/@@ @@ " c 21 5  +8)+8C =) 45F4GyQ'',,u%C$c(:(?(?(D(D(FF FFQ;#m*<< J   ?)s>#DcSSKJn Sn///nURHynUR5U:XaJUSR UR S55 USR UR 5S5 MaUR5S:XdMwSnM{ /nUbc[U5HTup[U [5(aXS;a[SU S 35eM3X"US5;dMCUR U 5 MV X4$) aCheck whether the block already has fetch operation. Return false if the block does not have any fetch operation. If some fetch operation have been appended to the block, check that the info contained in these fetch operation matches the fetch_targets. Raise exception when any mismatch is found. Return true when the block has fetch operation with matching info. Args: block: a block instance (typically global block of a program) fetch_targets: a list of fetch_target_data fetch_op: the operator name of fetch Return: A boolean value that indicates whether a block has fetch operators that match the info contained in fetch_targets. r)ValueSetFrrbzbuiltin.set_parameterTzFound fetch_target[z)] is type(str) and doesn't have fetch op.) paddle.autograd.backward_utilsrrvrbappendoperand_sourceattrs enumerater;strrz) r{rrrris_startup_program fetch_inforneed_fetch_inforS fetch_vars r0+has_fetch_operations_and_is_startup_programrs(8bJii 779 qM !2!21!5 6 qM F!3 4 WWY1 1!%  O %m4LA)S))qM1#-aS0YZ2(:a="99&&y15  ..r/cUR5nUR5nX7R;aURU5nO3UR U[ R RRSS9nXGR;aURU5n O3UR U[ R RRSS9n [XqU5(dq[U5HbupURU 5(a-URU 5n URSSU/0SU /0SU 0S9 MH[R"SU S 35 Md U(aS n OS n [!XrXM5(d_[U5HPup[#U[$[&45(dS U S [)U535eUR+U SU/0SU /0SU 0S9 MR U$)NTrbrw persistablergrsrtrrwinputsoutputsr The variable : is not found in program. It is not declared or is pruned.fetch_v2fetchWrong type for fetch_list[]: )clone global_blockvarsrf create_varrrKrLFEED_MINIBATCH FETCH_LISTrrhas_var _prepend_oprnrorr;rrrw append_op)programrg fetch_list feed_var_namefetch_var_name use_fetch_v2 tmp_programrfeed_varrrSrboutrrfs r0_add_feed_fetch_opsrs--/K++-L)))##M2**%%44+  *** $$^4  ++%%00,  l- @ @ GA##D))"&&t,(((,"SEN !* ) #D6)cd' .   +FAcHc?33 ,QCs49+> 3  " "cU| ,aj #  , r/c SSKnUR5nSn[XAX%5upgU(aURR U5 [ U5Hup[ U [5(dSUS[U 535eU(a%URRU R5n URRX[U5-U5n SU lM SSS5 gg!,(df  g=f)Nr pd_op.fetchrrT)paddlerrstatic program_guardrr;rrw_pir_ops parameterrbrrr) rrrrrrrrrS fetch_inputrs r0_add_pir_fetch_opsrs'')LH3 n (O  ]] ( ( 1"+O"<!+u5503tK7H6IJ5&"(//";";K._to_strns| c8 $ $88==? " S ! !J X & &r#w<  U # #svv; CH'MMN Nr/r,)r;tupler>join)rfritemss r0rrmsb O#u!f#t'* +stWT]s +xx{s| ,sA#c[U[5(a1[U5[UR55-[ X#5-$UR R 5[UR55-[ X#5-$r3)r; PirProgramr raw_address_get_program_cache_keyr^cached_hash_str)rr5rgrs r0)_get_strong_program_cache_key_for_new_exersx':&& L%##%& '$T6 7  LL ( ( *%##%& '$T6 7 r/cSn[U[R5(a UROUnU"URS5[ [ U55-[X5-$)Nc/n[URR55HnURU5 M SR U5$)N )r>rkeysrr)r{ block_strvar_names r0_get_varname_from_block>_get_strong_program_cache_key.._get_varname_from_blocks> UZZ__./H   X &0yy##r/r)r;rr*_programblocksrrr)rrgrr inner_programs r0_get_strong_program_cache_keyrsg$ gx77 8 8   4 4Q 78 bk    2 3r/c>/n[U[5(a[UR55nOK[U[[45(a0[ U5H!up4U[UR55- nM# /nUb[[ [U55nX%-$r3)r;dictr>rrrmapr)rgrfeed_var_namesrSeachfetch_var_namess r0_get_feed_fetch_var_namesrsN$diik* D4- ( ( GA d499;/ /N'Os<<=  ++r/c*[[X55$r3)rr)rgrs r0rrs (: ;;r/cD[U[R5(dUcS5e[U5n[R"U5(a&[R "U5R U5nO[U[[45(aQ[R "U5nUR[R:Xa [S5eUR U5nO[S[U5S35e[R"5nURX5 U$)a Convert numpy.ndarray to Tensor, its only support Tensor without LoD information. For higher dimensional sequence data, please use DenseTensor directly. Examples: .. code-block:: python >>> import numpy as np >>> import paddle.base as base >>> place = base.CPUPlace() >>> exe = base.Executor(place) >>> data = np.array((100, 200, 300)) >>> np_outs = map(lambda x: base.executor._as_lodtensor(x, place), data) Args: data(numpy.ndarray|list|tuple|scalar): a instance of array, scalar, list or tuple data(core.Place): the place of created tensor dtype(str|paddle.dtype|np.dtype, optional): the expected data type of created tensor Returns: DenseTensor z:The dtype should be given when feed data is not np.ndarrayz Failed to convert input data to a regular ndarray : * Usually this means the input data contains nested lists with different lengths. Please consider using 'base.create_lod_tensor' to convert it to a LoD-Tensor.zConvert data of type z to Tensor is not supported)r;rDndarrayrisscalarrEastyper>rreobject_rrwrr?set)dataplacererGs r0 _as_lodtensorrs2 dBJJ ' '  H  e$ ;;t  88D>((/D tUm , ,88D>DzzRZZ'd ;;u%D'T |3NO     F JJt Mr/cP[U[R5=(d$ [UR[R5nU(aZ[U[R5(aUO URnUR(a[ R "S[5 gg)Nz-Standalone executor is not used for inferenceFT)r;rr*_graph _is_inferencernro UserWarning)rrcompiledcompiled_programs r0_can_use_interpreter_corers'8#;#;< 00AH'8#;#;<<    ) ) MM?  r/c0[R"U5 gr3)loggingwarning)msgs r0 _warning_oncer s OOCr/c4\rSrSrSSjrSr\S5rSrg) FetchHandleriNc&UceXlX lgr3)var_dict period_secs)selfrrs r0__init__FetchHandler.__init__ s### &r/cUHMn[X5[RLdM#[RR USXSS35 MO [RR S5 g)Nz[0]: r r)rwrDrsysstdoutwrite)rres_dictkeys r0handlerFetchHandler.handlersZCHM"bjj0   C5hmA.>-?q!AB r/c[S5 g)Na class FetchHandlerExample(FetchHandler): def handler(self, res_dict): print(res_dict["auc"]) print("auc: {}, {}".format(res_dict["auc"], time.ctime())) auc = Variable() var_dict = {"auc": auc} handler = FetchHandlerExample(var_dict=var_dict) )printr.r/r0helpFetchHandler.helps   r/)rr)N<) __name__ __module__ __qualname____firstlineno__rr staticmethodr __static_attributes__r.r/r0rrs '      r/rc6\rSrSrSrSSjrS SjrSrSrg) _StandaloneExecutori$c[R"5UlURRU5 X lX0lUR 5Ulgr3)rPlace_place set_place_plan_scope_create_new_executor_new_exe)rrplanr5s r0r_StandaloneExecutor.__init__%s;jjl  e$  113 r/c<URRX5R5nU(aB[USS9n[ S5S(d#[ X@R R55$U$UR R5S:a [S5eU$)a Args: feed_names(list): This parameter represents the input names of the model. fetch_list(list): This parameter represents the Tensors that need to be returned after the model runs. The default is None. return_numpy(bool): This parameter indicates whether convert the fetched Tensors (the Tensor specified in the fetch list) to numpy.ndarray. if it is False, the type of the return value is a list of :code:`DenseTensor`. The default is True. TrFLAGS_enable_pir_in_executorrz;`merge_tensor` does not support when return_numpy is False.) r2run _move_to_listr=rrr/rrB)r feed_namesrenable_job_schedule_profilertensorss r0r7_StandaloneExecutor.run,s--##  -/  wT2G;<.&gzz/I/I/KLLNzz))+a/"QNr/c<URRU5nU$r3)r2 run_profile)rr9 program_descs r0r>_StandaloneExecutor.run_profileIs}}00< r/cr[R"URURUR5nU$r3)rStandaloneExecutorr-r/r0)rnew_exes r0r1(_StandaloneExecutor._create_new_executorMs'))$++tzz4;;Or/)r2r-r/r0N)TF)returnzcore.ProgramDesc) r#r$r%r&rr7r>r1r(r.r/r0r*r*$s4KP:r/r*cR\rSrSr"SS5rSrSrSrSrSr S r S r S r g ) _ExecutorCacheiRc,\rSrSrSSjrSrSrSrg)_ExecutorCache._CachedDataiSNc XlX lX0lX@lXPlX`lXplXl[UR[R5(aURR(dG[R"URR5R5URl [![#URRUR URUR55Ulg[![#URUR URUR55Ulgr3)rrgrrrrr5r3r;rr*rrIrGraphr to_programhashrr rrrgrrrrr5r3s r0r#_ExecutorCache._CachedData.__init__Ts#LI(O!. 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Args: place(paddle.CPUPlace()|paddle.CUDAPlace(n)|str|None): This parameter represents which device the executor runs on. When this parameter is None, PaddlePaddle will set the default device according to its installation version. If Paddle is CPU version, the default device would be set to `CPUPlace()` . If Paddle is GPU version, the default device would be set to `CUDAPlace(0)` . Default is None. If ``place`` is string, it can be ``cpu``, and ``gpu:x``, where ``x`` is the index of the GPUs. Note: users only pass one Place or None to initialize Executor when using multiple-cards. Other APIs will override the cards. See `document for multiple-cards `_ Returns: Executor Examples: .. code-block:: python >>> import paddle >>> import numpy >>> import os >>> # Executor is only used in static graph mode >>> paddle.enable_static() >>> # Set place explicitly. >>> # use_cuda = True >>> # place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() >>> # exe = paddle.static.Executor(place) >>> # If you don't set place, PaddlePaddle sets the default device. >>> exe = paddle.static.Executor() >>> train_program = paddle.static.Program() >>> startup_program = paddle.static.Program() >>> with paddle.static.program_guard(train_program, startup_program): ... data = paddle.static.data(name='X', shape=[None, 1], dtype='float32') ... hidden = paddle.static.nn.fc(data, 10) ... loss = paddle.mean(hidden) ... paddle.optimizer.SGD(learning_rate=0.01).minimize(loss) ... >>> # Run the startup program once and only once. >>> # Not need to optimize/compile the startup program. >>> exe.run(startup_program) >>> # Run the main program directly without compile. >>> x = numpy.random.random(size=(10, 1)).astype('float32') >>> loss_data, = exe.run(train_program, feed={"X": x}, fetch_list=[loss.name]) >>> # Or, compiled the program and run. See `CompiledProgram` >>> # for more details. >>> compiled_prog = paddle.static.CompiledProgram( ... train_program) >>> loss_data, = exe.run(compiled_prog, feed={"X": x}, fetch_list=[loss.name]) r(rNcPUc[R"5nX lO"U5Ul0Ul0Ul0Ul0Ul0Ul0Ul 0Ul [R"5nURUR5 [R"U5UlSUl0UlSUlSUl[(R*"S5Ul[/5Ul[R2R55UlSUlg)NF__auto_checkpoint_executor__)r_current_expected_place_r_get_paddle_placeprogram_caches ctx_cachestrainer_caches scope_cachesmicro_scope_cache var_cachespruned_program_cachesrr,r.r_default_executor_closedpruned_program_scope_caches_prepare_to_run_calledr3rgenerate_auto_checkpoint_namerG_executor_cacheop_proto_and_checker_makerkOpRoleAttrName op_role_keyr:)rrexpected_placers r0rExecutor.__init__s =&??AN'J"44U;DJ  !#%'" JJL DJJ!%q!1 +-(&+# %0%9%9 *& " ./::JJL,1)r/cURUR;=(aT [UR5UR5[[R R R5-$r3)r attr_namesint all_attrsrrOpRoleOptimize)rrs r0_is_optimizer_opExecutor._is_optimizer_op6s[2==0AS LLN4++ ,6 //66?? @6A Ar/cXUR5 URR5 gr3)closerrcrVs r0__del__Executor.__del__;s  ""$r/cXlgr3)r3)rr3s r0 _set_planExecutor._set_planBs r/c:URRUS5$r3)rgetrprogram_cache_keys r0_get_scope_cacheExecutor._get_scope_cacheEs  $$%6==r/c:URRUS5$r3)rrrs r0_get_ctx_cacheExecutor._get_ctx_cacheHs""#4d;;r/c:URRUS5$r3)rrrs r0_get_trainer_cacheExecutor._get_trainer_cacheK""&&'8$??r/c:URRUS5$r3)rrrs r0_get_program_cacheExecutor._get_program_cacheNrr/c X RU'gr3)rrrrs r0_add_program_cacheExecutor._add_program_cacheQs18-.r/c:URRUS5$r3)rrrs r0_get_pruned_program_cache"Executor._get_pruned_program_cacheTs))--.?FFr/c X RU'gr3)rrs r0_add_pruned_program_cache"Executor._add_pruned_program_cacheWs8?""#45r/c:URRUS5$r3)rrrs r0_get_pruned_program_scope_cache(Executor._get_pruned_program_scope_cacheZs//334EtLLr/c X RU'gr3)rrs r0_add_pruned_program_scope_cache(Executor._add_pruned_program_scope_cache]s>E(():;r/c X RU'gr3)r)r ctx_cache_keyctxs r0_add_ctx_cacheExecutor._add_ctx_cache`s), &r/c X RU'gr3)r)rtrainer_cache_keyr s r0_add_trainer_cacheExecutor._add_trainer_cachecs14-.r/c X RU'gr3)r)rscope_cache_keyr5s r0_add_scope_cacheExecutor._add_scope_cachefs-2/*r/c X RU'gr3)r)rr micro_scopess r0_add_micro_scopes_cache Executor._add_micro_scopes_cacheis4@01r/c:URRUS5$r3)rrrs r0_get_micro_scopes_cache Executor._get_micro_scopes_cachels%%))*;TBBr/c"[SUS35 g)Nz"use_program_cache is force set to z! by FLAGS_FORCE_USE_PROGRAM_CACHE)r )ruse_program_caches r0_log_force_set_program_cache%Executor._log_force_set_program_cacheos01B0CCd e r/c UR5nURGH%nURR5S:XGaURR S5SnX'nUR U5n U R [RRR:waJ[U[R5(d [XRU R 5n[X5 URR!S5n Sn [#U 5U (a[R$"XHXz5 GMU/n Sn UR&(aSUR&;aUR&SSn [)UR*5(aUR+5SOUR*SnX-S:Xde[,R."[,R0"U5U S5n [3U 5HNnU S:a#[XURU R 5OXn[R$"UUUX-U-5 MP GM& g g) Nrgrtrrr6rrmnum_micro_batches)rrvr^rwryrfrerrKrLSTRINGSr;r?rrrlrrset_feed_variablercallablercrDsplitrErQ)rrrgrr5rrrcur_feedrfr pir_flag_namemicro_cur_feednum_micro_batch batch_sizerS micro_feeds r0 _feed_dataExecutor._feed_datats++- ""Bww||~'#%77>>%#8#; 1"&&'7899 4 4 < <<%h0@0@AA#0$jj#))$*#8ggll5) > ]+M:**)9'/ZN&'O--,0E0EE*1*?*?,+-+/ (77%NN,Q/!)!2# *;q@@@)+HHX.*#?3 /2* . 14::syy"0!2 #..!&)1A5 4g#r/c [5nUHnUSnURU5 USnUSn USn XrR5;a U (aMDX'n [U [R 5(d[ XRU5n [XX5 [R"X;US5 M [UR55H5n X;dM URU 5 [R"SU S35 M7 g)Nrrr z The value r)raddrr;rr?rrrqr$r>poprnro) rrrgr5rfeed_target_names data_op_inforrris_persistabler' feed_names r0_pir_feed_dataExecutor._pir_feed_datasE)L+A   ! !"2 3#AH$QI)!_Nyy{2~-Hh(8(899(::xH %I   " "54Da H#*(diik*I1#   +ef+r/c[[U55Vs/sHn[R"X2U5PM nnU$s snfr3)rQrArget_fetch_variable)rrrr5rSoutss r0 _fetch_dataExecutor._fetch_datasF3z?+ +  # #E1 =+    s!<c/n/nSn[U5HupV[U[5(aUH nU"X#U5 M M.[U[5(ac[US[[45(d.[ SUSUSUS[ US5R S3 5eUSH nU"X#U5 M MU"X#U5 M X24$)a Split optimize_ops from fetch_list, which provided to specify program pruning. Args: fetch_list(list): The original fetch_list. Possible types of fetch_list are: fetch_list = ['loss'] fetch_list = [[sgd, sgd], 'loss'] fetch_list = [([sgd, sgd], [(param, grad)]), 'loss'] Returns: optimize_ops(list): The optimize operators splited from fetch_list. fetch_list(list): The updated fetch_list which does not contain optimize operators. c[U[5(a2UR5(aURU5 g[ S5e[U[ [ 45(aURU5 g[ S[U5S35e)Nz0The operator in fetch_list is not an optimize_opzLThe item in fetch_list should be str, variable or optimize_op, but received .)r;r_is_optimize_oprrrrrw) _optimize_ops _fetch_listrs r0 _get_targets@Executor._split_optimize_ops_in_fetch_list.._get_targetss$))''))!((.#JD8S/22""4(bcghlcmbnnopr/rzRequires fetch_list[z8][0] shall be one of (list, tuple) when type(fetch_list[z']) is `tuple`, but received fetch_list[z][0]'s type is `z`.)rr;r>rrrwr#)clsrrBrCrDindexrrSs r0!_split_optimize_ops_in_fetch_list*Executor._split_optimize_ops_in_fetch_lists   %Z0KE$%%A Q?D%((!$q'D%=99#.ug5mnsmtu\]b\ccstxy}~y@tAtJtJsKKMNaA Q?!]>1"))r/c[U[R5nU(a5UR(a URnO[R "S5 gUn/n[U[ 5(a[UR55nOK[U[[45(a0[U5H!upU[U R55- nM# U(dNURH>n U RH+n U R5(dMURU 5 M- M@ X4-n URX|5n U(a4Xl[ R""U R$5UlSUlU$U nU$)a Prune operators and variables which are not needed to generate :code:`fetch_list` and optimize operators. Prune operators and variables which are needed to generate variables to be fed. Notes: This is a very low level API. Users should not use this API directly. Args: program(Program): the origin program feed(list|dict): feed dict or list. fetch_list(list|Variable): A list of variables need to be fetched optimize_ops(list[Operator]): A list of optimizer operators Returns: Program: A new, pruned program. z[The program holds no _program, maybe it is constructed by graph, which can't be pruned yet.NF)r;rr*rrnrorr>rrrrrvrAr_prune_with_inputrGraphr^r _compiled)rFrrgr optimize_opsrorigin_programr9rSrr{rtargetspruned_programs r0_prune_programExecutor._prune_programs4,gx'?'?@ !(!1!1 q$N dD ! !diik*J tUm , ,$T?d499;// +'..))B))++$++B/$/ +'99*N  . !ZZ(;(;rrr2rr)rFrrgrrr6rSrs r0 _update_feedExecutor._update_feedCs.gx'?'?@ &//<<>  V "//1L dD ! !!$))+. #++I66HHY'MM' {2lm/ tUm , ,$T?!%diik!2I'// ::+ +I;6pq"3+ r/cUR(daSUlURR5H!upURR U5 AM# URR 5 gg)a Close the executor. This interface is used for distributed training (PServers mode). This executor can not be used after calling the interface, because this interface releases resources associated with the current Trainer. Returns: None Examples: .. code-block:: python >>> import paddle >>> cpu = paddle.CPUPlace() >>> exe = paddle.static.Executor(cpu) >>> # execute training or testing >>> exe.close() TN)rritemsrrelease_trainerr)rktrainer_instances r0rExecutor.closevs`(||DL'+':':'@'@'B#&&667GH$(C  " " ( ( * r/cUR(agURR5H!upURR U5 AM# URR 5 g)z' flush all trainer param to root_scope N)rrrXrrYrc)rrr[s r0flushExecutor.flushsV << #'#6#6#<#<#> A  " " 2 23C D $? !!#r/c gr3r. rrrgrrrr5rr use_prunes r0r7 Executor.runs"%r/c gr3r.ras r0r7rcsr/c gr3r.ras r0r7rcs14r/c [RRSS5n U bU S;nURU5 [ 5(aUR UUUUUUUS9n U $UR UUUUUUUUU S9 n [R"5 U $)a Run the specified :code:`Program` or :code:`CompiledProgram`. It should be noted that the executor will execute all the operators in :code:`Program` or :code:`CompiledProgram` without pruning some operators of the :code:`Program` or :code:`CompiledProgram` according to fetch_list. And you could specify the scope to store the :code:`Tensor` during the executor running if the scope is not set, the executor will use the global scope, i.e. :code:`paddle.static.global_scope()`. Args: program(Program|CompiledProgram): This parameter represents the :code:`Program` or :code:`CompiledProgram` to be executed. If this parameter is not provided, that parameter is None, the program will be set to :code:`paddle.static.default_main_program()`. The default is None. feed(list|dict): This parameter represents the input Tensors of the model. If it is single card training, the feed is dict type, and if it is multi-card training, the parameter feed can be dict or list of Tensors. If the parameter type is dict, the data in the feed will be split and sent to multiple devices (CPU/GPU), that is to say, the input data will be evenly sent to different devices, so you should make sure the number of samples of the current mini-batch must be greater than the number of places; if the parameter type is list, those data are copied directly to each device, so the length of this list should be equal to the number of places. The default is None. fetch_list(list): This parameter represents the Tensors that need to be returned after the model runs. The default is None. feed_var_name(str): This parameter represents the name of the input Tensor of the feed operator. The default is "feed". fetch_var_name(str): This parameter represents the name of the output Tensor of the fetch operator. The default is "fetch". scope(Scope): the scope used to run this program, you can switch it to different scope. default is :code:`paddle.static.global_scope()` return_numpy(bool): This parameter indicates whether convert the fetched Tensors (the Tensor specified in the fetch list) to numpy.ndarray. if it is False, the type of the return value is a list of :code:`DenseTensor`. The default is True. use_program_cache(bool): This parameter indicates whether the input :code:`Program` is cached. If the parameter is True, the model may run faster in the following cases: the input program is :code:`paddle.static.Program`, and the parameters(program, feed Tensor name and fetch_list Tensor) of this interface remains unchanged during running. The default is False. use_prune(bool): This parameter indicates whether the input :code:`Program` will be pruned. If the parameter is True, the program will be pruned according to the given feed and fetch_list, which means the operators and variables in program that generate :code:`feed` and are not needed to generate :code:`fetch_list` will be pruned. The default is False, which means the program will not pruned and all the operators and variables will be executed during running. Note that if the tuple returned from :code:`Optimizer.minimize()` is passed to :code:`fetch_list`, :code:`use_prune` will be overridden to True, and the program will be pruned. Returns: List: The fetched result list. Examples: .. code-block:: python :name: code-example-1 >>> # doctest: +SKIP("This has diff in xdoctest env") >>> import paddle >>> import numpy >>> # First create the Executor. >>> paddle.enable_static() >>> place = paddle.CPUPlace() # paddle.CUDAPlace(0) >>> exe = paddle.static.Executor(place) >>> data = paddle.static.data(name='X', shape=[None, 1], dtype='float32') >>> hidden = paddle.static.nn.fc(data, 10) >>> loss = paddle.mean(hidden) >>> adam = paddle.optimizer.Adam() >>> adam.minimize(loss) >>> i = paddle.zeros(shape=[1], dtype='int64') >>> array = paddle.tensor.array_write(x=loss, i=i) >>> # Run the startup program once and only once. >>> exe.run(paddle.static.default_startup_program()) >>> x = numpy.random.random(size=(10, 1)).astype('float32') >>> loss_val, array_val = exe.run( ... feed={'X': x}, ... fetch_list=[loss.name, array.name] # type: ignore[union-attr] ... ) >>> print(array_val) >>> # doctest: +SKIP("Random output") [array(0.16870381, dtype=float32)] >>> # doctest: -SKIP .. code-block:: python :name: code-example-2 >>> # doctest: +SKIP("This has diff in xdoctest env") >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> import numpy as np >>> # First create the Executor. >>> paddle.enable_static() >>> place = paddle.CUDAPlace(0) >>> exe = paddle.static.Executor(place) >>> data = paddle.static.data(name='X', shape=[None, 1], dtype='float32') >>> class_dim = 2 >>> prediction = paddle.static.nn.fc(data, class_dim) >>> loss = paddle.mean(prediction) >>> adam = paddle.optimizer.Adam() >>> adam.minimize(loss) >>> # Run the startup program once and only once. >>> exe.run(paddle.static.default_startup_program()) >>> build_strategy = paddle.static.BuildStrategy() >>> binary = paddle.static.CompiledProgram( ... paddle.static.default_main_program(), ... build_strategy=build_strategy ... ) >>> batch_size = 6 >>> x = np.random.random(size=(batch_size, 1)).astype('float32') >>> prediction, = exe.run( ... binary, ... feed={'X': x}, ... fetch_list=[prediction.name] ... ) >>> # If the user uses two GPU cards to run this python code, the printed result will be >>> # (6, class_dim). The first dimension value of the printed result is the batch_size. >>> print("The prediction shape: {}".format(np.array(prediction).shape)) The prediction shape: (6, 2) >>> print(prediction) >>> # doctest: +SKIP("Random output") [[-0.37789783 -0.19921964] [-0.3577645 -0.18863106] [-0.24274671 -0.12814042] [-0.24635398 -0.13003758] [-0.49232286 -0.25939852] [-0.44514108 -0.2345845 ]] >>> # doctest: -SKIP FLAGS_FORCE_USE_PROGRAM_CACHEN)rrtTrrrs)rrgrrrr5r) rrgrrrr5rrrb) renvironrrr _run_pir_impl _run_implrupdate_autotune_status) rrrgrrrr5rrrbforce_use_program_cacheress r0r7rcsj#%**.. +T#  # . 7<!   - -.? @ ==$$%+-)%C, ..%+-)"3#! C  ' ' ) r/c UR(a [S5eUSLn Uc [5nURU5n[ U[ 5(aUR (aUR Sn [R"U 5n [R"5n U RU 5 [R"U 5Ul SUR ;aUR Sn[ U[ 5(aF[UR5R 5S:XaU (aSn ["R$"U 5 Uc ['5nUnUnUR)U5unnU(aSn U (Ga[+XU5nUR-U5nUc[ U[.R05(aUR3[5[7U555n[8R8"U5nUUlUR3[5[7U555c$UR=[5[7U55U5 UR?XUU5nURAUU5 OUnURCUU5nUn[EXRF5(GaUc0nO6[ U[H[J45(a[U5S:XdS5eUSn[ U[L5(d[OS [QU535eURCX5n0n[ U[.R05(d)[ URR[.R05(a[ U[.R05(aUO URRnURTnUbURV(aS S /nUHWn[XRZ"US 5n[ U[45(aUR]5nUS :XaSOS n[_U5UU'MY [a[LRcUS55 URdRgUUUUUURFU5unnURiXXF5 [kUS5(GacSSK6J7n [ URpU5(dS5eURpnU"5nUR5RrURtn[vRx"U/5R{[}UR~55n [R"UURt5n![U [R"55n"[R"5(a["R$"S5 OW[R"5(a!U!RU"U!R55 OU!RU"URF5 UR[IUR55UUR5n#[aU5 U#$[ U[.R05n$U (Ga&U$(aURR5n%OUR5n%U%RrHn&U%RRU&R55n'U%RrU&n(U'R5S LdMPU'RQ5[RRR:XdMU'R5SLdMU(RSLdMU(RSLdMU(RS LdMU&U;dM[SU&35e [R"X5 URX`RF5 UR(dSUR35eURURU5$)N"Attempted to use a closed Executor heter_placestartup_programrNow you are using default_main_program, but there are no operators in the program to be executed. Please ensure you create model correctly or you can pass the Program or the CompiledProgram manually.TrNot compiled with data parallel7feed requires dict as its Parameter. But you passed in FLAGS_new_executor_serial_run!FLAGS_new_executor_sequential_runFrsrj LRSchedulermust be LRSchedulerCaution!!! When capturing CUDA Graph, the learning rate scheduler would not take any effect! Please set the learning rate manually before each batch!zNeed feed data for variable z0Program must have _is_inference = True, but get )]rrBr_check_fetch_listr;r _heter_pipeline_optrrrr,r.rrrArrvrnror1rHrrrr*rrrrH_share_vars_fromrrRrrUrrr>rrrrwrr{sequential_runrrlowerboolrfromkeysrrfr-r~paddle.optimizer.lrrxrjr _var_namerDrErrreget_variable_tensorrCPUPlaceis_cuda_graph_capturingis_compiled_with_ipu _copy_fromr-r7rr:rr^rencoderrKrL DENSE_TENSORr_ stop_gradientis_databelong_to_optimizerraacp_auto_checkpointr}r_run_inference _executor))rrrgrrrr5rrrbuse_default_main_programrpr error_info_origin_fetch_list_origin_programrN cache_keycached_pruned_programprogram_scope_cacherQ stored_flagrr schedule_flagflagrrCrxrjlr_valuelr_varrrG cpu_tensorretrrvarnamevardescvarobjs) r0rjExecutor._run_impls <<CD D#*d? ?*,G++J7 gw ' 'G,G,G"55mDK#55kBK A KK $%)]]1%5D " G$?$??!556GH w ( (G((*../14'C  j) = NE(!#'#I#I $  L I 51I%)$B$B9$M !$,gx'?'?@@*.*N*NB/0+'#ii0G/BG,<<? 34  <<? 34g"&!4!4:|"..y.I!6$$^T:D$G $Wjj 9 9|D4-004yA~H'HH~AwdD))MdSWj\Z$$W3DK'8#;#;<<  8 8AA "'8+C+CDD ! 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For more information please refer to the executor.run(...).c34># UH nT"U5v M g7fr3r.).0v is_fetch_vars r0 -Executor._check_fetch_list..s41|AszRequire fetch_list[z5] 's type shall be one of (Value, str), but received r@)rwrrallextendr>rr#)rr is_tuple_listrmrSrfrs @r0r{Executor._check_fetch_listsJ B  I  # #< Z((  %%)*%5$67% % (  +FAC   3s##4444JJtCy)JJsO)!,abfgjbkbtbtauuvw, r/c[[[U55S-S5nUR[U55 SSS5 UR(aNSUR;a=[SS5nUR[URS55 SSS5 ggg!,(df  Nn=f!,(df  g=f)Nz_train_desc.prototxtw fleet_desczfleet_desc.prototxt)openrrr _fleet_opt)rrtrainerfouts r0_dump_debug_infoExecutor._dump_debug_infos #bk"%;;S AT JJs7| $B   ,'2D2D"D+S1T 3w11,?@A21#E B A21sB%2(B6% B36 Cc [URR55nXC:aUn[SUSUS35 XCUSS-:a[SXC-SUS35 XC-USS'UR USSU-5 U$)Nz/Pipeline training: setting the pipeline num to z" is enough because there are only z filesconcurrency_listrzBPipeline training: setting the 1st element in concurrency_list to )rAdataset get_filelistr set_thread)r pipeline_optr pipeline_numfilelist_lengths r0_adjust_pipeline_resource"Executor._adjust_pipeline_resourcesgoo::<=  )*L A/ARRtvEuFFLM  L9K,LQ,OO O TUdUtTuvXYhXiiop  / + ,Q / <(:;A>MNr/c| /n/nSnSnUR5RHnURU5(a OURS5(dM3UR S5S:waUR S5OUnUbXG:wa"UR /5 UR U5 USR U5 UnM [ U5nSn Sn X:aX:aX9U:XaU S- n X:a X9U:XaMX:XaOX9n U S- n U n Sn X:a'X9U :waX9U:waSn OU S- n X:a X9U :waMX:XaOHU (dMt[X5H)n X-HnURSU 5 M XU 'U S- n M+ X:aMSn/n/n[U5HLn UbXCU :wa$UR /5 UR X95 USRX)5 X9nMN [5nUR5RHPnUR5RU5nUR(aM5URUR5 MR [ U5n[5n[5n[U5V s/sHn /PM nn [U5Hn [5n[5nXHnUR H4nUR#U5HnUU;dM URU5 M M6 UR$H,nUR'U5HnURU5 M M. 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M~r/c  URbSSKn Ub [S5e/n UR5RR 5H'n U R (dMU RU 5 M) U RR5RS5nURS5 URS5 URS/5 URU 5 GOURGbpURSnURSnUS:waSUR;aSSKn Ub [S 5e/n UR5RR 5H'n U R (dMU RU 5 M) U RR5RS 5nURS5 URS5 URS/5 URU 5 OUc [S 5e[ R""U5n[$R&"5nUR)U5 [$R*"U5UlOUc [S 5eUR/5 /nUR(Ga URS nUHXn[1U[25(a UR4nOUnUUR5R;dMGURU5 MZ [7URS /US SS9URS 'URS R9S5nUR:HInUR<S:XdMUR?S[$R@RBRD5 MK SnURGUUUUUUUU S9unnURIU5 URK5 URcURcURMUUS9 URNSLa1URPRN(a[RRT"S5 URWURPRX5 URSL=(dW URZSL=(aB URZR]SS5=(a URZR]SS5S:HnUSLa@UR,R_UR`URc5X2Rd5nOUc/n[gUSU5nURiU5nUcRUR,R_UR`URc5X2Rd5nURkUU5 OURmURd5 U byURoS5nUU 5nURs5 UR,RuU5 URw5 USLaUR,RyU5 O;UR,RuU5 USLaUR,RyU5 UR{5 UR}5 U(a:URS5R5nUR5n[U5$g)Nr(dataset should be None for pipeline modeFileInstantDatasetrNonepipeline_stagerp is_fl_modez.dataset should be None for heter pipeline modeInMemoryDatasetz)dataset is need and should be initializedsection_programrgrrrgrrrop_rolerrr5rrrrrrrF0dataset should call set_use_ps_gpu in PsGpu moderdump_fields_pathr)CrrrBrrvaluesrrbaseDatasetFactorycreate_datasetset_batch_sizer set_filelist set_use_varr|rrrr,r.rr_prepare_to_runr;rrbrr{rvrwrrrrr _set_infer_gen_trainer_descrr proto_descr r _dynamic_adjust_before_trainrrrinit_for_datasetr^_descrrrr ResetDatasetget_worker_scoper!rrun_from_datasetstoprY_dynamic_adjust_after_train_finish_to_runrget_fetch_listr8r=)rrrr5ris_inferrrrr fetch_handlerrrrfstage_idrprreal_fetch_list real_programrr main_blockrrreused_trainerr[rscope0 fetch_monitorarrr;s r0_run_from_datasetExecutor._run_from_dataset s  , ""#MNNI++-2299;;;;$$S)<kk002AA$G  " "1 %   q !  & *    *  ( ( 4223CDH!55mDK1}w'B'BB!**L !#I&335::AAC;;;%,,S1 D%kk88:II)G**1-&&q)((&2'' 2?&C$55kBK A KK $%)]]1%5D ""#NOO!  "001BCL' i22%.^^N%.N!\%>%>%@%E%EE#**95 (8K--.?@*$& 8G ! !"3 4!../@AGGJJ nn77g%LL!77>>GG %J..!!%/  w 8$!!#  (**2%%gw%G    &7+=+=+H+H OON O,,W-?-?-J-JK 44D@   d * E""&&|U; E""&&'92>"D  U "&&77LL'--/5// ! 5gtZPI#66yA '#'#9#9#J#JLL'--/5//$ '' 3CD --goo>  $%66q9F/ FM    !  " " 3 34D E    &&&667GH  " " 3 34D E&&&667GH++- ..)88:C'')GG$ $r/c ^^^TRceUbS5e[TST5n URU 5n U (aU bU $SSKmUU4SjnU"5nUU4SjnU"5unnUTRS'SmUR TUUUUTUU S9unnUR U5 UR 5 URSLa1URR(a[R"S5 URURR5 UR5nURRTR UX2R"5nUUU/n U (aUR%X5 U $) Nr rc>/nTR5RR5H'nUR(dMUR U5 M) TR R 5RS5nURS5 URS5 URS/5 URU5 UR5 U$)Nr rr ) rrrrrrrrrrrrr)rrfrrrs r0 _get_dataset4Executor._prepare_pipeline_ctx.._get_datasetz sI++-2299;;;;$$S)<kk002AA$G  " "1 %   q !  & *    *  # # %Nr/c>TRSn/nTHWn[U[5(a URnOUnX0R 5R ;dMFUR U5 MY [U/USSS9nURS5nURHInURS:XdMURS[RRR5 MK X4$)Nrrgrrrr)rr;rrbrrrrr{rvrwrrrrr)r/r.rrr0rrrs r0_get_real_program_fetch_listDExecutor._prepare_pipeline_ctx.._get_real_program_fetch_list s"001BCL O' i22%.^^N%.N!%>%>%@%E%EE#**95 (/$*$& L&++A.J nn77g%LL!77>>GG % 0 0r/rrFr)rrrrrrrrr r r r!rr#rr"r^rr )rrrr5rr+rrrrr,rrr r9r<r/r.r trainer_descr[rs ` ` @r0_prepare_pipeline_ctxExecutor._prepare_pipeline_ctx` su$$000J JJ1'4L !!), J . 1:)E(F% o3?/0 ..!!%/  w 8$!!#    &7+=+=+H+H OON O,,W-?-?-J-JK}} 11BB LL, o'78     / r/cUR5nUR5nX5R;aURU5nO3UR U[ R RRSS9n[XRU5(dq[U5HbupxURU5(a-URU5n URSSU/0SU /0SU0S9 MH[R"SUS 35 Md U$) NTrrgrsrtrrrr)rrrrfrrrKrLrrrrrrnro) rrrgrrrrrSrbrs r0 _add_feed_opsExecutor._add_feed_ops smmo "//1 -- -#'' 6H#.."\\))88 /H",mDD$T?''--&**40C ,,# #hZ0!&$aj -MM'v-gh+r/cUR5nUR5nX6R;aURU5nO3UR U[ R RRSS9nU(aSnOSn[XbX85(d_[U5HPup[U [[45(dSU S[U 535eURUSU /0SU/0S U 0S 9 MR U$) NTrrrrrrsrtrr)rrrrfrrrKrLrrrr;rrrwr) rFrrrrrrrrrSrfs r0_add_fetch_opsExecutor._add_fetch_ops s mmo "//1 .. .$((8I$//#\\))44 0I !HH# n  $J/!##7703tCykB7&&!#<"YK0 !* ' 0r/cUR5nUR5n[UR5n[ [ U55H3nURUR U:XdM"URU5 M5 U$r3)rrrArvreversedrQrw _remove_op)rFr fetch_op_namerrop_numrs r0_remove_fetch_opsExecutor._remove_fetch_ops smmmo "//1 \%%&E&M*C$))]:'',+r/c URUUUUUUUUU U U 5 up>> import paddle >>> paddle.enable_static() >>> place = paddle.CPUPlace() # you can set place = paddle.CUDAPlace(0) to use gpu >>> exe = paddle.static.Executor(place) >>> x = paddle.static.data(name="x", shape=[None, 10, 10], dtype="int64") >>> y = paddle.static.data(name="y", shape=[None, 1], dtype="int64", lod_level=1) >>> dataset = paddle.base.DatasetFactory().create_dataset() >>> dataset.set_use_var([x, y]) >>> dataset.set_thread(1) >>> # you should set your own filelist, e.g. filelist = ["dataA.txt"] >>> filelist = [] # type: ignore[var-annotated] >>> dataset.set_filelist(filelist) >>> exe.run(paddle.static.default_startup_program()) >>> exe.infer_from_dataset(program=paddle.static.default_main_program(), ... dataset=dataset) Tr5 rrrr5rrrrrr,s r0infer_from_datasetExecutor.infer_from_datasetS s6D%%            r/c 8URUSUSUUUUS9up(URS5 UR5 URXS9 URR UR UR5US5n URRU 5 U $)NrrFr) rrrrrr"r^r#r&) rrr5rrrrr,rr[s r0start_heter_trainerExecutor.start_heter_trainer s..!!%/   5!!!# g?11BB LL'--/5$  //0@A r/c 6URUUUUSUUUUU 5 $)ap Train from a pre-defined Dataset. Dataset is defined in paddle.base.dataset. Given a program, either a program or compiled program, train_from_dataset will consume all data samples in dataset. Input scope can be given by users. By default, scope is global_scope(). The total number of thread run in training is `thread`. Thread number used in training will be minimum value of threadnum in Dataset and the value of thread in this interface. Debug can be set so that executor will display Run-Time for all operators and the throughputs of current training task. Note: train_from_dataset will destroy all resources created within executor for each run. Args: program(Program|CompiledProgram): the program that needs to be run, if not provided, then default_main_program (not compiled) will be used. dataset(paddle.base.Dataset): dataset created outside this function, a user should provide a well-defined dataset before calling this function. Please check the document of Dataset if needed. scope(Scope): the scope used to run this program, you can switch it to different scope for each run. default is global_scope thread(int): number of thread a user wants to run in this function. Default is 0, which means using thread num of dataset debug(bool): whether a user wants to run train_from_dataset fetch_list(Tensor List): fetch Tensor list, each variable will be printed during training fetch_info(String List): print information for each Tensor, its length should be equal to fetch_list print_period(int): the number of mini-batches for each print, default is 100 fetch_handler(FetchHandler): a user define class for fetch output. Returns: None Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> place = paddle.CPUPlace() # you can set place = paddle.CUDAPlace(0) to use gpu >>> exe = paddle.static.Executor(place) >>> x = paddle.static.data(name="x", shape=[None, 10, 10], dtype="int64") >>> y = paddle.static.data(name="y", shape=[None, 1], dtype="int64", lod_level=1) >>> dataset = paddle.base.DatasetFactory().create_dataset() >>> dataset.set_use_var([x, y]) >>> dataset.set_thread(1) >>> # you should set your own filelist, e.g. filelist = ["dataA.txt"] >>> filelist = [] # type: ignore[var-annotated] >>> dataset.set_filelist(filelist) >>> exe.run(paddle.static.default_startup_program()) >>> exe.train_from_dataset(program=paddle.static.default_main_program(), ... dataset=dataset) FrRrSs r0train_from_datasetExecutor.train_from_dataset s6B%%            r/)rrrrrrr:rrrr3rrrrrrr3)rzPlaceLike | NonerEr )rEr )rr>)NNN) .........)r Program | CompiledProgram | Nonerg;dict[str, npt.NDArray[Any]] | list[npt.NDArray[Any]] | Noner,str | Tensor | Sequence[str | Tensor] | Nonerrrrr5core._Scope | Nonerz Literal[True]rrrbrrEzlist[npt.NDArray[Any]])rr\rgr]rr^rrrrr5r_rzLiteral[False]rrrbrrEz list[Tensor])rr\rgr]rr^rrrrr5r_rrrrrbrrEz%list[Tensor] | list[npt.NDArray[Any]]) NNNrgrNTFF)NN)rr rEzlist[int] | None)NNNrFNNd) NNNrFFNNr`N) NNNrFFNNr`NFFr) NNNrFNNr`N)rr\rz&DatasetBase | _FleetDatasetBase | Noner5r_rrrrrlist[Tensor] | Nonerlist[str] | Nonerrr,FetchHandler | NonerEr )NNFNNr`N)rzProgram | Noner5r_rrrrcrrdrrr,rerEzcore.TrainerBase)6r#r$r%r&__doc____annotations__rrrrrrrrrrrrrr rrrrrr-r7r< classmethodrHrRrUrr^rr7rjrirr{rrrrr5r?rBrErLrOrTrWrZr(r.r/r0rrsC=~ M2>A %><@@9G@MF-53AC 6p<1*1*f?C;;z**X +6 $58LOCF !$'&)"% %1 %J %A %  %  %" %$ %  % %  % %58LOCF !$''*"% 1 J A     " %       58LOCF !$' "% 41 4J 4A 4  4  4" 4 4  4 4 / 4 4 xtG$(*.'+-1M 1M 8M " M  M  M (M %M M +M  M b#'$(*.'+-1* * "*  * ( * % * * +*  * \59:>$(*.'+-1L 1L 8L " L  L  L (L %L L +L  L L r/r)rE core._Scope)r5rirEri)r5rirEzGenerator[None, None, None]ra)rrb)r)NTr3)f __future__rrHr rrrn functoolsrtypingrrrrnumpyrDrr paddle.base.frameworkr paddle.base.libpaddle.pirr r rrrrrrrrr data_feederrrrrrrrrrrincubate.checkpointr rtrainer_factoryr!r"wrapped_decoratorr#collections.abcr$r% numpy.typingnptr&paddle._typingr'paddle._typing.device_liker(paddle.base.datasetr)(paddle.distributed.fleet.dataset.dataset_FleetDatasetBase paddle.staticr*__all__Scoper- NativeConfigInferNativeConfigAnalysisConfigInferAnalysisConfigr1r7r9r=rOrTrlrqrrrrrrrrrrrrrrrr rr*rGrr.r/r0rs#  88.5 54&   8@<3(1/.  **,%%)),''T+\$ F+\,^%R7>*\7D*/\LQ=@'0#$7D0  ( ,<1h,    8++\@/@/F E E r/