o p ic@sddlZddlZddlZddlZddlZddlmmZ ddl mZddl m Z ddl mZddlmZddlmZddlmZdd lmZdd lmZmZmZmZgZGd d d Zd ddZd ddZddZ ddZ!ddZ"ddZ#ddZ$ddZ%ddZ&dS)!N)core) convert_dtype)switch_to_static_graph)PirTranslatedLayer)TranslatedLayer)layers) logging_utils)func_to_source_codeparse_arg_and_kwargsparse_varargs_name type_namec@seZdZdZdddZddZddZed d Zed d Z d dZ ddZ e ddZ e ddZe ddZe ddZe ddZe ddZdS) FunctionSpecz8 Wrapper class for a function for class method. NcCst|r|j|_t|j|_n||_d|_|dur"d|_d|_ n| ||_t j |j|_ t|\|_|_t||_|jdurXtt|ddttfrZ|j|jj7_dSdSdS)N__self__)inspectismethod__func___dygraph_functionweakrefrefr_class_instance _input_spec_flat_input_spec_verify_input_specpaddleutilsflattenr _arg_names_default_kwargsr Z varargs_name isinstancegetattrrrZ_input_args_names)selffunction input_specr$i/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/paddle/jit/dy2static/function_spec.py__init__.s$     zFunctionSpec.__init__c Cst|jt|kr8d|jjdt|jd|jdt|d|d }|r4t|dr4|d7}t|t|t|}t t|t|jD]6}|j|}||vr\| ||||=qF||j vrttd |jjd |d |d |d | |j |qFt ||fS) a Moves kwargs with default value into arguments list to keep `args` contain the same length value as function definition. For example: Given function definition: `def foo(x, a=1, b=2)`, when calling it by `foo(23)`, the args is `[23]`, kwargs is `{a=1, b=2}`. In this function, it will return args with `[23, 1, 2]`, kwargs with `{}` Args: args(tuple): tuple of input arguments value of decorated function. kwargs(dict): dict of input keyword arguments value of decorated function. Return: New arguments tuple containing default kwargs value. zThe decorated function `z ` requires z arguments: , but received z with .rzP Maybe the function has more than one decorator, we don't support this for now.`z()` requires `z,` arguments, but not found in input `args`: z and `kwargs`: ) lenrdygraph_function__name__risclassNotImplementedError ValueErrorlistrangeappendrtuple)r!argskwargs error_msgiarg_namer$r$r%unified_args_and_kwargsFs$4    z$FunctionSpec.unified_args_and_kwargscsg}g}|jdur6|rt|jjd|dt|t|jkr/tdt|dt|jt||j}n#t||j}dd|D}tt | |fdd t |D}t |j|}||fS) a Converts input arguments into InputSpec. 1. If specific input_spec, use them to construct feed layers. 2. If input_spec is None, consider all Tensor and Numpy.ndarray as feed layers Args: args(tuple): tuple of input arguments value of function containing default kwargs value. kwargs(dict): kwargs arguments received by **kwargs. Return: Same nest structure with args and kwargs by replacing value with InputSpec. Nz# got unexpected keyword arguments: z;. 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Args: input_with_spec(tuple): input arguments by replacing argument with InputSpec. main_program(Program): main program for inserting feed layer. r to_placements stop_gradientFfeed_)nameshapedtypeDistributedInputSpecN)/paddle.distributed.auto_parallel.placement_typerKrrr ir_staticZ program_guardrErstatic InputSpecr inputdatarNrOrrPrL7paddle.distributed.auto_parallel.static.dist_input_specrR dims_mappingZ_pir_opsZ shard_tensormeshr2pack_sequence_as)r!input_with_spec main_programrKflat_input_specZ function_argsZprogram_inputsr7var_specrLZ feed_valuerR placementsZdist_feed_valuer$r$r%pir_to_static_inputs_with_specsD           z+FunctionSpec.pir_to_static_inputs_with_specc Cstj|}g}|}t|D]e\}}t|tjjrnt|dd}|j |j p*d||j |j dd|d} ddl m} ddlm} t|| rmdd lm} | } | | }|j|j_|j|j_|jd |jd | |n|} || qtj||S) rIrLFrMT)rNrOrPZis_dataZneed_check_feedrLrrQ)DistributedTensor)get_default_distributed_context process_meshrZ)rrrZ global_blockrErrUrVr Z create_varrNrOrPrYrRZ3paddle.distributed.auto_parallel.static.dist_tensorrcZ4paddle.distributed.auto_parallel.static.dist_contextrdr[ dist_attrrerZZmark_annotatedZadd_dist_tensor_for_programr2r\)r!r]r^r_inputsblockr7r`rLZ feed_layerrRrcrdZdefault_dist_ctxZ dist_tensorr$r$r%to_static_inputs_with_specs:           z'FunctionSpec.to_static_inputs_with_speccCs*t|ttfstdt|dt|S)zJ Verifies the `input_spec` and its element type is valid. zBThe type(input_spec) should be one of (tuple, list), but received r()rr3r0 TypeErrorr )r!r#r$r$r%rs zFunctionSpec._verify_input_speccCsd|jjd|j|jS)Nz function: {}({}), input_spec: {},)formatr+r,joinrrr!r$r$r%__repr__s  zFunctionSpec.__repr__cCs*|jdurdS|durtd|S)NzFThe instance of class has been deleted, please re-create the instance.)r RuntimeErrorrnr$r$r%class_instance$s  zFunctionSpec.class_instancecCs|jdur |j|jS|jSN)rqr__get__rnr$r$r%r+.s zFunctionSpec.dygraph_functioncC|jSrr)rrnr$r$r% args_name5zFunctionSpec.args_namecCrtrr)rrnr$r$r%r#9rvzFunctionSpec.input_speccCrtrr)rrnr$r$r%r_=rvzFunctionSpec.flat_input_speccCs t|jSrr)r r+rnr$r$r%codeAs zFunctionSpec.coderr)r, __module__ __qualname____doc__r&r9rHrrbrirropropertyrqr+rur#r_rwr$r$r$r%r)s. *8 5 3      rTcCjt}|dur3t|tjr*|r%|}dd|D}tt||}|S|j}|Stdt ||S)z Returns parameters of decorated layers. If set `include_sublayer` True, the parameters created in sub layers will be added. NcSg|]}|jqSr$rN)r:pr$r$r%r<Pz"get_parameters..:Type of `layer_instance` should be nn.Layer, but received ) collections OrderedDictrrLayer parameterszip _parametersrjr )layer_instanceinclude_sublayerparamsnamesr$r$r%get_parametersFs  rcCr|)z Returns Variable buffers of decorated layers. If set `include_sublayer` True, the Variable buffers created in sub layers will be added. NcSr}r$r~)r:bufferr$r$r%r<frzget_buffers..r) rrrrrbuffersr_buffersrjr )rrrrr$r$r% get_buffers\s  rc Cs,ddlm}ddlm}g}ttj|D]v\}}t|t j r+tj j |}d|_n\t|tjjrJ|j}|r?||}ntj j |}||_n=t|tjjjtjjfr|j}|ry|j}||j|}||j|j|j|||j d}n tj |j|j|j}||_|!|qtj"||}|S)NrrJrQT)rPrNr[raZ local_shape)#rSrKrYrRrErrrrnpndarrayrUrV from_numpyrLreagerTensorZis_distZ from_dtensor from_tensorbase frameworkVariablepirValuerfrerZrOrPrNZ _local_shaper2r\) r4rKrRrGr> input_varrLr[rar$r$r%_replace_value_with_input_specqsJ        rc CsLt|t|ks Jttt|d}g}tt||D]\}\}}d}ttj|D]n\}} t| tj rBtj j | } d| _ n1t| tjjrW| j } tj j | } | | _ nt| tjjjrq| j } tj | j| j| j} | | _ n| } t| tj tjjtjjjfrdt||d|d|| _|d7}|| q-qtj||}|S)NrrTz_jst.r()r*strrErrrrrrrrUrVrrLrrrrrrrrOrPrNzfillr2r\) r4 arg_namesZ order_digitrGorderargZ name_prefixindexr>Z origin_inputrrLr$r$r%rBsB  " rBc sdfdd }tttfrag}|dtD]\}}t||}||qttkrTtdD]}t|tjj t j frSt dt|dq<|td|Sttri}|dD]\}} |vrt| |||<qr| ||<qr|Sttjjr tgd} t| tjjstd d j| _t| rj| _| St d d | d | SS)aD Replaces tensor in structured `inputs` by InputSpec in `input_spec`. Args: inputs(list|dict): nested structure list or dict. input_spec(list|dict): same nested structure list or dict as inputs. Return: Same structure with inputs by replacing the element with specified InputSpec. Fcsft|t|urtdt|dt|d|r/t|t|kr1tdtdtdSdS)Nztype(input) should be r'r(zBRequires len(inputs) >= len(input_spec), but received len(inputs):z < len(input_spec):)typerjr*r/)rWspec check_lengthr#rgr$r%check_type_and_lensz1convert_to_input_spec..check_type_and_lenTNzThe inputs contain `z` without specifying InputSpec, its shape and dtype will be treated immutable. Please specific InputSpec information in `@to_static` if you expect them as mutable inputs.rz1Give input spec into a non-tensorable arguments `z`.z;input spec is not compatible with real inputs. input_spec: z , real_spec:  )F)rr3r0rErAr2r*rrrrrr warnr extenddictitemsrrUrVrrprN spec_greaterrO) rgr#rr]r7rZout_specZ rest_inputrNrWZ real_specr$rr%rAsT         rAcCsDt|}|dt|}t|D]\}}||}t||||<q|S)ao Adds default name according to argument name from decorated function if without specifying InputSpec.name The naming rule are as followed: 1. If InputSpec.name is not None, do nothing. 2. If each argument `x` corresponds to an InputSpec, using the argument name like `x` 3. If the arguments `inputs` corresponds to a list(InputSpec), using name like `inputs_0`, `inputs_1` 4. If the arguments `input_dic` corresponds to a dict(InputSpec), using key as name. For example: # case 1: foo(x, y) foo = to_static(foo, input_spec=[InputSpec([None, 10]), InputSpec([None])]) print([in_var.name for in_var in foo.inputs]) # [x, y] # case 2: foo(inputs) where inputs is a list foo = to_static(foo, input_spec=[[InputSpec([None, 10]), InputSpec([None])]]) print([in_var.name for in_var in foo.inputs]) # [inputs_0, inputs_1] # case 3: foo(inputs) where inputs is a dict foo = to_static(foo, input_spec=[{'x': InputSpec([None, 10]), 'y': InputSpec([None])}]) print([in_var.name for in_var in foo.inputs]) # [x, y] N)r0r*rE_replace_spec_name)rur]Zcandidate_arg_namesr7r8r#r$r$r%rFs rFcCst|tjjr|jdur||_|St|ttfr4g}t|D]\}}|d|}|t ||q|St|t rMi}| D] \}}t ||||<q?|S|S)zL Replaces InputSpec.name with given `name` while not specifying it. N_) rrrUrVrNr0r3rEr2rrr)rNr#Zprocessed_specsr7rnew_namer;r$r$r%r5s   rcsbddtj|D}|ddtj|D7}difddfdd|D}t|S)a Generator hash spec with args/kwargs InputSpec names. Consider the following InputSpecs with same shape/dtype except for name: 1. [InputSpec([3,3], 'float32', 'x'), InputSpec([3,3], 'float32', 'x')] 2. [InputSpec([3,3], 'float32', 'x'), InputSpec([3,3], 'float32', 'y')] Under @to_static, we should generate two different program not just one, because the former has one input ('x'), but the latter has two input ('x', 'y'). cS g|] }t|tjjr|jqSr$rrrUrVrNr:rr$r$r%r<U  z$_hash_spec_names..cSrr$rrr$r$r%r<Zrrcs |vr |<d7|S)Nrr$r~)r7name_idsr$r%to_idxasz _hash_spec_names..to_idxcsg|]}|qSr$r$)r:rN)rr$r%r<hr=)rrrr3)Z args_specsZ kwargs_specsZ spec_namesvaluer$)r7rrr%_hash_spec_namesLs   rcCsdd}||j|jS)NcSsBt|t|kr dSt||D]\}}|dkr||krdSqdS)NFT)r*r)Z first_shapeZ second_shapeZfirst_nZsecond_nr$r$r%_shape_greaternsz$spec_greater.._shape_greater)rO)firstotherrr$r$r%rmsr)T)'rrrnumpyrrZpaddle.pir.corerrrTZ paddle.baseZpaddle.base.data_feederrZpaddle.base.dygraph.baserZpaddle.jit.pir_translated_layerrZpaddle.jit.translated_layerrZpaddle.nn.layerrr rr r r r __all__rrrrrBrArFrrrr$r$r$r%s6         0&K# !