%VjiP >ddlZddlZddlmZmZmZddlZddlmcm cm cm Z ddl mcm cm Zddlm Z ddlmZmZddlmZddlmZddlmZddlmZdd lmZmZejj ZGd d ej Z!d ed edede"e#e$efde%e!e!ff dZ&d ed ede"e#e$efdee%eej'e(feej'e)fffdZ*d ed edefdZ+d ed ede)fdZ,d ede-e)fdZ.d ed ede#fdZ/dede#defdZ0deddfdZ1dZ2e2dej'dej'dej'fdZ3e2dej'dej'dej'fdZ4e2dej'dej'dej'fd Z5d ede6fd!Z7d ed ed"e)defd#Z8dS)$N)CallableOptionalUnion)FakeQuantizeBase ObserverBase)_is_activation_post_process)getattr_from_fqn) GraphModule)Node)NSNodeTargetType NSResultsTypeceZdZejZejZejZejZejZ dS)NodeInputOrOutputTypeN) __name__ __module__ __qualname__enumautoFP32INT8FP16UNKNOWN FP32_OR_INT8N/root/voice-cloning/.venv/lib/python3.11/site-packages/torch/ao/ns/fx/utils.pyrrsQ 49;;D 49;;D 49;;DdikkG 49;;LLLrrnodegm logger_clsnode_type_to_io_type_mapreturncn|d}|d}|d}|d}|d}|d} |d} |d} |jd kr|j|vrtjtjfS|j|vrtjtjfS|j|vrtjtjfS|j|vrAt ||d } t| tsJt| |||\} }||fStj tj fS|jd kr;|jd ksJt|jtsJt||jtfd | D}t|ttfs|rAt ||d } t| tsJt| |||\} }||fStfd |D}tfd| D}|rtjtjfS|rtjtjfStj tj fS|jdkr:|jdkrKt ||d }t|tsJt||||\} }|tjfS|jdkrwt ||d }t|tsJt||||\} }t ||d}|t jus J|d|tjfS|j| vrAt ||d } t| tsJt| |||\} }||fStj tj fStj tj fS)Nfuns_io_type_fp32funs_io_type_fp16funs_io_type_int8funs_io_type_fp32_or_int8mods_io_type_fp32mods_io_type_int8mods_io_type_fp32_or_int8meths_io_type_fp32_or_int8 call_functionr call_modulec38K|]}t|VdSN isinstance.0 target_typemods r z7get_node_first_input_and_output_type..NsA1 1  sK ( (1 1 1 1 1 1 rc38K|]}t|VdSr/r0r2s rr6z7get_node_first_input_and_output_type..aA) )  sK ( () ) ) ) ) ) rc38K|]}t|VdSr/r0r2s rr6z7get_node_first_input_and_output_type..er8r call_method dequantizetor z handling needs to be added)optargetrrrrget_normalized_nth_inputr1r $get_node_first_input_and_output_typerstrr anyrrtorchfloat16)rrr r!FUNS_IO_TYPE_FP32FUNS_IO_TYPE_FP16FUNS_IO_TYPE_INT8FUNS_IO_TYPE_FP32_OR_INT8MODS_IO_TYPE_FP32MODS_IO_TYPE_INT8MODS_IO_TYPE_FP32_OR_INT8METHS_IO_TYPE_FP32_OR_INT8 first_arg_prev_node_input_typeprev_node_output_type"is_known_fp32_or_int8_input_moduleis_known_fp32_input_moduleis_known_int8_input_module prev_nodecur_node_dtype_targetr5s @rr@r@&s11DE01DE01DE 89T U01DE01DE 89T U!9:V!W w/!! ;+ + +).0E0JK K ;+ + +).0E0JK K [- - -).0E0JK K [5 5 50r1==Ii.. . ..52z+C %%*+@A A)13H3PQ Q M ! !w-''''$+s+++++r4;//-01 1 1 1 81 1 1 . . * sZ7GH I I B1 B 1r1==Ii.. . ..52z+C %%*+@A A%() ) ) ) 0) ) ) & & "&)) ) ) ) 0) ) ) & & " & R).0E0JK K ' R).0E0JK K)13H3PQ Q M ! ! ;, & &1r1==Ii.. . ..52z+C %%*+@+EF F [D 1r1==Ii.. . ..52z+C %% %=T2q$I$I !(EM999(EEE:99*+@+EF F [6 6 60r1==Ii.. . ..52z+C %%*+@A A%-/D/LMM%-/D/LMMrcvt||d}t|tsdS|d}d}|jdkrh|jt jkr|||ddS|jtjtj tj tj fvr|||ddSdS|jd krt|jtsJt||jttjtjtjt$jtjtjtjtjtjtjtjtjtjtjtjtjtj tj!t$j"t$j#t$j$t$jt$j%t$j&frj'j(fStSfd |D}|rtU|||SdS) z{ Returns the qparams (scale, zero_point) of the first input to `node`, if they can be inferred from the graph. rNr*cft|||}t|||}t|trt|jtsJt|trt|jtsJt ||j}t ||j}||fSr/)r?r1r r>rAr )rr scale_arg_idx zp_arg_idx scale_nodezp_node scale_objzp_objs r _get_scale_zp_from_function_argsz@get_node_input_qparams.._get_scale_zp_from_function_argss-dB FF *4Z@@*d++R :;Lc0R0RRRR'4((LZ-L-LLLL$R):;; !"gn556""rr,r r-c38K|]}t|VdSr/r0)r3r4 module_objs rr6z)get_node_input_qparams..sA1 1  z; / /1 1 1 1 1 1 r)+r?r1r r=r>rCquantize_per_tensortoqaddadd_relumulmul_relurAr nnqLinearConv1dConv2dnniq ConvReLU2dConv3d BatchNorm2d BatchNorm3dConvTranspose1dConvTranspose2dELU GroupNormInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm Hardswish LeakyReLUReLU6BNReLU2dBNReLU3d ConvReLU1d ConvReLU3d LinearReLUscale zero_pointrBget_node_input_qparams)rrr!rSrKr]rPras @rrrs)r155I i & &t 89T U###|&&  u8 8 833Ir1aHH H  #'3<#,!O O O33Ir1aHH Ht  & &)*C00000%b)*:;;      ## """      1    =:$j&;< <-01 1 1 1 81 1 1 . . * . S))R9QRR R 4rc|jdkrt||j}t|rt |jdksJt |jdtsJ|jd}t |jtsJt||j}t|rIt |jdksJt |jdtsJ|jd}|S)a If node is not an observer, returns it. If node is an observer, navigates up the graph and returns the first parent which is not an observer. For example, graph: (node_non_obs), node = node_non_obs : returns node_non_obs graph: (node_non_obs -> obs0), node = obs0 : returns node_non_obs graph: (node_non_obs -> obs0 -> fq0), node = fq0 : returns node_non_obs r-r r) r=r r>rlenargsr1r rArrnode_objs rreturn_first_non_observer_noders w-#B 44 &x 0 0 $ty>>Q&&&&dilD11 1 119Qr1nnLSTMrs rget_number_of_non_param_argsrsB" w-#B 44 h ( ( 1 1rc^tjdkrgSjdkrjtjtjjjtjfvs4jtj tjjj tj fvrfdtdD}|SdgS)a- Returns the indices of args of the node which we should attach loggers to, if input logging is enabled. For example, * for (x + y), returns [0, 1] * for (1 + y), returns [1] * for (x + 1), returns [0] * for (linear(x, w, b)) returns [0] * by default, returns [0] rr,cZg|]'}tj|tk%|(Sr)typerr )r3irs r z4get_arg_indices_of_inputs_to_log..?s1DDDdil););t)C)C!)C)C)Crr^) rrr=r>rCrdops quantizedoperatorrfrange)rresults` r get_arg_indices_of_inputs_to_logr,s 49~~  w/!!  59#6#:HLIII ;59ei&9&=x|L L LDDDDU1XXDDD 3Jrcd}|jdvrtj|j}nP|jdkrEt |jt sJt ||j}tj|}|S)z Returns a string representation of the type of the function or module pointed to by this node, or '' for other node types. )r,r:r-)r=rCtypenamer>r1rAr )rrr4 target_mods rget_target_type_strrDsu K w222nT[11 M ! !$+s+++++%b$+66 nZ00 rresults model_namec i}|D]k\}}d}|D]B}|D]+\}}||kr t|sJ|dd}+,C||||<f|||<l|S)a  Rekeys the layer name of a results dictionary to use node names from `model_name`. For example, transforms {'base_op_1_0': {'node_output': {'model_a': [{'ref_node_name': 'linear1', ...}]}}} into {'linear1': {'node_output': {'model_a': [{'ref_node_name': 'linear1', ...}]}}} Note: we cannot use these node names directly because they are not guaranteed to be consistent across models. This is why we extract the results first and rekey afterwards. Nr ref_node_name)itemsvaluesr) rr new_resultsold_layer_nameresult_type_to_resultsnew_layer_namemodel_name_to_resultscur_model_namelist_of_resultss r'rekey_logger_info_on_node_name_of_modelrSs,K29--// A A..%;%B%B%D%D   !3H3N3N3P3P  /!Z///////%4Q%7%HNN    %*@K ' '*@K ' ' rc d}|D]X}|D]A}|D]*\}}t|dkr|dd|}n+|r|D]|}|D]c}||}|D]D\}}||kr tt|D]}||d}|||d<Ed{dSdS)ay If `fqn` entries are filled in for one of the models in `results`, copies them over to any models which do not have them filled out. A common use case benefitting from this is comparing a model prepared by quantization to a quantized model. 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Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats r^)rCsqrtsumrrs rcompute_normalized_l2_errorrs9 :A!|((**adZZ\\9 : ::rc|dd}|dd}tjj||S)z Computes the cosine similarity between `x` and `y`. Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats r )reshaperCr functionalcosine_similarityrs rcompute_cosine_similarityrsE !RA !RA 8  0 0A 6 66rc|jdkrM|jtjtjt jt jtjtjfvrdSdS)Nr,FT)r=r>rCrdrfrcatstack)rs rop_type_supports_shadowingrsM w/!! ; I I L L I K    5 4ridxc ||d}|l|\}}t|t|z|ksJ|t|kr||St||St|jt|jz|ksJ|t|jkr |j|S|t|jz}t|j|S#t $rt|jt|jz|ksJ|t|jkr|j|cYS|t|jz}t|j|cYSwxYw)zu Given a node, gets the n'th input to that node, normalizing args and kwargs to the best of its ability. 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