iY dddlZddlZddlmZddlZddlmcmcmcm Z ddl mcmcm Z ddl mZddlmZmZddlmZddlmZddlmZddlmZdd lmZmZej8jZGd d ej<Zd ed edede e!e"efde#eeff dZ$d ed ede e!e"efde#ejJe&zejJe'zfdzfdZ(d ed edefdZ)d ed ede'fdZ*d ede+e'fdZ,d ed ede!fdZ-dede!defdZ.deddfdZ/dZ0e0dejJdejJdejJfdZ1e0dejJdejJdejJfdZ2e0dejJdejJdejJfd Z3d ede4fd!Z5d ed ed"e'defd#Z6y)$N)Callable)FakeQuantizeBase ObserverBase)_is_activation_post_process)getattr_from_fqn) GraphModule)Node)NSNodeTargetType NSResultsTypeceZdZejZejZejZejZejZ y)NodeInputOrOutputTypeN) __name__ __module__ __qualname__enumautoFP32INT8FP16UNKNOWN FP32_OR_INT8N/var/www/html/engine/venv/lib/python3.12/site-packages/torch/ao/ns/fx/utils.pyrrsE 499;D 499;D 499;DdiikG 499;Lrrnodegm logger_clsnode_type_to_io_type_mapreturnc|d}|d}|d}|d}|d}|d} |d} |d} |jd k(r|j|vr tjtjfS|j|vr tjtjfS|j|vr tj tj fS|j|vrIt ||d } t| tstd t| t| |||\} }||fStjtjfS|jd k(r{|jd k7rtd |jdt|jts!tdt|jt||jtfd| D}t|t t"fs|rIt ||d } t| tstd t| t| |||\} }||fStfd|D}tfd| D}|r tjtjfS|r tj tj fStjtjfS|jdk(rp|jdk(rWt ||d }t|tstd t|t||||\} }|tjfS|jdk(rt ||d }t|tstd t|t||||\} }t ||d}|t$j&urt|d|tjfS|j| vrIt ||d } t| tstd t| t| |||\} }||fStjtjfStjtjfS)Nfuns_io_type_fp32funs_io_type_fp16funs_io_type_int8funs_io_type_fp32_or_int8mods_io_type_fp32mods_io_type_int8mods_io_type_fp32_or_int8meths_io_type_fp32_or_int8 call_functionrExpected Node, got call_modulezExpected call_module, got ''zExpected str, but got c36K|]}t|ywN isinstance.0 target_typemods r z7get_node_first_input_and_output_type..Rs 1  sK (1 c36K|]}t|ywr/r0r2s rr6z7get_node_first_input_and_output_type..f )  sK () r7c36K|]}t|ywr/r0r2s rr6z7get_node_first_input_and_output_type..jr9r7 call_method dequantizetor z handling needs to be added)optargetrrrrget_normalized_nth_inputr1r AssertionErrortype$get_node_first_input_and_output_typerstrranyrrtorchfloat16)rrrrFUNS_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 @rrCrC&s11DE01DE01DE 89T U01DE01DE 89T U!9:V!W ww/! ;;+ +)..0E0J0JK K ;;+ +)..0E0J0JK K [[- -)..0E0J0JK K [[5 50r1=Ii.$':4 ?:K%LMM52z+C %%*+@A A)113H3P3PQ Q M ! 77m # #>twwiq!IJ J$++s+ #9$t{{:K9L!MN Nr4;;/-01 81 . * sZ7GH I11r1=Ii.$':4 ?:K%LMM52z+C %%*+@A A%() 0) & "&)) 0) & " &)..0E0J0JK K ')..0E0J0JK K)113H3P3PQ Q M ! ;;, &1r1=Ii.$':4 ?:K%LMM52z+C %%*+@+E+EF F [[D 1r1=Ii.$':4 ?:K%LMM52z+C %% %=T2q$I !$EMM9$,--HI*+@+E+EF F [[6 60r1=Ii.$':4 ?:K%LMM52z+C %%*+@A A%--/D/L/LMM%--/D/L/LMMrct||d}t|tsy|d}d}|jdk(r}|jt j ur |||ddS|jtjtjtjtjfvr |||ddSy|jd k(rt|jts!td t|jt||jtt j"t j$t j&t(j*t j,t j.t j0t j2t j4t j6t j8t j:t j<t j>t j@t jBt jDt jFt(jHt(jJt(jLt(j*t(jNt(jPfrjRjTfStWfd |D}|r tY|||Sy) z{ Returns the qparams (scale, zero_point) of the first input to `node`, if they can be inferred from the graph. rNr(ct|||}t|||}t|tstdt |t|j t s!tdt |j t|tstdt |t|j t s!tdt |j t||j }t||j }||fS)Nr+Expected str, got )r@r1r rArBr?rDr)rr scale_arg_idx zp_arg_idx scale_nodezp_node scale_objzp_objs r _get_scale_zp_from_function_argsz@get_node_input_qparams.._get_scale_zp_from_function_argss-dB F *4Z@*d+ #6tJ7G6H!IJ J*++S1 #5d:;L;L6M5N!OP P'4( #6tG}o!FG G'..#. #5d7>>6J5K!LM M$R):):; !"gnn56""rr*r r,rZc36K|]}t|ywr/r0)r3r4 module_objs rr6z)get_node_input_qparams..s 1  z; /1 r7)-r@r1r r>r?rFquantize_per_tensortoqaddadd_relumulmul_relurDrArBrnnqLinearConv1dConv2dnniq ConvReLU2dConv3d BatchNorm2d BatchNorm3dConvTranspose1dConvTranspose2dELU GroupNormInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm Hardswish LeakyReLUReLU6BNReLU2dBNReLU3d ConvReLU1d ConvReLU3d LinearReLUscale zero_pointrEget_node_input_qparams)rrrrVrNrarSres @rrrs)r15I i & 89T U #||&   u88 83Ir1aH H   #''3<<#,,!O O3Ir1aH H  &)**C0 #5d9;K;K6L5M!NO O%b)*:*:;      #### """"""      1  :$$j&;&;< <-01 81 . * .))R9QR R rcF|jdk(rt||j}t|rnt |j dk7r!t dt |j t|j dts$t dt|j d|j d}t|jts!t dt|jt||j}t|rt |j dk7r!t dt |j t|j dts$t dt|j d|j d}|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 z)Expected node.args to have length 1, got rr+rZ) r>rr?rlenargsrAr1r rBrDrrnode_objs rreturn_first_non_observer_nodersT ww-#B 4 &x 0499~"$?DII?OPdiilD1$':4 ! ;M:N%OPP99QQ&(CC NCST"$))A,5(+>tDIIaL?Q>R)STTyy| Krc|jdk(r1t||j}t|tj ryy)aO Assumes that all non-param args occur first. Returns the number of non-param args expected for a node. For example, for F.linear(x, weight, bias) Returns 1, because x is a non-param arg and weight and bias are params. For lstm_mod(x, hid) Returns 2, because both x and hid are non-param args. r,rbr )r>rr?r1nnLSTMrs rget_number_of_non_param_argsr+s6" ww-#B 4 h ( rct|jdk(rgS|jdk(r|jtj tj jj tj fvsO|jtjtj jjtjfvr9tdDcgc]#}t|j|tus"|%}}|SdgScc}w)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*rb) rrr>r?rFrhops quantizedoperatorrjrangerBr )riresults r get_arg_indices_of_inputs_to_logrEs 499~  ww/!  599#6#6#:#:HLLII ;;599eii&9&9&=&=x||L L"1XDdiil);t)C!DD 3JEs #D9DcRd}|jdvr!tj|j}|S|jdk(rft |jt s!t dt|jt||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,rZ) r>rFtypenamer?r1rDrArBr)rrr4 target_mods rget_target_type_strr]s K ww22nnT[[1  M !$++s+ #5d4;;6G5H!IJ J%b$++6 nnZ0 rresults model_nameci}|jD]h\}}d}|jD]A}|jD],\}}||k(r"t|dk(r td|dd}-.C||||<d|||<j|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. Nrz(Expected list_of_results to be not empty ref_node_name)itemsvaluesrrA) 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_modelrms,K29--/ A..%;%B%B%D  !3H3N3N3P /!Z/?+q0,-WXX%4Q%7%HN     %*@K '*@K ' A rcd}|jD]L}|jD]6}|jD] \}}t|dkDs|dd|}nnn|rw|jD]c}|jD]N}||}|jD]4\}}||k(r tt|D]}||d}|||d<6Peyy)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. In this case, the model prepared by quantization would have `fqn` entries, and the quantized model would not. Nrfqn)rrrr) rmodel_name_with_fqnsrrr model_resultsref_model_resultsrrs rmaybe_add_missing_fqnsrs- ").."2%;%B%B%D  !-B-H-H-J ) M}%)$Q'.:/9,      &-nn&6 6 ")?)F)F)H 6%$9:N$O!1F1L1L1N6-J !%99 "3}#566/25925 a(/6 6 6 6rcfdS)Ncf|^}}}t|trt|ts t|trFt|tr6g}t||D]#\}}||g|}|j  |i|%|St|t j rRt|t j r8|jr|j}|jr|j}|jt jk7s|jt jk7ry||g|} |i|Sr/) r1tuplelistzipappendrFTensor is_quantizedr<dtypefloat) rkwargsa0a1a_otherrel0el1new_argsfinners rrzGmaybe_dequantize_first_two_tensor_args_and_handle_tuples..innersB r5 !jU&; r4 ZD%9GBK ;S/w/uh9&9: ;N ELL )jU\\.J]]_]]_ 88u{{ "bhh%++&=%W%(%f%%rr)rrs`@r8maybe_dequantize_first_two_tensor_args_and_handle_tuplesrs&2 Lrxyctj|}tj||z }dtj||z zS)z Computes the SQNR between `x` and `y`. Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats )rFnormlog10)rrPsPns r compute_sqnrrs; AB AE B  BG$ $$rc|tj||z dzj|dzjz S)z Computes the normalized L2 error between `x` and `y`. Args: x: Tensor or tuple of tensors y: Tensor or tuple of tensors Return: float or tuple of floats rb)rFsqrtsumrrs rcompute_normalized_l2_errorrs3 ::A!|((*adZZ\9 ::rc|jdd}|jdd}tjjj ||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 )reshaperFr functionalcosine_similarityrs rcompute_cosine_similarityrsA !RA !RA 88   0 0A 66rc|jdk(ri|jtjtjt jt jtj tjfvryy)Nr*FT)r>r?rFrhrjrcatstack)rs rop_type_supports_shadowingr sQ ww/! ;; II II LL LL II KK    ridxc n |j|d}|t|\}}t|t|z|kr&td|dt|t|z|t|kr||St|j |St|j t|j z|kr:td|dt|j t|j z|t|j kr|j |S|t|j z}t|j j |S#t$rt|j t|j z|kr;td|dt|j t|j zd|t|j kr|j |cYS|t|j z}t|j j |cYSwxYw)zu Given a node, gets the n'th input to that node, normalizing args and kwargs to the best of its ability. T)normalize_to_only_use_kwargsNzIndex z out of range: total = )normalized_argumentsrrArrrr RuntimeError)rrrnorm_args_and_kwargs norm_args norm_kwargs kwargs_idxs rr@r@s %:#88 T 9  +%9 "I{9~K 00C7$SE!8Y#kJZ9Z8[\S^# ~%K..01#66499~DKK 00C7$SE!8TYY#dkkJZ9Z8[\S^#yy~% 3tyy>1 DKK..01*==  : tyy>C , , 3 4S^c$++FV5V4WX  TYY 99S> !s499~-J **,-j9 9 :s,A,E/E BE=EBH43>H43H4)7rrcollections.abcrrFtorch.ao.nn.intrinsic.quantizedaor intrinsicrrptorch.ao.nn.quantizedrltorch.nntorch.ao.quantizationrrtorch.ao.quantization.observerrtorch.ao.quantization.utilsrtorch.fxrtorch.fx.graphr ns_typesr r rrgEnumrdictrDsetrrCrrintrrrrrrrrrrrrboolrr@rrrrs $ ..##@F8 5 ii DII DN DNDNDN#3,<(=#=> DN  "7 78 DNNU UU#3,<(=#=>U 5<<% !3 34t; Up# ## #L     44DI0 d    % %%%P 6M 6d 6F:: %ELL %U\\ %ell %: % : ;5<< ;ELL ;U\\ ;: ;:77%,,75<<7:7& T d *:4*:[*:s*:t*:r