o I hsP @shddlZddlZddlmZmZmZddlZddlmm m m Z ddl mm m Zddlm Z ddlmZmZddlmZddlmZddlmZddlmZdd lmZmZejj ZGd d d ej Z!d ed edede"e#e$efde%e!e!ff ddZ&d ed ede"e#e$efdee%eej'e(feej'e)fffddZ*d ed edefddZ+d ed ede)fddZ,d ede-e)fddZ.d ed ede#fddZ/dede#defdd Z0deddfd!d"Z1d#d$Z2e2d%ej'd&ej'dej'fd'd(Z3e2d%ej'd&ej'dej'fd)d*Z4e2d%ej'd&ej'dej'fd+d,Z5d ede6fd-d.Z7d ed ed/e)defd0d1Z8dS)2N)CallableOptionalUnion)FakeQuantizeBase ObserverBase)_is_activation_post_process)getattr_from_fqn) GraphModule)Node)NSNodeTargetType NSResultsTypec@s4eZdZeZeZeZeZeZ dS)NodeInputOrOutputTypeN) __name__ __module__ __qualname__enumautoFP32INT8FP16UNKNOWN FP32_OR_INT8rrH/var/www/vscode/kcb/lib/python3.10/site-packages/torch/ao/ns/fx/utils.pyrs  rnodegm logger_clsnode_type_to_io_type_mapreturncs|d}|d}|d}|d}|d}|d} |d} |d} |jd krk|j|vr0tjtjfS|j|vr;tjtjfS|j|vrFtjtjfS|j|vret||d } t| tsXJt | |||\} }||fStj tj fS|jd kr|jd kswJt|jt sJt ||jt fd d | D}t|ttfs|rt||d } t| tsJt | |||\} }||fSt fdd |D}t fdd | D}|rtjtjfS|rtjtjfStj tj fS|jdkrc|jdkrt||d }t|tsJt ||||\} }|tjfS|jdkrN  z7get_node_first_input_and_output_type..c3r*r+r,r.r1rrr3`r4c3r*r+r,r.r1rrr3cr4 call_method dequantizetor z handling needs to be added)optargetrrrrget_normalized_nth_inputr-r $get_node_first_input_and_output_typerstrranyrrtorchfloat16)rrrrFUNS_IO_TYPE_FP32FUNS_IO_TYPE_FP16FUNS_IO_TYPE_INT8FUNS_IO_TYPE_FP32_OR_INT8MODS_IO_TYPE_FP32MODS_IO_TYPE_INT8MODS_IO_TYPE_FP32_OR_INT8METHS_IO_TYPE_FP32_OR_INT8 first_arg_prev_node_input_typeprev_node_output_type"is_known_fp32_or_int8_input_moduleis_known_fp32_input_moduleis_known_int8_input_module prev_nodecur_node_dtype_targetrr1rr;&s                               r;cs@t||d}t|ts dS|d}dd}|jdkr=|jtjkr'|||ddS|jtjtj tj tj fvr;|||dd SdS|jd krt|jt sJJt ||jttjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtjtj tj!tj"tj#tj$tjtj%tj&frj'j(fSt)fd d |D}|rt*|||SdS) z{ Returns the qparams (scale, zero_point) of the first input to `node`, if they can be inferred from the graph. rNr&cSslt|||}t|||}t|trt|jtsJt|tr$t|jts&Jt||j}t||j}||fSr+)r:r-r r9r<r)rr scale_arg_idx zp_arg_idx scale_nodezp_node scale_objzp_objrrr _get_scale_zp_from_function_argss    z@get_node_input_qparams.._get_scale_zp_from_function_argsr(r r)c3r*r+r,r. module_objrrr3r4z)get_node_input_qparams..)+r:r-r r8r9r>quantize_per_tensortoqaddadd_relumulmul_relur<rnnqLinearConv1dConv2dnniq ConvReLU2dConv3d BatchNorm2d BatchNorm3dConvTranspose1dConvTranspose2dELU GroupNormInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm Hardswish LeakyReLUReLU6BNReLU2dBNReLU3d ConvReLU1d ConvReLU3d LinearReLUscale zero_pointr=get_node_input_qparams)rrrrNrFrVrKrrYrr|sb       r|cCs|jdkrQt||j}t|rQt|jdksJt|jdts"J|jd}t|jts/Jt||j}t|rQt|jdksBJt|jdtsLJ|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) r8rr9rlenargsr-r r<rrnode_objrrrreturn_first_non_observer_nodes    rcCs*|jdkrt||j}t|tjrdSdS)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)rWr )r8rr9r-nnLSTMrrrrget_number_of_non_param_argss   rcsptjdkr gSjdkr5jtjtjjjtjfvs(jtj tjjj tj fvr5fddt dD}|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(cs"g|] }tj|tkr|qSr)typer~r )r/irrr ;s"z4get_arg_indices_of_inputs_to_log..rW) r}r~r8r9r>r]ops quantizedoperatorr_range)rresultrrr get_arg_indices_of_inputs_to_log(s  rcCsRd}|jdvrt|j}|S|jdkr't|jtsJt||j}t|}|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(r5r))r8r>typenamer9r-r<r)rrr0 target_modrrrget_target_type_str@s     rresults model_namec Cszi}|D]4\}}d}|D]}|D]\}}||kr+t|s$J|dd}qqq|dur6|||<q|||<q|S)a  Rekeys the layer name of a results dictionary to use node names from `model_name`. 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In this case, the model prepared by quantization would have `fqn` entries, and the quantized model would not. Nrfqn)rrr}r) rmodel_name_with_fqnsrrr model_resultsref_model_resultsrrrrrmaybe_add_missing_fqnsvs4      rcsfddS)Nc s|^}}}t|trt|tst|tr8t|tr8g}t||D]\}}||g|R}||i|q |St|tjrRt|tjrR|jrK|}|jrR|}|j tj ks^|j tj kr`dS||g|R}|i|Sr+) r-tuplelistzipappendr>Tensor is_quantizedr6dtypefloat) r~kwargsa0a1a_otherrel0el1new_argsfinnerrrrs( zGmaybe_dequantize_first_two_tensor_args_and_handle_tuples..innerr)rrrr8maybe_dequantize_first_two_tensor_args_and_handle_tuplessrxycCs*t|}t||}dt||S)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 )r>normlog10)rrPsPnrrr compute_sqnrs rcCs"t||d|dS)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 rW)r>sqrtsumrrrrrcompute_normalized_l2_errors" rcCs(|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 )reshaper>r functionalcosine_similarityrrrrcompute_cosine_similaritys  rcCs4|jdkr|jtjtjtjtjtjtjfvrdSdS)Nr(FT)r8r9r>r]r_rcatstackrrrrop_type_supports_shadowings  ridxcCs"z[|j|dd}|dur0|\}}t|t||ksJ|t|kr'||WSt||WSt|jt|j|ks>J|t|jkrK|j|WS|t|j}t|j|WStyt|jt|j|kspJ|t|jkr~|j|YS|t|j}t|j|YSw)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_kwargsN)normalized_argumentsr}rrr~r RuntimeError)rrrnorm_args_and_kwargs norm_args norm_kwargs kwargs_idxrrrr:s,    r:)9rrtypingrrrr>torch.ao.nn.intrinsic.quantizedaor intrinsicrretorch.ao.nn.quantizedratorch.nntorch.ao.quantizationrrtorch.ao.quantization.observerrtorch.ao.quantization.utilsrtorch.fxr torch.fx.graphr ns_typesr r rr\Enumrdictr<setrr;rrintr|rrrrrrrrrrrboolrr:rrrrs        {" P   '"