o eiX @sXddlZddlZddlmZddlZddlmmmm Z ddl mmm Z ddl mZddlmZmZddlmZddlmZddlmZddlmZdd lmZmZejj ZGd d d ejZd ed edede e!e"efde#eeff ddZ$d ed ede e!e"efde#ej%e&Bej%e'BfdBfddZ(d ed edefddZ)d ed ede'fddZ*d ede+e'fddZ,d ed ede!fddZ-dede!defdd Z.deddfd!d"Z/d#d$Z0e0d%ej%d&ej%dej%fd'd(Z1e0d%ej%d&ej%dej%fd)d*Z2e0d%ej%d&ej%dej%fd+d,Z3d ede4fd-d.Z5d ed ed/e'defd0d1Z6dS)2N)Callable)FakeQuantizeBase ObserverBase)_is_activation_post_process)getattr_from_fqn) GraphModule)Node)NSNodeTargetType NSResultsTypec@s4eZdZeZeZeZeZeZ dS)NodeInputOrOutputTypeN) __name__ __module__ __qualname__enumautoFP32INT8FP16UNKNOWN FP32_OR_INT8rr^/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/site-packages/torch/ao/ns/fx/utils.pyr s  r nodegm logger_clsnode_type_to_io_type_mapreturncs8|d}|d}|d}|d}|d}|d} |d} |d} |jd krr|j|vr0tjtjfS|j|vr;tjtjfS|j|vrFtjtjfS|j|vrlt||d } t| ts_t d t | t | |||\} }||fStj tj fS|jd kr|jd krt d |jdt|jt st dt |jt||jtfdd| D}t|ttfs|rt||d } t| tst d t | t | |||\} }||fStfdd|D}tfdd| D}|rtjtjfS|rtjtjfStj tj fS|jdkr|jdkr,t||d }t|tst d t |t ||||\} }|tjfS|jdkrht||d }t|tsGt d t |t ||||\} }t||d}|tjurct |d|tjfS|j| vrt||d } t| tst d t | t | |||\} }||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_functionrExpected Node, got call_modulezExpected call_module, got ''zExpected str, but got c3|]}t|VqdSN isinstance.0 target_typemodrr R  z7get_node_first_input_and_output_type..c3r*r+r,r.r1rrr3fr4c3r*r+r,r.r1rrr3jr4 call_method dequantizetor z handling needs to be added)optargetr rrrget_normalized_nth_inputr-rAssertionErrortype$get_node_first_input_and_output_typerstrranyrrtorchfloat16)rrrrFUNS_IO_TYPE_FP32FUNS_IO_TYPE_FP16FUNS_IO_TYPE_INT8FUNS_IO_TYPE_FP32_OR_INT8MODS_IO_TYPE_FP32MODS_IO_TYPE_INT8MODS_IO_TYPE_FP32_OR_INT8METHS_IO_TYPE_FP32_OR_INT8 first_arg_prev_node_input_typeprev_node_output_type"is_known_fp32_or_int8_input_moduleis_known_fp32_input_moduleis_known_int8_input_module prev_nodecur_node_dtype_targetrr1rr=&s                                       r=csPt||d}t|ts dS|d}dd}|jdkr=|jtjur'|||ddS|jtjtj tj tj fvr;|||dd SdS|jd krt|jt sRt d t|jt||jttjtjtjtjtjtjtjtjtjtjtjtjtjtjtj tj!tj"tj#tj$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$cSst|||}t|||}t|tstdt|t|jts*tdt|jt|ts8tdt|t|jtsHtdt|jt||j}t||j}||fS)Nr'Expected str, got )r:r-rr;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(rRc3r*r+r,r. module_objrrr3r4z)get_node_input_qparams..)-r:r-rr8r9r@quantize_per_tensortoqaddadd_relumulmul_relur>r;r<rnnqLinearConv1dConv2dnniq ConvReLU2dConv3d BatchNorm2d BatchNorm3dConvTranspose1dConvTranspose2dELU GroupNormInstanceNorm1dInstanceNorm2dInstanceNorm3d LayerNorm Hardswish LeakyReLUReLU6BNReLU2dBNReLU3d ConvReLU1d ConvReLU3d LinearReLUscale zero_pointr?get_node_input_qparams)rrrrPrHrYrMrr\rrsd         rcCs|jdkr}t||j}t|r}t|jdkr tdt|jt|jdts4tdt |jd|jd}t|jt sItdt |jt||j}t|r}t|jdkrdtdt|jt|jdtsxtdt |jd|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 z)Expected node.args to have length 1, got rr'rR) r8rr9rlenargsr;r-rr<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(rZr )r8rr9r-nnLSTMrrrrget_number_of_non_param_args+s   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|tur|qSr)r<rr)r/irrr Xs"z4get_arg_indices_of_inputs_to_log..rZ) rrr8r9r@r`ops quantizedoperatorrbrange)rresultrrr get_arg_indices_of_inputs_to_logEs  rcCsbd}|jdvrt|j}|S|jdkr/t|jts$tdt|jt||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(rR) r8r@typenamer9r-r>r;r<r)rrr0 target_modrrrget_target_type_str]s     rresults model_namec Csi}|D]8\}}d}|D] }|D]\}}||kr/t|dkr(td|dd}qqq|dur:|||<q|||<q|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)itemsvaluesrr;) rr new_resultsold_layer_nameresult_type_to_resultsnew_layer_namemodel_name_to_resultscur_model_namelist_of_resultsrrr'rekey_logger_info_on_node_name_of_modelms    rc Csd}|D]$}|D]}|D]\}}t|dkr(|dddur(|}nq|rb|D]2}|D])}||}|D]\}}||krJqAtt|D]}||d}|||d<qPqAq7q1dSdS)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_resultsrrrrrmaybe_add_missing_fqnss4      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) rkwargsa0a1a_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 rZ)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`rbrcatstackrrrrop_type_supports_shadowing s  ridxc Cszy|j|dd}|dur>|\}}t|t||kr*td|dt|t||t|kr5||WSt||WSt|jt|j|kr\td|dt|jt|j|t|jkri|j|WS|t|j}t|j|WStyt|jt|j|krtd|dt|jt|jd|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_kwargsNzIndex z out of range: total = )normalized_argumentsrr;rrrr RuntimeError)rrrnorm_args_and_kwargs norm_args norm_kwargs kwargs_idxrrrr:sB    r:)7rrcollections.abcrr@torch.ao.nn.intrinsic.quantizedaor intrinsicrrhtorch.ao.nn.quantizedrdtorch.nntorch.ao.quantizationrrtorch.ao.quantization.observerrtorch.ao.quantization.utilsrtorch.fxrtorch.fx.graphrns_typesr r rr_Enumr dictr>setrr=rrintrrrrrrrrrrrrboolrr:rrrrs          X &  (#