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Utils shared by different modes of quantization (eager/graph) N) QuantType)TupleAnyUnionCallableDictOptional)is_parametrized) OrderedDict) signature)getfullargspecztorch.ao.quantization.utilsc@seZdZdZdS) MatchAllNodezo A node pattern that matches all nodes, used in defining fusion patterns in FX Graph Mode Quantization N)__name__ __module__ __qualname____doc__rr?/tmp/pip-unpacked-wheel-gikjz4vx/torch/ao/quantization/utils.pyr sr reluZrelu_ contiguousdetachZdetach_ hardsigmoidZ hardsigmoid_Zpermuterepeatrepeat_interleaveZreshapeZresize_shapesigmoidZsigmoid_sizesqueezeZsqueeze_tanhZtanh_ transpose unsqueezeZ unsqueeze_viewcCsR|jdko|jtk}|jdko&|jtk}|jdkoFt|t|jtk}|||fS)N call_functionZ call_methodZ call_module)optarget func_list method_listtypestrmodule_type_list)nodemodulesZis_call_functionZis_call_methodZis_call_modulerrr check_nodegs r,cCs|}|||SN)copyupdate)Z default_dictZadditional_dictdrrrget_combined_dictns r1cCs|tjkp|tjkSr-)torchper_tensor_affineZper_tensor_symmetricqschemerrr is_per_tensorss r6cCs|tjtjtjfkSr-)r2per_channel_affineZ per_channel_affine_float_qparamsper_channel_symmetricr4rrris_per_channelwsr9)objfqnreturncCstt|d|S)zO Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz. .) functoolsreducegetattrsplit)r:r;rrrgetattr_from_fqn|srBcCst|dr|jnd}|j}||d}|s,|St|r|dk s t|}|}t|dr,|jnd}|j|j|fS)zk returns the qconfig tuple for qconfig: (activation_dtype, weight_dtype, activation_compute_dtype) N compute_dtype)rKrOrQrFrcrC)rMrOrQrcrrrrYs  rYcCs|dk s t|}|}tjtjtjg}|j|krft|drP|j |krPt j S|j|kr`t j St j S|jtjkrt|dr|j |krt j S|jtjkrt j Std|j|jdS)NrczKUnrecognized dtype combination in get_quant_type: activation({}),weight({}))rKrOrQr2rSrTr_rCrFrcrZDYNAMICZSTATICZ WEIGHT_ONLYrU ExceptionrL)rMrOrQZ static_dtypesrrrrIs&     rI)min_valmax_valr<cCs|dks|dkr&tddS|dks>|dkr~|tdkrd|tdkrdtddS||kstd||nt||kstd||dS)z Checks if the given minimum and maximum values are valid, meaning that they exist and the min value is less than the max value. rzMmust run observer before calling calculate_qparams. Returning default values.Finfz-infz!min {} should be less than max {}T) ZnumelwarningswarnZdimfloatrKrLr2all)rerfrrrcheck_min_max_valids* rl) quant_min quant_maxhas_customized_qrangerC reduce_ranger<c Cs&|r|tjkrd\}}nd\}}||}}|dk rD|dk rD||}}||d} |tjkrzd| krndksntdn(|tjkrd| krdksntd |r|d |d }}nd|tjkr|rd \}}nd \}}nB|tjkr|rd \}}nd\}}n|tjkrd\}}nd\}}||fS)ztCalculates actual qmin and qmax based on the quantization range, observer datatype and if range is reduced. )r)rNrzRquantization range should be positive and not exceed the maximum bit range (=256).lzYquantization range should be positive and not exceed the maximum bit range (=4294967296).)i?)i)rrw)irq)r)r2r]rTrKrS) rmrnrorCrpZinitial_quant_minZinitial_quant_maxZcustom_quant_minZcustom_quant_maxZ qrange_lenrrrcalculate_qmin_qmax1sP                rycCs8|dd}t|dkr$d|dfS|d|dfSdS)z, Turn 'foo.bar' into ['foo', 'bar'] r=rsrN)rsplitlen)r$rrrr _parent_namefs   r~cCs:t|jdkrdSt|r2t|jdko0d|jkSdSdS)z Checks if module._modules is empty or if module is a parametrization, checks that module._modules only has the 'parametrizations' module rTrsZparametrizationsFN)r|Z_modulesr )modulerrr)has_no_children_ignoring_parametrizationsps r)root submoduler<cCs&|D]\}}||kr|SqdS)aS Get the path (fully qualified name) of a submodule Example:: >> class M(torch.nn.Module): def __init__(self): self.linear = torch.nn.Linear(5, 5) def forward(self, x): return self.linear(x) >> m = M() >> l = m.linear >> _get_path_of_module(m, l) "linear" N)Z named_modules)rrnprrr_get_path_of_module}s r)flocr<csfdd|DS)z Get local keyword arguments Example:: >> def f(self, a, b=9): pass >> loc = {"a": 6, "c": 7} >> _get_signature_locals(f, loc) {"a": 6} cs$i|]\}}|tjkr||qSr)r parameters).0kvrrr sz)_get_signature_locals..)items)rrrrr_get_signature_localss rzOrderedDict[str, Any])rr<cCsfi}t|jD]J\}}|j|jk r2|j||<q|j|jkrHd||<q|j|jkri||<qt|S)z Get all default keyword arguments from function signature Example:: >> def f(self, a, b=9): pass >> _get_default_kwargs(f) {"b": 9} r) r rrdefaultemptykindVAR_POSITIONAL VAR_KEYWORDr )rkwargsnameparamrrr_get_default_kwargss       r)funcrr<cCs@t|}t||}|}|D]\}}||kr"|||<q"|S)a) Given a function and local function arguments, normalize the keyword arguments by filling in default arguments from function signature Example:: >> def f(self, key1=3, key2=3): pass >> loc = {"key2": 6} >> _normalize_kwargs(f, loc) {"key1": 3, "key2": 6} )rrr.r)rrZdefault_kwargsZ local_kwargsnormalized_kwargsattrvalrrr_normalize_kwargss   rcCstt|jS)z Get number of positional args for a function Example:: >> def f(self, key1=3, key2=3): pass >> _get_num_pos_args(f) 3 )r|r argsrrrr_get_num_pos_argss r.)modelexample_inputsr<csJ|ifdd}tjjj|tjj_z ||W5tjj_XS)a Given a model and its example inputs, return a dictionary from fully qualified name of submodules to example_inputs for that submodule, e.g. {"linear1": (tensor1,), "linear2": (tensor2,), "sub": (tensor3,), "sub.linear1": (tensor4,), ...} Used to make quantizing submodules easier now that FX Graph Mode Quantization requries example inputs. Also works for keyword arguments with default values, we would flatten keyword arguments as positional arguments and fill in the missing keyword args with default values, e.g. if we have a forward function: def forward(self, x, key1=3, key2=3): ... and we call it with self.submodule(x, key2=6) we'll get example_inputs: (x, 3, 6) user can also override `key1` with positional arguments as well: for self.submodule(x, 5, key2=6) we'll get: (x, 5, 6) variable positional arguments and variable positional keyword arguments in forward function are not supported currently, so please make sure no submodules is using them. c st|}t|j|}t|jd}|t|}|rP|rP|jdd|d8}q2||t |}t |}|dk r||<|f||S)NrsF)last) listr.rZforwardrr|popitemextendvaluestupler) selfrrZsubmodule_example_inputsrnum_argsZ num_to_popZsubmodule_example_inputs_tupler;Zfqn_to_example_inputsZorig_module_callrrr_patched_module_calls      z7get_fqn_to_example_inputs.._patched_module_call)r2nnModule__call__)rrrrrrget_fqn_to_example_inputss    rPattern)jrrhr>r2Z torch.ao.quantization.quant_typertypingrrrrrrZtorch.nn.utils.parametrizer collectionsr inspectr r rrr rZReLUZReLU6ZAdaptiveAvgPool1dZAdaptiveAvgPool2dZAdaptiveAvgPool3dZ AvgPool1dZ AvgPool2dZ AvgPool3dZ MaxPool1dZ MaxPool2dZ MaxPool3dZIdentityZ HardsigmoidZSigmoidZTanhr)Z functionalZadaptive_avg_pool1dZadaptive_avg_pool2dZadaptive_avg_pool3dZeluZ hardswishZ instance_normZ layer_normZ leaky_reluZsiluZmishZdropoutZ max_pool1dZ max_pool2dZ max_pool3drZhardtanhZ hardtanh_rrrrrstacksumrr catr%Zmeanr&r,r1r6r9r(rBrHrNrPrRrXrVr\r^r`raboolrbrYrIZTensorrlintrCryr~rrrrrrrr__all__rrrrs      (     5  "    ;