[Th7$ XSSKrSSKJr SSKJr SSKJr SSKJrJ r J r J r J r J r Jr SSKJrJr SSKJr SS KJr SS KJrJr SS KJr SS KJrJrJr SS KJ r /SQr!S\S\ S\4Sjr"S\S\ S\4Sjr#\RHRJRLRN\RHRJRLRP\RHRJRRRN\RHRTRV/r,S\S\-4Sjr.SS\S\-S\-S\4Sjjr/g)N) constant_fold)DuplicateDQPass)PortNodeMetaForQDQ)DerivedQuantizationSpecFixedQParamsQuantizationSpecQuantizationAnnotationQuantizationSpecQuantizationSpecBase QuantizerSharedQuantizationSpec) GraphModuleNode) PassManager)prepare)_fold_conv_bn_qat_fuse_conv_bn_qat) reference_representation_rewrite)_disallow_eval_train_fuse_conv_bn__get_node_name_to_scope)#_convert_to_reference_decomposed_fx) prepare_pt2eprepare_qat_pt2e convert_pt2emodel quantizerreturncf[RRS5 URn[ U5n[ U5 UR U5nURU5 URU5 [UUSURS9nURRU5 [U5nU$)aPrepare a model for post training quantization Args: * `model` (torch.fx.GraphModule): a model captured by `torch.export.export_for_training` API. * `quantizer`: A backend specific quantizer that conveys how user want the model to be quantized. Tutorial for how to write a quantizer can be found here: https://pytorch.org/tutorials/prototype/pt2e_quantizer.html Return: A GraphModule with observer (based on quantizer annotation), ready for calibration Example:: import torch from torch.ao.quantization.quantize_pt2e import prepare_pt2e from torch.ao.quantization.quantizer import ( XNNPACKQuantizer, get_symmetric_quantization_config, ) class M(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(5, 10) def forward(self, x): return self.linear(x) # initialize a floating point model float_model = M().eval() # define calibration function def calibrate(model, data_loader): model.eval() with torch.no_grad(): for image, target in data_loader: model(image) # Step 1. program capture # NOTE: this API will be updated to torch.export API in the future, but the captured # result shoud mostly stay the same m = torch.export.export_for_training(m, *example_inputs).module() # we get a model with aten ops # Step 2. quantization # backend developer will write their own Quantizer and expose methods to allow # users to express how they # want the model to be quantized quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config()) m = prepare_pt2e(m, quantizer) # run calibration # calibrate(m, sample_inference_data) z+quantization_api.quantize_pt2e.prepare_pt2eFis_qatobs_or_fq_callback) torch_C_log_api_usage_oncemetarrtransform_for_annotationannotatevalidaterprepare_obs_or_fq_callbackupdaterrroriginal_graph_metanode_name_to_scopes [/var/www/auris/envauris/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.pyrrst HH  !NO**075  . .u 5E u u  $??  E  JJ)*  'E Lcf[RRS5 URn[ U5nUR U5nUR U5 URU5 [U5 [UUSURS9nURRU5 [U5nU$)aPrepare a model for quantization aware training Args: * `model` (torch.fx.GraphModule): see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e` * `quantizer`: see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e` Return: A GraphModule with fake quant modules (based on quantizer annotation), ready for quantization aware training Example:: import torch from torch.ao.quantization.quantize_pt2e import prepare_qat_pt2e from torch.ao.quantization.quantizer import ( XNNPACKQuantizer, get_symmetric_quantization_config, ) class M(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(5, 10) def forward(self, x): return self.linear(x) # initialize a floating point model float_model = M().eval() # define the training loop for quantization aware training def train_loop(model, train_data): model.train() for image, target in data_loader: ... # Step 1. program capture # NOTE: this API will be updated to torch.export API in the future, but the captured # result shoud mostly stay the same m = torch.export.export_for_training(m, *example_inputs).module() # we get a model with aten ops # Step 2. quantization # backend developer will write their own Quantizer and expose methods to allow # users to express how they # want the model to be quantized quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config()) m = prepare_qat_pt2e(m, quantizer) # run quantization aware training train_loop(prepared_model, train_loop) z/quantization_api.quantize_pt2e.prepare_qat_pt2eTr ) r#r$r%r&rr'r(r)rrr*r+rr,s r/rrnsp HH  !RS**07  . .u 5E u ue  $??  E  JJ)*  'E Lr0ncTURS:H=(a UR[;$)a@If there is any pure ops between get_attr and quantize op they will be const propagated e.g. get_attr(weight) -> transpose -> quantize -> dequantize* (Note: dequantize op is not going to be constant propagated) This filter is added because we don't want to constant fold the things that are not related to quantization call_function)optarget _QUANT_OPS)r2s r/_quant_node_constraintr8s! 44? " =qxx:'==r0use_reference_representation fold_quantizec[RRS5 [U[5(d[ SUS35eUR n[U5n[U5n[[5/5nU"U5Rn[[5/5nU"U5RnU(a[U[5 U(a [U5nUR R!U5 [#U5nU$)aConvert a calibrated/trained model to a quantized model Args: * `model` (torch.fx.GraphModule): calibrated/trained model * `use_reference_representation` (bool): boolean flag to indicate whether to produce referece representation or not * `fold_quantize` (bool): boolean flag for whether fold the quantize op or not Returns: quantized model, either in q/dq representation or reference representation Example:: # prepared_model: the model produced by `prepare_pt2e`/`prepare_qat_pt2e` and calibration/training # `convert_pt2e` produces a quantized model that represents quantized computation with # quantize dequantize ops and fp32 ops by default. # Please refer to # https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html#convert-the-calibrated-model-to-a-quantized-model # for detailed explanation of output quantized model quantized_model = convert_pt2e(prepared_model) z+quantization_api.quantize_pt2e.convert_pt2ezjUnexpected argument type for `use_reference_representation`, please make sure you intend to pass argument z to convert_pt2e)r#r$r% isinstancebool ValueErrorr&rrrr graph_modulerrr8rr+r)rr9r:r-pms r/rrs4 HH  !NO 2D 9 9 <rVs4 ?HL':!@AUU< L LLL^J JJJ\ II""66>> II""66== II""77?? 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