o "i,$ @sHddlZddlmZddlmZddlmZddlmZmZddl m Z m Z m Z ddl mZdd lmZdd lmZmZmZmZmZmZmZdd lmZdd lmZdd lmZddlm Z gdZ!dededefddZ"dededefddZ#ej$j%j&j'ej$j%j&j(ej$j%j)j'gZ*dede+fddZ,  d dede+de+defddZ-dS)!N) GraphModule)Node)prepare)_fuse_conv_bn_qat_fold_conv_bn_qat)_get_node_name_to_scope_fuse_conv_bn__disallow_eval_train) reference_representation_rewrite)#_convert_to_reference_decomposed_fx) QuantizerQuantizationSpecBaseQuantizationSpecFixedQParamsQuantizationSpecSharedQuantizationSpecDerivedQuantizationSpecQuantizationAnnotation) PassManager)DuplicateDQPass)PortNodeMetaForQDQ) constant_fold) prepare_pt2eprepare_qat_pt2e convert_pt2emodel quantizerreturncCsftjd|j}t|}t|||||||t ||dd}|j |t |}|S)aRPrepare a model for post training quantization Args: * `model` (torch.fx.GraphModule): a model captured by `torch.export` API in the short term we are using `torch._export.capture_pre_autograd_graph`, in the long term we'll migrate to some `torch.export` 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._export import capture_pre_autograd_graph from torch.ao.quantization.quantizer import ( XNNPACKQuantizer, get_symmetric_quantization_config, ) class M(torch.nn.Module): def __init__(self): 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 = capture_pre_autograd_graph(m, *example_inputs) # 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_qat) torch_C_log_api_usage_oncemetarr transform_for_annotationannotatevalidaterupdater rroriginal_graph_metanode_name_to_scoper+a/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/ao/quantization/quantize_pt2e.pyr&s =    rcCsftjd|j}t|}||||||t|t ||dd}|j |t |}|S)a_Prepare 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._export import capture_pre_autograd_graph from torch.ao.quantization.quantizer import ( XNNPACKQuantizer, get_symmetric_quantization_config, ) class M(torch.nn.Module): def __init__(self): 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 = capture_pre_autograd_graph(m, *example_inputs) # 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 r(r+r+r,rrs 9    rncCs|jdko |jtvS)aTIf 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)r-r+r+r,_quant_node_constraintsr2FTuse_reference_representation fold_quantizecCstjdt|tstd|d|j}t|}t|}t t g}||j }t t g}||j }|r;t |t|rAt|}|j|t|}|S)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#r rrr graph_modulerrr2r r'r )rr3r4r)pmr+r+r,rs*        r)FT).r torch.fxrr pt2e.preparerpt2e.qat_utilsrr pt2e.utilsrr r pt2e.representationr quantize_fxr torch.ao.quantization.quantizerr rrrrrr"torch.fx.passes.infra.pass_managerr,torch.ao.quantization.pt2e.duplicate_dq_passr-torch.ao.quantization.pt2e.port_metadata_passr%torch._export.passes.constant_foldingr__all__rropsquantized_decomposedquantize_per_tensordefaulttensorquantize_per_channelr1r6r2rr+r+r+r,sX     $    L I