U di#@sddlZddlmZddlmZddlmZddZddZd d Z d d Z d"ddZ d ej fddZ d#ddZd$ddZd d ej dfddZd%ddZd&ddZddd d ej fddZd'ddZd(d d!ZdS))N)QConfig) QuantType)wrap_cpp_modulecCs&t|tjjs"tdtt|dS)Nz$input must be a script module, got: ) isinstancetorchjitZ ScriptModule ValueErrorstrtypemodelr F/tmp/pip-unpacked-wheel-ua33x9lu/torch/ao/quantization/quantize_jit.py_check_is_script_modulesrcCs|jdstddS)Nforwardz0input script module does not have forward method)_cZ _has_methodrr r r r_check_forward_method s rcCs(ttj|jtj|jdS)zInstantiate the activation and weight observer modules and script them, these observer module instances will be deepcopied during prepare_jit step. ) activationweight)rrrscriptrrr)Zqconfigr r rscript_qconfigsrcCsdd|DS)z~Helper function used by `prepare_jit`. Apply `script_qconfig` for all entries in `qconfig_dict` that is not None. cSs"i|]\}}||rt|ndqSN)r).0kvr r r sz'script_qconfig_dict..)items) qconfig_dictr r rscript_qconfig_dictsrFcCs:tjd|j}tj|}|r.||nt|}|S)z Fuse conv - bn module Works for eval model only. Args: model: TorchScript model from scripting or tracing z.quantization_api.quantize_jit.fuse_conv_bn_jit)r_C_log_api_usage_oncerZ_jit_pass_fold_convbn _reconstructr)r inplacemodel_cr r rfuse_conv_bn_jits   r$cCsrt|t|tdd|Ds.tdt|}t||}tj |j d|||}|rf| |nt |}|S)Ncss|]}t|tVqdSr)rr )rxr r r 2sz_prepare_jit..z4qconfig_dict should only contain names(str) as keys.r) rrallkeysrrr$rrZ_jit_pass_insert_observersrr!r)r rr" quant_typeZscripted_qconfig_dictr#r r r _prepare_jit/s    r*cCstjdt|||tjdS)Nz)quantization_api.quantize_jit.prepare_jitr))rrr r*rSTATICr rr"r r r prepare_jitAs r.cCstjdt|||tjdS)Nz1quantization_api.quantize_jit.prepare_dynamic_jitr+)rrr r*rDYNAMICr-r r rprepare_dynamic_jitEs r0cCst|||j}tj|d|||}|sltdd|D}|sP||dkr\g}tj |||}|r|| |nt |}tj |j tj|j |S)Nrcss|]}|jjdkVqdS)ZxpuN)Zdevicer )rpr r rr&Psz_convert_jit..)revalrrrZ_jit_pass_insert_quant_dequantr' parameterscpuZ_jit_pass_quant_finalizer!r_jit_pass_constant_propagationgraph _jit_pass_dce)r r"debugr)preserved_attrsr#Zis_xpur r r _convert_jitIs" r:cCs tjdt|||tj|dS)Nz)quantization_api.quantize_jit.convert_jitr)r9)rrr r:rr,r r"r8r9r r r convert_jit`s r=cCs tjdt|||tj|dS)Nz1quantization_api.quantize_jit.convert_dynamic_jitr;)rrr r:rr/r<r r rconvert_dynamic_jitds r>cCs|tjkr$t|||}t|d|}n>|s0td|sAssertionErrorr.r=rrr5r6r7)r rrun_fnrun_argsr"r8r)r r r _quantize_jiths      rBc Cs$tjdt||||||tjdS)aQuantize the input float TorchScript model with post training static quantization. First it will prepare the model for calibration, then it calls `run_fn` which will run the calibration step, after that we will convert the model to a quantized model. Args: `model`: input float TorchScript model `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and qconfig for that module as value, empty key means the qconfig will be applied to whole model unless it's overwritten by more specific configurations, the qconfig for each module is either found in the dictionary or fallback to the qconfig of parent module. Right now qconfig_dict is the only way to configure how the model is quantized, and it is done in the granularity of module, that is, we only support one type of qconfig for each torch.nn.Module, and the qconfig for sub module will override the qconfig for parent module, empty string means global configuration. `run_fn`: a calibration function for calibrating the prepared model `run_args`: positional arguments for `run_fn` `inplace`: carry out model transformations in-place, the original module is mutated `debug`: flag for producing a debug friendly model (preserve weight attribute) Return: Quantized TorchSciprt model. Example: ```python import torch from torch.ao.quantization import get_default_qconfig from torch.ao.quantization import quantize_jit ts_model = torch.jit.script(float_model.eval()) # or torch.jit.trace(float_model, input) qconfig = get_default_qconfig('fbgemm') def calibrate(model, data_loader): model.eval() with torch.no_grad(): for image, target in data_loader: model(image) quantized_model = quantize_jit( ts_model, {'': qconfig}, calibrate, [data_loader_test]) ``` z*quantization_api.quantize_jit.quantize_jitr+)rrr rBrr,)r rr@rAr"r8r r r quantize_jitys2 rCcCs tjdt||||tjdS)a Quantize the input float TorchScript model with post training dynamic quantization. Currently only qint8 quantization of torch.nn.Linear is supported. Args: `model`: input float TorchScript model `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and qconfig for that module as value, please see detailed descriptions in :func:`~torch.ao.quantization.quantize_jit` `inplace`: carry out model transformations in-place, the original module is mutated `debug`: flag for producing a debug friendly model (preserve weight attribute) Return: Quantized TorchSciprt model. Example: ```python import torch from torch.ao.quantization import per_channel_dynamic_qconfig from torch.ao.quantization import quantize_dynmiac_jit ts_model = torch.jit.script(float_model.eval()) # or torch.jit.trace(float_model, input) qconfig = get_default_qconfig('fbgemm') def calibrate(model, data_loader): model.eval() with torch.no_grad(): for image, target in data_loader: model(image) quantized_model = quantize_dynamic_jit( ts_model, {'': qconfig}, calibrate, [data_loader_test]) ``` z2quantization_api.quantize_jit.quantize_dynamic_jit)r"r8r))rrr rBrr/)r rr"r8r r rquantize_dynamic_jits& rD)F)F)F)FFN)FFN)FF)FF)rZtorch.ao.quantization.qconfigrZ torch.ao.quantization.quant_typerZtorch.jit._recursiverrrrrr$r,r*r.r0r:r=r>rBrCrDr r r rs&           5