^j zddlZddlmZddlmcmZddlmZddlm Z m Z m Z dgZ GddejZ y)N) LinearReLU)is_parametrized$transfer_parametrizations_and_paramstype_before_parametrizationsLinearcfeZdZdZej Z d dfd ZdZe ddZ dZ xZ S) ra A linear module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Linear`, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation. Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to default. Attributes: weight: fake quant module for weight c||d}t||||fi||s td||_|j ||_y)N)devicedtypez'qconfig must be provided for QAT module)factory_kwargs)super__init__AssertionErrorqconfigweightweight_fake_quant) self in_features out_featuresbiasrr r r __class__s i/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/torch/ao/nn/qat/modules/linear.pyrzLinear.__init__"sN%+U; lDKNK !JK K !(~!Ncvtj||j|j|jSN)Flinearrrr)rinputs rforwardzLinear.forward2s(xxt55dkkBDIINNrc ht||jk7rEtd|jd|jjdt|jt |ds td|j s tdt|t k(r|d}|j }||j|j|jdu| }t|d rt||d n|j|_ t|d rt||d |S|j|_ |S) zCreate a qat module from a float module or qparams_dict Args: `mod` a float module, either produced by torch.ao.quantization utilities or directly from user zqat.z.from_float only works for z, got rz,Input float module must have qconfig definedz,Input float module must have a valid qconfigrN)rrrr) r _FLOAT_MODULEr__name__hasattrrrrrrrrr)clsmoduse_precomputed_fake_quantr qat_linears r from_floatzLinear.from_float5s& ( ,0A0A A s||n$?$$--.f5QRU5V5_5_4`b sI& !OP P{{ !OP P ' , :a&C++ OO   %   3 ) 0j( K # J  3 ' 0j& I"hhJOrctjj|j|j|j du}tjj |jj|_|j r>s9 , *SRYYSr