U Kc@sZddlZddlmmmZddlmZddlmmm Z Gdddej Z dS)N)_quantize_weightcsheZdZdZdZdejffdd ZddZdd Z d d Z fd d Z e ddZ e ddZZS)Lineara A dynamic quantized linear module with floating point tensor as inputs and outputs. 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 :class:`torch.nn.Linear`, attributes will be randomly initialized at module creation time and will be overwritten later Attributes: weight (Tensor): the non-learnable quantized weights of the module which are of shape :math:`(\text{out\_features}, \text{in\_features})`. bias (Tensor): the non-learnable floating point bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized to zero. Examples:: >>> m = nn.quantized.dynamic.Linear(20, 30) >>> input = torch.randn(128, 20) >>> # xdoctest: +SKIP >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) Tcs"tt|j||||dd|_dS)Ndtyper)superr__init__version)self in_features out_featuresZbias_r __class__P/tmp/pip-unpacked-wheel-gikjz4vx/torch/ao/nn/quantized/dynamic/modules/linear.pyr"szLinear.__init__cCs|jjtjkrR|jdks"|jdkr8tjj||jj}q~tjjj||jjdd}n,|jjtjkrvtjj ||jj}nt d| |jS)NrT)Z reduce_rangez.Unsupported dtype on dynamic quantized linear!) _packed_paramsrtorchqint8r ops quantizedZlinear_dynamicfloat16Zlinear_dynamic_fp16 RuntimeErrorto)r xYrrrforward*s$zLinear.forwardcCsdS)NZDynamicQuantizedLinearr)r rrr _get_name:szLinear._get_namecCs>d|j|j|jj}|jjtjkr:|d|7}|S)Nz)in_features={}, out_features={}, dtype={}z , qscheme={}) formatr r rrrrweightZqscheme)r Zextra_repr_strrrr extra_repr=szLinear.extra_reprc s2|dd}||_tt||||d|||dS)Nr F)getr rr_load_from_state_dict) r Z state_dictprefixZlocal_metadatastrictZ missing_keysZunexpected_keysZ error_msgsr r rrr!Es zLinear._load_from_state_dictcCs>tjjtjjjjtjjjjjtj jj j jg}t ||ksRt dtdd|Dt|dsdt dt |tjkrz|d}|jdk r|jjdk r|j}nddlm}|}|j}|tjtjfkst d |||j|tjkrt|j|}n |tjkr|j}ntd ||j|j|d }|||j|S) zCreate a dynamic quantized module from a float module or qparams_dict Args: mod (Module): a float module, either produced by torch.ao.quantization utilities or provided by the user zYsz%Linear.from_float..qconfigz,Input float module must have qconfig definedrN)default_dynamic_qconfigzTThe only supported dtypes for dynamic quantized linear are qint8 and float16 got: {}z9Unsupported dtype specified for dynamic quantized Linear!r) rnnrmodulesZlinearZNonDynamicallyQuantizableLinear intrinsicZfusedZ LinearReLUaoZqatZdynamictypeAssertionErrorstrhasattrnnir'rZtorch.ao.quantization.qconfigr(rrrrrfloatrr r set_weight_biasbias)clsmodZ float_modulesZweight_observerr(rqweightqlinearrrr from_floatLs:       zLinear.from_floatcCs2||j|j|jd}|}|j}||||S)a  Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized module Args: ref_qlinear (Module): a reference quantized module, either produced by torch.ao.quantization functions or provided by the user r)r r Z weight_dtypeZget_quantized_weightr4r3)r5Z ref_qlinearr8r7r4rrrfrom_referencess  zLinear.from_reference)r$ __module__ __qualname____doc___versionrrrrrrr! classmethodr9r: __classcell__rrr rrs  &r) rZtorch.ao.nn.quantizedr,r)rZnnqZ#torch.ao.nn.quantized.modules.utilsrZtorch.ao.nn.intrinsicr+r1rrrrrs