o i@sZdZddlZddlZddlmZddlmmZeeej j Z Gdddej Z dS)zDynamic Convolution module.N)nncs.eZdZdZ  dfdd ZddZZS)DynamicConvolutionaDynamic Convolution layer. This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq Args: wshare (int): the number of kernel of convolution n_feat (int): the number of features dropout_rate (float): dropout_rate kernel_size (int): kernel size (length) use_kernel_mask (bool): Use causal mask or not for convolution kernel use_bias (bool): Use bias term or not. Fcstt|||dksJ||_||_||_||_d|_t ||d|_ t |||_ t ||jd||_ tj |j jt|_||_|jrXtt||_dSdS)z$Construct Dynamic Convolution layer.rN)superr__init__wshareuse_kernel_mask dropout_rate kernel_sizeattnrLinearlinear1linear2 linear_weightinitxavier_uniformweightGLUactuse_bias ParametertorchTensorbias)selfrn_featr r r r __class__`/home/ubuntu/.local/lib/python3.10/site-packages/funasr/models/transformer/utils/dynamic_conv.pyrs   zDynamicConvolution.__init__cCs2|}|\}}}|j} |j} ||}||}||} tj| |j|j d} | ||| |  dd } t j|| ||| d| jd} | || ||| dtd} | |j} | || || f|| d|| || d||| df| | dt| dd|} |jrt t j|||jdd} | | d ktd} tj| dd } | |_| || ||} | dd }| || t|| | dd}t | |}| dd |||}|j r||j! ddd}| dd}|d ur|js| dd }||dkd }|"|}|S) a&Forward of 'Dynamic Convolution'. This function takes query, key and value but uses only quert. This is just for compatibility with self-attention layer (attention.py) Args: query (torch.Tensor): (batch, time1, d_model) input tensor key (torch.Tensor): (batch, time2, d_model) NOT USED value (torch.Tensor): (batch, time2, d_model) NOT USED mask (torch.Tensor): (batch, time1, time2) mask Return: x (torch.Tensor): (batch, time1, d_model) output )trainingrr)dtypez-inf)devicerg)dimN)#sizerr rrrFdropoutr r!view transpose contiguousrzerosr"fill_floattor$ as_stridedcopy_narrowintr trilones unsqueeze masked_fillsoftmaxr bmmrrr)rquerykeyvaluemaskxBTCHkr weight_new kernel_maskrrr forward=sD   $" <"    zDynamicConvolution.forward)FF)__name__ __module__ __qualname____doc__rrG __classcell__rrrr r s !r)rKnumpyrrtorch.nn.functional functionalr(r/finfofloat32min MIN_VALUEModulerrrrr s