o iZ@sZdZddlZddlZddlmZddlmmZeeej j Z Gdddej Z dS)z-Lightweight 2-Dimensional Convolution module.N)nncs.eZdZdZ  dfdd ZddZZS)LightweightConvolution2DaLightweight 2-Dimensional 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. Fc stt|||dksJ||_||_||_||_t|d|_t ||d|_ t |d||_ t |_t t|jd|dd|_t tdd|dd|_||_|jrgt t||_t|jt|d}t|jt|dd}tj||fddd|_dS)z6Construct Lightweight 2-Dimensional Convolution layer.rdimN)superr__init__wshareuse_kernel_mask dropout_rate kernel_sizeint padding_sizerLinearlinear1linear2GLUact ParametertorchTensoruniform_weightweight_fuse_biasbiaszerosonescat unsqueeze kernel_mask) selfr n_featr rr r kernel_mask0 kernel_mask1 __class___/home/ubuntu/.local/lib/python3.10/site-packages/funasr/models/transformer/utils/lightconv2d.pyr s$   z!LightweightConvolution2D.__init__cCs|}|\}}}|j} ||}||}tj|jdd} tj| |j|j d} t j ||d|j |j |jd| } tj|d|||| |j||d|||} |ddd| |}tj|j|j|j d} |jr|j|j |_| |jdktd } tj| dd} tj|| |j|jd|||}|jr||jddd}|dd}t || fd}|d ur|js|dd }||d kd}||}|S) a8Forward of 'Lightweight 2-Dimensional Convolution'. This function takes query, key and value but uses only query. 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 rr)trainingr)devicedtype)paddinggroupsrgz-infNr)sizer rrFsoftmaxrdropoutr r+rrrr,r-copy_conv1dviewr transpose contiguousrr r"to masked_fillfloatrrr r)r#querykeyvaluemaskxBTCHr weight_newxfrr)r)r*forward@s>       z LightweightConvolution2D.forward)FF)__name__ __module__ __qualname____doc__r rH __classcell__r)r)r'r*r s $r)rLnumpyrrtorch.nn.functional functionalr2r<finfofloat32min MIN_VALUEModulerr)r)r)r*s