昇腾AI原生创新算子挑战赛（S1赛季）复盘- MseLossGrad算子
发表于2024-05-08 22:38:08
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- 正文前感谢昇腾各位工作人员，没有你们的辛勤就没有我们的进步
- 本文立意交流大赛MseLossGrad算子编译过程
- MseLossGrad算子是反向算子，怎么计算的就是一个难点，连公式都不知，那怎么继续编写算子
- 通过对提供的案例进行分析，得到计算公式如下
- 由公式可知有标量计算
- 那么如何从输入string “mean” 转换成 float型，再输入op_kernel参与计算便是其中另一难点
- 下列展示测试案例
- 第一个测试案例
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reduction = "mean" if 'mean' == reduction: reduce_elts = 1.0 for i in input_predict.shape: reduce_elts *= i cof = (reduce_elts**(-1)) * 2.0 else: cof = 2.0 sub_res = input_predict - input_label norm_grad = sub_res * cof golden = norm_grad * input_doutconst char *pvalue = context->GetAttrs()->GetStr(0); float reduce_elts = 1.0; float cof = 1.0; uint8_t i =0; uint8_t j =0; printf("getstr %s\r\n", pvalue); if(strcmp(pvalue,"mean") == 0) { for(i = 0;i<dimNum0;i++) { reduce_elts = reduce_elts*shape0[i]; } cof = 1.0/reduce_elts*2.0; print("cof info %f",cof); tiling.set_cof(cof);//获取属性值 } else { tiling.set_cof(2.0);//获取属性值 }__aicore__ inline void Compute(int32_t progress) { LocalTensor<DTYPE_PREDICT> inLocal = inQueueIN.DeQue<DTYPE_PREDICT>(); LocalTensor<DTYPE_PREDICT> predictLocal = inLocal; LocalTensor<DTYPE_LABEL> labelLocal = inLocal[this->tileLength]; LocalTensor<DTYPE_LABEL> doutLocal = inLocal[2*this->tileLength]; LocalTensor<DTYPE_Y> outLocal = outQueueOUT.AllocTensor<DTYPE_Y>(); //采用div + Muls + Add实现 Sub(outLocal, predictLocal, labelLocal, this->tileLength); Muls(outLocal,outLocal,static_cast<DTYPE_Y>(this->cof),this->tileLength); Mul(outLocal,outLocal,doutLocal,this->tileLength); outQueueOUT.EnQue<DTYPE_Y>(outLocal); inQueueIN.FreeTensor(inLocal); }