[GitHub] sxjscience commented on a change in pull request #9740: add axis support and gradient for L2norm

[GitBox <gi...@apache.org>]

sxjscience commented on a change in pull request #9740: add axis support and gradient for L2norm
URL: https://github.com/apache/incubator-mxnet/pull/9740#discussion_r177294267
 
 

 ##########
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##########
 @@ -862,15 +905,47 @@ void L2NormComputeImpl(mshadow::Stream<xpu> *s,
   });
 }
 
+template<typename xpu>
+void SqRootForL2(const OpContext& ctx, OpReqType req, const TBlob &output) {
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  MSHADOW_REAL_TYPE_SWITCH(output.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+      DType* out_data = output.dptr<DType>();
+      using namespace mxnet_op;
+      Kernel<op_with_req<mshadow_op::square_root, Req>, xpu>::Launch(
+        s, output.Size(), out_data, out_data);
+    });
+  });
+}
+
+struct square {
+  /*! \brief map a to result using defined operation */
+  template<typename DType>
+  MSHADOW_XINLINE static DType Map(DType a) {
+    return a * a;
+  }
+};
 
 Review comment:
   I think square is supported in `mshadow_op::square`.

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