[GitHub] ZiyueHuang commented on a change in pull request #9479: add norm operator for sparse ndarray

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

ZiyueHuang commented on a change in pull request #9479: add norm operator for sparse ndarray 
URL: https://github.com/apache/incubator-mxnet/pull/9479#discussion_r162623029
 
 

 ##########
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##########
 @@ -816,20 +816,94 @@ struct ReduceGrad {
   }
 };
 
+inline bool L2NormStorageType(const nnvm::NodeAttrs& attrs,
+                              const int dev_mask,
+                              DispatchMode* dispatch_mode,
+                              std::vector<int>* in_attrs,
+                              std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  const int in_stype = in_attrs->at(0);
+  int& out_stype = out_attrs->at(0);
+  bool dispatched = false;
+  if (!dispatched && in_stype == kDefaultStorage) {
+    // dns -> dns
+    dispatched = storage_type_assign(&out_stype, kDefaultStorage, dispatch_mode,
+                                     DispatchMode::kFCompute);
+  }
+  if (!dispatched && (in_stype == kCSRStorage || in_stype == kRowSparseStorage)) {
+    // csr/rsp -> dns
+    dispatched = storage_type_assign(&out_stype, kDefaultStorage, dispatch_mode,
+                                     DispatchMode::kFComputeEx);
+  }
+  if (!dispatched) {
+    dispatched = dispatch_fallback(out_attrs, dispatch_mode);
+  }
+  return dispatched;
+}
+
+template<typename xpu>
+void L2NormComputeImpl(mshadow::Stream<xpu> *s,
+                       const TBlob& input,
+                       const OpReqType req,
+                       const TBlob& output) {
+  MSHADOW_REAL_TYPE_SWITCH(output.type_flag_, DType, {
+    MXNET_ASSIGN_REQ_SWITCH(req, Req, {
+      mshadow::Tensor<xpu, 1, DType> out = output.get<xpu, 1, DType>(s);
+      mshadow::Tensor<xpu, 1, DType> in = input.get_with_shape<xpu, 1, DType>(
+        mshadow::Shape1(input.shape_.Size()), s);
+      mshadow::VectorDot(out, in, in);
+      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);
+    });
+  });
+}
+
+
 template<typename xpu>
 void L2NormCompute(const nnvm::NodeAttrs& attrs,
                    const OpContext& ctx,
                    const std::vector<TBlob>& inputs,
                    const std::vector<OpReqType>& req,
                    const std::vector<TBlob>& outputs) {
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
-  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
-    mshadow::Tensor<xpu, 1, DType> out = outputs[0].get<xpu, 1, DType>(s);
-    mshadow::Tensor<xpu, 1, DType> in = inputs[0].get_with_shape<xpu, 1, DType>(
-      mshadow::Shape1(inputs[0].shape_.Size()), s);
-    mshadow::VectorDot(out, in, in);
-    ASSIGN_DISPATCH(out, req[0], mshadow::expr::F<mxnet::op::mshadow_op::square_root>(out));
-  });
+  L2NormComputeImpl(s, inputs[0], req[0], outputs[0]);
 
 Review comment:
   Would it be better if `if (req == kNullOp) return;` before `L2NormComputeImpl` since we'll save the computation of `VectorDot`? Just as line 880 in `L2NormComputeSparseImpl`.

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