haojin2 commented on a change in pull request #10208: [MXNET-117] Sparse 
operator broadcast_mul/div(csr, dense) = csr
URL: https://github.com/apache/incubator-mxnet/pull/10208#discussion_r179052324
 
 

 ##########
 File path: src/operator/tensor/elemwise_binary_broadcast_op.h
 ##########
 @@ -185,6 +230,84 @@ void BinaryBroadcastCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename OP>
+void BinaryBroadcastCsrDnsCsrImpl(const OpContext& ctx,
+                                  const NDArray& csr,
+                                  const NDArray& dns,
+                                  const OpReqType req,
+                                  const NDArray& output) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  using namespace csr;
+  CHECK(req != kAddTo && req != kWriteInplace);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  bool col_vec;
+  if (dns.shape().ndim() == 1) {
+    col_vec = false;
+  } else {
+    col_vec = (dns.shape()[0] == csr.shape()[0])? true : false;
+  }
+
+  if (csr.storage_initialized()) {
+    const nnvm::dim_t nnz = csr.storage_shape()[0];
+    const nnvm::dim_t num_rows = output.shape()[0];
+    output.CheckAndAlloc({Shape1(num_rows + 1), Shape1(nnz)});
+
+    MSHADOW_TYPE_SWITCH(output.dtype(), DType, {
+      MSHADOW_IDX_TYPE_SWITCH(output.aux_type(kIdx), CType, {
+        MSHADOW_IDX_TYPE_SWITCH(output.aux_type(kIndPtr), RType, {
+          MXNET_ASSIGN_REQ_SWITCH(req, req_type, {
+            Kernel<csr_dns_csr_broadcast_kernel<req_type, OP>, xpu>::Launch(
+              s, num_rows, csr.data().dptr<DType>(), 
csr.aux_data(kIdx).dptr<CType>(),
+              csr.aux_data(kIndPtr).dptr<RType>(), dns.data().dptr<DType>(),
+              output.data().dptr<DType>(), csr.shape()[1], col_vec);
+            Copy(output.aux_data(kIdx).FlatTo1D<xpu, CType>(),
+                 csr.aux_data(kIdx).FlatTo1D<xpu, CType>());
+            Copy(output.aux_data(kIndPtr).FlatTo1D<xpu, RType>(),
+                 csr.aux_data(kIndPtr).FlatTo1D<xpu, RType>());
+          });
+        });
+      });
+    });
+  // If input csr is an empty matrix, fill zeros and return
+  } else {
+    FillZerosCsrImpl(s, output);
+    return;
+  }
+}
+
+template<typename xpu, typename OP>
+void BinaryBroadcastComputeCsrEx(const nnvm::NodeAttrs& attrs,
+                                 const OpContext& ctx,
+                                 const std::vector<NDArray>& inputs,
+                                 const std::vector<OpReqType>& req,
+                                 const std::vector<NDArray>& outputs) {
+  CHECK_EQ(inputs.size(), 2U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  CHECK_LE(inputs[1].shape().ndim(), 2U) << "input dense matrix should have 
less than 2 dimensions";
+  const NDArray& lhs = inputs[0];
+  const NDArray& rhs = inputs[1];
+  const NDArray& out = outputs[0];
+  const auto lhs_stype = lhs.storage_type();
+  const auto rhs_stype = rhs.storage_type();
+  const auto out_stype = out.storage_type();
+  // If the input is not a vector
+  if ((rhs.shape().ndim() != 1U) && (rhs.shape()[0] != 1) && (rhs.shape()[1] 
!= 1)) {
+    // Currently do not support elementwise_mul/div(csr, dense) = csr, log and 
exit
+    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
+  } else {
+    if (req[0] != kNullOp) {
+      // broadcast(CSR, Dense(1D)) = CSR
+      if (lhs_stype == kCSRStorage && rhs_stype == kDefaultStorage && 
out_stype == kCSRStorage) {
 
 Review comment:
   Made it work

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