[GitHub] cjolivier01 commented on a change in pull request #10150: [WIP] [DO NOT MERGE] Sparse operator broadcast_mul/div(csr, dense) = csr

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

cjolivier01 commented on a change in pull request #10150: [WIP] [DO NOT MERGE] Sparse operator broadcast_mul/div(csr, dense) = csr
URL: https://github.com/apache/incubator-mxnet/pull/10150#discussion_r175332618
 
 

 ##########
 File path: src/operator/tensor/elemwise_binary_broadcast_op.h
 ##########
 @@ -185,6 +227,75 @@ 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_EQ(dns.shape().ndim(), 1) << "input dense should be a vector";
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  bool col_vec = (dns.shape()[0] == csr.shape()[0])? true : false;
+  if (!csr.storage_initialized()) {
+    FillZerosCsrImpl(s, output);
+    return;
+  }
+  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<DType, CType, RType, 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>());
+        });
+      });
+    });
+  });
+}
+
+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 auto in1_stype = inputs[0].storage_type();
+  const auto in2_stype = inputs[1].storage_type();
+  const auto out_stype = outputs[0].storage_type();
+  if (!(inputs[1].shape().ndim() == 1U)) {
+    ElemwiseBinaryOp::ComputeEx<xpu, OP>(attrs, ctx, inputs, req, outputs);
+  } else {
+    if (req[0] != kNullOp) {
+      // broadcast(CSR, Dense(1D)) = CSR
+      if (in1_stype == kCSRStorage && in2_stype == kDefaultStorage && out_stype == kCSRStorage) {
+        BinaryBroadCastCsrDnsCsrImpl<xpu, OP>(ctx, inputs[0], inputs[1], req[0], outputs[0]);
+      // broadcast(CSR, Dense(1D)) = Dense
+      //} else if (in1_stype == kCSRStorage && in2_stype == kDefaultStorage &&
+      //           out_stype == kDefaultStorage) {
+      //  BinaryBroadCastCsrDnsDnsImpl(ctx, inputs[0], input[1], req[0], outputs[0]);
+      } else {
+        LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
 
 Review comment:
   would catching this in the storage type inference and then doing a fallback not work for this case?

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