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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/03/04 01:33:57 UTC

[GitHub] [incubator-mxnet] samskalicky commented on a change in pull request #17569: Adding sparse support to MXTensor for custom operators

samskalicky commented on a change in pull request #17569: Adding sparse support to MXTensor for custom operators
URL: https://github.com/apache/incubator-mxnet/pull/17569#discussion_r387401876
 
 

 ##########
 File path: include/mxnet/lib_api.h
 ##########
 @@ -1111,16 +1197,49 @@ extern "C" {
 
     // create a vector of tensors for inputs
     std::vector<MXTensor> inputs(num_in);
+    // create a vector for sparse inputs
+    std::vector<MXInSparse> in_sparse(num_in);
+
     for (int i = 0; i < num_in; i++) {
-      inputs[i].setTensor(indata[i], (MXDType)intypes[i], inshapes[i], indims[i],
-                          inIDs[i], {indev_type[i], indev_id[i]});
+      // Dense representation.
+      if(!in_indices_shapes) {
+        inputs[i].setTensor(indata[i], (MXDType)intypes[i], inshapes[i], indims[i],
+                            inIDs[i], {indev_type[i], indev_id[i]}, kDefaultStorage);
+      }
+      // Sparse representation.
+      else {
+        MXStorageType type;
+        if(!in_indptr_shapes) {
+          type = kRowSparseStorage;
+          in_sparse[i].set(indata[i], inshapes[i], indims[i], in_indices[i], in_indices_shapes[i]);
+        }
+        else {
+          type = kCSRStorage;
+          in_sparse[i].set(indata[i], inshapes[i], indims[i], in_indices[i],
+                           in_indices_shapes[i], in_indptr[i], in_indptr_shapes[i]);
+        }
+        inputs[i].setTensor((void*)(&in_sparse[i]), (MXDType)intypes[i], inshapes[i], indims[i],
+                            inIDs[i], {indev_type[i], indev_id[i]}, type);
+      }
     }
 
     // create a vector of tensors for outputs
     std::vector<MXTensor> outputs(num_out);
+    // create a vector for sparse outputs
+    std::vector<MXOutSparse> out_sparse;
 
 Review comment:
   This assumes we should allocate a sparse tensor for every input, before even checking if the input is sparse. Maybe theres a better way to do it 

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