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_r387836987
##########
File path: include/mxnet/lib_api.h
##########
@@ -1193,19 +1312,57 @@ extern "C" {
const int64_t** outshapes, int* outdims, void**
outdata, int* outtypes,
size_t* outIDs, const char** outdev_type, int*
outdev_id, int num_out,
xpu_malloc_t cpu_malloc, void* cpu_alloc,
- xpu_malloc_t gpu_malloc, void* gpu_alloc, void*
stream) {
+ xpu_malloc_t gpu_malloc, void* gpu_alloc, void*
stream,
+ void** in_indices, void** in_indptr,
+ int64_t* in_indices_shapes, int64_t*
in_indptr_shapes,
+ std::vector<std::vector<float>>& tmp_data,
+ std::vector<std::vector<int64_t>>& col_idx,
+ std::vector<std::vector<int64_t>>& row_ptr) {
// 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]);
Review comment:
some sparse operators produce dense outputs, not sure we can guarantee all
or nothing
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