guanxinq 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_r392525710
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File path: src/c_api/c_api.cc
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@@ -572,12 +645,30 @@ int MXLoadLib(const char *path) {
DispatchMode* dispatch_mode,
std::vector<int>* in_stypes,
std::vector<int>* out_stypes) {
- // TODO(ziyimu): remove this dense enforce check after supporting sparse
tensor
- CHECK(mxnet::common::ContainsOnlyStorage(*in_stypes,
mxnet::kDefaultStorage))
- << "Error input tensors are not dense for custom operator '" << name_str
<< "'";
- // set outputs as dense
- return op::storage_type_assign(out_stypes, mxnet::kDefaultStorage,
- dispatch_mode, DispatchMode::kFComputeEx);
+ // convert attributes to vector of char*
+ std::vector<const char*> attr_keys, attr_vals;
+ for (auto kv : attrs.dict) {
+ attr_keys.push_back(kv.first.c_str());
+ attr_vals.push_back(kv.second.c_str());
+ }
+ // copy input types from in_stype
+ std::vector<int> instypes(*in_stypes);
+
+ // output types will be populated by inferType function
+ std::vector<int> outstypes(out_stypes->size());
+
+ CHECK(callInferSType(stype_fp, attr_keys.data(), attr_vals.data(),
attr_keys.size(),
Review comment:
Do we have to do so? Just like the InferType function, do we need to have
default implementation for float32?
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