sandeep-krishnamurthy commented on a change in pull request #15245: FP16
Support for C Predict API
URL: https://github.com/apache/incubator-mxnet/pull/15245#discussion_r299708932
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File path: src/c_api/c_predict_api.cc
##########
@@ -187,21 +206,41 @@ int _CreatePartialOut(const char* symbol_json_str,
try {
mxnet::ShapeVector in_shapes;
+ nnvm::DTypeVector in_types;
for (std::string key : sym.ListInputNames(Symbol::kAll)) {
if (known_shape.count(key) != 0) {
in_shapes.push_back(known_shape[key]);
} else {
in_shapes.emplace_back();
}
}
+
+ for (std::string key : sym.ListInputNames(Symbol::kAll)) {
+ if (arg_types.count(key) != 0) {
+ in_types.push_back(arg_types[key]);
+ } else if (aux_types.count(key) != 0) {
+ in_types.push_back(aux_types[key]);
+ }
+ }
nnvm::Graph g; g.outputs = sym.outputs;
g = mxnet::exec::InferShape(std::move(g), std::move(in_shapes),
"__shape__");
+ g = mxnet::exec::InferType(std::move(g), std::move(in_types), "__dtype__");
bool infer_complete = (g.GetAttr<size_t>("shape_num_unknown_nodes") == 0);
+ // This is tricky for AMP Use case, for example, with only weights input
types
+ // cannot be inferred in AMP. Thus for AMP converted model type_dict will
be
+ // required
+ bool infer_type_complete = (g.GetAttr<size_t>("dtype_num_unknown_nodes")
== 0);
CHECK(infer_complete)
<< "The shape information of is not enough to get the shapes";
+ CHECK(infer_type_complete)
+ << "The type information is not enough, please provide input arg_types
"
+ "with provided_arg_dtype_names and provided_arg_dtypes";
Review comment:
I think this will not be clear for an MXNet user, he is not setting any
provided_arg_dtype_names and provided_arg_dtypes parameters, so if something
fails, how to debug?
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