vvchernov commented on code in PR #11358:
URL: https://github.com/apache/tvm/pull/11358#discussion_r967164258
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src/runtime/vm/vm.cc:
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@@ -143,8 +143,15 @@ PackedFunc VirtualMachine::GetFunction(const std::string&
name,
} else {
auto it = inputs_.find(func_name);
ICHECK(it != inputs_.end()) << "Input has not been set for function "
<< func_name;
- const std::vector<ObjectRef>& func_args = it->second;
- *rv = Invoke(func, func_args);
+ const std::vector<ObjectRef>& input_args = it->second;
+ if (set_outputs_enabled_.count(func_name) &&
set_outputs_enabled_[func_name]) {
+ ICHECK(outputs_.count(func_name))
+ << "Outputs have not been set for function " << func_name;
+ *rv = Invoke(func, input_args, outputs_[func_name]);
+ set_outputs_enabled_[func_name] = false;
Review Comment:
It depends on scenario of using output tensors allocated outside. I see two
options: 1. In-place scenario: memory for output tensors is allocated once.
Each new inference (invoke) writes result in this memory. 2. We should insert
new inputs/outputs for each new infer. Just now the second scenario is
implemented and in that case `outputs_` can be cleared. But in the first
scenario we should store `outputs_`. What do you think should I support the
first scenario instead of second one or both ones or keep the current one?
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