[GitHub] [incubator-mxnet] nikudyshko opened a new issue #18668: InferArgsMap does not infer dtype

[GitBox <gi...@apache.org>]

nikudyshko opened a new issue #18668:
URL: https://github.com/apache/incubator-mxnet/issues/18668


   ## Description 
   While creating a neural network with MXNet cpp-api, if set `dtype` of know args to `1 (corresponds float64)`, `InferArgsMap` will not transfer this type to infered args. This causes error, when creating Executor. 
   
   ### Error Message
   [22:30:58] C:\Users\UserName\Desktop\neural_nets\Mark1\Mark1\mxnet\include\dmlc\logging.h:429: [22:30:58] C:\Users\UserName\Desktop\neural_nets\Mark1\Mark1\mxnet\include\mxnet-cpp\executor.hpp:85: Check failed: MXExecutorBindEX(symbol.GetHandle(), context.GetDeviceType(), context.GetDeviceId(), group_to_ctx.size(), map_keys.data(), dev_types.data(), dev_ids.data(), arg_handles.size(), arg_handles.data(), grad_handles.data(), grad_reqs_uint.data(), aux_handles.size(), aux_handles.data(), shared_exec_handle, &handle_) == 0 (-1 vs. 0) :
   
   ## To Reproduce 
   ```cpp
   #include <chrono>
   #include "mxnet-cpp/MxNetCpp.h"
   
   using namespace mxnet::cpp;
   
   Symbol mlp(const std::vector<int>& layers) {
   	auto x = Symbol::Variable("X");
   	auto label = Symbol::Variable("label");
   
   	std::vector<Symbol> weights(layers.size());
   	std::vector<Symbol> biases(layers.size());
   	std::vector<Symbol> outputs(layers.size());
   
   	for (size_t i = 0; i < layers.size(); ++i) {
   		weights[i] = Symbol::Variable("w" + std::to_string(i));
   		biases[i] = Symbol::Variable("b" + std::to_string(i));
   		Symbol fc = FullyConnected(
   			i == 0 ? x : outputs[i - 1],  // data
   			weights[i],
   			biases[i],
   			layers[i]);
   		outputs[i] = i == layers.size() - 1 ? fc : Activation(fc, ActivationActType::kRelu);
   	}
   
   	return SoftmaxOutput(outputs.back(), label);
   }
   
   int main()
   { 
   	const int image_size = 28;
   	const std::vector<int> layers{ 128, 64, 10 };
   	const int batch_size = 100;
   	const int max_epoch = 10;
   	const float learning_rate = 0.1;
   	const float weight_decay = 1e-2;
   
   	auto net = mlp(layers);
   
   	Context ctx = Context::cpu();  // Use CPU for training
   
   	std::map<std::string, NDArray> args;
   	args["X"] = NDArray(Shape(batch_size, image_size * image_size), ctx, true, 1); // 1 should correspond to float64 
   	args["label"] = NDArray(Shape(batch_size), ctx, true, 1); // 1 should correspond to float64 
   	// Let MXNet infer shapes other parameters such as weights
   	net.InferArgsMap(ctx, &args, args); 
   
   	for (auto& arg : args)
   		std::cout << arg.first << ' ' << arg.second.GetDType() << '\n'; 
   	// Output here: 
   	// X 1
   	// b0 0
   	// b1 0
   	// b2 0
   	// label 1
   	// w0 0
   	// w1 0
   	// w2 0 
   	// All 0 should be 1 
   
   	// Create executor by binding parameters to the model
   	auto* exec = net.SimpleBind(ctx, args); // Error here 
   
   	return 0;  
   } 
   ```
   
   ### Steps to reproduce
   Just compile and run the code above 
   
   ## What have you tried to solve it?
   I've tried to add `dtype`-infering to the code of `InferArgsMap`. This allowed me to create a network, but than an had been thrown on args destruction. 
   
   ## Environment 
   Win10 64-bit, VS2019, MXNet
   


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