void printNodeInfo(const onnx::NodeProto &node)
{
std::cout
<< "name: " << node.name()
<< "op_type: " << node.op_type()
<< "domain: " << node.domain()
<< "input: " << node.input(0)
<< "output: " << node.output(0)
<< std::endl;
}
inline auto make_softmax_primitive(
std::unordered_map<std::string, const mkldnn::memory> const&
/*parameter_memory_table*/,
std::unordered_map<std::string, std::tuple<const mkldnn::memory,
mkldnn::memory::format>> const&
variable_memory_table,
std::set<std::string> const& required_output_set,
onnx::NodeProto const& node, mkldnn::engine const& engine) {
constexpr auto softmax_axis = 1;
auto const& input_memory_and_origin_format =
find_value(variable_memory_table, node.input(0));
auto const& input_memory = std::get<0>(input_memory_and_origin_format);
auto input_origin_format = std::get<1>(input_memory_and_origin_format);
auto input_output_dims = extract_dims(input_memory);
auto const& output_name = node.output(0);
std::vector<mkldnn::memory>
temp_variable_memory_list; // for temporary memory's life
auto op_desc = mkldnn::softmax_forward::desc(
mkldnn::prop_kind::forward_inference,
input_memory.get_primitive_desc().desc(), softmax_axis);
auto op_pd = mkldnn::softmax_forward::primitive_desc(op_desc, engine);
std::vector<mkldnn::primitive> net;
std::vector<std::pair<
std::string, std::tuple<mkldnn::memory, mkldnn::memory::format>>>
variable_memory_list;
std::vector<std::pair<std::string, array>> output_name_and_arr_list;
manage_output_memory(required_output_set, output_name, dtype_t::float_,
input_output_dims, input_origin_format,
input_memory.get_primitive_desc(),
variable_memory_list, temp_variable_memory_list,
output_name_and_arr_list, net, engine,
[&input_memory, &op_pd](auto& op_output_memory) {
return mkldnn::softmax_forward(
op_pd, input_memory, op_output_memory);
});
return std::make_tuple(net, variable_memory_list,
temp_variable_memory_list,
output_name_and_arr_list);
}
inline auto make_fc_primitive(
std::unordered_map<std::string, const mkldnn::memory> const&
parameter_memory_table,
std::unordered_map<std::string, std::tuple<const mkldnn::memory,
mkldnn::memory::format>> const&
variable_memory_table,
std::set<std::string> const& required_output_set,
onnx::NodeProto const& node, mkldnn::engine const& engine) {
auto attribute_table = instant::make_attribute_table(node);
auto axis = load_attribute_int(attribute_table, "axis");
assert(axis == 1);
auto axis_w = load_attribute_int(attribute_table, "axis_w");
assert(axis_w == 1);
auto const& input_memory_and_origin_format =
find_value(variable_memory_table, node.input(0));
auto const& input_memory = std::get<0>(input_memory_and_origin_format);
auto input_origin_format = std::get<1>(input_memory_and_origin_format);
auto const& weight_memory =
find_value(parameter_memory_table, node.input(1));
auto const& bias_memory =
find_value(parameter_memory_table, node.input(2));
auto input_dims = extract_dims(input_memory);
auto weight_dims = extract_dims(weight_memory);
auto bias_dims = extract_dims(bias_memory);
mkldnn::memory::dims output_dims{input_dims[0], bias_dims[0]};
auto const& output_name = node.output(0);
auto fc_input_md =
mkldnn::memory::desc({input_dims}, mkldnn::memory::data_type::f32,
mkldnn::memory::format::any);
auto fc_weight_md =
mkldnn::memory::desc({weight_dims}, mkldnn::memory::data_type::f32,
mkldnn::memory::format::any);
auto fc_output_md =
mkldnn::memory::desc({output_dims}, mkldnn::memory::data_type::f32,
mkldnn::memory::format::any);
mkldnn::inner_product_forward::desc fc_desc(
mkldnn::prop_kind::forward_inference, fc_input_md, fc_weight_md,
bias_memory.get_primitive_desc().desc(), fc_output_md);
auto fc_pd =
mkldnn::inner_product_forward::primitive_desc(fc_desc, engine);
std::vector<mkldnn::primitive> net;
std::vector<mkldnn::memory>
temp_variable_memory_list; // for temporary memory's life
auto fc_input_memory = input_memory;
if(mkldnn::memory::primitive_desc(fc_pd.src_primitive_desc()) !=
input_memory.get_primitive_desc()) {
fc_input_memory = mkldnn::memory(fc_pd.src_primitive_desc());
temp_variable_memory_list.push_back(fc_input_memory);
net.push_back(mkldnn::reorder(input_memory, fc_input_memory));
}
auto fc_weight_memory = weight_memory;
if(mkldnn::memory::primitive_desc(fc_pd.weights_primitive_desc()) !=
weight_memory.get_primitive_desc()) {
fc_weight_memory = mkldnn::memory(fc_pd.weights_primitive_desc());
temp_variable_memory_list.push_back(fc_weight_memory);
net.push_back(mkldnn::reorder(weight_memory, fc_weight_memory));
}
std::vector<std::pair<
std::string, std::tuple<mkldnn::memory, mkldnn::memory::format>>>
variable_memory_list;
std::vector<std::pair<std::string, array>> output_name_and_arr_list;
manage_output_memory(
required_output_set, output_name, dtype_t::float_, output_dims,
input_origin_format, fc_pd.dst_primitive_desc(), variable_memory_list,
temp_variable_memory_list, output_name_and_arr_list, net, engine,
[&fc_pd, &fc_input_memory, &fc_weight_memory,
&bias_memory](auto& op_output_memory) {
return mkldnn::inner_product_forward(
fc_pd, fc_input_memory, fc_weight_memory, bias_memory,
op_output_memory);
});
return std::make_tuple(net, variable_memory_list,
temp_variable_memory_list,
output_name_and_arr_list);
}