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Merged list of tensor custom data type support and recent advances in…
… PT FE organization and new ops support (basically control flow)
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src/frontends/pytorch/include/openvino/frontend/pytorch/c_torchscript_decoder.hpp
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182 changes: 182 additions & 0 deletions
182
src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,182 @@ | ||
| #pragma once | ||
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| #include <openvino/frontend/exception.hpp> | ||
| #include <openvino/frontend/node_context.hpp> | ||
| #include <openvino/frontend/pytorch/decoder.hpp> | ||
| #include <openvino/opsets/opset8.hpp> | ||
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| #include "exception.hpp" | ||
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| namespace ov { | ||
| namespace frontend { | ||
| namespace pytorch { | ||
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| typedef std::map<size_t, Output<Node>> TensorMap; | ||
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| class NodeContext : public frontend::NodeContext { | ||
| public: | ||
| NodeContext(std::shared_ptr<Decoder> decoder, | ||
| TensorMap* tensor_map, | ||
| ParameterVector* external_parameters, | ||
| const TensorMap& ext_tensor_map) | ||
| : // TODO: why the following ctor is explicit? | ||
| frontend::NodeContext(decoder->get_op_type()), | ||
| m_decoder(decoder), | ||
| m_tensor_map(tensor_map), | ||
| m_ext_tensor_map(ext_tensor_map), | ||
| m_external_parameters(external_parameters) {} | ||
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| // Do not search for input in tensor map; try to access it as a constant of specified type T and return its value | ||
| template <typename T> | ||
| T const_input(size_t index) const; | ||
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| size_t get_input_size() const override { | ||
| return m_decoder->inputs().size(); | ||
| }; | ||
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| // Search for input in tensor map and return an output port for already converted op | ||
| // TODO: int due to base class uses it, but naturally it should be size_t for PT | ||
| Output<Node> get_input(int index) const override { | ||
| // std::cerr << "Trying to map input to ngraph..."; | ||
| OV_FRONTEND_REQUIRE(!m_decoder->input_is_none(index)); | ||
| auto input = m_decoder->input(index); | ||
| OV_FRONTEND_REQUIRE(m_tensor_map->count(input)); | ||
| return m_tensor_map->at(input); | ||
| } | ||
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| // TODO: upstream to base class | ||
| OutputVector inputs() const { | ||
| OutputVector res; | ||
| for (size_t input : m_decoder->inputs()) { | ||
| // std::cerr << "Searching for input: " << input->unique() << "\n"; | ||
| OV_FRONTEND_REQUIRE(m_tensor_map->find(input) != m_tensor_map->end()); | ||
| res.push_back(m_tensor_map->at(input)); | ||
| } | ||
| return res; | ||
| } | ||
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| bool input_is_none(size_t index) const { | ||
| return m_decoder->input_is_none(index); | ||
| } | ||
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| // Convert the resulting value of this node to ngraph Constant; works correctly only for nodes that produce | ||
| // constant value, naturally for prim::Constant | ||
| OutputVector as_constant() const { | ||
| return m_decoder->as_constant(); | ||
| } | ||
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| /* | ||
| TODO: Should be uncommented when explicit NodeContext ctor won't require passing op_type | ||
| const std::string& get_op_type() const override { | ||
| return m_decoder->get_op_type(); | ||
| } | ||
| */ | ||
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| std::string get_schema() const { | ||
| return m_decoder->get_schema(); | ||
| } | ||
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| size_t num_of_outputs() const { | ||
| return m_decoder->num_of_outputs(); | ||
| } | ||
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| std::vector<size_t> outputs() const { | ||
| return m_decoder->outputs(); | ||
| } | ||
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| std::shared_ptr<Node> mark_node(std::shared_ptr<Node> ov_node) const { | ||
| return m_decoder->mark_node(ov_node); | ||
| } | ||
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| void mark_nodes(std::vector<std::shared_ptr<Node>> ov_nodes) const { | ||
| return m_decoder->mark_nodes(ov_nodes); | ||
| } | ||
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| Output<Node> mark_output(Output<Node> ov_output) const { | ||
| return m_decoder->mark_node(ov_output.get_node_shared_ptr()); | ||
| } | ||
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| Any get_attribute_as_any(const std::string&) const override { | ||
| throw std::runtime_error( | ||
| "There is no any named attributes in Pytorch node, query by attribute name is not implemented"); | ||
| } | ||
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| void mutate_input(size_t index, Output<Node> ov_output) { | ||
| OV_FRONTEND_REQUIRE(!m_decoder->input_is_none(index)); | ||
| auto input = m_decoder->input(index); | ||
| OV_FRONTEND_REQUIRE(m_tensor_map->count(input)); | ||
| m_tensor_map->at(input).get_tensor().set_names({std::to_string(input) + "_"}); | ||
| // TODO: find out why this doesn't work | ||
| ov_output.get_tensor().add_names({std::to_string(input)}); | ||
| (*m_tensor_map)[input] = ov_output; | ||
| m_mutated_tensors.insert(input); | ||
| } | ||
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| std::set<size_t> get_mutated_tensors() const { | ||
| return m_mutated_tensors; | ||
| } | ||
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| std::shared_ptr<Decoder> get_decoder() const { | ||
| return m_decoder; | ||
| } | ||
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| void add_tensor_to_context(size_t index, Output<Node> ov_output) { | ||
| if (m_tensor_map->count(index)) { | ||
| std::cerr << "[ WARNING ] Current context has tensor. Rewriting." << std::endl; | ||
| } | ||
| ov_output.get_tensor().add_names({std::to_string(index)}); | ||
| (*m_tensor_map)[index] = ov_output; | ||
| } | ||
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| Output<Node> get_tensor_from_model(size_t index) { | ||
| if (m_tensor_map->find(index) != m_tensor_map->end()) { | ||
| return m_tensor_map->at(index); | ||
| } else { | ||
| return Output<Node>(); | ||
| } | ||
| } | ||
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| Output<Node> get_tensor_from_model_or_create_input(size_t index) { | ||
| if (m_tensor_map->find(index) != m_tensor_map->end()) { | ||
| return m_tensor_map->at(index); | ||
| } else { | ||
| // nested subgraphs case | ||
| auto parameter = std::make_shared<opset8::Parameter>(element::dynamic, PartialShape::dynamic()); | ||
| parameter->get_output_tensor(0).add_names({std::to_string(index)}); | ||
| (*m_tensor_map)[index] = parameter; | ||
| m_external_parameters->push_back(parameter); | ||
| std::cout << "Nested case, created: " << parameter << std::endl; | ||
| return parameter; | ||
| } | ||
| } | ||
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| Output<Node> get_input_from_visible_context(size_t index) { | ||
| OV_FRONTEND_REQUIRE(index < get_input_size()); | ||
| auto input_tensor = get_input(index); | ||
| auto input_node = input_tensor.get_node_shared_ptr(); | ||
| if (std::dynamic_pointer_cast<opset8::Parameter>(input_node)) { | ||
| // We need to look into external context for inputs that would be feed into this parameter | ||
| auto name = input_node->get_output_tensor(0).get_any_name(); | ||
| size_t tensor_idx = (size_t)std::stoll(name); | ||
| if (m_ext_tensor_map.count(tensor_idx)) { | ||
| input_tensor = m_ext_tensor_map.at(tensor_idx); | ||
| } | ||
| } | ||
| return input_tensor; | ||
| } | ||
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| std::shared_ptr<ov::Model> convert_subgraph(size_t index); | ||
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| private: | ||
| std::shared_ptr<opset8::Constant> get_constant_at_input(size_t index) const; | ||
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| std::shared_ptr<Decoder> m_decoder; | ||
| std::set<size_t> m_mutated_tensors; | ||
| TensorMap* m_tensor_map; | ||
| const TensorMap& m_ext_tensor_map; | ||
| ParameterVector* m_external_parameters; | ||
| }; | ||
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| } // namespace pytorch | ||
| } // namespace frontend | ||
| } // namespace ov |
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