export.cpp

#include <torch/csrc/jit/serialization/export.h>

#include <ATen/ATen.h>
#include <ATen/Utils.h>
#include <ATen/core/functional.h>
#include <c10/macros/Macros.h>
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <c10/util/accumulate.h>
#include <c10/util/irange.h>
#include <torch/csrc/autograd/symbolic.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/runtime/instruction.h>
#include <torch/csrc/jit/serialization/import_export_constants.h>
#include <torch/csrc/jit/serialization/import_export_functions.h>
#include <torch/csrc/jit/serialization/import_export_helpers.h>
#include <torch/csrc/jit/serialization/onnx.h>
#include <torch/csrc/onnx/back_compat.h>
#include <torch/csrc/onnx/onnx.h>
#include <torch/version.h>
#include <atomic>

C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wnewline-eof")
#include <onnx/checker.h>
C10_DIAGNOSTIC_POP()
#include <onnx/onnx_pb.h>
#include <onnx/proto_utils.h>
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wsuggest-override")
#include <onnx/shape_inference/implementation.h>
C10_DIAGNOSTIC_POP()

#include <fstream>
#include <memory>
#include <regex>
#include <set>
#include <string>
#include <vector>

namespace torch::jit {

static std::string get_little_endian_data(const at::Tensor& t) {
#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
  return std::string(
      static_cast<char*>(t.data_ptr()), t.element_size() * t.numel());
#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
  const size_t element_size = t.element_size();
  const size_t num_elements = t.numel();

  std::vector<char> data_copy{
      static_cast<char*>(t.data_ptr()),
      static_cast<char*>(t.data_ptr()) + element_size * num_elements};

  for (size_t i = 0; i < num_elements; ++i) {
    char* start_byte = data_copy.data() + i * element_size;
    char* end_byte = start_byte + element_size - 1;
    /* keep swapping */
    for (size_t count = 0; count < element_size / 2; ++count) {
      std::swap(*start_byte, *end_byte);
      ++start_byte;
      --end_byte;
    }
  }

  return std::string(data_copy.data(), element_size * num_elements);
#else
#error Unexpected or undefined __BYTE_ORDER__
#endif
}

void writeArchiveAndTensors(
    const std::string& archive_name,
    const char* data,
    size_t size,
    const std::vector<at::Tensor>& tensors,
    caffe2::serialize::PyTorchStreamWriter& out) {
  std::string prefix = archive_name + "/";
  size_t i = 0;
  for (const auto& td : tensors) {
    WriteableTensorData writable_td = getWriteableTensorData(td);
    std::string fname = prefix + std::to_string(i++);
    out.writeRecord(fname, writable_td.data(), writable_td.sizeInBytes());
  }
  std::string fname = archive_name + ".pkl";
  out.writeRecord(fname, data, size);
}

namespace {
namespace onnx_torch = ::torch::onnx;
namespace onnx = ::ONNX_NAMESPACE;

const static int kInvalidOpsetVersion = -1;
const static int kMainOpsetVersion = 19;
// Based on OP_SET_ID_VERSION_MAP in
// https://github.com/onnx/onnx/blob/master/onnx/helper.py.
constexpr static std::array<int64_t, kMainOpsetVersion + 1>
    kOpsetVersionToIRVersion = {
        kInvalidOpsetVersion,
        3, // opset 1
        kInvalidOpsetVersion,
        kInvalidOpsetVersion,
        kInvalidOpsetVersion,
        3, // opset 5
        3, // opset 6
        3, // opset 7
        3, // opset 8
        4, // opset 9
        5, // opset 10
        6, // opset 11
        7, // opset 12
        7, // opset 13
        7, // opset 14
        8, // opset 15
        8, // opset 16
        8, // opset 17
        8, // opset 18
        9, // opset 19
};

std::string getNodeStackTraceString(const Node* n) {
  return n->sourceRange().str();
}

void validateBlock(
    Block* b,
    onnx_torch::OperatorExportTypes operator_export_type) {
  for (auto node : b->nodes()) {
    for (Block* sub_block : node->blocks()) {
      validateBlock(sub_block, operator_export_type);
    }
    // Macro'ed so we get a marginally better line number on failed export
#define FAIL_EXPORT(name)                          \
  throw std::runtime_error(                        \
      std::string("ONNX export failed: ") + name + \
      "\n\nGraph we tried to export:\n" + b->owningGraph()->toString());
    // Special error messages for certain types of operators
    if (node->kind() == prim::PythonOp) {
      if (operator_export_type !=
          onnx_torch::OperatorExportTypes::ONNX_FALLTHROUGH) {
        auto py_node = static_cast<PythonOp*>(node);
        FAIL_EXPORT(
            "Couldn't export Python operator " + py_node->name() +
            "\n\nDefined at:\n" + getNodeStackTraceString(node))
      }
    } else {
#ifdef BUILD_CAFFE2
      // Assuming this is a Caffe2 change as it only modifies an aten op
      // for operator_export_type == ONNX_ATEN_FALLBACK, which is a common
      // pattern for Caffe2-specific scenarios.
      if (node->kind() == aten::expand) {
        if (operator_export_type ==
            onnx_torch::OperatorExportTypes::ONNX_ATEN_FALLBACK) {
          WithInsertPoint guard(node);
          auto* new_node =
              b->owningGraph()->insertNode(b->owningGraph()->create(
                  Symbol(::c10::aten::ATen),
                  node->inputs(),
                  node->outputs().size()));
          for (size_t i = 0; i < node->outputs().size(); ++i) {
            node->output(i)->replaceAllUsesWith(new_node->output(i));
          }
          new_node->s_(Symbol::fromQualString("attr::operator"), "expand");
        }
      }
#endif
      if (node->kind() == prim::PackPadded || node->kind() == prim::PadPacked) {
        if (operator_export_type !=
            onnx_torch::OperatorExportTypes::ONNX_FALLTHROUGH) {
          FAIL_EXPORT(
              "Cannot export individual pack_padded_sequence or pad_packed_sequence; these operations must occur in pairs.\n\nUsage of this operation occurred at:\n" +
              getNodeStackTraceString(node));
        }
      }
      bool is_aten_enabled = operator_export_type ==
              onnx_torch::OperatorExportTypes::ONNX_ATEN_FALLBACK ||
          operator_export_type == onnx_torch::OperatorExportTypes::ONNX_ATEN ||
          operator_export_type ==
              onnx_torch::OperatorExportTypes::ONNX_FALLTHROUGH;
      if (node->kind().is_aten() && !is_aten_enabled && !node->mustBeNone()) {
        FAIL_EXPORT(
            "Couldn't export operator " + node->kind().toDisplayString() +
            "\n\nDefined at:\n" + getNodeStackTraceString(node));
      }
    }
#undef FAIL_EXPORT
  }
}

void validateGraph(
    const std::shared_ptr<Graph>& graph,
    onnx_torch::OperatorExportTypes operator_export_type) {
  validateBlock(graph->block(), operator_export_type);
}

std::string GetFileRootPath(const std::string& rootPath) {
  std::string rootPath_ = rootPath;
  // First, making slash consistent.
  std::replace(rootPath_.begin(), rootPath_.end(), '\\', '/');
  // Second, remove trailing slashes, if any
  std::regex trailer("/+$");
  std::string root = std::regex_replace(rootPath_, trailer, std::string());
  std::string folder = root.substr(0, root.find_last_of('/'));
  if (folder == rootPath_) { // If no root folder specified, select cwd.
    return std::string(".");
  }
  return folder;
}

std::string GetExternalFileName(
    const c10::optional<std::string>& external_ref) {
  auto tensorName = external_ref.value();
  const std::string illegalChars = "\\/:?\"<>|";
  for (char& i : tensorName) {
    if (illegalChars.find(i) != std::string::npos) {
      i = '_';
    }
  }
  return tensorName;
}

void CloseFile(FILE* fp) {
  fclose(fp);
}

void CreateExternalFile(
    const at::Tensor& tensor,
    const std::string& tensorName,
    const std::string& onnx_file_path) {
  auto folder = GetFileRootPath(onnx_file_path);
  std::string fullFilePath = folder + "/" + tensorName;
  std::unique_ptr<FILE, decltype(&CloseFile)> fp(
      fopen(fullFilePath.c_str(), "wb"), &CloseFile);
  if (fp == nullptr) {
    throw std::runtime_error(
        std::string("ONNX export failed. Could not open file or directory: ") +
        fullFilePath);
  }
  std::string s = get_little_endian_data(tensor);
  fwrite(s.c_str(), tensor.element_size(), tensor.numel(), fp.get());
} // fclose() called here through CloseFile(), if FILE* is not a null pointer.

class GraphEncoder {
 public:
  GraphEncoder(
      const std::shared_ptr<Graph>& graph,
      int64_t onnx_opset_version,
      onnx_torch::OperatorExportTypes operator_export_type,
      const std::map<std::string, at::Tensor>& initializers,
      const std::unordered_map<
          std::string,
          std::unordered_map<int64_t, std::string>>& dynamic_axes,
      bool defer_weight_export,
      bool strip_doc,
      bool keep_initializers_as_inputs,
      const std::map<std::string, int>& custom_opsets,
      bool add_node_names,
      bool use_external_data_format,
      const std::string& onnx_file_path,
      const NodeAttrNameMap& node_attr_to_name = {});

  std::shared_ptr<onnx::ModelProto> get_model_proto() {
    return model_proto_;
  }

  SymbolDimMap get_symbol_dim_param_map() {
    return symbol_dim_map_;
  }

  RawDataExportMap get_raw_data_export_map() {
    return raw_data_export_map_;
  }

  bool get_use_external_data_format() {
    return use_external_data_format_;
  }

  NodeNameMap get_onnx_node_names() {
    return onnx_node_name_map_;
  }

 private:
  // Using std::map instead of std::unordered_map for initializers
  // in EncodeGraph constructor so that the order in which initializers
  // get written to the ONNX graph is always the deterministic and
  // predictable. While this is not a ONNX requirement, it is needed
  // for testing purposes in tests that use _export_to_pretty_string()
  // for validating ONNX graphs.
  void EncodeGraph(
      onnx::GraphProto* graph_proto,
      const std::shared_ptr<Graph>& graph,
      const std::map<std::string, at::Tensor>& initializers =
          std::map<std::string, at::Tensor>(),
      const std::
          unordered_map<std::string, std::unordered_map<int64_t, std::string>>&
              dynamic_axes = std::unordered_map<
                  std::string,
                  std::unordered_map<int64_t, std::string>>(),
      bool keep_initializers_as_inputs = true,
      bool add_node_names = true,
      bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  void EncodeBlock(
      onnx::GraphProto* graph_proto,
      const Block* block,
      const std::map<std::string, at::Tensor>& initializers =
          std::map<std::string, at::Tensor>(),
      const std::
          unordered_map<std::string, std::unordered_map<int64_t, std::string>>&
              dynamic_axes = std::unordered_map<
                  std::string,
                  std::unordered_map<int64_t, std::string>>(),
      bool keep_initializers_as_inputs = true,
      bool add_node_names = true,
      bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  void AddInitializersIntoGraphProto(
      onnx::GraphProto* graph_proto,
      const Block* block,
      const std::map<std::string, at::Tensor>& initializers =
          std::map<std::string, at::Tensor>(),
      bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  unsigned long long int GetGraphProtoSize(
      onnx::GraphProto* graph_proto,
      const std::shared_ptr<Graph>& graph,
      bool add_node_names,
      bool use_external_data_format,
      const std::string& onnx_file_path,
      const std::map<std::string, at::Tensor>& initializers =
          std::map<std::string, at::Tensor>());

  void EncodeNode(
      onnx::GraphProto* graph_proto,
      onnx::NodeProto* node_proto,
      const Node* node,
      bool add_node_names = true,
      bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  void EncodeTypeProto(
      onnx::TypeProto* type_proto,
      const TypePtr& node_type,
      const std::string& name);

  void EncodeLocalFunctionOpsetImport(
      onnx::FunctionProto* func_proto,
      const Node* n,
      std::unordered_set<std::string>& custom_domains);

  void EncodeLocalFunction(
      onnx::GraphProto* graph_proto,
      onnx::FunctionProto* func_proto,
      const Node* n,
      bool add_node_names = true,
      bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  void EncodeTensor(
      onnx::TensorProto* tensor_proto,
      const at::Tensor& tensor,
      const c10::optional<std::string> external_ref = {},
      const bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  void EncodeIntermediateValueInfo(
      onnx::GraphProto* graph_proto,
      const Value* n);

  void EncodeValueInfo(
      onnx::GraphProto* graph_proto,
      onnx::ValueInfoProto* v,
      const Value* n,
      const std::
          unordered_map<std::string, std::unordered_map<int64_t, std::string>>&
              dynamic_axes = std::unordered_map<
                  std::string,
                  std::unordered_map<int64_t, std::string>>());

  void EncodeValueInfoType(
      onnx::TypeProto* onnx_type,
      const TypePtr node_type,
      const Value* n,
      const std::unordered_map<
          std::string,
          std::unordered_map<int64_t, std::string>>& dynamic_axes);

  void AddAttribute(
      onnx::NodeProto* node_proto,
      const jit::Symbol name,
      const std::string& ref_attr_name,
      const AttributeKind attr_kind);

  void AddAttribute(
      onnx::NodeProto* node_proto,
      const jit::Node* node,
      const jit::Symbol name,
      const bool use_external_data_format = false,
      const std::string& onnx_file_path = std::string());

  void AddAttribute(onnx::FunctionProto* func_proto, const std::string& name);

  void TensorTypeToONNXType(
      const TensorTypePtr& tensor_type,
      const std::string& dim_name_prefix,
      const std::string& name,
      const std::unordered_map<
          std::string,
          std::unordered_map<int64_t, std::string>>& dynamic_axes,
      onnx::TypeProto_Tensor* onnx_tensor_type,
      bool assign_dim_param = true);

  SymbolDimMap symbol_dim_map_;
  std::shared_ptr<onnx::ModelProto> model_proto_;
  size_t num_blocks_;
  size_t num_op_nodes_;
  size_t num_external_data_;
  onnx_torch::OperatorExportTypes operator_export_type_;
  bool strip_doc_;
  std::set<std::string> domains_;
  RawDataExportMap raw_data_export_map_;
  bool defer_weight_export_;
  bool use_external_data_format_;
  int64_t onnx_opset_version_;
  std::map<std::string, int> custom_opsets_;
  std::shared_ptr<Graph> graph_;
  NodeAttrNameMap node_attr_to_name_;
  NodeNameMap onnx_node_name_map_;
  // For large models, the parameters can be stored in separate binary files.
  // This parameter sets a threshold on the number of elements in the parameter
  // tensor, beyond which the parameter is stored in a separate file (if
  // use_external_data_format_ is True). This threshold is in place
  // so as not to create too many external files.
  const size_t ParamSizeThresholdForExternalStorage = 1024;
};

onnx::TensorProto_DataType ATenTypeToOnnxType(at::ScalarType at_type) {
  switch (at_type) {
    case at::kDouble:
      return onnx::TensorProto_DataType_DOUBLE;
    case at::kFloat:
      return onnx::TensorProto_DataType_FLOAT;
    case at::kHalf:
      return onnx::TensorProto_DataType_FLOAT16;
    case at::kByte:
      return onnx::TensorProto_DataType_UINT8;
    case at::kChar:
      return onnx::TensorProto_DataType_INT8;
    case at::kShort:
      return onnx::TensorProto_DataType_INT16;
    case at::kInt:
      return onnx::TensorProto_DataType_INT32;
    case at::kLong:
      return onnx::TensorProto_DataType_INT64;
    case at::kBool:
      return onnx::TensorProto_DataType_BOOL;
    case at::kQInt8:
      return onnx::TensorProto_DataType_INT8;
    case at::kQUInt8:
      return onnx::TensorProto_DataType_UINT8;
    case at::kQInt32:
      return onnx::TensorProto_DataType_INT32;
    case at::kBFloat16:
      return onnx::TensorProto_DataType_BFLOAT16;
    case at::kFloat8_e4m3fn:
      return onnx_torch::TensorProto_DataType_FLOAT8E4M3FN;
    case at::kFloat8_e5m2:
      return onnx_torch::TensorProto_DataType_FLOAT8E5M2;
    case at::kFloat8_e4m3fnuz:
      return onnx_torch::TensorProto_DataType_FLOAT8E4M3FNUZ;
    case at::kFloat8_e5m2fnuz:
      return onnx_torch::TensorProto_DataType_FLOAT8E5M2FNUZ;
    default:
      TORCH_CHECK(
          false,
          "ScalarType ",
          toString(at_type),
          " is an unexpected tensor scalar type");
  }
}

onnx::AttributeProto_AttributeType ATenAttributeKindToOnnxAttributeType(
    AttributeKind at_kind,
    const jit::Symbol name) {
  switch (at_kind) {
    case AttributeKind::f:
      return onnx::AttributeProto_AttributeType_FLOAT;
    case AttributeKind::fs:
      return onnx::AttributeProto_AttributeType_FLOATS;
    case AttributeKind::i:
      return onnx::AttributeProto_AttributeType_INT;
    case AttributeKind::is:
      return onnx::AttributeProto_AttributeType_INTS;
    case AttributeKind::s:
      return onnx::AttributeProto_AttributeType_STRING;
    case AttributeKind::ss:
      return onnx::AttributeProto_AttributeType_STRINGS;
    case AttributeKind::t:
      return onnx::AttributeProto_AttributeType_TENSOR;
    case AttributeKind::ts:
      return onnx::AttributeProto_AttributeType_TENSORS;
    case AttributeKind::ty:
      return onnx::AttributeProto_AttributeType_TYPE_PROTO;
    case AttributeKind::tys:
      return onnx::AttributeProto_AttributeType_TYPE_PROTOS;
    case AttributeKind::g:
      return onnx::AttributeProto_AttributeType_GRAPH;
    case AttributeKind::gs:
      return onnx::AttributeProto_AttributeType_GRAPHS;
    default:
      std::ostringstream err_msg;
      err_msg << "attribute \"" << name.toDisplayString()
              << "\" has unexpected kind: " << toString(at_kind);
      throw std::runtime_error(err_msg.str());
  }
}

GraphEncoder::GraphEncoder(
    const std::shared_ptr<Graph>& graph,
    int64_t onnx_opset_version,
    onnx_torch::OperatorExportTypes operator_export_type,
    const std::map<std::string, at::Tensor>& initializers,
    const std::unordered_map<
        std::string,
        std::unordered_map<int64_t, std::string>>& dynamic_axes,
    bool defer_weight_export,
    bool strip_doc,
    bool keep_initializers_as_inputs,
    const std::map<std::string, int>& custom_opsets,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path,
    const NodeAttrNameMap& node_attr_to_name)
    : model_proto_(std::make_shared<onnx::ModelProto>()),
      num_blocks_(0),
      num_op_nodes_(0),
      num_external_data_(0),
      operator_export_type_(operator_export_type),
      strip_doc_(strip_doc),
      defer_weight_export_(defer_weight_export),
      use_external_data_format_(use_external_data_format),
      onnx_opset_version_(onnx_opset_version),
      custom_opsets_(custom_opsets),
      graph_(graph),
      node_attr_to_name_(node_attr_to_name) {
  model_proto_->set_producer_name("pytorch");
  TORCH_CHECK(
      onnx_opset_version > 0 &&
          static_cast<size_t>(onnx_opset_version) <
              kOpsetVersionToIRVersion.size() &&
          kOpsetVersionToIRVersion[onnx_opset_version] != kInvalidOpsetVersion,
      "Unsupported onnx_opset_version: ",
      onnx_opset_version);

  model_proto_->set_ir_version(kOpsetVersionToIRVersion[onnx_opset_version]);
  model_proto_->set_producer_version(TORCH_VERSION);
  validateGraph(graph, operator_export_type);

  // If graph proto size exceed maximum protobuf size of 2GB, set
  // use_external_data_format to true.
  if (!use_external_data_format &&
      GetGraphProtoSize(
          model_proto_->mutable_graph(),
          graph,
          add_node_names,
          use_external_data_format,
          onnx_file_path,
          initializers) > INT_MAX) {
    GRAPH_DEBUG(
        "Exporting model exceed maximum protobuf size of 2GB. Storing model parameters in external data files");
    use_external_data_format = true;
    // use_external_data_format_ is one of graph_encoder private variable set
    // for return `use_external_data_format` value.
    use_external_data_format_ = use_external_data_format;
  }

  if (use_external_data_format) {
    TORCH_CHECK(
        !onnx_file_path.empty(),
        "The serialized model is larger than the 2GiB limit imposed by the protobuf library. ",
        "Therefore the output file must be a file path, so that the ONNX external data can ",
        "be written to the same directory. Please specify the output file name.");
  }

  auto* imp = model_proto_->add_opset_import();
  // This is the version of ONNX operator set we are targeting
  imp->set_version(onnx_opset_version);

  EncodeGraph(
      model_proto_->mutable_graph(),
      graph,
      initializers,
      dynamic_axes,
      keep_initializers_as_inputs,
      add_node_names,
      use_external_data_format,
      onnx_file_path);

  for (const std::string& domain : domains_) {
    auto* opset = model_proto_->add_opset_import();
    opset->set_domain(domain);
    //  Check if domain version is registered. If not, set to version 1
    auto it = custom_opsets.find(domain);
    if (it == custom_opsets.end())
      opset->set_version(1);
    else {
      opset->set_version(it->second);
    }
  }

  for (auto const& custom_opset : custom_opsets) {
    if (!std::count(domains_.begin(), domains_.end(), custom_opset.first)) {
      TORCH_WARN(
          "Custom opset domain: '",
          custom_opset.first,
          "' provided is not used in the model. ",
          "Please verify custom opset domain names.");
    }
  }
}

void GraphEncoder::TensorTypeToONNXType(
    const TensorTypePtr& tensor_type,
    const std::string& dim_name_prefix,
    const std::string& name,
    const std::unordered_map<
        std::string,
        std::unordered_map<int64_t, std::string>>& dynamic_axes,
    onnx::TypeProto_Tensor* onnx_tensor_type,
    bool assign_dim_param) {
  if (tensor_type->dim()) {
    onnx::TensorShapeProto* shape = onnx_tensor_type->mutable_shape();
    auto sizes = tensor_type->symbolic_sizes().sizes().value();
    for (const auto i : c10::irange(sizes.size())) {
      shape->add_dim();
      if ((dynamic_axes.find(name) != dynamic_axes.end()) &&
          (dynamic_axes.at(name).find(i) != dynamic_axes.at(name).end())) {
        shape->mutable_dim(i)->set_dim_param(dynamic_axes.at(name).at(i));
        if (!sizes[i].is_static()) {
          symbol_dim_map_[sizes[i]] = dynamic_axes.at(name).at(i);
        }
      } else if (sizes[i].is_static()) {
        shape->mutable_dim(i)->set_dim_value(sizes[i].static_size());
      } else if (assign_dim_param) {
        if (symbol_dim_map_.find(sizes[i]) == symbol_dim_map_.end()) {
          symbol_dim_map_[sizes[i]] =
              dim_name_prefix + name + "_dim_" + std::to_string(i);
        }
        shape->mutable_dim(i)->set_dim_param(symbol_dim_map_[sizes[i]]);
      }
    }
  }
  if (tensor_type->scalarType()) {
    onnx_tensor_type->set_elem_type(
        ATenTypeToOnnxType(tensor_type->scalarType().value()));
  }
}

void GraphEncoder::EncodeValueInfoType(
    onnx::TypeProto* onnx_type,
    const TypePtr node_type,
    const Value* n,
    const std::unordered_map<
        std::string,
        std::unordered_map<int64_t, std::string>>& dynamic_axes) {
  std::string dim_name_prefix;
  if (n->node()->kind() != prim::Param) {
    dim_name_prefix = n->node()->kind().toUnqualString();
  }
  if (TensorTypePtr tensor_type = node_type->cast<TensorType>()) {
    if (tensor_type->dim() || tensor_type->scalarType()) {
      // Encode type if either shape or dtype exists.
      onnx::TypeProto_Tensor* onnx_tensor_type =
          onnx_type->mutable_tensor_type();
      // Do not assign dim_param for sequence tensor type.
      // Sequence of tensors could differ in dimension size.
      // Use a dimension with neither dim_value nor dim_param set
      // to denote an unknown dimension.
      // Create and assign dim_param for normal tensor type.
      auto is_sequence_tensor = static_cast<bool>(n->type()->cast<ListType>());
      TensorTypeToONNXType(
          tensor_type,
          dim_name_prefix,
          n->debugName(),
          dynamic_axes,
          onnx_tensor_type,
          !is_sequence_tensor);
    }
  } else if (BoolTypePtr bool_type = node_type->cast<BoolType>()) {
    onnx::TypeProto_Tensor* onnx_tensor_type = onnx_type->mutable_tensor_type();
    onnx_tensor_type->set_elem_type(ATenTypeToOnnxType(at::kBool));
  } else if (IntTypePtr int_type = node_type->cast<IntType>()) {
    onnx::TypeProto_Tensor* onnx_tensor_type = onnx_type->mutable_tensor_type();
    onnx_tensor_type->set_elem_type(ATenTypeToOnnxType(at::kLong));
  } else if (FloatTypePtr float_type = node_type->cast<FloatType>()) {
    onnx::TypeProto_Tensor* onnx_tensor_type = onnx_type->mutable_tensor_type();
    onnx_tensor_type->set_elem_type(ATenTypeToOnnxType(at::kFloat));
  } else if (ListTypePtr list_type = node_type->cast<ListType>()) {
    auto list_elem_type = list_type->getElementType();
    onnx::TypeProto_Sequence* sequence_type =
        onnx_type->mutable_sequence_type();
    onnx::TypeProto* onnx_tensor_type = sequence_type->mutable_elem_type();
    EncodeValueInfoType(onnx_tensor_type, list_elem_type, n, dynamic_axes);
  } else if (OptionalTypePtr optional_type = node_type->cast<OptionalType>()) {
    auto elem_type = optional_type->getElementType();
    if (TensorTypePtr tensor_type = elem_type->cast<TensorType>()) {
      onnx::TypeProto_Optional* onnx_optional_type =
          onnx_type->mutable_optional_type();
      onnx::TypeProto_Tensor* onnx_tensor_type =
          onnx_optional_type->mutable_elem_type()->mutable_tensor_type();
      TensorTypeToONNXType(
          tensor_type,
          dim_name_prefix,
          n->debugName(),
          dynamic_axes,
          onnx_tensor_type);
    } else if (ListTypePtr inner_node_type = elem_type->cast<ListType>()) {
      auto list_elem_type = inner_node_type->getElementType();
      if (TensorTypePtr tensor_type = list_elem_type->cast<TensorType>()) {
        onnx::TypeProto_Optional* onnx_optional_type =
            onnx_type->mutable_optional_type();
        onnx::TypeProto_Sequence* onnx_optional_sequence_type =
            onnx_optional_type->mutable_elem_type()->mutable_sequence_type();
        onnx::TypeProto_Tensor* onnx_tensor_type =
            onnx_optional_sequence_type->mutable_elem_type()
                ->mutable_tensor_type();
        TensorTypeToONNXType(
            tensor_type,
            dim_name_prefix,
            n->debugName(),
            dynamic_axes,
            onnx_tensor_type);
      }
    }
  }
}

void GraphEncoder::EncodeValueInfo(
    onnx::GraphProto* graph_proto,
    onnx::ValueInfoProto* v,
    const Value* n,
    const std::unordered_map<
        std::string,
        std::unordered_map<int64_t, std::string>>& dynamic_axes) {
  std::string name = n->debugName();
  v->set_name(name);
  EncodeValueInfoType(v->mutable_type(), n->type(), n, dynamic_axes);
}

void GraphEncoder::EncodeGraph(
    onnx::GraphProto* graph_proto,
    const std::shared_ptr<Graph>& graph,
    const std::map<std::string, at::Tensor>& initializers,
    const std::unordered_map<
        std::string,
        std::unordered_map<int64_t, std::string>>& dynamic_axes,
    bool keep_initializers_as_inputs,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path) {
  EncodeBlock(
      graph_proto,
      graph->block(),
      initializers,
      dynamic_axes,
      keep_initializers_as_inputs,
      add_node_names,
      use_external_data_format,
      onnx_file_path);
}

void GraphEncoder::EncodeBlock(
    onnx::GraphProto* graph_proto,
    const Block* block,
    const std::map<std::string, at::Tensor>& initializers,
    const std::unordered_map<
        std::string,
        std::unordered_map<int64_t, std::string>>& dynamic_axes,
    bool keep_initializers_as_inputs,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path) {
  TORCH_INTERNAL_ASSERT(graph_proto != nullptr);
  if (nullptr == block->owningNode()) {
    // Top level main graph.
    graph_proto->set_name("main_graph");
  } else {
    // TODO: Set more meaningful name for sub-graphs.
    std::string block_name = "sub_graph";
    if (num_blocks_) {
      block_name += std::to_string(num_blocks_);
    }
    num_blocks_++;
    graph_proto->set_name(block_name);
  }

  // Since ONNX IR VERSION 4, initializers do not have to
  // be a subset of graph inputs. We use keep_initializers_as_inputs
  // argument to determine whether to add initializers
  // as inputs or not. If keep_initializers_as_inputs=false,
  // we only add non-parameter inputs as inputs to ONNX graph, and
  // not the initializers (parameters). If keep_initializers_as_inputs
  // =true, we add initializers as inputs too. Setting
  // keep_initializers_as_inputs=false allows better
  // optimizations, such as constant-folding, on ONNX graphs
  // by backends/optimizers.
  if (keep_initializers_as_inputs) {
    for (auto input : block->inputs()) {
      onnx::ValueInfoProto* v = graph_proto->add_input();
      EncodeValueInfo(graph_proto, v, input, dynamic_axes);
    }
  } else {
    for (auto input : block->inputs()) {
      auto it = initializers.find(input->debugName());
      if (it == initializers.end()) {
        onnx::ValueInfoProto* v = graph_proto->add_input();
        EncodeValueInfo(graph_proto, v, input, dynamic_axes);
      }
    }
  }
  for (auto output : block->outputs()) {
    onnx::ValueInfoProto* v = graph_proto->add_output();
    EncodeValueInfo(graph_proto, v, output, dynamic_axes);
  }
  for (auto node : block->nodes()) {
    if (node->mustBeNone()) {
      // None nodes are used to implement optional inputs. One
      // way to "not provide" an optional input is to create an
      // Undefined node, and pass its output as that input.
      continue;
    }
    if (node->kind() == ::c10::Symbol::onnx("LocalFunctionDef")) {
      auto* func_proto = model_proto_->add_functions();
      EncodeLocalFunction(
          graph_proto,
          func_proto,
          node,
          add_node_names,
          use_external_data_format,
          onnx_file_path);
      continue;
    }
    auto* n_proto = graph_proto->add_node();
    EncodeNode(
        graph_proto,
        n_proto,
        node,
        add_node_names,
        use_external_data_format,
        onnx_file_path);
  }
  AddInitializersIntoGraphProto(
      graph_proto,
      block,
      initializers,
      use_external_data_format,
      onnx_file_path);
}

void GraphEncoder::AddInitializersIntoGraphProto(
    onnx::GraphProto* graph_proto,
    const Block* block,
    const std::map<std::string, at::Tensor>& initializers,
    bool use_external_data_format,
    const std::string& onnx_file_path) {
  TORCH_INTERNAL_ASSERT(block->inputs().size() >= initializers.size());
  for (auto input : block->inputs()) {
    auto name_tensor_pair = initializers.find(input->debugName());
    if (name_tensor_pair == initializers.end()) {
      continue;
    }
    auto p = graph_proto->add_initializer();
    p->set_name(name_tensor_pair->first);
    EncodeTensor(
        p,
        name_tensor_pair->second,
        name_tensor_pair->first,
        use_external_data_format,
        onnx_file_path);
  }
}

unsigned long long int GraphEncoder::GetGraphProtoSize(
    onnx::GraphProto* graph_proto,
    const std::shared_ptr<Graph>& graph,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path,
    const std::map<std::string, at::Tensor>& initializers) {
  // Model size = sum(size(initializers)) + sum(size(onnx_constant_nodes))

  // Add up all Initializers
  onnx::GraphProto graph_proto_copy = onnx::GraphProto(*graph_proto);
  unsigned long long int size = graph_proto_copy.ByteSizeLong();
  for (auto input : graph->inputs()) {
    auto name_tensor_pair = initializers.find(input->debugName());
    if (name_tensor_pair == initializers.end()) {
      continue;
    }
    auto tensor_proto = graph_proto_copy.add_initializer();
    const at::Tensor& tensor = name_tensor_pair->second;
    for (auto d : tensor.sizes()) {
      tensor_proto->add_dims(d);
    }
    tensor_proto->set_data_type(ATenTypeToOnnxType(tensor.scalar_type()));

    // Don't actually copy the buffer into tensor_proto since that is expensive.
    // All we actually need is its size.
    size += tensor_proto->ByteSizeLong();
    size += tensor.element_size() * tensor.numel();
  }

  // Add up all onnx::Constant nodes that are Tensors
  for (const auto& node : graph->nodes()) {
    if (node->kind() == ::c10::onnx::Constant &&
        node->hasAttribute(attr::value) &&
        node->kindOf(attr::value) == AttributeKind::t) {
      at::Tensor tensor = node->t(attr::value);

      // Don't actually copy the buffer into n_proto since that is expensive.
      // All we actually need is its size.
      auto* n_proto = graph_proto_copy.add_node();
      EncodeNode(
          &graph_proto_copy,
          n_proto,
          node,
          add_node_names,
          use_external_data_format,
          onnx_file_path);

      // Calculate the size of the tensor in bytes
      size += n_proto->ByteSizeLong();
      size += tensor.element_size() * tensor.numel();
    }
  }
  return size;
}

void GraphEncoder::EncodeNode(
    onnx::GraphProto* graph_proto,
    onnx::NodeProto* node_proto,
    const Node* node,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path) {
  if (!strip_doc_) {
    node_proto->set_doc_string(node->sourceRange().str());
  }
  for (auto input : node->inputs()) {
    if (input->node()->mustBeNone()) {
      node_proto->add_input("");
    } else {
      node_proto->add_input(input->debugName());
    }
  }
  for (auto output : node->outputs()) {
    node_proto->add_output(output->debugName());
    EncodeIntermediateValueInfo(graph_proto, output);
  }
  if (!node->kind().is_onnx()) {
    std::string domain;
    if (node->kind().is_aten() || node->kind().is_caffe2()) {
      domain = node->kind().domainString();
    } else { //  Custom namespace and domain
      domain = node->kind().ns().toUnqualString();
    }
    // TODO: set correct domain for function proto.
    domains_.insert(domain);
    node_proto->set_domain(domain);
  }
  if (operator_export_type_ == onnx_torch::OperatorExportTypes::ONNX) {
    TORCH_INTERNAL_ASSERT(
        !node->kind().is_aten() && !node->kind().is_prim() &&
        !node->kind().is_attr());
  }
  node_proto->set_op_type(node->kind().toUnqualString());
  const auto node_name_attribute_symbol =
      Symbol::attr(::torch::onnx::kOnnxNodeNameAttribute);
  if (add_node_names) {
    std::string node_name =
        node_proto->op_type() + "_" + std::to_string(num_op_nodes_);
    if (node->hasAttribute(node_name_attribute_symbol)) {
      node_name = node->s(node_name_attribute_symbol);
    }
    node_proto->set_name(node_name);
    onnx_node_name_map_[node] = node_name;
    num_op_nodes_++;
  }
  auto attrs_it = node_attr_to_name_.find(node);
  for (auto attr_name : node->attributeNames()) {
    if (attr_name == node_name_attribute_symbol) {
      // Skip the node name attribute.
      continue;
    }
    if (attrs_it != node_attr_to_name_.end()) {
      auto attr_it = attrs_it->second.find(attr_name.toUnqualString());
      if (attr_it != attrs_it->second.end()) {
        AddAttribute(
            node_proto, attr_name, attr_it->second, node->kindOf(attr_name));
        continue;
      }
    }
    AddAttribute(
        node_proto, node, attr_name, use_external_data_format, onnx_file_path);
  }
  if (node->kind() == ::c10::onnx::Loop) {
    TORCH_INTERNAL_ASSERT(node->blocks().size() == 1);

    auto body = node_proto->add_attribute();
    body->set_name("body");
    body->set_type(onnx::AttributeProto_AttributeType_GRAPH);
    auto g = body->mutable_g();
    EncodeBlock(
        g,
        node->blocks()[0],
        {},
        {},
        true,
        true,
        use_external_data_format,
        onnx_file_path);
  }
  if (node->kind() == ::c10::onnx::If) {
    TORCH_INTERNAL_ASSERT(node->blocks().size() == 2);

    auto then_branch = node_proto->add_attribute();
    then_branch->set_name("then_branch");
    then_branch->set_type(onnx::AttributeProto_AttributeType_GRAPH);
    auto true_g = then_branch->mutable_g();
    EncodeBlock(
        true_g,
        node->blocks()[0],
        {},
        {},
        true,
        true,
        use_external_data_format,
        onnx_file_path);

    auto else_branch = node_proto->add_attribute();
    else_branch->set_name("else_branch");
    else_branch->set_type(onnx::AttributeProto_AttributeType_GRAPH);
    auto false_g = else_branch->mutable_g();
    EncodeBlock(
        false_g,
        node->blocks()[1],
        {},
        {},
        true,
        true,
        use_external_data_format,
        onnx_file_path);
  }
}

void GraphEncoder::AddAttribute(
    onnx::NodeProto* node_proto,
    const jit::Symbol name,
    const std::string& ref_attr_name,
    const AttributeKind attr_kind) {
  auto attr = node_proto->add_attribute();
  TORCH_INTERNAL_ASSERT(name.is_attr());
  attr->set_name(name.toUnqualString());
  attr->set_ref_attr_name(ref_attr_name);
  attr->set_type(ATenAttributeKindToOnnxAttributeType(attr_kind, name));
}

void GraphEncoder::AddAttribute(
    onnx::NodeProto* node_proto,
    const jit::Node* node,
    const jit::Symbol name,
    const bool use_external_data_format,
    const std::string& onnx_file_path) {
  auto createAttributeTensorName =
      [](const onnx::NodeProto* node_proto,
         onnx::TensorProto* tensor_proto,
         const jit::Symbol attr_name,
         size_t& num_external_data) -> std::string {
    if (tensor_proto->has_name()) {
      return tensor_proto->name();
    }
    if (!node_proto->has_name()) {
      auto name = node_proto->op_type() + "_" + attr_name.toDisplayString() +
          "_" + std::to_string(num_external_data);
      num_external_data++;
      return name;
    } else {
      return node_proto->name() + "_" + attr_name.toDisplayString();
    }
  };

  auto attr = node_proto->add_attribute();
  TORCH_INTERNAL_ASSERT(name.is_attr());
  attr->set_name(name.toUnqualString());
  attr->set_type(
      ATenAttributeKindToOnnxAttributeType(node->kindOf(name), name));
  switch (node->kindOf(name)) {
    case AttributeKind::f:
      attr->set_f(node->f(name));
      break;
    case AttributeKind::fs:
      for (auto& v : node->fs(name))
        // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
        attr->add_floats(v);
      break;
    case AttributeKind::i:
      attr->set_i(node->i(name));
      break;
    case AttributeKind::is:
      for (auto& v : node->is(name))
        attr->add_ints(v);
      break;
    case AttributeKind::s:
      attr->set_s(node->s(name));
      break;
    case AttributeKind::ss:
      for (auto& v : node->ss(name))
        attr->add_strings(v);
      break;
    case AttributeKind::t: {
      auto t = attr->mutable_t();
      if (use_external_data_format && !t->has_name()) {
        t->set_name(
            createAttributeTensorName(node_proto, t, name, num_external_data_));
      }
      EncodeTensor(
          t, node->t(name), {}, use_external_data_format, onnx_file_path);
    } break;
    case AttributeKind::ts:
      for (auto& v : node->ts(name)) {
        auto t = attr->add_tensors();
        if (use_external_data_format && !t->has_name()) {
          t->set_name(createAttributeTensorName(
              node_proto, t, name, num_external_data_));
        }
        EncodeTensor(t, v, {}, use_external_data_format, onnx_file_path);
      }
      break;
    case AttributeKind::ty: {
      attr->set_type(onnx::AttributeProto_AttributeType_TYPE_PROTO);
      auto tp = attr->mutable_tp();
      const TypePtr& node_type = node->ty(name);
      EncodeTypeProto(
          tp, node_type, node_proto->op_type() + "_" + name.toDisplayString());
    } break;
    case AttributeKind::tys: {
      attr->set_type(onnx::AttributeProto_AttributeType_TYPE_PROTOS);
      size_t index = 0;
      for (auto& v : node->tys(name)) {
        auto tp = attr->add_type_protos();
        EncodeTypeProto(
            tp,
            v,
            node_proto->op_type() + "_" + name.toDisplayString() + "_" +
                std::to_string(index));
        index++;
      }
    } break;
    case AttributeKind::g: {
      auto g = attr->mutable_g();
      EncodeGraph(
          g,
          node->g(name),
          {},
          {},
          true,
          true,
          use_external_data_format,
          onnx_file_path);
    } break;
    case AttributeKind::gs:
      for (auto& v : node->gs(name)) {
        auto g = attr->add_graphs();
        EncodeGraph(
            g, v, {}, {}, true, true, use_external_data_format, onnx_file_path);
      }
      break;
    default:
      std::ostringstream err_msg;
      err_msg << "attribute \"" << name.toDisplayString()
              << "\" has unexpected kind: " << toString(node->kindOf(name));
      throw std::runtime_error(err_msg.str());
  }
}

void GraphEncoder::AddAttribute(
    onnx::FunctionProto* func_proto,
    const std::string& name) {
  TORCH_INTERNAL_ASSERT(nullptr != func_proto);
  func_proto->add_attribute(name);
}

void GraphEncoder::EncodeLocalFunctionOpsetImport(
    onnx::FunctionProto* func_proto,
    const Node* n,
    std::unordered_set<std::string>& custom_domains) {
  if (!n->kind().is_onnx()) {
    std::string domain;
    if (n->kind().is_aten() || n->kind().is_caffe2()) {
      domain = n->kind().domainString();
    } else { //  Custom namespace and domain
      domain = n->kind().ns().toUnqualString();
    }
    domains_.insert(domain);

    if (custom_domains.find(domain) == custom_domains.end()) {
      custom_domains.insert(domain);

      auto* custom_imp = func_proto->add_opset_import();
      custom_imp->set_domain(domain);
      //  Check if domain version is registered. If not, set to version 1
      auto it = custom_opsets_.find(domain);
      if (it == custom_opsets_.end())
        custom_imp->set_version(1);
      else {
        custom_imp->set_version(it->second);
      }
    }
  }

  for (auto* b : n->blocks()) {
    for (auto* sub_n : b->nodes()) {
      EncodeLocalFunctionOpsetImport(func_proto, sub_n, custom_domains);
    }
  }
}

void GraphEncoder::EncodeLocalFunction(
    onnx::GraphProto* graph_proto,
    onnx::FunctionProto* func_proto,
    const Node* n,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path) {
  const auto fsub_g = n->g(Symbol::attr("graph"));
  func_proto->set_name(n->s(::c10::attr::name));

  for (auto input : fsub_g->inputs()) {
    func_proto->add_input(input->debugName());
  }
  for (auto output : fsub_g->outputs()) {
    func_proto->add_output(output->debugName());
  }

  // encode attributes names
  if (n->hasAttribute(Symbol::attr("attributes"))) {
    for (auto attr_name : n->ss(Symbol::attr("attributes"))) {
      AddAttribute(func_proto, attr_name);
    }
  }

  auto* imp = func_proto->add_opset_import();
  // This is the version of ONNX operator set we are targeting
  imp->set_version(onnx_opset_version_);

  // add for custom domain as well.
  const auto& domain = n->s(Symbol::attr("domain"));
  func_proto->set_domain(domain);
  domains_.insert(domain);
  std::unordered_set<std::string> custom_domains;

  for (auto* fsub_n : fsub_g->nodes()) {
    if (fsub_n->mustBeNone()) {
      // None nodes are used to implement optional inputs. One
      // way to "not provide" an optional input is to create an
      // Undefined node, and pass its output as that input.
      continue;
    }
    auto* n_proto = func_proto->add_node();
    EncodeNode(
        graph_proto,
        n_proto,
        fsub_n,
        add_node_names,
        use_external_data_format,
        onnx_file_path);
    EncodeLocalFunctionOpsetImport(func_proto, fsub_n, custom_domains);
  }
}

void GraphEncoder::EncodeTypeProto(
    onnx::TypeProto* type_proto,
    const TypePtr& node_type,
    const std::string& name) {
  if (TensorTypePtr tensor_type = node_type->cast<TensorType>()) {
    onnx::TypeProto_Tensor* onnx_tensor_type =
        type_proto->mutable_tensor_type();
    TensorTypeToONNXType(tensor_type, "", name, {}, onnx_tensor_type);
  } else if (ListTypePtr list_type = node_type->cast<ListType>()) {
    onnx::TypeProto_Sequence* seq_type = type_proto->mutable_sequence_type();
    auto elem_type = list_type->getElementType();
    EncodeTypeProto(seq_type->mutable_elem_type(), elem_type, name);
  }
}

void GraphEncoder::EncodeTensor(
    onnx::TensorProto* tensor_proto,
    const at::Tensor& tensor,
    const c10::optional<std::string> external_ref,
    const bool use_external_data_format,
    const std::string& onnx_file_path) {
  for (auto d : tensor.sizes()) {
    tensor_proto->add_dims(d);
  }
  tensor_proto->set_data_type(ATenTypeToOnnxType(tensor.scalar_type()));
  at::Tensor t;
  // CPU's HalfTensor doesn't have contiguous(), so first calling contiguous()
  // TODO We don't call .cpu() on quantized tensors as it fails when calling
  // aten::empty() on quantized tensors beyond certain size. Issue #29435.
  if (tensor.is_quantized()) {
    t = tensor.contiguous();
  } else {
    t = tensor.contiguous().cpu();
  }

  // Either defer_weight_export should be true and external_ref must be present,
  // or use_external_data_format should be true, not both at the same time. They
  // can both be false at the same time (for ONNX export for regular model
  // size).
  TORCH_INTERNAL_ASSERT(
      !((defer_weight_export_ && external_ref) && use_external_data_format));
  // Add a buffer to the raw_data_export_map for the caller to dump into an
  // external data store. If external_ref is not specified, we instead dump
  // the contiguous data into the protobuf itself
  if (defer_weight_export_ && external_ref) {
    // For now, we use the name of the tensor as the external lookup name to
    // avoid ONNX protobuf changes.
    TORCH_INTERNAL_ASSERT(external_ref.value() == tensor_proto->name());
    TORCH_INTERNAL_ASSERT(
        raw_data_export_map_.count(external_ref.value()) == 0);
    raw_data_export_map_[external_ref.value()] = t;
    tensor_proto->set_raw_data("__EXTERNAL");
  } else {
    TORCH_INTERNAL_ASSERT(t.is_contiguous());
    size_t tensorSize = static_cast<size_t>(c10::multiply_integers(
        std::begin(tensor.sizes()), std::end(tensor.sizes())));
    if (use_external_data_format &&
        tensorSize > ParamSizeThresholdForExternalStorage) {
      TORCH_INTERNAL_ASSERT(!onnx_file_path.empty());
      TORCH_INTERNAL_ASSERT(tensor_proto->has_name());
      auto tensorName = GetExternalFileName(tensor_proto->name());
      CreateExternalFile(t, tensorName, onnx_file_path);
      onnx::StringStringEntryProto* location =
          tensor_proto->mutable_external_data()->Add();
      location->set_key("location");
      location->set_value(tensorName);
      tensor_proto->set_data_location(onnx::TensorProto_DataLocation_EXTERNAL);
    } else {
      // According to ParseData function's comments in onnx, tensor data is
      // always little endian.
      tensor_proto->set_raw_data(get_little_endian_data(t));
    }
  }
}

void GraphEncoder::EncodeIntermediateValueInfo(
    onnx::GraphProto* graph_proto,
    const Value* v) {
  // Motivation is to encode ValueInfo for onnx local function nodes.
  auto n = v->node();
  if (n->kind().is_onnx() || n->kind().is_aten()) {
    // Encode value info only for non-onnx or non-ATen nodes.
    return;
  }
  if (n->owningGraph() != graph_.get()) {
    // Encode value info only for node in main graph.
    return;
  }
  for (const auto* o : graph_->outputs()) {
    // Do not encode value info for graph outputs.
    if (o == v) {
      return;
    }
  }
  auto v_info_p = graph_proto->add_value_info();
  EncodeValueInfo(graph_proto, v_info_p, v);
}

} // namespace

std::string pretty_print_onnx(
    const std::shared_ptr<Graph>& graph,
    const std::map<std::string, at::Tensor>& initializers,
    int64_t onnx_opset_version,
    bool defer_weight_export,
    ::torch::onnx::OperatorExportTypes operator_export_type,
    bool google_printer,
    bool keep_initializers_as_inputs,
    const std::map<std::string, int>& custom_opsets,
    bool add_node_names) {
  auto graph_encoder = GraphEncoder(
      graph,
      onnx_opset_version,
      operator_export_type,
      initializers,
      std::unordered_map<
          std::string,
          std::unordered_map<int64_t, std::string>>{},
      defer_weight_export,
      true,
      keep_initializers_as_inputs,
      custom_opsets,
      add_node_names,
      false,
      std::string());
  if (google_printer) {
    return graph_encoder.get_model_proto()->DebugString();
  }
  return prettyPrint(*graph_encoder.get_model_proto());
}

std::tuple<
    std::shared_ptr<::ONNX_NAMESPACE::ModelProto>,
    RawDataExportMap,
    SymbolDimMap,
    bool,
    NodeNameMap>
export_onnx(
    const std::shared_ptr<Graph>& graph,
    const std::map<std::string, at::Tensor>& initializers,
    int64_t onnx_opset_version,
    const std::unordered_map<
        std::string,
        std::unordered_map<std::int64_t, std::string>>& dynamic_axes,
    bool defer_weight_export,
    ::torch::onnx::OperatorExportTypes operator_export_type,
    bool strip_doc_string,
    bool keep_initializers_as_inputs,
    const std::map<std::string, int>& custom_opsets,
    bool add_node_names,
    bool use_external_data_format,
    const std::string& onnx_file_path,
    const NodeAttrNameMap& node_attr_to_name) {
  auto graph_encoder = GraphEncoder(
      graph,
      onnx_opset_version,
      operator_export_type,
      initializers,
      dynamic_axes,
      defer_weight_export,
      strip_doc_string,
      keep_initializers_as_inputs,
      custom_opsets,
      add_node_names,
      use_external_data_format,
      onnx_file_path,
      node_attr_to_name);
  GRAPH_DEBUG("onnx proto:", prettyPrint(*graph_encoder.get_model_proto()));
  return std::make_tuple(
      graph_encoder.get_model_proto(),
      graph_encoder.get_raw_data_export_map(),
      graph_encoder.get_symbol_dim_param_map(),
      graph_encoder.get_use_external_data_format(),
      graph_encoder.get_onnx_node_names());
}

std::string serialize_model_proto_to_string(
    const std::shared_ptr<::ONNX_NAMESPACE::ModelProto>& model_proto) {
  return model_proto->SerializeAsString();
}

void check_onnx_proto(const std::string& proto_string) {
  onnx::ModelProto model;
  if (!ParseProtoFromBytes(&model, proto_string.c_str(), proto_string.size())) {
    throw std::runtime_error("Invalid ONNX proto string.");
    return;
  }
  // 1. baseline check
  // These two checks prevent broken graph being generated
  // And errors out exporting if that happens.
  onnx::checker::check_model(model);
  onnx::shape_inference::InferShapes(model);
  // 2. full check
  // apply strict mode shape type inference check which examines
  // whether it's a valid ONNX graph or not. As for some users, they
  // don't need a fully valid ONNX graph to run their model, we simply
  // add this information as warning message if it fails.
  try {
    auto* schema_registry = onnx::OpSchemaRegistry::Instance();
    onnx::ShapeInferenceOptions options{
        /*check_type=*/true,
        /*error_mode=*/true};
    onnx::shape_inference::InferShapes(model, schema_registry, options);
  } catch (const onnx::InferenceError& ex) {
    TORCH_WARN(
        "The exported ONNX model failed ONNX shape inference. "
        "The model will not be executable by the ONNX Runtime. "
        "If this is unintended and you believe there is a bug, "
        "please report an issue at https://github.com/pytorch/pytorch/issues. "
        "Error reported by strict ONNX shape inference: ",
        ex.what());
  }
}

} // namespace torch::jit