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9fff8155c362da777e7ce31b85fb2dc7cfced2d5
pytorch/torch/csrc/jit/serialization/export.cpp
Yuanyuan Chen 9fff8155c3 [2/N] Fix clang-tidy readability checks (#164652) 
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
2025-10-06 01:06:01 +00:00

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#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/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/jit/serialization/pickler.h>
#include <torch/csrc/onnx/back_compat.h>
#include <torch/csrc/onnx/onnx.h>
#include <torch/version.h>
#include <onnx/checker.h>
#include <onnx/onnx_pb.h>
#include <onnx/proto_utils.h>
#include <onnx/shape_inference/implementation.h>
#include <memory>
#include <optional>
#include <regex>
#include <set>
#include <sstream>
#include <string>
#include <utility>
#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;
constexpr int kInvalidOpsetVersion = -1;
constexpr int kMainOpsetVersion = 23;
// 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
9, // opset 20
10, // opset 21
10, // opset 22
11, // opset 23
};
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 {
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 std::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,
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 std::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_{0};
size_t num_op_nodes_{0};
size_t num_external_data_{0};
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.
static constexpr size_t ParamSizeThresholdForExternalStorage = 1024;
};
static 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");
}
}
static 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,
NodeAttrNameMap node_attr_to_name)
: model_proto_(std::make_shared<onnx::ModelProto>()),
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_(std::move(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.");
}
}
}
// NOLINTBEGIN(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
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()));
}
}
// NOLINTEND(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
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(static_cast<float>(node->f(name)));
break;
case AttributeKind::fs:
for (auto& v : node->fs(name))
attr->add_floats(static_cast<float>(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 (const 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 std::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_val=*/true,
/*strict_mode_val=*/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
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