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3979cb0656
pytorch/torch/csrc/jit/serialization/export.cpp
Richard Barnes 3979cb0656 irange for size_t (#55320) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/55320

Test Plan: Sandcastle

Reviewed By: ngimel

Differential Revision: D27572577

fbshipit-source-id: 97710fd2bb1303006b05828a0d1343b0b59ccb03
2021-06-03 01:04:13 -07:00

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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/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/onnx.h>
#include <onnx/checker.h>
#include <onnx/onnx_pb.h>
#include <onnx/proto_utils.h>
#include <fstream>
#include <memory>
#include <regex>
#include <set>
#include <string>
#include <vector>
namespace torch {
namespace jit {
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;
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());
if (node->kind() == prim::PythonOp) {
auto py_node = static_cast<PythonOp*>(node);
FAIL_EXPORT(
"Couldn't export Python operator " + py_node->name() +
"\n\nDefined at:\n" + getNodeStackTraceString(node))
} else {
// Special error messages for certain types of operators
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::onnx::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");
}
}
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);
// this is run on an onnx graph which doesn't have side effects.
// ignore side effects in dead code elimination.
EliminateDeadCode(
graph->block(),
true,
DCESideEffectPolicy::ALLOW_DELETING_NODES_WITH_SIDE_EFFECTS);
}
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);
}
fwrite(tensor.data_ptr(), tensor.element_size(), tensor.numel(), fp.get());
} // fclose() called here through CloseFile(), if FILE* is not a null pointer.
class EncoderBase {
public:
EncoderBase(
onnx_torch::OperatorExportTypes operator_export_type,
bool strip_doc);
onnx::ModelProto get_model_proto() {
return model_proto_;
}
SymbolDimMap get_symbol_dim_param_map() {
return symbol_dim_map_;
}
protected:
// 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());
virtual 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()) = 0;
virtual void EncodeIntermediateValueInfo(
onnx::GraphProto* graph_proto,
const Value* n) {}
virtual 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 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());
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
SymbolDimMap symbol_dim_map_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
onnx::ModelProto model_proto_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
size_t num_blocks_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
size_t num_op_nodes_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
size_t num_external_data_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
onnx_torch::OperatorExportTypes operator_export_type_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
bool strip_doc_;
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
std::set<std::string> domains_;
// 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 API
// argument use_external_data_format is set to True). This threshold is in
// place so as not to create too many external files.
// NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
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;
default:
AT_ERROR("unexpected tensor scalar type");
}
}
EncoderBase::EncoderBase(
onnx_torch::OperatorExportTypes operator_export_type,
bool strip_doc)
: num_blocks_(0),
num_op_nodes_(0),
num_external_data_(0),
operator_export_type_(operator_export_type),
strip_doc_(strip_doc) {
model_proto_.set_producer_name("pytorch");
// we pin IR version to version 6 (12/11/2019) instead of using
// onnx::IR_VERSION. with this change, the test_operators.py will be more
// stable. only bump it when it's necessary
model_proto_.set_ir_version(onnx_torch::IR_VERSION);
// TODO: set the producer version using appropriate function call
model_proto_.set_producer_version(onnx_torch::PRODUCER_VERSION);
}
void EncoderBase::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);
auto tensorTypeToONNXType = [&dynamic_axes, &name, n, this](
const TensorTypePtr& t,
onnx::TypeProto_Tensor* tensor_type) {
if (t->dim()) {
onnx::TensorShapeProto* shape = tensor_type->mutable_shape();
auto sizes = t->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 (symbol_dim_map_.find(sizes[i]) == symbol_dim_map_.end()) {
if (n->node()->kind() == prim::Param) {
symbol_dim_map_[sizes[i]] = name + "_dim_" + std::to_string(i);
} else {
std::string op_type = n->node()->kind().toUnqualString();
symbol_dim_map_[sizes[i]] =
op_type + name + "_dim_" + std::to_string(i);
}
}
shape->mutable_dim(i)->set_dim_param(symbol_dim_map_[sizes[i]]);
}
}
}
if (t->scalarType()) {
tensor_type->set_elem_type(ATenTypeToOnnxType(t->scalarType().value()));
}
};
if (TensorTypePtr node_type = n->type()->cast<TensorType>()) {
if (node_type->dim() || node_type->scalarType()) {
// Encode type if either shape or dtype exists.
onnx::TypeProto* onnx_type = v->mutable_type();
onnx::TypeProto_Tensor* tensor_type = onnx_type->mutable_tensor_type();
tensorTypeToONNXType(node_type, tensor_type);
}
} else if (BoolTypePtr node_type = n->type()->cast<BoolType>()) {
onnx::TypeProto* onnx_type = v->mutable_type();
onnx::TypeProto_Tensor* tensor_type = onnx_type->mutable_tensor_type();
tensor_type->set_elem_type(ATenTypeToOnnxType(at::kBool));
} else if (ListTypePtr list_type = n->type()->cast<ListType>()) {
auto elem_type = list_type->getElementType();
if (TensorTypePtr inner_node_type = elem_type->cast<TensorType>()) {
onnx::TypeProto* onnx_type = v->mutable_type();
onnx::TypeProto_Sequence* sequence_type =
onnx_type->mutable_sequence_type();
onnx::TypeProto_Tensor* tensor_type =
sequence_type->mutable_elem_type()->mutable_tensor_type();
tensorTypeToONNXType(inner_node_type, tensor_type);
}
}
}
void EncoderBase::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 EncoderBase::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) {
AT_ASSERT(graph_proto != nullptr);
std::string block_name = "torch-jit-export";
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()) {
bool is_raw_export =
operator_export_type_ == onnx_torch::OperatorExportTypes::RAW;
if (node->mustBeNone() && !is_raw_export) {
// 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 p_n = graph_proto->add_node();
if (!strip_doc_) {
p_n->set_doc_string(node->sourceRange().str());
}
for (auto input : node->inputs()) {
if (input->node()->mustBeNone() && !is_raw_export) {
p_n->add_input("");
} else {
p_n->add_input(input->debugName());
}
}
for (auto output : node->outputs()) {
p_n->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();
}
domains_.insert(domain);
p_n->set_domain(domain);
}
if (is_raw_export) {
AT_ASSERT(!node->kind().is_onnx());
} else if (operator_export_type_ == onnx_torch::OperatorExportTypes::ONNX) {
AT_ASSERT(
!node->kind().is_aten() && !node->kind().is_prim() &&
!node->kind().is_attr());
}
p_n->set_op_type(node->kind().toUnqualString());
if (add_node_names) {
p_n->set_name(p_n->op_type() + "_" + std::to_string(num_op_nodes_));
num_op_nodes_++;
}
for (auto attr_name : node->attributeNames()) {
AddAttribute(
p_n, node, attr_name, use_external_data_format, onnx_file_path);
}
if (is_raw_export && node->blocks().size() > 0) {
auto blocks = p_n->add_attribute();
blocks->set_name("_blocks");
blocks->set_type(onnx::AttributeProto_AttributeType_GRAPHS);
for (auto block : node->blocks()) {
auto graph = blocks->add_graphs();
EncodeBlock(graph, block, initializers);
}
}
if (node->kind() == ::c10::onnx::Loop) {
AT_ASSERT(node->blocks().size() == 1);
auto body = p_n->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) {
AT_ASSERT(node->blocks().size() == 2);
auto true_branch = p_n->add_attribute();
true_branch->set_name("then_branch");
true_branch->set_type(onnx::AttributeProto_AttributeType_GRAPH);
auto true_g = true_branch->mutable_g();
EncodeBlock(
true_g,
node->blocks()[0],
{},
{},
true,
true,
use_external_data_format,
onnx_file_path);
auto false_branch = p_n->add_attribute();
false_branch->set_name("else_branch");
false_branch->set_type(onnx::AttributeProto_AttributeType_GRAPH);
auto false_g = false_branch->mutable_g();
EncodeBlock(
false_g,
node->blocks()[1],
{},
{},
true,
true,
use_external_data_format,
onnx_file_path);
}
}
AddInitializersIntoGraphProto(
graph_proto,
block,
initializers,
use_external_data_format,
onnx_file_path);
}
void EncoderBase::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) {
AT_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);
}
}
void EncoderBase::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();
AT_ASSERT(name.is_attr());
attr->set_name(name.toUnqualString());
switch (node->kindOf(name)) {
case AttributeKind::f:
attr->set_f(node->f(name));
attr->set_type(onnx::AttributeProto_AttributeType_FLOAT);
break;
case AttributeKind::fs:
attr->set_type(onnx::AttributeProto_AttributeType_FLOATS);
for (auto& v : node->fs(name))
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
attr->add_floats(v);
break;
case AttributeKind::i:
attr->set_type(onnx::AttributeProto_AttributeType_INT);
attr->set_i(node->i(name));
break;
case AttributeKind::is:
attr->set_type(onnx::AttributeProto_AttributeType_INTS);
for (auto& v : node->is(name))
attr->add_ints(v);
break;
case AttributeKind::s:
attr->set_type(onnx::AttributeProto_AttributeType_STRING);
attr->set_s(node->s(name));
break;
case AttributeKind::ss:
attr->set_type(onnx::AttributeProto_AttributeType_STRINGS);
for (auto& v : node->ss(name))
attr->add_strings(v);
break;
case AttributeKind::t: {
attr->set_type(onnx::AttributeProto_AttributeType_TENSOR);
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:
attr->set_type(onnx::AttributeProto_AttributeType_TENSORS);
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::g: {
attr->set_type(onnx::AttributeProto_AttributeType_GRAPH);
auto g = attr->mutable_g();
EncodeGraph(
g,
node->g(name),
{},
{},
true,
true,
use_external_data_format,
onnx_file_path);
} break;
case AttributeKind::gs:
attr->set_type(onnx::AttributeProto_AttributeType_GRAPHS);
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:
throw std::runtime_error("unexpected attribute kind");
}
}
class GraphEncoder : public EncoderBase {
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);
RawDataExportMap get_raw_data_export_map() {
return raw_data_export_map_;
}
private:
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()) override;
RawDataExportMap raw_data_export_map_;
bool defer_weight_export_;
};
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)
: EncoderBase(operator_export_type, strip_doc),
defer_weight_export_(defer_weight_export) {
if (operator_export_type != onnx_torch::OperatorExportTypes::RAW) {
validateGraph(graph, operator_export_type);
}
if (use_external_data_format) {
TORCH_CHECK(
!onnx_file_path.empty(),
"For large model export, f in torch.onnx.export must be a non-empty string "
"specifying the location of the model.");
}
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::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).
AT_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.
AT_ASSERT(external_ref.value() == tensor_proto->name());
AT_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 {
AT_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) {
AT_ASSERT(!onnx_file_path.empty());
AT_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 {
tensor_proto->set_raw_data(std::string(
static_cast<char*>(t.data_ptr()), t.element_size() * t.numel()));
}
}
}
} // 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());
}
// export_raw_ir will export IR ops without turning them into ONNX ops.
// The output will use the ONNX protobuf format, but the ops will not
// conform to the ONNX op specification. Thus, the output will not
// be interpretable by a ONNX-compatible framework. However, PyTorch or
// libtorch will be able to import the IR and play it back.
std::tuple<
std::shared_ptr<::ONNX_NAMESPACE::ModelProto>,
RawDataExportMap,
SymbolDimMap>
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) {
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);
const size_t proto_size = graph_encoder.get_model_proto().ByteSizeLong();
TORCH_CHECK(
proto_size <= INT_MAX,
"Exporting model exceed maximum protobuf size of 2GB. "
"Please call torch.onnx.export with use_external_data_format=True.");
GRAPH_DEBUG("onnx proto:", prettyPrint(graph_encoder.get_model_proto()));
return std::make_tuple(
std::make_shared<::ONNX_NAMESPACE::ModelProto>(
graph_encoder.get_model_proto()),
graph_encoder.get_raw_data_export_map(),
graph_encoder.get_symbol_dim_param_map());
}
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;
}
onnx::checker::check_model(model);
}
} // namespace jit
} // namespace torch
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