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5be0baefa2cd4202810d6971366c439a44dda69c
pytorch/torch/csrc/jit/export.cpp
Luca Antiga 5be0baefa2 Use streams in JIT serialization, allow JIT serialization to/from buffer (#11932) 
Summary:
This PR replaces the use of `std::FILE` with `istream`/`ostream` for JIT serialization.
It uses this mechanism to add the possibility to serialize to/from binary buffers, in addition to files, both in `libtorch` and from Python.

`getExportImportCopy` in `test_jit.py` has been updated so that both file and buffer codepaths are exercised during tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11932

Differential Revision: D10084303

Pulled By: apaszke

fbshipit-source-id: b850801b3932922fa1dbac6fdaed5063d58bc20d
2018-09-28 07:54:27 -07:00

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#include "torch/csrc/jit/export.h"
#include "torch/csrc/jit/serialization.h"
#include "torch/csrc/autograd/symbolic.h"
#include "onnx/onnx_pb.h"
#include "torch/csrc/onnx/onnx.h"
#include "torch/csrc/utils/functional.h"
#include <torch/csrc/jit/assertions.h>
#include "torch/csrc/jit/passes/dead_code_elimination.h"
#include <ATen/ATen.h>
#include <ATen/core/optional.h>
#include <memory>
#include <vector>
#include <string>
#include <sstream>
#include <fstream>
namespace torch { namespace jit {
namespace {
namespace onnx_torch = ::torch::onnx;
namespace onnx = ::ONNX_NAMESPACE;
std::string getExportableSchemaStringForMethod(const script::Method& method) {
const auto& schema = method.getSchema();
for (const auto& argument : schema.arguments) {
AT_CHECK(
!argument.default_value,
"Default arguments in script graphs may currently not be exported.");
}
std::ostringstream stream;
stream << schema;
return stream.str();
}
std::string getNodeStackTraceString(const Node* n) {
std::stringstream ss;
if (n->getSourceLocation()) {
n->getSourceLocation()->highlight(ss);
} else {
ss << "<unknown location>";
}
return ss.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());
IR_IF(node, PythonOp)
auto py_node = static_cast<torch::jit::PythonOp*>(value);
FAIL_EXPORT(
"Couldn't export Python operator " + py_node->name() +
"\n\nDefined at:\n" + getNodeStackTraceString(node))
IR_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(::torch::jit::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");
} else {
FAIL_EXPORT(
"Could not export a broadcasted operation; ONNX likely does not support this form of broadcasting.\n\nBroadcast occurred at:\n" +
getNodeStackTraceString(node));
}
}
if (node->kind() == prim::PackPadded || node->kind() == prim::PadPacked) {
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_fallback = operator_export_type == onnx_torch::OperatorExportTypes::ONNX_ATEN_FALLBACK;
if (!node->kind().is_onnx() && !is_aten_fallback && node->kind() != prim::Undefined) {
FAIL_EXPORT(
"Couldn't export operator " + node->kind().toDisplayString() + "\n\nDefined at:\n" +
getNodeStackTraceString(node));
}
IR_END()
#undef FAIL_EXPORT
}
}
void validateGraph(const std::shared_ptr<Graph>& graph, onnx_torch::OperatorExportTypes operator_export_type) {
validateBlock(graph->block(), operator_export_type);
EliminateDeadCode(graph);
}
class EncoderBase {
public:
EncoderBase(onnx_torch::OperatorExportTypes operator_export_type, bool strip_doc);
onnx::ModelProto get_model_proto() {
return model_proto_;
}
protected:
void EncodeGraph(onnx::GraphProto *graph_proto,
const std::shared_ptr<Graph> &graph,
const std::vector<at::Tensor> &initializers = {});
void EncodeBlock(onnx::GraphProto *graph_proto,
const Block *block,
const std::vector<at::Tensor> &initializers = {});
virtual void EncodeTensor(onnx::TensorProto *tensor_proto,
const at::Tensor &tensor,
const at::optional<std::string> external_ref = {}) = 0;
virtual void EncodeIntermediateValueInfo(onnx::GraphProto *graph_proto,
const Value* n) {};
virtual void EncodeValueInfo(onnx::GraphProto *graph_proto,
onnx::ValueInfoProto* v,
const Value* n);
void AddAttribute(onnx::NodeProto *node_proto, const jit::Node *node, const jit::Symbol name);
onnx::ModelProto model_proto_;
size_t num_blocks_;
onnx_torch::OperatorExportTypes operator_export_type_;
bool strip_doc_;
};
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;
default:
AT_ERROR("unexpected tensor scalar type");
}
}
EncoderBase::EncoderBase(onnx_torch::OperatorExportTypes operator_export_type, bool strip_doc)
: num_blocks_(0),
operator_export_type_(operator_export_type),
strip_doc_(strip_doc) {
model_proto_.set_producer_name("pytorch");
model_proto_.set_ir_version(onnx::IR_VERSION);
model_proto_.set_producer_version("0.4");
}
void EncoderBase::EncodeValueInfo(
onnx::GraphProto *graph_proto,
onnx::ValueInfoProto* v,
const Value* n) {
v->set_name(n->uniqueName());
onnx::TypeProto* t = v->mutable_type();
onnx::TypeProto_Tensor* tensor_type = t->mutable_tensor_type();
onnx::TensorShapeProto* shape = tensor_type->mutable_shape();
if (CompleteTensorTypePtr node_type = n->type()->cast<CompleteTensorType>()) {
const std::vector<std::int64_t>& sizes = node_type->sizes();
for (size_t i = 0; i < sizes.size(); i++) {
shape->add_dim();
shape->mutable_dim(i)->set_dim_value(sizes[i]);
}
tensor_type->set_elem_type(ATenTypeToOnnxType(node_type->scalarType()));
} else {
tensor_type->set_elem_type(onnx::TensorProto_DataType_UNDEFINED);
}
}
void EncoderBase::EncodeGraph(
onnx::GraphProto *graph_proto,
const std::shared_ptr<Graph> &graph,
const std::vector<at::Tensor> &initializers) {
EncodeBlock(graph_proto, graph->block(), initializers);
}
void EncoderBase::EncodeBlock(
onnx::GraphProto *graph_proto, const Block *block,
const std::vector<at::Tensor> &initializers) {
JIT_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);
for (auto input : block->inputs()) {
onnx::ValueInfoProto* v = graph_proto->add_input();
EncodeValueInfo(graph_proto, v, input);
}
for (auto output : block->outputs()) {
onnx::ValueInfoProto* v = graph_proto->add_output();
EncodeValueInfo(graph_proto, v, output);
}
for (auto node : block->nodes()) {
bool is_raw_export = operator_export_type_ == onnx_torch::OperatorExportTypes::RAW;
if (node->kind() == prim::Undefined && !is_raw_export) {
// Undefined 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 (node->getSourceLocation() && !strip_doc_) {
std::stringstream ss;
node->getSourceLocation()->highlight(ss);
p_n->set_doc_string(ss.str());
}
for(auto input : node->inputs()) {
if (input->node()->kind() == prim::Undefined && !is_raw_export) {
p_n->add_input("");
} else {
p_n->add_input(input->uniqueName());
}
}
for(auto output : node->outputs()) {
p_n->add_output(output->uniqueName());
EncodeIntermediateValueInfo(graph_proto, output);
}
if (is_raw_export) {
JIT_ASSERT(!node->kind().is_onnx());
p_n->set_domain(node->kind().domainString());
}
else if (operator_export_type_ == onnx_torch::OperatorExportTypes::ONNX) {
JIT_ASSERT(node->kind().is_onnx());
}
p_n->set_op_type(node->kind().toUnqualString());
for(auto attr_name : node->attributeNames()) {
AddAttribute(p_n, node, attr_name);
}
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() == torch::jit::onnx::Loop) {
JIT_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]);
}
if (node->kind() == torch::jit::onnx::If) {
JIT_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]);
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]);
}
}
auto num_initializers = initializers.size();
JIT_ASSERT(block->inputs().size() >= num_initializers);
size_t inputs_count = block->inputs().size() - num_initializers;
for (auto & tensor : initializers) {
// TODO: stop using positions to determine which initializers
// match to which inputs
std::string name = graph_proto->input(inputs_count++).name();
auto p = graph_proto->add_initializer();
p->set_name(name);
EncodeTensor(p, tensor, name);
}
}
void EncoderBase::AddAttribute(onnx::NodeProto *node_proto, const jit::Node *node, const jit::Symbol name) {
auto attr = node_proto->add_attribute();
JIT_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))
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();
EncodeTensor(t, node->t(name));
} break;
case AttributeKind::ts:
attr->set_type(onnx::AttributeProto_AttributeType_TENSORS);
for(auto & v : node->ts(name)) {
auto t = attr->add_tensors();
EncodeTensor(t, v);
}
break;
case AttributeKind::g: {
attr->set_type(onnx::AttributeProto_AttributeType_GRAPH);
auto g = attr->mutable_g();
EncodeGraph(g, node->g(name));
} 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);
}
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::vector<at::Tensor> &initializers,
bool defer_weight_export,
bool strip_doc);
RawDataExportMap get_raw_data_export_map() {
return raw_data_export_map_;
}
private:
virtual void EncodeTensor(onnx::TensorProto *tensor_proto,
const at::Tensor &tensor,
const at::optional<std::string> external_ref = {}) 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::vector<at::Tensor> &initializers,
bool defer_weight_export,
bool strip_doc)
: 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);
}
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);
}
void GraphEncoder::EncodeTensor(
onnx::TensorProto *tensor_proto,
const at::Tensor &tensor,
const at::optional<std::string> external_ref) {
for(auto d : tensor.sizes()) {
tensor_proto->add_dims(d);
}
tensor_proto->set_data_type(ATenTypeToOnnxType(tensor.type().scalarType()));
// CPU's HalfTensor doesn't have contiguous(), so first calling contiguous()
auto t = tensor.contiguous().cpu();
// 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.
JIT_ASSERT(external_ref.value() == tensor_proto->name());
JIT_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 {
JIT_ASSERT(t.is_contiguous());
tensor_proto->set_raw_data(std::string(static_cast<char*>(t.data_ptr()), t.type().elementSizeInBytes() * t.numel()));
}
}
class ModuleEncoder: public EncoderBase {
public:
ModuleEncoder(const script::Module &module,
std::ostream& out);
private:
void EncodeModule(onnx::GraphProto *graph_proto, const script::Module &module);
void EncodeParameters(onnx::GraphProto *graph_proto,
const script::Module &module,
const std::string prefix);
void EncodeParameter(onnx::TensorProto *tensor_proto,
const script::NamedParameter &parameter,
const std::string prefix);
void EncodeMethods(onnx::GraphProto *graph_proto,
const script::Module &module,
const std::string prefix);
void EncodeMethod(onnx::NodeProto *node_proto,
script::Method &method,
const std::string prefix);
virtual void EncodeTensor(onnx::TensorProto *tensor_proto,
const at::Tensor &tensor,
const at::optional<std::string> external_ref = {}) override;
virtual void EncodeIntermediateValueInfo(onnx::GraphProto *graph_proto,
const Value* n) override;
virtual void EncodeValueInfo(onnx::GraphProto *graph_proto,
onnx::ValueInfoProto* v,
const Value* n) override;
void EncodeTypeInfo(onnx::GraphProto *graph_proto,
onnx::ValueInfoProto* v,
const TypePtr& type,
const std::string& name);
PyTorchStreamWriter stream_writer_;
// Used to deduplicate tensor storages
std::unordered_map<const void*, uint64_t> storage_dedup_map_;
// Used to keep track of Parameter names so Methods can refer to them
std::unordered_map<at::Tensor*, std::string> parameter_map_;
// Used to create sequential dummy names for node types
size_t type_counter_ = 0;
};
ModuleEncoder::ModuleEncoder(
const script::Module &module,
std::ostream& out)
: EncoderBase(onnx_torch::OperatorExportTypes::RAW, false),
stream_writer_(out) {
model_proto_.set_doc_string("THIS PROTO IS NOT STANDARD ONNX");
EncodeModule(model_proto_.mutable_graph(), module);
}
void ModuleEncoder::EncodeIntermediateValueInfo(onnx::GraphProto *graph_proto, const Value *n) {
auto v = graph_proto->add_value_info();
EncodeTypeInfo(graph_proto, v, n->type(), n->uniqueName());
}
void ModuleEncoder::EncodeTypeInfo(
onnx::GraphProto *graph_proto,
onnx::ValueInfoProto* v,
const TypePtr& type,
const std::string& name) {
v->set_name(name);
onnx::TypeProto* type_proto = v->mutable_type();
onnx::TypeProto_Tensor* tensortype_proto = type_proto->mutable_tensor_type();
onnx::TensorShapeProto* shape_proto = tensortype_proto->mutable_shape();
// Use TypeProto fields to encode types.
// denotation stores the type as a string
auto kind = type->kind();
if (kind == TypeKind::DynamicType) {
type_proto->set_denotation("DynamicType");
tensortype_proto->set_elem_type(onnx::TensorProto_DataType_UNDEFINED);
} else if (kind == TypeKind::TensorType) {
type_proto->set_denotation("TensorType");
// encode the number of dimensions by pushing that number of ones into the shape proto
auto tensor_type = type->expect<TensorType>();
for (int i = 0; i < tensor_type->dim(); i++) {
shape_proto->add_dim();
shape_proto->mutable_dim(i)->set_dim_value(1);
}
tensortype_proto->set_elem_type(ATenTypeToOnnxType(tensor_type->scalarType()));
} else if (kind == TypeKind::CompleteTensorType) {
type_proto->set_denotation("CompleteTensorType");
CompleteTensorTypePtr node_type = type->cast<CompleteTensorType>();
// store the sizes and strides in the dims field of TensorShapeProto
size_t i = 0;
for (auto &size : node_type->sizes()) {
shape_proto->add_dim();
shape_proto->mutable_dim(i)->set_dim_value(size);
i++;
}
for (auto &stride : node_type->strides()) {
shape_proto->add_dim();
shape_proto->mutable_dim(i)->set_dim_value(stride);
i++;
}
tensortype_proto->set_elem_type(ATenTypeToOnnxType(node_type->scalarType()));
} else if (kind == TypeKind::TupleType) {
type_proto->set_denotation("TupleType");
TupleTypePtr node_type = type->cast<TupleType>();
auto elements = node_type->elements();
// Generate a name for and encode each subtype in the value_info field of the GraphProto.
for (size_t i = 0; i < elements.size(); i++) {
std::string name = "#" + std::to_string(type_counter_++);
shape_proto->add_dim();
shape_proto->mutable_dim(i)->set_dim_param(name);
onnx::ValueInfoProto* subtype_proto = graph_proto->add_value_info();
EncodeTypeInfo(graph_proto, subtype_proto, elements[i], name);
}
} else if (kind == TypeKind::ListType) {
type_proto->set_denotation("ListType");
ListTypePtr node_type = type->cast<ListType>();
// Generate a name for and encode the subtype in the value_info field of the GraphProto.
std::string name = "#" + std::to_string(type_counter_++);
shape_proto->add_dim();
shape_proto->mutable_dim(0)->set_dim_param(name);
onnx::ValueInfoProto* subtype_proto = graph_proto->add_value_info();
EncodeTypeInfo(graph_proto, subtype_proto, node_type->getElementType(), name);
} else if (kind == TypeKind::NumberType) {
type_proto->set_denotation("NumberType");
} else if (kind == TypeKind::FloatType) {
type_proto->set_denotation("FloatType");
} else if (kind == TypeKind::IntType) {
type_proto->set_denotation("IntType");
} else if (kind == TypeKind::NoneType) {
type_proto->set_denotation("NoneType");
} else if (kind == TypeKind::GeneratorType) {
type_proto->set_denotation("GeneratorType");
} else if (kind == TypeKind::StringType) {
type_proto->set_denotation("StringType");
} else if (kind == TypeKind::VarType) {
type_proto->set_denotation("TypeVar:" + type->expect<VarType>()->name());
} else if (kind == TypeKind::WorldType) {
type_proto->set_denotation("WorldType");
} else {
throw std::runtime_error("unexpected type kind");
}
}
void ModuleEncoder::EncodeValueInfo(
onnx::GraphProto *graph_proto,
onnx::ValueInfoProto* v,
const Value* n) {
EncodeTypeInfo(graph_proto, v, n->type(), n->uniqueName());
}
void ModuleEncoder::EncodeModule(
onnx::GraphProto *graph_proto,
const script::Module &module) {
EncodeParameters(graph_proto, module, "");
EncodeMethods(graph_proto, module, "");
auto str = model_proto_.SerializeAsString();
stream_writer_.writeRecord(str.data(), str.size());
}
void ModuleEncoder::EncodeParameters(
onnx::GraphProto *graph_proto,
const script::Module &module,
const std::string prefix) {
// Encode each parameter as a initializer in the proto
for (auto &parameter : module.get_parameters()) {
auto tensor_proto = graph_proto->add_initializer();
EncodeParameter(tensor_proto, parameter.value, prefix);
}
for (auto &submodule : module.get_modules()) {
EncodeParameters(graph_proto, *submodule.value.module, prefix + submodule.key + ".");
}
}
void ModuleEncoder::EncodeParameter(
onnx::TensorProto *tensor_proto,
const script::NamedParameter &parameter,
const std::string prefix) {
auto tensor = parameter.slot();
// Name will be prefixed by submodule. e.g. submodule_foo.parameter_bar
auto name = prefix + parameter.name;
tensor_proto->set_name(name);
parameter_map_[tensor] = name;
// Parameters have these fields, but tensors do not
tensor_proto->add_int64_data(parameter.is_buffer);
tensor_proto->add_int64_data(tensor->requires_grad());
EncodeTensor(tensor_proto, *tensor, name);
}
void ModuleEncoder::EncodeMethods(
onnx::GraphProto *graph_proto,
const script::Module &module,
const std::string prefix) {
// Encode each parameter as a initializer in the proto
for (auto &method : module.get_methods()) {
auto node_proto = graph_proto->add_node();
EncodeMethod(node_proto, *method.value, prefix);
}
for (auto &submodule : module.get_modules()) {
EncodeMethods(graph_proto, *submodule.value.module, prefix + submodule.key + ".");
}
}
void ModuleEncoder::EncodeMethod(
onnx::NodeProto *node_proto,
script::Method &method,
const std::string prefix) {
node_proto->set_name(prefix + method.name());
if (method.is_optimized()) {
// mark that this method was optimized
node_proto->set_domain("optimized");
}
// We store the schema string in the docstring.
node_proto->set_doc_string(getExportableSchemaStringForMethod(method));
// Store member_inputs of Method in input
for (auto &member_input : method.params()) {
auto it = parameter_map_.find(member_input);
JIT_ASSERT(it != parameter_map_.end());
node_proto->add_input(it->second);
}
auto attr_proto = node_proto->add_attribute();
attr_proto->set_type(onnx::AttributeProto_AttributeType_GRAPH);
for (auto node : method.graph()->nodes()) {
if (node->kind() == prim::PythonOp) {
auto py_node = static_cast<torch::jit::PythonOp*>(node);
throw std::runtime_error(
"Couldn't export Python operator " + py_node->name() +
"\n\nDefined at:\n" + getNodeStackTraceString(node));
}
}
EncodeBlock(attr_proto->mutable_g(), method.graph()->block(), {});
}
void ModuleEncoder::EncodeTensor(
onnx::TensorProto *tensor_proto,
const at::Tensor &tensor,
const at::optional<std::string> external_ref) {
auto storage_ptr = tensor.storage().unsafeGetStorageImpl();
auto dedup_it = storage_dedup_map_.find(storage_ptr);
if (dedup_it != storage_dedup_map_.end()) {
tensor_proto->add_int64_data(dedup_it->second);
} else {
at::Tensor t = tensor;
if (tensor.storage().device_type() == at::DeviceType::CUDA) {
// NB: This new tensor is created to support cuda tensors.
// Storages can be mutated when converting tensors from cuda to cpu,
// and we need a cpu tensor to copy data from.
t = at::getType(tensor).tensor(
tensor.storage(),
/* storageOffset = */ 0,
/* size = */ { static_cast<int64_t>(tensor.type().elementSizeInBytes() * tensor.storage().size()) },
/* strides = */ { 1 })
.cpu();
}
auto record_number = stream_writer_.writeRecord(
static_cast<char*>(t.storage().data()), t.type().elementSizeInBytes() * t.storage().size());
tensor_proto->add_int64_data(record_number);
storage_dedup_map_[storage_ptr] = record_number;
}
for (auto &d : tensor.sizes()) {
tensor_proto->add_dims(d);
}
tensor_proto->set_data_type(ATenTypeToOnnxType(tensor.type().scalarType()));
tensor_proto->add_int64_data(tensor.storage_offset());
for (auto &d : tensor.strides()) {
tensor_proto->add_int64_data(d);
}
}
// Pretty printing
namespace {
constexpr char indent_char = ' ';
constexpr size_t indent_multiplier = 2;
std::string idt(size_t indent) {
return std::string(indent * indent_multiplier, indent_char);
}
std::string nlidt(size_t indent) {
return std::string("\n") + idt(indent);
}
void dump(const onnx::TensorProto& tensor, std::ostream& stream) {
stream << "TensorProto shape: [";
for (int i = 0; i < tensor.dims_size(); ++i) {
stream << tensor.dims(i) << (i == tensor.dims_size() - 1 ? "" : " ");
}
stream << "]";
}
void dump(const onnx::TensorShapeProto& shape, std::ostream& stream) {
for (int i = 0; i < shape.dim_size(); ++i) {
auto &dim = shape.dim(i);
if (dim.has_dim_value()) {
stream << dim.dim_value();
} else {
stream << "?";
}
stream << (i == shape.dim_size() - 1 ? "" : " ");
}
}
void dump(const onnx::TypeProto_Tensor& tensor_type, std::ostream& stream) {
stream << "Tensor dims: ";
dump(tensor_type.shape(), stream);
}
void dump(const onnx::TypeProto& type, std::ostream& stream) {
dump(type.tensor_type(), stream);
}
void dump(const onnx::ValueInfoProto& value_info, std::ostream& stream) {
stream << "{name: \"" << value_info.name()
<< "\", type:";
dump(value_info.type(), stream);
stream << "}";
}
void dump(const onnx::GraphProto& graph, std::ostream& stream, size_t indent);
void dump(const onnx::AttributeProto& attr, std::ostream& stream, size_t indent) {
stream << "{ name: '" << attr.name() << "', type: ";
if (attr.has_f()) {
stream << "float, value: " << attr.f();
} else if (attr.has_i()) {
stream << "int, value: " << attr.i();
} else if (attr.has_s()) {
stream << "string, value: '" << attr.s() << "'";
} else if (attr.has_g()) {
stream << "graph, value:\n";
dump(attr.g(), stream, indent+1);
stream << nlidt(indent);
} else if (attr.has_t()) {
stream << "tensor, value:";
dump(attr.t(), stream);
} else if (attr.floats_size()) {
stream << "floats, values: [";
for (int i = 0; i < attr.floats_size(); ++i)
stream << attr.floats(i) << (i == attr.floats_size() - 1 ? "" : " ");
stream << "]";
} else if (attr.ints_size()) {
stream << "ints, values: [";
for (int i = 0; i < attr.ints_size(); ++i)
stream << attr.ints(i) << (i == attr.ints_size() - 1 ? "" : " ");
stream << "]";
} else if (attr.strings_size()) {
stream << "strings, values: [";
for (int i = 0; i < attr.strings_size(); ++i)
stream << "'" << attr.strings(i) << "'" << (i == attr.strings_size() - 1 ? "" : " ");
stream << "]";
} else if (attr.tensors_size()) {
stream << "tensors, values: [";
for (auto& t : attr.tensors()) {
dump(t, stream);
}
stream << "]";
} else if (attr.graphs_size()) {
stream << "graphs, values: [";
for (auto& g : attr.graphs()) {
dump(g, stream, indent+1);
}
stream << "]";
} else {
stream << "UNKNOWN";
}
stream << "}";
}
void dump(const onnx::NodeProto& node, std::ostream& stream, size_t indent) {
stream << "Node {type: \"" << node.op_type() << "\", inputs: [";
for (int i = 0; i < node.input_size(); ++i) {
stream << node.input(i) << (i == node.input_size() - 1 ? "" : ",");
}
stream << "], outputs: [";
for (int i = 0; i < node.output_size(); ++i) {
stream << node.output(i) << (i == node.output_size() - 1 ? "" : ",");
}
stream << "], attributes: [";
for (int i = 0; i < node.attribute_size(); ++i) {
dump(node.attribute(i), stream, indent+1);
stream << (i == node.attribute_size() - 1 ? "" : ",");
}
stream << "]}";
}
void dump(const onnx::GraphProto& graph, std::ostream& stream, size_t indent) {
stream << idt(indent) << "GraphProto {" << nlidt(indent+1)
<< "name: \"" << graph.name() << "\"" << nlidt(indent+1)
<< "inputs: [";
for (int i = 0; i < graph.input_size(); ++i) {
dump(graph.input(i), stream);
stream << (i == graph.input_size() - 1 ? "" : ",");
}
stream << "]" << nlidt(indent+1)
<< "outputs: [";
for (int i = 0; i < graph.output_size(); ++i) {
dump(graph.output(i), stream);
stream << (i == graph.output_size() - 1 ? "" : ",");
}
stream << "]" << nlidt(indent+1)
<< "initializers: [";
for (int i = 0; i < graph.initializer_size(); ++i) {
dump(graph.initializer(i), stream);
stream << (i == graph.initializer_size() - 1 ? "" : ",");
}
stream << "]" << nlidt(indent+1)
<< "nodes: [" << nlidt(indent+2);
for (int i = 0; i < graph.node_size(); ++i) {
dump(graph.node(i), stream, indent+2);
if (i != graph.node_size() - 1) stream << "," << nlidt(indent+2);
}
stream << nlidt(indent+1) << "]\n" << idt(indent) << "}\n";
}
void dump(const onnx::OperatorSetIdProto& operator_set_id, std::ostream& stream) {
stream << "OperatorSetIdProto { domain: " << operator_set_id.domain() << "}";
}
void dump(const onnx::ModelProto& model, std::ostream& stream, size_t indent) {
stream << idt(indent)
<< "ModelProto {" << nlidt(indent+1)
<< "producer_name: \"" << model.producer_name() << "\"" << nlidt(indent+1)
<< "domain: \"" << model.domain() << "\"" << nlidt(indent+1)
<< "doc_string: \"" << model.doc_string() << "\"";
if (model.has_graph()) {
stream << nlidt(indent+1) << "graph:\n";
dump(model.graph(), stream, indent+2);
}
if (model.opset_import_size()) {
stream << idt(indent+1) << "opset_import: [";
for (auto &opset_imp : model.opset_import()) {
dump(opset_imp, stream);
}
stream << "],\n";
}
stream << idt(indent) << "}\n";
}
} // namespace
std::string prettyPrint(const onnx::ModelProto& model) {
std::stringstream ss;
dump(model, ss, 0);
return ss.str();
}
}
std::string PrettyPrintExportedGraph(
const std::shared_ptr<Graph> &graph,
const std::vector<at::Tensor> &initializers,
int64_t onnx_opset_version,
bool defer_weight_export,
::torch::onnx::OperatorExportTypes operator_export_type,
bool google_printer) {
auto graph_encoder = GraphEncoder(
graph, onnx_opset_version, operator_export_type, initializers, defer_weight_export, true);
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::string, RawDataExportMap> ExportGraph(
const std::shared_ptr<Graph> &graph,
const std::vector<at::Tensor> &initializers,
int64_t onnx_opset_version,
bool defer_weight_export,
::torch::onnx::OperatorExportTypes operator_export_type) {
auto graph_encoder = GraphEncoder(
graph, onnx_opset_version, operator_export_type, initializers, defer_weight_export, false);
return std::make_tuple(graph_encoder.get_model_proto().SerializeAsString(),
graph_encoder.get_raw_data_export_map());
}
void ExportModule(const script::Module& module, std::ostream& out) {
ModuleEncoder(module, out);
}
void ExportModule(const script::Module& module, const std::string &filename) {
std::ofstream out(filename, std::ios_base::binary);
ExportModule(module, out);
}
}}
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