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import_data.cpp
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import_data.cpp
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#include <torch/csrc/jit/mobile/import_data.h>
#include <ATen/Functions.h>
#include <ATen/core/ivalue.h>
#include <c10/util/irange.h>
#include <torch/csrc/jit/api/compilation_unit.h>
#include <torch/csrc/jit/mobile/file_format.h>
#include <torch/csrc/jit/mobile/flatbuffer_loader.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/import_export_common.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/mobile/observer.h>
#include <torch/csrc/jit/mobile/type_parser.h>
#include <torch/csrc/jit/runtime/instruction.h>
#include <torch/csrc/jit/serialization/unpickler.h>
#include <torch/custom_class.h>
#include <caffe2/serialize/in_memory_adapter.h>
#include <string>
#include <vector>
namespace torch::jit {
using caffe2::serialize::PyTorchStreamReader;
namespace {
/**
* Given a ZIP file containing a file named "data.pkl", uses Pickle to
* deserialize the file and returns the IValue inside it.
*/
class IValueUnpickler final {
public:
explicit IValueUnpickler(std::unique_ptr<PyTorchStreamReader> reader);
c10::IValue deserialize(std::optional<at::Device> device);
private:
c10::IValue readArchive(
const std::string& archive_name,
std::shared_ptr<mobile::CompilationUnit> mcu,
std::optional<at::Device> device);
std::shared_ptr<CompilationUnit> compilation_unit_;
std::unique_ptr<PyTorchStreamReader> reader_;
};
IValueUnpickler::IValueUnpickler(std::unique_ptr<PyTorchStreamReader> reader)
: compilation_unit_(std::make_shared<CompilationUnit>()),
reader_(std::move(reader)) {}
c10::IValue IValueUnpickler::deserialize(std::optional<at::Device> device) {
auto mcu = std::make_shared<mobile::CompilationUnit>();
return readArchive("data", mcu, device);
}
c10::IValue IValueUnpickler::readArchive(
const std::string& archive_name,
std::shared_ptr<mobile::CompilationUnit> mcu,
std::optional<at::Device> device) {
std::stringstream picklename;
picklename << archive_name << ".pkl";
at::DataPtr pickle_ptr;
size_t pickle_size = 0;
std::tie(pickle_ptr, pickle_size) = reader_->getRecord(picklename.str());
size_t bytes_read = 0;
auto data = reinterpret_cast<const char*>(pickle_ptr.get());
auto reader = [&](char* buffer, size_t len) -> size_t {
if (bytes_read >= pickle_size) {
return 0;
}
len = std::min(pickle_size - bytes_read, len);
// Copy len bytes into buffer
const char* start = data + bytes_read;
std::memcpy(buffer, start, len);
bytes_read += len;
return len;
};
static const c10::QualifiedName torchPrefix = "__torch__";
auto type_resolver = [&](const c10::QualifiedName& qn) {
TypePtr type;
// HACK: first we check whether the name starts with `__torch__` to tell if
// it's "supposed" to be a class type. This is a reliable check today, but
// there is no guarantee that this is the case. The real solution is to
// merge type parsers so we can share class resolution logic.
if (torchPrefix.isPrefixOf(qn)) {
if (compilation_unit_->get_class(qn) == nullptr) {
auto typeptr = ClassType::create(qn, compilation_unit_, true);
compilation_unit_->register_type(typeptr);
}
type = compilation_unit_->get_class(qn);
} else {
type = c10::parseType(qn.qualifiedName());
}
return c10::StrongTypePtr(compilation_unit_, type);
};
auto obj_loader = [&](const at::StrongTypePtr& type, IValue input) {
auto cls = type.type_->expect<at::ClassType>();
auto qn = cls->name();
c10::QualifiedName method_name(qn.value(), "__setstate__");
auto setstate = mcu->find_function(method_name);
auto find_custom_class_with_setstate = [&qn]() -> c10::ClassTypePtr {
auto custom_class_type = torch::jit::getCustomClass(qn->qualifiedName());
if (custom_class_type && custom_class_type->findMethod("__setstate__")) {
return custom_class_type;
}
return nullptr;
};
if (setstate) {
auto obj = c10::ivalue::Object::create(type, 0);
Stack stack({obj, input});
setstate->run(stack);
return obj;
} else if (auto custom_class_type = find_custom_class_with_setstate()) {
auto obj = c10::ivalue::Object::create(
c10::StrongTypePtr(nullptr, custom_class_type), 1);
Stack stack({obj, input});
custom_class_type->getMethod("__setstate__").run(stack);
return obj;
} else {
auto dict = std::move(input).toGenericDict();
size_t ndict = dict.size();
auto obj = c10::ivalue::Object::create(type, ndict);
auto it = dict.begin();
for (const auto i : c10::irange(ndict)) {
std::stringstream name;
name << it->key();
cls->addOrCheckAttribute(name.str(), it->key().type());
obj->setSlot(i, it->value());
++it;
}
return obj;
}
};
auto read_record = [&](const std::string& name) {
std::stringstream ss;
ss << archive_name << "/" << name;
return std::get<0>(reader_->getRecord(ss.str()));
};
Unpickler unpickler(
reader,
std::move(type_resolver),
std::move(obj_loader),
std::move(read_record),
device,
false,
nullptr);
return unpickler.parse_ivalue();
}
/**
* Extracts and returns the parameter map serialized as ZIP + Pickle in @p rai.
*/
std::map<std::string, at::Tensor> load_parameters_from_zip(
std::unique_ptr<ReadAdapterInterface> rai,
std::optional<c10::Device> device) {
auto reader = std::make_unique<PyTorchStreamReader>(std::move(rai));
IValueUnpickler unpickler(std::move(reader));
auto result = unpickler.deserialize(device).toGenericDict();
std::map<std::string, at::Tensor> map;
for (const auto& e : result) {
auto key = e.key().toStringRef();
auto value = e.value().toTensor().tensor_data();
map[key] = value;
}
return map;
}
} // namespace
/**
* Extracts the parameter map stored in @p module. Expects a layout
* compatible with the one created by #_save_parameters().
*/
std::map<std::string, at::Tensor> mobile_module_to_parameter_map(
const mobile::Module& module) {
// Safely look for a slot with the expected name. Note that
// c10::ivalue::Object::getAttr() is not safe if the attribute isn't present.
auto obj = module._ivalue();
const std::vector<IValue>& slots = obj->slots();
for (const auto i : c10::irange(slots.size())) {
if (obj->type()->getAttributeName(i) ==
mobile::internal::kSavedParametersAttributeName) {
// Found a slot with the right name; make sure it's a
// Dict<string, Tensor>.
c10::IValue data = slots[i];
if (data.isGenericDict()) {
auto data_dict = data.toGenericDict();
// The key and value should be DynamicTypes that wrap String and Tensor.
c10::DynamicType* keyType =
data_dict.keyType()->castRaw<c10::DynamicType>();
c10::DynamicType* valueType =
data_dict.valueType()->castRaw<c10::DynamicType>();
if (keyType != nullptr &&
keyType->fallback()->kind() == TypeKind::StringType &&
valueType != nullptr &&
valueType->fallback()->kind() == TypeKind::TensorType) {
// Name and type are good; copy the contents to the output map.
std::map<std::string, at::Tensor> params;
for (const auto& e : data_dict) {
// The source Tensor points into the flatbuffer data associated with
// the Module. But, this Tensor needs to outlive the Module, since
// the caller of _load_parameters() won't have a pointer to the
// Module. So, return a deep copy.
const auto& source = e.value().toTensor();
at::Tensor copy = at::empty_like(source); // Must be the same shape.
copy.copy_(source);
params[e.key().toStringRef()] = copy;
}
return params;
}
}
}
}
TORCH_CHECK(
false,
"Could not find Dict<string, Tensor> named '",
mobile::internal::kSavedParametersAttributeName,
"' in deserialized mobile::Module");
}
static std::map<std::string, at::Tensor> _load_parameters_bytes(
const std::shared_ptr<char>& data,
size_t size,
std::optional<at::Device> device) {
TORCH_CHECK(size >= kFileFormatHeaderSize, "Unrecognized data format");
FileFormat format = getFileFormat(data.get());
// Call the appropriate parser.
std::map<std::string, at::Tensor> map;
switch (format) {
case FileFormat::FlatbufferFileFormat: {
auto m = parse_flatbuffer_no_object(data, size, device);
map = mobile_module_to_parameter_map(m);
break;
}
case FileFormat::ZipFileFormat: {
auto rai = std::make_unique<caffe2::serialize::MemoryReadAdapter>(
data.get(), size);
map = load_parameters_from_zip(std::move(rai), device);
break;
}
default:
TORCH_CHECK(false, "Unrecognized data format");
}
return map;
}
std::map<std::string, at::Tensor> _load_parameters(
std::istream& in,
std::optional<at::Device> device) {
auto [data, size] = get_stream_content(in);
return _load_parameters_bytes(data, size, device);
}
std::map<std::string, at::Tensor> _load_parameters(
const std::string& filename,
std::optional<at::Device> device) {
auto [data, size] = get_file_content(filename.c_str());
return _load_parameters_bytes(data, size, device);
}
} // namespace torch::jit