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unpickler.cpp
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#include <ATen/ATen.h>
#include <ATen/core/Dict.h>
#ifdef USE_RPC
#include <torch/csrc/distributed/rpc/rref_context.h>
#endif
#include <torch/csrc/jit/api/function_impl.h>
#include <torch/csrc/jit/mobile/type_parser.h>
#include <torch/csrc/jit/serialization/pickler.h>
#include <torch/csrc/jit/serialization/storage_context.h>
#include <torch/csrc/jit/serialization/unpickler.h>
#include <torch/csrc/utils/byte_order.h>
#include <string>
#include <utility>
namespace torch::jit {
using ::c10::IValue;
static void restoreAccurateTypeTagsIfPossible(const IValue& root) {
if (root.isObject()) {
restoreAccurateTypeTags(root, root.type());
}
}
// Pickled objects are stored in a form compatible with Python pickling.
// In torchscript List[T]/Dict[K, V] are statically typed and contain
// dynamic type tags that allow T, K, and V to be recovered. But this
// info is not stored in the Python pickling information. However, we
// can recover this information from the static type of the top-level
// object being unpickled, because we have a record of the type of the
// objects it contains as attributes.
// `IfPossible` - we can only do this recovery when we have an object as
// the top-level unpickled thing (which is guaranteed for Modules, but
// not for torch.load/torch.save). Otherwise we do not know the types
// of the contained objects and cannot restore the tags.
void restoreAccurateTypeTags(const IValue& root, const TypePtr& type_tag) {
struct Work {
TypePtr type;
IValue value;
};
std::vector<Work> to_process = {{type_tag, root}};
std::unordered_set<const void*> scanned;
while (!to_process.empty()) {
Work w = std::move(to_process.back());
to_process.pop_back();
// ensure we only scan each pointer value once, otherwise this
// can become exponential (and if we allow recursive data in the future,
// it would not terminiate).
if (w.value.isPtrType()) {
const void* key = w.value.internalToPointer();
auto it = scanned.find(key);
if (it != scanned.end()) {
continue;
}
scanned.emplace_hint(it, key);
}
auto kind = w.type->kind();
if (auto dyn = w.type->castRaw<c10::DynamicType>()) {
kind = dyn->dynamicKind();
}
switch (kind) {
case TensorType::Kind:
case StorageType::Kind:
case NumberType::Kind:
case FloatType::Kind:
case ComplexType::Kind:
case IntType::Kind:
case NoneType::Kind:
case GeneratorType::Kind:
case QuantizerType::Kind:
case BoolType::Kind:
case VarType::Kind:
case CapsuleType::Kind:
case PyObjectType::Kind:
case StringType::Kind:
case FunctionType::Kind:
case DeviceObjType::Kind:
case StreamObjType::Kind:
case QSchemeType::Kind:
case LayoutType::Kind:
case MemoryFormatType::Kind:
case ScalarTypeType::Kind:
case RRefType::Kind:
case AnyType::Kind:
case AnyListType::Kind:
case AnyTupleType::Kind:
case AnyClassType::Kind:
case AnyEnumType::Kind:
// no op, there is nothing to tag
break;
case c10::SymIntType::Kind:
// TODO: Can this really show up though? :think:
TORCH_CHECK(!w.value.toSymInt().is_heap_allocated());
// no op, there is nothing to tag
break;
case c10::SymFloatType::Kind:
TORCH_CHECK(!w.value.toSymFloat().is_symbolic());
// no op, there is nothing to tag
break;
case c10::SymBoolType::Kind:
TORCH_CHECK(!w.value.toSymBool().is_heap_allocated());
// no op, there is nothing to tag
break;
case DynamicType::Kind:
case UnionType::Kind:
case EnumType::Kind:
// TODO(gmagogsfm): Implement serialization/deserialization of Enum.
TORCH_INTERNAL_ASSERT(false);
case TupleType::Kind: {
auto t = w.value.toTuple();
for (size_t i = 0; i < w.type->containedTypeSize(); ++i) {
Work elem = {w.type->containedType(i), t->elements().at(i)};
to_process.emplace_back(std::move(elem));
}
} break;
case FutureType::Kind: {
auto f = w.value.toFuture();
if (f->completed()) {
Work elem = {w.type->containedType(0), f->value()};
to_process.emplace_back(std::move(elem));
}
} break;
case AwaitType::Kind: {
auto aw = w.value.toAwait();
if (aw->completed()) {
Work elem = {w.type->containedType(0), aw->wait()};
to_process.emplace_back(std::move(elem));
}
} break;
case OptionalType::Kind: {
if (!w.value.isNone()) {
Work elem = {w.type->containedType(0), w.value};
to_process.emplace_back(std::move(elem));
}
} break;
case ListType::Kind: {
// specialized lists do not need their type refined, so we can exit
// early here
if (!w.value.isList()) {
break;
}
auto elem_type = w.type->containedType(0);
auto lst = w.value.toList();
lst.unsafeSetElementType(elem_type);
for (const IValue& item : lst) {
Work elem = {elem_type, item};
to_process.emplace_back(std::move(elem));
}
} break;
case DictType::Kind: {
auto d = w.value.toGenericDict();
auto keyType = w.type->containedType(0);
auto valType = w.type->containedType(1);
d.unsafeSetKeyType(keyType);
d.unsafeSetValueType(valType);
for (const auto& item : d) {
Work kelem = {keyType, item.key()};
Work velem = {valType, item.value()};
to_process.emplace_back(std::move(kelem));
to_process.emplace_back(std::move(velem));
}
} break;
// in both cases the dynamic type is a class, and we are going to tag with
// the dynamic type
case InterfaceType::Kind:
case ClassType::Kind: {
auto obj = w.value.toObject();
auto typ = obj->type(); // note: intentionally using the dynamic type,
// the static type is potentially less accurate
for (size_t i = 0; i < typ->numAttributes(); ++i) {
Work elem = {typ->getAttribute(i), obj->getSlot(i)};
to_process.emplace_back(std::move(elem));
}
};
}
}
}
namespace {
template <typename T>
bool is(const Type& type) {
if (type.kind() == T::Kind) {
return true;
}
if (auto dyn = type.castRaw<c10::DynamicType>()) {
return dyn->tag() == c10::DynamicTypeTrait<T>::tagValue();
}
return false;
}
} // namespace
static void restoreContainerTypeTags(
const IValue& ivalue,
const TypePtr& type) {
if (is<DictType>(*type)) {
auto dict = ivalue.toGenericDict();
dict.unsafeSetKeyType(type->containedType(0));
dict.unsafeSetValueType(type->containedType(1));
} else if (is<ListType>(*type)) {
ivalue.toList().unsafeSetElementType(type->containedType(0));
} else {
TORCH_CHECK(
false, "Unknown type for tag restoration: " + type->annotation_str());
}
}
IValue Unpickler::parse_ivalue() {
run();
TORCH_CHECK(
stack_.size() == 1,
"Unpickler expected 1 element on the stack, but found ",
stack_.size());
if (version_ <= 2) {
// See [type tag serialization]
restoreAccurateTypeTagsIfPossible(stack_[0]);
}
return stack_[0];
}
double Unpickler::readFloat() {
AT_ASSERT(sizeof(double) == 8);
double big_endian = read<double>();
#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
double little_endian = 0;
// Pickle floats are big endian, so reverse the bytes
auto big_endian_ptr = reinterpret_cast<const char*>(&big_endian);
std::reverse_copy(
big_endian_ptr,
big_endian_ptr + sizeof(big_endian),
reinterpret_cast<char*>(&little_endian));
return little_endian;
#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
return big_endian;
#else
#error Unexpected or undefined __BYTE_ORDER__
#endif
}
void Unpickler::run() {
// Expect a PROTO opcode and protocol number at the start of blob
auto opcode = readOpCode();
TORCH_CHECK(
opcode == PickleOpCode::PROTO,
"Expected PROTO opcode at the start"
" of pickle archive, found ",
int(static_cast<uint8_t>(opcode)));
uint8_t protocol = read<uint8_t>();
TORCH_CHECK(
protocol == 2,
"Only Pickle protocol 2 is supported, found protocol = ",
protocol);
while (true) {
PickleOpCode opcode = readInstruction();
if (opcode == PickleOpCode::STOP) {
return;
}
}
}
void Unpickler::setInput(size_t memo_id) {
AT_ASSERT(!stack_.empty());
if (memo_id >= memo_table_.size()) {
memo_table_.insert(
memo_table_.end(), memo_id - memo_table_.size(), IValue());
memo_table_.push_back(stack_.back());
} else {
memo_table_[memo_id] = stack_.back();
}
}
// emplace_back on bool vectors does not exist on some systems
// avoid it by calling push_back for bool
template <typename T>
inline void append(std::vector<T>& a, T&& e) {
a.emplace_back(std::forward<T>(e));
}
template <>
// NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
inline void append<bool>(std::vector<bool>& a, bool&& e) {
a.push_back(e);
}
static std::vector<int64_t> tupleToIntList(const IValue& v) {
return fmap(v.toTupleRef().elements(), [](const IValue& v) -> int64_t {
return v.toInt();
});
}
// note we cannot use toIntList, toDoubleList because during unpickling the
// lists are not yet tagged
template <typename T>
static std::vector<T> convertList(const IValue& v) {
return fmap(v.toListRef(), [](const IValue& elem) { return elem.to<T>(); });
}
PickleOpCode Unpickler::readInstruction() {
auto opcode = readOpCode();
switch (opcode) {
case PickleOpCode::EMPTY_LIST: {
stack_.emplace_back(c10::impl::GenericList(AnyType::get()));
} break;
case PickleOpCode::EMPTY_TUPLE: {
if (empty_tuple_.isNone()) {
// we only need one object, since tuples are not mutable.
empty_tuple_ = c10::ivalue::Tuple::create(std::vector<IValue>());
}
stack_.emplace_back(empty_tuple_);
} break;
case PickleOpCode::BINPUT: {
size_t memo_id = read<uint8_t>();
setInput(memo_id);
} break;
case PickleOpCode::LONG_BINPUT: {
TORCH_CHECK(
std::numeric_limits<size_t>::max() >=
std::numeric_limits<uint32_t>::max(),
"Found a LONG_BINPUT opcode, but size_t on this system is "
"not big enough to decode it");
size_t memo_id = read<uint32_t>();
setInput(memo_id);
} break;
case PickleOpCode::MARK: {
// Mark location of the container ivalue in the stack
marks_.push_back(stack_.size());
} break;
case PickleOpCode::NEWTRUE: {
stack_.emplace_back(true);
} break;
case PickleOpCode::NEWFALSE: {
stack_.emplace_back(false);
} break;
case PickleOpCode::NONE: {
stack_.emplace_back();
} break;
case PickleOpCode::BININT1: {
uint8_t value = read<uint8_t>();
stack_.emplace_back(int64_t(value));
} break;
case PickleOpCode::BININT2: {
uint16_t value = from_le16(read<uint16_t>());
stack_.emplace_back(int64_t(value));
} break;
case PickleOpCode::BININT: {
int32_t value = from_le32(read<int32_t>());
stack_.emplace_back(int64_t(value));
} break;
case PickleOpCode::LONG1: {
// Only read LONG1s with 8 as the length
uint8_t length = read<uint8_t>();
TORCH_CHECK(length == 8, "Expected length to be 8, got ", int(length));
stack_.emplace_back(int64_t(from_le64(read<int64_t>())));
} break;
case PickleOpCode::BINUNICODE: {
uint32_t length = from_le32(read<uint32_t>());
stack_.emplace_back(readBytes(length));
} break;
case PickleOpCode::BINUNICODE8: {
int64_t length = from_le64(read<int64_t>());
stack_.emplace_back(readBytes(length));
} break;
case PickleOpCode::BINFLOAT:
stack_.emplace_back(readFloat());
break;
case PickleOpCode::TUPLE: {
TORCH_CHECK(!marks_.empty(), "Parsing error: marks_ is empty");
size_t start = marks_.back();
marks_.pop_back();
std::vector<IValue> elements;
TORCH_CHECK(
stack_.size() >= start,
"Parsing error: wrong start index ",
start,
" for stack_ of size ",
stack_.size());
const auto tupleSize = stack_.size() - start;
switch (tupleSize) {
case 3: {
auto e3 = pop(stack_);
auto e2 = pop(stack_);
auto e1 = pop(stack_);
stack_.emplace_back(c10::ivalue::Tuple::create(
std::move(e1), std::move(e2), std::move(e3)));
break;
}
case 2: {
auto e2 = pop(stack_);
auto e1 = pop(stack_);
stack_.emplace_back(
c10::ivalue::Tuple::create(std::move(e1), std::move(e2)));
break;
}
case 1:
stack_.emplace_back(c10::ivalue::Tuple::create(pop(stack_)));
break;
default: {
elements.reserve(stack_.size() - start);
auto start_it = stack_.begin() + static_cast<std::ptrdiff_t>(start);
for (auto it = start_it; it != stack_.end(); ++it) {
elements.emplace_back(std::move(*it));
}
stack_.erase(start_it, stack_.end());
stack_.emplace_back(c10::ivalue::Tuple::create(std::move(elements)));
break;
}
}
} break;
case PickleOpCode::TUPLE1: {
TORCH_CHECK(
!stack_.empty(),
"Parsing error: stack_ contains ",
stack_.size(),
" elements, at least 1 expected");
stack_.emplace_back(c10::ivalue::Tuple::create(pop(stack_)));
} break;
case PickleOpCode::TUPLE2: {
TORCH_CHECK(
stack_.size() > 1,
"Parsing error: stack_ contains ",
stack_.size(),
" elements, at least 2 expected");
auto e2 = pop(stack_);
auto e1 = pop(stack_);
stack_.emplace_back(
c10::ivalue::Tuple::create(std::move(e1), std::move(e2)));
} break;
case PickleOpCode::TUPLE3: {
TORCH_CHECK(
stack_.size() > 2,
"Parsing error: stack_ contains ",
stack_.size(),
" elements, at least 3 expected");
auto e3 = pop(stack_);
auto e2 = pop(stack_);
auto e1 = pop(stack_);
stack_.emplace_back(c10::ivalue::Tuple::create(
std::move(e1), std::move(e2), std::move(e3)));
} break;
case PickleOpCode::EMPTY_DICT:
stack_.emplace_back(
c10::impl::GenericDict(AnyType::get(), AnyType::get()));
break;
case PickleOpCode::APPENDS: {
TORCH_CHECK(!marks_.empty(), "Parsing error: marks_ is empty");
size_t start = marks_.back();
TORCH_CHECK(
start > 0 && start <= stack_.size(),
"Parsing error: wrong start index ",
start,
" for stack_ of size ",
stack_.size());
auto list_ivalue = stack_.at(start - 1);
readList(list_ivalue);
} break;
case PickleOpCode::APPEND: {
TORCH_CHECK(
stack_.size() >= 2, "Parsing error: missing elements in stack_.");
auto list_ivalue = stack_.at(stack_.size() - 2);
readListElements(list_ivalue, stack_.size() - 1);
} break;
case PickleOpCode::LIST: {
IValue list_ivalue = c10::impl::GenericList(AnyType::get());
readList(list_ivalue);
stack_.push_back(std::move(list_ivalue));
} break;
case PickleOpCode::DICT: {
TORCH_CHECK(!marks_.empty(), "Parsing error: marks_ is empty");
size_t start = marks_.back();
marks_.pop_back();
TORCH_CHECK(
stack_.size() > start,
"Parsing error: wrong start index ",
start,
" for stack_ which of size ",
stack_.size());
auto dict = c10::impl::GenericDict(AnyType::get(), AnyType::get());
TORCH_CHECK(
(stack_.size() - start) % 2 == 0,
"Parsing error: stack_ is of size ",
stack_.size(),
" and start index is ",
start,
", but stack_ is iterated by two elements at a time");
for (size_t i = start; i < stack_.size(); i += 2) {
dict.insert_or_assign(stack_[i], stack_[i + 1]);
}
stack_.erase(
stack_.begin() + static_cast<std::ptrdiff_t>(start), stack_.end());
stack_.emplace_back(std::move(dict));
} break;
case PickleOpCode::SETITEMS: {
TORCH_CHECK(!marks_.empty(), "Parsing error: marks_ is empty");
size_t start = marks_.back();
marks_.pop_back();
TORCH_CHECK(
start > 0 && start <= stack_.size(),
"Parsing error: wrong start index for stack_");
auto dict = stack_.at(start - 1).toGenericDict();
TORCH_CHECK(
(stack_.size() - start) % 2 == 0,
"Parsing error: stack_ is of size ",
stack_.size(),
" and start index is ",
start,
", but stack_ is iterated by two elemenst at a time");
for (size_t i = start; i < stack_.size(); i += 2) {
dict.insert_or_assign(stack_[i], stack_[i + 1]);
}
stack_.erase(
stack_.begin() + static_cast<std::ptrdiff_t>(start), stack_.end());
} break;
case PickleOpCode::BINGET: {
auto pos = read<uint8_t>();
TORCH_CHECK(
memo_table_.size() > pos,
"Parsing error: out of bounds access at ",
(size_t)pos,
" to memo_table_ which is of size ",
memo_table_.size());
stack_.push_back(memo_table_.at(pos));
} break;
case PickleOpCode::LONG_BINGET: {
auto pos = read<uint32_t>();
TORCH_CHECK(
memo_table_.size() > pos,
"Parsing error: out of bounds access at ",
(size_t)pos,
" to memo_table_ which is of size ",
memo_table_.size());
stack_.push_back(memo_table_.at(pos));
} break;
case PickleOpCode::STOP:
break;
case PickleOpCode::GLOBAL: {
// Module name, it's not needed for anything
auto module_name = readString();
auto class_name = readString();
readGlobal(module_name, class_name);
} break;
case PickleOpCode::NEWOBJ: {
TORCH_CHECK(!stack_.empty(), "Parsing error: stack_ is empty");
// pop empty tuple, the actual action is stored in the globals_stack_
stack_.pop_back();
} break;
// because we have NEWOBJ do nothing, BUILD and REDUCE end up doing
// the same thing
case PickleOpCode::BUILD:
case PickleOpCode::REDUCE: {
// stack is: <functor_idx> <functor_arg>
// extract <functor_idx> and remove from the stack:
TORCH_CHECK(
stack_.size() > 1,
"Parsing error: stack_ contains ",
stack_.size(),
" elements, at least 2 expected");
std::swap(*(stack_.end() - 2), *(stack_.end() - 1));
size_t idx = stack_.back().toInt();
stack_.pop_back();
// stack is: <functor_arg>
TORCH_CHECK(
idx < globals_.size(),
"Parsing error: out of bounds access to globals_");
globals_.at(idx)();
} break;
case PickleOpCode::BINPERSID: {
TORCH_CHECK(!stack_.empty(), "Parsing error: stack_ is empty");
auto tuple = pop(stack_).toTuple();
const auto& args = tuple->elements();
AT_ASSERT(
args.at(0).toStringRef() == "storage",
"unknown PERSID key ",
args.at(0).toStringRef());
at::ScalarType type = args.at(1).toScalarType();
const std::string& key = args.at(2).toStringRef();
at::Device device(args.at(3).toStringRef());
// remap device location if it's not meta
if (device_ && !device.is_meta()) {
device = *device_;
}
at::Storage storage;
if (storage_context_ != nullptr && storage_context_->hasStorage(key)) {
// for torch.package logic where storage may be loaded already
storage = storage_context_->getStorage(key);
} else {
int64_t numel = args.at(4).toInt();
auto dtype = scalarTypeToTypeMeta(type);
at::DataPtr storage_ptr;
if (numel > 0) {
// If there are no elements in the tensor, there's no point in
// reading a zero (0) byte file from the input stream and paying
// that cost.
storage_ptr = read_record_(key);
}
storage = at::Storage(
c10::Storage::use_byte_size_t(),
numel * dtype.itemsize(),
std::move(storage_ptr),
/*allocator=*/nullptr,
/*resizable=*/false); // NB: we didn't set any allocator for the
// tensor
if (storage_context_ != nullptr) {
storage_context_->addStorage(key, storage);
}
}
auto options = at::device(at::kCPU).dtype(type);
if (use_storage_device_) {
options = options.device(storage.device());
device = storage.device();
}
at::Tensor tensor;
if (options.backend() == c10::Backend::QuantizedCPU) {
tensor = at::_empty_affine_quantized({}, options, 0, 0)
.set_(storage, 0, {}, {});
} else {
tensor = at::empty({0}, options).set_(storage);
}
if (device.is_cuda() || device.is_xpu() || device.is_meta() ||
device.is_mtia() || device.is_hpu() || device.is_mps() ||
device.is_privateuseone()) {
tensor = tensor.to(device, tensor.scalar_type());
} else if (device.type() != DeviceType::CPU) {
TORCH_CHECK(
false,
"supported devices include CPU, CUDA, HPU and ",
c10::get_privateuse1_backend(),
" however got ",
DeviceTypeName(device.type(), false));
}
stack_.emplace_back(std::move(tensor));
} break;
case PickleOpCode::SETITEM: {
// At this OpCode, stack looks like
// | Stack Bottom |
// | ...... |
// | Dict | -> (stack_size - 3)
// | Key | -> (stack_size - 2)
// | Value | -> (stack_size - 1)
TORCH_CHECK(
stack_.size() >= 3,
"Parsing error: stack doesn't have enough elements");
auto stack_size = stack_.size();
auto dict_pos = stack_size - 3;
auto key_pos = stack_size - 2;
auto val_pos = stack_size - 1;
TORCH_CHECK(
(dict_pos < stack_size) && (key_pos < stack_size) &&
(val_pos < stack_size),
"Parsing error: attempted out-of-bounds access while processing SETITEM opcode");
auto dict = stack_.at(dict_pos).toGenericDict();
dict.insert_or_assign(stack_.at(key_pos), stack_.at(val_pos));
stack_.erase(
stack_.begin() + static_cast<std::ptrdiff_t>(key_pos), stack_.end());
} break;
default: {
TORCH_CHECK(
false,
"Unknown opcode for unpickling at ",
// NOLINTNEXTLINE(performance-no-int-to-ptr)
reinterpret_cast<void*>(opcode),
": ",
int(static_cast<uint8_t>(opcode)));
} break;
}
return opcode;
}
void Unpickler::readGlobal(
const std::string& module_name,
const std::string& class_name) {
if (this->skip_next_read_global) {
// See [NOTE] skip_next_read_global
this->skip_next_read_global--;
if (this->skip_next_read_global == 1) {
// Pass through to the correct handler
} else if (this->skip_next_read_global == 0) {
// Corresponds to the type of `Tensor` being unpickled
if (module_name != "torch" || class_name != "Tensor") {
TORCH_WARN(
"Trying to load a Subclassed Tensor, it will be converted to at::Tensor in C++");
}
stack_.emplace_back(int64_t(globals_.size() - 1));
return;
} else {
TORCH_CHECK(false, "INVALID VALUES")
}
}
// TODO [unpickler refactor] __main__ isn't used by the pickler anymore, this
// is only here for bc-compatibility reasons
if (module_name == "__main__") {
if (class_name == "TensorID") {
globals_.emplace_back([this] {
auto setitem_data = stack_.back();
stack_.pop_back();
TORCH_INTERNAL_ASSERT(
!tensor_table_.empty(),
"Pickler tried to write a tensor but had no tensor table to write to");
stack_.emplace_back(tensor_table_.at(setitem_data.toInt()));
});
} else if (class_name == "IntList") {
globals_.emplace_back([this] {
stack_.back().toList().unsafeSetElementType(IntType::get());
});
} else {
TORCH_CHECK(false, "Unknown pickler class id", class_name);
}
} else if (module_name == "torch.jit._pickle") {
if (class_name == "build_tensor_from_id") {
globals_.emplace_back([this] {
// Pop reduce arg off the stack
auto data = stack_.back().toTupleRef().elements().at(0);
stack_.pop_back();
TORCH_CHECK(
!tensor_table_.empty(),
"Found a tensor table reference but Unpickler"
" has no tensor table\n");
stack_.emplace_back(tensor_table_.at(data.toInt()));
});
} else if (class_name == "restore_type_tag") {
globals_.emplace_back([this] {
auto tuple = stack_.back().toTuple();
const auto& data = tuple->elements();
auto type_str = data.at(1).toStringRef();
stack_.pop_back();
TypePtr type = nullptr;
auto entry = type_cache_.find(type_str);
if (entry != type_cache_.end()) {
type = entry->second;
} else {
if (type_resolver_ == nullptr) {
// If we haven't injected a custom way of retrieving types from
// names, use a barebones type parser.
type = type_parser_(type_str);
} else {
type = type_resolver_(type_str).type_;
}
type_cache_[type_str] = type;
}
// TODO: Use lookahead to avoid creating the tuple and immediately
// destroying it here
restoreContainerTypeTags(data.at(0), type);
stack_.emplace_back(data.at(0));
});
} else {
TypePtr elem_type = nullptr;
if (class_name == "build_intlist") {
elem_type = IntType::get();
} else if (class_name == "build_tensorlist") {
elem_type = TensorType::get();
} else if (class_name == "build_doublelist") {
elem_type = FloatType::get();
} else if (class_name == "build_boollist") {
elem_type = BoolType::get();
} else {
TORCH_CHECK(false, "Unknown pickler class id ", class_name);
}
// Unpickle a list specialization (e.g. List[Tensor], List[int], ...)
globals_.emplace_back([this, elem_type] {
// Pop reduce arg off the stack
auto data = stack_.back().toTupleRef().elements().at(0).toList();
stack_.pop_back();
data.unsafeSetElementType(elem_type);
stack_.emplace_back(std::move(data));
});
}
} else if (
module_name == "torch._utils" &&
(class_name == "_rebuild_tensor_v2" ||
class_name == "_rebuild_qtensor")) {
// Unpickle a tensor
bool quantized = class_name == "_rebuild_qtensor";
rebuildTensor(quantized);
} else if (
module_name == "torch._tensor" &&
(class_name == "_rebuild_from_type_v2")) {
// Unpickle a Tensor with Python attributes or
// a Subclassed Tensor.
rebuildTensorFromTypeV2();
} else if (
module_name == "torch._utils" && class_name == "_rebuild_sparse_tensor") {
rebuildSparseTensor();
} else if (module_name == "builtins" && class_name == "complex") {
globals_.emplace_back([this] {
auto tuple = pop(stack_).toTuple();
const auto& elems = tuple->elements();
AT_ASSERT(elems.size() == 2);
auto complex =
c10::complex<double>(elems.at(0).toDouble(), elems.at(1).toDouble());
stack_.emplace_back(complex);
});
} else if (module_name == "collections" && class_name == "OrderedDict") {
// collections.OrderedDict is used in tensor serialization for a tensor's
// backward hooks (but they are not actually saved with this Pickler)
globals_.emplace_back([this] {
// drop the Tuple that was argument to OrderedDict, and replace it
// with None OrderedDicts only appear in tensor deserialization and
// their value is never used
stack_.back() = IValue();
});
} else if (module_name == "torch" && class_name == "device") {
globals_.emplace_back([this] {
auto device_string = stack_.back().toTupleRef().elements().at(0);
stack_.pop_back();
stack_.emplace_back(c10::Device(device_string.toStringRef()));
});
stack_.emplace_back(int64_t(globals_.size() - 1));
return;
} else if (module_name == "torch.distributed.rpc" && class_name == "rref") {
#ifdef USE_RPC
return rebuildRRef();
#else
TORCH_INTERNAL_ASSERT(
false,
"RRef unpickling is only supported with the distributed package");
#endif
} else if (module_name == "torch") {
// Try to manually resolve several global enums
// NOTE: this does not put a global into the global table,
// like the other branches here because no REDUCE or BUILD will
// be called on this value. Instead, we just put it on the stack
// and return early
std::optional<c10::ScalarType> scalar_type;
#define CHECK_SCALAR(_, name) \
if (class_name == #name "Storage") { \
scalar_type = c10::k##name; \
}
AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(CHECK_SCALAR)
#undef CHECK_SCALAR
if (scalar_type.has_value()) {
stack_.emplace_back(int64_t(*scalar_type));
return;
}
std::optional<at::QScheme> qscheme;
for (int i = 0; i < at::COMPILE_TIME_NUM_QSCHEMES; ++i) {
if (class_name == toString(static_cast<at::QScheme>(i))) {
qscheme = static_cast<at::QScheme>(i);
}
}
if (qscheme.has_value()) {
stack_.emplace_back(int64_t(*qscheme));
return;
}
TORCH_CHECK(
false,
"Unpickler found unknown torch global, 'torch.",
class_name,
"'");
} else {
TORCH_CHECK(
type_resolver_,
"Unpickler found unknown type ",
module_name,
".",
class_name);
at::StrongTypePtr type =
type_resolver_(c10::QualifiedName(module_name, class_name));
if (auto enum_type = type.type_->cast<c10::EnumType>()) {
globals_.emplace_back([this, enum_type] {
auto val = stack_.back();
stack_.pop_back();
for (const auto& p : enum_type->enumNamesValues()) {
if (p.second == val) {
auto enum_holder = c10::make_intrusive<at::ivalue::EnumHolder>(
enum_type, p.first, p.second);
stack_.emplace_back(std::move(enum_holder));
return;
}
}
});
} else {
// Otherwise, global is a class/object type.
globals_.emplace_back([this, type] {
auto val = stack_.back();
stack_.pop_back();
auto obj = obj_loader_(type, val);
stack_.emplace_back(std::move(obj));
});
}
}
stack_.emplace_back(int64_t(globals_.size() - 1));
}
void Unpickler::rebuildSparseTensor() {
globals_.emplace_back([this] {
auto tup = pop(stack_).toTuple();
const auto& elements = tup->elements();
size_t idx = 0;
auto layout = elements.at(idx++).toInt();
at::Tensor result;
switch (layout) {
case static_cast<int>(c10::Layout::Sparse): {
std::vector<int64_t> size = tupleToIntList(elements.at(idx++));
bool requires_grad = elements.at(idx++).toBool();
auto& indices_tensor = elements.at(idx++).toTensor();
auto& values_tensor = elements.at(idx++).toTensor();
auto options = values_tensor.options()
.layout(c10::Layout::Sparse)
.requires_grad(requires_grad);
result = at::_sparse_coo_tensor_unsafe(
indices_tensor, values_tensor, size, options);
result = autograd::make_variable(result, options.requires_grad());
break;
}
case static_cast<int>(c10::Layout::SparseCsr): {
std::vector<int64_t> size = tupleToIntList(elements.at(idx++));
bool requires_grad = elements.at(idx++).toBool();
auto& crow_indices = elements.at(idx++).toTensor();
auto& col_indices = elements.at(idx++).toTensor();
auto& values_tensor = elements.at(idx++).toTensor();
auto options = values_tensor.options()
.layout(c10::Layout::SparseCsr)
.requires_grad(requires_grad);
result = at::_sparse_csr_tensor_unsafe(
crow_indices, col_indices, values_tensor, size, options);
result =
autograd::make_variable(std::move(result), options.requires_grad());
break;
}
default:
TORCH_CHECK(
false,
"Unsupported sparse tensor layout type in serialization ",
static_cast<c10::Layout>(layout));
break;
}
stack_.emplace_back(std::move(result));
});
}
void Unpickler::rebuildTensor(bool quantized) {
globals_.emplace_back([this, quantized] {
auto tup = pop(stack_).toTuple();
const auto& elements = tup->elements();
size_t idx = 0;
auto& storage_tensor = elements.at(idx++).toTensor();
int64_t storage_offset = elements.at(idx++).toInt();
std::vector<int64_t> size = tupleToIntList(elements.at(idx++));
std::vector<int64_t> stride = tupleToIntList(elements.at(idx++));
at::Tensor result;
if (quantized) {
auto qparams_tuple = elements.at(idx++).toTuple();
const auto& qparams = qparams_tuple->elements();
auto qscheme = static_cast<at::QScheme>(qparams.at(0).toInt());
switch (qscheme) {
case at::kPerTensorAffine: {
double q_scale = qparams.at(1).toDouble();
int64_t q_zero_point = qparams.at(2).toInt();
result = at::_empty_affine_quantized(
{0}, storage_tensor.options(), q_scale, q_zero_point);
} break;
case at::kPerChannelAffineFloatQParams:
case at::kPerChannelAffine: {
const auto& scales = qparams.at(1).toTensor();
const auto& zero_points = qparams.at(2).toTensor();
int64_t axis = qparams.at(3).toInt();
result = at::_empty_per_channel_affine_quantized(
{0}, scales, zero_points, axis, storage_tensor.options());
} break;
default:
TORCH_CHECK(
false,
"Unsupported tensor quantization type in serialization ",
toString(qscheme));
break;
}
} else {
result = at::empty({0}, storage_tensor.options());
}
bool requires_grad = elements.at(idx++).toBool();
idx++; // backwards hooks is empty
at::TensorImpl* impl = result.unsafeGetTensorImpl();
impl->set_storage_keep_dtype(storage_tensor.storage());
impl->set_storage_offset(storage_offset);
impl->set_sizes_and_strides(size, stride);
result = autograd::make_variable(result, requires_grad);
// Handle if math_bits were pickled.
// See `args` of _reduce_ex_internal
// for a regular tensor (final else case).
// Tensors pickled before this patch didn't
// have this argument for storing MathBits,
// in that case, we do nothing.
// NOTE: `math_bits` is the 7th arg.
// NOTE: This is only meant for regular tensor and not quantized
// which also has 7 args serialized.
if (!quantized && elements.size() == 7) {
auto math_bits = elements.at(idx++).toGenericDict();
torch::jit::setTensorMetadata(result, math_bits);