#pragma once
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#include <string>
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#include <vector>
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#include <ATen/core/ivalue.h>
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#include <ATen/core/jit_type.h>
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#include <c10/util/ArrayRef.h>
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#include <torch/csrc/utils/disallow_copy.h>
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namespace torch {
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namespace jit {
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// See Python's pickletools.py for a detailed description of each of these codes
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enum class PickleOpCode : char {
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MARK = '(',
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STOP = '.',
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POP = '0',
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POP_MARK = '1',
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DUP = '2',
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FLOAT = 'F',
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INT = 'I',
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BININT = 'J',
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BININT1 = 'K',
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LONG = 'L',
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BININT2 = 'M',
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NONE = 'N',
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PERSID = 'P',
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BINPERSID = 'Q',
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REDUCE = 'R',
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STRING = 'S',
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BINSTRING = 'T',
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SHORT_BINSTRING = 'U',
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UNICODE = 'V',
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BINUNICODE = 'X',
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APPEND = 'a',
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BUILD = 'b',
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GLOBAL = 'c',
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DICT = 'd',
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EMPTY_DICT = '}',
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APPENDS = 'e',
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GET = 'g',
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BINGET = 'h',
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INST = 'i',
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LONG_BINGET = 'j',
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LIST = 'l',
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EMPTY_LIST = ']',
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OBJ = 'o',
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PUT = 'p',
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BINPUT = 'q',
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LONG_BINPUT = 'r',
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SETITEM = 's',
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TUPLE = 't',
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EMPTY_TUPLE = ')',
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SETITEMS = 'u',
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BINFLOAT = 'G',
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// Protocol 2
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PROTO = '\x80',
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NEWOBJ = '\x81',
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EXT1 = '\x82',
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EXT2 = '\x83',
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EXT4 = '\x84',
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TUPLE1 = '\x85',
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TUPLE2 = '\x86',
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TUPLE3 = '\x87',
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NEWTRUE = '\x88',
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NEWFALSE = '\x89',
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LONG1 = '\x8a',
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LONG4 = '\x8b',
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// Protocol 3 (Python 3.x)
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BINBYTES = 'B',
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SHORT_BINBYTES = 'C',
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// Protocol 4
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SHORT_BINUNICODE = '\x8c',
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BINUNICODE8 = '\x8d',
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BINBYTES8 = '\x8e',
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EMPTY_SET = '\x8f',
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ADDITEMS = '\x90',
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FROZENSET = '\x91',
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NEWOBJ_EX = '\x92',
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STACK_GLOBAL = '\x93',
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MEMOIZE = '\x94',
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FRAME = '\x95'
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};
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enum PicklerClass : uint8_t {
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// A reference to the tensor table
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TENSOR = 0,
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// List[int]
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INTLIST = 1,
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// List[Tensor]
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TENSORLIST = 2,
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// List[float]
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DOUBLELIST = 3,
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// List[bool]
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BOOLLIST = 4
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};
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using ::c10::IValue;
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struct WriteableTensorData {
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const char* data() const {
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return static_cast<const char*>(tensor_.storage().data());
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}
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size_t sizeInBytes() const {
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return size_;
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}
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size_t numel() const {
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return tensor_.storage().numel();
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}
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private:
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friend WriteableTensorData getWriteableTensorData(const at::Tensor& tensor);
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at::Tensor tensor_;
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uint64_t size_;
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};
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class Pickler {
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TH_DISALLOW_COPY_AND_ASSIGN(Pickler);
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public:
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Pickler(
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std::function<void(const char*, size_t)> writer,
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std::vector<at::Tensor>* tensor_table)
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: writer_(writer), tensor_table_(tensor_table) {}
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// Push protocol onto the stack
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void protocol();
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// Push STOP PickleOpCode onto the stack
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void stop();
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void pushIValue(const IValue& ivalue);
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void startTuple();
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void endTuple();
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const std::vector<WriteableTensorData>& tensorData() {
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return tensor_data_;
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}
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void pushEmptyDict();
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void pushDict(const IValue& ivalue);
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void pushInt(int64_t value);
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void pushLong(const std::string& data);
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private:
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void pushIValueImpl(const IValue& ivalue);
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void pushDouble(double value);
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void pushGenericList(const IValue& ivalue);
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void pushIntList(const IValue& ivalue);
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void pushList(const IValue& ivalue);
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void pushTensor(const IValue& ivalue);
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void pushTensorReference(const IValue& ivalue);
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void pushLiteralTensor(const IValue& ivalue);
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void pushTuple(const IValue& ivalue);
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void pushString(const std::string& string);
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// unmemoized version
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void pushStringImpl(const std::string& string);
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void pushStorageOfTensor(const at::Tensor& tensor);
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void pushBinGet(uint32_t memo_id);
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void pushClass(PicklerClass cls);
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void pushSpecializedList(
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const IValue& ivalue,
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PicklerClass cls,
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const std::function<void(const IValue&)>& item_pusher);
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void pushGlobal(
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const std::string& module_name,
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const std::string& class_name);
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// raw string data is appended directly to the byte stream
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void pushBytes(const std::string& string);
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void pushTensorData(const at::Tensor& tensor);
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// Add a BINPUT op and return the memoization id used
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size_t pushNextBinPut();
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const void* getPointer(const IValue& ivalue);
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// These convert values to bytes and add them to the stack (NB: since T is to
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// the left of a '::', its type cannot be deduced by the compiler so one must
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// explicitly instantiate the template, i.e. push<int>(int) works, push(int)
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// does not)
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template <typename T>
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void push(typename std::common_type<T>::type value) {
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const char* begin = reinterpret_cast<const char*>(&value);
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writer_(begin, sizeof(T));
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}
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// Stream to write binary data to
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std::function<void(const char*, size_t)> writer_;
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// Stack of opcodes/data
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std::vector<char> stack_;
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// External table of tensors to serialize. If this is missing, then tensors
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// are serialized directly into the pickle
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std::vector<at::Tensor>* tensor_table_;
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// TODO: only use this if necessary (add a pass to find all shared ivalues,
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// and only memoize those)
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uint32_t memo_id_ = 0;
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// Memoization of IValues that have been written (index in table is used for
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// BINPUT opcodes) to enable shared references
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std::unordered_map<const void*, uint32_t> memoized_ivalue_map_;
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// because we de-dup ivalues based on their raw pointer address in the above
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// map we need to keep all the memoized values alive during the pickle.
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// Otherwise, it is possible that a raw address gets reused for another
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// object, and we will alias it to the old object at that address.
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std::vector<IValue> memoized_ivalues_;
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// List of tensor storages to serialize in the same binary as the pickle data
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// similar to ivalues, they are memoized using BINPUT
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std::vector<WriteableTensorData> tensor_data_;
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std::unordered_map<const void*, uint32_t> memoized_storage_map_;
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std::unordered_map<std::string, uint32_t> memoized_globals_map_;
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std::unordered_map<std::string, uint32_t> memoized_strings_map_;
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};
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// returns a (tensor, record_size) for a tensor, converting it to a CPU tensor
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// if necessary
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WriteableTensorData getWriteableTensorData(const at::Tensor& tensor);
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// return the value of the tensor's storage pointer
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uint64_t getStorageKey(const at::Tensor& tensor);
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// if the cls has __getstate__/__setstate__
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// assert they have the right schema and return true,
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// otherwise return false
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bool checkHasValidSetGetState(const std::shared_ptr<c10::ClassType>& cls);
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} // namespace jit
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} // namespace torch
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