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# Unpickler restricted to loading only state dicts
# Restrict constructing types to a list defined in _get_allowed_globals()
# Restrict BUILD operation to `Tensor`, `Parameter` and `OrderedDict` types only
# Restrict APPEND/APPENDS to `list`
# In `GLOBALS` operation do not do class lookup by name, but rather rely on dictionary
# defined by `_get_allowed_globals()` method, that contains:
# - torch types (Storage, dtypes, Tensor, `torch.Size`),
# - `torch._utils._rebuild` functions.
# - `torch.nn.Parameter`
# - `collections.OrderedDict`
# Based of https://github.com/python/cpython/blob/main/Lib/pickle.py
# Expected to be useful for loading PyTorch model weights
# For example:
# data = urllib.request.urlopen('https://download.pytorch.org/models/resnet50-0676ba61.pth').read()
# buf = io.BytesIO(data)
# weights = torch.load(buf, weights_only = True)
import functools as _functools
from collections import OrderedDict
from pickle import (
APPEND,
APPENDS,
BINGET,
BININT,
BININT1,
BININT2,
BINPERSID,
BINPUT,
BINUNICODE,
BUILD,
bytes_types,
decode_long,
EMPTY_DICT,
EMPTY_LIST,
EMPTY_SET,
EMPTY_TUPLE,
GLOBAL,
LONG1,
LONG_BINGET,
LONG_BINPUT,
MARK,
NEWFALSE,
NEWOBJ,
NEWTRUE,
NONE,
PROTO,
REDUCE,
SETITEM,
SETITEMS,
SHORT_BINSTRING,
STOP,
TUPLE,
TUPLE1,
TUPLE2,
TUPLE3,
UnpicklingError,
)
from struct import unpack
from sys import maxsize
from typing import Any, Dict, List
import torch
# Unpickling machinery
@_functools.lru_cache(maxsize=1)
def _get_allowed_globals():
rc: Dict[str, Any] = {
"collections.OrderedDict": OrderedDict,
"torch.nn.parameter.Parameter": torch.nn.Parameter,
"torch.serialization._get_layout": torch.serialization._get_layout,
"torch.Size": torch.Size,
"torch.Tensor": torch.Tensor,
}
# dtype
for t in [
torch.complex32,
torch.complex64,
torch.complex128,
torch.float16,
torch.float32,
torch.float64,
torch.int8,
torch.int16,
torch.int32,
torch.int64,
]:
rc[str(t)] = t
# Tensor classes
for tt in torch._tensor_classes:
rc[f"{tt.__module__}.{tt.__name__}"] = tt
# Storage classes
for ts in torch._storage_classes:
rc[f"{ts.__module__}.{ts.__name__}"] = ts
# Rebuild functions
for f in [
torch._utils._rebuild_parameter,
torch._utils._rebuild_tensor,
torch._utils._rebuild_tensor_v2,
torch._utils._rebuild_sparse_tensor,
torch._utils._rebuild_meta_tensor_no_storage,
torch._utils._rebuild_sparse_csr_tensor,
]:
rc[f"torch._utils.{f.__name__}"] = f
return rc
class Unpickler:
def __init__(self, file, *, encoding: str = "bytes"):
self.encoding = encoding
self.readline = file.readline
self.read = file.read
self.memo: Dict[int, Any] = {}
def load(self):
"""Read a pickled object representation from the open file.
Return the reconstituted object hierarchy specified in the file.
"""
self.metastack = []
self.stack: List[Any] = []
self.append = self.stack.append
read = self.read
readline = self.readline
while True:
key = read(1)
if not key:
raise EOFError
assert isinstance(key, bytes_types)
# Risky operators
if key[0] == GLOBAL[0]:
module = readline()[:-1].decode("utf-8")
name = readline()[:-1].decode("utf-8")
full_path = f"{module}.{name}"
if full_path in _get_allowed_globals():
self.append(_get_allowed_globals()[full_path])
else:
raise RuntimeError(f"Unsupported class {full_path}")
elif key[0] == NEWOBJ[0]:
args = self.stack.pop()
cls = self.stack.pop()
if cls is not torch.nn.Parameter:
raise RuntimeError(f"Trying to instantiate unsupported class {cls}")
self.append(torch.nn.Parameter(*args))
elif key[0] == REDUCE[0]:
args = self.stack.pop()
func = self.stack[-1]
if func not in _get_allowed_globals().values():
raise RuntimeError(
f"Trying to call reduce for unrecognized function {func}"
)
self.stack[-1] = func(*args)
elif key[0] == BUILD[0]:
state = self.stack.pop()
inst = self.stack[-1]
if type(inst) is torch.Tensor:
# Legacy unpickling
inst.set_(*state)
elif type(inst) is torch.nn.Parameter:
inst.__setstate__(state)
elif type(inst) is OrderedDict:
inst.__dict__.update(state)
else:
raise RuntimeError(
f"Can only build Tensor, parameter or dict objects, but got {type(inst)}"
)
# Stack manipulation
elif key[0] == APPEND[0]:
item = self.stack.pop()
list_obj = self.stack[-1]
if type(list_obj) is not list:
raise RuntimeError(
f"Can only append to lists, but got {type(list_obj)}"
)
list_obj.append(item)
elif key[0] == APPENDS[0]:
items = self.pop_mark()
list_obj = self.stack[-1]
if type(list_obj) is not list:
raise RuntimeError(
f"Can only extend lists, but got {type(list_obj)}"
)
list_obj.extend(items)
elif key[0] == SETITEM[0]:
(v, k) = (self.stack.pop(), self.stack.pop())
self.stack[-1][k] = v
elif key[0] == SETITEMS[0]:
items = self.pop_mark()
for i in range(0, len(items), 2):
self.stack[-1][items[i]] = items[i + 1]
elif key[0] == MARK[0]:
self.metastack.append(self.stack)
self.stack = []
self.append = self.stack.append
elif key[0] == TUPLE[0]:
items = self.pop_mark()
self.append(tuple(items))
elif key[0] == TUPLE1[0]:
self.stack[-1] = (self.stack[-1],)
elif key[0] == TUPLE2[0]:
self.stack[-2:] = [(self.stack[-2], self.stack[-1])]
elif key[0] == TUPLE3[0]:
self.stack[-3:] = [(self.stack[-3], self.stack[-2], self.stack[-1])]
# Basic types construction
elif key[0] == NONE[0]:
self.append(None)
elif key[0] == NEWFALSE[0]:
self.append(False)
elif key[0] == NEWTRUE[0]:
self.append(True)
elif key[0] == EMPTY_TUPLE[0]:
self.append(())
elif key[0] == EMPTY_LIST[0]:
self.append([])
elif key[0] == EMPTY_DICT[0]:
self.append({})
elif key[0] == EMPTY_SET[0]:
self.append(set())
elif key[0] == BININT[0]:
self.append(unpack("<i", read(4))[0])
elif key[0] == BININT1[0]:
self.append(self.read(1)[0])
elif key[0] == BININT2[0]:
self.append(unpack("<H", read(2))[0])
elif key[0] == BINUNICODE[0]:
strlen = unpack("<I", read(4))[0]
if strlen > maxsize:
raise RuntimeError("String is too long")
strval = str(read(strlen), "utf-8", "surrogatepass")
self.append(strval)
elif key[0] == SHORT_BINSTRING[0]:
strlen = read(1)[0]
strdata = read(strlen)
if self.encoding != "bytes":
strdata = strdata.decode(self.encoding, "strict")
self.append(strdata)
elif key[0] == BINPERSID[0]:
pid = self.stack.pop()
# Only allow persistent load of storage
if type(pid) is not tuple and not type(pid) is not int:
raise RuntimeError(
f"persistent_load id must be tuple or int, but got {type(pid)}"
)
if (
type(pid) is tuple
and len(pid) > 0
and torch.serialization._maybe_decode_ascii(pid[0]) != "storage"
):
raise RuntimeError(
f"Only persistent_load of storage is allowed, but got {pid[0]}"
)
self.append(self.persistent_load(pid))
elif key[0] in [BINGET[0], LONG_BINGET[0]]:
idx = (read(1) if key[0] == BINGET[0] else unpack("<I", read(4)))[0]
self.append(self.memo[idx])
elif key[0] in [BINPUT[0], LONG_BINPUT[0]]:
i = (read(1) if key[0] == BINPUT[0] else unpack("<I", read(4)))[0]
if i < 0:
raise ValueError("negative argument")
self.memo[i] = self.stack[-1]
elif key[0] == LONG1[0]:
n = read(1)[0]
data = read(n)
self.append(decode_long(data))
# First and last deserializer ops
elif key[0] == PROTO[0]:
# Read and ignore proto version
read(1)[0]
elif key[0] == STOP[0]:
rc = self.stack.pop()
return rc
else:
raise RuntimeError(f"Unsupported operand {key[0]}")
# Return a list of items pushed in the stack after last MARK instruction.
def pop_mark(self):
items = self.stack
self.stack = self.metastack.pop()
self.append = self.stack.append
return items
def persistent_load(self, pid):
raise UnpicklingError("unsupported persistent id encountered")
def load(file, *, encoding: str = "ASCII"):
return Unpickler(file, encoding=encoding).load()
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