Custom unpickler to avoid pickle's arbitrary code execution vulnerability

torch_lazy_loader.py

@@ -50,9 +50,12 @@ import itertools
 import zipfile
 import pickle
 import torch
+import numpy as np
+import collections
+import _codecs
 import utils
 from torch.nn import Module
-from typing import Any, Callable, Dict, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
 
 _EXTRA_STATE_KEY_SUFFIX = '_extra_state'
@@ -111,8 +114,50 @@ class LazyTensor:
         tensor._backward_hooks = self.backward_hooks
         return tensor
 
+class RestrictedUnpickler(pickle.Unpickler):
+    def original_persistent_load(self, saved_id):
+        return super().persistent_load(saved_id)
 
-class _LazyUnpickler(pickle.Unpickler):
+    def forced_persistent_load(self, saved_id):
+        if saved_id[0] != "storage":
+            raise pickle.UnpicklingError("`saved_id[0]` must be 'storage'")
+        return self.original_persistent_load(saved_id)
+
+    def find_class(self, module, name):
+        if module == "collections" and name == "OrderedDict":
+            return collections.OrderedDict
+        elif module == "torch._utils" and name == "_rebuild_tensor_v2":
+            return torch._utils._rebuild_tensor_v2
+        elif module == "torch" and name in (
+            "DoubleStorage",
+            "FloatStorage",
+            "HalfStorage",
+            "LongStorage",
+            "IntStorage",
+            "ShortStorage",
+            "CharStorage",
+            "ByteStorage",
+            "BoolStorage",
+            "BFloat16Storage",
+        ):
+            return getattr(torch, name)
+        elif module == "numpy.core.multiarray" and name == "scalar":
+            return np.core.multiarray.scalar
+        elif module == "numpy" and name == "dtype":
+            return np.dtype
+        elif module == "_codecs" and name == "encode":
+            return _codecs.encode
+        else:
+            # Forbid everything else.
+            qualified_name = name if module == "__builtin__" else f"{module}.{name}"
+            raise pickle.UnpicklingError(f"`{qualified_name}` is forbidden; the model you are loading probably contains malicious code")
+
+    def load(self, *args, **kwargs):
+        self.original_persistent_load = getattr(self, "persistent_load", pickle.Unpickler.persistent_load)
+        self.persistent_load = self.forced_persistent_load
+        return super().load(*args, **kwargs)
+
+class _LazyUnpickler(RestrictedUnpickler):
     lazy_loaded_storages: Dict[str, LazyTensor]
 
     def __init__(self, *args, **kwargs):
@@ -127,7 +172,6 @@ class _LazyUnpickler(pickle.Unpickler):
         return LazyTensor(storage_type, key, location)
 
     def load(self, *args, **kwargs):
-        self.persistent_load = self.forced_persistent_load
         retval = super().load(*args, **kwargs)
         self.lazy_loaded_storages = {}
         return retval
@@ -213,16 +257,33 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, miss
                     unexpected_keys.append(key)
 
 
+@contextlib.contextmanager
+def use_custom_unpickler(unpickler: Type[pickle.Unpickler] = RestrictedUnpickler):
+    try:
+        old_unpickler = pickle.Unpickler
+        pickle.Unpickler = unpickler
+
+        old_pickle_load = pickle.load
+
+        def new_pickle_load(*args, **kwargs):
+            return pickle.Unpickler(*args, **kwargs).load()
+
+        pickle.load = new_pickle_load
+
+        yield
+
+    finally:
+        pickle.Unpickler = old_unpickler
+        pickle.load = old_pickle_load
+
 @contextlib.contextmanager
 def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, dematerialized_modules=False, use_accelerate_init_empty_weights=False):
     if not enable:
-        yield False
+        with use_custom_unpickler(RestrictedUnpickler):
+            yield False
         return
 
     try:
-        old_unpickler = pickle.Unpickler
-        pickle.Unpickler = _LazyUnpickler
-
         old_rebuild_tensor = torch._utils._rebuild_tensor
         torch._utils._rebuild_tensor = _rebuild_tensor
 
@@ -261,10 +322,10 @@ def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, demate
                 old_load_from_state_dict = torch.nn.Module._load_from_state_dict
                 torch.nn.Module._load_from_state_dict = _load_from_state_dict
 
-        yield True
+        with use_custom_unpickler(_LazyUnpickler):
+            yield True
 
     finally:
-        pickle.Unpickler = old_unpickler
         torch._utils._rebuild_tensor = old_rebuild_tensor
         torch.load = old_torch_load
         if dematerialized_modules:

tpu_mtj_backend.py

@@ -955,6 +955,7 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
 
     import torch
     import torch.utils.dlpack
+    import torch_lazy_loader
     from tqdm.auto import tqdm
 
     move_xmap = jax.experimental.maps.xmap(
@@ -996,8 +997,9 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
             continue
         layer = checkpoint_layer - 2
         shards = []
-        for checkpoint_shard in range(checkpoint_shards):
+        with torch_lazy_loader.use_custom_unpickler(torch_lazy_loader.RestrictedUnpickler):
-            shards.append(torch.load(path_template.format(layer=checkpoint_layer, shard=checkpoint_shard), map_location="cpu"))
+            for checkpoint_shard in range(checkpoint_shards):
+                shards.append(torch.load(path_template.format(layer=checkpoint_layer, shard=checkpoint_shard), map_location="cpu"))
         for key in shards[0]:
             if key == "attention.rotary_emb.inv_freq":
                 continue