## Semantic
The semantic is
(1) By default `torch.serialization.skip_data(materialize_fake_tensors=False)` will make `torch.save` skip writing storages (but reserve space for them in the checkpoint).
```python
import torch
import torch.nn as nn
sd = nn.Linear(3, 5).state_dict()
with torch.serialization.skip_data():
torch.save(sd, 'foo.pt')
print(torch.load('foo.pt', weights_only=True))
```
(2) With `torch.serialization.skip_data(materialize_fake_tensors=True)`If FakeTensor is passed to `torch.save` the pickler will treat these FakeTensors as being "materialized" space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor)
```python
import torch
import torch.nn as nn
from torch._subclasses.fake_tensor import FakeTensorMode
with FakeTensorMode():
m = nn.Linear(3, 5, dtype=torch.float16, device='cuda')
sd = m.state_dict()
with torch.serialization.skip_data(materialize_fake_tensors=True):
torch.save(sd, 'bla.pt')
print(torch.load('bla.pt', weights_only=True))
# OrderedDict([('weight', tensor([[0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.]], device='cuda:0', dtype=torch.float16)), ('bias', tensor([0., 0., 0., 0., 0.], device='cuda:0', dtype=torch.float16))])
```
## Follow Ups
- [ ] `torch.load` semantic for skip_data context manager
- [ ] Mechanism for getting offsets of storages saved via this method (for writing in a separate pass)
Differential Revision: [D62238610](https://our.internmc.facebook.com/intern/diff/D62238610)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134504
Approved by: https://github.com/albanD
## Semantic
The semantic is
(1) By default `torch.serialization.skip_data(materialize_fake_tensors=False)` will make `torch.save` skip writing storages (but reserve space for them in the checkpoint).
```python
import torch
import torch.nn as nn
sd = nn.Linear(3, 5).state_dict()
with torch.serialization.skip_data():
torch.save(sd, 'foo.pt')
print(torch.load('foo.pt', weights_only=True))
```
(2) With `torch.serialization.skip_data(materialize_fake_tensors=True)`If FakeTensor is passed to `torch.save` the pickler will treat these FakeTensors as being "materialized" space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor)
```python
import torch
import torch.nn as nn
from torch._subclasses.fake_tensor import FakeTensorMode
with FakeTensorMode():
m = nn.Linear(3, 5, dtype=torch.float16, device='cuda')
sd = m.state_dict()
with torch.serialization.skip_data(materialize_fake_tensors=True):
torch.save(sd, 'bla.pt')
print(torch.load('bla.pt', weights_only=True))
# OrderedDict([('weight', tensor([[0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.]], device='cuda:0', dtype=torch.float16)), ('bias', tensor([0., 0., 0., 0., 0.], device='cuda:0', dtype=torch.float16))])
```
## Follow Ups
- [ ] `torch.load` semantic for skip_data context manager
- [ ] Mechanism for getting offsets of storages saved via this method (for writing in a separate pass)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134504
Approved by: https://github.com/albanD
## Semantic
The semantic is
(1) By default `torch.serialization.skip_data(materialize_fake_tensors=False)` will make `torch.save` skip writing storages (but reserve space for them in the checkpoint).
```python
import torch
import torch.nn as nn
sd = nn.Linear(3, 5).state_dict()
with torch.serialization.skip_data():
torch.save(sd, 'foo.pt')
print(torch.load('foo.pt', weights_only=True))
```
(2) With `torch.serialization.skip_data(materialize_fake_tensors=True)`If FakeTensor is passed to `torch.save` the pickler will treat these FakeTensors as being "materialized" space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor)
```python
import torch
import torch.nn as nn
from torch._subclasses.fake_tensor import FakeTensorMode
with FakeTensorMode():
m = nn.Linear(3, 5, dtype=torch.float16, device='cuda')
sd = m.state_dict()
with torch.serialization.skip_data(materialize_fake_tensors=True):
torch.save(sd, 'bla.pt')
print(torch.load('bla.pt', weights_only=True))
# OrderedDict([('weight', tensor([[0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.]], device='cuda:0', dtype=torch.float16)), ('bias', tensor([0., 0., 0., 0., 0.], device='cuda:0', dtype=torch.float16))])
```
## Follow Ups
- [ ] `torch.load` semantic for skip_data context manager
- [ ] Mechanism for getting offsets of storages saved via this method (for writing in a separate pass)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134504
Approved by: https://github.com/albanD
Fixes#133163
Debugged in collaboration with @hariveliki
The `byte` type is demanding the global `_codecs.encode`. That means, the following currently works:
```python
import torch
torch.save(b'hello', '/tmp/dummy.pth')
torch.serialization.add_safe_globals([_codecs.encode])
torch.load('/tmp/dummy.pth', weights_only=True)
```
Similarly, `bytearray` needs `builtins.bytearray`.
Following the `torch.loads` docs promise, both types should be supported without `add_safe_globals` as they are both primitive types:
> weights_only: Indicates whether unpickler should be restricted to
> loading only tensors, primitive types, dictionaries
> and any types added via :func:`torch.serialization.add_safe_globals`.
This PR adds both `_codecs.encode` and `builtins.bytearray` to `_get_allowed_globals` and test for saving and loading of both types with and without `weights_only`.
Co-authored-by: hariveliki <98284163+hariveliki@users.noreply.github.com>
Co-authored-by: mikaylagawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133189
Approved by: https://github.com/mikaylagawarecki
Also bold certain text in the error message as suggested
<img width="3000" alt="Screenshot 2024-07-19 at 5 56 48 PM" src="https://github.com/user-attachments/assets/378f20c5-c6b2-4e53-8eaf-0bd26c3a6b60">
With a GLOBAL like `os.execv` the error message is now as such
```python
File "/data/users/mg1998/pytorch/torch/serialization.py", line 1256, in load
raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
Trying to load unsupported GLOBAL posix.execv whose module posix is blocked.
Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131259
Approved by: https://github.com/malfet, https://github.com/albanD
As @vmoens pointed out, the current error message does not make the "either/or" between setting `weights_only=False` and using `add_safe_globals` clear enough, and should print the code for the user to call `add_safe_globals`
New formatting looks like such
In the case that `add_safe_globals` can be used
```python
>>> import torch
>>> from torch.testing._internal.two_tensor import TwoTensor
>>> torch.save(TwoTensor(torch.randn(2), torch.randn(2)), "two_tensor.pt")
>>> torch.load("two_tensor.pt", weights_only=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/users/mg1998/pytorch/torch/serialization.py", line 1225, in load
raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options
(1) Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.
WeightsUnpickler error: Unsupported global: GLOBAL torch.testing._internal.two_tensor.TwoTensor was not an allowed global by default. Please use `torch.serialization.add_safe_globals([TwoTensor])` to allowlist this global if you trust this class/function.
Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
```
For other issues (unsupported bytecode)
```python
>>> import torch
>>> t = torch.randn(2, 3)
>>> torch.save(t, "protocol_5.pt", pickle_protocol=5)
>>> torch.load("protocol_5.pt", weights_only=True)
/data/users/mg1998/pytorch/torch/_weights_only_unpickler.py:359: UserWarning: Detected pickle protocol 5 in the checkpoint, which was not the default pickle protocol used by `torch.load` (2). The weights_only Unpickler might not support all instructions implemented by this protocol, please file an issue for adding support if you encounter this.
warnings.warn(
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/users/mg1998/pytorch/torch/serialization.py", line 1225, in load
raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
Please file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Unsupported operand 149
Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
```
Old formatting would have been like:
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/users/mg1998/pytorch/torch/serialization.py", line 1203, in load
raise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you get the file from a trusted source. Alternatively, to load with `weights_only` please check the recommended steps in the following error message. WeightsUnpickler error: Unsupported global: GLOBAL torch.testing._internal.two_tensor.TwoTensor was not an allowed global by default. Please use `torch.serialization.add_safe_globals` to allowlist this global if you trust this class/function.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129705
Approved by: https://github.com/albanD, https://github.com/vmoens
ghstack dependencies: #129239, #129396, #129509
Since we use [`DEFAULT_PROTOCOL=2`](https://github.com/pytorch/pytorch/blob/main/torch/serialization.py#L62), some functions/classes that were renamed from python 2-->3 will be pickled with their python2 name. This PR ensures that when a mod `GLOBAL <python2_mod>.<python2_name> ` is encountered, [following the strategy used by pickle](https://github.com/python/cpython/blob/main/Lib/pickle.py#L1590C13-L1593C63) it is properly mapped to `<python3_mod>.<python3_name>`.
This fix ensures that `add_safe_globals` works properly for such functions/classes (i.e. users will allowlist the python3 func and the weights_only unpickler will do the appropriate translation when checking whether a class was allowlisted).
An example is as follows:
`__builtin__` was named to `builtins`, see the [release notes for Python 3.0](https://docs.python.org/3/whatsnew/3.0.html)
> Renamed module `__builtin__` to [`builtins`](https://docs.python.org/3/library/builtins.html#module-builtins) (removing the underscores, adding an ‘s’). The __builtins__ variable found in most global namespaces is unchanged. To modify a builtin, you should use [builtins](https://docs.python.org/3/library/builtins.html#module-builtins), not `__builtins__`!
However, since we use [`DEFAULT_PROTOCOL=2`](https://github.com/pytorch/pytorch/blob/main/torch/serialization.py#L62), builtins will be pickled with their module string as `__builtin__`.
```python
>>> import pickle
>>> import pickletools
>>> print.__module__
'builtins'
>>> with open('print.pkl', 'wb') as f:
>>> pickle.dump(print, f, protocol=2) # 2 because this is the default protocol used by pytorch
>>> with open('print.pkl', 'rb') as f:
>>> pickletools.dis(f)
0: \x80 PROTO 2
2: c GLOBAL '__builtin__ print' # pickle saves the module string as __builtin__ !!! :(
21: q BINPUT 0
23: . STOP
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129244
Approved by: https://github.com/albanD
#### Conditions for allowlisting tensor subclasses
We allow tensor subclasses types that
(1) Do not override `__setstate__`, `__getattr__`, `__setattr__`, `__get__`, `__set__` or `__getattribute__` of `torch.Tensor` (`torch.Tensor` does not have a definition of `__getattr__`, `__get__` or `__set__` so we check that these are `None`)
(2) Use the generic `tp_alloc`
(3) Are in a module that *has been imported by the user*
to be pushed onto the stack as strings by `GLOBAL` instructions, while storing the type in a dict
The strings will be converted to the classes as appropriate when executing `REBUILD` with `_rebuild_from_type_v2`
*Note that we use `inspect.getattr_static(sys.modules[module], name)` to get the class/function as this method claims to have no code execution.
The rationale for the 3 conditions above is as follows:
The rebuild func provided by `Tensor.__reduce_ex__` is `torch._tensor._rebuild_from_type_v2`, which is defined as such (note the call to `getattr`, `Tensor.__setstate__` and the call to `as_subclass` as well as the call to `_set_obj_state` which calls `setattr`)
4e66aaa010/torch/_tensor.py (L57-L71)
`as_subclass` is implemented with a call to `THPVariable_NewWithVar`
that will eventually call `tp_alloc` here
4e66aaa010/torch/csrc/autograd/python_variable.cpp (L2053)
The `func` arg to `_rebuild_from_type_v2` for wrapper subclasses is `Tensor.rebuild_wrapper_subclass`, which will similarly call into `THPVariable_NewWithVar` and hit the above `tp_alloc`
**Note that we do not call `tp_init` or `tp_new` (i.e. `cls.__init__` or `cls.__new__`) when unpickling**
### How do we check something is a tensor subclass/constraints around imports
In order to check whether `bla` is a tensor subclass in the bytecode `GLOBAL module.name`, we need to do an `issubclass` check, which entails converting the global string to the appropriate type. We *do not* arbitrarily import modules but will perform this check as long as the given subclass (given by `module.name`) has already been imported by the user (i.e. `module in sys.modules` and `issubclass(getattr(sys[modules], name), torch.Tensor)`
This PR also allowlisted `torch._utils._rebuild_wrapper_subclass` and `torch.device` (used by `_rebuild_wrapper_subclass`)
### API for allow listing
This PR also added `torch.serialization.{add/get/clear}_safe_globals` that enables user to allowlist globals they have deemed safe and manipulate this list (for example they could allowlist a tensor subclass with a custom `__setstate__` if they have checked that this is safe).
Next steps:
- Add testing and allowlist required classes for all in-core tensor subclasses (e.g. `DTensor`, `FakeTensor` etc.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124331
Approved by: https://github.com/albanD
Add `PyTorchFileWriter.write_record_metadata(record_name, num_bytes)` that
- writes the zipfile header/end of central directory metadata for an entry*
- reserves `num_bytes` in the zipfile for the payload.
*Since the payload is not provided, the CRC32 computation is skipped and 0s are written in the corresponding entry of the zipfile header
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125184
Approved by: https://github.com/albanD
Fixes#124528
Going over the options for our MapAllocator and what they do, I don't think any other of them need to be piped up to `torch.load`
4f29103749/aten/src/ATen/MapAllocator.h (L8-L16)
~However, I wonder if this `MmapVisibility(Enum)` is a good way to represent "or-ing" together of `mmap` flags if we want to extend it in the future. I looked over the flags for [`mmap(2)`](https://man7.org/linux/man-pages/man2/mmap.2.html), and could not immediately see how most of them would be useful for `torch.load` (would maybe `MAP_LOCKED` (like `mlock`) or `MAP_HUGE` ever be worthwhile?)~
Using the flags provided by the python `mmap` library so that we can extend the allowed flags and pipe them down to the cpp `mmap` call if there is a need for other flags in the future
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124889
Approved by: https://github.com/albanD
Adds the following to allowed globals for the `weights_only` unpickler
- [x] `torch._utils._rebuild_qtensor` and qtensor related types
- [x] `torch._utils._rebuild_parameter_with_state` (used deserializing a parameter that has user-defined attributes like `Param.foo`)
The remaining rebuild functions that have not been allowlisted are
- [x] `torch._utils._rebuild_wrapper_subclass` (allowlisted in above PR)
- [ ] `torch._utils._rebuild_device_tensor_from_numpy`
- [ ] `torch._utils._rebuild_xla_tensor` (legacy)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124330
Approved by: https://github.com/albanD
Fixes https://github.com/pytorch/pytorch/issues/98921
There were two issues detected:
- `MultiStepLR`: issue is described in https://github.com/pytorch/pytorch/issues/98921, this is resolved by allowlisting `collections.Counter`
- `OneCycleLR`: `state_dict['anneal_func']` is either `<function OneCycleLR._annealing_cos at 0x7f364186f5b0>` or
`<function OneCycleLR._annealing_linear at 0x7f39aa483640>` depending on the `anneal_func` kwarg.
This leads to `WeightsUnpickler error: Unsupported class __builtin__.getattr` from the `weights_only` Unpickler.
Fixed the above in a BC-compatible manner by adding `OneCyclicLR._anneal_func_type` as a string attribute and removing `OneCyclicLR.anneal_func`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123775
Approved by: https://github.com/albanD, https://github.com/malfet
By finally breaking FC promise on new dtypes by serializing untyped
storage and tensor dtypes
- Add `_rebuild_tensor_v3` that takes an extra dtype argument
- In `Tensor.__reduce_ex__` serialize tensor using untyped storage for
v3_dtypes (which are at the moment limited to float8 dtypes)
Test plan: `python -c "import torch;x=torch.arange(10).to(dtype=torch.float8_e4m3fn);torch.save(x, 'pt.pt');print(torch.load('pt.pt'))"`
Fixes https://github.com/pytorch/pytorch/issues/114634
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114662
Approved by: https://github.com/ngimel
Use the same strategy as for unsafe pickler, i.e. use dummy `torch.serialization.StorageType` to represent legacy typed storage classes during deserialization. Add `_dtype` property to be able to use it for both new and legacy format deserialization.
Parametrize `test_serialization_new_format_old_format_compat`
Add regression test to validate that loading legacy modes can be done
without any warnings
Before the change:
```
% python test_serialization.py -v -k test_serialization_new_format_old_format_compat_
test_serialization_new_format_old_format_compat_cpu (__main__.TestBothSerializationCPU) ... ok
test_serialization_new_format_old_format_compat_safe_cpu (__main__.TestBothSerializationCPU) ... /Users/nshulga/git/pytorch/pytorch/torch/_utils.py:836: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
return self.fget.__get__(instance, owner)()
ok
----------------------------------------------------------------------
Ran 2 tests in 0.116s
OK
```
Without the change but update test to catch warnings:
```
% python test_serialization.py -v -k test_serialization_new_format_old_format_compat_
test_serialization_new_format_old_format_compat_weights_only_False_cpu (__main__.TestBothSerializationCPU) ... ok
test_serialization_new_format_old_format_compat_weights_only_True_cpu (__main__.TestBothSerializationCPU) ... FAIL
======================================================================
FAIL: test_serialization_new_format_old_format_compat_weights_only_True_cpu (__main__.TestBothSerializationCPU)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/nshulga/git/pytorch/pytorch/torch/testing/_internal/common_utils.py", line 2536, in wrapper
method(*args, **kwargs)
File "/Users/nshulga/git/pytorch/pytorch/torch/testing/_internal/common_device_type.py", line 415, in instantiated_test
result = test(self, **param_kwargs)
File "/Users/nshulga/git/pytorch/pytorch/test/test_serialization.py", line 807, in test_serialization_new_format_old_format_compat
self.assertTrue(len(w) == 0, msg=f"Expected no warnings but got {[str(x) for x in w]}")
AssertionError: False is not true : Expected no warnings but got ["{message : UserWarning('TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()'), category : 'UserWarning', filename : '/Users/nshulga/git/pytorch/pytorch/torch/_utils.py', lineno : 836, line : None}"]
To execute this test, run the following from the base repo dir:
python test/test_serialization.py -k test_serialization_new_format_old_format_compat_weights_only_True_cpu
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
----------------------------------------------------------------------
Ran 2 tests in 0.109s
FAILED (failures=1)
```
Fixes problem reported in https://github.com/pytorch/pytorch/issues/52181#issuecomment-1715738910
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113614
Approved by: https://github.com/kit1980, https://github.com/albanD
This was discussed in feedback from the original version of my "reorder proxy/fake" PR. This PR allows calls to `tensor.untyped_storage()` to **always** return a python storage object to the user. Previously, we would error loudly if we detected that the storage had a null dataptr.
Instead, I updated the python bindings for the python storage methods that I saw involve data access, to throw an error later, only if you try to access those methods (e.g. `storage.data_ptr()` will now raise an error if the data ptr is null).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107417
Approved by: https://github.com/albanD, https://github.com/ezyang, https://github.com/zou3519
