Commit Graph

6 Commits

Author SHA1 Message Date
Nikita Shulga
c01f5118a6 Add float to list of allowed ops (#94910)
By adding `BINFLOAT` op support

Fixes https://github.com/pytorch/pytorch/issues/94670
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94910
Approved by: https://github.com/albanD
2023-02-15 23:13:21 +00:00
Pearu Peterson
50e2e4faf3 Sparse CSC/BSR/BSC serialization and pickle support (#89553)
Fixes https://github.com/pytorch/pytorch/issues/89497

Pull Request resolved: https://github.com/pytorch/pytorch/pull/89553
Approved by: https://github.com/cpuhrsch
2022-11-23 20:56:48 +00:00
kshitij12345
f74946324e [fix] allow saving python attr on Tensor and Parameter via torch.save (#81616)
Fixes: https://github.com/pytorch/pytorch/issues/72129

TODO:
* [x] Fix for Parameter

Benchmark
(Measurable diff for small tensors)
```
[-------------- Save and Load --------------]
                    |  After PR  |  Before PR
1 threads: ----------------------------------
      ()            |    111.7   |     106.9
      (4, 4)        |    114.4   |     109.2
      (128, 128)    |    135.2   |     128.3
      (1024, 1024)  |   1431.9   |    1431.3

Times are in microseconds (us).
```

<details>

<summary> Benchmark Script </summary>

```python
import torch
from torch.testing._internal.common_utils import BytesIOContext
from torch.utils import benchmark
import pickle

shapes = ((), (4, 4), (128, 128), (1024, 1024))

sizes = [1, 64, 1024, 10000]
results = []

def save_load_fn(t):
    with BytesIOContext() as f:
        torch.save(t, f)
        f.seek(0)
        torch.load(f)

for shape in shapes:
    t = torch.randn(shape)
    label = 'Save and Load'
    sub_label = f'{shape}'
    results.append(benchmark.Timer(
        stmt='save_load_fn(t)',
        globals={'t': t, 'save_load_fn':save_load_fn},
        label=label,
        sub_label=sub_label,
        description='Before PR',
    ).blocked_autorange(min_run_time=2))

compare = benchmark.Compare(results)
compare.print()

with open('before_pr.pkl', 'wb') as f:
    pickle.dump(results, f)

# with open('after_pr.pkl', 'rb') as f:
#     after_pr = pickle.load(f)

# with open('before_pr.pkl', 'rb') as f:
#     before_pr = pickle.load(f)

# compare = benchmark.Compare(after_pr + before_pr)
# compare.print()
```

</details>

NOTE : **BC-Breaking** : After this PR, all tensors (also regular tensors) will be serialised using `_rebuild_from_type_v2`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/81616
Approved by: https://github.com/albanD, https://github.com/kurtamohler
2022-11-11 21:11:12 +00:00
PyTorch MergeBot
78a0ca29d9 Revert "[fix] allow saving python attr on Tensor and Parameter via torch.save (#81616)"
This reverts commit 54b6188cc6.

Reverted https://github.com/pytorch/pytorch/pull/81616 on behalf of https://github.com/mehtanirav due to Internal publishing is broken
2022-11-07 18:51:16 +00:00
Kshiteej K
54b6188cc6 [fix] allow saving python attr on Tensor and Parameter via torch.save (#81616)
Fixes: https://github.com/pytorch/pytorch/issues/72129

TODO:
* [x] Fix for Parameter

Benchmark
(Measurable diff for small tensors)
```
[-------------- Save and Load --------------]
                    |  After PR  |  Before PR
1 threads: ----------------------------------
      ()            |    111.7   |     106.9
      (4, 4)        |    114.4   |     109.2
      (128, 128)    |    135.2   |     128.3
      (1024, 1024)  |   1431.9   |    1431.3

Times are in microseconds (us).
```

<details>

<summary> Benchmark Script </summary>

```python
import torch
from torch.testing._internal.common_utils import BytesIOContext
from torch.utils import benchmark
import pickle

shapes = ((), (4, 4), (128, 128), (1024, 1024))

sizes = [1, 64, 1024, 10000]
results = []

def save_load_fn(t):
    with BytesIOContext() as f:
        torch.save(t, f)
        f.seek(0)
        torch.load(f)

for shape in shapes:
    t = torch.randn(shape)
    label = 'Save and Load'
    sub_label = f'{shape}'
    results.append(benchmark.Timer(
        stmt='save_load_fn(t)',
        globals={'t': t, 'save_load_fn':save_load_fn},
        label=label,
        sub_label=sub_label,
        description='Before PR',
    ).blocked_autorange(min_run_time=2))

compare = benchmark.Compare(results)
compare.print()

with open('before_pr.pkl', 'wb') as f:
    pickle.dump(results, f)

# with open('after_pr.pkl', 'rb') as f:
#     after_pr = pickle.load(f)

# with open('before_pr.pkl', 'rb') as f:
#     before_pr = pickle.load(f)

# compare = benchmark.Compare(after_pr + before_pr)
# compare.print()
```

</details>

NOTE : **BC-Breaking** : After this PR, all tensors (also regular tensors) will be serialised using `_rebuild_from_type_v2`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/81616
Approved by: https://github.com/albanD, https://github.com/kurtamohler
2022-11-03 09:57:47 +00:00
Nikita Shulga
961ebca225 Add weights_only option to torch.load (#86812)
This addresses the security issue in default Python's `unpickler` that allows arbitrary code execution while unpickling.
Restrict classes allowed to be unpicked to in `None`, `int`, `bool`, `str`, `float`, `list`, `tuple`, `dict`/`OrderedDict` as well as `torch.Size`, `torch.nn.Param` as well as  `torch.Tensor` and `torch.Storage` variants.

Defaults `weights_only` is set to `False`,  but allows global override to safe only load via `TORCH_FORCE_WEIGHTS_ONLY_LOAD` environment variable.

To some extent, addresses https://github.com/pytorch/pytorch/issues/52596
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86812
Approved by: https://github.com/ezyang
2022-10-21 01:09:50 +00:00