Add torch.serialization.skip_data context manager #134504

mikaylagawarecki · 2024-08-26T20:55:27Z

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).

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)

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)

Stack from ghstack (oldest at bottom):

-> Add torch.serialization.skip_data context manager #134504

Differential Revision: D62238610

[ghstack-poisoned]

pytorch-bot · 2024-08-26T20:55:29Z

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[ghstack-poisoned]

The semantic is (1) If real tensors are passed, storages bytes will not be written but zipfile metadata + sufficient space will be saved in the checkpoint for ```python import torch import torch.nn as nn sd = nn.Linear(3, 5).state_dict() torch.save(sd, 'foo.pt', metadata_only=True) print(torch.load('foo.pt', weights_only=True)) # OrderedDict([('weight', tensor([[0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.]])), ('bias', tensor([0., 0., 0., 0., 0.]))]) ``` (2) If FakeTensor is passed, space will be saved in the checkpoint for the associated storage ```python from torch._subclasses.fake_tensor import FakeTensorMode with FakeTensorMode(): m = nn.Linear(3, 5, dtype=torch.float16, device='cuda') sd = m.state_dict() torch.save(sd, 'bla.pt', metadata_only=True) 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))]) ``` [ghstack-poisoned]

torch/_tensor.py

mikaylagawarecki · 2024-08-26T21:41:04Z

torch/serialization.py

            # pointing to the same data all have the same dtype. If storage is
            # not allocated, don't perform this check
-            if storage.data_ptr() != 0:
+            if str(storage.device) != "meta" and storage.data_ptr() != 0:


Using str(storage.device) != "meta" to avoid the deprecation message mentioned above when accessing FakeTensor data_ptr

...are there other cases when device is not meta but storage data_ptr is 0?

ok, think this fix is good then to avoid accessing data_ptr specifically for FakeTensor storage

The semantic is (1) If real tensors are passed, storages bytes will not be written but zipfile metadata + sufficient space will be saved in the checkpoint for ```python import torch import torch.nn as nn sd = nn.Linear(3, 5).state_dict() torch.save(sd, 'foo.pt', metadata_only=True) print(torch.load('foo.pt', weights_only=True)) # OrderedDict([('weight', tensor([[0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.]])), ('bias', tensor([0., 0., 0., 0., 0.]))]) ``` (2) If FakeTensor is passed to `torch.save(metadata_only=True)` space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor) ```python from torch._subclasses.fake_tensor import FakeTensorMode with FakeTensorMode(): m = nn.Linear(3, 5, dtype=torch.float16, device='cuda') sd = m.state_dict() torch.save(sd, 'bla.pt', metadata_only=True) 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))]) ``` [ghstack-poisoned]

ghstack-source-id: 2ded66d Pull Request resolved: #134504

The semantic is (1) If real tensors are passed, storages bytes will not be written but zipfile metadata + sufficient space will be saved in the checkpoint for ```python import torch import torch.nn as nn sd = nn.Linear(3, 5).state_dict() torch.save(sd, 'foo.pt', metadata_only=True) print(torch.load('foo.pt', weights_only=True)) # OrderedDict([('weight', tensor([[0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.], # [0., 0., 0.]])), ('bias', tensor([0., 0., 0., 0., 0.]))]) ``` (2) If FakeTensor is passed to `torch.save(metadata_only=True)` space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor) ```python from torch._subclasses.fake_tensor import FakeTensorMode with FakeTensorMode(): m = nn.Linear(3, 5, dtype=torch.float16, device='cuda') sd = m.state_dict() torch.save(sd, 'bla.pt', metadata_only=True) 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))]) ``` [ghstack-poisoned]

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))]) ``` [ghstack-poisoned]

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))]) ``` [ghstack-poisoned]

ghstack-source-id: 5257374 Pull Request resolved: #134504

albanD · 2024-08-27T20:31:37Z

torch/_tensor.py

+        )
        state = torch._utils._get_obj_state(self)
-        if type(self) is Tensor and not state:
+        # Ignore all state when using FakeTensor with skip_data(materialize_fake_tensors) because FakeTensor has


I think this is sub-optimal in the long term. We should have a clearer contract on how our FakeTensor -> real Tensor with no data.
Dropping every single field from it might not be acceptable for everyone and might be interesting if there was a method to extract such Tensor from it easily.

This sounds ok as a temporary solution here I think

I agree, technically what we want is to drop every attribute that is in the dict of a "normal" FakeTensor, but keep anything else, is that right?

But agreed that for now it seems tricky to handle this because I'm not sure what is in a "normal" FakeTensor's dict is stable

torch/_tensor.py

albanD · 2024-08-27T20:44:03Z

torch/_tensor.py

-                    ),
+                (
+                    isinstance(
+                        self, torch._subclasses.functional_tensor.FunctionalTensor


Should we just split functional + fake Tensor to another condition just above? I have to admit I can't read this condition anymore :D

albanD · 2024-08-27T20:45:46Z

torch/serialization.py

+# (1) map_location (needed for wrapper subclasses/third party devices to torch._utils)
+# (2) skip_data (needed for torch.Tensor.__reduce_ex__ for skip_data ctx)
+# (3) materialize_fake_tensors (needed for torch.Tensor.__reduce_ex__ for skip_data ctx)
+_serialization_tls = threading.local()


Ho I would have expected you just did torch._C._stash_obj_in_tls("_serialization_tls", _serialization_tls) here and nothing else below?

Or if we don't know how to do it. It's ok to remote it all and open an issue as a TODO for later.

Removed and opened issue here #134680

torch/serialization.py

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))]) ``` [ghstack-poisoned]

## 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) [ghstack-poisoned]

mikaylagawarecki · 2024-09-05T13:23:08Z

@mikaylagawarecki has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

## 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) [ghstack-poisoned]

ghstack-source-id: 9258c78 Pull Request resolved: #134504

mikaylagawarecki · 2024-09-05T13:52:22Z

@mikaylagawarecki has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

mikaylagawarecki · 2024-09-05T14:05:52Z

Repro given no longer fails, see D62238610

albanD

SGTM!

mikaylagawarecki · 2024-09-05T14:42:54Z

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mikaylagawarecki · 2024-09-05T14:57:52Z

@pytorchbot merge

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## 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: pytorch#134504 Approved by: https://github.com/albanD

…4504)" This reverts commit 202600b. Reverted pytorch#134504 on behalf of https://github.com/mikaylagawarecki due to This is breaking Windows docs tests due to NamedTemporaryFile on Windows not working well ([comment](pytorch#134504 (comment)))

## 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: pytorch#134504 Approved by: https://github.com/albanD

…4504)" This reverts commit 94db935. Reverted pytorch#134504 on behalf of https://github.com/kit1980 due to See D62082697 ([comment](pytorch#134504 (comment)))

## 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: pytorch#134504 Approved by: https://github.com/albanD

Add metadata_only flag to torch.save

920d15f

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mikaylagawarecki mentioned this pull request Aug 26, 2024

Prototype changes to create fake checkpoints with empty storages #134503

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Update on "Add metadata_only flag to torch.save"

fe1f39e

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mikaylagawarecki commented Aug 26, 2024

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Add metadata_only flag to torch.save

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ghstack-source-id: 2ded66d Pull Request resolved: #134504

mikaylagawarecki changed the title ~~Add metadata_only flag to torch.save~~ Add torch.serialization.skip_data context manager Aug 27, 2024

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mikaylagawarecki added a commit that referenced this pull request Aug 27, 2024

Add metadata_only flag to torch.save

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ghstack-source-id: 5257374 Pull Request resolved: #134504

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mikaylagawarecki mentioned this pull request Aug 28, 2024

Use torch._C._stash_obj_in_tls for global state in serialization #134680

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Add metadata_only flag to torch.save

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ghstack-source-id: 9258c78 Pull Request resolved: #134504

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Add torch.serialization.skip_data context manager #134504

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mikaylagawarecki commented Sep 5, 2024

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mikaylagawarecki commented Aug 26, 2024 •

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mikaylagawarecki Aug 28, 2024 •

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