Summary:
When default hooks are set, they are pushed onto a stack.
When nesting context-manager, only the inner-most hooks will
be applied.
There is special care needed to update the TLS code. See also https://github.com/pytorch/pytorch/issues/70940 (i.e. do we need to be storing the enabled flag as well?)
Fixes https://github.com/pytorch/pytorch/issues/70134
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70932
Reviewed By: mruberry
Differential Revision: D33530370
Pulled By: albanD
fbshipit-source-id: 3197d585d77563f36c175d3949115a0776b309f4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62909
This PR makes saved tensors default hooks thread local.
This allows using default hooks in a multithreaded context.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D30165416
Pulled By: Varal7
fbshipit-source-id: 10a7d580661d3d94bdaf398c4e076b7bea11c16b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61927
This is a refactor of `SavedVariable.cpp` to prevent ever defining the `data_` tensor if default hooks are set.
Before the refactor:
```c++
data_ = variable.tensor_data(); // this is wasteful if hooks are defined
register_hooks(Engine::get_default_engine().get_default_saved_variable_hooks());
```
After the refactor:
```c++
if (get_default_hooks_()) {
save_metadata_(variable);
register_hooks_(get_default_hooks_(), variable);
return;
}
save_metadata_(variable);
data_ = variable.tensor_data(); // only needed if hooks are not defined
```
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D29848524
Pulled By: Varal7
fbshipit-source-id: abca1eee37a17b47841e28d8a576490913fce1ce
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62564
If the user runs code that registers default saved tensor hooks from
multiple threads, it will fail with a nice error message most of the
time. This commit handles the very rare case where a race condition
would have made it fail silently.
Relanding previous PR #61957
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D30045406
Pulled By: Varal7
fbshipit-source-id: d04f74c99affbbf655e53cfc2acd42f7c5b4e6eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62563
Expose a pair of functions to Python users: torch.autograd.graph.set_saved_tensors_default_hooks(pack, unpack) and torch.autograd.graph.reset_saved_tensors_default_hooks().
These functions control the hooks applied to saved tensors: all tensors saved in that context will be packed using the pack function, then unpacked accordingly when needed.
Currently, this works by simply calling register_hooks (cf #60975) directly at the end of the constructor of a SavedVariable. This could be optimized further by not performing the copy before registering default hooks, but this would require a small refactor. Edit: the refactor is done in #61927.
A current limitation is that if users create tensors in this context, they will not be able to register additional hooks on the saved tensor.
For instance, to perform something like #28997, one could define a pack function that saves to disk whenever the tensor size is too big and returns a filename, then unpack simply reads the content of the file and outputs a tensor, e.g.:
```
def pack(x):
name = os.path.join(tmp_dir, str(uuid.uuid4()))
torch.save(x, name)
return name
def unpack(name):
return torch.load(name)
```
Relanding previous PR: https://github.com/pytorch/pytorch/pull/61834
Original PR led to timeout error in: https://www.internalfb.com/mast/job/yuguo-release_canary_offline_training-inlinecvrp_a-canary_offline_train_28a7ecfc
Now passing: https://www.internalfb.com/mast/job/quach-release_canary_offline_training-inlinecvrp_a-canary_offline_train_9bb57e98
The difference with the new version is we don't need to acquire the GIL when calling `PyDefaultSavedVariableHooks::get_hooks`.
Test Plan: Imported from OSS
Reviewed By: iramazanli
Differential Revision: D30045405
Pulled By: Varal7
fbshipit-source-id: 7f6c07af3a56fe8835d5edcc815c15ea4fb4e332
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61957
If the user runs code that registers default saved tensor hooks from
multiple threads, it will fail with a nice error message most of the
time. This commit handles the very rare case where a race condition
would have made it fail silently.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D29848525
Pulled By: Varal7
fbshipit-source-id: eb9bdcfbeed857a988834651246390ea14eedd33
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61834
Expose a pair of functions to Python users: torch.autograd.graph.set_saved_tensors_default_hooks(pack, unpack) and torch.autograd.graph.reset_saved_tensors_default_hooks().
These functions control the hooks applied to saved tensors: all tensors saved in that context will be packed using the pack function, then unpacked accordingly when needed.
Currently, this works by simply calling register_hooks (cf #60975) directly at the end of the constructor of a SavedVariable. This could be optimized further by not performing the copy before registering default hooks, but this would require a small refactor. Edit: the refactor is done in #61927.
A current limitation is that if users create tensors in this context, they will not be able to register additional hooks on the saved tensor.
For instance, to perform something like #28997, one could define a pack function that saves to disk whenever the tensor size is too big and returns a filename, then unpack simply reads the content of the file and outputs a tensor, e.g.:
```
def pack(x):
name = os.path.join(tmp_dir, str(uuid.uuid4()))
torch.save(x, name)
return name
def unpack(name):
return torch.load(name)
```
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D29792193
Pulled By: Varal7
fbshipit-source-id: 33e931230ef59faa3ec8b5d11ef7c05539bce77c
