Memory leak detected on ROCm. Skip until it can be addressed.
PYTORCH_TEST_WITH_ROCM=1 PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=1 python test_eager_transforms.py -k test_compile_vmap_hessian_cuda
See #117642 for moving rocm CI to unstable due to this test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118009
Approved by: https://github.com/jeanschmidt
Using `@skipifTorchDynamo` is wrong, the correct usage is
`@skipIfTorchDynamo()` or `@skipIfTorchDynamo("msg")`. This would cause
tests to stop existing.
Added an assertion for this and fixed the incorrect callsites.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117114
Approved by: https://github.com/voznesenskym
After this refactor:
* ```TorchVariable``` definition and all references are removed.
* All ```is_allowed``` references except one are removed.
- The only left one is in ```torch/_dynamo/decorators:_disallow_in_graph_helper```. It was called when users put ```disallow_in_graph``` decorator on a function. Since we use the lists in ```trace_rules``` to decide the function's trace rule, so the decorator would only be used as customer function rather than torch functions. I'll defer this to a separate decorator refactor PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116312
Approved by: https://github.com/jansel
After this refactor:
* ```TorchVariable``` definition and all references are removed.
* All ```is_allowed``` references except one are removed.
- The only left one is in ```torch/_dynamo/decorators:_disallow_in_graph_helper```. It was called when users put ```disallow_in_graph``` decorator on a function. Since we use the lists in ```trace_rules``` to decide the function's trace rule, so the decorator would only be used as customer function rather than torch functions. I'll defer this to a separate decorator refactor PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116312
Approved by: https://github.com/jansel
Manually enable `capture_func_transforms` for testing as plan is to default `capture_func_transforms` to False in 2.1. (enable it so that we still test the support on release branch).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107122
Approved by: https://github.com/zou3519
D47969512 was the original diff to revert this, but the diff train doesn't work well, so I have to split it into two part: this OSS PR and another separate diff to revert the fbcode change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106562
Approved by: https://github.com/angelayi
Summary:
We are working toward full model compilation, where when compilation error happens, we just fall back to eager mode rather than error out.
But at the same time, we should fix these issues if they are bugs. We will:
* 1/ log warnings in OSS;
* 2/ log warnings and write them into Scuba in fbcode;
to prevent us from ignoring these issues.
Test Plan: Manual test
Differential Revision: D47506314
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105307
Approved by: https://github.com/jansel
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new `Buffer` class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the `register_buffer` method has not been changed. The `persistent` parameter in the `Buffer` type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new `Buffer` type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the `Buffer` type can be used as a drop in replacement for `register_buffer` as it just leads to `register_buffer` being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104069
Approved by: https://github.com/mikaylagawarecki
This PR:
- adds a mechanism to turn any RAII guard into a Python Context Manager
- turns ExcludeDispatchKeyGuard into a context manager, and purges usages
of the older torch._C.ExcludeDispatchKeyGuard from the codebase.
The mechanism is that given a RAII guard, we construct a context
manager object that holds an optional guard. When we enter the context
manager we populate the guard, when we exit we reset it.
We don't delete torch._C.ExcludeDispatchKeyGuard for BC reasons (people
are using it in fbcode). If this code actually sticks
(it is using C++17 and that worries me a bit), then I'll apply the
change to other RAII guards we have, otherwise, we can write our own
std::apply.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102037
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
Fixes https://github.com/pytorch/pytorch/issues/96887
We error out in BOTH the case when graph is created and when it is not created.
Still bc-breaking, but not as severe because we are limiting to the case where someone uses setup_context.
This makes setup_context and non-setup_context versions diverge in their behavior
- With the non-setup_context version, saved variables are assumed to have the grad_fn of the inputs.
- But now with the setup_context version, we produce an error for this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97212
Approved by: https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/96887
We error out in BOTH the case when graph is created and when it is not created.
Still bc-breaking, but not as severe because we are limiting to the case where someone uses setup_context.
This makes setup_context and non-setup_context versions diverge in their behavior
- With the non-setup_context version, saved variables are assumed to have the grad_fn of the inputs.
- But now with the setup_context version, we produce an error for this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97212
Approved by: https://github.com/zou3519
Twice this week I have had people confuse "operator defined with Python
operator registration aka torch.library" and "PyOperator which is used
to define control flow operators and other operators that cannot be
represented in JIT schema." Renaming PyOperator for clarity.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97493
Approved by: https://github.com/SherlockNoMad
Fixes#96347
This PR:
- Makes the functorch tests run as a part of the "default" shards
- Delete the functorch CI shard from all CI job configurations (if it exists)
- Increase the "default" shard count by 1 for each job, unless it was
previously set to 1, to accommodate the new functorch tests and not
regress time-to-signal.
- Adds a bunch of skips for ROCM and torchdynamo configurations. We can
investigate them later.
NB: I might go through some more iterations to figure out what other
skips need to be added, but this iteration of the PR seems to pass most CI.
suite.
Test Plan:
- wait for CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96464
Approved by: https://github.com/huydhn
Applies some more harmless pyupgrades. This one gets rid of deprecated aliases in unit_tests and more upgrades yield for loops into yield from generators which are more performance and propagates more information / exceptions from original generator. This is the modern recommended way of forwarding generators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94309
Approved by: https://github.com/albanD
