This PR replaces c10::call_once calls with static initialization when possible. C++11 semantics guarantees that static initialization is atomic. Static initialization also has lower cost than using c10::call_once.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166381
Approved by: https://github.com/malfet
These happen when building with CMAKE_BUILD_TYPE=RelWithAssert
This should fix two types of failures that started with https://github.com/pytorch/pytorch/pull/163665
Disclaimer that I used a lot of AI since I don't how pybind works or what refcounts and pointers are, so idk if this is a good solution, or even a solution at all (fwiw the tests pass now)
The first one type is
Truncated:
```
default_pg, _ = _new_process_group_helper(
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 2096, in _new_process_group_helper
backend_class = creator_fn(dist_backend_opts, backend_options)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/distributed/fake_pg.py", line 25, in _create_fake_pg
return FakeProcessGroup._create_internal(
RuntimeError: new_refcount != 1 INTERNAL ASSERT FAILED at "/var/lib/jenkins/workspace/c10/util/intrusive_ptr.h":319, please report a bug to PyTorch. intrusive_ptr: Cannot increase refcount after it reached zero.
Exception raised from retain_ at /var/lib/jenkins/workspace/c10/util/intrusive_ptr.h:319 (most recent call first):
C++ CapturedTraceback:
#4 std::_Function_handler<std::shared_ptr<c10::LazyValue<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > const> (), c10::SetStackTraceFetcher(std::function<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > ()>)::{lambda()#1}>::_M_invoke(std::_Any_data const&) from Logging.cpp:0
#5 c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) from ??:0
#6 c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) from ??:0
#7 c10::detail::torchInternalAssertFail(char const*, char const*, unsigned int, char const*, char const*) from ??:0
#8 void pybind11::class_<c10d::FakeProcessGroup, (anonymous namespace)::IntrusivePtrNoGilDestructor<c10d::FakeProcessGroup> >::init_instance<(anonymous namespace)::IntrusivePtrNoGilDestructor<c10d::FakeProcessGroup>, 0>(pybind11::detail::instance*, void const*) from init.cpp:0
#9 pybind11::detail::type_caster_generic::cast(void const*, pybind11::return_value_policy, pybind11::handle, pybind11::detail::type_info const*, void* (*)(void const*), void* (*)(void const*), void const*) from :0
#10 pybind11::cpp_function::initialize<torch::distributed::c10d::(anonymous namespace)::c10d_init(_object*, _object*)::{lambda(int, int, c10::intrusive_ptr<c10d::FakeProcessGroup::Options, c10::detail::intrusive_target_default_null_type<c10d::FakeProcessGroup::Options> >)#127}, c10::intrusive_ptr<c10d::FakeProcessGroup, c10::detail::intrusive_target_default_null_type<c10d::FakeProcessGroup> >, int, int, c10::intrusive_ptr<c10d::FakeProcessGroup::Options, c10::detail::intrusive_target_default_null_type<c10d::FakeProcessGroup::Options> >, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::arg, pybind11::arg, pybind11::arg_v>(torch::distributed::c10d::(anonymous namespace)::c10d_init(_object*, _object*)::{lambda(int, int, c10::intrusive_ptr<c10d::FakeProcessGroup::Options, c10::detail::intrusive_target_default_null_type<c10d::FakeProcessGroup::Options> >)#127}&&, c10::intrusive_ptr<c10d::FakeProcessGroup, c10::detail::intrusive_target_default_null_type<c10d::FakeProcessGroup> > (*)(int, int, c10::intrusive_ptr<c10d::FakeProcessGroup::Options, c10::detail::intrusive_target_default_null_type<c10d::FakeProcessGroup::Options> >), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&, pybind11::arg_v const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&) from init.cpp:0
```
and I fix it here by getting rid of `DontIncreaseRefcount` and using make_intrusive to do the ref count handling instead. However, I also had to move the constructor to be public, which I think is not good, based on the reasoning of the original PR
The other one type is
```
Traceback (most recent call last):
File "/var/lib/jenkins/workspace/test/test_testing.py", line 2415, in test_no_warning_on_import
self.assertEqual(out, "")
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 4233, in assertEqual
raise error_metas.pop()[0].to_error( # type: ignore[index]
AssertionError: String comparison failed: "/opt/conda/envs/py_3.10/lib/python3.10/s[352 chars]):\n" != ''
- /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/distributed/__init__.py:29: FutureWarning: pybind11-bound class 'torch._C._distributed_c10d.FakeProcessGroup' is using an old-style placement-new '__init__' which has been deprecated. See the upgrade guide in pybind11's docs. This message is only visible when compiled in debug mode.
- if is_available() and not torch._C._c10d_init():
To execute this test, run the following from the base repo dir:
python test/test_testing.py TestImports.test_no_warning_on_import
```
which I fix by getting rid of the `__init__` which I think is ok since it'll just error if you try to make one?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165479
Approved by: https://github.com/ezyang
A LocalTensor is a tensor subclass which simulates a tensor that is
distributed across SPMD ranks. A LocalTensor might be size N, but in fact
there are world_size shards/replicas of it stored internally. When you do a
plain PyTorch operation on it, we apply the operation to each shard; when you
do a collective, we do the mathematically equivalent operation on the local
shards. A LocalTensor is associated with a list of ranks which specify
which ranks it holds local tensors for.
NB, this is NOT a DataParallel like abstraction where you can run operations
on multiple different GPUs. It is intended purely for *debugging* purposes,
the overhead is almost certainly too high to keep eight GPUs (even the C++
autograd needs multithreading to keep up!) (It might potentially be possible
to trace through this with torch.compile and then compile it with CUDA graphs
but this is currently a non-goal.)
In order to handle MPMD, we provide a helper decorator that allows you to
run a function with no side effects for each LocalTensor shard and combine
results back into LocalTensor or LocalIntNode.
Note: This PR convert all DTensor ops and some DTensor tests to illustrate
intended usage and ensure conrrectness. In subsequent PR more tests will be
converted. DUring test conversion we aim to share as much as possible of
test logic between multi-process / multi-threaded and local tensor tests.
We would like to developers to be able to run both flavors of the tests.
Note: This work is based on the original proposal
by @ezyang (WIP PR https://github.com/pytorch/pytorch/pull/162753).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164537
Approved by: https://github.com/ezyang
This PR includes a couple of changes to extend FlightRecorder dump by PyTorch watchdog
- New knobs to control FR dump as suggested in the public documentation even for watchdog
(TORCH_INCLUDE_STACK_TRACE, TORCH_INCLUDE_ONLY_ACTIVE)
- Trigger the flight recorder dump on exceptions which could be triggered by any CUDA / host side error
(TORCH_NCCL_EXTRA_DUMP_ON_EXEC)
-> Can be used as a snapshot of the workload progress for post-mortem analysis
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164591
Approved by: https://github.com/fduwjj
We also want to have a python side API for users to reset FR recording for FR entries. We don't need to reset the PGNCCL's member counter since we are creating new PGNCCL anyway. FR is a global ring buffer, so we need to reset it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164988
Approved by: https://github.com/tushar00jain
ghstack dependencies: #164752
Modified `multimem_one_shot_all_reduce_out` function to accept a `root` argument, making it a `multimem_reduce` op.
The original `multimem_one_shot_all_reduce` op becomes a caller of the `multimem_reduce`, with each rank providing its own rank id as root.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164517
Approved by: https://github.com/ngimel
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
Modified `multimem_one_shot_all_reduce_out` function to accept a `root` argument, making it a `multimem_reduce` op.
The original `multimem_one_shot_all_reduce` op becomes a caller of the `multimem_reduce`, with each rank providing its own rank id as root.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164517
Approved by: https://github.com/ngimel
Fixes#162129. Added validation in _rank_not_in_group() to check if ```FakeProcessGroup``` is properly initialized before use, raising a clear error message if ```torch.distributed.init_process_group(backend='fake')``` hasn't been called first.
This prevents silent failures and ensures proper dispatch system integration for all distributed operations.
Added test case test_fake_process_group_direct_usage_error() that validates the error is raised for ```all_reduce``` and ```all_to_all_single``` operations.
Please let me know if additional distributed operators should be tested or if any other updates are needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163665
Approved by: https://github.com/ezyang
