1. Reworked `MultiProcContinousTest` to spawn processes during `setUpClass` instead of `main` (so that we can support multiple TestClass'es in one file).
2. The child processes are now an infinite loop, monitoring test IDs passed from main process via a task queue. Reciprocally, the child processes inform the main process completion of a test via a completion queue.
3. Added a test template.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153653
Approved by: https://github.com/d4l3k, https://github.com/fegin, https://github.com/fduwjj
Change logging.error to logging.exception to log additional information when relevant. A few places have slipped in logging.errors in try except since I last did a clean up here and the rule is stabilized so I am enabling it codebase wide. I have NOQA'd much of our custom exception stack trace handling for RPC calls and distributed and tried to a fix a few errors based on whether we immediately reraised it or if we didn't print any exception handling where it could be useful.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153473
Approved by: https://github.com/albanD, https://github.com/cyyever
Using `logging.basicConfig` to set root logger's level is not a good behavior. Fix common_distributed.py to set level for current logger only, because it affects downstream's 3rd-party testing plugins.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152319
Approved by: https://github.com/Skylion007
This adds lazy initialization support to ProcessGroupGloo via `TORCH_GLOO_LAZY_INIT` or via `create_device(..., lazy_init=True)`
This is still a draft PR as there's one race condition when doing coalesced operations that needs to be fixed upstream in Gloo first. Depends on https://github.com/facebookincubator/gloo/pull/427 landing first
This also updates the gloo submodule to include the required changes.
Test plan:
added lazy init test variants
```
pytest -v test/distributed/test_c10d_gloo.py -k Lazy
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150801
Approved by: https://github.com/fduwjj
Allows test classes using MPCT to set their own timeout as a class
property, which is good enough since the processgroup is shared across
test instances and the timeout is set at processgroup init.
Also sets a default timeout of 2 minutes, which is probably (?) long
enough for reasonable tests, but can be changed if it causes flakyness.
It's preferable to have as short default timeout as possible, since when
debugging tests getting a timeout quickly helps.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145099
Approved by: https://github.com/d4l3k, https://github.com/fduwjj
ghstack dependencies: #145010, #145011
Adding `destroy_pg_upon_exit` property to allow derived Test classes to control whether auto destroy is desired.
(Otherwise, derived test classes will need to rewrite the `_run()` method, leading to duplicated code of `_run()` and if one needs to add things to `_run` in the future, more code change is needed.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141192
Approved by: https://github.com/wconstab
# Motivation
This pr is an extension of #131758. As described in #131758, these changes are looking to make distributed UTs more accessible to users of all device types.
It is a demonstration of a few changes discussed by @kwen2501 and @jgong5 in the discussion for #131758(https://github.com/pytorch/pytorch/pull/131758#discussion_r1762422784)
This PR contains two types of changes, the first is to the common distributed folder where we have added a new class derived from MultiProcessTestCase which helps abstracts out the process group creation /deletion and other functionality for a given device.
The new generalized content can be added by deriving from this base class.
Also includes other misc changes for gaudi support
The second changed file is test_functional_api. a test file in common distributed. This file is a POC for how we can use this new class to write more device agnostic distributed test cases.
The following changes have been made to test_functional_api.py:
-Functionality has been added to test for non cuda devices using intel HPU as an example
-Multiple set up steps previously required by MultiProcessTestCase have been abstracted out
-Misc adaptations to allow for general call to accelerators while adding test skips instead explicitly skipping for multiple GPUs
-Skipifhpu flags have been added to enable skipping a few Multithreaded test cases which are as yet not supported on HPUs
NOTE: Within test functional api, there are tests which require the use of some multithreading functions which are as yet not supported on HPUs. These have been skipped for hpu using skipHPU decorator.
I will be raising a separate PR to improve usability pf said decorators in a device agnostic setting in the manner suggested by @kwen2501 in a comment on this PR.
This pr is a cleaned up version of a previous PR(#136988) which I closed due to human error. I have addressed some of the comments made by @kwen2501 in this as well
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138216
Approved by: https://github.com/kwen2501, https://github.com/guangyey
Faced with an annoying string of warnings like this when running tests,
<img width="1644" alt="Screenshot 2024-11-15 at 11 23 21 AM" src="https://github.com/user-attachments/assets/91ff4e1d-3c29-4510-9a61-46e7df68a212">
My choices seem to be (1) call destroy_process_group() at the end of
each test fn, (2) do this in some wrapper, (3) do it in the base test
class.
Since tests in MultiProcessTestCase are responsible for calling
init_process_group themselves, they should also be responsible for
calling destroy (or at least method (3) would be asymmetric and may
result in double-destroy).
But it doesn't feel worth it to go add a destroy call manually to each
test, and try/except for a possible second destroy call seems like a
happy middle ground.
Note: tests that want to ensure that destroy runs cleanly can and should
still call destroy _inside_ the test, and this change does not affect
that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140820
Approved by: https://github.com/fegin
Faced with an annoying string of warnings like this when running tests,
<img width="1644" alt="Screenshot 2024-11-15 at 11 23 21 AM" src="https://github.com/user-attachments/assets/91ff4e1d-3c29-4510-9a61-46e7df68a212">
My choices seem to be (1) call destroy_process_group() at the end of
each test fn, (2) do this in some wrapper, (3) do it in the base test
class.
Since tests in MultiProcessTestCase are responsible for calling
init_process_group themselves, they should also be responsible for
calling destroy (or at least method (3) would be asymmetric and may
result in double-destroy).
But it doesn't feel worth it to go add a destroy call manually to each
test, and try/except for a possible second destroy call seems like a
happy middle ground.
Note: tests that want to ensure that destroy runs cleanly can and should
still call destroy _inside_ the test, and this change does not affect
that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140820
Approved by: https://github.com/fegin
ghstack dependencies: #140460, #140815
This PR contains multiple fixes for issue https://github.com/pytorch/pytorch/issues/135279:
## First part:
Moves the GPU guard (`cudaSetDevice`) before the `currentStreamCaptureStatusMayInitCtx` call.
As its name suggests, it May Init Ctx.
## Second part:
Even with the above fix, additional contexts are still observed during Work object destruction, e.g.
```
work = dist.all_reduce(tensor, async_op=True)
time.sleep(5) <-- no additional context yet
del work <-- additional context shows up
```
### Debug process
Chasing it down to destruction of a `Future` object -- a member variable of `Work`.
Then further down to the following member of `Future`:
```
std::vector<c10::Event> events_;
```
When the `events_` are destroyed, we hit the road down to:
1f3a793790/c10/cuda/impl/CUDAGuardImpl.h (L106-L121)
When there is no "preset" CUDA context (**which is the case for python garbage collector**), line 112: `c10::cuda::GetDevice(&orig_device)` will set `orig_device` to 0. Then, at line 120, `c10::cuda::SetDevice(orig_device)` will "officially" set the context to device 0 --
**that's where rank 1, 2, ... can create extra context on device 0!**
### Solution
This PR adds an explicit destructor to `Future`. In this destructor, destroy each event with a device guard.
## Test
Added test_extra_cuda_context, implemented via
- `pynvml` (if available), or
- memory consumption check.
`python test/distributed/test_c10d_nccl.py -k test_extra_cuda_context`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135273
Approved by: https://github.com/fduwjj, https://github.com/wconstab, https://github.com/eqy
ghstack dependencies: #137161
This PR contains multiple fixes for issue https://github.com/pytorch/pytorch/issues/135279:
## First part:
Moves the GPU guard (`cudaSetDevice`) before the `currentStreamCaptureStatusMayInitCtx` call.
As its name suggests, it May Init Ctx.
## Second part:
Even with the above fix, additional contexts are still observed during Work object destruction, e.g.
```
work = dist.all_reduce(tensor, async_op=True)
time.sleep(5) <-- no additional context yet
del work <-- additional context shows up
```
### Debug process
Chasing it down to destruction of a `Future` object -- a member variable of `Work`.
Then further down to the following member of `Future`:
```
std::vector<c10::Event> events_;
```
When the `events_` are destroyed, we hit the road down to:
1f3a793790/c10/cuda/impl/CUDAGuardImpl.h (L106-L121)
When there is no "preset" CUDA context (**which is the case for python garbage collector**), line 112: `c10::cuda::GetDevice(&orig_device)` will set `orig_device` to 0. Then, at line 120, `c10::cuda::SetDevice(orig_device)` will "officially" set the context to device 0 --
**that's where rank 1, 2, ... can create extra context on device 0!**
### Solution
This PR adds an explicit destructor to `Future`. In this destructor, destroy each event with a device guard.
## Test
Added test_extra_cuda_context, implemented via
- `pynvml` (if available), or
- memory consumption check.
`python test/distributed/test_c10d_nccl.py -k test_extra_cuda_context`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135273
Approved by: https://github.com/fduwjj, https://github.com/wconstab, https://github.com/eqy
skip_if_rocm is used only in multiprocess case (when UT test class is a child of MultiProcessTestCase). Each individual process can exit with a skip code. If used for single process UT, it will cause the UT to fail as the process returns a non-zero exit code. Use skipIfRocm in single process UTs.
To avoid the above confusion, this PR renamed skip_if_rocm to skip_if_rocm_multiprocess.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136161
Approved by: https://github.com/jithunnair-amd, https://github.com/kwen2501, https://github.com/fegin
This PR creates these `GroupedSchedulerNode`s:
- One for each all-gather code block (cast + copy-in + all-gather)
- One for each all-gather-wait code block (all-gather-wait + copy-out)
- One for each reduce-scatter code block (copy-in + reduce-scatter)
- One for each reduce-scatter-wait code block (reduce-scatter-wait)
This serves two goals:
- Prevent outside ops from being fused into these op groups, in order to have more predicable memory usage.
- Make it easier to specify the dependency e.g. from `i+1` all-gather group node to the `i` all-gather-wait group node, to enforce FSDP2 comm ordering (i.e. "serialization of comms").
The actual "reorder-for-FSDP-compute-comm-overlap" PR will come next.
Test commands:
- `pytest -rA test/distributed/test_compute_comm_reordering.py::TestComputeCommReorderingMultiProc`
- `pytest -rA test/distributed/_composable/fsdp/test_fully_shard_compile.py::TestFullyShardCompile::test_transformer_backend_inductor`
- `pytest -rA test/distributed/_composable/fsdp/test_fully_shard_compile.py::TestFullyShardCompile::test_nested_fully_shard_backend_inductor`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131510
Approved by: https://github.com/yifuwang
Summary: `test/distributed/_composable/test_replicate_with_compiler.py` exercises inductor. This change introduces a version of MultiProcessTestCase that derives from the inductor TestCase class to make sure we always get a clean cache dir.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129494
Approved by: https://github.com/eellison
**Summary**
This PR switches the default TCPStore server backend to a new implementation that utilizes [`libuv`](https://github.com/libuv/libuv) for significantly lower initialization time and better scalability:
<img width="714" alt="image" src="https://github.com/pytorch/pytorch/assets/12968408/18503011-da5d-4104-8ba9-abc456438b02">
We hope this improvement would benefit users from a much shorter startup time in large-scale jobs. Eventually, we hope to fully replace the old TCPStore backend implementation with the libuv one.
**What it changes**
This PR changes the underlying TCPStore server backend to `libuv` if users don't explicitly specify to use the old TCPStore server. This change is not supposed to cause any user notice except significant faster TCPStore startup for large-scale jobs.
One thing to note is, we do not support the initialization approach where user passes in a socket for libuv backend. We plan to support it as a next step but we choose to disable it before fully testing. If you are initializing TCPStore in this approach, you can see the next section to remain using the old TCPStore server.
**Fallback/Remain using the old TCPStore server**
For users who want to stay with the old TCPStore backend, there're 3 ways:
1. If user is directly instantiating TCPStore object, user can pass in argument `use_libuv=False` to use the old TCPStore server backend e.g. `store = torch.distributed.TCPStore(..., use_libuv=False)`.
2. Or, specify the TCPStore backend option in `init_method` when calling default ProcessGroup init, e.g. `torch.distributed.init_process_group(..., init_method="{YOUR_RENDEZVOUS_METHOD}://{YOUR_HOSTNAME}:{YOUR_PORT}?use_libuv=0")`
3. Or, user can set environment variable `USE_LIBUV` to `"0"` when launching.
These 3 approach are in order of precedence. That being said, if user specifies `use_libuv=0` in `init_method` and also sets environment var `USE_LIBUV="1"`, the former will take effect and the TCPStore backend instantiated will be the old one instead of the one using libuv.
**Operating Systems Compatibility**
From the CI signals, we believe the new implementation has the same behavior as the old TCPStore server on all supported platforms. If you notice any behavior discrepancy, please file an issue with `oncall: distributed` label.
**Test Plan**
`pytest test/distributed/test_store.py`
<img width="2548" alt="image" src="https://github.com/pytorch/pytorch/assets/12968408/dc0aebeb-6d5a-4daa-b98c-e56bd39aa588">
note: `TestMultiThreadedWait::test_wait` is a broken test that has been there for some time.
`test/distributed/elastic/utils/distributed_test.py`
<img width="2558" alt="image" src="https://github.com/pytorch/pytorch/assets/12968408/a6a3266d-b798-41c4-94d2-152056a034f6">
**TODO**
1. Update the doc at
- https://pytorch.org/docs/stable/distributed.html#distributed-key-value-store
- https://pytorch.org/docs/stable/distributed.html#tcp-initialization
2. Make torch elastic rendezvous to use libuv TCPStore as well. See `torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py` cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @penguinwu @fegin @wanchaol @fduwjj @wz337 @tianyu-l @wconstab @yf225 @chauhang @d4l3k @kurman
3. Test if libuv backend is okay with initialization with socket. Change `LibUvTCPStoreTest::test_take_over_listen_socket`.
**Test Plan**
`pytest test/distributed/test_store.py`
<img width="2548" alt="image" src="https://github.com/pytorch/pytorch/assets/12968408/dc0aebeb-6d5a-4daa-b98c-e56bd39aa588">
note: `TestMultiThreadedWait::test_wait` is a broken test that has been there for some time.
`test/distributed/elastic/utils/distributed_test.py`
<img width="2558" alt="image" src="https://github.com/pytorch/pytorch/assets/12968408/a6a3266d-b798-41c4-94d2-152056a034f6">
Differential Revision: [D58259591](https://our.internmc.facebook.com/intern/diff/D58259591)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127957
Approved by: https://github.com/kurman
ghstack dependencies: #127956
