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
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
- Enable communication of tensors with Complex datatype in ProcessGroupGloo, similar to how ProcessGroupNCCL handles it.
- Move a function, which checks if Complex datatype is supported by a reduce operation, from ProcessGroupNCCL.cpp into a new file to be shared with ProcessGroupGloo.
Fixes#156632
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156633
Approved by: https://github.com/d4l3k
Instead of implicitly creating nccl comm inside mem pool registration for symmetric memory, we decide to error it out so that we only support eager init case when the nccl comm is already initiated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160145
Approved by: https://github.com/kwen2501
Summary: We added group split in D78300794 and remote_group_merge in D78450094. We first want to upstream this change to PGNCCLx as well so that NCCLx can use this new API and we can continue our c10d clean up in https://github.com/pytorch/pytorch/pull/158488.
Test Plan:
CI
```
buck test -c hpc_comms.use_ncclx=stable comms/ncclx/pg/tests:test_c10d_ncclx -- test_group_split_and_merge
```
Rollback Plan:
Differential Revision: D78521060
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158790
Approved by: https://github.com/d4l3k
Summary:
ncclUniqueID is only relevant when a comm is created using ncclCommCreate or ncclCommCreateConfig. If a comm is created with ncclCommSplit, this field is unset, causing its usage to create unexpected behavior.
This patch creates a unique hash key for each comm, irrespective of how the comm is created.
Test Plan:
CI
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156790
Approved by: https://github.com/fduwjj, https://github.com/kwen2501
At the end of the scope when std::async is launched, a wait will be called which could makes the code blocking, this is not expected for monitoring thread. Instead, let's use a vector to contain the reference to it. So no blocking will happen. And at the end of loop, wait will still be called but it is ok since all the checks or dump has already been finished.
Differential Revision: [D77190380](https://our.internmc.facebook.com/intern/diff/D77190380)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156653
Approved by: https://github.com/kwen2501
Summary: We noticed std::future_error: Broken promise errors in logging, so let's disable for now and will investigate more.
Test Plan:
CI
Rollback Plan:
Differential Revision: D76929722
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156362
Approved by: https://github.com/fegin
Summary: We want to build a logging table for tracking the collective time spent on GPU for all internal workloads. Since we have a cudaEventQuery for both the start and end of a collective (We rolled out ECudaEventStart (enableTiming) fully already), we plan to add this logging table inside the watchdog of PyTorch ProcessGroupNCCL so that we get to know the duration of collectives.
Test Plan:
CI + dry run.
Rollback Plan:
Differential Revision: D76552340
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156008
Approved by: https://github.com/fegin, https://github.com/eqy
While looking into a case when FR dump (actual dump not monitoring thread) takes 30 mins, I realized that our global write lock is grabbed too early so the second effort to dump FR without stack trace will fail because of a deadlock because the global write lock is still hold. So we should only grab the lock when we are ready to write so that we are less likely to keep the lock forever. Also I did an audit to the lock within FR as well and found that there is one place we can shrink as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155949
Approved by: https://github.com/Skylion007
By extracting both monitor thread and watchdog thread into a separate class this will help us learn what dependencies we have for each thread and it will kind of simplify the consolidation work for each thread (consolidating from thread per PG instance to per PG class)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155831
Approved by: https://github.com/d4l3k, https://github.com/kwen2501
This is the start of a series of efforts to consolidating auxiliary threads in PGNCCL, aka watchdog and heartbeat_monitoring threads. Right now we launch these two threads per PG instances, i.e., if users create hundred or thousand instances of PG or subPGs, we will end up with that twice many side threads which is not efficient. We have a RFC to consolidate them (https://github.com/pytorch/pytorch/issues/146956). Right now both threads are assigned with so many functionalities so it is hard to do the consolidations in one shot, we will try to split it into at least two steps (PRs) to make it easier to test and review.
We did our first attemp in https://github.com/pytorch/pytorch/pull/153668 but we also want to try to see if we can make monitoring thread a class. This PR is doing the first step to make monitoring thread a class. The next step to also extract watchdog to be a separate class so that we know its dependency.
What we did in this PR:
1. Move all related variables and methods into a class named `HeartbeatMonitor`.
2. Correct some errors in the original logics inside monitoring thread loop.
3. Move the error propagation check to watchdog thread which is more relevant. This is totally fine since we rolled out EventCache out fully so watchdog hang is rare now.
Today there are two major functions inside heartbeat monitoring thread today:
1. Check the heartbeat of watchdog thread every 8 minutes. If no heartbeat detected and we are sure monitoring thread has not been stopped, we will kill the program by SIG_ABORT.
2. We check TCPStore every 30 sec to see if any watchdog timeout happens on other ranks, if so we will initiate a dump signal on the current rank as well. (We do this only in the default PG)
Differential Revision: [D75799278](https://our.internmc.facebook.com/intern/diff/D75799278)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153977
Approved by: https://github.com/kwen2501, https://github.com/d4l3k
This is the start of a series of efforts to consolidating auxiliary threads in PGNCCL, aka watchdog and heartbeat_monitoring threads. Right now we launch these two threads per PG instances, i.e., if users create hundred or thousand instances of PG or subPGs, we will end up with that twice many side threads which is not efficient. We have a RFC to consolidate them (https://github.com/pytorch/pytorch/issues/146956). Right now both threads are assigned with so many functionalities so it is hard to do the consolidations in one shot, we will try to split it into at least two steps (PRs) to make it easier to test and review.
We did our first attemp in https://github.com/pytorch/pytorch/pull/153668 but we also want to try to see if we can make monitoring thread a class. This PR is doing the first step to make monitoring thread a class. The next step to also extract watchdog to be a separate class so that we know its dependency.
What we did in this PR:
1. Move all related variables and methods into a class named `HeartbeatMonitor`.
2. Correct some errors in the original logics inside monitoring thread loop.
3. Move the error propagation check to watchdog thread which is more relevant. This is totally fine since we rolled out EventCache out fully so watchdog hang is rare now.
Today there are two major functions inside heartbeat monitoring thread today:
1. Check the heartbeat of watchdog thread every 8 minutes. If no heartbeat detected and we are sure monitoring thread has not been stopped, we will kill the program by SIG_ABORT.
2. We check TCPStore every 30 sec to see if any watchdog timeout happens on other ranks, if so we will initiate a dump signal on the current rank as well. (We do this only in the default PG)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153977
Approved by: https://github.com/kwen2501, https://github.com/d4l3k
Removes MemPoolContext from custom user mempools. The ground truth for which pool should be used is in graph_pools active pool, and MemPoolContext just introduced an opportunity for the pool pointed to by MemPoolContext and active pool in graph_pools to go out of sync (see all the asserts in the code to make sure that happens, and yet it still could happen in a multithread scenario, see my recent PRs (#153990).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154042
Approved by: https://github.com/albanD, https://github.com/syed-ahmed
Work around issues like #153960, #152623
NCCL 2.26 seems to introduce random hang in non-blocking API mode. This PR opts out of non-blocking mode to work around it. Previously torch turned it on by default in eager init (i.e. `device_id` passed) to avoid init overhead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154055
Approved by: https://github.com/atalman
