Summary: Pass process group info into NcclWork
Test Plan: buck2 run mode/dev-nosan kineto/libkineto/fb/integration_tests:pytorch_execution_trace_integration_test
Differential Revision: D61677160
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134269
Approved by: https://github.com/wconstab
This adds logs if we can't acquire locks in NCCLUtils and ProcessGroupNCCL for 30s.
This is motivated by some deadlocks were seeing and it's unclear if it's in NCCL or on the PyTorch side of things.
This required replacing most `std::mutex` with `std::timed_mutex` and `std::condition_variable_any` as appropriate.
Test plan:
existing CI for regressions
will add unit tests on `C10D_LOCK_GUARD`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134131
Approved by: https://github.com/c-p-i-o, https://github.com/fduwjj
Summary:
Split out `seq_id` into `collective_seq_id` and `p2p_seq_id`. The main idea here is that collectives that go to all machines should have identical `collective_seq_id` and therefore it makes it easier to spot if one of machines isn't handling a collective operation.
Next, we can attempt to match up p2p operations to ensure that the sender(s)/receivers(s) are in sync.
Resolves issue: https://github.com/pytorch/pytorch/issues/125173
Test Plan:
Unit tests.
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125727
Approved by: https://github.com/zdevito
Summary:
We found that some dumps are missing when monitoring thread timeout.
This is likely due to multiple PGs could still dump the same records
at the same time. So we should allow only PG0 to actualy dump
Test Plan:
unit test
python test/run_test.py --cpp --verbose -i cpp/ProcessGroupNCCLErrorsTest
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125356
Approved by: https://github.com/c-p-i-o
Fix and test issues with both coalesced and individual send/recv ops
Considered an alternate approach and then ditched it
- alternate approach: #119757
- reason ditched: prefer recording individual collective events inside
coalescing region instead of just the event at the end of the region,
which also would not have tensor sizes or opnames without additional
state variables added
Another approach also ditched
- record events on workEnqueue instead of initWork
- reason ditched: too messy to get input/output shapes tagged on
recording when recording in workEnqueue. Adding the info onto the
Work obj would be possible, but adds to overhead of copying Works
which we do on every collective. We can get info off the input/output
tensors directly in initWork, but we don't want to keep refs to those
tensors alive while the work is Enqueued, so we'd have to specifically
copy size lists or something.
This PR instead avoids creating a work inside pointToPoint when
coalescing is active. Instead, only at endCoalescing() is a work finally
intialized and enqueued. But it adds a record() call inside
pointToPoint() instead of creating a work, during coalescing. This
record() call picks up tensor shapes and op names.
It ALSO changes initWork to accept a 'record' argument. This defaults to
false, and should only be set to true if the caller ensures the work
will be enqueued by workEnqueue, ensuring its cuda events are live when
used by flight recorder's update_state().
The testing uncovers some odd pre-existing behavior and leaves them
alone for now. We could change some of these
- seq starts off at 1, not 0 for first op (but this is inconistent)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120270
Approved by: https://github.com/shuqiangzhang
ghstack dependencies: #120724
Summary:
The current dump timeout logic is a bit cumbersome as it needs 2 times: 1.
timeout, 2. wake up time. And in theory the caller just needs to wait
for a max of timeout value for the dump and declare the dump to be
either successful or not. Also we unify the async call using std::async
instead of a customized async lauch function for each operation.
Test Plan:
Unit tests
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120331
Approved by: https://github.com/wconstab
Today watchdog's sleep interval is 1s. That's a bit long compared to modern GPU link's (or network link's) speed.
Take DDP and Ampere for example:
DDP's bucket size = 25 MB
Ampere's NVLink speed = 250 GB/s
25 MB / 250 GB/s = 100 ms.
So we are updating the interval to 100 ms.
Update:
25 MB / 250 GB/s = 0.1 ms
But let's see how it goes so far between making the checking more aggressive.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117297
Approved by: https://github.com/fduwjj
Previously, we have the writer register to each NCCL PG(backend), so for every pg, we have a NCCL PG instance, so if we use some customized writer when multiple sub-PGs are used, we need to ensure user to register the writer for every backend which indicates a bad UX. Furthermore, the debug info is global, so it does not make sense to have the writer for each instance. We even have a static mutex in the `dumpDebuggingInfo` to ensure we serialize the write, that makes it more obvious that we can make the writer a singleton so that we only have one writer instance for all PG instances.
Although the rationale is clear, the implementation may vary a lot. So this PR is RFC for now to see if this implementation makes sense or not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116489
Approved by: https://github.com/kwen2501
Replaces the "always sleep 30 sec before abort" with "wait up to 30 sec
for the future to complete then abort". The difference in this case is
the abort happens as soon as the dump finishes up to a maximum, instead
of always waiting the maximum.
Allows multiple calls to dump, which will be serialized.
Renames tryWriteDebugInfo to launchAsyncDebugDump in spirit of the
change to support more than one launch and to always launch rather than
only launching on the first call.
Adds a test for dumping on timeout.
This reverts commit ac7d14baad53fa7d63119418f760190f289d8a01.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115332
Approved by: https://github.com/fduwjj
Replaces the "always sleep 30 sec before abort" with "wait up to 30 sec
for the future to complete then abort". The difference in this case is
the abort happens as soon as the dump finishes up to a maximum, instead
of always waiting the maximum.
Allows multiple calls to dump, which will be serialized.
Renames `tryWriteDebugInfo` to `launchAsyncDebugDump` in spirit of the
change to support more than one launch and to always launch rather than
only launching on the first call.
Adds a test for dumping on timeout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115176
Approved by: https://github.com/zdevito
NCCL_ prefix should only be used for NCCL library's environment variables. We currently use a few environment variables in PyTorch with the NCCL_ prefix that are the NCCL library does not understand.
This patch renames such environment variables to use the TORCH_NCCL_ prefix instead. We still maintain the old NCCL_ variables, but throw a warning when they are used.
The following env changes have been made:
`NCCL_BLOCKING_WAIT` -> `TORCH_NCCL_BLOCKING_WAIT`
`NCCL_ENABLE_TIMING` -> `TORCH_NCCL_ENABLE_TIMING`
`NCCL_DESYNC_DEBUG` -> `TORCH_NCCL_DESYNC_DEBUG`
`NCCL_ASYNC_ERROR_HANDLING` -> `TORCH_NCCL_ASYNC_ERROR_HANDLING`
`ENABLE_NCCL_HEALTH_CHECK` -> `TORCH_ENABLE_NCCL_HEALTH_CHECK`
`NCCL_USE_TENSOR_REGISTER_ALLOCATOR_HOOK` -> `TORCH_NCCL_USE_TENSOR_REGISTER_ALLOCATOR_HOOK`
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114077
Approved by: https://github.com/fduwjj
Summary:
The getCvar* functions allow us to provide multiple environment variables for the same value. This allows us to deprecate some variables in favor of others, while still allowing users to temporarily use the old variables for some time.
Test Plan: OSS CI
Reviewed By: fduwjj, XilunWu
Differential Revision: D51225487
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113797
Approved by: https://github.com/fduwjj
This PR is to enable the store of NCCL flight recorder to storage and make it configurable by letting users register their own way of storing the debug info. We will then provide users a script to offline parse and process the dumped blobs.
One thing, this PR is not trying to resolve is to decide where to dump the debug info. I will send a follow-up PR to address that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113503
Approved by: https://github.com/zdevito
This PR has the following goals:
1. Detect unhealthy nccl watchdog thread by implementing a heartbeat. NCCL watchdog sometimes can hang for several reasons such as nccl/cuda API bugs or unexpected blocking behaviors. This is the last resort to ensure that we don't silently keep the training job run for hours.
2. Sometimes, the process gets stuck in the destroy of NCCL PG, and this PR will ensure that we will eventually abort it after some time (by default 2 mins)
3. Once heartbeat cannot be heard, we dump debug information (for now, we just use the flight recorder implemented in https://github.com/pytorch/pytorch/pull/110960/files) to disk. (How and where to dump the debug info will be addressed in the following PR).
4. Finally, we initiate std::abort via `LOG(FATAL)` to kill the process.
To clarify further what this PR is trying to solve, we first list are four cases when a NCCL PG can end up with:
- case 1: ncclwatchdog gets stuck (maybe some blocking API) and heartbeat monitor kills it during regular heartbeat monitor loop.
- case 2: ncclwatchdog timeout and desync report or destroy kicked in(let's call it shutdown) but this shutdown takes so long and heartbeat believes it has to kills the process anyway.
- case 3: ncclwatchdog aborts the process (heartbeat monitor not involved)
- case 4: program exits cleanly (heartbeat monitor not involved)
As we can see here, this PR is trying to address case one and two and we also want to ensure adding one more monitor thread does not interfere what we are currently doing in case three and four. That's why we added two flags `terminateHeartbeatMonitorThread_` and `collectiveDebugInfoMode_`.
For case three and four, either `monitorWakeUpCV_` will be waked up in the destructor or `terminateHeartbeatMonitorThread_` will be set to true. So that monitor thread will just exit ASAP.
For case one, both `terminateHeartbeatMonitorThread_` and `collectiveDebugInfoMode_` will still false when monitor thread see there are no heartbeat, so it will directly kill the process. For case two, either `terminateHeartbeatMonitorThread_` and `collectiveDebugInfoMode_` will be true, the monitor thread will wait extra time before killing the process.
Differential Revision: [D51146305](https://our.internmc.facebook.com/intern/diff/D51146305)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112518
Approved by: https://github.com/kwen2501, https://github.com/wconstab
Keep a buffer of the last 16384 nccl work actions, including the stack
trace that launched the event.
When torch._C._distributed_c10d._dump_nccl_trace(), it an dump these to
a pickled archive.
For each action we get:
process_group_id, seq_id, collective_name, size_of_first_tensor, stack trace
state - issued, started, completed (based on cuda events and queried if
necessary when the dump is requested)
I tested that it is possible to query event state when the streams are
otherwise stuck.
Differential Revision: [D50138956](https://our.internmc.facebook.com/intern/diff/D50138956)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110960
Approved by: https://github.com/wconstab
Fixes#97191
This PR aims to propagate collective exceptions (async error or timeout) up to the program, so as to avoid silent stuck job.
### Previous output in #97191
```
Rank 0 is the problematic rank
Rank 4 completed
Rank 5 completed
Rank 3 completed
Rank 6 completed
Rank 2 completed
Rank 7 completed
Rank 1 completed
[E ProcessGroupNCCL.cpp:464] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=ALLREDUCE, Timeout(ms)=10000) ran for 10917 milliseconds before timing out.
Rank 0 completed
[E ProcessGroupNCCL.cpp:478] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:483] To avoid data inconsistency, we are taking the entire process down.
```
Although it says that it is taking the process down, it sometimes fails to do so.
### New output after this PR:
```
...
[E ProcessGroupNCCL.cpp:459] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=ALLREDUCE, Timeout(ms)=10000) ran for 10599 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:473] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:479] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:818] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=ALLREDUCE, Timeout(ms)=10000) ran for 10599 milliseconds before timing out.
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -6) local_rank: 0 (pid: 194470) of binary: /data/home/kw2501/repos/pytorch-dev-env/bin/python
Traceback (most recent call last):
File "/pytorch-dev-env/bin/torchrun", line 33, in <module>
sys.exit(load_entry_point('torch', 'console_scripts', 'torchrun')())
File "/pytorch-dev/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/pytorch-dev/torch/distributed/run.py", line 794, in main
run(args)
File "/pytorch-dev/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/pytorch-dev/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/pytorch-dev/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
hang.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-03-20_22:00:42
host : node0
rank : 0 (local_rank: 0)
exitcode : -6 (pid: 194470)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 194470
============================================================
```
The log suggests that TorchX monitor is triggered, and job is torn down.
### Major changes in this PR:
1. Merge ncclWatchDog thread and workCleanupLoop thread into one so that the watch action and the throw action are streamlined.
Previously, ncclWatchDog is responsible for watching comm error and timeout, and workCleanupLoop is responsible for watching Work item error and throwing exception. This two-thread design is not streamlined, raising the chance of missing the throw. Also, it is duplicated to watch at multiple level.
2. Rethrow exception at watchdog thread.
3. Clean up a bunch of duplicated functions, e.g. `checkAndThrowException` and `handleNcclException`.
4. Turn on ASYNC_ERROR_HANDLING by default
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97066
Approved by: https://github.com/rohan-varma
Fixes#97191
This PR aims to propagate collective exceptions (async error or timeout) up to the program, so as to avoid silent stuck job.
### Previous output in #97191
```
Rank 0 is the problematic rank
Rank 4 completed
Rank 5 completed
Rank 3 completed
Rank 6 completed
Rank 2 completed
Rank 7 completed
Rank 1 completed
[E ProcessGroupNCCL.cpp:464] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=ALLREDUCE, Timeout(ms)=10000) ran for 10917 milliseconds before timing out.
Rank 0 completed
[E ProcessGroupNCCL.cpp:478] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:483] To avoid data inconsistency, we are taking the entire process down.
```
Although it says that it is taking the process down, it sometimes fails to do so.
### New output after this PR:
```
...
[E ProcessGroupNCCL.cpp:459] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=ALLREDUCE, Timeout(ms)=10000) ran for 10599 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:473] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:479] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:818] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=ALLREDUCE, Timeout(ms)=10000) ran for 10599 milliseconds before timing out.
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -6) local_rank: 0 (pid: 194470) of binary: /data/home/kw2501/repos/pytorch-dev-env/bin/python
Traceback (most recent call last):
File "/pytorch-dev-env/bin/torchrun", line 33, in <module>
sys.exit(load_entry_point('torch', 'console_scripts', 'torchrun')())
File "/pytorch-dev/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/pytorch-dev/torch/distributed/run.py", line 794, in main
run(args)
File "/pytorch-dev/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/pytorch-dev/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/pytorch-dev/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
hang.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-03-20_22:00:42
host : node0
rank : 0 (local_rank: 0)
exitcode : -6 (pid: 194470)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 194470
============================================================
```
The log suggests that TorchX monitor is triggered, and job is torn down.
### Major changes in this PR:
1. Merge ncclWatchDog thread and workCleanupLoop thread into one so that the watch action and the throw action are streamlined.
Previously, ncclWatchDog is responsible for watching comm error and timeout, and workCleanupLoop is responsible for watching Work item error and throwing exception. This two-thread design is not streamlined, raising the chance of missing the throw. Also, it is duplicated to watch at multiple level.
2. Rethrow exception at watchdog thread.
3. Clean up a bunch of duplicated functions, e.g. `checkAndThrowException` and `handleNcclException`.
4. Turn on ASYNC_ERROR_HANDLING by default
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97066
Approved by: https://github.com/rohan-varma
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera, https://github.com/huydhn
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68310
Enable desync root cause analysis by recording the last footprint of collective calls. When timeout we parse the store trace and figure out the root cause of the desync issue. This feature is built based on async error handling.
Test Plan:
Standalone test
* Typical desync - P467288969
* Mismatched collectives - P467288916
* Mismatched broadcast size - P467288873
DDP benchmark
* DDP benchmark desync - P467433483, P467520195
No perf regression:
* w/o this diff https://www.internalfb.com/intern/fblearner/details/308379789?tab=Outputs
* w/ this diff https://www.internalfb.com/intern/fblearner/details/308534088?tab=Outputs
Reviewed By: mingzhe09088
Differential Revision: D32348647
fbshipit-source-id: 43e7e96e3fa2be0ac66c1325bceb639b461a8b3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60543
Since now c10d is part of libtorch, it would also be nice if the sources lived all in one place.
ghstack-source-id: 132306292
Test Plan: It builds
Reviewed By: cbalioglu
Differential Revision: D29062002
fbshipit-source-id: d9e1301e9d73e1643fa0f0119cd2d618f1ad52e6
