We are considering consolidating data source for logging and flight recorder so that we don't build multiple paths for debugging information. Before we do any merging, we want to first ensure that the PG status is also included in flight recorder. Also, we can leverage this information to validate our FR dump as well. Because the dump is not synced so we might potentially see some variants in the dump.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131268
Approved by: https://github.com/shuqiangzhang
Some features of third-party devices depend on TraceUtils.h, so some of the CUDA code was removed and split into NCCLUtils files.
In addition, some common functions still remain in TraceUtils.h since I'm not sure if other devices will use them later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126969
Approved by: https://github.com/c-p-i-o
Some features of third-party devices depend on TraceUtils.h, so some of the CUDA code was removed and split into NCCLUtils files.
In addition, some common functions still remain in TraceUtils.h since I'm not sure if other devices will use them later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126969
Approved by: https://github.com/c-p-i-o
This is a duplicate of: https://github.com/pytorch/pytorch/pull/127421 which we can't merge. its landed internally already
Summary:
`ncclCommCreateFromRanks` - described in this [document](https://docs.google.com/document/d/1QIRkAO4SAQ6eFBpxE51JmRKRAH2bwAHn8OIj69XuFqQ/edit#heading=h.5g71oqe3soez), replaces `ncclCommSplit` in NCCLX versions 2.21.5+. The difference is that `ncclCommCreateFromRanks` is given a list of active ranks and is collective only over those ranks as opposed to `ncclCommSplit` for which you give it a color for every rank including NO_COLOR for inactive ranks and the collective is over the entire world.
This diff connects `ncclCommCreateFromRanks` to `c10d`
`ncclCommSplit` will still be available at the NCCL API but, in this diff, is not used starting at version 2.21.5
Split the python test and implementation of `split()` for internal FB and external OSS builds.
The diff defines `"USE_C10D_NCCL_FBCODE"` as a compiler option. When defined, we use the version of split in the newly created `NCCLUtils.cpp` in the `fb` directory. The `fb` directory is not *shipit*-ed to *github*.
The same API is used for `split()` in both the `ncclx` and `nccl` versions adding `ranks` to the API. This argument is not used in the `nccl` version nor in the 2.18 `ncclx` version where `ncclCommSplit()` is used instead of `ncclCommCreateFromRanks()` in `ncclx`
This diff was squashed with D57343946 - see D57343946 for additional review comments.
Test Plan:
for 2.18.3-1 and 2.21.5-1 versions:
```
buck2 run fbcode//mode/opt -c param.use_nccl=True -c fbcode.nvcc_arch=a100 -c hpc_comms.use_ncclx="$VERSION" -c fbcode.enable_gpu_sections=true fbcode//caffe2/test/distributed/fb:test_comm_split_subgroup_x
```
```
BUILD SUCCEEDED
...
ok
----------------------------------------------------------------------
Ran 1 test in 10.210s
OK
~/scripts
```
OSS build:
`[cmodlin@devgpu003.vll5 ~/fbsource/third-party/ncclx/v2.21.5-1 (e56338cfa)]$ ./maint/oss_build.sh`
OSS build output:
```
...
ncclCommHash 197dce9b413e2775
nccl commDesc example_pg
Dump from comm 0x4708aa0 rings: [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]
Dump from comm 0x4708aa0 commDesc: example_pg
Dump from comm 0x4708aa0 nRanks: 1
Dump from comm 0x4708aa0 nNodes: 1
Dump from comm 0x4708aa0 node: 0
Dump from comm 0x4708aa0 localRanks: 1
Dump from comm 0x4708aa0 localRank: 0
Dump from comm 0x4708aa0 rank: 0
Dump from comm 0x4708aa0 commHash: "197dce9b413e2775"
2024-05-24T09:02:54.385543 devgpu003:3040664:3040744 [0][AsyncJob]ctran/backends/ib/CtranIb.cc:143 NCCL WARN CTRAN-IB : No active device found.
2024-05-24T09:02:54.385607 devgpu003:3040664:3040744 [0][AsyncJob]ctran/mapper/CtranMapper.cc:187 NCCL WARN CTRAN: IB backend not enabled
Created NCCL_SPLIT_TYPE_NODE type splitComm 0x11c76d0, rank 0
~/fbsource/third-party/ncclx/v2.21.5-1
```
Reviewed By: wconstab, wesbland
Differential Revision: D56907877
Fixes #ISSUE_NUMBER
Co-authored-by: Cory Modlin <cmodlin@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127982
Approved by: https://github.com/izaitsevfb
Summary:
User-defined PyTorch modules that uses `C10D_NCCL_CHECK` run into undefined symbol errors
when loaded by `torch.library.load()`, because they have not been exported. This change
exports the symbols needed to resolve those runtime errors.
Test Plan: PyTorch CI
Differential Revision: D57977944
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127550
Approved by: https://github.com/Skylion007
Summary: It would be useful to log the destination of the trace dump in either manifold or local file for the users to quickly locate the dump
Test Plan: Modified unit tests
Differential Revision: D54972069
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122345
Approved by: https://github.com/wconstab
Summary:
This PR is mainly about flight recorder side of changes that takes a
map of maps as input, and dump it as picklable. Also add functions that
should be compiled only when NCCL_COMM_DUMP is defined
Test Plan:
Integration tests with NCCL would be done later, here we only do the
c10d side of dump test, aka,NCCLTraceTest
Testing the dump function is a bit tricky as we don't have
existing C++ unit tests for them. So we still use the Python NCCLTraceTest with
the python binding of _dump_nccl_trace(), we manually fed the
dump_nccl_trace with a map of test info, and assert the pickle result and
print the converted python dict:
```
(sqzhang_1) [sqzhang@devgpu009.cln1 ~/pytorch (main)]$ python
test/distributed/test_c10d_nccl.py NCCLTraceTest
NCCL version 2.19.3+cuda12.0
[rank0]:[E ProcessGroupNCCL.cpp:1200] [PG 0 Rank 0] ProcessGroupNCCL
preparing to dump debug info.
.NCCL version 2.19.3+cuda12.0
.NCCL version 2.19.3+cuda12.0
{'ncclID2': {'Key2': 'Value2', 'Key1': 'Value1'}, 'ncclID1': {'Key2':
'Value2', 'Key1': 'Value1'}}
{'ncclID2': {'Key2': 'Value2', 'Key1': 'Value1'}, 'ncclID1': {'Key2':
'Value2', 'Key1': 'Value1'}}
.NCCL version 2.19.3+cuda12.0
{'ncclID2': {'Key2': 'Value2', 'Key1': 'Value1'}, 'ncclID1': {'Key2':
'Value2', 'Key1': 'Value1'}}
{'ncclID2': {'Key2': 'Value2', 'Key1': 'Value1'}, 'ncclID1': {'Key2':
'Value2', 'Key1': 'Value1'}}
.NCCL version 2.19.3+cuda12.0
.NCCL version 2.19.3+cuda12.0
.NCCL version 2.19.3+cuda12.0
.NCCL version 2.19.3+cuda12.0
.
----------------------------------------------------------------------
Ran 8 tests in 95.761s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120063
Approved by: https://github.com/wconstab
resolve#117749
Summary:
Updated the PR with the following intentions:
1. identify eagerMode init (as opposed to lazy init), in which case we will create NCCL comms without guarantees that they are fully initialized if NONBLOCKING mode is also enabled.
2. Python users can do their other works (e.g., model init) between invoking init_process_group and their first collective call.
3. c10D would guarantee/wait for communicators to be initialized before issuing the first collective call.
4. For NCCL collective calls, the contract between python users and c10d is not changed much from blocking calls (C10d would wait the NCCL call to be ncclSuccess, or timeout, whichever happens first).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118256
Approved by: https://github.com/kwen2501
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
- [c10d] (retry) Opportunistically use `ncclCommSplit` when creating new NCCL groups (#112889)
- Guard use of `split_from` with a `hasattr` check for cases when NCCL (or RCCL) lacks `ncclCommSplit`
Fixes cause of revert of original PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114385
Approved by: https://github.com/huydhn
Currently `ncclCommInitRankConfig` is always used when creating new
communicator groups. This is wasteful as it creates non-shared pairs
of endpoint queues as well as costs time to re-establish
communication.
This change is transparent and opportunistic; when `dist.new_group` is
called, it will use the existing, healthy world process group to
select the right ranks to include in the process group.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112889
Approved by: https://github.com/kwen2501
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
Summary:
We need to register all cache segments allocated by allocator, so that NCCL can apply zero copy algorithms at collective and point-to-point operations.
How to track and register all cache segments:
- It registers a register and a deregister hook to cache allocator as action tracker callbacks, tracking SEGMENT_ALLOC and SEGMENT_FREE trace entries, respectively. When SEGMENT_ALLOC is tracked, the register hook will register to the PG's communicators on the same device. Similarly, when SEGMENT_FREE is tracked, the deregister hook handles deregistration before cudaFree.
- When a new NCCL communicator is created, it dumps the snapspot from cache allocator to register all existing cache segments at once.
- When a NCCL communicator is aborted, it deregisters all segments that have been registered by this communicator
Test Plan: See test in D50726971
Reviewed By: wconstab
Differential Revision: D50726970
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112850
Approved by: https://github.com/wconstab
NCCL 2.17+ introduces some user configurable parameters for NCCL communicators using [ncclConfig_t](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#c.ncclConfig_t) datatype and [ncclCommInitRankConfig](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/comms.html#ncclcomminitrankconfig). This PR enables that feature.
A user can tune the parameters as follows:
```
import torch.distributed as dist
nccl_options = dist.ProcessGroupNCCL.Options()
nccl_options.config.max_ctas = 32
nccl_options.config.min_ctas = 8
nccl_options.config.cga_cluster_size = 2
dist.init_process_group(backend='nccl', init_method='env://', pg_options=nccl_options)
my_group = dist.new_group(pg_options=nccl_options)
```
The default values of these parameters are what is initialized by `NCCL_CONFIG_INITIALIZER`. Only for DistributedDataParallel, this PR sets the default value of cga_cluster_size to 2 (a heuristic that works well especially for DDP workloads).
Tuning these parameters can lead to improvement in end-to-end performance, since it affects the communication-computation overlap for NCCL kernels.
CC: @ptrblck @kwen2501
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97394
Approved by: https://github.com/kwen2501
Support for nonblocking NCCL communicators/fault tolerance/checking which was added in 2.14 as an experimental feature.
Enabled via the environment variable:
```
TORCH_NCCL_USE_COMM_NONBLOCKING=1
```
CC @ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95715
Approved by: https://github.com/kwen2501
Support for nonblocking NCCL communicators/fault tolerance/checking which was added in 2.14 as an experimental feature.
Enabled via the environment variable:
```
TORCH_NCCL_USE_COMM_NONBLOCKING=1
```
CC @ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95715
Approved by: https://github.com/kwen2501
Currently all of the distributed errors are thrown from the `TORCH_CHECK` macro which throws a generic `RuntimeError`. This change introduced a new error type `DistBackendError` which derives from `RuntimeError` to signify there was an error with the backend communication library. This allows for better error handling and analysis at higher levels in the stack. Motivation: https://docs.google.com/document/d/1j6VPOkC6znscliFuiDWMuMV1_fH4Abgdq7TCHMcXai4/edit#heading=h.a9rc38misyx8
Changes:
- introduce new error type
- Update `C10D_NCCL_CHECK`
Sample script to demonstrate new error type
```python
# python -m torch.distributed.run --nproc_per_node=2 <script>.py
import torch
import torch.distributed as dist
if __name__ == "__main__":
dist.init_process_group("nccl")
dist.broadcast(torch.tensor([1, 2, 3]).cuda(), 0)
```
Differential Revision: [D40998803](https://our.internmc.facebook.com/intern/diff/D40998803)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88134
Approved by: https://github.com/rohan-varma
