I hit this in tests when calling `init_process_group(init_method="tcp://localhost:0", ...)`. You can't use port 0 due to the bug in the conditional and will get error `ValueError: Error initializing torch.distributed using tcp:// rendezvous: port number missing`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154156
Approved by: https://github.com/d4l3k, 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
This adds a `reduce_scatter` implementation for ProcessGroupGloo. This is a pretty naive implementation as it does 1 allreduce per rank but may be useful for testing in FSDP etc. There was an existing implementation of reduce_scatter_tensor/reduce_scatter_tensor_coalesed that has a very similar implementation but requires a fixed tensor size per rank.
If users find these functions to be too slow we can address them as issues arise.
Gloo now supports all major distributed operations. Quite a few of these were added by @rohan-varma and @yifuwang but they didn't update the support chart. We also have `CUDAWork` variants of most operations so those were also added to the chart.
Test plan:
```
pytest -v test/distributed/test_c10d_gloo.py -k reduce_scatter
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149869
Approved by: https://github.com/fduwjj
This adds AVG support to ProcessGroupGloo to better support FSDP on CPU. I expect there will be more issues but this is easy enough to support in a naive fashion.
This applies to both reduce and allreduce.
This is a simple SUM + division and may not be the most numerically stable but that's expected. FSDP for low precision data types implements pre/post divide and uses SUM instead.
Test plan:
```
pytest -v test/distributed/test_c10d_gloo.py
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149781
Approved by: https://github.com/fduwjj
This PR
* makes changes to the workflow files and scripts so we can run CI workflows on the MI300 runners
* skips and fixes several tests, failed on MI300, observed in https://github.com/pytorch/pytorch/pull/140989
Skipped due to unsupported Float8_e4m3fn data type on MI300 (need to update test code to use datatypes supported by MI300):
- distributed.tensor.parallel.test_micro_pipeline_tp.py::MicroPipelineTPTest::test_fuse_all_gather_scaled_matmul_A_dims_\*_gather_dim_\* (24 tests across inductor/distributed configs)
- distributed.tensor.parallel.test_micro_pipeline_tp.py::test_fuse_scaled_matmul_reduce_scatter_A_dims_\*_scatter_dim_\* (12 tests across inductor/distributed configs))
- inductor.test_loop_ordering::LoopOrderingTest::test_fp8_cast_and_t
- inductor.test_loop_ordering::LoopOrderingTest::test_fp8_pattern_2
Skipped due to AssertionError on MI300:
- inductor.test_mkldnn_pattern_matcher.py::test_qconv2d_int8_mixed_bf16
- distributed._tools.test_sac_ilp::TestSACILP::test_sac_ilp_case1
Skipped:
- test_cuda.py::TestCudaMallocAsync::test_clock_speed
- test_cuda.py::TestCudaMallocAsync::test_power_draw
- test_torch.py::TestTorchDeviceTypeCUDA::test_deterministic_cumsum_cuda
Skipped flaky tests on MI300:
- distributed.test_c10d_gloo.py::ProcessGroupGlooTest::test_gather_stress_cuda
- inductor.test_cpu_repro::CPUReproTests::test_lstm_packed_unbatched_False* (256 tests)
Fixed:
- test_matmul_cuda.py::TestFP8MatmulCudaCUDA::test_float8_basics_cuda
Features:
- inductor/test_fp8.py - declare a new function to convert FP8 datatypes to ROCm supported FP8 datatypes. It keeps test names for CUDA and ROCm and allows to enable Inductor FP8 tests on CPU
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143673
Approved by: https://github.com/jeffdaily, https://github.com/malfet, https://github.com/pruthvistony
Co-authored-by: saienduri <saimanas.enduri@amd.com>
Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143753
Approved by: https://github.com/Skylion007
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
### Motivation
Despite our plan to reduce gloo usage, it is still being widely used as testing tool (in both the PyTorch CI and user tests) for code that only uses nccl in real world scenario. There's some coverage issues around all-gather and reduce-scatter variants, which are currently worked around in ugly ways (e.g. [this](b9e86bc93d/torch/distributed/_functional_collectives_impl.py (L216-L219)) and [this](b9e86bc93d/torch/distributed/_functional_collectives_impl.py (L262-L272))). For native funcol I ran into the same issues but I'd rather just fix the coverage.
### This PR
We already have a fallback impl for `_reduce_scatter_base`, which is composed from all-reduce + scatter. The scatter was not necessary. It introduces extra communication, sync point, and forced the impl to fail on `asyncOp=True`. This PR does the following:
- Simulate reduce-scatter with `allreduce(inp).chunk(world_size)[rank]`. This is still 2x communication than a real reduce-scatter (since all-reduce = reduce-scatter + all-gather), but it's strictly better than what we have now.
- By doing the above, the comm becomes async and we don't have to fail on `asyncOp=True`.
- The general logic is implemented in `reduce_scatter_tensor_coalesced`. `_reduce_scatter_base` just calls it with single input/output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118911
Approved by: https://github.com/shuqiangzhang
ghstack dependencies: #118910
### Motivation
Despite our plan to reduce gloo usage, it is still being widely used as testing tool (in both the PyTorch CI and user tests) for code that only uses nccl in real world scenario. There's some coverage issues around all-gather and reduce-scatter variants, which are currently worked around in ugly ways (e.g. [this](b9e86bc93d/torch/distributed/_functional_collectives_impl.py (L216-L219)) and [this](b9e86bc93d/torch/distributed/_functional_collectives_impl.py (L262-L272))). For native funcol I ran into the same issues but I'd rather just fix the coverage.
**I think it's reasonable to think of this as a fix rather than adding new features. This is orthogonal to the potential reduction of gloo usage**.
### This PR
This PR adds `ProcessGroupGloo::allgather_into_tensor_coalesced`. This is very straightforward - `ProcessGroupGloo` already supports `allgather_coalesced`, to which we can funnel `allgather_into_tensor_coalesced`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118910
Approved by: https://github.com/shuqiangzhang
Work.result() returns a vector of tensors. This signature is problematic as some collectives may just return one tensor (e.g all-reduce), while some others may return multiple tensors (e.g. all-gather).
It would be clearer/easier for users to directly access the result via the tensor/tensorlist passed to the collective APIs.
Deprecating work.result() would also allow us to remove the `outputs_` field in the Work class, avoiding an "artificial" reference to the tensor, which could potentially hold up the tensor's memory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117565
Approved by: https://github.com/wconstab
Fixes#111422
allreduce_sparse_cuda gets dispatched to allreduce_sparse which doesnt exist for gloo. However, gloo has an existing implementation so this is just fixing the dispatching to that.
The reason CI didn't catch this is because we are calling the backend directly. Added a test which calls the public API (dist.XYZ) and goes through the dispatcher
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111485
Approved by: https://github.com/fduwjj
We have a plethora of error types for various errors raised from c10d. These include `RuntimeError`, `TimeoutError`, `SocketError`, `DistBackendError` etc.
This results in messy code during error handling somewhat like this:
```
if "NCCL" in exception_str:
...
if "Timed out initializing process group in store based barrier on rank" in exception_str:
...
if "The client socket has timed out after" in exception_str:
...
if "Broken pipe" in exception_str:
...
if "Connection reset by peer" in exception_str:
...
```
To address this issue, in this PR I've ensured added these error types:
1. **DistError** - the base type of all distributed errors
2. **DistBackendError** - this already existed and referred to PG backend errors
3. **DistStoreError** - for errors originating from the store
4. **DistNetworkError** - for general network errors coming from the socket library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108191
Approved by: https://github.com/H-Huang
We have a plethora of error types for various errors raised from c10d. These include `RuntimeError`, `TimeoutError`, `SocketError`, `DistBackendError` etc.
This results in messy code during error handling somewhat like this:
```
if "NCCL" in exception_str:
...
if "Timed out initializing process group in store based barrier on rank" in exception_str:
...
if "The client socket has timed out after" in exception_str:
...
if "Broken pipe" in exception_str:
...
if "Connection reset by peer" in exception_str:
...
```
To address this issue, in this PR I've ensured added these error types:
1. **DistError** - the base type of all distributed errors
2. **DistBackendError** - this already existed and referred to PG backend errors
3. **DistStoreError** - for errors originating from the store
4. **DistNetworkError** - for general network errors coming from the socket library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107651
Approved by: https://github.com/H-Huang
The test was marked as flaky in #59965. However, it is not failing anymore so it can be enabled.
This PR enables only one test, but it will only run in local tests because the test suite is disabled in CI.
#94495 is a superset of this PR which enables the full test suite. The CI run there shows this test passing.
Fixes#59965
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103317
Approved by: https://github.com/kit1980
Summary: with the new c10d API, we don't need all ranks to call new_group. Integrate with the new API, so that every rank just call new_group 3 times, with a local barrier with the members within the group.
Reviewed By: xunnanxu, eeggl
Differential Revision: D45315615
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100518
Approved by: https://github.com/kumpera
Add use_local_synchronization argument to new_group.
When this argument is True, is change new_group to do a store_barrier only on the ranks that are park of the group and not the whole cluster.
This addressess both scalability and composability problems associated with new_group.
Fixes#81291.
This is relanding #84224
As part of the original PR I did a quick benchmark of creating 3 PGs per rank using both functions and perf is the following:
new_group use_local_synchronization=False:
| World Size | Time (in secs) |
| --- | ----------- |
| 4 | 0.12 |
| 8 | 0.25 |
| 16 | 0.51 |
| 32 | 0.87 |
| 64 | 1.50 |
| 128 | 2.87 |
new_group use_local_synchronization=True:
| World Size | Time (in secs) |
| --- | ----------- |
| 4 | 0.05 |
| 8 | 0.04 |
| 16 | 0.03 |
| 32 | 0.03 |
| 64 | 0.04 |
| 128 | 0.04 |
Scaling for `use_local_synchronization=False` is sub linear because the number of process groups created as a multiple of world_size decreases as we go up. It's 6 with world_size 4 and 192 with world_size 128.
Scaling for `use_local_synchronization=True` is constant as the number of store barriers executed per rank remains constant at 3.
Setup:
1 AWS host, backend gloo.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99931
Approved by: https://github.com/xw285cornell
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88330
### Implementation
Move backend-specific (NCCL, Gloo, etc) collective implementations to corresponding `Backend` class. Update ProcessGroup to support multiple backends and use dispatcher to calls backends based on tensor device type.
### Changes
#### c++ changes (ProcessGroup files, `Ops.cpp`, `init.cpp`)
- Update pybind definitions for new process group base class and new backend class
- Update pybinded backend class with collective definitions to keep BC with Python PG instances (e.g. `dist.ProcessGroupGloo`, `dist.ProcessGroupNCCL`) which are used in tests
- Switch `ProcessGroupGloo`, `ProcessGroupNCCL`, `ProcessGroupMPI`, `ProcessGroupUCC` to derive from the `Backend` class.
- Update CPU/CUDA `Ops.cpp` and `OpsImpl.cpp` to perform this dispatching by querying the backend using the device type
- Update internal dispatched implementation of `barrier` to use a tensor which allows operation to be dispatched.
- Update `allgather` collective to use `TensorList`. For some reason it was using the default implementation of `allgather` rather than dispatching it correctly. I still don't understand why and had originally filed an issue in 85122.
#### python changes (`distributed_c10d.py`, test files)
- Add BackendConfig class to specify the default configurations of backends and `get_backend_config()` API
- `get_backend()` deprecation warning
- `init_process_group` how returns a generic `ProcessGroup` object, it contains a list of backends (the ones stated above) which it will dispatch operations to.
- `new_group` updated to return the same as above
- Update `test_c10d_gloo.py`, Update `DistributedDataParallelTest` to use `init_process_group`, Update `ReducerTest`, update `test_broadcast_coalesced_gloo` to move from PG instance and gloo options
- Update `test_c10d_nccl.py`, Update `DistributedDataParallelTest` to use `init_process_group`
- Specific tests updated: `test_Backend_enum_class`
### Changes missing
- lazy initialization of backends
- support parsing of BackendConfig
### open questions
- Pure Python PG extensions (https://github.com/pytorch/pytorch/pull/66338)
# Example
This is a basic script (using 2 backends within a process group)
```python
# python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 basic_scenario.py
import torch.distributed as dist
import torch
import os
if __name__ == "__main__":
rank = os.environ.get("RANK")
# initialize with both gloo and nccl
dist.init_process_group()
# with gloo
dist.all_reduce(torch.tensor([1.0]))
print(f"Rank {rank} finished")
# with nccl
dist.all_reduce(torch.tensor([1.0], device=f"cuda:{rank}"))
```
Test Plan: Imported from OSS
Differential Revision: D42069829
Pulled By: H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90997
Approved by: https://github.com/awgu, https://github.com/fduwjj
