[Core][Distributed] Refactor ipc buffer init in CustomAllreduce

[hanzhi713]

As discussed with @youkaichao, we use cuda API to share tensors instead of replying _share_cuda_, which won't break with expandable segment or future pytorch upgrade.

Additional changes:

1. Improved some comments, especially about cuda graph ipc registration.
2. Consolidated all_reduce methods.
3. Use vector<int64_t> instead of pytorch tensor for handle during cuda graph ipc registration process. The use of Tensor was introduced in [Kernel][Misc] Use TORCH_LIBRARY instead of PYBIND11_MODULE for custom ops #5047. We sticked to non-tensor type to remove this complexity.

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[mgoin]

cc @tlrmchlsmth

[tlrmchlsmth]

@hanzhi713 and @youkaichao could you share a few more details on what's going on?

Looks like this is related to #9815 -- is the idea to be more stable across Pytorch versions? Do you see any downsides to this?

[hanzhi713]

@hanzhi713 and @youkaichao could you share a few more details on what's going on?

Looks like this is related to #9815 -- is the idea to be more stable across Pytorch versions? Do you see any downsides to this?

Yes, we want to rely less on internal API to prevent future breaking. A downside to the current approach is that it doesn't support expandable_segments:True due to a current pytorch limitation.

An alternative design I see is to do the one-time allocation of IPC-enabled buffers ourselves through CUDA C++ (i.e. cudaMalloc + ipc handle calls).

[youkaichao]

#10064 will make this pr easier. we don't need to depend on pytorch's internal apis

[youkaichao]

please merge main to use the functionality from #10064

[hanzhi713]

Done. @youkaichao PTAL, thanks!

[youkaichao]

thanks for the great contribution! do you have some updated perf numbers? I assume it should not affect the performance.

[youkaichao]

nit: please fix the format.

[hanzhi713]

There're no perf changes from the C++. I can run the python side benchmark to be sure.

[hanzhi713]

@youkaichao I can confirm that there's no perf difference with benchmarks/benchmark_latency.py

[youkaichao]

RuntimeError: Inplace update to inference tensor outside InferenceMode is not allowed.You can make a clone to get a normal tensor before doing inplace

there are some errors in the ci actually @hanzhi713

[youkaichao]

use broadcast as before in _gather_ipc_meta ?

code for reference:

        for i, rank in enumerate(ranks):
            dist.broadcast_object_list(all_data[i],
                                       src=rank,
                                       group=self.group,
                                       device="cpu")

[hanzhi713]

Yeah it looks like we still need to use broadcast here

[youkaichao]

to locally test it, run pytest -v -s tests/basic_correctness/test_basic_correctness.py::test_models_distributed[facebook/opt-125m-mp--A100]

[hanzhi713]

My old benchmark script had --enforce-eager there which didn't catch this

[hanzhi713]

Should be fixed now.

[youkaichao]

@hanzhi713 thanks again for the great contribution!