Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_grpc_destroy_bug
minqiyang
7 years ago
commit
2c4fb585db
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# Distributed Training with NCCL2
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We design a pattern that can enable training with `ParallelExecutor` and
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using [NCCL2](https://developer.nvidia.com/nccl) as it's collective
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communication library.
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In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast`
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to do multi GPU training. And if we initialize NCCL2 communicators as
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ranks in a distributed environment, we can simply run the `ParallelExecutor`
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as a distributed program! The only thing that may be different than in
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the single node version is that we need to broadcast the NCCL unique ID
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to all the nodes, and initialize communicators using that ID, so NCCL2
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will know each other as ranks.
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To achieve this feature, we introduce a new operator: `gen_nccl_id` op,
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so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in
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what ever platform you like.
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It have two running modes:
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1. Generate and broadcast mode, which should be used on trainer 0;
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1. Listen and fetch mode, which should be used on trainers other than 0.
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In both two modes, this op can save the NCCL ID into current scope as a
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persistable variable, Then we can insert this op at the end of
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"startup program" of fluid, so that all workers can get the same ID to
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initialize NCCL communicator objects.
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<img src="src/ncc2_design.png">
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The above figure indicates the general process when training with NCCL2
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distributed. Each trainer have the number of communicators equal to the
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number of GPUs, but the ranks should match the global ranks number: here
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we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should
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be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1.
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