From 46034faf97e7a135756d36391f5c4a970fed92ab Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 29 Aug 2017 16:05:10 -0700 Subject: [PATCH] Design doc: operator based parameter server. --- doc/design/ops/dist_train.md | 82 +++++++++++++++++++++++++ doc/design/ops/src/dist-graph.graffle | Bin 0 -> 4915 bytes doc/design/ops/src/dist-graph.png | Bin 0 -> 133866 bytes doc/design/ops/src/local-graph.graffle | Bin 0 -> 2515 bytes doc/design/ops/src/local-graph.png | Bin 0 -> 31493 bytes 5 files changed, 82 insertions(+) create mode 100644 doc/design/ops/dist_train.md create mode 100644 doc/design/ops/src/dist-graph.graffle create mode 100644 doc/design/ops/src/dist-graph.png create mode 100644 doc/design/ops/src/local-graph.graffle create mode 100644 doc/design/ops/src/local-graph.png diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md new file mode 100644 index 0000000000..0380826b0d --- /dev/null +++ b/doc/design/ops/dist_train.md @@ -0,0 +1,82 @@ +# Design Doc: Operation Graph Based Parameter Server + +## Abstract + +We propose an approach to implment the parameter server. In this +approach, there is no fundimental difference between the trainer and +the parameter server: they both run sub-graphs, but sub-graphs of +different purposes. + +## Background + +The previous implementations of the parameter server does not run a +sub-graph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server. + +It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given during the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension. + +## Design + +### Graph Converter + +The *graph converter* converts user-defined operation (OP) graph into +sub-graphs to be scheduled on different nodes. + +1. The user-defined OP graph will be cut into sub-graphs of +different purposes (e.g., trainer, parameter server) to run on +different workers. + +1. OPs will be added to the subgraphs, so the subgraphs can +communicate with each other. We will need these OPs: *send*, *recv*, +*gradient accumulator*, *string accumulator*, *loop forever*. + +Below is an example of converting the user defined graph to the +sub-graphs for the trainer and the parameter server: + + + +After converting: + + + +1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. +1. Operators are added to the sub-graphs. + - *send* operator sends data and sender's address to the destination. + - *recv* operator receives data and sender's address from the + destination. It will block until data has been received. + - *gradient accumulator* operator accumulates *N* pieces of + gradients. N=1 in Async-SGD, N>1 in Sync-SGD. + - *string accumulator* accumulates *N* pieces of strings into a + list of strings. N=1 in Async-SGD, N>1 in Sync-SGD. + - *loop forever* runs itself as a target forever. + +### Benefits + +- Model parallelism become easier to implement: it's an extension to + the trainer - parameter server approach. we already have the + communication OPs, but need to extend the graph converter. + +- User-defined optimizer is easier to add - user can now express it as + a subgraph. + +- No more duplication logic inside the trainer and the parameter + server in the background section. + +### Challenges + +- It might be hard for the graph converter to cut a general graph + (without any hint for which sub-graph is the optimizer). We may need + to label which sub-graph inside the OP graph is the optimizer. + +- It's important to balance the parameter shards of on multiple + parameter server. 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