From c2a16b5c91c5a719cecfe6f1516258c13d8295c1 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 31 Aug 2017 16:50:51 -0700 Subject: [PATCH] update OP based parameter server design --- doc/design/ops/dist_train.md | 65 +++++++++++++++++--------- doc/design/ops/src/dist-graph.graffle | Bin 6059 -> 6430 bytes doc/design/ops/src/dist-graph.png | Bin 228040 -> 227532 bytes 3 files changed, 43 insertions(+), 22 deletions(-) diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md index 8e92c87a59b..29940ac59de 100644 --- a/doc/design/ops/dist_train.md +++ b/doc/design/ops/dist_train.md @@ -4,13 +4,13 @@ We propose an approach to implement the parameter server. In this approach, there is no fundamental difference between the trainer and -the parameter server: they both run sub-graphs, but sub-graphs of +the parameter server: they both run subgraphs, but subgraphs of different purposes. ## Background The previous implementations of the parameter server does not run a -sub-graph. parameter initialization, optimizer computation, network +subgraph. parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the trainer and the parameter server. @@ -26,35 +26,40 @@ server becomes a natural extension. ### Graph Converter The *graph converter* converts the user-defined operation (OP) graph -into sub-graphs to be scheduled on different nodes. +into subgraphs to be scheduled on different nodes with the following +steps: -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. OP placement: the OPs will be placed on different nodes according + to heuristic that minimizes estimated total computation + time. Currently we will use a simple heuristic that puts parameter + varable on parameter server workers and everything else on trainer + 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*. +1. Add communication OPs to enable the communication between nodes. + +We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. Below is an example of converting the user defined graph to the -sub-graphs for the trainer and the parameter server: +subgraphs 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. +1. Operators are added to the subgraphs. + - *Send* sends data to the connected *Recv* operator. The + scheduler on the receive node will only schedule *Recv* operator + to run when the *Send* operator has ran (the *Send* OP will mark + the *Recv* OP runnable automatically). + - *Enueue* enqueues the input variable, it can block until space + become available in the queue. + - *Dequeue* outputs configurable numbers of tensors from the + queue. It will block until the queue have the required number of + tensors. + ### Benefits @@ -71,8 +76,8 @@ After converting: ### 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. + (without any hint for which subgraph is the optimizer). We may need + to label which subgraph inside the OP graph is the optimizer. - It's important to balance the parameter shards of on multiple parameter server. If a single parameter is very big (some @@ -80,3 +85,19 @@ After converting: automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable). + +### Discussion + +- In the "Aync SGD" figure, the "W" variable on the parameter server + could be read and wrote concurrently, what is our locking strategy? + +- Does our current tensor design supports enqueue (put the input tensor + into the queue tensor)? + +- *Dequeue* OP will have variable numbers of output (depends on the + `min_count` attribute), does our current design support it? (similar + question for the *Add* OP) + + +References: +[1] (TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems)[https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf] diff --git a/doc/design/ops/src/dist-graph.graffle b/doc/design/ops/src/dist-graph.graffle index 6353f736042ed6056d4f59b0cb4072ae2f7008aa..941399c6ced8d5f65b6c595522b770c88259df4b 100644 GIT binary patch literal 6430 zcmV+(8R6z1iwFP!000030PS5_bK6F?eolS`ub=L%k)Ym}jN>U;a%^Y3#Zv4f<*F%& zgk(%8fB_*pO3MHJHb9BQ(f}6#+;QjgVDEZbL^n=*D z@}fuR;nDra`!43%e&3DFDc?=bJniI0k(+$@=HWPw%q>4MBXq*+z`u&Zo00R{e0mzW zmzP6NK0LZ>l4*^laEKW_kyLAzY4Pyr{>+mR+{EGW;J-KHxHMeMOXGd)_J2>V9}LX3 zM<4zEwA0;#xNDZQ7~db8j_L2e53!WkVMK9>C%@G}?+`0W9BqCJNtw`8(Ju$Rzs)D< zXg>X3>4Tp6k2#s%RDJo9yZhvdCqs8!zWCWkcjV26UFh@tbk^bio=NuhC(X{{+o3mW zd4JQBgXWas@3W(s!;|Buk^6ZzuGz?5z0MBDyrAi`y#F-&FnI}M@3%0#UO>K1+~AWt zejWK&{!Fs&-v&D9q3qMk^f>eX@=EP7=JKFNsFLMg;qc}1eHyJ~|!>Kk)d0UYihJMp(hq+YyZ+!0z4O~qshewXR&1LJ<7wEx 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