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Opened 10月 11, 2016 by saxon_zh@saxon_zhGuest

Implementing DC-ASGD

Created by: yingfeng

DC-ASGD is a remarkable work to resolve the convergence problem of ASGD using delayed compensation from Microsoft. According to the author's claim, the performance of DC-ASGD could be the same with ASGD while the accuracy of model would not be affected.
The major step of DC-ASGD could be seen as follows: Worker Side: a1

PS Side: a2

However, there are some shortcomings within the algorithm above:

  • DC-ASGD requires PS server side to store M copies of weights, while M equals to number of workers. It is of high memory wasting
  • An alternative solution of DC-ASGD as mentioned in the paper to get avoid of saving M copies of weights is to let worker push both gradients and weights to PS server, however, it also has the shortcoming of bandwidth wasting

I have a proposal to implement an improved version of DC-ASGD without above shortcomings:

  1. Pull W_t to worker
  2. Compute G_m at worker side
  3. Compute W_t+1 at worker side using the same algorithm mentioned in the above algorithm 2. This is the major differenciation
  4. Push W_t+1 to PS server

Such a proposal does not need a high memory wasting at PS server side, while the bandwidth is also the same. The major issue is: at PS server side, there has to exist a lock, because each worker will push w_t+1 to server side, and will cover the existing weight matrix.

Implementing such kinds of DC-ASGD to paddle is easy, however, I'm not sure about the correctness and the eventual convergence, as well as the effects of lock at PS side.

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标识: paddlepaddle/Paddle#185
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