Design Doc: Parameter Server

Abstract

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 subgraphs, but subgraphs of different purposes.

Background

The previous implementations of the parameter server do not run a fluid sub-program. Parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the trainer as well as the parameter server.

It would be great if we can write code once and use them on both: the trainer and the parameter server, since this reduces code duplication and improves extensibility. Given that after the current refactoring, we are representing everything as a computation graph on the trainer. Representing everything as a computation graph on the parameter server becomes a natural extension.

Design

Distributed Transpiler

The Distributed Transpiler converts the user-defined fluid program into sub-programs to be scheduled on different nodes with the following steps:

  1. OP placement: the OPs will be placed on different nodes according to a heuristic that minimizes the estimated total computation time. Currently we will use a simple heuristic that puts parameter variable on parameter server workers and everything else on trainer workers.
  2. 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 subgraphs for the trainer and the parameter server:

After converting:

  1. The parameter variable W and its optimizer program are placed on the parameter server.
  2. Operators are added to the program.
  3. 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).
  4. Enqueue enqueues the input variable, it can block until space become available in the queue.
  5. Dequeue outputs configurable numbers of tensors from the queue. It will block until the queue has the required number of tensors.

Sparse Update

For embedding layers, the gradient may have many rows containing only 0 when training, if the gradient uses a dense tensor to do parameter optimization, it could spend unnecessary memory, slow down the calculations and waste the bandwidth while doing distributed training. In Fluid, we introduce SelectedRows to represent a list of rows containing non-zero gradient data. So when we do parameter optimization both locally and remotely, we only need to send those non-zero rows to the optimizer operators:

Benefits

  • Model parallelism becomes easier to implement: it is an extension to the trainer - parameter server approach. We can have several "Transpilers" to achieve different goals.
  • User-defined optimizer is easier to add - user can now express it as a sub-program.
  • No more duplication logic inside the trainer and the parameter server mentioned in the background section.

Challenges

  • It is important to balance the parameter shards on multiple parameter servers. If a single parameter is very big (for example: some word-embedding, fully connected, softmax layer), we need to automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable).
  • In the "Async SGD" figure, the "W" variable on the parameter server could be read and written concurrently. See here for more details about concurrent program in Fluid.

Discussion

  • Can the Enqueue OP be implemented under our current tensor design (put the input tensor into the queue tensor)?
  • Dequeue OP will have variable numbers of output (depending 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