Xinghai Sun
8 years ago
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# Design Doc: Operation Graph Based Parameter Server
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## Abstract
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We propose an approach to implement the parameter server. In this
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approach, there is no fundamental difference between the trainer and
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the parameter server: they both run subgraphs, but subgraphs of
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different purposes.
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## Background
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The previous implementations of the parameter server does not run a
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subgraph. parameter initialization, optimizer computation, network
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communication and checkpointing are implemented twice on both the
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trainer and the parameter server.
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It would be great if we can write code once and use them on both the
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trainer and the parameter server: reduces code duplication and
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improves extensibility. Given that after the current refactor, we are
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representing everything as a computing graph on the
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trainer. Representing everything as a computing graph on the parameter
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server becomes a natural extension.
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## Design
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### Graph Converter
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The *graph converter* converts the user-defined operation (OP) graph
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into subgraphs to be scheduled on different nodes with the following
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steps:
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1. OP placement: the OPs will be placed on different nodes according
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to heuristic that minimizes estimated total computation
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time. Currently we will use a simple heuristic that puts parameter
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varable on parameter server workers and everything else on trainer
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workers.
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1. Add communication OPs to enable the communication between nodes.
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We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
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Below is an example of converting the user defined graph to the
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subgraphs for the trainer and the parameter server:
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<img src="src/local-graph.png" width="300"/>
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After converting:
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<img src="src/dist-graph.png" width="700"/>
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1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
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1. Operators are added to the subgraphs.
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- *Send* sends data to the connected *Recv* operator. The
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scheduler on the receive node will only schedule *Recv* operator
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to run when the *Send* operator has ran (the *Send* OP will mark
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the *Recv* OP runnable automatically).
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- *Enueue* enqueues the input variable, it can block until space
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become available in the queue.
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- *Dequeue* outputs configurable numbers of tensors from the
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queue. It will block until the queue have the required number of
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tensors.
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### Benefits
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- Model parallelism become easier to implement: it's an extension to
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the trainer - parameter server approach. we already have the
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communication OPs, but need to extend the graph converter's
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placement functionality.
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- User-defined optimizer is easier to add - user can now express it as
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a subgraph.
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- No more duplication logic inside the trainer and the parameter
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server mentioned in the background section.
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### Challenges
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- It might be hard for the graph converter to cut a general graph
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(without any hint for which subgraph is the optimizer). We may need
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to label which subgraph inside the OP graph is the optimizer.
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- It's important to balance the parameter shards of on multiple
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parameter server. If a single parameter is very big (some
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word-embedding, fully connected, softmax layer), we need to
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automatically partition the single parameter onto different
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parameter servers when possible (only element-wise optimizer depends
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on the parameter variable).
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### Discussion
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- In the "Aync SGD" figure, the "W" variable on the parameter server
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could be read and wrote concurrently, what is our locking strategy?
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E.g., each variable have a lock cpp method to be invoked by every
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OP, or, have a lock OP.
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- Can the Enqueue OP be implemented under our current tensor design
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(puts the input tensor into the queue tensor)?
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- *Dequeue* OP will have variable numbers of output (depends on the
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`min_count` attribute), does our current design support it? (similar
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question for the *Add* OP)
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### References:
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[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
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## Background
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PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
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PaddlePaddle use proto message to describe compile time graph for
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1. Computation graph should be able to be saved to a file.
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1. In distributed training, the graph will be serialized and send to multiple workers.
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The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.
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| |compile time|runtime|
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|---|---|---|
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|Data|VarDesc(proto)|Variable(cpp)|
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|Operation|OpDesc(proto)|Operator(cpp)|
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## Definition of VarDesc
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A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.
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```proto
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message VarDesc {
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required string name = 1;
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optional LoDTensorDesc lod_tensor = 2;
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}
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```
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## Definition of LodTensorDesc
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```proto
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enum DataType {
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BOOL = 0;
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INT16 = 1;
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INT32 = 2;
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INT64 = 3;
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FP16 = 4;
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FP32 = 5;
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FP64 = 6;
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}
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message LoDTensorDesc {
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required DataType data_type = 1;
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repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
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optional int32 lod_level = 3 [default=0];
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}
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```
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## Definition of Variable in Python
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In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
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```python
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image = Variable(dims=[-1, 640, 480])
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# fc1 and fc2 are both Variable
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fc1 = layer.fc(input=image, output_size=10)
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fc2 = layer.fc(input=fc1, output_size=20)
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```
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### what should class `Variable` Have
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1. `name`.a name of string type is used to mark the value of the Variable.
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1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.
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1. `operator`. Variable should record which operator produce itself. The reaon is:
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- we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
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In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.
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```python
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import VarDesc
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import LoDTensorDesc
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import framework
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def AddInitialOperator(variable, initializer):
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# add an initialize Operator to block to init this Variable
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class Variable(object):
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def __init__(self, name, dims, type, initializer):
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self._block = get_default_block()
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self._name = name
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self.op = None
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tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
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_var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
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self._var = framework.CreateVar(_var_desc)
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self._block.add_var(self)
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# add initial op according to initializer
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if initializer is not None:
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AddInitialOperator(self, initializer)
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def dims(self):
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return self._var.dims()
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def data_type(self):
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return self._var.data_type()
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def to_proto(self):
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pass
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```
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Then we can use this Variable to create a fc layer in Python.
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```python
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import paddle as pd
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def flatten_size(X, num_flatten_dims):
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prod = 1 # of last num_flatten_dims
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for i in xrange(num_flatten_dims):
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prod = prod * X.dims[-i-1]
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return prod
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def layer.fc(X, output_size, num_flatten_dims):
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W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
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b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
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out = Variable(type=FP32)
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y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
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pd.InferShape(y)
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return out
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x = Variable(dims=[-1, 640, 480])
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y = layer.fc(x, output_size=100)
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z = layer.fc(y, output_size=200)
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paddle.eval(targets=[z], ...)
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print(z)
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```
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