README.md

Dynamic Graph on Fluid

PaddlePaddle Fluid is targeting the autodiff without tape, which, however, is very challenging and we are still way from there. DyNet and PyTorch provide a good design idea, the tape, that significantly eases the challenge. Also, DyNet provides a C++ API that is as convenient as Python but with higher efficiency and could conveniently integrate with industrial/production systems. This package, tape, combines the good of

1. tape from PyTorch and DyNet
2. C++ API and core from DyNet
3. rich set of operators from PaddlePaddle

Overview

We can implement Dynet-like Tape(See this survey) by wrapping Paddle Fluid's Operator and Variable.

The user API is straight forward since

1. it is imperative. And it uses host language's control flow logic.
2. it avoids extra concepts such as Scope and Executor.

All of these benefits come at the cost of just adding one line reset_global_tape at every iteration.

Code Structure

In short, the Tape contains a vector of OpHandles. And an OpHandle contains its type, the pointers to the Variables, and necessary attributes.

class Variable {
public:
  VriableHandle Grad(); // returns its gradient variable
private:
  framework::VarDesc desc_; // compile time infershape, necessary for lazy execution
  framework::Variable var_; // run time variable, holds data memory
};

using VariableHandle = shared_ptr<Variable>;

struct OpHandle {
  string type_;
  map<string, vector<VariableHandle>> inputs_;
  map<string, vector<VariableHandle>> outputs_;
  AttributeMap attrs_;
};

class Tape {
public:
  void AddOp(OpHandle); // add op
  void Forward();       // execute the tape_
  void Backward();      // execute the backward of the tape_
private:
  vector<OpHandle> tape_;
};

We uses Function to indicate layers. It takes care of parameter initialization and AddOp to the Tape when it is called.

class Linear {
 public:
  Linear(int in_dim, int out_dim, const std::string &act)
      : w_(new Variable("LinearWeight")),
        b_(new Variable("LinearBias")),
        act_(act) {
    Tape init_tape;

    std::string initializer = "fill_constant";
    framework::AttributeMap attrs;
    attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
    attrs["shape"] = std::vector<int>{in_dim, out_dim};
    attrs["value"] = 1.0f;
    init_tape.AddOp(initializer, {}, {{"Out", {w_}}}, attrs);

    attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
    attrs["shape"] = std::vector<int>{out_dim};
    attrs["value"] = 1.0f;
    init_tape.AddOp(initializer, {}, {{"Out", {b_}}}, attrs);

    init_tape.Forward();
  }

  VariableHandle operator()(VariableHandle input) {
    VariableHandle pre_bias(new Variable("linear"));
    get_global_tape().AddOp("mul",
                            {{"X", {input}}, {"Y", {w_}}},
                            {{"Out", {pre_bias}}},
                            {{"x_num_col_dims", 1}, {"y_num_col_dims", 1}});
    VariableHandle pre_act(new Variable("linear"));
    get_global_tape().AddOp("elementwise_add",
                            {{"X", {pre_bias}}, {"Y", {b_}}},
                            {{"Out", {pre_act}}},
                            {{"axis", 1}});
    VariableHandle post_act(new Variable("linear"));
    get_global_tape().AddOp(act_,
                            {{"X", {pre_act}}},
                            {{"Out", {post_act}}},
                            {});
    return post_act;
  }

  std::vector<VariableHandle> Params() { return {w_, b_}; }

 private:
  VariableHandle w_;
  VariableHandle b_;
  std::string act_;
};

User API

// Model function
paddle::tape::Linear linear1(3, 3, "relu"); // init weight and bias
paddle::tape::Linear linear2(3, 3, "relu"); // init weight and bias
paddle::tape::Mean mean;

// Optimizer
paddle::tape::SGD sgd(0.001);

// Data Feeder
paddle::tape::Fill data_feeder(...);
VariableHandle input(new paddle::tape::Variable("input"));
VariableHandle label(new paddle::tape::Variable("label"));

for (int i = 0; i < 2; ++i) {
  reset_global_tape();

  data_feeder(input, label);

  auto loss = softmax(linear2(linear1(input)), label); // compile time InferShape & InferVarType
  LOG(INFO) << loss.value(); // Run forward up to loss

  // Run backward, store gradient of w at w->Grad()
  get_global_tape.Backward(loss);

  // Update w
  sgd(linear1.Params());
  sgd(linear2.Params());
}

digraph G {

subgraph cluster_0 {
            node [shape=record,style=filled];
	style=filled;
	color=lightgrey;
            linear1 [label="{type: mul | {input | {<before_mul1>X: before_mul1 |<weight1> Y: weight1}} |  {output |<before_bias1> Out: before_bias1}}"];
            elementwise_add1 [label="{type: elementwise_add | {input | {<before_bias1>X: before_bias1 |<bias1> Y: bias1}} |  {output |<before_act1> Out: before_act1}}"];
            relu1 [label="{type: relu | {input | {<before_act1>X: before_act1 }} |  {output |<after_act1> Out: after_act1}}"];

	linear1 -> elementwise_add1->relu1;
	label = "forward tape";
}

    linear1:before_mul1->before_mul1
    linear1:weight1->weight1
    linear1:before_bias1->before_bias1

    elementwise_add1:bias1->bias1
    elementwise_add1:before_bias1->before_bias1
    elementwise_add1:before_act1->before_act1

    relu1:before_act1->before_act1
    relu1:after_act1->after_act1

subgraph cluster_1 {
            node [shape=record,style=filled];
	style=filled;
	color=lightgrey;
            linear1_grad [label="{type: mul_grad | {input | {<before_mul1>X: before_mul1 |<weight1> Y: weight1|<before_bias1_grad> Out_grad: before_bias1_grad}} |  {output |{<before_mul1_grad>X_grad: before_mul1_grad |<weight1_grad> Y_grad: weight1_grad}}}"];

            elementwise_add1_grad [label="{type: elementwise_add_grad | {input | <before_act1_grad> Out_grad: before_act1_grad} |  {output |{<before_bias1_grad>X_grad: before_bias1_grad |<bias1_grad> Y_grad: bias1_grad}}}"];

            relu1_grad [label="{type: relu_grad |  {input |<after_act1_grad> Out_grad: after_act1_grad} | {ouput | {<before_act1_grad>X_grad: before_act1_grad }}}"];

	linear1_grad -> elementwise_add1_grad ->relu1_grad [dir=back];
            label = "backward tape";
}

    relu1_grad:after_act1_grad->after_act1_grad
    relu1_grad:before_act1_grad->before_act1_grad

    elementwise_add1_grad:before_act1_grad->before_act1_grad
    elementwise_add1_grad:before_bias1_grad->before_bias1_grad
    elementwise_add1_grad:bias1_grad->bias1_grad

    linear1_grad:before_mul1->before_mul1
    linear1_grad:weight1->weight1
    linear1_grad:before_bias1_grad->before_bias1_grad
    linear1_grad:before_mul1_grad->before_mul1_grad
    linear1_grad:weight1_grad->weight1_grad


subgraph cluster_2 {
            node [shape=record];
            label = "Linear1";
            weight1
            bias1
}

    weight1 -> weight1_grad [ label="Grad()", style="dashed" ];
    bias1 -> bias1_grad [ label="Grad()", style="dashed"];

}

Code Reuse

We want to stay close to Paddle Fluid as much as possible.

Reuse All Operators

As all Ops are registered at OpInfoMap, the effort of adding a new Function is about 10 lines of code, similar to expose an operator to Python.

Reuse Compile Time InferShape and InferVarType

Note that all the symbolic information is stored at tape::Varaible::desc_, instead of ProgramDesc.block.vars, we create a temporary BlockDesc to do InferShape and InferVarType every time we AddOp to the tape.

Reuse Operator::Run

We use smart pointer, instead of Scope, to manage memory. So we create a temporary Scope for every Operator::Run().

Possible Feature

Release Memory on Backward

We can release memory aggressively. During backward, we can delete the OpHandle once we have finished its backward. Since all the variable is managed by smart pointer, the memory is automatically released when its ref_count goes to 0.

Kernel Fusion

As a symbolic representation of the Tape is constructed first before the actual execution, it would be possible to perform graph optimization. One use case is kernel fusion.