Julia Custom Operators


Currently, embedding Julia suffers from multithreading issues: calling Julia from a non-Julia thread is not supported in ADCME. When TensorFlow kernel codes are executed concurrently, it is difficult to invoke the Julia functions. See issue.

In scientific and engineering applications, the operators provided by TensorFlow are not sufficient for high performance computing. In addition, constraining oneself to TensorFlow environment sacrifices the powerful scientific computing ecosystems provided by other languages such as Julia and Python. For example, one might want to code a finite volume method for a sophisticated fluid dynamics problem; it is hard to have the flexible syntax to achieve this goal, obtain performance boost from existing fast solvers such as AMG, and benefit from many other third-party packages within TensorFlow. This motivates us to find a way to "plugin" custom operators to TensorFlow.

We have already introduced how to incooperate C++ custom operators. For many researchers, they usually prototype the solvers in a high level language such as MATLAB, Julia or Python. To enjoy the parallelism and automatic differentiation feature of TensorFlow, they need to port them into C/C++. However, this is also cumbersome sometimes, espeically the original solvers depend on many packages in the high-level language.

We solve this problem by incorporating Julia functions directly into TensorFlow. That is, for any Julia functions, we can immediately convert it to a TensorFlow operator. At runtime, when this operator is executed, the corresponding Julia function is executed. That implies we have the Julia speed. Most importantly, the function is perfectly compitable with the native Julia environment; third-party packages, global variables, nested functions, etc. all work smoothly. Since Julia has the ability to call other languages in a quite elegant and simple manner, such as C/C++, Python, R, Java, this means it is possible to incoporate packages/codes from any supported languages into TensorFlow ecosystem. We need to point out that in TensorFlow, tf.numpy_function can be used to convert a Python function to a TensorFlow operator. However, in the runtime, the speed for this operator falls back to Python (or numpy operation for related parts). This is a drawback.

The key for implementing the mechanism is embedding Julia in C++. Still we need to create a C++ dynamic library for TensorFlow. However, the library is only an interface for invoking Julia code. At runtime, jl_get_function is called to search for the related function in the main module. C++ arrays, which include all the relavant data, are passed to this function through jl_call. It requires routine convertion from C++ arrays to Julia array interfaces jl_array_t*. However, those bookkeeping tasks are programatic and possibly will be automated in the future. Afterwards,Julia returns the result to C++ and thereafter the data are passed to the next operator.

There are two caveats in the implementation. The first is that due to GIL of Python, we must take care of the thread lock while interfacing with Julia. This was done by putting a guard around th eJulia interface

PyGILState_STATE py_threadstate;
py_threadstate = PyGILState_Ensure();
// code here 

The second is the memory mangement of Julia arrays. This was done by defining gabage collection markers explicitly

jl_value_t **args;
JL_GC_PUSHARGS(args, 6); // args can now hold 2 `jl_value_t*` objects
args[0] = ...
args[1] = ...
# do something

This technique is remarkable and puts together one of the best langages in scientific computing and that in machine learning. The work that can be built on ADCME is enormous and significantly reduce the development time.


Here we present a simple example. Suppose we want to compute the Jacobian of a two layer neural network $\frac{\partial y}{\partial x}$

\[y = W_2\tanh(W_1x+b_1)+b_2\]

where $x, b_1, b_2, y\in \mathbb{R}^{10}$, $W_1, W_2\in \mathbb{R}^{100}$. In TensorFlow, this can be done by computing the gradients $\frac{\partial y_i}{\partial x}$ for each $i$. In Julia, we can use ForwardDiff to do it automatically.

function twolayer(J, x, w1, w2, b1, b2)
    f = x -> begin
        w1 = reshape(w1, 10, 10)
        w2 = reshape(w2, 10, 10)
        z = w2*tanh.(w1*x+b1)+b2
    J[:] = ForwardDiff.jacobian(f, x)[:]

To make a custom operator, we first generate a wrapper

using ADCME

We modify custom_op.txt

double x(?)
double w1(?)
double b1(?)
double w2(?)
double b2(?)
double y(?) -> output

and run


Three files are generatedCMakeLists.txt, TwoLayer.cpp and gradtest.jl. Now create a new file TwoLayer.h

#include "julia.h"
#include "Python.h"

void forward(double *y, const double *x, const double *w1, const double *w2, const double *b1, const double *b2, int n){
    PyGILState_STATE py_threadstate;
    py_threadstate = PyGILState_Ensure();
    jl_value_t* array_type = jl_apply_array_type((jl_value_t*)jl_float64_type, 1);
    jl_value_t **args;
    JL_GC_PUSHARGS(args, 6); // args can now hold 2 `jl_value_t*` objects
    args[0] = (jl_value_t*)jl_ptr_to_array_1d(array_type, y, n*n, 0);
    args[1] = (jl_value_t*)jl_ptr_to_array_1d(array_type, const_cast<double*>(x), n, 0);
    args[2] = (jl_value_t*)jl_ptr_to_array_1d(array_type, const_cast<double*>(w1), n*n, 0);
    args[3] = (jl_value_t*)jl_ptr_to_array_1d(array_type, const_cast<double*>(w2), n*n, 0);
    args[4] = (jl_value_t*)jl_ptr_to_array_1d(array_type, const_cast<double*>(b1), n, 0);
    args[5] = (jl_value_t*)jl_ptr_to_array_1d(array_type, const_cast<double*>(b2), n, 0);
    auto fun = jl_get_function(jl_main_module, "twolayer");
  	if (fun==NULL) jl_errorf("Function not found in Main module.");
    else jl_call(fun, args, 6);
    if (jl_exception_occurred())
        printf("%s \n", jl_typeof_str(jl_exception_occurred()));

Most of the codes have been explanined except jl_ptr_to_array_1d. This function generates a Julia array wrapper from C++ arrays. The last argument 0 indicates that Julia is not responsible for gabage collection. TwoLayer.cpp should also be modified according to https://github.com/kailaix/ADCME.jl/blob/master/examples/twolayer_jacobian/TwoLayer.cpp.

Finally, we can test in gradtest.jl

two_layer = load_op("build/libTwoLayer", "two_layer")

w1 = rand(100)
w2 = rand(100)
b1 = rand(10)
b2 = rand(10)
x = rand(10)
J = rand(100)
twolayer(J, x, w1, w2, b1, b2)

y = two_layer(constant(x), constant(w1), constant(b1), constant(w2), constant(b2))
sess = Session(); init(sess)
J0 = run(sess, y)
@show norm(J-J0)

Embedded in Modules

If the custom operator is intended to be used in a precompiled module, we can load the dynamic library at initialization

global my_op 
function __init__()
	global my_op = load_op("$(@__DIR__)/path/to/libMyOp", "my_op")

The corresponding Julia function called by my_op must be exported in the module (such that it is in the Main module when invoked). One such example is given in MyModule

Quick Reference for Implementing C++ Custom Operators in ADCME

  1. Set output shape
c->set_output(0, c->Vector(n));
c->set_output(0, c->Matrix(m, n));
c->set_output(0, c->Scalar());
  1. Names

.Input and .Ouput : names must be in lower case, no _, only letters.

  1. TensorFlow Input/Output to TensorFlow Tensors

Obtain flat arrays

  1. Scalars

Allocate scalars using TensorShape()

  1. Allocate Shapes

Although you can use -1 for shape reference, you must allocate exact shapes in Compute