TensorFlow is a highly popular open-source library used for machine learning applications. One of its powerful features is the ability to extend its functionalities using custom operations (Ops). This can be particularly useful when you need to optimize performance by writing custom operations in C++ or when implementing functionalities that aren't provided out-of-the-box.
In this article, we'll walk through how to create and load custom Ops in TensorFlow using the load_op_library function. We'll also look at how to integrate them into your TensorFlow workflows.
Understanding Custom Ops
Custom Ops are user-defined operations that can be integrated into the TensorFlow graph. They can offer optimized computation capabilities which might not be available with standard TensorFlow operations.
Why Use Custom Ops?
- Performance Optimization: You can implement operations in C++ to enhance speed, especially for computationally intensive tasks.
- Extended Functionality: Custom Ops let you tap into unique functionalities not covered by the default TensorFlow ops.
Creating a Custom Op
We'll go through a step-by-step example of how to implement a simple custom operation, compile it, and load it into a TensorFlow environment.
Step 1: Write the C++ Code for the Custom Op
Create a C++ file named custom_op.cc. Here is where you define the logic of your custom operation.
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
using namespace tensorflow;
REGISTER_OP("MyCustomOp")
.Input("input: float")
.Output("output: float");
class MyCustomOp : public OpKernel {
public:
explicit MyCustomOp(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
const Tensor& input_tensor = context->input(0);
auto input = input_tensor.flat();
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(), &output_tensor));
auto output = output_tensor->flat();
for (int i = 0; i < input.size(); ++i) {
output(i) = input(i) * 2.0;
}
}
};
REGISTER_KERNEL_BUILDER(Name("MyCustomOp").Device(DEVICE_CPU), MyCustomOp);
This example defines a custom op named MyCustomOp that takes a tensor, doubles each element, and outputs the result.
Step 2: Compile the C++ Code into a Shared Library
Use the following command to compile your C++ code into a shared library:
g++ -std=c++11 -shared custom_op.cc -o custom_op.so -fPIC -I $(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
This command compiles the C++ file into a shared object file that TensorFlow can load.
Loading the Custom Op into TensorFlow
After compiling the custom operation, the next task is to load it into your Python TensorFlow program. This is where the load_op_library function comes in.
Step 3: Load the Custom Op in a Python Script
Create a Python script to load and utilize the compiled custom operation.
import tensorflow as tf
# Load the custom op library
custom_op_path = './custom_op.so'
my_custom_op_lib = tf.load_op_library(custom_op_path)
# Use the custom op
with tf.Graph().as_default():
input_tensor = tf.constant([1.0, 2.0, 3.0], dtype=tf.float32)
output_tensor = my_custom_op_lib.my_custom_op(input=input_tensor)
with tf.compat.v1.Session() as sess:
result = sess.run(output_tensor)
print(result) # Expected output: [2.0, 4.0, 6.0]
In this code snippet, we load the custom operation using tf.load_op_library and execute it within a TensorFlow session. The output should display the elements of the tensor doubled, verifying that the custom operation is working as expected.
Conclusion
Integrating custom ops in TensorFlow can enhance the efficiency of your machine learning applications by providing a way to run optimized, high-performance operations written in C++. This article has provided an introductory tutorial on writing, compiling, and utilizing custom ops in TensorFlow using the load_op_library function, helping you extend the capabilities of your deep learning models.
