How to create custom ops in TensorFlow graph?

Introduction to Custom Ops

Creating custom operations in a TensorFlow graph can be essential for advanced users who need functionality that goes beyond the library's built-in operations. It allows you to define new operations for performance tuning, incorporating legacy code, or adding features that are not natively supported in TensorFlow. Below, you will find a guide explaining how to implement custom ops efficiently.

Understand the Use Case

• Identify parts of your model where existing TensorFlow operations are insufficient or underperforming.

 

• Determine whether writing a custom op will meet your performance, accuracy, or functionality requirements.

Set Up Environment

• Ensure you have a development environment with TensorFlow installed.

 

• Have access to build tools like Bazel, which is essential for compiling custom ops.

Define the Custom Op

Start by defining the computation of the custom op in C++ (or another supported language). Here's a basic structure in C++:

#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 {
    // Grab the input tensor
    const Tensor& input_tensor = context->input(0);

    // Create an output tensor
    Tensor* output_tensor = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
                                                     &output_tensor));

    // Set the output tensor to zero (or perform computation)
    auto output_flat = output_tensor->flat<float>();
    for (int i = 0; i < input_tensor.NumElements(); ++i) {
      output_flat(i) = 0; // Example operation
    }
  }
};

REGISTER_KERNEL_BUILDER(Name("MyCustomOp").Device(DEVICE_CPU), MyCustomOp);

Compile the Custom Op

Compiling your custom TensorFlow op requires configuring and running Bazel. Create a BUILD file in the same directory as the C++ code:

load("//tensorflow:tensorflow.bzl", "tf_cc_binary")

tf_cc_binary(
    name = "my_custom_op",
    srcs = ["my_custom_op.cc"],
    deps = [
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "//tensorflow/core:util",
    ],
)

Then, compile your custom operation:

bazel build //path/to:my_custom_op

Integrate with TensorFlow Python

Create a Python wrapper around your custom op to integrate it with TensorFlow:

import tensorflow as tf
from tensorflow.python.framework import ops

my_custom_op_module = tf.load_op_library('path/to/compiled/my_custom_op.so')

@ops.RegisterGradient("MyCustomOp")
def _my_custom_op_grad(op, grad):
    return [grad]  # Example: identity gradient

def my_custom_op(input_tensor):
    return my_custom_op_module.my_custom_op(input_tensor)

Testing the Custom Op

• Test the custom operation in isolation to ensure it behaves as expected.

 

• Integrate it into a TensorFlow model and observe any improvements or changes.

 

• Compare its performance with equivalent default operations for benchmarking.

By following these steps, you can successfully create and integrate custom ops into a TensorFlow graph, allowing for extended capabilities and optimizations specific to your use case.