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.
