How to use custom operators

TensorFlow Lite currently supports a subset of TensorFlow operators. However, it does support the use of user-provided implementations (as known as custom implementations) if the model contains an operator that is not supported.

Let’s walk through this via an example. Assume we are using the Sin operator and that we are building a very simple model for a function y = sin(x + offset), where offset is trainable.

The code to train the TensorFlow model will be something like:

offset = tf.get_variable("offset", [1,], tf.float32)
x = tf.placeholder(tf.float32, shape=(None,))
y = tf.sin(x + offset)
y_ = tf.placeholder(tf.float32, shape=(None,))
loss = tf.reduce_sum(tf.square(y - y_))
optimizer = tf.train.GradientDescentOptimizer(0.001)
train = optimizer.minimize(loss)

If you convert this model to Tensorflow Lite format using the TensorFlow Lite Optimizing Converter with --allow_custom_ops argument, and run it with the default interpreter, the interpreter will raise the following error messages:

Didn't find custom op for name 'Sin'
Registration failed.

All we need to do to use the op in TensorFlow Lite is define two functions (Prepare and Eval), and construct a TfLiteRegistration. This code would look something like this:

TfLiteStatus SinPrepare(TfLiteContext* context, TfLiteNode* node) {
  using namespace tflite;
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  const TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);

  int num_dims = NumDimensions(input);

  TfLiteIntArray* output_size = TfLiteIntArrayCreate(num_dims);
  for (int i=0; i<num_dims; ++i) {
    output_size->data[i] = input->dims->data[i];

  return context->ResizeTensor(context, output, output_size);

TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) {
  using namespace tflite;
  const TfLiteTensor* input = GetInput(context, node,0);
  TfLiteTensor* output = GetOutput(context, node,0);

  float* input_data = input->data.f;
  float* output_data = output->data.f;

  size_t count = 1;
  int num_dims = NumDimensions(input);
  for (int i = 0; i < num_dims; ++i) {
    count *= input->dims->data[i];

  for (size_t i=0; i<count; ++i) {
    output_data[i] = sin(input_data[i]);
  return kTfLiteOk;

TfLiteRegistration* Register_SIN() {
  static TfLiteRegistration r = {nullptr, nullptr, SinPrepare, SinEval};
  return &r;

When initializing the OpResolver, add the custom op into the resolver, this will register the operator with Tensorflow Lite so that TensorFlow Lite can use the new implementation.

tflite::ops::builtin::BuiltinOpResolver builtins;
builtins.AddCustom("Sin", Register_SIN());

Note that a similar process as above can be followed for supporting for a set of operations instead of a single operator.

Best Practices for writing custom operators

  1. Optimize memory allocations and de-allocations cautiously. It is more efficient to allocate memory in Prepare() instead of Invoke(), and allocate memory before a loop instead of in every iteration. Use temporary tensors data rather than mallocing yourself (see item 2). Use pointers/references instead of copying as much as possible.

  2. If a data structure will persist during the entire operation, we advise pre-allocating the memory using temporary tensors. You may need to use OpData struct to reference the tensor indices in other functions. See example in the kernel for convolution. A sample code snippet is below

    auto* op_data = reinterpret_cast<OpData*>(node->user_data);
    node->temporaries = TfLiteIntArrayCreate(1);
    node->temporaries->data[0] = op_data->temp_tensor_index;
    TfLiteTensor* temp_tensor = &context->tensors[op_data->temp_tensor_index];
    temp_tensor->type =  kTfLiteFloat32;
    temp_tensor->allocation_type = kTfLiteArenaRw;
  3. If it doesn't cost too much wasted memory, prefer using a static fixed size array (or in Resize() pre-allocated std::vector) rather than using a dynamically allocating std::vector every iteration of execution.

  4. Avoid instantiating standard library container templates that don't already exist, because they affect binary size. For example, if you need a std::map in your operation that doesn't exist in other kernels, using a std::vector with direct indexing mapping could work while keeping the binary size small. See what other kernels use to gain insight (or ask).

  5. Check the pointer to the memory returned by malloc. If this pointer is nullptr, no operations should be performed using that pointer. If you malloc() in a function and have an error exit, deallocate memory before you exit.

  6. Use TF_LITE_ENSURE(context, condition) to check for a specific condition. Your code must not leave memory hanging when TF_LITE_ENSURE is done, i.e., these should be done before any resources are allocated that will leak.

Special TF Graph Attributes

When tflite_convert converts a TensorFlow graph into TFLite format, it makes some assumption about custom operations that might be not correct. In this case, the generated graph may not execute.

It is possible to add aditional information about your custom op output to TF graph before it is converted. The following attributes are supported:

  • _output_quantized a boolean attribute, true if the operation outputs are quantized
  • _output_types a list of types for output tensors
  • _output_shapes a list of shapes for output tensors

Setting the Attributes

This is an example how the attributes can be set:

frozen_graph_def = tf.graph_util.convert_variables_to_constants(...)
for node in frozen_graph_def.node:
    if node.op == 'sin':
      node.attr['_output_quantized'].b = False
tflite_model = tf.lite.toco_convert(

Note: After the attributes are set, the graph can not be executed by Tensorflow, therefore it should be done just before the conversion.