7. Custom operators

This section explains how to implement a custom operator (op) in PopART. Code from the Leaky ReLU custom op example in the Graphcore GitHub repository will be used to illustrate the concepts.

7.1. Overview

You need to write some C++ classes to implement the op.

One is an implementations of the op as PopART’s intermediate representation (IR). This is used during PopART’s compilation process to transform and optimise the graph. There is also a Poplar implementation of the op, which provides the code that is run when the graph is executed. If the op will be used for training, then you also need gradient versions of these.

These classes are compiled to create a shared object library that can be linked with a Python program when it is run.

You also need to define an “operator identifier”. This consists of a unique combination of domain, operator name and operator version strings. This is used to register the custom op classes with PopART so that it can be used.

There are two ways of using the new custom op: from the builder API or from an ONNX file.

  • Builder API: You can include the new op with the builder API using the domain, op name and op version that match the custom op definition.

  • ONNX file: You can reference the op from an ONNX file using a NodeProto definition that matches the custom op definition.

The custom op will then be instantiated from the shared object library and treated like any other op in PopART.

You can also provide an “op definition” when you register the custom op. PopART will use that to check that the correct inputs, outputs and attributes are provided, and are of the expected types.

7.1.1. Custom op classes

The two key base classes in PopART that define an op are:

  • Op: the intermediate representation (IR) of an op in PopART. This provides methods that are called during PopART’s optimisation passes and transformations of the compute graph. This representation of the op is decoupled from the Poplar implementation.

  • Opx: a Poplar implementation of the op. This is the code that will actually be run on the IPU.

If the op is required for training, then a GradOp and GradOpx must also be defined for the gradient operation (see Fig. 7.1 and Fig. 7.2).

To make these classes visible to PopART, you must instantiate OpCreator and OpxCreator objects. These map from the string identifier of the new op (for example, “LeakyRelu”; see Section 7.3.1, Define the op identifier) to constructors for your newly-defined Op and Opx C++ classes.


Fig. 7.1 Op class diagram


Fig. 7.2 Opx class diagram

These classes are compiled to create a shared object library that can be dynamically linked into the Python program at runtime, as shown below:

import ctypes


You can see how this is done in the LeakyReLU example.

7.2. Implementing a custom op

Some of the examples in the GitHub repository have a single C++ file that defines all of the classes for a custom op. Although this can make it easier to see everything in one place, it can be more difficult to follow. So, in this section the main elements of the LeakyRelu example are extracted with some more detailed descriptions of each method.

7.2.1. The op class

The Op base class provides the methods necessary for the PopART IR passes and transformations.

The main methods that you need to override or implement are:

  • Attributes should be passed into the constructor and corresponding accessors defined.

  • clone(): returns a copy of the op. Usually, this means returning a std::make_unique copy of the op. This must be implemented.

  • setup(): sets the shape and type of the arguments to the op. This must set the type and shape information for all the output TensorInfo objects.

  • appendAttributes(): appends attributes when serialising the op to a stream. This is used for some debugging purposes but also for generating the PopART IR hash. This hash is used to determine whether a Poplar cache can be reused so it is important that op attributes which may alter the Poplar compilation are appended to this stream. If this method is overridden, then it must also call the base class method.

  • appendOutlineAttributes(): determines which ops are functionally equivalent during outlining.

  • getGradOps(): returns a vector of GradOp object for each Op in the forward graph to automatically generate the backward pass. There can be a separate grad op for each input (this is usually cleaner to implement) or a single grad op that generates gradients for all inputs.

    The mapping from the index of each output tensor of the grad op to the index of each input tensor of the non-grad op is configured using the gradOutToNonGradIn() method that should be overridden in the GradOp definitions (see below).

  • getSubgraphValue(): this is used by outlining algorithm to determine whether or not to outline ops. There are high and low bounding values retrieved by getHighSubgraphValue() (for expensive ops such as Conv) or getLowSubgraphValue() (for inexpensive ops such as Relu).

  • requiresRandomSeed(): this is set to false by default. This should be overridden and set to true if an IPU random seed tensor is required by the op. If so it will be connected to inTensor(getSeedInIndex()) by the IR process.

  • inplacePriorityDefault(): if the op can be replaced by an in-place variant of itself, this method should be overridden to return a vector of <OperatorIdentifier, float> tuples in descending order of preference. For example, the LeakyReLU implementation for this is:

    return {{Onnx::CustomOperators::LeakyReluInplace, 10}};
  • getInplaceVariant(): this is called to instantiate a particular in-place variant of the Op with a specified OperatorIdentifier from the vector returned by inplacePriorityDefault().

LeakyReluOp example

class LeakyReluOp : public popart::Op {
  LeakyReluOp(const popart::OperatorIdentifier &_opid, float _alpha,
              const popart::Op::Settings &settings_)
      : popart::Op(_opid, settings_), alpha(_alpha) {}

  std::unique_ptr<Op> clone() const final {
    return std::make_unique<LeakyReluOp>(*this);

  void setup() final { outInfo(0) = inInfo(0); }

  void appendAttributes(popart::OpSerialiserBase &os) const override {
    os.appendAttribute("alpha", getAlpha());

  void appendOutlineAttributes(popart::OpSerialiserBase &os) const override {
    os.appendAttribute("alpha", getAlpha());

  std::vector<std::unique_ptr<popart::Op>> getGradOps() {
    std::vector<std::unique_ptr<Op>> upops;
    upops.emplace_back(new LeakyReluGradOp(*this));
    return upops;

  float getSubgraphValue() const final { return getHighSubgraphValue(); }

  bool requiresRandomSeed() const override { return false; }

  // Attributes
  float getAlpha() const { return alpha; }

  float alpha;

7.2.2. The grad op class

class LeakyReluGradOp : public popart::Op {
  LeakyReluGradOp::LeakyReluGradOp(const LeakyReluOp &fwdOp)
      : popart::Op(CustomGradOperators::LeakyReluGrad_6, fwdOp.settings),
        alpha(fwdOp.getAlpha()) {}

  std::unique_ptr<popart::Op> clone() const final {
    return std::make_unique<LeakyReluGradOp>(*this);
  void setup() final { outInfo(0) = inInfo(0); };

  const std::vector<popart::GradInOutMapper> &gradInputInfo() const {
    static const std::vector<popart::GradInOutMapper> inInfo = {
        {0, 0, popart::GradOpInType::GradOut},
        {1, 0, popart::GradOpInType::In}};
    return inInfo;

  // The Grad Op has 1 output, which is the gradient of the only input
  const std::map<int, int> &gradOutToNonGradIn() const {
    static const std::map<int, int> outInfo = {{0, 0}};
    return outInfo;

  bool requiresRandomSeed() const override { return false; }

  // an estimate of how valuable sub-graph matching will be
  float getSubgraphValue() const final { return getHighSubgraphValue(); }

  float getAlpha() const { return alpha; }

  // Implementation defined below
  void appendAttributes(popart::OpSerialiserBase &os) const override {
    os.appendAttribute("alpha", getAlpha());

  // Implementation defined below
  void appendOutlineAttributes(popart::OpSerialiserBase &os) const override {
    os.appendAttribute("alpha", getAlpha());

  float alpha;

7.2.3. The opx class

The Opx class provides a grow() function that implements the corresponding Op definition as Poplar or PopLibs calls using the provided program::Sequence. Since OpxCreator uses a generic constructor, you should also check that the Op passed in is of the expected type and matches the OperatorIdentifier.

class LeakyReluOpx : public popart::popx::Opx {
  LeakyReluOpx(popart::Op *op, popart::popx::Devicex *devicex)
      : popart::popx::Opx(op, devicex) {
        op, {CustomOperators::LeakyRelu_1, CustomOperators::LeakyRelu_6});

  void grow(poplar::program::Sequence &prog) const final {

    auto op = getOp<LeakyReluOp>();

    poplar::Tensor input = getInTensor(0);

    float alpha = op.getAlpha();

    // x < 0.0f ? alpha * x : x
    auto expression = pe::Select(pe::Mul(pe::Const(alpha), pe::_1), pe::_1,
                                 pe::Lt(pe::_1, pe::Const(0.0f)));

    popops::mapInPlace(graph(), expression, {input}, prog,
                       debugContext("LeakyRelu"), poplar::OptionFlags());

    setOutTensor(0, input);

7.2.4. The grad opx class

class LeakyReluGradOpx : public popart::popx::Opx {
  LeakyReluGradOpx(popart::Op *op, popart::popx::Devicex *devicex)
      : popart::popx::Opx(op, devicex) {
    verifyOp<LeakyReluGradOp>(op, {CustomGradOperators::LeakyReluGrad_1,

  void grow(poplar::program::Sequence &prog) const final {

    auto op = getOp<LeakyReluGradOp>();

    poplar::Tensor grad = getInTensor(0);
    poplar::Tensor input = getInTensor(1);

    float alpha = op.getAlpha();

    // (grad * (x < 0.0f ? alpha : 1))
    pe::Mul expression = pe::Mul(pe::Select(pe::Const(alpha), pe::Const(1.0f),
                                            pe::Lt(pe::_2, pe::Const(0.0f))),

    auto output =
        popops::map(graph(), expression, {grad, input}, prog,
                    debugContext("LeakyReluGrad"), poplar::OptionFlags());

    setOutTensor(0, output);

7.3. Making the op available to PopART

After you have written the classes that implement the op, you will need to make the op available to PopART. This means defining an op identifier and using the op creator class to register the op with PopART.

7.3.1. Define the op identifier

The first step is to define an OperatorIdentifier with the domain, op name and op version so that the op can be be found by the builder.customOp() call in PopART or by a reference to the op in an ONNX file.

The OperatorIdentifier is a structure with the components domain, opName and opVersion.

For example, from leaky_relu_custom_op.cpp:

namespace CustomOperators {
  const popart::OperatorIdentifier LeakyRelu_1 = {"ai.onnx", "LeakyRelu", 1};
  const popart::OperatorIdentifier LeakyRelu_6 = {"ai.onnx", "LeakyRelu", 6};
} // namespace CustomOperators

namespace CustomGradOperators {
  const popart::OperatorIdentifier LeakyReluGrad_1 = {"ai.onnx", "LeakyReluGrad", 1};
  const popart::OperatorIdentifier LeakyReluGrad_6 = {"ai.onnx", "LeakyReluGrad", 6};
} // namespace CustomGradOperators

7.3.2. Define the op creator

The op creator registers the the new Op with PopART.

The OperatorIdentifier and a factory function that generates the new Op class are passed to the constructor of OpCreator to create a mapping. When your program loads the shared object library, this OpCreator is instantiated and registers the new Op.

You can also pass in an OpDefinition that allows the inputs, outputs and attributes to be checked against those provided in the model implementation.

The GradOp class will be implicitly created when the overridden method getGradOps() is called during the backwards pass.

namespace {
static OpDefinition::DataTypes T = {DataType::FLOAT16, DataType::FLOAT};

static OpDefinition
    leakyReluOpDef({OpDefinition::Inputs({{"input", T}}),
                    OpDefinition::Outputs({{"output", T}}),
                    OpDefinition::Attributes({{"alpha", {"*"}}})});

static OpCreator<LeakyReluOp> leakyReluOpCreator(
    popart::OpDefinitions({{Onnx::Operators::LeakyRelu_1, leakyReluOpDef},
                          {Onnx::Operators::LeakyRelu_6, leakyReluOpDef}}),
    [](const OpCreatorInfo &info) {
      float alpha = info.attributes.getAttribute<popart::Attributes::Float>(
          "alpha", 1e-2f);
      // default epsilon is 10**(-2)
      return std::make_unique<LeakyReluOp>(info.opid, alpha, info.settings);
} // namespace

7.3.3. Define the opx creator

You add the Opx definitions in a similar to the Op. In this case, a generic constructor of the Opx is always used of the form Opx(Op *op, Devicex *devicex). For example:

static popart::popx::OpxCreator<LeakyReluOpx> LeakyReluOpxCreator(
    {CustomOperators::LeakyRelu_1, CustomOperators::LeakyRelu_6});
static popart::popx::OpxCreator<LeakyReluGradOpx>

7.4. ONNX schema and shape inference

To enable ONNX to use the op as part of an ONNX model, you must define a schema for it. This includes inputs, outputs, domain, and versions.

To register an OpSchema, you can use the macro ONNX_OPERATOR_SCHEMA(name) and then append the various functions in the class. See schema.h for more examples.

namespace ONNX {

void LeakyReluShapeInference(InferenceContext &ctx) {

static const char LeakyReluDoc[] = "Performs a leaky ReLU operation on the input.";

        .Input(0, "X", "Input tensor", "T")
        .Output(0, "Y", "Output tensor", "T")
            {"tensor(float)", "tensor(int32)", "tensor(float16)"},
            "Constrain input and output types to signed numeric tensors.")

static bool registerOps() {
  auto &d = ONNX_NAMESPACE::OpSchemaRegistry::DomainToVersionRange::Instance();
  d.AddDomainToVersion("com.acme", 1, 1);

      GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(comAcme, 1, LeakyRelu)>());

  return true;

} // namespace ONNX

In the same namespace you can define the shape inference for the op. This allows ONNX to infer from the shape of the inputs the shape of the outputs. With simple operations, such as this example, the output shape is the same as the first input, so you can use the ONNX function propagateShapeAndTypeFromFirstInput from shape_inference.h.

There are other methods to use for shape inference in ONNX contained in that header. For example, numpy-style broadcasting, shape from attributes, and so on. Defining shape inference is optional, however you may encounter issues with operations later in your model if ONNX is not able to infer the input shape of an operation from earlier inputs.

7.5. Using the op in a program

The op can be referenced, using the values in the op identifier, in a Python program using the builder. For example, from run_leaky_relu.py:

output_tensor = builder.customOp(opName="LeakyRelu",
                                 attributes={"alpha": alpha})[0]

Or the op can be referenced from an ONNX file using a NodeProto definition that matches the domain, name and version of the op.

If you are using PyTorch you can also call this custom op using PopTorch.