Custom C++ and CUDA Operators

Created On: Jun 18, 2024 | Last Updated: Aug 29, 2024 | Last Verified: Nov 05, 2024

Author: Richard Zou

What you will learn

• How to integrate custom operators written in C++/CUDA with PyTorch

• How to test custom operators using torch.library.opcheck

Prerequisites

• PyTorch 2.4 or later

• Basic understanding of C++ and CUDA programming

PyTorch offers a large library of operators that work on Tensors (e.g. torch.add, torch.sum, etc). However, you may wish to bring a new custom operator to PyTorch. This tutorial demonstrates the blessed path to authoring a custom operator written in C++/CUDA.

For our tutorial, we’ll demonstrate how to author a fused multiply-add C++ and CUDA operator that composes with PyTorch subsystems. The semantics of the operation are as follows:

def mymuladd(a: Tensor, b: Tensor, c: float):
    return a * b + c

You can find the end-to-end working example for this tutorial here .

Setting up the Build System

If you are developing custom C++/CUDA code, it must be compiled. Note that if you’re interfacing with a Python library that already has bindings to precompiled C++/CUDA code, you might consider writing a custom Python operator instead (Python Custom Operators).

Use torch.utils.cpp_extension to compile custom C++/CUDA code for use with PyTorch C++ extensions may be built either “ahead of time” with setuptools, or “just in time” via load_inline; we’ll focus on the “ahead of time” flavor.

Using cpp_extension is as simple as writing the following setup.py:

from setuptools import setup, Extension
from torch.utils import cpp_extension

setup(name="extension_cpp",
      ext_modules=[
          cpp_extension.CppExtension("extension_cpp", ["muladd.cpp"])],
      cmdclass={'build_ext': cpp_extension.BuildExtension})

If you need to compile CUDA code (for example, .cu files), then instead use torch.utils.cpp_extension.CUDAExtension. Please see how extension-cpp for an example for how this is set up.

Defining the custom op and adding backend implementations

First, let’s write a C++ function that computes mymuladd:

at::Tensor mymuladd_cpu(at::Tensor a, const at::Tensor& b, double c) {
  TORCH_CHECK(a.sizes() == b.sizes());
  TORCH_CHECK(a.dtype() == at::kFloat);
  TORCH_CHECK(b.dtype() == at::kFloat);
  TORCH_INTERNAL_ASSERT(a.device().type() == at::DeviceType::CPU);
  TORCH_INTERNAL_ASSERT(b.device().type() == at::DeviceType::CPU);
  at::Tensor a_contig = a.contiguous();
  at::Tensor b_contig = b.contiguous();
  at::Tensor result = torch::empty(a_contig.sizes(), a_contig.options());
  const float* a_ptr = a_contig.data_ptr<float>();
  const float* b_ptr = b_contig.data_ptr<float>();
  float* result_ptr = result.data_ptr<float>();
  for (int64_t i = 0; i < result.numel(); i++) {
    result_ptr[i] = a_ptr[i] * b_ptr[i] + c;
  }
  return result;
}

In order to use this from PyTorch’s Python frontend, we need to register it as a PyTorch operator using the TORCH_LIBRARY API. This will automatically bind the operator to Python.

Operator registration is a two step-process:

• Defining the operator - This step ensures that PyTorch is aware of the new operator.

• Registering backend implementations - In this step, implementations for various backends, such as CPU and CUDA, are associated with the operator.

Defining an operator

To define an operator, follow these steps:

1. select a namespace for an operator. We recommend the namespace be the name of your top-level project; we’ll use “extension_cpp” in our tutorial.

2. provide a schema string that specifies the input/output types of the operator and if an input Tensors will be mutated. We support more types in addition to Tensor and float; please see The Custom Operators Manual for more details.

   • If you are authoring an operator that can mutate its input Tensors, please see here (Creating mutable operators) for how to specify that.

TORCH_LIBRARY(extension_cpp, m) {
   // Note that "float" in the schema corresponds to the C++ double type
   // and the Python float type.
   m.def("mymuladd(Tensor a, Tensor b, float c) -> Tensor");
 }

This makes the operator available from Python via torch.ops.extension_cpp.mymuladd.

Registering backend implementations for an operator

Use TORCH_LIBRARY_IMPL to register a backend implementation for the operator.

TORCH_LIBRARY_IMPL(extension_cpp, CPU, m) {
  m.impl("mymuladd", &mymuladd_cpu);
}

If you also have a CUDA implementation of myaddmul, you can register it in a separate TORCH_LIBRARY_IMPL block:

__global__ void muladd_kernel(int numel, const float* a, const float* b, float c, float* result) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < numel) result[idx] = a[idx] * b[idx] + c;
}

at::Tensor mymuladd_cuda(const at::Tensor& a, const at::Tensor& b, double c) {
  TORCH_CHECK(a.sizes() == b.sizes());
  TORCH_CHECK(a.dtype() == at::kFloat);
  TORCH_CHECK(b.dtype() == at::kFloat);
  TORCH_INTERNAL_ASSERT(a.device().type() == at::DeviceType::CUDA);
  TORCH_INTERNAL_ASSERT(b.device().type() == at::DeviceType::CUDA);
  at::Tensor a_contig = a.contiguous();
  at::Tensor b_contig = b.contiguous();
  at::Tensor result = torch::empty(a_contig.sizes(), a_contig.options());
  const float* a_ptr = a_contig.data_ptr<float>();
  const float* b_ptr = b_contig.data_ptr<float>();
  float* result_ptr = result.data_ptr<float>();

  int numel = a_contig.numel();
  muladd_kernel<<<(numel+255)/256, 256>>>(numel, a_ptr, b_ptr, c, result_ptr);
  return result;
}

TORCH_LIBRARY_IMPL(extension_cpp, CUDA, m) {
  m.impl("mymuladd", &mymuladd_cuda);
}

Adding torch.compile support for an operator

To add torch.compile support for an operator, we must add a FakeTensor kernel (also known as a “meta kernel” or “abstract impl”). FakeTensors are Tensors that have metadata (such as shape, dtype, device) but no data: the FakeTensor kernel for an operator specifies how to compute the metadata of output tensors given the metadata of input tensors. The FakeTensor kernel should return dummy Tensors of your choice with the correct Tensor metadata (shape/strides/dtype/device).

We recommend that this be done from Python via the torch.library.register_fake API, though it is possible to do this from C++ as well (see The Custom Operators Manual for more details).

# Important: the C++ custom operator definitions should be loaded first
# before calling ``torch.library`` APIs that add registrations for the
# C++ custom operator(s). The following import loads our
# C++ custom operator definitions.
# See the next section for more details.
from . import _C

@torch.library.register_fake("extension_cpp::mymuladd")
def _(a, b, c):
    torch._check(a.shape == b.shape)
    torch._check(a.dtype == torch.float)
    torch._check(b.dtype == torch.float)
    torch._check(a.device == b.device)
    return torch.empty_like(a)

Setting up hybrid Python/C++ registration

In this tutorial, we defined a custom operator in C++, added CPU/CUDA implementations in C++, and added FakeTensor kernels and backward formulas in Python. The order in which these registrations are loaded (or imported) matters (importing in the wrong order will lead to an error).

To use the custom operator with hybrid Python/C++ registrations, we must first load the C++ library that holds the custom operator definition and then call the torch.library registration APIs. This can happen in one of two ways:

1. If you’re following this tutorial, importing the Python C extension module we created will load the C++ custom operator definitions.

2. If your C++ custom operator is located in a shared library object, you can also use torch.ops.load_library("/path/to/library.so") to load it.

Adding training (autograd) support for an operator

Use torch.library.register_autograd to add training support for an operator. Prefer this over directly using Python torch.autograd.Function or C++ torch::autograd::Function; you must use those in a very specific way to avoid silent incorrectness (see The Custom Operators Manual for more details).

def _backward(ctx, grad):
    a, b = ctx.saved_tensors
    grad_a, grad_b = None, None
    if ctx.needs_input_grad[0]:
        grad_a = grad * b
    if ctx.needs_input_grad[1]:
        grad_b = grad * a
    return grad_a, grad_b, None

def _setup_context(ctx, inputs, output):
    a, b, c = inputs
    saved_a, saved_b = None, None
    if ctx.needs_input_grad[0]:
        saved_b = b
    if ctx.needs_input_grad[1]:
        saved_a = a
    ctx.save_for_backward(saved_a, saved_b)

# This code adds training support for the operator. You must provide us
# the backward formula for the operator and a `setup_context` function
# to save values to be used in the backward.
torch.library.register_autograd(
    "extension_cpp::mymuladd", _backward, setup_context=_setup_context)

Note that the backward must be a composition of PyTorch-understood operators. If you wish to use another custom C++ or CUDA kernel in your backwards pass, it must be wrapped into a custom operator.

If we had our own custom mymul kernel, we would need to wrap it into a custom operator and then call that from the backward:

// New! a mymul_cpu kernel
at::Tensor mymul_cpu(const at::Tensor& a, const at::Tensor& b) {
  TORCH_CHECK(a.sizes() == b.sizes());
  TORCH_CHECK(a.dtype() == at::kFloat);
  TORCH_CHECK(b.dtype() == at::kFloat);
  TORCH_CHECK(a.device().type() == at::DeviceType::CPU);
  TORCH_CHECK(b.device().type() == at::DeviceType::CPU);
  at::Tensor a_contig = a.contiguous();
  at::Tensor b_contig = b.contiguous();
  at::Tensor result = torch::empty(a_contig.sizes(), a_contig.options());
  const float* a_ptr = a_contig.data_ptr<float>();
  const float* b_ptr = b_contig.data_ptr<float>();
  float* result_ptr = result.data_ptr<float>();
  for (int64_t i = 0; i < result.numel(); i++) {
    result_ptr[i] = a_ptr[i] * b_ptr[i];
  }
  return result;
}

TORCH_LIBRARY(extension_cpp, m) {
  m.def("mymuladd(Tensor a, Tensor b, float c) -> Tensor");
  // New! defining the mymul operator
  m.def("mymul(Tensor a, Tensor b) -> Tensor");
}


TORCH_LIBRARY_IMPL(extension_cpp, CPU, m) {
  m.impl("mymuladd", &mymuladd_cpu);
  // New! registering the cpu kernel for the mymul operator
  m.impl("mymul", &mymul_cpu);
}

def _backward(ctx, grad):
    a, b = ctx.saved_tensors
    grad_a, grad_b = None, None
    if ctx.needs_input_grad[0]:
        grad_a = torch.ops.extension_cpp.mymul.default(grad, b)
    if ctx.needs_input_grad[1]:
        grad_b = torch.ops.extension_cpp.mymul.default(grad, a)
    return grad_a, grad_b, None


def _setup_context(ctx, inputs, output):
    a, b, c = inputs
    saved_a, saved_b = None, None
    if ctx.needs_input_grad[0]:
        saved_b = b
    if ctx.needs_input_grad[1]:
        saved_a = a
    ctx.save_for_backward(saved_a, saved_b)


# This code adds training support for the operator. You must provide us
# the backward formula for the operator and a `setup_context` function
# to save values to be used in the backward.
torch.library.register_autograd(
    "extension_cpp::mymuladd", _backward, setup_context=_setup_context)

Testing an operator

Use torch.library.opcheck to test that the custom op was registered correctly. Note that this function does not test that the gradients are mathematically correct – plan to write separate tests for that, either manual ones or by using torch.autograd.gradcheck.

def sample_inputs(device, *, requires_grad=False):
    def make_tensor(*size):
        return torch.randn(size, device=device, requires_grad=requires_grad)

    def make_nondiff_tensor(*size):
        return torch.randn(size, device=device, requires_grad=False)

    return [
        [make_tensor(3), make_tensor(3), 1],
        [make_tensor(20), make_tensor(20), 3.14],
        [make_tensor(20), make_nondiff_tensor(20), -123],
        [make_nondiff_tensor(2, 3), make_tensor(2, 3), -0.3],
    ]

def reference_muladd(a, b, c):
    return a * b + c

samples = sample_inputs(device, requires_grad=True)
samples.extend(sample_inputs(device, requires_grad=False))
for args in samples:
    # Correctness test
    result = torch.ops.extension_cpp.mymuladd(*args)
    expected = reference_muladd(*args)
    torch.testing.assert_close(result, expected)

    # Use opcheck to check for incorrect usage of operator registration APIs
    torch.library.opcheck(torch.ops.extension_cpp.mymuladd.default, args)

Creating mutable operators

You may wish to author a custom operator that mutates its inputs. Use Tensor(a!) to specify each mutable Tensor in the schema; otherwise, there will be undefined behavior. If there are multiple mutated Tensors, use different names (for example, Tensor(a!), Tensor(b!), Tensor(c!)) for each mutable Tensor.

Let’s author a myadd_out(a, b, out) operator, which writes the contents of a+b into out.

// An example of an operator that mutates one of its inputs.
void myadd_out_cpu(const at::Tensor& a, const at::Tensor& b, at::Tensor& out) {
  TORCH_CHECK(a.sizes() == b.sizes());
  TORCH_CHECK(b.sizes() == out.sizes());
  TORCH_CHECK(a.dtype() == at::kFloat);
  TORCH_CHECK(b.dtype() == at::kFloat);
  TORCH_CHECK(out.dtype() == at::kFloat);
  TORCH_CHECK(out.is_contiguous());
  TORCH_INTERNAL_ASSERT(a.device().type() == at::DeviceType::CPU);
  TORCH_INTERNAL_ASSERT(b.device().type() == at::DeviceType::CPU);
  TORCH_INTERNAL_ASSERT(out.device().type() == at::DeviceType::CPU);
  at::Tensor a_contig = a.contiguous();
  at::Tensor b_contig = b.contiguous();
  const float* a_ptr = a_contig.data_ptr<float>();
  const float* b_ptr = b_contig.data_ptr<float>();
  float* result_ptr = out.data_ptr<float>();
  for (int64_t i = 0; i < out.numel(); i++) {
    result_ptr[i] = a_ptr[i] + b_ptr[i];
  }
}

When defining the operator, we must specify that it mutates the out Tensor in the schema:

TORCH_LIBRARY(extension_cpp, m) {
  m.def("mymuladd(Tensor a, Tensor b, float c) -> Tensor");
  m.def("mymul(Tensor a, Tensor b) -> Tensor");
  // New!
  m.def("myadd_out(Tensor a, Tensor b, Tensor(a!) out) -> ()");
}

TORCH_LIBRARY_IMPL(extension_cpp, CPU, m) {
  m.impl("mymuladd", &mymuladd_cpu);
  m.impl("mymul", &mymul_cpu);
  // New!
  m.impl("myadd_out", &myadd_out_cpu);
}

Note

Do not return any mutated Tensors as outputs of the operator as this will cause incompatibility with PyTorch subsystems like torch.compile.

Conclusion

In this tutorial, we went over the recommended approach to integrating Custom C++ and CUDA operators with PyTorch. The TORCH_LIBRARY/torch.library APIs are fairly low-level. For more information about how to use the API, see The Custom Operators Manual.