flashinfer: Skill for Claude Code

Tutorial: Adding a New Kernel to FlashInfer

This tutorial walks through adding a simple element-wise scale operation to FlashInfer. We'll implement scale(x, factor) = x * factor to demonstrate the complete workflow.

Goal

Add a new operation that scales each element of a tensor by a scalar factor:

• Input: tensor x and scalar factor
• Output: x * factor (element-wise)
• Support multiple dtypes (FP16, BF16, FP32)

Step 1: Define CUDA Kernel in include/

Create include/flashinfer/scale.cuh:

#pragma once
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cuda_bf16.h>

namespace flashinfer {

/*!
 * \brief Element-wise scale kernel
 * \tparam T Data type (half, __nv_bfloat16, float)
 * \param input Input tensor
 * \param output Output tensor
 * \param factor Scale factor
 * \param n Number of elements
 */
template <typename T>
__global__ void ScaleKernel(const T* input, T* output, T factor, int n) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < n) {
    output[idx] = input[idx] * factor;
  }
}

/*!
 * \brief Launch scale kernel
 * \tparam T Data type
 * \param input Input pointer
 * \param output Output pointer
 * \param factor Scale factor
 * \param n Number of elements
 * \param stream CUDA stream
 */
template <typename T>
cudaError_t ScaleLauncher(const T* input, T* output, T factor, int n,
                          cudaStream_t stream = nullptr) {
  const int threads = 256;
  const int blocks = (n + threads - 1) / threads;

  ScaleKernel<T><<<blocks, threads, 0, stream>>>(input, output, factor, n);

  return cudaGetLastError();
}

}  // namespace flashinfer

Key points:

• Framework-agnostic (no Torch headers)
• Uses raw pointers
• Template-based for dtype flexibility
• Only includes what's needed (cuda_runtime, cuda_fp16, cuda_bf16)

Step 2: Create Launcher in csrc/

Create csrc/scale.cu:

#include "flashinfer/scale.cuh"

using namespace flashinfer;

void scale_launcher(TensorView input, TensorView output,
                    float factor) {
  CHECK_INPUT(input);
  CHECK_INPUT(output);
  TVM_FFI_ICHECK_EQ(input.dtype(), output.dtype());
  int n = input.numel();
  auto stream = get_stream(input.device());

  DISPATCH_DLPACK_DTYPE_TO_CTYPE_FP32_FP16(input.dtype(), DType, [&] {
    cudaError_t status = ScaleLauncher<DType>(
      input.data_ptr<DType>(),
      output.data_ptr<DType>(),
      static_cast<DType>(factor),
      n,
      stream
    );
    TVM_FFI_ICHECK(status == cudaSuccess)
        << "Failed to run ScaleLauncher: " << cudaGetErrorString(status);
    return true;
  });
}