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cudaErrorInvalidDeviceFunction (8)cudaErrorInvalidDeviceFunction (error code 8) occurs when CUDA cannot find or execute the requested kernel function on the device. This typically happens when there's a mismatch between the compiled code architecture and the GPU, or when the kernel function wasn't properly compiled for device execution. This error is particularly common when distributing CUDA applications across different GPU architectures, using dynamic parallelism incorrectly, or when there are issues with the build system not properly compiling device code. This guide covers the root causes, step-by-step debugging approaches, and best practices for ensuring your kernel functions are properly compiled and accessible.
CUDA error: invalid device function cudaErrorInvalidDeviceFunction: invalid device function RuntimeError: CUDA error: invalid device function Error: the provided PTX was compiled with an unsupported toolchain
Ensure your code is compiled for the correct GPU compute capability.
# Check your GPU compute capability
nvidia-smi --query-gpu=compute_cap --format=csv
# CMake configuration for multiple architectures
set(CMAKE_CUDA_ARCHITECTURES "60;70;75;80;86")
# Or nvcc compilation
nvcc -arch=sm_75 kernel.cu -o program
# For PyTorch extensions, set in setup.py
from torch.utils.cpp_extension import CUDAExtension
ext_modules=[
CUDAExtension('my_extension',
['extension.cu'],
extra_compile_args={'nvcc': ['-arch=sm_75']})
]Verify that your kernel has the correct qualifiers and signature.
// Correct kernel declaration
__global__ void myKernel(int* data, int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
data[idx] = idx;
}
}
// Device function (not directly launchable)
__device__ void helperFunction(int x) {
// Can only be called from kernel or device function
}
// Launch the kernel
myKernel<<<blocks, threads>>>(d_data, size);If launching kernels from device code, enable dynamic parallelism.
// Compile with dynamic parallelism support
nvcc -arch=sm_75 -rdc=true -lcudadevrt kernel.cu
// CMakeLists.txt
set_property(TARGET myprogram PROPERTY CUDA_SEPARABLE_COMPILATION ON)
// Kernel launching another kernel
__global__ void parentKernel() {
// Launch child kernel from device
childKernel<<<1, 256>>>();
cudaDeviceSynchronize();
}
__global__ void childKernel() {
printf("Child kernel running\n");
}For multi-file projects, ensure proper linking of device code.
// kernel.cuh (header)
__global__ void myKernel(int* data);
// kernel.cu (implementation)
#include "kernel.cuh"
__global__ void myKernel(int* data) {
// Implementation
}
// CMakeLists.txt
set_property(TARGET mylib PROPERTY CUDA_SEPARABLE_COMPILATION ON)
set_property(TARGET mylib PROPERTY CUDA_RESOLVE_DEVICE_SYMBOLS ON)
# Or with nvcc
nvcc -dc kernel.cu -o kernel.o
nvcc -dlink kernel.o -o link.o
nvcc kernel.o link.o -o programWhen using function pointers, ensure they point to valid device functions.
// Define device function pointer type
typedef void (*KernelFunc)(int*);
// Get device function pointer
__device__ void deviceFunc(int* data) {
data[threadIdx.x] = threadIdx.x;
}
// Get pointer to device function
__global__ void wrapperKernel(int* data) {
deviceFunc(data);
}
// On host, get pointer using cudaMemcpyFromSymbol
KernelFunc h_func;
cudaMemcpyFromSymbol(&h_func, deviceFunc, sizeof(KernelFunc));Aggressive optimizations might remove unused functions.
// Prevent function from being optimized away
__global__ void __attribute__((used)) myKernel(int* data) {
// Kernel code
}
// Or disable aggressive optimizations
nvcc -O1 kernel.cu // Instead of -O3
// CMake
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O1")
// Keep debug symbols for verification
nvcc -g -G kernel.cuFunction is missing __global__ qualifier, so it cannot be launched as a kernel. CUDA sees it as a host function.
// Missing __global__ qualifier
void myKernel(int* data) {
int idx = threadIdx.x;
data[idx] = idx;
}
int main() {
int* d_data;
cudaMalloc(&d_data, 256 * sizeof(int));
// This will fail - myKernel is not a device function
myKernel<<<1, 256>>>(d_data);
return 0;
}Kernel has proper __global__ qualifier and includes error checking after launch to catch any issues immediately.
// Correct kernel with __global__ qualifier
__global__ void myKernel(int* data) {
int idx = threadIdx.x;
data[idx] = idx;
}
int main() {
int* d_data;
cudaMalloc(&d_data, 256 * sizeof(int));
// Properly launches kernel on device
myKernel<<<1, 256>>>(d_data);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
printf("Kernel launch failed: %s\n", cudaGetErrorString(err));
}
cudaDeviceSynchronize();
cudaFree(d_data);
return 0;
}This is usually due to architecture mismatch. The kernel was compiled for a specific compute capability that the other GPU doesn't support. Compile with multiple -gencode flags to support different architectures, or use -arch=sm_XX matching your target GPU.
No, only functions declared with __global__ can be launched as kernels. __device__ functions can only be called from other device code (kernels or device functions). If you need to launch it, change the qualifier to __global__.
Use cuobjdump to inspect the compiled binary: cuobjdump -symbols myprogram.o. Look for your kernel name in the symbol table. If missing, it wasn't compiled or was optimized away.
Relocatable device code (-rdc=true) allows device code to reference symbols from other compilation units. It's required for dynamic parallelism and multi-file CUDA projects where kernels call functions defined in other files.
General kernel launch failures
PTX compilation and architecture issues
Device availability problems
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