Question about memory management for custom cuda.parallel operators

[KalyanChakravarthyK]

I've been working with the cuda.parallel module and noticed some memory usage patterns that I'm trying to better understand. When implementing custom operators:

What's the recommended approach for managing temporary GPU memory allocations in custom operators to avoid memory leaks?
Are there best practices for determining optimal chunk sizes when processing large tensors to balance throughput and memory usage?
Is there documentation on how the memory pool interacts with custom operators, particularly for asynchronous operations?

[shwina]

Hi @KalyanChakravarthyKodela - thanks for your question. If possible it would be helpful to see an example of kind of operations you're doing that lead to memory leaks. Are you able to share a representative code snippet/example?

[KalyanChakravarthyK]

Hi @shwina, thanks for your response!

#include <torch/extension.h>
#include <cuda_runtime.h>

// ======================
// 1. Kernel with PROPER memory management
// ======================
__global__ void leaky_kernel(float* input, float* output, int size) {
    // UNSAFE: Dynamic allocation in kernel (will leak)
    float* temp = new float[10]; 
    // ... computations ...
    delete[] temp;  // Still unsafe - kernel allocations are tricky
}

__global__ void safe_kernel(float* input, float* output, int size, float* temp_storage) {
    // SAFE: Pre-allocated memory passed as argument
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        temp_storage[idx] = input[idx] * 2.0f;
        output[idx] = temp_storage[idx] + 1.0f;
    }
}

// ======================
// 2. Memory Manager (RAII)
// ======================
class GPUMemoryGuard {
public:
    GPUMemoryGuard(size_t bytes) : ptr_(nullptr), bytes_(bytes) {
        cudaMalloc(&ptr_, bytes_); 
    }
    
    ~GPUMemoryGuard() {
        if (ptr_) cudaFree(ptr_);
    }
    
    void* get() const { return ptr_; }
    
private:
    void* ptr_;
    size_t bytes_;
};

// ======================
// 3. Custom Operator
// ======================
torch::Tensor safe_forward(torch::Tensor input) {
    auto output = torch::empty_like(input);
    const int num_elements = input.numel();
    
    // RAII memory management
    GPUMemoryGuard temp_storage(num_elements * sizeof(float));
    
    // Optimal block size calculation
    const int threads = 256;
    const int blocks = (num_elements + threads - 1) / threads;
    
    // Launch kernel with proper stream synchronization
    auto stream = at::cuda::getCurrentCUDAStream();
    safe_kernel<<<blocks, threads, 0, stream>>>(
        input.data_ptr<float>(),
        output.data_ptr<float>(),
        num_elements,
        static_cast<float*>(temp_storage.get())
    );
    
    // Error checking
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    
    return output;
}

// ======================
// 4. BAD EXAMPLE (leaks)
// ======================
torch::Tensor leaky_forward(torch::Tensor input) {
    auto output = torch::empty_like(input);
    float* d_temp;
    
    // Manual allocation without guard
    cudaMalloc(&d_temp, input.numel() * sizeof(float));
    
    if (some_condition) {
        return output;  // LEAK: Early return before cudaFree
    }
    
    // ... kernel launches ...
    
    // Potentially skipped in error cases
    cudaFree(d_temp);
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("safe_forward", &safe_forward, "Safe memory managed version");
    m.def("leaky_forward", &leaky_forward, "Leaky version for comparison");
}

[oleksandr-pavlyk]

RAII based allocation on host is the best practice. Be aware that cudaFree command may perform implicit synchronization. Consider using faster stream-ordered allocation API: cudaMallocAsync/cudaFreeAsync.

See https://developer.nvidia.com/blog/using-cuda-stream-ordered-memory-allocator-part-1/ and https://developer.nvidia.com/blog/using-cuda-stream-ordered-memory-allocator-part-2/ for more details.