## GELU Instructions to Run

1. Make sure you have the CUDA toolkit installed on your machine.
2. Save the above code to a file, say `gelu.cu`.
3. Compile the code using `nvcc` (NVIDIA CUDA Compiler):

```
nvcc gelu.cu -o gelu
```

4. Run the compiled binary:

```
./gelu
```

You should see the GELU output for the sample data.

-----

## gelu_binding.cpp

To integrate the CUDA C code into a Python module, you can use the `pybind11` library. `pybind11` is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code.

Here's how you can integrate the GELU CUDA C code into your Python module:

### Steps:

1. **Install pybind11 and set up the environment**:
   - You can install `pybind11` via pip:
     ```
     pip install pybind11
     ```
   - Ensure you have the CUDA toolkit installed on your machine.

2. **Create a binding code**:
   - Make a new file named `gelu_binding.cpp`. This will be the binding code between the CUDA C and Python.

   ```cpp
   #include <pybind11/pybind11.h>
   #include <pybind11/numpy.h>
   #include <math.h>
   #include <cuda_runtime.h>

   // The CUDA kernel and other functions remain unchanged

   // Wrapper function for pybind11
   pybind11::array_t<float> py_gelu(pybind11::array_t<float> input_array) {
       pybind11::buffer_info buf_info = input_array.request();
       float *ptr = static_cast<float *>(buf_info.ptr);
       int size = buf_info.size;

       float *output = new float[size];
       gelu(ptr, output, size);

       // Return a numpy array
       return pybind11::array_t<float>(buf_info.size, output);
   }

   PYBIND11_MODULE(gelu_module, m) {
       m.def("gelu", &py_gelu, "GELU function on GPU");
   }
   ```

3. **Compile the binding code**:
   - Use `c++` with the `-fPIC` flag to produce position-independent code, which is required for shared libraries. Also, link against the necessary CUDA libraries.

   ```
   c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` gelu_binding.cpp -o gelu_module`python3-config --extension-suffix` -L/usr/local/cuda/lib64 -lcudart
   ```

   This will produce a shared library named `gelu_module.cpython-xxm-x86_64-linux-gnu.so` (name may vary depending on your platform and Python version).

4. **Integrate with your module**:
   - Move the generated shared library into your module's directory.
   - In your `main.py` or any of the `vX.py` files where you wish to use the GELU function, you can now import and use the GELU function as:

   ```python
   import gelu_module

   def some_function():
       data = [1.0, 2.0, 3.0, 4.0]
       result = gelu_module.gelu(data)
       print(result)
   ```

5. **Run your Python code**:
   - Simply run your Python code as usual. The GELU function will be executed on the GPU using the CUDA toolkit.

Note: Ensure that you have the necessary permissions to write and execute files in your module directory. Also, you may need to adjust the library and include paths depending on your CUDA installation.