Thank you! --- Part 2
## Model’s Translation Capability
Thanks for this great question. As you mentioned, there are entities, libraries, keywords, and syntaxes from the source language (e.g., C++) that may not be valid in the target language (e.g., CUDA). `std::unique_ptr` and other C++ Standard Template Libraries (STL) belong to this category and must be avoided in the translation. The pre-training process in CodeRosetta (esp. Weighted Token Dropping) equips the model to gain a semantic understanding of source and target languages. It reduces the occurrences of invalid tokens in the translation. Nonetheless, there may be cases in which the model fails to correctly translate from common entities in the source code to valid entities in the target code.
Looking into our dataset, there was no occurrence of `std::unique_ptr` in the test set. However, as the training set has `std::unique_ptr`, we intended to test the capability of CodeRosetta by developing a C++ code that uses `std::unique_ptr`, we made sure this sample does not exist in the training set:
### Reference C++ Code
```
void initializeArray(std::unique_ptr<int[]>& array, int size) {
for (int i = 0; i < size; ++i) {
array[i] = i * 10;
}
}
```
### CodeRosetta CUDA Generation
```
__global__ void initializeArray(int *array, int size) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < size) {
array[i] = i * 10;
}
}
```
This sample is about array initialization. As can be seen, the model is able to provide the CUDA code. It removes the `std::unique_ptr` from the CUDA kernel as it is not supported in the device code. In CUDA, `std::unique_ptr` is called in the host kernel, which is the main function. Our model focuses on the generation of device function and hence is successful in this case.
Moreover, from the test set, we checked where our model could potentially fail. Here is an example of a translation that CodeRosetta struggles with:
### Reference C++ Code:
```
void getRho(const int numOfNucl, const double* psi, const double* occNo, double* rho, const char debug) {
*rho = 0;
for (int i = 0; i < numOfNucl; ++i) {
*rho += occNo[i] * psi[i] * psi[i];
}
if (debug == 1) {
printf("DEBUG ▁ print ▁ of ▁ RHO:\n▁ RHO ▁ = ▁ %f\nThis ▁ is ▁ the ▁ last ▁ line (RHO).\n\n", *rho);
}
}
```
### CodeRosetta CUDA Generation:
```
__global__ void getRho(const int numOfNucl, const double *psi, const double *occNo, double *rho, const char debug) {
*rho = 0;
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < numOfNucl; i += blockDim.x * gridDim.x) {
*rho += occNo[i] * psi[i] * psi[i];
}
if (debug == 1) {
printf("DEBUG print of RHO:\nRHO = %f\nThis is the last line (RHO).\n\n", *rho);
}
}
```
In this example, CodeRosetta, along with all larger models like GPT-4, Gemini Ultra, and Gemini Pro, failed to translate the code correctly. We verified the generated code by executing it. The line `*rho = 0;` initializes the variable rho to zero. However, if this kernel is executed by multiple threads across different blocks simultaneously (which is very likely), all threads will write to the same memory location. This results in a race condition where multiple threads are simultaneously updating the value of *rho without any synchronization mechanism, such as atomic operations or reduction techniques. As a result, each thread's write could overwrite others, leading to incorrect and unpredictable outcomes. To rectify this issue, `rho` should be set to 0 in the host code, and `atomicAdd` should be included in the computation.
---
We hope these clarifications address your feedback and questions effectively and you find our rebuttal and additional discussion satisfactory.
We would greatly appreciate it if these additional results and clarifications lead you to reevaluate our work.
At the end, if there are any other lingering questions or requests, please do not hesitate to ask. Our goal is to work with experts like you to improve the quality of our paper and make our research more valuable for the community.
Thank you,
The Authors