| using namespace neuroflow; | |
| int main() { | |
| std::cout << "GEMM detail test..." << std::endl; | |
| Tensor A({2, 64}); // input | |
| Tensor B({32, 64}); // weight | |
| Tensor C({2, 32}); // output | |
| std::cout << "A shape: [" << A.shape_[0] << ", " << A.shape_[1] << "]" << std::endl; | |
| std::cout << "B shape: [" << B.shape_[0] << ", " << B.shape_[1] << "]" << std::endl; | |
| std::cout << "C shape: [" << C.shape_[0] << ", " << C.shape_[1] << "]" << std::endl; | |
| // Fill data | |
| float* a = A.as_fp32(); | |
| float* b = B.as_fp32(); | |
| for (size_t i = 0; i < A.numel(); ++i) a[i] = 0.1f * i; | |
| for (size_t i = 0; i < B.numel(); ++i) b[i] = 0.01f * i; | |
| std::cout << "Calling gemm..." << std::endl; | |
| TensorOps::gemm(A, B, C); // C = A @ B^T ? | |
| std::cout << "C numel: " << C.numel() << std::endl; | |
| std::cout << "C data_size: " << C.data_size_ << std::endl; | |
| float* c = C.as_fp32(); | |
| std::cout << "First 5 C values: "; | |
| for (size_t i = 0; i < 5; ++i) std::cout << c[i] << " "; | |
| std::cout << std::endl; | |
| std::cout << "After gemm, trying to allocate new tensor..." << std::endl; | |
| Tensor new_t({10}); | |
| std::cout << "Success!" << std::endl; | |
| return 0; | |
| } | |