#include "ggml/ggml.h" #include #include #include #include bool is_close(float a, float b, float epsilon) { return fabs(a - b) < epsilon; } int main(int argc, const char ** argv) { struct ggml_init_params params = { .mem_size = 1024*1024*1024, .mem_buffer = NULL, }; struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_LBFGS); //struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM); opt_params.n_threads = (argc > 1) ? atoi(argv[1]) : 8; const int NP = 1 << 12; const int NF = 1 << 8; struct ggml_context * ctx0 = ggml_init(params); struct ggml_tensor * F = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, NF, NP); struct ggml_tensor * l = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, NP); // regularization weight struct ggml_tensor * lambda = ggml_new_f32(ctx0, 1e-5f); srand(0); for (int j = 0; j < NP; j++) { const float ll = j < NP/2 ? 1.0f : -1.0f; ((float *)l->data)[j] = ll; for (int i = 0; i < NF; i++) { ((float *)F->data)[j*NF + i] = ((ll > 0 && i < NF/2 ? 1.0f : ll < 0 && i >= NF/2 ? 1.0f : 0.0f) + ((float)rand()/(float)RAND_MAX - 0.5f)*0.1f)/(0.5f*NF); } } { // initial guess struct ggml_tensor * x = ggml_set_f32(ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, NF), 0.0f); ggml_set_param(ctx0, x); // f = sum[(fj*x - l)^2]/n + lambda*|x^2| struct ggml_tensor * f = ggml_add(ctx0, ggml_div(ctx0, ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, ggml_mul_mat(ctx0, F, x), l) ) ), ggml_new_f32(ctx0, NP) ), ggml_mul(ctx0, ggml_sum(ctx0, ggml_sqr(ctx0, x)), lambda) ); enum ggml_opt_result res = ggml_opt(NULL, opt_params, f); assert(res == GGML_OPT_OK); // print results for (int i = 0; i < 16; i++) { printf("x[%3d] = %g\n", i, ((float *)x->data)[i]); } printf("...\n"); for (int i = NF - 16; i < NF; i++) { printf("x[%3d] = %g\n", i, ((float *)x->data)[i]); } printf("\n"); for (int i = 0; i < NF; ++i) { if (i < NF/2) { assert(is_close(((float *)x->data)[i], 1.0f, 1e-2f)); } else { assert(is_close(((float *)x->data)[i], -1.0f, 1e-2f)); } } } ggml_free(ctx0); return 0; }