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#include "ggml.h"
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#define MAX_NARGS 2
#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdouble-promotion"
#endif
//
// logging
//
#define GGML_DEBUG 0
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
#define GGML_PRINT(...) printf(__VA_ARGS__)
float frand(void) {
return (float)rand()/(float)RAND_MAX;
}
int irand(int n) {
return rand()%n;
}
void get_random_dims(int64_t * dims, int ndims) {
dims[0] = dims[1] = dims[2] = dims[3] = 1;
for (int i = 0; i < ndims; i++) {
dims[i] = 1 + irand(4);
}
}
void get_random_dims_minmax(int64_t * dims, int ndims, int min, int max) {
dims[0] = dims[1] = dims[2] = dims[3] = 1;
for (int i = 0; i < ndims; i++) {
dims[i] = min + irand(max-min);
}
}
struct ggml_tensor * get_random_tensor(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
float fmin,
float fmax) {
struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne);
switch (ndims) {
case 1:
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin;
}
break;
case 2:
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
break;
case 3:
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
}
break;
case 4:
for (int i3 = 0; i3 < ne[3]; i3++) {
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
}
}
break;
default:
assert(false);
};
return result;
}
float get_element(const struct ggml_tensor * t, int idx) {
return ((float *)t->data)[idx];
}
void set_element(struct ggml_tensor * t, int idx, float value) {
((float *)t->data)[idx] = value;
}
int main(void) {
struct ggml_init_params params = {
/* .mem_size = */ 1024*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
int64_t ne1[4] = {4, 128, 1, 1};
int64_t ne2[4] = {4, 256, 1, 1};;
int64_t ne3[4] = {128, 256, 1, 1};
struct ggml_tensor * a = get_random_tensor(ctx, 2, ne1, -1, +1);
struct ggml_tensor * b = get_random_tensor(ctx, 2, ne2, -1, +1);
ggml_set_param(ctx, a);
ggml_set_param(ctx, b);
struct ggml_tensor * c = get_random_tensor(ctx, 2, ne3, -1, +1);
struct ggml_tensor * ab = ggml_mul_mat(ctx, a, b);
struct ggml_tensor * d = ggml_sub(ctx, c, ab);
struct ggml_tensor * e = ggml_sum(ctx, ggml_sqr(ctx, d));
struct ggml_cgraph ge = ggml_build_forward(e);
ggml_graph_reset(&ge);
ggml_graph_compute_with_ctx(ctx, &ge, /*n_threads*/ 1);
const float fe = ggml_get_f32_1d(e, 0);
printf("%s: e = %.4f\n", __func__, fe);
struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM);
ggml_opt(ctx, opt_params, e);
ggml_graph_reset(&ge);
ggml_graph_compute_with_ctx(ctx, &ge, /*n_threads*/ 1);
const float fe_opt = ggml_get_f32_1d(e, 0);
printf("%s: original e = %.4f\n", __func__, fe);
printf("%s: optimized e = %.4f\n", __func__, fe_opt);
const bool success = (fe_opt <= fe);
assert(success);
ggml_free(ctx);
return success ? 0 : -1;
}
// int64_t ne1[4] = {4, 128, 1, 1};
// int64_t ne2[4] = {4, 256, 1, 1};;
// int64_t ne3[4] = {128, 256, 1, 1};
// main: original e = 25890.9375
// main: optimized e = 10094.7031
// int64_t ne1[4] = {8, 128, 1, 1};
// int64_t ne2[4] = {8, 256, 1, 1};;
// int64_t ne3[4] = {128, 256, 1, 1};
// main: original e = 39429.5078
// main: optimized e = 9275.8936
// int64_t ne1[4] = {16, 128, 1, 1};
// int64_t ne2[4] = {16, 256, 1, 1};;
// int64_t ne3[4] = {128, 256, 1, 1};
// main: original e = 68371.1328
// main: optimized e = 7854.4502
// int64_t ne1[4] = {32, 128, 1, 1};
// int64_t ne2[4] = {32, 256, 1, 1};;
// int64_t ne3[4] = {128, 256, 1, 1};
// main: original e = 126061.1953
// main: optimized e = 5451.0166
// int64_t ne1[4] = {4, 1024, 1, 1};
// int64_t ne2[4] = {4, 2048, 1, 1};;
// int64_t ne3[4] = {1024, 2048, 1, 1};
// main: original e = 1620817.8750
// main: optimized e = 698387.6875
// another run on M1
// int64_t ne1[4] = {4, 1024, 1, 1};
// int64_t ne2[4] = {4, 2048, 1, 1};;
// int64_t ne3[4] = {1024, 2048, 1, 1};
// main: original e = 1629595.6250
// main: optimized e = 698169.1250
// int64_t ne1[4] = {32, 1024, 1, 1};
// int64_t ne2[4] = {32, 2048, 1, 1};;
// int64_t ne3[4] = {1024, 2048, 1, 1};
// main: original e = 8146770.5000
// main: optimized e = 651119.1250
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