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