| | #include "ggml/ggml.h" |
| |
|
| | #include <stdio.h> |
| | #include <stdlib.h> |
| |
|
| | int main(int argc, const char ** argv) { |
| | const int n_threads = 2; |
| |
|
| | struct ggml_init_params params = { |
| | .mem_size = 128*1024*1024, |
| | .mem_buffer = NULL, |
| | .no_alloc = false, |
| | }; |
| |
|
| | struct ggml_context * ctx0 = ggml_init(params); |
| |
|
| | { |
| | struct ggml_tensor * x = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| |
|
| | ggml_set_param(ctx0, x); |
| |
|
| | struct ggml_tensor * a = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| | struct ggml_tensor * b = ggml_mul(ctx0, x, x); |
| | struct ggml_tensor * f = ggml_mul(ctx0, b, a); |
| |
|
| | |
| | |
| |
|
| | ggml_print_objects(ctx0); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(f); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x, 2.0f); |
| | ggml_set_f32(a, 3.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(f->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("f = %f\n", ggml_get_f32_1d(f, 0)); |
| | printf("df/dx = %f\n", ggml_get_f32_1d(x->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(f, 0) == 12.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x->grad, 0) == 12.0f); |
| |
|
| | ggml_set_f32(x, 3.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(f->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("f = %f\n", ggml_get_f32_1d(f, 0)); |
| | printf("df/dx = %f\n", ggml_get_f32_1d(x->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(f, 0) == 27.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x->grad, 0) == 18.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-1-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-1-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| | struct ggml_tensor * x3 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| |
|
| | ggml_set_f32(x1, 3.0f); |
| | ggml_set_f32(x2, 1.0f); |
| | ggml_set_f32(x3, 0.0f); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| |
|
| | struct ggml_tensor * y = ggml_add(ctx0, ggml_mul(ctx0, x1, x1), ggml_mul(ctx0, x1, x2)); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f\n", ggml_get_f32_1d(x1->grad, 0)); |
| | printf("df/dx2 = %f\n", ggml_get_f32_1d(x2->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 12.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 7.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 3.0f); |
| |
|
| | struct ggml_tensor * g1 = x1->grad; |
| | struct ggml_tensor * g2 = x2->grad; |
| |
|
| | struct ggml_cgraph gbb = ggml_build_backward(ctx0, &gb, true); |
| |
|
| | ggml_graph_reset(&gb); |
| | ggml_set_f32(g1->grad, 1.0f); |
| | ggml_set_f32(g2->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gbb, n_threads); |
| |
|
| | printf("H * [1, 1] = [ %f %f ]\n", ggml_get_f32_1d(x1->grad, 0), ggml_get_f32_1d(x2->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 1.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-2-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-2-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| |
|
| | struct ggml_tensor * y = ggml_mul(ctx0, ggml_add(ctx0, ggml_mul(ctx0, x1, x1), ggml_mul(ctx0, x1, x2)), x1); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x1, 3.0f); |
| | ggml_set_f32(x2, 4.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f\n", ggml_get_f32_1d(x1->grad, 0)); |
| | printf("df/dx2 = %f\n", ggml_get_f32_1d(x2->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 63.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 51.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 9.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-3-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-3-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| | struct ggml_tensor * x3 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| | ggml_set_param(ctx0, x3); |
| |
|
| | struct ggml_tensor * y = ggml_mul(ctx0, ggml_mul(ctx0, ggml_mul(ctx0, x1, x1), ggml_mul(ctx0, x2, x2)), x3); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x1, 1.0f); |
| | ggml_set_f32(x2, 2.0f); |
| | ggml_set_f32(x3, 3.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f\n", ggml_get_f32_1d(x1->grad, 0)); |
| | printf("df/dx2 = %f\n", ggml_get_f32_1d(x2->grad, 0)); |
| | printf("df/dx3 = %f\n", ggml_get_f32_1d(x3->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 12.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 24.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 12.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x3->grad, 0) == 4.0f); |
| |
|
| | struct ggml_tensor * g1 = x1->grad; |
| | struct ggml_tensor * g2 = x2->grad; |
| | struct ggml_tensor * g3 = x3->grad; |
| |
|
| | struct ggml_cgraph gbb = ggml_build_backward(ctx0, &gb, true); |
| |
|
| | ggml_graph_reset(&gb); |
| | ggml_set_f32(g1->grad, 1.0f); |
| | ggml_set_f32(g2->grad, 1.0f); |
| | ggml_set_f32(g3->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gbb, n_threads); |
| |
|
| | printf("H * [1, 1, 1] = [ %f %f %f ]\n", |
| | ggml_get_f32_1d(x1->grad, 0), |
| | ggml_get_f32_1d(x2->grad, 0), |
| | ggml_get_f32_1d(x3->grad, 0)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 56.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 34.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x3->grad, 0) == 12.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-4-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-4-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| |
|
| | struct ggml_tensor * y = ggml_sum(ctx0, ggml_mul(ctx0, x1, x2)); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x1, 3.0f); |
| | ggml_set_f32(x2, 5.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f %f %f\n", |
| | ggml_get_f32_1d(x1->grad, 0), |
| | ggml_get_f32_1d(x1->grad, 1), |
| | ggml_get_f32_1d(x1->grad, 2)); |
| | printf("df/dx2 = %f %f %f\n", |
| | ggml_get_f32_1d(x2->grad, 0), |
| | ggml_get_f32_1d(x2->grad, 1), |
| | ggml_get_f32_1d(x2->grad, 2)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 45.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 5.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 1) == 5.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 1) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 2) == 5.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 2) == 3.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-5-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-5-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| |
|
| | struct ggml_tensor * y = |
| | ggml_sum(ctx0, |
| | ggml_add(ctx0, |
| | ggml_mul(ctx0, x1, x2), |
| | ggml_mul(ctx0, |
| | ggml_repeat(ctx0, ggml_new_f32(ctx0, -2.0f), x1), |
| | ggml_mul(ctx0, x1, x1) |
| | ) |
| | ) |
| | ); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x1, 3.0f); |
| | ggml_set_f32(x2, 5.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f %f %f\n", |
| | ggml_get_f32_1d(x1->grad, 0), |
| | ggml_get_f32_1d(x1->grad, 1), |
| | ggml_get_f32_1d(x1->grad, 2)); |
| | printf("df/dx2 = %f %f %f\n", |
| | ggml_get_f32_1d(x2->grad, 0), |
| | ggml_get_f32_1d(x2->grad, 1), |
| | ggml_get_f32_1d(x2->grad, 2)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == -9.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == -7.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 1) == -7.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 2) == -7.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 1) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 2) == 3.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-6-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-6-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| |
|
| | struct ggml_tensor * y = |
| | ggml_sum(ctx0, |
| | ggml_sub(ctx0, |
| | ggml_mul(ctx0, x1, x2), |
| | ggml_mul(ctx0, |
| | ggml_mul(ctx0, x1, x1), |
| | ggml_repeat(ctx0, ggml_new_f32(ctx0, -2.0f), x1) |
| | ) |
| | ) |
| | ); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x1, 3.0f); |
| | ggml_set_f32(x2, 5.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f %f %f\n", |
| | ggml_get_f32_1d(x1->grad, 0), |
| | ggml_get_f32_1d(x1->grad, 1), |
| | ggml_get_f32_1d(x1->grad, 2)); |
| | printf("df/dx2 = %f %f %f\n", |
| | ggml_get_f32_1d(x2->grad, 0), |
| | ggml_get_f32_1d(x2->grad, 1), |
| | ggml_get_f32_1d(x2->grad, 2)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 99.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 17.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 1) == 17.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 2) == 17.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 1) == 3.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 2) == 3.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-7-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-7-backward.dot"); |
| | } |
| |
|
| | |
| |
|
| | { |
| | struct ggml_tensor * x1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| | struct ggml_tensor * x2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 3); |
| |
|
| | ggml_set_param(ctx0, x1); |
| | ggml_set_param(ctx0, x2); |
| |
|
| | struct ggml_tensor * y = |
| | ggml_abs(ctx0, |
| | ggml_sub(ctx0, x1, x2) |
| | ); |
| |
|
| | struct ggml_cgraph gf = ggml_build_forward(y); |
| | struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); |
| |
|
| | ggml_set_f32(x1, 3.0f); |
| | ggml_set_f32(x2, 5.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f %f %f\n", |
| | ggml_get_f32_1d(x1->grad, 0), |
| | ggml_get_f32_1d(x1->grad, 1), |
| | ggml_get_f32_1d(x1->grad, 2)); |
| | printf("df/dx2 = %f %f %f\n", |
| | ggml_get_f32_1d(x2->grad, 0), |
| | ggml_get_f32_1d(x2->grad, 1), |
| | ggml_get_f32_1d(x2->grad, 2)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 2.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == -1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 1) == -1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 2) == -1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == 1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 1) == 1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 2) == 1.0f); |
| |
|
| | ggml_set_f32(x1, 7.0f); |
| | ggml_set_f32(x2, 5.0f); |
| |
|
| | ggml_graph_reset(&gf); |
| | ggml_set_f32(y->grad, 1.0f); |
| |
|
| | ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); |
| |
|
| | printf("y = %f\n", ggml_get_f32_1d(y, 0)); |
| | printf("df/dx1 = %f %f %f\n", |
| | ggml_get_f32_1d(x1->grad, 0), |
| | ggml_get_f32_1d(x1->grad, 1), |
| | ggml_get_f32_1d(x1->grad, 2)); |
| | printf("df/dx2 = %f %f %f\n", |
| | ggml_get_f32_1d(x2->grad, 0), |
| | ggml_get_f32_1d(x2->grad, 1), |
| | ggml_get_f32_1d(x2->grad, 2)); |
| |
|
| | GGML_ASSERT(ggml_get_f32_1d(y, 0) == 2.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 0) == 1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 1) == 1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x1->grad, 2) == 1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 0) == -1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 1) == -1.0f); |
| | GGML_ASSERT(ggml_get_f32_1d(x2->grad, 2) == -1.0f); |
| |
|
| | ggml_graph_dump_dot(&gf, NULL, "test1-8-forward.dot"); |
| | ggml_graph_dump_dot(&gb, &gf, "test1-8-backward.dot"); |
| | } |
| |
|
| | ggml_free(ctx0); |
| |
|
| | return 0; |
| | } |
| |
|
