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ggml/tests/test1.c

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#include "ggml/ggml.h"
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
int main(int argc, const char ** argv) {
struct ggml_init_params params = {
.mem_size = 128*1024*1024,
.mem_buffer = NULL,
};
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);
// a*x^2
// 2*a*x
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(ctx0, &gb);
printf("f = %f\n", ggml_get_f32_1d(f, 0));
printf("df/dx = %f\n", ggml_get_f32_1d(x->grad, 0));
assert(ggml_get_f32_1d(f, 0) == 12.0f);
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(ctx0, &gb);
printf("f = %f\n", ggml_get_f32_1d(f, 0));
printf("df/dx = %f\n", ggml_get_f32_1d(x->grad, 0));
assert(ggml_get_f32_1d(f, 0) == 27.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 12.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == 7.0f);
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(ctx0, &gbb);
printf("H * [1, 1] = [ %f %f ]\n", ggml_get_f32_1d(x1->grad, 0), ggml_get_f32_1d(x2->grad, 0));
assert(ggml_get_f32_1d(x1->grad, 0) == 3.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 63.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == 51.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 12.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == 24.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == 12.0f);
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(ctx0, &gbb);
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));
assert(ggml_get_f32_1d(x1->grad, 0) == 56.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == 34.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 45.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == 5.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == 3.0f);
assert(ggml_get_f32_1d(x1->grad, 1) == 5.0f);
assert(ggml_get_f32_1d(x2->grad, 1) == 3.0f);
assert(ggml_get_f32_1d(x1->grad, 2) == 5.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == -9.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == -7.0f);
assert(ggml_get_f32_1d(x1->grad, 1) == -7.0f);
assert(ggml_get_f32_1d(x1->grad, 2) == -7.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == 3.0f);
assert(ggml_get_f32_1d(x2->grad, 1) == 3.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 99.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == 17.0f);
assert(ggml_get_f32_1d(x1->grad, 1) == 17.0f);
assert(ggml_get_f32_1d(x1->grad, 2) == 17.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == 3.0f);
assert(ggml_get_f32_1d(x2->grad, 1) == 3.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 2.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == -1.0f);
assert(ggml_get_f32_1d(x1->grad, 1) == -1.0f);
assert(ggml_get_f32_1d(x1->grad, 2) == -1.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == 1.0f);
assert(ggml_get_f32_1d(x2->grad, 1) == 1.0f);
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(ctx0, &gb);
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));
assert(ggml_get_f32_1d(y, 0) == 2.0f);
assert(ggml_get_f32_1d(x1->grad, 0) == 1.0f);
assert(ggml_get_f32_1d(x1->grad, 1) == 1.0f);
assert(ggml_get_f32_1d(x1->grad, 2) == 1.0f);
assert(ggml_get_f32_1d(x2->grad, 0) == -1.0f);
assert(ggml_get_f32_1d(x2->grad, 1) == -1.0f);
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;
}
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