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test1.zig
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const std = @import("std");
const c = @cImport({
@cInclude("stdio.h");
@cInclude("stdlib.h");
@cInclude("ggml/ggml.h");
});
pub fn main() !void {
const params = .{
.mem_size = 128*1024*1024,
.mem_buffer = null,
.no_alloc = false,
};
const ctx0 = c.ggml_init(params);
defer c.ggml_free(ctx0);
{
const x = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
c.ggml_set_param(ctx0, x);
const a = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
const b = c.ggml_mul(ctx0, x, x);
const f = c.ggml_mul(ctx0, b, a);
// a*x^2
// 2*a*x
c.ggml_print_objects(ctx0);
const gf = c.ggml_build_forward(f);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x, 2.0);
_ = c.ggml_set_f32(a, 3.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(f.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("f = {d:.6}\n", .{c.ggml_get_f32_1d(f, 0)});
std.debug.print("df/dx = {d:.6}\n", .{c.ggml_get_f32_1d(x.*.grad, 0)});
try std.testing.expect(c.ggml_get_f32_1d(f, 0) == 12.0);
try std.testing.expect(c.ggml_get_f32_1d(x.*.grad, 0) == 12.0);
_ = c.ggml_set_f32(x, 3.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(f.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("f = {d:.6}\n", .{c.ggml_get_f32_1d(f, 0)});
std.debug.print("df/dx = {d:.6}\n", .{c.ggml_get_f32_1d(x.*.grad, 0)});
try std.testing.expect(c.ggml_get_f32_1d(f, 0) == 27.0);
try std.testing.expect(c.ggml_get_f32_1d(x.*.grad, 0) == 18.0);
c.ggml_graph_dump_dot(&gf, null, "test1-1-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-1-backward.dot");
}
/////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
const x3 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
_ = c.ggml_set_f32(x1, 3.0);
_ = c.ggml_set_f32(x2, 1.0);
_ = c.ggml_set_f32(x3, 0.0);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
const y = c.ggml_add(ctx0, c.ggml_mul(ctx0, x1, x1), c.ggml_mul(ctx0, x1, x2));
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6}\n", .{c.ggml_get_f32_1d(x1.*.grad, 0)});
std.debug.print("df/dx2 = {d:.6}\n", .{c.ggml_get_f32_1d(x2.*.grad, 0)});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 12.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 7.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 3.0);
const g1 = x1.*.grad;
const g2 = x2.*.grad;
const gbb = c.ggml_build_backward(ctx0, @constCast(&gb), true);
c.ggml_graph_reset(@constCast(&gb));
_ = c.ggml_set_f32(g1.*.grad, 1.0);
_ = c.ggml_set_f32(g2.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gbb));
std.debug.print("H * [1, 1] = [ {d:.6} {d:.6} ]\n", .{c.ggml_get_f32_1d(x1.*.grad, 0), c.ggml_get_f32_1d(x2.*.grad, 0)});
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 1.0);
c.ggml_graph_dump_dot(&gf, null, "test1-2-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-2-backward.dot");
}
///////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
const y = c.ggml_mul(ctx0, c.ggml_add(ctx0, c.ggml_mul(ctx0, x1, x1), c.ggml_mul(ctx0, x1, x2)), x1);
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x1, 3.0);
_ = c.ggml_set_f32(x2, 4.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6}\n", .{c.ggml_get_f32_1d(x1.*.grad, 0)});
std.debug.print("df/dx2 = {d:.6}\n", .{c.ggml_get_f32_1d(x2.*.grad, 0)});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 63.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 51.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 9.0);
c.ggml_graph_dump_dot(&gf, null, "test1-3-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-3-backward.dot");
}
///////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
const x3 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 1);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
c.ggml_set_param(ctx0, x3);
const y = c.ggml_mul(ctx0, c.ggml_mul(ctx0, c.ggml_mul(ctx0, x1, x1), c.ggml_mul(ctx0, x2, x2)), x3);
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x1, 1.0);
_ = c.ggml_set_f32(x2, 2.0);
_ = c.ggml_set_f32(x3, 3.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6}\n", .{c.ggml_get_f32_1d(x1.*.grad, 0)});
std.debug.print("df/dx2 = {d:.6}\n", .{c.ggml_get_f32_1d(x2.*.grad, 0)});
std.debug.print("df/dx3 = {d:.6}\n", .{c.ggml_get_f32_1d(x3.*.grad, 0)});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 12.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 24.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 12.0);
try std.testing.expect(c.ggml_get_f32_1d(x3.*.grad, 0) == 4.0);
const g1 = x1.*.grad;
const g2 = x2.*.grad;
const g3 = x3.*.grad;
const gbb = c.ggml_build_backward(ctx0, @constCast(&gb), true);
c.ggml_graph_reset(@constCast(&gb));
_ = c.ggml_set_f32(g1.*.grad, 1.0);
_ = c.ggml_set_f32(g2.*.grad, 1.0);
_ = c.ggml_set_f32(g3.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gbb));
std.debug.print("H * [1, 1, 1] = [ {d:.6} {d:.6} {d:.6}]\n",
.{
c.ggml_get_f32_1d(x1.*.grad, 0),
c.ggml_get_f32_1d(x2.*.grad, 0),
c.ggml_get_f32_1d(x3.*.grad, 0),
});
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 56.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 34.0);
try std.testing.expect(c.ggml_get_f32_1d(x3.*.grad, 0) == 12.0);
c.ggml_graph_dump_dot(&gf, null, "test1-4-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-4-backward.dot");
}
///////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
const y = c.ggml_sum(ctx0, c.ggml_mul(ctx0, x1, x2));
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x1, 3.0);
_ = c.ggml_set_f32(x2, 5.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x1.*.grad, 0),
c.ggml_get_f32_1d(x1.*.grad, 1),
c.ggml_get_f32_1d(x1.*.grad, 2),
});
std.debug.print("df/dx2 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x2.*.grad, 0),
c.ggml_get_f32_1d(x2.*.grad, 1),
c.ggml_get_f32_1d(x2.*.grad, 2),
});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 45.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 5.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 1) == 5.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 1) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 2) == 5.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 2) == 3.0);
c.ggml_graph_dump_dot(&gf, null, "test1-5-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-5-backward.dot");
}
///////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
const y =
c.ggml_sum(ctx0,
c.ggml_add(ctx0,
c.ggml_mul(ctx0, x1, x2),
c.ggml_mul(ctx0,
c.ggml_repeat(ctx0, c.ggml_new_f32(ctx0, -2.0), x1),
c.ggml_mul(ctx0, x1, x1)
)
)
);
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x1, 3.0);
_ = c.ggml_set_f32(x2, 5.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x1.*.grad, 0),
c.ggml_get_f32_1d(x1.*.grad, 1),
c.ggml_get_f32_1d(x1.*.grad, 2),
});
std.debug.print("df/dx2 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x2.*.grad, 0),
c.ggml_get_f32_1d(x2.*.grad, 1),
c.ggml_get_f32_1d(x2.*.grad, 2),
});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == -9.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == -7.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 1) == -7.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 2) == -7.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 1) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 2) == 3.0);
c.ggml_graph_dump_dot(&gf, null, "test1-6-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-6-backward.dot");
}
///////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
const y =
c.ggml_sum(ctx0,
c.ggml_sub(ctx0,
c.ggml_mul(ctx0, x1, x2),
c.ggml_mul(ctx0,
c.ggml_mul(ctx0, x1, x1),
c.ggml_repeat(ctx0, c.ggml_new_f32(ctx0, -2.0), x1)
)
)
);
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x1, 3.0);
_ = c.ggml_set_f32(x2, 5.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x1.*.grad, 0),
c.ggml_get_f32_1d(x1.*.grad, 1),
c.ggml_get_f32_1d(x1.*.grad, 2),
});
std.debug.print("df/dx2 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x2.*.grad, 0),
c.ggml_get_f32_1d(x2.*.grad, 1),
c.ggml_get_f32_1d(x2.*.grad, 2),
});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 99.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 17.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 1) == 17.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 2) == 17.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 1) == 3.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 2) == 3.0);
c.ggml_graph_dump_dot(&gf, null, "test1-7-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-7-backward.dot");
}
///////////////////////////////////////////////////////////////
{
const x1 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
const x2 = c.ggml_new_tensor_1d(ctx0, c.GGML_TYPE_F32, 3);
c.ggml_set_param(ctx0, x1);
c.ggml_set_param(ctx0, x2);
const y =
c.ggml_abs(ctx0,
c.ggml_sub(ctx0, x1, x2)
);
const gf = c.ggml_build_forward(y);
const gb = c.ggml_build_backward(ctx0, @constCast(&gf), false);
_ = c.ggml_set_f32(x1, 3.0);
_ = c.ggml_set_f32(x2, 5.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x1.*.grad, 0),
c.ggml_get_f32_1d(x1.*.grad, 1),
c.ggml_get_f32_1d(x1.*.grad, 2),
});
std.debug.print("df/dx2 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x2.*.grad, 0),
c.ggml_get_f32_1d(x2.*.grad, 1),
c.ggml_get_f32_1d(x2.*.grad, 2),
});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 2.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == -1.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 1) == -1.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 2) == -1.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == 1.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 1) == 1.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 2) == 1.0);
_ = c.ggml_set_f32(x1, 7.0);
_ = c.ggml_set_f32(x2, 5.0);
c.ggml_graph_reset(@constCast(&gf));
_ = c.ggml_set_f32(y.*.grad, 1.0);
c.ggml_graph_compute(ctx0, @constCast(&gb));
std.debug.print("y = {d:.6}\n", .{c.ggml_get_f32_1d(y, 0)});
std.debug.print("df/dx1 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x1.*.grad, 0),
c.ggml_get_f32_1d(x1.*.grad, 1),
c.ggml_get_f32_1d(x1.*.grad, 2),
});
std.debug.print("df/dx2 = {d:.6} {d:.6} {d:.6}\n",
.{
c.ggml_get_f32_1d(x2.*.grad, 0),
c.ggml_get_f32_1d(x2.*.grad, 1),
c.ggml_get_f32_1d(x2.*.grad, 2),
});
try std.testing.expect(c.ggml_get_f32_1d(y, 0) == 2.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 0) == 1.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 1) == 1.0);
try std.testing.expect(c.ggml_get_f32_1d(x1.*.grad, 2) == 1.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 0) == -1.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 1) == -1.0);
try std.testing.expect(c.ggml_get_f32_1d(x2.*.grad, 2) == -1.0);
c.ggml_graph_dump_dot(&gf, null, "test1-8-forward.dot");
c.ggml_graph_dump_dot(&gb, &gf, "test1-8-backward.dot");
}
_ = try std.io.getStdIn().reader().readByte();
}