package ggml
import (
"maps"
"math"
"slices"
"strconv"
"strings"
"testing"
"github.com/google/go-cmp/cmp"
)
func TestTensorLayers(t *testing.T) {
tensors := make(map[string]*Tensor)
for _, name := range []string{
"token_embd.weight",
"blk.0.attn_k.weight",
"blk.0.attn_output.weight",
"blk.0.attn_q.weight",
"blk.0.attn_v.weight",
"blk.0.attn_norm.weight",
"blk.0.ffn_down.weight",
"blk.0.ffn_gate.weight",
"blk.0.ffn_up.weight",
"blk.0.ffn_norm.weight",
"output_norm.weight",
"mm.0.bias",
"mm.0.weight",
"v.blk.0.attn_k.weight",
"v.blk.0.attn_output.weight",
"v.blk.0.attn_q.weight",
"v.blk.0.attn_v.weight",
"v.blk.0.attn_norm.weight",
"v.blk.0.ffn_down.weight",
"v.blk.0.ffn_gate.weight",
"v.blk.0.ffn_up.weight",
"v.blk.0.ffn_norm.weight",
"v.patch_embd.weight",
"v.position_embd.gate",
"v.position_embd.weight",
} {
tensors[name] = &Tensor{Name: name}
}
cases := []struct {
name string
items []*Tensor
want map[string]Layer
}{
{
name: "text",
items: slices.Collect(func(yield func(*Tensor) bool) {
for k, v := range tensors {
if !strings.HasPrefix(k, "mm.") && !strings.HasPrefix(k, "v.") {
if !yield(v) {
return
}
}
}
}),
want: map[string]Layer{
"blk.0": {
"attn_k.weight": tensors["blk.0.attn_k.weight"],
"attn_q.weight": tensors["blk.0.attn_q.weight"],
"attn_v.weight": tensors["blk.0.attn_v.weight"],
"attn_output.weight": tensors["blk.0.attn_output.weight"],
"attn_norm.weight": tensors["blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["blk.0.ffn_norm.weight"],
},
"token_embd": {"weight": tensors["token_embd.weight"]},
"output_norm": {"weight": tensors["output_norm.weight"]},
},
},
{
name: "vision",
items: slices.Collect(func(yield func(*Tensor) bool) {
for k, v := range tensors {
if strings.HasPrefix(k, "mm.") || strings.HasPrefix(k, "v.") {
if !yield(v) {
return
}
}
}
}),
want: map[string]Layer{
"mm.0": {
"bias": tensors["mm.0.bias"],
"weight": tensors["mm.0.weight"],
},
"v.blk.0": {
"attn_k.weight": tensors["v.blk.0.attn_k.weight"],
"attn_q.weight": tensors["v.blk.0.attn_q.weight"],
"attn_v.weight": tensors["v.blk.0.attn_v.weight"],
"attn_output.weight": tensors["v.blk.0.attn_output.weight"],
"attn_norm.weight": tensors["v.blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["v.blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["v.blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["v.blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["v.blk.0.ffn_norm.weight"],
},
"v": {
"patch_embd.weight": tensors["v.patch_embd.weight"],
"position_embd.gate": tensors["v.position_embd.gate"],
"position_embd.weight": tensors["v.position_embd.weight"],
},
},
},
{
name: "vision and text",
items: slices.Collect(maps.Values(tensors)),
want: map[string]Layer{
"blk.0": {
"attn_k.weight": tensors["blk.0.attn_k.weight"],
"attn_q.weight": tensors["blk.0.attn_q.weight"],
"attn_v.weight": tensors["blk.0.attn_v.weight"],
"attn_output.weight": tensors["blk.0.attn_output.weight"],
"attn_norm.weight": tensors["blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["blk.0.ffn_norm.weight"],
},
"token_embd": {"weight": tensors["token_embd.weight"]},
"output_norm": {"weight": tensors["output_norm.weight"]},
"mm.0": {
"bias": tensors["mm.0.bias"],
"weight": tensors["mm.0.weight"],
},
"v.blk.0": {
"attn_k.weight": tensors["v.blk.0.attn_k.weight"],
"attn_q.weight": tensors["v.blk.0.attn_q.weight"],
"attn_v.weight": tensors["v.blk.0.attn_v.weight"],
"attn_output.weight": tensors["v.blk.0.attn_output.weight"],
"attn_norm.weight": tensors["v.blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["v.blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["v.blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["v.blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["v.blk.0.ffn_norm.weight"],
},
"v": {
"patch_embd.weight": tensors["v.patch_embd.weight"],
"position_embd.gate": tensors["v.position_embd.gate"],
"position_embd.weight": tensors["v.position_embd.weight"],
},
},
},
}
for _, tt := range cases {
t.Run(tt.name, func(t *testing.T) {
got := Tensors{items: tt.items}.GroupLayers()
if diff := cmp.Diff(got, tt.want); diff != "" {
t.Errorf("unexpected layers (-got +want):\n%s", diff)
}
})
}
}
// ref: https://github.com/ggml-org/llama.cpp/blob/a82c9e7c23ef6db48cebfa194dc9cebbc4ac3552/ggml/src/ggml.c#L572
func TestTensorTypes(t *testing.T) {
cases := []struct {
kind uint32
blockSize uint64
typeSize uint64
}{
{0, 1, 4},
{1, 1, 2},
{2, 32, 18},
{3, 32, 20},
{6, 32, 22},
{7, 32, 24},
{8, 32, 34},
{9, 32, 36},
{10, 256, 84},
{11, 256, 110},
{12, 256, 144},
{13, 256, 176},
{14, 256, 210},
{15, 256, 292},
{16, 256, 66},
{17, 256, 74},
{18, 256, 98},
{19, 256, 50},
{20, 32, 18},
{21, 256, 110},
{22, 256, 82},
{23, 256, 136},
{24, 1, 1},
{25, 1, 2},
{26, 1, 4},
{27, 1, 8},
{28, 1, 8},
{29, 256, 56},
{30, 1, 2},
}
for _, tt := range cases {
t.Run(strconv.Itoa(int(tt.kind)), func(t *testing.T) {
tensor := Tensor{Kind: tt.kind}
if tensor.blockSize() != tt.blockSize {
t.Errorf("unexpected block size: got=%d want=%d", tensor.blockSize(), tt.blockSize)
}
if tensor.typeSize() != tt.typeSize {
t.Errorf("unexpected type size: got=%d want=%d", tensor.typeSize(), tt.typeSize)
}
})
}
}
func TestKeyValue(t *testing.T) {
kv := KV{
"general.architecture": "test",
"test.strings": &array[string]{size: 3, values: []string{"a", "b", "c"}},
"test.float32s": &array[float32]{size: 3, values: []float32{1.0, 2.0, 3.0}},
"test.int32s": &array[int32]{size: 3, values: []int32{1, 2, 3}},
"test.uint32s": &array[uint32]{size: 3, values: []uint32{1, 2, 3}},
}
if diff := cmp.Diff(kv.Strings("strings"), []string{"a", "b", "c"}); diff != "" {
t.Errorf("unexpected strings (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Strings("nonexistent.strings"), []string(nil)); diff != "" {
t.Errorf("unexpected strings (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Strings("default.strings", []string{"ollama"}), []string{"ollama"}); diff != "" {
t.Errorf("unexpected strings (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Floats("float32s"), []float32{1.0, 2.0, 3.0}); diff != "" {
t.Errorf("unexpected float32s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Floats("nonexistent.float32s"), []float32(nil)); diff != "" {
t.Errorf("unexpected float32s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Floats("default.float32s", []float32{math.MaxFloat32}), []float32{math.MaxFloat32}); diff != "" {
t.Errorf("unexpected float32s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Ints("int32s"), []int32{1, 2, 3}); diff != "" {
t.Errorf("unexpected int8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Ints("nonexistent.int32s"), []int32(nil)); diff != "" {
t.Errorf("unexpected int8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Ints("default.int32s", []int32{math.MaxInt32}), []int32{math.MaxInt32}); diff != "" {
t.Errorf("unexpected int8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Uints("uint32s"), []uint32{1, 2, 3}); diff != "" {
t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Uints("nonexistent.uint32s"), []uint32(nil)); diff != "" {
t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Uints("default.uint32s", []uint32{math.MaxUint32}), []uint32{math.MaxUint32}); diff != "" {
t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
}
}