ollama/fs/ggml/ggml_test.go

package ggml

import (
	"maps"
	"slices"
	"strings"
	"testing"

	"github.com/google/go-cmp/cmp"
)

type ggufModel struct {
	kv      KV
	tensors Tensors
}

func (m *ggufModel) KV() KV           { return m.kv }
func (m *ggufModel) Tensors() Tensors { return m.tensors }

func TestGraphNoVocab(t *testing.T) {
	g := &GGML{
		container: &containerGGUF{},
		model: &ggufModel{
			kv: KV{
				"general.architecture": "llama",
				"block_count":          uint32(1),
			},
			tensors: Tensors{
				items: []*Tensor{},
			},
		},
	}

	// This should not panic
	_, _, _ = g.GraphSize(1, 1, "f16")
}

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)
			}
		})
	}
}