memory_test.go 4.36 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
package llm

import (
	"bytes"
	"encoding/binary"
	"fmt"
	"os"
	"testing"

	"github.com/ollama/ollama/api"
	"github.com/ollama/ollama/envconfig"
	"github.com/ollama/ollama/gpu"
	"github.com/stretchr/testify/assert"
	"github.com/stretchr/testify/require"
)

func TestEstimateGPULayers(t *testing.T) {
	envconfig.Debug = true
	modelName := "dummy"
	f, err := os.CreateTemp(t.TempDir(), modelName)
Daniel Hiltgen's avatar
Daniel Hiltgen committed
21
	require.NoError(t, err)
22
23
24
	defer f.Close()
	gguf := NewGGUFV3(binary.LittleEndian)
	inputLayerCount := 5
25

26
	tensors := []Tensor{
27
28
29
30
31
32
		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
		{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
		{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
		{Name: "blk.3.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
		{Name: "blk.4.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
33
	}
Daniel Hiltgen's avatar
Daniel Hiltgen committed
34
	assert.Len(t, tensors, inputLayerCount+1)
35
36
37
38
39
40
41
42
43
44
45
46
47
48
	err = gguf.Encode(f, KV{
		"general.architecture":          "llama",
		"general.name":                  "name",
		"llama.context_length":          uint32(32),
		"llama.embedding_length":        uint32(4096),
		"llama.block_count":             uint32(inputLayerCount),
		"llama.attention.head_count":    uint32(32),
		"llama.attention.head_count_kv": uint32(32),
		"tokenizer.ggml.tokens":         []string{" "},
		"tokenizer.ggml.scores":         []float32{0},
		"tokenizer.ggml.token_type":     []int32{0},
	}, tensors)
	require.NoError(t, err)

49
50
51
52
	ggml, err := LoadModel(f.Name(), 0)
	if err != nil {
		t.Fatal(err)
	}
53
54
55
56
57
58
59
60
61

	// Simple CPU scenario
	gpus := []gpu.GpuInfo{
		{
			Library: "cpu",
		},
	}
	projectors := []string{}
	opts := api.DefaultOptions()
Daniel Hiltgen's avatar
Daniel Hiltgen committed
62
63
64
65
66
	t.Run("cpu", func(t *testing.T) {
		estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
		assert.Equal(t, 0, estimate.Layers)
		assert.Equal(t, uint64(0), estimate.Graph)
	})
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87

	// derived from the dummy ggml file above
	graphPartialOffload := uint64(202377216)
	graphFullOffload := uint64(171968512)
	layerSize := uint64(33554436)
	projectorSize := uint64(0)
	memoryLayerOutput := uint64(4)

	// Dual CUDA scenario with assymetry
	gpuMinimumMemory := uint64(2048)
	gpus = []gpu.GpuInfo{
		{
			Library:       "cuda",
			MinimumMemory: gpuMinimumMemory,
		},
		{
			Library:       "cuda",
			MinimumMemory: gpuMinimumMemory,
		},
	}
	// Nested array: GPU0 layer space, GPU1 layer space, expected gpu0, expected gpu1
Daniel Hiltgen's avatar
Daniel Hiltgen committed
88
89
90
91
	for i, s := range []struct {
		layer0, layer1   uint64
		expect0, expect1 uint64
	}{
92
93
94
95
96
97
98
99
100
		{1, 1, 1, 1},
		{2, 1, 2, 1},
		{2, 2, 2, 2},
		{1, 2, 1, 2},
		{3, 3, 3, 3},
		{4, 4, 3, 3},
		{6, 6, 3, 3},
		{0, 3, 0, 3},
	} {
Daniel Hiltgen's avatar
Daniel Hiltgen committed
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
		t.Run(fmt.Sprintf("%v", s), func(t *testing.T) {
			gpus[0].FreeMemory = 0
			gpus[1].FreeMemory = 0
			gpus[0].FreeMemory += projectorSize
			if s.layer0 > 0 {
				gpus[0].FreeMemory += memoryLayerOutput
			} else {
				gpus[1].FreeMemory += memoryLayerOutput
			}
			gpus[0].FreeMemory += gpuMinimumMemory + layerSize + s.layer0*layerSize + 1
			gpus[1].FreeMemory += gpuMinimumMemory + layerSize + s.layer1*layerSize + 1
			gpus[0].FreeMemory += max(graphFullOffload, graphPartialOffload)
			gpus[1].FreeMemory += max(graphFullOffload, graphPartialOffload)
			estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
			assert.Equal(t, int(s.expect0+s.expect1), estimate.Layers, "scenario %d: %v", i, s)
			assert.Equal(t, fmt.Sprintf("%d,%d", s.expect0, s.expect1), estimate.TensorSplit, "scenario %d: %v", i, s)
			var layerSums uint64
			for _, b := range estimate.GPUSizes {
				layerSums += b
			}
			if estimate.Layers < inputLayerCount+1 {
				assert.Less(t, estimate.VRAMSize, estimate.TotalSize, "scenario %d: %v %+v", i, s, estimate)
				assert.Equal(t, estimate.VRAMSize, layerSums, "scenario %d: %v %+v", i, s, estimate)
			} else {
				assert.Equal(t, estimate.VRAMSize, estimate.TotalSize, "scenario %d: %v %+v", i, s, estimate)
				assert.Equal(t, estimate.TotalSize, layerSums, "scenario %d: %v %+v", i, s, estimate)
			}
		})
129
130
	}
}