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8a257ec00a76d3dd90f0106fba1e56215588efae
ollama/x/server/show_test.go

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package server
import (
"bytes"
"encoding/binary"
"encoding/json"
"os"
"path/filepath"
"testing"
"github.com/ollama/ollama/manifest"
)
func TestBuildModelInfo(t *testing.T) {
tests := []struct {
name string
config modelConfig
totalTensorBytes int64
tensorCount int64
wantArch string
wantContextLen int
wantEmbedLen int
wantBlockCount int
wantParamCount int64
}{
{
name: "gemma3 model with model_type",
config: modelConfig{
ModelType: "gemma3",
HiddenSize: 2560,
NumHiddenLayers: 34,
MaxPositionEmbeddings: 131072,
IntermediateSize: 10240,
NumAttentionHeads: 8,
NumKeyValueHeads: 4,
VocabSize: 262144,
TorchDtype: "bfloat16",
},
totalTensorBytes: 8_600_000_150, // ~4.3B params * 2 bytes + 150 bytes header
tensorCount: 1,
wantArch: "gemma3",
wantContextLen: 131072,
wantEmbedLen: 2560,
wantBlockCount: 34,
wantParamCount: 4_300_000_000,
},
{
name: "llama model with architectures array",
config: modelConfig{
Architectures: []string{"LlamaForCausalLM"},
HiddenSize: 4096,
NumHiddenLayers: 32,
MaxPositionEmbeddings: 4096,
IntermediateSize: 11008,
NumAttentionHeads: 32,
NumKeyValueHeads: 32,
VocabSize: 32000,
TorchDtype: "float16",
},
totalTensorBytes: 14_000_000_150, // ~7B params * 2 bytes + 150 bytes header
tensorCount: 1,
wantArch: "llama",
wantContextLen: 4096,
wantEmbedLen: 4096,
wantBlockCount: 32,
wantParamCount: 7_000_000_000,
},
{
name: "multimodal model with text_config",
config: modelConfig{
Architectures: []string{"Gemma3ForConditionalGeneration"},
HiddenSize: 1152, // vision hidden size
TextConfig: &struct {
HiddenSize int `json:"hidden_size"`
MaxPositionEmbeddings int `json:"max_position_embeddings"`
NumHiddenLayers int `json:"num_hidden_layers"`
}{
HiddenSize: 2560,
MaxPositionEmbeddings: 131072,
NumHiddenLayers: 34,
},
NumAttentionHeads: 8,
NumKeyValueHeads: 4,
VocabSize: 262144,
TorchDtype: "bfloat16",
},
totalTensorBytes: 8_600_000_150,
tensorCount: 1,
wantArch: "gemma3",
wantContextLen: 131072,
wantEmbedLen: 2560,
wantBlockCount: 34,
wantParamCount: 4_300_000_000,
},
{
name: "float32 model",
config: modelConfig{
ModelType: "test",
HiddenSize: 512,
NumHiddenLayers: 6,
MaxPositionEmbeddings: 2048,
TorchDtype: "float32",
},
totalTensorBytes: 400_000_150, // 100M params * 4 bytes + 150 bytes header
tensorCount: 1,
wantArch: "test",
wantContextLen: 2048,
wantEmbedLen: 512,
wantBlockCount: 6,
wantParamCount: 100_000_000,
},
{
name: "multiple tensors with header overhead",
config: modelConfig{
ModelType: "test",
HiddenSize: 256,
NumHiddenLayers: 4,
MaxPositionEmbeddings: 1024,
TorchDtype: "bfloat16",
},
totalTensorBytes: 2_001_500, // 1M params * 2 bytes + 10 tensors * 150 bytes
tensorCount: 10,
wantArch: "test",
wantContextLen: 1024,
wantEmbedLen: 256,
wantBlockCount: 4,
wantParamCount: 1_000_000,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
info := buildModelInfo(tt.config, tt.totalTensorBytes, tt.tensorCount)
// Check architecture
if arch, ok := info["general.architecture"].(string); !ok || arch != tt.wantArch {
t.Errorf("architecture = %v, want %v", info["general.architecture"], tt.wantArch)
}
// Check context length
contextKey := tt.wantArch + ".context_length"
if contextLen, ok := info[contextKey].(int); !ok || contextLen != tt.wantContextLen {
t.Errorf("context_length = %v, want %v", info[contextKey], tt.wantContextLen)
}
// Check embedding length
embedKey := tt.wantArch + ".embedding_length"
if embedLen, ok := info[embedKey].(int); !ok || embedLen != tt.wantEmbedLen {
t.Errorf("embedding_length = %v, want %v", info[embedKey], tt.wantEmbedLen)
}
// Check block count
blockKey := tt.wantArch + ".block_count"
if blockCount, ok := info[blockKey].(int); !ok || blockCount != tt.wantBlockCount {
t.Errorf("block_count = %v, want %v", info[blockKey], tt.wantBlockCount)
}
// Check parameter count
if paramCount, ok := info["general.parameter_count"].(int64); !ok || paramCount != tt.wantParamCount {
t.Errorf("parameter_count = %v, want %v", info["general.parameter_count"], tt.wantParamCount)
}
})
}
}
func TestBuildModelInfo_ArchitectureConversion(t *testing.T) {
tests := []struct {
name string
architectures []string
modelType string
wantArch string
}{
{
name: "LlamaForCausalLM",
architectures: []string{"LlamaForCausalLM"},
wantArch: "llama",
},
{
name: "Gemma3ForCausalLM",
architectures: []string{"Gemma3ForCausalLM"},
wantArch: "gemma3",
},
{
name: "Gemma3ForConditionalGeneration",
architectures: []string{"Gemma3ForConditionalGeneration"},
wantArch: "gemma3",
},
{
name: "Qwen2ForCausalLM",
architectures: []string{"Qwen2ForCausalLM"},
wantArch: "qwen2",
},
{
name: "model_type takes precedence",
architectures: []string{"LlamaForCausalLM"},
modelType: "custom",
wantArch: "custom",
},
{
name: "empty architectures with model_type",
architectures: nil,
modelType: "mymodel",
wantArch: "mymodel",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
config := modelConfig{
Architectures: tt.architectures,
ModelType: tt.modelType,
}
info := buildModelInfo(config, 0, 0)
if arch, ok := info["general.architecture"].(string); !ok || arch != tt.wantArch {
t.Errorf("architecture = %v, want %v", info["general.architecture"], tt.wantArch)
}
})
}
}
func TestBuildModelInfo_BytesPerParam(t *testing.T) {
tests := []struct {
name string
dtype string
totalBytes int64
tensorCount int64
wantParamCount int64
}{
{
name: "bfloat16",
dtype: "bfloat16",
totalBytes: 2_000_150, // 1M * 2 + 150
tensorCount: 1,
wantParamCount: 1_000_000,
},
{
name: "float16",
dtype: "float16",
totalBytes: 2_000_150,
tensorCount: 1,
wantParamCount: 1_000_000,
},
{
name: "float32",
dtype: "float32",
totalBytes: 4_000_150, // 1M * 4 + 150
tensorCount: 1,
wantParamCount: 1_000_000,
},
{
name: "int8",
dtype: "int8",
totalBytes: 1_000_150, // 1M * 1 + 150
tensorCount: 1,
wantParamCount: 1_000_000,
},
{
name: "unknown dtype defaults to 2 bytes",
dtype: "unknown",
totalBytes: 2_000_150,
tensorCount: 1,
wantParamCount: 1_000_000,
},
{
name: "empty dtype defaults to 2 bytes",
dtype: "",
totalBytes: 2_000_150,
tensorCount: 1,
wantParamCount: 1_000_000,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
config := modelConfig{
ModelType: "test",
TorchDtype: tt.dtype,
}
info := buildModelInfo(config, tt.totalBytes, tt.tensorCount)
if paramCount, ok := info["general.parameter_count"].(int64); !ok || paramCount != tt.wantParamCount {
t.Errorf("parameter_count = %v, want %v", info["general.parameter_count"], tt.wantParamCount)
}
})
}
}
func TestParseSafetensorsHeader(t *testing.T) {
tests := []struct {
name string
header map[string]any
wantDtype string
wantShape []int64
wantQuantType string
wantGroupSize string
wantErr bool
}{
{
name: "simple tensor",
header: map[string]any{
"weight": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 262144},
"data_offsets": []int64{0, 1342177280},
},
},
wantDtype: "BF16",
wantShape: []int64{2560, 262144},
},
{
name: "tensor keyed by name",
header: map[string]any{
"model.layers.0.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 2560},
"data_offsets": []int64{0, 13107200},
},
},
wantDtype: "BF16",
wantShape: []int64{2560, 2560},
},
{
name: "with int4 quant metadata",
header: map[string]any{
"__metadata__": map[string]any{
"quant_type": "int4",
"group_size": "32",
},
"model.layers.0.mlp.up_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{2560, 320},
"data_offsets": []int64{0, 3276800},
},
"model.layers.0.mlp.up_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 80},
"data_offsets": []int64{3276800, 3686400},
},
"model.layers.0.mlp.up_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 80},
"data_offsets": []int64{3686400, 4096000},
},
},
wantDtype: "U32",
wantShape: []int64{2560, 320},
wantQuantType: "int4",
wantGroupSize: "32",
},
{
name: "int8 quant metadata",
header: map[string]any{
"__metadata__": map[string]any{
"quant_type": "int8",
"group_size": "64",
},
"model.layers.0.mlp.down_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{2560, 640},
"data_offsets": []int64{0, 6553600},
},
"model.layers.0.mlp.down_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 40},
"data_offsets": []int64{6553600, 6963200},
},
},
wantDtype: "U32",
wantShape: []int64{2560, 640},
wantQuantType: "int8",
wantGroupSize: "64",
},
{
name: "with old-style format metadata",
header: map[string]any{
"__metadata__": map[string]any{
"format": "pt",
},
"bias": map[string]any{
"dtype": "F32",
"shape": []int64{1024},
"data_offsets": []int64{0, 4096},
},
},
wantDtype: "F32",
wantShape: []int64{1024},
},
{
name: "float16 tensor",
header: map[string]any{
"layer.weight": map[string]any{
"dtype": "F16",
"shape": []int64{512, 512, 3, 3},
"data_offsets": []int64{0, 4718592},
},
},
wantDtype: "F16",
wantShape: []int64{512, 512, 3, 3},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
// Create safetensors format: 8-byte size + JSON header
headerJSON, err := json.Marshal(tt.header)
if err != nil {
t.Fatalf("failed to marshal header: %v", err)
}
var buf bytes.Buffer
if err := binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON))); err != nil {
t.Fatalf("failed to write header size: %v", err)
}
buf.Write(headerJSON)
info, err := parseSafetensorsHeader(&buf)
if (err != nil) != tt.wantErr {
t.Errorf("parseSafetensorsHeader() error = %v, wantErr %v", err, tt.wantErr)
return
}
if tt.wantErr {
return
}
if info.Dtype != tt.wantDtype {
t.Errorf("Dtype = %v, want %v", info.Dtype, tt.wantDtype)
}
if len(info.Shape) != len(tt.wantShape) {
t.Errorf("Shape length = %v, want %v", len(info.Shape), len(tt.wantShape))
} else {
for i, s := range info.Shape {
if s != tt.wantShape[i] {
t.Errorf("Shape[%d] = %v, want %v", i, s, tt.wantShape[i])
}
}
}
if info.QuantType != tt.wantQuantType {
t.Errorf("QuantType = %v, want %v", info.QuantType, tt.wantQuantType)
}
if info.GroupSize != tt.wantGroupSize {
t.Errorf("GroupSize = %v, want %v", info.GroupSize, tt.wantGroupSize)
}
})
}
}
func TestParseSafetensorsHeader_Errors(t *testing.T) {
tests := []struct {
name string
data []byte
wantErr string
}{
{
name: "empty data",
data: []byte{},
wantErr: "failed to read header size",
},
{
name: "truncated header size",
data: []byte{0x01, 0x02, 0x03},
wantErr: "failed to read header size",
},
{
name: "header size too large",
data: func() []byte {
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(2*1024*1024)) // 2MB
return buf.Bytes()
}(),
wantErr: "header size too large",
},
{
name: "truncated header",
data: func() []byte {
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(100))
buf.Write([]byte("short"))
return buf.Bytes()
}(),
wantErr: "failed to read header",
},
{
name: "invalid JSON",
data: func() []byte {
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(10))
buf.Write([]byte("not json!!"))
return buf.Bytes()
}(),
wantErr: "failed to parse header",
},
{
name: "no tensors in header",
data: func() []byte {
header := map[string]any{
"__metadata__": map[string]any{"format": "pt"},
}
headerJSON, _ := json.Marshal(header)
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
buf.Write(headerJSON)
return buf.Bytes()
}(),
wantErr: "no tensor found in header",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
_, err := parseSafetensorsHeader(bytes.NewReader(tt.data))
if err == nil {
t.Error("expected error, got nil")
return
}
if !bytes.Contains([]byte(err.Error()), []byte(tt.wantErr)) {
t.Errorf("error = %v, want error containing %v", err, tt.wantErr)
}
})
}
}
func TestGetTensorInfoFromManifest(t *testing.T) {
// Create a temp directory for blobs and set OLLAMA_MODELS
tempDir := t.TempDir()
t.Setenv("OLLAMA_MODELS", tempDir)
blobDir := filepath.Join(tempDir, "blobs")
if err := os.MkdirAll(blobDir, 0o755); err != nil {
t.Fatalf("failed to create blobs dir: %v", err)
}
// Create test tensor blobs with __metadata__
tensors := []struct {
name string
digest string
dtype string
shape []int64
}{
{
name: "model.embed_tokens.weight",
digest: "sha256:abc123abc123abc123abc123abc123abc123abc123abc123abc123abc123abc0",
dtype: "BF16",
shape: []int64{262144, 2560},
},
{
name: "model.layers.0.self_attn.q_proj.weight",
digest: "sha256:def456def456def456def456def456def456def456def456def456def456def0",
dtype: "BF16",
shape: []int64{2560, 2560},
},
{
name: "model.norm.weight",
digest: "sha256:789789789789789789789789789789789789789789789789789789789789abc0",
dtype: "F32",
shape: []int64{2560},
},
}
// Create blob files with tensor keyed by name
var layers []manifest.Layer
for _, tensor := range tensors {
header := map[string]any{
tensor.name: map[string]any{
"dtype": tensor.dtype,
"shape": tensor.shape,
"data_offsets": []int64{0, 1000},
},
}
headerJSON, _ := json.Marshal(header)
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
buf.Write(headerJSON)
// Write blob file using the digest format expected by GetBlobsPath
blobPath, err := manifest.BlobsPath(tensor.digest)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(blobPath, buf.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write blob: %v", err)
}
layers = append(layers, manifest.Layer{
MediaType: manifest.MediaTypeImageTensor,
Digest: tensor.digest,
Size: int64(buf.Len() + 1000), // header + fake data
Name: tensor.name,
})
}
// Add a non-tensor layer (should be skipped)
layers = append(layers, manifest.Layer{
MediaType: "application/vnd.ollama.image.json",
Digest: "sha256:0000000000000000000000000000000000000000000000000000000000000000",
Size: 100,
Name: "config.json",
})
mf := &manifest.Manifest{
SchemaVersion: 2,
MediaType: "application/vnd.docker.distribution.manifest.v2+json",
Layers: layers,
}
result, err := getTensorInfoFromManifest(mf)
if err != nil {
t.Fatalf("getTensorInfoFromManifest() error = %v", err)
}
if len(result) != 3 {
t.Errorf("got %d tensors, want 3", len(result))
}
// Verify each tensor
for i, tensor := range tensors {
if i >= len(result) {
break
}
if result[i].Name != tensor.name {
t.Errorf("tensor[%d].Name = %v, want %v", i, result[i].Name, tensor.name)
}
if result[i].Type != tensor.dtype {
t.Errorf("tensor[%d].Type = %v, want %v", i, result[i].Type, tensor.dtype)
}
if len(result[i].Shape) != len(tensor.shape) {
t.Errorf("tensor[%d].Shape length = %v, want %v", i, len(result[i].Shape), len(tensor.shape))
}
}
}
func TestGetTensorInfoFromManifest_Quantized(t *testing.T) {
// Create a temp directory for blobs and set OLLAMA_MODELS
tempDir := t.TempDir()
t.Setenv("OLLAMA_MODELS", tempDir)
blobDir := filepath.Join(tempDir, "blobs")
if err := os.MkdirAll(blobDir, 0o755); err != nil {
t.Fatalf("failed to create blobs dir: %v", err)
}
// Create a combined quantized blob with __metadata__
header := map[string]any{
"__metadata__": map[string]string{
"quant_type": "int4",
"group_size": "32",
},
"model.layers.0.mlp.up_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{2560, 320}, // packed: 2560 / 8 = 320
"data_offsets": []int64{0, 3276800},
},
"model.layers.0.mlp.up_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 80}, // 2560 / 32 = 80
"data_offsets": []int64{3276800, 3686400},
},
"model.layers.0.mlp.up_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 80},
"data_offsets": []int64{3686400, 4096000},
},
}
headerJSON, _ := json.Marshal(header)
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
buf.Write(headerJSON)
digest := "sha256:aabb11aabb11aabb11aabb11aabb11aabb11aabb11aabb11aabb11aabb11aabb"
blobPath, err := manifest.BlobsPath(digest)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(blobPath, buf.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write blob: %v", err)
}
mf := &manifest.Manifest{
SchemaVersion: 2,
MediaType: "application/vnd.docker.distribution.manifest.v2+json",
Layers: []manifest.Layer{
{
MediaType: manifest.MediaTypeImageTensor,
Digest: digest,
Size: int64(buf.Len() + 4096000),
Name: "model.layers.0.mlp.up_proj.weight",
},
},
}
result, err := getTensorInfoFromManifest(mf)
if err != nil {
t.Fatalf("getTensorInfoFromManifest() error = %v", err)
}
if len(result) != 1 {
t.Fatalf("got %d tensors, want 1", len(result))
}
tensor := result[0]
if tensor.Name != "model.layers.0.mlp.up_proj.weight" {
t.Errorf("Name = %v, want model.layers.0.mlp.up_proj.weight", tensor.Name)
}
if tensor.Type != "INT4" {
t.Errorf("Type = %v, want INT4", tensor.Type)
}
// Shape should be unpacked: 320 * 8 = 2560
if len(tensor.Shape) != 2 || tensor.Shape[0] != 2560 || tensor.Shape[1] != 2560 {
t.Errorf("Shape = %v, want [2560, 2560]", tensor.Shape)
}
}
func TestGetParameterCountFromManifest(t *testing.T) {
// Create a temp directory for blobs and set OLLAMA_MODELS
tempDir := t.TempDir()
t.Setenv("OLLAMA_MODELS", tempDir)
blobDir := filepath.Join(tempDir, "blobs")
if err := os.MkdirAll(blobDir, 0o755); err != nil {
t.Fatalf("failed to create blobs dir: %v", err)
}
// Unquantized tensor: [4,5] = 20 params
header1 := map[string]any{
"model.embed_tokens.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{4, 5},
"data_offsets": []int64{0, 40},
},
}
header1JSON, _ := json.Marshal(header1)
var buf1 bytes.Buffer
binary.Write(&buf1, binary.LittleEndian, uint64(len(header1JSON)))
buf1.Write(header1JSON)
digest1 := "sha256:1111111111111111111111111111111111111111111111111111111111111111"
blobPath1, err := manifest.BlobsPath(digest1)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(blobPath1, buf1.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write blob1: %v", err)
}
// Quantized int4 tensor with packed shape [10,2] -> unpacked [10,16] = 160 params
header2 := map[string]any{
"__metadata__": map[string]string{
"quant_type": "int4",
"group_size": "32",
},
"model.layers.0.mlp.up_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{10, 2},
"data_offsets": []int64{0, 80},
},
"model.layers.0.mlp.up_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{10, 1},
"data_offsets": []int64{80, 100},
},
"model.layers.0.mlp.up_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{10, 1},
"data_offsets": []int64{100, 120},
},
}
header2JSON, _ := json.Marshal(header2)
var buf2 bytes.Buffer
binary.Write(&buf2, binary.LittleEndian, uint64(len(header2JSON)))
buf2.Write(header2JSON)
digest2 := "sha256:2222222222222222222222222222222222222222222222222222222222222222"
blobPath2, err := manifest.BlobsPath(digest2)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(blobPath2, buf2.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write blob2: %v", err)
}
mf := &manifest.Manifest{
SchemaVersion: 2,
MediaType: "application/vnd.docker.distribution.manifest.v2+json",
Layers: []manifest.Layer{
{
MediaType: manifest.MediaTypeImageTensor,
Digest: digest1,
Size: int64(buf1.Len() + 40),
Name: "model.embed_tokens.weight",
},
{
MediaType: manifest.MediaTypeImageTensor,
Digest: digest2,
Size: int64(buf2.Len() + 120),
Name: "model.layers.0.mlp.up_proj.weight",
},
},
}
paramCount, err := getParameterCountFromManifest(mf)
if err != nil {
t.Fatalf("getParameterCountFromManifest() error = %v", err)
}
const want int64 = 180 // 20 + 160
if paramCount != want {
t.Errorf("parameter_count = %d, want %d", paramCount, want)
}
}
func TestGetParameterCountFromManifest_MixedQuantizedPacked(t *testing.T) {
// Create a temp directory for blobs and set OLLAMA_MODELS
tempDir := t.TempDir()
t.Setenv("OLLAMA_MODELS", tempDir)
blobDir := filepath.Join(tempDir, "blobs")
if err := os.MkdirAll(blobDir, 0o755); err != nil {
t.Fatalf("failed to create blobs dir: %v", err)
}
// Packed mixed-precision blob (no global metadata):
// - gate_proj: int4 packed [5,8] + scale [5,2] => unpacked [5,64] = 320 params
// - down_proj: int8 packed [5,16] + scale [5,1] => unpacked [5,64] = 320 params
header := map[string]any{
"model.layers.0.mlp.experts.0.gate_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{5, 8},
"data_offsets": []int64{0, 160},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{5, 2},
"data_offsets": []int64{160, 180},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{5, 2},
"data_offsets": []int64{180, 200},
},
"model.layers.0.mlp.experts.0.down_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{5, 16},
"data_offsets": []int64{200, 520},
},
"model.layers.0.mlp.experts.0.down_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{5, 1},
"data_offsets": []int64{520, 530},
},
"model.layers.0.mlp.experts.0.down_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{5, 1},
"data_offsets": []int64{530, 540},
},
}
headerJSON, _ := json.Marshal(header)
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
buf.Write(headerJSON)
digest := "sha256:3333333333333333333333333333333333333333333333333333333333333333"
blobPath, err := manifest.BlobsPath(digest)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(blobPath, buf.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write blob: %v", err)
}
mf := &manifest.Manifest{
SchemaVersion: 2,
MediaType: "application/vnd.docker.distribution.manifest.v2+json",
Layers: []manifest.Layer{
{
MediaType: manifest.MediaTypeImageTensor,
Digest: digest,
Size: int64(buf.Len() + 540),
Name: "model.layers.0.mlp.experts",
},
},
}
paramCount, err := getParameterCountFromManifest(mf)
if err != nil {
t.Fatalf("getParameterCountFromManifest() error = %v", err)
}
const want int64 = 640 // 320 + 320
if paramCount != want {
t.Errorf("parameter_count = %d, want %d", paramCount, want)
}
}
func TestParseSafetensorsAllHeaders(t *testing.T) {
tests := []struct {
name string
header map[string]any
wantCount int
wantNames []string
wantDtypes []string
wantQuants []string
wantErr bool
}{
{
name: "single tensor blob",
header: map[string]any{
"model.layers.0.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 2560},
"data_offsets": []int64{0, 13107200},
},
},
wantCount: 1,
wantNames: []string{"model.layers.0.weight"},
wantDtypes: []string{"BF16"},
wantQuants: []string{""},
},
{
name: "packed unquantized blob",
header: map[string]any{
"model.layers.0.mlp.experts.0.down_proj.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 10240},
"data_offsets": []int64{0, 52428800},
},
"model.layers.0.mlp.experts.0.gate_proj.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 2560},
"data_offsets": []int64{52428800, 104857600},
},
"model.layers.0.mlp.experts.0.up_proj.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 2560},
"data_offsets": []int64{104857600, 157286400},
},
},
wantCount: 3,
wantNames: []string{
"model.layers.0.mlp.experts.0.down_proj.weight",
"model.layers.0.mlp.experts.0.gate_proj.weight",
"model.layers.0.mlp.experts.0.up_proj.weight",
},
wantDtypes: []string{"BF16", "BF16", "BF16"},
wantQuants: []string{"", "", ""},
},
{
name: "packed quantized blob with global metadata",
header: map[string]any{
"__metadata__": map[string]any{
"quant_type": "int4",
"group_size": "32",
},
"model.layers.0.mlp.experts.0.gate_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{10240, 320},
"data_offsets": []int64{0, 13107200},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{13107200, 14745600},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{14745600, 16384000},
},
"model.layers.0.mlp.experts.0.up_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{10240, 320},
"data_offsets": []int64{16384000, 29491200},
},
"model.layers.0.mlp.experts.0.up_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{29491200, 31129600},
},
"model.layers.0.mlp.experts.0.up_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{31129600, 32768000},
},
},
wantCount: 2,
wantNames: []string{
"model.layers.0.mlp.experts.0.gate_proj.weight",
"model.layers.0.mlp.experts.0.up_proj.weight",
},
wantDtypes: []string{"U32", "U32"},
wantQuants: []string{"int4", "int4"},
},
{
name: "packed mixed-precision blob (no global metadata)",
header: map[string]any{
"model.layers.0.mlp.experts.0.gate_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{10240, 320},
"data_offsets": []int64{0, 13107200},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{13107200, 14745600},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{14745600, 16384000},
},
"model.layers.0.mlp.experts.0.down_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{2560, 2560},
"data_offsets": []int64{16384000, 42598400},
},
"model.layers.0.mlp.experts.0.down_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 160},
"data_offsets": []int64{42598400, 43417600},
},
},
wantCount: 2,
wantNames: []string{
"model.layers.0.mlp.experts.0.down_proj.weight",
"model.layers.0.mlp.experts.0.gate_proj.weight",
},
wantDtypes: []string{"U32", "U32"},
wantQuants: []string{"int8", "int4"},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
headerJSON, err := json.Marshal(tt.header)
if err != nil {
t.Fatalf("failed to marshal header: %v", err)
}
var buf bytes.Buffer
if err := binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON))); err != nil {
t.Fatalf("failed to write header size: %v", err)
}
buf.Write(headerJSON)
results, err := parseSafetensorsAllHeaders(&buf)
if (err != nil) != tt.wantErr {
t.Errorf("parseSafetensorsAllHeaders() error = %v, wantErr %v", err, tt.wantErr)
return
}
if tt.wantErr {
return
}
if len(results) != tt.wantCount {
t.Fatalf("got %d tensors, want %d", len(results), tt.wantCount)
}
for i, info := range results {
if info.Name != tt.wantNames[i] {
t.Errorf("tensor[%d].Name = %v, want %v", i, info.Name, tt.wantNames[i])
}
if info.Dtype != tt.wantDtypes[i] {
t.Errorf("tensor[%d].Dtype = %v, want %v", i, info.Dtype, tt.wantDtypes[i])
}
if info.QuantType != tt.wantQuants[i] {
t.Errorf("tensor[%d].QuantType = %v, want %v", i, info.QuantType, tt.wantQuants[i])
}
}
})
}
}
func TestGetTensorInfoFromManifest_Packed(t *testing.T) {
// Create a temp directory for blobs and set OLLAMA_MODELS
tempDir := t.TempDir()
t.Setenv("OLLAMA_MODELS", tempDir)
blobDir := filepath.Join(tempDir, "blobs")
if err := os.MkdirAll(blobDir, 0o755); err != nil {
t.Fatalf("failed to create blobs dir: %v", err)
}
// Create a packed blob with multiple expert tensors (mixed quantization)
header := map[string]any{
"model.layers.0.mlp.experts.0.gate_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{10240, 320},
"data_offsets": []int64{0, 13107200},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{13107200, 14745600},
},
"model.layers.0.mlp.experts.0.gate_proj.weight.bias": map[string]any{
"dtype": "BF16",
"shape": []int64{10240, 80},
"data_offsets": []int64{14745600, 16384000},
},
"model.layers.0.mlp.experts.0.down_proj.weight": map[string]any{
"dtype": "U32",
"shape": []int64{2560, 2560},
"data_offsets": []int64{16384000, 42598400},
},
"model.layers.0.mlp.experts.0.down_proj.weight.scale": map[string]any{
"dtype": "BF16",
"shape": []int64{2560, 160},
"data_offsets": []int64{42598400, 43417600},
},
}
headerJSON, _ := json.Marshal(header)
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
buf.Write(headerJSON)
packedDigest := "sha256:aaaa000000000000000000000000000000000000000000000000000000000001"
blobPath, err := manifest.BlobsPath(packedDigest)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(blobPath, buf.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write packed blob: %v", err)
}
// Also create a regular (single-tensor) blob
singleHeader := map[string]any{
"model.embed_tokens.weight": map[string]any{
"dtype": "BF16",
"shape": []int64{262144, 2560},
"data_offsets": []int64{0, 1342177280},
},
}
singleHeaderJSON, _ := json.Marshal(singleHeader)
var singleBuf bytes.Buffer
binary.Write(&singleBuf, binary.LittleEndian, uint64(len(singleHeaderJSON)))
singleBuf.Write(singleHeaderJSON)
singleDigest := "sha256:bbbb000000000000000000000000000000000000000000000000000000000002"
singleBlobPath, err := manifest.BlobsPath(singleDigest)
if err != nil {
t.Fatalf("failed to get blob path: %v", err)
}
if err := os.WriteFile(singleBlobPath, singleBuf.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write single blob: %v", err)
}
mf := &manifest.Manifest{
SchemaVersion: 2,
MediaType: "application/vnd.docker.distribution.manifest.v2+json",
Layers: []manifest.Layer{
{
MediaType: manifest.MediaTypeImageTensor,
Digest: singleDigest,
Size: int64(singleBuf.Len()),
Name: "model.embed_tokens.weight",
},
{
MediaType: manifest.MediaTypeImageTensor,
Digest: packedDigest,
Size: int64(buf.Len()),
Name: "model.layers.0.mlp.experts", // group prefix
},
},
}
result, err := getTensorInfoFromManifest(mf)
if err != nil {
t.Fatalf("getTensorInfoFromManifest() error = %v", err)
}
// Should have 3 tensors: 1 single + 2 packed main tensors
if len(result) != 3 {
t.Fatalf("got %d tensors, want 3. Tensors: %v", len(result), result)
}
// First tensor should be the single blob
if result[0].Name != "model.embed_tokens.weight" {
t.Errorf("tensor[0].Name = %v, want model.embed_tokens.weight", result[0].Name)
}
if result[0].Type != "BF16" {
t.Errorf("tensor[0].Type = %v, want BF16", result[0].Type)
}
// Packed tensors should have their actual names (sorted)
packedNames := make(map[string]bool)
for _, r := range result[1:] {
packedNames[r.Name] = true
}
if !packedNames["model.layers.0.mlp.experts.0.down_proj.weight"] {
t.Error("missing packed tensor: model.layers.0.mlp.experts.0.down_proj.weight")
}
if !packedNames["model.layers.0.mlp.experts.0.gate_proj.weight"] {
t.Error("missing packed tensor: model.layers.0.mlp.experts.0.gate_proj.weight")
}
}
func TestReadSafetensorsHeader(t *testing.T) {
// Create a temp file with a valid safetensors header
tempDir := t.TempDir()
header := map[string]any{
"test_tensor": map[string]any{
"dtype": "BF16",
"shape": []int64{1024, 768},
"data_offsets": []int64{0, 1572864},
},
}
headerJSON, _ := json.Marshal(header)
var buf bytes.Buffer
binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
buf.Write(headerJSON)
filePath := filepath.Join(tempDir, "test.safetensors")
if err := os.WriteFile(filePath, buf.Bytes(), 0o644); err != nil {
t.Fatalf("failed to write test file: %v", err)
}
info, err := readSafetensorsHeader(filePath)
if err != nil {
t.Fatalf("readSafetensorsHeader() error = %v", err)
}
if info.Dtype != "BF16" {
t.Errorf("Dtype = %v, want BF16", info.Dtype)
}
if len(info.Shape) != 2 || info.Shape[0] != 1024 || info.Shape[1] != 768 {
t.Errorf("Shape = %v, want [1024, 768]", info.Shape)
}
}
func TestReadSafetensorsHeader_FileNotFound(t *testing.T) {
_, err := readSafetensorsHeader("/nonexistent/path/file.safetensors")
if err == nil {
t.Error("expected error for nonexistent file")
}
}
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