Merge pull request #3478 from ollama/mxyng/tensor-layer

refactor tensor query

format/bytes.go

@@ -50,7 +50,7 @@ func HumanBytes(b int64) string {
 	}
 }
 
-func HumanBytes2(b int64) string {
+func HumanBytes2(b uint64) string {
 	switch {
 	case b >= MebiByte:
 		return fmt.Sprintf("%.1f MiB", float64(b)/MebiByte)

gpu/gpu.go

@@ -243,7 +243,7 @@ func getCPUMem() (memInfo, error) {
 	return ret, nil
 }
 
-func CheckVRAM() (int64, error) {
+func CheckVRAM() (uint64, error) {
 	userLimit := os.Getenv("OLLAMA_MAX_VRAM")
 	if userLimit != "" {
 		avail, err := strconv.ParseInt(userLimit, 10, 64)
@@ -251,11 +251,11 @@ func CheckVRAM() (int64, error) {
 			return 0, fmt.Errorf("Invalid OLLAMA_MAX_VRAM setting %s: %s", userLimit, err)
 		}
 		slog.Info(fmt.Sprintf("user override OLLAMA_MAX_VRAM=%d", avail))
-		return avail, nil
+		return uint64(avail), nil
 	}
 	gpuInfo := GetGPUInfo()
 	if gpuInfo.FreeMemory > 0 && (gpuInfo.Library == "cuda" || gpuInfo.Library == "rocm") {
-		return int64(gpuInfo.FreeMemory), nil
+		return gpuInfo.FreeMemory, nil
 	}
 
 	return 0, fmt.Errorf("no GPU detected") // TODO - better handling of CPU based memory determiniation

gpu/gpu_darwin.go

@@ -17,7 +17,7 @@ import (
 )
 
 // CheckVRAM returns the free VRAM in bytes on Linux machines with NVIDIA GPUs
-func CheckVRAM() (int64, error) {
+func CheckVRAM() (uint64, error) {
 	userLimit := os.Getenv("OLLAMA_MAX_VRAM")
 	if userLimit != "" {
 		avail, err := strconv.ParseInt(userLimit, 10, 64)
@@ -25,15 +25,14 @@ func CheckVRAM() (int64, error) {
 			return 0, fmt.Errorf("Invalid OLLAMA_MAX_VRAM setting %s: %s", userLimit, err)
 		}
 		slog.Info(fmt.Sprintf("user override OLLAMA_MAX_VRAM=%d", avail))
-		return avail, nil
+		return uint64(avail), nil
 	}
 
 	if runtime.GOARCH == "amd64" {
 		// gpu not supported, this may not be metal
 		return 0, nil
 	}
-	recommendedMaxVRAM := int64(C.getRecommendedMaxVRAM())
-	return recommendedMaxVRAM, nil
+	return uint64(C.getRecommendedMaxVRAM()), nil
 }
 
 func GetGPUInfo() GpuInfo {

gpu/types.go

@@ -15,7 +15,7 @@ type GpuInfo struct {
 	Variant string `json:"variant,omitempty"`
 
 	// MinimumMemory represents the minimum memory required to use the GPU
-	MinimumMemory int64 `json:"-"`
+	MinimumMemory uint64 `json:"-"`
 
 	// TODO add other useful attributes about the card here for discovery information
 }

llm/ggla.go

@@ -49,7 +49,7 @@ func (llm *ggla) KV() KV {
 	return llm.kv
 }
 
-func (llm *ggla) Tensors() []*Tensor {
+func (llm *ggla) Tensors() Tensors {
 	return llm.tensors
 }
 

llm/ggml.go

@@ -13,16 +13,6 @@ type GGML struct {
 	model
 }
 
-func (ggml *GGML) LayerSize(prefix string) (n int64) {
-	for _, t := range ggml.Tensors() {
-		if strings.HasPrefix(t.Name, prefix) {
-			n += int64(t.size())
-		}
-	}
-
-	return
-}
-
 const (
 	fileTypeF32 uint32 = iota
 	fileTypeF16
@@ -101,7 +91,7 @@ func fileType(fileType uint32) string {
 
 type model interface {
 	KV() KV
-	Tensors() []*Tensor
+	Tensors() Tensors
 }
 
 type KV map[string]any
@@ -167,6 +157,36 @@ func (kv KV) ContextLength() uint64 {
 	return kv.u64(fmt.Sprintf("%s.context_length", kv.Architecture()))
 }
 
+type Tensors []*Tensor
+
+func (ts Tensors) Layers() map[string]Layer {
+	layers := make(map[string]Layer)
+	for _, t := range ts {
+		parts := strings.Split(t.Name, ".")
+		if parts[0] == "blk" {
+			parts = parts[1:]
+		}
+
+		if _, ok := layers[parts[0]]; !ok {
+			layers[parts[0]] = make(Layer)
+		}
+
+		layers[parts[0]][strings.Join(parts[1:], ".")] = t
+	}
+
+	return layers
+}
+
+type Layer map[string]*Tensor
+
+func (l Layer) size() (size uint64) {
+	for _, t := range l {
+		size += t.size()
+	}
+
+	return size
+}
+
 type Tensor struct {
 	Name   string `json:"name"`
 	Kind   uint32 `json:"kind"`
@@ -304,49 +324,52 @@ func DecodeGGML(rs io.ReadSeeker) (*GGML, int64, error) {
 	}, offset, nil
 }
 
-func (llm GGML) GraphSize(context, batch int) (int64, bool) {
-	embeddingLength := llm.KV().EmbeddingLength()
-	headCount := llm.KV().HeadCount()
-	headCountKV := llm.KV().HeadCountKV()
-	vocabLength := len(llm.KV()["tokenizer.ggml.tokens"].([]any))
-
-	var attnQKVWeight1 uint64 = 0
-	for _, t := range llm.Tensors() {
-		if strings.HasSuffix(t.Name, ".attn_qkv.weight") && len(t.Shape) >= 2 {
-			attnQKVWeight1 = t.Shape[1]
-			break
-		}
-	}
-
-	var ffnGate1 uint64 = 0
-	for _, t := range llm.Tensors() {
-		if strings.Index(t.Name, ".ffn_gate") > 0 && len(t.Shape) >= 2 {
-			ffnGate1 = t.Shape[1]
-			break
-		}
-	}
+func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload uint64) {
+	embedding := llm.KV().EmbeddingLength()
+	heads := llm.KV().HeadCount()
+	headsKV := llm.KV().HeadCountKV()
+	vocab := uint64(len(llm.KV()["tokenizer.ggml.tokens"].([]any)))
 
 	switch llm.KV().Architecture() {
-	case "gemma", "command-r":
-		return 4 * int64(batch) * int64(embeddingLength+uint64(vocabLength)), true
-	case "phi2":
-		return max(
-			4*int64(batch)*int64(embeddingLength+uint64(vocabLength)),
-			4*int64(batch)*int64(1+4*embeddingLength+uint64(context)+attnQKVWeight1+uint64(context)*headCount),
-		), true
-	case "qwen2":
-		return max(
-			4*int64(batch)*int64(embeddingLength+uint64(vocabLength)),
-			4*int64(batch)*int64(1+2*embeddingLength+uint64(context)+uint64(context)*headCount),
-		), true
 	case "llama":
-		if ffnGate1 > 0 {
-			// moe
-			return 4 * int64(batch) * int64(2+3*embeddingLength+uint64(context)+uint64(context)*headCount+2*headCountKV+ffnGate1), true
-		}
-	
-		return 4 * int64(batch) * int64(1+4*embeddingLength+uint64(context)+uint64(context)*headCount), true
+		fullOffload = 4 * batch * (1 + 4*embedding + context*(1+heads))
+
+		partialOffload = 4 * batch * embedding
+		partialOffload += max(
+			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embedding/heads*headsKV),
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+		)
+	case "gemma":
+		fullOffload = 4 * batch * (embedding + vocab)
+		partialOffload = 4*batch*(2*embedding+vocab+1) + embedding*vocab*105/128
+	case "command-r":
+		fullOffload = max(
+			4*batch*(embedding+vocab),
+			4*batch*(2+4*embedding+context*(1+heads)),
+		)
+
+		partialOffload = max(
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+			4*batch*(1+2*embedding+context*(1+heads))+ 4*embedding*context+embedding*embedding*9/16,
+		)
+	case "qwen2":
+		fullOffload = max(
+			4*batch*(embedding+vocab),
+			4*batch*(1+2*embedding+context+context*heads),
+		)
+
+		partialOffload = max(
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+			4*(batch*(1+2*embedding+context*(1+heads))+embedding*(1+context)),
+		)
+	case "phi2":
+		fullOffload = max(
+			4*batch*(embedding+vocab),
+			4*batch*(1+4*embedding+context+context*heads),
+		)
+
+		partialOffload = 4*batch*(2*embedding+vocab) + embedding*vocab*105/128
 	}
 
-	return 0, false
+	return
 }

llm/gguf.go

@@ -109,7 +109,7 @@ func (llm *gguf) KV() KV {
 	return llm.kv
 }
 
-func (llm *gguf) Tensors() []*Tensor {
+func (llm *gguf) Tensors() Tensors {
 	return llm.tensors
 }
 

llm/server.go

@@ -41,10 +41,6 @@ var cpuOnlyFamilies = []string{
 }
 
 func NewLlamaServer(model string, adapters, projectors []string, opts api.Options) (*LlamaServer, error) {
-	if _, err := os.Stat(model); err != nil {
-		return nil, err
-	}
-
 	f, err := os.Open(model)
 	if err != nil {
 		return nil, err
@@ -65,67 +61,79 @@ func NewLlamaServer(model string, adapters, projectors []string, opts api.Option
 		opts.NumCtx = 4
 	}
 
-	availableMemory, _ := gpu.CheckVRAM()
+	memoryAvailable, _ := gpu.CheckVRAM()
 	info := gpu.GetGPUInfo()
 
-	usedMemory := info.MinimumMemory
+	memoryMinimum := info.MinimumMemory
 	for _, projector := range projectors {
-		usedMemory += projectorMemoryRequirements(projector)
+		memoryMinimum += projectorMemoryRequirements(projector)
 
 		// multimodal models require at least 2048 context
 		opts.NumCtx = max(opts.NumCtx, 2048)
 	}
 
 	// fp16 k,v = (1 (k) + 1 (v)) * sizeof(float16) * n_ctx * n_layer * n_embd / n_head * n_head_kv
-	kv := 2 * 2 * int64(opts.NumCtx) * int64(ggml.KV().BlockCount()) * int64(ggml.KV().EmbeddingLength()) / int64(ggml.KV().HeadCount()) * int64(ggml.KV().HeadCountKV())
+	var kv uint64 = 2 * 2 * uint64(opts.NumCtx) * ggml.KV().BlockCount() * ggml.KV().EmbeddingLength() / ggml.KV().HeadCount() * ggml.KV().HeadCountKV()
 
-	graph, ok := ggml.GraphSize(opts.NumCtx, min(opts.NumCtx, opts.NumBatch))
-	if !ok {
-		graph = int64(ggml.KV().GQA()) * kv / 6
+	graphPartialOffload, graphFullOffload := ggml.GraphSize(uint64(opts.NumCtx), uint64(min(opts.NumCtx, opts.NumBatch)))
+	if graphPartialOffload == 0 {
+		graphPartialOffload = ggml.KV().GQA() * kv / 6
 	}
 
-	usedMemory += graph
-
-	if (usedMemory > availableMemory || slices.Contains(cpuOnlyFamilies, ggml.KV().Architecture())) && info.Library != "metal" {
-		info.Library = "cpu"
+	if graphFullOffload == 0 {
+		graphFullOffload = graphPartialOffload
 	}
 
-	requiredMemory := usedMemory
+	// memoryRequiredTotal represents the memory required for full GPU offloading (all layers)
+	memoryRequiredTotal := memoryMinimum + graphFullOffload
+
+	// memoryRequiredPartial represents the memory required for partial GPU offloading (n > 0, n < layers)
+	memoryRequiredPartial := memoryMinimum + graphPartialOffload
+
+	if info.Library != "metal" {
+		if memoryRequiredPartial > memoryAvailable || slices.Contains(cpuOnlyFamilies, ggml.KV().Architecture()) {
+			info.Library = "cpu"
+		}
+	}
 
-	var layers int
+	var layerCount int
+	layers := ggml.Tensors().Layers()
 	for i := 0; i < int(ggml.KV().BlockCount()); i++ {
-		layerMemory := ggml.LayerSize(fmt.Sprintf("blk.%d.", i)) + kv/int64(ggml.KV().BlockCount())
-		requiredMemory += layerMemory
+		memoryLayer := layers[fmt.Sprintf("%d", i)].size()
+
+		// KV is proportional to the number of layers
+		memoryLayer += kv / ggml.KV().BlockCount()
 
-		if availableMemory > usedMemory+layerMemory && (opts.NumGPU < 0 || layers < opts.NumGPU) {
-			usedMemory += layerMemory
-			layers++
+		memoryRequiredTotal += memoryLayer
+		if memoryAvailable > memoryRequiredPartial+memoryLayer {
+			memoryRequiredPartial += memoryLayer
+			layerCount++
 		}
 	}
 
-	memOutputLayer := ggml.LayerSize("output.")
-	requiredMemory += memOutputLayer
+	memoryLayerOutput := layers["output"].size()
+	memoryRequiredTotal += memoryLayerOutput
+	if memoryAvailable > memoryRequiredTotal {
+		layerCount = int(ggml.KV().BlockCount()) + 1
+		memoryRequiredPartial = memoryRequiredTotal
+	}
 
-	// only offload output layer if all repeating layers are offloaded
-	if layers >= int(ggml.KV().BlockCount()) && availableMemory > usedMemory+memOutputLayer {
-		usedMemory += memOutputLayer
-		layers++
+	if opts.NumGPU < 0 {
+		opts.NumGPU = layerCount
 	}
 
 	slog.Info(
 		"offload to gpu",
-		"layers", layers,
-		"required", format.HumanBytes2(requiredMemory),
-		"used", format.HumanBytes2(usedMemory),
-		"available", format.HumanBytes2(availableMemory),
+		"reallayers", opts.NumGPU,
+		"layers", layerCount,
+		"required", format.HumanBytes2(memoryRequiredTotal),
+		"used", format.HumanBytes2(memoryRequiredPartial),
+		"available", format.HumanBytes2(memoryAvailable),
 		"kv", format.HumanBytes2(kv),
-		"graph", format.HumanBytes2(graph),
+		"fulloffload", format.HumanBytes2(graphFullOffload),
+		"partialoffload", format.HumanBytes2(graphPartialOffload),
 	)
 
-	if opts.NumGPU < 0 && info.Library != "cpu" {
-		opts.NumGPU = layers
-	}
-
 	if len(adapters) > 1 {
 		return nil, errors.New("ollama supports only one lora adapter, but multiple were provided")
 	}
@@ -282,7 +290,7 @@ func NewLlamaServer(model string, adapters, projectors []string, opts api.Option
 	return nil, finalErr
 }
 
-func projectorMemoryRequirements(filename string) int64 {
+func projectorMemoryRequirements(filename string) uint64 {
 	file, err := os.Open(filename)
 	if err != nil {
 		return 0
@@ -294,18 +302,12 @@ func projectorMemoryRequirements(filename string) int64 {
 		return 0
 	}
 
-	prefixes := make(map[string]struct{})
-	for _, layer := range ggml.Tensors() {
-		parts := strings.Split(layer.Name, ".")
-		prefixes[strings.Join(parts[:2], ".")] = struct{}{}
-	}
-
-	var ask int64
-	for prefix := range prefixes {
-		ask += ggml.LayerSize(prefix)
+	var mem uint64
+	for _, layer := range ggml.Tensors().Layers() {
+		mem += layer.size()
 	}
 
-	return ask
+	return mem
 }
 
 type ServerStatus int