Files
ollama/ml/device.go
Daniel Hiltgen bc8909fb38 Use runners for GPU discovery (#12090)
This revamps how we discover GPUs in the system by leveraging the Ollama
runner.  This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs.  Now the runner does that implicitly based on the actual
device list.  In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.

Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.

Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.
2025-10-01 15:12:32 -07:00

339 lines
8.9 KiB
Go

package ml
import (
"context"
"encoding/binary"
"fmt"
"hash/maphash"
"log/slog"
"slices"
"sort"
"strconv"
"strings"
"github.com/ollama/ollama/format"
)
// GPULayers is a set of layers to be allocated on a single GPU
type GPULayers struct {
DeviceID
// Layers is a set of layer indicies to load
Layers []int
}
func (g GPULayers) String() string {
if len(g.Layers) == 0 {
return ""
}
slices.Sort(g.Layers)
contiguous := true
base := g.Layers[0]
for i := range g.Layers {
if g.Layers[i] != base+i {
contiguous = false
break
}
}
if contiguous {
return fmt.Sprintf("ID:%v Layers:%v(%v..%v)", g.ID, len(g.Layers), g.Layers[0], g.Layers[len(g.Layers)-1])
} else {
return fmt.Sprintf("ID:%v Layers:%v%v", g.ID, len(g.Layers), g.Layers)
}
}
// GPULayersList is a set of layer allocations across multiple GPUs
type GPULayersList []GPULayers
func (l GPULayersList) String() string {
if l.Sum() > 0 {
return fmt.Sprintf("%v%v", l.Sum(), []GPULayers(l))
} else {
return fmt.Sprintf("%v", []GPULayers(l))
}
}
// Sum is the total number of layers assigned across all GPUs
func (l GPULayersList) Sum() int {
var sum int
for _, g := range l {
sum += len(g.Layers)
}
return sum
}
var h maphash.Hash
// Hash is an identifier of this layer assignment
func (l GPULayersList) Hash() uint64 {
h.Reset()
for _, g := range l {
if len(g.Layers) > 0 {
h.WriteString(g.ID + g.Library)
for _, l := range g.Layers {
binary.Write(&h, binary.NativeEndian, int64(l))
}
}
}
return h.Sum64()
}
// ErrNoMem is returned when panicing due to insufficient memory. It includes
// the attempted memory allocation.
type ErrNoMem struct {
BackendMemory
}
func (e ErrNoMem) Error() string {
return fmt.Sprintf("insufficient memory - required allocations: %+v", e.BackendMemory)
}
// Minimal unique device identification
type DeviceID struct {
// ID is an identifier for the device for matching with system
// management libraries. The ID is only unique for other devices
// using the same Library.
// This ID represents a "post filtered" view of the enumerated devices
// if the ID is numeric
ID string `json:"id"`
// Library identifies which library is used for the device (e.g. CUDA, ROCm, etc.)
Library string `json:"backend,omitempty"`
}
// DeviceMemory provides a breakdown of the memory needed
// per device, such as a CPU or GPU.
type DeviceMemory struct {
DeviceID
// Name is the name of the device as labeled by the backend. It
// may not be persistent across instances of the runner.
Name string
// Weights is the per-layer memory needed for the model weights.
Weights []uint64
// Cache is the per-layer memory needed for the KV cache.
Cache []uint64
// Graph is the size of the compute graph. It is not per-layer.
Graph uint64
}
func sumMemory(mem []uint64) uint64 {
var sum uint64
for _, m := range mem {
sum += m
}
return sum
}
// Size returns the total size of the memory required by this device
func (m DeviceMemory) Size() uint64 {
return sumMemory(m.Weights) + sumMemory(m.Cache) + m.Graph
}
func memoryPresent(mem []uint64) bool {
return slices.ContainsFunc(mem, func(m uint64) bool { return m != 0 })
}
func (m DeviceMemory) LogValue() slog.Value {
var attrs []slog.Attr
if memoryPresent(m.Weights) {
attrs = append(attrs, slog.Any("Weights", m.Weights))
}
if memoryPresent(m.Cache) {
attrs = append(attrs, slog.Any("Cache", m.Cache))
}
if m.Graph != 0 {
attrs = append(attrs, slog.Any("Graph", m.Graph))
}
if len(attrs) > 0 && m.ID != "" {
attrs = append([]slog.Attr{slog.String("ID", m.ID)}, attrs...)
}
return slog.GroupValue(attrs...)
}
// BackendMemory provides the amount of memory required to load the model
// per device based on the BackendParams. In some cases, not all required
// allocations will be known at this point. However, the size of the most recent
// allocation is guaranteed to be provided so that if it failed, the caller can
// accommodate that to make forward progress.
type BackendMemory struct {
// InputWeights are always located on the CPU and cannot be moved
InputWeights uint64
// CPU model components are located in system memory. This does not
// include unified memory allocated through the GPU.
CPU DeviceMemory
// GPU model components are located on one or more GPUs.
GPUs []DeviceMemory
}
func (m BackendMemory) LogValue() slog.Value {
var attrs []slog.Attr
if m.InputWeights != 0 {
attrs = append(attrs, slog.Any("InputWeights", m.InputWeights))
}
attrs = append(attrs, slog.Any(m.CPU.Name, m.CPU))
for _, g := range m.GPUs {
attrs = append(attrs, slog.Any(g.Name, g))
}
return slog.GroupValue(attrs...)
}
// Log prints a high level summary of the memory
func (m BackendMemory) Log(level slog.Level) {
var total uint64
for _, gpu := range m.GPUs {
if sum := sumMemory(gpu.Weights); sum > 0 {
slog.Log(context.TODO(), level, "model weights", "device", gpu.Name, "size", format.HumanBytes2(sum))
total += sum
}
}
if sum := m.InputWeights + sumMemory(m.CPU.Weights); sum > 0 {
slog.Log(context.TODO(), level, "model weights", "device", m.CPU.Name, "size", format.HumanBytes2(sum))
total += sum
}
for _, gpu := range m.GPUs {
if sum := sumMemory(gpu.Cache); sum > 0 {
slog.Log(context.TODO(), level, "kv cache", "device", gpu.Name, "size", format.HumanBytes2(sum))
total += sum
}
}
if sum := sumMemory(m.CPU.Cache); sum > 0 {
slog.Log(context.TODO(), level, "kv cache", "device", m.CPU.Name, "size", format.HumanBytes2(sum))
total += sum
}
for _, gpu := range m.GPUs {
if sum := gpu.Graph; sum > 0 {
slog.Log(context.TODO(), level, "compute graph", "device", gpu.Name, "size", format.HumanBytes2(sum))
total += sum
}
}
if sum := m.CPU.Graph; sum > 0 {
slog.Log(context.TODO(), level, "compute graph", "device", m.CPU.Name, "size", format.HumanBytes2(sum))
total += sum
}
if total > 0 {
slog.Log(context.TODO(), level, "total memory", "size", format.HumanBytes2(total))
}
}
type DeviceInfo struct {
DeviceID
// Name is the name of the device as labeled by the backend. It
// may not be persistent across instances of the runner.
Name string `json:"name"`
// Description is the longer user-friendly identification of the device
Description string `json:"description"`
// FilterID is populated with the unfiltered device ID if a numeric ID is used
// so the device can be included.
FilteredID string `json:"filtered_id,omitempty"`
// Integrated is set true for integrated GPUs, false for Discrete GPUs
Integrated bool `json:"integration,omitempty"`
// PCIID is the bus, device and domain ID of the device for deduplication
// when discovered by multiple backends
PCIID string `json:"pci_id,omitempty"`
// TotalMemory is the total amount of memory the device can use for loading models
TotalMemory uint64 `json:"total_memory"`
// FreeMemory is the amount of memory currently available on the device for loading models
FreeMemory uint64 `json:"free_memory,omitempty"`
// ComputeMajor is the major version of capabilities of the device
// if unsupported by the backend, -1 will be returned
ComputeMajor int
// ComputeMinor is the minor version of capabilities of the device
// if unsupported by the backend, -1 will be returned
ComputeMinor int
// Driver Information
DriverMajor int `json:"driver_major,omitempty"`
DriverMinor int `json:"driver_minor,omitempty"`
// Where backends were loaded from
LibraryPath []string
}
func (d DeviceInfo) Compute() string {
// AMD gfx is encoded into the major minor in hex form
if strings.EqualFold(d.Library, "ROCm") {
return fmt.Sprintf("gfx%x%02x", d.ComputeMajor, d.ComputeMinor)
}
return strconv.Itoa(d.ComputeMajor) + "." + strconv.Itoa(d.ComputeMinor)
}
func (d DeviceInfo) Driver() string {
return strconv.Itoa(d.DriverMajor) + "." + strconv.Itoa(d.DriverMinor)
}
type DeviceComparison int
const (
UniqueDevice DeviceComparison = iota
SameBackendDevice // The device is the same, and the library/backend is the same
DuplicateDevice // The same physical device but different library/backend (overlapping device)
)
func (a DeviceInfo) Compare(b DeviceInfo) DeviceComparison {
if a.PCIID != b.PCIID {
return UniqueDevice
}
if a.Library == b.Library {
return SameBackendDevice
}
return DuplicateDevice
}
// For a SameBackendDevice, return true if b is better than a
// e.g. newer GPU library version
func (a DeviceInfo) IsBetter(b DeviceInfo) bool {
aLib := a.LibraryPath[len(a.LibraryPath)-1]
bLib := b.LibraryPath[len(b.LibraryPath)-1]
if aLib == bLib {
return false
}
aLibSplit := strings.SplitN(aLib, "_", 2)
bLibSplit := strings.SplitN(bLib, "_", 2)
if len(aLibSplit) < 2 || len(bLibSplit) < 2 {
return false
}
if aLibSplit[0] != bLibSplit[0] {
slog.Debug("unexpected libraries", "a", aLib, "b", bLib)
return false
}
if aLibSplit[1] == bLibSplit[1] {
return false
}
cmp := []string{aLibSplit[1], bLibSplit[1]}
sort.Sort(sort.Reverse(sort.StringSlice(cmp)))
return cmp[0] == bLibSplit[1]
}