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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.
213 lines
5.7 KiB
Go
213 lines
5.7 KiB
Go
package discover
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import (
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"context"
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"log/slog"
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"path/filepath"
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"runtime"
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"strings"
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"github.com/ollama/ollama/format"
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"github.com/ollama/ollama/ml"
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)
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type memInfo struct {
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TotalMemory uint64 `json:"total_memory,omitempty"`
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FreeMemory uint64 `json:"free_memory,omitempty"`
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FreeSwap uint64 `json:"free_swap,omitempty"` // TODO split this out for system only
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}
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// Beginning of an `ollama info` command
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type GpuInfo struct { // TODO better name maybe "InferenceProcessor"?
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ml.DeviceID
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memInfo
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// Optional variant to select (e.g. versions, cpu feature flags)
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Variant string `json:"variant"`
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// MinimumMemory represents the minimum memory required to use the GPU
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MinimumMemory uint64 `json:"-"`
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// Any extra PATH/LD_LIBRARY_PATH dependencies required for the Library to operate properly
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DependencyPath []string `json:"lib_path,omitempty"`
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// Set to true if we can NOT reliably discover FreeMemory. A value of true indicates
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// the FreeMemory is best effort, and may over or under report actual memory usage
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// False indicates FreeMemory can generally be trusted on this GPU
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UnreliableFreeMemory bool
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// GPU information
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filterID string // AMD Workaround: The numeric ID of the device used to filter out other devices
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Name string `json:"name"` // user friendly name if available
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Compute string `json:"compute"` // Compute Capability or gfx
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// Driver Information - TODO no need to put this on each GPU
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DriverMajor int `json:"driver_major,omitempty"`
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DriverMinor int `json:"driver_minor,omitempty"`
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// TODO other performance capability info to help in scheduling decisions
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}
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func (gpu GpuInfo) RunnerName() string {
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if gpu.Variant != "" {
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return gpu.Library + "_" + gpu.Variant
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}
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return gpu.Library
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}
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type CPUInfo struct {
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GpuInfo
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CPUs []CPU
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}
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// CPU type represents a CPU Package occupying a socket
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type CPU struct {
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ID string `cpuinfo:"processor"`
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VendorID string `cpuinfo:"vendor_id"`
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ModelName string `cpuinfo:"model name"`
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CoreCount int
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EfficiencyCoreCount int // Performance = CoreCount - Efficiency
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ThreadCount int
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}
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type GpuInfoList []GpuInfo
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func (l GpuInfoList) ByLibrary() []GpuInfoList {
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resp := []GpuInfoList{}
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libs := []string{}
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for _, info := range l {
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found := false
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requested := info.Library
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if info.Variant != "" {
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requested += "_" + info.Variant
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}
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for i, lib := range libs {
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if lib == requested {
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resp[i] = append(resp[i], info)
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found = true
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break
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}
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}
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if !found {
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libs = append(libs, requested)
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resp = append(resp, []GpuInfo{info})
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}
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}
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return resp
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}
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func LogDetails(devices []ml.DeviceInfo) {
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for _, dev := range devices {
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var libs []string
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for _, dir := range dev.LibraryPath {
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if strings.Contains(dir, filepath.Join("lib", "ollama")) {
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libs = append(libs, filepath.Base(dir))
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}
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}
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typeStr := "discrete"
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if dev.Integrated {
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typeStr = "iGPU"
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}
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slog.Info("inference compute",
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"id", dev.ID,
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"library", dev.Library,
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"compute", dev.Compute(),
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"name", dev.Name,
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"description", dev.Description,
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"libdirs", strings.Join(libs, ","),
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"driver", dev.Driver(),
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"pci_id", dev.PCIID,
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"type", typeStr,
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"total", format.HumanBytes2(dev.TotalMemory),
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"available", format.HumanBytes2(dev.FreeMemory),
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)
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}
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// CPU inference
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if len(devices) == 0 {
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dev, _ := GetCPUMem()
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slog.Info("inference compute",
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"id", "cpu",
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"library", "cpu",
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"compute", "",
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"name", "cpu",
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"description", "cpu",
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"libdirs", "ollama",
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"driver", "",
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"pci_id", "",
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"type", "",
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"total", format.HumanBytes2(dev.TotalMemory),
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"available", format.HumanBytes2(dev.FreeMemory),
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)
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}
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}
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// Sort by Free Space
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type ByFreeMemory []GpuInfo
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func (a ByFreeMemory) Len() int { return len(a) }
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func (a ByFreeMemory) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
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func (a ByFreeMemory) Less(i, j int) bool { return a[i].FreeMemory < a[j].FreeMemory }
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type SystemInfo struct {
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System CPUInfo `json:"system"`
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GPUs []GpuInfo `json:"gpus"`
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}
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// Return the optimal number of threads to use for inference
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func (si SystemInfo) GetOptimalThreadCount() int {
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if len(si.System.CPUs) == 0 {
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// Fall back to Go's num CPU
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return runtime.NumCPU()
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}
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coreCount := 0
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for _, c := range si.System.CPUs {
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coreCount += c.CoreCount - c.EfficiencyCoreCount
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}
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return coreCount
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}
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// For each GPU, check if it does NOT support flash attention
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func (l GpuInfoList) FlashAttentionSupported() bool {
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for _, gpu := range l {
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supportsFA := gpu.Library == "cpu" ||
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gpu.Name == "Metal" || gpu.Library == "Metal" ||
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(gpu.Library == "CUDA" && gpu.DriverMajor >= 7) ||
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gpu.Library == "ROCm"
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if !supportsFA {
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return false
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}
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}
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return true
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}
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type BaseRunner interface {
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// GetPort returns the localhost port number the runner is running on
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GetPort() int
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// HasExited indicates if the runner is no longer running. This can be used during
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// bootstrap to detect if a given filtered device is incompatible and triggered an assert
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HasExited() bool
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}
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type RunnerDiscovery interface {
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BaseRunner
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// GetDeviceInfos will perform a query of the underlying device libraries
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// for device identification and free VRAM information
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// During bootstrap scenarios, this routine may take seconds to complete
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GetDeviceInfos(ctx context.Context) []ml.DeviceInfo
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}
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type FilteredRunnerDiscovery interface {
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RunnerDiscovery
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// GetActiveDeviceIDs returns the filtered set of devices actively in
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// use by this runner for running models. If the runner is a bootstrap runner, no devices
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// will be active yet so no device IDs are returned.
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// This routine will not query the underlying device and will return immediately
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GetActiveDeviceIDs() []ml.DeviceID
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}
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