ollama/gpu/gpu.go

//go:build linux || windows

package gpu

/*
#cgo linux LDFLAGS: -lrt -lpthread -ldl -lstdc++ -lm
#cgo windows LDFLAGS: -lpthread

#include "gpu_info.h"

*/
import "C"
import (
	"fmt"
	"log"
	"runtime"
	"sync"
	"unsafe"

	"github.com/jmorganca/ollama/api"
)

type handles struct {
	cuda *C.cuda_handle_t
	rocm *C.rocm_handle_t
}

var gpuMutex sync.Mutex
var gpuHandles *handles = nil

// Note: gpuMutex must already be held
func initGPUHandles() {
	// TODO - if the ollama build is CPU only, don't do these checks as they're irrelevant and confusing
	log.Printf("Detecting GPU type")
	gpuHandles = &handles{nil, nil}
	var resp C.cuda_init_resp_t
	C.cuda_init(&resp)
	if resp.err != nil {
		log.Printf("CUDA not detected: %s", C.GoString(resp.err))
		C.free(unsafe.Pointer(resp.err))

		var resp C.rocm_init_resp_t
		C.rocm_init(&resp)
		if resp.err != nil {
			log.Printf("ROCm not detected: %s", C.GoString(resp.err))
			C.free(unsafe.Pointer(resp.err))
		} else {
			log.Printf("Radeon GPU detected")
			rocm := resp.rh
			gpuHandles.rocm = &rocm
		}
	} else {
		log.Printf("Nvidia GPU detected")
		cuda := resp.ch
		gpuHandles.cuda = &cuda
	}
}

func GetGPUInfo() GpuInfo {
	// TODO - consider exploring lspci (and equivalent on windows) to check for
	// GPUs so we can report warnings if we see Nvidia/AMD but fail to load the libraries
	gpuMutex.Lock()
	defer gpuMutex.Unlock()
	if gpuHandles == nil {
		initGPUHandles()
	}

	var memInfo C.mem_info_t
	resp := GpuInfo{"", 0, 0}
	if gpuHandles.cuda != nil {
		C.cuda_check_vram(*gpuHandles.cuda, &memInfo)
		if memInfo.err != nil {
			log.Printf("error looking up CUDA GPU memory: %s", C.GoString(memInfo.err))
			C.free(unsafe.Pointer(memInfo.err))
		} else {
			resp.Library = "cuda"
		}
	} else if gpuHandles.rocm != nil {
		C.rocm_check_vram(*gpuHandles.rocm, &memInfo)
		if memInfo.err != nil {
			log.Printf("error looking up ROCm GPU memory: %s", C.GoString(memInfo.err))
			C.free(unsafe.Pointer(memInfo.err))
		} else {
			resp.Library = "rocm"
		}
	}
	if resp.Library == "" {
		C.cpu_check_ram(&memInfo)
		// In the future we may offer multiple CPU variants to tune CPU features
		if runtime.GOOS == "windows" {
			resp.Library = "cpu"
		} else {
			resp.Library = "default"
		}
	}
	if memInfo.err != nil {
		log.Printf("error looking up CPU memory: %s", C.GoString(memInfo.err))
		C.free(unsafe.Pointer(memInfo.err))
		return resp
	}
	resp.FreeMemory = uint64(memInfo.free)
	resp.TotalMemory = uint64(memInfo.total)
	return resp
}

func CheckVRAM() (int64, error) {
	gpuInfo := GetGPUInfo()
	if gpuInfo.FreeMemory > 0 && (gpuInfo.Library == "cuda" || gpuInfo.Library == "rocm") {
		return int64(gpuInfo.FreeMemory), nil
	}
	return 0, fmt.Errorf("no GPU detected") // TODO - better handling of CPU based memory determiniation
}

func NumGPU(numLayer, fileSizeBytes int64, opts api.Options) int {
	if opts.NumGPU != -1 {
		return opts.NumGPU
	}
	info := GetGPUInfo()
	if info.Library == "cpu" || info.Library == "default" {
		return 0
	}

	/*
		Calculate bytes per layer, this will roughly be the size of the model file divided by the number of layers.
		We can store the model weights and the kv cache in vram,
		to enable kv chache vram storage add two additional layers to the number of layers retrieved from the model file.
	*/
	bytesPerLayer := uint64(fileSizeBytes / numLayer)

	// 75% of the absolute max number of layers we can fit in available VRAM, off-loading too many layers to the GPU can cause OOM errors
	layers := int(info.FreeMemory/bytesPerLayer) * 3 / 4

	log.Printf("%d MB VRAM available, loading up to %d %s GPU layers out of %d", info.FreeMemory/(1024*1024), layers, info.Library, numLayer)

	return layers
}