DGNum/infrastructure
12
6
Fork 3
Code Issues 16 Pull requests 11 Projects 1 Releases Packages Wiki Activity Actions

feat(krz01): move the GPU stuff to the host for now

Browse source 
We also add a K80 specific patch for ollama.

Signed-off-by: Ryan Lahfa <ryan@dgnum.eu>
This commit is contained in:
Ryan Lahfa 2024-10-08 18:44:21 +02:00
parent 8160b2762f
commit 4bedb3f497
3 changed files with 213 additions and 44 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

179
machines/krz01/K80-support.patch Normal file
View file

@ -0,0 +1,179 @@
From 2abd226ff3093c5a9e18a618fba466853e7ebaf7 Mon Sep 17 00:00:00 2001
From: Raito Bezarius <masterancpp@gmail.com>
Date: Tue, 8 Oct 2024 18:27:41 +0200
Subject: [PATCH] K80 support
Signed-off-by: Raito Bezarius <masterancpp@gmail.com>
---
docs/development.md | 6 +++-
docs/gpu.md | 1 +
gpu/amd_linux.go | 6 +++-
gpu/gpu.go | 63 ++++++++++++++++++++++++++++++++++++-----
scripts/build_docker.sh | 2 +-
scripts/build_linux.sh | 2 +-
6 files changed, 69 insertions(+), 11 deletions(-)
diff --git a/docs/development.md b/docs/development.md
index 2f7b9ecf..9da35931 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -51,7 +51,11 @@ Typically the build scripts will auto-detect CUDA, however, if your Linux distro
or installation approach uses unusual paths, you can specify the location by
specifying an environment variable `CUDA_LIB_DIR` to the location of the shared
libraries, and `CUDACXX` to the location of the nvcc compiler. You can customize
-a set of target CUDA architectures by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "50;60;70")
+a set of target CUDA architectures by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "35;37;50;60;70")
+
+To support GPUs older than Compute Capability 5.0, you will need to use an older version of
+the Driver from [Unix Driver Archive](https://www.nvidia.com/en-us/drivers/unix/) (tested with 470) and [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive) (tested with cuda V11). When you build Ollama, you will need to set two environment variable to adjust the minimum compute capability Ollama supports via `export GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/gpu.CudaComputeMajorMin=3\" \"-X=github.com/ollama/ollama/gpu.CudaComputeMinorMin=5\"'"` and the `CMAKE_CUDA_ARCHITECTURES`. To find the Compute Capability of your older GPU, refer to [GPU Compute Capability](https://developer.nvidia.com/cuda-gpus).
+
Then generate dependencies:
diff --git a/docs/gpu.md b/docs/gpu.md
index a6b559f0..66627611 100644
--- a/docs/gpu.md
+++ b/docs/gpu.md
@@ -28,6 +28,7 @@ Check your compute compatibility to see if your card is supported:
| 5.0 | GeForce GTX | `GTX 750 Ti` `GTX 750` `NVS 810` |
| | Quadro | `K2200` `K1200` `K620` `M1200` `M520` `M5000M` `M4000M` `M3000M` `M2000M` `M1000M` `K620M` `M600M` `M500M` |
+For building locally to support older GPUs, see [developer.md](./development.md#linux-cuda-nvidia)
### GPU Selection
diff --git a/gpu/amd_linux.go b/gpu/amd_linux.go
index 6b08ac2e..768fb97a 100644
--- a/gpu/amd_linux.go
+++ b/gpu/amd_linux.go
@@ -159,7 +159,11 @@ func AMDGetGPUInfo() []GpuInfo {
return []GpuInfo{}
}
- if int(major) < RocmComputeMin {
+ minVer, err := strconv.Atoi(RocmComputeMajorMin)
+ if err != nil {
+ slog.Error("invalid RocmComputeMajorMin setting", "value", RocmComputeMajorMin, "error", err)
+ }
+ if int(major) < minVer {
slog.Warn(fmt.Sprintf("amdgpu too old gfx%d%x%x", major, minor, patch), "gpu", gpuID)
continue
}
diff --git a/gpu/gpu.go b/gpu/gpu.go
index 781e23df..60d68c33 100644
--- a/gpu/gpu.go
+++ b/gpu/gpu.go
@@ -16,6 +16,7 @@ import (
"os"
"path/filepath"
"runtime"
+ "strconv"
"strings"
"sync"
"unsafe"
@@ -38,9 +39,11 @@ const (
var gpuMutex sync.Mutex
// With our current CUDA compile flags, older than 5.0 will not work properly
-var CudaComputeMin = [2]C.int{5, 0}
+// (string values used to allow ldflags overrides at build time)
+var CudaComputeMajorMin = "5"
+var CudaComputeMinorMin = "0"
-var RocmComputeMin = 9
+var RocmComputeMajorMin = "9"
// TODO find a better way to detect iGPU instead of minimum memory
const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU
@@ -175,11 +178,57 @@ func GetGPUInfo() GpuInfoList {
var memInfo C.mem_info_t
resp := []GpuInfo{}
- // NVIDIA first
- for i := 0; i < gpuHandles.deviceCount; i++ {
- // TODO once we support CPU compilation variants of GPU libraries refine this...
- if cpuVariant == "" && runtime.GOARCH == "amd64" {
- continue
+ // Load ALL libraries
+ cHandles = initCudaHandles()
+ minMajorVer, err := strconv.Atoi(CudaComputeMajorMin)
+ if err != nil {
+ slog.Error("invalid CudaComputeMajorMin setting", "value", CudaComputeMajorMin, "error", err)
+ }
+ minMinorVer, err := strconv.Atoi(CudaComputeMinorMin)
+ if err != nil {
+ slog.Error("invalid CudaComputeMinorMin setting", "value", CudaComputeMinorMin, "error", err)
+ }
+
+ // NVIDIA
+ for i := range cHandles.deviceCount {
+ if cHandles.cudart != nil || cHandles.nvcuda != nil {
+ gpuInfo := CudaGPUInfo{
+ GpuInfo: GpuInfo{
+ Library: "cuda",
+ },
+ index: i,
+ }
+ var driverMajor int
+ var driverMinor int
+ if cHandles.cudart != nil {
+ C.cudart_bootstrap(*cHandles.cudart, C.int(i), &memInfo)
+ } else {
+ C.nvcuda_bootstrap(*cHandles.nvcuda, C.int(i), &memInfo)
+ driverMajor = int(cHandles.nvcuda.driver_major)
+ driverMinor = int(cHandles.nvcuda.driver_minor)
+ }
+ if memInfo.err != nil {
+ slog.Info("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
+ C.free(unsafe.Pointer(memInfo.err))
+ continue
+ }
+
+ if int(memInfo.major) < minMajorVer || (int(memInfo.major) == minMajorVer && int(memInfo.minor) < minMinorVer) {
+ slog.Info(fmt.Sprintf("[%d] CUDA GPU is too old. Compute Capability detected: %d.%d", i, memInfo.major, memInfo.minor))
+ continue
+ }
+ gpuInfo.TotalMemory = uint64(memInfo.total)
+ gpuInfo.FreeMemory = uint64(memInfo.free)
+ gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
+ gpuInfo.Compute = fmt.Sprintf("%d.%d", memInfo.major, memInfo.minor)
+ gpuInfo.MinimumMemory = cudaMinimumMemory
+ gpuInfo.DependencyPath = depPath
+ gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
+ gpuInfo.DriverMajor = driverMajor
+ gpuInfo.DriverMinor = driverMinor
+
+ // TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
+ cudaGPUs = append(cudaGPUs, gpuInfo)
}
gpuInfo := GpuInfo{
Library: "cuda",
diff --git a/scripts/build_docker.sh b/scripts/build_docker.sh
index e91c56ed..c03bc25f 100755
--- a/scripts/build_docker.sh
+++ b/scripts/build_docker.sh
@@ -3,7 +3,7 @@
set -eu
export VERSION=${VERSION:-$(git describe --tags --first-parent --abbrev=7 --long --dirty --always | sed -e "s/^v//g")}
-export GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=$VERSION\" \"-X=github.com/ollama/ollama/server.mode=release\"'"
+export GOFLAGS=${GOFLAGS:-"'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=$VERSION\" \"-X=github.com/ollama/ollama/server.mode=release\"'"}
# We use 2 different image repositories to handle combining architecture images into multiarch manifest
# (The ROCm image is x86 only and is not a multiarch manifest)
diff --git a/scripts/build_linux.sh b/scripts/build_linux.sh
index 27c4ff1f..e7e6d0dd 100755
--- a/scripts/build_linux.sh
+++ b/scripts/build_linux.sh
@@ -3,7 +3,7 @@
set -eu
export VERSION=${VERSION:-$(git describe --tags --first-parent --abbrev=7 --long --dirty --always | sed -e "s/^v//g")}
-export GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=$VERSION\" \"-X=github.com/ollama/ollama/server.mode=release\"'"
+export GOFLAGS=${GOFLAGS:-"'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=$VERSION\" \"-X=github.com/ollama/ollama/server.mode=release\"'"}
BUILD_ARCH=${BUILD_ARCH:-"amd64 arm64"}
export AMDGPU_TARGETS=${AMDGPU_TARGETS:=""}
--
2.46.0

20
machines/krz01/_configuration.nix
View file

@ -1,4 +1,4 @@
{ lib, ... }:
{ lib, pkgs, ... }:
lib.extra.mkConfig {
enabledModules = [
@ -30,7 +30,23 @@ lib.extra.mkConfig {
# We are going to use CUDA here.
nixpkgs.config.cudaSupport = true;
services.ollama = {
enable = true;
package =
(pkgs.ollama.override {
cudaPackages = pkgs.cudaPackages_11;
gcc12 = pkgs.gcc11;
}).overrideAttrs
(old: {
CMAKE_CUDA_ARCHITECTURES = "35";
ldflags = old.ldflags ++ [
# K80 is 3.5
"-X=github.com/ollama/ollama/gpu.CudaComputeMajorMin=3"
"-X=github.com/ollama/ollama/gpu.CudaComputeMinorMin=5"
];
patches = (old.patches or [ ]) ++ [ ./K80-support.patch ];
});
};
users.users.root.hashedPassword = "$y$j9T$eNZQgDN.J5y7KTG2hXgat1$J1i5tjx5dnSZu.C9B7swXi5zMFIkUnmRrnmyLHFAt8/";
};

58
machines/krz01/microvm-ml01.nix
View file

@ -1,48 +1,22 @@
_: {
microvm.autostart = [ "ml01" ];
microvm.vms.ml01 = {
config =
{ config, ... }:
{
nixpkgs.config.cudaSupport = true;
nixpkgs.config.nvidia.acceptLicense = true;
# Tesla K80 is not supported by the latest driver.
hardware.nvidia.package = config.boot.kernelPackages.nvidia_x11_legacy470;
# Don't ask.
services.xserver.videoDrivers = [ "nvidia" ];
networking.hostName = "ml01";
services.ollama = {
enable = true;
listenAddress = "0.0.0.0:11434";
sandbox = true;
acceleration = "cuda";
};
microvm = {
hypervisor = "cloud-hypervisor";
vcpu = 4;
mem = 4096;
balloonMem = 2048;
devices = [
# The nVidia Tesla K80
{
bus = "pci";
path = "0000:44:00.0";
}
{
bus = "pci";
path = "0000:45:00.0";
}
];
shares = [
{
source = "/nix/store";
mountPoint = "/nix/.ro-store";
tag = "ro-store";
proto = "virtiofs";
}
];
};
config = {
networking.hostName = "ml01";
microvm = {
hypervisor = "cloud-hypervisor";
vcpu = 4;
mem = 4096;
balloonMem = 2048;
shares = [
{
source = "/nix/store";
mountPoint = "/nix/.ro-store";
tag = "ro-store";
proto = "virtiofs";
}
];
};
};
};
}