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README.md
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......@@ -12,49 +12,55 @@
##### Docker
docker pull docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-py3.10-dtk24.04.3-ubuntu20.04
```bash
docker pull docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-py3.10-dtk24.04.3-ubuntu20.04
docker run -i -t -d --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home -v /opt/hyhal:/opt/hyhal:ro -v --group-add video --shm-size 16G --name {容器名} {镜像ID}
docker run -i -t -d --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home -v /opt/hyhal:/opt/hyhal:ro -v --group-add video --shm-size 16G --name {容器名} {镜像ID}
```
1、下载源码
git clone -b 0.4.7 http://developer.sourcefind.cn/codes/OpenDAS/ollama.git --depth=1
cd ollama
2、安装依赖包
cd llm/llama.cpp
pip install -r requirements.txt
```bash
git clone -b 0.4.7 http://developer.sourcefind.cn/codes/OpenDAS/ollama.git --depth=1
cd ollama
```
#### 编译
##### 环境设置
export AMDGPU_TARGETS=当前设备型号（如：gfx906，gfx928等）
export HSA_OVERRIDE_GFX_VERSION=设备型号（如: gfx906对应9.0.6；gfx928对应9.2.8）
export LIBRARY_PATH=/opt/dtk/llvm/lib/clang/15.0.0/lib/linux/:$LIBRARY_PATH
export HIP_VISIBLE_DEVICES=所有设备号（0,1,2,3,4,5,6,...）/选择设备号
```bash
export AMDGPU_TARGETS=当前设备型号（如：gfx906，gfx928等）
export HSA_OVERRIDE_GFX_VERSION=设备型号（如: gfx906对应9.0.6；gfx928对应9.2.8）
export LIBRARY_PATH=/opt/dtk/llvm/lib/clang/15.0.0/lib/linux/:$LIBRARY_PATH
export HIP_VISIBLE_DEVICES=所有设备号（0,1,2,3,4,5,6,...）/选择设备号
```
安装go
wget https://go.dev/dl/go1.22.8.linux-amd64.tar.gz
cd ../..
tar -C /usr/local -xzf go1.22.8.linux-amd64.tar.gz
export PATH=$PATH:/usr/local/go/bin
```bash
wget https://go.dev/dl/go1.22.8.linux-amd64.tar.gz
tar -C /usr/local -xzf go1.22.8.linux-amd64.tar.gz
export PATH=$PATH:/usr/local/go/bin
# 修改go下载源，提升速度（按需设置）
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
# 修改go下载源，提升速度（按需设置）
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
```
##### 运行编译
export LD_LIBRARY_PATH=/path/to/ollama/llama/build/linux-amd64/runners/rocm:$LD_LIBRARY_PATH
make -j 16
go build .
```bash
export LD_LIBRARY_PATH=/path/to/ollama/llama/build/linux-amd64/runners/rocm:$LD_LIBRARY_PATH (path/to换成自己ollama路径)
make -j 16
go build .
```
## 验证
./ollama serve （选择可用设备，可通过上条命令输出结果查看）
./ollama run llama3.1
```bash
./ollama serve （选择可用设备，可通过上条命令输出结果查看）
./ollama run llama3.1
```
更多使用方式请参考[原项目](https://github.com/ollama/ollama)。
......
docs/tutorials/fly-gpu.md deleted 100644 → 0
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# Running Ollama on Fly.io GPU Instances
Ollama runs with little to no configuration on [Fly.io GPU instances](https://fly.io/docs/gpus/gpu-quickstart/). If you don't have access to GPUs yet, you'll need to [apply for access](https://fly.io/gpu/) on the waitlist. Once you're accepted, you'll get an email with instructions on how to get started.
Create a new app with `fly apps create`:
```bash
fly apps create
```
Then create a `fly.toml` file in a new folder that looks like this:
```toml
app = "sparkling-violet-709"
primary_region = "ord"
vm.size = "a100-40gb" # see https://fly.io/docs/gpus/gpu-quickstart/ for more info
[build]
image = "ollama/ollama"
[http_service]
internal_port = 11434
force_https = false
auto_stop_machines = true
auto_start_machines = true
min_machines_running = 0
processes = ["app"]
[mounts]
source = "models"
destination = "/root/.ollama"
initial_size = "100gb"
```
Then create a [new private IPv6 address](https://fly.io/docs/reference/private-networking/#flycast-private-load-balancing) for your app:
```bash
fly ips allocate-v6 --private
```
Then deploy your app:
```bash
fly deploy
```
And finally you can access it interactively with a new Fly.io Machine:
```
fly machine run -e OLLAMA_HOST=http://your-app-name.flycast --shell ollama/ollama
```
```bash
$ ollama run openchat:7b-v3.5-fp16
>>> How do I bake chocolate chip cookies?
To bake chocolate chip cookies, follow these steps:
1. Preheat the oven to 375°F (190°C) and line a baking sheet with parchment paper or silicone baking mat.
2. In a large bowl, mix together 1 cup of unsalted butter (softened), 3/4 cup granulated sugar, and 3/4
cup packed brown sugar until light and fluffy.
3. Add 2 large eggs, one at a time, to the butter mixture, beating well after each addition. Stir in 1
teaspoon of pure vanilla extract.
4. In a separate bowl, whisk together 2 cups all-purpose flour, 1/2 teaspoon baking soda, and 1/2 teaspoon
salt. Gradually add the dry ingredients to the wet ingredients, stirring until just combined.
5. Fold in 2 cups of chocolate chips (or chunks) into the dough.
6. Drop rounded tablespoons of dough onto the prepared baking sheet, spacing them about 2 inches apart.
7. Bake for 10-12 minutes, or until the edges are golden brown. The centers should still be slightly soft.
8. Allow the cookies to cool on the baking sheet for a few minutes before transferring them to a wire rack
to cool completely.
Enjoy your homemade chocolate chip cookies!
```
When you set it up like this, it will automatically turn off when you're done using it. Then when you access it again, it will automatically turn back on. This is a great way to save money on GPU instances when you're not using them. If you want a persistent wake-on-use connection to your Ollama instance, you can set up a [connection to your Fly network using WireGuard](https://fly.io/docs/reference/private-networking/#discovering-apps-through-dns-on-a-wireguard-connection). Then you can access your Ollama instance at `http://your-app-name.flycast`.
And that's it!
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# Using LangChain with Ollama using JavaScript
In this tutorial, we are going to use JavaScript with LangChain and Ollama to learn about something just a touch more recent. In August 2023, there was a series of wildfires on Maui. There is no way an LLM trained before that time can know about this, since their training data would not include anything as recent as that. So we can find the [Wikipedia article about the fires](https://en.wikipedia.org/wiki/2023_Hawaii_wildfires) and ask questions about the contents.
To get started, let's just use **LangChain** to ask a simple question to a model. To do this with JavaScript, we need to install **LangChain**:
```bash
npm install @langchain/community
```
Now we can start building out our JavaScript:
```javascript
import { Ollama } from "@langchain/community/llms/ollama";
const ollama = new Ollama({
baseUrl: "http://localhost:11434",
model: "llama3.1",
});
const answer = await ollama.invoke(`why is the sky blue?`);
console.log(answer);
```
That will get us the same thing as if we ran `ollama run llama3.1 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
```bash
npm install cheerio
```
```javascript
import { CheerioWebBaseLoader } from "langchain/document_loaders/web/cheerio";
const loader = new CheerioWebBaseLoader("https://en.wikipedia.org/wiki/2023_Hawaii_wildfires");
const data = await loader.load();
```
That will load the document. Although this page is smaller than the Odyssey, it is certainly bigger than the context size for most LLMs. So we are going to need to split into smaller pieces, and then select just the pieces relevant to our question. This is a great use for a vector datastore. In this example, we will use the **MemoryVectorStore** that is part of **LangChain**. But there is one more thing we need to get the content into the datastore. We have to run an embeddings process that converts the tokens in the text into a series of vectors. And for that, we are going to use **Tensorflow**. There is a lot of stuff going on in this one. First, install the **Tensorflow** components that we need.
```javascript
npm install @tensorflow/tfjs-core@3.6.0 @tensorflow/tfjs-converter@3.6.0 @tensorflow-models/universal-sentence-encoder@1.3.3 @tensorflow/tfjs-node@4.10.0
```
If you just install those components without the version numbers, it will install the latest versions, but there are conflicts within **Tensorflow**, so you need to install the compatible versions.
```javascript
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import "@tensorflow/tfjs-node";
import { TensorFlowEmbeddings } from "langchain/embeddings/tensorflow";
// Split the text into 500 character chunks. And overlap each chunk by 20 characters
const textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 500,
chunkOverlap: 20
});
const splitDocs = await textSplitter.splitDocuments(data);
// Then use the TensorFlow Embedding to store these chunks in the datastore
const vectorStore = await MemoryVectorStore.fromDocuments(splitDocs, new TensorFlowEmbeddings());
```
To connect the datastore to a question asked to a LLM, we need to use the concept at the heart of **LangChain**: the chain. Chains are a way to connect a number of activities together to accomplish a particular tasks. There are a number of chain types available, but for this tutorial we are using the **RetrievalQAChain**.
```javascript
import { RetrievalQAChain } from "langchain/chains";
const retriever = vectorStore.asRetriever();
const chain = RetrievalQAChain.fromLLM(ollama, retriever);
const result = await chain.call({query: "When was Hawaii's request for a major disaster declaration approved?"});
console.log(result.text)
```
So we created a retriever, which is a way to return the chunks that match a query from a datastore. And then connect the retriever and the model via a chain. Finally, we send a query to the chain, which results in an answer using our document as a source. The answer it returned was correct, August 10, 2023.
And that is a simple introduction to what you can do with **LangChain** and **Ollama.**
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# Using LangChain with Ollama in Python
Let's imagine we are studying the classics, such as **the Odyssey** by **Homer**. We might have a question about Neleus and his family. If you ask llama2 for that info, you may get something like:
> I apologize, but I'm a large language model, I cannot provide information on individuals or families that do not exist in reality. Neleus is not a real person or character, and therefore does not have a family or any other personal details. My apologies for any confusion. Is there anything else I can help you with?
This sounds like a typical censored response, but even llama2-uncensored gives a mediocre answer:
> Neleus was a legendary king of Pylos and the father of Nestor, one of the Argonauts. His mother was Clymene, a sea nymph, while his father was Neptune, the god of the sea.
So let's figure out how we can use **LangChain** with Ollama to ask our question to the actual document, the Odyssey by Homer, using Python.
Let's start by asking a simple question that we can get an answer to from the **Llama2** model using **Ollama**. First, we need to install the **LangChain** package:
`pip install langchain_community`
Then we can create a model and ask the question:
```python
from langchain_community.llms import Ollama
ollama = Ollama(
base_url='http://localhost:11434',
model="llama3"
)
print(ollama.invoke("why is the sky blue"))
```
Notice that we are defining the model and the base URL for Ollama.
Now let's load a document to ask questions against. I'll load up the Odyssey by Homer, which you can find at Project Gutenberg. We will need **WebBaseLoader** which is part of **LangChain** and loads text from any webpage. On my machine, I also needed to install **bs4** to get that to work, so run `pip install bs4`.
```python
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://www.gutenberg.org/files/1727/1727-h/1727-h.htm")
data = loader.load()
```
This file is pretty big. Just the preface is 3000 tokens. Which means the full document won't fit into the context for the model. So we need to split it up into smaller pieces.
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
```
It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb`
We also need to pull embedding model: `ollama pull nomic-embed-text`
```python
from langchain.embeddings import OllamaEmbeddings
from langchain.vectorstores import Chroma
oembed = OllamaEmbeddings(base_url="http://localhost:11434", model="nomic-embed-text")
vectorstore = Chroma.from_documents(documents=all_splits, embedding=oembed)
```
Now let's ask a question from the document. **Who was Neleus, and who is in his family?** Neleus is a character in the Odyssey, and the answer can be found in our text.
```python
question="Who is Neleus and who is in Neleus' family?"
docs = vectorstore.similarity_search(question)
len(docs)
```
This will output the number of matches for chunks of data similar to the search.
The next thing is to send the question and the relevant parts of the docs to the model to see if we can get a good answer. But we are stitching two parts of the process together, and that is called a chain. This means we need to define a chain:
```python
from langchain.chains import RetrievalQA
qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
res = qachain.invoke({"query": question})
print(res['result'])
```
The answer received from this chain was:
> Neleus is a character in Homer's "Odyssey" and is mentioned in the context of Penelope's suitors. Neleus is the father of Chloris, who is married to Neleus and bears him several children, including Nestor, Chromius, Periclymenus, and Pero. Amphinomus, the son of Nisus, is also mentioned as a suitor of Penelope and is known for his good natural disposition and agreeable conversation.
It's not a perfect answer, as it implies Neleus married his daughter when actually Chloris "was the youngest daughter to Amphion son of Iasus and king of Minyan Orchomenus, and was Queen in Pylos".
I updated the chunk_overlap for the text splitter to 20 and tried again and got a much better answer:
> Neleus is a character in Homer's epic poem "The Odyssey." He is the husband of Chloris, who is the youngest daughter of Amphion son of Iasus and king of Minyan Orchomenus. Neleus has several children with Chloris, including Nestor, Chromius, Periclymenus, and Pero.
And that is a much better answer.
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# Running Ollama on NVIDIA Jetson Devices
Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) and should run out of the box with the standard installation instructions.
The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack), but should also work on JetPack 6.0.
- Install Ollama via standard Linux command (ignore the 404 error): `curl https://ollama.com/install.sh | sh`
- Pull the model you want to use (e.g. mistral): `ollama pull mistral`
- Start an interactive session: `ollama run mistral`
And that's it!
# Running Ollama in Docker
When running GPU accelerated applications in Docker, it is highly recommended to use [dusty-nv jetson-containers repo](https://github.com/dusty-nv/jetson-containers).
\ No newline at end of file
gpu/amd_common.go deleted 100644 → 0
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//go:build linux || windows
package gpu
import (
"errors"
"log/slog"
"os"
"path/filepath"
"runtime"
"strings"
)
// Determine if the given ROCm lib directory is usable by checking for existence of some glob patterns
func rocmLibUsable(libDir string) bool {
slog.Debug("evaluating potential rocm lib dir " + libDir)
for _, g := range ROCmLibGlobs {
res, _ := filepath.Glob(filepath.Join(libDir, g))
if len(res) == 0 {
return false
}
}
return true
}
func GetSupportedGFX(libDir string) ([]string, error) {
var ret []string
files, err := filepath.Glob(filepath.Join(libDir, "rocblas", "library", "TensileLibrary_lazy_gfx*.dat"))
if err != nil {
return nil, err
}
for _, file := range files {
ret = append(ret, strings.TrimSuffix(strings.TrimPrefix(filepath.Base(file), "TensileLibrary_lazy_"), ".dat"))
}
return ret, nil
}
func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
ids := []string{}
for _, info := range gpuInfo {
if info.Library != "rocm" {
// TODO shouldn't happen if things are wired correctly...
slog.Debug("rocmGetVisibleDevicesEnv skipping over non-rocm device", "library", info.Library)
continue
}
ids = append(ids, info.ID)
}
return "HIP_VISIBLE_DEVICES", strings.Join(ids, ",")
}
func commonAMDValidateLibDir() (string, error) {
// Favor our bundled version
// Installer payload location if we're running the installed binary
exe, err := os.Executable()
if err == nil {
rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
if rocmLibUsable(rocmTargetDir) {
slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
return rocmTargetDir, nil
}
}
// Prefer explicit HIP env var
hipPath := os.Getenv("HIP_PATH")
if hipPath != "" {
hipLibDir := filepath.Join(hipPath, "bin")
if rocmLibUsable(hipLibDir) {
slog.Debug("detected ROCM via HIP_PATH=" + hipPath)
return hipLibDir, nil
}
}
// Scan the LD_LIBRARY_PATH or PATH
pathEnv := "LD_LIBRARY_PATH"
if runtime.GOOS == "windows" {
pathEnv = "PATH"
}
paths := os.Getenv(pathEnv)
for _, path := range filepath.SplitList(paths) {
d, err := filepath.Abs(path)
if err != nil {
continue
}
if rocmLibUsable(d) {
return d, nil
}
}
// Well known location(s)
for _, path := range RocmStandardLocations {
if rocmLibUsable(path) {
return path, nil
}
}
return "", errors.New("no suitable rocm found, falling back to CPU")
}
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package gpu
import (
"errors"
"fmt"
"log/slog"
"syscall"
"unsafe"
"golang.org/x/sys/windows"
)
const (
hipSuccess = 0
hipErrorNoDevice = 100
)
type hipDevicePropMinimal struct {
Name [256]byte
unused1 [140]byte
GcnArchName [256]byte // gfx####
iGPU int // Doesn't seem to actually report correctly
unused2 [128]byte
}
// Wrap the amdhip64.dll library for GPU discovery
type HipLib struct {
dll windows.Handle
hipGetDeviceCount uintptr
hipGetDeviceProperties uintptr
hipMemGetInfo uintptr
hipSetDevice uintptr
hipDriverGetVersion uintptr
}
func NewHipLib() (*HipLib, error) {
// At runtime we depend on v6, so discover GPUs with the same library for a consistent set of GPUs
h, err := windows.LoadLibrary("amdhip64_6.dll")
if err != nil {
return nil, fmt.Errorf("unable to load amdhip64_6.dll, please make sure to upgrade to the latest amd driver: %w", err)
}
hl := &HipLib{}
hl.dll = h
hl.hipGetDeviceCount, err = windows.GetProcAddress(hl.dll, "hipGetDeviceCount")
if err != nil {
return nil, err
}
hl.hipGetDeviceProperties, err = windows.GetProcAddress(hl.dll, "hipGetDeviceProperties")
if err != nil {
return nil, err
}
hl.hipMemGetInfo, err = windows.GetProcAddress(hl.dll, "hipMemGetInfo")
if err != nil {
return nil, err
}
hl.hipSetDevice, err = windows.GetProcAddress(hl.dll, "hipSetDevice")
if err != nil {
return nil, err
}
hl.hipDriverGetVersion, err = windows.GetProcAddress(hl.dll, "hipDriverGetVersion")
if err != nil {
return nil, err
}
return hl, nil
}
// The hip library only evaluates the HIP_VISIBLE_DEVICES variable at startup
// so we have to unload/reset the library after we do our initial discovery
// to make sure our updates to that variable are processed by llama.cpp
func (hl *HipLib) Release() {
err := windows.FreeLibrary(hl.dll)
if err != nil {
slog.Warn("failed to unload amdhip64.dll", "error", err)
}
hl.dll = 0
}
func (hl *HipLib) AMDDriverVersion() (driverMajor, driverMinor int, err error) {
if hl.dll == 0 {
return 0, 0, errors.New("dll has been unloaded")
}
var version int
status, _, err := syscall.SyscallN(hl.hipDriverGetVersion, uintptr(unsafe.Pointer(&version)))
if status != hipSuccess {
return 0, 0, fmt.Errorf("failed call to hipDriverGetVersion: %d %s", status, err)
}
slog.Debug("hipDriverGetVersion", "version", version)
driverMajor = version / 10000000
driverMinor = (version - (driverMajor * 10000000)) / 100000
return driverMajor, driverMinor, nil
}
func (hl *HipLib) HipGetDeviceCount() int {
if hl.dll == 0 {
slog.Error("dll has been unloaded")
return 0
}
var count int
status, _, err := syscall.SyscallN(hl.hipGetDeviceCount, uintptr(unsafe.Pointer(&count)))
if status == hipErrorNoDevice {
slog.Info("AMD ROCm reports no devices found")
return 0
}
if status != hipSuccess {
slog.Warn("failed call to hipGetDeviceCount", "status", status, "error", err)
}
return count
}
func (hl *HipLib) HipSetDevice(device int) error {
if hl.dll == 0 {
return errors.New("dll has been unloaded")
}
status, _, err := syscall.SyscallN(hl.hipSetDevice, uintptr(device))
if status != hipSuccess {
return fmt.Errorf("failed call to hipSetDevice: %d %s", status, err)
}
return nil
}
func (hl *HipLib) HipGetDeviceProperties(device int) (*hipDevicePropMinimal, error) {
if hl.dll == 0 {
return nil, errors.New("dll has been unloaded")
}
var props hipDevicePropMinimal
status, _, err := syscall.SyscallN(hl.hipGetDeviceProperties, uintptr(unsafe.Pointer(&props)), uintptr(device))
if status != hipSuccess {
return nil, fmt.Errorf("failed call to hipGetDeviceProperties: %d %s", status, err)
}
return &props, nil
}
// free, total, err
func (hl *HipLib) HipMemGetInfo() (uint64, uint64, error) {
if hl.dll == 0 {
return 0, 0, errors.New("dll has been unloaded")
}
var totalMemory uint64
var freeMemory uint64
status, _, err := syscall.SyscallN(hl.hipMemGetInfo, uintptr(unsafe.Pointer(&freeMemory)), uintptr(unsafe.Pointer(&totalMemory)))
if status != hipSuccess {
return 0, 0, fmt.Errorf("failed call to hipMemGetInfo: %d %s", status, err)
}
return freeMemory, totalMemory, nil
}
gpu/amd_linux.go deleted 100644 → 0
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package gpu
import (
"bufio"
"errors"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"slices"
"sort"
"strconv"
"strings"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
)
// Discovery logic for AMD/ROCm GPUs
const (
// DriverVersionFile = "/sys/module/amdgpu/version"
DriverVersionFile = "/sys/module/hydcu/version"
AMDNodesSysfsDir = "/sys/class/kfd/kfd/topology/nodes/"
GPUPropertiesFileGlob = AMDNodesSysfsDir + "*/properties"
// Prefix with the node dir
GPUTotalMemoryFileGlob = "mem_banks/*/properties" // size_in_bytes line
// Direct Rendering Manager sysfs location
DRMDeviceDirGlob = "/sys/class/drm/card*/device"
DRMTotalMemoryFile = "mem_info_vram_total"
DRMUsedMemoryFile = "mem_info_vram_used"
// In hex; properties file is in decimal
DRMUniqueIDFile = "unique_id"
DRMVendorFile = "vendor"
DRMDeviceFile = "device"
)
var (
// Used to validate if the given ROCm lib is usable
// ROCmLibGlobs = []string{"libhipblas.so.2*", "rocblas"} // TODO - probably include more coverage of files here...
ROCmLibGlobs = []string{"libhipblas.so.0.1","rocblas"}
// RocmStandardLocations = []string{"/opt/rocm/lib", "/usr/lib64"}
RocmStandardLocations = []string{"/opt/dtk/lib", "/usr/lib64"}
)
// Gather GPU information from the amdgpu driver if any supported GPUs are detected
func AMDGetGPUInfo() []RocmGPUInfo {
resp := []RocmGPUInfo{}
if !AMDDetected() {
return resp
}
// Opportunistic logging of driver version to aid in troubleshooting
driverMajor, driverMinor, err := AMDDriverVersion()
if err != nil {
// TODO - if we see users crash and burn with the upstreamed kernel this can be adjusted to hard-fail rocm support and fallback to CPU
slog.Warn("ollama recommends running the https://www.amd.com/en/support/linux-drivers", "error", err)
}
// Determine if the user has already pre-selected which GPUs to look at, then ignore the others
var visibleDevices []string
hipVD := envconfig.HipVisibleDevices() // zero based index only
rocrVD := envconfig.RocrVisibleDevices() // zero based index or UUID, but consumer cards seem to not support UUID
gpuDO := envconfig.GpuDeviceOrdinal() // zero based index
switch {
// TODO is this priorty order right?
case hipVD != "":
visibleDevices = strings.Split(hipVD, ",")
case rocrVD != "":
visibleDevices = strings.Split(rocrVD, ",")
// TODO - since we don't yet support UUIDs, consider detecting and reporting here
// all our test systems show GPU-XX indicating UUID is not supported
case gpuDO != "":
visibleDevices = strings.Split(gpuDO, ",")
}
gfxOverride := envconfig.HsaOverrideGfxVersion()
var supported []string
libDir := ""
// The amdgpu driver always exposes the host CPU(s) first, but we have to skip them and subtract
// from the other IDs to get alignment with the HIP libraries expectations (zero is the first GPU, not the CPU)
matches, _ := filepath.Glob(GPUPropertiesFileGlob)
sort.Slice(matches, func(i, j int) bool {
// /sys/class/kfd/kfd/topology/nodes/<number>/properties
a, err := strconv.ParseInt(filepath.Base(filepath.Dir(matches[i])), 10, 64)
if err != nil {
slog.Debug("parse err", "error", err, "match", matches[i])
return false
}
b, err := strconv.ParseInt(filepath.Base(filepath.Dir(matches[j])), 10, 64)
if err != nil {
slog.Debug("parse err", "error", err, "match", matches[i])
return false
}
return a < b
})
cpuCount := 0
for _, match := range matches {
slog.Debug("evaluating amdgpu node " + match)
fp, err := os.Open(match)
if err != nil {
slog.Debug("failed to open sysfs node", "file", match, "error", err)
continue
}
defer fp.Close()
nodeID, err := strconv.Atoi(filepath.Base(filepath.Dir(match)))
if err != nil {
slog.Debug("failed to parse node ID", "error", err)
continue
}
scanner := bufio.NewScanner(fp)
isCPU := false
var major, minor, patch uint64
var vendor, device, uniqueID uint64
for scanner.Scan() {
line := strings.TrimSpace(scanner.Text())
// Note: we could also use "cpu_cores_count X" where X is greater than zero to detect CPUs
if strings.HasPrefix(line, "gfx_target_version") {
ver := strings.Fields(line)
// Detect CPUs
if len(ver) == 2 && ver[1] == "0" {
slog.Debug("detected CPU " + match)
isCPU = true
break
}
if len(ver) != 2 || len(ver[1]) < 5 {
slog.Warn("malformed "+match, "gfx_target_version", line)
// If this winds up being a CPU, our offsets may be wrong
continue
}
l := len(ver[1])
var err1, err2, err3 error
patch, err1 = strconv.ParseUint(ver[1][l-2:l], 10, 32)
minor, err2 = strconv.ParseUint(ver[1][l-4:l-2], 10, 32)
major, err3 = strconv.ParseUint(ver[1][:l-4], 10, 32)
if err1 != nil || err2 != nil || err3 != nil {
slog.Debug("malformed int " + line)
continue
}
} else if strings.HasPrefix(line, "vendor_id") {
ver := strings.Fields(line)
if len(ver) != 2 {
slog.Debug("malformed", "vendor_id", line)
continue
}
vendor, err = strconv.ParseUint(ver[1], 10, 64)
if err != nil {
slog.Debug("malformed", "vendor_id", line, "error", err)
}
} else if strings.HasPrefix(line, "device_id") {
ver := strings.Fields(line)
if len(ver) != 2 {
slog.Debug("malformed", "device_id", line)
continue
}
device, err = strconv.ParseUint(ver[1], 10, 64)
if err != nil {
slog.Debug("malformed", "device_id", line, "error", err)
}
} else if strings.HasPrefix(line, "unique_id") {
ver := strings.Fields(line)
if len(ver) != 2 {
slog.Debug("malformed", "unique_id", line)
continue
}
uniqueID, err = strconv.ParseUint(ver[1], 10, 64)
if err != nil {
slog.Debug("malformed", "unique_id", line, "error", err)
}
}
// TODO - any other properties we want to extract and record?
// vendor_id + device_id -> pci lookup for "Name"
// Other metrics that may help us understand relative performance between multiple GPUs
}
// Note: while ./mem_banks/*/used_memory exists, it doesn't appear to take other VRAM consumers
// into consideration, so we instead map the device over to the DRM driver sysfs nodes which
// do reliably report VRAM usage.
if isCPU {
cpuCount++
continue
}
// CPUs are always first in the list
gpuID := nodeID - cpuCount
// Shouldn't happen, but just in case...
if gpuID < 0 {
slog.Error("unexpected amdgpu sysfs data resulted in negative GPU ID, please set OLLAMA_DEBUG=1 and report an issue")
return nil
}
if int(major) < RocmComputeMin {
slog.Warn(fmt.Sprintf("amdgpu too old gfx%d%x%x", major, minor, patch), "gpu", gpuID)
continue
}
// Look up the memory for the current node
totalMemory := uint64(0)
usedMemory := uint64(0)
var usedFile string
mapping := []struct {
id uint64
filename string
}{
{vendor, DRMVendorFile},
{device, DRMDeviceFile},
{uniqueID, DRMUniqueIDFile}, // Not all devices will report this
}
slog.Debug("mapping amdgpu to drm sysfs nodes", "amdgpu", match, "vendor", vendor, "device", device, "unique_id", uniqueID)
// Map over to DRM location to find the total/free memory
drmMatches, _ := filepath.Glob(DRMDeviceDirGlob)
for _, devDir := range drmMatches {
matched := true
for _, m := range mapping {
if m.id == 0 {
// Null ID means it didn't populate, so we can't use it to match
continue
}
filename := filepath.Join(devDir, m.filename)
buf, err := os.ReadFile(filename)
if err != nil {
slog.Debug("failed to read sysfs node", "file", filename, "error", err)
matched = false
break
}
// values here are in hex, strip off the lead 0x and parse so we can compare the numeric (decimal) values in amdgpu
cmp, err := strconv.ParseUint(strings.TrimPrefix(strings.TrimSpace(string(buf)), "0x"), 16, 64)
if err != nil {
slog.Debug("failed to parse sysfs node", "file", filename, "error", err)
matched = false
break
}
if cmp != m.id {
matched = false
break
}
}
if !matched {
continue
}
// Found the matching DRM directory
slog.Debug("matched", "amdgpu", match, "drm", devDir)
totalFile := filepath.Join(devDir, DRMTotalMemoryFile)
buf, err := os.ReadFile(totalFile)
if err != nil {
slog.Debug("failed to read sysfs node", "file", totalFile, "error", err)
break
}
totalMemory, err = strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
if err != nil {
slog.Debug("failed to parse sysfs node", "file", totalFile, "error", err)
break
}
usedFile = filepath.Join(devDir, DRMUsedMemoryFile)
usedMemory, err = getFreeMemory(usedFile)
if err != nil {
slog.Debug("failed to update used memory", "error", err)
}
break
}
// iGPU detection, remove this check once we can support an iGPU variant of the rocm library
if totalMemory < IGPUMemLimit {
slog.Info("unsupported Radeon iGPU detected skipping", "id", gpuID, "total", format.HumanBytes2(totalMemory))
continue
}
var name string
// TODO - PCI ID lookup
if vendor > 0 && device > 0 {
name = fmt.Sprintf("%04x:%04x", vendor, device)
}
slog.Debug("amdgpu memory", "gpu", gpuID, "total", format.HumanBytes2(totalMemory))
slog.Debug("amdgpu memory", "gpu", gpuID, "available", format.HumanBytes2(totalMemory-usedMemory))
gpuInfo := RocmGPUInfo{
GpuInfo: GpuInfo{
Library: "rocm",
memInfo: memInfo{
TotalMemory: totalMemory,
FreeMemory: (totalMemory - usedMemory),
},
ID: strconv.Itoa(gpuID),
Name: name,
Compute: fmt.Sprintf("gfx%d%x%x", major, minor, patch),
MinimumMemory: rocmMinimumMemory,
DriverMajor: driverMajor,
DriverMinor: driverMinor,
},
usedFilepath: usedFile,
}
// If the user wants to filter to a subset of devices, filter out if we aren't a match
if len(visibleDevices) > 0 {
include := false
for _, visible := range visibleDevices {
if visible == gpuInfo.ID {
include = true
break
}
}
if !include {
slog.Info("filtering out device per user request", "id", gpuInfo.ID, "visible_devices", visibleDevices)
continue
}
}
// Final validation is gfx compatibility - load the library if we haven't already loaded it
// even if the user overrides, we still need to validate the library
if libDir == "" {
libDir, err = AMDValidateLibDir()
if err != nil {
slog.Warn("unable to verify rocm library, will use cpu", "error", err)
return nil
}
}
gpuInfo.DependencyPath = libDir
if gfxOverride == "" {
// Only load supported list once
if len(supported) == 0 {
supported, err = GetSupportedGFX(libDir)
if err != nil {
slog.Warn("failed to lookup supported GFX types, falling back to CPU mode", "error", err)
return nil
}
slog.Debug("rocm supported GPUs", "types", supported)
}
gfx := gpuInfo.Compute
if !slices.Contains[[]string, string](supported, gfx) {
slog.Warn("amdgpu is not supported", "gpu", gpuInfo.ID, "gpu_type", gfx, "library", libDir, "supported_types", supported)
// TODO - consider discrete markdown just for ROCM troubleshooting?
slog.Warn("See https://github.com/ollama/ollama/blob/main/docs/gpu.md#overrides for HSA_OVERRIDE_GFX_VERSION usage")
continue
} else {
slog.Info("amdgpu is supported", "gpu", gpuInfo.ID, "gpu_type", gfx)
}
} else {
slog.Info("skipping rocm gfx compatibility check", "HSA_OVERRIDE_GFX_VERSION", gfxOverride)
}
// Check for env var workarounds
if name == "1002:687f" { // Vega RX 56
gpuInfo.EnvWorkarounds = append(gpuInfo.EnvWorkarounds, [2]string{"HSA_ENABLE_SDMA", "0"})
}
// The GPU has passed all the verification steps and is supported
resp = append(resp, gpuInfo)
}
if len(resp) == 0 {
slog.Info("no compatible amdgpu devices detected")
}
return resp
}
// Quick check for AMD driver so we can skip amdgpu discovery if not present
func AMDDetected() bool {
// Some driver versions (older?) don't have a version file, so just lookup the parent dir
sysfsDir := filepath.Dir(DriverVersionFile)
_, err := os.Stat(sysfsDir)
if errors.Is(err, os.ErrNotExist) {
slog.Debug("amdgpu driver not detected " + sysfsDir)
return false
} else if err != nil {
slog.Debug("error looking up amd driver", "path", sysfsDir, "error", err)
return false
}
return true
}
// Prefer to use host installed ROCm, as long as it meets our minimum requirements
// failing that, tell the user how to download it on their own
func AMDValidateLibDir() (string, error) {
libDir, err := commonAMDValidateLibDir()
if err == nil {
return libDir, nil
}
// Well known ollama installer path
installedRocmDir := "/usr/share/ollama/lib/rocm"
if rocmLibUsable(installedRocmDir) {
return installedRocmDir, nil
}
// If we still haven't found a usable rocm, the user will have to install it on their own
slog.Warn("amdgpu detected, but no compatible rocm library found. Either install rocm v6, or follow manual install instructions at https://github.com/ollama/ollama/blob/main/docs/linux.md#manual-install")
return "", errors.New("no suitable rocm found, falling back to CPU")
}
func AMDDriverVersion() (driverMajor, driverMinor int, err error) {
_, err = os.Stat(DriverVersionFile)
if err != nil {
return 0, 0, fmt.Errorf("amdgpu version file missing: %s %w", DriverVersionFile, err)
}
fp, err := os.Open(DriverVersionFile)
if err != nil {
return 0, 0, err
}
defer fp.Close()
verString, err := io.ReadAll(fp)
if err != nil {
return 0, 0, err
}
pattern := `\A(\d+)\.(\d+).*`
regex := regexp.MustCompile(pattern)
match := regex.FindStringSubmatch(string(verString))
if len(match) < 2 {
return 0, 0, fmt.Errorf("malformed version string %s", string(verString))
}
driverMajor, err = strconv.Atoi(match[1])
if err != nil {
return 0, 0, err
}
driverMinor, err = strconv.Atoi(match[2])
if err != nil {
return 0, 0, err
}
return driverMajor, driverMinor, nil
}
func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
if len(gpus) == 0 {
return nil
}
for i := range gpus {
usedMemory, err := getFreeMemory(gpus[i].usedFilepath)
if err != nil {
return err
}
slog.Debug("updating rocm free memory", "gpu", gpus[i].ID, "name", gpus[i].Name, "before", format.HumanBytes2(gpus[i].FreeMemory), "now", format.HumanBytes2(gpus[i].TotalMemory-usedMemory))
gpus[i].FreeMemory = gpus[i].TotalMemory - usedMemory
}
return nil
}
func getFreeMemory(usedFile string) (uint64, error) {
buf, err := os.ReadFile(usedFile)
if err != nil {
return 0, fmt.Errorf("failed to read sysfs node %s %w", usedFile, err)
}
usedMemory, err := strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
if err != nil {
slog.Debug("failed to parse sysfs node", "file", usedFile, "error", err)
return 0, fmt.Errorf("failed to parse sysfs node %s %w", usedFile, err)
}
return usedMemory, nil
}
gpu/amd_windows.go deleted 100644 → 0
View file @ 0cb78a2f
package gpu
import (
"bytes"
"errors"
"log/slog"
"os"
"path/filepath"
"slices"
"strconv"
"strings"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
)
const (
// TODO We're lookinng for this exact name to detect iGPUs since hipGetDeviceProperties never reports integrated==true
iGPUName = "AMD Radeon(TM) Graphics"
)
var (
// Used to validate if the given ROCm lib is usable
ROCmLibGlobs = []string{"hipblas.dll", "rocblas"} // This is not sufficient to discern v5 vs v6
RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\6.1\\bin"} // TODO glob?
)
func AMDGetGPUInfo() []RocmGPUInfo {
resp := []RocmGPUInfo{}
hl, err := NewHipLib()
if err != nil {
slog.Debug(err.Error())
return nil
}
defer hl.Release()
driverMajor, driverMinor, err := hl.AMDDriverVersion()
if err != nil {
// For now this is benign, but we may eventually need to fail compatibility checks
slog.Debug("error looking up amd driver version", "error", err)
}
// Note: the HIP library automatically handles subsetting to any HIP_VISIBLE_DEVICES the user specified
count := hl.HipGetDeviceCount()
if count == 0 {
return nil
}
libDir, err := AMDValidateLibDir()
if err != nil {
slog.Warn("unable to verify rocm library, will use cpu", "error", err)
return nil
}
var supported []string
gfxOverride := envconfig.HsaOverrideGfxVersion()
if gfxOverride == "" {
supported, err = GetSupportedGFX(libDir)
if err != nil {
slog.Warn("failed to lookup supported GFX types, falling back to CPU mode", "error", err)
return nil
}
} else {
slog.Info("skipping rocm gfx compatibility check", "HSA_OVERRIDE_GFX_VERSION", gfxOverride)
}
slog.Debug("detected hip devices", "count", count)
// TODO how to determine the underlying device ID when visible devices is causing this to subset?
for i := range count {
err = hl.HipSetDevice(i)
if err != nil {
slog.Warn("set device", "id", i, "error", err)
continue
}
props, err := hl.HipGetDeviceProperties(i)
if err != nil {
slog.Warn("get properties", "id", i, "error", err)
continue
}
n := bytes.IndexByte(props.Name[:], 0)
name := string(props.Name[:n])
// TODO is UUID actually populated on windows?
// Can luid be used on windows for setting visible devices (and is it actually set?)
n = bytes.IndexByte(props.GcnArchName[:], 0)
gfx := string(props.GcnArchName[:n])
slog.Debug("hip device", "id", i, "name", name, "gfx", gfx)
// slog.Info(fmt.Sprintf("[%d] Integrated: %d", i, props.iGPU)) // DOESN'T REPORT CORRECTLY! Always 0
// TODO Why isn't props.iGPU accurate!?
if strings.EqualFold(name, iGPUName) {
slog.Info("unsupported Radeon iGPU detected skipping", "id", i, "name", name, "gfx", gfx)
continue
}
if gfxOverride == "" {
// Strip off Target Features when comparing
if !slices.Contains[[]string, string](supported, strings.Split(gfx, ":")[0]) {
slog.Warn("amdgpu is not supported", "gpu", i, "gpu_type", gfx, "library", libDir, "supported_types", supported)
// TODO - consider discrete markdown just for ROCM troubleshooting?
slog.Warn("See https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md for HSA_OVERRIDE_GFX_VERSION usage")
continue
} else {
slog.Debug("amdgpu is supported", "gpu", i, "gpu_type", gfx)
}
}
freeMemory, totalMemory, err := hl.HipMemGetInfo()
if err != nil {
slog.Warn("get mem info", "id", i, "error", err)
continue
}
// iGPU detection, remove this check once we can support an iGPU variant of the rocm library
if totalMemory < IGPUMemLimit {
slog.Info("amdgpu appears to be an iGPU, skipping", "gpu", i, "total", format.HumanBytes2(totalMemory))
continue
}
slog.Debug("amdgpu memory", "gpu", i, "total", format.HumanBytes2(totalMemory))
slog.Debug("amdgpu memory", "gpu", i, "available", format.HumanBytes2(freeMemory))
gpuInfo := RocmGPUInfo{
GpuInfo: GpuInfo{
Library: "rocm",
memInfo: memInfo{
TotalMemory: totalMemory,
FreeMemory: freeMemory,
},
// Free memory reporting on Windows is not reliable until we bump to ROCm v6.2
UnreliableFreeMemory: true,
ID: strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
DependencyPath: libDir,
MinimumMemory: rocmMinimumMemory,
Name: name,
Compute: gfx,
DriverMajor: driverMajor,
DriverMinor: driverMinor,
},
index: i,
}
resp = append(resp, gpuInfo)
}
return resp
}
func AMDValidateLibDir() (string, error) {
libDir, err := commonAMDValidateLibDir()
if err == nil {
return libDir, nil
}
// Installer payload (if we're running from some other location)
localAppData := os.Getenv("LOCALAPPDATA")
appDir := filepath.Join(localAppData, "Programs", "Ollama")
rocmTargetDir := filepath.Join(appDir, "rocm")
if rocmLibUsable(rocmTargetDir) {
slog.Debug("detected ollama installed ROCm at " + rocmTargetDir)
return rocmTargetDir, nil
}
// Should not happen on windows since we include it in the installer, but stand-alone binary might hit this
slog.Warn("amdgpu detected, but no compatible rocm library found. Please install ROCm")
return "", errors.New("no suitable rocm found, falling back to CPU")
}
func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
if len(gpus) == 0 {
return nil
}
hl, err := NewHipLib()
if err != nil {
slog.Debug(err.Error())
return nil
}
defer hl.Release()
for i := range gpus {
err := hl.HipSetDevice(gpus[i].index)
if err != nil {
return err
}
freeMemory, _, err := hl.HipMemGetInfo()
if err != nil {
slog.Warn("get mem info", "id", i, "error", err)
continue
}
slog.Debug("updating rocm free memory", "gpu", gpus[i].ID, "name", gpus[i].Name, "before", format.HumanBytes2(gpus[i].FreeMemory), "now", format.HumanBytes2(freeMemory))
gpus[i].FreeMemory = freeMemory
}
return nil
}
gpu/assets.go deleted 100644 → 0
View file @ 0cb78a2f
package gpu
import (
"errors"
"fmt"
"log/slog"
"os"
"path/filepath"
"runtime"
"strconv"
"strings"
"sync"
"syscall"
"time"
"github.com/ollama/ollama/envconfig"
)
var (
lock sync.Mutex
payloadsDir = ""
)
func PayloadsDir() (string, error) {
lock.Lock()
defer lock.Unlock()
var err error
if payloadsDir == "" {
runnersDir := envconfig.RunnersDir()
if runnersDir != "" {
payloadsDir = runnersDir
return payloadsDir, nil
}
// The remainder only applies on non-windows where we still carry payloads in the main executable
cleanupTmpDirs()
tmpDir := envconfig.TmpDir()
if tmpDir == "" {
tmpDir, err = os.MkdirTemp("", "ollama")
if err != nil {
return "", fmt.Errorf("failed to generate tmp dir: %w", err)
}
} else {
err = os.MkdirAll(tmpDir, 0o755)
if err != nil {
return "", fmt.Errorf("failed to generate tmp dir %s: %w", tmpDir, err)
}
}
// Track our pid so we can clean up orphaned tmpdirs
n := filepath.Join(tmpDir, "ollama.pid")
if err := os.WriteFile(n, []byte(strconv.Itoa(os.Getpid())), 0o644); err != nil {
return "", fmt.Errorf("failed to write pid file %s: %w", n, err)
}
// We create a distinct subdirectory for payloads within the tmpdir
// This will typically look like /tmp/ollama3208993108/runners on linux
payloadsDir = filepath.Join(tmpDir, "runners")
}
return payloadsDir, nil
}
// Best effort to clean up prior tmpdirs
func cleanupTmpDirs() {
matches, err := filepath.Glob(filepath.Join(os.TempDir(), "ollama*", "ollama.pid"))
if err != nil {
return
}
for _, match := range matches {
raw, err := os.ReadFile(match)
if errors.Is(err, os.ErrNotExist) {
slog.Debug("not a ollama runtime directory, skipping", "path", match)
continue
} else if err != nil {
slog.Warn("could not read ollama.pid, skipping", "path", match, "error", err)
continue
}
pid, err := strconv.Atoi(string(raw))
if err != nil {
slog.Warn("invalid pid, skipping", "path", match, "error", err)
continue
}
p, err := os.FindProcess(pid)
if err == nil && !errors.Is(p.Signal(syscall.Signal(0)), os.ErrProcessDone) {
slog.Warn("process still running, skipping", "pid", pid, "path", match)
continue
}
if err := os.Remove(match); err != nil {
slog.Warn("could not cleanup stale pidfile", "path", match, "error", err)
}
runners := filepath.Join(filepath.Dir(match), "runners")
if err := os.RemoveAll(runners); err != nil {
slog.Warn("could not cleanup stale runners", "path", runners, "error", err)
}
if err := os.Remove(filepath.Dir(match)); err != nil {
slog.Warn("could not cleanup stale tmpdir", "path", filepath.Dir(match), "error", err)
}
}
}
func Cleanup() {
lock.Lock()
defer lock.Unlock()
runnersDir := envconfig.RunnersDir()
if payloadsDir != "" && runnersDir == "" && runtime.GOOS != "windows" {
// We want to fully clean up the tmpdir parent of the payloads dir
tmpDir := filepath.Clean(filepath.Join(payloadsDir, ".."))
slog.Debug("cleaning up", "dir", tmpDir)
err := os.RemoveAll(tmpDir)
if err != nil {
// On windows, if we remove too quickly the llama.dll may still be in-use and fail to remove
time.Sleep(1000 * time.Millisecond)
err = os.RemoveAll(tmpDir)
if err != nil {
slog.Warn("failed to clean up", "dir", tmpDir, "err", err)
}
}
}
}
func UpdatePath(dir string) {
if runtime.GOOS == "windows" {
tmpDir := filepath.Dir(dir)
pathComponents := strings.Split(os.Getenv("PATH"), ";")
i := 0
for _, comp := range pathComponents {
if strings.EqualFold(comp, dir) {
return
}
// Remove any other prior paths to our temp dir
if !strings.HasPrefix(strings.ToLower(comp), strings.ToLower(tmpDir)) {
pathComponents[i] = comp
i++
}
}
newPath := strings.Join(append([]string{dir}, pathComponents...), ";")
slog.Info("updating", "PATH", newPath)
os.Setenv("PATH", newPath)
}
// linux and darwin rely on rpath
}
gpu/cpu_common.go deleted 100644 → 0
View file @ 0cb78a2f
package gpu
import (
"os"
"path/filepath"
"runtime"
"strings"
"golang.org/x/sys/cpu"
)
func GetCPUCapability() CPUCapability {
if cpu.X86.HasAVX2 {
return CPUCapabilityAVX2
}
if cpu.X86.HasAVX {
return CPUCapabilityAVX
}
// else LCD
return CPUCapabilityNone
}
func IsNUMA() bool {
if runtime.GOOS != "linux" {
// numa support in llama.cpp is linux only
return false
}
ids := map[string]interface{}{}
packageIds, _ := filepath.Glob("/sys/devices/system/cpu/cpu*/topology/physical_package_id")
for _, packageId := range packageIds {
id, err := os.ReadFile(packageId)
if err == nil {
ids[strings.TrimSpace(string(id))] = struct{}{}
}
}
return len(ids) > 1
}
gpu/cuda_common.go deleted 100644 → 0
View file @ 0cb78a2f
//go:build linux || windows
package gpu
import (
"log/slog"
"strings"
)
func cudaGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
ids := []string{}
for _, info := range gpuInfo {
if info.Library != "cuda" {
// TODO shouldn't happen if things are wired correctly...
slog.Debug("cudaGetVisibleDevicesEnv skipping over non-cuda device", "library", info.Library)
continue
}
ids = append(ids, info.ID)
}
return "CUDA_VISIBLE_DEVICES", strings.Join(ids, ",")
}
gpu/gpu.go deleted 100644 → 0
View file @ 0cb78a2f
//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/slog"
"os"
"path/filepath"
"runtime"
"strings"
"sync"
"unsafe"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
)
type cudaHandles struct {
deviceCount int
cudart *C.cudart_handle_t
nvcuda *C.nvcuda_handle_t
nvml *C.nvml_handle_t
}
type oneapiHandles struct {
oneapi *C.oneapi_handle_t
deviceCount int
}
const (
cudaMinimumMemory = 457 * format.MebiByte
rocmMinimumMemory = 457 * format.MebiByte
// TODO OneAPI minimum memory
)
var (
gpuMutex sync.Mutex
bootstrapped bool
cpuCapability CPUCapability
cpus []CPUInfo
cudaGPUs []CudaGPUInfo
nvcudaLibPath string
cudartLibPath string
oneapiLibPath string
nvmlLibPath string
rocmGPUs []RocmGPUInfo
oneapiGPUs []OneapiGPUInfo
)
// With our current CUDA compile flags, older than 5.0 will not work properly
var CudaComputeMin = [2]C.int{5, 0}
var RocmComputeMin = 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
// Jetson devices have JETSON_JETPACK="x.y.z" factory set to the Jetpack version installed.
// Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
var CudaTegra string = os.Getenv("JETSON_JETPACK")
// Note: gpuMutex must already be held
func initCudaHandles() *cudaHandles {
// TODO - if the ollama build is CPU only, don't do these checks as they're irrelevant and confusing
cHandles := &cudaHandles{}
// Short Circuit if we already know which library to use
if nvmlLibPath != "" {
cHandles.nvml, _ = LoadNVMLMgmt([]string{nvmlLibPath})
return cHandles
}
if nvcudaLibPath != "" {
cHandles.deviceCount, cHandles.nvcuda, _ = LoadNVCUDAMgmt([]string{nvcudaLibPath})
return cHandles
}
if cudartLibPath != "" {
cHandles.deviceCount, cHandles.cudart, _ = LoadCUDARTMgmt([]string{cudartLibPath})
return cHandles
}
slog.Debug("searching for GPU discovery libraries for NVIDIA")
var cudartMgmtPatterns []string
// Aligned with driver, we can't carry as payloads
nvcudaMgmtPatterns := NvcudaGlobs
if runtime.GOOS == "windows" {
localAppData := os.Getenv("LOCALAPPDATA")
cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
}
tmpDir, _ := PayloadsDir()
if tmpDir != "" {
// TODO - add "payloads" for subprocess
cudartMgmtPatterns = []string{filepath.Join(tmpDir, "cuda*", CudartMgmtName)}
}
cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
if len(NvmlGlobs) > 0 {
nvmlLibPaths := FindGPULibs(NvmlMgmtName, NvmlGlobs)
if len(nvmlLibPaths) > 0 {
nvml, libPath := LoadNVMLMgmt(nvmlLibPaths)
if nvml != nil {
slog.Debug("nvidia-ml loaded", "library", libPath)
cHandles.nvml = nvml
nvmlLibPath = libPath
}
}
}
nvcudaLibPaths := FindGPULibs(NvcudaMgmtName, nvcudaMgmtPatterns)
if len(nvcudaLibPaths) > 0 {
deviceCount, nvcuda, libPath := LoadNVCUDAMgmt(nvcudaLibPaths)
if nvcuda != nil {
slog.Debug("detected GPUs", "count", deviceCount, "library", libPath)
cHandles.nvcuda = nvcuda
cHandles.deviceCount = deviceCount
nvcudaLibPath = libPath
return cHandles
}
}
cudartLibPaths := FindGPULibs(CudartMgmtName, cudartMgmtPatterns)
if len(cudartLibPaths) > 0 {
deviceCount, cudart, libPath := LoadCUDARTMgmt(cudartLibPaths)
if cudart != nil {
slog.Debug("detected GPUs", "library", libPath, "count", deviceCount)
cHandles.cudart = cudart
cHandles.deviceCount = deviceCount
cudartLibPath = libPath
return cHandles
}
}
return cHandles
}
// Note: gpuMutex must already be held
func initOneAPIHandles() *oneapiHandles {
oHandles := &oneapiHandles{}
// Short Circuit if we already know which library to use
if oneapiLibPath != "" {
oHandles.deviceCount, oHandles.oneapi, _ = LoadOneapiMgmt([]string{oneapiLibPath})
return oHandles
}
oneapiLibPaths := FindGPULibs(OneapiMgmtName, OneapiGlobs)
if len(oneapiLibPaths) > 0 {
oHandles.deviceCount, oHandles.oneapi, oneapiLibPath = LoadOneapiMgmt(oneapiLibPaths)
}
return oHandles
}
func GetCPUInfo() GpuInfoList {
gpuMutex.Lock()
if !bootstrapped {
gpuMutex.Unlock()
GetGPUInfo()
} else {
gpuMutex.Unlock()
}
return GpuInfoList{cpus[0].GpuInfo}
}
func GetGPUInfo() GpuInfoList {
// 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()
needRefresh := true
var cHandles *cudaHandles
var oHandles *oneapiHandles
defer func() {
if cHandles != nil {
if cHandles.cudart != nil {
C.cudart_release(*cHandles.cudart)
}
if cHandles.nvcuda != nil {
C.nvcuda_release(*cHandles.nvcuda)
}
if cHandles.nvml != nil {
C.nvml_release(*cHandles.nvml)
}
}
if oHandles != nil {
if oHandles.oneapi != nil {
// TODO - is this needed?
C.oneapi_release(*oHandles.oneapi)
}
}
}()
if !bootstrapped {
slog.Info("looking for compatible GPUs")
needRefresh = false
cpuCapability = GetCPUCapability()
var memInfo C.mem_info_t
mem, err := GetCPUMem()
if err != nil {
slog.Warn("error looking up system memory", "error", err)
}
cpus = []CPUInfo{
{
GpuInfo: GpuInfo{
memInfo: mem,
Library: "cpu",
Variant: cpuCapability,
ID: "0",
},
},
}
// Fallback to CPU mode if we're lacking required vector extensions on x86
if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
slog.Warn("CPU does not have minimum vector extensions, GPU inference disabled", "required", GPURunnerCPUCapability, "detected", cpuCapability)
bootstrapped = true
// No need to do any GPU discovery, since we can't run on them
return GpuInfoList{cpus[0].GpuInfo}
}
// On windows we bundle the nvidia library one level above the runner dir
depPath := ""
if runtime.GOOS == "windows" && envconfig.RunnersDir() != "" {
depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir()), "cuda")
}
// Load ALL libraries
cHandles = initCudaHandles()
// 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 memInfo.major < CudaComputeMin[0] || (memInfo.major == CudaComputeMin[0] && memInfo.minor < CudaComputeMin[1]) {
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
// query the management library as well so we can record any skew between the two
// which represents overhead on the GPU we must set aside on subsequent updates
if cHandles.nvml != nil {
C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)
if memInfo.err != nil {
slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
C.free(unsafe.Pointer(memInfo.err))
} else {
if memInfo.free != 0 && uint64(memInfo.free) > gpuInfo.FreeMemory {
gpuInfo.OSOverhead = uint64(memInfo.free) - gpuInfo.FreeMemory
slog.Info("detected OS VRAM overhead",
"id", gpuInfo.ID,
"library", gpuInfo.Library,
"compute", gpuInfo.Compute,
"driver", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor),
"name", gpuInfo.Name,
"overhead", format.HumanBytes2(gpuInfo.OSOverhead),
)
}
}
}
// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
cudaGPUs = append(cudaGPUs, gpuInfo)
}
}
// Intel
if envconfig.IntelGPU() {
oHandles = initOneAPIHandles()
if oHandles != nil && oHandles.oneapi != nil {
// On windows we bundle the oneapi library one level above the runner dir
depPath = ""
if runtime.GOOS == "windows" && envconfig.RunnersDir() != "" {
depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir()), "oneapi")
}
for d := range oHandles.oneapi.num_drivers {
if oHandles.oneapi == nil {
// shouldn't happen
slog.Warn("nil oneapi handle with driver count", "count", int(oHandles.oneapi.num_drivers))
continue
}
devCount := C.oneapi_get_device_count(*oHandles.oneapi, C.int(d))
for i := range devCount {
gpuInfo := OneapiGPUInfo{
GpuInfo: GpuInfo{
Library: "oneapi",
},
driverIndex: int(d),
gpuIndex: int(i),
}
// TODO - split bootstrapping from updating free memory
C.oneapi_check_vram(*oHandles.oneapi, C.int(d), i, &memInfo)
// TODO - convert this to MinimumMemory based on testing...
var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
memInfo.free = C.uint64_t(totalFreeMem)
gpuInfo.TotalMemory = uint64(memInfo.total)
gpuInfo.FreeMemory = uint64(memInfo.free)
gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
gpuInfo.DependencyPath = depPath
oneapiGPUs = append(oneapiGPUs, gpuInfo)
}
}
}
}
rocmGPUs = AMDGetGPUInfo()
bootstrapped = true
if len(cudaGPUs) == 0 && len(rocmGPUs) == 0 && len(oneapiGPUs) == 0 {
slog.Info("no compatible GPUs were discovered")
}
}
// For detected GPUs, load library if not loaded
// Refresh free memory usage
if needRefresh {
mem, err := GetCPUMem()
if err != nil {
slog.Warn("error looking up system memory", "error", err)
} else {
slog.Debug("updating system memory data",
slog.Group(
"before",
"total", format.HumanBytes2(cpus[0].TotalMemory),
"free", format.HumanBytes2(cpus[0].FreeMemory),
"free_swap", format.HumanBytes2(cpus[0].FreeSwap),
),
slog.Group(
"now",
"total", format.HumanBytes2(mem.TotalMemory),
"free", format.HumanBytes2(mem.FreeMemory),
"free_swap", format.HumanBytes2(mem.FreeSwap),
),
)
cpus[0].FreeMemory = mem.FreeMemory
cpus[0].FreeSwap = mem.FreeSwap
}
var memInfo C.mem_info_t
if cHandles == nil && len(cudaGPUs) > 0 {
cHandles = initCudaHandles()
}
for i, gpu := range cudaGPUs {
if cHandles.nvml != nil {
C.nvml_get_free(*cHandles.nvml, C.int(gpu.index), &memInfo.free, &memInfo.total, &memInfo.used)
} else if cHandles.cudart != nil {
C.cudart_bootstrap(*cHandles.cudart, C.int(gpu.index), &memInfo)
} else if cHandles.nvcuda != nil {
C.nvcuda_get_free(*cHandles.nvcuda, C.int(gpu.index), &memInfo.free, &memInfo.total)
memInfo.used = memInfo.total - memInfo.free
} else {
// shouldn't happen
slog.Warn("no valid cuda library loaded to refresh vram usage")
break
}
if memInfo.err != nil {
slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
C.free(unsafe.Pointer(memInfo.err))
continue
}
if memInfo.free == 0 {
slog.Warn("error looking up nvidia GPU memory")
continue
}
if cHandles.nvml != nil && gpu.OSOverhead > 0 {
// When using the management library update based on recorded overhead
memInfo.free -= C.uint64_t(gpu.OSOverhead)
}
slog.Debug("updating cuda memory data",
"gpu", gpu.ID,
"name", gpu.Name,
"overhead", format.HumanBytes2(gpu.OSOverhead),
slog.Group(
"before",
"total", format.HumanBytes2(gpu.TotalMemory),
"free", format.HumanBytes2(gpu.FreeMemory),
),
slog.Group(
"now",
"total", format.HumanBytes2(uint64(memInfo.total)),
"free", format.HumanBytes2(uint64(memInfo.free)),
"used", format.HumanBytes2(uint64(memInfo.used)),
),
)
cudaGPUs[i].FreeMemory = uint64(memInfo.free)
}
if oHandles == nil && len(oneapiGPUs) > 0 {
oHandles = initOneAPIHandles()
}
for i, gpu := range oneapiGPUs {
if oHandles.oneapi == nil {
// shouldn't happen
slog.Warn("nil oneapi handle with device count", "count", oHandles.deviceCount)
continue
}
C.oneapi_check_vram(*oHandles.oneapi, C.int(gpu.driverIndex), C.int(gpu.gpuIndex), &memInfo)
// TODO - convert this to MinimumMemory based on testing...
var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
memInfo.free = C.uint64_t(totalFreeMem)
oneapiGPUs[i].FreeMemory = uint64(memInfo.free)
}
err = RocmGPUInfoList(rocmGPUs).RefreshFreeMemory()
if err != nil {
slog.Debug("problem refreshing ROCm free memory", "error", err)
}
}
resp := []GpuInfo{}
for _, gpu := range cudaGPUs {
resp = append(resp, gpu.GpuInfo)
}
for _, gpu := range rocmGPUs {
resp = append(resp, gpu.GpuInfo)
}
for _, gpu := range oneapiGPUs {
resp = append(resp, gpu.GpuInfo)
}
if len(resp) == 0 {
resp = append(resp, cpus[0].GpuInfo)
}
return resp
}
func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
var ldPaths []string
var patterns []string
gpuLibPaths := []string{}
slog.Debug("Searching for GPU library", "name", baseLibName)
switch runtime.GOOS {
case "windows":
ldPaths = strings.Split(os.Getenv("PATH"), ";")
case "linux":
ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), ":")
default:
return gpuLibPaths
}
// Start with whatever we find in the PATH/LD_LIBRARY_PATH
for _, ldPath := range ldPaths {
d, err := filepath.Abs(ldPath)
if err != nil {
continue
}
patterns = append(patterns, filepath.Join(d, baseLibName+"*"))
}
patterns = append(patterns, defaultPatterns...)
slog.Debug("gpu library search", "globs", patterns)
for _, pattern := range patterns {
// Nvidia PhysX known to return bogus results
if strings.Contains(pattern, "PhysX") {
slog.Debug("skipping PhysX cuda library path", "path", pattern)
continue
}
// Ignore glob discovery errors
matches, _ := filepath.Glob(pattern)
for _, match := range matches {
// Resolve any links so we don't try the same lib multiple times
// and weed out any dups across globs
libPath := match
tmp := match
var err error
for ; err == nil; tmp, err = os.Readlink(libPath) {
if !filepath.IsAbs(tmp) {
tmp = filepath.Join(filepath.Dir(libPath), tmp)
}
libPath = tmp
}
new := true
for _, cmp := range gpuLibPaths {
if cmp == libPath {
new = false
break
}
}
if new {
gpuLibPaths = append(gpuLibPaths, libPath)
}
}
}
slog.Debug("discovered GPU libraries", "paths", gpuLibPaths)
return gpuLibPaths
}
func LoadCUDARTMgmt(cudartLibPaths []string) (int, *C.cudart_handle_t, string) {
var resp C.cudart_init_resp_t
resp.ch.verbose = getVerboseState()
for _, libPath := range cudartLibPaths {
lib := C.CString(libPath)
defer C.free(unsafe.Pointer(lib))
C.cudart_init(lib, &resp)
if resp.err != nil {
slog.Debug("Unable to load cudart", "library", libPath, "error", C.GoString(resp.err))
C.free(unsafe.Pointer(resp.err))
} else {
return int(resp.num_devices), &resp.ch, libPath
}
}
return 0, nil, ""
}
func LoadNVCUDAMgmt(nvcudaLibPaths []string) (int, *C.nvcuda_handle_t, string) {
var resp C.nvcuda_init_resp_t
resp.ch.verbose = getVerboseState()
for _, libPath := range nvcudaLibPaths {
lib := C.CString(libPath)
defer C.free(unsafe.Pointer(lib))
C.nvcuda_init(lib, &resp)
if resp.err != nil {
// Decide what log level based on the type of error message to help users understand why
msg := C.GoString(resp.err)
switch resp.cudaErr {
case C.CUDA_ERROR_INSUFFICIENT_DRIVER, C.CUDA_ERROR_SYSTEM_DRIVER_MISMATCH:
slog.Warn("version mismatch between driver and cuda driver library - reboot or upgrade may be required", "library", libPath, "error", msg)
case C.CUDA_ERROR_NO_DEVICE:
slog.Info("no nvidia devices detected", "library", libPath)
case C.CUDA_ERROR_UNKNOWN:
slog.Warn("unknown error initializing cuda driver library", "library", libPath, "error", msg)
slog.Warn("see https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md for more information")
default:
if strings.Contains(msg, "wrong ELF class") {
slog.Debug("skipping 32bit library", "library", libPath)
} else {
slog.Info("unable to load cuda driver library", "library", libPath, "error", msg)
}
}
C.free(unsafe.Pointer(resp.err))
} else {
return int(resp.num_devices), &resp.ch, libPath
}
}
return 0, nil, ""
}
func LoadNVMLMgmt(nvmlLibPaths []string) (*C.nvml_handle_t, string) {
var resp C.nvml_init_resp_t
resp.ch.verbose = getVerboseState()
for _, libPath := range nvmlLibPaths {
lib := C.CString(libPath)
defer C.free(unsafe.Pointer(lib))
C.nvml_init(lib, &resp)
if resp.err != nil {
slog.Info(fmt.Sprintf("Unable to load NVML management library %s: %s", libPath, C.GoString(resp.err)))
C.free(unsafe.Pointer(resp.err))
} else {
return &resp.ch, libPath
}
}
return nil, ""
}
func LoadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string) {
var resp C.oneapi_init_resp_t
num_devices := 0
resp.oh.verbose = getVerboseState()
for _, libPath := range oneapiLibPaths {
lib := C.CString(libPath)
defer C.free(unsafe.Pointer(lib))
C.oneapi_init(lib, &resp)
if resp.err != nil {
slog.Debug("Unable to load oneAPI management library", "library", libPath, "error", C.GoString(resp.err))
C.free(unsafe.Pointer(resp.err))
} else {
for i := range resp.oh.num_drivers {
num_devices += int(C.oneapi_get_device_count(resp.oh, C.int(i)))
}
return num_devices, &resp.oh, libPath
}
}
return 0, nil, ""
}
func getVerboseState() C.uint16_t {
if envconfig.Debug() {
return C.uint16_t(1)
}
return C.uint16_t(0)
}
// Given the list of GPUs this instantiation is targeted for,
// figure out the visible devices environment variable
//
// If different libraries are detected, the first one is what we use
func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
if len(l) == 0 {
return "", ""
}
switch l[0].Library {
case "cuda":
return cudaGetVisibleDevicesEnv(l)
case "rocm":
return rocmGetVisibleDevicesEnv(l)
case "oneapi":
return oneapiGetVisibleDevicesEnv(l)
default:
slog.Debug("no filter required for library " + l[0].Library)
return "", ""
}
}
gpu/gpu_darwin.go deleted 100644 → 0
View file @ 0cb78a2f
//go:build darwin
package gpu
/*
#cgo CFLAGS: -x objective-c
#cgo LDFLAGS: -framework Foundation -framework CoreGraphics -framework Metal
#include "gpu_info_darwin.h"
*/
import "C"
import (
"runtime"
"github.com/ollama/ollama/format"
)
const (
metalMinimumMemory = 512 * format.MebiByte
)
func GetGPUInfo() GpuInfoList {
mem, _ := GetCPUMem()
if runtime.GOARCH == "amd64" {
return []GpuInfo{
{
Library: "cpu",
Variant: GetCPUCapability(),
memInfo: mem,
},
}
}
info := GpuInfo{
Library: "metal",
ID: "0",
}
info.TotalMemory = uint64(C.getRecommendedMaxVRAM())
// TODO is there a way to gather actual allocated video memory? (currentAllocatedSize doesn't work)
info.FreeMemory = info.TotalMemory
info.MinimumMemory = metalMinimumMemory
return []GpuInfo{info}
}
func GetCPUInfo() GpuInfoList {
mem, _ := GetCPUMem()
return []GpuInfo{
{
Library: "cpu",
Variant: GetCPUCapability(),
memInfo: mem,
},
}
}
func GetCPUMem() (memInfo, error) {
return memInfo{
TotalMemory: uint64(C.getPhysicalMemory()),
FreeMemory: uint64(C.getFreeMemory()),
// FreeSwap omitted as Darwin uses dynamic paging
}, nil
}
func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
// No-op on darwin
return "", ""
}
gpu/gpu_info.h deleted 100644 → 0
View file @ 0cb78a2f
#ifndef __APPLE__
#ifndef __GPU_INFO_H__
#define __GPU_INFO_H__
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#ifndef _WIN32
#include <dlfcn.h>
#define LOAD_LIBRARY(lib, flags) dlopen(lib, flags)
#define LOAD_SYMBOL(handle, sym) dlsym(handle, sym)
#define LOAD_ERR() strdup(dlerror())
#define UNLOAD_LIBRARY(handle) dlclose(handle)
#else
#include <windows.h>
#define LOAD_LIBRARY(lib, flags) LoadLibrary(lib)
#define LOAD_SYMBOL(handle, sym) GetProcAddress(handle, sym)
#define UNLOAD_LIBRARY(handle) FreeLibrary(handle)
#define LOAD_ERR() ({\
LPSTR messageBuffer = NULL; \
size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS, \
NULL, GetLastError(), MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&messageBuffer, 0, NULL); \
char *resp = strdup(messageBuffer); \
LocalFree(messageBuffer); \
resp; \
})
#endif
#define LOG(verbose, ...) \
do { \
if (verbose) { \
fprintf(stderr, __VA_ARGS__); \
} \
} while (0)
#ifdef __cplusplus
extern "C" {
#endif
#define GPU_ID_LEN 64
#define GPU_NAME_LEN 96
typedef struct mem_info {
char *err; // If non-nill, caller responsible for freeing
char gpu_id[GPU_ID_LEN];
char gpu_name[GPU_NAME_LEN];
uint64_t total;
uint64_t free;
uint64_t used;
// Compute Capability
int major;
int minor;
int patch;
} mem_info_t;
void cpu_check_ram(mem_info_t *resp);
#ifdef __cplusplus
}
#endif
#include "gpu_info_cudart.h"
#include "gpu_info_nvcuda.h"
#include "gpu_info_nvml.h"
#include "gpu_info_oneapi.h"
#endif // __GPU_INFO_H__
#endif // __APPLE__
gpu/gpu_info_cudart.c deleted 100644 → 0
View file @ 0cb78a2f
#ifndef __APPLE__ // TODO - maybe consider nvidia support on intel macs?
#include <string.h>
#include "gpu_info_cudart.h"
void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
cudartReturn_t ret;
resp->err = NULL;
resp->num_devices = 0;
const int buflen = 256;
char buf[buflen + 1];
int i;
struct lookup {
char *s;
void **p;
} l[] = {
{"cudaSetDevice", (void *)&resp->ch.cudaSetDevice},
{"cudaDeviceSynchronize", (void *)&resp->ch.cudaDeviceSynchronize},
{"cudaDeviceReset", (void *)&resp->ch.cudaDeviceReset},
{"cudaMemGetInfo", (void *)&resp->ch.cudaMemGetInfo},
{"cudaGetDeviceCount", (void *)&resp->ch.cudaGetDeviceCount},
{"cudaDeviceGetAttribute", (void *)&resp->ch.cudaDeviceGetAttribute},
{"cudaDriverGetVersion", (void *)&resp->ch.cudaDriverGetVersion},
{"cudaGetDeviceProperties", (void *)&resp->ch.cudaGetDeviceProperties},
{NULL, NULL},
};
resp->ch.handle = LOAD_LIBRARY(cudart_lib_path, RTLD_LAZY);
if (!resp->ch.handle) {
char *msg = LOAD_ERR();
LOG(resp->ch.verbose, "library %s load err: %s\n", cudart_lib_path, msg);
snprintf(buf, buflen,
"Unable to load %s library to query for Nvidia GPUs: %s",
cudart_lib_path, msg);
free(msg);
resp->err = strdup(buf);
return;
}
for (i = 0; l[i].s != NULL; i++) {
*l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
if (!*(l[i].p)) {
char *msg = LOAD_ERR();
LOG(resp->ch.verbose, "dlerr: %s\n", msg);
UNLOAD_LIBRARY(resp->ch.handle);
resp->ch.handle = NULL;
snprintf(buf, buflen, "symbol lookup for %s failed: %s", l[i].s,
msg);
free(msg);
resp->err = strdup(buf);
return;
}
}
ret = (*resp->ch.cudaSetDevice)(0);
if (ret != CUDART_SUCCESS) {
LOG(resp->ch.verbose, "cudaSetDevice err: %d\n", ret);
UNLOAD_LIBRARY(resp->ch.handle);
resp->ch.handle = NULL;
if (ret == CUDA_ERROR_INSUFFICIENT_DRIVER) {
resp->err = strdup("your nvidia driver is too old or missing. If you have a CUDA GPU please upgrade to run ollama");
return;
}
snprintf(buf, buflen, "cudart init failure: %d", ret);
resp->err = strdup(buf);
return;
}
int version = 0;
cudartDriverVersion_t driverVersion;
driverVersion.major = 0;
driverVersion.minor = 0;
// Report driver version if we're in verbose mode, ignore errors
ret = (*resp->ch.cudaDriverGetVersion)(&version);
if (ret != CUDART_SUCCESS) {
LOG(resp->ch.verbose, "cudaDriverGetVersion failed: %d\n", ret);
} else {
driverVersion.major = version / 1000;
driverVersion.minor = (version - (driverVersion.major * 1000)) / 10;
LOG(resp->ch.verbose, "CUDA driver version: %d-%d\n", driverVersion.major, driverVersion.minor);
}
ret = (*resp->ch.cudaGetDeviceCount)(&resp->num_devices);
if (ret != CUDART_SUCCESS) {
LOG(resp->ch.verbose, "cudaGetDeviceCount err: %d\n", ret);
UNLOAD_LIBRARY(resp->ch.handle);
resp->ch.handle = NULL;
snprintf(buf, buflen, "unable to get device count: %d", ret);
resp->err = strdup(buf);
return;
}
}
void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
resp->err = NULL;
cudartMemory_t memInfo = {0,0,0};
cudartReturn_t ret;
const int buflen = 256;
char buf[buflen + 1];
if (h.handle == NULL) {
resp->err = strdup("cudart handle isn't initialized");
return;
}
ret = (*h.cudaSetDevice)(i);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "cudart device failed to initialize");
resp->err = strdup(buf);
return;
}
cudaDeviceProp_t props;
ret = (*h.cudaGetDeviceProperties)(&props, i);
if (ret != CUDART_SUCCESS) {
LOG(h.verbose, "[%d] device properties lookup failure: %d\n", i, ret);
snprintf(&resp->gpu_id[0], GPU_ID_LEN, "%d", i);
resp->major = 0;
resp->minor = 0;
} else {
int allNull = 1;
for (int j = 0; j < 16; j++) {
if (props.uuid.bytes[j] != 0) {
allNull = 0;
break;
}
}
if (allNull != 0) {
snprintf(&resp->gpu_id[0], GPU_ID_LEN, "%d", i);
} else {
// GPU-d110a105-ac29-1d54-7b49-9c90440f215b
snprintf(&resp->gpu_id[0], GPU_ID_LEN,
"GPU-%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
props.uuid.bytes[0],
props.uuid.bytes[1],
props.uuid.bytes[2],
props.uuid.bytes[3],
props.uuid.bytes[4],
props.uuid.bytes[5],
props.uuid.bytes[6],
props.uuid.bytes[7],
props.uuid.bytes[8],
props.uuid.bytes[9],
props.uuid.bytes[10],
props.uuid.bytes[11],
props.uuid.bytes[12],
props.uuid.bytes[13],
props.uuid.bytes[14],
props.uuid.bytes[15]
);
}
resp->major = props.major;
resp->minor = props.minor;
// TODO add other useful properties from props
}
ret = (*h.cudaMemGetInfo)(&memInfo.free, &memInfo.total);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "cudart device memory info lookup failure %d", ret);
resp->err = strdup(buf);
return;
}
resp->total = memInfo.total;
resp->free = memInfo.free;
resp->used = memInfo.used;
LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
}
void cudart_release(cudart_handle_t h) {
LOG(h.verbose, "releasing cudart library\n");
UNLOAD_LIBRARY(h.handle);
h.handle = NULL;
}
#endif // __APPLE__
\ No newline at end of file
gpu/gpu_info_cudart.h deleted 100644 → 0
View file @ 0cb78a2f
#ifndef __APPLE__
#ifndef __GPU_INFO_CUDART_H__
#define __GPU_INFO_CUDART_H__
#include "gpu_info.h"
// Just enough typedef's to dlopen/dlsym for memory information
typedef enum cudartReturn_enum {
CUDART_SUCCESS = 0,
CUDART_ERROR_INVALID_VALUE = 1,
CUDART_ERROR_MEMORY_ALLOCATION = 2,
CUDART_ERROR_INSUFFICIENT_DRIVER = 35,
// Other values omitted for now...
} cudartReturn_t;
typedef enum cudartDeviceAttr_enum {
cudartDevAttrComputeCapabilityMajor = 75,
cudartDevAttrComputeCapabilityMinor = 76,
// TODO - not yet wired up but may be useful for Jetson or other
// integrated GPU scenarios with shared memory
cudaDevAttrIntegrated = 18
} cudartDeviceAttr_t;
typedef void *cudartDevice_t; // Opaque is sufficient
typedef struct cudartMemory_st {
size_t total;
size_t free;
size_t used;
} cudartMemory_t;
typedef struct cudartDriverVersion {
int major;
int minor;
} cudartDriverVersion_t;
typedef struct cudaUUID {
unsigned char bytes[16];
} cudaUUID_t;
typedef struct cudaDeviceProp {
char name[256]; /**< ASCII string identifying device */
cudaUUID_t uuid; /**< 16-byte unique identifier */
char luid[8]; /**< 8-byte locally unique identifier. Value is undefined on TCC and non-Windows platforms */
unsigned int luidDeviceNodeMask; /**< LUID device node mask. Value is undefined on TCC and non-Windows platforms */
size_t totalGlobalMem; /**< Global memory available on device in bytes */
size_t sharedMemPerBlock; /**< Shared memory available per block in bytes */
int regsPerBlock; /**< 32-bit registers available per block */
int warpSize; /**< Warp size in threads */
size_t memPitch; /**< Maximum pitch in bytes allowed by memory copies */
int maxThreadsPerBlock; /**< Maximum number of threads per block */
int maxThreadsDim[3]; /**< Maximum size of each dimension of a block */
int maxGridSize[3]; /**< Maximum size of each dimension of a grid */
int clockRate; /**< Clock frequency in kilohertz */
size_t totalConstMem; /**< Constant memory available on device in bytes */
int major; /**< Major compute capability */
int minor; /**< Minor compute capability */
size_t textureAlignment; /**< Alignment requirement for textures */
size_t texturePitchAlignment; /**< Pitch alignment requirement for texture references bound to pitched memory */
int deviceOverlap; /**< Device can concurrently copy memory and execute a kernel. Deprecated. Use instead asyncEngineCount. */
int multiProcessorCount; /**< Number of multiprocessors on device */
int kernelExecTimeoutEnabled; /**< Specified whether there is a run time limit on kernels */
int integrated; /**< Device is integrated as opposed to discrete */
int canMapHostMemory; /**< Device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer */
int computeMode; /**< Compute mode (See ::cudaComputeMode) */
int maxTexture1D; /**< Maximum 1D texture size */
int maxTexture1DMipmap; /**< Maximum 1D mipmapped texture size */
int maxTexture1DLinear; /**< Deprecated, do not use. Use cudaDeviceGetTexture1DLinearMaxWidth() or cuDeviceGetTexture1DLinearMaxWidth() instead. */
int maxTexture2D[2]; /**< Maximum 2D texture dimensions */
int maxTexture2DMipmap[2]; /**< Maximum 2D mipmapped texture dimensions */
int maxTexture2DLinear[3]; /**< Maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory */
int maxTexture2DGather[2]; /**< Maximum 2D texture dimensions if texture gather operations have to be performed */
int maxTexture3D[3]; /**< Maximum 3D texture dimensions */
int maxTexture3DAlt[3]; /**< Maximum alternate 3D texture dimensions */
int maxTextureCubemap; /**< Maximum Cubemap texture dimensions */
int maxTexture1DLayered[2]; /**< Maximum 1D layered texture dimensions */
int maxTexture2DLayered[3]; /**< Maximum 2D layered texture dimensions */
int maxTextureCubemapLayered[2];/**< Maximum Cubemap layered texture dimensions */
int maxSurface1D; /**< Maximum 1D surface size */
int maxSurface2D[2]; /**< Maximum 2D surface dimensions */
int maxSurface3D[3]; /**< Maximum 3D surface dimensions */
int maxSurface1DLayered[2]; /**< Maximum 1D layered surface dimensions */
int maxSurface2DLayered[3]; /**< Maximum 2D layered surface dimensions */
int maxSurfaceCubemap; /**< Maximum Cubemap surface dimensions */
int maxSurfaceCubemapLayered[2];/**< Maximum Cubemap layered surface dimensions */
size_t surfaceAlignment; /**< Alignment requirements for surfaces */
int concurrentKernels; /**< Device can possibly execute multiple kernels concurrently */
int ECCEnabled; /**< Device has ECC support enabled */
int pciBusID; /**< PCI bus ID of the device */
int pciDeviceID; /**< PCI device ID of the device */
int pciDomainID; /**< PCI domain ID of the device */
int tccDriver; /**< 1 if device is a Tesla device using TCC driver, 0 otherwise */
int asyncEngineCount; /**< Number of asynchronous engines */
int unifiedAddressing; /**< Device shares a unified address space with the host */
int memoryClockRate; /**< Peak memory clock frequency in kilohertz */
int memoryBusWidth; /**< Global memory bus width in bits */
int l2CacheSize; /**< Size of L2 cache in bytes */
int persistingL2CacheMaxSize; /**< Device's maximum l2 persisting lines capacity setting in bytes */
int maxThreadsPerMultiProcessor;/**< Maximum resident threads per multiprocessor */
int streamPrioritiesSupported; /**< Device supports stream priorities */
int globalL1CacheSupported; /**< Device supports caching globals in L1 */
int localL1CacheSupported; /**< Device supports caching locals in L1 */
size_t sharedMemPerMultiprocessor; /**< Shared memory available per multiprocessor in bytes */
int regsPerMultiprocessor; /**< 32-bit registers available per multiprocessor */
int managedMemory; /**< Device supports allocating managed memory on this system */
int isMultiGpuBoard; /**< Device is on a multi-GPU board */
int multiGpuBoardGroupID; /**< Unique identifier for a group of devices on the same multi-GPU board */
int hostNativeAtomicSupported; /**< Link between the device and the host supports native atomic operations */
int singleToDoublePrecisionPerfRatio; /**< Ratio of single precision performance (in floating-point operations per second) to double precision performance */
int pageableMemoryAccess; /**< Device supports coherently accessing pageable memory without calling cudaHostRegister on it */
int concurrentManagedAccess; /**< Device can coherently access managed memory concurrently with the CPU */
int computePreemptionSupported; /**< Device supports Compute Preemption */
int canUseHostPointerForRegisteredMem; /**< Device can access host registered memory at the same virtual address as the CPU */
int cooperativeLaunch; /**< Device supports launching cooperative kernels via ::cudaLaunchCooperativeKernel */
int cooperativeMultiDeviceLaunch; /**< Deprecated, cudaLaunchCooperativeKernelMultiDevice is deprecated. */
size_t sharedMemPerBlockOptin; /**< Per device maximum shared memory per block usable by special opt in */
int pageableMemoryAccessUsesHostPageTables; /**< Device accesses pageable memory via the host's page tables */
int directManagedMemAccessFromHost; /**< Host can directly access managed memory on the device without migration. */
int maxBlocksPerMultiProcessor; /**< Maximum number of resident blocks per multiprocessor */
int accessPolicyMaxWindowSize; /**< The maximum value of ::cudaAccessPolicyWindow::num_bytes. */
size_t reservedSharedMemPerBlock; /**< Shared memory reserved by CUDA driver per block in bytes */
} cudaDeviceProp_t;
typedef struct cudart_handle {
void *handle;
uint16_t verbose;
cudartReturn_t (*cudaSetDevice)(int device);
cudartReturn_t (*cudaDeviceSynchronize)(void);
cudartReturn_t (*cudaDeviceReset)(void);
cudartReturn_t (*cudaMemGetInfo)(size_t *, size_t *);
cudartReturn_t (*cudaGetDeviceCount)(int *);
cudartReturn_t (*cudaDeviceGetAttribute)(int* value, cudartDeviceAttr_t attr, int device);
cudartReturn_t (*cudaDriverGetVersion) (int *driverVersion);
cudartReturn_t (*cudaGetDeviceProperties) (cudaDeviceProp_t* prop, int device);
} cudart_handle_t;
typedef struct cudart_init_resp {
char *err; // If err is non-null handle is invalid
cudart_handle_t ch;
int num_devices;
} cudart_init_resp_t;
void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp);
void cudart_bootstrap(cudart_handle_t ch, int device_id, mem_info_t *resp);
// TODO - if we keep this library longer term, add cudart_get_free
void cudart_release(cudart_handle_t ch);
#endif // __GPU_INFO_CUDART_H__
#endif // __APPLE__
gpu/gpu_info_darwin.h deleted 100644 → 0
View file @ 0cb78a2f
#import <Metal/Metal.h>
#include <stdint.h>
uint64_t getRecommendedMaxVRAM();
uint64_t getPhysicalMemory();
uint64_t getFreeMemory();
gpu/gpu_info_darwin.m deleted 100644 → 0
View file @ 0cb78a2f
#import <Foundation/Foundation.h>
#import <mach/mach.h>
#include "gpu_info_darwin.h"
uint64_t getRecommendedMaxVRAM() {
id<MTLDevice> device = MTLCreateSystemDefaultDevice();
uint64_t result = device.recommendedMaxWorkingSetSize;
CFRelease(device);
return result;
}
// getPhysicalMemory returns the total physical memory in bytes
uint64_t getPhysicalMemory() {
return [NSProcessInfo processInfo].physicalMemory;
}
// getFreeMemory returns the total free memory in bytes, including inactive
// memory that can be reclaimed by the system.
uint64_t getFreeMemory() {
mach_port_t host_port = mach_host_self();
mach_msg_type_number_t host_size = sizeof(vm_statistics64_data_t) / sizeof(integer_t);
vm_size_t pagesize;
vm_statistics64_data_t vm_stat;
host_page_size(host_port, &pagesize);
if (host_statistics64(host_port, HOST_VM_INFO64, (host_info64_t)&vm_stat, &host_size) != KERN_SUCCESS) {
return 0;
}
uint64_t free_memory = (uint64_t)vm_stat.free_count * pagesize;
free_memory += (uint64_t)vm_stat.speculative_count * pagesize;
free_memory += (uint64_t)vm_stat.inactive_count * pagesize;
return free_memory;
}
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