Compare commits

..

1 Commits

Author SHA1 Message Date
Blake Mizerany
49c126fde8 build.go: introduce a friendlier way to build Ollama
This commit introduces a more friendly way to build Ollama dependencies
and the binary without abusing `go generate` and removing the
unnecessary extra steps it brings with it.

This script also provides nicer feedback to the user about what is
happening during the build process.

At the end, it prints a helpful message to the user about what to do
next (e.g. run the new local Ollama).
2024-04-09 13:52:08 -07:00
98 changed files with 2606 additions and 5706 deletions

View File

@@ -1,60 +0,0 @@
name: Bug report
labels: [bug]
description: Something isn't working right.
body:
- type: textarea
id: description
attributes:
label: What is the issue?
description: What happened? What did you expect to happen?
validations:
required: true
- type: dropdown
id: os
attributes:
label: OS
description: Which operating system are you using?
multiple: true
options:
- Linux
- macOS
- Windows
- Docker
- WSL2
validations:
required: false
- type: dropdown
id: gpu
attributes:
label: GPU
description: Which GPU are you using?
multiple: true
options:
- Nvidia
- AMD
- Intel
- Apple
- Other
validations:
required: false
- type: dropdown
id: cpu
attributes:
label: CPU
description: Which CPU are you using?
multiple: true
options:
- Intel
- AMD
- Apple
- Other
validations:
required: false
- type: input
id: version
attributes:
label: Ollama version
description: What version of Ollama are you using? (`ollama --version`)
placeholder: e.g., 0.1.32
validations:
required: false

View File

@@ -0,0 +1,18 @@
name: Model request
description: Request a new model for the library
labels: [mr]
body:
- type: markdown
attributes:
value: |
Please check if your Model request is [already available](https://ollama.com/search) or that you cannot [import it](https://github.com/ollama/ollama/blob/main/docs/import.md#import-a-model) yourself.
Tell us about which Model you'd like to see in the library!
- type: textarea
id: problem
attributes:
label: What model would you like?
description: Please provide a link to the model.
- type: markdown
attributes:
value: |
Thanks for filing a model request!

View File

@@ -1,6 +0,0 @@
---
name: Feature request
about: Request a new feature
labels: feature request
---

View File

@@ -0,0 +1,41 @@
name: Feature request
description: Propose a new feature
labels: [needs-triage, fr]
body:
- type: markdown
attributes:
value: |
Please check if your feature request is [already filed](https://github.com/ollama/ollama/issues).
Tell us about your idea!
- type: textarea
id: problem
attributes:
label: What are you trying to do?
description: Tell us about the problem you're trying to solve.
validations:
required: false
- type: textarea
id: solution
attributes:
label: How should we solve this?
description: If you have an idea of how you'd like to see this feature work, let us know.
validations:
required: false
- type: textarea
id: alternative
attributes:
label: What is the impact of not solving this?
description: (How) Are you currently working around the issue?
validations:
required: false
- type: textarea
id: context
attributes:
label: Anything else?
description: Any additional context to share, e.g., links
validations:
required: false
- type: markdown
attributes:
value: |
Thanks for filing a feature request!

View File

@@ -1,5 +0,0 @@
---
name: Model request
about: Request support for a new model to be added to Ollama
labels: model request
---

125
.github/ISSUE_TEMPLATE/90_bug_report.yml vendored Normal file
View File

@@ -0,0 +1,125 @@
name: Bug report
description: File a bug report. If you need help, please join our Discord server.
labels: [needs-triage, bug]
body:
- type: markdown
attributes:
value: |
Please check if your bug is [already filed](https://github.com/ollama/ollama/issues) before filing a new one.
- type: textarea
id: what-happened
attributes:
label: What is the issue?
description: What happened? What did you expect to happen?
validations:
required: true
- type: textarea
id: what-was-expected
attributes:
label: What did you expect to see?
description: What did you expect to see/happen instead?
validations:
required: false
- type: textarea
id: steps
attributes:
label: Steps to reproduce
description: What are the steps you took that hit this issue?
validations:
required: false
- type: textarea
id: changes
attributes:
label: Are there any recent changes that introduced the issue?
description: If so, what are those changes?
validations:
required: false
- type: dropdown
id: os
attributes:
label: OS
description: What OS are you using? You may select more than one.
multiple: true
options:
- Linux
- macOS
- Windows
- Other
validations:
required: false
- type: dropdown
id: architecture
attributes:
label: Architecture
description: What architecture are you using? You may select more than one.
multiple: true
options:
- arm64
- amd64
- x86
- Other
- type: dropdown
id: platform
attributes:
label: Platform
description: What platform are you using? You may select more than one.
multiple: true
options:
- Docker
- WSL
- WSL2
validations:
required: false
- type: input
id: ollama-version
attributes:
label: Ollama version
description: What Ollama version are you using? (`ollama --version`)
placeholder: e.g., 1.14.4
validations:
required: false
- type: dropdown
id: gpu
attributes:
label: GPU
description: What GPU, if any, are you using? You may select more than one.
multiple: true
options:
- Nvidia
- AMD
- Intel
- Apple
- Other
validations:
required: false
- type: textarea
id: gpu-info
attributes:
label: GPU info
description: What GPU info do you have? (`nvidia-smi`, `rocminfo`, `system_profiler SPDisplaysDataType`, etc.)
validations:
required: false
- type: dropdown
id: cpu
attributes:
label: CPU
description: What CPU are you using? You may select more than one.
multiple: true
options:
- Intel
- AMD
- Apple
- Other
validations:
required: false
- type: textarea
id: other-software
attributes:
label: Other software
description: What other software are you using that might be related to this issue?
validations:
required: false
- type: markdown
attributes:
value: |
Thanks for filing a bug report!

View File

@@ -30,7 +30,7 @@ jobs:
security set-key-partition-list -S apple-tool:,apple:,codesign: -s -k password build.keychain
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- name: Build Darwin
env:
@@ -86,7 +86,7 @@ jobs:
write-host "plugin installed"
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- run: go get ./...
- run: |
@@ -95,7 +95,8 @@ jobs:
cd $env:GITHUB_WORKSPACE
$env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
$env:PATH="$gopath;$env:PATH"
go generate -x ./...
$env:GOARCH = ""; go run build.go -f -d -target=${{ matrix.arch }}
name: go generate
- uses: actions/upload-artifact@v4
with:
@@ -103,7 +104,6 @@ jobs:
path: |
llm/build/**/bin/*
llm/build/**/*.a
dist/windows-amd64/**
# ROCm generation step
generate-windows-rocm:
@@ -140,7 +140,7 @@ jobs:
write-host "plugin installed"
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- name: 'Install ROCm'
run: |
@@ -174,9 +174,7 @@ jobs:
- uses: actions/upload-artifact@v4
with:
name: generate-windows-rocm
path: |
llm/build/**/bin/*
dist/windows-amd64/**
path: llm/build/**/bin/*
- uses: actions/upload-artifact@v4
with:
name: windows-rocm-deps
@@ -217,7 +215,7 @@ jobs:
write-host "plugin installed"
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- name: 'Install CUDA'
run: |
@@ -256,9 +254,7 @@ jobs:
- uses: actions/upload-artifact@v4
with:
name: generate-windows-cuda
path: |
llm/build/**/bin/*
dist/windows-amd64/**
path: llm/build/**/bin/*
- uses: actions/upload-artifact@v4
with:
name: windows-cuda-deps
@@ -305,24 +301,29 @@ jobs:
write-host "plugin installed"
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- run: go get
- uses: actions/download-artifact@v4
with:
name: generate-windows-cpu
path: llm/build
- uses: actions/download-artifact@v4
with:
name: generate-windows-cuda
path: llm/build
- uses: actions/download-artifact@v4
with:
name: windows-cuda-deps
path: dist/deps
- uses: actions/download-artifact@v4
with:
name: windows-rocm-deps
path: dist/deps
- uses: actions/download-artifact@v4
with:
name: generate-windows-rocm
path: llm/build
- run: dir llm/build
- run: |
$gopath=(get-command go).source | split-path -parent
@@ -331,13 +332,13 @@ jobs:
$env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
$env:PATH="$gopath;$env:PATH"
$env:OLLAMA_SKIP_GENERATE="1"
$env:NVIDIA_DIR=$(resolve-path ".\dist\deps")
$env:HIP_PATH=$(resolve-path ".\dist\deps")
& .\scripts\build_windows.ps1
- uses: actions/upload-artifact@v4
with:
name: dist-windows
path: |
dist/OllamaSetup.exe
dist/ollama-windows-*.zip
path: dist/*.exe
# Linux x86 assets built using the container based build
build-linux-amd64:

View File

@@ -10,11 +10,13 @@ concurrency:
group: ${{ github.workflow }}-$${{ github.pull_request.number || github.run_id }}
cancel-in-progress: true
on:
pull_request:
paths:
- '**/*'
- '!docs/**'
- '!examples/**'
- '!README.md'
jobs:
@@ -31,9 +33,7 @@ jobs:
- id: changes
run: |
changed() {
git diff-tree -r --no-commit-id --name-only \
$(git merge-base ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }}) \
${{ github.event.pull_request.head.sha }} \
git diff-tree -r --no-commit-id --name-only ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }} \
| xargs python3 -c "import sys; print(any([x.startswith('$1') for x in sys.argv[1:]]))"
}
@@ -62,7 +62,7 @@ jobs:
- uses: actions/checkout@v4
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- run: go get ./...
- run: |
@@ -73,10 +73,12 @@ jobs:
$env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
$env:PATH="$gopath;$gccpath;$env:PATH"
echo $env:PATH
go generate -x ./...
$env:GOARCH = ""; go run build.go -f -d -target=${{ matrix.arch }}
if: ${{ startsWith(matrix.os, 'windows-') }}
name: 'Windows Go Generate'
- run: go generate -x ./...
- run: |
GOARCH= go run build.go -f -d -target=${{ matrix.arch }}
if: ${{ ! startsWith(matrix.os, 'windows-') }}
name: 'Unix Go Generate'
- uses: actions/upload-artifact@v4
@@ -104,20 +106,18 @@ jobs:
- uses: actions/checkout@v4
- uses: actions/setup-go@v4
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- run: go get ./...
- run: |
git config --global --add safe.directory /__w/ollama/ollama
go generate -x ./...
GOARCH= go run build.go -f -d -target=${{ matrix.arch }}
env:
OLLAMA_SKIP_CPU_GENERATE: '1'
- uses: actions/upload-artifact@v4
with:
name: cuda-${{ matrix.cuda-version }}-libraries
path: |
llm/build/**/bin/*
dist/windows-amd64/**
path: llm/build/**/bin/*
generate-rocm:
needs: [changes]
if: ${{ needs.changes.outputs.GENERATE_ROCM == 'True' }}
@@ -137,20 +137,18 @@ jobs:
- uses: actions/checkout@v4
- uses: actions/setup-go@v4
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- run: go get ./...
- run: |
git config --global --add safe.directory /__w/ollama/ollama
go generate -x ./...
GOARCH= go run build.go -f -d -target=${{ matrix.arch }}
env:
OLLAMA_SKIP_CPU_GENERATE: '1'
- uses: actions/upload-artifact@v4
with:
name: rocm-${{ matrix.rocm-version }}-libraries
path: |
llm/build/**/bin/*
dist/windows-amd64/**
path: llm/build/**/bin/*
# ROCm generation step
generate-windows-rocm:
@@ -161,7 +159,7 @@ jobs:
- uses: actions/checkout@v4
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- name: 'Install ROCm'
run: |
@@ -183,8 +181,9 @@ jobs:
$env:PATH="$gopath;$env:PATH"
$env:OLLAMA_SKIP_CPU_GENERATE="1"
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
go generate -x ./...
name: go generate
$env:GOARCH = ""; go run build.go -f -d -target=${{ matrix.arch }}
name: go run build.go
env:
OLLAMA_SKIP_CPU_GENERATE: '1'
# TODO - do we need any artifacts?
@@ -198,7 +197,7 @@ jobs:
- uses: actions/checkout@v4
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- name: 'Install CUDA'
run: |
@@ -217,7 +216,7 @@ jobs:
- name: 'Verify CUDA'
run: nvcc -V
- run: go get ./...
- name: go generate
- name: go run build.go
run: |
$gopath=(get-command go).source | split-path -parent
$cudabin=(get-command nvcc).source | split-path
@@ -226,7 +225,8 @@ jobs:
$env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
$env:PATH="$gopath;$cudabin;$env:PATH"
$env:OLLAMA_SKIP_CPU_GENERATE="1"
go generate -x ./...
$env:GOARCH = ""; go run build.go -f -d -target=${{ matrix.arch }}
env:
OLLAMA_SKIP_CPU_GENERATE: '1'
# TODO - do we need any artifacts?
@@ -253,7 +253,7 @@ jobs:
submodules: recursive
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: false
- run: |
case ${{ matrix.arch }} in
@@ -269,9 +269,14 @@ jobs:
mkdir -p llm/build/darwin/$ARCH/stub/bin
touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
if: ${{ startsWith(matrix.os, 'macos-') }}
- run: |
mkdir -p llm/build/windows/$ARCH/stub/bin
touch llm/build/windows/$ARCH/stub/bin/ollama_llama_server
if: ${{ startsWith(matrix.os, 'windows-') }}
shell: bash
- uses: golangci/golangci-lint-action@v4
with:
args: --timeout 8m0s -v
args: --timeout 8m0s
test:
strategy:
matrix:
@@ -293,8 +298,15 @@ jobs:
submodules: recursive
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
go-version: '1.22'
cache: true
- run: |
GOARCH= go run build.go -f -d -target=${{ matrix.arch }}
if: ${{ ! startsWith(matrix.os, 'windows-') }}
- run: |
$env:GOARCH = ""; go run build.go -f -d -target=${{ matrix.arch }}
if: ${{ startsWith(matrix.os, 'windows-') }}
- run: go get
- run: |
case ${{ matrix.arch }} in
amd64) echo ARCH=x86_64 ;;
@@ -309,10 +321,13 @@ jobs:
mkdir -p llm/build/darwin/$ARCH/stub/bin
touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
if: ${{ startsWith(matrix.os, 'macos-') }}
- run: |
mkdir -p llm/build/windows/$ARCH/stub/bin
touch llm/build/windows/$ARCH/stub/bin/ollama_llama_server
if: ${{ startsWith(matrix.os, 'windows-') }}
shell: bash
- run: go generate ./...
- run: go build
- run: go test -v ./...
- run: |
go test -v ./...
- uses: actions/upload-artifact@v4
with:
name: ${{ matrix.os }}-binaries

View File

@@ -18,7 +18,7 @@ ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
COPY --from=llm-code / /go/src/github.com/ollama/ollama/
WORKDIR /go/src/github.com/ollama/ollama/llm/generate
ARG CGO_CFLAGS
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
RUN OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
FROM --platform=linux/arm64 nvidia/cuda:$CUDA_VERSION-devel-rockylinux8 AS cuda-build-arm64
ARG CMAKE_VERSION
@@ -28,7 +28,7 @@ ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
COPY --from=llm-code / /go/src/github.com/ollama/ollama/
WORKDIR /go/src/github.com/ollama/ollama/llm/generate
ARG CGO_CFLAGS
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
RUN OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS rocm-build-amd64
ARG CMAKE_VERSION
@@ -40,9 +40,9 @@ COPY --from=llm-code / /go/src/github.com/ollama/ollama/
WORKDIR /go/src/github.com/ollama/ollama/llm/generate
ARG CGO_CFLAGS
ARG AMDGPU_TARGETS
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
RUN OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
RUN mkdir /tmp/scratch && \
for dep in $(zcat /go/src/github.com/ollama/ollama/llm/build/linux/x86_64/rocm*/bin/deps.txt.gz) ; do \
for dep in $(cat /go/src/github.com/ollama/ollama/llm/llama.cpp/build/linux/x86_64/rocm*/lib/deps.txt) ; do \
cp ${dep} /tmp/scratch/ || exit 1 ; \
done && \
(cd /opt/rocm/lib && tar cf - rocblas/library) | (cd /tmp/scratch/ && tar xf - ) && \
@@ -64,11 +64,11 @@ WORKDIR /go/src/github.com/ollama/ollama/llm/generate
FROM --platform=linux/amd64 cpu-builder-amd64 AS static-build-amd64
RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu-build-amd64
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
RUN OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx-build-amd64
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
RUN OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx2-build-amd64
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
RUN OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
FROM --platform=linux/arm64 centos:7 AS cpu-builder-arm64
ARG CMAKE_VERSION
@@ -84,7 +84,7 @@ WORKDIR /go/src/github.com/ollama/ollama/llm/generate
FROM --platform=linux/arm64 cpu-builder-arm64 AS static-build-arm64
RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
FROM --platform=linux/arm64 cpu-builder-arm64 AS cpu-build-arm64
RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
RUN OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
# Intermediate stage used for ./scripts/build_linux.sh

View File

@@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
## Quickstart
To run and chat with [Llama 3](https://ollama.com/library/llama3):
To run and chat with [Llama 2](https://ollama.com/library/llama2):
```
ollama run llama3
ollama run llama2
```
## Model library
@@ -49,18 +49,21 @@ Here are some example models that can be downloaded:
| Model | Parameters | Size | Download |
| ------------------ | ---------- | ----- | ------------------------------ |
| Llama 3 | 8B | 4.7GB | `ollama run llama3` |
| Llama 3 | 70B | 40GB | `ollama run llama3:70b` |
| Phi-3 | 3,8B | 2.3GB | `ollama run phi3` |
| Llama 2 | 7B | 3.8GB | `ollama run llama2` |
| Mistral | 7B | 4.1GB | `ollama run mistral` |
| Dolphin Phi | 2.7B | 1.6GB | `ollama run dolphin-phi` |
| Phi-2 | 2.7B | 1.7GB | `ollama run phi` |
| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
| Starling | 7B | 4.1GB | `ollama run starling-lm` |
| Code Llama | 7B | 3.8GB | `ollama run codellama` |
| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
| Llama 2 13B | 13B | 7.3GB | `ollama run llama2:13b` |
| Llama 2 70B | 70B | 39GB | `ollama run llama2:70b` |
| Orca Mini | 3B | 1.9GB | `ollama run orca-mini` |
| Vicuna | 7B | 3.8GB | `ollama run vicuna` |
| LLaVA | 7B | 4.5GB | `ollama run llava` |
| Gemma | 2B | 1.4GB | `ollama run gemma:2b` |
| Gemma | 7B | 4.8GB | `ollama run gemma:7b` |
| Solar | 10.7B | 6.1GB | `ollama run solar` |
> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -94,16 +97,16 @@ See the [guide](docs/import.md) on importing models for more information.
### Customize a prompt
Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3` model:
Models from the Ollama library can be customized with a prompt. For example, to customize the `llama2` model:
```
ollama pull llama3
ollama pull llama2
```
Create a `Modelfile`:
```
FROM llama3
FROM llama2
# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
@@ -138,7 +141,7 @@ ollama create mymodel -f ./Modelfile
### Pull a model
```
ollama pull llama3
ollama pull llama2
```
> This command can also be used to update a local model. Only the diff will be pulled.
@@ -146,13 +149,13 @@ ollama pull llama3
### Remove a model
```
ollama rm llama3
ollama rm llama2
```
### Copy a model
```
ollama cp llama3 my-model
ollama cp llama2 my-llama2
```
### Multiline input
@@ -176,7 +179,7 @@ The image features a yellow smiley face, which is likely the central focus of th
### Pass in prompt as arguments
```
$ ollama run llama3 "Summarize this file: $(cat README.md)"
$ ollama run llama2 "Summarize this file: $(cat README.md)"
Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
```
@@ -198,16 +201,10 @@ Install `cmake` and `go`:
brew install cmake go
```
Then generate dependencies:
```
go generate ./...
```
Then build the binary:
```
go build .
go run build.go
```
More detailed instructions can be found in the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
@@ -223,7 +220,7 @@ Next, start the server:
Finally, in a separate shell, run a model:
```
./ollama run llama3
./ollama run llama2
```
## REST API
@@ -234,7 +231,7 @@ Ollama has a REST API for running and managing models.
```
curl http://localhost:11434/api/generate -d '{
"model": "llama3",
"model": "llama2",
"prompt":"Why is the sky blue?"
}'
```
@@ -243,7 +240,7 @@ curl http://localhost:11434/api/generate -d '{
```
curl http://localhost:11434/api/chat -d '{
"model": "llama3",
"model": "mistral",
"messages": [
{ "role": "user", "content": "why is the sky blue?" }
]
@@ -256,17 +253,16 @@ See the [API documentation](./docs/api.md) for all endpoints.
### Web & Desktop
- [Open WebUI](https://github.com/open-webui/open-webui)
- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
- [Lollms-Webui](https://github.com/ParisNeo/lollms-webui)
- [LibreChat](https://github.com/danny-avila/LibreChat)
- [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
- [Saddle](https://github.com/jikkuatwork/saddle)
- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
- [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
- [Open WebUI](https://github.com/open-webui/open-webui)
- [Ollamac](https://github.com/kevinhermawan/Ollamac)
- [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md)
- [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
@@ -288,13 +284,9 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
- [QA-Pilot: Chat with Code Repository](https://github.com/reid41/QA-Pilot)
- [ChatOllama: Open Source Chatbot based on Ollama with Knowledge Bases](https://github.com/sugarforever/chat-ollama)
- [CRAG Ollama Chat: Simple Web Search with Corrective RAG](https://github.com/Nagi-ovo/CRAG-Ollama-Chat)
- [RAGFlow: Open-source Retrieval-Augmented Generation engine based on deep document understanding](https://github.com/infiniflow/ragflow)
- [chat: chat web app for teams](https://github.com/swuecho/chat)
- [Lobe Chat](https://github.com/lobehub/lobe-chat) with [Integrating Doc](https://lobehub.com/docs/self-hosting/examples/ollama)
- [Ollama RAG Chatbot: Local Chat with multiples PDFs using Ollama and RAG.](https://github.com/datvodinh/rag-chatbot.git)
### Terminal
@@ -310,17 +302,15 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Oatmeal](https://github.com/dustinblackman/oatmeal)
- [cmdh](https://github.com/pgibler/cmdh)
- [ooo](https://github.com/npahlfer/ooo)
- [shell-pilot](https://github.com/reid41/shell-pilot)
- [tenere](https://github.com/pythops/tenere)
- [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/).
- [typechat-cli](https://github.com/anaisbetts/typechat-cli)
- [ShellOracle](https://github.com/djcopley/ShellOracle)
- [tlm](https://github.com/yusufcanb/tlm)
- [podman-ollama](https://github.com/ericcurtin/podman-ollama)
### Database
- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md)
- [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
### Package managers
@@ -381,7 +371,3 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
- [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
### Supported backends
- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.

View File

@@ -1,9 +1,3 @@
// Package api implements the client-side API for code wishing to interact
// with the ollama service. The methods of the [Client] type correspond to
// the ollama REST API as described in https://github.com/ollama/ollama/blob/main/docs/api.md
//
// The ollama command-line client itself uses this package to interact with
// the backend service.
package api
import (
@@ -11,6 +5,7 @@ import (
"bytes"
"context"
"encoding/json"
"errors"
"fmt"
"io"
"net"
@@ -24,8 +19,6 @@ import (
"github.com/ollama/ollama/version"
)
// Client encapsulates client state for interacting with the ollama
// service. Use [ClientFromEnvironment] to create new Clients.
type Client struct {
base *url.URL
http *http.Client
@@ -47,15 +40,6 @@ func checkError(resp *http.Response, body []byte) error {
return apiError
}
// ClientFromEnvironment creates a new [Client] using configuration from the
// environment variable OLLAMA_HOST, which points to the network host and
// port on which the ollama service is listenting. The format of this variable
// is:
//
// <scheme>://<host>:<port>
//
// If the variable is not specified, a default ollama host and port will be
// used.
func ClientFromEnvironment() (*Client, error) {
defaultPort := "11434"
@@ -91,13 +75,6 @@ func ClientFromEnvironment() (*Client, error) {
}, nil
}
func NewClient(base *url.URL, http *http.Client) *Client {
return &Client{
base: base,
http: http,
}
}
func (c *Client) do(ctx context.Context, method, path string, reqData, respData any) error {
var reqBody io.Reader
var data []byte
@@ -214,14 +191,8 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
return nil
}
// GenerateResponseFunc is a function that [Client.Generate] invokes every time
// a response is received from the service. If this function returns an error,
// [Client.Generate] will stop generating and return this error.
type GenerateResponseFunc func(GenerateResponse) error
// Generate generates a response for a given prompt. The req parameter should
// be populated with prompt details. fn is called for each response (there may
// be multiple responses, e.g. in case streaming is enabled).
func (c *Client) Generate(ctx context.Context, req *GenerateRequest, fn GenerateResponseFunc) error {
return c.stream(ctx, http.MethodPost, "/api/generate", req, func(bts []byte) error {
var resp GenerateResponse
@@ -233,15 +204,8 @@ func (c *Client) Generate(ctx context.Context, req *GenerateRequest, fn Generate
})
}
// ChatResponseFunc is a function that [Client.Chat] invokes every time
// a response is received from the service. If this function returns an error,
// [Client.Chat] will stop generating and return this error.
type ChatResponseFunc func(ChatResponse) error
// Chat generates the next message in a chat. [ChatRequest] may contain a
// sequence of messages which can be used to maintain chat history with a model.
// fn is called for each response (there may be multiple responses, e.g. if case
// streaming is enabled).
func (c *Client) Chat(ctx context.Context, req *ChatRequest, fn ChatResponseFunc) error {
return c.stream(ctx, http.MethodPost, "/api/chat", req, func(bts []byte) error {
var resp ChatResponse
@@ -253,14 +217,8 @@ func (c *Client) Chat(ctx context.Context, req *ChatRequest, fn ChatResponseFunc
})
}
// PullProgressFunc is a function that [Client.Pull] invokes every time there
// is progress with a "pull" request sent to the service. If this function
// returns an error, [Client.Pull] will stop the process and return this error.
type PullProgressFunc func(ProgressResponse) error
// Pull downloads a model from the ollama library. fn is called each time
// progress is made on the request and can be used to display a progress bar,
// etc.
func (c *Client) Pull(ctx context.Context, req *PullRequest, fn PullProgressFunc) error {
return c.stream(ctx, http.MethodPost, "/api/pull", req, func(bts []byte) error {
var resp ProgressResponse
@@ -343,7 +301,18 @@ func (c *Client) Embeddings(ctx context.Context, req *EmbeddingRequest) (*Embedd
}
func (c *Client) CreateBlob(ctx context.Context, digest string, r io.Reader) error {
return c.do(ctx, http.MethodPost, fmt.Sprintf("/api/blobs/%s", digest), r, nil)
if err := c.do(ctx, http.MethodHead, fmt.Sprintf("/api/blobs/%s", digest), nil, nil); err != nil {
var statusError StatusError
if !errors.As(err, &statusError) || statusError.StatusCode != http.StatusNotFound {
return err
}
if err := c.do(ctx, http.MethodPost, fmt.Sprintf("/api/blobs/%s", digest), r, nil); err != nil {
return err
}
}
return nil
}
func (c *Client) Version(ctx context.Context) (string, error) {

View File

@@ -2,7 +2,6 @@ package api
import (
"encoding/json"
"errors"
"fmt"
"math"
"os"
@@ -34,46 +33,18 @@ func (e StatusError) Error() string {
type ImageData []byte
// GenerateRequest describes a request sent by [Client.Generate]. While you
// have to specify the Model and Prompt fields, all the other fields have
// reasonable defaults for basic uses.
type GenerateRequest struct {
// Model is the model name; it should be a name familiar to Ollama from
// the library at https://ollama.com/library
Model string `json:"model"`
Model string `json:"model"`
Prompt string `json:"prompt"`
System string `json:"system"`
Template string `json:"template"`
Context []int `json:"context,omitempty"`
Stream *bool `json:"stream,omitempty"`
Raw bool `json:"raw,omitempty"`
Format string `json:"format"`
KeepAlive *Duration `json:"keep_alive,omitempty"`
Images []ImageData `json:"images,omitempty"`
// Prompt is the textual prompt to send to the model.
Prompt string `json:"prompt"`
// System overrides the model's default system message/prompt.
System string `json:"system"`
// Template overrides the model's default prompt template.
Template string `json:"template"`
// Context is the context parameter returned from a previous call to
// Generate call. It can be used to keep a short conversational memory.
Context []int `json:"context,omitempty"`
// Stream specifies whether the response is streaming; it is true by default.
Stream *bool `json:"stream,omitempty"`
// Raw set to true means that no formatting will be applied to the prompt.
Raw bool `json:"raw,omitempty"`
// Format specifies the format to return a response in.
Format string `json:"format"`
// KeepAlive controls how long the model will stay loaded in memory following
// this request.
KeepAlive *Duration `json:"keep_alive,omitempty"`
// Images is an optional list of base64-encoded images accompanying this
// request, for multimodal models.
Images []ImageData `json:"images,omitempty"`
// Options lists model-specific options. For example, temperature can be
// set through this field, if the model supports it.
Options map[string]interface{} `json:"options"`
}
@@ -138,24 +109,19 @@ type Options struct {
// Runner options which must be set when the model is loaded into memory
type Runner struct {
UseNUMA bool `json:"numa,omitempty"`
NumCtx int `json:"num_ctx,omitempty"`
NumBatch int `json:"num_batch,omitempty"`
NumGQA int `json:"num_gqa,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"`
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
NumThread int `json:"num_thread,omitempty"`
// Unused: RopeFrequencyBase is ignored. Instead the value in the model will be used
RopeFrequencyBase float32 `json:"rope_frequency_base,omitempty"`
// Unused: RopeFrequencyScale is ignored. Instead the value in the model will be used
RopeFrequencyScale float32 `json:"rope_frequency_scale,omitempty"`
UseNUMA bool `json:"numa,omitempty"`
NumCtx int `json:"num_ctx,omitempty"`
NumBatch int `json:"num_batch,omitempty"`
NumGQA int `json:"num_gqa,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"`
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
NumThread int `json:"num_thread,omitempty"`
}
type EmbeddingRequest struct {
@@ -171,11 +137,10 @@ type EmbeddingResponse struct {
}
type CreateRequest struct {
Model string `json:"model"`
Path string `json:"path"`
Modelfile string `json:"modelfile"`
Stream *bool `json:"stream,omitempty"`
Quantization string `json:"quantization,omitempty"`
Model string `json:"model"`
Path string `json:"path"`
Modelfile string `json:"modelfile"`
Stream *bool `json:"stream,omitempty"`
// Name is deprecated, see Model
Name string `json:"name"`
@@ -308,7 +273,7 @@ func (m *Metrics) Summary() {
}
}
var ErrInvalidOpts = errors.New("invalid options")
var ErrInvalidOpts = fmt.Errorf("invalid options")
func (opts *Options) FromMap(m map[string]interface{}) error {
valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
@@ -396,10 +361,8 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
func DefaultOptions() Options {
return Options{
// options set on request to runner
NumPredict: -1,
// set a minimal num_keep to avoid issues on context shifts
NumKeep: 4,
NumPredict: -1,
NumKeep: 0,
Temperature: 0.8,
TopK: 40,
TopP: 0.9,
@@ -417,16 +380,16 @@ func DefaultOptions() Options {
Runner: Runner{
// options set when the model is loaded
NumCtx: 2048,
NumBatch: 512,
NumGPU: -1, // -1 here indicates that NumGPU should be set dynamically
NumGQA: 1,
NumThread: 0, // let the runtime decide
LowVRAM: false,
F16KV: true,
UseMLock: false,
UseMMap: true,
UseNUMA: false,
NumCtx: 2048,
NumBatch: 512,
NumGPU: -1, // -1 here indicates that NumGPU should be set dynamically
NumGQA: 1,
NumThread: 0, // let the runtime decide
LowVRAM: false,
F16KV: true,
UseMLock: false,
UseMMap: true,
UseNUMA: false,
},
}
}

View File

@@ -9,6 +9,7 @@ import (
"os"
"os/exec"
"path/filepath"
"syscall"
"time"
"github.com/ollama/ollama/api"
@@ -86,29 +87,19 @@ func SpawnServer(ctx context.Context, command string) (chan int, error) {
// Re-wire context done behavior to attempt a graceful shutdown of the server
cmd.Cancel = func() error {
if cmd.Process != nil {
err := terminate(cmd)
if err != nil {
slog.Warn("error trying to gracefully terminate server", "err", err)
return cmd.Process.Kill()
}
cmd.Process.Signal(os.Interrupt) //nolint:errcheck
tick := time.NewTicker(10 * time.Millisecond)
defer tick.Stop()
for {
select {
case <-tick.C:
exited, err := isProcessExited(cmd.Process.Pid)
if err != nil {
return err
}
if exited {
return nil
// OS agnostic "is it still running"
if proc, err := os.FindProcess(int(cmd.Process.Pid)); err != nil || errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
return nil //nolint:nilerr
}
case <-time.After(5 * time.Second):
slog.Warn("graceful server shutdown timeout, killing", "pid", cmd.Process.Pid)
return cmd.Process.Kill()
cmd.Process.Kill() //nolint:errcheck
}
}
}

View File

@@ -4,35 +4,9 @@ package lifecycle
import (
"context"
"errors"
"fmt"
"os"
"os/exec"
"syscall"
)
func getCmd(ctx context.Context, cmd string) *exec.Cmd {
return exec.CommandContext(ctx, cmd, "serve")
}
func terminate(cmd *exec.Cmd) error {
return cmd.Process.Signal(os.Interrupt)
}
func isProcessExited(pid int) (bool, error) {
proc, err := os.FindProcess(pid)
if err != nil {
return false, fmt.Errorf("failed to find process: %v", err)
}
err = proc.Signal(syscall.Signal(0))
if err != nil {
if errors.Is(err, os.ErrProcessDone) || errors.Is(err, syscall.ESRCH) {
return true, nil
}
return false, fmt.Errorf("error signaling process: %v", err)
}
return false, nil
}

View File

@@ -2,88 +2,12 @@ package lifecycle
import (
"context"
"fmt"
"os/exec"
"syscall"
"golang.org/x/sys/windows"
)
func getCmd(ctx context.Context, exePath string) *exec.Cmd {
cmd := exec.CommandContext(ctx, exePath, "serve")
cmd.SysProcAttr = &syscall.SysProcAttr{
HideWindow: true,
CreationFlags: windows.CREATE_NEW_PROCESS_GROUP,
}
cmd.SysProcAttr = &syscall.SysProcAttr{HideWindow: true, CreationFlags: 0x08000000}
return cmd
}
func terminate(cmd *exec.Cmd) error {
dll, err := windows.LoadDLL("kernel32.dll")
if err != nil {
return err
}
defer dll.Release() // nolint: errcheck
pid := cmd.Process.Pid
f, err := dll.FindProc("AttachConsole")
if err != nil {
return err
}
r1, _, err := f.Call(uintptr(pid))
if r1 == 0 && err != syscall.ERROR_ACCESS_DENIED {
return err
}
f, err = dll.FindProc("SetConsoleCtrlHandler")
if err != nil {
return err
}
r1, _, err = f.Call(0, 1)
if r1 == 0 {
return err
}
f, err = dll.FindProc("GenerateConsoleCtrlEvent")
if err != nil {
return err
}
r1, _, err = f.Call(windows.CTRL_BREAK_EVENT, uintptr(pid))
if r1 == 0 {
return err
}
r1, _, err = f.Call(windows.CTRL_C_EVENT, uintptr(pid))
if r1 == 0 {
return err
}
return nil
}
const STILL_ACTIVE = 259
func isProcessExited(pid int) (bool, error) {
hProcess, err := windows.OpenProcess(windows.PROCESS_QUERY_INFORMATION, false, uint32(pid))
if err != nil {
return false, fmt.Errorf("failed to open process: %v", err)
}
defer windows.CloseHandle(hProcess) // nolint: errcheck
var exitCode uint32
err = windows.GetExitCodeProcess(hProcess, &exitCode)
if err != nil {
return false, fmt.Errorf("failed to get exit code: %v", err)
}
if exitCode == STILL_ACTIVE {
return false, nil
}
return true, nil
}

View File

@@ -88,12 +88,15 @@ DialogFontSize=12
[Files]
Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
Source: "..\dist\windows-amd64\*.dll"; DestDir: "{app}"; Flags: ignoreversion 64bit
Source: "..\dist\windows-amd64\ollama_runners\*"; DestDir: "{app}\ollama_runners"; Flags: ignoreversion 64bit recursesubdirs
Source: "..\dist\windeps\*.dll"; DestDir: "{app}"; Flags: ignoreversion 64bit
Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
#if DirExists("..\dist\windows-amd64\rocm")
Source: "..\dist\windows-amd64\rocm\*"; DestDir: "{app}\rocm\"; Flags: ignoreversion recursesubdirs
; Assumes v5.7, may need adjustments for v6
#if GetEnv("HIP_PATH") != ""
Source: "{#GetEnv('HIP_PATH')}\bin\hipblas.dll"; DestDir: "{app}\rocm\"; Flags: ignoreversion
Source: "{#GetEnv('HIP_PATH')}\bin\rocblas.dll"; DestDir: "{app}\rocm\"; Flags: ignoreversion
; amdhip64.dll dependency comes from the driver and must be installed already
Source: "{#GetEnv('HIP_PATH')}\bin\rocblas\library\*"; DestDir: "{app}\rocm\rocblas\library\"; Flags: ignoreversion
#endif
@@ -129,7 +132,7 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi
;FinishedHeadingLabel=Run your first model
;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n ollama run llama3
;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n ollama run llama2
;ClickFinish=%n
[Registry]

View File

@@ -24,5 +24,10 @@ func NewTray() (commontray.OllamaTray, error) {
return nil, fmt.Errorf("failed to load icon %s: %w", iconName, err)
}
return InitPlatformTray(icon, updateIcon)
tray, err := InitPlatformTray(icon, updateIcon)
if err != nil {
return nil, err
}
return tray, nil
}

192
build.go Normal file
View File

@@ -0,0 +1,192 @@
//go:build ignore
package main
import (
"cmp"
"errors"
"flag"
"log"
"os"
"os/exec"
"path/filepath"
"runtime"
)
// Flags
var (
flagForce = flag.Bool("f", false, "force re-generation of dependencies")
flagSkipBuild = flag.Bool("d", false, "generate dependencies only (e.g. skip 'go build .')")
// Flags to set GOARCH and GOOS explicitly for cross-platform builds,
// e.g., in CI to target a different platform than the build matrix
// default. These allows us to run generate without a separate build
// step for building the script binary for the host ARCH and then
// runing the generate script for the target ARCH. Instead, we can
// just run `go run build.go -target=$GOARCH` to generate the
// deps.
flagGOARCH = flag.String("target", "", "sets GOARCH to use when generating dependencies and building")
)
func buildEnv() []string {
return append(os.Environ(),
"GOARCH="+cmp.Or(*flagGOARCH, runtime.GOARCH),
)
}
func main() {
log.SetFlags(0)
flag.Usage = func() {
log.Printf("Usage: go run build.go [flags]")
log.Println()
log.Println("Flags:")
flag.PrintDefaults()
log.Println()
log.Println("This script builds the Ollama server binary and generates the llama.cpp")
log.Println("bindings for the current platform. It assumes that the current working")
log.Println("directory is the root directory of the Ollama project.")
log.Println()
log.Println("If the -d flag is provided, the script will only generate the dependencies")
log.Println("and skip building the Ollama server binary.")
log.Println()
log.Println("If the -f flag is provided, the script will force re-generation of the")
log.Println("dependencies.")
log.Println()
log.Println("If the -target flag is provided, the script will set GOARCH to the value")
log.Println("of the flag. This is useful for cross-platform builds.")
log.Println()
log.Println("The script will check for the required dependencies (cmake, gcc) and")
log.Println("print their version.")
log.Println()
log.Println("The script will also check if it is being run from the root directory of")
log.Println("the Ollama project.")
log.Println()
os.Exit(1)
}
flag.Parse()
log.Printf("=== Building Ollama ===")
defer func() {
log.Printf("=== Done building Ollama ===")
log.Println()
log.Println("To run the Ollama server, use:")
log.Println()
log.Println(" ./ollama serve")
log.Println()
}()
if flag.NArg() > 0 {
flag.Usage()
}
if !inRootDir() {
log.Fatalf("Please run this script from the root directory of the Ollama project.")
}
if err := checkDependencies(); err != nil {
log.Fatalf("Failed dependency check: %v", err)
}
if err := buildLlammaCPP(); err != nil {
log.Fatalf("Failed to build llama.cpp: %v", err)
}
if err := goBuildOllama(); err != nil {
log.Fatalf("Failed to build ollama Go binary: %v", err)
}
}
// checkDependencies does a quick check to see if the required dependencies are
// installed on the system and functioning enough to print their version.
//
// TODO(bmizerany): Check the actual version of the dependencies? Seems a
// little daunting given diff versions might print diff things. This should
// be good enough for now.
func checkDependencies() error {
var err error
check := func(name string, args ...string) {
log.Printf("=== Checking for %s ===", name)
defer log.Printf("=== Done checking for %s ===\n\n", name)
cmd := exec.Command(name, args...)
cmd.Stdout = os.Stdout
cmd.Stderr = os.Stderr
err = errors.Join(err, cmd.Run())
}
check("cmake", "--version")
check("gcc", "--version")
return err
}
func goBuildOllama() error {
log.Println("=== Building Ollama binary ===")
defer log.Printf("=== Done building Ollama binary ===\n\n")
if *flagSkipBuild {
log.Println("Skipping 'go build -o ollama .'")
return nil
}
cmd := exec.Command("go", "build", "-o", "ollama", ".")
cmd.Stdout = os.Stdout
cmd.Stderr = os.Stderr
cmd.Env = buildEnv()
return cmd.Run()
}
// buildLlammaCPP generates the llama.cpp bindings for the current platform.
//
// It assumes that the current working directory is the root directory of the
// Ollama project.
func buildLlammaCPP() error {
log.Println("=== Generating dependencies ===")
defer log.Printf("=== Done generating dependencies ===\n\n")
if *flagForce {
if err := os.RemoveAll(filepath.Join("llm", "build")); err != nil {
return err
}
}
if isDirectory(filepath.Join("llm", "build")) {
log.Println("llm/build already exists; skipping. Use -f to force re-generate.")
return nil
}
scriptDir, err := filepath.Abs(filepath.Join("llm", "generate"))
if err != nil {
return err
}
var cmd *exec.Cmd
switch runtime.GOOS {
case "windows":
script := filepath.Join(scriptDir, "gen_windows.ps1")
cmd = exec.Command("powershell", "-ExecutionPolicy", "Bypass", "-File", script)
case "linux":
script := filepath.Join(scriptDir, "gen_linux.sh")
cmd = exec.Command("bash", script)
case "darwin":
script := filepath.Join(scriptDir, "gen_darwin.sh")
cmd = exec.Command("bash", script)
default:
log.Fatalf("Unsupported OS: %s", runtime.GOOS)
}
cmd.Dir = filepath.Join("llm", "generate")
cmd.Stdout = os.Stdout
cmd.Stderr = os.Stderr
cmd.Env = buildEnv()
log.Printf("Running GOOS=%s GOARCH=%s %s", runtime.GOOS, runtime.GOARCH, cmd.Args)
return cmd.Run()
}
func isDirectory(path string) bool {
info, err := os.Stat(path)
if err != nil {
return false
}
return info.IsDir()
}
// inRootDir returns true if the current working directory is the root
// directory of the Ollama project. It looks for a file named "go.mod".
func inRootDir() bool {
_, err := os.Stat("go.mod")
return err == nil
}

View File

@@ -17,7 +17,6 @@ import (
"os"
"os/signal"
"path/filepath"
"regexp"
"runtime"
"strings"
"syscall"
@@ -54,6 +53,8 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
p := progress.NewProgress(os.Stderr)
defer p.Stop()
bars := make(map[string]*progress.Bar)
modelfile, err := os.ReadFile(filename)
if err != nil {
return err
@@ -94,16 +95,71 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return err
}
// TODO make this work w/ adapters
if fi.IsDir() {
// this is likely a safetensors or pytorch directory
// TODO make this work w/ adapters
tempfile, err := tempZipFiles(path)
tf, err := os.CreateTemp("", "ollama-tf")
if err != nil {
return err
}
defer os.RemoveAll(tempfile)
defer os.RemoveAll(tf.Name())
path = tempfile
zf := zip.NewWriter(tf)
files, err := filepath.Glob(filepath.Join(path, "model-*.safetensors"))
if err != nil {
return err
}
if len(files) == 0 {
return fmt.Errorf("no safetensors files were found in '%s'", path)
}
// add the safetensor config file + tokenizer
files = append(files, filepath.Join(path, "config.json"))
files = append(files, filepath.Join(path, "added_tokens.json"))
files = append(files, filepath.Join(path, "tokenizer.model"))
for _, fn := range files {
f, err := os.Open(fn)
if os.IsNotExist(err) && strings.HasSuffix(fn, "added_tokens.json") {
continue
} else if err != nil {
return err
}
fi, err := f.Stat()
if err != nil {
return err
}
h, err := zip.FileInfoHeader(fi)
if err != nil {
return err
}
h.Name = filepath.Base(fn)
h.Method = zip.Store
w, err := zf.CreateHeader(h)
if err != nil {
return err
}
_, err = io.Copy(w, f)
if err != nil {
return err
}
}
if err := zf.Close(); err != nil {
return err
}
if err := tf.Close(); err != nil {
return err
}
path = tf.Name()
}
digest, err := createBlob(cmd, client, path)
@@ -111,17 +167,10 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return err
}
name := c.Name
if c.Name == "model" {
name = "from"
}
re := regexp.MustCompile(fmt.Sprintf(`(?im)^(%s)\s+%s\s*$`, name, c.Args))
modelfile = re.ReplaceAll(modelfile, []byte("$1 @"+digest))
modelfile = bytes.ReplaceAll(modelfile, []byte(c.Args), []byte("@"+digest))
}
}
bars := make(map[string]*progress.Bar)
fn := func(resp api.ProgressResponse) error {
if resp.Digest != "" {
spinner.Stop()
@@ -145,9 +194,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return nil
}
quantization, _ := cmd.Flags().GetString("quantization")
request := api.CreateRequest{Name: args[0], Modelfile: string(modelfile), Quantization: quantization}
request := api.CreateRequest{Name: args[0], Modelfile: string(modelfile)}
if err := client.Create(cmd.Context(), &request, fn); err != nil {
return err
}
@@ -155,114 +202,6 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return nil
}
func tempZipFiles(path string) (string, error) {
tempfile, err := os.CreateTemp("", "ollama-tf")
if err != nil {
return "", err
}
defer tempfile.Close()
zipfile := zip.NewWriter(tempfile)
defer zipfile.Close()
detectContentType := func(path string) (string, error) {
f, err := os.Open(path)
if err != nil {
return "", err
}
defer f.Close()
var b bytes.Buffer
b.Grow(512)
if _, err := io.CopyN(&b, f, 512); err != nil && !errors.Is(err, io.EOF) {
return "", err
}
contentType, _, _ := strings.Cut(http.DetectContentType(b.Bytes()), ";")
return contentType, nil
}
glob := func(pattern, contentType string) ([]string, error) {
matches, err := filepath.Glob(pattern)
if err != nil {
return nil, err
}
for _, safetensor := range matches {
if ct, err := detectContentType(safetensor); err != nil {
return nil, err
} else if ct != contentType {
return nil, fmt.Errorf("invalid content type: expected %s for %s", ct, safetensor)
}
}
return matches, nil
}
var files []string
if st, _ := glob(filepath.Join(path, "model*.safetensors"), "application/octet-stream"); len(st) > 0 {
// safetensors files might be unresolved git lfs references; skip if they are
// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
files = append(files, st...)
} else if pt, _ := glob(filepath.Join(path, "pytorch_model*.bin"), "application/zip"); len(pt) > 0 {
// pytorch files might also be unresolved git lfs references; skip if they are
// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
files = append(files, pt...)
} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/octet-stream"); len(pt) > 0 {
// pytorch files might also be unresolved git lfs references; skip if they are
// covers consolidated.x.pth, consolidated.pth
files = append(files, pt...)
} else {
return "", errors.New("no safetensors or torch files found")
}
// add configuration files, json files are detected as text/plain
js, err := glob(filepath.Join(path, "*.json"), "text/plain")
if err != nil {
return "", err
}
files = append(files, js...)
if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
// tokenizer.model might be a unresolved git lfs reference; error if it is
files = append(files, tks...)
} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
files = append(files, tks...)
}
for _, file := range files {
f, err := os.Open(file)
if err != nil {
return "", err
}
defer f.Close()
fi, err := f.Stat()
if err != nil {
return "", err
}
zfi, err := zip.FileInfoHeader(fi)
if err != nil {
return "", err
}
zf, err := zipfile.CreateHeader(zfi)
if err != nil {
return "", err
}
if _, err := io.Copy(zf, f); err != nil {
return "", err
}
}
return tempfile.Name(), nil
}
func createBlob(cmd *cobra.Command, client *api.Client, path string) (string, error) {
bin, err := os.Open(path)
if err != nil {
@@ -287,6 +226,14 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string) (string, er
}
func RunHandler(cmd *cobra.Command, args []string) error {
if os.Getenv("OLLAMA_MODELS") != "" {
return errors.New("OLLAMA_MODELS must only be set for 'ollama serve'")
}
if err := checkServerHeartbeat(cmd, args); err != nil {
return err
}
client, err := api.ClientFromEnvironment()
if err != nil {
return err
@@ -996,7 +943,6 @@ func NewCLI() *cobra.Command {
}
createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile (default \"Modelfile\")")
createCmd.Flags().StringP("quantization", "q", "", "Quantization level.")
showCmd := &cobra.Command{
Use: "show MODEL",
@@ -1013,11 +959,10 @@ func NewCLI() *cobra.Command {
showCmd.Flags().Bool("system", false, "Show system message of a model")
runCmd := &cobra.Command{
Use: "run MODEL [PROMPT]",
Short: "Run a model",
Args: cobra.MinimumNArgs(1),
PreRunE: checkServerHeartbeat,
RunE: RunHandler,
Use: "run MODEL [PROMPT]",
Short: "Run a model",
Args: cobra.MinimumNArgs(1),
RunE: RunHandler,
}
runCmd.Flags().Bool("verbose", false, "Show timings for response")

View File

@@ -1,16 +1,21 @@
package convert
import (
"bytes"
"cmp"
"encoding/binary"
"encoding/json"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"slices"
"strings"
"github.com/d4l3k/go-bfloat16"
"github.com/mitchellh/mapstructure"
"github.com/x448/float16"
"google.golang.org/protobuf/proto"
"github.com/ollama/ollama/convert/sentencepiece"
@@ -18,23 +23,19 @@ import (
)
type Params struct {
Architectures []string `json:"architectures"`
VocabSize int `json:"vocab_size"`
HiddenSize int `json:"hidden_size"` // n_embd
HiddenLayers int `json:"num_hidden_layers"` // n_layer
ContextSize int `json:"max_position_embeddings"`
IntermediateSize int `json:"intermediate_size"`
AttentionHeads int `json:"num_attention_heads"` // n_head
KeyValHeads int `json:"num_key_value_heads"`
NormEPS float64 `json:"rms_norm_eps"`
BoSTokenID int `json:"bos_token_id"`
EoSTokenID int `json:"eos_token_id"`
HeadDimension int `json:"head_dim"`
PaddingTokenID int `json:"pad_token_id"`
RopeFrequencyBase float64 `json:"rope_theta"`
Experts int `json:"num_local_experts"`
ExpertsUsed int `json:"num_experts_per_tok"`
Architectures []string `json:"architectures"`
VocabSize int `json:"vocab_size"`
HiddenSize int `json:"hidden_size"` // n_embd
HiddenLayers int `json:"num_hidden_layers"` // n_layer
ContextSize int `json:"max_position_embeddings"`
IntermediateSize int `json:"intermediate_size"`
AttentionHeads int `json:"num_attention_heads"` // n_head
KeyValHeads int `json:"num_key_value_heads"`
NormEPS float64 `json:"rms_norm_eps"`
BoSTokenID int `json:"bos_token_id"`
EoSTokenID int `json:"eos_token_id"`
HeadDimension int `json:"head_dim"`
PaddingTokenID int `json:"pad_token_id"`
ByteOrder
}
@@ -44,45 +45,157 @@ type ByteOrder interface {
binary.AppendByteOrder
}
type MetaData struct {
Type string `mapstructure:"dtype"`
Shape []int `mapstructure:"shape"`
Offsets []int `mapstructure:"data_offsets"`
}
type ModelArch interface {
GetTensors() error
LoadVocab() error
WriteGGUF() (string, error)
}
type ModelFormat interface {
GetLayerName(string) (string, error)
GetTensors(string, *Params) ([]llm.Tensor, error)
GetParams(string) (*Params, error)
GetModelArch(string, string, *Params) (ModelArch, error)
}
type ModelData struct {
Path string
Name string
Params *Params
Vocab *Vocab
Tensors []llm.Tensor
Format ModelFormat
}
func GetModelFormat(dirname string) (ModelFormat, error) {
files, err := filepath.Glob(filepath.Join(dirname, "*"))
func ReadSafeTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
f, err := os.Open(fn)
if err != nil {
return nil, 0, err
}
defer f.Close()
var jsonSize uint64
if err := binary.Read(f, binary.LittleEndian, &jsonSize); err != nil {
return nil, 0, err
}
buf := make([]byte, jsonSize)
_, err = io.ReadFull(f, buf)
if err != nil {
return nil, 0, err
}
d := json.NewDecoder(bytes.NewBuffer(buf))
d.UseNumber()
var parsed map[string]interface{}
if err = d.Decode(&parsed); err != nil {
return nil, 0, err
}
var keys []string
for k := range parsed {
keys = append(keys, k)
}
slices.Sort(keys)
slog.Info("converting layers")
var tensors []llm.Tensor
for _, k := range keys {
vals := parsed[k].(map[string]interface{})
var data MetaData
if err = mapstructure.Decode(vals, &data); err != nil {
return nil, 0, err
}
var size uint64
var kind uint32
switch len(data.Shape) {
case 0:
// metadata
continue
case 1:
// convert to float32
kind = 0
size = uint64(data.Shape[0] * 4)
case 2:
// convert to float16
kind = 1
size = uint64(data.Shape[0] * data.Shape[1] * 2)
}
ggufName, err := GetTensorName(k)
if err != nil {
slog.Error("%v", err)
return nil, 0, err
}
shape := []uint64{0, 0, 0, 0}
for i := range data.Shape {
shape[i] = uint64(data.Shape[i])
}
t := llm.Tensor{
Name: ggufName,
Kind: kind,
Offset: offset,
Shape: shape[:],
}
t.WriterTo = safetensorWriterTo{
t: &t,
params: params,
bo: params.ByteOrder,
filename: fn,
start: uint64(data.Offsets[0]),
end: uint64(data.Offsets[1]),
padding: 8 + jsonSize,
}
slog.Debug(fmt.Sprintf("%v", t))
tensors = append(tensors, t)
offset += size
}
return tensors, offset, nil
}
func GetSafeTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
var tensors []llm.Tensor
files, err := filepath.Glob(filepath.Join(dirpath, "/model-*.safetensors"))
if err != nil {
return nil, err
}
for _, fn := range files {
slog.Debug(fmt.Sprintf("file = %s", fn))
if strings.HasSuffix(fn, ".safetensors") {
return &SafetensorFormat{}, nil
} else if strings.HasSuffix(fn, ".bin") {
slog.Debug("model is torch")
return &TorchFormat{}, nil
var offset uint64
for _, f := range files {
var t []llm.Tensor
var err error
t, offset, err = ReadSafeTensors(f, offset, params)
if err != nil {
slog.Error("%v", err)
return nil, err
}
tensors = append(tensors, t...)
}
return tensors, nil
}
func GetParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "config.json"))
if err != nil {
return nil, err
}
defer f.Close()
var params Params
d := json.NewDecoder(f)
err = d.Decode(&params)
if err != nil {
return nil, err
}
return nil, fmt.Errorf("couldn't determine model format")
params.ByteOrder = binary.LittleEndian
return &params, nil
}
// Details on gguf's tokenizer can be found at:
@@ -93,7 +206,7 @@ type Vocab struct {
Types []int32
}
func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
func LoadSentencePieceTokens(dirpath string, vocabSize int) (*Vocab, error) {
slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
if err != nil {
@@ -173,8 +286,8 @@ func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
}
slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
if params.VocabSize > len(v.Tokens) {
missingTokens := params.VocabSize - len(v.Tokens)
if vocabSize > len(v.Tokens) {
missingTokens := vocabSize - len(v.Tokens)
slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
for cnt := 0; cnt < missingTokens; cnt++ {
v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
@@ -185,3 +298,136 @@ func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
return v, nil
}
func GetTensorName(n string) (string, error) {
tMap := map[string]string{
"model.embed_tokens.weight": "token_embd.weight",
"model.layers.(\\d+).input_layernorm.weight": "blk.$1.attn_norm.weight",
"model.layers.(\\d+).mlp.down_proj.weight": "blk.$1.ffn_down.weight",
"model.layers.(\\d+).mlp.gate_proj.weight": "blk.$1.ffn_gate.weight",
"model.layers.(\\d+).mlp.up_proj.weight": "blk.$1.ffn_up.weight",
"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
"model.layers.(\\d+).self_attn.k_proj.weight": "blk.$1.attn_k.weight",
"model.layers.(\\d+).self_attn.o_proj.weight": "blk.$1.attn_output.weight",
"model.layers.(\\d+).self_attn.q_proj.weight": "blk.$1.attn_q.weight",
"model.layers.(\\d+).self_attn.v_proj.weight": "blk.$1.attn_v.weight",
"lm_head.weight": "output.weight",
"model.norm.weight": "output_norm.weight",
}
v, ok := tMap[n]
if ok {
return v, nil
}
// quick hack to rename the layers to gguf format
for k, v := range tMap {
re := regexp.MustCompile(k)
newName := re.ReplaceAllString(n, v)
if newName != n {
return newName, nil
}
}
return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
}
type safetensorWriterTo struct {
t *llm.Tensor
params *Params
bo ByteOrder
filename string
start, end, padding uint64
handler func(w io.Writer, r safetensorWriterTo, f *os.File) error
}
func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
f, err := os.Open(r.filename)
if err != nil {
return 0, err
}
defer f.Close()
if _, err = f.Seek(int64(r.padding+r.start), 0); err != nil {
return 0, err
}
// use the handler if one is present
if r.handler != nil {
return 0, r.handler(w, r, f)
}
remaining := r.end - r.start
bufSize := uint64(10240)
var finished bool
for {
data := make([]byte, min(bufSize, remaining))
b, err := io.ReadFull(f, data)
remaining -= uint64(b)
if err == io.EOF || remaining <= 0 {
finished = true
} else if err != nil {
return 0, err
}
// convert bfloat16 -> ieee float32
tDataF32 := bfloat16.DecodeFloat32(data)
switch r.t.Kind {
case 0:
if err := binary.Write(w, r.bo, tDataF32); err != nil {
return 0, err
}
case 1:
// convert float32 -> float16
tempBuf := make([]uint16, len(data)/2)
for cnt, v := range tDataF32 {
tDataF16 := float16.Fromfloat32(v)
tempBuf[cnt] = uint16(tDataF16)
}
if err := binary.Write(w, binary.LittleEndian, tempBuf); err != nil {
return 0, err
}
}
if finished {
break
}
}
return 0, nil
}
func GetModelArchFromParams(name, dirPath string, params *Params) (ModelArch, error) {
switch len(params.Architectures) {
case 0:
return nil, fmt.Errorf("No architecture specified to convert")
case 1:
switch params.Architectures[0] {
case "MistralForCausalLM":
return &MistralModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
},
}, nil
case "GemmaForCausalLM":
return &GemmaModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
},
}, nil
default:
return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
}
}
return nil, fmt.Errorf("Unknown error")
}

View File

@@ -65,14 +65,13 @@ func addOnes(data []float32, vectorSize int) ([]float32, error) {
}
func (m *GemmaModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
t, err := GetSafeTensors(m.Path, m.Params)
if err != nil {
return err
}
slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
m.Tensors = []llm.Tensor{}
for _, l := range t {
if strings.HasSuffix(l.Name, "norm.weight") {
wt := l.WriterTo.(safetensorWriterTo)
@@ -86,7 +85,7 @@ func (m *GemmaModel) GetTensors() error {
}
func (m *GemmaModel) LoadVocab() error {
v, err := LoadSentencePieceTokens(m.Path, m.Params)
v, err := LoadSentencePieceTokens(m.Path, m.Params.VocabSize)
if err != nil {
return err
}

View File

@@ -1,176 +0,0 @@
package convert
import (
"encoding/binary"
"fmt"
"io"
"log/slog"
"os"
"regexp"
"strings"
"github.com/nlpodyssey/gopickle/pytorch"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
type LlamaModel struct {
ModelData
}
func llamaLayerHandler(w io.Writer, r torchWriterTo) error {
slog.Debug(fmt.Sprintf("repacking layer '%s'", r.t.Name))
data := r.storage.(*pytorch.HalfStorage).Data
tData := make([]uint16, len(data))
for cnt, v := range data {
tData[cnt] = uint16(float16.Fromfloat32(v))
}
var err error
var heads uint32
if strings.Contains(r.t.Name, "attn_q") {
heads = uint32(r.params.AttentionHeads)
} else if strings.Contains(r.t.Name, "attn_k") {
heads = uint32(r.params.KeyValHeads)
if heads == 0 {
heads = uint32(r.params.AttentionHeads)
}
} else {
return fmt.Errorf("unknown layer type")
}
slog.Debug(fmt.Sprintf("heads = %d", heads))
tData, err = llamaRepack(tData, int(heads), r.t.Shape)
if err != nil {
return err
}
if err = binary.Write(w, r.bo, tData); err != nil {
return err
}
return nil
}
func llamaRepack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data))
origShape := n.Shape().Clone()
// reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf
if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil {
return nil, err
}
if err := n.T(0, 2, 1, 3); err != nil {
return nil, err
}
if err := n.Reshape(origShape...); err != nil {
return nil, err
}
if err := n.Transpose(); err != nil {
return nil, err
}
newN, err := native.SelectU16(n, 1)
if err != nil {
return nil, err
}
var fullTensor []uint16
for _, v := range newN {
fullTensor = append(fullTensor, v...)
}
return fullTensor, nil
}
func (m *LlamaModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
m.Tensors = []llm.Tensor{}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
return err
}
for _, l := range t {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
slog.Debug(fmt.Sprintf("setting handler for: %s", l.Name))
wt := l.WriterTo.(torchWriterTo)
wt.handler = llamaLayerHandler
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
}
return nil
}
func (m *LlamaModel) LoadVocab() error {
var v *Vocab
var err error
slog.Debug("loading vocab")
v, err = LoadSentencePieceTokens(m.Path, m.Params)
if err != nil {
return err
}
slog.Debug("vocab loaded")
m.Vocab = v
return nil
}
func (m *LlamaModel) WriteGGUF() (string, error) {
kv := llm.KV{
"general.architecture": "llama",
"general.name": m.Name,
"llama.vocab_size": uint32(len(m.Vocab.Tokens)),
"llama.context_length": uint32(m.Params.ContextSize),
"llama.embedding_length": uint32(m.Params.HiddenSize),
"llama.block_count": uint32(m.Params.HiddenLayers),
"llama.feed_forward_length": uint32(m.Params.IntermediateSize),
"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
"llama.attention.head_count": uint32(m.Params.AttentionHeads),
"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
"general.file_type": uint32(1),
"tokenizer.ggml.model": "llama",
"tokenizer.ggml.tokens": m.Vocab.Tokens,
"tokenizer.ggml.scores": m.Vocab.Scores,
"tokenizer.ggml.token_type": m.Vocab.Types,
"tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID),
"tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID),
"tokenizer.ggml.unknown_token_id": uint32(0),
"tokenizer.ggml.add_bos_token": true,
"tokenizer.ggml.add_eos_token": false,
}
f, err := os.CreateTemp("", "ollama-gguf")
if err != nil {
return "", err
}
defer f.Close()
mod := llm.NewGGUFV3(m.Params.ByteOrder)
if err := mod.Encode(f, kv, m.Tensors); err != nil {
return "", err
}
slog.Debug(fmt.Sprintf("gguf file = %s", f.Name()))
return f.Name(), nil
}

View File

@@ -97,7 +97,7 @@ func repack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
}
func (m *MistralModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
t, err := GetSafeTensors(m.Path, m.Params)
if err != nil {
return err
}
@@ -124,7 +124,7 @@ func (m *MistralModel) GetTensors() error {
}
func (m *MistralModel) LoadVocab() error {
v, err := LoadSentencePieceTokens(m.Path, m.Params)
v, err := LoadSentencePieceTokens(m.Path, m.Params.VocabSize)
if err != nil {
return err
}

View File

@@ -1,96 +0,0 @@
package convert
import (
"os"
"regexp"
"github.com/ollama/ollama/llm"
)
type MixtralModel struct {
ModelData
}
func (m *MixtralModel) GetTensors() error {
t, err := m.Format.GetTensors(m.Path, m.Params)
if err != nil {
return err
}
m.Tensors = []llm.Tensor{}
pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
re, err := regexp.Compile(pattern)
if err != nil {
return err
}
for _, l := range t {
matches := re.FindAllStringSubmatch(l.Name, -1)
if len(matches) > 0 {
wt := l.WriterTo.(safetensorWriterTo)
wt.handler = mistralLayerHandler
l.WriterTo = wt
}
m.Tensors = append(m.Tensors, l)
}
return nil
}
func (m *MixtralModel) LoadVocab() error {
v, err := LoadSentencePieceTokens(m.Path, m.Params)
if err != nil {
return err
}
m.Vocab = v
return nil
}
func (m *MixtralModel) WriteGGUF() (string, error) {
kv := llm.KV{
"general.architecture": "llama",
"general.name": m.Name,
"llama.block_count": uint32(m.Params.HiddenLayers),
"llama.context_length": uint32(m.Params.ContextSize),
"llama.embedding_length": uint32(m.Params.HiddenSize),
"llama.feed_forward_length": uint32(m.Params.IntermediateSize),
"llama.attention.head_count": uint32(m.Params.AttentionHeads),
"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
"llama.rope.freq_base": float32(m.Params.RopeFrequencyBase),
"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
"llama.expert_count": uint32(m.Params.Experts),
"llama.expert_used_count": uint32(m.Params.ExpertsUsed),
"llama.vocab_size": uint32(len(m.Vocab.Tokens)),
"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
"general.file_type": uint32(1),
"tokenizer.ggml.model": "llama",
"tokenizer.ggml.tokens": m.Vocab.Tokens,
"tokenizer.ggml.scores": m.Vocab.Scores,
"tokenizer.ggml.token_type": m.Vocab.Types,
"tokenizer.ggml.bos_token_id": uint32(m.Params.BoSTokenID),
"tokenizer.ggml.eos_token_id": uint32(m.Params.EoSTokenID),
"tokenizer.ggml.unknown_token_id": uint32(0),
"tokenizer.ggml.add_bos_token": true,
"tokenizer.ggml.add_eos_token": false,
}
f, err := os.CreateTemp("", "ollama-gguf")
if err != nil {
return "", err
}
defer f.Close()
mod := llm.NewGGUFV3(m.Params.ByteOrder)
if err := mod.Encode(f, kv, m.Tensors); err != nil {
return "", err
}
return f.Name(), nil
}

View File

@@ -1,317 +0,0 @@
package convert
import (
"bytes"
"encoding/binary"
"encoding/json"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"slices"
"github.com/d4l3k/go-bfloat16"
"github.com/mitchellh/mapstructure"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
type safetensorWriterTo struct {
t *llm.Tensor
params *Params
bo ByteOrder
filename string
start, end, padding uint64
handler func(w io.Writer, r safetensorWriterTo, f *os.File) error
}
type tensorMetaData struct {
Type string `mapstructure:"dtype"`
Shape []int `mapstructure:"shape"`
Offsets []int `mapstructure:"data_offsets"`
}
type SafetensorFormat struct{}
func (m *SafetensorFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
slog.Debug("getting tensor data")
var tensors []llm.Tensor
files, err := filepath.Glob(filepath.Join(dirpath, "/model-*.safetensors"))
if err != nil {
return nil, err
}
var offset uint64
for _, f := range files {
var t []llm.Tensor
var err error
t, offset, err = m.readTensors(f, offset, params)
if err != nil {
slog.Error("%v", err)
return nil, err
}
tensors = append(tensors, t...)
}
slog.Debug(fmt.Sprintf("all tensors = %d", len(tensors)))
return tensors, nil
}
func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
f, err := os.Open(fn)
if err != nil {
return nil, 0, err
}
defer f.Close()
var jsonSize uint64
if err := binary.Read(f, binary.LittleEndian, &jsonSize); err != nil {
return nil, 0, err
}
buf := make([]byte, jsonSize)
_, err = io.ReadFull(f, buf)
if err != nil {
return nil, 0, err
}
d := json.NewDecoder(bytes.NewBuffer(buf))
d.UseNumber()
var parsed map[string]interface{}
if err = d.Decode(&parsed); err != nil {
return nil, 0, err
}
var keys []string
for k := range parsed {
keys = append(keys, k)
}
slices.Sort(keys)
slog.Info("converting layers")
var tensors []llm.Tensor
for _, k := range keys {
vals := parsed[k].(map[string]interface{})
var data tensorMetaData
if err = mapstructure.Decode(vals, &data); err != nil {
slog.Error("couldn't decode properly")
return nil, 0, err
}
var size uint64
var kind uint32
switch len(data.Shape) {
case 0:
// metadata
continue
case 1:
// convert to float32
kind = 0
size = uint64(data.Shape[0] * 4)
case 2:
// convert to float16
kind = 1
size = uint64(data.Shape[0] * data.Shape[1] * 2)
}
ggufName, err := m.GetLayerName(k)
if err != nil {
slog.Error("%v", err)
return nil, 0, err
}
shape := []uint64{0, 0, 0, 0}
for i := range data.Shape {
shape[i] = uint64(data.Shape[i])
}
t := llm.Tensor{
Name: ggufName,
Kind: kind,
Offset: offset,
Shape: shape[:],
}
t.WriterTo = safetensorWriterTo{
t: &t,
params: params,
bo: params.ByteOrder,
filename: fn,
start: uint64(data.Offsets[0]),
end: uint64(data.Offsets[1]),
padding: 8 + jsonSize,
}
offset += size
tensors = append(tensors, t)
}
slog.Debug(fmt.Sprintf("total tensors for file = %d", len(tensors)))
slog.Debug(fmt.Sprintf("offset = %d", offset))
return tensors, offset, nil
}
func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "config.json"))
if err != nil {
return nil, err
}
defer f.Close()
var params Params
d := json.NewDecoder(f)
err = d.Decode(&params)
if err != nil {
return nil, err
}
params.ByteOrder = binary.LittleEndian
return &params, nil
}
func (m *SafetensorFormat) GetLayerName(n string) (string, error) {
directMap := map[string]string{
"model.embed_tokens.weight": "token_embd.weight",
"lm_head.weight": "output.weight",
"model.norm.weight": "output_norm.weight",
}
tMap := map[string]string{
"model.layers.(\\d+).input_layernorm.weight": "blk.$1.attn_norm.weight",
"model.layers.(\\d+).mlp.down_proj.weight": "blk.$1.ffn_down.weight",
"model.layers.(\\d+).mlp.gate_proj.weight": "blk.$1.ffn_gate.weight",
"model.layers.(\\d+).mlp.up_proj.weight": "blk.$1.ffn_up.weight",
"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
"model.layers.(\\d+).self_attn.k_proj.weight": "blk.$1.attn_k.weight",
"model.layers.(\\d+).self_attn.o_proj.weight": "blk.$1.attn_output.weight",
"model.layers.(\\d+).self_attn.q_proj.weight": "blk.$1.attn_q.weight",
"model.layers.(\\d+).self_attn.v_proj.weight": "blk.$1.attn_v.weight",
"model.layers.(\\d+).block_sparse_moe.gate.weight": "blk.$1.ffn_gate_inp.weight",
"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w1.weight": "blk.$1.ffn_gate.$2.weight",
"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w2.weight": "blk.$1.ffn_down.$2.weight",
"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w3.weight": "blk.$1.ffn_up.$2.weight",
}
v, ok := directMap[n]
if ok {
return v, nil
}
// quick hack to rename the layers to gguf format
for k, v := range tMap {
re := regexp.MustCompile(k)
newName := re.ReplaceAllString(n, v)
if newName != n {
return newName, nil
}
}
return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
}
func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
f, err := os.Open(r.filename)
if err != nil {
return 0, err
}
defer f.Close()
if _, err = f.Seek(int64(r.padding+r.start), 0); err != nil {
return 0, err
}
// use the handler if one is present
if r.handler != nil {
return 0, r.handler(w, r, f)
}
remaining := r.end - r.start
bufSize := uint64(10240)
var finished bool
for {
data := make([]byte, min(bufSize, remaining))
b, err := io.ReadFull(f, data)
remaining -= uint64(b)
if err == io.EOF || remaining <= 0 {
finished = true
} else if err != nil {
return 0, err
}
// convert bfloat16 -> ieee float32
tDataF32 := bfloat16.DecodeFloat32(data)
switch r.t.Kind {
case 0:
if err := binary.Write(w, r.bo, tDataF32); err != nil {
return 0, err
}
case 1:
// convert float32 -> float16
tempBuf := make([]uint16, len(data)/2)
for cnt, v := range tDataF32 {
tDataF16 := float16.Fromfloat32(v)
tempBuf[cnt] = uint16(tDataF16)
}
if err := binary.Write(w, r.bo, tempBuf); err != nil {
return 0, err
}
}
if finished {
break
}
}
return 0, nil
}
func (m *SafetensorFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
switch len(params.Architectures) {
case 0:
return nil, fmt.Errorf("No architecture specified to convert")
case 1:
switch params.Architectures[0] {
case "MistralForCausalLM":
return &MistralModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
case "MixtralForCausalLM":
return &MixtralModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
case "GemmaForCausalLM":
return &GemmaModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
default:
return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
}
}
return nil, fmt.Errorf("Unknown error")
}

View File

@@ -1,286 +0,0 @@
package convert
import (
"encoding/binary"
"encoding/json"
"fmt"
"io"
"log/slog"
"os"
"path/filepath"
"regexp"
"strings"
"github.com/nlpodyssey/gopickle/pytorch"
"github.com/nlpodyssey/gopickle/types"
"github.com/x448/float16"
"github.com/ollama/ollama/llm"
)
type torchWriterTo struct {
t *llm.Tensor
params *Params
bo ByteOrder
storage pytorch.StorageInterface
handler func(w io.Writer, r torchWriterTo) error
}
type TorchFormat struct{}
func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
slog.Debug("getting torch tensors")
files, err := filepath.Glob(filepath.Join(dirpath, "pytorch_model-*.bin"))
if err != nil {
slog.Error("didn't find any torch files")
return nil, err
}
var offset uint64
var tensors []llm.Tensor
for _, fn := range files {
m, err := pytorch.Load(fn)
if err != nil {
slog.Error(fmt.Sprintf("error unpickling: %q", err))
return []llm.Tensor{}, err
}
for _, k := range m.(*types.Dict).Keys() {
if strings.HasSuffix(k.(string), "self_attn.rotary_emb.inv_freq") {
continue
}
t, _ := m.(*types.Dict).Get(k)
tshape := t.(*pytorch.Tensor).Size
var size uint64
var kind uint32
switch len(tshape) {
case 0:
continue
case 1:
// convert to float32
kind = 0
size = uint64(tshape[0] * 4)
case 2:
// convert to float16
kind = 1
size = uint64(tshape[0] * tshape[1] * 2)
}
ggufName, err := tf.GetLayerName(k.(string))
if err != nil {
slog.Error("%v", err)
return nil, err
}
slog.Debug(fmt.Sprintf("finding name for '%s' -> '%s'", k.(string), ggufName))
shape := []uint64{0, 0, 0, 0}
for i := range tshape {
shape[i] = uint64(tshape[i])
}
tensor := llm.Tensor{
Name: ggufName,
Kind: kind,
Offset: offset, // calculate the offset
Shape: shape[:],
}
tensor.WriterTo = torchWriterTo{
t: &tensor,
params: params,
bo: params.ByteOrder,
storage: t.(*pytorch.Tensor).Source,
}
tensors = append(tensors, tensor)
offset += size
}
}
return tensors, nil
}
func getAltParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "params.json"))
if err != nil {
slog.Error("no params.json")
return nil, err
}
defer f.Close()
type TorchParams struct {
HiddenSize int `json:"dim"`
AttentionHeads int `json:"n_heads"`
KeyValHeads int `json:"n_kv_heads"`
HiddenLayers int `json:"n_layers"`
RopeTheta int `json:"rope_theta"`
NormEPS float64 `json:"norm_eps"`
}
var tparams TorchParams
d := json.NewDecoder(f)
err = d.Decode(&tparams)
if err != nil {
return nil, err
}
params := &Params{
HiddenSize: tparams.HiddenSize,
AttentionHeads: tparams.AttentionHeads,
KeyValHeads: tparams.KeyValHeads,
HiddenLayers: tparams.HiddenLayers,
NormEPS: tparams.NormEPS,
}
switch {
case tparams.RopeTheta == 1000000:
// Codellama
params.ContextSize = 16384
case tparams.NormEPS == 1e-06:
// llama2
slog.Debug("Found llama2 - setting context size to 4096")
params.ContextSize = 4096
default:
params.ContextSize = 2048
}
params.ByteOrder = binary.LittleEndian
return params, nil
}
func (m *TorchFormat) GetParams(dirpath string) (*Params, error) {
f, err := os.Open(filepath.Join(dirpath, "config.json"))
if err != nil {
if os.IsNotExist(err) {
// try params.json instead
return getAltParams(dirpath)
} else {
return nil, err
}
}
var params Params
d := json.NewDecoder(f)
err = d.Decode(&params)
if err != nil {
return nil, err
}
params.ByteOrder = binary.LittleEndian
return &params, nil
}
func (m *TorchFormat) GetLayerName(n string) (string, error) {
directMap := map[string]string{
"tok_embeddings.weight": "token_embd.weight",
"output.weight": "output.weight",
"norm.weight": "output_norm.weight",
"rope.freqs": "rope_freqs.weight",
"model.embed_tokens.weight": "token_embd.weight",
"lm_head.weight": "output.weight",
"model.norm.weight": "output_norm.weight",
}
lMap := map[string]string{
"layers.(\\d+).attention_norm.weight": "blk.$1.attn_norm.weight",
"layers.(\\d+).attention_output_norm.weight": "blk.$1.attn_norm.weight",
"layers.(\\d+).feed_forward.w2.weight": "blk.$1.ffn_down.weight",
"layers.(\\d+).feed_forward.w1.weight": "blk.$1.ffn_gate.weight",
"layers.(\\d+).feed_forward.w3.weight": "blk.$1.ffn_up.weight",
"layers.(\\d+).ffn_norm.weight": "blk.$1.ffn_norm.weight",
"layers.(\\d+).attention.wk.weight": "blk.$1.attn_k.weight",
"layers.(\\d+).attention.wo.weight": "blk.$1.attn_output.weight",
"layers.(\\d+).attention.wq.weight": "blk.$1.attn_q.weight",
"layers.(\\d+).attention.wv.weight": "blk.$1.attn_v.weight",
"model.layers.(\\d+).input_layernorm.weight": "blk.$1.attn_norm.weight",
"model.layers.(\\d+).mlp.down_proj.weight": "blk.$1.ffn_down.weight",
"model.layers.(\\d+).mlp.gate_proj.weight": "blk.$1.ffn_gate.weight",
"model.layers.(\\d+).mlp.up_proj.weight": "blk.$1.ffn_up.weight",
"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
"model.layers.(\\d+).self_attn.k_proj.weight": "blk.$1.attn_k.weight",
"model.layers.(\\d+).self_attn.o_proj.weight": "blk.$1.attn_output.weight",
"model.layers.(\\d+).self_attn.q_proj.weight": "blk.$1.attn_q.weight",
"model.layers.(\\d+).self_attn.v_proj.weight": "blk.$1.attn_v.weight",
}
v, ok := directMap[n]
if ok {
return v, nil
}
// quick hack to rename the layers to gguf format
for k, v := range lMap {
re := regexp.MustCompile(k)
newName := re.ReplaceAllString(n, v)
if newName != n {
return newName, nil
}
}
return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
}
func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
// use the handler if one is present
if r.handler != nil {
return 0, r.handler(w, r)
}
switch r.storage.(type) {
case *pytorch.FloatStorage:
slog.Warn(fmt.Sprintf("unexpected storage found for layer '%s'; skipping", r.t.Name))
return 0, nil
case *pytorch.HalfStorage:
switch r.t.Kind {
case 0:
data := r.storage.(*pytorch.HalfStorage).Data
slog.Debug(fmt.Sprintf("%35s F32 (%d)", r.t.Name, len(data)))
if err := binary.Write(w, r.bo, data); err != nil {
return 0, err
}
case 1:
data := r.storage.(*pytorch.HalfStorage).Data
tData := make([]uint16, len(data))
for cnt, v := range data {
tData[cnt] = uint16(float16.Fromfloat32(v))
}
slog.Debug(fmt.Sprintf("%35s F16 (%d)", r.t.Name, len(tData)))
if err := binary.Write(w, r.bo, tData); err != nil {
return 0, err
}
}
}
return 0, nil
}
func (m *TorchFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
switch len(params.Architectures) {
case 0:
return nil, fmt.Errorf("No architecture specified to convert")
case 1:
switch params.Architectures[0] {
case "LlamaForCausalLM":
return &LlamaModel{
ModelData{
Name: name,
Path: dirPath,
Params: params,
Format: m,
},
}, nil
default:
return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
}
}
return nil, fmt.Errorf("Unknown error")
}

View File

@@ -90,7 +90,7 @@ The final response in the stream also includes additional data about the generat
- `load_duration`: time spent in nanoseconds loading the model
- `prompt_eval_count`: number of tokens in the prompt
- `prompt_eval_duration`: time spent in nanoseconds evaluating the prompt
- `eval_count`: number of tokens in the response
- `eval_count`: number of tokens the response
- `eval_duration`: time in nanoseconds spent generating the response
- `context`: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
- `response`: empty if the response was streamed, if not streamed, this will contain the full response

View File

@@ -23,13 +23,7 @@ export OLLAMA_DEBUG=1
Get the required libraries and build the native LLM code:
```bash
go generate ./...
```
Then build ollama:
```bash
go build .
go run build.go
```
Now you can run `ollama`:
@@ -38,6 +32,16 @@ Now you can run `ollama`:
./ollama
```
### Rebuilding the native code
If at any point you need to rebuild the native code, you can run the
build.go script again using the `-f` flag to force a rebuild, and,
optionally, the `-d` flag to skip building the Go binary:
```bash
go run build.go -f -d
```
### Linux
#### Linux CUDA (NVIDIA)
@@ -53,16 +57,10 @@ 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
set set of target CUDA architectues by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "50;60;70")
Then generate dependencies:
```
go generate ./...
```
Then build the binary:
```
go build .
go run build.go
```
#### Linux ROCm (AMD)
@@ -78,21 +76,17 @@ install (typically `/opt/rocm`), and `CLBlast_DIR` to the location of the
CLBlast install (typically `/usr/lib/cmake/CLBlast`). You can also customize
the AMD GPU targets by setting AMDGPU_TARGETS (e.g. `AMDGPU_TARGETS="gfx1101;gfx1102"`)
```
go generate ./...
```
Then build the binary:
```
go build .
go run build.go
```
ROCm requires elevated privileges to access the GPU at runtime. On most distros you can add your user account to the `render` group, or run as root.
#### Advanced CPU Settings
By default, running `go generate ./...` will compile a few different variations
By default, running `go run build.go` will compile a few different variations
of the LLM library based on common CPU families and vector math capabilities,
including a lowest-common-denominator which should run on almost any 64 bit CPU
somewhat slowly. At runtime, Ollama will auto-detect the optimal variation to
@@ -102,8 +96,7 @@ like to use. For example, to compile an optimized binary for an Intel i9-9880H,
you might use:
```
OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on" go generate ./...
go build .
OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on" go run build.go
```
#### Containerized Linux Build
@@ -124,8 +117,7 @@ Install required tools:
```powershell
$env:CGO_ENABLED="1"
go generate ./...
go build .
go run build.go
```
#### Windows CUDA (NVIDIA)
@@ -142,4 +134,4 @@ In addition to the common Windows development tools described above, install AMD
- [AMD HIP](https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html)
- [Strawberry Perl](https://strawberryperl.com/)
Lastly, add `ninja.exe` included with MSVC to the system path (e.g. `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja`).
Lastly, add `ninja.exe` included with MSVC to the system path (e.g. `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja`).

View File

@@ -228,7 +228,3 @@ To unload the model and free up memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama2", "keep_alive": 0}'
```
Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
If you wish to override the `OLLAMA_KEEP_ALIVE` setting, use the `keep_alive` API parameter with the `/api/generate` or `/api/chat` API endpoints.

View File

@@ -139,6 +139,9 @@ PARAMETER <parameter> <parametervalue>
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| num_gqa | The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b | int | num_gqa 1 |
| num_gpu | The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. | int | num_gpu 50 |
| num_thread | Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). | int | num_thread 8 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |

View File

@@ -18,7 +18,7 @@ const ollama = new Ollama({
model: "llama2",
});
const answer = await ollama.invoke(`why is the sky blue?`);
const answer = await ollama.call(`why is the sky blue?`);
console.log(answer);
```

View File

@@ -1,15 +1,38 @@
# 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.
With some minor configuration, Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/). The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack).
The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack), but should also work on JetPack 6.0.
NVIDIA Jetson devices are Linux-based embedded AI computers that are purpose-built for AI applications.
Jetsons have an integrated GPU that is wired directly to the memory controller of the machine. For this reason, the `nvidia-smi` command is unrecognized, and Ollama proceeds to operate in "CPU only"
mode. This can be verified by using a monitoring tool like jtop.
In order to address this, we simply pass the path to the Jetson's pre-installed CUDA libraries into `ollama serve` (while in a tmux session). We then hardcode the num_gpu parameters into a cloned
version of our target model.
Prerequisites:
- curl
- tmux
Here are the steps:
- Install Ollama via standard Linux command (ignore the 404 error): `curl https://ollama.com/install.sh | sh`
- Stop the Ollama service: `sudo systemctl stop ollama`
- Start Ollama serve in a tmux session called ollama_jetson and reference the CUDA libraries path: `tmux has-session -t ollama_jetson 2>/dev/null || tmux new-session -d -s ollama_jetson
'LD_LIBRARY_PATH=/usr/local/cuda/lib64 ollama serve'`
- Pull the model you want to use (e.g. mistral): `ollama pull mistral`
- Start an interactive session: `ollama run mistral`
- Create a new Modelfile specifically for enabling GPU support on the Jetson: `touch ModelfileMistralJetson`
- In the ModelfileMistralJetson file, specify the FROM model and the num_gpu PARAMETER as shown below:
```
FROM mistral
PARAMETER num_gpu 999
```
- Create a new model from your Modelfile: `ollama create mistral-jetson -f ./ModelfileMistralJetson`
- Run the new model: `ollama run mistral-jetson`
If you run a monitoring tool like jtop you should now see that Ollama is using the Jetson's integrated GPU.
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).

View File

@@ -14,7 +14,7 @@ As this is a preview release, you should expect a few bugs here and there. If
you run into a problem you can reach out on
[Discord](https://discord.gg/ollama), or file an
[issue](https://github.com/ollama/ollama/issues).
Logs will often be helpful in diagnosing the problem (see
Logs will often be helpful in dianosing the problem (see
[Troubleshooting](#troubleshooting) below)
## System Requirements

View File

@@ -1,51 +0,0 @@
package main
import (
"context"
"fmt"
"log"
"github.com/ollama/ollama/api"
)
func main() {
client, err := api.ClientFromEnvironment()
if err != nil {
log.Fatal(err)
}
messages := []api.Message{
api.Message{
Role: "system",
Content: "Provide very brief, concise responses",
},
api.Message{
Role: "user",
Content: "Name some unusual animals",
},
api.Message{
Role: "assistant",
Content: "Monotreme, platypus, echidna",
},
api.Message{
Role: "user",
Content: "which of these is the most dangerous?",
},
}
ctx := context.Background()
req := &api.ChatRequest{
Model: "llama2",
Messages: messages,
}
respFunc := func(resp api.ChatResponse) error {
fmt.Print(resp.Message.Content)
return nil
}
err = client.Chat(ctx, req, respFunc)
if err != nil {
log.Fatal(err)
}
}

View File

@@ -1,40 +0,0 @@
package main
import (
"context"
"fmt"
"log"
"github.com/ollama/ollama/api"
)
func main() {
client, err := api.ClientFromEnvironment()
if err != nil {
log.Fatal(err)
}
// By default, GenerateRequest is streaming.
req := &api.GenerateRequest{
Model: "gemma",
Prompt: "how many planets are there?",
}
ctx := context.Background()
respFunc := func(resp api.GenerateResponse) error {
// Only print the response here; GenerateResponse has a number of other
// interesting fields you want to examine.
// In streaming mode, responses are partial so we call fmt.Print (and not
// Println) in order to avoid spurious newlines being introduced. The
// model will insert its own newlines if it wants.
fmt.Print(resp.Response)
return nil
}
err = client.Generate(ctx, req, respFunc)
if err != nil {
log.Fatal(err)
}
fmt.Println()
}

View File

@@ -1,37 +0,0 @@
package main
import (
"context"
"fmt"
"log"
"github.com/ollama/ollama/api"
)
func main() {
client, err := api.ClientFromEnvironment()
if err != nil {
log.Fatal(err)
}
req := &api.GenerateRequest{
Model: "gemma",
Prompt: "how many planets are there?",
// set streaming to false
Stream: new(bool),
}
ctx := context.Background()
respFunc := func(resp api.GenerateResponse) error {
// Only print the response here; GenerateResponse has a number of other
// interesting fields you want to examine.
fmt.Println(resp.Response)
return nil
}
err = client.Generate(ctx, req, respFunc)
if err != nil {
log.Fatal(err)
}
}

View File

@@ -1,47 +0,0 @@
package main
import (
"context"
"fmt"
"log"
"os"
"github.com/ollama/ollama/api"
)
func main() {
if len(os.Args) <= 1 {
log.Fatal("usage: <image name>")
}
imgData, err := os.ReadFile(os.Args[1])
if err != nil {
log.Fatal(err)
}
client, err := api.ClientFromEnvironment()
if err != nil {
log.Fatal(err)
}
req := &api.GenerateRequest{
Model: "llava",
Prompt: "describe this image",
Images: []api.ImageData{imgData},
}
ctx := context.Background()
respFunc := func(resp api.GenerateResponse) error {
// In streaming mode, responses are partial so we call fmt.Print (and not
// Println) in order to avoid spurious newlines being introduced. The
// model will insert its own newlines if it wants.
fmt.Print(resp.Response)
return nil
}
err = client.Generate(ctx, req, respFunc)
if err != nil {
log.Fatal(err)
}
fmt.Println()
}

View File

@@ -1,31 +0,0 @@
package main
import (
"context"
"fmt"
"log"
"github.com/ollama/ollama/api"
)
func main() {
client, err := api.ClientFromEnvironment()
if err != nil {
log.Fatal(err)
}
ctx := context.Background()
req := &api.PullRequest{
Model: "mistral",
}
progressFunc := func(resp api.ProgressResponse) error {
fmt.Printf("Progress: status=%v, total=%v, completed=%v\n", resp.Status, resp.Total, resp.Completed)
return nil
}
err = client.Pull(ctx, req, progressFunc)
if err != nil {
log.Fatal(err)
}
}

View File

@@ -15,7 +15,6 @@ const (
KibiByte = Byte * 1024
MebiByte = KibiByte * 1024
GibiByte = MebiByte * 1024
)
func HumanBytes(b int64) string {
@@ -51,7 +50,7 @@ func HumanBytes(b int64) string {
}
}
func HumanBytes2(b uint64) string {
func HumanBytes2(b int64) string {
switch {
case b >= MebiByte:
return fmt.Sprintf("%.1f MiB", float64(b)/MebiByte)

7
go.mod
View File

@@ -19,10 +19,7 @@ require (
golang.org/x/sync v0.3.0
)
require (
github.com/nlpodyssey/gopickle v0.3.0
github.com/pdevine/tensor v0.0.0-20240228013915-64ccaa8d9ca9
)
require github.com/pdevine/tensor v0.0.0-20240228013915-64ccaa8d9ca9
require (
github.com/apache/arrow/go/arrow v0.0.0-20201229220542-30ce2eb5d4dc // indirect
@@ -71,7 +68,7 @@ require (
golang.org/x/net v0.17.0 // indirect
golang.org/x/sys v0.13.0
golang.org/x/term v0.13.0
golang.org/x/text v0.14.0 // indirect
golang.org/x/text v0.13.0 // indirect
google.golang.org/protobuf v1.30.0
gopkg.in/yaml.v3 v3.0.1 // indirect
)

6
go.sum
View File

@@ -122,8 +122,6 @@ github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd h1:TRLaZ9cD/w
github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=
github.com/modern-go/reflect2 v1.0.2 h1:xBagoLtFs94CBntxluKeaWgTMpvLxC4ur3nMaC9Gz0M=
github.com/modern-go/reflect2 v1.0.2/go.mod h1:yWuevngMOJpCy52FWWMvUC8ws7m/LJsjYzDa0/r8luk=
github.com/nlpodyssey/gopickle v0.3.0 h1:BLUE5gxFLyyNOPzlXxt6GoHEMMxD0qhsE4p0CIQyoLw=
github.com/nlpodyssey/gopickle v0.3.0/go.mod h1:f070HJ/yR+eLi5WmM1OXJEGaTpuJEUiib19olXgYha0=
github.com/olekukonko/tablewriter v0.0.5 h1:P2Ga83D34wi1o9J6Wh1mRuqd4mF/x/lgBS7N7AbDhec=
github.com/olekukonko/tablewriter v0.0.5/go.mod h1:hPp6KlRPjbx+hW8ykQs1w3UBbZlj6HuIJcUGPhkA7kY=
github.com/pdevine/tensor v0.0.0-20240228013915-64ccaa8d9ca9 h1:DV4iXjNn6fGeDl1AkZ1I0QB/0DBjrc7kPpxHrmuDzW4=
@@ -238,8 +236,8 @@ golang.org/x/term v0.13.0/go.mod h1:LTmsnFJwVN6bCy1rVCoS+qHT1HhALEFxKncY3WNNh4U=
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.14.0 h1:ScX5w1eTa3QqT8oi6+ziP7dTV1S2+ALU0bI+0zXKWiQ=
golang.org/x/text v0.14.0/go.mod h1:18ZOQIKpY8NJVqYksKHtTdi31H5itFRjB5/qKTNYzSU=
golang.org/x/text v0.13.0 h1:ablQoSUd0tRdKxZewP80B+BaqeKJuVhuRxj/dkrun3k=
golang.org/x/text v0.13.0/go.mod h1:TvPlkZtksWOMsz7fbANvkp4WM8x/WCo/om8BMLbz+aE=
golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=

View File

@@ -7,7 +7,7 @@ import (
"log/slog"
"os"
"path/filepath"
"runtime"
"strconv"
"strings"
)
@@ -35,64 +35,22 @@ func GetSupportedGFX(libDir string) ([]string, error) {
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)
func amdSetVisibleDevices(ids []int, skip map[int]interface{}) {
// Set the visible devices if not already set
// TODO - does sort order matter?
devices := []string{}
for i := range ids {
if _, skipped := skip[i]; skipped {
continue
}
ids = append(ids, info.ID)
devices = append(devices, strconv.Itoa(i))
}
val := strings.Join(devices, ",")
err := os.Setenv("HIP_VISIBLE_DEVICES", val)
if err != nil {
slog.Warn(fmt.Sprintf("failed to set env: %s", err))
} else {
slog.Info("Setting HIP_VISIBLE_DEVICES=" + val)
}
return "HIP_VISIBLE_DEVICES", strings.Join(ids, ",")
}
func commonAMDValidateLibDir() (string, error) {
// We try to favor system paths first, so that we can wire up the subprocess to use
// the system version. Only use our bundled version if the system version doesn't work
// This gives users a more recovery options if versions have subtle problems at runtime
// 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)
if rocmLibUsable(RocmStandardLocation) {
return RocmStandardLocation, nil
}
// 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
}
}
return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
}

View File

@@ -69,7 +69,7 @@ func NewHipLib() (*HipLib, error) {
func (hl *HipLib) Release() {
err := windows.FreeLibrary(hl.dll)
if err != nil {
slog.Warn("failed to unload amdhip64.dll", "error", err)
slog.Warn(fmt.Sprintf("failed to unload amdhip64.dll: %s", err))
}
hl.dll = 0
}
@@ -98,7 +98,7 @@ func (hl *HipLib) HipGetDeviceCount() int {
return 0
}
if status != hipSuccess {
slog.Warn("failed call to hipGetDeviceCount", "status", status, "error", err)
slog.Warn(fmt.Sprintf("failed call to hipGetDeviceCount: %d %s", status, err))
}
return count
}

View File

@@ -11,8 +11,6 @@ import (
"slices"
"strconv"
"strings"
"github.com/ollama/ollama/format"
)
// Discovery logic for AMD/ROCm GPUs
@@ -26,6 +24,9 @@ const (
GPUTotalMemoryFileGlob = "mem_banks/*/properties" // size_in_bytes line
GPUUsedMemoryFileGlob = "mem_banks/*/used_memory"
RocmStandardLocation = "/opt/rocm/lib"
// TODO find a better way to detect iGPU instead of minimum memory
IGPUMemLimit = 1024 * 1024 * 1024 // 512G is what they typically report, so anything less than 1G must be iGPU
)
var (
@@ -34,11 +35,14 @@ var (
)
// Gather GPU information from the amdgpu driver if any supported GPUs are detected
func AMDGetGPUInfo() []GpuInfo {
resp := []GpuInfo{}
// HIP_VISIBLE_DEVICES will be set if we detect a mix of unsupported and supported devices
// and the user hasn't already set this variable
func AMDGetGPUInfo(resp *GpuInfo) {
// TODO - DRY this out with windows
if !AMDDetected() {
return resp
return
}
skip := map[int]interface{}{}
// Opportunistic logging of driver version to aid in troubleshooting
ver, err := AMDDriverVersion()
@@ -46,117 +50,160 @@ func AMDGetGPUInfo() []GpuInfo {
slog.Info("AMD Driver: " + ver)
} else {
// 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)
slog.Warn(fmt.Sprintf("ollama recommends running the https://www.amd.com/en/support/linux-drivers: %s", err))
}
// Determine if the user has already pre-selected which GPUs to look at, then ignore the others
var visibleDevices []string
hipVD := os.Getenv("HIP_VISIBLE_DEVICES") // zero based index only
rocrVD := os.Getenv("ROCR_VISIBLE_DEVICES") // zero based index or UUID, but consumer cards seem to not support UUID
gpuDO := os.Getenv("GPU_DEVICE_ORDINAL") // 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, ",")
// If the user has specified exactly which GPUs to use, look up their memory
visibleDevices := os.Getenv("HIP_VISIBLE_DEVICES")
if visibleDevices != "" {
ids := []int{}
for _, idStr := range strings.Split(visibleDevices, ",") {
id, err := strconv.Atoi(idStr)
if err != nil {
slog.Warn(fmt.Sprintf("malformed HIP_VISIBLE_DEVICES=%s %s", visibleDevices, err))
} else {
ids = append(ids, id)
}
}
amdProcMemLookup(resp, nil, ids)
return
}
// Gather GFX version information from all detected cards
gfx := AMDGFXVersions()
verStrings := []string{}
for i, v := range gfx {
verStrings = append(verStrings, v.ToGFXString())
if v.Major == 0 {
// Silently skip CPUs
skip[i] = struct{}{}
continue
}
if v.Major < 9 {
// TODO consider this a build-time setting if we can support 8xx family GPUs
slog.Warn(fmt.Sprintf("amdgpu [%d] too old %s", i, v.ToGFXString()))
skip[i] = struct{}{}
}
}
slog.Info(fmt.Sprintf("detected amdgpu versions %v", verStrings))
// Abort if all GPUs are skipped
if len(skip) >= len(gfx) {
slog.Info("all detected amdgpus are skipped, falling back to CPU")
return
}
// If we got this far, then we have at least 1 GPU that's a ROCm candidate, so make sure we have a lib
libDir, err := AMDValidateLibDir()
if err != nil {
slog.Warn(fmt.Sprintf("unable to verify rocm library, will use cpu: %s", err))
return
}
updateLibPath(libDir)
gfxOverride := os.Getenv("HSA_OVERRIDE_GFX_VERSION")
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)
cpuCount := 0
for _, match := range matches {
slog.Debug("evaluating amdgpu node " + match)
fp, err := os.Open(match)
if gfxOverride == "" {
supported, err := GetSupportedGFX(libDir)
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
slog.Warn(fmt.Sprintf("failed to lookup supported GFX types, falling back to CPU mode: %s", err))
return
}
slog.Debug(fmt.Sprintf("rocm supported GPU types %v", supported))
scanner := bufio.NewScanner(fp)
isCPU := false
var major, minor, patch 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
}
for i, v := range gfx {
if !slices.Contains[[]string, string](supported, v.ToGFXString()) {
slog.Warn(fmt.Sprintf("amdgpu [%d] %s is not supported by %s %v", i, v.ToGFXString(), libDir, 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")
skip[i] = struct{}{}
} else {
slog.Info(fmt.Sprintf("amdgpu [%d] %s is supported", i, v.ToGFXString()))
}
// 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
}
} else {
slog.Debug("skipping rocm gfx compatibility check with HSA_OVERRIDE_GFX_VERSION=" + gfxOverride)
}
if isCPU {
cpuCount++
if len(skip) >= len(gfx) {
slog.Info("all detected amdgpus are skipped, falling back to CPU")
return
}
ids := make([]int, len(gfx))
i := 0
for k := range gfx {
ids[i] = k
i++
}
amdProcMemLookup(resp, skip, ids)
if resp.memInfo.DeviceCount == 0 {
return
}
if len(skip) > 0 {
amdSetVisibleDevices(ids, skip)
}
}
func updateLibPath(libDir string) {
ldPaths := []string{}
if val, ok := os.LookupEnv("LD_LIBRARY_PATH"); ok {
ldPaths = strings.Split(val, ":")
}
for _, d := range ldPaths {
if d == libDir {
return
}
}
val := strings.Join(append(ldPaths, libDir), ":")
slog.Debug("updated lib path", "LD_LIBRARY_PATH", val)
os.Setenv("LD_LIBRARY_PATH", val)
}
// Walk the sysfs nodes for the available GPUs and gather information from them
// skipping over any devices in the skip map
func amdProcMemLookup(resp *GpuInfo, skip map[int]interface{}, ids []int) {
resp.memInfo.DeviceCount = 0
resp.memInfo.TotalMemory = 0
resp.memInfo.FreeMemory = 0
slog.Debug("discovering VRAM for amdgpu devices")
if len(ids) == 0 {
entries, err := os.ReadDir(AMDNodesSysfsDir)
if err != nil {
slog.Warn(fmt.Sprintf("failed to read amdgpu sysfs %s - %s", AMDNodesSysfsDir, err))
return
}
for _, node := range entries {
if !node.IsDir() {
continue
}
id, err := strconv.Atoi(node.Name())
if err != nil {
slog.Warn("malformed amdgpu sysfs node id " + node.Name())
continue
}
ids = append(ids, id)
}
}
slog.Debug(fmt.Sprintf("amdgpu devices %v", ids))
for _, id := range ids {
if _, skipped := skip[id]; skipped {
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 []GpuInfo{}
}
if int(major) < RocmComputeMin {
slog.Warn(fmt.Sprintf("amdgpu too old gfx%d%d%x", major, minor, patch), "gpu", gpuID)
continue
}
// Look up the memory for the current node
totalMemory := uint64(0)
usedMemory := uint64(0)
propGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(nodeID), GPUTotalMemoryFileGlob)
// Adjust for sysfs vs HIP ids
propGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(id+1), GPUTotalMemoryFileGlob)
propFiles, err := filepath.Glob(propGlob)
if err != nil {
slog.Warn("error looking up total GPU memory", "glob", propGlob, "error", err)
slog.Warn(fmt.Sprintf("error looking up total GPU memory: %s %s", propGlob, err))
}
// 1 or more memory banks - sum the values of all of them
for _, propFile := range propFiles {
fp, err := os.Open(propFile)
if err != nil {
slog.Warn("failed to open sysfs node", "file", propFile, "erroir", err)
slog.Warn(fmt.Sprintf("failed to open sysfs node file %s: %s", propFile, err))
continue
}
defer fp.Close()
@@ -179,113 +226,49 @@ func AMDGetGPUInfo() []GpuInfo {
}
}
if totalMemory == 0 {
slog.Warn("amdgpu reports zero total memory", "gpu", gpuID)
slog.Warn(fmt.Sprintf("amdgpu [%d] reports zero total memory, skipping", id))
skip[id] = struct{}{}
continue
}
usedGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(nodeID), GPUUsedMemoryFileGlob)
if totalMemory < IGPUMemLimit {
slog.Info(fmt.Sprintf("amdgpu [%d] appears to be an iGPU with %dM reported total memory, skipping", id, totalMemory/1024/1024))
skip[id] = struct{}{}
continue
}
usedGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(id), GPUUsedMemoryFileGlob)
usedFiles, err := filepath.Glob(usedGlob)
if err != nil {
slog.Warn("error looking up used GPU memory", "glob", usedGlob, "error", err)
slog.Warn(fmt.Sprintf("error looking up used GPU memory: %s %s", usedGlob, err))
continue
}
for _, usedFile := range usedFiles {
fp, err := os.Open(usedFile)
if err != nil {
slog.Warn("failed to open sysfs node", "file", usedFile, "error", err)
slog.Warn(fmt.Sprintf("failed to open sysfs node file %s: %s", usedFile, err))
continue
}
defer fp.Close()
data, err := io.ReadAll(fp)
if err != nil {
slog.Warn("failed to read sysfs node", "file", usedFile, "error", err)
slog.Warn(fmt.Sprintf("failed to read sysfs node file %s: %s", usedFile, err))
continue
}
used, err := strconv.ParseUint(strings.TrimSpace(string(data)), 10, 64)
if err != nil {
slog.Warn("malformed used memory", "data", string(data), "error", err)
slog.Warn(fmt.Sprintf("malformed used memory %s: %s", string(data), err))
continue
}
usedMemory += used
}
// 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", gpuID, "total", format.HumanBytes2(totalMemory))
continue
}
slog.Info("amdgpu memory", "gpu", gpuID, "total", format.HumanBytes2(totalMemory))
slog.Info("amdgpu memory", "gpu", gpuID, "available", format.HumanBytes2(totalMemory-usedMemory))
gpuInfo := GpuInfo{
Library: "rocm",
memInfo: memInfo{
TotalMemory: totalMemory,
FreeMemory: (totalMemory - usedMemory),
},
ID: fmt.Sprintf("%d", gpuID),
// Name: not exposed in sysfs directly, would require pci device id lookup
Major: int(major),
Minor: int(minor),
Patch: int(patch),
MinimumMemory: rocmMinimumMemory,
}
// 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 []GpuInfo{}
}
}
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 []GpuInfo{}
}
slog.Debug("rocm supported GPUs", "types", supported)
}
gfx := fmt.Sprintf("gfx%d%d%x", gpuInfo.Major, gpuInfo.Minor, gpuInfo.Patch)
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.Debug("skipping rocm gfx compatibility check with HSA_OVERRIDE_GFX_VERSION=" + gfxOverride)
}
// The GPU has passed all the verification steps and is supported
resp = append(resp, gpuInfo)
slog.Info(fmt.Sprintf("[%d] amdgpu totalMemory %dM", id, totalMemory/1024/1024))
slog.Info(fmt.Sprintf("[%d] amdgpu freeMemory %dM", id, (totalMemory-usedMemory)/1024/1024))
resp.memInfo.DeviceCount++
resp.memInfo.TotalMemory += totalMemory
resp.memInfo.FreeMemory += (totalMemory - usedMemory)
}
if len(resp) == 0 {
slog.Info("no compatible amdgpu devices detected")
if resp.memInfo.DeviceCount > 0 {
resp.Library = "rocm"
}
return resp
}
// Quick check for AMD driver so we can skip amdgpu discovery if not present
@@ -297,24 +280,87 @@ func AMDDetected() bool {
slog.Debug("amdgpu driver not detected " + sysfsDir)
return false
} else if err != nil {
slog.Debug("error looking up amd driver", "path", sysfsDir, "error", err)
slog.Debug(fmt.Sprintf("error looking up amd driver %s %s", sysfsDir, err))
return false
}
return true
}
func setupLink(source, target string) error {
if err := os.RemoveAll(target); err != nil {
return fmt.Errorf("failed to remove old rocm directory %s %w", target, err)
}
if err := os.Symlink(source, target); err != nil {
return fmt.Errorf("failed to create link %s => %s %w", source, target, err)
}
slog.Debug(fmt.Sprintf("host rocm linked %s => %s", source, target))
return nil
}
// Ensure the AMD rocm lib dir is wired up
// 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()
// We rely on the rpath compiled into our library to find rocm
// so we establish a symlink to wherever we find it on the system
// to <payloads>/rocm
payloadsDir, err := PayloadsDir()
if err != nil {
return "", err
}
// If we already have a rocm dependency wired, nothing more to do
rocmTargetDir := filepath.Clean(filepath.Join(payloadsDir, "..", "rocm"))
if rocmLibUsable(rocmTargetDir) {
return rocmTargetDir, nil
}
// next to the running binary
exe, err := os.Executable()
if err == nil {
return libDir, nil
peerDir := filepath.Dir(exe)
if rocmLibUsable(peerDir) {
slog.Debug("detected ROCM next to ollama executable " + peerDir)
return rocmTargetDir, setupLink(peerDir, rocmTargetDir)
}
peerDir = filepath.Join(filepath.Dir(exe), "rocm")
if rocmLibUsable(peerDir) {
slog.Debug("detected ROCM next to ollama executable " + peerDir)
return rocmTargetDir, setupLink(peerDir, rocmTargetDir)
}
}
// Well known ollama installer path
installedRocmDir := "/usr/share/ollama/lib/rocm"
if rocmLibUsable(installedRocmDir) {
return installedRocmDir, nil
return rocmTargetDir, setupLink(installedRocmDir, rocmTargetDir)
}
// Prefer explicit HIP env var
hipPath := os.Getenv("HIP_PATH")
if hipPath != "" {
hipLibDir := filepath.Join(hipPath, "lib")
if rocmLibUsable(hipLibDir) {
slog.Debug("detected ROCM via HIP_PATH=" + hipPath)
return rocmTargetDir, setupLink(hipLibDir, rocmTargetDir)
}
}
// Scan the library path for potential matches
ldPaths := strings.Split(os.Getenv("LD_LIBRARY_PATH"), ":")
for _, ldPath := range ldPaths {
d, err := filepath.Abs(ldPath)
if err != nil {
continue
}
if rocmLibUsable(d) {
return rocmTargetDir, setupLink(d, rocmTargetDir)
}
}
// Well known location(s)
if rocmLibUsable("/opt/rocm/lib") {
return rocmTargetDir, setupLink("/opt/rocm/lib", rocmTargetDir)
}
// If we still haven't found a usable rocm, the user will have to install it on their own
@@ -338,3 +384,68 @@ func AMDDriverVersion() (string, error) {
}
return strings.TrimSpace(string(verString)), nil
}
func AMDGFXVersions() map[int]Version {
// The amdgpu driver always exposes the host CPU as node 0, but we have to skip that and subtract one
// from the other IDs to get alignment with the HIP libraries expectations (zero is the first GPU, not the CPU)
res := map[int]Version{}
matches, _ := filepath.Glob(GPUPropertiesFileGlob)
for _, match := range matches {
fp, err := os.Open(match)
if err != nil {
slog.Debug(fmt.Sprintf("failed to open sysfs node file %s: %s", match, err))
continue
}
defer fp.Close()
i, err := strconv.Atoi(filepath.Base(filepath.Dir(match)))
if err != nil {
slog.Debug(fmt.Sprintf("failed to parse node ID %s", err))
continue
}
if i == 0 {
// Skipping the CPU
continue
}
// Align with HIP IDs (zero is first GPU, not CPU)
i -= 1
scanner := bufio.NewScanner(fp)
for scanner.Scan() {
line := strings.TrimSpace(scanner.Text())
if strings.HasPrefix(line, "gfx_target_version") {
ver := strings.Fields(line)
if len(ver) != 2 || len(ver[1]) < 5 {
if ver[1] != "0" {
slog.Debug("malformed " + line)
}
res[i] = Version{
Major: 0,
Minor: 0,
Patch: 0,
}
continue
}
l := len(ver[1])
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
}
res[i] = Version{
Major: uint(major),
Minor: uint(minor),
Patch: uint(patch),
}
}
}
}
return res
}
func (v Version) ToGFXString() string {
return fmt.Sprintf("gfx%d%d%d", v.Major, v.Minor, v.Patch)
}

View File

@@ -7,10 +7,7 @@ import (
"os"
"path/filepath"
"slices"
"strconv"
"strings"
"github.com/ollama/ollama/format"
)
const (
@@ -25,32 +22,36 @@ var (
ROCmLibGlobs = []string{"hipblas.dll", "rocblas"} // TODO - probably include more coverage of files here...
)
func AMDGetGPUInfo() []GpuInfo {
resp := []GpuInfo{}
func AMDGetGPUInfo(resp *GpuInfo) {
hl, err := NewHipLib()
if err != nil {
slog.Debug(err.Error())
return nil
return
}
defer hl.Release()
skip := map[int]interface{}{}
ids := []int{}
resp.memInfo.DeviceCount = 0
resp.memInfo.TotalMemory = 0
resp.memInfo.FreeMemory = 0
ver, err := hl.AMDDriverVersion()
if err == nil {
slog.Info("AMD Driver: " + ver)
} else {
// For now this is benign, but we may eventually need to fail compatibility checks
slog.Debug("error looking up amd driver version", "error", err)
slog.Debug(fmt.Sprintf("error looking up amd driver version: %s", err))
}
// Note: the HIP library automatically handles subsetting to any HIP_VISIBLE_DEVICES the user specified
// Note: the HIP library automatically handles HIP_VISIBLE_DEVICES
count := hl.HipGetDeviceCount()
if count == 0 {
return nil
return
}
libDir, err := AMDValidateLibDir()
if err != nil {
slog.Warn("unable to verify rocm library, will use cpu", "error", err)
return nil
slog.Warn(fmt.Sprintf("unable to verify rocm library, will use cpu: %s", err))
return
}
var supported []string
@@ -58,120 +59,95 @@ func AMDGetGPUInfo() []GpuInfo {
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
slog.Warn(fmt.Sprintf("failed to lookup supported GFX types, falling back to CPU mode: %s", err))
return
}
} else {
slog.Debug("skipping rocm gfx compatibility check with HSA_OVERRIDE_GFX_VERSION=" + gfxOverride)
}
slog.Info("detected hip devices", "count", count)
// TODO how to determine the underlying device ID when visible devices is causing this to subset?
slog.Info(fmt.Sprintf("detected %d hip devices", count))
for i := 0; i < count; i++ {
ids = append(ids, i)
err = hl.HipSetDevice(i)
if err != nil {
slog.Warn("set device", "id", i, "error", err)
slog.Warn(fmt.Sprintf("[%d] %s", i, err))
skip[i] = struct{}{}
continue
}
props, err := hl.HipGetDeviceProperties(i)
if err != nil {
slog.Warn("get properties", "id", i, "error", err)
slog.Warn(fmt.Sprintf("[%d] %s", i, err))
skip[i] = struct{}{}
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?)
slog.Info(fmt.Sprintf("[%d] Name: %s", i, name))
n = bytes.IndexByte(props.GcnArchName[:], 0)
gfx := string(props.GcnArchName[:n])
slog.Info("hip device", "id", i, "name", name, "gfx", gfx)
var major, minor, patch string
switch len(gfx) {
case 6:
major, minor, patch = gfx[3:4], gfx[4:5], gfx[5:]
case 7:
major, minor, patch = gfx[3:5], gfx[5:6], gfx[6:]
}
slog.Info(fmt.Sprintf("[%d] GcnArchName: %s", i, 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("iGPU detected skipping", "id", i)
slog.Info(fmt.Sprintf("iGPU detected [%d] skipping", i))
skip[i] = struct{}{}
continue
}
if gfxOverride == "" {
if !slices.Contains[[]string, string](supported, gfx) {
slog.Warn("amdgpu is not supported", "gpu", i, "gpu_type", gfx, "library", libDir, "supported_types", supported)
slog.Warn(fmt.Sprintf("amdgpu [%d] %s is not supported by %s %v", i, gfx, libDir, 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")
skip[i] = struct{}{}
continue
} else {
slog.Info("amdgpu is supported", "gpu", i, "gpu_type", gfx)
slog.Info(fmt.Sprintf("amdgpu [%d] %s is supported", i, gfx))
}
}
freeMemory, totalMemory, err := hl.HipMemGetInfo()
totalMemory, freeMemory, err := hl.HipMemGetInfo()
if err != nil {
slog.Warn("get mem info", "id", i, "error", err)
slog.Warn(fmt.Sprintf("[%d] %s", i, 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
}
// TODO revisit this once ROCm v6 is available on windows.
// v5.7 only reports VRAM used by this process, so it's completely wrong and unusable
slog.Info("amdgpu memory", "gpu", i, "total", format.HumanBytes2(totalMemory))
slog.Info("amdgpu memory", "gpu", i, "available", format.HumanBytes2(freeMemory))
gpuInfo := GpuInfo{
Library: "rocm",
memInfo: memInfo{
TotalMemory: totalMemory,
FreeMemory: freeMemory,
},
ID: fmt.Sprintf("%d", i), // TODO this is probably wrong if we specify visible devices
DependencyPath: libDir,
MinimumMemory: rocmMinimumMemory,
}
if major != "" {
gpuInfo.Major, err = strconv.Atoi(major)
if err != nil {
slog.Info("failed to parse version", "version", gfx, "error", err)
}
}
if minor != "" {
gpuInfo.Minor, err = strconv.Atoi(minor)
if err != nil {
slog.Info("failed to parse version", "version", gfx, "error", err)
}
}
if patch != "" {
// Patch rev is hex; e.g. gfx90a
p, err := strconv.ParseInt(patch, 16, 0)
if err != nil {
slog.Info("failed to parse version", "version", gfx, "error", err)
} else {
gpuInfo.Patch = int(p)
}
}
if gpuInfo.Major < RocmComputeMin {
slog.Warn(fmt.Sprintf("amdgpu [%s] too old gfx%d%d%x", gpuInfo.ID, gpuInfo.Major, gpuInfo.Minor, gpuInfo.Patch))
continue
}
resp = append(resp, gpuInfo)
// TODO according to docs, freeMem may lie on windows!
slog.Info(fmt.Sprintf("[%d] Total Mem: %d", i, totalMemory))
slog.Info(fmt.Sprintf("[%d] Free Mem: %d", i, freeMemory))
resp.memInfo.DeviceCount++
resp.memInfo.TotalMemory += totalMemory
resp.memInfo.FreeMemory += freeMemory
}
return resp
if resp.memInfo.DeviceCount > 0 {
resp.Library = "rocm"
}
// Abort if all GPUs are skipped
if len(skip) >= count {
slog.Info("all detected amdgpus are skipped, falling back to CPU")
return
}
if len(skip) > 0 {
amdSetVisibleDevices(ids, skip)
}
UpdatePath(libDir)
}
func AMDValidateLibDir() (string, error) {
libDir, err := commonAMDValidateLibDir()
// On windows non-admins typically can't create links
// so instead of trying to rely on rpath and a link in
// $LibDir/rocm, we instead rely on setting PATH to point
// to the location of the ROCm library
// Installer payload location if we're running the installed binary
exe, err := os.Executable()
if err == nil {
return libDir, nil
rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
if rocmLibUsable(rocmTargetDir) {
slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
return rocmTargetDir, nil
}
}
// Installer payload (if we're running from some other location)
@@ -183,6 +159,21 @@ func AMDValidateLibDir() (string, error) {
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
}
}
// Well known location(s)
if rocmLibUsable(RocmStandardLocation) {
return RocmStandardLocation, 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 "", fmt.Errorf("no suitable rocm found, falling back to CPU")

View File

@@ -24,51 +24,6 @@ func PayloadsDir() (string, error) {
defer lock.Unlock()
var err error
if payloadsDir == "" {
runnersDir := os.Getenv("OLLAMA_RUNNERS_DIR")
// On Windows we do not carry the payloads inside the main executable
if runtime.GOOS == "windows" && runnersDir == "" {
appExe, err := os.Executable()
if err != nil {
slog.Error("failed to lookup executable path", "error", err)
return "", err
}
cwd, err := os.Getwd()
if err != nil {
slog.Error("failed to lookup working directory", "error", err)
return "", err
}
var paths []string
for _, root := range []string{appExe, cwd} {
paths = append(paths,
filepath.Join(root),
filepath.Join(root, "windows-"+runtime.GOARCH),
filepath.Join(root, "dist", "windows-"+runtime.GOARCH),
)
}
// Try a few variations to improve developer experience when building from source in the local tree
for _, p := range paths {
candidate := filepath.Join(p, "ollama_runners")
_, err := os.Stat(candidate)
if err == nil {
runnersDir = candidate
break
}
}
if runnersDir == "" {
err = fmt.Errorf("unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama_runners'")
slog.Error("incomplete distribution", "error", err)
return "", err
}
}
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 := os.Getenv("OLLAMA_TMPDIR")
if tmpDir == "" {
@@ -125,7 +80,7 @@ func cleanupTmpDirs() {
}
err = os.RemoveAll(d)
if err != nil {
slog.Debug("unable to cleanup stale tmpdir", "path", d, "error", err)
slog.Debug(fmt.Sprintf("unable to cleanup stale tmpdir %s: %s", d, err))
}
}
}
@@ -133,8 +88,7 @@ func cleanupTmpDirs() {
func Cleanup() {
lock.Lock()
defer lock.Unlock()
runnersDir := os.Getenv("OLLAMA_RUNNERS_DIR")
if payloadsDir != "" && runnersDir == "" && runtime.GOOS != "windows" {
if payloadsDir != "" {
// 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)
@@ -166,7 +120,7 @@ func UpdatePath(dir string) {
}
}
newPath := strings.Join(append([]string{dir}, pathComponents...), ";")
slog.Info("updating", "PATH", newPath)
slog.Info(fmt.Sprintf("Updating PATH to %s", newPath))
os.Setenv("PATH", newPath)
}
// linux and darwin rely on rpath

View File

@@ -1,22 +0,0 @@
//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, ",")
}

View File

@@ -16,6 +16,7 @@ import (
"os"
"path/filepath"
"runtime"
"strconv"
"strings"
"sync"
"unsafe"
@@ -24,8 +25,8 @@ import (
)
type handles struct {
deviceCount int
cudart *C.cudart_handle_t
nvml *C.nvml_handle_t
cudart *C.cudart_handle_t
}
const (
@@ -38,10 +39,26 @@ 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}
var RocmComputeMin = 9
// Possible locations for the nvidia-ml library
var NvmlLinuxGlobs = []string{
"/usr/local/cuda/lib64/libnvidia-ml.so*",
"/usr/lib/x86_64-linux-gnu/nvidia/current/libnvidia-ml.so*",
"/usr/lib/x86_64-linux-gnu/libnvidia-ml.so*",
"/usr/lib/wsl/lib/libnvidia-ml.so*",
"/usr/lib/wsl/drivers/*/libnvidia-ml.so*",
"/opt/cuda/lib64/libnvidia-ml.so*",
"/usr/lib*/libnvidia-ml.so*",
"/usr/lib/aarch64-linux-gnu/nvidia/current/libnvidia-ml.so*",
"/usr/lib/aarch64-linux-gnu/libnvidia-ml.so*",
"/usr/local/lib*/libnvidia-ml.so*",
// 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
// TODO: are these stubs ever valid?
"/opt/cuda/targets/x86_64-linux/lib/stubs/libnvidia-ml.so*",
}
var NvmlWindowsGlobs = []string{
"c:\\Windows\\System32\\nvml.dll",
}
var CudartLinuxGlobs = []string{
"/usr/local/cuda/lib64/libcudart.so*",
@@ -71,18 +88,26 @@ func initGPUHandles() *handles {
// TODO - if the ollama build is CPU only, don't do these checks as they're irrelevant and confusing
gpuHandles := &handles{}
gpuHandles := &handles{nil, nil}
var nvmlMgmtName string
var nvmlMgmtPatterns []string
var cudartMgmtName string
var cudartMgmtPatterns []string
tmpDir, _ := PayloadsDir()
switch runtime.GOOS {
case "windows":
nvmlMgmtName = "nvml.dll"
nvmlMgmtPatterns = make([]string, len(NvmlWindowsGlobs))
copy(nvmlMgmtPatterns, NvmlWindowsGlobs)
cudartMgmtName = "cudart64_*.dll"
localAppData := os.Getenv("LOCALAPPDATA")
cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", cudartMgmtName)}
cudartMgmtPatterns = append(cudartMgmtPatterns, CudartWindowsGlobs...)
case "linux":
nvmlMgmtName = "libnvidia-ml.so"
nvmlMgmtPatterns = make([]string, len(NvmlLinuxGlobs))
copy(nvmlMgmtPatterns, NvmlLinuxGlobs)
cudartMgmtName = "libcudart.so*"
if tmpDir != "" {
// TODO - add "payloads" for subprocess
@@ -93,21 +118,31 @@ func initGPUHandles() *handles {
return gpuHandles
}
slog.Info("Detecting GPUs")
slog.Info("Detecting GPU type")
cudartLibPaths := FindGPULibs(cudartMgmtName, cudartMgmtPatterns)
if len(cudartLibPaths) > 0 {
deviceCount, cudart, libPath := LoadCUDARTMgmt(cudartLibPaths)
cudart := LoadCUDARTMgmt(cudartLibPaths)
if cudart != nil {
slog.Info("detected GPUs", "library", libPath, "count", deviceCount)
slog.Info("Nvidia GPU detected via cudart")
gpuHandles.cudart = cudart
gpuHandles.deviceCount = deviceCount
return gpuHandles
}
}
// TODO once we build confidence, remove this and the gpu_info_nvml.[ch] files
nvmlLibPaths := FindGPULibs(nvmlMgmtName, nvmlMgmtPatterns)
if len(nvmlLibPaths) > 0 {
nvml := LoadNVMLMgmt(nvmlLibPaths)
if nvml != nil {
slog.Info("Nvidia GPU detected via nvidia-ml")
gpuHandles.nvml = nvml
return gpuHandles
}
}
return gpuHandles
}
func GetGPUInfo() GpuInfoList {
func GetGPUInfo() GpuInfo {
// TODO - consider exploring lspci (and equivalent on windows) to check for
// GPUs so we can report warnings if we see Nvidia/AMD but fail to load the libraries
gpuMutex.Lock()
@@ -115,6 +150,9 @@ func GetGPUInfo() GpuInfoList {
gpuHandles := initGPUHandles()
defer func() {
if gpuHandles.nvml != nil {
C.nvml_release(*gpuHandles.nvml)
}
if gpuHandles.cudart != nil {
C.cudart_release(*gpuHandles.cudart)
}
@@ -127,63 +165,72 @@ 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
}
gpuInfo := GpuInfo{
Library: "cuda",
}
C.cudart_check_vram(*gpuHandles.cudart, C.int(i), &memInfo)
resp := GpuInfo{}
if gpuHandles.nvml != nil && (cpuVariant != "" || runtime.GOARCH != "amd64") {
C.nvml_check_vram(*gpuHandles.nvml, &memInfo)
if memInfo.err != nil {
slog.Info("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
slog.Info(fmt.Sprintf("[nvidia-ml] error looking up NVML GPU memory: %s", C.GoString(memInfo.err)))
C.free(unsafe.Pointer(memInfo.err))
continue
} else if memInfo.count > 0 {
// Verify minimum compute capability
var cc C.nvml_compute_capability_t
C.nvml_compute_capability(*gpuHandles.nvml, &cc)
if cc.err != nil {
slog.Info(fmt.Sprintf("[nvidia-ml] error looking up NVML GPU compute capability: %s", C.GoString(cc.err)))
C.free(unsafe.Pointer(cc.err))
} else if cc.major > CudaComputeMin[0] || (cc.major == CudaComputeMin[0] && cc.minor >= CudaComputeMin[1]) {
slog.Info(fmt.Sprintf("[nvidia-ml] NVML CUDA Compute Capability detected: %d.%d", cc.major, cc.minor))
resp.Library = "cuda"
resp.MinimumMemory = cudaMinimumMemory
} else {
slog.Info(fmt.Sprintf("[nvidia-ml] CUDA GPU is too old. Falling back to CPU mode. Compute Capability detected: %d.%d", cc.major, cc.minor))
}
}
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.Major = int(memInfo.major)
gpuInfo.Minor = int(memInfo.minor)
gpuInfo.MinimumMemory = cudaMinimumMemory
// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
resp = append(resp, gpuInfo)
}
// Then AMD
resp = append(resp, AMDGetGPUInfo()...)
if len(resp) == 0 {
C.cpu_check_ram(&memInfo)
} else if gpuHandles.cudart != nil && (cpuVariant != "" || runtime.GOARCH != "amd64") {
C.cudart_check_vram(*gpuHandles.cudart, &memInfo)
if memInfo.err != nil {
slog.Info("error looking up CPU memory", "error", C.GoString(memInfo.err))
slog.Info(fmt.Sprintf("[cudart] error looking up CUDART GPU memory: %s", C.GoString(memInfo.err)))
C.free(unsafe.Pointer(memInfo.err))
} else if memInfo.count > 0 {
// Verify minimum compute capability
var cc C.cudart_compute_capability_t
C.cudart_compute_capability(*gpuHandles.cudart, &cc)
if cc.err != nil {
slog.Info(fmt.Sprintf("[cudart] error looking up CUDA compute capability: %s", C.GoString(cc.err)))
C.free(unsafe.Pointer(cc.err))
} else if cc.major > CudaComputeMin[0] || (cc.major == CudaComputeMin[0] && cc.minor >= CudaComputeMin[1]) {
slog.Info(fmt.Sprintf("[cudart] CUDART CUDA Compute Capability detected: %d.%d", cc.major, cc.minor))
resp.Library = "cuda"
resp.MinimumMemory = cudaMinimumMemory
} else {
slog.Info(fmt.Sprintf("[cudart] CUDA GPU is too old. Falling back to CPU mode. Compute Capability detected: %d.%d", cc.major, cc.minor))
}
}
} else {
AMDGetGPUInfo(&resp)
if resp.Library != "" {
resp.MinimumMemory = rocmMinimumMemory
return resp
}
gpuInfo := GpuInfo{
Library: "cpu",
Variant: cpuVariant,
}
gpuInfo.TotalMemory = uint64(memInfo.total)
gpuInfo.FreeMemory = uint64(memInfo.free)
gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
resp = append(resp, gpuInfo)
}
if resp.Library == "" {
C.cpu_check_ram(&memInfo)
resp.Library = "cpu"
resp.Variant = cpuVariant
}
if memInfo.err != nil {
slog.Info(fmt.Sprintf("error looking up CPU memory: %s", C.GoString(memInfo.err)))
C.free(unsafe.Pointer(memInfo.err))
return resp
}
resp.DeviceCount = uint32(memInfo.count)
resp.FreeMemory = uint64(memInfo.free)
resp.TotalMemory = uint64(memInfo.total)
return resp
}
func GetCPUMem() (memInfo, error) {
func getCPUMem() (memInfo, error) {
var ret memInfo
var info C.mem_info_t
C.cpu_check_ram(&info)
@@ -196,11 +243,29 @@ func GetCPUMem() (memInfo, error) {
return ret, nil
}
func CheckVRAM() (int64, error) {
userLimit := os.Getenv("OLLAMA_MAX_VRAM")
if userLimit != "" {
avail, err := strconv.ParseInt(userLimit, 10, 64)
if err != nil {
return 0, fmt.Errorf("Invalid OLLAMA_MAX_VRAM setting %s: %s", userLimit, err)
}
slog.Info(fmt.Sprintf("user override OLLAMA_MAX_VRAM=%d", avail))
return avail, nil
}
gpuInfo := GetGPUInfo()
if gpuInfo.FreeMemory > 0 && (gpuInfo.Library == "cuda" || gpuInfo.Library == "rocm") {
return int64(gpuInfo.FreeMemory), nil
}
return 0, fmt.Errorf("no GPU detected") // TODO - better handling of CPU based memory determiniation
}
func FindGPULibs(baseLibName string, patterns []string) []string {
// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
var ldPaths []string
gpuLibPaths := []string{}
slog.Debug("Searching for GPU library", "name", baseLibName)
slog.Info(fmt.Sprintf("Searching for GPU management library %s", baseLibName))
switch runtime.GOOS {
case "windows":
@@ -218,7 +283,7 @@ func FindGPULibs(baseLibName string, patterns []string) []string {
}
patterns = append(patterns, filepath.Join(d, baseLibName+"*"))
}
slog.Debug("gpu library search", "globs", patterns)
slog.Debug(fmt.Sprintf("gpu management search paths: %v", patterns))
for _, pattern := range patterns {
// Ignore glob discovery errors
matches, _ := filepath.Glob(pattern)
@@ -246,11 +311,28 @@ func FindGPULibs(baseLibName string, patterns []string) []string {
}
}
}
slog.Debug("discovered GPU libraries", "paths", gpuLibPaths)
slog.Info(fmt.Sprintf("Discovered GPU libraries: %v", gpuLibPaths))
return gpuLibPaths
}
func LoadCUDARTMgmt(cudartLibPaths []string) (int, *C.cudart_handle_t, string) {
func LoadNVMLMgmt(nvmlLibPaths []string) *C.nvml_handle_t {
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
}
}
return nil
}
func LoadCUDARTMgmt(cudartLibPaths []string) *C.cudart_handle_t {
var resp C.cudart_init_resp_t
resp.ch.verbose = getVerboseState()
for _, libPath := range cudartLibPaths {
@@ -258,13 +340,13 @@ func LoadCUDARTMgmt(cudartLibPaths []string) (int, *C.cudart_handle_t, string) {
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))
slog.Info(fmt.Sprintf("Unable to load cudart CUDA management library %s: %s", libPath, C.GoString(resp.err)))
C.free(unsafe.Pointer(resp.err))
} else {
return int(resp.num_devices), &resp.ch, libPath
return &resp.ch
}
}
return 0, nil, ""
return nil
}
func getVerboseState() C.uint16_t {
@@ -273,22 +355,3 @@ func getVerboseState() C.uint16_t {
}
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)
default:
slog.Debug("no filter required for library " + l[0].Library)
return "", ""
}
}

View File

@@ -9,41 +9,52 @@ package gpu
*/
import "C"
import (
"fmt"
"log/slog"
"os"
"runtime"
"strconv"
)
func GetGPUInfo() GpuInfoList {
mem, _ := GetCPUMem()
// CheckVRAM returns the free VRAM in bytes on Linux machines with NVIDIA GPUs
func CheckVRAM() (int64, error) {
userLimit := os.Getenv("OLLAMA_MAX_VRAM")
if userLimit != "" {
avail, err := strconv.ParseInt(userLimit, 10, 64)
if err != nil {
return 0, fmt.Errorf("Invalid OLLAMA_MAX_VRAM setting %s: %s", userLimit, err)
}
slog.Info(fmt.Sprintf("user override OLLAMA_MAX_VRAM=%d", avail))
return avail, nil
}
if runtime.GOARCH == "amd64" {
return []GpuInfo{
{
Library: "cpu",
Variant: GetCPUVariant(),
memInfo: mem,
},
// gpu not supported, this may not be metal
return 0, nil
}
recommendedMaxVRAM := int64(C.getRecommendedMaxVRAM())
return recommendedMaxVRAM, nil
}
func GetGPUInfo() GpuInfo {
mem, _ := getCPUMem()
if runtime.GOARCH == "amd64" {
return GpuInfo{
Library: "cpu",
Variant: GetCPUVariant(),
memInfo: mem,
}
}
info := GpuInfo{
return GpuInfo{
Library: "metal",
ID: "0",
memInfo: mem,
}
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 = 0
return []GpuInfo{info}
}
func GetCPUMem() (memInfo, error) {
func getCPUMem() (memInfo, error) {
return memInfo{
TotalMemory: uint64(C.getPhysicalMemory()),
TotalMemory: 0,
FreeMemory: 0,
DeviceCount: 0,
}, nil
}
func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
// No-op on darwin
return "", ""
}

View File

@@ -38,17 +38,12 @@
extern "C" {
#endif
#define GPU_ID_LEN 64
typedef struct mem_info {
char *err; // If non-nill, caller responsible for freeing
char gpu_id[GPU_ID_LEN];
uint64_t total;
uint64_t free;
// Compute Capability
int major;
int minor;
unsigned int count;
int igpu_index; // If >= 0, we detected an integrated GPU to ignore
char *err; // If non-nill, caller responsible for freeing
} mem_info_t;
void cpu_check_ram(mem_info_t *resp);
@@ -57,6 +52,7 @@ void cpu_check_ram(mem_info_t *resp);
}
#endif
#include "gpu_info_nvml.h"
#include "gpu_info_cudart.h"
#endif // __GPU_INFO_H__

View File

@@ -8,11 +8,9 @@ void cpu_check_ram(mem_info_t *resp) {
MEMORYSTATUSEX info;
info.dwLength = sizeof(info);
if (GlobalMemoryStatusEx(&info) != 0) {
resp->count = 1;
resp->total = info.ullTotalPhys;
resp->free = info.ullAvailPhys;
resp->major = 0;
resp->minor = 0;
snprintf(&resp->gpu_id[0], GPU_ID_LEN, "0");
} else {
resp->err = LOAD_ERR();
}
@@ -29,11 +27,9 @@ void cpu_check_ram(mem_info_t *resp) {
if (sysinfo(&info) != 0) {
resp->err = strdup(strerror(errno));
} else {
resp->count = 1;
resp->total = info.totalram * info.mem_unit;
resp->free = info.freeram * info.mem_unit;
resp->major = 0;
resp->minor = 0;
snprintf(&resp->gpu_id[0], GPU_ID_LEN, "0");
}
return;
}

View File

@@ -6,7 +6,6 @@
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;
@@ -22,7 +21,6 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
{"cudaGetDeviceCount", (void *)&resp->ch.cudaGetDeviceCount},
{"cudaDeviceGetAttribute", (void *)&resp->ch.cudaDeviceGetAttribute},
{"cudaDriverGetVersion", (void *)&resp->ch.cudaDriverGetVersion},
{"cudaGetDeviceProperties", (void *)&resp->ch.cudaGetDeviceProperties},
{NULL, NULL},
};
@@ -38,7 +36,13 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
return;
}
// TODO once we've squashed the remaining corner cases remove this log
LOG(resp->ch.verbose, "wiring cudart library functions in %s\n", cudart_lib_path);
for (i = 0; l[i].s != NULL; i++) {
// TODO once we've squashed the remaining corner cases remove this log
LOG(resp->ch.verbose, "dlsym: %s\n", l[i].s);
*l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
if (!l[i].p) {
char *msg = LOAD_ERR();
@@ -59,7 +63,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
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");
resp->err = strdup("your nvidia driver is too old or missing, please upgrade to run ollama");
return;
}
snprintf(buf, buflen, "cudart init failure: %d", ret);
@@ -81,95 +85,110 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
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_check_vram(cudart_handle_t h, int i, mem_info_t *resp) {
void cudart_check_vram(cudart_handle_t h, mem_info_t *resp) {
resp->err = NULL;
cudartMemory_t memInfo = {0,0,0};
cudartReturn_t ret;
const int buflen = 256;
char buf[buflen + 1];
int i;
if (h.handle == NULL) {
resp->err = strdup("cudart handle isn't initialized");
return;
}
ret = (*h.cudaSetDevice)(i);
// cudaGetDeviceCount takes int type, resp-> count is uint
int deviceCount;
ret = (*h.cudaGetDeviceCount)(&deviceCount);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "cudart device failed to initialize");
snprintf(buf, buflen, "unable to get device count: %d", ret);
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
resp->count = (unsigned int)deviceCount;
}
ret = (*h.cudaMemGetInfo)(&memInfo.free, &memInfo.total);
resp->total = 0;
resp->free = 0;
for (i = 0; i < resp-> count; i++) {
ret = (*h.cudaSetDevice)(i);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "cudart device failed to initialize");
resp->err = strdup(buf);
return;
}
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;
}
LOG(h.verbose, "[%d] CUDA totalMem %lu\n", i, memInfo.total);
LOG(h.verbose, "[%d] CUDA freeMem %lu\n", i, memInfo.free);
resp->total += memInfo.total;
resp->free += memInfo.free;
}
}
void cudart_compute_capability(cudart_handle_t h, cudart_compute_capability_t *resp) {
resp->err = NULL;
resp->major = 0;
resp->minor = 0;
int major = 0;
int minor = 0;
cudartReturn_t ret;
const int buflen = 256;
char buf[buflen + 1];
int i;
if (h.handle == NULL) {
resp->err = strdup("cudart handle not initialized");
return;
}
int devices;
ret = (*h.cudaGetDeviceCount)(&devices);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "cudart device memory info lookup failure %d", ret);
snprintf(buf, buflen, "unable to get cudart device count: %d", ret);
resp->err = strdup(buf);
return;
}
resp->total = memInfo.total;
resp->free = memInfo.free;
for (i = 0; i < devices; i++) {
ret = (*h.cudaSetDevice)(i);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "cudart device failed to initialize");
resp->err = strdup(buf);
return;
}
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] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
ret = (*h.cudaDeviceGetAttribute)(&major, cudartDevAttrComputeCapabilityMajor, i);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "device compute capability lookup failure %d: %d", i, ret);
resp->err = strdup(buf);
return;
}
ret = (*h.cudaDeviceGetAttribute)(&minor, cudartDevAttrComputeCapabilityMinor, i);
if (ret != CUDART_SUCCESS) {
snprintf(buf, buflen, "device compute capability lookup failure %d: %d", i, ret);
resp->err = strdup(buf);
return;
}
// Report the lowest major.minor we detect as that limits our compatibility
if (resp->major == 0 || resp->major > major ) {
resp->major = major;
resp->minor = minor;
} else if ( resp->major == major && resp->minor > minor ) {
resp->minor = minor;
}
}
}
void cudart_release(cudart_handle_t h) {

View File

@@ -6,8 +6,7 @@
// Just enough typedef's to dlopen/dlsym for memory information
typedef enum cudartReturn_enum {
CUDART_SUCCESS = 0,
CUDA_ERROR_INVALID_VALUE = 1,
CUDA_ERROR_MEMORY_ALLOCATION = 2,
CUDART_UNSUPPORTED = 1,
CUDA_ERROR_INSUFFICIENT_DRIVER = 35,
// Other values omitted for now...
} cudartReturn_t;
@@ -15,11 +14,6 @@ typedef enum cudartReturn_enum {
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
@@ -34,92 +28,6 @@ typedef struct cudartDriverVersion {
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;
@@ -130,17 +38,23 @@ typedef struct cudart_handle {
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;
typedef struct cudart_compute_capability {
char *err;
int major;
int minor;
} cudart_compute_capability_t;
void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp);
void cudart_check_vram(cudart_handle_t ch, int device_id, mem_info_t *resp);
void cudart_check_vram(cudart_handle_t ch, mem_info_t *resp);
void cudart_compute_capability(cudart_handle_t th, cudart_compute_capability_t *cc);
void cudart_release(cudart_handle_t ch);
#endif // __GPU_INFO_CUDART_H__

View File

@@ -1,4 +1,3 @@
#import <Metal/Metal.h>
#include <stdint.h>
uint64_t getRecommendedMaxVRAM();
uint64_t getPhysicalMemory();

View File

@@ -1,13 +1,11 @@
// go:build darwin
//go:build darwin
#include "gpu_info_darwin.h"
uint64_t getRecommendedMaxVRAM() {
id<MTLDevice> device = MTLCreateSystemDefaultDevice();
uint64_t result = device.recommendedMaxWorkingSetSize;
CFRelease(device);
return result;
uint64_t getRecommendedMaxVRAM()
{
id<MTLDevice> device = MTLCreateSystemDefaultDevice();
uint64_t result = device.recommendedMaxWorkingSetSize;
CFRelease(device);
return result;
}
uint64_t getPhysicalMemory() {
return [[NSProcessInfo processInfo] physicalMemory];
}

221
gpu/gpu_info_nvml.c Normal file
View File

@@ -0,0 +1,221 @@
#ifndef __APPLE__ // TODO - maybe consider nvidia support on intel macs?
#include <string.h>
#include "gpu_info_nvml.h"
void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
nvmlReturn_t ret;
resp->err = NULL;
const int buflen = 256;
char buf[buflen + 1];
int i;
struct lookup {
char *s;
void **p;
} l[] = {
{"nvmlInit_v2", (void *)&resp->ch.nvmlInit_v2},
{"nvmlShutdown", (void *)&resp->ch.nvmlShutdown},
{"nvmlDeviceGetHandleByIndex", (void *)&resp->ch.nvmlDeviceGetHandleByIndex},
{"nvmlDeviceGetMemoryInfo", (void *)&resp->ch.nvmlDeviceGetMemoryInfo},
{"nvmlDeviceGetCount_v2", (void *)&resp->ch.nvmlDeviceGetCount_v2},
{"nvmlDeviceGetCudaComputeCapability", (void *)&resp->ch.nvmlDeviceGetCudaComputeCapability},
{"nvmlSystemGetDriverVersion", (void *)&resp->ch.nvmlSystemGetDriverVersion},
{"nvmlDeviceGetName", (void *)&resp->ch.nvmlDeviceGetName},
{"nvmlDeviceGetSerial", (void *)&resp->ch.nvmlDeviceGetSerial},
{"nvmlDeviceGetVbiosVersion", (void *)&resp->ch.nvmlDeviceGetVbiosVersion},
{"nvmlDeviceGetBoardPartNumber", (void *)&resp->ch.nvmlDeviceGetBoardPartNumber},
{"nvmlDeviceGetBrand", (void *)&resp->ch.nvmlDeviceGetBrand},
{NULL, NULL},
};
resp->ch.handle = LOAD_LIBRARY(nvml_lib_path, RTLD_LAZY);
if (!resp->ch.handle) {
char *msg = LOAD_ERR();
LOG(resp->ch.verbose, "library %s load err: %s\n", nvml_lib_path, msg);
snprintf(buf, buflen,
"Unable to load %s library to query for Nvidia GPUs: %s",
nvml_lib_path, msg);
free(msg);
resp->err = strdup(buf);
return;
}
// TODO once we've squashed the remaining corner cases remove this log
LOG(resp->ch.verbose, "wiring nvidia management library functions in %s\n", nvml_lib_path);
for (i = 0; l[i].s != NULL; i++) {
// TODO once we've squashed the remaining corner cases remove this log
LOG(resp->ch.verbose, "dlsym: %s\n", l[i].s);
*l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
if (!l[i].p) {
resp->ch.handle = NULL;
char *msg = LOAD_ERR();
LOG(resp->ch.verbose, "dlerr: %s\n", msg);
UNLOAD_LIBRARY(resp->ch.handle);
snprintf(buf, buflen, "symbol lookup for %s failed: %s", l[i].s,
msg);
free(msg);
resp->err = strdup(buf);
return;
}
}
ret = (*resp->ch.nvmlInit_v2)();
if (ret != NVML_SUCCESS) {
LOG(resp->ch.verbose, "nvmlInit_v2 err: %d\n", ret);
UNLOAD_LIBRARY(resp->ch.handle);
resp->ch.handle = NULL;
snprintf(buf, buflen, "nvml vram init failure: %d", ret);
resp->err = strdup(buf);
return;
}
// Report driver version if we're in verbose mode, ignore errors
ret = (*resp->ch.nvmlSystemGetDriverVersion)(buf, buflen);
if (ret != NVML_SUCCESS) {
LOG(resp->ch.verbose, "nvmlSystemGetDriverVersion failed: %d\n", ret);
} else {
LOG(resp->ch.verbose, "CUDA driver version: %s\n", buf);
}
}
void nvml_check_vram(nvml_handle_t h, mem_info_t *resp) {
resp->err = NULL;
nvmlDevice_t device;
nvmlMemory_t memInfo = {0};
nvmlReturn_t ret;
const int buflen = 256;
char buf[buflen + 1];
int i;
if (h.handle == NULL) {
resp->err = strdup("nvml handle isn't initialized");
return;
}
ret = (*h.nvmlDeviceGetCount_v2)(&resp->count);
if (ret != NVML_SUCCESS) {
snprintf(buf, buflen, "unable to get device count: %d", ret);
resp->err = strdup(buf);
return;
}
resp->total = 0;
resp->free = 0;
for (i = 0; i < resp->count; i++) {
ret = (*h.nvmlDeviceGetHandleByIndex)(i, &device);
if (ret != NVML_SUCCESS) {
snprintf(buf, buflen, "unable to get device handle %d: %d", i, ret);
resp->err = strdup(buf);
return;
}
ret = (*h.nvmlDeviceGetMemoryInfo)(device, &memInfo);
if (ret != NVML_SUCCESS) {
snprintf(buf, buflen, "device memory info lookup failure %d: %d", i, ret);
resp->err = strdup(buf);
return;
}
if (h.verbose) {
nvmlBrandType_t brand = 0;
// When in verbose mode, report more information about
// the card we discover, but don't fail on error
ret = (*h.nvmlDeviceGetName)(device, buf, buflen);
if (ret != NVML_SUCCESS) {
LOG(h.verbose, "nvmlDeviceGetName failed: %d\n", ret);
} else {
LOG(h.verbose, "[%d] CUDA device name: %s\n", i, buf);
}
ret = (*h.nvmlDeviceGetBoardPartNumber)(device, buf, buflen);
if (ret != NVML_SUCCESS) {
LOG(h.verbose, "nvmlDeviceGetBoardPartNumber failed: %d\n", ret);
} else {
LOG(h.verbose, "[%d] CUDA part number: %s\n", i, buf);
}
ret = (*h.nvmlDeviceGetSerial)(device, buf, buflen);
if (ret != NVML_SUCCESS) {
LOG(h.verbose, "nvmlDeviceGetSerial failed: %d\n", ret);
} else {
LOG(h.verbose, "[%d] CUDA S/N: %s\n", i, buf);
}
ret = (*h.nvmlDeviceGetVbiosVersion)(device, buf, buflen);
if (ret != NVML_SUCCESS) {
LOG(h.verbose, "nvmlDeviceGetVbiosVersion failed: %d\n", ret);
} else {
LOG(h.verbose, "[%d] CUDA vbios version: %s\n", i, buf);
}
ret = (*h.nvmlDeviceGetBrand)(device, &brand);
if (ret != NVML_SUCCESS) {
LOG(h.verbose, "nvmlDeviceGetBrand failed: %d\n", ret);
} else {
LOG(h.verbose, "[%d] CUDA brand: %d\n", i, brand);
}
}
LOG(h.verbose, "[%d] CUDA totalMem %ld\n", i, memInfo.total);
LOG(h.verbose, "[%d] CUDA freeMem %ld\n", i, memInfo.free);
resp->total += memInfo.total;
resp->free += memInfo.free;
}
}
void nvml_compute_capability(nvml_handle_t h, nvml_compute_capability_t *resp) {
resp->err = NULL;
resp->major = 0;
resp->minor = 0;
nvmlDevice_t device;
int major = 0;
int minor = 0;
nvmlReturn_t ret;
const int buflen = 256;
char buf[buflen + 1];
int i;
if (h.handle == NULL) {
resp->err = strdup("nvml handle not initialized");
return;
}
unsigned int devices;
ret = (*h.nvmlDeviceGetCount_v2)(&devices);
if (ret != NVML_SUCCESS) {
snprintf(buf, buflen, "unable to get device count: %d", ret);
resp->err = strdup(buf);
return;
}
for (i = 0; i < devices; i++) {
ret = (*h.nvmlDeviceGetHandleByIndex)(i, &device);
if (ret != NVML_SUCCESS) {
snprintf(buf, buflen, "unable to get device handle %d: %d", i, ret);
resp->err = strdup(buf);
return;
}
ret = (*h.nvmlDeviceGetCudaComputeCapability)(device, &major, &minor);
if (ret != NVML_SUCCESS) {
snprintf(buf, buflen, "device compute capability lookup failure %d: %d", i, ret);
resp->err = strdup(buf);
return;
}
// Report the lowest major.minor we detect as that limits our compatibility
if (resp->major == 0 || resp->major > major ) {
resp->major = major;
resp->minor = minor;
} else if ( resp->major == major && resp->minor > minor ) {
resp->minor = minor;
}
}
}
void nvml_release(nvml_handle_t h) {
LOG(h.verbose, "releasing nvml library\n");
UNLOAD_LIBRARY(h.handle);
h.handle = NULL;
}
#endif // __APPLE__

57
gpu/gpu_info_nvml.h Normal file
View File

@@ -0,0 +1,57 @@
#ifndef __APPLE__
#ifndef __GPU_INFO_NVML_H__
#define __GPU_INFO_NVML_H__
#include "gpu_info.h"
// Just enough typedef's to dlopen/dlsym for memory information
typedef enum nvmlReturn_enum {
NVML_SUCCESS = 0,
// Other values omitted for now...
} nvmlReturn_t;
typedef void *nvmlDevice_t; // Opaque is sufficient
typedef struct nvmlMemory_st {
unsigned long long total;
unsigned long long free;
unsigned long long used;
} nvmlMemory_t;
typedef enum nvmlBrandType_enum
{
NVML_BRAND_UNKNOWN = 0,
} nvmlBrandType_t;
typedef struct nvml_handle {
void *handle;
uint16_t verbose;
nvmlReturn_t (*nvmlInit_v2)(void);
nvmlReturn_t (*nvmlShutdown)(void);
nvmlReturn_t (*nvmlDeviceGetHandleByIndex)(unsigned int, nvmlDevice_t *);
nvmlReturn_t (*nvmlDeviceGetMemoryInfo)(nvmlDevice_t, nvmlMemory_t *);
nvmlReturn_t (*nvmlDeviceGetCount_v2)(unsigned int *);
nvmlReturn_t (*nvmlDeviceGetCudaComputeCapability)(nvmlDevice_t, int* major, int* minor);
nvmlReturn_t (*nvmlSystemGetDriverVersion) (char* version, unsigned int length);
nvmlReturn_t (*nvmlDeviceGetName) (nvmlDevice_t device, char* name, unsigned int length);
nvmlReturn_t (*nvmlDeviceGetSerial) (nvmlDevice_t device, char* serial, unsigned int length);
nvmlReturn_t (*nvmlDeviceGetVbiosVersion) (nvmlDevice_t device, char* version, unsigned int length);
nvmlReturn_t (*nvmlDeviceGetBoardPartNumber) (nvmlDevice_t device, char* partNumber, unsigned int length);
nvmlReturn_t (*nvmlDeviceGetBrand) (nvmlDevice_t device, nvmlBrandType_t* type);
} nvml_handle_t;
typedef struct nvml_init_resp {
char *err; // If err is non-null handle is invalid
nvml_handle_t ch;
} nvml_init_resp_t;
typedef struct nvml_compute_capability {
char *err;
int major;
int minor;
} nvml_compute_capability_t;
void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp);
void nvml_check_vram(nvml_handle_t ch, mem_info_t *resp);
void nvml_compute_capability(nvml_handle_t ch, nvml_compute_capability_t *cc);
void nvml_release(nvml_handle_t ch);
#endif // __GPU_INFO_NVML_H__
#endif // __APPLE__

View File

@@ -9,16 +9,23 @@ import (
func TestBasicGetGPUInfo(t *testing.T) {
info := GetGPUInfo()
assert.Greater(t, len(info), 0)
assert.Contains(t, "cuda rocm cpu metal", info[0].Library)
if info[0].Library != "cpu" {
assert.Greater(t, info[0].TotalMemory, uint64(0))
assert.Greater(t, info[0].FreeMemory, uint64(0))
assert.Contains(t, "cuda rocm cpu metal", info.Library)
switch runtime.GOOS {
case "darwin":
// TODO - remove this once MacOS returns some size for CPU
return
case "linux", "windows":
assert.Greater(t, info.TotalMemory, uint64(0))
assert.Greater(t, info.FreeMemory, uint64(0))
assert.Greater(t, info.DeviceCount, uint32(0))
default:
return
}
}
func TestCPUMemInfo(t *testing.T) {
info, err := GetCPUMem()
info, err := getCPUMem()
assert.NoError(t, err)
switch runtime.GOOS {
case "darwin":

View File

@@ -3,6 +3,7 @@ package gpu
type memInfo struct {
TotalMemory uint64 `json:"total_memory,omitempty"`
FreeMemory uint64 `json:"free_memory,omitempty"`
DeviceCount uint32 `json:"device_count,omitempty"`
}
// Beginning of an `ollama info` command
@@ -14,51 +15,13 @@ type GpuInfo struct {
Variant string `json:"variant,omitempty"`
// MinimumMemory represents the minimum memory required to use the GPU
MinimumMemory uint64 `json:"-"`
MinimumMemory int64 `json:"-"`
// Any extra PATH/LD_LIBRARY_PATH dependencies required for the Library to operate properly
DependencyPath string `json:"lib_path,omitempty"`
// GPU information
ID string `json:"gpu_id"` // string to use for selection of this specific GPU
Name string `json:"name"` // user friendly name if available
Major int `json:"major,omitempty"` // Major compatibility version (CC or gfx)
Minor int `json:"minor,omitempty"` // Minor compatibility version (CC or gfx)
Patch int `json:"patch,omitempty"` // Patch compatibility only matters on AMD
// TODO other performance capability info to help in scheduling decisions
// TODO add other useful attributes about the card here for discovery information
}
type GpuInfoList []GpuInfo
// Split up the set of gpu info's by Library and variant
func (l GpuInfoList) ByLibrary() []GpuInfoList {
resp := []GpuInfoList{}
libs := []string{}
for _, info := range l {
found := false
requested := info.Library
if info.Variant != "" {
requested += "_" + info.Variant
}
for i, lib := range libs {
if lib == requested {
resp[i] = append(resp[i], info)
found = true
break
}
}
if !found {
libs = append(libs, info.Library)
resp = append(resp, []GpuInfo{info})
}
}
return resp
type Version struct {
Major uint
Minor uint
Patch uint
}
// Sort by Free Space
type ByFreeMemory []GpuInfo
func (a ByFreeMemory) Len() int { return len(a) }
func (a ByFreeMemory) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
func (a ByFreeMemory) Less(i, j int) bool { return a[i].FreeMemory < a[j].FreeMemory }

View File

@@ -4,14 +4,11 @@ package integration
import (
"context"
"log/slog"
"os"
"runtime"
"net/http"
"testing"
"time"
"github.com/ollama/ollama/api"
"github.com/stretchr/testify/require"
)
func TestOrcaMiniBlueSky(t *testing.T) {
@@ -27,44 +24,5 @@ func TestOrcaMiniBlueSky(t *testing.T) {
"seed": 123,
},
}
GenerateTestHelper(ctx, t, req, []string{"rayleigh", "scattering"})
}
func TestUnicodeModelDir(t *testing.T) {
// This is only useful for Windows with utf-16 characters, so skip this test for other platforms
if runtime.GOOS != "windows" {
t.Skip("Unicode test only applicable to windows")
}
// Only works for local testing
if os.Getenv("OLLAMA_TEST_EXISTING") != "" {
t.Skip("TestUnicodeModelDir only works for local testing, skipping")
}
modelDir, err := os.MkdirTemp("", "ollama_埃")
require.NoError(t, err)
defer os.RemoveAll(modelDir)
slog.Info("unicode", "OLLAMA_MODELS", modelDir)
oldModelsDir := os.Getenv("OLLAMA_MODELS")
if oldModelsDir == "" {
defer os.Unsetenv("OLLAMA_MODELS")
} else {
defer os.Setenv("OLLAMA_MODELS", oldModelsDir)
}
err = os.Setenv("OLLAMA_MODELS", modelDir)
require.NoError(t, err)
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
req := api.GenerateRequest{
Model: "orca-mini",
Prompt: "why is the sky blue?",
Stream: &stream,
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},
}
GenerateTestHelper(ctx, t, req, []string{"rayleigh", "scattering"})
GenerateTestHelper(ctx, t, &http.Client{}, req, []string{"rayleigh", "scattering"})
}

View File

@@ -1,225 +0,0 @@
//go:build integration
package integration
import (
"context"
"log/slog"
"os"
"strconv"
"sync"
"testing"
"time"
"github.com/ollama/ollama/api"
"github.com/stretchr/testify/require"
)
func TestMultiModelConcurrency(t *testing.T) {
var (
req = [2]api.GenerateRequest{
{
Model: "orca-mini",
Prompt: "why is the ocean blue?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
}, {
Model: "tinydolphin",
Prompt: "what is the origin of the us thanksgiving holiday?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
},
}
resp = [2][]string{
[]string{"sunlight"},
[]string{"england", "english", "massachusetts", "pilgrims"},
}
)
var wg sync.WaitGroup
wg.Add(len(req))
ctx, cancel := context.WithTimeout(context.Background(), time.Second*120)
defer cancel()
for i := 0; i < len(req); i++ {
go func(i int) {
defer wg.Done()
GenerateTestHelper(ctx, t, req[i], resp[i])
}(i)
}
wg.Wait()
}
func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute) // GTX 750 2G card takes ~9 minutes
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req, resp := GenerateRequests()
// Get the server running (if applicable) warm the model up with a single initial request
DoGenerate(ctx, t, client, req[0], resp[0], 60*time.Second, 5*time.Second)
var wg sync.WaitGroup
wg.Add(len(req))
for i := 0; i < len(req); i++ {
go func(i int) {
defer wg.Done()
for j := 0; j < 5; j++ {
slog.Info("Starting", "req", i, "iter", j)
// On slower GPUs it can take a while to process the 4 concurrent requests
// so we allow a much longer initial timeout
DoGenerate(ctx, t, client, req[i], resp[i], 90*time.Second, 5*time.Second)
}
}(i)
}
wg.Wait()
}
// Stress the system if we know how much VRAM it has, and attempt to load more models than will fit
func TestMultiModelStress(t *testing.T) {
vram := os.Getenv("OLLAMA_MAX_VRAM")
if vram == "" {
t.Skip("OLLAMA_MAX_VRAM not specified, can't pick the right models for the stress test")
}
max, err := strconv.ParseUint(vram, 10, 64)
require.NoError(t, err)
const MB = uint64(1024 * 1024)
type model struct {
name string
size uint64 // Approximate amount of VRAM they typically use when fully loaded in VRAM
}
smallModels := []model{
{
name: "orca-mini",
size: 2992 * MB,
},
{
name: "phi",
size: 2616 * MB,
},
{
name: "gemma:2b",
size: 2364 * MB,
},
{
name: "stable-code:3b",
size: 2608 * MB,
},
{
name: "starcoder2:3b",
size: 2166 * MB,
},
}
mediumModels := []model{
{
name: "llama2",
size: 5118 * MB,
},
{
name: "mistral",
size: 4620 * MB,
},
{
name: "orca-mini:7b",
size: 5118 * MB,
},
{
name: "dolphin-mistral",
size: 4620 * MB,
},
{
name: "gemma:7b",
size: 5000 * MB,
},
// TODO - uncomment this once #3565 is merged and this is rebased on it
// {
// name: "codellama:7b",
// size: 5118 * MB,
// },
}
// These seem to be too slow to be useful...
// largeModels := []model{
// {
// name: "llama2:13b",
// size: 7400 * MB,
// },
// {
// name: "codellama:13b",
// size: 7400 * MB,
// },
// {
// name: "orca-mini:13b",
// size: 7400 * MB,
// },
// {
// name: "gemma:7b",
// size: 5000 * MB,
// },
// {
// name: "starcoder2:15b",
// size: 9100 * MB,
// },
// }
var chosenModels []model
switch {
case max < 10000*MB:
slog.Info("selecting small models")
chosenModels = smallModels
// case max < 30000*MB:
default:
slog.Info("selecting medium models")
chosenModels = mediumModels
// default:
// slog.Info("selecting large models")
// chosenModels = largModels
}
req, resp := GenerateRequests()
for i := range req {
if i > len(chosenModels) {
break
}
req[i].Model = chosenModels[i].name
}
ctx, cancel := context.WithTimeout(context.Background(), 15*time.Minute) // TODO baseline -- 10m too short
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
// Make sure all the models are pulled before we get started
for _, r := range req {
require.NoError(t, PullIfMissing(ctx, client, r.Model))
}
var wg sync.WaitGroup
consumed := uint64(256 * MB) // Assume some baseline usage
for i := 0; i < len(req); i++ {
// Always get at least 2 models, but dont' overshoot VRAM too much or we'll take too long
if i > 1 && consumed > max {
slog.Info("achieved target vram exhaustion", "count", i, "vramMB", max/1024/1024, "modelsMB", consumed/1024/1024)
break
}
consumed += chosenModels[i].size
slog.Info("target vram", "count", i, "vramMB", max/1024/1024, "modelsMB", consumed/1024/1024)
wg.Add(1)
go func(i int) {
defer wg.Done()
for j := 0; j < 3; j++ {
slog.Info("Starting", "req", i, "iter", j, "model", req[i].Model)
DoGenerate(ctx, t, client, req[i], resp[i], 90*time.Second, 5*time.Second)
}
}(i)
}
wg.Wait()
}

View File

@@ -4,6 +4,7 @@ package integration
import (
"context"
"net/http"
"testing"
"time"
@@ -24,5 +25,5 @@ func TestContextExhaustion(t *testing.T) {
"num_ctx": 128,
},
}
GenerateTestHelper(ctx, t, req, []string{"once", "upon", "lived"})
GenerateTestHelper(ctx, t, &http.Client{}, req, []string{"once", "upon", "lived"})
}

View File

@@ -5,6 +5,7 @@ package integration
import (
"context"
"encoding/base64"
"net/http"
"testing"
"time"
@@ -28,11 +29,10 @@ func TestIntegrationMultimodal(t *testing.T) {
},
}
// Note: sometimes it returns "the ollamas" sometimes "the ollams"
resp := "the ollam"
resp := "the ollamas"
ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
defer cancel()
GenerateTestHelper(ctx, t, req, []string{resp})
GenerateTestHelper(ctx, t, &http.Client{}, req, []string{resp})
}
const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb

View File

@@ -4,6 +4,8 @@ package integration
import (
"context"
"net/http"
"sync"
"testing"
"time"
@@ -43,5 +45,25 @@ var (
func TestIntegrationSimpleOrcaMini(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), time.Second*120)
defer cancel()
GenerateTestHelper(ctx, t, req[0], resp[0])
GenerateTestHelper(ctx, t, &http.Client{}, req[0], resp[0])
}
// TODO
// The server always loads a new runner and closes the old one, which forces serial execution
// At present this test case fails with concurrency problems. Eventually we should try to
// get true concurrency working with n_parallel support in the backend
func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
var wg sync.WaitGroup
wg.Add(len(req))
ctx, cancel := context.WithTimeout(context.Background(), time.Second*120)
defer cancel()
for i := 0; i < len(req); i++ {
go func(i int) {
defer wg.Done()
GenerateTestHelper(ctx, t, &http.Client{}, req[i], resp[i])
}(i)
}
wg.Wait()
}
// TODO - create a parallel test with 2 different models once we support concurrency

View File

@@ -5,14 +5,13 @@ package integration
import (
"bytes"
"context"
"errors"
"encoding/json"
"fmt"
"io"
"log/slog"
"math/rand"
"net"
"net/http"
"net/url"
"os"
"path/filepath"
"runtime"
@@ -24,13 +23,9 @@ import (
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/app/lifecycle"
"github.com/stretchr/testify/require"
"github.com/stretchr/testify/assert"
)
func Init() {
lifecycle.InitLogging()
}
func FindPort() string {
port := 0
if a, err := net.ResolveTCPAddr("tcp", "localhost:0"); err == nil {
@@ -46,7 +41,7 @@ func FindPort() string {
return strconv.Itoa(port)
}
func GetTestEndpoint() (*api.Client, string) {
func GetTestEndpoint() (string, string) {
defaultPort := "11434"
ollamaHost := os.Getenv("OLLAMA_HOST")
@@ -72,20 +67,16 @@ func GetTestEndpoint() (*api.Client, string) {
port = FindPort()
}
slog.Info("server connection", "host", host, "port", port)
return api.NewClient(
&url.URL{
Scheme: scheme,
Host: net.JoinHostPort(host, port),
},
http.DefaultClient), fmt.Sprintf("%s:%s", host, port)
url := fmt.Sprintf("%s:%s", host, port)
slog.Info("server connection", "url", url)
return scheme, url
}
// TODO make fanicier, grab logs, etc.
var serverMutex sync.Mutex
var serverReady bool
func startServer(ctx context.Context, ollamaHost string) error {
func StartServer(ctx context.Context, ollamaHost string) error {
// Make sure the server has been built
CLIName, err := filepath.Abs("../ollama")
if err != nil {
@@ -134,76 +125,67 @@ func startServer(ctx context.Context, ollamaHost string) error {
return nil
}
func PullIfMissing(ctx context.Context, client *api.Client, modelName string) error {
func PullIfMissing(ctx context.Context, client *http.Client, scheme, testEndpoint, modelName string) error {
slog.Info("checking status of model", "model", modelName)
showReq := &api.ShowRequest{Name: modelName}
showCtx, cancel := context.WithDeadlineCause(
ctx,
time.Now().Add(5*time.Second),
fmt.Errorf("show for existing model %s took too long", modelName),
)
defer cancel()
_, err := client.Show(showCtx, showReq)
var statusError api.StatusError
switch {
case errors.As(err, &statusError) && statusError.StatusCode == http.StatusNotFound:
break
case err != nil:
requestJSON, err := json.Marshal(showReq)
if err != nil {
return err
default:
}
req, err := http.NewRequest("POST", scheme+"://"+testEndpoint+"/api/show", bytes.NewReader(requestJSON))
if err != nil {
return err
}
// Make the request with the HTTP client
response, err := client.Do(req.WithContext(ctx))
if err != nil {
return err
}
defer response.Body.Close()
if response.StatusCode == 200 {
slog.Info("model already present", "model", modelName)
return nil
}
slog.Info("model missing", "model", modelName)
slog.Info("model missing", "status", response.StatusCode)
stallDuration := 30 * time.Second // This includes checksum verification, which can take a while on larger models
stallTimer := time.NewTimer(stallDuration)
fn := func(resp api.ProgressResponse) error {
// fmt.Print(".")
if !stallTimer.Reset(stallDuration) {
return fmt.Errorf("stall was detected, aborting status reporting")
}
return nil
}
stream := true
pullReq := &api.PullRequest{Name: modelName, Stream: &stream}
var pullError error
done := make(chan int)
go func() {
pullError = client.Pull(ctx, pullReq, fn)
done <- 0
}()
select {
case <-stallTimer.C:
return fmt.Errorf("download stalled")
case <-done:
return pullError
requestJSON, err = json.Marshal(pullReq)
if err != nil {
return err
}
req, err = http.NewRequest("POST", scheme+"://"+testEndpoint+"/api/pull", bytes.NewReader(requestJSON))
if err != nil {
return err
}
slog.Info("pulling", "model", modelName)
response, err = client.Do(req.WithContext(ctx))
if err != nil {
return err
}
defer response.Body.Close()
if response.StatusCode != 200 {
return fmt.Errorf("failed to pull model") // TODO more details perhaps
}
slog.Info("model pulled", "model", modelName)
return nil
}
var serverProcMutex sync.Mutex
// Returns an Client, the testEndpoint, and a cleanup function, fails the test on errors
// Starts the server if needed
func InitServerConnection(ctx context.Context, t *testing.T) (*api.Client, string, func()) {
client, testEndpoint := GetTestEndpoint()
if os.Getenv("OLLAMA_TEST_EXISTING") == "" {
serverProcMutex.Lock()
fp, err := os.CreateTemp("", "ollama-server-*.log")
if err != nil {
t.Fatalf("failed to generate log file: %s", err)
}
lifecycle.ServerLogFile = fp.Name()
fp.Close()
require.NoError(t, startServer(ctx, testEndpoint))
}
func GenerateTestHelper(ctx context.Context, t *testing.T, client *http.Client, genReq api.GenerateRequest, anyResp []string) {
return client, testEndpoint, func() {
// TODO maybe stuff in an init routine?
lifecycle.InitLogging()
requestJSON, err := json.Marshal(genReq)
if err != nil {
t.Fatalf("Error serializing request: %v", err)
}
defer func() {
if os.Getenv("OLLAMA_TEST_EXISTING") == "" {
defer serverProcMutex.Unlock()
if t.Failed() {
@@ -221,118 +203,63 @@ func InitServerConnection(ctx context.Context, t *testing.T) (*api.Client, strin
os.Stderr.Write(data)
slog.Warn("END OF SERVER")
}
err := os.Remove(lifecycle.ServerLogFile)
err = os.Remove(lifecycle.ServerLogFile)
if err != nil && !os.IsNotExist(err) {
slog.Warn("failed to cleanup", "logfile", lifecycle.ServerLogFile, "error", err)
}
}
}
}
func GenerateTestHelper(ctx context.Context, t *testing.T, genReq api.GenerateRequest, anyResp []string) {
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
require.NoError(t, PullIfMissing(ctx, client, genReq.Model))
DoGenerate(ctx, t, client, genReq, anyResp, 30*time.Second, 10*time.Second)
}
func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq api.GenerateRequest, anyResp []string, initialTimeout, streamTimeout time.Duration) {
stallTimer := time.NewTimer(initialTimeout)
var buf bytes.Buffer
fn := func(response api.GenerateResponse) error {
// fmt.Print(".")
buf.Write([]byte(response.Response))
if !stallTimer.Reset(streamTimeout) {
return fmt.Errorf("stall was detected while streaming response, aborting")
}
return nil
}
stream := true
genReq.Stream = &stream
done := make(chan int)
var genErr error
go func() {
genErr = client.Generate(ctx, &genReq, fn)
done <- 0
}()
scheme, testEndpoint := GetTestEndpoint()
select {
case <-stallTimer.C:
if buf.Len() == 0 {
t.Errorf("generate never started. Timed out after :%s", initialTimeout.String())
} else {
t.Errorf("generate stalled. Response so far:%s", buf.String())
if os.Getenv("OLLAMA_TEST_EXISTING") == "" {
serverProcMutex.Lock()
fp, err := os.CreateTemp("", "ollama-server-*.log")
if err != nil {
t.Fatalf("failed to generate log file: %s", err)
}
case <-done:
require.NoError(t, genErr, "failed with %s request prompt %s ", genReq.Model, genReq.Prompt)
// Verify the response contains the expected data
response := buf.String()
atLeastOne := false
for _, resp := range anyResp {
if strings.Contains(strings.ToLower(response), resp) {
atLeastOne = true
break
}
}
require.True(t, atLeastOne, "none of %v found in %s", anyResp, response)
slog.Info("test pass", "model", genReq.Model, "prompt", genReq.Prompt, "contains", anyResp, "response", response)
case <-ctx.Done():
t.Error("outer test context done while waiting for generate")
lifecycle.ServerLogFile = fp.Name()
fp.Close()
assert.NoError(t, StartServer(ctx, testEndpoint))
}
}
// Generate a set of requests
// By default each request uses orca-mini as the model
func GenerateRequests() ([]api.GenerateRequest, [][]string) {
return []api.GenerateRequest{
{
Model: "orca-mini",
Prompt: "why is the ocean blue?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
}, {
Model: "orca-mini",
Prompt: "why is the color of dirt brown?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
}, {
Model: "orca-mini",
Prompt: "what is the origin of the us thanksgiving holiday?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
}, {
Model: "orca-mini",
Prompt: "what is the origin of independence day?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
}, {
Model: "orca-mini",
Prompt: "what is the composition of air?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
},
},
[][]string{
[]string{"sunlight"},
[]string{"soil", "organic", "earth", "black", "tan"},
[]string{"england", "english", "massachusetts", "pilgrims"},
[]string{"fourth", "july", "declaration", "independence"},
[]string{"nitrogen", "oxygen", "carbon", "dioxide"},
err = PullIfMissing(ctx, client, scheme, testEndpoint, genReq.Model)
if err != nil {
t.Fatalf("Error pulling model: %v", err)
}
// Make the request and get the response
req, err := http.NewRequest("POST", scheme+"://"+testEndpoint+"/api/generate", bytes.NewReader(requestJSON))
if err != nil {
t.Fatalf("Error creating request: %v", err)
}
// Set the content type for the request
req.Header.Set("Content-Type", "application/json")
// Make the request with the HTTP client
response, err := client.Do(req.WithContext(ctx))
if err != nil {
t.Fatalf("Error making request: %v", err)
}
defer response.Body.Close()
body, err := io.ReadAll(response.Body)
assert.NoError(t, err)
assert.Equal(t, response.StatusCode, 200, string(body))
// Verify the response is valid JSON
var payload api.GenerateResponse
err = json.Unmarshal(body, &payload)
if err != nil {
assert.NoError(t, err, body)
}
// Verify the response contains the expected data
atLeastOne := false
for _, resp := range anyResp {
if strings.Contains(strings.ToLower(payload.Response), resp) {
atLeastOne = true
break
}
}
assert.True(t, atLeastOne, "none of %v found in %s", anyResp, payload.Response)
}

View File

@@ -39,10 +39,6 @@
#include "httplib.h"
#include "json.hpp"
#if defined(_WIN32)
#include <windows.h>
#endif
#include <cstddef>
#include <thread>
#include <chrono>
@@ -2774,28 +2770,8 @@ inline void signal_handler(int signal) {
shutdown_handler(signal);
}
#if defined(_WIN32)
char* wchar_to_char(const wchar_t* wstr) {
if (wstr == nullptr) return nullptr;
// Determine the number of bytes needed for the UTF-8 string
int bytes = WideCharToMultiByte(CP_UTF8, 0, wstr, -1, nullptr, 0, nullptr, nullptr);
char* str = new char[bytes];
// Convert the wide-character string to a UTF-8 string
WideCharToMultiByte(CP_UTF8, 0, wstr, -1, str, bytes, nullptr, nullptr);
return str;
}
int wmain(int argc, wchar_t **wargv) {
char** argv = new char*[argc];
for (int i = 0; i < argc; ++i) {
argv[i] = wchar_to_char(wargv[i]);
}
#else
int main(int argc, char **argv) {
#endif
int main(int argc, char **argv)
{
#if SERVER_VERBOSE != 1
log_disable();
#endif
@@ -3306,11 +3282,6 @@ int main(int argc, char **argv) {
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
for (int i = 0; i < argc; ++i) {
delete[] argv[i];
}
delete[] argv;
#endif
llama.queue_tasks.start_loop();
svr.stop();

View File

@@ -21,7 +21,7 @@ init_vars() {
# TODO - add additional optimization flags...
CMAKE_DEFS="-DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off ${CMAKE_DEFS}"
fi
case $(uname -s) in
case $(uname -s) in
"Darwin")
LIB_EXT="dylib"
WHOLE_ARCHIVE="-Wl,-force_load"

View File

@@ -1,6 +1,6 @@
#!/bin/bash
# This script is intended to run inside the go generate
# working directory must be ./llm/generate/
# This script is intended to run inside the `go run build.go` script, which
# sets the working directory to the correct location: ./llm/generate/.
# TODO - add hardening to detect missing tools (cmake, etc.)
@@ -89,10 +89,10 @@ case "${GOARCH}" in
;;
*)
echo "GOARCH must be set"
echo "this script is meant to be run from within go generate"
echo "this script is meant to be run from within 'go run build.go'"
exit 1
;;
esac
cleanup
echo "go generate completed. LLM runners: $(cd ${BUILD_DIR}/..; echo *)"
echo "code generation completed. LLM runners: $(cd ${BUILD_DIR}/..; echo *)"

View File

@@ -1,6 +1,6 @@
#!/bin/bash
# This script is intended to run inside the go generate
# working directory must be llm/generate/
# This script is intended to run with the `go run build.go` script, which
# sets the working directory to the correct location: ./llm/generate/.
# First we build one or more CPU based LLM libraries
#
@@ -57,21 +57,21 @@ init_vars
git_module_setup
apply_patches
init_vars
if [ -z "${OLLAMA_SKIP_STATIC_GENERATE}" -o "${OLLAMA_CPU_TARGET}" = "static" ]; then
# Builds by default, allows skipping, forces build if OLLAMA_CPU_TARGET="static"
# Enables optimized Dockerfile builds using a blanket skip and targeted overrides
# Static build for linking into the Go binary
init_vars
CMAKE_TARGETS="--target llama --target ggml"
CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DLLAMA_NATIVE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="../build/linux/${ARCH}_static"
echo "Building static library"
build
fi
init_vars
if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "static" ]; then
# Static build for linking into the Go binary
init_vars
CMAKE_TARGETS="--target llama --target ggml"
CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DLLAMA_NATIVE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="../build/linux/${ARCH}_static"
echo "Building static library"
build
fi
# Users building from source can tune the exact flags we pass to cmake for configuring
# llama.cpp, and we'll build only 1 CPU variant in that case as the default.
if [ -n "${OLLAMA_CUSTOM_CPU_DEFS}" ]; then
@@ -165,22 +165,14 @@ if [ -d "${CUDA_LIB_DIR}" ]; then
fi
if [ "${ARCH}" == "arm64" ]; then
echo "ARM CPU detected - disabling unsupported AVX instructions"
# ARM-based CPUs such as M1 and Tegra do not support AVX extensions.
#
# CUDA compute < 6.0 lacks proper FP16 support on ARM.
# Disabling has minimal performance effect while maintaining compatibility.
# CUDA compute < 6.0 lacks proper FP16 support on ARM.
# Disabling has minimal performance effect while maintaining compatibility.
ARM64_DEFS="-DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_CUDA_F16=off"
fi
# Users building from source can tune the exact flags we pass to cmake for configuring llama.cpp
if [ -n "${OLLAMA_CUSTOM_CUDA_DEFS}" ]; then
echo "OLLAMA_CUSTOM_CUDA_DEFS=\"${OLLAMA_CUSTOM_CUDA_DEFS}\""
CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
echo "Building custom CUDA GPU"
else
CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DLLAMA_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
fi
CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS}"
CMAKE_DEFS="-DLLAMA_CUDA=on -DLLAMA_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS}"
BUILD_DIR="../build/linux/${ARCH}/cuda${CUDA_VARIANT}"
EXTRA_LIBS="-L${CUDA_LIB_DIR} -lcudart -lcublas -lcublasLt -lcuda"
build
@@ -225,12 +217,6 @@ if [ -d "${ROCM_PATH}" ]; then
fi
init_vars
CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DLLAMA_HIPBLAS=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
# Users building from source can tune the exact flags we pass to cmake for configuring llama.cpp
if [ -n "${OLLAMA_CUSTOM_ROCM_DEFS}" ]; then
echo "OLLAMA_CUSTOM_ROCM_DEFS=\"${OLLAMA_CUSTOM_ROCM_DEFS}\""
CMAKE_DEFS="${CMAKE_DEFS} ${OLLAMA_CUSTOM_ROCM_DEFS}"
echo "Building custom ROCM GPU"
fi
BUILD_DIR="../build/linux/${ARCH}/rocm${ROCM_VARIANT}"
EXTRA_LIBS="-L${ROCM_PATH}/lib -L/opt/amdgpu/lib/x86_64-linux-gnu/ -Wl,-rpath,\$ORIGIN/../../rocm/ -lhipblas -lrocblas -lamdhip64 -lrocsolver -lamd_comgr -lhsa-runtime64 -lrocsparse -ldrm -ldrm_amdgpu"
build
@@ -251,4 +237,4 @@ if [ -d "${ROCM_PATH}" ]; then
fi
cleanup
echo "go generate completed. LLM runners: $(cd ${BUILD_DIR}/..; echo *)"
echo "code generation completed. LLM runners: $(cd ${BUILD_DIR}/..; echo *)"

View File

@@ -26,25 +26,15 @@ function amdGPUs {
$GPU_LIST -join ';'
}
function init_vars {
if (!$script:SRC_DIR) {
$script:SRC_DIR = $(resolve-path "..\..\")
}
if (!$script:llamacppDir) {
$script:llamacppDir = "../llama.cpp"
}
if (!$script:cmakeTargets) {
$script:cmakeTargets = @("ollama_llama_server")
}
$script:SRC_DIR = $(resolve-path "..\..\")
$script:llamacppDir = "../llama.cpp"
$script:cmakeDefs = @(
"-DBUILD_SHARED_LIBS=on",
"-DLLAMA_NATIVE=off"
)
$script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
$script:cmakeTargets = @("ollama_llama_server")
$script:ARCH = "amd64" # arm not yet supported.
$script:DIST_BASE = "${script:SRC_DIR}\dist\windows-${script:ARCH}\ollama_runners"
md "$script:DIST_BASE" -ea 0 > $null
if ($env:CGO_CFLAGS -contains "-g") {
$script:cmakeDefs += @("-DCMAKE_VERBOSE_MAKEFILE=on", "-DLLAMA_SERVER_VERBOSE=on", "-DCMAKE_BUILD_TYPE=RelWithDebInfo")
$script:config = "RelWithDebInfo"
@@ -65,6 +55,7 @@ function init_vars {
} else {
$script:CUDA_LIB_DIR=$env:CUDA_LIB_DIR
}
$script:GZIP=(get-command -ea 'silentlycontinue' gzip).path
$script:DUMPBIN=(get-command -ea 'silentlycontinue' dumpbin).path
if ($null -eq $env:CMAKE_CUDA_ARCHITECTURES) {
$script:CMAKE_CUDA_ARCHITECTURES="50;52;61;70;75;80"
@@ -143,18 +134,21 @@ function sign {
}
}
function install {
write-host "Installing binaries to dist dir ${script:distDir}"
mkdir ${script:distDir} -ErrorAction SilentlyContinue
function compress {
if ($script:GZIP -eq $null) {
write-host "gzip not installed, not compressing files"
return
}
write-host "Compressing binaries..."
$binaries = dir "${script:buildDir}/bin/*.exe"
foreach ($file in $binaries) {
copy-item -Path $file -Destination ${script:distDir} -Force
& "$script:GZIP" --best -f $file
}
write-host "Installing dlls to dist dir ${script:distDir}"
write-host "Compressing dlls..."
$dlls = dir "${script:buildDir}/bin/*.dll"
foreach ($file in $dlls) {
copy-item -Path $file -Destination ${script:distDir} -Force
& "$script:GZIP" --best -f $file
}
}
@@ -175,191 +169,123 @@ function cleanup {
}
}
init_vars
git_module_setup
apply_patches
# -DLLAMA_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
# -DLLAMA_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
# -DLLAMA_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
$script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
function build_static() {
if ((-not "${env:OLLAMA_SKIP_STATIC_GENERATE}") -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "static"))) {
# GCC build for direct linking into the Go binary
init_vars
# cmake will silently fallback to msvc compilers if mingw isn't in the path, so detect and fail fast
# as we need this to be compiled by gcc for golang to be able to link with itx
write-host "Checking for MinGW..."
# error action ensures we exit on failure
get-command gcc
get-command mingw32-make
$oldTargets = $script:cmakeTargets
$script:cmakeTargets = @("llama", "ggml")
$script:cmakeDefs = @(
"-G", "MinGW Makefiles"
"-DCMAKE_C_COMPILER=gcc.exe",
"-DCMAKE_CXX_COMPILER=g++.exe",
"-DBUILD_SHARED_LIBS=off",
"-DLLAMA_NATIVE=off",
"-DLLAMA_AVX=off",
"-DLLAMA_AVX2=off",
"-DLLAMA_AVX512=off",
"-DLLAMA_F16C=off",
"-DLLAMA_FMA=off")
$script:buildDir="../build/windows/${script:ARCH}_static"
write-host "Building static library"
build
$script:cmakeTargets = $oldTargets
} else {
write-host "Skipping CPU generation step as requested"
}
}
function build_cpu() {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) {
# remaining llama.cpp builds use MSVC
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=off", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu"
$script:distDir="$script:DIST_BASE\cpu"
write-host "Building LCD CPU"
build
sign
install
} else {
write-host "Skipping CPU generation step as requested"
}
}
function build_cpu_avx() {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx"))) {
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu_avx"
$script:distDir="$script:DIST_BASE\cpu_avx"
write-host "Building AVX CPU"
build
sign
install
} else {
write-host "Skipping CPU AVX generation step as requested"
}
}
function build_cpu_avx2() {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx2"))) {
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=on", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=on", "-DLLAMA_F16C=on") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu_avx2"
$script:distDir="$script:DIST_BASE\cpu_avx2"
write-host "Building AVX2 CPU"
build
sign
install
} else {
write-host "Skipping CPU AVX2 generation step as requested"
}
}
function build_cuda() {
if ((-not "${env:OLLAMA_SKIP_CUDA_GENERATE}") -and ("${script:CUDA_LIB_DIR}")) {
# Then build cuda as a dynamically loaded library
$nvcc = "$script:CUDA_LIB_DIR\nvcc.exe"
$script:CUDA_VERSION=(get-item ($nvcc | split-path | split-path)).Basename
if ($null -ne $script:CUDA_VERSION) {
$script:CUDA_VARIANT="_"+$script:CUDA_VERSION
}
init_vars
$script:buildDir="../build/windows/${script:ARCH}/cuda$script:CUDA_VARIANT"
$script:distDir="$script:DIST_BASE\cuda$script:CUDA_VARIANT"
$script:cmakeDefs += @("-A", "x64", "-DLLAMA_CUDA=ON", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR", "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}")
if ($null -ne $env:OLLAMA_CUSTOM_CUDA_DEFS) {
write-host "OLLAMA_CUSTOM_CUDA_DEFS=`"${env:OLLAMA_CUSTOM_CUDA_DEFS}`""
$script:cmakeDefs +=@("${env:OLLAMA_CUSTOM_CUDA_DEFS}")
write-host "building custom CUDA GPU"
}
build
sign
install
write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\"
cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
} else {
write-host "Skipping CUDA generation step"
}
}
function build_rocm() {
if ((-not "${env:OLLAMA_SKIP_ROCM_GENERATE}") -and ("${env:HIP_PATH}")) {
$script:ROCM_VERSION=(get-item $env:HIP_PATH).Basename
if ($null -ne $script:ROCM_VERSION) {
$script:ROCM_VARIANT="_v"+$script:ROCM_VERSION
}
init_vars
$script:buildDir="../build/windows/${script:ARCH}/rocm$script:ROCM_VARIANT"
$script:distDir="$script:DIST_BASE\rocm$script:ROCM_VARIANT"
$script:cmakeDefs += @(
"-G", "Ninja",
"-DCMAKE_C_COMPILER=clang.exe",
"-DCMAKE_CXX_COMPILER=clang++.exe",
"-DLLAMA_HIPBLAS=on",
"-DHIP_PLATFORM=amd",
"-DLLAMA_AVX=on",
"-DLLAMA_AVX2=off",
"-DCMAKE_POSITION_INDEPENDENT_CODE=on",
"-DAMDGPU_TARGETS=$(amdGPUs)",
"-DGPU_TARGETS=$(amdGPUs)"
)
# Make sure the ROCm binary dir is first in the path
$env:PATH="$env:HIP_PATH\bin;$env:PATH"
# We have to clobber the LIB var from the developer shell for clang to work properly
$env:LIB=""
if ($null -ne $env:OLLAMA_CUSTOM_ROCM_DEFS) {
write-host "OLLAMA_CUSTOM_ROCM_DEFS=`"${env:OLLAMA_CUSTOM_ROCM_DEFS}`""
$script:cmakeDefs += @("${env:OLLAMA_CUSTOM_ROCM_DEFS}")
write-host "building custom ROCM GPU"
}
write-host "Building ROCm"
build
# Ninja doesn't prefix with config name
${script:config}=""
if ($null -ne $script:DUMPBIN) {
& "$script:DUMPBIN" /dependents "${script:buildDir}/bin/ollama_llama_server.exe" | select-string ".dll"
}
sign
install
# Assumes v5.7, may need adjustments for v6
rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
md "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\rocblas\library\" -ea 0 > $null
cp "${env:HIP_PATH}\bin\hipblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
cp "${env:HIP_PATH}\bin\rocblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
# amdhip64.dll dependency comes from the driver and must be installed on the host to use AMD GPUs
cp "${env:HIP_PATH}\bin\rocblas\library\*" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\rocblas\library\"
} else {
write-host "Skipping ROCm generation step"
}
}
if ($null -eq ${env:OLLAMA_SKIP_CPU_GENERATE}) {
# GCC build for direct linking into the Go binary
init_vars
if ($($args.count) -eq 0) {
git_module_setup
apply_patches
build_static
build_cpu
build_cpu_avx
build_cpu_avx2
build_cuda
build_rocm
# cmake will silently fallback to msvc compilers if mingw isn't in the path, so detect and fail fast
# as we need this to be compiled by gcc for golang to be able to link with itx
write-host "Checking for MinGW..."
# error action ensures we exit on failure
get-command gcc
get-command mingw32-make
$script:cmakeTargets = @("llama", "ggml")
$script:cmakeDefs = @(
"-G", "MinGW Makefiles"
"-DCMAKE_C_COMPILER=gcc.exe",
"-DCMAKE_CXX_COMPILER=g++.exe",
"-DBUILD_SHARED_LIBS=off",
"-DLLAMA_NATIVE=off",
"-DLLAMA_AVX=off",
"-DLLAMA_AVX2=off",
"-DLLAMA_AVX512=off",
"-DLLAMA_F16C=off",
"-DLLAMA_FMA=off")
$script:buildDir="../build/windows/${script:ARCH}_static"
write-host "Building static library"
build
cleanup
write-host "`ngo generate completed. LLM runners: $(get-childitem -path $script:DIST_BASE)"
# remaining llama.cpp builds use MSVC
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=off", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu"
write-host "Building LCD CPU"
build
sign
compress
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu_avx"
write-host "Building AVX CPU"
build
sign
compress
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=on", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=on", "-DLLAMA_F16C=on") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu_avx2"
write-host "Building AVX2 CPU"
build
sign
compress
} else {
for ( $i = 0; $i -lt $args.count; $i++ ) {
write-host "performing $($args[$i])"
& $($args[$i])
}
}
write-host "Skipping CPU generation step as requested"
}
if ($null -ne $script:CUDA_LIB_DIR) {
# Then build cuda as a dynamically loaded library
$nvcc = "$script:CUDA_LIB_DIR\nvcc.exe"
$script:CUDA_VERSION=(get-item ($nvcc | split-path | split-path)).Basename
if ($null -ne $script:CUDA_VERSION) {
$script:CUDA_VARIANT="_"+$script:CUDA_VERSION
}
init_vars
$script:buildDir="../build/windows/${script:ARCH}/cuda$script:CUDA_VARIANT"
$script:cmakeDefs += @("-A", "x64", "-DLLAMA_CUDA=ON", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR", "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}")
build
sign
compress
}
if ($null -ne $env:HIP_PATH) {
$script:ROCM_VERSION=(get-item $env:HIP_PATH).Basename
if ($null -ne $script:ROCM_VERSION) {
$script:ROCM_VARIANT="_v"+$script:ROCM_VERSION
}
init_vars
$script:buildDir="../build/windows/${script:ARCH}/rocm$script:ROCM_VARIANT"
$script:cmakeDefs += @(
"-G", "Ninja",
"-DCMAKE_C_COMPILER=clang.exe",
"-DCMAKE_CXX_COMPILER=clang++.exe",
"-DLLAMA_HIPBLAS=on",
"-DHIP_PLATFORM=amd",
"-DLLAMA_AVX=on",
"-DLLAMA_AVX2=off",
"-DCMAKE_POSITION_INDEPENDENT_CODE=on",
"-DAMDGPU_TARGETS=$(amdGPUs)",
"-DGPU_TARGETS=$(amdGPUs)"
)
# Make sure the ROCm binary dir is first in the path
$env:PATH="$env:HIP_PATH\bin;$env:PATH"
# We have to clobber the LIB var from the developer shell for clang to work properly
$env:LIB=""
write-host "Building ROCm"
build
# Ninja doesn't prefix with config name
${script:config}=""
if ($null -ne $script:DUMPBIN) {
& "$script:DUMPBIN" /dependents "${script:buildDir}/bin/ollama_llama_server.exe" | select-string ".dll"
}
sign
compress
}
cleanup
write-host "`ncode generation completed. LLM runners: $(get-childitem -path ${script:SRC_DIR}\llm\build\windows\${script:ARCH})"

View File

@@ -1,3 +0,0 @@
package generate
//go:generate bash ./gen_darwin.sh

View File

@@ -1,3 +0,0 @@
package generate
//go:generate bash ./gen_linux.sh

View File

@@ -1,3 +0,0 @@
package generate
//go:generate powershell -ExecutionPolicy Bypass -File ./gen_windows.ps1

View File

@@ -49,7 +49,7 @@ func (llm *ggla) KV() KV {
return llm.kv
}
func (llm *ggla) Tensors() Tensors {
func (llm *ggla) Tensors() []*Tensor {
return llm.tensors
}

View File

@@ -13,6 +13,16 @@ type GGML struct {
model
}
func (ggml *GGML) LayerSize(prefix string) (n int64) {
for _, t := range ggml.Tensors() {
if strings.HasPrefix(t.Name, prefix) {
n += int64(t.size())
}
}
return
}
const (
fileTypeF32 uint32 = iota
fileTypeF16
@@ -91,7 +101,7 @@ func fileType(fileType uint32) string {
type model interface {
KV() KV
Tensors() Tensors
Tensors() []*Tensor
}
type KV map[string]any
@@ -157,37 +167,6 @@ func (kv KV) ContextLength() uint64 {
return kv.u64(fmt.Sprintf("%s.context_length", kv.Architecture()))
}
type Tensors []*Tensor
func (ts Tensors) Layers() map[string]Layer {
layers := make(map[string]Layer)
for _, t := range ts {
parts := strings.Split(t.Name, ".")
if parts[0] == "blk" {
// join first and second part, e.g. blk.%d
parts = append([]string{fmt.Sprintf("%s.%s", parts[0], parts[1])}, parts[2:]...)
}
if _, ok := layers[parts[0]]; !ok {
layers[parts[0]] = make(Layer)
}
layers[parts[0]][strings.Join(parts[1:], ".")] = t
}
return layers
}
type Layer map[string]*Tensor
func (l Layer) size() (size uint64) {
for _, t := range l {
size += t.size()
}
return size
}
type Tensor struct {
Name string `json:"name"`
Kind uint32 `json:"kind"`
@@ -325,77 +304,49 @@ func DecodeGGML(rs io.ReadSeeker) (*GGML, int64, error) {
}, offset, nil
}
func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload uint64) {
embedding := llm.KV().EmbeddingLength()
heads := llm.KV().HeadCount()
headsKV := llm.KV().HeadCountKV()
vocab := uint64(len(llm.KV()["tokenizer.ggml.tokens"].([]any)))
func (llm GGML) GraphSize(context, batch int) (int64, bool) {
embeddingLength := llm.KV().EmbeddingLength()
headCount := llm.KV().HeadCount()
headCountKV := llm.KV().HeadCountKV()
vocabLength := len(llm.KV()["tokenizer.ggml.tokens"].([]any))
layers := llm.Tensors().Layers()
switch llm.KV().Architecture() {
case "llama":
fullOffload = 4 * batch * (1 + 4*embedding + context*(1+heads))
partialOffload = 4 * batch * embedding
partialOffload += max(
4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embedding/heads*headsKV),
4*batch*(embedding+vocab)+embedding*vocab*105/128,
)
if ffnGateExpsWeight, ok := layers["blk.0"]["ffn_gate_exps.weight"]; ok {
// mixtral 8x22b
ff := uint64(llm.KV()["llama.feed_forward_length"].(uint32))
partialOffload = max(
3*ffnGateExpsWeight.size()+4*batch*(2*ff+headsKV+embedding+context+embedding/heads*headsKV),
4*(context*batch*heads+context*embedding/heads*headsKV+batch*1024+embedding/heads*headsKV*batch),
)
} else if ffnGateWeight, ok := layers["blk.0"]["ffn_gate.0.weight"]; ok {
// mixtral 8x7b
ffnGateWeight1 := ffnGateWeight.Shape[1]
fullOffload = 4 * batch * (2 + 3*embedding + context*(1+heads) + 2*headsKV + ffnGateWeight1)
partialOffload = max(
4*batch*(3+embedding/heads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
4*batch*(1+2*embedding+context*(1+heads))+embedding*(6*context*headsKV/heads+embedding*9/16),
)
var attnQKVWeight1 uint64 = 0
for _, t := range llm.Tensors() {
if strings.HasSuffix(t.Name, ".attn_qkv.weight") && len(t.Shape) >= 2 {
attnQKVWeight1 = t.Shape[1]
break
}
case "gemma":
fullOffload = 4 * batch * (embedding + vocab)
partialOffload = 4*batch*(2*embedding+vocab+1) + embedding*vocab*105/128
case "command-r":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(2+4*embedding+context*(1+heads)),
)
partialOffload = max(
4*batch*(embedding+vocab)+embedding*vocab*105/128,
4*batch*(1+2*embedding+context*(1+heads))+4*embedding*context+embedding*embedding*9/16,
)
case "qwen2":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(1+2*embedding+context+context*heads),
)
partialOffload = max(
4*batch*(embedding+vocab)+embedding*vocab*105/128,
4*(batch*(1+2*embedding+context*(1+heads))+embedding*(1+context)),
)
case "phi2":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(1+4*embedding+context+context*heads),
)
partialOffload = 4*batch*(2*embedding+vocab) + embedding*vocab*105/128
case "stablelm":
fullOffload = 4 * batch * (context*(1+heads) + 3*embedding + 2)
partialOffload = max(
4*batch*(vocab+2*embedding),
fullOffload,
)
}
return
var ffnGate1 uint64 = 0
for _, t := range llm.Tensors() {
if strings.Index(t.Name, ".ffn_gate") > 0 && len(t.Shape) >= 2 {
ffnGate1 = t.Shape[1]
break
}
}
switch llm.KV().Architecture() {
case "gemma", "command-r":
return 4 * int64(batch) * int64(embeddingLength+uint64(vocabLength)), true
case "phi2":
return max(
4*int64(batch)*int64(embeddingLength+uint64(vocabLength)),
4*int64(batch)*int64(1+4*embeddingLength+uint64(context)+attnQKVWeight1+uint64(context)*headCount),
), true
case "qwen2":
return max(
4*int64(batch)*int64(embeddingLength+uint64(vocabLength)),
4*int64(batch)*int64(1+2*embeddingLength+uint64(context)+uint64(context)*headCount),
), true
case "llama":
if ffnGate1 > 0 {
// moe
return 4 * int64(batch) * int64(2+3*embeddingLength+uint64(context)+uint64(context)*headCount+2*headCountKV+ffnGate1), true
}
return 4 * int64(batch) * int64(1+4*embeddingLength+uint64(context)+uint64(context)*headCount), true
}
return 0, false
}

View File

@@ -6,8 +6,6 @@ import (
"fmt"
"io"
"strings"
"log/slog"
)
type containerGGUF struct {
@@ -54,7 +52,6 @@ func (c *containerGGUF) Decode(rs io.ReadSeeker) (model, error) {
}
model := newGGUF(c)
slog.Debug(fmt.Sprintf("model = %#v", model))
if err := model.Decode(rs); err != nil {
return nil, err
}
@@ -112,7 +109,7 @@ func (llm *gguf) KV() KV {
return llm.kv
}
func (llm *gguf) Tensors() Tensors {
func (llm *gguf) Tensors() []*Tensor {
return llm.tensors
}
@@ -251,12 +248,8 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
}
for _, tensor := range llm.tensors {
if _, err := rs.Seek(int64(tensor.size()), io.SeekCurrent); err != nil {
return err
}
padding := llm.padding(int64(tensor.size()), int64(alignment))
if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
padded := (int64(tensor.size()) + int64(alignment) - 1) & ^(int64(alignment) - 1)
if _, err := rs.Seek(padded, io.SeekCurrent); err != nil {
return err
}
}
@@ -458,18 +451,15 @@ var ggufKVOrder = map[string][]string{
"llama": {
"general.architecture",
"general.name",
"llama.vocab_size",
"llama.context_length",
"llama.embedding_length",
"llama.block_count",
"llama.feed_forward_length",
"llama.rope.dimension_count",
"llama.attention.head_count",
"llama.attention.head_count_kv",
"llama.attention.layer_norm_rms_epsilon",
"llama.rope.freq_base",
"llama.rope.dimension_count",
"llama.expert_count",
"llama.expert_used_count",
"gemma.context_length",
"gemma.embedding_length",
"gemma.block_count",
@@ -519,17 +509,11 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
return err
}
kvCheck := make(map[string]bool)
for k := range kv {
kvCheck[k] = false
}
for _, k := range ggufKVOrder["llama"] {
v, ok := kv[k]
if !ok {
continue
}
kvCheck[k] = true
if err := binary.Write(ws, llm.ByteOrder, uint64(len(k))); err != nil {
return err
@@ -577,20 +561,12 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
return err
}
}
default:
return fmt.Errorf("improper type for '%s'", k)
}
if err != nil {
return err
}
}
for k, v := range kvCheck {
if !v {
return fmt.Errorf("Didn't know how to write kv %s", k)
}
}
for _, tensor := range tensors {
if err := binary.Write(ws, llm.ByteOrder, uint64(len(tensor.Name))); err != nil {
return err
@@ -600,11 +576,9 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
return err
}
dims := 0
for cnt := 0; cnt < len(tensor.Shape); cnt++ {
if tensor.Shape[cnt] > 0 {
dims++
}
dims := 1
if tensor.Shape[1] > 0 {
dims = 2
}
if err := binary.Write(ws, llm.ByteOrder, uint32(dims)); err != nil {
@@ -631,9 +605,8 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
return err
}
var alignment int64 = 32
padding := llm.padding(offset, alignment)
if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding))); err != nil {
padding := llm.padding(offset, 32)
if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding-offset))); err != nil {
return err
}
@@ -647,8 +620,8 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
return err
}
padding := llm.padding(offset, alignment)
if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding))); err != nil {
padding := llm.padding(offset, 32)
if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding-offset))); err != nil {
return err
}
}
@@ -657,5 +630,5 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
}
func (gguf) padding(offset, align int64) int64 {
return (align - offset%align) % align
return (offset + align - 1) / align * align
}

View File

@@ -6,81 +6,10 @@ package llm
// #cgo windows,amd64 LDFLAGS: ${SRCDIR}/build/windows/amd64_static/libllama.a -static -lstdc++
// #cgo linux,amd64 LDFLAGS: ${SRCDIR}/build/linux/x86_64_static/libllama.a -lstdc++
// #cgo linux,arm64 LDFLAGS: ${SRCDIR}/build/linux/arm64_static/libllama.a -lstdc++
// #include <stdlib.h>
// #include "llama.h"
import "C"
import (
"fmt"
"unsafe"
)
// SystemInfo is an unused example of calling llama.cpp functions using CGo
func SystemInfo() string {
return C.GoString(C.llama_print_system_info())
}
func Quantize(infile, outfile, filetype string) error {
cinfile := C.CString(infile)
defer C.free(unsafe.Pointer(cinfile))
coutfile := C.CString(outfile)
defer C.free(unsafe.Pointer(coutfile))
params := C.llama_model_quantize_default_params()
params.nthread = -1
switch filetype {
case "F32":
params.ftype = fileTypeF32
case "F16":
params.ftype = fileTypeF16
case "Q4_0":
params.ftype = fileTypeQ4_0
case "Q4_1":
params.ftype = fileTypeQ4_1
case "Q4_1_F16":
params.ftype = fileTypeQ4_1_F16
case "Q8_0":
params.ftype = fileTypeQ8_0
case "Q5_0":
params.ftype = fileTypeQ5_0
case "Q5_1":
params.ftype = fileTypeQ5_1
case "Q2_K":
params.ftype = fileTypeQ2_K
case "Q3_K_S":
params.ftype = fileTypeQ3_K_S
case "Q3_K_M":
params.ftype = fileTypeQ3_K_M
case "Q3_K_L":
params.ftype = fileTypeQ3_K_L
case "Q4_K_S":
params.ftype = fileTypeQ4_K_S
case "Q4_K_M":
params.ftype = fileTypeQ4_K_M
case "Q5_K_S":
params.ftype = fileTypeQ5_K_S
case "Q5_K_M":
params.ftype = fileTypeQ5_K_M
case "Q6_K":
params.ftype = fileTypeQ6_K
case "IQ2_XXS":
params.ftype = fileTypeIQ2_XXS
case "IQ2_XS":
params.ftype = fileTypeIQ2_XS
case "Q2_K_S":
params.ftype = fileTypeQ2_K_S
case "Q3_K_XS":
params.ftype = fileTypeQ3_K_XS
case "IQ3_XXS":
params.ftype = fileTypeIQ3_XXS
default:
return fmt.Errorf("unknown filetype: %s", filetype)
}
if retval := C.llama_model_quantize(cinfile, coutfile, &params); retval != 0 {
return fmt.Errorf("llama_model_quantize: %d", retval)
}
return nil
}

View File

@@ -2,5 +2,5 @@ package llm
import "embed"
// unused on windows
//go:embed build/windows/*/*/bin/*
var libEmbed embed.FS

View File

@@ -1,188 +0,0 @@
package llm
import (
"fmt"
"log/slog"
"os"
"strconv"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
)
// This algorithm looks for a complete fit to determine if we need to unload other models
func PredictServerFit(allGpus gpu.GpuInfoList, ggml *GGML, adapters, projectors []string, opts api.Options) (bool, uint64) {
var estimatedVRAM uint64
if opts.NumCtx > int(ggml.KV().ContextLength()) {
slog.Warn("requested context length is greater than model max context length", "requested", opts.NumCtx, "model", ggml.KV().ContextLength())
opts.NumCtx = int(ggml.KV().ContextLength())
}
if opts.NumCtx < 4 {
opts.NumCtx = 4
}
// Split up the GPUs by type and try them
for _, gpus := range allGpus.ByLibrary() {
var layerCount int
layerCount, estimatedVRAM = EstimateGPULayers(gpus, ggml, projectors, opts)
if opts.NumGPU < 0 {
if layerCount > 0 && layerCount >= int(ggml.KV().BlockCount()+1) {
return true, estimatedVRAM
}
} else {
if layerCount > 0 && layerCount >= opts.NumGPU {
return true, estimatedVRAM
}
}
}
return false, estimatedVRAM
}
// Given a model and one or more GPU targets, predict how many layers and bytes we can load
// The GPUs provided must all be the same Library
func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts api.Options) (int, uint64) {
if gpus[0].Library == "cpu" {
return 0, 0
}
var memoryAvailable uint64
for _, info := range gpus {
memoryAvailable += info.FreeMemory
}
userLimit := os.Getenv("OLLAMA_MAX_VRAM")
if userLimit != "" {
avail, err := strconv.ParseUint(userLimit, 10, 64)
if err != nil {
slog.Error("invalid setting, ignoring", "OLLAMA_MAX_VRAM", userLimit, "error", err)
} else {
slog.Info("user override memory limit", "OLLAMA_MAX_VRAM", avail, "actual", memoryAvailable)
memoryAvailable = avail
}
}
slog.Debug("evaluating", "library", gpus[0].Library, "gpu_count", len(gpus), "available", format.HumanBytes2(memoryAvailable))
// TODO - this is probably wrong, first GPU vs secondaries will have different overheads
memoryMinimum := gpus[0].MinimumMemory
for _, projector := range projectors {
memoryMinimum += projectorMemoryRequirements(projector)
// multimodal models require at least 2048 context
opts.NumCtx = max(opts.NumCtx, 2048)
}
// fp16 k,v = (1 (k) + 1 (v)) * sizeof(float16) * n_ctx * n_layer * n_embd / n_head * n_head_kv
var kv uint64 = 2 * 2 * uint64(opts.NumCtx) * ggml.KV().BlockCount() * ggml.KV().EmbeddingLength() / ggml.KV().HeadCount() * ggml.KV().HeadCountKV()
graphPartialOffload, graphFullOffload := ggml.GraphSize(uint64(opts.NumCtx), uint64(min(opts.NumCtx, opts.NumBatch)))
if graphPartialOffload == 0 {
graphPartialOffload = ggml.KV().GQA() * kv / 6
}
if graphFullOffload == 0 {
graphFullOffload = graphPartialOffload
}
graphFullOffload *= uint64(len(gpus))
graphPartialOffload *= uint64(len(gpus))
// memoryRequiredTotal represents the memory required for full GPU offloading (all layers)
memoryRequiredTotal := memoryMinimum + graphFullOffload
// memoryRequiredPartial represents the memory required for partial GPU offloading (n > 0, n < layers)
memoryRequiredPartial := memoryMinimum + graphPartialOffload
if memoryRequiredPartial > memoryAvailable {
slog.Debug("insufficient VRAM to load any model layers")
return 0, 0
}
layers := ggml.Tensors().Layers()
var memoryLayerOutput uint64
if layer, ok := layers["output_norm"]; ok {
memoryLayerOutput += layer.size()
}
if layer, ok := layers["output"]; ok {
memoryLayerOutput += layer.size()
} else if layer, ok := layers["token_embd"]; ok {
memoryLayerOutput += layer.size()
}
if gpus[0].Library == "metal" && opts.UseMMap {
// memory is preallocated for output tensors
memoryRequiredTotal += memoryLayerOutput
memoryRequiredPartial += memoryLayerOutput
}
var layerCount int
for i := 0; i < int(ggml.KV().BlockCount()); i++ {
memoryLayer := layers[fmt.Sprintf("blk.%d", i)].size()
// KV is proportional to the number of layers
memoryLayer += kv / ggml.KV().BlockCount()
memoryRequiredTotal += memoryLayer
if memoryAvailable > memoryRequiredPartial+memoryLayer {
memoryRequiredPartial += memoryLayer
layerCount++
}
}
if gpus[0].Library != "metal" || !opts.UseMMap {
// memory was not preallocated for output tensors
memoryRequiredTotal += memoryLayerOutput
}
if memoryAvailable > memoryRequiredTotal {
layerCount = int(ggml.KV().BlockCount()) + 1
memoryRequiredPartial = memoryRequiredTotal
}
memoryWeights := memoryRequiredTotal - memoryMinimum - graphFullOffload - kv
slog.Info(
"offload to gpu",
slog.Group(
"layers",
// actual number of layers offloaded
"real", opts.NumGPU,
// estimated number of layers that can be offloaded
"estimate", layerCount,
),
slog.Group(
"memory",
// memory available for offloading
"available", format.HumanBytes2(memoryAvailable),
slog.Group(
"required",
// memory required for full offloading
"full", format.HumanBytes2(memoryRequiredTotal),
// memory required to offload layers.estimate layers
"partial", format.HumanBytes2(memoryRequiredPartial),
// memory of KV cache
"kv", format.HumanBytes2(kv),
),
slog.Group(
"weights",
// memory of the weights
"total", format.HumanBytes2(memoryWeights),
// memory of repeating layers
"repeating", format.HumanBytes2(memoryWeights-memoryLayerOutput),
// memory of non-repeating layers
"nonrepeating", format.HumanBytes2(memoryLayerOutput),
),
slog.Group(
"graph",
// memory of graph when fully offloaded
"full", format.HumanBytes2(graphFullOffload),
// memory of graph when not fully offloaded
"partial", format.HumanBytes2(graphPartialOffload),
),
),
)
return layerCount, uint64(memoryRequiredPartial)
}

View File

@@ -1,12 +0,0 @@
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index e431c7f7..f077e688 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -3,6 +3,7 @@
// I'll gradually clean and extend it
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
+#include "common.h"
#include "log.h"
#include "ggml.h"
#include "ggml-alloc.h"

View File

@@ -1,45 +0,0 @@
diff --git a/ggml-metal.m b/ggml-metal.m
index 0207b787..b5e9884b 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1396,27 +1396,23 @@ static enum ggml_status ggml_metal_graph_compute(
// to the matrix-vector kernel
int ne11_mm_min = 1;
-#if 0
// the numbers below are measured on M2 Ultra for 7B and 13B models
// these numbers do not translate to other devices or model sizes
// TODO: need to find a better approach
- if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) {
- switch (src0t) {
- case GGML_TYPE_F16: ne11_mm_min = 2; break;
- case GGML_TYPE_Q8_0: ne11_mm_min = 7; break;
- case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
- case GGML_TYPE_Q3_K: ne11_mm_min = 7; break;
- case GGML_TYPE_Q4_0:
- case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
- case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
- case GGML_TYPE_Q5_0: // not tested yet
- case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
- case GGML_TYPE_Q5_K: ne11_mm_min = 7; break;
- case GGML_TYPE_Q6_K: ne11_mm_min = 7; break;
- default: ne11_mm_min = 1; break;
- }
+ switch (src0t) {
+ case GGML_TYPE_F16: ne11_mm_min = 2; break;
+ case GGML_TYPE_Q8_0: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
+ case GGML_TYPE_Q3_K: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q4_0:
+ case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
+ case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
+ case GGML_TYPE_Q5_0: // not tested yet
+ case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
+ case GGML_TYPE_Q5_K: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q6_K: ne11_mm_min = 7; break;
+ default: ne11_mm_min = 1; break;
}
-#endif
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel

View File

@@ -9,7 +9,6 @@ import (
"log/slog"
"os"
"path/filepath"
"runtime"
"strings"
"golang.org/x/exp/slices"
@@ -18,7 +17,7 @@ import (
"github.com/ollama/ollama/gpu"
)
var errPayloadMissing = errors.New("expected payloads not included in this build of ollama")
var errPayloadMissing = fmt.Errorf("expected payloads not included in this build of ollama")
func Init() error {
payloadsDir, err := gpu.PayloadsDir()
@@ -26,15 +25,13 @@ func Init() error {
return err
}
if runtime.GOOS != "windows" {
slog.Info("extracting embedded files", "dir", payloadsDir)
binGlob := "build/*/*/*/bin/*"
slog.Info("extracting embedded files", "dir", payloadsDir)
binGlob := "build/*/*/*/bin/*"
// extract server libraries
err = extractFiles(payloadsDir, binGlob)
if err != nil {
return fmt.Errorf("extract binaries: %v", err)
}
// extract server libraries
err = extractFiles(payloadsDir, binGlob)
if err != nil {
return fmt.Errorf("extract binaries: %v", err)
}
var variants []string
@@ -141,23 +138,6 @@ func serversForGpu(info gpu.GpuInfo) []string {
return servers
}
// Return the optimal server for this CPU architecture
func serverForCpu() string {
if runtime.GOOS == "darwin" && runtime.GOARCH == "arm64" {
return "metal"
}
variant := gpu.GetCPUVariant()
availableServers := availableServers()
if variant != "" {
for cmp := range availableServers {
if cmp == "cpu_"+variant {
return cmp
}
}
}
return "cpu"
}
// extract extracts the embedded files to the target directory
func extractFiles(targetDir string, glob string) error {
files, err := fs.Glob(libEmbed, glob)

View File

@@ -17,43 +17,30 @@ import (
"os/exec"
"path/filepath"
"runtime"
"slices"
"strconv"
"strings"
"time"
"golang.org/x/sync/semaphore"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
)
type LlamaServer interface {
Ping(ctx context.Context) error
WaitUntilRunning(ctx context.Context) error
Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error
Embedding(ctx context.Context, prompt string) ([]float64, error)
Tokenize(ctx context.Context, content string) ([]int, error)
Detokenize(ctx context.Context, tokens []int) (string, error)
Close() error
EstimatedVRAM() uint64
}
// llmServer is an instance of the llama.cpp server
type llmServer struct {
// LlamaServer is an instance of the llama.cpp server
type LlamaServer struct {
port int
cmd *exec.Cmd
done chan error // Channel to signal when the process exits
status *StatusWriter
options api.Options
// TODO - this should be broken down by GPU
estimatedVRAM uint64 // Estimated usage of VRAM by the loaded model
sem *semaphore.Weighted
}
func LoadModel(model string) (*GGML, error) {
var cpuOnlyFamilies = []string{
"mamba",
}
func NewLlamaServer(model string, adapters, projectors []string, opts api.Options) (*LlamaServer, error) {
if _, err := os.Stat(model); err != nil {
return nil, err
}
@@ -65,13 +52,10 @@ func LoadModel(model string) (*GGML, error) {
defer f.Close()
ggml, _, err := DecodeGGML(f)
return ggml, err
}
if err != nil {
return nil, err
}
// NewLlamaServer will run a server for the given GPUs
// The gpu list must be a single family.
func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options) (LlamaServer, error) {
var err error
if opts.NumCtx > int(ggml.KV().ContextLength()) {
slog.Warn("requested context length is greater than model max context length", "requested", opts.NumCtx, "model", ggml.KV().ContextLength())
opts.NumCtx = int(ggml.KV().ContextLength())
@@ -81,51 +65,75 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
opts.NumCtx = 4
}
cpuRunner := ""
var estimatedVRAM uint64
var systemMemory uint64
if (len(gpus) == 1 && gpus[0].Library == "cpu") || opts.NumGPU == 0 {
availableMemory, _ := gpu.CheckVRAM()
info := gpu.GetGPUInfo()
// TODO evaluate system memory to see if we should block the load, or force an unload of another CPU runner
usedMemory := info.MinimumMemory
for _, projector := range projectors {
usedMemory += projectorMemoryRequirements(projector)
cpuRunner = serverForCpu()
} else {
if gpus[0].Library == "metal" {
memInfo, err := gpu.GetCPUMem()
if err != nil {
slog.Error("failed to lookup system memory", "error", err)
} else {
systemMemory = memInfo.TotalMemory
slog.Debug("system memory", "total", format.HumanBytes2(systemMemory))
}
}
var layers int
layers, estimatedVRAM = EstimateGPULayers(gpus, ggml, projectors, opts)
// multimodal models require at least 2048 context
opts.NumCtx = max(opts.NumCtx, 2048)
}
if gpus[0].Library == "metal" && estimatedVRAM > systemMemory {
// disable partial offloading when model is greater than total system memory as this
// can lead to locking up the system
opts.NumGPU = 0
} else if opts.NumGPU < 0 && layers > 0 && gpus[0].Library != "cpu" {
opts.NumGPU = layers
// fp16 k,v = (1 (k) + 1 (v)) * sizeof(float16) * n_ctx * n_layer * n_embd / n_head * n_head_kv
kv := 2 * 2 * int64(opts.NumCtx) * int64(ggml.KV().BlockCount()) * int64(ggml.KV().EmbeddingLength()) / int64(ggml.KV().HeadCount()) * int64(ggml.KV().HeadCountKV())
graph, ok := ggml.GraphSize(opts.NumCtx, min(opts.NumCtx, opts.NumBatch))
if !ok {
graph = int64(ggml.KV().GQA()) * kv / 6
}
usedMemory += graph
if (usedMemory > availableMemory || slices.Contains(cpuOnlyFamilies, ggml.KV().Architecture())) && info.Library != "metal" {
info.Library = "cpu"
}
requiredMemory := usedMemory
var layers int
for i := 0; i < int(ggml.KV().BlockCount()); i++ {
layerMemory := ggml.LayerSize(fmt.Sprintf("blk.%d.", i)) + kv/int64(ggml.KV().BlockCount())
requiredMemory += layerMemory
if availableMemory > usedMemory+layerMemory && (opts.NumGPU < 0 || layers < opts.NumGPU) {
usedMemory += layerMemory
layers++
}
}
// Loop through potential servers
finalErr := fmt.Errorf("no suitable llama servers found")
memOutputLayer := ggml.LayerSize("output.")
requiredMemory += memOutputLayer
// only offload output layer if all repeating layers are offloaded
if layers >= int(ggml.KV().BlockCount()) && availableMemory > usedMemory+memOutputLayer {
usedMemory += memOutputLayer
layers++
}
slog.Info(
"offload to gpu",
"layers", layers,
"required", format.HumanBytes2(requiredMemory),
"used", format.HumanBytes2(usedMemory),
"available", format.HumanBytes2(availableMemory),
"kv", format.HumanBytes2(kv),
"graph", format.HumanBytes2(graph),
)
if opts.NumGPU < 0 && info.Library != "cpu" {
opts.NumGPU = layers
}
if len(adapters) > 1 {
return nil, errors.New("ollama supports only one lora adapter, but multiple were provided")
}
availableServers := availableServers()
var servers []string
if cpuRunner != "" {
servers = []string{cpuRunner}
} else {
servers = serversForGpu(gpus[0]) // All GPUs in the list are matching Library and Variant
}
demandLib := strings.Trim(os.Getenv("OLLAMA_LLM_LIBRARY"), "\"' ")
servers := serversForGpu(info)
demandLib := os.Getenv("OLLAMA_LLM_LIBRARY")
if demandLib != "" {
serverPath := availableServers[demandLib]
if serverPath == "" {
@@ -137,7 +145,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
}
if len(servers) == 0 {
return nil, fmt.Errorf("no servers found for %v", gpus)
return nil, fmt.Errorf("no servers found for %v", info)
}
params := []string{
@@ -194,26 +202,10 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
params = append(params, "--numa")
}
// "--cont-batching", // TODO - doesn't seem to have any noticeable perf change for multiple requests
numParallel := 1
if onp := os.Getenv("OLLAMA_NUM_PARALLEL"); onp != "" {
numParallel, err = strconv.Atoi(onp)
if err != nil || numParallel <= 0 {
err = fmt.Errorf("invalid OLLAMA_NUM_PARALLEL=%s must be greater than zero - %w", onp, err)
slog.Error("misconfiguration", "error", err)
return nil, err
}
}
params = append(params, "--parallel", fmt.Sprintf("%d", numParallel))
// Loop through potential servers
var finalErr error
for i := 0; i < len(servers); i++ {
dir := availableServers[servers[i]]
if dir == "" {
// Shouldn't happen
finalErr = fmt.Errorf("[%d] server %s not listed in available servers %v", i, servers[i], availableServers)
slog.Error("sever list inconsistent", "error", finalErr)
continue
}
// Find an availableServers port, retry on each iterration in case the failure was a port conflict race
port := 0
@@ -236,60 +228,30 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
}
// append the server directory to LD_LIBRARY_PATH/PATH
libraryPaths := []string{dir}
if libraryPath, ok := os.LookupEnv(pathEnv); ok {
// Append our runner directory to the path
// This will favor system libraries over our bundled library dependencies
libraryPaths = append(filepath.SplitList(libraryPath), libraryPaths...)
}
// Note: we always put the dependency path first
// since this was the exact version we verified for AMD GPUs
// and we favor what the user had in their path
if gpus[0].DependencyPath != "" {
// TODO refine for multi-gpu support
libraryPaths = append([]string{gpus[0].DependencyPath}, libraryPaths...)
}
server := filepath.Join(dir, "ollama_llama_server")
if runtime.GOOS == "windows" {
server = server + ".exe"
}
// Detect tmp cleaners wiping out the file
_, err := os.Stat(server)
if errors.Is(err, os.ErrNotExist) {
slog.Warn("llama server disappeared, reinitializing payloads", "path", server, "error", err)
err = Init()
if err != nil {
slog.Warn("failed to reinitialize payloads", "error", err)
return nil, err
}
s := &LlamaServer{
port: port,
cmd: exec.Command(server, finalParams...),
status: NewStatusWriter(os.Stderr),
options: opts,
}
s := &llmServer{
port: port,
cmd: exec.Command(server, finalParams...),
status: NewStatusWriter(os.Stderr),
options: opts,
estimatedVRAM: estimatedVRAM,
sem: semaphore.NewWeighted(int64(numParallel)),
}
libEnv := fmt.Sprintf("%s=%s", pathEnv, strings.Join(libraryPaths, string(filepath.ListSeparator)))
slog.Debug(libEnv)
s.cmd.Env = append(os.Environ(), libEnv)
s.cmd.Stdout = os.Stdout
s.cmd.Stderr = s.status
// TODO - multiple GPU selection logic...
key, val := gpu.GpuInfoList(gpus).GetVisibleDevicesEnv()
if key != "" {
s.cmd.Env = append(s.cmd.Env, key+"="+val)
}
slog.Info("starting llama server", "cmd", s.cmd.String())
// Log at debug as the environment is inherited and might contain sensitive information
slog.Debug("subprocess", "environment", s.cmd.Env)
if err = s.cmd.Start(); err != nil {
msg := ""
@@ -307,13 +269,12 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
_ = s.cmd.Wait()
}()
// TODO - make sure this is all wired up correctly
// if err = s.WaitUntilRunning(); err != nil {
// slog.Error("error starting llama server", "server", servers[i], "error", err)
// s.Close()
// finalErr = err
// continue
// }
if err = s.waitUntilRunning(); err != nil {
slog.Error("error starting llama server", "server", servers[i], "error", err)
s.Close()
finalErr = err
continue
}
return s, nil
}
@@ -321,7 +282,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
return nil, finalErr
}
func projectorMemoryRequirements(filename string) uint64 {
func projectorMemoryRequirements(filename string) int64 {
file, err := os.Open(filename)
if err != nil {
return 0
@@ -333,12 +294,18 @@ func projectorMemoryRequirements(filename string) uint64 {
return 0
}
var mem uint64
for _, layer := range ggml.Tensors().Layers() {
mem += layer.size()
prefixes := make(map[string]struct{})
for _, layer := range ggml.Tensors() {
parts := strings.Split(layer.Name, ".")
prefixes[strings.Join(parts[:2], ".")] = struct{}{}
}
return mem
var ask int64
for prefix := range prefixes {
ask += ggml.LayerSize(prefix)
}
return ask
}
type ServerStatus int
@@ -351,21 +318,6 @@ const ( // iota is reset to 0
ServerStatusError
)
func (s ServerStatus) ToString() string {
switch s {
case ServerStatusReady:
return "llm server ready"
case ServerStatusNoSlotsAvaialble:
return "llm busy - no slots available"
case ServerStatusLoadingModel:
return "llm server loading model"
case ServerStatusNotResponding:
return "llm server not responding"
default:
return "llm server error"
}
}
type ServerStatusResp struct {
Status string `json:"status"`
SlotsIdle int `json:"slots_idle"`
@@ -373,7 +325,7 @@ type ServerStatusResp struct {
Error string `json:"error"`
}
func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
func (s *LlamaServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
// Fail fast if its exited
if s.cmd.ProcessState != nil {
msg := ""
@@ -420,7 +372,7 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
}
}
func (s *llmServer) Ping(ctx context.Context) error {
func (s *LlamaServer) Ping(ctx context.Context) error {
_, err := s.getServerStatus(ctx)
if err != nil {
slog.Debug("server unhealthy", "error", err)
@@ -429,10 +381,9 @@ func (s *llmServer) Ping(ctx context.Context) error {
return nil
}
func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
func (s *LlamaServer) waitUntilRunning() error {
start := time.Now()
// TODO we need to wire up a better way to detect hangs during model load and startup of the server
expiresAt := time.Now().Add(10 * time.Minute) // be generous with timeout, large models can take a while to load
expiresAt := time.Now().Add(3 * time.Minute) // be generous with timeout, large models can take a while to load
ticker := time.NewTicker(50 * time.Millisecond)
defer ticker.Stop()
@@ -440,9 +391,6 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
var lastStatus ServerStatus = -1
for {
select {
case <-ctx.Done():
slog.Info("context expired before server started")
return fmt.Errorf("timed out waiting for llama runner to start: %w", ctx.Err())
case err := <-s.done:
msg := ""
if s.status != nil && s.status.LastErrMsg != "" {
@@ -466,9 +414,9 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
return fmt.Errorf("llama runner process no longer running: %d %s", s.cmd.ProcessState.ExitCode(), msg)
}
c, cancel := context.WithTimeout(ctx, 200*time.Millisecond)
ctx, cancel := context.WithTimeout(context.Background(), 200*time.Millisecond)
defer cancel()
status, err := s.getServerStatus(c)
status, err := s.getServerStatus(ctx)
if err != nil && lastStatus != status {
slog.Debug("server not yet available", "error", err)
lastStatus = status
@@ -554,19 +502,7 @@ type CompletionResponse struct {
EvalDuration time.Duration
}
func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {
if err := s.sem.Acquire(ctx, 1); err != nil {
slog.Error("Failed to acquire semaphore", "error", err)
return err
}
defer s.sem.Release(1)
// only allow maximum 10 "context shifts" to avoid infinite generation
if req.Options.NumPredict < 0 || req.Options.NumPredict > 10*s.options.NumCtx {
req.Options.NumPredict = 10 * s.options.NumCtx
slog.Debug("setting token limit to 10x num_ctx", "num_ctx", s.options.NumCtx, "num_predict", req.Options.NumPredict)
}
func (s *LlamaServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {
request := map[string]any{
"prompt": req.Prompt,
"stream": true,
@@ -597,7 +533,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
if err != nil {
return err
} else if status != ServerStatusReady {
return fmt.Errorf("unexpected server status: %s", status.ToString())
return fmt.Errorf("unexpected server status: %d", status)
}
if req.Format == "json" {
@@ -744,18 +680,13 @@ type EmbeddingResponse struct {
Embedding []float64 `json:"embedding"`
}
func (s *llmServer) Embedding(ctx context.Context, prompt string) ([]float64, error) {
if err := s.sem.Acquire(ctx, 1); err != nil {
slog.Error("Failed to acquire semaphore", "error", err)
return nil, err
}
defer s.sem.Release(1)
func (s *LlamaServer) Embedding(ctx context.Context, prompt string) ([]float64, error) {
// Make sure the server is ready
status, err := s.getServerStatus(ctx)
if err != nil {
return nil, err
} else if status != ServerStatusReady {
return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
return nil, fmt.Errorf("unexpected server status: %d", status)
}
data, err := json.Marshal(TokenizeRequest{Content: prompt})
@@ -801,13 +732,13 @@ type TokenizeResponse struct {
Tokens []int `json:"tokens"`
}
func (s *llmServer) Tokenize(ctx context.Context, content string) ([]int, error) {
func (s *LlamaServer) Tokenize(ctx context.Context, content string) ([]int, error) {
// Make sure the server is ready
status, err := s.getServerStatus(ctx)
if err != nil {
return nil, err
} else if status != ServerStatusReady && status != ServerStatusNoSlotsAvaialble {
return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
} else if status != ServerStatusReady {
return nil, fmt.Errorf("unexpected server status: %d", status)
}
data, err := json.Marshal(TokenizeRequest{Content: content})
@@ -853,13 +784,13 @@ type DetokenizeResponse struct {
Content string `json:"content"`
}
func (s *llmServer) Detokenize(ctx context.Context, tokens []int) (string, error) {
func (s *LlamaServer) Detokenize(ctx context.Context, tokens []int) (string, error) {
// Make sure the server is ready
status, err := s.getServerStatus(ctx)
if err != nil {
return "", err
} else if status != ServerStatusReady && status != ServerStatusNoSlotsAvaialble {
return "", fmt.Errorf("unexpected server status: %s", status.ToString())
} else if status != ServerStatusReady {
return "", fmt.Errorf("unexpected server status: %d", status)
}
data, err := json.Marshal(DetokenizeRequest{Tokens: tokens})
@@ -897,7 +828,7 @@ func (s *llmServer) Detokenize(ctx context.Context, tokens []int) (string, error
return decoded.Content, nil
}
func (s *llmServer) Close() error {
func (s *LlamaServer) Close() error {
if s.cmd != nil {
slog.Debug("stopping llama server")
return s.cmd.Process.Kill()
@@ -906,10 +837,6 @@ func (s *llmServer) Close() error {
return nil
}
func (s *llmServer) EstimatedVRAM() uint64 {
return s.estimatedVRAM
}
func parseDurationMs(ms float64) time.Duration {
dur, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
if err != nil {

View File

@@ -14,7 +14,7 @@ go build .
Then run the desktop app with `npm start`:
```
cd macapp
cd app
npm install
npm start
```

View File

@@ -30,7 +30,7 @@ function checkEnv() {
$script:INNO_SETUP_DIR=(get-item "C:\Program Files*\Inno Setup*\")[0]
$script:DEPS_DIR="${script:SRC_DIR}\dist\windows-amd64"
$script:DEPS_DIR="${script:SRC_DIR}\dist\windeps"
$env:CGO_ENABLED="1"
echo "Checking version"
if (!$env:VERSION) {
@@ -81,8 +81,8 @@ function buildOllama() {
/csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} ollama.exe
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
New-Item -ItemType Directory -Path .\dist\windows-amd64\ -Force
cp .\ollama.exe .\dist\windows-amd64\
New-Item -ItemType Directory -Path .\dist -Force
cp .\ollama.exe .\dist\ollama-windows-amd64.exe
}
function buildApp() {
@@ -101,6 +101,7 @@ function buildApp() {
function gatherDependencies() {
write-host "Gathering runtime dependencies"
cd "${script:SRC_DIR}"
rm -ea 0 -recurse -force -path "${script:DEPS_DIR}"
md "${script:DEPS_DIR}" -ea 0 > $null
# TODO - this varies based on host build system and MSVC version - drive from dumpbin output
@@ -109,6 +110,9 @@ function gatherDependencies() {
cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\"
cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\"
cp "${script:NVIDIA_DIR}\cudart64_*.dll" "${script:DEPS_DIR}\"
cp "${script:NVIDIA_DIR}\cublas64_*.dll" "${script:DEPS_DIR}\"
cp "${script:NVIDIA_DIR}\cublasLt64_*.dll" "${script:DEPS_DIR}\"
cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\"
if ("${env:KEY_CONTAINER}") {
@@ -120,6 +124,7 @@ function gatherDependencies() {
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
}
}
function buildInstaller() {
@@ -134,18 +139,12 @@ function buildInstaller() {
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
function distZip() {
write-host "Generating stand-alone distribution zip file ${script:SRC_DIR}\dist\ollama-windows-amd64.zip"
Compress-Archive -Path "${script:SRC_DIR}\dist\windows-amd64\*" -DestinationPath "${script:SRC_DIR}\dist\ollama-windows-amd64.zip" -Force
}
try {
checkEnv
buildOllama
buildApp
gatherDependencies
buildInstaller
distZip
} catch {
write-host "Build Failed"
write-host $_

View File

@@ -247,8 +247,7 @@ func (b *blobDownload) downloadChunk(ctx context.Context, requestURL *url.URL, w
}
if !part.lastUpdated.IsZero() && time.Since(part.lastUpdated) > 5*time.Second {
const msg = "%s part %d stalled; retrying. If this persists, press ctrl-c to exit, then 'ollama pull' to find a faster connection."
slog.Info(fmt.Sprintf(msg, b.Digest[7:19], part.N))
slog.Info(fmt.Sprintf("%s part %d stalled; retrying", b.Digest[7:19], part.N))
// reset last updated
part.lastUpdated = time.Time{}
return errPartStalled

View File

@@ -29,7 +29,6 @@ import (
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/llm"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/types/model"
"github.com/ollama/ollama/version"
)
@@ -285,7 +284,7 @@ func realpath(mfDir, from string) string {
return abspath
}
func CreateModel(ctx context.Context, name, modelFileDir, quantization string, commands []parser.Command, fn func(resp api.ProgressResponse)) error {
func CreateModel(ctx context.Context, name, modelFileDir string, commands []parser.Command, fn func(resp api.ProgressResponse)) error {
deleteMap := make(map[string]struct{})
if manifest, _, err := GetManifest(ParseModelPath(name)); err == nil {
for _, layer := range append(manifest.Layers, manifest.Config) {
@@ -323,7 +322,7 @@ func CreateModel(ctx context.Context, name, modelFileDir, quantization string, c
pathName := realpath(modelFileDir, c.Args)
ggufName, err := convertModel(name, pathName, fn)
ggufName, err := convertSafetensors(name, pathName, fn)
if err != nil {
var pathErr *fs.PathError
switch {
@@ -338,27 +337,8 @@ func CreateModel(ctx context.Context, name, modelFileDir, quantization string, c
if ggufName != "" {
pathName = ggufName
slog.Debug(fmt.Sprintf("new image layer path: %s", pathName))
defer os.RemoveAll(ggufName)
if quantization != "" {
quantization = strings.ToUpper(quantization)
fn(api.ProgressResponse{Status: fmt.Sprintf("quantizing %s model to %s", "F16", quantization)})
tempfile, err := os.CreateTemp(filepath.Dir(ggufName), quantization)
if err != nil {
return err
}
defer os.RemoveAll(tempfile.Name())
if err := llm.Quantize(ggufName, tempfile.Name(), quantization); err != nil {
return err
}
if err := tempfile.Close(); err != nil {
return err
}
pathName = tempfile.Name()
}
}
bin, err := os.Open(pathName)
@@ -634,7 +614,7 @@ func CreateModel(ctx context.Context, name, modelFileDir, quantization string, c
return nil
}
func convertModel(name, path string, fn func(resp api.ProgressResponse)) (string, error) {
func convertSafetensors(name, path string, fn func(resp api.ProgressResponse)) (string, error) {
r, err := zip.OpenReader(path)
if err != nil {
return "", err
@@ -669,22 +649,17 @@ func convertModel(name, path string, fn func(resp api.ProgressResponse)) (string
rc.Close()
}
mf, err := convert.GetModelFormat(tempDir)
params, err := convert.GetParams(tempDir)
if err != nil {
return "", err
}
params, err := mf.GetParams(tempDir)
mArch, err := convert.GetModelArchFromParams(name, tempDir, params)
if err != nil {
return "", err
}
mArch, err := mf.GetModelArch(name, tempDir, params)
if err != nil {
return "", err
}
fn(api.ProgressResponse{Status: "processing tensors"})
fn(api.ProgressResponse{Status: "processing safetensors"})
if err := mArch.GetTensors(); err != nil {
return "", err
}
@@ -702,39 +677,36 @@ func convertModel(name, path string, fn func(resp api.ProgressResponse)) (string
return path, nil
}
func CopyModel(src, dst model.Name) error {
if !dst.IsFullyQualified() {
return model.Unqualified(dst)
}
if !src.IsFullyQualified() {
return model.Unqualified(src)
}
manifests, err := GetManifestPath()
func CopyModel(src, dest string) error {
srcModelPath := ParseModelPath(src)
srcPath, err := srcModelPath.GetManifestPath()
if err != nil {
return err
}
dstpath := filepath.Join(manifests, dst.Filepath())
if err := os.MkdirAll(filepath.Dir(dstpath), 0o755); err != nil {
return err
}
srcpath := filepath.Join(manifests, src.Filepath())
srcfile, err := os.Open(srcpath)
destModelPath := ParseModelPath(dest)
destPath, err := destModelPath.GetManifestPath()
if err != nil {
return err
}
defer srcfile.Close()
dstfile, err := os.Create(dstpath)
if err != nil {
if err := os.MkdirAll(filepath.Dir(destPath), 0o755); err != nil {
return err
}
defer dstfile.Close()
_, err = io.Copy(dstfile, srcfile)
return err
// copy the file
input, err := os.ReadFile(srcPath)
if err != nil {
fmt.Println("Error reading file:", err)
return err
}
err = os.WriteFile(destPath, input, 0o644)
if err != nil {
fmt.Println("Error reading file:", err)
return err
}
return nil
}
func deleteUnusedLayers(skipModelPath *ModelPath, deleteMap map[string]struct{}, dryRun bool) error {
@@ -1141,7 +1113,7 @@ func GetSHA256Digest(r io.Reader) (string, int64) {
return fmt.Sprintf("sha256:%x", h.Sum(nil)), n
}
var errUnauthorized = errors.New("unauthorized")
var errUnauthorized = fmt.Errorf("unauthorized")
func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.URL, headers http.Header, body io.ReadSeeker, regOpts *registryOptions) (*http.Response, error) {
for i := 0; i < 2; i++ {
@@ -1259,7 +1231,7 @@ func parseRegistryChallenge(authStr string) registryChallenge {
}
}
var errDigestMismatch = errors.New("digest mismatch, file must be downloaded again")
var errDigestMismatch = fmt.Errorf("digest mismatch, file must be downloaded again")
func verifyBlob(digest string) error {
fp, err := GetBlobsPath(digest)

View File

@@ -15,8 +15,11 @@ import (
"os"
"os/signal"
"path/filepath"
"reflect"
"runtime"
"strconv"
"strings"
"sync"
"syscall"
"time"
@@ -29,15 +32,13 @@ import (
"github.com/ollama/ollama/llm"
"github.com/ollama/ollama/openai"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/types/model"
"github.com/ollama/ollama/version"
)
var mode string = gin.DebugMode
type Server struct {
addr net.Addr
sched *Scheduler
addr net.Addr
}
func init() {
@@ -52,8 +53,84 @@ func init() {
gin.SetMode(mode)
}
var loaded struct {
mu sync.Mutex
llama *llm.LlamaServer
expireTimer *time.Timer
model string
adapters []string
projectors []string
*api.Options
}
var defaultSessionDuration = 5 * time.Minute
// load a model into memory if it is not already loaded, it is up to the caller to lock loaded.mu before calling this function
func load(c *gin.Context, model *Model, opts api.Options, sessionDuration time.Duration) error {
ctx, cancel := context.WithTimeout(c, 10*time.Second)
defer cancel()
needLoad := loaded.llama == nil || // is there a model loaded?
loaded.model != model.ModelPath || // has the base model changed?
!reflect.DeepEqual(loaded.adapters, model.AdapterPaths) || // have the adapters changed?
!reflect.DeepEqual(loaded.projectors, model.ProjectorPaths) || // have the adapters changed?
!reflect.DeepEqual(loaded.Options.Runner, opts.Runner) || // have the runner options changed?
loaded.llama.Ping(ctx) != nil
if needLoad {
if loaded.llama != nil {
slog.Info("changing loaded model")
loaded.llama.Close()
loaded.llama = nil
loaded.model = ""
loaded.adapters = nil
loaded.projectors = nil
loaded.Options = nil
}
llama, err := llm.NewLlamaServer(model.ModelPath, model.AdapterPaths, model.ProjectorPaths, opts)
if err != nil {
// some older models are not compatible with newer versions of llama.cpp
// show a generalized compatibility error until there is a better way to
// check for model compatibility
if errors.Is(llm.ErrUnsupportedFormat, err) || strings.Contains(err.Error(), "failed to load model") {
err = fmt.Errorf("%v: this model may be incompatible with your version of Ollama. If you previously pulled this model, try updating it by running `ollama pull %s`", err, model.ShortName)
}
return err
}
loaded.model = model.ModelPath
loaded.adapters = model.AdapterPaths
loaded.projectors = model.ProjectorPaths
loaded.llama = llama
loaded.Options = &opts
}
if loaded.expireTimer == nil {
loaded.expireTimer = time.AfterFunc(sessionDuration, func() {
loaded.mu.Lock()
defer loaded.mu.Unlock()
if loaded.llama != nil {
loaded.llama.Close()
}
loaded.llama = nil
loaded.model = ""
loaded.adapters = nil
loaded.projectors = nil
loaded.Options = nil
})
}
loaded.expireTimer.Reset(sessionDuration)
return nil
}
func modelOptions(model *Model, requestOpts map[string]interface{}) (api.Options, error) {
opts := api.DefaultOptions()
if err := opts.FromMap(model.Options); err != nil {
@@ -73,7 +150,9 @@ func isSupportedImageType(image []byte) bool {
return slices.Contains(allowedTypes, contentType)
}
func (s *Server) GenerateHandler(c *gin.Context) {
func GenerateHandler(c *gin.Context) {
loaded.mu.Lock()
defer loaded.mu.Unlock()
checkpointStart := time.Now()
var req api.GenerateRequest
@@ -141,16 +220,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
sessionDuration = req.KeepAlive.Duration
}
rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, sessionDuration)
var runner *runnerRef
select {
case runner = <-rCh:
case err = <-eCh:
if errors.Is(err, context.Canceled) {
c.JSON(499, gin.H{"error": "request canceled"})
return
}
if err := load(c, model, opts, sessionDuration); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
@@ -201,7 +271,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
sb.Reset()
if req.Context != nil {
prev, err := runner.llama.Detokenize(c.Request.Context(), req.Context)
prev, err := loaded.llama.Detokenize(c.Request.Context(), req.Context)
if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
@@ -223,6 +293,9 @@ func (s *Server) GenerateHandler(c *gin.Context) {
defer close(ch)
fn := func(r llm.CompletionResponse) {
// Update model expiration
loaded.expireTimer.Reset(sessionDuration)
// Build up the full response
if _, err := generated.WriteString(r.Content); err != nil {
ch <- gin.H{"error": err.Error()}
@@ -254,7 +327,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
}
// TODO (jmorganca): encode() should not strip special tokens
tokens, err := runner.llama.Tokenize(c.Request.Context(), p)
tokens, err := loaded.llama.Tokenize(c.Request.Context(), p)
if err != nil {
ch <- gin.H{"error": err.Error()}
return
@@ -282,7 +355,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
Images: images,
Options: opts,
}
if err := runner.llama.Completion(c.Request.Context(), req, fn); err != nil {
if err := loaded.llama.Completion(c.Request.Context(), req, fn); err != nil {
ch <- gin.H{"error": err.Error()}
}
}()
@@ -344,7 +417,10 @@ func getDefaultSessionDuration() time.Duration {
return defaultSessionDuration
}
func (s *Server) EmbeddingsHandler(c *gin.Context) {
func EmbeddingsHandler(c *gin.Context) {
loaded.mu.Lock()
defer loaded.mu.Unlock()
var req api.EmbeddingRequest
err := c.ShouldBindJSON(&req)
switch {
@@ -389,16 +465,7 @@ func (s *Server) EmbeddingsHandler(c *gin.Context) {
sessionDuration = req.KeepAlive.Duration
}
rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, sessionDuration)
var runner *runnerRef
select {
case runner = <-rCh:
case err = <-eCh:
if errors.Is(err, context.Canceled) {
c.JSON(499, gin.H{"error": "request canceled"})
return
}
if err := load(c, model, opts, sessionDuration); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
@@ -409,7 +476,7 @@ func (s *Server) EmbeddingsHandler(c *gin.Context) {
return
}
embedding, err := runner.llama.Embedding(c.Request.Context(), req.Prompt)
embedding, err := loaded.llama.Embedding(c.Request.Context(), req.Prompt)
if err != nil {
slog.Info(fmt.Sprintf("embedding generation failed: %v", err))
c.JSON(http.StatusInternalServerError, gin.H{"error": "failed to generate embedding"})
@@ -422,7 +489,7 @@ func (s *Server) EmbeddingsHandler(c *gin.Context) {
c.JSON(http.StatusOK, resp)
}
func (s *Server) PullModelHandler(c *gin.Context) {
func PullModelHandler(c *gin.Context) {
var req api.PullRequest
err := c.ShouldBindJSON(&req)
switch {
@@ -471,7 +538,7 @@ func (s *Server) PullModelHandler(c *gin.Context) {
streamResponse(c, ch)
}
func (s *Server) PushModelHandler(c *gin.Context) {
func PushModelHandler(c *gin.Context) {
var req api.PushRequest
err := c.ShouldBindJSON(&req)
switch {
@@ -520,7 +587,7 @@ func (s *Server) PushModelHandler(c *gin.Context) {
streamResponse(c, ch)
}
func (s *Server) CreateModelHandler(c *gin.Context) {
func CreateModelHandler(c *gin.Context) {
var req api.CreateRequest
err := c.ShouldBindJSON(&req)
switch {
@@ -580,7 +647,7 @@ func (s *Server) CreateModelHandler(c *gin.Context) {
ctx, cancel := context.WithCancel(c.Request.Context())
defer cancel()
if err := CreateModel(ctx, model, filepath.Dir(req.Path), req.Quantization, commands, fn); err != nil {
if err := CreateModel(ctx, model, filepath.Dir(req.Path), commands, fn); err != nil {
ch <- gin.H{"error": err.Error()}
}
}()
@@ -593,7 +660,7 @@ func (s *Server) CreateModelHandler(c *gin.Context) {
streamResponse(c, ch)
}
func (s *Server) DeleteModelHandler(c *gin.Context) {
func DeleteModelHandler(c *gin.Context) {
var req api.DeleteRequest
err := c.ShouldBindJSON(&req)
switch {
@@ -638,7 +705,7 @@ func (s *Server) DeleteModelHandler(c *gin.Context) {
c.JSON(http.StatusOK, nil)
}
func (s *Server) ShowModelHandler(c *gin.Context) {
func ShowModelHandler(c *gin.Context) {
var req api.ShowRequest
err := c.ShouldBindJSON(&req)
switch {
@@ -738,7 +805,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
return resp, nil
}
func (s *Server) ListModelsHandler(c *gin.Context) {
func ListModelsHandler(c *gin.Context) {
models := make([]api.ModelResponse, 0)
manifestsPath, err := GetManifestPath()
if err != nil {
@@ -798,39 +865,39 @@ func (s *Server) ListModelsHandler(c *gin.Context) {
c.JSON(http.StatusOK, api.ListResponse{Models: models})
}
func (s *Server) CopyModelHandler(c *gin.Context) {
var r api.CopyRequest
if err := c.ShouldBindJSON(&r); errors.Is(err, io.EOF) {
func CopyModelHandler(c *gin.Context) {
var req api.CopyRequest
err := c.ShouldBindJSON(&req)
switch {
case errors.Is(err, io.EOF):
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
return
} else if err != nil {
case err != nil:
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
src := model.ParseName(r.Source)
if !src.IsValid() {
_ = c.Error(fmt.Errorf("source %q is invalid", r.Source))
}
dst := model.ParseName(r.Destination)
if !dst.IsValid() {
_ = c.Error(fmt.Errorf("destination %q is invalid", r.Destination))
}
if len(c.Errors) > 0 {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": c.Errors.Errors()})
if req.Source == "" || req.Destination == "" {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "source add destination are required"})
return
}
if err := CopyModel(src, dst); errors.Is(err, os.ErrNotExist) {
c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model %q not found", r.Source)})
} else if err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
if err := ParseModelPath(req.Destination).Validate(); err != nil {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
if err := CopyModel(req.Source, req.Destination); err != nil {
if os.IsNotExist(err) {
c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found", req.Source)})
} else {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
}
return
}
}
func (s *Server) HeadBlobHandler(c *gin.Context) {
func HeadBlobHandler(c *gin.Context) {
path, err := GetBlobsPath(c.Param("digest"))
if err != nil {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
@@ -845,25 +912,7 @@ func (s *Server) HeadBlobHandler(c *gin.Context) {
c.Status(http.StatusOK)
}
func (s *Server) CreateBlobHandler(c *gin.Context) {
path, err := GetBlobsPath(c.Param("digest"))
if err != nil {
c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
}
_, err = os.Stat(path)
switch {
case errors.Is(err, os.ErrNotExist):
// noop
case err != nil:
c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
default:
c.Status(http.StatusOK)
return
}
func CreateBlobHandler(c *gin.Context) {
layer, err := NewLayer(c.Request.Body, "")
if err != nil {
c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
@@ -992,27 +1041,27 @@ func (s *Server) GenerateRoutes() http.Handler {
allowedHostsMiddleware(s.addr),
)
r.POST("/api/pull", s.PullModelHandler)
r.POST("/api/generate", s.GenerateHandler)
r.POST("/api/chat", s.ChatHandler)
r.POST("/api/embeddings", s.EmbeddingsHandler)
r.POST("/api/create", s.CreateModelHandler)
r.POST("/api/push", s.PushModelHandler)
r.POST("/api/copy", s.CopyModelHandler)
r.DELETE("/api/delete", s.DeleteModelHandler)
r.POST("/api/show", s.ShowModelHandler)
r.POST("/api/blobs/:digest", s.CreateBlobHandler)
r.HEAD("/api/blobs/:digest", s.HeadBlobHandler)
r.POST("/api/pull", PullModelHandler)
r.POST("/api/generate", GenerateHandler)
r.POST("/api/chat", ChatHandler)
r.POST("/api/embeddings", EmbeddingsHandler)
r.POST("/api/create", CreateModelHandler)
r.POST("/api/push", PushModelHandler)
r.POST("/api/copy", CopyModelHandler)
r.DELETE("/api/delete", DeleteModelHandler)
r.POST("/api/show", ShowModelHandler)
r.POST("/api/blobs/:digest", CreateBlobHandler)
r.HEAD("/api/blobs/:digest", HeadBlobHandler)
// Compatibility endpoints
r.POST("/v1/chat/completions", openai.Middleware(), s.ChatHandler)
r.POST("/v1/chat/completions", openai.Middleware(), ChatHandler)
for _, method := range []string{http.MethodGet, http.MethodHead} {
r.Handle(method, "/", func(c *gin.Context) {
c.String(http.StatusOK, "Ollama is running")
})
r.Handle(method, "/api/tags", s.ListModelsHandler)
r.Handle(method, "/api/tags", ListModelsHandler)
r.Handle(method, "/api/version", func(c *gin.Context) {
c.JSON(http.StatusOK, gin.H{"version": version.Version})
})
@@ -1066,9 +1115,7 @@ func Serve(ln net.Listener) error {
}
}
ctx, done := context.WithCancel(context.Background())
sched := InitScheduler(ctx)
s := &Server{addr: ln.Addr(), sched: sched}
s := &Server{addr: ln.Addr()}
r := s.GenerateRoutes()
slog.Info(fmt.Sprintf("Listening on %s (version %s)", ln.Addr(), version.Version))
@@ -1081,8 +1128,9 @@ func Serve(ln net.Listener) error {
signal.Notify(signals, syscall.SIGINT, syscall.SIGTERM)
go func() {
<-signals
done()
sched.unloadAllRunners()
if loaded.llama != nil {
loaded.llama.Close()
}
gpu.Cleanup()
os.Exit(0)
}()
@@ -1090,12 +1138,12 @@ func Serve(ln net.Listener) error {
if err := llm.Init(); err != nil {
return fmt.Errorf("unable to initialize llm library %w", err)
}
s.sched.Run(ctx)
// At startup we retrieve GPU information so we can get log messages before loading a model
// This will log warnings to the log in case we have problems with detected GPUs
_ = gpu.GetGPUInfo()
if runtime.GOOS == "linux" { // TODO - windows too
// check compatibility to log warnings
if _, err := gpu.CheckVRAM(); err != nil {
slog.Info(err.Error())
}
}
return srvr.Serve(ln)
}
@@ -1151,9 +1199,9 @@ func streamResponse(c *gin.Context, ch chan any) {
}
// ChatPrompt builds up a prompt from a series of messages for the currently `loaded` model
func chatPrompt(ctx context.Context, runner *runnerRef, template string, messages []api.Message, numCtx int) (string, error) {
func chatPrompt(ctx context.Context, template string, messages []api.Message, numCtx int) (string, error) {
encode := func(s string) ([]int, error) {
return runner.llama.Tokenize(ctx, s)
return loaded.llama.Tokenize(ctx, s)
}
prompt, err := ChatPrompt(template, messages, numCtx, encode)
@@ -1164,7 +1212,10 @@ func chatPrompt(ctx context.Context, runner *runnerRef, template string, message
return prompt, nil
}
func (s *Server) ChatHandler(c *gin.Context) {
func ChatHandler(c *gin.Context) {
loaded.mu.Lock()
defer loaded.mu.Unlock()
checkpointStart := time.Now()
var req api.ChatRequest
@@ -1221,16 +1272,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
sessionDuration = req.KeepAlive.Duration
}
rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, sessionDuration)
var runner *runnerRef
select {
case runner = <-rCh:
case err = <-eCh:
if errors.Is(err, context.Canceled) {
c.JSON(499, gin.H{"error": "request canceled"})
return
}
if err := load(c, model, opts, sessionDuration); err != nil {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
@@ -1247,7 +1289,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
}, req.Messages...)
}
prompt, err := chatPrompt(c.Request.Context(), runner, model.Template, req.Messages, opts.NumCtx)
prompt, err := chatPrompt(c.Request.Context(), model.Template, req.Messages, opts.NumCtx)
if err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
@@ -1290,6 +1332,8 @@ func (s *Server) ChatHandler(c *gin.Context) {
defer close(ch)
fn := func(r llm.CompletionResponse) {
// Update model expiration
loaded.expireTimer.Reset(sessionDuration)
resp := api.ChatResponse{
Model: req.Model,
@@ -1312,7 +1356,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
ch <- resp
}
if err := runner.llama.Completion(c.Request.Context(), llm.CompletionRequest{
if err := loaded.llama.Completion(c.Request.Context(), llm.CompletionRequest{
Prompt: prompt,
Format: req.Format,
Images: images,

View File

@@ -61,7 +61,7 @@ func Test_Routes(t *testing.T) {
fn := func(resp api.ProgressResponse) {
t.Logf("Status: %s", resp.Status)
}
err = CreateModel(context.TODO(), name, "", "", commands, fn)
err = CreateModel(context.TODO(), name, "", commands, fn)
assert.Nil(t, err)
}

View File

@@ -1,546 +0,0 @@
package server
import (
"context"
"errors"
"fmt"
"log/slog"
"os"
"reflect"
"sort"
"strconv"
"strings"
"sync"
"time"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llm"
"golang.org/x/exp/slices"
)
type LlmRequest struct {
ctx context.Context //nolint:containedctx
model *Model
opts api.Options
sessionDuration time.Duration
successCh chan *runnerRef
errCh chan error
}
type Scheduler struct {
pendingReqCh chan *LlmRequest
finishedReqCh chan *LlmRequest
expiredCh chan *runnerRef
unloadedCh chan interface{}
loaded map[string]*runnerRef
loadedMu sync.Mutex
loadFn func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList)
newServerFn func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error)
getGpuFn func() gpu.GpuInfoList
}
// TODO set this to zero after a release or two, to enable multiple models by default
var loadedMax = 1 // Maximum runners; < 1 maps to as many as will fit in VRAM (unlimited for CPU runners)
var maxQueuedRequests = 10 // TODO configurable
var numParallel = 1
func InitScheduler(ctx context.Context) *Scheduler {
maxRunners := os.Getenv("OLLAMA_MAX_LOADED_MODELS")
if maxRunners != "" {
m, err := strconv.Atoi(maxRunners)
if err != nil {
slog.Error("invalid setting", "OLLAMA_MAX_LOADED_MODELS", maxRunners, "error", err)
} else {
loadedMax = m
}
}
if onp := os.Getenv("OLLAMA_NUM_PARALLEL"); onp != "" {
p, err := strconv.Atoi(onp)
if err != nil || p <= 0 {
slog.Error("invalid parallel setting, must be greater than zero", "OLLAMA_NUM_PARALLEL", onp, "error", err)
} else {
numParallel = p
}
}
sched := &Scheduler{
pendingReqCh: make(chan *LlmRequest, maxQueuedRequests),
finishedReqCh: make(chan *LlmRequest, maxQueuedRequests),
expiredCh: make(chan *runnerRef, maxQueuedRequests),
unloadedCh: make(chan interface{}, maxQueuedRequests),
loaded: make(map[string]*runnerRef),
newServerFn: llm.NewLlamaServer,
getGpuFn: gpu.GetGPUInfo,
}
sched.loadFn = sched.load
return sched
}
// context must be canceled to decrement ref count and release the runner
func (s *Scheduler) GetRunner(c context.Context, model *Model, opts api.Options, sessionDuration time.Duration) (chan *runnerRef, chan error) {
req := &LlmRequest{
ctx: c,
model: model,
opts: opts,
sessionDuration: sessionDuration,
successCh: make(chan *runnerRef),
errCh: make(chan error, 1),
}
// context split across parallel threads
opts.NumCtx = opts.NumCtx * numParallel
select {
case s.pendingReqCh <- req:
default:
req.errCh <- fmt.Errorf("server busy, please try again. maximum pending requests exceeded")
}
return req.successCh, req.errCh
}
// Returns immediately, spawns go routines for the scheduler which will shutdown when ctx is done
func (s *Scheduler) Run(ctx context.Context) {
slog.Debug("starting llm scheduler")
go func() {
s.processPending(ctx)
}()
go func() {
s.processCompleted(ctx)
}()
}
func (s *Scheduler) processPending(ctx context.Context) {
for {
select {
case <-ctx.Done():
slog.Debug("shutting down scheduler pending loop")
return
case pending := <-s.pendingReqCh:
// Block other requests until we get this pending request running
for {
var runnerToExpire *runnerRef
s.loadedMu.Lock()
runner := s.loaded[pending.model.ModelPath]
loadedCount := len(s.loaded)
s.loadedMu.Unlock()
if runner != nil {
if runner.needsReload(ctx, pending) {
runnerToExpire = runner
} else {
// Runner is usable, return it
pending.useLoadedRunner(runner, s.finishedReqCh)
break
}
} else if loadedMax > 0 && loadedCount >= loadedMax {
slog.Debug("max runners achieved, unloading one to make room", "runner_count", loadedCount)
runnerToExpire = s.findRunnerToUnload(pending)
} else {
// Either no models are loaded or below loadedMax
// Get a refreshed GPU list
gpus := s.getGpuFn()
// Load model for fitting
ggml, err := llm.LoadModel(pending.model.ModelPath)
if err != nil {
pending.errCh <- err
break
}
// No models loaded. Load the model but prefer the best fit.
if loadedCount == 0 {
slog.Debug("loading first model", "model", pending.model.ModelPath)
g := pickBestFitGPUs(pending, ggml, gpus)
if g != nil {
gpus = g
}
s.loadFn(pending, ggml, gpus)
break
}
// More than one loaded model, so we have to see if the new one fits
// Update free memory from currently loaded models
s.updateFreeSpace(gpus)
gpus = pickBestFitGPUs(pending, ggml, gpus)
if gpus != nil {
slog.Debug("new model fits with existing models, loading")
s.loadFn(pending, ggml, gpus)
break
}
runnerToExpire = s.findRunnerToUnload(pending)
}
if runnerToExpire == nil {
// Shouildn't happen
slog.Error("runner to expire was nil!")
continue
}
// Trigger an expiration to unload once it's done
runnerToExpire.refMu.Lock()
slog.Debug("resetting model to expire immediately to make room", "model", runnerToExpire.model, "refCount", runnerToExpire.refCount)
if runnerToExpire.expireTimer != nil {
runnerToExpire.expireTimer.Stop()
runnerToExpire.expireTimer = nil
}
runnerToExpire.sessionDuration = 0
if runnerToExpire.refCount <= 0 {
s.expiredCh <- runnerToExpire
}
runnerToExpire.refMu.Unlock()
// Wait for the unload to happen
// Note: at this point we're queueing up all incoming requests, even if they were for
// a different model that's loaded and not scheduled to be removed.
slog.Debug("waiting for pending requests to complete and unload to occur", "model", runnerToExpire.model)
select {
case <-ctx.Done():
slog.Debug("shutting down scheduler pending loop")
return
case <-s.unloadedCh:
slog.Debug("unload completed", "model", runnerToExpire.model)
continue
}
}
case <-s.unloadedCh:
// An unload request when there are no pending request can be ignored
slog.Debug("ignoring unload event with no pending requests")
}
}
}
func (s *Scheduler) processCompleted(ctx context.Context) {
// Process completed requests, expired timers, and unloading models
for {
select {
case <-ctx.Done():
slog.Debug("shutting down scheduler completed loop")
return
case finished := <-s.finishedReqCh:
s.loadedMu.Lock()
runner := s.loaded[finished.model.ModelPath]
s.loadedMu.Unlock()
if runner == nil {
slog.Error("finished requeset signal received after model unloaded", "model", finished.model.ModelPath)
continue
}
runner.refMu.Lock()
runner.refCount--
if runner.refCount <= 0 {
if runner.sessionDuration <= 0 {
slog.Debug("runner with zero duration has gone idle, expiring to unload", "model", runner.model)
if runner.expireTimer != nil {
runner.expireTimer.Stop()
runner.expireTimer = nil
}
s.expiredCh <- runner
} else if runner.expireTimer == nil {
slog.Debug("runner with non-zero duration has gone idle, adding timer", "model", runner.model, "duration", runner.sessionDuration)
runner.expireTimer = time.AfterFunc(runner.sessionDuration, func() {
slog.Debug("timer expired, expiring to unload", "model", runner.model)
runner.refMu.Lock()
defer runner.refMu.Unlock()
if runner.expireTimer != nil {
runner.expireTimer.Stop()
}
s.expiredCh <- runner
})
} else {
slog.Debug("runner with non-zero duration has gone idle, resetting timer", "model", runner.model, "duration", runner.sessionDuration)
runner.expireTimer.Reset(runner.sessionDuration)
}
}
slog.Debug("after processing request finished event", "model", runner.model, "refCount", runner.refCount)
runner.refMu.Unlock()
case runner := <-s.expiredCh:
slog.Debug("runner expired event received", "model", runner.model)
runner.refMu.Lock()
if runner.refCount > 0 {
// Shouldn't happen, but safeguard to ensure no leaked runners
slog.Debug("expired event with positive ref count, retrying", "model", runner.model, "refCount", runner.refCount)
go func(runner *runnerRef) {
// We can't unload yet, but want to as soon as the current request completes
// So queue up another expired event
time.Sleep(10 * time.Millisecond)
s.expiredCh <- runner
}(runner)
runner.refMu.Unlock()
continue
}
slog.Debug("got lock to unload", "model", runner.model)
runner.unload()
s.loadedMu.Lock()
delete(s.loaded, runner.model)
s.loadedMu.Unlock()
slog.Debug("runner released", "model", runner.model)
runner.refMu.Unlock()
slog.Debug("sending an unloaded event", "model", runner.model)
s.unloadedCh <- struct{}{}
}
}
}
// Complete the pending request and send the runner back to the requester
// Wires up a finished event after the request context is completed
// Updates session duration, and resets expiration timer
func (pending *LlmRequest) useLoadedRunner(runner *runnerRef, finished chan *LlmRequest) {
runner.refMu.Lock()
defer runner.refMu.Unlock()
runner.refCount++
runner.sessionDuration = pending.sessionDuration
pending.successCh <- runner
go func() {
<-pending.ctx.Done()
slog.Debug("context for request finished")
finished <- pending
}()
}
func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList) {
llama, err := s.newServerFn(gpus, req.model.ModelPath, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts)
if err != nil {
// some older models are not compatible with newer versions of llama.cpp
// show a generalized compatibility error until there is a better way to
// check for model compatibility
if errors.Is(llm.ErrUnsupportedFormat, err) || strings.Contains(err.Error(), "failed to load model") {
err = fmt.Errorf("%v: this model may be incompatible with your version of Ollama. If you previously pulled this model, try updating it by running `ollama pull %s`", err, req.model.ShortName)
}
slog.Info("NewLlamaServer failed", "model", req.model.ModelPath, "error", err)
req.errCh <- err
return
}
runner := &runnerRef{}
runner.model = req.model.ModelPath
runner.adapters = req.model.AdapterPaths
runner.projectors = req.model.ProjectorPaths
runner.llama = llama
runner.Options = &req.opts
runner.sessionDuration = req.sessionDuration
runner.gpus = gpus
runner.estimatedVRAM = llama.EstimatedVRAM()
runner.loading = true
runner.refCount = 1
runner.refMu.Lock()
s.loadedMu.Lock()
s.loaded[req.model.ModelPath] = runner
slog.Info("loaded runners", "count", len(s.loaded))
s.loadedMu.Unlock()
go func() {
defer runner.refMu.Unlock()
if err = llama.WaitUntilRunning(req.ctx); err != nil {
slog.Error("error loading llama server", "error", err)
runner.refCount--
req.errCh <- err
slog.Debug("triggering expiration for failed load", "model", runner.model)
s.expiredCh <- runner
return
}
slog.Debug("finished setting up runner", "model", req.model.ModelPath)
runner.loading = false
go func() {
<-req.ctx.Done()
slog.Debug("context for request finished")
s.finishedReqCh <- req
}()
req.successCh <- runner
}()
}
func (s *Scheduler) updateFreeSpace(allGpus gpu.GpuInfoList) {
type predKey struct {
Library string
ID string
}
predMap := map[predKey]uint64{} // Sum up the total predicted usage per GPU for all runners
s.loadedMu.Lock()
for _, r := range s.loaded {
r.refMu.Lock()
gpuIDs := make([]string, 0, len(r.gpus))
if r.llama != nil {
// TODO this should be broken down by GPU instead of assuming uniform spread
estimatedVRAMPerGPU := r.llama.EstimatedVRAM() / uint64(len(r.gpus))
for _, gpu := range r.gpus {
gpuIDs = append(gpuIDs, gpu.ID)
}
for _, gpu := range allGpus {
if slices.Contains(gpuIDs, gpu.ID) {
predMap[predKey{gpu.Library, gpu.ID}] += estimatedVRAMPerGPU
}
}
} else {
slog.Warn("unexpected nil runner reference, memory prediction may be incorrect")
}
r.refMu.Unlock()
}
s.loadedMu.Unlock()
// Now that we've summed up all the GPU usage predictions across all the loaded runners, update the gpu list
for i := range allGpus {
if p, ok := predMap[predKey{allGpus[i].Library, allGpus[i].ID}]; ok {
slog.Debug("gpu reported", "gpu", allGpus[i].ID, "library", allGpus[i].Library, "available", format.HumanBytes2(allGpus[i].FreeMemory))
if p > allGpus[i].TotalMemory {
// Shouldn't happen
slog.Warn("predicted usage exceeds VRAM", "gpu", allGpus[i].ID, "totalMemory", allGpus[i].TotalMemory, "predicted", p)
allGpus[i].FreeMemory = 0
} else if (allGpus[i].TotalMemory - p) < allGpus[i].FreeMemory { // predicted free is smaller than reported free, use it
// TODO maybe we should just always trust our numbers, since cuda's free memory reporting is laggy
// and we might unload models we didn't actually need to. The risk is if some other GPU intensive app is loaded
// after we start our first runner, then we'll never acount for that, so picking the smallest free value seems prudent.
allGpus[i].FreeMemory = allGpus[i].TotalMemory - p
}
slog.Info("updated VRAM", "gpu", allGpus[i].ID, "library", allGpus[i].Library, "total", format.HumanBytes2(allGpus[i].TotalMemory), "available", format.HumanBytes2(allGpus[i].FreeMemory))
}
}
}
type runnerRef struct {
refMu sync.Mutex
// refCond sync.Cond // Signaled on transition from 1 -> 0 refCount
refCount uint // prevent unloading if > 0
// unloading bool // set to true when we are trying to unload the runner
llama llm.LlamaServer
loading bool // True only during initial load, then false forever
gpus gpu.GpuInfoList // Recorded at time of provisioning
estimatedVRAM uint64
sessionDuration time.Duration
expireTimer *time.Timer
model string
adapters []string
projectors []string
*api.Options
}
// The refMu must already be held when calling unload
func (runner *runnerRef) unload() {
if runner.llama != nil {
runner.llama.Close()
}
runner.llama = nil
runner.adapters = nil
runner.projectors = nil
runner.Options = nil
runner.gpus = nil
}
func (runner *runnerRef) needsReload(ctx context.Context, req *LlmRequest) bool {
slog.Debug("evaluating already loaded", "model", req.model.ModelPath)
runner.refMu.Lock()
defer runner.refMu.Unlock()
timeout := 10 * time.Second
if runner.loading {
timeout = 2 * time.Minute // Initial load can take a long time for big models on slow systems...
}
// Don't reload runner if num_gpu=-1 was provided
optsExisting := runner.Options.Runner
optsNew := req.opts.Runner
if optsNew.NumGPU < 0 {
optsExisting.NumGPU = -1
optsNew.NumGPU = -1
}
ctx, cancel := context.WithTimeout(ctx, timeout)
defer cancel()
if !reflect.DeepEqual(runner.adapters, req.model.AdapterPaths) || // have the adapters changed?
!reflect.DeepEqual(runner.projectors, req.model.ProjectorPaths) || // have the projectors changed?
!reflect.DeepEqual(optsExisting, optsNew) || // have the runner options changed?
runner.llama.Ping(ctx) != nil {
return true
}
return false
}
type ByDuration []*runnerRef
func (a ByDuration) Len() int { return len(a) }
func (a ByDuration) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
func (a ByDuration) Less(i, j int) bool {
// uint64 to turn negative time (never unload) to largest
return uint64(a[i].sessionDuration) < uint64(a[j].sessionDuration)
}
// TODO - future consideration to pick runners based on size
// type BySize []*runnerRef
// func (a BySize) Len() int { return len(a) }
// func (a BySize) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
// func (a BySize) Less(i, j int) bool { return a[i].estimatedVRAM < a[j].estimatedVRAM }
// pickBestFitGPUs will try to find the optimal placement of the model in the available GPUs where the model fully fits
// If the model can not be fit fully within the available GPU(s) nil is returned
func pickBestFitGPUs(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList) gpu.GpuInfoList {
var estimatedVRAM uint64
for _, gl := range gpus.ByLibrary() {
var ok bool
sgl := append(make(gpu.GpuInfoList, 0, len(gl)), gl...)
// TODO - potentially sort by performance capability, existing models loaded, etc.
// Note: at present, this will favor more VRAM over faster GPU speed in mixed setups
sort.Sort(sort.Reverse(gpu.ByFreeMemory(sgl)))
// First attempt to fit the model into a single GPU
for _, g := range sgl {
if ok, estimatedVRAM = llm.PredictServerFit([]gpu.GpuInfo{g}, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
slog.Debug("new model will fit in available VRAM in single GPU, loading", "model", req.model.ModelPath, "gpu", g.ID, "available", g.FreeMemory, "required", format.HumanBytes2(estimatedVRAM))
return []gpu.GpuInfo{g}
}
}
// TODO future refinements
// - if multiple Libraries, see if any single GPU in any Library will fit
// - try subsets of GPUs instead of just falling back to 1 or all in a family
// Now try all the GPUs
if ok, estimatedVRAM = llm.PredictServerFit(gl, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
slog.Debug("new model will fit in available VRAM, loading", "model", req.model.ModelPath, "library", gl[0].Library, "required", format.HumanBytes2(estimatedVRAM))
return gl
}
}
return nil
}
// findRunnerToUnload finds a runner to unload to make room for a new model
func (s *Scheduler) findRunnerToUnload(req *LlmRequest) *runnerRef {
s.loadedMu.Lock()
runnerList := make([]*runnerRef, 0, len(s.loaded))
for _, r := range s.loaded {
runnerList = append(runnerList, r)
}
s.loadedMu.Unlock()
// In the future we can enhance the algorithm to be smarter about picking the optimal runner to unload
// e.g., if we have multiple options, will one make room for the request?
sort.Sort(ByDuration(runnerList))
// First try to find a runner that's already idle
for _, runner := range runnerList {
runner.refMu.Lock()
rc := runner.refCount
runner.refMu.Unlock()
if rc == 0 {
slog.Debug("found an idle runner to unload")
return runner
}
}
// None appear idle, just wait for the one with the shortest duration
slog.Debug("no idle runners, picking the shortest duration", "count", len(runnerList))
return runnerList[0]
}
func (s *Scheduler) unloadAllRunners() {
s.loadedMu.Lock()
defer s.loadedMu.Unlock()
for model, runner := range s.loaded {
if runner.llama != nil {
slog.Debug("shutting down runner", "model", model)
runner.llama.Close()
}
}
}

View File

@@ -1,562 +0,0 @@
package server
import (
"bytes"
"context"
"encoding/binary"
"fmt"
"log/slog"
"os"
"testing"
"time"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/app/lifecycle"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llm"
"github.com/stretchr/testify/assert"
"github.com/stretchr/testify/require"
)
func init() {
os.Setenv("OLLAMA_DEBUG", "1")
lifecycle.InitLogging()
}
func TestInitScheduler(t *testing.T) {
ctx, done := context.WithCancel(context.Background())
defer done()
initialMax := loadedMax
s := InitScheduler(ctx)
require.Equal(t, initialMax, loadedMax)
require.NotNil(t, s.loaded)
os.Setenv("OLLAMA_MAX_LOADED_MODELS", "blue")
s = InitScheduler(ctx)
require.Equal(t, initialMax, loadedMax)
require.NotNil(t, s.loaded)
os.Setenv("OLLAMA_MAX_LOADED_MODELS", "0")
s = InitScheduler(ctx)
require.Equal(t, 0, loadedMax)
require.NotNil(t, s.loaded)
}
func TestLoad(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 5*time.Millisecond)
defer done()
s := InitScheduler(ctx)
var ggml *llm.GGML // value not used in tests
req := &LlmRequest{
ctx: ctx,
model: &Model{ModelPath: "foo"},
successCh: make(chan *runnerRef, 1),
errCh: make(chan error, 1),
sessionDuration: 2,
}
// Fail to load model first
s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error) {
return nil, fmt.Errorf("something failed to load model blah")
}
gpus := gpu.GpuInfoList{}
s.load(req, ggml, gpus)
require.Len(t, req.successCh, 0)
require.Len(t, req.errCh, 1)
require.Len(t, s.loaded, 0)
err := <-req.errCh
require.Contains(t, err.Error(), "this model may be incompatible")
server := &mockLlm{estimatedVRAM: 10}
s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error) {
return server, nil
}
s.load(req, ggml, gpus)
select {
case err := <-req.errCh:
require.NoError(t, err)
case resp := <-req.successCh:
require.Equal(t, uint64(10), resp.estimatedVRAM)
require.Equal(t, uint(1), resp.refCount)
require.Len(t, s.loaded, 1)
}
req.model.ModelPath = "dummy_model_path"
server.waitResp = fmt.Errorf("wait failure")
s.load(req, ggml, gpus)
select {
case err := <-req.errCh:
require.Contains(t, err.Error(), "wait failure")
case resp := <-req.successCh:
t.Errorf("unexpected success %v", resp)
}
runner := s.loaded["dummy_model_path"]
require.NotNil(t, runner)
require.Equal(t, uint(0), runner.refCount)
time.Sleep(1 * time.Millisecond)
require.Len(t, s.expiredCh, 1)
}
type bundle struct {
ctx context.Context //nolint:containedctx
ctxDone func()
srv *mockLlm
req *LlmRequest
ggml *llm.GGML
}
func (scenario *bundle) newServer(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error) {
return scenario.srv, nil
}
func newScenario(t *testing.T, ctx context.Context, modelName string, estimatedVRAM uint64) *bundle {
scenario := &bundle{}
scenario.ctx, scenario.ctxDone = context.WithCancel(ctx)
t.Helper()
f, err := os.CreateTemp(t.TempDir(), modelName)
assert.Nil(t, err)
defer f.Close()
gguf := llm.NewGGUFV3(binary.LittleEndian)
err = gguf.Encode(f, llm.KV{
"general.architecture": "llama",
"general.name": "name",
"llama.context_length": uint32(32),
"llama.embedding_length": uint32(4096),
"llama.block_count": uint32(1),
"llama.attention.head_count": uint32(32),
"llama.attention.head_count_kv": uint32(32),
"tokenizer.ggml.tokens": []string{" "},
"tokenizer.ggml.scores": []float32{0},
"tokenizer.ggml.token_type": []int32{0},
}, []llm.Tensor{
{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
})
assert.Nil(t, err)
fname := f.Name()
model := &Model{Name: modelName, ModelPath: fname}
scenario.ggml, err = llm.LoadModel(model.ModelPath)
require.NoError(t, err)
scenario.req = &LlmRequest{
ctx: scenario.ctx,
model: model,
sessionDuration: 5 * time.Millisecond,
successCh: make(chan *runnerRef, 1),
errCh: make(chan error, 1),
}
scenario.srv = &mockLlm{estimatedVRAM: estimatedVRAM}
return scenario
}
func TestRequests(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
defer done()
// Same model, same request
scenario1a := newScenario(t, ctx, "ollama-model-1", 10)
scenario1a.req.sessionDuration = 0
scenario1b := newScenario(t, ctx, "ollama-model-1", 11)
scenario1b.req.model = scenario1a.req.model
scenario1b.ggml = scenario1a.ggml
scenario1b.req.sessionDuration = 0
// simple reload of same model
scenario2a := newScenario(t, ctx, "ollama-model-1", 20)
scenario2a.req.model = scenario1a.req.model
scenario2a.ggml = scenario1a.ggml
// Multiple loaded models
scenario3a := newScenario(t, ctx, "ollama-model-3a", 1*format.GigaByte)
scenario3b := newScenario(t, ctx, "ollama-model-3b", 24*format.GigaByte)
scenario3c := newScenario(t, ctx, "ollama-model-3c", 30) // Needs prior unloaded
s := InitScheduler(ctx)
s.getGpuFn = func() gpu.GpuInfoList {
g := gpu.GpuInfo{Library: "metal"}
g.TotalMemory = 24 * format.GigaByte
g.FreeMemory = 12 * format.GigaByte
return []gpu.GpuInfo{g}
}
s.newServerFn = scenario1a.newServer
slog.Info("scenario1a")
s.pendingReqCh <- scenario1a.req
require.Len(t, s.pendingReqCh, 1)
s.Run(ctx)
select {
case resp := <-scenario1a.req.successCh:
require.Equal(t, resp.llama, scenario1a.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, scenario1a.req.errCh, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
// Same runner as first request due to not needing a reload
s.newServerFn = scenario1b.newServer
slog.Info("scenario1b")
s.pendingReqCh <- scenario1b.req
select {
case resp := <-scenario1b.req.successCh:
require.Equal(t, resp.llama, scenario1a.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, scenario1b.req.errCh, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
// Trigger a reload
s.newServerFn = scenario2a.newServer
scenario2a.req.model.AdapterPaths = []string{"new"}
slog.Info("scenario2a")
s.pendingReqCh <- scenario2a.req
// finish first two requests, so model can reload
time.Sleep(1 * time.Millisecond)
scenario1a.ctxDone()
scenario1b.ctxDone()
select {
case resp := <-scenario2a.req.successCh:
require.Equal(t, resp.llama, scenario2a.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, scenario2a.req.errCh, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
loadedMax = 1
s.newServerFn = scenario3a.newServer
slog.Info("scenario3a")
s.pendingReqCh <- scenario3a.req
// finish prior request, so new model can load
time.Sleep(1 * time.Millisecond)
scenario2a.ctxDone()
select {
case resp := <-scenario3a.req.successCh:
require.Equal(t, resp.llama, scenario3a.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, scenario3a.req.errCh, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
require.Len(t, s.loaded, 1)
loadedMax = 0
s.newServerFn = scenario3b.newServer
slog.Info("scenario3b")
s.pendingReqCh <- scenario3b.req
select {
case resp := <-scenario3b.req.successCh:
require.Equal(t, resp.llama, scenario3b.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, scenario3b.req.errCh, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
require.Len(t, s.loaded, 2)
// Try to load a model that wont fit
s.newServerFn = scenario3c.newServer
slog.Info("scenario3c")
require.Len(t, s.loaded, 2)
scenario3a.ctxDone() // Won't help since this one isn't big enough to make room
time.Sleep(2 * time.Millisecond)
s.pendingReqCh <- scenario3c.req
// finish prior request, so new model can load
time.Sleep(6 * time.Millisecond)
require.Len(t, s.loaded, 1)
scenario3b.ctxDone()
select {
case resp := <-scenario3c.req.successCh:
require.Equal(t, resp.llama, scenario3c.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, scenario3c.req.errCh, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
require.Len(t, s.loaded, 1)
}
func TestGetRunner(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 20*time.Millisecond)
defer done()
// Same model, same request
scenario1a := newScenario(t, ctx, "ollama-model-1a", 10)
scenario1a.req.sessionDuration = 0
scenario1b := newScenario(t, ctx, "ollama-model-1b", 10)
scenario1b.req.sessionDuration = 0
scenario1c := newScenario(t, ctx, "ollama-model-1c", 10)
scenario1c.req.sessionDuration = 0
maxQueuedRequests = 1
s := InitScheduler(ctx)
s.getGpuFn = func() gpu.GpuInfoList {
g := gpu.GpuInfo{Library: "metal"}
g.TotalMemory = 24 * format.GigaByte
g.FreeMemory = 12 * format.GigaByte
return []gpu.GpuInfo{g}
}
s.newServerFn = scenario1a.newServer
slog.Info("scenario1a")
successCh1a, errCh1a := s.GetRunner(scenario1a.ctx, scenario1a.req.model, scenario1a.req.opts, scenario1a.req.sessionDuration)
require.Len(t, s.pendingReqCh, 1)
slog.Info("scenario1b")
successCh1b, errCh1b := s.GetRunner(scenario1b.ctx, scenario1b.req.model, scenario1b.req.opts, scenario1b.req.sessionDuration)
require.Len(t, s.pendingReqCh, 1)
require.Len(t, successCh1b, 0)
require.Len(t, errCh1b, 1)
err := <-errCh1b
require.Contains(t, err.Error(), "server busy")
s.Run(ctx)
select {
case resp := <-successCh1a:
require.Equal(t, resp.llama, scenario1a.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, errCh1a, 0)
case <-ctx.Done():
t.Errorf("timeout")
}
scenario1a.ctxDone()
require.Len(t, s.loaded, 1)
scenario1c.req.model.ModelPath = "bad path"
slog.Info("scenario1c")
successCh1c, errCh1c := s.GetRunner(scenario1c.ctx, scenario1c.req.model, scenario1c.req.opts, scenario1c.req.sessionDuration)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, successCh1c, 0)
require.Len(t, errCh1c, 0)
time.Sleep(5 * time.Millisecond)
require.Len(t, s.loaded, 0)
require.Len(t, errCh1c, 1)
err = <-errCh1c
require.Contains(t, err.Error(), "bad path")
scenario1b.ctxDone()
}
// TODO - add one scenario that triggers the bogus finished event with positive ref count
func TestPrematureExpired(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
defer done()
// Same model, same request
scenario1a := newScenario(t, ctx, "ollama-model-1a", 10)
s := InitScheduler(ctx)
s.getGpuFn = func() gpu.GpuInfoList {
g := gpu.GpuInfo{Library: "metal"}
g.TotalMemory = 24 * format.GigaByte
g.FreeMemory = 12 * format.GigaByte
return []gpu.GpuInfo{g}
}
s.newServerFn = scenario1a.newServer
successCh1a, errCh1a := s.GetRunner(scenario1a.ctx, scenario1a.req.model, scenario1a.req.opts, scenario1a.req.sessionDuration)
require.Len(t, s.pendingReqCh, 1)
s.Run(ctx)
select {
case resp := <-successCh1a:
require.Equal(t, resp.llama, scenario1a.srv)
require.Len(t, s.pendingReqCh, 0)
require.Len(t, errCh1a, 0)
require.Len(t, s.loaded, 1)
slog.Info("sending premature expired event now")
s.expiredCh <- resp // Shouldn't happen in real life, but make sure its safe
case <-ctx.Done():
t.Errorf("timeout")
}
time.Sleep(scenario1a.req.sessionDuration)
scenario1a.ctxDone()
time.Sleep(20 * time.Millisecond)
require.LessOrEqual(t, len(s.finishedReqCh), 1)
time.Sleep(10 * time.Millisecond)
require.Len(t, s.finishedReqCh, 0)
s.loadedMu.Lock()
require.Len(t, s.loaded, 0)
s.loadedMu.Unlock()
// also shouldn't happen in real life
s.finishedReqCh <- scenario1a.req
time.Sleep(5 * time.Millisecond)
}
func TestUseLoadedRunner(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 5*time.Millisecond)
req := &LlmRequest{
ctx: ctx,
successCh: make(chan *runnerRef, 1),
sessionDuration: 2,
}
finished := make(chan *LlmRequest)
llm1 := &mockLlm{}
r1 := &runnerRef{llama: llm1, sessionDuration: 1}
req.useLoadedRunner(r1, finished)
require.Equal(t, uint(1), r1.refCount)
require.Equal(t, time.Duration(2), r1.sessionDuration)
select {
case success := <-req.successCh:
require.Equal(t, r1, success)
case <-ctx.Done():
t.Errorf("timeout")
}
done()
fin := <-finished
require.Equal(t, req, fin)
}
func TestUpdateFreeSpace(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 5*time.Millisecond)
defer done()
gpus := gpu.GpuInfoList{
{
Library: "a",
ID: "1",
},
{
Library: "a",
ID: "2",
},
}
gpus[0].TotalMemory = 1000
gpus[0].FreeMemory = 900
gpus[1].TotalMemory = 2000
gpus[1].FreeMemory = 1900
llm1 := &mockLlm{estimatedVRAM: 100}
llm2 := &mockLlm{estimatedVRAM: 200}
r1 := &runnerRef{llama: llm1, gpus: gpus}
r2 := &runnerRef{llama: llm2, gpus: gpus}
s := InitScheduler(ctx)
s.loaded["a"] = r1
s.loaded["b"] = r2
s.updateFreeSpace(gpus)
require.Equal(t, uint64(850), gpus[0].FreeMemory)
require.Equal(t, uint64(1850), gpus[1].FreeMemory)
}
func TestFindRunnerToUnload(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 5*time.Millisecond)
defer done()
req := &LlmRequest{ctx: ctx}
r1 := &runnerRef{refCount: 1, sessionDuration: 1}
r2 := &runnerRef{sessionDuration: 2}
s := InitScheduler(ctx)
s.loaded["a"] = r1
s.loaded["b"] = r2
resp := s.findRunnerToUnload(req)
require.Equal(t, r2, resp)
r2.refCount = 1
resp = s.findRunnerToUnload(req)
require.Equal(t, r1, resp)
}
func TestNeedsReload(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 5*time.Millisecond)
defer done()
llm := &mockLlm{}
runner := &runnerRef{
adapters: []string{"adapter1"},
projectors: []string{"projector1"},
Options: &api.Options{},
llama: llm,
}
req := &LlmRequest{
model: &Model{
AdapterPaths: []string{"adapter2"},
ProjectorPaths: []string{"projector2"},
},
opts: api.Options{},
}
resp := runner.needsReload(ctx, req)
require.True(t, resp)
req.model.AdapterPaths = runner.adapters
resp = runner.needsReload(ctx, req)
require.True(t, resp)
req.model.ProjectorPaths = runner.projectors
runner.loading = true
req.opts.NumBatch = 1234
resp = runner.needsReload(ctx, req)
require.True(t, resp)
req.opts.NumBatch = runner.Options.NumBatch
llm.pingResp = fmt.Errorf("foo")
resp = runner.needsReload(ctx, req)
require.True(t, resp)
llm.pingResp = nil
resp = runner.needsReload(ctx, req)
require.False(t, resp)
req.opts.NumGPU = 99
resp = runner.needsReload(ctx, req)
require.True(t, resp)
req.opts.NumGPU = -1
resp = runner.needsReload(ctx, req)
require.False(t, resp)
}
func TestUnloadAllRunners(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 5*time.Millisecond)
defer done()
llm1 := &mockLlm{}
llm2 := &mockLlm{}
s := InitScheduler(ctx)
s.unloadAllRunners()
r1 := &runnerRef{llama: llm1}
r2 := &runnerRef{llama: llm2}
s.loaded["a"] = r1
s.loaded["b"] = r2
s.unloadAllRunners()
require.True(t, llm1.closeCalled)
require.True(t, llm2.closeCalled)
}
func TestUnload(t *testing.T) {
llm1 := &mockLlm{}
r1 := &runnerRef{llama: llm1}
r2 := &runnerRef{adapters: []string{"A"}}
r1.unload()
require.True(t, llm1.closeCalled)
r2.unload()
require.Nil(t, r2.adapters)
}
type mockLlm struct {
pingResp error
waitResp error
completionResp error
embeddingResp []float64
embeddingRespErr error
tokenizeResp []int
tokenizeRespErr error
detokenizeResp string
detonekizeRespErr error
closeResp error
closeCalled bool
estimatedVRAM uint64
}
func (s *mockLlm) Ping(ctx context.Context) error { return s.pingResp }
func (s *mockLlm) WaitUntilRunning(ctx context.Context) error { return s.waitResp }
func (s *mockLlm) Completion(ctx context.Context, req llm.CompletionRequest, fn func(llm.CompletionResponse)) error {
return s.completionResp
}
func (s *mockLlm) Embedding(ctx context.Context, prompt string) ([]float64, error) {
return s.embeddingResp, s.embeddingRespErr
}
func (s *mockLlm) Tokenize(ctx context.Context, content string) ([]int, error) {
return s.tokenizeResp, s.tokenizeRespErr
}
func (s *mockLlm) Detokenize(ctx context.Context, tokens []int) (string, error) {
return s.detokenizeResp, s.detonekizeRespErr
}
func (s *mockLlm) Close() error {
s.closeCalled = true
return s.closeResp
}
func (s *mockLlm) EstimatedVRAM() uint64 { return s.estimatedVRAM }

View File

@@ -1,313 +0,0 @@
// Package model contains types and utilities for parsing, validating, and
// working with model names and digests.
package model
import (
"cmp"
"errors"
"fmt"
"log/slog"
"path/filepath"
"strings"
)
// Errors
var (
// ErrUnqualifiedName represents an error where a name is not fully
// qualified. It is not used directly in this package, but is here
// to avoid other packages inventing their own error type.
// Additionally, it can be conveniently used via [Unqualified].
ErrUnqualifiedName = errors.New("unqualified name")
)
// Unqualified is a helper function that returns an error with
// ErrUnqualifiedName as the cause and the name as the message.
func Unqualified(n Name) error {
return fmt.Errorf("%w: %s", ErrUnqualifiedName, n)
}
// MissingPart is used to indicate any part of a name that was "promised" by
// the presence of a separator, but is missing.
//
// The value was chosen because it is deemed unlikely to be set by a user,
// not a valid part name valid when checked by [Name.IsValid], and easy to
// spot in logs.
const MissingPart = "!MISSING!"
// DefaultName returns a name with the default values for the host, namespace,
// and tag parts. The model and digest parts are empty.
//
// - The default host is ("registry.ollama.ai")
// - The default namespace is ("library")
// - The default tag is ("latest")
func DefaultName() Name {
return Name{
Host: "registry.ollama.ai",
Namespace: "library",
Tag: "latest",
}
}
type partKind int
const (
kindHost partKind = iota
kindNamespace
kindModel
kindTag
kindDigest
)
func (k partKind) String() string {
switch k {
case kindHost:
return "host"
case kindNamespace:
return "namespace"
case kindModel:
return "model"
case kindTag:
return "tag"
case kindDigest:
return "digest"
default:
return "unknown"
}
}
// Name is a structured representation of a model name string, as defined by
// [ParseNameNoDefaults].
//
// It is not guaranteed to be valid. Use [Name.IsValid] to check if the name
// is valid.
//
// It is not directly comparable with other Names. Use [Name.Equal] and
// [Name.MapHash] for determining equality and using as a map key.
type Name struct {
Host string
Namespace string
Model string
Tag string
RawDigest string
}
// ParseName parses and assembles a Name from a name string. The
// format of a valid name string is:
//
// s:
// { host } "/" { namespace } "/" { model } ":" { tag } "@" { digest }
// { host } "/" { namespace } "/" { model } ":" { tag }
// { host } "/" { namespace } "/" { model } "@" { digest }
// { host } "/" { namespace } "/" { model }
// { namespace } "/" { model } ":" { tag } "@" { digest }
// { namespace } "/" { model } ":" { tag }
// { namespace } "/" { model } "@" { digest }
// { namespace } "/" { model }
// { model } ":" { tag } "@" { digest }
// { model } ":" { tag }
// { model } "@" { digest }
// { model }
// "@" { digest }
// host:
// pattern: alphanum { alphanum | "-" | "_" | "." | ":" }*
// length: [1, 350]
// namespace:
// pattern: alphanum { alphanum | "-" | "_" }*
// length: [2, 80]
// model:
// pattern: alphanum { alphanum | "-" | "_" | "." }*
// length: [2, 80]
// tag:
// pattern: alphanum { alphanum | "-" | "_" | "." }*
// length: [1, 80]
// digest:
// pattern: alphanum { alphanum | "-" | ":" }*
// length: [2, 80]
//
// Most users should use [ParseName] instead, unless need to support
// different defaults than DefaultName.
//
// The name returned is not guaranteed to be valid. If it is not valid, the
// field values are left in an undefined state. Use [Name.IsValid] to check
// if the name is valid.
func ParseName(s string) Name {
return Merge(ParseNameBare(s), DefaultName())
}
// ParseNameBare parses s as a name string and returns a Name. No merge with
// [DefaultName] is performed.
func ParseNameBare(s string) Name {
var n Name
var promised bool
s, n.RawDigest, promised = cutLast(s, "@")
if promised && n.RawDigest == "" {
n.RawDigest = MissingPart
}
s, n.Tag, _ = cutPromised(s, ":")
s, n.Model, promised = cutPromised(s, "/")
if !promised {
n.Model = s
return n
}
s, n.Namespace, promised = cutPromised(s, "/")
if !promised {
n.Namespace = s
return n
}
n.Host = s
return n
}
// Merge merges the host, namespace, and tag parts of the two names,
// preferring the non-empty parts of a.
func Merge(a, b Name) Name {
a.Host = cmp.Or(a.Host, b.Host)
a.Namespace = cmp.Or(a.Namespace, b.Namespace)
a.Tag = cmp.Or(a.Tag, b.Tag)
return a
}
// String returns the name string, in the format that [ParseNameNoDefaults]
// accepts as valid, if [Name.IsValid] reports true; otherwise the empty
// string is returned.
func (n Name) String() string {
var b strings.Builder
if n.Host != "" {
b.WriteString(n.Host)
b.WriteByte('/')
}
if n.Namespace != "" {
b.WriteString(n.Namespace)
b.WriteByte('/')
}
b.WriteString(n.Model)
if n.Tag != "" {
b.WriteByte(':')
b.WriteString(n.Tag)
}
if n.RawDigest != "" {
b.WriteByte('@')
b.WriteString(n.RawDigest)
}
return b.String()
}
// IsValid reports whether all parts of the name are present and valid. The
// digest is a special case, and is checked for validity only if present.
func (n Name) IsValid() bool {
if n.RawDigest != "" && !isValidPart(kindDigest, n.RawDigest) {
return false
}
return n.IsFullyQualified()
}
// IsFullyQualified returns true if all parts of the name are present and
// valid without the digest.
func (n Name) IsFullyQualified() bool {
var parts = []string{
n.Host,
n.Namespace,
n.Model,
n.Tag,
}
for i, part := range parts {
if !isValidPart(partKind(i), part) {
return false
}
}
return true
}
// Filepath returns a canonical filepath that represents the name with each part from
// host to tag as a directory in the form:
//
// {host}/{namespace}/{model}/{tag}
//
// It uses the system's filepath separator and ensures the path is clean.
//
// It panics if the name is not fully qualified. Use [Name.IsFullyQualified]
// to check if the name is fully qualified.
func (n Name) Filepath() string {
if !n.IsFullyQualified() {
panic("illegal attempt to get filepath of invalid name")
}
return filepath.Join(
strings.ToLower(n.Host),
strings.ToLower(n.Namespace),
strings.ToLower(n.Model),
strings.ToLower(n.Tag),
)
}
// LogValue returns a slog.Value that represents the name as a string.
func (n Name) LogValue() slog.Value {
return slog.StringValue(n.String())
}
func isValidLen(kind partKind, s string) bool {
switch kind {
case kindHost:
return len(s) >= 1 && len(s) <= 350
case kindTag:
return len(s) >= 1 && len(s) <= 80
default:
return len(s) >= 2 && len(s) <= 80
}
}
func isValidPart(kind partKind, s string) bool {
if !isValidLen(kind, s) {
return false
}
for i := range s {
if i == 0 {
if !isAlphanumeric(s[i]) {
return false
}
continue
}
switch s[i] {
case '_', '-':
case '.':
if kind == kindNamespace {
return false
}
case ':':
if kind != kindHost && kind != kindDigest {
return false
}
default:
if !isAlphanumeric(s[i]) {
return false
}
}
}
return true
}
func isAlphanumeric(c byte) bool {
return c >= 'A' && c <= 'Z' || c >= 'a' && c <= 'z' || c >= '0' && c <= '9'
}
func cutLast(s, sep string) (before, after string, ok bool) {
i := strings.LastIndex(s, sep)
if i >= 0 {
return s[:i], s[i+len(sep):], true
}
return s, "", false
}
// cutPromised cuts the last part of s at the last occurrence of sep. If sep is
// found, the part before and after sep are returned as-is unless empty, in
// which case they are returned as MissingPart, which will cause
// [Name.IsValid] to return false.
func cutPromised(s, sep string) (before, after string, ok bool) {
before, after, ok = cutLast(s, sep)
if !ok {
return before, after, false
}
return cmp.Or(before, MissingPart), cmp.Or(after, MissingPart), true
}

View File

@@ -1,237 +0,0 @@
package model
import (
"reflect"
"testing"
)
const (
part80 = "88888888888888888888888888888888888888888888888888888888888888888888888888888888"
part350 = "33333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333"
)
func TestParseNameParts(t *testing.T) {
cases := []struct {
in string
want Name
wantValidDigest bool
}{
{
in: "host/namespace/model:tag",
want: Name{
Host: "host",
Namespace: "namespace",
Model: "model",
Tag: "tag",
},
},
{
in: "host/namespace/model",
want: Name{
Host: "host",
Namespace: "namespace",
Model: "model",
},
},
{
in: "namespace/model",
want: Name{
Namespace: "namespace",
Model: "model",
},
},
{
in: "model",
want: Name{
Model: "model",
},
},
{
in: "h/nn/mm:t",
want: Name{
Host: "h",
Namespace: "nn",
Model: "mm",
Tag: "t",
},
},
{
in: part80 + "/" + part80 + "/" + part80 + ":" + part80,
want: Name{
Host: part80,
Namespace: part80,
Model: part80,
Tag: part80,
},
},
{
in: part350 + "/" + part80 + "/" + part80 + ":" + part80,
want: Name{
Host: part350,
Namespace: part80,
Model: part80,
Tag: part80,
},
},
{
in: "@digest",
want: Name{
RawDigest: "digest",
},
wantValidDigest: false,
},
{
in: "model@sha256:123",
want: Name{
Model: "model",
RawDigest: "sha256:123",
},
wantValidDigest: true,
},
}
for _, tt := range cases {
t.Run(tt.in, func(t *testing.T) {
got := ParseNameBare(tt.in)
if !reflect.DeepEqual(got, tt.want) {
t.Errorf("parseName(%q) = %v; want %v", tt.in, got, tt.want)
}
})
}
}
var testCases = map[string]bool{ // name -> valid
"host/namespace/model:tag": true,
"host/namespace/model": false,
"namespace/model": false,
"model": false,
"@sha256-1000000000000000000000000000000000000000000000000000000000000000": false,
"model@sha256-1000000000000000000000000000000000000000000000000000000000000000": false,
"model@sha256:1000000000000000000000000000000000000000000000000000000000000000": false,
// long (but valid)
part80 + "/" + part80 + "/" + part80 + ":" + part80: true,
part350 + "/" + part80 + "/" + part80 + ":" + part80: true,
"h/nn/mm:t@sha256-1000000000000000000000000000000000000000000000000000000000000000": true, // bare minimum part sizes
"h/nn/mm:t@sha256:1000000000000000000000000000000000000000000000000000000000000000": true, // bare minimum part sizes
"m": false, // model too short
"n/mm:": false, // namespace too short
"h/n/mm:t": false, // namespace too short
"@t": false, // digest too short
"mm@d": false, // digest too short
// invalids
"^": false,
"mm:": false,
"/nn/mm": false,
"//": false,
"//mm": false,
"hh//": false,
"//mm:@": false,
"00@": false,
"@": false,
// not starting with alphanum
"-hh/nn/mm:tt@dd": false,
"hh/-nn/mm:tt@dd": false,
"hh/nn/-mm:tt@dd": false,
"hh/nn/mm:-tt@dd": false,
"hh/nn/mm:tt@-dd": false,
"": false,
// hosts
"host:https/namespace/model:tag": true,
// colon in non-host part before tag
"host/name:space/model:tag": false,
}
func TestNameparseNameDefault(t *testing.T) {
const name = "xx"
n := ParseName(name)
got := n.String()
want := "registry.ollama.ai/library/xx:latest"
if got != want {
t.Errorf("parseName(%q).String() = %q; want %q", name, got, want)
}
}
func TestNameIsValid(t *testing.T) {
var numStringTests int
for s, want := range testCases {
n := ParseNameBare(s)
t.Logf("n: %#v", n)
got := n.IsValid()
if got != want {
t.Errorf("parseName(%q).IsValid() = %v; want %v", s, got, want)
}
// Test roundtrip with String
if got {
got := ParseNameBare(s).String()
if got != s {
t.Errorf("parseName(%q).String() = %q; want %q", s, got, s)
}
numStringTests++
}
}
if numStringTests == 0 {
t.Errorf("no tests for Name.String")
}
}
func TestNameIsValidPart(t *testing.T) {
cases := []struct {
kind partKind
s string
want bool
}{
{kind: kindHost, s: "", want: false},
{kind: kindHost, s: "a", want: true},
{kind: kindHost, s: "a.", want: true},
{kind: kindHost, s: "a.b", want: true},
{kind: kindHost, s: "a:123", want: true},
{kind: kindHost, s: "a:123/aa/bb", want: false},
{kind: kindNamespace, s: "bb", want: true},
{kind: kindNamespace, s: "a.", want: false},
{kind: kindModel, s: "-h", want: false},
{kind: kindDigest, s: "sha256-1000000000000000000000000000000000000000000000000000000000000000", want: true},
}
for _, tt := range cases {
t.Run(tt.s, func(t *testing.T) {
got := isValidPart(tt.kind, tt.s)
if got != tt.want {
t.Errorf("isValidPart(%s, %q) = %v; want %v", tt.kind, tt.s, got, tt.want)
}
})
}
}
func FuzzName(f *testing.F) {
for s := range testCases {
f.Add(s)
}
f.Fuzz(func(t *testing.T, s string) {
n := ParseNameBare(s)
if n.IsValid() {
parts := [...]string{n.Host, n.Namespace, n.Model, n.Tag, n.RawDigest}
for _, part := range parts {
if part == ".." {
t.Errorf("unexpected .. as valid part")
}
if len(part) > 350 {
t.Errorf("part too long: %q", part)
}
}
if n.String() != s {
t.Errorf("String() = %q; want %q", n.String(), s)
}
}
})
}

View File

@@ -1,2 +0,0 @@
go test fuzz v1
string("00@")

View File

@@ -1,15 +0,0 @@
// Copyright (c) Tailscale Inc & AUTHORS
// SPDX-License-Identifier: BSD-3-Clause
// Package structs contains the Incomparable type.
package structs
// Incomparable is a zero-width incomparable type. If added as the
// first field in a struct, it marks that struct as not comparable
// (can't do == or be a map key) and usually doesn't add any width to
// the struct (unless the struct has only small fields).
//
// By making a struct incomparable, you can prevent misuse (prevent
// people from using ==), but also you can shrink generated binaries,
// as the compiler can omit equality funcs from the binary.
type Incomparable [0]func()