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@@ -2,65 +2,56 @@
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||||
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<div align="center">
|
||||
|
||||
<b>LLM inference in C/C++</b>
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://github.com/ggml-org/llama.cpp/releases)
|
||||
[](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
|
||||
[](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
|
||||
[](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)
|
||||
|
||||
[Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc)
|
||||
[manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev branches](https://github.com/ggml-org/llama.cpp-dev/blob/master/README-features.md) / [compile times](https://github.com/ggml-org/llama.cpp-dev/blob/master/README-compile-times.md) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
|
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|
||||
LLM inference in C/C++
|
||||
|
||||
## Recent API changes
|
||||
|
||||
- [Changelog for `libllama` API](https://github.com/ggml-org/llama.cpp/issues/9289)
|
||||
- [Changelog for `llama-server` REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
|
||||
|
||||
## Hot topics
|
||||
|
||||
- **Hugging Face cache migration: models downloaded with `-hf` are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.**
|
||||
- **[guide : using the new WebUI of llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/16938)**
|
||||
- [guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)
|
||||
- [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)
|
||||
- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
|
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- Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
|
||||
- VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
|
||||
- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
|
||||
- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
|
||||
- Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
|
||||
- WebGPU support is now available in the browser, see a blog/demo introducing it [here](https://reeselevine.github.io/llamas-on-the-web/).
|
||||
|
||||
----
|
||||
</div>
|
||||
|
||||
## Quick start
|
||||
|
||||
Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:
|
||||
A few options to get `llama.cpp` installed on your machine:
|
||||
|
||||
- Install `llama.cpp` using [brew, nix, winget, or conda-forge](docs/install.md)
|
||||
- Visit https://llama.app and follow the instructions
|
||||
- Run with Docker - see our [Docker documentation](docs/docker.md)
|
||||
- Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases)
|
||||
- Build from source by cloning this repository - check out [our build guide](docs/build.md)
|
||||
|
||||
Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more.
|
||||
|
||||
Example command:
|
||||
Once installed:
|
||||
|
||||
```sh
|
||||
# Use a local model file
|
||||
llama-cli -m my_model.gguf
|
||||
|
||||
# Or download and run a model directly from Hugging Face
|
||||
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
# Download and run a model directly from Hugging Face
|
||||
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF
|
||||
|
||||
# Launch OpenAI-compatible API server
|
||||
llama-server -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
|
||||
```
|
||||
|
||||
<table align="center">
|
||||
<tr>
|
||||
<td align="center" width=50%>
|
||||
<img width="1310" height="888" alt="VLM session with `llama cli`" src="https://github.com/user-attachments/assets/88726b48-1713-48aa-a525-95a02e78afc4" />
|
||||
<i>VLM session with <b>llama cli</b></i>
|
||||
</td>
|
||||
<td align="center">
|
||||
<img width="1392" height="958" alt="Built-in web UI against `llama serve` running Qwen 3.6" src="https://github.com/user-attachments/assets/b402f972-2e32-4def-8771-8d849f08cf2e" />
|
||||
<i>Built-in web UI against <b>llama serve</b></i>
|
||||
</td>
|
||||
</tr>
|
||||
<table>
|
||||
|
||||
## Description
|
||||
|
||||
The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
|
||||
range of hardware - locally and in the cloud.
|
||||
The main goal of `llama.cpp` is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on
|
||||
a wide range of hardware - locally and in the cloud.
|
||||
|
||||
- Plain C/C++ implementation without any dependencies
|
||||
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
|
||||
@@ -71,467 +62,40 @@ range of hardware - locally and in the cloud.
|
||||
- Vulkan and SYCL backend support
|
||||
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
|
||||
|
||||
The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggml-org/ggml) library.
|
||||
|
||||
<details>
|
||||
<summary>Models</summary>
|
||||
|
||||
Typically finetunes of the base models below are supported as well.
|
||||
|
||||
Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md)
|
||||
|
||||
#### Text-only
|
||||
|
||||
- [X] LLaMA 🦙
|
||||
- [x] LLaMA 2 🦙🦙
|
||||
- [x] LLaMA 3 🦙🦙🦙
|
||||
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
|
||||
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
|
||||
- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
|
||||
- [x] [Jamba](https://huggingface.co/ai21labs)
|
||||
- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
|
||||
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
|
||||
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
|
||||
- [X] [BERT](https://github.com/ggml-org/llama.cpp/pull/5423)
|
||||
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
|
||||
- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
|
||||
- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
|
||||
- [X] [Starcoder models](https://github.com/ggml-org/llama.cpp/pull/3187)
|
||||
- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
|
||||
- [X] [MPT](https://github.com/ggml-org/llama.cpp/pull/3417)
|
||||
- [X] [Bloom](https://github.com/ggml-org/llama.cpp/pull/3553)
|
||||
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
|
||||
- [X] [StableLM models](https://huggingface.co/stabilityai)
|
||||
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
|
||||
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
|
||||
- [x] [PLaMo-13B](https://github.com/ggml-org/llama.cpp/pull/3557)
|
||||
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
|
||||
- [x] [PhiMoE](https://github.com/ggml-org/llama.cpp/pull/11003)
|
||||
- [x] [GPT-2](https://huggingface.co/gpt2)
|
||||
- [x] [Orion 14B](https://github.com/ggml-org/llama.cpp/pull/5118)
|
||||
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
|
||||
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
|
||||
- [x] [Gemma](https://ai.google.dev/gemma)
|
||||
- [x] [Mamba](https://github.com/state-spaces/mamba)
|
||||
- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
|
||||
- [x] [Xverse](https://huggingface.co/models?search=xverse)
|
||||
- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
|
||||
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
|
||||
- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
|
||||
- [x] [OLMo](https://allenai.org/olmo)
|
||||
- [x] [OLMo 2](https://allenai.org/olmo)
|
||||
- [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924)
|
||||
- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
|
||||
- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
|
||||
- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
|
||||
- [x] [Smaug](https://huggingface.co/models?search=Smaug)
|
||||
- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B)
|
||||
- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
|
||||
- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
|
||||
- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
|
||||
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
|
||||
- [x] [GLM-4-0414](https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e)
|
||||
- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
|
||||
- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
|
||||
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
|
||||
- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
|
||||
- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
|
||||
- [x] [RWKV-7](https://huggingface.co/collections/shoumenchougou/rwkv7-gxx-gguf)
|
||||
- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
|
||||
- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
|
||||
- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
|
||||
- [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
|
||||
- [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
|
||||
- [x] [Liquid LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2)
|
||||
- [x] [Liquid LFM2.5 models](https://huggingface.co/collections/LiquidAI/lfm25)
|
||||
- [x] [Liquid Nanos](https://huggingface.co/collections/LiquidAI/liquid-nanos)
|
||||
- [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
|
||||
- [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
|
||||
- [x] [Mellum models](https://huggingface.co/JetBrains/models?search=mellum)
|
||||
|
||||
#### Multimodal
|
||||
|
||||
- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
|
||||
- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
|
||||
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
|
||||
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
|
||||
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
|
||||
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
|
||||
- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
|
||||
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
|
||||
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
|
||||
- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
|
||||
- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
|
||||
- [x] [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Bindings</summary>
|
||||
|
||||
- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama)
|
||||
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
|
||||
- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
|
||||
- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
|
||||
- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
|
||||
- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli)
|
||||
- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
|
||||
- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
|
||||
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
|
||||
- Ruby: [docusealco/rllama](https://github.com/docusealco/rllama)
|
||||
- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
|
||||
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
|
||||
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
|
||||
- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
|
||||
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
|
||||
- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
|
||||
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
|
||||
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
|
||||
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
|
||||
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
|
||||
- Java: [QuasarByte/llama-cpp-jna](https://github.com/QuasarByte/llama-cpp-jna)
|
||||
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
|
||||
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
|
||||
- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
|
||||
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggml-org/llama.cpp/pull/6326)
|
||||
- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
|
||||
- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
|
||||
- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
|
||||
- Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
|
||||
- Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma)
|
||||
- Android: [llama.android](/examples/llama.android)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>UIs</summary>
|
||||
|
||||
*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
|
||||
|
||||
- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
|
||||
- [BonzAI App](https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988) (proprietary)
|
||||
- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
|
||||
- [Dot](https://github.com/alexpinel/Dot) (GPL)
|
||||
- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
|
||||
- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0)
|
||||
- [janhq/jan](https://github.com/janhq/jan) (AGPL)
|
||||
- [johnbean393/Sidekick](https://github.com/johnbean393/Sidekick) (MIT)
|
||||
- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
|
||||
- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
|
||||
- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
|
||||
- [LARS](https://github.com/abgulati/LARS) (AGPL)
|
||||
- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
|
||||
- [LlamaLib](https://github.com/undreamai/LlamaLib) (Apache-2.0)
|
||||
- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
|
||||
- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
|
||||
- [LMStudio](https://lmstudio.ai/) (proprietary)
|
||||
- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
|
||||
- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
|
||||
- [MindMac](https://mindmac.app) (proprietary)
|
||||
- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
|
||||
- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
|
||||
- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0)
|
||||
- [nat/openplayground](https://github.com/nat/openplayground) (MIT)
|
||||
- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT)
|
||||
- [ollama/ollama](https://github.com/ollama/ollama) (MIT)
|
||||
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
|
||||
- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
|
||||
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT)
|
||||
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT)
|
||||
- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
|
||||
- [ramalama](https://github.com/containers/ramalama) (MIT)
|
||||
- [semperai/amica](https://github.com/semperai/amica) (MIT)
|
||||
- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
|
||||
- [Autopen](https://github.com/blackhole89/autopen) (GPL)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Tools</summary>
|
||||
|
||||
- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from Hugging Face Hub and convert them to GGML
|
||||
- [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
|
||||
- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
|
||||
- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
|
||||
- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
|
||||
- [unslothai/unsloth](https://github.com/unslothai/unsloth) – 🦥 exports/saves fine-tuned and trained models to GGUF (Apache-2.0)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Infrastructure</summary>
|
||||
|
||||
- [Paddler](https://github.com/intentee/paddler) - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
|
||||
- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
|
||||
- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
|
||||
- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
|
||||
- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
|
||||
- [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes.
|
||||
- [LLMKube](https://github.com/defilantech/llmkube) - Kubernetes operator for llama.cpp with multi-GPU and Apple Silicon Metal
|
||||
support"
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Games</summary>
|
||||
|
||||
- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
|
||||
|
||||
</details>
|
||||
|
||||
The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-org/ggml) library.
|
||||
|
||||
## Supported backends
|
||||
|
||||
| Backend | Target devices |
|
||||
| --- | --- |
|
||||
| [Metal](docs/build.md#metal-build) | Apple Silicon |
|
||||
| [BLAS](docs/build.md#blas-build) | All |
|
||||
| [BLIS](docs/backend/BLIS.md) | All |
|
||||
| [SYCL](docs/backend/SYCL.md) | Intel GPU |
|
||||
| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
|
||||
| [MUSA](docs/build.md#musa) | Moore Threads GPU |
|
||||
| [CANN](docs/build.md#cann) | Ascend NPU |
|
||||
| [CUDA](docs/build.md#cuda) | Nvidia GPU |
|
||||
| [HIP](docs/build.md#hip) | AMD GPU |
|
||||
| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
|
||||
| [Vulkan](docs/build.md#vulkan) | GPU |
|
||||
| [CANN](docs/build.md#cann) | Ascend NPU |
|
||||
| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
|
||||
| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
|
||||
| [WebGPU](docs/build.md#webgpu) | All |
|
||||
| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
|
||||
| [Hexagon [In Progress]](docs/backend/snapdragon/README.md) | Snapdragon |
|
||||
| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
|
||||
| [MUSA](docs/build.md#musa) | Moore Threads GPU |
|
||||
| [Metal](docs/build.md#metal-build) | Apple Silicon |
|
||||
| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
|
||||
| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
|
||||
| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
|
||||
| [SYCL](docs/backend/SYCL.md) | Intel GPU |
|
||||
| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
|
||||
| [Vulkan](docs/build.md#vulkan) | GPU |
|
||||
| [WebGPU](docs/build.md#webgpu) | All |
|
||||
| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
|
||||
|
||||
## Obtaining and quantizing models
|
||||
## Documentation
|
||||
|
||||
The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
|
||||
|
||||
- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
|
||||
- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
|
||||
|
||||
You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, by using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
|
||||
|
||||
```sh
|
||||
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
```
|
||||
|
||||
By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. The `MODEL_ENDPOINT` must point to a Hugging Face compatible API endpoint.
|
||||
|
||||
After downloading a model, use the CLI tools to run it locally - see below.
|
||||
|
||||
`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
|
||||
|
||||
The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
|
||||
|
||||
- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
|
||||
- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
|
||||
- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
|
||||
- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)
|
||||
|
||||
To learn more about model quantization, [read this documentation](tools/quantize/README.md)
|
||||
|
||||
## [`llama-cli`](tools/cli)
|
||||
|
||||
#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
|
||||
|
||||
- <details open>
|
||||
<summary>Run in conversation mode</summary>
|
||||
|
||||
Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
|
||||
|
||||
```bash
|
||||
llama-cli -m model.gguf
|
||||
|
||||
# > hi, who are you?
|
||||
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
|
||||
#
|
||||
# > what is 1+1?
|
||||
# Easy peasy! The answer to 1+1 is... 2!
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Run in conversation mode with custom chat template</summary>
|
||||
|
||||
```bash
|
||||
# use the "chatml" template (use -h to see the list of supported templates)
|
||||
llama-cli -m model.gguf -cnv --chat-template chatml
|
||||
|
||||
# use a custom template
|
||||
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Constrain the output with a custom grammar</summary>
|
||||
|
||||
```bash
|
||||
llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
|
||||
|
||||
# {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
|
||||
```
|
||||
|
||||
The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md).
|
||||
|
||||
For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## [`llama-server`](tools/server)
|
||||
|
||||
#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
|
||||
|
||||
- <details open>
|
||||
<summary>Start a local HTTP server with default configuration on port 8080</summary>
|
||||
|
||||
```bash
|
||||
llama-server -m model.gguf --port 8080
|
||||
|
||||
# Basic web UI can be accessed via browser: http://localhost:8080
|
||||
# Chat completion endpoint: http://localhost:8080/v1/chat/completions
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Support multiple-users and parallel decoding</summary>
|
||||
|
||||
```bash
|
||||
# up to 4 concurrent requests, each with 4096 max context
|
||||
llama-server -m model.gguf -c 16384 -np 4
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Enable speculative decoding</summary>
|
||||
|
||||
```bash
|
||||
# the draft.gguf model should be a small variant of the target model.gguf
|
||||
llama-server -m model.gguf -md draft.gguf
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Serve an embedding model</summary>
|
||||
|
||||
```bash
|
||||
# use the /embedding endpoint
|
||||
llama-server -m model.gguf --embedding --pooling cls -ub 8192
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Serve a reranking model</summary>
|
||||
|
||||
```bash
|
||||
# use the /reranking endpoint
|
||||
llama-server -m model.gguf --reranking
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Constrain all outputs with a grammar</summary>
|
||||
|
||||
```bash
|
||||
# custom grammar
|
||||
llama-server -m model.gguf --grammar-file grammar.gbnf
|
||||
|
||||
# JSON
|
||||
llama-server -m model.gguf --grammar-file grammars/json.gbnf
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## [`llama-perplexity`](tools/perplexity)
|
||||
|
||||
#### A tool for measuring the [perplexity](tools/perplexity/README.md) [^1] (and other quality metrics) of a model over a given text.
|
||||
|
||||
- <details open>
|
||||
<summary>Measure the perplexity over a text file</summary>
|
||||
|
||||
```bash
|
||||
llama-perplexity -m model.gguf -f file.txt
|
||||
|
||||
# [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
|
||||
# Final estimate: PPL = 5.4007 +/- 0.67339
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Measure KL divergence</summary>
|
||||
|
||||
```bash
|
||||
# TODO
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
[^1]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
|
||||
|
||||
## [`llama-bench`](tools/llama-bench)
|
||||
|
||||
#### Benchmark the performance of the inference for various parameters.
|
||||
|
||||
- <details open>
|
||||
<summary>Run default benchmark</summary>
|
||||
|
||||
```bash
|
||||
llama-bench -m model.gguf
|
||||
|
||||
# Output:
|
||||
# | model | size | params | backend | threads | test | t/s |
|
||||
# | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
|
||||
# | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | pp512 | 5765.41 ± 20.55 |
|
||||
# | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | tg128 | 197.71 ± 0.81 |
|
||||
#
|
||||
# build: 3e0ba0e60 (4229)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## [`llama-simple`](examples/simple)
|
||||
|
||||
#### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
|
||||
|
||||
- <details>
|
||||
<summary>Basic text completion</summary>
|
||||
|
||||
```bash
|
||||
llama-simple -m model.gguf
|
||||
|
||||
# Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## Contributing
|
||||
|
||||
- Contributors can open PRs
|
||||
- Collaborators will be invited based on contributions
|
||||
- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
|
||||
- Any help with managing issues, PRs and projects is very appreciated!
|
||||
- See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
|
||||
- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
|
||||
- Make sure to read this: [Inference at the edge](https://github.com/ggml-org/llama.cpp/discussions/205)
|
||||
- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
|
||||
|
||||
## Other documentation
|
||||
#### Tools
|
||||
|
||||
- [cli](tools/cli/README.md)
|
||||
- [completion](tools/completion/README.md)
|
||||
- [server](tools/server/README.md)
|
||||
- [GBNF grammars](grammars/README.md)
|
||||
|
||||
#### Development documentation
|
||||
#### Development
|
||||
|
||||
- [How to build](docs/build.md)
|
||||
- [Running on Docker](docs/docker.md)
|
||||
@@ -539,63 +103,19 @@ To learn more about model quantization, [read this documentation](tools/quantize
|
||||
- [Multi-GPU usage](docs/multi-gpu.md)
|
||||
- [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
|
||||
- [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks)
|
||||
- [XCFramework](docs/xcframework.md)
|
||||
- [Completions](docs/completions.md)
|
||||
- [Models](docs/models.md)
|
||||
|
||||
#### Seminal papers and background on the models
|
||||
## Contributing
|
||||
|
||||
If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
|
||||
- LLaMA:
|
||||
- [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
|
||||
- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
|
||||
- GPT-3
|
||||
- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
|
||||
- GPT-3.5 / InstructGPT / ChatGPT:
|
||||
- [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
|
||||
- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
|
||||
- Contributors can open PRs
|
||||
- Collaborators will be invited based on contributions
|
||||
- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
|
||||
- Any help with managing issues, PRs and projects is very appreciated!
|
||||
- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
|
||||
|
||||
## XCFramework
|
||||
The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS,
|
||||
and macOS. It can be used in Swift projects without the need to compile the
|
||||
library from source. For example:
|
||||
```swift
|
||||
// swift-tools-version: 5.10
|
||||
// The swift-tools-version declares the minimum version of Swift required to build this package.
|
||||
|
||||
import PackageDescription
|
||||
|
||||
let package = Package(
|
||||
name: "MyLlamaPackage",
|
||||
targets: [
|
||||
.executableTarget(
|
||||
name: "MyLlamaPackage",
|
||||
dependencies: [
|
||||
"LlamaFramework"
|
||||
]),
|
||||
.binaryTarget(
|
||||
name: "LlamaFramework",
|
||||
url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
|
||||
checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
|
||||
)
|
||||
]
|
||||
)
|
||||
```
|
||||
The above example is using an intermediate build `b5046` of the library. This can be modified
|
||||
to use a different version by changing the URL and checksum.
|
||||
|
||||
## Completions
|
||||
Command-line completion is available for some environments.
|
||||
|
||||
#### Bash Completion
|
||||
```bash
|
||||
$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
|
||||
$ source ~/.llama-completion.bash
|
||||
```
|
||||
Optionally this can be added to your `.bashrc` or `.bash_profile` to load it
|
||||
automatically. For example:
|
||||
```console
|
||||
$ echo "source ~/.llama-completion.bash" >> ~/.bashrc
|
||||
```
|
||||
|
||||
## Dependencies
|
||||
## Acknowledgements
|
||||
|
||||
- [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license
|
||||
- [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
|
||||
|
||||
@@ -1476,6 +1476,20 @@ std::string common_get_model_endpoint() {
|
||||
return model_endpoint;
|
||||
}
|
||||
|
||||
char * common_get_model_or_exit(int argc, char * argv[]) {
|
||||
if (argc > 1) {
|
||||
return argv[1];
|
||||
}
|
||||
|
||||
char * path = getenv("LLAMACPP_TEST_MODELFILE");
|
||||
if (!path || strlen(path) == 0) {
|
||||
fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE=<gguf_model_path> to silence this warning and run this test.\n\033[0m");
|
||||
exit(EXIT_SUCCESS);
|
||||
}
|
||||
|
||||
return path;
|
||||
}
|
||||
|
||||
common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
|
||||
auto * mem = llama_get_memory(ctx);
|
||||
if (mem == nullptr) {
|
||||
|
||||
+3
-4
@@ -294,10 +294,6 @@ struct common_params_sampling {
|
||||
|
||||
bool backend_sampling = false;
|
||||
|
||||
bool has_logit_bias() const {
|
||||
return !logit_bias.empty();
|
||||
}
|
||||
|
||||
// print the parameters into a string
|
||||
std::string print() const;
|
||||
};
|
||||
@@ -934,6 +930,9 @@ void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adap
|
||||
// model endpoint from env
|
||||
std::string common_get_model_endpoint();
|
||||
|
||||
// for testing purposes
|
||||
char * common_get_model_or_exit(int, char*[]);
|
||||
|
||||
//
|
||||
// Context utils
|
||||
//
|
||||
|
||||
+13
-2
@@ -310,8 +310,19 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
|
||||
}
|
||||
}
|
||||
|
||||
if (params.has_logit_bias()) {
|
||||
samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
|
||||
// logit bias: user biases + model suppress tokens (-INFINITY)
|
||||
{
|
||||
std::vector<llama_logit_bias> merged = params.logit_bias;
|
||||
|
||||
int32_t n_suppress = 0;
|
||||
const llama_token * suppress = llama_vocab_get_suppress_tokens(vocab, &n_suppress);
|
||||
for (int32_t i = 0; i < n_suppress; ++i) {
|
||||
merged.push_back({ suppress[i], -INFINITY });
|
||||
}
|
||||
|
||||
if (!merged.empty()) {
|
||||
samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), merged.size(), merged.data()));
|
||||
}
|
||||
}
|
||||
|
||||
if (params.mirostat == 0) {
|
||||
|
||||
+40
-3
@@ -1,6 +1,8 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Iterable, TYPE_CHECKING
|
||||
import re
|
||||
|
||||
from typing import Callable, Iterable, TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
|
||||
@@ -213,12 +215,47 @@ class Glm4MoeLiteModel(DeepseekV2Model):
|
||||
class GlmMoeDsaModel(DeepseekV2Model):
|
||||
model_arch = gguf.MODEL_ARCH.GLM_DSA
|
||||
skip_mtp = False
|
||||
supports_mtp_export = True
|
||||
|
||||
# Trunk layer count, stashed before indexing so the classmethod
|
||||
# filter_tensors can identify the appended NextN/MTP block (mirrors
|
||||
# HYV3Model / Step35Model).
|
||||
_n_main_layers: int | None = None
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
|
||||
self.block_count = self.hparams["num_hidden_layers"]
|
||||
if not self.no_mtp:
|
||||
self.block_count += self.hparams.get("num_nextn_predict_layers", 0)
|
||||
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
|
||||
|
||||
def index_tensors(self, remote_hf_model_id: str | None = None):
|
||||
type(self)._n_main_layers = self.hparams["num_hidden_layers"]
|
||||
return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
|
||||
|
||||
@classmethod
|
||||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
if (titem := super().filter_tensors(item)) is None:
|
||||
return None
|
||||
name, gen = titem
|
||||
|
||||
# GLM-5.2 appends the NextN/MTP block past num_hidden_layers
|
||||
# (model.layers.78 -> blk.78 in the 79-block file).
|
||||
assert cls._n_main_layers is not None
|
||||
is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
|
||||
|
||||
# --no-mtp: drop the appended NextN block entirely.
|
||||
if is_mtp and cls.no_mtp:
|
||||
return None
|
||||
# --mtp: keep ONLY NextN-block tensors plus the shared embeddings/
|
||||
# norm/lm_head (so the resulting GGUF carries just the draft head).
|
||||
if cls.mtp_only and not is_mtp and name not in (
|
||||
"model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
|
||||
):
|
||||
return None
|
||||
|
||||
return name, gen
|
||||
|
||||
def set_vocab(self):
|
||||
return self._set_vocab_glm()
|
||||
|
||||
@@ -230,7 +267,7 @@ class GlmMoeDsaModel(DeepseekV2Model):
|
||||
self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
|
||||
|
||||
# NextN/MTP prediction layers
|
||||
if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
|
||||
if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
|
||||
self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
|
||||
|
||||
# DSA indexer parameters
|
||||
|
||||
@@ -179,12 +179,12 @@ class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
|
||||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
name, gen = item
|
||||
|
||||
if not name.startswith("visual.") and not name.startswith("audio_tower."):
|
||||
return None
|
||||
|
||||
if name.startswith("thinker."):
|
||||
name = name.replace("thinker.", "")
|
||||
|
||||
if not name.startswith("visual.") and not name.startswith("audio_tower."):
|
||||
return None
|
||||
|
||||
if "audio_bos_eos_token" in name:
|
||||
# this tensor is left unused in transformers code
|
||||
# https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
# Completions
|
||||
|
||||
Command-line completion is available for some environments.
|
||||
|
||||
## Bash Completion
|
||||
|
||||
```bash
|
||||
$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
|
||||
$ source ~/.llama-completion.bash
|
||||
```
|
||||
|
||||
Optionally this can be added to your `.bashrc` or `.bash_profile` to load it
|
||||
automatically. For example:
|
||||
|
||||
```console
|
||||
$ echo "source ~/.llama-completion.bash" >> ~/.bashrc
|
||||
```
|
||||
@@ -0,0 +1,26 @@
|
||||
# Obtaining and quantizing models
|
||||
|
||||
The [Hugging Face](https://huggingface.co) platform hosts [thousands of models](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
|
||||
|
||||
- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
|
||||
|
||||
You can use any `llama.cpp`-compatible model from [Hugging Face](https://huggingface.co/) using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
|
||||
|
||||
```sh
|
||||
llama cli -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
```
|
||||
|
||||
You can use the same CLI invocation to download from other sites, by pointing the `MODEL_ENDPOINT` environment variable to an endpoint compatible with the Hugging Face API.
|
||||
`llama.cpp` can also run models you have downloaded locally to your filesystem.
|
||||
|
||||
After downloading a model, use the CLI tools to run it locally - see below.
|
||||
|
||||
`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
|
||||
To learn more about model quantization, [read this documentation](../tools/quantize/README.md)
|
||||
|
||||
The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
|
||||
|
||||
- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
|
||||
- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
|
||||
- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
|
||||
- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)
|
||||
@@ -0,0 +1,31 @@
|
||||
# XCFramework
|
||||
|
||||
The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS,
|
||||
and macOS. It can be used in Swift projects without the need to compile the
|
||||
library from source. For example:
|
||||
|
||||
```swift
|
||||
// swift-tools-version: 5.10
|
||||
// The swift-tools-version declares the minimum version of Swift required to build this package.
|
||||
|
||||
import PackageDescription
|
||||
|
||||
let package = Package(
|
||||
name: "MyLlamaPackage",
|
||||
targets: [
|
||||
.executableTarget(
|
||||
name: "MyLlamaPackage",
|
||||
dependencies: [
|
||||
"LlamaFramework"
|
||||
]),
|
||||
.binaryTarget(
|
||||
name: "LlamaFramework",
|
||||
url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
|
||||
checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
|
||||
)
|
||||
]
|
||||
)
|
||||
```
|
||||
|
||||
The above example is using an intermediate build `b5046` of the library. This can be modified
|
||||
to use a different version by changing the URL and checksum.
|
||||
+1
-1
@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
|
||||
|
||||
### GGML Version
|
||||
set(GGML_VERSION_MAJOR 0)
|
||||
set(GGML_VERSION_MINOR 17)
|
||||
set(GGML_VERSION_MINOR 18)
|
||||
set(GGML_VERSION_PATCH 0)
|
||||
set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
|
||||
|
||||
|
||||
@@ -6,9 +6,9 @@
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define RPC_PROTO_MAJOR_VERSION 4
|
||||
#define RPC_PROTO_MAJOR_VERSION 5
|
||||
#define RPC_PROTO_MINOR_VERSION 0
|
||||
#define RPC_PROTO_PATCH_VERSION 3
|
||||
#define RPC_PROTO_PATCH_VERSION 0
|
||||
|
||||
#ifdef __cplusplus
|
||||
static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION");
|
||||
|
||||
@@ -469,6 +469,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
|
||||
return (src0->type == GGML_TYPE_F32 ||
|
||||
((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
|
||||
src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
|
||||
case GGML_OP_CONV_2D:
|
||||
return ggml_is_contiguous(op->src[0]);
|
||||
default:
|
||||
return true;
|
||||
}
|
||||
|
||||
@@ -2739,7 +2739,7 @@ static block_q8_0x4 make_block_q8_0x4(block_q8_0 * in, unsigned int blck_size_in
|
||||
return out;
|
||||
}
|
||||
|
||||
static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
|
||||
static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, int blck_size_interleave) {
|
||||
block_q4_0x4 out;
|
||||
|
||||
for (int i = 0; i < 4; i++) {
|
||||
|
||||
@@ -977,6 +977,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_Q1_0> {
|
||||
static constexpr int qi = QI1_0;
|
||||
};
|
||||
|
||||
template<>
|
||||
struct ggml_cuda_type_traits<GGML_TYPE_Q2_0> {
|
||||
static constexpr int qk = QK2_0;
|
||||
static constexpr int qr = QR2_0;
|
||||
static constexpr int qi = QI2_0;
|
||||
};
|
||||
|
||||
template<>
|
||||
struct ggml_cuda_type_traits<GGML_TYPE_Q4_0> {
|
||||
static constexpr int qk = QK4_0;
|
||||
|
||||
@@ -126,6 +126,7 @@ void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const float * X_D = (const float *) input->data;
|
||||
float * Y_D = (float *) dst->data;
|
||||
|
||||
GGML_ASSERT(ggml_is_contiguous(input));
|
||||
GGML_ASSERT(ggml_is_contiguous(kernel));
|
||||
GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
|
||||
|
||||
|
||||
@@ -459,6 +459,8 @@ to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0:
|
||||
return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
|
||||
case GGML_TYPE_Q2_0:
|
||||
return dequantize_block_cont_cuda<QK2_0, QR2_0, dequantize_q2_0>;
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_row_q4_0_cuda;
|
||||
case GGML_TYPE_Q4_1:
|
||||
@@ -514,6 +516,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0:
|
||||
return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
|
||||
case GGML_TYPE_Q2_0:
|
||||
return dequantize_block_cont_cuda<QK2_0, QR2_0, dequantize_q2_0>;
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_row_q4_0_cuda;
|
||||
case GGML_TYPE_Q4_1:
|
||||
@@ -572,6 +576,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0:
|
||||
return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
|
||||
case GGML_TYPE_Q2_0:
|
||||
return dequantize_block_cont_cuda<QK2_0, QR2_0, dequantize_q2_0>;
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_row_q4_0_cuda;
|
||||
case GGML_TYPE_Q4_1:
|
||||
@@ -629,6 +635,8 @@ to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) {
|
||||
return convert_unary_cuda<float>;
|
||||
case GGML_TYPE_Q1_0:
|
||||
return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
|
||||
case GGML_TYPE_Q2_0:
|
||||
return dequantize_block_cuda<QK2_0, QR2_0, dequantize_q2_0>;
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
|
||||
case GGML_TYPE_Q4_1:
|
||||
@@ -652,6 +660,8 @@ to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type) {
|
||||
return convert_unary_cuda<float, nv_bfloat16>;
|
||||
case GGML_TYPE_Q1_0:
|
||||
return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
|
||||
case GGML_TYPE_Q2_0:
|
||||
return dequantize_block_cuda<QK2_0, QR2_0, dequantize_q2_0>;
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
|
||||
case GGML_TYPE_Q4_1:
|
||||
@@ -675,6 +685,8 @@ to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type) {
|
||||
return convert_unary_cuda<half, float>;
|
||||
case GGML_TYPE_Q1_0:
|
||||
return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
|
||||
case GGML_TYPE_Q2_0:
|
||||
return dequantize_block_cuda<QK2_0, QR2_0, dequantize_q2_0>;
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
|
||||
case GGML_TYPE_Q4_1:
|
||||
|
||||
@@ -23,6 +23,26 @@ static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const in
|
||||
v.y = (2*bit_1 - 1) * d;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ void dequantize_q2_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
|
||||
const block_q2_0 * x = (const block_q2_0 *) vx;
|
||||
|
||||
const float d = x[ib].d;
|
||||
|
||||
// Q2_0: 2 bits per element, 4 elements per byte.
|
||||
// Stored code c in {0,1,2,3} maps to symbol s = c - 1 in {-1, 0, +1, +2}.
|
||||
const int byte_index_0 = iqs / 4;
|
||||
const int bit_offset_0 = (iqs % 4) * 2;
|
||||
|
||||
const int byte_index_1 = (iqs + 1) / 4;
|
||||
const int bit_offset_1 = ((iqs + 1) % 4) * 2;
|
||||
|
||||
const int c0 = (x[ib].qs[byte_index_0] >> bit_offset_0) & 0x3;
|
||||
const int c1 = (x[ib].qs[byte_index_1] >> bit_offset_1) & 0x3;
|
||||
|
||||
v.x = (c0 - 1) * d;
|
||||
v.y = (c1 - 1) * d;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
|
||||
const block_q4_0 * x = (const block_q4_0 *) vx;
|
||||
|
||||
|
||||
@@ -320,6 +320,10 @@ static void ggml_cuda_get_rows_switch_src0_type(
|
||||
get_rows_cuda_q<QK1_0, QR1_0, dequantize_q1_0>(src0_d, src1_d, dst_d,
|
||||
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q2_0:
|
||||
get_rows_cuda_q<QK2_0, QR2_0, dequantize_q2_0>(src0_d, src1_d, dst_d,
|
||||
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
get_rows_cuda_q<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_d, dst_d,
|
||||
ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
|
||||
|
||||
@@ -1836,6 +1836,20 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
|
||||
ggml_cuda_mul_mat_vec_f(ctx, src0, src1, nullptr, dst);
|
||||
return;
|
||||
}
|
||||
// A transposed vector can still use MMVQ (i.e. ne01 == 1)
|
||||
if (ne01 == 1 && ne11 > MMVF_MAX_BATCH_SIZE && ne2 == 1 && ne3 == 1
|
||||
&& src0->type == GGML_TYPE_F32
|
||||
&& ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)
|
||||
&& ggml_cuda_should_use_mmvf(src1->type, cc, src1->ne, src1->nb, /*ne11 =*/ 1)) {
|
||||
ggml_tensor dst_vec = *dst;
|
||||
dst_vec.ne[0] = ne11;
|
||||
dst_vec.ne[1] = 1;
|
||||
dst_vec.nb[1] = dst_vec.nb[0]*ne11;
|
||||
dst_vec.nb[2] = dst_vec.nb[1];
|
||||
dst_vec.nb[3] = dst_vec.nb[1];
|
||||
ggml_cuda_mul_mat_vec_f(ctx, src1, src0, nullptr, &dst_vec);
|
||||
return;
|
||||
}
|
||||
if (ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, ne11, /*mul_mat_id =*/ false)) {
|
||||
ggml_cuda_mul_mat_f(ctx, src0, src1, nullptr, dst);
|
||||
return;
|
||||
@@ -4802,6 +4816,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
||||
case GGML_TYPE_F32:
|
||||
case GGML_TYPE_F16:
|
||||
case GGML_TYPE_Q1_0:
|
||||
case GGML_TYPE_Q2_0:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
@@ -4840,6 +4855,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
||||
case GGML_TYPE_BF16:
|
||||
case GGML_TYPE_I32:
|
||||
case GGML_TYPE_Q1_0:
|
||||
case GGML_TYPE_Q2_0:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
@@ -5089,7 +5105,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
||||
case GGML_OP_IM2COL:
|
||||
case GGML_OP_IM2COL_3D:
|
||||
case GGML_OP_CONV_2D:
|
||||
return true;
|
||||
return (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]));
|
||||
case GGML_OP_CONV_2D_DW:
|
||||
return op->src[0]->type == GGML_TYPE_F32;
|
||||
case GGML_OP_CONV_TRANSPOSE_2D:
|
||||
|
||||
@@ -16,6 +16,23 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
CASE(GGML_TYPE_Q1_0, 256, 1, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 256, 1, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
|
||||
@@ -7,6 +7,14 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
CASE(GGML_TYPE_Q1_0, 512, 1, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q1_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
CASE(GGML_TYPE_Q2_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 512, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q4_0, 512, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
CASE(GGML_TYPE_Q4_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
|
||||
|
||||
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 64, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
|
||||
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
|
||||
@@ -0,0 +1,290 @@
|
||||
static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna3_5(ggml_type type, int J, bool fallback) {
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
|
||||
return ggml_cuda_mmq_config(GGML_TYPE_COUNT, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, 256, false, true);
|
||||
}
|
||||
@@ -0,0 +1,290 @@
|
||||
static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna3(ggml_type type, int J, bool fallback) {
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
|
||||
return ggml_cuda_mmq_config(GGML_TYPE_COUNT, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, 256, false, true);
|
||||
}
|
||||
@@ -1,77 +1,89 @@
|
||||
static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna4(ggml_type type, int J, bool fallback) {
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
@@ -79,66 +91,62 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
@@ -146,105 +154,105 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
@@ -252,27 +260,27 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
|
||||
@@ -95,6 +95,103 @@ template <ggml_type type, int J, bool fallback> static __device__ __forceinline_
|
||||
}
|
||||
}
|
||||
|
||||
template <ggml_type type, int J, bool fallback> static __device__ __forceinline__ void ggml_cuda_mmq_load_tiles_q2_0(
|
||||
const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
|
||||
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
||||
constexpr int nwarps = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size;
|
||||
constexpr int I = ggml_cuda_mmq_get_I(type, J, fallback);
|
||||
constexpr int sram_stride = ggml_cuda_mmq_get_sram_stride(type, J, fallback);
|
||||
|
||||
#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
|
||||
int * x_qs = (int *) x_tile;
|
||||
float * x_df = (float *) (x_qs + 2*MMQ_TILE_NE_K);
|
||||
#else
|
||||
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, I);
|
||||
int * x_qs = (int *) x_tile;
|
||||
float * x_df = (float *) (x_qs + txs.qs);
|
||||
#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
|
||||
|
||||
constexpr int blocks_per_iter = MMQ_ITER_K / QK2_0;
|
||||
constexpr int threads_per_row = blocks_per_iter * QI2_0;
|
||||
constexpr int nrows = warp_size / threads_per_row;
|
||||
constexpr int scale_entries_per_block = QK2_0 / QK8_1;
|
||||
constexpr int scale_entries_per_row = blocks_per_iter * scale_entries_per_block;
|
||||
|
||||
const int txi = threadIdx.x % threads_per_row;
|
||||
const int kbx = txi / QI2_0;
|
||||
const int kqsx = txi % QI2_0;
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < I; i0 += nrows*nwarps) {
|
||||
int i = i0 + threadIdx.y*nrows + threadIdx.x/threads_per_row;
|
||||
|
||||
if (fallback) {
|
||||
i = min(i, i_max);
|
||||
}
|
||||
|
||||
const block_q2_0 * bxi = (const block_q2_0 *) x + kbx0 + i*stride + kbx;
|
||||
// Each 32-element chunk occupies 8 bytes of qs (32 elements * 2 bits = 64 bits)
|
||||
const int qs_offset = 8*kqsx;
|
||||
const int qs0 = bxi->qs[qs_offset + 0] | (bxi->qs[qs_offset + 1] << 8) |
|
||||
(bxi->qs[qs_offset + 2] << 16) | (bxi->qs[qs_offset + 3] << 24);
|
||||
const int qs1 = bxi->qs[qs_offset + 4] | (bxi->qs[qs_offset + 5] << 8) |
|
||||
(bxi->qs[qs_offset + 6] << 16) | (bxi->qs[qs_offset + 7] << 24);
|
||||
|
||||
// Unpack 32 2-bit codes into 8 int32s, each holding 4 signed int8s in {-1,0,1,2}.
|
||||
int unpacked_bytes[8];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
const int shift = j * 8;
|
||||
const int codes = (qs0 >> shift) & 0xFF;
|
||||
const int c0 = ((codes >> 0) & 0x3) - 1;
|
||||
const int c1 = ((codes >> 2) & 0x3) - 1;
|
||||
const int c2 = ((codes >> 4) & 0x3) - 1;
|
||||
const int c3 = ((codes >> 6) & 0x3) - 1;
|
||||
unpacked_bytes[j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
const int shift = j * 8;
|
||||
const int codes = (qs1 >> shift) & 0xFF;
|
||||
const int c0 = ((codes >> 0) & 0x3) - 1;
|
||||
const int c1 = ((codes >> 2) & 0x3) - 1;
|
||||
const int c2 = ((codes >> 4) & 0x3) - 1;
|
||||
const int c3 = ((codes >> 6) & 0x3) - 1;
|
||||
unpacked_bytes[4 + j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
|
||||
}
|
||||
|
||||
const int dst_offset = kbx*(scale_entries_per_block*QI8_0) + kqsx*QI8_0;
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 8; ++j) {
|
||||
#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
|
||||
x_qs[i*sram_stride + dst_offset + j] = unpacked_bytes[j];
|
||||
#else
|
||||
x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j] = unpacked_bytes[j];
|
||||
#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
|
||||
}
|
||||
}
|
||||
|
||||
const int ksx = threadIdx.x % scale_entries_per_row;
|
||||
const int scale_block = ksx / scale_entries_per_block;
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < I; i0 += nwarps) {
|
||||
int i = i0 + threadIdx.y;
|
||||
|
||||
if (fallback) {
|
||||
i = min(i, i_max);
|
||||
}
|
||||
|
||||
const block_q2_0 * bxi = (const block_q2_0 *) x + kbx0 + i*stride + scale_block;
|
||||
|
||||
#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
|
||||
x_df[i*sram_stride + ksx] = bxi->d;
|
||||
#else
|
||||
x_df[i*(2*MMQ_TILE_NE_K/QI8_0) + i/(QI8_0/2) + ksx] = bxi->d;
|
||||
#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
|
||||
}
|
||||
}
|
||||
|
||||
template <ggml_type type, int J, bool fallback> static __device__ __forceinline__ void ggml_cuda_mmq_load_tiles_q4_0(
|
||||
const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
|
||||
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
||||
|
||||
@@ -10,6 +10,9 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
|
||||
case GGML_TYPE_Q1_0:
|
||||
mul_mat_q_case<GGML_TYPE_Q1_0>(ctx, args, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q2_0:
|
||||
mul_mat_q_case<GGML_TYPE_Q2_0>(ctx, args, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
mul_mat_q_case<GGML_TYPE_Q4_0>(ctx, args, stream);
|
||||
break;
|
||||
@@ -262,6 +265,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
|
||||
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0:
|
||||
case GGML_TYPE_Q2_0:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
@@ -296,6 +300,15 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
|
||||
return false;
|
||||
}
|
||||
|
||||
// MMQ tiles require at least 48 KiB per-block shared memory; fall back to BLAS otherwise.
|
||||
{
|
||||
const int id = ggml_cuda_get_device();
|
||||
const size_t smpbo = ggml_cuda_info().devices[id].smpbo;
|
||||
if (smpbo < 48 * 1024) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if (turing_mma_available(cc)) {
|
||||
return true;
|
||||
}
|
||||
|
||||
@@ -60,6 +60,7 @@ static_assert(sizeof(block_fp4_mmq) == sizeof(block_q8_1_mmq), "Unexpected b
|
||||
static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
|
||||
switch (type_x) {
|
||||
case GGML_TYPE_Q1_0:
|
||||
case GGML_TYPE_Q2_0:
|
||||
return MMQ_Q8_1_DS_LAYOUT_D4;
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
@@ -218,6 +219,8 @@ struct ggml_cuda_mmq_config {
|
||||
|
||||
#include "mmq-config-cdna.cuh"
|
||||
#include "mmq-config-rdna2.cuh"
|
||||
#include "mmq-config-rdna3.cuh"
|
||||
#include "mmq-config-rdna3-5.cuh"
|
||||
#include "mmq-config-rdna4.cuh"
|
||||
|
||||
#undef CASE
|
||||
@@ -227,9 +230,15 @@ static __host__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(const ggml_type ty
|
||||
if (GGML_CUDA_CC_IS_CDNA(cc)) {
|
||||
return ggml_cuda_mmq_get_config_cdna(type, J, fallback);
|
||||
}
|
||||
if (amd_wmma_available(cc)) {
|
||||
if (GGML_CUDA_CC_IS_RDNA4(cc)) {
|
||||
return ggml_cuda_mmq_get_config_rdna4(type, J, fallback);
|
||||
}
|
||||
if (GGML_CUDA_CC_IS_RDNA3_5(cc)) {
|
||||
return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
|
||||
}
|
||||
if (GGML_CUDA_CC_IS_RDNA3(cc)) { // covers RDNA 3.0
|
||||
return ggml_cuda_mmq_get_config_rdna3(type, J, fallback);
|
||||
}
|
||||
return ggml_cuda_mmq_get_config_rdna2(type, J, fallback);
|
||||
}
|
||||
if (blackwell_mma_available(cc)) {
|
||||
@@ -245,8 +254,12 @@ static constexpr __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(ggml_t
|
||||
#ifdef GGML_USE_HIP
|
||||
#ifdef CDNA
|
||||
return ggml_cuda_mmq_get_config_cdna(type, J, fallback);
|
||||
#elif defined(AMD_WMMA_AVAILABLE)
|
||||
#elif defined(RDNA4)
|
||||
return ggml_cuda_mmq_get_config_rdna4(type, J, fallback);
|
||||
#elif defined(RDNA3_5)
|
||||
return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
|
||||
#elif defined(RDNA3)
|
||||
return ggml_cuda_mmq_get_config_rdna3(type, J, fallback);
|
||||
#else
|
||||
return ggml_cuda_mmq_get_config_rdna2(type, J, fallback);
|
||||
#endif // CDNA
|
||||
@@ -373,6 +386,7 @@ static constexpr __device__ int ggml_cuda_mmq_get_rows_per_warp(ggml_type type,
|
||||
static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int I) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0: return MMQ_DP4A_TXS_Q8_0;
|
||||
case GGML_TYPE_Q2_0: return MMQ_DP4A_TXS_Q8_0;
|
||||
case GGML_TYPE_Q4_0: return MMQ_DP4A_TXS_Q4_0;
|
||||
case GGML_TYPE_Q4_1: return MMQ_DP4A_TXS_Q4_1;
|
||||
case GGML_TYPE_Q5_0: return MMQ_DP4A_TXS_Q8_0;
|
||||
@@ -530,6 +544,12 @@ static constexpr __device__ ggml_cuda_mmq_util_funcs ggml_cuda_mmq_get_util_func
|
||||
ggml_cuda_mmq_load_tiles_q1_0<type, J, fallback>,
|
||||
ggml_cuda_mmq_vec_dot_q8_0_q8_1_dp4a<type, J, fallback>,
|
||||
ggml_cuda_mmq_write_back_dp4a<type, J, fallback>);
|
||||
case GGML_TYPE_Q2_0:
|
||||
return ggml_cuda_mmq_util_funcs(
|
||||
VDR_Q2_0_Q8_1_MMQ,
|
||||
ggml_cuda_mmq_load_tiles_q2_0<type, J, fallback>,
|
||||
ggml_cuda_mmq_vec_dot_q8_0_q8_1_dp4a<type, J, fallback>,
|
||||
ggml_cuda_mmq_write_back_dp4a<type, J, fallback>);
|
||||
case GGML_TYPE_Q4_0:
|
||||
return ggml_cuda_mmq_util_funcs(
|
||||
VDR_Q4_0_Q8_1_MMQ,
|
||||
@@ -688,6 +708,12 @@ static constexpr __device__ ggml_cuda_mmq_util_funcs ggml_cuda_mmq_get_util_func
|
||||
ggml_cuda_mmq_load_tiles_q1_0<type, J, fallback>,
|
||||
ggml_cuda_mmq_vec_dot_q8_0_q8_1_mma<type, J, fallback, MMQ_Q8_1_DS_LAYOUT_D4>,
|
||||
ggml_cuda_mmq_write_back_mma<type, J, fallback>);
|
||||
case GGML_TYPE_Q2_0:
|
||||
return ggml_cuda_mmq_util_funcs(
|
||||
-1,
|
||||
ggml_cuda_mmq_load_tiles_q2_0<type, J, fallback>,
|
||||
ggml_cuda_mmq_vec_dot_q8_0_q8_1_mma<type, J, fallback, MMQ_Q8_1_DS_LAYOUT_D4>,
|
||||
ggml_cuda_mmq_write_back_mma<type, J, fallback>);
|
||||
case GGML_TYPE_Q4_0:
|
||||
return ggml_cuda_mmq_util_funcs(
|
||||
-1,
|
||||
@@ -1538,6 +1564,7 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
|
||||
template void mul_mat_q_case<type>(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \
|
||||
|
||||
extern DECL_MMQ_CASE(GGML_TYPE_Q1_0);
|
||||
extern DECL_MMQ_CASE(GGML_TYPE_Q2_0);
|
||||
extern DECL_MMQ_CASE(GGML_TYPE_Q4_0);
|
||||
extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
|
||||
extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
|
||||
|
||||
@@ -10,6 +10,7 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_
|
||||
static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0: return vec_dot_q1_0_q8_1;
|
||||
case GGML_TYPE_Q2_0: return vec_dot_q2_0_q8_1;
|
||||
case GGML_TYPE_Q4_0: return vec_dot_q4_0_q8_1;
|
||||
case GGML_TYPE_Q4_1: return vec_dot_q4_1_q8_1;
|
||||
case GGML_TYPE_Q5_0: return vec_dot_q5_0_q8_1;
|
||||
@@ -38,6 +39,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
|
||||
static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q1_0: return VDR_Q1_0_Q8_1_MMVQ;
|
||||
case GGML_TYPE_Q2_0: return VDR_Q2_0_Q8_1_MMVQ;
|
||||
case GGML_TYPE_Q4_0: return VDR_Q4_0_Q8_1_MMVQ;
|
||||
case GGML_TYPE_Q4_1: return VDR_Q4_1_Q8_1_MMVQ;
|
||||
case GGML_TYPE_Q5_0: return VDR_Q5_0_Q8_1_MMVQ;
|
||||
@@ -1010,6 +1012,12 @@ static void mul_mat_vec_q_switch_type(
|
||||
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
|
||||
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q2_0:
|
||||
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_0>
|
||||
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
|
||||
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
|
||||
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_0>
|
||||
(vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
|
||||
|
||||
@@ -36,6 +36,7 @@ SOURCE_FATTN_MMA_CASE = "DECL_FATTN_MMA_F16_CASE({head_size_kq}, {head_size_v},
|
||||
|
||||
TYPES_MMQ = [
|
||||
"GGML_TYPE_Q1_0",
|
||||
"GGML_TYPE_Q2_0",
|
||||
"GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
|
||||
"GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K",
|
||||
"GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S",
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../mmq.cuh"
|
||||
|
||||
DECL_MMQ_CASE(GGML_TYPE_Q2_0);
|
||||
@@ -109,6 +109,9 @@ static __device__ __forceinline__ uint32_t unpack_ksigns(const uint8_t v) {
|
||||
#define VDR_Q1_0_Q8_1_MMVQ 1 // Process one 32-element chunk at a time for parallelism
|
||||
#define VDR_Q1_0_Q8_1_MMQ 4 // Q1_0 has 128 bits (4 ints) per block
|
||||
|
||||
#define VDR_Q2_0_Q8_1_MMVQ 1 // Process one 32-element chunk at a time for parallelism
|
||||
#define VDR_Q2_0_Q8_1_MMQ 2 // Q2_0 group 64: 128 bits (4 ints) per block, 2 32-element chunks
|
||||
|
||||
#define VDR_Q4_0_Q8_1_MMVQ 2
|
||||
#define VDR_Q4_0_Q8_1_MMQ 4
|
||||
|
||||
@@ -722,6 +725,64 @@ static __device__ __forceinline__ float vec_dot_q1_0_q8_1(
|
||||
return d1 * d8 * sumi;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ float vec_dot_q2_0_q8_1(
|
||||
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
|
||||
|
||||
const block_q2_0 * bq2_0 = (const block_q2_0 *) vbq + kbx;
|
||||
|
||||
// Q2_0 (group 64): 64 elements with ONE scale, 2 bits per element (4 elements per byte)
|
||||
// Q8_1: 32 elements per block with individual scales
|
||||
// iqs selects which of the 2 chunks of 32 elements to process (0-1)
|
||||
|
||||
const float d2 = bq2_0->d;
|
||||
|
||||
// Process only the chunk specified by iqs
|
||||
const block_q8_1 * bq8_1_chunk = bq8_1 + iqs;
|
||||
|
||||
// Load 64 bits (8 bytes) for this chunk from Q2_0: bytes [8*iqs, 8*iqs+8)
|
||||
const int offset = iqs * 8;
|
||||
const int v0 = bq2_0->qs[offset + 0] | (bq2_0->qs[offset + 1] << 8) |
|
||||
(bq2_0->qs[offset + 2] << 16) | (bq2_0->qs[offset + 3] << 24);
|
||||
const int v1 = bq2_0->qs[offset + 4] | (bq2_0->qs[offset + 5] << 8) |
|
||||
(bq2_0->qs[offset + 6] << 16) | (bq2_0->qs[offset + 7] << 24);
|
||||
|
||||
// Unpack 32 2-bit codes into 8 int32s, each holding 4 signed int8 symbols in {-1,0,1,2}.
|
||||
// Stored code c in {0,1,2,3} -> symbol s = c - 1.
|
||||
int vi_bytes[8];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
const int shift = j * 8;
|
||||
const int codes = (v0 >> shift) & 0xFF;
|
||||
const int c0 = ((codes >> 0) & 0x3) - 1;
|
||||
const int c1 = ((codes >> 2) & 0x3) - 1;
|
||||
const int c2 = ((codes >> 4) & 0x3) - 1;
|
||||
const int c3 = ((codes >> 6) & 0x3) - 1;
|
||||
vi_bytes[j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
const int shift = j * 8;
|
||||
const int codes = (v1 >> shift) & 0xFF;
|
||||
const int c0 = ((codes >> 0) & 0x3) - 1;
|
||||
const int c1 = ((codes >> 2) & 0x3) - 1;
|
||||
const int c2 = ((codes >> 4) & 0x3) - 1;
|
||||
const int c3 = ((codes >> 6) & 0x3) - 1;
|
||||
vi_bytes[4 + j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
|
||||
}
|
||||
|
||||
// Compute dot product for this 32-element chunk
|
||||
int sumi = 0;
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 8; ++j) {
|
||||
const int u = get_int_b4(bq8_1_chunk->qs, j);
|
||||
sumi = ggml_cuda_dp4a(vi_bytes[j], u, sumi);
|
||||
}
|
||||
|
||||
// Apply Q2_0's single scale and this chunk's Q8_1 scale
|
||||
const float d8 = __low2float(bq8_1_chunk->ds);
|
||||
return d2 * d8 * sumi;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
|
||||
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
|
||||
|
||||
|
||||
@@ -213,7 +213,7 @@ typedef void * ggml_metal_rset_t;
|
||||
// a collection of residency sets (non-owning)
|
||||
typedef struct ggml_metal_rsets * ggml_metal_rsets_t;
|
||||
|
||||
ggml_metal_rsets_t ggml_metal_rsets_init(void);
|
||||
ggml_metal_rsets_t ggml_metal_rsets_init(ggml_metal_device_t dev);
|
||||
void ggml_metal_rsets_free(ggml_metal_rsets_t rsets);
|
||||
|
||||
//
|
||||
|
||||
@@ -557,7 +557,32 @@ struct ggml_metal_rsets {
|
||||
dispatch_group_t d_group;
|
||||
};
|
||||
|
||||
ggml_metal_rsets_t ggml_metal_rsets_init(void) {
|
||||
#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
|
||||
static void ggml_metal_dummy_work(ggml_metal_device_t dev) {
|
||||
if (dev->mtl_queue == nil) {
|
||||
return;
|
||||
}
|
||||
|
||||
@autoreleasepool {
|
||||
// perform a minimal dummy operation on the GPU
|
||||
id<MTLBuffer> buf = [dev->mtl_device newBufferWithLength:1 options:MTLResourceStorageModePrivate];
|
||||
id<MTLCommandBuffer> cmd_buf = [dev->mtl_queue commandBuffer];
|
||||
|
||||
{
|
||||
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
|
||||
|
||||
[encoder fillBuffer:buf range:NSMakeRange(0, 1) value:0];
|
||||
|
||||
[encoder endEncoding];
|
||||
}
|
||||
|
||||
[cmd_buf commit];
|
||||
[buf release];
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
ggml_metal_rsets_t ggml_metal_rsets_init(ggml_metal_device_t dev) {
|
||||
ggml_metal_rsets_t res = calloc(1, sizeof(struct ggml_metal_rsets));
|
||||
|
||||
res->lock = [[NSLock alloc] init];
|
||||
@@ -610,6 +635,15 @@ ggml_metal_rsets_t ggml_metal_rsets_init(void) {
|
||||
#endif
|
||||
});
|
||||
|
||||
#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
|
||||
if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
|
||||
// workaround for residency set memory not being released if no GPU operation occurs
|
||||
// https://developer.apple.com/forums/thread/839089
|
||||
// https://github.com/ggml-org/llama.cpp/issues/25937
|
||||
ggml_metal_dummy_work(dev);
|
||||
}
|
||||
#endif
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
@@ -864,7 +898,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
|
||||
}
|
||||
|
||||
if (dev->props.use_residency_sets) {
|
||||
dev->rsets = ggml_metal_rsets_init();
|
||||
dev->rsets = ggml_metal_rsets_init(dev);
|
||||
} else {
|
||||
dev->rsets = nil;
|
||||
}
|
||||
@@ -1484,6 +1518,7 @@ static void ggml_metal_buffer_rset_free(ggml_metal_buffer_t buf) {
|
||||
if (buf->rset) {
|
||||
[buf->rset endResidency];
|
||||
[buf->rset removeAllAllocations];
|
||||
[buf->rset commit];
|
||||
[buf->rset release];
|
||||
}
|
||||
}
|
||||
|
||||
@@ -71,6 +71,7 @@ enum rpc_cmd {
|
||||
RPC_CMD_HELLO,
|
||||
RPC_CMD_DEVICE_COUNT,
|
||||
RPC_CMD_GRAPH_RECOMPUTE,
|
||||
RPC_CMD_MEMSET_TENSOR,
|
||||
RPC_CMD_COUNT,
|
||||
};
|
||||
|
||||
@@ -152,6 +153,13 @@ struct rpc_msg_buffer_clear_req {
|
||||
uint8_t value;
|
||||
};
|
||||
|
||||
struct rpc_msg_memset_tensor_req {
|
||||
rpc_tensor tensor;
|
||||
uint64_t offset;
|
||||
uint64_t size;
|
||||
uint8_t value;
|
||||
};
|
||||
|
||||
struct rpc_msg_set_tensor_hash_req {
|
||||
rpc_tensor tensor;
|
||||
uint64_t offset;
|
||||
@@ -462,6 +470,19 @@ static enum ggml_status ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_
|
||||
return GGML_STATUS_SUCCESS;
|
||||
}
|
||||
|
||||
static void ggml_backend_rpc_buffer_memset_tensor(
|
||||
ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
|
||||
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
|
||||
rpc_msg_memset_tensor_req request = {
|
||||
/* .tensor = */ serialize_tensor(tensor),
|
||||
/* .offset = */ offset,
|
||||
/* .size = */ size,
|
||||
/* .value = */ value,
|
||||
};
|
||||
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_MEMSET_TENSOR, &request, sizeof(request), nullptr, 0);
|
||||
RPC_STATUS_ASSERT(status);
|
||||
}
|
||||
|
||||
static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
|
||||
rpc_tensor rpc_tensor = serialize_tensor(tensor);
|
||||
@@ -531,7 +552,7 @@ static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = {
|
||||
/* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer,
|
||||
/* .get_base = */ ggml_backend_rpc_buffer_get_base,
|
||||
/* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor,
|
||||
/* .memset_tensor = */ NULL,
|
||||
/* .memset_tensor = */ ggml_backend_rpc_buffer_memset_tensor,
|
||||
/* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor,
|
||||
/* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor,
|
||||
/* .set_tensor_2d = */ NULL,
|
||||
@@ -831,6 +852,7 @@ public:
|
||||
bool buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response);
|
||||
bool free_buffer(const rpc_msg_free_buffer_req & request);
|
||||
bool buffer_clear(const rpc_msg_buffer_clear_req & request);
|
||||
bool memset_tensor(const rpc_msg_memset_tensor_req & request);
|
||||
bool set_tensor(const std::vector<uint8_t> & input);
|
||||
bool set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rpc_msg_set_tensor_hash_rsp & response);
|
||||
bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response);
|
||||
@@ -989,6 +1011,52 @@ bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) {
|
||||
return true;
|
||||
}
|
||||
|
||||
bool rpc_server::memset_tensor(const rpc_msg_memset_tensor_req & request) {
|
||||
struct ggml_init_params params {
|
||||
/*.mem_size =*/ ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
ggml_context_ptr ctx_ptr { ggml_init(params) };
|
||||
GGML_ASSERT(ctx_ptr != nullptr);
|
||||
ggml_context * ctx = ctx_ptr.get();
|
||||
ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
|
||||
if (tensor == nullptr || tensor->buffer == nullptr) {
|
||||
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
const uint64_t tensor_size = ggml_nbytes(tensor);
|
||||
if (request.offset > tensor_size || request.size > tensor_size - request.offset) {
|
||||
GGML_LOG_ERROR("[%s] tensor region (offset=%" PRIu64 ", size=%" PRIu64 ") out of tensor bounds [0, %" PRIu64 ")\n",
|
||||
__func__, request.offset, request.size, tensor_size);
|
||||
return false;
|
||||
}
|
||||
|
||||
const uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(tensor->buffer);
|
||||
const uint64_t buffer_size = ggml_backend_buffer_get_size(tensor->buffer);
|
||||
if (request.tensor.data < buffer_start) {
|
||||
GGML_LOG_ERROR("[%s] tensor data before buffer start\n", __func__);
|
||||
return false;
|
||||
}
|
||||
const uint64_t data_offset = request.tensor.data - buffer_start;
|
||||
if (data_offset > buffer_size ||
|
||||
request.offset > buffer_size - data_offset ||
|
||||
request.size > buffer_size - data_offset - request.offset) {
|
||||
GGML_LOG_ERROR("[%s] tensor region out of buffer bounds\n", __func__);
|
||||
return false;
|
||||
}
|
||||
if (tensor->buffer->iface.memset_tensor == nullptr) {
|
||||
GGML_LOG_ERROR("[%s] memset not implemented by backend buffer\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
LOG_DBG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 ", value: %u\n",
|
||||
__func__, (void *) tensor->buffer, tensor->data, request.offset, request.size, request.value);
|
||||
ggml_backend_tensor_memset(tensor, request.value, request.offset, request.size);
|
||||
return true;
|
||||
}
|
||||
|
||||
ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) {
|
||||
// Validate tensor type before using it
|
||||
if (tensor->type >= GGML_TYPE_COUNT) {
|
||||
@@ -1585,6 +1653,19 @@ static void rpc_serve_client(const std::vector<ggml_backend_t> & backends, const
|
||||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_MEMSET_TENSOR: {
|
||||
rpc_msg_memset_tensor_req request;
|
||||
if (!recv_msg(sock, &request, sizeof(request))) {
|
||||
return;
|
||||
}
|
||||
if (!server.memset_tensor(request)) {
|
||||
return;
|
||||
}
|
||||
if (!send_msg(sock, nullptr, 0)) {
|
||||
return;
|
||||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_SET_TENSOR: {
|
||||
std::vector<uint8_t> input;
|
||||
if (!recv_msg(sock, input)) {
|
||||
|
||||
@@ -306,29 +306,43 @@ static __dpct_inline__ T op_trunc(T x) {
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T, typename F>
|
||||
static void unary_op_flat_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> & item_ct1, F func) {
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
dst[i] = func(x[i]);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T, typename F>
|
||||
static void unary_op_generic_kernel(
|
||||
const T * x,
|
||||
T * dst,
|
||||
const int k,
|
||||
const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3,
|
||||
const sycl::uint3 ne0_fd, const sycl::uint3 ne1_fd, const sycl::uint3 ne2_fd,
|
||||
const size_t nb0, const size_t nb1, const size_t nb2, const size_t nb3,
|
||||
const size_t nbd0, const size_t nbd1, const size_t nbd2, const size_t nbd3,
|
||||
const sycl::nd_item<1> & item_ct1,
|
||||
F func) {
|
||||
|
||||
(void) ne3;
|
||||
// 32-bit index math: k is int, so every logical index fits u32. 64-bit integer div/mod is
|
||||
// emulated on Xe and dominates this kernel otherwise, and even the 32-bit divide is worth
|
||||
// avoiding -- the divisors are launch-invariant, so the magic numbers are precomputed
|
||||
// host-side and each division becomes a multiply-high plus a shift.
|
||||
// Byte offsets are widened back to size_t only for the final address math.
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
const int64_t i0 = i % ne0;
|
||||
const int64_t i1 = (i / ne0) % ne1;
|
||||
const int64_t i2 = (i / (ne0*ne1)) % ne2;
|
||||
const int64_t i3 = i / (ne0*ne1*ne2);
|
||||
sycl::uint2 dm = fast_div_modulo((uint32_t) i, ne0_fd);
|
||||
const uint32_t i0 = dm.y();
|
||||
dm = fast_div_modulo(dm.x(), ne1_fd);
|
||||
const uint32_t i1 = dm.y();
|
||||
dm = fast_div_modulo(dm.x(), ne2_fd);
|
||||
const uint32_t i2 = dm.y();
|
||||
const uint32_t i3 = dm.x();
|
||||
|
||||
const char * src_base = (const char *) x;
|
||||
char * dst_base = (char *) dst;
|
||||
|
||||
const T * srcp = (const T *)(src_base + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3 );
|
||||
T * dstp = (T *)(dst_base + i0*nbd0 + i1*nbd1 + i2*nbd2 + i3*nbd3);
|
||||
const T * srcp = (const T *)(src_base + (size_t) i0*nb0 + (size_t) i1*nb1 + (size_t) i2*nb2 + (size_t) i3*nb3 );
|
||||
T * dstp = (T *)(dst_base + (size_t) i0*nbd0 + (size_t) i1*nbd1 + (size_t) i2*nbd2 + (size_t) i3*nbd3);
|
||||
|
||||
*dstp = func(*srcp);
|
||||
}
|
||||
@@ -407,46 +421,51 @@ static void clamp(const T * x, T * dst, const float min, const float max, const
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void gated_op_fused_geglu(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
static void gated_op_fused_geglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
const int64_t j0 = (i / n) * o0 + (i % n);
|
||||
const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
|
||||
const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
|
||||
const int64_t j0 = rc.x() * o0 + rc.y();
|
||||
const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
|
||||
dst[i] = op_gelu(x[j0]) * g[j1];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void gated_op_fused_reglu(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
static void gated_op_fused_reglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
const int64_t j0 = (i / n) * o0 + (i % n);
|
||||
const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
|
||||
const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
|
||||
const int64_t j0 = rc.x() * o0 + rc.y();
|
||||
const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
|
||||
dst[i] = op_relu(x[j0]) * g[j1];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void gated_op_fused_swiglu(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
static void gated_op_fused_swiglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
const int64_t j0 = (i / n) * o0 + (i % n);
|
||||
const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
|
||||
const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
|
||||
const int64_t j0 = rc.x() * o0 + rc.y();
|
||||
const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
|
||||
dst[i] = op_silu(x[j0]) * g[j1];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void gated_op_fused_geglu_erf(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
static void gated_op_fused_geglu_erf(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
const int64_t j0 = (i / n) * o0 + (i % n);
|
||||
const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
|
||||
const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
|
||||
const int64_t j0 = rc.x() * o0 + rc.y();
|
||||
const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
|
||||
dst[i] = op_gelu_erf(x[j0]) * g[j1];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
|
||||
SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
|
||||
const int64_t j0 = (i / n) * o0 + (i % n);
|
||||
const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
|
||||
const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
|
||||
const int64_t j0 = rc.x() * o0 + rc.y();
|
||||
const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
|
||||
dst[i] = op_gelu_quick(x[j0]) * g[j1];
|
||||
}
|
||||
}
|
||||
@@ -529,6 +548,10 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c
|
||||
GGML_ASSERT(dst->ne[0] == nc);
|
||||
GGML_ASSERT(ggml_is_contiguous_1(dst->src[0]));
|
||||
GGML_ASSERT(ggml_is_contiguous(dst));
|
||||
// The fused GLU kernels index with 32-bit fastdiv, which is exact only for indices below
|
||||
// 2^31. A dst that large is ~8 GB at f32, and the grid sizing already narrows to 32 bits,
|
||||
// so assert the bound rather than carry a second code path for it.
|
||||
GGML_ASSERT(ggml_nelements(dst) < ((int64_t) 1 << 31));
|
||||
const int32_t swapped = ((const int32_t *) dst->op_params)[1];
|
||||
void * src0_d = src0->data;
|
||||
void * src1_d = src1 ? src1->data : src0->data;
|
||||
@@ -597,7 +620,6 @@ static inline void ggml_sycl_op_unary(
|
||||
const int64_t ne0 = dst->ne[0];
|
||||
const int64_t ne1 = dst->ne[1];
|
||||
const int64_t ne2 = dst->ne[2];
|
||||
const int64_t ne3 = dst->ne[3];
|
||||
|
||||
const size_t nb0 = src0->nb[0];
|
||||
const size_t nb1 = src0->nb[1];
|
||||
@@ -609,24 +631,42 @@ static inline void ggml_sycl_op_unary(
|
||||
const size_t nbd2 = dst->nb[2];
|
||||
const size_t nbd3 = dst->nb[3];
|
||||
|
||||
// Hot unary ops (FFN/GDN silu, sigmoid, ...) run on contiguous tensors;
|
||||
// skip the strided index math entirely for them.
|
||||
const bool contiguous = ggml_is_contiguous(src0) && ggml_is_contiguous(dst);
|
||||
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
|
||||
[=](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
|
||||
|
||||
const int num_blocks = ceil_div(k_elements, 256);
|
||||
|
||||
stream->parallel_for(
|
||||
sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
|
||||
sycl::range<1>(256)),
|
||||
[=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
unary_op_generic_kernel(
|
||||
src, dst_ptr, k_elements,
|
||||
ne0, ne1, ne2, ne3,
|
||||
nb0, nb1, nb2, nb3,
|
||||
nbd0, nbd1, nbd2, nbd3,
|
||||
item_ct1,
|
||||
func
|
||||
);
|
||||
});
|
||||
if (contiguous) {
|
||||
stream->parallel_for(
|
||||
sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
|
||||
sycl::range<1>(256)),
|
||||
[=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
unary_op_flat_kernel(src, dst_ptr, k_elements, item_ct1, func);
|
||||
});
|
||||
} else {
|
||||
// Launch-invariant divisors: compute the magic numbers once on the host so the
|
||||
// kernel never issues an integer divide. Only the strided path needs them.
|
||||
const sycl::uint3 ne0_fd = init_fastdiv_values((uint32_t) ne0);
|
||||
const sycl::uint3 ne1_fd = init_fastdiv_values((uint32_t) ne1);
|
||||
const sycl::uint3 ne2_fd = init_fastdiv_values((uint32_t) ne2);
|
||||
stream->parallel_for(
|
||||
sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
|
||||
sycl::range<1>(256)),
|
||||
[=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
unary_op_generic_kernel(
|
||||
src, dst_ptr, k_elements,
|
||||
ne0_fd, ne1_fd, ne2_fd,
|
||||
nb0, nb1, nb2, nb3,
|
||||
nbd0, nbd1, nbd2, nbd3,
|
||||
item_ct1,
|
||||
func
|
||||
);
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
@@ -930,10 +970,11 @@ static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tens
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
|
||||
[](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
|
||||
const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
|
||||
const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
|
||||
main_stream->parallel_for(
|
||||
sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
|
||||
sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
|
||||
gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -942,10 +983,11 @@ static inline void ggml_sycl_op_reglu(ggml_backend_sycl_context & ctx, ggml_tens
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
|
||||
[](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
|
||||
const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_RELU_BLOCK_SIZE); // Using RELU block size for reglu
|
||||
const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
|
||||
main_stream->parallel_for(
|
||||
sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_RELU_BLOCK_SIZE)),
|
||||
sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
|
||||
gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -954,10 +996,11 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
|
||||
[](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
|
||||
const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_SILU_BLOCK_SIZE); // Using SILU block size for swiglu
|
||||
const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
|
||||
main_stream->parallel_for(
|
||||
sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_SILU_BLOCK_SIZE)),
|
||||
sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
|
||||
gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -1057,10 +1100,11 @@ static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
|
||||
[](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
|
||||
const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
|
||||
const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
|
||||
main_stream->parallel_for(
|
||||
sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
|
||||
sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
|
||||
gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
|
||||
});
|
||||
});
|
||||
}
|
||||
@@ -1069,10 +1113,11 @@ static inline void ggml_sycl_op_geglu_quick(ggml_backend_sycl_context & ctx, ggm
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
|
||||
[](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
|
||||
const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
|
||||
const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
|
||||
main_stream->parallel_for(
|
||||
sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
|
||||
sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
|
||||
gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
|
||||
gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -1481,6 +1481,11 @@ struct vk_op_binary_push_constants {
|
||||
float param1; float param2; int32_t param3;
|
||||
};
|
||||
|
||||
// Distinct type with the same layout so concat can overload tensor offset initialization.
|
||||
struct vk_op_concat_push_constants : vk_op_binary_push_constants {};
|
||||
static_assert(sizeof(vk_op_concat_push_constants) == sizeof(vk_op_binary_push_constants));
|
||||
static_assert(std::is_standard_layout_v<vk_op_concat_push_constants>);
|
||||
|
||||
struct vk_op_multi_add_push_constants {
|
||||
// shape for dst
|
||||
uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23;
|
||||
@@ -2246,6 +2251,40 @@ static uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const gg
|
||||
return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));;
|
||||
}
|
||||
|
||||
static uint32_t ggml_vk_concat_unit_size(ggml_type type) {
|
||||
const uint32_t type_size = ggml_type_size(type);
|
||||
|
||||
if (!ggml_is_quantized(type)) {
|
||||
return type_size;
|
||||
}
|
||||
|
||||
// Use the widest existing concat shader that evenly divides a quant block.
|
||||
if (type_size % 8 == 0) {
|
||||
return 8;
|
||||
}
|
||||
if (type_size % 4 == 0) {
|
||||
return 4;
|
||||
}
|
||||
if (type_size % 2 == 0) {
|
||||
return 2;
|
||||
}
|
||||
return 1;
|
||||
}
|
||||
|
||||
static bool ggml_vk_concat_supported(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) {
|
||||
if (src0->type != src1->type || src0->type != dst->type) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!ggml_is_quantized(src0->type)) {
|
||||
const size_t type_size = ggml_type_size(src0->type);
|
||||
return type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8;
|
||||
}
|
||||
|
||||
// Quantized tensor rows are block-aligned when created.
|
||||
return ggml_is_contiguous_rows(src0) && ggml_is_contiguous_rows(src1) && ggml_is_contiguous_rows(dst);
|
||||
}
|
||||
|
||||
template <typename T> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
GGML_UNUSED(p);
|
||||
GGML_UNUSED(src0);
|
||||
@@ -10896,14 +10935,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
|
||||
}
|
||||
return nullptr;
|
||||
case GGML_OP_CONCAT: {
|
||||
if (src0->type != src1->type || src0->type != dst->type) {
|
||||
if (!ggml_vk_concat_supported(src0, src1, dst)) {
|
||||
return nullptr;
|
||||
}
|
||||
if (ggml_blck_size(src0->type) != 1) {
|
||||
return nullptr;
|
||||
}
|
||||
const size_t type_size = ggml_type_size(src0->type);
|
||||
switch (type_size) {
|
||||
switch (ggml_vk_concat_unit_size(src0->type)) {
|
||||
case 1:
|
||||
return ctx->device->pipeline_concat_i8;
|
||||
case 2:
|
||||
@@ -11595,6 +11630,18 @@ template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_concat_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t unit_size = ggml_vk_concat_unit_size(dst->type);
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / unit_size;
|
||||
const uint32_t b_offset = get_misalign_bytes(ctx, src1) / unit_size;
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / unit_size;
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
|
||||
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
@@ -11630,7 +11677,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
|
||||
}
|
||||
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
|
||||
std::cerr << "), " << ggml_op_name(op) << ")");
|
||||
GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT
|
||||
GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || op == GGML_OP_CONCAT || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT
|
||||
GGML_ASSERT(dst->buffer != nullptr);
|
||||
const uint64_t ne00 = src0->ne[0];
|
||||
const uint64_t ne01 = src0->ne[1];
|
||||
@@ -11885,6 +11932,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
|
||||
ne *= ggml_type_size(src0->type) / 2;
|
||||
}
|
||||
}
|
||||
if (op == GGML_OP_CONCAT && ggml_is_quantized(dst->type)) {
|
||||
ne = ne / ggml_blck_size(dst->type) * ggml_type_size(dst->type) / ggml_vk_concat_unit_size(dst->type);
|
||||
}
|
||||
// copy_to_quant has block size of 32, and each thread does QUANT_K elements.
|
||||
// Splitting into 512x512xZ wouldn't work well since each workgroup does 1024 elements.
|
||||
// So divide by block size here before splitting into 512x512 groups.
|
||||
@@ -12525,18 +12575,28 @@ static void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subc
|
||||
static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
int * op_params = (int *)dst->op_params;
|
||||
|
||||
const uint32_t src0_type_size = ggml_type_size(src0->type);
|
||||
const uint32_t src1_type_size = ggml_type_size(src1->type);
|
||||
const uint32_t dst_type_size = ggml_type_size(dst->type);
|
||||
const uint32_t unit_size = ggml_vk_concat_unit_size(dst->type);
|
||||
const uint32_t units_per_block = ggml_type_size(dst->type) / unit_size;
|
||||
const uint32_t block_size = ggml_blck_size(dst->type);
|
||||
const bool quantized = ggml_is_quantized(dst->type);
|
||||
|
||||
ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, {
|
||||
(uint32_t)ggml_nelements(dst),
|
||||
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
|
||||
(uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
|
||||
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
|
||||
// Address dimension 0 in packed storage units; higher strides may be noncontiguous.
|
||||
const uint32_t ne00 = src0->ne[0] / block_size * units_per_block;
|
||||
const uint32_t ne10 = src1->ne[0] / block_size * units_per_block;
|
||||
const uint32_t ne20 = dst->ne[0] / block_size * units_per_block;
|
||||
const uint32_t nb00 = quantized ? 1 : src0->nb[0] / unit_size;
|
||||
const uint32_t nb10 = quantized ? 1 : src1->nb[0] / unit_size;
|
||||
const uint32_t nb20 = quantized ? 1 : dst->nb[0] / unit_size;
|
||||
|
||||
vk_op_concat_push_constants pc {{
|
||||
ne20 * (uint32_t)dst->ne[1] * (uint32_t)dst->ne[2] * (uint32_t)dst->ne[3],
|
||||
ne00, (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], nb00, (uint32_t)src0->nb[1] / unit_size, (uint32_t)src0->nb[2] / unit_size, (uint32_t)src0->nb[3] / unit_size,
|
||||
ne10, (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], nb10, (uint32_t)src1->nb[1] / unit_size, (uint32_t)src1->nb[2] / unit_size, (uint32_t)src1->nb[3] / unit_size,
|
||||
ne20, (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], nb20, (uint32_t) dst->nb[1] / unit_size, (uint32_t) dst->nb[2] / unit_size, (uint32_t) dst->nb[3] / unit_size,
|
||||
0,
|
||||
0.0f, 0.0f, op_params[0],
|
||||
});
|
||||
}};
|
||||
ggml_vk_op_f32<vk_op_concat_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, std::move(pc));
|
||||
}
|
||||
|
||||
static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
||||
@@ -17872,12 +17932,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
|
||||
return op->src[0]->type == op->src[1]->type && op->src[0]->type == op->type &&
|
||||
(op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_I32);
|
||||
case GGML_OP_CONCAT: {
|
||||
if (op->src[0]->type != op->src[1]->type || op->src[0]->type != op->type) {
|
||||
return false;
|
||||
}
|
||||
const size_t type_size = ggml_type_size(op->type);
|
||||
return ggml_blck_size(op->type) == 1 &&
|
||||
(type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8);
|
||||
return ggml_vk_concat_supported(op->src[0], op->src[1], op);
|
||||
}
|
||||
case GGML_OP_ADD1:
|
||||
return (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32)
|
||||
@@ -18016,10 +18071,17 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
|
||||
case GGML_OP_CONV_2D:
|
||||
case GGML_OP_CONV_TRANSPOSE_2D:
|
||||
{
|
||||
const bool transpose = op->op == GGML_OP_CONV_TRANSPOSE_2D;
|
||||
const int64_t cout = !transpose ? op->src[0]->ne[3] : op->src[0]->ne[2];
|
||||
const int64_t cin = !transpose ? op->src[0]->ne[2] : op->src[0]->ne[3];
|
||||
|
||||
// Channel-contiguous format is not supported yet.
|
||||
return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
|
||||
(op->src[0]->nb[0] == sizeof(float) || op->src[0]->nb[0] == sizeof(ggml_fp16_t) ) &&
|
||||
op->src[1]->type == GGML_TYPE_F32 &&
|
||||
op->type == GGML_TYPE_F32 &&
|
||||
cout == op->ne[2] &&
|
||||
cin == op->src[1]->ne[2] &&
|
||||
ggml_is_contiguous(op->src[0]) &&
|
||||
ggml_is_contiguous(op->src[1]) &&
|
||||
ggml_is_contiguous(op));
|
||||
|
||||
@@ -591,7 +591,8 @@ struct ggml_webgpu_flash_attn_common_pipeline_key {
|
||||
ggml_type dst_type;
|
||||
uint32_t head_dim_qk;
|
||||
uint32_t head_dim_v;
|
||||
bool kv_direct;
|
||||
bool k_direct;
|
||||
bool v_direct;
|
||||
bool kv_overlap;
|
||||
bool has_mask;
|
||||
bool has_sinks;
|
||||
@@ -600,8 +601,9 @@ struct ggml_webgpu_flash_attn_common_pipeline_key {
|
||||
bool operator==(const ggml_webgpu_flash_attn_common_pipeline_key & other) const {
|
||||
return q_type == other.q_type && k_type == other.k_type && v_type == other.v_type &&
|
||||
dst_type == other.dst_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v &&
|
||||
kv_direct == other.kv_direct && kv_overlap == other.kv_overlap && has_mask == other.has_mask &&
|
||||
has_sinks == other.has_sinks && uses_logit_softcap == other.uses_logit_softcap;
|
||||
k_direct == other.k_direct && v_direct == other.v_direct && kv_overlap == other.kv_overlap &&
|
||||
has_mask == other.has_mask && has_sinks == other.has_sinks &&
|
||||
uses_logit_softcap == other.uses_logit_softcap;
|
||||
}
|
||||
};
|
||||
|
||||
@@ -613,7 +615,8 @@ inline void ggml_webgpu_flash_attn_hash_common_pipeline_key(size_t &
|
||||
ggml_webgpu_hash_combine(seed, key.dst_type);
|
||||
ggml_webgpu_hash_combine(seed, key.head_dim_qk);
|
||||
ggml_webgpu_hash_combine(seed, key.head_dim_v);
|
||||
ggml_webgpu_hash_combine(seed, key.kv_direct);
|
||||
ggml_webgpu_hash_combine(seed, key.k_direct);
|
||||
ggml_webgpu_hash_combine(seed, key.v_direct);
|
||||
ggml_webgpu_hash_combine(seed, key.kv_overlap);
|
||||
ggml_webgpu_hash_combine(seed, key.has_mask);
|
||||
ggml_webgpu_hash_combine(seed, key.has_sinks);
|
||||
@@ -687,12 +690,13 @@ inline bool ggml_webgpu_flash_attn_float_vec4_aligned(const ggml_tensor * K,
|
||||
ggml_webgpu_flash_attn_float_vec4_aligned(V, storage_offset_alignment);
|
||||
}
|
||||
|
||||
inline bool ggml_webgpu_flash_attn_kv_direct(const ggml_tensor * Q,
|
||||
const ggml_tensor * K,
|
||||
const ggml_tensor * V,
|
||||
uint32_t kv_direct_align) {
|
||||
return K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16 && (Q->ne[0] % kv_direct_align == 0) &&
|
||||
(K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
|
||||
inline bool ggml_webgpu_flash_attn_k_direct(const ggml_tensor * Q, const ggml_tensor * K, uint32_t kv_direct_align) {
|
||||
return (K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q8_0 || K->type == GGML_TYPE_Q4_0) &&
|
||||
(Q->ne[0] % kv_direct_align == 0) && (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
|
||||
}
|
||||
|
||||
inline bool ggml_webgpu_flash_attn_v_direct(const ggml_tensor * Q, const ggml_tensor * V, uint32_t kv_direct_align) {
|
||||
return ggml_webgpu_flash_attn_k_direct(Q, V, kv_direct_align);
|
||||
}
|
||||
|
||||
inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_common_pipeline_key(
|
||||
@@ -706,10 +710,11 @@ inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_co
|
||||
key.dst_type = context.dst->type;
|
||||
key.head_dim_qk = (uint32_t) context.src0->ne[0];
|
||||
key.head_dim_v = (uint32_t) context.src2->ne[0];
|
||||
key.kv_direct = ggml_webgpu_flash_attn_kv_direct(context.src0, context.src1, context.src2, kv_direct_align);
|
||||
key.kv_overlap = kv_overlap;
|
||||
key.has_mask = context.src3 != nullptr;
|
||||
key.has_sinks = context.src4 != nullptr;
|
||||
key.k_direct = ggml_webgpu_flash_attn_k_direct(context.src0, context.src1, kv_direct_align);
|
||||
key.v_direct = ggml_webgpu_flash_attn_v_direct(context.src0, context.src2, kv_direct_align);
|
||||
key.kv_overlap = kv_overlap;
|
||||
key.has_mask = context.src3 != nullptr;
|
||||
key.has_sinks = context.src4 != nullptr;
|
||||
key.uses_logit_softcap = ggml_get_op_params_f32(context.dst, 2) != 0.0f;
|
||||
return key;
|
||||
}
|
||||
@@ -794,9 +799,13 @@ inline std::vector<std::string> ggml_webgpu_flash_attn_common_defines(
|
||||
defines.push_back("LOGIT_SOFTCAP");
|
||||
variant += "_lgsc";
|
||||
}
|
||||
if (key.kv_direct) {
|
||||
defines.push_back("KV_DIRECT");
|
||||
variant += "_kvdirect";
|
||||
if (key.k_direct) {
|
||||
defines.push_back("K_DIRECT");
|
||||
variant += "_k_direct";
|
||||
}
|
||||
if (key.v_direct) {
|
||||
defines.push_back("V_DIRECT");
|
||||
variant += "_v_direct";
|
||||
}
|
||||
if (key.kv_overlap) {
|
||||
defines.push_back("KV_OVERLAP");
|
||||
@@ -815,6 +824,12 @@ inline std::vector<std::string> ggml_webgpu_flash_attn_common_defines(
|
||||
|
||||
if (ggml_is_quantized(key.k_type) || ggml_is_quantized(key.v_type)) {
|
||||
defines.push_back("U32_DEQUANT_HELPERS");
|
||||
if (ggml_is_quantized(key.k_type)) {
|
||||
defines.push_back("LOADERS_QUANTIZED_K");
|
||||
}
|
||||
if (ggml_is_quantized(key.v_type)) {
|
||||
defines.push_back("LOADERS_QUANTIZED_V");
|
||||
}
|
||||
}
|
||||
|
||||
return defines;
|
||||
@@ -2792,12 +2807,14 @@ class ggml_webgpu_shader_lib {
|
||||
ggml_webgpu_flash_attn_pipeline_key key = {};
|
||||
key.common = ggml_webgpu_flash_attn_make_common_pipeline_key(
|
||||
context, decisions.use_sg_matrix ? context.sg_mat_k : 1u, kv_overlap);
|
||||
key.common.kv_direct = decisions.use_sg_matrix && key.common.kv_direct;
|
||||
key.use_sg_matrix = decisions.use_sg_matrix;
|
||||
key.common.k_direct &= decisions.use_sg_matrix && key.common.k_type == GGML_TYPE_F16;
|
||||
key.common.v_direct &= decisions.use_sg_matrix && key.common.v_type == GGML_TYPE_F16;
|
||||
key.use_sg_matrix = decisions.use_sg_matrix;
|
||||
|
||||
const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(
|
||||
context.wg_mem_limit_bytes, decisions.q_tile, decisions.use_sg_matrix ? context.sg_mat_n : 1u,
|
||||
key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask, key.common.kv_direct);
|
||||
key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask,
|
||||
key.common.k_direct || key.common.v_direct);
|
||||
GGML_ASSERT(max_kv_tile > 0);
|
||||
|
||||
decisions.kv_tile = decisions.use_sg_matrix ?
|
||||
@@ -2809,7 +2826,7 @@ class ggml_webgpu_shader_lib {
|
||||
std::min(context.max_wg_size, std::max(GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE,
|
||||
GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size));
|
||||
|
||||
if (key.common.kv_direct) {
|
||||
if (key.common.k_direct || key.common.v_direct) {
|
||||
decisions.kv_tile = std::min(decisions.kv_tile, GGML_WEBGPU_KV_SEQ_PAD);
|
||||
while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) {
|
||||
decisions.kv_tile -= decisions.use_sg_matrix ? context.sg_mat_n : context.min_subgroup_size;
|
||||
@@ -2856,9 +2873,9 @@ class ggml_webgpu_shader_lib {
|
||||
}
|
||||
|
||||
ggml_webgpu_flash_attn_vec_decisions decisions = {};
|
||||
decisions.kv_tile =
|
||||
ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk,
|
||||
key.common.head_dim_v, key.common.has_mask, key.common.kv_direct);
|
||||
decisions.kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk,
|
||||
key.common.head_dim_v, key.common.has_mask,
|
||||
key.common.k_direct || key.common.v_direct);
|
||||
decisions.wg_size = context.max_subgroup_size;
|
||||
|
||||
std::string variant = "flash_attn_vec";
|
||||
@@ -2870,12 +2887,10 @@ class ggml_webgpu_shader_lib {
|
||||
variant += "_mask_blk";
|
||||
}
|
||||
|
||||
uint32_t d_split = context.min_subgroup_size;
|
||||
if (key.common.k_type == GGML_TYPE_F16 && key.common.v_type == GGML_TYPE_F16) {
|
||||
const uint32_t D = key.common.head_dim_qk | key.common.head_dim_v;
|
||||
const uint32_t D_lsb = D & (~(D - 1u));
|
||||
d_split = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u));
|
||||
}
|
||||
uint32_t d_split = context.min_subgroup_size;
|
||||
const uint32_t D = key.common.head_dim_qk | key.common.head_dim_v;
|
||||
const uint32_t D_lsb = D & (~(D - 1u));
|
||||
d_split = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u));
|
||||
|
||||
defines.push_back(std::string("D_SPLIT=") + std::to_string(d_split));
|
||||
variant += "_dsplit" + std::to_string(d_split);
|
||||
|
||||
@@ -3839,7 +3839,8 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer
|
||||
const auto & capabilities = ctx->webgpu_global_ctx->capabilities;
|
||||
if (ggml_webgpu_flash_attn_use_vec_path(ctx->webgpu_global_ctx, Q, K, V)) {
|
||||
const bool kv_direct =
|
||||
ggml_webgpu_flash_attn_kv_direct(Q, K, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH);
|
||||
ggml_webgpu_flash_attn_k_direct(Q, K, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH) ||
|
||||
ggml_webgpu_flash_attn_v_direct(Q, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH);
|
||||
const uint32_t kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile(
|
||||
capabilities.limits.maxComputeWorkgroupStorageSize, (uint32_t) Q->ne[0], (uint32_t) V->ne[0],
|
||||
mask != nullptr, kv_direct);
|
||||
@@ -4448,9 +4449,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
|
||||
const uint32_t q_tile =
|
||||
use_subgroup_matrix ? capabilities.sg_mat_m : GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE;
|
||||
const uint32_t kv_granularity = use_subgroup_matrix ? capabilities.sg_mat_n : 1u;
|
||||
const bool kv_direct = use_subgroup_matrix ?
|
||||
ggml_webgpu_flash_attn_kv_direct(src0, src1, src2, capabilities.sg_mat_k) :
|
||||
false;
|
||||
const bool kv_direct = use_subgroup_matrix ?
|
||||
ggml_webgpu_flash_attn_k_direct(src0, src1, capabilities.sg_mat_k) ||
|
||||
ggml_webgpu_flash_attn_v_direct(src0, src2, capabilities.sg_mat_k) :
|
||||
false;
|
||||
const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(
|
||||
capabilities.limits.maxComputeWorkgroupStorageSize, q_tile, kv_granularity, (uint32_t) src0->ne[0],
|
||||
(uint32_t) src2->ne[0], op->src[3] != nullptr, kv_direct);
|
||||
|
||||
@@ -9,6 +9,12 @@ fn get_byte_i32(value: u32, index: u32) -> i32 {
|
||||
#endif
|
||||
|
||||
#ifdef U32_DEQUANT_HELPERS
|
||||
|
||||
fn f16_from_u16(bits: u32) -> f16 {
|
||||
let packed = unpack2x16float(bits);
|
||||
return f16(packed[0]);
|
||||
}
|
||||
|
||||
#ifdef DECLARE_BYTE_LOADERS_SRC
|
||||
fn load_u16_at_src(byte_offset: u32) -> u32 {
|
||||
let word = src[byte_offset / 4u];
|
||||
@@ -36,7 +42,7 @@ fn load_f16_as_f32_at_src(byte_offset: u32) -> f32 {
|
||||
let d_bits = (word >> shift) & 0xFFFFu;
|
||||
return unpack2x16float(d_bits)[0];
|
||||
}
|
||||
#endif
|
||||
#endif // DECLARE_BYTE_LOADERS_SRC
|
||||
|
||||
#ifdef DECLARE_BYTE_LOADERS_SRC0
|
||||
fn load_u16_at_src0(byte_offset: u32) -> u32 {
|
||||
@@ -72,8 +78,47 @@ fn load_f16_as_f32_at_src0(byte_offset: u32) -> f32 {
|
||||
let d_bits = (word >> shift) & 0xFFFFu;
|
||||
return unpack2x16float(d_bits)[0];
|
||||
}
|
||||
#endif
|
||||
#endif
|
||||
#endif // DECLARE_BYTE_LOADERS_SRC0
|
||||
|
||||
#ifdef LOADERS_QUANTIZED_K
|
||||
fn load_k_u16_at(byte_offset: u32) -> u32 {
|
||||
let word = K[byte_offset / 4u];
|
||||
let shift = (byte_offset & 2u) * 8u;
|
||||
return (word >> shift) & 0xFFFFu;
|
||||
}
|
||||
|
||||
fn load_k_u32_at(byte_offset: u32) -> u32 {
|
||||
let word_idx = byte_offset / 4u;
|
||||
let shift = (byte_offset & 3u) * 8u;
|
||||
let lo = K[word_idx];
|
||||
if (shift == 0u) {
|
||||
return lo;
|
||||
}
|
||||
let hi = K[word_idx + 1u];
|
||||
return (lo >> shift) | (hi << (32u - shift));
|
||||
}
|
||||
#endif // LOADERS_QUANTIZED_K
|
||||
|
||||
#ifdef LOADERS_QUANTIZED_V
|
||||
fn load_v_u16_at(byte_offset: u32) -> u32 {
|
||||
let word = V[byte_offset / 4u];
|
||||
let shift = (byte_offset & 2u) * 8u;
|
||||
return (word >> shift) & 0xFFFFu;
|
||||
}
|
||||
|
||||
fn load_v_u32_at(byte_offset: u32) -> u32 {
|
||||
let word_idx = byte_offset / 4u;
|
||||
let shift = (byte_offset & 3u) * 8u;
|
||||
let lo = V[word_idx];
|
||||
if (shift == 0u) {
|
||||
return lo;
|
||||
}
|
||||
let hi = V[word_idx + 1u];
|
||||
return (lo >> shift) | (hi << (32u - shift));
|
||||
}
|
||||
#endif // LOADERS_QUANTIZED_V
|
||||
|
||||
#endif // U32_DEQUANT_HELPERS
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -138,7 +138,7 @@ const FLOAT_MIN: f32 = -1.0e9;
|
||||
// The number of Q rows processed per workgroup
|
||||
var<workgroup> q_shmem: array<f16, Q_TILE * HEAD_DIM_QK>;
|
||||
|
||||
#ifndef KV_DIRECT
|
||||
#if !defined(K_DIRECT) || !defined(V_DIRECT)
|
||||
const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
|
||||
// we can reuse the same shmem for K and V since we only need one at a time
|
||||
var<workgroup> kv_shmem: array<f16, kv_shmem_size>;
|
||||
@@ -183,13 +183,12 @@ fn load_kx4(buf: ptr<storage, array<vec4<K_TYPE>>, read_write>, scalar_index: u3
|
||||
return (*buf)[scalar_index >> 2u];
|
||||
}
|
||||
|
||||
#ifndef KV_DIRECT
|
||||
#if !defined(K_DIRECT) || !defined(V_DIRECT)
|
||||
#define QUANT_SHMEM kv_shmem
|
||||
#define QUANT_OUT_TYPE f16
|
||||
#include "quant_inner_loops.tmpl"
|
||||
#include "flash_attn_quant_staging.tmpl"
|
||||
|
||||
#if !defined(K_Q4_0) && !defined(K_Q8_0)
|
||||
#if !defined(K_DIRECT) && !defined(K_Q4_0) && !defined(K_Q8_0)
|
||||
fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) {
|
||||
for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) {
|
||||
let k_row = elem_idx / HEAD_DIM_QK;
|
||||
@@ -204,7 +203,7 @@ fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u
|
||||
}
|
||||
#endif
|
||||
|
||||
#if !defined(V_Q4_0) && !defined(V_Q8_0)
|
||||
#if !defined(V_DIRECT) && !defined(V_Q4_0) && !defined(V_Q8_0)
|
||||
fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) {
|
||||
for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE) {
|
||||
let v_row = elem_idx / HEAD_DIM_V;
|
||||
@@ -296,7 +295,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
}
|
||||
|
||||
// load k tile into shared memory
|
||||
#ifndef KV_DIRECT
|
||||
#ifndef K_DIRECT
|
||||
load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
|
||||
#endif
|
||||
|
||||
@@ -306,7 +305,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
// TODO: this loop seems to be the current largest bottleneck
|
||||
// this bracket exists to scope the lifetime of variables, reducing register pressure
|
||||
{
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
let k_block_row = kv_tile + subgroup_id * SG_MAT_N;
|
||||
var k_global_offset = k_head_offset + k_block_row * params.stride_k1;
|
||||
#else
|
||||
@@ -318,7 +317,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
|
||||
var q_cur = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, 0u, false, HEAD_DIM_QK);
|
||||
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
var k_cur = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + 0u, true, params.stride_k1);
|
||||
#else
|
||||
var k_cur = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + 0u, true, HEAD_DIM_QK);
|
||||
@@ -328,7 +327,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
for (; t + 1u < HEAD_DIM_QK / SG_MAT_K; t += 2u) {
|
||||
let h0 = t * SG_MAT_K;
|
||||
var q0 = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, h0, false, HEAD_DIM_QK);
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
var k0 = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + h0, true, params.stride_k1);
|
||||
#else
|
||||
var k0 = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + h0, true, HEAD_DIM_QK);
|
||||
@@ -339,7 +338,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
|
||||
let h1 = (t + 1u) * SG_MAT_K;
|
||||
var q1g = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, h1, false, HEAD_DIM_QK);
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
var k1g = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + h1, true, params.stride_k1);
|
||||
#else
|
||||
var k1g = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + h1, true, HEAD_DIM_QK);
|
||||
@@ -353,7 +352,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
if (t < HEAD_DIM_QK / SG_MAT_K) {
|
||||
let h = t * SG_MAT_K;
|
||||
var qn = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, h, false, HEAD_DIM_QK);
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
var kn = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + h, true, params.stride_k1);
|
||||
#else
|
||||
var kn = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + h, true, HEAD_DIM_QK);
|
||||
@@ -365,7 +364,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
|
||||
acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc);
|
||||
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
k_global_offset += num_subgroups * SG_MAT_N * params.stride_k1;
|
||||
#else
|
||||
k_block_offset += num_subgroups * SG_MAT_N * HEAD_DIM_QK;
|
||||
@@ -436,7 +435,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
}
|
||||
|
||||
// load v tile into shared memory
|
||||
#ifndef KV_DIRECT
|
||||
#ifndef V_DIRECT
|
||||
load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
|
||||
#endif
|
||||
|
||||
@@ -464,7 +463,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
);
|
||||
|
||||
// load V submatrix from global or shared memory
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef V_DIRECT
|
||||
let v_block_row = kv_tile + kv_block * SG_MAT_N;
|
||||
let v_global_offset = v_head_offset + v_block_row * params.stride_v1 + head_dim_block;
|
||||
var v_sg_mat: subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K> = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(
|
||||
|
||||
@@ -1,3 +1,5 @@
|
||||
#include "quant_inner_loops.tmpl"
|
||||
|
||||
#define BLOCK_SIZE 32
|
||||
#define BLOCKS_K ((HEAD_DIM_QK + BLOCK_SIZE - 1) / BLOCK_SIZE)
|
||||
#define BLOCKS_V ((HEAD_DIM_V + BLOCK_SIZE - 1) / BLOCK_SIZE)
|
||||
@@ -26,49 +28,6 @@
|
||||
#define V_BYTES_PER_INNER_LOOP 4u
|
||||
#endif
|
||||
|
||||
#if defined(K_Q4_0) || defined(K_Q8_0)
|
||||
fn load_k_u16_at(byte_offset: u32) -> u32 {
|
||||
let word = K[byte_offset / 4u];
|
||||
let shift = (byte_offset & 2u) * 8u;
|
||||
return (word >> shift) & 0xFFFFu;
|
||||
}
|
||||
|
||||
fn load_k_u32_at(byte_offset: u32) -> u32 {
|
||||
let word_idx = byte_offset / 4u;
|
||||
let shift = (byte_offset & 3u) * 8u;
|
||||
let lo = K[word_idx];
|
||||
if (shift == 0u) {
|
||||
return lo;
|
||||
}
|
||||
let hi = K[word_idx + 1u];
|
||||
return (lo >> shift) | (hi << (32u - shift));
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(V_Q4_0) || defined(V_Q8_0)
|
||||
fn load_v_u16_at(byte_offset: u32) -> u32 {
|
||||
let word = V[byte_offset / 4u];
|
||||
let shift = (byte_offset & 2u) * 8u;
|
||||
return (word >> shift) & 0xFFFFu;
|
||||
}
|
||||
|
||||
fn load_v_u32_at(byte_offset: u32) -> u32 {
|
||||
let word_idx = byte_offset / 4u;
|
||||
let shift = (byte_offset & 3u) * 8u;
|
||||
let lo = V[word_idx];
|
||||
if (shift == 0u) {
|
||||
return lo;
|
||||
}
|
||||
let hi = V[word_idx + 1u];
|
||||
return (lo >> shift) | (hi << (32u - shift));
|
||||
}
|
||||
#endif
|
||||
|
||||
fn f16_from_u16(bits: u32) -> f16 {
|
||||
let packed = unpack2x16float(bits);
|
||||
return f16(packed[0]);
|
||||
}
|
||||
|
||||
#if defined(K_Q4_0) || defined(K_Q8_0)
|
||||
fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) {
|
||||
for (var elem_idx = local_x * K_NQ; elem_idx < kv_count * HEAD_DIM_QK; elem_idx += WG_SIZE * K_NQ) {
|
||||
|
||||
@@ -153,7 +153,6 @@ var<workgroup> p_shmem: array<f16, Q_TILE * KV_TILE>;
|
||||
|
||||
#define QUANT_SHMEM kv_shmem
|
||||
#define QUANT_OUT_TYPE f16
|
||||
#include "quant_inner_loops.tmpl"
|
||||
#include "flash_attn_quant_staging.tmpl"
|
||||
|
||||
#if !defined(K_Q4_0) && !defined(K_Q8_0)
|
||||
@@ -270,7 +269,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
local_scores[slot] = FLOAT_MIN;
|
||||
}
|
||||
|
||||
#ifndef KV_DIRECT
|
||||
// The tile path stages K/V in shared memory so each tile can be reused across
|
||||
// Q_TILE query rows. It therefore does not use the direct path.
|
||||
#ifndef K_DIRECT
|
||||
load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
|
||||
#endif
|
||||
|
||||
@@ -333,7 +334,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
|
||||
workgroupBarrier();
|
||||
|
||||
#ifndef KV_DIRECT
|
||||
// The tile path stages K/V in shared memory so each tile can be reused across
|
||||
// Q_TILE query rows. It therefore does not use the direct path.
|
||||
#ifndef V_DIRECT
|
||||
load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
|
||||
#endif
|
||||
|
||||
|
||||
@@ -196,49 +196,35 @@ struct Params {
|
||||
|
||||
// Just a very small float value.
|
||||
const FLOAT_MIN: f32 = -1.0e9;
|
||||
const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
|
||||
|
||||
var<workgroup> q_shmem: array<f32, HEAD_DIM_QK>;
|
||||
|
||||
#ifndef KV_DIRECT
|
||||
const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
|
||||
// we can reuse the same shmem for K and V since we only need one at a time
|
||||
var<workgroup> kv_shmem: array<f32, kv_shmem_size>;
|
||||
#endif
|
||||
|
||||
var<workgroup> o_shmem: array<f32, HEAD_DIM_V>;
|
||||
// note that we reuse the same storage for both since we only need one at a time
|
||||
var<workgroup> inter_shmem: array<f32, KV_TILE>;
|
||||
|
||||
#ifdef MASK
|
||||
// storage for mask values
|
||||
var<workgroup> mask_shmem: array<f32, KV_TILE>;
|
||||
#endif
|
||||
|
||||
// note that we reuse the same storage for both since we only need one at a time
|
||||
var<workgroup> inter_shmem: array<f32, KV_TILE>;
|
||||
|
||||
// Storage for row max and exp sum during online softmax
|
||||
fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 {
|
||||
var v = select(FLOAT_MIN,
|
||||
inter_shmem[kv_idx] * params.scale,
|
||||
kv_idx < KV_TILE);
|
||||
#ifdef LOGIT_SOFTCAP
|
||||
v = params.logit_softcap * tanh(v);
|
||||
#if defined(K_DIRECT) || defined(V_DIRECT)
|
||||
// Shared memory for scale factor (d) in quantized K/V. Multiple threads use the same value,
|
||||
// so caching it is more efficient, even on the direct path.
|
||||
var<workgroup> d_shmem: array<f32, kv_shmem_size / 32>;
|
||||
#endif
|
||||
#ifdef MASK
|
||||
if (apply_mask) {
|
||||
var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE);
|
||||
v += select(mask_val, slope * mask_val, has_bias);
|
||||
}
|
||||
#endif
|
||||
return v;
|
||||
}
|
||||
|
||||
#ifndef KV_DIRECT
|
||||
// K/V shared memory handling
|
||||
#if !defined(K_DIRECT) || !defined(V_DIRECT)
|
||||
|
||||
// we can reuse the same shmem for K and V since we only need one at a time
|
||||
var<workgroup> kv_shmem: array<f32, kv_shmem_size>;
|
||||
|
||||
#define QUANT_SHMEM kv_shmem
|
||||
#define QUANT_OUT_TYPE f32
|
||||
#include "quant_inner_loops.tmpl"
|
||||
#include "flash_attn_quant_staging.tmpl"
|
||||
|
||||
#if !defined(K_Q4_0) && !defined(K_Q8_0)
|
||||
#if !defined(K_DIRECT) && !defined(K_Q4_0) && !defined(K_Q8_0)
|
||||
fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) {
|
||||
for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE * 4u) {
|
||||
let k_row = elem_idx / HEAD_DIM_QK;
|
||||
@@ -256,7 +242,7 @@ fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u
|
||||
}
|
||||
#endif
|
||||
|
||||
#if !defined(V_Q4_0) && !defined(V_Q8_0)
|
||||
#if !defined(V_DIRECT) && !defined(V_Q4_0) && !defined(V_Q8_0)
|
||||
fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) {
|
||||
for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE * 4u) {
|
||||
let v_row = elem_idx / HEAD_DIM_V;
|
||||
@@ -273,7 +259,24 @@ fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u
|
||||
}
|
||||
}
|
||||
#endif
|
||||
#endif // !defined(K_DIRECT) || !defined(V_DIRECT)
|
||||
|
||||
// Storage for row max and exp sum during online softmax
|
||||
fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 {
|
||||
var v = select(FLOAT_MIN,
|
||||
inter_shmem[kv_idx] * params.scale,
|
||||
kv_idx < KV_TILE);
|
||||
#ifdef LOGIT_SOFTCAP
|
||||
v = params.logit_softcap * tanh(v);
|
||||
#endif
|
||||
#ifdef MASK
|
||||
if (apply_mask) {
|
||||
var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE);
|
||||
v += select(mask_val, slope * mask_val, has_bias);
|
||||
}
|
||||
#endif
|
||||
return v;
|
||||
}
|
||||
|
||||
@compute @workgroup_size(WG_SIZE)
|
||||
fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
@@ -355,12 +358,31 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
inter_shmem[elem_idx] = 0.0;
|
||||
}
|
||||
|
||||
// load k tile into shared memory
|
||||
#ifndef KV_DIRECT
|
||||
load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
|
||||
#ifdef K_DIRECT
|
||||
// load only the scale factor (d) from each quantized block into shared memory on the direct path.
|
||||
#if defined(K_Q8_0)
|
||||
for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_QK; j += WG_SIZE * 32) {
|
||||
let kv_row = kv_tile + j / HEAD_DIM_QK;
|
||||
let block_idx = (j % HEAD_DIM_QK) / 32;
|
||||
let block_byte_base = 34 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
|
||||
let d = f32(f16_from_u16(load_k_u16_at(block_byte_base)));
|
||||
d_shmem[j / 32] = d;
|
||||
}
|
||||
#elif defined(K_Q4_0)
|
||||
for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_QK; j += WG_SIZE * 32) {
|
||||
let kv_row = kv_tile + j / HEAD_DIM_QK;
|
||||
let block_idx = (j % HEAD_DIM_QK) / 32;
|
||||
let block_byte_base = 18 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
|
||||
let d = f32(f16_from_u16(load_k_u16_at(block_byte_base)));
|
||||
d_shmem[j / 32] = d;
|
||||
}
|
||||
#endif
|
||||
#else
|
||||
// load k tile into shared memory
|
||||
load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
|
||||
#endif // defined(K_DIRECT)
|
||||
|
||||
workgroupBarrier();
|
||||
workgroupBarrier();
|
||||
|
||||
// accumulate q block * k block into registers across the entire KV tile
|
||||
if (!skip_tile) {
|
||||
@@ -381,9 +403,40 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
q_shmem[q_off + 1u],
|
||||
q_shmem[q_off + 2u],
|
||||
q_shmem[q_off + 3u]);
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef K_DIRECT
|
||||
#if defined(K_Q8_0)
|
||||
let kv_row = kv_tile + kv_idx;
|
||||
let block_idx = (i * 4u) / 32;
|
||||
let id_in_block = (i * 4u) % 32;
|
||||
let block_byte_base = 34 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
|
||||
let q_byte_base = block_byte_base + 2u;
|
||||
let d = d_shmem[(kv_idx * HEAD_DIM_QK) / 32 + block_idx];
|
||||
let q8u4 = load_k_u32_at(q_byte_base + id_in_block);
|
||||
let kv = vec4<f32>(
|
||||
d * f32(get_byte_i32(q8u4, 0)),
|
||||
d * f32(get_byte_i32(q8u4, 1)),
|
||||
d * f32(get_byte_i32(q8u4, 2)),
|
||||
d * f32(get_byte_i32(q8u4, 3)),
|
||||
);
|
||||
#elif defined(K_Q4_0)
|
||||
let kv_row = kv_tile + kv_idx;
|
||||
let block_idx = (i * 4u) / 32;
|
||||
let id_in_block = (i * 4u) % 32;
|
||||
let phase = id_in_block / 16;
|
||||
let block_byte_base = 18 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
|
||||
let q_byte_base = block_byte_base + 2u;
|
||||
let d = d_shmem[(kv_idx * HEAD_DIM_QK) / 32 + block_idx];
|
||||
let q8u4 = load_k_u32_at(q_byte_base + (id_in_block - phase * 16u));
|
||||
let kv = vec4<f32>(
|
||||
d * (f32((get_byte(q8u4, 0) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
d * (f32((get_byte(q8u4, 1) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
d * (f32((get_byte(q8u4, 2) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
d * (f32((get_byte(q8u4, 3) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
);
|
||||
#else
|
||||
let idx = k_head_offset + (kv_tile + kv_idx) * params.stride_k1 + (i * 4u);
|
||||
let kv = vec4<f32>(K[idx >> 2u]);
|
||||
#endif
|
||||
#else
|
||||
let idx = kv_idx * HEAD_DIM_QK + (i * 4u);
|
||||
let kv = vec4<f32>(
|
||||
@@ -391,7 +444,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
kv_shmem[idx + 1u],
|
||||
kv_shmem[idx + 2u],
|
||||
kv_shmem[idx + 3u]);
|
||||
#endif
|
||||
#endif // defined(K_DIRECT)
|
||||
partial_sum += dot(qv, kv);
|
||||
}
|
||||
}
|
||||
@@ -473,12 +526,32 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
}
|
||||
}
|
||||
|
||||
// load v tile into shared memory
|
||||
#ifndef KV_DIRECT
|
||||
load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
|
||||
#endif
|
||||
|
||||
workgroupBarrier();
|
||||
#ifdef V_DIRECT
|
||||
// load only `d` of quantized block into shared memory in the direct path
|
||||
#if defined(V_Q8_0)
|
||||
for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_V; j += WG_SIZE * 32) {
|
||||
let v_row = kv_tile + j / HEAD_DIM_V;
|
||||
let block_idx = (j % HEAD_DIM_V) / 32;
|
||||
let block_byte_base = 34 * (v_head_offset + v_row * params.stride_v1 + block_idx);
|
||||
let d = f32(f16_from_u16(load_v_u16_at(block_byte_base)));
|
||||
d_shmem[j / 32] = d;
|
||||
}
|
||||
#elif defined(V_Q4_0)
|
||||
for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_V; j += WG_SIZE * 32) {
|
||||
let v_row = kv_tile + j / HEAD_DIM_V;
|
||||
let block_idx = (j % HEAD_DIM_V) / 32;
|
||||
let block_byte_base = 18 * (v_head_offset + v_row * params.stride_v1 + block_idx);
|
||||
let d = f32(f16_from_u16(load_v_u16_at(block_byte_base)));
|
||||
d_shmem[j / 32] = d;
|
||||
}
|
||||
#endif
|
||||
#else
|
||||
// load v tile into shared memory
|
||||
load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
|
||||
#endif // V_DIRECT
|
||||
|
||||
workgroupBarrier();
|
||||
|
||||
if (!skip_tile) {
|
||||
// we have P (KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem
|
||||
@@ -501,9 +574,38 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
}
|
||||
|
||||
let p = inter_shmem[kv_idx];
|
||||
#ifdef KV_DIRECT
|
||||
#ifdef V_DIRECT
|
||||
#if defined(V_Q8_0)
|
||||
let block_idx = (vec_col * 4u) / 32;
|
||||
let id_in_block = (vec_col * 4u) % 32;
|
||||
let block_byte_base = 34 * (v_head_offset + v_row * params.stride_v1 + block_idx);
|
||||
let q_byte_base = block_byte_base + 2u;
|
||||
let d = d_shmem[(kv_idx * HEAD_DIM_V) / 32 + block_idx];
|
||||
let q8u4 = load_v_u32_at(q_byte_base + id_in_block);
|
||||
let v4 = vec4<f32>(
|
||||
d * f32(get_byte_i32(q8u4, 0)),
|
||||
d * f32(get_byte_i32(q8u4, 1)),
|
||||
d * f32(get_byte_i32(q8u4, 2)),
|
||||
d * f32(get_byte_i32(q8u4, 3)),
|
||||
);
|
||||
#elif defined(V_Q4_0)
|
||||
let block_idx = (vec_col * 4u) / 32;
|
||||
let id_in_block = (vec_col * 4u) % 32;
|
||||
let phase = id_in_block / 16;
|
||||
let block_byte_base = 18 * (v_head_offset + v_row * params.stride_v1 + block_idx);
|
||||
let q_byte_base = block_byte_base + 2u;
|
||||
let d = d_shmem[(kv_idx * HEAD_DIM_V) / 32 + block_idx];
|
||||
let q8u4 = load_v_u32_at(q_byte_base + (id_in_block - phase * 16u));
|
||||
let v4 = vec4<f32>(
|
||||
d * (f32((get_byte(q8u4, 0) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
d * (f32((get_byte(q8u4, 1) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
d * (f32((get_byte(q8u4, 2) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
d * (f32((get_byte(q8u4, 3) >> (phase * 4u)) & 0xFu) - 8.0),
|
||||
);
|
||||
#else
|
||||
let v_idx = v_head_offset + v_row * params.stride_v1 + vec_col * 4u;
|
||||
let v4 = vec4<f32>(V[v_idx >> 2u]);
|
||||
#endif
|
||||
#else
|
||||
let v_idx = kv_idx * HEAD_DIM_V + vec_col * 4u;
|
||||
let v4 = vec4<f32>(
|
||||
@@ -511,7 +613,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
|
||||
kv_shmem[v_idx + 1u],
|
||||
kv_shmem[v_idx + 2u],
|
||||
kv_shmem[v_idx + 3u]);
|
||||
#endif
|
||||
#endif // defined(V_DIRECT)
|
||||
lo += p * v4;
|
||||
}
|
||||
|
||||
|
||||
@@ -4440,8 +4440,11 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B,
|
||||
MODEL_TENSOR.LAYER_OUT_NORM,
|
||||
MODEL_TENSOR.NEXTN_EH_PROJ,
|
||||
MODEL_TENSOR.NEXTN_EMBED_TOKENS,
|
||||
MODEL_TENSOR.NEXTN_ENORM,
|
||||
MODEL_TENSOR.NEXTN_HNORM,
|
||||
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
|
||||
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
|
||||
],
|
||||
MODEL_ARCH.STEP35: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
|
||||
@@ -1102,6 +1102,9 @@ extern "C" {
|
||||
LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
|
||||
LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab);
|
||||
|
||||
// model-specific suppress tokens (gguf key: tokenizer.ggml.suppress_tokens)
|
||||
LLAMA_API const llama_token * llama_vocab_get_suppress_tokens(const struct llama_vocab * vocab, int32_t * n_suppress_tokens);
|
||||
|
||||
LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
|
||||
LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
|
||||
LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab);
|
||||
|
||||
@@ -1 +1 @@
|
||||
9be313313c8ecb9488911bd64550190e3ed80f38
|
||||
06ca97616793248fadb410ea8d69c7511b2005e4
|
||||
|
||||
+17
-5
@@ -474,6 +474,9 @@ llama_context::llama_context(
|
||||
}
|
||||
|
||||
llama_context::~llama_context() {
|
||||
// wait for any pending asynchronous copies into the output buffers before they are freed
|
||||
synchronize();
|
||||
|
||||
if (!model.hparams.no_alloc) {
|
||||
for (size_t i = 0; i < backend_ptrs.size(); ++i) {
|
||||
ggml_backend_t backend = backend_ptrs[i];
|
||||
@@ -1417,13 +1420,17 @@ int llama_context::encode(const llama_batch & batch_inp) {
|
||||
// micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
|
||||
GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
|
||||
|
||||
// TODO: this clear of the buffer can easily be forgotten - need something better
|
||||
// sync first so any in-flight async copies into embd_seq complete before it is freed
|
||||
if (!embd_seq.empty()) {
|
||||
synchronize();
|
||||
}
|
||||
embd_seq.clear();
|
||||
|
||||
if (t_compute_start_us == 0) {
|
||||
t_compute_start_us = ggml_time_us();
|
||||
}
|
||||
|
||||
// TODO: this clear of the buffer can easily be forgotten - need something better
|
||||
embd_seq.clear();
|
||||
|
||||
sched_reserve();
|
||||
|
||||
n_queued_tokens += n_tokens;
|
||||
@@ -1762,13 +1769,18 @@ int llama_context::decode(const llama_batch & batch_inp) {
|
||||
|
||||
GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
|
||||
|
||||
// TODO: this clear of the buffer can easily be forgotten - need something better
|
||||
// sync first so any in-flight async copies into embd_seq complete before it is freed
|
||||
if (!embd_seq.empty()) {
|
||||
synchronize();
|
||||
}
|
||||
embd_seq.clear();
|
||||
|
||||
if (t_compute_start_us == 0) {
|
||||
t_compute_start_us = ggml_time_us();
|
||||
}
|
||||
n_queued_tokens += n_tokens_all;
|
||||
|
||||
// TODO: this clear of the buffer can easily be forgotten - need something better
|
||||
embd_seq.clear();
|
||||
output_swaps.clear();
|
||||
|
||||
sched_reserve();
|
||||
|
||||
+53
-2
@@ -2072,7 +2072,6 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
||||
res = nullptr;
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
{
|
||||
res = new llama_kv_cache_dsa(
|
||||
*this,
|
||||
@@ -2089,6 +2088,56 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
||||
nullptr,
|
||||
nullptr);
|
||||
} break;
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
{
|
||||
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) {
|
||||
// The NextN/MTP draft head runs dense MLA (no DSA indexer), so the
|
||||
// MTP context uses a plain attention KV cache holding only the
|
||||
// nextn layer(s) - same pattern as the hybrid Qwen3.5 MTP context.
|
||||
llama_kv_cache::layer_filter_cb filter =
|
||||
[&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
|
||||
res = new llama_kv_cache(
|
||||
*this,
|
||||
hparams,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
1,
|
||||
hparams.n_swa,
|
||||
hparams.swa_type,
|
||||
nullptr,
|
||||
filter,
|
||||
nullptr,
|
||||
nullptr);
|
||||
} else {
|
||||
// Main context: DSA cache for the trunk layers only - the nextn
|
||||
// layer(s) are never attended by the trunk graph.
|
||||
llama_kv_cache::layer_filter_cb filter = nullptr;
|
||||
if (hparams.n_layer_nextn > 0) {
|
||||
filter = [&](uint32_t il) { return il < hparams.n_layer(); };
|
||||
}
|
||||
|
||||
res = new llama_kv_cache_dsa(
|
||||
*this,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
1,
|
||||
hparams.n_swa,
|
||||
hparams.swa_type,
|
||||
filter,
|
||||
nullptr);
|
||||
}
|
||||
} break;
|
||||
// Models that need standard caching should rely on recurrent/hybrid
|
||||
// checks
|
||||
default:
|
||||
@@ -2194,7 +2243,9 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
||||
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
}
|
||||
|
||||
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3) && hparams.n_layer_nextn > 0) {
|
||||
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA ||
|
||||
arch == LLM_ARCH_MIMO2) &&
|
||||
hparams.n_layer_nextn > 0) {
|
||||
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
|
||||
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
} else {
|
||||
|
||||
+16
-1
@@ -2578,7 +2578,14 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
if (suppress_idx != -1) {
|
||||
const int n = gguf_get_arr_n(ctx, suppress_idx);
|
||||
const int32_t * data = (const int32_t *) gguf_get_arr_data(ctx, suppress_idx);
|
||||
suppress_tokens.assign(data, data + n);
|
||||
// drop out-of-range ids
|
||||
suppress_tokens.reserve(n);
|
||||
for (int i = 0; i < n; ++i) {
|
||||
const int32_t id = data[i];
|
||||
if (id >= 0 && id < (int) id_to_token.size()) {
|
||||
suppress_tokens.push_back(id);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -4205,6 +4212,14 @@ bool llama_vocab_get_add_sep(const struct llama_vocab * vocab) {
|
||||
return vocab->get_add_sep();
|
||||
}
|
||||
|
||||
const llama_token * llama_vocab_get_suppress_tokens(const struct llama_vocab * vocab, int32_t * n_suppress_tokens) {
|
||||
const std::vector<llama_token> & tokens = vocab->get_suppress_tokens();
|
||||
if (n_suppress_tokens) {
|
||||
*n_suppress_tokens = (int32_t) tokens.size();
|
||||
}
|
||||
return tokens.data();
|
||||
}
|
||||
|
||||
llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) {
|
||||
return vocab->token_fim_pre();
|
||||
}
|
||||
|
||||
@@ -142,33 +142,6 @@ static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, in
|
||||
idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
|
||||
}
|
||||
|
||||
// TODO @ngxson : maybe improve this in the future
|
||||
class llm_graph_input_logits_bias : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_logits_bias(const llama_vocab & vocab) {
|
||||
arr.resize(vocab.n_tokens(), 0.0f);
|
||||
for (llama_token id : vocab.get_suppress_tokens()) {
|
||||
if (0 <= id && id < (int32_t)vocab.n_tokens()) {
|
||||
arr[id] = -INFINITY;
|
||||
}
|
||||
}
|
||||
}
|
||||
virtual ~llm_graph_input_logits_bias() = default;
|
||||
|
||||
void set_input(const llama_ubatch * /*ubatch*/) override {
|
||||
const int64_t n_vocab = arr.size();
|
||||
ggml_backend_tensor_set(logits_bias, arr.data(), 0, n_vocab*ggml_element_size(logits_bias));
|
||||
}
|
||||
|
||||
bool can_reuse(const llm_graph_params & /*params*/) override {
|
||||
return true;
|
||||
}
|
||||
|
||||
ggml_tensor * logits_bias = nullptr; // F32 [n_vocab]
|
||||
|
||||
std::vector<float> arr;
|
||||
};
|
||||
|
||||
llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params),
|
||||
model(model),
|
||||
@@ -429,16 +402,6 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
|
||||
cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
|
||||
}
|
||||
|
||||
// apply logits bias if needed (e.g. for gemma4_unified patch)
|
||||
// this is to mirror the suppress_tokens patch on transformers, to avoid model from outputing <image|> and <audio|> tokens (which is a known issue related to the checkpoint)
|
||||
// TODO: maybe handle this inside the sampling system in the future
|
||||
if (!model.vocab.get_suppress_tokens().empty()) {
|
||||
auto inp_bias = std::make_unique<llm_graph_input_logits_bias>(model.vocab);
|
||||
inp_bias->logits_bias = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, inp_bias->arr.size());
|
||||
cur = ggml_add(ctx0, cur, inp_bias->logits_bias);
|
||||
res->add_input(std::move(inp_bias));
|
||||
}
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
|
||||
+271
-10
@@ -72,15 +72,27 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
|
||||
|
||||
switch (hparams.n_layer()) {
|
||||
case 78: type = LLM_TYPE_744B_A40B; break;
|
||||
case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer
|
||||
case 79:
|
||||
type = LLM_TYPE_744B_A40B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
||||
void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
const int64_t n_expert_shared = hparams.n_expert_shared;
|
||||
|
||||
// MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft).
|
||||
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
|
||||
// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
|
||||
// tensors live in a separate file (or were stripped at conversion). Mark
|
||||
// MTP tensors NOT_REQUIRED so the trunk loads cleanly.
|
||||
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
|
||||
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
|
||||
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
|
||||
const bool is_mla = hparams.is_mla();
|
||||
if (!is_mla) {
|
||||
throw std::runtime_error("GLM_DSA architecture requires MLA");
|
||||
@@ -109,12 +121,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer_all; ++i) {
|
||||
int flags = 0;
|
||||
if (i >= n_layer) {
|
||||
// skip all tensors in the NextN layers
|
||||
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
|
||||
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
|
||||
}
|
||||
// NextN/MTP layers (i >= n_layer) are full decoder blocks used by the
|
||||
// LLM_GRAPH_TYPE_DECODER_MTP draft head; load them like qwen35moe/step35/hy_v3.
|
||||
const int flags = (i >= n_layer) ? mtp_flags : trunk_flags;
|
||||
|
||||
auto & layer = layers[i];
|
||||
|
||||
@@ -167,7 +176,7 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
}
|
||||
|
||||
// NextN/MTP tensors (preserved but unused) - conditionally load for last n_layer_nextn
|
||||
// NextN/MTP tensors - the NextN-specific wiring around the extra decoder block
|
||||
if (i >= n_layer) {
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
|
||||
@@ -182,6 +191,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const {
|
||||
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
|
||||
return std::make_unique<graph_mtp>(*this, params);
|
||||
}
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
@@ -469,7 +481,9 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
|
||||
}
|
||||
}
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
// when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,
|
||||
// so the early output masking has to be skipped (it is applied after the final norm instead)
|
||||
if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
@@ -532,6 +546,14 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
|
||||
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
// post-norm hidden state feeds the NextN/MTP draft head
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
@@ -543,3 +565,242 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// LLM_GRAPH_TYPE_DECODER_MTP draft head for GLM-5.2 (GLM_DSA).
|
||||
// Semantics mirror the deepseek-family NextN/MTP layer:
|
||||
// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->
|
||||
// full glm_dsa decoder block (dense MLA attention + sigmoid-gated MoE FFN
|
||||
// with shared expert, exactly as the trunk deepseek2 graph builds it) ->
|
||||
// shared_head_norm (fallback output_norm) -> shared LM head.
|
||||
// The DSA indexer is not used at runtime (same as the trunk graph).
|
||||
llama_model_glm_dsa::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
|
||||
: llm_graph_context(params) {
|
||||
GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM_DSA MTP requires n_layer_nextn > 0");
|
||||
GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM_DSA MTP currently only supports a single MTP block");
|
||||
GGML_ASSERT(hparams.is_mla() && "GLM_DSA MTP requires MLA");
|
||||
|
||||
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
|
||||
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
|
||||
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
|
||||
"nextn_layer_offset out of range [0, n_layer_nextn)");
|
||||
const auto & layer = model.layers[il];
|
||||
|
||||
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
|
||||
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
|
||||
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
|
||||
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
|
||||
|
||||
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
|
||||
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
|
||||
|
||||
const uint32_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
|
||||
// See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY.
|
||||
GGML_ASSERT(ext_factor >= 0.0f);
|
||||
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
|
||||
|
||||
const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
|
||||
const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
|
||||
|
||||
// TODO: extract in a common llm_graph_context::build_inp_embd_h()
|
||||
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
|
||||
|
||||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
ggml_set_input(inp->tokens);
|
||||
|
||||
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
|
||||
ggml_set_input(inp->embd);
|
||||
|
||||
ggml_tensor * tok_embd;
|
||||
if (ubatch.token) {
|
||||
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
|
||||
|
||||
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
|
||||
} else {
|
||||
tok_embd = inp->embd;
|
||||
}
|
||||
cb(tok_embd, "mtp_tok_embd", il);
|
||||
|
||||
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
|
||||
ggml_set_input(inp->h);
|
||||
ggml_set_name(inp->h, "mtp_h_input");
|
||||
|
||||
ggml_tensor * h_embd = inp->h;
|
||||
|
||||
res->add_input(std::move(inp));
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
// MLA with the absorption optimization uses a K-only cache (V is a view of K)
|
||||
auto * inp_attn = build_attn_inp_k();
|
||||
|
||||
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(h_norm, "mtp_hnorm", il);
|
||||
|
||||
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(e_norm, "mtp_enorm", il);
|
||||
|
||||
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
|
||||
cb(concat, "mtp_concat", il);
|
||||
|
||||
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
|
||||
cb(cur, "mtp_eh_proj", il);
|
||||
|
||||
ggml_tensor * inpSA = cur;
|
||||
|
||||
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_attn_norm", il);
|
||||
|
||||
// self-attention: dense MLA, same construction as the deepseek2 trunk graph
|
||||
{
|
||||
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
|
||||
cb(q, "mtp_q", il);
|
||||
|
||||
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(q, "mtp_q", il);
|
||||
|
||||
q = ggml_mul_mat(ctx0, layer.wq_b, q);
|
||||
cb(q, "mtp_q", il);
|
||||
|
||||
// split into {n_embd_head_qk_nope, n_head, n_tokens}
|
||||
ggml_tensor * q_nope =
|
||||
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
|
||||
cb(q_nope, "mtp_q_nope", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, n_head, n_tokens}
|
||||
ggml_tensor * q_pe = ggml_view_3d(
|
||||
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "mtp_q_pe", il);
|
||||
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
|
||||
cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);
|
||||
|
||||
// split into {kv_lora_rank, n_tokens}
|
||||
ggml_tensor * kv_cmpr =
|
||||
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, 1, n_tokens}
|
||||
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
||||
cb(k_pe, "mtp_k_pe", il);
|
||||
|
||||
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(q_pe, "mtp_q_pe", il);
|
||||
|
||||
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(k_pe, "mtp_k_pe", il);
|
||||
|
||||
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr", il);
|
||||
|
||||
// {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "mtp_q_nope_perm", il);
|
||||
|
||||
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);
|
||||
|
||||
// {kv_lora_rank, n_head, n_tokens}
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
|
||||
// note: rope must go first for in-place context shifting in build_rope_shift()
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "mtp_Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "mtp_Kcur", il);
|
||||
|
||||
// {kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "mtp_Vcur", il);
|
||||
|
||||
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
|
||||
cur = build_attn(inp_attn,
|
||||
layer.wo, NULL, layer.wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);
|
||||
cb(cur, "mtp_attn_out", il);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "mtp_ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_ffn_norm", il);
|
||||
|
||||
// MoE FFN with shared expert - same construction as the deepseek2 trunk graph
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
layer.ffn_gate_inp,
|
||||
layer.ffn_up_exps,
|
||||
layer.ffn_gate_exps,
|
||||
layer.ffn_down_exps,
|
||||
layer.ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr,
|
||||
layer.ffn_gate_up_exps,
|
||||
layer.ffn_up_exps_s,
|
||||
layer.ffn_gate_exps_s,
|
||||
layer.ffn_down_exps_s);
|
||||
cb(moe_out, "mtp_ffn_moe_out", il);
|
||||
|
||||
// FFN shared expert
|
||||
ggml_tensor * ffn_shexp =
|
||||
build_ffn(cur,
|
||||
layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s,
|
||||
layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s,
|
||||
layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(ffn_shexp, "mtp_ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "mtp_ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "mtp_post_ffn", il);
|
||||
|
||||
// shared_head_norm applied after the decoder block, before the shared LM head.
|
||||
// The post-norm hidden state seeds the next MTP step.
|
||||
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
|
||||
? layer.nextn.shared_head_norm
|
||||
: model.output_norm;
|
||||
GGML_ASSERT(head_norm_w && "GLM_DSA MTP: missing both nextn.shared_head_norm and output_norm");
|
||||
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
cb(cur, "mtp_shared_head_norm", -1);
|
||||
|
||||
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
|
||||
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
|
||||
GGML_ASSERT(head_w && "GLM_DSA MTP: missing LM head (nextn.shared_head_head or model.output)");
|
||||
cur = build_lora_mm(head_w, cur, head_s);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
res->t_logits = cur;
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
+181
-21
@@ -25,9 +25,13 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
|
||||
void llama_model_mimo2::load_arch_tensors(llama_model_loader & ml) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
|
||||
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
|
||||
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
|
||||
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
@@ -40,41 +44,46 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
|
||||
uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
|
||||
uint32_t n_head = hparams.n_head(i);
|
||||
|
||||
// NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support
|
||||
const bool is_nextn = i >= n_layer;
|
||||
const int skip = is_nextn ? TENSOR_SKIP : 0;
|
||||
const int flags = is_nextn ? mtp_flags : 0;
|
||||
|
||||
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, skip);
|
||||
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, flags);
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, skip);
|
||||
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
|
||||
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | flags);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, skip);
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
|
||||
|
||||
// non-MoE branch
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags);
|
||||
|
||||
// MoE branch
|
||||
int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip);
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
|
||||
|
||||
if (is_nextn) {
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, skip);
|
||||
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, skip);
|
||||
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip);
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, flags);
|
||||
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, flags);
|
||||
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_mimo2::build_arch_graph(const llm_graph_params & params) const {
|
||||
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
|
||||
return std::make_unique<graph_mtp>(*this, params);
|
||||
}
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
@@ -89,6 +98,8 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
const float v_scale = hparams.f_attn_value_scale;
|
||||
const bool emit_h_nextn = cparams.embeddings_nextn;
|
||||
const bool crop_last_layer = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked);
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
@@ -168,7 +179,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
|
||||
}
|
||||
}
|
||||
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
if (il == n_layer - 1 && crop_last_layer) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
@@ -218,6 +229,15 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
|
||||
|
||||
cur = inpL;
|
||||
|
||||
if (emit_h_nextn) {
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
}
|
||||
|
||||
cur = build_norm(cur,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, -1);
|
||||
@@ -233,3 +253,143 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// Mirrors MiMo's appended NextN block: normalize and fuse token and hidden inputs, run the decoder block,
|
||||
// expose its pre-head-norm state to the next draft step, then apply the shared output norm and LM head.
|
||||
// Converted checkpoints may store that shared norm as layer_out_norm, so it remains in the fallback chain.
|
||||
llama_model_mimo2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
|
||||
: llm_graph_context(params) {
|
||||
GGML_ASSERT(hparams.n_layer_nextn > 0 && "MIMO2 MTP requires n_layer_nextn > 0");
|
||||
|
||||
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
|
||||
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
|
||||
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
|
||||
"nextn_layer_offset out of range [0, n_layer_nextn)");
|
||||
|
||||
const auto & layer = model.layers[il];
|
||||
GGML_ASSERT(layer.nextn.eh_proj && "MIMO2 MTP block missing nextn.eh_proj");
|
||||
GGML_ASSERT(layer.nextn.enorm && "MIMO2 MTP block missing nextn.enorm");
|
||||
GGML_ASSERT(layer.nextn.hnorm && "MIMO2 MTP block missing nextn.hnorm");
|
||||
GGML_ASSERT(layer.wqkv && "MIMO2 MTP requires fused attn_qkv");
|
||||
|
||||
const uint32_t n_head_l = hparams.n_head(il);
|
||||
const uint32_t n_head_kv_l = hparams.n_head_kv(il);
|
||||
|
||||
const float freq_base_l = model.get_rope_freq_base(cparams, il);
|
||||
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
|
||||
const float v_scale = hparams.f_attn_value_scale;
|
||||
|
||||
auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
|
||||
|
||||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
ggml_set_input(inp->tokens);
|
||||
|
||||
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
|
||||
ggml_set_input(inp->embd);
|
||||
ggml_set_name(inp->embd, "mtp_h_input");
|
||||
|
||||
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
|
||||
ggml_tensor * h_input = inp->embd;
|
||||
ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
|
||||
cb(tok_embd, "mtp_tok_embd", il);
|
||||
|
||||
res->add_input(std::move(inp));
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
auto * inp_attn = build_attn_inp_kv_iswa();
|
||||
|
||||
ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(h_norm, "mtp_hnorm", il);
|
||||
|
||||
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(e_norm, "mtp_enorm", il);
|
||||
|
||||
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
|
||||
cb(concat, "mtp_concat", il);
|
||||
|
||||
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
|
||||
cb(cur, "mtp_eh_proj", il);
|
||||
|
||||
ggml_tensor * inpSA = cur;
|
||||
|
||||
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_attn_norm", il);
|
||||
|
||||
ggml_tensor * qkv = build_lora_mm(layer.wqkv, cur, layer.wqkv_s);
|
||||
cb(qkv, "mtp_wqkv", il);
|
||||
|
||||
const size_t row_k = ggml_row_size(qkv->type, n_embd_head_k);
|
||||
const size_t row_v = ggml_row_size(qkv->type, n_embd_head_v);
|
||||
const size_t row_full = qkv->nb[1];
|
||||
const size_t k_off = row_k * n_head_l;
|
||||
const size_t v_off = k_off + row_k * n_head_kv_l;
|
||||
|
||||
ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l, n_tokens, row_k, row_full, 0);
|
||||
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off);
|
||||
ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
|
||||
cb(Qcur, "mtp_Qcur", il);
|
||||
cb(Kcur, "mtp_Kcur", il);
|
||||
cb(Vcur, "mtp_Vcur", il);
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
layer.wo, nullptr, layer.wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, nullptr,
|
||||
1.0f / sqrtf(float(n_embd_head_k)), il);
|
||||
cb(cur, "mtp_attn_out", il);
|
||||
|
||||
if (v_scale) {
|
||||
cur = ggml_scale(ctx0, cur, v_scale);
|
||||
cb(cur, "mtp_attn_out_scaled", il);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "mtp_ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_ffn_norm", il);
|
||||
|
||||
GGML_ASSERT(layer.ffn_gate && layer.ffn_down && layer.ffn_up && "MIMO2 MTP requires dense FFN tensors");
|
||||
cur = build_ffn(cur,
|
||||
layer.ffn_up, layer.ffn_up_b, nullptr,
|
||||
layer.ffn_gate, layer.ffn_gate_b, nullptr,
|
||||
layer.ffn_down, layer.ffn_down_b, nullptr,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "mtp_ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "mtp_post_ffn", il);
|
||||
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
|
||||
? layer.nextn.shared_head_norm
|
||||
: (layer.layer_out_norm ? layer.layer_out_norm : model.output_norm);
|
||||
GGML_ASSERT(head_norm_w && "MIMO2 MTP missing head norm fallback");
|
||||
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
|
||||
cb(cur, "mtp_shared_head_norm", -1);
|
||||
|
||||
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
|
||||
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
|
||||
GGML_ASSERT(head_w && "MIMO2 MTP missing LM head fallback");
|
||||
cur = build_lora_mm(head_w, cur, head_s);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
res->t_logits = cur;
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
+21
-72
@@ -2,7 +2,6 @@
|
||||
#include "llama-kv-cache.h"
|
||||
#include <cmath>
|
||||
#include <vector>
|
||||
#include <algorithm>
|
||||
#include <cstdint>
|
||||
|
||||
// MiniMax-M3: MiniMax-M2 style GQA (per-head QK-norm, partial rotary) with
|
||||
@@ -126,68 +125,6 @@ public:
|
||||
int64_t nblk;
|
||||
};
|
||||
|
||||
// pooled score of a block with no visible token: -inf from the mask, or -FLT_MAX from the
|
||||
// max-pool identity when every element of the block is -inf
|
||||
static inline bool msa_score_masked(float x) { return x <= -1e30f; }
|
||||
|
||||
// MSA block selection (batch regime)
|
||||
// CPU custom op, the token-level expansion and the combination with the causal mask happen on the GPU.
|
||||
static void msa_block_mask_op(struct ggml_tensor * dst, int ith, int nth, void * userdata) {
|
||||
const struct ggml_tensor * bs = dst->src[0];
|
||||
const struct ggml_tensor * bias = dst->src[1];
|
||||
const msa_params * p = (const msa_params *) userdata;
|
||||
|
||||
const int nblk = (int) bs->ne[0];
|
||||
const int Hd = (int) bs->ne[1];
|
||||
const int S = (int) bs->ne[2];
|
||||
|
||||
GGML_ASSERT(bs->type == GGML_TYPE_F32 && ggml_is_contiguous(bs));
|
||||
GGML_ASSERT(bias->type == GGML_TYPE_F32 && ggml_is_contiguous(bias));
|
||||
GGML_ASSERT(dst->type == GGML_TYPE_F16 && ggml_is_contiguous(dst));
|
||||
GGML_ASSERT(dst->ne[0] == nblk && dst->ne[1] == S && dst->ne[2] == Hd);
|
||||
GGML_ASSERT(bias->ne[0] == nblk && bias->ne[1] == S);
|
||||
|
||||
const int topk = p->topk_blocks < nblk ? p->topk_blocks : nblk;
|
||||
|
||||
const ggml_fp16_t f16_zero = ggml_fp32_to_fp16(0.0f);
|
||||
const ggml_fp16_t f16_ninf = ggml_fp32_to_fp16(-INFINITY);
|
||||
|
||||
std::vector<float> rank(nblk);
|
||||
std::vector<char> valid(nblk);
|
||||
std::vector<int> ord(nblk);
|
||||
|
||||
ggml_fp16_t * out = (ggml_fp16_t *) dst->data;
|
||||
|
||||
for (int i = ith; i < S; i += nth) {
|
||||
const float * bias_col = (const float *) bias->data + (size_t) i * nblk;
|
||||
for (int h = 0; h < Hd; ++h) {
|
||||
const float * bs_col = (const float *) bs->data + ((size_t) i * Hd + h) * nblk;
|
||||
|
||||
for (int bk = 0; bk < nblk; ++bk) {
|
||||
// a block is selectable if it has a visible token or is locally forced
|
||||
valid[bk] = !msa_score_masked(bs_col[bk]) || bias_col[bk] > 0.0f;
|
||||
rank [bk] = bias_col[bk] > 0.0f ? bias_col[bk] : bs_col[bk];
|
||||
ord [bk] = bk;
|
||||
}
|
||||
|
||||
std::partial_sort(ord.begin(), ord.begin() + topk, ord.end(),
|
||||
[&](int a, int b) { return rank[a] > rank[b]; });
|
||||
|
||||
ggml_fp16_t * dst_col = out + ((size_t) h * S + i) * nblk;
|
||||
for (int bk = 0; bk < nblk; ++bk) {
|
||||
dst_col[bk] = f16_ninf;
|
||||
}
|
||||
for (int t = 0; t < topk; ++t) {
|
||||
const int bk = ord[t];
|
||||
if (!valid[bk]) {
|
||||
break; // sorted desc: first invalid -> fewer than topk selectable blocks
|
||||
}
|
||||
dst_col[bk] = f16_zero;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// One FA call for all GQA groups (and at multi-stream decode, all streams) by mapping them onto the FA sequence dim (ne[3])
|
||||
ggml_tensor * llama_model_minimax_m3::graph::build_attn_msa_fa(
|
||||
ggml_tensor * q_cur, // [D, HQ, T]
|
||||
@@ -433,8 +370,8 @@ llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_
|
||||
msa_mf->nb[1], msa_mf->nb[1], st*msa_mf->nb[3]);
|
||||
ggml_tensor * km_s = ggml_view_3d(ctx0, msa_kqm, n_kv, n_tps, 1,
|
||||
msa_kqm->nb[1], msa_kqm->nb[3], st*msa_kqm->nb[3]);
|
||||
ggml_tensor * bias_s = ggml_view_2d(ctx0, msa_loc->bias, nblk, n_tps,
|
||||
msa_loc->bias->nb[1], st*n_tps*msa_loc->bias->nb[1]);
|
||||
ggml_tensor * bias_s = ggml_view_3d(ctx0, msa_loc->bias, nblk, 1, n_tps,
|
||||
msa_loc->bias->nb[1], msa_loc->bias->nb[1], st*n_tps*msa_loc->bias->nb[1]);
|
||||
ggml_tensor * q_s = ggml_view_3d(ctx0, Qcur, D, n_head, n_tps,
|
||||
Qcur->nb[1], Qcur->nb[2], st*n_tps*Qcur->nb[2]);
|
||||
ggml_tensor * k_s = ggml_view_4d(ctx0, k, D, HKV, n_kv, 1,
|
||||
@@ -453,15 +390,27 @@ llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_
|
||||
ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0);
|
||||
cb(bs, "msa_bs", il);
|
||||
|
||||
// block-level 0/-inf keep mask on the CPU, tiny transfer
|
||||
ggml_tensor * srcs[2] = { bs, bias_s };
|
||||
ggml_tensor * bm = ggml_custom_4d(ctx0, GGML_TYPE_F16,
|
||||
nblk, n_tps, Hd, 1,
|
||||
srcs, 2, msa_block_mask_op, GGML_N_TASKS_MAX,
|
||||
const_cast<msa_params *>(&mm.msa_p));
|
||||
// bias the scores so locally-forced blocks always rank first
|
||||
ggml_tensor * bsf = ggml_add(ctx0, bs, bias_s); // [nblk, Hd, n_tps]
|
||||
cb(bsf, "msa_bsf", il);
|
||||
|
||||
ggml_tensor * idx = ggml_top_k(ctx0, bsf, K); // [K, Hd, n_tps] i32
|
||||
|
||||
ggml_tensor * ninf = ggml_cast(ctx0,
|
||||
ggml_scale_bias(ctx0, bias_s, 0.0f, -1e30f),
|
||||
GGML_TYPE_F16); // [nblk, 1, n_tps]
|
||||
ninf = ggml_repeat_4d(ctx0, ninf, nblk, Hd, n_tps, 1);
|
||||
ggml_tensor * zero = ggml_scale(ctx0,
|
||||
ggml_cast(ctx0, idx, GGML_TYPE_F32), 0.0f);
|
||||
ggml_tensor * bm = ggml_set_rows(ctx0,
|
||||
ggml_reshape_3d(ctx0, ninf, 1, nblk, Hd*n_tps),
|
||||
ggml_reshape_3d(ctx0, zero, 1, K, Hd*n_tps),
|
||||
ggml_reshape_2d(ctx0, idx, K, Hd*n_tps));
|
||||
bm = ggml_reshape_3d(ctx0, bm, nblk, Hd, n_tps);
|
||||
bm = ggml_cont(ctx0, ggml_permute(ctx0, bm, 0, 2, 1, 3)); // [nblk, n_tps, Hd]
|
||||
cb(bm, "msa_block_mask", il);
|
||||
|
||||
// expand block -> token granularity on the GPU (j = bk*blk + t),
|
||||
// expand block -> token granularity (j = bk*blk + t),
|
||||
// then combine with the causal mask in place
|
||||
ggml_tensor * bmx = ggml_repeat_4d(ctx0,
|
||||
ggml_reshape_3d(ctx0, bm, 1, nblk, n_tps*Hd),
|
||||
|
||||
@@ -1237,6 +1237,10 @@ struct llama_model_glm_dsa : public llama_model_base {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct graph_mtp : public llm_graph_context {
|
||||
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
@@ -2123,6 +2127,10 @@ struct llama_model_mimo2 : public llama_model_base {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct graph_mtp : public llm_graph_context {
|
||||
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
|
||||
@@ -87,7 +87,7 @@ function(llama_build_and_test source)
|
||||
set(multiValueArgs ARGS)
|
||||
cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
|
||||
|
||||
set(TEST_SOURCES ${source} ${LLAMA_TEST_UNPARSED_ARGUMENTS} get-model.cpp)
|
||||
set(TEST_SOURCES ${source} ${LLAMA_TEST_UNPARSED_ARGUMENTS})
|
||||
|
||||
if (NOT DEFINED LLAMA_TEST_LABEL)
|
||||
set(LLAMA_TEST_LABEL "main")
|
||||
@@ -148,7 +148,7 @@ if (LLAMA_LLGUIDANCE)
|
||||
llama_build_and_test(test-grammar-llguidance.cpp ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf)
|
||||
endif ()
|
||||
|
||||
llama_build(test-recurrent-state-rollback.cpp get-model.cpp)
|
||||
llama_build(test-recurrent-state-rollback.cpp)
|
||||
|
||||
if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
|
||||
# these tests are disabled on Windows because they use internal functions not exported with LLAMA_API (when building with shared libraries)
|
||||
@@ -278,6 +278,10 @@ set_tests_properties(test-state-restore-fragmented PROPERTIES FIXTURES_REQUIRED
|
||||
llama_build_and_test(test-save-load-state.cpp LABEL "model" ARGS -m "${MODEL_DEST}")
|
||||
set_tests_properties(test-save-load-state PROPERTIES FIXTURES_REQUIRED test-download-model)
|
||||
|
||||
if (APPLE)
|
||||
llama_build(test-rset-release.cpp)
|
||||
endif()
|
||||
|
||||
if (NOT GGML_BACKEND_DL)
|
||||
# these tests use the backends directly and cannot be built with dynamic loading
|
||||
llama_build_and_test(test-barrier.cpp)
|
||||
|
||||
@@ -1,21 +0,0 @@
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <cstring>
|
||||
|
||||
#include "get-model.h"
|
||||
|
||||
char * get_model_or_exit(int argc, char *argv[]) {
|
||||
char * model_path;
|
||||
if (argc > 1) {
|
||||
model_path = argv[1];
|
||||
|
||||
} else {
|
||||
model_path = getenv("LLAMACPP_TEST_MODELFILE");
|
||||
if (!model_path || strlen(model_path) == 0) {
|
||||
fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE=<gguf_model_path> to silence this warning and run this test.\n\033[0m");
|
||||
exit(EXIT_SUCCESS);
|
||||
}
|
||||
}
|
||||
|
||||
return model_path;
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
#pragma once
|
||||
char * get_model_or_exit(int, char*[]);
|
||||
@@ -1,15 +1,13 @@
|
||||
// ref: https://github.com/ggml-org/llama.cpp/issues/4952#issuecomment-1892864763
|
||||
|
||||
#include <cstdio>
|
||||
#include <string>
|
||||
#include <thread>
|
||||
|
||||
#include "llama.h"
|
||||
#include "get-model.h"
|
||||
#include "common.h"
|
||||
|
||||
// This creates a new context inside a pthread and then tries to exit cleanly.
|
||||
int main(int argc, char ** argv) {
|
||||
auto * model_path = get_model_or_exit(argc, argv);
|
||||
auto * model_path = common_get_model_or_exit(argc, argv);
|
||||
|
||||
std::thread([&model_path]() {
|
||||
llama_backend_init();
|
||||
|
||||
@@ -1350,18 +1350,22 @@ struct test_case {
|
||||
|
||||
// check if the backends support the ops
|
||||
bool supported = true;
|
||||
std::string unsupported_str;
|
||||
for (ggml_backend_t backend : {backend1, backend2}) {
|
||||
for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
|
||||
if (!ggml_backend_supports_op(backend, t)) {
|
||||
supported = false;
|
||||
break;
|
||||
if (unsupported_str.empty()) {
|
||||
unsupported_str = std::string(ggml_backend_name(backend));
|
||||
} else {
|
||||
unsupported_str += ", " + std::string(ggml_backend_name(backend));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!supported) {
|
||||
// Create test result for unsupported operation
|
||||
test_result result(ggml_backend_name(backend1), current_op_name, vars(), "test",
|
||||
test_result result(unsupported_str, current_op_name, vars(), "test",
|
||||
false, false, "not supported");
|
||||
|
||||
print_test_result_locked(output_printer, result);
|
||||
@@ -8009,6 +8013,7 @@ static const ggml_type other_types[] = {
|
||||
GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
|
||||
GGML_TYPE_Q8_0,
|
||||
GGML_TYPE_Q1_0,
|
||||
GGML_TYPE_Q2_0,
|
||||
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
|
||||
GGML_TYPE_Q5_K,
|
||||
GGML_TYPE_Q6_K,
|
||||
@@ -8324,7 +8329,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_conv_2d(
|
||||
{ act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
|
||||
{ act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
|
||||
kernel_type, 1, 1, 0, 0, 1, 1, false));
|
||||
kernel_type, 1, 1, 0, 0, 1, 1, false)); // bool cwhn = false
|
||||
test_cases.emplace_back(new test_conv_2d(
|
||||
{ act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
|
||||
{ act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
|
||||
kernel_type, 1, 1, 0, 0, 1, 1, true)); // bool cwhn = true
|
||||
}
|
||||
}
|
||||
#endif
|
||||
@@ -8353,7 +8362,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
calc_conv_output_size(H, KH, s1, p1, d1) > 0) {
|
||||
for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
|
||||
test_cases.emplace_back(new test_conv_2d(
|
||||
{ W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false));
|
||||
{ W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false)); // bool cwhn = false
|
||||
test_cases.emplace_back(new test_conv_2d(
|
||||
{ W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, true)); // bool cwhn = true
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -8365,7 +8376,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
}
|
||||
}
|
||||
for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
|
||||
test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false));
|
||||
test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false)); // bool cwhn = false
|
||||
test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, true)); // bool cwhn = true
|
||||
}
|
||||
|
||||
// sycl backend will limit task global_range < MAX_INT
|
||||
@@ -8833,6 +8845,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_MXFP4, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));
|
||||
|
||||
// m == 1, with n on both sides of MMVF_MAX_BATCH_SIZE (8): mmvf below, operand swap above
|
||||
for (int64_t n : {1, 7, 8, 9, 16, 128, 512}) {
|
||||
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 1, n, 2048, {1, 1}, {1, 1}));
|
||||
}
|
||||
|
||||
#if 0
|
||||
{
|
||||
@@ -9559,6 +9575,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_Q4_0));
|
||||
test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q1_0));
|
||||
test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_F16));
|
||||
test_cases.emplace_back(new test_flash_attn_ext(128, 128, 4, {1, 1}, 96, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_Q2_0));
|
||||
test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_Q4_0));
|
||||
test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q2_0));
|
||||
test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_F16));
|
||||
|
||||
// large-KV F16 cases (Qwen3.6-27B geometry and a llama-class control): the upstream matrix
|
||||
// stops at kv=1024, blind to long-context FA bugs (e.g. the oneDNN SDPA ordering race on BMG).
|
||||
@@ -9744,7 +9764,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
|
||||
test_cases.emplace_back(new test_conv_2d(
|
||||
{ act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
|
||||
{ act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
|
||||
kernel_type, 1, 1, 0, 0, 1, 1, false));
|
||||
kernel_type, 1, 1, 0, 0, 1, 1, false)); // bool cwhn = false
|
||||
test_cases.emplace_back(new test_conv_2d(
|
||||
{ act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
|
||||
{ act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
|
||||
kernel_type, 1, 1, 0, 0, 1, 1, true)); // bool cwhn = true
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
#include "ggml.h"
|
||||
#include "llama.h"
|
||||
#include "llama-cpp.h"
|
||||
#include "get-model.h"
|
||||
#include "common.h"
|
||||
|
||||
#ifdef NDEBUG
|
||||
@@ -1136,7 +1135,7 @@ int main(int argc, char ** argv) {
|
||||
test_args args = parse_cli(argc, argv);
|
||||
|
||||
if (args.model.empty()) {
|
||||
args.model = get_model_or_exit(1, argv);
|
||||
args.model = common_get_model_or_exit(1, argv);
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
#include "llama.h"
|
||||
#include "get-model.h"
|
||||
#include "common.h"
|
||||
|
||||
#include <cstdlib>
|
||||
|
||||
int main(int argc, char *argv[] ) {
|
||||
auto * model_path = get_model_or_exit(argc, argv);
|
||||
auto * model_path = common_get_model_or_exit(argc, argv);
|
||||
auto * file = fopen(model_path, "r");
|
||||
if (file == nullptr) {
|
||||
fprintf(stderr, "no model at '%s' found\n", model_path);
|
||||
|
||||
@@ -216,18 +216,18 @@ static std::string snapshot_file_from_name(const std::string & name) {
|
||||
}
|
||||
|
||||
static const remote_model_spec model_specs[] = {
|
||||
{ "ggml-org/Qwen3-0.6B-GGUF", "Q8_0" },
|
||||
{ "ggml-org/GLM-4.6V-GGUF", "Q8_0" },
|
||||
{ "ggml-org/Step-3.5-Flash-GGUF", "Q4_K" },
|
||||
{ "ggml-org/Qwen3-Coder-Next-GGUF", "Q8_0" },
|
||||
{ "ggml-org/Qwen3-14B-GGUF", "Q8_0" },
|
||||
{ "ggml-org/Nemotron-Nano-3-30B-A3B-GGUF", "Q8_0" },
|
||||
{ "ggml-org/gpt-oss-120b-GGUF", "mxfp4" },
|
||||
{ "ggml-org/gemma-3-4b-it-GGUF", "Q8_0" },
|
||||
{ "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" },
|
||||
{ "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF", "IQ1_M" },
|
||||
{ "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", "IQ1_S" }, // TODO: swap with ggml-org if/when it's released
|
||||
{ "bartowski/Qwen_Qwen3.5-27B-GGUF", "Q8_0" }, // TODO: swap with ggml-org if/when it's released
|
||||
{ "ggml-org/Qwen3-0.6B-GGUF", "Q8_0" },
|
||||
{ "ggml-org/GLM-4.6V-GGUF", "Q8_0" },
|
||||
{ "ggml-org/Step-3.5-Flash-GGUF", "Q4_K" },
|
||||
{ "ggml-org/Qwen3-Coder-Next-GGUF", "Q8_0" },
|
||||
{ "ggml-org/Qwen3-14B-GGUF", "Q8_0" },
|
||||
{ "ggml-org/NVIDIA-Nemotron-Nano-3-30B-A3B-GGUF", "Q8_0" },
|
||||
{ "ggml-org/gpt-oss-120b-GGUF", "mxfp4" },
|
||||
{ "ggml-org/gemma-3-4b-it-GGUF", "Q8_0" },
|
||||
{ "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" },
|
||||
{ "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF", "IQ1_M" },
|
||||
//{ "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", "IQ1_S" }, // TODO: swap with ggml-org if/when it's released
|
||||
{ "ggml-org/Qwen3.6-27B-GGUF", "Q8_0" },
|
||||
};
|
||||
|
||||
static const int n_model_specs = (int) (sizeof(model_specs) / sizeof(model_specs[0]));
|
||||
|
||||
@@ -0,0 +1,53 @@
|
||||
// ref: https://github.com/ggml-org/llama.cpp/issues/25937
|
||||
// only works reliably when run with a large model that occupies 3GB+ of wired memory
|
||||
// thus, this test is not run by default
|
||||
// example model to run with: google/gemma-4-E4B-it-qat-q4_0-gguf
|
||||
|
||||
#include "llama.h"
|
||||
#include "common.h"
|
||||
|
||||
#include <cstdint>
|
||||
#include <mach/mach.h>
|
||||
#include <mach/mach_host.h>
|
||||
#include <unistd.h>
|
||||
|
||||
static uint64_t wired_memory() {
|
||||
vm_statistics64_data_t vmstat;
|
||||
mach_msg_type_number_t count = HOST_VM_INFO64_COUNT;
|
||||
if (host_statistics64(mach_host_self(), HOST_VM_INFO64, (host_info64_t)&vmstat, &count) != KERN_SUCCESS) {
|
||||
return UINT64_MAX;
|
||||
}
|
||||
return static_cast<uint64_t>(vmstat.wire_count) * vm_kernel_page_size;
|
||||
}
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
auto * model_path = common_get_model_or_exit(argc, argv);
|
||||
|
||||
llama_backend_init();
|
||||
|
||||
const uint64_t wired_initial = wired_memory();
|
||||
|
||||
llama_model_params params = llama_model_default_params();
|
||||
params.load_mode = LLAMA_LOAD_MODE_NONE;
|
||||
struct llama_model* model = llama_model_load_from_file(model_path, params);
|
||||
|
||||
const uint64_t wired_loaded = wired_memory();
|
||||
const uint64_t wired_delta = wired_loaded - wired_initial;
|
||||
// system memory fluctuates, so we need to allocate enough to reliably detect the release
|
||||
GGML_ASSERT(wired_delta > 2'000'000'000); // 2GB
|
||||
|
||||
llama_model_free(model);
|
||||
|
||||
const uint64_t t_start_ms = ggml_time_ms();
|
||||
|
||||
// expect most of the allocated memory to be released within 10 seconds
|
||||
// we allow for some tolerance due to system-wide memory fluctuations
|
||||
while (wired_memory() > wired_loaded - 0.75 * wired_delta) {
|
||||
GGML_ASSERT(ggml_time_ms() - t_start_ms < 10'000);
|
||||
usleep(100'000); // 100ms
|
||||
}
|
||||
|
||||
llama_backend_free();
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -33,7 +33,7 @@ enum resize_algo {
|
||||
RESIZE_ALGO_BILINEAR, // stretch to target resolution
|
||||
RESIZE_ALGO_BICUBIC, // center-crop when aspect ratio doesn't match
|
||||
RESIZE_ALGO_BICUBIC_PILLOW,
|
||||
// RESIZE_ALGO_LANCZOS, // TODO
|
||||
RESIZE_ALGO_LANCZOS,
|
||||
};
|
||||
|
||||
// Padding style for img_tool::resize
|
||||
|
||||
+50
-10
@@ -68,6 +68,9 @@ struct img_tool {
|
||||
case RESIZE_ALGO_BICUBIC_PILLOW:
|
||||
resize_bicubic_pillow(src, dst, target_resolution.width, target_resolution.height);
|
||||
break;
|
||||
case RESIZE_ALGO_LANCZOS:
|
||||
resize_lanczos_pillow(src, dst, target_resolution.width, target_resolution.height);
|
||||
break;
|
||||
default:
|
||||
throw std::runtime_error("Unsupported resize algorithm");
|
||||
}
|
||||
@@ -97,6 +100,9 @@ struct img_tool {
|
||||
case RESIZE_ALGO_BICUBIC_PILLOW:
|
||||
resize_bicubic_pillow(src, resized_image, new_width, new_height);
|
||||
break;
|
||||
case RESIZE_ALGO_LANCZOS:
|
||||
resize_lanczos_pillow(src, resized_image, new_width, new_height);
|
||||
break;
|
||||
default:
|
||||
throw std::runtime_error("Unsupported resize algorithm");
|
||||
}
|
||||
@@ -337,22 +343,50 @@ private:
|
||||
}
|
||||
}
|
||||
|
||||
// Bicubic resize function using Pillow's ImagingResample algorithm
|
||||
// Pillow-compatible separable resampling (Bicubic and Lanczos)
|
||||
// Adapted from https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Resample.c
|
||||
//
|
||||
// Key Difference with resize_bicubic:
|
||||
// 1. Uses separable filtering: horizontal pass followed by vertical pass
|
||||
// Key properties:
|
||||
// 1. Separable filtering: horizontal pass followed by vertical pass
|
||||
// 2. Pre-computes normalized filter coefficients for each output pixel
|
||||
// 3. Applies convolution using fixed-point integer arithmetic for performance
|
||||
// 3. Fixed-point integer arithmetic (22 fractional bits) for speed and determinism
|
||||
static bool resize_bicubic_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
|
||||
return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/false);
|
||||
}
|
||||
|
||||
// Lanczos-3 (support radius 3), matches Pillow's Image.LANCZOS
|
||||
static bool resize_lanczos_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
|
||||
return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/true);
|
||||
}
|
||||
|
||||
static bool resize_pillow(
|
||||
const clip_image_u8 & img,
|
||||
clip_image_u8 & dst,
|
||||
int target_width,
|
||||
int target_height,
|
||||
bool use_lanczos) {
|
||||
// Fixed-point precision: 22 bits = 32 (int32_t) - 8 (uint8_t pixels) - 2 (headroom for accumulation)
|
||||
// This allows encoding fractional weights as integers: weight * 2^22
|
||||
const int PRECISION_BITS = 32 - 8 - 2;
|
||||
|
||||
// Bicubic filter function with a = -0.5 (Note that GGML/PyTorch takes a = -0.75)
|
||||
// Resample filter: Lanczos-3 (support [-3, 3]) or bicubic with a = -0.5 (support [-2, 2])
|
||||
// Note: GGML/PyTorch bicubic uses a = -0.75, Pillow uses a = -0.5
|
||||
// Returns filter weight for distance x from pixel center
|
||||
// Support: [-2, 2], meaning the filter influences pixels within 2 units of distance
|
||||
auto bicubic_filter = [](double x) -> double {
|
||||
auto resample_filter = [use_lanczos](double x) -> double {
|
||||
if (use_lanczos) {
|
||||
if (-3.0 <= x && x < 3.0) {
|
||||
auto sinc = [](double v) {
|
||||
if (v == 0.0) {
|
||||
return 1.0;
|
||||
}
|
||||
const double pi_v = v * 3.141592653589793238462643383279502884;
|
||||
return std::sin(pi_v) / pi_v;
|
||||
};
|
||||
return sinc(x) * sinc(x / 3.0);
|
||||
}
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
constexpr double a = -0.5;
|
||||
if (x < 0.0) {
|
||||
x = -x;
|
||||
@@ -366,8 +400,8 @@ private:
|
||||
return 0.0; // Zero outside [-2, 2]
|
||||
};
|
||||
|
||||
// Filter support radius: bicubic extends 2 pixels in each direction
|
||||
constexpr double filter_support = 2.0;
|
||||
// Filter support radius: 2 for bicubic, 3 for lanczos
|
||||
const double filter_support = use_lanczos ? 3.0 : 2.0;
|
||||
|
||||
// Clipping function for 8-bit values
|
||||
auto clip8 = [](int val) -> uint8_t {
|
||||
@@ -434,7 +468,7 @@ private:
|
||||
// Compute filter weights for each contributing input pixel
|
||||
for (x = 0; x < xmax; x++) {
|
||||
// Distance from input pixel center to output pixel center in input space
|
||||
double w = bicubic_filter((x + xmin - center + 0.5) * ss);
|
||||
double w = resample_filter((x + xmin - center + 0.5) * ss);
|
||||
pre_weights[xx * ksize + x] = w;
|
||||
ww += w; // Accumulate for normalization
|
||||
}
|
||||
@@ -463,6 +497,12 @@ private:
|
||||
const double fxp_scale = std::ldexp(1.0, PRECISION_BITS); // 1.0 * 2^PRECISION_BITS
|
||||
|
||||
for (int i = 0; i < outSize * ksize; i++) {
|
||||
if (use_lanczos) {
|
||||
// Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice
|
||||
const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5);
|
||||
weights[i] = static_cast<int32_t>(rounded);
|
||||
continue;
|
||||
}
|
||||
double tmp_val = pre_weights[i] * fxp_scale;
|
||||
if (pre_weights[i] < 0) {
|
||||
tmp_val -= 0.5;
|
||||
|
||||
@@ -78,31 +78,41 @@ struct server_batch {
|
||||
};
|
||||
std::vector<token> tokens;
|
||||
int32_t n_tokens_alloc = 0;
|
||||
int32_t n_embd = 0;
|
||||
|
||||
// track if given slot can be batched with slots already in the batch
|
||||
server_slot * slot_batched = nullptr;
|
||||
|
||||
// in embd mode, we temporarily swap out the tokens arr and restore it on clear()
|
||||
bool has_embd = false;
|
||||
llama_token * tokens_ptr = nullptr;
|
||||
std::vector<float> embd;
|
||||
|
||||
float alora_scale = -1.0f;
|
||||
size_t alora_disabled_id = 0;
|
||||
|
||||
server_batch() {
|
||||
batch.token = nullptr; // sentinel: uninitialized batch
|
||||
batch.pos = nullptr; // sentinel: uninitialized batch
|
||||
}
|
||||
|
||||
~server_batch() {
|
||||
if (batch.token != nullptr) {
|
||||
if (batch.pos != nullptr) {
|
||||
clear();
|
||||
llama_batch_free(batch);
|
||||
}
|
||||
}
|
||||
|
||||
void init(int32_t n_tokens_alloc) {
|
||||
void init(int32_t n_tokens_alloc, int32_t n_embd) {
|
||||
this->n_tokens_alloc = n_tokens_alloc;
|
||||
this->n_embd = n_embd;
|
||||
batch = llama_batch_init(n_tokens_alloc, 0, 1);
|
||||
tokens_ptr = batch.token;
|
||||
tokens.reserve(n_tokens_alloc);
|
||||
}
|
||||
|
||||
bool add(int32_t id_slot, llama_token token, llama_pos pos, bool output) {
|
||||
GGML_ASSERT(batch.token != nullptr);
|
||||
GGML_ASSERT(!has_embd); // cannot mix tokens + embd in same batch
|
||||
GGML_ASSERT(batch.pos != nullptr);
|
||||
if ((int32_t)tokens.size() >= n_tokens_alloc) {
|
||||
return false;
|
||||
}
|
||||
@@ -110,13 +120,30 @@ struct server_batch {
|
||||
return true;
|
||||
}
|
||||
|
||||
bool add(int32_t id_slot, const std::vector<float> & embd_in, llama_pos pos, bool output) {
|
||||
GGML_ASSERT(batch.pos != nullptr);
|
||||
if ((int32_t)tokens.size() >= n_tokens_alloc) {
|
||||
return false;
|
||||
}
|
||||
tokens.push_back({ id_slot, LLAMA_TOKEN_NULL, pos, output });
|
||||
has_embd = true;
|
||||
embd.insert(embd.end(), embd_in.begin(), embd_in.end());
|
||||
return true;
|
||||
}
|
||||
|
||||
void clear() {
|
||||
tokens.clear();
|
||||
embd.clear();
|
||||
common_batch_clear(batch);
|
||||
slot_batched = nullptr;
|
||||
alora_scale = -1.0f;
|
||||
alora_disabled_id = 0;
|
||||
batch_rendered = false;
|
||||
has_embd = false;
|
||||
if (batch.token == nullptr) {
|
||||
batch.token = tokens_ptr;
|
||||
batch.embd = nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
int32_t size() const {
|
||||
@@ -129,25 +156,33 @@ struct server_batch {
|
||||
}
|
||||
|
||||
void render() {
|
||||
GGML_ASSERT(batch.token != nullptr);
|
||||
GGML_ASSERT(!batch_rendered);
|
||||
GGML_ASSERT(batch.pos != nullptr);
|
||||
common_batch_clear(batch);
|
||||
for (int32_t i = 0; i < size(); i++) {
|
||||
const auto & t = tokens[i];
|
||||
common_batch_add(batch, t.token, t.pos, { t.id_slot }, t.output);
|
||||
}
|
||||
if (has_embd) {
|
||||
batch.token = nullptr; // will be restored on clear()
|
||||
batch.embd = embd.data();
|
||||
}
|
||||
batch_rendered = true;
|
||||
}
|
||||
|
||||
llama_batch get_view(int32_t off, int32_t n_tokens) const {
|
||||
GGML_ASSERT(batch.token != nullptr);
|
||||
GGML_ASSERT(batch.pos != nullptr);
|
||||
GGML_ASSERT(batch_rendered);
|
||||
GGML_ASSERT(off >= 0 && off < size());
|
||||
GGML_ASSERT(n_tokens > 0 && off + n_tokens <= size());
|
||||
|
||||
auto * token = batch.token ? batch.token + off : nullptr;
|
||||
auto * embd = batch.embd ? batch.embd + off * n_embd : nullptr;
|
||||
|
||||
llama_batch view = {
|
||||
n_tokens,
|
||||
batch.token + off,
|
||||
nullptr,
|
||||
token,
|
||||
embd,
|
||||
batch.pos + off,
|
||||
batch.n_seq_id + off,
|
||||
batch.seq_id + off,
|
||||
@@ -270,6 +305,10 @@ struct server_slot {
|
||||
|
||||
llama_token sampled; // in speculative mode, this is the last accepted token
|
||||
|
||||
// for TTS models, this is the embd generated from prev step, decode this to generate next hidden state
|
||||
// corresponding to one token position (size = n_embd)
|
||||
std::vector<float> inp_embd;
|
||||
|
||||
// stats
|
||||
size_t n_sent_text = 0; // number of sent text character
|
||||
|
||||
@@ -378,7 +417,9 @@ struct server_slot {
|
||||
bool can_batch_with(server_slot & other_slot) const {
|
||||
GGML_ASSERT(task);
|
||||
|
||||
return task->type == other_slot.task->type && are_lora_equal(lora, other_slot.lora);
|
||||
return task->type == other_slot.task->type
|
||||
&& inp_embd.size() == other_slot.inp_embd.size()
|
||||
&& are_lora_equal(lora, other_slot.lora);
|
||||
}
|
||||
|
||||
bool has_budget(const common_params & global_params) {
|
||||
@@ -444,7 +485,11 @@ struct server_slot {
|
||||
// no speculative decoding
|
||||
i_batch = batch.size();
|
||||
|
||||
add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true);
|
||||
if (!inp_embd.empty()) {
|
||||
add_ok &= batch.add(id, inp_embd, prompt.tokens.pos_next(), true);
|
||||
} else {
|
||||
add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true);
|
||||
}
|
||||
|
||||
SLT_DBG(*this, "slot decode token, id=%d, n_ctx = %d, n_tokens = %d, truncated = %d\n",
|
||||
sampled, n_ctx, prompt.n_tokens(), truncated);
|
||||
@@ -1334,7 +1379,8 @@ private:
|
||||
// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
|
||||
{
|
||||
const int32_t n_batch = llama_n_batch(ctx_tgt);
|
||||
batch.init(std::max(n_batch, params_base.n_parallel));
|
||||
const int32_t n_embd = llama_model_n_embd_inp(model_tgt);
|
||||
batch.init(std::max(n_batch, params_base.n_parallel), n_embd);
|
||||
}
|
||||
|
||||
if (params_base.cache_ram_mib != 0) {
|
||||
@@ -1537,7 +1583,7 @@ private:
|
||||
|
||||
// find the slot that has at least n% prompt similarity
|
||||
if (slot_prompt_similarity != 0.0f) {
|
||||
float sim_best = 0;
|
||||
float f_sim_best = 0;
|
||||
|
||||
for (server_slot & slot : slots) {
|
||||
if (task.id_slot != -1 && slot.id != task.id_slot) {
|
||||
@@ -1546,6 +1592,7 @@ private:
|
||||
|
||||
// skip the slot if it is not available
|
||||
if (slot.is_processing()) {
|
||||
SLT_TRC(slot, " - skipping, is_processing = %d\n", slot.is_processing());
|
||||
continue;
|
||||
}
|
||||
|
||||
@@ -1553,26 +1600,30 @@ private:
|
||||
|
||||
// skip the slot if it does not contains cached tokens
|
||||
if (tokens.empty()) {
|
||||
SLT_TRC(slot, "%s", " - skipping, slot is empty\n");
|
||||
continue;
|
||||
}
|
||||
|
||||
// fraction of the Longest Common Prefix length with respect to the input prompt length
|
||||
const float sim_cur = float(tokens.get_common_prefix(task.tokens)) / task.tokens.size();
|
||||
const size_t lcp_len = tokens.get_common_prefix(task.tokens);
|
||||
const float f_sim_cur = float(lcp_len) / task.tokens.size();
|
||||
|
||||
SLT_TRC(slot, " - checking sim = %.3f (%zu/%zu) > %.3f\n", f_sim_cur, lcp_len, task.tokens.size(), slot_prompt_similarity);
|
||||
|
||||
// select the current slot if the criteria match
|
||||
if (sim_cur > sim_best && sim_cur > slot_prompt_similarity) {
|
||||
sim_best = sim_cur;
|
||||
if (f_sim_cur > f_sim_best && f_sim_cur > slot_prompt_similarity) {
|
||||
f_sim_best = f_sim_cur;
|
||||
|
||||
ret = &slot;
|
||||
}
|
||||
}
|
||||
|
||||
if (ret != nullptr) {
|
||||
const float f_keep = (sim_best*task.tokens.size()) / ret->prompt.tokens.size();
|
||||
const float f_keep = (f_sim_best*task.tokens.size()) / ret->prompt.tokens.size();
|
||||
|
||||
if (task.id_slot == -1) {
|
||||
SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
|
||||
sim_best, slot_prompt_similarity, f_keep);
|
||||
SLT_INF(*ret, "selected slot by LCP similarity, f_sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
|
||||
f_sim_best, slot_prompt_similarity, f_keep);
|
||||
}
|
||||
|
||||
// if we are about to lose a large portion of the existing context - save it in the prompt cache
|
||||
@@ -3573,6 +3624,15 @@ private:
|
||||
n_empty_consecutive = 0;
|
||||
}
|
||||
|
||||
// TODO @ngxson : dft model may have different n_embd than the tgt model, so we check & reject if that's the case
|
||||
// this case is not currently used by any models, but may need to be supported in the future
|
||||
if (spec && batch.has_embd) {
|
||||
if (llama_model_n_embd_inp(model_dft) != llama_model_n_embd_inp(model_tgt)) {
|
||||
SRV_ERR("%s", "unsupported batch.has_embd + spec case\n");
|
||||
throw std::runtime_error("unsupported batch.has_embd + spec case");
|
||||
}
|
||||
}
|
||||
|
||||
const int ret = llama_decode(ctx_tgt, batch_view);
|
||||
|
||||
metrics.on_decoded(slots);
|
||||
|
||||
@@ -1742,9 +1742,9 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
|
||||
const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);
|
||||
|
||||
float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
|
||||
float sim_best = float(lcp_best) / tokens_new.size();
|
||||
float f_sim_best = float(lcp_best) / tokens_new.size();
|
||||
|
||||
SRV_TRC(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
|
||||
SRV_TRC(" - looking for better prompt, base f_keep = %.3f, f_sim = %.3f\n", f_keep_best, f_sim_best);
|
||||
|
||||
auto it_best = states.end();
|
||||
|
||||
@@ -1753,25 +1753,25 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
|
||||
const int lcp_cur = it->prompt.tokens.get_common_prefix(tokens_new);
|
||||
|
||||
const float f_keep_cur = float(lcp_cur) / it->prompt.tokens.size();
|
||||
const float sim_cur = float(lcp_cur) / tokens_new.size();
|
||||
const float f_sim_cur = float(lcp_cur) / tokens_new.size();
|
||||
|
||||
SRV_TRC(" - prompt with length %7zu, lcp = %7d, f_keep = %.3f, sim = %.3f\n", it->prompt.tokens.size(), lcp_cur, f_keep_cur, sim_cur);
|
||||
SRV_TRC(" - prompt with length %7zu, lcp = %7d, f_keep = %.3f, f_sim = %.3f\n", it->prompt.tokens.size(), lcp_cur, f_keep_cur, f_sim_cur);
|
||||
|
||||
// don't trash large prompts
|
||||
if (f_keep_cur < 0.25f) {
|
||||
continue;
|
||||
}
|
||||
|
||||
if (f_keep_best < f_keep_cur && sim_best < sim_cur) {
|
||||
if (f_keep_best < f_keep_cur && f_sim_best < f_sim_cur) {
|
||||
f_keep_best = f_keep_cur;
|
||||
sim_best = sim_cur;
|
||||
f_sim_best = f_sim_cur;
|
||||
|
||||
it_best = it;
|
||||
}
|
||||
}
|
||||
|
||||
if (it_best != states.end()) {
|
||||
SRV_TRC(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
|
||||
SRV_TRC(" - found better prompt with f_keep = %.3f, f_sim = %.3f\n", f_keep_best, f_sim_best);
|
||||
|
||||
{
|
||||
auto & data = it_best->data.main;
|
||||
|
||||
+17
-2
@@ -48,6 +48,9 @@
|
||||
}: Props = $props();
|
||||
|
||||
let dropdownOpen = $state(false);
|
||||
// The system message action moves focus to the message editor, so the menu
|
||||
// must not restore focus to the trigger on close
|
||||
let suppressCloseAutoFocus = false;
|
||||
|
||||
function handleMcpSettingsClick() {
|
||||
dropdownOpen = false;
|
||||
@@ -96,7 +99,16 @@
|
||||
</Tooltip.Content>
|
||||
</Tooltip.Root>
|
||||
|
||||
<DropdownMenu.Content align="start" class="w-52">
|
||||
<DropdownMenu.Content
|
||||
align="start"
|
||||
class="w-52"
|
||||
onCloseAutoFocus={(e) => {
|
||||
if (suppressCloseAutoFocus) {
|
||||
suppressCloseAutoFocus = false;
|
||||
e.preventDefault();
|
||||
}
|
||||
}}
|
||||
>
|
||||
<ChatFormActionAddReasoningSubmenu />
|
||||
|
||||
<DropdownMenu.Separator />
|
||||
@@ -148,7 +160,10 @@
|
||||
|
||||
<DropdownMenu.Item
|
||||
class="flex cursor-pointer items-center gap-2"
|
||||
onclick={onSystemPromptClick}
|
||||
onclick={() => {
|
||||
suppressCloseAutoFocus = true;
|
||||
onSystemPromptClick?.();
|
||||
}}
|
||||
>
|
||||
<MessageSquare class={ICON_CLASS_DEFAULT} />
|
||||
|
||||
|
||||
@@ -2,6 +2,7 @@
|
||||
import { goto } from '$app/navigation';
|
||||
import { getChatActionsContext, setMessageEditContext } from '$lib/contexts';
|
||||
import { chatStore, pendingEditMessageId } from '$lib/stores/chat.svelte';
|
||||
import { isMobile } from '$lib/stores/viewport.svelte';
|
||||
import { conversationsStore } from '$lib/stores/conversations.svelte';
|
||||
import { DatabaseService } from '$lib/services/database.service';
|
||||
import { SYSTEM_MESSAGE_PLACEHOLDER } from '$lib/constants';
|
||||
@@ -46,7 +47,14 @@
|
||||
assistantMessages: number;
|
||||
messageTypes: string[];
|
||||
} | null>(null);
|
||||
let editedContent = $derived(message.content);
|
||||
// The system message placeholder must never surface as editable content; keeping
|
||||
// it in the derived (not just in handleEdit) guards against prop invalidation
|
||||
// reverting the override while editing
|
||||
let editedContent = $derived(
|
||||
message.role === MessageRole.SYSTEM && message.content === SYSTEM_MESSAGE_PLACEHOLDER
|
||||
? ''
|
||||
: message.content
|
||||
);
|
||||
|
||||
let rawEditContent = $derived.by(() => {
|
||||
if (message.role !== MessageRole.ASSISTANT) return undefined;
|
||||
@@ -265,6 +273,12 @@
|
||||
chatActions.navigateToSibling(siblingId);
|
||||
}
|
||||
|
||||
// After the system message flow ends, hand focus to the main chat form
|
||||
function focusMainChatForm() {
|
||||
if (isMobile.current) return;
|
||||
document.querySelector<HTMLTextAreaElement>('.chat-screen-form-wrapper textarea')?.focus();
|
||||
}
|
||||
|
||||
async function handleSaveEdit() {
|
||||
if (message.role === MessageRole.SYSTEM) {
|
||||
// System messages: update in place without branching
|
||||
@@ -276,6 +290,8 @@
|
||||
isEditing = false;
|
||||
if (conversationDeleted) {
|
||||
goto(ROUTES.START);
|
||||
} else {
|
||||
focusMainChatForm();
|
||||
}
|
||||
return;
|
||||
}
|
||||
@@ -285,6 +301,7 @@
|
||||
if (index !== -1) {
|
||||
conversationsStore.updateMessageAtIndex(index, { content: newContent });
|
||||
}
|
||||
focusMainChatForm();
|
||||
} else if (message.role === MessageRole.USER) {
|
||||
const finalExtras = await getMergedExtras();
|
||||
chatActions.editWithBranching(message, editedContent.trim(), finalExtras);
|
||||
|
||||
@@ -106,15 +106,23 @@
|
||||
onFileRemove?.(fileId);
|
||||
}
|
||||
|
||||
// Auto-focus must not steal focus already claimed elsewhere (e.g. the system
|
||||
// message editor opened just before a navigation)
|
||||
function focusFormUnlessCaptured() {
|
||||
const active = document.activeElement;
|
||||
if (active instanceof HTMLTextAreaElement || active instanceof HTMLInputElement) return;
|
||||
chatFormRef?.focus();
|
||||
}
|
||||
|
||||
onMount(() => {
|
||||
if (!isMobile.current) {
|
||||
setTimeout(() => chatFormRef?.focus(), 100);
|
||||
setTimeout(focusFormUnlessCaptured, 100);
|
||||
}
|
||||
});
|
||||
|
||||
afterNavigate((navigation) => {
|
||||
if (navigation?.from != null && !isMobile.current) {
|
||||
setTimeout(() => chatFormRef?.focus(), 100);
|
||||
setTimeout(focusFormUnlessCaptured, 100);
|
||||
}
|
||||
});
|
||||
|
||||
@@ -127,7 +135,7 @@
|
||||
|
||||
$effect(() => {
|
||||
if (previousIsLoading && !isLoading) {
|
||||
setTimeout(() => chatFormRef?.focus(), 10);
|
||||
setTimeout(focusFormUnlessCaptured, 10);
|
||||
}
|
||||
|
||||
previousIsLoading = isLoading;
|
||||
|
||||
@@ -11,7 +11,7 @@
|
||||
<div
|
||||
class={[
|
||||
'pointer-events-none mb-4 hidden px-4 text-center text-balance',
|
||||
isEmpty && 'mb-[calc(50dvh-8rem)] md:mb-6 pointer-events-auto block!'
|
||||
isEmpty && 'mb-[calc(50dvh-8rem)] md:mb-8 pointer-events-auto block!'
|
||||
]}
|
||||
>
|
||||
<h1 class="mb-2 text-2xl font-semibold tracking-tight md:text-3xl">Hello there</h1>
|
||||
|
||||
@@ -31,14 +31,19 @@ export class DatabaseService {
|
||||
* Creates a new conversation.
|
||||
*
|
||||
* @param name - Name of the conversation
|
||||
* @param fields - Optional extra fields (e.g. reasoningEffort)
|
||||
* @returns The created conversation
|
||||
*/
|
||||
static async createConversation(name: string): Promise<DatabaseConversation> {
|
||||
static async createConversation(
|
||||
name: string,
|
||||
fields?: Partial<Omit<DatabaseConversation, 'id' | 'name' | 'lastModified'>>
|
||||
): Promise<DatabaseConversation> {
|
||||
const conversation: DatabaseConversation = {
|
||||
id: uuid(),
|
||||
name,
|
||||
lastModified: Date.now(),
|
||||
currNode: ''
|
||||
currNode: '',
|
||||
...fields
|
||||
};
|
||||
|
||||
await db[IDXDB_TABLES.conversations].add(conversation);
|
||||
@@ -137,7 +142,7 @@ export class DatabaseService {
|
||||
* @param systemPrompt - The system prompt content (must be non-empty)
|
||||
* @param parentId - Parent message ID (typically the root message)
|
||||
* @returns The created system message
|
||||
* @throws Error if systemPrompt is empty
|
||||
* @throws Error if systemPrompt is empty or the parent message does not exist
|
||||
*/
|
||||
static async createSystemMessage(
|
||||
convId: string,
|
||||
@@ -149,27 +154,30 @@ export class DatabaseService {
|
||||
throw new Error('Cannot create system message with empty content');
|
||||
}
|
||||
|
||||
const systemMessage: DatabaseMessage = {
|
||||
id: uuid(),
|
||||
convId,
|
||||
type: MessageRole.SYSTEM,
|
||||
timestamp: Date.now(),
|
||||
role: MessageRole.SYSTEM,
|
||||
content: trimmedPrompt,
|
||||
parent: parentId,
|
||||
children: []
|
||||
};
|
||||
return await db.transaction('rw', db[IDXDB_TABLES.messages], async () => {
|
||||
const parentMessage = await db[IDXDB_TABLES.messages].get(parentId);
|
||||
if (!parentMessage) {
|
||||
throw new Error(`Parent message ${parentId} not found`);
|
||||
}
|
||||
|
||||
await db[IDXDB_TABLES.messages].add(systemMessage);
|
||||
const systemMessage: DatabaseMessage = {
|
||||
id: uuid(),
|
||||
convId,
|
||||
type: MessageRole.SYSTEM,
|
||||
timestamp: Date.now(),
|
||||
role: MessageRole.SYSTEM,
|
||||
content: trimmedPrompt,
|
||||
parent: parentId,
|
||||
children: []
|
||||
};
|
||||
|
||||
const parentMessage = await db[IDXDB_TABLES.messages].get(parentId);
|
||||
if (parentMessage) {
|
||||
await db[IDXDB_TABLES.messages].add(systemMessage);
|
||||
await db[IDXDB_TABLES.messages].update(parentId, {
|
||||
children: [...parentMessage.children, systemMessage.id]
|
||||
});
|
||||
}
|
||||
|
||||
return systemMessage;
|
||||
return systemMessage;
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -442,7 +450,8 @@ export class DatabaseService {
|
||||
}
|
||||
|
||||
/**
|
||||
* Updates a conversation.
|
||||
* Updates a conversation. `lastModified` is never stamped implicitly;
|
||||
* pass it in `updates` to bump the conversation in recency ordering.
|
||||
*
|
||||
* @param id - Conversation ID
|
||||
* @param updates - Partial updates to apply
|
||||
@@ -452,10 +461,7 @@ export class DatabaseService {
|
||||
id: string,
|
||||
updates: Partial<Omit<DatabaseConversation, 'id'>>
|
||||
): Promise<void> {
|
||||
await db[IDXDB_TABLES.conversations].update(id, {
|
||||
...updates,
|
||||
lastModified: Date.now()
|
||||
});
|
||||
await db[IDXDB_TABLES.conversations].update(id, updates);
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -473,7 +479,7 @@ export class DatabaseService {
|
||||
* @returns The new pinned status
|
||||
*/
|
||||
static async toggleConversationPin(id: string): Promise<boolean> {
|
||||
const conversation = await db.conversations.get(id);
|
||||
const conversation = await db[IDXDB_TABLES.conversations].get(id);
|
||||
if (!conversation) {
|
||||
throw new Error(`Conversation ${id} not found`);
|
||||
}
|
||||
@@ -497,7 +503,6 @@ export class DatabaseService {
|
||||
const result = new Map<string, boolean>();
|
||||
if (cleanIds.length === 0) return result;
|
||||
|
||||
const now = Date.now();
|
||||
await db.transaction('rw', db[IDXDB_TABLES.conversations], async () => {
|
||||
const convs = await db[IDXDB_TABLES.conversations].bulkGet(cleanIds);
|
||||
const updates: DatabaseConversation[] = [];
|
||||
@@ -505,7 +510,7 @@ export class DatabaseService {
|
||||
const conv = convs[i];
|
||||
if (!conv) continue;
|
||||
const newPinned = !conv.pinned;
|
||||
updates.push({ ...conv, pinned: newPinned, lastModified: now });
|
||||
updates.push({ ...conv, pinned: newPinned });
|
||||
result.set(cleanIds[i], newPinned);
|
||||
}
|
||||
if (updates.length === 0) return;
|
||||
|
||||
@@ -1658,7 +1658,8 @@ class ChatStore {
|
||||
generateConversationTitle(newContent, Boolean(config().titleGenerationUseFirstLine))
|
||||
);
|
||||
const messagesToRemove = conversationsStore.activeMessages.slice(messageIndex + 1);
|
||||
for (const message of messagesToRemove) await DatabaseService.deleteMessage(message.id);
|
||||
if (messagesToRemove.length > 0)
|
||||
await DatabaseService.deleteMessageCascading(activeConv.id, messagesToRemove[0].id);
|
||||
conversationsStore.sliceActiveMessages(messageIndex + 1);
|
||||
conversationsStore.updateConversationTimestamp();
|
||||
this.setChatLoading(activeConv.id, true);
|
||||
@@ -1690,7 +1691,7 @@ class ChatStore {
|
||||
const { index: messageIndex } = result;
|
||||
try {
|
||||
const messagesToRemove = conversationsStore.activeMessages.slice(messageIndex);
|
||||
for (const message of messagesToRemove) await DatabaseService.deleteMessage(message.id);
|
||||
await DatabaseService.deleteMessageCascading(activeConv.id, messagesToRemove[0].id);
|
||||
conversationsStore.sliceActiveMessages(messageIndex);
|
||||
conversationsStore.updateConversationTimestamp();
|
||||
this.setChatLoading(activeConv.id, true);
|
||||
@@ -2037,7 +2038,7 @@ class ChatStore {
|
||||
timings
|
||||
});
|
||||
|
||||
conversationsStore.updateConversationTimestamp();
|
||||
conversationsStore.updateConversationTimestamp(msg.convId);
|
||||
|
||||
this.setChatLoading(msg.convId, false);
|
||||
this.clearChatStreaming(msg.convId);
|
||||
|
||||
@@ -111,6 +111,9 @@ class ConversationsStore {
|
||||
| ((messageId: string, updates: Partial<DatabaseMessage>) => void)
|
||||
| null = null;
|
||||
|
||||
/** In-flight init run; shared by concurrent callers, reset on failure to allow retry */
|
||||
private initPromise: Promise<void> | null = null;
|
||||
|
||||
/**
|
||||
*
|
||||
*
|
||||
@@ -121,19 +124,25 @@ class ConversationsStore {
|
||||
|
||||
/**
|
||||
* Initialize the store by loading conversations from database.
|
||||
* Must be called once after app startup.
|
||||
* Safe to call multiple times: concurrent callers share a single run,
|
||||
* and a failed run can be retried by calling again.
|
||||
*/
|
||||
async init(): Promise<void> {
|
||||
if (!browser) return;
|
||||
if (this.isInitialized) return;
|
||||
init(): Promise<void> {
|
||||
if (!browser) return Promise.resolve();
|
||||
if (this.initPromise) return this.initPromise;
|
||||
|
||||
try {
|
||||
await MigrationService.runAllMigrations();
|
||||
await this.loadConversations();
|
||||
this.isInitialized = true;
|
||||
} catch (error) {
|
||||
console.error('Failed to initialize conversations:', error);
|
||||
}
|
||||
this.initPromise = (async () => {
|
||||
try {
|
||||
await MigrationService.runAllMigrations();
|
||||
await this.loadConversations();
|
||||
this.isInitialized = true;
|
||||
} catch (error) {
|
||||
console.error('Failed to initialize conversations:', error);
|
||||
this.initPromise = null;
|
||||
}
|
||||
})();
|
||||
|
||||
return this.initPromise;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -237,15 +246,11 @@ class ConversationsStore {
|
||||
*/
|
||||
async createConversation(name?: string): Promise<string> {
|
||||
const conversationName = name || `Chat ${new Date().toLocaleString()}`;
|
||||
const conversation = await DatabaseService.createConversation(conversationName);
|
||||
|
||||
// No MCP override list is seeded: getAllMcpServerOverrides resolves
|
||||
// servers without a per-conversation override to `mcpServers[i].enabled`,
|
||||
// and only explicit toggles are stored on the conversation.
|
||||
|
||||
// Inherit the global reasoning default into the new conversation
|
||||
conversation.reasoningEffort = this.pendingReasoningEffort;
|
||||
await DatabaseService.updateConversation(conversation.id, {
|
||||
const conversation = await DatabaseService.createConversation(conversationName, {
|
||||
reasoningEffort: this.pendingReasoningEffort
|
||||
});
|
||||
|
||||
@@ -358,10 +363,7 @@ class ConversationsStore {
|
||||
async deleteAll(): Promise<void> {
|
||||
try {
|
||||
const allConversations = await DatabaseService.getAllConversations();
|
||||
|
||||
for (const conv of allConversations) {
|
||||
await DatabaseService.deleteConversation(conv.id);
|
||||
}
|
||||
await DatabaseService.bulkDeleteConversations(allConversations.map((c) => c.id));
|
||||
|
||||
this.clearActiveConversation();
|
||||
this.conversations = [];
|
||||
@@ -412,7 +414,9 @@ class ConversationsStore {
|
||||
}
|
||||
|
||||
toast.success(
|
||||
convIds.length === 1 ? 'Conversation deleted' : `${convIds.length} conversations deleted`
|
||||
idsToRemove.size === 1
|
||||
? 'Conversation deleted'
|
||||
: `${idsToRemove.size} conversations deleted`
|
||||
);
|
||||
} catch (error) {
|
||||
console.error('Failed to bulk delete conversations:', error);
|
||||
@@ -443,7 +447,6 @@ class ConversationsStore {
|
||||
const newPinned = updates.get(this.conversations[i].id);
|
||||
if (newPinned !== undefined) this.conversations[i].pinned = newPinned;
|
||||
}
|
||||
this.conversations = [...this.conversations];
|
||||
|
||||
toast.success(
|
||||
convIds.length === 1
|
||||
@@ -552,7 +555,6 @@ class ConversationsStore {
|
||||
|
||||
if (convIndex !== -1) {
|
||||
this.conversations[convIndex].name = name;
|
||||
this.conversations = [...this.conversations];
|
||||
}
|
||||
|
||||
if (this.activeConversation?.id === convId) {
|
||||
@@ -576,7 +578,6 @@ class ConversationsStore {
|
||||
|
||||
if (convIndex !== -1) {
|
||||
this.conversations[convIndex].pinned = newPinnedState;
|
||||
this.conversations = [...this.conversations];
|
||||
}
|
||||
|
||||
if (this.activeConversation?.id === convId) {
|
||||
@@ -591,18 +592,33 @@ class ConversationsStore {
|
||||
}
|
||||
|
||||
/**
|
||||
* Updates conversation lastModified timestamp and moves it to top of list
|
||||
* Marks a conversation as recently active: stamps lastModified (persisted)
|
||||
* and moves it to the top of the list. Only message-activity flows call
|
||||
* this; metadata updates (rename, pin, settings) do not.
|
||||
*
|
||||
* @param convId - Conversation that produced the activity, defaults to the active one
|
||||
*/
|
||||
updateConversationTimestamp(): void {
|
||||
if (!this.activeConversation) return;
|
||||
updateConversationTimestamp(convId?: string): void {
|
||||
const targetId = convId ?? this.activeConversation?.id;
|
||||
if (!targetId) return;
|
||||
|
||||
const chatIndex = this.conversations.findIndex((c) => c.id === this.activeConversation!.id);
|
||||
const now = Date.now();
|
||||
|
||||
const chatIndex = this.conversations.findIndex((c) => c.id === targetId);
|
||||
|
||||
if (chatIndex !== -1) {
|
||||
this.conversations[chatIndex].lastModified = Date.now();
|
||||
this.conversations[chatIndex].lastModified = now;
|
||||
const updatedConv = this.conversations.splice(chatIndex, 1)[0];
|
||||
this.conversations = [updatedConv, ...this.conversations];
|
||||
}
|
||||
|
||||
if (this.activeConversation?.id === targetId) {
|
||||
this.activeConversation = { ...this.activeConversation, lastModified: now };
|
||||
}
|
||||
|
||||
DatabaseService.updateConversation(targetId, { lastModified: now }).catch((error) =>
|
||||
console.error('Failed to update conversation timestamp:', error)
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -773,7 +789,6 @@ class ConversationsStore {
|
||||
if (convIndex !== -1) {
|
||||
this.conversations[convIndex].mcpServerOverrides =
|
||||
newOverrides.length > 0 ? newOverrides : undefined;
|
||||
this.conversations = [...this.conversations];
|
||||
}
|
||||
}
|
||||
|
||||
@@ -837,7 +852,6 @@ class ConversationsStore {
|
||||
const convIndex = this.conversations.findIndex((c) => c.id === this.activeConversation!.id);
|
||||
if (convIndex !== -1) {
|
||||
this.conversations[convIndex].reasoningEffort = effort;
|
||||
this.conversations = [...this.conversations];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,364 +0,0 @@
|
||||
/**
|
||||
* @deprecated Legacy migration utility — remove at some point in the future once all users have migrated to the new structured agentic message format.
|
||||
*
|
||||
* Converts old marker-based agentic messages to the new structured format
|
||||
* with separate messages per turn.
|
||||
*
|
||||
* Old format: Single assistant message with markers in content:
|
||||
* <<<reasoning_content_start>>>...<<<reasoning_content_end>>>
|
||||
* <<<AGENTIC_TOOL_CALL_START>>>...<<<AGENTIC_TOOL_CALL_END>>>
|
||||
*
|
||||
* New format: Separate messages per turn:
|
||||
* - assistant (content + reasoningContent + toolCalls)
|
||||
* - tool (toolCallId + content)
|
||||
* - assistant (next turn)
|
||||
* - ...
|
||||
*/
|
||||
|
||||
import { LEGACY_AGENTIC_REGEX, LEGACY_REASONING_TAGS } from '$lib/constants';
|
||||
import { DatabaseService } from '$lib/services/database.service';
|
||||
import { MessageRole, MessageType } from '$lib/enums';
|
||||
import type { DatabaseMessage } from '$lib/types/database';
|
||||
|
||||
const MIGRATION_DONE_KEY = 'llama-ui-migration-v2-done';
|
||||
/** @deprecated Use {@link MIGRATION_DONE_KEY} instead */
|
||||
const DEPRECATED_MIGRATION_DONE_KEY = 'llama-webui-migration-v2-done';
|
||||
|
||||
/**
|
||||
* @deprecated Part of legacy migration — remove with the migration module.
|
||||
* Check if migration has been performed.
|
||||
*/
|
||||
export function isMigrationNeeded(): boolean {
|
||||
try {
|
||||
// Check new key first, fall back to deprecated old key
|
||||
if (localStorage.getItem(MIGRATION_DONE_KEY)) return false;
|
||||
if (localStorage.getItem(DEPRECATED_MIGRATION_DONE_KEY)) {
|
||||
// Migrate to new key
|
||||
try {
|
||||
localStorage.setItem(MIGRATION_DONE_KEY, String(Date.now()));
|
||||
localStorage.removeItem(DEPRECATED_MIGRATION_DONE_KEY);
|
||||
} catch {
|
||||
// Ignore storage errors
|
||||
}
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark migration as done.
|
||||
*/
|
||||
function markMigrationDone(): void {
|
||||
try {
|
||||
localStorage.setItem(MIGRATION_DONE_KEY, String(Date.now()));
|
||||
} catch {
|
||||
// Ignore localStorage errors
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if a message has legacy markers in its content.
|
||||
*/
|
||||
function hasLegacyMarkers(message: DatabaseMessage): boolean {
|
||||
if (!message.content) return false;
|
||||
return LEGACY_AGENTIC_REGEX.HAS_LEGACY_MARKERS.test(message.content);
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract reasoning content from legacy marker format.
|
||||
*/
|
||||
function extractLegacyReasoning(content: string): { reasoning: string; cleanContent: string } {
|
||||
let reasoning = '';
|
||||
let cleanContent = content;
|
||||
|
||||
// Extract all reasoning blocks
|
||||
const re = new RegExp(LEGACY_AGENTIC_REGEX.REASONING_EXTRACT.source, 'g');
|
||||
let match;
|
||||
while ((match = re.exec(content)) !== null) {
|
||||
reasoning += match[1];
|
||||
}
|
||||
|
||||
// Remove reasoning tags from content
|
||||
cleanContent = cleanContent
|
||||
.replace(new RegExp(LEGACY_AGENTIC_REGEX.REASONING_BLOCK.source, 'g'), '')
|
||||
.replace(LEGACY_AGENTIC_REGEX.REASONING_OPEN, '');
|
||||
|
||||
return { reasoning, cleanContent };
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse legacy content with tool call markers into structured turns.
|
||||
*/
|
||||
interface ParsedTurn {
|
||||
textBefore: string;
|
||||
toolCalls: Array<{
|
||||
name: string;
|
||||
args: string;
|
||||
result: string;
|
||||
}>;
|
||||
}
|
||||
|
||||
function parseLegacyToolCalls(content: string): ParsedTurn[] {
|
||||
const turns: ParsedTurn[] = [];
|
||||
const regex = new RegExp(LEGACY_AGENTIC_REGEX.COMPLETED_TOOL_CALL.source, 'g');
|
||||
|
||||
let lastIndex = 0;
|
||||
let currentTurn: ParsedTurn = { textBefore: '', toolCalls: [] };
|
||||
let match;
|
||||
|
||||
while ((match = regex.exec(content)) !== null) {
|
||||
const textBefore = content.slice(lastIndex, match.index).trim();
|
||||
|
||||
// If there's text between tool calls and we already have tool calls,
|
||||
// that means a new turn started (text after tool results = new LLM turn)
|
||||
if (textBefore && currentTurn.toolCalls.length > 0) {
|
||||
turns.push(currentTurn);
|
||||
currentTurn = { textBefore, toolCalls: [] };
|
||||
} else if (textBefore && currentTurn.toolCalls.length === 0) {
|
||||
currentTurn.textBefore = textBefore;
|
||||
}
|
||||
|
||||
currentTurn.toolCalls.push({
|
||||
name: match[1],
|
||||
args: match[2],
|
||||
result: match[3].replace(/^\n+|\n+$/g, '')
|
||||
});
|
||||
|
||||
lastIndex = match.index + match[0].length;
|
||||
}
|
||||
|
||||
// Any remaining text after the last tool call
|
||||
const remainingText = content.slice(lastIndex).trim();
|
||||
|
||||
if (currentTurn.toolCalls.length > 0) {
|
||||
turns.push(currentTurn);
|
||||
}
|
||||
|
||||
// If there's text after all tool calls, it's the final assistant response
|
||||
if (remainingText) {
|
||||
// Remove any partial/open markers
|
||||
const cleanRemaining = remainingText
|
||||
.replace(LEGACY_AGENTIC_REGEX.AGENTIC_TOOL_CALL_OPEN, '')
|
||||
.trim();
|
||||
if (cleanRemaining) {
|
||||
turns.push({ textBefore: cleanRemaining, toolCalls: [] });
|
||||
}
|
||||
}
|
||||
|
||||
// If no tool calls found at all, return the original content as a single turn
|
||||
if (turns.length === 0) {
|
||||
turns.push({ textBefore: content.trim(), toolCalls: [] });
|
||||
}
|
||||
|
||||
return turns;
|
||||
}
|
||||
|
||||
/**
|
||||
* Migrate a single conversation's messages from legacy format to new format.
|
||||
*/
|
||||
async function migrateConversation(convId: string): Promise<number> {
|
||||
const allMessages = await DatabaseService.getConversationMessages(convId);
|
||||
let migratedCount = 0;
|
||||
|
||||
for (const message of allMessages) {
|
||||
if (message.role !== MessageRole.ASSISTANT) continue;
|
||||
if (!hasLegacyMarkers(message)) {
|
||||
// Still check for reasoning-only markers (no tool calls)
|
||||
if (message.content?.includes(LEGACY_REASONING_TAGS.START)) {
|
||||
const { reasoning, cleanContent } = extractLegacyReasoning(message.content);
|
||||
await DatabaseService.updateMessage(message.id, {
|
||||
content: cleanContent.trim(),
|
||||
reasoningContent: reasoning || undefined
|
||||
});
|
||||
migratedCount++;
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
// Has agentic markers - full migration needed
|
||||
const { reasoning, cleanContent } = extractLegacyReasoning(message.content);
|
||||
const turns = parseLegacyToolCalls(cleanContent);
|
||||
|
||||
// Parse existing toolCalls JSON to try to match IDs
|
||||
let existingToolCalls: Array<{
|
||||
id: string;
|
||||
function?: { name: string; arguments: string };
|
||||
}> = [];
|
||||
if (message.toolCalls) {
|
||||
try {
|
||||
existingToolCalls = JSON.parse(message.toolCalls);
|
||||
} catch {
|
||||
// Ignore
|
||||
}
|
||||
}
|
||||
|
||||
// First turn uses the existing message
|
||||
const firstTurn = turns[0];
|
||||
if (!firstTurn) continue;
|
||||
|
||||
// Match tool calls from the first turn to existing IDs
|
||||
const firstTurnToolCalls = firstTurn.toolCalls.map((tc, i) => {
|
||||
const existing =
|
||||
existingToolCalls.find((e) => e.function?.name === tc.name) || existingToolCalls[i];
|
||||
return {
|
||||
id: existing?.id || `legacy_tool_${i}`,
|
||||
type: 'function' as const,
|
||||
function: { name: tc.name, arguments: tc.args }
|
||||
};
|
||||
});
|
||||
|
||||
// Update the existing message for the first turn
|
||||
await DatabaseService.updateMessage(message.id, {
|
||||
content: firstTurn.textBefore,
|
||||
reasoningContent: reasoning || undefined,
|
||||
toolCalls: firstTurnToolCalls.length > 0 ? JSON.stringify(firstTurnToolCalls) : ''
|
||||
});
|
||||
|
||||
let currentParentId = message.id;
|
||||
let toolCallIdCounter = existingToolCalls.length;
|
||||
|
||||
// Create tool result messages for the first turn
|
||||
for (let i = 0; i < firstTurn.toolCalls.length; i++) {
|
||||
const tc = firstTurn.toolCalls[i];
|
||||
const toolCallId = firstTurnToolCalls[i]?.id || `legacy_tool_${i}`;
|
||||
|
||||
const toolMsg = await DatabaseService.createMessageBranch(
|
||||
{
|
||||
convId,
|
||||
type: MessageType.TEXT,
|
||||
role: MessageRole.TOOL,
|
||||
content: tc.result,
|
||||
toolCallId,
|
||||
timestamp: message.timestamp + i + 1,
|
||||
toolCalls: '',
|
||||
children: []
|
||||
},
|
||||
currentParentId
|
||||
);
|
||||
currentParentId = toolMsg.id;
|
||||
}
|
||||
|
||||
// Create messages for subsequent turns
|
||||
for (let turnIdx = 1; turnIdx < turns.length; turnIdx++) {
|
||||
const turn = turns[turnIdx];
|
||||
|
||||
const turnToolCalls = turn.toolCalls.map((tc, i) => {
|
||||
const idx = toolCallIdCounter + i;
|
||||
const existing = existingToolCalls[idx];
|
||||
return {
|
||||
id: existing?.id || `legacy_tool_${idx}`,
|
||||
type: 'function' as const,
|
||||
function: { name: tc.name, arguments: tc.args }
|
||||
};
|
||||
});
|
||||
toolCallIdCounter += turn.toolCalls.length;
|
||||
|
||||
// Create assistant message for this turn
|
||||
const assistantMsg = await DatabaseService.createMessageBranch(
|
||||
{
|
||||
convId,
|
||||
type: MessageType.TEXT,
|
||||
role: MessageRole.ASSISTANT,
|
||||
content: turn.textBefore,
|
||||
timestamp: message.timestamp + turnIdx * 100,
|
||||
toolCalls: turnToolCalls.length > 0 ? JSON.stringify(turnToolCalls) : '',
|
||||
children: [],
|
||||
model: message.model
|
||||
},
|
||||
currentParentId
|
||||
);
|
||||
currentParentId = assistantMsg.id;
|
||||
|
||||
// Create tool result messages for this turn
|
||||
for (let i = 0; i < turn.toolCalls.length; i++) {
|
||||
const tc = turn.toolCalls[i];
|
||||
const toolCallId = turnToolCalls[i]?.id || `legacy_tool_${toolCallIdCounter + i}`;
|
||||
|
||||
const toolMsg = await DatabaseService.createMessageBranch(
|
||||
{
|
||||
convId,
|
||||
type: MessageType.TEXT,
|
||||
role: MessageRole.TOOL,
|
||||
content: tc.result,
|
||||
toolCallId,
|
||||
timestamp: message.timestamp + turnIdx * 100 + i + 1,
|
||||
toolCalls: '',
|
||||
children: []
|
||||
},
|
||||
currentParentId
|
||||
);
|
||||
currentParentId = toolMsg.id;
|
||||
}
|
||||
}
|
||||
|
||||
// Re-parent any children of the original message to the last created message
|
||||
// (the original message's children list was the next user message or similar)
|
||||
if (message.children.length > 0 && currentParentId !== message.id) {
|
||||
for (const childId of message.children) {
|
||||
// Skip children we just created (they were already properly parented)
|
||||
const child = allMessages.find((m) => m.id === childId);
|
||||
if (!child) continue;
|
||||
// Only re-parent non-tool messages that were original children
|
||||
if (child.role !== MessageRole.TOOL) {
|
||||
await DatabaseService.updateMessage(childId, { parent: currentParentId });
|
||||
// Add to new parent's children
|
||||
const newParent = await DatabaseService.getConversationMessages(convId).then((msgs) =>
|
||||
msgs.find((m) => m.id === currentParentId)
|
||||
);
|
||||
if (newParent && !newParent.children.includes(childId)) {
|
||||
await DatabaseService.updateMessage(currentParentId, {
|
||||
children: [...newParent.children, childId]
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
// Clear re-parented children from the original message
|
||||
await DatabaseService.updateMessage(message.id, { children: [] });
|
||||
}
|
||||
|
||||
migratedCount++;
|
||||
}
|
||||
|
||||
return migratedCount;
|
||||
}
|
||||
|
||||
/**
|
||||
* @deprecated Part of legacy migration — remove with the migration module.
|
||||
* Run the full migration across all conversations.
|
||||
* This should be called once at app startup if migration is needed.
|
||||
*/
|
||||
export async function runLegacyMigration(): Promise<void> {
|
||||
if (!isMigrationNeeded()) return;
|
||||
|
||||
if (import.meta.env.DEV && import.meta.env.VITE_DEBUG)
|
||||
console.log('[Migration] Starting legacy message format migration...');
|
||||
|
||||
try {
|
||||
const conversations = await DatabaseService.getAllConversations();
|
||||
let totalMigrated = 0;
|
||||
|
||||
for (const conv of conversations) {
|
||||
const count = await migrateConversation(conv.id);
|
||||
totalMigrated += count;
|
||||
}
|
||||
|
||||
if (import.meta.env.DEV && import.meta.env.VITE_DEBUG) {
|
||||
if (totalMigrated > 0) {
|
||||
console.log(
|
||||
`[Migration] Migrated ${totalMigrated} messages across ${conversations.length} conversations`
|
||||
);
|
||||
} else {
|
||||
console.log('[Migration] No legacy messages found, marking as done');
|
||||
}
|
||||
}
|
||||
|
||||
markMigrationDone();
|
||||
} catch (error) {
|
||||
console.error('[Migration] Failed to migrate legacy messages:', error);
|
||||
// Still mark as done to avoid infinite retry loops
|
||||
markMigrationDone();
|
||||
}
|
||||
}
|
||||
Vendored
+1
-1
@@ -41,7 +41,7 @@ if (LLAMA_BUILD_BORINGSSL)
|
||||
set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
|
||||
|
||||
set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
|
||||
set(BORINGSSL_VERSION "0.20260713.0" CACHE STRING "BoringSSL version")
|
||||
set(BORINGSSL_VERSION "0.20260728.0" CACHE STRING "BoringSSL version")
|
||||
|
||||
message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user