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llama.cpp
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Merge remote-tracking branch 'upstream/master'

This commit is contained in:
Reese Levine 2025-11-11 16:39:20 -08:00
parent c201d0de77 c273d75375
commit 6db729813f
115 changed files with 52441 additions and 13452 deletions
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2
.devops/nix/package.nix
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@ -34,6 +34,7 @@
rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
enableCurl ? true,
useVulkan ? false,
useRpc ? false,
llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
# It's necessary to consistently use backendStdenv when building with CUDA support,
@ -175,6 +176,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
(cmakeBool "GGML_METAL" useMetalKit)
(cmakeBool "GGML_VULKAN" useVulkan)
(cmakeBool "GGML_STATIC" enableStatic)
(cmakeBool "GGML_RPC" useRpc)
]
++ optionals useCuda [
(

24
.devops/vulkan.Dockerfile
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@ -1,4 +1,4 @@
ARG UBUNTU_VERSION=24.04
ARG UBUNTU_VERSION=25.10
FROM ubuntu:$UBUNTU_VERSION AS build
@ -7,32 +7,16 @@ FROM ubuntu:$UBUNTU_VERSION AS build
# Install build tools
RUN apt update && apt install -y git build-essential cmake wget xz-utils
# Install Vulkan SDK
ARG VULKAN_VERSION=1.4.321.1
RUN ARCH=$(uname -m) && \
wget -qO /tmp/vulkan-sdk.tar.xz https://sdk.lunarg.com/sdk/download/${VULKAN_VERSION}/linux/vulkan-sdk-linux-${ARCH}-${VULKAN_VERSION}.tar.xz && \
mkdir -p /opt/vulkan && \
tar -xf /tmp/vulkan-sdk.tar.xz -C /tmp --strip-components=1 && \
mv /tmp/${ARCH}/* /opt/vulkan/ && \
rm -rf /tmp/*
# Install cURL and Vulkan SDK dependencies
RUN apt install -y libcurl4-openssl-dev curl \
libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev
# Set environment variables
ENV VULKAN_SDK=/opt/vulkan
ENV PATH=$VULKAN_SDK/bin:$PATH
ENV LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH
ENV CMAKE_PREFIX_PATH=$VULKAN_SDK:$CMAKE_PREFIX_PATH
ENV PKG_CONFIG_PATH=$VULKAN_SDK/lib/pkgconfig:$PKG_CONFIG_PATH
libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
cmake --build build --config Release -j$(nproc)
RUN mkdir -p /app/lib && \
@ -50,7 +34,7 @@ RUN mkdir -p /app/full \
FROM ubuntu:$UBUNTU_VERSION AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl libvulkan-dev \
&& apt-get install -y libgomp1 curl libvulkan1 mesa-vulkan-drivers \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

8
.editorconfig
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@ -60,3 +60,11 @@ end_of_line = unset
charset = unset
trim_trailing_whitespace = unset
insert_final_newline = unset
[benches/**]
indent_style = unset
indent_size = unset
end_of_line = unset
charset = unset
trim_trailing_whitespace = unset
insert_final_newline = unset

47
.github/workflows/build.yml vendored
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@ -1691,3 +1691,50 @@ jobs:
run: |
GG_BUILD_KLEIDIAI=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
ggml-ci-arm64-graviton4-kleidiai:
runs-on: ah-ubuntu_22_04-c8g_8x
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Dependencies
id: depends
run: |
set -euxo pipefail
sudo apt-get update
sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
apt-get install -y \
build-essential \
libcurl4-openssl-dev \
python3-venv \
gpg \
wget \
time \
git-lfs
git lfs install
# install the latest cmake
sudo install -d /usr/share/keyrings
wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
| gpg --dearmor \
| sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
| sudo tee /etc/apt/sources.list.d/kitware.list
sudo apt-get update
sudo apt-get install -y cmake
- name: ccache
uses: ggml-org/ccache-action@v1.2.16
with:
key: ggml-ci-arm64-graviton4-kleidiai
evict-old-files: 1d
- name: Test
id: ggml-ci
run: |
GG_BUILD_KLEIDIAI=1 \
GG_BUILD_EXTRA_TESTS_0=1 \
bash ./ci/run.sh ./tmp/results ./tmp/mnt

1
CMakeLists.txt
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@ -210,6 +210,7 @@ endif()
if (LLAMA_BUILD_COMMON)
add_subdirectory(common)
add_subdirectory(vendor/cpp-httplib)
endif()
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)

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benches/dgx-spark/aime25_openai__gpt-oss-120b-high_temp1.0_20251109_094547.html Normal file
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benches/dgx-spark/aime25_openai__gpt-oss-120b-high_temp1.0_20251109_094547.json Normal file
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@ -0,0 +1,6 @@
{
"chars": 2296.1916666666666,
"chars:std": 986.051306946325,
"score": 0.925,
"score:std": 0.26339134382131846
}

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benches/dgx-spark/dgx-spark.md Normal file
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@ -0,0 +1,264 @@
## System info
```bash
uname --all
Linux spark-17ed 6.11.0-1016-nvidia #16-Ubuntu SMP PREEMPT_DYNAMIC Sun Sep 21 16:52:46 UTC 2025 aarch64 aarch64 aarch64 GNU/Linux
g++ --version
g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
nvidia-smi
Sun Nov 2 10:43:25 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GB10 On | 0000000F:01:00.0 Off | N/A |
| N/A 35C P8 4W / N/A | Not Supported | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
```
## ggml-org/gpt-oss-20b-GGUF
Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
- `llama-batched-bench`
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 512 | 32 | 1 | 544 | 0.374 | 1369.01 | 0.383 | 83.64 | 0.757 | 719.01 |
| 512 | 32 | 2 | 1088 | 0.274 | 3741.35 | 0.659 | 97.14 | 0.933 | 1166.66 |
| 512 | 32 | 4 | 2176 | 0.526 | 3896.47 | 0.817 | 156.73 | 1.342 | 1621.08 |
| 512 | 32 | 8 | 4352 | 1.044 | 3925.10 | 0.987 | 259.44 | 2.030 | 2143.56 |
| 512 | 32 | 16 | 8704 | 2.076 | 3945.84 | 1.248 | 410.32 | 3.324 | 2618.60 |
| 512 | 32 | 32 | 17408 | 4.170 | 3929.28 | 1.630 | 628.40 | 5.799 | 3001.76 |
| 4096 | 32 | 1 | 4128 | 1.083 | 3782.66 | 0.394 | 81.21 | 1.477 | 2795.13 |
| 4096 | 32 | 2 | 8256 | 2.166 | 3782.72 | 0.725 | 88.28 | 2.891 | 2856.14 |
| 4096 | 32 | 4 | 16512 | 4.333 | 3780.88 | 0.896 | 142.82 | 5.230 | 3157.38 |
| 4096 | 32 | 8 | 33024 | 8.618 | 3802.14 | 1.155 | 221.69 | 9.773 | 3379.08 |
| 4096 | 32 | 16 | 66048 | 17.330 | 3781.73 | 1.598 | 320.34 | 18.928 | 3489.45 |
| 4096 | 32 | 32 | 132096 | 34.671 | 3780.48 | 2.336 | 438.35 | 37.007 | 3569.51 |
| 8192 | 32 | 1 | 8224 | 2.233 | 3668.56 | 0.438 | 72.98 | 2.671 | 3078.44 |
| 8192 | 32 | 2 | 16448 | 4.425 | 3702.95 | 0.756 | 84.66 | 5.181 | 3174.95 |
| 8192 | 32 | 4 | 32896 | 8.859 | 3698.64 | 0.967 | 132.38 | 9.826 | 3347.72 |
| 8192 | 32 | 8 | 65792 | 17.714 | 3699.57 | 1.277 | 200.52 | 18.991 | 3464.35 |
| 8192 | 32 | 16 | 131584 | 35.494 | 3692.84 | 1.841 | 278.12 | 37.335 | 3524.46 |
| 8192 | 32 | 32 | 263168 | 70.949 | 3694.82 | 2.798 | 365.99 | 73.747 | 3568.53 |
- `llama-bench`
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 3714.25 ± 20.36 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 86.58 ± 0.43 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 3445.17 ± 17.85 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 81.72 ± 0.53 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 3218.78 ± 11.34 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 74.86 ± 0.64 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 2732.83 ± 7.17 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 71.57 ± 0.51 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 2119.75 ± 12.81 |
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 62.33 ± 0.24 |
build: eeee367de (6989)
## ggml-org/gpt-oss-120b-GGUF
Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
- `llama-batched-bench`
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 512 | 32 | 1 | 544 | 0.571 | 897.18 | 0.543 | 58.96 | 1.113 | 488.60 |
| 512 | 32 | 2 | 1088 | 0.593 | 1725.37 | 1.041 | 61.45 | 1.635 | 665.48 |
| 512 | 32 | 4 | 2176 | 1.043 | 1963.15 | 1.334 | 95.95 | 2.377 | 915.36 |
| 512 | 32 | 8 | 4352 | 2.099 | 1951.63 | 1.717 | 149.07 | 3.816 | 1140.45 |
| 512 | 32 | 16 | 8704 | 4.207 | 1947.12 | 2.311 | 221.56 | 6.518 | 1335.35 |
| 512 | 32 | 32 | 17408 | 8.422 | 1945.36 | 3.298 | 310.46 | 11.720 | 1485.27 |
| 4096 | 32 | 1 | 4128 | 2.138 | 1915.88 | 0.571 | 56.09 | 2.708 | 1524.12 |
| 4096 | 32 | 2 | 8256 | 4.266 | 1920.25 | 1.137 | 56.27 | 5.404 | 1527.90 |
| 4096 | 32 | 4 | 16512 | 8.564 | 1913.02 | 1.471 | 86.99 | 10.036 | 1645.29 |
| 4096 | 32 | 8 | 33024 | 17.092 | 1917.19 | 1.979 | 129.33 | 19.071 | 1731.63 |
| 4096 | 32 | 16 | 66048 | 34.211 | 1915.65 | 2.850 | 179.66 | 37.061 | 1782.15 |
| 4096 | 32 | 32 | 132096 | 68.394 | 1916.44 | 4.381 | 233.72 | 72.775 | 1815.13 |
| 8192 | 32 | 1 | 8224 | 4.349 | 1883.45 | 0.620 | 51.65 | 4.969 | 1655.04 |
| 8192 | 32 | 2 | 16448 | 8.674 | 1888.83 | 1.178 | 54.33 | 9.852 | 1669.48 |
| 8192 | 32 | 4 | 32896 | 17.351 | 1888.55 | 1.580 | 81.01 | 18.931 | 1737.68 |
| 8192 | 32 | 8 | 65792 | 34.743 | 1886.31 | 2.173 | 117.80 | 36.916 | 1782.20 |
| 8192 | 32 | 16 | 131584 | 69.413 | 1888.29 | 3.297 | 155.28 | 72.710 | 1809.70 |
| 8192 | 32 | 32 | 263168 | 138.903 | 1887.24 | 5.004 | 204.63 | 143.907 | 1828.73 |
- `llama-bench`
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 1919.36 ± 5.01 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 60.40 ± 0.30 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 1825.30 ± 6.37 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 56.94 ± 0.29 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 1739.19 ± 6.00 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 52.51 ± 0.42 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 1536.75 ± 4.27 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 49.33 ± 0.27 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 1255.85 ± 3.26 |
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 42.99 ± 0.18 |
build: eeee367de (6989)
## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
- `llama-batched-bench`
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 512 | 32 | 1 | 544 | 0.398 | 1285.90 | 0.530 | 60.41 | 0.928 | 586.27 |
| 512 | 32 | 2 | 1088 | 0.386 | 2651.65 | 0.948 | 67.50 | 1.334 | 815.38 |
| 512 | 32 | 4 | 2176 | 0.666 | 3076.37 | 1.209 | 105.87 | 1.875 | 1160.71 |
| 512 | 32 | 8 | 4352 | 1.325 | 3091.39 | 1.610 | 158.98 | 2.935 | 1482.65 |
| 512 | 32 | 16 | 8704 | 2.664 | 3075.58 | 2.150 | 238.19 | 4.813 | 1808.39 |
| 512 | 32 | 32 | 17408 | 5.336 | 3070.31 | 2.904 | 352.59 | 8.240 | 2112.50 |
| 4096 | 32 | 1 | 4128 | 1.444 | 2836.81 | 0.581 | 55.09 | 2.025 | 2038.81 |
| 4096 | 32 | 2 | 8256 | 2.872 | 2852.14 | 1.084 | 59.06 | 3.956 | 2086.99 |
| 4096 | 32 | 4 | 16512 | 5.744 | 2852.32 | 1.440 | 88.90 | 7.184 | 2298.47 |
| 4096 | 32 | 8 | 33024 | 11.463 | 2858.68 | 2.068 | 123.78 | 13.531 | 2440.65 |
| 4096 | 32 | 16 | 66048 | 22.915 | 2859.95 | 3.018 | 169.67 | 25.933 | 2546.90 |
| 4096 | 32 | 32 | 132096 | 45.956 | 2852.10 | 4.609 | 222.18 | 50.565 | 2612.39 |
| 8192 | 32 | 1 | 8224 | 3.063 | 2674.72 | 0.693 | 46.20 | 3.755 | 2189.92 |
| 8192 | 32 | 2 | 16448 | 6.109 | 2681.87 | 1.214 | 52.71 | 7.323 | 2245.98 |
| 8192 | 32 | 4 | 32896 | 12.197 | 2686.63 | 1.682 | 76.11 | 13.878 | 2370.30 |
| 8192 | 32 | 8 | 65792 | 24.409 | 2684.94 | 2.556 | 100.17 | 26.965 | 2439.95 |
| 8192 | 32 | 16 | 131584 | 48.753 | 2688.50 | 3.994 | 128.20 | 52.747 | 2494.64 |
| 8192 | 32 | 32 | 263168 | 97.508 | 2688.42 | 6.528 | 156.86 | 104.037 | 2529.57 |
- `llama-bench`
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 2925.55 ± 4.25 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 62.80 ± 0.27 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 2531.01 ± 6.79 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 55.86 ± 0.33 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 2244.39 ± 5.33 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 45.95 ± 0.33 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 1783.17 ± 3.68 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 39.07 ± 0.10 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 1241.90 ± 3.13 |
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 29.92 ± 0.06 |
build: eeee367de (6989)
## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
- `llama-batched-bench`
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 512 | 32 | 1 | 544 | 0.211 | 2421.57 | 1.055 | 30.33 | 1.266 | 429.57 |
| 512 | 32 | 2 | 1088 | 0.419 | 2441.34 | 1.130 | 56.65 | 1.549 | 702.32 |
| 512 | 32 | 4 | 2176 | 0.873 | 2345.54 | 1.174 | 108.99 | 2.048 | 1062.74 |
| 512 | 32 | 8 | 4352 | 1.727 | 2371.85 | 1.254 | 204.22 | 2.980 | 1460.19 |
| 512 | 32 | 16 | 8704 | 3.452 | 2373.22 | 1.492 | 343.16 | 4.944 | 1760.56 |
| 512 | 32 | 32 | 17408 | 6.916 | 2368.93 | 1.675 | 611.51 | 8.591 | 2026.36 |
| 4096 | 32 | 1 | 4128 | 1.799 | 2277.26 | 1.084 | 29.51 | 2.883 | 1431.91 |
| 4096 | 32 | 2 | 8256 | 3.577 | 2290.01 | 1.196 | 53.50 | 4.774 | 1729.51 |
| 4096 | 32 | 4 | 16512 | 7.172 | 2284.36 | 1.313 | 97.50 | 8.485 | 1946.00 |
| 4096 | 32 | 8 | 33024 | 14.341 | 2284.96 | 1.520 | 168.46 | 15.860 | 2082.18 |
| 4096 | 32 | 16 | 66048 | 28.675 | 2285.44 | 1.983 | 258.21 | 30.658 | 2154.33 |
| 4096 | 32 | 32 | 132096 | 57.354 | 2285.32 | 2.640 | 387.87 | 59.994 | 2201.82 |
| 8192 | 32 | 1 | 8224 | 3.701 | 2213.75 | 1.119 | 28.59 | 4.820 | 1706.34 |
| 8192 | 32 | 2 | 16448 | 7.410 | 2211.19 | 1.272 | 50.31 | 8.682 | 1894.56 |
| 8192 | 32 | 4 | 32896 | 14.802 | 2213.83 | 1.460 | 87.68 | 16.261 | 2022.96 |
| 8192 | 32 | 8 | 65792 | 29.609 | 2213.35 | 1.781 | 143.74 | 31.390 | 2095.93 |
| 8192 | 32 | 16 | 131584 | 59.229 | 2212.96 | 2.495 | 205.17 | 61.725 | 2131.79 |
| 8192 | 32 | 32 | 263168 | 118.449 | 2213.15 | 3.714 | 275.75 | 122.162 | 2154.25 |
- `llama-bench`
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 2272.74 ± 4.68 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 30.66 ± 0.02 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 2107.80 ± 9.55 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 29.71 ± 0.05 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 1937.80 ± 6.75 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 28.86 ± 0.04 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 1641.12 ± 1.78 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 27.24 ± 0.04 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 1296.02 ± 2.67 |
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 23.78 ± 0.03 |
build: eeee367de (6989)
## ggml-org/gemma-3-4b-it-qat-GGUF
Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
- `llama-batched-bench`
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 512 | 32 | 1 | 544 | 0.094 | 5434.73 | 0.394 | 81.21 | 0.488 | 1114.15 |
| 512 | 32 | 2 | 1088 | 0.168 | 6091.68 | 0.498 | 128.52 | 0.666 | 1633.41 |
| 512 | 32 | 4 | 2176 | 0.341 | 6010.68 | 0.542 | 236.37 | 0.882 | 2466.43 |
| 512 | 32 | 8 | 4352 | 0.665 | 6161.46 | 0.678 | 377.74 | 1.342 | 3241.72 |
| 512 | 32 | 16 | 8704 | 1.323 | 6193.19 | 0.902 | 567.41 | 2.225 | 3911.74 |
| 512 | 32 | 32 | 17408 | 2.642 | 6202.03 | 1.231 | 832.03 | 3.872 | 4495.36 |
| 4096 | 32 | 1 | 4128 | 0.701 | 5840.49 | 0.439 | 72.95 | 1.140 | 3621.23 |
| 4096 | 32 | 2 | 8256 | 1.387 | 5906.82 | 0.574 | 111.48 | 1.961 | 4210.12 |
| 4096 | 32 | 4 | 16512 | 2.758 | 5940.33 | 0.651 | 196.58 | 3.409 | 4843.33 |
| 4096 | 32 | 8 | 33024 | 5.491 | 5967.56 | 0.876 | 292.40 | 6.367 | 5187.12 |
| 4096 | 32 | 16 | 66048 | 10.978 | 5969.58 | 1.275 | 401.69 | 12.253 | 5390.38 |
| 4096 | 32 | 32 | 132096 | 21.944 | 5972.93 | 1.992 | 514.16 | 23.936 | 5518.73 |
| 8192 | 32 | 1 | 8224 | 1.402 | 5841.91 | 0.452 | 70.73 | 1.855 | 4434.12 |
| 8192 | 32 | 2 | 16448 | 2.793 | 5865.34 | 0.637 | 100.55 | 3.430 | 4795.51 |
| 8192 | 32 | 4 | 32896 | 5.564 | 5889.64 | 0.770 | 166.26 | 6.334 | 5193.95 |
| 8192 | 32 | 8 | 65792 | 11.114 | 5896.44 | 1.122 | 228.07 | 12.237 | 5376.51 |
| 8192 | 32 | 16 | 131584 | 22.210 | 5901.38 | 1.789 | 286.15 | 24.000 | 5482.74 |
| 8192 | 32 | 32 | 263168 | 44.382 | 5906.56 | 3.044 | 336.38 | 47.426 | 5549.02 |
- `llama-bench`
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 5810.04 ± 21.71 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 84.54 ± 0.18 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 5288.04 ± 3.54 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 78.82 ± 1.37 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 4960.43 ± 16.64 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 74.13 ± 0.30 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 4495.92 ± 31.11 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 72.37 ± 0.29 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 3746.90 ± 40.01 |
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 63.02 ± 0.20 |
build: eeee367de (6989)

11
benches/dgx-spark/run-aime-120b-t8-x8-high.log Normal file
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7
ci/run.sh
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@ -121,7 +121,12 @@ fi
if [ -n "${GG_BUILD_KLEIDIAI}" ]; then
echo ">>===== Enabling KleidiAI support"
CANDIDATES=("armv9-a+dotprod+i8mm" "armv8.6-a+dotprod+i8mm" "armv8.2-a+dotprod")
CANDIDATES=(
"armv9-a+dotprod+i8mm+sve2"
"armv9-a+dotprod+i8mm"
"armv8.6-a+dotprod+i8mm"
"armv8.2-a+dotprod"
)
CPU=""
for cpu in "${CANDIDATES[@]}"; do

8
common/CMakeLists.txt
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@ -56,6 +56,8 @@ add_library(${TARGET} STATIC
common.h
console.cpp
console.h
download.cpp
download.h
http.h
json-partial.cpp
json-partial.h
@ -77,10 +79,11 @@ if (BUILD_SHARED_LIBS)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
# TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
set(LLAMA_COMMON_EXTRA_LIBS build_info)
# Use curl to download model url
if (LLAMA_CURL)
# Use curl to download model url
find_package(CURL)
if (NOT CURL_FOUND)
message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF")
@ -88,6 +91,9 @@ if (LLAMA_CURL)
target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
include_directories(${CURL_INCLUDE_DIRS})
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARIES})
else()
# otherwise, use cpp-httplib
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
endif()
if (LLAMA_OPENSSL)

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common/arg.cpp
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4
common/arg.h
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@ -59,8 +59,8 @@ struct common_arg {
common_arg & set_sparam();
bool in_example(enum llama_example ex);
bool is_exclude(enum llama_example ex);
bool get_value_from_env(std::string & output);
bool has_value_from_env();
bool get_value_from_env(std::string & output) const;
bool has_value_from_env() const;
std::string to_string();
};

33
common/common.cpp
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@ -910,6 +910,39 @@ std::string fs_get_cache_file(const std::string & filename) {
return cache_directory + filename;
}
std::vector<common_file_info> fs_list_files(const std::string & path) {
std::vector<common_file_info> files;
if (path.empty()) return files;
std::filesystem::path dir(path);
if (!std::filesystem::exists(dir) || !std::filesystem::is_directory(dir)) {
return files;
}
for (const auto & entry : std::filesystem::directory_iterator(dir)) {
try {
// Only include regular files (skip directories)
const auto & p = entry.path();
if (std::filesystem::is_regular_file(p)) {
common_file_info info;
info.path = p.string();
info.name = p.filename().string();
try {
info.size = static_cast<size_t>(std::filesystem::file_size(p));
} catch (const std::filesystem::filesystem_error &) {
info.size = 0;
}
files.push_back(std::move(info));
}
} catch (const std::filesystem::filesystem_error &) {
// skip entries we cannot inspect
continue;
}
}
return files;
}
//
// Model utils

10
common/common.h
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@ -460,7 +460,8 @@ struct common_params {
float slot_prompt_similarity = 0.1f;
// batched-bench params
bool is_pp_shared = false;
bool is_pp_shared = false;
bool is_tg_separate = false;
std::vector<int32_t> n_pp;
std::vector<int32_t> n_tg;
@ -611,6 +612,13 @@ bool fs_create_directory_with_parents(const std::string & path);
std::string fs_get_cache_directory();
std::string fs_get_cache_file(const std::string & filename);
struct common_file_info {
std::string path;
std::string name;
size_t size = 0; // in bytes
};
std::vector<common_file_info> fs_list_files(const std::string & path);
//
// Model utils
//

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55
common/download.h Normal file
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@ -0,0 +1,55 @@
#pragma once
#include <string>
struct common_params_model;
//
// download functionalities
//
struct common_cached_model_info {
std::string manifest_path;
std::string user;
std::string model;
std::string tag;
size_t size = 0; // GGUF size in bytes
std::string to_string() const {
return user + "/" + model + ":" + tag;
}
};
struct common_hf_file_res {
std::string repo; // repo name with ":tag" removed
std::string ggufFile;
std::string mmprojFile;
};
/**
* Allow getting the HF file from the HF repo with tag (like ollama), for example:
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
* - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
* Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
*
* Return pair of <repo, file> (with "repo" already having tag removed)
*
* Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
*/
common_hf_file_res common_get_hf_file(
const std::string & hf_repo_with_tag,
const std::string & bearer_token,
bool offline);
// returns true if download succeeded
bool common_download_model(
const common_params_model & model,
const std::string & bearer_token,
bool offline);
// returns list of cached models
std::vector<common_cached_model_info> common_list_cached_models();
// resolve and download model from Docker registry
// return local path to downloaded model file
std::string common_docker_resolve_model(const std::string & docker);

119
convert_hf_to_gguf.py
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@ -218,8 +218,7 @@ class ModelBase:
logger.info(f"gguf: indexing model part '{part_name}'")
ctx: ContextManager[Any]
if is_safetensors:
from safetensors import safe_open
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
ctx = cast(ContextManager[Any], gguf.utility.SafetensorsLocal(self.dir_model / part_name))
else:
ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
@ -228,18 +227,18 @@ class ModelBase:
for name in model_part.keys():
if is_safetensors:
data: gguf.utility.LocalTensor = model_part[name]
if self.lazy:
data = model_part.get_slice(name)
data_gen = lambda data=data: LazyTorchTensor.from_safetensors_slice(data) # noqa: E731
data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data) # noqa: E731
else:
data = model_part.get_tensor(name)
data_gen = lambda data=data: data # noqa: E731
dtype = LazyTorchTensor._dtype_str_map[data.dtype]
data_gen = lambda data=data, dtype=dtype: torch.from_numpy(data.mmap_bytes()).view(dtype).reshape(data.shape) # noqa: E731
else:
data = model_part[name]
data_torch: Tensor = model_part[name]
if self.lazy:
data_gen = lambda data=data: LazyTorchTensor.from_eager(data) # noqa: E731
data_gen = lambda data=data_torch: LazyTorchTensor.from_eager(data) # noqa: E731
else:
data_gen = lambda data=data: data # noqa: E731
data_gen = lambda data=data_torch: data # noqa: E731
tensors[name] = data_gen
# verify tensor name presence and identify potentially missing files
@ -278,15 +277,14 @@ class ModelBase:
# The scale is inverted
return data / scale.float()
def dequant_simple(weight: Tensor, scale: Tensor) -> Tensor:
def dequant_simple(weight: Tensor, scale: Tensor, block_size: Sequence[int] | None = None) -> Tensor:
scale = scale.float()
if (weight_block_size := quant_config.get("weight_block_size")):
# TODO: make sure it's a list of integers
for i, size in enumerate(weight_block_size):
if block_size is not None:
for i, size in enumerate(block_size):
scale = scale.repeat_interleave(size, i)
# unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
scale = scale[tuple(slice(0, size) for size in weight.shape)]
# unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
scale = scale[tuple(slice(0, size) for size in weight.shape)]
return weight.float() * scale
@ -333,6 +331,40 @@ class ModelBase:
return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T
def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int):
assert w.dtype == torch.int32
shape = tuple(shape_tensor.tolist())
assert len(shape) == 2
mask = (1 << num_bits) - 1
shifts = torch.arange(0, 32 - (num_bits - 1), num_bits, dtype=torch.int32)
if self.lazy:
shifts = LazyTorchTensor.from_eager(shifts)
if zero_point is None:
offset = 1 << (num_bits - 1)
else:
assert len(zero_point.shape) == 2
offset = (zero_point.unsqueeze(1) >> shifts.reshape(1, -1, 1)) & mask
offset = offset.reshape(-1, zero_point.shape[1])
# trim padding, and prepare for broadcast
# NOTE: the zero-point is packed along dim 0
offset = offset[:shape[0], :].unsqueeze(-1)
# extract values
# NOTE: the weights are packed along dim 1
unpacked = (w.unsqueeze(-1) >> shifts.reshape(1, 1, -1)) & mask
unpacked = unpacked.reshape(shape[0], -1)
# trim padding
unpacked = unpacked[:, :shape[1]]
# prepare for broadcast of the scale
unpacked = unpacked.reshape(shape[0], (unpacked.shape[-1] + group_size - 1) // group_size, group_size)
unpacked = unpacked - offset
return (unpacked * scale.unsqueeze(-1).float()).reshape(shape)
if quant_method == "bitnet":
for name in self.model_tensors.keys():
if name.endswith(".weight_scale"):
@ -342,12 +374,13 @@ class ModelBase:
self.model_tensors[weight_name] = lambda w=w, s=s: dequant_bitnet(w(), s())
tensors_to_remove.append(name)
elif quant_method == "fp8":
block_size = quant_config.get("weight_block_size")
for name in self.model_tensors.keys():
if name.endswith(".weight_scale_inv"):
weight_name = name.removesuffix("_scale_inv")
w = self.model_tensors[weight_name]
s = self.model_tensors[name]
self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s())
self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
tensors_to_remove.append(name)
elif quant_method == "gptq":
for name in self.model_tensors.keys():
@ -371,6 +404,49 @@ class ModelBase:
".scales",
)
]
elif quant_method == "compressed-tensors":
quant_format = quant_config["format"]
groups = quant_config["config_groups"]
if len(groups) > 1:
raise NotImplementedError("Can't handle multiple config groups for compressed-tensors yet")
weight_config = tuple(groups.values())[0]["weights"]
if quant_format == "float-quantized" or quant_format == "int-quantized" or quant_format == "naive-quantized":
block_size = weight_config.get("block_structure", None)
strategy = weight_config.get("strategy")
assert strategy == "channel" or strategy == "block"
assert weight_config.get("group_size") is None # didn't find a model using this yet
for name in self.model_tensors.keys():
if name.endswith(".weight_scale"):
weight_name = name.removesuffix("_scale")
w = self.model_tensors[weight_name]
s = self.model_tensors[name]
self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
tensors_to_remove.append(name)
elif quant_format == "pack-quantized":
assert weight_config.get("strategy") == "group"
assert weight_config.get("type", "int") == "int"
num_bits = weight_config.get("num_bits")
group_size = weight_config.get("group_size")
assert isinstance(num_bits, int)
assert isinstance(group_size, int)
for name in self.model_tensors.keys():
if name.endswith(".weight_packed"):
base_name = name.removesuffix("_packed")
w = self.model_tensors[name]
scale = self.model_tensors[base_name + "_scale"]
shape = self.model_tensors[base_name + "_shape"]
zero_point = self.model_tensors.get(base_name + "_zero_point", lambda: None)
new_tensors[base_name] = (
lambda w=w, scale=scale, shape=shape, zero_point=zero_point: dequant_packed(
w(), scale(), shape(), zero_point(), num_bits, group_size,
)
)
tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")]
if (base_name + "_zero_point") in self.model_tensors:
tensors_to_remove.append(base_name + "_zero_point")
else:
raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported")
else:
raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
@ -7278,6 +7354,7 @@ class PLMModel(TextModel):
@ModelBase.register("T5ForConditionalGeneration")
@ModelBase.register("MT5ForConditionalGeneration")
@ModelBase.register("UMT5ForConditionalGeneration")
@ModelBase.register("UMT5Model")
class T5Model(TextModel):
model_arch = gguf.MODEL_ARCH.T5
@ -10002,6 +10079,16 @@ class LazyTorchTensor(gguf.LazyBase):
lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[...] if len(s.get_shape()) == 0 else s[:])
return cast(torch.Tensor, lazy)
@classmethod
def from_local_tensor(cls, t: gguf.utility.LocalTensor) -> Tensor:
def load_tensor(tensor: gguf.utility.LocalTensor) -> Tensor:
dtype = cls._dtype_str_map[tensor.dtype]
return torch.from_numpy(tensor.mmap_bytes()).view(dtype).reshape(tensor.shape)
dtype = cls._dtype_str_map[t.dtype]
shape = t.shape
lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(t,), func=lambda r: load_tensor(r))
return cast(torch.Tensor, lazy)
@classmethod
def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
dtype = cls._dtype_str_map[remote_tensor.dtype]

42
docs/build.md
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@ -178,6 +178,48 @@ GeForce RTX 3070 8.6
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
```
### Overriding the CUDA Version
If you have multiple CUDA installations on your system and want to compile llama.cpp for a specific one, e.g. for CUDA 11.7 installed under `/opt/cuda-11.7`:
```bash
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_COMPILER=/opt/cuda-11.7/bin/nvcc -DCMAKE_INSTALL_RPATH="/opt/cuda-11.7/lib64;\$ORIGIN" -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
```
#### Fixing Compatibility Issues with Old CUDA and New glibc
If you try to use an old CUDA version (e.g. v11.7) with a new glibc version you can get errors like this:
```
/usr/include/bits/mathcalls.h(83): error: exception specification is
incompatible with that of previous function "cospi"
/opt/cuda-11.7/bin/../targets/x86_64-linux/include/crt/math_functions.h(5545):
here
```
It seems the least bad solution is to patch the CUDA installation to declare the correct signatures.
Replace the following lines in `/path/to/your/cuda/installation/targets/x86_64-linux/include/crt/math_functions.h`:
```C++
// original lines
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double cospi(double x);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float cospif(float x);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double sinpi(double x);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float sinpif(float x);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double rsqrt(double x);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float rsqrtf(float x);
// edited lines
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double cospi(double x) noexcept (true);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float cospif(float x) noexcept (true);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double sinpi(double x) noexcept (true);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float sinpif(float x) noexcept (true);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double rsqrt(double x) noexcept (true);
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float rsqrtf(float x) noexcept (true);
```
### Runtime CUDA environmental variables
You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime.

2
docs/ops.md
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@ -24,7 +24,7 @@ Legend:
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ |
| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ |
| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ |
| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
| CONV_2D | ❌ | ❌ | ✅ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ |
| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |

32
docs/ops/SYCL.csv
View File

@ -9307,37 +9307,37 @@
"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","SYCL"
"SYCL0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","yes","SYCL"
"SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","no","SYCL"
"SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","yes","SYCL"
"SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","SYCL"
"SYCL0","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","SYCL"
"SYCL0","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","SYCL"

Can't render this file because it is too large.

7
examples/gguf/gguf.cpp
View File

@ -184,8 +184,13 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
const char * name = gguf_get_tensor_name (ctx, i);
const size_t size = gguf_get_tensor_size (ctx, i);
const size_t offset = gguf_get_tensor_offset(ctx, i);
const auto type = gguf_get_tensor_type (ctx, i);
printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset);
const char * type_name = ggml_type_name(type);
const size_t type_size = ggml_type_size(type);
const size_t n_elements = size / type_size;
printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu, type = %s, n_elts = %zu\n", __func__, i, name, size, offset, type_name, n_elements);
}
}

2
ggml/CMakeLists.txt
View File

@ -168,7 +168,7 @@ option(GGML_RV_ZFH "ggml: enable riscv zfh" ON)
option(GGML_RV_ZVFH "ggml: enable riscv zvfh" ON)
option(GGML_RV_ZICBOP "ggml: enable riscv zicbop" ON)
option(GGML_XTHEADVECTOR "ggml: enable xtheadvector" OFF)
option(GGML_VXE "ggml: enable vxe" ON)
option(GGML_VXE "ggml: enable vxe" ${GGML_NATIVE})
option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
set(GGML_CPU_ARM_ARCH "" CACHE STRING "ggml: CPU architecture for ARM")

16
ggml/src/CMakeLists.txt
View File

@ -211,6 +211,11 @@ add_library(ggml-base
ggml-quants.h
gguf.cpp)
set_target_properties(ggml-base PROPERTIES
VERSION ${GGML_VERSION}
SOVERSION ${GGML_VERSION_MAJOR}
)
target_include_directories(ggml-base PRIVATE .)
if (GGML_BACKEND_DL)
target_compile_definitions(ggml-base PUBLIC GGML_BACKEND_DL)
@ -220,6 +225,11 @@ add_library(ggml
ggml-backend-reg.cpp)
add_library(ggml::ggml ALIAS ggml)
set_target_properties(ggml PROPERTIES
VERSION ${GGML_VERSION}
SOVERSION ${GGML_VERSION_MAJOR}
)
if (GGML_BACKEND_DIR)
if (NOT GGML_BACKEND_DL)
message(FATAL_ERROR "GGML_BACKEND_DIR requires GGML_BACKEND_DL")
@ -259,6 +269,12 @@ function(ggml_add_backend_library backend)
target_compile_definitions(${backend} PUBLIC GGML_BACKEND_SHARED)
endif()
# Set versioning properties for all backend libraries
set_target_properties(${backend} PROPERTIES
VERSION ${GGML_VERSION}
SOVERSION ${GGML_VERSION_MAJOR}
)
if(NOT GGML_AVAILABLE_BACKENDS)
set(GGML_AVAILABLE_BACKENDS "${backend}"
CACHE INTERNAL "List of backends for cmake package")

45
ggml/src/ggml-cpu/CMakeLists.txt
View File

@ -126,25 +126,36 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
)
if (NOT ARM_MCPU_RESULT)
string(REGEX MATCH "-mcpu=[^ ']+" ARM_MCPU_FLAG "${ARM_MCPU}")
string(REGEX MATCH "-march=[^ ']+" ARM_MARCH_FLAG "${ARM_MCPU}")
# on some old GCC we need to read -march=
if (ARM_MARCH_FLAG AND NOT "${ARM_MARCH_FLAG}" STREQUAL "-march=native")
set(ARM_NATIVE_FLAG "${ARM_MARCH_FLAG}")
elseif(ARM_MCPU_FLAG AND NOT "${ARM_MCPU_FLAG}" STREQUAL "-mcpu=native")
set(ARM_NATIVE_FLAG "${ARM_MCPU_FLAG}")
endif()
endif()
if ("${ARM_MCPU_FLAG}" STREQUAL "")
set(ARM_MCPU_FLAG -mcpu=native)
message(STATUS "ARM -mcpu not found, -mcpu=native will be used")
if ("${ARM_NATIVE_FLAG}" STREQUAL "")
set(ARM_NATIVE_FLAG -mcpu=native)
message(WARNING "ARM -march/-mcpu not found, -mcpu=native will be used")
else()
message(STATUS "ARM detected flags: ${ARM_NATIVE_FLAG}")
endif()
include(CheckCXXSourceRuns)
function(check_arm_feature tag code)
set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+${tag}")
set(CMAKE_REQUIRED_FLAGS "${ARM_NATIVE_FLAG}+${tag}")
check_cxx_source_runs("${code}" GGML_MACHINE_SUPPORTS_${tag})
if (GGML_MACHINE_SUPPORTS_${tag})
set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+${tag}" PARENT_SCOPE)
set(ARM_NATIVE_FLAG_FIX "${ARM_NATIVE_FLAG_FIX}+${tag}" PARENT_SCOPE)
else()
set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+no${tag}")
set(CMAKE_REQUIRED_FLAGS "${ARM_NATIVE_FLAG}+no${tag}")
check_cxx_source_compiles("int main() { return 0; }" GGML_MACHINE_SUPPORTS_no${tag})
if (GGML_MACHINE_SUPPORTS_no${tag})
set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+no${tag}" PARENT_SCOPE)
set(ARM_NATIVE_FLAG_FIX "${ARM_NATIVE_FLAG_FIX}+no${tag}" PARENT_SCOPE)
endif()
endif()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
@ -155,7 +166,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
check_arm_feature(sve "#include <arm_sve.h>\nint main() { svfloat32_t _a, _b; volatile svfloat32_t _c = svadd_f32_z(svptrue_b8(), _a, _b); return 0; }")
check_arm_feature(sme "#include <arm_sme.h>\n__arm_locally_streaming int main() { __asm__ volatile(\"smstart; smstop;\"); return 0; }")
list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}")
list(APPEND ARCH_FLAGS "${ARM_NATIVE_FLAG}${ARM_NATIVE_FLAG_FIX}")
else()
if (GGML_CPU_ARM_ARCH)
list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH})
@ -579,6 +590,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
${KLEIDIAI_SRC}/kai/ukernels/
${KLEIDIAI_SRC}/kai/ukernels/matmul/
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/)
@ -597,23 +609,34 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32_neon.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c)
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qai8dxp_f32.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi8cxp_qsi8cx_neon.c)
if (NOT DOTPROD_ENABLED MATCHES -1)
list(APPEND GGML_KLEIDIAI_SOURCES
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.c)
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod.c)
endif()
if (NOT I8MM_ENABLED MATCHES -1)
list(APPEND GGML_KLEIDIAI_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm.c)
list(APPEND GGML_KLEIDIAI_SOURCES
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm.c)
endif()
if (NOT SME_ENABLED MATCHES -1)
list(APPEND GGML_KLEIDIAI_SOURCES
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa_asm.S
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot_asm.S
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.c
${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa_asm.S
${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_bf16p2vlx2_f32_sme.c

454
ggml/src/ggml-cpu/arch/arm/quants.c
View File

@ -2044,6 +2044,26 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
}
#ifdef __ARM_FEATURE_SVE
static inline svuint32_t ggml_decode_q4scales_and_mins_for_mmla(const uint32_t * vx_scales) {
const svbool_t pg_all = svptrue_pat_b32(SV_VL4);
const svbool_t pg_false = svpfalse_b(); // 0x0000
const svbool_t pg_lo_8 = svwhilelt_b8_s32(0, 8); // 0x00ff
const svbool_t pg_odd = svzip1_b32(pg_false, pg_lo_8);
svuint32_t vutmp_hi, vutmp_lo;
svuint32_t vx01 = svld1_u32(pg_lo_8, vx_scales);
vutmp_hi = svzip1_u32(vx01, vx01);
vutmp_hi = svlsr_n_u32_m(pg_odd, vutmp_hi, 2);
vutmp_hi = svreinterpret_u32_u64(svand_n_u64_x(pg_all, svreinterpret_u64_u32(vutmp_hi), UINT64_C(0x303030303f3f3f3f)));
const svuint32_t vx2 = svdup_u32(vx_scales[2]);
vutmp_lo = svlsr_u32_x(pg_all, vx2, svreinterpret_u32_s32(svindex_s32(-2, 2)));
vutmp_lo = svand_n_u32_z(pg_odd, vutmp_lo, UINT32_C(0x0f0f0f0f));
svuint32_t vutmp = svorr_u32_z(pg_all, vutmp_hi, vutmp_lo);
return vutmp;
}
#endif
void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
assert(n % QK_K == 0);
#ifdef __ARM_FEATURE_MATMUL_INT8
@ -2066,8 +2086,220 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
static const uint32_t kmask3 = 0x03030303;
uint32_t utmp[4];
#ifdef __ARM_FEATURE_SVE
const int vector_length = ggml_cpu_get_sve_cnt()*8;
#endif
#if defined(__ARM_FEATURE_MATMUL_INT8)
#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
if (nrc == 2) {
svbool_t pg32_2 = svptrue_pat_b32(SV_VL2);
const block_q4_K * GGML_RESTRICT vx0 = vx;
const block_q8_K * GGML_RESTRICT vy0 = vy;
const block_q4_K * GGML_RESTRICT vx1 = (const block_q4_K *) ((const uint8_t*)vx + bx);
const block_q8_K * GGML_RESTRICT vy1 = (const block_q8_K *) ((const uint8_t*)vy + by);
union {
uint32_t u32[8];
uint64_t u64[4];
} new_utmp;
svfloat32_t sumf1 = svdup_n_f32(0);
switch (vector_length) {
case 128:
{
svbool_t pg_false = svpfalse_b();
svbool_t pg_lo_8 = svwhilelt_b8_s32(0, 8);
svbool_t vmins_mask1= svzip1_b32(pg_lo_8, pg_false);
svbool_t vmins_mask2 = svzip1_b32(pg_false, pg_lo_8);
svbool_t pg128_all = svptrue_pat_b8(SV_VL16);
for (int i = 0; i < nb; ++i) {
svfloat32_t vy_d = svuzp1_f32(svdup_n_f32(vy0[i].d), svdup_n_f32(vy1[i].d));
svfloat32_t vx_d = svzip1_f32(svdup_n_f32(GGML_FP16_TO_FP32(vx0[i].d)), svdup_n_f32(GGML_FP16_TO_FP32(vx1[i].d)));
svfloat32_t svsuper_block_scales = svmul_f32_x(pg128_all, vy_d, vx_d);
svfloat32_t vx_dmins = svzip1_f32(svdup_n_f32(GGML_FP16_TO_FP32(vx0[i].dmin)), svdup_n_f32(GGML_FP16_TO_FP32(vx1[i].dmin)));
svfloat32_t vy_dmins = svuzp1_f32(svdup_n_f32(vy0[i].d), svdup_n_f32(vy1[i].d));
svfloat32_t svdmins = svmul_n_f32_x(pg128_all, svmul_f32_x(pg128_all, vy_dmins, vx_dmins), -1);
const uint8_t * GGML_RESTRICT q4_0 = vx0[i].qs;
const int8_t * GGML_RESTRICT q8_0 = vy0[i].qs;
const uint8_t * GGML_RESTRICT q4_1 = vx1[i].qs;
const int8_t * GGML_RESTRICT q8_1 = vy1[i].qs;
svint16_t lo = svld1_s16(pg128_all, vy0[i].bsums + 0);
svint16_t hi = svld1_s16(pg128_all, vy0[i].bsums + 8);
svint16_t sum_tmp1 = svuzp1_s16(lo, hi);
svint16_t sum_tmp2 = svuzp2_s16(lo, hi);
svint16_t svq8sums_0 = svadd_s16_x(pg128_all, sum_tmp1, sum_tmp2);
lo = svld1_s16(pg128_all, vy1[i].bsums + 0);
hi = svld1_s16(pg128_all, vy1[i].bsums + 8);
sum_tmp1 = svuzp1(lo, hi);
sum_tmp2 = svuzp2(lo, hi);
svint16_t svq8sums_1 = svadd_s16_x(pg128_all, sum_tmp1, sum_tmp2);
svuint32_t decoded_scales0 = ggml_decode_q4scales_and_mins_for_mmla((const uint32_t *)vx0[i].scales);
svuint32_t decoded_scales1 = ggml_decode_q4scales_and_mins_for_mmla((const uint32_t *)vx1[i].scales);
svuint32x2_t decoded_scales = svcreate2_u32(decoded_scales0, decoded_scales1);
svst2_u32(pg128_all, new_utmp.u32, decoded_scales);
svint16_t svmins8_0 = svreinterpret_s16_u16(svunpklo_u16(svreinterpret_u8_u32(svuzp1_u32(svld1_u32(vmins_mask1, new_utmp.u32+4), svdup_n_u32(0)))));
svint16_t svmins8_1 = svreinterpret_s16_u16(svunpklo_u16(svreinterpret_u8_u32(svuzp2_u32(svld1_u32(vmins_mask2, new_utmp.u32+4), svdup_n_u32(0)))));
svint32_t svsumfs_tmp1 = svreinterpret_s32_s64(svdot_s64(svdup_n_s64(0), svq8sums_0, svmins8_0));
svint32_t svsumfs_tmp2 = svreinterpret_s32_s64(svdot_s64(svdup_n_s64(0), svq8sums_0, svmins8_1));
svint32_t svsumfs_tmp3 = svtrn1_s32(svsumfs_tmp1, svsumfs_tmp2);
svint32_t svsumfs_tmp4 = svreinterpret_s32_s64(svdot_s64(svdup_n_s64(0), svq8sums_1, svmins8_0));
svint32_t svsumfs_tmp5 = svreinterpret_s32_s64(svdot_s64(svdup_n_s64(0), svq8sums_1, svmins8_1));
svint32_t svsumfs_tmp6 = svtrn1_s32(svsumfs_tmp4, svsumfs_tmp5);
svint32_t svsumfs_tmp7 = svreinterpret_s32_s64(svtrn2_s64(svreinterpret_s64_s32(svsumfs_tmp3), svreinterpret_s64_s32(svsumfs_tmp6)));
svint32_t svsumfs_tmp8 = svreinterpret_s32_s64(svtrn1_s64(svreinterpret_s64_s32(svsumfs_tmp3), svreinterpret_s64_s32(svsumfs_tmp6)));
svint32_t svsumfs_tmp = svadd_s32_x(pg128_all, svsumfs_tmp7, svsumfs_tmp8);
svint32_t svscales, sumi1, sumi2;
svint32_t acc_sumif1 = svdup_n_s32(0);
svint32_t acc_sumif2 = svdup_n_s32(0);
svint8_t q4bytes_0_l, q4bytes_0_h, q4bytes_1_l, q4bytes_1_h, l0, l1, l2, l3,
q8bytes_0_h, q8bytes_0_l, q8bytes_1_h, q8bytes_1_l, r0, r1, r2, r3;
#pragma GCC unroll 1
for (int j = 0; j < QK_K/64; ++j) {
q4bytes_0_l = svreinterpret_s8_u8(svand_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_0), 0xf));
q4bytes_1_l = svreinterpret_s8_u8(svand_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_1), 0xf));
q4bytes_0_h = svreinterpret_s8_u8(svand_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_0+16), 0xf));
q4bytes_1_h = svreinterpret_s8_u8(svand_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_1+16), 0xf));
l0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q4bytes_0_l), svreinterpret_s64_s8(q4bytes_1_l)));
l1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q4bytes_0_l), svreinterpret_s64_s8(q4bytes_1_l)));
l2 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q4bytes_0_h), svreinterpret_s64_s8(q4bytes_1_h)));
l3 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q4bytes_0_h), svreinterpret_s64_s8(q4bytes_1_h)));
q8bytes_0_h = svld1_s8(pg128_all, q8_0);
q8bytes_1_h = svld1_s8(pg128_all, q8_1);
q8bytes_0_l = svld1_s8(pg128_all, q8_0+16);
q8bytes_1_l = svld1_s8(pg128_all, q8_1+16);
r0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0_h), svreinterpret_s64_s8(q8bytes_1_h)));
r1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0_h), svreinterpret_s64_s8(q8bytes_1_h)));
r2 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0_l), svreinterpret_s64_s8(q8bytes_1_l)));
r3 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0_l), svreinterpret_s64_s8(q8bytes_1_l)));
sumi1 = svmmla_s32(svmmla_s32(svmmla_s32(svmmla_s32(svdup_n_s32(0), r0, l0), r1, l1), r2, l2), r3, l3);
svscales = svreinterpret_s32_u32(svlsr_n_u32_x(pg128_all, svlsl_n_u32_x(pg128_all, svreinterpret_u32_u64(svdup_n_u64(new_utmp.u64[j/2])), 8*(4-2*(j%2)-1)), 24));
acc_sumif1 = svmla_s32_x(pg128_all, acc_sumif1, svscales, sumi1);
q4bytes_0_l = svreinterpret_s8_u8(svlsr_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_0), 4));
q4bytes_1_l = svreinterpret_s8_u8(svlsr_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_1), 4));
q4bytes_0_h = svreinterpret_s8_u8(svlsr_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_0+16), 4));
q4bytes_1_h = svreinterpret_s8_u8(svlsr_n_u8_x(pg128_all, svld1_u8(pg128_all, q4_1+16), 4));
l0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q4bytes_0_l), svreinterpret_s64_s8(q4bytes_1_l)));
l1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q4bytes_0_l), svreinterpret_s64_s8(q4bytes_1_l)));
l2 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q4bytes_0_h), svreinterpret_s64_s8(q4bytes_1_h)));
l3 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q4bytes_0_h), svreinterpret_s64_s8(q4bytes_1_h)));
q8bytes_0_h = svld1_s8(pg128_all, q8_0+32);
q8bytes_1_h = svld1_s8(pg128_all, q8_1+32);
q8bytes_0_l = svld1_s8(pg128_all, q8_0+48);
q8bytes_1_l = svld1_s8(pg128_all, q8_1+48);
r0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0_h), svreinterpret_s64_s8(q8bytes_1_h)));
r1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0_h), svreinterpret_s64_s8(q8bytes_1_h)));
r2 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0_l), svreinterpret_s64_s8(q8bytes_1_l)));
r3 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0_l), svreinterpret_s64_s8(q8bytes_1_l)));
sumi2 = svmmla_s32(svmmla_s32(svmmla_s32(svmmla_s32(svdup_n_s32(0), r0, l0), r1, l1), r2, l2), r3, l3);
svscales = svreinterpret_s32_u32(svlsr_n_u32_x(pg128_all, svlsl_n_u32_x(pg128_all, svreinterpret_u32_u64(svdup_n_u64(new_utmp.u64[j/2])), 8*(4-2*(j%2)-2)), 24));
acc_sumif2 = svmla_s32_x(pg128_all, acc_sumif2, svscales, sumi2);
q4_0 += 32; q4_1 += 32; q8_0 += 64; q8_1 += 64;
}
sumf1 = svmla_f32_x(pg128_all,
svmla_f32_x(pg128_all,
sumf1,
svcvt_f32_x(pg128_all,
svadd_s32_x(pg128_all, acc_sumif1, acc_sumif2)),
svsuper_block_scales),
svdmins,
svcvt_f32_s32_x(pg128_all, svsumfs_tmp));
} //end of for nb
} // end of case 128
break;
case 256:
case 512:
{
const svbool_t pg32_4 = svptrue_pat_b32(SV_VL4);
const svbool_t pg8_16 = svptrue_pat_b8(SV_VL16);
const svbool_t pg256_all = svptrue_pat_b8(SV_ALL);
for (int i = 0; i < nb; ++i) {
const uint8_t * GGML_RESTRICT q4_0 = vx0[i].qs;
const int8_t * GGML_RESTRICT q8_0 = vy0[i].qs;
const uint8_t * GGML_RESTRICT q4_1 = vx1[i].qs;
const int8_t * GGML_RESTRICT q8_1 = vy1[i].qs;
svint32_t svscales, sumi1, sumi2;
svint32_t acc_sumif1 = svdup_n_s32(0);
svint32_t acc_sumif2 = svdup_n_s32(0);
svint8_t l0, l1, l2, l3, r0, r1, r2, r3;
svfloat32_t vx_d = svzip1_f32(svdup_n_f32(GGML_FP16_TO_FP32(vx0[i].d)), svdup_n_f32(GGML_FP16_TO_FP32(vx1[i].d)));
svfloat64_t vy_d_tmp = svreinterpret_f64_f32(svuzp1_f32(svdup_n_f32(vy0[i].d), svdup_n_f32(vy1[i].d)));
svfloat32_t vy_d = svreinterpret_f32_f64(svuzp1_f64(vy_d_tmp, vy_d_tmp));
svfloat32_t svsuper_block_scales = svmul_f32_z(pg32_4, vy_d, vx_d);
svfloat32_t vx_dmins = svzip1_f32(svdup_n_f32(GGML_FP16_TO_FP32(vx0[i].dmin)), svdup_n_f32(GGML_FP16_TO_FP32(vx1[i].dmin)));
svfloat64_t vy_dmins_tmp = svreinterpret_f64_f32(svuzp1_f32(svdup_n_f32(vy0[i].d), svdup_n_f32(vy1[i].d)));
svfloat32_t vy_dmins = svreinterpret_f32_f64(svuzp1_f64(vy_dmins_tmp, vy_dmins_tmp));
svfloat32_t svdmins = svmul_n_f32_x(pg32_4, svmul_f32_x(pg32_4, vx_dmins, vy_dmins), -1);
svint16_t rc1 = svuzp1_s16(svld1_s16(pg256_all, vy0[i].bsums), svld1_s16(pg256_all, vy1[i].bsums));
svint16_t rc2 = svuzp2_s16(svld1_s16(pg256_all, vy0[i].bsums), svld1_s16(pg256_all, vy1[i].bsums));
svint16_t svq8sums = svadd_s16_x(pg256_all, rc1, rc2);
svuint32_t decoded_scales0 = ggml_decode_q4scales_and_mins_for_mmla((const uint32_t *)vx0[i].scales);
svuint32_t decoded_scales1 = ggml_decode_q4scales_and_mins_for_mmla((const uint32_t *)vx1[i].scales);
svuint32x2_t decoded_scales = svcreate2_u32(decoded_scales0, decoded_scales1);
svst2_u32(pg8_16, new_utmp.u32, decoded_scales);
svint16_t new_svq8sums_0 = svreinterpret_s16_u64(svtrn1_u64(svreinterpret_u64_s16(svq8sums), svreinterpret_u64_s16(svq8sums)));
svint16_t new_svq8sums_1 = svreinterpret_s16_u64(svtrn2_u64(svreinterpret_u64_s16(svq8sums), svreinterpret_u64_s16(svq8sums)));
svuint64_t new_mins_0 = svdup_u64(new_utmp.u64[2]);
svuint64_t new_mins_1 = svdup_u64(new_utmp.u64[3]);
svint16_t new_svmins8_0 = svreinterpret_s16_u16(svunpklo_u16(svreinterpret_u8_u64(new_mins_0)));
svint16_t new_svmins8_1 = svreinterpret_s16_u16(svunpklo_u16(svreinterpret_u8_u64(new_mins_1)));
svint64_t dot_prod_0 = svdot_s64(svdup_s64(0), new_svmins8_0, new_svq8sums_0);
svint64_t dot_prod_1 = svdot_s64(dot_prod_0, new_svmins8_1, new_svq8sums_1);
svfloat32_t converted_dot_prod_1 = svcvt_f32_s64_x(pg256_all, dot_prod_1);
svfloat32_t svsumfs_tmp = svuzp1_f32(converted_dot_prod_1, converted_dot_prod_1);
#pragma GCC unroll 1
for (int j = 0; j < QK_K/64; ++j) {
svuint8_t q4bytes_0 = svand_n_u8_x(pg256_all, svld1_u8(pg256_all, q4_0), 0xf);
svuint8_t q4bytes_1 = svand_n_u8_x(pg256_all, svld1_u8(pg256_all, q4_1), 0xf);
svuint8_t q4bytes_2 = svlsr_n_u8_x(pg256_all, svld1_u8(pg256_all, q4_0), 4);
svuint8_t q4bytes_3 = svlsr_n_u8_x(pg256_all, svld1_u8(pg256_all, q4_1), 4);
l0 = svreinterpret_s8_u64(svzip1_u64(svreinterpret_u64_u8(q4bytes_0), svreinterpret_u64_u8(q4bytes_1)));
l1 = svreinterpret_s8_u64(svzip2_u64(svreinterpret_u64_u8(q4bytes_0), svreinterpret_u64_u8(q4bytes_1)));
l2 = svreinterpret_s8_u64(svzip1_u64(svreinterpret_u64_u8(q4bytes_2), svreinterpret_u64_u8(q4bytes_3)));
l3 = svreinterpret_s8_u64(svzip2_u64(svreinterpret_u64_u8(q4bytes_2), svreinterpret_u64_u8(q4bytes_3)));
svint8_t q8bytes_0 = svld1_s8(pg256_all, q8_0);
svint8_t q8bytes_1 = svld1_s8(pg256_all, q8_1);
svint8_t q8bytes_2 = svld1_s8(pg256_all, q8_0+32);
svint8_t q8bytes_3 = svld1_s8(pg256_all, q8_1+32);
r0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0), svreinterpret_s64_s8(q8bytes_1)));
r1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0), svreinterpret_s64_s8(q8bytes_1)));
r2 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_2), svreinterpret_s64_s8(q8bytes_3)));
r3 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_2), svreinterpret_s64_s8(q8bytes_3)));
sumi1 = svmmla(svmmla(svdup_n_s32(0), r0, l0), r1, l1);
svscales = svreinterpret_s32_u32(svlsr_n_u32_x(pg256_all, svlsl_n_u32_x(pg256_all, svreinterpret_u32_u64(svdup_n_u64(new_utmp.u64[j/2])), 8*(4-2*(j%2)-1)), 24));
acc_sumif1 = svmla_s32_x(pg256_all, acc_sumif1, svscales, sumi1);
sumi2 = svmmla(svmmla(svdup_n_s32(0), r2, l2), r3, l3);
svscales = svreinterpret_s32_u32(svlsr_n_u32_x(pg256_all, svlsl_n_u32_x(pg256_all, svreinterpret_u32_u64(svdup_n_u64(new_utmp.u64[j/2])), 8*(4-2*(j%2)-2)), 24));
acc_sumif2 = svmla_s32_x(pg256_all, acc_sumif2, svscales, sumi2);
q4_0 += 32; q4_1 += 32; q8_0 += 64; q8_1 += 64;
}
svint32_t acc_sumif = svadd_s32_x(pg256_all, acc_sumif1, acc_sumif2);
svint32_t swap_acc_sumif = svext_s32(acc_sumif, acc_sumif, 4);
acc_sumif = svadd_s32_x(pg32_4, acc_sumif, swap_acc_sumif);
sumf1 = svmla_f32_x(pg32_4,
svmla_f32_x(pg32_4,
sumf1,
svcvt_f32_x(pg32_4, acc_sumif),
svsuper_block_scales),
svdmins,
svsumfs_tmp);
} // end of for nb
} // end of case 256-512
break;
default:
assert(false && "Unsupported vector length");
break;
}
svst1_f32(pg32_2, s, sumf1);
svst1_f32(pg32_2, s + bs, svreinterpret_f32_u8(svext_u8(svreinterpret_u8_f32(sumf1), svdup_n_u8(0), 8)));
return;
}
#elif defined(__ARM_FEATURE_MATMUL_INT8)
if (nrc == 2) {
const block_q4_K * GGML_RESTRICT x0 = x;
const block_q4_K * GGML_RESTRICT x1 = (const block_q4_K *) ((const uint8_t *)vx + bx);
@ -2235,7 +2467,6 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
const uint8_t * GGML_RESTRICT q4 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
const int vector_length = ggml_cpu_get_sve_cnt()*8;
const svuint8_t m4b = svdup_n_u8(0xf);
const svint32_t mzero = svdup_n_s32(0);
svint32_t sumi1 = svdup_n_s32(0);
@ -2480,7 +2711,201 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
const int nb = n / QK_K;
#if defined(__ARM_FEATURE_MATMUL_INT8)
#ifdef __ARM_FEATURE_SVE
const int vector_length = ggml_cpu_get_sve_cnt()*8;
#endif
#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
if (nrc == 2) {
const svbool_t pg32_2 = svptrue_pat_b32(SV_VL2);
svfloat32_t sum = svdup_n_f32(0);
const block_q6_K * GGML_RESTRICT vx0 = vx;
const block_q8_K * GGML_RESTRICT vy0 = vy;
const block_q6_K * GGML_RESTRICT vx1 = (const block_q6_K *) ((const uint8_t*)vx + bx);
const block_q8_K * GGML_RESTRICT vy1 = (const block_q8_K *) ((const uint8_t*)vy + by);
switch (vector_length) {
case 128:
{
const svbool_t pg128_all = svptrue_pat_b8(SV_ALL);
for (int i = 0; i < nb; ++i) {
const uint8_t * GGML_RESTRICT ql0 = vx0[i].ql;
const uint8_t * GGML_RESTRICT qh0 = vx0[i].qh;
const uint8_t * GGML_RESTRICT ql1 = vx1[i].ql;
const uint8_t * GGML_RESTRICT qh1 = vx1[i].qh;
const int8_t * GGML_RESTRICT q80 = vy0[i].qs;
const int8_t * GGML_RESTRICT q81 = vy1[i].qs;
const int8_t * GGML_RESTRICT scale0 = vx0[i].scales;
const int8_t * GGML_RESTRICT scale1 = vx1[i].scales;
svfloat32_t vy_d = svuzp1_f32(svdup_n_f32(vy0[i].d), svdup_n_f32(vy1[i].d));
svfloat32_t vx_d = svzip1_f32(svdup_n_f32(GGML_FP16_TO_FP32(vx0[i].d)), svdup_n_f32(GGML_FP16_TO_FP32(vx1[i].d)));
svfloat32_t svsuper_block_scales = svmul_f32_x(pg128_all, vy_d, vx_d);
// process q8sum summation 128 bit route
const svint16_t q8sums_01 = svld1_s16(pg128_all, vy0[i].bsums);
const svint16_t q8sums_02 = svld1_s16(pg128_all, vy0[i].bsums + 8);
const svint16_t q8sums_11 = svld1_s16(pg128_all, vy1[i].bsums);
const svint16_t q8sums_12 = svld1_s16(pg128_all, vy1[i].bsums + 8);
const svint64x2_t q6scales_0_tmp = svld2_s64(pg128_all, (const int64_t *)scale0);
const svint16_t q6scales_01 = svunpklo_s16(svreinterpret_s8_s64(svget2_s64(q6scales_0_tmp, 0)));
const svint16_t q6scales_02 = svunpklo_s16(svreinterpret_s8_s64(svget2_s64(q6scales_0_tmp, 1)));
const svint64x2_t q6scales_1_tmp = svld2_s64(pg128_all, (const int64_t *)scale1);
const svint16_t q6scales_11 = svunpklo_s16(svreinterpret_s8_s64(svget2_s64(q6scales_1_tmp, 0)));
const svint16_t q6scales_12 = svunpklo_s16(svreinterpret_s8_s64(svget2_s64(q6scales_1_tmp, 1)));
const svint64_t prod = svdup_n_s64(0);
svint32_t isum_tmp1 = svreinterpret_s32_s64(svdot_s64(svdot_s64(prod, q8sums_01, q6scales_01), q8sums_02, q6scales_02));
svint32_t isum_tmp2 = svreinterpret_s32_s64(svdot_s64(svdot_s64(prod, q8sums_01, q6scales_11), q8sums_02, q6scales_12));
svint32_t isum_tmp3 = svtrn1_s32(isum_tmp1, isum_tmp2);
svint32_t isum_tmp4 = svreinterpret_s32_s64(svdot_s64(svdot_s64(prod, q8sums_11, q6scales_01), q8sums_12, q6scales_02));
svint32_t isum_tmp5 = svreinterpret_s32_s64(svdot_s64(svdot_s64(prod, q8sums_11, q6scales_11), q8sums_12, q6scales_12));
svint32_t isum_tmp6 = svtrn1_s32(isum_tmp4, isum_tmp5);
svint32_t isum_tmp7 = svreinterpret_s32_s64(svtrn2_s64(svreinterpret_s64_s32(isum_tmp3), svreinterpret_s64_s32(isum_tmp6)));
svint32_t isum_tmp8 = svreinterpret_s32_s64(svtrn1_s64(svreinterpret_s64_s32(isum_tmp3), svreinterpret_s64_s32(isum_tmp6)));
svint32_t svisum_mins = svadd_s32_x(pg128_all, isum_tmp7, isum_tmp8);
// process mmla
svint8_t l0, l1, r0, r1;
svint32_t isum_tmp = svdup_n_s32(0);
for (int j = 0; j < QK_K/128; ++j) {
for (int k = 0; k < 8; ++k) {
svuint8_t qhbits_0 = svld1_u8(pg128_all, qh0+16*(k%2));
svuint8_t qhbits_1 = svld1_u8(pg128_all, qh1+16*(k%2));
svuint8_t q6bits_0 = svld1_u8(pg128_all, ql0+16*(k%4));
svuint8_t q6bits_1 = svld1_u8(pg128_all, ql1+16*(k%4));
const int ql_pos = (k/4)*4;
svuint8_t q6bytes_0_lo = (ql_pos < 4) ? svand_n_u8_x(pg128_all, q6bits_0, 0xf) : svlsr_n_u8_x(pg128_all, q6bits_0, 4);
svuint8_t q6bytes_1_lo = (ql_pos < 4) ? svand_n_u8_x(pg128_all, q6bits_1, 0xf) : svlsr_n_u8_x(pg128_all, q6bits_1, 4);
const int qh_pos = (k/2)*2;
svuint8_t q6bytes_0_hi = svand_n_u8_x(pg128_all, qhbits_0, 0x3 << qh_pos);
svuint8_t q6bytes_1_hi = svand_n_u8_x(pg128_all, qhbits_1, 0x3 << qh_pos);
svint8_t q6bytes_0, q6bytes_1;
if (qh_pos <= 4) {
q6bytes_0 = svreinterpret_s8_u8(svmla_n_u8_x(pg128_all, q6bytes_0_lo, q6bytes_0_hi, 1 << (4 - qh_pos)));
q6bytes_1 = svreinterpret_s8_u8(svmla_n_u8_x(pg128_all, q6bytes_1_lo, q6bytes_1_hi, 1 << (4 - qh_pos)));
} else {
q6bytes_0 = svreinterpret_s8_u8(svorr_u8_x(pg128_all, q6bytes_0_lo, svlsr_n_u8_x(pg128_all, q6bytes_0_hi, (qh_pos - 4))));
q6bytes_1 = svreinterpret_s8_u8(svorr_u8_x(pg128_all, q6bytes_1_lo, svlsr_n_u8_x(pg128_all, q6bytes_1_hi, (qh_pos - 4))));
}
svint8_t q8bytes_0 = svld1_s8(pg128_all, q80+16*(k%8));
svint8_t q8bytes_1 = svld1_s8(pg128_all, q81+16*(k%8));
l0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q6bytes_0), svreinterpret_s64_s8(q6bytes_1)));
l1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q6bytes_0), svreinterpret_s64_s8(q6bytes_1)));
r0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0), svreinterpret_s64_s8(q8bytes_1)));
r1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0), svreinterpret_s64_s8(q8bytes_1)));
svint32_t svscale = svzip1_s32(svdup_n_s32(scale0[k]), svdup_n_s32(scale1[k]));
isum_tmp = svmla_s32_x(pg128_all, isum_tmp, svmmla_s32(svmmla_s32(svdup_n_s32(0), r0, l0), r1, l1), svscale);
}
qh0 += 32; qh1 += 32;
ql0 += 64; ql1 += 64;
q80 += 128; q81 += 128;
scale0 += 8; scale1 += 8;
}
sum = svmla_f32_x(pg128_all, sum,
svcvt_f32_x(pg128_all, svmla_s32_x(pg128_all, isum_tmp,
svisum_mins, svdup_n_s32(-32))),
svsuper_block_scales);
}
} // end of case 128
break;
case 256:
case 512:
{
const svbool_t pg256_all = svptrue_pat_b8(SV_ALL);
const svbool_t pg32_4 = svptrue_pat_b32(SV_VL4);
for (int i = 0; i < nb; ++i) {
const uint8_t * GGML_RESTRICT ql0 = vx0[i].ql;
const uint8_t * GGML_RESTRICT qh0 = vx0[i].qh;
const uint8_t * GGML_RESTRICT ql1 = vx1[i].ql;
const uint8_t * GGML_RESTRICT qh1 = vx1[i].qh;
const int8_t * GGML_RESTRICT q80 = vy0[i].qs;
const int8_t * GGML_RESTRICT q81 = vy1[i].qs;
const int8_t * GGML_RESTRICT scale0 = vx0[i].scales;
const int8_t * GGML_RESTRICT scale1 = vx1[i].scales;
svfloat32_t vx_d = svzip1_f32(svdup_n_f32(GGML_FP16_TO_FP32(vx0[i].d)), svdup_n_f32(GGML_FP16_TO_FP32(vx1[i].d)));
svfloat64_t vy_d_tmp = svreinterpret_f64_f32(svuzp1_f32(svdup_n_f32(vy0[i].d), svdup_n_f32(vy1[i].d)));
svfloat32_t vy_d = svreinterpret_f32_f64(svuzp1_f64(vy_d_tmp, vy_d_tmp));
svfloat32_t svsuper_block_scales = svmul_f32_x(pg32_4, vy_d, vx_d);
// process q8sum summation 256 bit route
const svint16_t q8sums_0 = svld1_s16(pg256_all, vy0[i].bsums);
const svint16_t q8sums_1 = svld1_s16(pg256_all, vy1[i].bsums);
const svint16_t q6scales_0 = svunpklo_s16(svld1_s8(pg256_all, scale0));
const svint16_t q6scales_1 = svunpklo_s16(svld1_s8(pg256_all, scale1));
const svint64_t prod = svdup_n_s64(0);
svint32_t isum_tmp1 = svreinterpret_s32_s64(svdot_s64(prod, q8sums_0, q6scales_0));
svint32_t isum_tmp2 = svreinterpret_s32_s64(svdot_s64(prod, q8sums_0, q6scales_1));
svint32_t isum_tmp3 = svreinterpret_s32_s64(svdot_s64(prod, q8sums_1, q6scales_0));
svint32_t isum_tmp4 = svreinterpret_s32_s64(svdot_s64(prod, q8sums_1, q6scales_1));
svint32_t isum_tmp5 = svtrn1_s32(isum_tmp1, isum_tmp2);
svint32_t isum_tmp6 = svtrn1_s32(isum_tmp3, isum_tmp4);
svint32_t isum_tmp7 = svreinterpret_s32_s64(svtrn2_s64(svreinterpret_s64_s32(isum_tmp5), svreinterpret_s64_s32(isum_tmp6)));
svint32_t isum_tmp8 = svreinterpret_s32_s64(svtrn1_s64(svreinterpret_s64_s32(isum_tmp5), svreinterpret_s64_s32(isum_tmp6)));
svint32_t isum_tmp9 = svadd_s32_x(pg256_all, isum_tmp7, isum_tmp8);
svint32_t isum_tmp10 = svreinterpret_s32_u8(svext_u8(svreinterpret_u8_s32(isum_tmp9), svreinterpret_u8_s32(isum_tmp9), 16));
svint32_t svisum_mins = svadd_s32_z(pg32_4, isum_tmp9, isum_tmp10);
// process mmla
svint8_t l0, l1, r0, r1;
svint32_t isum_tmp = svdup_n_s32(0);
for (int j = 0; j < QK_K/128; ++j) {
for (int k = 0; k < 8; k+=2) { // process 2 block
svuint8_t qhbits_0 = svld1_u8(pg256_all, qh0);
svuint8_t qhbits_1 = svld1_u8(pg256_all, qh1);
svuint8_t q6bits_0 = svld1_u8(pg256_all, ql0+32*((k%4)/2));
svuint8_t q6bits_1 = svld1_u8(pg256_all, ql1+32*((k%4)/2));
const int ql_pos = (k/4)*4;
svuint8_t q6bytes_0_lo = (ql_pos < 4) ? svand_n_u8_x(pg256_all, q6bits_0, 0xf) : svlsr_n_u8_x(pg256_all, q6bits_0, 4);
svuint8_t q6bytes_1_lo = (ql_pos < 4) ? svand_n_u8_x(pg256_all, q6bits_1, 0xf) : svlsr_n_u8_x(pg256_all, q6bits_1, 4);
const int qh_pos = (k/2)*2;
svuint8_t q6bytes_0_hi = svand_n_u8_x(pg256_all, qhbits_0, 0x3 << qh_pos);
svuint8_t q6bytes_1_hi = svand_n_u8_x(pg256_all, qhbits_1, 0x3 << qh_pos);
svint8_t q6bytes_0, q6bytes_1;
if (qh_pos <= 4) {
q6bytes_0 = svreinterpret_s8_u8(svmla_n_u8_x(pg256_all, q6bytes_0_lo, q6bytes_0_hi, 1 << (4 - qh_pos)));
q6bytes_1 = svreinterpret_s8_u8(svmla_n_u8_x(pg256_all, q6bytes_1_lo, q6bytes_1_hi, 1 << (4 - qh_pos)));
} else {
q6bytes_0 = svreinterpret_s8_u8(svorr_u8_x(pg256_all, q6bytes_0_lo, svlsr_n_u8_x(pg256_all, q6bytes_0_hi, (qh_pos - 4))));
q6bytes_1 = svreinterpret_s8_u8(svorr_u8_x(pg256_all, q6bytes_1_lo, svlsr_n_u8_x(pg256_all, q6bytes_1_hi, (qh_pos - 4))));
}
svint8_t q8bytes_0 = svld1_s8(pg256_all, q80+32*(k/2));
svint8_t q8bytes_1 = svld1_s8(pg256_all, q81+32*(k/2));
l0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q6bytes_0), svreinterpret_s64_s8(q6bytes_1)));
l1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q6bytes_0), svreinterpret_s64_s8(q6bytes_1)));
r0 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0), svreinterpret_s64_s8(q8bytes_1)));
r1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0), svreinterpret_s64_s8(q8bytes_1)));
svint32_t svscale0 = svzip1_s32(svdup_n_s32(scale0[k]), svdup_n_s32(scale1[k]));
svint32_t svscale1 = svzip1_s32(svdup_n_s32(scale0[k+1]), svdup_n_s32(scale1[k+1]));
isum_tmp = svmla_s32_x(pg256_all, isum_tmp, svmmla_s32(svdup_n_s32(0), r0, l0), svscale0);
isum_tmp = svmla_s32_x(pg256_all, isum_tmp, svmmla_s32(svdup_n_s32(0), r1, l1), svscale1);
}
qh0 += 32; qh1 += 32;
ql0 += 64; ql1 += 64;
q80 += 128; q81 += 128;
scale0 += 8; scale1 += 8;
} // end of for
svint32_t swap_isum_tmp = svext_s32(isum_tmp, isum_tmp, 4);
isum_tmp = svadd_s32_x(pg32_4, isum_tmp, swap_isum_tmp);
sum = svmla_f32_x(pg32_4, sum,
svcvt_f32_x(pg32_4, svmla_s32_x(pg32_4, isum_tmp,
svisum_mins, svdup_n_s32(-32))),
svsuper_block_scales);
}
} // end of case 256
break;
default:
assert(false && "Unsupported vector length");
break;
} // end of switch
svst1_f32(pg32_2, s, sum);
svst1_f32(pg32_2, s + bs, svreinterpret_f32_u8(svext_u8(svreinterpret_u8_f32(sum), svdup_n_u8(0), 8)));
return;
}
#elif defined(__ARM_FEATURE_MATMUL_INT8)
if (nrc == 2) {
const block_q6_K * GGML_RESTRICT x0 = x;
const block_q6_K * GGML_RESTRICT x1 = (const block_q6_K *) ((const uint8_t *)vx + bx);
@ -2594,27 +3019,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
// adjust bias, apply superblock scale
{
int32_t bias[4];
#ifdef __ARM_FEATURE_SVE
const svbool_t pg16_8 = svptrue_pat_b16(SV_VL8);
const svbool_t pg8_8 = svptrue_pat_b8(SV_VL8);
const svint16_t y0_q8sums_0 = svld1_s16(pg16_8, y0->bsums);
const svint16_t y0_q8sums_1 = svld1_s16(pg16_8, y0->bsums + 8);
const svint16_t y1_q8sums_0 = svld1_s16(pg16_8, y1->bsums);
const svint16_t y1_q8sums_1 = svld1_s16(pg16_8, y1->bsums + 8);
const svint16_t x0_q6scales_0 = svunpklo_s16(svld1_s8(pg8_8, x0->scales));
const svint16_t x0_q6scales_1 = svunpklo_s16(svld1_s8(pg8_8, x0->scales + 8));
const svint16_t x1_q6scales_0 = svunpklo_s16(svld1_s8(pg8_8, x1->scales));
const svint16_t x1_q6scales_1 = svunpklo_s16(svld1_s8(pg8_8, x1->scales + 8));
const svint64_t zero = svdup_n_s64(0);
bias[0] = svaddv_s64(svptrue_b64(), svadd_s64_x(svptrue_b64(), svdot_s64(zero, y0_q8sums_0, x0_q6scales_0),
svdot_s64(zero, y0_q8sums_1, x0_q6scales_1)));
bias[1] = svaddv_s64(svptrue_b64(), svadd_s64_x(svptrue_b64(), svdot_s64(zero, y1_q8sums_0, x0_q6scales_0),
svdot_s64(zero, y1_q8sums_1, x0_q6scales_1)));
bias[2] = svaddv_s64(svptrue_b64(), svadd_s64_x(svptrue_b64(), svdot_s64(zero, y0_q8sums_0, x1_q6scales_0),
svdot_s64(zero, y0_q8sums_1, x1_q6scales_1)));
bias[3] = svaddv_s64(svptrue_b64(), svadd_s64_x(svptrue_b64(), svdot_s64(zero, y1_q8sums_0, x1_q6scales_0),
svdot_s64(zero, y1_q8sums_1, x1_q6scales_1)));
#else
// NEON doesn't support int16 dot product, fallback to separated mul and add
const int16x8x2_t q8sums0 = vld1q_s16_x2(y0->bsums);
const int16x8x2_t q8sums1 = vld1q_s16_x2(y1->bsums);
@ -2646,7 +3050,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
vmull_s16(vget_high_s16(q8sums1.val[1]), vget_high_s16(q6scales1.val[1]))));
bias[3] = vaddvq_s32(prod);
#endif
const int32x4_t vibias = vmulq_n_s32(vld1q_s32(bias), 32);
const float32x4_t superblock_scale = {
@ -2672,7 +3075,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
#endif
#ifdef __ARM_FEATURE_SVE
const int vector_length = ggml_cpu_get_sve_cnt()*8;
float sum = 0;
svuint8_t m4b = svdup_n_u8(0xf);
svint32_t vzero = svdup_n_s32(0);

163
ggml/src/ggml-cpu/arch/riscv/quants.c
View File

@ -580,16 +580,19 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
const float dmin = -y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
uint8_t *patmp = atmp;
int vsums;
int tmp;
int tmp, t1, t2, t3, t4, t5, t6, t7;
__asm__ __volatile__(
"vsetivli zero, 16, e8, m1\n\t"
"vmv.v.x v8, zero\n\t"
"lb zero, 15(%[sc])\n\t"
"vle8.v v1, (%[sc])\n\t"
"vle8.v v2, (%[bsums])\n\t"
"addi %[tmp], %[bsums], 16\n\t"
"vand.vi v0, v1, 0xF\n\t"
"vsrl.vi v1, v1, 4\n\t"
"vle8.v v3, (%[tmp])\n\t"
"vse8.v v0, (%[scale])\n\t"
"vsetivli zero, 16, e16, m2\n\t"
"vle16.v v2, (%[bsums])\n\t"
"vzext.vf2 v0, v1\n\t"
"vwmul.vv v4, v0, v2\n\t"
"vsetivli zero, 16, e32, m4\n\t"
@ -608,46 +611,89 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int j = 0; j < QK_K/128; ++j) {
__asm__ __volatile__(
"vsetvli zero, %[vl32], e8, m2\n\t"
"lb zero, 31(%[q2])\n\t"
"addi %[tmp], %[q2], 16\n\t"
"addi %[t1], %[q8], 16\n\t"
"vsetivli zero, 16, e8, m1\n\t"
"vle8.v v0, (%[q2])\n\t"
"vle8.v v1, (%[tmp])\n\t"
"vsrl.vi v2, v0, 2\n\t"
"vsrl.vi v3, v1, 2\n\t"
"vsrl.vi v4, v0, 4\n\t"
"vsrl.vi v6, v0, 6\n\t"
"vand.vi v0, v0, 0x3\n\t"
"vand.vi v2, v2, 0x3\n\t"
"vand.vi v4, v4, 0x3\n\t"
"vsetvli zero, %[vl128], e8, m8\n\t"
"addi %[tmp], %[q8], 32\n\t"
"vle8.v v8, (%[q8])\n\t"
"vsetvli zero, %[vl64], e8, m4\n\t"
"vle8.v v9, (%[t1])\n\t"
"addi %[t1], %[t1], 32\n\t"
"vsrl.vi v5, v1, 4\n\t"
"vsrl.vi v6, v0, 6\n\t"
"vsrl.vi v7, v1, 6\n\t"
"vle8.v v10, (%[tmp])\n\t"
"vle8.v v11, (%[t1])\n\t"
"addi %[tmp], %[tmp], 32\n\t"
"addi %[t1], %[t1], 32\n\t"
"vand.vi v0, v0, 0x3\n\t"
"vand.vi v1, v1, 0x3\n\t"
"vand.vi v2, v2, 0x3\n\t"
"vle8.v v12, (%[tmp])\n\t"
"vle8.v v13, (%[t1])\n\t"
"addi %[tmp], %[tmp], 32\n\t"
"addi %[t1], %[t1], 32\n\t"
"vand.vi v3, v3, 0x3\n\t"
"vand.vi v4, v4, 0x3\n\t"
"vand.vi v5, v5, 0x3\n\t"
"vle8.v v14, (%[tmp])\n\t"
"vle8.v v15, (%[t1])\n\t"
"vwmul.vv v16, v0, v8\n\t"
"vwmul.vv v18, v1, v9\n\t"
"vwmul.vv v20, v2, v10\n\t"
"vwmul.vv v22, v3, v11\n\t"
"vwmul.vv v24, v4, v12\n\t"
"vsetivli zero, 16, e16, m2\n\t"
"vwmul.vv v26, v5, v13\n\t"
"vwmul.vv v28, v6, v14\n\t"
"vwmul.vv v30, v7, v15\n\t"
"vsetivli zero, 8, e16, m1\n\t"
"vmv.v.x v0, zero\n\t"
"vwredsum.vs v10, v16, v0\n\t"
"lbu %[tmp], 0(%[scale])\n\t"
"vwredsum.vs v8, v16, v0\n\t"
"vwredsum.vs v9, v18, v0\n\t"
"vwredsum.vs v8, v20, v0\n\t"
"vwredsum.vs v7, v22, v0\n\t"
"vwredsum.vs v11, v24, v0\n\t"
"vwredsum.vs v12, v26, v0\n\t"
"vwredsum.vs v13, v28, v0\n\t"
"vwredsum.vs v14, v30, v0\n\t"
"lbu %[t1], 1(%[scale])\n\t"
"vwredsum.vs v10, v20, v0\n\t"
"vwredsum.vs v11, v22, v0\n\t"
"lbu %[t2], 2(%[scale])\n\t"
"vwredsum.vs v12, v24, v0\n\t"
"vwredsum.vs v13, v26, v0\n\t"
"lbu %[t3], 3(%[scale])\n\t"
"vwredsum.vs v14, v28, v0\n\t"
"vwredsum.vs v15, v30, v0\n\t"
"lbu %[t4], 4(%[scale])\n\t"
"vwredsum.vs v8, v17, v8\n\t"
"vwredsum.vs v9, v19, v9\n\t"
"lbu %[t5], 5(%[scale])\n\t"
"vwredsum.vs v10, v21, v10\n\t"
"vwredsum.vs v11, v23, v11\n\t"
"lbu %[t6], 6(%[scale])\n\t"
"vwredsum.vs v12, v25, v12\n\t"
"vwredsum.vs v13, v27, v13\n\t"
"lbu %[t7], 7(%[scale])\n\t"
"vwredsum.vs v14, v29, v14\n\t"
"vwredsum.vs v15, v31, v15\n\t"
"vsetivli zero, 4, e32, m1\n\t"
"vslideup.vi v10, v9, 1\n\t"
"vslideup.vi v8, v7, 1\n\t"
"vslideup.vi v11, v12, 1\n\t"
"vslideup.vi v13, v14, 1\n\t"
"vslideup.vi v10, v8, 2\n\t"
"vslideup.vi v11, v13, 2\n\t"
"vsetivli zero, 8, e32, m2\n\t"
"vle8.v v15, (%[scale])\n\t"
"vzext.vf4 v12, v15\n\t"
"vmul.vv v10, v10, v12\n\t"
"vredsum.vs v0, v10, v0\n\t"
"vmul.vx v0, v8, %[tmp]\n\t"
"vmul.vx v1, v9, %[t1]\n\t"
"vmacc.vx v0, %[t2], v10\n\t"
"vmacc.vx v1, %[t3], v11\n\t"
"vmacc.vx v0, %[t4], v12\n\t"
"vmacc.vx v1, %[t5], v13\n\t"
"vmacc.vx v0, %[t6], v14\n\t"
"vmacc.vx v1, %[t7], v15\n\t"
"vmv.x.s %[tmp], v0\n\t"
"add %[isum], %[isum], %[tmp]"
: [tmp] "=&r" (tmp), [isum] "+&r" (isum)
"vmv.x.s %[t1], v1\n\t"
"add %[isum], %[isum], %[tmp]\n\t"
"add %[isum], %[isum], %[t1]"
: [tmp] "=&r" (tmp), [t1] "=&r" (t1), [t2] "=&r" (t2), [t3] "=&r" (t3)
, [t4] "=&r" (t4), [t5] "=&r" (t5), [t6] "=&r" (t6), [t7] "=&r" (t7)
, [isum] "+&r" (isum)
: [q2] "r" (q2), [scale] "r" (patmp), [q8] "r" (q8)
, [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
: "memory"
, "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
, "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
@ -929,7 +975,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
const int8_t * restrict q8 = y[i].qs;
int8_t * scale = (int8_t *)utmp;
int tmp;
int tmp, t1, t2, t3, t4, t5, t6, t7;
__asm__ __volatile__(
"vsetivli zero, 12, e8, m1\n\t"
"vle8.v v0, (%[s6b])\n\t"
@ -967,19 +1013,23 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
int isum = 0;
for (int j = 0; j < QK_K; j += 128) {
__asm__ __volatile__(
"lb zero, 31(%[q3])\n\t"
"vsetvli zero, %[vl32], e8, m2, ta, mu\n\t"
"vle8.v v8, (%[q3])\n\t"
"vsrl.vi v10, v8, 2\n\t"
"vsrl.vi v12, v8, 4\n\t"
"vsrl.vi v14, v8, 6\n\t"
"lb zero, 64(%[q8])\n\t"
"vand.vi v8, v8, 3\n\t"
"vand.vi v10, v10, 3\n\t"
"vand.vi v12, v12, 3\n\t"
"vle8.v v2, (%[qh])\n\t"
"lb zero, 127(%[q8])\n\t"
"vand.vx v4, v2, %[m]\n\t"
"slli %[m], %[m], 1\n\t"
"vmseq.vx v0, v4, zero\n\t"
"vadd.vi v8, v8, -4, v0.t\n\t"
"lb zero, 0(%[q8])\n\t"
"vand.vx v4, v2, %[m]\n\t"
"slli %[m], %[m], 1\n\t"
"vmseq.vx v0, v4, zero\n\t"
@ -994,34 +1044,43 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
"vadd.vi v14, v14, -4, v0.t\n\t"
"vsetvli zero, %[vl128], e8, m8\n\t"
"vle8.v v0, (%[q8])\n\t"
"lb %[tmp], 0(%[scale])\n\t"
"lb %[t1], 1(%[scale])\n\t"
"lb %[t2], 2(%[scale])\n\t"
"lb %[t3], 3(%[scale])\n\t"
"vsetvli zero, %[vl64], e8, m4\n\t"
"vwmul.vv v16, v0, v8\n\t"
"vwmul.vv v24, v4, v12\n\t"
"vsetivli zero, 16, e16, m2\n\t"
"vmv.v.x v0, zero\n\t"
"vwredsum.vs v10, v16, v0\n\t"
"vwredsum.vs v8, v16, v0\n\t"
"lb %[t4], 4(%[scale])\n\t"
"lb %[t5], 5(%[scale])\n\t"
"vwredsum.vs v9, v18, v0\n\t"
"vwredsum.vs v8, v20, v0\n\t"
"vwredsum.vs v7, v22, v0\n\t"
"vwredsum.vs v11, v24, v0\n\t"
"vwredsum.vs v12, v26, v0\n\t"
"vwredsum.vs v13, v28, v0\n\t"
"vwredsum.vs v14, v30, v0\n\t"
"vwredsum.vs v10, v20, v0\n\t"
"vwredsum.vs v11, v22, v0\n\t"
"vwredsum.vs v12, v24, v0\n\t"
"lb %[t6], 6(%[scale])\n\t"
"lb %[t7], 7(%[scale])\n\t"
"vwredsum.vs v13, v26, v0\n\t"
"vwredsum.vs v14, v28, v0\n\t"
"vwredsum.vs v15, v30, v0\n\t"
"vsetivli zero, 4, e32, m1\n\t"
"vslideup.vi v10, v9, 1\n\t"
"vslideup.vi v8, v7, 1\n\t"
"vslideup.vi v11, v12, 1\n\t"
"vslideup.vi v13, v14, 1\n\t"
"vslideup.vi v10, v8, 2\n\t"
"vslideup.vi v11, v13, 2\n\t"
"vsetivli zero, 8, e32, m2\n\t"
"vle8.v v15, (%[scale])\n\t"
"vsext.vf4 v12, v15\n\t"
"vmul.vv v10, v10, v12\n\t"
"vredsum.vs v0, v10, v0\n\t"
"vmul.vx v0, v8, %[tmp]\n\t"
"vmul.vx v1, v9, %[t1]\n\t"
"vmacc.vx v0, %[t2], v10\n\t"
"vmacc.vx v1, %[t3], v11\n\t"
"vmacc.vx v0, %[t4], v12\n\t"
"vmacc.vx v1, %[t5], v13\n\t"
"vmacc.vx v0, %[t6], v14\n\t"
"vmacc.vx v1, %[t7], v15\n\t"
"vmv.x.s %[tmp], v0\n\t"
"add %[isum], %[isum], %[tmp]"
: [tmp] "=&r" (tmp), [m] "+&r" (m), [isum] "+&r" (isum)
"vmv.x.s %[t1], v1\n\t"
"add %[isum], %[isum], %[tmp]\n\t"
"add %[isum], %[isum], %[t1]"
: [tmp] "=&r" (tmp), [t1] "=&r" (t1), [t2] "=&r" (t2), [t3] "=&r" (t3)
, [t4] "=&r" (t4), [t5] "=&r" (t5), [t6] "=&r" (t6), [t7] "=&r" (t7)
, [m] "+&r" (m), [isum] "+&r" (isum)
: [vl128] "r" (128), [vl64] "r" (64), [vl32] "r" (32)
, [q3] "r" (q3), [qh] "r" (qh), [scale] "r" (scale), [q8] "r" (q8)
: "memory"

44
ggml/src/ggml-cpu/ggml-cpu.c
View File

@ -1807,22 +1807,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_cont(params, tensor);
} break;
case GGML_OP_RESHAPE:
{
ggml_compute_forward_reshape(params, tensor);
} break;
case GGML_OP_VIEW:
{
ggml_compute_forward_view(params, tensor);
} break;
case GGML_OP_PERMUTE:
{
ggml_compute_forward_permute(params, tensor);
} break;
case GGML_OP_TRANSPOSE:
{
ggml_compute_forward_transpose(params, tensor);
} break;
case GGML_OP_GET_ROWS:
{
ggml_compute_forward_get_rows(params, tensor);
@ -2042,6 +2026,22 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
// nop
} break;
case GGML_OP_RESHAPE:
{
// nop
} break;
case GGML_OP_PERMUTE:
{
// nop
} break;
case GGML_OP_VIEW:
{
// nop
} break;
case GGML_OP_TRANSPOSE:
{
// nop
} break;
case GGML_OP_COUNT:
{
GGML_ABORT("fatal error");
@ -2884,6 +2884,11 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
struct ggml_tensor * node = cgraph->nodes[node_n];
if (ggml_op_is_empty(node->op)) {
// skip NOPs
continue;
}
ggml_compute_forward(&params, node);
if (state->ith == 0 && cplan->abort_callback &&
@ -3269,6 +3274,13 @@ void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
__m128 y_vec = _mm_cvtph_ps(x_vec);
_mm_storeu_ps(y + i, y_vec);
}
#elif defined(__riscv_zvfh)
for (int vl; i < n; i += vl) {
vl = __riscv_vsetvl_e16m1(n - i);
vfloat16m1_t vx = __riscv_vle16_v_f16m1((_Float16 *)&x[i], vl);
vfloat32m2_t vy = __riscv_vfwcvt_f_f_v_f32m2(vx, vl);
__riscv_vse32_v_f32m2(&y[i], vy, vl);
}
#endif
for (; i < n; ++i) {

283
ggml/src/ggml-cpu/kleidiai/kernels.cpp
View File

@ -4,6 +4,7 @@
// KleidiAI micro-kernels
#include "kai_matmul_clamp_f32_qsi8d32p_qsi4c32p_interface.h"
#include "kai_matmul_clamp_f32_qai8dxp_qsi8cxp_interface.h"
#include "kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod.h"
#include "kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod.h"
#include "kai_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod.h"
@ -11,20 +12,31 @@
#include "kai_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa.h"
#include "kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.h"
#include "kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.h"
#include "kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.h"
#include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot.h"
#include "kai_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod.h"
#include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod.h"
#include "kai_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod.h"
#include "kai_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm.h"
#include "kai_lhs_pack_bf16p2vlx2_f32_sme.h"
#include "kai_lhs_quant_pack_qsi8d32p_f32.h"
#include "kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.h"
#include "kai_lhs_quant_pack_qsi8d32p_f32_neon.h"
#include "kai_lhs_quant_pack_qai8dxp_f32.h"
#include "kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.h"
#include "kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.h"
#include "kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.h"
#include "kai_rhs_pack_nxk_qsi8cxp_qsi8cx_neon.h"
#include "kai_common.h"
#include "simd-mappings.h"
#define GGML_COMMON_DECL_CPP
#include "ggml-common.h"
#include "kernels.h"
#define NELEMS(x) sizeof(x) / sizeof(*x)
@ -55,6 +67,14 @@ static inline void kernel_run_fn10(size_t m, size_t n, size_t k, size_t /*bl*/,
Fn(m, n, k, lhs, rhs, dst, dst_stride_row, dst_stride_col, clamp_min, clamp_max);
}
template<void(*Fn)(size_t,size_t,size_t,const void*,const void*,float*,size_t,size_t,float,float)>
static inline void kernel_run_float_fn10(size_t m, size_t n, size_t k, size_t /*bl*/,
const void* lhs, const void* rhs, void* dst,
size_t dst_stride_row, size_t dst_stride_col,
float clamp_min, float clamp_max) {
Fn(m, n, k, lhs, rhs, static_cast<float*>(dst), dst_stride_row, dst_stride_col, clamp_min, clamp_max);
}
template<size_t(*Fn)(size_t,size_t,size_t,size_t,size_t,size_t)>
static inline size_t lhs_ps_fn6(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr) {
return Fn(m, k, bl, mr, kr, sr);
@ -93,6 +113,12 @@ static inline void lhs_pack_void_fn9(size_t m, size_t k, size_t /*bl*/, size_t m
Fn(m, k, mr, kr, sr, m_idx_start, lhs, lhs_stride, lhs_packed);
}
template<void(*Fn)(size_t,size_t,size_t,size_t,size_t,size_t,const float*,size_t,void*)>
static inline void lhs_pack_float_fn9_no_bl(size_t m, size_t k, size_t /*bl*/, size_t mr, size_t kr, size_t sr,
size_t m_idx_start, const void * lhs, size_t lhs_stride, void * lhs_packed) {
Fn(m, k, mr, kr, sr, m_idx_start, static_cast<const float*>(lhs), lhs_stride, lhs_packed);
}
template<size_t(*Fn)(size_t,size_t,size_t,size_t,size_t)>
static inline size_t rhs_ps_fn5(size_t n, size_t k, size_t nr, size_t kr, size_t bl) {
return Fn(n, k, nr, kr, bl);
@ -124,6 +150,18 @@ static inline void rhs_pack_fn12(size_t num_groups, size_t n, size_t k, size_t n
static_cast<const kai_rhs_pack_qs4cxs1s0_param*>(params));
}
template<void(*Fn)(size_t,size_t,size_t,size_t,size_t,size_t,const int8_t*,const float*,const float*,void*,size_t,const struct kai_rhs_pack_qsi8cx_params*)>
static inline void rhs_pack_scale_fn12(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t /*bl*/,
size_t /*rhs_stride*/, const void* rhs, const void* bias, const void* scale,
void* rhs_packed, size_t extra_bytes, const void* params) {
Fn(num_groups, n, k, nr, kr, sr,
static_cast<const int8_t*>(rhs),
static_cast<const float*>(bias),
static_cast<const float*>(scale),
rhs_packed, extra_bytes,
static_cast<const kai_rhs_pack_qsi8cx_params*>(params));
}
template<void(*Fn)(size_t,size_t,size_t,size_t,size_t,size_t,size_t,const void*,const void*,const void*,void*,size_t,const void*)>
static inline void rhs_pack_fn13(size_t num_groups, size_t n, size_t k, size_t nr, size_t kr, size_t sr, size_t /*bl*/,
size_t rhs_stride, const void* rhs, const void* bias, const void* scale,
@ -213,6 +251,57 @@ static void dequantize_row_qsi4c32ps1s0scalef16(
GGML_UNUSED(kr);
}
static void dequantize_row_qsi8cxp(
const void *packed_data,
int32_t row_idx,
int64_t k,
float *out,
size_t nr,
size_t packed_row_stride,
size_t kr,
size_t bl,
size_t num_bytes_multiplier
) {
GGML_UNUSED(bl);
GGML_UNUSED(num_bytes_multiplier);
const size_t k_internal = ((size_t) k + QK8_0 - 1) / QK8_0 * QK8_0;
const size_t group_idx = row_idx / nr;
const size_t row_in_group = row_idx % nr;
const uint8_t * group_ptr = static_cast<const uint8_t *>(packed_data) + group_idx * packed_row_stride;
const int8_t * data_base = reinterpret_cast<const int8_t *>(group_ptr);
const size_t num_blocks = k_internal / kr;
for (size_t block = 0; block < num_blocks; ++block) {
const int8_t * block_ptr = data_base + (block * nr + row_in_group) * kr;
for (size_t i = 0; i < kr; ++i) {
const size_t k_idx = block * kr + i;
if (k_idx < (size_t) k) {
out[k_idx] = static_cast<float>(block_ptr[i]);
}
}
}
const uint8_t * sums_ptr = group_ptr + nr * k_internal;
GGML_UNUSED(sums_ptr);
const float * scale_ptr = reinterpret_cast<const float *>(sums_ptr + nr * sizeof(int32_t));
const float scale = scale_ptr[row_in_group];
if (scale == 0.0f) {
for (size_t i = 0; i < (size_t) k; ++i) {
out[i] = 0.0f;
}
return;
}
for (size_t i = 0; i < (size_t) k; ++i) {
out[i] *= scale;
}
}
static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
#if defined(__ARM_FEATURE_SME)
{
@ -548,6 +637,174 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
#endif
};
static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = {
#if defined(__ARM_FEATURE_SME)
{
/* SME GEMM */
{
/* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa,
/* .get_lhs_offset_ex = */ &kernel_offs_fn2<kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa>,
/* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2<kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa>,
/* .run_kernel_ex = */ &kernel_run_float_fn10<kai_run_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa>,
},
/* .gemm_lhs_info = */ {
/* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32,
/* .get_packed_offset_ex = */ &lhs_offs_fn5<kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32>,
/* .packed_size_ex = */ &lhs_ps_fn5<kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32>,
/* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl<kai_run_lhs_quant_pack_qai8dxp_f32>,
},
/* SME GEMV */
{
/* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot,
/* .get_lhs_offset_ex = */ &kernel_offs_fn2<kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot>,
/* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2<kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot>,
/* .run_kernel_ex = */ &kernel_run_float_fn10<kai_run_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot>,
},
/* .gemv_lhs_info = */ {
/* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32,
/* .get_packed_offset_ex = */ &lhs_offs_fn5<kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32>,
/* .packed_size_ex = */ &lhs_ps_fn5<kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32>,
/* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl<kai_run_lhs_quant_pack_qai8dxp_f32>,
},
/* .rhs_info = */ {
/* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon,
/* .to_float = */ dequantize_row_qsi8cxp,
/* .packed_size_ex = */ &rhs_ps_fn5<kai_get_rhs_packed_size_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
/* .packed_stride_ex = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
/* .pack_func_ex = */ &rhs_pack_scale_fn12<kai_run_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
},
/* .required_cpu = */ CPU_FEATURE_SME,
/* .lhs_type = */ GGML_TYPE_F32,
/* .rhs_type = */ GGML_TYPE_Q8_0,
/* .op_type = */ GGML_TYPE_F32,
},
#endif
#if defined(__ARM_FEATURE_MATMUL_INT8)
{
/* I8MM GEMM */
{
/* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm,
/* .get_lhs_offset_ex = */ &kernel_offs_fn2<kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm>,
/* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2<kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm>,
/* .run_kernel_ex = */ &kernel_run_float_fn10<kai_run_matmul_clamp_f32_qai8dxp4x8_qsi8cxp4x8_16x4_neon_i8mm>,
},
/* .gemm_lhs_info = */ {
/* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32,
/* .get_packed_offset_ex = */ &lhs_offs_fn5<kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32>,
/* .packed_size_ex = */ &lhs_ps_fn5<kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32>,
/* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl<kai_run_lhs_quant_pack_qai8dxp_f32>,
},
/* I8MM GEMV (dotprod fallback) */
{
/* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod,
/* .get_lhs_offset_ex = */ &kernel_offs_fn2<kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod>,
/* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2<kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod>,
/* .run_kernel_ex = */ &kernel_run_float_fn10<kai_run_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod>,
},
/* .gemv_lhs_info = */ {
/* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32,
/* .get_packed_offset_ex = */ &lhs_offs_fn5<kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32>,
/* .packed_size_ex = */ &lhs_ps_fn5<kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32>,
/* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl<kai_run_lhs_quant_pack_qai8dxp_f32>,
},
/* .rhs_info = */ {
/* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon,
/* .to_float = */ dequantize_row_qsi8cxp,
/* .packed_size_ex = */ &rhs_ps_fn5<kai_get_rhs_packed_size_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
/* .packed_stride_ex = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
/* .pack_func_ex = */ &rhs_pack_scale_fn12<kai_run_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
},
/* .required_cpu = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
/* .lhs_type = */ GGML_TYPE_F32,
/* .rhs_type = */ GGML_TYPE_Q8_0,
/* .op_type = */ GGML_TYPE_F32,
},
#endif
#if defined(__ARM_FEATURE_DOTPROD)
{
/* DOTPROD GEMM */
{
/* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod,
/* .get_lhs_offset_ex = */ &kernel_offs_fn2<kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod>,
/* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2<kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod>,
/* .run_kernel_ex = */ &kernel_run_float_fn10<kai_run_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod>,
},
/* .gemm_lhs_info = */ {
/* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32,
/* .get_packed_offset_ex = */ &lhs_offs_fn5<kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32>,
/* .packed_size_ex = */ &lhs_ps_fn5<kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32>,
/* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl<kai_run_lhs_quant_pack_qai8dxp_f32>,
},
/* DOTPROD GEMV */
{
/* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod,
/* .get_lhs_offset_ex = */ &kernel_offs_fn2<kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod>,
/* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2<kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod>,
/* .run_kernel_ex = */ &kernel_run_float_fn10<kai_run_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod>,
},
/* .gemv_lhs_info = */ {
/* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32,
/* .get_packed_offset_ex = */ &lhs_offs_fn5<kai_get_lhs_packed_offset_lhs_quant_pack_qai8dxp_f32>,
/* .packed_size_ex = */ &lhs_ps_fn5<kai_get_lhs_packed_size_lhs_quant_pack_qai8dxp_f32>,
/* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl<kai_run_lhs_quant_pack_qai8dxp_f32>,
},
/* .rhs_info = */ {
/* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon,
/* .to_float = */ dequantize_row_qsi8cxp,
/* .packed_size_ex = */ &rhs_ps_fn5<kai_get_rhs_packed_size_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
/* .packed_stride_ex = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
/* .pack_func_ex = */ &rhs_pack_scale_fn12<kai_run_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
},
/* .required_cpu = */ CPU_FEATURE_DOTPROD,
/* .lhs_type = */ GGML_TYPE_F32,
/* .rhs_type = */ GGML_TYPE_Q8_0,
/* .op_type = */ GGML_TYPE_F32,
},
#endif
};
ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, const ggml_tensor * tensor) {
ggml_kleidiai_kernels * kernel = nullptr;
@ -562,6 +819,17 @@ ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, c
break;
}
}
if (!kernel) {
for (size_t i = 0; i < NELEMS(gemm_gemv_kernels_q8); ++i) {
if ((cpu_features & gemm_gemv_kernels_q8[i].required_cpu) == gemm_gemv_kernels_q8[i].required_cpu &&
gemm_gemv_kernels_q8[i].lhs_type == tensor->src[1]->type &&
gemm_gemv_kernels_q8[i].rhs_type == tensor->src[0]->type &&
gemm_gemv_kernels_q8[i].op_type == tensor->type) {
kernel = &gemm_gemv_kernels_q8[i];
break;
}
}
}
#endif
}
@ -582,3 +850,18 @@ ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q4_0(cpu_feature features)
return kernels;
}
ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q8_0(cpu_feature features) {
ggml_kleidiai_kernels * kernels = nullptr;
#if defined(__ARM_FEATURE_SME) || defined(__ARM_FEATURE_DOTPROD) || defined(__ARM_FEATURE_MATMUL_INT8)
for (size_t i = 0; i < NELEMS(gemm_gemv_kernels_q8); ++i) {
if ((features & gemm_gemv_kernels_q8[i].required_cpu) == gemm_gemv_kernels_q8[i].required_cpu) {
kernels = &gemm_gemv_kernels_q8[i];
break;
}
}
#endif
return kernels;
}

1
ggml/src/ggml-cpu/kleidiai/kernels.h
View File

@ -87,3 +87,4 @@ struct ggml_kleidiai_kernels {
ggml_kleidiai_kernels * ggml_kleidiai_select_kernels(cpu_feature cpu_features, const ggml_tensor * tensor);
ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q4_0(cpu_feature features);
ggml_kleidiai_kernels * ggml_kleidiai_select_kernels_q8_0(cpu_feature features);

277
ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
View File

@ -5,10 +5,13 @@
#include <assert.h>
#include <atomic>
#include <cfloat>
#include <cmath>
#include <algorithm>
#include <stdexcept>
#include <stdint.h>
#include <string.h>
#include <string>
#include <vector>
#if defined(__linux__)
#include <asm/hwcap.h>
#include <sys/auxv.h>
@ -38,8 +41,9 @@
struct ggml_kleidiai_context {
cpu_feature features;
ggml_kleidiai_kernels * kernels;
} static ctx = { CPU_FEATURE_NONE, NULL };
ggml_kleidiai_kernels * kernels_q4;
ggml_kleidiai_kernels * kernels_q8;
} static ctx = { CPU_FEATURE_NONE, NULL, NULL };
static const char* cpu_feature_to_string(cpu_feature f) {
switch (f) {
@ -73,10 +77,14 @@ static void init_kleidiai_context(void) {
if (sme_enabled != 0) {
ctx.features |= ggml_cpu_has_sme() ? CPU_FEATURE_SME : CPU_FEATURE_NONE;
}
ctx.kernels = ggml_kleidiai_select_kernels_q4_0(ctx.features);
ctx.kernels_q4 = ggml_kleidiai_select_kernels_q4_0(ctx.features);
ctx.kernels_q8 = ggml_kleidiai_select_kernels_q8_0(ctx.features);
#ifndef NDEBUG
if (ctx.kernels) {
GGML_LOG_DEBUG("kleidiai: using kernel with CPU feature %s\n", cpu_feature_to_string(ctx.kernels->required_cpu));
if (ctx.kernels_q4) {
GGML_LOG_DEBUG("kleidiai: using q4 kernel with CPU feature %s\n", cpu_feature_to_string(ctx.kernels_q4->required_cpu));
}
if (ctx.kernels_q8) {
GGML_LOG_DEBUG("kleidiai: using q8 kernel with CPU feature %s\n", cpu_feature_to_string(ctx.kernels_q8->required_cpu));
}
#endif
}
@ -130,6 +138,9 @@ class tensor_traits : public ggml::cpu::tensor_traits {
if (kernels->rhs_type == GGML_TYPE_Q4_0) {
if (!lhs_info->packed_size_ex) return false;
size = lhs_info->packed_size_ex(m, k, QK4_0, mr, kr, sr);
} else if (kernels->rhs_type == GGML_TYPE_Q8_0) {
if (!lhs_info->packed_size_ex) return false;
size = lhs_info->packed_size_ex(m, k, QK8_0, mr, kr, sr);
} else if (kernels->rhs_type == GGML_TYPE_F16) {
if (!lhs_info->packed_size_ex || !kernels->rhs_info.packed_size_ex) return false;
const int64_t lhs_batch_size0 = op->src[1]->ne[2];
@ -149,11 +160,13 @@ class tensor_traits : public ggml::cpu::tensor_traits {
if (dst->op == GGML_OP_MUL_MAT) {
if (dst->src[0]->type == GGML_TYPE_Q4_0) {
return compute_forward_q4_0(params, dst);
} else if (dst->src[0]->type == GGML_TYPE_Q8_0) {
return compute_forward_q8_0(params, dst);
} else if (dst->src[0]->type == GGML_TYPE_F16) {
return compute_forward_fp16(params, dst);
}
} else if (dst->op == GGML_OP_GET_ROWS) {
if (dst->src[0]->type == GGML_TYPE_Q4_0) {
if (dst->src[0]->type == GGML_TYPE_Q4_0 || dst->src[0]->type == GGML_TYPE_Q8_0) {
return compute_forward_get_rows(params, dst);
}
}
@ -400,19 +413,120 @@ class tensor_traits : public ggml::cpu::tensor_traits {
return true;
}
bool compute_forward_get_rows(struct ggml_compute_params * params, struct ggml_tensor * dst) {
GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q4_0);
if (!ctx.kernels) {
return false;
}
bool compute_forward_q8_0(struct ggml_compute_params * params, struct ggml_tensor * dst) {
GGML_ASSERT(dst->src[0]->type == GGML_TYPE_Q8_0);
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
rhs_packing_info * rhs_info = &ctx.kernels->rhs_info;
kernel_info * kernel = &ctx.kernels->gemm;
ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst);
if (!kernels) {
return false;
}
bool is_gemv = src1->ne[1] == 1;
kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
if (!kernel || !lhs_info->get_packed_offset_ex || !lhs_info->pack_func_ex ||
!kernel->get_rhs_packed_offset_ex || !kernel->run_kernel_ex || !kernel->get_dst_offset) {
return false;
}
const int ith = params->ith;
const int nth_raw = params->nth;
const int nth = nth_raw > 0 ? nth_raw : 1;
const size_t k = ne00;
const size_t m = ne11;
const size_t n = ne01;
size_t mr = kernel->get_mr();
size_t kr = kernel->get_kr();
size_t sr = kernel->get_sr();
const uint8_t * lhs = static_cast<const uint8_t *>(src1->data);
uint8_t * lhs_packed = static_cast<uint8_t *>(params->wdata);
const uint8_t * rhs_packed = static_cast<const uint8_t *>(src0->data);
const size_t n_step = kernel->get_n_step();
const size_t num_n_per_thread = kai_roundup(kai_roundup(n, nth) / nth, n_step);
const size_t n_start = ith * num_n_per_thread;
size_t n_to_process = 0;
if (n_start < n) {
n_to_process = num_n_per_thread;
if ((n_start + n_to_process) > n) {
n_to_process = n - n_start;
}
}
const size_t num_m_per_thread = kai_roundup(m, mr * nth) / nth;
const size_t m_start = ith * num_m_per_thread;
size_t m_to_process = num_m_per_thread;
if ((m_start + m_to_process) > m) {
m_to_process = m - m_start;
}
if (m_start < m) {
const size_t src_stride = src1->nb[1];
const float * src_ptr = reinterpret_cast<const float *>(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1]));
const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(m_start, k, 0, mr, kr, sr);
void * lhs_packed_ptr = static_cast<void *>(lhs_packed + lhs_packed_offset);
lhs_info->pack_func_ex(m_to_process, k, 0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr);
}
ggml_barrier(params->threadpool);
const size_t dst_stride = dst->nb[1];
const size_t lhs_packed_offset = lhs_info->get_packed_offset_ex(0, k, 0, mr, kr, sr);
const size_t rhs_packed_offset = kernel->get_rhs_packed_offset_ex(n_start, k, 0);
const size_t dst_offset = kernel->get_dst_offset(0, n_start, dst_stride);
const void * rhs_ptr = static_cast<const void *>(rhs_packed + rhs_packed_offset);
const void * lhs_ptr = static_cast<const void *>(lhs_packed + lhs_packed_offset);
float * dst_ptr = reinterpret_cast<float *>(static_cast<uint8_t *>(dst->data) + dst_offset);
if (n_to_process > 0) {
kernel->run_kernel_ex(m, n_to_process, k, 0, lhs_ptr, rhs_ptr, dst_ptr, dst_stride,
sizeof(float), -FLT_MAX, FLT_MAX);
}
return true;
}
bool compute_forward_get_rows(struct ggml_compute_params * params, struct ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
ggml_kleidiai_kernels * kernels = nullptr;
size_t block_len = 0;
size_t num_bytes_multiplier = 0;
if (dst->src[0]->type == GGML_TYPE_Q4_0) {
if (!ctx.kernels_q4) {
return false;
}
kernels = ctx.kernels_q4;
block_len = QK4_0;
num_bytes_multiplier = sizeof(uint16_t);
} else if (dst->src[0]->type == GGML_TYPE_Q8_0) {
if (!ctx.kernels_q8) {
return false;
}
kernels = ctx.kernels_q8;
block_len = QK8_0;
num_bytes_multiplier = sizeof(float);
} else {
return false;
}
rhs_packing_info * rhs_info = &kernels->rhs_info;
kernel_info * kernel = &kernels->gemm;
if (!rhs_info->to_float || !kernel->get_nr) {
return false;
}
@ -423,8 +537,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
const size_t block_rows = kernel->get_nr();
const size_t kr = kernel->get_kr();
const size_t num_bytes_multiplier = sizeof(uint16_t);
const size_t packed_stride = rhs_info->packed_stride(nc, block_rows, kr, QK4_0);
const size_t packed_stride = rhs_info->packed_stride(nc, block_rows, kr, block_len);
const int ith = params->ith;
const int nth = params->nth;
@ -439,7 +552,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
GGML_ASSERT(row_idx >= 0 && row_idx < src0->ne[1]);
float *out = (float *)((char *)dst->data + i * nb1);
rhs_info->to_float(src0->data, row_idx, nc, out, block_rows, packed_stride, kr, QK4_0, num_bytes_multiplier);
rhs_info->to_float(src0->data, row_idx, nc, out, block_rows, packed_stride, kr, block_len, num_bytes_multiplier);
}
return true;
@ -447,21 +560,91 @@ class tensor_traits : public ggml::cpu::tensor_traits {
public:
int repack(struct ggml_tensor * tensor, const void * data, size_t data_size) {
GGML_ASSERT(tensor->type == GGML_TYPE_Q4_0);
GGML_ASSERT(ctx.kernels);
const size_t n = tensor->ne[1];
const size_t k = tensor->ne[0];
size_t nr = ctx.kernels->gemm.get_nr();
size_t kr = ctx.kernels->gemm.get_kr();
size_t sr = ctx.kernels->gemm.get_sr();
struct kai_rhs_pack_qs4cxs1s0_param params;
params.lhs_zero_point = 1;
params.rhs_zero_point = 8;
ctx.kernels->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, QK4_0, 0, (const uint8_t*)data, nullptr, nullptr, tensor->data, 0, &params);
if (tensor->type == GGML_TYPE_Q4_0) {
if (!ctx.kernels_q4) {
return -1;
}
size_t nr = ctx.kernels_q4->gemm.get_nr();
size_t kr = ctx.kernels_q4->gemm.get_kr();
size_t sr = ctx.kernels_q4->gemm.get_sr();
struct kai_rhs_pack_qs4cxs1s0_param params;
params.lhs_zero_point = 1;
params.rhs_zero_point = 8;
ctx.kernels_q4->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, QK4_0, 0,
static_cast<const uint8_t *>(data),
nullptr, nullptr, tensor->data, 0, &params);
GGML_UNUSED(data_size);
return 0;
} else if (tensor->type == GGML_TYPE_Q8_0) {
if (!ctx.kernels_q8) {
return -1;
}
const size_t row_stride = tensor->nb[1];
const size_t k_blocks = (k + QK8_0 - 1) / QK8_0;
std::vector<int8_t> qdata(n * k, 0);
std::vector<float> scales(n, 0.0f);
for (size_t row = 0; row < n; ++row) {
const auto * row_blocks = reinterpret_cast<const block_q8_0 *>(
static_cast<const uint8_t *>(data) + row * row_stride);
float max_abs = 0.0f;
for (size_t block = 0; block < k_blocks; ++block) {
const block_q8_0 & blk = row_blocks[block];
const float d = GGML_FP16_TO_FP32(blk.d);
for (size_t l = 0; l < QK8_0; ++l) {
const size_t linear_idx = block * QK8_0 + l;
if (linear_idx >= k) {
break;
}
const float value = d * blk.qs[l];
max_abs = std::max(max_abs, std::fabs(value));
}
}
float scale = max_abs > 0.0f ? max_abs / 127.0f : 0.0f;
scales[row] = scale;
const float inv_scale = scale > 0.0f ? 1.0f / scale : 0.0f;
for (size_t block = 0; block < k_blocks; ++block) {
const block_q8_0 & blk = row_blocks[block];
const float d = GGML_FP16_TO_FP32(blk.d);
for (size_t l = 0; l < QK8_0; ++l) {
const size_t linear_idx = block * QK8_0 + l;
if (linear_idx >= k) {
break;
}
const float value = d * blk.qs[l];
int32_t q = scale > 0.0f ? static_cast<int32_t>(std::lround(value * inv_scale)) : 0;
q = std::clamp(q, -127, 127);
qdata[row * k + linear_idx] = static_cast<int8_t>(q);
}
}
}
size_t nr = ctx.kernels_q8->gemm.get_nr();
size_t kr = ctx.kernels_q8->gemm.get_kr();
size_t sr = ctx.kernels_q8->gemm.get_sr();
struct kai_rhs_pack_qsi8cx_params params;
params.lhs_zero_point = 1;
params.scale_multiplier = 1.0f;
ctx.kernels_q8->rhs_info.pack_func_ex(1, n, k, nr, kr, sr, 0, 0,
qdata.data(), nullptr, scales.data(),
tensor->data, 0, &params);
GGML_UNUSED(data_size);
return 0;
}
return 0;
GGML_UNUSED(data_size);
return -1;
}
};
@ -518,27 +701,45 @@ static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alignment(ggml_backend_b
}
static size_t ggml_backend_cpu_kleidiai_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) {
GGML_ASSERT(tensor->type == GGML_TYPE_Q4_0);
GGML_ASSERT(ctx.kernels);
const size_t n = tensor->ne[1];
const size_t k = tensor->ne[0];
const size_t nr = ctx.kernels->gemm.get_nr();
const size_t kr = ctx.kernels->gemm.get_kr();
return ctx.kernels->rhs_info.packed_size_ex(n, k, nr, kr, QK4_0);
GGML_UNUSED(buft);
const size_t n = tensor->ne[1];
const size_t k = tensor->ne[0];
ggml_kleidiai_kernels * kernels = nullptr;
size_t block_len = 0;
if (tensor->type == GGML_TYPE_Q4_0) {
GGML_ASSERT(ctx.kernels_q4);
kernels = ctx.kernels_q4;
block_len = QK4_0;
} else if (tensor->type == GGML_TYPE_Q8_0) {
GGML_ASSERT(ctx.kernels_q8);
kernels = ctx.kernels_q8;
block_len = QK8_0;
} else {
return 0;
}
const size_t nr = kernels->gemm.get_nr();
const size_t kr = kernels->gemm.get_kr();
const size_t packed = kernels->rhs_info.packed_size_ex(n, k, nr, kr, block_len);
const size_t raw = ggml_nbytes(tensor);
return packed > raw ? packed : raw;
}
namespace ggml::cpu::kleidiai {
class extra_buffer_type : ggml::cpu::extra_buffer_type {
bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
if ((op->op == GGML_OP_MUL_MAT || op->op == GGML_OP_GET_ROWS) &&
op->src[0]->type == GGML_TYPE_Q4_0 &&
(op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q8_0) &&
op->src[0]->buffer &&
(ggml_n_dims(op->src[0]) == 2) &&
op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type() && ctx.kernels) {
op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) {
if (((op->src[0]->type == GGML_TYPE_Q4_0) ? ctx.kernels_q4 : ctx.kernels_q8) == nullptr) {
return false;
}
if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
return false;
}

357
ggml/src/ggml-cpu/ops.cpp
View File

@ -4455,46 +4455,6 @@ void ggml_compute_forward_cont(
ggml_compute_forward_dup(params, dst);
}
// ggml_compute_forward_reshape
void ggml_compute_forward_reshape(
const ggml_compute_params * params,
ggml_tensor * dst) {
// NOP
GGML_UNUSED(params);
GGML_UNUSED(dst);
}
// ggml_compute_forward_view
void ggml_compute_forward_view(
const ggml_compute_params * params,
ggml_tensor * dst) {
// NOP
GGML_UNUSED(params);
GGML_UNUSED(dst);
}
// ggml_compute_forward_permute
void ggml_compute_forward_permute(
const ggml_compute_params * params,
ggml_tensor * dst) {
// NOP
GGML_UNUSED(params);
GGML_UNUSED(dst);
}
// ggml_compute_forward_transpose
void ggml_compute_forward_transpose(
const ggml_compute_params * params,
ggml_tensor * dst) {
// NOP
GGML_UNUSED(params);
GGML_UNUSED(dst);
}
// ggml_compute_forward_get_rows
static void ggml_compute_forward_get_rows_q(
@ -5543,7 +5503,28 @@ static void ggml_mrope_cache_init(
}
}
static void ggml_compute_forward_rope_f32(
template<typename T>
static void rotate_pairs(const int64_t n, const int64_t n_offset, const float * cache, const T * src_data, T * dst_data, const int scale = 2) {
for (int64_t i0 = 0; i0 < n; i0 += 2) {
const int64_t ic = i0/scale; // hack for GGML_ROPE_TYPE_NORMAL, where we need ic = i0; for all other cases, ic = i0/2
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const T * const src = src_data + ic;
T * dst = dst_data + ic;
const float x0 = type_conversion_table<T>::to_f32(src[0]);
const float x1 = type_conversion_table<T>::to_f32(src[n_offset]);
dst[0] = type_conversion_table<T>::from_f32(x0*cos_theta - x1*sin_theta);
dst[n_offset] = type_conversion_table<T>::from_f32(x0*sin_theta + x1*cos_theta);
}
}
template<typename T> //float or ggml_fp16_t
static void ggml_compute_forward_rope_flt(
const ggml_compute_params * params,
ggml_tensor * dst,
const bool forward) {
@ -5552,6 +5533,9 @@ static void ggml_compute_forward_rope_f32(
const ggml_tensor * src1 = dst->src[1];
const ggml_tensor * src2 = dst->src[2];
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_I32);
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
int sections[4];
@ -5574,7 +5558,8 @@ static void ggml_compute_forward_rope_f32(
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
//printf("n_past = %d, ne2 = %d\n", n_past, ne2);
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(nb0 == nb00);
GGML_ASSERT(nb0 == sizeof(T));
const int ith = params->ith;
const int nth = params->nth;
@ -5599,12 +5584,11 @@ static void ggml_compute_forward_rope_f32(
float corr_dims[2];
ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; // ggml_rope_multi, multimodal rotary position embedding
const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE; // qwen3vl apply interleaved mrope
const bool mrope_used = mode & GGML_ROPE_TYPE_MROPE; // ggml_rope_multi, note: also true for vision (24 & 8 == true) and for imrope
const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
if (is_mrope) {
if (mrope_used) {
GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
}
@ -5630,7 +5614,7 @@ static void ggml_compute_forward_rope_f32(
for (int64_t i2 = 0; i2 < ne2; i2++) { // seq-len
float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
if (!is_mrope) {
if (!mrope_used) {
const int64_t p = pos[i2];
ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
}
@ -5648,269 +5632,36 @@ static void ggml_compute_forward_rope_f32(
if (ir++ < ir0) continue;
if (ir > ir1) break;
if (is_neox || is_mrope) {
if (is_vision){
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const int64_t ic = i0/2;
T * src = (T *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
T * dst_data = (T *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1);
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[n_dims] = x0*sin_theta + x1*cos_theta;
}
} else {
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const int64_t ic = i0/2;
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims/2];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
}
}
} else {
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
const float x0 = src[0];
const float x1 = src[1];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[1] = x0*sin_theta + x1*cos_theta;
}
switch (mode) {
case GGML_ROPE_TYPE_NORMAL:
rotate_pairs<T>(n_dims, 1, cache, src, dst_data, 1);
break;
case GGML_ROPE_TYPE_NEOX:
case GGML_ROPE_TYPE_MROPE:
case GGML_ROPE_TYPE_IMROPE:
rotate_pairs<T>(n_dims, n_dims/2, cache, src, dst_data);
break;
case GGML_ROPE_TYPE_VISION:
rotate_pairs<T>(ne0, n_dims, cache, src, dst_data);
break;
default:
GGML_ABORT("rope type not supported");
}
if (is_vision) {
for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
const int64_t ic = i0/2;
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[n_dims] = x0*sin_theta + x1*cos_theta;
}
} else {
if (!is_vision) {
// fill the remain channels with data from src tensor
for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
const T * const src = (T *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
T * dst_data = (T *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
}
}
}
// TODO: deduplicate f16/f32 code
static void ggml_compute_forward_rope_f16(
const ggml_compute_params * params,
ggml_tensor * dst,
const bool forward) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const ggml_tensor * src2 = dst->src[2];
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
int sections[4];
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
//const int n_ctx = ((int32_t *) dst->op_params)[3];
const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
memcpy(&sections, (int32_t *) dst->op_params + 11, sizeof(int)*4);
GGML_TENSOR_UNARY_OP_LOCALS
//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
//printf("n_past = %d, ne2 = %d\n", n_past, ne2);
GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
const int ith = params->ith;
const int nth = params->nth;
const int nr = ggml_nrows(dst);
GGML_ASSERT(n_dims <= ne0);
GGML_ASSERT(n_dims % 2 == 0);
// rows per thread
const int dr = (nr + nth - 1)/nth;
// row range for this thread
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
// row index used to determine which thread to use
int ir = 0;
const float theta_scale = powf(freq_base, -2.0f/n_dims);
float corr_dims[2];
ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE;
const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
if (is_mrope) {
GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
}
if (is_vision) {
GGML_ASSERT(n_dims == ne0/2);
}
const float * freq_factors = NULL;
if (src2 != NULL) {
GGML_ASSERT(src2->type == GGML_TYPE_F32);
GGML_ASSERT(src2->ne[0] >= n_dims / 2);
freq_factors = (const float *) src2->data;
}
// backward process uses inverse rotation by cos and sin.
// cos and sin build a rotation matrix, where the inverse is the transpose.
// this essentially just switches the sign of sin.
const float sin_sign = forward ? 1.0f : -1.0f;
const int32_t * pos = (const int32_t *) src1->data;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = 0; i2 < ne2; i2++) {
float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
if (!is_mrope) {
const int64_t p = pos[i2];
ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
}
else {
const int64_t p_t = pos[i2];
const int64_t p_h = pos[i2 + ne2];
const int64_t p_w = pos[i2 + ne2 * 2];
const int64_t p_e = pos[i2 + ne2 * 3];
ggml_mrope_cache_init(
p_t, p_h, p_w, p_e, sections, is_imrope, is_vision,
freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
}
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
if (is_neox || is_mrope) {
if (is_vision) {
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const int64_t ic = i0/2;
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = GGML_CPU_FP16_TO_FP32(src[0]);
const float x1 = GGML_CPU_FP16_TO_FP32(src[n_dims]);
dst_data[0] = GGML_CPU_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[n_dims] = GGML_CPU_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
} else {
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const int64_t ic = i0/2;
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = GGML_CPU_FP16_TO_FP32(src[0]);
const float x1 = GGML_CPU_FP16_TO_FP32(src[n_dims/2]);
dst_data[0] = GGML_CPU_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[n_dims/2] = GGML_CPU_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
}
} else {
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
const float x0 = GGML_CPU_FP16_TO_FP32(src[0]);
const float x1 = GGML_CPU_FP16_TO_FP32(src[1]);
dst_data[0] = GGML_CPU_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[1] = GGML_CPU_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
}
if (is_vision) {
for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
const int64_t ic = i0/2;
const float cos_theta = cache[i0 + 0];
const float sin_theta = cache[i0 + 1];
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = GGML_CPU_FP16_TO_FP32(src[0]);
const float x1 = GGML_CPU_FP16_TO_FP32(src[n_dims]);
dst_data[0] = GGML_CPU_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[n_dims] = GGML_CPU_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
} else {
for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
} //attn-heads
}
}
}
@ -5924,11 +5675,11 @@ void ggml_compute_forward_rope(
switch (src0->type) {
case GGML_TYPE_F16:
{
ggml_compute_forward_rope_f16(params, dst, true);
ggml_compute_forward_rope_flt<ggml_fp16_t>(params, dst, true);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_rope_f32(params, dst, true);
ggml_compute_forward_rope_flt<float>(params, dst, true);
} break;
default:
{
@ -5948,11 +5699,11 @@ void ggml_compute_forward_rope_back(
switch (src0->type) {
case GGML_TYPE_F16:
{
ggml_compute_forward_rope_f16(params, dst, false);
ggml_compute_forward_rope_flt<ggml_fp16_t>(params, dst, false);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_rope_f32(params, dst, false);
ggml_compute_forward_rope_flt<float>(params, dst, false);
} break;
default:
{

4
ggml/src/ggml-cpu/ops.h
View File

@ -51,10 +51,6 @@ void ggml_compute_forward_scale(const struct ggml_compute_params * params, struc
void ggml_compute_forward_set(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_cpy(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_cont(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_reshape(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_view(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_permute(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_transpose(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_get_rows(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_get_rows_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_set_rows(const struct ggml_compute_params * params, struct ggml_tensor * dst);

1
ggml/src/ggml-cuda/CMakeLists.txt
View File

@ -124,6 +124,7 @@ if (CUDAToolkit_FOUND)
if (GGML_CUDA_DEBUG)
list(APPEND CUDA_FLAGS -lineinfo)
add_compile_definitions(GGML_CUDA_DEBUG)
endif()
if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8")

101
ggml/src/ggml-cuda/cpy.cu
View File

@ -7,6 +7,10 @@
typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
const int CUDA_CPY_TILE_DIM_2D = 32; // 2D tile dimension for transposed blocks
const int CUDA_CPY_BLOCK_NM = 8; // block size of 3rd dimension if available
const int CUDA_CPY_BLOCK_ROWS = 8; // block dimension for marching through rows
template <cpy_kernel_t cpy_1>
static __global__ void cpy_flt(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
@ -35,6 +39,55 @@ static __global__ void cpy_flt(const char * cx, char * cdst, const int ne,
cpy_1(cx + x_offset, cdst + dst_offset);
}
template <typename T>
static __global__ void cpy_flt_transpose(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13) {
const T* src = reinterpret_cast<const T*>(cx);
T* dst = reinterpret_cast<T*>(cdst);
const int64_t nmat = ne / (ne00 * ne01);
const int64_t n = ne00 * ne01;
const int x = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.x;
const int y = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
const int tx = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.x; // transpose block offset
const int ty = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
__shared__ float tile[CUDA_CPY_TILE_DIM_2D][CUDA_CPY_TILE_DIM_2D+1];
#pragma unroll
for (int i = 0; i < CUDA_CPY_BLOCK_NM; ++i) {
const unsigned int imat = blockIdx.z * CUDA_CPY_BLOCK_NM + i;
if (imat >= nmat)
break;
#pragma unroll
for (int j = 0; j < CUDA_CPY_TILE_DIM_2D; j += CUDA_CPY_BLOCK_ROWS) {
if(x < ne01 && y + j < ne00){
const int row = threadIdx.y+j;
const int col = threadIdx.x * sizeof(float)/sizeof(T);
T *tile2 = reinterpret_cast<T*>(tile[row]);
tile2[col] = src[imat*n + (y+j)*ne01 + x];
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < CUDA_CPY_TILE_DIM_2D; j += CUDA_CPY_BLOCK_ROWS) {
if (ty + j < ne01 && tx < ne00) {
const int col = (threadIdx.y+j)*sizeof(float)/sizeof(T);
const T *tile2 = reinterpret_cast<const T*>(tile[threadIdx.x]);
dst[imat*n + (ty+j)*ne00 + tx] = tile2[col];
}
}
}
}
static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
float * cdstf = (float *)(cdsti);
@ -136,15 +189,36 @@ cudaStream_t stream) {
(cx, cdst, ne);
}
template<typename src_t, typename dst_t>
template<typename src_t, typename dst_t, bool transposed = false>
static void ggml_cpy_flt_cuda(
const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
cpy_flt<cpy_1_flt<src_t, dst_t>><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
if (transposed) {
GGML_ASSERT(ne == ne00*ne01*ne02); // ne[3] is 1 assumed
int ne00n, ne01n, ne02n;
if (nb00 <= nb02) { // most likely safe to handle nb00 = nb02 case here
ne00n = ne00;
ne01n = ne01;
ne02n = ne02;
} else if (nb00 > nb02) {
ne00n = ne00;
ne01n = ne01*ne02;
ne02n = 1;
}
dim3 dimGrid( (ne01n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D,
(ne00n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D,
(ne/(ne01n*ne00n) + CUDA_CPY_BLOCK_NM - 1) / CUDA_CPY_BLOCK_NM);
dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
cpy_flt_transpose<dst_t><<<dimGrid, dimBlock, 0, stream>>>
(cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
} else {
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
cpy_flt<cpy_1_flt<src_t, dst_t>><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
}
}
static void ggml_cpy_f32_q8_0_cuda(
@ -310,6 +384,7 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
char * src1_ddc = (char *) src1->data;
const bool contiguous_srcs = ggml_is_contiguous(src0) && ggml_is_contiguous(src1);
const bool can_be_transposed = nb01 == (int64_t)ggml_element_size(src0) && src0->ne[3] == 1;
if (src0->type == src1->type && contiguous_srcs) {
GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
@ -322,7 +397,11 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
}
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
ggml_cpy_flt_cuda<float, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
if (can_be_transposed) {
ggml_cpy_flt_cuda<float, float, true> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else {
ggml_cpy_flt_cuda<float, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
}
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) {
if (contiguous_srcs) {
ggml_cpy_flt_contiguous_cuda<float, nv_bfloat16> (src0_ddc, src1_ddc, ne, main_stream);
@ -361,7 +440,11 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
} else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) {
ggml_cpy_q5_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
ggml_cpy_flt_cuda<half, half> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
if (can_be_transposed) {
ggml_cpy_flt_cuda<half, half, true> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else {
ggml_cpy_flt_cuda<half, half> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
}
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) {
if (contiguous_srcs) {
ggml_cpy_flt_contiguous_cuda<half, nv_bfloat16> (src0_ddc, src1_ddc, ne, main_stream);
@ -375,7 +458,11 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
ggml_cpy_flt_cuda<half, float> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
}
} else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) {
ggml_cpy_flt_cuda<nv_bfloat16, nv_bfloat16> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
if (can_be_transposed) {
ggml_cpy_flt_cuda<nv_bfloat16, nv_bfloat16, true> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else {
ggml_cpy_flt_cuda<nv_bfloat16, nv_bfloat16> (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
}
} else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) {
if (contiguous_srcs) {
ggml_cpy_flt_contiguous_cuda<nv_bfloat16, half> (src0_ddc, src1_ddc, ne, main_stream);

51
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -27,7 +27,6 @@
#include "ggml-cuda/mmq.cuh"
#include "ggml-cuda/mmvf.cuh"
#include "ggml-cuda/mmvq.cuh"
#include "ggml-cuda/moe-expert-reduce.cuh"
#include "ggml-cuda/norm.cuh"
#include "ggml-cuda/opt-step-adamw.cuh"
#include "ggml-cuda/opt-step-sgd.cuh"
@ -2113,7 +2112,7 @@ static bool ggml_cuda_should_fuse_mul_mat_vec_f(const ggml_tensor * tensor) {
src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, is_mul_mat_id ? src1->ne[2] : src1->ne[1]);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, is_mul_mat_id ? src1->ne[2] : src1->ne[1]);
const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft) ||
ggml_backend_buft_is_cuda_split(src1->buffer->buft);
@ -2207,16 +2206,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
const int cc = ggml_cuda_info().devices[id].cc;
const int warp_size = ggml_cuda_info().devices[id].warp_size;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
}
} else {
const int cc = ggml_cuda_info().devices[ctx.device].cc;
const int warp_size = ggml_cuda_info().devices[ctx.device].warp_size;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
use_mul_mat_f = use_mul_mat_f && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
}
@ -2287,7 +2286,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
return;
}
if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src1->ne[2], /*mul_mat_id=*/true)) {
if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src0->nb, src1->ne[2], /*mul_mat_id=*/true)) {
ggml_cuda_mul_mat_f(ctx, src0, src1, ids, dst);
return;
}
@ -3152,8 +3151,6 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
#ifdef GGML_CUDA_DEBUG
const int nodes_fused = i - prev_i - 1;
prev_i = i;
@ -3199,31 +3196,6 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue;
}
if (node->op == GGML_OP_MUL) {
int current_node = i + 1;
int num_views = 0;
int num_adds = 0;
while (current_node < cgraph->n_nodes && cgraph->nodes[current_node]->op == GGML_OP_VIEW) {
num_views++;
current_node++;
}
while (current_node < cgraph->n_nodes && cgraph->nodes[current_node]->op == GGML_OP_ADD &&
num_adds < num_views - 1) {
num_adds++;
current_node++;
}
if (num_adds == num_views - 1 && num_views > 0) {
ggml_tensor * dst_node = cgraph->nodes[current_node - 1];
if (ggml_cuda_should_use_moe_expert_reduce(cgraph, i, current_node)) {
ggml_cuda_op_moe_expert_reduce(*cuda_ctx, node->src[0], node->src[1], dst_node);
i += num_views + num_adds;
continue;
}
}
}
if (node->op == GGML_OP_ADD) {
int n_fuse = 0;
ggml_op ops[8];
@ -3302,6 +3274,13 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue;
}
// we don't support repeating adds
if (bias_op == GGML_OP_ADD &&
(!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
!ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
continue;
}
const ggml_tensor * src0 = up_n->src[0];
const ggml_tensor * src1 = up_n->src[1];
const ggml_tensor * ids = up_n->src[2];
@ -3411,6 +3390,10 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
continue;
}
if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
continue;
}
ggml_cuda_mm_fusion_args_host fusion_data{};
fusion_data.x_bias = bias_tensor;

18
ggml/src/ggml-cuda/mmf.cu
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@ -119,15 +119,27 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr
}
}
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne, const int src1_ncols, bool mul_mat_id) {
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne,
const size_t * src0_nb, const int src1_ncols, bool mul_mat_id) {
if (ggml_is_quantized(type)) {
return false;
}
if (src0_ne[0] % (warp_size * (4/ggml_type_size(type))) != 0) {
const size_t ts = ggml_type_size(type);
if (src0_ne[0] % (warp_size * (4/ts)) != 0) {
return false;
}
if (src0_nb[0] != ts) {
return false;
}
// Pointers not aligned to the size of half2/nv_bfloat162/float2 would result in a crash:
for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
if (src0_nb[i] % (2*ts) != 0) {
return false;
}
}
if (src0_ne[1] % MMF_ROWS_PER_BLOCK != 0) {
return false;
}

2
ggml/src/ggml-cuda/mmf.cuh
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@ -17,7 +17,7 @@ struct mmf_ids_data {
void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, const int src1_ncols, bool mul_mat_id);
bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, const size_t * src0_nb, const int src1_ncols, bool mul_mat_id);
template <typename T, int rows_per_block, int cols_per_block, int nwarps, bool has_ids>
__launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1)

2
ggml/src/ggml-cuda/mmq.cuh
View File

@ -3494,7 +3494,7 @@ static __global__ void mul_mat_q_stream_k_fixup(
const int col_diff = col_high - col_low;
for (int j = threadIdx.y*warp_size + threadIdx.x; j < mmq_x; j += nwarps*warp_size) {
ids_dst_shared[j] = ids_dst[col_low + j];
ids_dst_shared[j] = ids_dst[col_low + jt*mmq_x + j];
}
__syncthreads();

15
ggml/src/ggml-cuda/mmvf.cu
View File

@ -716,10 +716,23 @@ void ggml_cuda_op_mul_mat_vec_f(
GGML_UNUSED_VARS(ctx, src1, dst, src1_ddq_i, src1_ncols, src1_padded_row_size);
}
bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, const size_t * src0_nb, int64_t ne11) {
if (src0_ne[0] % 2 != 0) {
return false;
}
const size_t ts = ggml_type_size(type);
if (src0_nb[0] != ts) {
return false;
}
// Pointers not aligned to the size of half2/nv_bfloat162/float2 would result in a crash:
for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
if (src0_nb[i] % (2*ts) != 0) {
return false;
}
}
switch (type) {
case GGML_TYPE_F32:
if (GGML_CUDA_CC_IS_NVIDIA(cc)) {

2
ggml/src/ggml-cuda/mmvf.cuh
View File

@ -9,4 +9,4 @@ void ggml_cuda_op_mul_mat_vec_f(
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream);
bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);
bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, const size_t * src0_nb, int64_t ne11);

168
ggml/src/ggml-cuda/moe-expert-reduce.cu
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@ -1,168 +0,0 @@
#include "moe-expert-reduce.cuh"
// This kernel is a fusion of the expert weight reduce, common in MoE models
template <int n_expert_used_template>
__global__ void moe_expert_reduce_cuda(const float * __restrict__ experts,
const float * __restrict__ weights,
float * __restrict__ dst,
const int n_expert_used,
const int n_cols) {
const int row = blockIdx.x;
const int col = blockIdx.y * blockDim.x + threadIdx.x;
if (col >= n_cols) {
return;
}
experts += row * n_cols * n_expert_used;
weights += row * n_expert_used;
dst += row * n_cols;
float acc = 0.f;
if constexpr (n_expert_used_template == 0) {
for (int expert = 0; expert < n_expert_used; ++expert) {
ggml_cuda_mad(acc, experts[col], weights[expert]);
experts += n_cols;
}
dst[col] = acc;
} else {
#pragma unroll
for (int i = 0; i < n_expert_used_template; ++i) {
ggml_cuda_mad(acc, experts[col], weights[i]);
experts += n_cols;
}
dst[col] = acc;
}
}
static void launch_moe_expert_reduce(ggml_backend_cuda_context & ctx,
const float * experts,
const float * weights,
float * dst,
const int n_expert_used,
const int n_cols,
const int n_rows) {
const int block_size = 32;
const int n_blocks_x = n_rows;
const int n_blocks_y = (n_cols + block_size - 1) / block_size;
dim3 block_dims(block_size);
dim3 grid_dims(n_blocks_x, n_blocks_y);
cudaStream_t stream = ctx.stream();
switch (n_expert_used) {
case 1:
moe_expert_reduce_cuda<1>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 2:
moe_expert_reduce_cuda<2>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 4:
moe_expert_reduce_cuda<4>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 6:
moe_expert_reduce_cuda<6>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 8:
moe_expert_reduce_cuda<8>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 16:
moe_expert_reduce_cuda<16>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 32:
moe_expert_reduce_cuda<32>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 64:
moe_expert_reduce_cuda<64>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
case 128:
moe_expert_reduce_cuda<128>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
default:
moe_expert_reduce_cuda<0>
<<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
break;
}
}
bool ggml_cuda_should_use_moe_expert_reduce(const ggml_cgraph * cgraph, int start_index, int end_index) {
const ggml_tensor * mul = cgraph->nodes[start_index];
if (mul->op != GGML_OP_MUL || !ggml_is_contiguous(mul->src[0]) || !ggml_is_contiguous(mul->src[1])) {
return false;
}
int current_node = start_index + 1;
size_t current_offset = 0;
std::vector<const ggml_tensor *> view_nodes;
//check if all are views of the expert in increasing order
while (current_node < end_index && cgraph->nodes[current_node]->op == GGML_OP_VIEW) {
const ggml_tensor * node = cgraph->nodes[current_node];
if (node->view_src != mul) {
return false;
}
if (node->view_offs < current_offset) {
return false;
}
current_offset = node->view_offs;
current_node++;
view_nodes.push_back(node);
}
//check if all the adds are in increasing order
const ggml_tensor * prev_add_src = view_nodes.empty() ? nullptr : view_nodes[0];
int num_adds = 0;
int num_views = view_nodes.size();
while (current_node < end_index && cgraph->nodes[current_node]->op == GGML_OP_ADD) {
const ggml_tensor * add_node = cgraph->nodes[current_node];
bool is_first_op_ok = num_views > num_adds ? add_node->src[0] == prev_add_src : false;
bool is_second_op_ok = num_views > num_adds ? add_node->src[1] == view_nodes[num_adds + 1] : false;
if (!is_first_op_ok || !is_second_op_ok) {
return false;
}
prev_add_src = add_node;
num_adds++;
current_node++;
}
if (num_views != num_adds + 1) {
return false;
}
return true;
}
void ggml_cuda_op_moe_expert_reduce(ggml_backend_cuda_context & ctx,
const ggml_tensor * experts,
const ggml_tensor * weights,
ggml_tensor * dst) {
const int n_rows = experts->ne[2];
const int n_expert_used = experts->ne[1];
const int n_cols = experts->ne[0];
GGML_ASSERT(experts->type == GGML_TYPE_F32);
GGML_ASSERT(weights->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(experts));
GGML_ASSERT(ggml_is_contiguous(weights));
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const float * experts_d = (const float *) experts->data;
const float * weights_d = (const float *) weights->data;
float * dst_d = (float *) dst->data;
launch_moe_expert_reduce(ctx, experts_d, weights_d, dst_d, n_expert_used, n_cols, n_rows);
}

11
ggml/src/ggml-cuda/moe-expert-reduce.cuh
View File

@ -1,11 +0,0 @@
#include "common.cuh"
#include "ggml.h"
#include <initializer_list>
void ggml_cuda_op_moe_expert_reduce(ggml_backend_cuda_context & ctx,
const ggml_tensor * experts,
const ggml_tensor * weights,
ggml_tensor * dst);
bool ggml_cuda_should_use_moe_expert_reduce(const ggml_cgraph * cgraph, int start_index, int end_index);

93
ggml/src/ggml-cuda/upscale.cu
View File

@ -81,6 +81,70 @@ static __global__ void upscale_f32_bilinear(const float * x, float * dst,
dst[index] = result;
}
namespace bicubic_interpolation {
// https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm
__device__ const float a = -0.75f; // use alpha = -0.75 (same as PyTorch)
static __device__ float weight1(float x) { return ((a + 2) * x - (a + 3)) * x * x + 1; };
static __device__ float weight2(float x) { return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a; };
static __device__ float bicubic(float p0, float p1, float p2, float p3, float x) {
const float w0 = weight2(x + 1);
const float w1 = weight1(x + 0);
const float w2 = weight1(1 - x);
const float w3 = weight2(2 - x);
return p0 * w0 + p1 * w1 + p2 * w2 + p3 * w3;
};
} // namespace bicubic_interpolation
static __global__ void upscale_f32_bicubic(const float * x, float * dst,
const int nb00, const int nb01, const int nb02, const int nb03,
const int ne00_src, const int ne01_src,
const int ne10_dst, const int ne11_dst, const int ne12_dst, const int ne13_dst,
const float sf0, const float sf1, const float sf2, const float sf3,
const float pixel_offset) {
using bicubic_interpolation::bicubic;
const int64_t index = threadIdx.x + blockIdx.x * blockDim.x;
const int64_t dst_total_elements = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
if (index >= dst_total_elements) {
return;
}
const int i10_dst = index % ne10_dst;
const int i11_dst = (index / ne10_dst) % ne11_dst;
const int i12_dst = (index / (ne10_dst * ne11_dst)) % ne12_dst;
const int i13_dst = index / (ne10_dst * ne11_dst * ne12_dst);
const int i02_src = (int)(i12_dst / sf2);
const int i03_src = (int)(i13_dst / sf3);
const float y_src_f = ((float)i11_dst + pixel_offset) / sf1 - pixel_offset;
const int y0_src = (int)floorf(y_src_f);
const float dy = y_src_f - (float)y0_src;
const float x_src_f = ((float)i10_dst + pixel_offset) / sf0 - pixel_offset;
const int x0_src = (int)floorf(x_src_f);
const float dx = x_src_f - (float)x0_src;
const char * x_base = (const char *)x + (int64_t)i02_src * nb02 + (int64_t)i03_src * nb03;
auto load = [=](int x_off, int y_off) -> float {
int i00_src = max(0, min(x0_src + x_off, ne00_src - 1));
int i01_src = max(0, min(y0_src + y_off, ne01_src - 1));
return *(const float *)(x_base + (int64_t)i00_src * nb00 + (int64_t)i01_src * nb01);
};
const float result = bicubic(
bicubic(load(-1,-1), load(0,-1), load(1,-1), load(2,-1), dx),
bicubic(load(-1, 0), load(0, 0), load(1, 0), load(2, 0), dx),
bicubic(load(-1, 1), load(0, 1), load(1, 1), load(2, 1), dx),
bicubic(load(-1, 2), load(0, 2), load(1, 2), load(2, 2), dx), dy);
dst[index] = result;
}
static void upscale_f32_cuda(const float * x, float * dst,
const int nb00, const int nb01, const int nb02, const int nb03,
const int ne10, const int ne11, const int ne12, const int ne13,
@ -104,6 +168,18 @@ static void upscale_f32_bilinear_cuda(const float * x, float * dst,
upscale_f32_bilinear<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
}
static void upscale_f32_bicubic_cuda(const float * x, float * dst,
const int nb00, const int nb01, const int nb02, const int nb03,
const int ne00_src, const int ne01_src,
const int ne10_dst, const int ne11_dst, const int ne12_dst, const int ne13_dst,
const float sf0, const float sf1, const float sf2, const float sf3,
const float pixel_offset, cudaStream_t stream) {
const int64_t dst_size = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
const int64_t num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
upscale_f32_bicubic<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
}
void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
@ -121,17 +197,22 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float sf2 = (float)dst->ne[2]/src0->ne[2];
const float sf3 = (float)dst->ne[3]/src0->ne[3];
float pixel_offset = 0.5f;
if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
sf0 = dst->ne[0] > 1 && src0->ne[0] > 1 ? (float)(dst->ne[0] - 1) / (src0->ne[0] - 1) : sf0;
sf1 = dst->ne[1] > 1 && src0->ne[1] > 1 ? (float)(dst->ne[1] - 1) / (src0->ne[1] - 1) : sf1;
pixel_offset = 0.0f;
}
if (mode == GGML_SCALE_MODE_NEAREST) {
upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream);
} else if (mode == GGML_SCALE_MODE_BILINEAR) {
float pixel_offset = 0.5f;
if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
sf0 = dst->ne[0] > 1 && src0->ne[0] > 1 ? (float)(dst->ne[0] - 1) / (src0->ne[0] - 1) : sf0;
sf1 = dst->ne[1] > 1 && src0->ne[1] > 1 ? (float)(dst->ne[1] - 1) / (src0->ne[1] - 1) : sf1;
pixel_offset = 0.0f;
}
upscale_f32_bilinear_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
src0->ne[0], src0->ne[1], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
sf0, sf1, sf2, sf3, pixel_offset, stream);
} else if (mode == GGML_SCALE_MODE_BICUBIC) {
upscale_f32_bicubic_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
src0->ne[0], src0->ne[1], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
sf0, sf1, sf2, sf3, pixel_offset, stream);
}
}

65
ggml/src/ggml-hexagon/ggml-hexagon.cpp
View File

@ -367,7 +367,13 @@ struct ggml_backend_hexagon_buffer_context {
ggml_backend_hexagon_buffer_context(ggml_hexagon_session * sess, size_t size, bool repack) {
size += 4 * 1024; // extra page for padding
this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS | RPCMEM_HEAP_NOREG, size);
if (rpcmem_alloc2) {
this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS | RPCMEM_HEAP_NOREG, size);
} else {
GGML_LOG_INFO("ggml-hex: %s rpcmem_alloc2 not found, falling back to rpcmem_alloc\n", sess->name.c_str());
this->base = (uint8_t *) rpcmem_alloc(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS | RPCMEM_HEAP_NOREG, size);
}
if (!this->base) {
GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer : size %zu\n", sess->name.c_str(), size);
throw std::runtime_error("ggml-hex: rpcmem_alloc failed (see log for details)");
@ -1679,12 +1685,13 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
}
// Get session URI
char htp_uri[256];
sprintf(htp_uri, "file:///libggml-htp-v%u.so?htp_iface_skel_handle_invoke&_modver=1.0", opt_arch);
char session_uri[256];
{
struct remote_rpc_get_uri u;
char htp_uri[256];
snprintf(htp_uri, sizeof(htp_uri), "file:///libggml-htp-v%u.so?htp_iface_skel_handle_invoke&_modver=1.0", opt_arch);
struct remote_rpc_get_uri u = {};
u.session_id = this->session_id;
u.domain_name = const_cast<char *>(CDSP_DOMAIN_NAME);
u.domain_name_len = strlen(CDSP_DOMAIN_NAME);
@ -1695,8 +1702,12 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
int err = remote_session_control(FASTRPC_GET_URI, (void *) &u, sizeof(u));
if (err != AEE_SUCCESS) {
GGML_LOG_ERROR("ggml-hex: failed to get URI for session %d : error 0x%x\n", dev_id, err);
throw std::runtime_error("ggml-hex: remote_session_control(get-uri) failed (see log for details)");
// fallback to single session uris
int htp_URI_domain_len = strlen(htp_uri) + MAX_DOMAIN_NAMELEN;
snprintf(session_uri, htp_URI_domain_len, "%s%s", htp_uri, my_domain->uri);
GGML_LOG_WARN("ggml-hex: failed to get URI for session %d : error 0x%x. Falling back to single session URI: %s\n", dev_id, err, session_uri);
}
}
@ -3145,26 +3156,17 @@ static inline bool op_reuse_src1(const ggml_tensor * op1, const ggml_tensor * op
return (op0 && op0->src[1] == op1->src[1]);
}
static inline bool is_compute_op(ggml_tensor *node)
{
return !(ggml_op_is_empty(node->op) || ggml_is_empty(node));
}
// scan the graph and figure out last compute op index
static inline int last_compute_op(ggml_cgraph * graph) {
int last;
int last = 0;
for (int i = 0; i < graph->n_nodes; ++i) {
ggml_tensor * node = graph->nodes[i];
switch (node->op) {
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_MUL:
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_RMS_NORM:
case GGML_OP_GLU:
case GGML_OP_ADD_ID:
last = i;
break;
default:
break;
if (is_compute_op(graph->nodes[i])) {
last = i;
}
}
@ -3183,6 +3185,10 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
for (int i = 0; i < graph->n_nodes; ++i) {
ggml_tensor * node = graph->nodes[i];
if (!is_compute_op(node)) {
continue;
}
uint32_t flags = 0;
// skip quantizer if src1 is reused
@ -3234,14 +3240,6 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
ggml_hexagon_rope(node, flags);
break;
// non-compute ops
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
break;
default:
GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node));
}
@ -3668,6 +3666,11 @@ ggml_hexagon_registry::ggml_hexagon_registry(ggml_backend_reg_t reg) {
}
}
if(opt_arch < 75) {
opt_ndev = 1;
GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n");
}
GGML_LOG_INFO("ggml-hex: Hexagon Arch version v%d\n", opt_arch);
// Create devices / sessions

1
ggml/src/ggml-hexagon/htp-utils.h
View File

@ -64,6 +64,7 @@ extern "C" {
# pragma weak remote_handle64_control
# pragma weak fastrpc_mmap
# pragma weak fastrpc_munmap
# pragma weak rpcmem_alloc2
#endif
#if !defined(_WINDOWS)

74
ggml/src/ggml-hexagon/htp/binary-ops.c
View File

@ -34,6 +34,11 @@ static hvx_elemwise_f32_func func_table_HVX[] = { hvx_mul_f32, hvx_add_f32,
static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_opt, hvx_add_f32_opt, hvx_sub_f32_opt };
#define htp_binary_preamble \
const struct htp_tensor * src0 = &octx->src0; \
const struct htp_tensor * src1 = &octx->src1; \
const struct htp_tensor * src2 = &octx->src2; \
struct htp_tensor * dst = &octx->dst; \
\
const uint32_t ne00 = src0->ne[0]; \
const uint32_t ne01 = src0->ne[1]; \
const uint32_t ne02 = src0->ne[2]; \
@ -62,16 +67,15 @@ static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_opt, hvx_add_f
const uint32_t nb0 = dst->nb[0]; \
const uint32_t nb1 = dst->nb[1]; \
const uint32_t nb2 = dst->nb[2]; \
const uint32_t nb3 = dst->nb[3];
const uint32_t nb3 = dst->nb[3]; \
\
const uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread;
static void binary_job_f32_per_thread(const struct htp_tensor * src0,
const struct htp_tensor * src1,
struct htp_tensor * dst,
uint8_t * spad_data,
uint32_t nth,
uint32_t ith,
uint32_t src0_nrows_per_thread,
enum htp_op op) {
static void binary_job_f32_per_thread(struct htp_ops_context * octx,
uint8_t * spad_data,
uint32_t nth,
uint32_t ith,
enum htp_op op) {
htp_binary_preamble;
const size_t src0_row_size = nb01;
@ -107,16 +111,23 @@ static void binary_job_f32_per_thread(const struct htp_tensor * src0,
uint8_t * restrict spad_data_th = spad_data + (ith * src0_row_size);
const uint32_t nr0 = ne00 / ne10;
const uint8_t * restrict src0_ptr = (const uint8_t *) src0->data + (src0_start_row * src0_row_size);
uint8_t * restrict dst_ptr = (uint8_t *) dst->data + (src0_start_row * dst_row_size);
const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
const uint8_t * restrict src1_ptr = NULL;
const uint32_t ne02_ne01 = ne02 * ne01;
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
src1_ptr = data_src1 + (ir % src1_nrows) * src1_row_size;
const uint32_t i03 = fastdiv(ir, &octx->src0_div21);
const uint32_t i02 = fastdiv(ir - i03 * ne02_ne01, &octx->src0_div1);
const uint32_t i01 = (ir - i03 * ne02_ne01 - i02 * ne01);
const uint32_t i13 = fastmodulo(i03, ne13, &octx->src1_div3);
const uint32_t i12 = fastmodulo(i02, ne12, &octx->src1_div2);
const uint32_t i11 = fastmodulo(i01, ne11, &octx->src1_div1);
const uint8_t * restrict src1_ptr = data_src1 + i13 * nb13 + i12 * nb12 + i11 * src1_row_size;
if (ir + 1 < src0_end_row) {
htp_l2fetch(src0_ptr + ne00, 1, src0_row_size, src0_row_size);
@ -125,6 +136,7 @@ static void binary_job_f32_per_thread(const struct htp_tensor * src0,
}
}
const uint32_t nr0 = ne00 / ne10;
if (nr0 > 1) {
if ((1 == is_aligned) && (nr0 == ne00)) {
hvx_bcast_fp32_a(spad_data_th, *(float *) src1_ptr, nr0);
@ -149,22 +161,17 @@ static void binary_job_f32_per_thread(const struct htp_tensor * src0,
(unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
}
static void binary_add_id_job_f32_per_thread(const struct htp_tensor * src0,
const struct htp_tensor * src1,
const struct htp_tensor * src2,
struct htp_tensor * dst,
uint8_t * spad_data,
uint32_t nth,
uint32_t ith,
uint32_t src0_nrows_per_thread,
hvx_elemwise_f32_func func_HVX) {
static void binary_add_id_job_f32_per_thread(struct htp_ops_context * octx,
uint8_t * spad_data,
uint32_t nth,
uint32_t ith,
hvx_elemwise_f32_func func_HVX) {
htp_binary_preamble;
const size_t src0_row_size = nb01;
const size_t src1_row_size = nb11;
const size_t dst_row_size = nb1;
const uint32_t ne02_ne01 = ne02 * ne01;
const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows
const uint32_t src0_start_row = src0_nrows_per_thread * ith;
@ -187,10 +194,11 @@ static void binary_add_id_job_f32_per_thread(const struct htp_tensor * src0,
const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
uint8_t * restrict data_dst = (uint8_t *) dst->data;
const uint32_t ne02_ne01 = ne02 * ne01;
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
// src0 indices
const uint32_t i03 = ir / ne02_ne01;
const uint32_t i02 = (ir - i03 * ne02_ne01) / ne01;
const uint32_t i03 = fastdiv(ir, &octx->src0_div21);
const uint32_t i02 = fastdiv(ir - i03 * ne02_ne01, &octx->src0_div1);
const uint32_t i01 = (ir - i03 * ne02_ne01 - i02 * ne01);
// src1 indices
@ -234,13 +242,11 @@ static void binary_job_dispatcher_f32(unsigned int n, unsigned int i, void * dat
case HTP_OP_MUL:
case HTP_OP_ADD:
case HTP_OP_SUB:
binary_job_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->src1_spad.data, n, i,
octx->src0_nrows_per_thread, octx->op);
binary_job_f32_per_thread(octx, octx->src1_spad.data, n, i, octx->op);
break;
case HTP_OP_ADD_ID:
binary_add_id_job_f32_per_thread(&octx->src0, &octx->src1, &octx->src2, &octx->dst, octx->src0_spad.data, n,
i, octx->src0_nrows_per_thread, hvx_add_f32);
binary_add_id_job_f32_per_thread(octx, octx->src0_spad.data, n, i, hvx_add_f32);
break;
default:
@ -321,6 +327,16 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) {
octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs;
octx->src0_div21 = init_fastdiv_values(src0->ne[2] * src0->ne[1]);
octx->src0_div3 = init_fastdiv_values(src0->ne[3]);
octx->src0_div2 = init_fastdiv_values(src0->ne[2]);
octx->src0_div1 = init_fastdiv_values(src0->ne[1]);
octx->src1_div21 = init_fastdiv_values(src1->ne[2] * src1->ne[1]);
octx->src1_div3 = init_fastdiv_values(src1->ne[3]);
octx->src1_div2 = init_fastdiv_values(src1->ne[2]);
octx->src1_div1 = init_fastdiv_values(src1->ne[1]);
worker_pool_run_func(octx->ctx->worker_pool, binary_op_func, octx, n_jobs);
}

8
ggml/src/ggml-hexagon/htp/htp-msg.h
View File

@ -119,10 +119,10 @@ static const char * htp_type_name(uint32_t t) {
#define HTP_MAX_DIMS 4
struct htp_tensor {
uint32_t data; // Buffer offset in the messages, and data pointer on the NSP
uint32_t type; // Data type
uint32_t ne[HTP_MAX_DIMS]; // Number of elements
uint32_t nb[HTP_MAX_DIMS]; // Stride in bytes (see ggml.h ggml_tensor)
uint32_t data; // Buffer offset in the messages, and data pointer on the NSP
uint32_t type; // Data type
uint32_t ne[HTP_MAX_DIMS]; // Number of elements
uint32_t nb[HTP_MAX_DIMS]; // Stride in bytes (see ggml.h ggml_tensor)
};
#define HTP_MAX_OP_PARAMS 64

11
ggml/src/ggml-hexagon/htp/htp-ops.h
View File

@ -4,6 +4,7 @@
#include "htp-ctx.h"
#include "htp-msg.h"
#include "worker-pool.h"
#include "ops-utils.h"
#include <assert.h>
#include <stdint.h>
@ -38,6 +39,16 @@ struct htp_ops_context {
uint32_t src0_nrows_per_thread;
uint32_t src1_nrows_per_thread;
struct fastdiv_values src0_div1; // fastdiv values for ne1
struct fastdiv_values src0_div2; // fastdiv values for ne2
struct fastdiv_values src0_div3; // fastdiv values for ne3
struct fastdiv_values src0_div21; // fastdiv values for ne2 * ne1
struct fastdiv_values src1_div1; // fastdiv values for ne1
struct fastdiv_values src1_div2; // fastdiv values for ne2
struct fastdiv_values src1_div3; // fastdiv values for ne3
struct fastdiv_values src1_div21; // fastdiv values for ne2 * ne1
uint32_t flags;
};

33
ggml/src/ggml-hexagon/htp/ops-utils.h
View File

@ -31,6 +31,39 @@ static inline uint32_t htp_round_up(uint32_t n, uint32_t m) {
return m * ((n + m - 1) / m);
}
// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
// Precompute mp (m' in the paper) and L such that division
// can be computed using a multiply (high 32b of 64b result)
// and a shift:
//
// n/d = (mulhi(n, mp) + n) >> L;
struct fastdiv_values {
uint32_t mp;
uint32_t l;
};
static inline struct fastdiv_values init_fastdiv_values(uint32_t d) {
struct fastdiv_values result = { 0, 0 };
// compute L = ceil(log2(d));
while (result.l < 32 && ((uint32_t) 1 << result.l) < d) {
++(result.l);
}
result.mp = (uint32_t) (((uint64_t) 1 << 32) * (((uint64_t) 1 << result.l) - d) / d + 1);
return result;
}
static inline uint32_t fastdiv(uint32_t n, const struct fastdiv_values * vals) {
// Compute high 32 bits of n * mp
const uint32_t hi = (uint32_t) (((uint64_t) n * vals->mp) >> 32); // mulhi(n, mp)
// add n, apply bit shift
return (hi + n) >> vals->l;
}
static inline uint32_t fastmodulo(uint32_t n, uint32_t d, const struct fastdiv_values * vals) {
return n - fastdiv(n, vals) * d;
}
static inline void htp_l2fetch(const void * p, uint32_t height, uint32_t width, uint32_t stride) {
const uint64_t control = Q6_P_combine_RR(stride, Q6_R_combine_RlRl(width, height));
asm volatile(" l2fetch(%0,%1) " : : "r"(p), "r"(control));

11
ggml/src/ggml-metal/ggml-metal-context.m
View File

@ -35,7 +35,6 @@ struct ggml_metal {
// additional, inference-time compiled pipelines
ggml_metal_pipelines_t pipelines_ext;
bool use_bfloat;
bool use_fusion;
bool use_concurrency;
bool use_graph_optimize;
@ -121,11 +120,10 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
}
}
const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev);
//const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev);
res->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
res->use_bfloat = props_dev->has_bfloat;
res->use_fusion = getenv("GGML_METAL_FUSION_DISABLE") == nil;
res->use_concurrency = getenv("GGML_METAL_CONCURRENCY_DISABLE") == nil;
@ -147,7 +145,6 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
memset(res->fuse_cnt, 0, sizeof(res->fuse_cnt));
GGML_LOG_INFO("%s: use bfloat = %s\n", __func__, res->use_bfloat ? "true" : "false");
GGML_LOG_INFO("%s: use fusion = %s\n", __func__, res->use_fusion ? "true" : "false");
GGML_LOG_INFO("%s: use concurrency = %s\n", __func__, res->use_concurrency ? "true" : "false");
GGML_LOG_INFO("%s: use graph optimize = %s\n", __func__, res->use_graph_optimize ? "true" : "false");
@ -292,7 +289,7 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor,
// queue the copy operation into the queue of the Metal context
// this will be queued at the end, after any currently ongoing GPU operations
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[encoder copyFromBuffer:buf_src
@ -303,6 +300,7 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor,
[encoder endEncoding];
[cmd_buf commit];
[buf_src release];
// do not wait here for completion
//[cmd_buf waitUntilCompleted];
@ -333,7 +331,7 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te
// queue the copy operation into the queue of the Metal context
// this will be queued at the end, after any currently ongoing GPU operations
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[encoder copyFromBuffer:bid_src.metal
@ -344,6 +342,7 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te
[encoder endEncoding];
[cmd_buf commit];
[buf_dst release];
// do not wait here for completion
//[cmd_buf waitUntilCompleted];

5
ggml/src/ggml-metal/ggml-metal-device.h
View File

@ -95,7 +95,9 @@ void ggml_metal_encoder_end_encoding(ggml_metal_encoder_t encoder);
typedef struct ggml_metal_library * ggml_metal_library_t;
ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev);
ggml_metal_library_t ggml_metal_library_init (ggml_metal_device_t dev);
ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev, const char * source, bool verbose);
void ggml_metal_library_free(ggml_metal_library_t lib);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline (ggml_metal_library_t lib, const char * name);
@ -193,6 +195,7 @@ struct ggml_metal_device_props {
bool has_simdgroup_mm;
bool has_unified_memory;
bool has_bfloat;
bool has_tensor;
bool use_residency_sets;
bool use_shared_buffers;

213
ggml/src/ggml-metal/ggml-metal-device.m
View File

@ -21,8 +21,9 @@
#define GGML_METAL_HAS_RESIDENCY_SETS 1
#endif
// overload of MTLGPUFamilyMetal3 (not available in some environments)
// overload of MTLGPUFamilyMetalX (not available in some environments)
static const NSInteger MTLGPUFamilyMetal3_GGML = 5001;
static const NSInteger MTLGPUFamilyMetal4_GGML = 5002;
// virtual address for GPU memory allocations
static atomic_uintptr_t g_addr_device = 0x000000400ULL;
@ -261,6 +262,10 @@ ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_BF16"];
}
if (ggml_metal_device_get_props(dev)->has_tensor) {
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_TENSOR"];
}
#if GGML_METAL_EMBED_LIBRARY
[prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
#endif
@ -298,6 +303,72 @@ ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
return res;
}
ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev, const char * source, bool verbose) {
if (source == NULL) {
GGML_LOG_ERROR("%s: source is NULL\n", __func__);
return NULL;
}
id<MTLDevice> device = ggml_metal_device_get_obj(dev);
id<MTLLibrary> library = nil;
NSError * error = nil;
const int64_t t_start = ggml_time_us();
NSString * src = [[NSString alloc] initWithBytes:source
length:strlen(source)
encoding:NSUTF8StringEncoding];
if (!src) {
GGML_LOG_ERROR("%s: failed to create NSString from source\n", __func__);
return NULL;
}
@autoreleasepool {
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
MTLCompileOptions * options = [MTLCompileOptions new];
options.preprocessorMacros = prep;
library = [device newLibraryWithSource:src options:options error:&error];
if (error) {
if (verbose) {
GGML_LOG_ERROR("%s: error compiling source: %s\n", __func__, [[error description] UTF8String]);
} else {
GGML_LOG_ERROR("%s: error compiling source\n", __func__);
}
library = nil;
}
[options release];
}
[src release];
if (!library) {
if (verbose) {
GGML_LOG_ERROR("%s: failed to create Metal library from source\n", __func__);
}
return NULL;
}
if (verbose) {
GGML_LOG_INFO("%s: compiled in %.3f sec\n", __func__, (ggml_time_us() - t_start) / 1e6);
}
ggml_metal_library_t res = calloc(1, sizeof(struct ggml_metal_library));
if (!res) {
GGML_LOG_ERROR("%s: calloc failed\n", __func__);
return NULL;
}
res->obj = library;
res->device = device;
res->pipelines = ggml_metal_pipelines_init();
return res;
}
void ggml_metal_library_free(ggml_metal_library_t lib) {
if (!lib) {
return;
@ -345,9 +416,9 @@ ggml_metal_pipeline_t ggml_metal_library_compile_pipeline(ggml_metal_library_t l
if (!mtl_function) {
ggml_critical_section_end();
GGML_LOG_ERROR("%s: error: failed to compile pipeline: base = '%s', name = '%s'\n", __func__, base, name);
GGML_LOG_ERROR("%s: failed to compile pipeline: base = '%s', name = '%s'\n", __func__, base, name);
if (error) {
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
GGML_LOG_ERROR("%s: %s\n", __func__, [[error description] UTF8String]);
}
return nil;
@ -355,13 +426,21 @@ ggml_metal_pipeline_t ggml_metal_library_compile_pipeline(ggml_metal_library_t l
res->obj = [lib->device newComputePipelineStateWithFunction:mtl_function error:&error];
ggml_metal_pipelines_add(lib->pipelines, name, res);
[mtl_function release];
GGML_LOG_DEBUG("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, name, (void *) res->obj,
(int) res->obj.maxTotalThreadsPerThreadgroup,
(int) res->obj.threadExecutionWidth);
if (res->obj.maxTotalThreadsPerThreadgroup == 0 || res->obj.threadExecutionWidth == 0) {
ggml_critical_section_end();
GGML_LOG_ERROR("%s: incompatible pipeline %s\n", __func__, name);
return nil;
}
ggml_metal_pipelines_add(lib->pipelines, name, res);
}
ggml_critical_section_end();
@ -469,6 +548,128 @@ ggml_metal_device_t ggml_metal_device_init(void) {
dev->props.has_bfloat = [dev->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
dev->props.has_bfloat |= [dev->mtl_device supportsFamily:MTLGPUFamilyApple6];
if (getenv("GGML_METAL_BF16_DISABLE") != NULL) {
dev->props.has_bfloat = false;
}
dev->props.has_tensor = [dev->mtl_device supportsFamily:MTLGPUFamilyMetal4_GGML];
if (getenv("GGML_METAL_TENSOR_DISABLE") != NULL) {
dev->props.has_tensor = false;
}
// note: disable the tensor API by default for old chips because with the current implementation it is not useful
// - M2 Ultra: ~5% slower
// - M4, M4 Max: no significant difference
//
// TODO: try to update the tensor API kernels to at least match the simdgroup performance
if (getenv("GGML_METAL_TENSOR_ENABLE") == NULL &&
![[dev->mtl_device name] containsString:@"M5"] &&
![[dev->mtl_device name] containsString:@"M6"] &&
![[dev->mtl_device name] containsString:@"A19"] &&
![[dev->mtl_device name] containsString:@"A20"]) {
GGML_LOG_WARN("%s: tensor API disabled for pre-M5 and pre-A19 devices\n", __func__);
dev->props.has_tensor = false;
}
// double-check that the tensor API compiles
if (dev->props.has_tensor) {
const char * src_tensor_f16 = "\n"
"#include <metal_stdlib> \n"
"#include <metal_tensor> \n"
"#include <MetalPerformancePrimitives/MetalPerformancePrimitives.h> \n"
" \n"
"using namespace metal; \n"
"using namespace mpp::tensor_ops; \n"
" \n"
"kernel void dummy_kernel( \n"
" tensor<device half, dextents<int32_t, 2>> A [[buffer(0)]], \n"
" tensor<device half, dextents<int32_t, 2>> B [[buffer(1)]], \n"
" device float * C [[buffer(2)]], \n"
" uint2 tgid [[threadgroup_position_in_grid]]) \n"
"{ \n"
" auto tA = A.slice(0, (int)tgid.y); \n"
" auto tB = B.slice((int)tgid.x, 0); \n"
" \n"
" matmul2d< \n"
" matmul2d_descriptor(8, 8, dynamic_extent), \n"
" execution_simdgroups<4>> mm; \n"
" \n"
" auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>(); \n"
" \n"
" auto sA = tA.slice(0, 0); \n"
" auto sB = tB.slice(0, 0); \n"
" mm.run(sB, sA, cT); \n"
" \n"
" auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(4, 4)); \n"
" \n"
" cT.store(tC); \n"
"}";
GGML_LOG_INFO("%s: testing tensor API for f16 support\n", __func__);
ggml_metal_library_t lib = ggml_metal_library_init_from_source(dev, src_tensor_f16, false);
if (lib == NULL) {
GGML_LOG_WARN("%s: - the tensor API is not supported in this environment - disabling\n", __func__);
dev->props.has_tensor = false;
} else {
ggml_metal_pipeline_t ppl = ggml_metal_library_compile_pipeline(lib, "dummy_kernel", "dummy_kernel", nil);
if (!ppl) {
GGML_LOG_WARN("%s: - the tensor API is not supported in this environment - disabling\n", __func__);
dev->props.has_tensor = false;
}
ggml_metal_library_free(lib);
}
}
// try to compile a dummy kernel to determine if the tensor API is supported for bfloat
if (dev->props.has_tensor && dev->props.has_bfloat) {
const char * src_tensor_bf16 = "\n"
"#include <metal_stdlib> \n"
"#include <metal_tensor> \n"
"#include <MetalPerformancePrimitives/MetalPerformancePrimitives.h> \n"
" \n"
"using namespace metal; \n"
"using namespace mpp::tensor_ops; \n"
" \n"
"kernel void dummy_kernel( \n"
" tensor<device bfloat, dextents<int32_t, 2>> A [[buffer(0)]], \n"
" tensor<device bfloat, dextents<int32_t, 2>> B [[buffer(1)]], \n"
" device float * C [[buffer(2)]], \n"
" uint2 tgid [[threadgroup_position_in_grid]]) \n"
"{ \n"
" auto tA = A.slice(0, (int)tgid.y); \n"
" auto tB = B.slice((int)tgid.x, 0); \n"
" \n"
" matmul2d< \n"
" matmul2d_descriptor(8, 8, dynamic_extent), \n"
" execution_simdgroups<4>> mm; \n"
" \n"
" auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>(); \n"
" \n"
" auto sA = tA.slice(0, 0); \n"
" auto sB = tB.slice(0, 0); \n"
" mm.run(sB, sA, cT); \n"
" \n"
" auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(4, 4)); \n"
" \n"
" cT.store(tC); \n"
"}";
GGML_LOG_INFO("%s: testing tensor API for bfloat support\n", __func__);
ggml_metal_library_t lib = ggml_metal_library_init_from_source(dev, src_tensor_bf16, false);
if (lib == NULL) {
GGML_LOG_WARN("%s: - the tensor API does not support bfloat - disabling bfloat support\n", __func__);
dev->props.has_bfloat = false;
} else {
ggml_metal_pipeline_t ppl = ggml_metal_library_compile_pipeline(lib, "dummy_kernel", "dummy_kernel", nil);
if (!ppl) {
GGML_LOG_WARN("%s: - the tensor API does not support bfloat - disabling bfloat support\n", __func__);
dev->props.has_bfloat = false;
}
ggml_metal_library_free(lib);
}
}
dev->props.use_residency_sets = true;
#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
@ -476,7 +677,6 @@ ggml_metal_device_t ggml_metal_device_init(void) {
#endif
dev->props.use_shared_buffers = dev->props.has_unified_memory;
if (getenv("GGML_METAL_SHARED_BUFFERS_DISABLE") != NULL) {
dev->props.use_shared_buffers = false;
}
@ -529,6 +729,7 @@ ggml_metal_device_t ggml_metal_device_init(void) {
GGML_LOG_INFO("%s: simdgroup matrix mul. = %s\n", __func__, dev->props.has_simdgroup_mm ? "true" : "false");
GGML_LOG_INFO("%s: has unified memory = %s\n", __func__, dev->props.has_unified_memory ? "true" : "false");
GGML_LOG_INFO("%s: has bfloat = %s\n", __func__, dev->props.has_bfloat ? "true" : "false");
GGML_LOG_INFO("%s: has tensor = %s\n", __func__, dev->props.has_tensor ? "true" : "false");
GGML_LOG_INFO("%s: use residency sets = %s\n", __func__, dev->props.use_residency_sets ? "true" : "false");
GGML_LOG_INFO("%s: use shared buffers = %s\n", __func__, dev->props.use_shared_buffers ? "true" : "false");

5
ggml/src/ggml-metal/ggml-metal-ops.cpp
View File

@ -1036,6 +1036,11 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
nth = std::min(nth, nk0);
if (nth*nrptg > ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nth = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline);
nrptg = 1;
}
ggml_metal_kargs_set_rows args = {
/*.nk0 =*/ nk0,
/*.ne01 =*/ ne01,

543
ggml/src/ggml-metal/ggml-metal.metal
View File

@ -9,6 +9,12 @@ __embed_ggml-common.h__
#include <metal_stdlib>
#ifdef GGML_METAL_HAS_TENSOR
#include <metal_tensor>
#include <MetalPerformancePrimitives/MetalPerformancePrimitives.h>
#endif
using namespace metal;
#define MAX(x, y) ((x) > (y) ? (x) : (y))
@ -1742,7 +1748,7 @@ kernel void kernel_op_sum_f32(
float sumf = 0;
for (int64_t i0 = tpitg.x; i0 < args.np; i0 += ntg.x) {
for (uint64_t i0 = tpitg.x; i0 < args.np; i0 += ntg.x) {
sumf += src0[i0];
}
@ -5467,6 +5473,7 @@ template [[host_name("kernel_flash_attn_ext_q8_0_dk576_dv512")]] kernel flash_at
#undef FA_TYPES
#undef FA_TYPES_BF
#undef FA_TYPES_F32
constant bool FC_flash_attn_ext_vec_has_mask [[function_constant(FC_FLASH_ATTN_EXT_VEC + 0)]];
constant bool FC_flash_attn_ext_vec_has_sinks [[function_constant(FC_FLASH_ATTN_EXT_VEC + 1)]];
@ -6088,6 +6095,7 @@ template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk576_dv512")]] kernel flas
template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk576_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q8_0, 8, dequantize_q8_0_t4, block_q8_0, 8, dequantize_q8_0_t4, 576, 512, 2>;
#undef FA_TYPES
#undef FA_TYPES_F32
constant int32_t FC_flash_attn_ext_vec_reduce_DV [[function_constant(FC_FLASH_ATTN_EXT_VEC_REDUCE + 0)]];
constant int32_t FC_flash_attn_ext_vec_reduce_NWG [[function_constant(FC_FLASH_ATTN_EXT_VEC_REDUCE + 1)]];
@ -8141,17 +8149,6 @@ kernel void kernel_set_rows_f(
constant bool FC_mul_mm_bc_inp [[function_constant(FC_MUL_MM + 0)]];
constant bool FC_mul_mm_bc_out [[function_constant(FC_MUL_MM + 1)]];
#define BLOCK_SIZE_M 64 // 8 simdgroup matrices from matrix A
#define BLOCK_SIZE_N 32 // 4 simdgroup matrices from matrix B
#define BLOCK_SIZE_K 32
#define THREAD_MAT_M 4 // each thread take 4 simdgroup matrices from matrix A
#define THREAD_MAT_N 2 // each thread take 2 simdgroup matrices from matrix B
#define THREAD_PER_BLOCK 128
#define THREAD_PER_ROW 2 // 2 thread for each row in matrix A to load numbers
#define THREAD_PER_COL 4 // 4 thread for each row in matrix B to load numbers
#define SG_MAT_SIZE 64 // simdgroup matrix is of shape 8x8
#define SG_MAT_ROW 8
// each block_q contains 16*nl weights
template<typename S0, typename S0_4x4, typename S0_8x8, typename S1, typename S1_2x4, typename S1_8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &), typename T0, typename T0_4x4, typename T1, typename T1_2x4>
kernel void kernel_mul_mm(
@ -8167,18 +8164,48 @@ kernel void kernel_mul_mm(
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
const int r0 = tgpig.y;
const int r1 = tgpig.x;
threadgroup float * sc = (threadgroup float *)(shmem);
constexpr int NR0 = 64;
constexpr int NR1 = 32;
constexpr int NK = 32;
constexpr int NL0 = NK/16;
constexpr int NL1 = NK/8;
const int im = tgpig.z;
const int r0 = tgpig.y*NR0;
const int r1 = tgpig.x*NR1;
// if this block is of 64x32 shape or smaller
const short n_rows = (args.ne0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (args.ne0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M;
const short n_cols = (args.ne1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? (args.ne1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N;
const short nr0 = (args.ne0 - r0 < NR0) ? (args.ne0 - r0) : NR0;
const short nr1 = (args.ne1 - r1 < NR1) ? (args.ne1 - r1) : NR1;
// a thread shouldn't load data outside of the matrix
const short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
const short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
const short lr0 = ((short)tiitg/NL0) < nr0 ? ((short)tiitg/NL0) : nr0 - 1; // 0 .. 63
const short lr1 = ((short)tiitg/NL1) < nr1 ? ((short)tiitg/NL1) : nr1 - 1; // 0 .. 31
const short il0 = (tiitg % NL0);
short il = il0;
const int i12 = im%args.ne12;
const int i13 = im/args.ne12;
const uint64_t offset0 = (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
const short offset1 = il0/nl;
device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
const short iy = 8*(tiitg % NL1);
device const T1 * y = (device const T1 *)(src1
+ args.nb13*i13
+ args.nb12*i12
+ args.nb11*(r1 + lr1)
+ args.nb10*iy);
#ifndef GGML_METAL_HAS_TENSOR
S0_8x8 ma[4];
S1_8x8 mb[2];
@ -8187,36 +8214,36 @@ kernel void kernel_mul_mm(
for (short i = 0; i < 8; i++){
mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
}
#else
auto tA = tensor<threadgroup S0, dextents<int32_t, 2>, tensor_inline>(sa, dextents<int32_t, 2>(NK, NR0));
auto tB = tensor<threadgroup S1, dextents<int32_t, 2>, tensor_inline>(sb, dextents<int32_t, 2>(NR1, NK ));
short il = (tiitg % THREAD_PER_ROW);
mpp::tensor_ops::matmul2d<
mpp::tensor_ops::matmul2d_descriptor(NR1, NR0, NK, false, true, false, mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
execution_simdgroups<4>> mm;
const int i12 = im%args.ne12;
const int i13 = im/args.ne12;
auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>();
#endif
const uint64_t offset0 = (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
const short offset1 = il/nl;
device const block_q * x = (device const block_q *)(src0
+ args.nb01*(r0*BLOCK_SIZE_M + thread_row) + offset0) + offset1;
const short iy = (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL));
device const T1 * y = (device const T1 *)(src1
+ args.nb13*i13
+ args.nb12*i12
+ args.nb11*(r1*BLOCK_SIZE_N + thread_col)
+ args.nb10*iy);
for (int loop_k = 0; loop_k < args.ne00; loop_k += BLOCK_SIZE_K) {
for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
#ifndef GGML_METAL_HAS_TENSOR
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
*(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \
+ (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \
+ (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = loop_k + 16*il + i < args.ne00 ? ((device T0 *) x)[i] : 0;
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
*(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
}
} else {
S0_4x4 temp_a;
@ -8225,91 +8252,203 @@ kernel void kernel_mul_mm(
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
*(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \
+ (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \
+ (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = temp_a[i/4][i%4];
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
// NOTE: this is massively slower.. WTF?
//sa[64*ib + 8*ly + lx] = temp_a[i/4][i%4];
*(sa + 64*ib + 8*ly + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
sb[32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL) + i] = loop_k + iy + i < args.ne00 ? (S1) ((device T1 *) y)[i] : 0;
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
const short ib = 4*sx + sy;
*(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
*(threadgroup S1_2x4 *)(sb + 32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL)) = (S1_2x4)(*((device T1_2x4 *) y));
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short dx = sx;
const short dy = sy;
const short ly = (tiitg/NL1)%8;
const short ib = 4*sx + sy;
*(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
}
#else
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
const short lx = i%8;
const short ly = (tiitg/NL0)%8;
//const short lx = (tiitg/NL0)%8;
//const short ly = i%8;
*(sa + NK*(8*sy + ly) + 8*sx + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
}
} else {
S0_4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
const short lx = i%8;
const short ly = (tiitg/NL0)%8;
//const short lx = (tiitg/NL0)%8;
//const short ly = i%8;
*(sa + NK*(8*sy + ly) + 8*sx + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
*(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
//const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
*(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)(*((device T1_2x4 *) y));
}
#endif
il = (il + 2 < nl) ? il + 2 : il % 2;
x = (il < 2) ? x + (2 + nl - 1)/nl : x;
y += BLOCK_SIZE_K;
y += NK;
threadgroup_barrier(mem_flags::mem_threadgroup);
#ifndef GGML_METAL_HAS_TENSOR
// load matrices from threadgroup memory and conduct outer products
threadgroup const S0 * lsma = (sa + THREAD_MAT_M*SG_MAT_SIZE*(sgitg%2));
threadgroup const S1 * lsmb = (sb + THREAD_MAT_N*SG_MAT_SIZE*(sgitg/2));
threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
#pragma unroll(4)
for (short ik = 0; ik < BLOCK_SIZE_K/8; ik++) {
FOR_UNROLL (short ik = 0; ik < NK/8; ik++) {
simdgroup_barrier(mem_flags::mem_none);
#pragma unroll(4)
for (short i = 0; i < 4; i++) {
simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i);
}
#pragma unroll(2)
for (short i = 0; i < 2; i++) {
simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i);
FOR_UNROLL (short i = 0; i < 4; i++) {
simdgroup_load(ma[i], lsma + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
#pragma unroll(8)
for (short i = 0; i < 8; i++){
FOR_UNROLL (short i = 0; i < 2; i++) {
simdgroup_load(mb[i], lsmb + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 8; i++){
simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]);
}
lsma += (BLOCK_SIZE_M/SG_MAT_ROW)*SG_MAT_SIZE;
lsmb += (BLOCK_SIZE_N/SG_MAT_ROW)*SG_MAT_SIZE;
lsma += 8*64;
lsmb += 4*64;
}
#else
auto sA = tA.slice(0, 0);
auto sB = tB.slice(0, 0);
mm.run(sB, sA, cT);
#endif
}
if (!FC_mul_mm_bc_out || ((r0 + 1) * BLOCK_SIZE_M <= args.ne0 && (r1 + 1) * BLOCK_SIZE_N <= args.ne1)) {
if (!FC_mul_mm_bc_out || (r0 + NR0 <= args.ne0 && r1 + NR1 <= args.ne1)) {
// if no bounds checks on the output are needed, we can directly write to device memory
#ifdef GGML_METAL_HAS_TENSOR
device float * C = (device float *) dst +
(BLOCK_SIZE_M * r0 + 32*(sgitg & 1)) + \
(BLOCK_SIZE_N * r1 + 16*(sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0;
r0 + \
r1 * args.ne0 + im*args.ne1*args.ne0;
auto tC = tensor<device float, dextents<int32_t, 2>, tensor_inline>(C, dextents<int32_t, 2>(args.ne0, NR1));
cT.store(tC);
#else
device float * C = (device float *) dst +
(r0 + 32*(sgitg & 1)) + \
(r1 + 16*(sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0;
for (short i = 0; i < 8; i++) {
simdgroup_store(mc[i], C + 8 * (i%4) + 8 * args.ne0 * (i/4), args.ne0);
simdgroup_store(mc[i], C + 8*(i%4) + 8*args.ne0*(i/4), args.ne0, 0, false);
}
#endif
} else {
// block is smaller than 64x32, we should avoid writing data outside of the matrix
threadgroup_barrier(mem_flags::mem_threadgroup);
threadgroup float * temp_str = ((threadgroup float *) shmem) \
+ 32*(sgitg&1) + (16*(sgitg >> 1))*BLOCK_SIZE_M;
threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
#ifdef GGML_METAL_HAS_TENSOR
auto tC = tensor<threadgroup float, dextents<int32_t, 2>, tensor_inline>(sc, dextents<int32_t, 2>(NR0, NR1));
cT.store(tC);
#else
for (short i = 0; i < 8; i++) {
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M);
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
}
#endif
threadgroup_barrier(mem_flags::mem_threadgroup);
if (sgitg == 0) {
for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
device float * D = (device float *) dst + (r0*BLOCK_SIZE_M) + (r1*BLOCK_SIZE_N + j)*args.ne0 + im*args.ne1*args.ne0;
for (int j = tiitg; j < nr1; j += NR1) {
device float * D = (device float *) dst + r0 + (r1 + j)*args.ne0 + im*args.ne1*args.ne0;
device float4 * D4 = (device float4 *) D;
threadgroup float * C = temp_str + (j*BLOCK_SIZE_M);
threadgroup float * C = temp_str + (j*NR0);
threadgroup float4 * C4 = (threadgroup float4 *) C;
int i = 0;
for (; i < n_rows/4; i++) {
for (; i < nr0/4; i++) {
*(D4 + i) = *(C4 + i);
}
i *= 4;
for (; i < n_rows; i++) {
for (; i < nr0; i++) {
*(D + i) = *(C + i);
}
}
@ -8394,31 +8533,63 @@ kernel void kernel_mul_mm_id(
ushort tiitg[[thread_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
const int r0 = tgpig.y;
const int r1 = tgpig.x;
threadgroup float * sc = (threadgroup float *)(shmem);
constexpr int NR0 = 64;
constexpr int NR1 = 32;
constexpr int NK = 32;
constexpr int NL0 = NK/16;
constexpr int NL1 = NK/8;
const int im = tgpig.z; // expert
const int r0 = tgpig.y*NR0;
const int r1 = tgpig.x*NR1;
device const uint32_t * tpe_u32 = (device const uint32_t *) (htpe);
device const int32_t * ids_i32 = (device const int32_t *) (hids);
const int32_t neh1 = tpe_u32[im];
if (r1*BLOCK_SIZE_N >= neh1) {
if (r1 >= neh1) {
return;
}
// if this block is of 64x32 shape or smaller
const short n_rows = (args.ne0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (args.ne0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M;
const short n_cols = ( neh1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? ( neh1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N;
const short nr0 = (args.ne0 - r0 < NR0) ? (args.ne0 - r0) : NR0;
const short nr1 = ( neh1 - r1 < NR1) ? ( neh1 - r1) : NR1;
// a thread shouldn't load data outside of the matrix
const short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
const short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
const short lr0 = ((short)tiitg/NL0) < nr0 ? ((short)tiitg/NL0) : nr0 - 1; // 0 .. 63
const short lr1 = ((short)tiitg/NL1) < nr1 ? ((short)tiitg/NL1) : nr1 - 1; // 0 .. 31
const short il0 = (tiitg % NL0);
short il = il0;
const int id = ids_i32[im*args.ne21 + r1 + lr1];
const short i11 = (id % args.ne20) % args.ne11;
const short i12 = (id / args.ne20);
const short i13 = 0;
const uint64_t offset0 = im*args.nb02 + i13*args.nb03;
const short offset1 = il0/nl;
device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
const short iy = 8*(tiitg % NL1);
device const T1 * y = (device const T1 *)(src1
+ args.nb13*i13
+ args.nb12*i12
+ args.nb11*i11
+ args.nb10*iy);
#ifndef GGML_METAL_HAS_TENSOR
S0_8x8 ma[4];
S1_8x8 mb[2];
@ -8427,39 +8598,36 @@ kernel void kernel_mul_mm_id(
for (short i = 0; i < 8; i++){
mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
}
#else
auto tA = tensor<threadgroup S0, dextents<int32_t, 2>, tensor_inline>(sa, dextents<int32_t, 2>(NK, NR0));
auto tB = tensor<threadgroup S1, dextents<int32_t, 2>, tensor_inline>(sb, dextents<int32_t, 2>(NR1, NK ));
short il = (tiitg % THREAD_PER_ROW);
mpp::tensor_ops::matmul2d<
mpp::tensor_ops::matmul2d_descriptor(NR1, NR0, NK, false, true, false, mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
execution_simdgroups<4>> mm;
const int id = ids_i32[im*args.ne21 + r1*BLOCK_SIZE_N + thread_col];
auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>();
#endif
const short i11 = (id % args.ne20) % args.ne11;
const short i12 = (id / args.ne20);
const short i13 = 0;
const uint64_t offset0 = im*args.nb02 + i13*args.nb03;
const short offset1 = il/nl;
device const block_q * x = (device const block_q *)(src0
+ args.nb01*(r0*BLOCK_SIZE_M + thread_row) + offset0) + offset1;
const short iy = (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL));
device const T1 * y = (device const T1 *)(src1
+ args.nb13*i13
+ args.nb12*i12
+ args.nb11*i11
+ args.nb10*iy);
for (int loop_k = 0; loop_k < args.ne00; loop_k += BLOCK_SIZE_K) {
for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
#ifndef GGML_METAL_HAS_TENSOR
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
*(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \
+ (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \
+ (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = loop_k + 16*il + i < args.ne00 ? ((device T0 *) x)[i] : 0;
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
*(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
}
} else {
S0_4x4 temp_a;
@ -8468,85 +8636,188 @@ kernel void kernel_mul_mm_id(
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
*(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \
+ (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \
+ (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = temp_a[i/4][i%4];
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
// NOTE: this is massively slower.. WTF?
//sa[64*ib + 8*ly + lx] = temp_a[i/4][i%4];
*(sa + 64*ib + 8*ly + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
sb[32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL) + i] = loop_k + iy + i < args.ne00 ? (S1) ((device T1 *) y)[i] : 0;
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
const short ib = 4*sx + sy;
*(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
*(threadgroup S1_2x4 *)(sb + 32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL)) = (S1_2x4)(*((device T1_2x4 *) y));
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short dx = sx;
const short dy = sy;
const short ly = (tiitg/NL1)%8;
const short ib = 4*sx + sy;
*(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
}
#else
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
const short lx = i%8;
const short ly = (tiitg/NL0)%8;
//const short lx = (tiitg/NL0)%8;
//const short ly = i%8;
*(sa + NK*(8*sy + ly) + 8*sx + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
}
} else {
S0_4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
const short lx = i%8;
const short ly = (tiitg/NL0)%8;
//const short lx = (tiitg/NL0)%8;
//const short ly = i%8;
*(sa + NK*(8*sy + ly) + 8*sx + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
*(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
//const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
*(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)(*((device T1_2x4 *) y));
}
#endif
il = (il + 2 < nl) ? il + 2 : il % 2;
x = (il < 2) ? x + (2 + nl - 1)/nl : x;
y += BLOCK_SIZE_K;
y += NK;
threadgroup_barrier(mem_flags::mem_threadgroup);
#ifndef GGML_METAL_HAS_TENSOR
// load matrices from threadgroup memory and conduct outer products
threadgroup const S0 * lsma = (sa + THREAD_MAT_M*SG_MAT_SIZE*(sgitg%2));
threadgroup const S1 * lsmb = (sb + THREAD_MAT_N*SG_MAT_SIZE*(sgitg/2));
threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
#pragma unroll(4)
for (short ik = 0; ik < BLOCK_SIZE_K/8; ik++) {
#pragma unroll(4)
for (short i = 0; i < 4; i++) {
simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i);
FOR_UNROLL (short ik = 0; ik < NK/8; ik++) {
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 4; i++) {
simdgroup_load(ma[i], lsma + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
#pragma unroll(2)
for (short i = 0; i < 2; i++) {
simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i);
FOR_UNROLL (short i = 0; i < 2; i++) {
simdgroup_load(mb[i], lsmb + 64*i, 8, 0, false);
}
#pragma unroll(8)
for (short i = 0; i < 8; i++){
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 8; i++){
simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]);
}
lsma += (BLOCK_SIZE_M/SG_MAT_ROW)*SG_MAT_SIZE;
lsmb += (BLOCK_SIZE_N/SG_MAT_ROW)*SG_MAT_SIZE;
lsma += 8*64;
lsmb += 4*64;
}
#else
auto sA = tA.slice(0, 0);
auto sB = tB.slice(0, 0);
mm.run(sB, sA, cT);
#endif
}
// block is smaller than 64x32, we should avoid writing data outside of the matrix
threadgroup_barrier(mem_flags::mem_threadgroup);
threadgroup float * temp_str = ((threadgroup float *) shmem) \
+ 32*(sgitg&1) + (16*(sgitg >> 1))*BLOCK_SIZE_M;
#ifdef GGML_METAL_HAS_TENSOR
auto tC = tensor<threadgroup float, dextents<int32_t, 2>, tensor_inline>(sc, dextents<int32_t, 2>(NR0, NR1));
cT.store(tC);
#else
threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
#pragma unroll(8)
for (short i = 0; i < 8; i++) {
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M);
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
}
#endif
threadgroup_barrier(mem_flags::mem_threadgroup);
for (short j = sgitg; j < n_cols; j += 4) {
const int id = ids_i32[im*args.ne21 + r1*BLOCK_SIZE_N + j];
for (short j = sgitg; j < nr1; j += 4) {
const int id = ids_i32[im*args.ne21 + r1 + j];
const short ide = id % args.ne20;
const short idt = id / args.ne20;
device float * D = (device float *) dst + (r0*BLOCK_SIZE_M) + ide*args.ne0 + idt*args.ne1*args.ne0;
device float * D = (device float *) dst + r0 + ide*args.ne0 + idt*args.ne1*args.ne0;
device float4 * D4 = (device float4 *) D;
threadgroup float * C = (threadgroup float *) shmem + (j*BLOCK_SIZE_M);
threadgroup float * C = (threadgroup float *) shmem + j*NR0;
threadgroup float4 * C4 = (threadgroup float4 *) C;
int i = tiisg;
for (; i < n_rows/4; i += 32) {
for (; i < nr0/4; i += 32) {
*(D4 + i) = *(C4 + i);
}
i = (4*(n_rows/4)) + tiisg;
for (; i < n_rows; i += 32) {
i = (4*(nr0/4)) + tiisg;
for (; i < nr0; i += 32) {
*(D + i) = *(C + i);
}
}

45
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@ -53,6 +53,37 @@
bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor);
// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
// Precompute mp (m' in the paper) and L such that division
// can be computed using a multiply (high 32b of 64b result)
// and a shift:
//
// n/d = (mulhi(n, mp) + n) >> L;
struct fastdiv_vals {
uint32_t mp;
uint32_t L;
uint32_t d;
uint32_t pad;
};
static_assert(sizeof(fastdiv_vals) == 16, "fastdiv_vals size incorrect");
static fastdiv_vals init_fastdiv_values(uint64_t d_64) {
GGML_ASSERT(d_64 != 0);
GGML_ASSERT(d_64 <= std::numeric_limits<uint32_t>::max());
uint32_t d = (uint32_t)d_64;
// compute L = ceil(log2(d));
uint32_t L = 0;
while (L < 32 && (uint32_t{ 1 } << L) < d) {
L++;
}
uint32_t mp = (uint32_t) ((uint64_t{ 1 } << 32) * ((uint64_t{ 1 } << L) - d) / d + 1);
// pack divisor as well to reduce error surface
return { mp, L, d, 0 };
}
enum GPU_FAMILY {
ADRENO,
INTEL,
@ -2944,8 +2975,11 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
case GGML_OP_PAD:
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
case GGML_OP_UPSCALE: {
ggml_scale_mode mode = (ggml_scale_mode)(ggml_get_op_params_i32(op, 0) & 0xFF);
return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
(mode == GGML_SCALE_MODE_NEAREST || mode == GGML_SCALE_MODE_BILINEAR);
}
case GGML_OP_CONV_2D:
return (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16) ||
(op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||
@ -4461,6 +4495,9 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
GGML_ABORT("not implemented");
}
fastdiv_vals ne11_ = init_fastdiv_values(ne11);
fastdiv_vals ne12_ = init_fastdiv_values(ne12);
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
@ -4471,8 +4508,8 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(fastdiv_vals), &ne11_));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(fastdiv_vals), &ne12_));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb10));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb12));

51
ggml/src/ggml-opencl/kernels/set_rows.cl
View File

@ -1,5 +1,16 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
// v = { mp, L, d }
inline uint fastdiv(uint n, uint4 v) {
uint msbs;
msbs = mul_hi(n, v.s0);
return (msbs + n) >> v.s1;
}
inline uint fastmod(uint n, uint4 v) {
uint q = fastdiv(n, v);
return n - q * v.s2;
}
kernel void kernel_set_rows_f32_i64(
global char * src0,
ulong offset0,
@ -11,8 +22,8 @@ kernel void kernel_set_rows_f32_i64(
ulong nb01,
ulong nb02,
ulong nb03,
int ne11,
int ne12,
uint4 ne11,
uint4 ne12,
ulong nb10,
ulong nb11,
ulong nb12,
@ -33,8 +44,10 @@ kernel void kernel_set_rows_f32_i64(
return;
}
int i12 = i03%ne12;
int i11 = i02%ne11;
//int i12 = i03%ne12;
//int i11 = i02%ne11;
int i12 = fastmod(i03, ne12);
int i11 = fastmod(i02, ne11);
int i10 = i01;
long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
@ -58,8 +71,8 @@ kernel void kernel_set_rows_f16_i64(
ulong nb01,
ulong nb02,
ulong nb03,
int ne11,
int ne12,
uint4 ne11,
uint4 ne12,
ulong nb10,
ulong nb11,
ulong nb12,
@ -80,8 +93,10 @@ kernel void kernel_set_rows_f16_i64(
return;
}
int i12 = i03%ne12;
int i11 = i02%ne11;
//int i12 = i03%ne12;
//int i11 = i02%ne11;
int i12 = fastmod(i03, ne12);
int i11 = fastmod(i02, ne11);
int i10 = i01;
long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
@ -105,8 +120,8 @@ kernel void kernel_set_rows_f32_i32(
ulong nb01,
ulong nb02,
ulong nb03,
int ne11,
int ne12,
uint4 ne11,
uint4 ne12,
ulong nb10,
ulong nb11,
ulong nb12,
@ -127,8 +142,10 @@ kernel void kernel_set_rows_f32_i32(
return;
}
int i12 = i03%ne12;
int i11 = i02%ne11;
//int i12 = i03%ne12;
//int i11 = i02%ne11;
int i12 = fastmod(i03, ne12);
int i11 = fastmod(i02, ne11);
int i10 = i01;
int i1 = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
@ -152,8 +169,8 @@ kernel void kernel_set_rows_f16_i32(
ulong nb01,
ulong nb02,
ulong nb03,
int ne11,
int ne12,
uint4 ne11,
uint4 ne12,
ulong nb10,
ulong nb11,
ulong nb12,
@ -174,8 +191,10 @@ kernel void kernel_set_rows_f16_i32(
return;
}
int i12 = i03%ne12;
int i11 = i02%ne11;
//int i12 = i03%ne12;
//int i11 = i02%ne11;
int i12 = fastmod(i03, ne12);
int i11 = fastmod(i02, ne11);
int i10 = i01;
int i1 = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];

99
ggml/src/ggml-sycl/concat.cpp
View File

@ -11,9 +11,13 @@
//
#include "concat.hpp"
#include "common.hpp"
static void concat_f32_dim0(const float *x, const float *y, float *dst,
static inline size_t elem_size(ggml_type t) {
return ggml_type_size(t) / ggml_blck_size(t);
}
template <typename T>
static void concat_T_dim0(const T *x, const T *y, T *dst,
const int ne0, const int ne00,
const sycl::nd_item<3> &item_ct1) {
int nidx = item_ct1.get_local_id(2) +
@ -36,7 +40,8 @@ static void concat_f32_dim0(const float *x, const float *y, float *dst,
}
}
static void concat_f32_dim1(const float *x, const float *y, float *dst,
template <typename T>
static void concat_T_dim1(const T *x, const T *y, T *dst,
const int ne0, const int ne01,
const sycl::nd_item<3> &item_ct1) {
int nidx = item_ct1.get_local_id(2) +
@ -59,7 +64,8 @@ static void concat_f32_dim1(const float *x, const float *y, float *dst,
}
}
static void concat_f32_dim2(const float *x, const float *y, float *dst,
template <typename T>
static void concat_T_dim2(const T *x, const T *y, T *dst,
const int ne0, const int ne02,
const sycl::nd_item<3> &item_ct1) {
int nidx = item_ct1.get_local_id(2) +
@ -82,45 +88,35 @@ static void concat_f32_dim2(const float *x, const float *y, float *dst,
}
}
static void concat_f32_sycl(const float *x, const float *y, float *dst,
template <typename T>
static void concat_T_sycl(const T *x, const T *y, T *dst,
int ne00, int ne01, int ne02, int ne0, int ne1,
int ne2, int dim, queue_ptr stream) {
int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
sycl::range<3> gridDim(ne2, ne1, num_blocks);
switch (dim) {
case 0:
stream->parallel_for(
sycl::nd_range<3>(gridDim *
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1);
});
break;
stream->parallel_for(sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) { concat_T_dim0<T>(x, y, dst, ne0, ne00, item_ct1); });
break;
case 1:
stream->parallel_for(
sycl::nd_range<3>(gridDim *
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1);
});
break;
stream->parallel_for(sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) { concat_T_dim1<T>(x, y, dst, ne0, ne01, item_ct1); });
break;
// dim >=2 will be dispatched to the default path
default:
stream->parallel_for(
sycl::nd_range<3>(gridDim *
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1);
});
break;
stream->parallel_for(sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) { concat_T_dim2<T>(x, y, dst, ne0, ne02, item_ct1); });
break;
}
}
// non-contiguous kernel (slow)
static void concat_f32_sycl_non_cont(
template<typename T>
static void concat_T_sycl_non_cont(
queue_ptr stream, const char *src0, const char *src1, char *dst,
int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03, uint64_t nb00,
uint64_t nb01, uint64_t nb02, uint64_t nb03, int64_t /*ne10*/,
@ -137,24 +133,25 @@ static void concat_f32_sycl_non_cont(
int64_t o[4] = { 0, 0, 0, 0 };
o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
const float * x;
const T * x;
for (int i0 = item_ct1.get_local_id(2); i0 < ne0; i0 += item_ct1.get_local_range(2)) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const float *) (src0 + (i3) *nb03 + (i2) *nb02 + (i1) *nb01 + (i0) *nb00);
x = (const T *) (src0 + (i3) *nb03 + (i2) *nb02 + (i1) *nb01 + (i0) *nb00);
} else {
x = (const float *) (src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 + (i1 - o[1]) * nb11 +
x = (const T *) (src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 + (i1 - o[1]) * nb11 +
(i0 - o[0]) * nb10);
}
float *y = (float *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);
T *y = (T *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);
*y = *x;
}
});
}
void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
template <typename T>
void concat_impl_sycl(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
@ -163,15 +160,14 @@ void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
const int32_t dim = ((int32_t *) dst->op_params)[0];
if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
const float * src0_d = (const float *) src0->data;
const float * src1_d = (const float *) src1->data;
float * dst_d = (float *) dst->data;
const T * src0_d = (const T *) src0->data;
const T * src1_d = (const T *) src1->data;
T * dst_d = (T *) dst->data;
size_t type_size = elem_size(dst->type);
if (dim != 3) {
for (int i3 = 0; i3 < dst->ne[3]; i3++) {
concat_f32_sycl(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4),
dst_d + i3 * (dst->nb[3] / 4), src0->ne[0], src0->ne[1], src0->ne[2], dst->ne[0],
concat_T_sycl<T>(src0_d + i3 * (src0->nb[3] / type_size), src1_d + i3 * (src1->nb[3] / type_size),
dst_d + i3 * (dst->nb[3] / type_size), src0->ne[0], src0->ne[1], src0->ne[2], dst->ne[0],
dst->ne[1], dst->ne[2], dim, stream);
}
} else {
@ -179,13 +175,28 @@ void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
const size_t size1 = ggml_nbytes(src1);
SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait()));
SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d + size0 / 4, src1_d, size1).wait()));
SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d + size0 / type_size, src1_d, size1).wait()));
}
} else {
concat_f32_sycl_non_cont(stream, (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
concat_T_sycl_non_cont<T>(stream, (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0->nb[0], src0->nb[1],
src0->nb[2], src0->nb[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3], dst->ne[0], dst->ne[1], dst->ne[2],
dst->ne[3], dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
}
}
void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
switch (dst->type) {
case GGML_TYPE_F32:
concat_impl_sycl<float>(ctx, dst);
break;
case GGML_TYPE_I32:
concat_impl_sycl<int32_t>(ctx, dst);
break;
default:
GGML_ASSERT(false && "ggml_sycl_op_concat: unsupported type");
break;
}
}

6
ggml/src/ggml-sycl/ggml-sycl.cpp
View File

@ -4534,16 +4534,12 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
}
return false;
}
case GGML_OP_CONCAT:
{
ggml_type src0_type = op->src[0]->type;
return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
}
case GGML_OP_REPEAT_BACK:
{
ggml_type src0_type = op->src[0]->type;
return src0_type == GGML_TYPE_F32;
}
case GGML_OP_CONCAT:
case GGML_OP_DUP:
case GGML_OP_ARGMAX:
case GGML_OP_NONE:

1963
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

File diff suppressed because it is too large Load Diff

75
ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
View File

@ -62,14 +62,8 @@ layout(push_constant) uniform parameter {
uint32_t nb3;
// fastdiv helper values
uint32_t KWmp; uint32_t KWL;
uint32_t KWKHmp; uint32_t KWKHL;
uint32_t OWmp; uint32_t OWL;
uint32_t OWOHmp; uint32_t OWOHL;
#ifdef TRANSPOSE
uint32_t s0mp; uint32_t s0L;
uint32_t s1mp; uint32_t s1L;
#endif
}
p;
@ -84,6 +78,15 @@ layout(constant_id = 4) const uint TS_K = 8;
layout(constant_id = 5) const uint use_collectives = 1;
layout(constant_id = 6) const uint SHMEM_PAD = 4;
layout(constant_id = 7) const uint s0 = 1;
layout(constant_id = 8) const uint s1 = 1;
layout(constant_id = 9) const uint p0 = 0;
layout(constant_id = 10) const uint p1 = 0;
layout(constant_id = 11) const uint d0 = 1;
layout(constant_id = 12) const uint d1 = 1;
layout(constant_id = 13) const uint KW = 1;
layout(constant_id = 14) const uint KH = 1;
uint32_t tid = gl_LocalInvocationID.x;
const uint32_t WG_SIZE = gl_WorkGroupSize.x;
@ -92,7 +95,7 @@ uint splitWork(uint work_size, uint block_size) {
}
uint32_t K = p.Cout;
uint32_t CRS = p.Cin * p.KH * p.KW;
uint32_t CRS = p.Cin * KH * KW;
uint32_t NPQ = p.N * p.OH * p.OW;
uint32_t n_elems_out = K * NPQ;
@ -187,7 +190,7 @@ void main() {
}
#endif
/* Advance block in CRS dim */
for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) {
[[dont_unroll]] for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) {
uint32_t CRS_idx_a;
uint32_t Cin_idx_a;
uint32_t KH_idx_a;
@ -200,10 +203,10 @@ void main() {
uint32_t cached_KW_idx;
if (use_collectives == 1) {
cached_CRS_idx = B_idx_CRS * BS_CRS + gl_SubgroupInvocationID;
cached_Cin_idx = fastdiv(cached_CRS_idx, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
uint32_t cached_CRS_remainder = (cached_CRS_idx - cached_Cin_idx * p.KW * p.KH);
cached_KH_idx = fastdiv(cached_CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
cached_KW_idx = cached_CRS_remainder - cached_KH_idx * p.KW;
cached_Cin_idx = cached_CRS_idx / (KW * KH);
uint32_t cached_CRS_remainder = cached_CRS_idx % (KW * KH);
cached_KH_idx = cached_CRS_remainder / KW;
cached_KW_idx = cached_CRS_remainder % KW;
CRS_idx_a = subgroupShuffle(cached_CRS_idx, Ac);
Cin_idx_a = subgroupShuffle(cached_Cin_idx, Ac);
@ -211,21 +214,21 @@ void main() {
KW_idx_a = subgroupShuffle(cached_KW_idx, Ac);
} else {
CRS_idx_a = B_idx_CRS * BS_CRS + Ac; // Global CRS_idx_a (column index of A)
Cin_idx_a = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
uint32_t CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
KH_idx_a = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
KW_idx_a = CRS_remainder - KH_idx_a * p.KW;
Cin_idx_a = CRS_idx_a / (KW * KH);
uint32_t CRS_remainder = CRS_idx_a % (KW * KH);
KH_idx_a = CRS_remainder / KW;
KW_idx_a = CRS_remainder % KW;
}
#else
CRS_idx_a = B_idx_CRS * BS_CRS + Ac; // Global CRS_idx_a (column index of A)
Cin_idx_a = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH); / (p.KW * p.KH);
CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
KH_idx_a = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
KW_idx_a = CRS_remainder - KH_idx_a * p.KW;
Cin_idx_a = CRS_idx_a / (KW * KH);
CRS_remainder = CRS_idx_a % (KW * KH);
KH_idx_a = CRS_remainder / KW;
KW_idx_a = CRS_remainder % KW;
#endif
/* Load kernel to A_block: (BS_K x BS_CRS)*/
for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) {
UNROLL for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) {
uint32_t B_ly = r_offset + Ar;
uint32_t B_lx = Ac;
uint32_t K_idx = B_idx_K * BS_K + B_ly; /* Global K_idx (row index of A)*/
@ -262,27 +265,27 @@ void main() {
KW_idx_b = subgroupShuffle(cached_KW_idx, r_offset + Br);
} else {
CRS_idx_b = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
Cin_idx_b = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
KH_idx_b = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
KW_idx_b = CRS_remainder - KH_idx_b * p.KW;
Cin_idx_b = CRS_idx_b / (KW * KH);
uint32_t CRS_remainder = CRS_idx_b % (KW * KH);
KH_idx_b = CRS_remainder / KW;
KW_idx_b = CRS_remainder % KW;
}
#else
CRS_idx_b = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
Cin_idx_b = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
KH_idx_b = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
KW_idx_b = CRS_remainder - KH_idx_b * p.KW;
Cin_idx_b = CRS_idx_b / (KW * KH);
uint32_t CRS_remainder = CRS_idx_b % (KW * KH);
KH_idx_b = CRS_remainder / KW;
KW_idx_b = CRS_remainder % KW;
#endif
#ifdef TRANSPOSE
uint32_t H_idx_x_s1 = OH_idx - KH_idx_b * p.d1 + p.p1;
uint32_t W_idx_x_s0 = OW_idx - KW_idx_b * p.d0 + p.p0;
uint32_t H_idx = fastdiv(H_idx_x_s1, p.s1mp, p.s1L);
uint32_t W_idx = fastdiv(W_idx_x_s0, p.s0mp, p.s0L);
uint32_t H_idx_x_s1 = OH_idx - KH_idx_b * d1 + p1;
uint32_t W_idx_x_s0 = OW_idx - KW_idx_b * d0 + p0;
uint32_t H_idx = H_idx_x_s1 / s1;
uint32_t W_idx = W_idx_x_s0 / s0;
#else
uint32_t H_idx = OH_idx * p.s1 + KH_idx_b * p.d1 - p.p1;
uint32_t W_idx = OW_idx * p.s0 + KW_idx_b * p.d0 - p.p0;
uint32_t H_idx = OH_idx * s1 + KH_idx_b * d1 - p1;
uint32_t W_idx = OW_idx * s0 + KW_idx_b * d0 - p0;
#endif
uint32_t src_idx =
min(max(W_idx + H_idx * p.nb11 + Cin_idx_b * p.nb12 + N_idx * p.nb13, 0), p.Cin * p.N * p.W * p.H - 1);
@ -290,7 +293,7 @@ void main() {
if (CRS_idx_b >= CRS || NPQ_idx >= NPQ
|| H_idx >= p.H || W_idx >= p.W // Lower bound checks aren't necessary. (idx >= 0x80000000 for such case)
#ifdef TRANSPOSE
|| (H_idx_x_s1 - H_idx * p.s1 != 0) || (W_idx_x_s0 - W_idx * p.s0 != 0)
|| (H_idx_x_s1 - H_idx * s1 != 0) || (W_idx_x_s0 - W_idx * s0 != 0)
#endif
) {
val = 0.0;

8
ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.glsl
View File

@ -3,6 +3,9 @@
#include "rte.glsl"
#include "utils.glsl"
#if RMS_NORM_ROPE_FUSION
#include "rope_params.glsl"
#endif
layout (push_constant) uniform parameter
{
@ -12,11 +15,16 @@ layout (push_constant) uniform parameter
uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
uint misalign_offsets;
float param1; float param2; int param3;
#if RMS_NORM_ROPE_FUSION
rope_params rope;
#endif
} p;
#if !RMS_NORM_ROPE_FUSION
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
#endif
// true if src0/src1 are the same shape and the indices can be reused without additional modulus
layout(constant_id = 0) const bool norepeat = false;

19
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl
View File

@ -49,6 +49,7 @@ layout (push_constant) uniform parameter
uint batch_stride_d;
uint enable_bias;
uint enable_scale;
#ifdef MUL_MAT_ID
uint nei0;
@ -129,6 +130,12 @@ void reduce_result(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t
temp[j][n] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
#endif
}
#ifdef MUL_MAT_ID
if (p.enable_scale != 0) {
const uint expert_idx = gl_GlobalInvocationID.y;
temp[j][n] *= FLOAT_TYPE(data_bias[expert_idx]);
}
#endif
data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
}
}
@ -171,6 +178,12 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
temp[j][n] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
#endif
}
#ifdef MUL_MAT_ID
if (p.enable_scale != 0) {
const uint expert_idx = gl_GlobalInvocationID.y;
temp[j][n] *= FLOAT_TYPE(data_bias[expert_idx]);
}
#endif
data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
}
}
@ -203,6 +216,12 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
tmpsh[j][n][0] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
#endif
}
#ifdef MUL_MAT_ID
if (p.enable_scale != 0) {
const uint expert_idx = gl_GlobalInvocationID.y;
tmpsh[j][n][0] *= FLOAT_TYPE(data_bias[expert_idx]);
}
#endif
data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(tmpsh[j][n][0]);
}
}

33
ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
View File

@ -100,7 +100,6 @@ layout (push_constant) uniform parameter
layout (constant_id = 0) const uint BLOCK_SIZE = 64;
layout (constant_id = 1) const uint BM = 64;
layout (constant_id = 2) const uint BN = 64;
layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant
layout (constant_id = 4) const uint WM = 32;
layout (constant_id = 5) const uint WN = 32;
layout (constant_id = 6) const uint WMITER = 2;
@ -109,6 +108,14 @@ layout (constant_id = 8) const uint TN = 2;
layout (constant_id = 9) const uint TK = 1; // Only needed for coopmat
layout (constant_id = 10) const uint WARP = 32;
#if defined(DATA_A_F32) || defined(DATA_A_F16)
#define BK 32
#define BK_STEP 4
#else
layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant
#define BK_STEP 2
#endif
#ifdef COOPMAT
#define SHMEM_STRIDE (BK / 2 + 4)
#else
@ -244,8 +251,13 @@ void main() {
}
#else
ACC_TYPE_VEC2 sums[WMITER * TM * WNITER * TN/2];
#if defined(DATA_A_F32) || defined(DATA_A_F16)
FLOAT_TYPE_VEC4 cache_a[WMITER * TM];
FLOAT_TYPE_VEC4 cache_b;
#else
FLOAT_TYPE_VEC2 cache_a[WMITER * TM];
FLOAT_TYPE_VEC2 cache_b;
#endif
[[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN/2; i++) {
sums[i] = ACC_TYPE_VEC2(0.0f, 0.0f);
@ -283,24 +295,41 @@ void main() {
}
}
#else
[[unroll]] for (uint i = 0; i < BK / 2; i++) {
[[unroll]] for (uint i = 0; i < BK / BK_STEP; i++) {
// Load from shared into cache
[[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
[[unroll]] for (uint j = 0; j < TM; j++) {
#if defined(DATA_A_F32) || defined(DATA_A_F16)
cache_a[wsir * TM + j].xy = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + 2 * i ];
cache_a[wsir * TM + j].zw = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + 2 * i + 1];
#else
cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + i];
#endif
}
}
[[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
[[unroll]] for (uint cc = 0; cc < TN; cc++) {
#if defined(DATA_A_F32) || defined(DATA_A_F16)
cache_b.xy = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + 2 * i ];
cache_b.zw = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + 2 * i + 1];
#else
cache_b = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + i];
#endif
[[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
[[unroll]] for (uint cr = 0; cr < TM / 2; cr++) {
// [WNITER][TN][WMITER][TM / 2] -> [wsic][cc][wsir][cr]
const uint sums_idx = (wsic * TN + cc) * WMITER * (TM / 2) + wsir * (TM / 2) + cr;
#if defined(DATA_A_F32) || defined(DATA_A_F16)
sums[sums_idx].x = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].y), ACC_TYPE(cache_b.y),
fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].z), ACC_TYPE(cache_b.z), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].w), ACC_TYPE(cache_b.w), sums[sums_idx].x))));
sums[sums_idx].y = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].y), ACC_TYPE(cache_b.y),
fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].z), ACC_TYPE(cache_b.z), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].w), ACC_TYPE(cache_b.w), sums[sums_idx].y))));
#else
sums[sums_idx].x = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr ].y), ACC_TYPE(cache_b.y), sums[sums_idx].x));
sums[sums_idx].y = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].y), ACC_TYPE(cache_b.y), sums[sums_idx].y));
#endif
}
}
}

6
ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
View File

@ -211,7 +211,9 @@ void main() {
const uint iqs = loadr_a;
[[unroll]] for (uint k_step = 0; k_step < BK_STEP; k_step++) {
block_a_to_shmem(k_step * BM + buf_ib, ib + k_step, iqs);
if (block + k_step * BK < end_k) {
block_a_to_shmem(k_step * BM + buf_ib, ib + k_step, iqs);
}
}
}
[[unroll]] for (uint l = 0; loadc_b + l < BN; l += loadstride_b) {
@ -226,7 +228,7 @@ void main() {
const uint iqs = loadr_b;
[[unroll]] for (uint k_step = 0; k_step < BK_STEP; k_step++) {
block_b_to_shmem(k_step * BN + buf_ib, ib + k_step, iqs);
block_b_to_shmem(k_step * BN + buf_ib, ib + k_step, iqs, block + k_step * BK < end_k);
}
}

33
ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl
View File

@ -469,19 +469,30 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
#endif
#ifdef MMQ_SHMEM
void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
const uint ib_outer = ib / 4;
const uint ib_inner = ib % 4;
void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs, const bool is_in_bounds) {
if (is_in_bounds) {
const uint ib_outer = ib / 4;
const uint ib_inner = ib % 4;
if (iqs == 0) {
buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
if (iqs == 0) {
buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
}
const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
buf_b[buf_ib].qs[iqs * 4 ] = values.x;
buf_b[buf_ib].qs[iqs * 4 + 1] = values.y;
buf_b[buf_ib].qs[iqs * 4 + 2] = values.z;
buf_b[buf_ib].qs[iqs * 4 + 3] = values.w;
} else {
if (iqs == 0) {
buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(0.0f);
}
buf_b[buf_ib].qs[iqs * 4 ] = 0;
buf_b[buf_ib].qs[iqs * 4 + 1] = 0;
buf_b[buf_ib].qs[iqs * 4 + 2] = 0;
buf_b[buf_ib].qs[iqs * 4 + 3] = 0;
}
const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
buf_b[buf_ib].qs[iqs * 4 ] = values.x;
buf_b[buf_ib].qs[iqs * 4 + 1] = values.y;
buf_b[buf_ib].qs[iqs * 4 + 2] = values.z;
buf_b[buf_ib].qs[iqs * 4 + 3] = values.w;
}
void block_b_to_registers(const uint ib) {

2
ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp
View File

@ -61,7 +61,7 @@ void quantize() {
const uint a_idx = ib * 8 + iqs;
vec4 vals = a_idx < p.ne ? data_a[a_idx] : vec4(0.0f);
vec4 vals = a_idx < p.ne / 4 ? data_a[a_idx] : vec4(0.0f);
const vec4 abs_vals = abs(vals);
// Find absolute max for each block

44
ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp
View File

@ -3,6 +3,32 @@
#include "generic_binary_head.glsl"
#include "types.glsl"
#if RMS_NORM_ROPE_FUSION
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
// data is passed from rms_norm -> rope through shared memory.
// rms_norm calls this data_d, rope calls this rope_data_a.
// Binding 2 is not used
shared FLOAT_TYPE rope_data_a[1024];
#define data_d rope_data_a
layout (binding = 3) readonly buffer R_Y {int rope_data_pos[];};
layout (binding = 4) readonly buffer R_Z {float rope_data_ff[];};
layout (binding = 5) writeonly buffer R_D {ROPE_D_TYPE rope_data_d[];};
layout (binding = 6) readonly buffer R_I {uvec2 rope_data_i[];}; // indices for set_rows
#include "rope_params.glsl"
#include "rope_funcs.glsl"
#define GGML_ROPE_TYPE_NORMAL 0
#define GGML_ROPE_TYPE_NEOX 2
#define GGML_ROPE_TYPE_MROPE 8
#define GGML_ROPE_TYPE_VISION 24
#endif
#extension GL_EXT_control_flow_attributes : enable
#define BLOCK_SIZE 512
@ -28,8 +54,12 @@ void rms_norm(uint num_iters) {
uint32_t a_offset = samp*stride_sample + channel*stride_channel + row*stride_row + get_aoffset();
uint32_t b_offset = src1_idx(0, row, channel, samp) + get_boffset();
#if RMS_NORM_ROPE_FUSION
// Per-row offset in shared memory
uint32_t d_offset = 0;
#else
uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset();
#endif
FLOAT_TYPE sum = FLOAT_TYPE(0.0f); // partial sum for thread in warp
[[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) {
@ -79,6 +109,18 @@ void rms_norm(uint num_iters) {
data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]));
}
}
#if RMS_NORM_ROPE_FUSION
barrier();
rope_params rp = p.rope;
uint rope_row = (samp*nchannels + channel)*nrows + row;
for (uint t = 2*tid; t < ncols; t += 2*BLOCK_SIZE) {
if (rp.rope_mode == GGML_ROPE_TYPE_NEOX) {
rope_neox(t, rope_row, rp);
} else if (rp.rope_mode == GGML_ROPE_TYPE_NORMAL) {
rope_norm(t, rope_row, rp);
}
}
#endif
}
void main() {

227
ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl Normal file
View File

@ -0,0 +1,227 @@
float rope_yarn_ramp(const float low, const float high, const uint i0) {
const float y = (i0 / 2 - low) / max(0.001f, high - low);
return 1.0f - min(1.0f, max(0.0f, y));
}
uint rope_a_coord(const uint i0, const uint i01, const uint i02, rope_params p) {
#if RMS_NORM_ROPE_FUSION
// Per-row offset in shared memory
const uint ix = i0;
#else
const uint ix = i02*p.nb02 + i01*p.nb01 + i0;
#endif
return ix;
}
void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta, rope_params p) {
float mscale = p.attn_factor;
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = p.freq_scale * theta_extrap;
float theta = theta_interp;
if (p.ext_factor != 0.0f) {
float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
// Get n-d magnitude scaling corrected for interpolation
mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
}
// Backprogagation uses inverted rotation
if (p.is_back != 0) {
theta = -theta;
}
cos_theta = cos(theta) * mscale;
sin_theta = sin(theta) * mscale;
}
void rope_norm(const uint i0, const uint i1, rope_params p) {
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
if (i0 >= ne0) {
return;
}
// i1 is actually i2*nb2+i1, but the rows are contiguous
const uint i01 = i1 % ne1;
const uint i02 = i1 / ne1;
uint idst = i1*ne0 + i0;
const uint ix = rope_a_coord(i0, i01, i02, p);
// Fusion optimization: ROPE + VIEW + SET_ROWS..
// The rope output is viewed as a 1D tensor and offset based on a row index in data_i.
if (p.set_rows_stride != 0) {
idst = i01*ne0 + i0;
idst += rope_data_i[i02].x * p.set_rows_stride;
}
if (i0 >= p.n_dims) {
rope_data_d[idst + 0] = ROPE_D_TYPE(rope_data_a[ix + 0]);
rope_data_d[idst + 1] = ROPE_D_TYPE(rope_data_a[ix + 1]);
return;
}
const float theta_base = rope_data_pos[i02] * pow(p.theta_scale, i0/2.0f);
const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
const float x0 = float(rope_data_a[ix + 0]);
const float x1 = float(rope_data_a[ix + 1]);
rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
rope_data_d[idst + 1] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
}
void rope_neox(const uint i0, const uint i1, rope_params p) {
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
if (i0 >= ne0) {
return;
}
const uint i01 = i1 % ne1;
const uint i02 = i1 / ne1;
uint idst = i1*ne0 + i0/2;
const uint ix = rope_a_coord(i0/2, i01, i02, p);
// Fusion optimization: ROPE + VIEW + SET_ROWS..
// The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i.
if (p.set_rows_stride != 0) {
idst = i01*ne0 + i0/2;
idst += rope_data_i[i02].x * p.set_rows_stride;
}
if (i0 >= p.n_dims) {
rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]);
rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]);
return;
}
const float theta_base = rope_data_pos[i02] * pow(p.theta_scale, i0/2.0f);
const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
const float x0 = float(rope_data_a[ix + 0]);
const float x1 = float(rope_data_a[ix + p.n_dims/2]);
rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
}
void rope_multi(const uint i0, const uint i1, rope_params p) {
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
uint ne2 = p.ne02;
if (i0 >= ne0) {
return;
}
const uint i01 = i1 % ne1;
const uint i02 = i1 / ne1;
const uint idst = i1*ne0 + i0/2;
const uint ix = rope_a_coord(i0/2, i01, i02, p);
if (i0 >= p.n_dims) {
rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]);
rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]);
return;
}
const int sect_dims = p.sections[0] + p.sections[1] + p.sections[2] + p.sections[3];
const int sec_w = p.sections[1] + p.sections[0];
const uint sector = (i0 / 2) % sect_dims;
float theta_base = 0.0;
if (p.is_imrope != 0) {
if (sector % 3 == 1 && sector < 3 * p.sections[1]) {
theta_base = rope_data_pos[i02 + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
} else if (sector % 3 == 2 && sector < 3 * p.sections[2]) {
theta_base = rope_data_pos[i02 + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
} else if (sector % 3 == 0 && sector < 3 * p.sections[0]) {
theta_base = rope_data_pos[i02]*pow(p.theta_scale, i0/2.0f);
} else {
theta_base = rope_data_pos[i02 + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
}
} else {
if (sector < p.sections[0]) {
theta_base = rope_data_pos[i02]*pow(p.theta_scale, i0/2.0f);
}
else if (sector >= p.sections[0] && sector < sec_w) {
theta_base = rope_data_pos[i02 + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
}
else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
theta_base = rope_data_pos[i02 + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
}
else if (sector >= sec_w + p.sections[2]) {
theta_base = rope_data_pos[i02 + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
}
}
const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
const float x0 = float(rope_data_a[ix + 0]);
const float x1 = float(rope_data_a[ix + p.n_dims/2]);
rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
}
void rope_vision(const uint i0, const uint i1, rope_params p) {
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
uint ne2 = p.ne02;
if (i0 >= ne0) {
return;
}
const uint i01 = i1 % ne1;
const uint i02 = i1 / ne1;
const uint idst = i1*ne0 + i0/2;
const uint ix = rope_a_coord(i0/2, i01, i02, p);
const int sect_dims = p.sections[0] + p.sections[1];
const int sec_w = p.sections[1] + p.sections[0];
const uint sector = (i0 / 2) % sect_dims;
float theta_base = 0.0;
if (sector < p.sections[0]) {
const uint p0 = sector;
theta_base = rope_data_pos[i02]*pow(p.theta_scale, p0);
}
else if (sector >= p.sections[0] && sector < sec_w) {
const uint p0 = sector - p.sections[0];
theta_base = rope_data_pos[i02 + ne2]*pow(p.theta_scale, p0);
}
const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
const float x0 = float(rope_data_a[ix + 0]);
const float x1 = float(rope_data_a[ix + p.n_dims]);
rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
rope_data_d[idst + p.n_dims] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
}

56
ggml/src/ggml-vulkan/vulkan-shaders/rope_head.glsl
View File

@ -3,56 +3,18 @@
#extension GL_EXT_shader_16bit_storage : require
#include "rte.glsl"
#include "rope_params.glsl"
layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
layout (binding = 1) readonly buffer Y {int data_pos[];};
layout (binding = 2) readonly buffer Z {float data_ff[];};
layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
layout (binding = 4) readonly buffer I {uvec2 data_i[];}; // indices for set_rows
layout (binding = 0) readonly buffer X {A_TYPE rope_data_a[];};
layout (binding = 1) readonly buffer Y {int rope_data_pos[];};
layout (binding = 2) readonly buffer Z {float rope_data_ff[];};
layout (binding = 3) writeonly buffer D {ROPE_D_TYPE rope_data_d[];};
layout (binding = 4) readonly buffer I {uvec2 rope_data_i[];}; // indices for set_rows
layout (push_constant) uniform parameter {
uint ncols;
uint n_dims;
float freq_scale;
uint p_delta_rows;
float freq_base;
float ext_factor;
float attn_factor;
float corr_dims[2];
float theta_scale;
uint has_ff;
uint ne02;
uint s1;
uint s2;
int sections[4];
uint is_imrope;
uint is_back;
uint set_rows_stride;
} p;
rope_params pc;
};
float rope_yarn_ramp(const float low, const float high, const uint i0) {
const float y = (i0 / 2 - low) / max(0.001f, high - low);
return 1.0f - min(1.0f, max(0.0f, y));
}
void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
float mscale = p.attn_factor;
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = p.freq_scale * theta_extrap;
float theta = theta_interp;
if (p.ext_factor != 0.0f) {
float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
// Get n-d magnitude scaling corrected for interpolation
mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
}
// Backprogagation uses inverted rotation
if (p.is_back != 0) {
theta = -theta;
}
cos_theta = cos(theta) * mscale;
sin_theta = sin(theta) * mscale;
}

67
ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp
View File

@ -1,70 +1,11 @@
#version 450
#include "rope_head.glsl"
#include "rope_funcs.glsl"
void main() {
const uint i0 = 2*gl_GlobalInvocationID.y;
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
uint ne2 = p.ne02;
if (i0 >= ne0) {
return;
}
const uint row_dst = gl_GlobalInvocationID.x;
const uint row_x = row_dst % ne1;
const uint channel_x = row_dst / ne1;
const uint idst = row_dst*ne0 + i0/2;
const uint ix = channel_x*p.s2 + row_x*p.s1 + i0/2;
if (i0 >= p.n_dims) {
data_d[idst + i0/2 + 0] = data_a[ix + i0/2 + 0];
data_d[idst + i0/2 + 1] = data_a[ix + i0/2 + 1];
return;
}
const int sect_dims = p.sections[0] + p.sections[1] + p.sections[2] + p.sections[3];
const int sec_w = p.sections[1] + p.sections[0];
const uint sector = (i0 / 2) % sect_dims;
float theta_base = 0.0;
if (p.is_imrope != 0) {
if (sector % 3 == 1 && sector < 3 * p.sections[1]) {
theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
} else if (sector % 3 == 2 && sector < 3 * p.sections[2]) {
theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
} else if (sector % 3 == 0 && sector < 3 * p.sections[0]) {
theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
} else {
theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
}
} else {
if (sector < p.sections[0]) {
theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
}
else if (sector >= p.sections[0] && sector < sec_w) {
theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
}
else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
}
else if (sector >= sec_w + p.sections[2]) {
theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
}
}
const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
const float x0 = float(data_a[ix + 0]);
const float x1 = float(data_a[ix + p.n_dims/2]);
data_d[idst + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
data_d[idst + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
// i1 is actually i2*nb2+i1, but the rows are contiguous
const uint i1 = gl_GlobalInvocationID.x;
rope_multi(i0, i1, pc);
}

45
ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp
View File

@ -1,48 +1,11 @@
#version 450
#include "rope_head.glsl"
#include "rope_funcs.glsl"
void main() {
const uint i0 = 2*gl_GlobalInvocationID.y;
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
if (i0 >= ne0) {
return;
}
const uint row_dst = gl_GlobalInvocationID.x;
const uint row_x = row_dst % ne1;
const uint channel_x = row_dst / ne1;
uint idst = row_dst*ne0 + i0/2;
const uint ix = channel_x*p.s2 + row_x*p.s1 + i0/2;
// Fusion optimization: ROPE + VIEW + SET_ROWS..
// The rope output is viewed as a 1D tensor and offset based on a row index in data_i.
if (p.set_rows_stride != 0) {
idst = row_x*ne0 + i0/2;
idst += data_i[channel_x].x * p.set_rows_stride;
}
if (i0 >= p.n_dims) {
data_d[idst + i0/2 + 0] = D_TYPE(data_a[ix + i0/2 + 0]);
data_d[idst + i0/2 + 1] = D_TYPE(data_a[ix + i0/2 + 1]);
return;
}
const float theta_base = data_pos[channel_x] * pow(p.theta_scale, i0/2.0f);
const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
const float x0 = float(data_a[ix + 0]);
const float x1 = float(data_a[ix + p.n_dims/2]);
data_d[idst + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
data_d[idst + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
// i1 is actually i2*nb2+i1, but the rows are contiguous
const uint i1 = gl_GlobalInvocationID.x;
rope_neox(i0, i1, pc);
}

45
ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp
View File

@ -1,48 +1,11 @@
#version 450
#include "rope_head.glsl"
#include "rope_funcs.glsl"
void main() {
const uint i0 = 2*gl_GlobalInvocationID.y;
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
if (i0 >= ne0) {
return;
}
const uint row_dst = gl_GlobalInvocationID.x;
const uint row_x = row_dst % ne1;
const uint channel_x = row_dst / ne1;
uint idst = row_dst*ne0 + i0;
const uint ix = channel_x*p.s2 + row_x*p.s1 + i0;
// Fusion optimization: ROPE + VIEW + SET_ROWS..
// The rope output is viewed as a 1D tensor and offset based on a row index in data_i.
if (p.set_rows_stride != 0) {
idst = row_x*ne0 + i0;
idst += data_i[channel_x].x * p.set_rows_stride;
}
if (i0 >= p.n_dims) {
data_d[idst + 0] = D_TYPE(data_a[ix + 0]);
data_d[idst + 1] = D_TYPE(data_a[ix + 1]);
return;
}
const float theta_base = data_pos[channel_x] * pow(p.theta_scale, i0/2.0f);
const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
const float x0 = float(data_a[ix + 0]);
const float x1 = float(data_a[ix + 1]);
data_d[idst + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
data_d[idst + 1] = D_TYPE(x0*sin_theta + x1*cos_theta);
// i1 is actually i2*nb2+i1, but the rows are contiguous
const uint i1 = gl_GlobalInvocationID.x;
rope_norm(i0, i1, pc);
}

27
ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl Normal file
View File

@ -0,0 +1,27 @@
#if !defined(GGML_ROPE_PARAMS)
#define GGML_ROPE_PARAMS
#include "rte.glsl"
struct rope_params {
uint rope_mode;
uint ncols;
uint n_dims;
float freq_scale;
uint p_delta_rows;
float freq_base;
float ext_factor;
float attn_factor;
float corr_dims[2];
float theta_scale;
uint has_ff;
uint ne02;
uint nb01;
uint nb02;
int sections[4];
uint is_imrope;
uint is_back;
uint set_rows_stride;
};
#endif // !defined(GGML_ROPE_PARAMS)

44
ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp
View File

@ -1,47 +1,11 @@
#version 450
#include "rope_head.glsl"
#include "rope_funcs.glsl"
void main() {
const uint i0 = 2*gl_GlobalInvocationID.y;
uint ne0 = p.ncols;
uint ne1 = p.p_delta_rows;
uint ne2 = p.ne02;
if (i0 >= ne0) {
return;
}
const uint row_dst = gl_GlobalInvocationID.x;
const uint row_x = row_dst % ne1;
const uint channel_x = row_dst / ne1;
const uint idst = row_dst*ne0 + i0/2;
const uint ix = channel_x*p.s2 + row_x*p.s1 + i0/2;
const int sect_dims = p.sections[0] + p.sections[1];
const int sec_w = p.sections[1] + p.sections[0];
const uint sector = (i0 / 2) % sect_dims;
float theta_base = 0.0;
if (sector < p.sections[0]) {
const uint p0 = sector;
theta_base = data_pos[channel_x]*pow(p.theta_scale, p0);
}
else if (sector >= p.sections[0] && sector < sec_w) {
const uint p0 = sector - p.sections[0];
theta_base = data_pos[channel_x + ne2]*pow(p.theta_scale, p0);
}
const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
const float x0 = float(data_a[ix + 0]);
const float x1 = float(data_a[ix + p.n_dims]);
data_d[idst + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
data_d[idst + p.n_dims] = D_TYPE(x0*sin_theta + x1*cos_theta);
// i1 is actually i2*nb2+i1, but the rows are contiguous
const uint i1 = gl_GlobalInvocationID.x;
rope_vision(i0, i1, pc);
}

37
ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp
View File

@ -20,6 +20,7 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
// from ggml.h: enum ggml_scale_mode, enum ggml_scale_flag
#define NEAREST 0
#define BILINEAR 1
#define BICUBIC 2
layout (constant_id = 0) const uint scale_mode = 0;
@ -61,6 +62,39 @@ float interpolate_bilinear(uint i10, uint i11, uint i12, uint i13) {
return fetch_bilinear(c0, c1, d, i12, i13);
}
// Bicubic interpolation with alpha = -0.75
// https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm
const vec4 bcoeffs1 = vec4( 1.25, -2.25, 0.0, 1.0);
const vec4 bcoeffs2 = vec4(-0.75, 3.75, -6.0, 3.0);
vec4 powers(float x) { return vec4(x*x*x, x*x, x, 1); }
float bicubic(float p0, float p1, float p2, float p3, float x) {
return p0 * dot(bcoeffs2, powers(x + 1)) +
p1 * dot(bcoeffs1, powers(x )) +
p2 * dot(bcoeffs1, powers(1 - x)) +
p3 * dot(bcoeffs2, powers(2 - x));
}
#define FETCH(a,b) data_a[base + clamp(i.x+(a), 0, res.x) * p.nb00 + clamp(i.y+(b), 0, res.y) * p.nb01]
float interpolate_bicubic(uint i10, uint i11, uint i12, uint i13) {
const ivec2 res = ivec2(p.ne00 - 1, p.ne01 - 1);
const vec2 coord = (vec2(i10, i11) + p.pixel_offset) / vec2(p.sf0, p.sf1) - p.pixel_offset;
const vec2 d = fract(coord);
const ivec2 i = ivec2(floor(coord));
const uint i02 = uint(i12 / p.sf2);
const uint i03 = uint(i13 / p.sf3);
const uint base = p.a_offset + i03 * p.nb03 + i02 * p.nb02;
return bicubic(
bicubic(FETCH(-1,-1), FETCH(0,-1), FETCH(1,-1), FETCH(2,-1), d.x),
bicubic(FETCH(-1, 0), FETCH(0, 0), FETCH(1, 0), FETCH(2, 0), d.x),
bicubic(FETCH(-1, 1), FETCH(0, 1), FETCH(1, 1), FETCH(2, 1), d.x),
bicubic(FETCH(-1, 2), FETCH(0, 2), FETCH(1, 2), FETCH(2, 2), d.x), d.y);
}
void main() {
const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
@ -81,6 +115,9 @@ void main() {
case BILINEAR:
result = interpolate_bilinear(i10, i11, i12, i13);
break;
case BICUBIC:
result = interpolate_bicubic(i10, i11, i12, i13);
break;
}
data_d[p.d_offset + idx] = D_TYPE(result);

40
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View File

@ -18,6 +18,7 @@
#include <algorithm>
#include <sys/stat.h>
#include <sys/types.h>
#include <filesystem>
#ifdef _WIN32
#define NOMINMAX
@ -695,6 +696,8 @@ void process_shaders() {
string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("rms_norm_partials_f32", "rms_norm_partials.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("rms_norm_mul_rope_f32_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"ROPE_D_TYPE", "float"}, {"RMS_NORM_ROPE_FUSION", "1"}}));
string_to_spv("rms_norm_mul_rope_f32_f16_rte", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RMS_NORM_ROPE_FUSION", "1"}, {"RTE16", "1"}}));
string_to_spv("rms_norm_back_f32", "rms_norm_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("l2_norm_f32", "l2_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
@ -840,25 +843,25 @@ void process_shaders() {
string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_norm_f32_f16", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
string_to_spv("rope_norm_f32_f16_rte", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_norm_f32_f16", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_norm_f32_f16_rte", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("rope_neox_f16_rte", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_neox_f32_f16", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
string_to_spv("rope_neox_f32_f16_rte", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_neox_f16_rte", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_neox_f32_f16", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_neox_f32_f16_rte", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_multi_f32", "rope_multi.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("rope_multi_f16", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("rope_multi_f16_rte", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_multi_f32", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
string_to_spv("rope_multi_f16", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_multi_f16_rte", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_vision_f32", "rope_vision.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("rope_vision_f16", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("rope_vision_f16_rte", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("rope_vision_f32", "rope_vision.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
string_to_spv("rope_vision_f16", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
string_to_spv("rope_vision_f16_rte", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}});
@ -1078,6 +1081,11 @@ int main(int argc, char** argv) {
if (args.find("--glslc") != args.end()) {
GLSLC = args["--glslc"]; // Path to glslc
if (!std::filesystem::exists(GLSLC) || !std::filesystem::is_regular_file(GLSLC)) {
std::cerr << "Error: glslc not found at " << GLSLC << std::endl;
return EXIT_FAILURE;
}
}
if (args.find("--source") != args.end()) {
input_filepath = args["--source"]; // The shader source file to compile

11
gguf-py/gguf/lazy.py
View File

@ -48,13 +48,18 @@ class LazyMeta(ABCMeta):
# NOTE: doing this from a metaclass is very convenient
# TODO: make this even more comprehensive
for binary_op in (
"lt", "le", "eq", "ne", "ge", "gt", "not"
"abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul",
"neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor",
"lt", "le", "eq", "ne", "ge", "gt",
"add", "and", "floordiv", "lshift", "mod", "mul", "matmul",
"or", "pow", "rshift", "sub", "truediv", "xor",
"iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor",
"radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor",
):
attr_name = f"__{binary_op}__"
# evaluation on the meta tensor is needed in case there's broadcasting
namespace[attr_name] = mk_wrap(attr_name, meta_noop=False)
for unary_op in ("not", "abs", "invert", "neg", "pos"):
attr_name = f"__{unary_op}__"
# the result of these operators usually has the same shape and dtype as the input,
# so evaluation on the meta tensor can be skipped.
namespace[attr_name] = mk_wrap(attr_name, meta_noop=True)

80
gguf-py/gguf/utility.py
View File

@ -1,10 +1,12 @@
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
import os
import json
import numpy as np
def fill_templated_filename(filename: str, output_type: str | None) -> str:
@ -177,6 +179,10 @@ class SafetensorRemote:
except KeyError as e:
raise ValueError(f"Missing key in metadata for tensor '{name}': {e}, meta = {meta}")
# order by name (same as default safetensors behavior)
# ref: https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L606
res = dict(sorted(res.items(), key=lambda t: t[0]))
return res
@classmethod
@ -266,3 +272,77 @@ class SafetensorRemote:
if os.environ.get("HF_TOKEN"):
headers["Authorization"] = f"Bearer {os.environ['HF_TOKEN']}"
return headers
@dataclass
class LocalTensorRange:
filename: Path
offset: int
size: int
@dataclass
class LocalTensor:
dtype: str
shape: tuple[int, ...]
data_range: LocalTensorRange
def mmap_bytes(self) -> np.ndarray:
return np.memmap(self.data_range.filename, offset=self.data_range.offset, shape=self.data_range.size)
class SafetensorsLocal:
"""
Read a safetensors file from the local filesystem.
Custom parsing gives a bit more control over the memory usage.
The official safetensors library doesn't expose file ranges.
"""
ALIGNMENT = 8 # bytes
tensors: dict[str, LocalTensor]
def __init__(self, filename: Path):
with open(filename, "rb") as f:
metadata_length = int.from_bytes(f.read(8), byteorder='little')
file_size = os.stat(filename).st_size
if file_size < 8 + metadata_length:
raise ValueError(f"Could not read complete metadata. Need {8 + metadata_length} bytes, got {file_size}")
metadata_str = f.read(metadata_length).decode('utf-8')
try:
metadata = json.loads(metadata_str)
except json.JSONDecodeError as e:
raise ValueError(f"Failed to parse safetensors metadata as JSON: {e}")
data_start_offset = f.tell()
alignment = self.ALIGNMENT
if data_start_offset % alignment != 0:
data_start_offset += alignment - (data_start_offset % alignment)
tensors: dict[str, LocalTensor] = {}
for name, meta in metadata.items():
if name == "__metadata__":
# ignore metadata, it's not a tensor
continue
tensors[name] = LocalTensor(
dtype=meta["dtype"],
shape=tuple(meta["shape"]),
data_range=LocalTensorRange(
filename,
data_start_offset + meta["data_offsets"][0],
meta["data_offsets"][1] - meta["data_offsets"][0],
),
)
# order by name (same as default safetensors behavior)
# ref: https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L606
self.tensors = dict(sorted(tensors.items(), key=lambda t: t[0]))
def __enter__(self, *args, **kwargs):
del args, kwargs # unused
return self.tensors
def __exit__(self, *args, **kwargs):
del args, kwargs # unused

2
include/llama.h
View File

@ -463,6 +463,7 @@ extern "C" {
// NOTE: After creating a llama_context, it is recommended to query the actual values using these functions
// In some cases the requested values via llama_context_params may differ from the actual values used by the context
// ref: https://github.com/ggml-org/llama.cpp/pull/17046#discussion_r2503085732
LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_ctx_seq (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
@ -485,6 +486,7 @@ extern "C" {
LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model);

17
scripts/sync_vendor.py
View File

@ -20,3 +20,20 @@ vendor = {
for url, filename in vendor.items():
print(f"downloading {url} to {filename}") # noqa: NP100
urllib.request.urlretrieve(url, filename)
# split cpp/h files for httplib
# see: https://github.com/yhirose/cpp-httplib/blob/master/split.py
if 'httplib.h' in filename:
border = '// ----------------------------------------------------------------------------'
with open(filename, 'r') as f:
content = f.read()
header, implementation, footer = content.split(border, 2)
fname_cpp = filename.replace('.h', '.cpp')
with open(filename, 'w') as fh:
fh.write(header)
fh.write(footer)
with open(fname_cpp, 'w') as fc:
fc.write('#include "httplib.h"\n')
fc.write('namespace httplib {\n')
fc.write(implementation.replace('\ninline ', '\n'))
fc.write('} // namespace httplib\n')

5
src/CMakeLists.txt
View File

@ -132,6 +132,11 @@ add_library(llama
models/graph-context-mamba.cpp
)
set_target_properties(llama PROPERTIES
VERSION ${LLAMA_INSTALL_VERSION}
SOVERSION 0
)
target_include_directories(llama PRIVATE .)
target_include_directories(llama PUBLIC ../include)
target_compile_features (llama PRIVATE cxx_std_17) # don't bump

12
src/llama-context.cpp
View File

@ -21,6 +21,8 @@ llama_context::llama_context(
llama_context_params params) :
model(model),
balloc(std::make_unique<llama_batch_allocr>(model.hparams.n_pos_per_embd())) {
// TODO warning when creating llama_context with awkward ctx size that is not a power of 2,
// may need to be backend-dependent
LLAMA_LOG_INFO("%s: constructing llama_context\n", __func__);
t_start_us = model.t_start_us;
@ -112,10 +114,14 @@ llama_context::llama_context(
}
}
// ref: https://github.com/ggml-org/llama.cpp/pull/17046#discussion_r2503085732
cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256);
if (cparams.kv_unified) {
cparams.n_ctx_seq = cparams.n_ctx;
} else {
cparams.n_ctx_seq = cparams.n_ctx / cparams.n_seq_max;
cparams.n_ctx_seq = GGML_PAD(cparams.n_ctx_seq, 256);
if (cparams.n_ctx_seq == 0) {
throw std::runtime_error("n_ctx_seq == 0");
@ -821,7 +827,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
const auto & hparams = model.hparams;
const int64_t n_embd = hparams.n_embd;
const int64_t n_embd = hparams.n_embd_inp();
const int64_t n_vocab = model.vocab.n_tokens();
// note: during encode, we always pass the full sequence starting from pos = 0
@ -990,7 +996,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
const auto & hparams = model.hparams;
const int64_t n_vocab = vocab.n_tokens();
const int64_t n_embd = hparams.n_embd;
const int64_t n_embd = hparams.n_embd_inp();
// when computing embeddings, all tokens are output
const bool output_all = cparams.embeddings;
@ -2148,7 +2154,7 @@ void llama_context::opt_epoch_iter(
batch.logits [pos_batch] = true;
}
if (!balloc->init(batch, model.vocab, nullptr, model.hparams.n_embd, cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max, true)) {
if (!balloc->init(batch, model.vocab, nullptr, model.hparams.n_embd_inp(), cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max, true)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return;
}

4
src/llama-graph.cpp
View File

@ -1142,7 +1142,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
// input embeddings with optional lora
ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
const int64_t n_embd = hparams.n_embd;
const int64_t n_embd = hparams.n_embd_inp();
auto inp = std::make_unique<llm_graph_input_embd>();
@ -1279,7 +1279,7 @@ ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
// return cur;
//}
const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd;
const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd_inp();
const auto n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc);

10
src/llama-hparams.cpp
View File

@ -60,6 +60,16 @@ uint32_t llama_hparams::n_gqa(uint32_t il) const {
return n_head/n_head_kv;
}
uint32_t llama_hparams::n_embd_inp() const {
uint32_t n_embd_inp = n_embd;
if (n_deepstack_layers > 0) {
n_embd_inp += n_embd * n_deepstack_layers;
}
return n_embd_inp;
}
uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
const uint32_t n_head_kv = this->n_head_kv(il);

3
src/llama-hparams.h
View File

@ -227,6 +227,9 @@ struct llama_hparams {
uint32_t n_gqa(uint32_t il = 0) const;
// dimension of main + auxiliary input embeddings
uint32_t n_embd_inp() const;
// dimension of key embeddings across all k-v heads
uint32_t n_embd_k_gqa(uint32_t il = 0) const;

4
src/llama-kv-cache-iswa.cpp
View File

@ -45,7 +45,9 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
const uint32_t size_base = kv_size;
uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*(unified ? n_seq_max : 1) + n_ubatch, n_pad));
// note: the SWA cache is always padded to 256 for performance
// https://github.com/ggml-org/llama.cpp/issues/17037
uint32_t size_swa = GGML_PAD(std::min(size_base, hparams.n_swa*(unified ? n_seq_max : 1) + n_ubatch), 256);
// when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
if (swa_full) {

7
src/llama-memory-recurrent.cpp
View File

@ -151,7 +151,8 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
p1 = std::numeric_limits<llama_pos>::max();
}
// models like Mamba or RWKV can't have a state partially erased
// models like Mamba or RWKV can't have a state partially erased at the end
// of the sequence because their state isn't preserved for previous tokens
if (seq_id >= (int64_t) size) {
// could be fatal
return false;
@ -160,8 +161,8 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
int32_t & tail_id = cells[seq_id].tail;
if (tail_id >= 0) {
const auto & cell = cells[tail_id];
// partial intersection is invalid
if ((0 < p0 && p0 < cell.pos) || (0 < p1 && p1 <= cell.pos)) {
// partial intersection is invalid if it includes the final pos
if (0 < p0 && p0 <= cell.pos && p1 > cell.pos) {
//printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: partial intersection is invalid, so returning false\n");
return false;
}

23
src/llama-model.cpp
View File

@ -276,8 +276,8 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
} break;
case GGML_OP_IM2COL:
{
const int n_embd = hparams.n_embd;
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
const int n_embd_inp = hparams.n_embd_inp();
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd_inp, w->ne[1], 1, 1);
op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
} break;
case GGML_OP_SCALE:
@ -1039,9 +1039,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case 64: type = LLM_TYPE_32B; break;
default: type = LLM_TYPE_UNKNOWN;
}
// since vision model stacks deepstack features along feature dim
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
hparams.n_embd *= hparams.n_deepstack_layers + 1;
} break;
case LLM_ARCH_QWEN3MOE:
{
@ -1065,9 +1062,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case 94: type = LLM_TYPE_235B_A22B; break;
default: type = LLM_TYPE_UNKNOWN;
}
// since vision model stacks deepstack features along feature dim
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
hparams.n_embd *= hparams.n_deepstack_layers + 1;
} break;
case LLM_ARCH_PHI2:
{
@ -3341,10 +3335,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_QWEN3:
case LLM_ARCH_QWEN3VL:
{
// for model loading, the weights only have the main embd
// so we need to divide by the number of deepstack layers + 1
// n_embd is const int so we declare a new variable
int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@ -3380,10 +3370,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_QWEN3MOE:
case LLM_ARCH_QWEN3VLMOE:
{
// for model loading, the weights only have the main embd
// so we need to divide by the number of deepstack layers + 1
// n_embd is const int so we declare a new variable
int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@ -6535,6 +6521,7 @@ void llama_model::print_info() const {
if (!hparams.vocab_only) {
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_embd_inp = %u\n", __func__, hparams.n_embd_inp());
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str());
LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
@ -7380,6 +7367,10 @@ int32_t llama_model_n_embd(const llama_model * model) {
return model->hparams.n_embd;
}
int32_t llama_model_n_embd_inp(const llama_model * model) {
return model->hparams.n_embd_inp();
}
int32_t llama_model_n_layer(const llama_model * model) {
return model->hparams.n_layer;
}

9
src/models/ernie4-5.cpp
View File

@ -1,7 +1,5 @@
#include "models.h"
llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
@ -19,6 +17,8 @@ llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_grap
auto * inp_attn = build_attn_inp_kv();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@ -67,9 +67,8 @@ llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_grap
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);

3
src/models/openai-moe-iswa.cpp
View File

@ -11,6 +11,8 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@ -69,7 +71,6 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}

3
src/models/qwen3vl-moe.cpp
View File

@ -1,9 +1,8 @@
#include "models.h"
llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
const size_t n_deepstack_layers = hparams.n_deepstack_layers;
const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
const int64_t n_embd = hparams.n_embd;
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

5
src/models/qwen3vl.cpp
View File

@ -1,13 +1,10 @@
#include "models.h"
llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
const size_t n_deepstack_layers = hparams.n_deepstack_layers;
const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
const int64_t n_embd = hparams.n_embd;
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);

428
tests/test-backend-ops.cpp
View File

@ -289,6 +289,10 @@ static double mean_abs_asymm(const float * a, const float * b, const size_t n, c
// utils for printing the variables of the test cases
static std::string var_to_str(const std::string & x) {
return x;
}
template<typename T>
static std::string var_to_str(const T & x) {
return std::to_string(x);
@ -340,7 +344,8 @@ static std::string var_to_str(ggml_scale_mode mode) {
switch (mode) {
case GGML_SCALE_MODE_NEAREST: return "nearest";
case GGML_SCALE_MODE_BILINEAR: return "bilinear";
default: return std::to_string(mode);
case GGML_SCALE_MODE_BICUBIC: return "bicubic";
default: return std::to_string(mode);
}
}
@ -2311,6 +2316,79 @@ struct test_rope_set_rows : public test_case {
}
};
// GGML_OP_RMS_NORM + GGML_OP_MUL + GGML_OP_ROPE (+ GGML_OP_VIEW + GGML_OP_SET_ROWS)
struct test_rms_norm_mul_rope : public test_case {
const std::array<int64_t, 4> ne;
const float eps;
const bool multi_add; // test a sequence of adds feeding into rms_norm
const bool set_rows;
int mode;
std::string op_desc(ggml_tensor * t) override {
GGML_UNUSED(t);
return "RMS_NORM_MUL_ROPE";
}
bool run_whole_graph() override { return true; }
std::string vars() override {
return VARS_TO_STR5(ne, eps, multi_add, set_rows, mode);
}
test_rms_norm_mul_rope(std::array<int64_t, 4> ne, float eps = 1e-6f, bool multi_add = false,
bool set_rows = false, int mode = GGML_ROPE_TYPE_NORMAL)
: ne(ne), eps(eps), multi_add(multi_add), set_rows(set_rows), mode(mode) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1);
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1);
ggml_tensor * c = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1);
if (multi_add) {
a = ggml_add(ctx, ggml_add(ctx, a, b), c);
}
a = ggml_mul(ctx, ggml_rms_norm(ctx, a, eps), b);
ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]);
ggml_tensor * rope = ggml_rope(ctx, a, pos, ne[0], mode);
ggml_tensor * out;
if (set_rows) {
ggml_tensor * view = ggml_view_2d(ctx, rope, ne[0] * ne[1], ne[2], rope->nb[2], 0);
ggml_tensor * dst = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, ne[0] * ne[1], ne[2] * ne[3], 1, 1);
ggml_set_name(dst, "dst");
ggml_tensor * row_idxs = ggml_new_tensor_3d(ctx, GGML_TYPE_I64, ne[2], 1, 1);
ggml_set_name(row_idxs, "row_idxs");
out = ggml_set_rows(ctx, dst, view, row_idxs);
ggml_set_name(out, "out");
} else {
out = rope;
}
return out;
}
void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I64 || t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) {
continue;
}
init_set_rows_row_ids(t, ne[2]);
} else {
init_tensor_uniform(t);
}
}
}
};
// GGML_OP_ARGMAX
struct test_argmax : public test_case {
const ggml_type type;
@ -2593,9 +2671,10 @@ struct test_cpy : public test_case {
const std::array<int64_t, 4> permute_dst;
bool _src_use_permute;
bool _dst_use_permute;
bool _src_transpose;
std::string vars() override {
return VARS_TO_STR5(type_src, type_dst, ne, permute_src, permute_dst);
return VARS_TO_STR6(type_src, type_dst, ne, permute_src, permute_dst, _src_transpose);
}
double max_nmse_err() override {
@ -2633,10 +2712,12 @@ struct test_cpy : public test_case {
test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 1},
std::array<int64_t, 4> permute_src = {0, 0, 0, 0},
std::array<int64_t, 4> permute_dst = {0, 0, 0, 0})
std::array<int64_t, 4> permute_dst = {0, 0, 0, 0},
bool transpose_src = false)
: type_src(type_src), type_dst(type_dst), ne(ne), permute_src(permute_src), permute_dst(permute_dst),
_src_use_permute(permute_src[0] + permute_src[1] + permute_src[2] + permute_src[3] > 0),
_dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[3] > 0) {}
_dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[3] > 0),
_src_transpose(transpose_src){}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
@ -2648,6 +2729,11 @@ struct test_cpy : public test_case {
ggml_set_name(src, "src_permuted");
}
if (_src_transpose) {
src = ggml_transpose(ctx, src);
ggml_set_name(src, "src_transposed");
}
ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
ggml_set_name(dst, "dst");
@ -3394,11 +3480,11 @@ struct test_mul_mat : public test_case {
const std::array<int64_t, 2> bs; // dims 3 and 4
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
const std::array<int64_t, 4> per; // permutation of dimensions
const bool v; // whether a and b are non-contiguous views
const int64_t k_v; // size of k in memory, resulting in a non-contiguous view for k_v > k, no view for k_v == 0
const uint32_t o; // number of outputs
std::string vars() override {
return VARS_TO_STR10(type_a, type_b, m, n, k, bs, nr, per, v, o);
return VARS_TO_STR10(type_a, type_b, m, n, k, bs, nr, per, k_v, o);
}
double max_nmse_err() override {
@ -3419,8 +3505,8 @@ struct test_mul_mat : public test_case {
std::array<int64_t, 2> bs = {10, 10},
std::array<int64_t, 2> nr = {2, 2},
std::array<int64_t, 4> per = {0, 1, 2, 3},
bool v = false, uint32_t o = 1)
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per), v(v), o(o) {}
int64_t k_v = 0, uint32_t o = 1)
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per), k_v(k_v), o(o) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
@ -3430,7 +3516,7 @@ struct test_mul_mat : public test_case {
const int npermuted = (per[0] != 0) + (per[1] != 1) + (per[2] != 2) + (per[3] != 3);
if (npermuted > 0) {
GGML_ASSERT(npermuted == 2);
GGML_ASSERT(!v); // not handled
GGML_ASSERT(k_v == 0); // not handled
GGML_ASSERT(!ggml_is_quantized(type_a) || per[0] == 0);
GGML_ASSERT(!ggml_is_quantized(type_b) || per[0] == 0);
@ -3454,29 +3540,21 @@ struct test_mul_mat : public test_case {
ggml_set_name(a, "a_permuted");
ggml_set_name(b, "b_permuted");
} else {
if (v) {
a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k*2, n, bs[0]*nr[0], bs[1]*nr[1]);
const int64_t k_physical = k_v == 0 ? k : k_v;
a = ggml_new_tensor_4d(ctx, type_a, k_physical, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k_physical, n, bs[0]*nr[0], bs[1]*nr[1]);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
ggml_set_param(a);
}
ggml_set_param(b);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
ggml_set_param(a);
}
ggml_set_param(b);
}
if (k_v != 0) {
GGML_ASSERT(k_v > k);
a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0);
b = ggml_view_4d(ctx, b, k, n, bs[0]*nr[0], bs[1]*nr[1], b->nb[1], b->nb[2], b->nb[3], 0);
} else {
a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
ggml_set_param(a);
}
ggml_set_param(b);
}
}
ggml_set_name(a, "a");
ggml_set_name(b, "b");
@ -3501,6 +3579,27 @@ struct test_mul_mat : public test_case {
}
};
static void init_mul_mat_id_tensors(ggml_context * ctx, int n_mats) {
std::random_device rd;
std::default_random_engine rng(rd());
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
// ids
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<int32_t> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i % n_mats;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
}
} else {
init_tensor_uniform(t);
}
}
}
// GGML_OP_MUL_MAT_ID
struct test_mul_mat_id : public test_case {
const ggml_type type_a;
@ -3511,10 +3610,9 @@ struct test_mul_mat_id : public test_case {
const int64_t m;
const int64_t n;
const int64_t k;
const uint32_t o; // number of outputs
std::string vars() override {
return VARS_TO_STR9(type_a, type_b, n_mats, n_used, b, m, n, k, o);
return VARS_TO_STR8(type_a, type_b, n_mats, n_used, b, m, n, k);
}
double max_nmse_err() override {
@ -3528,9 +3626,69 @@ struct test_mul_mat_id : public test_case {
test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int n_mats = 8, int n_used = 2, bool b = false,
int64_t m = 32, int64_t n = 32, int64_t k = 32, uint32_t o = 1)
int64_t m = 32, int64_t n = 32, int64_t k = 32)
: type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
m(m), n(n), k(k), o(o) {
m(m), n(n), k(k) {
GGML_ASSERT(n_used <= n_mats);
}
ggml_tensor * build_graph(ggml_context * ctx) override {
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
ggml_tensor * as = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
ggml_set_name(as, "as");
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
ggml_set_name(ids, "ids");
if (n_used != n_mats) {
ids = ggml_view_2d(ctx, ids, n_used, n, ids->nb[1], 0);
ggml_set_name(ids, "view_of_ids");
}
ggml_tensor * b = ggml_new_tensor_3d(ctx, type_b, k, this->b ? 1 : n_used, n);
ggml_set_name(b, "b");
ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids);
ggml_set_name(out, "out");
return out;
}
void initialize_tensors(ggml_context * ctx) override {
init_mul_mat_id_tensors(ctx, n_mats);
}
};
// GGML_OP_MUL_MAT_ID + GGML_OP_ADD or GGML_OP_MUL
struct test_mul_mat_id_fusion : public test_case {
const ggml_type type_a;
const ggml_type type_b;
const int n_mats;
const int n_used;
const bool b; // broadcast b matrix
const int64_t m;
const int64_t n;
const int64_t k;
const uint32_t o; // number of outputs
const bool mul;
std::string vars() override {
return VARS_TO_STR10(type_a, type_b, n_mats, n_used, b, m, n, k, o, mul);
}
double max_nmse_err() override {
return 5e-4;
}
uint64_t op_flops(ggml_tensor * t) override {
GGML_UNUSED(t);
return 2 * m * k * n * n_used;
}
test_mul_mat_id_fusion(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int n_mats = 8, int n_used = 2, bool b = false,
int64_t m = 32, int64_t n = 32, int64_t k = 32, uint32_t o = 1, bool mul = false)
: type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
m(m), n(n), k(k), o(o), mul(mul) {
GGML_ASSERT(n_used <= n_mats);
}
@ -3559,35 +3717,25 @@ struct test_mul_mat_id : public test_case {
out = ggml_add(ctx, out, out2);
}
if (mul) {
std::array<int64_t, 4> ne { 1, out->ne[1], out->ne[2], out->ne[3] };
ne[0] = 1;
ggml_tensor * m = ggml_new_tensor(ctx, out->type, 4, ne.data());
out = ggml_mul(ctx, out, m);
}
return out;
}
void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
std::random_device rd;
std::default_random_engine rng(rd());
// ids
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<int32_t> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i % n_mats;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
}
} else {
init_tensor_uniform(t);
}
}
init_mul_mat_id_tensors(ctx, n_mats);
}
bool run_whole_graph() override { return o > 1; }
bool run_whole_graph() override { return true; }
std::string op_desc(ggml_tensor * t) override {
GGML_UNUSED(t);
return ggml_op_name(GGML_OP_MUL_MAT_ID);
return "MUL_MAT_ID_FUSION";
}
};
@ -4826,60 +4974,6 @@ struct test_topk_moe: public test_case {
}
};
struct test_moe_expert_reduce : public test_case {
const int64_t n_embd;
const int64_t n_tokens;
const int64_t n_expert_used;
test_moe_expert_reduce(int64_t n_embd = 64, int64_t n_tokens = 5, int64_t n_expert_used = 4)
: n_embd(n_embd), n_tokens(n_tokens), n_expert_used(n_expert_used) {
GGML_ASSERT(n_expert_used > 1);
}
std::string vars() override {
return VARS_TO_STR3(n_embd, n_tokens, n_expert_used);
}
std::string op_desc(ggml_tensor * t) override {
GGML_UNUSED(t);
return "MOE_EXPERT_REDUCE";
}
bool run_whole_graph() override { return true; }
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * experts = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, n_expert_used, n_tokens);
ggml_set_name(experts, "experts");
ggml_tensor * weights = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 1, n_expert_used, n_tokens);
ggml_set_name(weights, "weights");
ggml_tensor * weighted = ggml_mul(ctx, experts, weights);
ggml_set_name(weighted, "weighted_experts");
std::vector<ggml_tensor *> expert_views(n_expert_used);
for (int64_t i = 0; i < n_expert_used; ++i) {
expert_views[i] = ggml_view_2d(ctx, weighted, n_embd, n_tokens, weighted->nb[2], i * weighted->nb[1]);
std::string name = "expert_view_" + std::to_string(i);
ggml_set_name(expert_views[i], name.c_str());
ggml_build_forward_expand(gf, expert_views[i]);
}
ggml_tensor * moe_out = expert_views[0];
for (int64_t i = 1; i < n_expert_used; ++i) {
moe_out = ggml_add(ctx, moe_out, expert_views[i]);
std::string name = "expert_add_" + std::to_string(i - 1);
ggml_set_name(moe_out, name.c_str());
}
ggml_set_name(moe_out, "moe_out");
return moe_out;
}
};
struct test_mul_mat_vec_fusion : public test_case {
const ggml_type type;
const ggml_glu_op glu_op;
@ -4928,8 +5022,10 @@ struct test_mul_mat_vec_fusion : public test_case {
ggml_tensor * build_graph(ggml_context * ctx) override {
if (!use_id) {
std::array<int64_t, 4> ne = {k, m, 1, 1};
std::array<int64_t, 4> ne0 = {k, n, 1, 1};
const int channels = 4;
const int samples = 2;
std::array<int64_t, 4> ne = { k, m, channels, samples };
std::array<int64_t, 4> ne0 = { k, n, channels, samples };
ggml_tensor * cur = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data());
ggml_tensor * gate = with_gate ? ggml_new_tensor(ctx, type, 4, ne0.data()) : nullptr;
@ -4937,14 +5033,14 @@ struct test_mul_mat_vec_fusion : public test_case {
ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
if (with_bias) {
std::array<int64_t, 4> bias_ne = {ffn_up->ne[0], 1, 1, 1};
std::array<int64_t, 4> bias_ne = { ffn_up->ne[0], 1, channels, samples };
ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
ffn_up = ggml_add(ctx, ffn_up, up_bias);
}
ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr;
if (with_bias && with_gate) {
std::array<int64_t, 4> bias_ne = {ffn_gate->ne[0], 1, 1, 1};
std::array<int64_t, 4> bias_ne = { ffn_gate->ne[0], 1, channels, samples };
ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
}
@ -4988,24 +5084,7 @@ struct test_mul_mat_vec_fusion : public test_case {
init_tensor_uniform(t);
}
} else {
std::random_device rd;
std::default_random_engine rng(rd());
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
// ids
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<int32_t> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i % n_mats;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
}
} else {
init_tensor_uniform(t);
}
}
init_mul_mat_id_tensors(ctx, n_mats);
}
}
@ -5161,7 +5240,9 @@ struct test_interpolate : public test_case {
const uint32_t mode = GGML_SCALE_MODE_NEAREST;
std::string vars() override {
return VARS_TO_STR4(type, ne, ne_tgt, mode);
ggml_scale_mode mode = (ggml_scale_mode)(this->mode & 0xFF);
std::string flags = (this->mode & GGML_SCALE_FLAG_ALIGN_CORNERS) ? "align_corners" : "none";
return VARS_TO_STR5(type, ne, ne_tgt, mode, flags);
}
test_interpolate(ggml_type type = GGML_TYPE_F32,
@ -6658,6 +6739,14 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_I32, {256, 2, 3, 4}, {1, 0, 2, 3}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_I32, GGML_TYPE_F32, {256, 2, 3, 4}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_I32, GGML_TYPE_F32, {256, 2, 3, 4}, {1, 0, 2, 3}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {256, 4, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 4, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 4, 3, 3}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_BF16, GGML_TYPE_BF16, {256, 4, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_BF16, GGML_TYPE_BF16, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 1, 4, 1}, {1, 2, 0, 3}, {0, 0, 0, 0}));
test_cases.emplace_back(new test_cont());
test_cases.emplace_back(new test_cont(GGML_TYPE_F32, {2, 1, 1 ,1}));
@ -6760,6 +6849,22 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
}
}
for (auto multi_add : {false, true}) {
for (auto set_rows : {false, true}) {
for (auto rope : {GGML_ROPE_TYPE_NORMAL, GGML_ROPE_TYPE_NEOX}) {
test_cases.emplace_back(new test_rms_norm_mul_rope({768, 1, 1, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({768, 3, 1, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({768, 3, 5, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({128, 32, 2, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({128, 4, 2, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({128, 32, 50, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({128, 4, 50, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({8192, 2, 2, 1}, 1e-6f, multi_add, set_rows, rope));
test_cases.emplace_back(new test_rms_norm_mul_rope({8192, 2, 2, 1}, 1e-6f, multi_add, set_rows, rope));
}
}
}
test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, {64, 5, 4, 3}, 1e-12f));
for (int64_t d_conv : {3, 4}) {
@ -6903,9 +7008,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 193, {1, 1}, {4, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 67, {1, 1}, {4, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 16, 32, 32, { 1, 1}, {1, 1}, {0, 1, 2, 3}, true, 3));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 16, 32, 32, { 1, 1}, {1, 1}, {0, 1, 2, 3}, 64, 3));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 64, 77, 77, {12,1}, {1,1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 576, 512, 576, {1,1}, {1,1}));
#if 0
// test the mat-mat path for Metal
for (int k = 1; k < 512; ++k) {
@ -6929,7 +7036,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
for (uint32_t k = 0; k < 2; ++k) {
for (ggml_type type: {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 1056 + m, 1, 128 + k, {bs, bs2}, {nr, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 128 + m, 1, 1056 + k, {bs, bs2}, {nr, 1}, {0, 1, 2, 3}, true));
test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 128 + m, 1, 1056 + k, {bs, bs2}, {nr, 1}, {0, 1, 2, 3}, 2*1056 + k));
}
}
}
@ -6951,7 +7058,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
}
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, false, 32, 32, 32, 3));
test_cases.emplace_back(new test_mul_mat_id_fusion(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, false, 32, 32, 32, 3));
// gpt-oss issue with Vulkan mmq_id
test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_MXFP4, GGML_TYPE_F32, 32, 2, false, 2880, 32, 2880));
@ -6988,6 +7095,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
}
}
for (int bs : {1, 4, 512}) {
for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q4_K}) {
for (ggml_type type_b : {GGML_TYPE_F32}) {
// test with mul after (ffn_moe_weighted)
test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 128, 8, false, 768, bs, 2048, 1, true));
}
}
}
for (ggml_type type_a : base_types) {
for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
for (int n : {1, 16}) {
@ -7196,15 +7312,17 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2, 8, 8192, 1}, order)); // bailingmoe2 (group selection)
}
for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR}) {
for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR, GGML_SCALE_MODE_BICUBIC}) {
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode));
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode, true));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, mode));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {5, 7, 11, 13}, {2, 5, 7, 11}, mode));
}
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {1, 4, 3, 2}, {2, 8, 3, 2}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {4, 1, 3, 2}, {1, 1, 3, 2}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
for (ggml_scale_mode mode : {GGML_SCALE_MODE_BILINEAR, GGML_SCALE_MODE_BICUBIC}) {
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, mode | GGML_SCALE_FLAG_ALIGN_CORNERS));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {1, 4, 3, 2}, {2, 8, 3, 2}, mode | GGML_SCALE_FLAG_ALIGN_CORNERS));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {4, 1, 3, 2}, {1, 1, 3, 2}, mode | GGML_SCALE_FLAG_ALIGN_CORNERS));
}
test_cases.emplace_back(new test_sum());
test_cases.emplace_back(new test_sum_rows());
@ -7333,10 +7451,6 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true));
test_cases.emplace_back(new test_topk_moe({ 32, 22, 1, 1 }, 8, /*with_norm*/ false, /*delayed_softmax*/ true));
test_cases.emplace_back(new test_moe_expert_reduce(1024, 5, 4));
test_cases.emplace_back(new test_moe_expert_reduce(80, 3, 6));
test_cases.emplace_back(new test_moe_expert_reduce(80, 3, 7));
#if 0
// these tests are disabled to save execution time, sbut they can be handy for debugging
test_cases.emplace_back(new test_llama(2, true));
@ -7402,6 +7516,18 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_Q4_0, {8192, 512, 2, 1}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_Q4_0, GGML_TYPE_F32, {8192, 512, 2, 1}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {768*1024, 256, 1, 1}, {1, 0, 2, 3}, {0, 0, 0, 0}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {768*1024, 256, 1, 1}, {1, 0, 2, 3}, {0, 0, 0, 0}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {768, 1024, 256, 1}, {1, 0, 2, 3}, {0, 0, 0, 0}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_BF16, GGML_TYPE_BF16, {768, 1024, 256, 1}, {1, 0, 2, 3}, {0, 0, 0, 0}));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {768*1024, 256, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {768, 1024, 256, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {768*1024, 256, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {768, 1024, 256, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_cpy(GGML_TYPE_BF16, GGML_TYPE_BF16, {768, 1024, 256, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {4096, 4096, 5, 1}, false, false, GGML_TYPE_F32, {1, 1}, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {12888, 256, 5, 1}, false, false, GGML_TYPE_F32, {1, 1}, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 4096, 5, 1}, false, false, GGML_TYPE_F32, {1, 1}, 1.0f, 0.0f));
@ -7422,7 +7548,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8, 1}, {4, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8, 1}, {4, 1}, {0, 1, 2, 3}, true));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8, 1}, {4, 1}, {0, 1, 2, 3}, 2*16416));
for (int bs : {1, 2, 3, 4, 5, 8, 512}) {
for (ggml_type type_a : all_types) {
@ -7436,7 +7562,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) {
for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) {
for (ggml_type type_b : {GGML_TYPE_F32}) {
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 128, 8, false, 768, bs, 2048, 1));
test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 128, 8, false, 768, bs, 2048, 1));
}
}
}
@ -7444,7 +7570,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) {
for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) {
for (ggml_type type_b : {GGML_TYPE_F32}) {
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 1792, bs, 2048, 1));
test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 32, 4, false, 1792, bs, 2048, 1));
}
}
}
@ -7454,7 +7580,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
for (int bs : {1, 4, 8, 512}) {
for (ggml_type type_a : {GGML_TYPE_MXFP4}) {
for (ggml_type type_b : {GGML_TYPE_F32}) {
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 2880, bs, 2880, 1));
test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 32, 4, false, 2880, bs, 2880, 1));
}
}
}
@ -7494,6 +7620,22 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_add_id(GGML_TYPE_F32, GGML_TYPE_F32, 2880, 32, 4, n_token));
}
for (bool fw : {true, false}) { // fw == forward
for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
for (bool ff : {false, true}) { // freq_factors
for (float v : { 0, 1 }) {
test_cases.emplace_back(new test_rope(type, {128, 32, 512, 1}, 128, GGML_ROPE_TYPE_NORMAL, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // llama 7B
test_cases.emplace_back(new test_rope(type, {128, 64, 512, 1}, 128, GGML_ROPE_TYPE_NORMAL, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // llama 65B
test_cases.emplace_back(new test_rope(type, { 80, 32, 512, 1}, 20, GGML_ROPE_TYPE_NEOX, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // neox (stablelm)
test_cases.emplace_back(new test_rope(type, { 64, 8, 512, 1}, 64, GGML_ROPE_TYPE_NEOX, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // neox (falcon 40B)
test_cases.emplace_back(new test_rope(type, {128, 12, 512, 1}, 128, GGML_ROPE_TYPE_MROPE, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,m-rope (qwen2vl 2B)
test_cases.emplace_back(new test_rope(type, {128, 12, 2, 1}, 128, GGML_ROPE_TYPE_IMROPE, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,imrope (qwen3vl 2B)
test_cases.emplace_back(new test_rope(type, { 80, 16, 2, 1}, 80, GGML_ROPE_TYPE_VISION, 512, 1.0f, 0.0f, 1.0f, ff, v, fw)); // rope_multi,m-rope (qwen2vl ViT)
}
}
}
}
std::vector<std::array<int64_t, 4>> reduce_rows_cases = {
{ 8192, 1, 1, 1 },
{ 8192, 8192, 1, 1 },

11
tests/test-rope.cpp
View File

@ -138,7 +138,7 @@ int main(int /*argc*/, const char ** /*argv*/) {
struct ggml_tensor * x;
// rope f32
for (int m = 0; m < 6; ++m) {
for (int m = 0; m < 5; ++m) {
const int ndims = 4;
const int64_t n_rot = 128;
@ -153,7 +153,7 @@ int main(int /*argc*/, const char ** /*argv*/) {
x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
int mode = -1;
if (m < 3) {
if (m < 2) {
struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
@ -163,8 +163,8 @@ int main(int /*argc*/, const char ** /*argv*/) {
((int32_t *) p1->data)[i] = n_past_2 - n_past_0;
((int32_t *) p2->data)[i] = n_past_2 + i;
}
// test mode 0, 2, 4 (standard, GPT-NeoX, GLM)
mode = m == 0 ? 0 : m == 1 ? 2 : 4;
// test mode 0, 2 (standard, GPT-NeoX)
mode = m == 0 ? GGML_ROPE_TYPE_NORMAL : GGML_ROPE_TYPE_NEOX;
// 100, 101, 102, ..., 172
r0 = ggml_rope(ctx0, x, p0, n_rot, mode);
@ -180,7 +180,8 @@ int main(int /*argc*/, const char ** /*argv*/) {
struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2] * 4);
int sections[4] = {16, 24, 24, 0};
mode = (m == 3) ? GGML_ROPE_TYPE_MROPE : (m == 4) ? GGML_ROPE_TYPE_VISION : GGML_ROPE_TYPE_IMROPE;
mode = (m == 2) ? GGML_ROPE_TYPE_MROPE : (m == 3) ? GGML_ROPE_TYPE_VISION : GGML_ROPE_TYPE_IMROPE;
for (int i = 0; i < ne[2]; ++i) {
for (int j = 0; j < 4; ++j) {

44
tools/batched-bench/batched-bench.cpp
View File

@ -23,7 +23,8 @@ int main(int argc, char ** argv) {
common_init();
int is_pp_shared = params.is_pp_shared;
int is_pp_shared = params.is_pp_shared;
int is_tg_separate = params.is_tg_separate;
std::vector<int> n_pp = params.n_pp;
std::vector<int> n_tg = params.n_tg;
@ -72,8 +73,8 @@ int main(int argc, char ** argv) {
// decode in batches of ctx_params.n_batch tokens
auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch, bool synchronize) {
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
llama_batch batch_view = {
n_tokens,
@ -113,7 +114,7 @@ int main(int argc, char ** argv) {
if (!params.batched_bench_output_jsonl) {
LOG("\n");
LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, int(params.flash_attn_type), params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, is_tg_separate = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, int(params.flash_attn_type), is_pp_shared, is_tg_separate, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG("\n");
LOG("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
LOG("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
@ -172,16 +173,35 @@ int main(int argc, char ** argv) {
const auto t_tg_start = ggml_time_us();
for (int i = 0; i < tg; ++i) {
common_batch_clear(batch);
if (is_tg_separate) {
// decode pattern:
// 0 0 0 ... 1 1 1 ... 2 2 2 ... 3 3 3 ...
for (int j = 0; j < pl; ++j) {
common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
}
for (int i = 0; i < tg; ++i) {
common_batch_clear(batch);
if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
}
}
} else {
// decode pattern:
// 0123 0123 0123 ...
for (int i = 0; i < tg; ++i) {
common_batch_clear(batch);
for (int j = 0; j < pl; ++j) {
common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
}
if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
}
}

47
tools/llama-bench/llama-bench.cpp
View File

@ -1919,6 +1919,12 @@ struct sql_printer : public printer {
}
};
struct ctx_state {
int depth = 0; // in tokens
std::vector<uint8_t> buf; // the llama_context state buffer
};
static bool test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);
@ -2051,6 +2057,10 @@ int main(int argc, char ** argv) {
llama_model * lmodel = nullptr;
const cmd_params_instance * prev_inst = nullptr;
// store the llama_context state at the previous depth that we performed a test
// ref: https://github.com/ggml-org/llama.cpp/pull/16944#issuecomment-3478151721
ctx_state cstate;
int params_idx = 0;
auto params_count = params_instances.size();
for (const auto & inst : params_instances) {
@ -2134,14 +2144,37 @@ int main(int argc, char ** argv) {
llama_memory_clear(llama_get_memory(ctx), false);
if (t.n_depth > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count,
i + 1, params.reps);
bool is_cached = t.n_depth == cstate.depth;
if (is_cached) {
// if previously we have computed at this depth, just restore the state
const size_t ret = llama_state_seq_set_data(ctx, cstate.buf.data(), cstate.buf.size(), 0);
if (ret == 0) {
// if the old state is incompatible with the current context - reprocess from scratch
is_cached = false;
}
}
bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads);
if (!res) {
fprintf(stderr, "%s: error: failed to run depth\n", __func__);
exit(1);
if (!is_cached) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count,
i + 1, params.reps);
}
bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads);
if (!res) {
fprintf(stderr, "%s: error: failed to run depth\n", __func__);
exit(1);
}
// store the context state for reuse in later runs
cstate.depth = t.n_depth;
cstate.buf.resize(llama_state_seq_get_size(ctx, 0));
llama_state_seq_get_data(ctx, cstate.buf.data(), cstate.buf.size(), 0);
} else {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d (cached)\n", params_idx, params_count,
i + 1, params.reps);
}
}
}

6
tools/main/main.cpp
View File

@ -354,7 +354,11 @@ int main(int argc, char ** argv) {
}
// remove any "future" tokens that we might have inherited from the previous session
llama_memory_seq_rm(mem, -1, n_matching_session_tokens, -1);
if (!llama_memory_seq_rm(mem, -1, n_matching_session_tokens, -1)) {
LOG_INF("%s: unable to resuse common prefix\n", __func__);
n_matching_session_tokens = 0;
llama_memory_seq_rm(mem, -1, -1, -1);
}
}
LOG_DBG("recalculate the cached logits (check): embd_inp.size() %zu, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu\n",

5
tools/mtmd/CMakeLists.txt
View File

@ -13,6 +13,11 @@ add_library(mtmd
mtmd-helper.h
)
set_target_properties(mtmd PROPERTIES
VERSION ${LLAMA_INSTALL_VERSION}
SOVERSION 0
)
target_link_libraries (mtmd PUBLIC ggml llama)
target_link_libraries (mtmd PRIVATE Threads::Threads)
target_include_directories(mtmd PUBLIC .)

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