mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-07-01 10:07:44 +02:00
Compare commits
45 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Xuan-Son Nguyen
|
9a96352729 | ||
|
Alberto Cabrera Pérez
|
c03a5a46f0 | ||
|
k4ss4n
|
6948adc90d | ||
|
Piotr Wilkin (ilintar)
|
854b09f0d7 | ||
|
Daniel Bevenius
|
66d403c480 | ||
|
Raul Torres
|
f0bfe54f55 | ||
|
hipudding
|
52e38faf8c | ||
|
Georgi Gerganov
|
a0d585537c | ||
|
Xuan-Son Nguyen
|
98e57ca422 | ||
|
Tarek Dakhran
|
262364e31d | ||
|
손희준
|
820ebfa6f4 | ||
|
Sascha Rogmann
|
292f6908cd | ||
|
Georgi Gerganov
|
81ddc60cb3 | ||
|
Georgi Gerganov
|
972f323e73 | ||
|
Kevin Pouget
|
f5e7734ff2 | ||
|
Hugo
|
1e8924fd65 | ||
|
Piotr Wilkin (ilintar)
|
39bf692af1 | ||
|
Oliver Simons
|
e06088da0f | ||
|
Adrien Gallouët
|
5fa1c190d9 | ||
|
Georgi Gerganov
|
eb449cdfa4 | ||
|
ddh0
|
5999b50eb0 | ||
|
Sigbjørn Skjæret
|
9a5f57795c | ||
|
Georgi Gerganov
|
96441c955e | ||
|
Georgi Gerganov
|
8872ad2125 | ||
|
Georgi Gerganov
|
34ba7b5a2f | ||
|
forforever73
|
b83111815e | ||
|
Alex Trotta
|
3228e77287 | ||
|
Abhijit Ramesh
|
7fbd36c50c | ||
|
Nechama Krashinski
|
537eadb1b9 | ||
|
Jeff Bolz
|
db6adb3c88 | ||
|
Georgi Gerganov
|
dfde5993ea | ||
|
Lasse Lauwerys
|
06bf3796f4 | ||
|
ymcki
|
3688c4f504 | ||
|
Jeff Bolz
|
1946e46f4c | ||
|
Jeff Bolz
|
f9bd518a6b | ||
|
Georgi Gerganov
|
7fcf1ef45d | ||
|
Daniel Bevenius
|
e696cfc016 | ||
|
Georgi Gerganov
|
3e21647666 | ||
|
Georgi Gerganov
|
22cae83218 | ||
|
Jeff Bolz
|
449ec2ab07 | ||
|
Georgi Gerganov
|
3795cc1e89 | ||
|
Sigbjørn Skjæret
|
b828e18c75 | ||
|
Adrien Gallouët
|
a4ea7a188f | ||
|
Georgi Gerganov
|
7a4f97d196 | ||
|
Oleksandr Kuvshynov
|
a498c75ad1 |
@@ -54,6 +54,7 @@ RUN apt-get update \
|
||||
build-essential \
|
||||
git \
|
||||
python3 \
|
||||
python3-dev \
|
||||
python3-pip \
|
||||
python3-wheel \
|
||||
&& pip install --break-system-packages --upgrade setuptools \
|
||||
|
||||
@@ -295,6 +295,7 @@ jobs:
|
||||
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
|
||||
-DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
|
||||
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
|
||||
|
||||
- name: Build (no OpenMP)
|
||||
@@ -307,6 +308,7 @@ jobs:
|
||||
-DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
|
||||
-DGGML_OPENMP=OFF
|
||||
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
|
||||
|
||||
- name: Test
|
||||
@@ -468,7 +470,7 @@ jobs:
|
||||
export GGML_VK_VISIBLE_DEVICES=0
|
||||
export GGML_VK_DISABLE_F16=1
|
||||
# This is using llvmpipe and runs slower than other backends
|
||||
ctest -L main --verbose --timeout 4200
|
||||
ctest -L main --verbose --timeout 4800
|
||||
|
||||
ubuntu-24-cmake-webgpu:
|
||||
runs-on: ubuntu-24.04
|
||||
|
||||
@@ -0,0 +1,73 @@
|
||||
name: Server-Metal
|
||||
|
||||
on:
|
||||
workflow_dispatch: # allows manual triggering
|
||||
inputs:
|
||||
sha:
|
||||
description: 'Commit SHA1 to build'
|
||||
required: false
|
||||
type: string
|
||||
slow_tests:
|
||||
description: 'Run slow tests'
|
||||
required: true
|
||||
type: boolean
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
paths: ['.github/workflows/server-metal.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
|
||||
|
||||
env:
|
||||
LLAMA_LOG_COLORS: 1
|
||||
LLAMA_LOG_PREFIX: 1
|
||||
LLAMA_LOG_TIMESTAMPS: 1
|
||||
LLAMA_LOG_VERBOSITY: 10
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
server-metal:
|
||||
runs-on: [self-hosted, macOS, ARM64]
|
||||
|
||||
name: server-metal (${{ matrix.wf_name }})
|
||||
strategy:
|
||||
matrix:
|
||||
build_type: [Release]
|
||||
wf_name: ["GPUx1"]
|
||||
include:
|
||||
- build_type: Release
|
||||
extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
|
||||
wf_name: "GPUx1, backend-sampling"
|
||||
- build_type: Release
|
||||
extra_args: "GGML_METAL_DEVICES=2"
|
||||
wf_name: "GPUx2"
|
||||
- build_type: Release
|
||||
extra_args: "GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1"
|
||||
wf_name: "GPUx2, backend-sampling"
|
||||
fail-fast: false
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v6
|
||||
with:
|
||||
fetch-depth: 0
|
||||
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
|
||||
|
||||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
cmake -B build -DGGML_SCHED_NO_REALLOC=ON
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
|
||||
|
||||
- name: Tests
|
||||
id: server_integration_tests
|
||||
if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
|
||||
run: |
|
||||
cd tools/server/tests
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
export ${{ matrix.extra_args }}
|
||||
pytest -v -x -m "not slow"
|
||||
@@ -8,10 +8,6 @@ on:
|
||||
description: 'Commit SHA1 to build'
|
||||
required: false
|
||||
type: string
|
||||
slow_tests:
|
||||
description: 'Run slow tests'
|
||||
required: true
|
||||
type: boolean
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
@@ -101,119 +97,3 @@ jobs:
|
||||
if: ${{ always() && steps.playwright.conclusion == 'success' }}
|
||||
run: npm run test:e2e
|
||||
working-directory: tools/server/webui
|
||||
|
||||
server-build:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
strategy:
|
||||
matrix:
|
||||
sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
|
||||
build_type: [RelWithDebInfo]
|
||||
include:
|
||||
- build_type: Release
|
||||
sanitizer: ""
|
||||
fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
|
||||
|
||||
steps:
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
run: |
|
||||
sudo apt-get update
|
||||
sudo apt-get -y install \
|
||||
build-essential \
|
||||
xxd \
|
||||
git \
|
||||
cmake \
|
||||
curl \
|
||||
wget \
|
||||
language-pack-en \
|
||||
libssl-dev
|
||||
|
||||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v6
|
||||
with:
|
||||
fetch-depth: 0
|
||||
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
|
||||
|
||||
- name: Python setup
|
||||
id: setup_python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Tests dependencies
|
||||
id: test_dependencies
|
||||
run: |
|
||||
pip install -r tools/server/tests/requirements.txt
|
||||
|
||||
- name: Setup Node.js for WebUI
|
||||
uses: actions/setup-node@v6
|
||||
with:
|
||||
node-version: "22"
|
||||
cache: "npm"
|
||||
cache-dependency-path: "tools/server/webui/package-lock.json"
|
||||
|
||||
- name: Install WebUI dependencies
|
||||
run: npm ci
|
||||
working-directory: tools/server/webui
|
||||
|
||||
- name: Build WebUI
|
||||
run: npm run build
|
||||
working-directory: tools/server/webui
|
||||
|
||||
- name: Build (no OpenMP)
|
||||
id: cmake_build_no_openmp
|
||||
if: ${{ matrix.sanitizer == 'THREAD' }}
|
||||
run: |
|
||||
cmake -B build \
|
||||
-DGGML_NATIVE=OFF \
|
||||
-DLLAMA_BUILD_SERVER=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
|
||||
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
|
||||
-DGGML_OPENMP=OFF ;
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
|
||||
|
||||
- name: Build (sanitizers)
|
||||
id: cmake_build_sanitizers
|
||||
if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
|
||||
run: |
|
||||
cmake -B build \
|
||||
-DGGML_NATIVE=OFF \
|
||||
-DLLAMA_BUILD_SERVER=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
|
||||
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
|
||||
|
||||
- name: Build (sanitizers)
|
||||
id: cmake_build
|
||||
if: ${{ matrix.sanitizer == '' }}
|
||||
run: |
|
||||
cmake -B build \
|
||||
-DGGML_NATIVE=OFF \
|
||||
-DLLAMA_BUILD_SERVER=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
|
||||
|
||||
- name: Tests
|
||||
id: server_integration_tests
|
||||
if: ${{ matrix.sanitizer == '' }}
|
||||
env:
|
||||
GITHUB_ACTIONS: "true"
|
||||
run: |
|
||||
cd tools/server/tests
|
||||
./tests.sh
|
||||
|
||||
- name: Tests (sanitizers)
|
||||
id: server_integration_tests_sanitizers
|
||||
if: ${{ matrix.sanitizer != '' }}
|
||||
run: |
|
||||
cd tools/server/tests
|
||||
LLAMA_SANITIZE=1 ./tests.sh
|
||||
|
||||
- name: Slow tests
|
||||
id: server_integration_tests_slow
|
||||
if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
|
||||
run: |
|
||||
cd tools/server/tests
|
||||
SLOW_TESTS=1 ./tests.sh
|
||||
|
||||
@@ -81,18 +81,14 @@ jobs:
|
||||
-DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
|
||||
-DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
|
||||
-DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
|
||||
cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
|
||||
|
||||
- name: Python setup
|
||||
id: setup_python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Tests dependencies
|
||||
id: test_dependencies
|
||||
run: |
|
||||
pip install -r tools/server/tests/requirements.txt
|
||||
pip-install: -r tools/server/tests/requirements.txt
|
||||
|
||||
- name: Tests
|
||||
id: server_integration_tests
|
||||
@@ -102,6 +98,14 @@ jobs:
|
||||
export ${{ matrix.extra_args }}
|
||||
pytest -v -x -m "not slow"
|
||||
|
||||
- name: Slow tests
|
||||
id: server_integration_tests_slow
|
||||
if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
|
||||
run: |
|
||||
cd tools/server/tests
|
||||
export ${{ matrix.extra_args }}
|
||||
SLOW_TESTS=1 pytest -v -x
|
||||
|
||||
server-windows:
|
||||
runs-on: windows-2022
|
||||
|
||||
@@ -124,11 +128,7 @@ jobs:
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Tests dependencies
|
||||
id: test_dependencies
|
||||
run: |
|
||||
pip install -r tools/server/tests/requirements.txt
|
||||
pip-install: -r tools/server/tests/requirements.txt
|
||||
|
||||
- name: Tests
|
||||
id: server_integration_tests
|
||||
|
||||
@@ -109,6 +109,7 @@ option(LLAMA_BUILD_TOOLS "llama: build tools" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_TOOLS_INSTALL "llama: install tools" ${LLAMA_TOOLS_INSTALL_DEFAULT})
|
||||
option(LLAMA_TESTS_INSTALL "llama: install tests" ON)
|
||||
|
||||
# 3rd party libs
|
||||
option(LLAMA_HTTPLIB "llama: httplib for downloading functionality" ON)
|
||||
|
||||
@@ -288,6 +288,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
|
||||
| [WebGPU [In Progress]](docs/build.md#webgpu) | All |
|
||||
| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
|
||||
| [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
|
||||
| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
|
||||
|
||||
## Obtaining and quantizing models
|
||||
|
||||
|
||||
+209
-162
@@ -8,7 +8,7 @@ g++ --version
|
||||
g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
|
||||
|
||||
nvidia-smi
|
||||
Sun Nov 2 10:43:25 2025
|
||||
Thu Feb 5 13:49:40 2026
|
||||
+-----------------------------------------------------------------------------------------+
|
||||
| NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 |
|
||||
+-----------------------------------------+------------------------+----------------------+
|
||||
@@ -17,7 +17,7 @@ Sun Nov 2 10:43:25 2025
|
||||
| | | 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 47C P0 13W / N/A | Not Supported | 0% Default |
|
||||
| | | N/A |
|
||||
+-----------------------------------------+------------------------+----------------------+
|
||||
```
|
||||
@@ -29,46 +29,46 @@ 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
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 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 |
|
||||
| 512 | 32 | 1 | 544 | 0.270 | 1895.57 | 0.399 | 80.13 | 0.669 | 812.60 |
|
||||
| 512 | 32 | 2 | 1088 | 0.230 | 4451.23 | 0.583 | 109.71 | 0.813 | 1337.56 |
|
||||
| 512 | 32 | 4 | 2176 | 0.437 | 4688.87 | 0.820 | 156.03 | 1.257 | 1730.91 |
|
||||
| 512 | 32 | 8 | 4352 | 0.863 | 4744.23 | 0.942 | 271.79 | 1.805 | 2410.73 |
|
||||
| 512 | 32 | 16 | 8704 | 1.725 | 4748.19 | 1.173 | 436.38 | 2.899 | 3002.85 |
|
||||
| 512 | 32 | 32 | 17408 | 3.437 | 4767.38 | 1.503 | 681.49 | 4.939 | 3524.40 |
|
||||
| 4096 | 32 | 1 | 4128 | 0.907 | 4513.91 | 0.407 | 78.54 | 1.315 | 3139.56 |
|
||||
| 4096 | 32 | 2 | 8256 | 1.796 | 4560.42 | 0.625 | 102.37 | 2.422 | 3409.45 |
|
||||
| 4096 | 32 | 4 | 16512 | 3.596 | 4555.66 | 0.888 | 144.11 | 4.485 | 3681.93 |
|
||||
| 4096 | 32 | 8 | 33024 | 7.184 | 4561.44 | 1.098 | 233.11 | 8.282 | 3987.51 |
|
||||
| 4096 | 32 | 16 | 66048 | 14.369 | 4560.82 | 1.503 | 340.74 | 15.872 | 4161.30 |
|
||||
| 4096 | 32 | 32 | 132096 | 28.760 | 4557.52 | 2.162 | 473.59 | 30.922 | 4271.95 |
|
||||
| 8192 | 32 | 1 | 8224 | 1.859 | 4405.59 | 0.430 | 74.36 | 2.290 | 3591.61 |
|
||||
| 8192 | 32 | 2 | 16448 | 3.698 | 4430.02 | 0.656 | 97.59 | 4.354 | 3777.47 |
|
||||
| 8192 | 32 | 4 | 32896 | 7.403 | 4426.10 | 0.957 | 133.82 | 8.360 | 3934.97 |
|
||||
| 8192 | 32 | 8 | 65792 | 14.802 | 4427.63 | 1.222 | 209.44 | 16.024 | 4105.87 |
|
||||
| 8192 | 32 | 16 | 131584 | 29.596 | 4428.67 | 1.741 | 294.13 | 31.337 | 4199.00 |
|
||||
| 8192 | 32 | 32 | 263168 | 59.169 | 4430.42 | 2.619 | 390.92 | 61.789 | 4259.17 |
|
||||
|
||||
|
||||
- `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 |
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | dio | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 | 4505.82 ± 12.90 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 | 83.43 ± 0.59 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d4096 | 4158.34 ± 18.84 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d4096 | 79.22 ± 0.60 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d8192 | 3993.81 ± 17.55 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d8192 | 75.22 ± 1.05 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d16384 | 3449.98 ± 12.13 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d16384 | 70.36 ± 0.37 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d32768 | 2689.42 ± 18.89 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d32768 | 61.65 ± 0.30 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
build: 11fb327bf (7941)
|
||||
|
||||
## ggml-org/gpt-oss-120b-GGUF
|
||||
|
||||
@@ -77,46 +77,46 @@ 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
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 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 |
|
||||
| 512 | 32 | 1 | 544 | 0.445 | 1151.80 | 0.560 | 57.14 | 1.005 | 541.53 |
|
||||
| 512 | 32 | 2 | 1088 | 0.472 | 2169.85 | 0.874 | 73.27 | 1.345 | 808.65 |
|
||||
| 512 | 32 | 4 | 2176 | 0.826 | 2480.33 | 1.299 | 98.51 | 2.125 | 1023.94 |
|
||||
| 512 | 32 | 8 | 4352 | 1.644 | 2491.67 | 1.608 | 159.18 | 3.252 | 1338.20 |
|
||||
| 512 | 32 | 16 | 8704 | 3.292 | 2488.35 | 2.117 | 241.85 | 5.409 | 1609.13 |
|
||||
| 512 | 32 | 32 | 17408 | 6.604 | 2481.07 | 2.898 | 353.31 | 9.502 | 1832.04 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.698 | 2412.65 | 0.580 | 55.21 | 2.277 | 1812.66 |
|
||||
| 4096 | 32 | 2 | 8256 | 3.399 | 2409.88 | 0.934 | 68.53 | 4.333 | 1905.27 |
|
||||
| 4096 | 32 | 4 | 16512 | 6.823 | 2401.21 | 1.411 | 90.72 | 8.234 | 2005.30 |
|
||||
| 4096 | 32 | 8 | 33024 | 13.574 | 2413.97 | 1.841 | 139.07 | 15.415 | 2142.31 |
|
||||
| 4096 | 32 | 16 | 66048 | 27.176 | 2411.52 | 2.609 | 196.26 | 29.785 | 2217.49 |
|
||||
| 4096 | 32 | 32 | 132096 | 54.359 | 2411.23 | 3.905 | 262.20 | 58.264 | 2267.19 |
|
||||
| 8192 | 32 | 1 | 8224 | 3.491 | 2346.81 | 0.613 | 52.23 | 4.103 | 2004.21 |
|
||||
| 8192 | 32 | 2 | 16448 | 6.939 | 2361.03 | 0.981 | 65.21 | 7.921 | 2076.56 |
|
||||
| 8192 | 32 | 4 | 32896 | 13.888 | 2359.40 | 1.511 | 84.71 | 15.399 | 2136.21 |
|
||||
| 8192 | 32 | 8 | 65792 | 27.756 | 2361.18 | 2.034 | 125.86 | 29.790 | 2208.56 |
|
||||
| 8192 | 32 | 16 | 131584 | 55.554 | 2359.34 | 3.021 | 169.49 | 58.575 | 2246.41 |
|
||||
| 8192 | 32 | 32 | 263168 | 111.036 | 2360.89 | 4.537 | 225.72 | 115.573 | 2277.08 |
|
||||
|
||||
|
||||
- `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 |
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | dio | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 | 2443.91 ± 7.47 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 | 58.72 ± 0.20 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d4096 | 2309.84 ± 3.63 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d4096 | 55.67 ± 0.35 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d8192 | 2216.68 ± 10.16 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d8192 | 52.87 ± 0.43 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d16384 | 1956.31 ± 6.39 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d16384 | 49.45 ± 0.20 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d32768 | 1567.08 ± 11.79 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d32768 | 42.76 ± 0.14 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
build: 11fb327bf (7941)
|
||||
|
||||
## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
|
||||
|
||||
@@ -125,46 +125,46 @@ 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
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 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 |
|
||||
| 512 | 32 | 1 | 544 | 0.393 | 1303.73 | 0.548 | 58.36 | 0.941 | 578.10 |
|
||||
| 512 | 32 | 2 | 1088 | 0.387 | 2648.68 | 0.910 | 70.35 | 1.296 | 839.27 |
|
||||
| 512 | 32 | 4 | 2176 | 0.659 | 3107.63 | 1.302 | 98.33 | 1.961 | 1109.77 |
|
||||
| 512 | 32 | 8 | 4352 | 1.322 | 3099.35 | 1.669 | 153.42 | 2.990 | 1455.43 |
|
||||
| 512 | 32 | 16 | 8704 | 2.639 | 3104.63 | 2.212 | 231.44 | 4.851 | 1794.32 |
|
||||
| 512 | 32 | 32 | 17408 | 5.284 | 3100.80 | 2.955 | 346.53 | 8.239 | 2112.93 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.417 | 2890.36 | 0.598 | 53.51 | 2.015 | 2048.45 |
|
||||
| 4096 | 32 | 2 | 8256 | 2.829 | 2895.62 | 1.019 | 62.82 | 3.848 | 2145.60 |
|
||||
| 4096 | 32 | 4 | 16512 | 5.656 | 2896.96 | 1.528 | 83.79 | 7.183 | 2298.71 |
|
||||
| 4096 | 32 | 8 | 33024 | 11.338 | 2890.02 | 2.127 | 120.36 | 13.465 | 2452.53 |
|
||||
| 4096 | 32 | 16 | 66048 | 22.709 | 2885.96 | 3.104 | 164.97 | 25.812 | 2558.79 |
|
||||
| 4096 | 32 | 32 | 132096 | 45.301 | 2893.35 | 4.723 | 216.80 | 50.024 | 2640.63 |
|
||||
| 8192 | 32 | 1 | 8224 | 3.022 | 2711.09 | 0.678 | 47.20 | 3.700 | 2222.89 |
|
||||
| 8192 | 32 | 2 | 16448 | 6.039 | 2713.01 | 1.149 | 55.70 | 7.188 | 2288.21 |
|
||||
| 8192 | 32 | 4 | 32896 | 12.050 | 2719.35 | 1.785 | 71.69 | 13.835 | 2377.67 |
|
||||
| 8192 | 32 | 8 | 65792 | 24.113 | 2717.90 | 2.629 | 97.39 | 26.741 | 2460.31 |
|
||||
| 8192 | 32 | 16 | 131584 | 48.178 | 2720.58 | 4.099 | 124.91 | 52.277 | 2517.06 |
|
||||
| 8192 | 32 | 32 | 263168 | 96.401 | 2719.31 | 6.696 | 152.93 | 103.097 | 2552.63 |
|
||||
|
||||
|
||||
- `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 |
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | dio | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 | 2986.97 ± 18.87 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 | 61.06 ± 0.23 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d4096 | 2633.45 ± 6.26 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d4096 | 54.77 ± 0.28 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d8192 | 2354.14 ± 3.84 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d8192 | 48.02 ± 0.40 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d16384 | 1908.86 ± 4.25 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d16384 | 40.23 ± 0.10 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d32768 | 1348.17 ± 2.00 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d32768 | 30.21 ± 0.04 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
build: 11fb327bf (7941)
|
||||
|
||||
## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
|
||||
|
||||
@@ -173,46 +173,46 @@ 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
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 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 |
|
||||
| 512 | 32 | 1 | 544 | 0.212 | 2420.12 | 1.100 | 29.10 | 1.311 | 414.85 |
|
||||
| 512 | 32 | 2 | 1088 | 0.428 | 2393.89 | 1.185 | 54.00 | 1.613 | 674.56 |
|
||||
| 512 | 32 | 4 | 2176 | 0.894 | 2290.41 | 1.229 | 104.17 | 2.123 | 1025.02 |
|
||||
| 512 | 32 | 8 | 4352 | 1.758 | 2330.36 | 1.319 | 194.15 | 3.076 | 1414.70 |
|
||||
| 512 | 32 | 16 | 8704 | 3.508 | 2335.21 | 1.543 | 331.90 | 5.051 | 1723.33 |
|
||||
| 512 | 32 | 32 | 17408 | 7.035 | 2328.93 | 1.738 | 589.21 | 8.773 | 1984.29 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.831 | 2237.25 | 1.125 | 28.44 | 2.956 | 1396.42 |
|
||||
| 4096 | 32 | 2 | 8256 | 3.642 | 2249.48 | 1.253 | 51.07 | 4.895 | 1686.64 |
|
||||
| 4096 | 32 | 4 | 16512 | 7.274 | 2252.26 | 1.380 | 92.72 | 8.655 | 1907.81 |
|
||||
| 4096 | 32 | 8 | 33024 | 14.576 | 2248.09 | 1.617 | 158.29 | 16.193 | 2039.37 |
|
||||
| 4096 | 32 | 16 | 66048 | 29.138 | 2249.17 | 2.081 | 246.01 | 31.219 | 2115.63 |
|
||||
| 4096 | 32 | 32 | 132096 | 58.275 | 2249.19 | 2.814 | 363.87 | 61.089 | 2162.34 |
|
||||
| 8192 | 32 | 1 | 8224 | 3.757 | 2180.26 | 1.184 | 27.03 | 4.941 | 1664.37 |
|
||||
| 8192 | 32 | 2 | 16448 | 7.522 | 2178.05 | 1.341 | 47.73 | 8.863 | 1855.77 |
|
||||
| 8192 | 32 | 4 | 32896 | 15.043 | 2178.25 | 1.548 | 82.69 | 16.591 | 1982.74 |
|
||||
| 8192 | 32 | 8 | 65792 | 30.111 | 2176.49 | 1.937 | 132.13 | 32.048 | 2052.90 |
|
||||
| 8192 | 32 | 16 | 131584 | 60.405 | 2169.90 | 2.706 | 189.21 | 63.111 | 2084.97 |
|
||||
| 8192 | 32 | 32 | 263168 | 120.439 | 2176.58 | 3.993 | 256.46 | 124.432 | 2114.96 |
|
||||
|
||||
|
||||
- `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 |
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | dio | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 | 2250.28 ± 6.41 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 | 29.43 ± 0.02 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d4096 | 2100.19 ± 8.96 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d4096 | 28.61 ± 0.02 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d8192 | 2007.56 ± 4.16 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d8192 | 27.38 ± 0.09 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d16384 | 1779.11 ± 6.42 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d16384 | 25.72 ± 0.03 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d32768 | 1471.23 ± 1.71 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d32768 | 22.51 ± 0.02 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
build: 11fb327bf (7941)
|
||||
|
||||
## ggml-org/gemma-3-4b-it-qat-GGUF
|
||||
|
||||
@@ -221,44 +221,91 @@ 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
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 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 |
|
||||
| 512 | 32 | 1 | 544 | 0.092 | 5566.97 | 0.412 | 77.63 | 0.504 | 1078.95 |
|
||||
| 512 | 32 | 2 | 1088 | 0.161 | 6345.67 | 0.522 | 122.70 | 0.683 | 1593.06 |
|
||||
| 512 | 32 | 4 | 2176 | 0.325 | 6309.87 | 0.562 | 227.68 | 0.887 | 2453.87 |
|
||||
| 512 | 32 | 8 | 4352 | 0.643 | 6374.42 | 0.685 | 373.67 | 1.328 | 3277.94 |
|
||||
| 512 | 32 | 16 | 8704 | 1.277 | 6413.64 | 0.915 | 559.47 | 2.192 | 3970.01 |
|
||||
| 512 | 32 | 32 | 17408 | 2.518 | 6506.57 | 1.249 | 819.61 | 3.767 | 4620.64 |
|
||||
| 4096 | 32 | 1 | 4128 | 0.674 | 6079.68 | 0.453 | 70.60 | 1.127 | 3662.88 |
|
||||
| 4096 | 32 | 2 | 8256 | 1.335 | 6137.82 | 0.627 | 102.03 | 1.962 | 4208.11 |
|
||||
| 4096 | 32 | 4 | 16512 | 2.657 | 6167.35 | 0.749 | 170.92 | 3.405 | 4848.71 |
|
||||
| 4096 | 32 | 8 | 33024 | 5.307 | 6173.91 | 0.974 | 262.89 | 6.281 | 5257.53 |
|
||||
| 4096 | 32 | 16 | 66048 | 10.610 | 6176.96 | 1.379 | 371.42 | 11.988 | 5509.40 |
|
||||
| 4096 | 32 | 32 | 132096 | 21.213 | 6178.89 | 2.122 | 482.50 | 23.335 | 5660.82 |
|
||||
| 8192 | 32 | 1 | 8224 | 1.359 | 6027.34 | 0.467 | 68.52 | 1.826 | 4503.48 |
|
||||
| 8192 | 32 | 2 | 16448 | 2.699 | 6069.68 | 0.653 | 98.03 | 3.352 | 4906.68 |
|
||||
| 8192 | 32 | 4 | 32896 | 5.366 | 6106.74 | 0.818 | 156.55 | 6.184 | 5319.96 |
|
||||
| 8192 | 32 | 8 | 65792 | 10.755 | 6093.50 | 1.174 | 218.04 | 11.929 | 5515.22 |
|
||||
| 8192 | 32 | 16 | 131584 | 21.484 | 6100.82 | 1.829 | 279.90 | 23.314 | 5644.11 |
|
||||
| 8192 | 32 | 32 | 263168 | 42.950 | 6103.40 | 3.058 | 334.91 | 46.008 | 5720.05 |
|
||||
|
||||
|
||||
- `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 |
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | dio | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 | 5948.74 ± 10.61 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 | 81.05 ± 0.20 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d4096 | 5652.69 ± 34.29 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d4096 | 76.37 ± 0.58 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d8192 | 5509.57 ± 40.69 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d8192 | 71.61 ± 0.80 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d16384 | 5340.86 ± 36.92 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d16384 | 70.89 ± 0.34 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | pp2048 @ d32768 | 5023.30 ± 13.52 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | 1 | tg32 @ d32768 | 62.28 ± 0.30 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
build: 11fb327bf (7941)
|
||||
|
||||
## ggml-org/GLM-4.7-Flash-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 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 | 0.433 | 1181.83 | 0.693 | 46.16 | 1.126 | 482.94 |
|
||||
| 512 | 32 | 2 | 1088 | 0.439 | 2334.46 | 1.034 | 61.89 | 1.473 | 738.75 |
|
||||
| 512 | 32 | 4 | 2176 | 0.772 | 2654.46 | 1.459 | 87.76 | 2.230 | 975.77 |
|
||||
| 512 | 32 | 8 | 4352 | 1.541 | 2658.78 | 2.043 | 125.31 | 3.583 | 1214.47 |
|
||||
| 512 | 32 | 16 | 8704 | 3.083 | 2656.91 | 2.675 | 191.42 | 5.758 | 1511.62 |
|
||||
| 512 | 32 | 32 | 17408 | 6.159 | 2660.12 | 3.615 | 283.24 | 9.774 | 1780.98 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.915 | 2139.30 | 0.725 | 44.14 | 2.640 | 1563.83 |
|
||||
| 4096 | 32 | 2 | 8256 | 3.834 | 2136.40 | 1.119 | 57.21 | 4.953 | 1666.81 |
|
||||
| 4096 | 32 | 4 | 16512 | 7.636 | 2145.72 | 1.631 | 78.49 | 9.266 | 1781.93 |
|
||||
| 4096 | 32 | 8 | 33024 | 15.295 | 2142.40 | 2.344 | 109.21 | 17.639 | 1872.20 |
|
||||
| 4096 | 32 | 16 | 66048 | 30.573 | 2143.62 | 3.773 | 135.70 | 34.346 | 1923.04 |
|
||||
| 4096 | 32 | 32 | 132096 | 61.282 | 2138.82 | 5.795 | 176.71 | 67.077 | 1969.31 |
|
||||
| 8192 | 32 | 1 | 8224 | 4.510 | 1816.24 | 0.760 | 42.11 | 5.270 | 1560.44 |
|
||||
| 8192 | 32 | 2 | 16448 | 9.036 | 1813.19 | 1.206 | 53.06 | 10.242 | 1605.91 |
|
||||
| 8192 | 32 | 4 | 32896 | 18.070 | 1813.43 | 1.783 | 71.80 | 19.852 | 1657.03 |
|
||||
| 8192 | 32 | 8 | 65792 | 36.125 | 1814.15 | 2.635 | 97.14 | 38.760 | 1697.41 |
|
||||
| 8192 | 32 | 16 | 131584 | 72.367 | 1811.20 | 4.954 | 103.34 | 77.322 | 1701.77 |
|
||||
| 8192 | 32 | 32 | 263168 | 144.501 | 1814.13 | 8.103 | 126.37 | 152.604 | 1724.51 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | dio | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | --: | --------------: | -------------------: |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | pp2048 | 2364.18 ± 11.43 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | tg32 | 48.68 ± 0.12 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | pp2048 @ d4096 | 1684.13 ± 1.24 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | tg32 @ d4096 | 44.62 ± 0.22 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | pp2048 @ d8192 | 1314.68 ± 1.41 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | tg32 @ d8192 | 42.59 ± 0.11 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | pp2048 @ d16384 | 914.05 ± 3.32 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | tg32 @ d16384 | 38.72 ± 0.13 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | pp2048 @ d32768 | 567.20 ± 0.90 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | CUDA | 99 | 2048 | 1 | 1 | tg32 @ d32768 | 32.65 ± 0.09 |
|
||||
|
||||
build: 11fb327bf (7941)
|
||||
|
||||
@@ -0,0 +1,298 @@
|
||||
## System info
|
||||
|
||||
```bash
|
||||
uname -a
|
||||
Darwin gg-studio 25.2.0 Darwin Kernel Version 25.2.0: Tue Nov 18 21:07:05 PST 2025; root:xnu-12377.61.12~1/RELEASE_ARM64_T6020 arm64
|
||||
|
||||
g++ --version
|
||||
Apple clang version 17.0.0 (clang-1700.3.19.1)
|
||||
Target: arm64-apple-darwin25.2.0
|
||||
```
|
||||
|
||||
## 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, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
|
||||
|
||||
| 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 | 0.215 | 2381.35 | 0.245 | 130.45 | 0.460 | 1181.81 |
|
||||
| 512 | 32 | 2 | 1088 | 0.379 | 2701.43 | 0.382 | 167.56 | 0.761 | 1429.67 |
|
||||
| 512 | 32 | 4 | 2176 | 0.721 | 2839.27 | 0.604 | 211.76 | 1.326 | 1641.32 |
|
||||
| 512 | 32 | 8 | 4352 | 1.433 | 2858.30 | 1.033 | 247.75 | 2.466 | 1764.57 |
|
||||
| 512 | 32 | 16 | 8704 | 2.853 | 2871.12 | 1.570 | 326.11 | 4.423 | 1967.77 |
|
||||
| 512 | 32 | 32 | 17408 | 5.699 | 2874.95 | 1.910 | 536.15 | 7.609 | 2287.88 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.552 | 2638.56 | 0.334 | 95.72 | 1.887 | 2188.00 |
|
||||
| 4096 | 32 | 2 | 8256 | 3.084 | 2655.88 | 0.404 | 158.54 | 3.488 | 2366.86 |
|
||||
| 4096 | 32 | 4 | 16512 | 6.151 | 2663.78 | 0.652 | 196.39 | 6.802 | 2427.37 |
|
||||
| 4096 | 32 | 8 | 33024 | 12.288 | 2666.77 | 1.135 | 225.47 | 13.423 | 2460.27 |
|
||||
| 4096 | 32 | 16 | 66048 | 24.563 | 2668.12 | 1.762 | 290.55 | 26.325 | 2508.97 |
|
||||
| 4096 | 32 | 32 | 132096 | 49.114 | 2668.73 | 2.398 | 426.94 | 51.512 | 2564.35 |
|
||||
| 8192 | 32 | 1 | 8224 | 3.345 | 2448.78 | 0.275 | 116.46 | 3.620 | 2271.76 |
|
||||
| 8192 | 32 | 2 | 16448 | 6.665 | 2458.11 | 0.425 | 150.71 | 7.090 | 2319.91 |
|
||||
| 8192 | 32 | 4 | 32896 | 13.315 | 2460.92 | 0.691 | 185.21 | 14.006 | 2348.63 |
|
||||
| 8192 | 32 | 8 | 65792 | 26.611 | 2462.73 | 1.212 | 211.16 | 27.823 | 2364.62 |
|
||||
| 8192 | 32 | 16 | 131584 | 53.232 | 2462.27 | 1.919 | 266.83 | 55.151 | 2385.88 |
|
||||
| 8192 | 32 | 32 | 263168 | 110.455 | 2373.30 | 2.752 | 372.03 | 113.208 | 2324.64 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | threads | n_ubatch | fa | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 | 2713.40 ± 3.56 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | tg32 | 129.97 ± 3.90 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d4096 | 2324.59 ± 3.01 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d4096 | 123.38 ± 0.17 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d8192 | 1989.82 ± 30.11 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d8192 | 117.39 ± 0.33 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d16384 | 1556.54 ± 6.22 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d16384 | 109.75 ± 0.42 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d32768 | 1122.63 ± 1.45 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d32768 | 98.25 ± 0.08 |
|
||||
|
||||
build: b828e18c7 (7948)
|
||||
|
||||
## 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, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
|
||||
|
||||
| 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 | 0.426 | 1200.92 | 0.361 | 88.56 | 0.788 | 690.64 |
|
||||
| 512 | 32 | 2 | 1088 | 0.683 | 1500.14 | 0.545 | 117.35 | 1.228 | 886.02 |
|
||||
| 512 | 32 | 4 | 2176 | 1.204 | 1701.56 | 0.847 | 151.19 | 2.050 | 1061.34 |
|
||||
| 512 | 32 | 8 | 4352 | 2.402 | 1705.20 | 1.455 | 176.00 | 3.857 | 1128.45 |
|
||||
| 512 | 32 | 16 | 8704 | 4.802 | 1705.90 | 2.349 | 217.93 | 7.152 | 1217.08 |
|
||||
| 512 | 32 | 32 | 17408 | 9.593 | 1707.85 | 3.665 | 279.42 | 13.258 | 1313.01 |
|
||||
| 4096 | 32 | 1 | 4128 | 2.581 | 1587.08 | 0.390 | 82.12 | 2.970 | 1389.67 |
|
||||
| 4096 | 32 | 2 | 8256 | 5.124 | 1598.79 | 0.589 | 108.62 | 5.713 | 1445.10 |
|
||||
| 4096 | 32 | 4 | 16512 | 10.231 | 1601.47 | 0.928 | 137.98 | 11.158 | 1479.80 |
|
||||
| 4096 | 32 | 8 | 33024 | 20.468 | 1600.94 | 1.606 | 159.38 | 22.074 | 1496.04 |
|
||||
| 4096 | 32 | 16 | 66048 | 40.924 | 1601.42 | 2.639 | 193.99 | 43.563 | 1516.15 |
|
||||
| 4096 | 32 | 32 | 132096 | 81.819 | 1601.98 | 4.466 | 229.29 | 86.284 | 1530.94 |
|
||||
| 8192 | 32 | 1 | 8224 | 5.517 | 1484.74 | 0.409 | 78.16 | 5.927 | 1387.58 |
|
||||
| 8192 | 32 | 2 | 16448 | 11.008 | 1488.43 | 0.622 | 102.92 | 11.629 | 1414.34 |
|
||||
| 8192 | 32 | 4 | 32896 | 22.002 | 1489.29 | 0.987 | 129.66 | 22.990 | 1430.90 |
|
||||
| 8192 | 32 | 8 | 65792 | 46.051 | 1423.11 | 1.858 | 137.79 | 47.909 | 1373.27 |
|
||||
| 8192 | 32 | 16 | 131584 | 97.680 | 1341.85 | 2.872 | 178.28 | 100.552 | 1308.62 |
|
||||
| 8192 | 32 | 32 | 263168 | 176.407 | 1486.02 | 5.048 | 202.85 | 181.455 | 1450.32 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | threads | n_ubatch | fa | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 | 1648.69 ± 1.80 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | tg32 | 85.60 ± 0.52 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d4096 | 1429.86 ± 1.01 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d4096 | 82.03 ± 0.12 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d8192 | 1257.90 ± 1.81 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d8192 | 78.23 ± 0.33 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d16384 | 1013.49 ± 0.70 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d16384 | 73.20 ± 0.28 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d32768 | 721.11 ± 0.58 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d32768 | 65.52 ± 0.10 |
|
||||
|
||||
build: b828e18c7 (7948)
|
||||
|
||||
## 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, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
|
||||
|
||||
| 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 | 0.243 | 2109.23 | 0.419 | 76.34 | 0.662 | 821.84 |
|
||||
| 512 | 32 | 2 | 1088 | 0.406 | 2521.40 | 0.575 | 111.36 | 0.981 | 1109.27 |
|
||||
| 512 | 32 | 4 | 2176 | 0.744 | 2751.65 | 0.841 | 152.22 | 1.585 | 1372.71 |
|
||||
| 512 | 32 | 8 | 4352 | 1.479 | 2770.20 | 1.330 | 192.48 | 2.809 | 1549.53 |
|
||||
| 512 | 32 | 16 | 8704 | 2.951 | 2776.20 | 2.572 | 199.05 | 5.523 | 1575.93 |
|
||||
| 512 | 32 | 32 | 17408 | 5.899 | 2777.64 | 2.603 | 393.34 | 8.502 | 2047.54 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.901 | 2154.15 | 0.474 | 67.58 | 2.375 | 1738.14 |
|
||||
| 4096 | 32 | 2 | 8256 | 3.788 | 2162.89 | 0.652 | 98.17 | 4.439 | 1859.69 |
|
||||
| 4096 | 32 | 4 | 16512 | 7.564 | 2166.18 | 0.990 | 129.24 | 8.554 | 1930.34 |
|
||||
| 4096 | 32 | 8 | 33024 | 15.121 | 2166.98 | 1.632 | 156.82 | 16.754 | 1971.12 |
|
||||
| 4096 | 32 | 16 | 66048 | 30.241 | 2167.09 | 3.166 | 161.72 | 33.407 | 1977.04 |
|
||||
| 4096 | 32 | 32 | 132096 | 60.474 | 2167.42 | 3.780 | 270.93 | 64.254 | 2055.86 |
|
||||
| 8192 | 32 | 1 | 8224 | 4.733 | 1730.92 | 0.483 | 66.29 | 5.215 | 1576.85 |
|
||||
| 8192 | 32 | 2 | 16448 | 9.459 | 1732.09 | 0.722 | 88.58 | 10.182 | 1615.46 |
|
||||
| 8192 | 32 | 4 | 32896 | 18.912 | 1732.65 | 1.120 | 114.26 | 20.032 | 1642.14 |
|
||||
| 8192 | 32 | 8 | 65792 | 37.797 | 1733.91 | 1.873 | 136.67 | 39.670 | 1658.49 |
|
||||
| 8192 | 32 | 16 | 131584 | 84.133 | 1557.92 | 3.718 | 137.72 | 87.850 | 1497.82 |
|
||||
| 8192 | 32 | 32 | 263168 | 157.550 | 1663.88 | 4.854 | 210.98 | 162.403 | 1620.46 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | threads | n_ubatch | fa | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 | 2453.11 ± 1.70 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | tg32 | 78.97 ± 0.46 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d4096 | 1569.46 ± 1.97 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d4096 | 71.18 ± 0.37 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d8192 | 1145.51 ± 1.16 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d8192 | 65.11 ± 0.36 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d16384 | 741.04 ± 0.74 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d16384 | 56.87 ± 0.14 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d32768 | 431.31 ± 0.31 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d32768 | 45.26 ± 0.11 |
|
||||
|
||||
build: b828e18c7 (7948)
|
||||
|
||||
## 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, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
|
||||
|
||||
| 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 | 0.339 | 1509.22 | 0.409 | 78.17 | 0.749 | 726.67 |
|
||||
| 512 | 32 | 2 | 1088 | 0.646 | 1584.93 | 0.483 | 132.45 | 1.129 | 963.45 |
|
||||
| 512 | 32 | 4 | 2176 | 1.258 | 1627.50 | 0.585 | 218.67 | 1.844 | 1180.21 |
|
||||
| 512 | 32 | 8 | 4352 | 2.506 | 1634.41 | 1.005 | 254.83 | 3.511 | 1239.64 |
|
||||
| 512 | 32 | 16 | 8704 | 5.007 | 1635.99 | 1.595 | 321.07 | 6.602 | 1318.38 |
|
||||
| 512 | 32 | 32 | 17408 | 10.007 | 1637.19 | 1.676 | 611.12 | 11.683 | 1490.03 |
|
||||
| 4096 | 32 | 1 | 4128 | 2.730 | 1500.46 | 0.431 | 74.31 | 3.160 | 1306.12 |
|
||||
| 4096 | 32 | 2 | 8256 | 5.446 | 1504.33 | 0.524 | 122.04 | 5.970 | 1382.91 |
|
||||
| 4096 | 32 | 4 | 16512 | 10.875 | 1506.59 | 0.662 | 193.45 | 11.537 | 1431.28 |
|
||||
| 4096 | 32 | 8 | 33024 | 21.749 | 1506.61 | 1.158 | 221.11 | 22.907 | 1441.64 |
|
||||
| 4096 | 32 | 16 | 66048 | 43.477 | 1507.36 | 1.901 | 269.32 | 45.378 | 1455.49 |
|
||||
| 4096 | 32 | 32 | 132096 | 86.954 | 1507.37 | 2.325 | 440.42 | 89.279 | 1479.59 |
|
||||
| 8192 | 32 | 1 | 8224 | 5.940 | 1379.21 | 0.449 | 71.20 | 6.389 | 1287.20 |
|
||||
| 8192 | 32 | 2 | 16448 | 11.865 | 1380.84 | 0.559 | 114.59 | 12.424 | 1323.92 |
|
||||
| 8192 | 32 | 4 | 32896 | 23.723 | 1381.25 | 0.728 | 175.80 | 24.452 | 1345.35 |
|
||||
| 8192 | 32 | 8 | 65792 | 47.434 | 1381.63 | 1.279 | 200.09 | 48.713 | 1350.60 |
|
||||
| 8192 | 32 | 16 | 131584 | 94.864 | 1381.69 | 2.198 | 232.97 | 97.061 | 1355.68 |
|
||||
| 8192 | 32 | 32 | 263168 | 189.743 | 1381.57 | 3.052 | 335.50 | 192.795 | 1365.01 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | threads | n_ubatch | fa | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 | 1565.91 ± 0.86 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | tg32 | 79.68 ± 0.39 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d4096 | 1317.41 ± 1.02 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d4096 | 74.70 ± 0.04 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d8192 | 1134.65 ± 0.76 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d8192 | 71.31 ± 0.12 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d16384 | 886.46 ± 0.78 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d16384 | 65.93 ± 0.06 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d32768 | 612.21 ± 0.30 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d32768 | 56.83 ± 0.02 |
|
||||
|
||||
build: b828e18c7 (7948)
|
||||
|
||||
## 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, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
|
||||
|
||||
| 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 | 0.186 | 2748.06 | 0.235 | 136.28 | 0.421 | 1291.78 |
|
||||
| 512 | 32 | 2 | 1088 | 0.342 | 2990.95 | 0.312 | 204.99 | 0.655 | 1662.15 |
|
||||
| 512 | 32 | 4 | 2176 | 0.662 | 3092.69 | 0.404 | 316.97 | 1.066 | 2041.21 |
|
||||
| 512 | 32 | 8 | 4352 | 1.317 | 3110.41 | 0.579 | 441.80 | 1.896 | 2294.97 |
|
||||
| 512 | 32 | 16 | 8704 | 2.625 | 3120.23 | 1.207 | 424.08 | 3.833 | 2270.93 |
|
||||
| 512 | 32 | 32 | 17408 | 5.242 | 3125.34 | 1.299 | 788.23 | 6.541 | 2661.19 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.408 | 2909.90 | 0.296 | 108.07 | 1.704 | 2422.95 |
|
||||
| 4096 | 32 | 2 | 8256 | 2.793 | 2933.40 | 0.325 | 197.00 | 3.118 | 2648.25 |
|
||||
| 4096 | 32 | 4 | 16512 | 5.567 | 2943.22 | 0.440 | 291.07 | 6.006 | 2749.05 |
|
||||
| 4096 | 32 | 8 | 33024 | 11.114 | 2948.23 | 0.640 | 400.26 | 11.754 | 2809.59 |
|
||||
| 4096 | 32 | 16 | 66048 | 22.217 | 2949.76 | 1.327 | 385.83 | 23.544 | 2805.26 |
|
||||
| 4096 | 32 | 32 | 132096 | 44.420 | 2950.77 | 1.553 | 659.30 | 45.973 | 2873.36 |
|
||||
| 8192 | 32 | 1 | 8224 | 2.860 | 2864.58 | 0.250 | 127.90 | 3.110 | 2644.42 |
|
||||
| 8192 | 32 | 2 | 16448 | 5.702 | 2873.63 | 0.335 | 191.07 | 6.036 | 2724.77 |
|
||||
| 8192 | 32 | 4 | 32896 | 11.383 | 2878.69 | 0.456 | 280.72 | 11.839 | 2778.63 |
|
||||
| 8192 | 32 | 8 | 65792 | 22.750 | 2880.75 | 0.671 | 381.48 | 23.421 | 2809.14 |
|
||||
| 8192 | 32 | 16 | 131584 | 45.484 | 2881.74 | 1.406 | 364.04 | 46.890 | 2806.22 |
|
||||
| 8192 | 32 | 32 | 263168 | 90.956 | 2882.10 | 1.793 | 570.98 | 92.749 | 2837.41 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | threads | n_ubatch | fa | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 | 2923.59 ± 3.10 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | tg32 | 134.28 ± 1.29 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d4096 | 2748.21 ± 3.05 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d4096 | 133.11 ± 0.08 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d8192 | 2641.45 ± 2.31 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d8192 | 125.85 ± 0.35 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d16384 | 2446.20 ± 2.94 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d16384 | 125.00 ± 0.12 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d32768 | 2129.18 ± 7.43 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d32768 | 113.14 ± 0.10 |
|
||||
|
||||
build: b828e18c7 (7948)
|
||||
|
||||
## ggml-org/GLM-4.7-Flash-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
|
||||
|
||||
| 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 | 0.326 | 1568.69 | 0.522 | 61.28 | 0.849 | 641.09 |
|
||||
| 512 | 32 | 2 | 1088 | 0.528 | 1939.42 | 0.744 | 86.07 | 1.272 | 855.63 |
|
||||
| 512 | 32 | 4 | 2176 | 0.968 | 2114.85 | 1.105 | 115.85 | 2.073 | 1049.56 |
|
||||
| 512 | 32 | 8 | 4352 | 1.928 | 2124.62 | 1.684 | 151.99 | 3.612 | 1204.82 |
|
||||
| 512 | 32 | 16 | 8704 | 3.844 | 2131.34 | 3.141 | 162.99 | 6.985 | 1246.11 |
|
||||
| 512 | 32 | 32 | 17408 | 7.683 | 2132.38 | 3.924 | 260.95 | 11.608 | 1499.71 |
|
||||
| 4096 | 32 | 1 | 4128 | 3.280 | 1248.75 | 0.723 | 44.29 | 4.003 | 1031.33 |
|
||||
| 4096 | 32 | 2 | 8256 | 6.545 | 1251.63 | 0.930 | 68.85 | 7.475 | 1104.53 |
|
||||
| 4096 | 32 | 4 | 16512 | 13.080 | 1252.64 | 1.454 | 88.03 | 14.534 | 1136.12 |
|
||||
| 4096 | 32 | 8 | 33024 | 26.154 | 1252.90 | 2.388 | 107.20 | 28.542 | 1157.04 |
|
||||
| 4096 | 32 | 16 | 66048 | 52.297 | 1253.14 | 4.724 | 108.37 | 57.022 | 1158.30 |
|
||||
| 4096 | 32 | 32 | 132096 | 104.578 | 1253.34 | 7.266 | 140.93 | 111.844 | 1181.08 |
|
||||
| 8192 | 32 | 1 | 8224 | 9.623 | 851.31 | 0.767 | 41.72 | 10.390 | 791.54 |
|
||||
| 8192 | 32 | 2 | 16448 | 20.916 | 783.32 | 1.148 | 55.74 | 22.064 | 745.45 |
|
||||
| 8192 | 32 | 4 | 32896 | 43.509 | 753.14 | 1.833 | 69.82 | 45.342 | 725.51 |
|
||||
| 8192 | 32 | 8 | 65792 | 79.621 | 823.10 | 3.180 | 80.50 | 82.801 | 794.58 |
|
||||
| 8192 | 32 | 16 | 131584 | 153.770 | 852.39 | 6.502 | 78.74 | 160.272 | 821.00 |
|
||||
| 8192 | 32 | 32 | 263168 | 307.539 | 852.39 | 10.839 | 94.48 | 318.378 | 826.59 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | threads | n_ubatch | fa | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 | 1629.33 ± 0.27 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | tg32 | 59.58 ± 0.13 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d4096 | 732.67 ± 0.42 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d4096 | 47.44 ± 0.15 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d8192 | 474.33 ± 0.33 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d8192 | 40.20 ± 0.20 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d16384 | 277.46 ± 0.09 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d16384 | 31.50 ± 0.93 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | pp2048 @ d32768 | 151.44 ± 0.05 |
|
||||
| deepseek2 30B.A3B Q8_0 | 29.65 GiB | 29.94 B | MTL,BLAS | 16 | 2048 | 1 | tg32 @ d32768 | 21.81 ± 0.01 |
|
||||
|
||||
build: b828e18c7 (7948)
|
||||
@@ -3437,16 +3437,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
|
||||
params.speculative.ngram_size_m = value;
|
||||
}
|
||||
).set_examples({LLAMA_EXAMPLE_SERVER}));
|
||||
add_opt(common_arg(
|
||||
{"--spec-ngram-check-rate"}, "N",
|
||||
string_format("ngram check rate for ngram-simple/ngram-map speculative decoding (default: %d)", params.speculative.ngram_check_rate),
|
||||
[](common_params & params, int value) {
|
||||
if (value < 1) {
|
||||
throw std::invalid_argument("ngram check rate must be at least 1");
|
||||
}
|
||||
params.speculative.ngram_check_rate = value;
|
||||
}
|
||||
).set_examples({LLAMA_EXAMPLE_SERVER}));
|
||||
add_opt(common_arg(
|
||||
{"--spec-ngram-min-hits"}, "N",
|
||||
string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits),
|
||||
|
||||
+35
-4
@@ -380,15 +380,46 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
|
||||
return msgs;
|
||||
}
|
||||
|
||||
json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
|
||||
static json render_message_to_json(const std::vector<common_chat_msg> & msgs, const jinja::caps & c) {
|
||||
if (!c.supports_string_content && !c.supports_typed_content) {
|
||||
LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
|
||||
}
|
||||
|
||||
bool only_string_accepted = c.supports_string_content && !c.supports_typed_content;
|
||||
bool only_typed_accepted = !c.supports_string_content && c.supports_typed_content;
|
||||
|
||||
json messages = json::array();
|
||||
for (const auto & msg : msgs) {
|
||||
json jmsg = msg.to_json_oaicompat(concat_typed_text);
|
||||
messages.push_back(jmsg);
|
||||
if (only_string_accepted) {
|
||||
json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
|
||||
messages.push_back(jmsg);
|
||||
} else if (only_typed_accepted) {
|
||||
json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
|
||||
if (jmsg.at("content").is_string()) {
|
||||
jmsg["content"] = json::array({
|
||||
json{
|
||||
{"type", "text"},
|
||||
{"text", jmsg.at("content").get<std::string>()},
|
||||
}
|
||||
});
|
||||
}
|
||||
messages.push_back(jmsg);
|
||||
} else {
|
||||
json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
|
||||
messages.push_back(jmsg);
|
||||
}
|
||||
}
|
||||
return messages;
|
||||
}
|
||||
|
||||
// DEPRECATED: only used in tests
|
||||
json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
|
||||
jinja::caps c;
|
||||
c.supports_string_content = true;
|
||||
c.supports_typed_content = !concat_typed_text;
|
||||
return render_message_to_json(msgs, c);
|
||||
}
|
||||
|
||||
std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & tools) {
|
||||
std::vector<common_chat_tool> result;
|
||||
|
||||
@@ -3020,7 +3051,7 @@ static common_chat_params common_chat_templates_apply_jinja(
|
||||
: *tmpls->template_default;
|
||||
const auto & src = tmpl.source();
|
||||
const auto & caps = tmpl.original_caps();
|
||||
params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content);
|
||||
params.messages = render_message_to_json(inputs.messages, tmpl.original_caps());
|
||||
params.add_generation_prompt = inputs.add_generation_prompt;
|
||||
params.tool_choice = inputs.tool_choice;
|
||||
params.reasoning_format = inputs.reasoning_format;
|
||||
|
||||
@@ -240,6 +240,8 @@ bool common_chat_templates_support_enable_thinking(const common_chat_templates *
|
||||
|
||||
// Parses a JSON array of messages in OpenAI's chat completion API format.
|
||||
std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);
|
||||
|
||||
// DEPRECATED: only used in tests
|
||||
nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
|
||||
|
||||
std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);
|
||||
|
||||
@@ -269,7 +269,6 @@ struct common_params_speculative {
|
||||
|
||||
uint16_t ngram_size_n = 12; // ngram size for lookup
|
||||
uint16_t ngram_size_m = 48; // mgram size for speculative tokens
|
||||
uint16_t ngram_check_rate = 1; // check rate for ngram lookup
|
||||
uint16_t ngram_min_hits = 1; // minimum hits at ngram/mgram lookup for mgram to be proposed
|
||||
|
||||
std::shared_ptr<common_ngram_mod> ngram_mod;
|
||||
|
||||
@@ -63,7 +63,8 @@ static void caps_print_stats(value & v, const std::string & path) {
|
||||
|
||||
std::map<std::string, bool> caps::to_map() const {
|
||||
return {
|
||||
{"requires_typed_content", requires_typed_content},
|
||||
{"supports_string_content", supports_string_content},
|
||||
{"supports_typed_content", supports_typed_content},
|
||||
{"supports_tools", supports_tools},
|
||||
{"supports_tool_calls", supports_tool_calls},
|
||||
{"supports_parallel_tool_calls", supports_parallel_tool_calls},
|
||||
@@ -89,7 +90,7 @@ caps caps_get(jinja::program & prog) {
|
||||
return v->stats.ops.find(op_name) != v->stats.ops.end();
|
||||
};
|
||||
|
||||
// case: typed content requirement
|
||||
// case: typed content support
|
||||
caps_try_execute(
|
||||
prog,
|
||||
[&]() {
|
||||
@@ -105,12 +106,16 @@ caps caps_get(jinja::program & prog) {
|
||||
// tools
|
||||
return json{nullptr};
|
||||
},
|
||||
[&](bool, value & messages, value &) {
|
||||
[&](bool success, value & messages, value &) {
|
||||
auto & content = messages->at(0)->at("content");
|
||||
caps_print_stats(content, "messages[0].content");
|
||||
if (has_op(content, "selectattr") || has_op(content, "array_access")) {
|
||||
// accessed as an array
|
||||
result.requires_typed_content = true;
|
||||
result.supports_typed_content = true;
|
||||
}
|
||||
if (!success) {
|
||||
// failed to execute with content as string
|
||||
result.supports_string_content = false;
|
||||
}
|
||||
}
|
||||
);
|
||||
|
||||
+3
-1
@@ -14,7 +14,9 @@ struct caps {
|
||||
bool supports_parallel_tool_calls = true;
|
||||
bool supports_preserve_reasoning = false; // support assistant message with reasoning_content
|
||||
|
||||
bool requires_typed_content = false; // default: use string content
|
||||
// one of the 2 content capabilities must be true
|
||||
bool supports_string_content = true;
|
||||
bool supports_typed_content = false;
|
||||
|
||||
// for reporting on server
|
||||
std::map<std::string, bool> to_map() const;
|
||||
|
||||
@@ -446,6 +446,12 @@ value for_statement::execute_impl(context & ctx) {
|
||||
|
||||
value iterable_val = iter_expr->execute(scope);
|
||||
|
||||
// mark the variable being iterated as used for stats
|
||||
if (ctx.is_get_stats) {
|
||||
iterable_val->stats.used = true;
|
||||
iterable_val->stats.ops.insert("array_access");
|
||||
}
|
||||
|
||||
if (iterable_val->is_undefined()) {
|
||||
JJ_DEBUG("%s", "For loop iterable is undefined, skipping loop");
|
||||
iterable_val = mk_val<value_array>();
|
||||
|
||||
@@ -231,10 +231,9 @@ void common_ngram_map_draft(common_ngram_map & map,
|
||||
GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
|
||||
}
|
||||
|
||||
// Only check every check_rate tokens to save compute
|
||||
// i.e., perform check if (cur_len - idx_last_check) >= check_rate
|
||||
if (map.idx_last_check + map.check_rate > cur_len) {
|
||||
return;
|
||||
if (map.idx_last_check > cur_len) {
|
||||
// Should not happen because of common_ngram_map_begin().
|
||||
GGML_ABORT("%s: map.idx_last_check > cur_len: %zu > %zu", __func__, map.idx_last_check, cur_len);
|
||||
}
|
||||
map.idx_last_check = cur_len;
|
||||
|
||||
|
||||
+3
-5
@@ -24,7 +24,6 @@
|
||||
struct common_ngram_simple_config {
|
||||
uint16_t size_ngram; // size of n-grams to lookup in self-mode
|
||||
uint16_t size_mgram; // size of m-grams to draft in self-mode
|
||||
uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token
|
||||
};
|
||||
|
||||
// Searches for a n-gram in the history and checks whether a draft sequence should be generated.
|
||||
@@ -66,15 +65,14 @@ struct common_ngram_map {
|
||||
bool key_only; // true if only key n-grams are used, no values.
|
||||
|
||||
std::vector<common_ngram_map_key> keys; // key n-grams which occur several times in token-history
|
||||
uint16_t check_rate; // check for speculative decoding without draft model for each check_rate token
|
||||
uint16_t min_hits; // minimum number of key hits to consider a draft
|
||||
|
||||
bool show_key_map_stats = false; // true, if statitics of the key_map should be printed.
|
||||
bool show_key_map_stats = false; // true, if statistics of the key_map should be printed.
|
||||
|
||||
common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys,
|
||||
uint16_t check_rate, uint16_t min_hits)
|
||||
uint16_t min_hits)
|
||||
: size_key(sz_key), size_value(sz_value), key_only(only_keys),
|
||||
check_rate(check_rate), min_hits(min_hits) {
|
||||
min_hits(min_hits) {
|
||||
key_map.resize(COMMON_NGRAM_HASH_MAP_SIZE); // 2^18 hash entries, 0 entries if key_map shouldn't be used
|
||||
}
|
||||
|
||||
|
||||
+60
-31
@@ -113,13 +113,14 @@ static bool common_speculative_are_compatible(
|
||||
struct common_speculative_state {
|
||||
const enum common_speculative_type type;
|
||||
|
||||
// TODO: rename to n_call_draft, n_gen_drafts, n_acc_drafts, n_gen_tokens, n_acc_tokens
|
||||
// TODO: add n_call_begin, n_call_accept
|
||||
size_t drafts_call_count = 0; // number of times this implementation was called.
|
||||
size_t drafts_generated_count = 0; // number of times a draft or part was generated by this implementation.
|
||||
size_t drafts_accepted_count = 0; // number of times a draft or part was accepted by the target model.
|
||||
size_t drafts_generated_tokens = 0; // number of tokens generated by this implementation.
|
||||
size_t drafts_accepted_tokens = 0; // number of tokens accepted by the target model.
|
||||
size_t n_call_begin = 0; // number of times this implementation was called for refresh.
|
||||
size_t n_call_draft = 0; // number of times this implementation was called for generation.
|
||||
size_t n_call_accept = 0; // number of times this implementation was called for accumulation.
|
||||
|
||||
size_t n_gen_drafts = 0; // number of times a draft or part was generated by this implementation.
|
||||
size_t n_acc_drafts = 0; // number of times a draft or part was accepted by the target model.
|
||||
size_t n_gen_tokens = 0; // number of tokens generated by this implementation.
|
||||
size_t n_acc_tokens = 0; // number of tokens accepted by the target model.
|
||||
|
||||
// TODO: track performance of most recent calls
|
||||
const bool gen_perf = true; // whether to generate performance stats.
|
||||
@@ -465,8 +466,6 @@ struct common_speculative_state_eagle3 : public common_speculative_state {
|
||||
struct common_speculative_state_ngram_simple : public common_speculative_state {
|
||||
common_ngram_simple_config config;
|
||||
|
||||
uint16_t check_id = 0; // used to control the frequency of generating drafts
|
||||
|
||||
common_speculative_state_ngram_simple(
|
||||
enum common_speculative_type type,
|
||||
common_ngram_simple_config config)
|
||||
@@ -481,11 +480,6 @@ struct common_speculative_state_ngram_simple : public common_speculative_state {
|
||||
const llama_tokens & prompt_tgt,
|
||||
llama_token id_last,
|
||||
llama_tokens & result) override {
|
||||
++check_id;
|
||||
if (check_id < config.check_rate) {
|
||||
return;
|
||||
}
|
||||
check_id = 0;
|
||||
|
||||
result = common_ngram_simple_draft(config, prompt_tgt, id_last);
|
||||
GGML_UNUSED(params);
|
||||
@@ -752,10 +746,9 @@ static common_ngram_map get_common_ngram_map(const common_speculative_config & c
|
||||
uint16_t size_key = config.params.ngram_size_n;
|
||||
uint16_t size_value = config.params.ngram_size_m;
|
||||
bool key_only = (config.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
|
||||
uint16_t check_rate = config.params.ngram_check_rate;
|
||||
uint16_t min_hits = config.params.ngram_min_hits;
|
||||
|
||||
return common_ngram_map(size_key, size_value, key_only, check_rate, min_hits);
|
||||
return common_ngram_map(size_key, size_value, key_only, min_hits);
|
||||
}
|
||||
|
||||
static common_speculative_state_ngram_cache create_state_ngram_cache(
|
||||
@@ -805,6 +798,42 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
|
||||
return it->second;
|
||||
}
|
||||
|
||||
bool common_speculative_is_compat(llama_context * ctx_tgt) {
|
||||
auto * mem = llama_get_memory(ctx_tgt);
|
||||
if (mem == nullptr) {
|
||||
return false;
|
||||
}
|
||||
|
||||
bool res = true;
|
||||
|
||||
llama_memory_clear(mem, true);
|
||||
|
||||
// eval 2 tokens to check if the context is compatible
|
||||
std::vector<llama_token> tmp;
|
||||
tmp.push_back(0);
|
||||
tmp.push_back(0);
|
||||
|
||||
int ret = llama_decode(ctx_tgt, llama_batch_get_one(tmp.data(), tmp.size()));
|
||||
if (ret != 0) {
|
||||
LOG_ERR("%s: llama_decode() failed: %d\n", __func__, ret);
|
||||
res = false;
|
||||
goto done;
|
||||
}
|
||||
|
||||
// try to remove the last tokens
|
||||
if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
|
||||
LOG_WRN("%s: the target context does not support partial sequence removal\n", __func__);
|
||||
res = false;
|
||||
goto done;
|
||||
}
|
||||
|
||||
done:
|
||||
llama_memory_clear(mem, true);
|
||||
llama_synchronize(ctx_tgt);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
// initialization of the speculative decoding system
|
||||
//
|
||||
common_speculative * common_speculative_init(
|
||||
@@ -895,12 +924,10 @@ common_speculative * common_speculative_init(
|
||||
|
||||
uint16_t ngram_size_key = ngram_map.size_key;
|
||||
uint16_t mgram_size_value = ngram_map.size_value;
|
||||
uint16_t check_rate = ngram_map.check_rate;
|
||||
|
||||
auto config_simple = common_ngram_simple_config {
|
||||
/* .size_ngram = */ ngram_size_key,
|
||||
/* .size_mgram = */ mgram_size_value,
|
||||
/* .check_rate = */ check_rate
|
||||
/* .size_mgram = */ mgram_size_value
|
||||
};
|
||||
auto state = std::make_unique<common_speculative_state_ngram_simple>(
|
||||
/* .type = */ config.type,
|
||||
@@ -961,6 +988,7 @@ void common_speculative_begin(common_speculative * spec, const llama_tokens & pr
|
||||
for (auto & impl : spec->impls) {
|
||||
common_time_meas tm(impl->t_begin_us, !impl->gen_perf);
|
||||
impl->begin(prompt);
|
||||
impl->n_call_begin++;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -977,17 +1005,17 @@ llama_tokens common_speculative_draft(
|
||||
{
|
||||
common_time_meas tm(impl->t_draft_us, !impl->gen_perf);
|
||||
impl->draft(params, prompt_tgt, id_last, result);
|
||||
impl->drafts_call_count++;
|
||||
impl->n_call_draft++;
|
||||
}
|
||||
|
||||
if (!result.empty()) {
|
||||
LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
|
||||
common_speculative_type_to_str(impl.get()->type).c_str(), prompt_tgt.size(),
|
||||
impl.get()->drafts_call_count, result.size());
|
||||
impl.get()->n_call_draft, result.size());
|
||||
|
||||
spec->curr_impl = impl.get(); // set current implementation for stats
|
||||
impl->drafts_generated_count++;
|
||||
impl->drafts_generated_tokens += result.size();
|
||||
impl->n_gen_drafts++;
|
||||
impl->n_gen_tokens += result.size();
|
||||
|
||||
break; // We have a draft, so break out of the loop and return it.
|
||||
}
|
||||
@@ -1008,11 +1036,12 @@ void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
|
||||
{
|
||||
common_time_meas tm(impl->t_accept_us, !impl->gen_perf);
|
||||
if (n_accepted > 0) {
|
||||
impl->drafts_accepted_count++;
|
||||
impl->drafts_accepted_tokens += n_accepted;
|
||||
impl->n_acc_drafts++;
|
||||
impl->n_acc_tokens += n_accepted;
|
||||
}
|
||||
|
||||
impl->accept(n_accepted);
|
||||
impl->n_call_accept++;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1033,13 +1062,13 @@ void common_speculative_print_stats(const common_speculative * spec) {
|
||||
str_perf = "";
|
||||
}
|
||||
|
||||
LOG_INF("statistics %s: #calls = %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
|
||||
LOG_INF("statistics %s: #calls(b,g,a) = %zu %zu %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
|
||||
common_speculative_type_to_str(impl->type).c_str(),
|
||||
impl->drafts_call_count,
|
||||
impl->drafts_generated_count,
|
||||
impl->drafts_accepted_count,
|
||||
impl->drafts_generated_tokens,
|
||||
impl->drafts_accepted_tokens,
|
||||
impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
|
||||
impl->n_gen_drafts,
|
||||
impl->n_acc_drafts,
|
||||
impl->n_gen_tokens,
|
||||
impl->n_acc_tokens,
|
||||
str_perf.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@@ -14,6 +14,10 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
|
||||
// convert type to string
|
||||
std::string common_speculative_type_to_str(enum common_speculative_type type);
|
||||
|
||||
// check if the llama_context is compatible for speculative decoding
|
||||
// note: clears the memory of the context
|
||||
bool common_speculative_is_compat(llama_context * ctx_tgt);
|
||||
|
||||
common_speculative * common_speculative_init(
|
||||
common_params_speculative & params,
|
||||
llama_context * ctx_tgt);
|
||||
|
||||
+394
-25
@@ -586,6 +586,10 @@ class ModelBase:
|
||||
gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
|
||||
gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
|
||||
gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
|
||||
# Kimi KDA conv weights should be F32
|
||||
gguf.MODEL_TENSOR.SSM_CONV1D_Q,
|
||||
gguf.MODEL_TENSOR.SSM_CONV1D_K,
|
||||
gguf.MODEL_TENSOR.SSM_CONV1D_V,
|
||||
)
|
||||
)
|
||||
or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
|
||||
@@ -903,10 +907,10 @@ class TextModel(ModelBase):
|
||||
if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
|
||||
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
|
||||
logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
|
||||
if (n_experts := self.hparams.get("num_local_experts")) is not None:
|
||||
if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
|
||||
self.gguf_writer.add_expert_count(n_experts)
|
||||
logger.info(f"gguf: expert count = {n_experts}")
|
||||
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
|
||||
if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)) is not None:
|
||||
self.gguf_writer.add_expert_used_count(n_experts_used)
|
||||
logger.info(f"gguf: experts used count = {n_experts_used}")
|
||||
if (n_expert_groups := self.hparams.get("n_group")) is not None:
|
||||
@@ -916,7 +920,7 @@ class TextModel(ModelBase):
|
||||
self.gguf_writer.add_expert_group_used_count(n_group_used)
|
||||
logger.info(f"gguf: expert groups used count = {n_group_used}")
|
||||
|
||||
if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func"], optional=True)) is not None:
|
||||
if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
|
||||
if score_func == "sigmoid":
|
||||
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
|
||||
elif score_func == "softmax":
|
||||
@@ -4105,37 +4109,29 @@ class Qwen2MoeModel(TextModel):
|
||||
# Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
|
||||
if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
|
||||
mapped = f"{name}.weight" if not name.endswith(".weight") else name
|
||||
# Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
|
||||
# Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
|
||||
# Need PyTorch: (128, 2048, 768) [reversed of GGML]
|
||||
# So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
|
||||
permuted = data_torch.permute(0, 2, 1).contiguous()
|
||||
yield from super().modify_tensors(permuted, mapped, bid)
|
||||
# HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
|
||||
yield from super().modify_tensors(data_torch, mapped, bid)
|
||||
return
|
||||
|
||||
if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
|
||||
if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
|
||||
if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
|
||||
raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
|
||||
split_dim = data_torch.shape[-1] // 2
|
||||
gate = data_torch[..., :split_dim].contiguous()
|
||||
up = data_torch[..., split_dim:].contiguous()
|
||||
# Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
|
||||
# Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
|
||||
# Need PyTorch: (128, 768, 2048) [reversed of GGML]
|
||||
# So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
|
||||
base_name = name.removesuffix(".weight")
|
||||
base = base_name.rsplit('.', 1)[0]
|
||||
mapped_gate = f"{base}.gate_proj.weight"
|
||||
mapped_up = f"{base}.up_proj.weight"
|
||||
perm_gate = gate.permute(0, 2, 1).contiguous()
|
||||
perm_up = up.permute(0, 2, 1).contiguous()
|
||||
yield from super().modify_tensors(perm_gate, mapped_gate, bid)
|
||||
yield from super().modify_tensors(perm_up, mapped_up, bid)
|
||||
# HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
|
||||
n_ff = data_torch.shape[-2] // 2
|
||||
gate = data_torch[..., :n_ff, :].contiguous()
|
||||
up = data_torch[..., n_ff:, :].contiguous()
|
||||
# gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
|
||||
base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
|
||||
mapped_gate = f"{base_name}.gate_proj.weight"
|
||||
mapped_up = f"{base_name}.up_proj.weight"
|
||||
yield from super().modify_tensors(gate, mapped_gate, bid)
|
||||
yield from super().modify_tensors(up, mapped_up, bid)
|
||||
return
|
||||
|
||||
if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
|
||||
# skip visual tensors
|
||||
return
|
||||
|
||||
if name.find("experts") != -1:
|
||||
n_experts = self.hparams["num_experts"]
|
||||
assert bid is not None
|
||||
@@ -4531,6 +4527,35 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
|
||||
if name.startswith("model.visual."):
|
||||
return
|
||||
|
||||
# Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
|
||||
if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
|
||||
name = name.replace("language_model.", "")
|
||||
mapped = f"{name}.weight" if not name.endswith(".weight") else name
|
||||
permuted = data_torch.permute(0, 2, 1).contiguous()
|
||||
yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
|
||||
return
|
||||
|
||||
if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
|
||||
name = name.replace("language_model.", "")
|
||||
if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
|
||||
raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
|
||||
split_dim = data_torch.shape[-1] // 2
|
||||
gate = data_torch[..., :split_dim].contiguous()
|
||||
up = data_torch[..., split_dim:].contiguous()
|
||||
# Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
|
||||
# Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
|
||||
# Need PyTorch: (128, 768, 2048) [reversed of GGML]
|
||||
# So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
|
||||
base_name = name.removesuffix(".weight")
|
||||
base = base_name.rsplit('.', 1)[0]
|
||||
mapped_gate = f"{base}.gate_proj.weight"
|
||||
mapped_up = f"{base}.up_proj.weight"
|
||||
perm_gate = gate.permute(0, 2, 1).contiguous()
|
||||
perm_up = up.permute(0, 2, 1).contiguous()
|
||||
yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
|
||||
yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
|
||||
return
|
||||
|
||||
yield from super().modify_tensors(data_torch, name, bid)
|
||||
|
||||
|
||||
@@ -5013,6 +5038,221 @@ class CodeShellModel(TextModel):
|
||||
self.gguf_writer.add_rope_scaling_factor(1.0)
|
||||
|
||||
|
||||
@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
|
||||
class KimiLinearModel(TextModel):
|
||||
"""Kimi-Linear model with hybrid MLA+KDA architecture"""
|
||||
model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
|
||||
|
||||
_experts: list[dict[str, Tensor]] | None = None
|
||||
|
||||
def set_vocab(self):
|
||||
try:
|
||||
self._set_vocab_gpt2()
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
|
||||
tokpre = self.get_vocab_base_pre(tokenizer)
|
||||
|
||||
if tokpre == "kimi-k2":
|
||||
# Build merges list using the approach similar to HunYuanMoE
|
||||
merges = []
|
||||
vocab = {}
|
||||
mergeable_ranks = tokenizer.model._mergeable_ranks
|
||||
for token, rank in mergeable_ranks.items():
|
||||
vocab[QwenModel.token_bytes_to_string(token)] = rank
|
||||
if len(token) == 1:
|
||||
continue
|
||||
merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
|
||||
if len(merged) == 2:
|
||||
merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
|
||||
# Build token list
|
||||
vocab_size = self.hparams["vocab_size"]
|
||||
special_tokens = tokenizer.special_tokens
|
||||
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
|
||||
tokens: list[str] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
tokens.append(f"[PAD{i}]")
|
||||
toktypes.append(gguf.TokenType.UNUSED)
|
||||
else:
|
||||
token = reverse_vocab[i]
|
||||
tokens.append(token)
|
||||
if i in special_tokens.values():
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||||
self.gguf_writer.add_tokenizer_pre(tokpre)
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
self.gguf_writer.add_token_merges(merges)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
# override eos id in config.json with tiktoken eos id
|
||||
self.gguf_writer.add_eos_token_id(tokenizer.eos_id)
|
||||
else:
|
||||
raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
# note: To enable MLA KV cache, attention needs to be converted into MQA (ie: GQA with 1 group)
|
||||
self.hparams["num_key_value_heads"] = 1
|
||||
|
||||
super().set_gguf_parameters()
|
||||
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
|
||||
|
||||
# KDA & MLA params
|
||||
# Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
|
||||
linear_attn_config = self.hparams["linear_attn_config"]
|
||||
# n_head == 0 for KDA layers, n_head > 0 for MLA layers
|
||||
# full_attention_layers list will be used to distingush layer type
|
||||
_num_kv_heads = list()
|
||||
_full_attn_layers = linear_attn_config["full_attn_layers"]
|
||||
for il in range(self.hparams["num_hidden_layers"]):
|
||||
if il + 1 in _full_attn_layers:
|
||||
_num_kv_heads.append(self.hparams["num_key_value_heads"])
|
||||
else:
|
||||
_num_kv_heads.append(0)
|
||||
assert len(_num_kv_heads) == self.hparams["num_hidden_layers"]
|
||||
self.gguf_writer.add_head_count_kv(_num_kv_heads)
|
||||
|
||||
if (ssm_d_conv := linear_attn_config.get("short_conv_kernel_size")) is not None:
|
||||
self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
|
||||
if (kda_head_dim := linear_attn_config.get("head_dim")) is not None:
|
||||
self.gguf_writer.add_kda_head_dim(kda_head_dim)
|
||||
|
||||
# MLA params - use add_* methods that handle arch substitution
|
||||
# Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
|
||||
if (q_lora_rank := self.find_hparam(["q_lora_rank", "n_lora_q"], optional=True)) is not None:
|
||||
self.gguf_writer.add_q_lora_rank(q_lora_rank)
|
||||
# To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
|
||||
kv_lora_rank = self.find_hparam(["kv_lora_rank", "n_lora_kv"], optional=False)
|
||||
self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
|
||||
|
||||
# MLA head dimensions
|
||||
# Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
|
||||
qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
|
||||
# Rotation - use qk_rope_head_dim for Kimi
|
||||
qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim", "n_rot"], optional=False)
|
||||
self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
|
||||
self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
|
||||
v_head_dim = self.hparams.get("v_head_dim")
|
||||
|
||||
# Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
|
||||
if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
|
||||
self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
|
||||
elif qk_nope_head_dim is not None:
|
||||
n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
|
||||
self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
|
||||
|
||||
# n_embd_head_v_mla = v_head_dim
|
||||
if (n_embd_head_v_mla := self.hparams.get("n_embd_head_v_mla")) is not None:
|
||||
self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
|
||||
elif v_head_dim is not None:
|
||||
self.gguf_writer.add_value_length_mla(v_head_dim)
|
||||
|
||||
# moe_intermediate_size (1024 for Kimi)
|
||||
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
|
||||
# num_shared_experts (1 for Kimi)
|
||||
self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
|
||||
# first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
|
||||
self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
|
||||
# Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
|
||||
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
|
||||
|
||||
def prepare_tensors(self):
|
||||
super().prepare_tensors()
|
||||
if self._experts is not None:
|
||||
experts = [k for d in self._experts for k in d.keys()]
|
||||
if len(experts) > 0:
|
||||
raise ValueError(f"Unprocessed experts: {experts}")
|
||||
|
||||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||||
logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")
|
||||
|
||||
# Handle KDA conv1d weights
|
||||
# HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
|
||||
# llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
|
||||
# GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
|
||||
# Memory layouts match: both have conv_step (d_conv) changing fastest
|
||||
if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
|
||||
# HF shape: [d_inner, d_conv] e.g. [4096, 4]
|
||||
# Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
|
||||
if data_torch.ndim == 2:
|
||||
d_inner, d_conv = data_torch.shape
|
||||
# Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
|
||||
data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
|
||||
logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
|
||||
elif data_torch.ndim == 3:
|
||||
# Already 3D [d_inner, 1, d_conv] from unsqueeze
|
||||
d_inner, _, d_conv = data_torch.shape
|
||||
data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
|
||||
logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
|
||||
|
||||
# Kimi specific bias
|
||||
if name.endswith("e_score_correction_bias"):
|
||||
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
|
||||
|
||||
# Handle A_log: iHF stores as [1, 1, num_heads, 1]
|
||||
# llama.cpp expects ggml ne = [1, num_heads, 1, 1]
|
||||
# GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
|
||||
if name.endswith(".A_log"):
|
||||
data_torch = -torch.exp(data_torch)
|
||||
if name.endswith(".dt_bias"):
|
||||
name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
|
||||
logger.info("Changed dt_bias to dt_proj.bias")
|
||||
|
||||
# process the experts separately
|
||||
if name.find("block_sparse_moe.experts") != -1:
|
||||
n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=False)
|
||||
assert bid is not None
|
||||
|
||||
if self._experts is None:
|
||||
self._experts = [{} for _ in range(self.block_count)]
|
||||
|
||||
self._experts[bid][name] = data_torch
|
||||
|
||||
if len(self._experts[bid]) >= n_experts * 3:
|
||||
# merge the experts into a single 3d tensor
|
||||
# w1: gate, w2: down, w3: up
|
||||
for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
|
||||
("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
|
||||
("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
|
||||
datas: list[Tensor] = []
|
||||
for xid in range(n_experts):
|
||||
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
|
||||
datas.append(self._experts[bid][ename])
|
||||
del self._experts[bid][ename]
|
||||
data_torch = torch.stack(datas, dim=0)
|
||||
new_name = self.format_tensor_name(tname, bid)
|
||||
yield from super().modify_tensors(data_torch, new_name, bid)
|
||||
return
|
||||
|
||||
# note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
|
||||
if name.endswith("kv_b_proj.weight"):
|
||||
name_kb = name.replace("kv_b_proj", "k_b_proj")
|
||||
name_vb = name.replace("kv_b_proj", "v_b_proj")
|
||||
n_head_kv = self.hparams["num_key_value_heads"]
|
||||
v_head_dim = self.find_hparam(["n_embd_head_v_mla", "v_head_dim"], optional=False)
|
||||
qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
|
||||
logger.info("Split kv_b n_head_kv %d\n" % n_head_kv)
|
||||
assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
|
||||
kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
|
||||
k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
|
||||
k_b = k_b.transpose(1, 2)
|
||||
yield from super().modify_tensors(k_b, name_kb, bid)
|
||||
yield from super().modify_tensors(v_b, name_vb, bid)
|
||||
return
|
||||
|
||||
yield from super().modify_tensors(data_torch, name, bid)
|
||||
|
||||
|
||||
@ModelBase.register("InternLM2ForCausalLM")
|
||||
class InternLM2Model(TextModel):
|
||||
model_arch = gguf.MODEL_ARCH.INTERNLM2
|
||||
@@ -7693,6 +7933,135 @@ class MimoV2Model(TextModel):
|
||||
raise ValueError(f"Unprocessed experts: {experts}")
|
||||
|
||||
|
||||
@ModelBase.register("Step3p5ForCausalLM")
|
||||
class Step35Model(TextModel):
|
||||
model_arch = gguf.MODEL_ARCH.STEP35
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
rope_theta = self.hparams.get("rope_theta")
|
||||
if isinstance(rope_theta, list):
|
||||
self.hparams["rope_theta"] = float(rope_theta[0])
|
||||
self.hparams["local_rope_theta"] = float(rope_theta[1])
|
||||
self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
|
||||
self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
|
||||
|
||||
super().set_gguf_parameters()
|
||||
|
||||
layer_types = self.hparams.get("layer_types") or []
|
||||
partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
|
||||
attn_other = self.hparams.get("attention_other_setting") or {}
|
||||
|
||||
n_head_base = self.hparams["num_attention_heads"]
|
||||
n_kv_base = self.hparams["num_attention_groups"]
|
||||
|
||||
n_head_swa = attn_other.get("num_attention_heads", n_head_base)
|
||||
n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
|
||||
|
||||
layer_types = layer_types[: self.block_count]
|
||||
partial_rotary_factors = partial_rotary_factors[: self.block_count]
|
||||
assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
|
||||
head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
|
||||
kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
|
||||
swa_pat = [lt == "sliding_attention" for lt in layer_types]
|
||||
|
||||
self.gguf_writer.add_head_count(head_arr)
|
||||
self.gguf_writer.add_head_count_kv(kv_arr)
|
||||
|
||||
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
|
||||
self.gguf_writer.add_sliding_window_pattern(swa_pat)
|
||||
|
||||
self.gguf_writer.add_value_length(self.hparams["head_dim"])
|
||||
|
||||
# MoE params
|
||||
self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
|
||||
self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
|
||||
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
|
||||
self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
|
||||
|
||||
if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
|
||||
self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
|
||||
if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
|
||||
self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
|
||||
|
||||
# leading dense blocks
|
||||
leading_dense = 0
|
||||
moe_layers_enum = self.hparams.get("moe_layers_enum")
|
||||
if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
|
||||
moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
|
||||
if moe_layers:
|
||||
leading_dense = max(0, moe_layers[0])
|
||||
self.gguf_writer.add_leading_dense_block_count(leading_dense)
|
||||
self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
|
||||
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
|
||||
|
||||
# Optional per-layer SwiGLU clamps.
|
||||
if (limits := self.hparams.get("swiglu_limits")) is not None:
|
||||
limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
|
||||
self.gguf_writer.add_swiglu_clamp_exp(limits_f)
|
||||
if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
|
||||
limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
|
||||
self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
|
||||
|
||||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
|
||||
# remove mtp layers
|
||||
if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
|
||||
il = int(m.group(1))
|
||||
n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
|
||||
if il >= n_main:
|
||||
return
|
||||
if name.endswith("norm.weight"):
|
||||
data_torch += 1.0
|
||||
# Map router bias (expert selection bias) to a GGUF bias tensor
|
||||
if name.endswith(".moe.router_bias"):
|
||||
name += ".bias"
|
||||
|
||||
if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
|
||||
data_torch = data_torch.squeeze().contiguous()
|
||||
|
||||
yield from super().modify_tensors(data_torch, name, bid)
|
||||
|
||||
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
|
||||
# Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
|
||||
# llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
|
||||
rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
|
||||
rope_type = rope_params.get("rope_type") or ""
|
||||
if rope_type.lower() != "llama3":
|
||||
return
|
||||
|
||||
# Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
|
||||
rope_theta = self.hparams.get("rope_theta", 10000.0)
|
||||
if isinstance(rope_theta, list):
|
||||
rope_theta = rope_theta[0]
|
||||
base = float(rope_theta)
|
||||
if (dim := self.hparams.get("head_dim")) is None:
|
||||
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
|
||||
dim = int(dim)
|
||||
|
||||
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
|
||||
|
||||
factor = float(rope_params.get("factor", 8.0))
|
||||
low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
|
||||
high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
|
||||
old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
|
||||
|
||||
low_freq_wavelen = old_context_len / low_freq_factor
|
||||
high_freq_wavelen = old_context_len / high_freq_factor
|
||||
|
||||
rope_factors: list[float] = []
|
||||
for freq in freqs:
|
||||
wavelen = 2 * math.pi / float(freq)
|
||||
if wavelen < high_freq_wavelen:
|
||||
rope_factors.append(1.0)
|
||||
elif wavelen > low_freq_wavelen:
|
||||
rope_factors.append(factor)
|
||||
else:
|
||||
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
|
||||
rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
|
||||
|
||||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
|
||||
|
||||
|
||||
@ModelBase.register("PanguEmbeddedForCausalLM")
|
||||
class PanguEmbeddedModel(TextModel):
|
||||
model_arch = gguf.MODEL_ARCH.PANGU_EMBED
|
||||
|
||||
@@ -0,0 +1,180 @@
|
||||
# GGML-VirtGPU Backend
|
||||
|
||||
The GGML-VirtGPU backend enables GGML applications to run machine
|
||||
learning computations on host hardware while the application itself
|
||||
runs inside a virtual machine. It uses host-guest shared memory to
|
||||
efficiently share data buffers between the two sides.
|
||||
|
||||
This backend relies on the virtio-gpu, and VirglRenderer API Remoting
|
||||
(APIR) component. The backend is split into two libraries:
|
||||
- a GGML implementation (the "remoting frontend"), running in the
|
||||
guest and interacting with the virtgpu device
|
||||
- a VirglRenderer APIR compatible library (the "remoting backend"),
|
||||
running in the host and interacting with Virglrenderer and an actual
|
||||
GGML device backend.
|
||||
|
||||
## OS support
|
||||
|
||||
| OS | Status | Backend | CI testing | Notes
|
||||
| -------- | ----------------- | ----------- | ----------- | -----
|
||||
| MacOS 14 | Supported | ggml-metal | X | Working when compiled on MacOS 14
|
||||
| MacOS 15 | Supported | ggml-metal | X | Working when compiled on MacOS 14 or MacOS 15
|
||||
| MacOS 26 | Not tested | | |
|
||||
| Linux | Under development | ggml-vulkan | not working | Working locally, CI running into deadlocks
|
||||
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
The GGML-VirtGPU backend consists of three main components:
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
%% Nodes
|
||||
|
||||
subgraph GuestVM ["Guest VM - Frontend"]
|
||||
App([GGML Application<br/>llama.cpp, etc.])
|
||||
|
||||
direction TB
|
||||
Interface[GGML Backend Interface]
|
||||
Comm["GGML-VirtGPU<br/>(hypercalls + shared mem)"]
|
||||
|
||||
App --> Interface
|
||||
Interface --> Comm
|
||||
end
|
||||
|
||||
API[virtio-gpu / virglrenderer API]
|
||||
|
||||
subgraph HostSystem [Host System - Backend]
|
||||
direction TB
|
||||
Dispatcher[GGML-VirtGPU-Backend]
|
||||
BackendLib[GGML Backend library<br/>Metal / Vulkan / CPU / ...]
|
||||
|
||||
Dispatcher --> BackendLib
|
||||
end
|
||||
|
||||
%% Connections
|
||||
Comm --> API
|
||||
API --> HostSystem
|
||||
```
|
||||
|
||||
### Key Components
|
||||
|
||||
1. **Guest-side Frontend** (`ggml-virtgpu/`): Implements the GGML backend interface and forwards operations to the host
|
||||
2. **Host-side Backend** (`ggml-virtgpu/backend/`): Receives forwarded operations and executes them on actual hardware backends
|
||||
3. **Communication Layer**: Uses virtio-gpu hypercalls and shared memory for efficient data transfer
|
||||
|
||||
## Features
|
||||
|
||||
- **Dynamic backend loading** on the host side (CPU, CUDA, Metal, etc.)
|
||||
- **Zero-copy data transfer** via host-guest shared memory pages
|
||||
|
||||
## Communication Protocol
|
||||
|
||||
### Hypercalls and Shared Memory
|
||||
|
||||
The backend uses two primary communication mechanisms:
|
||||
|
||||
1. **Hypercalls (`DRM_IOCTL_VIRTGPU_EXECBUFFER`)**: Trigger remote execution from guest to host
|
||||
2. **Shared Memory Pages**: Zero-copy data transfer for tensors and parameters
|
||||
|
||||
#### Shared Memory Layout
|
||||
|
||||
Each connection uses two shared memory buffers:
|
||||
|
||||
- **Data Buffer** (24 MiB): For command/response data and tensor transfers
|
||||
- **Reply Buffer** (16 KiB): For command replies and status information
|
||||
- **Data Buffers**: Dynamically allocated host-guest shared buffers
|
||||
served as GGML buffers.
|
||||
|
||||
### APIR Protocol
|
||||
|
||||
The Virglrender API Remoting protocol defines three command types:
|
||||
|
||||
- `HANDSHAKE`: Protocol version negotiation and capability discovery
|
||||
- `LOADLIBRARY`: Dynamic loading of backend libraries on the host
|
||||
- `FORWARD`: API function call forwarding
|
||||
|
||||
### Binary Serialization
|
||||
|
||||
Commands and data are serialized using a custom binary protocol with:
|
||||
|
||||
- Fixed-size encoding for basic types
|
||||
- Variable-length arrays with size prefixes
|
||||
- Buffer bounds checking
|
||||
- Error recovery mechanisms
|
||||
|
||||
## Supported Operations
|
||||
|
||||
### Device Operations
|
||||
- Device enumeration and capability queries
|
||||
- Memory information (total/free)
|
||||
- Backend type detection
|
||||
|
||||
### Buffer Operations
|
||||
- Buffer allocation and deallocation
|
||||
- Tensor data transfer (host ↔ guest)
|
||||
- Memory copying and clearing
|
||||
|
||||
### Computation Operations
|
||||
- Graph execution forwarding
|
||||
|
||||
## Build Requirements
|
||||
|
||||
### Guest-side Dependencies
|
||||
- `libdrm` for DRM/virtio-gpu communication
|
||||
- C++20 compatible compiler
|
||||
- CMake 3.14+
|
||||
|
||||
### Host-side Dependencies
|
||||
- virglrenderer with APIR support (pending upstream review)
|
||||
- Target backend libraries (libggml-metal, libggml-vulkan, etc.)
|
||||
|
||||
## Configuration
|
||||
|
||||
### Environment Variables
|
||||
|
||||
- `GGML_VIRTGPU_BACKEND_LIBRARY`: Path to the host-side backend library
|
||||
- `GGML_VIRTGPU_DEBUG`: Enable debug logging
|
||||
|
||||
### Build Options
|
||||
|
||||
- `GGML_VIRTGPU`: Enable the VirtGPU backend (`ON` or `OFF`, default: `OFF`)
|
||||
- `GGML_VIRTGPU_BACKEND`: Build the host-side backend component (`ON`, `OFF` or `ONLY`, default: `OFF`)
|
||||
|
||||
### System Requirements
|
||||
|
||||
- VM with virtio-gpu support
|
||||
- VirglRenderer with APIR patches
|
||||
- Compatible backend libraries on host
|
||||
|
||||
## Limitations
|
||||
|
||||
- **VM-specific**: Only works in virtual machines with virtio-gpu support
|
||||
- **Host dependency**: Requires properly configured host-side backend
|
||||
- **Latency**: Small overhead from VM escaping for each operation
|
||||
|
||||
|
||||
* This work is pending upstream changes in the VirglRenderer
|
||||
project.
|
||||
* The backend can be tested with Virglrenderer compiled from source
|
||||
using this PR:
|
||||
https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590
|
||||
* This work is pending changes in the VMM/hypervisor running the
|
||||
virtual machine, which need to know how to route the newly
|
||||
introduced APIR capset.
|
||||
* The environment variable `VIRGL_ROUTE_VENUS_TO_APIR=1` allows
|
||||
using the Venus capset, until the relevant hypervisors have been
|
||||
patched. However, setting this flag breaks the Vulkan/Venus normal
|
||||
behavior.
|
||||
* The environment variable `GGML_REMOTING_USE_APIR_CAPSET` tells the
|
||||
`ggml-virtgpu` backend to use the APIR capset. This will become
|
||||
the default when the relevant hypervisors have been patched.
|
||||
|
||||
* This work focused on improving the performance of llama.cpp running
|
||||
on MacOS containers, and is mainly tested on this platform. The
|
||||
linux support (via `krun`) is in progress.
|
||||
|
||||
## See Also
|
||||
|
||||
- [Development and Testing](VirtGPU/development.md)
|
||||
- [Backend configuration](VirtGPU/configuration.md)
|
||||
@@ -0,0 +1,174 @@
|
||||
# GGML-VirtGPU Backend Configuration
|
||||
|
||||
This document describes the environment variables used by the ggml-virtgpu backend system, covering both the frontend (guest-side) and backend (host-side) components.
|
||||
|
||||
## Environment Variables Overview
|
||||
|
||||
The ggml-virtgpu backend uses environment variables for configuration across three main components:
|
||||
- **Frontend (Guest)**: GGML applications running in VMs
|
||||
- **Hypervisor**: Virglrenderer/APIR system
|
||||
- **Backend (Host)**: Host-side GGML backend integration
|
||||
|
||||
## Frontend (Guest-side) Configuration
|
||||
|
||||
### GGML_REMOTING_USE_APIR_CAPSET
|
||||
- **Location**: `ggml/src/ggml-virtgpu/virtgpu.cpp`
|
||||
- **Type**: Boolean flag (presence-based)
|
||||
- **Purpose**: Controls which virtio-gpu capability set to use for communication
|
||||
- **Values**:
|
||||
- Set (any value): Use the APIR capset (long-term setup)
|
||||
- Unset: Use the Venus capset (easier for testing with an unmodified hypervisor)
|
||||
- **Default**: Unset (Venus capset)
|
||||
- **Usage**:
|
||||
```bash
|
||||
export GGML_REMOTING_USE_APIR_CAPSET=1 # Use APIR capset
|
||||
# or leave unset for Venus capset
|
||||
```
|
||||
|
||||
## Hypervisor (Virglrenderer/APIR) Configuration
|
||||
|
||||
These environment variables are used during the transition phase for
|
||||
running with an unmodified hypervisor (not supporting the
|
||||
VirglRenderer APIR component). They will be removed in the future, and
|
||||
the hypervisor will instead configure VirglRenderer with the APIR
|
||||
_Configuration Key_.
|
||||
|
||||
### VIRGL_APIR_BACKEND_LIBRARY
|
||||
- **Location**: `virglrenderer/src/apir/apir-context.c`
|
||||
- **Configuration Key**: `apir.load_library.path`
|
||||
- **Type**: File path string
|
||||
- **Purpose**: Path to the APIR backend library that virglrenderer should dynamically load
|
||||
- **Required**: Yes
|
||||
- **Example**:
|
||||
```bash
|
||||
export VIRGL_APIR_BACKEND_LIBRARY="/path/to/libggml-remotingbackend.so"
|
||||
```
|
||||
|
||||
### VIRGL_ROUTE_VENUS_TO_APIR
|
||||
- **Location**: `virglrenderer/src/apir/apir-renderer.h`
|
||||
- **Type**: Boolean flag (presence-based)
|
||||
- **Purpose**: Temporary workaround to route Venus capset calls to APIR during hypervisor transition period
|
||||
- **Status**: will be removed once hypervisors support APIR natively
|
||||
- **Warning**: Breaks normal Vulkan/Venus functionality
|
||||
- **Usage**:
|
||||
```bash
|
||||
export VIRGL_ROUTE_VENUS_TO_APIR=1 # For testing with an unmodified hypervisor
|
||||
```
|
||||
|
||||
### VIRGL_APIR_LOG_TO_FILE
|
||||
- **Location**: `virglrenderer/src/apir/apir-renderer.c`
|
||||
- **Environment Variable**: `VIRGL_APIR_LOG_TO_FILE`
|
||||
- **Type**: File path string
|
||||
- **Purpose**: Enable debug logging from the VirglRenderer APIR component to specified file
|
||||
- **Required**: No (optional debugging)
|
||||
- **Default**: Logging to `stderr`
|
||||
- **Usage**:
|
||||
```bash
|
||||
export VIRGL_APIR_LOG_TO_FILE="/tmp/apir-debug.log"
|
||||
```
|
||||
|
||||
## Backend (Host-side) Configuration
|
||||
|
||||
These environment variables are used during the transition phase for
|
||||
running with an unmodified hypervisor (not supporting the
|
||||
VirglRenderer APIR component). They will be removed in the future, and
|
||||
the hypervisor will instead configure VirglRenderer with the APIR
|
||||
_Configuration Key_.
|
||||
|
||||
### APIR_LLAMA_CPP_GGML_LIBRARY_PATH
|
||||
- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
|
||||
- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
|
||||
- **Configuration Key**: `ggml.library.path`
|
||||
- **Type**: File path string
|
||||
- **Purpose**: Path to the actual GGML backend library (Metal, CUDA, Vulkan, etc.)
|
||||
- **Required**: **Yes** - backend initialization fails without this
|
||||
- **Examples**:
|
||||
```bash
|
||||
# macOS with Metal backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
|
||||
|
||||
# Linux with CUDA backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-cuda.so"
|
||||
|
||||
# macOS or Linux with Vulkan backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-vulkan.so"
|
||||
```
|
||||
|
||||
### APIR_LLAMA_CPP_GGML_LIBRARY_REG
|
||||
- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
|
||||
- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
|
||||
- **Configuration Key**: `ggml.library.reg`
|
||||
- **Type**: Function symbol name string
|
||||
- **Purpose**: Name of the backend registration function to call after loading the library
|
||||
- **Required**: No (defaults to `ggml_backend_init`)
|
||||
- **Default**: `ggml_backend_init`
|
||||
- **Examples**:
|
||||
```bash
|
||||
# Metal backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
|
||||
|
||||
# CUDA backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_cuda_reg"
|
||||
|
||||
# Vulkan backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_vulkan_reg"
|
||||
|
||||
# Generic fallback (default)
|
||||
# export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_init"
|
||||
```
|
||||
|
||||
### APIR_LLAMA_CPP_LOG_TO_FILE
|
||||
- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp:62`
|
||||
- **Environment Variable**: `APIR_LLAMA_CPP_LOG_TO_FILE`
|
||||
- **Type**: File path string
|
||||
- **Purpose**: Enable debug logging from the GGML backend to specified file
|
||||
- **Required**: No (optional debugging)
|
||||
- **Usage**:
|
||||
```bash
|
||||
export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml-backend-debug.log"
|
||||
```
|
||||
|
||||
## Configuration Flow
|
||||
|
||||
The configuration system works as follows:
|
||||
|
||||
1. **Hypervisor Setup**: Virglrenderer loads the APIR backend library specified by `VIRGL_APIR_BACKEND_LIBRARY`
|
||||
|
||||
2. **Context Creation**: When an APIR context is created, it populates a configuration table with environment variables:
|
||||
- `apir.load_library.path` ← `VIRGL_APIR_BACKEND_LIBRARY`
|
||||
- `ggml.library.path` ← `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
|
||||
- `ggml.library.reg` ← `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
|
||||
- this step will eventually be performed by the hypervisor itself, with command-line arguments instead of environment variables.
|
||||
|
||||
3. **Backend Initialization**: The backend queries the configuration via callbacks:
|
||||
- `virgl_cbs->get_config(ctx_id, "ggml.library.path")` returns the library path
|
||||
- `virgl_cbs->get_config(ctx_id, "ggml.library.reg")` returns the registration function
|
||||
|
||||
4. **Library Loading**: The backend dynamically loads and initializes the specified GGML library
|
||||
|
||||
## Error Messages
|
||||
|
||||
Common error scenarios and their messages:
|
||||
|
||||
- **Missing library path**: `"cannot open the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_PATH' not defined"`
|
||||
- **Missing registration function**: `"cannot register the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_REG' not defined"`
|
||||
|
||||
## Example Complete Configuration
|
||||
|
||||
Here's an example configuration for a macOS host with Metal backend:
|
||||
|
||||
```bash
|
||||
# Hypervisor environment
|
||||
export VIRGL_APIR_BACKEND_LIBRARY="/opt/llama.cpp/lib/libggml-virtgpu-backend.dylib"
|
||||
|
||||
# Backend configuration
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
|
||||
|
||||
# Optional logging
|
||||
export VIRGL_APIR_LOG_TO_FILE="/tmp/apir.log"
|
||||
export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml.log"
|
||||
|
||||
# Guest configuration
|
||||
export GGML_REMOTING_USE_APIR_CAPSET=1
|
||||
```
|
||||
@@ -0,0 +1,220 @@
|
||||
# Development and Testing
|
||||
|
||||
## Development
|
||||
|
||||
### Code Generation
|
||||
|
||||
The backend uses code generation from YAML configuration:
|
||||
|
||||
```bash
|
||||
# Regenerate protocol code
|
||||
cd ggml-virtgpu/
|
||||
python regenerate_remoting.py
|
||||
```
|
||||
|
||||
### Adding New Operations
|
||||
|
||||
1. Add function definition to `ggmlremoting_functions.yaml`
|
||||
2. Regenerate code with `regenerate_remoting.py`
|
||||
3. Implement guest-side forwarding in `virtgpu-forward-*.cpp`
|
||||
4. Implement host-side handling in `backend-dispatched-*.cpp`
|
||||
|
||||
## Testing
|
||||
|
||||
This document provides instructions for building and testing the GGML-VirtGPU backend on macOS with containers.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
The testing setup requires:
|
||||
|
||||
- macOS host system
|
||||
- Container runtime with `libkrun` provider (podman machine)
|
||||
- Access to development patchset for VirglRenderer
|
||||
|
||||
### Required Patchsets
|
||||
|
||||
The backend requires patches that are currently under review:
|
||||
|
||||
- **Virglrenderer APIR upstream PR**: https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590 (for reference)
|
||||
- **MacOS Virglrenderer (for krunkit)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-macos
|
||||
- **Linux Virglrenderer (for krun)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-linux
|
||||
|
||||
### Build Instructions
|
||||
|
||||
#### 1. Build ggml-virtgpu-backend (Host-side, macOS)
|
||||
|
||||
```bash
|
||||
# Build the backend that runs natively on macOS
|
||||
mkdir llama.cpp
|
||||
cd llama.cpp
|
||||
git clone https://github.com/ggml-org/llama.cpp.git src
|
||||
cd src
|
||||
|
||||
LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
|
||||
|
||||
cmake -S . -B $LLAMA_MAC_BUILD \
|
||||
-DGGML_NATIVE=OFF \
|
||||
-DLLAMA_CURL=ON \
|
||||
-DGGML_REMOTINGBACKEND=ONLY \
|
||||
-DGGML_METAL=ON
|
||||
|
||||
TARGETS="ggml-metal"
|
||||
cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $TARGETS
|
||||
|
||||
# Build additional tools for native benchmarking
|
||||
EXTRA_TARGETS="llama-run llama-bench"
|
||||
cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
|
||||
```
|
||||
|
||||
#### 2. Build virglrenderer (Host-side, macOS)
|
||||
|
||||
```bash
|
||||
# Build virglrenderer with APIR support
|
||||
mkdir virglrenderer
|
||||
git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
|
||||
cd src
|
||||
|
||||
VIRGL_BUILD_DIR=$PWD/build
|
||||
|
||||
# -Dvenus=true and VIRGL_ROUTE_VENUS_TO_APIR=1 route the APIR requests via the Venus backend, for easier testing without a patched hypervisor
|
||||
|
||||
meson setup $VIRGL_BUILD_DIR \
|
||||
-Dvenus=true \
|
||||
-Dapir=true
|
||||
|
||||
ninja -C $VIRGL_BUILD_DIR
|
||||
```
|
||||
|
||||
#### 3. Build ggml-virtgpu (Guest-side, Linux)
|
||||
|
||||
Option A: Build from a script:
|
||||
|
||||
```bash
|
||||
# Inside a Linux container
|
||||
mkdir llama.cpp
|
||||
git clone https://github.com/ggml-org/llama.cpp.git src
|
||||
cd src
|
||||
|
||||
LLAMA_LINUX_BUILD=$PWD//build-virtgpu
|
||||
|
||||
cmake -S . -B $LLAMA_LINUX_BUILD \
|
||||
-DGGML_VIRTGPU=ON
|
||||
|
||||
ninja -C $LLAMA_LINUX_BUILD
|
||||
```
|
||||
|
||||
Option B: Build container image with frontend:
|
||||
|
||||
```bash
|
||||
cat << EOF > remoting.containerfile
|
||||
FROM quay.io/fedora/fedora:43
|
||||
USER 0
|
||||
|
||||
WORKDIR /app/remoting
|
||||
|
||||
ARG LLAMA_CPP_REPO="https://github.com/ggml-org/llama.cpp.git"
|
||||
ARG LLAMA_CPP_VERSION="master"
|
||||
ARG LLAMA_CPP_CMAKE_FLAGS="-DGGML_VIRTGPU=ON"
|
||||
ARG LLAMA_CPP_CMAKE_BUILD_FLAGS="--parallel 4"
|
||||
|
||||
RUN dnf install -y git cmake gcc gcc-c++ libcurl-devel libdrm-devel
|
||||
|
||||
RUN git clone "\${LLAMA_CPP_REPO}" src \\
|
||||
&& git -C src fetch origin \${LLAMA_CPP_VERSION} \\
|
||||
&& git -C src reset --hard FETCH_HEAD
|
||||
|
||||
RUN mkdir -p build \\
|
||||
&& cd src \\
|
||||
&& set -o pipefail \\
|
||||
&& cmake -S . -B ../build \${LLAMA_CPP_CMAKE_FLAGS} \\
|
||||
&& cmake --build ../build/ \${LLAMA_CPP_CMAKE_BUILD_FLAGS}
|
||||
|
||||
ENTRYPOINT ["/app/remoting/src/build/bin/llama-server"]
|
||||
EOF
|
||||
|
||||
mkdir -p empty_dir
|
||||
podman build -f remoting.containerfile ./empty_dir -t localhost/llama-cpp.virtgpu
|
||||
```
|
||||
|
||||
### Environment Setup
|
||||
|
||||
#### Set krunkit Environment Variables
|
||||
|
||||
```bash
|
||||
# Define the base directories (adapt these paths to your system)
|
||||
VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build
|
||||
LLAMA_MAC_BUILD=$HOME/remoting/llama.cpp/build-backend
|
||||
|
||||
# For krunkit to load the custom virglrenderer library
|
||||
export DYLD_LIBRARY_PATH=$VIRGL_BUILD_DIR/src
|
||||
|
||||
# For Virglrenderer to load the ggml-remotingbackend library
|
||||
export VIRGL_APIR_BACKEND_LIBRARY="$LLAMA_MAC_BUILD/bin/libggml-virtgpu-backend.dylib"
|
||||
|
||||
# For llama.cpp remotingbackend to load the ggml-metal backend
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="$LLAMA_MAC_BUILD/bin/libggml-metal.dylib"
|
||||
export APIR_LLAMA_CPP_GGML_LIBRARY_REG=ggml_backend_metal_reg
|
||||
```
|
||||
|
||||
#### Launch Container Environment
|
||||
|
||||
```bash
|
||||
# Set container provider to libkrun
|
||||
export CONTAINERS_MACHINE_PROVIDER=libkrun
|
||||
podman machine start
|
||||
```
|
||||
|
||||
#### Verify Environment
|
||||
|
||||
Confirm that krunkit is using the correct virglrenderer library:
|
||||
|
||||
```bash
|
||||
lsof -c krunkit | grep virglrenderer
|
||||
# Expected output:
|
||||
# krunkit 50574 user txt REG 1,14 2273912 10849442 ($VIRGL_BUILD_DIR/src)/libvirglrenderer.1.dylib
|
||||
```
|
||||
|
||||
### Running Tests
|
||||
|
||||
#### Launch Test Container
|
||||
|
||||
```bash
|
||||
# Optional model caching
|
||||
mkdir -p models
|
||||
PODMAN_CACHE_ARGS="-v models:/models --user root:root --cgroupns host --security-opt label=disable -w /models"
|
||||
|
||||
podman run $PODMAN_CACHE_ARGS -it --rm --device /dev/dri localhost/llama-cpp.virtgpu
|
||||
```
|
||||
|
||||
#### Test llama.cpp in Container
|
||||
|
||||
```bash
|
||||
|
||||
# Run performance benchmark
|
||||
/app/remoting/build/bin/llama-bench -m ./llama3.2
|
||||
```
|
||||
|
||||
Expected output (performance may vary):
|
||||
```
|
||||
| model | size | params | backend | ngl | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------: | -------------------: |
|
||||
| llama 3B Q4_K - Medium | 1.87 GiB | 3.21 B | ggml-virtgpu | 99 | pp512 | 991.30 ± 0.66 |
|
||||
| llama 3B Q4_K - Medium | 1.87 GiB | 3.21 B | ggml-virtgpu | 99 | tg128 | 85.71 ± 0.11 |
|
||||
```
|
||||
|
||||
### Troubleshooting
|
||||
|
||||
#### SSH Environment Variable Issues
|
||||
|
||||
⚠️ **Warning**: Setting `DYLD_LIBRARY_PATH` from SSH doesn't work on macOS. Here is a workaround:
|
||||
|
||||
**Workaround 1: Replace system library**
|
||||
```bash
|
||||
VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build # ⚠️ adapt to your system
|
||||
BREW_VIRGL_DIR=/opt/homebrew/Cellar/virglrenderer/0.10.4d/lib
|
||||
VIRGL_LIB=libvirglrenderer.1.dylib
|
||||
|
||||
cd $BREW_VIRGL_DIR
|
||||
mv $VIRGL_LIB ${VIRGL_LIB}.orig
|
||||
ln -s $VIRGL_BUILD_DIR/src/$VIRGL_LIB
|
||||
```
|
||||
+1
-1
@@ -22,7 +22,7 @@ Legend:
|
||||
| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
|
||||
| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
|
||||
| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
|
||||
| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
|
||||
| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
|
||||
| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
|
||||
| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
|
||||
|
||||
+4
-4
@@ -77,8 +77,8 @@
|
||||
"SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
|
||||
"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
|
||||
"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
|
||||
"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
|
||||
"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
|
||||
"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
|
||||
"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
@@ -161,8 +161,8 @@
|
||||
"SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
|
||||
"SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
"SYCL0","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
|
||||
"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
|
||||
"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
|
||||
"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
|
||||
"SYCL0","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
"SYCL0","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
|
||||
"SYCL0","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
|
||||
|
||||
|
Can't render this file because it is too large.
|
+6
-7
@@ -119,8 +119,6 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
|
||||
of lookup n-gram (default: 12)
|
||||
--spec-ngram-size-m N ngram size M for ngram-simple/ngram-map speculative decoding, length
|
||||
of draft m-gram (default: 48)
|
||||
--spec-ngram-check-rate N ngram check rate for ngram-simple/ngram-map speculative decoding
|
||||
(default: 1)
|
||||
--spec-ngram-min-hits N minimum hits for ngram-map speculative decoding (default: 1)
|
||||
```
|
||||
|
||||
@@ -153,10 +151,6 @@ Sets the size M of the draft m-gram for n-gram map based speculative decoding.
|
||||
The m-gram size determines how many tokens to draft when a match is found.
|
||||
Larger values can provide more speedup but may reduce acceptance rate.
|
||||
|
||||
### `--spec-ngram-check-rate R`
|
||||
|
||||
This option aims at performance if the n-gram lookup in history is to costly. A lookup will be executed at every R tokens (default is 1, every token).
|
||||
|
||||
### `--spec-ngram-min-hits H`
|
||||
|
||||
This option defines how often a key has to appear in the token history to be used as a draft (default is 1).
|
||||
@@ -175,7 +169,12 @@ draft acceptance rate = 0.70312 ( 90 accepted / 128 generated)
|
||||
statistics ngram_mod: #calls = 810, #gen drafts = 15, #acc drafts = 15, #gen tokens = 960, #acc tokens = 730, dur(b,g,a) = 0.149, 0.347, 0.005 ms
|
||||
```
|
||||
|
||||
- `#calls`: number of calls of this implementations
|
||||
```
|
||||
statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts = 26, #gen tokens = 1248, #acc tokens = 968, dur(b,g,a) = 2.234, 1.427, 0.016 ms
|
||||
```
|
||||
|
||||
|
||||
- `#calls(b,g,a)`: number of calls of begin (new prompt), generation and accumulation of this implementations
|
||||
- `#gen drafts`: number of drafts generated by this implementation
|
||||
- `#acc drafts`: number of drafts accepted (partially) by the main model
|
||||
- `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
|
||||
|
||||
@@ -471,9 +471,10 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
|
||||
|
||||
int best_score = 0;
|
||||
fs::path best_path;
|
||||
std::error_code ec;
|
||||
|
||||
for (const auto & search_path : search_paths) {
|
||||
if (std::error_code ec; !fs::exists(search_path, ec)) {
|
||||
if (!fs::exists(search_path, ec)) {
|
||||
if (ec) {
|
||||
GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str());
|
||||
} else {
|
||||
@@ -483,7 +484,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
|
||||
}
|
||||
fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
|
||||
for (const auto & entry : dir_it) {
|
||||
if (entry.is_regular_file()) {
|
||||
if (entry.is_regular_file(ec)) {
|
||||
auto filename = entry.path().filename();
|
||||
auto ext = entry.path().extension();
|
||||
if (filename.native().find(file_prefix) == 0 && ext == file_extension) {
|
||||
|
||||
@@ -3286,130 +3286,223 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Performs expert-specific matrix multiplication (MoE) with
|
||||
* quantized precision using the CANN backend.
|
||||
* @brief Performs quantized matrix multiplication for Mixture of Experts (MoE)
|
||||
* models using the CANN backend.
|
||||
*
|
||||
* This function executes a matrix multiplication operation tailored for
|
||||
* Mixture of Experts (MoE) models, where the input tensor is multiplied
|
||||
* with expert-specific quantized weight matrices. It leverages the CANN
|
||||
* backend to perform efficient low-precision computations and stores the
|
||||
* quantized result in the destination tensor `dst`.
|
||||
* This function implements MUL_MAT_ID operation for quantized weight matrices
|
||||
* (Q4_0 and Q8_0 formats). It selects expert-specific weight matrices based on
|
||||
* the provided expert indices, and computes matrix multiplication using CANN's
|
||||
* WeightQuantBatchMatmulV2 operator.
|
||||
*
|
||||
* Quantization techniques reduce memory footprint and improve performance
|
||||
* by using lower-bit representations (e.g., int8) instead of floating-point.
|
||||
* This function is designed to work with such formats and may incorporate
|
||||
* optimizations like identity-based fast paths or routing masks for sparse
|
||||
* expert selection.
|
||||
* The function performs the following steps:
|
||||
* 1. Converts input/output tensors to F16 format if necessary
|
||||
* 2. Uses IndexSelect to extract expert-specific weights and scales based on indices
|
||||
* 3. Performs quantized matrix multiplication for each expert using WeightQuantBatchMatmulV2
|
||||
* 4. Converts output back to the target type if needed
|
||||
*
|
||||
* @param ctx The context for executing CANN backend operations.
|
||||
* @param dst The destination tensor where the quantized MoE multiplication result
|
||||
* will be stored.
|
||||
* Tensor shapes:
|
||||
* - dst: [M, K, N, 1] - output tensor
|
||||
* - src0: [D, M, A, 1] - quantized weight matrices (Q4_0 or Q8_0)
|
||||
* - src1: [D, B, N, 1] - input activations (B = K for per-expert input, or B = 1 for broadcast)
|
||||
* - ids: [K, N] - expert indices for routing
|
||||
*
|
||||
* @note This function assumes quantized data types and is designed for
|
||||
* MoE architectures with potential sparse expert routing.
|
||||
* @param ctx The CANN backend context for operation execution.
|
||||
* @param dst The destination tensor where the multiplication result will be stored.
|
||||
*
|
||||
* @note Only Q4_0 and Q8_0 quantization formats are supported.
|
||||
* @note The function handles automatic type conversion to/from F16 as needed by the hardware.
|
||||
*/
|
||||
static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
|
||||
// TODO: Use aclnnGroupedMatMul
|
||||
//dst [M, K, N, 1]
|
||||
ggml_tensor * src0 = dst->src[0]; //src0 [D, M, A, 1]
|
||||
ggml_tensor * src1 = dst->src[1]; //src1 [D, B, N, 1], B = K or B = 1
|
||||
ggml_tensor * ids = dst->src[2]; //ids [K, N]
|
||||
// dst: [M, K, N, 1]
|
||||
// src0: [D, M, A, 1] - quantized weights
|
||||
// src1: [D, B, N, 1] - input activations, B = K or B = 1
|
||||
// ids: [K, N] - expert indices
|
||||
ggml_tensor * src0 = dst->src[0];
|
||||
ggml_tensor * src1 = dst->src[1];
|
||||
ggml_tensor * ids = dst->src[2];
|
||||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS
|
||||
GGML_ASSERT(src0->ne[3] == 1);
|
||||
GGML_ASSERT(src1->ne[3] == 1);
|
||||
GGML_ASSERT(dst->ne[3] == 1);
|
||||
GGML_ASSERT(src1->ne[2] == ids->ne[1]);
|
||||
|
||||
// copy index from npu to cpu
|
||||
int64_t n_as = ne02; // A
|
||||
int64_t n_ids = ids->ne[0]; // K
|
||||
const int64_t n_batches = ids->ne[1];
|
||||
const int64_t n_select_experts = ids->ne[0];
|
||||
const enum ggml_type type = src0->type;
|
||||
|
||||
std::vector<char> ids_host(ggml_nbytes(ids));
|
||||
ACL_CHECK(aclrtMemcpyAsync(ids_host.data(), ggml_nbytes(ids), ids->data, ggml_nbytes(ids),
|
||||
ACL_MEMCPY_DEVICE_TO_HOST, ctx.stream()));
|
||||
ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
|
||||
const int32_t group_size = QK8_0; // Both Q4_0 and Q8_0 use group size of 32
|
||||
GGML_ASSERT(group_size == QK4_0);
|
||||
|
||||
char * src0_original = (char *) src0->data;
|
||||
char * src1_original = (char *) src1->data;
|
||||
char * dst_original = (char *) dst->data;
|
||||
// Calculate element size for quantized weights
|
||||
const float weight_elem_size =
|
||||
(type == GGML_TYPE_Q4_0) ? 0.5f :
|
||||
(type == GGML_TYPE_Q8_0) ? 1.0f :
|
||||
(GGML_ABORT("MUL_MAT_ID only supports Q4_0 and Q8_0"), 0.0f);
|
||||
|
||||
ggml_tensor src0_row = *src0;
|
||||
ggml_tensor src1_row = *src1;
|
||||
ggml_tensor dst_row = *dst;
|
||||
// Calculate scale offset in memory
|
||||
const size_t weight_size = src0->ne[0] * src0->ne[1] * src0->ne[2] * weight_elem_size;
|
||||
const size_t scale_elem_size = sizeof(uint16_t);
|
||||
char * scale_data = (char *) src0->data + weight_size;
|
||||
|
||||
const enum ggml_type type = dst->src[0]->type;
|
||||
float weight_elem_size;
|
||||
if (type == GGML_TYPE_Q4_0) {
|
||||
weight_elem_size = float(sizeof(uint8_t)) / 2;
|
||||
} else if (type == GGML_TYPE_Q8_0) {
|
||||
weight_elem_size = float(sizeof(uint8_t));
|
||||
} else {
|
||||
GGML_ABORT("MUL_MAT_ID only support quant type Q4_0 and Q8_0 ");
|
||||
}
|
||||
// Allocate buffers for selected expert weights and scales
|
||||
const size_t selected_weight_size = src0->ne[0] * src0->ne[1] * n_select_experts * weight_elem_size;
|
||||
ggml_cann_pool_alloc selected_weight_alloc(ctx.pool(), selected_weight_size);
|
||||
void * selected_weight_buffer = selected_weight_alloc.get();
|
||||
|
||||
// src0_row [D, M, 1, 1] weight without permute
|
||||
src0_row.ne[2] = 1;
|
||||
src0_row.ne[3] = 1;
|
||||
src0_row.nb[0] = weight_elem_size;
|
||||
src0_row.nb[1] = weight_elem_size * ne00;
|
||||
src0_row.nb[2] = weight_elem_size * ne00;
|
||||
src0_row.nb[3] = weight_elem_size * ne00;
|
||||
size_t weight_stride = ne00 * ne01 * weight_elem_size;
|
||||
size_t weight_size = weight_stride * ne02 * ne03;
|
||||
const size_t selected_scale_size = (src0->ne[0] / group_size) * src0->ne[1] * n_select_experts * scale_elem_size;
|
||||
ggml_cann_pool_alloc selected_scale_alloc(ctx.pool(), selected_scale_size);
|
||||
void * selected_scale_buffer = selected_scale_alloc.get();
|
||||
|
||||
// scale [D, M, 1, 1] -> scale && permute
|
||||
size_t scale_elem_size = sizeof(uint16_t);
|
||||
size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
|
||||
// Helper lambda to allocate and cast tensor to F16 if needed
|
||||
constexpr size_t f16_elem_size = sizeof(uint16_t);
|
||||
auto prepare_f16_buffer = [&](ggml_tensor * tensor, ggml_cann_pool_alloc & allocator,
|
||||
bool need_cast = false) -> void * {
|
||||
if (tensor->type == GGML_TYPE_F16) {
|
||||
return tensor->data;
|
||||
}
|
||||
|
||||
// src1_row [D, 1, 1, 1] -> input
|
||||
src1_row.ne[1] = 1;
|
||||
src1_row.ne[2] = 1;
|
||||
src1_row.ne[3] = 1;
|
||||
src1_row.nb[2] = nb11;
|
||||
src1_row.nb[3] = nb11;
|
||||
size_t total_size = f16_elem_size;
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
total_size *= tensor->ne[i];
|
||||
}
|
||||
void * buffer = allocator.alloc(total_size);
|
||||
|
||||
// dst_row [M, 1, 1, 1] -> out
|
||||
dst_row.ne[1] = 1;
|
||||
dst_row.ne[2] = 1;
|
||||
dst_row.ne[3] = 1;
|
||||
dst_row.nb[2] = nb1;
|
||||
dst_row.nb[3] = nb1;
|
||||
if (need_cast == false) {
|
||||
return buffer;
|
||||
}
|
||||
|
||||
//create weight for one row
|
||||
ggml_cann_pool_alloc weight_allocator(ctx.pool());
|
||||
void * weight_buffer = weight_allocator.alloc(nb02);
|
||||
for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
|
||||
for (int64_t id = 0; id < n_ids; id++) {
|
||||
// expert index
|
||||
int32_t i02 = *(int32_t *) (ids_host.data() + iid1 * ids->nb[1] + id * ids->nb[0]);
|
||||
GGML_ASSERT(i02 >= 0 && i02 < n_as);
|
||||
int64_t ne[GGML_MAX_DIMS];
|
||||
size_t nb[GGML_MAX_DIMS] = { f16_elem_size };
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
ne[i] = tensor->ne[i];
|
||||
if (i > 0) {
|
||||
nb[i] = nb[i - 1] * ne[i - 1];
|
||||
}
|
||||
}
|
||||
|
||||
// If B = 1 (broadcast), always use 0; otherwise, use id.
|
||||
int64_t i11 = (ne11 == 1 ? 0 : id);
|
||||
int64_t i12 = iid1;
|
||||
acl_tensor_ptr src_tensor = ggml_cann_create_tensor(tensor);
|
||||
acl_tensor_ptr f16_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
|
||||
aclnn_cast(ctx, src_tensor.get(), f16_tensor.get(), ACL_FLOAT16);
|
||||
|
||||
int64_t i1 = id;
|
||||
int64_t i2 = i12;
|
||||
return buffer;
|
||||
};
|
||||
|
||||
void * src0_tmp_ptr = src0_original + i02 * weight_stride;
|
||||
void * scale_tmp_ptr = src0_original + weight_size + i02 * scale_stride;
|
||||
void * src1_tmp_ptr = src1_original + i11 * nb11 + i12 * nb12;
|
||||
void * dst_tmp_ptr = dst_original + i1 * nb1 + i2 * nb2;
|
||||
// Prepare input and output buffers
|
||||
ggml_cann_pool_alloc input_alloc(ctx.pool());
|
||||
void * input_buffer = prepare_f16_buffer(src1, input_alloc, true);
|
||||
|
||||
// mem cpy
|
||||
ACL_CHECK(aclrtMemcpyAsync(weight_buffer, weight_stride, src0_tmp_ptr, weight_stride,
|
||||
ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
|
||||
void * scale_buffer = (char *) weight_buffer + weight_stride;
|
||||
ACL_CHECK(aclrtMemcpyAsync(scale_buffer, scale_stride, scale_tmp_ptr, scale_stride,
|
||||
ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
|
||||
ggml_cann_pool_alloc output_alloc(ctx.pool());
|
||||
void * output_buffer = prepare_f16_buffer(dst, output_alloc, false);
|
||||
|
||||
src0_row.data = weight_buffer;
|
||||
src1_row.data = src1_tmp_ptr;
|
||||
dst_row.data = dst_tmp_ptr;
|
||||
dst_row.src[0] = &src0_row;
|
||||
dst_row.src[1] = &src1_row;
|
||||
// Process each batch
|
||||
for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
|
||||
// Create index tensor for current batch
|
||||
const size_t index_offset = batch_idx * ids->nb[1];
|
||||
acl_tensor_ptr batch_indices = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, index_offset);
|
||||
|
||||
ggml_cann_mul_mat(ctx, &dst_row);
|
||||
// Select quantized weights using expert indices
|
||||
// Q4_0 stores 2 values per byte, Q8_0 stores 1 value per byte
|
||||
const int64_t weight_d = (type == GGML_TYPE_Q4_0) ? src0->ne[0] / 2 : src0->ne[0];
|
||||
const int64_t weight_m = src0->ne[1];
|
||||
const int64_t weight_n_experts = src0->ne[2];
|
||||
|
||||
int64_t weight_ne[3] = { weight_d, weight_m, weight_n_experts };
|
||||
size_t weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t), weight_d * weight_m * sizeof(int8_t) };
|
||||
|
||||
acl_tensor_ptr all_weights =
|
||||
ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, 3);
|
||||
|
||||
int64_t selected_weight_ne[3] = { weight_d, weight_m, n_select_experts };
|
||||
size_t selected_weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t),
|
||||
weight_d * weight_m * sizeof(int8_t) };
|
||||
|
||||
acl_tensor_ptr selected_weights = ggml_cann_create_tensor(selected_weight_buffer, ACL_INT8, sizeof(int8_t),
|
||||
selected_weight_ne, selected_weight_nb, 3);
|
||||
|
||||
GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_weights.get(), 0, batch_indices.get(), selected_weights.get());
|
||||
|
||||
// Select scales using the same expert indices
|
||||
const int64_t scale_d = src0->ne[0] / group_size;
|
||||
int64_t scale_ne[3] = { scale_d, weight_m, weight_n_experts };
|
||||
size_t scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size, scale_d * weight_m * scale_elem_size };
|
||||
|
||||
acl_tensor_ptr all_scales =
|
||||
ggml_cann_create_tensor(scale_data, ACL_FLOAT16, scale_elem_size, scale_ne, scale_nb, 3);
|
||||
|
||||
int64_t selected_scale_ne[3] = { scale_d, weight_m, n_select_experts };
|
||||
size_t selected_scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size,
|
||||
scale_d * weight_m * scale_elem_size };
|
||||
|
||||
acl_tensor_ptr selected_scales = ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size,
|
||||
selected_scale_ne, selected_scale_nb, 3);
|
||||
|
||||
GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_scales.get(), 0, batch_indices.get(), selected_scales.get());
|
||||
|
||||
// Process each expert for current batch
|
||||
// IndexSelect output layout: [D, M, K] in contiguous format
|
||||
// WeightQuantBatchMatmulV2 expects: [M, D] with row-major stride
|
||||
for (int64_t expert_idx = 0; expert_idx < n_select_experts; expert_idx++) {
|
||||
// Determine input offset: broadcast if src1->ne[1]==1, otherwise use per-expert input
|
||||
const size_t input_offset =
|
||||
(batch_idx * src1->ne[1] + (src1->ne[1] == 1 ? 0 : expert_idx)) * src1->ne[0] * f16_elem_size;
|
||||
const size_t output_offset = (batch_idx * dst->ne[1] + expert_idx) * dst->ne[0] * f16_elem_size;
|
||||
|
||||
// Create weight view for current expert: [D, M, K] -> [M, D]
|
||||
int64_t weight_view_ne[2] = { weight_m, src0->ne[0] };
|
||||
float weight_view_nb[2] = { src0->ne[0] * weight_elem_size, weight_elem_size };
|
||||
const size_t weight_view_offset = expert_idx * selected_weight_nb[2];
|
||||
|
||||
acl_tensor_ptr weight_view =
|
||||
ggml_cann_create_tensor(selected_weight_buffer, ggml_cann_type_mapping(type), weight_elem_size,
|
||||
weight_view_ne, weight_view_nb, 2, ACL_FORMAT_ND, weight_view_offset);
|
||||
|
||||
// Create scale view for current expert: [D, M, K] -> [M, D]
|
||||
int64_t scale_view_ne[2] = { weight_m, scale_d };
|
||||
size_t scale_view_nb[2] = { selected_scale_nb[1], selected_scale_nb[0] };
|
||||
const size_t scale_view_offset = expert_idx * selected_scale_nb[2];
|
||||
|
||||
acl_tensor_ptr scale_view =
|
||||
ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size, scale_view_ne,
|
||||
scale_view_nb, 2, ACL_FORMAT_ND, scale_view_offset);
|
||||
|
||||
// Create input activation tensor [D, 1]
|
||||
int64_t input_ne[2] = { src1->ne[0], 1 };
|
||||
size_t input_nb[2] = { f16_elem_size, src1->ne[0] * f16_elem_size };
|
||||
|
||||
acl_tensor_ptr input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, f16_elem_size, input_ne,
|
||||
input_nb, 2, ACL_FORMAT_ND, input_offset);
|
||||
|
||||
// Create output tensor [M, 1]
|
||||
int64_t output_ne[2] = { dst->ne[0], 1 };
|
||||
size_t output_nb[2] = { f16_elem_size, dst->ne[0] * f16_elem_size };
|
||||
|
||||
acl_tensor_ptr output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, output_ne,
|
||||
output_nb, 2, ACL_FORMAT_ND, output_offset);
|
||||
|
||||
// Perform quantized matrix multiplication
|
||||
GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, input_tensor.get(), weight_view.get(),
|
||||
scale_view.get(), nullptr, nullptr, nullptr, nullptr, group_size,
|
||||
output_tensor.get());
|
||||
}
|
||||
}
|
||||
return;
|
||||
|
||||
// Cast output back to original type if we used a temporary F16 buffer
|
||||
if (dst->type != GGML_TYPE_F16) {
|
||||
int64_t ne[GGML_MAX_DIMS];
|
||||
size_t nb[GGML_MAX_DIMS] = { f16_elem_size };
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
ne[i] = dst->ne[i];
|
||||
if (i > 0) {
|
||||
nb[i] = nb[i - 1] * ne[i - 1];
|
||||
}
|
||||
}
|
||||
|
||||
acl_tensor_ptr f16_output =
|
||||
ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
|
||||
acl_tensor_ptr dst_tensor = ggml_cann_create_tensor(dst);
|
||||
|
||||
aclnn_cast(ctx, f16_output.get(), dst_tensor.get(), ggml_cann_type_mapping(dst->type));
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
|
||||
|
||||
@@ -794,19 +794,44 @@ struct ggml_backend_cann_buffer_context {
|
||||
~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
|
||||
};
|
||||
|
||||
// cann buffer type
|
||||
/**
|
||||
* @brief Check if a buffer is a CANN buffer.
|
||||
*
|
||||
* This function checks if a given buffer is a CANN buffer by comparing its
|
||||
* `get_name` function pointer to `ggml_backend_cann_buffer_get_name`.
|
||||
*
|
||||
* @param buffer The buffer to check.
|
||||
* @return true if the buffer is a CANN buffer, false otherwise.
|
||||
* @brief Structure representing context information for a specific backend
|
||||
* buffer type.
|
||||
*/
|
||||
static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft);
|
||||
struct ggml_backend_cann_buffer_type_context {
|
||||
int32_t device; /**< Device identifier associated with the buffer context. */
|
||||
std::string name; /**< Name associated with the buffer context. */
|
||||
};
|
||||
|
||||
static bool ggml_backend_buffer_is_cann(ggml_backend_buffer_t buffer) {
|
||||
return ggml_backend_buft_is_cann(buffer->buft);
|
||||
/**
|
||||
* @brief Retrieves the name associated with a CANN buffer type.
|
||||
*
|
||||
* This function returns the descriptive name associated with the specified
|
||||
* CANN buffer type context.
|
||||
*
|
||||
* @param buft Pointer to the buffer type context.
|
||||
* @return Const pointer to the C-style string containing the name.
|
||||
*/
|
||||
static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
|
||||
|
||||
return buft_ctx->name.c_str();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Checks if the backend buffer type is associated with the CANN backend.
|
||||
*
|
||||
* This function checks whether the provided backend buffer type is associated
|
||||
* with the CANN backend based on the comparison of its name retrieval function
|
||||
* pointer.
|
||||
*
|
||||
* @param buft Pointer to the backend buffer type to check.
|
||||
* @return bool Returns true if the buffer type is associated with the CANN
|
||||
* backend, otherwise false.
|
||||
*/
|
||||
static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
|
||||
return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -1271,7 +1296,7 @@ static void ggml_backend_cann_buffer_get_tensor(ggml_backend_buffer_t buffer,
|
||||
static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
|
||||
const ggml_tensor * src,
|
||||
ggml_tensor * dst) {
|
||||
if (ggml_backend_buffer_is_cann(src->buffer)) {
|
||||
if (ggml_backend_buft_is_cann(src->buffer->buft)) {
|
||||
ggml_backend_cann_buffer_context * src_ctx = (ggml_backend_cann_buffer_context *) src->buffer->context;
|
||||
ggml_backend_cann_buffer_context * dst_ctx = (ggml_backend_cann_buffer_context *) buffer->context;
|
||||
|
||||
@@ -1335,31 +1360,6 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
|
||||
/* .reset = */ NULL,
|
||||
};
|
||||
|
||||
// cann buffer type
|
||||
/**
|
||||
* @brief Structure representing context information for a specific backend
|
||||
* buffer type.
|
||||
*/
|
||||
struct ggml_backend_cann_buffer_type_context {
|
||||
int32_t device; /**< Device identifier associated with the buffer context. */
|
||||
std::string name; /**< Name associated with the buffer context. */
|
||||
};
|
||||
|
||||
/**
|
||||
* @brief Retrieves the name associated with a CANN buffer type.
|
||||
*
|
||||
* This function returns the descriptive name associated with the specified
|
||||
* CANN buffer type context.
|
||||
*
|
||||
* @param buft Pointer to the buffer type context.
|
||||
* @return Const pointer to the C-style string containing the name.
|
||||
*/
|
||||
static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
|
||||
|
||||
return buft_ctx->name.c_str();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Allocates a new CANN buffer of the specified type and size.
|
||||
*
|
||||
@@ -1997,7 +1997,7 @@ static bool ggml_backend_cann_cpy_tensor_async(ggml_backend_t backend_src,
|
||||
|
||||
GGML_ASSERT(!is_matmul_weight((const ggml_tensor *) src));
|
||||
|
||||
if (!ggml_backend_buffer_is_cann(src->buffer) || !ggml_backend_buffer_is_cann(dst->buffer)) {
|
||||
if (!ggml_backend_buft_is_cann(src->buffer->buft) || !ggml_backend_buft_is_cann(dst->buffer->buft)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
@@ -2523,21 +2523,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
|
||||
GGML_UNUSED(dev);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Checks if the backend buffer type is associated with the CANN backend.
|
||||
*
|
||||
* This function checks whether the provided backend buffer type is associated
|
||||
* with the CANN backend based on the comparison of its name retrieval function
|
||||
* pointer.
|
||||
*
|
||||
* @param buft Pointer to the backend buffer type to check.
|
||||
* @return bool Returns true if the buffer type is associated with the CANN
|
||||
* backend, otherwise false.
|
||||
*/
|
||||
static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
|
||||
return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Records an event on the CANN backend stream.
|
||||
*
|
||||
|
||||
@@ -43,6 +43,7 @@
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
|
||||
@@ -55,7 +56,8 @@
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
# define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
|
||||
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
|
||||
@@ -76,6 +78,7 @@
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
|
||||
@@ -84,6 +87,7 @@
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
|
||||
@@ -107,6 +111,7 @@
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
|
||||
@@ -119,6 +124,7 @@
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
|
||||
@@ -143,6 +149,7 @@
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
|
||||
@@ -155,6 +162,7 @@
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
|
||||
@@ -186,6 +194,7 @@
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
|
||||
@@ -197,6 +206,7 @@
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
|
||||
@@ -227,6 +237,7 @@
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
|
||||
@@ -239,6 +250,7 @@
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
|
||||
@@ -271,6 +283,7 @@
|
||||
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
|
||||
#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
|
||||
#define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
|
||||
@@ -283,6 +296,7 @@
|
||||
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
|
||||
#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
|
||||
#define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
|
||||
|
||||
@@ -1072,6 +1072,195 @@ void ggml_gemv_q5_K_8x8_q8_K(int n,
|
||||
ggml_gemv_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemv_q6_K_8x4_q8_K(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
const void * GGML_RESTRICT vx,
|
||||
const void * GGML_RESTRICT vy,
|
||||
int nr,
|
||||
int nc) {
|
||||
constexpr int qk = QK_K;
|
||||
const int nb = n / qk;
|
||||
|
||||
constexpr int ncols_interleaved = 8;
|
||||
constexpr int blocklen = 4;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nc % ncols_interleaved == 0);
|
||||
|
||||
UNUSED(nb);
|
||||
UNUSED(ncols_interleaved);
|
||||
UNUSED(blocklen);
|
||||
|
||||
#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
|
||||
constexpr int col_groups = ncols_interleaved / 4;
|
||||
const uint8x16_t m4b = vdupq_n_u8(0x0f);
|
||||
const uint8x16_t mask_lo = vdupq_n_u8(0x03);
|
||||
const uint8x16_t mask_hi = vdupq_n_u8(0x30);
|
||||
|
||||
// 1x8 tile = 2 x 4
|
||||
float32x4_t acc_f32[2];
|
||||
|
||||
const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy;
|
||||
|
||||
for (int x = 0; x < nc / ncols_interleaved; x++) {
|
||||
const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb);
|
||||
|
||||
for (int i = 0; i < col_groups; i++) {
|
||||
acc_f32[i] = vdupq_n_f32(0);
|
||||
}
|
||||
|
||||
for (int b = 0; b < nb; b++) {
|
||||
float32x4_t q6_d_0 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d)); // d0 d1 d2 d3
|
||||
float32x4_t q6_d_1 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4)); // d4 d5 d6 d7
|
||||
float32x4_t q8_d = vdupq_n_f32(q8_ptr[b].d);
|
||||
float32x4_t sb_scale_0 = vmulq_f32(q6_d_0, q8_d);
|
||||
float32x4_t sb_scale_1 = vmulq_f32(q6_d_1, q8_d);
|
||||
|
||||
int32x4_t acc[col_groups];
|
||||
for (int i = 0; i < col_groups; i++) {
|
||||
acc[i] = vdupq_n_s32(0);
|
||||
}
|
||||
|
||||
// Load all 16 scales once and widen to int16 (Q6_K has 16 scales per block)
|
||||
// Reused for bias and dequantization later
|
||||
int16_t q6_scales[16 * 8];
|
||||
for (int i = 0; i < 16; i++) {
|
||||
int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8));
|
||||
vst1q_s16(q6_scales + i * 8, scales);
|
||||
}
|
||||
|
||||
// Compute bias per column using q8 bsums and preloaded scales to skip the -32 shift
|
||||
int32x4_t bias_lo = vdupq_n_s32(0);
|
||||
int32x4_t bias_hi = vdupq_n_s32(0);
|
||||
|
||||
// Load bsums in chunks of 4 to process with vectorized operations
|
||||
for (int i = 0; i < 16; i += 4) {
|
||||
int16x4_t bsums_vec = vld1_s16(q8_ptr[b].bsums + i);
|
||||
int16x4_t scales_lo_0 = vld1_s16(q6_scales + (i + 0) * 8);
|
||||
int16x4_t scales_hi_0 = vld1_s16(q6_scales + (i + 0) * 8 + 4);
|
||||
int16x4_t scales_lo_1 = vld1_s16(q6_scales + (i + 1) * 8);
|
||||
int16x4_t scales_hi_1 = vld1_s16(q6_scales + (i + 1) * 8 + 4);
|
||||
int16x4_t scales_lo_2 = vld1_s16(q6_scales + (i + 2) * 8);
|
||||
int16x4_t scales_hi_2 = vld1_s16(q6_scales + (i + 2) * 8 + 4);
|
||||
int16x4_t scales_lo_3 = vld1_s16(q6_scales + (i + 3) * 8);
|
||||
int16x4_t scales_hi_3 = vld1_s16(q6_scales + (i + 3) * 8 + 4);
|
||||
|
||||
bias_lo = vmlal_lane_s16(bias_lo, scales_lo_0, bsums_vec, 0);
|
||||
bias_hi = vmlal_lane_s16(bias_hi, scales_hi_0, bsums_vec, 0);
|
||||
bias_lo = vmlal_lane_s16(bias_lo, scales_lo_1, bsums_vec, 1);
|
||||
bias_hi = vmlal_lane_s16(bias_hi, scales_hi_1, bsums_vec, 1);
|
||||
bias_lo = vmlal_lane_s16(bias_lo, scales_lo_2, bsums_vec, 2);
|
||||
bias_hi = vmlal_lane_s16(bias_hi, scales_hi_2, bsums_vec, 2);
|
||||
bias_lo = vmlal_lane_s16(bias_lo, scales_lo_3, bsums_vec, 3);
|
||||
bias_hi = vmlal_lane_s16(bias_hi, scales_hi_3, bsums_vec, 3);
|
||||
}
|
||||
bias_lo = vshlq_n_s32(bias_lo, 5);
|
||||
bias_hi = vshlq_n_s32(bias_hi, 5);
|
||||
|
||||
// Process two 128-value halves per superblock
|
||||
for (int half = 0; half < 2; half++) {
|
||||
const uint8_t * ql_base = q6_ptr[b].ql + half * 512;
|
||||
const uint8_t * qh_base = q6_ptr[b].qh + half * 256;
|
||||
|
||||
// A subblock (sb) is a set of weights that share the scale
|
||||
// Since q6_K scales are per 16 elements
|
||||
// num sbs -> 256 elements / (16 elements/scale * 2 elements/byte * 2 halves)
|
||||
for (int sb = 0; sb < QK_K / 64; sb++) {
|
||||
const int8_t * q8_base_l = q8_ptr[b].qs + half * 128 + sb * 16;
|
||||
const int8_t * q8_base_h = q8_base_l + 64;
|
||||
|
||||
// Load and duplicate q8 values (each register covers four interleaved columns of q6)
|
||||
int8x16_t q8_l[4];
|
||||
int8x16_t q8_h[4];
|
||||
for (int i = 0; i < 4; i++) {
|
||||
q8_l[i] = (int8x16_t) vld1q_dup_s32((const int32_t *) (q8_base_l + i * 4));
|
||||
q8_h[i] = (int8x16_t) vld1q_dup_s32((const int32_t *) (q8_base_h + i * 4));
|
||||
}
|
||||
|
||||
const int ql_off_base = sb * QK_K / 2;
|
||||
const int qh_off_base = ql_off_base & 255; // wraps after 256 bytes
|
||||
|
||||
// Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1)
|
||||
uint8x16x4_t q6_ql_0 = vld1q_u8_x4(ql_base + ql_off_base);
|
||||
uint8x16x4_t q6_ql_1 = vld1q_u8_x4(ql_base + ql_off_base + 64);
|
||||
uint8x16x4_t q6_qh_0 = vld1q_u8_x4(qh_base + qh_off_base);
|
||||
uint8x16x4_t q6_qh_1 = vld1q_u8_x4(qh_base + qh_off_base + 64);
|
||||
|
||||
// Adjust qh for subblocks 2 and 3 (shift right by 2)
|
||||
if (sb > 1) {
|
||||
q6_qh_0.val[0] = vshrq_n_u8(q6_qh_0.val[0], 2);
|
||||
q6_qh_0.val[1] = vshrq_n_u8(q6_qh_0.val[1], 2);
|
||||
q6_qh_0.val[2] = vshrq_n_u8(q6_qh_0.val[2], 2);
|
||||
q6_qh_0.val[3] = vshrq_n_u8(q6_qh_0.val[3], 2);
|
||||
q6_qh_1.val[0] = vshrq_n_u8(q6_qh_1.val[0], 2);
|
||||
q6_qh_1.val[1] = vshrq_n_u8(q6_qh_1.val[1], 2);
|
||||
q6_qh_1.val[2] = vshrq_n_u8(q6_qh_1.val[2], 2);
|
||||
q6_qh_1.val[3] = vshrq_n_u8(q6_qh_1.val[3], 2);
|
||||
}
|
||||
|
||||
const uint8x16_t q6_ql[8] = { q6_ql_0.val[0], q6_ql_0.val[1], q6_ql_0.val[2], q6_ql_0.val[3],
|
||||
q6_ql_1.val[0], q6_ql_1.val[1], q6_ql_1.val[2], q6_ql_1.val[3] };
|
||||
const uint8x16_t q6_qh[8] = { q6_qh_0.val[0], q6_qh_0.val[1], q6_qh_0.val[2], q6_qh_0.val[3],
|
||||
q6_qh_1.val[0], q6_qh_1.val[1], q6_qh_1.val[2], q6_qh_1.val[3] };
|
||||
|
||||
// Process column groups (0-3, 4-7)
|
||||
for (int g = 0; g < col_groups; g++) {
|
||||
int32x4_t sb_acc_l = vdupq_n_s32(0);
|
||||
int32x4_t sb_acc_h = vdupq_n_s32(0);
|
||||
|
||||
for (int chunk = 0; chunk < 4; chunk++) {
|
||||
const int idx = chunk * 2 + g;
|
||||
|
||||
const uint8x16_t q6_qs_l = q6_ql[idx];
|
||||
const uint8x16_t q6_qs_h = q6_qh[idx];
|
||||
|
||||
// Extract high 2 bits for upper nibble reconstruction
|
||||
const uint8x16_t q6_qs_hh = vandq_u8(q6_qs_h, mask_hi);
|
||||
|
||||
// q6 = (low4 | high2<<4), without -32 bias (handled via bsums)
|
||||
const int8x16_t q6_l =
|
||||
vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_qs_l, m4b), vandq_u8(q6_qs_h, mask_lo), 4));
|
||||
const int8x16_t q6_h = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_qs_l, 4), q6_qs_hh));
|
||||
|
||||
sb_acc_l = vdotq_s32(sb_acc_l, q6_l, q8_l[chunk]);
|
||||
sb_acc_h = vdotq_s32(sb_acc_h, q6_h, q8_h[chunk]);
|
||||
}
|
||||
|
||||
const int scale_idx_l = half * 8 + sb;
|
||||
const int scale_idx_h = half * 8 + sb + 4;
|
||||
|
||||
const int32x4_t scale_vec_l = vmovl_s16(vld1_s16(q6_scales + scale_idx_l * 8 + g * 4));
|
||||
const int32x4_t scale_vec_h = vmovl_s16(vld1_s16(q6_scales + scale_idx_h * 8 + g * 4));
|
||||
|
||||
acc[g] = vmlaq_s32(acc[g], sb_acc_l, scale_vec_l);
|
||||
acc[g] = vmlaq_s32(acc[g], sb_acc_h, scale_vec_h);
|
||||
}
|
||||
}
|
||||
} // for half
|
||||
|
||||
// Bias correction
|
||||
acc[0] = vsubq_s32(acc[0], bias_lo);
|
||||
acc[1] = vsubq_s32(acc[1], bias_hi);
|
||||
|
||||
// Apply superblock scale (no mins for q6_K)
|
||||
// acc[g] has [c0, c1, c2, c3]
|
||||
float32x4_t w_0123 = vmulq_f32(vcvtq_f32_s32(acc[0]), sb_scale_0);
|
||||
float32x4_t w_4567 = vmulq_f32(vcvtq_f32_s32(acc[1]), sb_scale_1);
|
||||
|
||||
acc_f32[0] = vaddq_f32(acc_f32[0], w_0123);
|
||||
acc_f32[1] = vaddq_f32(acc_f32[1], w_4567);
|
||||
} // for b
|
||||
|
||||
int base = x * ncols_interleaved;
|
||||
vst1q_f32(s + base, acc_f32[0]);
|
||||
vst1q_f32(s + base + 4, acc_f32[1]);
|
||||
} // for x
|
||||
return;
|
||||
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
|
||||
ggml_gemv_q6_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemv_q6_K_8x8_q8_K(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
@@ -1177,15 +1366,14 @@ void ggml_gemv_q6_K_8x8_q8_K(int n,
|
||||
q8_h[i] = (int8x16_t) vld1q_dup_s64((const int64_t *) (q8_base_h + i * 8));
|
||||
}
|
||||
|
||||
// TODO: Test other qh repack patterns to reduce loads
|
||||
const int ql_off_base = sb * QK_K / 2;
|
||||
const int qh_off_base = ql_off_base & 255; // wraps after 256 bytes
|
||||
|
||||
// Load 4 vectors at once (64 bytes each for ql_0, ql_1, qh_0, qh_1)
|
||||
ggml_uint8x16x4_t q6_ql_0 = ggml_vld1q_u8_x4(ql_base + ql_off_base);
|
||||
ggml_uint8x16x4_t q6_ql_1 = ggml_vld1q_u8_x4(ql_base + ql_off_base + 64);
|
||||
ggml_uint8x16x4_t q6_qh_0 = ggml_vld1q_u8_x4(qh_base + qh_off_base);
|
||||
ggml_uint8x16x4_t q6_qh_1 = ggml_vld1q_u8_x4(qh_base + qh_off_base + 64);
|
||||
uint8x16x4_t q6_ql_0 = vld1q_u8_x4(ql_base + ql_off_base);
|
||||
uint8x16x4_t q6_ql_1 = vld1q_u8_x4(ql_base + ql_off_base + 64);
|
||||
uint8x16x4_t q6_qh_0 = vld1q_u8_x4(qh_base + qh_off_base);
|
||||
uint8x16x4_t q6_qh_1 = vld1q_u8_x4(qh_base + qh_off_base + 64);
|
||||
|
||||
// Adjust qh for subblocks 2 and 3 (shift right by 2)
|
||||
if (sb > 1) {
|
||||
@@ -3474,6 +3662,208 @@ void ggml_gemm_q5_K_8x8_q8_K(int n,
|
||||
ggml_gemm_q5_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemm_q6_K_8x4_q8_K(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
const void * GGML_RESTRICT vx,
|
||||
const void * GGML_RESTRICT vy,
|
||||
int nr,
|
||||
int nc) {
|
||||
constexpr int qk = QK_K;
|
||||
const int nb = n / qk;
|
||||
|
||||
constexpr int ncols_interleaved = 8;
|
||||
constexpr int blocklen = 4;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nr % 4 == 0);
|
||||
assert(nc % ncols_interleaved == 0);
|
||||
|
||||
UNUSED(nb);
|
||||
UNUSED(ncols_interleaved);
|
||||
UNUSED(blocklen);
|
||||
|
||||
#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
|
||||
constexpr int q8_k_blocklen = 4;
|
||||
constexpr int col_groups = ncols_interleaved / 4;
|
||||
constexpr int acc_size = q8_k_blocklen * col_groups; // 4 rows, 2 column groups
|
||||
const uint8x16_t m4b = vdupq_n_u8(0x0f);
|
||||
const uint8x16_t mask_lo = vdupq_n_u8(0x03);
|
||||
const uint8x16_t mask_hi = vdupq_n_u8(0x30);
|
||||
const int8x16_t m32s = vdupq_n_s8(32);
|
||||
|
||||
float32x4_t acc_f32[acc_size];
|
||||
|
||||
for (int y = 0; y < nr / q8_k_blocklen; y++) {
|
||||
const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb);
|
||||
|
||||
for (int x = 0; x < nc / ncols_interleaved; x++) {
|
||||
const block_q6_Kx8 * GGML_RESTRICT q6_ptr = (const block_q6_Kx8 *) vx + (x * nb);
|
||||
|
||||
for (int i = 0; i < acc_size; i++) {
|
||||
acc_f32[i] = vdupq_n_f32(0);
|
||||
}
|
||||
|
||||
for (int b = 0; b < nb; b++) {
|
||||
float32x4_t q6_d_0123 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d));
|
||||
float32x4_t q6_d_4567 = vcvt_f32_f16(vld1_f16((const __fp16 *) q6_ptr[b].d + 4));
|
||||
float32x4_t q8_d_0123 = vld1q_f32(q8_ptr[b].d);
|
||||
|
||||
float32x4_t sbd_scale_0123[q8_k_blocklen];
|
||||
float32x4_t sbd_scale_4567[q8_k_blocklen];
|
||||
|
||||
sbd_scale_0123[0] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 0);
|
||||
sbd_scale_4567[0] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 0);
|
||||
sbd_scale_0123[1] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 1);
|
||||
sbd_scale_4567[1] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 1);
|
||||
sbd_scale_0123[2] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 2);
|
||||
sbd_scale_4567[2] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 2);
|
||||
sbd_scale_0123[3] = vmulq_laneq_f32(q6_d_0123, q8_d_0123, 3);
|
||||
sbd_scale_4567[3] = vmulq_laneq_f32(q6_d_4567, q8_d_0123, 3);
|
||||
|
||||
int32x4_t acc_s32[acc_size];
|
||||
for (int i = 0; i < acc_size; i++) {
|
||||
acc_s32[i] = vdupq_n_s32(0);
|
||||
}
|
||||
|
||||
int16_t q6_scales[8 * 16];
|
||||
for (int i = 0; i < 16; i++) {
|
||||
int16x8_t scales = vmovl_s8(vld1_s8(q6_ptr[b].scales + i * 8));
|
||||
vst1q_s16(q6_scales + i * 8, scales);
|
||||
}
|
||||
|
||||
for (int half = 0; half < 2; half++) {
|
||||
const uint8_t * ql_base = q6_ptr[b].ql + half * 512;
|
||||
const uint8_t * qh_base = q6_ptr[b].qh + half * 256;
|
||||
|
||||
for (int sb = 0; sb < QK_K / 64; sb++) {
|
||||
int32x4_t acc_lo[acc_size];
|
||||
int32x4_t acc_hi[acc_size];
|
||||
for (int i = 0; i < acc_size; i++) {
|
||||
acc_lo[i] = vdupq_n_s32(0);
|
||||
acc_hi[i] = vdupq_n_s32(0);
|
||||
}
|
||||
|
||||
const int8_t * q8_base_l = q8_ptr[b].qs + half * 512 + sb * 64;
|
||||
const int8_t * q8_base_h = q8_ptr[b].qs + half * 512 + 256 + sb * 64;
|
||||
|
||||
// 4 rows * 16 elements per scale
|
||||
// 4 reads of 16 bytes each
|
||||
constexpr int reads_per_sb = 4;
|
||||
int8x16_t q8_l[reads_per_sb];
|
||||
int8x16_t q8_h[reads_per_sb];
|
||||
for (int k = 0; k < reads_per_sb; k++) {
|
||||
q8_l[k] = vld1q_s8(q8_base_l + 16 * k);
|
||||
q8_h[k] = vld1q_s8(q8_base_h + 16 * k);
|
||||
}
|
||||
|
||||
const int ql_off_base = sb * QK_K / 2;
|
||||
const int qh_off_base = ql_off_base & 255;
|
||||
|
||||
uint8x16_t q6_ql_0123[reads_per_sb];
|
||||
uint8x16_t q6_ql_4567[reads_per_sb];
|
||||
uint8x16_t q6_qh_0123[reads_per_sb];
|
||||
uint8x16_t q6_qh_4567[reads_per_sb];
|
||||
|
||||
for (int k = 0; k < reads_per_sb; k++) {
|
||||
q6_ql_0123[k] = vld1q_u8(ql_base + ql_off_base + k * 32);
|
||||
q6_ql_4567[k] = vld1q_u8(ql_base + ql_off_base + k * 32 + 16);
|
||||
q6_qh_0123[k] = vld1q_u8(qh_base + qh_off_base + k * 32);
|
||||
q6_qh_4567[k] = vld1q_u8(qh_base + qh_off_base + k * 32 + 16);
|
||||
}
|
||||
|
||||
if (sb > 1) {
|
||||
for (int k = 0; k < reads_per_sb; k++) {
|
||||
q6_qh_0123[k] = vshrq_n_u8(q6_qh_0123[k], 2);
|
||||
q6_qh_4567[k] = vshrq_n_u8(q6_qh_4567[k], 2);
|
||||
}
|
||||
}
|
||||
|
||||
for (int k = 0; k < reads_per_sb; k++) {
|
||||
// q = (ql | qh) - 32
|
||||
const uint8x16_t hbit_lo_0123 = vandq_u8(q6_qh_0123[k], mask_lo);
|
||||
const uint8x16_t hbit_hi_0123 = vandq_u8(q6_qh_0123[k], mask_hi);
|
||||
const uint8x16_t hbit_lo_4567 = vandq_u8(q6_qh_4567[k], mask_lo);
|
||||
const uint8x16_t hbit_hi_4567 = vandq_u8(q6_qh_4567[k], mask_hi);
|
||||
|
||||
const int8x16_t q6_0123_lo = vsubq_s8(
|
||||
vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_ql_0123[k], m4b), hbit_lo_0123, 4)), m32s);
|
||||
const int8x16_t q6_0123_hi = vsubq_s8(
|
||||
vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_ql_0123[k], 4), hbit_hi_0123)), m32s);
|
||||
|
||||
acc_lo[0] = vdotq_laneq_s32(acc_lo[0], q6_0123_lo, q8_l[k], 0); // 0..3 r0 c0123
|
||||
acc_lo[1] = vdotq_laneq_s32(acc_lo[1], q6_0123_lo, q8_l[k], 1); // 0..3 r1 c0123
|
||||
acc_lo[2] = vdotq_laneq_s32(acc_lo[2], q6_0123_lo, q8_l[k], 2); // 0..3 r2 c0123
|
||||
acc_lo[3] = vdotq_laneq_s32(acc_lo[3], q6_0123_lo, q8_l[k], 3); // 0..3 r3 c0123
|
||||
|
||||
acc_hi[0] = vdotq_laneq_s32(acc_hi[0], q6_0123_hi, q8_h[k], 0); // 64..67 r0 c0123
|
||||
acc_hi[1] = vdotq_laneq_s32(acc_hi[1], q6_0123_hi, q8_h[k], 1); // 64..67 r1 c0123
|
||||
acc_hi[2] = vdotq_laneq_s32(acc_hi[2], q6_0123_hi, q8_h[k], 2); // 64..67 r2 c0123
|
||||
acc_hi[3] = vdotq_laneq_s32(acc_hi[3], q6_0123_hi, q8_h[k], 3); // 64..67 r3 c0123
|
||||
|
||||
const int8x16_t q6_4567_lo = vsubq_s8(
|
||||
vreinterpretq_s8_u8(vsliq_n_u8(vandq_u8(q6_ql_4567[k], m4b), hbit_lo_4567, 4)), m32s);
|
||||
const int8x16_t q6_4567_hi = vsubq_s8(
|
||||
vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6_ql_4567[k], 4), hbit_hi_4567)), m32s);
|
||||
|
||||
acc_lo[4] = vdotq_laneq_s32(acc_lo[4], q6_4567_lo, q8_l[k], 0); // 0..3 r0 c4567
|
||||
acc_lo[5] = vdotq_laneq_s32(acc_lo[5], q6_4567_lo, q8_l[k], 1); // 0..3 r1 c4567
|
||||
acc_lo[6] = vdotq_laneq_s32(acc_lo[6], q6_4567_lo, q8_l[k], 2); // 0..3 r2 c4567
|
||||
acc_lo[7] = vdotq_laneq_s32(acc_lo[7], q6_4567_lo, q8_l[k], 3); // 0..3 r3 c4567
|
||||
|
||||
acc_hi[4] = vdotq_laneq_s32(acc_hi[4], q6_4567_hi, q8_h[k], 0); // 64..67 r0 c4567
|
||||
acc_hi[5] = vdotq_laneq_s32(acc_hi[5], q6_4567_hi, q8_h[k], 1); // 64..67 r1 c4567
|
||||
acc_hi[6] = vdotq_laneq_s32(acc_hi[6], q6_4567_hi, q8_h[k], 2); // 64..67 r2 c4567
|
||||
acc_hi[7] = vdotq_laneq_s32(acc_hi[7], q6_4567_hi, q8_h[k], 3); // 64..67 r3 c4567
|
||||
}
|
||||
|
||||
// Scale and bias
|
||||
const int scale_idx_l = half * 8 + sb;
|
||||
const int scale_idx_h = half * 8 + sb + 4;
|
||||
|
||||
for (int g = 0; g < col_groups; g++) {
|
||||
const int16x4_t scales_l16 = vld1_s16(q6_scales + scale_idx_l * 8 + g * 4);
|
||||
const int16x4_t scales_h16 = vld1_s16(q6_scales + scale_idx_h * 8 + g * 4);
|
||||
const int32x4_t scale_vec_l = vmovl_s16(scales_l16);
|
||||
const int32x4_t scale_vec_h = vmovl_s16(scales_h16);
|
||||
const int acc_offset = g * q8_k_blocklen;
|
||||
|
||||
for (int row = 0; row < q8_k_blocklen; row++) {
|
||||
const int idx = row * 2 + g;
|
||||
acc_s32[idx] = vmlaq_s32(acc_s32[idx], acc_lo[acc_offset + row], scale_vec_l);
|
||||
acc_s32[idx] = vmlaq_s32(acc_s32[idx], acc_hi[acc_offset + row], scale_vec_h);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Finally we apply the superblock scales
|
||||
for (int row = 0; row < q8_k_blocklen; row++) {
|
||||
const int idx0 = 2 * row;
|
||||
const int idx1 = 2 * row + 1;
|
||||
const int32x4_t acc_0123 = acc_s32[idx0];
|
||||
const int32x4_t acc_4567 = acc_s32[idx1];
|
||||
|
||||
acc_f32[idx0] = vmlaq_f32(acc_f32[idx0], vcvtq_f32_s32(acc_0123), sbd_scale_0123[row]);
|
||||
acc_f32[idx1] = vmlaq_f32(acc_f32[idx1], vcvtq_f32_s32(acc_4567), sbd_scale_4567[row]);
|
||||
}
|
||||
} // for b
|
||||
|
||||
for (int i = 0; i < q8_k_blocklen; i++) {
|
||||
int row = y * q8_k_blocklen + i;
|
||||
for (int j = 0; j < 2; j++) {
|
||||
int col = x * ncols_interleaved + j * 4;
|
||||
int offset = row * bs + col;
|
||||
vst1q_f32(s + offset, acc_f32[2 * i + j]);
|
||||
}
|
||||
}
|
||||
} // for x
|
||||
} // for y
|
||||
return;
|
||||
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
|
||||
ggml_gemm_q6_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemm_q6_K_8x8_q8_K(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
|
||||
@@ -7629,8 +7629,7 @@ static void ggml_compute_forward_pad_f32(
|
||||
|
||||
const ggml_tensor * src0 = dst->src[0];
|
||||
|
||||
GGML_ASSERT(src0->nb[0] == sizeof(float));
|
||||
GGML_ASSERT( dst->nb[0] == sizeof(float));
|
||||
assert(dst->nb[0] == sizeof(float));
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
+229
-196
@@ -256,6 +256,200 @@ template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTR
|
||||
ggml_quantize_mat_q8_K_4x8(x, vy, n_per_row);
|
||||
}
|
||||
|
||||
template <int M, int N>
|
||||
static void ggml_gemv_q6_K_NxM_q8_K_generic_impl(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
const void * GGML_RESTRICT vx,
|
||||
const void * GGML_RESTRICT vy,
|
||||
int nr,
|
||||
int nc) {
|
||||
constexpr int blocklen = M;
|
||||
constexpr int ncols_interleaved = N;
|
||||
const int qk = QK_K;
|
||||
const int nb = n / qk;
|
||||
const int blocks_per_half = 64 / blocklen;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nc % ncols_interleaved == 0);
|
||||
|
||||
UNUSED(bs);
|
||||
UNUSED(nr);
|
||||
|
||||
float sumf[8];
|
||||
|
||||
const block_q8_K * a_ptr = (const block_q8_K *) vy;
|
||||
for (int x = 0; x < nc / ncols_interleaved; x++) {
|
||||
const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
|
||||
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
sumf[j] = 0.0f;
|
||||
}
|
||||
|
||||
for (int l = 0; l < nb; l++) {
|
||||
for (int k = 0; k < (qk / (2 * blocklen)); k++) {
|
||||
const int base_l = (k / blocks_per_half) * 128 + (k % blocks_per_half) * blocklen;
|
||||
const int base_h = base_l + 64;
|
||||
|
||||
const int scale_idx_l = base_l / 16;
|
||||
const int scale_idx_h = base_h / 16;
|
||||
|
||||
const int qh_shift_l = ((base_l % 128) / 32) * 2;
|
||||
const int qh_shift_h = ((base_h % 128) / 32) * 2;
|
||||
|
||||
const int qh_half_l = (base_l / 128) * 32;
|
||||
const int qh_half_h = (base_h / 128) * 32;
|
||||
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
const int8_t scale_l = b_ptr[l].scales[scale_idx_l * ncols_interleaved + j];
|
||||
const int8_t scale_h = b_ptr[l].scales[scale_idx_h * ncols_interleaved + j];
|
||||
|
||||
int sumi_l = 0;
|
||||
int sumi_h = 0;
|
||||
|
||||
for (int i = 0; i < blocklen; i++) {
|
||||
const int ql_pos = k * ncols_interleaved * blocklen + j * blocklen + i;
|
||||
const int l_4 = b_ptr[l].ql[ql_pos] & 0xF;
|
||||
const int hi_4 = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
|
||||
|
||||
const int qh_idx_l = qh_half_l + ((base_l + i) % 32);
|
||||
const int qh_chunk_l = qh_idx_l / blocklen;
|
||||
const int qh_pos_l = qh_idx_l % blocklen;
|
||||
const int qh_offset_l = qh_chunk_l * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_l;
|
||||
const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
|
||||
|
||||
const int qh_idx_h = qh_half_h + ((base_h + i) % 32);
|
||||
const int qh_chunk_h = qh_idx_h / blocklen;
|
||||
const int qh_pos_h = qh_idx_h % blocklen;
|
||||
const int qh_offset_h = qh_chunk_h * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_h;
|
||||
const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
|
||||
|
||||
const int q_l = ((hi_2_l << 4) | l_4) - 32;
|
||||
const int q_h = ((hi_2_h << 4) | hi_4) - 32;
|
||||
|
||||
const int8_t a_l = a_ptr[l].qs[base_l + i];
|
||||
const int8_t a_h = a_ptr[l].qs[base_h + i];
|
||||
|
||||
sumi_l += q_l * a_l;
|
||||
sumi_h += q_h * a_h;
|
||||
}
|
||||
|
||||
sumf[j] +=
|
||||
(sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
s[x * ncols_interleaved + j] = sumf[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <int M, int N>
|
||||
static void ggml_gemm_q6_K_NxM_q8_K_generic_impl(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
const void * GGML_RESTRICT vx,
|
||||
const void * GGML_RESTRICT vy,
|
||||
int nr,
|
||||
int nc) {
|
||||
constexpr int blocklen = M;
|
||||
constexpr int ncols_interleaved = N;
|
||||
const int qk = QK_K;
|
||||
const int nb = n / qk;
|
||||
const int blocks_per_half = 64 / blocklen;
|
||||
const int q8_half_stride = 512;
|
||||
const int q8_low_high_step = 256;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nr % 4 == 0);
|
||||
assert(nc % ncols_interleaved == 0);
|
||||
|
||||
UNUSED(bs);
|
||||
|
||||
float sumf[4][8];
|
||||
|
||||
for (int y = 0; y < nr / 4; y++) {
|
||||
const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
|
||||
for (int x = 0; x < nc / ncols_interleaved; x++) {
|
||||
const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
|
||||
|
||||
for (int m = 0; m < 4; m++) {
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
sumf[m][j] = 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
for (int l = 0; l < nb; l++) {
|
||||
for (int k = 0; k < (qk / (2 * blocklen)); k++) {
|
||||
const int base_l = (k / blocks_per_half) * 128 + (k % blocks_per_half) * blocklen;
|
||||
const int base_h = base_l + 64;
|
||||
|
||||
const int scale_idx_l = base_l / 16;
|
||||
const int scale_idx_h = base_h / 16;
|
||||
|
||||
const int qh_shift_l = ((base_l % 128) / 32) * 2;
|
||||
const int qh_shift_h = ((base_h % 128) / 32) * 2;
|
||||
|
||||
const int qh_half_l = (base_l / 128) * 32;
|
||||
const int qh_half_h = (base_h / 128) * 32;
|
||||
|
||||
const int q8_base = (k / blocks_per_half) * q8_half_stride + (k % blocks_per_half) * (blocklen * 4);
|
||||
|
||||
for (int m = 0; m < 4; m++) {
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
const int8_t scale_l = b_ptr[l].scales[scale_idx_l * ncols_interleaved + j];
|
||||
const int8_t scale_h = b_ptr[l].scales[scale_idx_h * ncols_interleaved + j];
|
||||
|
||||
int sumi_l = 0;
|
||||
int sumi_h = 0;
|
||||
|
||||
for (int i = 0; i < blocklen; i++) {
|
||||
const int ql_pos = k * ncols_interleaved * blocklen + j * blocklen + i;
|
||||
const int l_4 = b_ptr[l].ql[ql_pos] & 0xF;
|
||||
const int hi_4 = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
|
||||
|
||||
const int qh_idx_l = qh_half_l + ((base_l + i) % 32);
|
||||
const int qh_chunk_l = qh_idx_l / blocklen;
|
||||
const int qh_pos_l = qh_idx_l % blocklen;
|
||||
const int qh_offset_l =
|
||||
qh_chunk_l * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_l;
|
||||
const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
|
||||
|
||||
const int qh_idx_h = qh_half_h + ((base_h + i) % 32);
|
||||
const int qh_chunk_h = qh_idx_h / blocklen;
|
||||
const int qh_pos_h = qh_idx_h % blocklen;
|
||||
const int qh_offset_h =
|
||||
qh_chunk_h * (blocklen * ncols_interleaved) + j * blocklen + qh_pos_h;
|
||||
const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
|
||||
|
||||
const int q_l = ((hi_2_l << 4) | l_4) - 32;
|
||||
const int q_h = ((hi_2_h << 4) | hi_4) - 32;
|
||||
|
||||
const int8_t q8_l = a_ptr[l].qs[q8_base + m * blocklen + i];
|
||||
const int8_t q8_h = a_ptr[l].qs[q8_base + m * blocklen + i + q8_low_high_step];
|
||||
|
||||
sumi_l += q_l * q8_l;
|
||||
sumi_h += q_h * q8_h;
|
||||
}
|
||||
|
||||
sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) *
|
||||
a_ptr[l].d[m];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int m = 0; m < 4; m++) {
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
|
||||
void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
@@ -704,94 +898,12 @@ void ggml_gemv_q5_K_8x8_q8_K_generic(int n,
|
||||
}
|
||||
|
||||
|
||||
void ggml_gemv_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
ggml_gemv_q6_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemv_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
constexpr int qk = QK_K;
|
||||
const int nb = n / qk;
|
||||
const int ncols_interleaved = 8;
|
||||
const int blocklen = 8;
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nc % ncols_interleaved == 0);
|
||||
|
||||
UNUSED(bs);
|
||||
UNUSED(nr);
|
||||
|
||||
float sumf[8];
|
||||
|
||||
const block_q8_K * a_ptr = (const block_q8_K *) vy;
|
||||
for (int x = 0; x < nc / ncols_interleaved; x++) {
|
||||
const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
|
||||
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
sumf[j] = 0.0f;
|
||||
}
|
||||
|
||||
for (int l = 0; l < nb; l++) {
|
||||
|
||||
|
||||
for (int k = 0; k < 16; k++) {
|
||||
// k = 0.. 7 weights 0-63 low, 64-127 high
|
||||
// k = 8..15 weights 128-191 low, 192-255 high
|
||||
const int base_l = (k / 8) * 128 + (k % 8) * 8;
|
||||
const int base_h = base_l + 64;
|
||||
|
||||
const int scale_idx_l = base_l / 16;
|
||||
const int scale_idx_h = base_h / 16;
|
||||
|
||||
// Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half
|
||||
const int qh_shift_l = ((base_l % 128) / 32) * 2;
|
||||
const int qh_shift_h = ((base_h % 128) / 32) * 2;
|
||||
|
||||
// qh_half: offset to the correct 32-byte half (0 or 32)
|
||||
const int qh_half_l = (base_l / 128) * 32;
|
||||
const int qh_half_h = (base_h / 128) * 32;
|
||||
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
// Interleaved scales
|
||||
const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j];
|
||||
const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j];
|
||||
|
||||
int sumi_l = 0;
|
||||
int sumi_h = 0;
|
||||
|
||||
for (int i = 0; i < blocklen; i++) {
|
||||
const int ql_pos = k * 64 + j * 8 + i;
|
||||
const int l_4 = b_ptr[l].ql[ql_pos] & 0xF;
|
||||
const int hi_4 = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
|
||||
|
||||
// qh indexing with 8-byte interleaving (like q5_K)
|
||||
const int qh_byte_l = qh_half_l + ((base_l + i) % 32);
|
||||
const int qh_chunk_l = qh_byte_l / 8;
|
||||
const int qh_pos_l = qh_byte_l % 8;
|
||||
const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l;
|
||||
const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
|
||||
|
||||
const int qh_byte_h = qh_half_h + ((base_h + i) % 32);
|
||||
const int qh_chunk_h = qh_byte_h / 8;
|
||||
const int qh_pos_h = qh_byte_h % 8;
|
||||
const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h;
|
||||
const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
|
||||
|
||||
const int q_l = ((hi_2_l << 4) | l_4) - 32;
|
||||
const int q_h = ((hi_2_h << 4) | hi_4) - 32;
|
||||
|
||||
const int8_t a_l = a_ptr[l].qs[base_l + i];
|
||||
const int8_t a_h = a_ptr[l].qs[base_h + i];
|
||||
|
||||
sumi_l += q_l * a_l;
|
||||
sumi_h += q_h * a_h;
|
||||
}
|
||||
|
||||
sumf[j] +=
|
||||
(sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
s[x * ncols_interleaved + j] = sumf[j];
|
||||
}
|
||||
}
|
||||
ggml_gemv_q6_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
@@ -1485,109 +1597,12 @@ void ggml_gemm_q5_K_8x8_q8_K_generic(int n,
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_gemm_q6_K_8x8_q8_K_generic(int n,
|
||||
float * GGML_RESTRICT s,
|
||||
size_t bs,
|
||||
const void * GGML_RESTRICT vx,
|
||||
const void * GGML_RESTRICT vy,
|
||||
int nr,
|
||||
int nc) {
|
||||
const int qk = QK_K;
|
||||
const int nb = n / qk;
|
||||
const int ncols_interleaved = 8;
|
||||
const int blocklen = 8;
|
||||
void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
ggml_gemm_q6_K_NxM_q8_K_generic_impl<4, 8>(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
assert(n % qk == 0);
|
||||
assert(nr % 4 == 0);
|
||||
assert(nc % ncols_interleaved == 0);
|
||||
|
||||
UNUSED(bs);
|
||||
|
||||
float sumf[4][8];
|
||||
|
||||
for (int y = 0; y < nr / 4; y++) {
|
||||
const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
|
||||
for (int x = 0; x < nc / ncols_interleaved; x++) {
|
||||
const block_q6_Kx8 * b_ptr = (const block_q6_Kx8 *) vx + (x * nb);
|
||||
|
||||
for (int m = 0; m < 4; m++) {
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
sumf[m][j] = 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
for (int l = 0; l < nb; l++) {
|
||||
for (int k = 0; k < 16; k++) {
|
||||
// k = 0.. 7 weights 0-63 low, 64-127 high
|
||||
// k = 8..15 weights 128-191 low, 192-255 high
|
||||
const int base_l = (k / 8) * 128 + (k % 8) * 8;
|
||||
const int base_h = base_l + 64;
|
||||
|
||||
const int scale_idx_l = base_l / 16;
|
||||
const int scale_idx_h = base_h / 16;
|
||||
|
||||
// Bit shift cycles 0,2,4,6 for each 32-value group within a 128-value half
|
||||
const int qh_shift_l = ((base_l % 128) / 32) * 2;
|
||||
const int qh_shift_h = ((base_h % 128) / 32) * 2;
|
||||
|
||||
// qh_half: offset to the correct 32-byte half (0 or 32)
|
||||
const int qh_half_l = (base_l / 128) * 32;
|
||||
const int qh_half_h = (base_h / 128) * 32;
|
||||
|
||||
// Activation base indices for q8_Kx4 interleaved format
|
||||
// Layout: 128-value halves (k/8), then 8-value sub-blocks (k%8) with stride 32
|
||||
const int q8_base = (k / 8) * 512 + (k % 8) * 32;
|
||||
|
||||
for (int m = 0; m < 4; m++) {
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
// Interleaved scales
|
||||
const int8_t scale_l = b_ptr[l].scales[scale_idx_l * 8 + j];
|
||||
const int8_t scale_h = b_ptr[l].scales[scale_idx_h * 8 + j];
|
||||
|
||||
int sumi_l = 0;
|
||||
int sumi_h = 0;
|
||||
|
||||
for (int i = 0; i < blocklen; i++) {
|
||||
const int ql_pos = k * 64 + j * 8 + i;
|
||||
const int l_4 = b_ptr[l].ql[ql_pos] & 0xF;
|
||||
const int hi_4 = (b_ptr[l].ql[ql_pos] >> 4) & 0xF;
|
||||
|
||||
const int qh_idx_l = qh_half_l + ((base_l + i) % 32);
|
||||
const int qh_chunk_l = qh_idx_l / 8;
|
||||
const int qh_pos_l = qh_idx_l % 8;
|
||||
const int qh_offset_l = qh_chunk_l * 64 + j * 8 + qh_pos_l;
|
||||
const int hi_2_l = (b_ptr[l].qh[qh_offset_l] >> qh_shift_l) & 0x3;
|
||||
|
||||
const int qh_idx_h = qh_half_h + ((base_h + i) % 32);
|
||||
const int qh_chunk_h = qh_idx_h / 8;
|
||||
const int qh_pos_h = qh_idx_h % 8;
|
||||
const int qh_offset_h = qh_chunk_h * 64 + j * 8 + qh_pos_h;
|
||||
const int hi_2_h = (b_ptr[l].qh[qh_offset_h] >> qh_shift_h) & 0x3;
|
||||
|
||||
const int q_l = ((hi_2_l << 4) | l_4) - 32;
|
||||
const int q_h = ((hi_2_h << 4) | hi_4) - 32;
|
||||
|
||||
const int8_t q8_l = a_ptr[l].qs[q8_base + m * 8 + i];
|
||||
const int8_t q8_h = a_ptr[l].qs[q8_base + m * 8 + i + 256];
|
||||
|
||||
sumi_l += q_l * q8_l;
|
||||
sumi_h += q_h * q8_h;
|
||||
}
|
||||
|
||||
sumf[m][j] += (sumi_l * scale_l + sumi_h * scale_h) * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) *
|
||||
a_ptr[l].d[m];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int m = 0; m < 4; m++) {
|
||||
for (int j = 0; j < ncols_interleaved; j++) {
|
||||
s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
void ggml_gemm_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
ggml_gemm_q6_K_NxM_q8_K_generic_impl<8, 8>(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
@@ -2097,18 +2112,18 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in
|
||||
}
|
||||
|
||||
const int end_ls = QK_K * 4 / blck_size_interleave;
|
||||
// Interleave Q6_K quants by taking 8 bytes at a time
|
||||
// Interleave Q6_K quants by taking blck_size_interleave bytes at a time
|
||||
for (int i = 0; i < end_ls; ++i) {
|
||||
int src_id = i % n_blocks;
|
||||
int src_offset = (i / n_blocks) * blck_size_interleave;
|
||||
int dst_offset = i * blck_size_interleave;
|
||||
|
||||
uint64_t elem_ls;
|
||||
memcpy(&elem_ls, &in[src_id].ql[src_offset], sizeof(uint64_t));
|
||||
memcpy(&out.ql[dst_offset], &elem_ls, sizeof(uint64_t));
|
||||
memcpy(&elem_ls, &in[src_id].ql[src_offset], blck_size_interleave);
|
||||
memcpy(&out.ql[dst_offset], &elem_ls, blck_size_interleave);
|
||||
}
|
||||
|
||||
// Interleave high bits using same 8-byte pattern as low bits
|
||||
// Interleave high bits using same chunk size as low bits
|
||||
const int end_hs = end_ls / 2;
|
||||
for (int i = 0; i < end_hs; ++i) {
|
||||
int src_id = i % n_blocks;
|
||||
@@ -2116,8 +2131,8 @@ static block_q6_Kx8 make_block_q6_Kx8(block_q6_K * in, unsigned int blck_size_in
|
||||
int dst_offset = i * blck_size_interleave;
|
||||
|
||||
uint64_t elem_hs;
|
||||
memcpy(&elem_hs, &in[src_id].qh[src_offset], sizeof(uint64_t));
|
||||
memcpy(&out.qh[dst_offset], &elem_hs, sizeof(uint64_t));
|
||||
memcpy(&elem_hs, &in[src_id].qh[src_offset], blck_size_interleave);
|
||||
memcpy(&out.qh[dst_offset], &elem_hs, blck_size_interleave);
|
||||
}
|
||||
|
||||
// The below logic is designed so as to unpack and rearrange scales in Q6_K
|
||||
@@ -2262,7 +2277,7 @@ static int repack_q5_K_to_q5_K_8_bl(struct ggml_tensor * t,
|
||||
|
||||
static int repack_q6_K_to_q6_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
|
||||
GGML_ASSERT(t->type == GGML_TYPE_Q6_K);
|
||||
GGML_ASSERT(interleave_block == 8);
|
||||
GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
|
||||
constexpr int nrows_interleaved = 8;
|
||||
|
||||
block_q6_Kx8 * dst = (block_q6_Kx8 *)t->data;
|
||||
@@ -2511,6 +2526,10 @@ template <> int repack<block_q5_K, 8, 8>(struct ggml_tensor * t, const void * da
|
||||
return repack_q5_K_to_q5_K_8_bl(t, 8, data, data_size);
|
||||
}
|
||||
|
||||
template <> int repack<block_q6_K, 4, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
|
||||
return repack_q6_K_to_q6_K_8_bl(t, 4, data, data_size);
|
||||
}
|
||||
|
||||
template <> int repack<block_q6_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
|
||||
return repack_q6_K_to_q6_K_8_bl(t, 8, data, data_size);
|
||||
}
|
||||
@@ -2575,6 +2594,10 @@ template <> void gemv<block_q5_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
|
||||
ggml_gemv_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
template <> void gemv<block_q6_K, 4, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
|
||||
ggml_gemv_q6_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
template <> void gemv<block_q6_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
|
||||
ggml_gemv_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
@@ -2634,6 +2657,10 @@ template <> void gemm<block_q5_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
|
||||
ggml_gemm_q5_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
template <> void gemm<block_q6_K, 4, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
|
||||
ggml_gemm_q6_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
|
||||
template <> void gemm<block_q6_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
|
||||
ggml_gemm_q6_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
|
||||
}
|
||||
@@ -3043,6 +3070,7 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
|
||||
static const ggml::cpu::repack::tensor_traits<block_q5_K, 8, 8, GGML_TYPE_Q8_K> q5_K_8x8_q8_K;
|
||||
|
||||
// instance for Q6_K
|
||||
static const ggml::cpu::repack::tensor_traits<block_q6_K, 4, 8, GGML_TYPE_Q8_K> q6_K_8x4_q8_K;
|
||||
static const ggml::cpu::repack::tensor_traits<block_q6_K, 8, 8, GGML_TYPE_Q8_K> q6_K_8x8_q8_K;
|
||||
|
||||
// instance for Q2
|
||||
@@ -3107,6 +3135,11 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
|
||||
return &q6_K_8x8_q8_K;
|
||||
}
|
||||
}
|
||||
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
|
||||
if (cur->ne[1] % 8 == 0) {
|
||||
return &q6_K_8x4_q8_K;
|
||||
}
|
||||
}
|
||||
} else if (cur->type == GGML_TYPE_IQ4_NL) {
|
||||
if (ggml_cpu_has_avx2()) {
|
||||
if (cur->ne[1] % 8 == 0) {
|
||||
|
||||
@@ -112,6 +112,7 @@ void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
|
||||
void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
@@ -122,6 +123,7 @@ void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
|
||||
void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q5_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
@@ -142,6 +144,7 @@ void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
void ggml_gemv_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
@@ -152,6 +155,7 @@ void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
void ggml_gemm_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q5_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
|
||||
@@ -2979,8 +2979,7 @@ static bool ggml_cuda_graph_node_properties_match(ggml_tensor * node, ggml_cuda_
|
||||
}
|
||||
}
|
||||
|
||||
if ((node->op == GGML_OP_SCALE || node->op == GGML_OP_GLU) &&
|
||||
memcmp(props->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
|
||||
if (memcmp(props->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
|
||||
return false;
|
||||
}
|
||||
|
||||
@@ -4835,8 +4834,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
||||
case GGML_OP_SUM_ROWS:
|
||||
case GGML_OP_MEAN:
|
||||
case GGML_OP_GROUP_NORM:
|
||||
case GGML_OP_PAD:
|
||||
return ggml_is_contiguous(op->src[0]);
|
||||
case GGML_OP_PAD:
|
||||
return true;
|
||||
case GGML_OP_UPSCALE:
|
||||
case GGML_OP_PAD_REFLECT_1D:
|
||||
case GGML_OP_ARANGE:
|
||||
|
||||
+13
-10
@@ -7,7 +7,7 @@ __device__ __forceinline__ int64_t wrap_around(int64_t coord, int64_t size) {
|
||||
return (coord + size) % size;
|
||||
}
|
||||
|
||||
static __global__ void pad_f32(const float * src, float * dst,
|
||||
static __global__ void pad_f32(const float * src, size_t s00, size_t s01, size_t s02, size_t s03, float * dst,
|
||||
const int lp0, const int rp0, const int lp1, const int rp1,
|
||||
const int lp2, const int rp2, const int lp3, const int rp3,
|
||||
const int ne0, const int ne1, const int ne2, const int ne3,
|
||||
@@ -34,11 +34,8 @@ static __global__ void pad_f32(const float * src, float * dst,
|
||||
const int64_t i01 = i1 - lp1;
|
||||
const int64_t i02 = i2 - lp2;
|
||||
const int64_t i03 = i3 - lp3;
|
||||
const int64_t ne02 = ne2 - lp2 - rp2;
|
||||
const int64_t ne01 = ne1 - lp1 - rp1;
|
||||
const int64_t ne00 = ne0 - lp0 - rp0;
|
||||
|
||||
const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
|
||||
const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
|
||||
|
||||
dst[dst_idx] = src[src_idx];
|
||||
} else {
|
||||
@@ -57,21 +54,21 @@ static __global__ void pad_f32(const float * src, float * dst,
|
||||
const int64_t i02 = wrap_around(i2 - lp2, ne02);
|
||||
const int64_t i03 = wrap_around(i3 - lp3, ne03);
|
||||
|
||||
const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
|
||||
const int64_t src_idx = i03 * s03 + i02 * s02 + i01 * s01 + i00 * s00;
|
||||
|
||||
dst[dst_idx] = src[src_idx];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
static void pad_f32_cuda(const float * src, float * dst,
|
||||
static void pad_f32_cuda(const float * src, size_t s00, size_t s01, size_t s02, size_t s03, float * dst,
|
||||
const int lp0, const int rp0, const int lp1, const int rp1,
|
||||
const int lp2, const int rp2, const int lp3, const int rp3,
|
||||
const int ne0, const int ne1, const int ne2, const int ne3,
|
||||
const bool circular, cudaStream_t stream) {
|
||||
int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
|
||||
dim3 gridDim(num_blocks, ne1, ne2 * ne3);
|
||||
pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, dst,
|
||||
pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, s00, s01, s02, s03, dst,
|
||||
lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
|
||||
ne0, ne1, ne2, ne3, circular);
|
||||
}
|
||||
@@ -82,9 +79,10 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
float * dst_d = (float *) dst->data;
|
||||
cudaStream_t stream = ctx.stream();
|
||||
|
||||
GGML_TENSOR_UNARY_OP_LOCALS;
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(ggml_is_contiguous(src0));
|
||||
|
||||
const int32_t lp0 = ((const int32_t *) (dst->op_params))[0];
|
||||
const int32_t rp0 = ((const int32_t *) (dst->op_params))[1];
|
||||
@@ -96,7 +94,12 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const int32_t rp3 = ((const int32_t *) (dst->op_params))[7];
|
||||
const int32_t circular = ((const int32_t *) (dst->op_params))[8];
|
||||
|
||||
pad_f32_cuda(src0_d, dst_d,
|
||||
const size_t s00 = nb00 / ggml_type_size(src0->type);
|
||||
const size_t s01 = nb01 / ggml_type_size(src0->type);
|
||||
const size_t s02 = nb02 / ggml_type_size(src0->type);
|
||||
const size_t s03 = nb03 / ggml_type_size(src0->type);
|
||||
|
||||
pad_f32_cuda(src0_d, s00, s01, s02, s03, dst_d,
|
||||
lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
|
||||
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
|
||||
(bool) circular, stream);
|
||||
|
||||
+232
-132
@@ -43,10 +43,15 @@ static __device__ void rope_yarn(
|
||||
template <bool forward, bool has_ff, typename T, typename D>
|
||||
static __global__ void rope_norm(const T * x,
|
||||
D * dst,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int32_t * pos,
|
||||
const float freq_scale,
|
||||
@@ -59,23 +64,23 @@ static __global__ void rope_norm(const T * x,
|
||||
const int set_rows_stride) {
|
||||
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
|
||||
|
||||
if (i0 >= ne0) {
|
||||
if (i0 >= ne00) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
const int row_x = row_dst % ne1;
|
||||
const int channel_x = row_dst / ne1;
|
||||
|
||||
int idst = row_dst * ne0 + i0;
|
||||
const int ix = channel_x*s2 + row_x*s1 + i0;
|
||||
const uint32_t i3 = row_dst / (ne01 * ne02);
|
||||
const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
|
||||
const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
|
||||
|
||||
int idst = i0 + i1 * s1 + i2 * s2 + i3 * s3;
|
||||
const int ix = i0 + i1 * s01 + i2 * s02 + i3 * s03;
|
||||
// Fusion optimization: ROPE + VIEW + SET_ROWS.
|
||||
// The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
|
||||
if (set_rows_stride != 0) {
|
||||
idst = row_x * ne0 + i0;
|
||||
idst += row_indices[channel_x] * set_rows_stride;
|
||||
idst = i1 * s1 + i0;
|
||||
idst += row_indices[i2] * set_rows_stride;
|
||||
}
|
||||
|
||||
const auto & store_coaelsced = [&](float x0, float x1) {
|
||||
@@ -92,7 +97,7 @@ static __global__ void rope_norm(const T * x,
|
||||
return;
|
||||
}
|
||||
|
||||
const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
|
||||
const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
|
||||
|
||||
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
|
||||
|
||||
@@ -110,10 +115,15 @@ static __global__ void rope_norm(const T * x,
|
||||
template <bool forward, bool has_ff, typename T, typename D>
|
||||
static __global__ void rope_neox(const T * x,
|
||||
D * dst,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int32_t * pos,
|
||||
const float freq_scale,
|
||||
@@ -126,23 +136,24 @@ static __global__ void rope_neox(const T * x,
|
||||
const int set_rows_stride) {
|
||||
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
|
||||
|
||||
if (i0 >= ne0) {
|
||||
if (i0 >= ne00) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
const int row_x = row_dst % ne1;
|
||||
const int channel_x = row_dst / ne1;
|
||||
const uint32_t i3 = row_dst / (ne01 * ne02);
|
||||
const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
|
||||
const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
|
||||
|
||||
int idst = row_dst * ne0 + i0 / 2;
|
||||
const int ix = channel_x*s2 + row_x*s1 + i0/2;
|
||||
int idst = i0 / 2 + i1 * s1 + i2 * s2 + i3 * s3;
|
||||
const int ix = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
|
||||
|
||||
// Fusion optimization: ROPE + VIEW + SET_ROWS.
|
||||
// The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
|
||||
if (set_rows_stride != 0) {
|
||||
idst = row_x * ne0 + i0 / 2;
|
||||
idst += row_indices[channel_x] * set_rows_stride;
|
||||
idst = i1 * s1 + i0 / 2;
|
||||
idst += row_indices[i2] * set_rows_stride;
|
||||
}
|
||||
|
||||
if (i0 >= n_dims) {
|
||||
@@ -152,7 +163,7 @@ static __global__ void rope_neox(const T * x,
|
||||
return;
|
||||
}
|
||||
|
||||
const float theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
|
||||
const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
|
||||
|
||||
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
|
||||
|
||||
@@ -168,24 +179,42 @@ static __global__ void rope_neox(const T * x,
|
||||
dst[idst + n_dims / 2] = ggml_cuda_cast<D>(x0 * sin_theta + x1 * cos_theta);
|
||||
}
|
||||
|
||||
template<bool forward, bool has_ff, typename T>
|
||||
static __global__ void rope_multi(
|
||||
const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2,
|
||||
const int n_dims, const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
|
||||
const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors, const mrope_sections sections, const bool is_imrope) {
|
||||
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
|
||||
template <bool forward, bool has_ff, typename T>
|
||||
static __global__ void rope_multi(const T * x,
|
||||
T * dst,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int32_t * pos,
|
||||
const float freq_scale,
|
||||
const float ext_factor,
|
||||
const float attn_factor,
|
||||
const rope_corr_dims corr_dims,
|
||||
const float theta_scale,
|
||||
const float * freq_factors,
|
||||
const mrope_sections sections,
|
||||
const bool is_imrope) {
|
||||
const int i0 = 2 * (blockDim.y * blockIdx.y + threadIdx.y);
|
||||
|
||||
if (i0 >= ne0) {
|
||||
if (i0 >= ne00) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
const int row_x = row_dst % ne1;
|
||||
const int channel_x = row_dst / ne1;
|
||||
const uint32_t i3 = row_dst / (ne01 * ne02);
|
||||
const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
|
||||
const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
|
||||
|
||||
const int idst = row_dst*ne0 + i0/2;
|
||||
const int ix = channel_x*s2 + row_x*s1 + i0/2;
|
||||
int idst = i0 / 2 + i1 * s1 + i2 * s2 + i3 * s3;
|
||||
const int ix = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
|
||||
|
||||
if (i0 >= n_dims) {
|
||||
dst[idst + i0/2 + 0] = x[ix + i0/2 + 0];
|
||||
@@ -200,27 +229,24 @@ static __global__ void rope_multi(
|
||||
|
||||
float theta_base = 0.0;
|
||||
if (is_imrope) {
|
||||
if (sector % 3 == 1 && sector < 3 * sections.v[1]) { // h
|
||||
theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
|
||||
} else if (sector % 3 == 2 && sector < 3 * sections.v[2]) { // w
|
||||
theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
|
||||
} else if (sector % 3 == 0 && sector < 3 * sections.v[0]) { // t
|
||||
theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
|
||||
if (sector % 3 == 1 && sector < 3 * sections.v[1]) { // h
|
||||
theta_base = pos[i2 + ne02 * 1] * powf(theta_scale, i0 / 2.0f);
|
||||
} else if (sector % 3 == 2 && sector < 3 * sections.v[2]) { // w
|
||||
theta_base = pos[i2 + ne02 * 2] * powf(theta_scale, i0 / 2.0f);
|
||||
} else if (sector % 3 == 0 && sector < 3 * sections.v[0]) { // t
|
||||
theta_base = pos[i2] * powf(theta_scale, i0 / 2.0f);
|
||||
} else {
|
||||
theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
|
||||
theta_base = pos[i2 + ne02 * 3] * powf(theta_scale, i0 / 2.0f);
|
||||
}
|
||||
} else {
|
||||
if (sector < sections.v[0]) {
|
||||
theta_base = pos[channel_x]*powf(theta_scale, i0/2.0f);
|
||||
}
|
||||
else if (sector >= sections.v[0] && sector < sec_w) {
|
||||
theta_base = pos[channel_x + ne2 * 1]*powf(theta_scale, i0/2.0f);
|
||||
}
|
||||
else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
|
||||
theta_base = pos[channel_x + ne2 * 2]*powf(theta_scale, i0/2.0f);
|
||||
}
|
||||
else if (sector >= sec_w + sections.v[2]) {
|
||||
theta_base = pos[channel_x + ne2 * 3]*powf(theta_scale, i0/2.0f);
|
||||
theta_base = pos[i2] * powf(theta_scale, i0 / 2.0f);
|
||||
} else if (sector >= sections.v[0] && sector < sec_w) {
|
||||
theta_base = pos[i2 + ne02 * 1] * powf(theta_scale, i0 / 2.0f);
|
||||
} else if (sector >= sec_w && sector < sec_w + sections.v[2]) {
|
||||
theta_base = pos[i2 + ne02 * 2] * powf(theta_scale, i0 / 2.0f);
|
||||
} else if (sector >= sec_w + sections.v[2]) {
|
||||
theta_base = pos[i2 + ne02 * 3] * powf(theta_scale, i0 / 2.0f);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -238,37 +264,53 @@ static __global__ void rope_multi(
|
||||
dst[idst + n_dims/2] = x0*sin_theta + x1*cos_theta;
|
||||
}
|
||||
|
||||
template<bool forward, bool has_ff, typename T>
|
||||
static __global__ void rope_vision(
|
||||
const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims,
|
||||
const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
|
||||
const float theta_scale, const float * freq_factors, const mrope_sections sections) {
|
||||
template <bool forward, bool has_ff, typename T>
|
||||
static __global__ void rope_vision(const T * x,
|
||||
T * dst,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int32_t * pos,
|
||||
const float freq_scale,
|
||||
const float ext_factor,
|
||||
const float attn_factor,
|
||||
const rope_corr_dims corr_dims,
|
||||
const float theta_scale,
|
||||
const float * freq_factors,
|
||||
const mrope_sections sections) {
|
||||
const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
|
||||
|
||||
if (i0 >= ne0) {
|
||||
if (i0 >= ne00) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int row_dst = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
const int row_x = row_dst % ne1;
|
||||
const int channel_x = row_dst / ne1;
|
||||
const uint32_t i3 = row_dst / (ne01 * ne02);
|
||||
const uint32_t i2 = (row_dst - i3 * ne01 * ne02) / ne01;
|
||||
const uint32_t i1 = row_dst - i3 * ne01 * ne02 - i2 * ne01;
|
||||
|
||||
const int idst = row_dst*ne0 + i0/2;
|
||||
const int ix = channel_x*s2 + row_x*s1 + i0/2;
|
||||
int idst = i0 / 2 + i1 * s1 + i2 * s2 + i3 * s3;
|
||||
const int ix = i0 / 2 + i1 * s01 + i2 * s02 + i3 * s03;
|
||||
|
||||
const int sect_dims = sections.v[0] + sections.v[1];
|
||||
const int sec_w = sections.v[1] + sections.v[0];
|
||||
const int sector = (i0 / 2) % sect_dims;
|
||||
const int sec_w = sections.v[1] + sections.v[0];
|
||||
const int sector = (i0 / 2) % sect_dims;
|
||||
|
||||
float theta_base = 0.0;
|
||||
if (sector < sections.v[0]) {
|
||||
const int p = sector;
|
||||
theta_base = pos[channel_x]*powf(theta_scale, p);
|
||||
}
|
||||
else if (sector >= sections.v[0] && sector < sec_w) {
|
||||
theta_base = pos[i2] * powf(theta_scale, p);
|
||||
} else if (sector >= sections.v[0] && sector < sec_w) {
|
||||
const int p = sector - sections.v[0];
|
||||
theta_base = pos[channel_x + ne2]*powf(theta_scale, p);
|
||||
theta_base = pos[i2 + ne02] * powf(theta_scale, p);
|
||||
}
|
||||
|
||||
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
|
||||
@@ -288,10 +330,15 @@ static __global__ void rope_vision(
|
||||
template <bool forward, typename T, typename D>
|
||||
static void rope_norm_cuda(const T * x,
|
||||
D * dst,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int nr,
|
||||
const int32_t * pos,
|
||||
@@ -304,31 +351,36 @@ static void rope_norm_cuda(const T * x,
|
||||
const int64_t * row_indices,
|
||||
const int set_rows_stride,
|
||||
cudaStream_t stream) {
|
||||
GGML_ASSERT(ne0 % 2 == 0);
|
||||
GGML_ASSERT(ne00 % 2 == 0);
|
||||
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
|
||||
const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
||||
const int n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
|
||||
const dim3 block_nums(nr, n_blocks_x, 1);
|
||||
|
||||
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
||||
const float theta_scale = powf(freq_base, -2.0f / n_dims);
|
||||
|
||||
if (freq_factors == nullptr) {
|
||||
rope_norm<forward, false><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
|
||||
freq_factors, row_indices, set_rows_stride);
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
|
||||
} else {
|
||||
rope_norm<forward, true><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
|
||||
freq_factors, row_indices, set_rows_stride);
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
|
||||
}
|
||||
}
|
||||
|
||||
template <bool forward, typename T, typename D>
|
||||
static void rope_neox_cuda(const T * x,
|
||||
D * dst,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int nr,
|
||||
const int32_t * pos,
|
||||
@@ -341,55 +393,92 @@ static void rope_neox_cuda(const T * x,
|
||||
const int64_t * row_indices,
|
||||
const int set_rows_stride,
|
||||
cudaStream_t stream) {
|
||||
GGML_ASSERT(ne0 % 2 == 0);
|
||||
GGML_ASSERT(ne00 % 2 == 0);
|
||||
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
|
||||
const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
||||
const int n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
|
||||
const dim3 block_nums(nr, n_blocks_x, 1);
|
||||
|
||||
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
||||
const float theta_scale = powf(freq_base, -2.0f / n_dims);
|
||||
|
||||
if (freq_factors == nullptr) {
|
||||
rope_neox<forward, false><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
|
||||
freq_factors, row_indices, set_rows_stride);
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
|
||||
} else {
|
||||
rope_neox<forward, true><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
|
||||
freq_factors, row_indices, set_rows_stride);
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, row_indices, set_rows_stride);
|
||||
}
|
||||
}
|
||||
|
||||
template<bool forward, typename T>
|
||||
static void rope_multi_cuda(
|
||||
const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
|
||||
const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
|
||||
const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, const bool is_imrope, cudaStream_t stream) {
|
||||
GGML_ASSERT(ne0 % 2 == 0);
|
||||
template <bool forward, typename T>
|
||||
static void rope_multi_cuda(const T * x,
|
||||
T * dst,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int nr,
|
||||
const int32_t * pos,
|
||||
const float freq_scale,
|
||||
const float freq_base,
|
||||
const float ext_factor,
|
||||
const float attn_factor,
|
||||
const rope_corr_dims corr_dims,
|
||||
const float * freq_factors,
|
||||
const mrope_sections sections,
|
||||
const bool is_imrope,
|
||||
cudaStream_t stream) {
|
||||
GGML_ASSERT(ne00 % 2 == 0);
|
||||
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
|
||||
const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
||||
const int n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
|
||||
const dim3 block_nums(nr, n_blocks_x, 1);
|
||||
|
||||
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
||||
const float theta_scale = powf(freq_base, -2.0f / n_dims);
|
||||
|
||||
if (freq_factors == nullptr) {
|
||||
rope_multi<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
|
||||
} else {
|
||||
rope_multi<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, sections, is_imrope);
|
||||
}
|
||||
}
|
||||
|
||||
template<bool forward, typename T>
|
||||
static void rope_vision_cuda(
|
||||
const T * x, T * dst, const int ne0, const int ne1, const int ne2, const int s1, const int s2, const int n_dims, const int nr,
|
||||
const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
|
||||
const rope_corr_dims corr_dims, const float * freq_factors, const mrope_sections sections, cudaStream_t stream) {
|
||||
GGML_ASSERT(ne0 % 2 == 0);
|
||||
template <bool forward, typename T>
|
||||
static void rope_vision_cuda(const T * x,
|
||||
T * dst,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int s01,
|
||||
const int s02,
|
||||
const int s03,
|
||||
const int s1,
|
||||
const int s2,
|
||||
const int s3,
|
||||
const int n_dims,
|
||||
const int nr,
|
||||
const int32_t * pos,
|
||||
const float freq_scale,
|
||||
const float freq_base,
|
||||
const float ext_factor,
|
||||
const float attn_factor,
|
||||
const rope_corr_dims corr_dims,
|
||||
const float * freq_factors,
|
||||
const mrope_sections sections,
|
||||
cudaStream_t stream) {
|
||||
GGML_ASSERT(ne00 % 2 == 0);
|
||||
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
|
||||
const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
||||
const int n_blocks_x = (ne00 + 2 * CUDA_ROPE_BLOCK_SIZE - 1) / (2 * CUDA_ROPE_BLOCK_SIZE);
|
||||
const dim3 block_nums(nr, n_blocks_x, 1);
|
||||
// break down (head_dim, heads, seq) into (CUDA_ROPE_BLOCK_SIZE, x, heads * seq)
|
||||
// where x ~= ceil(head_dim / CUDA_ROPE_BLOCK_SIZE);
|
||||
@@ -398,11 +487,11 @@ static void rope_vision_cuda(
|
||||
|
||||
if (freq_factors == nullptr) {
|
||||
rope_vision<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, sections);
|
||||
} else {
|
||||
rope_vision<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
|
||||
x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor,
|
||||
x, dst, ne00, ne01, ne02, s01, s02, s03, s1, s2, s3, n_dims, pos, freq_scale, ext_factor,
|
||||
attn_factor, corr_dims, theta_scale, freq_factors, sections);
|
||||
}
|
||||
}
|
||||
@@ -445,6 +534,11 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
|
||||
|
||||
const size_t s01 = src0->nb[1] / ggml_type_size(src0->type);
|
||||
const size_t s02 = src0->nb[2] / ggml_type_size(src0->type);
|
||||
const size_t s03 = src0->nb[3] / ggml_type_size(src0->type);
|
||||
|
||||
const size_t s1 = dst->nb[1] / ggml_type_size(dst->type);
|
||||
const size_t s2 = dst->nb[2] / ggml_type_size(dst->type);
|
||||
const size_t s3 = dst->nb[3] / ggml_type_size(dst->type);
|
||||
|
||||
//const int n_past = ((int32_t *) dst->op_params)[0];
|
||||
const int n_dims = ((int32_t *) dst->op_params)[1];
|
||||
@@ -495,57 +589,63 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
|
||||
// compute
|
||||
if (is_neox) {
|
||||
if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
|
||||
rope_neox_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
|
||||
nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
|
||||
freq_factors, row_indices, set_rows_stride, stream);
|
||||
rope_neox_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02,
|
||||
s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
|
||||
ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
|
||||
set_rows_stride, stream);
|
||||
} else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
|
||||
rope_neox_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
|
||||
nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
|
||||
freq_factors, row_indices, set_rows_stride, stream);
|
||||
rope_neox_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
|
||||
s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
|
||||
ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
|
||||
set_rows_stride, stream);
|
||||
} else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
|
||||
rope_neox_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
|
||||
pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
|
||||
freq_factors, row_indices, set_rows_stride, stream);
|
||||
rope_neox_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
|
||||
s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
|
||||
ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
|
||||
set_rows_stride, stream);
|
||||
} else {
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
} else if (is_mrope && !is_vision) {
|
||||
if (src0->type == GGML_TYPE_F32) {
|
||||
rope_multi_cuda<forward>(
|
||||
(const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
|
||||
freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, is_imrope, stream);
|
||||
rope_multi_cuda<forward>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
|
||||
s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
|
||||
corr_dims, freq_factors, sections, is_imrope, stream);
|
||||
} else if (src0->type == GGML_TYPE_F16) {
|
||||
rope_multi_cuda<forward>(
|
||||
(const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
|
||||
freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, is_imrope, stream);
|
||||
rope_multi_cuda<forward>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
|
||||
s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
|
||||
corr_dims, freq_factors, sections, is_imrope, stream);
|
||||
} else {
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
} else if (is_vision) {
|
||||
if (src0->type == GGML_TYPE_F32) {
|
||||
rope_vision_cuda<forward>(
|
||||
(const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
|
||||
freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
|
||||
rope_vision_cuda<forward>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
|
||||
s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
|
||||
corr_dims, freq_factors, sections, stream);
|
||||
} else if (src0->type == GGML_TYPE_F16) {
|
||||
rope_vision_cuda<forward>(
|
||||
(const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
|
||||
freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, stream);
|
||||
rope_vision_cuda<forward>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02, s03, s1,
|
||||
s2, s3, n_dims, nr, pos, freq_scale, freq_base, ext_factor, attn_factor,
|
||||
corr_dims, freq_factors, sections, stream);
|
||||
} else {
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
} else {
|
||||
if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
|
||||
rope_norm_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
|
||||
nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
|
||||
freq_factors, row_indices, set_rows_stride, stream);
|
||||
rope_norm_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, ne02, s01, s02,
|
||||
s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
|
||||
ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
|
||||
set_rows_stride, stream);
|
||||
} else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
|
||||
rope_norm_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
|
||||
nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
|
||||
freq_factors, row_indices, set_rows_stride, stream);
|
||||
rope_norm_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
|
||||
s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
|
||||
ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
|
||||
set_rows_stride, stream);
|
||||
} else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
|
||||
rope_norm_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
|
||||
pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
|
||||
freq_factors, row_indices, set_rows_stride, stream);
|
||||
rope_norm_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, ne02, s01, s02,
|
||||
s03, s1, s2, s3, n_dims, nr, pos, freq_scale, freq_base,
|
||||
ext_factor, attn_factor, corr_dims, freq_factors, row_indices,
|
||||
set_rows_stride, stream);
|
||||
} else {
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
|
||||
@@ -394,7 +394,7 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con
|
||||
[encoder endEncoding];
|
||||
|
||||
ggml_metal_event_t ev_cpy = ggml_metal_get_ev_cpy(ctx_src);
|
||||
ggml_metal_event_record(ctx_src, ev_cpy);
|
||||
ggml_metal_event_encode_signal(ev_cpy, cmd_buf);
|
||||
|
||||
[cmd_buf commit];
|
||||
|
||||
@@ -415,7 +415,7 @@ bool ggml_metal_cpy_tensor_async(ggml_metal_t ctx_src, ggml_metal_t ctx_dst, con
|
||||
|
||||
enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) {
|
||||
// number of nodes encoded by the main thread (empirically determined)
|
||||
const int n_main = 64;
|
||||
const int n_main = MAX(64, 0.1*gf->n_nodes);
|
||||
|
||||
// number of threads in addition to the main thread
|
||||
const int n_cb = ctx->n_cb;
|
||||
|
||||
@@ -176,6 +176,26 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows(ggml_me
|
||||
return res;
|
||||
}
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||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag(ggml_metal_library_t lib, const ggml_tensor * op) {
|
||||
char base[256];
|
||||
char name[256];
|
||||
|
||||
const int n = op->src[0]->ne[0];
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||||
|
||||
snprintf(base, 256, "kernel_diag_%s", ggml_type_name(op->src[0]->type));
|
||||
snprintf(name, 256, "%s_n=%d", base, n);
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||||
|
||||
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
|
||||
if (!res.pipeline) {
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||||
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
|
||||
}
|
||||
|
||||
res.nsg = 1;
|
||||
res.smem = 0;
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat(ggml_metal_library_t lib, ggml_type tsrc) {
|
||||
char base[256];
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||||
char name[256];
|
||||
@@ -1372,34 +1392,78 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_flash_attn_ext_v
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||||
GGML_UNUSED(op);
|
||||
}
|
||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(
|
||||
ggml_metal_library_t lib,
|
||||
ggml_op op,
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||||
int32_t n_fuse,
|
||||
bool row) {
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||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin(ggml_metal_library_t lib, const ggml_tensor * op, int32_t n_fuse) {
|
||||
char base[256];
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||||
char name[256];
|
||||
|
||||
const char * op_str = "undefined";
|
||||
switch (op) {
|
||||
case GGML_OP_ADD: op_str = "add"; break;
|
||||
case GGML_OP_SUB: op_str = "sub"; break;
|
||||
case GGML_OP_MUL: op_str = "mul"; break;
|
||||
case GGML_OP_DIV: op_str = "div"; break;
|
||||
int op_num = -1;
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||||
|
||||
switch (op->op) {
|
||||
case GGML_OP_ADD: op_num = 0; break;
|
||||
case GGML_OP_SUB: op_num = 1; break;
|
||||
case GGML_OP_MUL: op_num = 2; break;
|
||||
case GGML_OP_DIV: op_num = 3; break;
|
||||
default: GGML_ABORT("fatal error");
|
||||
};
|
||||
|
||||
if (row) {
|
||||
snprintf(base, 256, "kernel_%s_row_c4_fuse_%d", op_str, n_fuse);
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||||
} else {
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||||
snprintf(base, 256, "kernel_%s_fuse_%d", op_str, n_fuse);
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||||
}
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||||
const char * t0_str = ggml_type_name(op->src[0]->type);
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||||
const char * t1_str = ggml_type_name(op->src[1]->type);
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||||
const char * t_str = ggml_type_name(op->type);
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||||
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||||
snprintf(name, 256, "%s", base);
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||||
const bool is_c4 = (op->src[0]->ne[0] % 4 == 0) && (op->src[1]->ne[0] % 4 == 0);
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||||
|
||||
const bool is_rb = ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && (ggml_nrows(op->src[1]) == 1) && ggml_nelements(op) < 65536;
|
||||
|
||||
snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s%s", t0_str, t1_str, t_str, is_c4 ? "_4" : "");
|
||||
snprintf(name, 256, "%s_op=%d_nf=%d_rb=%d", base, op_num, n_fuse, is_rb);
|
||||
|
||||
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
|
||||
if (!res.pipeline) {
|
||||
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
|
||||
ggml_metal_cv_t cv = ggml_metal_cv_init();
|
||||
|
||||
ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0);
|
||||
ggml_metal_cv_set_int16(cv, n_fuse, FC_BIN + 1);
|
||||
ggml_metal_cv_set_bool (cv, is_rb, FC_BIN + 2);
|
||||
|
||||
res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
|
||||
|
||||
ggml_metal_cv_free(cv);
|
||||
}
|
||||
|
||||
res.c4 = is_c4;
|
||||
res.cnt = is_rb;
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one(ggml_metal_library_t lib, ggml_op op) {
|
||||
char base[256];
|
||||
char name[256];
|
||||
|
||||
int op_num = -1;
|
||||
|
||||
switch (op) {
|
||||
case GGML_OP_ADD: op_num = 0; break;
|
||||
case GGML_OP_SUB: op_num = 1; break;
|
||||
case GGML_OP_MUL: op_num = 2; break;
|
||||
case GGML_OP_DIV: op_num = 3; break;
|
||||
default: GGML_ABORT("fatal error");
|
||||
};
|
||||
|
||||
snprintf(base, 256, "kernel_bin_fuse_%s_%s_%s", "f32", "f32", "f32");
|
||||
snprintf(name, 256, "%s_op=%d_nf=%d", base, op_num, 1);
|
||||
|
||||
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
|
||||
if (!res.pipeline) {
|
||||
ggml_metal_cv_t cv = ggml_metal_cv_init();
|
||||
|
||||
ggml_metal_cv_set_int16(cv, op_num, FC_BIN + 0);
|
||||
ggml_metal_cv_set_int16(cv, 1, FC_BIN + 1);
|
||||
ggml_metal_cv_set_bool (cv, false, FC_BIN + 2);
|
||||
|
||||
res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
|
||||
|
||||
ggml_metal_cv_free(cv);
|
||||
}
|
||||
|
||||
return res;
|
||||
|
||||
@@ -53,6 +53,9 @@ struct ggml_metal_pipeline_with_params {
|
||||
int nr1;
|
||||
|
||||
size_t smem;
|
||||
|
||||
bool c4;
|
||||
bool cnt;
|
||||
};
|
||||
|
||||
int ggml_metal_pipeline_max_theads_per_threadgroup(struct ggml_metal_pipeline_with_params pipeline);
|
||||
@@ -108,6 +111,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_1d
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_2d (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows (ggml_metal_library_t lib, enum ggml_type tsrc);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows (ggml_metal_library_t lib, enum ggml_type tidx, enum ggml_type tdst);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat (ggml_metal_library_t lib, enum ggml_type tsrc);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_glu (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
@@ -133,7 +137,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse );
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin_one (ggml_metal_library_t lib, enum ggml_op op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_norm (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
|
||||
|
||||
@@ -346,10 +346,12 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_meta
|
||||
|
||||
struct ggml_metal_pipeline_with_params res = {
|
||||
/*.pipeline =*/ nil,
|
||||
/*.nsg =*/ 0,
|
||||
/*.nr0 =*/ 0,
|
||||
/*.nr1 =*/ 0,
|
||||
/*.nsg =*/ 0,
|
||||
/*.smem =*/ 0,
|
||||
/*.c4 =*/ false,
|
||||
/*.cnt =*/ false,
|
||||
};
|
||||
|
||||
res.pipeline = ggml_metal_pipelines_get(lib->pipelines, name);
|
||||
@@ -362,10 +364,12 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline(ggml_meta
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_metal_library_t lib, const char * base, const char * name, ggml_metal_cv_t cv) {
|
||||
struct ggml_metal_pipeline_with_params res = {
|
||||
/*.pipeline =*/ nil,
|
||||
/*.nsg =*/ 0,
|
||||
/*.nr0 =*/ 0,
|
||||
/*.nr1 =*/ 0,
|
||||
/*.nsg =*/ 0,
|
||||
/*.smem =*/ 0,
|
||||
/*.c4 =*/ false,
|
||||
/*.cnt =*/ false,
|
||||
};
|
||||
|
||||
[lib->lock lock];
|
||||
@@ -1054,7 +1058,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
|
||||
case GGML_OP_MUL:
|
||||
case GGML_OP_DIV:
|
||||
case GGML_OP_ADD_ID:
|
||||
return op->src[0]->type == GGML_TYPE_F32;
|
||||
return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32;
|
||||
case GGML_OP_ACC:
|
||||
case GGML_OP_REPEAT:
|
||||
case GGML_OP_SCALE:
|
||||
@@ -1152,8 +1156,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
|
||||
return has_simdgroup_reduction;
|
||||
case GGML_OP_RWKV_WKV6:
|
||||
case GGML_OP_RWKV_WKV7:
|
||||
case GGML_OP_SOLVE_TRI:
|
||||
return true;
|
||||
case GGML_OP_SOLVE_TRI:
|
||||
case GGML_OP_MUL_MAT:
|
||||
case GGML_OP_MUL_MAT_ID:
|
||||
return has_simdgroup_reduction;
|
||||
@@ -1235,6 +1239,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
|
||||
return false;
|
||||
};
|
||||
}
|
||||
case GGML_OP_DIAG:
|
||||
return true;
|
||||
case GGML_OP_OPT_STEP_ADAMW:
|
||||
case GGML_OP_OPT_STEP_SGD:
|
||||
return has_simdgroup_reduction;
|
||||
|
||||
@@ -80,6 +80,7 @@
|
||||
#define FC_SSM_CONV 900
|
||||
#define FC_SOLVE_TRI 1000
|
||||
#define FC_COUNT_EQUAL 1100
|
||||
#define FC_BIN 1200
|
||||
|
||||
// op-specific constants
|
||||
#define OP_FLASH_ATTN_EXT_NQPSG 8
|
||||
@@ -792,6 +793,25 @@ typedef struct {
|
||||
uint64_t nb3;
|
||||
} ggml_metal_kargs_set_rows;
|
||||
|
||||
typedef struct {
|
||||
int32_t ne00;
|
||||
int32_t ne01;
|
||||
int32_t ne02;
|
||||
int32_t ne03;
|
||||
uint64_t nb00;
|
||||
uint64_t nb01;
|
||||
uint64_t nb02;
|
||||
uint64_t nb03;
|
||||
int32_t ne0;
|
||||
int32_t ne1;
|
||||
int32_t ne2;
|
||||
int32_t ne3;
|
||||
uint64_t nb0;
|
||||
uint64_t nb1;
|
||||
uint64_t nb2;
|
||||
uint64_t nb3;
|
||||
} ggml_metal_kargs_diag;
|
||||
|
||||
typedef struct {
|
||||
int64_t ne00;
|
||||
int64_t ne01;
|
||||
|
||||
@@ -361,6 +361,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
|
||||
{
|
||||
n_fuse = ggml_metal_op_set_rows(ctx, idx);
|
||||
} break;
|
||||
case GGML_OP_DIAG:
|
||||
{
|
||||
n_fuse = ggml_metal_op_diag(ctx, idx);
|
||||
} break;
|
||||
case GGML_OP_L2_NORM:
|
||||
{
|
||||
n_fuse = ggml_metal_op_l2_norm(ctx, idx);
|
||||
@@ -703,7 +707,7 @@ int ggml_metal_op_acc(ggml_metal_op_t ctx, int idx) {
|
||||
/*.o1 =*/ { 0 },
|
||||
};
|
||||
|
||||
auto pipeline = ggml_metal_library_get_pipeline_bin(lib, GGML_OP_ADD, 1, false);
|
||||
auto pipeline = ggml_metal_library_get_pipeline_bin_one(lib, GGML_OP_ADD);
|
||||
|
||||
ggml_metal_encoder_set_pipeline(enc, pipeline);
|
||||
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
|
||||
@@ -1259,6 +1263,48 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
int ggml_metal_op_diag(ggml_metal_op_t ctx, int idx) {
|
||||
ggml_tensor * op = ctx->node(idx);
|
||||
|
||||
ggml_metal_library_t lib = ctx->lib;
|
||||
ggml_metal_encoder_t enc = ctx->enc;
|
||||
|
||||
GGML_TENSOR_LOCALS(int32_t, ne0, op->src[0], ne);
|
||||
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
|
||||
GGML_TENSOR_LOCALS(int32_t, ne, op, ne);
|
||||
GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
|
||||
|
||||
ggml_metal_kargs_diag args = {
|
||||
/*.ne00 =*/ne00,
|
||||
/*.ne01 =*/ne01,
|
||||
/*.ne02 =*/ne02,
|
||||
/*.ne03 =*/ne03,
|
||||
/*.nb00 =*/nb00,
|
||||
/*.nb01 =*/nb01,
|
||||
/*.nb02 =*/nb02,
|
||||
/*.nb03 =*/nb03,
|
||||
/*.ne0 =*/ne0,
|
||||
/*.ne1 =*/ne1,
|
||||
/*.ne2 =*/ne2,
|
||||
/*.ne3 =*/ne3,
|
||||
/*.nb0 =*/nb0,
|
||||
/*.nb1 =*/nb1,
|
||||
/*.nb2 =*/nb2,
|
||||
/*.nb3 =*/nb3,
|
||||
};
|
||||
|
||||
auto pipeline = ggml_metal_library_get_pipeline_diag(lib, op);
|
||||
|
||||
ggml_metal_encoder_set_pipeline(enc, pipeline);
|
||||
ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 2);
|
||||
|
||||
ggml_metal_encoder_dispatch_threadgroups(enc, ne1, ne2, ne3, 32, 1, 1);
|
||||
|
||||
return 1;
|
||||
}
|
||||
|
||||
int ggml_metal_op_soft_max(ggml_metal_op_t ctx, int idx) {
|
||||
ggml_tensor * op = ctx->node(idx);
|
||||
|
||||
@@ -2849,8 +2895,6 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
|
||||
GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
|
||||
GGML_ASSERT(ggml_is_contiguous_rows(op->src[1]));
|
||||
|
||||
bool bcast_row = false;
|
||||
|
||||
ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
|
||||
ggml_metal_buffer_id bid_src1 = ggml_metal_get_buffer_id(op->src[1]);
|
||||
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);
|
||||
@@ -2944,18 +2988,7 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
|
||||
|
||||
struct ggml_metal_pipeline_with_params pipeline;
|
||||
|
||||
if (ggml_nelements(op->src[1]) == ne10 && ggml_is_contiguous(op->src[1]) && ne00 % 4 == 0 && ne10 % 4 == 0) {
|
||||
GGML_ASSERT(ggml_is_contiguous(op->src[0]));
|
||||
|
||||
// src1 is a row
|
||||
GGML_ASSERT(ne11 == 1);
|
||||
|
||||
pipeline = ggml_metal_library_get_pipeline_bin(lib, op->op, n_fuse, true);
|
||||
|
||||
bcast_row = true;
|
||||
} else {
|
||||
pipeline = ggml_metal_library_get_pipeline_bin(lib, op->op, n_fuse, false);
|
||||
}
|
||||
pipeline = ggml_metal_library_get_pipeline_bin(lib, op, n_fuse);
|
||||
|
||||
if (n_fuse > 1) {
|
||||
bid_dst = ggml_metal_get_buffer_id(ctx->node(idx + n_fuse - 1));
|
||||
@@ -2969,20 +3002,28 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
|
||||
}
|
||||
}
|
||||
|
||||
if (pipeline.c4) {
|
||||
args.ne00 = ne00/4;
|
||||
args.ne10 = ne10/4;
|
||||
args.ne0 = ne0/4;
|
||||
}
|
||||
|
||||
ggml_metal_encoder_set_pipeline(enc, pipeline);
|
||||
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
|
||||
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
|
||||
ggml_metal_encoder_set_buffer (enc, bid_src1, 2);
|
||||
ggml_metal_encoder_set_buffer (enc, bid_dst, 3);
|
||||
|
||||
if (bcast_row) {
|
||||
const int64_t n = ggml_nelements(op)/4;
|
||||
if (pipeline.cnt) {
|
||||
const int n = pipeline.c4 ? ggml_nelements(op)/4 : ggml_nelements(op);
|
||||
|
||||
ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
|
||||
} else {
|
||||
int nth = 32;
|
||||
const int nth_max = MIN(256, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
|
||||
|
||||
while (16*nth < ne0 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
|
||||
int nth = 1;
|
||||
|
||||
while (2*nth < args.ne0 && nth < nth_max) {
|
||||
nth *= 2;
|
||||
}
|
||||
|
||||
|
||||
@@ -56,6 +56,7 @@ int ggml_metal_op_sum_rows (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_cumsum (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_get_rows (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_set_rows (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_diag (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_soft_max (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_ssm_conv (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_ssm_scan (ggml_metal_op_t ctx, int idx);
|
||||
|
||||
@@ -895,11 +895,13 @@ enum ggml_sort_order {
|
||||
GGML_SORT_ORDER_DESC,
|
||||
};
|
||||
|
||||
// general-purpose kernel for addition, subtraction, multiplication and division of two tensors
|
||||
// pros: works for non-contiguous tensors, supports broadcast across all dims
|
||||
// cons: not very efficient
|
||||
template <int F>
|
||||
kernel void kernel_add_fuse_impl(
|
||||
// OP: 0 - add, 1 - sub, 2 - mul, 3 - div
|
||||
constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
|
||||
constant short FC_bin_f [[function_constant(FC_BIN + 1)]];
|
||||
constant bool FC_bin_rb [[function_constant(FC_BIN + 2)]];
|
||||
|
||||
template <typename T0, typename T1, typename T>
|
||||
kernel void kernel_bin_fuse_impl(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
@@ -907,138 +909,152 @@ kernel void kernel_add_fuse_impl(
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
#define FC_OP FC_bin_op
|
||||
#define FC_F FC_bin_f
|
||||
#define FC_RB FC_bin_rb
|
||||
|
||||
const int i13 = i03%args.ne13;
|
||||
const int i12 = i02%args.ne12;
|
||||
const int i11 = i01%args.ne11;
|
||||
if (FC_RB) {
|
||||
// row broadcast
|
||||
const uint i0 = tgpig.x;
|
||||
const uint i1 = i0%args.ne10;
|
||||
|
||||
device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
|
||||
device float * dst_ptr = (device float *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs);
|
||||
device const T0 * src0_row = (device const T0 *) (src0);
|
||||
device T * dst_row = (device T *) (dst);
|
||||
|
||||
device const float * src1_ptr[F];
|
||||
for (short j = 0; j < F; ++j) {
|
||||
src1_ptr[j] = (device const float *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
|
||||
}
|
||||
if (FC_F == 1) {
|
||||
device const T1 * src1_row = (device const T1 *) (src1 + args.o1[0]);
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = i0%args.ne10;
|
||||
if (FC_OP == 0) {
|
||||
dst_row[i0] = src0_row[i0] + src1_row[i1];
|
||||
}
|
||||
|
||||
float res = src0_ptr[i0];
|
||||
if (FC_OP == 1) {
|
||||
dst_row[i0] = src0_row[i0] - src1_row[i1];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (short j = 0; j < F; ++j) {
|
||||
res += src1_ptr[j][i10];
|
||||
}
|
||||
if (FC_OP == 2) {
|
||||
dst_row[i0] = src0_row[i0] * src1_row[i1];
|
||||
}
|
||||
|
||||
dst_ptr[i0] = res;
|
||||
}
|
||||
}
|
||||
if (FC_OP == 3) {
|
||||
dst_row[i0] = src0_row[i0] / src1_row[i1];
|
||||
}
|
||||
} else {
|
||||
T0 res = src0_row[i0];
|
||||
|
||||
typedef decltype(kernel_add_fuse_impl<2>) kernel_add_fuse_t;
|
||||
if (FC_OP == 0) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res += ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_add_fuse_1")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<1>;
|
||||
template [[host_name("kernel_add_fuse_2")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<2>;
|
||||
template [[host_name("kernel_add_fuse_3")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<3>;
|
||||
template [[host_name("kernel_add_fuse_4")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<4>;
|
||||
template [[host_name("kernel_add_fuse_5")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<5>;
|
||||
template [[host_name("kernel_add_fuse_6")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<6>;
|
||||
template [[host_name("kernel_add_fuse_7")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<7>;
|
||||
template [[host_name("kernel_add_fuse_8")]] kernel kernel_add_fuse_t kernel_add_fuse_impl<8>;
|
||||
if (FC_OP == 1) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res -= ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_sub_fuse_1(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
if (FC_OP == 2) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res *= ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
const int i13 = i03%args.ne13;
|
||||
const int i12 = i02%args.ne12;
|
||||
const int i11 = i01%args.ne11;
|
||||
if (FC_OP == 3) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res /= ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
|
||||
device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
|
||||
device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs;
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = i0%args.ne10;
|
||||
*((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) - *((device float *)(src1_ptr + i10*args.nb10));
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_mul_fuse_1(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
|
||||
const int i13 = i03%args.ne13;
|
||||
const int i12 = i02%args.ne12;
|
||||
const int i11 = i01%args.ne11;
|
||||
|
||||
device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
|
||||
device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
|
||||
device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs;
|
||||
|
||||
if (args.ne10 == 1) {
|
||||
const float x = *((device float *)(src1_ptr));
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
*((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * x;
|
||||
dst_row[i0] = res;
|
||||
}
|
||||
} else {
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = i0%args.ne10;
|
||||
*((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * *((device float *)(src1_ptr + i10*args.nb10));
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int i13 = i03%args.ne13;
|
||||
const int i12 = i02%args.ne12;
|
||||
const int i11 = i01%args.ne11;
|
||||
|
||||
device const T0 * src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
|
||||
device T * dst_ptr = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs);
|
||||
|
||||
if (FC_F == 1) {
|
||||
device const T1 * src1_ptr = (device const T1 *) (src1 + args.o1[0] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = i0%args.ne10;
|
||||
|
||||
if (FC_OP == 0) {
|
||||
dst_ptr[i0] = src0_ptr[i0] + src1_ptr[i10];
|
||||
}
|
||||
|
||||
if (FC_OP == 1) {
|
||||
dst_ptr[i0] = src0_ptr[i0] - src1_ptr[i10];
|
||||
}
|
||||
|
||||
if (FC_OP == 2) {
|
||||
dst_ptr[i0] = src0_ptr[i0] * src1_ptr[i10];
|
||||
}
|
||||
|
||||
if (FC_OP == 3) {
|
||||
dst_ptr[i0] = src0_ptr[i0] / src1_ptr[i10];
|
||||
}
|
||||
}
|
||||
} else {
|
||||
device const T1 * src1_ptr[8];
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
src1_ptr[j] = (device const T1 *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
|
||||
}
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = i0%args.ne10;
|
||||
|
||||
T res = src0_ptr[i0];
|
||||
|
||||
if (FC_OP == 0) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res += src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 1) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res -= src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 2) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res *= src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 3) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res /= src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
dst_ptr[i0] = res;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#undef FC_OP
|
||||
#undef FC_F
|
||||
#undef FC_RB
|
||||
}
|
||||
|
||||
kernel void kernel_div_fuse_1(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
typedef decltype(kernel_bin_fuse_impl<float, float, float>) kernel_bin_fuse_t;
|
||||
|
||||
const int i13 = i03%args.ne13;
|
||||
const int i12 = i02%args.ne12;
|
||||
const int i11 = i01%args.ne11;
|
||||
|
||||
device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs;
|
||||
device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + args.o1[0];
|
||||
device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs;
|
||||
|
||||
if (args.ne10 == 1) {
|
||||
const float x = 1.0f / *((device float *)(src1_ptr));
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
*((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * x;
|
||||
}
|
||||
} else {
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = i0%args.ne10;
|
||||
*((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) / *((device float *)(src1_ptr + i10*args.nb10));
|
||||
}
|
||||
}
|
||||
}
|
||||
template [[host_name("kernel_bin_fuse_f32_f32_f32")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float, float, float>;
|
||||
template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float4, float4, float4>;
|
||||
|
||||
kernel void kernel_add_id(
|
||||
constant ggml_metal_kargs_add_id & args,
|
||||
@@ -1057,7 +1073,7 @@ kernel void kernel_add_id(
|
||||
const size_t nb1 = args.ne0 * sizeof(float);
|
||||
const size_t nb2 = args.ne1 * nb1;
|
||||
|
||||
device float * dst_row = (device float *)((device char *)dst + i1*nb1 + i2*nb2);
|
||||
device float * dst_row = (device float *)((device char *)dst + i1*nb1 + i2*nb2);
|
||||
device const float * src0_row = (device const float *)((device char *)src0 + i1*args.nb01 + i2*args.nb02);
|
||||
device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
|
||||
|
||||
@@ -1098,141 +1114,6 @@ template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat
|
||||
template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
|
||||
template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
|
||||
|
||||
// assumption: src1 is a row
|
||||
// broadcast src1 into src0
|
||||
template <short F>
|
||||
kernel void kernel_add_row_c4_fuse_impl(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const uint nb = args.ne00/4;
|
||||
const uint i = tpig % nb;
|
||||
|
||||
device const float4 * src0_row = (device const float4 *) (src0);
|
||||
device float4 * dst_row = (device float4 *) (dst);
|
||||
|
||||
float4 res = src0_row[tpig];
|
||||
|
||||
#pragma unroll(F)
|
||||
for (short j = 0; j < F; ++j) {
|
||||
res += ((device const float4 *) (src1 + args.o1[j]))[i];
|
||||
}
|
||||
|
||||
dst_row[tpig] = res;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_add_row_c4_fuse_impl<1>) kernel_add_row_c4_fuse_t;
|
||||
|
||||
template [[host_name("kernel_add_row_c4_fuse_1")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<1>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_2")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<2>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_3")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<3>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_4")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<4>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_5")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<5>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_6")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<6>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_7")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<7>;
|
||||
template [[host_name("kernel_add_row_c4_fuse_8")]] kernel kernel_add_row_c4_fuse_t kernel_add_row_c4_fuse_impl<8>;
|
||||
|
||||
template <short F>
|
||||
kernel void kernel_sub_row_c4_fuse_impl(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
|
||||
const uint nb = args.ne00/4;
|
||||
const uint i = tpig % nb;
|
||||
|
||||
device const float4 * src0_row = (device const float4 *) (src0);
|
||||
device float4 * dst_row = (device float4 *) (dst);
|
||||
|
||||
device const float4 * src1_row[F];
|
||||
for (short j = 0; j < F; ++j) {
|
||||
src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
|
||||
}
|
||||
|
||||
float4 res = src0_row[tpig];
|
||||
|
||||
#pragma unroll(F)
|
||||
for (short j = 0; j < F; ++j) {
|
||||
res -= src1_row[j][i];
|
||||
}
|
||||
|
||||
dst_row[tpig] = res;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_sub_row_c4_fuse_impl<1>) kernel_sub_row_c4_fuse_t;
|
||||
|
||||
template [[host_name("kernel_sub_row_c4_fuse_1")]] kernel kernel_sub_row_c4_fuse_t kernel_sub_row_c4_fuse_impl<1>;
|
||||
|
||||
template <short F>
|
||||
kernel void kernel_mul_row_c4_fuse_impl(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
|
||||
const uint nb = args.ne00/4;
|
||||
const uint i = tpig % nb;
|
||||
|
||||
device const float4 * src0_row = (device const float4 *) (src0);
|
||||
device float4 * dst_row = (device float4 *) (dst);
|
||||
|
||||
device const float4 * src1_row[F];
|
||||
for (short j = 0; j < F; ++j) {
|
||||
src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
|
||||
}
|
||||
|
||||
float4 res = src0_row[tpig];
|
||||
|
||||
#pragma unroll(F)
|
||||
for (short j = 0; j < F; ++j) {
|
||||
res *= src1_row[j][i];
|
||||
}
|
||||
|
||||
dst_row[tpig] = res;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_mul_row_c4_fuse_impl<1>) kernel_mul_row_c4_fuse_t;
|
||||
|
||||
template [[host_name("kernel_mul_row_c4_fuse_1")]] kernel kernel_mul_row_c4_fuse_t kernel_mul_row_c4_fuse_impl<1>;
|
||||
|
||||
template <short F>
|
||||
kernel void kernel_div_row_c4_fuse_impl(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
|
||||
const uint nb = args.ne00/4;
|
||||
const uint i = tpig % nb;
|
||||
|
||||
device const float4 * src0_row = (device const float4 *) (src0);
|
||||
device float4 * dst_row = (device float4 *) (dst);
|
||||
|
||||
device const float4 * src1_row[F];
|
||||
for (short j = 0; j < F; ++j) {
|
||||
src1_row[j] = (device const float4 *) (src1 + args.o1[j]);
|
||||
}
|
||||
|
||||
float4 res = src0_row[tpig];
|
||||
|
||||
#pragma unroll(F)
|
||||
for (short j = 0; j < F; ++j) {
|
||||
res /= src1_row[j][i];
|
||||
}
|
||||
|
||||
dst_row[tpig] = res;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_div_row_c4_fuse_impl<1>) kernel_div_row_c4_fuse_t;
|
||||
|
||||
template [[host_name("kernel_div_row_c4_fuse_1")]] kernel kernel_div_row_c4_fuse_t kernel_div_row_c4_fuse_impl<1>;
|
||||
|
||||
kernel void kernel_scale_f32(
|
||||
constant ggml_metal_kargs_scale & args,
|
||||
device const float * src0,
|
||||
@@ -5285,6 +5166,7 @@ constant int32_t FC_flash_attn_ext_blk_ncpsg [[function_constant(FC_FLASH_ATTN_E
|
||||
// scan the blocks of the mask that are not masked
|
||||
// 0 - masked (i.e. full of -INF, skip)
|
||||
// 1 - not masked (i.e. at least one element of the mask is not -INF)
|
||||
// 2 - all zero
|
||||
kernel void kernel_flash_attn_ext_blk(
|
||||
constant ggml_metal_kargs_flash_attn_ext_blk & args,
|
||||
device const char * mask,
|
||||
@@ -5306,27 +5188,29 @@ kernel void kernel_flash_attn_ext_blk(
|
||||
|
||||
device const half * mask_src = (device const half *) (mask + (i1*Q)*args.nb31 + i2*args.nb32 + i3*args.nb33) + i0*C + tiisg;
|
||||
|
||||
// fast route
|
||||
if (res == 0) {
|
||||
if (simd_max(*mask_src) > -MAXHALF/2) {
|
||||
res = 1;
|
||||
}
|
||||
}
|
||||
|
||||
// detailed check of the elements of the block
|
||||
if ((C > NW || Q > 1) && res == 0) {
|
||||
half m = -MAXHALF;
|
||||
half mmin = MAXHALF;
|
||||
half mmax = -MAXHALF;
|
||||
|
||||
FOR_UNROLL (short j = 0; j < Q; ++j) {
|
||||
FOR_UNROLL (short ii = 0; ii < C/NW; ++ii) {
|
||||
m = max(m, mask_src[ii*NW]);
|
||||
mmin = min(mmin, mask_src[ii*NW]);
|
||||
mmax = max(mmax, mask_src[ii*NW]);
|
||||
}
|
||||
|
||||
mask_src += args.nb31/2;
|
||||
}
|
||||
|
||||
if (simd_max(m) > -MAXHALF/2) {
|
||||
res = 1;
|
||||
mmin = simd_min(mmin);
|
||||
mmax = simd_max(mmax);
|
||||
|
||||
if (mmax > -MAXHALF) {
|
||||
if (mmin == 0.0 && mmax == 0.0) {
|
||||
res = 2;
|
||||
} else {
|
||||
res = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5568,9 +5452,13 @@ void kernel_flash_attn_ext_impl(
|
||||
ic = 0;
|
||||
}
|
||||
|
||||
char blk_cur = 1;
|
||||
|
||||
// read the mask into shared mem
|
||||
if (FC_flash_attn_ext_has_mask) {
|
||||
if (blk[ic0] == 0) {
|
||||
blk_cur = blk[ic0];
|
||||
|
||||
if (blk_cur == 0) {
|
||||
FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
|
||||
pm2[jj] += NW;
|
||||
}
|
||||
@@ -5578,16 +5466,22 @@ void kernel_flash_attn_ext_impl(
|
||||
continue;
|
||||
}
|
||||
|
||||
FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
|
||||
const short j = jj*NSG + sgitg;
|
||||
if (blk_cur == 1) {
|
||||
FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
|
||||
const short j = jj*NSG + sgitg;
|
||||
|
||||
if (FC_flash_attn_ext_bc_mask) {
|
||||
sm2[j*SH + tiisg] = (iq1 + j) < args.ne31 ? pm2[jj][tiisg] : half2(-MAXHALF, -MAXHALF);
|
||||
} else {
|
||||
sm2[j*SH + tiisg] = pm2[jj][tiisg];
|
||||
if (FC_flash_attn_ext_bc_mask) {
|
||||
sm2[j*SH + tiisg] = (iq1 + j) < args.ne31 ? pm2[jj][tiisg] : half2(-MAXHALF, -MAXHALF);
|
||||
} else {
|
||||
sm2[j*SH + tiisg] = pm2[jj][tiisg];
|
||||
}
|
||||
|
||||
pm2[jj] += NW;
|
||||
}
|
||||
} else if (blk_cur == 2) {
|
||||
FOR_UNROLL (short jj = 0; jj < NQ; ++jj) {
|
||||
pm2[jj] += NW;
|
||||
}
|
||||
|
||||
pm2[jj] += NW;
|
||||
}
|
||||
|
||||
#if 0
|
||||
@@ -5752,10 +5646,12 @@ void kernel_flash_attn_ext_impl(
|
||||
}
|
||||
|
||||
// mqk = mqk + slope*mask
|
||||
if (FC_flash_attn_ext_has_bias) {
|
||||
s2 += s2_t(sm2[j*SH + tiisg])*slope;
|
||||
} else {
|
||||
s2 += s2_t(sm2[j*SH + tiisg]);
|
||||
if (blk_cur != 2) {
|
||||
if (FC_flash_attn_ext_has_bias) {
|
||||
s2 += s2_t(sm2[j*SH + tiisg])*slope;
|
||||
} else {
|
||||
s2 += s2_t(sm2[j*SH + tiisg]);
|
||||
}
|
||||
}
|
||||
|
||||
M[jj] = simd_max(max(M[jj], max(s2[0], s2[1])));
|
||||
@@ -8815,6 +8711,26 @@ kernel void kernel_set_rows_f(
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_diag_f32(
|
||||
constant ggml_metal_kargs_diag & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]]) {
|
||||
constexpr short NW = N_SIMDWIDTH;
|
||||
|
||||
const int32_t i3 = tgpig.z;
|
||||
const int32_t i2 = tgpig.y;
|
||||
const int32_t i1 = tgpig.x;
|
||||
|
||||
device const float * src0_ptr = (device const float *)(src0 + i2*args.nb02 + i3*args.nb03);
|
||||
device float * dst_ptr = (device float *)(dst + i1*args.nb01 + i2*args.nb2 + i3*args.nb3);
|
||||
|
||||
for (int i0 = tiitg; i0 < args.ne0; i0 += NW) {
|
||||
dst_ptr[i0] = i0 == i1 ? src0_ptr[i0] : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
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)]];
|
||||
|
||||
|
||||
@@ -836,16 +836,9 @@ static inline void ggml_sycl_op_floor(ggml_backend_sycl_context & ctx, ggml_tens
|
||||
}
|
||||
|
||||
static inline void ggml_sycl_op_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
|
||||
ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
|
||||
[](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
|
||||
const int num_blocks = ceil_div(k_elements, 256);
|
||||
stream->parallel_for(
|
||||
sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
|
||||
sycl::range<1>(256)),
|
||||
[=](sycl::nd_item<1> item_ct1) {
|
||||
unary_op_ceil_kernel(src, dst_ptr, k_elements, item_ct1);
|
||||
});
|
||||
});
|
||||
ggml_sycl_detail::ggml_sycl_op_unary(ctx, dst, [](auto x) {
|
||||
return op_ceil(x);
|
||||
});
|
||||
}
|
||||
|
||||
static inline void ggml_sycl_op_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
|
||||
|
||||
@@ -4591,9 +4591,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
|
||||
case GGML_UNARY_OP_EXP:
|
||||
case GGML_UNARY_OP_SOFTPLUS:
|
||||
case GGML_UNARY_OP_ELU:
|
||||
case GGML_UNARY_OP_CEIL:
|
||||
return true;
|
||||
case GGML_UNARY_OP_FLOOR:
|
||||
case GGML_UNARY_OP_CEIL:
|
||||
case GGML_UNARY_OP_ROUND:
|
||||
case GGML_UNARY_OP_TRUNC:
|
||||
#if defined (GGML_SYCL_F16)
|
||||
|
||||
@@ -402,18 +402,19 @@ enum FaCodePath {
|
||||
};
|
||||
|
||||
struct vk_fa_pipeline_state {
|
||||
vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc)
|
||||
: HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc) {}
|
||||
vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc, uint32_t flags)
|
||||
: HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc), flags(flags) {}
|
||||
|
||||
uint32_t HSK, HSV;
|
||||
bool small_rows, small_cache;
|
||||
FaCodePath path;
|
||||
bool aligned;
|
||||
bool f32acc;
|
||||
uint32_t flags;
|
||||
|
||||
bool operator<(const vk_fa_pipeline_state &b) const {
|
||||
return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc) <
|
||||
std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc);
|
||||
return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, flags) <
|
||||
std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc, b.flags);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -820,6 +821,8 @@ struct vk_device_struct {
|
||||
|
||||
std::map<vk_fa_pipeline_state, vk_pipeline> pipeline_flash_attn_f32_f16[GGML_TYPE_COUNT];
|
||||
|
||||
std::map<std::pair<uint32_t, uint32_t>, vk_pipeline> pipeline_fa_mask_opt;
|
||||
|
||||
vk_pipeline pipeline_flash_attn_split_k_reduce;
|
||||
vk_pipeline pipeline_count_experts;
|
||||
|
||||
@@ -1549,6 +1552,18 @@ struct vk_op_flash_attn_split_k_reduce_push_constants {
|
||||
uint32_t sinks;
|
||||
};
|
||||
|
||||
struct vk_op_flash_attn_mask_opt_push_constants {
|
||||
uint32_t nem0;
|
||||
uint32_t nem1;
|
||||
uint32_t nem2;
|
||||
uint32_t nbm1;
|
||||
uint32_t nbm2;
|
||||
uint32_t nbm3;
|
||||
uint32_t nbd1;
|
||||
uint32_t nbd2;
|
||||
uint32_t nbd3;
|
||||
};
|
||||
|
||||
// Allow pre-recording command buffers
|
||||
struct vk_staging_memcpy {
|
||||
vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
|
||||
@@ -1757,6 +1772,7 @@ class vk_perf_logger {
|
||||
" k(" << k->ne[0] << "," << k->ne[1] << "," << k->ne[2] << "," << k->ne[3] << "), " <<
|
||||
" v(" << v->ne[0] << "," << v->ne[1] << "," << v->ne[2] << "," << v->ne[3] << "), " <<
|
||||
" m(" << (m?m->ne[0]:0) << "," << (m?m->ne[1]:0) << "," << (m?m->ne[2]:0) << "," << (m?m->ne[3]:0) << ")";
|
||||
*n_flops = 2ull * q->ne[1] * q->ne[2] * (k->ne[0] + v->ne[0]) * k->ne[1] * q->ne[3];
|
||||
return name.str();
|
||||
}
|
||||
if (node->op == GGML_OP_TOP_K) {
|
||||
@@ -3177,7 +3193,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
||||
return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache)[0], 1, 1};
|
||||
};
|
||||
|
||||
auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) -> std::vector<uint32_t> {
|
||||
auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache, uint32_t flags) -> std::vector<uint32_t> {
|
||||
// For large number of rows, 128 invocations seems to work best.
|
||||
// For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we
|
||||
// can't use 256 for D==80.
|
||||
@@ -3209,7 +3225,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
||||
// AMD prefers loading K directly from global memory
|
||||
const uint32_t k_load_shmem = device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 ? 1 : 0;
|
||||
|
||||
return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem};
|
||||
return {wg_size, rows_cols[0], rows_cols[1], hsk, hsv, clamp, D_split, device->subgroup_size, k_load_shmem, flags};
|
||||
};
|
||||
|
||||
#define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
|
||||
@@ -3221,18 +3237,19 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
||||
FaCodePath path = fa.first.path; \
|
||||
bool aligned = fa.first.aligned; \
|
||||
bool f32acc = fa.first.f32acc; \
|
||||
uint32_t flags = fa.first.flags; \
|
||||
if (path == FAPATH) { \
|
||||
if (aligned) { \
|
||||
if (f32acc) { \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,flags), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
} else { \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache,flags), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
} \
|
||||
} else { \
|
||||
if (f32acc) { \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,flags), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
} else { \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 7, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache,flags), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? device->subgroup_size : 0)); \
|
||||
} \
|
||||
} \
|
||||
} \
|
||||
@@ -4028,6 +4045,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
||||
ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, sizeof(vk_op_flash_attn_split_k_reduce_push_constants), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
|
||||
|
||||
for (auto &it : device->pipeline_fa_mask_opt) {
|
||||
auto BrBc = it.first;
|
||||
ggml_vk_create_pipeline(device, it.second, "fa_mask_opt", fa_mask_opt_len, fa_mask_opt_data, "main", 2, sizeof(vk_op_flash_attn_mask_opt_push_constants), {1, 1, 1}, {128, 128 / device->subgroup_size, BrBc.first, BrBc.second}, 1, true, true, device->subgroup_size);
|
||||
}
|
||||
|
||||
if (device->subgroup_clustered && device->subgroup_require_full_support) {
|
||||
ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_subgroup_len, quantize_q8_1_x4_subgroup_data, "main", 2, sizeof(vk_quantize_q8_1_push_constants), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1, true, true);
|
||||
} else {
|
||||
@@ -5561,9 +5583,9 @@ static void ggml_vk_instance_init() {
|
||||
// Check if there are two physical devices corresponding to the same GPU
|
||||
// This handles the case where the same GPU appears with different drivers (e.g., RADV + AMDVLK on Linux),
|
||||
// see https://github.com/ggml-org/llama.cpp/pull/7582 for original deduplication.
|
||||
// However, for MoltenVK on macOS, multiple GPUs on the same card may report the same UUID,
|
||||
// see https://github.com/KhronosGroup/MoltenVK/issues/2683. Until this is fixed, we'll only deduplicate
|
||||
// when drivers differ (same driver + same UUID = likely different GPUs)
|
||||
// MoltenVK on macOS may report the same UUID for distinct GPUs on multi-GPU cards,
|
||||
// see https://github.com/KhronosGroup/MoltenVK/issues/2683. Skip when both old/new
|
||||
// driver is MoltenVK
|
||||
auto old_device = std::find_if(
|
||||
vk_instance.device_indices.begin(),
|
||||
vk_instance.device_indices.end(),
|
||||
@@ -5580,11 +5602,9 @@ static void ggml_vk_instance_init() {
|
||||
old_id.deviceLUIDValid && new_id.deviceLUIDValid &&
|
||||
std::equal(std::begin(old_id.deviceLUID), std::end(old_id.deviceLUID), std::begin(new_id.deviceLUID))
|
||||
);
|
||||
bool both_molten_vk = (new_driver.driverID == vk::DriverId::eMoltenvk && old_driver.driverID == vk::DriverId::eMoltenvk);
|
||||
|
||||
// Only deduplicate if same UUID AND different drivers
|
||||
// (same driver + same UUID on MoltenVK = likely different GPUs on multi-GPU card)
|
||||
bool different_driver = (old_driver.driverID != new_driver.driverID);
|
||||
return same_uuid && different_driver;
|
||||
return same_uuid && !both_molten_vk;
|
||||
}
|
||||
);
|
||||
if (old_device == vk_instance.device_indices.end()) {
|
||||
@@ -8402,8 +8422,6 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
|
||||
const uint32_t acctype = f32acc ? 4 : 2;
|
||||
const uint32_t f16vec4 = 8;
|
||||
|
||||
const uint32_t tmpsh = (Bc / MatBc) * sizeof(float);
|
||||
|
||||
const uint32_t qstride = hsk_pad / 4 + 2;
|
||||
const uint32_t Qf = Br * qstride * f16vec4;
|
||||
|
||||
@@ -8420,7 +8438,7 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
|
||||
|
||||
const uint32_t slope = Br * acctype;
|
||||
|
||||
const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + slope;
|
||||
const uint32_t total_size = Qf + Psh + sfsh + ksh + slope;
|
||||
const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
|
||||
|
||||
VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", kv_type=" << kv_type << ", total_size=" << total_size << ", supported=" << supported);
|
||||
@@ -8447,6 +8465,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
|
||||
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
|
||||
|
||||
const uint32_t nem0 = mask ? mask->ne[0] : 0;
|
||||
const uint32_t nem1 = mask ? mask->ne[1] : 0;
|
||||
const uint32_t nem2 = mask ? mask->ne[2] : 0;
|
||||
const uint32_t nem3 = mask ? mask->ne[3] : 0;
|
||||
@@ -8576,7 +8595,26 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
|
||||
|
||||
bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32;
|
||||
|
||||
vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc);
|
||||
float scale = 1.0f;
|
||||
float max_bias = 0.0f;
|
||||
float logit_softcap = 0.0f;
|
||||
|
||||
memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float));
|
||||
memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
|
||||
memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));
|
||||
|
||||
if (logit_softcap != 0) {
|
||||
scale /= logit_softcap;
|
||||
}
|
||||
|
||||
// Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
|
||||
bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768;
|
||||
|
||||
uint32_t flags = (use_mask_opt ? 1 : 0) |
|
||||
(mask != nullptr ? 2 : 0) |
|
||||
(logit_softcap != 0 ? 4 : 0);
|
||||
|
||||
vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc, flags);
|
||||
|
||||
vk_pipeline pipeline = nullptr;
|
||||
|
||||
@@ -8627,23 +8665,33 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
|
||||
ggml_vk_preallocate_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
{
|
||||
// Request descriptor sets
|
||||
if (split_k > 1) {
|
||||
ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
|
||||
auto rows_cols = fa_rows_cols(path, HSK, HSV, !aligned, k->type, small_rows, small_cache);
|
||||
const uint32_t Br = rows_cols[0];
|
||||
const uint32_t Bc = rows_cols[1];
|
||||
|
||||
const uint32_t mask_opt_num_dwords = CEIL_DIV(nem0, 16 * Bc);
|
||||
const uint64_t mask_opt_size = sizeof(uint32_t) * mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2 * nem3;
|
||||
|
||||
vk_pipeline pipeline_fa_mask_opt = nullptr;
|
||||
if (use_mask_opt) {
|
||||
std::lock_guard<std::recursive_mutex> guard(ctx->device->mutex);
|
||||
auto &pipelines = ctx->device->pipeline_fa_mask_opt;
|
||||
auto it = pipelines.find({Br, Bc});
|
||||
if (it != pipelines.end()) {
|
||||
pipeline_fa_mask_opt = it->second;
|
||||
} else {
|
||||
pipelines[{Br, Bc}] = pipeline_fa_mask_opt = std::make_shared<vk_pipeline_struct>();
|
||||
}
|
||||
}
|
||||
assert(pipeline_fa_mask_opt);
|
||||
ggml_pipeline_request_descriptor_sets(ctx, pipeline_fa_mask_opt, 1);
|
||||
|
||||
float scale = 1.0f;
|
||||
float max_bias = 0.0f;
|
||||
float logit_softcap = 0.0f;
|
||||
|
||||
memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float));
|
||||
memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
|
||||
memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));
|
||||
|
||||
if (logit_softcap != 0) {
|
||||
scale /= logit_softcap;
|
||||
if (ctx->prealloc_size_y < mask_opt_size) {
|
||||
ctx->prealloc_size_y = mask_opt_size;
|
||||
ggml_vk_preallocate_buffers(ctx, subctx);
|
||||
}
|
||||
if (ctx->prealloc_y_need_sync) {
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
}
|
||||
|
||||
const uint32_t n_head_kv = neq2;
|
||||
@@ -8657,8 +8705,29 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
|
||||
vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
|
||||
vk_subbuffer mask_buf = mask ? ggml_vk_tensor_subbuffer(ctx, mask) : q_buf;
|
||||
vk_subbuffer sinks_buf = sinks ? ggml_vk_tensor_subbuffer(ctx, sinks) : q_buf;
|
||||
vk_subbuffer mask_opt_buf = use_mask_opt ? ggml_vk_subbuffer(ctx, ctx->prealloc_y, 0) : q_buf;
|
||||
|
||||
uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | ((mask != nullptr) << 16) | n_head_log2;
|
||||
uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | n_head_log2;
|
||||
|
||||
if (use_mask_opt)
|
||||
{
|
||||
const vk_op_flash_attn_mask_opt_push_constants opt_pc = {
|
||||
nem0,
|
||||
nem1,
|
||||
nem2,
|
||||
(uint32_t)(mask->nb[1] / sizeof(ggml_fp16_t)),
|
||||
(uint32_t)(mask->nb[2] / sizeof(ggml_fp16_t)),
|
||||
(uint32_t)(mask->nb[3] / sizeof(ggml_fp16_t)),
|
||||
mask_opt_num_dwords,
|
||||
mask_opt_num_dwords * CEIL_DIV(nem1, Br),
|
||||
mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2,
|
||||
};
|
||||
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline_fa_mask_opt,
|
||||
{ mask_buf, mask_opt_buf }, opt_pc,
|
||||
{ mask_opt_num_dwords, CEIL_DIV(nem1, Br), nem2 * nem3 });
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
const vk_flash_attn_push_constants pc = { N, KV,
|
||||
(uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
|
||||
@@ -8674,13 +8743,15 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
|
||||
gqa_ratio, split_kv, split_k };
|
||||
|
||||
if (split_k > 1) {
|
||||
ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
|
||||
|
||||
if (ctx->prealloc_split_k_need_sync) {
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
workgroups_x *= pipeline->wg_denoms[0];
|
||||
vk_subbuffer split_k_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
|
||||
{q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf},
|
||||
{q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf, mask_opt_buf},
|
||||
// We only use split_k when group query attention is enabled, which means
|
||||
// there's no more than one tile of rows (i.e. workgroups_x would have been
|
||||
// one). We reuse workgroups_x to mean the number of splits, so we need to
|
||||
@@ -8699,7 +8770,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
|
||||
workgroups_x *= pipeline->wg_denoms[0];
|
||||
}
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
|
||||
{q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf},
|
||||
{q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf, mask_opt_buf},
|
||||
pc, { workgroups_x, workgroups_y, workgroups_z });
|
||||
}
|
||||
}
|
||||
|
||||
@@ -94,6 +94,10 @@ void main() {
|
||||
}
|
||||
}
|
||||
|
||||
const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc);
|
||||
// mo_offset will point to the tile starting at row i*Br and col 0
|
||||
uint32_t mo_offset = mo_stride * i;
|
||||
|
||||
#if BLOCK_SIZE > 1
|
||||
uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
|
||||
uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
|
||||
@@ -104,15 +108,28 @@ void main() {
|
||||
uint32_t m_offset = gqa_iq1*KV;
|
||||
if (p.nem2 != 1 || p.nem3 != 1) {
|
||||
m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV;
|
||||
mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride;
|
||||
}
|
||||
|
||||
uint32_t mask_opt = 0;
|
||||
uint32_t mask_opt_idx = ~0;
|
||||
|
||||
[[dont_unroll]]
|
||||
for (uint32_t j = start_j; j < end_j; ++j) {
|
||||
|
||||
if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
|
||||
if (USE_MASK_OPT && mask_opt_idx != j / 16) {
|
||||
mask_opt_idx = j / 16;
|
||||
mask_opt = data_mask_opt[mo_offset + mask_opt_idx];
|
||||
}
|
||||
uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3;
|
||||
if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) {
|
||||
// skip this block
|
||||
continue;
|
||||
}
|
||||
// Only load if the block is not all zeros
|
||||
if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) {
|
||||
bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
|
||||
|
||||
float max_mask = NEG_FLT_MAX_OVER_2;
|
||||
[[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) {
|
||||
uint32_t c = (idx + tid) % Bc;
|
||||
uint32_t r = (idx + tid) / Bc;
|
||||
@@ -120,25 +137,12 @@ void main() {
|
||||
if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) {
|
||||
float m = float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]);
|
||||
masksh[c][r] = m;
|
||||
max_mask = max(max_mask, m);
|
||||
} else {
|
||||
masksh[c][r] = float(0);
|
||||
}
|
||||
}
|
||||
}
|
||||
// skip the block if the mask is entirely -inf
|
||||
bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2);
|
||||
barrier();
|
||||
if (gl_SubgroupInvocationID == 0) {
|
||||
tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f;
|
||||
}
|
||||
barrier();
|
||||
[[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
|
||||
max_mask = max(max_mask, tmpsh[s]);
|
||||
}
|
||||
if (max_mask <= NEG_FLT_MAX_OVER_2) {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
float Sf[Br][cols_per_thread];
|
||||
@@ -177,7 +181,7 @@ void main() {
|
||||
}
|
||||
}
|
||||
|
||||
if (p.logit_softcap != 0.0f) {
|
||||
if (LOGIT_SOFTCAP) {
|
||||
[[unroll]] for (uint32_t r = 0; r < Br; ++r) {
|
||||
[[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
|
||||
Sf[r][c] = p.logit_softcap * tanh(Sf[r][c]);
|
||||
@@ -185,7 +189,7 @@ void main() {
|
||||
}
|
||||
}
|
||||
|
||||
if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
|
||||
if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) {
|
||||
[[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
|
||||
[[unroll]] for (uint32_t r = 0; r < Br; ++r) {
|
||||
float mvf = masksh[c * cols_per_iter + col_tid][r];
|
||||
@@ -256,9 +260,6 @@ void main() {
|
||||
barrier();
|
||||
}
|
||||
|
||||
// prevent race on tmpsh
|
||||
barrier();
|
||||
|
||||
// reduce across threads
|
||||
|
||||
[[unroll]] for (uint32_t r = 0; r < Br; ++r) {
|
||||
|
||||
@@ -10,6 +10,11 @@ layout (constant_id = 5) const uint32_t Clamp = 0;
|
||||
layout (constant_id = 6) const uint32_t D_split = 16;
|
||||
layout (constant_id = 7) const uint32_t SubGroupSize = 32;
|
||||
layout (constant_id = 8) const uint32_t K_LOAD_SHMEM = 0;
|
||||
layout (constant_id = 9) const uint32_t Flags = 0;
|
||||
|
||||
const bool USE_MASK_OPT = (Flags & 1) != 0;
|
||||
const bool MASK_ENABLE = (Flags & 2) != 0;
|
||||
const bool LOGIT_SOFTCAP = (Flags & 4) != 0;
|
||||
|
||||
// Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths
|
||||
const uint32_t HSK_pad = (HSK + 15) & ~15;
|
||||
@@ -59,13 +64,17 @@ layout (push_constant) uniform parameter {
|
||||
} p;
|
||||
|
||||
#define SINK_ENABLE_BIT (1<<24)
|
||||
#define MASK_ENABLE_BIT (1<<16)
|
||||
#define N_LOG2_MASK 0xFFFF
|
||||
|
||||
layout (binding = 4) readonly buffer S {float data_s[];};
|
||||
|
||||
layout (binding = 5) writeonly buffer O {D_TYPE data_o[];};
|
||||
|
||||
layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];};
|
||||
|
||||
#define MASK_OPT_ALL_NEG_INF 1
|
||||
#define MASK_OPT_ALL_ZERO 2
|
||||
|
||||
#define BINDING_IDX_K 0
|
||||
#define BINDING_IDX_V 1
|
||||
#if defined(DATA_A_F32)
|
||||
@@ -231,3 +240,7 @@ void init_indices()
|
||||
// and breaking the alignment detection.
|
||||
m_stride = (p.gqa_ratio > 1) ? (p.gqa_ratio >> 16) : KV;
|
||||
}
|
||||
|
||||
// Bias applied to softmax to stay in fp16 range.
|
||||
// Based on ggml-cuda issue https://github.com/ggml-org/llama.cpp/issues/18606
|
||||
const float FATTN_KQ_MAX_OFFSET = 3.0f*0.6931f;
|
||||
|
||||
@@ -42,8 +42,6 @@ D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TY
|
||||
return elem;
|
||||
}
|
||||
|
||||
shared float tmpsh[row_split];
|
||||
|
||||
const uint32_t qstride = HSK_pad / 4 + 2; // in units of f16vec4
|
||||
shared f16vec4 Qf[Br * qstride];
|
||||
|
||||
@@ -134,6 +132,10 @@ void main() {
|
||||
}
|
||||
}
|
||||
|
||||
const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc);
|
||||
// mo_offset will point to the tile starting at row i*Br and col 0
|
||||
uint32_t mo_offset = mo_stride * i;
|
||||
|
||||
#if BLOCK_SIZE > 1
|
||||
uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
|
||||
uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
|
||||
@@ -144,66 +146,74 @@ void main() {
|
||||
uint32_t m_offset = gqa_iq1*KV;
|
||||
if (p.nem2 != 1 || p.nem3 != 1) {
|
||||
m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV;
|
||||
mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride;
|
||||
}
|
||||
|
||||
uint32_t mask_opt = 0;
|
||||
uint32_t mask_opt_idx = ~0;
|
||||
|
||||
[[dont_unroll]]
|
||||
for (uint32_t j = start_j; j < end_j; ++j) {
|
||||
|
||||
f16vec4 mask_cache[Bc * Br / 4 / WorkGroupSize];
|
||||
if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
|
||||
bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
|
||||
[[unroll]] for (uint32_t idx = 0; idx < mask_cache.length(); ++idx) {
|
||||
mask_cache[idx] = f16vec4(0);
|
||||
}
|
||||
|
||||
float max_mask = NEG_FLT_MAX_OVER_2;
|
||||
[[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
|
||||
uint32_t c = (idx + tid) / (Br / 4);
|
||||
uint32_t r = (idx + tid) % (Br / 4);
|
||||
if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) {
|
||||
if ((!KV_bounds_check || j * Bc + c < KV)) {
|
||||
f16vec4 m;
|
||||
if (!nem1_bounds_check || i * Br + r * 4 + 3 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 3) * m_stride + (j * Bc + c)]);
|
||||
max_mask = max(max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])), float(m[3]));
|
||||
} else if (i * Br + r * 4 + 2 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
|
||||
0.0);
|
||||
max_mask = max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2]));
|
||||
} else if (i * Br + r * 4 + 1 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
|
||||
0.0,
|
||||
0.0);
|
||||
max_mask = max(max(max_mask, float(m[0])), float(m[1]));
|
||||
} else if (i * Br + r * 4 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
0.0,
|
||||
0.0,
|
||||
0.0);
|
||||
max_mask = max(max_mask, float(m[0]));
|
||||
} else {
|
||||
m = f16vec4(0.0);
|
||||
if (MASK_ENABLE) {
|
||||
|
||||
if (USE_MASK_OPT && mask_opt_idx != j / 16) {
|
||||
mask_opt_idx = j / 16;
|
||||
mask_opt = data_mask_opt[mo_offset + mask_opt_idx];
|
||||
}
|
||||
uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3;
|
||||
if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) {
|
||||
// skip this block
|
||||
continue;
|
||||
}
|
||||
// Only load if the block is not all zeros
|
||||
if (mask_opt_bits != MASK_OPT_ALL_ZERO) {
|
||||
bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
|
||||
|
||||
float max_mask = NEG_FLT_MAX_OVER_2;
|
||||
[[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
|
||||
uint32_t c = (idx + tid) / (Br / 4);
|
||||
uint32_t r = (idx + tid) % (Br / 4);
|
||||
if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) {
|
||||
if ((!KV_bounds_check || j * Bc + c < KV)) {
|
||||
f16vec4 m;
|
||||
if (!nem1_bounds_check || i * Br + r * 4 + 3 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 3) * m_stride + (j * Bc + c)]);
|
||||
max_mask = max(max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])), float(m[3]));
|
||||
} else if (i * Br + r * 4 + 2 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)],
|
||||
0.0);
|
||||
max_mask = max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2]));
|
||||
} else if (i * Br + r * 4 + 1 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)],
|
||||
0.0,
|
||||
0.0);
|
||||
max_mask = max(max(max_mask, float(m[0])), float(m[1]));
|
||||
} else if (i * Br + r * 4 < p.nem1) {
|
||||
m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)],
|
||||
0.0,
|
||||
0.0,
|
||||
0.0);
|
||||
max_mask = max(max_mask, float(m[0]));
|
||||
} else {
|
||||
m = f16vec4(0.0);
|
||||
}
|
||||
mask_cache[idx / WorkGroupSize] = m;
|
||||
}
|
||||
mask_cache[idx / WorkGroupSize] = m;
|
||||
}
|
||||
}
|
||||
}
|
||||
// skip the block if the mask is entirely -inf
|
||||
bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2);
|
||||
barrier();
|
||||
if (gl_SubgroupInvocationID == 0) {
|
||||
tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f;
|
||||
}
|
||||
barrier();
|
||||
[[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
|
||||
max_mask = max(max_mask, tmpsh[s]);
|
||||
}
|
||||
if (max_mask <= NEG_FLT_MAX_OVER_2) {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
if (K_LOAD_SHMEM != 0) {
|
||||
@@ -293,7 +303,7 @@ void main() {
|
||||
coopMatStore(SfMat, sfsh, coord, sfshstride, gl_CooperativeMatrixLayoutRowMajor);
|
||||
barrier();
|
||||
|
||||
if (p.logit_softcap != 0.0f) {
|
||||
if (LOGIT_SOFTCAP) {
|
||||
[[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
|
||||
uint32_t c = (idx + tid) / (Br / 4);
|
||||
uint32_t r = (idx + tid) % (Br / 4);
|
||||
@@ -304,7 +314,7 @@ void main() {
|
||||
barrier();
|
||||
}
|
||||
|
||||
if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
|
||||
if (MASK_ENABLE) {
|
||||
[[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) {
|
||||
uint32_t c = (idx + tid) / (Br / 4);
|
||||
uint32_t r = (idx + tid) % (Br / 4);
|
||||
|
||||
@@ -117,7 +117,7 @@ void main() {
|
||||
Qf16 = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSK_pad, gl_MatrixUseA>(Q);
|
||||
Qf16 *= float16_t(p.scale);
|
||||
|
||||
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(0);
|
||||
coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(0);
|
||||
|
||||
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> L, M;
|
||||
|
||||
@@ -138,48 +138,53 @@ void main() {
|
||||
coopMatPerElementNV(slopeMat, slopeMat, perElemOpComputeSlope, iq2);
|
||||
}
|
||||
|
||||
const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc);
|
||||
// mo_offset will point to the tile starting at row i*Br and col 0
|
||||
uint32_t mo_offset = mo_stride * i;
|
||||
|
||||
uint32_t m_offset = gqa_iq1*KV * 2 /*sizeof(float16_t)*/;
|
||||
if (p.nem2 != 1 || p.nem3 != 1) {
|
||||
m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV * 2 /*sizeof(float16_t)*/;
|
||||
mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride;
|
||||
}
|
||||
|
||||
uint32_t mask_opt = 0;
|
||||
uint32_t mask_opt_idx = ~0;
|
||||
|
||||
[[dont_unroll]]
|
||||
for (uint32_t j = start_j; j < end_j; ++j) {
|
||||
|
||||
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv;
|
||||
if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
|
||||
bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
|
||||
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv = coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0);
|
||||
if (MASK_ENABLE) {
|
||||
|
||||
if (nem1_bounds_check) {
|
||||
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
|
||||
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
|
||||
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
|
||||
tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t
|
||||
if (USE_MASK_OPT && mask_opt_idx != j / 16) {
|
||||
mask_opt_idx = j / 16;
|
||||
mask_opt = data_mask_opt[mo_offset + mask_opt_idx];
|
||||
}
|
||||
uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3;
|
||||
if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) {
|
||||
// skip this block
|
||||
continue;
|
||||
}
|
||||
// Only load if the block is not all zeros
|
||||
if (mask_opt_bits != MASK_OPT_ALL_ZERO) {
|
||||
bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0;
|
||||
|
||||
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mvmax;
|
||||
if (nem1_bounds_check) {
|
||||
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
|
||||
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
|
||||
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
|
||||
tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t
|
||||
|
||||
coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
|
||||
coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
|
||||
} else {
|
||||
tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp);
|
||||
// Don't clamp against nem1 when GQA is enabled
|
||||
uint32_t m_height = p.gqa_ratio > 1 ? ~0 : p.nem1;
|
||||
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, m_height, KV);
|
||||
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
|
||||
|
||||
// skip the block if the mask is entirely -inf
|
||||
coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16);
|
||||
if (mvmax[0] <= NEG_FLT_MAX_OVER_2) {
|
||||
continue;
|
||||
}
|
||||
} else {
|
||||
tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp);
|
||||
// Don't clamp against nem1 when GQA is enabled
|
||||
uint32_t m_height = p.gqa_ratio > 1 ? ~0 : p.nem1;
|
||||
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, m_height, KV);
|
||||
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
|
||||
|
||||
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mvmax;
|
||||
|
||||
coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
|
||||
|
||||
// skip the block if the mask is entirely -inf
|
||||
coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16);
|
||||
if (mvmax[0] <= NEG_FLT_MAX_OVER_2) {
|
||||
continue;
|
||||
coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -192,14 +197,14 @@ void main() {
|
||||
coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose DECODEFUNC);
|
||||
S = coopMatMulAdd(Qf16, K_T, S);
|
||||
|
||||
if (p.logit_softcap != 0.0f) {
|
||||
if (LOGIT_SOFTCAP) {
|
||||
[[unroll]]
|
||||
for (int k = 0; k < S.length(); ++k) {
|
||||
S[k] = ACC_TYPE(p.logit_softcap)*tanh(S[k]);
|
||||
}
|
||||
}
|
||||
|
||||
if ((p.mask_n_head_log2 & MASK_ENABLE_BIT) != 0) {
|
||||
if (MASK_ENABLE) {
|
||||
S += slopeMat*coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(mv);
|
||||
}
|
||||
|
||||
@@ -218,6 +223,8 @@ void main() {
|
||||
|
||||
coopMatReduceNV(rowmax, S, gl_CooperativeMatrixReduceRowNV, maxReduce);
|
||||
|
||||
rowmax += coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(FATTN_KQ_MAX_OFFSET);
|
||||
|
||||
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> Mold = M;
|
||||
|
||||
// M = max(rowmax, Mold)
|
||||
@@ -260,11 +267,8 @@ void main() {
|
||||
// resize eM by using smear/reduce
|
||||
coopMatReduceNV(eMdiag, eM, gl_CooperativeMatrixReduceRowNV, smearReduce);
|
||||
|
||||
// multiply with fp16 accumulation, then add to O.
|
||||
coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> PV = coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(0);
|
||||
PV = coopMatMulAdd(P_A, V, PV);
|
||||
|
||||
O = eMdiag * O + coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(PV);
|
||||
O *= coopmat<float16_t, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(eMdiag);
|
||||
O = coopMatMulAdd(P_A, V, O);
|
||||
}
|
||||
|
||||
// If there is split_k, then the split_k resolve shader does the final
|
||||
@@ -306,7 +310,7 @@ void main() {
|
||||
if (sink > Mr[i]) {
|
||||
ms = exp(Mr[i] - sink);
|
||||
|
||||
O[i] *= ms;
|
||||
O[i] *= float16_t(ms);
|
||||
} else {
|
||||
vs = exp(sink - Mr[i]);
|
||||
}
|
||||
@@ -320,15 +324,16 @@ void main() {
|
||||
Ldiag[k] = (Ldiag[k] == 0.0) ? ACC_TYPE(0.0) : (ACC_TYPE(1.0) / Ldiag[k]);
|
||||
}
|
||||
|
||||
O = Ldiag*O;
|
||||
coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(O);
|
||||
|
||||
O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(Ldiag)*O_D;
|
||||
|
||||
#if defined(ACC_TYPE_MAX)
|
||||
[[unroll]] for (uint i = 0; i < O.length(); ++i) { O[i] = clamp(O[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
|
||||
[[unroll]] for (uint i = 0; i < O_D.length(); ++i) { O_D[i] = clamp(O_D[i], D_TYPE(-ACC_TYPE_MAX), D_TYPE(ACC_TYPE_MAX)); }
|
||||
#endif
|
||||
|
||||
uint32_t o_offset = gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV;
|
||||
|
||||
coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, HSV_pad, gl_MatrixUseAccumulator>(O);
|
||||
if (p.gqa_ratio > 1) {
|
||||
coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N);
|
||||
} else {
|
||||
|
||||
@@ -0,0 +1,142 @@
|
||||
#version 450
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : enable
|
||||
#extension GL_EXT_shader_16bit_storage : enable
|
||||
#extension GL_KHR_shader_subgroup_arithmetic : enable
|
||||
|
||||
layout (constant_id = 0) const uint BLOCK_SIZE = 128;
|
||||
layout (constant_id = 1) const uint NUM_SUBGROUPS = 4;
|
||||
layout (constant_id = 2) const uint Br = 32;
|
||||
layout (constant_id = 3) const uint Bc = 32;
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout (binding = 0) readonly buffer A {float16_t data_a[];};
|
||||
layout (binding = 0) readonly buffer Av4 {f16vec4 data_av4[];};
|
||||
layout (binding = 1) writeonly buffer D {uint data_d[];};
|
||||
|
||||
layout (push_constant) uniform parameter {
|
||||
uint nem0;
|
||||
uint nem1;
|
||||
uint nem2;
|
||||
uint nbm1;
|
||||
uint nbm2;
|
||||
uint nbm3;
|
||||
uint nbd1;
|
||||
uint nbd2;
|
||||
uint nbd3;
|
||||
};
|
||||
|
||||
#define MASK_OPT_ALL_NEG_INF 1
|
||||
#define MASK_OPT_ALL_ZERO 2
|
||||
|
||||
shared float minsh[NUM_SUBGROUPS];
|
||||
shared float maxsh[NUM_SUBGROUPS];
|
||||
|
||||
// For each Br x Bc block of the mask (input) buffer, read all values and check
|
||||
// if it's all -inf or all zero. Write out a two-bit code indicating which it is
|
||||
// (or zero for neither). Each workgroup processes 16 tiles and writes out a
|
||||
// 32-bit result mask.
|
||||
//
|
||||
// TODO: This is a lot of work per workgroup, might make sense to split this into
|
||||
// more workgroups in the future.
|
||||
void main() {
|
||||
// Each workgroup handles a row
|
||||
const uint tid = gl_LocalInvocationIndex;
|
||||
const uint i0 = gl_WorkGroupID.x;
|
||||
const uint i1 = gl_WorkGroupID.y;
|
||||
const uint i2 = gl_WorkGroupID.z % nem2;
|
||||
const uint i3 = gl_WorkGroupID.z / nem2;
|
||||
|
||||
float FLT_MAX_OVER_2 = uintBitsToFloat(0x7EFFFFFF);
|
||||
|
||||
uint result = 0;
|
||||
|
||||
// Fast path for fully in-bounds blocks where we can do f16vec4 loads
|
||||
if ((nem0 % Bc) == 0 && (nem1 % Br) == 0 &&
|
||||
((Br * Bc) % (BLOCK_SIZE * 4)) == 0) {
|
||||
[[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) {
|
||||
float min_v = FLT_MAX_OVER_2;
|
||||
float max_v = -FLT_MAX_OVER_2;
|
||||
[[unroll]] for (uint i = 0; i < Br * Bc / 4; i += BLOCK_SIZE) {
|
||||
uint j0 = (i + tid) % (Bc / 4);
|
||||
uint j1 = (i + tid) / (Bc / 4);
|
||||
|
||||
j0 *= 4;
|
||||
j0 += (i0 * 16 + block_x) * Bc;
|
||||
j1 += i1 * Br;
|
||||
|
||||
vec4 f = vec4(data_av4[(j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3) / 4]);
|
||||
[[unroll]] for (int c = 0; c < 4; ++c) {
|
||||
min_v = min(min_v, f[c]);
|
||||
max_v = max(max_v, f[c]);
|
||||
}
|
||||
}
|
||||
min_v = subgroupMin(min_v);
|
||||
max_v = subgroupMax(max_v);
|
||||
if (gl_SubgroupInvocationID == 0) {
|
||||
minsh[gl_SubgroupID] = min_v;
|
||||
maxsh[gl_SubgroupID] = max_v;
|
||||
}
|
||||
barrier();
|
||||
if (tid == 0) {
|
||||
[[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) {
|
||||
min_v = min(min_v, minsh[i]);
|
||||
max_v = max(max_v, maxsh[i]);
|
||||
}
|
||||
if (max_v <= -FLT_MAX_OVER_2) {
|
||||
result |= 1 << (2*block_x);
|
||||
}
|
||||
if (min_v == 0.0f && max_v == 0.0f) {
|
||||
result |= 2 << (2*block_x);
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
}
|
||||
} else {
|
||||
[[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) {
|
||||
float min_v = FLT_MAX_OVER_2;
|
||||
float max_v = -FLT_MAX_OVER_2;
|
||||
[[unroll]] for (uint i = 0; i < Br * Bc; i += BLOCK_SIZE) {
|
||||
if ((Br * Bc % BLOCK_SIZE) != 0 && i + tid >= Br * Bc) {
|
||||
continue;
|
||||
}
|
||||
uint j0 = (i + tid) % Bc;
|
||||
uint j1 = (i + tid) / Bc;
|
||||
|
||||
j0 += (i0 * 16 + block_x) * Bc;
|
||||
j1 += i1 * Br;
|
||||
|
||||
if (j0 < nem0 && j1 < nem1) {
|
||||
float f = float(data_a[j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3]);
|
||||
min_v = min(min_v, f);
|
||||
max_v = max(max_v, f);
|
||||
}
|
||||
}
|
||||
min_v = subgroupMin(min_v);
|
||||
max_v = subgroupMax(max_v);
|
||||
if (gl_SubgroupInvocationID == 0) {
|
||||
minsh[gl_SubgroupID] = min_v;
|
||||
maxsh[gl_SubgroupID] = max_v;
|
||||
}
|
||||
barrier();
|
||||
if (tid == 0) {
|
||||
[[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) {
|
||||
min_v = min(min_v, minsh[i]);
|
||||
max_v = max(max_v, maxsh[i]);
|
||||
}
|
||||
if (max_v <= -FLT_MAX_OVER_2) {
|
||||
result |= 1 << (2*block_x);
|
||||
}
|
||||
if (min_v == 0.0f && max_v == 0.0f) {
|
||||
result |= 2 << (2*block_x);
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
}
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
data_d[i0 + i1 * nbd1 + i2 * nbd2 + i3 * nbd3] = result;
|
||||
}
|
||||
}
|
||||
@@ -790,6 +790,8 @@ void process_shaders() {
|
||||
string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {});
|
||||
string_to_spv("fa_split_k_reduce", "flash_attn_split_k_reduce.comp", {});
|
||||
|
||||
string_to_spv("fa_mask_opt", "flash_attn_mask_opt.comp", {});
|
||||
|
||||
string_to_spv("quantize_q8_1", "quantize_q8_1.comp", {});
|
||||
string_to_spv("quantize_q8_1_subgroup", "quantize_q8_1.comp", {{"USE_SUBGROUPS", "1"}});
|
||||
|
||||
|
||||
@@ -465,4 +465,73 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Binary **/
|
||||
|
||||
struct ggml_webgpu_binary_pipeline_key {
|
||||
int type;
|
||||
int op;
|
||||
bool inplace;
|
||||
bool overlap;
|
||||
|
||||
bool operator==(const ggml_webgpu_binary_pipeline_key & other) const {
|
||||
return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap;
|
||||
}
|
||||
};
|
||||
|
||||
struct ggml_webgpu_binary_pipeline_key_hash {
|
||||
size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const {
|
||||
size_t seed = 0;
|
||||
ggml_webgpu_hash_combine(seed, key.type);
|
||||
ggml_webgpu_hash_combine(seed, key.op);
|
||||
ggml_webgpu_hash_combine(seed, key.inplace);
|
||||
ggml_webgpu_hash_combine(seed, key.overlap);
|
||||
return seed;
|
||||
}
|
||||
};
|
||||
|
||||
struct ggml_webgpu_binary_shader_lib_context {
|
||||
ggml_webgpu_binary_pipeline_key key;
|
||||
uint32_t max_wg_size;
|
||||
};
|
||||
|
||||
inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
|
||||
pre_wgsl::Preprocessor & preprocessor,
|
||||
const char * shader_src,
|
||||
const ggml_webgpu_binary_shader_lib_context & context) {
|
||||
std::vector<std::string> defines;
|
||||
std::string op_name = ggml_op_name((ggml_op) context.key.op);
|
||||
std::string variant = op_name;
|
||||
|
||||
defines.push_back(std::string("OP_") + op_name);
|
||||
|
||||
switch (context.key.type) {
|
||||
case GGML_TYPE_F32:
|
||||
defines.push_back("TYPE_F32");
|
||||
variant += "_f32";
|
||||
break;
|
||||
case GGML_TYPE_F16:
|
||||
defines.push_back("TYPE_F16");
|
||||
variant += "_f16";
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("Unsupported type for binary shader");
|
||||
}
|
||||
|
||||
if (context.key.inplace) {
|
||||
defines.push_back("INPLACE");
|
||||
variant += "_inplace";
|
||||
} else if (context.key.overlap) {
|
||||
defines.push_back("OVERLAP");
|
||||
variant += "_overlap";
|
||||
}
|
||||
|
||||
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
|
||||
ggml_webgpu_processed_shader result;
|
||||
result.wgsl = preprocessor.preprocess(shader_src, defines);
|
||||
result.variant = variant;
|
||||
ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
|
||||
decisions->wg_size = context.max_wg_size;
|
||||
result.decisions = decisions;
|
||||
return result;
|
||||
}
|
||||
#endif // GGML_WEBGPU_SHADER_LIB_HPP
|
||||
|
||||
@@ -348,13 +348,12 @@ struct webgpu_context_struct {
|
||||
|
||||
std::unordered_map<ggml_webgpu_set_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_set_rows_pipeline_key_hash>
|
||||
set_rows_pipelines;
|
||||
std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines; // src_type, vectorized
|
||||
std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines; // src_type, vectorized
|
||||
|
||||
std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines; // src_type, dst_type
|
||||
std::map<int, std::map<int, webgpu_pipeline>> add_pipelines; // type, inplace
|
||||
std::map<int, std::map<int, webgpu_pipeline>> sub_pipelines; // type, inplace
|
||||
std::map<int, std::map<int, webgpu_pipeline>> mul_pipelines; // type, inplace
|
||||
std::map<int, std::map<int, webgpu_pipeline>> div_pipelines; // type, inplace
|
||||
std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines; // src_type, dst_type
|
||||
|
||||
std::unordered_map<ggml_webgpu_binary_pipeline_key, webgpu_pipeline, ggml_webgpu_binary_pipeline_key_hash>
|
||||
binary_pipelines;
|
||||
|
||||
std::map<int, webgpu_pipeline> rms_norm_pipelines; // inplace
|
||||
std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> rope_pipelines; // type, ff, inplace
|
||||
@@ -823,6 +822,28 @@ static bool ggml_webgpu_tensor_equal(ggml_tensor * a, ggml_tensor * b) {
|
||||
(ggml_webgpu_tensor_offset(a) == ggml_webgpu_tensor_offset(b));
|
||||
}
|
||||
|
||||
// Used to determine if two tensors share the same buffer and their byte ranges overlap,
|
||||
static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) {
|
||||
return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
|
||||
ggml_webgpu_tensor_offset(a) < (ggml_webgpu_tensor_offset(b) + ggml_nbytes(b)) &&
|
||||
ggml_webgpu_tensor_offset(b) < (ggml_webgpu_tensor_offset(a) + ggml_nbytes(a));
|
||||
}
|
||||
|
||||
struct binary_overlap_flags {
|
||||
bool inplace; // src0 == dst
|
||||
bool overlap; // src1 == dst
|
||||
};
|
||||
|
||||
static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0,
|
||||
ggml_tensor * src1,
|
||||
ggml_tensor * dst) {
|
||||
binary_overlap_flags flags = {};
|
||||
flags.inplace = ggml_webgpu_tensor_equal(src0, dst);
|
||||
flags.overlap = ggml_webgpu_tensor_overlap(src1, dst);
|
||||
|
||||
return flags;
|
||||
}
|
||||
|
||||
static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
|
||||
uint32_t ne = (uint32_t) ggml_nelements(dst);
|
||||
|
||||
@@ -1375,14 +1396,42 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
|
||||
return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
|
||||
}
|
||||
|
||||
static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
|
||||
ggml_tensor * src0,
|
||||
ggml_tensor * src1,
|
||||
ggml_tensor * dst,
|
||||
webgpu_pipeline & pipeline,
|
||||
bool inplace) {
|
||||
static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
|
||||
ggml_tensor * src0,
|
||||
ggml_tensor * src1,
|
||||
ggml_tensor * dst) {
|
||||
binary_overlap_flags flags = ggml_webgpu_detect_binary_overlap(src0, src1, dst);
|
||||
|
||||
ggml_webgpu_binary_pipeline_key pipeline_key = {
|
||||
.type = dst->type,
|
||||
.op = dst->op,
|
||||
.inplace = flags.inplace,
|
||||
.overlap = flags.overlap,
|
||||
};
|
||||
ggml_webgpu_binary_shader_lib_context shader_lib_ctx = {
|
||||
.key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
|
||||
};
|
||||
|
||||
webgpu_pipeline pipeline;
|
||||
auto it = ctx->binary_pipelines.find(pipeline_key);
|
||||
if (it != ctx->binary_pipelines.end()) {
|
||||
pipeline = it->second;
|
||||
} else {
|
||||
ggml_webgpu_processed_shader processed =
|
||||
ggml_webgpu_preprocess_binary_shader(ctx->p, wgsl_binary, shader_lib_ctx);
|
||||
pipeline =
|
||||
ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
|
||||
pipeline.context = processed.decisions;
|
||||
ctx->binary_pipelines.emplace(pipeline_key, pipeline);
|
||||
}
|
||||
|
||||
ggml_webgpu_generic_shader_decisions decisions =
|
||||
*static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
|
||||
|
||||
uint32_t ne = (uint32_t) ggml_nelements(dst);
|
||||
|
||||
std::vector<uint32_t> params = {
|
||||
(uint32_t) ggml_nelements(dst),
|
||||
ne,
|
||||
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
|
||||
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
|
||||
(uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
|
||||
@@ -1399,24 +1448,30 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
|
||||
(uint32_t) src1->ne[3],
|
||||
};
|
||||
|
||||
std::vector<wgpu::BindGroupEntry> entries = {
|
||||
{ .binding = 0,
|
||||
.buffer = ggml_webgpu_tensor_buf(src0),
|
||||
.offset = ggml_webgpu_tensor_align_offset(ctx, src0),
|
||||
.size = ggml_webgpu_tensor_binding_size(ctx, src0) },
|
||||
{ .binding = 1,
|
||||
.buffer = ggml_webgpu_tensor_buf(src1),
|
||||
.offset = ggml_webgpu_tensor_align_offset(ctx, src1),
|
||||
.size = ggml_webgpu_tensor_binding_size(ctx, src1) }
|
||||
};
|
||||
if (!inplace) {
|
||||
std::vector<wgpu::BindGroupEntry> entries;
|
||||
|
||||
entries.push_back({
|
||||
.binding = 0,
|
||||
.buffer = ggml_webgpu_tensor_buf(src0),
|
||||
.offset = ggml_webgpu_tensor_align_offset(ctx, src0),
|
||||
.size = ggml_webgpu_tensor_binding_size(ctx, src0),
|
||||
});
|
||||
|
||||
entries.push_back({
|
||||
.binding = 1,
|
||||
.buffer = ggml_webgpu_tensor_buf(src1),
|
||||
.offset = ggml_webgpu_tensor_align_offset(ctx, src1),
|
||||
.size = ggml_webgpu_tensor_binding_size(ctx, src1),
|
||||
});
|
||||
|
||||
if (!flags.inplace && !flags.overlap) {
|
||||
entries.push_back({ .binding = 2,
|
||||
.buffer = ggml_webgpu_tensor_buf(dst),
|
||||
.offset = ggml_webgpu_tensor_align_offset(ctx, dst),
|
||||
.size = ggml_webgpu_tensor_binding_size(ctx, dst) });
|
||||
}
|
||||
|
||||
uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
|
||||
uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
|
||||
return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
|
||||
}
|
||||
|
||||
@@ -2038,25 +2093,10 @@ static std::optional<webgpu_command> ggml_webgpu_encode_node(webgpu_context ctx,
|
||||
return std::nullopt;
|
||||
#endif
|
||||
case GGML_OP_ADD:
|
||||
{
|
||||
int inplace = ggml_webgpu_tensor_equal(src0, node);
|
||||
return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipelines[node->type][inplace], inplace);
|
||||
}
|
||||
case GGML_OP_SUB:
|
||||
{
|
||||
int inplace = ggml_webgpu_tensor_equal(src0, node);
|
||||
return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipelines[node->type][inplace], inplace);
|
||||
}
|
||||
case GGML_OP_MUL:
|
||||
{
|
||||
int inplace = ggml_webgpu_tensor_equal(src0, node);
|
||||
return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipelines[node->type][inplace], inplace);
|
||||
}
|
||||
case GGML_OP_DIV:
|
||||
{
|
||||
int inplace = ggml_webgpu_tensor_equal(src0, node);
|
||||
return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipelines[node->type][inplace], inplace);
|
||||
}
|
||||
return ggml_webgpu_binary_op(ctx, src0, src1, node);
|
||||
case GGML_OP_RMS_NORM:
|
||||
return ggml_webgpu_rms_norm(ctx, src0, node);
|
||||
case GGML_OP_ROPE:
|
||||
@@ -2665,58 +2705,6 @@ static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants);
|
||||
}
|
||||
|
||||
static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) {
|
||||
std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
|
||||
|
||||
webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32, "add_f32", constants);
|
||||
webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16, "add_f16", constants);
|
||||
webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants);
|
||||
webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants);
|
||||
}
|
||||
|
||||
static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) {
|
||||
std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
|
||||
|
||||
webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32, "sub_f32", constants);
|
||||
webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16, "sub_f16", constants);
|
||||
webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants);
|
||||
webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants);
|
||||
}
|
||||
|
||||
static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) {
|
||||
std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
|
||||
|
||||
webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32, "mul_f32", constants);
|
||||
webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16, "mul_f16", constants);
|
||||
webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants);
|
||||
webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants);
|
||||
}
|
||||
|
||||
static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) {
|
||||
std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
|
||||
|
||||
webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32, "div_f32", constants);
|
||||
webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16, "div_f16", constants);
|
||||
webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants);
|
||||
webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] =
|
||||
ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants);
|
||||
}
|
||||
|
||||
static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) {
|
||||
std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE);
|
||||
|
||||
@@ -3018,10 +3006,6 @@ static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) {
|
||||
ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_get_rows_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_add_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_sub_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_mul_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_div_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_rope_pipeline(webgpu_ctx);
|
||||
ggml_webgpu_init_glu_pipeline(webgpu_ctx);
|
||||
|
||||
@@ -1,188 +0,0 @@
|
||||
#define(VARIANTS)
|
||||
|
||||
[
|
||||
{
|
||||
"SHADER_NAME": "add_f32",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "+"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "add_f16",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "+"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "add_f32_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "+"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "add_f16_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "+"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "mul_f32",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "*"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "mul_f16",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "*"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "mul_f32_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "*"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "mul_f16_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "*"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "sub_f32",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "-"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "sub_f16",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "-"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "sub_f32_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "-"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "sub_f16_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "-"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "div_f32",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "/"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "div_f16",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "/"
|
||||
},
|
||||
"DECLS": ["NOT_INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "div_f32_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f32",
|
||||
"OP": "/"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
},
|
||||
{
|
||||
"SHADER_NAME": "div_f16_inplace",
|
||||
"REPLS": {
|
||||
"TYPE" : "f16",
|
||||
"OP": "/"
|
||||
},
|
||||
"DECLS": ["INPLACE"]
|
||||
}
|
||||
]
|
||||
|
||||
#end(VARIANTS)
|
||||
|
||||
#define(DECLS)
|
||||
|
||||
#decl(NOT_INPLACE)
|
||||
|
||||
fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
|
||||
dst[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
|
||||
}
|
||||
|
||||
@group(0) @binding(2)
|
||||
var<storage, read_write> dst: array<{{TYPE}}>;
|
||||
|
||||
@group(0) @binding(3)
|
||||
var<uniform> params: Params;
|
||||
|
||||
#enddecl(NOT_INPLACE)
|
||||
|
||||
#decl(INPLACE)
|
||||
|
||||
fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
|
||||
src0[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
|
||||
}
|
||||
|
||||
@group(0) @binding(2)
|
||||
var<uniform> params: Params;
|
||||
|
||||
#enddecl(INPLACE)
|
||||
|
||||
#end(DECLS)
|
||||
|
||||
|
||||
#define(SHADER)
|
||||
|
||||
enable f16;
|
||||
|
||||
#include "binary_head.tmpl"
|
||||
|
||||
@group(0) @binding(0)
|
||||
var<storage, read_write> src0: array<{{TYPE}}>;
|
||||
|
||||
@group(0) @binding(1)
|
||||
var<storage, read_write> src1: array<{{TYPE}}>;
|
||||
|
||||
DECLS
|
||||
|
||||
override wg_size: u32;
|
||||
@compute @workgroup_size(wg_size)
|
||||
fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
|
||||
if (gid.x < params.ne) {
|
||||
update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
|
||||
}
|
||||
}
|
||||
|
||||
#end(SHADER)
|
||||
@@ -0,0 +1,107 @@
|
||||
enable f16;
|
||||
|
||||
struct Params {
|
||||
ne: u32,
|
||||
|
||||
// offsets in elements
|
||||
offset_src0: u32,
|
||||
offset_src1: u32,
|
||||
offset_dst: u32,
|
||||
|
||||
stride_src1_0: u32,
|
||||
stride_src1_1: u32,
|
||||
stride_src1_2: u32,
|
||||
stride_src1_3: u32,
|
||||
|
||||
a_ne0: u32,
|
||||
a_ne1: u32,
|
||||
a_ne2: u32,
|
||||
|
||||
b_ne0: u32,
|
||||
b_ne1: u32,
|
||||
b_ne2: u32,
|
||||
b_ne3: u32,
|
||||
};
|
||||
|
||||
fn src1_index(_i: u32) -> u32 {
|
||||
var i = _i;
|
||||
let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
|
||||
i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
|
||||
let a_i2 = i / (params.a_ne1 * params.a_ne0);
|
||||
i = i % (params.a_ne1 * params.a_ne0);
|
||||
let a_i1 = i / params.a_ne0;
|
||||
let a_i0 = i % params.a_ne0;
|
||||
|
||||
// handle repetition of b
|
||||
// index loops back to the beginning and repeats after elements are exhausted = modulo
|
||||
let b_i0 = a_i0 % params.b_ne0;
|
||||
let b_i1 = a_i1 % params.b_ne1;
|
||||
let b_i2 = a_i2 % params.b_ne2;
|
||||
let b_i3 = a_i3 % params.b_ne3;
|
||||
|
||||
// compute index for position in b's flat array
|
||||
return b_i0 * params.stride_src1_0 +
|
||||
b_i1 * params.stride_src1_1 +
|
||||
b_i2 * params.stride_src1_2 +
|
||||
b_i3 * params.stride_src1_3;
|
||||
}
|
||||
|
||||
#ifdef TYPE_F32
|
||||
#define DataType f32
|
||||
#endif
|
||||
#ifdef TYPE_F16
|
||||
#define DataType f16
|
||||
#endif
|
||||
|
||||
@group(0) @binding(0)
|
||||
var<storage, read_write> src0: array<DataType>;
|
||||
|
||||
@group(0) @binding(1)
|
||||
var<storage, read_write> src1 : array<DataType>;
|
||||
|
||||
#ifdef INPLACE
|
||||
@group(0) @binding(2)
|
||||
var<uniform> params: Params;
|
||||
|
||||
#elif defined(OVERLAP)
|
||||
@group(0) @binding(2)
|
||||
var<uniform> params: Params;
|
||||
|
||||
#else
|
||||
@group(0) @binding(2)
|
||||
var<storage, read_write> dst: array<DataType>;
|
||||
|
||||
@group(0) @binding(3)
|
||||
var<uniform> params: Params;
|
||||
#endif
|
||||
|
||||
fn op(a: DataType, b: DataType) -> DataType {
|
||||
#ifdef OP_ADD
|
||||
return a + b;
|
||||
#elif defined(OP_SUB)
|
||||
return a - b;
|
||||
#elif defined(OP_MUL)
|
||||
return a * b;
|
||||
#elif defined(OP_DIV)
|
||||
return a / b;
|
||||
#endif
|
||||
}
|
||||
|
||||
fn update(dst_i: u32, src0_i: u32, src1_i: u32){
|
||||
let result = op(src0[src0_i], src1[src1_i]);
|
||||
|
||||
#ifdef INPLACE
|
||||
src0[dst_i] = result;
|
||||
#elif defined(OVERLAP)
|
||||
src1[dst_i] = result;
|
||||
#else
|
||||
dst[dst_i] = result;
|
||||
#endif
|
||||
}
|
||||
|
||||
@compute @workgroup_size(WG_SIZE)
|
||||
fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
|
||||
if (gid.x < params.ne) {
|
||||
update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
|
||||
}
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
struct Params {
|
||||
ne: u32,
|
||||
|
||||
// offsets in elements
|
||||
offset_src0: u32,
|
||||
offset_src1: u32,
|
||||
offset_dst: u32,
|
||||
|
||||
stride_src1_0: u32,
|
||||
stride_src1_1: u32,
|
||||
stride_src1_2: u32,
|
||||
stride_src1_3: u32,
|
||||
|
||||
a_ne0: u32,
|
||||
a_ne1: u32,
|
||||
a_ne2: u32,
|
||||
|
||||
b_ne0: u32,
|
||||
b_ne1: u32,
|
||||
b_ne2: u32,
|
||||
b_ne3: u32,
|
||||
};
|
||||
|
||||
fn src1_index(_i: u32) -> u32 {
|
||||
var i = _i;
|
||||
let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
|
||||
i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
|
||||
let a_i2 = i / (params.a_ne1 * params.a_ne0);
|
||||
i = i % (params.a_ne1 * params.a_ne0);
|
||||
let a_i1 = i / params.a_ne0;
|
||||
let a_i0 = i % params.a_ne0;
|
||||
|
||||
// handle repetition of b
|
||||
// index loops back to the beginning and repeats after elements are exhausted = modulo
|
||||
let b_i0 = a_i0 % params.b_ne0;
|
||||
let b_i1 = a_i1 % params.b_ne1;
|
||||
let b_i2 = a_i2 % params.b_ne2;
|
||||
let b_i3 = a_i3 % params.b_ne3;
|
||||
|
||||
// compute index for position in b's flat array
|
||||
return b_i0 * params.stride_src1_0 +
|
||||
b_i1 * params.stride_src1_1 +
|
||||
b_i2 * params.stride_src1_2 +
|
||||
b_i3 * params.stride_src1_3;
|
||||
}
|
||||
+115
-20
@@ -146,6 +146,8 @@ class Keys:
|
||||
ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx"
|
||||
ALTUP_NUM_INPUTS = "{arch}.altup.num_inputs"
|
||||
EMBD_LENGTH_PER_LAYER_INP = "{arch}.embedding_length_per_layer_input"
|
||||
SWIGLU_CLAMP_EXP = "{arch}.swiglu_clamp_exp"
|
||||
SWIGLU_CLAMP_SHEXP = "{arch}.swiglu_clamp_shexp"
|
||||
DENSE_FEAT_IN_SIZE = "{arch}.{dense}_feat_in"
|
||||
DENSE_FEAT_OUT_SIZE = "{arch}.{dense}_feat_out"
|
||||
|
||||
@@ -179,20 +181,20 @@ class Keys:
|
||||
TEMPERATURE_SCALE = "{arch}.attention.temperature_scale"
|
||||
|
||||
class Rope:
|
||||
DIMENSION_COUNT = "{arch}.rope.dimension_count"
|
||||
DIMENSION_SECTIONS = "{arch}.rope.dimension_sections"
|
||||
FREQ_BASE = "{arch}.rope.freq_base"
|
||||
FREQ_BASE_SWA = "{arch}.rope.freq_base_swa"
|
||||
SCALING_TYPE = "{arch}.rope.scaling.type"
|
||||
SCALING_FACTOR = "{arch}.rope.scaling.factor"
|
||||
SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor"
|
||||
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
|
||||
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
|
||||
SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier"
|
||||
SCALING_YARN_EXT_FACTOR = "{arch}.rope.scaling.yarn_ext_factor"
|
||||
SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor"
|
||||
SCALING_YARN_BETA_FAST = "{arch}.rope.scaling.yarn_beta_fast"
|
||||
SCALING_YARN_BETA_SLOW = "{arch}.rope.scaling.yarn_beta_slow"
|
||||
DIMENSION_COUNT = "{arch}.rope.dimension_count"
|
||||
DIMENSION_SECTIONS = "{arch}.rope.dimension_sections"
|
||||
FREQ_BASE = "{arch}.rope.freq_base"
|
||||
FREQ_BASE_SWA = "{arch}.rope.freq_base_swa"
|
||||
SCALING_TYPE = "{arch}.rope.scaling.type"
|
||||
SCALING_FACTOR = "{arch}.rope.scaling.factor"
|
||||
SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor"
|
||||
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
|
||||
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
|
||||
SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier"
|
||||
SCALING_YARN_EXT_FACTOR = "{arch}.rope.scaling.yarn_ext_factor"
|
||||
SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor"
|
||||
SCALING_YARN_BETA_FAST = "{arch}.rope.scaling.yarn_beta_fast"
|
||||
SCALING_YARN_BETA_SLOW = "{arch}.rope.scaling.yarn_beta_slow"
|
||||
|
||||
class Split:
|
||||
LLM_KV_SPLIT_NO = "split.no"
|
||||
@@ -207,6 +209,9 @@ class Keys:
|
||||
GROUP_COUNT = "{arch}.ssm.group_count"
|
||||
DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms"
|
||||
|
||||
class KDA:
|
||||
HEAD_DIM = "{arch}.kda.head_dim"
|
||||
|
||||
class WKV:
|
||||
HEAD_SIZE = "{arch}.wkv.head_size"
|
||||
|
||||
@@ -459,8 +464,10 @@ class MODEL_ARCH(IntEnum):
|
||||
PANGU_EMBED = auto()
|
||||
MISTRAL3 = auto()
|
||||
MIMO2 = auto()
|
||||
STEP35 = auto()
|
||||
LLAMA_EMBED = auto()
|
||||
MAINCODER = auto()
|
||||
KIMI_LINEAR = auto()
|
||||
|
||||
|
||||
class VISION_PROJECTOR_TYPE(IntEnum):
|
||||
@@ -551,6 +558,14 @@ class MODEL_TENSOR(IntEnum):
|
||||
SSM_NORM = auto()
|
||||
SSM_OUT = auto()
|
||||
SSM_BETA_ALPHA = auto() # qwen3next
|
||||
SSM_CONV1D_Q = auto() # Kimi Linear
|
||||
SSM_CONV1D_K = auto() # Kimi Linear
|
||||
SSM_CONV1D_V = auto() # Kimi Linear
|
||||
SSM_F_A = auto() # Kimi Linear
|
||||
SSM_F_B = auto() # Kimi Linear
|
||||
SSM_BETA = auto() # Kimi Linear
|
||||
SSM_G_A = auto() # Kimi Linear
|
||||
SSM_G_B = auto() # Kimi Linear
|
||||
TIME_MIX_W0 = auto()
|
||||
TIME_MIX_W1 = auto()
|
||||
TIME_MIX_W2 = auto()
|
||||
@@ -880,8 +895,10 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
||||
MODEL_ARCH.PANGU_EMBED: "pangu-embedded",
|
||||
MODEL_ARCH.MISTRAL3: "mistral3",
|
||||
MODEL_ARCH.MIMO2: "mimo2",
|
||||
MODEL_ARCH.STEP35: "step35",
|
||||
MODEL_ARCH.LLAMA_EMBED: "llama-embed",
|
||||
MODEL_ARCH.MAINCODER: "maincoder",
|
||||
MODEL_ARCH.KIMI_LINEAR: "kimi-linear",
|
||||
}
|
||||
|
||||
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
|
||||
@@ -969,6 +986,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
||||
MODEL_TENSOR.SSM_NORM: "blk.{bid}.ssm_norm",
|
||||
MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
|
||||
MODEL_TENSOR.SSM_BETA_ALPHA: "blk.{bid}.ssm_ba",
|
||||
MODEL_TENSOR.SSM_CONV1D_Q: "blk.{bid}.ssm_conv1d_q", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_CONV1D_K: "blk.{bid}.ssm_conv1d_k", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_CONV1D_V: "blk.{bid}.ssm_conv1d_v", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_F_A: "blk.{bid}.ssm_f_a", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_F_B: "blk.{bid}.ssm_f_b", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_BETA: "blk.{bid}.ssm_beta", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_G_A: "blk.{bid}.ssm_g_a", # Kimi Linear
|
||||
MODEL_TENSOR.SSM_G_B: "blk.{bid}.ssm_g_b", # Kimi Linear
|
||||
MODEL_TENSOR.TIME_MIX_W0: "blk.{bid}.time_mix_w0",
|
||||
MODEL_TENSOR.TIME_MIX_W1: "blk.{bid}.time_mix_w1",
|
||||
MODEL_TENSOR.TIME_MIX_W2: "blk.{bid}.time_mix_w2",
|
||||
@@ -3343,6 +3368,32 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B,
|
||||
],
|
||||
MODEL_ARCH.STEP35: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_Q_NORM,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_K_NORM,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_GATE,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
MODEL_TENSOR.FFN_GATE_INP,
|
||||
MODEL_TENSOR.FFN_GATE_EXP,
|
||||
MODEL_TENSOR.FFN_DOWN_EXP,
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
MODEL_TENSOR.FFN_UP_SHEXP,
|
||||
MODEL_TENSOR.FFN_GATE_SHEXP,
|
||||
MODEL_TENSOR.FFN_DOWN_SHEXP,
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B,
|
||||
],
|
||||
MODEL_ARCH.LLAMA_EMBED: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
@@ -3379,6 +3430,47 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.KIMI_LINEAR: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.ATTN_Q_A,
|
||||
MODEL_TENSOR.ATTN_Q_B,
|
||||
MODEL_TENSOR.ATTN_KV_A_MQA,
|
||||
MODEL_TENSOR.ATTN_KV_B,
|
||||
MODEL_TENSOR.ATTN_K_B,
|
||||
MODEL_TENSOR.ATTN_V_B,
|
||||
MODEL_TENSOR.ATTN_Q_A_NORM,
|
||||
MODEL_TENSOR.ATTN_KV_A_NORM,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
MODEL_TENSOR.FFN_GATE_INP,
|
||||
MODEL_TENSOR.FFN_GATE_EXP,
|
||||
MODEL_TENSOR.FFN_DOWN_EXP,
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
MODEL_TENSOR.SSM_CONV1D_Q,
|
||||
MODEL_TENSOR.SSM_CONV1D_K,
|
||||
MODEL_TENSOR.SSM_CONV1D_V,
|
||||
MODEL_TENSOR.SSM_F_A,
|
||||
MODEL_TENSOR.SSM_F_B,
|
||||
MODEL_TENSOR.SSM_BETA,
|
||||
MODEL_TENSOR.SSM_A,
|
||||
MODEL_TENSOR.SSM_G_A,
|
||||
MODEL_TENSOR.SSM_G_B,
|
||||
MODEL_TENSOR.SSM_DT,
|
||||
MODEL_TENSOR.SSM_NORM,
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B,
|
||||
MODEL_TENSOR.FFN_GATE_SHEXP,
|
||||
MODEL_TENSOR.FFN_DOWN_SHEXP,
|
||||
MODEL_TENSOR.FFN_UP_SHEXP,
|
||||
],
|
||||
# TODO
|
||||
}
|
||||
|
||||
@@ -3691,12 +3783,12 @@ KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS
|
||||
KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
|
||||
|
||||
# RoPE
|
||||
KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT
|
||||
KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE
|
||||
KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE
|
||||
KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR
|
||||
KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
|
||||
KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED
|
||||
KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT
|
||||
KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE
|
||||
KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE
|
||||
KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR
|
||||
KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
|
||||
KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED
|
||||
|
||||
# SSM
|
||||
KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL
|
||||
@@ -3706,6 +3798,9 @@ KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK
|
||||
KEY_SSM_GROUP_COUNT = Keys.SSM.GROUP_COUNT
|
||||
KEY_SSM_DT_B_C_RMS = Keys.SSM.DT_B_C_RMS
|
||||
|
||||
# KDA
|
||||
KEY_KDA_HEAD_DIM = Keys.KDA.HEAD_DIM
|
||||
|
||||
# tokenization
|
||||
KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL
|
||||
KEY_TOKENIZER_PRE = Keys.Tokenizer.PRE
|
||||
|
||||
@@ -824,6 +824,12 @@ class GGUFWriter:
|
||||
def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
|
||||
self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
|
||||
|
||||
def add_swiglu_clamp_exp(self, values: Sequence[float]) -> None:
|
||||
self.add_array(Keys.LLM.SWIGLU_CLAMP_EXP.format(arch=self.arch), values)
|
||||
|
||||
def add_swiglu_clamp_shexp(self, values: Sequence[float]) -> None:
|
||||
self.add_array(Keys.LLM.SWIGLU_CLAMP_SHEXP.format(arch=self.arch), values)
|
||||
|
||||
def add_expert_group_scale(self, value: float) -> None:
|
||||
self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)
|
||||
|
||||
@@ -980,6 +986,9 @@ class GGUFWriter:
|
||||
def add_ssm_dt_b_c_rms(self, value: bool) -> None:
|
||||
self.add_bool(Keys.SSM.DT_B_C_RMS.format(arch=self.arch), value)
|
||||
|
||||
def add_kda_head_dim(self, value: int) -> None:
|
||||
self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value)
|
||||
|
||||
def add_tokenizer_model(self, model: str) -> None:
|
||||
self.add_string(Keys.Tokenizer.MODEL, model)
|
||||
|
||||
|
||||
@@ -359,6 +359,7 @@ class TensorNameMap:
|
||||
|
||||
MODEL_TENSOR.ATTN_GATE: (
|
||||
"model.layers.{bid}.self_attn.gate_proj", # afmoe
|
||||
"model.layers.{bid}.self_attn.g_proj", # step3.5 head-wise attention gate
|
||||
),
|
||||
|
||||
# Feed-forward norm
|
||||
@@ -423,6 +424,7 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.mlp.router.gate", # afmoe
|
||||
"layers.{bid}.gate", # mistral-large
|
||||
"backbone.layers.{bid}.mixer.gate", # nemotron-h-moe
|
||||
"model.layers.{bid}.moe.gate", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
|
||||
@@ -438,6 +440,8 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.block_sparse_moe.e_score_correction", # minimax-m2
|
||||
"backbone.layers.{bid}.mixer.gate.e_score_correction", # nemotron-h-moe
|
||||
"model.layers.{bid}.mlp.e_score_correction", # exaone-moe
|
||||
"model.layers.{bid}.block_sparse_moe.gate.e_score_correction", # kimi
|
||||
"model.layers.{bid}.moe.router_bias", # step3.5 expert selection bias
|
||||
),
|
||||
|
||||
# Feed-forward up
|
||||
@@ -492,6 +496,7 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.feed_forward.experts.up_proj", # llama4
|
||||
"encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe
|
||||
"model.layers.{bid}.block_sparse_moe.experts.up", # smallthinker
|
||||
"model.layers.{bid}.moe.up_proj", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_UP_SHEXP: (
|
||||
@@ -502,6 +507,8 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.mlp.shared_mlp.up_proj", # hunyuan
|
||||
"layers.{bid}.shared_experts.w3", # mistral-large
|
||||
"backbone.layers.{bid}.mixer.shared_experts.up_proj", # nemotron-h-moe
|
||||
"model.layers.{bid}.block_sparse_moe.shared_experts.up_proj", # kimi
|
||||
"model.layers.{bid}.share_expert.up_proj", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_UP_CHEXP: (
|
||||
@@ -541,6 +548,7 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged)
|
||||
"model.layers.{bid}.feed_forward.experts.gate_proj", # llama4
|
||||
"model.layers.{bid}.block_sparse_moe.experts.gate", # smallthinker
|
||||
"model.layers.{bid}.moe.gate_proj", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_GATE_SHEXP: (
|
||||
@@ -549,6 +557,8 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4
|
||||
"model.layers.{bid}.mlp.shared_mlp.gate_proj", # hunyuan
|
||||
"layers.{bid}.shared_experts.w1", # mistral-large
|
||||
"model.layers.{bid}.block_sparse_moe.shared_experts.gate_proj", # kimi
|
||||
"model.layers.{bid}.share_expert.gate_proj", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_GATE_CHEXP: (
|
||||
@@ -603,6 +613,7 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.feed_forward.experts.down_proj", # llama4
|
||||
"encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe
|
||||
"model.layers.{bid}.block_sparse_moe.experts.down", # smallthinker
|
||||
"model.layers.{bid}.moe.down_proj", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_DOWN_SHEXP: (
|
||||
@@ -613,6 +624,8 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.mlp.shared_mlp.down_proj", # hunyuan
|
||||
"layers.{bid}.shared_experts.w2", # mistral-large
|
||||
"backbone.layers.{bid}.mixer.shared_experts.down_proj", # nemotron-h-moe
|
||||
"model.layers.{bid}.block_sparse_moe.shared_experts.down_proj", # kimi
|
||||
"model.layers.{bid}.share_expert.down_proj", # step3.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_DOWN_CHEXP: (
|
||||
@@ -759,6 +772,7 @@ class TensorNameMap:
|
||||
"model.layers.layers.{bid}.mixer.dt_proj", # plamo2
|
||||
"model.layers.{bid}.linear_attn.dt_proj", # qwen3next
|
||||
"backbone.layers.{bid}.mixer.dt", # nemotron-h-moe
|
||||
"model.layers.{bid}.self_attn.dt_proj", # kimi
|
||||
),
|
||||
|
||||
MODEL_TENSOR.SSM_DT_NORM: (
|
||||
@@ -772,6 +786,7 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.mamba.A_log", # jamba falcon-h1 granite-hybrid
|
||||
"model.layers.layers.{bid}.mixer.A_log", # plamo2
|
||||
"model.layers.{bid}.linear_attn.A_log", # qwen3next
|
||||
"model.layers.{bid}.self_attn.A_log", # kimi
|
||||
),
|
||||
|
||||
MODEL_TENSOR.SSM_B_NORM: (
|
||||
@@ -797,6 +812,7 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.mamba.norm", # falcon-h1 granite-hybrid
|
||||
"model.layers.{bid}.linear_attn.norm", # qwen3next
|
||||
"backbone.layers.{bid}.mixer.norm", # mamba2
|
||||
"model.layers.{bid}.self_attn.o_norm", # kimi
|
||||
),
|
||||
|
||||
MODEL_TENSOR.SSM_OUT: (
|
||||
@@ -811,6 +827,31 @@ class TensorNameMap:
|
||||
"model.layers.{bid}.linear_attn.in_proj_ba", # qwen3next
|
||||
),
|
||||
|
||||
# Kimi Linear KDA (using SSM_ prefix for consistency)
|
||||
MODEL_TENSOR.SSM_CONV1D_Q: (
|
||||
"model.layers.{bid}.self_attn.q_conv1d",
|
||||
),
|
||||
MODEL_TENSOR.SSM_CONV1D_K: (
|
||||
"model.layers.{bid}.self_attn.k_conv1d",
|
||||
),
|
||||
MODEL_TENSOR.SSM_CONV1D_V: (
|
||||
"model.layers.{bid}.self_attn.v_conv1d",
|
||||
),
|
||||
MODEL_TENSOR.SSM_F_A: (
|
||||
"model.layers.{bid}.self_attn.f_a_proj",
|
||||
),
|
||||
MODEL_TENSOR.SSM_F_B: (
|
||||
"model.layers.{bid}.self_attn.f_b_proj",
|
||||
),
|
||||
MODEL_TENSOR.SSM_BETA: (
|
||||
"model.layers.{bid}.self_attn.b_proj",
|
||||
),
|
||||
MODEL_TENSOR.SSM_G_A: (
|
||||
"model.layers.{bid}.self_attn.g_a_proj",
|
||||
),
|
||||
MODEL_TENSOR.SSM_G_B: (
|
||||
"model.layers.{bid}.self_attn.g_b_proj",
|
||||
),
|
||||
MODEL_TENSOR.TIME_MIX_W0: (
|
||||
"model.layers.{bid}.attention.w0", # rwkv7
|
||||
),
|
||||
|
||||
@@ -23,7 +23,7 @@ numpy = ">=1.17"
|
||||
tqdm = ">=4.27"
|
||||
pyyaml = ">=5.1"
|
||||
requests = ">=2.25"
|
||||
sentencepiece = { version = ">=0.1.98,<=0.2.0", optional = true }
|
||||
sentencepiece = { version = ">=0.1.98,<0.3.0", optional = true }
|
||||
PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true }
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
|
||||
+1
-1
@@ -17,7 +17,7 @@ classifiers = [
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9"
|
||||
numpy = "^1.25.0"
|
||||
sentencepiece = ">=0.1.98,<=0.2.0"
|
||||
sentencepiece = ">=0.1.98,<0.3.0"
|
||||
transformers = ">=4.35.2,<5.0.0"
|
||||
protobuf = ">=4.21.0,<5.0.0"
|
||||
gguf = { path = "./gguf-py" }
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
numpy~=1.26.4
|
||||
sentencepiece~=0.2.0
|
||||
sentencepiece>=0.1.98,<0.3.0
|
||||
|
||||
transformers>=4.57.1,<5.0.0
|
||||
|
||||
|
||||
Regular → Executable
+34
-26
@@ -7,47 +7,54 @@ ARGS_BB="-c 270336 -npp 512,4096,8192 -npl 1,2,4,8,16,32 -ntg 32"
|
||||
ARGS_B="-d 0,4096,8192,16384,32768 -p 2048 -n 32"
|
||||
|
||||
QUICK=0
|
||||
DIO=0
|
||||
while (( "$#" )); do
|
||||
case "$1" in
|
||||
--quick) QUICK=1; shift ;;
|
||||
*) shift ;;
|
||||
esac
|
||||
case "$1" in
|
||||
--quick) QUICK=1; shift ;;
|
||||
--dio) DIO=1; shift ;;
|
||||
*) shift ;;
|
||||
esac
|
||||
done
|
||||
|
||||
if (( QUICK )); then
|
||||
ARGS_BB="-c 20480 -npp 512,4096 -npl 1,2,4 -ntg 32"
|
||||
ARGS_B="-d 0 -p 2048 -n 32"
|
||||
ARGS_BB="-c 20480 -npp 512,4096 -npl 1,2,4 -ntg 32"
|
||||
ARGS_B="-d 0 -p 2048 -n 32"
|
||||
fi
|
||||
|
||||
if (( DIO )); then
|
||||
ARGS_BB="${ARGS_BB} --no-mmap --direct-io"
|
||||
ARGS_B="${ARGS_B} -mmp 0 -dio 1"
|
||||
fi
|
||||
|
||||
run_model() {
|
||||
local HFR=$1
|
||||
local HFF=$2
|
||||
local HFR=$1
|
||||
local HFF=$2
|
||||
|
||||
printf "## ${HFR}\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf "Model: https://huggingface.co/${HFR}\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf "## ${HFR}\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf "Model: https://huggingface.co/${HFR}\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
|
||||
printf -- "- \`llama-batched-bench\`\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf -- "- \`llama-batched-bench\`\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
|
||||
./bin/llama-batched-bench \
|
||||
-hfr "${HFR}" -hff "${HFF}" \
|
||||
-m "${HFF}" -fa 1 -ub 2048 --no-mmap \
|
||||
${ARGS_BB} | tee -a "$RESULTS"
|
||||
./bin/llama-batched-bench \
|
||||
-hfr "${HFR}" -hff "${HFF}" \
|
||||
-m "${HFF}" -fa 1 -ub 2048 \
|
||||
${ARGS_BB} | tee -a "$RESULTS"
|
||||
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
|
||||
printf -- "- \`llama-bench\`\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf -- "- \`llama-bench\`\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
|
||||
./bin/llama-bench \
|
||||
-m "${HFF}" -fa 1 -ub 2048 -mmp 0 \
|
||||
${ARGS_B} | tee -a "$RESULTS"
|
||||
./bin/llama-bench \
|
||||
-m "${HFF}" -fa 1 -ub 2048 \
|
||||
${ARGS_B} | tee -a "$RESULTS"
|
||||
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
printf "\n" | tee -a "$RESULTS"
|
||||
|
||||
printf "\n"
|
||||
printf "\n"
|
||||
}
|
||||
|
||||
run_model "ggml-org/gpt-oss-20b-GGUF" "gpt-oss-20b-mxfp4.gguf"
|
||||
@@ -55,6 +62,7 @@ run_model "ggml-org/gpt-oss-120b-GGUF" "gpt-oss-120b-mxfp4-
|
||||
run_model "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF" "qwen3-coder-30b-a3b-instruct-q8_0.gguf"
|
||||
run_model "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF" "qwen2.5-coder-7b-q8_0.gguf"
|
||||
run_model "ggml-org/gemma-3-4b-it-qat-GGUF" "gemma-3-4b-it-qat-Q4_0.gguf"
|
||||
run_model "ggml-org/GLM-4.7-Flash-GGUF" "GLM-4.7-Flash-Q8_0.gguf"
|
||||
|
||||
if [[ -f models-extra.txt ]]; then
|
||||
while read -r HFR HFF; do
|
||||
|
||||
+3
-1
@@ -31,7 +31,7 @@ add_library(llama
|
||||
llama-model-saver.cpp
|
||||
llama-model.cpp
|
||||
llama-quant.cpp
|
||||
llama-sampling.cpp
|
||||
llama-sampler.cpp
|
||||
llama-vocab.cpp
|
||||
unicode-data.cpp
|
||||
unicode.cpp
|
||||
@@ -84,6 +84,7 @@ add_library(llama
|
||||
models/internlm2.cpp
|
||||
models/jais.cpp
|
||||
models/jamba.cpp
|
||||
models/kimi-linear.cpp
|
||||
models/lfm2.cpp
|
||||
models/llada-moe.cpp
|
||||
models/llada.cpp
|
||||
@@ -134,6 +135,7 @@ add_library(llama
|
||||
models/stablelm.cpp
|
||||
models/starcoder.cpp
|
||||
models/starcoder2.cpp
|
||||
models/step35-iswa.cpp
|
||||
models/t5-dec.cpp
|
||||
models/t5-enc.cpp
|
||||
models/wavtokenizer-dec.cpp
|
||||
|
||||
+118
-16
@@ -117,9 +117,11 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
||||
{ LLM_ARCH_RND1, "rnd1" },
|
||||
{ LLM_ARCH_PANGU_EMBED, "pangu-embedded" },
|
||||
{ LLM_ARCH_MISTRAL3, "mistral3" },
|
||||
{ LLM_ARCH_MIMO2, "mimo2" },
|
||||
{ LLM_ARCH_MIMO2, "mimo2" },
|
||||
{ LLM_ARCH_STEP35, "step35" },
|
||||
{ LLM_ARCH_LLAMA_EMBED, "llama-embed" },
|
||||
{ LLM_ARCH_MAINCODER, "maincoder" },
|
||||
{ LLM_ARCH_KIMI_LINEAR, "kimi-linear" },
|
||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||
};
|
||||
|
||||
@@ -161,6 +163,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
||||
{ LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" },
|
||||
{ LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
|
||||
{ LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH, "%s.expert_chunk_feed_forward_length" },
|
||||
{ LLM_KV_SWIGLU_CLAMP_EXP, "%s.swiglu_clamp_exp" },
|
||||
{ LLM_KV_SWIGLU_CLAMP_SHEXP, "%s.swiglu_clamp_shexp" },
|
||||
{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
|
||||
{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
|
||||
{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
|
||||
@@ -219,21 +223,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
||||
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
|
||||
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
|
||||
|
||||
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
|
||||
{ LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" },
|
||||
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
|
||||
{ LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" },
|
||||
{ LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
|
||||
{ LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
|
||||
{ LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
|
||||
{ LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" },
|
||||
{ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
|
||||
{ LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR, "%s.rope.scaling.yarn_ext_factor" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_BETA_FAST, "%s.rope.scaling.yarn_beta_fast" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, "%s.rope.scaling.yarn_beta_slow" },
|
||||
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
|
||||
{ LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" },
|
||||
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
|
||||
{ LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" },
|
||||
{ LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
|
||||
{ LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
|
||||
{ LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
|
||||
{ LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" },
|
||||
{ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
|
||||
{ LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR, "%s.rope.scaling.yarn_ext_factor" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_BETA_FAST, "%s.rope.scaling.yarn_beta_fast" },
|
||||
{ LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, "%s.rope.scaling.yarn_beta_slow" },
|
||||
|
||||
{ LLM_KV_SPLIT_NO, "split.no" },
|
||||
{ LLM_KV_SPLIT_COUNT, "split.count" },
|
||||
@@ -246,6 +250,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
||||
{ LLM_KV_SSM_GROUP_COUNT, "%s.ssm.group_count" },
|
||||
{ LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" },
|
||||
|
||||
{ LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" },
|
||||
|
||||
{ LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
|
||||
|
||||
{ LLM_KV_POSNET_EMBEDDING_LENGTH, "%s.posnet.embedding_length" },
|
||||
@@ -371,6 +377,15 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
|
||||
{ LLM_TENSOR_SSM_DT_NORM, "blk.%d.ssm_dt_norm" },
|
||||
{ LLM_TENSOR_SSM_B_NORM, "blk.%d.ssm_b_norm" },
|
||||
{ LLM_TENSOR_SSM_C_NORM, "blk.%d.ssm_c_norm" },
|
||||
{ LLM_TENSOR_SSM_CONV1D_Q, "blk.%d.ssm_conv1d_q" },
|
||||
{ LLM_TENSOR_SSM_CONV1D_K, "blk.%d.ssm_conv1d_k" },
|
||||
{ LLM_TENSOR_SSM_CONV1D_V, "blk.%d.ssm_conv1d_v" },
|
||||
{ LLM_TENSOR_SSM_F_A, "blk.%d.ssm_f_a" },
|
||||
{ LLM_TENSOR_SSM_F_B, "blk.%d.ssm_f_b" },
|
||||
{ LLM_TENSOR_SSM_BETA, "blk.%d.ssm_beta" },
|
||||
{ LLM_TENSOR_SSM_G_A, "blk.%d.ssm_g_a" },
|
||||
{ LLM_TENSOR_SSM_G_B, "blk.%d.ssm_g_b" },
|
||||
{ LLM_TENSOR_SSM_NORM, "blk.%d.ssm_norm" },
|
||||
{ LLM_TENSOR_ATTN_Q_A_NORM, "blk.%d.attn_q_a_norm" },
|
||||
{ LLM_TENSOR_ATTN_KV_A_NORM, "blk.%d.attn_kv_a_norm" },
|
||||
{ LLM_TENSOR_ATTN_Q_A, "blk.%d.attn_q_a" },
|
||||
@@ -2267,6 +2282,35 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
|
||||
LLM_TENSOR_FFN_UP_EXPS,
|
||||
LLM_TENSOR_FFN_EXP_PROBS_B,
|
||||
};
|
||||
case LLM_ARCH_STEP35:
|
||||
return {
|
||||
LLM_TENSOR_TOKEN_EMBD,
|
||||
LLM_TENSOR_OUTPUT_NORM,
|
||||
LLM_TENSOR_OUTPUT,
|
||||
LLM_TENSOR_ROPE_FREQS,
|
||||
LLM_TENSOR_ROPE_FACTORS_LONG,
|
||||
LLM_TENSOR_ROPE_FACTORS_SHORT,
|
||||
LLM_TENSOR_ATTN_NORM,
|
||||
LLM_TENSOR_ATTN_Q,
|
||||
LLM_TENSOR_ATTN_Q_NORM,
|
||||
LLM_TENSOR_ATTN_K,
|
||||
LLM_TENSOR_ATTN_K_NORM,
|
||||
LLM_TENSOR_ATTN_V,
|
||||
LLM_TENSOR_ATTN_GATE,
|
||||
LLM_TENSOR_ATTN_OUT,
|
||||
LLM_TENSOR_FFN_NORM,
|
||||
LLM_TENSOR_FFN_GATE,
|
||||
LLM_TENSOR_FFN_DOWN,
|
||||
LLM_TENSOR_FFN_UP,
|
||||
LLM_TENSOR_FFN_GATE_INP,
|
||||
LLM_TENSOR_FFN_GATE_EXPS,
|
||||
LLM_TENSOR_FFN_DOWN_EXPS,
|
||||
LLM_TENSOR_FFN_UP_EXPS,
|
||||
LLM_TENSOR_FFN_GATE_SHEXP,
|
||||
LLM_TENSOR_FFN_UP_SHEXP,
|
||||
LLM_TENSOR_FFN_DOWN_SHEXP,
|
||||
LLM_TENSOR_FFN_EXP_PROBS_B,
|
||||
};
|
||||
case LLM_ARCH_GPTJ:
|
||||
case LLM_ARCH_UNKNOWN:
|
||||
return {
|
||||
@@ -2289,6 +2333,54 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
|
||||
LLM_TENSOR_FFN_DOWN,
|
||||
LLM_TENSOR_FFN_UP,
|
||||
};
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
return {
|
||||
LLM_TENSOR_TOKEN_EMBD,
|
||||
LLM_TENSOR_OUTPUT_NORM,
|
||||
LLM_TENSOR_OUTPUT,
|
||||
LLM_TENSOR_ROPE_FREQS,
|
||||
LLM_TENSOR_ATTN_NORM,
|
||||
LLM_TENSOR_ATTN_Q,
|
||||
LLM_TENSOR_ATTN_K,
|
||||
LLM_TENSOR_ATTN_V,
|
||||
LLM_TENSOR_ATTN_OUT,
|
||||
LLM_TENSOR_FFN_NORM,
|
||||
// Dense FFN (layer 0 only)
|
||||
LLM_TENSOR_FFN_GATE,
|
||||
LLM_TENSOR_FFN_DOWN,
|
||||
LLM_TENSOR_FFN_UP,
|
||||
// MoE FFN (layers 1+)
|
||||
LLM_TENSOR_FFN_GATE_INP,
|
||||
LLM_TENSOR_FFN_GATE_EXPS,
|
||||
LLM_TENSOR_FFN_DOWN_EXPS,
|
||||
LLM_TENSOR_FFN_UP_EXPS,
|
||||
LLM_TENSOR_FFN_EXP_PROBS_B,
|
||||
// Shared experts
|
||||
LLM_TENSOR_FFN_GATE_SHEXP,
|
||||
LLM_TENSOR_FFN_DOWN_SHEXP,
|
||||
LLM_TENSOR_FFN_UP_SHEXP,
|
||||
// KDA (using SSM_ enum prefix, keeping GGUF names for backward compat)
|
||||
LLM_TENSOR_SSM_CONV1D_Q,
|
||||
LLM_TENSOR_SSM_CONV1D_K,
|
||||
LLM_TENSOR_SSM_CONV1D_V,
|
||||
LLM_TENSOR_SSM_F_A,
|
||||
LLM_TENSOR_SSM_F_B,
|
||||
LLM_TENSOR_SSM_BETA,
|
||||
LLM_TENSOR_SSM_A,
|
||||
LLM_TENSOR_SSM_G_A,
|
||||
LLM_TENSOR_SSM_G_B,
|
||||
LLM_TENSOR_SSM_DT,
|
||||
LLM_TENSOR_SSM_NORM,
|
||||
// MLA
|
||||
LLM_TENSOR_ATTN_Q_A,
|
||||
LLM_TENSOR_ATTN_Q_B,
|
||||
LLM_TENSOR_ATTN_Q_A_NORM,
|
||||
LLM_TENSOR_ATTN_KV_A_MQA,
|
||||
LLM_TENSOR_ATTN_KV_B,
|
||||
LLM_TENSOR_ATTN_K_B,
|
||||
LLM_TENSOR_ATTN_V_B,
|
||||
LLM_TENSOR_ATTN_KV_A_NORM,
|
||||
};
|
||||
default:
|
||||
GGML_ABORT("unknown architecture for tensor mapping");
|
||||
}
|
||||
@@ -2392,6 +2484,15 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
|
||||
{LLM_TENSOR_SSM_C_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_SSM_D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_SSM_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
// Kimi KDA - Conv tensors are 4D [d_conv, 1, d_inner, 1], reshaped to 2D at runtime
|
||||
{LLM_TENSOR_SSM_CONV1D_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_SSM_CONV1D_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_SSM_CONV1D_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_SSM_F_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_SSM_F_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_SSM_BETA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_SSM_G_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_SSM_G_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_TIME_MIX_LERP_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_TIME_MIX_LN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_CHANNEL_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
@@ -2573,6 +2674,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
|
||||
case LLM_ARCH_NEMOTRON_H:
|
||||
case LLM_ARCH_NEMOTRON_H_MOE:
|
||||
case LLM_ARCH_QWEN3NEXT:
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
return true;
|
||||
default:
|
||||
return false;
|
||||
|
||||
@@ -122,8 +122,10 @@ enum llm_arch {
|
||||
LLM_ARCH_PANGU_EMBED,
|
||||
LLM_ARCH_MISTRAL3,
|
||||
LLM_ARCH_MIMO2,
|
||||
LLM_ARCH_STEP35,
|
||||
LLM_ARCH_LLAMA_EMBED,
|
||||
LLM_ARCH_MAINCODER,
|
||||
LLM_ARCH_KIMI_LINEAR,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
@@ -165,6 +167,8 @@ enum llm_kv {
|
||||
LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
|
||||
LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
|
||||
LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,
|
||||
LLM_KV_SWIGLU_CLAMP_EXP,
|
||||
LLM_KV_SWIGLU_CLAMP_SHEXP,
|
||||
LLM_KV_USE_PARALLEL_RESIDUAL,
|
||||
LLM_KV_TENSOR_DATA_LAYOUT,
|
||||
LLM_KV_EXPERT_COUNT,
|
||||
@@ -250,6 +254,8 @@ enum llm_kv {
|
||||
LLM_KV_SSM_GROUP_COUNT,
|
||||
LLM_KV_SSM_DT_B_C_RMS,
|
||||
|
||||
LLM_KV_KDA_HEAD_DIM,
|
||||
|
||||
LLM_KV_WKV_HEAD_SIZE,
|
||||
|
||||
LLM_KV_TOKENIZER_MODEL,
|
||||
@@ -398,6 +404,15 @@ enum llm_tensor {
|
||||
LLM_TENSOR_SSM_NORM,
|
||||
LLM_TENSOR_SSM_OUT,
|
||||
LLM_TENSOR_SSM_BETA_ALPHA, // qwen3next
|
||||
// Kimi Linear KDA (using SSM_ prefix for consistency)
|
||||
LLM_TENSOR_SSM_CONV1D_Q, // kimi: Q conv1d weight
|
||||
LLM_TENSOR_SSM_CONV1D_K, // kimi: K conv1d weight
|
||||
LLM_TENSOR_SSM_CONV1D_V, // kimi: V conv1d weight
|
||||
LLM_TENSOR_SSM_F_A, // kimi: forget gate projection A
|
||||
LLM_TENSOR_SSM_F_B, // kimi: forget gate projection B
|
||||
LLM_TENSOR_SSM_BETA, // kimi: beta mixing coefficient
|
||||
LLM_TENSOR_SSM_G_A, // kimi: output gate projection A
|
||||
LLM_TENSOR_SSM_G_B, // kimi: output gate projection B
|
||||
LLM_TENSOR_TIME_MIX_W0,
|
||||
LLM_TENSOR_TIME_MIX_W1,
|
||||
LLM_TENSOR_TIME_MIX_W2,
|
||||
|
||||
@@ -2013,7 +2013,7 @@ void llama_context::output_reorder() {
|
||||
//
|
||||
|
||||
uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
|
||||
if (model.arch == LLM_ARCH_QWEN3NEXT) {
|
||||
if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
|
||||
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
|
||||
}
|
||||
uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
#include "llama-impl.h"
|
||||
#include "llama-vocab.h"
|
||||
#include "llama-sampling.h"
|
||||
#include "llama-sampler.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <algorithm>
|
||||
|
||||
@@ -13,6 +13,8 @@
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <cstring>
|
||||
#include <numeric>
|
||||
#include <sstream>
|
||||
#include <unordered_set>
|
||||
|
||||
void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
|
||||
@@ -533,6 +535,50 @@ bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
|
||||
return res;
|
||||
}
|
||||
|
||||
// TODO: Hybrid input classes are a bit redundant.
|
||||
// Instead of creating a hybrid input, the graph can simply create 2 separate inputs.
|
||||
// Refactoring is required in the future.
|
||||
void llm_graph_input_mem_hybrid_k::set_input(const llama_ubatch * ubatch) {
|
||||
mctx->get_attn()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
|
||||
|
||||
mctx->get_attn()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
|
||||
|
||||
const int64_t n_rs = mctx->get_recr()->get_n_rs();
|
||||
|
||||
if (inp_rs->s_copy) {
|
||||
GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
|
||||
int32_t * data = (int32_t *) inp_rs->s_copy->data;
|
||||
|
||||
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
|
||||
for (uint32_t i = 0; i < n_rs; ++i) {
|
||||
data[i] = mctx->get_recr()->s_copy(i);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool llm_graph_input_mem_hybrid_k::can_reuse(const llm_graph_params & params) {
|
||||
const auto * mctx = static_cast<const llama_memory_hybrid_context *>(params.mctx);
|
||||
|
||||
this->mctx = mctx;
|
||||
|
||||
bool res = true;
|
||||
|
||||
res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
|
||||
|
||||
res &= inp_attn->self_kq_mask->ne[0] == mctx->get_attn()->get_n_kv();
|
||||
res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
|
||||
|
||||
res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
|
||||
|
||||
res &= inp_rs->s_copy_main->ne[0] == params.ubatch.n_seqs;
|
||||
res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
|
||||
|
||||
res &= inp_rs->head == mctx->get_recr()->get_head();
|
||||
res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
void llm_graph_input_mem_hybrid_iswa::set_input(const llama_ubatch * ubatch) {
|
||||
const auto * attn_ctx = mctx->get_attn();
|
||||
|
||||
@@ -970,6 +1016,26 @@ ggml_tensor * llm_graph_context::build_ffn(
|
||||
switch (type_op) {
|
||||
case LLM_FFN_SILU:
|
||||
if (gate && type_gate == LLM_FFN_PAR) {
|
||||
// Step35: HF clamps gate (after SiLU) and up before multiplication
|
||||
if (arch == LLM_ARCH_STEP35 && il >= 0) {
|
||||
const float limit = hparams.swiglu_clamp_shexp[il];
|
||||
constexpr float eps = 1e-6f;
|
||||
if (limit > eps) {
|
||||
ggml_tensor * gate_act = ggml_silu(ctx0, cur);
|
||||
cb(gate_act, "ffn_silu", il);
|
||||
gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
|
||||
cb(gate_act, "ffn_silu_clamped", il);
|
||||
|
||||
tmp = ggml_clamp(ctx0, tmp, -limit, limit);
|
||||
cb(tmp, "ffn_up_clamped", il);
|
||||
|
||||
cur = ggml_mul(ctx0, gate_act, tmp);
|
||||
cb(cur, "ffn_swiglu_limited", il);
|
||||
type_gate = LLM_FFN_SEQ;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
cur = ggml_swiglu_split(ctx0, cur, tmp);
|
||||
cb(cur, "ffn_swiglu", il);
|
||||
type_gate = LLM_FFN_SEQ;
|
||||
@@ -1272,6 +1338,25 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
|
||||
switch (type_op) {
|
||||
case LLM_FFN_SILU:
|
||||
if (gate_exps) {
|
||||
// Step35: per-layer clamp for routed experts
|
||||
if (arch == LLM_ARCH_STEP35 && il >= 0) {
|
||||
const float limit = hparams.swiglu_clamp_exp[il];
|
||||
constexpr float eps = 1e-6f;
|
||||
if (limit > eps) {
|
||||
ggml_tensor * gate_act = ggml_silu(ctx0, cur);
|
||||
cb(gate_act, "ffn_moe_silu", il);
|
||||
gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
|
||||
cb(gate_act, "ffn_moe_silu_clamped", il);
|
||||
|
||||
up = ggml_clamp(ctx0, up, -limit, limit);
|
||||
cb(up, "ffn_moe_up_clamped", il);
|
||||
|
||||
cur = ggml_mul(ctx0, gate_act, up);
|
||||
cb(cur, "ffn_moe_swiglu_limited", il);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
cur = ggml_swiglu_split(ctx0, cur, up);
|
||||
cb(cur, "ffn_moe_swiglu", il);
|
||||
} else {
|
||||
@@ -2268,6 +2353,17 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
|
||||
return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
|
||||
}
|
||||
|
||||
llm_graph_input_mem_hybrid_k * llm_graph_context::build_inp_mem_hybrid_k() const {
|
||||
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
|
||||
|
||||
auto inp_rs = build_rs_inp_impl (ctx0, ubatch, mctx_cur->get_recr());
|
||||
auto inp_attn = build_attn_inp_k_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
|
||||
|
||||
auto inp = std::make_unique<llm_graph_input_mem_hybrid_k>(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);
|
||||
|
||||
return (llm_graph_input_mem_hybrid_k *) res->add_input(std::move(inp));
|
||||
}
|
||||
|
||||
llm_graph_input_mem_hybrid_iswa * llm_graph_context::build_inp_mem_hybrid_iswa() const {
|
||||
const auto * mctx_cur = static_cast<const llama_memory_hybrid_iswa_context *>(mctx);
|
||||
|
||||
|
||||
@@ -433,6 +433,34 @@ public:
|
||||
const llama_memory_hybrid_context * mctx;
|
||||
};
|
||||
|
||||
class llm_graph_input_mem_hybrid_k : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_mem_hybrid_k(
|
||||
const llama_cparams & cparams,
|
||||
std::unique_ptr<llm_graph_input_attn_k> inp_attn,
|
||||
std::unique_ptr<llm_graph_input_rs> inp_rs,
|
||||
const llama_memory_hybrid_context * mctx) :
|
||||
inp_attn(std::move(inp_attn)),
|
||||
inp_rs(std::move(inp_rs)),
|
||||
cparams(cparams),
|
||||
mctx(mctx) { }
|
||||
virtual ~llm_graph_input_mem_hybrid_k() = default;
|
||||
|
||||
void set_input(const llama_ubatch * ubatch) override;
|
||||
|
||||
bool can_reuse(const llm_graph_params & params) override;
|
||||
|
||||
std::unique_ptr<llm_graph_input_attn_k> inp_attn;
|
||||
std::unique_ptr<llm_graph_input_rs> inp_rs;
|
||||
|
||||
llm_graph_input_attn_k * get_attn() const { return inp_attn.get(); }
|
||||
llm_graph_input_rs * get_recr() const { return inp_rs.get(); }
|
||||
|
||||
const llama_cparams cparams;
|
||||
|
||||
const llama_memory_hybrid_context * mctx;
|
||||
};
|
||||
|
||||
class llm_graph_input_mem_hybrid_iswa : public llm_graph_input_i {
|
||||
public:
|
||||
llm_graph_input_mem_hybrid_iswa(
|
||||
@@ -960,6 +988,7 @@ struct llm_graph_context {
|
||||
//
|
||||
|
||||
llm_graph_input_mem_hybrid * build_inp_mem_hybrid() const;
|
||||
llm_graph_input_mem_hybrid_k * build_inp_mem_hybrid_k() const;
|
||||
|
||||
llm_graph_input_mem_hybrid_iswa * build_inp_mem_hybrid_iswa() const;
|
||||
|
||||
|
||||
@@ -139,6 +139,13 @@ uint32_t llama_hparams::n_embd_r() const {
|
||||
return n_embd * (n_shortconv_l_cache - 1);
|
||||
}
|
||||
|
||||
if (n_embd_head_kda != 0) {
|
||||
// for Kimi KDA layers
|
||||
// Conv state for Q, K, V: 3 * (d_conv - 1) * n_head * head_dim
|
||||
const uint32_t d_inner = n_head() * n_embd_head_kda; // 32 * 128 = 4096
|
||||
return 3 * (ssm_d_conv > 0 ? ssm_d_conv - 1 : 3) * d_inner;
|
||||
}
|
||||
|
||||
// TODO: maybe support other convolution strides than 1
|
||||
// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
|
||||
// Corresponds to Mamba's conv_states size
|
||||
@@ -151,6 +158,13 @@ uint32_t llama_hparams::n_embd_s() const {
|
||||
return n_embd * wkv_head_size;
|
||||
}
|
||||
|
||||
if (n_embd_head_kda != 0) {
|
||||
// for Kimi KDA layers
|
||||
// Full recurrent state: head_dim * head_dim * n_head
|
||||
// h tensor shape for delta attention: [head_dim, head_dim, n_head]
|
||||
return n_embd_head_kda * n_embd_head_kda * n_head(); // 128 * 128 * 32 = 524288
|
||||
}
|
||||
|
||||
// corresponds to Mamba's ssm_states size
|
||||
return ssm_d_state * ssm_d_inner;
|
||||
}
|
||||
|
||||
@@ -137,6 +137,9 @@ struct llama_hparams {
|
||||
uint32_t ssm_dt_rank = 0;
|
||||
uint32_t ssm_n_group = 0;
|
||||
|
||||
// for Kimi Linear KDA
|
||||
uint32_t n_embd_head_kda = 0;
|
||||
|
||||
// for hybrid state space models
|
||||
std::array<bool, LLAMA_MAX_LAYERS> recurrent_layer_arr;
|
||||
|
||||
@@ -203,6 +206,11 @@ struct llama_hparams {
|
||||
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
|
||||
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
|
||||
|
||||
|
||||
// Step35: optional per-layer clamps for (Swi)GLU
|
||||
std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_exp; // clamping for expert FFN
|
||||
std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_shexp; // shared expert
|
||||
|
||||
// this value n_pattern means that every nth layer is dense (i.e. non-SWA)
|
||||
// dense_first means whether the pattern is start with a dense layer
|
||||
// note that if n_pattern == 0, all layers are SWA
|
||||
|
||||
@@ -218,7 +218,9 @@ llama_memory_context_ptr llama_kv_cache_iswa::init_update(llama_context * lctx,
|
||||
}
|
||||
|
||||
bool llama_kv_cache_iswa::get_can_shift() const {
|
||||
return kv_base->get_size() == kv_swa->get_size();
|
||||
return kv_base->get_can_shift() &&
|
||||
kv_swa->get_can_shift() &&
|
||||
kv_base->get_size() == kv_swa->get_size();
|
||||
}
|
||||
|
||||
void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
|
||||
|
||||
@@ -974,6 +974,10 @@ void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch &
|
||||
}
|
||||
|
||||
bool llama_kv_cache::get_can_shift() const {
|
||||
// Step35 uses per-layer RoPE dims; K-shift assumes a single global n_rot.
|
||||
if (model.arch == LLM_ARCH_STEP35) {
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
@@ -125,10 +125,12 @@ const char * llm_type_name(llm_type type) {
|
||||
case LLM_TYPE_21B_A3B: return "21B.A3B";
|
||||
case LLM_TYPE_30B_A3B: return "30B.A3B";
|
||||
case LLM_TYPE_31B_A3_5B: return "31B.A3.5B";
|
||||
case LLM_TYPE_48B_A3B: return "48B.A3B";
|
||||
case LLM_TYPE_80B_A3B: return "80B.A3B";
|
||||
case LLM_TYPE_100B_A6B: return "100B.A6B";
|
||||
case LLM_TYPE_102B_A12B: return "102B.A12B";
|
||||
case LLM_TYPE_106B_A12B: return "106B.A12B";
|
||||
case LLM_TYPE_196B_A11B: return "196B.A11B";
|
||||
case LLM_TYPE_230B_A10B: return "230B.A10B";
|
||||
case LLM_TYPE_235B_A22B: return "235B.A22B";
|
||||
case LLM_TYPE_300B_A47B: return "300B.A47B";
|
||||
@@ -559,6 +561,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
||||
std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
|
||||
std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
|
||||
std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
|
||||
std::fill(hparams.swiglu_clamp_exp.begin(), hparams.swiglu_clamp_exp.end(), 0.0f);
|
||||
std::fill(hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.end(), 0.0f);
|
||||
|
||||
ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false);
|
||||
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
|
||||
@@ -2450,6 +2454,66 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl);
|
||||
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
|
||||
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
|
||||
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot);
|
||||
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
||||
ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda);
|
||||
|
||||
// MLA qk_rope_head_dim (for reference)
|
||||
// qk_rope_head_dim = 64, qk_nope_head_dim = 128, qk_head_dim = 192
|
||||
|
||||
// Mark KDA layers as recurrent using n_head_kv pattern (like Jamba)
|
||||
// Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention)
|
||||
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
||||
hparams.recurrent_layer_arr[i] = hparams.n_head_kv(i) == 0; // KDA layers are recurrent
|
||||
}
|
||||
|
||||
// MoE parameters - Kimi uses moe_intermediate_size = 1024
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
||||
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
||||
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 27: type = LLM_TYPE_48B_A3B; break; // Kimi-Linear-48B-A3B
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_STEP35:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
|
||||
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
|
||||
|
||||
// MoE + SWA parameters
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
||||
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
||||
|
||||
// Step35 uses sigmoid gating by default (if not set in GGUF)
|
||||
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
|
||||
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
|
||||
}
|
||||
|
||||
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
||||
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
|
||||
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
||||
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer, false);
|
||||
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 45: type = LLM_TYPE_196B_A11B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
default: throw std::runtime_error("unsupported model architecture");
|
||||
}
|
||||
|
||||
@@ -6752,6 +6816,141 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
// Check for KDA specific tensors to determine layer type or if it's a mixed model
|
||||
// Assuming KDA layer if KDA tensors are present
|
||||
|
||||
// KDA uses head_dim = 128 (from linear_attn_config.head_dim)
|
||||
const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda;
|
||||
const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda;
|
||||
const int64_t ssm_d_conv = hparams.ssm_d_conv;
|
||||
|
||||
// Try loading KDA specific tensors (using SSM_ prefix)
|
||||
// Conv1d weights: try 4D first, then 3D (quantization may remove trailing 1)
|
||||
// 4D: [d_conv, 1, d_inner, 1], 3D: [d_conv, 1, d_inner]
|
||||
layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
|
||||
if (!layer.ssm_q_conv) {
|
||||
layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
|
||||
if (layer.ssm_q_conv) {
|
||||
// KDA Layer - Conv1d weights may be 3D or 4D
|
||||
layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
|
||||
if (!layer.ssm_k_conv) {
|
||||
layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, n_embd_head_k_kda * n_head}, 0);
|
||||
}
|
||||
layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head, 1}, TENSOR_NOT_REQUIRED);
|
||||
if (!layer.ssm_v_conv) {
|
||||
layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, n_embd_head_v_kda * n_head}, 0);
|
||||
}
|
||||
|
||||
// q, k, v projections
|
||||
// Python: q_proj, k_proj, v_proj
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k_kda * n_head}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_head_k_kda * n_head}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_head_v_kda * n_head}, 0);
|
||||
|
||||
// KDA specific projections
|
||||
// f_a_proj, f_b_proj
|
||||
layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); // head_dim
|
||||
layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0); // projection_size
|
||||
|
||||
// b_proj (beta mixing coefficient)
|
||||
layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0);
|
||||
|
||||
// A_log - Shape in GGUF: [1, num_heads, 1, 1] (4D) or [1, num_heads] (2D after quantization) Note: -exp(A_log) is applied in convert_hf_to_gguf.py
|
||||
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head, 1, 1}, TENSOR_NOT_REQUIRED);
|
||||
if (!layer.ssm_a) {
|
||||
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
|
||||
}
|
||||
|
||||
// dt_bias - shape [n_embd_head_k_kda * n_head] = [4096]
|
||||
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_embd_head_k_kda * n_head}, 0);
|
||||
|
||||
// g_a_proj, g_b_proj (output gate)
|
||||
layer.ssm_g_a = create_tensor(tn(LLM_TENSOR_SSM_G_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0);
|
||||
layer.ssm_g_b = create_tensor(tn(LLM_TENSOR_SSM_G_B, "weight", i), {n_embd_head_k_kda, n_embd_head_k_kda * n_head}, 0);
|
||||
|
||||
// o_norm (reusing SSM_NORM)
|
||||
layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); // FusedRMSNormGated
|
||||
|
||||
// o_proj
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0);
|
||||
|
||||
} else {
|
||||
// MLA Layer - use MLA-specific head dimensions
|
||||
const int64_t q_lora_rank = hparams.n_lora_q;
|
||||
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
||||
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
|
||||
|
||||
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, TENSOR_NOT_REQUIRED);
|
||||
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
|
||||
|
||||
if (layer.attn_q_a_norm) {
|
||||
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
|
||||
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
|
||||
} else {
|
||||
// Kimi MLA without Q compression: wq = [n_embd, n_head * n_embd_head_k_mla]
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
|
||||
}
|
||||
|
||||
// Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA)
|
||||
// Note: hparams.n_rot may be 72 (from conversion) but actual is 64
|
||||
const int64_t qk_rope_head_dim = hparams.n_rot; // From config: qk_rope_head_dim
|
||||
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0);
|
||||
// Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled)
|
||||
layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED);
|
||||
if (!layer.wkv_b) { // MLA KV cache enabled
|
||||
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0);
|
||||
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
|
||||
}
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
|
||||
}
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
// MoE intermediate size (different from dense FFN)
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
|
||||
// Kimi uses n_layer_dense_lead to determine which layers use dense FFN vs MoE
|
||||
// first_k_dense_replace = 1 means layer 0 uses dense FFN, layers 1+ use MoE
|
||||
if (i < (int) hparams.n_layer_dense_lead) {
|
||||
// Dense FFN layer - use normal n_ff
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
} else {
|
||||
// MoE layer - use n_ff_exp (1024) instead of n_ff (9216)
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
||||
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
||||
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
|
||||
|
||||
// Shared experts use moe_intermediate_size * num_shared_experts
|
||||
// Kimi: shared_expert_intermediate_size = 1024 * 1 = 1024
|
||||
// Tensors are 2D: [n_embd, n_ff_shexp] or [n_ff_shexp, n_embd]
|
||||
const int64_t n_ff_shexp_actual = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1);
|
||||
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp_actual, n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp_actual}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_COGVLM:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
@@ -6940,6 +7139,72 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_STEP35:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
|
||||
// ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
|
||||
uint32_t n_rot_max = 0;
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
n_rot_max = std::max(n_rot_max, hparams.n_rot);
|
||||
}
|
||||
if (n_rot_max == 0) {
|
||||
n_rot_max = n_rot;
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
const uint32_t n_head_l = hparams.n_head(i);
|
||||
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
|
||||
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
// optional rope factors (llama3) / longrope tensors
|
||||
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
|
||||
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
||||
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
||||
} else {
|
||||
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
||||
}
|
||||
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head_l}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
|
||||
|
||||
// head-wise attention gate (Step35 self_attn.g_proj)
|
||||
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
// dense MLP (leading dense blocks)
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
// MoE routed experts + selection bias (router_bias)
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
// shared expert MLP
|
||||
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_MAINCODER:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
@@ -8086,6 +8351,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
||||
{
|
||||
llm = std::make_unique<llm_build_mimo2_iswa>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
{
|
||||
llm = std::make_unique<llm_build_kimi_linear>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_STEP35:
|
||||
{
|
||||
llm = std::make_unique<llm_build_step35_iswa>(*this, params);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
@@ -8235,6 +8508,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_WAVTOKENIZER_DEC:
|
||||
case LLM_ARCH_NEMOTRON_H:
|
||||
case LLM_ARCH_NEMOTRON_H_MOE:
|
||||
case LLM_ARCH_KIMI_LINEAR:
|
||||
return LLAMA_ROPE_TYPE_NONE;
|
||||
|
||||
// use what we call a normal RoPE, operating on pairs of consecutive head values
|
||||
@@ -8330,6 +8604,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_AFMOE:
|
||||
case LLM_ARCH_QWEN3NEXT:
|
||||
case LLM_ARCH_MIMO2:
|
||||
case LLM_ARCH_STEP35:
|
||||
return LLAMA_ROPE_TYPE_NEOX;
|
||||
|
||||
case LLM_ARCH_QWEN2VL:
|
||||
|
||||
@@ -118,10 +118,12 @@ enum llm_type {
|
||||
LLM_TYPE_21B_A3B, // Ernie MoE small
|
||||
LLM_TYPE_30B_A3B,
|
||||
LLM_TYPE_31B_A3_5B,
|
||||
LLM_TYPE_48B_A3B, // Kimi Linear
|
||||
LLM_TYPE_80B_A3B, // Qwen3 Next
|
||||
LLM_TYPE_100B_A6B,
|
||||
LLM_TYPE_102B_A12B, // Solar-Open
|
||||
LLM_TYPE_106B_A12B, // GLM-4.5-Air
|
||||
LLM_TYPE_196B_A11B, // Step3.5-Flash
|
||||
LLM_TYPE_230B_A10B, // Minimax M2
|
||||
LLM_TYPE_235B_A22B,
|
||||
LLM_TYPE_300B_A47B, // Ernie MoE big
|
||||
@@ -411,6 +413,18 @@ struct llama_layer {
|
||||
struct ggml_tensor * ffn_act_beta = nullptr;
|
||||
struct ggml_tensor * ffn_act_eps = nullptr;
|
||||
|
||||
// Kimi Linear KDA (using ssm_ prefix for consistency)
|
||||
// Note: ssm_dt_b already exists above (mamba bias), reused for Kimi dt_bias
|
||||
struct ggml_tensor * ssm_q_conv = nullptr;
|
||||
struct ggml_tensor * ssm_k_conv = nullptr;
|
||||
struct ggml_tensor * ssm_v_conv = nullptr;
|
||||
struct ggml_tensor * ssm_f_a = nullptr;
|
||||
struct ggml_tensor * ssm_f_b = nullptr;
|
||||
struct ggml_tensor * ssm_beta = nullptr;
|
||||
struct ggml_tensor * ssm_g_a = nullptr;
|
||||
struct ggml_tensor * ssm_g_b = nullptr;
|
||||
struct ggml_tensor * ssm_o_norm = nullptr;
|
||||
|
||||
struct llama_layer_posnet posnet;
|
||||
|
||||
struct llama_layer_convnext convnext;
|
||||
|
||||
+2
-2
@@ -787,9 +787,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
|
||||
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight");
|
||||
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
|
||||
|
||||
// do not quantize Mamba's small yet 2D weights
|
||||
// do not quantize Mamba /Kimi's small conv1d weights
|
||||
// NOTE: can't use LLM_TN here because the layer number is not known
|
||||
quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
|
||||
quantize &= name.find("ssm_conv1d") == std::string::npos;
|
||||
quantize &= name.find("shortconv.conv.weight") == std::string::npos;
|
||||
|
||||
// do not quantize RWKV's small yet 2D weights
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
#include "llama-sampling.h"
|
||||
#include "llama-sampler.h"
|
||||
|
||||
#include "llama-impl.h"
|
||||
#include "llama-vocab.h"
|
||||
@@ -1,7 +1,5 @@
|
||||
#pragma once
|
||||
|
||||
// TODO: rename llama-sampling.h/.cpp to llama-sampler.h/.cpp ?
|
||||
|
||||
#include "llama.h"
|
||||
|
||||
#include <vector>
|
||||
+28
-17
@@ -1752,26 +1752,33 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|
||||
// read bpe merges and populate bpe ranks
|
||||
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
|
||||
// Kimi-K2 uses custom tokenization without traditional BPE merges
|
||||
const bool is_kimi_k2 = (tokenizer_pre == "kimi-k2");
|
||||
|
||||
if (merges_keyidx == -1) {
|
||||
throw std::runtime_error("cannot find tokenizer merges in model file\n");
|
||||
}
|
||||
|
||||
const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
|
||||
for (int i = 0; i < n_merges; i++) {
|
||||
const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
|
||||
//GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
|
||||
|
||||
std::string first;
|
||||
std::string second;
|
||||
|
||||
const size_t pos = word.find(' ', 1);
|
||||
|
||||
if (pos != std::string::npos) {
|
||||
first = word.substr(0, pos);
|
||||
second = word.substr(pos + 1);
|
||||
if (!is_kimi_k2) {
|
||||
throw std::runtime_error("cannot find tokenizer merges in model file\n");
|
||||
}
|
||||
// Kimi-K2 doesn't need merges, skip
|
||||
LLAMA_LOG_INFO("%s: Kimi-K2 tokenizer detected, skipping BPE merges\n", __func__);
|
||||
} else {
|
||||
const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
|
||||
for (int i = 0; i < n_merges; i++) {
|
||||
const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
|
||||
//GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
|
||||
|
||||
bpe_ranks.emplace(std::make_pair(first, second), i);
|
||||
std::string first;
|
||||
std::string second;
|
||||
|
||||
const size_t pos = word.find(' ', 1);
|
||||
|
||||
if (pos != std::string::npos) {
|
||||
first = word.substr(0, pos);
|
||||
second = word.substr(pos + 1);
|
||||
}
|
||||
|
||||
bpe_ranks.emplace(std::make_pair(first, second), i);
|
||||
}
|
||||
}
|
||||
|
||||
// default special tokens
|
||||
@@ -2226,6 +2233,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|| t.first == "<|end_of_text|>" // granite
|
||||
|| t.first == "<EOT>"
|
||||
|| t.first == "_<EOT>"
|
||||
|| t.first == "[EOT]" // Kimi-K2
|
||||
|| t.first == "<｜end▁of▁sentence｜>" // DeepSeek
|
||||
|| t.first == "<end_of_utterance>" // smoldocling
|
||||
) {
|
||||
@@ -2322,6 +2330,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|| t.first == "<fim-pad>"
|
||||
|| t.first == "<fim_pad>" // Granite
|
||||
|| t.first == "<PAD>"
|
||||
|| t.first == "[PAD]" // Kimi-K2
|
||||
) {
|
||||
special_fim_pad_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
@@ -2424,6 +2433,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|| t.first == "<|eom_id|>"
|
||||
|| t.first == "<EOT>"
|
||||
|| t.first == "_<EOT>"
|
||||
|| t.first == "[EOT]" // Kimi-K2
|
||||
|| t.first == "[EOS]" // Kimi-K2
|
||||
|| t.first == "<|end_of_text|>"
|
||||
|| t.first == "<end_of_utterance>" // smoldocling
|
||||
) {
|
||||
|
||||
@@ -0,0 +1,772 @@
|
||||
#include "models.h"
|
||||
#include "ggml.h"
|
||||
|
||||
#define CHUNK_SIZE 64
|
||||
|
||||
// Causal Conv1d function for Q,K,V
|
||||
// When qkv is 0, it is Q, 1 is K, 2 is V
|
||||
static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all, ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w, int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) {
|
||||
const int64_t d_inner = head_dim * n_head;
|
||||
const int64_t conv_state_size = (d_conv - 1) * d_inner;
|
||||
const int64_t n_embd_r_total = 3 * conv_state_size; // Q + K + V
|
||||
|
||||
// conv_state_all is [n_embd_r_total, n_seqs], split into Q, K, V
|
||||
// Each conv state is [(d_conv-1) * d_inner] per sequence, need to reshape to [d_conv-1, d_inner, n_seqs]
|
||||
// Memory layout: for each seq, Q state is first conv_state_size elements, then K, then V
|
||||
// conv_state_all has stride: nb[0] = element_size, nb[1] = n_embd_r_total * element_size
|
||||
// View Q conv state: offset 0, size conv_state_size per seq
|
||||
// conv_state_all is [n_embd_r_total, n_seqs] with memory layout:
|
||||
// state[i + seq * n_embd_r_total] where i = conv_step + channel * (d_conv-1) + {0, conv_state_size, 2*conv_state_size} for Q/K/V
|
||||
// We want [d_conv-1, d_inner, n_seqs] view:
|
||||
// nb1 = (d_conv-1) * element_size (stride between channels)
|
||||
// nb2 = n_embd_r_total * element_size (stride between seqs)
|
||||
ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs,
|
||||
(d_conv - 1) * ggml_element_size(conv_state_all), // nb1: stride between channels
|
||||
n_embd_r_total * ggml_element_size(conv_state_all), // nb2: stride between seqs
|
||||
qkv * conv_state_size * ggml_element_size(conv_state_all));
|
||||
|
||||
// Causal Conv1d function for Q,K,V
|
||||
// When qkv is 0, it is Q, 1 is K, 2 is V
|
||||
// Step 1: Q, K, V projections -> [d_inner, n_tokens]
|
||||
ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x);
|
||||
|
||||
// Reshape input: {d_inner, n_tokens} -> {d_inner, n_seq_tokens, n_seqs}
|
||||
ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs);
|
||||
|
||||
// Concat Q conv state and current input: {d_conv-1 + n_seq_tokens, d_inner, n_seqs}
|
||||
ggml_tensor * conv_x = ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0);
|
||||
|
||||
// Save last (d_conv-1) columns back to Q conv state
|
||||
ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
|
||||
conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
|
||||
ggml_build_forward_expand(gf,
|
||||
ggml_cpy(ctx0, last_conv_x,
|
||||
ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
|
||||
(kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
|
||||
// Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner]
|
||||
// GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv]
|
||||
// vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step]
|
||||
// ggml_ssm_conv computes: c[conv_step + channel * d_conv]
|
||||
// GGUF layout: [d_conv, 1, d_inner] or [d_conv, 1, d_inner, 1] -> reshape to [d_conv, d_inner]
|
||||
// Reshape conv weight from [d_conv, 1, d_inner, 1] to [d_conv, d_inner] for ggml_ssm_conv
|
||||
ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner);
|
||||
|
||||
// Apply conv1d
|
||||
// ggml_ssm_conv output: {d_inner, n_seq_tokens, n_seqs}
|
||||
ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight);
|
||||
// Reshape to 2D for bias add: {d_inner, n_tokens}
|
||||
Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens);
|
||||
Xcur = ggml_silu(ctx0, Xcur);
|
||||
|
||||
return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs);
|
||||
}
|
||||
|
||||
llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context_mamba(params), model(model) {
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
cb(inpL, "model.embed_tokens", -1);
|
||||
|
||||
// Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM)
|
||||
// So we don't need inp_pos
|
||||
|
||||
auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr;
|
||||
auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr;
|
||||
auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr();
|
||||
auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr;
|
||||
auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr;
|
||||
|
||||
// Output ids for selecting which tokens to output
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
ggml_tensor * chunked_causal_mask =
|
||||
ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
|
||||
GGML_TRI_TYPE_LOWER);
|
||||
|
||||
ggml_tensor * chunked_identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
|
||||
ggml_tensor * chunked_diag_mask = ggml_add(ctx0, chunked_causal_mask, chunked_identity);
|
||||
|
||||
ggml_build_forward_expand(gf, chunked_causal_mask);
|
||||
ggml_build_forward_expand(gf, chunked_identity);
|
||||
ggml_build_forward_expand(gf, chunked_diag_mask);
|
||||
|
||||
// Kimi dimension constants
|
||||
const int64_t n_head = hparams.n_head();
|
||||
const int64_t head_dim = hparams.n_embd_head_kda;
|
||||
const int64_t d_conv = hparams.ssm_d_conv;
|
||||
const int64_t d_inner = n_head * head_dim; // 32 * 128 = 4096
|
||||
const int64_t n_seqs = ubatch.n_seqs;
|
||||
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
|
||||
|
||||
// Verify batch consistency for recurrent layers
|
||||
GGML_ASSERT(n_seqs != 0);
|
||||
GGML_ASSERT(ubatch.equal_seqs());
|
||||
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
|
||||
|
||||
// MLA params
|
||||
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
|
||||
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();
|
||||
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
||||
// qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot
|
||||
// Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim]
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot; // config.qk_rope_head_dim
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; // 192 - 64 = 128
|
||||
// Attention scale for MLA
|
||||
const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla);
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
const auto & layer = model.layers[il];
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
// Attention Norm
|
||||
cur = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// Check layer type by checking which tensors exist
|
||||
// KDA layers have ssm_a_log tensor, MLA layers have wkv_a_mqa tensor
|
||||
bool is_kda = (layer.ssm_a != nullptr);
|
||||
bool is_mla = (layer.wkv_a_mqa != nullptr);
|
||||
|
||||
if (is_kda) {
|
||||
// === KDA Layer (Kimi Delta Attention) with Recurrent State ===
|
||||
// Reference: vLLM kda.py
|
||||
const auto * mctx_cur = inp_rs->mctx;
|
||||
const auto kv_head = mctx_cur->get_head();
|
||||
|
||||
// Get conv states from r_l tensor (Q, K, V each have separate state)
|
||||
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
|
||||
cb(conv_states_all, "conv_states_all", il);
|
||||
ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
|
||||
ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
|
||||
ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
|
||||
ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head);
|
||||
|
||||
// g1 = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias)
|
||||
ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur);
|
||||
ggml_tensor * g1 = ggml_mul_mat(ctx0, layer.ssm_f_b, f_a);
|
||||
cb(g1, "g1 f_b(f_a(cur))", il);
|
||||
g1 = ggml_add(ctx0, g1, layer.ssm_dt_b);
|
||||
g1 = ggml_softplus(ctx0, g1);
|
||||
g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens);
|
||||
|
||||
// A_log shape is [1, n_head] or [1, n_head, 1, 1], need to broadcast to [head_dim, n_head, n_tokens]. No need to -exp(a_log) because it was done in convert_hf_to_gguf.py
|
||||
// Reshape to [1, n_head, 1] for broadcasting with g1 [head_dim, n_head, n_tokens]
|
||||
ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head, 1);
|
||||
g1 = ggml_mul(ctx0, g1, A);
|
||||
cb(g1, "kda_g1", il);
|
||||
|
||||
// Compute beta (mixing coefficient)
|
||||
ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur);
|
||||
beta = ggml_reshape_4d(ctx0, beta, n_head, 1, n_seq_tokens, n_seqs);
|
||||
cb(beta, "kda_beta", il);
|
||||
|
||||
// Reshape for KDA recurrence
|
||||
// {n_embd, n_tokens} -> {n_embd, n_seq_tokens, n_seqs}
|
||||
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
|
||||
|
||||
g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs);
|
||||
|
||||
// Get SSM state and compute KDA recurrence using ggml_kda_scan
|
||||
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
|
||||
ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs);
|
||||
state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs);
|
||||
// Choose between build_kda_chunking and build_kda_recurrent based on n_tokens
|
||||
std::pair<ggml_tensor *, ggml_tensor *> attn_out = n_seq_tokens == 1 ?
|
||||
build_kda_autoregressive(Qcur, Kcur, Vcur, g1, beta, state, il) :
|
||||
build_kda_chunking(Qcur, Kcur, Vcur, g1, beta, state, chunked_causal_mask, chunked_identity, chunked_diag_mask, il);
|
||||
|
||||
ggml_tensor * output = attn_out.first;
|
||||
ggml_tensor * new_state = attn_out.second;
|
||||
cb(output, "attn_output", il);
|
||||
cb(new_state, "new_state", il);
|
||||
|
||||
// Update the recurrent states
|
||||
ggml_build_forward_expand(gf,
|
||||
ggml_cpy(ctx0, new_state,
|
||||
ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
|
||||
kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
|
||||
|
||||
// Output gating g2 = g_b(g_a(x))
|
||||
ggml_tensor * cur_2d = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
|
||||
ggml_tensor * g_a = ggml_mul_mat(ctx0, layer.ssm_g_a, cur_2d);
|
||||
ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.ssm_g_b, g_a);
|
||||
cb(g2, "g2 g_b(g_a(cur_2d))", il);
|
||||
g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_seq_tokens * n_seqs);
|
||||
|
||||
// Apply o_norm with sigmoid gating
|
||||
// Note: Kimi model uses sigmoid gating, not SiLU (despite FusedRMSNormGated default being swish)
|
||||
// Formula: output = RMSNorm(x) * sigmoid(g)
|
||||
ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, output, head_dim, n_head, n_seq_tokens * n_seqs);
|
||||
ggml_tensor * normed = build_norm(attn_out_final, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(normed, "kda_normed", il);
|
||||
ggml_tensor * gate = ggml_sigmoid(ctx0, g2);
|
||||
ggml_tensor * gated = ggml_mul(ctx0, normed, gate);
|
||||
|
||||
// Output projection
|
||||
gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens);
|
||||
cur = ggml_mul_mat(ctx0, layer.wo, gated);
|
||||
cb(cur, "kda_out", il);
|
||||
|
||||
} else if (is_mla) {
|
||||
// === MLA Layer (Multi-head Latent Attention) without KV Cache ===
|
||||
// Reference: vLLM mla.py
|
||||
// Step 1: Q projection and reshape
|
||||
// vLLM Kimi: q = q_proj(hidden_states), then view as [n_tokens, n_head, qk_head_dim]
|
||||
// Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM)
|
||||
ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.wq, cur);
|
||||
|
||||
// Step 2: KV compression
|
||||
// kv_cmpr_pe = kv_a_proj_with_mqa(hidden_states) -> [kv_lora_rank + qk_rope_head_dim, n_tokens]
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
|
||||
|
||||
// Split: kv_cmpr = kv_lora[:kv_lora_rank], k_pe = kv_lora[kv_lora_rank:]
|
||||
ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
||||
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
||||
// Note: Kimi MLA does NOT apply RoPE (rotary_emb=None in vLLM)
|
||||
// k_pe is used directly without RoPE
|
||||
// Normalize kv_c
|
||||
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
|
||||
if (layer.wk_b && layer.wv_b) { // MLA KV cache enabled
|
||||
// extract q_nope
|
||||
ggml_tensor * q_nope =
|
||||
ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla),
|
||||
ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0);
|
||||
cb(q_nope, "q_nope", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, n_head, n_tokens}
|
||||
ggml_tensor * q_pe = ggml_view_3d(
|
||||
ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla),
|
||||
ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, ggml_row_size(Qcur->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "q_pe", il);
|
||||
|
||||
// {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "q_nope_perm", il);
|
||||
|
||||
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed", il);
|
||||
|
||||
// {kv_lora_rank, n_head, n_tokens}
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
|
||||
// note: rope must go first for in-place context shifting in build_rope_shift()
|
||||
Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
||||
cb(kv_cmpr, "kv_cmpr_reshape", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
// {kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
cur = build_attn(inp_attn_k, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il);
|
||||
cb(cur, "mla_out", il);
|
||||
} else { // MLA KV cache disabled. Fall back to MHA KV cache.
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens);
|
||||
cb(Qcur, "mla_Q", il);
|
||||
// KV decompression: kv = kv_b_proj(kv_c_normed)
|
||||
ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr);
|
||||
const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla;
|
||||
|
||||
// Split kv into k_nope and v
|
||||
ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
|
||||
ggml_row_size(kv->type, kv_per_head),
|
||||
ggml_row_size(kv->type, kv_per_head * n_head), 0);
|
||||
ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens,
|
||||
ggml_row_size(kv->type, kv_per_head),
|
||||
ggml_row_size(kv->type, kv_per_head * n_head),
|
||||
ggml_row_size(kv->type, n_embd_head_qk_nope));
|
||||
Vcur = ggml_cont(ctx0, Vcur);
|
||||
cb(Vcur, "mla_V", il);
|
||||
|
||||
// Concatenate k_nope + k_pe (broadcast k_pe to all heads)
|
||||
// K = [k_nope, k_pe] where k_nope is [qk_nope_head_dim, n_head, n_tokens]
|
||||
// and k_pe is [qk_rope_head_dim, 1, n_tokens] broadcast to all heads
|
||||
// Need to broadcast k_pe from [qk_rope, 1, n_tokens] to [qk_rope, n_head, n_tokens]
|
||||
ggml_tensor * k_pe_target = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens);
|
||||
ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target);
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, k_pe_repeated, k_nope, 0);
|
||||
cb(Kcur, "mla_K", il);
|
||||
|
||||
// Direct softmax attention (with MHA KV cache)
|
||||
// Use build_attn with inp_attn for proper mask handling
|
||||
cur = build_attn(inp_attn_kv, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il);
|
||||
cb(cur, "mla_out", il);
|
||||
}
|
||||
} else {
|
||||
// Unknown layer type - this should not happen
|
||||
GGML_ABORT("Kimi layer is neither KDA nor MLA - missing required tensors");
|
||||
}
|
||||
|
||||
// On last layer, select only the output tokens
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
// Residual
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// FFN Norm
|
||||
cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
if ((uint32_t) il < hparams.n_layer_dense_lead) {
|
||||
// Dense FFN layer
|
||||
cur = build_ffn(cur,
|
||||
layer.ffn_up, NULL, NULL,
|
||||
layer.ffn_gate, NULL, NULL,
|
||||
layer.ffn_down, NULL, NULL,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
// MoE layer
|
||||
// Kimi uses moe_renormalize=True and routed_scaling_factor (stored as expert_weights_scale) = 2.446
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
layer.ffn_gate_inp,
|
||||
layer.ffn_up_exps,
|
||||
layer.ffn_gate_exps,
|
||||
layer.ffn_down_exps,
|
||||
layer.ffn_exp_probs_b,
|
||||
hparams.n_expert,
|
||||
hparams.n_expert_used,
|
||||
LLM_FFN_SILU, true,
|
||||
true, hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// Shared expert
|
||||
{
|
||||
ggml_tensor * ffn_shexp = build_ffn(cur,
|
||||
layer.ffn_up_shexp, NULL, NULL,
|
||||
layer.ffn_gate_shexp, NULL, NULL,
|
||||
layer.ffn_down_shexp, NULL, NULL,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(ffn_shexp, "ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
}
|
||||
// Residual
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
inpL = cur;
|
||||
}
|
||||
cur = inpL;
|
||||
|
||||
// Final Norm
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// Output
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
/*
|
||||
This is a ggml implementation of the naive_chunk_kda function of
|
||||
https://github.com/fla-org/flash-linear-attention/blob/main/fla/ops/kda/naive.py
|
||||
*/
|
||||
std::pair<ggml_tensor *, ggml_tensor *> llm_build_kimi_linear::build_kda_chunking(
|
||||
ggml_tensor * q,
|
||||
ggml_tensor * k,
|
||||
ggml_tensor * v,
|
||||
ggml_tensor * gk,
|
||||
ggml_tensor * beta,
|
||||
ggml_tensor * state,
|
||||
ggml_tensor * causal_mask,
|
||||
ggml_tensor * identity,
|
||||
ggml_tensor * diag_mask,
|
||||
int il) {
|
||||
GGML_ASSERT(ggml_is_contiguous(state));
|
||||
|
||||
const int64_t S_k = q->ne[0];
|
||||
const int64_t H_k = q->ne[1];
|
||||
const int64_t n_tokens = q->ne[2];
|
||||
const int64_t n_seqs = q->ne[3];
|
||||
|
||||
const int64_t S_v = v->ne[0];
|
||||
const int64_t H_v = v->ne[1];
|
||||
|
||||
GGML_ASSERT(v->ne[2] == n_tokens);
|
||||
GGML_ASSERT(k->ne[2] == n_tokens);
|
||||
GGML_ASSERT(gk->ne[0] == S_v && gk->ne[1] == H_v && gk->ne[2] == n_tokens && gk->ne[3] == n_seqs);
|
||||
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
|
||||
GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v && state->ne[2] == H_v && state->ne[3] == n_seqs);
|
||||
|
||||
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
|
||||
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
|
||||
|
||||
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
|
||||
|
||||
// TODO: can this ever be false?
|
||||
const bool use_qk_l2norm = true;
|
||||
|
||||
if (use_qk_l2norm) {
|
||||
const float eps_norm = hparams.f_norm_rms_eps;
|
||||
|
||||
q = ggml_l2_norm(ctx0, q, eps_norm);
|
||||
k = ggml_l2_norm(ctx0, k, eps_norm);
|
||||
}
|
||||
|
||||
const float scale = 1.0f / sqrtf(S_v);
|
||||
|
||||
beta = ggml_sigmoid(ctx0, beta);
|
||||
|
||||
cb(q, "q_in", il);
|
||||
cb(k, "k_in", il);
|
||||
cb(v, "v_in", il);
|
||||
cb(beta, "beta_in", il);
|
||||
cb(gk, "gk_in", il);
|
||||
|
||||
q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
|
||||
k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_k, n_tokens, H_k, n_seqs);
|
||||
v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
|
||||
gk = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
|
||||
|
||||
beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
|
||||
state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
|
||||
|
||||
cb(q, "q_perm", il);
|
||||
cb(k, "k_perm", il);
|
||||
cb(v, "v_perm", il);
|
||||
cb(beta, "beta_perm", il);
|
||||
cb(gk, "gk_perm", il);
|
||||
cb(state, "state_in", il);
|
||||
|
||||
GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
|
||||
GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
|
||||
GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
|
||||
GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
|
||||
|
||||
// Do padding
|
||||
const int64_t chunk_size = CHUNK_SIZE;
|
||||
|
||||
const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
|
||||
const int64_t n_chunks = (n_tokens + pad) / chunk_size;
|
||||
|
||||
q = ggml_pad(ctx0, q, 0, pad, 0, 0);
|
||||
k = ggml_pad(ctx0, k, 0, pad, 0, 0);
|
||||
v = ggml_pad(ctx0, v, 0, pad, 0, 0);
|
||||
gk = ggml_pad(ctx0, gk, 0, pad, 0, 0);
|
||||
beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
|
||||
|
||||
cb(q, "q_pad", il);
|
||||
cb(k, "k_pad", il);
|
||||
cb(v, "v_pad", il);
|
||||
cb(beta, "beta_pad", il);
|
||||
cb(gk, "gk_pad", il);
|
||||
|
||||
ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
|
||||
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
|
||||
|
||||
cb(v_beta, "v_beta", il);
|
||||
cb(k_beta, "k_beta", il);
|
||||
|
||||
const int64_t HB = H_k * n_seqs;
|
||||
|
||||
q = ggml_cont_4d(ctx0, q, S_k, chunk_size, n_chunks, HB);
|
||||
k = ggml_cont_4d(ctx0, k, S_k, chunk_size, n_chunks, HB);
|
||||
k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, HB);
|
||||
v = ggml_cont_4d(ctx0, v, S_v, chunk_size, n_chunks, HB);
|
||||
v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, HB);
|
||||
|
||||
gk = ggml_cont_4d(ctx0, gk, S_k, chunk_size, n_chunks, HB);
|
||||
beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, HB);
|
||||
|
||||
// switch for cumsum
|
||||
gk = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk, 1, 0, 2, 3), chunk_size, S_k, n_chunks, HB);
|
||||
cb(gk, "gk", il);
|
||||
ggml_tensor * gk_cumsum = ggml_cumsum(ctx0, gk);
|
||||
cb(gk_cumsum, "gk_cumsum", il);
|
||||
|
||||
/*
|
||||
Compute Akk and Aqk loop together
|
||||
Akk loop:
|
||||
for i in range(BT):
|
||||
k_i = k[..., i, :] # k_i [B,H,NT,S]
|
||||
g_i = g[..., i:i+1, :] # g_i [B,H,NT,1,S]
|
||||
A[..., i] = torch.einsum('... c d, ... d -> ... c', k * (g - g_i).exp(), k_i)
|
||||
Aqk loop:
|
||||
for j in range(BT):
|
||||
k_j = k[:, :, i, j]
|
||||
g_j = g[:, :, i, j:j+1, :]
|
||||
A[..., j] = torch.einsum('... c d, ... d -> ... c', q_i * (g_i - g_j).exp(), k_j)
|
||||
*/
|
||||
const int64_t CHB = n_chunks * H_k * n_seqs;
|
||||
ggml_tensor * gkcs_i = ggml_reshape_4d(ctx0, gk_cumsum, chunk_size, 1, S_k, CHB); // [chunk_size, 1, S_k, CHB]
|
||||
ggml_tensor * gkcs_j = ggml_reshape_4d(ctx0, gkcs_i, 1, chunk_size, S_k, CHB); // [1, chunk_size, S_k, CHB]
|
||||
|
||||
ggml_tensor * gkcs_j_bc = ggml_repeat_4d(ctx0, gkcs_j, chunk_size, chunk_size, S_k, CHB); // [1, chunk_size, S_k, CHB] -> [chunk_size, chunk_size, S_k, CHB]
|
||||
// decay_mask [chunk_size,chunk_size,S_k,CHB]
|
||||
ggml_tensor * decay_mask = ggml_sub(ctx0, gkcs_j_bc, gkcs_i);
|
||||
cb(decay_mask, "decay_mask", il);
|
||||
|
||||
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
|
||||
cb(decay_mask, "decay_masked", il);
|
||||
decay_mask = ggml_exp(ctx0, decay_mask);
|
||||
decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
|
||||
|
||||
// decay_mask [S_k,BT_j,BT_i,CHB] *Note* second and third chunk_sizes are switched
|
||||
decay_mask = ggml_cont_4d(ctx0, ggml_permute(ctx0, decay_mask, 2, 1, 0, 3), S_k, chunk_size, chunk_size, CHB);
|
||||
|
||||
ggml_tensor * k_i = ggml_reshape_4d(ctx0, k, S_k, chunk_size, 1, CHB);
|
||||
ggml_tensor * k_j = ggml_reshape_4d(ctx0, k, S_k, 1, chunk_size, CHB);
|
||||
ggml_tensor * q_i = ggml_reshape_4d(ctx0, q, S_k, chunk_size, 1, CHB);
|
||||
|
||||
ggml_tensor * decay_k_i = ggml_mul(ctx0, decay_mask, k_i);
|
||||
ggml_tensor * decay_q_i = ggml_mul(ctx0, decay_mask, q_i);
|
||||
|
||||
// decay_k_i [S.BT,BT,CHB] @ k_j [S,1,BT,CHB] = Akk [BT,1,BT,CHB]
|
||||
ggml_tensor * Akk = ggml_mul_mat(ctx0, decay_k_i, k_j);
|
||||
ggml_tensor * Aqk = ggml_mul_mat(ctx0, decay_q_i, k_j);
|
||||
Akk = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, Akk, chunk_size, chunk_size, n_chunks, HB)));
|
||||
Aqk = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, Aqk, chunk_size, chunk_size, n_chunks, HB)));
|
||||
cb(Akk, "Akk", il);
|
||||
cb(Aqk, "Aqk", il);
|
||||
|
||||
Akk = ggml_mul(ctx0, Akk, beta);
|
||||
Akk = ggml_neg(ctx0, ggml_mul(ctx0, Akk, causal_mask));
|
||||
cb(Akk, "attn_pre_solve", il);
|
||||
|
||||
Aqk = ggml_mul(ctx0, Aqk, diag_mask);
|
||||
Aqk = ggml_scale(ctx0, Aqk, scale); // scale q
|
||||
cb(Aqk, "Aqk_masked", il);
|
||||
|
||||
// for i in range(1, chunk_size):
|
||||
// row = attn[..., i, :i].clone()
|
||||
// sub = attn[..., :i, :i].clone()
|
||||
// attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
|
||||
// attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
|
||||
//
|
||||
// We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
|
||||
ggml_tensor * attn_lower = ggml_mul(ctx0, Akk, causal_mask);
|
||||
ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
|
||||
|
||||
ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, Akk, true, true, false);
|
||||
Akk = ggml_mul(ctx0, lin_solve, causal_mask);
|
||||
Akk = ggml_add(ctx0, Akk, identity);
|
||||
|
||||
cb(Akk, "attn_solved", il);
|
||||
|
||||
// switch back for downstream
|
||||
gk_cumsum = ggml_cont_4d(ctx0, ggml_permute(ctx0, gk_cumsum, 1, 0, 2, 3), S_k, chunk_size, n_chunks, HB);
|
||||
ggml_tensor * gkexp = ggml_exp(ctx0, gk_cumsum);
|
||||
cb(gk_cumsum, "gk_cumsum", il);
|
||||
|
||||
// u = (A*beta[..., None, :]) @ v aka U_[t]
|
||||
ggml_tensor * vb = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), Akk);
|
||||
|
||||
ggml_tensor * kbeta_gkexp = ggml_mul(ctx0, k_beta, gkexp);
|
||||
cb(kbeta_gkexp, "kbeta_gkexp", il);
|
||||
|
||||
ggml_tensor * k_cumdecay = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gkexp)), Akk);
|
||||
cb(k_cumdecay, "k_cumdecay", il);
|
||||
|
||||
ggml_tensor * core_attn_out = nullptr;
|
||||
ggml_tensor * new_state = ggml_dup(ctx0, state);
|
||||
|
||||
cb(new_state, "new_state", il);
|
||||
|
||||
for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
|
||||
// extract one chunk worth of data
|
||||
auto chunkify = [=](ggml_tensor * t) {
|
||||
return ggml_cont(ctx0, ggml_view_4d(ctx0, t, t->ne[0], chunk_size, 1, t->ne[3],
|
||||
t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
|
||||
};
|
||||
auto chunkify_A = [=](ggml_tensor * t) {
|
||||
return ggml_cont(ctx0, ggml_view_4d(ctx0, t, chunk_size, chunk_size, 1, t->ne[3],
|
||||
t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
|
||||
};
|
||||
|
||||
|
||||
// k [S,BT,NT,H*B] => k_chunk [S,BT,1,H*B]
|
||||
ggml_tensor * k_chunk = chunkify(k);
|
||||
ggml_tensor * q_chunk = chunkify(q);
|
||||
ggml_tensor * vb_chunk = chunkify(vb);
|
||||
|
||||
// gk_cumsum [S,BT,NT,H*B] => gk_cs_chunk [S,BT,1,H*B]
|
||||
ggml_tensor * gk_cs_chunk = chunkify(gk_cumsum);
|
||||
ggml_tensor * k_cumdecay_chunk = chunkify(k_cumdecay);
|
||||
ggml_tensor * gkexp_chunk = ggml_exp(ctx0, gk_cs_chunk);
|
||||
ggml_tensor * Aqk_chunk = chunkify_A(Aqk);
|
||||
|
||||
ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
|
||||
|
||||
// new_state [S,S,1,H*B] k_cumdecay_chunk [S,BT,1,H*B]
|
||||
// v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state or W_[t] @ S_[t]
|
||||
ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
|
||||
|
||||
// v_new = v_i - v_prime or U_[t] - W_[t]*S_[t]
|
||||
ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, vb_chunk, v_prime), v_prime);
|
||||
ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
|
||||
|
||||
// q_chunk [S,BT,1,H*B] gkexp_chunk [S,BT,1,H*B]
|
||||
// attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
|
||||
// or Gamma_[t]*Q_]t] @ S
|
||||
ggml_tensor * q_gk_exp = ggml_mul(ctx0, q_chunk, gkexp_chunk);
|
||||
ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_gk_exp);
|
||||
attn_inter = ggml_scale(ctx0, attn_inter, scale); // scale q
|
||||
|
||||
// v_new_t [S,BT,1,H*B] Aqk [BT,BT,1,H*B]
|
||||
// core_attn_out[:, :, i] = attn_inter + attn @ v_new or A' @ (U_[t] - W_[t]*S_[t])
|
||||
ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, Aqk_chunk);
|
||||
|
||||
// o[:, :, i] = (q_i * g_i.exp()) @ S + A @ v_i
|
||||
ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
|
||||
|
||||
core_attn_out = core_attn_out == nullptr ? core_attn_out_chunk : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 1);
|
||||
|
||||
ggml_tensor * gk_cum_last =
|
||||
ggml_cont(ctx0, ggml_view_4d(ctx0, gk_cs_chunk, gk_cs_chunk->ne[0], 1, gk_cs_chunk->ne[2], gk_cs_chunk->ne[3],
|
||||
gk_cs_chunk->nb[1], gk_cs_chunk->nb[2], gk_cs_chunk->nb[3],
|
||||
gk_cs_chunk->nb[1] * (gk_cs_chunk->ne[1] - 1)));
|
||||
|
||||
ggml_tensor * gkexp_last = ggml_exp(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, gk_cum_last)));
|
||||
|
||||
ggml_tensor * gk_diff = ggml_neg(ctx0, ggml_sub(ctx0, gk_cs_chunk, gk_cum_last));
|
||||
|
||||
ggml_tensor * gk_diff_exp = ggml_exp(ctx0, gk_diff);
|
||||
|
||||
ggml_tensor * key_gkdiff = ggml_mul(ctx0, k_chunk, gk_diff_exp);
|
||||
|
||||
// rearrange((g_i[:,:,-1:] - g_i).exp()*k_i, 'b h c k -> b h k c') @ (U_[t] - W_[t] @ S)
|
||||
ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gkdiff)));
|
||||
|
||||
new_state = ggml_add(ctx0,
|
||||
ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gkexp_last, gkexp_last->ne[0], gkexp_last->ne[1], H_v, n_seqs)),
|
||||
ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
|
||||
}
|
||||
|
||||
core_attn_out = ggml_cont_4d(ctx0, core_attn_out, S_v, chunk_size * n_chunks, H_v, n_seqs);
|
||||
|
||||
// truncate padded tokens
|
||||
ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
|
||||
S_v, n_tokens, H_v, n_seqs,
|
||||
ggml_row_size(core_attn_out->type, S_v),
|
||||
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
|
||||
ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
|
||||
output_tokens = ggml_cont(ctx0, output_tokens);
|
||||
// permute back to (S_v, H_v, n_tokens, n_seqs)
|
||||
output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
|
||||
output_tokens = ggml_cont(ctx0, output_tokens);
|
||||
|
||||
cb(new_state, "output_state", il);
|
||||
|
||||
return {output_tokens, new_state};
|
||||
}
|
||||
|
||||
std::pair<ggml_tensor *, ggml_tensor *> llm_build_kimi_linear::build_kda_autoregressive(
|
||||
ggml_tensor * q,
|
||||
ggml_tensor * k,
|
||||
ggml_tensor * v,
|
||||
ggml_tensor * gk,
|
||||
ggml_tensor * beta,
|
||||
ggml_tensor * state,
|
||||
int il) {
|
||||
GGML_ASSERT(ggml_is_contiguous(v));
|
||||
GGML_ASSERT(ggml_is_contiguous(gk));
|
||||
|
||||
const int64_t S_k = q->ne[0];
|
||||
const int64_t H_k = q->ne[1];
|
||||
const int64_t n_tokens = q->ne[2];
|
||||
const int64_t n_seqs = q->ne[3];
|
||||
|
||||
const int64_t S_v = v->ne[0];
|
||||
const int64_t H_v = v->ne[1];
|
||||
|
||||
GGML_ASSERT(n_tokens == 1);
|
||||
GGML_ASSERT(v->ne[2] == n_tokens);
|
||||
GGML_ASSERT(k->ne[2] == n_tokens);
|
||||
GGML_ASSERT(gk->ne[0] == S_k && gk->ne[1] == H_k && gk->ne[2] == n_tokens && gk->ne[3] == n_seqs);
|
||||
GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
|
||||
GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_k && state->ne[2] == H_v && state->ne[3] == n_seqs);
|
||||
|
||||
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
|
||||
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
|
||||
|
||||
GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
|
||||
|
||||
const float eps_norm = hparams.f_norm_rms_eps;
|
||||
|
||||
q = ggml_l2_norm(ctx0, q, eps_norm);
|
||||
k = ggml_l2_norm(ctx0, k, eps_norm);
|
||||
|
||||
const float scale = 1.0f / sqrtf(S_v);
|
||||
|
||||
q = ggml_scale(ctx0, q, scale);
|
||||
beta = ggml_sigmoid(ctx0, beta);
|
||||
|
||||
cb(q, "q_in", il);
|
||||
cb(k, "k_in", il);
|
||||
cb(v, "v_in", il);
|
||||
cb(beta, "beta_in", il);
|
||||
cb(gk, "gk_in", il);
|
||||
|
||||
// g [H,1,B,1] g_t [1,H,B,1] => [1,1,H,B]
|
||||
// gk [S,H,1,B] => [S,1,H,B] gk_t [1,S,H,B]
|
||||
// beta [H,1,1,B] beta_t [1,H,1,B] => [1,1,H,B]
|
||||
gk = ggml_reshape_4d(ctx0, gk, S_k, 1, H_k, n_seqs);
|
||||
ggml_tensor * gk_t = ggml_cont(ctx0, ggml_transpose(ctx0, gk));
|
||||
ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
|
||||
|
||||
// Apply exponential to gk_t
|
||||
gk_t = ggml_exp(ctx0, gk_t);
|
||||
// Apply the gated delta rule for the single timestep
|
||||
// last_recurrent_state = last_recurrent_state * gk_t
|
||||
// S = S * g_i[..., None].exp()
|
||||
state = ggml_mul(ctx0, state, gk_t);
|
||||
|
||||
ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
|
||||
|
||||
// state [S,S,H,B] k [S,1,H,B] k_state [S_v,1,H,B]
|
||||
k = ggml_reshape_4d(ctx0, k, S_k, 1, H_k, n_seqs);
|
||||
ggml_tensor * k_state = ggml_mul_mat(ctx0, state_t, k);
|
||||
|
||||
// v_i - (k_i[..., None] * S).sum(-2)
|
||||
v = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
|
||||
ggml_tensor * v_diff = ggml_sub(ctx0, v, k_state);
|
||||
|
||||
// b_i[..., None] * k_i
|
||||
ggml_tensor * k_beta = ggml_mul(ctx0, k, beta_t);
|
||||
|
||||
// S = S + torch.einsum('b h k, b h v -> b h k v', b_i[..., None] * k_i, v_i - (k_i[..., None] * S).sum(-2))
|
||||
// v_diff_t [1,S_v,H,B] k_beta_t [1,S_k,H,B] state [S_v,S_k,H,B]
|
||||
state = ggml_add(ctx0, state, ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_diff)), ggml_cont(ctx0, ggml_transpose(ctx0, k_beta))));
|
||||
|
||||
q = ggml_reshape_4d(ctx0, q, S_k, 1, H_k, n_seqs);
|
||||
state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
|
||||
ggml_tensor * core_attn_out = ggml_mul_mat(ctx0, state_t, q);
|
||||
// core_attn_out should be [S_v, 1, H_v, n_seqs] after this
|
||||
cb(core_attn_out, "output_tokens", il);
|
||||
cb(state, "new_state", il);
|
||||
|
||||
return {core_attn_out, state};
|
||||
}
|
||||
|
||||
@@ -288,6 +288,33 @@ struct llm_build_jamba : public llm_graph_context_mamba {
|
||||
llm_build_jamba(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_kimi_linear : public llm_graph_context_mamba {
|
||||
llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params);
|
||||
|
||||
std::pair<ggml_tensor *, ggml_tensor *> build_kda_autoregressive(
|
||||
ggml_tensor * q,
|
||||
ggml_tensor * k,
|
||||
ggml_tensor * v,
|
||||
ggml_tensor * gk,
|
||||
ggml_tensor * beta,
|
||||
ggml_tensor * state,
|
||||
int il);
|
||||
|
||||
std::pair<ggml_tensor *, ggml_tensor *> build_kda_chunking(
|
||||
ggml_tensor * q,
|
||||
ggml_tensor * k,
|
||||
ggml_tensor * v,
|
||||
ggml_tensor * gk,
|
||||
ggml_tensor * beta,
|
||||
ggml_tensor * state,
|
||||
ggml_tensor * causal_mask,
|
||||
ggml_tensor * identity,
|
||||
ggml_tensor * diag_mask,
|
||||
int il);
|
||||
|
||||
const llama_model & model;
|
||||
};
|
||||
|
||||
struct llm_build_lfm2 : public llm_graph_context {
|
||||
const llama_model & model;
|
||||
|
||||
@@ -556,6 +583,10 @@ struct llm_build_starcoder : public llm_graph_context {
|
||||
llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_step35_iswa : public llm_graph_context {
|
||||
llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_t5_dec : public llm_graph_context {
|
||||
llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
@@ -0,0 +1,168 @@
|
||||
#include "models.h"
|
||||
|
||||
llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
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;
|
||||
|
||||
const uint32_t n_head_l = hparams.n_head(il);
|
||||
const uint32_t n_head_kv_l = hparams.n_head_kv(il);
|
||||
|
||||
const float freq_base_l = model.get_rope_freq_base(cparams, il);
|
||||
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// dump pre-attn RMSNorm input to pinpoint layer boundary issues
|
||||
cb(cur, "attn_norm_in", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
|
||||
|
||||
// Q/K per-head RMSNorm (Step35 q_norm / k_norm)
|
||||
if (model.layers[il].attn_q_norm) {
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
}
|
||||
if (model.layers[il].attn_k_norm) {
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(Kcur, "Kcur_normed", il);
|
||||
}
|
||||
|
||||
// RoPE (partial rotary factors per layer)
|
||||
const bool is_swa = hparams.is_swa(il);
|
||||
ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
|
||||
const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2);
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, rope_factors,
|
||||
n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, rope_factors,
|
||||
n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur_pos", il);
|
||||
cb(Kcur, "Kcur_pos", il);
|
||||
|
||||
const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
|
||||
ggml_tensor * attn_out = build_attn(inp_attn,
|
||||
nullptr, nullptr,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(attn_out, "attn_out", il);
|
||||
// head-wise attention gate: sigmoid(g_proj(x)) in torch
|
||||
if (model.layers[il].wqkv_gate) {
|
||||
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]
|
||||
cb(gate, "attn_gate", il);
|
||||
|
||||
gate = ggml_sigmoid(ctx0, gate);
|
||||
cb(gate, "attn_gate_sigmoid", il);
|
||||
|
||||
// reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]
|
||||
ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
|
||||
ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens);
|
||||
cb(gate_3d, "attn_gate_3d", il);
|
||||
|
||||
attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
|
||||
cb(attn_3d, "attn_gated_3d", il);
|
||||
|
||||
attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
|
||||
cb(attn_out, "attn_gated", il);
|
||||
}
|
||||
|
||||
// output projection
|
||||
cur = build_lora_mm(model.layers[il].wo, attn_out);
|
||||
cb(cur, "attn_proj", il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1 && 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);
|
||||
|
||||
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
// feed-forward
|
||||
if (model.layers[il].ffn_gate_inp == nullptr) {
|
||||
// dense MLP
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, nullptr,
|
||||
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
// MoE routed experts
|
||||
const bool norm_w = hparams.expert_weights_norm;
|
||||
const float w_scale = hparams.expert_weights_scale;
|
||||
const bool scale_w = w_scale != 0.0f;
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
model.layers[il].ffn_gate_inp,
|
||||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
model.layers[il].ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU,
|
||||
norm_w, scale_w, w_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// shared expert MLP (always added on MoE layers in Step35)
|
||||
ggml_tensor * sh_out = build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, nullptr, nullptr,
|
||||
model.layers[il].ffn_gate_shexp, nullptr, nullptr,
|
||||
model.layers[il].ffn_down_shexp, nullptr, nullptr,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(sh_out, "ffn_shared_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, sh_out);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
+13
-36
@@ -497,49 +497,26 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
|
||||
return bpe_offsets;
|
||||
}
|
||||
|
||||
// use std::wregex to split the text
|
||||
static std::vector<size_t> unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector<size_t> & offsets) {
|
||||
std::wregex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
|
||||
template <typename CharT>
|
||||
static std::vector<size_t> unicode_regex_split_stl(const std::basic_string<CharT> & text, const std::basic_string<CharT> & regex, const std::vector<size_t> & offsets) {
|
||||
using BidirIt = typename std::basic_string<CharT>::const_iterator;
|
||||
#ifdef _MSC_VER
|
||||
// Bypass bug in MSVC: https://github.com/ggml-org/llama.cpp/issues/17830
|
||||
constexpr auto regex_flags = std::regex_constants::ECMAScript;
|
||||
#else
|
||||
constexpr auto regex_flags = std::regex_constants::optimize | std::regex_constants::nosubs;
|
||||
#endif
|
||||
std::basic_regex<CharT> expr(regex, regex_flags);
|
||||
std::vector<size_t> bpe_offsets; // store the offset of each word
|
||||
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
|
||||
size_t start = 0;
|
||||
for (auto offset : offsets) {
|
||||
std::wcregex_iterator it(wtext.data() + start, wtext.data() + start + offset, expr);
|
||||
std::wcregex_iterator end;
|
||||
std::regex_iterator<BidirIt> it(text.begin() + start, text.begin() + start + offset, expr);
|
||||
std::regex_iterator<BidirIt> end;
|
||||
|
||||
int64_t start_idx = 0;
|
||||
while (it != end) {
|
||||
std::wcmatch match = *it;
|
||||
if (match.position() > start_idx) {
|
||||
bpe_offsets.emplace_back(match.position() - start_idx);
|
||||
}
|
||||
bpe_offsets.emplace_back(match.length());
|
||||
start_idx = match.position() + match.length();
|
||||
++it;
|
||||
}
|
||||
|
||||
if (start_idx < (int64_t) offset) {
|
||||
bpe_offsets.emplace_back(offset - start_idx);
|
||||
}
|
||||
start += offset;
|
||||
}
|
||||
|
||||
return bpe_offsets;
|
||||
}
|
||||
|
||||
// use std::regex to split the text
|
||||
static std::vector<size_t> unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
|
||||
std::regex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
|
||||
std::vector<size_t> bpe_offsets; // store the offset of each word
|
||||
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
|
||||
size_t start = 0;
|
||||
for (auto offset : offsets) {
|
||||
std::cregex_iterator it(text.data() + start, text.data() + start + offset, expr);
|
||||
std::cregex_iterator end;
|
||||
|
||||
int64_t start_idx = 0;
|
||||
while (it != end) {
|
||||
std::cmatch match = *it;
|
||||
std::match_results<BidirIt> match = *it;
|
||||
if (match.position() > start_idx) {
|
||||
bpe_offsets.emplace_back(match.position() - start_idx);
|
||||
}
|
||||
|
||||
@@ -11,7 +11,9 @@ function(llama_build source)
|
||||
|
||||
add_executable(${TEST_TARGET} ${TEST_SOURCES})
|
||||
target_link_libraries(${TEST_TARGET} PRIVATE common)
|
||||
install(TARGETS ${TEST_TARGET} RUNTIME)
|
||||
if (LLAMA_TESTS_INSTALL)
|
||||
install(TARGETS ${TEST_TARGET} RUNTIME)
|
||||
endif()
|
||||
endfunction()
|
||||
|
||||
function(llama_test target)
|
||||
@@ -100,7 +102,9 @@ function(llama_build_and_test source)
|
||||
endif()
|
||||
|
||||
add_executable(${TEST_TARGET} ${TEST_SOURCES})
|
||||
install(TARGETS ${TEST_TARGET} RUNTIME)
|
||||
if (LLAMA_TESTS_INSTALL)
|
||||
install(TARGETS ${TEST_TARGET} RUNTIME)
|
||||
endif()
|
||||
target_link_libraries(${TEST_TARGET} PRIVATE common)
|
||||
|
||||
add_test(
|
||||
|
||||
+20
-14
@@ -169,20 +169,22 @@ static void init_tensor_kq_mask(ggml_tensor * tensor, float min = -1.0f, float m
|
||||
const int blck0 = 128;
|
||||
const int blck1 = 64;
|
||||
|
||||
// number of INF blocks
|
||||
const int n_inf_blocks = 0.1*(ne0*ne1*ne2*ne3)/(blck0*blck1);
|
||||
// number of INF/zero blocks
|
||||
const int n_inf_zero_blocks = 0.2*(ne0*ne1*ne2*ne3)/(blck0*blck1);
|
||||
|
||||
for (int b = 0; b < n_inf_blocks; b++) {
|
||||
for (int b = 0; b < n_inf_zero_blocks; b++) {
|
||||
const int p3 = (rd() % ne3);
|
||||
const int p2 = (rd() % ne2);
|
||||
const int p1 = (rd() % ne1);
|
||||
const int p0 = (rd() % ne0);
|
||||
|
||||
bool inf = rd() & 1;
|
||||
|
||||
for (int i1 = 0; i1 < blck1 && p1 + i1 < ne1; i1++) {
|
||||
const int idx = p3*ne2*ne1*ne0 + p2*ne1*ne0 + (p1 + i1)*ne0 + p0;
|
||||
|
||||
for (int i0 = 0; i0 < blck0 && p0 + i0 < ne0; i0++) {
|
||||
data_f32[idx + i0] = -INFINITY;
|
||||
data_f32[idx + i0] = inf ? -INFINITY : 0.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -5892,33 +5894,36 @@ struct test_pad_ext : public test_case {
|
||||
const int rp2;
|
||||
const int lp3;
|
||||
const int rp3;
|
||||
const bool v;
|
||||
const int tfrm; // 0 - none, 1 - non-cont, 2 - perm
|
||||
const bool circular;
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v, circular);
|
||||
return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, tfrm, circular);
|
||||
}
|
||||
|
||||
test_pad_ext(ggml_type type = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne_a = {512, 512, 3, 1},
|
||||
int lp0 = 1, int rp0 = 1, int lp1 = 1, int rp1 = 1,
|
||||
int lp2 = 1, int rp2 = 1, int lp3 = 1, int rp3 = 1,
|
||||
bool v = false, bool circular = false)
|
||||
int tfrm = 0, bool circular = false)
|
||||
: type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3),
|
||||
v(v), circular(circular) {}
|
||||
tfrm(tfrm), circular(circular) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
|
||||
ggml_set_name(a, "a");
|
||||
|
||||
if (v) {
|
||||
if (tfrm == 1) {
|
||||
a = ggml_view_4d(ctx, a, (a->ne[0] + 1) / 2, (a->ne[1] + 1) / 2, (a->ne[2] + 1) / 2, (a->ne[3] + 1) / 2, a->nb[1], a->nb[2], a->nb[3], 0);
|
||||
ggml_set_name(a, "view of a");
|
||||
} else if (tfrm == 2) {
|
||||
a = ggml_permute(ctx, a, 2, 1, 0, 3);
|
||||
ggml_set_name(a, "permuted a");
|
||||
}
|
||||
|
||||
ggml_tensor * out = circular
|
||||
? ggml_pad_ext_circular(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3)
|
||||
: ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
|
||||
: ggml_pad_ext (ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
|
||||
ggml_set_name(out, "out");
|
||||
|
||||
return out;
|
||||
@@ -8196,10 +8201,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 200, 64, 4, 4 }));
|
||||
test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 384, 64, 4, 4 }));
|
||||
|
||||
for (bool v : {false, true}) {
|
||||
for (int tfrm : {0, 1, 2}) {
|
||||
for (bool circular : {false, true}) {
|
||||
test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v, circular));
|
||||
test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v, circular));
|
||||
test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, tfrm, circular));
|
||||
test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, tfrm, circular));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -8229,6 +8234,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
||||
for (ggml_prec prec : {GGML_PREC_F32, GGML_PREC_DEFAULT}) {
|
||||
if (hsk != 128 && prec == GGML_PREC_DEFAULT) continue;
|
||||
for (ggml_type type_KV : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
|
||||
if (type_KV != GGML_TYPE_F16 && hsk != 64 && hsk != 72) continue;
|
||||
test_cases.emplace_back(new test_flash_attn_ext(
|
||||
hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV));
|
||||
// run fewer test cases permuted
|
||||
@@ -8517,7 +8523,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
|
||||
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, {128, 12, 512, 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)
|
||||
}
|
||||
}
|
||||
|
||||
+131
-5
@@ -10,6 +10,7 @@
|
||||
#include "ggml-backend.h"
|
||||
#include "gguf.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <cstdlib>
|
||||
@@ -1116,9 +1117,8 @@ struct clip_model_loader {
|
||||
case PROJECTOR_TYPE_LFM2:
|
||||
{
|
||||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||||
// ref: https://huggingface.co/LiquidAI/LFM2-VL-3B/blob/main/preprocessor_config.json
|
||||
// config above specifies number of tokens after downsampling, while here it is before, relax lowerbound to 64
|
||||
hparams.set_limit_image_tokens(64, 1024);
|
||||
// ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json
|
||||
hparams.set_limit_image_tokens(64, 256);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PIXTRAL:
|
||||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||||
@@ -2807,6 +2807,119 @@ private:
|
||||
}
|
||||
};
|
||||
|
||||
// ref: https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py
|
||||
// some of the logic is similar to llava_uhd, but with different hyperparameters and some logic is unique (e.g. grid layout)
|
||||
struct lfm2_vl_image_processor {
|
||||
// ref: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B/blob/main/processor_config.json
|
||||
static constexpr int min_tiles = 2;
|
||||
static constexpr int max_tiles = 10;
|
||||
static constexpr float max_pixels_tolerance = 2.0f;
|
||||
static constexpr int tile_size = 512;
|
||||
|
||||
static llava_uhd::slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) {
|
||||
llava_uhd::slice_instructions inst;
|
||||
const auto & params = ctx->model.hparams;
|
||||
const int align_size = params.patch_size * params.n_merge;
|
||||
|
||||
inst.interpolation_overview = img_tool::RESIZE_ALGO_BILINEAR;
|
||||
inst.interpolation_refined = img_tool::RESIZE_ALGO_BILINEAR;
|
||||
inst.overview_size = img_tool::calc_size_preserved_ratio(original_size, align_size, params.image_min_pixels, params.image_max_pixels);
|
||||
|
||||
// tile if either dimension exceeds tile_size with tolerance
|
||||
const bool needs_tiling = original_size.width > tile_size * max_pixels_tolerance || original_size.height > tile_size * max_pixels_tolerance;
|
||||
|
||||
if (!needs_tiling) {
|
||||
inst.refined_size = clip_image_size{0, 0};
|
||||
inst.grid_size = clip_image_size{0, 0};
|
||||
return inst;
|
||||
}
|
||||
|
||||
const clip_image_size grid = get_grid_layout(original_size.height, original_size.width);
|
||||
|
||||
inst.grid_size = grid;
|
||||
inst.refined_size = clip_image_size{tile_size * grid.width, tile_size * grid.height};
|
||||
|
||||
LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d, grid size: %d x %d\n",
|
||||
__func__,
|
||||
original_size.width, original_size.height,
|
||||
inst.overview_size.width, inst.overview_size.height,
|
||||
inst.refined_size.width, inst.refined_size.height,
|
||||
grid.width, grid.height);
|
||||
|
||||
for (int row = 0; row < grid.height; row++) {
|
||||
for (int col = 0; col < grid.width; col++) {
|
||||
llava_uhd::slice_coordinates slice;
|
||||
slice.x = col * tile_size;
|
||||
slice.y = row * tile_size;
|
||||
slice.size = clip_image_size{tile_size, tile_size};
|
||||
inst.slices.push_back(slice);
|
||||
LOG_DBG("%s: slice %d: x=%d, y=%d, size=%d x %d\n",
|
||||
__func__, (int)inst.slices.size() - 1,
|
||||
slice.x, slice.y, slice.size.width, slice.size.height);
|
||||
}
|
||||
}
|
||||
|
||||
return inst;
|
||||
}
|
||||
|
||||
private:
|
||||
static clip_image_size find_closest_aspect_ratio(
|
||||
float aspect_ratio,
|
||||
const std::vector<clip_image_size> & target_ratios,
|
||||
int width, int height) {
|
||||
float best_ratio_diff = std::numeric_limits<float>::max();
|
||||
clip_image_size best_ratio = {1, 1};
|
||||
const float area = static_cast<float>(width * height);
|
||||
|
||||
for (const auto & ratio : target_ratios) {
|
||||
const float target_aspect_ratio = static_cast<float>(ratio.width) / ratio.height;
|
||||
const float ratio_diff = std::abs(aspect_ratio - target_aspect_ratio);
|
||||
if (ratio_diff < best_ratio_diff) {
|
||||
best_ratio_diff = ratio_diff;
|
||||
best_ratio = ratio;
|
||||
} else if (ratio_diff == best_ratio_diff) {
|
||||
const float target_area = static_cast<float>(tile_size * tile_size * ratio.width * ratio.height);
|
||||
if (area > 0.5f * target_area) {
|
||||
best_ratio = ratio;
|
||||
}
|
||||
}
|
||||
}
|
||||
return best_ratio;
|
||||
}
|
||||
|
||||
static std::vector<clip_image_size> get_target_ratios() {
|
||||
std::vector<clip_image_size> ratios;
|
||||
for (int n = min_tiles; n <= max_tiles; n++) {
|
||||
for (int w = 1; w <= n; w++) {
|
||||
for (int h = 1; h <= n; h++) {
|
||||
if (w * h >= min_tiles && w * h <= max_tiles) {
|
||||
bool found = false;
|
||||
for (const auto & r : ratios) {
|
||||
if (r.width == w && r.height == h) {
|
||||
found = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!found) {
|
||||
ratios.push_back({w, h});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
std::sort(ratios.begin(), ratios.end(), [](const clip_image_size & a, const clip_image_size & b) {
|
||||
return a.width * a.height < b.width * b.height;
|
||||
});
|
||||
return ratios;
|
||||
}
|
||||
|
||||
static clip_image_size get_grid_layout(int height, int width) {
|
||||
const float aspect_ratio = static_cast<float>(width) / height;
|
||||
const auto ratios = get_target_ratios();
|
||||
return find_closest_aspect_ratio(aspect_ratio, ratios, width, height);
|
||||
}
|
||||
};
|
||||
|
||||
// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
|
||||
// res_imgs memory is being allocated here, previous allocations will be freed if found
|
||||
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, struct clip_image_f32_batch * res_imgs) {
|
||||
@@ -3021,6 +3134,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
|
||||
} break;
|
||||
|
||||
case PROJECTOR_TYPE_LFM2:
|
||||
{
|
||||
auto const inst = lfm2_vl_image_processor::get_slice_instructions(ctx, original_size);
|
||||
std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
|
||||
|
||||
for (size_t i = 0; i < imgs.size(); ++i) {
|
||||
clip_image_f32_ptr res(clip_image_f32_init());
|
||||
normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
|
||||
res_imgs->entries.push_back(std::move(res));
|
||||
}
|
||||
|
||||
res_imgs->grid_x = inst.grid_size.width;
|
||||
res_imgs->grid_y = inst.grid_size.height;
|
||||
} break;
|
||||
|
||||
case PROJECTOR_TYPE_KIMIVL:
|
||||
{
|
||||
GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
|
||||
@@ -3032,8 +3159,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
|
||||
const std::array<uint8_t, 3> pad_color = {122, 116, 104};
|
||||
|
||||
clip_image_u8 resized_img;
|
||||
const bool pad = (ctx->proj_type() != PROJECTOR_TYPE_LFM2);
|
||||
img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, pad, pad_color);
|
||||
img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
|
||||
clip_image_f32_ptr res(clip_image_f32_init());
|
||||
normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
|
||||
res_imgs->entries.push_back(std::move(res));
|
||||
|
||||
+16
-3
@@ -85,6 +85,7 @@ enum mtmd_slice_tmpl {
|
||||
MTMD_SLICE_TMPL_MINICPMV_2_6,
|
||||
MTMD_SLICE_TMPL_LLAMA4,
|
||||
MTMD_SLICE_TMPL_IDEFICS3,
|
||||
MTMD_SLICE_TMPL_LFM2,
|
||||
};
|
||||
|
||||
const char * mtmd_default_marker() {
|
||||
@@ -307,9 +308,19 @@ struct mtmd_context {
|
||||
img_end = "<|im_end|>";
|
||||
|
||||
} else if (proj == PROJECTOR_TYPE_LFM2) {
|
||||
img_beg = "<|image_start|>";
|
||||
img_end = "<|image_end|>";
|
||||
|
||||
// multi-tile:
|
||||
// <|image_start|>
|
||||
// <|img_row_1_col_1|> (tile) <|img_row_1_col_2|> (tile) ...
|
||||
// <|img_thumbnail|> (thumbnail)
|
||||
// <|image_end|>
|
||||
// single-tile:
|
||||
// <|image_start|> (image) <|image_end|>
|
||||
img_beg = "<|image_start|>";
|
||||
img_end = "<|image_end|>";
|
||||
slice_tmpl = MTMD_SLICE_TMPL_LFM2;
|
||||
sli_img_start_tmpl = "<|img_row_%d_col_%d|>";
|
||||
tok_ov_img_start = {lookup_token("<|img_thumbnail|>")};
|
||||
ov_img_first = false;
|
||||
} else if (proj == PROJECTOR_TYPE_GLM4V) {
|
||||
img_beg = "<|begin_of_image|>";
|
||||
img_end = "<|end_of_image|>";
|
||||
@@ -562,11 +573,13 @@ struct mtmd_tokenizer {
|
||||
}
|
||||
|
||||
// handle llava-uhd style preprocessing
|
||||
const bool has_tiling_grid = batch_f32.grid_x > 0 && batch_f32.grid_y > 0;
|
||||
if (
|
||||
ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
|
||||
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
|
||||
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
|
||||
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_IDEFICS3
|
||||
|| (ctx->slice_tmpl == MTMD_SLICE_TMPL_LFM2 && has_tiling_grid)
|
||||
) {
|
||||
const int n_col = batch_f32.grid_x;
|
||||
const int n_row = batch_f32.grid_y;
|
||||
|
||||
+40
-19
@@ -119,27 +119,48 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
|
||||
[[noreturn]]
|
||||
static void usage(const char * executable) {
|
||||
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable);
|
||||
printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file] [--prune-layers] [--keep-split] [--override-kv]\n");
|
||||
printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--tensor-type-file]\n");
|
||||
printf(" [--prune-layers] [--keep-split] [--override-kv]\n");
|
||||
printf(" model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
|
||||
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
|
||||
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
|
||||
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
|
||||
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
|
||||
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
|
||||
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
|
||||
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
|
||||
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
|
||||
printf(" --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
|
||||
printf(" Advanced option to selectively quantize tensors. May be specified multiple times.\n");
|
||||
printf(" --tensor-type-file tensor_type.txt: list of tensors to quantize to specific ggml_type. example: --tensor-type-file tensor_type_list.txt\n");
|
||||
printf(" Advanced option to selectively quantize a long list of tensors. Format to be tensor_name=ggml_type, separated by spaces/newline.\n");
|
||||
printf(" --prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model\n");
|
||||
printf(" Advanced option to remove all tensors from the given layers\n");
|
||||
printf(" --keep-split: will generate quantized model in the same shards as input\n");
|
||||
printf(" --allow-requantize\n");
|
||||
printf(" allow requantizing tensors that have already been quantized\n");
|
||||
printf(" WARNING: this can severely reduce quality compared to quantizing\n");
|
||||
printf(" from 16bit or 32bit!\n");
|
||||
printf(" --leave-output-tensor\n");
|
||||
printf(" leave output.weight un(re)quantized\n");
|
||||
printf(" increases model size but may also increase quality, especially when requantizing\n");
|
||||
printf(" --pure\n");
|
||||
printf(" disable k-quant mixtures and quantize all tensors to the same type\n");
|
||||
printf(" --imatrix file_name\n");
|
||||
printf(" use data in file_name as importance matrix for quant optimizations\n");
|
||||
printf(" --include-weights tensor_name\n");
|
||||
printf(" use importance matrix for this/these tensor(s)\n");
|
||||
printf(" --exclude-weights tensor_name\n");
|
||||
printf(" do not use importance matrix for this/these tensor(s)\n");
|
||||
printf(" --output-tensor-type ggml_type\n");
|
||||
printf(" use this ggml_type for the output.weight tensor\n");
|
||||
printf(" --token-embedding-type ggml_type\n");
|
||||
printf(" use this ggml_type for the token embeddings tensor\n");
|
||||
printf(" --tensor-type tensor_name=ggml_type\n");
|
||||
printf(" quantize this tensor to this ggml_type\n");
|
||||
printf(" this is an advanced option to selectively quantize tensors. may be specified multiple times.\n");
|
||||
printf(" example: --tensor-type attn_q=q8_0\n");
|
||||
printf(" --tensor-type-file tensor_types.txt\n");
|
||||
printf(" list of tensors to quantize to a specific ggml_type\n");
|
||||
printf(" this is an advanced option to selectively quantize a long list of tensors.\n");
|
||||
printf(" the file should use the same format as above, separated by spaces or newlines.\n");
|
||||
printf(" --prune-layers L0,L1,L2...\n");
|
||||
printf(" comma-separated list of layer numbers to prune from the model\n");
|
||||
printf(" WARNING: this is an advanced option, use with care.\n");
|
||||
printf(" --keep-split\n");
|
||||
printf(" generate quantized model in the same shards as input\n");
|
||||
printf(" --override-kv KEY=TYPE:VALUE\n");
|
||||
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
|
||||
printf("Note: --include-weights and --exclude-weights cannot be used together\n");
|
||||
printf("\nAllowed quantization types:\n");
|
||||
printf(" override model metadata by key in the quantized model. may be specified multiple times.\n");
|
||||
printf(" WARNING: this is an advanced option, use with care.\n\n");
|
||||
printf("note: --include-weights and --exclude-weights cannot be used together\n\n");
|
||||
printf("-----------------------------------------------------------------------------\n");
|
||||
printf(" allowed quantization types\n");
|
||||
printf("-----------------------------------------------------------------------------\n\n");
|
||||
for (const auto & it : QUANT_OPTIONS) {
|
||||
if (it.name != "COPY") {
|
||||
printf(" %2d or ", it.ftype);
|
||||
|
||||
+49
-15
@@ -1,12 +1,7 @@
|
||||
#if defined(_MSC_VER)
|
||||
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
|
||||
#endif
|
||||
|
||||
#include "ggml-rpc.h"
|
||||
#ifdef _WIN32
|
||||
# define NOMINMAX
|
||||
# define DIRECTORY_SEPARATOR '\\'
|
||||
# include <locale>
|
||||
# include <windows.h>
|
||||
# include <fcntl.h>
|
||||
# include <io.h>
|
||||
@@ -15,23 +10,43 @@
|
||||
# include <unistd.h>
|
||||
# include <sys/stat.h>
|
||||
#endif
|
||||
#include <codecvt>
|
||||
#include <string>
|
||||
#include <stdio.h>
|
||||
#include <vector>
|
||||
#include <filesystem>
|
||||
#include <algorithm>
|
||||
#include <thread>
|
||||
#include <regex>
|
||||
|
||||
namespace fs = std::filesystem;
|
||||
#if defined(__linux__)
|
||||
#include <sys/types.h>
|
||||
#include <pwd.h>
|
||||
#endif
|
||||
|
||||
// NOTE: this is copied from common.cpp to avoid linking with libcommon
|
||||
#ifdef _WIN32
|
||||
static std::wstring utf8_to_wstring(const std::string & str) {
|
||||
if (str.empty()) {
|
||||
return std::wstring();
|
||||
}
|
||||
|
||||
int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);
|
||||
|
||||
if (size <= 0) {
|
||||
return std::wstring();
|
||||
}
|
||||
|
||||
std::wstring wstr(size, 0);
|
||||
MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);
|
||||
|
||||
return wstr;
|
||||
}
|
||||
#endif
|
||||
|
||||
// NOTE: this is copied from common.cpp to avoid linking with libcommon
|
||||
// returns true if successful, false otherwise
|
||||
static bool fs_create_directory_with_parents(const std::string & path) {
|
||||
#ifdef _WIN32
|
||||
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
|
||||
std::wstring wpath = converter.from_bytes(path);
|
||||
std::wstring wpath = utf8_to_wstring(path);
|
||||
|
||||
// if the path already exists, check whether it's a directory
|
||||
const DWORD attributes = GetFileAttributesW(wpath.c_str());
|
||||
@@ -44,9 +59,16 @@ static bool fs_create_directory_with_parents(const std::string & path) {
|
||||
// process path from front to back, procedurally creating directories
|
||||
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
|
||||
const std::wstring subpath = wpath.substr(0, pos_slash);
|
||||
const wchar_t * test = subpath.c_str();
|
||||
|
||||
const bool success = CreateDirectoryW(test, NULL);
|
||||
pos_slash += 1;
|
||||
|
||||
// skip the drive letter, in some systems it can return an access denied error
|
||||
if (subpath.length() == 2 && subpath[1] == ':') {
|
||||
continue;
|
||||
}
|
||||
|
||||
const bool success = CreateDirectoryW(subpath.c_str(), NULL);
|
||||
|
||||
if (!success) {
|
||||
const DWORD error = GetLastError();
|
||||
|
||||
@@ -60,8 +82,6 @@ static bool fs_create_directory_with_parents(const std::string & path) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
pos_slash += 1;
|
||||
}
|
||||
|
||||
return true;
|
||||
@@ -115,13 +135,27 @@ static std::string fs_get_cache_directory() {
|
||||
#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
|
||||
if (std::getenv("XDG_CACHE_HOME")) {
|
||||
cache_directory = std::getenv("XDG_CACHE_HOME");
|
||||
} else {
|
||||
} else if (std::getenv("HOME")) {
|
||||
cache_directory = std::getenv("HOME") + std::string("/.cache/");
|
||||
} else {
|
||||
#if defined(__linux__)
|
||||
/* no $HOME is defined, fallback to getpwuid */
|
||||
struct passwd *pw = getpwuid(getuid());
|
||||
if ((!pw) || (!pw->pw_dir)) {
|
||||
throw std::runtime_error("Failed to find $HOME directory");
|
||||
}
|
||||
|
||||
cache_directory = std::string(pw->pw_dir) + std::string("/.cache/");
|
||||
#else /* defined(__linux__) */
|
||||
throw std::runtime_error("Failed to find $HOME directory");
|
||||
#endif /* defined(__linux__) */
|
||||
}
|
||||
#elif defined(__APPLE__)
|
||||
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
|
||||
#elif defined(_WIN32)
|
||||
cache_directory = std::getenv("LOCALAPPDATA");
|
||||
#elif defined(__EMSCRIPTEN__)
|
||||
GGML_ABORT("not implemented on this platform");
|
||||
#else
|
||||
# error Unknown architecture
|
||||
#endif
|
||||
|
||||
@@ -740,6 +740,11 @@ private:
|
||||
|
||||
slots.clear();
|
||||
|
||||
const bool can_spec = common_speculative_is_compat(ctx);
|
||||
if (!can_spec) {
|
||||
SRV_WRN("%s", "speculative decoding not supported by this context\n");
|
||||
}
|
||||
|
||||
// initialize slots
|
||||
for (int i = 0; i < params_base.n_parallel; i++) {
|
||||
server_slot slot;
|
||||
@@ -752,7 +757,7 @@ private:
|
||||
slot.prompt.tokens.has_mtmd = mctx != nullptr;
|
||||
|
||||
// try speculative decoding
|
||||
{
|
||||
if (can_spec) {
|
||||
slot.spec = common_speculative_init(params_base.speculative, slot.ctx);
|
||||
if (slot.spec) {
|
||||
if (mctx) {
|
||||
@@ -2502,7 +2507,8 @@ private:
|
||||
slot.n_prompt_tokens_processed++;
|
||||
|
||||
// process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
|
||||
if (do_checkpoint && slot.task->n_tokens() - slot.prompt.n_tokens() == 64) {
|
||||
const int n_last = std::min(n_batch, 512);
|
||||
if (do_checkpoint && slot.task->n_tokens() == slot.prompt.n_tokens() + n_last) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
@@ -3578,6 +3584,8 @@ void server_routes::init_routes() {
|
||||
auto res = create_response();
|
||||
std::vector<raw_buffer> files;
|
||||
json body = convert_responses_to_chatcmpl(json::parse(req.body));
|
||||
SRV_DBG("%s\n", "Request converted: OpenAI Responses -> OpenAI Chat Completions");
|
||||
SRV_DBG("converted request: %s\n", body.dump().c_str());
|
||||
json body_parsed = oaicompat_chat_params_parse(
|
||||
body,
|
||||
meta->chat_params,
|
||||
@@ -3594,6 +3602,8 @@ void server_routes::init_routes() {
|
||||
auto res = create_response();
|
||||
std::vector<raw_buffer> files;
|
||||
json body = convert_anthropic_to_oai(json::parse(req.body));
|
||||
SRV_DBG("%s\n", "Request converted: Anthropic -> OpenAI Chat Completions");
|
||||
SRV_DBG("converted request: %s\n", body.dump().c_str());
|
||||
json body_parsed = oaicompat_chat_params_parse(
|
||||
body,
|
||||
meta->chat_params,
|
||||
@@ -3610,6 +3620,8 @@ void server_routes::init_routes() {
|
||||
auto res = create_response();
|
||||
std::vector<raw_buffer> files;
|
||||
json body = convert_anthropic_to_oai(json::parse(req.body));
|
||||
SRV_DBG("%s\n", "Request converted: Anthropic -> OpenAI Chat Completions");
|
||||
SRV_DBG("converted request: %s\n", body.dump().c_str());
|
||||
json body_parsed = oaicompat_chat_params_parse(
|
||||
body,
|
||||
meta->chat_params,
|
||||
|
||||
@@ -80,7 +80,6 @@ json task_params::to_json(bool only_metrics) const {
|
||||
{"speculative.type", common_speculative_type_to_str(speculative.type)},
|
||||
{"speculative.ngram_size_n", speculative.ngram_size_n},
|
||||
{"speculative.ngram_size_m", speculative.ngram_size_m},
|
||||
{"speculative.ngram_c_rate", speculative.ngram_check_rate},
|
||||
{"speculative.ngram_m_hits", speculative.ngram_min_hits},
|
||||
{"timings_per_token", timings_per_token},
|
||||
{"post_sampling_probs", post_sampling_probs},
|
||||
@@ -144,7 +143,6 @@ json task_params::to_json(bool only_metrics) const {
|
||||
{"speculative.type", common_speculative_type_to_str(speculative.type)},
|
||||
{"speculative.ngram_size_n", speculative.ngram_size_n},
|
||||
{"speculative.ngram_size_m", speculative.ngram_size_m},
|
||||
{"speculative.ngram_c_rate", speculative.ngram_check_rate},
|
||||
{"speculative.ngram_m_hits", speculative.ngram_min_hits},
|
||||
{"timings_per_token", timings_per_token},
|
||||
{"post_sampling_probs", post_sampling_probs},
|
||||
@@ -257,12 +255,10 @@ task_params server_task::params_from_json_cmpl(
|
||||
|
||||
params.speculative.ngram_size_n = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
|
||||
params.speculative.ngram_size_m = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m);
|
||||
params.speculative.ngram_check_rate = json_value(data, "speculative.ngram_c_rate", defaults.speculative.ngram_check_rate);
|
||||
params.speculative.ngram_min_hits = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits);
|
||||
|
||||
params.speculative.ngram_size_n = std::max(std::min(1, (int) params.speculative.ngram_size_n), 1024);
|
||||
params.speculative.ngram_size_m = std::max(std::min(1, (int) params.speculative.ngram_size_m), 1024);
|
||||
params.speculative.ngram_check_rate = std::max(std::min(1, (int) params.speculative.ngram_check_rate), 1024);
|
||||
params.speculative.ngram_min_hits = std::max(std::min(1, (int) params.speculative.ngram_min_hits), 1024);
|
||||
|
||||
// Use OpenAI API logprobs only if n_probs wasn't provided
|
||||
|
||||
+2
-2
@@ -34,7 +34,7 @@ $ build/bin/llama-quantize models/outetts-0.2-0.5B-f16.gguf \
|
||||
```
|
||||
The quantized model will be `models/outetts-0.2-0.5B-q8_0.gguf`.
|
||||
|
||||
Next we do something simlar for the audio decoder. First download or checkout
|
||||
Next we do something similar for the audio decoder. First download or checkout
|
||||
the model for the voice decoder:
|
||||
```console
|
||||
$ pushd models
|
||||
@@ -42,7 +42,7 @@ $ git clone --branch main --single-branch --depth 1 https://huggingface.co/novat
|
||||
$ cd WavTokenizer-large-speech-75token && git lfs install && git lfs pull
|
||||
$ popd
|
||||
```
|
||||
This model file is PyTorch checkpoint (.ckpt) and we first need to convert it to
|
||||
This model file is a PyTorch checkpoint (.ckpt) and we first need to convert it to
|
||||
huggingface format:
|
||||
```console
|
||||
(venv) python tools/tts/convert_pt_to_hf.py \
|
||||
|
||||
Vendored
+1
-1
@@ -39,7 +39,7 @@ if (LLAMA_BUILD_BORINGSSL)
|
||||
set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
|
||||
|
||||
set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
|
||||
set(BORINGSSL_VERSION "0.20251002.0" CACHE STRING "BoringSSL version")
|
||||
set(BORINGSSL_VERSION "0.20260204.0" CACHE STRING "BoringSSL version")
|
||||
|
||||
message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user